xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

0c726a91b132ee60f058b25c0fba09ddeb748fb2

5,324

py

Python

tests/rlax_dqn/test_experience_buffer.py

vsois/hanabi-agents

86a5bc16a07631ef8307f117b8831a681da1cc71

[ "MIT" ]

1

2021-02-18T13:59:45.000Z

tests/rlax_dqn/test_experience_buffer.py

vsois/hanabi-agents

86a5bc16a07631ef8307f117b8831a681da1cc71

[ "MIT" ]

null

tests/rlax_dqn/test_experience_buffer.py

vsois/hanabi-agents

86a5bc16a07631ef8307f117b8831a681da1cc71

[ "MIT" ]

null

import numpy as onp from hanabi_agents.rlax_dqn.experience_buffer import ExperienceBuffer def test_ctor(): obs_len = 5 lm_len = 3 reward_len = 1 capacity = 7 exp_buf = ExperienceBuffer(obs_len, lm_len, reward_len, capacity) assert exp_buf._obs_tm1_buf.shape == (capacity, obs_len) assert exp_buf._obs_t_buf.shape == (capacity, obs_len) assert exp_buf._act_tm1_buf.shape == (capacity, 1) assert exp_buf._lms_t_buf.shape == (capacity, lm_len) assert exp_buf._rew_t_buf.shape == (capacity, 1) assert exp_buf.capacity == capacity assert exp_buf.size == 0 assert exp_buf.cur_idx == 0 def test_add_transition(): obs_len = 5 lm_len = 3 reward_len = 1 capacity = 7 exp_buf = ExperienceBuffer(obs_len, lm_len, reward_len, capacity) trns_size = 4 obs1 = onp.random.randint(0, 2, (trns_size, obs_len)) obs2 = onp.random.randint(0, 2, (trns_size, obs_len)) while onp.all(obs1 == obs2): obs2 = onp.random.randint(0, 2, (trns_size, obs_len)) assert not onp.all(obs1 == obs2) acts = onp.random.randint(0, lm_len + 1, (trns_size, 1)) rew = onp.random.random((trns_size, 1)) lms = onp.random.randint(0, 2, (trns_size, lm_len)) term = onp.random.randint(0, 2, (trns_size, 1)) exp_buf.add_transitions( obs1, acts, rew, obs2, lms, term) assert exp_buf.size == trns_size assert exp_buf.cur_idx == trns_size assert onp.all(exp_buf._obs_tm1_buf[:trns_size] == obs1) assert onp.all(exp_buf._act_tm1_buf[:trns_size] == acts) assert onp.all(exp_buf._rew_t_buf[:trns_size] == rew) assert onp.all(exp_buf._obs_t_buf[:trns_size] == obs2) assert onp.all(exp_buf._lms_t_buf[:trns_size] == lms) assert onp.all(exp_buf._terminal_t_buf[:trns_size] == term) def test_add_transition_fill_capacity(): obs_len = 5 lm_len = 3 reward_len = 1 capacity = 7 exp_buf = ExperienceBuffer(obs_len, lm_len, reward_len, capacity) trns_size = capacity obs1 = onp.random.randint(0, 2, (trns_size, obs_len)) obs2 = onp.random.randint(0, 2, (trns_size, obs_len)) while onp.all(obs1 == obs2): obs2 = onp.random.randint(0, 2, (trns_size, obs_len)) assert not onp.all(obs1 == obs2) acts = onp.random.randint(0, lm_len + 1, (trns_size, 1)) rew = onp.random.random((trns_size, 1)) lms = onp.random.randint(0, 2, (trns_size, lm_len)) term = onp.random.randint(0, 2, (trns_size, 1)) exp_buf.add_transitions( obs1, acts, rew, obs2, lms, term) assert exp_buf.size == capacity assert exp_buf.cur_idx == 0 assert onp.all(exp_buf._obs_tm1_buf == obs1) assert onp.all(exp_buf._act_tm1_buf == acts) assert onp.all(exp_buf._rew_t_buf == rew) assert onp.all(exp_buf._obs_t_buf == obs2) assert onp.all(exp_buf._lms_t_buf == lms) assert onp.all(exp_buf._terminal_t_buf == term) def test_add_transition_capacity_overflow(): obs_len = 5 lm_len = 3 reward_len = 1 capacity = 7 exp_buf = ExperienceBuffer(obs_len, lm_len, reward_len, capacity) trns_size = capacity + 1 obs1 = onp.random.randint(0, 2, (trns_size, obs_len)) obs2 = onp.random.randint(0, 2, (trns_size, obs_len)) while onp.all(obs1 == obs2): obs2 = onp.random.randint(0, 2, (trns_size, obs_len)) assert not onp.all(obs1 == obs2) acts = onp.random.randint(0, lm_len + 1, (trns_size, 1)) rew = onp.random.random((trns_size, 1)) lms = onp.random.randint(0, 2, (trns_size, lm_len)) term = onp.random.randint(0, 2, (trns_size, 1)) exp_buf.add_transitions( obs1, acts, rew, obs2, lms, term) assert exp_buf.size == capacity assert exp_buf.cur_idx == 1 obs1[:1] = obs1[-1:] acts[:1] = acts[-1:] rew[:1] = rew[-1:] obs2[:1] = obs2[-1:] lms[:1] = lms[-1:] term[:1] = term[-1:] assert onp.all(exp_buf._obs_tm1_buf == obs1[:-1]) assert onp.all(exp_buf._act_tm1_buf == acts[:-1]) assert onp.all(exp_buf._rew_t_buf == rew[:-1]) assert onp.all(exp_buf._obs_t_buf == obs2[:-1]) assert onp.all(exp_buf._lms_t_buf == lms[:-1]) assert onp.all(exp_buf._terminal_t_buf == term[:-1]) def test_getter(): obs_len = 5 lm_len = 3 reward_len = 1 capacity = 7 exp_buf = ExperienceBuffer(obs_len, lm_len, reward_len, capacity) trns_size = capacity obs1 = onp.random.randint(0, 2, (trns_size, obs_len)) obs2 = onp.random.randint(0, 2, (trns_size, obs_len)) acts = onp.random.randint(0, lm_len + 1, (trns_size, 1)) rew = onp.random.random((trns_size, 1)) lms = onp.random.randint(0, 2, (trns_size, lm_len)) term = onp.random.randint(0, 2, (trns_size, 1)) exp_buf.add_transitions( obs1, acts, rew, obs2, lms, term) indices = [1, 3, 6] samples = exp_buf[indices] assert onp.all(samples.observation_tm1 == obs1[indices]) assert onp.all(samples.action_tm1 == acts[indices]) assert onp.all(samples.reward_t == rew[indices]) assert onp.all(samples.observation_t == obs2[indices]) assert onp.all(samples.legal_moves_t == lms[indices]) assert onp.all(samples.terminal_t == term[indices])

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plugins/snowflake/dbt/adapters/snowflake/__version__.py

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[ "Apache-2.0" ]

null

plugins/snowflake/dbt/adapters/snowflake/__version__.py

alexells/dbt

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[ "Apache-2.0" ]

null

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gym_simplifiedtetris/helpers/__init__.py

OliverOverend/gym-simplifiedtetristemp

832a0e99b52ec0c13bad1badc0dc1ba6453a6981

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2021-10-04T19:38:14.000Z

2022-03-15T09:15:09.000Z

gym_simplifiedtetris/helpers/__init__.py

OliverOverend/gym-simplifiedtetristemp

832a0e99b52ec0c13bad1badc0dc1ba6453a6981

[ "MIT" ]

2

2021-10-05T18:19:29.000Z

2021-10-05T18:29:37.000Z

gym_simplifiedtetris/helpers/__init__.py

OliverOverend/gym-simplifiedtetristemp

832a0e99b52ec0c13bad1badc0dc1ba6453a6981

[ "MIT" ]

3

2021-11-19T20:50:07.000Z

2022-03-24T16:37:37.000Z

"""Initialise the helpers package.""" from gym_simplifiedtetris.helpers.eval_agent import eval_agent from gym_simplifiedtetris.helpers.train_q_learning import train_q_learning __all__ = ["eval_agent", "train_q_learning"]

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python/anyascii/_data/_08a.py

casept/anyascii

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[ "ISC" ]

null

python/anyascii/_data/_08a.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_08a.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

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b='Yan Yan Ding Fu Qiu Qiu Jiao Hong Ji Fan Xun Diao Hong Chai Tao Xu Jie Yi Ren Xun Yin Shan Qi Tuo Ji Xun Yin E Fen Ya Yao Song Shen Yin Xin Jue Xiao Ne Chen You Zhi Xiong Fang Xin Chao She Yan Sa Zhun Xu Yi Yi Su Chi He Shen He Xu Zhen Zhu Zheng Gou Zi Zi Zhan Gu Fu Jian Die Ling Di Yang Li Nao Pan Zhou Gan Yi Ju Yao Zha Yi Yi Qu Zhao Ping Bi Xiong Qu Ba Da Zu Tao Zhu Ci Zhe Yong Xu Xun Yi Huang He Shi Cha Xiao Shi Hen Cha Gou Gui Quan Hui Jie Hua Gai Xiang Wei Shen Zhou Tong Mi Zhan Ming E Hui Yan Xiong Gua Er Bing Tiao Yi Lei Zhu Kuang Kua Wu Yu Teng Ji Zhi Ren Cu Lang E Kuang Ei Shi Ting Dan Bei Chan You Keng Qiao Qin Shua An Yu Xiao Cheng Jie Xian Wu Wu Gao Song Bu Hui Jing Shuo Zhen Shuo Du Hua Chang Shui Jie Ke Qu Cong Xiao Sui Wang Xian Fei Chi Ta Yi Ni Yin Diao Pi Zhuo Chan Chen Zhun Ji Qi Tan Zhui Wei Ju Qing Dong Zheng Ze Zou Qian Zhuo Liang Jian Chu Hao Lun Shen Biao Hua Pian Yu Die Xu Pian Shi Xuan Shi Hun Hua E Zhong Di Xie Fu Pu Ting Jian Qi Yu Zi Zhuan Xi Hui Yin An Xian Nan Chen Feng Zhu Yang Yan Huang Xuan Ge Nuo Qi'

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py

Python

server/test_api.py

SOLARMA/hackafake-backend

d2f2a7c144cde0446649cdde776cd1e05ccb4f85

[ "BSD-3-Clause" ]

1

2021-11-05T11:52:43.000Z

server/test_api.py

hackafake/hackafake-backend

d2f2a7c144cde0446649cdde776cd1e05ccb4f85

[ "BSD-3-Clause" ]

30

2018-04-18T07:14:40.000Z

2022-01-10T07:39:24.000Z

server/test_api.py

SOLARMA/hackafake-backend

d2f2a7c144cde0446649cdde776cd1e05ccb4f85

[ "BSD-3-Clause" ]

null

from flask import Blueprint from flask_json import as_json from random import randint test_bp = Blueprint('test', __name__)

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43

py

Python

stats/models/__init__.py

48ix/stats

4b7ae032c4db3d7e01ee48e4af071d793753da1a

[ "MIT" ]

null

stats/models/__init__.py

48ix/stats

4b7ae032c4db3d7e01ee48e4af071d793753da1a

[ "MIT" ]

null

stats/models/__init__.py

48ix/stats

4b7ae032c4db3d7e01ee48e4af071d793753da1a

[ "MIT" ]

1

2020-10-22T00:00:42.000Z

"""Configuration & API Response Models."""

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py

Python

orgdynamic/insertdatetime.py

Sinamore/orgextended

60f6ae8e347697a4ffad3f0b89889c31031de9d1

[ "MIT" ]

120

2020-11-24T16:22:18.000Z

2022-03-26T08:25:52.000Z

orgdynamic/insertdatetime.py

Sinamore/orgextended

60f6ae8e347697a4ffad3f0b89889c31031de9d1

[ "MIT" ]

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2021-01-11T11:10:19.000Z

2022-03-14T13:33:10.000Z

orgdynamic/insertdatetime.py

Sinamore/orgextended

60f6ae8e347697a4ffad3f0b89889c31031de9d1

[ "MIT" ]

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2021-02-16T08:03:22.000Z

2022-02-11T12:22:24.000Z

import sublime import datetime def Execute(view, params): return [str(datetime.datetime.now())]

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Python

ptgaze/head_pose_estimation/__init__.py

YW-Ma/pytorch_mpiigaze_demo

9d097b34c92ea53b1510d830b0d3c535fa42f20b

[ "MIT" ]

1

2021-11-04T01:54:56.000Z

ptgaze/head_pose_estimation/__init__.py

YW-Ma/pytorch_mpiigaze_demo

9d097b34c92ea53b1510d830b0d3c535fa42f20b

[ "MIT" ]

null

ptgaze/head_pose_estimation/__init__.py

YW-Ma/pytorch_mpiigaze_demo

9d097b34c92ea53b1510d830b0d3c535fa42f20b

[ "MIT" ]

null

from .face_landmark_estimator import LandmarkEstimator from .head_pose_normalizer import HeadPoseNormalizer

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py

Python

coworks/middleware/__init__.py

sidneyarcidiacono/coworks

7f51b83e8699ced991d16a5a43ad19e569b6e814

[ "MIT" ]

null

coworks/middleware/__init__.py

sidneyarcidiacono/coworks

7f51b83e8699ced991d16a5a43ad19e569b6e814

[ "MIT" ]

null

coworks/middleware/__init__.py

sidneyarcidiacono/coworks

7f51b83e8699ced991d16a5a43ad19e569b6e814

[ "MIT" ]

null

from .xray import XRayMiddleware

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src/python/serif/model/base_model.py

BBN-E/text-open

c508f6caeaa51a43cdb0bc27d8ed77e5750fdda9

[ "Apache-2.0" ]

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2022-03-24T19:56:45.000Z

src/python/serif/model/base_model.py

BBN-E/text-open

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src/python/serif/model/base_model.py

BBN-E/text-open

c508f6caeaa51a43cdb0bc27d8ed77e5750fdda9

[ "Apache-2.0" ]

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from abc import ABC from abc import abstractmethod class BaseModel(ABC): def __init__(self,**kwargs): pass @abstractmethod def process(self, serif_doc): pass def reload_model(self): pass

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6518fd0bb2c5ab7c54f5be577d99c4e0b951785d

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Python

tests/test_all.py

biosimulations/biosimulations-modeldb

85b211c799d82e47a9d0424fd425958bab6add4c

[ "MIT" ]

null

tests/test_all.py

biosimulations/biosimulations-modeldb

85b211c799d82e47a9d0424fd425958bab6add4c

[ "MIT" ]

3

2022-02-28T14:15:04.000Z

2022-03-21T10:34:04.000Z

tests/test_all.py

biosimulations/biosimulations-modeldb

85b211c799d82e47a9d0424fd425958bab6add4c

[ "MIT" ]

null

from biosimulations_modeldb import __main__ from biosimulations_modeldb._version import __version__ from biosimulations_modeldb.core import ( get_project_ids, get_project, get_paper_metadata, get_metadata_for_project, export_project_metadata_for_project_to_omex_metadata, init_combine_archive_from_dir, create_sedml_for_xpp_file, build_combine_archive_for_project, make_directories, import_project, import_projects, TAXA, ARTICLE_FIGURES_COMBINE_ARCHIVE_SUBDIRECTORY, ) from biosimulations_modeldb.config import get_config from biosimulators_utils.combine.data_model import CombineArchiveContent, CombineArchiveContentFormat from biosimulators_utils.combine.io import CombineArchiveReader from biosimulators_utils.omex_meta.io import BiosimulationsOmexMetaReader from biosimulators_utils.ref.data_model import JournalArticle from biosimulators_utils.sedml.io import SedmlSimulationWriter, SedmlSimulationReader from biosimulators_utils.sedml.validation import validate_doc from unittest import mock import Bio.Entrez import biosimulations_modeldb.__main__ import capturer import git import os import requests import requests_cache import shutil import tempfile import unittest Bio.Entrez.email = 'biosimulations.daemon@gmail.com' class MockCrossRefSessionResponse: def raise_for_status(self): pass def json(self): return { 'message': { 'title': [''], 'container-title': [''], 'volume': '', 'published': { 'date-parts': [ [ 2021, 12, 31, ] ] } } } class MockCrossRefSession: def get(self, url): return MockCrossRefSessionResponse() class MockS3Bucket: def __init__(self, name): pass def upload_file(self, *args, **kwargs): pass class TestCase(unittest.TestCase): def setUp(self): self.case_dirname = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.case_dirname) @classmethod def setUpClass(cls): cls.dirname = tempfile.mkdtemp() git.Repo.init(cls.dirname) cls.pkg_dirname = os.path.join(cls.dirname, 'biosimulations_modeldb') os.mkdir(cls.pkg_dirname) @classmethod def tearDownClass(cls): shutil.rmtree(cls.dirname) def test_get_project_ids(self): config = get_config( sessions_dirname=os.path.join(self.pkg_dirname, 'source'), ) ids = get_project_ids(config, 'XPP') self.assertIn(35358, ids) self.assertGreater(len(ids), 100) def test_get_paper_metadata(self): config = get_config( sessions_dirname=os.path.join(self.pkg_dirname, 'source'), ) paper = { 'object_id': 5225, } get_paper_metadata(paper, config) self.assertEqual(paper, { 'object_id': 5225, 'uris': { 'doi': None, 'pubmed': '8792224', 'url': None, }, 'citation': paper['citation'], }) self.assertEqual(paper['citation'].authors, ['Pinsky PF', 'Rinzel J']) self.assertEqual(paper['citation'].title, 'Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons') self.assertEqual(paper['citation'].journal, 'J Comput Neurosci') self.assertEqual(paper['citation'].volume, '1') self.assertEqual(paper['citation'].issue, None) self.assertEqual(paper['citation'].pages, '39-60') self.assertEqual(paper['citation'].year, 1994) def test_get_paper_metadata_without_pages(self): config = get_config( sessions_dirname=os.path.join(self.pkg_dirname, 'source'), ) def json(): return { 'title': {'value': 'XYZ'}, 'authors': {'value': [{'object_name': 'abc'}]}, 'journal': {'value': 'def'}, 'year': {'value': 2022}, } def get(url): return mock.Mock( raise_for_status=lambda: None, json=json, ) config['source_session'] = mock.Mock( get=get ) paper = { 'object_id': 186225, } get_paper_metadata(paper, config) self.assertEqual(paper, { 'object_id': 186225, 'uris': { 'doi': None, 'pubmed': None, 'url': None, }, 'citation': paper['citation'], }) self.assertEqual(paper['citation'].authors, ['abc']) self.assertEqual(paper['citation'].title, 'XYZ') self.assertEqual(paper['citation'].journal, 'def') self.assertEqual(paper['citation'].volume, None) self.assertEqual(paper['citation'].issue, None) self.assertEqual(paper['citation'].pages, None) self.assertEqual(paper['citation'].year, 2022) def test_get_paper_metadata_with_pages(self): config = get_config( sessions_dirname=os.path.join(self.pkg_dirname, 'source'), ) def json(): return { 'title': {'value': 'XYZ'}, 'authors': {'value': [{'object_name': 'abc'}]}, 'journal': {'value': 'def'}, 'volume': {'value': 'ghi'}, 'first_page': {'value': '1'}, 'last_page': {'value': '2'}, 'year': {'value': 2022}, 'doi': {'value': 'jkl'}, 'pubmed_id': {'value': 'mno'}, 'url': {'value': 'pqr'}, } def get(url): return mock.Mock( raise_for_status=lambda: None, json=json, ) config['source_session'] = mock.Mock( get=get ) paper = { 'object_id': 186225, } get_paper_metadata(paper, config) self.assertEqual(paper, { 'object_id': 186225, 'uris': { 'doi': 'jkl', 'pubmed': 'mno', 'url': 'pqr', }, 'citation': paper['citation'], }) self.assertEqual(paper['citation'].authors, ['abc']) self.assertEqual(paper['citation'].title, 'XYZ') self.assertEqual(paper['citation'].journal, 'def') self.assertEqual(paper['citation'].volume, 'ghi') self.assertEqual(paper['citation'].issue, None) self.assertEqual(paper['citation'].pages, '1-2') self.assertEqual(paper['citation'].year, 2022) def test_get_project(self): config = get_config( sessions_dirname=os.path.join(self.pkg_dirname, 'source'), ) project = get_project(35358, config) self.assertEqual(project['id'], 35358) self.assertEqual(project['name'], 'CA3 pyramidal cell: rhythmogenesis in a reduced Traub model (Pinsky, Rinzel 1994)') self.assertEqual(project['created'], '2004-02-09T17:12:24') def test_get_metadata_for_project(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 136097, 'species': { 'value': [{'object_name': 'Drosophila'}], }, 'region': { 'value': [{'object_name': 'Auditory brainstem'}], }, 'model_paper': { 'value': [{ 'object_id': 136105, 'uris': { 'doi': '10.1093/chemse/bjp032', 'pubmed': None, 'url': None, }, 'citation': JournalArticle(), }], }, } description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '136097'), config) self.assertTrue(description.startswith('This is the readme for the model associated with the AChems abstract')) self.assertEqual(taxa, [{'uri': 'http://identifiers.org/taxonomy:7215', 'label': 'Drosophila'}]) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/doi:10.1093/chemse/bjp032') self.assertTrue(references[0]['label'].startswith('Abstracts from the Thirty-first Annual Meeting')) self.assertEqual(thumbnails, []) # read from cache description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '136097'), config) self.assertTrue(description.startswith('This is the readme for the model associated with the AChems abstract')) self.assertEqual(taxa, [{'uri': 'http://identifiers.org/taxonomy:7215', 'label': 'Drosophila'}]) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/doi:10.1093/chemse/bjp032') self.assertTrue(references[0]['label'].startswith('Abstracts from the Thirty-first Annual Meeting')) self.assertEqual(thumbnails, []) def test_get_metadata_for_project_no_description(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 58712, 'region': { 'value': [{'object_name': 'Drosophila'}], } } description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '58712'), config) self.assertEqual(description, None) self.assertEqual(taxa, [{'uri': 'http://identifiers.org/taxonomy:7215', 'label': 'Drosophila'}]) description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '58712'), config) self.assertEqual(description, None) self.assertEqual(taxa, [{'uri': 'http://identifiers.org/taxonomy:7215', 'label': 'Drosophila'}]) def test_get_metadata_for_project_no_annotated_taxonomy(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 57910, 'species': { 'value': [{'object_name': 'Escherichia coli'}], } } with self.assertRaisesRegex(ValueError, 'Taxonomy must be annotated'): description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '57910'), config) def test_get_metadata_for_project_with_images(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 57910, 'model_paper': { 'value': [{ 'object_id': 57915, 'uris': { 'doi': None, 'pubmed': '9192303', 'url': None, }, 'citation': JournalArticle(), }], }, } description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '57910'), config) self.assertTrue(description.startswith('This is the readme.txt for the model associated with the paper')) self.assertEqual(taxa, []) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/doi:10.1111/j.1469-7793.1997.313bn.x') self.assertTrue(references[0]['label'].startswith('Nicoletta Chiesa,')) self.assertEqual(thumbnails, [{ 'local_filename': os.path.join(config['source_projects_dirname'], '57910', 'samplerun.jpg'), 'archive_filename': 'samplerun.jpg', 'format': 'jpeg', }]) description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '57910'), config) self.assertTrue(description.startswith('This is the readme.txt for the model associated with the paper')) self.assertEqual(taxa, []) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/doi:10.1111/j.1469-7793.1997.313bn.x') self.assertTrue(references[0]['label'].startswith('Nicoletta Chiesa,')) self.assertEqual(thumbnails, [{ 'local_filename': os.path.normpath(os.path.join(config['source_projects_dirname'], '57910', 'samplerun.jpg')), 'archive_filename': 'samplerun.jpg', 'format': 'jpeg', }]) def test_get_metadata_for_project_no_doi_pubmed(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 76879, 'model_paper': { 'value': [{ 'object_id': 57915, 'uris': { 'doi': None, 'pubmed': None, 'url': 'http://example.com', }, 'citation': JournalArticle(title='Example'), }], }, } description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '76879'), config) self.assertTrue(description.startswith('From Excitatory and Inhibitory Interactions in')) self.assertEqual(taxa, []) self.assertEqual(references, [{ 'uri': 'http://example.com', 'label': 'Example.', }]) self.assertEqual(thumbnails, []) description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '76879'), config) self.assertTrue(description.startswith('From Excitatory and Inhibitory Interactions in')) self.assertEqual(taxa, []) self.assertEqual(references, [{ 'uri': 'http://example.com', 'label': 'Example.', }]) self.assertEqual(thumbnails, []) def test_get_metadata_for_project_no_citations(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 45513, } description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '45513'), config) self.assertTrue(description.startswith('This is the readme.txt for the models associated with the paper')) self.assertEqual(taxa, []) self.assertEqual(references, []) self.assertEqual(thumbnails, []) description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '45513'), config) self.assertTrue(description.startswith('This is the readme.txt for the models associated with the paper')) self.assertEqual(taxa, []) self.assertEqual(references, []) self.assertEqual(thumbnails, []) def test_get_metadata_for_project_with_pmc_images(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 83558, 'model_paper': { 'value': [{ 'object_id': 1234, 'uris': { 'doi': None, 'pubmed': '16965177', 'url': None, }, 'citation': JournalArticle(), }], }, } description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '83558'), config) self.assertTrue(description.startswith('This is the readme for the model code associated with the publication:')) self.assertEqual(taxa, []) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/doi:10.1371/journal.pcbi.0020119') self.assertTrue(references[0]['label'].startswith('Maria Lindskog,')) self.assertEqual(thumbnails[0]['id'], 'pcbi.0020119/pcbi-0020119-g001') self.assertEqual(thumbnails[0]['local_filename'], os.path.join( config['source_thumbnails_dirname'], 'PMC1562452', 'PMC1562452', 'pcbi.0020119.g001.jpg')) self.assertEqual(thumbnails[0]['archive_filename'], os.path.join( ARTICLE_FIGURES_COMBINE_ARCHIVE_SUBDIRECTORY, 'PMC1562452', 'pcbi.0020119.g001.jpg')) self.assertEqual(thumbnails[0]['format'], 'jpeg') self.assertEqual(thumbnails[0]['label'], 'Figure 1') self.assertTrue(thumbnails[0]['caption'].startswith('<title ')) description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '83558'), config) self.assertTrue(description.startswith('This is the readme for the model code associated with the publication:')) self.assertEqual(taxa, []) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/doi:10.1371/journal.pcbi.0020119') self.assertTrue(references[0]['label'].startswith('Maria Lindskog,')) self.assertEqual(thumbnails[0]['id'], 'pcbi.0020119/pcbi-0020119-g001') self.assertEqual(thumbnails[0]['local_filename'], os.path.join( config['source_thumbnails_dirname'], 'PMC1562452', 'PMC1562452', 'pcbi.0020119.g001.jpg')) self.assertEqual(thumbnails[0]['archive_filename'], os.path.join( ARTICLE_FIGURES_COMBINE_ARCHIVE_SUBDIRECTORY, 'PMC1562452', 'pcbi.0020119.g001.jpg')) self.assertEqual(thumbnails[0]['format'], 'jpeg') self.assertEqual(thumbnails[0]['label'], 'Figure 1') self.assertTrue(thumbnails[0]['caption'].startswith('<title ')) def test_get_metadata_for_project_without_doi(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 64171, 'model_paper': { 'value': [{ 'object_id': 55860, 'uris': { 'doi': None, 'pubmed': '15239590', 'url': None, }, 'citation': JournalArticle(), }], }, } description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '64171'), config) self.assertTrue(description.startswith('This is the readme.txt for the models associated with the paper')) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/pubmed:15239590') self.assertTrue(references[0]['label'].startswith('Wu SN.')) description, taxa, references, thumbnails = get_metadata_for_project( project, os.path.join(config['source_projects_dirname'], '64171'), config) self.assertTrue(description.startswith('This is the readme.txt for the models associated with the paper')) self.assertEqual(len(references), 1) self.assertEqual(references[0]['uri'], 'http://identifiers.org/pubmed:15239590') self.assertTrue(references[0]['label'].startswith('Wu SN.')) def test_export_project_metadata_for_project_to_omex_metadata(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], ) config['source_projects_dirname'] = base_config['source_projects_dirname'] if not os.path.isdir(config['source_thumbnails_dirname']): os.makedirs(config['source_thumbnails_dirname']) if not os.path.isdir(os.path.join(config['final_metadata_dirname'])): os.makedirs(os.path.join(config['final_metadata_dirname'])) project = { 'id': 57910, 'name': 'abc', 'notes': { 'value': 'def', }, 'model_type': { 'value': [{ 'object_id': '123', 'object_name': 'My model type', }] }, 'region': { 'value': [ { 'object_id': '456', 'object_name': 'Generic', }, { 'object_id': '789', 'object_name': 'My region (xyz)', }, ] }, 'other_type': { 'value': 'My other type', }, 'implemented_by': { 'value': [ { 'object_name': 'Jane Doe [email at domain]', }, { 'object_name': 'Jack Doer', }, ], }, 'public_submitter_name': { 'value': 'John Doe' }, 'public_submitter_email': { 'value': 'email2@domain', }, 'created': '2022-01-01', 'ver_date': '2022-01-02', } description = 'ghi' taxa = [{ 'uri': 'http://identifiers.org/taxonomy:7215', 'label': 'Drosophila', }] references = [{ 'uri': 'http://identifiers.org/doi:10.1093/chemse/bjp032', 'label': 'jkl', }] thumbnails = [{ 'local_filename': os.path.join(config['source_projects_dirname'], '57910', 'samplerun.jpg'), 'archive_filename': './samplerun.jpg', 'format': 'jpeg', }] metadata_filename = os.path.join(self.case_dirname, 'metadata.rdf') export_project_metadata_for_project_to_omex_metadata(project, description, taxa, references, thumbnails, metadata_filename, config) metadata, errors, warnings = BiosimulationsOmexMetaReader().run( metadata_filename, working_dir=os.path.join(config['source_projects_dirname'], '57910')) self.assertEqual(errors, []) self.assertEqual(warnings, []) expected_metadata = { "uri": '.', "combine_archive_uri": 'http://omex-library.org/57910.omex', 'title': 'abc', 'abstract': 'def', 'keywords': [], 'description': 'ghi', 'taxa': taxa, 'encodes': [ { 'uri': 'http://modeldb.science/ModelList?id=123', 'label': 'My model type', }, { 'uri': None, 'label': 'My other type', }, { 'uri': 'http://modeldb.science/ModelList?id=789', 'label': 'My region', }, ], 'thumbnails': [ thumbnail['archive_filename'] for thumbnail in thumbnails ], 'sources': [], 'predecessors': [], 'successors': [], 'see_also': [], 'creators': [ { 'uri': 'mailto:email@domain', 'label': 'Jane Doe', }, { 'uri': None, 'label': 'Jack Doer', }, ], 'contributors': [ { 'uri': 'mailto:email2@domain', 'label': 'John Doe', }, { 'uri': 'https://senselab.med.yale.edu/', 'label': 'Sense Lab at Yale University', }, { 'uri': 'http://identifiers.org/orcid:0000-0002-2605-5080', 'label': 'Jonathan R. Karr', }, ], 'identifiers': [ { 'uri': 'https://identifiers.org/modeldb:57910', 'label': 'modeldb:57910', }, ], 'citations': references, 'references': [], 'license': None, 'funders': [], 'created': '2022-01-01', 'modified': [ '2022-01-02', ], 'other': [], } self.assertEqual(metadata, [expected_metadata]) with self.assertRaisesRegex(ValueError, 'metadata is not valid'): with mock.patch.object(BiosimulationsOmexMetaReader, 'run', return_value=[None, [['My error']], []]): export_project_metadata_for_project_to_omex_metadata( project, description, taxa, references, thumbnails, metadata_filename, config) def test_init_combine_archive_from_dir(self): config = get_config() dirname = os.path.join(config['source_projects_dirname'], '57910') init_combine_archive_from_dir(dirname) with open(os.path.join(self.case_dirname, 'test1.unknown'), 'w') as file: file.write('here') with open(os.path.join(self.case_dirname, 'test2.xml'), 'w') as file: file.write('<sbml xmlns="http://www.sbml.org/sbml/"></sbml>') with open(os.path.join(self.case_dirname, 'test3.xml'), 'w') as file: file.write('<nml xmlns="http://morphml.org/neuroml/schema"></nml>') with open(os.path.join(self.case_dirname, 'test4.xml'), 'w') as file: file.write('<xml></xml>') with open(os.path.join(self.case_dirname, 'test5.xml'), 'w') as file: file.write('<xml></xml') with open(os.path.join(self.case_dirname, 'test6.jpg'), 'w') as file: file.write('here') with open(os.path.join(self.case_dirname, 'desktop.ini'), 'w') as file: file.write('here') with self.assertWarnsRegex(UserWarning, 'is not known'): archive = init_combine_archive_from_dir(self.case_dirname) archive.contents.sort(key=lambda content: content.location) self.assertEqual(len(archive.contents), 5) self.assertEqual(archive.contents[0].location, 'test1.unknown') self.assertEqual(archive.contents[0].format, CombineArchiveContentFormat.OTHER.value) self.assertEqual(archive.contents[1].location, 'test2.xml') self.assertEqual(archive.contents[1].format, CombineArchiveContentFormat.SBML.value) self.assertEqual(archive.contents[2].location, 'test3.xml') self.assertEqual(archive.contents[2].format, CombineArchiveContentFormat.NeuroML.value) self.assertEqual(archive.contents[3].location, 'test4.xml') self.assertEqual(archive.contents[3].format, CombineArchiveContentFormat.XML.value) self.assertEqual(archive.contents[4].location, 'test5.xml') self.assertEqual(archive.contents[4].format, CombineArchiveContentFormat.OTHER.value) def test_create_sedml_for_xpp_file(self): config = get_config() dirname = os.path.join(config['source_projects_dirname'], '35358') sed_doc = create_sedml_for_xpp_file(35358, dirname, 'booth_bose.ode') sedml_filename = os.path.join(dirname, '_test_.sedml') SedmlSimulationWriter().run(sed_doc, sedml_filename) sed_doc_2 = SedmlSimulationReader().run(sedml_filename) os.remove(sedml_filename) # SedmlSimulationWriter().run(sed_doc, 'test.sedml', # validate_semantics=False, # validate_models_with_languages=False, # validate_targets_with_model_sources=False) errors, warnings = validate_doc(sed_doc, dirname) self.assertEqual(errors, []) def test_create_sedml_for_xpp_file_with_set_file(self): config = get_config() dirname = os.path.join(config['source_projects_dirname'], '116867') sed_doc = create_sedml_for_xpp_file(116867, dirname, 'rubin_terman_pd.ode') sedml_filename = os.path.join(dirname, '_test_.sedml') SedmlSimulationWriter().run(sed_doc, sedml_filename) sed_doc_2 = SedmlSimulationReader().run(sedml_filename) os.remove(sedml_filename) # SedmlSimulationWriter().run(sed_doc, 'test.sedml', # validate_semantics=False, # validate_models_with_languages=False, # validate_targets_with_model_sources=False) errors, warnings = validate_doc(sed_doc, dirname) self.assertEqual(errors, []) def test_build_combine_archive_for_project(self): config = get_config() id = 57910 source_project_dirname = os.path.join(config['source_projects_dirname'], '57910') final_project_dirname = os.path.join(self.case_dirname, 'archive') archive_filename = os.path.join(self.case_dirname, 'archive.omex') extra_contents = { os.path.join(config['source_projects_dirname'], '57910', 'samplerun.jpg'): CombineArchiveContent( location='samplerun.jpg', format=CombineArchiveContentFormat.JPEG.value, ), 'MANIFEST.in': CombineArchiveContent( location='MANIFEST.in', format=CombineArchiveContentFormat.TEXT.value, ), } build_combine_archive_for_project(id, source_project_dirname, final_project_dirname, archive_filename, extra_contents) archive_dirname = os.path.join(self.case_dirname, 'unpacked') archive = CombineArchiveReader().run(archive_filename, archive_dirname) def test_build_combine_archive_for_project_extra_content_in_sub_dir(self): config = get_config() id = 57910 source_project_dirname = os.path.join(config['source_projects_dirname'], '57910') final_project_dirname = os.path.join(self.case_dirname, 'archive') archive_filename = os.path.join(self.case_dirname, 'archive.omex') extra_contents = { os.path.join(config['source_projects_dirname'], '57910', 'samplerun.jpg'): CombineArchiveContent( location=os.path.join('subdir', 'samplerun.jpg'), format=CombineArchiveContentFormat.JPEG.value, ), 'MANIFEST.in': CombineArchiveContent( location='MANIFEST.in', format=CombineArchiveContentFormat.TEXT.value, ), } build_combine_archive_for_project(id, source_project_dirname, final_project_dirname, archive_filename, extra_contents) archive_dirname = os.path.join(self.case_dirname, 'unpacked') archive = CombineArchiveReader().run(archive_filename, archive_dirname) self.assertTrue(os.path.isfile(os.path.join(archive_dirname, 'subdir', 'samplerun.jpg'))) def test_import_project(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, source_dirname=os.path.join(self.pkg_dirname, 'source'), sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], issues_filename=base_config['issues_filename'], status_filename=os.path.join(self.pkg_dirname, 'final', 'status.yml'), max_projects=1, bucket_name='bucket', ) make_directories(config) config['cross_ref_session'] = MockCrossRefSession() project = get_project(57910, config) auth = '' with mock.patch('biosimulators_utils.biosimulations.utils.run_simulation_project', return_value='*' * 32): with mock.patch('boto3.resource', return_value=mock.Mock(Bucket=MockS3Bucket)): import_project(project, True, auth, config) def test_import_projects(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, source_dirname=os.path.join(self.pkg_dirname, 'source'), sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], issues_filename=base_config['issues_filename'], status_filename=os.path.join(self.pkg_dirname, 'final', 'status.yml'), max_projects=1, bucket_name='bucket', ) config['cross_ref_session'] = MockCrossRefSession() with mock.patch('biosimulators_utils.biosimulations.utils.run_simulation_project', return_value='*' * 32): with mock.patch('biosimulators_utils.biosimulations.utils.get_authorization_for_client', return_value='xxx yyy'): with mock.patch('boto3.resource', return_value=mock.Mock(Bucket=MockS3Bucket)): import_projects(config) def test_import_projects_dry_run(self): base_config = get_config() config = get_config( base_dirname=self.pkg_dirname, source_dirname=os.path.join(self.pkg_dirname, 'source'), sessions_dirname=os.path.join(self.pkg_dirname, 'source'), final_dirname=os.path.join(self.pkg_dirname, 'final'), curators_filename=base_config['curators_filename'], issues_filename=base_config['issues_filename'], status_filename=os.path.join(self.pkg_dirname, 'final', 'status.yml'), max_projects=1, bucket_name='bucket', ) config['cross_ref_session'] = MockCrossRefSession() config['dry_run'] = True config['simulate_projects'] = False config['publish_projects'] = False with mock.patch('biosimulators_utils.biosimulations.utils.get_authorization_for_client', return_value='xxx yyy'): with mock.patch('boto3.resource', return_value=mock.Mock(Bucket=MockS3Bucket)): import_projects(config) def test_cli(self): base_config = get_config() with mock.patch.dict('os.environ', { 'BASE_DIRNAME': self.pkg_dirname, 'SOURCE_DIRNAME': os.path.join(self.pkg_dirname, 'source'), 'SESSIONS_DIRNAME': os.path.join(self.pkg_dirname, 'source'), 'FINAL_DIRNAME': os.path.join(self.pkg_dirname, 'final'), 'CURATORS_FILENAME': base_config['curators_filename'], 'ISSUES_FILENAME': base_config['issues_filename'], 'STATUS_FILENAME': os.path.join(self.pkg_dirname, 'final', 'status.yml'), 'BUCKET_NAME': 'bucket', }): def mock_get_config(**args): config = get_config(**args) config['cross_ref_session'] = MockCrossRefSession() return config with mock.patch('biosimulators_utils.biosimulations.utils.run_simulation_project', return_value='*' * 32): with mock.patch('biosimulators_utils.biosimulations.utils.get_authorization_for_client', return_value='xxx yyy'): with mock.patch('boto3.resource', return_value=mock.Mock(Bucket=MockS3Bucket)): import biosimulations_modeldb.config with mock.patch.object(biosimulations_modeldb.__main__, 'get_config', side_effect=mock_get_config): with __main__.App(argv=[ 'run-projects-and-publish', '--max-projects', '1', ]) as app: app.run() def test_cli_help(self): with mock.patch('sys.argv', ['', '--help']): with self.assertRaises(SystemExit): __main__.main() def test_version(self): with __main__.App(argv=['--version']) as app: with capturer.CaptureOutput(merged=False, relay=False) as captured: with self.assertRaises(SystemExit) as cm: app.run() self.assertEqual(cm.exception.code, 0) stdout = captured.stdout.get_text() self.assertEqual(stdout, __version__) self.assertEqual(captured.stderr.get_text(), '')

43.64657

139

0.587096

4,239

41,988

5.598962

0.101203

0.031853

0.044662

0.031263

0.782211

0.742732

0.730345

0.716988

0.707255

0.698028

0

0.026136

0.284677

41,988

961

140

43.691988

0.764075

0.011884

0

0.55711

0

0.002331

0.210757

0.062417

0

0.149184

1

0.045455

false

0.003497

0.0338

0.006993

0.092075

0

null

0

1

0

null

0

5

652816f11f719180a48274a87a9d126eb1b683bb

17

py

Python

Practicals/Steve/py_test.py

TonyJenkins/cfs2160-2019-python-public

3bf9f9a905dfeb9f5e664ef5b27905acb224f422

[ "Unlicense" ]

12

2019-10-10T10:43:20.000Z

2020-01-25T12:42:25.000Z

Practicals/Steve/py_test.py

TonyJenkins/cfs2160-2019-python-public

3bf9f9a905dfeb9f5e664ef5b27905acb224f422

[ "Unlicense" ]

null

Practicals/Steve/py_test.py

TonyJenkins/cfs2160-2019-python-public

3bf9f9a905dfeb9f5e664ef5b27905acb224f422

[ "Unlicense" ]

6

2019-10-03T14:41:17.000Z

2019-12-07T10:59:33.000Z

print("GIT TEST")

17

0.705882

3

17

4

1

0

0.058824

17

1

17

0.75

0

0.444444

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

6539cbbadc6ac844afbbb24025ba63f75bfe45cd

179

py

Python

src/puzzle/__init__.py

hat27/puzzle

b96071dc90ec280b50aa0e9f39986e4ad5dac37a

[ "MIT" ]

2

2017-12-23T15:15:21.000Z

2018-02-27T04:15:30.000Z

src/puzzle/__init__.py

hat27/puzzle

b96071dc90ec280b50aa0e9f39986e4ad5dac37a

[ "MIT" ]

null

src/puzzle/__init__.py

hat27/puzzle

b96071dc90ec280b50aa0e9f39986e4ad5dac37a

[ "MIT" ]

null

#-*- coding: utf8 -*- __author__ = "Gou.Hattori" __version__ = "0.0.6" from . import pz_env from . import pz_config from . import PzLog from . import Piece from . import Puzzle

16.272727

26

0.698324

26

179

4.423077

0.615385

0.434783

0.208696

0

0.027211

0.178771

179

10

27

17.9

0.755102

0.111732

0

0.101266

0

1

0

false

0

0.714286

0

0.714286

0

1

0

null

1

0

null

0

1

0

1

0

5

6545e588c6f8c13476f3e1fe251a168fb862a8a1

127

py

Python

template_demo/__main__.py

dls-controls/template_demo

1534f871f0427d96d7aef666107fc5a251444000

[ "Apache-2.0" ]

null

template_demo/__main__.py

dls-controls/template_demo

1534f871f0427d96d7aef666107fc5a251444000

[ "Apache-2.0" ]

null

template_demo/__main__.py

dls-controls/template_demo

1534f871f0427d96d7aef666107fc5a251444000

[ "Apache-2.0" ]

null

from template_demo import cli # test with: # pipenv run python -m template_demo if __name__ == "__main__": cli.main()

18.142857

40

0.692913

18

127

4.333333

0.777778

0.307692

0

0.212598

127

6

41

21.166667

0.78

0.385827

0

0.106667

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

e8ea202df726649c91974b6c50c9cc50c6f2a0e7

127

py

Python

Introducao python/exercicios/ex046.py

Luis12368/python

23352d75ad13bcfd09ea85ab422fdc6ae1fcc5e7

[ "MIT" ]

null

Introducao python/exercicios/ex046.py

Luis12368/python

23352d75ad13bcfd09ea85ab422fdc6ae1fcc5e7

[ "MIT" ]

null

Introducao python/exercicios/ex046.py

Luis12368/python

23352d75ad13bcfd09ea85ab422fdc6ae1fcc5e7

[ "MIT" ]

null

from time import sleep from time import sleep for c in range(10, -1, -1): print(c) sleep(0.5) print('FELIZ ANO NOVO')

18.142857

27

0.653543

24

127

3.458333

0.666667

0.192771

0.337349

0.457831

0

0.060606

0.220472

127

7

28

18.142857

0.777778

0

0.333333

0

0.109375

0

1

0

false

0

0.333333

0

0.333333

1

0

null

0

1

0

1

0

null

0

1

0

5

3311a2a2d201f24798d091f5acc49b88e07eab84

78

py

Python

dsp/__init__.py

shivarao101/dsp

09ca228eb1761ca9af36b810a8ac0f81ab7eba91

[ "MIT" ]

null

dsp/__init__.py

shivarao101/dsp

09ca228eb1761ca9af36b810a8ac0f81ab7eba91

[ "MIT" ]

null

dsp/__init__.py

shivarao101/dsp

09ca228eb1761ca9af36b810a8ac0f81ab7eba91

[ "MIT" ]

null

from Addition import Addition from basicdspalgorithm import basicdspalgorithm

39

47

0.897436

8

78

8.75

0.5

0

0.102564

78

2

47

39

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

332d8523fb13b98cf21a1d2a2a001744ba74cb16

107

py

Python

ext/datawald_mage2agency/datawald_mage2agency/__init__.py

ideabosque/DataWald-AWS

3ea905a40526dad3cb0eff92167c1e4230aa4aa9

[ "MIT" ]

null

ext/datawald_mage2agency/datawald_mage2agency/__init__.py

ideabosque/DataWald-AWS

3ea905a40526dad3cb0eff92167c1e4230aa4aa9

[ "MIT" ]

null

ext/datawald_mage2agency/datawald_mage2agency/__init__.py

ideabosque/DataWald-AWS

3ea905a40526dad3cb0eff92167c1e4230aa4aa9

[ "MIT" ]

null

__all__ = ["datawald_mage2agency"] from .mage2agency import Mage2Agency from .mage2agent import Mage2Agent

26.75

36

0.831776

11

107

7.636364

0.545455

0.357143

0

0.052083

0.102804

107

3

37

35.666667

0.822917

0

0.186916

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

334a9189319d570714eb458ee0b7dd41960aa733

145

py

Python

gsplines/basis/__init__.py

rafaelrojasmiliani/gsplines

663b10f6d53b498a1e892d9eb32a345153de36d2

[ "MIT" ]

3

2021-08-28T01:42:40.000Z

2021-12-02T22:39:45.000Z

gsplines/basis/__init__.py

rafaelrojasmiliani/gsplines

663b10f6d53b498a1e892d9eb32a345153de36d2

[ "MIT" ]

null

gsplines/basis/__init__.py

rafaelrojasmiliani/gsplines

663b10f6d53b498a1e892d9eb32a345153de36d2

[ "MIT" ]

null

from .basis0010 import cBasis0010 from .basis1010 import cBasis1010 from .basis1000 import cBasis1000 from .basislagrange import cBasisLagrange

24.166667

41

0.855172

16

145

7.75

0.625

0

0.1875

0.117241

145

5

42

29

0.78125

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

686de6875a3a931363ddee27fb0e7a7572a53973

26

py

Python

fffit/tests/base_test.py

helpscott/fffit

22f83c3e804304398822bfdc335704cacab5efc5

[ "MIT" ]

null

fffit/tests/base_test.py

helpscott/fffit

22f83c3e804304398822bfdc335704cacab5efc5

[ "MIT" ]

null

fffit/tests/base_test.py

helpscott/fffit

22f83c3e804304398822bfdc335704cacab5efc5

[ "MIT" ]

4

2021-05-13T19:51:08.000Z

2021-12-08T01:22:20.000Z

class BaseTest: pass

6.5

15

0.653846

3

26

5.666667

1

0

0.307692

26

3

16

8.666667

0.944444

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

68714220d17acb2e9475094f27b4c9ac2f82ca5b

4,391

py

Python

uaa-python/app/database/article_db.py

suomitek/cubeai

cc4c0f5f445a552d239910da63944307c1f06e37

[ "Apache-2.0" ]

null

uaa-python/app/database/article_db.py

suomitek/cubeai

cc4c0f5f445a552d239910da63944307c1f06e37

[ "Apache-2.0" ]

null

uaa-python/app/database/article_db.py

suomitek/cubeai

cc4c0f5f445a552d239910da63944307c1f06e37

[ "Apache-2.0" ]

null

from app.globals.globals import g from app.domain.article import Article from app.utils.pageable import gen_pageable async def get_articles(where, pageable): pageable = gen_pageable(pageable) sql = 'SELECT * FROM article {} {}'.format(where, pageable) sql_total_count = 'SELECT COUNT(*) FROM article {}'.format(where) async with await g.db.pool.Connection() as conn: async with conn.cursor() as cursor: await cursor.execute(sql) records = cursor.fetchall() article_list = [] for record in records: article = Article() article.from_record(record) article_list.append(article.__dict__) await cursor.execute(sql_total_count) total_count = cursor.fetchone() return total_count[0], article_list async def get_articles_by_uuid(uuid): sql = 'SELECT * FROM article WHERE uuid = "{}" limit 1'.format(uuid) async with await g.db.pool.Connection() as conn: async with conn.cursor() as cursor: await cursor.execute(sql) records = cursor.fetchall() article_list = [] for record in records: article = Article() article.from_record(record) article_list.append(article.__dict__) return article_list async def get_article(id): sql = 'SELECT * FROM article WHERE id = "{}" limit 1'.format(id) async with await g.db.pool.Connection() as conn: async with conn.cursor() as cursor: await cursor.execute(sql) records = cursor.fetchall() article_list = [] for record in records: article = Article() article.from_record(record) article_list.append(article.__dict__) return article_list[0] async def create_article(article): sql = ''' INSERT INTO article ( uuid, author_login, author_name, subject_1, subject_2, subject_3, title, summary, tag_1, tag_2, tag_3, picture_url, content, display_order, created_date, modified_date ) VALUES ( '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}') '''.format( article.uuid, article.authorLogin, article.authorName, article.subject1, article.subject2, article.subject3, article.title, article.summary, article.tag1, article.tag2, article.tag3, article.pictureUrl, article.content, article.displayOrder, article.createdDate, article.modifiedDate ) async with await g.db.pool.Connection() as conn: async with conn.cursor() as cursor: await cursor.execute(sql) await conn.commit() async def update_article(article): sql = ''' UPDATE article SET uuid = '{}', author_login = '{}', author_name = '{}', subject_1 = '{}', subject_2 = '{}', subject_3 = '{}', title = '{}', summary = '{}', tag_1 = '{}', tag_2 = '{}', tag_3 = '{}', picture_url = '{}', content = '{}', display_order = '{}', created_date = '{}', modified_date = '{}' WHERE id = {} '''.format( article.uuid, article.authorLogin, article.authorName, article.subject1, article.subject2, article.subject3, article.title, article.summary, article.tag1, article.tag2, article.tag3, article.pictureUrl, article.content, article.displayOrder, article.createdDate, article.modifiedDate, article.id ) async with await g.db.pool.Connection() as conn: async with conn.cursor() as cursor: await cursor.execute(sql) await conn.commit() async def delete_article(id): sql = 'DELETE FROM article WHERE id = "{}"'.format(id) async with await g.db.pool.Connection() as conn: async with conn.cursor() as cursor: await cursor.execute(sql) await conn.commit()

27.616352

114

0.53974

441

4,391

5.229025

0.185941

0.046834

0.05464

0.063747

0.774935

0.738942

0

0.009692

0.342063

4,391

158

115

27.791139

0.788508

0

0.544776

0

0.278297

0

1

0

false

0

0.022388

0

0.044776

0

null

0

1

0

null

0

5

688558631c3f21e329b1a10a009f7799c0302723

152

py

Python

organizations/admin.py

intherenzone/CRM

d0f3cffed01e5fddfc39c2281b26b2f376b71152

[ "MIT" ]

2

2018-02-15T15:33:00.000Z

2018-02-15T16:29:12.000Z

organizations/admin.py

intherenzone/CRM

d0f3cffed01e5fddfc39c2281b26b2f376b71152

[ "MIT" ]

1

2018-08-31T08:54:22.000Z

organizations/admin.py

intherenzone/CRM

d0f3cffed01e5fddfc39c2281b26b2f376b71152

[ "MIT" ]

12

2017-11-02T22:32:32.000Z

2018-04-12T05:13:25.000Z

from django.contrib import admin # Register your models here. from organizations.models import Organization admin.site.register(Organization)

19

46

0.789474

18

152

6.666667

0.666667

0

0.157895

152

7

47

21.714286

0.9375

0.171053

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

68adadf00f180a57f63efb3f9a9f9223596b55d9

142

py

Python

pyazblob/errors.py

RobertoPrevato/PyAzBlob

3031d30ef029a3d49ee8eccc9b2732249548e2ff

[ "MIT" ]

4

2017-08-09T08:03:50.000Z

2020-10-06T20:15:30.000Z

pyazblob/errors.py

RobertoPrevato/PyAzBlob

3031d30ef029a3d49ee8eccc9b2732249548e2ff

[ "MIT" ]

null

pyazblob/errors.py

RobertoPrevato/PyAzBlob

3031d30ef029a3d49ee8eccc9b2732249548e2ff

[ "MIT" ]

2

2018-12-18T06:13:16.000Z

2020-02-19T10:13:49.000Z

class ConfigurationError(Exception): pass class ApplicationError(Exception): pass class UploadFailure(ApplicationError): pass

12.909091

38

0.760563

12

142

9

0.5

0.240741

0.333333

0

0.176056

142

10

39

14.2

0.923077

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

d79c27f412fc25919add065122c8e7d7661be02f

142

py

Python

backend/api/streamflow_api.py

jossM/streamflow

5c01db1439b25709c0a78a962b42142bfa692279

[ "Apache-2.0" ]

null

backend/api/streamflow_api.py

jossM/streamflow

5c01db1439b25709c0a78a962b42142bfa692279

[ "Apache-2.0" ]

null

backend/api/streamflow_api.py

jossM/streamflow

5c01db1439b25709c0a78a962b42142bfa692279

[ "Apache-2.0" ]

null

# coding=utf-8 from fastapi import FastAPI from api.tasks import add_tasks_resources app = FastAPI() # all routes add_tasks_resources(app)

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null

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1

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0416d564be2ff3cc37d1278e1285d2a45ecd3639

45

py

Python

tests/unit/bivariate/__init__.py

pvk-developer/Copulas

4de54e946ecb1e2bf831874e6a00a7d04d302804

[ "MIT" ]

71

2018-06-20T12:07:34.000Z

2020-01-03T21:43:01.000Z

tests/unit/bivariate/__init__.py

Hooddi/Copulas

86dc1304fe4ffb51302fc37801d7f18c4ab4d88d

[ "MIT" ]

75

2018-06-20T09:46:07.000Z

2019-12-23T15:04:19.000Z

tests/unit/bivariate/__init__.py

Hooddi/Copulas

86dc1304fe4ffb51302fc37801d7f18c4ab4d88d

[ "MIT" ]

25

2018-06-24T18:01:11.000Z

2020-01-02T14:30:09.000Z

"""Copulas bivariate unit testing module."""

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6.6

1

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1

45

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0.844444

0

null

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0

1

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1

0

null

0

1

0

5

f08e67ddfa07697c7452af1d6f00b386ef656e5b

283

py

Python

src/oscar_accounts/core.py

n8snyder/django-oscar-accounts

1d473f6ccf795989c7ced9356b4ce20c642debe0

[ "BSD-3-Clause" ]

149

2015-01-09T18:36:57.000Z

2022-01-19T05:22:11.000Z

src/oscar_accounts/core.py

n8snyder/django-oscar-accounts

1d473f6ccf795989c7ced9356b4ce20c642debe0

[ "BSD-3-Clause" ]

124

2015-01-21T05:27:40.000Z

2022-02-01T11:05:08.000Z

src/oscar_accounts/core.py

n8snyder/django-oscar-accounts

1d473f6ccf795989c7ced9356b4ce20c642debe0

[ "BSD-3-Clause" ]

89

2015-01-10T08:14:14.000Z

2021-11-04T10:51:29.000Z

from oscar.core.loading import get_model from oscar_accounts import names Account = get_model('oscar_accounts', 'Account') def redemptions_account(): return Account.objects.get(name=names.REDEMPTIONS) def lapsed_account(): return Account.objects.get(name=names.LAPSED)

20.214286

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283

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13

55

21.769231

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0.074205

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false

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0.285714

0.857143

0

null

0

1

0

null

0

1

0

1

0

5

f0a9d6d837d611db77b32f5e0c18ba59616040be

57

py

Python

netlens/visualization/__init__.py

deepfx/netlens

5bce3cac60076c52974e0526aeaf36e2710fc352

[ "MIT" ]

15

2020-01-20T16:15:11.000Z

2020-11-15T11:47:27.000Z

netlens/visualization/__init__.py

deepfx/netlens

5bce3cac60076c52974e0526aeaf36e2710fc352

[ "MIT" ]

null

netlens/visualization/__init__.py

deepfx/netlens

5bce3cac60076c52974e0526aeaf36e2710fc352

[ "MIT" ]

null

from .param import ImageParam from .render import OptVis

19

29

0.824561

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5.875

0.75

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57

2

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28.5

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true

0

1

0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

5

f0c07744eac22946d1ad429741f26e7d34dfa712

131

py

Python

fastapi_crud/src/app/main.py

balapitchuka/fastapi_snippets

a5efc9bd3520412761131fcf4a3c9d11b1053ceb

[ "MIT" ]

null

fastapi_crud/src/app/main.py

balapitchuka/fastapi_snippets

a5efc9bd3520412761131fcf4a3c9d11b1053ceb

[ "MIT" ]

null

fastapi_crud/src/app/main.py

balapitchuka/fastapi_snippets

a5efc9bd3520412761131fcf4a3c9d11b1053ceb

[ "MIT" ]

null

from fastapi import FastAPI app = FastAPI() @app.get("/hello") def hello(): return {"message" : "fastapi is up and running"}

16.375

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18

131

4.833333

0.722222

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131

8

52

16.375

0.813084

0

0.287879

0

1

0.2

false

0

0.2

0.6

0

1

0

null

1

0

1

0

null

0

1

0

5

9bc5b730fb4b0c4f0c18ff636e7a072cb10953dd

30

py

Python

rosetta/tests/views.py

SergeyKubrak/django-rosetta

76e8387f8c838565adb8d6d0b6060c2b8c690436

[ "MIT" ]

24

2016-08-06T18:10:54.000Z

2022-03-04T11:47:39.000Z

rosetta/tests/views.py

SergeyKubrak/django-rosetta

76e8387f8c838565adb8d6d0b6060c2b8c690436

[ "MIT" ]

1

2017-03-28T02:36:50.000Z

2017-03-28T07:18:57.000Z

rosetta/tests/views.py

SergeyKubrak/django-rosetta

76e8387f8c838565adb8d6d0b6060c2b8c690436

[ "MIT" ]

13

2017-03-28T02:35:32.000Z

2022-02-21T23:36:15.000Z

def dummy(request): pass

7.5

19

0.633333

4

30

4.75

1

0

0.266667

30

3

20

10

0.863636

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9bd5df0e0b3f6b4c2a6e9501cdd160e14a89c0b2

149

py

Python

myexman/__init__.py

Yif-Yang/simclr-pytorch

e962762016837d81a5a358407b552bad418ab162

[ "MIT" ]

90

2020-12-10T14:07:16.000Z

2022-03-31T18:55:47.000Z

myexman/__init__.py

Yif-Yang/simclr-pytorch

e962762016837d81a5a358407b552bad418ab162

[ "MIT" ]

9

2020-12-23T09:53:11.000Z

2022-01-28T12:47:49.000Z

myexman/__init__.py

Yif-Yang/simclr-pytorch

e962762016837d81a5a358407b552bad418ab162

[ "MIT" ]

19

2021-01-03T13:35:54.000Z

2022-01-21T01:56:52.000Z

from .parser import ( ExParser, simpleroot ) from .index import ( Index ) from . import index from . import parser __version__ = '0.0.2'

13.545455

21

0.66443

19

149

5

0.473684

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0.315789

0.442105

0

0.026549

0.241611

149

10

22

14.9

0.814159

0

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1

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false

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0

1

0

null

1

0

null

0

1

0

5

9bdd82ba6d9db095c6234b0f9915023b5d572910

27

py

Python

tests/test_extensions/extension1.py

CraftSpider/SpiderTools

4bf155feec7cb983e8d283d93552902ec85178a2

[ "MIT" ]

5

2019-10-14T13:50:02.000Z

2021-09-23T18:48:27.000Z

tests/test_extensions/extension1.py

CraftSpider/SpiderTools

4bf155feec7cb983e8d283d93552902ec85178a2

[ "MIT" ]

null

tests/test_extensions/extension1.py

CraftSpider/SpiderTools

4bf155feec7cb983e8d283d93552902ec85178a2

[ "MIT" ]

null

def setup(bot): pass

5.4

15

0.555556

4

27

3.75

1

0

0.333333

27

4

16

6.75

0.833333

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9beca84460505c1fced09e281c900f29916becd5

140

py

Python

ai_random.py

sun2125/class

e0058eb35dae903b04945ec9f329e9cbfcc48110

[ "MIT" ]

null

ai_random.py

sun2125/class

e0058eb35dae903b04945ec9f329e9cbfcc48110

[ "MIT" ]

null

ai_random.py

sun2125/class

e0058eb35dae903b04945ec9f329e9cbfcc48110

[ "MIT" ]

null

def bet(game, round, funds, game_record, round_record): import random return random.randint(0, funds[0]) if round < 9 else funds[0]

35

65

0.707143

23

140

4.217391

0.608696

0.123711

0

0.034783

0.178571

140

3

66

46.666667

0.808696

0

1

0.333333

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

504495d7e9ca06307d0cd6bd72ba3d91caa6a1d3

131

py

Python

emoji/config.py

jdherg/octopus-holdings

0db5b9b4e4e0c10e03063128e3e034926e1c5a6f

[ "MIT" ]

38

2015-07-11T00:03:10.000Z

2021-09-24T20:23:30.000Z

emoji/config.py

jdherg/octopus-holdings

0db5b9b4e4e0c10e03063128e3e034926e1c5a6f

[ "MIT" ]

7

2016-02-11T21:50:10.000Z

2021-09-22T15:46:54.000Z

emoji/config.py

jdherg/octopus-holdings

0db5b9b4e4e0c10e03063128e3e034926e1c5a6f

[ "MIT" ]

3

2016-09-26T02:40:53.000Z

2017-05-24T18:21:20.000Z

import json import pathlib with open(pathlib.Path(__file__).with_name("emoji_config.json")) as f: EMOJI_CONFIG = json.load(f)

21.833333

70

0.755725

21

131

4.380952

0.619048

0.23913

0.326087

0

0.122137

131

5

71

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0.8

0

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0.5

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null

1

0

1

0

null

0

1

0

5

acd7ad9d1e49c326f16de93a726d83a2e34e2818

291

py

Python

sac/__init__.py

hbutsuak95/iv_rl

0f72a8f077a238237027ea96b7d1160c35ac9959

[ "MIT" ]

9

2022-01-16T11:27:00.000Z

2022-03-13T14:04:48.000Z

sac/__init__.py

hbutsuak95/iv_rl

0f72a8f077a238237027ea96b7d1160c35ac9959

[ "MIT" ]

null

sac/__init__.py

hbutsuak95/iv_rl

0f72a8f077a238237027ea96b7d1160c35ac9959

[ "MIT" ]

null

from .sac import SACTrainer, VarSACTrainer from .ensembleSAC import EnsembleSAC, VarEnsembleSAC from .iv_sac import IV_EnsembleSAC, IV_VarEnsembleSAC, IV_VarSAC from .sunrise import SunriseSAC, Sunrise_VarEnsembleSAC from .uwac import UWACSAC, UWAC_VarEnsembleSAC from .main import run_sac

36.375

64

0.852234

37

291

6.513514

0.405405

0.224066

0

0.106529

291

7

65

41.571429

0.926923

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

ace96b29847d94e0ad359533ffc8ada9431b620b

140

py

Python

modules/import_specific_attributes.py

magicalcarpet/the_complete_python_course

0ac0c5015a93607d7d29258ac0a3fc38dda81bd2

[ "MIT" ]

null

modules/import_specific_attributes.py

magicalcarpet/the_complete_python_course

0ac0c5015a93607d7d29258ac0a3fc38dda81bd2

[ "MIT" ]

null

modules/import_specific_attributes.py

magicalcarpet/the_complete_python_course

0ac0c5015a93607d7d29258ac0a3fc38dda81bd2

[ "MIT" ]

null

from calculator import creator, add, subtract from math import sqrt print(creator) print(add(2, 5)) print(subtract(10, 3)) print(sqrt(49))

17.5

45

0.75

23

140

4.565217

0.608696

0

0.056911

0.121429

140

7

46

20

0.796748

0

1

0

true

0

0.333333

0

0.333333

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

acedab4b50c728019dba842d5d62c4836acfcfc5

2,536

py

Python

Comd/level.py

Hotkota/Am

da55c4a710e6c90577e1db1f93f107d171462959

[ "MIT" ]

3

2020-08-18T04:45:36.000Z

2021-01-22T15:58:44.000Z

Comd/level.py

Hotkota/Am

da55c4a710e6c90577e1db1f93f107d171462959

[ "MIT" ]

null

Comd/level.py

Hotkota/Am

da55c4a710e6c90577e1db1f93f107d171462959

[ "MIT" ]

null

import sqlite3 import discord from discord.ext import commands class Level(commands.Cog): def __init__(self, client): self.client = client @commands.command(aliases = ["level", "lvl", "xp", "опыт"]) async def уровень(self, ctx, *, arg): if arg != 18: pass else: if member.bot: await ctx.send("У ботов нет профиля") else: with sqlite3.connect("../am/data/DB/Database.db") as conn: cursor = conn.cursor() for row in cursor.execute(f"SELECT lvl, xp, name FROM users where id={arg}").fetchall(): emb = discord.Embed(title = f"Профиль {row[1]}",colour = discord.Color.red()) emb.description = f"Уровень: **{row[0]}**\nопыт: **{row[1]}**\nДо нового уровня: **{(5*row[0]**2+50*row[0]+100)-row[1]}**" await ctx.send(embed = emb) @commands.command(aliases = ["level", "lvl", "xp", "опыт"]) async def уровень(self, ctx, member: discord.Member): if member.bot: await ctx.send("У ботов нет профиля") else: with sqlite3.connect("../am/data/DB/Database.db") as conn: cursor = conn.cursor() for row in cursor.execute(f"SELECT lvl, xp FROM users where id={member.id}").fetchall(): emb = discord.Embed(title = f"Профиль {ctx.message.author.name}",colour = discord.Color.red()) emb.set_thumbnail(url = ctx.message.author.avatar_url) emb.description = f"Уровень: **{row[0]}**\nопыт: **{row[1]}**\nДо нового уровня: **{(5*row[0]**2+50*row[0]+100)-row[1]}**" await ctx.send(embed = emb) @уровень.error async def Level_error(self, ctx, error): if isinstance(error, commands.MissingRequiredArgument): with sqlite3.connect("../am/data/DB/Database.db") as conn: cursor = conn.cursor() for row in cursor.execute(f"SELECT lvl, xp FROM users where id={ctx.message.author.id}").fetchall(): emb = discord.Embed(title = f"Профиль {ctx.message.author.name}",colour = discord.Color.red()) emb.set_thumbnail(url = ctx.message.author.avatar_url) emb.description = f"Уровень: **{row[0]}**\nопыт: **{row[1]}**\nДо нового уровня: **{(5*row[0]**2+50*row[0]+100)-row[1]}**" await ctx.send(embed = emb) def setup(client): client.add_cog(Level(client))

50.72

146

0.554416

325

2,536

4.295385

0.255385

0.025788

0.04298

0.770057

0.755014

0.729226

0

0.023639

0.282729

2,536

50

147

50.72

0.743815

0

0.568182

0

0.068182

0.266456

0.106819

0

1

0.045455

false

0.022727

0.068182

0

0.136364

0

null

0

1

0

null

0

5

acfa4cd8be4c7fb3a584bfa15fcebe72e1973b20

13,799

py

Python

networkapiclient/OptionPool.py

shildenbrand/GloboNetworkAPI-client-python

728ea9d13e3004e62586f5eb6ae2eae2bc41a50e

[ "Apache-2.0" ]

16

2015-05-09T16:33:01.000Z

2019-10-24T19:06:03.000Z

networkapiclient/OptionPool.py

shildenbrand/GloboNetworkAPI-client-python

728ea9d13e3004e62586f5eb6ae2eae2bc41a50e

[ "Apache-2.0" ]

3

2019-08-09T20:18:12.000Z

2019-11-11T17:23:48.000Z

networkapiclient/OptionPool.py

shildenbrand/GloboNetworkAPI-client-python

728ea9d13e3004e62586f5eb6ae2eae2bc41a50e

[ "Apache-2.0" ]

15

2015-02-03T17:10:59.000Z

2021-05-14T21:01:37.000Z

# -*- coding:utf-8 -*- # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from networkapiclient.exception import InvalidParameterError from networkapiclient.utils import is_valid_int_param from networkapiclient.ApiGenericClient import ApiGenericClient class OptionPool(ApiGenericClient): def __init__(self, networkapi_url, user, password, user_ldap=None): """Class constructor receives parameters to connect to the networkAPI. :param networkapi_url: URL to access the network API. :param user: User for authentication. :param password: Password for authentication. """ super( OptionPool, self).__init__( networkapi_url, user, password, user_ldap) def add(self, tipo_opcao, nome_opcao): """Inserts a new Option Pool and returns its identifier. :param tipo_opcao: Type. String with a maximum of 50 characters and respect [a-zA-Z\_-] :param nome_opcao_txt: Name Option. String with a maximum of 50 characters and respect [a-zA-Z\_-] :return: Following dictionary: :: {'id': < id > , 'type':<type>, 'name':<name>} :raise InvalidParameterError: The value of tipo_opcao or nome_opcao_txt is invalid. :raise DataBaseError: Networkapi failed to access the database. :raise XMLError: Networkapi failed to generate the XML response. """ #optionpool_map = dict() #optionpool_map['type'] = tipo_opcao #optionpool_map['name'] = nome_opcao url='api/pools/options/save/' return self.post(url, {'type': tipo_opcao, "name":nome_opcao }) def modify(self, id_option_pool, tipo_opcao, nome_opcao): """Change Option Pool from by id. :param id_option_pool: Identifier of the Option Pool. Integer value and greater than zero. :param tipo_opcao: Type. String with a maximum of 50 characters and respect [a-zA-Z\_-] :param nome_opcao_txt: Name Option. String with a maximum of 50 characters and respect [a-zA-Z\_-] :return: None :raise InvalidParameterError: Option Pool identifier is null or invalid. :raise InvalidParameterError: The value of tipo_opcao or nome_opcao_txt is invalid. :raise optionpoolNotFoundError: Option pool not registered. :raise DataBaseError: Networkapi failed to access the database. :raise XMLError: Networkapi failed to generate the XML response. """ if not is_valid_int_param(id_option_pool): raise InvalidParameterError( u'The identifier of Option Pool is invalid or was not informed.') #optionpool_map = dict() #optionpool_map['type'] = tipo_opcao #optionpool_map['name'] = nome_opcao_txt url = 'api/pools/options/' + str(id_option_pool) + '/' return self.put(url,{'type': tipo_opcao, "name":nome_opcao } ) def remove(self, id_option_pool): """Remove Option pool by identifier and all Environment related . :param id_option_pool: Identifier of the Option Pool. Integer value and greater than zero. :return: None :raise InvalidParameterError: Option Pool identifier is null and invalid. :raise optionpoolNotFoundError: Option Pool not registered. :raise optionpoolError: Option Pool associated with Pool. :raise DataBaseError: Networkapi failed to access the database. :raise XMLError: Networkapi failed to generate the XML response. """ if not is_valid_int_param(id_option_pool): raise InvalidParameterError( u'The identifier of Option Pool is invalid or was not informed.') url = 'api/pools/options/' + str(id_option_pool) + '/' return self.delete(url) def get_option_pool(self, id_option_pool): """Search Option Pool by id. :param id_option_pool: Identifier of the Option Pool. Integer value and greater than zero. :return: Following dictionary: :: {‘id’: < id_option_pool >, ‘type’: < tipo_opcao >, ‘name’: < nome_opcao_txt >} :raise InvalidParameterError: Option Pool identifier is null and invalid. :raise optionpoolNotFoundError: Option Pool not registered. :raise DataBaseError: Networkapi failed to access the database. :raise XMLError: Networkapi failed to generate the XML response. """ if not is_valid_int_param(id_option_pool): raise InvalidParameterError( u'The identifier of Option Pool is invalid or was not informed.') url = 'api/pools/options/' + str(id_option_pool) + '/' return self.get(url) def get_all_option_pool(self, option_type=None): """Get all Option Pool. :return: Dictionary with the following structure: :: {[{‘id’: < id >, ‘type’: < tipo_opcao >, ‘name’: < nome_opcao_txt >}, ... other option pool ...] } :raise optionpoolNotFoundError: Option Pool not registered. :raise DataBaseError: Can't connect to networkapi database. :raise XMLError: Failed to generate the XML response. """ if option_type: url = 'api/pools/options/?type='+option_type else: url = 'api/pools/options/' return self.get(url) def get_all_environment_option_pool(self, id_environment=None, option_id=None, option_type=None): """Get all Option VIP by Environment . :return: Dictionary with the following structure: :: {[{‘id’: < id >, option: { 'id': <id> 'type':<type> 'name':<name> } environment: { 'id':<id> .... all environment info } etc to option pools ...] } :raise EnvironmentVipNotFoundError: Environment Pool not registered. :raise DataBaseError: Can't connect to networkapi database. :raise XMLError: Failed to generate the XML response. """ url='api/pools/environment_options/' if id_environment: if option_id: if option_type: url = url + "?environment_id=" + str(id_environment)+ "&option_id=" + str(option_id) + "&option_type=" + option_type else: url = url + "?environment_id=" + str(id_environment)+ "&option_id=" + str(option_id) else: if option_type: url = url + "?environment_id=" + str(id_environment) + "&option_type=" + option_type else: url = url + "?environment_id=" + str(id_environment) elif option_id: if option_type: url = url + "?option_id=" + str(option_id) + "&option_type=" + option_type else: url = url + "?option_id=" + str(option_id) elif option_type: url = url + "?option_type=" + option_type return self.get(url) def associate_environment_option_pool(self, id_option_pool, id_environment): """Create a relationship of optionpool with Environment. :param id_option_pool: Identifier of the Option Pool. Integer value and greater than zero. :param id_environment: Identifier of the Environment . Integer value and greater than zero. :return: Dictionary with the following structure: {‘id’: < id >, option: { 'id': <id> 'type':<type> 'name':<name> } environment: { 'id':<id> .... all environment info } } :raise InvalidParameterError: Option Pool/Environment Pool identifier is null and/or invalid. :raise optionpoolNotFoundError: Option Pool not registered. :raise EnvironmentVipNotFoundError: Environment Pool not registered. :raise optionpoolError: Option Pool is already associated with the environment pool. :raise UserNotAuthorizedError: User does not have authorization to make this association. :raise DataBaseError: Networkapi failed to access the database. :raise XMLError: Networkapi failed to generate the XML response. """ if not is_valid_int_param(id_option_pool): raise InvalidParameterError( u'The identifier of Option Pool is invalid or was not informed.') if not is_valid_int_param(id_environment): raise InvalidParameterError( u'The identifier of Environment Pool is invalid or was not informed.') url= 'api/pools/environment_options/save/' return self.post(url, {'option_id': id_option_pool,"environment_id":id_environment }) def get_environment_option_pool(self, environment_option_id ): """Get Environment Option Pool by id . :return: Dictionary with the following structure: :: {‘id’: < id >, option: { 'id': <id> 'type':<type> 'name':<name> } environment: { 'id':<id> .... all environment info } } :raise EnvironmentVipNotFoundError: Environment Pool not registered. :raise DataBaseError: Can't connect to networkapi database. :raise XMLError: Failed to generate the XML response. """ url = 'api/pools/environment_options/' + str(environment_option_id) + '/' return self.get(url) def disassociate_environment_option_pool(self, environment_option_id): """Remove a relationship of optionpool with Environment. :param id_option_pool: Identifier of the Option Pool. Integer value and greater than zero. :param id_environment: Identifier of the Environment Pool. Integer value and greater than zero. :return: { 'id': < environment_option_id> } :raise InvalidParameterError: Option Pool/Environment Pool identifier is null and/or invalid. :raise optionpoolNotFoundError: Option Pool not registered. :raise EnvironmentVipNotFoundError: Environment VIP not registered. :raise optionpoolError: Option pool is not associated with the environment pool :raise UserNotAuthorizedError: User does not have authorization to make this association. :raise DataBaseError: Networkapi failed to access the database. :raise XMLError: Networkapi failed to generate the XML response. """ if not is_valid_int_param(environment_option_id): raise InvalidParameterError( u'The identifier of Option Pool is invalid or was not informed.') if not is_valid_int_param(environment_option_id): raise InvalidParameterError( u'The identifier of Environment Pool is invalid or was not informed.') url = 'api/pools/environment_options/' + str(environment_option_id) + '/' return self.delete(url) def modify_environment_option_pool(self, environment_option_id, id_option_pool,id_environment ): """Remove a relationship of optionpool with Environment. :param id_option_pool: Identifier of the Option Pool. Integer value and greater than zero. :param id_environment: Identifier of the Environment Pool. Integer value and greater than zero. :return: Dictionary with the following structure: :: {‘id’: < id >, option: { 'id': <id> 'type':<type> 'name':<name> } environment: { 'id':<id> .... all environment info } } :raise InvalidParameterError: Option Pool/Environment Pool identifier is null and/or invalid. :raise optionpoolNotFoundError: Option Pool not registered. :raise EnvironmentVipNotFoundError: Environment VIP not registered. :raise optionpoolError: Option pool is not associated with the environment pool :raise UserNotAuthorizedError: User does not have authorization to make this association. :raise DataBaseError: Networkapi failed to access the database. :raise XMLError: Networkapi failed to generate the XML response. """ if not is_valid_int_param(environment_option_id): raise InvalidParameterError( u'The identifier of Environment Option Pool is invalid or was not informed.') #optionpool_map = dict() #optionpool_map['option'] = option_id #optionpool_map['environment'] = environment_id url = 'api/pools/environment_options/' + str(environment_option_id) + '/' return self.put(url, {'option_id': id_option_pool,"environment_id":id_environment })

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4a107ca1f19ec60ef21d3c3f2c805d46aff92d02

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py

Python

__init__.py

sickless/flask_private_area

09b2f9382c0426f5ed63488f9fd8ca6d4b3f751c

[ "BSD-3-Clause" ]

null

__init__.py

sickless/flask_private_area

09b2f9382c0426f5ed63488f9fd8ca6d4b3f751c

[ "BSD-3-Clause" ]

null

__init__.py

sickless/flask_private_area

09b2f9382c0426f5ed63488f9fd8ca6d4b3f751c

[ "BSD-3-Clause" ]

null

from .flask_private_area import app

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py

Python

enthought/scripting/util.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/scripting/util.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/scripting/util.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from apptools.scripting.util import *

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4a2086e58a509af6536a764a7ee169742c362e19

131

py

Python

recipes-support/pot-watcher/files/play-sound.py

masselstine/meta-alexa

bbb44fa29a73d1cc9670b24a031acfdcf100e8d1

[ "MIT" ]

null

recipes-support/pot-watcher/files/play-sound.py

masselstine/meta-alexa

bbb44fa29a73d1cc9670b24a031acfdcf100e8d1

[ "MIT" ]

null

recipes-support/pot-watcher/files/play-sound.py

masselstine/meta-alexa

bbb44fa29a73d1cc9670b24a031acfdcf100e8d1

[ "MIT" ]

null

#!/usr/bin/python3 import pexpect pexpect.run("ssh 192.168.42.1 'aplay /root/beedoo.wav'", events={'(?i)password':'incendia\n'})

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py

Python

torch/ao/nn/__init__.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

60,067

2017-01-18T17:21:31.000Z

2022-03-31T21:37:45.000Z

torch/ao/nn/__init__.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

66,955

2017-01-18T17:21:38.000Z

2022-03-31T23:56:11.000Z

torch/ao/nn/__init__.py

Hacky-DH/pytorch

80dc4be615854570aa39a7e36495897d8a040ecc

[ "Intel" ]

19,210

2017-01-18T17:45:04.000Z

2022-03-31T23:51:56.000Z

from torch.ao.nn import sparse

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py

Python

crpm/test_noop.py

dmontemayor/CRPM

e896831fad7bed42d17574b137e600fc5adbf6b0

[ "MIT" ]

null

crpm/test_noop.py

dmontemayor/CRPM

e896831fad7bed42d17574b137e600fc5adbf6b0

[ "MIT" ]

null

crpm/test_noop.py

dmontemayor/CRPM

e896831fad7bed42d17574b137e600fc5adbf6b0

[ "MIT" ]

null

"""NOOP test """ def test_noop(): """This test does nothing. """

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py

Python

DataAnalysis/Data_analysis_script.py

RafaBO/Cell-tracing

598cc796d3b25f1d66ab3431274d0f11c310a370

[ "MIT", "BSD-3-Clause" ]

176

2018-09-24T10:04:14.000Z

2022-03-30T18:38:09.000Z

DataAnalysis/Data_analysis_script.py

tb901029/Usiigaci

263f599e40f31e81c07d78bb756e689b67cc086f

[ "MIT", "BSD-3-Clause" ]

24

2018-11-08T14:12:56.000Z

2021-12-10T23:26:26.000Z

DataAnalysis/Data_analysis_script.py

tb901029/Usiigaci

263f599e40f31e81c07d78bb756e689b67cc086f

[ "MIT", "BSD-3-Clause" ]

70

2018-09-07T03:53:06.000Z

2022-03-29T12:59:48.000Z

''' Single cell tracking data processing script Hsieh-Fu Tsai (hsiehfutsai@gmail.com), Tyler Sloan(info@quorumetrix.com), Amy Shen(amy.shen@oist.jp) purpose: this notebook aims to be a general tool for analysis of single cell migration data with use of opensource tools. Input data: the script can process cell tracking data from ImageJ, Lineage Mapper, Metamorph, or Usiigaci tracker. If you use this code, please cite the following paper: Hsieh-Fu Tsai, Joanna Gajda, Tyler Sloan, Andrei Rares, Amy Shen, Usiigaci: Label-free instance-aware cell tracking in phase contrast microscopy using Mask R-CNN. Version: v1.0 2018.08.19 License: This script is released under MIT license Copyright <2018> <Okinawa Institute of Science and Technology Graduate University> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' #import libraries import numpy as np import pandas as pd import scipy from IPython.core.display import display import matplotlib.pyplot as plt from mpl_toolkits.mplot3d.art3d import Line3DCollection from matplotlib.collections import LineCollection from matplotlib import colors as mcolors from matplotlib.colors import ListedColormap, BoundaryNorm import seaborn as sns import os from itertools import groupby from operator import itemgetter import imageio from read_roi import read_roi_file from read_roi import read_roi_zip #Definition #define the frames throughout the experiments n_frames = 61 # define the time interval between each frame t_inc = 10 # in minutes print("Total frame of time lapse is %d" %(n_frames)) print("Time interval is %d minutes"%(t_inc)) #define the data location location = r'C:\Users\Davince\Dropbox (OIST)\Deeplearning_system\tracking project\Testautomaticfinding' #define the location type = 'folder, 'csv location_type ='folder' #define the data_type = 'ImageJ', 'Usiigaci', LineageMapper', or 'Metamorph' data_type = 'Usiigaci' #input data loading if data_type=='ImageJ': if location_type == 'csv': df_ij = pd.read_csv(location) n_cells_ij = int(len(df_ij) / n_frames) timestamps = np.linspace(0, n_frames*t_inc, n_frames+1) print("Cell track numbers is %d"%(n_cells_ij)) elif data_type=='LineageMapper': if location_type=='csv': df_LM = pd.read_csv(location) count = df_LM['Cell ID'].value_counts() cell_ids_LM = count[count==n_frames].index.tolist() n_cells_LM = int(len(cell_ids_LM)) timestamps = np.linspace(0, n_frames*t_inc, n_frames+1) print("Cell track number is: " + str(n_cells_LM)) col_names = df_LM.columns.tolist() selected_df = pd.DataFrame(columns=col_names) for i in cell_ids_LM: selected_df = selected_df.append(df_LM.loc[df_LM['Cell ID']==i].copy()) selected_df.reset_index(drop=True, inplace=True) elif data_type=='Metamorph': if location_type=='csv': df_meta = pd.read_csv(location) count = df_meta['Object #'].value_counts() cell_ids_meta = count[count==n_frames].index.tolist() n_cells_meta = int(len(cell_ids_meta)) timestamps = np.linspace(0, n_frames*t_inc, n_frames+1) print("Cell track number is:" + str(n_cells_meta)) col_names = df_meta.columns.tolist() selected_df = pd.DataFrame(columns=col_names) for i in cell_ids_meta: selected_df = selected_df.append(df_meta.loc[df_meta['Object #']==i].copy()) selected_df.reset_index(drop=True, inplace=True) elif data_type=='Usiigaci': if location_type=='csv': df_usiigaci = pd.read_csv(location) count = df_usiigaci['particle'].value_counts() cell_ids_usiigaci = count[count==n_frames].index.tolist() # finding only cells that exist through all the framee n_cells_usiigaci = int(len(cell_ids_usiigaci)) timestamps = np.linspace(0, n_frames*t_inc, n_frames+1) print("Cell track number is:" + str(n_cells_usiigaci)) col_names = df_usiigaci.columns.tolist() selected_df = pd.DataFrame(columns=col_names) for i in cell_ids_usiigaci: selected_df = selected_df.append(df_usiigaci.loc[df_usiigaci['particle']==i].copy()) selected_df.reset_index(drop=True, inplace=True) if location_type == 'folder': #looks for tracks.csv in nested folders all_files = [] sub_directory = [] for root, dirs, files in os.walk(location): for file in files: if file.endswith("tracks.csv"): relativePath = os.path.relpath(root, location) if relativePath == ".": relativePath = "" all_files.append((relativePath.count(os.path.sep),relativePath, file)) all_files.sort(reverse=True) for (count, folder), files in groupby(all_files, itemgetter(0, 1)): sub_directory.append(folder) print("Found the following directories containing Usiigaci tracked results:") print("\n".join(str(x) for x in sub_directory)) print("Making new ids and concatenate dataframe") frame_list = [] for i in range(0, len(sub_directory)): path = os.path.join(location, str(sub_directory[i]+"\\tracks.csv")) replicate_id = sub_directory[i].split('_')[0] df_usiigaci = pd.read_csv(path) #number of index is cell_number = df_usiigaci.index.size new_id_list = [] for i in range(0, df_usiigaci.index.size): new_id = replicate_id + "_" + str(df_usiigaci.iloc[i, 0]) new_id_list.append(new_id) df_usiigaci['newid'] = new_id_list frame_list.append(df_usiigaci) #display(df) #create new pandas dataframe with all the csv data. df_combined = pd.concat(frame_list, ignore_index=True) df_combined.to_csv(os.path.join(location + "\\combined.csv")) count = df_combined['newid'].value_counts() cell_ids_usiigaci = count[count==n_frames].index.tolist() # finding only cells that exist through all the framee n_cells_usiigaci = int(len(cell_ids_usiigaci)) timestamps = np.linspace(0, n_frames*t_inc, n_frames+1) print("Cell track number is:" + str(n_cells_usiigaci)) col_names = df_usiigaci.columns.tolist() selected_df = pd.DataFrame(columns=col_names) for i in cell_ids_usiigaci: selected_df = selected_df.append(df_combined.loc[df_combined['newid']==i].copy()) selected_df.reset_index(drop=True, inplace=True) selected_df.to_csv(os.path.join(location+"\\selected.csv")) #display(selected_df) else: print("Data loading error") #start processing data: if data_type=='ImageJ': print("processing ImageJ data") # Process the data into a numpy time-array props_t_array = [] props_t_array = np.empty([n_cells_ij, 14, n_frames]) # Creates a time array, formatted like a spreadsheet, cells in rows, columns for X and Y, and t in Z #print(np.shape(props_t_array)) cell_dfs = [] ind_i = 0 i_cell = 0 for i in range(1,len(df_ij)): # Using 1 instead of zero here avoids indexing -1, but won't skip first row being copied because ind_i initialized as zero above. if(df_ij.loc[i-1,'Slice'] > df_ij.loc[i,'Slice']): ind_f = i - 1 sub_df = df_ij.loc[ind_i:ind_f,:] ind_i = i # Copy the measurements of interest into the numpy array props_t_array[i_cell,0,:] = sub_df['X'] # This will be a problem if the number of frames ever differs between cells. props_t_array[i_cell,1,:] = sub_df['Y'] props_t_array[i_cell,2,:] = sub_df['Area'] props_t_array[i_cell,3,:] = sub_df['Perim.'] props_t_array[i_cell,4,:] = sub_df['Angle'] props_t_array[i_cell,5,:] = sub_df['Circ.'] cell_dfs.append(sub_df) # add also to a list of dataframes i_cell = i_cell + 1 if(i == len(df_ij) - 1): # A special case for the last cell in the results file. ind_f = i sub_df =df_ij.loc[ind_i:ind_f,:] # Copy the measurements of interest into the numpy array props_t_array[i_cell,0,:] = sub_df['X'] # This will be a problem if the number of frames ever differs between cells. props_t_array[i_cell,1,:] = sub_df['Y'] props_t_array[i_cell,2,:] = sub_df['Area'] props_t_array[i_cell,3,:] = sub_df['Perim.'] props_t_array[i_cell,4,:] = sub_df['Angle'] props_t_array[i_cell,5,:] = sub_df['Circ.'] # Correct the position coordinates so that all cells start at the same location in the plot. zerod_t_array = np.empty([n_cells_ij, 2, n_frames]) # Creates a time array, formatted like a spreadsheet, cells in rows, columns for X and Y, and t in Z for i in range(0,n_cells_ij): for j in range(0,n_frames): zerod_t_array[i,0,j] = props_t_array[i,0,j] - props_t_array[i,0,0] zerod_t_array[i,1,j] = props_t_array[i,1,j] - props_t_array[i,1,0] #print(props_t_array.to_string()) n_cells = n_cells_ij elif data_type=='Usiigaci': print("processing Usiigaci data") # Process the data into a numpy time-array props_t_array = [] props_t_array = np.empty([n_cells_usiigaci, 14, n_frames]) # Creates a time array, formatted like a spreadsheet, cells in rows, columns for X and Y, and t in Z n_rows_csv=len(selected_df) print('Number of cells: '+ str(n_cells_usiigaci)) print('Number of rows: '+str(n_rows_csv)) if(int(n_rows_csv / n_cells_usiigaci) != n_frames): # We can use this to parse the file print('Error: improper number of rows in tracked file for the number of cells and timepoints.') cell_dfs = [] ind_i = 0 for i_cell in range(0,n_cells_usiigaci): ind_f = ind_i + n_frames - 1 sub_df = selected_df.loc[ind_i:ind_f,:] props_t_array[i_cell,0,:] = sub_df['x'] props_t_array[i_cell,1,:] = sub_df['y'] props_t_array[i_cell,2,:] = sub_df['area'] props_t_array[i_cell,3,:] = sub_df['perimeter'] props_t_array[i_cell,4,:] = sub_df['angle'] #props_t_array[i_cell,5,:] = sub_df['solidity'] # Display the current dataframe and portion of the numpy array. #display(sub_df) #print(props_t_array[i_cell,0:2,:]) ind_i = ind_i + n_frames n_cells = n_cells_usiigaci elif data_type=='LineageMapper': print("processing lineage mapper data") props_t_array = [] props_t_array = np.empty([n_cells_LM, 14, n_frames]) #print(np.shape(props_t_array)) n_rows_csv = len(selected_df) print('Number of cells: ' + str(n_cells_LM)) print('Number of rows: ' + str(n_rows_csv)) if(int(n_rows_csv / n_cells_LM) != n_frames): # We can use this to parse the file print('Error: improper number of rows in trk file for the number of cells and timepoints.') cell_dfs = [] ind_i = 0 for i_cell in range(0,n_cells_LM): ind_f = ind_i + n_frames - 1 sub_df = selected_df.loc[ind_i:ind_f,:] props_t_array[i_cell,0,:] = sub_df['X Coordinate'] props_t_array[i_cell,1,:] = sub_df['Y Coordinate'] # Display the current dataframe and portion of the numpy array. #display(sub_df) #print(props_t_array[i_cell,0:2,:]) ind_i = ind_i + n_frames n_cells = n_cells_LM elif data_type=='Metamorph': print("processing metamorph data") props_t_array = [] props_t_array = np.empty([n_cells_meta, 14, n_frames]) #print(np.shape(props_t_array)) n_rows_csv = len(selected_df) print('Number of cells: ' + str(n_cells_meta)) print('Number of rows: ' + str(n_rows_csv)) if(int(n_rows_csv / n_cells_meta) != n_frames): # We can use this to parse the file print('Error: improper number of rows in the file for the number of cells and timepoints.') cell_dfs = [] ind_i = 0 for i_cell in range(0,n_cells_meta): ind_f = ind_i + n_frames - 1 sub_df = selected_df.loc[ind_i:ind_f,:] props_t_array[i_cell,0,:] = sub_df['X'] props_t_array[i_cell,1,:] = sub_df['Y'] # Display the current dataframe and portion of the numpy array. #print(props_t_array[i_cell,0:2,:]) ind_i = ind_i + n_frames n_cells = n_cells_meta else: print("no data found") #Calculation for cell migration parameters if data_type=='ImageJ': for i in range(0,n_cells): for j in range(0, n_frames): #Segment length if(j > 0): segment = np.sqrt(pow((props_t_array[i,0,j]-props_t_array[i,0,j-1]),2) + pow((props_t_array[i,1,j]-props_t_array[i,1,j-1]),2)) else: segment = 0 props_t_array[i,6,j] = segment # Cumulative path length if(j > 0): cumulative = cumulative + segment else: cumulative = 0 props_t_array[i,7,j] = cumulative # Orientation # CURRENTLY: If data_imageJ is false, then this is dealing with NaNs from the empty column of the array. axis_angle = props_t_array[i,4,j] # Angle of the long axis of the cell: Angle (IJ)?? orientation = np.cos(2 * np.radians(axis_angle)) props_t_array[i,8,j] = orientation # Euclidean distance (From start to current frame) if(j > 0): euc_dist = np.sqrt(pow((props_t_array[i,0,j]-props_t_array[i,0,0]),2) + pow((props_t_array[i,1,j]-props_t_array[i,1,0]),2)) else: euc_dist = 0 props_t_array[i,9,j] = euc_dist # Migration speed if(j > 0): speed = euc_dist / (j*t_inc / 60) # Microns per hour, since t_inc is in minutes else: speed = 0 # Or should it be NaN?? props_t_array[i,10,j] = speed # Directedness (Using the calculation from Paul's spreadsheet, where directedness = deltax / radius (euc_distance)) if(j > 0): # Doesn't make sense to calculate this on the first frame. directedness = (props_t_array[i,0,j]-props_t_array[i,0,0]) / euc_dist else: directedness = 0 props_t_array[i,11,j] = directedness # Turn angle if(j > 0): # Doesn't make sense to calculate this on the first frame. turn_angle_radians = np.arctan((props_t_array[i,1,j] - props_t_array[i,1,j-1]) / (props_t_array[i,0,j] - props_t_array[i,0,j-1])) turn_angle = np.degrees(turn_angle_radians) else: turn_angle = 0 props_t_array[i,12,j] = turn_angle # Endpoint directionality ratio (confinement ratio, meandering index) if(j > 0): ep_dr = cumulative / euc_dist # This is problematic because segment uses i+1 - i, whereas euc_dist uses i - 0. else: ep_dr = 0 #endpoint direcionality ratio is defined arbitrarily 0 at first frame # Direction autocorrelation if(j > 0): dir_auto = np.cos(props_t_array[i,12,j] - props_t_array[i,12,j-1]) else: dir_auto = 0 props_t_array[i,13,j] = dir_auto elif data_type=='Usiigaci': for i in range(0,n_cells): for j in range(0, n_frames): #Segment length if(j > 0): segment = np.sqrt(pow((props_t_array[i,0,j]-props_t_array[i,0,j-1]),2) + pow((props_t_array[i,1,j]-props_t_array[i,1,j-1]),2)) else: segment = 0 props_t_array[i,6,j] = segment # Cumulative path length if(j > 0): cumulative = cumulative + segment else: cumulative = 0 props_t_array[i,7,j] = cumulative # Orientation # CURRENTLY: If data_imageJ is false, then this is dealing with NaNs from the empty column of the array. orientation = np.cos(2*props_t_array[i,4,j]) # Angle of the long axis of the cell: Angle (IJ)?? props_t_array[i,8,j] = orientation # Euclidean distance (From start to current frame) if(j > 0): euc_dist = np.sqrt(pow((props_t_array[i,0,j]-props_t_array[i,0,0]),2) + pow((props_t_array[i,1,j]-props_t_array[i,1,0]),2)) else: euc_dist = 0 props_t_array[i,9,j] = euc_dist # Migration speed if(j > 0): speed = euc_dist / (j*t_inc / 60) # Microns per hour, since t_inc is in minutes else: speed = 0 # Or should it be NaN?? props_t_array[i,10,j] = speed # Directedness (Using the calculation from Paul's spreadsheet, where directedness = deltax / radius (euc_distance)) if(j > 0): # Doesn't make sense to calculate this on the first frame. directedness = (props_t_array[i,0,j]-props_t_array[i,0,0]) / euc_dist else: directedness = 0 props_t_array[i,11,j] = directedness # Turn angle if(j > 0): # Doesn't make sense to calculate this on the first frame. turn_angle_radians = np.arctan((props_t_array[i,1,j] - props_t_array[i,1,j-1]) / (props_t_array[i,0,j] - props_t_array[i,0,j-1])) turn_angle = np.degrees(turn_angle_radians) else: turn_angle = 0 props_t_array[i,12,j] = turn_angle # Endpoint directionality ratio (confinement ratio, meandering index) if(j > 0): ep_dr = cumulative / euc_dist # This is problematic because segment uses i+1 - i, whereas euc_dist uses i - 0. else: ep_dr = 0 #endpoint direcionality ratio is defined arbitrarily 0 at first frame # Direction autocorrelation if(j > 0): dir_auto = np.cos(props_t_array[i,12,j] - props_t_array[i,12,j-1]) else: dir_auto = 0 props_t_array[i,13,j] = dir_auto else: for i in range(0,n_cells): for j in range(0, n_frames): #Segment length if(j > 0): segment = np.sqrt(pow((props_t_array[i,0,j]-props_t_array[i,0,j-1]),2) + pow((props_t_array[i,1,j]-props_t_array[i,1,j-1]),2)) else: segment = 0 props_t_array[i,6,j] = segment # Cumulative path length if(j > 0): cumulative = cumulative + segment else: cumulative = 0 props_t_array[i,7,j] = cumulative # Euclidean distance (From start to current frame) if(j > 0): euc_dist = np.sqrt(pow((props_t_array[i,0,j]-props_t_array[i,0,0]),2) + pow((props_t_array[i,1,j]-props_t_array[i,1,0]),2)) else: euc_dist = 0 props_t_array[i,9,j] = euc_dist # Migration speed if(j > 0): speed = euc_dist / (j*t_inc / 60) # Microns per hour, since t_inc is in minutes else: speed = 0 # Or should it be NaN?? props_t_array[i,10,j] = speed # Directedness (Using the calculation from Paul's spreadsheet, where directedness = deltax / radius (euc_distance)) if(j > 0): # Doesn't make sense to calculate this on the first frame. directedness = (props_t_array[i,0,j]-props_t_array[i,0,0]) / euc_dist else: directedness = 0 props_t_array[i,11,j] = directedness # Turn angle if(j > 0): # Doesn't make sense to calculate this on the first frame. turn_angle_radians = np.arctan((props_t_array[i,1,j] - props_t_array[i,1,j-1]) / (props_t_array[i,0,j] - props_t_array[i,0,j-1])) turn_angle = np.degrees(turn_angle_radians) else: turn_angle = 0 props_t_array[i,12,j] = turn_angle # Endpoint directionality ratio (confinement ratio, meandering index) if(j > 0): ep_dr = cumulative / euc_dist # This is problematic because segment uses i+1 - i, whereas euc_dist uses i - 0. else: ep_dr = 0 #endpoint direcionality ratio is defined arbitrarily 0 at first frame # Direction autocorrelation if(j > 0): dir_auto = np.cos(props_t_array[i,12,j] - props_t_array[i,12,j-1]) else: dir_auto = 0 props_t_array[i,13,j] = dir_auto # Correct the position coordinates so that all cells start at the same location in the plot. zerod_t_array = np.empty([n_cells, 2, n_frames]) # Creates a time array, formatted like a spreadsheet, cells in rows, columns for X and Y, and t in Z for i in range(0,n_cells): for j in range(0,n_frames): zerod_t_array[i,0,j] = props_t_array[i,0,j] - props_t_array[i,0,0] zerod_t_array[i,1,j] = props_t_array[i,1,j] - props_t_array[i,1,0] #export the descriptive statistics to a csv file stats_df = pd.DataFrame(columns=['cell_id','time', 'x_pos_microns', 'y_pos_microns', 'x_pos_corr', 'y_pos_corr', 'area', 'perimeter', 'angle', 'circularity', 'segment_length', 'cumulative_path_length', 'orientation', 'euclidean_distance', 'speed', 'directedness', 'turn_angle', 'direction_autocorrelation', 'solidity']) #deleted velocity stats_df.round(4) summary_cell_df = pd.DataFrame(columns=['cell_id', 'avg_area', 'avg_perimeter', 'avg_angle', 'avg_circularity', 'avg_segment_length', 'total_path_length', 'avg_orientation', 'euclidean_distance', 'avg_speed', 'avg_velocity', 'avg_directedness', 'avg_turn_angle', 'avg_direction_autocorrelation', 'avg_solidity']) summary_cell_df.round(2) summary_timepoint_df = pd.DataFrame(columns=['time', 'avg_area', 'avg_perimeter', 'avg_angle', 'avg_circularity', 'avg_segment_length', 'total_path_length', 'avg_orientation', 'euclidean_distance', 'avg_speed', 'avg_velocity','avg_directedness', 'avg_turn_angle', 'avg_direction_autocorrelation', 'avg_solidity', 'std_orientation', 'sem_orientation','sem_speed','sem_directedness']) summary_timepoint_df.round(2) t = np.linspace(0,(n_frames-1)*t_inc,n_frames) i_row = 0 if data_type=='ImageJ': for i in range(0,len(props_t_array[:,0,0])): for j in range(0,len(props_t_array[0,0,:])): stats_df.loc[i_row] = i_row stats_df['cell_id'][i_row] = i + 1 stats_df['time'][i_row] = t[j] stats_df['x_pos_microns'][i_row] = props_t_array[i,0,j] stats_df['y_pos_microns'][i_row] = props_t_array[i,1,j] stats_df['x_pos_corr'][i_row] = zerod_t_array[i,0,j] stats_df['y_pos_corr'][i_row] = zerod_t_array[i,1,j] stats_df['area'][i_row] = props_t_array[i,2,j] stats_df['perimeter'][i_row] = props_t_array[i,3,j] stats_df['angle'][i_row] = props_t_array[i,4,j] stats_df['circularity'][i_row] = props_t_array[i,5,j] stats_df['segment_length'][i_row] = props_t_array[i,6,j] stats_df['cumulative_path_length'][i_row] = props_t_array[i,7,j] stats_df['orientation'][i_row] = props_t_array[i,8,j] stats_df['euclidean_distance'][i_row] = props_t_array[i,9,j] stats_df['speed'][i_row] = props_t_array[i,10,j] stats_df['directedness'][i_row] = props_t_array[i,11,j] stats_df['turn_angle'][i_row] = props_t_array[i,12,j] stats_df['direction_autocorrelation'][i_row] = props_t_array[i,13,j] i_row = i_row + 1 elif data_type=='Usiigaci': for i in range(0,len(props_t_array[:,0,0])): for j in range(0,len(props_t_array[0,0,:])): stats_df.loc[i_row] = i_row stats_df['cell_id'][i_row] = i + 1 stats_df['time'][i_row] = t[j] stats_df['x_pos_microns'][i_row] = props_t_array[i,0,j] stats_df['y_pos_microns'][i_row] = props_t_array[i,1,j] stats_df['x_pos_corr'][i_row] = zerod_t_array[i,0,j] stats_df['y_pos_corr'][i_row] = zerod_t_array[i,1,j] stats_df['area'][i_row] = props_t_array[i,2,j] stats_df['perimeter'][i_row] = props_t_array[i,3,j] stats_df['angle'][i_row] = props_t_array[i,4,j] stats_df['solidity'][i_row] = props_t_array[i,5,j] stats_df['segment_length'][i_row] = props_t_array[i,6,j] stats_df['cumulative_path_length'][i_row] = props_t_array[i,7,j] stats_df['orientation'][i_row] = props_t_array[i,8,j] stats_df['euclidean_distance'][i_row] = props_t_array[i,9,j] stats_df['speed'][i_row] = props_t_array[i,10,j] stats_df['directedness'][i_row] = props_t_array[i,11,j] stats_df['turn_angle'][i_row] = props_t_array[i,12,j] stats_df['direction_autocorrelation'][i_row] = props_t_array[i,13,j] i_row = i_row + 1 else: for i in range(0,len(props_t_array[:,0,0])): for j in range(0,len(props_t_array[0,0,:])): stats_df.loc[i_row] = i_row stats_df['cell_id'][i_row] = i + 1 stats_df['time'][i_row] = t[j] stats_df['x_pos_microns'][i_row] = props_t_array[i,0,j] stats_df['y_pos_microns'][i_row] = props_t_array[i,1,j] stats_df['x_pos_corr'][i_row] = zerod_t_array[i,0,j] stats_df['y_pos_corr'][i_row] = zerod_t_array[i,1,j] stats_df['segment_length'][i_row] = props_t_array[i,6,j] stats_df['cumulative_path_length'][i_row] = props_t_array[i,7,j] stats_df['euclidean_distance'][i_row] = props_t_array[i,9,j] stats_df['speed'][i_row] = props_t_array[i,10,j] stats_df['directedness'][i_row] = props_t_array[i,11,j] stats_df['turn_angle'][i_row] = props_t_array[i,12,j] stats_df['direction_autocorrelation'][i_row] = props_t_array[i,13,j] i_row = i_row + 1 #create avg statistics of individual cells if data_type=='ImageJ': for i in range(0,len(props_t_array[:,0,0])): summary_cell_df.loc[i] = i summary_cell_df['cell_id'][i] = i + 1 summary_cell_df['avg_area'][i] = np.mean(props_t_array[i,2,:]) summary_cell_df['avg_perimeter'][i] = np.mean(props_t_array[i,3,:]) summary_cell_df['avg_angle'][i] = np.mean(props_t_array[i,4,:]) summary_cell_df['avg_circularity'][i] = np.mean(props_t_array[i,5,:]) summary_cell_df['avg_segment_length'][i] = np.mean(props_t_array[i,6,1:]) # substract time point 0 summary_cell_df['total_path_length'][i] = props_t_array[i,7,-1] # Total path length is cumulative path length at final timepoint summary_cell_df['avg_orientation'][i] = np.mean(props_t_array[i,8,:]) summary_cell_df['euclidean_distance'][i] = props_t_array[i,9,-1] # Linear distance between first and last point summary_cell_df['avg_speed'][i] = np.mean(props_t_array[i,10,1:]) #subtract time point 0 summary_cell_df['avg_velocity'][i] = props_t_array[i,9,-1] / ((t[-1] - t[0]) / 60) # Total Euclidean distance per hour. summary_cell_df['avg_directedness'][i] = np.nanmean(props_t_array[i,11,1:])#subtract time point 0 summary_cell_df['avg_turn_angle'][i] = np.nanmean(props_t_array[i,12,1:])#subtract time point 0 summary_cell_df['avg_direction_autocorrelation'][i] = np.nanmean(props_t_array[i,13,1:])#subtract time point 0 elif data_type=='Usiigaci': for i in range(0,len(props_t_array[:,0,0])): summary_cell_df.loc[i] = i summary_cell_df['cell_id'][i] = i + 1 summary_cell_df['avg_area'][i] = np.mean(props_t_array[i,2,:]) summary_cell_df['avg_perimeter'][i] = np.mean(props_t_array[i,3,:]) summary_cell_df['avg_angle'][i] = np.mean(props_t_array[i,4,:]) summary_cell_df['avg_solidity'][i] = np.mean(props_t_array[i,5,:]) summary_cell_df['avg_segment_length'][i] = np.mean(props_t_array[i,6,1:]) # substract time point 0 summary_cell_df['total_path_length'][i] = props_t_array[i,7,-1] # Total path length is cumulative path length at final timepoint summary_cell_df['avg_orientation'][i] = np.mean(props_t_array[i,8,:]) summary_cell_df['euclidean_distance'][i] = props_t_array[i,9,-1] # Linear distance between first and last point summary_cell_df['avg_speed'][i] = np.mean(props_t_array[i,10,1:]) #subtract time point 0 summary_cell_df['avg_velocity'][i] = props_t_array[i,9,-1] / ((t[-1] - t[0]) / 60) # Total Euclidean distance per hour. summary_cell_df['avg_directedness'][i] = np.nanmean(props_t_array[i,11,1:])#subtract time point 0 summary_cell_df['avg_turn_angle'][i] = np.nanmean(props_t_array[i,12,1:])#subtract time point 0 summary_cell_df['avg_direction_autocorrelation'][i] = np.nanmean(props_t_array[i,13,1:])#subtract time point 0 else: for i in range(0,len(props_t_array[:,0,0])): summary_cell_df.loc[i] = i summary_cell_df['cell_id'][i] = i + 1 summary_cell_df['avg_segment_length'][i] = np.mean(props_t_array[i,6,1:]) # substract time point 0 summary_cell_df['total_path_length'][i] = props_t_array[i,7,-1] # Total path length is cumulative path length at final timepoint summary_cell_df['euclidean_distance'][i] = props_t_array[i,9,-1] # Linear distance between first and last point summary_cell_df['avg_speed'][i] = np.mean(props_t_array[i,10,1:]) #subtract time point 0 summary_cell_df['avg_velocity'][i] = props_t_array[i,9,-1] / ((t[-1] - t[0]) / 60) # Total Euclidean distance per hour. summary_cell_df['avg_directedness'][i] = np.nanmean(props_t_array[i,11,1:])#subtract time point 0 summary_cell_df['avg_turn_angle'][i] = np.nanmean(props_t_array[i,12,1:])#subtract time point 0 summary_cell_df['avg_direction_autocorrelation'][i] = np.nanmean(props_t_array[i,13,1:])#subtract time point 0 #individual time point statistics if data_type=='ImageJ': for i in range(0,len(props_t_array[0,0,:])): summary_timepoint_df.loc[i] = i summary_timepoint_df['time'][i] = i*t_inc summary_timepoint_df['avg_area'][i] = np.mean(props_t_array[:,2,i]) summary_timepoint_df['avg_perimeter'][i] = np.mean(props_t_array[:,3,i]) summary_timepoint_df['avg_angle'][i] = np.mean(props_t_array[:,4,i]) summary_timepoint_df['avg_circularity'][i] = np.mean(props_t_array[:,5,i]) summary_timepoint_df['avg_segment_length'][i] = np.mean(props_t_array[:,6,i]) summary_timepoint_df['total_path_length'][i] = np.mean(props_t_array[:,7,i]) # Total path length is cumulative path length at final timepoint summary_timepoint_df['avg_orientation'][i] = np.mean(props_t_array[:,8,i]) summary_timepoint_df['std_orientation'][i] = np.std(props_t_array[:,8,i]) summary_timepoint_df['sem_orientation'][i] = scipy.stats.sem(props_t_array[:,8,i]) summary_timepoint_df['euclidean_distance'][i] = np.mean(props_t_array[:,9,i]) # Linear distance between first and last point summary_timepoint_df['avg_speed'][i] = np.mean(props_t_array[:,10,i]) summary_timepoint_df['sem_speed'][i] = scipy.stats.sem(props_t_array[:,10,i]) summary_timepoint_df['avg_velocity'][i] = np.mean(props_t_array[:,9,i]) / ((t[-1] - t[0]) / 60) # Total Euclidean distance per hour. summary_timepoint_df['avg_directedness'][i] = np.nanmean(props_t_array[:,11,i]) summary_timepoint_df['sem_directedness'][i] = scipy.stats.sem(props_t_array[:,11,i]) summary_timepoint_df['avg_turn_angle'][i] = np.nanmean(props_t_array[:,12,i]) summary_timepoint_df['avg_direction_autocorrelation'][i] = np.nanmean(props_t_array[:,13,i]) elif data_type=='Usiigaci': for i in range(0,len(props_t_array[0,0,:])): summary_timepoint_df.loc[i] = i summary_timepoint_df['time'][i] = i*t_inc summary_timepoint_df['avg_area'][i] = np.mean(props_t_array[:,2,i]) summary_timepoint_df['avg_perimeter'][i] = np.mean(props_t_array[:,3,i]) summary_timepoint_df['avg_angle'][i] = np.mean(props_t_array[:,4,i]) summary_timepoint_df['avg_solidity'][i] = np.mean(props_t_array[:,5,i]) summary_timepoint_df['avg_segment_length'][i] = np.mean(props_t_array[:,6,i]) summary_timepoint_df['total_path_length'][i] = np.mean(props_t_array[:,7,i]) # Total path length is cumulative path length at final timepoint summary_timepoint_df['avg_orientation'][i] = np.mean(props_t_array[:,8,i]) summary_timepoint_df['std_orientation'][i] = np.std(props_t_array[:,8,i]) summary_timepoint_df['sem_orientation'][i] = scipy.stats.sem(props_t_array[:,8,i]) summary_timepoint_df['euclidean_distance'][i] = np.mean(props_t_array[:,9,i]) # Linear distance between first and last point summary_timepoint_df['avg_speed'][i] = np.mean(props_t_array[:,10,i]) summary_timepoint_df['sem_speed'][i] = scipy.stats.sem(props_t_array[:,10,i]) summary_timepoint_df['avg_velocity'][i] = np.mean(props_t_array[:,9,i]) / ((t[-1] - t[0]) / 60) # Total Euclidean distance per hour. summary_timepoint_df['avg_directedness'][i] = np.nanmean(props_t_array[:,11,i]) summary_timepoint_df['sem_directedness'][i] = scipy.stats.sem(props_t_array[:,11,i]) summary_timepoint_df['avg_turn_angle'][i] = np.nanmean(props_t_array[:,12,i]) summary_timepoint_df['avg_direction_autocorrelation'][i] = np.nanmean(props_t_array[:,13,i]) else: for i in range(0,len(props_t_array[0,0,:])): summary_timepoint_df.loc[i] = i summary_timepoint_df['time'][i] = i*t_inc summary_timepoint_df['avg_segment_length'][i] = np.mean(props_t_array[:,6,i]) summary_timepoint_df['total_path_length'][i] = np.mean(props_t_array[:,7,i]) # Total path length is cumulative path length at final timepoint summary_timepoint_df['euclidean_distance'][i] = np.mean(props_t_array[:,9,i]) # Linear distance between first and last point summary_timepoint_df['avg_speed'][i] = np.mean(props_t_array[:,10,i]) summary_timepoint_df['sem_speed'][i] = scipy.stats.sem(props_t_array[:,10,i]) summary_timepoint_df['avg_velocity'][i] = np.mean(props_t_array[:,9,i]) / ((t[-1] - t[0]) / 60) # Total Euclidean distance per hour. summary_timepoint_df['avg_directedness'][i] = np.nanmean(props_t_array[:,11,i]) summary_timepoint_df['sem_directedness'][i] = scipy.stats.sem(props_t_array[:,11,i]) summary_timepoint_df['avg_turn_angle'][i] = np.nanmean(props_t_array[:,12,i]) summary_timepoint_df['avg_direction_autocorrelation'][i] = np.nanmean(props_t_array[:,13,i]) export_path = 'export//spreadsheets//' if not os.path.exists(export_path): os.makedirs(export_path) stats_df.to_csv(export_path + "cell_migration_descriptive_statistics.csv", header=True, index=False) summary_cell_df.to_csv(export_path + "cell_migration_summary.csv", header=True, index=False) summary_timepoint_df.to_csv(export_path+"timepoint_migration_summary.csv", header=True, index=False) #Drawing plots # Set plot limits xmin = -500 xmax = 500 ymin = -500 ymax = 500 frames = [] fig = plt.figure(frameon=True,facecolor='w') fig.set_size_inches(10,10) #ax = plt.Axes(fig, [0., 0., 1., 1.]) #ax.set_axis_off() #fig.add_axes(ax) export_path = 'export//scatter//' if not os.path.exists(export_path): os.makedirs(export_path) frames = [] fig = plt.figure(frameon=True,facecolor='w') fig.set_size_inches(10,10) #ax = plt.Axes(fig, [0., 0., 1., 1.]) #ax.set_axis_off() #fig.add_axes(ax) cell_colors = np.linspace(0,1,n_cells) print("graphing raw scatter plot") for t in range(0,n_frames): ax=plt.subplot(111) ax.clear() ax.scatter(props_t_array[:,0,t],props_t_array[:,1,t],s=20, alpha=1, c=cell_colors) #ax.axis('equal') ax.axis([xmin, xmax, ymin, ymax]) # Setting the axes like this avoid the zero values in the preallocated empty array. #ax.text(250, 1050, 'Distribution of cell positions at t = ' + str(int(timestamps[t])) + ' minutes', fontsize=15) ax.text(xmin + (xmax - xmin) / 8, ymax + 5, 'Distribution of cell positions at t = ' + str(int(timestamps[t])) + ' minutes', fontsize=15) ax.set_xlabel('X position ($\mu$m)', fontsize=15) ax.set_ylabel('Y position ($\mu$m)', fontsize=15) # Draw the figure fig.canvas.draw() # Convert to numpy array, and append to list #np_fig = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') np_fig = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) np_fig = np_fig.reshape(fig.canvas.get_width_height()[::-1] + (3,)) frames.append(np_fig) imageio.mimsave(export_path + '/scatter_raw.gif', frames) np.fig=[] frames = [] fig = plt.figure(frameon=True,facecolor='w') fig.set_size_inches(10,10) print("graphing zeroed scatter plot") for t in range(0,n_frames): ax = plt.subplot(111) ax.clear() ax.scatter(zerod_t_array[:,0,t],zerod_t_array[:,1,t],s=20, alpha=1, c=cell_colors) #ax.axis('equal') #plt.axis('off') ax.axis([xmin, xmax, ymin, ymax]) # Setting the axes like this avoid the zero values in the preallocated empty array. #ax.text(-50, 90, 'Distribution of cell positions (zeroed) at t = ' + str(int(timestamps[t])) + ' minutes', fontsize=15) ax.text(xmin + (xmax - xmin) / 8, ymax + 5, 'Distribution of cell positions (zeroed) at t = ' + str(int(timestamps[t])) + ' minutes', fontsize=15) ax.set_xlabel('Relative X position ($\mu$m)', fontsize=15) ax.set_ylabel('Relative Y position ($\mu$m)', fontsize=15) # Draw the figure fig.canvas.draw() #uncomment this one if you want to save individual time point into a file #plt.savefig(export_path + 'scatter%d.png'%(t), format='png', dpi=600) # Convert to numpy array, and append to list #np_fig = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') np_fig = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) np_fig = np_fig.reshape(fig.canvas.get_width_height()[::-1] + (3,)) frames.append(np_fig) imageio.mimsave(export_path + '/scatter_zeroed.gif', frames) print("graphing 2D trajectory plots") #2D hair ball plots with each cell track is one color x = zerod_t_array[:,0,:] y = zerod_t_array[:,1,:] t = np.linspace(0,n_frames*t_inc,n_frames) fig = plt.figure(figsize = (10,10),facecolor='w') ax = fig.add_subplot(111) export_path = 'export//2d_hairball//' if not os.path.exists(export_path): os.makedirs(export_path) segs = np.zeros((n_cells, n_frames, 2), float) segs[:, :, 0] = x segs[:, :, 1] = y ax.set_xlim(xmin, xmax) ax.set_ylim(ymin, ymax) ax.set_xlabel('X position ($\mu$m)') ax.set_ylabel('Y position ($\mu$m)') colors = [mcolors.to_rgba(c) for c in plt.rcParams['axes.prop_cycle'].by_key()['color']] line_segments = LineCollection(segs,colors=colors, cmap=plt.get_cmap('jet')) ax.add_collection(line_segments) ax.set_title('Cell migration trajectories') #plt.axis('equal') plt.savefig(export_path + '2d_hairball.png', format='png', dpi=600) #2D hair ball trajectory plot with color coed accroding to elapsed time (Imaris Like trajectory) t = np.linspace(0,n_frames*t_inc,n_frames) fig = plt.figure(figsize = (10,10),facecolor='w') ax = fig.add_subplot(111) export_path = 'export//2d_hairball//' if not os.path.exists(export_path): os.makedirs(export_path) for n in range(0,n_cells): x = zerod_t_array[n,0,:] y = zerod_t_array[n,1,:] # Remove the nans from the array x = x[~np.isnan(x)] y = y[~np.isnan(y)] # Set the segments in the correct format points = np.array([x, y]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1) # Axis limits and titles ax.set_xlim(xmin, xmax) ax.set_ylim(ymin, ymax) ax.set_xlabel('X position ($\mu$m)') ax.set_ylabel('Y position ($\mu$m)') # plt.axis('equal') # Set the colormap cmap=plt.get_cmap('jet') line_segments = LineCollection(segments,array=t, cmap=cmap) ax.add_collection(line_segments) axcb = fig.colorbar(line_segments) axcb.set_label('Time (minutes)') ax.set_title('Cell migration trajectories') #plt.axis('equal') plt.savefig(export_path + '2d_hairball_time_cmap.png', format='png', dpi=600) # 2D hairball with color of entire trjactory is mapped by color x = zerod_t_array[:,0,:] y = zerod_t_array[:,1,:] end_x_pos = np.empty([len(x[:,0]),1]) for i in range(0,len(end_x_pos)): # For each cell x_vals = np.copy(np.squeeze(x[i,:])) x_vals = x_vals[~np.isnan(x_vals)] if(len(x_vals) > 0): end_x_pos[i] = x_vals[-1] fig = plt.figure(figsize = (10,10),facecolor='w') ax = fig.add_subplot(111) export_path = 'export//2d_hairball//' if not os.path.exists(export_path): os.makedirs(export_path) segs = np.zeros((n_cells, n_frames, 2), float) segs[:, :, 0] = x segs[:, :, 1] = y #ax.set_xlim(np.nanmin(x), np.nanmax(x)) #ax.set_ylim(np.nanmin(y), np.nanmax(y)) ax.set_xlim(xmin, xmax) ax.set_ylim(ymin, ymax) ax.set_xlabel('X position ($\mu$m)') ax.set_ylabel('Y position ($\mu$m)') line_segments = LineCollection(segs,array=np.squeeze(end_x_pos), cmap=plt.get_cmap('jet')) ax.add_collection(line_segments) ax.set_title('Cell migration trajectories') axcb = fig.colorbar(line_segments, orientation="horizontal", pad=0.1) axcb.set_label('Final x position ($\mu$m)') plt.savefig(export_path + '2d_hairball_cmap_endPos.png', format='png', dpi=600) #2d hair ball plot with final position color coding by X direction (Ibidi like) x = zerod_t_array[:,0,:] y = zerod_t_array[:,1,:] x_1 = x[x[:,-1] < 0] y_1 = y[x[:,-1] < 0] x_2 = x[x[:,-1] > 0] y_2 = y[x[:,-1] > 0] t = np.linspace(0,(n_frames-1)*t_inc,n_frames) fig = plt.figure(figsize = (10,10),facecolor='w') ax = fig.add_subplot(111) export_path = 'export//2d_hairball//' if not os.path.exists(export_path): os.makedirs(export_path) segs_1 = np.zeros((len(x_1[:,0]), n_frames, 2), float) segs_1[:, :, 0] = x_1 segs_1[:, :, 1] = y_1 segs_2 = np.zeros((len(x_2[:,0]), n_frames, 2), float) segs_2[:, :, 0] = x_2 segs_2[:, :, 1] = y_2 #ax.set_xlim(np.nanmin(x)-5, np.nanmax(x)+5) #ax.set_ylim(np.nanmin(y)-5, np.nanmax(y)+5) ax.set_xlim(xmin, xmax) ax.set_ylim(ymin, ymax) ax.set_xlabel('X position ($\mu$m)') ax.set_ylabel('Y position ($\mu$m)') line_segments_1 = LineCollection(segs_1,colors='red') ax.add_collection(line_segments_1) line_segments_2 = LineCollection(segs_2,colors='black') ax.add_collection(line_segments_2) ax.set_title('Cell migration trajectories') ax.scatter(x_1[:,-1],y_1[:,-1],s=50, c='red') ax.scatter(x_2[:,-1],y_2[:,-1],s=50, c='black') plt.savefig(export_path + '2d_hairball_cmap_endPos_2color.png', format='png', dpi=600) #3D hairball plot with time as z axis frames = [] fig = plt.figure(figsize = (15,15)) ax = fig.add_subplot(111, projection='3d') export_path = 'export//3d_hairball//' if not os.path.exists(export_path): os.makedirs(export_path) for n in range(0,n_cells): x = zerod_t_array[n,0,:] y = zerod_t_array[n,1,:] t = np.linspace(0,n_frames*t_inc,n_frames) ax.plot(x, y, t)#, c=) ax.set_xlabel('X position ($\mu$m)') ax.set_ylabel('Y position ($\mu$m)') ax.set_zlabel('Time (minutes)') print("graphing 3D trajectory") for angle in range(0, 360): ax.view_init(30, angle) # Draw the figure fig.canvas.draw() # Convert to numpy array, and append to list #np_fig = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') np_fig = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) np_fig = np_fig.reshape(fig.canvas.get_width_height()[::-1] + (3,)) frames.append(np_fig) imageio.mimsave(export_path + '/3d_hairball_test.gif', frames) #plot violin plot for cell area perimeter, orientation, circularity, speed, directedness, turn angle, and direction autocorrelation print("plotting violin plots") export_path = 'export//Violin plots//' if not os.path.exists(export_path): os.makedirs(export_path) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) t=np.linspace(0,(n_frames-1)*t_inc, n_frames) ax.violinplot(np.squeeze(props_t_array[:,2,:]), positions=t, widths=10, showmeans=False, showextrema=True, showmedians=True) ax.set_title('cell area') ax.set_ylabel('cellarea($\mu m^2$)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'area_violin.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax.violinplot(np.squeeze(props_t_array[:,3,:]),positions=t, widths = 10, showmeans=True, showextrema=True, showmedians=False) ax.set_title('Cell perimeter') ax.set_ylabel('Cell Perimeter ($\mu$m)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'perimeter_violin.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax.violinplot(np.squeeze(props_t_array[:,4,:]),positions=t, widths = 10, showmeans=True, showextrema=True, showmedians=False) ax.set_title('Orientation angle') ax.set_ylabel('Angle (degrees)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'angle_violin.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax.violinplot(np.squeeze(props_t_array[:,5,:]),positions=t, widths = 10, showmeans=True, showextrema=True, showmedians=False) ax.set_title('Circularity') ax.set_ylabel('Circularity (a.u.)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'circularity_violin.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax.violinplot(np.squeeze(props_t_array[:,10,:]),positions=t, widths = 10, showmeans=True, showextrema=True, showmedians=False) ax.set_title('Speed') ax.set_ylabel('Speed ($\mu$m/h)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'speed_violin.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax.violinplot(np.squeeze(props_t_array[:,11,:]),positions=t, widths = 10, showmeans=True, showextrema=True, showmedians=False) ax.set_title('Directedness') ax.set_ylabel('Directedness(a.u.)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'directedness_violin.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax.violinplot(np.squeeze(props_t_array[:,12,:]),positions=t, widths = 10, showmeans=True, showextrema=True, showmedians=False) ax.set_title('Turn angle') ax.set_ylabel('Turn angle (degrees)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'turnangle_violin.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax.violinplot(np.squeeze(props_t_array[:,13,:]),positions=t, widths = 10, showmeans=True, showextrema=True, showmedians=False) ax.set_title('Direction autocorrelation') ax.set_ylabel('Direction autocorrelation(a.u.)') ax.set_xlabel('Time (minutes)') plt.savefig(export_path + 'direction_autocorrelation_violin.png', format='png', dpi=600) #box plots export_path = 'export//boxplots//' if not os.path.exists(export_path): os.makedirs(export_path) t = np.linspace(0,(n_frames-1)*t_inc,n_frames) print("plotting box plots") linewidth = 1 fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,2,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,2,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Cell area') ax.set_ylabel('Cell area ($\mu m^2$)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'cellarea_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,3,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,3,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Cell perimeter') ax.set_ylabel('Cell perimeter ($\mu m$)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'perimeter_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,4,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,4,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Orientation angle') ax.set_ylabel('Orientation angle (degrees)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'angle_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,8,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,8,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Orientation index') ax.set_ylabel('Orientation index') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'orientation_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,5,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,5,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Circularity') ax.set_ylabel('Circularity (a.u.)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'circularity_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,10,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,10,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Speed') ax.set_ylabel('Speed ($\mu$m/h)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'speed_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,11,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,11,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Directedness') ax.set_ylabel('Directedness(a.u.)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'directedness_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,12,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,12,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Turn angle') ax.set_ylabel('Turn angle (degrees)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'turnangle_boxplot.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.boxplot(data=np.squeeze(props_t_array[:,13,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) ax = sns.swarmplot(data=np.squeeze(props_t_array[:,13,:]), orient="v", linewidth=linewidth, ax=ax) ax.set_title('Direction autocorrelation') ax.set_ylabel('Direction autocorrelation(a.u.)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'direction_autocorrelation_boxplot.png', format='png', dpi=600) export_path = 'export//Timeseries plots//' if not os.path.exists(export_path): os.makedirs(export_path) t = np.linspace(0,(n_frames-1)*t_inc,n_frames) print("plotting time series plot") linewidth = 1 fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax=sns.tsplot(np.squeeze(props_t_array[:,2,:]),time=t, condition='Cell Area', value='Cell Area ($\mu m^2$)', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Cell area') ax.set_ylabel('Cell area ($\mu m^2$)') ax.set_xlabel('Time (Minutes)') plt.savefig(export_path + 'cellarea_timeseries.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax =sns.tsplot(np.squeeze(props_t_array[:,3,:]),time=t, condition='Cell Perimeter', value='Cell Perimeter ($\mu$m)', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Cell perimeter') ax.set_ylabel('Cell perimeter ($\mu m$)') ax.set_xlabel('Time (Minutes)') plt.savefig(export_path + 'perimeter_timeseries.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.tsplot(np.squeeze(props_t_array[:,4,:]),time=t, condition='Orientation angle', value='Orientation Angle (degrees)', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Orientation angle') ax.set_ylabel('Orientation angle (degrees)') ax.set_xlabel('Time (Minutes)') plt.savefig(export_path + 'angle_timeseries.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.tsplot(np.squeeze(props_t_array[:,8,:]),time=t, condition='Orientation', value='Orientation', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Orientation') ax.set_ylabel('Orientation') ax.set_xlabel('Time (Minutes)') plt.savefig(export_path + 'orientation_timeseries.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.tsplot(np.squeeze(props_t_array[:,5,:]),time=t, condition='Circularity', value='Circularity (a.u.)', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Circularity') ax.set_ylabel('Circularity (a.u.)') ax.set_xlabel('Time (Minutes)') plt.savefig(export_path + 'circularity_timeseries.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.tsplot(np.squeeze(props_t_array[:,10,1:-1]),time=t[1:-1], condition='Speed', value='Speed ($\mu$m)/h', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Speed') ax.set_ylabel('Speed ($\mu$m/h)') ax.set_xlabel('Time (Minutes))') plt.savefig(export_path + 'speed_timeseries.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) ax = sns.tsplot(np.squeeze(props_t_array[:,11,1:-1]),time=t[1:-1], condition='Directedness', value='Directedness (a.u.)', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Directedness') ax.set_ylabel('Directedness(a.u.)') ax.set_xlabel('Time (Minutes)') plt.savefig(export_path + 'directedness_timeseries.png', format='png', dpi=600) fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) #ax = sns.boxplot(data=np.squeeze(props_t_array[:,12,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) #ax = sns.swarmplot(data=np.squeeze(props_t_array[:,12,:]), orient="v", linewidth=linewidth, ax=ax) ax = sns.tsplot(np.squeeze(props_t_array[:,12,1:-1]),time=t[1:-1], condition='Turn angle', value='Turn angle (degree)', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Turn angle') ax.set_ylabel('Turn angle (degrees)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'turnangle_timeseries.png', format='png', dpi=600) #plt.show() fig = plt.figure(figsize=(18,10), facecolor='w') ax = fig.add_subplot(111) #ax = sns.boxplot(data=np.squeeze(props_t_array[:,13,:]),orient="v",linewidth=linewidth,fliersize=2, ax=ax) #ax = sns.swarmplot(data=np.squeeze(props_t_array[:,13,:]), orient="v", linewidth=linewidth, ax=ax) sns.tsplot(np.squeeze(props_t_array[:,13,1:-1]),time=t[1:-1], condition='Direction autocorrelation', value='Direction autocorrelation (a.u.)', err_style="ci_band", ci=[0,95], ax=ax) ax.set_title('Direction autocorrelation') ax.set_ylabel('Direction autocorrelation(a.u.)') ax.set_xlabel('Time (frame)') plt.savefig(export_path + 'direction_autocorrelation_timeseries.png', format='png', dpi=600) export_path = 'export//frequency_histogram_subplots//' if not os.path.exists(export_path): os.makedirs(export_path) print("plotting frequency histogram subplots") sns.set_style({'lines.linewidth': 8.0},{'axes.linewidth': 2.0}) frames = [] fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4, figsize=(20,5), facecolor='w') for t in range(0,n_frames): ax1.clear() ax2.clear() ax3.clear() ax4.clear() #speed ax1.hist(props_t_array[:,10,t], color='white',edgecolor='black', linewidth=4,range=(0, np.nanmax(props_t_array[:,10,:]))) ax1.set_xlabel('Speed ($\mu$m/h)', fontsize=20) ax1.set_ylabel('Frequency', fontsize=20) ax1.set_xlim(0, np.nanmax(props_t_array[:,10,:])) ax1.set_ylim(0, n_cells) #angle ax2.hist(props_t_array[:,4,t], color='white',edgecolor='black', linewidth=4,range=(0, 180)) #props_t_array[:,8,t]) ax2.set_xlabel('$\Phi$ (degrees)', fontsize=20) ax2.set_ylabel('Frequency', fontsize=20) ax2.set_xlim(0, 180) ax2.set_ylim(0, n_cells) #turn angle ax3.hist(props_t_array[:,12,t], color='white',edgecolor='black', linewidth=4,range=(np.nanmin(props_t_array[:,12,:]), np.nanmax(props_t_array[:,12,:]))) ax3.set_xlabel('α(degrees)', fontsize=20) ax3.set_ylabel('Frequency', fontsize=20) ax3.set_xlim(np.nanmin(props_t_array[:,12,:]), np.nanmax(props_t_array[:,12,:])) ax3.set_ylim(0, n_cells) # plot of 2 variables p1=sns.kdeplot(zerod_t_array[:,0,t], shade=False, color="b", ax=ax4) p1=sns.kdeplot(zerod_t_array[:,1,t], shade=False, color="r", ax=ax4) ax4.text(-150, 0.06, '$\Delta$X', fontsize=30, color='blue') ax4.text(-150, 0.05, '$\Delta$Y', fontsize=30, color='red') ax4.set_xlabel('Change in position ($\mu$m)', fontsize=20) ax4.set_xlim(np.nanmin(zerod_t_array[:,:,:])-5, np.nanmax(zerod_t_array[:,:,:])+5) ax4.set_ylim(0, 0.1) # Draw the figure fig.canvas.draw() # Convert to numpy array, and append to list #np_fig = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') np_fig = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) np_fig = np_fig.reshape(fig.canvas.get_width_height()[::-1] + (3,)) frames.append(np_fig) imageio.mimsave(export_path + '/frequency_histogram_subplots.gif', frames)

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null

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5

c5e9ce1c9d44b8b8c824cbf18c1610d9b643e32c

173

py

Python

django_bootstrap_wysiwyg/utils.py

Prithvi45/django-bootstrap-wysiwyg

7ec93c29221207d793070c2956814b36dcc175a5

[ "MIT" ]

9

2015-02-03T07:01:38.000Z

2017-10-18T09:08:18.000Z

django_bootstrap_wysiwyg/utils.py

Prithvi45/django-bootstrap-wysiwyg

7ec93c29221207d793070c2956814b36dcc175a5

[ "MIT" ]

4

2015-01-06T13:44:59.000Z

2020-06-04T19:24:46.000Z

django_bootstrap_wysiwyg/utils.py

laplacesdemon/django-bootstrap-wysiwyg

7ec93c29221207d793070c2956814b36dcc175a5

[ "MIT" ]

8

2015-01-06T13:45:21.000Z

2020-11-24T17:32:58.000Z

from django.conf import settings def setting(name, default=None): """returns the setting value or default if not exists""" return getattr(settings, name, default)

24.714286

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0

5

c5f36d201b018d8df09e797ed4f7b19e11ee49e5

224

py

Python

share/lib/python/neuron/rxd/geometry3d/__init__.py

tommorse/nrn

73236b12977118ae0a98d7dbbed60973994cdaee

[ "BSD-3-Clause" ]

1

2020-05-28T17:21:52.000Z

share/lib/python/neuron/rxd/geometry3d/__init__.py

tommorse/nrn

73236b12977118ae0a98d7dbbed60973994cdaee

[ "BSD-3-Clause" ]

2

2019-11-09T23:02:28.000Z

2019-11-18T00:17:10.000Z

share/lib/python/neuron/rxd/geometry3d/__init__.py

tommorse/nrn

73236b12977118ae0a98d7dbbed60973994cdaee

[ "BSD-3-Clause" ]

1

2018-12-18T13:52:16.000Z

from .surface import surface from .triangularMesh import TriangularMesh from .voxelize import voxelize #from .voxelize2 import voxelize2 from .scalarField import ScalarField from .FullJoinMorph import fullmorph as voxelize2

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a89b95e54048c41e56dc4855c2cecee7bed6ef41

3,990

py

Python

tests/api_resources/test_subscription_schedule.py

STejas6/stripe-python

428f82d41023b54bed618a4eb96f48eb87ea940f

[ "MIT" ]

29

2019-09-05T18:40:53.000Z

2022-03-10T22:00:57.000Z

tests/api_resources/test_subscription_schedule.py

STejas6/stripe-python

428f82d41023b54bed618a4eb96f48eb87ea940f

[ "MIT" ]

3

2020-08-25T17:23:05.000Z

2021-10-03T19:47:39.000Z

tests/api_resources/test_subscription_schedule.py

STejas6/stripe-python

428f82d41023b54bed618a4eb96f48eb87ea940f

[ "MIT" ]

15

2019-11-05T23:43:27.000Z

2022-03-02T12:48:53.000Z

from __future__ import absolute_import, division, print_function import stripe TEST_RESOURCE_ID = "sub_sched_123" TEST_REVISION_ID = "sub_sched_rev_123" class TestSubscriptionScheduleSchedule(object): def test_is_listable(self, request_mock): resources = stripe.SubscriptionSchedule.list() request_mock.assert_requested("get", "/v1/subscription_schedules") assert isinstance(resources.data, list) assert isinstance(resources.data[0], stripe.SubscriptionSchedule) def test_is_retrievable(self, request_mock): resource = stripe.SubscriptionSchedule.retrieve(TEST_RESOURCE_ID) request_mock.assert_requested( "get", "/v1/subscription_schedules/%s" % TEST_RESOURCE_ID ) assert isinstance(resource, stripe.SubscriptionSchedule) def test_is_creatable(self, request_mock): resource = stripe.SubscriptionSchedule.create(customer="cus_123") request_mock.assert_requested("post", "/v1/subscription_schedules") assert isinstance(resource, stripe.SubscriptionSchedule) def test_is_saveable(self, request_mock): resource = stripe.SubscriptionSchedule.retrieve(TEST_RESOURCE_ID) resource.metadata["key"] = "value" resource.save() request_mock.assert_requested( "post", "/v1/subscription_schedules/%s" % TEST_RESOURCE_ID ) def test_is_modifiable(self, request_mock): resource = stripe.SubscriptionSchedule.modify( TEST_RESOURCE_ID, metadata={"key": "value"} ) request_mock.assert_requested( "post", "/v1/subscription_schedules/%s" % TEST_RESOURCE_ID ) assert isinstance(resource, stripe.SubscriptionSchedule) def test_can_cancel(self, request_mock): resource = stripe.SubscriptionSchedule.retrieve(TEST_RESOURCE_ID) resource = resource.cancel() request_mock.assert_requested( "post", "/v1/subscription_schedules/%s/cancel" % TEST_RESOURCE_ID ) assert isinstance(resource, stripe.SubscriptionSchedule) def test_can_cancel_classmethod(self, request_mock): resource = stripe.SubscriptionSchedule.cancel(TEST_RESOURCE_ID) request_mock.assert_requested( "post", "/v1/subscription_schedules/%s/cancel" % TEST_RESOURCE_ID ) assert isinstance(resource, stripe.SubscriptionSchedule) def test_can_release(self, request_mock): resource = stripe.SubscriptionSchedule.retrieve(TEST_RESOURCE_ID) resource = resource.release() request_mock.assert_requested( "post", "/v1/subscription_schedules/%s/release" % TEST_RESOURCE_ID ) assert isinstance(resource, stripe.SubscriptionSchedule) def test_can_release_classmethod(self, request_mock): resource = stripe.SubscriptionSchedule.release(TEST_RESOURCE_ID) request_mock.assert_requested( "post", "/v1/subscription_schedules/%s/release" % TEST_RESOURCE_ID ) assert isinstance(resource, stripe.SubscriptionSchedule) class TestSubscriptionScheduleRevisions(object): def test_is_listable(self, request_mock): resources = stripe.SubscriptionSchedule.list_revisions( TEST_RESOURCE_ID ) request_mock.assert_requested( "get", "/v1/subscription_schedules/%s/revisions" % TEST_RESOURCE_ID ) assert isinstance(resources.data, list) assert isinstance( resources.data[0], stripe.SubscriptionScheduleRevision ) def test_is_retrievable(self, request_mock): resource = stripe.SubscriptionSchedule.retrieve_revision( TEST_RESOURCE_ID, TEST_REVISION_ID ) request_mock.assert_requested( "get", "/v1/subscription_schedules/%s/revisions/%s" % (TEST_RESOURCE_ID, TEST_REVISION_ID), ) assert isinstance(resource, stripe.SubscriptionScheduleRevision)

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1

0

null

0

5

a8ad475afe927fcb71002a4d2296fbcee84a839b

5,983

py

Python

test/test_route53_interface.py

fossabot/route53-ddns

364cc08fc3d4d2a5278c86463b88ce776c220ba8

[ "MIT" ]

null

test/test_route53_interface.py

fossabot/route53-ddns

364cc08fc3d4d2a5278c86463b88ce776c220ba8

[ "MIT" ]

null

test/test_route53_interface.py

fossabot/route53-ddns

364cc08fc3d4d2a5278c86463b88ce776c220ba8

[ "MIT" ]

null

from route53_ddns import route53_interface from unittest.mock import MagicMock, call, patch import pytest @patch("route53_ddns.route53_interface.route53") def test_get_hosted_zone_id_raises_keyerror_if_no_zone(route53_mock): route53_mock.list_hosted_zones.return_value = {"HostedZones": []} with pytest.raises(KeyError): route53_interface.get_hosted_zone_id("my.zone") @patch("route53_ddns.route53_interface.route53") def test_get_hosted_zone_id_value_error_if_not_found(route53_mock): route53_mock.list_hosted_zones.return_value = {"HostedZones": [{"Name": "another.zone"}]} with pytest.raises(ValueError): route53_interface.get_hosted_zone_id("my.zone") @patch("route53_ddns.route53_interface.route53") def test_get_hosted_zone_id_returns_right_Value(route53_mock): route53_mock.list_hosted_zones.return_value = {"HostedZones": [{"Name": "my.zone.", "Id": "expected_id"}]} zone_id = route53_interface.get_hosted_zone_id("my.zone") assert zone_id == "expected_id" @patch('route53_ddns.route53_interface.sleep') @patch("route53_ddns.route53_interface.route53") def test_wait_for_change_completion(route53_mock, sleep_mock): sleep_mock.return_value = None route53_mock.get_change.side_effect = [ {"ChangeInfo": {"Status": "PENDING"}}, {"ChangeInfo": {"Status": "PENDING"}}, {"ChangeInfo": {"Status": "INSYNC"}}, ] route53_interface.wait_for_change_completion(change_id="change_id") route53_mock.get_change.assert_has_calls([ call(Id="change_id"), call(Id="change_id"), call(Id="change_id") ]) @patch("route53_ddns.route53_interface.route53") def test_get_current_ip_not_found(route53_mock): route53_mock.list_resource_record_sets.return_value = { "ResourceRecordSets": [ { "Name": "record.other.zone.", "Type": "CNAME", "TTL": 3600 } ] } assert route53_interface.get_current_ip(zone_id="my.zone", record_name="record") == None @patch("route53_ddns.route53_interface.route53") def test_get_current_ip_found_wrong_type(route53_mock): route53_mock.list_resource_record_sets.return_value = { "ResourceRecordSets": [ { "Name": "record.my.zone.", "Type": "CNAME", "TTL": 3600, "ResourceRecords": [{"Value": "other.domain"}] } ] } with pytest.raises(ValueError): route53_interface.get_current_ip(zone_id="my.zone", record_name="record.my.zone") @patch("route53_ddns.route53_interface.route53") def test_get_current_ip_too_many_entries(route53_mock): route53_mock.list_resource_record_sets.return_value = { "ResourceRecordSets": [ { "Name": "record.my.zone.", "Type": "A", "TTL": 3600, "ResourceRecords": [{"Value": "10.0.0.1"}, {"Value": "10.0.0.2"}] } ] } with pytest.raises(ValueError): route53_interface.get_current_ip(zone_id="my.zone", record_name="record.my.zone") @patch("route53_ddns.route53_interface.route53") def test_get_current_ip_found_ok(route53_mock): route53_mock.list_resource_record_sets.return_value = { "ResourceRecordSets": [ { "Name": "record.my.zone.", "Type": "A", "TTL": 3600, "ResourceRecords": [{"Value": "10.0.0.1"}] } ] } assert route53_interface.get_current_ip(zone_id="my.zone", record_name="record.my.zone") == "10.0.0.1" @patch("route53_ddns.route53_interface.get_current_ip") @patch("route53_ddns.route53_interface.get_hosted_zone_id") @patch("route53_ddns.route53_interface.route53") def test_update_record_nothing_to_do(route53_mock, get_hosted_zone_id_mock, get_current_ip_mock): get_hosted_zone_id_mock.return_value = "zone_id" get_current_ip_mock.return_value = "10.0.0.1" route53_interface.update_record("my.zone", "record.my.zone", "10.0.0.1") route53_mock.change_resource_record_sets.assert_not_called() @patch("route53_ddns.route53_interface.get_current_ip") @patch("route53_ddns.route53_interface.get_hosted_zone_id") @patch("route53_ddns.route53_interface.route53") def test_update_record_dryrun(route53_mock, get_hosted_zone_id_mock, get_current_ip_mock): get_hosted_zone_id_mock.return_value = "zone_id" get_current_ip_mock.return_value = "10.0.0.2" route53_interface.update_record("my.zone", "record.my.zone", "10.0.0.1", dryrun=True) route53_mock.change_resource_record_sets.assert_not_called() @patch("route53_ddns.route53_interface.wait_for_change_completion") @patch("route53_ddns.route53_interface.get_current_ip") @patch("route53_ddns.route53_interface.get_hosted_zone_id") @patch("route53_ddns.route53_interface.route53") def test_update_record(route53_mock, get_hosted_zone_id_mock, get_current_ip_mock, wait_for_change_completion): get_hosted_zone_id_mock.return_value = "zone_id" get_current_ip_mock.return_value = "10.0.0.2" route53_mock.change_resource_record_sets.return_value = {"ChangeInfo": {"Id": "change-id"}} wait_for_change_completion.return_value = None route53_interface.update_record("my.zone", "record.my.zone", "10.0.0.1") route53_mock.change_resource_record_sets.assert_called_once_with( HostedZoneId="zone_id", ChangeBatch={ "Comment": f"Updating record to 10.0.0.1", "Changes": [ { "Action": "UPSERT", "ResourceRecordSet": { "Name": "record.my.zone", "Type": "A", "TTL": 60, "ResourceRecords": [{"Value": "10.0.0.1"}], }, } ], }, ) wait_for_change_completion.assert_called_once()

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0.052301

0.20107

5,983

168

112

35.613095

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0.107692

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null

0

1

0

1

0

null

0

5

a8b56a64f15368e4ce35094fa893ace696dac90d

193

py

Python

tests/time_config.py

Toloka/toloka-airflow

cd7cc8bb755453c6ae2ff7a87445bc27e965e7f1

[ "Apache-2.0" ]

5

2022-01-25T11:50:51.000Z

2022-02-03T10:06:27.000Z

tests/time_config.py

Toloka/toloka-airflow

cd7cc8bb755453c6ae2ff7a87445bc27e965e7f1

[ "Apache-2.0" ]

1

2022-01-25T12:29:31.000Z

2022-02-03T10:05:40.000Z

tests/time_config.py

Toloka/toloka-airflow

cd7cc8bb755453c6ae2ff7a87445bc27e965e7f1

[ "Apache-2.0" ]

null

from datetime import datetime, timedelta, timezone DATA_INTERVAL_START = datetime(2021, 9, 13, tzinfo=timezone(timedelta(hours=3))) DATA_INTERVAL_END = DATA_INTERVAL_START + timedelta(days=1)

38.6

80

0.80829

27

193

5.555556

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0.24

0.226667

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1

0

1

0

null

0

1

0

5

a8c0c69125f0d2db08af6df25e3a2693a5d1a985

51

py

Python

protonets/data/__init__.py

gabrielhuang/prototypical-networks

e363420b627c4a558eccde6b72e179b632d183c5

[ "MIT" ]

26

2019-02-21T17:01:19.000Z

2021-12-12T08:26:38.000Z

protonets/data/__init__.py

gabrielhuang/centroid-networks

e363420b627c4a558eccde6b72e179b632d183c5

[ "MIT" ]

null

protonets/data/__init__.py

gabrielhuang/centroid-networks

e363420b627c4a558eccde6b72e179b632d183c5

[ "MIT" ]

null

from . import omniglot, miniimagenet, omniglot_ccn

25.5

50

0.823529

6

51

6.833333

0.833333

0

0.117647

51

1

51

0.911111

0

1

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

76bbe259863b7c662cdd4fdb8d0a26d65afaafef

292

py

Python

binwalk/__init__.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

binwalk/__init__.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

binwalk/__init__.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

__all__ = ['scan', 'execute', 'Modules', 'ModuleException'] from binwalk.core.module import Modules, ModuleException # Convenience functions def scan(*args, **kwargs): return Modules(*args, **kwargs).execute() def execute(*args, **kwargs): return Modules(*args, **kwargs).execute()

29.2

59

0.705479

32

292

6.3125

0.5

0.19802

0.158416

0.227723

0.39604

0

0.123288

292

9

60

32.444444

0.789063

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0.333333

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false

0

0.166667

0.333333

0.833333

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null

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1

0

1

0

null

0

1

0

1

0

5

76bbe821286bbb6e65af0614717d8561ce4c07a1

73

py

Python

sample/django_sample/app/app/__init__.py

knroy/celery-rmq

63220db4bc82ae7767c18713bf3d19679d44aaf8

[ "MIT" ]

null

sample/django_sample/app/app/__init__.py

knroy/celery-rmq

63220db4bc82ae7767c18713bf3d19679d44aaf8

[ "MIT" ]

4

2021-03-30T13:14:35.000Z

2021-09-22T18:57:04.000Z

sample/django_sample/app/app/__init__.py

knroy/celery-rmq

63220db4bc82ae7767c18713bf3d19679d44aaf8

[ "MIT" ]

null

from .celery import app, app_provider __all__ = ['app', 'app_provider']

18.25

37

0.726027

10

73

4.7

0.6

0.255319

0.595745

0

0.136986

73

3

38

24.333333

0.746032

0

0.205479

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

76c8ce776b151bd002dfa301383757f026d469ee

53

py

Python

src/session/twitter/gui/configuration/__init__.py

Oire/TheQube

fcfd8a68b15948e0740642d635db24adef8cc314

[ "MIT" ]

21

2015-08-02T21:26:14.000Z

2019-12-27T09:57:44.000Z

src/session/twitter/gui/configuration/__init__.py

Oire/TheQube

fcfd8a68b15948e0740642d635db24adef8cc314

[ "MIT" ]

34

2015-01-12T00:38:14.000Z

2020-08-31T11:19:37.000Z

src/session/twitter/gui/configuration/__init__.py

Oire/TheQube

fcfd8a68b15948e0740642d635db24adef8cc314

[ "MIT" ]

15

2015-03-24T15:42:30.000Z

2020-09-24T20:26:42.000Z

from main import TwitterConfigDialog import panels

17.666667

37

0.849057

6

53

7.5

0.833333

0

0.150943

53

2

38

26.5

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4f5370f91f3a2cbea92a87a0fa913f73feda23f1

141

py

Python

addons/payment_sips/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

addons/payment_sips/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

addons/payment_sips/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

from . import models from . import controllers from odoo.addons.payment.models.payment_acquirer import create_missing_journal_for_acquirers

28.2

92

0.865248

19

141

6.157895

0.684211

0.17094

0

0.092199

141

4

93

35.25

0.914063

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4f87469359e14f5db5b194d722c1c3fafcd16e22

371

py

Python

lib/googlecloudsdk/third_party/apis/testing/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

null

lib/googlecloudsdk/third_party/apis/testing/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

null

lib/googlecloudsdk/third_party/apis/testing/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

2

2020-11-04T03:08:21.000Z

2020-11-05T08:14:41.000Z

"""Common imports for generated testing client library.""" # pylint:disable=wildcard-import import pkgutil from googlecloudsdk.third_party.apitools.base.py import * from googlecloudsdk.third_party.apis.testing.v1.testing_v1_client import * from googlecloudsdk.third_party.apis.testing.v1.testing_v1_messages import * __path__ = pkgutil.extend_path(__path__, __name__)

33.727273

76

0.830189

49

371

5.877551

0.510204

0.125

0.239583

0.291667

0.388889

0

0.01173

0.080863

371

10

77

37.1

0.832845

0.226415

0

1

0

1

0

false

0

0.8

0

0.8

0

null

0

1

0

1

0

null

0

1

0

1

0

5

8c0c95920311c5496c10f8bf576551b17c23311c

209

py

Python

operations/filters/face_detection.py

zylamarek/dataset-tools

d0f446a6da20b7394bab86bf2253de866dbfc7be

[ "MIT" ]

null

operations/filters/face_detection.py

zylamarek/dataset-tools

d0f446a6da20b7394bab86bf2253de866dbfc7be

[ "MIT" ]

6

2021-03-19T01:18:16.000Z

2022-03-11T23:49:18.000Z

operations/filters/face_detection.py

zylamarek/dataset-tools

d0f446a6da20b7394bab86bf2253de866dbfc7be

[ "MIT" ]

null

import face_recognition from .filter import Filter class FaceDetection(Filter): def apply_single(self, img): boxes = face_recognition.face_locations(img, model='hog') return bool(boxes)

20.9

65

0.722488

26

209

5.653846

0.692308

0.204082

0

0.191388

209

9

66

23.222222

0.869822

0

0.014354

0

1

0.166667

false

0

0.333333

0

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

8c2966d423f802dda3f0bd063fdc1707816a7577

370

py

Python

app/utility/schemas.py

syth0le/tg_reminder_bot

956f552c2c81732aaa41c1f006e31f4167e7cdff

[ "MIT" ]

null

app/utility/schemas.py

syth0le/tg_reminder_bot

956f552c2c81732aaa41c1f006e31f4167e7cdff

[ "MIT" ]

null

app/utility/schemas.py

syth0le/tg_reminder_bot

956f552c2c81732aaa41c1f006e31f4167e7cdff

[ "MIT" ]

null

from typing import NamedTuple class TemporaryReminder(NamedTuple): id: int title: str type: str is_done: bool date: str class PermanentReminder(NamedTuple): id: int title: str type: str is_done: bool frequency: int #hours date: str class Bookmark(NamedTuple): id: int title: str type: str is_done: bool

14.230769

36

0.640541

47

370

4.978723

0.404255

0.153846

0.192308

0.25641

0.512821

0

0.291892

370

25

37

14.8

0.89313

0.013514

0

0.736842

0

1

0

true

0

0.052632

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

8c3182dadc68a822d438fc04808aa6ddc6498ecf

146

py

Python

copywriting/signals.py

uncommitted-and-forgotten/django-copywriting

4a9fab437d255c71920420f6b478d77b5d5bbbfb

[ "MIT" ]

2

2015-03-10T15:45:08.000Z

2015-10-20T05:00:40.000Z

copywriting/signals.py

uncommitted-and-forgotten/django-copywriting

4a9fab437d255c71920420f6b478d77b5d5bbbfb

[ "MIT" ]

4

2015-06-10T08:23:55.000Z

2016-01-25T13:18:19.000Z

copywriting/signals.py

uncommitted-and-forgotten/django-copywriting

4a9fab437d255c71920420f6b478d77b5d5bbbfb

[ "MIT" ]

1

2021-08-28T15:16:45.000Z

from django.dispatch import Signal ready_to_review = Signal(providing_args=["articleID"]) ready_to_publish = Signal(providing_args=["articleID"])

36.5

55

0.815068

19

146

5.947368

0.631579

0.123894

0.336283

0.495575

0

0.068493

146

4

55

36.5

0.830882

0

0.122449

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

5

4fd80fa4ddbc1ae8806802522dce2b88389a66d9

141

py

Python

rotypes/__init__.py

Sait0Yuuki/ArknightsAutoHelper

5ecec0d120482c930181346cfdb8542090e169c1

[ "MIT" ]

1,035

2019-05-14T11:58:32.000Z

2022-03-16T15:09:53.000Z

rotypes/__init__.py

Sait0Yuuki/ArknightsAutoHelper

5ecec0d120482c930181346cfdb8542090e169c1

[ "MIT" ]

209

2019-05-11T13:19:57.000Z

2022-03-12T01:42:11.000Z

rotypes/__init__.py

Sait0Yuuki/ArknightsAutoHelper

5ecec0d120482c930181346cfdb8542090e169c1

[ "MIT" ]

254

2019-05-13T09:06:54.000Z

2022-03-16T09:47:44.000Z

from .types import HRESULT, GUID, REFGUID from . import roapi from .winstring import HSTRING from .inspectable import IUnknown, IInspectable

28.2

47

0.815603

18

141

6.388889

0.666667

0

0.134752

141

4

48

35.25

0.942623

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4fdc48164ee59b8899d54e3e68f83bca823c5e56

20,774

py

Python

pyunfurl/provider_data/noembed.py

tdarwin/pyunfurl

c49833977a1df6d39e6ae502c4f0c4ae38ed1193

[ "MIT" ]

5

2019-11-01T01:40:36.000Z

2022-01-11T10:37:23.000Z

pyunfurl/provider_data/noembed.py

tdarwin/pyunfurl

c49833977a1df6d39e6ae502c4f0c4ae38ed1193

[ "MIT" ]

3

2020-11-17T08:30:29.000Z

2022-02-16T15:00:46.000Z

pyunfurl/provider_data/noembed.py

tdarwin/pyunfurl

c49833977a1df6d39e6ae502c4f0c4ae38ed1193

[ "MIT" ]

5

2020-10-01T10:05:23.000Z

2022-02-14T14:41:30.000Z

NOEMBED_PROVIDER_LIST = [ [ "https?://(?:[^\\.]+\\.)?(?:youtu\\.be|youtube\\.com/embed)/([a-zA-Z0-9_-]+)", "http://noembed.com/embed", ], [ "https?://(?:[^\\.]+\\.)?youtube\\.com/watch/?\\?(?:.+&)?v=([^&]+)", "http://noembed.com/embed", ], [ "https?://www\\.globalgiving\\.org/((micro)?projects|funds)/.*", "http://noembed.com/embed", ], [ "https?://www\\.giantbomb\\.com/videos/[^/]+/\\d+-\\d+/?", "http://noembed.com/embed", ], ["http://bash\\.org/\\?(\\d+)", "http://noembed.com/embed"], ["http://amzn\\.com/([^/]+)", "http://noembed.com/embed"], [ "http://www\\.amazon\\.com/(?:.+/)?[gd]p/(?:product/)?(?:tags-on-product/)?([a-zA-Z0-9]+)", "http://noembed.com/embed", ], ["https://tube.switch.ch/videos/([a-z0-9]+)", "http://noembed.com/embed"], ["https?://db\\.tt/[a-zA-Z0-9]+", "http://noembed.com/embed"], [ "https?://www\\.(dropbox\\.com/s/.+\\.(?:jpg|png|gif))", "http://noembed.com/embed", ], ["https?://imgur\\.com/(?:[^\\/]+/)?[0-9a-zA-Z]+$", "http://noembed.com/embed"], ["https?://muki\\.io/(embed/)?(.+)", "http://noembed.com/embed"], ["https?://vine.co/v/[a-zA-Z0-9]+", "http://noembed.com/embed"], [ "https?://reports\\.zoho\\.com/ZDBDataSheetView\\.cc\\?OBJID=1432535000000003002&STANDALONE=true&INTERVAL=120&DATATYPESYMBOL=false&REMTOOLBAR=false&SEARCHBOX=true&INCLUDETITLE=true&INCLUDEDESC=true&SHOWHIDEOPT=true", "http://noembed.com/embed", ], ["https?://yfrog\\.us/.*", "http://noembed.com/embed"], ["https?://.*\\.yfrog\\.com/.*", "http://noembed.com/embed"], ["https?://.*\\.wizer\\.me/preview/.*", "http://noembed.com/embed"], ["https?://.*\\.wizer\\.me/learn/.*", "http://noembed.com/embed"], ["https?://.*\\.wiredrive\\.com/.*", "http://noembed.com/embed"], ["https?://vlipsy\\.com/.*", "http://noembed.com/embed"], ["https?://player\\.vimeo\\.com/video/.*", "http://noembed.com/embed"], ["https?://vimeo\\.com/ondemand/.*/.*", "http://noembed.com/embed"], ["https?://vimeo\\.com/groups/.*/videos/.*", "http://noembed.com/embed"], ["https?://vimeo\\.com/channels/.*/.*", "http://noembed.com/embed"], ["https?://vimeo\\.com/album/.*/video/.*", "http://noembed.com/embed"], ["https?://vimeo\\.com/.*", "http://noembed.com/embed"], ["https?://vidl\\.it/.*", "http://noembed.com/embed"], ["https?://www\\.videojug\\.com/interview/.*", "http://noembed.com/embed"], ["https?://www\\.videojug\\.com/film/.*", "http://noembed.com/embed"], ["https?://www\\.vevo\\.com/.*", "http://noembed.com/embed"], ["https?://veervr\\.tv/videos/.*", "http://noembed.com/embed"], ["https?://veer\\.tv/videos/.*", "http://noembed.com/embed"], ["https?://uttles\\.com/uttle/.*", "http://noembed.com/embed"], ["https?://utposts\\.com/products/.*", "http://noembed.com/embed"], ["https?://www\\.utposts\\.com/products/.*", "http://noembed.com/embed"], ["https?://.*\\.ustream\\.com/.*", "http://noembed.com/embed"], ["https?://.*\\.ustream\\.tv/.*", "http://noembed.com/embed"], ["https?://.*\\.uol\\.com\\.br/video/.*", "http://noembed.com/embed"], ["https?://.*\\.uol\\.com\\.br/view/.*", "http://noembed.com/embed"], ["https?://player\\.ubideo\\.com/.*", "http://noembed.com/embed"], ["https?://twitter\\.com/.*/status/.*", "http://noembed.com/embed"], ["https?://twitch\\.tv/.*", "http://noembed.com/embed"], ["https?://www\\.twitch\\.tv/.*", "http://noembed.com/embed"], ["https?://clips\\.twitch\\.tv/.*", "http://noembed.com/embed"], ["https?://www\\.topy\\.se/image/.*", "http://noembed.com/embed"], ["https?://www\\.tickcounter\\.com/worldclock/.*", "http://noembed.com/embed"], ["https?://www\\.tickcounter\\.com/ticker/.*", "http://noembed.com/embed"], ["https?://www\\.tickcounter\\.com/countup/.*", "http://noembed.com/embed"], ["https?://www\\.tickcounter\\.com/countdown/.*", "http://noembed.com/embed"], ["https?://theysaidso\\.com/image/.*", "http://noembed.com/embed"], ["https?://.*\\.nytimes\\.com/.*", "http://noembed.com/embed"], ["https?://nytimes\\.com/.*", "http://noembed.com/embed"], 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138

py

Python

telisar/npc/hightiefling.py

evilchili/telisar

4152de28ed03afecb579c6065414439146b8b169

[ "Unlicense" ]

1

2018-06-29T14:46:18.000Z

telisar/npc/hightiefling.py

evilchili/telisar

4152de28ed03afecb579c6065414439146b8b169

[ "Unlicense" ]

null

telisar/npc/hightiefling.py

evilchili/telisar

4152de28ed03afecb579c6065414439146b8b169

[ "Unlicense" ]

1

2018-06-29T14:47:07.000Z

from telisar.languages import infernal from telisar.npc import tiefling class NPC(tiefling.NPC): language = infernal.HighTiefling()

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null

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1

0

1

0

5

8b2f46ae7f36031e570a03cdbff059fa47bdd4e6

159

py

Python

NowCoder/test.py

windcry1/My-ACM-ICPC

b85b1c83b72c6b51731dae946a0df57c31d3e7a1

[ "MIT" ]

null

NowCoder/test.py

windcry1/My-ACM-ICPC

b85b1c83b72c6b51731dae946a0df57c31d3e7a1

[ "MIT" ]

null

NowCoder/test.py

windcry1/My-ACM-ICPC

b85b1c83b72c6b51731dae946a0df57c31d3e7a1

[ "MIT" ]

null

# >>> Author: WindCry1 # >>> Mail: lanceyu120@gmail.com # >>> Website: https://windcry1.com # >>> Date: 1/20/2020 8:18 PM from math import * from sys import *

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1

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5

8c7210c558be27ed471913cf0fc393f6fe20a814

77

py

Python

cowait/notebook/__init__.py

ProgHaj/cowait

e95c30faab8caf8b0413de4e1784529a3a06475d

[ "Apache-2.0" ]

2

2021-08-11T08:51:42.000Z

2021-08-11T08:55:19.000Z

cowait/notebook/__init__.py

ProgHaj/cowait

e95c30faab8caf8b0413de4e1784529a3a06475d

[ "Apache-2.0" ]

null

cowait/notebook/__init__.py

ProgHaj/cowait

e95c30faab8caf8b0413de4e1784529a3a06475d

[ "Apache-2.0" ]

null

# flake8: noqa: F401 from .task import NotebookTask from .spawn import task

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8c7695dac616728b5d996c32cfc219c4bfe4d1a8

309

py

Python

parlai/agents/sandy/sandy.py

vitouphy/ParlAI

49258b966b4567902f10ad806a22db19da87b989

[ "MIT" ]

null

parlai/agents/sandy/sandy.py

vitouphy/ParlAI

49258b966b4567902f10ad806a22db19da87b989

[ "MIT" ]

null

parlai/agents/sandy/sandy.py

vitouphy/ParlAI

49258b966b4567902f10ad806a22db19da87b989

[ "MIT" ]

null

from parlai.core.torch_agent import TorchAgent, Output class SandyAgent(TorchAgent): def train_step(self, batch): pass def eval_step(self, batch): # for each row in batch, convert tensor to back to text strings return Output([self.dict.vec2txt(row) for row in batch.text_vec])

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1

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1

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5

8caddab25081b472292d0007756ef92dbb4c8217

170

py

Python

exercicios_basico/ex047.py

montalvas/python

483c2097f6f91bfae127dafcb63e3006eeecad1d

[ "MIT" ]

null

exercicios_basico/ex047.py

montalvas/python

483c2097f6f91bfae127dafcb63e3006eeecad1d

[ "MIT" ]

null

exercicios_basico/ex047.py

montalvas/python

483c2097f6f91bfae127dafcb63e3006eeecad1d

[ "MIT" ]

null

#Todos os números pares entre 1 e 50 print('TODOS OS PARES ENTRE 1 E 50:') for c in range(1, 51): if c % 2 == 0: print('\033[34m{}\033[m'.format(c), end=' ')

28.333333

52

0.576471

34

170

2.882353

0.647059

0.142857

0.22449

0.244898

0.285714

0

0.147287

0.241176

170

6

52

28.333333

0.612403

0.205882

0

0.333333

0

0.166667

0

1

0

false

0

0.5

0

null

0

1

0

1

0

null

0

1

0

1

0

5

50784d54fd3949d0e7849502072e2633d4e53c9b

150

py

Python

fastapi_mailman/globals.py

daniel-herrero/fastapi-mailman

a174d0ec777d3330dc5464f71fafa7829db07bf1

[ "MIT" ]

6

2021-10-08T10:20:37.000Z

2022-03-30T08:56:10.000Z

fastapi_mailman/globals.py

daniel-herrero/fastapi-mailman

a174d0ec777d3330dc5464f71fafa7829db07bf1

[ "MIT" ]

2

2021-11-11T11:44:29.000Z

2022-03-08T06:54:54.000Z

fastapi_mailman/globals.py

daniel-herrero/fastapi-mailman

a174d0ec777d3330dc5464f71fafa7829db07bf1

[ "MIT" ]

1

2022-03-04T14:43:22.000Z

import typing as t if t.TYPE_CHECKING: from . import Mail Mailman = t.TypeVar("Mailman", bound=Mail) MAILMAN: t.Optional["Mailman"] = None

16.666667

46

0.686667

22

150

4.636364

0.636364

0.215686

0.235294

0

0.193333

150

8

47

18.75

0.842975

0

0.093333

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

5

50e1090fad0bbbe503dbbb07eedeccdf9de66b66

164

py

Python

queen.py

ChreSyr/iratus

bf4cfa514f5f8c59781af0c9c69bf65dea3bb873

[ "MIT" ]

null

queen.py

ChreSyr/iratus

bf4cfa514f5f8c59781af0c9c69bf65dea3bb873

[ "MIT" ]

null

queen.py

ChreSyr/iratus

bf4cfa514f5f8c59781af0c9c69bf65dea3bb873

[ "MIT" ]

null

from piece import RollingPiece class Queen(RollingPiece): LETTER = "q" moves = ((-1, -1), (-1, 1), (1, 1), (1, -1), (1, 0), (0, 1), (-1, 0), (0, -1))

14.909091

82

0.469512

26

164

2.961538

0.423077

0.233766

0.272727

0.311688

0.207792

0.116883

0

0.130081

0.25

164

10

83

16.4

0.495935

0

0.006211

0

1

0

false

0

0.25

0

1

0

1

0

null

1

0

1

0

null

0

5

50f60fd5d4eeb6c0ee5edd93f247a67dcb7b7a3a

48

py

Python

neo/Network/core/exceptions.py

volekerb/neo-python

5bdded2c339219355cf1d31ae58653b0f94c6e51

[ "MIT" ]

387

2017-07-17T18:25:54.000Z

2021-11-18T06:19:47.000Z

neo/Network/core/exceptions.py

volekerb/neo-python

5bdded2c339219355cf1d31ae58653b0f94c6e51

[ "MIT" ]

967

2017-08-19T15:48:03.000Z

2021-06-01T21:42:39.000Z

neo/Network/core/exceptions.py

volekerb/neo-python

5bdded2c339219355cf1d31ae58653b0f94c6e51

[ "MIT" ]

286

2017-07-17T03:44:36.000Z

2021-11-18T06:19:32.000Z

class DeserializationError(Exception): pass

16

38

0.791667

4

48

9.5

1

0

0.145833

48

2

39

24

0.926829

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

50fee9464d7606cd3192a09c000952bf246e0ef9

142

py

Python

src/django_mysql/exceptions.py

Juh10/django-mysql

d997be1321086e2b2c46574bc7882a2737a5c43c

[ "MIT" ]

null

src/django_mysql/exceptions.py

Juh10/django-mysql

d997be1321086e2b2c46574bc7882a2737a5c43c

[ "MIT" ]

null

src/django_mysql/exceptions.py

Juh10/django-mysql

d997be1321086e2b2c46574bc7882a2737a5c43c

[ "MIT" ]

null

from __future__ import annotations class TimeoutError(Exception): """ Indicates a database operation timed out in some way. """

17.75

57

0.711268

16

142

6.0625

1

0

0.21831

142

7

58

20.285714

0.873874

0.373239

0

1

0

true

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0fd2fdf8fc416d6086f4b68873ad97069e1c0da0

228

py

Python

src/fecc_object/SemicolonObject.py

castor91/fecc

bc46059c0d7a428d15b95050b70dec374b4bea28

[ "MIT" ]

1

2018-02-04T14:48:15.000Z

src/fecc_object/SemicolonObject.py

castor91/fecc

bc46059c0d7a428d15b95050b70dec374b4bea28

[ "MIT" ]

null

src/fecc_object/SemicolonObject.py

castor91/fecc

bc46059c0d7a428d15b95050b70dec374b4bea28

[ "MIT" ]

null

from AbstractObject import * class SemicolonObject(AbstractObject): def __init__(self, value): super(SemicolonObject, self).__init__(value) def generate(self, out_code): pass def __str__(self): return ''

20.727273

52

0.710526

25

228

5.96

0.64

0

0.188596

228

10

53

22.8

0.805405

0

1

0

1

0.5

false

0.166667

0.833333

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0fe42d1cbe70bbf9fb217edb565437b4f5814242

46

py

Python

tf/about/usefunc.py

ancient-data/text-fabric

c1ccd4a4dc451e94a789f138576576c5d7f13474

[ "MIT" ]

10

2017-10-30T22:38:00.000Z

2018-12-12T06:10:10.000Z

tf/about/usefunc.py

dirkroorda/text-fabric

c0a49f092ceda3e7bab91fd0f1aa84e2dc029cf4

[ "MIT" ]

37

2017-10-19T12:06:54.000Z

2018-12-13T10:18:23.000Z

tf/about/usefunc.py

dirkroorda/text-fabric

c0a49f092ceda3e7bab91fd0f1aa84e2dc029cf4

[ "MIT" ]

3

2018-02-28T12:37:21.000Z

2018-06-23T08:32:54.000Z

""" .. include:: ../docs/about/usefunc.md """

11.5

37

0.543478

5

46

5

1

0

0.108696

46

3

38

15.333333

0.609756

0.804348

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

ba08cc69799e83df0030dd0b6c586f291e109e64

124

py

Python

sugar_mole/api/apis/my_fox.py

Alexis-Jacob/sugar-mole

390b977aa1440a4551cf445cd0f62a6201467f81

[ "BSD-3-Clause" ]

null

sugar_mole/api/apis/my_fox.py

Alexis-Jacob/sugar-mole

390b977aa1440a4551cf445cd0f62a6201467f81

[ "BSD-3-Clause" ]

null

sugar_mole/api/apis/my_fox.py

Alexis-Jacob/sugar-mole

390b977aa1440a4551cf445cd0f62a6201467f81

[ "BSD-3-Clause" ]

null

from IAPI import IAPI class NetAtmo(IAPI): def __init__(self): self.name = "my fox" def name(self): return self.name

15.5

22

0.701613

20

124

4.15

0.6

0.192771

0

0.185484

124

8

23

15.5

0.821782

0

0.048

0

1

0.333333

false

0

0.166667

0.833333

0

1

0

null

0

1

0

null

0

1

0

1

0

5

ba189b10697b8e16a7afc4c843553a8a511418a5

107

py

Python

reddit_data_import_run.py

PervasiveWellbeingTech/inquire-web-backend

0a078943701472897c288ca1f2683ed749685e92

[ "Apache-2.0" ]

1

2020-10-07T09:35:47.000Z

reddit_data_import_run.py

PervasiveWellbeingTech/inquire-web-backend

0a078943701472897c288ca1f2683ed749685e92

[ "Apache-2.0" ]

1

2021-06-02T03:08:57.000Z

reddit_data_import_run.py

PervasiveWellbeingTech/inquire-web-backend

0a078943701472897c288ca1f2683ed749685e92

[ "Apache-2.0" ]

null

from scripts.load_reddit_may15_sql import run_full_import if __name__ == "__main__": run_full_import()

26.75

57

0.803738

16

107

4.4375

0.75

0.197183

0.366197

0

0.021277

0.121495

107

4

58

26.75

0.734043

0

0.074074

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

e85d818ec4ca24f5de147ad9d7dd17dad15329f8

355

py

Python

src/utils.py

bayuyuhartono-katadata/flask-project

000bafa4f593474eec2171b1b1ce392d64c5c47d

[ "MIT" ]

1

2019-10-28T06:53:36.000Z

src/utils.py

bayuyuhartono-katadata/flask-project

000bafa4f593474eec2171b1b1ce392d64c5c47d

[ "MIT" ]

1

2019-12-26T22:21:29.000Z

2019-12-29T12:47:33.000Z

src/utils.py

bayuyuhartono-katadata/flask-project

000bafa4f593474eec2171b1b1ce392d64c5c47d

[ "MIT" ]

1

2019-11-08T02:03:13.000Z

import os, base64 from werkzeug.routing import BaseConverter #print(base64.b64encode(os.urandom(64)).decode('utf-8')) def generateSecKey(): return base64.b64encode(os.urandom(64)).decode('utf-8') class RegexConverter(BaseConverter): def __init__(self, url, *regx): super(RegexConverter, self).__init__(url) self.regex = regx[0]

27.307692

59

0.715493

45

355

5.466667

0.577778

0.121951

0.138211

0.195122

0.292683

0

0.055738

0.140845

355

12

60

29.583333

0.75082

0.15493

0

1

0

0.016722

0

1

0.25

false

0

0.25

0.125

0.75

0

null

0

1

0

1

0

null

0

1

0

1

0

5

e87587486b66e78f3a8b0824b3ea7e13b73ce6a6

220

py

Python

lfs_compropago/admin.py

misaelnieto/lfs-compropago

6622d5021fb3a9a382d36e1e4e98116a69fbf45a

[ "MIT" ]

null

lfs_compropago/admin.py

misaelnieto/lfs-compropago

6622d5021fb3a9a382d36e1e4e98116a69fbf45a

[ "MIT" ]

null

lfs_compropago/admin.py

misaelnieto/lfs-compropago

6622d5021fb3a9a382d36e1e4e98116a69fbf45a

[ "MIT" ]

1

2016-02-08T17:36:41.000Z

# django imports from django.contrib import admin from .models import CompropagoTransaction from .models import CompropagoWebHookHit admin.site.register(CompropagoTransaction) admin.site.register(CompropagoWebHookHit)

24.444444

42

0.859091

23

220

8.217391

0.478261

0.10582

0.169312

0

0.086364

220

8

43

27.5

0.940299

0.063636

0

1

0

true

0

0.6

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e8788c9fc7223a38447cc88811a67ef9cd5dd611

218

py

Python

World/Object/Unit/Pet/model.py

sundayz/idewave-core

5bdb88892173c9c3e8c85f431cf9b5dbd9f23941

[ "Apache-2.0" ]

10

2019-06-29T19:24:52.000Z

2021-02-21T22:45:57.000Z

World/Object/Unit/Pet/model.py

sundayz/idewave-core

5bdb88892173c9c3e8c85f431cf9b5dbd9f23941

[ "Apache-2.0" ]

4

2019-08-15T07:03:36.000Z

2021-06-02T13:01:25.000Z

World/Object/Unit/Pet/model.py

sundayz/idewave-core

5bdb88892173c9c3e8c85f431cf9b5dbd9f23941

[ "Apache-2.0" ]

8

2019-06-30T22:47:48.000Z

2021-02-20T19:21:30.000Z

from World.Object.Unit.model import Unit from World.Object.Constants.HighGuid import HighGuid class Pet(Unit): def __init__(self): super().__init__() self.high_guid = HighGuid.HIGHGUID_PET.value

21.8

52

0.724771

29

218

5.103448

0.586207

0.121622

0.202703

0

0.178899

218

9

53

24.222222

0.826816

0

1

0.166667

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

e88127452f8513b689893088aac6b48c4dbe3287

271

py

Python

Online-Judges/CodingBat/Python/Logic-01/Logic_1-04-caught_speeding.py

shihab4t/Competitive-Programming

e8eec7d4f7d86bfa1c00b7fbbedfd6a1518f19be

[ "Unlicense" ]

3

2021-06-15T01:19:23.000Z

2022-03-16T18:23:53.000Z

Online-Judges/CodingBat/Python/Logic-01/Logic_1-04-caught_speeding.py

shihab4t/Competitive-Programming

e8eec7d4f7d86bfa1c00b7fbbedfd6a1518f19be

[ "Unlicense" ]

null

Online-Judges/CodingBat/Python/Logic-01/Logic_1-04-caught_speeding.py

shihab4t/Competitive-Programming

e8eec7d4f7d86bfa1c00b7fbbedfd6a1518f19be

[ "Unlicense" ]

null

def caught_speeding(speed, is_birthday): if speed <= 60 or is_birthday is True and speed <= 65: return 0 elif speed <= 80 or is_birthday is True and speed <= 85: return 1 elif speed > 80 or is_birthday is True and speed > 85: return 2

33.875

60

0.638376

45

271

3.733333

0.422222

0.238095

0.214286

0.25

0.690476

0.535714

0

0.078947

0.298893

271

7

61

38.714286

0.805263

0

1

0.142857

false

0

0.571429

0

null

1

0

1

0

null

0

1

0

5

e8829e8c852d3d7eb366143651c63280faa26f56

8,790

py

Python

Env/ellipsoid_gravity_utils.py

Aerospace-AI/Hovering-with-Altimetry

be19faa97f14b3ada53217f8941330750d0964c8

[ "MIT" ]

1

2021-06-17T11:02:46.000Z

Env/ellipsoid_gravity_utils.py

Aerospace-AI/Hovering-with-Altimetry

be19faa97f14b3ada53217f8941330750d0964c8

[ "MIT" ]

null

Env/ellipsoid_gravity_utils.py

Aerospace-AI/Hovering-with-Altimetry

be19faa97f14b3ada53217f8941330750d0964c8

[ "MIT" ]

null

import numpy as np def ellipsoid_gravity_field(x, a, b , c, rho): G = 6.6695e-11 # Gravitational constant [kg m**3/s**2] aa = a**2 bb = b**2 cc = c**2 errtol = 1e-10 # find the greatest root LAM B = -(x[0]**2 + x[1]**2 + x[2]**2 - aa - bb - cc) C = -cc*(x[0]**2 + x[1]**2) + bb*(cc - x[0]**2 - x[2]**2) + aa*(bb +cc - x[1]**2 - x[2]**2) D = -bb*cc*x[0]**2 + aa*(-cc*x[1]**2 + bb*(c - x[2])*(c + x[2])) poly = np.asarray([1, B, C, D]) LAM = np.max(np.roots(poly)) # find elliptic integrals F1 and E1 phi = np.arcsin(np.sqrt((aa-cc)/(LAM + aa)))# Argument phi, s.t. 0 < phi <= np.pi/2 k = np.sqrt((aa-bb)/(aa-cc)) # Modulus k, s.t. 0 < k < 1 F1 = lellipf(phi, k, errtol) E1 = lellipe(phi, k, errtol) fac1 = 4*np.pi*G*rho*a*b*c/np.sqrt(aa-cc) fac2 = np.sqrt((aa-cc)/((aa+LAM)*(bb+LAM)*(cc+LAM))) # Components of attraction. X = fac1/(aa-bb)*(E1-F1) Y = fac1*((-aa+cc)*E1/((aa-bb)*(bb-cc)) + F1/(aa-bb)+ (cc+LAM)*fac2/(bb-cc)) Z = fac1*(-E1 + (bb+LAM)*fac2)/(cc-bb) return np.asarray([X,Y,Z]), LAM # # lellipe(phi, k, errtol) # # Inputs: # # phi Input angle vector size 1xN. # k Input parameter vector size 1 or 1xN. # errtol Error tolerance for Carlson's algorithms. # # Matlab function to compute Legendre's (incomplete) elliptic integral # E(phi, k). Uses a vectorized implementation of Carlson's Duplication Algorithms # for symmetric elliptic integrals as found in "Computing Elliptic # Integrals by Duplication," by B. C. Carlson, Numer. Math. 33, 1-16 (1979) # and also found in ACM TOMS Algorithm 577. Section 4 in the paper cited # here describes how to convert between the symmetric elliptic integrals # and Legendre's elliptic integrals. # # Returns NaN's for any argument values outside input range. # def lellipe(phi, k, errtol): # Argument checking for vectorization: phivec = phi kvec = k snphi = np.sin(phivec) csphi = np.cos(phivec) snphi2 = snphi**2 csphi2 = csphi**2 k2 = kvec**2 y = 1.0 - k2*snphi2 onesvec = 1 f = snphi * rf(csphi2, y, onesvec, errtol) - k2 * snphi * snphi2 * rd(csphi2, y, onesvec, errtol)/3.0 return f # # lellipf(phi, k, errtol) # # Inputs: # # phi Input angle vector size 1 or 1xN. # k Input parameter vector size 1 or 1xN. # errtol Error tolerance for Carlson's algorithms. # # Matlab function to compute Legendre's (incomplete) elliptic integral # F(phi, k). Uses a vectorized implementation of Carlson's Duplication Algorithms # for symmetric elliptic integrals as found in "Computing Elliptic # Integrals by Duplication," by B. C. Carlson, Numer. Math. 33, 1-16 (1979) # and also found in ACM TOMS Algorithm 577. Section 4 in the paper cited # here describes how to convert between the symmetric elliptic integrals # and Legendre's elliptic integrals. # # Returns NaN's for any argument values outside input range. # def lellipf(phi, k, errtol): phivec = phi kvec = k snphi = np.sin(phivec) csphi = np.cos(phivec) csphi2 = csphi * csphi onesvec = 1 y = onesvec - kvec*kvec * snphi*snphi f = snphi * rf(csphi2, y, onesvec, errtol) return f # Elliptic Integrals by Duplication," by B. C. Carlson, Numer. Math. # 33, 1-16 (1979). # # Returns NaN's for any argument values outside input range. # # Algorithm is also from Carlson's ACM TOMS Algorithm 577. # # This code is a complete rewrite of the algorithm in vectorized form. # It was not produced by running a FORTRAN to Matlab converter. # # The following text is copied from ACM TOMS Algorithm 577 FORTRAN code: # # X AND Y ARE THE VARIABLES IN THE INTEGRAL RC(X,Y). # # ERRTOL IS SET TO THE DESIRED ERROR TOLERANCE. # RELATIVE ERROR DUE TO TRUNCATION IS LESS THAN # 16 * ERRTOL ** 6 / (1 - 2 * ERRTOL). # # SAMPLE CHOICES: ERRTOL RELATIVE TRUNCATION # ERROR LESS THAN # 1.D-3 3.D-19 # 3.D-3 2.D-16 # 1.D-2 3.D-13 # 3.D-2 2.D-10 # 1.D-1 3.D-7 # # Note by TRH: # # Absolute truncation error when the integrals are order 1 quantities # is closer to errtol, so be careful if you want high absolute precision. # # Thomas R. Hoffend Jr., Ph.D. # 3M Company # 3M Center Bldg. 236-GC-26 # St. Paul, MN 55144 # trhoffendjr@mmm.com # def rf(x, y, z, errtol): realmin = 1e-100 realmax = 1e100 # Argument limits as set by Carlson: LoLim = 5.0 * realmin UpLim = 5.0 * realmax # Define internally acceptable variable ranges for iterations: Xi = x Yi = y Zi = z # Carlson's duplication algorithm for Rf: Xn = Xi Yn = Yi Zn = Zi Mu = (Xn + Yn + Zn) / 3.0 Xndev = 2.0 - (Mu + Xn) / Mu Yndev = 2.0 - (Mu + Yn) / Mu Zndev = 2.0 - (Mu + Zn) / Mu epslon = np.max( np.abs([Xndev, Yndev, Zndev]) ) while epslon >= errtol: Xnroot = np.sqrt(Xn) Ynroot = np.sqrt(Yn) Znroot = np.sqrt(Zn) lambda1 = Xnroot * (Ynroot + Znroot) + Ynroot * Znroot Xn = 0.25 * (Xn + lambda1) Yn = 0.25 * (Yn + lambda1) Zn = 0.25 * (Zn + lambda1) Mu = (Xn + Yn + Zn) / 3.0 Xndev = 2.0 - (Mu + Xn) / Mu Yndev = 2.0 - (Mu + Yn) / Mu Zndev = 2.0 - (Mu + Zn) / Mu epslon = np.max( np.abs([Xndev , Yndev , Zndev]) ) C1 = 1.0 / 24.0 C2 = 3.0 / 44.0 C3 = 1.0 / 14.0 E2 = Xndev * Yndev - Zndev * Zndev E3 = Xndev * Yndev * Zndev S = 1.0 + (C1 * E2 - 0.1 - C2 * E3) * E2 + C3 * E3 f = S / np.sqrt(Mu) # Return NaN's where input argument was out of range: return f # # rd(x, y, z, errtol) # # Inputs: # # x Input vector size 1xN. # y Input vector size 1xN. # z Input vector size 1xN. # errtol Error tolerance. # # Matlab function to compute Carlson's symmetric elliptic integral Rd. # Implementation of Carlson's Duplication Algorithm 4 in "Computing # Elliptic Integrals by Duplication," by B. C. Carlson, Numer. Math. # 33, 1-16 (1979). # # Returns NaN's for any argument values outside input range. # # Algorithm is also from Carlson's ACM TOMS Algorithm 577. # # This code is a complete rewrite of the algorithm in vectorized form. # It was not produced by running a FORTRAN to Matlab converter. # # The following text is copied from ACM TOMS Algorithm 577 FORTRAN code: # # X AND Y ARE THE VARIABLES IN THE INTEGRAL RC(X,Y). # # ERRTOL IS SET TO THE DESIRED ERROR TOLERANCE. # RELATIVE ERROR DUE TO TRUNCATION IS LESS THAN # 16 * ERRTOL ** 6 / (1 - 2 * ERRTOL). # # SAMPLE CHOICES: ERRTOL RELATIVE TRUNCATION # ERROR LESS THAN # 1.D-3 3.D-19 # 3.D-3 2.D-16 # 1.D-2 3.D-13 # 3.D-2 2.D-10 # 1.D-1 3.D-7 # # Note by TRH: # # Absolute truncation error when the integrals are order 1 quantities # is closer to errtol, so be careful if you want high absolute precision. # # Thomas R. Hoffend Jr., Ph.D. # 3M Company # 3M Center Bldg. 236-GC-26 # St. Paul, MN 55144 # trhoffendjr@mmm.com # def rd(x, y, z, errtol ): realmin = 1e-100 realmax = 1e100 # Argument limits as set by Carlson: LoLim = 5.0 * realmin UpLim = 5.0 * realmax # Define internally acceptable variable ranges for iterations: Xi = x Yi = y Zi = z # Carlson's duplication algorithm for Rf: Xn = Xi Yn = Yi Zn = Zi sigma = 0.0 power4 = 1.0 Mu = (Xn + Yn + 3.0 * Zn) * 0.2 Xndev = (Mu - Xn) / Mu Yndev = (Mu - Yn) / Mu Zndev = (Mu - Zn) / Mu epslon = np.max( np.abs([Xndev, Yndev, Zndev]) ) while epslon >= errtol: Xnroot = np.sqrt(Xn) Ynroot = np.sqrt(Yn) Znroot = np.sqrt(Zn) lambda1 = Xnroot * (Ynroot + Znroot) + Ynroot * Znroot sigma = sigma + power4 / (Znroot * (Zn + lambda1)) power4 = 0.25 * power4 Xn = 0.25 * (Xn + lambda1) Yn = 0.25 * (Yn + lambda1) Zn = 0.25 * (Zn + lambda1) Mu = (Xn + Yn + 3.0 * Zn) * 0.2 Xndev = (Mu - Xn) / Mu Yndev = (Mu - Yn) / Mu Zndev = (Mu - Zn) / Mu epslon = np.max( np.abs([Xndev, Yndev, Zndev]) ) C1 = 3.0 / 14.0 C2 = 1.0 / 6.0 C3 = 9.0 / 22.0 C4 = 3.0 / 26.0 EA = Xndev * Yndev EB = Zndev * Zndev EC = EA - EB ED = EA - 6.0 * EB EF = ED + EC + EC S1 = ED * (-C1 + 0.25 * C3 * ED - 1.50 * C4 * Zndev * EF) S2 = Zndev * (C2 * EF + Zndev * (-C3 * EC + Zndev * C4 * EA)) f = 3.0 * sigma + power4 * (1.0 + S1 + S2) / (Mu * np.sqrt(Mu)) return f

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0.572127

1,389

8,790

3.61915

0.184305

0.014323

0.011936

0.022678

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null

0

1

0

null

0

5

e89e5b8db3ccc53b7cb13e274bbd9dce1db6ede9

173

py

Python

utilities/exceptions.py

canberkeh/word-finder

23e6c5b76a04d8ebbca5fc6ddd993940ce2d2df5

[ "MIT" ]

null

utilities/exceptions.py

canberkeh/word-finder

23e6c5b76a04d8ebbca5fc6ddd993940ce2d2df5

[ "MIT" ]

null

utilities/exceptions.py

canberkeh/word-finder

23e6c5b76a04d8ebbca5fc6ddd993940ce2d2df5

[ "MIT" ]

null

class DatabaseConnectionError(Exception): pass class DBCursorError(Exception): pass class QueryError(Exception): pass class ServiceError(Exception): pass

14.416667

41

0.751445

16

173

8.125

0.4375

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0.415385

0

0.179191

173

11

42

15.727273

0.915493

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1

0

null

1

0

null

0

1

0

5

e8acd0901e443a35c6dfa5bf054bb6e7e1bf906b

19

py

Python

email_reply_parser/version.py

Mahasweta-usc/email-reply-parser

c528e89656347331059d9e485af29fe855c56998

[ "MIT" ]

347

2015-01-05T02:11:52.000Z

2022-03-31T02:50:20.000Z

email_reply_parser/version.py

Mahasweta-usc/email-reply-parser

c528e89656347331059d9e485af29fe855c56998

[ "MIT" ]

31

2015-01-27T13:13:06.000Z

2022-03-24T17:21:20.000Z

email_reply_parser/version.py

Mahasweta-usc/email-reply-parser

c528e89656347331059d9e485af29fe855c56998

[ "MIT" ]

91

2015-01-24T00:33:20.000Z

2022-03-24T11:06:22.000Z

VERSION = '0.5.12'

9.5

18

0.578947

4

19

2.75

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0.25

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19

0.4375

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0.315789

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null

0

1

0

1

0

1

0

null

0

5

e8afd27b5c45ff247b46e1cb240f4e11b512c908

48

py

Python

__init__.py

leptoid/anime-dl

d72825c64ea06e1800e32b16dc95a8f3aee41c9a

[ "MIT" ]

2

2019-11-16T01:06:11.000Z

2020-07-24T02:34:16.000Z

__init__.py

leptoid/anime-dl

d72825c64ea06e1800e32b16dc95a8f3aee41c9a

[ "MIT" ]

null

__init__.py

leptoid/anime-dl

d72825c64ea06e1800e32b16dc95a8f3aee41c9a

[ "MIT" ]

null

import common import external import sites

9.6

16

0.770833

6

48

6.166667

0.666667

0

0.229167

48

4

17

12

1

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true

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null

0

1

0

null

0

1

0

1

0

5

2cd00dbd0aeccc705296607ac0d6faeb3ca31b78

84

py

Python

libaito/__init__.py

martinlatrille/RESTinPy

dfe56b87dd83130f60a44a329153e5a43398e5b0

[ "Apache-2.0" ]

null

libaito/__init__.py

martinlatrille/RESTinPy

dfe56b87dd83130f60a44a329153e5a43398e5b0

[ "Apache-2.0" ]

null

libaito/__init__.py

martinlatrille/RESTinPy

dfe56b87dd83130f60a44a329153e5a43398e5b0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import core import helpers import printers import settings

12

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84

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84

6

24

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null

0

1

0

1

0

1

0

5

2cdb4ea8628586e7bc85615a601d095537efecd0

100

py

Python

Apps/rsp/test/arg.py

zhanghongce/ila-mcm-fmcad18

e7045e38e45e758f2b0e0ecc7d4369f5014b8707

[ "MIT" ]

null

Apps/rsp/test/arg.py

zhanghongce/ila-mcm-fmcad18

e7045e38e45e758f2b0e0ecc7d4369f5014b8707

[ "MIT" ]

null

Apps/rsp/test/arg.py

zhanghongce/ila-mcm-fmcad18

e7045e38e45e758f2b0e0ecc7d4369f5014b8707

[ "MIT" ]

null

def f0(a, **l): print a def f1(a,b,c): print a+b+c d1 = {'a':1,'b':1,'c':1} f0(**d1) f1(**d1)

8.333333

24

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26

100

1.730769

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0

0.125

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100

11

25

9.090909

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null

1

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1

0

null

0

1

0

5

2ce334caa3c540df93a562fe752ae3122a0f3306

13,067

py

Python

ope_estimators.py

MasaKat0/off_policy_evaluation

4b54c321eee522e8c18b478fb455e7d144ab2332

[ "MIT" ]

null

ope_estimators.py

MasaKat0/off_policy_evaluation

4b54c321eee522e8c18b478fb455e7d144ab2332

[ "MIT" ]

null

ope_estimators.py

MasaKat0/off_policy_evaluation

4b54c321eee522e8c18b478fb455e7d144ab2332

[ "MIT" ]

null

import numpy as np from sklearn.linear_model import LinearRegression from sklearn.kernel_ridge import KernelRidge from sklearn.model_selection import GridSearchCV from kernel_regression import KernelLogit import warnings KernelRidge_hyp_param = {'alpha': [0.01, 0.1, 1], 'gamma': [0.01, 0.1, 1]} KernelLogit_sigma_list = np.array([0.01, 0.1, 1]) KernelLogit_lda_list = np.array([0.01, 0.1, 1]) def kernel_ridge_estimator(X, Y, Z, cv=2): model = KernelRidge(kernel='rbf') model = GridSearchCV( model, {'alpha': [0.01, 0.1, 1], 'gamma': [0.01, 0.1, 1]}, cv=cv) model.fit(X, Y) return model.predict(Z) def kernel_logit_estimator(X, Y, Z, cv=2): model, KX, KZ = KernelLogit(X, Y, Z, folds=cv, num_basis=100, sigma_list=KernelLogit_sigma_list, lda_list=KernelLogit_lda_list, algorithm='Ridge') model.fit(KX, Y) return model.predict_proba(KZ) class OPEestimators(): def __init__(self, classes, pi_evaluation, pi_behavior=None, variance=False): self.classes = classes self.pi_behavior = pi_behavior self.pi_evaluation = pi_evaluation self.variance = variance def fit(self, X, A, Y_matrix, est_type, outcome_estimator=kernel_ridge_estimator, policy_estimator=kernel_logit_estimator, warning_samples=10): self.X = X self.N_hst, self.dim = X.shape self.A = A self.Y = Y_matrix self.warning_samples = warning_samples self.outcome_estimator = kernel_ridge_estimator self.policy_estimator = kernel_logit_estimator warnings.simplefilter('ignore') if est_type == 'ipw': theta, var = self.ipw() if est_type == 'dm': theta, var = self.dm() if est_type == 'aipw_ddm': theta, var = self.aipw_ddm() if est_type == 'aipw': theta, var = self.aipw() if est_type == 'a2ipw': theta, var = self.a2ipw() if est_type == 'adr': theta, var = self.adr() if est_type == 'dr_ddm': theta, var = self.dr_ddm() if est_type == 'dr': theta, var = self.dr() if self.variance: return theta, var else: return theta def aipw_ddm(self, folds=2): theta_list = [] cv_fold = np.arange(folds) cv_split0 = np.floor(np.arange(self.N_hst)*folds/self.N_hst) cv_index = cv_split0[np.random.permutation(self.N_hst)] x_cv = [] a_cv = [] y_cv = [] pi_bhv_cv = [] pi_evl_cv = [] for k in cv_fold: x_cv.append(self.X[cv_index == k]) a_cv.append(self.A[cv_index == k]) y_cv.append(self.Y[cv_index == k]) pi_bhv_cv.append(self.pi_behavior[cv_index == k]) pi_evl_cv.append(self.pi_evaluation[cv_index == k]) for k in range(folds): count = 0 for j in range(folds): if j == k: x_te = x_cv[j] a_te = a_cv[j] y_te = y_cv[j] pi_bhv_te = pi_bhv_cv[j] pi_evl_te = pi_evl_cv[j] if j != k: if count == 0: x_tr = x_cv[j] a_tr = a_cv[j] y_tr = y_cv[j] pi_bhv_tr = pi_bhv_cv[j] pi_evl_tr = pi_evl_cv[j] count += 1 else: x_tr = np.append(x_tr, x_cv[j], axis=0) a_tr = np.append(a_tr, a_cv[j], axis=0) y_tr = np.append(y_tr, y_cv[j], axis=0) pi_bhv_tr = np.append(pi_bhv_tr, pi_bhv_cv[j], axis=0) pi_evl_tr = np.append(pi_evl_tr, pi_evl_cv[j], axis=0) densratio_matrix = pi_evl_te/pi_bhv_te f_matrix = np.zeros(shape=(len(x_te), len(self.classes))) for c in self.classes: f_matrix[:, c] = self.outcome_estimator( x_tr[a_tr[:, c] == 1], y_tr[:, c][a_tr[:, c] == 1], x_te) # weight = np.ones(shape=a_te.shape)*np.sum(a_te/pi_bhv_te, axis=0) weight = len(a_te) theta = np.sum(a_te*(y_te-f_matrix)*densratio_matrix / weight) + np.sum(f_matrix*pi_evl_te/weight) theta_list.append(theta) theta = np.mean(theta_list) densratio_matrix = self.pi_evaluation/self.pi_behavior f_matrix = np.zeros(shape=(self.N_hst, len(self.classes))) for c in self.classes: for t in range(self.N_hst): if np.sum(self.A[:t, c] == 1) > self.warning_samples: f_matrix[t, c] = self.outcome_estimator( self.X[:t][self.A[:t, c] == 1], self.Y[:t][:t, c][self.A[:t, c] == 1], [self.X[t]]) else: f_matrix[t, c] = 0 # weight = np.ones(shape=a_te.shape)*np.sum(a_te/pi_bhv_te, axis=0) score = np.sum(self.A*(self.Y-f_matrix)*densratio_matrix, axis=1) + np.sum(f_matrix*self.pi_evaluation, axis=1) var = np.mean((score - theta)**2) return theta, var def dr_ddm(self, folds=2): theta_list = [] cv_fold = np.arange(folds) cv_split0 = np.floor(np.arange(self.N_hst)*folds/self.N_hst) cv_index = cv_split0[np.random.permutation(self.N_hst)] x_cv = [] a_cv = [] y_cv = [] pi_evl_cv = [] for k in cv_fold: x_cv.append(self.X[cv_index == k]) a_cv.append(self.A[cv_index == k]) y_cv.append(self.Y[cv_index == k]) pi_evl_cv.append(self.pi_evaluation[cv_index == k]) for k in range(folds): count = 0 for j in range(folds): if j == k: x_te = x_cv[j] a_te = a_cv[j] y_te = y_cv[j] pi_evl_te = pi_evl_cv[j] if j != k: if count == 0: x_tr = x_cv[j] a_tr = a_cv[j] y_tr = y_cv[j] pi_evl_tr = pi_evl_cv[j] count += 1 else: x_tr = np.append(x_tr, x_cv[j], axis=0) a_tr = np.append(a_tr, a_cv[j], axis=0) y_tr = np.append(y_tr, y_cv[j], axis=0) pi_evl_tr = np.append(pi_evl_tr, pi_evl_cv[j], axis=0) a_temp = np.where(a_tr == 1)[1] pi_bhv_te = kernel_logit_estimator( x_tr, a_temp, x_te) densratio_matrix = pi_evl_te/pi_bhv_te f_matrix = np.zeros(shape=(len(x_te), len(self.classes))) for c in self.classes: f_matrix[:, c] = self.outcome_estimator( x_tr[a_tr[:, c] == 1], y_tr[:, c][a_tr[:, c] == 1], x_te) # weight = np.ones(shape=a_te.shape)*np.sum(a_te/pi_bhv_te, axis=0) weight = len(a_te) theta = np.sum(a_te*(y_te-f_matrix)*densratio_matrix / weight) + np.sum(f_matrix*pi_evl_te/weight) theta_list.append(theta) theta = np.mean(theta_list) a_temp = np.where(self.A == 1)[1] pi_behavior = kernel_logit_estimator(self.X, a_temp, self.X) densratio_matrix = self.pi_evaluation/pi_behavior f_matrix = np.zeros(shape=(self.N_hst, len(self.classes))) for c in self.classes: for t in range(self.N_hst): if np.sum(self.A[:t, c] == 1) > self.warning_samples: f_matrix[t, c] = self.outcome_estimator( self.X[:t][self.A[:t, c] == 1], self.Y[:t][:t, c][self.A[:t, c] == 1], [self.X[t]]) else: f_matrix[t, c] = 0 # weight = np.ones(shape=a_te.shape)*np.sum(a_te/pi_bhv_te, axis=0) score = np.sum(self.A*(self.Y-f_matrix)*densratio_matrix, axis=1) + np.sum(f_matrix*self.pi_evaluation, axis=1) var = np.mean((score - theta)**2) return theta, var def a2ipw(self): densratio_matrix = self.pi_evaluation/self.pi_behavior f_matrix = np.zeros(shape=(self.N_hst, len(self.classes))) for c in self.classes: for t in range(self.N_hst): if np.sum(self.A[:t, c] == 1) > self.warning_samples: f_matrix[t, c] = self.outcome_estimator( self.X[:t][self.A[:t, c] == 1], self.Y[:t][:t, c][self.A[:t, c] == 1], [self.X[t]]) else: f_matrix[t, c] = 0 # weight = np.ones(shape=a_te.shape)*np.sum(a_te/pi_bhv_te, axis=0) score = np.sum(self.A*(self.Y-f_matrix)*densratio_matrix, axis=1) + np.sum(f_matrix*self.pi_evaluation, axis=1) theta = np.mean(score) var = np.mean((score - theta)**2) return theta, var def adr(self): pi_behavior = np.copy(self.pi_evaluation) pi_behavior[:] = 0.5 for t in range(1, self.N_hst): if all(np.sum(self.A[:t, :] == 1, axis=0) > self.warning_samples): a_temp = np.where(self.A[:t] == 1)[1] pi_behavior[t, :] = kernel_logit_estimator( self.X[:t], a_temp, np.array([self.X[t]])) else: pi_behavior[t, :] = 0.5 densratio_matrix = self.pi_evaluation/pi_behavior f_matrix = np.zeros(shape=(self.N_hst, len(self.classes))) for c in self.classes: for t in range(self.N_hst): if np.sum(self.A[:t, c] == 1) > self.warning_samples: f_matrix[t, c] = self.outcome_estimator( self.X[:t][self.A[:t, c] == 1], self.Y[:t][:t, c][self.A[:t, c] == 1], [self.X[t]]) else: f_matrix[t, c] = 0 # weight = np.ones(shape=a_te.shape)*np.sum(a_te/pi_bhv_te, axis=0) score = np.sum(self.A*(self.Y-f_matrix)*densratio_matrix, axis=1) + np.sum(f_matrix*self.pi_evaluation, axis=1) theta = np.mean(score) var = np.mean((score - theta)**2) return theta, var def ipw(self): if self.pi_behavior is None: a_temp = np.where(self.A == 1)[1] self.pi_behavior = kernel_logit_estimator(self.X, a_temp, self.X) densratio = self.pi_evaluation/self.pi_behavior # weight = np.ones(shape=self.A.shape)*np.sum(self.A/self.pi_behavior, axis=0) score = np.sum(self.A*self.Y*densratio, axis=1) theta = np.mean(score) var = np.mean((score - theta)**2) return theta, var def dr(self): a_temp = np.where(self.A == 1)[1] pi_behavior = kernel_logit_estimator(self.X, a_temp, self.X) densratio = self.pi_evaluation/pi_behavior f_matrix = np.zeros(shape=(self.N_hst, len(self.classes))) for c in self.classes: f_matrix[:, c] = self.outcome_estimator( self.X[self.A[:, c] == 1], self.Y[:, c][self.A[:, c] == 1], self.X) # weight = np.ones(shape=self.A.shape)*np.sum(self.A/self.pi_behavior, axis=0) score = np.sum(self.A*(self.Y-f_matrix)*densratio, axis=1) + \ np.sum(f_matrix*self.pi_evaluation, axis=1) theta = np.mean(score) var = np.mean((score - theta)**2) return theta, var def aipw(self): densratio = self.pi_evaluation/self.pi_behavior f_matrix = np.zeros(shape=(self.N_hst, len(self.classes))) for c in self.classes: f_matrix[:, c] = self.outcome_estimator( self.X[self.A[:, c] == 1], self.Y[:, c][self.A[:, c] == 1], self.X) # weight = np.ones(shape=self.A.shape)*np.sum(self.A/self.pi_behavior, axis=0) score = np.sum(self.A*(self.Y-f_matrix)*densratio, axis=1) + \ np.sum(f_matrix*self.pi_evaluation, axis=1) theta = np.mean(score) var = np.mean((score - theta)**2) return theta, var def dm(self, method='Ridge'): f_matrix = np.zeros(shape=(self.N_hst, len(self.classes))) for c in self.classes: f_matrix[:, c] = self.outcome_estimator( self.X[self.A[:, c] == 1], self.Y[:, c][self.A[:, c] == 1], self.X) score = np.sum(f_matrix*self.pi_evaluation, axis=1) theta = np.mean(score) var = np.mean((score - theta)**2) return theta, var

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fa2ba789ab8bf3933288157a2f3dbc2cad5ba2b5

92

py

Python

enthought/units/geo_units.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/units/geo_units.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/units/geo_units.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from scimath.units.geo_units import *

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fa41eebf29bc158bda67e5a659c178e331050e78

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py

Python

pose_trackers/lighttrack/graph/unit_test/__init__.py

rcourivaud/video-to-pose3D

b908014fe2c531c075c11cee72bb798120f970c2

[ "MIT" ]

574

2019-07-12T08:35:18.000Z

2022-03-28T06:37:44.000Z

pose_trackers/lighttrack/graph/unit_test/__init__.py

rcourivaud/video-to-pose3D

b908014fe2c531c075c11cee72bb798120f970c2

[ "MIT" ]

55

2019-07-11T11:31:16.000Z

2022-03-11T23:54:54.000Z

pose_trackers/lighttrack/graph/unit_test/__init__.py

rcourivaud/video-to-pose3D

b908014fe2c531c075c11cee72bb798120f970c2

[ "MIT" ]

123

2019-09-06T07:08:40.000Z

2022-03-26T21:50:28.000Z

import os import sys sys.path.append(os.path.abspath("../utils/"))

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fa44ccefc9d9f47fbd3fe5e3e6e4301a6113318e

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py

Python

main/controllers/__init__.py

billtrn/Comment-Sentiment-Detector

3cacca439cf8ada10da021ca620008d8320eeacd

[ "MIT" ]

10

2021-05-19T11:24:19.000Z

2022-01-07T16:27:23.000Z

main/controllers/__init__.py

billtrn/Comment_Sentiment_Analysis

3cacca439cf8ada10da021ca620008d8320eeacd

[ "MIT" ]

1

2021-05-18T15:55:52.000Z

main/controllers/__init__.py

billtrn/Comment_Sentiment_Analysis

3cacca439cf8ada10da021ca620008d8320eeacd

[ "MIT" ]

null

def init_routes(): from . import api, index, prediction

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1

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5

fa5d4b9d08e8605dad0aceece94fed5dd3c5f867

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py

Python

pyMR/__init__.py

k4rth33k/pyMR

6d5caab73b7712e719716cf14c24c92c41d7c347

[ "MIT" ]

2

2020-07-19T05:37:26.000Z

2021-09-03T10:36:01.000Z

pyMR/__init__.py

k4rth33k/pyMR

6d5caab73b7712e719716cf14c24c92c41d7c347

[ "MIT" ]

null

pyMR/__init__.py

k4rth33k/pyMR

6d5caab73b7712e719716cf14c24c92c41d7c347

[ "MIT" ]

null

from .main import Master from .chunk import Chunks from .utils import Queue

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0

1

0

5

d72e24819bf2b965ae56363664d961a0a010f0e2

1,326

py

Python

tests/test_fp_data.py

hsamshod/hikvision-isapi-wrapper

7d4f9185baa503f53477d9ec39cf246c13eff41a

[ "MIT" ]

8

2020-12-23T09:10:31.000Z

2022-03-28T20:13:54.000Z

tests/test_fp_data.py

hsamshod/hikvision-isapi-wrapper

7d4f9185baa503f53477d9ec39cf246c13eff41a

[ "MIT" ]

null

tests/test_fp_data.py

hsamshod/hikvision-isapi-wrapper

7d4f9185baa503f53477d9ec39cf246c13eff41a

[ "MIT" ]

4

2021-01-18T18:36:37.000Z

2022-03-01T06:08:53.000Z

from types import SimpleNamespace import hikvision_isapi_wrapper as api import vcr @vcr.use_cassette('tests/vcr_cassettes/fp-data-add.yml', filter_headers=['Authorization']) def test_fp_data_add(): fp_lib_instance = api.FaceData() response = fp_lib_instance.face_data_add('blackFD', '1', '4', 'tessst', 'male', '19940226T000000+0500', 'Tashkent', 'https://i.ibb.co/P9rJSTQ/murod.jpg') assert isinstance(response, SimpleNamespace) assert response.statusString == 'OK', "Successful response should be OK" @vcr.use_cassette('tests/vcr_cassettes/fp-data-update.yml', filter_headers=['Authorization']) def test_fp_data_update(): fp_lib_instance = api.FaceData() response = fp_lib_instance.face_data_update('blackFD', '1', '4', 'tessst', 'male', '19940226T000000+0500', 'Tashkent', 'https://i.ibb.co/P9rJSTQ/murod.jpg') assert isinstance(response, SimpleNamespace) assert response.statusString == 'OK', "Successful response should be OK" @vcr.use_cassette('tests/vcr_cassettes/fp-data-delete.yml', filter_headers=['Authorization']) def test_fp_data_delete(): fp_lib_instance = api.FaceData() response = fp_lib_instance.face_data_delete('blackFD', '1', ['4',]) assert isinstance(response, SimpleNamespace) assert response.statusString == 'OK', "Successful response should be OK"

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1

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1

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null

0

5

d78138a9bda10e91002541895c1451189c1263ef

1,196

py

Python

tests/test_sectiongen.py

timskovjacobsen/conctools

74ef341f76fa49ca705175b6b4e618b847745859

[ "MIT" ]

8

2020-02-22T22:41:42.000Z

2021-06-14T13:44:31.000Z

tests/test_sectiongen.py

timskovjacobsen/conctools

74ef341f76fa49ca705175b6b4e618b847745859

[ "MIT" ]

4

2020-03-06T17:01:13.000Z

2020-06-02T12:43:01.000Z

tests/test_sectiongen.py

timskovjacobsen/conctools

74ef341f76fa49ca705175b6b4e618b847745859

[ "MIT" ]

1

2020-10-13T22:17:27.000Z

"""Tests for `sectiongen` module.""" import os import sys # import numpy as np # from numpy.testing import assert_almost_equal, assert_array_almost_equal # Import module to test import conctools._sectiongen as sg # TODO Adjust tests below after code to be tested was changed. # def test_neutral_axis_locs_traverse_upwards(): # # ----- Setup ----- # bounds = (-300, 300) # n_locations = 7 # desired = np.array([-300, -200, -100, 0, 100, 200, 300]) # # ----- Exercise ----- # locations = sg.neutral_axis_locs(bounds, n_locations, traverse_upwards=True) # # Unpack generator into array # actual = np.array([*locations]) # # ----- Verify ----- # assert_array_almost_equal(actual, desired) # def test_neutral_axis_locs_traverse_downwards(): # # ----- Setup ----- # bounds = (-300, 300) # n_locations = 7 # desired = np.array([300, 200, 100, 0, -100, -200, -300]) # # ----- Exercise ----- # locations = sg.neutral_axis_locs(bounds, n_locations, traverse_upwards=False) # # Unpack generator into array # actual = np.array([*locations]) # # ----- Verify ----- # assert_array_almost_equal(actual, desired)

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5

d78c129dfa2f795903d9b8552d0eb5894e83a9ed

247

py

Python

src/tlsscout/template_context_processors.py

gettis/tlsscout

55dd5a1dbc3329aa451bfd82aac9a0f68d52136f

[ "BSD-3-Clause" ]

9

2015-03-16T08:40:34.000Z

2020-10-13T15:15:38.000Z

src/tlsscout/template_context_processors.py

gettis/tlsscout

55dd5a1dbc3329aa451bfd82aac9a0f68d52136f

[ "BSD-3-Clause" ]

6

2015-03-22T19:32:52.000Z

2022-02-11T03:39:24.000Z

src/tlsscout/template_context_processors.py

gettis/tlsscout

55dd5a1dbc3329aa451bfd82aac9a0f68d52136f

[ "BSD-3-Clause" ]

8

2015-05-02T13:21:40.000Z

2020-09-30T17:59:49.000Z

from django.conf import settings def anon_access(request): return { 'ALLOW_ANONYMOUS_VIEWING': settings.ALLOW_ANONYMOUS_VIEWING } def signup_enabled(request): return { 'SIGNUP_ENABLED': settings.ENABLE_SIGNUP }

17.642857

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null

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1

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5

ad104614d925bb0fb61481e176a823da78cc50d8

402

py

Python

environment.py

bathicodes/Distributor

c24879ef142798fb0dcdb7fe9ca5e7dbcc3f3168

[ "MIT" ]

null

environment.py

bathicodes/Distributor

c24879ef142798fb0dcdb7fe9ca5e7dbcc3f3168

[ "MIT" ]

null

environment.py

bathicodes/Distributor

c24879ef142798fb0dcdb7fe9ca5e7dbcc3f3168

[ "MIT" ]

null

from pathlib import Path # -------------------- path_osx for mac os -------------------- # home_osx = str(Path.home()) def path_osx(): return f"{home_osx}/Desktop/Distributor" def documents_osx(): return f"{home_osx}/Documents/" def music_osx(): return f"{home_osx}/Music/" def pictures_osx(): return f"{home_osx}/Pictures/" def movies_osx(): return f"{home_osx}/Movies/"

21.157895

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null

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null

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1

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5

ad20d7bff083994381269345ed5c48c7e9c5d508

367

py

Python

metu_cafeteria_menu/exceptions.py

th0th/metu-cafeteria-menu

af62990a27f1250f82b92ab3fb4a848df0dac880

[ "MIT" ]

1

2020-12-29T11:57:48.000Z

metu_cafeteria_menu/exceptions.py

th0th/metu-cafeteria-menu

af62990a27f1250f82b92ab3fb4a848df0dac880

[ "MIT" ]

null

metu_cafeteria_menu/exceptions.py

th0th/metu-cafeteria-menu

af62990a27f1250f82b92ab3fb4a848df0dac880

[ "MIT" ]

null

class RequestException(Exception): def __init__(self, parent_exception, message): self.parent_exception = parent_exception self.message = message def __str__(self): return self.message class DateException(Exception): def __init__(self, message): self.message = message def __str__(self): return self.message

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null

0

1

0

1

0

5

ad500f072701d02195cf2e35de508971670fd1cf

766

py

Python

novice/02-02/pytest/test_sample.py

fakihAlim/zimera

69271dbcfe9d8f9b2ef72e6f6c8ce0ae4c57a9c9

[ "MIT" ]

null

novice/02-02/pytest/test_sample.py

fakihAlim/zimera

69271dbcfe9d8f9b2ef72e6f6c8ce0ae4c57a9c9

[ "MIT" ]

null

novice/02-02/pytest/test_sample.py

fakihAlim/zimera

69271dbcfe9d8f9b2ef72e6f6c8ce0ae4c57a9c9

[ "MIT" ]

null

def func(x): return x + 1 def test_answer(): assert func(3) == 4 # --- HASILNYA --- # (py39-nlp) C:\Users\DeLL\My Documents\github\zimera\novice\02-02\pytest>pytest test_sample.py # ================================================= test session starts ================================================= # platform win32 -- Python 3.9.7, pytest-6.2.5, py-1.11.0, pluggy-1.0.0 # rootdir: C:\Users\DeLL\My Documents\github\zimera\novice\02-02\pytest # plugins: anyio-2.2.0, hypothesis-6.32.1 # collected 1 item # test_sample.py . [100%] # ================================================== 1 passed in 0.03s ==================================================

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null

0

1

0

null

0

1

0

1

0

5

ad59e744139b2ed89cfb03e9ff98043640993360

1,061

py

Python

mysite/myapp/migrations/0010_auto_20190416_0314.py

Pdhenson/QuestLog

8cfe7061fa7ec6b7cf18cea8800763d35a852f79

[ "MIT" ]

null

mysite/myapp/migrations/0010_auto_20190416_0314.py

Pdhenson/QuestLog

8cfe7061fa7ec6b7cf18cea8800763d35a852f79

[ "MIT" ]

null

mysite/myapp/migrations/0010_auto_20190416_0314.py

Pdhenson/QuestLog

8cfe7061fa7ec6b7cf18cea8800763d35a852f79

[ "MIT" ]

null

# Generated by Django 2.2 on 2019-04-16 03:14 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('myapp', '0009_steps_quest'), ] operations = [ migrations.AlterField( model_name='steps', name='step_five', field=models.CharField(default='', max_length=255), ), migrations.AlterField( model_name='steps', name='step_four', field=models.CharField(default='', max_length=255), ), migrations.AlterField( model_name='steps', name='step_one', field=models.CharField(default='', max_length=255), ), migrations.AlterField( model_name='steps', name='step_three', field=models.CharField(default='', max_length=255), ), migrations.AlterField( model_name='steps', name='step_two', field=models.CharField(default='', max_length=255), ), ]

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1,061

5.539216

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0.176991

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0.256637

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0.046025

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1,061

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64

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false

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0.03125

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null

0

1

0

1

0

1

0

null

0

5

ad88b386a9b6cf7da7ffeec1bca533e736da0ba4

109

py

Python

app/web/__main__.py

art-solopov/zodb_book_mgmt

d3ab28911168dff097125f374ef720059b5acbd4

[ "MIT" ]

null

app/web/__main__.py

art-solopov/zodb_book_mgmt

d3ab28911168dff097125f374ef720059b5acbd4

[ "MIT" ]

null

app/web/__main__.py

art-solopov/zodb_book_mgmt

d3ab28911168dff097125f374ef720059b5acbd4

[ "MIT" ]

null

import bottle from .base import base_app bottle.run(app=base_app, host='localhost', port='8080', debug=True)

27.25

67

0.770642

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109

4.555556

0.666667

0.170732

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0.091743

109

3

68

36.333333

0.787879

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0.119266

0

1

0

true

0

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0.666667

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null

0

1

0

null

0

1

0

1

0

1

0

5

d1097cda901f0bb3fcfddf658f900dcec49d36d4

252

py

Python

jobportal/tests.py

zobeltran/webdevtproject

5c00c726863b1411e85fca48e44883cefe62b9dd

[ "Apache-2.0" ]

null

jobportal/tests.py

zobeltran/webdevtproject

5c00c726863b1411e85fca48e44883cefe62b9dd

[ "Apache-2.0" ]

null

jobportal/tests.py

zobeltran/webdevtproject

5c00c726863b1411e85fca48e44883cefe62b9dd

[ "Apache-2.0" ]

null

from django.test import TestCase # Create your tests here. # # class EmployeeRegistrationTest(TestCase): # def test_index(self): # resp = self.client.get('accounts/employee/register.html') # self.assertEqual(resp.status_code, 200)

28

67

0.710317

30

252

5.9

0.833333

0

0.014423

0.174603

252

8

68

31.5

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0

1

0

true

0

1

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1

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null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

d13935aee39e7625eb8390645147f92fb938f99e

200

py

Python

example_test.py

Alsrec/EC-500A2

0572b8ad7bff2b202a639ddb30c7f0c4e24d6f55

[ "MIT" ]

null

example_test.py

Alsrec/EC-500A2

0572b8ad7bff2b202a639ddb30c7f0c4e24d6f55

[ "MIT" ]

null

example_test.py

Alsrec/EC-500A2

0572b8ad7bff2b202a639ddb30c7f0c4e24d6f55

[ "MIT" ]

null

from example import* def test_add3(): assert add3(1, 2, 3) == 6 assert add3(5, 5, 5,) == 15 assert add3("EA", "Z", "Y") == "EAZY" def test_numpyaround(): assert numpyaround(1.222, 1) == 1.2

20

39

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34

200

3.441176

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0.25641

0

0.125

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200

9

40

22.222222

0.60625

0

0.04

0

0.571429

1

0.285714

true

0

0.142857

0

0.428571

0

null

1

0

1

0

null

0

1

0

1

0

5

d156751c8c97c1f04813a94da89ac9952b28a502

363

py

Python

parser/team28/models/objects/columns_select.py

webdev188/tytus

847071edb17b218f51bb969d335a8ec093d13f94

[ "MIT" ]

35

2020-12-07T03:11:43.000Z

2021-04-15T17:38:16.000Z

parser/team28/models/objects/columns_select.py

webdev188/tytus

847071edb17b218f51bb969d335a8ec093d13f94

[ "MIT" ]

47

2020-12-09T01:29:09.000Z

2021-01-13T05:37:50.000Z

parser/team28/models/objects/columns_select.py

webdev188/tytus

847071edb17b218f51bb969d335a8ec093d13f94

[ "MIT" ]

556

2020-12-07T03:13:31.000Z

2021-06-17T17:41:10.000Z

class ColumnsSelect(object): def __init__(self, values) : self._values = values def __repr__(self): return str(vars(self)) @property def values(self): return self._values @values.setter def values(self, values): self._values = values # column = ColumnsSelect([1,2,34,51,2]) # print(column.values)

22.6875

39

0.614325

43

363

4.930233

0.44186

0.235849

0.226415

0.188679

0.245283

0

0.026316

0.267218

363

16

40

22.6875

0.770677

0.15978

0

0.181818

0

1

0.363636

false

0

0.181818

0.636364

0

null

1

0

null

0

1

0

1

0

5

0f39f8313e77473f36e6a452d3c204193ddac78c

82

py

Python

engine/models/models.py

LloydTao/ecm3423-fur-effect

fefa73665b459dfd1648dca97a95e8313cf53dd5

[ "MIT" ]

null

engine/models/models.py

LloydTao/ecm3423-fur-effect

fefa73665b459dfd1648dca97a95e8313cf53dd5

[ "MIT" ]

null

engine/models/models.py

LloydTao/ecm3423-fur-effect

fefa73665b459dfd1648dca97a95e8313cf53dd5

[ "MIT" ]

null

import numpy as np import pygame from OpenGL.GL import * class Model: pass

9.111111

23

0.719512

13

82

4.538462

0.846154

0

0.243902

82

8

24

10.25

0.951613

0

1

0

true

0.2

0.6

0

0.8

0

1

0

null

0

1

0

null

0

1

0

5

0f4498c69b394978b204d57728ba7b082d792cec

52

py

Python

booster/pipeline/__init__.py

vlievin/booster-pytorch

a8f447160c30224112731a25f90f6f97126a34b2

[ "MIT" ]

4

2019-12-10T06:41:29.000Z

2021-08-06T13:34:59.000Z

booster/pipeline/__init__.py

vlievin/booster-pytorch

a8f447160c30224112731a25f90f6f97126a34b2

[ "MIT" ]

null

booster/pipeline/__init__.py

vlievin/booster-pytorch

a8f447160c30224112731a25f90f6f97126a34b2

[ "MIT" ]

1

2020-08-20T16:12:53.000Z

from .pipeline import Pipeline, DataParallelPipeline

52

0.884615

5

52

9.2

0.8

0

0.076923

52

1

52

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0f7a2d452ecd4eaa893409c49dc4736ef33dc536

134

py

Python

roman/__init__.py

drMJ2/roman

51469b3b1e76a92cc9986106a642bcd2ef3365ad

[ "MIT" ]

null

roman/__init__.py

drMJ2/roman

51469b3b1e76a92cc9986106a642bcd2ef3365ad

[ "MIT" ]

1

2020-09-18T21:13:24.000Z

roman/__init__.py

drMJ2/roman

51469b3b1e76a92cc9986106a642bcd2ef3365ad

[ "MIT" ]

null

from .robot import * from .ur import arm from .ur.arm import Joints, Tool from .rq import hand from .rq.hand import GraspMode, Finger

22.333333

38

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23

134

4.434783

0.478261

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0

0.164179

134

5

39

26.8

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0

1

0

true

0

1

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1

0

null

0

1

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null

0

1

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1

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0

5