diff --git "a/1456.jsonl" "b/1456.jsonl" new file mode 100644--- /dev/null +++ "b/1456.jsonl" @@ -0,0 +1,378 @@ +{"seq_id": "523591089", "text": "import pandas as pd\nfrom sklearn.datasets import make_classification\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import GridSearchCV\nimport pickle as pkl\nimport gzip\n\n\nclass ModelBuilder:\n def __init__(self):\n self.n_samples = 100000\n self.n_features = 5\n\n def run(self):\n print(\"Model creation started\")\n\n X, y = make_classification(self.n_samples, self.n_features, random_state=42)\n model = Pipeline(steps=[\n ('scaler', StandardScaler()),\n ('clf', RandomForestClassifier())\n ])\n\n params = {\n \"clf__n_estimators\": [10, 20, 50]\n }\n\n X = pd.DataFrame(X, columns=['X%i' % i for i in range(5)])\n\n gsearch = GridSearchCV(model, param_grid=params, cv=2)\n gsearch.fit(X, y)\n\n _best_model = gsearch.best_estimator_\n\n print(\"Saving model to the disk\")\n\n with gzip.GzipFile('model.pkl', 'w') as fhandler:\n pkl.dump(_best_model, fhandler)\n\n print(\"Model written\")\n\n\nif __name__ == '__main__':\n builder = ModelBuilder()\n builder.run()\n", "sub_path": "model_builder.py", "file_name": "model_builder.py", "file_ext": "py", "file_size_in_byte": 1209, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "sklearn.datasets.make_classification", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 31, "usage_type": "call"}, {"api_name": "gzip.GzipFile", "line_number": 38, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "102397615", "text": "# -*- coding: utf-8 -*-\nimport telebot\nimport constant\nimport random\nimport game\n\n\nbot = telebot.TeleBot(constant.token)\n\nglobal joke_number, game1\njoke_number = 0\ngame1 = False\n\n@bot.message_handler(commands=['start'])\ndef start_keyboard(message):\n initial_keyboard = telebot.types.ReplyKeyboardMarkup(True, False)\n initial_keyboard.row('/start','/stop')\n initial_keyboard.row(constant.messages_keys['greeting'][2], constant.messages_keys['joke'])\n initial_keyboard.row(constant.messages_keys['random_coin'], constant.messages_keys['game'])\n bot.send_message(message.from_user.id, constant.messages_reply['greeting'], reply_markup=initial_keyboard)\n\n@bot.message_handler(commands=['stop'])\ndef stop_keyboard(message):\n hide_keybord = telebot.types.ReplyKeyboardRemove()\n bot.send_message(message.from_user.id, '👀', reply_markup=hide_keybord)\n\n@bot.message_handler(content_types=[\"text\"])\ndef conversation(message):\n global joke_number, game1\n if constant.messages_keys['game'] in message.text:\n game1 = True\n if game1 == False:\n for greeting in constant.messages_keys['greeting']:\n if greeting in message.text:\n bot.send_message(message.chat.id, constant.messages_reply['greeting'][0])\n if constant.messages_keys['joke'] in message.text:\n bot.send_message(message.chat.id, constant.messages_reply_joke[joke_number])\n if joke_number == len(constant.messages_reply_joke)-1:\n joke_number = 0\n else:\n joke_number += 1\n elif constant.messages_keys['random_coin'] in message.text:\n bot.send_message(message.chat.id, random.choice(constant.messages_reply['random_coin']))\n else:\n if message.text != constant.messages_keys['greeting'][2]:\n bot.send_message(message.chat.id, constant.messages_reply['error'])\n elif game1 == True:\n game.game_sapper(message.chat.id, message.text)\n if game.gg == True:\n game1 = False\n game.gg = False\n \n\n\nif __name__ == '__main__':\n bot.polling(none_stop=True)", "sub_path": "app/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2114, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "telebot.TeleBot", "line_number": 8, "usage_type": "call"}, {"api_name": "constant.token", "line_number": 8, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 16, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 16, "usage_type": "attribute"}, {"api_name": "constant.messages_keys", "line_number": 18, "usage_type": "attribute"}, {"api_name": "constant.messages_keys", "line_number": 19, "usage_type": "attribute"}, {"api_name": "constant.messages_reply", "line_number": 20, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardRemove", "line_number": 24, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 24, "usage_type": "attribute"}, {"api_name": "constant.messages_keys", "line_number": 30, "usage_type": "attribute"}, {"api_name": "constant.messages_keys", "line_number": 33, "usage_type": "attribute"}, {"api_name": "constant.messages_reply", "line_number": 35, "usage_type": "attribute"}, {"api_name": "constant.messages_keys", "line_number": 36, "usage_type": "attribute"}, {"api_name": "constant.messages_reply_joke", "line_number": 37, "usage_type": "attribute"}, {"api_name": "constant.messages_reply_joke", "line_number": 38, "usage_type": "attribute"}, {"api_name": "constant.messages_keys", "line_number": 42, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 43, "usage_type": "call"}, {"api_name": "constant.messages_reply", "line_number": 43, "usage_type": "attribute"}, {"api_name": "constant.messages_keys", "line_number": 45, "usage_type": "attribute"}, {"api_name": "constant.messages_reply", "line_number": 46, "usage_type": "attribute"}, {"api_name": "game.game_sapper", "line_number": 48, "usage_type": "call"}, {"api_name": "game.gg", "line_number": 49, "usage_type": "attribute"}, {"api_name": "game.gg", "line_number": 51, "usage_type": "attribute"}]} +{"seq_id": "173622200", "text": "# -------------------------------------------------------------------------------------\n# Calls contour tracing in natural images script with different number of\n# puncturing bubbles\n# -------------------------------------------------------------------------------------\nimport numpy as np\nimport torch\n\nfrom train_contour_trace_natural_images import main\nimport models.new_piech_models as new_piech_models\nimport models.new_control_models as new_control_models\n\n\nif __name__ == '__main__':\n\n lr_arr = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5]\n\n data_set_parameters = {\n 'data_set_dir': './data/pathfinder_natural_images_2',\n }\n\n for lr in lr_arr:\n\n train_parameters = {\n 'random_seed': 15,\n 'train_batch_size': 32,\n 'test_batch_size': 32,\n 'learning_rate': lr,\n 'num_epochs': 100,\n 'lateral_w_reg_weight': 0.0001,\n 'lateral_w_reg_gaussian_sigma': 10,\n 'clip_negative_lateral_weights': True,\n 'lr_sched_step_size': 80,\n 'lr_sched_gamma': 0.5,\n 'punc_n_bubbles': 200,\n 'punc_fwhm': np.array([7, 9, 11, 13, 15, 17])\n }\n\n # # Create Model\n # cont_int_layer = new_piech_models.CurrentSubtractInhibitLayer(\n # lateral_e_size=15, lateral_i_size=15, n_iters=5, use_recurrent_batch_norm=True)\n # cont_int_layer = new_piech_models.CurrentDivisiveInhibitLayer(\n # lateral_e_size=15, lateral_i_size=15, n_iters=5, use_recurrent_batch_norm=True)\n\n cont_int_layer = new_control_models.ControlMatchParametersLayer(\n lateral_e_size=15, lateral_i_size=15)\n # cont_int_layer = new_control_models.ControlMatchIterationsLayer(\n # lateral_e_size=15, lateral_i_size=15, n_iters=5)\n # cont_int_layer = new_control_models.ControlRecurrentCnnLayer(\n # lateral_e_size=15, lateral_i_size=15, n_iters=5)\n\n scale_down_input_to_contour_integration_layer = 4\n net = new_piech_models.BinaryClassifierResnet50(cont_int_layer)\n\n main(\n net,\n train_params=train_parameters,\n data_set_params=data_set_parameters,\n base_results_store_dir=\n './results/contour_tracing_sensitivity_analysis/learning_rate/lr_{}'.format(lr),\n cont_int_scale=scale_down_input_to_contour_integration_layer,\n n_imgs_for_exp=5000\n )\n\n # ----------------------------------------------------------------------\n print(\"End\")\n import pdb\n pdb.set_trace()\n", "sub_path": "analyze_ct_learning_rate.py", "file_name": "analyze_ct_learning_rate.py", "file_ext": "py", "file_size_in_byte": 2562, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "models.new_control_models.ControlMatchParametersLayer", "line_number": 44, "usage_type": "call"}, {"api_name": "models.new_control_models", "line_number": 44, "usage_type": "name"}, {"api_name": "models.new_piech_models.BinaryClassifierResnet50", "line_number": 52, "usage_type": "call"}, {"api_name": "models.new_piech_models", "line_number": 52, "usage_type": "name"}, {"api_name": "train_contour_trace_natural_images.main", "line_number": 54, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "143576809", "text": "import boto3\nfrom moto import sqs\n\nfrom app.settings import LOCALSTACK, REGION, LOCALSTACK_ADDRESS\nfrom logzero import logger\n\ndef get_aws_resource(resource_type: str):\n \"\"\"Get an aws resource configured to use LocalStack if env var is set\"\"\"\n if LOCALSTACK:\n logger.warn(f\"Using localstack for {resource_type} resource\")\n return boto3.resource(\n resource_type,\n region_name=REGION,\n endpoint_url=LOCALSTACK_ADDRESS,\n aws_access_key_id=\"foo\",\n aws_secret_access_key=\"bar\",\n )\n else:\n return boto3.resource(resource_type, REGION)", "sub_path": "varnish-cleanup/app/aws_factory.py", "file_name": "aws_factory.py", "file_ext": "py", "file_size_in_byte": 620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "app.settings.LOCALSTACK", "line_number": 9, "usage_type": "name"}, {"api_name": "logzero.logger.warn", "line_number": 10, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 10, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 11, "usage_type": "call"}, {"api_name": "app.settings.REGION", "line_number": 13, "usage_type": "name"}, {"api_name": "app.settings.LOCALSTACK_ADDRESS", "line_number": 14, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 19, "usage_type": "call"}, {"api_name": "app.settings.REGION", "line_number": 19, "usage_type": "argument"}]} +{"seq_id": "41244988", "text": "#!/usr/bin/python\nimport numpy as np\nimport ase.build,ase.io\nfrom ase import Atoms\nimport argparse\n\nparser = argparse.ArgumentParser(\"Transfer (applicable) positions of cell 1 to cell 2\")\nparser.add_argument(\"cell1\", metavar=\"Cell 1\")\nparser.add_argument(\"cell2\", metavar=\"Cell 2\")\nparser.add_argument(\"-o\", dest=\"output\", type=str, default=\"POSCAR_out\", help=\"Name of output file\")\nparser.add_argument(\"-neg\", dest=\"allow_neg\", default=False, action=\"store_true\", help=\"Allow negative values in resulting POSCAR\")\nargs = parser.parse_args()\n\nfrom_cell = ase.io.read(args.cell1, format=\"vasp\")\nto_cell = ase.io.read(args.cell2, format=\"vasp\")\n\nfor cell in [from_cell, to_cell]:\n for atom in cell:\n rel_pos = np.linalg.solve(cell.get_cell()[:], atom.position)\n for i,p in enumerate(rel_pos):\n if p > 0.5:\n rel_pos[i] -= 1\n atom.position = np.matmul(cell.get_cell()[:], rel_pos)\n\nfor atom1 in from_cell:\n for atom2 in to_cell:\n diff = 0\n is_pair = True\n for i in range(3):\n diff += (atom1.position[i] - atom2.position[i])**2\n if np.sqrt(diff) > 0.8:\n is_pair = False\n break\n if is_pair:\n atom2.position = atom1.position\n break\nif not args.allow_neg:\n for cell in [from_cell, to_cell]:\n for atom in cell:\n rel_pos = np.linalg.solve(cell.get_cell()[:], atom.position)\n for i,p in enumerate(rel_pos):\n if p < 0:\n rel_pos[i] += 1\n atom.position = np.matmul(cell.get_cell()[:], rel_pos)\n\nase.io.write(args.output, to_cell, format=\"vasp\") \n \n\n", "sub_path": "processing/transfer_struct.py", "file_name": "transfer_struct.py", "file_ext": "py", "file_size_in_byte": 1674, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "ase.build.io.read", "line_number": 14, "usage_type": "call"}, {"api_name": "ase.build.io", "line_number": 14, "usage_type": "attribute"}, {"api_name": "ase.build", "line_number": 14, "usage_type": "name"}, {"api_name": "ase.build.io.read", "line_number": 15, "usage_type": "call"}, {"api_name": "ase.build.io", "line_number": 15, "usage_type": "attribute"}, {"api_name": "ase.build", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.linalg.solve", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 44, "usage_type": "call"}, {"api_name": "ase.build.io.write", "line_number": 46, "usage_type": "call"}, {"api_name": "ase.build.io", "line_number": 46, "usage_type": "attribute"}, {"api_name": "ase.build", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "632905642", "text": "# Copyright (c) 2013-2015 Siphon Contributors.\n# Distributed under the terms of the BSD 3-Clause License.\n# SPDX-License-Identifier: BSD-3-Clause\n\"\"\"Setup script for installing Siphon.\"\"\"\n\nfrom __future__ import print_function\n\nimport sys\n\nfrom setuptools import find_packages, setup\nimport versioneer\n\n\nver = versioneer.get_version()\n\n# Need to conditionally add enum support for older Python\ndependencies = ['numpy>=1.8', 'protobuf>=3.0.0a3', 'requests>=1.2', 'beautifulsoup4>=4.6',\n 'pandas']\nif sys.version_info < (3, 4):\n dependencies.append('enum34')\n\nsetup(\n name='siphon',\n version=ver,\n packages=find_packages(),\n author='Unidata Development Team',\n author_email='support-python@unidata.ucar.edu',\n license='BSD 3-Clause',\n url='https://github.com/Unidata/siphon',\n description=('A collection of Python utilities for interacting with the '\n 'Unidata technology stack.'),\n keywords='meteorology weather',\n classifiers=['Development Status :: 3 - Alpha',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Topic :: Scientific/Engineering',\n 'Intended Audience :: Science/Research',\n 'Operating System :: OS Independent',\n 'License :: OSI Approved :: BSD License'],\n\n python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*',\n install_requires=dependencies,\n extras_require={\n 'netcdf': 'netCDF4>=1.1.0',\n 'dev': 'ipython[all]>=3.1',\n 'test': ['pytest', 'pytest-flake8', 'pytest-runner',\n 'netCDF4>=1.1.0',\n 'flake8>3.2.0', 'flake8-builtins', 'flake8-comprehensions',\n 'flake8-copyright', 'flake8-docstrings', 'flake8-import-order',\n 'flake8-mutable', 'flake8-pep3101', 'flake8-print', 'flake8-quotes',\n 'flake8-rst-docstrings', 'pep8-naming',\n 'vcrpy~=1.5,!=1.7.0,!=1.7.1,!=1.7.2,!=1.7.3', 'xarray>=0.10.2'],\n 'doc': ['sphinx>=1.3,!=1.6.4', 'sphinx-gallery', 'doc8', 'm2r'],\n # SciPy needed for cartopy; we don't use cartopy[plotting] because\n # that will pull in GDAL.\n 'examples': ['matplotlib>=1.3', 'cartopy>=0.13.1', 'scipy', 'metpy']\n },\n\n download_url='https://github.com/Unidata/siphon/archive/v{}.tar.gz'.format(ver),\n cmdclass=versioneer.get_cmdclass(),\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "versioneer.get_version", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 19, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 22, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 25, "usage_type": "call"}, {"api_name": "versioneer.get_cmdclass", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "569771287", "text": "import os\nimport random\nimport argparse\nimport logging\nfrom tqdm import tqdm\n\nimport sys\n\nsys.path.append(os.path.abspath(\"../\"))\n\nlogging.getLogger().setLevel(logging.INFO)\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom training.dataset import VoiceDataset\nfrom training.tacotron2_model import Tacotron2, TextMelCollate, Tacotron2Loss\n\n\ndef eval_checkpoint(\n metadata_path,\n dataset_directory,\n checkpoint_folder,\n):\n # Hyperparams\n train_size = 0.8\n seed = 1234\n symbols = \"_-!'(),.:;? ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\"\n\n # Set seed\n torch.manual_seed(seed)\n random.seed(seed)\n\n # Load model\n logging.info(\"Loading model...\")\n model = Tacotron2()\n criterion = Tacotron2Loss()\n logging.info(\"Loaded model\")\n\n # Load data\n logging.info(\"Loading data...\")\n with open(metadata_path, encoding=\"utf-8\") as f:\n filepaths_and_text = [line.strip().split(\"|\") for line in f]\n\n random.shuffle(filepaths_and_text)\n int(len(filepaths_and_text) * train_size)\n test_files = filepaths_and_text[-100:]\n valset = VoiceDataset(test_files, dataset_directory, symbols, seed)\n collate_fn = TextMelCollate()\n\n # Data loaders\n val_loader = DataLoader(valset, num_workers=1, sampler=None, batch_size=1, pin_memory=False, collate_fn=collate_fn)\n logging.info(\"Loaded data\")\n\n for checkpoint in os.listdir(checkpoint_folder):\n checkpoint_path = os.path.join(checkpoint_folder, checkpoint)\n checkpoint_dict = torch.load(checkpoint_path, map_location=\"cpu\")\n model.load_state_dict(checkpoint_dict[\"state_dict\"])\n print(\"PROCESSING\", checkpoint)\n\n model.train()\n model.zero_grad()\n losses = []\n for _, batch in enumerate(tqdm(val_loader)):\n x, y = model.parse_batch(batch)\n y_pred = model(x)\n\n loss = criterion(y_pred, y)\n reduced_loss = loss.item()\n losses.append(reduced_loss)\n\n print(\"AVERAGE = \", sum(losses) / len(losses))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-m\", \"--metadata_path\", type=str, help=\"metadata path\")\n parser.add_argument(\"-d\", \"--dataset_directory\", type=str, help=\"directory to dataset\")\n parser.add_argument(\"-c\", \"--checkpoint_folder\", type=str, help=\"checkpoint folder\")\n\n args = parser.parse_args()\n eval_checkpoint(\n args.metadata_path,\n args.dataset_directory,\n args.checkpoint_folder,\n )\n", "sub_path": "research/eval_checkpoint.py", "file_name": "eval_checkpoint.py", "file_ext": "py", "file_size_in_byte": 2520, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 31, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 35, "usage_type": "call"}, {"api_name": "training.tacotron2_model.Tacotron2", "line_number": 36, "usage_type": "call"}, {"api_name": "training.tacotron2_model.Tacotron2Loss", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 41, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 45, "usage_type": "call"}, {"api_name": "training.dataset.VoiceDataset", "line_number": 48, "usage_type": "call"}, {"api_name": "training.tacotron2_model.TextMelCollate", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 53, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 57, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 64, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "212889258", "text": "from collections import deque\nclass Solution1(object):\n def canJump(self, nums):\n if len(nums) == 1:\n return True\n if len(nums) == 0 or nums[0] == 0:\n return False\n maxVisit = 0\n for i in range(len(nums)):\n if i <= maxVisit:\n maxVisit = max(maxVisit, nums[i] + i)\n if maxVisit >= len(nums) - 1:\n return True\n else:\n return False\n'''\n跳跃游戏\nhttps://leetcode-cn.com/problems/jump-game/\n这个是用贪心来做\n实际上是个隐式加速的BFS， 只不过一维数组的BFS可以优化成为greedy\n因为它是遍历走过的路，然后每次更新最远可达的路\n只是遍历的方式是先深度搜索搜到最深的地方，然后发现走不通就往回走看回去的路走不走得通\n贪心算法只是先标记一个最大能走通的，然后从近往远走\n\n下面是BFS的写法：\n'''\nclass Solution2(object):\n def canJump(self, nums):\n if len(nums) == 1:\n return True\n if len(nums) == 0:\n return False\n q = deque()\n visited = set()\n visited.add(0)\n q.append(0)\n while q:\n idx = q.pop()\n if idx >= len(nums)-1 or nums[idx] + idx >= len(nums) - 1:\n return True\n if idx >= 0 and nums[idx] == 0 or idx+nums[idx] in visited:\n idx -= 1\n while idx in visited:\n idx -= 1\n continue\n if idx == -1:return False\n else:\n q.append(idx)\n visited.add(idx)\n else:\n q.append(idx + nums[idx])\n visited.add(idx + nums[idx])\n return False\n\n'''\n我自己也是用\n另一种DP的想法： \ndp[i] 表示能够走到的最大的位置,直接转换题目思路不是是否能走到而是走到哪里做个判断\ndp[i] 指从0走到i后的下一个最大能走到的下标（前i个元素能到达的最远位置\n这样就和greedy是一样的了\n'''\nclass Solution2(object):\n def canJump(self, nums):\n if len(nums) == 1:\n return True\n if len(nums) == 0:\n return False\n dp = [0] * len(nums)\n dp[0] = nums[0]\n for i in range(1,len(nums)):\n if i > dp[i-1]:\n return False #第i个走不到\n else:\n dp[i] = max(dp[i-1], i + nums[i])\n if dp[i] >= len(nums) - 1:\n return True\n return False\n'''\n这边的dp可以优化，优化之后就是greedy了\n'''\n\na = Solution2()\nnums = [3,0,8,2,0,0,1]\nprint(a.canJump(nums))\n\n\n\n\n\n\n\n", "sub_path": "Graph_DFSandBFS/SpecialBFS_DP_Greedy/Leetcode_55/other_lc55.py", "file_name": "other_lc55.py", "file_ext": "py", "file_size_in_byte": 2710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "collections.deque", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "651706865", "text": "\"\"\"\nSupport for Ubiquiti EdgeOS routers.\nHEAVILY based on the AsusWRT component\nFor more details about this platform, please refer to the documentation at\nhttps://home-assistant.io/components/device_tracker.edgeos/\n\"\"\"\nimport logging\n\nfrom homeassistant.components.device_tracker import ATTR_SOURCE_TYPE, SOURCE_TYPE_ROUTER\nfrom homeassistant.components.device_tracker.config_entry import ScannerEntity\nfrom homeassistant.core import HomeAssistant\n\nfrom .helpers.const import *\nfrom .models.base_entity import EdgeOSEntity, async_setup_base_entry\nfrom .models.entity_data import EntityData\n\n_LOGGER = logging.getLogger(__name__)\nDEPENDENCIES = [DOMAIN]\n\nCURRENT_DOMAIN = DOMAIN_DEVICE_TRACKER\n\n\ndef get_device_tracker(hass: HomeAssistant, integration_name: str, entity: EntityData):\n device_tracker = EdgeOSScanner()\n device_tracker.initialize(hass, integration_name, entity, CURRENT_DOMAIN)\n\n return device_tracker\n\n\nasync def async_setup_entry(hass: HomeAssistant, entry, async_add_entities):\n \"\"\"Set up EdgeOS based off an entry.\"\"\"\n await async_setup_base_entry(\n hass, entry, async_add_entities, CURRENT_DOMAIN, get_device_tracker\n )\n\n\nasync def async_unload_entry(hass, config_entry):\n _LOGGER.info(f\"async_unload_entry {CURRENT_DOMAIN}: {config_entry}\")\n\n return True\n\n\nclass EdgeOSScanner(EdgeOSEntity, ScannerEntity):\n \"\"\"Represent a tracked device.\"\"\"\n\n @property\n def is_connected(self):\n \"\"\"Return true if the device is connected to the network.\"\"\"\n return self.entity.state\n\n @property\n def source_type(self):\n \"\"\"Return the source type, eg gps or router, of the device.\"\"\"\n return self.entity.attributes.get(ATTR_SOURCE_TYPE, SOURCE_TYPE_ROUTER)\n\n async def async_added_to_hass_local(self):\n _LOGGER.info(f\"Added new {self.name}\")\n\n def _immediate_update(self, previous_state: bool):\n if previous_state != self.entity.state:\n _LOGGER.debug(\n f\"{self.name} updated from {previous_state} to {self.entity.state}\"\n )\n\n super()._immediate_update(previous_state)\n", "sub_path": "custom_components/edgeos/device_tracker.py", "file_name": "device_tracker.py", "file_ext": "py", "file_size_in_byte": 2109, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 23, "usage_type": "name"}, {"api_name": "models.entity_data.EntityData", "line_number": 23, "usage_type": "name"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 30, "usage_type": "name"}, {"api_name": "models.base_entity.async_setup_base_entry", "line_number": 32, "usage_type": "call"}, {"api_name": "models.base_entity.EdgeOSEntity", "line_number": 43, "usage_type": "name"}, {"api_name": "homeassistant.components.device_tracker.config_entry.ScannerEntity", "line_number": 43, "usage_type": "name"}, {"api_name": "homeassistant.components.device_tracker.ATTR_SOURCE_TYPE", "line_number": 54, "usage_type": "argument"}, {"api_name": "homeassistant.components.device_tracker.SOURCE_TYPE_ROUTER", "line_number": 54, "usage_type": "argument"}]} +{"seq_id": "46120822", "text": "from flask import Flask, request, jsonify\nfrom flask_cors import CORS\nfrom bs4 import BeautifulSoup\nimport urllib2\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'the quick brown fox jumps over the lazy dog'\napp.config['CORS_HEADERS'] = 'Content-Type'\n\ncors = CORS(app)\n\ngithubURL = \"https://www.github.com/\"\n\n@app.route('/scrape', methods=['POST'])\ndef scrape():\n page = urllib2.urlopen(getPage(request.data))\n soup = BeautifulSoup(page, 'html.parser')\n\n repositories = []\n name = soup.find('span', attrs={'class': 'p-name'}).text\n username = soup.find('span', attrs={'class': 'p-nickname'}).text\n avatar = soup.find('a', attrs={'class': 'u-photo'}).get(\"href\")\n popularRepositories = soup.find(\"ol\", { \"class\" : \"gutter-condensed\" }).findAll(\"li\", recursive=False)\n repositorieCounter = soup.find(\"nav\", attrs={'class': 'UnderlineNav-body'}).select_one(\"a:nth-of-type(2)\").find(\"span\", attrs={\"class\": \"Counter\"}).text\n followersCounter = soup.find(\"nav\", attrs={'class': 'UnderlineNav-body'}).select_one(\"a:nth-of-type(5)\").find(\"span\", attrs={\"class\": \"Counter\"}).text\n followingCounter = soup.find(\"nav\", attrs={'class': 'UnderlineNav-body'}).select_one(\"a:last-child\").find(\"span\", attrs={\"class\": \"Counter\"}).text\n\n for repository in popularRepositories:\n link = githubURL + repository.find('a').get('href').encode('utf-8').strip(\"\\n\").strip()\n title = repository.find('span', attrs={'class': 'repo'}).text.encode('utf-8').strip(\"\\n\").strip()\n description = repository.find('p', attrs={'class': 'pinned-item-desc'}).text.encode('utf-8').strip(\"\\n\").strip()\n repositories.append([str(title), str(link), str(description)])\n\n githubData = {\n \"name\": name,\n \"username\": username,\n \"avatar\": avatar,\n \"repositorieCounter\": repositorieCounter,\n \"followersCounter\": followersCounter,\n \"followingCounter\": followingCounter,\n \"repositories\": repositories,\n }\n\n return jsonify(githubData)\n \ndef getPage(url):\n return githubURL + url\n\napp.run()", "sub_path": "src/scraper/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "242221239", "text": "import matplotlib.pyplot as plt\nimport matplotlib\nimport numpy as np\n\nfrom RSHelpers import circle\n\n#%%\n\n\n# Method to plot the 1st moment map\ndef plotMom1(self,which,xL=False,CB=False):\n \n # Get aperture size\n radius = self.radii[which]\n \n # Get unmasked minimu/maximum\n vmin = np.nanmin(self.mom1s[30])\n vmax = np.nanmax(self.mom1s[30])\n \n # Get desired 1st mom\n VCM = self.mom1s[30]\n \n # Get the median density value for colorbar\n half = (vmax-vmin)/2.0 + vmin\n \n # Set axis linewidths\n matplotlib.rcParams['axes.linewidth'] = 3\n\n # Plot 1st moment map\n ax = plt.imshow(circle(VCM,radius), origin='lower', vmin=vmin, vmax=vmax)\n \n # Set box to have fixed dimensions\n plt.gca().set_aspect('equal', adjustable='box')\n \n # No y label or y ticks necessary\n plt.ylabel('')\n plt.yticks([])\n \n # Create circular aperture object\n #circle = plt.Circle(self.center,radius=radius,color='k',fill=False,linewidth=3)\n \n # Add aperture object to plot\n #plt.gcf().gca().add_artist(circle)\n \n # If necessary set x axis label\n if xL == True:\n plt.xticks(self.xticks,self.xlabels,fontsize=5)\n plt.xlabel('L',fontsize=10)\n else:\n \n plt.xticks([])\n\n #If necessary add colorbar\n if CB == True:\n \n # Create colorbar subplot reference at top of 2nd row\n cax = plt.subplot(self.gs[1,1])\n \n # Create colorbar object\n y = plt.colorbar(ax, ticks = np.array([float(str(vmin + 0.01)[0:4]),\n float(str(half)[0:4]),\n float(str(vmax)[0:4])]), \n cax=cax, orientation=\"horizontal\")\n # Set colorbar tick positions to top\n y.ax.xaxis.set_ticks_position('top')\n \n # Set colorbar label position to top\n y.ax.xaxis.set_label_position('top')\n \n # Set desired colorbar tick size\n y.ax.tick_params(labelsize=5)\n \n # Set colorbar label\n y.set_label('$km s^{-1}$',fontsize=10)\n\n", "sub_path": "APJ_Paper/classes/plot/subclasses/observ_plot/class_methods/plot_mom1.py", "file_name": "plot_mom1.py", "file_ext": "py", "file_size_in_byte": 2140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "numpy.nanmin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 27, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "RSHelpers.circle", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "397121643", "text": "import pytest\nimport server.token_functions as TF\nimport server.channel_functions as CF \nimport server.channel_helper_functions as CHF\nfrom server.global_variables import reset_data, load_user\nfrom server.auth_functions import auth_login, auth_register, auth_logout\n\nreset_data()\nA = auth_register(\"z5226463@unsw.edu.au\", 'HoyaLee2019', \"Hoya\", \"Lee\")\nB = auth_register(\"z5000000@unsw.edu.au\", \"wsad1990\", \"Good\", \"Morning\")\ndatabaseListDict = load_user()\nPub_channel = CF.channels_create(A['token'], 'Num2', 'True')\n \n \n \n#Test following\n'''not logged in'''\ndef test_notLogin_invite():\n token = A['token']\n auth_logout(token)\n u_id = B['u_id']\n channelID = Pub_channel['channel_id']\n \n with pytest.raises(CF.AccessError):\n CF.channel_invite(token,channelID, u_id)\n \n \n'''Invalied channelID'''\ndef test_Invalid_public_channelID(): \n reset_data()\n A = auth_register(\"z5226463@unsw.edu.au\", 'HoyaLee2019', \"Hoya\", \"Lee\")\n B = auth_register(\"z5000000@unsw.edu.au\", \"wsad1990\", \"Good\", \"Morning\")\n token = A['token']\n u_id = B['u_id']\n channelID = Pub_channel['channel_id'] + 10086\n \n with pytest.raises(CHF.AccessError):\n CF.channel_invite(token, channelID, u_id)\n \n\n'''Invalid user_id for public channel'''\ndef test_invalid_user_id():\n token = A['token']\n u_id = B['u_id'] + 1\n channelID = Pub_channel['channel_id']\n \n with pytest.raises(CF.AccessError):\n CF.channel_invite(token, channelID, u_id)\n \n\n'''A test for channel that not exists'''\ndef test_channel_invite_notExists():\n token = A['token']\n u_id = B['u_id']\n channelID = None\n \n with pytest.raises(TypeError):\n CF.channel_invite(token, channelID, u_id)\n\n \n'''When the token not exists'''\ndef test_public_channel_invite_TKnot_exists():\n token = None\n u_id = B['u_id']\n channelID = Pub_channel['channel_id'] \n \n with pytest.raises(TypeError):\n CF.channel_invite(token, channelID, u_id)\n \n \n'''when the token can not be decode'''\ndef test_public_channel_invite_DETK_erro():\n token = 'hcuief7890995789agvhr'\n u_id = B['u_id']\n channelID = Pub_channel['channel_id'] \n \n with pytest.raises(TF.AccessError):\n CF.channel_invite(token, channelID, u_id)\n\n\n'''You must be in the channel to invite other members'''\ndef test_public_channel_invitor_NotIN():\n token = A['token']\n u_id = B['u_id']\n channelID = Pub_channel['channel_id']\n \n with pytest.raises(CHF.AccessError):\n CF.channel_invite(token, channelID, u_id)\n\n'''Invite successfully'''\ndef test_channel_invite_success():\n reset_data()\n A = auth_register(\"z5226463@unsw.edu.au\", 'HoyaLee2019', \"Hoya\", \"Lee\")\n B = auth_register(\"z5000000@unsw.edu.au\", \"wsad1990\", \"Good\", \"Morning\")\n Pub_channel = CF.channels_create(A['token'], 'Num2', 'True')\n channelID = Pub_channel['channel_id']\n token = A['token']\n u_id = B['u_id']\n \n CF.channel_invite(token, channelID, u_id)\n", "sub_path": "test_channel_invite_functions.py", "file_name": "test_channel_invite_functions.py", "file_ext": "py", "file_size_in_byte": 3025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "server.global_variables.reset_data", "line_number": 8, "usage_type": "call"}, {"api_name": "server.auth_functions.auth_register", "line_number": 9, "usage_type": "call"}, {"api_name": "server.auth_functions.auth_register", "line_number": 10, "usage_type": "call"}, {"api_name": "server.global_variables.load_user", "line_number": 11, "usage_type": "call"}, {"api_name": "server.channel_functions.channels_create", "line_number": 12, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 12, "usage_type": "name"}, {"api_name": "server.auth_functions.auth_logout", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 24, "usage_type": "call"}, {"api_name": "server.channel_functions.AccessError", "line_number": 24, "usage_type": "attribute"}, {"api_name": "server.channel_functions", "line_number": 24, "usage_type": "name"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 25, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 25, "usage_type": "name"}, {"api_name": "server.global_variables.reset_data", "line_number": 30, "usage_type": "call"}, {"api_name": "server.auth_functions.auth_register", "line_number": 31, "usage_type": "call"}, {"api_name": "server.auth_functions.auth_register", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 37, "usage_type": "call"}, {"api_name": "server.channel_helper_functions.AccessError", "line_number": 37, "usage_type": "attribute"}, {"api_name": "server.channel_helper_functions", "line_number": 37, "usage_type": "name"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 38, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 38, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "server.channel_functions.AccessError", "line_number": 47, "usage_type": "attribute"}, {"api_name": "server.channel_functions", "line_number": 47, "usage_type": "name"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 48, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 48, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 57, "usage_type": "call"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 58, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 58, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 67, "usage_type": "call"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 68, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 68, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 77, "usage_type": "call"}, {"api_name": "server.token_functions.AccessError", "line_number": 77, "usage_type": "attribute"}, {"api_name": "server.token_functions", "line_number": 77, "usage_type": "name"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 78, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 78, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 87, "usage_type": "call"}, {"api_name": "server.channel_helper_functions.AccessError", "line_number": 87, "usage_type": "attribute"}, {"api_name": "server.channel_helper_functions", "line_number": 87, "usage_type": "name"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 88, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 88, "usage_type": "name"}, {"api_name": "server.global_variables.reset_data", "line_number": 92, "usage_type": "call"}, {"api_name": "server.auth_functions.auth_register", "line_number": 93, "usage_type": "call"}, {"api_name": "server.auth_functions.auth_register", "line_number": 94, "usage_type": "call"}, {"api_name": "server.channel_functions.channels_create", "line_number": 95, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 95, "usage_type": "name"}, {"api_name": "server.channel_functions.channel_invite", "line_number": 100, "usage_type": "call"}, {"api_name": "server.channel_functions", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "179600084", "text": "from flask import make_response, jsonify, sessions\nfrom flask_restful import Resource\nfrom decorators import *\nfrom request_args import *\nimport datetime\nfrom constants import *\n\nclass LoanSchema(Resource):\n\n method_decorators = [token_required]\n # made loan post endpoint\n def post(self, current_user):\n # loan is only allowed for clients\n if current_user.get_role() == 'client':\n # check if the client doesn't already got an unpaid loan\n active_loan = Loan.query.filter(Loan.account_number == current_user.account_number, Loan.is_active == True).all()\n\n if not active_loan:\n args = loan_args.parse_args()\n\n #converting account type to client model\n current_user = Client.query.get(current_user.id)\n if current_user.get_account_type() == 'gold':\n days = GOLD_DAYS\n amount_limit = GOLD_LOAN_AMOUNT\n interest_rate = GOLD_RATE\n elif current_user.get_account_type() == 'silver':\n days = SILVER_DAYS\n amount_limit = SILVER_LOAN_AMOUNT\n interest_rate = SILVER_RATE\n else:\n days = BRONZE_DAYS\n amount_limit = BRONZE_LOAN_AMOUNT\n interest_rate = BRONZE_RATE\n\n print(amount_limit, args['amount'])\n if args['amount'] <= amount_limit:\n\n new_loan = Loan(current_user.account_number, args[\"amount\"], datetime.datetime.utcnow() + datetime.timedelta(days=days), interest_rate)\n central_account = Admin.query.filter(Admin.account_number == CENTRAL_ACCOUNT_NUMBER).first()\n \n # tranfer from central to the client\n central_account.bank_budget -= args['amount']\n current_user.balance += args['amount']\n \n db.session.add(new_loan)\n db.session.add(central_account)\n db.session.add(current_user)\n db.session.commit()\n\n \n\n return make_response({\"message\": f\"Loan taken successfully, amount to be paied is {new_loan.remaining_amount}\"}, 201)\n return make_response({\"message\": \"Can not take this ammount.\"}, 401)\n return make_response({\"message\": \"Please pay your current debt first.\"}, 401)\n return make_response({\"message\": \"Loan feature is only allowed for client accounts.\"}, 401)\n\n # made the function that retrieve active loans of a user\n def get(self, current_user):\n active_loan = Loan.query.filter(Loan.account_number == current_user.account_number, Loan.is_active == True).first()\n\n if active_loan:\n return jsonify(active_loan.serialize())\n return make_response({\"message\": \"No active loans.\"}, 400)\n\n # an update function for loan topup\n def put(self, current_user):\n active_loan = Loan.query.filter(Loan.account_number == current_user.account_number, Loan.is_active == True).first()\n #converting account type to client model\n current_user = Client.query.get(current_user.id)\n #central account\n central_account = Admin.query.filter(Admin.account_number == CENTRAL_ACCOUNT_NUMBER).first()\n\n if active_loan:\n args = loan_args.parse_args()\n topup = args['topup']\n\n if current_user.balance >= topup:\n if topup <= active_loan.remaining_amount:\n \n active_loan.remaining_amount -= topup\n current_user.balance -= topup\n central_account.bank_budget += topup\n\n #check if the user has finished the debt\n if active_loan.remaining_amount == 0:\n active_loan.is_active = False\n db.session.add(current_user)\n db.session.add(central_account)\n db.session.add(active_loan)\n db.session.commit()\n\n return make_response({'message': 'Congragulations you have finished your debt.'}, 201)\n \n db.session.add(current_user)\n db.session.add(central_account)\n db.session.add(active_loan)\n db.session.commit()\n return make_response({'message': f'Topup successful, you now have {active_loan.remaining_amount} debt left'}, 201)\n \n current_user.balance -= active_loan.remaining_amount\n central_account.bank_budget += active_loan.remaining_amount\n # returning amount from the extra topup provided\n returning_amount = topup - active_loan.remaining_amount\n active_loan.remaining_amount = 0\n active_loan.is_active = False\n\n db.session.add(current_user)\n db.session.add(central_account)\n db.session.add(active_loan)\n db.session.commit()\n\n\n return make_response({\"message\": f\"You have paid all the remaining amount, {returning_amount} has been returned to your account.\"}, 201)\n\n return make_response({'message': 'Insufficient balance'}, 401) \n\n return make_response({'message': \"Loan is not active.\"}, 401)\n\n # delete function for the loan\n def delete(self, current_user):\n active_loan = Loan.query.filter(Loan.account_number == current_user.account_number, Loan.is_active == True).first()\n #converting account type to client model\n current_user = Client.query.get(current_user.id)\n\n\n if active_loan:\n if current_user.balance >= active_loan.remaining_amount:\n current_user.balance -= active_loan.remaining_amount\n central_account = Admin.query.filter(Admin.account_number == CENTRAL_ACCOUNT_NUMBER).first()\n central_account.bank_budget += active_loan.remaining_amount\n active_loan.remaining_amount = 0\n active_loan.is_active = False\n\n db.session.add(current_user)\n db.session.add(central_account)\n db.session.add(active_loan)\n db.session.commit()\n\n return make_response({'message': 'Loan paid in full'}, 202)\n return make_response({'message': 'Insufficient balance'}, 401) \n return make_response({\"message\": \"loan deleted\"})\n\n", "sub_path": "features/loan/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 6609, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "flask_restful.Resource", "line_number": 8, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "200903085", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Mar 1 15:04:51 2021\r\n\r\n@author: brend\r\n\"\"\"\r\n\r\n\r\nimport math\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n#print(plt.style.available)\r\nplt.style.use('seaborn-colorblind')\r\n# plt.rcParams['figure.facecolor'] = '1'\r\n\r\nimport glob\r\nimport pandas as pd\r\n\r\nFocusWaist = 100*1e-6\r\n\r\ndef getData(data_file, NumSamples = 1):\r\n raw_data = np.asarray(pd.read_csv(data_file, delimiter = \"[\\],\\[\\t]\", engine='python',header=None))\r\n if NumSamples == 1:\r\n counts = raw_data[:,2]\r\n else:\r\n raw_counts = raw_data[:,2:2+NumSamples]\r\n TotalPulses = raw_counts.size\r\n print(TotalPulses)\r\n counts = np.mean(raw_counts, 1)\r\n \r\n times = raw_data[:,NumSamples + 17]\r\n times -= min(times)\r\n times = FixTimeOffset(times)\r\n datas = np.vstack((times, counts)).T\r\n return datas\r\ndef FixTimeOffset(data):#Fix timing when somehow it's less than 0.1 s between successive data points\r\n data = data.astype(float)\r\n dataDiff = np.diff(data)\r\n dataDiff2 = np.concatenate((dataDiff, np.array([0])))\r\n TimeDiff = np.mean(dataDiff)\r\n # print(f'Time Difference is {TimeDiff}')\r\n for i, x in enumerate(data):\r\n if i > 0:\r\n if data[i] - data[i-1] < 1e-1:\r\n # print(f\"time: {data[i-1]}, time2: {data[i]}\")\r\n data[i:] += TimeDiff\r\n return data\r\ndef plotBackground(ax, Ave):\r\n [BG_x0, BG_x1] = ax.get_xlim()\r\n BG_x = np.array([BG_x0, BG_x1])\r\n BG_y = np.ones(BG_x.shape)*Ave\r\n ax.plot(BG_x, BG_y, label='Average background')\r\n \r\ndata_filepath = r\"Neutral_Conditioning_Sacrif1*\"\r\ndata_files = glob.glob(data_filepath)\r\ndata_file_BG = [s for s in data_files if \"No-Ablation\" in s][0]\r\ndata_files_BeforeCond = [s for s in data_files if \"Before\" in s]\r\ndata_files_BeforeCond.pop(0)#Only need 1 example for figure\r\ndata_files_AfterCond = [s for s in data_files if \"After\" in s]\r\ndata_file_LongTerm = data_files_AfterCond[-1]\r\ndata_files_AfterCond.pop(-1)#Last file was long-term\r\nCondition_Fluences = np.array([130, 190])*1e-6/(math.pi*FocusWaist**2)#In J/m^2\r\n\r\ndata_BG = getData(data_file_BG)\r\nBG_Ave = np.mean(data_BG[:,1])\r\n\r\nfig = plt.figure(figsize=(20, 10))\r\nax1 = fig.add_subplot(111)\r\n\r\nfor i, data_file in enumerate(data_files_BeforeCond):\r\n data = getData(data_file)\r\n data_x = data[:,0]/60\r\n data_y = data[:,1]\r\n ax1.plot(data_x, data_y)\r\n \r\nLast_Offset = 0.18\r\nfor i, data_file in enumerate(data_files_AfterCond):\r\n data = getData(data_file)\r\n if i == 1:\r\n data[:,0] -= Last_Offset\r\n data = data[data[:,0] > 0]\r\n Condition_Fluence = Condition_Fluences[i]\r\n data_x = data[:,0]/60\r\n data_y = data[:,1]\r\n ax1.plot(data_x, data_y, label=f\"{Condition_Fluence/(100**2):0.3f} $\\mu J/cm^2$ Conditioning pulse energy\")\r\n\r\nplotBackground(ax1, BG_Ave)\r\nax1.legend(fontsize=20)\r\nax1.set_xlabel('Time / minute',fontsize=30, labelpad=20)\r\nax1.set_ylabel('Counts (a.u.)',fontsize=30, labelpad=23)\r\nax1.tick_params(axis='both', which='major', labelsize=20)\r\nax1.set_xlim(0, 2)\r\n\r\nplt.show()\r\n\r\nfig = plt.figure(figsize=(20, 10))\r\nax2 = fig.add_subplot(111)\r\n\r\nPulsesPer = 120\r\ndata = getData(data_file_LongTerm, PulsesPer)\r\ndata_y = data[:,1]\r\ndata_x = np.arange(data_y.size)*PulsesPer\r\nax2.plot(data_x, data_y, label=\"Average fluorescence each sweep\")\r\n#Need to get new BG\r\nplotBackground(ax2, BG_Ave)\r\nax2.legend(fontsize=20)\r\nax2.set_xlabel('Pulse number',fontsize=30, labelpad=20)\r\nax2.set_ylabel('Counts (a.u.)',fontsize=30, labelpad=23)\r\nax2.tick_params(axis='both', which='major', labelsize=20)\r\nax2.set_xlim(0, max(data_x))", "sub_path": "Paper_Data/Spot-Lifetimes/2020_11_04_Pitting-Conditioning/Condition-Sacrif1/Condition-Sacrificial-Area.py", "file_name": "Condition-Sacrificial-Area.py", "file_ext": "py", "file_size_in_byte": 3627, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 13, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 51, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "238873395", "text": "\"\"\"\nhttps://leetcode-cn.com/problems/trapping-rain-water-ii/\n\n\n给你一个 m x n 的矩阵，其中的值均为非负整数，代表二维高度图每个单元的高度，请计算图中形状最多能接多少体积的雨水。\n\n\n\n示例：\n\n给出如下 3x6 的高度图:\n[\n [1,4,3,1,3,2],\n [3,2,1,3,2,4],\n [2,3,3,2,3,1]\n]\n\n返回 4 。\n\n\n如上图所示，这是下雨前的高度图[[1,4,3,1,3,2],[3,2,1,3,2,4],[2,3,3,2,3,1]] 的状态。\n\n\n\n\n\n下雨后，雨水将会被存储在这些方块中。总的接雨水量是4。\n\n\n\n提示：\n\n1 <= m, n <= 110\n0 <= heightMap[i][j] <= 20000\n\n\"\"\"\nfrom heapq import heappush, heappop\nfrom typing import List, Tuple\n\n\nclass Solution:\n def trapRainWater(self, heightMap: List[List[int]]) -> int:\n if not heightMap or not heightMap[0]:\n return 0\n\n m, n = len(heightMap), len(heightMap[0])\n hp: List[Tuple[int, int, int]] = []\n visited = [[False for _ in range(n)] for _ in range(m)]\n # 将边缘放入 heap\n for r in range(m):\n for c in range(n):\n if r == 0 or r == m - 1 or c == 0 or c == n - 1:\n h = heightMap[r][c]\n heappush(hp, (h, r, c))\n visited[r][c] = True\n\n # 从小根堆里一次弹出 top, 更新 max_h\n # 将 top 的邻居放入 小根堆的同时，计算 邻居高度与 max_h 的差，这就是邻居的蓄水量，注意邻居比 max_h 高的情况\n lmt_h = -1\n water = 0\n while hp:\n h, r, c = heappop(hp)\n # 当前区域的高度瓶颈，如果 lmt_h 更新了，那么就说明，lmt_h 所属的区域没有更低的格子了\n lmt_h = max(lmt_h, h)\n for y, x in [(r - 1, c), (r + 1, c), (r, c - 1), (r, c + 1)]:\n if 0 <= y < m and 0 <= x < n and not visited[y][x]:\n cur_h = heightMap[y][x]\n # 当前区域的蓄水量，取决于当前区域的最大高度\n water += max(0, lmt_h - cur_h)\n heappush(hp, (cur_h, y, x))\n visited[y][x] = True\n\n return water\n", "sub_path": "week7/trapping_rain_water_ii.py", "file_name": "trapping_rain_water_ii.py", "file_ext": "py", "file_size_in_byte": 2156, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 47, "usage_type": "name"}, {"api_name": "heapq.heappush", "line_number": 54, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 62, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "161250880", "text": "import constants as c\nimport pygame\nfrom pygame.locals import *\nimport os\n\nclass Menu():\n\n # initialize the menu with buttons\n def __init__(self, MENUSTAT, screen, clock, background1):\n self.playButton = pygame.image.load(c.imagePath + c.playButton)\n self.helpButton = pygame.image.load(c.imagePath + c.helpButton)\n self.exitButton = pygame.image.load(c.imagePath + c.exitButton)\n self.MENUSTAT = MENUSTAT\n self.screen = screen\n self.clock = clock\n self.background1 = background1\n self.fpsClock = pygame.time.Clock()\n\n\n def formatButtons(self, menu, animateMenuX):\n formattedPlayButton = pygame.transform.scale(self.playButton, (100, 50)) # size change\n formattedHelpButton = pygame.transform.scale(self.helpButton, (100, 50)) # size change\n formattedExitButton = pygame.transform.scale(self.exitButton, (100, 50)) # size change\n\n self.screen.blit(menu, (animateMenuX, 0)) # add to layer starting left top corner position\n self.screen.blit(formattedPlayButton,\n (c.windowLength - int(c.windowLength / 2) - 250, c.windowWidth - int(c.windowWidth / 5)))\n self.screen.blit(formattedHelpButton,\n (c.windowLength - int(c.windowLength / 2), c.windowWidth - int(c.windowWidth / 5))) # middle\n self.screen.blit(formattedExitButton,\n (c.windowLength - int(c.windowLength / 2) + 250, c.windowWidth - int(c.windowWidth / 5)))\n\n return formattedPlayButton, formattedHelpButton, formattedExitButton\n\n\n def placeButtons(self, currentMouseX, currentMouseY, formattedPlayButton, formattedHelpButton, formattedExitButton):\n # if lowerbound < mousepos < higher bound and for y.. do button\n # difference is button size formatted\n if ((c.windowLength - int(c.windowLength / 2) - 250) < currentMouseX < (\n c.windowLength - int(c.windowLength / 2) - 150)) and (\n (c.windowWidth - int(c.windowWidth / 5)) < currentMouseY < (\n c.windowWidth - int(c.windowWidth / 5) + 50)):\n self.screen.blit(formattedPlayButton,\n (c.windowLength - int(c.windowLength / 2) - 250, c.windowWidth - int(c.windowWidth / 5)),\n special_flags=pygame.BLEND_RGBA_MULT)\n elif ((c.windowLength - int(c.windowLength / 2)) < currentMouseX < (\n c.windowLength - int(c.windowLength / 2) + 100)) and (\n (c.windowWidth - int(c.windowWidth / 5)) < currentMouseY < (\n c.windowWidth - int(c.windowWidth / 5) + 50)):\n self.screen.blit(formattedHelpButton,\n (c.windowLength - int(c.windowLength / 2), c.windowWidth - int(c.windowWidth / 5)),\n special_flags=pygame.BLEND_RGBA_MULT) # middle\n elif ((c.windowLength - int(c.windowLength / 2) + 250) < currentMouseX < (\n c.windowLength - int(c.windowLength / 2) + 350)) and (\n (c.windowWidth - int(c.windowWidth / 5)) < currentMouseY < (\n c.windowWidth - int(c.windowWidth / 5) + 50)):\n self.screen.blit(formattedExitButton,\n (c.windowLength - int(c.windowLength / 2) + 250, c.windowWidth - int(c.windowWidth / 5)),\n special_flags=pygame.BLEND_RGBA_MULT)\n\n def startMenu(self):\n animateMenuX = 0\n print(self.MENUSTAT)\n while self.MENUSTAT == True:\n mouse = pygame.mouse.get_pos()\n # check mouse pos and highlight button when mouse hovers above\n currentMouseX = mouse[0]\n currentMouseY = mouse[1]\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n quit()\n elif event.type == pygame.MOUSEBUTTONDOWN:\n # on right click on play\n if (event.button == 1) and ((c.windowLength - int(c.windowLength / 2) - 250) < currentMouseX < (\n c.windowLength - int(c.windowLength / 2) - 150)) and (\n (c.windowWidth - int(c.windowWidth / 5)) < currentMouseY < (\n c.windowWidth - int(c.windowWidth / 5) + 50)):\n print(\"Starting game ...\")\n menu = False\n running = True\n\n # prep load screen while game inits\n loadScreen = pygame.image.load(c.imagePath + c.loadingImage)\n formattedLoadScreen = pygame.transform.scale(loadScreen, (c.windowLength, c.windowWidth))\n self.screen.blit(formattedLoadScreen, (0, 0))\n pygame.display.update() # update visuals\n # TODO run the game\n #game(running, screen, background1, clock)\n\n # on right click on help (OPTIONS should rename latre)\n elif (event.button == 1) and ((c.windowLength - int(c.windowLength / 2)) < currentMouseX < (\n c.windowLength - int(c.windowLength / 2) + 100)) and (\n (c.windowWidth - int(c.windowWidth / 5)) < currentMouseY < (\n c.windowWidth - int(c.windowWidth / 5) + 50)):\n print(\"opening options ...\")\n # menu = False\n # running = True\n\n # show help/setuup screen ? then go back to menu screen\n\n # on right click on exit\n elif (event.button == 1) and ((c.windowLength - int(c.windowLength / 2) + 250) < currentMouseX < (\n c.windowLength - int(c.windowLength / 2) + 350)) and (\n (c.windowWidth - int(c.windowWidth / 5)) < currentMouseY < (\n c.windowWidth - int(c.windowWidth / 5) + 50)):\n print(\"Shutting down ...\")\n os._exit(0) # clean exit\n\n self.screen.fill((0, 0, 0))\n self.clock.tick(30)\n\n # menu animation\n menu = pygame.transform.scale(self.background1, (int(c.windowLength * 1.3), c.windowWidth)) # zoom in on bg\n\n formattedPlayButton, formattedHelpButton, formattedExitButton = self.formatButtons(menu, animateMenuX)\n\n ## message_to_screen(\"Players: \", red, screen)\n\n\n\n # # check mouse pos and highlight button when mouse hovers above\n currentMouseX = mouse[0]\n currentMouseY = mouse[1]\n\n self.placeButtons(currentMouseX, currentMouseY, formattedPlayButton, formattedHelpButton, formattedExitButton)\n\n pygame.display.update() # update visuals\n self.fpsClock.tick(30)\n\n animateMenuX -= 0.5\n if animateMenuX <= -400:\n animateMenuX = 0", "sub_path": "Duncan's Side Branch/MainMenu.py", "file_name": "MainMenu.py", "file_ext": "py", "file_size_in_byte": 6981, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "constants.imagePath", "line_number": 10, "usage_type": "attribute"}, {"api_name": "constants.playButton", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "constants.imagePath", "line_number": 11, "usage_type": "attribute"}, {"api_name": "constants.helpButton", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 12, "usage_type": "attribute"}, {"api_name": "constants.imagePath", "line_number": 12, "usage_type": "attribute"}, {"api_name": "constants.exitButton", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 23, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 27, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 27, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 29, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 29, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 31, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 31, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 39, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 40, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 41, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 42, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 44, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.BLEND_RGBA_MULT", "line_number": 45, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 46, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 47, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 48, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 49, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 51, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.BLEND_RGBA_MULT", "line_number": 52, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 53, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 54, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 55, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 56, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 58, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.BLEND_RGBA_MULT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 73, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 75, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 76, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 77, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 84, "usage_type": "attribute"}, {"api_name": "constants.imagePath", "line_number": 84, "usage_type": "attribute"}, {"api_name": "constants.loadingImage", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 85, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 85, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 87, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 87, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 92, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 93, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 94, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 95, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 103, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 104, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 105, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 108, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 114, "usage_type": "attribute"}, {"api_name": "constants.windowLength", "line_number": 114, "usage_type": "attribute"}, {"api_name": "constants.windowWidth", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 128, "usage_type": "attribute"}]} +{"seq_id": "386226080", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/nwinter/PycharmProjects/photon_projects/photon_core/photonai/optimization/performance_constraints.py\n# Compiled at: 2019-10-17 07:04:06\n# Size of source mod 2**32: 7110 bytes\nfrom enum import Enum\nimport numpy as np, inspect\nfrom photonai.processing.metrics import Scorer\nimport photonai.photonlogger.logger as logger\n\nclass PhotonBaseConstraint:\n __doc__ = '\\n The PHOTON base interface for any performance constraints that could speed up hyperparameter search.\\n After a particular configuration is tested in one fold, the performance constraint objects are called to\\n evaluate if the configuration is promising. If not, further testing in other folds is skipped to increase speed.\\n '\n ENUM_STRATEGY = Enum('strategy', 'first all mean')\n\n def __init__(self, strategy='first', metric='', threshold: float=None, margin: float=0, **kwargs):\n self._metric = None\n self._greater_is_better = None\n self._strategy = None\n self.metric = metric\n self.threshold = threshold\n self.margin = margin\n self.strategy = strategy\n\n @property\n def strategy(self):\n \"\"\"\n Getter for attribute strategy.\n :return:\n \"\"\"\n return self._strategy\n\n @strategy.setter\n def strategy(self, value):\n \"\"\"\n Setter for strategy. Checks if strategy is supported.\n :param value: String\n :return:\n \"\"\"\n try:\n self._strategy = PhotonBaseConstraint.ENUM_STRATEGY[value]\n except KeyError:\n raise KeyError('Your strategy: ' + str(value) + ' is not supported yet. Please use one of ' + str([x.name for x in PhotonBaseConstraint.ENUM_STRATEGY]))\n\n @property\n def metric(self):\n \"\"\"\n Getter for attribute metric.\n :return:\n \"\"\"\n return self._metric\n\n @metric.setter\n def metric(self, value):\n \"\"\"\n Setter for attribute metric.\n :param value: metric value\n :return:\n \"\"\"\n try:\n self._metric = value\n self._greater_is_better = Scorer.greater_is_better_distinction(self._metric)\n except NameError:\n self._metric = 'unknown'\n logger.warn('Your metric is not supported. Performance constraints are constantly False.')\n\n def shall_continue(self, config_item):\n \"\"\"\n Function to evaluate if the constraint is reached.\n If it returns True, the testing of the configuration is continued.\n If it returns False, further testing of the configuration is skipped\n to increase speed of the hyperparameter search.\n\n Parameters\n ----------\n * 'config_item' [MDBConfig]:\n All performance metrics and other scoring information for all configuration's performance.\n Can be used to evaluate if the configuration has any potential to serve the model's learning task.\n \"\"\"\n if self.metric == 'unknown':\n logger.warn('The metric is not known. Please check the metric: ' + self.metric + '. ' + 'Performance constraints are constantly True.')\n return True\n if self.metric not in config_item.inner_folds[0].validation.metrics:\n logger.warn('The metric is not calculated. Please insert ' + self.metric + ' to Hyperpipe.metrics. ' + 'Performance constraints are constantly False.')\n return False\n if self._greater_is_better:\n if self.strategy.name == 'first':\n if config_item.inner_folds[0].validation.metrics[self.metric] < self.threshold:\n return False\n elif self.strategy.name == 'all':\n if any((item < self.threshold for item in [x.validation.metrics[self.metric] for x in config_item.inner_folds])):\n return False\n elif self.strategy.name == 'mean':\n if np.mean([x.validation.metrics[self.metric] for x in config_item.inner_folds]) < self.threshold:\n return False\n return True\n if self.strategy.name == 'first':\n if config_item.inner_folds[0].validation.metrics[self.metric] > self.threshold:\n return False\n elif self.strategy.name == 'all':\n if any((item > self.threshold for item in [x.validation.metrics[self.metric] for x in config_item.inner_folds])):\n return False\n elif self.strategy.name == 'mean':\n if np.mean([x.validation.metrics[self.metric] for x in config_item.inner_folds]) > self.threshold:\n return False\n return True\n\n def copy_me(self):\n \"\"\"\n Copy self object.\n :return:\n \"\"\"\n new_me = type(self)(metric=(self.metric))\n signature = inspect.getfullargspec(self.__init__)[0]\n for attr in signature:\n if not attr == 'self':\n if hasattr(self, attr):\n if attr != 'strategy':\n setattr(new_me, attr, getattr(self, attr))\n if attr == 'strategy':\n setattr(new_me, attr, getattr(self, attr).name)\n\n return new_me\n\n\nclass MinimumPerformance(PhotonBaseConstraint):\n __doc__ = \"\\n Tests if a configuration performs better than a given limit for a particular metric.\\n\\n Example\\n -------\\n MinimumPerformance('accuracy', 0.96) tests if the configuration has at least a performance of 0.96 in\\n (the) [first, all, mean] fold(s).\\n If not further testing of the configuration is skipped, as it is regarded as not promising enough.\\n \"\n\n def __init__(self, metric='', threshold=1.0, strategy='first'):\n super(MinimumPerformance, self).__init__(strategy=strategy, metric=metric, threshold=threshold)\n\n\nclass DummyPerformance(PhotonBaseConstraint):\n __doc__ = \"\\n Tests if a configuration performs better than a given limit for a particular metric.\\n\\n Example\\n -------\\n DummyPerformance('accuracy', 0.1) tests if the configuration has at least a 10% better performance than the dummy\\n estimator. Distinguish between [first, all, mean] fold(s).\\n If not further testing of the configuration is skipped, as it is regarded as not promising enough.\\n \"\n\n def __init__(self, metric='', margin=0, strategy='first'):\n super(DummyPerformance, self).__init__(strategy=strategy, metric=metric, margin=margin)\n\n def set_dummy_performance(self, dummy_result):\n \"\"\"\n Set threshold with margin and dummy_performance value\n :param dummy_result: MDBScoreInformation type\n :return:\n \"\"\"\n self.threshold = dummy_result.validation.metrics[self.metric] + self.margin\n\n def copy_me(self):\n new_me = super(DummyPerformance, self).copy_me()\n if 'threshold' in self.__dict__.keys():\n new_me.threshold = self.threshold\n return new_me", "sub_path": "pycfiles/photonai-1.0.0b0.tar/performance_constraints.cpython-37.py", "file_name": "performance_constraints.cpython-37.py", "file_ext": "py", "file_size_in_byte": 7113, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "enum.Enum", "line_number": 15, "usage_type": "call"}, {"api_name": "photonai.processing.metrics.Scorer.greater_is_better_distinction", "line_number": 63, "usage_type": "call"}, {"api_name": "photonai.processing.metrics.Scorer", "line_number": 63, "usage_type": "name"}, {"api_name": "photonai.photonlogger.logger.warn", "line_number": 66, "usage_type": "call"}, {"api_name": "photonai.photonlogger.logger", "line_number": 66, "usage_type": "name"}, {"api_name": "photonai.photonlogger.logger.warn", "line_number": 82, "usage_type": "call"}, {"api_name": "photonai.photonlogger.logger", "line_number": 82, "usage_type": "name"}, {"api_name": "photonai.photonlogger.logger.warn", "line_number": 85, "usage_type": "call"}, {"api_name": "photonai.photonlogger.logger", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 105, "usage_type": "call"}, {"api_name": "inspect.getfullargspec", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "471574077", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\nHOW TO MAKE MONEYIN STOCKS(4TH) WILLIAM J. O'NEIL\nP166 绝对不建议投资者买入最近一季度每股收益同比增长幅度不到 18% 或 20% 的股票\nP168 最近一个季度的销售额增幅不应低于 25%\n\n增幅下限是最基本要求\nC= 可观或者加速增长的当季每股收益和每股销售收入, 如 高于 70% 或者 34% -> 53% -> 107% -> 126%\n\"\"\"\nimport pandas\n\nfrom config import config\nfrom indicator import finance as finance_ind\n\n\ndef finance(quote, period, backdays):\n df_finance = finance_ind.finance([quote.code[-1]])\n\n # 按季度\n cond1 = df_finance['dpnp_yoy_ratio'] > 18\n\n series = df_finance['totaloperatereve_yoy_ratio']\n cond2 = (series > 25) | ((series > 0) & (series.shift(periods=1) > 0) & (series.shift(periods=2) > 0))\n\n v = df_finance['dpnp_yoy_ratio_ins']\n cond3 = (v > 1) | (df_finance['dpnp_yoy_ratio'] > 70)\n\n # 按年度\n group = df_finance.groupby(pandas.Grouper(freq='4Q'))\n group_sum = group.sum()\n\n # 扣非每股收益\n dpnp = group_sum['dedu_parent_profit'][-1]\n dpnp_prev = group_sum['dedu_parent_profit'][-2]\n\n dpnp_yoy_ratio_4q = (dpnp / dpnp_prev) - 1\n cond4 = dpnp_yoy_ratio_4q > 0.25\n\n cond = cond1 & cond2 & cond3 & cond4\n\n return cond[-1]\n\n\ndef finance_ex(quote, period, backdays):\n df_finance = finance_ind.finance([quote.code[-1]])\n\n group = df_finance.groupby(pandas.Grouper(freq='4Q'))\n group_sum = group.sum()\n group_mean = group.mean()\n\n options = config.get_config_options()\n # 净资产收益率\n # roe1 = group_sum['roe'][-1]\n roe = round(100 * group_sum['eps'] / group_mean['bps'], 3)\n cond1 = roe > options['roe']\n\n # 收益稳定性\n # cond5 = df_finance['eps_std_rank'] < 25\n\n eps_std = df_finance['dpnp_yoy_ratio'].rolling(8).std()\n cond2 = eps_std < options['eps_std'] # 40 选出 18 只 # 3年 对应30\n\n cond = cond1 & cond2\n\n return cond[-1]\n", "sub_path": "selector/plugin/finance.py", "file_name": "finance.py", "file_ext": "py", "file_size_in_byte": 1953, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "indicator.finance.finance", "line_number": 18, "usage_type": "call"}, {"api_name": "indicator.finance", "line_number": 18, "usage_type": "name"}, {"api_name": "pandas.Grouper", "line_number": 30, "usage_type": "call"}, {"api_name": "indicator.finance.finance", "line_number": 46, "usage_type": "call"}, {"api_name": "indicator.finance", "line_number": 46, "usage_type": "name"}, {"api_name": "pandas.Grouper", "line_number": 48, "usage_type": "call"}, {"api_name": "config.config.get_config_options", "line_number": 52, "usage_type": "call"}, {"api_name": "config.config", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "194179773", "text": "from pathlib import Path\nfrom jinja2 import Environment, FileSystemLoader, Template\nimport markdown\nimport click\nfrom distutils.dir_util import copy_tree\nimport pkg_resources\nimport os\n\ncontent_dir, template_dir, output_dir = Path('content'), Path('templates'), Path('output')\nmd = markdown.Markdown(\n extensions=['meta', 'codehilite', 'fenced_code', 'toc', 'attr_list',\n 'tables', 'toc', 'markdown_captions', 'mdx_include']\n)\nenv = Environment(\n loader=FileSystemLoader(['templates', '.']), trim_blocks=True, lstrip_blocks=True\n)\nmain = click.group()(lambda: None)\n\ndef jinja_path(*patterns):\n items = []\n for pattern in patterns:\n for path in content_dir.glob(pattern):\n md.convert(path.read_text())\n items.append(\n {**{k:v[0] for k, v in md.Meta.items()},\n 'path':path.relative_to(content_dir).with_suffix('.html')}\n )\n return items\n\n@click.command(short_help=\"generate content\", help=\"generate output/ from content/\")\ndef build():\n for content_file in content_dir.rglob('*'):\n output_file = output_dir.joinpath(content_file.relative_to(content_dir))\n output_file.parent.mkdir(exist_ok=True)\n print('parsing ' + content_file.as_posix())\n\n if content_file.suffix.lower() == '.md':\n html = md.convert(content_file.read_text())\n if 'template' in md.Meta:\n template = env.get_template(\n template_dir.joinpath(md.Meta['template'][0]).as_posix()\n )\n output_file.with_suffix('.html').write_text(\n template.render(**{k:v[0] for k, v in md.Meta.items()}, content=html)\n )\n\n elif content_file.suffix == '.j2':\n template = env.get_template(content_file.as_posix())\n output_file.with_suffix('.html').write_text(template.render(path=jinja_path))\n\n elif content_file.is_file():\n if output_file.is_file():\n output_file.unlink()\n os.link(str(content_file), str(output_file))\n\n@click.command(short_help=\"initialize project\", help=\"initialize project\")\ndef init():\n copy_tree(pkg_resources.resource_filename(__name__, 'demo/'), '.')\n\nmain.add_command(build)\nmain.add_command(init)\n", "sub_path": "legoman/legoman.py", "file_name": "legoman.py", "file_ext": "py", "file_size_in_byte": 2294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "pathlib.Path", "line_number": 9, "usage_type": "call"}, {"api_name": "markdown.Markdown", "line_number": 10, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 14, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 15, "usage_type": "call"}, {"api_name": "click.group", "line_number": 17, "usage_type": "call"}, {"api_name": "os.link", "line_number": 54, "usage_type": "call"}, {"api_name": "click.command", "line_number": 30, "usage_type": "call"}, {"api_name": "distutils.dir_util.copy_tree", "line_number": 58, "usage_type": "call"}, {"api_name": "pkg_resources.resource_filename", "line_number": 58, "usage_type": "call"}, {"api_name": "click.command", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "181216692", "text": "import requests\n\nurl = \"https://httpbin.org/anything?foo=bar&foo=baz&baz=abc&key=value\"\n\npayload = { \"foo\": \"bar\" }\nheaders = {\n \"cookie\": \"foo=bar; bar=baz\",\n \"accept\": \"application/json\",\n \"content-type\": \"application/x-www-form-urlencoded\"\n}\n\nresponse = requests.post(url, data=payload, headers=headers)\n\nprint(response.text)", "sub_path": "src/targets/python/requests/fixtures/full.py", "file_name": "full.py", "file_ext": "py", "file_size_in_byte": 337, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "requests.post", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "255362351", "text": "# Copyright © 2019 Province of British Columbia\n#\n# Licensed under the Apache License, Version 2.0 (the 'License');\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an 'AS IS' BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Service to manage PAYBC services.\"\"\"\n\nfrom datetime import datetime\n\nfrom dateutil.parser import parse\nfrom flask import current_app\nfrom pay_api.models.base_model import db\nfrom pay_api.models.payment import Payment as PaymentModel\nfrom pay_api.models.statement import Statement as StatementModel\nfrom pay_api.models.statement_invoices import StatementInvoices as StatementInvoicesModel\nfrom pay_api.models.statement_settings import StatementSettings as StatementSettingsModel\nfrom pay_api.utils.enums import NotificationStatus, StatementFrequency\nfrom pay_api.utils.util import (\n get_first_and_last_dates_of_month, get_local_time, get_previous_day, get_previous_month_and_year,\n get_week_start_and_end_date)\n\n\nclass StatementTask: # pylint:disable=too-few-public-methods\n \"\"\"Task to generate statements.\"\"\"\n\n @classmethod\n def generate_statements(cls):\n \"\"\"Generate statements.\n\n Steps:\n 1. Get all payment accounts and it's active statement settings.\n \"\"\"\n current_time = get_local_time(datetime.now())\n # If today is sunday - generate all weekly statements for pervious week\n # If today is month beginning - generate all monthly statements for previous month\n # For every day generate all daily statements for previous day\n generate_weekly = current_time.weekday() == 6 # Sunday is 6\n generate_monthly = current_time.day == 1\n\n cls._generate_daily_statements(current_time)\n if generate_weekly:\n cls._generate_weekly_statements(current_time)\n if generate_monthly:\n cls._generate_monthly_statements(current_time)\n\n # Commit transaction\n db.session.commit()\n\n @classmethod\n def _generate_daily_statements(cls, current_time: datetime):\n \"\"\"Generate daily statements for all accounts with settings to generate daily.\"\"\"\n previous_day = get_previous_day(current_time)\n statement_settings = StatementSettingsModel.find_accounts_settings_by_frequency(previous_day,\n StatementFrequency.DAILY)\n current_app.logger.debug(f'Found {len(statement_settings)} accounts to generate DAILY statements')\n\n search_filter = {\n 'dateFilter': {\n 'startDate': previous_day.strftime('%m/%d/%Y'),\n 'endDate': previous_day.strftime('%m/%d/%Y')\n }\n }\n cls._create_statement_records(previous_day, search_filter, statement_settings)\n\n @classmethod\n def _generate_weekly_statements(cls, current_time: datetime):\n \"\"\"Generate weekly statements for all accounts with settings to generate weekly.\"\"\"\n statement_settings = StatementSettingsModel.find_accounts_settings_by_frequency(get_previous_day(current_time),\n StatementFrequency.WEEKLY)\n current_app.logger.debug(f'Found {len(statement_settings)} accounts to generate WEEKLY statements')\n search_filter = {\n 'weekFilter': {\n 'index': 1 # previous week\n }\n }\n\n cls._create_statement_records(current_time, search_filter, statement_settings)\n\n @classmethod\n def _generate_monthly_statements(cls, current_time: datetime):\n \"\"\"Generate monthly statements for all accounts with settings to generate monthly.\"\"\"\n statement_settings = StatementSettingsModel.find_accounts_settings_by_frequency(get_previous_day(current_time),\n StatementFrequency.MONTHLY)\n current_app.logger.debug(f'Found {len(statement_settings)} accounts to generate MONTHLY statements')\n last_month, last_month_year = get_previous_month_and_year()\n search_filter = {\n 'monthFilter': {\n 'month': last_month,\n 'year': last_month_year\n }\n }\n\n cls._create_statement_records(current_time, search_filter, statement_settings)\n\n @classmethod\n def _create_statement_records(cls, current_time, search_filter, statement_settings):\n statement_from = None\n statement_to = None\n if search_filter.get('dateFilter', None):\n statement_from = parse(search_filter.get('dateFilter').get('startDate'))\n statement_to = parse(search_filter.get('dateFilter').get('endDate'))\n elif search_filter.get('weekFilter', None):\n index = search_filter.get('weekFilter').get('index')\n statement_from, statement_to = get_week_start_and_end_date(index)\n elif search_filter.get('monthFilter', None):\n statement_from, statement_to = get_first_and_last_dates_of_month(\n search_filter.get('monthFilter').get('month'), search_filter.get('monthFilter').get('year'))\n for setting, pay_account in statement_settings:\n statement = StatementModel(\n frequency=setting.frequency,\n statement_settings_id=setting.id,\n payment_account_id=pay_account.id,\n created_on=current_time,\n from_date=statement_from,\n to_date=statement_to,\n notification_status_code=NotificationStatus.PENDING.value\n if pay_account.statement_notification_enabled is True\n else NotificationStatus.SKIP.value\n\n )\n # Add to DB session\n statement = statement.flush()\n\n purchases, total = PaymentModel.search_purchase_history( # pylint:disable=unused-variable\n auth_account_id=pay_account.auth_account_id,\n return_all=True,\n search_filter=search_filter,\n page=None,\n limit=None\n )\n\n for invoice in purchases:\n statement_invoice = StatementInvoicesModel(\n statement_id=statement.id,\n invoice_id=invoice.id\n )\n db.session.add(statement_invoice)\n\n # TODO Send an email notification\n", "sub_path": "jobs/payment-jobs/tasks/statement_task.py", "file_name": "statement_task.py", "file_ext": "py", "file_size_in_byte": 6785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "pay_api.utils.util.get_local_time", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "pay_api.models.base_model.db.session.commit", "line_number": 55, "usage_type": "call"}, {"api_name": "pay_api.models.base_model.db.session", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pay_api.models.base_model.db", "line_number": 55, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "name"}, {"api_name": "pay_api.utils.util.get_previous_day", "line_number": 60, "usage_type": "call"}, {"api_name": "pay_api.models.statement_settings.StatementSettings.find_accounts_settings_by_frequency", "line_number": 61, "usage_type": "call"}, {"api_name": "pay_api.models.statement_settings.StatementSettings", "line_number": 61, "usage_type": "name"}, {"api_name": "pay_api.utils.enums.StatementFrequency.DAILY", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pay_api.utils.enums.StatementFrequency", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.current_app.logger.debug", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "name"}, {"api_name": "pay_api.models.statement_settings.StatementSettings.find_accounts_settings_by_frequency", "line_number": 76, "usage_type": "call"}, {"api_name": "pay_api.models.statement_settings.StatementSettings", "line_number": 76, "usage_type": "name"}, {"api_name": "pay_api.utils.util.get_previous_day", "line_number": 76, "usage_type": "call"}, {"api_name": "pay_api.utils.enums.StatementFrequency.WEEKLY", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pay_api.utils.enums.StatementFrequency", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.current_app.logger.debug", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 78, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "name"}, {"api_name": "pay_api.models.statement_settings.StatementSettings.find_accounts_settings_by_frequency", "line_number": 90, "usage_type": "call"}, {"api_name": "pay_api.models.statement_settings.StatementSettings", "line_number": 90, "usage_type": "name"}, {"api_name": "pay_api.utils.util.get_previous_day", "line_number": 90, "usage_type": "call"}, {"api_name": "pay_api.utils.enums.StatementFrequency.MONTHLY", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pay_api.utils.enums.StatementFrequency", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.current_app.logger.debug", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 92, "usage_type": "name"}, {"api_name": "pay_api.utils.util.get_previous_month_and_year", "line_number": 93, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 108, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 109, "usage_type": "call"}, {"api_name": "pay_api.utils.util.get_week_start_and_end_date", "line_number": 112, "usage_type": "call"}, {"api_name": "pay_api.utils.util.get_first_and_last_dates_of_month", "line_number": 114, "usage_type": "call"}, {"api_name": "pay_api.models.statement.Statement", "line_number": 117, "usage_type": "call"}, {"api_name": "pay_api.utils.enums.NotificationStatus.PENDING", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pay_api.utils.enums.NotificationStatus", "line_number": 124, "usage_type": "name"}, {"api_name": "pay_api.utils.enums.NotificationStatus.SKIP", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pay_api.utils.enums.NotificationStatus", "line_number": 126, "usage_type": "name"}, {"api_name": "pay_api.models.payment.Payment.search_purchase_history", "line_number": 132, "usage_type": "call"}, {"api_name": "pay_api.models.payment.Payment", "line_number": 132, "usage_type": "name"}, {"api_name": "pay_api.models.statement_invoices.StatementInvoices", "line_number": 141, "usage_type": "call"}, {"api_name": "pay_api.models.base_model.db.session.add", "line_number": 145, "usage_type": "call"}, {"api_name": "pay_api.models.base_model.db.session", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pay_api.models.base_model.db", "line_number": 145, "usage_type": "name"}]} +{"seq_id": "19861931", "text": "from rest_framework.routers import DefaultRouter\nfrom App.views import UserViewSet, GroupViewSet\n\nfrom django.urls import path, include\nfrom App import views\n\nrouter = DefaultRouter()\nrouter.register('users', UserViewSet)\nrouter.register('groups', GroupViewSet)\n\nurlpatterns = [\n\n path('', views.home, name='home'),\n path('signup/', views.signup, name='signup'),\n path('accounts/', include('django.contrib.auth.urls')),\n path('secret/', views.secret_page, name='secret'),\n path('secret2/', views.SecretPage.as_view(), name='secret2'),\n\n]\n", "sub_path": "App/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 7, "usage_type": "call"}, {"api_name": "App.views.UserViewSet", "line_number": 8, "usage_type": "argument"}, {"api_name": "App.views.GroupViewSet", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "App.views.home", "line_number": 13, "usage_type": "attribute"}, {"api_name": "App.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "App.views.signup", "line_number": 14, "usage_type": "attribute"}, {"api_name": "App.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "App.views.secret_page", "line_number": 16, "usage_type": "attribute"}, {"api_name": "App.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "App.views.SecretPage.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "App.views.SecretPage", "line_number": 17, "usage_type": "attribute"}, {"api_name": "App.views", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "468459940", "text": "from sqlalchemy import (\n MetaData,\n Table,\n Column,\n Integer,\n NVARCHAR,\n Date,\n DateTime,\n ForeignKey,\n)\n\n\ndef upgrade(migrate_engine):\n meta = MetaData()\n meta.bind = migrate_engine\n\n Table(\"batch\", meta, autoload=True)\n\n request_details = Table(\n \"request_details\",\n meta,\n Column(\"id\", Integer, primary_key=True),\n Column(\"created_date\", DateTime()),\n Column(\"forename\", NVARCHAR(100)),\n Column(\"surname\", NVARCHAR(100)),\n Column(\"dob\", Date()),\n Column(\"sex\", NVARCHAR(10)),\n Column(\"postcode\", NVARCHAR(10)),\n Column(\"nhs_number\", NVARCHAR(10)),\n Column(\"system_number\", NVARCHAR(10)),\n Column(\"address1\", NVARCHAR(100)),\n Column(\"address2\", NVARCHAR(100)),\n Column(\"address3\", NVARCHAR(100)),\n Column(\"address4\", NVARCHAR(100)),\n Column(\"address5\", NVARCHAR(100)),\n Column(\"local_id\", NVARCHAR(100)),\n Column(\"batch_id\", Integer, ForeignKey(\"batch.id\"), index=True, nullable=False),\n )\n\n request_details.create()\n\n\ndef downgrade(migrate_engine):\n meta = MetaData()\n meta.bind = migrate_engine\n request_details = Table(\"request_details\", meta, autoload=True)\n request_details.drop()\n", "sub_path": "migrations/versions/002_Add_details_table.py", "file_name": "002_Add_details_table.py", "file_ext": "py", "file_size_in_byte": 1270, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "sqlalchemy.MetaData", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.NVARCHAR", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 37, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "149536129", "text": "#coding=utf-8\n#Author:XingShell\nfrom decision_stump import load_data\nfrom decision_stump import decision_stump_multi_d,err_out_counter\nimport numpy as np\nimport time\n\nmin = 1e-10\nclass ModelStump:\n def __init__(self,determined_dimension,theta_result,s_result):\n self.determined_dimension,self.theta_result,self.s_result = determined_dimension,theta_result,s_result\n def predict(self,x):\n x = np.array(x)\n return (np.sign(x[:,self.determined_dimension]-self.theta_result+min))*self.s_result\n def accurate(self,x,y):\n e_out = np.where(self.predict(x) == y, 0, 1).sum() / np.size(y, 0)\n return e_out\n def errorLabels(self,x,y):\n idx = [i for i,x in enumerate(np.where(self.predict(x) == y, 0, 1)) if x==1]\n return idx\n\nclass AdaboostStumps:\n def __init__(self,a,basemodels):\n self.a,self.basemodels = a,basemodels\n\n def predict(self, x):\n result = 0\n for i,w in enumerate(self.a):\n result += w*self.basemodels[i].predict(x)\n return np.sign(result)\n\n\n def accurate(self, x, y):\n e_out = np.where(self.predict(x) == y, 0, 1).sum() / np.size(y, 0)\n return e_out\n\n def errorLabels(self, x, y):\n idx = [i for i, x in enumerate(np.where(self.predict(x) == y, 0, 1)) if x == 1]\n return idx\n\n\n# def ModelError(x_datas,y_labels,ModelTree):\n# # 重新定制错误\n# errorIndex = []\n# for index,x in enumerate(x_datas):\n# ModelTree(x)\n\ndef AdaBoost(x_data, y_data,rounds=50):\n ms_list = []\n a = []\n N = len(y_data)\n W = [1/N]*N\n errorIndex = []\n determined_dimension, e_in_result, theta_result, s_result = decision_stump_multi_d(x_data, y_data,W)\n Model = (determined_dimension, theta_result, s_result)\n ms = ModelStump(*Model)\n errorIndex = ms.errorLabels(x_data, y_data)\n e_w = len(errorIndex)/len(y_data)\n for t in range(rounds):\n # 重新计算一遍错误向量\n errorIndex = ms.errorLabels(x_data,y_data)\n error_ratio = len(errorIndex)/len(y_data)\n if error_ratio == 0.5:\n return -1,None\n # print(e_w)\n # am = (1 / 2) * np.log((1 - error_ratio) / error_ratio)\n am = (1 / 2) * np.log((1 - e_w) / e_w)\n # if error_ratio<0.5:\n # print(errorIndex)\n a.append(am)\n ms_list.append(ms)\n am = np.abs(am)\n e_w = 0\n for i in range(len(y_data)):\n if i in errorIndex:\n W[i] *= np.exp(am)\n e_w += W[i]\n else:\n W[i] *= np.exp(-am)\n sum = np.sum(W)\n for index,w in enumerate(W):\n W[index] = w/sum\n # print(W)\n determined_dimension, e_in_result, theta_result, s_result = decision_stump_multi_d(x_data, y_data, W)\n Model = (determined_dimension, theta_result, s_result)\n ms = ModelStump(*Model)\n\n adaboost = AdaboostStumps(a,ms_list)\n e_in_result = adaboost.accurate(x_data,y_data)\n # print(a)\n print(\"E_IN:\", e_in_result)\n\n return e_in_result,adaboost\n\n\nif __name__ == '__main__':\n #开始时间\n start = time.time()\n x_train, y_train = load_data('data/train.txt')\n x_test, y_test = load_data('data/test.txt')\n _, Model = AdaBoost(x_train, y_train)\n # t = x_train,y_train\n # x_train, y_train = x_test,y_test\n # x_test, y_test = t\n yt = []\n ytest = []\n for r in range(1,50):\n # Model = AdaBoost(x_train,y_train,r)\n y,Model = AdaBoost(x_train,y_train,r)\n\n if y==-1:\n break\n yt.append(y)\n ytest.append(Model.accurate(x_test,y_test))\n #\n import matplotlib.pyplot as plt # 约定俗成的写法plt\n plt.plot(yt)\n plt.plot(ytest)\n plt.show()\n\n print(\"E_IN:\", Model.accurate(x_train, y_train))\n print(\"E_OUT:\",Model.accurate(x_test,y_test))\n\n\n", "sub_path": "决策树/adaboostBase.py", "file_name": "adaboostBase.py", "file_ext": "py", "file_size_in_byte": 3849, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 38, "usage_type": "call"}, {"api_name": "decision_stump.decision_stump_multi_d", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 80, "usage_type": "call"}, {"api_name": "decision_stump.decision_stump_multi_d", "line_number": 84, "usage_type": "call"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "decision_stump.load_data", "line_number": 99, "usage_type": "call"}, {"api_name": "decision_stump.load_data", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}]} +{"seq_id": "521707882", "text": "import collections\nclass Solution:\n def canFinish(self, numCourses, prerequisites):\n \"\"\"\n :type numCourses: int\n :type prerequisites: List[List[int]]\n :rtype: bool\n \"\"\"\n g = collections.defaultdict(list)\n for u,v in prerequisites:\n g[v].append(u)\n visited=[0]*numCourses\n def dfs(i):\n if visited[i]==1:return False\n if visited[i]==2:return True\n visited[i]=1\n for j in g[i]:\n if not dfs(j):return False\n visited[i]=2\n return True\n for i in range(numCourses):\n if not dfs(i):return False\n return True\n\nprint(Solution().canFinish(2,[[1,0]]))", "sub_path": "leetcode/0213课程表.py", "file_name": "0213课程表.py", "file_ext": "py", "file_size_in_byte": 722, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "collections.defaultdict", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "29830793", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.7-x86_64/egg/airflow/contrib/hooks/aws_firehose_hook.py\n# Compiled at: 2019-09-11 03:47:34\n# Size of source mod 2**32: 1837 bytes\nfrom airflow.contrib.hooks.aws_hook import AwsHook\n\nclass AwsFirehoseHook(AwsHook):\n __doc__ = '\\n Interact with AWS Kinesis Firehose.\\n :param delivery_stream: Name of the delivery stream\\n :type delivery_stream: str\\n :param region_name: AWS region name (example: us-east-1)\\n :type region_name: str\\n '\n\n def __init__(self, delivery_stream, region_name=None, *args, **kwargs):\n self.delivery_stream = delivery_stream\n self.region_name = region_name\n (super(AwsFirehoseHook, self).__init__)(*args, **kwargs)\n\n def get_conn(self):\n \"\"\"\n Returns AwsHook connection object.\n \"\"\"\n self.conn = self.get_client_type('firehose', self.region_name)\n return self.conn\n\n def put_records(self, records):\n \"\"\"\n Write batch records to Kinesis Firehose\n \"\"\"\n firehose_conn = self.get_conn()\n response = firehose_conn.put_record_batch(DeliveryStreamName=(self.delivery_stream),\n Records=records)\n return response", "sub_path": "pycfiles/apache_airflow_arup-1.10.5-py3.6/aws_firehose_hook.cpython-36.py", "file_name": "aws_firehose_hook.cpython-36.py", "file_ext": "py", "file_size_in_byte": 1345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "airflow.contrib.hooks.aws_hook.AwsHook", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "96818888", "text": "from lxml import etree\nimport zipfile\n\nooXMLns = {'w':'http://schemas.openxmlformats.org/wordprocessingml/2006/main'}\n\ndef get_comments(docxFileName, order=False):\n docxZip = zipfile.ZipFile(docxFileName)\n commentsXML = docxZip.read('word/comments.xml')\n et = etree.XML(commentsXML)\n comments = et.xpath('//w:comment',namespaces=ooXMLns)\n lst = []\n for c in comments:\n # attributes:\n # print(c.xpath('@w:author',namespaces=ooXMLns))\n # print(c.xpath('@w:date',namespaces=ooXMLns))\n # string value of the comment:\n # print(c.xpath('string(.)',namespaces=ooXMLns))\n lst.append(c.xpath('string(.)',namespaces=ooXMLns))\n if order:\n print('\\n'.join(sorted(lst)))\n return\n\n print('\\n'.join(lst))\n\nget_comments('1.docx', order=True)", "sub_path": "python_01/word/extract_comment.py", "file_name": "extract_comment.py", "file_ext": "py", "file_size_in_byte": 757, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "zipfile.ZipFile", "line_number": 7, "usage_type": "call"}, {"api_name": "lxml.etree.XML", "line_number": 9, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "526276528", "text": "#!/usr/bin/python3\n\nimport sys\nimport pandas as pd\nimport os, glob\nimport json\n\ndef o_f():\n outpath = \"out\"\n\n if not os.path.exists(outpath):\n os.makedirs(outpath)\n\n os.chdir(outpath)\n\ndef main(argv):\n srcDir = \".\"\n # csvs = \"*20[1-2][8|9|0].csv\"\n csvs = \"*.csv\"\n\n if len(argv) != 0:\n srcDir = argv[0]\n if len(argv) > 1:\n csvs = \"*.csv\"\n\n frame = pd.DataFrame()\n list_ = []\n\n # Specify datasets saved location/path\n os.chdir(srcDir)\n\n # Fetch all *.csv files\n for csvFile in glob.glob(csvs):\n # print csvFile\n\n # Specify encode and read csv contents \"SHIFT-JIS\"\n df= pd.read_csv(csvFile, encoding=\"SHIFT-JIS\")\n # print df\n\n df = df[df.columns[:7]]\n df.dropna(how='all')\n\n # df=df.rename(columns = {'two':'new_name'})\n df.columns.values[0] = \"tradeTime\"\n df.columns.values[1] = \"o\"\n df.columns.values[2] = \"h\"\n df.columns.values[3] = \"l\"\n df.columns.values[4] = \"c\"\n df.columns.values[5] = \"volume\"\n # modified price\n df.columns.values[6] = \"mp\"\n\n list_.append(df)\n\n # Concat\n frame = pd.concat(list_)\n\n # Remove odd rows\n frame = frame[frame.volume > 0] # [frame.volume != 0]\n\n col_name = frame.columns[0]\n # print \"[0]: \", col_name\n # print \"Dir: \", srcDir\n # frame = frame.rename(columns = {col_name: 'tradeTime'})\n # print frame.tradeTime\n\n # Drop duplicated\n frame.drop_duplicates(subset=[col_name], inplace=True)\n\n # Sort\n frame[col_name] = pd.to_datetime(frame.tradeTime)\n # frame.sort('tradeTime') This now sorts in date order (deprecated)\n frame.sort_values(by=[col_name], ascending=[False], inplace=True) # from ver 0.17\n frame[col_name] = frame[col_name].dt.strftime('%Y-%m-%d')\n\n # Reset idx : severl files have index 0\n frame = frame.reset_index(drop=True)\n\n # Calculate trade value\n frame['tradeValue'] = frame['volume'] * ((frame['h']+frame['l']+2*frame['c'])//4)\n frame['volume'] = frame['volume'] * ((frame['h']+frame['l']+2*frame['c'])//4) // frame['mp']\n\n # Output datasets\n o_f()\n\n # Convert all data\n frame.to_json('data.json', orient='records')\n\n # For report\n ldate = frame.at[0, 'tradeTime']\n # ldate = frame.get_value(0, 'tradeTime')\n item = {}\n item['code'] = srcDir[7:11]\n item['fromDate'] = ldate\n\n with open('item.json', 'w') as outfile:\n json.dump(item, outfile)\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n\n# Ref:\n## http://pandas.pydata.org/pandas-docs/stable/genindex.html\n## http://stackoverflow.com/questions/33642673/convert-csv-to-json-in-specific-format-using-python\n## http://www.nephridium-labs.com/blog/converting-between-json-and-csv-using-pandas/\n## https://github.com/nephridium/csv2json/blob/master/csv2json.py\n\n# $ pip install -U pandas --upgrade --proxy=http://id:pw@proxy.global.net:8080\n\n## * windows\n# > py -3 -m pip install --upgrade pip\n# > py -3 -m pip install pandas\n", "sub_path": "tf/sante/python3/readCsv3.py", "file_name": "readCsv3.py", "file_ext": "py", "file_size_in_byte": 2925, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "os.path.exists", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 12, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 30, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 71, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}]} +{"seq_id": "467228204", "text": "\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\" \n@author zhangbohan.dell@gmail.com\n@function:\n@create 1/10/2020 8:07 PM\n\"\"\"\nimport sys\n\nfrom PyQt5.QtWidgets import QApplication, QDialog\n\nfrom ui.mainUi import Ui_ncmTransfer\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n window = QDialog()\n ncmTransfer = Ui_ncmTransfer()\n ncmTransfer.setupUi(window)\n window.show()\n sys.exit(app.exec_())\n", "sub_path": "ui/start.py", "file_name": "start.py", "file_ext": "py", "file_size_in_byte": 423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 17, "usage_type": "call"}, {"api_name": "ui.mainUi.Ui_ncmTransfer", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "20671989", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom numpy.random import randn\nfrom datetime import datetime\n\n'''\nfig = plt.figure()\nfig.show()\n\nax1 = fig.add_subplot(2,2,1)\nax2 = fig.add_subplot(2,2,2)\nax3 = fig.add_subplot(2,2,3)\n\n\n\nplt.plot(randn(50).cumsum(),'k--')\n'''\nplt.close('all')\nfig = plt.figure()\nax = fig.add_subplot(1,1,1)\nfig.show()\n\ndata = pd.read_csv('D:\\Programs\\Python\\Data\\pydata-book-master\\ch08\\spx.csv',index_col=0,parse_dates=True)\nspx = data['SPX']\n\nspx.plot(ax=ax,style='k-')\n\ncrisis_data = [(datetime(2007,10,11),'Peak of bull market'),(datetime(2008,3,12),'Bear Stearns Fails'),\n (datetime(2008,9,15),'Lehman Bankruptcy')]\n\nfor date, label in crisis_data:\n ax.annotate(label,xy=(date,spx.asof(date)+50),xytext=(date,spx.asof(date)+200),arrowprops=dict(facecolor='black'),\n horizontalalignment='left',verticalalignment='top')\n\nax.set_xlim(['1/1/2007','1/1/2011'])\nax.set_ylim([600,1800])\nax.set_title('Important dates in 2008-2009 financial crisis')\n\n\n\n\n\n\n\n", "sub_path": "PythonForDataAnalysis/Matplotlib_Test.py", "file_name": "Matplotlib_Test.py", "file_ext": "py", "file_size_in_byte": 1024, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "matplotlib.pyplot.close", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "183247776", "text": "import matplotlib\nmatplotlib.use('TKAgg')\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport numpy as np\n\nx, y = np.meshgrid(np.arange(100), np.arange(100))\n\ndef getZ(mot):\n return np.sqrt((mot* x**2 + y**2))\n\nz = getZ(1)\nfig, ax = plt.subplots()\nim = ax.imshow(z)\n\ndef animate(i):\n im.set_data(getZ(mot=i)) # update the data\n return im\n\n# Init only required for blitting to give a clean slate.\ndef init():\n im.set_data(np.ma.array(x, mask=True))\n return im\n\nani = animation.FuncAnimation(fig, animate, np.arange(1, 500., 0.1), init_func=init,\n interval=25, blit=False)\n\nplt.show()\n\n\n", "sub_path": "Ph2150/other/anim_test.py", "file_name": "anim_test.py", "file_ext": "py", "file_size_in_byte": 658, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "matplotlib.use", "line_number": 2, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.ma.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 22, "usage_type": "attribute"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "648226418", "text": "# -*- coding: utf-8 -*-\n\n\nfrom os import path\n\nimport yaml\n\nfrom .helpers import get_test_cases, DATA_DIR, ROOT_DIR\n\n\ncases = get_test_cases([\n path.join(DATA_DIR, '*.yaml'),\n path.join(DATA_DIR, '*.yml'),\n path.join(ROOT_DIR, '*.yaml'),\n path.join(ROOT_DIR, '*.yml'),\n path.join(ROOT_DIR, '.*.yaml'),\n path.join(ROOT_DIR, '.*.yml'),\n])\n\n\ndef test_there_are_yaml_files_to_be_tested():\n assert len(cases) > 0\n\n\nfor case in cases:\n def _test():\n \"\"\"Tests whether YAML data file is a valid YAML document.\"\"\"\n with open(case['filename']) as f:\n assert yaml.load(f.read())\n\n globals()[case['fn_name']] = _test\n", "sub_path": "tests/valid_yaml_test.py", "file_name": "valid_yaml_test.py", "file_ext": "py", "file_size_in_byte": 657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "helpers.get_test_cases", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "helpers.DATA_DIR", "line_number": 12, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 12, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "helpers.DATA_DIR", "line_number": 13, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "helpers.ROOT_DIR", "line_number": 14, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "helpers.ROOT_DIR", "line_number": 15, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "helpers.ROOT_DIR", "line_number": 16, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "helpers.ROOT_DIR", "line_number": 17, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "89271604", "text": "import mock\n\nfrom kinto.core import scripts\n\nfrom .support import unittest\n\n\nclass InitSchemaTest(unittest.TestCase):\n def setUp(self):\n self.registry = mock.MagicMock()\n\n def test_migrate_calls_initialize_schema_on_backends(self):\n scripts.migrate({'registry': self.registry})\n self.assertTrue(self.registry.storage.initialize_schema.called)\n self.assertTrue(self.registry.cache.initialize_schema.called)\n self.assertTrue(self.registry.permission.initialize_schema.called)\n\n def test_migrate_in_read_only_display_warnings(self):\n with mock.patch('kinto.core.scripts.warnings.warn') as mocked:\n self.registry.settings = {'readonly': 'true'}\n scripts.migrate({'registry': self.registry})\n mocked.assert_any_call('Cannot migrate the storage backend '\n 'while in readonly mode.')\n mocked.assert_any_call('Cannot migrate the permission backend '\n 'while in readonly mode.')\n", "sub_path": "kinto/tests/core/test_scripts.py", "file_name": "test_scripts.py", "file_ext": "py", "file_size_in_byte": 1033, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "support.unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "support.unittest", "line_number": 8, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 10, "usage_type": "call"}, {"api_name": "kinto.core.scripts.migrate", "line_number": 13, "usage_type": "call"}, {"api_name": "kinto.core.scripts", "line_number": 13, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 19, "usage_type": "call"}, {"api_name": "kinto.core.scripts.migrate", "line_number": 21, "usage_type": "call"}, {"api_name": "kinto.core.scripts", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "152779649", "text": "import os\nimport time\nimport json\nimport subprocess\nimport RPi.GPIO as GPIO\n\n\n# connect the 4g internet\n# internet = subprocess.Popen([\"sudo\",\"wvdial\"])\n\n# start the epaper, gps, sendlog program\nepaper = subprocess.Popen([\"python\",\"epaper.py\"])\ngps = subprocess.Popen([\"sudo\",\"python\",\"gps.py\"])\nsendlog = subprocess.Popen([\"python\",\"sendlog.py\"])\n\n# the number of button's GPIO pin\ninput_pin = 31\noutput_pin = 33 \n\n# configutation for GPIO \nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(output_pin,GPIO.OUT)\nGPIO.setup(input_pin,GPIO.IN,pull_up_down=GPIO.PUD_DOWN)\nGPIO.output(output_pin,GPIO.HIGH)\n\n# mode\n# 0 = gps mode\n# 1 = rfid on duty\n# 2 = rfid off duty\nmode = 0\n\n# open the pipe file\nfifo = open(\"pipe\",\"w\")\n\n# create the data which will add into pipe\ncommand={\"father_process\":\"main\",\"mode\":mode}\n\n# begin\nwhile True:\n time.sleep(0.5)\n GPIO.wait_for_edge(31,GPIO.RISING)\n mode = (mode + 1) % 3\n command[\"mode\"] = mode\n json_str = json.dumps(command) + \"\\n\"\n fifo.write(json_str)\n fifo.flush()\n if mode == 0:\n rfid.kill()\n elif mode == 1:\n rfid = subprocess.Popen([\"python\",\"rfid.py\",\"on\"])\n elif mode == 2:\n rfid.kill()\n rfid = subprocess.Popen([\"python\",\"rfid.py\",\"off\"])\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "subprocess.Popen", "line_number": 12, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 13, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 14, "usage_type": "call"}, {"api_name": "RPi.GPIO.setmode", "line_number": 21, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 21, "usage_type": "name"}, {"api_name": "RPi.GPIO.BOARD", "line_number": 21, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 22, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 22, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 23, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 23, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.PUD_DOWN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 24, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 24, "usage_type": "name"}, {"api_name": "RPi.GPIO.HIGH", "line_number": 24, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "RPi.GPIO.wait_for_edge", "line_number": 41, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 41, "usage_type": "name"}, {"api_name": "RPi.GPIO.RISING", "line_number": 41, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 50, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "98197394", "text": "#!/usr/bin/env python3\nimport utils.datasets as ds\nimport machine_learning.naivebayes as nb\nimport utils.mesure as ms\n\n\ndef main():\n str0 = \"\"\n all_usr_funny = []\n all_usr_grade = []\n\n usr_vid, vid_usr, lusr, lvid = ds.combine_datas()\n del vid_usr\n test_system = ds.usr_specific(usr_vid, lvid, 10)\n for tested_usr in lusr:\n vecs_train = ds.list_features(\n test_system, [tested_usr], \"train\", lvid, \"vec\")\n vecs_test = ds.list_features(\n test_system, [tested_usr], \"test\", lvid, \"vec\")\n labels_test_funny = ds.list_features(\n test_system, [tested_usr], \"test\", lvid, \"funny\")\n labels_train_funny = ds.list_features(\n test_system, [tested_usr], \"train\", lvid, \"funny\")\n labels_train_grade = ds.list_features(\n test_system, [tested_usr], \"train\", lvid, \"grade\")\n labels_test_grade = ds.list_features(\n test_system, [tested_usr], \"test\", lvid, \"grade\")\n\n model_funny = nb.create_nb_model(vecs_train, labels_train_funny)\n pred_funny = nb.nb_predict(model_funny, vecs_test, labels_test_funny)\n\n model_grade = nb.create_nb_model(vecs_train, labels_train_grade)\n pred_grade = nb.nb_predict(model_grade, vecs_test, labels_test_grade)\n\n str0 += \"\\n\".join([\"\", tested_usr, ms.all_mesure_funny(pred_funny),\n ms.all_mesure_grade(pred_grade)])\n all_usr_funny.extend(pred_funny)\n all_usr_grade.extend(pred_grade)\n str0 += \"\\n\".join([\"\", \"all_usr\", ms.all_mesure_funny(all_usr_funny),\n ms.all_mesure_grade(all_usr_grade)])\n print(str0)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "scripts/test_system_nb.py", "file_name": "test_system_nb.py", "file_ext": "py", "file_size_in_byte": 1692, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "utils.datasets.combine_datas", "line_number": 12, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.datasets.usr_specific", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 14, "usage_type": "name"}, {"api_name": "utils.datasets.list_features", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 16, "usage_type": "name"}, {"api_name": "utils.datasets.list_features", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 18, "usage_type": "name"}, {"api_name": "utils.datasets.list_features", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 20, "usage_type": "name"}, {"api_name": "utils.datasets.list_features", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 22, "usage_type": "name"}, {"api_name": "utils.datasets.list_features", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 24, "usage_type": "name"}, {"api_name": "utils.datasets.list_features", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.datasets", "line_number": 26, "usage_type": "name"}, {"api_name": "machine_learning.naivebayes.create_nb_model", "line_number": 29, "usage_type": "call"}, {"api_name": "machine_learning.naivebayes", "line_number": 29, "usage_type": "name"}, {"api_name": "machine_learning.naivebayes.nb_predict", "line_number": 30, "usage_type": "call"}, {"api_name": "machine_learning.naivebayes", "line_number": 30, "usage_type": "name"}, {"api_name": "machine_learning.naivebayes.create_nb_model", "line_number": 32, "usage_type": "call"}, {"api_name": "machine_learning.naivebayes", "line_number": 32, "usage_type": "name"}, {"api_name": "machine_learning.naivebayes.nb_predict", "line_number": 33, "usage_type": "call"}, {"api_name": "machine_learning.naivebayes", "line_number": 33, "usage_type": "name"}, {"api_name": "utils.mesure.all_mesure_funny", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.mesure", "line_number": 35, "usage_type": "name"}, {"api_name": "utils.mesure.all_mesure_grade", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.mesure", "line_number": 36, "usage_type": "name"}, {"api_name": "utils.mesure.all_mesure_funny", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.mesure", "line_number": 39, "usage_type": "name"}, {"api_name": "utils.mesure.all_mesure_grade", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.mesure", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "249566063", "text": "from allauth.account.admin import EmailAddress\nfrom django.template.loader import render_to_string, get_template\nfrom django.core.mail import EmailMessage\nfrom datetime import datetime, timedelta\nfrom django.utils import timezone\nfrom twilio.rest import Client\nfrom django.conf import settings\nfrom pushbullet import Pushbullet, PushbulletError, PushError\nimport requests\nimport logging\nfrom urllib.parse import urlparse\nimport ipaddress\nfrom sentry_sdk import capture_exception\nimport smtplib\nimport backoff\n\nfrom lib.file_storage import save_file_obj\nfrom lib.utils import save_print_snapshot, last_pic_of_print\nfrom app.models import Printer, Print\nfrom app.telegram_bot import send_notification as send_telegram_notification\nfrom lib import site\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef send_failure_alert(printer, is_warning=True, print_paused=False):\n LOGGER.info(f'Printer {printer.user.id} {\"smells fishy\" if is_warning else \"is probably failing\"}. Sending Alerts')\n if not printer.current_print:\n LOGGER.warn(f'Trying to alert on printer without current print. printer_id: {printer.id}')\n return\n\n (_, rotated_jpg_url) = save_print_snapshot(\n printer.current_print,\n last_pic_of_print(printer.current_print, 'tagged'),\n unrotated_jpg_path=None,\n rotated_jpg_path=f'snapshots/{printer.id}/{printer.current_print.id}/{str(timezone.now().timestamp())}_rotated.jpg')\n\n # Calls wrapped in individual try/except because anyone of them could fail, and we still want the flow to continue\n\n try:\n if printer.user.alert_by_email:\n send_failure_alert_email(printer, rotated_jpg_url, is_warning, print_paused)\n except:\n capture_exception()\n\n try:\n send_failure_alert_pushbullet(printer, rotated_jpg_url, is_warning, print_paused)\n except:\n capture_exception()\n\n try:\n send_failure_alert_telegram(printer, rotated_jpg_url, is_warning, print_paused)\n except:\n capture_exception()\n\n try:\n if printer.user.is_pro and printer.user.alert_by_sms:\n send_failure_alert_sms(printer, is_warning, print_paused)\n except:\n capture_exception()\n\n try:\n if printer.user.is_pro:\n send_failure_alert_slack(printer, rotated_jpg_url, is_warning, print_paused)\n except:\n capture_exception()\n\n\ndef send_failure_alert_email(printer, rotated_jpg_url, is_warning, print_paused):\n if not settings.EMAIL_HOST:\n LOGGER.warn(\"Email settings are missing. Ignored send requests\")\n return\n\n subject = 'Your print {} on {} {}.'.format(\n printer.current_print.filename or '',\n printer.name,\n 'smells fishy' if is_warning else 'is probably failing')\n\n ctx = {\n 'printer': printer,\n 'print_paused': print_paused,\n 'is_warning': is_warning,\n 'view_link': site.build_full_url('/printers/'),\n 'cancel_link': site.build_full_url('/prints/{}/cancel/'.format(printer.current_print_id)),\n 'resume_link': site.build_full_url('/prints/{}/resume/'.format(printer.current_print_id)),\n }\n\n send_email(\n user=printer.user,\n subject=subject,\n mailing_list='alert',\n template_path='email/failure_alert.html',\n ctx=ctx,\n img_url=rotated_jpg_url,\n )\n\n\ndef send_failure_alert_sms(printer, is_warning, print_paused):\n if not settings.TWILIO_ENABLED:\n LOGGER.warn(\"Twilio settings are missing. Ignored send requests\")\n return\n\n if not printer.user.sms_eligible():\n return\n\n to_number = printer.user.phone_country_code + printer.user.phone_number\n\n pausing_msg = ''\n if print_paused:\n pausing_msg = 'Printer is paused. '\n elif printer.action_on_failure == Printer.PAUSE and is_warning:\n pausing_msg = 'Printer is NOT paused. '\n\n msg = 'The Spaghetti Detective - Your print {} on {} {}. {}Go check it at: {}'.format(\n printer.current_print.filename or '',\n printer.name,\n 'smells fishy' if is_warning else 'is probably failing',\n pausing_msg,\n site.build_full_url('/'))\n\n send_sms(msg, to_number)\n\n\ndef send_failure_alert_pushbullet(printer, rotated_jpg_url, is_warning, print_paused):\n if not printer.user.has_valid_pushbullet_token():\n return\n\n pausing_msg = ''\n if print_paused:\n pausing_msg = 'Printer is paused.'\n elif printer.action_on_failure == Printer.PAUSE and is_warning:\n pausing_msg = 'Printer is NOT paused because The Detective is not very sure about it.'\n\n pb = Pushbullet(printer.user.pushbullet_access_token)\n title = 'The Spaghetti Detective - Failure alert!'\n\n msg = 'Your print {} on {} {}.'.format(\n printer.current_print.filename or '',\n printer.name,\n 'smells fishy' if is_warning else 'is probably failing')\n link = site.build_full_url('/')\n body = '{}\\n{}\\nGo check it at: {}'.format(msg, pausing_msg, link)\n\n try:\n file_url = None\n try:\n file_url = rotated_jpg_url\n if not ipaddress.ip_address(urlparse(file_url).hostname).is_global:\n pb.upload_file(requests.get(file_url).content, 'Detected Failure.jpg')\n except:\n pass\n\n if file_url:\n pb.push_file(file_url=file_url, file_name=\"Detected Failure.jpg\", file_type=\"image/jpeg\", body=body, title=title)\n else:\n pb.push_link(title, link, body)\n except (PushError, PushbulletError) as e:\n LOGGER.error(e)\n\n\ndef send_failure_alert_telegram(printer, rotated_jpg_url, is_warning, print_paused):\n if not printer.user.telegram_chat_id:\n return\n\n try:\n photo = requests.get(rotated_jpg_url).content\n except:\n photo = None\n\n action = ''\n button_list = ['more_info']\n if print_paused:\n action = 'The print is paused.'\n button_list = ['cancel', 'resume', 'do_not_ask', 'more_info']\n elif printer.action_on_failure == Printer.PAUSE and is_warning:\n action = 'Printer is NOT paused because The Detective is not very sure about it.'\n button_list = ['cancel', 'more_info']\n\n notification_text = f\"\"\"Hi {printer.user.first_name or ''},\n\n_The Spaghetti Detective_ spotted some suspicious activity on your printer *{printer.name}*.\n\n{action}\"\"\"\n\n try:\n send_telegram_notification(printer, notification_text, photo, buttons=button_list)\n except requests.ConnectionError as e:\n LOGGER.error(e)\n\n\ndef send_failure_alert_slack(printer, rotated_jpg_url, is_warning, print_paused):\n if not printer.user.slack_access_token:\n return\n\n req = requests.get(\n url='https://slack.com/api/conversations.list',\n params={\n 'token': printer.user.slack_access_token,\n 'types': 'public_channel,private_channel'\n })\n req.raise_for_status()\n slack_channel_ids = [c['id'] for c in req.json()['channels'] if c['is_member']]\n\n for slack_channel_id in slack_channel_ids:\n msg = {\n \"channel\": slack_channel_id,\n \"blocks\": [\n {\n \"type\": \"section\",\n \"text\": {\n \"type\": \"mrkdwn\",\n \"text\": f\"*The Spaghetti Detective - Failure alert*\\n\\nYour print {printer.current_print.filename or ''} on {printer.name} {'smells fishy' if is_warning else 'is probably failing'}.\\nThe printer is {'paused' if print_paused else 'NOT paused'}.\\n<{site.build_full_url('/printers/')}|Check it out.>\"\n }\n }\n ]\n }\n try:\n msg['blocks'].append(\n {\n \"type\": \"image\",\n \"image_url\": rotated_jpg_url,\n \"alt_text\": \"Print snapshot\"\n }\n )\n except:\n pass\n\n req = requests.post(\n url='https://slack.com/api/chat.postMessage',\n headers={'Authorization': f'Bearer {printer.user.slack_access_token}'},\n json=msg\n )\n req.raise_for_status()\n\n\ndef send_print_notification(_print, extra_ctx={}):\n if _print.is_canceled():\n if not _print.printer.user.notify_on_canceled:\n return\n else:\n if not _print.printer.user.notify_on_done:\n return\n\n # Calls wrapped in individual try/except because anyone of them could fail, and we still want the flow to continue\n\n try:\n if _print.printer.user.print_notification_by_email:\n send_print_notification_email(_print, extra_ctx)\n except:\n capture_exception()\n\n try:\n if _print.printer.user.print_notification_by_pushbullet:\n send_print_notification_pushbullet(_print)\n except:\n capture_exception()\n\n try:\n if _print.printer.user.print_notification_by_telegram:\n send_print_notification_telegram(_print)\n except:\n capture_exception()\n\n try:\n if _print.printer.user.is_pro:\n send_print_notification_slack(_print)\n except:\n capture_exception()\n\n\ndef send_print_notification_email(_print, extra_ctx={}):\n subject = f'{_print.filename} is canceled.' if _print.is_canceled() else f'🙌 {_print.filename} is ready.'\n ctx = {\n 'print': _print,\n 'print_time': str(_print.ended_at() - _print.started_at).split('.')[0],\n 'timelapse_link': site.build_full_url(f'/prints/{_print.id}/'),\n }\n ctx.update(extra_ctx)\n send_email(\n user=_print.printer.user,\n subject=subject,\n mailing_list='print_notification',\n template_path='email/print_notification.html',\n ctx=ctx,\n img_url=_print.poster_url,\n )\n\n\ndef send_print_notification_telegram(_print):\n if not _print.printer.user.telegram_chat_id:\n return\n\n try:\n photo = requests.get(_print.poster_url).content\n except:\n photo = None\n\n notification_text = f\"\"\"Hi {_print.printer.user.first_name or ''},\n\nYour print job *{_print.filename}* {'has been canceled' if _print.is_canceled() else 'is done'} on printer {_print.printer.name}.\n\"\"\"\n try:\n send_telegram_notification(_print.printer, notification_text, photo)\n except requests.ConnectionError as e:\n LOGGER.error(e)\n\n\ndef send_print_notification_pushbullet(_print):\n if not _print.printer.user.has_valid_pushbullet_token():\n return\n\n pb = Pushbullet(_print.printer.user.pushbullet_access_token)\n\n title = 'The Spaghetti Detective - Print job notification'\n link = site.build_full_url('/')\n body = f\"Your print job {_print.filename} {'has been canceled' if _print.is_canceled() else 'is done'} on printer {_print.printer.name}.\"\n file_url = None\n try:\n file_url = _print.poster_url\n if not ipaddress.ip_address(urlparse(file_url).hostname).is_global:\n pb.upload_file(requests.get(file_url).content, 'Snapshot.jpg')\n except:\n pass\n\n try:\n if file_url:\n pb.push_file(file_url=file_url, file_name=\"Snapshot.jpg\", file_type=\"image/jpeg\", body=body, title=title)\n else:\n pb.push_link(title, link, body)\n except (PushError, PushbulletError) as e:\n LOGGER.error(e)\n\n\ndef send_print_notification_slack(_print):\n if not _print.printer.user.slack_access_token:\n return\n\n req = requests.get(\n url='https://slack.com/api/conversations.list',\n params={\n 'token': _print.user.slack_access_token,\n 'types': 'public_channel,private_channel'\n })\n req.raise_for_status()\n slack_channel_ids = [c['id'] for c in req.json()['channels'] if c['is_member']]\n\n for slack_channel_id in slack_channel_ids:\n msg = {\n \"channel\": slack_channel_id,\n \"blocks\": [\n {\n \"type\": \"section\",\n \"text\": {\n \"type\": \"mrkdwn\",\n \"text\": f\"*The Spaghetti Detective - Print job notification*\\n\\n*G-Code*: {_print.filename} \\n*Status*: {'Canceled' if _print.is_canceled() else 'Finished'}\\n*Printer*: <{site.build_full_url('/printers/')}|{_print.printer.name}>\"\n }\n }\n ]\n }\n if _print.poster_url:\n msg['blocks'].append(\n {\n \"type\": \"image\",\n \"image_url\": _print.poster_url,\n \"alt_text\": \"Print snapshot\"\n }\n )\n\n req = requests.post(\n url='https://slack.com/api/chat.postMessage',\n headers={'Authorization': f'Bearer {_print.user.slack_access_token}'},\n json=msg\n )\n req.raise_for_status()\n\n\n# Helpers\n@backoff.on_exception(backoff.expo,\n (smtplib.SMTPServerDisconnected,\n smtplib.SMTPSenderRefused,\n smtplib.SMTPResponseException,),\n max_tries=3)\ndef send_email(user, subject, mailing_list, template_path, ctx, img_url=None, verified_only=True, attachment=None):\n if not settings.EMAIL_HOST:\n LOGGER.warn(\"Email settings are missing. Ignored send requests\")\n return\n\n attachments = []\n if img_url:\n # https://github.com/TheSpaghettiDetective/TheSpaghettiDetective/issues/43\n try:\n if not ipaddress.ip_address(urlparse(img_url).hostname).is_global:\n attachments = [('Image.jpg', requests.get(img_url).content, 'image/jpeg')]\n except:\n pass\n\n ctx['img_url'] = None if attachments else img_url\n\n # By default email verification should be required for notifications but\n # maybe users will want to disable it on private servers\n if settings.ACCOUNT_EMAIL_VERIFICATION != 'none' and verified_only:\n emails = EmailAddress.objects.filter(user=user, verified=True)\n else:\n emails = EmailAddress.objects.filter(user=user)\n\n unsub_url = site.build_full_url(f'/unsubscribe_email/?unsub_token={user.unsub_token}&list={mailing_list}')\n for email in emails:\n ctx['unsub_url'] = unsub_url\n message = get_template(template_path).render(ctx)\n msg = EmailMessage(\n subject,\n message,\n to=(email.email,),\n from_email=settings.DEFAULT_FROM_EMAIL,\n attachments=attachments,\n headers={'List-Unsubscribe': f'<{unsub_url}>, '},)\n msg.content_subtype = 'html'\n if attachment:\n msg.attach_file(attachment)\n msg.send()\n\n\ndef send_sms(msg, to_number):\n twilio_client = Client(settings.TWILIO_ACCOUNT_SID, settings.TWILIO_AUTH_TOKEN)\n from_number = settings.TWILIO_FROM_NUMBER\n\n twilio_client.messages.create(body=msg, to=to_number, from_=from_number)\n", "sub_path": "data/codefile/thespaghettidetective@thespaghettidetective__b86b375__web$app$notifications.py.target.py", "file_name": "thespaghettidetective@thespaghettidetective__b86b375__web$app$notifications.py.target.py", "file_ext": "py", "file_size_in_byte": 14896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "lib.utils.save_print_snapshot", "line_number": 32, "usage_type": "call"}, {"api_name": "lib.utils.last_pic_of_print", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 36, "usage_type": "name"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 44, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 49, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 54, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 60, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 66, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_HOST", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 70, "usage_type": "name"}, {"api_name": "lib.site.build_full_url", "line_number": 83, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 83, "usage_type": "name"}, {"api_name": "lib.site.build_full_url", "line_number": 84, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 84, "usage_type": "name"}, {"api_name": "lib.site.build_full_url", "line_number": 85, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 85, "usage_type": "name"}, {"api_name": "django.conf.settings.TWILIO_ENABLED", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 99, "usage_type": "name"}, {"api_name": "app.models.Printer.PAUSE", "line_number": 111, "usage_type": "attribute"}, {"api_name": "app.models.Printer", "line_number": 111, "usage_type": "name"}, {"api_name": "lib.site.build_full_url", "line_number": 119, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 119, "usage_type": "name"}, {"api_name": "app.models.Printer.PAUSE", "line_number": 131, "usage_type": "attribute"}, {"api_name": "app.models.Printer", "line_number": 131, "usage_type": "name"}, {"api_name": "pushbullet.Pushbullet", "line_number": 134, "usage_type": "call"}, {"api_name": "lib.site.build_full_url", "line_number": 141, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 141, "usage_type": "name"}, {"api_name": "ipaddress.ip_address", "line_number": 148, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 148, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 149, "usage_type": "call"}, {"api_name": "pushbullet.PushError", "line_number": 157, "usage_type": "name"}, {"api_name": "pushbullet.PushbulletError", "line_number": 157, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 166, "usage_type": "call"}, {"api_name": "app.models.Printer.PAUSE", "line_number": 175, "usage_type": "attribute"}, {"api_name": "app.models.Printer", "line_number": 175, "usage_type": "name"}, {"api_name": "app.telegram_bot.send_notification", "line_number": 186, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 187, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 195, "usage_type": "call"}, {"api_name": "lib.site.build_full_url", "line_number": 212, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 212, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 228, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 250, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 256, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 262, "usage_type": "call"}, {"api_name": "sentry_sdk.capture_exception", "line_number": 268, "usage_type": "call"}, {"api_name": "lib.site.build_full_url", "line_number": 276, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 276, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 294, "usage_type": "call"}, {"api_name": "app.telegram_bot.send_notification", "line_number": 303, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 304, "usage_type": "attribute"}, {"api_name": "pushbullet.Pushbullet", "line_number": 312, "usage_type": "call"}, {"api_name": "lib.site.build_full_url", "line_number": 315, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 315, "usage_type": "name"}, {"api_name": "ipaddress.ip_address", "line_number": 320, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 320, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 321, "usage_type": "call"}, {"api_name": "pushbullet.PushError", "line_number": 330, "usage_type": "name"}, {"api_name": "pushbullet.PushbulletError", "line_number": 330, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 338, "usage_type": "call"}, {"api_name": "lib.site.build_full_url", "line_number": 355, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 355, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 369, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_HOST", "line_number": 384, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 384, "usage_type": "name"}, {"api_name": "ipaddress.ip_address", "line_number": 392, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 392, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 393, "usage_type": "call"}, {"api_name": "django.conf.settings.ACCOUNT_EMAIL_VERIFICATION", "line_number": 401, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 401, "usage_type": "name"}, {"api_name": "allauth.account.admin.EmailAddress.objects.filter", "line_number": 402, "usage_type": "call"}, {"api_name": "allauth.account.admin.EmailAddress.objects", "line_number": 402, "usage_type": "attribute"}, {"api_name": "allauth.account.admin.EmailAddress", "line_number": 402, "usage_type": "name"}, {"api_name": "allauth.account.admin.EmailAddress.objects.filter", "line_number": 404, "usage_type": "call"}, {"api_name": "allauth.account.admin.EmailAddress.objects", "line_number": 404, "usage_type": "attribute"}, {"api_name": "allauth.account.admin.EmailAddress", "line_number": 404, "usage_type": "name"}, {"api_name": "lib.site.build_full_url", "line_number": 406, "usage_type": "call"}, {"api_name": "lib.site", "line_number": 406, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 409, "usage_type": "call"}, {"api_name": "django.core.mail.EmailMessage", "line_number": 410, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 414, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 414, "usage_type": "name"}, {"api_name": "backoff.on_exception", "line_number": 378, "usage_type": "call"}, {"api_name": "backoff.expo", "line_number": 378, "usage_type": "attribute"}, {"api_name": "smtplib.SMTPServerDisconnected", "line_number": 379, "usage_type": "attribute"}, {"api_name": "smtplib.SMTPSenderRefused", "line_number": 380, "usage_type": "attribute"}, {"api_name": "smtplib.SMTPResponseException", "line_number": 381, "usage_type": "attribute"}, {"api_name": "twilio.rest.Client", "line_number": 424, "usage_type": "call"}, {"api_name": "django.conf.settings.TWILIO_ACCOUNT_SID", "line_number": 424, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 424, "usage_type": "name"}, {"api_name": "django.conf.settings.TWILIO_AUTH_TOKEN", "line_number": 424, "usage_type": "attribute"}, {"api_name": "django.conf.settings.TWILIO_FROM_NUMBER", "line_number": 425, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 425, "usage_type": "name"}]} +{"seq_id": "356337709", "text": "# Databricks notebook source\n# MAGIC %md\n# MAGIC COPYRIGHT: Columbia Sportswear 2019
\n# MAGIC DESCRIPTION: This notebook read the one trust privacy files from datalake and loads them to landing table. \n# MAGIC \n# MAGIC -----------------------------------------------------------------\n# MAGIC ###### MODIFICATION LOG\n# MAGIC | Programmmer | Change Request | Date | Change Description |\n# MAGIC |----------------------|-----------------|------------|--------------------------------------------------------------------|\n# MAGIC | Lakshmi Yalamanchili | OneTrust CCPA | 12/05/2019 | Initial Development |\n# MAGIC | jsingh | Removed hard coded path value | 01/09/2019 | Initial Development |\n# MAGIC | Lakshmi Yalamanchili | OneTrust CCPA | 09/28/2020 | Added logic to exit notebook and succeed if no file found |\n\n# COMMAND ----------\n\ndbutils.widgets.text(\"schmNm\", \"\", \"\")\ndbutils.widgets.text(\"tblNm\", \"\", \"\")\n\ndbutils.widgets.text(\"deltaTS\", \"\", \"\")\ndbutils.widgets.text(\"initFlg\", \"\", \"\")\n\n\ndbutils.widgets.get(\"deltaTS\")\ndeltaTS = getArgument(\"deltaTS\")\n\ndbutils.widgets.get(\"initFlg\")\ninitFlg = getArgument(\"initFlg\")\n\ndbutils.widgets.get(\"schmNm\")\nschmNm = getArgument(\"schmNm\")\n\ndbutils.widgets.get(\"tblNm\")\ntblNm = getArgument(\"tblNm\")\n\ndeltaTS = deltaTS.replace('T',' ')\n\n# COMMAND ----------\n\n# MAGIC %sql\n# MAGIC CREATE DATABASE IF NOT EXISTS EDW_LND_SVS_RES\n# MAGIC location '/mnt/entadls/native/restricted_consumer/edw_lnd/edw_lnd_svs_res';\n# MAGIC --drop table if exists EDW_LND_SVS_RES.ONETRUST;\n# MAGIC CREATE TABLE IF NOT EXISTS EDW_LND_SVS_RES.ONETRUST\n# MAGIC (\n# MAGIC Brand string\n# MAGIC ,DateUpdated timestamp\n# MAGIC ,Email string\n# MAGIC ,FirstName string\n# MAGIC ,LastName string\n# MAGIC ,IsVerified string\n# MAGIC ,RequestTypes string\n# MAGIC ,StateOfResidence string\n# MAGIC ,Status string\n# MAGIC ,EDW_HASH_CHK string\n# MAGIC ,EDW_CRT_TS timestamp\n# MAGIC ,EDW_UPDT_TS timestamp)\n# MAGIC USING DELTA\n# MAGIC LOCATION '/mnt/entadls/native/restricted_consumer/edw_lnd/edw_lnd_svs_res/onetrust';\n\n# COMMAND ----------\n\n#Truncate for Initial load\nif initFlg == \"X\" :\n\n truncTable = \"TRUNCATE TABLE {0}.{1}\".format(schmNm, tblNm)\n spark.sql(truncTable)\n\n# COMMAND ----------\n\n###if processing for today's date\n#import datetime \n#currentdate = datetime.datetime.now().strftime(\"%Y%m%d\") \n#print(currentdate)\n\n###if reading date from delta control\n#import datetime \n#datetime1 = spark.sql(\"select cast('{0}' as date) as dt\".format(deltaTS))\n#date_time = datetime1.select ('dt').collect()[0][0]\n#lastrundate = date_time.strftime('%Y%m%d')\n#print(lastrundate)\n\n# COMMAND ----------\n\nfrom datetime import datetime\nfrom datetime import timedelta \n\n#process all records from yesterday \npreviousday = datetime.now() + timedelta(days=-1)\ncurrentdate = previousday.strftime(\"%Y%m%d\") \nprint(currentdate)\n\n# COMMAND ----------\n\nimport os\nif not os.path.exists('/dbfs' + '/mnt/entadls/native/restricted_consumer/privacy/optout/v1/'+currentdate):\n dbutils.notebook.exit(\"Folder not found for the specified date:\" + currentdate)\n\n# COMMAND ----------\n\n#removed multiline option as files are placed in single line\ndf_OT_files=spark.read.json('/mnt/entadls/native/restricted_consumer/privacy/optout/v1/'+currentdate)\n#df_OT_files=spark.read.json('/mnt/entadls/native/restricted_consumer/privacy/optout/v1/'+'20200101')\n\n# COMMAND ----------\n\ndf_OT_files.cache()\ndf_OT_files.count()\n\n# COMMAND ----------\n\ndf_OT_files.createOrReplaceTempView(\"OT_files\")\n\n# COMMAND ----------\n\ndf_remove_dups = spark.sql(\"\"\"\nSelect\na.brand as Brand,\na.dateUpdated as DateUpdated,\na.email as Email,\na.firstName as FirstName,\na.lastName as LastName,\na.isVerified as IsVerified,\nconcat_ws(',',a.requestTypes) as RequestTypes,\na.stateofresidence as StateOfResidence,\na.status as Status,\nCAST(NULL AS string) as EDW_HASH_CHK,\nCURRENT_TIMESTAMP AS EDW_CRT_TS,\nCURRENT_TIMESTAMP AS EDW_UPDT_TS\nFROM\n(\nSelect \nbrand,\ndateUpdated,\nemail,\nfirstName,\nlastName,\nisVerified,\nRequestTypes,\nstateofresidence,\nstatus,\nROW_NUMBER() OVER (PARTITION BY email,brand ORDER BY dateUpdated DESC) as rnk\nfrom OT_files) a where a.rnk=1\"\"\")\n\n# COMMAND ----------\n\ndf_remove_dups.cache()\ndf_remove_dups.count()\n\n# COMMAND ----------\n\ndf_remove_dups.createOrReplaceTempView(\"OT_final_data\")\n\n# COMMAND ----------\n\n# MAGIC %sql\n# MAGIC INSERT INTO EDW_LND_SVS_RES.ONETRUST\n# MAGIC SELECT\n# MAGIC Brand,\n# MAGIC DateUpdated,\n# MAGIC Email,\n# MAGIC FirstName,\n# MAGIC LastName,\n# MAGIC IsVerified,\n# MAGIC RequestTypes,\n# MAGIC StateOfResidence,\n# MAGIC Status,\n# MAGIC EDW_HASH_CHK,\n# MAGIC EDW_CRT_TS,\n# MAGIC EDW_UPDT_TS\n# MAGIC from OT_final_data\n\n# COMMAND ----------\n\n# MAGIC %sql\n# MAGIC CREATE DATABASE IF NOT EXISTS EDW_LND_SVS_RES_VIEW;\n# MAGIC CREATE OR REPLACE VIEW EDW_LND_SVS_RES_VIEW.ONETRUST\n# MAGIC AS\n# MAGIC SELECT\n# MAGIC a.Brand,\n# MAGIC a.DateUpdated,\n# MAGIC a.Email,\n# MAGIC a.FirstName,\n# MAGIC a.LastName,\n# MAGIC a.IsVerified,\n# MAGIC a.RequestTypes,\n# MAGIC a.StateOfResidence,\n# MAGIC a.Status,\n# MAGIC a.EDW_HASH_CHK,\n# MAGIC a.EDW_CRT_TS,\n# MAGIC a.EDW_UPDT_TS\n# MAGIC FROM (\n# MAGIC SELECT\n# MAGIC Brand,\n# MAGIC DateUpdated,\n# MAGIC Email,\n# MAGIC FirstName,\n# MAGIC LastName,\n# MAGIC IsVerified,\n# MAGIC RequestTypes,\n# MAGIC StateOfResidence,\n# MAGIC Status,\n# MAGIC EDW_HASH_CHK,\n# MAGIC EDW_CRT_TS,\n# MAGIC EDW_UPDT_TS,\n# MAGIC ROW_NUMBER() OVER (PARTITION BY email,brand ORDER BY EDW_UPDT_TS DESC) as rnk\n# MAGIC FROM EDW_LND_SVS_RES.ONETRUST) a\n# MAGIC where a.rnk=1\n\n# COMMAND ----------\n\n#cache cleanup\ndf_OT_files.unpersist()\ndf_remove_dups.unpersist()\n\n# COMMAND ----------\n\n\n", "sub_path": "Prod/edw_lnd_svs_res/onetrust.py", "file_name": "onetrust.py", "file_ext": "py", "file_size_in_byte": 5684, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "datetime.datetime.now", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}]} +{"seq_id": "179149246", "text": "import torch\nimport torch.optim as optim\nfrom torch.autograd import Variable\n\nfrom bsg.bsg_hyper import *\nfrom bsg.bsg_vae import BSGvae\nfrom bsg.bsg_data import BSGdata\nfrom embedmodel import EmbedModel\nfrom torch.distributions.multivariate_normal import MultivariateNormal\nfrom torch.distributions import kl_divergence\n\n\nclass BSG(EmbedModel):\n\n def __init__(self, filename, pickle_name, model_location=''):\n super(BSG, self).__init__(filename, pickle_name, model_location, BSGvae, BSGdata, optim.Adam, HYPER_DICT)\n\n self.embed_dict = {\n 'w2i': self.data.w2i,\n 'model': self.model,\n 'optimizer': self.optimizer,\n 'total_epochs': self.init_epochs + self.epochs,\n 'hyper_params': HYPER_DICT\n }\n\n def get_context_words(self, w_i):\n c_i = torch.add(w_i.view(-1, 1), self.data.context_i.view(1, -1))\n return self.data.words[c_i]\n\n def lst_score_fn(self, candidates, sentence, index):\n self.model.training = False\n\n candidict = {}\n context = [self.data.w2i.get(sentence[i], 0) for i in (int(index) + self.data.context_i).data if\n 0 <= i < len(sentence)]\n context = Variable(torch.LongTensor(context).view(1,-1))\n candidates = [self.data.w2i.get(i, 0) for i in candidates]\n candidates = candidates\n\n w = self.data.w2i.get(sentence[int(index)], 0)\n w = Variable(torch.LongTensor([w]))\n\n mu_w, sigma_w = self.model(w, context, [], None, 1)\n mu_w = mu_w.squeeze()\n sigma_w = sigma_w.squeeze()\n\n p = MultivariateNormal(mu_w, torch.diag(sigma_w ** 2))\n\n for cand in candidates:\n cand_var = Variable(torch.LongTensor([cand]))\n mu_c, sigma_c = self.model(cand_var, context, [], None, 1)\n mu_c = mu_c.squeeze()\n sigma_c = sigma_c.squeeze()\n q = MultivariateNormal(mu_c, torch.diag(sigma_c ** 2))\n candidict[self.data.i2w[cand]] = kl_divergence(p, q)\n print(candidict)\n return candidict\n", "sub_path": "assignment2/bsg/bsg.py", "file_name": "bsg.py", "file_ext": "py", "file_size_in_byte": 2061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "embedmodel.EmbedModel", "line_number": 13, "usage_type": "name"}, {"api_name": "bsg.bsg_vae.BSGvae", "line_number": 16, "usage_type": "argument"}, {"api_name": "bsg.bsg_data.BSGdata", "line_number": 16, "usage_type": "argument"}, {"api_name": "torch.optim.Adam", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.add", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.distributions.multivariate_normal.MultivariateNormal", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.distributions.multivariate_normal.MultivariateNormal", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.distributions.kl_divergence", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "452174000", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 1 15:21:27 2020\n\n@author: joseramos\n@author: raul aguilar\n@author: julio lazo\n\nPROYECTO TEORIA ELECTROMAGNETICA\n\nPARA MEJORES RESULTADOS CORRER EN LA TERMINAL O IDLE PERO NO SPYDER\n\nalgoritmo para calcular la divergencia fue sacado de aca\nhttps://www.manongdao.com/article-250819.html\n\n\n\"\"\"\n\nimport numpy as np\n\nfrom mpl_toolkits.mplot3d import Axes3D \nimport matplotlib.pyplot as plt\n\nfrom matplotlib import cm\n\nfig = plt.figure()\n\nbw = cm.gray\nwarm = cm.coolwarm\n\n\n\"\"\"La pregunta 3.1 \n\"\"\"\n\nsin = np.sin\npi = np.pi\n\ndef h(y):\n return 2*y**3+5\n\ncosh = np.cosh\nsh = np.shape\n\na = 3\n\nb = a*1.5\n\ndx = 0.1\nx = np.arange(0,a,dx)\ny = np.linspace(0,b,len(x))\ndy = b/len(y)\n\nX,Y = np.meshgrid(x,y)\n\ndef cn(n,m):\n s = sin(m*pi/a*X)\n r = sin(n*pi/b*Y)\n t = np.exp(n*pi*a/b)\n I = np.trapz(f)\n I = I*2/b\n I = I/t\n return I\n\nn = [3,6,11,21]\n\ndef V(x,y,n):\n v = np.zeros((len(x),len(x)))\n for k in range(1,n):\n c = cn(k)\n r = 0\n s = 0\n for i in x:\n for j in y:\n v[s][r] += c*sin(k*pi*j/b)*np.exp(-k*pi*a/b)\n s += 1\n r += 1\n s = 0\n return v\n\n#x,y = np.meshgrid(x,y)\n\ndef divergence(f):\n num_dims = len(f)\n return np.ufunc.reduce(np.add, [np.gradient(f[i], axis=i) for i in range(num_dims)])\n\ndef graficarV(k, b):\n v = V(x,y,n[k])\n if b:\n plt.figure(k)\n plt.title('Potencial, con n = '+str(n[k]-1))\n plt.xlabel('X')\n plt.ylabel('Y') \n plt.pcolormesh(x,y,v,cmap = bw)\n plt.show()\n fig = plt.figure(10-k)\n ax = fig.gca(projection='3d')\n surf = ax.plot_surface(X,Y,v, cmap=cm.coolwarm,\n linewidth=0, antialiased=False)\n ax.set_ylabel('Y')\n ax.set_xlabel('X')\n ax.set_title('Potencial, con n='+str(n[k]-1))\n fig.colorbar(surf, shrink=0.5, aspect=5)\n plt.show()\n return v\n\ndef menu():\n global x\n global y\n global z\n global dy\n global dx\n global X\n global Y\n print('quiere graficar: \\n'\n '1. el campo electrico\\n'\n '2. el potencial\\n'\n '3. la carga superficial\\n'\n 'ingrese el numero de la opcion que desea por favor')\n ans = input('')\n if '2' in ans:\n dx = 0.01\n x = np.arange(-a,a,dx)\n y = np.linspace(0,b,len(x))\n dy = b/len(x)\n X,Y =np.meshgrid(x,y)\n for k in range(len(n)):\n v = graficarV(k,True)\n elif '1' in ans:\n z = np.linspace(0,1,3)\n dx = 0.3\n x = np.arange(-a,a,dx)\n y = np.linspace(0,b,len(x))\n dy = b/len(x)\n v = graficarV(3,False)\n v = np.expand_dims(v, axis = 2)\n v = np.repeat(v, 3, axis = 2)\n X,Y,Z =np.meshgrid(x,y,z)\n ex, ey, ez = np.gradient(v, dx, dy, 0.333)\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n ax.set_title('Campo eléctrico')\n ax.set_ylabel('Y')\n ax.set_xlabel('X')\n ax.quiver(X,Y,Z, ex, ey, ez, length=0.25, normalize = True)\n plt.show()\n dx = 0.3\n x = np.arange(-a,a,dx)\n y = np.linspace(0,b,len(x))\n dy = b/len(x)\n v = graficarV(3,False)\n X,Y =np.meshgrid(x,y)\n ex, ey = np.gradient(v, dx, dy)\n fig, ax = plt.subplots()\n ax.set_title('Campo eléctrico')\n ax.set_ylabel('Y')\n ax.set_xlabel('X')\n Q = ax.quiver(X,Y, ex, ey, linewidth = 0.01)\n plt.show()\n else:\n dx = 0.01\n x = np.arange(-a,a,dx)\n y = np.linspace(0,b,len(x))\n dy = b/len(x)\n z = np.arange(0,2,dx)\n X,Y =np.meshgrid(x,y)\n v = graficarV(3,False)\n ex, ey = np.gradient(v, dx, dy)\n densidad = divergence([ex,ey])\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n surf = ax.plot_surface(X,Y,densidad, cmap=cm.coolwarm,\n linewidth=0, antialiased=False)\n ax.set_ylabel('Y')\n ax.set_xlabel('X')\n ax.set_title('Densidad de superficie')\n fig.colorbar(surf, shrink=0.5, aspect=5)\n plt.show()\n \nmenu()\n", "sub_path": "31sinplanox0.py", "file_name": "31sinplanox0.py", "file_ext": "py", "file_size_in_byte": 4206, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.cm.gray", "line_number": 29, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.cm.coolwarm", "line_number": 30, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.cosh", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ufunc.reduce", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.ufunc", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.add", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.gradient", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.cm.coolwarm", "line_number": 98, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.cm.coolwarm", "line_number": 172, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}]} +{"seq_id": "381370387", "text": "\n\n\nfrom torch.utils.data import Dataset\nimport pandas as pd\nimport torch\nfrom transformers import GPT2Tokenizer,GPTNeoConfig,AutoTokenizer,BertTokenizer,T5Tokenizer\nfrom irt_to_nlp.config import ModelAvailable\nclass Loader(Dataset):\n \n def __init__(self,dir_csv_file:str,model:ModelAvailable) -> None:\n super().__init__()\n \n self.dir_csv_file=dir_csv_file\n self.data=pd.read_csv(self.dir_csv_file,index_col=\"Unnamed: 0\")\n self.y=self.data.pop(\"Dffclt\").to_numpy()\n self.X=list(self.data[\"sentence\"])\n self.model=model\n self.create_tokenizer()\n self.max_len=512\n\n def create_tokenizer(self):\n \n if self.model==ModelAvailable.customneogpt:\n self.tokenizer=GPT2Tokenizer.from_pretrained(\"EleutherAI/gpt-neo-1.3B\")\n # config=GPTNeoConfig()\n self.tokenizer.pad_token = self.tokenizer.eos_token\n # self.X=self.tokenizer(self.X,return_tensors=\"pt\", truncations=True,padding=True).input_ids\n \n elif self.model==ModelAvailable.bert_base_cased:\n self.tokenizer=AutoTokenizer.from_pretrained(\"bert-base-cased\")\n # self.X=self.tokenizer(self.X,return_tensors=\"pt\", padding=\"max_length\",truncation=True).input_ids\n \n elif self.model==ModelAvailable.distilbert_base_uncased:\n self.tokenizer=AutoTokenizer.from_pretrained('distilbert-base-uncased')\n # self.X=self.tokenizer(self.X,return_tensors=\"pt\", padding=\"max_length\",truncation=True).input_ids\n \n elif self.model==ModelAvailable.distilgpt2:\n self.tokenizer=AutoTokenizer.from_pretrained('distilgpt2')\n self.tokenizer.pad_token = self.tokenizer.eos_token\n # self.X=self.tokenizer(self.X,return_tensors=\"pt\", truncation=True,padding=\"max_length\").input_ids\n # print(self.X)\n \n elif self.model==ModelAvailable.bert_base_uncased:\n self.tokenizer=BertTokenizer.from_pretrained(\"bert-base-uncased\")\n # self.X=self.tokenizer(self.X,return_tensors=\"pt\",truncation=True,padding=True).input_ids\n # print(self.X)\n elif self.model==ModelAvailable.bert_base_multilingual_uncased_sentiment:\n \n self.tokenizer=AutoTokenizer.from_pretrained(\"nlptown/bert-base-multilingual-uncased-sentiment\")\n elif self.model==ModelAvailable.t5small:\n self.tokenizer=T5Tokenizer.from_pretrained(\"t5-small\")\n elif self.model==ModelAvailable.t5base:\n self.tokenizer=T5Tokenizer.from_pretrained(\"t5-base\")\n \n \n def __getitem__(self, index):\n # txt=self.data.iloc[index]\n # prompt=txt.values\n \n # input_ids=self.encodings[index]\n # input_ids=self.tokenizer(prompt[0],return_tensors=\"pt\").input_ids#,truncation=True,padding=True).input_ids\n review=self.X[index]\n encoding = self.tokenizer.encode_plus(\n review,\n add_special_tokens=True,\n max_length=self.max_len,\n return_token_type_ids=False,\n pad_to_max_length=True,\n return_attention_mask=True,\n return_tensors='pt',\n \n )\n input_ids=encoding.input_ids.flatten()\n attention_mask=encoding['attention_mask'].flatten()\n target=torch.tensor(self.y[index],dtype=torch.float)\n target=torch.unsqueeze(target,0)\n #pendiente aplicar transform simple a example\n return input_ids,attention_mask,target,index\n \n def __len__(self):\n \n return self.data.shape[0]\n \n \n \n \n\n\n", "sub_path": "irt_to_nlp/loader_nlp.py", "file_name": "loader_nlp.py", "file_ext": "py", "file_size_in_byte": 3689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 9, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "irt_to_nlp.config.ModelAvailable.customneogpt", "line_number": 24, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 24, "usage_type": "name"}, {"api_name": "transformers.GPT2Tokenizer.from_pretrained", "line_number": 25, "usage_type": "call"}, {"api_name": "transformers.GPT2Tokenizer", "line_number": 25, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable.bert_base_cased", "line_number": 30, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 30, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 31, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 31, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable.distilbert_base_uncased", "line_number": 34, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 34, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 35, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 35, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable.distilgpt2", "line_number": 38, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 38, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 39, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 39, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable.bert_base_uncased", "line_number": 44, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 44, "usage_type": "name"}, {"api_name": "transformers.BertTokenizer.from_pretrained", "line_number": 45, "usage_type": "call"}, {"api_name": "transformers.BertTokenizer", "line_number": 45, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable.bert_base_multilingual_uncased_sentiment", "line_number": 48, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 48, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 50, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 50, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable.t5small", "line_number": 51, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 51, "usage_type": "name"}, {"api_name": "transformers.T5Tokenizer.from_pretrained", "line_number": 52, "usage_type": "call"}, {"api_name": "transformers.T5Tokenizer", "line_number": 52, "usage_type": "name"}, {"api_name": "irt_to_nlp.config.ModelAvailable.t5base", "line_number": 53, "usage_type": "attribute"}, {"api_name": "irt_to_nlp.config.ModelAvailable", "line_number": 53, "usage_type": "name"}, {"api_name": "transformers.T5Tokenizer.from_pretrained", "line_number": 54, "usage_type": "call"}, {"api_name": "transformers.T5Tokenizer", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 76, "usage_type": "attribute"}, {"api_name": "torch.unsqueeze", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "548121245", "text": "import vrep, time, cv2, math, localization\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nserver_IP = \"127.0.0.1\"\nserver_port = 25000\nnome_sensor = []\nhandle_sensores = []\ndetect = [0,0,0,0,0,0,0,0]\nnoDetectionDist=5.0\nmaxDetectionDist=0.2\n\nRAIO_ROBO = 0.455/2\n\nsala_atual = 2\n\nsala_2 = [[-5.2754, 2.3],[-5.9254,-0.2],[-6.3504,1.7250],[-4.7003, 1.5],[-2.9504,-0.14998],[-2.9050,0.21059],[-3.0349,-0.39395],[1.9264,2.3452],[-2.3379,-4],[3.7764,2.3202],[4.3264,0.64525],[-0.47359,-4.6048],[4.4264,-1.7798],[1.4764,-4.5298],[5,-2.5548],[3.3,-4.34]]\n\n\n#sala_2 = [[-5.2754, 2.3],[-5.9254,-0.2],[-4.7003, 1.5],[-2.9504,-0.14998], [2.5746,1.05],[1.8227,1.9652],[-0.40229,1.2652], [2.6996, -2.95], [0.57465,-4.075],[-1.0959,-1.8270], [-1.4003,-4.1], [-1.3331,-2.2044], [-2.125,-1.425], [2.3747,0.67502]]\n\n\"\"\"sala_1 = [[-5.9, 1.3], [-5.8, 3.0], [-4.4, 3.9], [-3.3, 4.2], [-2.1, 6.0], [-2.2, 4.4], [-3.3, 4.2], [-4.4, 3.9], [-5.8, 4.5], [-5.9, 3.5], [-6.0,0.0]]\n\nsala_2 = [[-2.0,0.0], [0.0, 1.7], [1.3,1.7], [1.3, 3.8], [3.1,4.3], [4.5, 4.3], [4.5,6.2], [3.3,6.2], [3.1,4.3], [-0.5, 3.5], [1.8, 3.4]]\n\nsala_3 = [[1.2, 1.7], [3.2, 2.2], [3.7, 1.3], [3.6, -1.5], [2.8, -2.6], [0.8, -3.1], [0.6, -4.3], [-1.5, -4.2], [-1.3, -2.3]]\n\nsala_4 = [[-3.3, -2.4], [-3.5, -4.5], [-6.2, -4.5], [-6.2, -1.6]]\"\"\"\n\n#sala_2 = [[-3.0, 0.0], [0.0, 0.0], [1.0, 1.5], [2.0, 2.0], [3.0, 1.0], [4.0, 2.0], [4.0, -1.0], [3.0, -2.0], [2.0, -2.5], [1.0, -3.0], [0.5, -3.5], [0.0, -2.0], [-3.0, -2.3]]\n\n#sala_3 = [[-3.8, -4.2], [-6.0, -4.5], [-3.8, -4.2], [-3.0, -2.3], [-2.0, -2.2], [1.3,1.7]]\n\n#sala_4 = [[1.4, 3.8], [4.5, 5.4], [1.9, 4.0], [0.0, 3.5], [1.2, 4.0]]\n\n\nang_ultrassom = [90, 50, 30, 10, -10, -30, -50, -90]\n\n\n\n#---------------------Conecta no servidor---------------------------------\nclientID = vrep.simxStart(server_IP, server_port, True, True, 2000, 5)\n\nif (clientID!=-1):\n\tprint (\"Servidor Conectado!\")\n\t\n\n#------------------------------Inicializa Sensores ----------------------------\n\tfor i in range(0,8):\n\t\tnome_sensor.append(\"Pioneer_p3dx_ultrasonicSensor\" + str(i+1))\n\n\t\tres, handle = vrep.simxGetObjectHandle(clientID, nome_sensor[i], vrep.simx_opmode_oneshot_wait)\n\n\t\tif(res != vrep.simx_return_ok):\n\t\t\tprint (nome_sensor[i] + \" nao conectado\")\n\t\telse:\n\t\t\tprint (nome_sensor[i] + \" conectado\")\n\t\t\thandle_sensores.append(handle)\n\t\t\t\n\t#Vision sensor\t\t\n\tres, visionHandle = vrep.simxGetObjectHandle(clientID, \"Vision_sensor\", vrep.simx_opmode_oneshot_wait)\t\t\n\t\t\t\n#------------------------------Inicializa Motores ----------------------------\n\tresLeft, handle_motor_esq = vrep.simxGetObjectHandle(clientID, \"Pioneer_p3dx_leftMotor\", vrep.simx_opmode_oneshot_wait)\n\tif(resLeft != vrep.simx_return_ok):\n\t\tprint(\"Motor Esquerdo : Handle nao encontrado!\")\n\telse:\n\t\tprint(\"Motor Esquerdo: Conectado\")\n\n\tresRight, handle_motor_dir = vrep.simxGetObjectHandle(clientID, \"Pioneer_p3dx_rightMotor\", vrep.simx_opmode_oneshot_wait)\n\tif(resRight != vrep.simx_return_ok):\n\t\tprint(\"Motor Direito: Handle nao encontrado!\")\n\telse:\n\t\tprint(\"Motor Direito: Conectado\")\n\n#------------------------------Inicializa Robo ----------------------------\n\n\tresRobo, handle_robo = vrep.simxGetObjectHandle(clientID, \"Pioneer_p3dx\", vrep.simx_opmode_oneshot_wait)\n\n\nelse:\n\tprint(\"Servidor desconectado!\")\n\n\t\t\t\ndef ler_distancias(sensorHandle):\n\n\t\"\"\"\n\t\tEsse metodo ira ler a distancia de um conjunto de sensores ultrassonicos\n\t\tparametro: handle dos sensores\n\t\tretorna: distancias em metros\n\t\"\"\"\n\tdistancias = []\n\t\n\tfor sensor in sensorHandle:\n\t\treturnCode, detectionState, detectedPoint,_,_ = vrep.simxReadProximitySensor(clientID, sensor, vrep.simx_opmode_streaming)\n\t\tif (returnCode == vrep.simx_return_ok):\n\t\t\tif(detectionState != 0):\n\t\t\t\tdistancias.append(round(detectedPoint[2],5))\n\t\t\telse:\n\t\t\t\t#Muito distante\n\t\t\t\tdistancias.append(noDetectionDist)\n\t\telse:\n\t\t\t#print (\"Erro no sensor \"+str(i+1))\n\t\t\ttime.sleep(0.1)\n\treturn distancias\n\ndef get_angulo_alvo(x_robo, y_robo, ang_robo, x_alvo, y_alvo):\n\ttolerancia = 0.2\n\t\n\tang_alvo = math.atan2((y_alvo - y_robo),(x_alvo - x_robo))\n\tif (abs(x_robo - x_alvo) < tolerancia) and (abs(y_robo - y_alvo) < tolerancia):\n\t\tang_alvo = 0\n\t\n\treturn ang_alvo\n\ndef get_pos_atual():\n\tcode, position = vrep.simxGetObjectPosition(clientID, handle_robo, -1, vrep.simx_opmode_streaming)\n\t\n\twhile(code != vrep.simx_return_ok):\n\t\tcode, position = vrep.simxGetObjectPosition(clientID, handle_robo, -1, vrep.simx_opmode_streaming)\n\n\treturn position\n\ndef get_ang_atual():\n\tcode, ang = vrep.simxGetObjectOrientation(clientID, handle_robo, -1, vrep.simx_opmode_streaming)\n\twhile(code != vrep.simx_return_ok):\n\t\tcode, ang = vrep.simxGetObjectOrientation(clientID, handle_robo, -1, vrep.simx_opmode_streaming)\n\t\t\n\treturn ang[2]\n\ndef virar(angulo):\n\tprint(\"Virando para o angulo \", angulo*180.0/math.pi, \" graus\")\n\t#ang_inicial = get_ang_atual()\n\t\n\tif(get_ang_atual() < angulo):\n\t\tv_Left = -0.5\n\t\tv_Right = 0.5\n\telse:\n\t\tv_Left = 0.5\n\t\tv_Right = -0.5\n\t\t\n\twhile(abs(get_ang_atual() - angulo) > 0.01):\n\t\t#print abs(get_ang_atual() - angulo)\n\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, v_Right, vrep.simx_opmode_streaming)\n\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, v_Left, vrep.simx_opmode_streaming)\n\t\t#Atualiza localizacao\t\n\t\tthetaDir = vrep.simxGetJointPosition(clientID, handle_motor_dir, vrep.simx_opmode_streaming)[1]\n\t\tthetaEsq = vrep.simxGetJointPosition(clientID, handle_motor_esq, vrep.simx_opmode_streaming)[1]\n\t\tlocalizacao.setAngulos(thetaDir, thetaEsq)\n\t\t\ndef mover_para(x,y):\n\tprint(\"Movendo para \", x, \",\", y)\n\tprint(\"Angulo: \", get_ang_atual())\n\ttime.sleep(1)\n\tvel = 2\n\tang_alvo = get_angulo_alvo(get_pos_atual()[0], get_pos_atual()[1], get_ang_atual(), x, y)\n\tvirar(ang_alvo)\n\t\n\tchegou = False\n\t\n\twhile(not chegou):\n\n\t\tdist = ler_distancias(handle_sensores)\n\t\t\n\t\tif(dist):\n\t\t\tx_odom, y_odom = localizacao.getPosicao()\n\t\t\tsalva_dados(dist, get_pos_atual()[0], get_pos_atual()[1], x_odom, y_odom, get_ang_atual())\n\t\t\n\t\t\tif(dist[3] < 0.1):\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, -vel, vrep.simx_opmode_streaming)\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, vel, vrep.simx_opmode_streaming)\n\t\t\telif(dist[4] < 0.1):\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, vel, vrep.simx_opmode_streaming)\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, -vel, vrep.simx_opmode_streaming)\n\t\t\telif(dist[2] < 0.2):\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, -vel, vrep.simx_opmode_streaming)\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, vel, vrep.simx_opmode_streaming)\n\t\t\telif(dist[5] < 0.2):\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, vel, vrep.simx_opmode_streaming)\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, -vel, vrep.simx_opmode_streaming)\n\t\t\telse:\t\n\t\t\t\tang_alvo = get_angulo_alvo(get_pos_atual()[0], get_pos_atual()[1], get_ang_atual(), x, y)\n\t\t\t\tif(abs(get_ang_atual() - ang_alvo) > 0.1):\n\t\t\t\t\t#print(get_ang_atual())\n\t\t\t\t\tif((ang_alvo > 0 and dist[7] > 1.0) or (ang_alvo < 0 and dist[0] > 1.0)):\n\t\t\t\t\t\tvirar(ang_alvo)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, vel, vrep.simx_opmode_streaming)\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, vel, vrep.simx_opmode_streaming)\t\t\n\t\t\t\n\t\t\t#Atualiza localizacao\t\n\t\t\tthetaDir = vrep.simxGetJointPosition(clientID, handle_motor_dir, vrep.simx_opmode_streaming)[1]\n\t\t\tthetaEsq = vrep.simxGetJointPosition(clientID, handle_motor_esq, vrep.simx_opmode_streaming)[1]\n\t\t\tlocalizacao.setAngulos(thetaDir, thetaEsq)\n\t\t\t\t\n\t\t\tif(abs(get_pos_atual()[0]-x) < 0.1 and abs(get_pos_atual()[1]-y) < 0.1):\n\t\t\t\tchegou = True\n\t\t\t\tprint (\"chegou\")\n\t\t\t\t\n\t\t\t#else:\n\t\t\t#\tprint str(get_pos_atual()[0]),\",\",str(get_pos_atual()[1])\n\n\npontos_x = []\npontos_y = []\npontos = []\ntrajetoria_x = []\ntrajetoria_y = []\nodometria_x = []\nodometria_y = []\ndef salva_dados(dist, x_robo, y_robo, x_odom, y_odom, ang_robo):\n\tfor i in range(len(dist)):\n\t\tif(i == 0 or i == 2 or i == 5 or i == 7):\n\t\t\tif(dist[i] < noDetectionDist):\n\t\t\t\tx = x_robo + (dist[i] + RAIO_ROBO) * math.cos((ang_ultrassom[i]*math.pi/180) + ang_robo)\n\t\t\t\ty = y_robo + (dist[i] + RAIO_ROBO) * math.sin((ang_ultrassom[i]*math.pi/180) + ang_robo)\n\t\t\t\tif [x, y] not in pontos: \n\t\t\t\t\tpontos.append([x, y])\n\ttrajetoria_x.append(x_robo)\n\ttrajetoria_y.append(y_robo)\n\todometria_x.append(x_odom)\n\todometria_y.append(y_odom)\n\t\n\t\t\ndef plotar_mapa():\n\tplt.scatter(pontos_x, pontos_y, s=0.5)\n\tplt.plot(odometria_x, odometria_y, 'r--', trajetoria_x, trajetoria_y, 'g')\n\tplt.show()\n\t\t\t\ndisplay = False\n\nlocalizacao = localization.localizacao()\nlocalization.iniciar(clientID)\n#------------------------------ Loop principal ----------------------------\nwhile vrep.simxGetConnectionId(clientID) != -1:\n\n\tif(sala_atual == 2):\n flag=True\n for pos in sala_2:\n mover_para(pos[0], pos[1])\n\t\t\t \n\t\t \n\t\"\"\"elif(sala_atual == 2):\n\t\tfor pos in sala_2:\n\t\t\tmover_para(pos[0], pos[1])\n\telif(sala_atual == 3):\n\t\tfor pos in sala_3:\n\t\t\tmover_para(pos[0], pos[1])\n\telif(sala_atual == 4):\n\t\t\tfor pos in sala_4:\n\t\t\t\tmover_para(pos[0], pos[1])\"\"\"\n\tif(flag):\t\t\n\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, 0, vrep.simx_opmode_streaming)\n\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, 0, vrep.simx_opmode_streaming)\t\n\t\t\tprint (\"Fim\")\n\t\t\tfor p in pontos:\n\t\t\t\tpontos_x.append(p[0])\n\t\t\t\tpontos_y.append(p[1])\n\t\t\tplotar_mapa()\t\t\t\n\t\t\t\n\t\t\twhile(1):\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_dir, 0, vrep.simx_opmode_streaming)\n\t\t\t\tvrep.simxSetJointTargetVelocity(clientID, handle_motor_esq, 0, vrep.simx_opmode_streaming)\t\n\n\t\t\t\n\t#sala_atual+=1\n\n\t\n", "sub_path": "Trabalho1/explore_sala2.py", "file_name": "explore_sala2.py", "file_ext": "py", "file_size_in_byte": 9707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "vrep.simxStart", "line_number": 42, "usage_type": "call"}, {"api_name": "vrep.simxGetObjectHandle", "line_number": 52, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_oneshot_wait", "line_number": 52, "usage_type": "attribute"}, {"api_name": "vrep.simx_return_ok", "line_number": 54, "usage_type": "attribute"}, {"api_name": "vrep.simxGetObjectHandle", "line_number": 61, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_oneshot_wait", "line_number": 61, "usage_type": "attribute"}, {"api_name": "vrep.simxGetObjectHandle", "line_number": 64, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_oneshot_wait", "line_number": 64, "usage_type": "attribute"}, {"api_name": "vrep.simx_return_ok", "line_number": 65, "usage_type": "attribute"}, {"api_name": "vrep.simxGetObjectHandle", "line_number": 70, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_oneshot_wait", "line_number": 70, "usage_type": "attribute"}, {"api_name": "vrep.simx_return_ok", "line_number": 71, "usage_type": "attribute"}, {"api_name": "vrep.simxGetObjectHandle", "line_number": 78, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_oneshot_wait", "line_number": 78, "usage_type": "attribute"}, {"api_name": "vrep.simxReadProximitySensor", "line_number": 95, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 95, "usage_type": "attribute"}, {"api_name": "vrep.simx_return_ok", "line_number": 96, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 110, "usage_type": "call"}, {"api_name": "vrep.simxGetObjectPosition", "line_number": 117, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 117, "usage_type": "attribute"}, {"api_name": "vrep.simx_return_ok", "line_number": 119, "usage_type": "attribute"}, {"api_name": "vrep.simxGetObjectPosition", "line_number": 120, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 120, "usage_type": "attribute"}, {"api_name": "vrep.simxGetObjectOrientation", "line_number": 125, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 125, "usage_type": "attribute"}, {"api_name": "vrep.simx_return_ok", "line_number": 126, "usage_type": "attribute"}, {"api_name": "vrep.simxGetObjectOrientation", "line_number": 127, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 127, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 132, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 144, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 144, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 145, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 145, "usage_type": "attribute"}, {"api_name": "vrep.simxGetJointPosition", "line_number": 147, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 147, "usage_type": "attribute"}, {"api_name": "vrep.simxGetJointPosition", "line_number": 148, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 148, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 170, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 170, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 171, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 171, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 173, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 173, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 174, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 174, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 176, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 176, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 177, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 177, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 179, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 179, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 180, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 180, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 188, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 188, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 189, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 189, "usage_type": "attribute"}, {"api_name": "vrep.simxGetJointPosition", "line_number": 192, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 192, "usage_type": "attribute"}, {"api_name": "vrep.simxGetJointPosition", "line_number": 193, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 193, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 215, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 215, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 216, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 216, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "localization.localizacao", "line_number": 232, "usage_type": "call"}, {"api_name": "localization.iniciar", "line_number": 233, "usage_type": "call"}, {"api_name": "vrep.simxGetConnectionId", "line_number": 235, "usage_type": "call"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 253, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 253, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 254, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 254, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 262, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 262, "usage_type": "attribute"}, {"api_name": "vrep.simxSetJointTargetVelocity", "line_number": 263, "usage_type": "call"}, {"api_name": "vrep.simx_opmode_streaming", "line_number": 263, "usage_type": "attribute"}]} +{"seq_id": "375675198", "text": "from .utility import base, Representation\nfrom sqlalchemy import Column, INTEGER, VARCHAR, BOOLEAN\n\n\nclass Config(base, Representation):\n __tablename__ = \"configs\"\n\n id = Column(\n \"id\",\n INTEGER(),\n primary_key=True,\n autoincrement=True,\n )\n key = Column(\n \"key\",\n VARCHAR(length=32),\n )\n value = Column(\n \"value\",\n VARCHAR(length=255),\n )\n enabled = Column(\n \"enabled\",\n BOOLEAN(),\n default=True\n )\n", "sub_path": "zhoarder/database_model/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 504, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "utility.base", "line_number": 5, "usage_type": "name"}, {"api_name": "utility.Representation", "line_number": 5, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.INTEGER", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.BOOLEAN", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "625574945", "text": "import json\nimport logging\nfrom decimal import Decimal\n\nfrom bs4 import BeautifulSoup\n\nfrom storescraper.categories import ALL_IN_ONE, NOTEBOOK, CELL, UPS, \\\n WEARABLE, TABLET, MONITOR, COMPUTER_CASE, MOTHERBOARD, PROCESSOR, RAM, \\\n STORAGE_DRIVE, EXTERNAL_STORAGE_DRIVE, SOLID_STATE_DRIVE, VIDEO_CARD, \\\n KEYBOARD_MOUSE_COMBO, MOUSE, KEYBOARD, POWER_SUPPLY, HEADPHONES, \\\n GAMING_CHAIR, VIDEO_GAME_CONSOLE, PRINTER, MEMORY_CARD, USB_FLASH_DRIVE, \\\n STEREO_SYSTEM\nfrom storescraper.product import Product\nfrom storescraper.store import Store\nfrom storescraper.utils import session_with_proxy, remove_words\n\n\nclass Infosep(Store):\n @classmethod\n def categories(cls):\n return [\n ALL_IN_ONE,\n NOTEBOOK,\n CELL,\n WEARABLE,\n TABLET,\n MONITOR,\n COMPUTER_CASE,\n MOTHERBOARD,\n PROCESSOR,\n RAM,\n STORAGE_DRIVE,\n EXTERNAL_STORAGE_DRIVE,\n SOLID_STATE_DRIVE,\n VIDEO_CARD,\n KEYBOARD_MOUSE_COMBO,\n MOUSE,\n KEYBOARD,\n POWER_SUPPLY,\n HEADPHONES,\n GAMING_CHAIR,\n VIDEO_GAME_CONSOLE,\n PRINTER,\n MEMORY_CARD,\n USB_FLASH_DRIVE,\n STEREO_SYSTEM,\n UPS\n ]\n\n @classmethod\n def discover_urls_for_category(cls, category, extra_args=None):\n url_extensions = [\n ['todo-en-uno', ALL_IN_ONE],\n ['notebooks', NOTEBOOK],\n ['celulares', CELL],\n ['reloj-inteligente', WEARABLE],\n ['tablet', TABLET],\n ['monitores', MONITOR],\n ['gabinetes', COMPUTER_CASE],\n ['impresoras-laser', PRINTER],\n ['multifuncionales-laser', PRINTER],\n ['impresoras-de-tinta', PRINTER],\n ['multifuncionales', PRINTER],\n ['plotter', PRINTER],\n ['placas-madres', MOTHERBOARD],\n ['procesadores-intel', PROCESSOR],\n ['procesadores-amd', PROCESSOR],\n ['memorias-pc-notebook', RAM],\n ['disco-hdd', STORAGE_DRIVE],\n ['discos-ssd-externos', EXTERNAL_STORAGE_DRIVE],\n ['discos-ssd-internos', SOLID_STATE_DRIVE],\n ['discos-externos-25', EXTERNAL_STORAGE_DRIVE],\n ['tarjetas-de-video', VIDEO_CARD],\n ['kit-teclado-y-mouse', KEYBOARD_MOUSE_COMBO],\n ['mouse', MOUSE],\n ['teclado', KEYBOARD],\n ['fuente-de-poder-pc', POWER_SUPPLY],\n ['audifonos-gamer', HEADPHONES],\n ['audifonos', HEADPHONES],\n ['fuentes-gamer', POWER_SUPPLY],\n ['gabinetes-gamer', COMPUTER_CASE],\n ['memoria-hyperx', RAM],\n ['motherboard', MOTHERBOARD],\n ['monitor-gamer', MONITOR],\n ['notebook-gaming', NOTEBOOK],\n ['mouse-gamer', MOUSE],\n ['sillas-gamer', GAMING_CHAIR],\n ['teclado-y-mouse-gamer', KEYBOARD_MOUSE_COMBO],\n ['teclado-gamer', KEYBOARD],\n ['tarjetas-de-video-gamer', VIDEO_CARD],\n ['consolas-y-video-juegos', VIDEO_GAME_CONSOLE],\n ['memoria-micro-sdhc', MEMORY_CARD],\n ['pendrive', USB_FLASH_DRIVE],\n ['parlantes', STEREO_SYSTEM],\n ['ups-respaldo-de-energia', UPS],\n ]\n session = session_with_proxy(extra_args)\n product_urls = []\n for url_extension, local_category in url_extensions:\n if local_category != category:\n continue\n page = 1\n while True:\n if page > 10:\n raise Exception('page overflow: ' + url_extension)\n if page == 1:\n url_webpage = 'https://infosep.cl/categoria-producto/{}/' \\\n ''.format(url_extension)\n else:\n url_webpage = 'https://infosep.cl/categoria-producto/{}/' \\\n 'page/{}/'.format(url_extension, page)\n print(url_webpage)\n data = session.get(url_webpage).text\n soup = BeautifulSoup(data, 'html.parser')\n product_containers = soup.find('ul', 'products')\n if not product_containers:\n if page == 1:\n logging.warning('Empty category: ' + url_extension)\n break\n for container in product_containers.findAll('li'):\n product_url = container.find('a')['href']\n product_urls.append(product_url)\n page += 1\n return product_urls\n\n @classmethod\n def products_for_url(cls, url, category=None, extra_args=None):\n print(url)\n session = session_with_proxy(extra_args)\n response = session.get(url)\n soup = BeautifulSoup(response.text, 'html.parser')\n key = soup.find('link', {'rel': 'shortlink'})['href'].split('p=')[1]\n json_data = json.loads(\n soup.findAll('script', {'type': 'application/ld+json'})[-1]\n .text)\n\n if '@type' not in json_data:\n return []\n\n name = json_data['name']\n sku = json_data['sku']\n price = Decimal(json_data['offers'][0]['price'])\n\n if not price:\n return []\n\n if soup.find('p', 'stock in-stock'):\n stock = int(soup.find('p', 'stock in-stock').text.split()[0])\n else:\n stock = 0\n\n picture_urls = [tag['src'] for tag in soup.find(\n 'div', 'woocommerce-product-gallery').findAll('img')]\n\n p = Product(\n name,\n cls.__name__,\n category,\n url,\n url,\n key,\n stock,\n price,\n price,\n 'CLP',\n sku=sku,\n part_number=sku,\n picture_urls=picture_urls\n )\n return [p]\n", "sub_path": "storescraper/stores/infosep.py", "file_name": "infosep.py", "file_ext": "py", "file_size_in_byte": 5998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "storescraper.store.Store", "line_number": 18, "usage_type": "name"}, {"api_name": "storescraper.categories.ALL_IN_ONE", "line_number": 22, "usage_type": "name"}, {"api_name": "storescraper.categories.NOTEBOOK", "line_number": 23, "usage_type": "name"}, {"api_name": "storescraper.categories.CELL", "line_number": 24, "usage_type": "name"}, {"api_name": "storescraper.categories.WEARABLE", "line_number": 25, "usage_type": "name"}, {"api_name": "storescraper.categories.TABLET", "line_number": 26, "usage_type": "name"}, {"api_name": "storescraper.categories.MONITOR", "line_number": 27, "usage_type": "name"}, {"api_name": "storescraper.categories.COMPUTER_CASE", "line_number": 28, "usage_type": "name"}, {"api_name": "storescraper.categories.MOTHERBOARD", "line_number": 29, "usage_type": "name"}, {"api_name": "storescraper.categories.PROCESSOR", "line_number": 30, "usage_type": "name"}, {"api_name": "storescraper.categories.RAM", "line_number": 31, "usage_type": "name"}, {"api_name": "storescraper.categories.STORAGE_DRIVE", "line_number": 32, "usage_type": "name"}, {"api_name": "storescraper.categories.EXTERNAL_STORAGE_DRIVE", "line_number": 33, "usage_type": "name"}, {"api_name": "storescraper.categories.SOLID_STATE_DRIVE", "line_number": 34, "usage_type": "name"}, {"api_name": "storescraper.categories.VIDEO_CARD", "line_number": 35, "usage_type": "name"}, {"api_name": "storescraper.categories.KEYBOARD_MOUSE_COMBO", "line_number": 36, "usage_type": "name"}, {"api_name": "storescraper.categories.MOUSE", "line_number": 37, "usage_type": "name"}, {"api_name": "storescraper.categories.KEYBOARD", "line_number": 38, "usage_type": "name"}, {"api_name": "storescraper.categories.POWER_SUPPLY", "line_number": 39, "usage_type": "name"}, {"api_name": "storescraper.categories.HEADPHONES", "line_number": 40, "usage_type": "name"}, {"api_name": "storescraper.categories.GAMING_CHAIR", "line_number": 41, "usage_type": "name"}, {"api_name": "storescraper.categories.VIDEO_GAME_CONSOLE", "line_number": 42, "usage_type": "name"}, {"api_name": "storescraper.categories.PRINTER", "line_number": 43, "usage_type": "name"}, {"api_name": "storescraper.categories.MEMORY_CARD", "line_number": 44, "usage_type": "name"}, {"api_name": "storescraper.categories.USB_FLASH_DRIVE", "line_number": 45, "usage_type": "name"}, {"api_name": "storescraper.categories.STEREO_SYSTEM", "line_number": 46, "usage_type": "name"}, {"api_name": "storescraper.categories.UPS", "line_number": 47, "usage_type": "name"}, {"api_name": "storescraper.categories.ALL_IN_ONE", "line_number": 53, "usage_type": "name"}, {"api_name": "storescraper.categories.NOTEBOOK", "line_number": 54, "usage_type": "name"}, {"api_name": "storescraper.categories.CELL", "line_number": 55, "usage_type": "name"}, {"api_name": "storescraper.categories.WEARABLE", "line_number": 56, "usage_type": "name"}, {"api_name": "storescraper.categories.TABLET", "line_number": 57, "usage_type": "name"}, {"api_name": "storescraper.categories.MONITOR", "line_number": 58, "usage_type": "name"}, {"api_name": "storescraper.categories.COMPUTER_CASE", "line_number": 59, "usage_type": "name"}, {"api_name": "storescraper.categories.PRINTER", "line_number": 60, "usage_type": "name"}, {"api_name": "storescraper.categories.PRINTER", "line_number": 61, "usage_type": "name"}, {"api_name": "storescraper.categories.PRINTER", "line_number": 62, "usage_type": "name"}, {"api_name": "storescraper.categories.PRINTER", "line_number": 63, "usage_type": "name"}, {"api_name": "storescraper.categories.PRINTER", "line_number": 64, "usage_type": "name"}, {"api_name": "storescraper.categories.MOTHERBOARD", "line_number": 65, "usage_type": "name"}, {"api_name": "storescraper.categories.PROCESSOR", "line_number": 66, "usage_type": "name"}, {"api_name": "storescraper.categories.PROCESSOR", "line_number": 67, "usage_type": "name"}, {"api_name": "storescraper.categories.RAM", "line_number": 68, "usage_type": "name"}, {"api_name": "storescraper.categories.STORAGE_DRIVE", "line_number": 69, "usage_type": "name"}, {"api_name": "storescraper.categories.EXTERNAL_STORAGE_DRIVE", "line_number": 70, "usage_type": "name"}, {"api_name": "storescraper.categories.SOLID_STATE_DRIVE", "line_number": 71, "usage_type": "name"}, {"api_name": "storescraper.categories.EXTERNAL_STORAGE_DRIVE", "line_number": 72, "usage_type": "name"}, {"api_name": "storescraper.categories.VIDEO_CARD", "line_number": 73, "usage_type": "name"}, {"api_name": "storescraper.categories.KEYBOARD_MOUSE_COMBO", "line_number": 74, "usage_type": "name"}, {"api_name": "storescraper.categories.MOUSE", "line_number": 75, "usage_type": "name"}, {"api_name": "storescraper.categories.KEYBOARD", "line_number": 76, "usage_type": "name"}, {"api_name": "storescraper.categories.POWER_SUPPLY", "line_number": 77, "usage_type": "name"}, {"api_name": "storescraper.categories.HEADPHONES", "line_number": 78, "usage_type": "name"}, {"api_name": "storescraper.categories.HEADPHONES", "line_number": 79, "usage_type": "name"}, {"api_name": "storescraper.categories.POWER_SUPPLY", "line_number": 80, "usage_type": "name"}, {"api_name": "storescraper.categories.COMPUTER_CASE", "line_number": 81, "usage_type": "name"}, {"api_name": "storescraper.categories.RAM", "line_number": 82, "usage_type": "name"}, {"api_name": "storescraper.categories.MOTHERBOARD", "line_number": 83, "usage_type": "name"}, {"api_name": "storescraper.categories.MONITOR", "line_number": 84, "usage_type": "name"}, {"api_name": "storescraper.categories.NOTEBOOK", "line_number": 85, "usage_type": "name"}, {"api_name": "storescraper.categories.MOUSE", "line_number": 86, "usage_type": "name"}, {"api_name": "storescraper.categories.GAMING_CHAIR", "line_number": 87, "usage_type": "name"}, {"api_name": "storescraper.categories.KEYBOARD_MOUSE_COMBO", "line_number": 88, "usage_type": "name"}, {"api_name": "storescraper.categories.KEYBOARD", "line_number": 89, "usage_type": "name"}, {"api_name": "storescraper.categories.VIDEO_CARD", "line_number": 90, "usage_type": "name"}, {"api_name": "storescraper.categories.VIDEO_GAME_CONSOLE", "line_number": 91, "usage_type": "name"}, {"api_name": "storescraper.categories.MEMORY_CARD", "line_number": 92, "usage_type": "name"}, {"api_name": "storescraper.categories.USB_FLASH_DRIVE", "line_number": 93, "usage_type": "name"}, {"api_name": "storescraper.categories.STEREO_SYSTEM", "line_number": 94, "usage_type": "name"}, {"api_name": "storescraper.categories.UPS", "line_number": 95, "usage_type": "name"}, {"api_name": "storescraper.utils.session_with_proxy", "line_number": 97, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 114, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 118, "usage_type": "call"}, {"api_name": "storescraper.utils.session_with_proxy", "line_number": 129, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 131, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 133, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 142, "usage_type": "call"}, {"api_name": "storescraper.product.Product", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "489114680", "text": "# Licensed to Elasticsearch B.V. under one or more contributor\n# license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright\n# ownership. Elasticsearch B.V. licenses this file to you under\n# the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# \thttp://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\nimport typing as t\n\nfrom elastic_transport import ObjectApiResponse\n\nfrom ._base import NamespacedClient\nfrom .utils import SKIP_IN_PATH, _quote, _rewrite_parameters\n\n\nclass RollupClient(NamespacedClient):\n @_rewrite_parameters()\n def delete_job(\n self,\n *,\n id: str,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Deletes an existing rollup job.\n\n ``_\n\n :param id: The ID of the job to delete\n \"\"\"\n if id in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'id'\")\n __path = f\"/_rollup/job/{_quote(id)}\"\n __query: t.Dict[str, t.Any] = {}\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n __headers = {\"accept\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"DELETE\", __path, params=__query, headers=__headers\n )\n\n @_rewrite_parameters()\n def get_jobs(\n self,\n *,\n id: t.Optional[str] = None,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Retrieves the configuration, stats, and status of rollup jobs.\n\n ``_\n\n :param id: The ID of the job(s) to fetch. Accepts glob patterns, or left blank\n for all jobs\n \"\"\"\n if id not in SKIP_IN_PATH:\n __path = f\"/_rollup/job/{_quote(id)}\"\n else:\n __path = \"/_rollup/job\"\n __query: t.Dict[str, t.Any] = {}\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n __headers = {\"accept\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"GET\", __path, params=__query, headers=__headers\n )\n\n @_rewrite_parameters()\n def get_rollup_caps(\n self,\n *,\n id: t.Optional[str] = None,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Returns the capabilities of any rollup jobs that have been configured for a specific\n index or index pattern.\n\n ``_\n\n :param id: The ID of the index to check rollup capabilities on, or left blank\n for all jobs\n \"\"\"\n if id not in SKIP_IN_PATH:\n __path = f\"/_rollup/data/{_quote(id)}\"\n else:\n __path = \"/_rollup/data\"\n __query: t.Dict[str, t.Any] = {}\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n __headers = {\"accept\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"GET\", __path, params=__query, headers=__headers\n )\n\n @_rewrite_parameters()\n def get_rollup_index_caps(\n self,\n *,\n index: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]],\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Returns the rollup capabilities of all jobs inside of a rollup index (e.g. the\n index where rollup data is stored).\n\n ``_\n\n :param index: The rollup index or index pattern to obtain rollup capabilities\n from.\n \"\"\"\n if index in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'index'\")\n __path = f\"/{_quote(index)}/_rollup/data\"\n __query: t.Dict[str, t.Any] = {}\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n __headers = {\"accept\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"GET\", __path, params=__query, headers=__headers\n )\n\n @_rewrite_parameters(\n body_fields=True,\n ignore_deprecated_options={\"headers\"},\n )\n def put_job(\n self,\n *,\n id: str,\n cron: str,\n groups: t.Mapping[str, t.Any],\n index_pattern: str,\n page_size: int,\n rollup_index: str,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n headers: t.Optional[\n t.Mapping[str, t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]]\n ] = None,\n human: t.Optional[bool] = None,\n metrics: t.Optional[\n t.Union[t.List[t.Mapping[str, t.Any]], t.Tuple[t.Mapping[str, t.Any], ...]]\n ] = None,\n pretty: t.Optional[bool] = None,\n timeout: t.Optional[t.Union[\"t.Literal[-1]\", \"t.Literal[0]\", str]] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Creates a rollup job.\n\n ``_\n\n :param id: Identifier for the rollup job. This can be any alphanumeric string\n and uniquely identifies the data that is associated with the rollup job.\n The ID is persistent; it is stored with the rolled up data. If you create\n a job, let it run for a while, then delete the job, the data that the job\n rolled up is still be associated with this job ID. You cannot create a new\n job with the same ID since that could lead to problems with mismatched job\n configurations.\n :param cron: A cron string which defines the intervals when the rollup job should\n be executed. When the interval triggers, the indexer attempts to rollup the\n data in the index pattern. The cron pattern is unrelated to the time interval\n of the data being rolled up. For example, you may wish to create hourly rollups\n of your document but to only run the indexer on a daily basis at midnight,\n as defined by the cron. The cron pattern is defined just like a Watcher cron\n schedule.\n :param groups: Defines the grouping fields and aggregations that are defined\n for this rollup job. These fields will then be available later for aggregating\n into buckets. These aggs and fields can be used in any combination. Think\n of the groups configuration as defining a set of tools that can later be\n used in aggregations to partition the data. Unlike raw data, we have to think\n ahead to which fields and aggregations might be used. Rollups provide enough\n flexibility that you simply need to determine which fields are needed, not\n in what order they are needed.\n :param index_pattern: The index or index pattern to roll up. Supports wildcard-style\n patterns (`logstash-*`). The job attempts to rollup the entire index or index-pattern.\n :param page_size: The number of bucket results that are processed on each iteration\n of the rollup indexer. A larger value tends to execute faster, but requires\n more memory during processing. This value has no effect on how the data is\n rolled up; it is merely used for tweaking the speed or memory cost of the\n indexer.\n :param rollup_index: The index that contains the rollup results. The index can\n be shared with other rollup jobs. The data is stored so that it doesn’t interfere\n with unrelated jobs.\n :param headers:\n :param metrics: Defines the metrics to collect for each grouping tuple. By default,\n only the doc_counts are collected for each group. To make rollup useful,\n you will often add metrics like averages, mins, maxes, etc. Metrics are defined\n on a per-field basis and for each field you configure which metric should\n be collected.\n :param timeout: Time to wait for the request to complete.\n \"\"\"\n if id in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'id'\")\n if cron is None:\n raise ValueError(\"Empty value passed for parameter 'cron'\")\n if groups is None:\n raise ValueError(\"Empty value passed for parameter 'groups'\")\n if index_pattern is None:\n raise ValueError(\"Empty value passed for parameter 'index_pattern'\")\n if page_size is None:\n raise ValueError(\"Empty value passed for parameter 'page_size'\")\n if rollup_index is None:\n raise ValueError(\"Empty value passed for parameter 'rollup_index'\")\n __path = f\"/_rollup/job/{_quote(id)}\"\n __body: t.Dict[str, t.Any] = {}\n __query: t.Dict[str, t.Any] = {}\n if cron is not None:\n __body[\"cron\"] = cron\n if groups is not None:\n __body[\"groups\"] = groups\n if index_pattern is not None:\n __body[\"index_pattern\"] = index_pattern\n if page_size is not None:\n __body[\"page_size\"] = page_size\n if rollup_index is not None:\n __body[\"rollup_index\"] = rollup_index\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if headers is not None:\n __body[\"headers\"] = headers\n if human is not None:\n __query[\"human\"] = human\n if metrics is not None:\n __body[\"metrics\"] = metrics\n if pretty is not None:\n __query[\"pretty\"] = pretty\n if timeout is not None:\n __body[\"timeout\"] = timeout\n __headers = {\"accept\": \"application/json\", \"content-type\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"PUT\", __path, params=__query, headers=__headers, body=__body\n )\n\n @_rewrite_parameters(\n body_fields=True,\n )\n def rollup_search(\n self,\n *,\n index: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]],\n aggregations: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None,\n aggs: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n query: t.Optional[t.Mapping[str, t.Any]] = None,\n rest_total_hits_as_int: t.Optional[bool] = None,\n size: t.Optional[int] = None,\n typed_keys: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Enables searching rolled-up data using the standard query DSL.\n\n ``_\n\n :param index: The indices or index-pattern(s) (containing rollup or regular data)\n that should be searched\n :param aggregations:\n :param aggs:\n :param query:\n :param rest_total_hits_as_int: Indicates whether hits.total should be rendered\n as an integer or an object in the rest search response\n :param size: Must be zero if set, as rollups work on pre-aggregated data\n :param typed_keys: Specify whether aggregation and suggester names should be\n prefixed by their respective types in the response\n \"\"\"\n if index in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'index'\")\n __path = f\"/{_quote(index)}/_rollup_search\"\n __body: t.Dict[str, t.Any] = {}\n __query: t.Dict[str, t.Any] = {}\n if aggregations is not None:\n __body[\"aggregations\"] = aggregations\n if aggs is not None:\n __body[\"aggs\"] = aggs\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n if query is not None:\n __body[\"query\"] = query\n if rest_total_hits_as_int is not None:\n __query[\"rest_total_hits_as_int\"] = rest_total_hits_as_int\n if size is not None:\n __body[\"size\"] = size\n if typed_keys is not None:\n __query[\"typed_keys\"] = typed_keys\n __headers = {\"accept\": \"application/json\", \"content-type\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"POST\", __path, params=__query, headers=__headers, body=__body\n )\n\n @_rewrite_parameters()\n def start_job(\n self,\n *,\n id: str,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Starts an existing, stopped rollup job.\n\n ``_\n\n :param id: The ID of the job to start\n \"\"\"\n if id in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'id'\")\n __path = f\"/_rollup/job/{_quote(id)}/_start\"\n __query: t.Dict[str, t.Any] = {}\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n __headers = {\"accept\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"POST\", __path, params=__query, headers=__headers\n )\n\n @_rewrite_parameters()\n def stop_job(\n self,\n *,\n id: str,\n error_trace: t.Optional[bool] = None,\n filter_path: t.Optional[\n t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]]\n ] = None,\n human: t.Optional[bool] = None,\n pretty: t.Optional[bool] = None,\n timeout: t.Optional[t.Union[\"t.Literal[-1]\", \"t.Literal[0]\", str]] = None,\n wait_for_completion: t.Optional[bool] = None,\n ) -> ObjectApiResponse[t.Any]:\n \"\"\"\n Stops an existing, started rollup job.\n\n ``_\n\n :param id: The ID of the job to stop\n :param timeout: Block for (at maximum) the specified duration while waiting for\n the job to stop. Defaults to 30s.\n :param wait_for_completion: True if the API should block until the job has fully\n stopped, false if should be executed async. Defaults to false.\n \"\"\"\n if id in SKIP_IN_PATH:\n raise ValueError(\"Empty value passed for parameter 'id'\")\n __path = f\"/_rollup/job/{_quote(id)}/_stop\"\n __query: t.Dict[str, t.Any] = {}\n if error_trace is not None:\n __query[\"error_trace\"] = error_trace\n if filter_path is not None:\n __query[\"filter_path\"] = filter_path\n if human is not None:\n __query[\"human\"] = human\n if pretty is not None:\n __query[\"pretty\"] = pretty\n if timeout is not None:\n __query[\"timeout\"] = timeout\n if wait_for_completion is not None:\n __query[\"wait_for_completion\"] = wait_for_completion\n __headers = {\"accept\": \"application/json\"}\n return self.perform_request( # type: ignore[return-value]\n \"POST\", __path, params=__query, headers=__headers\n )\n", "sub_path": "elasticsearch/_sync/client/rollup.py", "file_name": "rollup.py", "file_ext": "py", "file_size_in_byte": 18567, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "_base.NamespacedClient", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 32, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 33, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 34, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 34, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 34, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 37, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 46, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 48, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 49, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 49, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 27, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 38, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 67, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 68, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 69, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 70, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 70, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 72, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 83, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 84, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 87, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 87, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 63, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 74, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 105, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 106, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 107, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 110, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 111, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 122, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 123, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 126, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 126, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 101, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 112, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 112, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 144, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 144, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 144, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 145, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 146, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 147, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 147, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 147, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 149, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 150, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 161, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 163, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 164, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 164, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 140, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 151, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 151, "usage_type": "attribute"}, {"api_name": "typing.Mapping", "line_number": 187, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 187, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 191, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 192, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 193, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 193, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 193, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 195, "usage_type": "attribute"}, {"api_name": "typing.Mapping", "line_number": 196, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 196, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 196, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 196, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 198, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 199, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 200, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 200, "usage_type": "attribute"}, {"api_name": "typing.Mapping", "line_number": 200, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 200, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 200, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 202, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 203, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 203, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 250, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 262, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 263, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 263, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 264, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 264, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 178, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 204, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 204, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 300, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 300, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 300, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.Mapping", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 302, "usage_type": "attribute"}, {"api_name": "typing.Mapping", "line_number": 302, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 302, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 303, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 304, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 305, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 305, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 305, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 307, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 308, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 309, "usage_type": "attribute"}, {"api_name": "typing.Mapping", "line_number": 309, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 309, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 310, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 311, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 312, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 330, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 332, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 333, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 333, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 334, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 334, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 294, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 313, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 313, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 365, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 366, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 367, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 367, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 367, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 369, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 370, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 379, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 381, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 382, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 382, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 360, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 371, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 371, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 401, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 402, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 403, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 403, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 403, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 405, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 406, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 407, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 407, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 408, "usage_type": "attribute"}, {"api_name": "utils.SKIP_IN_PATH", "line_number": 421, "usage_type": "name"}, {"api_name": "utils._quote", "line_number": 423, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 424, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 424, "usage_type": "attribute"}, {"api_name": "utils._rewrite_parameters", "line_number": 396, "usage_type": "call"}, {"api_name": "elastic_transport.ObjectApiResponse", "line_number": 409, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 409, "usage_type": "attribute"}]} +{"seq_id": "571670513", "text": "from typing import Type\n\nfrom kolas.endpoints import HandlesErrors\nfrom kolas.requests import Request\nfrom starlette.types import ASGIApp, Message, Receive, Scope, Send\n\n\nclass ErrorsMiddleware:\n def __init__(\n self, app: ASGIApp, error_view: Type[HandlesErrors],\n debug: bool = False,\n ) -> None:\n self.app = app\n self._error_view = error_view\n self._debug = debug\n\n async def __call__(self, scope: Scope, receive: Receive,\n send: Send) -> None:\n if scope[\"type\"] != \"http\":\n await self.app(scope, receive, send)\n return # pragma: no cover\n\n response_started = False\n\n async def _send(message: Message) -> None:\n nonlocal response_started\n\n if message[\"type\"] == \"http.response.start\":\n response_started = True\n await send(message)\n\n try:\n await self.app(scope, receive, _send)\n except Exception as exc:\n # in debug mode always re-raise exception\n # so ExceptionMiddleware can render exception trace.\n if self._debug:\n raise exc from None\n\n if response_started:\n msg = \"Caught handled exception, but response already started.\"\n raise RuntimeError(msg) from exc\n\n assert 'container' in scope\n self.container = scope['container']\n\n request = Request(scope, receive=receive)\n error_view = self.container.instantiate(\n self._error_view, scope=scope, receive=receive, send=_send\n )\n response = await error_view.handle_exception(exc, request)\n await response(scope, receive, _send)\n", "sub_path": "kolas/kolas/middleware/errors.py", "file_name": "errors.py", "file_ext": "py", "file_size_in_byte": 1741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "starlette.types.ASGIApp", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 10, "usage_type": "name"}, {"api_name": "kolas.endpoints.HandlesErrors", "line_number": 10, "usage_type": "name"}, {"api_name": "starlette.types.Scope", "line_number": 17, "usage_type": "name"}, {"api_name": "starlette.types.Receive", "line_number": 17, "usage_type": "name"}, {"api_name": "starlette.types.Send", "line_number": 18, "usage_type": "name"}, {"api_name": "starlette.types.Message", "line_number": 25, "usage_type": "name"}, {"api_name": "kolas.requests.Request", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "56008452", "text": "import os\nimport sys\nimport p3_preprocess\nfrom textblob import TextBlob\nfrom gt_ngram import gt_ngram\n\ndir_question = os.getcwd() + \"/question_dev.txt\"\nquestions = p3_preprocess.question_preprocess(dir_question)\n#questions is a dict with key = ID and value = question\n\ntext = {}\ndata = {}\n\nfor x in xrange(89, 321):\n\tdir_train = os.getcwd() + \"/doc_dev/\" + str(x) + \"/\"\n\ttext[str(x)+\"\\r\"] = {}\n\tfor y in xrange(1, 21):\n\t\tdata = p3_preprocess.doc_process(dir_train+str(y))\n\t\ttext[str(x)+\"\\r\"][str(y)] = data\n\t\tdata = []\n\t\"\"\"\n\tfor root, dirs, filenames in os.walk(dir_train):\n\t for i, f in enumerate(filenames):\n\n\t \tBecause of the low accuracy of the baseline method, the accuracy of\n\t \tusing 20 files to generate answers is the same as using 100 files.\n\t \tFor faster testing, we only use 20 files for now.\n\n\t \tif(i < 20):\n\t\t data = p3_preprocess.doc_process(root + f)\n\t\t text[str(x)+\"\\r\"][f] = data\n\t else:\n\t \tbreak\n\t\"\"\"\n\nguess = {}\nquestion_ngram = {}\n\nfor key in questions:\n\tguess[key] = dict((x, (\"\", sys.maxint, \"\")) for x in xrange(0, 5))\n\tquestion_ngram[key] = gt_ngram(questions[key])\n\n\n\tfor textfile in text[key]:\n\t\tfor x in xrange(0,len(text[key][textfile])):\n\t\t\tsent = text[key][textfile][x]\n\t\t\tperp = question_ngram[key].generate_perplexity(2, sent)\n\n\t\t\t#smaller perplexity: more similar to question\n\t\t\tif perp < guess[key][4][1]:\n\t\t\t\tanswer_blob = TextBlob(sent)\n\n\t\t\t\tanswers = answer_blob.noun_phrases\n\t\t\t\tif len(answers) > 0:\n\t\t\t\t\tanswer = \"\"\n\t\t\t\t\tfor phrase in answers:\n\t\t\t\t\t\tif phrase not in questions[key]:\n\t\t\t\t\t\t\tanswer = phrase\n\t\t\t\t\tif answer == \"\":\n\t\t\t\t\t\tanswer = answers[0]\n\t\t\t\telse:\n\t\t\t\t\tsent_list = sent.split()\n\t\t\t\t\tif len(sent_list)> 10:\n\t\t\t\t\t\tanswer = sent_list[0]\n\t\t\t\t\t\tfor n_words in range(1,10):\n\t\t\t\t\t\t\tanswer += ' ' + sent_list[n_words]\n\t\t\t\t\telse:\n\t\t\t\t\t\tanswer = sent\n\n\t\t\t\tguess[key][4] = [answer, perp, textfile]\n\t\t\t\tfor y in xrange(1,5):\n\t\t\t\t\tif perp < guess[key][4-y][1]:\n\t\t\t\t\t\tguess[key][5-y] = guess[key][4-y]\n\t\t\t\t\t\tguess[key][4-y] = [answer, perp, textfile]\n\t\t\t\t\telse:\n\t\t\t\t\t\tbreak\n\n#output:\nwith open(\"baseline_answer_dev.txt\", \"w\") as text_file:\n\tfor key in questions:\n\t\tfor x in xrange(0,5):\n\t\t\ttext_file.write(key.replace(\"\\r\", \"\") + \" \" + guess[key][x][2] + \" \" + guess[key][x][0] + \"\\n\")\n", "sub_path": "p3/baseline.py", "file_name": "baseline.py", "file_ext": "py", "file_size_in_byte": 2271, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "os.getcwd", "line_number": 7, "usage_type": "call"}, {"api_name": "p3_preprocess.question_preprocess", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 15, "usage_type": "call"}, {"api_name": "p3_preprocess.doc_process", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.maxint", "line_number": 40, "usage_type": "attribute"}, {"api_name": "gt_ngram.gt_ngram", "line_number": 41, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "298092797", "text": "import nacl.secret\nimport nacl.utils\nimport nacl.signing\nimport pickle\nimport os\nfrom socket import AF_INET, socket, SOCK_STREAM\nfrom nacl.public import PrivateKey, Box, PublicKey\nfrom nacl.signing import VerifyKey\nfrom threading import Thread\nfrom models.moviestore import MovieStore\nfrom views.movieview import MovieView\nfrom controllers.moviecontroller import MovieController\nfrom models.userstore import UserStore\nfrom views.userview import UserView\nfrom controllers.usercontroller import UserController\nfrom database.db import DB\nfrom database.Neo4J.graphdb import neo4jDB\nfrom common_functions import sign_and_encrypt, decrypt_and_verify\n\n\n#################################################### Sets up handling for incoming clients\ndef accept_incoming_connections():\n while True:\n client, client_address = SERVER.accept()\n print(\"%s:%s has connected.\" % client_address)\n \n client.send(pickle.dumps([bytes(pkserver), bytes(server_verify_key)]))\n combined_key = pickle.loads(client.recv(BUFSIZ))\n client_publickey = PublicKey(combined_key[0])\n\n client_verify_key = VerifyKey(combined_key[1])\n server_client_box = Box(skserver, client_publickey)\n\n symmetric_secret_key = nacl.utils.random(nacl.secret.SecretBox.KEY_SIZE)\n symmetric_secret_key_box_server = nacl.secret.SecretBox(symmetric_secret_key)\n \n nonce = nacl.utils.random(Box.NONCE_SIZE)\n encrypted = server_client_box.encrypt(symmetric_secret_key, nonce)\n client.send(encrypted)\n\n addresses[client] = client_address\n Thread(target=handle_client, args=(client,client_verify_key, symmetric_secret_key_box_server)).start()\n\n#################################################### Handles a single client connection\ndef handle_client(client, client_verify_key, box): # Takes client socket as argument.\n quit = False\n userId = 0\n userName = \"Anon\"\n #################################################### Login Screen\n while quit == False:\n client.send(sign_and_encrypt(box, server_signing_key, '1. Login\\n2. Signup:\\n3. Anon'))\n msg = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n\n if msg == '3': #################################################### Use Application as Anonymous\n quit = True\n else:\n client.send(sign_and_encrypt(box, server_signing_key, 'Username: '))\n username = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n client.send(sign_and_encrypt(box, server_signing_key, 'Password: '))\n password = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n\n if msg == '1': #################################################### Login\n result = user_controller.login(username, password)\n if result is not None:\n userId = result\n userName = username\n quit = True\n else:\n client.send\n \n elif msg == '2': #################################################### Signup\n result = user_controller.signup(username, password)\n if result is not None:\n userId = result\n userName = username\n quit = True\n\n #################################################### Home Page (Server functionality)\n quit = False\n while quit == False:\n welcome = \"\"\n if userId == 0:\n welcome = movie_view.print_anon_functions(userName)\n else:\n welcome = movie_view.print_user_functions(userName)\n client.send(sign_and_encrypt(box, server_signing_key, welcome))\n msg = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n while True:\n if msg == '1': #################################################### Search for a movie\n client.send(sign_and_encrypt(box, server_signing_key, \"Please enter the movie name you want to search:\\nTo specify year, use --year\\nExample: star wars --year 2018 \"))\n search_string = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n result = movie_controller.search_movie(search_string)\n result += '\\nPlease enter the movie name you want to search or type \"back\" to return to Home: '\n client.send(sign_and_encrypt(box, server_signing_key, result))\n next_search = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n while next_search != 'back':\n result = movie_controller.search_movie(next_search)\n result += '\\nPlease enter the movie name you want to search or type \"back\" to return to Home: '\n client.send(sign_and_encrypt(box, server_signing_key, result))\n next_search = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == '2': #################################################### Find movies similar to a movie\n client.send(sign_and_encrypt(box, server_signing_key, \"Please enter a movie ID you want to find other similar movies to: \"))\n search_string = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n result = movie_controller.get_similar_movies_by_genre(search_string)\n result += '\\nPlease enter a movie ID you want to find other similar movies to' \\\n ' or type \"back\" to return to Home: '\n client.send(sign_and_encrypt(box, server_signing_key, result))\n next_search = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n while next_search != 'back':\n result = movie_controller.get_similar_movies_by_genre(next_search)\n result += '\\nPlease enter a movie ID you want to find other similar movies to' \\\n ' or type \"back\" to return to Home: '\n client.send(sign_and_encrypt(box, server_signing_key, result))\n next_search = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n #################################################### User only functions\n if userId > 0:\n ######### Ratings\n\n if msg == '3': #################################################### Get movies user might be interested in by Precise algorithm\n result = movie_controller.get_precise_interested_movies(userId)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n if msg == '4': #################################################### Get movies user might be interested in by Quick algorithm\n result = movie_controller.get_quick_interested_movies(userId)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == '5': #################################################### Get movies rated by logged in user\n result = movie_controller.get_rated_movies(userId)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == '6': #################################################### Rate a movie\n client.send(sign_and_encrypt(box, server_signing_key, 'Movie ID: '))\n movie_id = int(decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ)))\n client.send(sign_and_encrypt(box, server_signing_key, 'Rating: '))\n rating = float(decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ)))\n result = movie_controller.set_movie_rating(userId, movie_id, rating)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == '7': #################################################### Delete rating of a movie\n client.send(sign_and_encrypt(box, server_signing_key, 'Movie ID: '))\n movie_id = int(decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ)))\n result = movie_controller.delete_movie_rating(userId, movie_id)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n ######### Reviews\n\n elif msg == '8': #################################################### View all your reviews\n result = movie_controller.list_reviews(userId)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == '9': #################################################### View review of a movie\n client.send(sign_and_encrypt(box, server_signing_key, 'Movie ID: '))\n movie_id = int(decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ)))\n result = movie_controller.read_review(userId, movie_id)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == '10': #################################################### Create/Update review of a movie\n client.send(sign_and_encrypt(box, server_signing_key, 'Movie ID: '))\n movie_id = int(decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ)))\n client.send(sign_and_encrypt(box, server_signing_key, 'Review Title: '))\n title = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n client.send(sign_and_encrypt(box, server_signing_key, 'Review Body: '))\n review = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n result = movie_controller.create_update_review(userId, movie_id, title, review)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == '11': #################################################### Delete review of a movie\n client.send(sign_and_encrypt(box, server_signing_key, 'Movie ID: '))\n movie_id = int(decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ)))\n result = movie_controller.delete_review(userId, movie_id)\n result += '\\nSend any key to return to Home'\n client.send(sign_and_encrypt(box, server_signing_key, result))\n back = decrypt_and_verify(box, client_verify_key, client.recv(BUFSIZ))\n break\n\n elif msg == 'quit': #################################################### Exit application\n client.send(sign_and_encrypt(box, server_signing_key, \"quit\"))\n client.close()\n del clients[client]\n print(clients)\n quit = True\n break\n else:\n break\n else:\n break\n\n\n#################################################### Establish Host & Port\nclients = {}\naddresses = {}\nHOST = ''\nPORT = 33000\nBUFSIZ = 1024\nADDR = (HOST, PORT)\nSERVER = socket(AF_INET, SOCK_STREAM)\nSERVER.bind(ADDR)\n\n#################################################### Check for private key file\nif not os.path.isfile('server_private_key'):\n skserver = PrivateKey.generate()\n f = open(\"server_private_key\", \"wb\")\n f.write(bytes(skserver))\n f.close()\nfile = open(\"server_private_key\", \"rb\")\nkey = file.read()\n\nskserver = PrivateKey(key)\npkserver = skserver.public_key\nserver_signing_key = nacl.signing.SigningKey(bytes(skserver))\nserver_verify_key = server_signing_key.verify_key\n\n#################################################### Establish Modules\ndb = DB()\ngraphdb = neo4jDB()\nmovie_store = MovieStore(db, graphdb)\nmovie_view = MovieView()\nmovie_controller = MovieController(movie_store, movie_view)\nuser_store = UserStore(db, graphdb)\nuser_view = UserView()\nuser_controller = UserController(user_store, user_view)\n\n#################################################### Run tests\nprint(\"Running test methods...\")\nfor res in user_store.test():\n print(res)\nfor res in movie_store.get_rated_movies(1):\n print(res)\nprint(\"Finished running test methods!\")\n\n#################################################### Listen for connections\nif __name__ == \"__main__\":\n SERVER.listen(5)\n print(\"Waiting for connection...\")\n ACCEPT_THREAD = Thread(target=accept_incoming_connections)\n ACCEPT_THREAD.start()\n ACCEPT_THREAD.join()\n SERVER.close()", "sub_path": "server_main.py", "file_name": "server_main.py", "file_ext": "py", "file_size_in_byte": 14178, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "pickle.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "nacl.public.PublicKey", "line_number": 29, "usage_type": "call"}, {"api_name": "nacl.signing.VerifyKey", "line_number": 31, "usage_type": "call"}, {"api_name": "nacl.public.Box", "line_number": 32, "usage_type": "call"}, {"api_name": "nacl.secret.utils.random", "line_number": 34, "usage_type": "call"}, {"api_name": "nacl.secret.utils", "line_number": 34, "usage_type": "attribute"}, {"api_name": "nacl.secret", "line_number": 34, "usage_type": "name"}, {"api_name": "nacl.secret.secret", "line_number": 34, "usage_type": "attribute"}, {"api_name": "nacl.secret.secret.SecretBox", "line_number": 35, "usage_type": "call"}, {"api_name": "nacl.secret.secret", "line_number": 35, "usage_type": "attribute"}, {"api_name": "nacl.secret", "line_number": 35, "usage_type": "name"}, {"api_name": "nacl.secret.utils.random", "line_number": 37, "usage_type": "call"}, {"api_name": "nacl.secret.utils", "line_number": 37, "usage_type": "attribute"}, {"api_name": "nacl.secret", "line_number": 37, "usage_type": "name"}, {"api_name": "nacl.public.Box.NONCE_SIZE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "nacl.public.Box", "line_number": 37, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 42, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 51, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 52, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 57, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 58, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 59, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 60, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 86, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 87, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 90, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 91, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 94, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 95, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 99, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 100, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 104, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 105, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 109, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 110, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 115, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 116, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 125, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 126, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 132, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 133, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 139, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 140, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 144, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 145, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 146, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 147, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 150, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 151, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 155, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 156, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 159, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 160, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 168, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 169, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 173, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 174, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 177, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 178, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 182, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 183, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 184, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 185, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 186, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 187, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 190, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 191, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 195, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 196, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 199, "usage_type": "call"}, {"api_name": "common_functions.decrypt_and_verify", "line_number": 200, "usage_type": "call"}, {"api_name": "common_functions.sign_and_encrypt", "line_number": 204, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 223, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 223, "usage_type": "argument"}, {"api_name": "socket.SOCK_STREAM", "line_number": 223, "usage_type": "argument"}, {"api_name": "os.path.isfile", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}, {"api_name": "nacl.public.PrivateKey.generate", "line_number": 228, "usage_type": "call"}, {"api_name": "nacl.public.PrivateKey", "line_number": 228, "usage_type": "name"}, {"api_name": "nacl.public.PrivateKey", "line_number": 235, "usage_type": "call"}, {"api_name": "nacl.secret.signing.SigningKey", "line_number": 237, "usage_type": "call"}, {"api_name": "nacl.secret.signing", "line_number": 237, "usage_type": "attribute"}, {"api_name": "nacl.secret", "line_number": 237, "usage_type": "name"}, {"api_name": "database.db.DB", "line_number": 241, "usage_type": "call"}, {"api_name": "database.Neo4J.graphdb.neo4jDB", "line_number": 242, "usage_type": "call"}, {"api_name": "models.moviestore.MovieStore", "line_number": 243, "usage_type": "call"}, {"api_name": "views.movieview.MovieView", "line_number": 244, "usage_type": "call"}, {"api_name": "controllers.moviecontroller.MovieController", "line_number": 245, "usage_type": "call"}, {"api_name": "models.userstore.UserStore", "line_number": 246, "usage_type": "call"}, {"api_name": "views.userview.UserView", "line_number": 247, "usage_type": "call"}, {"api_name": "controllers.usercontroller.UserController", "line_number": 248, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 262, "usage_type": "call"}]} +{"seq_id": "273755687", "text": "# 2017/08/06\n\nfrom trader import ETHTrader\nfrom datetime import datetime, timedelta\nimport gdax\nimport os\nimport random\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler \nfrom sklearn.externals import joblib\nfrom keras.models import load_model\n\nclass PricePredicter(ETHTrader):\n \n PREDICTION_FOLDER = 'predictions'\n PREDICTION_FILENAME = 'predictions.txt'\n \n def __init__(self):\n super(PricePredicter, self).__init__()\n self.date = datetime.today()\n self.public_client = gdax.PublicClient()\n \n self.positive_10 = [\n 'This is gonna be wild!',\n 'Ooooooh do I foresee a favorable spike?'\n ]\n self.positive_5 = [\n 'Mmmmm this is quite tasty',\n 'I am enjoying this'\n ]\n self.negative_5 = [\n 'GAHHHH! Brace for impact!',\n 'I feel a great panic approaching'\n ]\n self.negative_10 = [\n 'This is not very pretty. And I like pretty',\n 'Move the other way please'\n ]\n \n self.general_funny_commentary = [\n 'I want food. FEED ME',\n 'I demand you feed me',\n 'How many times do I have to tell you? MOAR FOOD',\n 'SQUAAAAAAAAAWK',\n 'Eeeeeeeegret',\n 'Look at my plummage *puffs out chest*',\n 'WEEEEE I can fly *flap flap*'\n 'Hmmm what else do birds do?'\n 'Am I amazing or am I really amazing?',\n \"Tell me I\\'m pretty\",\n 'Hodl hodl hodl~ Or did I mean hold?',\n 'Zzzzz so sleepy...'\n ]\n \n def run(self):\n model = self.locateMostRecent(ETHTrader.MODEL_FOLDER, self.today, ETHTrader.MODEL_NAME)\n self.regressor = load_model(model)\n self.gatherRealTimeData()\n self.makeAPrediction()\n self.makeRecommendation()\n self.recordPrediction()\n \n def gatherRealTimeData(self):\n print('Gathering real time data')\n order_book = self.public_client.get_product_order_book('ETH-USD', level=1)\n self.current_ask = float(order_book['asks'][0][0])\n self.current_bid = float(order_book['bids'][0][0])\n self._current_price = round((self.current_ask + self.current_bid) / 2, 2)\n self.current_price = np.array([self._current_price])\n print('Current Price: {0}'.format(self._current_price))\n \n def makeAPrediction(self):\n print('Predicting a single price')\n scaler_filename = self.locateMostRecent(ETHTrader.SCALER_FOLDER, self.date, ETHTrader.SCALER_NAME)\n self.sc = joblib.load(scaler_filename) \n self.current_price = self.sc.transform(self.current_price.reshape(-1, 1))\n self.current_price = np.reshape(self.current_price, (1, 1, 1))\n self.predicted_price = self.regressor.predict(self.current_price)\n self.predicted_price = self.sc.inverse_transform(self.predicted_price)\n self._predicted_price = round(self.predicted_price[0][0] / 100 * 100, 2)\n print('Predicted Price: {0}'.format(self._predicted_price))\n \n def makeRecommendation(self):\n self.commentary = ''\n self.change = round(self._predicted_price - self._current_price, 2)\n self.percent_change = round(self.change / self._current_price * 100, 2)\n if self.percent_change >= 5 or self.percent_change <= -5:\n self.commentary += self.generateSignificantMovementCommentary()\n else:\n self.commentary += self.selectRandomlyFromList(self.general_funny_commentary) + ' '\n self.commentary += self.includeActualPrediction() + '\\n'\n \n def generateSignificantMovementCommentary(self):\n if self.percent_change >= 10:\n return self.selectRandomlyFromList(self.positive_10)\n elif self.percent_change >= 5:\n return self.selectRandomlyFromList(self.positive_5)\n elif self.percent_change <= -10:\n return self.selectRandomlyFromList(self.negative_10)\n else:\n return self.selectRandomlyFromList(self.negative_5)\n \n def selectRandomlyFromList(self, lst):\n return random.choice(lst) + '\\n'\n \n def includeActualPrediction(self):\n return '(Current Price: {0}, \\n Predicted Price: {1}, \\n Predicted Change: {2}($), {3}%) \\n'.format(self._current_price, self._predicted_price, self.change, self.percent_change)\n \n def recordPrediction(self):\n self.date = self.dateObjectToString(self.date)\n self.makeFolders(PricePredicter.PREDICTION_FOLDER)\n prediction_file = self.generateFilePath(PricePredicter.PREDICTION_FOLDER, self.date, PricePredicter.PREDICTION_FILENAME)\n with open(prediction_file, 'a') as f:\n f.write(self.commentary)\n # also write to a file for messages\n with open(PricePredicter.PREDICTION_FILENAME, 'w') as f:\n f.write(self.commentary)\n\nif __name__ == '__main__': \n predicter = PricePredicter()\n predicter.run()\n", "sub_path": "price_predicter.py", "file_name": "price_predicter.py", "file_ext": "py", "file_size_in_byte": 5024, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "trader.ETHTrader", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "gdax.PublicClient", "line_number": 21, "usage_type": "call"}, {"api_name": "trader.ETHTrader.MODEL_FOLDER", "line_number": 56, "usage_type": "attribute"}, {"api_name": "trader.ETHTrader", "line_number": 56, "usage_type": "name"}, {"api_name": "trader.ETHTrader.MODEL_NAME", "line_number": 56, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "trader.ETHTrader.SCALER_FOLDER", "line_number": 74, "usage_type": "attribute"}, {"api_name": "trader.ETHTrader", "line_number": 74, "usage_type": "name"}, {"api_name": "trader.ETHTrader.SCALER_NAME", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 75, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 77, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "143172618", "text": "\"\"\"\r\nIntroducción a Matplot\r\n\"\"\"\r\n\r\n\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nxpoints = np.array([0, 6])\r\nypoints = np.array([0, 250])\r\n\r\nplt.plot(xpoints, ypoints)\r\nplt.show()\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nypoints = np.array([3, 8, 1, 10])\r\n\r\nplt.plot(ypoints, marker = 'o')\r\nplt.show()\r\n\r\n#%%\r\n\"\"\"\r\nMarker \tDescription\r\n'o' \tCircle \t\r\n'*' \tStar \t\r\n'.' \tPoint \t\r\n',' \tPixel \t\r\n'x' \tX \t\r\n'X' \tX (filled) \t\r\n'+' \tPlus \t\r\n'P' \tPlus (filled) \t\r\n's' \tSquare \t\r\n'D' \tDiamond \t\r\n'd' \tDiamond (thin) \t\r\n'p' \tPentagon \t\r\n'H' \tHexagon \t\r\n'h' \tHexagon \t\r\n'v' \tTriangle Down \t\r\n'^' \tTriangle Up \t\r\n'<' \tTriangle Left \t\r\n'>' \tTriangle Right \t\r\n'1' \tTri Down \t\r\n'2' \tTri Up \t\r\n'3' \tTri Left \t\r\n'4' \tTri Right \t\r\n'|' \tVline \t\r\n'_' \tHline\r\n\r\n\"\"\"\r\n#%%\r\n\"\"\"\r\nLine Syntax \tDescription\r\n'-' \tSolid line \t\r\n':' \tDotted line \t\r\n'--' \tDashed line \t\r\n'-.' \tDashed/dotted line\r\n\r\nColor Syntax \tDescription\r\n'r' \tRed \t\r\n'g' \tGreen \t\r\n'b' \tBlue \t\r\n'c' \tCyan \t\r\n'm' \tMagenta \t\r\n'y' \tYellow \t\r\n'k' \tBlack \t\r\n'w' \tWhite\r\n\r\n\"\"\"\r\n\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nypoints = np.array([3, 8, 1, 10])\r\n\r\nplt.plot(ypoints, 'o:r')\r\nplt.show()\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nypoints = np.array([3, 8, 1, 10])\r\n\r\n# plt.plot(ypoints, \r\n# marker = 'o', #Tipo de marcador\r\n# ms = 20, #Tamaño\r\n# mec = 'r') #Color borde\r\n\r\nplt.plot(ypoints,\r\n marker = 'o',\r\n ms = 20,\r\n mec = '#4CAF50', #Color borde\r\n mfc = 'r') #Color relleno\r\n\r\n\r\n\r\nplt.show()\r\n\r\n#%%\r\n\r\nplt.plot(ypoints, linewidth = '10')\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\ny1 = np.array([3, 8, 1, 10])\r\ny2 = np.array([6, 2, 7, 11])\r\n\r\nplt.plot(y1,linewidth = '10')\r\nplt.plot(y2,linewidth = '5')\r\n\r\nplt.show()\r\n\r\n#%%\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nx1 = np.array([0, 1, 2, 3])\r\ny1 = np.array([3, 8, 1, 10])\r\nx2 = np.array([0, 1, 2, 3])\r\ny2 = np.array([6, 2, 7, 11])\r\n\r\nplt.plot(x1, y1, x2, y2)\r\nplt.show()\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nx1 = np.array([0, 2, 10,20])\r\ny1 = np.array([3, 8, 1, 10])\r\n\r\nx2 = np.array([0, 1, 2, 3])\r\ny2 = np.array([6, 2, 7, 11])\r\n\r\nplt.plot(x1, y1, x2, y2)\r\nplt.show()\r\n\r\n#%%\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nx = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])\r\ny = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])\r\n\r\nfuente = {'family':'serif',\r\n 'color':'darkred',\r\n 'size':15}\r\n\r\nplt.plot(x, y)\r\n\r\nplt.xlabel(\"Average Pulse\")\r\nplt.ylabel(\"Calorie Burnage\")\r\n\r\n\r\nplt.title(\"Sports Watch Data\", fontdict=fuente, loc = 'left')\r\n\r\n\r\n\r\nplt.grid(color = 'green', linestyle = '--', linewidth = 0.5,axis='y')\r\n\r\nplt.show()\r\n\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n#plot 1:\r\nx = np.array([0, 1, 2, 3])\r\ny = np.array([3, 8, 1, 10])\r\n\r\n## FILAS, COLUMNAS, INDICES\r\n\r\nplt.subplot(1, 2, 1)\r\nplt.plot(x,y)\r\n\r\n#plot 2:\r\nx = np.array([0, 1, 2, 3])\r\ny = np.array([10, 20, 30, 40])\r\n\r\nplt.subplot(1, 2, 2)\r\nplt.plot(x,y)\r\n\r\nplt.show()\r\n\r\n#%%\r\n\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n#plot 1:\r\nx = np.array([0, 1, 2, 3])\r\ny = np.array([3, 8, 1, 10])\r\n\r\nplt.subplot(2, 2, 1)\r\nplt.plot(x,y)\r\n\r\n#plot 2:\r\nx = np.array([0, 1, 2, 3])\r\ny = np.array([10, 20, 30, 40])\r\n\r\nplt.subplot(2, 2, 4)\r\nplt.plot(x,y)\r\n\r\nplt.title(\"INCOME\")\r\n\r\nplt.suptitle(\"MY SHOP\")\r\n\r\nplt.show()\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\r\ny = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\r\ncolors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])\r\n\r\nsizes = np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])\r\n\r\nplt.scatter(x, y, c=colors, cmap='viridis', s=sizes, alpha=0.5)\r\n\r\nplt.colorbar()\r\n\r\nplt.show() \r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nx = np.array([\"A\", \"B\", \"C\", \"D\"])\r\ny = np.array([3, 8, 1, 10])\r\n\r\nplt.bar(x,y,alpha=0.5,width = 0.1, )\r\nplt.show()\r\n\r\n#%%\r\n\r\nx = [\"APPLES\", \"BANANAS\"]\r\ny = [400, 350]\r\nplt.bar(x, y)\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nx = np.array([\"A\", \"B\", \"C\", \"D\"])\r\ny = np.array([3, 8, 1, 10])\r\n\r\nplt.barh(x, y)\r\nplt.show()\r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\ny = np.array([35, 25, 25, 15])\r\nmylabels = [\"Apples\", \"Bananas\", \"Cherries\", \"Dates\"]\r\n\r\nplt.pie(y, labels = mylabels)\r\nplt.show() \r\n\r\n#%%\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\ny = np.array([35, 25, 25, 15])\r\nmylabels = [\"Apples\", \"Bananas\", \"Cherries\", \"Dates\"]\r\nmyexplode = [0.1, 0.5, 0, 0]\r\n\r\nplt.pie(y, labels = mylabels, explode = myexplode)\r\nplt.legend()\r\nplt.show() \r\n", "sub_path": "Semana 6/Matplot/INTRO_MATPLOT.py", "file_name": "INTRO_MATPLOT.py", "file_ext": "py", "file_size_in_byte": 4753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 240, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 267, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.barh", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}]} +{"seq_id": "401400234", "text": "# -*- coding: utf-8 -*-\n# author: itimor\n\n\nfrom django.conf import settings\nimport os\nfrom celery.schedules import crontab, timedelta\n\n# 为celery设置环境变量\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"core.settings\")\n\n# redis地址\nREDIS_URL = 'redis://127.0.0.1:6379/'\n# 设置代理人broker\nBROKER_URL = REDIS_URL + '0'\nCELERY_BROKER_URL = BROKER_URL\n# 设置结果存储，可用于跟踪结果\nCELERY_RESULT_BACKEND = 'django-db'\n# celery 的启动工作数量设置\nCELERY_WORKER_CONCURRENCY = 20\n# 任务预取功能，就是每个工作的进程／线程在获取任务的时候，会尽量多拿 n 个，以保证获取的通讯成本可以压缩。\nCELERYD_PREFETCH_MULTIPLIER = 20\n# 非常重要,有些情况下可以防止死锁\nCELERYD_FORCE_EXECV = True\n# celery 的 worker 执行多少个任务后进行重启操作\nCELERY_WORKER_MAX_TASKS_PER_CHILD = 100\n# 禁用所有速度限制，如果网络资源有限，不建议开足马力。\nCELERY_DISABLE_RATE_LIMITS = True\n# celery内容等消息的格式设置\nCELERY_ACCEPT_CONTENT = ['json']\nCELERY_TASK_SERIALIZER = 'json'\nCELERY_RESULT_SERIALIZER = 'json'\n\n# celery beat配置\nCELERY_TIMEZONE = settings.TIME_ZONE\nCELERY_ENABLE_UTC = False\nDJANGO_CELERY_BEAT_TZ_AWARE = False\nCELERYBEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'\nCELERY_BEAT_SCHEDULER = CELERYBEAT_SCHEDULER\n\n# 定时任务\nCELERYBEAT_SCHEDULE = {\n 'sum-task': {\n 'task': 'celery_tasks.tasks.add',\n 'schedule': timedelta(seconds=30),\n 'args': (3, 4)\n }\n}\n", "sub_path": "backend/celery_tasks/celeryconfig.py", "file_name": "celeryconfig.py", "file_ext": "py", "file_size_in_byte": 1549, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "os.environ.setdefault", "line_number": 10, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.conf.settings.TIME_ZONE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 35, "usage_type": "name"}, {"api_name": "celery.schedules.timedelta", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "263880913", "text": "#!/usr/bin/env python3\n# Copyright 2021 Jon Seager\n# See LICENSE file for licensing details.\n#\n# Learn more at: https://juju.is/docs/sdk\n\n\"\"\"Charm the service.\n\nRefer to the following post for a quick-start guide that will help you\ndevelop a new k8s charm using the Operator Framework:\n\n https://discourse.charmhub.io/t/4208\n\"\"\"\n\nimport logging\n\nfrom ops.charm import CharmBase\nfrom ops.main import main\nfrom ops.model import ActiveStatus\n\nlogger = logging.getLogger(__name__)\n\n\nclass HookTestCharm(CharmBase):\n def __init__(self, *args):\n super().__init__(*args)\n self.framework.observe(self.on.httpbin_pebble_ready, self._on_httpbin_pebble_ready)\n self.framework.observe(self.on.stop, self._on_stop)\n self.framework.observe(self.on.remove, self._on_remove)\n\n def _on_stop(self, event):\n logger.info(\"REMOVE EVENT\\nREMOVE EVENT\\nREMOVE EVENT\")\n\n def _on_remove(self, event):\n logger.info(\"REMOVE EVENT\\nREMOVE EVENT\\nREMOVE EVENT\")\n\n def _on_httpbin_pebble_ready(self, event):\n \"\"\"Define and start a workload using the Pebble API.\"\"\"\n container = event.workload\n pebble_layer = {\n \"services\": {\n \"httpbin\": {\n \"override\": \"replace\",\n \"summary\": \"httpbin\",\n \"command\": \"gunicorn -b 0.0.0.0:80 httpbin:app -k gevent\",\n \"startup\": \"enabled\",\n \"environment\": {},\n }\n },\n }\n container.add_layer(\"httpbin\", pebble_layer, combine=True)\n container.autostart()\n self.unit.status = ActiveStatus()\n\n\nif __name__ == \"__main__\":\n main(HookTestCharm)\n", "sub_path": "src/charm.py", "file_name": "charm.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "ops.charm.CharmBase", "line_number": 24, "usage_type": "name"}, {"api_name": "ops.model.ActiveStatus", "line_number": 53, "usage_type": "call"}, {"api_name": "ops.main.main", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "494555971", "text": "\"\"\"\nCe script permet d'envoyer un message à un ségment.\n\"\"\"\n\nimport requests\nimport Utils\n\ndef get_targets_from_segment(config, segment_id):\n \"\"\"Récupération des cibles\"\"\"\n url = config['API']['url'] + 'segments/' + segment_id + '/targets/'\n\n # Connexion à l'API\n print(\"Getting all targets from segment.\")\n headers = Utils.create_headers(config['API']['xKey'], 0)\n req = requests.get(url, headers=headers)\n req.raise_for_status()\n print(\"All targets acquired.\")\n return req.json()\n\ndef send_action_to_targets(config, targets, action_id):\n \"\"\"Envoie du message aux cibles\"\"\"\n\n print(\"Sending message to each target.\")\n for target in targets:\n url = config['API']['url'] + \"actions/\" + action_id + \"/targets/\" + target\n\n # SendMessage\n headers = Utils.create_headers(config['API']['xKey'], 0)\n req = requests.post(url, headers=headers)\n req.raise_for_status()\n print(\"Message sent.\")\n\ndef main():\n \"\"\"\n Pensez à modifier les ID.\n \"\"\"\n action_id = \"XXXXXXX\"\n segment_id = \"XXXXXX\"\n config = Utils.load_config()\n targets = get_targets_from_segment(config, segment_id)\n send_action_to_targets(config, targets, action_id)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "python/SendMessageOnSegment.py", "file_name": "SendMessageOnSegment.py", "file_ext": "py", "file_size_in_byte": 1260, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "Utils.create_headers", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "Utils.create_headers", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 29, "usage_type": "call"}, {"api_name": "Utils.load_config", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "436965598", "text": "\"\"\"\nDefinition of urls for Kases.\n\"\"\"\n\nfrom datetime import datetime\nfrom django.conf.urls import url\nfrom django.urls import path\nimport django.contrib.auth.views\nimport base.forms\nimport base.views\nimport case.views\nimport entity.views\nimport evidence.views\nimport task.views\nimport debug_toolbar\nfrom evidence.views import EvidenceList, EvidenceCreate, EvidenceTable, EvidenceDelete\nfrom evidence.views import EvidenceUpdate, EvidenceHome, EvidenceDetail\nfrom task.views import TaskList, TaskCreate, TaskTable, TaskDelete\nfrom task.views import TaskUpdate, TaskHome, TaskDetail\nfrom case.views import CaseList, CaseCreate, CaseTable, CaseDelete\nfrom case.views import CaseUpdate, CaseHome, CaseDetail, CaseNoteCreate\nfrom entity.views.company import CompanyCreate, CompanyDelete, CompanyDetail, CompanyUpdate\n#from entity.views.company import list, detail, create, update, delete\nfrom entity.views import company, person, group\n# Uncomment the next lines to enable the admin:\nfrom django.conf.urls import include\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = [\n\n # Case\n path('case//edit/', CaseUpdate.as_view(), name='case_edit'),\n path('case//', CaseDetail.as_view(), name='case_detail'),\n path('case//delete/', CaseDelete.as_view(), name='case_delete'),\n path('case/create/', CaseCreate.as_view(), name='case_create'),\n\n # Case Notes\n path('case//note/create/', CaseNoteCreate.as_view(), name='casenote_create'),\n #path('case//note//edit', CaseNoteUpdate.as_view(), name='casenote_update'),\n #path('case//note//detail', CaseDetail.as_view(), name='case_detail'),\n #path('case//note//delete', CaseDelete.as_view(), name='case_delete'),\n #path('case//note/list', CaseList.as_view(), name='case_list'),\n\n # Case Display\n path('case/list/', CaseList.as_view(), name='case_list'),\n path('case/table/', CaseTable.as_view(), name='case_table'),\n path('case/', CaseHome.as_view(), name='cases'), \n\n # Evidence\n path('evidence//edit/', EvidenceUpdate.as_view(), name='evidence_edit'),\n path('evidence//', EvidenceDetail.as_view(), name='evidence_detail'),\n path('evidence//delete/', EvidenceDelete.as_view(), name='evidence_delete'),\n path('evidence/create/', EvidenceCreate.as_view(), name='evidence_create'),\n path('evidence/', EvidenceHome.as_view(), name='evidence'),\n\n # Task\n path('task//edit/', TaskUpdate.as_view(), name='task_edit'),\n path('task//', TaskDetail.as_view(), name='task_detail'),\n path('task//delete/', TaskDelete.as_view(), name='task_delete'),\n path('task/create/', TaskCreate.as_view(), name='task_create'),\n path('task/', TaskHome.as_view(), name='tasks'), \n\n ## Entity\n path('companies/create/', company.create, name='company_create'),\n #path('companies/submit', CompanyFormView.as_view(), name='company'),\n path('companies//', company.detail, name='company_detail'),\n path('companies//delete/', company.delete, name='company_delete'),\n path('companies//edit/', company.update, name='company_update'),\n path('companies/page//', company.list, name='company_list_paginated'),\n path('companies/', company.list, name='company_list'),\n\n path('people/page//', person.list, name='contacts_person_list_paginated'),\n path('people/add/', person.create, name='contacts_person_create'),\n path('people/-/delete/', person.delete, name='contacts_person_delete'),\n path('people//delete/', person.delete, name='contacts_person_delete'),\n path('people/-/edit/', person.update, name='contacts_person_update'),\n path('people//edit/', person.update, name='contacts_person_update'),\n path('people/-/', person.detail, name='contacts_person_detail'),\n path('people//', person.detail, name='contacts_person_detail'),\n path('people/', person.list, name='contacts_person_list'),\n\n path('groups/page//', group.list, name='contacts_group_list_paginated'),\n path('groups/add/', group.create, name='contacts_group_create'),\n path('groups/-/delete/', group.delete, name='contacts_group_delete'),\n path('groups//delete/', group.delete, name='contacts_group_delete'),\n path('groups/-/edit/', group.update, name='contacts_group_update'),\n path('groups//edit/', group.update, name='contacts_group_update'),\n path('groups/-/', group.detail, name='contacts_group_detail'),\n path('groups//', group.detail, name='contacts_group_detail'),\n path('groups/', group.list, name='contacts_group_list'),\n \n # Debug Toolbar\n #path('__debug__/', include(debug_toolbar.urls)),\n \n # Needed for Django Authentication\n path('accounts/', include('django.contrib.auth.urls')),\n # Login\n path('login',\n django.contrib.auth.views.login,\n {\n 'template_name': 'registration/login.html',\n 'authentication_form': base.forms.BootstrapAuthenticationForm,\n 'extra_context':\n {\n 'title': 'Log in',\n 'year': datetime.now().year,\n }\n },\n name='login'),\n # Logout\n path('logout',\n django.contrib.auth.views.logout,\n {\n 'next_page': '/',\n },\n name='logout'),\n \n # Uncomment the admin/doc line below to enable admin documentation:\n path('admin/doc', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n path('admin', admin.site.urls),\n\n # Base CatchAll\n url(r'^$', CaseHome.as_view(), name='home'),\n]\n", "sub_path": "Kases/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 5844, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "case.views.CaseUpdate.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "case.views.CaseUpdate", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "case.views.CaseDetail.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "case.views.CaseDetail", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "case.views.CaseDelete.as_view", "line_number": 35, "usage_type": "call"}, {"api_name": "case.views.CaseDelete", "line_number": 35, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "case.views.CaseCreate.as_view", "line_number": 36, "usage_type": "call"}, {"api_name": "case.views.CaseCreate", "line_number": 36, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "case.views.CaseNoteCreate.as_view", "line_number": 39, "usage_type": "call"}, {"api_name": "case.views.CaseNoteCreate", "line_number": 39, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "case.views.CaseList.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "case.views.CaseList", "line_number": 46, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "case.views.CaseTable.as_view", "line_number": 47, "usage_type": "call"}, {"api_name": "case.views.CaseTable", "line_number": 47, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "case.views.CaseHome.as_view", "line_number": 48, "usage_type": "call"}, {"api_name": "case.views.CaseHome", "line_number": 48, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceUpdate.as_view", "line_number": 51, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceUpdate", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceDetail.as_view", "line_number": 52, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceDetail", "line_number": 52, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 53, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceDelete.as_view", "line_number": 53, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceDelete", "line_number": 53, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceCreate.as_view", "line_number": 54, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceCreate", "line_number": 54, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceHome.as_view", "line_number": 55, "usage_type": "call"}, {"api_name": "evidence.views.EvidenceHome", "line_number": 55, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 58, "usage_type": "call"}, {"api_name": "task.views.TaskUpdate.as_view", "line_number": 58, "usage_type": "call"}, {"api_name": "task.views.TaskUpdate", "line_number": 58, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 59, "usage_type": "call"}, {"api_name": "task.views.TaskDetail.as_view", "line_number": 59, "usage_type": "call"}, {"api_name": "task.views.TaskDetail", "line_number": 59, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 60, "usage_type": "call"}, {"api_name": "task.views.TaskDelete.as_view", "line_number": 60, "usage_type": "call"}, {"api_name": "task.views.TaskDelete", "line_number": 60, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 61, "usage_type": "call"}, {"api_name": "task.views.TaskCreate.as_view", "line_number": 61, "usage_type": "call"}, {"api_name": "task.views.TaskCreate", "line_number": 61, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 62, "usage_type": "call"}, {"api_name": "task.views.TaskHome.as_view", "line_number": 62, "usage_type": "call"}, {"api_name": "task.views.TaskHome", "line_number": 62, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 65, "usage_type": "call"}, {"api_name": "entity.views.company.create", "line_number": 65, "usage_type": "attribute"}, {"api_name": "entity.views.company", "line_number": 65, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 67, "usage_type": "call"}, {"api_name": "entity.views.company.detail", "line_number": 67, "usage_type": "attribute"}, {"api_name": "entity.views.company", "line_number": 67, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 68, "usage_type": "call"}, {"api_name": "entity.views.company.delete", "line_number": 68, "usage_type": "attribute"}, {"api_name": "entity.views.company", "line_number": 68, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 69, "usage_type": "call"}, {"api_name": "entity.views.company.update", "line_number": 69, "usage_type": "attribute"}, {"api_name": "entity.views.company", "line_number": 69, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 70, "usage_type": "call"}, {"api_name": "entity.views.company.list", "line_number": 70, "usage_type": "attribute"}, {"api_name": "entity.views.company", "line_number": 70, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 71, "usage_type": "call"}, {"api_name": "entity.views.company.list", "line_number": 71, "usage_type": "attribute"}, {"api_name": "entity.views.company", "line_number": 71, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 73, "usage_type": "call"}, {"api_name": "entity.views.person.list", "line_number": 73, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 73, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 74, "usage_type": "call"}, {"api_name": "entity.views.person.create", "line_number": 74, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 74, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 75, "usage_type": "call"}, {"api_name": "entity.views.person.delete", "line_number": 75, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 75, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 76, "usage_type": "call"}, {"api_name": "entity.views.person.delete", "line_number": 76, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 76, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 77, "usage_type": "call"}, {"api_name": "entity.views.person.update", "line_number": 77, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 77, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 78, "usage_type": "call"}, {"api_name": "entity.views.person.update", "line_number": 78, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 78, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 79, "usage_type": "call"}, {"api_name": "entity.views.person.detail", "line_number": 79, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 79, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 80, "usage_type": "call"}, {"api_name": "entity.views.person.detail", "line_number": 80, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 80, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 81, "usage_type": "call"}, {"api_name": "entity.views.person.list", "line_number": 81, "usage_type": "attribute"}, {"api_name": "entity.views.person", "line_number": 81, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 83, "usage_type": "call"}, {"api_name": "entity.views.group.list", "line_number": 83, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 83, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 84, "usage_type": "call"}, {"api_name": "entity.views.group.create", "line_number": 84, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 84, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 85, "usage_type": "call"}, {"api_name": "entity.views.group.delete", "line_number": 85, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 85, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 86, "usage_type": "call"}, {"api_name": "entity.views.group.delete", "line_number": 86, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 86, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 87, "usage_type": "call"}, {"api_name": "entity.views.group.update", "line_number": 87, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 87, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 88, "usage_type": "call"}, {"api_name": "entity.views.group.update", "line_number": 88, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 88, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 89, "usage_type": "call"}, {"api_name": "entity.views.group.detail", "line_number": 89, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 89, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 90, "usage_type": "call"}, {"api_name": "entity.views.group.detail", "line_number": 90, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 90, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 91, "usage_type": "call"}, {"api_name": "entity.views.group.list", "line_number": 91, "usage_type": "attribute"}, {"api_name": "entity.views.group", "line_number": 91, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 97, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 97, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 99, "usage_type": "call"}, {"api_name": "django.conf.urls.contrib", "line_number": 100, "usage_type": "attribute"}, {"api_name": "django.conf.urls", "line_number": 100, "usage_type": "name"}, {"api_name": "base.forms.forms", "line_number": 103, "usage_type": "attribute"}, {"api_name": "base.forms", "line_number": 103, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 112, "usage_type": "call"}, {"api_name": "django.conf.urls.contrib", "line_number": 113, "usage_type": "attribute"}, {"api_name": "django.conf.urls", "line_number": 113, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 120, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 120, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 123, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 123, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 123, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 126, "usage_type": "call"}, {"api_name": "case.views.CaseHome.as_view", "line_number": 126, "usage_type": "call"}, {"api_name": "case.views.CaseHome", "line_number": 126, "usage_type": "name"}]} +{"seq_id": "502445407", "text": "# -*-coding=utf-8-*-\n\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport time\nimport commands\nimport datetime\nimport sys\n\ndt = 0.01 #step length\n\nclass VehicleState:\n\tdef __init__(self, x=0.0, y=0.0, yaw=0.0,roadWheelAngle=0.0, \\\n\t\t\t\tsteeringSpeed=0.0, speed=0.0,wheel_base=2.0, \\\n\t\t\t\tmaxRoadWheelAngle=30.0/180.0*math.pi):\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.yaw = yaw\n\t\tself.roadWheelAngle = roadWheelAngle\n\t\tself.steeringSpeed = steeringSpeed\n\t\tself.speed = speed\n\t\tself.last_roadWheelAngle = roadWheelAngle\n\t\tself.wheel_base = wheel_base\n\t\tself.maxRoadWheelAngle = maxRoadWheelAngle\n\n\tdef update(self,steeringDirection=0):\n\t\tself.x = self.x+self.speed*math.cos(self.yaw)*dt\n\t\tself.y = self.y+self.speed * math.sin(self.yaw) * dt\n\t\tself.yaw = self.yaw+self.speed/self.wheel_base * math.tan(self.roadWheelAngle)*dt\n\t\tself.roadWheelAngle = self.roadWheelAngle+self.steeringSpeed* steeringDirection*dt\n\t\tif(self.roadWheelAngle > self.maxRoadWheelAngle):\n\t\t\tself.roadWheelAngle = self.maxRoadWheelAngle\n\t\telif(self.roadWheelAngle < -self.maxRoadWheelAngle):\n\t\t\tself.roadWheelAngle = -self.maxRoadWheelAngle\n\ndef coordinateTrans(x,y,x0=0.0,y0=0.0,theta0=0.0):\n\tX = (x-x0)*math.cos(theta0) + (y-y0)*math.sin(theta0)\n\tY = (x-x0)*(-math.sin(theta0))+(y-y0)*math.cos(theta0)\n\treturn X,Y\n\t\n\t\n\t\ndef lateralErr(x,y,yaw,path_x,path_y):\n\tdisList = [math.sqrt(math.pow(path_x[i]-x,2)+math.pow(path_y[i]-y,2)) for i in range(len(path_x))]\n\t\n\tdisMin = min(disList)\n\tindexOfDisMin = disList.index(disMin)\n\t\n\t#把离当前位置最近的路径点转换到车体坐标系\n\t#利用转换后的坐标判断横向偏差的±\n\tX,Y = coordinateTrans(path_x[indexOfDisMin],path_y[indexOfDisMin],x,y,yaw)\n\t#print(X,Y)\n\tif(Y>0):\n\t\treturn -disMin #Y大于0表明当前位置在路径右侧，右偏\n\telif(Y<0):\n\t\treturn disMin\n\telse:\n\t\treturn 0\n\ndef lat_yawErr(x,y,yaw,path_x,path_y):\n\tdisList = [math.sqrt(math.pow(path_x[i]-x,2)+math.pow(path_y[i]-y,2)) for i in range(len(path_x))]\n\t\n\tdisMin = min(disList)\n\tindexOfDisMin = disList.index(disMin)\n\t\n\t#把离当前位置最近的路径点转换到车体坐标系\n\t#利用转换后的坐标判断横向偏差的±\n\tX,Y = coordinateTrans(path_x[indexOfDisMin],path_y[indexOfDisMin],x,y,yaw)\n\n\tif(Y<0):\n\t\tdisMin = -disMin #Y<0表明当前位置在路径左侧，左偏\n\telif(Y==0):\n\t\tdisMin = 0\n\t\t\n\tif(indexOfDisMin==0):#第一个路径点\n\t\ttheta = math.atan2((path_y[indexOfDisMin+1] - path_y[indexOfDisMin]), \\\n\t\t\t\t(path_x[indexOfDisMin+1] - path_x[indexOfDisMin]))#与x轴夹角\n\telif(indexOfDisMin==len(path_x)-1):#最后一个路径点\n\t\ttheta = math.atan2((path_y[indexOfDisMin] - path_y[indexOfDisMin-1]), \\\n\t\t\t\t(path_x[indexOfDisMin] - path_x[indexOfDisMin-1]))#与x轴夹角\n\telse:\n\t\ttheta = math.atan2((path_y[indexOfDisMin+1] - path_y[indexOfDisMin-1]), \\\n\t\t\t\t(path_x[indexOfDisMin+1] - path_x[indexOfDisMin-1]))#与x轴夹角\n\tyawErr = yaw - theta\n\treturn disMin,yawErr,indexOfDisMin\n\ndef calCurvature(xList,yList,index):\n\tif(index==0 or index==len(xList)-1):\n\t\tprint('index error in calCurvature function')\n\t\texit()\n\telse:\n\t\ty_i =(yList[index+1]-yList[index])/(xList[index+1]-xList[index])\n\t\ty__i = (yList[index+1]+yList[index-1]-2*yList[index])/((xList[index+1]-xList[index])*(xList[index+1]-xList[index]))\n\t\tcurvature = math.fabs(y__i)/math.pow(1+y_i*y_i,1.5)\n\t\treturn curvature\n\ndef calTangentAngle(xList,yList,index):\n\tif(index==0 or index==len(xList)-1):\n\t\tprint('index error in calTangentAngle function')\n\t\texit()\n\telse:\n\t\tdy = yList[index+1] - yList[index-1]\n\t\tdx = xList[index+1] - xList[index-1]\n\t\treturn math.atan2(dy,dx)\n\ndef p2pDistance(x0,y0,x1,y1):\n\tdx = x1-x0\n\tdy = y1-y0\n\treturn math.sqrt(dx*dx+dy*dy)\n\ndef calCurvature2(xList,yList,index):\n\tif(index <5 or index >len(xList)-6):\n\t\tprint('index error in calCurvature2 function')\n\t\texit()\n\telse:\n\t\tarcLength = p2pDistance(xList[index-5],yList[index-5],xList[index],yList[index]) + \\\n\t\t\t\t\tp2pDistance(xList[index],yList[index],xList[index+5],yList[index+5])\n\t\trotateAngle = calTangentAngle(xList,yList,index+5) - calTangentAngle(xList,yList,index-5)\n\t\tprint(rotateAngle*180/math.pi)\n\t\treturn rotateAngle/arcLength\n\nglobal count \ncount = 0\n\t\t\ndef drawTangentLine(plt,x,y,theta):\n\tif(theta==0):\n\t\treturn\n\tglobal count\n\tcount = count+1\n\tif(count%5==0):\n\t\ty0 = y-x*math.tan(theta)\n\t\txArray = [0,x]\n\t\tyArray = [y0,y]\n\t\tplt.plot(xArray,yArray,'-*')\n\t\tplt.text(-3-count%10,y0,str(count))\n\t\t\ndef main():\n\n\tpath_x = []\n\tpath_y = []\n\tt_roadWheelAngleList =[]\n\t\n\twith open('path.txt','r') as f:\n\t\twhile True:\n\t\t\tline = f.readline()\n\t\t\tif not line:\n\t\t\t\tbreak\n\t\t\tx,y,roadWheelAngle,_ = line.split()\n\t\t\tpath_x.append(float(x))\n\t\t\tpath_y.append(float(y))\n\t\t\tt_roadWheelAngleList.append(float(roadWheelAngle))\n\t\n\t\n\tpointNum = len(path_x)\n\t\n\tsteeringDir=0\n\t\n\tfor i_ in range(101):\n\t\tp1 = 0.95\n\t\tp2 = -2.0 + i_*0.01\n\t\tlatErrList=[]\n\t\tyawErrList=[]\n\t\ttrackingEffect_x=[]\n\t\ttrackingEffect_y=[]\n\t\tvehicleState = VehicleState(x=0.0,y=1.0,yaw=0.0,roadWheelAngle=0.0, \\\n\t\t\t\t\t\t\t\tsteeringSpeed=5.0,speed=10.0)\n\t\t\t\t\t\t\t\t\n\t\ttest_name = 'Loc:('+str(vehicleState.x)+','+str(vehicleState.y)+')'+ \\\n\t\t\t\t' p1:'+str(p1)+' p2:'+str(p2)+ \\\n\t\t\t\t' steerSpeed:'+ str(vehicleState.steeringSpeed)+ \\\n\t\t\t\t' speed:'+str(vehicleState.speed)\n\t\t\t\t\t\t\t\t\n\t\twhile True:\n\t\t\t#print(lateralErr(vehicleState.x,vehicleState.y,vehicleState.yaw,path_x,path_y))\n\t\t\t#print(vehicleState.x,vehicleState.y)\n\t\t\tlatErr,yawErr,index = lat_yawErr(vehicleState.x,vehicleState.y,vehicleState.yaw,path_x,path_y)\n\t\t\tlatErrList.append(latErr)\n\t\t\tyawErrList.append(yawErr)\n\t\t\t#print(latErr,yawErr*180.0/math.pi)\n\t\t\tt_roadWheelAngle = t_roadWheelAngleList[index+1] + p1*latErr + p2*yawErr\n\t\t\tif(t_roadWheelAngle - vehicleState.roadWheelAngle>0.0):\n\t\t\t\tsteeringDir = 1\n\t\t\telif(t_roadWheelAngle - vehicleState.roadWheelAngle<0.0):\n\t\t\t\tsteeringDir = -1\n\t\t\telse:\n\t\t\t\tsteeringDir = 0\n\t\t\n\t\t\tif(math.fabs(t_roadWheelAngle-vehicleState.roadWheelAngle)<5.0/180*math.pi):\n\t\t\t\tvehicleState.steerSpeed = 1.0\n\t\t\telse:\n\t\t\t\tvehicleState.steerSpeed = 5.0\n\t\t\t\n\t\t\tvehicleState.update(steeringDir)\n\t\t\ttrackingEffect_x.append(vehicleState.x)\n\t\t\ttrackingEffect_y.append(vehicleState.y)\n\t\t\n\t\t\t#ax1.plot(vehicleState.x,vehicleState.y,'b.')\n\t\t\t#plt.pause(0.001)\n\t\t\n\t\t\tif(pointNum == index+2 ):\n\t\t\t\tbreak\n\t\tfig = plt.figure('new algorithm')\n\t\tax1 =fig.add_subplot(311)\n\t\tax1.plot(path_x,path_y,'r-',label='path')\n\t\tax1.legend(loc=1)\n\t\tax1.set_title(test_name)\n\t\n\t\tax1.plot(trackingEffect_x,trackingEffect_y,'b-')\n\t\tax2 = fig.add_subplot(312)\n\t\tax2.plot(trackingEffect_x,latErrList,'r-',label='latErr')\n\t\tax2.plot(trackingEffect_x,[0]*len(trackingEffect_x),'k-')\n\t\tax2.legend(loc=4)\n\t\n\t\tax3 =fig.add_subplot(313)\n\t\tax3.plot(trackingEffect_x,[x*180.0/math.pi for x in yawErrList],'r-')\n\t\tax3.plot(trackingEffect_x,[0]*len(trackingEffect_x),'k-')\n\n\t\tplt.savefig('figure/'+test_name+'.pdf')\n\t\n\t\t\n\t\n\t\tlatErrList = [math.fabs(x) for x in latErrList]\n\t\tLatMainErr = reduce(lambda x,y:x+y ,latErrList)*1.0/len(latErrList)\n\t\tax2.text(5,-1,'LatMainErr:%.2fcm'%(LatMainErr*100))\n\t\t\n\t\tyawErrList = [math.fabs(x) for x in yawErrList]\n\t\tYawMainErr = reduce(lambda x,y:x+y ,yawErrList)*1.0/len(yawErrList)\n\t\tax3.text(5,-1,'YawMainErr:%.2fdeg'%(YawMainErr*180.0/math.pi))\n\t\tplt.close()\n\t\twith open('debug.txt','a') as f:\n\t\t\tf.write('%.2f\\t%.2f\\t%.2f\\t%.2f\\t%.2f\\r\\n' %(p1,p2,vehicleState.speed,LatMainErr,YawMainErr*180.0/math.pi))\n\t\tprint('%d/100 has finish\\r\\n' %(i_))\n\t\t#plt.show()\n\n\t\t\t\nif __name__ == '__main__':\n\tmain()\n", "sub_path": "Python/pathTracking_autoDebug.py", "file_name": "pathTracking_autoDebug.py", "file_ext": "py", "file_size_in_byte": 7427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "math.pi", "line_number": 16, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 28, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 29, "usage_type": "call"}, {"api_name": "math.tan", "line_number": 30, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 38, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 38, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 39, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 39, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 45, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 62, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 62, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 77, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 80, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 83, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 95, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 95, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 105, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 110, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 120, "usage_type": "attribute"}, {"api_name": "math.tan", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "math.fabs", "line_number": 189, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 189, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 216, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "math.fabs", "line_number": 223, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 227, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 229, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 232, "usage_type": "attribute"}]} +{"seq_id": "333348874", "text": "# 100DaysOfPython\nimport requests\n\nBASE_URL = \"https://pixe.la\"\nTOKEN = \"100DaysOfPython\"\nHEADER = {\n \"X-USER-TOKEN\": TOKEN\n }\n\n\ndef post_request(endpoint, body, headers=None):\n response = requests.post(url=f'{BASE_URL}{endpoint}', json=body, headers=headers)\n response.raise_for_status()\n print(response.text)\n\n\ndef create_user():\n api_endpoint = '/v1/users'\n username = input(\"Enter a username: \")\n request_body = {\n \"token\": TOKEN,\n \"username\": username,\n \"agreeTermsOfService\": \"yes\",\n \"notMinor\": \"yes\"\n }\n post_request(api_endpoint, request_body)\n\n\ndef create_graph():\n username = input(\"Create graph for what username? \")\n graph_id = input(\"Enter a graph id: \")\n graph_name = input(\"Enter a name for the graph: \")\n graph_unit = input(\"Enter your unit of measurement name: \")\n graph_type = input(\"Unit quantity (int/float)? \")\n graph_color = input(\"Color (shibafu, momiji, sora, ichou, ajisai, kuro): \")\n api_endpoint = f'/v1/users/{username}/graphs'\n request_body = {\n \"id\": graph_id,\n \"name\": graph_name,\n \"unit\": graph_unit,\n \"type\": graph_type,\n \"color\": graph_color\n }\n post_request(api_endpoint, request_body, HEADER)\n\n\ndef post_a_pixel():\n username = input(\"Record for what username? \")\n graph_id = input(\"What is the graph ID? \")\n graph_date = input(\"Enter date (YYYYMMDD): \")\n graph_quantity = input(\"What is the quantity (int=0-9, float=0-9.0-9)? \")\n api_endpoint = f'/v1/users/{username}/graphs/{graph_id}'\n request_body = {\n \"date\": graph_date,\n \"quantity\": graph_quantity\n }\n post_request(api_endpoint, request_body, HEADER)\n\n\nwhile True:\n user_response = input(\"What would you like to do? \").lower()\n if user_response == \"exit\":\n exit()\n elif user_response == \"create user\":\n create_user()\n elif user_response == \"create graph\":\n create_graph()\n elif user_response == \"post a pixel\":\n post_a_pixel()\n else:\n print(\"\\nOops, that's not an option\\n\")\n\n", "sub_path": "day37/pixela/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2087, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "requests.post", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "245086779", "text": "import math\nimport random\nfrom dataclasses import dataclass\nfrom typing import Tuple\n\nimport numpy\nimport tcod\nimport tcod.event\n\nimport settings\nfrom engine import GameScene, core, colors\nfrom procgen.palettes import Palette\n\nRUGGEDNESS_BASE = 0.12\nRUGGEDNESS_SD = 0.01\n\n\n@dataclass\nclass PlanetTile(object):\n elevation: float = 0.0\n prevailing_wind: Tuple[float, float] = (0.0, 0.0)\n precipitation: float = 0.0\n temperature: float = 0.0\n biome: str = None\n\nclass PlanetGenTestScene(GameScene):\n \"\"\"Scene for testing height map functionality.\"\"\"\n\n def __init__(self):\n super().__init__()\n self.height = settings.MAP_HEIGHT\n self.width = settings.MAP_WIDTH\n self.layers = [1.0, 6.0, 20.0, 100.0]\n self.generators = [core.get_noise_generator(dimensions=3) for _ in range(len(self.layers))]\n self.burn = core.get_noise_generator(dimensions=3)\n self.update_screen = True\n self.map = numpy.zeros((self.width, self.height), order='F')\n self.water_level = random.uniform(0.0, 1.0)\n self.mountain_level = 0.95\n self.ruggedness = random.normalvariate(RUGGEDNESS_BASE, RUGGEDNESS_SD)\n self.freezing_zone = random.normalvariate(1.25, 0.25)\n self.tilt = random.randint(-5, 5)\n self.map_colors = Palette()\n\n def on_load(self):\n self.regenerate()\n self.render()\n\n def update(self):\n key_event = core.wait_for_char()\n if key_event.sym == tcod.event.K_SPACE:\n self.reset()\n elif key_event.sym == tcod.event.K_l:\n self.water_level += 0.01\n elif key_event.sym == tcod.event.K_k:\n self.water_level -= 0.01\n elif key_event.sym == tcod.event.K_p:\n self.mountain_level += 0.01\n elif key_event.sym == tcod.event.K_o:\n self.mountain_level -= 0.01\n elif key_event.sym == tcod.event.K_m:\n self.freezing_zone -= 0.1\n elif key_event.sym == tcod.event.K_n:\n self.freezing_zone += 0.1\n elif key_event.sym == tcod.event.K_x:\n self.ruggedness += 0.01\n print(self.ruggedness)\n elif key_event.sym == tcod.event.K_z:\n self.ruggedness -= 0.01\n print(self.ruggedness)\n elif key_event.sym == tcod.event.K_q:\n tcod.sys_save_screenshot()\n elif key_event.sym == tcod.event.K_ESCAPE:\n self.controller.pop_scene()\n self.regenerate()\n\n def reset(self):\n self.generators = [core.get_noise_generator() for _ in self.generators]\n self.map_colors = Palette()\n self.tilt = random.randint(-5, 5)\n self.freezing_zone = random.normalvariate(1.25, 0.25)\n self.ruggedness = random.normalvariate(RUGGEDNESS_BASE, RUGGEDNESS_SD)\n self.water_level = random.uniform(0.0, 1.0)\n\n def regenerate(self):\n for x in range(self.width):\n for y in range(self.height):\n self.map[x, y] = self.get_point(x, y)\n minimum = self.map.min()\n self.map -= minimum\n self.map /= self.map.max()\n self.update_screen = True\n\n def get_point(self, x, y):\n cylinder_x = x / settings.MAP_WIDTH\n cylinder_y = y / settings.MAP_HEIGHT\n\n x_in_rad = cylinder_x * 2 * math.pi\n y_in_rad = cylinder_y * math.pi\n y_sin = math.sin(y_in_rad + math.pi)\n\n r = 10.0\n a = r * math.sin(x_in_rad) * y_sin\n b = r * math.cos(x_in_rad) * y_sin\n c = r * math.cos(y_in_rad)\n\n output = 0\n base = 100.0 / (self.ruggedness * 3.0)\n for i in range(len(self.generators)):\n value = self.generators[i].get_point(\n (132+a) / self.layers[i],\n (123+b) / self.layers[i],\n (312+c) / self.layers[i]\n )\n output += base * value\n base /= (self.ruggedness * 3.0)\n return output\n\n def render(self):\n if self.update_screen:\n self.gui.root.clear()\n window = self.gui.root\n for index, v in numpy.ndenumerate(self.map):\n ground, rank = self.get_elevation_rank(v)\n if self.is_frozen(index):\n color = self.map_colors.tertiary[-1]\n elif ground:\n color = self.map_colors.primary[rank]\n else:\n color = self.map_colors.secondary[rank]\n if not ground:\n # water\n sym = '≈'\n elif rank == 9:\n sym = '▲'\n elif rank == 7:\n sym = '∩'\n elif rank <= 6:\n sym = random.choice([\"'\", ',', '.', 'ⁿ'])\n else:\n sym = '.'\n window.tiles[index[0], index[1]] = (\n ord(sym),\n (*color, 255),\n (*colors.darken(color, factor=0.8), 255)\n )\n self.update_screen = False\n\n def get_elevation_rank(self, value):\n if value <= self.water_level - 0.04:\n # deep water\n return 0, 0\n elif value <= self.water_level - 0.03:\n # normal water\n return 0, 1\n elif value <= self.water_level - 0.02:\n # shallow water\n return 0, 2\n elif value <= self.water_level - 0.01:\n return 0, 3\n elif value <= self.water_level:\n return 0, 4\n elif value < self.mountain_level - 0.7:\n # low lands\n return 1, 3\n elif value < self.mountain_level - 0.5:\n # low lands\n return 1, 4\n elif value < self.mountain_level - 0.3:\n # low lands\n return 1, 5\n elif value < self.mountain_level - 0.1:\n # midlands\n return 1, 6\n elif value < self.mountain_level:\n # highlands\n return 1, 7\n else:\n # mountain\n return 1, 9\n\n def is_frozen(self, p):\n elevation = self.map[p[0], p[1]]\n equator = (settings.MAP_HEIGHT // 2) + self.tilt\n steps_from_equator = (abs(equator - p[1]) + 1) * .1\n frosty_range = (elevation * 2 + steps_from_equator) / 3.0\n return frosty_range > self.freezing_zone\n\n", "sub_path": "scenes/planet_gen_test_scene.py", "file_name": "planet_gen_test_scene.py", "file_ext": "py", "file_size_in_byte": 6332, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "typing.Tuple", "line_number": 21, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 18, "usage_type": "name"}, {"api_name": "engine.GameScene", "line_number": 26, "usage_type": "name"}, {"api_name": "settings.MAP_HEIGHT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "settings.MAP_WIDTH", "line_number": 32, "usage_type": "attribute"}, {"api_name": "engine.core.get_noise_generator", "line_number": 34, "usage_type": "call"}, {"api_name": "engine.core", "line_number": 34, "usage_type": "name"}, {"api_name": "engine.core.get_noise_generator", "line_number": 35, "usage_type": "call"}, {"api_name": "engine.core", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 38, "usage_type": "call"}, {"api_name": "random.normalvariate", "line_number": 40, "usage_type": "call"}, {"api_name": "random.normalvariate", "line_number": 41, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 42, "usage_type": "call"}, {"api_name": "procgen.palettes.Palette", "line_number": 43, "usage_type": "call"}, {"api_name": "engine.core.wait_for_char", "line_number": 50, "usage_type": "call"}, {"api_name": "engine.core", "line_number": 50, "usage_type": "name"}, {"api_name": "tcod.event", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tcod.event", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tcod.sys_save_screenshot", "line_number": 72, "usage_type": "call"}, {"api_name": "tcod.event", "line_number": 73, "usage_type": "attribute"}, {"api_name": "engine.core.get_noise_generator", "line_number": 78, "usage_type": "call"}, {"api_name": "engine.core", "line_number": 78, "usage_type": "name"}, {"api_name": "procgen.palettes.Palette", "line_number": 79, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 80, "usage_type": "call"}, {"api_name": "random.normalvariate", "line_number": 81, "usage_type": "call"}, {"api_name": "random.normalvariate", "line_number": 82, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 83, "usage_type": "call"}, {"api_name": "settings.MAP_WIDTH", "line_number": 95, "usage_type": "attribute"}, {"api_name": "settings.MAP_HEIGHT", "line_number": 96, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 98, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 99, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 100, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 100, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 103, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 104, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 123, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 139, "usage_type": "call"}, {"api_name": "engine.colors.darken", "line_number": 145, "usage_type": "call"}, {"api_name": "engine.colors", "line_number": 145, "usage_type": "name"}, {"api_name": "settings.MAP_HEIGHT", "line_number": 184, "usage_type": "attribute"}]} +{"seq_id": "114120228", "text": "# Author: Abel \n# !/usr/bin/python3 \n# -*- coding: utf-8 -*- \n# This program is optimized for python 3.6\n# 从IPv6地址中提取IPv6前缀\n# 需要安装netaddr模块\n\nimport socket\nimport netifaces as ni\nimport netaddr as na\n\n\ndef extract_ipv6_info():\n \"\"\"Extracts IPv6 information\"\"\"\n print(\"IPV6 support built into Python: %s\" % socket.has_ipv6)\n for interface in ni.interfaces():\n all_addresses = ni.ifaddresses(interface)\n print(\"Interface %s:\" % interface)\n for family, addrs in all_addresses.items():\n fam_name = ni.address_families[family]\n print(' Address family: %s' % fam_name)\n\n for addr in addrs:\n if fam_name == 'AF_INET6':\n addr = addr['addr']\n has_eth_string = addr.split(\"%\")\n if has_eth_string:\n addr = addr.split(\"%\")[0]\n print(\" IP Address : %s\"% na.IPNetwork(addr))\n print(\" IP Version : %s\" % na.IPNetwork(addr).version)\n print(\" IP Prefix length: %s\" % na.IPNetwork(addr).prefixlen)\n print(\" Network : %s\" % na.IPNetwork(addr).network)\n print(\" Broadcast : %s\" % na.IPNetwork(addr).broadcast)\n\n\nif __name__ == \"__main__\":\n extract_ipv6_info()\n", "sub_path": "Chapter_three/python3/3_12_extract_ipv6_prefix.py", "file_name": "3_12_extract_ipv6_prefix.py", "file_ext": "py", "file_size_in_byte": 1367, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "socket.has_ipv6", "line_number": 15, "usage_type": "attribute"}, {"api_name": "netifaces.interfaces", "line_number": 16, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 17, "usage_type": "call"}, {"api_name": "netifaces.address_families", "line_number": 20, "usage_type": "attribute"}, {"api_name": "netaddr.IPNetwork", "line_number": 29, "usage_type": "call"}, {"api_name": "netaddr.IPNetwork", "line_number": 30, "usage_type": "call"}, {"api_name": "netaddr.IPNetwork", "line_number": 31, "usage_type": "call"}, {"api_name": "netaddr.IPNetwork", "line_number": 32, "usage_type": "call"}, {"api_name": "netaddr.IPNetwork", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "90313591", "text": "from tkinter import *\r\n\r\nimport time\r\nimport wave\r\nimport serial\r\nfrom pygame import mixer\r\nfrom Mainmenu import mainmenu\r\nfrom StopWatch import stopwatch\r\nfrom Timer import Timer\r\nfrom CameraStopWatch import Camera\r\n\r\nclass App(Tk):\r\n def __init__(self, *args, **kwargs):\r\n Tk.__init__(self, *args, **kwargs)\r\n\r\n self.title(\"Productivity Time\")\r\n self.geometry(\"320x440+0+0\")\r\n self.resizable(0,0)\r\n container = Frame(self)\r\n container.pack(side=\"top\", fill=\"both\", expand=True)\r\n container.grid_rowconfigure(0, weight=1)\r\n container.grid_columnconfigure(0, weight=1)\r\n\r\n self.frames = {}\r\n for F in (mainmenu, Timer, stopwatch,Camera):\r\n frame = F(container, self)\r\n self.frames[F.__name__] = frame\r\n frame.grid(row=0, column=0, sticky=\"nsew\")\r\n\r\n self.show_frame('mainmenu')\r\n\r\n def show_frame(self, cont):\r\n frame = self.frames[cont]\r\n frame.tkraise()\r\n\r\nser = serial.Serial('/dev/ttyACM0', 115200)\r\n\r\na = App()\r\na.mainloop()", "sub_path": "RPi Timer/clock.py", "file_name": "clock.py", "file_ext": "py", "file_size_in_byte": 1059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "Mainmenu.mainmenu", "line_number": 25, "usage_type": "name"}, {"api_name": "Timer.Timer", "line_number": 25, "usage_type": "name"}, {"api_name": "StopWatch.stopwatch", "line_number": 25, "usage_type": "name"}, {"api_name": "CameraStopWatch.Camera", "line_number": 25, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "630119283", "text": "\"\"\"\n爬取图吧(https://poi.mapbar.com)长沙市乡镇、街道、社区、小区、地铁站、公交站、道路等交通设施名\n生成自定义jieba分词词典\n输出文件：\nchangsha_transportation_ns.txt\n\"\"\"\nfrom lxml import etree\nfrom selenium import webdriver\n\ndriver = webdriver.Chrome()\ndata = []\n\n\ndef get_data(codes):\n url = 'https://poi.mapbar.com/changsha/' + codes + '/'\n driver.get(url)\n html = driver.page_source\n dom = etree.HTML(html, etree.HTMLParser(encoding='utf-8'))\n temp_data = dom.xpath('//div[@class=\"sortC\"]/dl/dd/a/text()')\n for i in temp_data:\n data.append(i)\n\n\ndata_list = ['G11', 'G12', 'G14', 'G15', 'G20', 'G21', 'G30', 'G31', 'G40', 'G50', 'G51', 'G60', 'G70', 'G80', 'G90',\n 'GA0', 'GA2', 'GF0']\nfor index in data_list:\n get_data(index)\n\nwith open('/home/asimov/PycharmProjects/wisdom_gov_affairs/question2/data/changsha_transportation_ns.txt', 'w') as f:\n for i in data:\n f.write(i)\n f.write(' ')\n f.write('ns')\n f.write('\\n')\n", "sub_path": "question2/图吧数据爬取/get_transportation.py", "file_name": "get_transportation.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 10, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 10, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 18, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 18, "usage_type": "name"}, {"api_name": "lxml.etree.HTMLParser", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "161428411", "text": "from requests_oauthlib import OAuth1\nimport json\nimport sys\nimport requests\nimport secret_data # file that contains OAuth credentials\n# import nltk # uncomment line after you install nltk\n\n## SI 206 - HW\n## COMMENT WITH:\n## Your section day/time:\n## Any names of people you worked with on this assignment:\n\n#usage should be python3 hw5_twitter.py \nusername = sys.argv[1]\nnum_tweets = sys.argv[2]\n\nconsumer_key = secret_data.CONSUMER_KEY\nconsumer_secret = secret_data.CONSUMER_SECRET\naccess_token = secret_data.ACCESS_KEY\naccess_secret = secret_data.ACCESS_SECRET\n\n#Code for OAuth starts\nurl = 'https://api.twitter.com/1.1/account/verify_credentials.json'\nauth = OAuth1(consumer_key, consumer_secret, access_token, access_secret)\nrequests.get(url, auth=auth)\n#Code for OAuth ends\n\n#Write your code below:\n#Code for Part 3:Caching\n#Finish parts 1 and 2 and then come back to this\n\n#Code for Part 1:Get Tweets\n\n#Code for Part 2:Analyze Tweets\n\n\n\nif __name__ == \"__main__\":\n if not consumer_key or not consumer_secret:\n print(\"You need to fill in client_key and client_secret in the secret_data.py file.\")\n exit()\n if not access_token or not access_secret:\n print(\"You need to fill in this API's specific OAuth URLs in this file.\")\n exit()\n", "sub_path": "hw5_twitter.py", "file_name": "hw5_twitter.py", "file_ext": "py", "file_size_in_byte": 1293, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "secret_data.CONSUMER_KEY", "line_number": 17, "usage_type": "attribute"}, {"api_name": "secret_data.CONSUMER_SECRET", "line_number": 18, "usage_type": "attribute"}, {"api_name": "secret_data.ACCESS_KEY", "line_number": 19, "usage_type": "attribute"}, {"api_name": "secret_data.ACCESS_SECRET", "line_number": 20, "usage_type": "attribute"}, {"api_name": "requests_oauthlib.OAuth1", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "70260953", "text": "#coding:utf-8\nimport pymysql\nmysql_info={\n \"host\":\"192.168.20.35\",\n \"user\":\"root\",\n \"password\":'begoit',\n 'db':'mysql'\n# 'charset':'utf8',\n# 'port':'3306'\n }\n\nclass MysqlUtil():\n \"\"\"\n mysql数据库相关操作\n 连接数据库信息：mysql_info\n 创建游标：mysql_excute\n 查询某个字段对应的字符串：mysql_getstring\n 查询一组数据：mysql_getrows\n 关闭mysql连接���mysql_close\n \"\"\"\n def __init__(self):\n self.db_info=mysql_info\n '''连接池方式'''\n self.conn=MysqlUtil._getConnect(self.db_info)\n\n @staticmethod\n def _getConnect(db_info):\n '''静态方法，从连接池中取出连接'''\n try:\n conn=pymysql.connect(host=db_info['host'],\n user=db_info['user'],\n passwd=db_info['password'],\n db=db_info['db'])\n# charset=db_info['charset'],\n# port=db_info['port'])\n\n return conn\n except Exception as a:\n print(\"数据库连接异常：%s\"%a)\n\n def mysql_execute(self,sql):\n '''执行sql语句'''\n cur=self.conn.cursor()\n try:\n cur.execute(sql)\n except Exception as a:\n self.conn.rollback() #sql执行异常后回滚\n print('执行SQL语句出现异常:%s'%a)\n else:\n cur.close()\n self.conn.commit() #sql无异常提交\n\n def mysql_getrows(self,sql):\n '''返回查询结果'''\n cur=self.conn.cursor()\n try:\n cur.execute(sql)\n except Exception as a:\n print('执行sql语句出现异常:%s'%a)\n else:\n rows=cur.fetchall()\n cur.close()\n return rows\n\n def mysql_getstring(self,sql):\n '''查询某个字段的对应值'''\n rows=self.mysql_getrows(sql)\n if rows!=None:\n for row in rows:\n for i in row:\n return i\n\n def mysql_close(self):\n '''关闭close mysql'''\n try:\n self.conn.close()\n except Exception as a:\n print('数据库关闭时异常:%s'%a)\n\n # MySQLdb.connect()     建立数据库连接\n # cur = conn.cursor()    #通过获取到的数据库连接conn下的cursor()方法来创建游标。\n # cur.execute()    #过游标cur 操作execute()方法可以写入纯sql语句。通过execute()方法中写如sql语句来对数据进行操作。\n # cur.close()     # cur.close() 关闭游标\n # conn.commit()   # conn.commit()方法在提交事物，在向数据库插入(或update)一条数据时必须要有这个方法，否则数据不会被真正的插入。\n # conn.rollback() # 发生错误时候回滚\n # conn.close()     # Conn.close()关闭数据库连接\n\nif __name__=='__main__':\n mysql=MysqlUtil()\n sql=\"select last_update from engine_cost where cost_name='io_block_read_cost'\"\n mysql.mysql_execute(sql)\n# print(aaa)\n print(mysql.mysql_getrows(sql))\n print(mysql.mysql_getstring(sql))\n mysql.mysql_close()\n", "sub_path": "common/get_mysql.py", "file_name": "get_mysql.py", "file_ext": "py", "file_size_in_byte": 3312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "pymysql.connect", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "641135109", "text": "# coding:utf-8\n# project/furniture/views.py\n\nfrom flask import render_template, Blueprint, url_for, redirect, flash, request\nfrom project.models import Tips, Types, Products, ProductsTips, Pictures\nfrom project import db\nfrom sqlalchemy.orm import aliased\nfrom sqlalchemy.sql import exists\nfrom .forms import TipEditForm, Form, StringField, TextAreaField, IntegerField, SelectField, FileField,\\\n SelectMultipleField\nfrom .apis import Page, up_to_qiniu\nimport uuid\nimport datetime\nfrom flask.ext.login import login_required\n\nfurniture_blueprint = Blueprint('furniture', __name__,)\nPAGE_SIZE = 5\n\n\ndef get_page_index(page_str):\n p = 1\n try:\n p = int(page_str)\n except ValueError as e:\n pass\n if p < 1:\n p = 1\n return p\n\n\ndef get_nav_types():\n nav_types = Types.query.filter().all()\n return nav_types\n\n# tips\n\n\n@furniture_blueprint.route('/tip_create', methods=['GET', 'POST'])\n@login_required\ndef tip_create():\n \n form = TipEditForm(request.form)\n if form.validate_on_submit():\n tip = Tips(tip_name=form.tip_name.data, tip_note=form.tip_note.data)\n db.session.add(tip)\n db.session.commit()\n flash(u'创建成功', 'success')\n return redirect(url_for('furniture.tip_list'))\n return render_template('furniture/tip_edit.html', form=form, nav_types=get_nav_types())\n\n\n@furniture_blueprint.route('/tip_edit/', methods=['GET', 'POST'])\n@login_required\ndef tip_edit(tip_id):\n\n # 先获取tip对象\n tip = db.session.query(Tips).get(tip_id)\n # 如果request.form空，使用tip对象\n form = TipEditForm(request.form, tip)\n if form.validate_on_submit():\n tip = Tips(tip_id=tip_id, tip_name=form.tip_name.data, tip_note=form.tip_note.data)\n db.session.merge(tip)\n db.session.commit()\n flash(u'更新成功', 'success')\n return redirect(url_for('furniture.tip_list'))\n return render_template('furniture/tip_edit.html', form=form, nav_types=get_nav_types())\n\n\n@furniture_blueprint.route('/tip_delete/')\n@login_required\ndef tip_delete(tip_id):\n tip = db.session.query(Tips).get(tip_id)\n if tip:\n db.session.delete(tip)\n db.session.commit()\n flash(u'删除成功', 'success')\n return redirect(url_for('furniture.tip_list'))\n else:\n flash(u'数据不存在', 'danger')\n return redirect(url_for('furniture.tip_list'))\n\n\n@furniture_blueprint.route('/tips/')\n@furniture_blueprint.route('/tips')\n@login_required\ndef tip_list(page_id=1):\n \n page_index = get_page_index(page_id)\n paginate = Tips.query.paginate(page_index, PAGE_SIZE, False)\n num = Tips.query.filter().count()\n page = Page(num, page_index, page_size=PAGE_SIZE)\n\n return render_template('furniture/tips.html', pagination=paginate, page=page, nav_types=get_nav_types())\n\n\n# types\n\n\n@furniture_blueprint.route('/types/')\n@furniture_blueprint.route('/types')\n@login_required\ndef types_list(page_id=1):\n\n # 转义 Types AS parent_types\n parent_types = aliased(Types, name='parent_types')\n page_index = get_page_index(page_id)\n # query = db.session.query(Types, parent_types).filter(Types.parent_type_id == parent_types.type_id)\n # query = query.outerjoin(parent_types, parent_types.parent_type_id == Types.type_id)\n num = Types.query.filter().count()\n page = Page(num, page_index, page_size=PAGE_SIZE)\n # 此次join查询，返回的types包含Types和parent_types，然后就可以获取到父类型的type_name了\n types = db.session.query(Types, parent_types)\\\n .outerjoin(parent_types, parent_types.type_id == Types.parent_type_id)\\\n .filter().all()[page.offset:page.offset+page.limit]\n\n return render_template('furniture/types.html', page=page, types=types, nav_types=get_nav_types())\n\n\ndef get_parent():\n parent_type_choices = [(0, u'无')]\n types = Types.query.filter().all()\n for this_type in types:\n parent_type_choices.append((this_type.type_id, this_type.type_name))\n return parent_type_choices\n\n\n@furniture_blueprint.route('/type_edit/', methods=['GET', 'POST'])\n@furniture_blueprint.route('/type_create', methods=['GET', 'POST'])\n@login_required\ndef type_edit(type_id=None):\n\n class TypesForm(Form):\n type_name = StringField(u'类型名称')\n type_note = TextAreaField(u'类型描述')\n parent_type_id = SelectField(u'上级类型', choices=get_parent())\n\n if type_id:\n this_type = db.session.query(Types).get(type_id)\n else:\n this_type = Types()\n form = TypesForm(request.form, this_type)\n if request.method == 'POST':\n this_type.type_name = form.type_name.data\n this_type.type_note = form.type_note.data\n this_type.parent_type_id = form.parent_type_id.data\n if type_id:\n db.session.merge(this_type)\n flash(u'更新成功', 'success')\n else:\n db.session.add(this_type)\n flash(u'新增成功', 'success')\n db.session.commit()\n return redirect(url_for('furniture.types_list'))\n return render_template('furniture/type_edit.html', form=form, nav_types=get_nav_types())\n\n\n'''\n@furniture_blueprint.route('/type_create2', methods=['GET', 'POST'])\n@login_required\ndef type_create():\n class TypesForm(Form):\n type_name = StringField(u'类型名称')\n type_note = TextAreaField(u'类型描述')\n parent_type_id = SelectField(u'上级类型', choices=get_parent())\n\n form = TypesForm(request.form)\n if request.method == 'POST':\n this_type = Types(type_name=form.type_name.data, type_note=form.type_note.data,\n parent_type_id=form.parent_type_id.data)\n db.session.add(this_type)\n db.session.commit()\n flash(u'新增成功', 'success')\n return redirect(url_for('furniture.types_list'))\n return render_template('furniture/type_edit.html', form=form)\n'''\n\n\n@furniture_blueprint.route('/type_delete/')\n@login_required\ndef type_delete(type_id):\n this_type = db.session.query(Types).get(type_id)\n if this_type:\n if db.session.query(Types).filter(exists().where(Types.parent_type_id == type_id)).count() > 0:\n flash(u'存在子类型，不可删除', 'danger')\n return redirect(url_for('furniture.types_list'))\n db.session.delete(this_type)\n db.session.commit()\n flash(u'删除成功', 'success')\n return redirect(url_for('furniture.types_list'))\n else:\n flash(u'数据不存在', 'danger')\n return redirect(url_for('furniture.types_list'))\n\n\ndef get_tips():\n tips_choices = [(0, u'无')]\n tips = Tips.query.filter().all()\n for tip in tips:\n tips_choices.append((tip.tip_id, tip.tip_name))\n return tips_choices\n\n\n# 验证文件格式\nALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'md', 'py'])\n\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\n\n\n@furniture_blueprint.route('/product_edit/', methods=['GET', 'POST'])\n@furniture_blueprint.route('/product_create', methods=['GET', 'POST'])\n@login_required\ndef product_edit(product_id=None):\n\n class ProductForm(Form):\n type_id = SelectField(u'产品类型', choices=get_parent())\n product_code = StringField(u'产品编码')\n product_name = StringField(u'产品名称')\n product_text = TextAreaField(u'产品简介')\n product_md = TextAreaField(u'产品详情')\n is_on_first = SelectField(u'是否显示在首页', choices=[(0, u'否'), (1, u'是')])\n is_rolling = SelectField(u'是否首页滚动展示', choices=[(0, u'否'), (1, u'是')])\n main_picture = FileField(u'产品主图')\n product_tips = SelectMultipleField(u'产品标签', choices=get_tips())\n\n if product_id:\n product = db.session.query(Products).get(product_id)\n else:\n product = Products()\n\n form = ProductForm(request.form, product)\n if request.method == 'POST':\n product.product_code = form.product_code.data\n product.product_name = form.product_name.data\n product.product_text = form.product_text.data\n product.product_md = form.product_md.data\n product.type_id = form.type_id.data\n product.is_on_first = form.is_on_first.data\n product.is_rolling = form.is_rolling.data\n\n # product.product_tips = form.product_tips.data\n # 处理主图片及其url\n main_picture = request.files['main_picture']\n if main_picture and allowed_file(main_picture.filename):\n up_to_qiniu(product.main_picture, '', 0)\n product.main_picture = str(uuid.uuid1()) + '.' + main_picture.filename.rsplit('.', 1)[1]\n up_to_qiniu(product.main_picture, main_picture, 1)\n product.main_picture_url = 'http://7xrwf4.com1.z0.glb.clouddn.com/%s' % product.main_picture\n # 保存数据\n if product_id:\n product.Update_time = datetime.datetime.now()\n db.session.merge(product)\n flash(u'更新成功', 'success')\n else:\n product.create_time = datetime.datetime.now()\n product.Update_time = datetime.datetime.now()\n db.session.add(product)\n flash(u'新增成功', 'success')\n db.session.flush()\n # 获取主键\n product_id = product.product_id\n db.session.commit()\n\n # 处理标签\n old_tips = db.session.query(ProductsTips).filter(ProductsTips.product_id == product_id).all()\n for old_tip in old_tips:\n db.session.delete(old_tip)\n db.session.commit()\n\n for tip in form.product_tips.data:\n product_tip = ProductsTips(product_id=product_id,\n tip_id=tip)\n db.session.add(product_tip)\n db.session.commit()\n\n # 获取到其它图片\n product_pictures = request.files.getlist('product_pictures')\n\n # 如果重新上传了图片，删掉原来的图片，再插入新的图片\n for product_picture in product_pictures:\n if product_picture and allowed_file(product_picture.filename):\n pictures = db.session.query(Pictures).filter(Pictures.product_id == product_id).all()\n for picture in pictures:\n up_to_qiniu(picture.picture_name, '', 0)\n db.session.delete(picture)\n db.session.commit()\n break\n\n for product_picture in product_pictures:\n if product_picture and allowed_file(product_picture.filename):\n product_picture.filename = str(uuid.uuid1()) + '.' + product_picture.filename.rsplit('.', 1)[1]\n up_to_qiniu(product_picture.filename, product_picture, 1)\n picture = Pictures(picture_name=product_picture.filename,\n picture_url='http://7xrwf4.com1.z0.glb.clouddn.com/%s' % product_picture.filename,\n product_id=product_id)\n db.session.add(picture)\n db.session.commit()\n\n return redirect(url_for('furniture.products_list'))\n\n # 查询标签，并组成列表\n tips = db.session.query(ProductsTips).filter(ProductsTips.product_id == product_id).all()\n form.product_tips.data = []\n for tip in tips:\n form.product_tips.data.append(str(tip.tip_id))\n return render_template('furniture/product_edit.html', form=form, nav_types=get_nav_types())\n\n\n@furniture_blueprint.route('/products')\n@furniture_blueprint.route('/products/')\n@login_required\ndef products_list(page_id=1):\n\n page_index = get_page_index(page_id)\n # paginate = Products.query.paginate(page_index, PAGE_SIZE, False)\n num = Products.query.filter().count()\n page = Page(num, page_index, page_size=PAGE_SIZE)\n products = db.session.query(Products, Types)\\\n .outerjoin(Types, Products.type_id == Types.type_id)\\\n .filter().all()[page.offset:page.offset+page.limit]\n\n return render_template('furniture/products.html', products=products, page=page, nav_types=get_nav_types())\n\n\n@furniture_blueprint.route('/product_delete/')\n@login_required\ndef product_delete(product_id):\n product = db.session.query(Products).get(product_id)\n\n if product:\n product_id = product.product_id\n up_to_qiniu(product.main_picture, '', 0)\n\n product_tips = db.session.query(ProductsTips).filter(ProductsTips.product_id == product_id).all()\n for product_tip in product_tips:\n db.session.delete(product_tip)\n db.session.commit()\n\n product_pictures = db.session.query(Pictures).filter(Pictures.product_id == product_id).all()\n for product_picture in product_pictures:\n up_to_qiniu(product_picture.picture_name, '', 0)\n db.session.delete(product_picture)\n db.session.commit()\n\n db.session.delete(product)\n db.session.commit()\n flash(u'删除成功', 'success')\n return redirect(url_for('furniture.products_list'))\n else:\n flash(u'产品不存在', 'danger')\n return redirect(url_for('furniture.products_list'))\n\n\n\n\n", "sub_path": "project/furniture/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "flask.Blueprint", "line_number": 16, "usage_type": "call"}, {"api_name": "project.models.Types.query.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "project.models.Types.query", "line_number": 32, "usage_type": "attribute"}, {"api_name": "project.models.Types", "line_number": 32, "usage_type": "name"}, {"api_name": "forms.TipEditForm", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "project.models.Tips", "line_number": 44, "usage_type": "call"}, {"api_name": "project.db.session.add", "line_number": 45, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 45, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 45, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 46, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 46, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 39, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 57, "usage_type": "call"}, {"api_name": "project.models.Tips", "line_number": 57, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 57, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 57, "usage_type": "name"}, {"api_name": "forms.TipEditForm", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "project.models.Tips", "line_number": 61, "usage_type": "call"}, {"api_name": "project.db.session.merge", "line_number": 62, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 62, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 62, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 63, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 63, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 53, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 72, "usage_type": "call"}, {"api_name": "project.models.Tips", "line_number": 72, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 72, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 72, "usage_type": "name"}, {"api_name": "project.db.session.delete", "line_number": 74, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 74, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 75, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 75, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 70, "usage_type": "name"}, {"api_name": "project.models.Tips.query.paginate", "line_number": 89, "usage_type": "call"}, {"api_name": "project.models.Tips.query", "line_number": 89, "usage_type": "attribute"}, {"api_name": "project.models.Tips", "line_number": 89, "usage_type": "name"}, {"api_name": "project.models.Tips.query.filter", "line_number": 90, "usage_type": "call"}, {"api_name": "project.models.Tips.query", "line_number": 90, "usage_type": "attribute"}, {"api_name": "project.models.Tips", "line_number": 90, "usage_type": "name"}, {"api_name": "apis.Page", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 85, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.aliased", "line_number": 105, "usage_type": "call"}, {"api_name": "project.models.Types", "line_number": 105, "usage_type": "argument"}, {"api_name": "project.models.Types.query.filter", "line_number": 109, "usage_type": "call"}, {"api_name": "project.models.Types.query", "line_number": 109, "usage_type": "attribute"}, {"api_name": "project.models.Types", "line_number": 109, "usage_type": "name"}, {"api_name": "apis.Page", "line_number": 110, "usage_type": "call"}, {"api_name": "project.db.session.query", "line_number": 112, "usage_type": "call"}, {"api_name": "project.models.Types", "line_number": 112, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 112, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 112, "usage_type": "name"}, {"api_name": "project.models.Types.parent_type_id", "line_number": 113, "usage_type": "attribute"}, {"api_name": "project.models.Types", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 101, "usage_type": "name"}, {"api_name": "project.models.Types.query.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "project.models.Types.query", "line_number": 121, "usage_type": "attribute"}, {"api_name": "project.models.Types", "line_number": 121, "usage_type": "name"}, {"api_name": "forms.Form", "line_number": 132, "usage_type": "name"}, {"api_name": "forms.StringField", "line_number": 133, "usage_type": "call"}, {"api_name": "forms.TextAreaField", "line_number": 134, "usage_type": "call"}, {"api_name": "forms.SelectField", "line_number": 135, "usage_type": "call"}, {"api_name": "project.db.session.query", "line_number": 138, "usage_type": "call"}, {"api_name": "project.models.Types", "line_number": 138, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 138, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 138, "usage_type": "name"}, {"api_name": "project.models.Types", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 142, "usage_type": "name"}, {"api_name": "project.db.session.merge", "line_number": 147, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 147, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 148, "usage_type": "call"}, {"api_name": "project.db.session.add", "line_number": 150, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 150, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 151, "usage_type": "call"}, {"api_name": "project.db.session.commit", "line_number": 152, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 152, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 152, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 154, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 129, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 181, "usage_type": "call"}, {"api_name": "project.models.Types", "line_number": 181, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 181, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 181, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 183, "usage_type": "call"}, {"api_name": "project.models.Types", "line_number": 183, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 183, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 183, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.exists", "line_number": 183, "usage_type": "call"}, {"api_name": "project.models.Types.parent_type_id", "line_number": 183, "usage_type": "attribute"}, {"api_name": "flask.flash", "line_number": 184, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 185, "usage_type": "call"}, {"api_name": "project.db.session.delete", "line_number": 186, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 186, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 186, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 187, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 187, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 187, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 191, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 192, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 192, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 179, "usage_type": "name"}, {"api_name": "project.models.Tips.query.filter", "line_number": 197, "usage_type": "call"}, {"api_name": "project.models.Tips.query", "line_number": 197, "usage_type": "attribute"}, {"api_name": "project.models.Tips", "line_number": 197, "usage_type": "name"}, {"api_name": "forms.Form", "line_number": 217, "usage_type": "name"}, {"api_name": "forms.SelectField", "line_number": 218, "usage_type": "call"}, {"api_name": "forms.StringField", "line_number": 219, "usage_type": "call"}, {"api_name": "forms.StringField", "line_number": 220, "usage_type": "call"}, {"api_name": "forms.TextAreaField", "line_number": 221, "usage_type": "call"}, {"api_name": "forms.TextAreaField", "line_number": 222, "usage_type": "call"}, {"api_name": "forms.SelectField", "line_number": 223, "usage_type": "call"}, {"api_name": "forms.SelectField", "line_number": 224, "usage_type": "call"}, {"api_name": "forms.FileField", "line_number": 225, "usage_type": "call"}, {"api_name": "forms.SelectMultipleField", "line_number": 226, "usage_type": "call"}, {"api_name": "project.db.session.query", "line_number": 229, "usage_type": "call"}, {"api_name": "project.models.Products", "line_number": 229, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 229, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 229, "usage_type": "name"}, {"api_name": "project.models.Products", "line_number": 231, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 233, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 233, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 234, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 234, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 245, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 245, "usage_type": "name"}, {"api_name": "apis.up_to_qiniu", "line_number": 247, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 248, "usage_type": "call"}, {"api_name": "apis.up_to_qiniu", "line_number": 249, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 253, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 253, "usage_type": "attribute"}, {"api_name": "project.db.session.merge", "line_number": 254, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 254, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 254, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 255, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 257, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 257, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 258, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 258, "usage_type": "attribute"}, {"api_name": "project.db.session.add", "line_number": 259, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 259, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 259, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 260, "usage_type": "call"}, {"api_name": "project.db.session.flush", "line_number": 261, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 261, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 261, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 264, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 264, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 264, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 267, "usage_type": "call"}, {"api_name": "project.models.ProductsTips", "line_number": 267, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 267, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 267, "usage_type": "name"}, {"api_name": "project.models.ProductsTips.product_id", "line_number": 267, "usage_type": "attribute"}, {"api_name": "project.db.session.delete", "line_number": 269, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 269, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 269, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 270, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 270, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 270, "usage_type": "name"}, {"api_name": "project.models.ProductsTips", "line_number": 273, "usage_type": "call"}, {"api_name": "project.db.session.add", "line_number": 275, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 275, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 275, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 276, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 276, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 276, "usage_type": "name"}, {"api_name": "flask.request.files.getlist", "line_number": 279, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 279, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 279, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 284, "usage_type": "call"}, {"api_name": "project.models.Pictures", "line_number": 284, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 284, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 284, "usage_type": "name"}, {"api_name": "project.models.Pictures.product_id", "line_number": 284, "usage_type": "attribute"}, {"api_name": "apis.up_to_qiniu", "line_number": 286, "usage_type": "call"}, {"api_name": "project.db.session.delete", "line_number": 287, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 287, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 287, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 288, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 288, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 288, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 293, "usage_type": "call"}, {"api_name": "apis.up_to_qiniu", "line_number": 294, "usage_type": "call"}, {"api_name": "project.models.Pictures", "line_number": 295, "usage_type": "call"}, {"api_name": "project.db.session.add", "line_number": 298, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 298, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 298, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 299, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 299, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 299, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 301, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 301, "usage_type": "call"}, {"api_name": "project.db.session.query", "line_number": 304, "usage_type": "call"}, {"api_name": "project.models.ProductsTips", "line_number": 304, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 304, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 304, "usage_type": "name"}, {"api_name": "project.models.ProductsTips.product_id", "line_number": 304, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 308, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 214, "usage_type": "name"}, {"api_name": "project.models.Products.query.filter", "line_number": 318, "usage_type": "call"}, {"api_name": "project.models.Products.query", "line_number": 318, "usage_type": "attribute"}, {"api_name": "project.models.Products", "line_number": 318, "usage_type": "name"}, {"api_name": "apis.Page", "line_number": 319, "usage_type": "call"}, {"api_name": "project.models.Types", "line_number": 321, "usage_type": "argument"}, {"api_name": "project.db.session.query", "line_number": 320, "usage_type": "call"}, {"api_name": "project.models.Products", "line_number": 320, "usage_type": "argument"}, {"api_name": "project.models.Types", "line_number": 320, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 320, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 320, "usage_type": "name"}, {"api_name": "project.models.Products.type_id", "line_number": 321, "usage_type": "attribute"}, {"api_name": "project.models.Products", "line_number": 321, "usage_type": "name"}, {"api_name": "project.models.Types.type_id", "line_number": 321, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 324, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 313, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 330, "usage_type": "call"}, {"api_name": "project.models.Products", "line_number": 330, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 330, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 330, "usage_type": "name"}, {"api_name": "apis.up_to_qiniu", "line_number": 334, "usage_type": "call"}, {"api_name": "project.db.session.query", "line_number": 336, "usage_type": "call"}, {"api_name": "project.models.ProductsTips", "line_number": 336, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 336, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 336, "usage_type": "name"}, {"api_name": "project.models.ProductsTips.product_id", "line_number": 336, "usage_type": "attribute"}, {"api_name": "project.db.session.delete", "line_number": 338, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 338, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 338, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 339, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 339, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 339, "usage_type": "name"}, {"api_name": "project.db.session.query", "line_number": 341, "usage_type": "call"}, {"api_name": "project.models.Pictures", "line_number": 341, "usage_type": "argument"}, {"api_name": "project.db.session", "line_number": 341, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 341, "usage_type": "name"}, {"api_name": "project.models.Pictures.product_id", "line_number": 341, "usage_type": "attribute"}, {"api_name": "apis.up_to_qiniu", "line_number": 343, "usage_type": "call"}, {"api_name": "project.db.session.delete", "line_number": 344, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 344, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 344, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 345, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 345, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 345, "usage_type": "name"}, {"api_name": "project.db.session.delete", "line_number": 347, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 347, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 347, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 348, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 348, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 348, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 349, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 350, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 350, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 352, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 353, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 353, "usage_type": "call"}, {"api_name": "flask.ext.login.login_required", "line_number": 328, "usage_type": "name"}]} +{"seq_id": "56780803", "text": "import sys\nimport logging\nimport logging.handlers\n\n\nclass Logger:\n \"\"\"\n Custom logging class that fits with how I prefere to log.\n There may be better or more proper ways of doing this but\n this works like a charm.\n This class stores all loggers into a property named loogers.\n A call to get_logger(name) will search for 'name' in loggers\n and if found return that logger, otherwise create a new one.\n \"\"\"\n\n # Default log level. This can be changed at initialization of the object\n loglevel = logging.INFO\n\n def __init__(self, log_file='/var/log/ucs.log',\n error_log_file='/var/log/ucs_err.log',\n log_size_MB=10, max_logs=8,\n formatter=logging.Formatter(\"%(asctime)s\\t%(name)s\\t%(levelname)s\\t%(message)s\"),\n log_level=logging.INFO):\n self.loggers = {}\n self.log_level = log_level\n self.log_file = log_file\n self.error_log_file = error_log_file\n\n self.formatter = formatter\n self.logsize = log_size_MB * 1048576\n self.max_logs = max_logs\n\n def get_logger(self, name):\n \"\"\"\n Search self.loggers for the 'name' parameter value and if found\n return the already created logger, otherwise create a new one.\n This sets up a file logger for info and error/exception and an\n output stream for debugging. These logger types cannot be changed\n without overriding this method.\n :param name: Name of the logger to be searched for\n :return: logger\n \"\"\"\n\n if self.loggers.get(name):\n return self.loggers.get(name)\n\n logger = logging.getLogger(name)\n logger.setLevel(self.log_level)\n\n dfh = logging.StreamHandler(stream=sys.stdout)\n dfh.setLevel(logging.DEBUG)\n dfh.setFormatter(self.formatter)\n\n lfh = logging.handlers.RotatingFileHandler(self.log_file,\n mode='a',\n maxBytes=self.logsize,\n backupCount=self.max_logs,\n encoding='utf8',\n delay=False)\n lfh.setLevel(logging.INFO)\n lfh.setFormatter(self.formatter)\n\n efh = logging.handlers.RotatingFileHandler(self.error_log_file,\n mode='a',\n maxBytes=self.logsize,\n backupCount=self.max_logs,\n encoding='utf8',\n delay=False)\n efh.setLevel(logging.ERROR)\n efh.setFormatter(self.formatter)\n\n logger.addHandler(lfh)\n logger.addHandler(efh)\n\n self.loggers.update({name: logger})\n\n return logger\n", "sub_path": "vspherecapacity/logging/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 2959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "65", "api": [{"api_name": "logging.INFO", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 23, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 50, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 51, "usage_type": "attribute"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 60, "usage_type": "attribute"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 63, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "316711479", "text": "import sys\nimport moviepy.editor as mp\nimport math\nimport os\nfrom mp3_tagger import MP3File, VERSION_1, VERSION_2, VERSION_BOTH\nfrom pysndfx import AudioEffectsChain\nfrom PIL import Image\n\n# Usage: